From 4fdd534e5b9a197f787da025c9d6b73818a9fdf8 Mon Sep 17 00:00:00 2001 From: Iaroslav Postovalov Date: Mon, 4 Apr 2022 18:58:41 +0700 Subject: [PATCH] Reduced for context receivers issue --- .github/workflows/build.yml | 32 - .github/workflows/pages.yml | 30 - .github/workflows/publish.yml | 49 - .space.kts | 3 - CHANGELOG.md | 165 -- README.md | 297 --- benchmarks/README.md | 4 - benchmarks/build.gradle.kts | 169 -- .../ExpressionsInterpretersBenchmark.kt | 105 - .../kmath/benchmarks/ArrayBenchmark.kt | 43 - .../kmath/benchmarks/BigIntBenchmark.kt | 112 - .../kmath/benchmarks/BufferBenchmark.kt | 39 - .../kscience/kmath/benchmarks/DotBenchmark.kt | 100 - .../ExpressionsInterpretersBenchmark.kt | 124 - .../kmath/benchmarks/JafamaBenchmark.kt | 42 - .../benchmarks/MatrixInverseBenchmark.kt | 53 - .../kmath/benchmarks/NDFieldBenchmark.kt | 105 - .../benchmarks/TensorAlgebraBenchmark.kt | 37 - .../kmath/benchmarks/ViktorBenchmark.kt | 65 - .../kmath/benchmarks/ViktorLogBenchmark.kt | 61 - 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kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorStructureND.kt diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml deleted file mode 100644 index cffef64b0..000000000 --- a/.github/workflows/build.yml +++ /dev/null @@ -1,32 +0,0 @@ -name: Gradle build - -on: - push: - branches: [ dev, master ] - pull_request: - -jobs: - build: - strategy: - matrix: - os: [ macOS-latest, windows-latest ] - runs-on: ${{matrix.os}} - timeout-minutes: 40 - steps: - - uses: actions/checkout@v3.0.0 - - uses: actions/setup-java@v3.0.0 - with: - java-version: 11 - distribution: liberica - - name: Cache konan - uses: actions/cache@v3.0.1 - with: - path: ~/.konan - key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }} - restore-keys: | - ${{ runner.os }}-gradle- - - name: Gradle Wrapper Validation - uses: gradle/wrapper-validation-action@v1.0.4 - - uses: gradle/gradle-build-action@v2.1.5 - with: - arguments: build diff --git a/.github/workflows/pages.yml b/.github/workflows/pages.yml deleted file mode 100644 index 9b76fd16e..000000000 --- a/.github/workflows/pages.yml +++ /dev/null @@ -1,30 +0,0 @@ -name: Dokka publication - -on: - push: - branches: [ master ] - -jobs: - build: - runs-on: ubuntu-20.04 - timeout-minutes: 40 - steps: - - uses: actions/checkout@v3.0.0 - - uses: actions/setup-java@v3.0.0 - with: - java-version: 11 - distribution: liberica - - name: Cache konan - uses: actions/cache@v3.0.1 - with: - path: ~/.konan - key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }} - restore-keys: | - ${{ runner.os }}-gradle- - - uses: gradle/gradle-build-action@v2.1.5 - with: - arguments: dokkaHtmlMultiModule --no-parallel - - uses: JamesIves/github-pages-deploy-action@4.2.5 - with: - branch: gh-pages - folder: build/dokka/htmlMultiModule diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml deleted file mode 100644 index 8e9b98dcb..000000000 --- a/.github/workflows/publish.yml +++ /dev/null @@ -1,49 +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@v3.0.0 - with: - java-version: 11 - distribution: liberica - - name: Cache konan - uses: actions/cache@v3.0.1 - with: - path: ~/.konan - key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }} - restore-keys: | - ${{ runner.os }}-gradle- - - uses: gradle/wrapper-validation-action@v1.0.4 - - name: Publish Windows Artifacts - if: matrix.os == 'windows-latest' - uses: gradle/gradle-build-action@v2.1.5 - with: - arguments: | - releaseAll - -Ppublishing.enabled=true - -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }} - -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }} - - name: Publish Mac Artifacts - if: matrix.os == 'macOS-latest' - uses: gradle/gradle-build-action@v2.1.5 - with: - arguments: | - releaseMacosX64 - releaseIosArm64 - releaseIosX64 - -Ppublishing.enabled=true - -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }} - -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }} diff --git a/.space.kts b/.space.kts deleted file mode 100644 index d70ad6d59..000000000 --- a/.space.kts +++ /dev/null @@ -1,3 +0,0 @@ -job("Build") { - gradlew("openjdk:11", "build") -} diff --git a/CHANGELOG.md b/CHANGELOG.md deleted file mode 100644 index a19b1f467..000000000 --- a/CHANGELOG.md +++ /dev/null @@ -1,165 +0,0 @@ -# KMath - -## [Unreleased] -### Added -- `ScaleOperations` interface -- `Field` extends `ScaleOperations` -- Basic integration API -- Basic MPP distributions and samplers -- `bindSymbolOrNull` -- Blocking chains and Statistics -- Multiplatform integration -- Integration for any Field element -- Extended operations for ND4J fields -- Jupyter Notebook integration module (kmath-jupyter) -- `@PerformancePitfall` annotation to mark possibly slow API -- Unified architecture for Integration and Optimization using features. -- `BigInt` operation performance improvement and fixes by @zhelenskiy (#328) -- Integration between `MST` and Symja `IExpr` -- Complex power -- Separate methods for UInt, Int and Number powers. NaN safety. -- Tensorflow prototype - -### Changed -- Exponential operations merged with hyperbolic functions -- Space is replaced by Group. Space is reserved for vector spaces. -- VectorSpace is now a vector space -- Buffer factories for primitives moved to MutableBuffer.Companion -- Rename `NDStructure` and `NDAlgebra` to `StructureND` and `AlgebraND` respectively -- `Real` -> `Double` -- DataSets are moved from functions to core -- Redesign advanced Chain API -- Redesign `MST`. Remove `MstExpression`. -- Move `MST` to core -- Separated benchmarks and examples -- Rewrite `kmath-ejml` without `ejml-simple` artifact, support sparse matrices -- Promote stability of kmath-ast and kmath-kotlingrad to EXPERIMENTAL. -- ColumnarData returns nullable column -- `MST` is made sealed interface -- Replace `MST.Symbolic` by `Symbol`, `Symbol` now implements MST -- Remove Any restriction on polynomials -- Add `out` variance to type parameters of `StructureND` and its implementations where possible -- Rename `DifferentiableMstExpression` to `KotlingradExpression` -- `FeatureSet` now accepts only `Feature`. It is possible to override keys and use interfaces. -- Use `Symbol` factory function instead of `StringSymbol` -- New discoverability pattern: `.algebra.` -- Adjusted commons-math API for linear solvers to match conventions. -- Buffer algebra does not require size anymore -- Operations -> Ops -- Default Buffer and ND algebras are now Ops and lack neutral elements (0, 1) as well as algebra-level shapes. -- Tensor algebra takes read-only structures as input and inherits AlgebraND - -### Deprecated -- Specialized `DoubleBufferAlgebra` - -### Removed -- Nearest in Domain. To be implemented in geometry package. -- Number multiplication and division in main Algebra chain -- `contentEquals` from Buffer. It moved to the companion. -- MSTExpression -- Expression algebra builders -- Complex and Quaternion no longer are elements. -- Second generic from DifferentiableExpression -- Algebra elements are completely removed. Use algebra contexts instead. - -### Fixed -- Ring inherits RingOperations, not GroupOperations -- Univariate histogram filling - -### Security - -## [0.2.0] -### Added -- `fun` annotation for SAM interfaces in library -- Explicit `public` visibility for all public APIs -- Better trigonometric and hyperbolic functions for `AutoDiffField` (https://github.com/mipt-npm/kmath/pull/140) -- Automatic README generation for features (#139) -- Native support for `memory`, `core` and `dimensions` -- `kmath-ejml` to supply EJML SimpleMatrix wrapper (https://github.com/mipt-npm/kmath/pull/136) -- A separate `Symbol` entity, which is used for global unbound symbol. -- A `Symbol` indexing scope. -- Basic optimization API for Commons-math. -- Chi squared optimization for array-like data in CM -- `Fitting` utility object in prob/stat -- ND4J support module submitting `NDStructure` and `NDAlgebra` over `INDArray` -- Coroutine-deterministic Monte-Carlo scope with a random number generator -- Some minor utilities to `kmath-for-real` -- Generic operation result parameter to `MatrixContext` -- New `MatrixFeature` interfaces for matrix decompositions -- Basic Quaternion vector support in `kmath-complex`. - -### Changed -- Package changed from `scientifik` to `space.kscience` -- Gradle version: 6.6 -> 6.8.2 -- Minor exceptions refactor (throwing `IllegalArgumentException` by argument checks instead of `IllegalStateException`) -- `Polynomial` secondary constructor made function -- Kotlin version: 1.3.72 -> 1.4.30 -- `kmath-ast` doesn't depend on heavy `kotlin-reflect` library -- Full autodiff refactoring based on `Symbol` -- `kmath-prob` renamed to `kmath-stat` -- Grid generators moved to `kmath-for-real` -- Use `Point` instead of specialized type in `kmath-for-real` -- Optimized dot product for buffer matrices moved to `kmath-for-real` -- EjmlMatrix context is an object -- Matrix LUP `inverse` renamed to `inverseWithLup` -- `NumericAlgebra` moved outside of regular algebra chain (`Ring` no longer implements it). -- Features moved to NDStructure and became transparent. -- Capitalization of LUP in many names changed to Lup. -- Refactored `NDStructure` algebra to be more simple, preferring under-the-hood conversion to explicit NDStructure types -- Refactor histograms. They are marked as prototype -- `Complex` and related features moved to a separate module `kmath-complex` -- Refactor AlgebraElement -- `symbol` method in `Algebra` renamed to `bindSymbol` to avoid ambiguity -- Add `out` projection to `Buffer` generic - -### Deprecated - -### Removed -- `kmath-koma` module because it doesn't support Kotlin 1.4. -- Support of `legacy` JS backend (we will support only IR) -- `toGrid` method. -- Public visibility of `BufferAccessor2D` -- `Real` class -- StructureND identity and equals - -### Fixed -- `symbol` method in `MstExtendedField` (https://github.com/mipt-npm/kmath/pull/140) - -### Security - -## [0.1.4] - -### Added -- Functional Expressions API -- Mathematical Syntax Tree, its interpreter and API -- String to MST parser (https://github.com/mipt-npm/kmath/pull/120) -- MST to JVM bytecode translator (https://github.com/mipt-npm/kmath/pull/94) -- FloatBuffer (specialized MutableBuffer over FloatArray) -- FlaggedBuffer to associate primitive numbers buffer with flags (to mark values infinite or missing, etc.) -- Specialized builder functions for all primitive buffers like `IntBuffer(25) { it + 1 }` (https://github.com/mipt-npm/kmath/pull/125) -- Interface `NumericAlgebra` where `number` operation is available to convert numbers to algebraic elements -- Inverse trigonometric functions support in ExtendedField (`asin`, `acos`, `atan`) (https://github.com/mipt-npm/kmath/pull/114) -- New space extensions: `average` and `averageWith` -- Local coding conventions -- Geometric Domains API in `kmath-core` -- Blocking chains in `kmath-coroutines` -- Full hyperbolic functions support and default implementations within `ExtendedField` -- Norm support for `Complex` - -### Changed -- `readAsMemory` now has `throws IOException` in JVM signature. -- Several functions taking functional types were made `inline`. -- Several functions taking functional types now have `callsInPlace` contracts. -- BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor optimizations -- `power(T, Int)` extension function has preconditions and supports `Field` -- Memory objects have more preconditions (overflow checking) -- `tg` function is renamed to `tan` (https://github.com/mipt-npm/kmath/pull/114) -- Gradle version: 6.3 -> 6.6 -- Moved probability distributions to commons-rng and to `kmath-prob` - -### Fixed -- Missing copy method in Memory implementation on JS (https://github.com/mipt-npm/kmath/pull/106) -- D3.dim value in `kmath-dimensions` -- Multiplication in integer rings in `kmath-core` (https://github.com/mipt-npm/kmath/pull/101) -- Commons RNG compatibility (https://github.com/mipt-npm/kmath/issues/93) -- Multiplication of BigInt by scalar diff --git a/README.md b/README.md deleted file mode 100644 index aea94f529..000000000 --- a/README.md +++ /dev/null @@ -1,297 +0,0 @@ -[![JetBrains Research](https://jb.gg/badges/research.svg)](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub) -[![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382) -![Gradle build](https://github.com/mipt-npm/kmath/workflows/Gradle%20build/badge.svg) -[![Maven Central](https://img.shields.io/maven-central/v/space.kscience/kmath-core.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:%22space.kscience%22) -[![Space](https://img.shields.io/badge/dynamic/xml?color=orange&label=Space&query=//metadata/versioning/latest&url=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fspace%2Fkscience%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/) - -# KMath - -Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based -analog to Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior -architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like -experience could be achieved with [kmath-for-real](/kmath-for-real) extension module. - -[Documentation site (**WIP**)](https://mipt-npm.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) - -# Goal - -* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native) - . -* Provide basic multiplatform implementations for those abstractions (without significant performance optimization). -* Provide bindings and wrappers with those abstractions for popular optimized platform libraries. - -## Non-goals - -* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in API. -* Provide the best performance out of the box. We have specialized libraries for that. Need only API wrappers for them. -* Cover all cases as immediately and in one bundle. We will modularize everything and add new features gradually. -* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like - for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better - experience for those, who want to work with specific types. - -## Features and stability - -KMath is a modular library. Different modules provide different features with different API stability guarantees. All -core modules are released with the same version, but with different API change policy. The features are described in -module definitions below. The module stability could have the following levels: - -* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could - break any moment. You can still use it, but be sure to fix the specific version. -* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked - with `@UnstableKmathAPI` or other stability warning annotations. -* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor - versions, but not in patch versions. API is protected - with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool. -* **STABLE**. The API stabilized. Breaking changes are allowed only in major releases. - -## Modules - - -### [benchmarks](benchmarks) -> -> -> **Maturity**: EXPERIMENTAL - -### [examples](examples) -> -> -> **Maturity**: EXPERIMENTAL - -### [kmath-ast](kmath-ast) -> -> -> **Maturity**: EXPERIMENTAL -> -> **Features:** -> - [expression-language](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt) : Expression language and its parser -> - [mst-jvm-codegen](kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode compiler -> - [mst-js-codegen](kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler -> - [rendering](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt) : Extendable MST rendering - - -### [kmath-commons](kmath-commons) -> -> -> **Maturity**: EXPERIMENTAL - -### [kmath-complex](kmath-complex) -> Complex numbers and quaternions. -> -> **Maturity**: PROTOTYPE -> -> **Features:** -> - [complex](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt) : Complex Numbers -> - [quaternion](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions - - -### [kmath-core](kmath-core) -> Core classes, algebra definitions, basic linear algebra -> -> **Maturity**: DEVELOPMENT -> -> **Features:** -> - [algebras](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces and fields. -> - [nd](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures and operations on them. -> - [linear](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition. -> - [buffers](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : One-dimensional structure -> - [expressions](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical expression once, users will be able to apply different types of -objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high -performance calculations to code generation. -> - [domains](kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains) : Domains -> - [autodiff](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation - - -### [kmath-coroutines](kmath-coroutines) -> -> -> **Maturity**: EXPERIMENTAL - -### [kmath-dimensions](kmath-dimensions) -> -> -> **Maturity**: PROTOTYPE - -### [kmath-ejml](kmath-ejml) -> -> -> **Maturity**: PROTOTYPE -> -> **Features:** -> - [ejml-vector](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt) : Point implementations. -> - [ejml-matrix](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt) : Matrix implementation. -> - [ejml-linear-space](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : LinearSpace implementations. - - -### [kmath-for-real](kmath-for-real) -> Extension module that should be used to achieve numpy-like behavior. -All operations are specialized to work with `Double` numbers without declaring algebraic contexts. -One can still use generic algebras though. -> -> **Maturity**: EXPERIMENTAL -> -> **Features:** -> - [DoubleVector](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt) : Numpy-like operations for Buffers/Points -> - [DoubleMatrix](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt) : Numpy-like operations for 2d real structures -> - [grids](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt) : Uniform grid generators - - -### [kmath-functions](kmath-functions) -> -> -> **Maturity**: EXPERIMENTAL -> -> **Features:** -> - [piecewise](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt) : Piecewise functions. -> - [polynomials](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt) : Polynomial functions. -> - [linear interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt) : Linear XY interpolator. -> - [spline interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt) : Cubic spline XY interpolator. -> - [integration](kmath-functions/#) : Univariate and multivariate quadratures - - -### [kmath-geometry](kmath-geometry) -> -> -> **Maturity**: PROTOTYPE - -### [kmath-histograms](kmath-histograms) -> -> -> **Maturity**: PROTOTYPE - -### [kmath-jafama](kmath-jafama) -> -> -> **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) -> -> -> **Maturity**: EXPERIMENTAL -> -> **Features:** -> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt) : MST based DifferentiableExpression. -> - [scalars-adapters](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt) : Conversions between Kotlin∇'s SFun and MST - - -### [kmath-memory](kmath-memory) -> An API and basic implementation for arranging objects in a continuous memory block. -> -> **Maturity**: DEVELOPMENT - -### [kmath-multik](kmath-multik) -> -> -> **Maturity**: PROTOTYPE - -### [kmath-nd4j](kmath-nd4j) -> -> -> **Maturity**: EXPERIMENTAL -> -> **Features:** -> - [nd4jarraystructure](kmath-nd4j/#) : NDStructure wrapper for INDArray -> - [nd4jarrayrings](kmath-nd4j/#) : Rings over Nd4jArrayStructure of Int and Long -> - [nd4jarrayfields](kmath-nd4j/#) : Fields over Nd4jArrayStructure of Float and Double - - -### [kmath-optimization](kmath-optimization) -> -> -> **Maturity**: EXPERIMENTAL - -### [kmath-stat](kmath-stat) -> -> -> **Maturity**: EXPERIMENTAL - -### [kmath-symja](kmath-symja) -> -> -> **Maturity**: PROTOTYPE - -### [kmath-tensorflow](kmath-tensorflow) -> -> -> **Maturity**: PROTOTYPE - -### [kmath-tensors](kmath-tensors) -> -> -> **Maturity**: PROTOTYPE -> -> **Features:** -> - [tensor algebra](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic linear algebra operations on tensors (plus, dot, etc.) -> - [tensor algebra with broadcasting](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt) : Basic linear algebra operations implemented with broadcasting. -> - [linear algebra operations](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Advanced linear algebra operations like LU decomposition, SVD, etc. - - -### [kmath-viktor](kmath-viktor) -> -> -> **Maturity**: DEVELOPMENT - - -## Multi-platform support - -KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the -[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features -are delegated to platform-specific implementations even if they could be provided in the common module for performance -reasons. Currently, the 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 -performance and flexibility. - -We expect to focus on creating convenient universal API first and then work on increasing performance for specific -cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized -native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be -better than SciPy. - -## Requirements - -KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for -execution to get better performance. - -### Repositories - -Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/) -repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of -[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could -be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy: - -```kotlin -repositories { - maven("https://repo.kotlin.link") -} - -dependencies { - api("space.kscience:kmath-core:$version") - // api("space.kscience:kmath-core-jvm:$version") for jvm-specific version -} -``` - -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 -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. diff --git a/benchmarks/README.md b/benchmarks/README.md deleted file mode 100644 index cd8fbafd3..000000000 --- a/benchmarks/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# Module benchmarks - - - diff --git a/benchmarks/build.gradle.kts b/benchmarks/build.gradle.kts deleted file mode 100644 index 22712816d..000000000 --- a/benchmarks/build.gradle.kts +++ /dev/null @@ -1,169 +0,0 @@ -@file:Suppress("UNUSED_VARIABLE") - -import space.kscience.kmath.benchmarks.addBenchmarkProperties - -plugins { - kotlin("multiplatform") - kotlin("plugin.allopen") - id("org.jetbrains.kotlinx.benchmark") -} - -allOpen.annotation("org.openjdk.jmh.annotations.State") -sourceSets.register("benchmarks") - -repositories { - mavenCentral() -} - -kotlin { - jvm() - - js(IR) { - nodejs() - } - - sourceSets { - all { - languageSettings { - progressiveMode = true - } - } - - val commonMain by getting { - dependencies { - implementation(project(":kmath-ast")) - implementation(project(":kmath-core")) - implementation(project(":kmath-coroutines")) - implementation(project(":kmath-complex")) - implementation(project(":kmath-stat")) - implementation(project(":kmath-dimensions")) - implementation(project(":kmath-for-real")) - implementation(project(":kmath-tensors")) - implementation("org.jetbrains.kotlinx:kotlinx-benchmark-runtime:0.4.2") - } - } - - 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(project(":kmath-multik")) - implementation(projects.kmath.kmathTensorflow) - implementation("org.tensorflow:tensorflow-core-platform:0.4.0") - implementation("org.nd4j:nd4j-native:1.0.0-M1") - // uncomment if your system supports AVX2 - // val os = System.getProperty("os.name") - // - // if (System.getProperty("os.arch") in arrayOf("x86_64", "amd64")) when { - // os.startsWith("Windows") -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:windows-x86_64-avx2") - // os == "Linux" -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:linux-x86_64-avx2") - // os == "Mac OS X" -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:macosx-x86_64-avx2") - // } else - // implementation("org.nd4j:nd4j-native-platform:1.0.0-beta7") - } - } - } -} - -// Configure benchmark -benchmark { - // Setup configurations - targets { - register("jvm") - register("js") - } - - fun kotlinx.benchmark.gradle.BenchmarkConfiguration.commonConfiguration() { - warmups = 2 - iterations = 5 - iterationTime = 2000 - iterationTimeUnit = "ms" - } - - configurations.register("buffer") { - commonConfiguration() - include("BufferBenchmark") - } - - configurations.register("nd") { - commonConfiguration() - include("NDFieldBenchmark") - } - - configurations.register("dot") { - commonConfiguration() - include("DotBenchmark") - } - - configurations.register("expressions") { - // Some extra precision - warmups = 2 - iterations = 10 - iterationTime = 10 - iterationTimeUnit = "s" - outputTimeUnit = "s" - include("ExpressionsInterpretersBenchmark") - } - - configurations.register("matrixInverse") { - commonConfiguration() - include("MatrixInverseBenchmark") - } - - configurations.register("bigInt") { - commonConfiguration() - include("BigIntBenchmark") - } - - configurations.register("jafamaDouble") { - commonConfiguration() - include("JafamaBenchmark") - } - - configurations.register("tensorAlgebra") { - commonConfiguration() - include("TensorAlgebraBenchmark") - } - - configurations.register("viktor") { - commonConfiguration() - include("ViktorBenchmark") - } - - configurations.register("viktorLog") { - commonConfiguration() - include("ViktorLogBenchmark") - } -} - -// Fix kotlinx-benchmarks bug -afterEvaluate { - val jvmBenchmarkJar by tasks.getting(org.gradle.jvm.tasks.Jar::class) { - duplicatesStrategy = DuplicatesStrategy.EXCLUDE - } -} - -kotlin.sourceSets.all { - with(languageSettings) { - optIn("kotlin.contracts.ExperimentalContracts") - optIn("kotlin.ExperimentalUnsignedTypes") - optIn("space.kscience.kmath.misc.UnstableKMathAPI") - } -} - -tasks.withType { - kotlinOptions { - jvmTarget = "11" - freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xlambdas=indy" - } -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL -} - -addBenchmarkProperties() diff --git a/benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt b/benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt deleted file mode 100644 index 126a2e648..000000000 --- a/benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt +++ /dev/null @@ -1,105 +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. - */ - -package space.kscience.kmath.benchmarks - -import kotlinx.benchmark.Benchmark -import kotlinx.benchmark.Blackhole -import kotlinx.benchmark.Scope -import kotlinx.benchmark.State -import space.kscience.kmath.expressions.* -import space.kscience.kmath.operations.Algebra -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.bindSymbol -import space.kscience.kmath.operations.invoke -import kotlin.math.sin -import kotlin.random.Random -import space.kscience.kmath.estree.compileToExpression as estreeCompileToExpression -import space.kscience.kmath.wasm.compileToExpression as wasmCompileToExpression - -@State(Scope.Benchmark) -class ExpressionsInterpretersBenchmark { - /** - * Benchmark case for [Expression] created with [expressionInExtendedField]. - */ - @Benchmark - fun functionalExpression(blackhole: Blackhole) = invokeAndSum(functional, blackhole) - - /** - * Benchmark case for [Expression] created with [toExpression]. - */ - @Benchmark - fun mstExpression(blackhole: Blackhole) = invokeAndSum(mst, blackhole) - - /** - * Benchmark case for [Expression] created with [compileToExpression]. - */ - @Benchmark - fun wasmExpression(blackhole: Blackhole) = invokeAndSum(wasm, blackhole) - - /** - * Benchmark case for [Expression] created with [compileToExpression]. - */ - @Benchmark - fun estreeExpression(blackhole: Blackhole) = invokeAndSum(estree, blackhole) - - /** - * Benchmark case for [Expression] implemented manually with `kotlin.math` functions. - */ - @Benchmark - fun rawExpression(blackhole: Blackhole) = invokeAndSum(raw, blackhole) - - /** - * Benchmark case for direct computation w/o [Expression]. - */ - @Benchmark - fun justCalculate(blackhole: Blackhole) { - val random = Random(0) - var sum = 0.0 - - repeat(times) { - val x = random.nextDouble() - sum += x * 2.0 + 2.0 / x - 16.0 / sin(x) - } - - blackhole.consume(sum) - } - - private fun invokeAndSum(expr: Expression, blackhole: Blackhole) { - val random = Random(0) - var sum = 0.0 - val m = HashMap() - - repeat(times) { - m[x] = random.nextDouble() - sum += expr(m) - } - - blackhole.consume(sum) - } - - private companion object { - private val x by symbol - private const val times = 1_000_000 - - private val functional = DoubleField.expression { - val x = bindSymbol(Symbol.x) - x * number(2.0) + 2.0 / x - 16.0 / sin(x) - } - - private val node = MstExtendedField { - x * 2.0 + number(2.0) / x - number(16.0) / sin(x) - } - - private val mst = node.toExpression(DoubleField) - private val wasm = node.wasmCompileToExpression(DoubleField) - private val estree = node.estreeCompileToExpression(DoubleField) - - private val raw = Expression { args -> - val x = args[x]!! - x * 2.0 + 2.0 / x - 16.0 / sin(x) - } - } -} diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt deleted file mode 100644 index ff933997f..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt +++ /dev/null @@ -1,43 +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. - */ - -package space.kscience.kmath.benchmarks - -import kotlinx.benchmark.Benchmark -import kotlinx.benchmark.Blackhole -import kotlinx.benchmark.Scope -import kotlinx.benchmark.State -import java.nio.IntBuffer - -@State(Scope.Benchmark) -internal class ArrayBenchmark { - @Benchmark - fun benchmarkArrayRead(blackhole: Blackhole) { - var res = 0 - for (i in 1..size) res += array[size - i] - blackhole.consume(res) - } - - @Benchmark - fun benchmarkBufferRead(blackhole: Blackhole) { - var res = 0 - for (i in 1..size) res += arrayBuffer[size - i] - blackhole.consume(res) - } - - @Benchmark - fun nativeBufferRead(blackhole: Blackhole) { - var res = 0 - for (i in 1..size) res += nativeBuffer[size - i] - blackhole.consume(res) - } - - private companion object { - private const val size = 1000 - private val array = IntArray(size) { it } - private val arrayBuffer = IntBuffer.wrap(array) - private val nativeBuffer = IntBuffer.allocate(size).also { for (i in 0 until size) it.put(i, i) } - } -} diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt deleted file mode 100644 index 188a48ca7..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt +++ /dev/null @@ -1,112 +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. - */ - -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.misc.UnstableKMathAPI -import space.kscience.kmath.operations.BigIntField -import space.kscience.kmath.operations.JBigIntegerField -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.operations.parseBigInteger -import java.math.BigInteger - - -@UnstableKMathAPI -@State(Scope.Benchmark) -internal class BigIntBenchmark { - - val kmSmallNumber = BigIntField.number(100) - val jvmSmallNumber = JBigIntegerField.number(100) - val kmNumber = BigIntField.number(Int.MAX_VALUE) - val jvmNumber = JBigIntegerField.number(Int.MAX_VALUE) - val kmLargeNumber = BigIntField { number(11).pow(100_000U) } - val jvmLargeNumber: BigInteger = JBigIntegerField { number(11).pow(100_000) } - val bigExponent = 50_000 - - @Benchmark - fun kmSmallAdd(blackhole: Blackhole) = BigIntField { - blackhole.consume(kmSmallNumber + kmSmallNumber + kmSmallNumber) - } - - @Benchmark - fun jvmSmallAdd(blackhole: Blackhole) = JBigIntegerField { - blackhole.consume(jvmSmallNumber + jvmSmallNumber + jvmSmallNumber) - } - - @Benchmark - fun kmAdd(blackhole: Blackhole) = BigIntField { - blackhole.consume(kmNumber + kmNumber + kmNumber) - } - - @Benchmark - fun jvmAdd(blackhole: Blackhole) = JBigIntegerField { - blackhole.consume(jvmNumber + jvmNumber + jvmNumber) - } - - @Benchmark - fun kmAddLarge(blackhole: Blackhole) = BigIntField { - blackhole.consume(kmLargeNumber + kmLargeNumber + kmLargeNumber) - } - - @Benchmark - fun jvmAddLarge(blackhole: Blackhole) = JBigIntegerField { - blackhole.consume(jvmLargeNumber + jvmLargeNumber + jvmLargeNumber) - } - - @Benchmark - fun kmMultiply(blackhole: Blackhole) = BigIntField { - blackhole.consume(kmNumber * kmNumber * kmNumber) - } - - @Benchmark - fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField { - blackhole.consume(kmLargeNumber*kmLargeNumber) - } - - @Benchmark - fun jvmMultiply(blackhole: Blackhole) = JBigIntegerField { - blackhole.consume(jvmNumber * jvmNumber * jvmNumber) - } - - @Benchmark - fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField { - blackhole.consume(jvmLargeNumber*jvmLargeNumber) - } - - @Benchmark - fun kmPower(blackhole: Blackhole) = BigIntField { - blackhole.consume(kmNumber.pow(bigExponent.toUInt())) - } - - @Benchmark - fun jvmPower(blackhole: Blackhole) = JBigIntegerField { - blackhole.consume(jvmNumber.pow(bigExponent)) - } - - @Benchmark - fun kmParsing16(blackhole: Blackhole) = JBigIntegerField { - blackhole.consume("0x7f57ed8b89c29a3b9a85c7a5b84ca3929c7b7488593".parseBigInteger()) - } - - @Benchmark - fun kmParsing10(blackhole: Blackhole) = JBigIntegerField { - blackhole.consume("236656783929183747565738292847574838922010".parseBigInteger()) - } - - @Benchmark - fun jvmParsing10(blackhole: Blackhole) = JBigIntegerField { - blackhole.consume("236656783929183747565738292847574838922010".toBigInteger(10)) - } - - @Benchmark - fun jvmParsing16(blackhole: Blackhole) = JBigIntegerField { - blackhole.consume("7f57ed8b89c29a3b9a85c7a5b84ca3929c7b7488593".toBigInteger(16)) - } -} diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt deleted file mode 100644 index 39819d407..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt +++ /dev/null @@ -1,39 +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. - */ - -package space.kscience.kmath.benchmarks - -import kotlinx.benchmark.Benchmark -import kotlinx.benchmark.Scope -import kotlinx.benchmark.State -import space.kscience.kmath.complex.Complex -import space.kscience.kmath.complex.complex -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.MutableBuffer - -@State(Scope.Benchmark) -internal class BufferBenchmark { - @Benchmark - fun genericDoubleBufferReadWrite() { - val buffer = DoubleBuffer(size) { it.toDouble() } - - (0 until size).forEach { - buffer[it] - } - } - - @Benchmark - fun complexBufferReadWrite() { - val buffer = MutableBuffer.complex(size / 2) { Complex(it.toDouble(), -it.toDouble()) } - - (0 until size / 2).forEach { - buffer[it] - } - } - - private companion object { - private const val size = 100 - } -} diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt deleted file mode 100644 index 7d5ae310b..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt +++ /dev/null @@ -1,100 +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. - */ - -package space.kscience.kmath.benchmarks - -import kotlinx.benchmark.Benchmark -import kotlinx.benchmark.Blackhole -import kotlinx.benchmark.Scope -import kotlinx.benchmark.State -import space.kscience.kmath.commons.linear.CMLinearSpace -import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM -import space.kscience.kmath.linear.invoke -import space.kscience.kmath.linear.linearSpace -import space.kscience.kmath.multik.multikAlgebra -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.tensorflow.produceWithTF -import space.kscience.kmath.tensors.core.DoubleTensorAlgebra -import space.kscience.kmath.tensors.core.tensorAlgebra -import kotlin.random.Random - -@State(Scope.Benchmark) -internal class DotBenchmark { - companion object { - val random = Random(12224) - const val dim = 1000 - - //creating invertible matrix - val matrix1 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ -> - random.nextDouble() - } - val matrix2 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ -> - random.nextDouble() - } - - val cmMatrix1 = CMLinearSpace { matrix1.toCM() } - val cmMatrix2 = CMLinearSpace { matrix2.toCM() } - - val ejmlMatrix1 = EjmlLinearSpaceDDRM { matrix1.toEjml() } - val ejmlMatrix2 = EjmlLinearSpaceDDRM { matrix2.toEjml() } - } - - - @Benchmark - fun tfDot(blackhole: Blackhole) { - blackhole.consume( - DoubleField.produceWithTF { - matrix1 dot matrix1 - } - ) - } - - @Benchmark - fun cmDotWithConversion(blackhole: Blackhole) = CMLinearSpace { - blackhole.consume(matrix1 dot matrix2) - } - - @Benchmark - fun cmDot(blackhole: Blackhole) = CMLinearSpace { - blackhole.consume(cmMatrix1 dot cmMatrix2) - } - - @Benchmark - fun ejmlDot(blackhole: Blackhole) = EjmlLinearSpaceDDRM { - blackhole.consume(ejmlMatrix1 dot ejmlMatrix2) - } - - @Benchmark - fun ejmlDotWithConversion(blackhole: Blackhole) = EjmlLinearSpaceDDRM { - blackhole.consume(matrix1 dot matrix2) - } - - @Benchmark - fun tensorDot(blackhole: Blackhole) = with(DoubleField.tensorAlgebra) { - blackhole.consume(matrix1 dot matrix2) - } - - @Benchmark - fun multikDot(blackhole: Blackhole) = with(DoubleField.multikAlgebra) { - blackhole.consume(matrix1 dot matrix2) - } - - @Benchmark - fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace(Buffer.Companion::auto)) { - blackhole.consume(matrix1 dot matrix2) - } - - @Benchmark - fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) { - blackhole.consume(matrix1 dot matrix2) - } - - @Benchmark - fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke { - blackhole.consume(matrix1 dot matrix2) - } -} diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt deleted file mode 100644 index db3524e67..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt +++ /dev/null @@ -1,124 +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. - */ - -package space.kscience.kmath.benchmarks - -import kotlinx.benchmark.Benchmark -import kotlinx.benchmark.Blackhole -import kotlinx.benchmark.Scope -import kotlinx.benchmark.State -import space.kscience.kmath.asm.compileToExpression -import space.kscience.kmath.expressions.* -import space.kscience.kmath.operations.Algebra -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.bindSymbol -import space.kscience.kmath.operations.invoke -import kotlin.math.sin -import kotlin.random.Random - -@State(Scope.Benchmark) -internal class ExpressionsInterpretersBenchmark { - /** - * Benchmark case for [Expression] created with [expressionInExtendedField]. - */ - @Benchmark - fun functionalExpression(blackhole: Blackhole) = invokeAndSum(functional, blackhole) - - /** - * Benchmark case for [Expression] created with [toExpression]. - */ - @Benchmark - fun mstExpression(blackhole: Blackhole) = invokeAndSum(mst, blackhole) - - /** - * Benchmark case for [Expression] created with [compileToExpression]. - */ - @Benchmark - fun asmGenericExpression(blackhole: Blackhole) = invokeAndSum(asmGeneric, blackhole) - - /** - * Benchmark case for [Expression] created with [compileToExpression]. - */ - @Benchmark - fun asmPrimitiveExpressionArray(blackhole: Blackhole) { - val random = Random(0) - var sum = 0.0 - val m = DoubleArray(1) - - repeat(times) { - m[xIdx] = random.nextDouble() - sum += asmPrimitive(m) - } - - blackhole.consume(sum) - } - - /** - * Benchmark case for [Expression] created with [compileToExpression]. - */ - @Benchmark - fun asmPrimitiveExpression(blackhole: Blackhole) = invokeAndSum(asmPrimitive, blackhole) - - /** - * Benchmark case for [Expression] implemented manually with `kotlin.math` functions. - */ - @Benchmark - fun rawExpression(blackhole: Blackhole) = invokeAndSum(raw, blackhole) - - /** - * Benchmark case for direct computation w/o [Expression]. - */ - @Benchmark - fun justCalculate(blackhole: Blackhole) { - val random = Random(0) - var sum = 0.0 - - repeat(times) { - val x = random.nextDouble() - sum += x * 2.0 + 2.0 / x - 16.0 / sin(x) - } - - blackhole.consume(sum) - } - - private fun invokeAndSum(expr: Expression, blackhole: Blackhole) { - val random = Random(0) - var sum = 0.0 - val m = HashMap() - - repeat(times) { - m[x] = random.nextDouble() - sum += expr(m) - } - - blackhole.consume(sum) - } - - private companion object { - private val x by symbol - private const val times = 1_000_000 - - private val functional = DoubleField.expression { - val x = bindSymbol(Symbol.x) - x * number(2.0) + 2.0 / x - 16.0 / sin(x) - } - - private val node = MstExtendedField { - x * 2.0 + number(2.0) / x - number(16.0) / sin(x) - } - - private val mst = node.toExpression(DoubleField) - - private val asmPrimitive = node.compileToExpression(DoubleField) - private val xIdx = asmPrimitive.indexer.indexOf(x) - - private val asmGeneric = node.compileToExpression(DoubleField as Algebra) - - private val raw = Expression { args -> - val x = args[x]!! - x * 2.0 + 2.0 / x - 16.0 / sin(x) - } - } -} diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/JafamaBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/JafamaBenchmark.kt deleted file mode 100644 index 5d4eee7c0..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/JafamaBenchmark.kt +++ /dev/null @@ -1,42 +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. - */ - -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())) } -} diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt deleted file mode 100644 index 4ff687aac..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt +++ /dev/null @@ -1,53 +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. - */ - -package space.kscience.kmath.benchmarks - -import kotlinx.benchmark.Benchmark -import kotlinx.benchmark.Blackhole -import kotlinx.benchmark.Scope -import kotlinx.benchmark.State -import space.kscience.kmath.commons.linear.CMLinearSpace -import space.kscience.kmath.commons.linear.lupSolver -import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM -import space.kscience.kmath.linear.invoke -import space.kscience.kmath.linear.linearSpace -import space.kscience.kmath.linear.lupSolver -import space.kscience.kmath.operations.algebra -import kotlin.random.Random - -@State(Scope.Benchmark) -internal class MatrixInverseBenchmark { - private companion object { - private val random = Random(1224) - private const val dim = 100 - - private val space = Double.algebra.linearSpace - - //creating invertible matrix - private val u = space.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 } - private val l = space.buildMatrix(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 } - private val matrix = space { l dot u } - } - - @Benchmark - fun kmathLupInversion(blackhole: Blackhole) { - blackhole.consume(Double.algebra.linearSpace.lupSolver().inverse(matrix)) - } - - @Benchmark - fun cmLUPInversion(blackhole: Blackhole) { - CMLinearSpace { - blackhole.consume(lupSolver().inverse(matrix)) - } - } - - @Benchmark - fun ejmlInverse(blackhole: Blackhole) { - EjmlLinearSpaceDDRM { - blackhole.consume(matrix.toEjml().inverse()) - } - } -} diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt deleted file mode 100644 index e3b3dde05..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt +++ /dev/null @@ -1,105 +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. - */ - -package space.kscience.kmath.benchmarks - -import kotlinx.benchmark.Benchmark -import kotlinx.benchmark.Blackhole -import kotlinx.benchmark.Scope -import kotlinx.benchmark.State -import org.jetbrains.kotlinx.multik.api.Multik -import org.jetbrains.kotlinx.multik.api.ones -import org.jetbrains.kotlinx.multik.ndarray.data.DN -import org.jetbrains.kotlinx.multik.ndarray.data.DataType -import space.kscience.kmath.multik.multikAlgebra -import space.kscience.kmath.nd.BufferedFieldOpsND -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.nd.ndAlgebra -import space.kscience.kmath.nd.one -import space.kscience.kmath.nd4j.nd4j -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.tensors.core.DoubleTensor -import space.kscience.kmath.tensors.core.one -import space.kscience.kmath.tensors.core.tensorAlgebra -import space.kscience.kmath.viktor.viktorAlgebra - -@State(Scope.Benchmark) -internal class NDFieldBenchmark { - @Benchmark - fun autoFieldAdd(blackhole: Blackhole) = with(autoField) { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - blackhole.consume(res) - } - - @Benchmark - fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - blackhole.consume(res) - } - - @Benchmark - fun boxingFieldAdd(blackhole: Blackhole) = with(genericField) { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - blackhole.consume(res) - } - - @Benchmark - fun multikAdd(blackhole: Blackhole) = with(multikField) { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - blackhole.consume(res) - } - - @Benchmark - fun viktorAdd(blackhole: Blackhole) = with(viktorField) { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - blackhole.consume(res) - } - - @Benchmark - fun tensorAdd(blackhole: Blackhole) = with(Double.tensorAlgebra) { - var res: DoubleTensor = one(shape) - repeat(n) { res = res + 1.0 } - blackhole.consume(res) - } - - @Benchmark - fun tensorInPlaceAdd(blackhole: Blackhole) = with(Double.tensorAlgebra) { - val res: DoubleTensor = one(shape) - repeat(n) { res += 1.0 } - blackhole.consume(res) - } - - @Benchmark - fun multikInPlaceAdd(blackhole: Blackhole) = with(DoubleField.multikAlgebra) { - val res = Multik.ones(shape, DataType.DoubleDataType).wrap() - repeat(n) { res += 1.0 } - blackhole.consume(res) - } - -// @Benchmark -// fun nd4jAdd(blackhole: Blackhole) = with(nd4jField) { -// var res: StructureND = 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 = intArrayOf(dim, dim) - private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto) - private val specializedField = DoubleField.ndAlgebra - private val genericField = BufferedFieldOpsND(DoubleField, Buffer.Companion::boxing) - private val nd4jField = DoubleField.nd4j - private val multikField = DoubleField.multikAlgebra - private val viktorField = DoubleField.viktorAlgebra - } -} diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/TensorAlgebraBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/TensorAlgebraBenchmark.kt deleted file mode 100644 index 38e064e53..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/TensorAlgebraBenchmark.kt +++ /dev/null @@ -1,37 +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. - */ - -package space.kscience.kmath.benchmarks - -import kotlinx.benchmark.Benchmark -import kotlinx.benchmark.Blackhole -import kotlinx.benchmark.Scope -import kotlinx.benchmark.State -import space.kscience.kmath.linear.linearSpace -import space.kscience.kmath.linear.matrix -import space.kscience.kmath.linear.symmetric -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.tensors.core.tensorAlgebra -import kotlin.random.Random - -@State(Scope.Benchmark) -internal class TensorAlgebraBenchmark { - companion object { - private val random = Random(12224) - private const val dim = 30 - - private val matrix = DoubleField.linearSpace.matrix(dim, dim).symmetric { _, _ -> random.nextDouble() } - } - - @Benchmark - fun tensorSymEigSvd(blackhole: Blackhole) = with(Double.tensorAlgebra) { - blackhole.consume(matrix.symEigSvd(1e-10)) - } - - @Benchmark - fun tensorSymEigJacobi(blackhole: Blackhole) = with(Double.tensorAlgebra) { - blackhole.consume(matrix.symEigJacobi(50, 1e-10)) - } -} \ No newline at end of file diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt deleted file mode 100644 index de301678c..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt +++ /dev/null @@ -1,65 +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. - */ - -package space.kscience.kmath.benchmarks - -import kotlinx.benchmark.Benchmark -import kotlinx.benchmark.Blackhole -import kotlinx.benchmark.Scope -import kotlinx.benchmark.State -import org.jetbrains.bio.viktor.F64Array -import space.kscience.kmath.nd.* -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.viktor.ViktorFieldND - -@State(Scope.Benchmark) -internal class ViktorBenchmark { - @Benchmark - fun automaticFieldAddition(blackhole: Blackhole) { - with(autoField) { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - blackhole.consume(res) - } - } - - @Benchmark - fun realFieldAddition(blackhole: Blackhole) { - with(realField) { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - blackhole.consume(res) - } - } - - @Benchmark - fun viktorFieldAddition(blackhole: Blackhole) { - with(viktorField) { - var res = one(shape) - repeat(n) { res += 1.0 } - blackhole.consume(res) - } - } - - @Benchmark - fun rawViktor(blackhole: Blackhole) { - val one = F64Array.full(init = 1.0, shape = intArrayOf(dim, dim)) - var res = one - repeat(n) { res = res + one } - blackhole.consume(res) - } - - private companion object { - private const val dim = 1000 - private const val n = 100 - private val shape = Shape(dim, dim) - - // automatically build context most suited for given type. - private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto) - private val realField = DoubleField.ndAlgebra - private val viktorField = ViktorFieldND(dim, dim) - } -} diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt deleted file mode 100644 index dfdd89d74..000000000 --- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt +++ /dev/null @@ -1,61 +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. - */ - -package space.kscience.kmath.benchmarks - -import kotlinx.benchmark.Benchmark -import kotlinx.benchmark.Blackhole -import kotlinx.benchmark.Scope -import kotlinx.benchmark.State -import org.jetbrains.bio.viktor.F64Array -import space.kscience.kmath.nd.BufferedFieldOpsND -import space.kscience.kmath.nd.Shape -import space.kscience.kmath.nd.ndAlgebra -import space.kscience.kmath.nd.one -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.viktor.ViktorFieldND - -@State(Scope.Benchmark) -internal class ViktorLogBenchmark { - @Benchmark - fun realFieldLog(blackhole: Blackhole) { - with(realField) { - val fortyTwo = structureND(shape) { 42.0 } - var res = one(shape) - repeat(n) { res = ln(fortyTwo) } - blackhole.consume(res) - } - } - - @Benchmark - fun viktorFieldLog(blackhole: Blackhole) { - with(viktorField) { - val fortyTwo = structureND(shape) { 42.0 } - var res = one - repeat(n) { res = ln(fortyTwo) } - blackhole.consume(res) - } - } - - @Benchmark - fun rawViktorLog(blackhole: Blackhole) { - val fortyTwo = F64Array.full(dim, dim, init = 42.0) - lateinit var res: F64Array - repeat(n) { res = fortyTwo.log() } - blackhole.consume(res) - } - - private companion object { - private const val dim = 1000 - private const val n = 100 - private val shape = Shape(dim, dim) - - // automatically build context most suited for given type. - private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto) - private val realField = DoubleField.ndAlgebra - private val viktorField = ViktorFieldND(dim, dim) - } -} diff --git a/docs/algebra.md b/docs/algebra.md deleted file mode 100644 index 20158a125..000000000 --- a/docs/algebra.md +++ /dev/null @@ -1,86 +0,0 @@ -# Algebraic Structures and Algebraic Elements - -The mathematical operations in KMath are generally separated from mathematical objects. This means that to perform an -operation, say `+`, one needs two objects of a type `T` and an algebra context, which draws appropriate operation up, -say `Group`. Next one needs to run the actual operation in the context: - -```kotlin -import space.kscience.kmath.operations.* - -val a: T = ... -val b: T = ... -val group: Group = ... - -val c = group { a + b } -``` - -At first glance, this distinction seems to be a needless complication, but in fact one needs to remember that in -mathematics, one could draw up different operations on same objects. For example, one could use different types of -geometry for vectors. - -## Algebraic Structures - -Primary mathematical contexts have the following hierarchy: - -`Field <: Ring <: Group <: Algebra` - -These interfaces follow real algebraic structures: - -- [Group](https://mathworld.wolfram.com/Group.html) defines addition, its identity element (i.e., 0) and additive - inverse (-x); -- [Ring](http://mathworld.wolfram.com/Ring.html) adds multiplication and its identity element (i.e., 1); -- [Field](http://mathworld.wolfram.com/Field.html) adds division operation. - -A typical implementation of `Field` is the `DoubleField` which works on doubles, and `VectorSpace` for `Space`. - -In some cases algebra context can hold additional operations like `exp` or `sin`, and then it inherits appropriate -interface. Also, contexts may have operations, which produce elements outside the context. For example, `Matrix.dot` -operation produces a matrix with new dimensions, which can be incompatible with initial matrix in linear operations. - -## Spaces and Fields - -KMath introduces contexts for builtin algebraic structures: - -```kotlin -import space.kscience.kmath.operations.* - -val c1 = Complex(1.0, 2.0) -val c2 = ComplexField.i - -val c3 = c1 + c2 -// or -val c3 = ComplexField { c1 + c2 } -``` - -Also, `ComplexField` features special operations to mix complex and real numbers, for example: - -```kotlin -import space.kscience.kmath.operations.* - -val c1 = Complex(1.0, 2.0) -val c2 = ComplexField { c1 - 1.0 } // Returns: Complex(re=0.0, im=2.0) -val c3 = ComplexField { c1 - i * 2.0 } -``` - -**Note**: In theory it is possible to add behaviors directly to the context, but as for now Kotlin does not support -that. Watch [KT-10468](https://youtrack.jetbrains.com/issue/KT-10468) and -[KEEP-176](https://github.com/Kotlin/KEEP/pull/176) for updates. - -## Nested fields - -Contexts allow one to build more complex structures. For example, it is possible to create a `Matrix` from complex -elements like so: - -```kotlin -val element = NDElement.complex(shape = intArrayOf(2, 2)) { index: IntArray -> - Complex(index[0].toDouble() - index[1].toDouble(), index[0].toDouble() + index[1].toDouble()) -} -``` - -The `element` in this example is a member of the `Field` of 2D structures, each element of which is a member of its own -`ComplexField`. It is important one does not need to create a special n-d class to hold complex numbers and implement -operations on it, one just needs to provide a field for its elements. - -**Note**: Fields themselves do not solve the problem of JVM boxing, but it is possible to solve with special contexts -like -`MemorySpec`. diff --git a/docs/buffers.md b/docs/buffers.md deleted file mode 100644 index e7573497e..000000000 --- a/docs/buffers.md +++ /dev/null @@ -1,20 +0,0 @@ -# Buffers - -Buffer is one of main building blocks of kmath. It is a basic interface allowing random-access read and write ( -with `MutableBuffer`). There are different types of buffers: - -* Primitive buffers wrapping like `DoubleBuffer` which are wrapping primitive arrays. -* Boxing `ListBuffer` wrapping a list -* Functionally defined `VirtualBuffer` which does not hold a state itself, but provides a function to calculate value -* `MemoryBuffer` allows direct allocation of objects in continuous memory block. - -Some kmath features require a `BufferFactory` class to operate properly. A general convention is to use functions -defined in -`Buffer` and `MutableBuffer` companion classes. For example factory `Buffer.Companion::auto` in most cases creates the -most suitable buffer for given reified type (for types with custom memory buffer it still better to use their -own `MemoryBuffer.create()` factory). - -## Buffer performance - -One should avoid using default boxing buffer wherever it is possible. Try to use primitive buffers or memory buffers -instead . diff --git a/docs/codestyle.md b/docs/codestyle.md deleted file mode 100644 index 73ba5f754..000000000 --- a/docs/codestyle.md +++ /dev/null @@ -1,27 +0,0 @@ -# Coding Conventions - -Generally, KMath code follows general [Kotlin coding conventions](https://kotlinlang.org/docs/reference/coding-conventions.html), but with a number of small changes and clarifications. - -## Utility Class Naming - -Filename should coincide with a name of one of the classes contained in the file or start with small letter and describe its contents. - -The code convention [here](https://kotlinlang.org/docs/reference/coding-conventions.html#source-file-names) says that file names should start with a capital letter even if file does not contain classes. Yet starting utility classes and aggregators with a small letter seems to be a good way to visually separate those files. - -This convention could be changed in future in a non-breaking way. - -## Private Variable Naming - -Private variables' names may start with underscore `_` for of the private mutable variable is shadowed by the public read-only value with the same meaning. - -This rule does not permit underscores in names, but it is sometimes useful to "underscore" the fact that public and private versions draw up the same entity. It is allowed only for private variables. - -This convention could be changed in future in a non-breaking way. - -## Functions and Properties One-liners - -Use one-liners when they occupy single code window line both for functions and properties with getters like -`val b: String get() = "fff"`. The same should be performed with multiline expressions when they could be -cleanly separated. - -There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook one-lines seem to better show that the property or function is easily calculated. diff --git a/docs/contexts.md b/docs/contexts.md deleted file mode 100644 index c26333860..000000000 --- a/docs/contexts.md +++ /dev/null @@ -1,73 +0,0 @@ -# Context-oriented mathematics - -## The problem - -A known problem for implementing mathematics in statically-typed languages (but not only in them) is that different sets -of mathematical operators can be defined on the same mathematical objects. Sometimes there is no single way to treat -some operations, including basic arithmetic operations, on a Java/Kotlin `Number`. Sometimes there are different ways to -define the same structure, such as Euclidean and elliptic geometry vector spaces over real vectors. Another problem -arises when one wants to add some kind of behavior to an existing entity. In dynamic languages those problems are -usually solved by adding dynamic context-specific behaviors at runtime, but this solution has a lot of drawbacks. - -## Context-oriented approach - -One possible solution to these problems is to divorce numerical representations from behaviors. For example in Kotlin -one can define a separate class representing some entity without any operations, ex. a complex number: - -```kotlin -data class Complex(val re: Double, val im: Double) -``` - -And then to define a separate class or singleton, representing an operation on those complex numbers: - -```kotlin -object ComplexOperations { - operator fun Complex.plus(other: Complex) = Complex(re + other.re, im + other.im) - operator fun Complex.minus(other: Complex) = Complex(re - other.re, im - other.im) -} -``` - -In Java, applying such external operations could be cumbersome, but Kotlin has a unique feature that allows us -implement this -naturally: [extensions with receivers](https://kotlinlang.org/docs/reference/extensions.html#extension-functions). In -Kotlin, an operation on complex number could be implemented as: - -```kotlin -with(ComplexOperations) { c1 + c2 - c3 } -``` - -Kotlin also allows the creation of functions with receivers: - -```kotlin -fun ComplexOperations.doSomethingWithComplex(c1: Complex, c2: Complex, c3: Complex) = c1 + c2 - c3 - -ComplexOperations.doComethingWithComplex(c1, c2, c3) -``` - -In fact, whole parts of a program may be run within a mathematical context or even multiple nested contexts. - -In KMath, contexts are not only responsible for operations, but also for raw object creation and advanced features. - -## Other possibilities - -### Type classes - -An obvious candidate to get more or less the same functionality is the type class, which allows one to bind a behavior -to a specific type without modifying the type itself. On the plus side, type classes do not require explicit context -declaration, so the code looks cleaner. On the minus side, if there are different sets of behaviors for the same types, -it is impossible to combine them into one module. Also, unlike type classes, context can have parameters or even state. -For example in KMath, sizes and strides for `NDElement` or `Matrix` could be moved to context to optimize performance in -case of a large amount of structures. - -### Wildcard imports and importing-on-demand - -Sometimes, one may wish to use a single context throughout a file. In this case, is possible to import all members from -a package or file, via `import context.complex.*`. Effectively, this is the same as enclosing an entire file with a -single context. However, when using multiple contexts, this technique can introduce operator ambiguity, due to namespace -pollution. If there are multiple scoped contexts that define the same operation, it is still possible to import -specific operations as needed, without using an explicit context with extension functions, for example: - -``` -import context.complex.op1 -import context.quaternion.op2 -``` diff --git a/docs/diagrams/core.puml b/docs/diagrams/core.puml deleted file mode 100644 index 87f8f2e2d..000000000 --- a/docs/diagrams/core.puml +++ /dev/null @@ -1,1020 +0,0 @@ -@startuml -interface "ColumnarData" { - size: Int -} -interface "XYColumnarData" { - x: Buffer - y: Buffer -} -interface "XYErrorColumnarData" { - yErr: Buffer -} -interface "XYZColumnarData" { - z: Buffer -} -interface "Domain" { - dimension: Int -} -interface "DoubleDomain" { - -} -class "HyperSquareDomain" { - lower: Buffer - upper: Buffer -} -class "UnconstrainedDomain" { - dimension: Int -} -class "UnivariateDomain" { - range: ClosedFloatingPointRange -} -interface "DifferentiableExpression" { - -} -interface "SpecialDifferentiableExpression" { - -} -abstract "FirstDerivativeExpression" { - -} -interface "AutoDiffProcessor" { - -} -interface "Expression" { - -} -interface "ExpressionAlgebra" { - -} -abstract "FunctionalExpressionAlgebra" { - algebra: A -} -class "FunctionalExpressionGroup" { - algebra: A -} -class "FunctionalExpressionRing" { - algebra: A -} -class "FunctionalExpressionField" { - algebra: A -} -class "FunctionalExpressionExtendedField" { - algebra: A -} -interface "MST" { - -} -class "Numeric" { - value: Number -} -class "Unary" { - operation: String - value: MST -} -class "Binary" { - operation: String - left: MST - right: MST -} -class "InnerAlgebra" { - algebra: Algebra - arguments: Map -} -class "MstNumericAlgebra" { - number() - bindSymbolOrNull() - bindSymbol() - unaryOperationFunction() - binaryOperationFunction() -} -class "MstGroup" { - zero: MST.Numericnumber() - bindSymbolOrNull() - add() - unaryPlus() - unaryMinus() - minus() - scale() - binaryOperationFunction() - unaryOperationFunction() -} -class "MstRing" { - zero: MST.Numeric - one: MST.Numericnumber() - bindSymbolOrNull() - add() - scale() - multiply() - unaryPlus() - unaryMinus() - minus() - binaryOperationFunction() - unaryOperationFunction() -} -class "MstField" { - zero: MST.Numeric - one: MST.NumericbindSymbolOrNull() - number() - add() - scale() - multiply() - divide() - unaryPlus() - unaryMinus() - minus() - binaryOperationFunction() - unaryOperationFunction() -} -class "MstExtendedField" { - zero: MST.Numeric - one: MST.NumericbindSymbolOrNull() - number() - sin() - cos() - tan() - asin() - acos() - atan() - sinh() - cosh() - tanh() - asinh() - acosh() - atanh() - add() - sqrt() - scale() - multiply() - divide() - unaryPlus() - unaryMinus() - minus() - power() - exp() - ln() - binaryOperationFunction() - unaryOperationFunction() -} -class "MstLogicAlgebra" { - bindSymbolOrNull() - const() - not() - and() - or() - xor() -} -class "AutoDiffValue" { - value: T -} -class "DerivationResult" { - value: T - derivativeValues: Map - context: Field -} -class "SimpleAutoDiffField" { - context: F - bindings: Map -} -class "AutoDiffVariableWithDerivative" { - identity: String - value: T - d: T -} -class "SimpleAutoDiffExpression" { - field: F - function: SimpleAutoDiffField -} -class "SimpleAutoDiffExtendedField" { - context: F - bindings: Map -} -interface "Symbol" { - identity: String -} -class "StringSymbol" { - identity: String -} -interface "SymbolIndexer" { - symbols: List -} -class "SimpleSymbolIndexer" { - symbols: List -} -class "BufferedLinearSpace" { - elementAlgebra: A - bufferFactory: BufferFactory -} -interface "LinearSolver" { - -} -interface "LinearSpace" { - elementAlgebra: A -} -class "LupDecomposition" { - context: LinearSpace - elementContext: Field - lu: Matrix - pivot: IntArray - even: Boolean -} -class "MatrixBuilder" { - linearSpace: LinearSpace - rows: Int - columns: Int -} -class "SymmetricMatrixFeature" { - -} -interface "MatrixFeature" { - -} -interface "DiagonalFeature" { - -} -class "ZeroFeature" { - -} -class "UnitFeature" { - -} -interface "InverseMatrixFeature" { - inverse: Matrix -} -interface "DeterminantFeature" { - determinant: T -} -class "LFeature" { - -} -class "UFeature" { - -} -interface "LUDecompositionFeature" { - l: Matrix - u: Matrix -} -interface "LupDecompositionFeature" { - l: Matrix - u: Matrix - p: Matrix -} -class "OrthogonalFeature" { - -} -interface "QRDecompositionFeature" { - q: Matrix - r: Matrix -} -interface "CholeskyDecompositionFeature" { - l: Matrix -} -interface "SingularValueDecompositionFeature" { - u: Matrix - s: Matrix - v: Matrix - singularValues: Point -} -class "MatrixWrapper" { - origin: Matrix - features: FeatureSet -} -class "TransposedFeature" { - original: Matrix -} -class "VirtualMatrix" { - rowNum: Int - colNum: Int - generator: (i:Int,j:Int)->T -} -class "UnstableKMathAPI" { - -} -class "PerformancePitfall" { - message: String -} -interface "Featured" { - -} -interface "Feature" { - key: FeatureKey -} -class "FeatureSet" { - features: Map -} -interface "Loggable" { - -} -class "ShapeMismatchException" { - expected: IntArray - actual: IntArray -} -interface "AlgebraND" { - shape: IntArray - elementContext: C -} -interface "GroupND" { - -} -interface "RingND" { - -} -interface "FieldND" { - -} -interface "BufferAlgebraND" { - strides: Strides - bufferFactory: BufferFactory - buffer: Buffer -} -class "BufferedGroupND" { - shape: IntArray - elementContext: A - bufferFactory: BufferFactory -} -class "BufferedRingND" { - shape: IntArray - elementContext: R - bufferFactory: BufferFactory -} -class "BufferedFieldND" { - shape: IntArray - elementContext: R - bufferFactory: BufferFactory -} -class "BufferND" { - strides: Strides - buffer: Buffer -} -class "MutableBufferND" { - strides: Strides - mutableBuffer: MutableBuffer -} -class "DoubleFieldND" { - shape: IntArray -} -class "ShortRingND" { - shape: IntArray -} -interface "Structure1D" { - dimension: Int -} -interface "MutableStructure1D" { - -} -class "Structure1DWrapper" { - structure: StructureND -} -class "MutableStructure1DWrapper" { - structure: MutableStructureND -} -class "Buffer1DWrapper" { - buffer: Buffer -} -class "MutableBuffer1DWrapper" { - buffer: MutableBuffer -} -interface "Structure2D" { - rowNum: Int - colNum: Int - shape: IntArray - rows: List - columns: List -} -interface "MutableStructure2D" { - rows: List - columns: List -} -class "Structure2DWrapper" { - structure: StructureND -} -class "MutableStructure2DWrapper" { - structure: MutableStructureND -} -interface "StructureFeature" { - -} -interface "StructureND" { - shape: IntArray - dimension: Int -} -interface "MutableStructureND" { - -} -interface "Strides" { - shape: IntArray - strides: IntArray - linearSize: Int -} -class "DefaultStrides" { - shape: IntArray -} -class "KMathContext" { - -} -interface "Algebra" { - -} -interface "GroupOperations" { - -} -interface "Group" { - zero: T -} -interface "RingOperations" { - -} -interface "Ring" { - one: T -} -interface "FieldOperations" { - -} -interface "Field" { - -} -interface "AlgebraElement" { - context: C -} -interface "GroupElement" { - -} -interface "RingElement" { - -} -interface "FieldElement" { - -} -class "BigIntField" { - zero: BigInt - one: BigIntnumber() - unaryMinus() - add() - scale() - multiply() - divide() - unaryPlus() - unaryMinus() -} -class "BigInt" { - sign: Byte - magnitude: Magnitude -} -interface "BufferAlgebra" { - bufferFactory: BufferFactory - elementAlgebra: A -} -class "BufferField" { - bufferFactory: BufferFactory - elementAlgebra: A - size: Int -} -interface "LogicAlgebra" { - -} -class "BooleanAlgebra" { - const() - not() - and() - or() - xor() -} -interface "ExtendedFieldOperations" { - -} -interface "ExtendedField" { - -} -class "DoubleField" { - zero: Double - one: Doublenumber() - binaryOperationFunction() - add() - multiply() - divide() - scale() - sin() - cos() - tan() - acos() - asin() - atan() - sinh() - cosh() - tanh() - asinh() - acosh() - atanh() - sqrt() - power() - exp() - ln() - norm() - unaryMinus() - plus() - minus() - times() - div() -} -class "FloatField" { - zero: Float - one: Floatnumber() - binaryOperationFunction() - add() - scale() - multiply() - divide() - sin() - cos() - tan() - acos() - asin() - atan() - sinh() - cosh() - tanh() - asinh() - acosh() - atanh() - sqrt() - power() - exp() - ln() - norm() - unaryMinus() - plus() - minus() - times() - div() -} -class "IntRing" { - zero: Int - one: Intnumber() - add() - multiply() - norm() - unaryMinus() - plus() - minus() - times() -} -class "ShortRing" { - zero: Short - one: Shortnumber() - add() - multiply() - norm() - unaryMinus() - plus() - minus() - times() -} -class "ByteRing" { - zero: Byte - one: Bytenumber() - add() - multiply() - norm() - unaryMinus() - plus() - minus() - times() -} -class "LongRing" { - zero: Long - one: Longnumber() - add() - multiply() - norm() - unaryMinus() - plus() - minus() - times() -} -interface "NumericAlgebra" { - -} -interface "ScaleOperations" { - -} -interface "NumbersAddOperations" { - -} -interface "TrigonometricOperations" { - -} -interface "PowerOperations" { - -} -interface "ExponentialOperations" { - -} -interface "Norm" { - -} -interface "Buffer" { - size: Int -} -interface "MutableBuffer" { - -} -class "ListBuffer" { - list: List -} -class "MutableListBuffer" { - list: MutableList -} -class "ArrayBuffer" { - array: Array -} -class "ReadOnlyBuffer" { - buffer: MutableBuffer -} -class "VirtualBuffer" { - size: Int - generator: (Int)->T -} -class "BufferAccessor2D" { - rowNum: Int - colNum: Int - factory: MutableBufferFactory -} -class "Row" { - buffer: MutableBuffer - rowIndex: Int -} -class "DoubleBuffer" { - array: DoubleArray -} -class "DoubleBufferFieldOperations" { - unaryMinus() - add() - multiply() - divide() - sin() - cos() - tan() - asin() - acos() - atan() - sinh() - cosh() - tanh() - asinh() - acosh() - atanh() - power() - exp() - ln() -} -class "DoubleL2Norm" { - norm() -} -class "DoubleBufferField" { - size: Int -} -enum "ValueFlag" { - NAN - MISSING - NEGATIVE_INFINITY - POSITIVE_INFINITY -} -interface "FlaggedBuffer" { - -} -class "FlaggedDoubleBuffer" { - values: DoubleArray - flags: ByteArray -} -class "FloatBuffer" { - array: FloatArray -} -class "IntBuffer" { - array: IntArray -} -class "LongBuffer" { - array: LongArray -} -class "MemoryBuffer" { - memory: Memory - spec: MemorySpec -} -class "MutableMemoryBuffer" { - memory: Memory - spec: MemorySpec -} -class "ShortBuffer" { - array: ShortArray -} -class "ExpressionFieldTest" { - x -} -class "InterpretTest" { - -} -class "SimpleAutoDiffTest" { - x - y - z -} -class "DoubleLUSolverTest" { - -} -class "MatrixTest" { - -} -class "CumulativeKtTest" { - -} -class "BigIntAlgebraTest" { - -} -class "BigIntConstructorTest" { - -} -class "BigIntConversionsTest" { - -} -class "BigIntOperationsTest" { - -} -class "DoubleFieldTest" { - -} -class "NDFieldTest" { - -} -class "NumberNDFieldTest" { - algebra - array1 - array2 -} -class "L2Norm" { - norm() -} -interface "AlgebraicVerifier" { - algebra: A -} -class "FieldVerifier" { - algebra: A - a: T - b: T - c: T - x: Number -} -class "RingVerifier" { - algebra: A - a: T - b: T - c: T - x: Number -} -class "SpaceVerifier" { - algebra: S - a: T - b: T - c: T - x: Number -} -class "JBigIntegerField" { - zero: BigInteger - one: BigIntegernumber() - add() - minus() - multiply() - unaryMinus() -} -abstract "JBigDecimalFieldBase" { - mathContext: MathContext -} -class "JBigDecimalField" { - mathContext: MathContext -} -"ColumnarData" <|--- XYColumnarData -"XYColumnarData" <|--- XYErrorColumnarData -"XYColumnarData" <|--- XYZColumnarData -"Domain" <|--- DoubleDomain -"DoubleDomain" <|--- HyperSquareDomain -"DoubleDomain" <|--- UnconstrainedDomain -"DoubleDomain" <|--- UnivariateDomain -"Expression" <|--- DifferentiableExpression -"DifferentiableExpression" <|--- SpecialDifferentiableExpression -"DifferentiableExpression" <|--- FirstDerivativeExpression -"Algebra" <|--- ExpressionAlgebra -"ExpressionAlgebra" <|--- FunctionalExpressionAlgebra -"FunctionalExpressionAlgebra" <|--- FunctionalExpressionGroup -"Group" <|--- FunctionalExpressionGroup -"FunctionalExpressionGroup" <|--- FunctionalExpressionRing -"Ring" <|--- FunctionalExpressionRing -"FunctionalExpressionRing" <|--- FunctionalExpressionField -"Field" <|--- FunctionalExpressionField -"ScaleOperations" <|--- FunctionalExpressionField -"FunctionalExpressionField" <|--- FunctionalExpressionExtendedField -"ExtendedField" <|--- FunctionalExpressionExtendedField -"MST" <|--- Numeric -"MST" <|--- Unary -"MST" <|--- Binary -"NumericAlgebra" <|--- InnerAlgebra -"NumericAlgebra" <|--- MstNumericAlgebra -"Group" <|--- MstGroup -"NumericAlgebra" <|--- MstGroup -"ScaleOperations" <|--- MstGroup -"Ring" <|--- MstRing -"NumbersAddOperations" <|--- MstRing -"ScaleOperations" <|--- MstRing -"Field" <|--- MstField -"NumbersAddOperations" <|--- MstField -"ScaleOperations" <|--- MstField -"ExtendedField" <|--- MstExtendedField -"NumericAlgebra" <|--- MstExtendedField -"LogicAlgebra" <|--- MstLogicAlgebra -"Field" <|--- SimpleAutoDiffField -"ExpressionAlgebra" <|--- SimpleAutoDiffField -"NumbersAddOperations" <|--- SimpleAutoDiffField -"AutoDiffValue" <|--- AutoDiffVariableWithDerivative -"Symbol" <|--- AutoDiffVariableWithDerivative -"FirstDerivativeExpression" <|--- SimpleAutoDiffExpression -"ExtendedField" <|--- SimpleAutoDiffExtendedField -"ScaleOperations" <|--- SimpleAutoDiffExtendedField -'"" <|--- SimpleAutoDiffExtendedField -"SimpleAutoDiffField" <|--- SimpleAutoDiffExtendedField -"MST" <|--- Symbol -"Symbol" <|--- StringSymbol -"SymbolIndexer" <|--- SimpleSymbolIndexer -"LinearSpace" <|--- BufferedLinearSpace -"LupDecompositionFeature" <|--- LupDecomposition -"DeterminantFeature" <|--- LupDecomposition -"MatrixFeature" <|--- SymmetricMatrixFeature -"StructureFeature" <|--- MatrixFeature -"MatrixFeature" <|--- DiagonalFeature -"DiagonalFeature" <|--- ZeroFeature -"DiagonalFeature" <|--- UnitFeature -"MatrixFeature" <|--- InverseMatrixFeature -"MatrixFeature" <|--- DeterminantFeature -"MatrixFeature" <|--- LFeature -"MatrixFeature" <|--- UFeature -"MatrixFeature" <|--- LUDecompositionFeature -"MatrixFeature" <|--- LupDecompositionFeature -"MatrixFeature" <|--- OrthogonalFeature -"MatrixFeature" <|--- QRDecompositionFeature -"MatrixFeature" <|--- CholeskyDecompositionFeature -"MatrixFeature" <|--- SingularValueDecompositionFeature -'"Matrixbyorigin{ -' -' -' @UnstableKMathAPI -' @Suppress -'overridefungetFeature:F? = -'features.getFeature -' -'overridefuntoString" -'}" <|--- MatrixWrapper -"MatrixFeature" <|--- TransposedFeature -"Matrix" <|--- VirtualMatrix -"Featured" <|--- FeatureSet -"RuntimeException" <|--- ShapeMismatchException -"Group" <|--- GroupND -"AlgebraND" <|--- GroupND -"Ring" <|--- RingND -"GroupND" <|--- RingND -"Field" <|--- FieldND -"RingND" <|--- FieldND -"AlgebraND" <|--- BufferAlgebraND -"GroupND" <|--- BufferedGroupND -"BufferAlgebraND" <|--- BufferedGroupND -"BufferedGroupND" <|--- BufferedRingND -"RingND" <|--- BufferedRingND -"BufferedRingND" <|--- BufferedFieldND -"FieldND" <|--- BufferedFieldND -"StructureND" <|--- BufferND -"MutableStructureND" <|--- MutableBufferND -"BufferND" <|--- MutableBufferND -"BufferedFieldND" <|--- DoubleFieldND -'" -'" <|--- DoubleFieldND -'"NumbersAddOperations" <|--- DoubleFieldND -'" -'" <|--- DoubleFieldND -'"ScaleOperations" <|--- DoubleFieldND -'" -'" <|--- DoubleFieldND -"ExtendedField" <|--- DoubleFieldND -"BufferedRingND" <|--- ShortRingND -'" -'" <|--- ShortRingND -"NumbersAddOperations" <|--- ShortRingND -"StructureND" <|--- Structure1D -"Buffer" <|--- Structure1D -"Structure1D" <|--- MutableStructure1D -"MutableStructureND" <|--- MutableStructure1D -"MutableBuffer" <|--- MutableStructure1D -"Structure1D" <|--- Structure1DWrapper -"MutableStructure1D" <|--- MutableStructure1DWrapper -"Structure1D" <|--- Buffer1DWrapper -"MutableStructure1D" <|--- MutableBuffer1DWrapper -"StructureND" <|--- Structure2D -"Structure2D" <|--- MutableStructure2D -"MutableStructureND" <|--- MutableStructure2D -"Structure2D" <|--- Structure2DWrapper -"MutableStructure2D" <|--- MutableStructure2DWrapper -"Feature" <|--- StructureFeature -"Featured" <|--- StructureND -"StructureND" <|--- MutableStructureND -"Strides" <|--- DefaultStrides -"Algebra" <|--- GroupOperations -"GroupOperations" <|--- Group -"GroupOperations" <|--- RingOperations -"Group" <|--- Ring -"RingOperations" <|--- Ring -"RingOperations" <|--- FieldOperations -"Ring" <|--- Field -"FieldOperations" <|--- Field -"ScaleOperations" <|--- Field -"NumericAlgebra" <|--- Field -"AlgebraElement" <|--- GroupElement -"GroupElement" <|--- RingElement -"RingElement" <|--- FieldElement -"Field" <|--- BigIntField -"NumbersAddOperations" <|--- BigIntField -"ScaleOperations" <|--- BigIntField -"Comparable" <|--- BigInt -"Algebra" <|--- BufferAlgebra -"BufferAlgebra" <|--- BufferField -"Field" <|--- BufferField -"Algebra" <|--- LogicAlgebra -"LogicAlgebra" <|--- BooleanAlgebra -"FieldOperations" <|--- ExtendedFieldOperations -'" -'" <|--- ExtendedFieldOperations -'"TrigonometricOperations" <|--- ExtendedFieldOperations -'" -'" <|--- ExtendedFieldOperations -'"PowerOperations" <|--- ExtendedFieldOperations -'" -'" <|--- ExtendedFieldOperations -"ExponentialOperations" <|--- ExtendedFieldOperations -"ExtendedFieldOperations" <|--- ExtendedField -"Field" <|--- ExtendedField -"NumericAlgebra" <|--- ExtendedField -"ScaleOperations" <|--- ExtendedField -"ExtendedField" <|--- DoubleField -"Norm" <|--- DoubleField -"ScaleOperations" <|--- DoubleField -"ExtendedField" <|--- FloatField -"Norm" <|--- FloatField -"Ring" <|--- IntRing -"Norm" <|--- IntRing -"NumericAlgebra" <|--- IntRing -"Ring" <|--- ShortRing -"Norm" <|--- ShortRing -"NumericAlgebra" <|--- ShortRing -"Ring" <|--- ByteRing -"Norm" <|--- ByteRing -"NumericAlgebra" <|--- ByteRing -"Ring" <|--- LongRing -"Norm" <|--- LongRing -"NumericAlgebra" <|--- LongRing -"Algebra" <|--- NumericAlgebra -"Algebra" <|--- ScaleOperations -"Ring" <|--- NumbersAddOperations -"NumericAlgebra" <|--- NumbersAddOperations -"Algebra" <|--- TrigonometricOperations -"Algebra" <|--- PowerOperations -"Algebra" <|--- ExponentialOperations -"Buffer" <|--- MutableBuffer -"Buffer" <|--- ListBuffer -"MutableBuffer" <|--- MutableListBuffer -"MutableBuffer" <|--- ArrayBuffer -"Buffer" <|--- ReadOnlyBuffer -"Buffer" <|--- VirtualBuffer -"MutableBuffer" <|--- Row -"MutableBuffer" <|--- DoubleBuffer -"ExtendedFieldOperations" <|--- DoubleBufferFieldOperations -"Norm" <|--- DoubleL2Norm -"ExtendedField" <|--- DoubleBufferField -"Norm" <|--- DoubleBufferField -"Buffer" <|--- FlaggedBuffer -"FlaggedBuffer" <|--- FlaggedDoubleBuffer -'" -'" <|--- FlaggedDoubleBuffer -"Buffer" <|--- FlaggedDoubleBuffer -"MutableBuffer" <|--- FloatBuffer -"MutableBuffer" <|--- IntBuffer -"MutableBuffer" <|--- LongBuffer -"Buffer" <|--- MemoryBuffer -"MemoryBuffer" <|--- MutableMemoryBuffer -'" -'" <|--- MutableMemoryBuffer -"MutableBuffer" <|--- MutableMemoryBuffer -"MutableBuffer" <|--- ShortBuffer -"Norm" <|--- L2Norm -"RingVerifier" <|--- FieldVerifier -"SpaceVerifier" <|--- RingVerifier -"AlgebraicVerifier" <|--- SpaceVerifier -"Ring" <|--- JBigIntegerField -"NumericAlgebra" <|--- JBigIntegerField -"Field" <|--- JBigDecimalFieldBase -"PowerOperations" <|--- JBigDecimalFieldBase -"NumericAlgebra" <|--- JBigDecimalFieldBase -"ScaleOperations" <|--- JBigDecimalFieldBase -"JBigDecimalFieldBase" <|--- JBigDecimalField -@enduml \ No newline at end of file diff --git a/docs/expressions.md b/docs/expressions.md deleted file mode 100644 index e6250110c..000000000 --- a/docs/expressions.md +++ /dev/null @@ -1,21 +0,0 @@ -# Expressions - -Expressions is a feature, which allows constructing lazily or immediately calculated parametric mathematical expressions. - -The potential use-cases for it (so far) are following: - -* lazy evaluation (in general simple lambda is better, but there are some border cases); -* automatic differentiation in single-dimension and in multiple dimensions; -* generation of mathematical syntax trees with subsequent code generation for other languages; -* symbolic computations, especially differentiation (and some other actions with `kmath-symja` integration with Symja's `IExpr`—integration, simplification, and more); -* visualization with `kmath-jupyter`. - -The workhorse of this API is `Expression` interface, which exposes single `operator fun invoke(arguments: Map): 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** diff --git a/docs/histograms.md b/docs/histograms.md deleted file mode 100644 index 6208deaff..000000000 --- a/docs/histograms.md +++ /dev/null @@ -1 +0,0 @@ -**TODO** \ No newline at end of file diff --git a/docs/images/KM.svg b/docs/images/KM.svg deleted file mode 100644 index 6f80e4d08..000000000 --- a/docs/images/KM.svg +++ /dev/null @@ -1,67 +0,0 @@ - - - -image/svg+xml - - - \ No newline at end of file diff --git a/docs/images/KM_mono.svg b/docs/images/KM_mono.svg deleted file mode 100644 index 8f8e470b2..000000000 --- a/docs/images/KM_mono.svg +++ /dev/null @@ -1,62 +0,0 @@ - - - -image/svg+xml - - \ No newline at end of file diff --git a/docs/images/KMath.svg b/docs/images/KMath.svg deleted file mode 100644 index f751d8eb9..000000000 --- a/docs/images/KMath.svg +++ /dev/null @@ -1,101 +0,0 @@ - - - -image/svg+xml - - - - - \ No newline at end of file diff --git a/docs/images/KMath_mono.svg b/docs/images/KMath_mono.svg deleted file mode 100644 index 8ca6c5e84..000000000 --- a/docs/images/KMath_mono.svg +++ /dev/null @@ -1,397 +0,0 @@ - - - -image/svg+xml - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/linear.md b/docs/linear.md deleted file mode 100644 index 2a05499ef..000000000 --- a/docs/linear.md +++ /dev/null @@ -1,31 +0,0 @@ -## Basic linear algebra layout - -KMath support for linear algebra organized in a context-oriented way, which means that operations are in most cases declared in context classes, and are not the members of classes that store data. This allows more flexible approach to maintain multiple back-ends. The new operations added as extensions to contexts instead of being member functions of data structures. - -The main context for linear algebra over matrices and vectors is `LinearSpace`, which defines addition and dot products of matrices and vectors: - -```kotlin -import space.kscience.kmath.linear.* - -LinearSpace.Companion.real { - val vec = buildVector(10) { i -> i.toDouble() } - val mat = buildMatrix(10, 10) { i, j -> i.toDouble() + j } - - // Addition - vec + vec - mat + mat - - // Multiplication by scalar - vec * 2.0 - mat * 2.0 - - // Dot product - mat dot vec - mat dot mat -} -``` - -## Backends overview - -### EJML -### Commons Math diff --git a/docs/nd-structure.md b/docs/nd-structure.md deleted file mode 100644 index 3e9203ec0..000000000 --- a/docs/nd-structure.md +++ /dev/null @@ -1,128 +0,0 @@ -# ND-structure generation and operations - -**TODO** - -# Performance for n-dimensional structures operations - -One of the most sought after features of mathematical libraries is the high-performance operations on n-dimensional -structures. In `kmath` performance depends on which particular context was used for operation. - -Let us consider following contexts: -```kotlin - // automatically build context most suited for given type. - val autoField = NDField.auto(DoubleField, dim, dim) - // specialized nd-field for Double. It works as generic Double field as well. - val specializedField = NDField.real(dim, dim) - //A generic boxing field. It should be used for objects, not primitives. - val genericField = NDField.buffered(DoubleField, dim, dim) -``` -Now let us perform several tests and see, which implementation is best suited for each case: - -## Test case - -To test performance we will take 2d-structures with `dim = 1000` and add a structure filled with `1.0` -to it `n = 1000` times. - -## Specialized -The code to run this looks like: -```kotlin - specializedField.run { - var res: NDBuffer = one - repeat(n) { - res += 1.0 - } - } -``` -The performance of this code is the best of all tests since it inlines all operations and is specialized for operation -with doubles. We will measure everything else relative to this one, so time for this test will be `1x` (real time -on my computer is about 4.5 seconds). The only problem with this approach is that it requires specifying type -from the beginning. Everyone does so anyway, so it is the recommended approach. - -## Automatic -Let's do the same with automatic field inference: -```kotlin - autoField.run { - var res = one - repeat(n) { - res += 1.0 - } - } -``` -Ths speed of this operation is approximately the same as for specialized case since `NDField.auto` just -returns the same `RealNDField` in this case. Of course, it is usually better to use specialized method to be sure. - -## Lazy -Lazy field does not produce a structure when asked, instead it generates an empty structure and fills it on-demand -using coroutines to parallelize computations. -When one calls -```kotlin - lazyField.run { - var res = one - repeat(n) { - res += 1.0 - } - } -``` -The result will be calculated almost immediately but the result will be empty. To get the full result -structure one needs to call all its elements. In this case computation overhead will be huge. So this field never -should be used if one expects to use the full result structure. Though if one wants only small fraction, it could -save a lot of time. - -This field still could be used with reasonable performance if call code is changed: -```kotlin - lazyField.run { - val res = one.map { - var c = 0.0 - repeat(n) { - c += 1.0 - } - c - } - - res.elements().forEach { it.second } - } -``` -In this case it completes in about `4x-5x` time due to boxing. - -## Boxing -The boxing field produced by -```kotlin - genericField.run { - var res: NDBuffer = one - repeat(n) { - res += 1.0 - } - } -``` -is the slowest one, because it requires boxing and unboxing the `double` on each operation. It takes about -`15x` time (**TODO: there seems to be a problem here, it should be slow, but not that slow**). This field should -never be used for primitives. - -## Element operation -Let us also check the speed for direct operations on elements: -```kotlin - var res = genericField.one - repeat(n) { - res += 1.0 - } -``` -One would expect to be at least as slow as field operation, but in fact, this one takes only `2x` time to complete. -It happens, because in this particular case it does not use actual `NDField` but instead calculated directly -via extension function. - -## What about python? - -Usually it is bad idea to compare the direct numerical operation performance in different languages, but it hard to -work completely without frame of reference. In this case, simple numpy code: -```python -import numpy as np - -res = np.ones((1000,1000)) -for i in range(1000): - res = res + 1.0 -``` -gives the completion time of about `1.1x`, which means that specialized kotlin code in fact is working faster (I think it is -because better memory management). Of course if one writes `res += 1.0`, the performance will be different, -but it would be different case, because numpy overrides `+=` with in-place operations. In-place operations are -available in `kmath` with `MutableNDStructure` but there is no field for it (one can still work with mapping -functions). \ No newline at end of file diff --git a/docs/readme.md b/docs/readme.md deleted file mode 100644 index 2953b7113..000000000 --- a/docs/readme.md +++ /dev/null @@ -1,14 +0,0 @@ -# Documentation - -* [Algebra](algebra.md): [context-based](contexts.md) operations on different primitives and structures. - -* [NDStructures](nd-structure.md) - -* [Linear algebra](linear.md): matrices, operations and linear equations solving. To be moved to separate module. - Currently, supports basic API and multiple library back-ends. - -* [Histograms](histograms.md): multidimensional histogram calculation and operations. - -* [Expressions](expressions.md) - -* Commons math integration diff --git a/docs/templates/ARTIFACT-TEMPLATE.md b/docs/templates/ARTIFACT-TEMPLATE.md deleted file mode 100644 index 1bac2a8ff..000000000 --- a/docs/templates/ARTIFACT-TEMPLATE.md +++ /dev/null @@ -1,26 +0,0 @@ -## Artifact: - -The Maven coordinates of this project are `${group}:${name}:${version}`. - -**Gradle:** -```gradle -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation '${group}:${name}:${version}' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("${group}:${name}:${version}") -} -``` \ No newline at end of file diff --git a/docs/templates/README-TEMPLATE.md b/docs/templates/README-TEMPLATE.md deleted file mode 100644 index 4ffa9e75f..000000000 --- a/docs/templates/README-TEMPLATE.md +++ /dev/null @@ -1,105 +0,0 @@ -[![JetBrains Research](https://jb.gg/badges/research.svg)](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub) -[![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382) -![Gradle build](https://github.com/mipt-npm/kmath/workflows/Gradle%20build/badge.svg) -[![Maven Central](https://img.shields.io/maven-central/v/space.kscience/kmath-core.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:%22space.kscience%22) -[![Space](https://img.shields.io/badge/dynamic/xml?color=orange&label=Space&query=//metadata/versioning/latest&url=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fspace%2Fkscience%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/) - -# KMath - -Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based -analog to Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior -architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like -experience could be achieved with [kmath-for-real](/kmath-for-real) extension module. - -[Documentation site (**WIP**)](https://mipt-npm.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) - -# Goal - -* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native) - . -* Provide basic multiplatform implementations for those abstractions (without significant performance optimization). -* Provide bindings and wrappers with those abstractions for popular optimized platform libraries. - -## Non-goals - -* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in API. -* Provide the best performance out of the box. We have specialized libraries for that. Need only API wrappers for them. -* Cover all cases as immediately and in one bundle. We will modularize everything and add new features gradually. -* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like - for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better - experience for those, who want to work with specific types. - -## Features and stability - -KMath is a modular library. Different modules provide different features with different API stability guarantees. All -core modules are released with the same version, but with different API change policy. The features are described in -module definitions below. The module stability could have the following levels: - -* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could - break any moment. You can still use it, but be sure to fix the specific version. -* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked - with `@UnstableKmathAPI` or other stability warning annotations. -* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor - versions, but not in patch versions. API is protected - with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool. -* **STABLE**. The API stabilized. Breaking changes are allowed only in major releases. - -## Modules - -${modules} - -## Multi-platform support - -KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the -[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features -are delegated to platform-specific implementations even if they could be provided in the common module for performance -reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and -feedback are also welcome. - -## Performance - -Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both -performance and flexibility. - -We expect to focus on creating convenient universal API first and then work on increasing performance for specific -cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized -native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be -better than SciPy. - -## Requirements - -KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for -execution to get better performance. - -### Repositories - -Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/) -repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of -[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could -be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy: - -```kotlin -repositories { - maven("https://repo.kotlin.link") -} - -dependencies { - api("${group}:kmath-core:$version") - // api("${group}:kmath-core-jvm:$version") for jvm-specific version -} -``` - -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 -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. diff --git a/examples/README.md b/examples/README.md deleted file mode 100644 index d4e1c5289..000000000 --- a/examples/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# Module examples - - - diff --git a/examples/build.gradle.kts b/examples/build.gradle.kts deleted file mode 100644 index 60f8f5aed..000000000 --- a/examples/build.gradle.kts +++ /dev/null @@ -1,70 +0,0 @@ -plugins { - kotlin("jvm") -} - -repositories { - mavenCentral() - maven("https://repo.kotlin.link") - maven("https://maven.pkg.jetbrains.space/kotlin/p/kotlin/kotlin-js-wrappers") -} - -dependencies { - implementation(project(":kmath-ast")) - implementation(project(":kmath-kotlingrad")) - implementation(project(":kmath-core")) - implementation(project(":kmath-coroutines")) - implementation(project(":kmath-commons")) - implementation(project(":kmath-complex")) - implementation(project(":kmath-optimization")) - implementation(project(":kmath-stat")) - implementation(project(":kmath-viktor")) - implementation(project(":kmath-dimensions")) - implementation(project(":kmath-ejml")) - implementation(project(":kmath-nd4j")) - implementation(project(":kmath-tensors")) - implementation(project(":kmath-symja")) - implementation(project(":kmath-for-real")) - //jafama - implementation(project(":kmath-jafama")) - //multik - implementation(project(":kmath-multik")) - - - implementation("org.nd4j:nd4j-native:1.0.0-beta7") - -// uncomment if your system supports AVX2 -// val os = System.getProperty("os.name") -// -// if (System.getProperty("os.arch") in arrayOf("x86_64", "amd64")) when { -// os.startsWith("Windows") -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:windows-x86_64-avx2") -// os == "Linux" -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:linux-x86_64-avx2") -// os == "Mac OS X" -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:macosx-x86_64-avx2") -// } else - implementation("org.nd4j:nd4j-native-platform:1.0.0-beta7") - - // multik implementation - implementation("org.jetbrains.kotlinx:multik-default:0.1.0") - - implementation("org.slf4j:slf4j-simple:1.7.32") - // plotting - implementation("space.kscience:plotlykt-server:0.5.0") -} - -kotlin.sourceSets.all { - with(languageSettings) { - optIn("kotlin.contracts.ExperimentalContracts") - optIn("kotlin.ExperimentalUnsignedTypes") - optIn("space.kscience.kmath.misc.UnstableKMathAPI") - } -} - -tasks.withType { - kotlinOptions { - jvmTarget = "11" - freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xopt-in=kotlin.RequiresOptIn" + "-Xlambdas=indy" - } -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/ast/astRendering.kt b/examples/src/main/kotlin/space/kscience/kmath/ast/astRendering.kt deleted file mode 100644 index c4f263f97..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/ast/astRendering.kt +++ /dev/null @@ -1,26 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.ast.rendering.FeaturedMathRendererWithPostProcess -import space.kscience.kmath.ast.rendering.LatexSyntaxRenderer -import space.kscience.kmath.ast.rendering.MathMLSyntaxRenderer -import space.kscience.kmath.ast.rendering.renderWithStringBuilder - -fun main() { - val mst = "exp(sqrt(x))-asin(2*x)/(2e10+x^3)/(-12)".parseMath() - val syntax = FeaturedMathRendererWithPostProcess.Default.render(mst) - println("MathSyntax:") - println(syntax) - println() - val latex = LatexSyntaxRenderer.renderWithStringBuilder(syntax) - println("LaTeX:") - println(latex) - println() - val mathML = MathMLSyntaxRenderer.renderWithStringBuilder(syntax) - println("MathML:") - println(mathML) -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/ast/expressions.kt b/examples/src/main/kotlin/space/kscience/kmath/ast/expressions.kt deleted file mode 100644 index 907f1bbe4..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/ast/expressions.kt +++ /dev/null @@ -1,26 +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. - */ - -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.invoke - -fun main() { - val expr = MstExtendedField { - x * 2.0 + number(2.0) / x - number(16.0) + asinh(x) / sin(x) - }.compileToExpression(DoubleField) - - val m = DoubleArray(expr.indexer.symbols.size) - val xIdx = expr.indexer.indexOf(x) - - repeat(10000000) { - m[xIdx] = 1.0 - expr(m) - } -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/ast/kotlingradSupport.kt b/examples/src/main/kotlin/space/kscience/kmath/ast/kotlingradSupport.kt deleted file mode 100644 index dec3bfb81..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/ast/kotlingradSupport.kt +++ /dev/null @@ -1,27 +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. - */ - -package space.kscience.kmath.ast - -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 - -/** - * In this example, *x2 − 4 x − 44* function is differentiated with Kotlin∇, and the - * derivation result is compared with valid derivative in a certain point. - */ -fun main() { - val actualDerivative = "x^2-4*x-44" - .parseMath() - .toKotlingradExpression(DoubleField) - .derivative(x) - - val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField) - check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0)) -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/ast/symjaSupport.kt b/examples/src/main/kotlin/space/kscience/kmath/ast/symjaSupport.kt deleted file mode 100644 index 7e09faeff..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/ast/symjaSupport.kt +++ /dev/null @@ -1,27 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.expressions.derivative -import space.kscience.kmath.expressions.invoke -import space.kscience.kmath.expressions.toExpression -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.symja.toSymjaExpression - -/** - * In this example, *x2 − 4 x − 44* function is differentiated with Symja, and the - * derivation result is compared with valid derivative in a certain point. - */ -fun main() { - val actualDerivative = "x^2-4*x-44" - .parseMath() - .toSymjaExpression(DoubleField) - .derivative(x) - - val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField) - check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0)) -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt b/examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt deleted file mode 100644 index 63e57bd8c..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt +++ /dev/null @@ -1,115 +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. - */ - -package space.kscience.kmath.fit - -import kotlinx.html.br -import kotlinx.html.h3 -import space.kscience.kmath.commons.expressions.DSProcessor -import space.kscience.kmath.commons.optimization.CMOptimizer -import space.kscience.kmath.distributions.NormalDistribution -import space.kscience.kmath.expressions.chiSquaredExpression -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.optimizeWith -import space.kscience.kmath.optimization.resultPoint -import space.kscience.kmath.optimization.resultValue -import space.kscience.kmath.real.DoubleVector -import space.kscience.kmath.real.map -import space.kscience.kmath.real.step -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.plotly.* -import space.kscience.plotly.models.ScatterMode -import space.kscience.plotly.models.TraceValues -import kotlin.math.pow -import kotlin.math.sqrt - -// Forward declaration of symbols that will be used in expressions. -private val a by symbol -private val b by symbol -private val c by symbol - -/** - * Shortcut to use buffers in plotly - */ -operator fun TraceValues.invoke(vector: DoubleVector) { - numbers = vector.asIterable() -} - -/** - * Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules. - */ -suspend fun main() { - //A generator for a normally distributed values - val generator = NormalDistribution(0.0, 1.0) - - //A chain/flow of random values with the given seed - val chain = generator.sample(RandomGenerator.default(112667)) - - - //Create a uniformly distributed x values like numpy.arrange - val x = 1.0..100.0 step 1.0 - - - //Perform an operation on each x value (much more effective, than numpy) - val y = x.map { it -> - val value = it.pow(2) + it + 1 - value + chain.next() * sqrt(value) - } - // this will also work, but less effective: - // val y = x.pow(2)+ x + 1 + chain.nextDouble() - - // create same errors for all xs - val yErr = y.map { sqrt(it) }//RealVector.same(x.size, sigma) - - // compute differentiable chi^2 sum for given model ax^2 + bx + c - val chi2 = DSProcessor.chiSquaredExpression(x, y, yErr) { arg -> - //bind variables to autodiff context - val a = bindSymbol(a) - val b = bindSymbol(b) - //Include default value for c if it is not provided as a parameter - val c = bindSymbolOrNull(c) ?: one - a * arg.pow(2) + b * arg + c - } - - //minimize the chi^2 in given starting point. Derivatives are not required, they are already included. - val result = chi2.optimizeWith( - CMOptimizer, - mapOf(a to 1.5, b to 0.9, c to 1.0), - FunctionOptimizationTarget.MINIMIZE - ) - - //display a page with plot and numerical results - val page = Plotly.page { - plot { - scatter { - mode = ScatterMode.markers - x(x) - y(y) - error_y { - array = yErr.toList() - } - name = "data" - } - scatter { - mode = ScatterMode.lines - x(x) - y(x.map { result.resultPoint[a]!! * it.pow(2) + result.resultPoint[b]!! * it + 1 }) - name = "fit" - } - } - br() - h3 { - +"Fit result: $result" - } - h3 { - +"Chi2/dof = ${result.resultValue / (x.size - 3)}" - } - } - - page.makeFile() -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/fit/qowFit.kt b/examples/src/main/kotlin/space/kscience/kmath/fit/qowFit.kt deleted file mode 100644 index d52976671..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/fit/qowFit.kt +++ /dev/null @@ -1,106 +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. - */ - -package space.kscience.kmath.fit - -import kotlinx.html.br -import kotlinx.html.h3 -import space.kscience.kmath.commons.expressions.DSProcessor -import space.kscience.kmath.data.XYErrorColumnarData -import space.kscience.kmath.distributions.NormalDistribution -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.expressions.binding -import space.kscience.kmath.expressions.symbol -import space.kscience.kmath.operations.asIterable -import space.kscience.kmath.operations.toList -import space.kscience.kmath.optimization.QowOptimizer -import space.kscience.kmath.optimization.chiSquaredOrNull -import space.kscience.kmath.optimization.fitWith -import space.kscience.kmath.optimization.resultPoint -import space.kscience.kmath.real.map -import space.kscience.kmath.real.step -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.plotly.* -import space.kscience.plotly.models.ScatterMode -import kotlin.math.abs -import kotlin.math.pow -import kotlin.math.sqrt - -// Forward declaration of symbols that will be used in expressions. -private val a by symbol -private val b by symbol -private val c by symbol - - -/** - * Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules. - */ -suspend fun main() { - //A generator for a normally distributed values - val generator = NormalDistribution(0.0, 1.0) - - //A chain/flow of random values with the given seed - val chain = generator.sample(RandomGenerator.default(112667)) - - - //Create a uniformly distributed x values like numpy.arrange - val x = 1.0..100.0 step 1.0 - - - //Perform an operation on each x value (much more effective, than numpy) - val y = x.map { it -> - val value = it.pow(2) + it + 1 - value + chain.next() * sqrt(value) - } - // this will also work, but less effective: - // val y = x.pow(2)+ x + 1 + chain.nextDouble() - - // create same errors for all xs - val yErr = y.map { sqrt(abs(it)) } - require(yErr.asIterable().all { it > 0 }) { "All errors must be strictly positive" } - - val result = XYErrorColumnarData.of(x, y, yErr).fitWith( - QowOptimizer, - DSProcessor, - mapOf(a to 0.9, b to 1.2, c to 2.0) - ) { arg -> - //bind variables to autodiff context - val a by binding - val b by binding - //Include default value for c if it is not provided as a parameter - val c = bindSymbolOrNull(c) ?: one - a * arg.pow(2) + b * arg + c - } - - //display a page with plot and numerical results - val page = Plotly.page { - plot { - scatter { - mode = ScatterMode.markers - x(x) - y(y) - error_y { - array = yErr.toList() - } - name = "data" - } - scatter { - mode = ScatterMode.lines - x(x) - y(x.map { result.model(result.resultPoint + (Symbol.x to it)) }) - name = "fit" - } - } - br() - h3 { - +"Fit result: ${result.resultPoint}" - } - h3 { - +"Chi2/dof = ${result.chiSquaredOrNull!! / (x.size - 3)}" - } - } - - page.makeFile() -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/functions/integrate.kt b/examples/src/main/kotlin/space/kscience/kmath/functions/integrate.kt deleted file mode 100644 index f60b1ab45..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/functions/integrate.kt +++ /dev/null @@ -1,23 +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. - */ - -package space.kscience.kmath.functions - -import space.kscience.kmath.integration.gaussIntegrator -import space.kscience.kmath.integration.integrate -import space.kscience.kmath.integration.value -import space.kscience.kmath.operations.DoubleField -import kotlin.math.pow - -fun main() { - //Define a function - val function: UnivariateFunction = { 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) - - //the value is nullable because in some cases the integration could not succeed - println(result.value) -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/functions/interpolate.kt b/examples/src/main/kotlin/space/kscience/kmath/functions/interpolate.kt deleted file mode 100644 index 8dbc7b7a4..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/functions/interpolate.kt +++ /dev/null @@ -1,54 +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. - */ - -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.plotly.Plotly -import space.kscience.plotly.UnstablePlotlyAPI -import space.kscience.plotly.makeFile -import space.kscience.plotly.models.functionXY -import space.kscience.plotly.scatter -import kotlin.math.PI -import kotlin.math.sin - -@OptIn(UnstablePlotlyAPI::class) -fun main() { - val data = (0..10).map { - val x = it.toDouble() / 5 * PI - x to sin(x) - } - - val polynomial: PiecewisePolynomial = SplineInterpolator( - DoubleField, ::DoubleBuffer - ).interpolatePolynomials(data) - - val function = polynomial.asFunction(DoubleField, 0.0) - - val cmInterpolate = org.apache.commons.math3.analysis.interpolation.SplineInterpolator().interpolate( - data.map { it.first }.toDoubleArray(), - data.map { it.second }.toDoubleArray() - ) - - Plotly.plot { - scatter { - name = "interpolated" - x.numbers = data.map { it.first } - y.numbers = x.doubles.map { function(it) } - } - scatter { - name = "original" - functionXY(0.0..(2 * PI), 0.1) { sin(it) } - } - scatter { - name = "cm" - x.numbers = data.map { it.first } - y.numbers = x.doubles.map { cmInterpolate.value(it) } - } - }.makeFile() -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/functions/interpolateSquare.kt b/examples/src/main/kotlin/space/kscience/kmath/functions/interpolateSquare.kt deleted file mode 100644 index feefedece..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/functions/interpolateSquare.kt +++ /dev/null @@ -1,45 +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. - */ - -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.real.map -import space.kscience.kmath.real.step -import space.kscience.plotly.Plotly -import space.kscience.plotly.UnstablePlotlyAPI -import space.kscience.plotly.makeFile -import space.kscience.plotly.models.functionXY -import space.kscience.plotly.scatter - -@OptIn(UnstablePlotlyAPI::class) -fun main() { - val function: UnivariateFunction = { x -> - if (x in 30.0..50.0) { - 1.0 - } else { - 0.0 - } - } - val xs = 0.0..100.0 step 0.5 - val ys = xs.map(function) - - val polynomial: PiecewisePolynomial = DoubleField.splineInterpolator.interpolatePolynomials(xs, ys) - - val polyFunction = polynomial.asFunction(DoubleField, 0.0) - - Plotly.plot { - scatter { - name = "interpolated" - functionXY(25.0..55.0, 0.1) { polyFunction(it) } - } - scatter { - name = "original" - functionXY(25.0..55.0, 0.1) { function(it) } - } - }.makeFile() -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/functions/matrixIntegration.kt b/examples/src/main/kotlin/space/kscience/kmath/functions/matrixIntegration.kt deleted file mode 100644 index 4f99aeb47..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/functions/matrixIntegration.kt +++ /dev/null @@ -1,34 +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. - */ - -package space.kscience.kmath.functions - -import space.kscience.kmath.integration.gaussIntegrator -import space.kscience.kmath.integration.integrate -import space.kscience.kmath.integration.value -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.nd.structureND -import space.kscience.kmath.nd.withNdAlgebra -import space.kscience.kmath.operations.algebra -import space.kscience.kmath.operations.invoke - -fun main(): Unit = Double.algebra { - withNdAlgebra(2, 2) { - - //Produce a diagonal StructureND - fun diagonal(v: Double) = structureND { (i, j) -> - if (i == j) v else 0.0 - } - - //Define a function in a nd space - val function: (Double) -> StructureND = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 } - - //get the result of the integration - val result = gaussIntegrator.integrate(0.0..10.0, function = function) - - //the value is nullable because in some cases the integration could not succeed - println(result.value) - } -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/jafama/JafamaDemo.kt b/examples/src/main/kotlin/space/kscience/kmath/jafama/JafamaDemo.kt deleted file mode 100644 index 9c3d0fdbe..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/jafama/JafamaDemo.kt +++ /dev/null @@ -1,15 +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. - */ - -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) }) -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/linear/dotPerformance.kt b/examples/src/main/kotlin/space/kscience/kmath/linear/dotPerformance.kt deleted file mode 100644 index a2d7d7c27..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/linear/dotPerformance.kt +++ /dev/null @@ -1,34 +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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.operations.algebra -import kotlin.random.Random -import kotlin.system.measureTimeMillis - -fun main() { - val random = Random(12224) - val dim = 1000 - - //creating invertible matrix - val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j -> - if (i <= j) random.nextDouble() else 0.0 - } - val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j -> - if (i <= j) random.nextDouble() else 0.0 - } - - val time = measureTimeMillis { - with(Double.algebra.linearSpace) { - repeat(10) { - matrix1 dot matrix2 - } - } - } - - println(time) - -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt b/examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt deleted file mode 100644 index a01ea7fe2..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt +++ /dev/null @@ -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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.real.* -import space.kscience.kmath.structures.DoubleBuffer - -fun main() { - val x0 = DoubleVector(0.0, 0.0, 0.0) - val sigma = DoubleVector(1.0, 1.0, 1.0) - - val gaussian: (Point) -> Double = { x -> - require(x.size == x0.size) - kotlin.math.exp(-((x - x0) / sigma).square().sum()) - } - - fun ((Point) -> Double).grad(x: Point): Point { - require(x.size == x0.size) - return DoubleBuffer(x.size) { i -> - val h = sigma[i] / 5 - val dVector = DoubleBuffer(x.size) { if (it == i) h else 0.0 } - val f1 = this(x + dVector / 2) - val f0 = this(x - dVector / 2) - (f1 - f0) / h - } - } - - println(gaussian.grad(x0)) - -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/operations/BigIntDemo.kt b/examples/src/main/kotlin/space/kscience/kmath/operations/BigIntDemo.kt deleted file mode 100644 index 51f439612..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/operations/BigIntDemo.kt +++ /dev/null @@ -1,11 +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. - */ - -package space.kscience.kmath.operations - -fun main() { - val res = BigIntField { number(1) * 2 } - println("bigint:$res") -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/operations/complexDemo.kt b/examples/src/main/kotlin/space/kscience/kmath/operations/complexDemo.kt deleted file mode 100644 index 2e1801cc2..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/operations/complexDemo.kt +++ /dev/null @@ -1,41 +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. - */ - -package space.kscience.kmath.operations - -import space.kscience.kmath.complex.Complex -import space.kscience.kmath.complex.algebra -import space.kscience.kmath.complex.bufferAlgebra -import space.kscience.kmath.complex.ndAlgebra -import space.kscience.kmath.nd.BufferND -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.nd.structureND - -fun main() = Complex.algebra { - val complex = 2 + 2 * i - println(complex * 8 - 5 * i) - - //flat buffer - val buffer = with(bufferAlgebra){ - buffer(8) { Complex(it, -it) }.map { Complex(it.im, it.re) } - } - println(buffer) - - // 2d element - val element: BufferND = ndAlgebra.structureND(2, 2) { (i, j) -> - Complex(i - j, i + j) - } - println(element) - - // 1d element operation - val result: StructureND = ndAlgebra{ - val a = structureND(8) { (it) -> i * it - it.toDouble() } - val b = 3 - val c = Complex(1.0, 1.0) - - (a pow b) + c - } - println(result) -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/operations/mixedNDOperations.kt b/examples/src/main/kotlin/space/kscience/kmath/operations/mixedNDOperations.kt deleted file mode 100644 index 62c9c8076..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/operations/mixedNDOperations.kt +++ /dev/null @@ -1,29 +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. - */ - -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.Shape -import space.kscience.kmath.nd.Structure2D -import space.kscience.kmath.nd.ndAlgebra -import space.kscience.kmath.viktor.ViktorStructureND -import space.kscience.kmath.viktor.viktorAlgebra - -fun main() { - val viktorStructure: ViktorStructureND = DoubleField.viktorAlgebra.structureND(Shape(2, 2)) { (i, j) -> - if (i == j) 2.0 else 0.0 - } - - val cmMatrix: Structure2D = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0) - - val res: DoubleBufferND = DoubleField.ndAlgebra { - exp(viktorStructure) + 2.0 * cmMatrix - } - - println(res) -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt b/examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt deleted file mode 100644 index 8e3cdf86f..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt +++ /dev/null @@ -1,71 +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. - */ - -package space.kscience.kmath.stat - -import kotlinx.coroutines.Dispatchers -import kotlinx.coroutines.async -import kotlinx.coroutines.runBlocking -import org.apache.commons.rng.sampling.distribution.BoxMullerNormalizedGaussianSampler -import org.apache.commons.rng.simple.RandomSource -import space.kscience.kmath.samplers.GaussianSampler -import java.time.Duration -import java.time.Instant -import org.apache.commons.rng.sampling.distribution.GaussianSampler as CMGaussianSampler - -private suspend fun runKMathChained(): Duration { - val generator = RandomGenerator.fromSource(RandomSource.MT, 123L) - val normal = GaussianSampler(7.0, 2.0) - val chain = normal.sample(generator) - val startTime = Instant.now() - var sum = 0.0 - - repeat(10000001) { counter -> - sum += chain.next() - - if (counter % 100000 == 0) { - val duration = Duration.between(startTime, Instant.now()) - val meanValue = sum / counter - println("Chain sampler completed $counter elements in $duration: $meanValue") - } - } - - return Duration.between(startTime, Instant.now()) -} - -private fun runApacheDirect(): Duration { - val rng = RandomSource.create(RandomSource.MT, 123L) - - val sampler = CMGaussianSampler.of( - BoxMullerNormalizedGaussianSampler.of(rng), - 7.0, - 2.0 - ) - - val startTime = Instant.now() - var sum = 0.0 - - repeat(10000001) { counter -> - sum += sampler.sample() - - if (counter % 100000 == 0) { - val duration = Duration.between(startTime, Instant.now()) - val meanValue = sum / counter - println("Direct sampler completed $counter elements in $duration: $meanValue") - } - } - - return Duration.between(startTime, Instant.now()) -} - -/** - * Comparing chain sampling performance with direct sampling performance - */ -fun main(): Unit = runBlocking(Dispatchers.Default) { - val directJob = async { runApacheDirect() } - val chainJob = async { runKMathChained() } - println("KMath Chained: ${chainJob.await()}") - println("Apache Direct: ${directJob.await()}") -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt b/examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt deleted file mode 100644 index 15654971f..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright 2018-2021 KMath contributors. - * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file. - */ - -package space.kscience.kmath.stat - -import kotlinx.coroutines.runBlocking -import space.kscience.kmath.chains.Chain -import space.kscience.kmath.chains.combineWithState -import space.kscience.kmath.distributions.NormalDistribution - -private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0) - -/** - * Averaging. - */ -private fun Chain.mean(): Chain = combineWithState(AveragingChainState(), { it.copy() }) { chain -> - val next = chain.next() - num++ - value += next - return@combineWithState value / num -} - - -fun main() { - val normal = NormalDistribution(0.0, 2.0) - val chain = normal.sample(RandomGenerator.default).mean() - - runBlocking { - repeat(10001) { counter -> - val mean = chain.next() - if (counter % 1000 == 0) { - println("[$counter] Average value is $mean") - } - } - } -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt b/examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt deleted file mode 100644 index d55f3df09..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt +++ /dev/null @@ -1,66 +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. - */ - -@file:Suppress("unused") - -package space.kscience.kmath.structures - -import space.kscience.kmath.complex.* -import space.kscience.kmath.linear.transpose -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.invoke -import kotlin.system.measureTimeMillis - -fun main() { - val dim = 1000 - val n = 1000 - - val realField = DoubleField.ndAlgebra(dim, dim) - val complexField: ComplexFieldND = ComplexField.ndAlgebra(dim, dim) - - val realTime = measureTimeMillis { - realField { - var res: StructureND = one - repeat(n) { - res += 1.0 - } - } - } - - println("Real addition completed in $realTime millis") - - val complexTime = measureTimeMillis { - complexField { - var res: StructureND = one - repeat(n) { - res += 1.0 - } - } - } - - println("Complex addition completed in $complexTime millis") -} - -fun complexExample() { - //Create a context for 2-d structure with complex values - ComplexField { - withNdAlgebra(4, 8) { - //a constant real-valued structure - val x = one * 2.5 - operator fun Number.plus(other: Complex) = Complex(this.toDouble() + other.re, other.im) - //a structure generator specific to this context - val matrix = structureND { (k, l) -> k + l * i } - //Perform sum - val sum = matrix + x + 1.0 - - //Represent the sum as 2d-structure and transpose - sum.as2D().transpose() - } - } -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt b/examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt deleted file mode 100644 index b680e267d..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt +++ /dev/null @@ -1,101 +0,0 @@ -/* - * Copyright 2018-2021 KMath contributors. - * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file. - */ - -package space.kscience.kmath.structures - -import kotlinx.coroutines.DelicateCoroutinesApi -import kotlinx.coroutines.GlobalScope -import org.nd4j.linalg.factory.Nd4j -import space.kscience.kmath.nd.* -import space.kscience.kmath.nd4j.nd4j -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.viktor.ViktorFieldND -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract -import kotlin.system.measureTimeMillis - -internal inline fun measureAndPrint(title: String, block: () -> Unit) { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - val time = measureTimeMillis(block) - println("$title completed in $time millis") -} - -@OptIn(DelicateCoroutinesApi::class) -fun main() { - // initializing Nd4j - Nd4j.zeros(0) - val dim = 1000 - val n = 1000 - val shape = Shape(dim, dim) - - - // automatically build context most suited for given type. - val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto) - // specialized nd-field for Double. It works as generic Double field as well. - val realField = DoubleField.ndAlgebra - //A generic boxing field. It should be used for objects, not primitives. - val boxingField = BufferedFieldOpsND(DoubleField, Buffer.Companion::boxing) - // Nd4j specialized field. - val nd4jField = DoubleField.nd4j - //viktor field - val viktorField = ViktorFieldND(dim, dim) - //parallel processing based on Java Streams - val parallelField = DoubleField.ndStreaming(dim, dim) - - measureAndPrint("Boxing addition") { - boxingField { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - } - } - - measureAndPrint("Specialized addition") { - realField { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - } - } - - measureAndPrint("Nd4j specialized addition") { - nd4jField { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - } - } - - measureAndPrint("Viktor addition") { - viktorField { - var res: StructureND = one - repeat(n) { res += 1.0 } - } - } - - measureAndPrint("Parallel stream addition") { - parallelField { - var res: StructureND = one - repeat(n) { res += 1.0 } - } - } - - measureAndPrint("Automatic field addition") { - autoField { - var res: StructureND = one(shape) - repeat(n) { res += 1.0 } - } - } - - measureAndPrint("Lazy addition") { - val res = realField.one(shape).mapAsync(GlobalScope) { - var c = 0.0 - repeat(n) { - c += 1.0 - } - c - } - - res.elements().forEach { it.second } - } -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt b/examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt deleted file mode 100644 index 548fb16c1..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt +++ /dev/null @@ -1,108 +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. - */ - -package space.kscience.kmath.structures - -import space.kscience.kmath.nd.* -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.ExtendedField -import space.kscience.kmath.operations.NumbersAddOps -import java.util.* -import java.util.stream.IntStream - -/** - * A demonstration implementation of NDField over Real using Java [java.util.stream.DoubleStream] for parallel - * execution. - */ -class StreamDoubleFieldND(override val shape: IntArray) : FieldND, - NumbersAddOps>, - ExtendedField> { - - private val strides = DefaultStrides(shape) - override val elementAlgebra: DoubleField get() = DoubleField - override val zero: BufferND by lazy { structureND(shape) { zero } } - override val one: BufferND by lazy { structureND(shape) { one } } - - override fun number(value: Number): BufferND { - val d = value.toDouble() // minimize conversions - return structureND(shape) { d } - } - - private val StructureND.buffer: DoubleBuffer - get() = when { - !shape.contentEquals(this@StreamDoubleFieldND.shape) -> throw ShapeMismatchException( - this@StreamDoubleFieldND.shape, - shape - ) - this is BufferND && this.indices == this@StreamDoubleFieldND.strides -> this.buffer as DoubleBuffer - else -> DoubleBuffer(strides.linearSize) { offset -> get(strides.index(offset)) } - } - - override fun structureND(shape: Shape, initializer: DoubleField.(IntArray) -> Double): BufferND { - val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset -> - val index = strides.index(offset) - DoubleField.initializer(index) - }.toArray() - - return BufferND(strides, array.asBuffer()) - } - - override fun StructureND.map( - transform: DoubleField.(Double) -> Double, - ): BufferND { - val array = Arrays.stream(buffer.array).parallel().map { DoubleField.transform(it) }.toArray() - return BufferND(strides, array.asBuffer()) - } - - override fun StructureND.mapIndexed( - transform: DoubleField.(index: IntArray, Double) -> Double, - ): BufferND { - val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset -> - DoubleField.transform( - strides.index(offset), - buffer.array[offset] - ) - }.toArray() - - return BufferND(strides, array.asBuffer()) - } - - override fun zip( - left: StructureND, - right: StructureND, - transform: DoubleField.(Double, Double) -> Double, - ): BufferND { - val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset -> - DoubleField.transform(left.buffer.array[offset], right.buffer.array[offset]) - }.toArray() - return BufferND(strides, array.asBuffer()) - } - - override fun StructureND.unaryMinus(): StructureND = map { -it } - - override fun scale(a: StructureND, value: Double): StructureND = a.map { it * value } - - override fun power(arg: StructureND, pow: Number): BufferND = arg.map { power(it, pow) } - - override fun exp(arg: StructureND): BufferND = arg.map { exp(it) } - - override fun ln(arg: StructureND): BufferND = arg.map { ln(it) } - - override fun sin(arg: StructureND): BufferND = arg.map { sin(it) } - override fun cos(arg: StructureND): BufferND = arg.map { cos(it) } - override fun tan(arg: StructureND): BufferND = arg.map { tan(it) } - override fun asin(arg: StructureND): BufferND = arg.map { asin(it) } - override fun acos(arg: StructureND): BufferND = arg.map { acos(it) } - override fun atan(arg: StructureND): BufferND = arg.map { atan(it) } - - override fun sinh(arg: StructureND): BufferND = arg.map { sinh(it) } - override fun cosh(arg: StructureND): BufferND = arg.map { cosh(it) } - override fun tanh(arg: StructureND): BufferND = arg.map { tanh(it) } - override fun asinh(arg: StructureND): BufferND = arg.map { asinh(it) } - override fun acosh(arg: StructureND): BufferND = arg.map { acosh(it) } - override fun atanh(arg: StructureND): BufferND = arg.map { atanh(it) } -} - -fun DoubleField.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(shape) diff --git a/examples/src/main/kotlin/space/kscience/kmath/structures/StructureReadBenchmark.kt b/examples/src/main/kotlin/space/kscience/kmath/structures/StructureReadBenchmark.kt deleted file mode 100644 index de36c664d..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/structures/StructureReadBenchmark.kt +++ /dev/null @@ -1,42 +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. - */ - -package space.kscience.kmath.structures - -import space.kscience.kmath.nd.BufferND -import space.kscience.kmath.nd.DefaultStrides -import kotlin.system.measureTimeMillis - -@Suppress("ASSIGNED_BUT_NEVER_ACCESSED_VARIABLE") -fun main() { - val n = 6000 - val array = DoubleArray(n * n) { 1.0 } - val buffer = DoubleBuffer(array) - val strides = DefaultStrides(intArrayOf(n, n)) - val structure = BufferND(strides, buffer) - - measureTimeMillis { - var res = 0.0 - strides.asSequence().forEach { res = structure[it] } - } // warmup - - val time1 = measureTimeMillis { - var res = 0.0 - strides.asSequence().forEach { res = structure[it] } - } - println("Structure reading finished in $time1 millis") - - val time2 = measureTimeMillis { - var res = 0.0 - strides.asSequence().forEach { res = buffer[strides.offset(it)] } - } - println("Buffer reading finished in $time2 millis") - - val time3 = measureTimeMillis { - var res = 0.0 - strides.asSequence().forEach { res = array[strides.offset(it)] } - } - println("Array reading finished in $time3 millis") -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt b/examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt deleted file mode 100644 index dea7095a8..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt +++ /dev/null @@ -1,37 +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. - */ - -package space.kscience.kmath.structures - -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.nd.mapToBuffer -import kotlin.system.measureTimeMillis - -@Suppress("UNUSED_VARIABLE") -fun main() { - val n = 6000 - val structure = StructureND.buffered(intArrayOf(n, n), Buffer.Companion::auto) { 1.0 } - structure.mapToBuffer { it + 1 } // warm-up - val time1 = measureTimeMillis { val res = structure.mapToBuffer { it + 1 } } - println("Structure mapping finished in $time1 millis") - val array = DoubleArray(n * n) { 1.0 } - - val time2 = measureTimeMillis { - val target = DoubleArray(n * n) - val res = array.forEachIndexed { index, value -> target[index] = value + 1 } - } - - println("Array mapping finished in $time2 millis") - - val buffer = DoubleBuffer(DoubleArray(n * n) { 1.0 }) - - val time3 = measureTimeMillis { - val target = DoubleBuffer(DoubleArray(n * n)) - val res = array.forEachIndexed { index, value -> - target[index] = value + 1 - } - } - println("Buffer mapping finished in $time3 millis") -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/structures/buffers.kt b/examples/src/main/kotlin/space/kscience/kmath/structures/buffers.kt deleted file mode 100644 index 889ea99bd..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/structures/buffers.kt +++ /dev/null @@ -1,23 +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. - */ - -package space.kscience.kmath.structures - -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.buffer -import space.kscience.kmath.operations.bufferAlgebra -import space.kscience.kmath.operations.withSize - -inline fun MutableBuffer.Companion.same( - n: Int, - value: R -): MutableBuffer = auto(n) { value } - - -fun main() { - with(DoubleField.bufferAlgebra.withSize(5)) { - println(number(2.0) + buffer(1, 2, 3, 4, 5)) - } -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/structures/typeSafeDimensions.kt b/examples/src/main/kotlin/space/kscience/kmath/structures/typeSafeDimensions.kt deleted file mode 100644 index c28b566b9..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/structures/typeSafeDimensions.kt +++ /dev/null @@ -1,35 +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. - */ - -package space.kscience.kmath.structures - -import space.kscience.kmath.dimensions.D2 -import space.kscience.kmath.dimensions.D3 -import space.kscience.kmath.dimensions.DMatrixContext -import space.kscience.kmath.dimensions.Dimension - -private fun DMatrixContext.simple() { - val m1 = produce { i, j -> (i + j).toDouble() } - val m2 = produce { i, j -> (i + j).toDouble() } - - //Dimension-safe addition - m1.transpose() + m2 -} - -private object D5 : Dimension { - override val dim: Int = 5 -} - -private fun DMatrixContext.custom() { - val m1 = produce { i, j -> (i + j).toDouble() } - val m2 = produce { i, j -> (i - j).toDouble() } - val m3 = produce { i, j -> (i - j).toDouble() } - (m1 dot m2) + m3 -} - -fun main(): Unit = with(DMatrixContext.real) { - simple() - custom() -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt deleted file mode 100644 index b42602988..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt +++ /dev/null @@ -1,68 +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. - */ - -package space.kscience.kmath.tensors - -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.tensors.core.DoubleTensor -import space.kscience.kmath.tensors.core.DoubleTensorAlgebra - -import kotlin.math.abs - -// OLS estimator using SVD - -fun main() { - //seed for random - val randSeed = 100500L - - // work in context with linear operations - DoubleTensorAlgebra { - // take coefficient vector from normal distribution - val alpha = randomNormal( - intArrayOf(5), - randSeed - ) + fromArray( - intArrayOf(5), - doubleArrayOf(1.0, 2.5, 3.4, 5.0, 10.1) - ) - - println("Real alpha:\n$alpha") - - // also take sample of size 20 from normal distribution for x - val x = randomNormal( - intArrayOf(20, 5), - randSeed - ) - - // calculate y and add gaussian noise (N(0, 0.05)) - val y = x dot alpha - y += y.randomNormalLike(randSeed) * 0.05 - - // now restore the coefficient vector with OSL estimator with SVD - val (u, singValues, v) = x.svd() - - // we have to make sure the singular values of the matrix are not close to zero - println("Singular values:\n$singValues") - - - // inverse Sigma matrix can be restored from singular values with diagonalEmbedding function - val sigma = diagonalEmbedding(singValues.map{ if (abs(it) < 1e-3) 0.0 else 1.0/it }) - - val alphaOLS = v dot sigma dot u.transpose() dot y - println("Estimated alpha:\n" + - "$alphaOLS") - - // figure out MSE of approximation - fun mse(yTrue: DoubleTensor, yPred: DoubleTensor): Double { - require(yTrue.shape.size == 1) - require(yTrue.shape contentEquals yPred.shape) - - val diff = yTrue - yPred - return diff.dot(diff).sqrt().value() - } - - println("MSE: ${mse(alpha, alphaOLS)}") - } -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt deleted file mode 100644 index aced0cf7d..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright 2018-2021 KMath contributors. - * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file. - */ - -package space.kscience.kmath.tensors - -import space.kscience.kmath.tensors.core.tensorAlgebra -import space.kscience.kmath.tensors.core.withBroadcast - - -// simple PCA - -fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with broadcast methods - val seed = 100500L - - // assume x is range from 0 until 10 - val x = fromArray( - intArrayOf(10), - DoubleArray(10) { it.toDouble() } - ) - - // take y dependent on x with noise - val y = 2.0 * x + (3.0 + x.randomNormalLike(seed) * 1.5) - - println("x:\n$x") - println("y:\n$y") - - // stack them into single dataset - val dataset = stack(listOf(x, y)).transpose() - - // normalize both x and y - val xMean = x.mean() - val yMean = y.mean() - - val xStd = x.std() - val yStd = y.std() - - val xScaled = (x - xMean) / xStd - val yScaled = (y - yMean) / yStd - - // save means ans standard deviations for further recovery - val mean = fromArray( - intArrayOf(2), - doubleArrayOf(xMean, yMean) - ) - println("Means:\n$mean") - - val std = fromArray( - intArrayOf(2), - doubleArrayOf(xStd, yStd) - ) - println("Standard deviations:\n$std") - - // calculate the covariance matrix of scaled x and y - val covMatrix = cov(listOf(xScaled, yScaled)) - println("Covariance matrix:\n$covMatrix") - - // and find out eigenvector of it - val (_, evecs) = covMatrix.symEig() - val v = evecs[0] - println("Eigenvector:\n$v") - - // reduce dimension of dataset - val datasetReduced = v dot stack(listOf(xScaled, yScaled)) - println("Reduced data:\n$datasetReduced") - - // we can restore original data from reduced data; - // for example, find 7th element of dataset. - val n = 7 - val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean - println("Original value:\n${dataset[n]}") - println("Restored value:\n$restored") -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.kt deleted file mode 100644 index a436ae1c3..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.kt +++ /dev/null @@ -1,42 +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. - */ - -package space.kscience.kmath.tensors - -import space.kscience.kmath.tensors.core.tensorAlgebra -import space.kscience.kmath.tensors.core.withBroadcast - - -// Dataset normalization - -fun main() = Double.tensorAlgebra.withBroadcast { // work in context with broadcast methods - // take dataset of 5-element vectors from normal distribution - val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5) - - dataset += fromArray( - intArrayOf(5), - doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // row means - ) - - - // find out mean and standard deviation of each column - val mean = dataset.mean(0, false) - val std = dataset.std(0, false) - - println("Mean:\n$mean") - println("Standard deviation:\n$std") - - // also, we can calculate other statistic as minimum and maximum of rows - println("Minimum:\n${dataset.min(0, false)}") - println("Maximum:\n${dataset.max(0, false)}") - - // now we can scale dataset with mean normalization - val datasetScaled = (dataset - mean) / std - - // find out mean and std of scaled dataset - - println("Mean of scaled:\n${datasetScaled.mean(0, false)}") - println("Mean of scaled:\n${datasetScaled.std(0, false)}") -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/linearSystemSolvingWithLUP.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/linearSystemSolvingWithLUP.kt deleted file mode 100644 index f465fc424..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/tensors/linearSystemSolvingWithLUP.kt +++ /dev/null @@ -1,93 +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. - */ - -package space.kscience.kmath.tensors - -import space.kscience.kmath.tensors.core.DoubleTensor -import space.kscience.kmath.tensors.core.tensorAlgebra -import space.kscience.kmath.tensors.core.withBroadcast - -// solving linear system with LUP decomposition - -fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear operations - - // set true value of x - val trueX = fromArray( - intArrayOf(4), - doubleArrayOf(-2.0, 1.5, 6.8, -2.4) - ) - - // and A matrix - val a = fromArray( - intArrayOf(4, 4), - doubleArrayOf( - 0.5, 10.5, 4.5, 1.0, - 8.5, 0.9, 12.8, 0.1, - 5.56, 9.19, 7.62, 5.45, - 1.0, 2.0, -3.0, -2.5 - ) - ) - - // calculate y value - val b = a dot trueX - - // check out A and b - println("A:\n$a") - println("b:\n$b") - - // solve `Ax = b` system using LUP decomposition - - // get P, L, U such that PA = LU - val (p, l, u) = a.lu() - - // check P is permutation matrix - println("P:\n$p") - // L is lower triangular matrix and U is upper triangular matrix - println("L:\n$l") - println("U:\n$u") - // and PA = LU - println("PA:\n${p dot a}") - println("LU:\n${l dot u}") - - /* Ax = b; - PAx = Pb; - LUx = Pb; - let y = Ux, then - Ly = Pb -- this system can be easily solved, since the matrix L is lower triangular; - Ux = y can be solved the same way, since the matrix L is upper triangular - */ - - - - // this function returns solution x of a system lx = b, l should be lower triangular - fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor { - val n = l.shape[0] - val x = zeros(intArrayOf(n)) - for (i in 0 until n) { - x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / l[intArrayOf(i, i)] - } - return x - } - - val y = solveLT(l, p dot b) - - // solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat - // create it by placing ones on side diagonal - val revMat = u.zeroesLike() - val n = revMat.shape[0] - for (i in 0 until n) { - revMat[intArrayOf(i, n - 1 - i)] = 1.0 - } - - // solution of system ux = b, u should be upper triangular - fun solveUT(u: DoubleTensor, b: DoubleTensor): DoubleTensor = revMat dot solveLT( - revMat dot u dot revMat, revMat dot b - ) - - val x = solveUT(u, y) - - println("True x:\n$trueX") - println("x founded with LU method:\n$x") -} \ No newline at end of file diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/multik.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/multik.kt deleted file mode 100644 index f2d1f0b41..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/tensors/multik.kt +++ /dev/null @@ -1,18 +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. - */ - -package space.kscience.kmath.tensors - -import org.jetbrains.kotlinx.multik.api.Multik -import org.jetbrains.kotlinx.multik.api.ndarray -import space.kscience.kmath.multik.multikAlgebra -import space.kscience.kmath.nd.one -import space.kscience.kmath.operations.DoubleField - -fun main(): Unit = with(DoubleField.multikAlgebra) { - val a = Multik.ndarray(intArrayOf(1, 2, 3)).asType().wrap() - val b = Multik.ndarray(doubleArrayOf(1.0, 2.0, 3.0)).wrap() - one(a.shape) - a + b * 3.0 -} diff --git a/examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt b/examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt deleted file mode 100644 index 5c41ab0f1..000000000 --- a/examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt +++ /dev/null @@ -1,239 +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. - */ - -package space.kscience.kmath.tensors - -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra -import space.kscience.kmath.tensors.core.DoubleTensor -import space.kscience.kmath.tensors.core.DoubleTensorAlgebra -import space.kscience.kmath.tensors.core.copyArray -import kotlin.math.sqrt - -const val seed = 100500L - -// Simple feedforward neural network with backpropagation training - -// interface of network layer -interface Layer { - fun forward(input: DoubleTensor): DoubleTensor - fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor -} - -// activation layer -open class Activation( - val activation: (DoubleTensor) -> DoubleTensor, - val activationDer: (DoubleTensor) -> DoubleTensor, -) : Layer { - override fun forward(input: DoubleTensor): DoubleTensor { - return activation(input) - } - - override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor { - return DoubleTensorAlgebra { outputError * activationDer(input) } - } -} - -fun relu(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra { - x.map { if (it > 0) it else 0.0 } -} - -fun reluDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra { - x.map { if (it > 0) 1.0 else 0.0 } -} - -// activation layer with relu activator -class ReLU : Activation(::relu, ::reluDer) - -fun sigmoid(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra { - 1.0 / (1.0 + (-x).exp()) -} - -fun sigmoidDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra { - sigmoid(x) * (1.0 - sigmoid(x)) -} - -// activation layer with sigmoid activator -class Sigmoid : Activation(::sigmoid, ::sigmoidDer) - -// dense layer -class Dense( - private val inputUnits: Int, - private val outputUnits: Int, - private val learningRate: Double = 0.1, -) : Layer { - - private val weights: DoubleTensor = DoubleTensorAlgebra { - randomNormal( - intArrayOf(inputUnits, outputUnits), - seed - ) * sqrt(2.0 / (inputUnits + outputUnits)) - } - - private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(intArrayOf(outputUnits)) } - - override fun forward(input: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra { - (input dot weights) + bias - } - - override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor = DoubleTensorAlgebra { - val gradInput = outputError dot weights.transpose() - - val gradW = input.transpose() dot outputError - val gradBias = outputError.mean(dim = 0, keepDim = false) * input.shape[0].toDouble() - - weights -= learningRate * gradW - bias -= learningRate * gradBias - - gradInput - } - -} - -// simple accuracy equal to the proportion of correct answers -fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double { - check(yPred.shape contentEquals yTrue.shape) - val n = yPred.shape[0] - var correctCnt = 0 - for (i in 0 until n) { - if (yPred[intArrayOf(i, 0)] == yTrue[intArrayOf(i, 0)]) { - correctCnt += 1 - } - } - return correctCnt.toDouble() / n.toDouble() -} - -// neural network class -@OptIn(ExperimentalStdlibApi::class) -class NeuralNetwork(private val layers: List) { - private fun softMaxLoss(yPred: DoubleTensor, yTrue: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra { - - val onesForAnswers = yPred.zeroesLike() - yTrue.copyArray().forEachIndexed { index, labelDouble -> - val label = labelDouble.toInt() - onesForAnswers[intArrayOf(index, label)] = 1.0 - } - - val softmaxValue = yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true) - - (-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble()) - } - - - private fun forward(x: DoubleTensor): List { - var input = x - - return buildList { - layers.forEach { layer -> - val output = layer.forward(input) - add(output) - input = output - } - } - } - - private fun train(xTrain: DoubleTensor, yTrain: DoubleTensor) { - val layerInputs = buildList { - add(xTrain) - addAll(forward(xTrain)) - } - - var lossGrad = softMaxLoss(layerInputs.last(), yTrain) - - layers.zip(layerInputs).reversed().forEach { (layer, input) -> - lossGrad = layer.backward(input, lossGrad) - } - } - - fun fit(xTrain: DoubleTensor, yTrain: DoubleTensor, batchSize: Int, epochs: Int) = DoubleTensorAlgebra { - fun iterBatch(x: DoubleTensor, y: DoubleTensor): Sequence> = sequence { - val n = x.shape[0] - val shuffledIndices = (0 until n).shuffled() - for (i in 0 until n step batchSize) { - val excerptIndices = shuffledIndices.drop(i).take(batchSize).toIntArray() - val batch = x.rowsByIndices(excerptIndices) to y.rowsByIndices(excerptIndices) - yield(batch) - } - } - - for (epoch in 0 until epochs) { - println("Epoch ${epoch + 1}/$epochs") - for ((xBatch, yBatch) in iterBatch(xTrain, yTrain)) { - train(xBatch, yBatch) - } - println("Accuracy:${accuracy(yTrain, predict(xTrain).argMax(1, true).asDouble())}") - } - } - - fun predict(x: DoubleTensor): DoubleTensor { - return forward(x).last() - } - -} - - -@OptIn(ExperimentalStdlibApi::class) -fun main() = BroadcastDoubleTensorAlgebra { - val features = 5 - val sampleSize = 250 - val trainSize = 180 - //val testSize = sampleSize - trainSize - - // take sample of features from normal distribution - val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5 - - x += fromArray( - intArrayOf(5), - doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // row means - ) - - - // define class like '1' if the sum of features > 0 and '0' otherwise - val y = fromArray( - intArrayOf(sampleSize, 1), - DoubleArray(sampleSize) { i -> - if (x[i].sum() > 0.0) { - 1.0 - } else { - 0.0 - } - } - ) - - // split train ans test - val trainIndices = (0 until trainSize).toList().toIntArray() - val testIndices = (trainSize until sampleSize).toList().toIntArray() - - val xTrain = x.rowsByIndices(trainIndices) - val yTrain = y.rowsByIndices(trainIndices) - - val xTest = x.rowsByIndices(testIndices) - val yTest = y.rowsByIndices(testIndices) - - // build model - val layers = buildList { - add(Dense(features, 64)) - add(ReLU()) - add(Dense(64, 16)) - add(ReLU()) - add(Dense(16, 2)) - add(Sigmoid()) - } - val model = NeuralNetwork(layers) - - // fit it with train data - model.fit(xTrain, yTrain, batchSize = 20, epochs = 10) - - // make prediction - val prediction = model.predict(xTest) - - // process raw prediction via argMax - val predictionLabels = prediction.argMax(1, true).asDouble() - - // find out accuracy - val acc = accuracy(yTest, predictionLabels) - println("Test accuracy:$acc") - -} diff --git a/kmath-ast/README.md b/kmath-ast/README.md deleted file mode 100644 index 4872a3a26..000000000 --- a/kmath-ast/README.md +++ /dev/null @@ -1,279 +0,0 @@ -# Module kmath-ast - -Extensions to MST API: transformations, dynamic compilation and visualization. - - - [expression-language](src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt) : Expression language and its parser - - [mst-jvm-codegen](src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode compiler - - [mst-js-codegen](src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler - - [rendering](src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt) : Extendable MST rendering - - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-ast:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-ast:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-ast:0.3.0-dev-20") -} -``` - -## 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. - -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: -1. `sin(x)` -2. `add(x, y)` - -## Dynamic expression code generation - -### On JVM - -`kmath-ast` JVM module supports runtime code generation to eliminate overhead of tree traversal. Code generator builds a -special implementation of `Expression` with implemented `invoke` function. - -For example, the following code: - -```kotlin -import space.kscience.kmath.asm.compileToExpression -import space.kscience.kmath.operations.DoubleField - -"x^3-x+3".parseMath().compileToExpression(DoubleField) -``` - -… leads to generation of bytecode, which can be decompiled to the following Java class: - -```java -import java.util.*; -import kotlin.jvm.functions.*; -import space.kscience.kmath.asm.internal.*; -import space.kscience.kmath.complex.*; -import space.kscience.kmath.expressions.*; - -public final class CompiledExpression_45045_0 implements Expression { - private final Object[] constants; - - public Complex invoke(Map arguments) { - Complex var2 = (Complex)MapIntrinsics.getOrFail(arguments, "x"); - return (Complex)((Function2)this.constants[0]).invoke(var2, (Complex)this.constants[1]); - } -} -``` - -For `LongRing`, `IntRing`, and `DoubleField` specialization is supported for better performance: - -```java -import java.util.*; -import space.kscience.kmath.asm.internal.*; -import space.kscience.kmath.expressions.*; - -public final class CompiledExpression_-386104628_0 implements DoubleExpression { - private final SymbolIndexer indexer; - - public SymbolIndexer getIndexer() { - return this.indexer; - } - - public double invoke(double[] arguments) { - double var2 = arguments[0]; - return Math.pow(var2, 3.0D) - var2 + 3.0D; - } - - public final Double invoke(Map arguments) { - double var2 = ((Double)MapIntrinsics.getOrFail(arguments, "x")).doubleValue(); - return Math.pow(var2, 3.0D) - var2 + 3.0D; - } -} -``` - -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. - -### On JS - -A similar feature is also available on JS. - -```kotlin -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.expressions.* -import space.kscience.kmath.operations.* -import space.kscience.kmath.estree.* - -MstField { x + 2 }.compileToExpression(DoubleField) -``` - -The code above returns expression implemented with such a JS function: - -```js -var executable = function (constants, arguments) { - return constants[1](constants[0](arguments, "x"), 2); -}; -``` - -JS also supports experimental expression optimization with [WebAssembly](https://webassembly.org/) IR generation. -Currently, only expressions inside `DoubleField` and `IntRing` are supported. - -```kotlin -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.expressions.* -import space.kscience.kmath.operations.* -import space.kscience.kmath.wasm.* - -MstField { x + 2 }.compileToExpression(DoubleField) -``` - -An example of emitted Wasm IR in the form of WAT: - -```lisp -(func \$executable (param \$0 f64) (result f64) - (f64.add - (local.get \$0) - (f64.const 2) - ) -) -``` - -#### Limitations - -- ESTree expression compilation uses `eval` which can be unavailable in several environments. -- WebAssembly isn't supported by old versions of browsers (see https://webassembly.org/roadmap/). - -## Rendering expressions - -kmath-ast also includes an extensible engine to display expressions in LaTeX or MathML syntax. - -Example usage: - -```kotlin -import space.kscience.kmath.ast.* -import space.kscience.kmath.ast.rendering.* -import space.kscience.kmath.misc.* - -@OptIn(UnstableKMathAPI::class) -public fun main() { - val mst = "exp(sqrt(x))-asin(2*x)/(2e10+x^3)/(12)+x^(2/3)".parseMath() - val syntax = FeaturedMathRendererWithPostProcess.Default.render(mst) - val latex = LatexSyntaxRenderer.renderWithStringBuilder(syntax) - println("LaTeX:") - println(latex) - println() - val mathML = MathMLSyntaxRenderer.renderWithStringBuilder(syntax) - println("MathML:") - println(mathML) -} -``` - -Result LaTeX: - -
- -![](https://latex.codecogs.com/gif.latex?%5Coperatorname{exp}%5C,%5Cleft(%5Csqrt{x}%5Cright)-%5Cfrac{%5Cfrac{%5Coperatorname{arcsin}%5C,%5Cleft(2%5C,x%5Cright)}{2%5Ctimes10^{10}%2Bx^{3}}}{12}+x^{2/3}) -
- -Result MathML (can be used with MathJax or other renderers): - -
- -```html - - - exp - - - - x - - - - - - - - - arcsin - - - 2 - - x - - - - 2 - × - - - 10 - - - 10 - - - + - - - x - - - 3 - - - - - - - 12 - - - + - - - x - - - 2 - / - 3 - - - - -``` - -
- -It is also possible to create custom algorithms of render, and even add support of other markup languages -(see API reference). diff --git a/kmath-ast/build.gradle.kts b/kmath-ast/build.gradle.kts deleted file mode 100644 index 15b1d0900..000000000 --- a/kmath-ast/build.gradle.kts +++ /dev/null @@ -1,86 +0,0 @@ -plugins { - kotlin("multiplatform") - id("ru.mipt.npm.gradle.common") -} - -kotlin.js { - nodejs { - testTask { - useMocha().timeout = "0" - } - } - - browser { - testTask { - useMocha().timeout = "0" - } - } -} - -kotlin.sourceSets { - filter { it.name.contains("test", true) } - .map(org.jetbrains.kotlin.gradle.plugin.KotlinSourceSet::languageSettings) - .forEach { it.optIn("space.kscience.kmath.misc.UnstableKMathAPI") } - - commonMain { - dependencies { - api("com.github.h0tk3y.betterParse:better-parse:0.4.2") - api(project(":kmath-core")) - } - } - - commonTest { - dependencies { - implementation(project(":kmath-complex")) - } - } - - jsMain { - dependencies { - implementation(npm("astring", "1.7.5")) - implementation(npm("binaryen", "101.0.0")) - implementation(npm("js-base64", "3.6.1")) - } - } - - jvmMain { - dependencies { - implementation("org.ow2.asm:asm-commons:9.2") - } - } -} - -//Workaround for https://github.com/Kotlin/dokka/issues/1455 -tasks.dokkaHtml { - dependsOn(tasks.build) -} - -if (System.getProperty("space.kscience.kmath.ast.dump.generated.classes") == "1") - tasks.jvmTest { - jvmArgs = (jvmArgs ?: emptyList()) + listOf("-Dspace.kscience.kmath.ast.dump.generated.classes=1") - } - -readme { - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL - propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md")) - - feature( - id = "expression-language", - ref = "src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt" - ) { "Expression language and its parser" } - - feature( - id = "mst-jvm-codegen", - ref = "src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt" - ) { "Dynamic MST to JVM bytecode compiler" } - - feature( - id = "mst-js-codegen", - ref = "src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt" - ) { "Dynamic MST to JS compiler" } - - feature( - id = "rendering", - ref = "src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt" - ) { "Extendable MST rendering" } -} diff --git a/kmath-ast/docs/README-TEMPLATE.md b/kmath-ast/docs/README-TEMPLATE.md deleted file mode 100644 index e9e22f4d4..000000000 --- a/kmath-ast/docs/README-TEMPLATE.md +++ /dev/null @@ -1,250 +0,0 @@ -# Module kmath-ast - -Extensions to MST API: transformations, dynamic compilation and visualization. - -${features} - -${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. - -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: -1. `sin(x)` -2. `add(x, y)` - -## Dynamic expression code generation - -### On JVM - -`kmath-ast` JVM module supports runtime code generation to eliminate overhead of tree traversal. Code generator builds a -special implementation of `Expression` with implemented `invoke` function. - -For example, the following code: - -```kotlin -import space.kscience.kmath.asm.compileToExpression -import space.kscience.kmath.operations.DoubleField - -"x^3-x+3".parseMath().compileToExpression(DoubleField) -``` - -… leads to generation of bytecode, which can be decompiled to the following Java class: - -```java -import java.util.*; -import kotlin.jvm.functions.*; -import space.kscience.kmath.asm.internal.*; -import space.kscience.kmath.complex.*; -import space.kscience.kmath.expressions.*; - -public final class CompiledExpression_45045_0 implements Expression { - private final Object[] constants; - - public Complex invoke(Map arguments) { - Complex var2 = (Complex)MapIntrinsics.getOrFail(arguments, "x"); - return (Complex)((Function2)this.constants[0]).invoke(var2, (Complex)this.constants[1]); - } -} -``` - -For `LongRing`, `IntRing`, and `DoubleField` specialization is supported for better performance: - -```java -import java.util.*; -import space.kscience.kmath.asm.internal.*; -import space.kscience.kmath.expressions.*; - -public final class CompiledExpression_-386104628_0 implements DoubleExpression { - private final SymbolIndexer indexer; - - public SymbolIndexer getIndexer() { - return this.indexer; - } - - public double invoke(double[] arguments) { - double var2 = arguments[0]; - return Math.pow(var2, 3.0D) - var2 + 3.0D; - } - - public final Double invoke(Map arguments) { - double var2 = ((Double)MapIntrinsics.getOrFail(arguments, "x")).doubleValue(); - return Math.pow(var2, 3.0D) - var2 + 3.0D; - } -} -``` - -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. - -### On JS - -A similar feature is also available on JS. - -```kotlin -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.expressions.* -import space.kscience.kmath.operations.* -import space.kscience.kmath.estree.* - -MstField { x + 2 }.compileToExpression(DoubleField) -``` - -The code above returns expression implemented with such a JS function: - -```js -var executable = function (constants, arguments) { - return constants[1](constants[0](arguments, "x"), 2); -}; -``` - -JS also supports experimental expression optimization with [WebAssembly](https://webassembly.org/) IR generation. -Currently, only expressions inside `DoubleField` and `IntRing` are supported. - -```kotlin -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.expressions.* -import space.kscience.kmath.operations.* -import space.kscience.kmath.wasm.* - -MstField { x + 2 }.compileToExpression(DoubleField) -``` - -An example of emitted Wasm IR in the form of WAT: - -```lisp -(func \$executable (param \$0 f64) (result f64) - (f64.add - (local.get \$0) - (f64.const 2) - ) -) -``` - -#### Limitations - -- ESTree expression compilation uses `eval` which can be unavailable in several environments. -- WebAssembly isn't supported by old versions of browsers (see https://webassembly.org/roadmap/). - -## Rendering expressions - -kmath-ast also includes an extensible engine to display expressions in LaTeX or MathML syntax. - -Example usage: - -```kotlin -import space.kscience.kmath.ast.* -import space.kscience.kmath.ast.rendering.* -import space.kscience.kmath.misc.* - -@OptIn(UnstableKMathAPI::class) -public fun main() { - val mst = "exp(sqrt(x))-asin(2*x)/(2e10+x^3)/(12)+x^(2/3)".parseMath() - val syntax = FeaturedMathRendererWithPostProcess.Default.render(mst) - val latex = LatexSyntaxRenderer.renderWithStringBuilder(syntax) - println("LaTeX:") - println(latex) - println() - val mathML = MathMLSyntaxRenderer.renderWithStringBuilder(syntax) - println("MathML:") - println(mathML) -} -``` - -Result LaTeX: - -
- -![](https://latex.codecogs.com/gif.latex?%5Coperatorname{exp}%5C,%5Cleft(%5Csqrt{x}%5Cright)-%5Cfrac{%5Cfrac{%5Coperatorname{arcsin}%5C,%5Cleft(2%5C,x%5Cright)}{2%5Ctimes10^{10}%2Bx^{3}}}{12}+x^{2/3}) -
- -Result MathML (can be used with MathJax or other renderers): - -
- -```html - - - exp - - - - x - - - - - - - - - arcsin - - - 2 - - x - - - - 2 - × - - - 10 - - - 10 - - - + - - - x - - - 3 - - - - - - - 12 - - - + - - - x - - - 2 - / - 3 - - - - -``` - -
- -It is also possible to create custom algorithms of render, and even add support of other markup languages -(see API reference). diff --git a/kmath-ast/reference/ArithmeticsEvaluator.g4 b/kmath-ast/reference/ArithmeticsEvaluator.g4 deleted file mode 100644 index dc47b23fb..000000000 --- a/kmath-ast/reference/ArithmeticsEvaluator.g4 +++ /dev/null @@ -1,59 +0,0 @@ -grammar ArithmeticsEvaluator; - -fragment DIGIT: '0'..'9'; -fragment LETTER: 'a'..'z'; -fragment CAPITAL_LETTER: 'A'..'Z'; -fragment UNDERSCORE: '_'; - -ID: (LETTER | UNDERSCORE | CAPITAL_LETTER) (LETTER | UNDERSCORE | DIGIT | CAPITAL_LETTER)*; -NUM: (DIGIT | '.')+ ([eE] [-+]? DIGIT+)?; -MUL: '*'; -DIV: '/'; -PLUS: '+'; -MINUS: '-'; -POW: '^'; -COMMA: ','; -LPAR: '('; -RPAR: ')'; -WS: [ \n\t\r]+ -> skip; - -num - : NUM - ; - -singular - : ID - ; - -unaryFunction - : ID LPAR subSumChain RPAR - ; - -binaryFunction - : ID LPAR subSumChain COMMA subSumChain RPAR - ; - -term - : num - | singular - | unaryFunction - | binaryFunction - | MINUS term - | LPAR subSumChain RPAR - ; - -powChain - : term (POW term)* - ; - -divMulChain - : powChain ((DIV | MUL) powChain)* - ; - -subSumChain - : divMulChain ((PLUS | MINUS) divMulChain)* - ; - -rootParser - : subSumChain EOF - ; diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/TypedMst.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/TypedMst.kt deleted file mode 100644 index 8a8b8797d..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/TypedMst.kt +++ /dev/null @@ -1,177 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.expressions.Expression -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Algebra -import space.kscience.kmath.operations.NumericAlgebra - -/** - * MST form where all values belong to the type [T]. It is optimal for constant folding, dynamic compilation, etc. - * - * @param T the type. - */ -@UnstableKMathAPI -public sealed interface TypedMst { - /** - * A node containing a unary operation. - * - * @param T the type. - * @property operation The identifier of operation. - * @property function The function implementing this operation. - * @property value The argument of this operation. - */ - public class Unary(public val operation: String, public val function: (T) -> T, public val value: TypedMst) : - TypedMst { - override fun equals(other: Any?): Boolean { - if (this === other) return true - if (other == null || this::class != other::class) return false - other as Unary<*> - if (operation != other.operation) return false - if (value != other.value) return false - return true - } - - override fun hashCode(): Int { - var result = operation.hashCode() - result = 31 * result + value.hashCode() - return result - } - - override fun toString(): String = "Unary(operation=$operation, value=$value)" - } - - /** - * A node containing binary operation. - * - * @param T the type. - * @property operation The identifier of operation. - * @property function The binary function implementing this operation. - * @property left The left operand. - * @property right The right operand. - */ - public class Binary( - public val operation: String, - public val function: Function, - public val left: TypedMst, - public val right: TypedMst, - ) : TypedMst { - override fun equals(other: Any?): Boolean { - if (this === other) return true - if (other == null || this::class != other::class) return false - - other as Binary<*> - - if (operation != other.operation) return false - if (left != other.left) return false - if (right != other.right) return false - - return true - } - - override fun hashCode(): Int { - var result = operation.hashCode() - result = 31 * result + left.hashCode() - result = 31 * result + right.hashCode() - return result - } - - override fun toString(): String = "Binary(operation=$operation, left=$left, right=$right)" - } - - /** - * The non-numeric constant value. - * - * @param T the type. - * @property value The held value. - * @property number The number this value corresponds. - */ - public class Constant(public val value: T, public val number: Number?) : TypedMst { - override fun equals(other: Any?): Boolean { - if (this === other) return true - if (other == null || this::class != other::class) return false - other as Constant<*> - if (value != other.value) return false - if (number != other.number) return false - return true - } - - override fun hashCode(): Int { - var result = value?.hashCode() ?: 0 - result = 31 * result + (number?.hashCode() ?: 0) - return result - } - - override fun toString(): String = "Constant(value=$value, number=$number)" - } - - /** - * The node containing a variable - * - * @param T the type. - * @property symbol The symbol of the variable. - */ - public class Variable(public val symbol: Symbol) : TypedMst { - override fun equals(other: Any?): Boolean { - if (this === other) return true - if (other == null || this::class != other::class) return false - other as Variable<*> - if (symbol != other.symbol) return false - return true - } - - override fun hashCode(): Int = symbol.hashCode() - override fun toString(): String = "Variable(symbol=$symbol)" - } -} - -/** - * Interprets the [TypedMst] node with this [Algebra] and [arguments]. - */ -@UnstableKMathAPI -public fun TypedMst.interpret(algebra: Algebra, arguments: Map): T = when (this) { - is TypedMst.Unary -> algebra.unaryOperation(operation, interpret(algebra, arguments)) - - is TypedMst.Binary -> when { - algebra is NumericAlgebra && left is TypedMst.Constant && left.number != null -> - algebra.leftSideNumberOperation(operation, left.number, right.interpret(algebra, arguments)) - - algebra is NumericAlgebra && right is TypedMst.Constant && right.number != null -> - algebra.rightSideNumberOperation(operation, left.interpret(algebra, arguments), right.number) - - else -> algebra.binaryOperation( - operation, - left.interpret(algebra, arguments), - right.interpret(algebra, arguments), - ) - } - - is TypedMst.Constant -> value - is TypedMst.Variable -> arguments.getValue(symbol) -} - -/** - * Interprets the [TypedMst] node with this [Algebra] and optional [arguments]. - */ -@UnstableKMathAPI -public fun TypedMst.interpret(algebra: Algebra, vararg arguments: Pair): T = interpret( - algebra, - when (arguments.size) { - 0 -> emptyMap() - 1 -> mapOf(arguments[0]) - else -> hashMapOf(*arguments) - }, -) - -/** - * Interpret this [TypedMst] node as expression. - */ -@UnstableKMathAPI -public fun TypedMst.toExpression(algebra: Algebra): Expression = Expression { arguments -> - interpret(algebra, arguments) -} diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/evaluateConstants.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/evaluateConstants.kt deleted file mode 100644 index 71fb154c9..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/evaluateConstants.kt +++ /dev/null @@ -1,93 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Algebra -import space.kscience.kmath.operations.NumericAlgebra -import space.kscience.kmath.operations.bindSymbolOrNull - -/** - * Evaluates constants in given [MST] for given [algebra] at the same time with converting to [TypedMst]. - */ -@UnstableKMathAPI -public fun MST.evaluateConstants(algebra: Algebra): TypedMst = when (this) { - is MST.Numeric -> TypedMst.Constant( - (algebra as? NumericAlgebra)?.number(value) ?: error("Numeric nodes are not supported by $algebra"), - value, - ) - - is MST.Unary -> when (val arg = value.evaluateConstants(algebra)) { - is TypedMst.Constant -> { - val value = algebra.unaryOperation( - operation, - arg.value, - ) - - TypedMst.Constant(value, if (value is Number) value else null) - } - - else -> TypedMst.Unary(operation, algebra.unaryOperationFunction(operation), arg) - } - - is MST.Binary -> { - val left = left.evaluateConstants(algebra) - val right = right.evaluateConstants(algebra) - - when { - left is TypedMst.Constant && right is TypedMst.Constant -> { - val value = when { - algebra is NumericAlgebra && left.number != null -> algebra.leftSideNumberOperation( - operation, - left.number, - right.value, - ) - - algebra is NumericAlgebra && right.number != null -> algebra.rightSideNumberOperation( - operation, - left.value, - right.number, - ) - - else -> algebra.binaryOperation( - operation, - left.value, - right.value, - ) - } - - TypedMst.Constant(value, if (value is Number) value else null) - } - - algebra is NumericAlgebra && left is TypedMst.Constant && left.number != null -> TypedMst.Binary( - operation, - algebra.leftSideNumberOperationFunction(operation), - left, - right, - ) - - algebra is NumericAlgebra && right is TypedMst.Constant && right.number != null -> TypedMst.Binary( - operation, - algebra.rightSideNumberOperationFunction(operation), - left, - right, - ) - - else -> TypedMst.Binary(operation, algebra.binaryOperationFunction(operation), left, right) - } - } - - is Symbol -> { - val boundSymbol = algebra.bindSymbolOrNull(this) - - if (boundSymbol != null) - TypedMst.Constant(boundSymbol, if (boundSymbol is Number) boundSymbol else null) - else - TypedMst.Variable(this) - } -} diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt deleted file mode 100644 index 012a6e65f..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt +++ /dev/null @@ -1,122 +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. - */ - -package space.kscience.kmath.ast - -import com.github.h0tk3y.betterParse.combinators.* -import com.github.h0tk3y.betterParse.grammar.Grammar -import com.github.h0tk3y.betterParse.grammar.parseToEnd -import com.github.h0tk3y.betterParse.grammar.parser -import com.github.h0tk3y.betterParse.grammar.tryParseToEnd -import com.github.h0tk3y.betterParse.lexer.Token -import com.github.h0tk3y.betterParse.lexer.TokenMatch -import com.github.h0tk3y.betterParse.lexer.literalToken -import com.github.h0tk3y.betterParse.lexer.regexToken -import com.github.h0tk3y.betterParse.parser.ParseResult -import com.github.h0tk3y.betterParse.parser.Parser -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.operations.FieldOps -import space.kscience.kmath.operations.GroupOps -import space.kscience.kmath.operations.PowerOperations -import space.kscience.kmath.operations.RingOps -import kotlin.math.floor - -/** - * better-parse implementation of grammar defined in the ArithmeticsEvaluator.g4. - * - * @author Alexander Nozik - * @author Iaroslav Postovalov - */ -public object ArithmeticsEvaluator : Grammar() { - private val num: Token by regexToken("[\\d.]+(?:[eE][-+]?\\d+)?".toRegex()) - private val id: Token by regexToken("[a-z_A-Z][\\da-z_A-Z]*".toRegex()) - private val lpar: Token by literalToken("(") - private val rpar: Token by literalToken(")") - private val comma: Token by literalToken(",") - private val mul: Token by literalToken("*") - private val pow: Token by literalToken("^") - private val div: Token by literalToken("/") - private val minus: Token by literalToken("-") - private val plus: Token by literalToken("+") - - @Suppress("unused") - private val ws: Token by regexToken("\\s+".toRegex(), ignore = true) - - // TODO Rewrite as custom parser to handle numbers with better precision. Currently, numbers like 1e10 are handled while they could be stored as longs without precision loss. - private val number: Parser by num use { - val d = text.toDoubleOrNull() - - MST.Numeric( - if (d == null || d == floor(d) && !d.isInfinite()) { - text.toLongOrNull() ?: text.toDouble() - } else - d - ) - } - - private val singular: Parser by id use { Symbol(text) } - - private val unaryFunction: Parser by (id and -lpar and parser(ArithmeticsEvaluator::subSumChain) and -rpar) - .map { (id, term) -> MST.Unary(id.text, term) } - - private val binaryFunction: Parser by id - .and(-lpar) - .and(parser(ArithmeticsEvaluator::subSumChain)) - .and(-comma) - .and(parser(ArithmeticsEvaluator::subSumChain)) - .and(-rpar) - .map { (id, left, right) -> MST.Binary(id.text, left, right) } - - private val term: Parser by number - .or(binaryFunction) - .or(unaryFunction) - .or(singular) - .or(-minus and parser(ArithmeticsEvaluator::term) map { MST.Unary(GroupOps.MINUS_OPERATION, it) }) - .or(-lpar and parser(ArithmeticsEvaluator::subSumChain) and -rpar) - - private val powChain: Parser by leftAssociative(term = term, operator = pow) { a, _, b -> - MST.Binary(PowerOperations.POW_OPERATION, a, b) - } - - private val divMulChain: Parser by leftAssociative( - term = powChain, - operator = div or mul use TokenMatch::type - ) { a, op, b -> - if (op == div) - MST.Binary(FieldOps.DIV_OPERATION, a, b) - else - MST.Binary(RingOps.TIMES_OPERATION, a, b) - } - - private val subSumChain: Parser by leftAssociative( - term = divMulChain, - operator = plus or minus use TokenMatch::type - ) { a, op, b -> - if (op == plus) - MST.Binary(GroupOps.PLUS_OPERATION, a, b) - else - MST.Binary(GroupOps.MINUS_OPERATION, a, b) - } - - override val rootParser: Parser by subSumChain -} - -/** - * Tries to parse the string into [MST] using [ArithmeticsEvaluator]. Returns [ParseResult] representing expression or - * error. - * - * @receiver the string to parse. - * @return the [MST] node. - */ -public fun String.tryParseMath(): ParseResult = ArithmeticsEvaluator.tryParseToEnd(this) - -/** - * Parses the string into [MST] using [ArithmeticsEvaluator]. - * - * @receiver the string to parse. - * @return the [MST] node. - */ -public fun String.parseMath(): MST = ArithmeticsEvaluator.parseToEnd(this) diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/LatexSyntaxRenderer.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/LatexSyntaxRenderer.kt deleted file mode 100644 index 2df3d3cc7..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/LatexSyntaxRenderer.kt +++ /dev/null @@ -1,150 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.misc.UnstableKMathAPI - -/** - * [SyntaxRenderer] implementation for LaTeX. - * - * The generated string is a valid LaTeX fragment to be used in the Math Mode. - * - * Example usage: - * - * ``` - * \documentclass{article} - * \begin{document} - * \begin{equation} - * %code generated by the syntax renderer - * \end{equation} - * \end{document} - * ``` - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public object LatexSyntaxRenderer : SyntaxRenderer { - override fun render(node: MathSyntax, output: Appendable): Unit = output.run { - fun render(syntax: MathSyntax) = render(syntax, output) - - when (node) { - is NumberSyntax -> append(node.string) - is SymbolSyntax -> append(node.string) - - is OperatorNameSyntax -> { - append("\\operatorname{") - append(node.name) - append('}') - } - - is SpecialSymbolSyntax -> when (node.kind) { - SpecialSymbolSyntax.Kind.INFINITY -> append("\\infty") - SpecialSymbolSyntax.Kind.SMALL_PI -> append("\\pi") - } - - is OperandSyntax -> { - if (node.parentheses) append("\\left(") - render(node.operand) - if (node.parentheses) append("\\right)") - } - - is UnaryOperatorSyntax -> { - render(node.prefix) - append("\\,") - render(node.operand) - } - - is UnaryPlusSyntax -> { - append('+') - render(node.operand) - } - - is UnaryMinusSyntax -> { - append('-') - render(node.operand) - } - - is RadicalSyntax -> { - append("\\sqrt") - append('{') - render(node.operand) - append('}') - } - - is ExponentSyntax -> if (node.useOperatorForm) { - append("\\operatorname{exp}\\,") - render(node.operand) - } else { - append("e^{") - render(node.operand) - append('}') - } - - is SuperscriptSyntax -> { - render(node.left) - append("^{") - render(node.right) - append('}') - } - - is SubscriptSyntax -> { - render(node.left) - append("_{") - render(node.right) - append('}') - } - - is BinaryOperatorSyntax -> { - render(node.prefix) - append("\\left(") - render(node.left) - append(',') - render(node.right) - append("\\right)") - } - - is BinaryPlusSyntax -> { - render(node.left) - append('+') - render(node.right) - } - - is BinaryMinusSyntax -> { - render(node.left) - append('-') - render(node.right) - } - - is FractionSyntax -> if (node.infix) { - render(node.left) - append('/') - render(node.right) - } else { - append("\\frac{") - render(node.left) - append("}{") - render(node.right) - append('}') - } - - is RadicalWithIndexSyntax -> { - append("\\sqrt") - append('[') - render(node.left) - append(']') - append('{') - render(node.right) - append('}') - } - - is MultiplicationSyntax -> { - render(node.left) - append(if (node.times) "\\times" else "\\,") - render(node.right) - } - } - } -} diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathMLSyntaxRenderer.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathMLSyntaxRenderer.kt deleted file mode 100644 index 8b5819b84..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathMLSyntaxRenderer.kt +++ /dev/null @@ -1,157 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.misc.UnstableKMathAPI - -/** - * [SyntaxRenderer] implementation for MathML. - * - * The generated XML string is a valid MathML instance. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public object MathMLSyntaxRenderer : SyntaxRenderer { - override fun render(node: MathSyntax, output: Appendable) { - output.append("") - renderPart(node, output) - output.append("") - } - - /** - * Renders a part of syntax returning a correct MathML tag not the whole MathML instance. - */ - public fun renderPart(node: MathSyntax, output: Appendable): Unit = output.run { - fun tag(tagName: String, vararg attr: Pair, block: () -> Unit = {}) { - append('<') - append(tagName) - - if (attr.isNotEmpty()) { - append(' ') - var count = 0 - - for ((name, value) in attr) { - if (++count > 1) append(' ') - append(name) - append("=\"") - append(value) - append('"') - } - } - - append('>') - block() - append("') - } - - fun render(syntax: MathSyntax) = renderPart(syntax, output) - - when (node) { - is NumberSyntax -> tag("mn") { append(node.string) } - is SymbolSyntax -> tag("mi") { append(node.string) } - is OperatorNameSyntax -> tag("mo") { append(node.name) } - - is SpecialSymbolSyntax -> when (node.kind) { - SpecialSymbolSyntax.Kind.INFINITY -> tag("mo") { append("∞") } - SpecialSymbolSyntax.Kind.SMALL_PI -> tag("mo") { append("π") } - } - - is OperandSyntax -> if (node.parentheses) { - tag("mfenced", "open" to "(", "close" to ")", "separators" to "") { - render(node.operand) - } - } else { - render(node.operand) - } - - is UnaryOperatorSyntax -> { - render(node.prefix) - tag("mspace", "width" to "0.167em") - render(node.operand) - } - - is UnaryPlusSyntax -> { - tag("mo") { append('+') } - render(node.operand) - } - - is UnaryMinusSyntax -> { - tag("mo") { append("-") } - render(node.operand) - } - - is RadicalSyntax -> tag("msqrt") { render(node.operand) } - - is ExponentSyntax -> if (node.useOperatorForm) { - tag("mo") { append("exp") } - tag("mspace", "width" to "0.167em") - render(node.operand) - } else { - tag("msup") { - tag("mrow") { - tag("mi") { append("e") } - } - tag("mrow") { render(node.operand) } - } - } - - is SuperscriptSyntax -> tag("msup") { - tag("mrow") { render(node.left) } - tag("mrow") { render(node.right) } - } - - is SubscriptSyntax -> tag("msub") { - tag("mrow") { render(node.left) } - tag("mrow") { render(node.right) } - } - - is BinaryOperatorSyntax -> { - render(node.prefix) - - tag("mfenced", "open" to "(", "close" to ")", "separators" to "") { - render(node.left) - tag("mo") { append(',') } - render(node.right) - } - } - - is BinaryPlusSyntax -> { - render(node.left) - tag("mo") { append('+') } - render(node.right) - } - - is BinaryMinusSyntax -> { - render(node.left) - tag("mo") { append('-') } - render(node.right) - } - - is FractionSyntax -> if (node.infix) { - render(node.left) - tag("mo") { append('/') } - render(node.right) - } else tag("mfrac") { - tag("mrow") { render(node.left) } - tag("mrow") { render(node.right) } - } - - is RadicalWithIndexSyntax -> tag("mroot") { - tag("mrow") { render(node.right) } - tag("mrow") { render(node.left) } - } - - is MultiplicationSyntax -> { - render(node.left) - if (node.times) tag("mo") { append("×") } else tag("mspace", "width" to "0.167em") - render(node.right) - } - } - } -} diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt deleted file mode 100644 index fdef35ebd..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt +++ /dev/null @@ -1,115 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.misc.UnstableKMathAPI - -/** - * Renders [MST] to [MathSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun interface MathRenderer { - /** - * Renders [MST] to [MathSyntax]. - */ - public fun render(mst: MST): MathSyntax -} - -/** - * Implements [MST] render process with sequence of features. - * - * @property features The applied features. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public open class FeaturedMathRenderer(public val features: List) : MathRenderer { - override fun render(mst: MST): MathSyntax { - for (feature in features) feature.render(this, mst)?.let { return it } - throw UnsupportedOperationException("Renderer $this has no appropriate feature to render node $mst.") - } - - /** - * Logical unit of [MST] rendering. - */ - public fun interface RenderFeature { - /** - * Renders [MST] to [MathSyntax] in the context of owning renderer. - */ - public fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? - } -} - -/** - * Extends [FeaturedMathRenderer] by adding post-processing stages. - * - * @property stages The applied stages. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public open class FeaturedMathRendererWithPostProcess( - features: List, - public val stages: List, -) : FeaturedMathRenderer(features) { - override fun render(mst: MST): MathSyntax { - val res = super.render(mst) - for (stage in stages) stage.perform(res) - return res - } - - /** - * Logical unit of [MathSyntax] post-processing. - */ - public fun interface PostProcessPhase { - /** - * Performs the specified action over [MathSyntax]. - */ - public fun perform(node: MathSyntax) - } - - public companion object { - /** - * The default setup of [FeaturedMathRendererWithPostProcess]. - */ - public val Default: FeaturedMathRendererWithPostProcess = FeaturedMathRendererWithPostProcess( - listOf( - // Printing known operations - BinaryPlus.Default, - BinaryMinus.Default, - UnaryPlus.Default, - UnaryMinus.Default, - Multiplication.Default, - Fraction.Default, - Power.Default, - SquareRoot.Default, - Exponent.Default, - InverseTrigonometricOperations.Default, - InverseHyperbolicOperations.Default, - - // Fallback option for unknown operations - printing them as operator - BinaryOperator.Default, - UnaryOperator.Default, - - // Pretty printing for some objects - PrettyPrintFloats.Default, - PrettyPrintIntegers.Default, - PrettyPrintPi.Default, - - // Printing terminal nodes as string - PrintNumeric, - PrintSymbol, - ), - listOf( - BetterExponent, - BetterFraction, - SimplifyParentheses.Default, - BetterMultiplication, - ), - ) - } -} diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathSyntax.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathSyntax.kt deleted file mode 100644 index ee23ab408..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathSyntax.kt +++ /dev/null @@ -1,381 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.misc.UnstableKMathAPI - -/** - * Syntax node for mathematical typography. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public sealed class MathSyntax { - /** - * The parent node of this syntax node. - */ - public var parent: MathSyntax? = null -} - -/** - * Terminal node, which should not have any children nodes. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public sealed class TerminalSyntax : MathSyntax() - -/** - * Node containing a certain operation. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public sealed class OperationSyntax : MathSyntax() { - /** - * The operation token. - */ - public abstract val operation: String -} - -/** - * Unary node, which has only one child. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public sealed class UnarySyntax : OperationSyntax() { - /** - * The operand of this node. - */ - public abstract val operand: MathSyntax -} - -/** - * Binary node, which has only two children. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public sealed class BinarySyntax : OperationSyntax() { - /** - * The left-hand side operand. - */ - public abstract val left: MathSyntax - - /** - * The right-hand side operand. - */ - public abstract val right: MathSyntax -} - -/** - * Represents a number. - * - * @property string The digits of number. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class NumberSyntax(public var string: String) : TerminalSyntax() - -/** - * Represents a symbol. - * - * @property string The symbol. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class SymbolSyntax(public var string: String) : TerminalSyntax() - -/** - * Represents special typing for operator name. - * - * @property name The operator name. - * @see BinaryOperatorSyntax - * @see UnaryOperatorSyntax - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class OperatorNameSyntax(public var name: String) : TerminalSyntax() - -/** - * Represents a usage of special symbols (e.g., *∞*). - * - * @property kind The kind of symbol. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class SpecialSymbolSyntax(public var kind: Kind) : TerminalSyntax() { - /** - * The kind of symbol. - */ - public enum class Kind { - /** - * The infinity (∞) symbol. - */ - INFINITY, - - /** - * The Pi (π) symbol. - */ - SMALL_PI; - } -} - -/** - * Represents operand of a certain operator wrapped with parentheses or not. - * - * @property operand The operand. - * @property parentheses Whether the operand should be wrapped with parentheses. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class OperandSyntax( - public val operand: MathSyntax, - public var parentheses: Boolean, -) : MathSyntax() { - init { - operand.parent = this - } -} - -/** - * Represents unary, prefix operator syntax (like *f(x)*). - * - * @property prefix The prefix. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class UnaryOperatorSyntax( - override val operation: String, - public var prefix: MathSyntax, - override val operand: OperandSyntax, -) : UnarySyntax() { - init { - operand.parent = this - } -} - -/** - * Represents prefix, unary plus operator (*+x*). - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class UnaryPlusSyntax( - override val operation: String, - override val operand: OperandSyntax, -) : UnarySyntax() { - init { - operand.parent = this - } -} - -/** - * Represents prefix, unary minus operator (*-x*). - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class UnaryMinusSyntax( - override val operation: String, - override val operand: OperandSyntax, -) : UnarySyntax() { - init { - operand.parent = this - } -} - -/** - * Represents radical with a node inside it (*√x*). - * - * @property operand The radicand. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class RadicalSyntax( - override val operation: String, - override val operand: MathSyntax, -) : UnarySyntax() { - init { - operand.parent = this - } -} - -/** - * Represents exponential function. - * - * @property operand The argument of function. - * @property useOperatorForm `true` if operator form is used (*exp (x)*), `false` if exponentiation form is used - * (*ex*). - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class ExponentSyntax( - override val operation: String, - override val operand: OperandSyntax, - public var useOperatorForm: Boolean, -) : UnarySyntax() { - init { - operand.parent = this - } -} - -/** - * Represents a syntax node with superscript (*x2*). - * - * @property left The node. - * @property right The superscript. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class SuperscriptSyntax( - override val operation: String, - override val left: MathSyntax, - override val right: MathSyntax, -) : BinarySyntax() { - init { - left.parent = this - right.parent = this - } -} - -/** - * Represents a syntax node with subscript (*xi*). - * - * @property left The node. - * @property right The subscript. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class SubscriptSyntax( - override val operation: String, - override val left: MathSyntax, - override val right: MathSyntax, -) : BinarySyntax() { - init { - left.parent = this - right.parent = this - } -} - -/** - * Represents binary, prefix operator syntax (like *f(a, b)*). - * - * @property prefix The prefix. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class BinaryOperatorSyntax( - override val operation: String, - public var prefix: MathSyntax, - override val left: MathSyntax, - override val right: MathSyntax, -) : BinarySyntax() { - init { - left.parent = this - right.parent = this - } -} - -/** - * Represents binary, infix addition (*42 + 42*). - * - * @param left The augend. - * @param right The addend. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class BinaryPlusSyntax( - override val operation: String, - override val left: OperandSyntax, - override val right: OperandSyntax, -) : BinarySyntax() { - init { - left.parent = this - right.parent = this - } -} - -/** - * Represents binary, infix subtraction (*42 − 42*). - * - * @param left The minuend. - * @param right The subtrahend. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class BinaryMinusSyntax( - override val operation: String, - override val left: OperandSyntax, - override val right: OperandSyntax, -) : BinarySyntax() { - init { - left.parent = this - right.parent = this - } -} - -/** - * Represents fraction with numerator and denominator. - * - * @property left The numerator. - * @property right The denominator. - * @property infix Whether infix (*1 / 2*) or normal (*½*) fraction should be made. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class FractionSyntax( - override val operation: String, - override val left: OperandSyntax, - override val right: OperandSyntax, - public var infix: Boolean, -) : BinarySyntax() { - init { - left.parent = this - right.parent = this - } -} - -/** - * Represents radical syntax with index (*3√x*). - * - * @property left The index. - * @property right The radicand. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class RadicalWithIndexSyntax( - override val operation: String, - override val left: MathSyntax, - override val right: MathSyntax, -) : BinarySyntax() { - init { - left.parent = this - right.parent = this - } -} - -/** - * Represents binary, infix multiplication in the form of coefficient (*2 x*) or with operator (*x × 2*). - * - * @property left The multiplicand. - * @property right The multiplier. - * @property times Whether the times (×) symbol should be used. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public data class MultiplicationSyntax( - override val operation: String, - override val left: OperandSyntax, - override val right: OperandSyntax, - public var times: Boolean, -) : BinarySyntax() { - init { - left.parent = this - right.parent = this - } -} diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/SyntaxRenderer.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/SyntaxRenderer.kt deleted file mode 100644 index 362c07d72..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/SyntaxRenderer.kt +++ /dev/null @@ -1,34 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.misc.UnstableKMathAPI - -/** - * Abstraction of writing [MathSyntax] as a string of an actual markup language. Typical implementation should - * involve traversal of MathSyntax with handling each subtype. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun interface SyntaxRenderer { - /** - * Renders the [MathSyntax] to [output]. - */ - public fun render(node: MathSyntax, output: Appendable) -} - -/** - * Calls [SyntaxRenderer.render] with given [node] and a new [StringBuilder] instance, and returns its content. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun SyntaxRenderer.renderWithStringBuilder(node: MathSyntax): String { - val sb = StringBuilder() - render(node, sb) - return sb.toString() -} diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/features.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/features.kt deleted file mode 100644 index 90f78a152..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/features.kt +++ /dev/null @@ -1,483 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.ast.rendering.FeaturedMathRenderer.RenderFeature -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import kotlin.reflect.KClass - -/** - * Prints any [Symbol] as a [SymbolSyntax] containing the [Symbol.identity] of it. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public val PrintSymbol: RenderFeature = RenderFeature { _, node -> - if (node !is Symbol) null - else SymbolSyntax(string = node.identity) -} - -/** - * Prints any [MST.Numeric] as a [NumberSyntax] containing the [Any.toString] result of it. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public val PrintNumeric: RenderFeature = RenderFeature { _, node -> - if (node !is MST.Numeric) - null - else - NumberSyntax(string = node.value.toString()) -} - -@UnstableKMathAPI -private fun printSignedNumberString(s: String): MathSyntax = if (s.startsWith('-')) - UnaryMinusSyntax( - operation = GroupOps.MINUS_OPERATION, - operand = OperandSyntax( - operand = NumberSyntax(string = s.removePrefix("-")), - parentheses = true, - ), - ) -else - NumberSyntax(string = s) - -/** - * Special printing for numeric types that are printed in form of - * *('-'? (DIGIT+ ('.' DIGIT+)? ('E' '-'? DIGIT+)? | 'Infinity')) | 'NaN'*. - * - * @property types The suitable types. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class PrettyPrintFloats(public val types: Set>) : RenderFeature { - override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? { - if (node !is MST.Numeric || node.value::class !in types) return null - - val toString = when (val v = node.value) { - is Float -> v.multiplatformToString() - is Double -> v.multiplatformToString() - else -> v.toString() - }.removeSuffix(".0") - - if (toString.contains('E', ignoreCase = true)) { - val (beforeE, afterE) = toString.split('E', ignoreCase = true) - val significand = beforeE.toDouble().toString().removeSuffix(".0") - val exponent = afterE.toDouble().toString().removeSuffix(".0") - - return MultiplicationSyntax( - operation = RingOps.TIMES_OPERATION, - left = OperandSyntax(operand = NumberSyntax(significand), parentheses = true), - right = OperandSyntax( - operand = SuperscriptSyntax( - operation = PowerOperations.POW_OPERATION, - left = NumberSyntax(string = "10"), - right = printSignedNumberString(exponent), - ), - parentheses = true, - ), - times = true, - ) - } - - if (toString.endsWith("Infinity")) { - val infty = SpecialSymbolSyntax(SpecialSymbolSyntax.Kind.INFINITY) - - if (toString.startsWith('-')) - return UnaryMinusSyntax( - operation = GroupOps.MINUS_OPERATION, - operand = OperandSyntax(operand = infty, parentheses = true), - ) - - return infty - } - - return printSignedNumberString(toString) - } - - public companion object { - /** - * The default instance containing [Float], and [Double]. - */ - public val Default: PrettyPrintFloats = PrettyPrintFloats(setOf(Float::class, Double::class)) - } -} - -/** - * Special printing for numeric types that are printed in form of *'-'? DIGIT+*. - * - * @property types The suitable types. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class PrettyPrintIntegers(public val types: Set>) : RenderFeature { - override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? = - if (node !is MST.Numeric || node.value::class !in types) - null - else - printSignedNumberString(node.value.toString()) - - public companion object { - /** - * The default instance containing [Byte], [Short], [Int], and [Long]. - */ - public val Default: PrettyPrintIntegers = - PrettyPrintIntegers(setOf(Byte::class, Short::class, Int::class, Long::class)) - } -} - -/** - * Special printing for symbols meaning Pi. - * - * @property symbols The allowed symbols. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class PrettyPrintPi(public val symbols: Set) : RenderFeature { - override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? = - if (node !is Symbol || node.identity !in symbols) - null - else - SpecialSymbolSyntax(kind = SpecialSymbolSyntax.Kind.SMALL_PI) - - public companion object { - /** - * The default instance containing `pi`. - */ - public val Default: PrettyPrintPi = PrettyPrintPi(setOf("pi")) - } -} - -/** - * Abstract printing of unary operations that discards [MST] if their operation is not in [operations] or its type is - * not [MST.Unary]. - * - * @param operations the allowed operations. If `null`, any operation is accepted. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public abstract class Unary(public val operations: Collection?) : RenderFeature { - /** - * The actual render function specialized for [MST.Unary]. - */ - protected abstract fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax? - - public final override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? = - if (node !is MST.Unary || operations != null && node.operation !in operations) - null - else - renderUnary(renderer, node) -} - -/** - * Abstract printing of unary operations that discards [MST] if their operation is not in [operations] or its type is - * not [MST.Binary]. - * - * @property operations the allowed operations. If `null`, any operation is accepted. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public abstract class Binary(public val operations: Collection?) : RenderFeature { - /** - * The actual render function specialized for [MST.Binary]. - */ - protected abstract fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax? - - public final override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? { - if (node !is MST.Binary || operations != null && node.operation !in operations) return null - return renderBinary(renderer, node) - } -} - -/** - * Handles binary nodes by producing [BinaryPlusSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class BinaryPlus(operations: Collection?) : Binary(operations) { - override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax = - BinaryPlusSyntax( - operation = node.operation, - left = OperandSyntax(parent.render(node.left), true), - right = OperandSyntax(parent.render(node.right), true), - ) - - public companion object { - /** - * The default instance configured with [GroupOps.PLUS_OPERATION]. - */ - public val Default: BinaryPlus = BinaryPlus(setOf(GroupOps.PLUS_OPERATION)) - } -} - -/** - * Handles binary nodes by producing [BinaryMinusSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class BinaryMinus(operations: Collection?) : Binary(operations) { - override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax = - BinaryMinusSyntax( - operation = node.operation, - left = OperandSyntax(operand = parent.render(node.left), parentheses = true), - right = OperandSyntax(operand = parent.render(node.right), parentheses = true), - ) - - public companion object { - /** - * The default instance configured with [GroupOps.MINUS_OPERATION]. - */ - public val Default: BinaryMinus = BinaryMinus(setOf(GroupOps.MINUS_OPERATION)) - } -} - -/** - * Handles unary nodes by producing [UnaryPlusSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class UnaryPlus(operations: Collection?) : Unary(operations) { - override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = UnaryPlusSyntax( - operation = node.operation, - operand = OperandSyntax(operand = parent.render(node.value), parentheses = true), - ) - - public companion object { - /** - * The default instance configured with [GroupOps.PLUS_OPERATION]. - */ - public val Default: UnaryPlus = UnaryPlus(setOf(GroupOps.PLUS_OPERATION)) - } -} - -/** - * Handles binary nodes by producing [UnaryMinusSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class UnaryMinus(operations: Collection?) : Unary(operations) { - override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = UnaryMinusSyntax( - operation = node.operation, - operand = OperandSyntax(operand = parent.render(node.value), parentheses = true), - ) - - public companion object { - /** - * The default instance configured with [GroupOps.MINUS_OPERATION]. - */ - public val Default: UnaryMinus = UnaryMinus(setOf(GroupOps.MINUS_OPERATION)) - } -} - -/** - * Handles binary nodes by producing [FractionSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class Fraction(operations: Collection?) : Binary(operations) { - override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax = FractionSyntax( - operation = node.operation, - left = OperandSyntax(operand = parent.render(node.left), parentheses = true), - right = OperandSyntax(operand = parent.render(node.right), parentheses = true), - infix = true, - ) - - public companion object { - /** - * The default instance configured with [FieldOps.DIV_OPERATION]. - */ - public val Default: Fraction = Fraction(setOf(FieldOps.DIV_OPERATION)) - } -} - -/** - * Handles binary nodes by producing [BinaryOperatorSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class BinaryOperator(operations: Collection?) : Binary(operations) { - override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax = - BinaryOperatorSyntax( - operation = node.operation, - prefix = OperatorNameSyntax(name = node.operation), - left = parent.render(node.left), - right = parent.render(node.right), - ) - - public companion object { - /** - * The default instance configured with `null`. - */ - public val Default: BinaryOperator = BinaryOperator(null) - } -} - -/** - * Handles unary nodes by producing [UnaryOperatorSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class UnaryOperator(operations: Collection?) : Unary(operations) { - override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = - UnaryOperatorSyntax( - operation = node.operation, - prefix = OperatorNameSyntax(node.operation), - operand = OperandSyntax(parent.render(node.value), true), - ) - - public companion object { - /** - * The default instance configured with `null`. - */ - public val Default: UnaryOperator = UnaryOperator(null) - } -} - -/** - * Handles binary nodes by producing [SuperscriptSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class Power(operations: Collection?) : Binary(operations) { - override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax = - SuperscriptSyntax( - operation = node.operation, - left = OperandSyntax(parent.render(node.left), true), - right = OperandSyntax(parent.render(node.right), true), - ) - - public companion object { - /** - * The default instance configured with [PowerOperations.POW_OPERATION]. - */ - public val Default: Power = Power(setOf(PowerOperations.POW_OPERATION)) - } -} - -/** - * Handles binary nodes by producing [RadicalSyntax] with no index. - */ -@UnstableKMathAPI -public class SquareRoot(operations: Collection?) : Unary(operations) { - override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = - RadicalSyntax(operation = node.operation, operand = parent.render(node.value)) - - public companion object { - /** - * The default instance configured with [PowerOperations.SQRT_OPERATION]. - */ - public val Default: SquareRoot = SquareRoot(setOf(PowerOperations.SQRT_OPERATION)) - } -} - -/** - * Handles unary nodes by producing [ExponentSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class Exponent(operations: Collection?) : Unary(operations) { - override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = ExponentSyntax( - operation = node.operation, - operand = OperandSyntax(operand = parent.render(node.value), parentheses = true), - useOperatorForm = true, - ) - - public companion object { - /** - * The default instance configured with [ExponentialOperations.EXP_OPERATION]. - */ - public val Default: Exponent = Exponent(setOf(ExponentialOperations.EXP_OPERATION)) - } -} - -/** - * Handles binary nodes by producing [MultiplicationSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class Multiplication(operations: Collection?) : Binary(operations) { - override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax = - MultiplicationSyntax( - operation = node.operation, - left = OperandSyntax(operand = parent.render(node.left), parentheses = true), - right = OperandSyntax(operand = parent.render(node.right), parentheses = true), - times = true, - ) - - public companion object { - /** - * The default instance configured with [RingOps.TIMES_OPERATION]. - */ - public val Default: Multiplication = Multiplication(setOf(RingOps.TIMES_OPERATION)) - } -} - -/** - * Handles binary nodes by producing inverse [UnaryOperatorSyntax] with *arc* prefix instead of *a*. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class InverseTrigonometricOperations(operations: Collection?) : Unary(operations) { - override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = - UnaryOperatorSyntax( - operation = node.operation, - prefix = OperatorNameSyntax(name = node.operation.replaceFirst("a", "arc")), - operand = OperandSyntax(operand = parent.render(node.value), parentheses = true), - ) - - public companion object { - /** - * The default instance configured with [TrigonometricOperations.ACOS_OPERATION], - * [TrigonometricOperations.ASIN_OPERATION], [TrigonometricOperations.ATAN_OPERATION]. - */ - public val Default: InverseTrigonometricOperations = InverseTrigonometricOperations(setOf( - TrigonometricOperations.ACOS_OPERATION, - TrigonometricOperations.ASIN_OPERATION, - TrigonometricOperations.ATAN_OPERATION, - )) - } -} - -/** - * Handles binary nodes by producing inverse [UnaryOperatorSyntax] with *ar* prefix instead of *a*. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class InverseHyperbolicOperations(operations: Collection?) : Unary(operations) { - override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = - UnaryOperatorSyntax( - operation = node.operation, - prefix = OperatorNameSyntax(name = node.operation.replaceFirst("a", "ar")), - operand = OperandSyntax(operand = parent.render(node.value), parentheses = true), - ) - - public companion object { - /** - * The default instance configured with [ExponentialOperations.ACOSH_OPERATION], - * [ExponentialOperations.ASINH_OPERATION], and [ExponentialOperations.ATANH_OPERATION]. - */ - public val Default: InverseHyperbolicOperations = InverseHyperbolicOperations(setOf( - ExponentialOperations.ACOSH_OPERATION, - ExponentialOperations.ASINH_OPERATION, - ExponentialOperations.ATANH_OPERATION, - )) - } -} diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt deleted file mode 100644 index 291399cee..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt +++ /dev/null @@ -1,9 +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. - */ - -package space.kscience.kmath.ast.rendering - -internal expect fun Double.multiplatformToString(): String -internal expect fun Float.multiplatformToString(): String diff --git a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/phases.kt b/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/phases.kt deleted file mode 100644 index c0271fbb5..000000000 --- a/kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/phases.kt +++ /dev/null @@ -1,320 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.ast.rendering.FeaturedMathRendererWithPostProcess.PostProcessPhase -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.FieldOps -import space.kscience.kmath.operations.GroupOps -import space.kscience.kmath.operations.PowerOperations -import space.kscience.kmath.operations.RingOps - -/** - * Removes unnecessary times (×) symbols from [MultiplicationSyntax]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public val BetterMultiplication: PostProcessPhase = PostProcessPhase { node -> - fun perform(node: MathSyntax): Unit = when (node) { - is NumberSyntax -> Unit - is SymbolSyntax -> Unit - is OperatorNameSyntax -> Unit - is SpecialSymbolSyntax -> Unit - is OperandSyntax -> perform(node.operand) - - is UnaryOperatorSyntax -> { - perform(node.prefix) - perform(node.operand) - } - - is UnaryPlusSyntax -> perform(node.operand) - is UnaryMinusSyntax -> perform(node.operand) - is RadicalSyntax -> perform(node.operand) - is ExponentSyntax -> perform(node.operand) - - is SuperscriptSyntax -> { - perform(node.left) - perform(node.right) - } - - is SubscriptSyntax -> { - perform(node.left) - perform(node.right) - } - - is BinaryOperatorSyntax -> { - perform(node.prefix) - perform(node.left) - perform(node.right) - } - - is BinaryPlusSyntax -> { - perform(node.left) - perform(node.right) - } - - is BinaryMinusSyntax -> { - perform(node.left) - perform(node.right) - } - - is FractionSyntax -> { - perform(node.left) - perform(node.right) - } - - is RadicalWithIndexSyntax -> { - perform(node.left) - perform(node.right) - } - - is MultiplicationSyntax -> { - node.times = node.right.operand is NumberSyntax && !node.right.parentheses - || node.left.operand is NumberSyntax && node.right.operand is FractionSyntax - || node.left.operand is NumberSyntax && node.right.operand is NumberSyntax - || node.left.operand is NumberSyntax && node.right.operand is SuperscriptSyntax && node.right.operand.left is NumberSyntax - - perform(node.left) - perform(node.right) - } - } - - perform(node) -} - -/** - * Chooses [FractionSyntax.infix] depending on the context. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public val BetterFraction: PostProcessPhase = PostProcessPhase { node -> - fun perform(node: MathSyntax, infix: Boolean = false): Unit = when (node) { - is NumberSyntax -> Unit - is SymbolSyntax -> Unit - is OperatorNameSyntax -> Unit - is SpecialSymbolSyntax -> Unit - is OperandSyntax -> perform(node.operand, infix) - - is UnaryOperatorSyntax -> { - perform(node.prefix, infix) - perform(node.operand, infix) - } - - is UnaryPlusSyntax -> perform(node.operand, infix) - is UnaryMinusSyntax -> perform(node.operand, infix) - is RadicalSyntax -> perform(node.operand, infix) - is ExponentSyntax -> perform(node.operand, infix) - - is SuperscriptSyntax -> { - perform(node.left, true) - perform(node.right, true) - } - - is SubscriptSyntax -> { - perform(node.left, true) - perform(node.right, true) - } - - is BinaryOperatorSyntax -> { - perform(node.prefix, infix) - perform(node.left, infix) - perform(node.right, infix) - } - - is BinaryPlusSyntax -> { - perform(node.left, infix) - perform(node.right, infix) - } - - is BinaryMinusSyntax -> { - perform(node.left, infix) - perform(node.right, infix) - } - - is FractionSyntax -> { - node.infix = infix - perform(node.left, infix) - perform(node.right, infix) - } - - is RadicalWithIndexSyntax -> { - perform(node.left, true) - perform(node.right, true) - } - - is MultiplicationSyntax -> { - perform(node.left, infix) - perform(node.right, infix) - } - } - - perform(node) -} - -/** - * Applies [ExponentSyntax.useOperatorForm] to [ExponentSyntax] when the operand contains a fraction, a - * superscript or a subscript to improve readability. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public val BetterExponent: PostProcessPhase = PostProcessPhase { node -> - fun perform(node: MathSyntax): Boolean { - return when (node) { - is NumberSyntax -> false - is SymbolSyntax -> false - is OperatorNameSyntax -> false - is SpecialSymbolSyntax -> false - is OperandSyntax -> perform(node.operand) - is UnaryOperatorSyntax -> perform(node.prefix) || perform(node.operand) - is UnaryPlusSyntax -> perform(node.operand) - is UnaryMinusSyntax -> perform(node.operand) - is RadicalSyntax -> true - - is ExponentSyntax -> { - val r = perform(node.operand) - node.useOperatorForm = r - r - } - - is SuperscriptSyntax -> true - is SubscriptSyntax -> true - is BinaryOperatorSyntax -> perform(node.prefix) || perform(node.left) || perform(node.right) - is BinaryPlusSyntax -> perform(node.left) || perform(node.right) - is BinaryMinusSyntax -> perform(node.left) || perform(node.right) - is FractionSyntax -> true - is RadicalWithIndexSyntax -> true - is MultiplicationSyntax -> perform(node.left) || perform(node.right) - } - } - - perform(node) -} - -/** - * Removes unnecessary parentheses from [OperandSyntax]. - * - * @property precedenceFunction Returns the precedence number for syntax node. Higher number is lower priority. - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public class SimplifyParentheses(public val precedenceFunction: (MathSyntax) -> Int) : - PostProcessPhase { - override fun perform(node: MathSyntax): Unit = when (node) { - is NumberSyntax -> Unit - is SymbolSyntax -> Unit - is OperatorNameSyntax -> Unit - is SpecialSymbolSyntax -> Unit - - is OperandSyntax -> { - val isRightOfSuperscript = - (node.parent is SuperscriptSyntax) && (node.parent as SuperscriptSyntax).right === node - - val precedence = precedenceFunction(node.operand) - - val needParenthesesByPrecedence = when (val parent = node.parent) { - null -> false - - is BinarySyntax -> { - val parentPrecedence = precedenceFunction(parent) - - parentPrecedence < precedence || - parentPrecedence == precedence && parentPrecedence != 0 && node === parent.right - } - - else -> precedence > precedenceFunction(parent) - } - - val isInsideExpOperator = - node.parent is ExponentSyntax && (node.parent as ExponentSyntax).useOperatorForm - - val isOnOrUnderNormalFraction = node.parent is FractionSyntax && !((node.parent as FractionSyntax).infix) - - node.parentheses = !isRightOfSuperscript - && (needParenthesesByPrecedence || node.parent is UnaryOperatorSyntax || isInsideExpOperator) - && !isOnOrUnderNormalFraction - - perform(node.operand) - } - - is UnaryOperatorSyntax -> { - perform(node.prefix) - perform(node.operand) - } - - is UnaryPlusSyntax -> perform(node.operand) - is UnaryMinusSyntax -> perform(node.operand) - is RadicalSyntax -> perform(node.operand) - is ExponentSyntax -> perform(node.operand) - - is SuperscriptSyntax -> { - perform(node.left) - perform(node.right) - } - - is SubscriptSyntax -> { - perform(node.left) - perform(node.right) - } - - is BinaryOperatorSyntax -> { - perform(node.prefix) - perform(node.left) - perform(node.right) - } - - is BinaryPlusSyntax -> { - perform(node.left) - perform(node.right) - } - - is BinaryMinusSyntax -> { - perform(node.left) - perform(node.right) - } - - is FractionSyntax -> { - perform(node.left) - perform(node.right) - } - - is MultiplicationSyntax -> { - perform(node.left) - perform(node.right) - } - - is RadicalWithIndexSyntax -> { - perform(node.left) - perform(node.right) - } - } - - public companion object { - /** - * The default configuration of [SimplifyParentheses] where power is 1, multiplicative operations are 2, - * additive operations are 3. - */ - public val Default: SimplifyParentheses = SimplifyParentheses { - when (it) { - is TerminalSyntax -> 0 - is UnarySyntax -> 2 - - is BinarySyntax -> when (it.operation) { - PowerOperations.POW_OPERATION -> 1 - RingOps.TIMES_OPERATION -> 3 - FieldOps.DIV_OPERATION -> 3 - GroupOps.MINUS_OPERATION -> 4 - GroupOps.PLUS_OPERATION -> 4 - else -> 0 - } - - else -> 0 - } - } - } -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerConsistencyWithInterpreter.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerConsistencyWithInterpreter.kt deleted file mode 100644 index 1edb5923e..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerConsistencyWithInterpreter.kt +++ /dev/null @@ -1,55 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.expressions.MstField -import space.kscience.kmath.expressions.MstRing -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.expressions.interpret -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.IntRing -import space.kscience.kmath.operations.invoke -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class TestCompilerConsistencyWithInterpreter { - @Test - fun intRing() = runCompilerTest { - val mst = MstRing { - binaryOperationFunction("+")( - unaryOperationFunction("+")( - (x - (2.toByte() + (scale( - add(number(1), number(1)), - 2.0, - ) + 1.toByte()))) * 3.0 - 1.toByte() - ), - - number(1), - ) * number(2) - } - - assertEquals( - mst.interpret(IntRing, x to 3), - mst.compile(IntRing, x to 3), - ) - } - - @Test - fun doubleField() = runCompilerTest { - val mst = MstField { - +(3 - 2 + 2 * number(1) + 1.0) + binaryOperationFunction("+")( - (3.0 - (x + (scale(add(number(1.0), number(1.0)), 2.0) + 1.0))) * 3 - 1.0 - + number(1), - number(1) / 2 + number(2.0) * one, - ) + zero - } - - assertEquals( - mst.interpret(DoubleField, x to 2.0), - mst.compile(DoubleField, x to 2.0), - ) - } -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerOperations.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerOperations.kt deleted file mode 100644 index be8a92f3e..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerOperations.kt +++ /dev/null @@ -1,88 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.expressions.MstExtendedField -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.expressions.invoke -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.invoke -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class TestCompilerOperations { - @Test - fun testUnaryPlus() = runCompilerTest { - val expr = MstExtendedField { +x }.compileToExpression(DoubleField) - assertEquals(2.0, expr(x to 2.0)) - } - - @Test - fun testUnaryMinus() = runCompilerTest { - val expr = MstExtendedField { -x }.compileToExpression(DoubleField) - assertEquals(-2.0, expr(x to 2.0)) - } - - @Test - fun testAdd() = runCompilerTest { - val expr = MstExtendedField { x + x }.compileToExpression(DoubleField) - assertEquals(4.0, expr(x to 2.0)) - } - - @Test - fun testSine() = runCompilerTest { - val expr = MstExtendedField { sin(x) }.compileToExpression(DoubleField) - assertEquals(0.0, expr(x to 0.0)) - } - - @Test - fun testCosine() = runCompilerTest { - val expr = MstExtendedField { cos(x) }.compileToExpression(DoubleField) - assertEquals(1.0, expr(x to 0.0)) - } - - @Test - fun testTangent() = runCompilerTest { - val expr = MstExtendedField { tan(x) }.compileToExpression(DoubleField) - assertEquals(0.0, expr(x to 0.0)) - } - - @Test - fun testArcSine() = runCompilerTest { - val expr = MstExtendedField { asin(x) }.compileToExpression(DoubleField) - assertEquals(0.0, expr(x to 0.0)) - } - - @Test - fun testArcCosine() = runCompilerTest { - val expr = MstExtendedField { acos(x) }.compileToExpression(DoubleField) - assertEquals(0.0, expr(x to 1.0)) - } - - @Test - fun testAreaHyperbolicSine() = runCompilerTest { - val expr = MstExtendedField { asinh(x) }.compileToExpression(DoubleField) - assertEquals(0.0, expr(x to 0.0)) - } - - @Test - fun testSubtract() = runCompilerTest { - val expr = MstExtendedField { x - x }.compileToExpression(DoubleField) - assertEquals(0.0, expr(x to 2.0)) - } - - @Test - fun testDivide() = runCompilerTest { - val expr = MstExtendedField { x / x }.compileToExpression(DoubleField) - assertEquals(1.0, expr(x to 2.0)) - } - - @Test - fun testPower() = runCompilerTest { - val expr = MstExtendedField { x pow 2 }.compileToExpression(DoubleField) - assertEquals(4.0, expr(x to 2.0)) - } -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerVariables.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerVariables.kt deleted file mode 100644 index 93ef97b0f..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerVariables.kt +++ /dev/null @@ -1,43 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.expressions.MstRing -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.expressions.Symbol.Companion.y -import space.kscience.kmath.expressions.invoke -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.IntRing -import space.kscience.kmath.operations.invoke -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertFailsWith - -internal class TestCompilerVariables { - @Test - fun testNoVariables() = runCompilerTest { - val expr = "0".parseMath().compileToExpression(DoubleField) - assertEquals(0.0, expr(), 0.0001) - } - - @Test - fun testOneVariable() = runCompilerTest { - val expr = MstRing { x }.compileToExpression(IntRing) - assertEquals(1, expr(x to 1)) - } - - @Test - fun testTwoVariables() = runCompilerTest { - val expr = "y+x/y+x".parseMath().compileToExpression(DoubleField) - assertEquals(8.0, expr(x to 4.0, y to 2.0)) - } - - @Test - fun testUndefinedVariableFails() = runCompilerTest { - val expr = MstRing { x }.compileToExpression(IntRing) - assertFailsWith { expr() } - } -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestFolding.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestFolding.kt deleted file mode 100644 index 954a0f330..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestFolding.kt +++ /dev/null @@ -1,52 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.operations.ByteRing -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.IntRing -import space.kscience.kmath.operations.pi -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.fail - -internal class TestFolding { - @Test - fun foldUnary() = assertEquals( - -1, - ("-(1)".parseMath().evaluateConstants(IntRing) as? TypedMst.Constant ?: fail()).value, - ) - - @Test - fun foldDeepUnary() = assertEquals( - 1, - ("-(-(1))".parseMath().evaluateConstants(IntRing) as? TypedMst.Constant ?: fail()).value, - ) - - @Test - fun foldBinary() = assertEquals( - 2, - ("1*2".parseMath().evaluateConstants(IntRing) as? TypedMst.Constant ?: fail()).value, - ) - - @Test - fun foldDeepBinary() = assertEquals( - 10, - ("1*2*5".parseMath().evaluateConstants(IntRing) as? TypedMst.Constant ?: fail()).value, - ) - - @Test - fun foldSymbol() = assertEquals( - DoubleField.pi, - ("pi".parseMath().evaluateConstants(DoubleField) as? TypedMst.Constant ?: fail()).value, - ) - - @Test - fun foldNumeric() = assertEquals( - 42.toByte(), - ("42".parseMath().evaluateConstants(ByteRing) as? TypedMst.Constant ?: fail()).value, - ) -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestParser.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestParser.kt deleted file mode 100644 index d0c3a789e..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestParser.kt +++ /dev/null @@ -1,59 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.complex.Complex -import space.kscience.kmath.complex.ComplexField -import space.kscience.kmath.expressions.interpret -import space.kscience.kmath.operations.Algebra -import space.kscience.kmath.operations.DoubleField -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class TestParser { - @Test - fun evaluateParsedMst() { - val mst = "2+2*(2+2)".parseMath() - val res = mst.interpret(ComplexField) - assertEquals(Complex(10.0, 0.0), res) - } - - @Test - fun evaluateMstSymbol() { - val mst = "i".parseMath() - val res = mst.interpret(ComplexField) - assertEquals(ComplexField.i, res) - } - - - @Test - fun evaluateMstUnary() { - val mst = "sin(0)".parseMath() - val res = mst.interpret(DoubleField) - assertEquals(0.0, res) - } - - @Test - fun evaluateMstBinary() { - val magicalAlgebra = object : Algebra { - override fun bindSymbolOrNull(value: String): String = value - - override fun unaryOperationFunction(operation: String): (arg: String) -> String { - throw NotImplementedError() - } - - override fun binaryOperationFunction(operation: String): (left: String, right: String) -> String = - when (operation) { - "magic" -> { left, right -> "$left ★ $right" } - else -> throw NotImplementedError() - } - } - - val mst = "magic(a, b)".parseMath() - val res = mst.interpret(magicalAlgebra) - assertEquals("a ★ b", res) - } -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestParserPrecedence.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestParserPrecedence.kt deleted file mode 100644 index 42cf5ce58..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestParserPrecedence.kt +++ /dev/null @@ -1,41 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.expressions.interpret -import space.kscience.kmath.operations.DoubleField -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class TestParserPrecedence { - @Test - fun test1(): Unit = assertEquals(6.0, "2*2+2".parseMath().interpret(f)) - - @Test - fun test2(): Unit = assertEquals(6.0, "2+2*2".parseMath().interpret(f)) - - @Test - fun test3(): Unit = assertEquals(10.0, "2^3+2".parseMath().interpret(f)) - - @Test - fun test4(): Unit = assertEquals(10.0, "2+2^3".parseMath().interpret(f)) - - @Test - fun test5(): Unit = assertEquals(16.0, "2^3*2".parseMath().interpret(f)) - - @Test - fun test6(): Unit = assertEquals(16.0, "2*2^3".parseMath().interpret(f)) - - @Test - fun test7(): Unit = assertEquals(18.0, "2+2^3*2".parseMath().interpret(f)) - - @Test - fun test8(): Unit = assertEquals(18.0, "2*2^3+2".parseMath().interpret(f)) - - private companion object { - private val f = DoubleField - } -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestFeatures.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestFeatures.kt deleted file mode 100644 index a40c785b9..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestFeatures.kt +++ /dev/null @@ -1,120 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.ast.rendering.TestUtils.testLatex -import space.kscience.kmath.expressions.MST.Numeric -import kotlin.test.Test - -internal class TestFeatures { - @Test - fun printSymbolic() = testLatex("x", "x") - - @Test - fun printNumeric() { - val num = object : Number() { - override fun toByte(): Byte = throw UnsupportedOperationException() - override fun toChar(): Char = throw UnsupportedOperationException() - override fun toDouble(): Double = throw UnsupportedOperationException() - override fun toFloat(): Float = throw UnsupportedOperationException() - override fun toInt(): Int = throw UnsupportedOperationException() - override fun toLong(): Long = throw UnsupportedOperationException() - override fun toShort(): Short = throw UnsupportedOperationException() - override fun toString(): String = "foo" - } - - testLatex(Numeric(num), "foo") - } - - @Test - fun prettyPrintFloats() { - testLatex(Numeric(Double.NaN), "NaN") - testLatex(Numeric(Double.POSITIVE_INFINITY), "\\infty") - testLatex(Numeric(Double.NEGATIVE_INFINITY), "-\\infty") - testLatex(Numeric(1.0), "1") - testLatex(Numeric(-1.0), "-1") - testLatex(Numeric(1.42), "1.42") - testLatex(Numeric(-1.42), "-1.42") - testLatex(Numeric(1.1e10), "1.1\\times10^{10}") - testLatex(Numeric(1.1e-10), "1.1\\times10^{-10}") - testLatex(Numeric(-1.1e-10), "-1.1\\times10^{-10}") - testLatex(Numeric(-1.1e10), "-1.1\\times10^{10}") - testLatex(Numeric(0.001), "0.001") - testLatex(Numeric(0.0000001), "1\\times10^{-7}") - - testLatex(Numeric(Float.NaN), "NaN") - testLatex(Numeric(Float.POSITIVE_INFINITY), "\\infty") - testLatex(Numeric(Float.NEGATIVE_INFINITY), "-\\infty") - testLatex(Numeric(1.0f), "1") - testLatex(Numeric(-1.0f), "-1") - testLatex(Numeric(1.42f), "1.42") - testLatex(Numeric(-1.42f), "-1.42") - testLatex(Numeric(1e10f), "1\\times10^{10}") - testLatex(Numeric(1e-10f), "1\\times10^{-10}") - testLatex(Numeric(-1e-10f), "-1\\times10^{-10}") - testLatex(Numeric(-1e10f), "-1\\times10^{10}") - testLatex(Numeric(0.001f), "0.001") - testLatex(Numeric(0.0000001f), "1\\times10^{-7}") - } - - @Test - fun prettyPrintIntegers() { - testLatex(Numeric(42), "42") - testLatex(Numeric(-42), "-42") - } - - @Test - fun prettyPrintPi() { - testLatex("pi", "\\pi") - } - - @Test - fun binaryPlus() = testLatex("2+2", "2+2") - - @Test - fun binaryMinus() = testLatex("2-2", "2-2") - - @Test - fun fraction() = testLatex("2/2", "\\frac{2}{2}") - - @Test - fun binaryOperator() = testLatex("f(x, y)", "\\operatorname{f}\\left(x,y\\right)") - - @Test - fun unaryOperator() = testLatex("f(x)", "\\operatorname{f}\\,\\left(x\\right)") - - @Test - fun power() = testLatex("x^y", "x^{y}") - - @Test - fun squareRoot() = testLatex("sqrt(x)", "\\sqrt{x}") - - @Test - fun exponential() = testLatex("exp(x)", "e^{x}") - - @Test - fun multiplication() = testLatex("x*1", "x\\times1") - - @Test - fun inverseTrigonometric() { - testLatex("asin(x)", "\\operatorname{arcsin}\\,\\left(x\\right)") - testLatex("acos(x)", "\\operatorname{arccos}\\,\\left(x\\right)") - testLatex("atan(x)", "\\operatorname{arctan}\\,\\left(x\\right)") - } - - @Test - fun inverseHyperbolic() { - testLatex("asinh(x)", "\\operatorname{arsinh}\\,\\left(x\\right)") - testLatex("acosh(x)", "\\operatorname{arcosh}\\,\\left(x\\right)") - testLatex("atanh(x)", "\\operatorname{artanh}\\,\\left(x\\right)") - } - -// @Test -// fun unaryPlus() { -// testLatex("+1", "+1") -// testLatex("+1", "++1") -// } -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestLatex.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestLatex.kt deleted file mode 100644 index 43f31baba..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestLatex.kt +++ /dev/null @@ -1,73 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.ast.rendering.TestUtils.testLatex -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.operations.GroupOps -import kotlin.test.Test - -internal class TestLatex { - @Test - fun number() = testLatex("42", "42") - - @Test - fun symbol() = testLatex("x", "x") - - @Test - fun operatorName() = testLatex("sin(1)", "\\operatorname{sin}\\,\\left(1\\right)") - - @Test - fun specialSymbol() { - testLatex(MST.Numeric(Double.POSITIVE_INFINITY), "\\infty") - testLatex("pi", "\\pi") - } - - @Test - fun operand() { - testLatex("sin(1)", "\\operatorname{sin}\\,\\left(1\\right)") - testLatex("1+1", "1+1") - } - - @Test - fun unaryOperator() = testLatex("sin(1)", "\\operatorname{sin}\\,\\left(1\\right)") - - @Test - fun unaryPlus() = testLatex(MST.Unary(GroupOps.PLUS_OPERATION, MST.Numeric(1)), "+1") - - @Test - fun unaryMinus() = testLatex("-x", "-x") - - @Test - fun radical() = testLatex("sqrt(x)", "\\sqrt{x}") - - @Test - fun superscript() = testLatex("x^y", "x^{y}") - - @Test - fun subscript() = testLatex(SubscriptSyntax("", SymbolSyntax("x"), NumberSyntax("123")), "x_{123}") - - @Test - fun binaryOperator() = testLatex("f(x, y)", "\\operatorname{f}\\left(x,y\\right)") - - @Test - fun binaryPlus() = testLatex("x+x", "x+x") - - @Test - fun binaryMinus() = testLatex("x-x", "x-x") - - @Test - fun fraction() = testLatex("x/x", "\\frac{x}{x}") - - @Test - fun radicalWithIndex() = testLatex(RadicalWithIndexSyntax("", SymbolSyntax("x"), SymbolSyntax("y")), "\\sqrt[x]{y}") - - @Test - fun multiplication() { - testLatex("x*1", "x\\times1") - testLatex("1*x", "1\\,x") - } -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestMathML.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestMathML.kt deleted file mode 100644 index 145055494..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestMathML.kt +++ /dev/null @@ -1,92 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.ast.rendering.TestUtils.testMathML -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.operations.GroupOps -import kotlin.test.Test - -internal class TestMathML { - @Test - fun number() = testMathML("42", "42") - - @Test - fun symbol() = testMathML("x", "x") - - @Test - fun operatorName() = testMathML( - "sin(1)", - "sin1", - ) - - @Test - fun specialSymbol() { - testMathML(MST.Numeric(Double.POSITIVE_INFINITY), "∞") - testMathML("pi", "π") - } - - @Test - fun operand() { - testMathML( - "sin(1)", - "sin1", - ) - - testMathML("1+1", "1+1") - } - - @Test - fun unaryOperator() = testMathML( - "sin(1)", - "sin1", - ) - - @Test - fun unaryPlus() = - testMathML(MST.Unary(GroupOps.PLUS_OPERATION, MST.Numeric(1)), "+1") - - @Test - fun unaryMinus() = testMathML("-x", "-x") - - @Test - fun radical() = testMathML("sqrt(x)", "x") - - @Test - fun superscript() = testMathML("x^y", "xy") - - @Test - fun subscript() = testMathML( - SubscriptSyntax("", SymbolSyntax("x"), NumberSyntax("123")), - "x123", - ) - - @Test - fun binaryOperator() = testMathML( - "f(x, y)", - "fx,y", - ) - - @Test - fun binaryPlus() = testMathML("x+x", "x+x") - - @Test - fun binaryMinus() = testMathML("x-x", "x-x") - - @Test - fun fraction() = testMathML("x/x", "xx") - - @Test - fun radicalWithIndex() = - testMathML(RadicalWithIndexSyntax("", SymbolSyntax("x"), SymbolSyntax("y")), - "yx") - - @Test - fun multiplication() { - testMathML("x*1", "x×1") - testMathML("1*x", "1x") - } -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestStages.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestStages.kt deleted file mode 100644 index 09ec127c7..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestStages.kt +++ /dev/null @@ -1,46 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.ast.rendering.TestUtils.testLatex -import kotlin.test.Test - -internal class TestStages { - @Test - fun betterMultiplication() { - testLatex("a*1", "a\\times1") - testLatex("1*(2/3)", "1\\times\\left(\\frac{2}{3}\\right)") - testLatex("1*1", "1\\times1") - testLatex("2e10", "2\\times10^{10}") - testLatex("2*x", "2\\,x") - testLatex("2*(x+1)", "2\\,\\left(x+1\\right)") - testLatex("x*y", "x\\,y") - } - - @Test - fun parentheses() { - testLatex("(x+1)", "x+1") - testLatex("x*x*x", "x\\,x\\,x") - testLatex("(x+x)*x", "\\left(x+x\\right)\\,x") - testLatex("x+x*x", "x+x\\,x") - testLatex("x+x^x*x+x", "x+x^{x}\\,x+x") - testLatex("(x+x)^x+x*x", "\\left(x+x\\right)^{x}+x\\,x") - testLatex("x^(x+x)", "x^{x+x}") - } - - @Test - fun exponent() { - testLatex("exp(x)", "e^{x}") - testLatex("exp(x/2)", "\\operatorname{exp}\\,\\left(\\frac{x}{2}\\right)") - testLatex("exp(x^2)", "\\operatorname{exp}\\,\\left(x^{2}\\right)") - } - - @Test - fun fraction() { - testLatex("x/y", "\\frac{x}{y}") - testLatex("x^(x/y)", "x^{x/y}") - } -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestUtils.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestUtils.kt deleted file mode 100644 index bf87b6fd0..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestUtils.kt +++ /dev/null @@ -1,46 +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. - */ - -package space.kscience.kmath.ast.rendering - -import space.kscience.kmath.ast.parseMath -import space.kscience.kmath.expressions.MST -import kotlin.test.assertEquals - -internal object TestUtils { - private fun mathSyntax(mst: MST) = FeaturedMathRendererWithPostProcess.Default.render(mst) - private fun latex(mst: MST) = LatexSyntaxRenderer.renderWithStringBuilder(mathSyntax(mst)) - private fun mathML(mst: MST) = MathMLSyntaxRenderer.renderWithStringBuilder(mathSyntax(mst)) - - internal fun testLatex(mst: MST, expectedLatex: String) = assertEquals( - expected = expectedLatex, - actual = latex(mst), - ) - - internal fun testLatex(expression: String, expectedLatex: String) = assertEquals( - expected = expectedLatex, - actual = latex(expression.parseMath()), - ) - - internal fun testLatex(expression: MathSyntax, expectedLatex: String) = assertEquals( - expected = expectedLatex, - actual = LatexSyntaxRenderer.renderWithStringBuilder(expression), - ) - - internal fun testMathML(mst: MST, expectedMathML: String) = assertEquals( - expected = "$expectedMathML", - actual = mathML(mst), - ) - - internal fun testMathML(expression: String, expectedMathML: String) = assertEquals( - expected = "$expectedMathML", - actual = mathML(expression.parseMath()), - ) - - internal fun testMathML(expression: MathSyntax, expectedMathML: String) = assertEquals( - expected = "$expectedMathML", - actual = MathMLSyntaxRenderer.renderWithStringBuilder(expression), - ) -} diff --git a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/utils.kt b/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/utils.kt deleted file mode 100644 index ec7436188..000000000 --- a/kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/utils.kt +++ /dev/null @@ -1,25 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.expressions.Expression -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.IntRing - -internal interface CompilerTestContext { - fun MST.compileToExpression(algebra: IntRing): Expression - fun MST.compile(algebra: IntRing, arguments: Map): Int - fun MST.compile(algebra: IntRing, vararg arguments: Pair): Int = compile(algebra, mapOf(*arguments)) - fun MST.compileToExpression(algebra: DoubleField): Expression - fun MST.compile(algebra: DoubleField, arguments: Map): Double - - fun MST.compile(algebra: DoubleField, vararg arguments: Pair): Double = - compile(algebra, mapOf(*arguments)) -} - -internal expect inline fun runCompilerTest(action: CompilerTestContext.() -> Unit) diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt deleted file mode 100644 index 521907d2c..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt +++ /dev/null @@ -1,18 +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. - */ - -package space.kscience.kmath.ast.rendering - -internal actual fun Double.multiplatformToString(): String { - val d = this - if (d >= 1e7 || d <= -1e7) return js("d.toExponential()") as String - return toString() -} - -internal actual fun Float.multiplatformToString(): String { - val d = this - if (d >= 1e7f || d <= -1e7f) return js("d.toExponential()") as String - return toString() -} diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt deleted file mode 100644 index a8b1aa2e1..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt +++ /dev/null @@ -1,52 +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. - */ - -package space.kscience.kmath.estree - -import space.kscience.kmath.ast.TypedMst -import space.kscience.kmath.ast.evaluateConstants -import space.kscience.kmath.estree.internal.ESTreeBuilder -import space.kscience.kmath.expressions.Expression -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.expressions.invoke -import space.kscience.kmath.internal.estree.BaseExpression -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Algebra - -/** - * Create a compiled expression with given [MST] and given [algebra]. - */ -@OptIn(UnstableKMathAPI::class) -public fun MST.compileToExpression(algebra: Algebra): Expression { - val typed = evaluateConstants(algebra) - if (typed is TypedMst.Constant) return Expression { typed.value } - - fun ESTreeBuilder.visit(node: TypedMst): BaseExpression = when (node) { - is TypedMst.Constant -> constant(node.value) - is TypedMst.Variable -> variable(node.symbol) - is TypedMst.Unary -> call(node.function, visit(node.value)) - - is TypedMst.Binary -> call( - node.function, - visit(node.left), - visit(node.right), - ) - } - - return ESTreeBuilder { visit(typed) }.instance -} - -/** - * Compile given MST to expression and evaluate it against [arguments] - */ -public fun MST.compile(algebra: Algebra, arguments: Map): T = - compileToExpression(algebra)(arguments) - -/** - * Compile given MST to expression and evaluate it against [arguments] - */ -public fun MST.compile(algebra: Algebra, vararg arguments: Pair): T = - compileToExpression(algebra)(*arguments) diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/internal/ESTreeBuilder.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/internal/ESTreeBuilder.kt deleted file mode 100644 index 10a6c4a16..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/internal/ESTreeBuilder.kt +++ /dev/null @@ -1,84 +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. - */ - -package space.kscience.kmath.estree.internal - -import space.kscience.kmath.expressions.Expression -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.internal.astring.generate -import space.kscience.kmath.internal.estree.* - -internal class ESTreeBuilder(val bodyCallback: ESTreeBuilder.() -> BaseExpression) { - private class GeneratedExpression(val executable: dynamic, val constants: Array) : Expression { - @Suppress("UNUSED_VARIABLE") - override fun invoke(arguments: Map): T { - val e = executable - val c = constants - val a = js("{}") - arguments.forEach { (key, value) -> a[key.identity] = value } - return js("e(c, a)").unsafeCast() - } - } - - val instance: Expression by lazy { - val node = Program( - sourceType = "script", - VariableDeclaration( - kind = "var", - VariableDeclarator( - id = Identifier("executable"), - init = FunctionExpression( - params = arrayOf(Identifier("constants"), Identifier("arguments")), - body = BlockStatement(ReturnStatement(bodyCallback())), - ), - ), - ), - ) - - eval(generate(node)) - GeneratedExpression(js("executable"), constants.toTypedArray()) - } - - private val constants = mutableListOf() - - fun constant(value: Any?) = when { - value == null || jsTypeOf(value) == "number" || jsTypeOf(value) == "string" || jsTypeOf(value) == "boolean" -> - SimpleLiteral(value) - - jsTypeOf(value) == "undefined" -> Identifier("undefined") - - else -> { - val idx = if (value in constants) constants.indexOf(value) else constants.also { it += value }.lastIndex - - MemberExpression( - computed = true, - optional = false, - `object` = Identifier("constants"), - property = SimpleLiteral(idx), - ) - } - } - - fun variable(name: Symbol): BaseExpression = call(getOrFail, Identifier("arguments"), SimpleLiteral(name.identity)) - - fun call(function: Function, vararg args: BaseExpression): BaseExpression = SimpleCallExpression( - optional = false, - callee = constant(function), - *args, - ) - - private companion object { - @Suppress("UNUSED_VARIABLE") - val getOrFail: (`object`: dynamic, key: String) -> dynamic = { `object`, key -> - val k = key - val o = `object` - - if (!(js("k in o") as Boolean)) - throw NoSuchElementException("Key $key is missing in the map.") - - js("o[k]") - } - } -} diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/internal/astring/astring.typealises.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/internal/astring/astring.typealises.kt deleted file mode 100644 index eb5c1e3dd..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/internal/astring/astring.typealises.kt +++ /dev/null @@ -1,8 +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. - */ - -package space.kscience.kmath.estree.internal.astring - -internal typealias Generator = Any diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/astring/astring.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/astring/astring.kt deleted file mode 100644 index cca2d83af..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/astring/astring.kt +++ /dev/null @@ -1,38 +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. - */ - -@file:JsModule("astring") -@file:JsNonModule - -package space.kscience.kmath.internal.astring - -import space.kscience.kmath.internal.estree.BaseNode - -internal external interface Options { - var indent: String? - get() = definedExternally - set(value) = definedExternally - var lineEnd: String? - get() = definedExternally - set(value) = definedExternally - var startingIndentLevel: Number? - get() = definedExternally - set(value) = definedExternally - var comments: Boolean? - get() = definedExternally - set(value) = definedExternally - var generator: Any? - get() = definedExternally - set(value) = definedExternally - var sourceMap: Any? - get() = definedExternally - set(value) = definedExternally -} - -internal external fun generate(node: BaseNode, options: Options /* Options & `T$0` */ = definedExternally): String - -internal external fun generate(node: BaseNode): String - -internal external var baseGenerator: Generator diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/astring/astring.typealises.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/astring/astring.typealises.kt deleted file mode 100644 index 93b4f6ce6..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/astring/astring.typealises.kt +++ /dev/null @@ -1,8 +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. - */ - -package space.kscience.kmath.internal.astring - -internal typealias Generator = Any diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/base64/base64.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/base64/base64.kt deleted file mode 100644 index 86e0cede7..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/base64/base64.kt +++ /dev/null @@ -1,54 +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. - */ - -@file:Suppress( - "INTERFACE_WITH_SUPERCLASS", - "OVERRIDING_FINAL_MEMBER", - "RETURN_TYPE_MISMATCH_ON_OVERRIDE", - "CONFLICTING_OVERLOADS", - "NO_EXPLICIT_VISIBILITY_IN_API_MODE_WARNING", - "ObjectPropertyName", - "ClassName", -) -@file:JsNonModule -@file:JsModule("js-base64") - -package space.kscience.kmath.internal.base64 - -import org.khronos.webgl.Uint8Array - -internal external var version: Any - -internal external var VERSION: Any - -internal external var btoaPolyfill: (bin: String) -> String - -internal external var _btoa: (bin: String) -> String - -internal external var fromUint8Array: (u8a: Uint8Array, urlsafe: Boolean) -> String - -internal external var utob: (u: String) -> String - -internal external var encode: (src: String, urlsafe: Boolean) -> String - -internal external var encodeURI: (src: String) -> String - -internal external var btou: (b: String) -> String - -internal external var atobPolyfill: (asc: String) -> String - -internal external var _atob: (asc: String) -> String - -internal external var toUint8Array: (a: String) -> Uint8Array - -internal external var decode: (src: String) -> String - -internal external var isValid: (src: Any) -> Boolean - -internal external var extendString: () -> Unit - -internal external var extendUint8Array: () -> Unit - -internal external var extendBuiltins: () -> Unit diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/binaryen/index.binaryen.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/binaryen/index.binaryen.kt deleted file mode 100644 index 42b6ac7d8..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/binaryen/index.binaryen.kt +++ /dev/null @@ -1,2239 +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. - */ - -@file:Suppress( - "INTERFACE_WITH_SUPERCLASS", - "OVERRIDING_FINAL_MEMBER", - "RETURN_TYPE_MISMATCH_ON_OVERRIDE", - "CONFLICTING_OVERLOADS", - "NO_EXPLICIT_VISIBILITY_IN_API_MODE_WARNING", - "PropertyName", - "ClassName", -) - -@file:JsModule("binaryen") -@file:JsNonModule - -package space.kscience.kmath.internal.binaryen - -import org.khronos.webgl.Uint8Array -import kotlin.js.Promise - -internal external var isReady: Boolean - -internal external var ready: Promise - -internal external var none: Type - -internal external var i32: Type - -internal external var i64: Type - -internal external var f32: Type - -internal external var f64: Type - -internal external var v128: Type - -internal external var funcref: Type - -internal external var anyref: Type - -internal external var nullref: Type - -internal external var exnref: Type - -internal external var unreachable: Type - -internal external var auto: Type - -internal external fun createType(types: Array): Type - -internal external fun expandType(type: Type): Array - -internal external enum class ExpressionIds { - Invalid, - Block, - If, - Loop, - Break, - Switch, - Call, - CallIndirect, - LocalGet, - LocalSet, - GlobalGet, - GlobalSet, - Load, - Store, - Const, - Unary, - Binary, - Select, - Drop, - Return, - Host, - Nop, - Unreachable, - AtomicCmpxchg, - AtomicRMW, - AtomicWait, - AtomicNotify, - AtomicFence, - SIMDExtract, - SIMDReplace, - SIMDShuffle, - SIMDTernary, - SIMDShift, - SIMDLoad, - MemoryInit, - DataDrop, - MemoryCopy, - MemoryFill, - RefNull, - RefIsNull, - RefFunc, - Try, - Throw, - Rethrow, - BrOnExn, - TupleMake, - TupleExtract, - Push, - Pop -} - -internal external var InvalidId: ExpressionIds - -internal external var BlockId: ExpressionIds - -internal external var IfId: ExpressionIds - -internal external var LoopId: ExpressionIds - -internal external var BreakId: ExpressionIds - -internal external var SwitchId: ExpressionIds - -internal external var CallId: ExpressionIds - -internal external var CallIndirectId: ExpressionIds - -internal external var LocalGetId: ExpressionIds - -internal external var LocalSetId: ExpressionIds - -internal external var GlobalGetId: ExpressionIds - -internal external var GlobalSetId: ExpressionIds - -internal external var LoadId: ExpressionIds - -internal external var StoreId: ExpressionIds - -internal external var ConstId: ExpressionIds - -internal external var UnaryId: ExpressionIds - -internal external var BinaryId: ExpressionIds - -internal external var SelectId: ExpressionIds - -internal external var DropId: ExpressionIds - -internal external var ReturnId: ExpressionIds - -internal external var HostId: ExpressionIds - -internal external var NopId: ExpressionIds - -internal external var UnreachableId: ExpressionIds - -internal external var AtomicCmpxchgId: ExpressionIds - -internal external var AtomicRMWId: ExpressionIds - -internal external var AtomicWaitId: ExpressionIds - -internal external var AtomicNotifyId: ExpressionIds - -internal external var AtomicFenceId: ExpressionIds - -internal external var SIMDExtractId: ExpressionIds - -internal external var SIMDReplaceId: ExpressionIds - -internal external var SIMDShuffleId: ExpressionIds - -internal external var SIMDTernaryId: ExpressionIds - -internal external var SIMDShiftId: ExpressionIds - -internal external var SIMDLoadId: ExpressionIds - -internal external var MemoryInitId: ExpressionIds - -internal external var DataDropId: ExpressionIds - -internal external var MemoryCopyId: ExpressionIds - -internal external var MemoryFillId: ExpressionIds - -internal external var RefNullId: ExpressionIds - -internal external var RefIsNullId: ExpressionIds - -internal external var RefFuncId: ExpressionIds - -internal external var TryId: ExpressionIds - -internal external var ThrowId: ExpressionIds - -internal external var RethrowId: ExpressionIds - -internal external var BrOnExnId: ExpressionIds - -internal external var TupleMakeId: ExpressionIds - -internal external var TupleExtractId: ExpressionIds - -internal external var PushId: ExpressionIds - -internal external var PopId: ExpressionIds - -internal external enum class ExternalKinds { - Function, - Table, - Memory, - Global, - Event -} - -internal external var ExternalFunction: ExternalKinds - -internal external var ExternalTable: ExternalKinds - -internal external var ExternalMemory: ExternalKinds - -internal external var ExternalGlobal: ExternalKinds - -internal external var ExternalEvent: ExternalKinds - -internal external enum class Features { - MVP, - Atomics, - MutableGlobals, - TruncSat, - SIMD, - BulkMemory, - SignExt, - ExceptionHandling, - TailCall, - ReferenceTypes, - Multivalue, - GC, - Memory64, - All -} - -internal external enum class Operations { - ClzInt32, - CtzInt32, - PopcntInt32, - NegFloat32, - AbsFloat32, - CeilFloat32, - FloorFloat32, - TruncFloat32, - NearestFloat32, - SqrtFloat32, - EqZInt32, - ClzInt64, - CtzInt64, - PopcntInt64, - NegFloat64, - AbsFloat64, - CeilFloat64, - FloorFloat64, - TruncFloat64, - NearestFloat64, - SqrtFloat64, - EqZInt64, - ExtendSInt32, - ExtendUInt32, - WrapInt64, - TruncSFloat32ToInt32, - TruncSFloat32ToInt64, - TruncUFloat32ToInt32, - TruncUFloat32ToInt64, - TruncSFloat64ToInt32, - TruncSFloat64ToInt64, - TruncUFloat64ToInt32, - TruncUFloat64ToInt64, - TruncSatSFloat32ToInt32, - TruncSatSFloat32ToInt64, - TruncSatUFloat32ToInt32, - TruncSatUFloat32ToInt64, - TruncSatSFloat64ToInt32, - TruncSatSFloat64ToInt64, - TruncSatUFloat64ToInt32, - TruncSatUFloat64ToInt64, - ReinterpretFloat32, - ReinterpretFloat64, - ConvertSInt32ToFloat32, - ConvertSInt32ToFloat64, - ConvertUInt32ToFloat32, - ConvertUInt32ToFloat64, - ConvertSInt64ToFloat32, - ConvertSInt64ToFloat64, - ConvertUInt64ToFloat32, - ConvertUInt64ToFloat64, - PromoteFloat32, - DemoteFloat64, - ReinterpretInt32, - ReinterpretInt64, - ExtendS8Int32, - ExtendS16Int32, - ExtendS8Int64, - ExtendS16Int64, - ExtendS32Int64, - AddInt32, - SubInt32, - MulInt32, - DivSInt32, - DivUInt32, - RemSInt32, - RemUInt32, - AndInt32, - OrInt32, - XorInt32, - ShlInt32, - ShrUInt32, - ShrSInt32, - RotLInt32, - RotRInt32, - EqInt32, - NeInt32, - LtSInt32, - LtUInt32, - LeSInt32, - LeUInt32, - GtSInt32, - GtUInt32, - GeSInt32, - GeUInt32, - AddInt64, - SubInt64, - MulInt64, - DivSInt64, - DivUInt64, - RemSInt64, - RemUInt64, - AndInt64, - OrInt64, - XorInt64, - ShlInt64, - ShrUInt64, - ShrSInt64, - RotLInt64, - RotRInt64, - EqInt64, - NeInt64, - LtSInt64, - LtUInt64, - LeSInt64, - LeUInt64, - GtSInt64, - GtUInt64, - GeSInt64, - GeUInt64, - AddFloat32, - SubFloat32, - MulFloat32, - DivFloat32, - CopySignFloat32, - MinFloat32, - MaxFloat32, - EqFloat32, - NeFloat32, - LtFloat32, - LeFloat32, - GtFloat32, - GeFloat32, - AddFloat64, - SubFloat64, - MulFloat64, - DivFloat64, - CopySignFloat64, - MinFloat64, - MaxFloat64, - EqFloat64, - NeFloat64, - LtFloat64, - LeFloat64, - GtFloat64, - GeFloat64, - MemorySize, - MemoryGrow, - AtomicRMWAdd, - AtomicRMWSub, - AtomicRMWAnd, - AtomicRMWOr, - AtomicRMWXor, - AtomicRMWXchg, - SplatVecI8x16, - ExtractLaneSVecI8x16, - ExtractLaneUVecI8x16, - ReplaceLaneVecI8x16, - SplatVecI16x8, - ExtractLaneSVecI16x8, - ExtractLaneUVecI16x8, - ReplaceLaneVecI16x8, - SplatVecI32x4, - ExtractLaneVecI32x4, - ReplaceLaneVecI32x4, - SplatVecI64x2, - ExtractLaneVecI64x2, - ReplaceLaneVecI64x2, - SplatVecF32x4, - ExtractLaneVecF32x4, - ReplaceLaneVecF32x4, - SplatVecF64x2, - ExtractLaneVecF64x2, - ReplaceLaneVecF64x2, - EqVecI8x16, - NeVecI8x16, - LtSVecI8x16, - LtUVecI8x16, - GtSVecI8x16, - GtUVecI8x16, - LeSVecI8x16, - LeUVecI8x16, - GeSVecI8x16, - GeUVecI8x16, - EqVecI16x8, - NeVecI16x8, - LtSVecI16x8, - LtUVecI16x8, - GtSVecI16x8, - GtUVecI16x8, - LeSVecI16x8, - LeUVecI16x8, - GeSVecI16x8, - GeUVecI16x8, - EqVecI32x4, - NeVecI32x4, - LtSVecI32x4, - LtUVecI32x4, - GtSVecI32x4, - GtUVecI32x4, - LeSVecI32x4, - LeUVecI32x4, - GeSVecI32x4, - GeUVecI32x4, - EqVecF32x4, - NeVecF32x4, - LtVecF32x4, - GtVecF32x4, - LeVecF32x4, - GeVecF32x4, - EqVecF64x2, - NeVecF64x2, - LtVecF64x2, - GtVecF64x2, - LeVecF64x2, - GeVecF64x2, - NotVec128, - AndVec128, - OrVec128, - XorVec128, - AndNotVec128, - BitselectVec128, - NegVecI8x16, - AnyTrueVecI8x16, - AllTrueVecI8x16, - ShlVecI8x16, - ShrSVecI8x16, - ShrUVecI8x16, - AddVecI8x16, - AddSatSVecI8x16, - AddSatUVecI8x16, - SubVecI8x16, - SubSatSVecI8x16, - SubSatUVecI8x16, - MulVecI8x16, - MinSVecI8x16, - MinUVecI8x16, - MaxSVecI8x16, - MaxUVecI8x16, - NegVecI16x8, - AnyTrueVecI16x8, - AllTrueVecI16x8, - ShlVecI16x8, - ShrSVecI16x8, - ShrUVecI16x8, - AddVecI16x8, - AddSatSVecI16x8, - AddSatUVecI16x8, - SubVecI16x8, - SubSatSVecI16x8, - SubSatUVecI16x8, - MulVecI16x8, - MinSVecI16x8, - MinUVecI16x8, - MaxSVecI16x8, - MaxUVecI16x8, - DotSVecI16x8ToVecI32x4, - NegVecI32x4, - AnyTrueVecI32x4, - AllTrueVecI32x4, - ShlVecI32x4, - ShrSVecI32x4, - ShrUVecI32x4, - AddVecI32x4, - SubVecI32x4, - MulVecI32x4, - MinSVecI32x4, - MinUVecI32x4, - MaxSVecI32x4, - MaxUVecI32x4, - NegVecI64x2, - AnyTrueVecI64x2, - AllTrueVecI64x2, - ShlVecI64x2, - ShrSVecI64x2, - ShrUVecI64x2, - AddVecI64x2, - SubVecI64x2, - AbsVecF32x4, - NegVecF32x4, - SqrtVecF32x4, - QFMAVecF32x4, - QFMSVecF32x4, - AddVecF32x4, - SubVecF32x4, - MulVecF32x4, - DivVecF32x4, - MinVecF32x4, - MaxVecF32x4, - AbsVecF64x2, - NegVecF64x2, - SqrtVecF64x2, - QFMAVecF64x2, - QFMSVecF64x2, - AddVecF64x2, - SubVecF64x2, - MulVecF64x2, - DivVecF64x2, - MinVecF64x2, - MaxVecF64x2, - TruncSatSVecF32x4ToVecI32x4, - TruncSatUVecF32x4ToVecI32x4, - TruncSatSVecF64x2ToVecI64x2, - TruncSatUVecF64x2ToVecI64x2, - ConvertSVecI32x4ToVecF32x4, - ConvertUVecI32x4ToVecF32x4, - ConvertSVecI64x2ToVecF64x2, - ConvertUVecI64x2ToVecF64x2, - LoadSplatVec8x16, - LoadSplatVec16x8, - LoadSplatVec32x4, - LoadSplatVec64x2, - LoadExtSVec8x8ToVecI16x8, - LoadExtUVec8x8ToVecI16x8, - LoadExtSVec16x4ToVecI32x4, - LoadExtUVec16x4ToVecI32x4, - LoadExtSVec32x2ToVecI64x2, - LoadExtUVec32x2ToVecI64x2, - NarrowSVecI16x8ToVecI8x16, - NarrowUVecI16x8ToVecI8x16, - NarrowSVecI32x4ToVecI16x8, - NarrowUVecI32x4ToVecI16x8, - WidenLowSVecI8x16ToVecI16x8, - WidenHighSVecI8x16ToVecI16x8, - WidenLowUVecI8x16ToVecI16x8, - WidenHighUVecI8x16ToVecI16x8, - WidenLowSVecI16x8ToVecI32x4, - WidenHighSVecI16x8ToVecI32x4, - WidenLowUVecI16x8ToVecI32x4, - WidenHighUVecI16x8ToVecI32x4, - SwizzleVec8x16 -} - -internal external var ClzInt32: Operations - -internal external var CtzInt32: Operations - -internal external var PopcntInt32: Operations - -internal external var NegFloat32: Operations - -internal external var AbsFloat32: Operations - -internal external var CeilFloat32: Operations - -internal external var FloorFloat32: Operations - -internal external var TruncFloat32: Operations - -internal external var NearestFloat32: Operations - -internal external var SqrtFloat32: Operations - -internal external var EqZInt32: Operations - -internal external var ClzInt64: Operations - -internal external var CtzInt64: Operations - -internal external var PopcntInt64: Operations - -internal external var NegFloat64: Operations - -internal external var AbsFloat64: Operations - -internal external var CeilFloat64: Operations - -internal external var FloorFloat64: Operations - -internal external var TruncFloat64: Operations - -internal external var NearestFloat64: Operations - -internal external var SqrtFloat64: Operations - -internal external var EqZInt64: Operations - -internal external var ExtendSInt32: Operations - -internal external var ExtendUInt32: Operations - -internal external var WrapInt64: Operations - -internal external var TruncSFloat32ToInt32: Operations - -internal external var TruncSFloat32ToInt64: Operations - -internal external var TruncUFloat32ToInt32: Operations - -internal external var TruncUFloat32ToInt64: Operations - -internal external var TruncSFloat64ToInt32: Operations - -internal external var TruncSFloat64ToInt64: Operations - -internal external var TruncUFloat64ToInt32: Operations - -internal external var TruncUFloat64ToInt64: Operations - -internal external var TruncSatSFloat32ToInt32: Operations - -internal external var TruncSatSFloat32ToInt64: Operations - -internal external var TruncSatUFloat32ToInt32: Operations - -internal external var TruncSatUFloat32ToInt64: Operations - -internal external var TruncSatSFloat64ToInt32: Operations - -internal external var TruncSatSFloat64ToInt64: Operations - -internal external var TruncSatUFloat64ToInt32: Operations - -internal external var TruncSatUFloat64ToInt64: Operations - -internal external var ReinterpretFloat32: Operations - -internal external var ReinterpretFloat64: Operations - -internal external var ConvertSInt32ToFloat32: Operations - -internal external var ConvertSInt32ToFloat64: Operations - -internal external var ConvertUInt32ToFloat32: Operations - -internal external var ConvertUInt32ToFloat64: Operations - -internal external var ConvertSInt64ToFloat32: Operations - -internal external var ConvertSInt64ToFloat64: Operations - -internal external var ConvertUInt64ToFloat32: Operations - -internal external var ConvertUInt64ToFloat64: Operations - -internal external var PromoteFloat32: Operations - -internal external var DemoteFloat64: Operations - -internal external var ReinterpretInt32: Operations - -internal external var ReinterpretInt64: Operations - -internal external var ExtendS8Int32: Operations - -internal external var ExtendS16Int32: Operations - -internal external var ExtendS8Int64: Operations - -internal external var ExtendS16Int64: Operations - -internal external var ExtendS32Int64: Operations - -internal external var AddInt32: Operations - -internal external var SubInt32: Operations - -internal external var MulInt32: Operations - -internal external var DivSInt32: Operations - -internal external var DivUInt32: Operations - -internal external var RemSInt32: Operations - -internal external var RemUInt32: Operations - -internal external var AndInt32: Operations - -internal external var OrInt32: Operations - -internal external var XorInt32: Operations - -internal external var ShlInt32: Operations - -internal external var ShrUInt32: Operations - -internal external var ShrSInt32: Operations - -internal external var RotLInt32: Operations - -internal external var RotRInt32: Operations - -internal external var EqInt32: Operations - -internal external var NeInt32: Operations - -internal external var LtSInt32: Operations - -internal external var LtUInt32: Operations - -internal external var LeSInt32: Operations - -internal external var LeUInt32: Operations - -internal external var GtSInt32: Operations - -internal external var GtUInt32: Operations - -internal external var GeSInt32: Operations - -internal external var GeUInt32: Operations - -internal external var AddInt64: Operations - -internal external var SubInt64: Operations - -internal external var MulInt64: Operations - -internal external var DivSInt64: Operations - -internal external var DivUInt64: Operations - -internal external var RemSInt64: Operations - -internal external var RemUInt64: Operations - -internal external var AndInt64: Operations - -internal external var OrInt64: Operations - -internal external var XorInt64: Operations - -internal external var ShlInt64: Operations - -internal external var ShrUInt64: Operations - -internal external var ShrSInt64: Operations - -internal external var RotLInt64: Operations - -internal external var RotRInt64: Operations - -internal external var EqInt64: Operations - -internal external var NeInt64: Operations - -internal external var LtSInt64: Operations - -internal external var LtUInt64: Operations - -internal external var LeSInt64: Operations - -internal external var LeUInt64: Operations - -internal external var GtSInt64: Operations - -internal external var GtUInt64: Operations - -internal external var GeSInt64: Operations - -internal external var GeUInt64: Operations - -internal external var AddFloat32: Operations - -internal external var SubFloat32: Operations - -internal external var MulFloat32: Operations - -internal external var DivFloat32: Operations - -internal external var CopySignFloat32: Operations - -internal external var MinFloat32: Operations - -internal external var MaxFloat32: Operations - -internal external var EqFloat32: Operations - -internal external var NeFloat32: Operations - -internal external var LtFloat32: Operations - -internal external var LeFloat32: Operations - -internal external var GtFloat32: Operations - -internal external var GeFloat32: Operations - -internal external var AddFloat64: Operations - -internal external var SubFloat64: Operations - -internal external var MulFloat64: Operations - -internal external var DivFloat64: Operations - -internal external var CopySignFloat64: Operations - -internal external var MinFloat64: Operations - -internal external var MaxFloat64: Operations - -internal external var EqFloat64: Operations - -internal external var NeFloat64: Operations - -internal external var LtFloat64: Operations - -internal external var LeFloat64: Operations - -internal external var GtFloat64: Operations - -internal external var GeFloat64: Operations - -internal external var MemorySize: Operations - -internal external var MemoryGrow: Operations - -internal external var AtomicRMWAdd: Operations - -internal external var AtomicRMWSub: Operations - -internal external var AtomicRMWAnd: Operations - -internal external var AtomicRMWOr: Operations - -internal external var AtomicRMWXor: Operations - -internal external var AtomicRMWXchg: Operations - -internal external var SplatVecI8x16: Operations - -internal external var ExtractLaneSVecI8x16: Operations - -internal external var ExtractLaneUVecI8x16: Operations - -internal external var ReplaceLaneVecI8x16: Operations - -internal external var SplatVecI16x8: Operations - -internal external var ExtractLaneSVecI16x8: Operations - -internal external var ExtractLaneUVecI16x8: Operations - -internal external var ReplaceLaneVecI16x8: Operations - -internal external var SplatVecI32x4: Operations - -internal external var ExtractLaneVecI32x4: Operations - -internal external var ReplaceLaneVecI32x4: Operations - -internal external var SplatVecI64x2: Operations - -internal external var ExtractLaneVecI64x2: Operations - -internal external var ReplaceLaneVecI64x2: Operations - -internal external var SplatVecF32x4: Operations - -internal external var ExtractLaneVecF32x4: Operations - -internal external var ReplaceLaneVecF32x4: Operations - -internal external var SplatVecF64x2: Operations - -internal external var ExtractLaneVecF64x2: Operations - -internal external var ReplaceLaneVecF64x2: Operations - -internal external var EqVecI8x16: Operations - -internal external var NeVecI8x16: Operations - -internal external var LtSVecI8x16: Operations - -internal external var LtUVecI8x16: Operations - -internal external var GtSVecI8x16: Operations - -internal external var GtUVecI8x16: Operations - -internal external var LeSVecI8x16: Operations - -internal external var LeUVecI8x16: Operations - -internal external var GeSVecI8x16: Operations - -internal external var GeUVecI8x16: Operations - -internal external var EqVecI16x8: Operations - -internal external var NeVecI16x8: Operations - -internal external var LtSVecI16x8: Operations - -internal external var LtUVecI16x8: Operations - -internal external var GtSVecI16x8: Operations - -internal external var GtUVecI16x8: Operations - -internal external var LeSVecI16x8: Operations - -internal external var LeUVecI16x8: Operations - -internal external var GeSVecI16x8: Operations - -internal external var GeUVecI16x8: Operations - -internal external var EqVecI32x4: Operations - -internal external var NeVecI32x4: Operations - -internal external var LtSVecI32x4: Operations - -internal external var LtUVecI32x4: Operations - -internal external var GtSVecI32x4: Operations - -internal external var GtUVecI32x4: Operations - -internal external var LeSVecI32x4: Operations - -internal external var LeUVecI32x4: Operations - -internal external var GeSVecI32x4: Operations - -internal external var GeUVecI32x4: Operations - -internal external var EqVecF32x4: Operations - -internal external var NeVecF32x4: Operations - -internal external var LtVecF32x4: Operations - -internal external var GtVecF32x4: Operations - -internal external var LeVecF32x4: Operations - -internal external var GeVecF32x4: Operations - -internal external var EqVecF64x2: Operations - -internal external var NeVecF64x2: Operations - -internal external var LtVecF64x2: Operations - -internal external var GtVecF64x2: Operations - -internal external var LeVecF64x2: Operations - -internal external var GeVecF64x2: Operations - -internal external var NotVec128: Operations - -internal external var AndVec128: Operations - -internal external var OrVec128: Operations - -internal external var XorVec128: Operations - -internal external var AndNotVec128: Operations - -internal external var BitselectVec128: Operations - -internal external var NegVecI8x16: Operations - -internal external var AnyTrueVecI8x16: Operations - -internal external var AllTrueVecI8x16: Operations - -internal external var ShlVecI8x16: Operations - -internal external var ShrSVecI8x16: Operations - -internal external var ShrUVecI8x16: Operations - -internal external var AddVecI8x16: Operations - -internal external var AddSatSVecI8x16: Operations - -internal external var AddSatUVecI8x16: Operations - -internal external var SubVecI8x16: Operations - -internal external var SubSatSVecI8x16: Operations - -internal external var SubSatUVecI8x16: Operations - -internal external var MulVecI8x16: Operations - -internal external var MinSVecI8x16: Operations - -internal external var MinUVecI8x16: Operations - -internal external var MaxSVecI8x16: Operations - -internal external var MaxUVecI8x16: Operations - -internal external var NegVecI16x8: Operations - -internal external var AnyTrueVecI16x8: Operations - -internal external var AllTrueVecI16x8: Operations - -internal external var ShlVecI16x8: Operations - -internal external var ShrSVecI16x8: Operations - -internal external var ShrUVecI16x8: Operations - -internal external var AddVecI16x8: Operations - -internal external var AddSatSVecI16x8: Operations - -internal external var AddSatUVecI16x8: Operations - -internal external var SubVecI16x8: Operations - -internal external var SubSatSVecI16x8: Operations - -internal external var SubSatUVecI16x8: Operations - -internal external var MulVecI16x8: Operations - -internal external var MinSVecI16x8: Operations - -internal external var MinUVecI16x8: Operations - -internal external var MaxSVecI16x8: Operations - -internal external var MaxUVecI16x8: Operations - -internal external var DotSVecI16x8ToVecI32x4: Operations - -internal external var NegVecI32x4: Operations - -internal external var AnyTrueVecI32x4: Operations - -internal external var AllTrueVecI32x4: Operations - -internal external var ShlVecI32x4: Operations - -internal external var ShrSVecI32x4: Operations - -internal external var ShrUVecI32x4: Operations - -internal external var AddVecI32x4: Operations - -internal external var SubVecI32x4: Operations - -internal external var MulVecI32x4: Operations - -internal external var MinSVecI32x4: Operations - -internal external var MinUVecI32x4: Operations - -internal external var MaxSVecI32x4: Operations - -internal external var MaxUVecI32x4: Operations - -internal external var NegVecI64x2: Operations - -internal external var AnyTrueVecI64x2: Operations - -internal external var AllTrueVecI64x2: Operations - -internal external var ShlVecI64x2: Operations - -internal external var ShrSVecI64x2: Operations - -internal external var ShrUVecI64x2: Operations - -internal external var AddVecI64x2: Operations - -internal external var SubVecI64x2: Operations - -internal external var AbsVecF32x4: Operations - -internal external var NegVecF32x4: Operations - -internal external var SqrtVecF32x4: Operations - -internal external var QFMAVecF32x4: Operations - -internal external var QFMSVecF32x4: Operations - -internal external var AddVecF32x4: Operations - -internal external var SubVecF32x4: Operations - -internal external var MulVecF32x4: Operations - -internal external var DivVecF32x4: Operations - -internal external var MinVecF32x4: Operations - -internal external var MaxVecF32x4: Operations - -internal external var AbsVecF64x2: Operations - -internal external var NegVecF64x2: Operations - -internal external var SqrtVecF64x2: Operations - -internal external var QFMAVecF64x2: Operations - -internal external var QFMSVecF64x2: Operations - -internal external var AddVecF64x2: Operations - -internal external var SubVecF64x2: Operations - -internal external var MulVecF64x2: Operations - -internal external var DivVecF64x2: Operations - -internal external var MinVecF64x2: Operations - -internal external var MaxVecF64x2: Operations - -internal external var TruncSatSVecF32x4ToVecI32x4: Operations - -internal external var TruncSatUVecF32x4ToVecI32x4: Operations - -internal external var TruncSatSVecF64x2ToVecI64x2: Operations - -internal external var TruncSatUVecF64x2ToVecI64x2: Operations - -internal external var ConvertSVecI32x4ToVecF32x4: Operations - -internal external var ConvertUVecI32x4ToVecF32x4: Operations - -internal external var ConvertSVecI64x2ToVecF64x2: Operations - -internal external var ConvertUVecI64x2ToVecF64x2: Operations - -internal external var LoadSplatVec8x16: Operations - -internal external var LoadSplatVec16x8: Operations - -internal external var LoadSplatVec32x4: Operations - -internal external var LoadSplatVec64x2: Operations - -internal external var LoadExtSVec8x8ToVecI16x8: Operations - -internal external var LoadExtUVec8x8ToVecI16x8: Operations - -internal external var LoadExtSVec16x4ToVecI32x4: Operations - -internal external var LoadExtUVec16x4ToVecI32x4: Operations - -internal external var LoadExtSVec32x2ToVecI64x2: Operations - -internal external var LoadExtUVec32x2ToVecI64x2: Operations - -internal external var NarrowSVecI16x8ToVecI8x16: Operations - -internal external var NarrowUVecI16x8ToVecI8x16: Operations - -internal external var NarrowSVecI32x4ToVecI16x8: Operations - -internal external var NarrowUVecI32x4ToVecI16x8: Operations - -internal external var WidenLowSVecI8x16ToVecI16x8: Operations - -internal external var WidenHighSVecI8x16ToVecI16x8: Operations - -internal external var WidenLowUVecI8x16ToVecI16x8: Operations - -internal external var WidenHighUVecI8x16ToVecI16x8: Operations - -internal external var WidenLowSVecI16x8ToVecI32x4: Operations - -internal external var WidenHighSVecI16x8ToVecI32x4: Operations - -internal external var WidenLowUVecI16x8ToVecI32x4: Operations - -internal external var WidenHighUVecI16x8ToVecI32x4: Operations - -internal external var SwizzleVec8x16: Operations - -internal external interface `T$2` { - fun get(index: Number, type: Type): ExpressionRef - fun set(index: Number, value: ExpressionRef): ExpressionRef - fun tee(index: Number, value: ExpressionRef, type: Type): ExpressionRef -} - -internal external interface `T$3` { - fun get(name: String, type: Type): ExpressionRef - fun set(name: String, value: ExpressionRef): ExpressionRef -} - -internal external interface `T$4` { - fun size(): ExpressionRef - fun grow(value: ExpressionRef): ExpressionRef - fun init(segment: Number, dest: ExpressionRef, offset: ExpressionRef, size: ExpressionRef): ExpressionRef - fun copy(dest: ExpressionRef, source: ExpressionRef, size: ExpressionRef): ExpressionRef - fun fill(dest: ExpressionRef, value: ExpressionRef, size: ExpressionRef): ExpressionRef -} - -internal external interface `T$5` { - fun drop(segment: Number): ExpressionRef -} - -internal external interface `T$6` { - fun f32(value: ExpressionRef): ExpressionRef - fun f64(value: ExpressionRef): ExpressionRef -} - -internal external interface `T$7` { - fun add(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun sub(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun and(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun or(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun xor(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun xchg(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun cmpxchg(offset: Number, ptr: ExpressionRef, expected: ExpressionRef, replacement: ExpressionRef): ExpressionRef -} - -internal external interface `T$8` { - fun load(offset: Number, ptr: ExpressionRef): ExpressionRef - fun load8_u(offset: Number, ptr: ExpressionRef): ExpressionRef - fun load16_u(offset: Number, ptr: ExpressionRef): ExpressionRef - fun store(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun store8(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun store16(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - var rmw: `T$7` - var rmw8_u: `T$7` - var rmw16_u: `T$7` - fun wait(ptr: ExpressionRef, expected: ExpressionRef, timeout: ExpressionRef): ExpressionRef -} - -internal external interface `T$9` { - fun load(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load8_s(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load8_u(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load16_s(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load16_u(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun store(offset: Number, align: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun store8(offset: Number, align: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun store16(offset: Number, align: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun const(value: Number): ExpressionRef - fun clz(value: ExpressionRef): ExpressionRef - fun ctz(value: ExpressionRef): ExpressionRef - fun popcnt(value: ExpressionRef): ExpressionRef - fun eqz(value: ExpressionRef): ExpressionRef - var trunc_s: `T$6` - var trunc_u: `T$6` - var trunc_s_sat: `T$6` - var trunc_u_sat: `T$6` - fun reinterpret(value: ExpressionRef): ExpressionRef - fun extend8_s(value: ExpressionRef): ExpressionRef - fun extend16_s(value: ExpressionRef): ExpressionRef - fun wrap(value: ExpressionRef): ExpressionRef - fun add(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun mul(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun div_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun div_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun rem_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun rem_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun and(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun or(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun xor(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun shl(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun shr_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun shr_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun rotl(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun rotr(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun eq(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ne(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - var atomic: `T$8` - fun pop(): ExpressionRef -} - -internal external interface `T$10` { - fun load(offset: Number, ptr: ExpressionRef): ExpressionRef - fun load8_u(offset: Number, ptr: ExpressionRef): ExpressionRef - fun load16_u(offset: Number, ptr: ExpressionRef): ExpressionRef - fun load32_u(offset: Number, ptr: ExpressionRef): ExpressionRef - fun store(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun store8(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun store16(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun store32(offset: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - var rmw: `T$7` - var rmw8_u: `T$7` - var rmw16_u: `T$7` - var rmw32_u: `T$7` - fun wait(ptr: ExpressionRef, expected: ExpressionRef, timeout: ExpressionRef): ExpressionRef -} - -internal external interface `T$11` { - fun load(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load8_s(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load8_u(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load16_s(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load16_u(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load32_s(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load32_u(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun store(offset: Number, align: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun store8(offset: Number, align: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun store16(offset: Number, align: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun store32(offset: Number, align: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun const(low: Number, high: Number): ExpressionRef - fun clz(value: ExpressionRef): ExpressionRef - fun ctz(value: ExpressionRef): ExpressionRef - fun popcnt(value: ExpressionRef): ExpressionRef - fun eqz(value: ExpressionRef): ExpressionRef - var trunc_s: `T$6` - var trunc_u: `T$6` - var trunc_s_sat: `T$6` - var trunc_u_sat: `T$6` - fun reinterpret(value: ExpressionRef): ExpressionRef - fun extend8_s(value: ExpressionRef): ExpressionRef - fun extend16_s(value: ExpressionRef): ExpressionRef - fun extend32_s(value: ExpressionRef): ExpressionRef - fun extend_s(value: ExpressionRef): ExpressionRef - fun extend_u(value: ExpressionRef): ExpressionRef - fun add(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun mul(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun div_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun div_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun rem_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun rem_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun and(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun or(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun xor(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun shl(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun shr_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun shr_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun rotl(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun rotr(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun eq(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ne(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - var atomic: `T$10` - fun pop(): ExpressionRef -} - -internal external interface `T$12` { - fun load(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun store(offset: Number, align: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun const(value: Number): ExpressionRef - fun const_bits(value: Number): ExpressionRef - fun neg(value: ExpressionRef): ExpressionRef - fun abs(value: ExpressionRef): ExpressionRef - fun ceil(value: ExpressionRef): ExpressionRef - fun floor(value: ExpressionRef): ExpressionRef - fun trunc(value: ExpressionRef): ExpressionRef - fun nearest(value: ExpressionRef): ExpressionRef - fun sqrt(value: ExpressionRef): ExpressionRef - fun reinterpret(value: ExpressionRef): ExpressionRef - var convert_s: `T$6` - var convert_u: `T$6` - fun demote(value: ExpressionRef): ExpressionRef - fun add(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun mul(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun div(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun copysign(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun min(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun max(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun eq(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ne(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun pop(): ExpressionRef -} - -internal external interface `T$13` { - fun load(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun store(offset: Number, align: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun const(value: Number): ExpressionRef - fun const_bits(low: Number, high: Number): ExpressionRef - fun neg(value: ExpressionRef): ExpressionRef - fun abs(value: ExpressionRef): ExpressionRef - fun ceil(value: ExpressionRef): ExpressionRef - fun floor(value: ExpressionRef): ExpressionRef - fun trunc(value: ExpressionRef): ExpressionRef - fun nearest(value: ExpressionRef): ExpressionRef - fun sqrt(value: ExpressionRef): ExpressionRef - fun reinterpret(value: ExpressionRef): ExpressionRef - var convert_s: `T$6` - var convert_u: `T$6` - fun promote(value: ExpressionRef): ExpressionRef - fun add(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun mul(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun div(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun copysign(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun min(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun max(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun eq(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ne(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun pop(): ExpressionRef -} - -internal external interface `T$14` { - fun load(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun store(offset: Number, align: Number, ptr: ExpressionRef, value: ExpressionRef): ExpressionRef - fun const(value: Number): ExpressionRef - fun not(value: ExpressionRef): ExpressionRef - fun and(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun or(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun xor(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun andnot(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun bitselect(left: ExpressionRef, right: ExpressionRef, cond: ExpressionRef): ExpressionRef - fun pop(): ExpressionRef -} - -internal external interface `T$15` { - fun splat(value: ExpressionRef): ExpressionRef - fun extract_lane_s(vec: ExpressionRef, index: ExpressionRef): ExpressionRef - fun extract_lane_u(vec: ExpressionRef, index: ExpressionRef): ExpressionRef - fun replace_lane(vec: ExpressionRef, index: ExpressionRef, value: ExpressionRef): ExpressionRef - fun eq(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ne(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun neg(value: ExpressionRef): ExpressionRef - fun any_true(value: ExpressionRef): ExpressionRef - fun all_true(value: ExpressionRef): ExpressionRef - fun shl(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun shr_s(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun shr_u(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun add(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun add_saturate_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun add_saturate_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub_saturate_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub_saturate_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun mul(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun min_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun min_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun max_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun max_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun avgr_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun narrow_i16x8_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun narrow_i16x8_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef -} - -internal external interface `T$16` { - fun splat(value: ExpressionRef): ExpressionRef - fun extract_lane_s(vec: ExpressionRef, index: ExpressionRef): ExpressionRef - fun extract_lane_u(vec: ExpressionRef, index: ExpressionRef): ExpressionRef - fun replace_lane(vec: ExpressionRef, index: ExpressionRef, value: ExpressionRef): ExpressionRef - fun eq(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ne(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun neg(value: ExpressionRef): ExpressionRef - fun any_true(value: ExpressionRef): ExpressionRef - fun all_true(value: ExpressionRef): ExpressionRef - fun shl(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun shr_s(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun shr_u(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun add(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun add_saturate_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun add_saturate_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub_saturate_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub_saturate_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun mul(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun min_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun min_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun max_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun max_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun avgr_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun narrow_i32x4_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun narrow_i32x4_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun widen_low_i8x16_s(value: ExpressionRef): ExpressionRef - fun widen_high_i8x16_s(value: ExpressionRef): ExpressionRef - fun widen_low_i8x16_u(value: ExpressionRef): ExpressionRef - fun widen_high_i8x16_u(value: ExpressionRef): ExpressionRef - fun load8x8_s(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load8x8_u(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef -} - -internal external interface `T$17` { - fun splat(value: ExpressionRef): ExpressionRef - fun extract_lane(vec: ExpressionRef, index: ExpressionRef): ExpressionRef - fun replace_lane(vec: ExpressionRef, index: ExpressionRef, value: ExpressionRef): ExpressionRef - fun eq(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ne(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge_s(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge_u(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun neg(value: ExpressionRef): ExpressionRef - fun any_true(value: ExpressionRef): ExpressionRef - fun all_true(value: ExpressionRef): ExpressionRef - fun shl(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun shr_s(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun shr_u(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun add(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun mul(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun trunc_sat_f32x4_s(value: ExpressionRef): ExpressionRef - fun trunc_sat_f32x4_u(value: ExpressionRef): ExpressionRef - fun widen_low_i16x8_s(value: ExpressionRef): ExpressionRef - fun widen_high_i16x8_s(value: ExpressionRef): ExpressionRef - fun widen_low_i16x8_u(value: ExpressionRef): ExpressionRef - fun widen_high_i16x8_u(value: ExpressionRef): ExpressionRef - fun load16x4_s(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load16x4_u(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef -} - -internal external interface `T$18` { - fun splat(value: ExpressionRef): ExpressionRef - fun extract_lane(vec: ExpressionRef, index: ExpressionRef): ExpressionRef - fun replace_lane(vec: ExpressionRef, index: ExpressionRef, value: ExpressionRef): ExpressionRef - fun neg(value: ExpressionRef): ExpressionRef - fun any_true(value: ExpressionRef): ExpressionRef - fun all_true(value: ExpressionRef): ExpressionRef - fun shl(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun shr_s(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun shr_u(vec: ExpressionRef, shift: ExpressionRef): ExpressionRef - fun add(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun trunc_sat_f64x2_s(value: ExpressionRef): ExpressionRef - fun trunc_sat_f64x2_u(value: ExpressionRef): ExpressionRef - fun load32x2_s(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef - fun load32x2_u(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef -} - -internal external interface `T$19` { - fun splat(value: ExpressionRef): ExpressionRef - fun extract_lane(vec: ExpressionRef, index: ExpressionRef): ExpressionRef - fun replace_lane(vec: ExpressionRef, index: ExpressionRef, value: ExpressionRef): ExpressionRef - fun eq(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ne(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun lt(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun gt(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun le(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun ge(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun abs(value: ExpressionRef): ExpressionRef - fun neg(value: ExpressionRef): ExpressionRef - fun sqrt(value: ExpressionRef): ExpressionRef - fun qfma(a: ExpressionRef, b: ExpressionRef, c: ExpressionRef): ExpressionRef - fun qfms(a: ExpressionRef, b: ExpressionRef, c: ExpressionRef): ExpressionRef - fun add(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun sub(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun mul(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun div(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun min(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun max(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun convert_i32x4_s(value: ExpressionRef): ExpressionRef - fun convert_i32x4_u(value: ExpressionRef): ExpressionRef -} - -internal external interface `T$20` { - fun shuffle(left: ExpressionRef, right: ExpressionRef, mask: Array): ExpressionRef - fun swizzle(left: ExpressionRef, right: ExpressionRef): ExpressionRef - fun load_splat(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef -} - -internal external interface `T$21` { - fun load_splat(offset: Number, align: Number, ptr: ExpressionRef): ExpressionRef -} - -internal external interface `T$22` { - fun pop(): ExpressionRef -} - -internal external interface `T$23` { - fun `null`(): ExpressionRef - fun is_null(value: ExpressionRef): ExpressionRef - fun func(name: String): ExpressionRef -} - -internal external interface `T$24` { - fun notify(ptr: ExpressionRef, notifyCount: ExpressionRef): ExpressionRef - fun fence(): ExpressionRef -} - -internal external interface `T$25` { - fun make(elements: Array): ExpressionRef - fun extract(tuple: ExpressionRef, index: Number): ExpressionRef -} - -internal external interface `T$26` { - var imported: Boolean - var segments: Array -} - -internal external interface `T$27` { - var binary: Uint8Array - var sourceMap: String? -} - -internal open external class Module { - open var ptr: Number - open fun block(label: String, children: Array, resultType: Type = definedExternally): ExpressionRef - open fun `if`( - condition: ExpressionRef, - ifTrue: ExpressionRef, - ifFalse: ExpressionRef = definedExternally - ): ExpressionRef - - open fun loop(label: String, body: ExpressionRef): ExpressionRef - open fun br( - label: String, - condition: ExpressionRef = definedExternally, - value: ExpressionRef = definedExternally - ): ExpressionRef - - open fun br_if( - label: String, - condition: ExpressionRef = definedExternally, - value: ExpressionRef = definedExternally - ): ExpressionRef - - open fun switch( - labels: Array, - defaultLabel: String, - condition: ExpressionRef, - value: ExpressionRef = definedExternally - ): ExpressionRef - - open fun call(name: String, operands: Array, returnType: Type): ExpressionRef - open fun return_call(name: String, operands: Array, returnType: Type): ExpressionRef - open fun call_indirect( - target: ExpressionRef, - operands: Array, - params: Type, - results: Type - ): ExpressionRef - - open fun return_call_indirect( - target: ExpressionRef, - operands: Array, - params: Type, - results: Type - ): ExpressionRef - - open var local: `T$2` - open var global: `T$3` - open var memory: `T$4` - open var data: `T$5` - open var i32: `T$9` - open var i64: `T$11` - open var f32: `T$12` - open var f64: `T$13` - open var v128: `T$14` - open var i8x16: `T$15` - open var i16x8: `T$16` - open var i32x4: `T$17` - open var i64x2: `T$18` - open var f32x4: `T$19` - open var f64x2: `T$19` - open var v8x16: `T$20` - open var v16x8: `T$21` - open var v32x4: `T$21` - open var v64x2: `T$21` - open var funcref: `T$22` - open var anyref: `T$22` - open var nullref: `T$22` - open var exnref: `T$22` - open var ref: `T$23` - open var atomic: `T$24` - open var tuple: `T$25` - open fun `try`(body: ExpressionRef, catchBody: ExpressionRef): ExpressionRef - open fun `throw`(event: String, operands: Array): ExpressionRef - open fun rethrow(exnref: ExpressionRef): ExpressionRef - open fun br_on_exn(label: String, event: String, exnref: ExpressionRef): ExpressionRef - open fun push(value: ExpressionRef): ExpressionRef - open fun select( - condition: ExpressionRef, - ifTrue: ExpressionRef, - ifFalse: ExpressionRef, - type: Type = definedExternally - ): ExpressionRef - - open fun drop(value: ExpressionRef): ExpressionRef - open fun `return`(value: ExpressionRef = definedExternally): ExpressionRef - open fun host(op: Operations, name: String, operands: Array): ExpressionRef - open fun nop(): ExpressionRef - open fun unreachable(): ExpressionRef - open fun addFunction(name: String, params: Type, results: Type, vars: Array, body: ExpressionRef): FunctionRef - open fun getFunction(name: String): FunctionRef - open fun removeFunction(name: String) - open fun getNumFunctions(): Number - open fun getFunctionByIndex(index: Number): FunctionRef - open fun addGlobal(name: String, type: Type, mutable: Boolean, init: ExpressionRef): GlobalRef - open fun getGlobal(name: String): GlobalRef - open fun removeGlobal(name: String) - open fun addEvent(name: String, attribute: Number, params: Type, results: Type): EventRef - open fun getEvent(name: String): EventRef - open fun removeEvent(name: String) - open fun addFunctionImport( - internalName: String, - externalModuleName: String, - externalBaseName: String, - params: Type, - results: Type - ) - - open fun addTableImport(internalName: String, externalModuleName: String, externalBaseName: String) - open fun addMemoryImport(internalName: String, externalModuleName: String, externalBaseName: String) - open fun addGlobalImport( - internalName: String, - externalModuleName: String, - externalBaseName: String, - globalType: Type - ) - - open fun addEventImport( - internalName: String, - externalModuleName: String, - externalBaseName: String, - attribute: Number, - params: Type, - results: Type - ) - - open fun addFunctionExport(internalName: String, externalName: String): ExportRef - open fun addTableExport(internalName: String, externalName: String): ExportRef - open fun addMemoryExport(internalName: String, externalName: String): ExportRef - open fun addGlobalExport(internalName: String, externalName: String): ExportRef - open fun removeExport(externalName: String) - open fun getNumExports(): Number - open fun getExportByIndex(index: Number): ExportRef - open fun setFunctionTable( - initial: Number, - maximum: Number, - funcNames: Array, - offset: ExpressionRef = definedExternally - ) - - open fun getFunctionTable(): `T$26` - open fun setMemory( - initial: Number, - maximum: Number, - exportName: String? = definedExternally, - segments: Array? = definedExternally, - flags: Array? = definedExternally, - shared: Boolean = definedExternally - ) - - open fun getNumMemorySegments(): Number - open fun getMemorySegmentInfoByIndex(index: Number): MemorySegmentInfo - open fun setStart(start: FunctionRef) - open fun getFeatures(): Features - open fun setFeatures(features: Features) - open fun addCustomSection(name: String, contents: Uint8Array) - open fun emitText(): String - open fun emitStackIR(optimize: Boolean = definedExternally): String - open fun emitAsmjs(): String - open fun validate(): Number - open fun optimize() - open fun optimizeFunction(func: String) - open fun optimizeFunction(func: FunctionRef) - open fun runPasses(passes: Array) - open fun runPassesOnFunction(func: String, passes: Array) - open fun runPassesOnFunction(func: FunctionRef, passes: Array) - open fun autoDrop() - open fun dispose() - open fun emitBinary(): Uint8Array - open fun emitBinary(sourceMapUrl: String?): `T$27` - open fun interpret() - open fun addDebugInfoFileName(filename: String): Number - open fun getDebugInfoFileName(index: Number): String? - open fun setDebugLocation( - func: FunctionRef, - expr: ExpressionRef, - fileIndex: Number, - lineNumber: Number, - columnNumber: Number - ) - - open fun copyExpression(expr: ExpressionRef): ExpressionRef -} - -internal external interface MemorySegment { - var offset: ExpressionRef - var data: Uint8Array - var passive: Boolean? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface TableElement { - var offset: ExpressionRef - var names: Array -} - -internal external fun wrapModule(ptr: Number): Module - -internal external fun getExpressionId(expression: ExpressionRef): Number - -internal external fun getExpressionType(expression: ExpressionRef): Type - -internal external fun getExpressionInfo(expression: ExpressionRef): ExpressionInfo - -internal external interface MemorySegmentInfo { - var offset: ExpressionRef - var data: Uint8Array - var passive: Boolean -} - -internal external interface ExpressionInfo { - var id: ExpressionIds - var type: Type -} - -internal external interface BlockInfo : ExpressionInfo { - var name: String - var children: Array -} - -internal external interface IfInfo : ExpressionInfo { - var condition: ExpressionRef - var ifTrue: ExpressionRef - var ifFalse: ExpressionRef -} - -internal external interface LoopInfo : ExpressionInfo { - var name: String - var body: ExpressionRef -} - -internal external interface BreakInfo : ExpressionInfo { - var name: String - var condition: ExpressionRef - var value: ExpressionRef -} - -internal external interface SwitchInfo : ExpressionInfo { - var names: Array - var defaultName: String? - var condition: ExpressionRef - var value: ExpressionRef -} - -internal external interface CallInfo : ExpressionInfo { - var isReturn: Boolean - var target: String - var operands: Array -} - -internal external interface CallIndirectInfo : ExpressionInfo { - var isReturn: Boolean - var target: ExpressionRef - var operands: Array -} - -internal external interface LocalGetInfo : ExpressionInfo { - var index: Number -} - -internal external interface LocalSetInfo : ExpressionInfo { - var isTee: Boolean - var index: Number - var value: ExpressionRef -} - -internal external interface GlobalGetInfo : ExpressionInfo { - var name: String -} - -internal external interface GlobalSetInfo : ExpressionInfo { - var name: String - var value: ExpressionRef -} - -internal external interface LoadInfo : ExpressionInfo { - var isAtomic: Boolean - var isSigned: Boolean - var offset: Number - var bytes: Number - var align: Number - var ptr: ExpressionRef -} - -internal external interface StoreInfo : ExpressionInfo { - var isAtomic: Boolean - var offset: Number - var bytes: Number - var align: Number - var ptr: ExpressionRef - var value: ExpressionRef -} - -internal external interface `T$28` { - var low: Number - var high: Number -} - -internal external interface ConstInfo : ExpressionInfo { - var value: dynamic /* Number | `T$28` */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface UnaryInfo : ExpressionInfo { - var op: Operations - var value: ExpressionRef -} - -internal external interface BinaryInfo : ExpressionInfo { - var op: Operations - var left: ExpressionRef - var right: ExpressionRef -} - -internal external interface SelectInfo : ExpressionInfo { - var ifTrue: ExpressionRef - var ifFalse: ExpressionRef - var condition: ExpressionRef -} - -internal external interface DropInfo : ExpressionInfo { - var value: ExpressionRef -} - -internal external interface ReturnInfo : ExpressionInfo { - var value: ExpressionRef -} - -internal external interface NopInfo : ExpressionInfo - -internal external interface UnreachableInfo : ExpressionInfo - -internal external interface HostInfo : ExpressionInfo { - var op: Operations - var nameOperand: String? - var operands: Array -} - -internal external interface AtomicRMWInfo : ExpressionInfo { - var op: Operations - var bytes: Number - var offset: Number - var ptr: ExpressionRef - var value: ExpressionRef -} - -internal external interface AtomicCmpxchgInfo : ExpressionInfo { - var bytes: Number - var offset: Number - var ptr: ExpressionRef - var expected: ExpressionRef - var replacement: ExpressionRef -} - -internal external interface AtomicWaitInfo : ExpressionInfo { - var ptr: ExpressionRef - var expected: ExpressionRef - var timeout: ExpressionRef - var expectedType: Type -} - -internal external interface AtomicNotifyInfo : ExpressionInfo { - var ptr: ExpressionRef - var notifyCount: ExpressionRef -} - -internal external interface AtomicFenceInfo : ExpressionInfo { - var order: Number -} - -internal external interface SIMDExtractInfo : ExpressionInfo { - var op: Operations - var vec: ExpressionRef - var index: ExpressionRef -} - -internal external interface SIMDReplaceInfo : ExpressionInfo { - var op: Operations - var vec: ExpressionRef - var index: ExpressionRef - var value: ExpressionRef -} - -internal external interface SIMDShuffleInfo : ExpressionInfo { - var left: ExpressionRef - var right: ExpressionRef - var mask: Array -} - -internal external interface SIMDTernaryInfo : ExpressionInfo { - var op: Operations - var a: ExpressionRef - var b: ExpressionRef - var c: ExpressionRef -} - -internal external interface SIMDShiftInfo : ExpressionInfo { - var op: Operations - var vec: ExpressionRef - var shift: ExpressionRef -} - -internal external interface SIMDLoadInfo : ExpressionInfo { - var op: Operations - var offset: Number - var align: Number - var ptr: ExpressionRef -} - -internal external interface MemoryInitInfo : ExpressionInfo { - var segment: Number - var dest: ExpressionRef - var offset: ExpressionRef - var size: ExpressionRef -} - -internal external interface MemoryDropInfo : ExpressionInfo { - var segment: Number -} - -internal external interface MemoryCopyInfo : ExpressionInfo { - var dest: ExpressionRef - var source: ExpressionRef - var size: ExpressionRef -} - -internal external interface MemoryFillInfo : ExpressionInfo { - var dest: ExpressionRef - var value: ExpressionRef - var size: ExpressionRef -} - -internal external interface RefNullInfo : ExpressionInfo - -internal external interface RefIsNullInfo : ExpressionInfo { - var value: ExpressionRef -} - -internal external interface RefFuncInfo : ExpressionInfo { - var func: String -} - -internal external interface TryInfo : ExpressionInfo { - var body: ExpressionRef - var catchBody: ExpressionRef -} - -internal external interface ThrowInfo : ExpressionInfo { - var event: String - var operands: Array -} - -internal external interface RethrowInfo : ExpressionInfo { - var exnref: ExpressionRef -} - -internal external interface BrOnExnInfo : ExpressionInfo { - var name: String - var event: String - var exnref: ExpressionRef -} - -internal external interface PopInfo : ExpressionInfo - -internal external interface PushInfo : ExpressionInfo { - var value: ExpressionRef -} - -internal external fun getFunctionInfo(func: FunctionRef): FunctionInfo - -internal external interface FunctionInfo { - var name: String - var module: String? - var base: String? - var params: Type - var results: Type - var vars: Array - var body: ExpressionRef -} - -internal external fun getGlobalInfo(global: GlobalRef): GlobalInfo - -internal external interface GlobalInfo { - var name: String - var module: String? - var base: String? - var type: Type - var mutable: Boolean - var init: ExpressionRef -} - -internal external fun getExportInfo(export_: ExportRef): ExportInfo - -internal external interface ExportInfo { - var kind: ExternalKinds - var name: String - var value: String -} - -internal external fun getEventInfo(event: EventRef): EventInfo - -internal external interface EventInfo { - var name: String - var module: String? - var base: String? - var attribute: Number - var params: Type - var results: Type -} - -internal external fun getSideEffects(expr: ExpressionRef, features: Features): SideEffects - -internal external enum class SideEffects { - None, - Branches, - Calls, - ReadsLocal, - WritesLocal, - ReadsGlobal, - WritesGlobal, - ReadsMemory, - WritesMemory, - ImplicitTrap, - IsAtomic, - Throws, - Any -} - -internal external fun emitText(expression: ExpressionRef): String - -internal external fun emitText(expression: Module): String - -internal external fun readBinary(data: Uint8Array): Module - -internal external fun parseText(text: String): Module - -internal external fun getOptimizeLevel(): Number - -internal external fun setOptimizeLevel(level: Number): Number - -internal external fun getShrinkLevel(): Number - -internal external fun setShrinkLevel(level: Number): Number - -internal external fun getDebugInfo(): Boolean - -internal external fun setDebugInfo(on: Boolean) - -internal external fun getLowMemoryUnused(): Boolean - -internal external fun setLowMemoryUnused(on: Boolean) - -internal external fun getPassArgument(key: String): String? - -internal external fun setPassArgument(key: String, value: String?) - -internal external fun clearPassArguments() - -internal external fun getAlwaysInlineMaxSize(): Number - -internal external fun setAlwaysInlineMaxSize(size: Number) - -internal external fun getFlexibleInlineMaxSize(): Number - -internal external fun setFlexibleInlineMaxSize(size: Number) - -internal external fun getOneCallerInlineMaxSize(): Number - -internal external fun setOneCallerInlineMaxSize(size: Number) - -internal external fun exit(status: Number) - -internal open external class Relooper(module: Module) { - open fun addBlock(expression: ExpressionRef): RelooperBlockRef - open fun addBranch(from: RelooperBlockRef, to: RelooperBlockRef, condition: ExpressionRef, code: ExpressionRef) - open fun addBlockWithSwitch(code: ExpressionRef, condition: ExpressionRef): RelooperBlockRef - open fun addBranchForSwitch( - from: RelooperBlockRef, - to: RelooperBlockRef, - indexes: Array, - code: ExpressionRef - ) - - open fun renderAndDispose(entry: RelooperBlockRef, labelHelper: Number): ExpressionRef -} diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/binaryen/index.binaryen.typealiases.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/binaryen/index.binaryen.typealiases.kt deleted file mode 100644 index 523b13b40..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/binaryen/index.binaryen.typealiases.kt +++ /dev/null @@ -1,16 +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. - */ - -@file:Suppress("PackageDirectoryMismatch", "NO_EXPLICIT_VISIBILITY_IN_API_MODE_WARNING", "KDocMissingDocumentation") - -package space.kscience.kmath.internal.binaryen - -internal typealias Type = Number -internal typealias ExpressionRef = Number -internal typealias FunctionRef = Number -internal typealias GlobalRef = Number -internal typealias ExportRef = Number -internal typealias EventRef = Number -internal typealias RelooperBlockRef = Number diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/emitter/emitter.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/emitter/emitter.kt deleted file mode 100644 index 1f7b09af8..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/emitter/emitter.kt +++ /dev/null @@ -1,18 +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. - */ - -package space.kscience.kmath.internal.emitter - -internal open external class Emitter { - constructor(obj: Any) - constructor() - - open fun on(event: String, fn: () -> Unit) - open fun off(event: String, fn: () -> Unit) - open fun once(event: String, fn: () -> Unit) - open fun emit(event: String, vararg any: Any) - open fun listeners(event: String): Array<() -> Unit> - open fun hasListeners(event: String): Boolean -} diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/estree/estree.extensions.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/estree/estree.extensions.kt deleted file mode 100644 index 3aa31f921..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/estree/estree.extensions.kt +++ /dev/null @@ -1,67 +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. - */ - -package space.kscience.kmath.internal.estree - -internal fun Program(sourceType: String, vararg body: dynamic) = object : Program { - override var type = "Program" - override var sourceType = sourceType - override var body = body -} - -internal fun VariableDeclaration(kind: String, vararg declarations: VariableDeclarator) = object : VariableDeclaration { - override var type = "VariableDeclaration" - override var declarations = declarations.toList().toTypedArray() - override var kind = kind -} - -internal fun VariableDeclarator(id: dynamic, init: dynamic) = object : VariableDeclarator { - override var type = "VariableDeclarator" - override var id = id - override var init = init -} - -internal fun Identifier(name: String) = object : Identifier { - override var type = "Identifier" - override var name = name -} - -internal fun FunctionExpression(params: Array, body: BlockStatement) = object : FunctionExpression { - override var params = params - override var type = "FunctionExpression" - override var body = body -} - -internal fun BlockStatement(vararg body: dynamic) = object : BlockStatement { - override var type = "BlockStatement" - override var body = body -} - -internal fun ReturnStatement(argument: dynamic) = object : ReturnStatement { - override var type = "ReturnStatement" - override var argument = argument -} - -internal fun SimpleLiteral(value: dynamic) = object : SimpleLiteral { - override var type = "Literal" - override var value = value -} - -internal fun MemberExpression(computed: Boolean, optional: Boolean, `object`: dynamic, property: dynamic) = - object : MemberExpression { - override var type = "MemberExpression" - override var computed = computed - override var optional = optional - override var `object` = `object` - override var property = property - } - -internal fun SimpleCallExpression(optional: Boolean, callee: dynamic, vararg arguments: dynamic) = - object : SimpleCallExpression { - override var type = "CallExpression" - override var optional = optional - override var callee = callee - override var arguments = arguments - } diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/estree/estree.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/estree/estree.kt deleted file mode 100644 index b62b8c06c..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/estree/estree.kt +++ /dev/null @@ -1,651 +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. - */ - -@file:Suppress("ClassName") - -package space.kscience.kmath.internal.estree - -import kotlin.js.RegExp - -internal external interface BaseNodeWithoutComments { - var type: String - var loc: SourceLocation? - get() = definedExternally - set(value) = definedExternally - var range: dynamic /* JsTuple */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface BaseNode : BaseNodeWithoutComments { - var leadingComments: Array? - get() = definedExternally - set(value) = definedExternally - var trailingComments: Array? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface Comment : BaseNodeWithoutComments { - override var type: String /* "Line" | "Block" */ - var value: String -} - -internal external interface SourceLocation { - var source: String? - get() = definedExternally - set(value) = definedExternally - var start: Position - var end: Position -} - -internal external interface Position { - var line: Number - var column: Number -} - -internal external interface Program : BaseNode { - override var type: String /* "Program" */ - var sourceType: String /* "script" | "module" */ - var body: Array - var comments: Array? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface Directive : BaseNode { - override var type: String /* "ExpressionStatement" */ - var expression: dynamic /* SimpleLiteral | RegExpLiteral */ - get() = definedExternally - set(value) = definedExternally - var directive: String -} - -internal external interface BaseFunction : BaseNode { - var params: Array - var generator: Boolean? - get() = definedExternally - set(value) = definedExternally - var async: Boolean? - get() = definedExternally - set(value) = definedExternally - var body: dynamic /* BlockStatement | ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface BaseStatement : BaseNode - -internal external interface EmptyStatement : BaseStatement { - override var type: String /* "EmptyStatement" */ -} - -internal external interface BlockStatement : BaseStatement { - override var type: String /* "BlockStatement" */ - var body: Array - var innerComments: Array? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ExpressionStatement : BaseStatement { - override var type: String /* "ExpressionStatement" */ - var expression: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface IfStatement : BaseStatement { - override var type: String /* "IfStatement" */ - var test: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var consequent: dynamic /* ExpressionStatement | BlockStatement | EmptyStatement | DebuggerStatement | WithStatement | ReturnStatement | LabeledStatement | BreakStatement | ContinueStatement | IfStatement | SwitchStatement | ThrowStatement | TryStatement | WhileStatement | DoWhileStatement | ForStatement | ForInStatement | ForOfStatement | FunctionDeclaration | VariableDeclaration | ClassDeclaration */ - get() = definedExternally - set(value) = definedExternally - var alternate: dynamic /* ExpressionStatement? | BlockStatement? | EmptyStatement? | DebuggerStatement? | WithStatement? | ReturnStatement? | LabeledStatement? | BreakStatement? | ContinueStatement? | IfStatement? | SwitchStatement? | ThrowStatement? | TryStatement? | WhileStatement? | DoWhileStatement? | ForStatement? | ForInStatement? | ForOfStatement? | FunctionDeclaration? | VariableDeclaration? | ClassDeclaration? */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface LabeledStatement : BaseStatement { - override var type: String /* "LabeledStatement" */ - var label: Identifier - var body: dynamic /* ExpressionStatement | BlockStatement | EmptyStatement | DebuggerStatement | WithStatement | ReturnStatement | LabeledStatement | BreakStatement | ContinueStatement | IfStatement | SwitchStatement | ThrowStatement | TryStatement | WhileStatement | DoWhileStatement | ForStatement | ForInStatement | ForOfStatement | FunctionDeclaration | VariableDeclaration | ClassDeclaration */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface BreakStatement : BaseStatement { - override var type: String /* "BreakStatement" */ - var label: Identifier? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ContinueStatement : BaseStatement { - override var type: String /* "ContinueStatement" */ - var label: Identifier? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface WithStatement : BaseStatement { - override var type: String /* "WithStatement" */ - var `object`: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var body: dynamic /* ExpressionStatement | BlockStatement | EmptyStatement | DebuggerStatement | WithStatement | ReturnStatement | LabeledStatement | BreakStatement | ContinueStatement | IfStatement | SwitchStatement | ThrowStatement | TryStatement | WhileStatement | DoWhileStatement | ForStatement | ForInStatement | ForOfStatement | FunctionDeclaration | VariableDeclaration | ClassDeclaration */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface SwitchStatement : BaseStatement { - override var type: String /* "SwitchStatement" */ - var discriminant: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var cases: Array -} - -internal external interface ReturnStatement : BaseStatement { - override var type: String /* "ReturnStatement" */ - var argument: dynamic /* ThisExpression? | ArrayExpression? | ObjectExpression? | FunctionExpression? | ArrowFunctionExpression? | YieldExpression? | SimpleLiteral? | RegExpLiteral? | UnaryExpression? | UpdateExpression? | BinaryExpression? | AssignmentExpression? | LogicalExpression? | MemberExpression? | ConditionalExpression? | SimpleCallExpression? | NewExpression? | SequenceExpression? | TemplateLiteral? | TaggedTemplateExpression? | ClassExpression? | MetaProperty? | Identifier? | AwaitExpression? | ImportExpression? | ChainExpression? */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ThrowStatement : BaseStatement { - override var type: String /* "ThrowStatement" */ - var argument: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface TryStatement : BaseStatement { - override var type: String /* "TryStatement" */ - var block: BlockStatement - var handler: CatchClause? - get() = definedExternally - set(value) = definedExternally - var finalizer: BlockStatement? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface WhileStatement : BaseStatement { - override var type: String /* "WhileStatement" */ - var test: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var body: dynamic /* ExpressionStatement | BlockStatement | EmptyStatement | DebuggerStatement | WithStatement | ReturnStatement | LabeledStatement | BreakStatement | ContinueStatement | IfStatement | SwitchStatement | ThrowStatement | TryStatement | WhileStatement | DoWhileStatement | ForStatement | ForInStatement | ForOfStatement | FunctionDeclaration | VariableDeclaration | ClassDeclaration */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface DoWhileStatement : BaseStatement { - override var type: String /* "DoWhileStatement" */ - var body: dynamic /* ExpressionStatement | BlockStatement | EmptyStatement | DebuggerStatement | WithStatement | ReturnStatement | LabeledStatement | BreakStatement | ContinueStatement | IfStatement | SwitchStatement | ThrowStatement | TryStatement | WhileStatement | DoWhileStatement | ForStatement | ForInStatement | ForOfStatement | FunctionDeclaration | VariableDeclaration | ClassDeclaration */ - get() = definedExternally - set(value) = definedExternally - var test: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ForStatement : BaseStatement { - override var type: String /* "ForStatement" */ - var init: dynamic /* VariableDeclaration? | ThisExpression? | ArrayExpression? | ObjectExpression? | FunctionExpression? | ArrowFunctionExpression? | YieldExpression? | SimpleLiteral? | RegExpLiteral? | UnaryExpression? | UpdateExpression? | BinaryExpression? | AssignmentExpression? | LogicalExpression? | MemberExpression? | ConditionalExpression? | SimpleCallExpression? | NewExpression? | SequenceExpression? | TemplateLiteral? | TaggedTemplateExpression? | ClassExpression? | MetaProperty? | Identifier? | AwaitExpression? | ImportExpression? | ChainExpression? */ - get() = definedExternally - set(value) = definedExternally - var test: dynamic /* ThisExpression? | ArrayExpression? | ObjectExpression? | FunctionExpression? | ArrowFunctionExpression? | YieldExpression? | SimpleLiteral? | RegExpLiteral? | UnaryExpression? | UpdateExpression? | BinaryExpression? | AssignmentExpression? | LogicalExpression? | MemberExpression? | ConditionalExpression? | SimpleCallExpression? | NewExpression? | SequenceExpression? | TemplateLiteral? | TaggedTemplateExpression? | ClassExpression? | MetaProperty? | Identifier? | AwaitExpression? | ImportExpression? | ChainExpression? */ - get() = definedExternally - set(value) = definedExternally - var update: dynamic /* ThisExpression? | ArrayExpression? | ObjectExpression? | FunctionExpression? | ArrowFunctionExpression? | YieldExpression? | SimpleLiteral? | RegExpLiteral? | UnaryExpression? | UpdateExpression? | BinaryExpression? | AssignmentExpression? | LogicalExpression? | MemberExpression? | ConditionalExpression? | SimpleCallExpression? | NewExpression? | SequenceExpression? | TemplateLiteral? | TaggedTemplateExpression? | ClassExpression? | MetaProperty? | Identifier? | AwaitExpression? | ImportExpression? | ChainExpression? */ - get() = definedExternally - set(value) = definedExternally - var body: dynamic /* ExpressionStatement | BlockStatement | EmptyStatement | DebuggerStatement | WithStatement | ReturnStatement | LabeledStatement | BreakStatement | ContinueStatement | IfStatement | SwitchStatement | ThrowStatement | TryStatement | WhileStatement | DoWhileStatement | ForStatement | ForInStatement | ForOfStatement | FunctionDeclaration | VariableDeclaration | ClassDeclaration */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface BaseForXStatement : BaseStatement { - var left: dynamic /* VariableDeclaration | Identifier | ObjectPattern | ArrayPattern | RestElement | AssignmentPattern | MemberExpression */ - get() = definedExternally - set(value) = definedExternally - var right: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var body: dynamic /* ExpressionStatement | BlockStatement | EmptyStatement | DebuggerStatement | WithStatement | ReturnStatement | LabeledStatement | BreakStatement | ContinueStatement | IfStatement | SwitchStatement | ThrowStatement | TryStatement | WhileStatement | DoWhileStatement | ForStatement | ForInStatement | ForOfStatement | FunctionDeclaration | VariableDeclaration | ClassDeclaration */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ForInStatement : BaseForXStatement { - override var type: String /* "ForInStatement" */ -} - -internal external interface DebuggerStatement : BaseStatement { - override var type: String /* "DebuggerStatement" */ -} - -internal external interface BaseDeclaration : BaseStatement - -internal external interface FunctionDeclaration : BaseFunction, BaseDeclaration { - override var type: String /* "FunctionDeclaration" */ - var id: Identifier? - override var body: BlockStatement -} - -internal external interface VariableDeclaration : BaseDeclaration { - override var type: String /* "VariableDeclaration" */ - var declarations: Array - var kind: String /* "var" | "let" | "const" */ -} - -internal external interface VariableDeclarator : BaseNode { - override var type: String /* "VariableDeclarator" */ - var id: dynamic /* Identifier | ObjectPattern | ArrayPattern | RestElement | AssignmentPattern | MemberExpression */ - get() = definedExternally - set(value) = definedExternally - var init: dynamic /* ThisExpression? | ArrayExpression? | ObjectExpression? | FunctionExpression? | ArrowFunctionExpression? | YieldExpression? | SimpleLiteral? | RegExpLiteral? | UnaryExpression? | UpdateExpression? | BinaryExpression? | AssignmentExpression? | LogicalExpression? | MemberExpression? | ConditionalExpression? | SimpleCallExpression? | NewExpression? | SequenceExpression? | TemplateLiteral? | TaggedTemplateExpression? | ClassExpression? | MetaProperty? | Identifier? | AwaitExpression? | ImportExpression? | ChainExpression? */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface BaseExpression : BaseNode - -internal external interface ChainExpression : BaseExpression { - override var type: String /* "ChainExpression" */ - var expression: dynamic /* SimpleCallExpression | MemberExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ThisExpression : BaseExpression { - override var type: String /* "ThisExpression" */ -} - -internal external interface ArrayExpression : BaseExpression { - override var type: String /* "ArrayExpression" */ - var elements: Array -} - -internal external interface ObjectExpression : BaseExpression { - override var type: String /* "ObjectExpression" */ - var properties: Array -} - -internal external interface Property : BaseNode { - override var type: String /* "Property" */ - var key: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var value: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression | ObjectPattern | ArrayPattern | RestElement | AssignmentPattern */ - get() = definedExternally - set(value) = definedExternally - var kind: String /* "init" | "get" | "set" */ - var method: Boolean - var shorthand: Boolean - var computed: Boolean -} - -internal external interface FunctionExpression : BaseFunction, BaseExpression { - var id: Identifier? - get() = definedExternally - set(value) = definedExternally - override var type: String /* "FunctionExpression" */ - override var body: BlockStatement -} - -internal external interface SequenceExpression : BaseExpression { - override var type: String /* "SequenceExpression" */ - var expressions: Array -} - -internal external interface UnaryExpression : BaseExpression { - override var type: String /* "UnaryExpression" */ - var operator: String /* "-" | "+" | "!" | "~" | "typeof" | "void" | "delete" */ - var prefix: Boolean - var argument: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface BinaryExpression : BaseExpression { - override var type: String /* "BinaryExpression" */ - var operator: String /* "==" | "!=" | "===" | "!==" | "<" | "<=" | ">" | ">=" | "<<" | ">>" | ">>>" | "+" | "-" | "*" | "/" | "%" | "**" | "|" | "^" | "&" | "in" | "instanceof" */ - var left: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var right: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface AssignmentExpression : BaseExpression { - override var type: String /* "AssignmentExpression" */ - var operator: String /* "=" | "+=" | "-=" | "*=" | "/=" | "%=" | "**=" | "<<=" | ">>=" | ">>>=" | "|=" | "^=" | "&=" */ - var left: dynamic /* Identifier | ObjectPattern | ArrayPattern | RestElement | AssignmentPattern | MemberExpression */ - get() = definedExternally - set(value) = definedExternally - var right: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface UpdateExpression : BaseExpression { - override var type: String /* "UpdateExpression" */ - var operator: String /* "++" | "--" */ - var argument: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var prefix: Boolean -} - -internal external interface LogicalExpression : BaseExpression { - override var type: String /* "LogicalExpression" */ - var operator: String /* "||" | "&&" | "??" */ - var left: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var right: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ConditionalExpression : BaseExpression { - override var type: String /* "ConditionalExpression" */ - var test: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var alternate: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var consequent: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface BaseCallExpression : BaseExpression { - var callee: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression | Super */ - get() = definedExternally - set(value) = definedExternally - var arguments: Array -} - -internal external interface SimpleCallExpression : BaseCallExpression { - override var type: String /* "CallExpression" */ - var optional: Boolean -} - -internal external interface NewExpression : BaseCallExpression { - override var type: String /* "NewExpression" */ -} - -internal external interface MemberExpression : BaseExpression, BasePattern { - override var type: String /* "MemberExpression" */ - var `object`: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression | Super */ - get() = definedExternally - set(value) = definedExternally - var property: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var computed: Boolean - var optional: Boolean -} - -internal external interface BasePattern : BaseNode - -internal external interface SwitchCase : BaseNode { - override var type: String /* "SwitchCase" */ - var test: dynamic /* ThisExpression? | ArrayExpression? | ObjectExpression? | FunctionExpression? | ArrowFunctionExpression? | YieldExpression? | SimpleLiteral? | RegExpLiteral? | UnaryExpression? | UpdateExpression? | BinaryExpression? | AssignmentExpression? | LogicalExpression? | MemberExpression? | ConditionalExpression? | SimpleCallExpression? | NewExpression? | SequenceExpression? | TemplateLiteral? | TaggedTemplateExpression? | ClassExpression? | MetaProperty? | Identifier? | AwaitExpression? | ImportExpression? | ChainExpression? */ - get() = definedExternally - set(value) = definedExternally - var consequent: Array -} - -internal external interface CatchClause : BaseNode { - override var type: String /* "CatchClause" */ - var param: dynamic /* Identifier? | ObjectPattern? | ArrayPattern? | RestElement? | AssignmentPattern? | MemberExpression? */ - get() = definedExternally - set(value) = definedExternally - var body: BlockStatement -} - -internal external interface Identifier : BaseNode, BaseExpression, BasePattern { - override var type: String /* "Identifier" */ - var name: String -} - -internal external interface SimpleLiteral : BaseNode, BaseExpression { - override var type: String /* "Literal" */ - var value: dynamic /* String? | Boolean? | Number? */ - get() = definedExternally - set(value) = definedExternally - var raw: String? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface `T$1` { - var pattern: String - var flags: String -} - -internal external interface RegExpLiteral : BaseNode, BaseExpression { - override var type: String /* "Literal" */ - var value: RegExp? - get() = definedExternally - set(value) = definedExternally - var regex: `T$1` - var raw: String? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ForOfStatement : BaseForXStatement { - override var type: String /* "ForOfStatement" */ - var await: Boolean -} - -internal external interface Super : BaseNode { - override var type: String /* "Super" */ -} - -internal external interface SpreadElement : BaseNode { - override var type: String /* "SpreadElement" */ - var argument: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ArrowFunctionExpression : BaseExpression, BaseFunction { - override var type: String /* "ArrowFunctionExpression" */ - var expression: Boolean - override var body: dynamic /* BlockStatement | ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface YieldExpression : BaseExpression { - override var type: String /* "YieldExpression" */ - var argument: dynamic /* ThisExpression? | ArrayExpression? | ObjectExpression? | FunctionExpression? | ArrowFunctionExpression? | YieldExpression? | SimpleLiteral? | RegExpLiteral? | UnaryExpression? | UpdateExpression? | BinaryExpression? | AssignmentExpression? | LogicalExpression? | MemberExpression? | ConditionalExpression? | SimpleCallExpression? | NewExpression? | SequenceExpression? | TemplateLiteral? | TaggedTemplateExpression? | ClassExpression? | MetaProperty? | Identifier? | AwaitExpression? | ImportExpression? | ChainExpression? */ - get() = definedExternally - set(value) = definedExternally - var delegate: Boolean -} - -internal external interface TemplateLiteral : BaseExpression { - override var type: String /* "TemplateLiteral" */ - var quasis: Array - var expressions: Array -} - -internal external interface TaggedTemplateExpression : BaseExpression { - override var type: String /* "TaggedTemplateExpression" */ - var tag: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var quasi: TemplateLiteral -} - -internal external interface `T$2` { - var cooked: String - var raw: String -} - -internal external interface TemplateElement : BaseNode { - override var type: String /* "TemplateElement" */ - var tail: Boolean - var value: `T$2` -} - -internal external interface AssignmentProperty : Property { - override var value: dynamic /* Identifier | ObjectPattern | ArrayPattern | RestElement | AssignmentPattern | MemberExpression */ - get() = definedExternally - set(value) = definedExternally - override var kind: String /* "init" */ - override var method: Boolean -} - -internal external interface ObjectPattern : BasePattern { - override var type: String /* "ObjectPattern" */ - var properties: Array -} - -internal external interface ArrayPattern : BasePattern { - override var type: String /* "ArrayPattern" */ - var elements: Array -} - -internal external interface RestElement : BasePattern { - override var type: String /* "RestElement" */ - var argument: dynamic /* Identifier | ObjectPattern | ArrayPattern | RestElement | AssignmentPattern | MemberExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface AssignmentPattern : BasePattern { - override var type: String /* "AssignmentPattern" */ - var left: dynamic /* Identifier | ObjectPattern | ArrayPattern | RestElement | AssignmentPattern | MemberExpression */ - get() = definedExternally - set(value) = definedExternally - var right: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface BaseClass : BaseNode { - var superClass: dynamic /* ThisExpression? | ArrayExpression? | ObjectExpression? | FunctionExpression? | ArrowFunctionExpression? | YieldExpression? | SimpleLiteral? | RegExpLiteral? | UnaryExpression? | UpdateExpression? | BinaryExpression? | AssignmentExpression? | LogicalExpression? | MemberExpression? | ConditionalExpression? | SimpleCallExpression? | NewExpression? | SequenceExpression? | TemplateLiteral? | TaggedTemplateExpression? | ClassExpression? | MetaProperty? | Identifier? | AwaitExpression? | ImportExpression? | ChainExpression? */ - get() = definedExternally - set(value) = definedExternally - var body: ClassBody -} - -internal external interface ClassBody : BaseNode { - override var type: String /* "ClassBody" */ - var body: Array -} - -internal external interface MethodDefinition : BaseNode { - override var type: String /* "MethodDefinition" */ - var key: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally - var value: FunctionExpression - var kind: String /* "constructor" | "method" | "get" | "set" */ - var computed: Boolean - var static: Boolean -} - -internal external interface ClassDeclaration : BaseClass, BaseDeclaration { - override var type: String /* "ClassDeclaration" */ - var id: Identifier? -} - -internal external interface ClassExpression : BaseClass, BaseExpression { - override var type: String /* "ClassExpression" */ - var id: Identifier? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface MetaProperty : BaseExpression { - override var type: String /* "MetaProperty" */ - var meta: Identifier - var property: Identifier -} - -internal external interface BaseModuleDeclaration : BaseNode - -internal external interface BaseModuleSpecifier : BaseNode { - var local: Identifier -} - -internal external interface ImportDeclaration : BaseModuleDeclaration { - override var type: String /* "ImportDeclaration" */ - var specifiers: Array - var source: dynamic /* SimpleLiteral | RegExpLiteral */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ImportSpecifier : BaseModuleSpecifier { - override var type: String /* "ImportSpecifier" */ - var imported: Identifier -} - -internal external interface ImportExpression : BaseExpression { - override var type: String /* "ImportExpression" */ - var source: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ImportDefaultSpecifier : BaseModuleSpecifier { - override var type: String /* "ImportDefaultSpecifier" */ -} - -internal external interface ImportNamespaceSpecifier : BaseModuleSpecifier { - override var type: String /* "ImportNamespaceSpecifier" */ -} - -internal external interface ExportNamedDeclaration : BaseModuleDeclaration { - override var type: String /* "ExportNamedDeclaration" */ - var declaration: dynamic /* FunctionDeclaration? | VariableDeclaration? | ClassDeclaration? */ - get() = definedExternally - set(value) = definedExternally - var specifiers: Array - var source: dynamic /* SimpleLiteral? | RegExpLiteral? */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ExportSpecifier : BaseModuleSpecifier { - override var type: String /* "ExportSpecifier" */ - var exported: Identifier -} - -internal external interface ExportDefaultDeclaration : BaseModuleDeclaration { - override var type: String /* "ExportDefaultDeclaration" */ - var declaration: dynamic /* FunctionDeclaration | VariableDeclaration | ClassDeclaration | ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ExportAllDeclaration : BaseModuleDeclaration { - override var type: String /* "ExportAllDeclaration" */ - var source: dynamic /* SimpleLiteral | RegExpLiteral */ - get() = definedExternally - set(value) = definedExternally -} - -internal external interface AwaitExpression : BaseExpression { - override var type: String /* "AwaitExpression" */ - var argument: dynamic /* ThisExpression | ArrayExpression | ObjectExpression | FunctionExpression | ArrowFunctionExpression | YieldExpression | SimpleLiteral | RegExpLiteral | UnaryExpression | UpdateExpression | BinaryExpression | AssignmentExpression | LogicalExpression | MemberExpression | ConditionalExpression | SimpleCallExpression | NewExpression | SequenceExpression | TemplateLiteral | TaggedTemplateExpression | ClassExpression | MetaProperty | Identifier | AwaitExpression | ImportExpression | ChainExpression */ - get() = definedExternally - set(value) = definedExternally -} diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/stream/stream.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/stream/stream.kt deleted file mode 100644 index 52be5530f..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/stream/stream.kt +++ /dev/null @@ -1,12 +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. - */ - -package space.kscience.kmath.internal.stream - -import space.kscience.kmath.internal.emitter.Emitter - -internal open external class Stream : Emitter { - open fun pipe(dest: Any, options: Any): Any -} diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/tsstdlib/lib.es2015.iterable.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/tsstdlib/lib.es2015.iterable.kt deleted file mode 100644 index 9c012e3a3..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/tsstdlib/lib.es2015.iterable.kt +++ /dev/null @@ -1,30 +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. - */ - -package space.kscience.kmath.internal.tsstdlib - -internal external interface IteratorYieldResult { - var done: Boolean? - get() = definedExternally - set(value) = definedExternally - var value: TYield -} - -internal external interface IteratorReturnResult { - var done: Boolean - var value: TReturn -} - -internal external interface Iterator { - fun next(vararg args: Any /* JsTuple<> | JsTuple */): dynamic /* IteratorYieldResult | IteratorReturnResult */ - val `return`: ((value: TReturn) -> dynamic)? - val `throw`: ((e: Any) -> dynamic)? -} - -internal typealias Iterator__1 = Iterator - -internal external interface Iterable - -internal external interface IterableIterator : Iterator__1 diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/tsstdlib/lib.es5.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/tsstdlib/lib.es5.kt deleted file mode 100644 index 0cd395f2c..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/tsstdlib/lib.es5.kt +++ /dev/null @@ -1,96 +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. - */ - -@file:Suppress("UNUSED_TYPEALIAS_PARAMETER", "DEPRECATION") - -package space.kscience.kmath.internal.tsstdlib - -import kotlin.js.RegExp - -internal typealias RegExpMatchArray = Array - -internal typealias RegExpExecArray = Array - -internal external interface RegExpConstructor { - @nativeInvoke - operator fun invoke(pattern: RegExp, flags: String = definedExternally): RegExp - - @nativeInvoke - operator fun invoke(pattern: RegExp): RegExp - - @nativeInvoke - operator fun invoke(pattern: String, flags: String = definedExternally): RegExp - - @nativeInvoke - operator fun invoke(pattern: String): RegExp - var prototype: RegExp - var `$1`: String - var `$2`: String - var `$3`: String - var `$4`: String - var `$5`: String - var `$6`: String - var `$7`: String - var `$8`: String - var `$9`: String - var lastMatch: String -} - -internal typealias Record = Any - -internal external interface ConcatArray { - var length: Number - - @nativeGetter - operator fun get(n: Number): T? - - @nativeSetter - operator fun set(n: Number, value: T) - fun join(separator: String = definedExternally): String - fun slice(start: Number = definedExternally, end: Number = definedExternally): Array -} - -internal external interface ArrayConstructor { - fun from(iterable: Iterable): Array - fun from(iterable: ArrayLike): Array - fun from(iterable: Iterable, mapfn: (v: T, k: Number) -> U, thisArg: Any = definedExternally): Array - fun from(iterable: Iterable, mapfn: (v: T, k: Number) -> U): Array - fun from(iterable: ArrayLike, mapfn: (v: T, k: Number) -> U, thisArg: Any = definedExternally): Array - fun from(iterable: ArrayLike, mapfn: (v: T, k: Number) -> U): Array - fun of(vararg items: T): Array - - @nativeInvoke - operator fun invoke(arrayLength: Number = definedExternally): Array - - @nativeInvoke - operator fun invoke(): Array - - @nativeInvoke - operator fun invoke(arrayLength: Number): Array - - @nativeInvoke - operator fun invoke(vararg items: T): Array - fun isArray(arg: Any): Boolean - var prototype: Array -} - -internal external interface ArrayLike { - var length: Number - - @nativeGetter - operator fun get(n: Number): T? - - @nativeSetter - operator fun set(n: Number, value: T) -} - -internal typealias Extract = Any - -internal external interface PromiseLike { - fun then( - onfulfilled: ((value: T) -> Any?)? = definedExternally, - onrejected: ((reason: Any) -> Any?)? = definedExternally - ): PromiseLike -} diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/webassembly/lib.dom.WebAssembly.module_dukat.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/webassembly/lib.dom.WebAssembly.module_dukat.kt deleted file mode 100644 index 90690abed..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/webassembly/lib.dom.WebAssembly.module_dukat.kt +++ /dev/null @@ -1,236 +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. - */ - -@file:JsQualifier("WebAssembly") - -@file:Suppress( - "INTERFACE_WITH_SUPERCLASS", - "OVERRIDING_FINAL_MEMBER", - "RETURN_TYPE_MISMATCH_ON_OVERRIDE", - "NO_EXPLICIT_VISIBILITY_IN_API_MODE_WARNING", - "ClassName", -) - -package space.kscience.kmath.internal.webassembly - -import space.kscience.kmath.internal.tsstdlib.PromiseLike -import org.khronos.webgl.ArrayBuffer -import org.khronos.webgl.ArrayBufferView -import org.khronos.webgl.Uint8Array -import org.w3c.fetch.Response -import kotlin.js.Promise - -@Suppress("NESTED_CLASS_IN_EXTERNAL_INTERFACE") -internal external interface CompileError { - companion object { - var prototype: CompileError - } -} - -@Suppress("NESTED_CLASS_IN_EXTERNAL_INTERFACE") -internal external interface Global { - var value: Any - fun valueOf(): Any - - companion object { - var prototype: Global - } -} - -@Suppress("NESTED_CLASS_IN_EXTERNAL_INTERFACE") -@JsName("Instance") -internal external interface Instance1 { - var exports: Exports - - companion object { - var prototype: Instance - } -} - -@Suppress("NESTED_CLASS_IN_EXTERNAL_INTERFACE") -internal external interface LinkError { - companion object { - var prototype: LinkError - } -} - -@Suppress("NESTED_CLASS_IN_EXTERNAL_INTERFACE") -internal external interface Memory { - var buffer: ArrayBuffer - fun grow(delta: Number): Number - - companion object { - var prototype: Memory - } -} - -@Suppress("NESTED_CLASS_IN_EXTERNAL_INTERFACE") -@JsName("Module") -internal external interface Module1 { - companion object { - var prototype: Module - fun customSections(moduleObject: Module, sectionName: String): Array - fun exports(moduleObject: Module): Array - fun imports(moduleObject: Module): Array - } -} - -@Suppress("NESTED_CLASS_IN_EXTERNAL_INTERFACE") -internal external interface RuntimeError { - companion object { - var prototype: RuntimeError - } -} - -@Suppress("NESTED_CLASS_IN_EXTERNAL_INTERFACE") -internal external interface Table { - var length: Number - fun get(index: Number): Function<*>? - fun grow(delta: Number): Number - fun set(index: Number, value: Function<*>?) - - companion object { - var prototype: Table - } -} - -internal external interface GlobalDescriptor { - var mutable: Boolean? - get() = definedExternally - set(value) = definedExternally - var value: String /* "f32" | "f64" | "i32" | "i64" */ -} - -internal external interface MemoryDescriptor { - var initial: Number - var maximum: Number? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface ModuleExportDescriptor { - var kind: String /* "function" | "global" | "memory" | "table" */ - var name: String -} - -internal external interface ModuleImportDescriptor { - var kind: String /* "function" | "global" | "memory" | "table" */ - var module: String - var name: String -} - -internal external interface TableDescriptor { - var element: String /* "anyfunc" */ - var initial: Number - var maximum: Number? - get() = definedExternally - set(value) = definedExternally -} - -internal external interface WebAssemblyInstantiatedSource { - var instance: Instance - var module: Module -} - -internal external fun compile(bytes: ArrayBufferView): Promise - -internal external fun compile(bytes: ArrayBuffer): Promise - -internal external fun compileStreaming(source: Response): Promise - -internal external fun compileStreaming(source: Promise): Promise - -internal external fun instantiate( - bytes: ArrayBufferView, - importObject: Imports = definedExternally, -): Promise - -internal external fun instantiate(bytes: ArrayBufferView): Promise - -internal external fun instantiate( - bytes: ArrayBuffer, - importObject: Imports = definedExternally, -): dynamic /* Promise | Promise */ - -internal external fun instantiate(bytes: ArrayBuffer): dynamic /* Promise | Promise */ - -internal external fun instantiate(moduleObject: Module, importObject: Imports = definedExternally): Promise - -internal external fun instantiate(moduleObject: Module): Promise - -internal external fun instantiateStreaming( - response: Response, - importObject: Imports = definedExternally, -): Promise - -internal external fun instantiateStreaming(response: Response): Promise - -internal external fun instantiateStreaming( - response: PromiseLike, - importObject: Imports = definedExternally, -): Promise - -internal external fun instantiateStreaming(response: PromiseLike): Promise - -internal external fun validate(bytes: ArrayBufferView): Boolean - -internal external fun validate(bytes: ArrayBuffer): Boolean - -internal external interface `T$0` { - var name: String - var kind: String -} - -internal external interface `T$1` { - var module: String - var name: String - var kind: String -} - -internal open external class Module { - constructor(bufferSource: ArrayBuffer) - constructor(bufferSource: Uint8Array) - - companion object { - fun customSections(module: Module, sectionName: String): Array - fun exports(module: Module): Array<`T$0`> - fun imports(module: Module): Array<`T$1`> - } -} - -@JsName("Instance") -internal open external class Instance(module: Module, importObject: dynamic = definedExternally) { - open var exports: dynamic -} - -@JsName("Memory") -internal open external class Memory1(memoryDescriptor: MemoryDescriptor) { - open var buffer: ArrayBuffer - open fun grow(numPages: Number): Number -} - -@JsName("Table") -internal open external class Table1(tableDescriptor: TableDescriptor) { - open var length: Number - open fun get(index: Number): Function<*> - open fun grow(numElements: Number): Number - open fun set(index: Number, value: Function<*>) -} - -internal external fun compile(bufferSource: Uint8Array): Promise - -internal external interface ResultObject { - var module: Module - var instance: Instance -} - -internal external fun instantiate( - bufferSource: Uint8Array, - importObject: Any = definedExternally, -): Promise - -internal external fun instantiate(bufferSource: Uint8Array): Promise - -internal external fun validate(bufferSource: Uint8Array): Boolean diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/webassembly/nonDeclarations.WebAssembly.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/webassembly/nonDeclarations.WebAssembly.kt deleted file mode 100644 index c5023c384..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/webassembly/nonDeclarations.WebAssembly.kt +++ /dev/null @@ -1,28 +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. - */ - -@file:Suppress( - "INTERFACE_WITH_SUPERCLASS", - "OVERRIDING_FINAL_MEMBER", - "RETURN_TYPE_MISMATCH_ON_OVERRIDE", - "CONFLICTING_OVERLOADS", - "NO_EXPLICIT_VISIBILITY_IN_API_MODE_WARNING", -) - -package space.kscience.kmath.internal.webassembly - -import space.kscience.kmath.internal.tsstdlib.Record - -internal typealias Exports = Record | Global | Memory | Table */> - -internal typealias ModuleImports = Record | Global | Memory | Table | Number */> - -internal typealias Imports = Record - -internal typealias CompileError1 = Error - -internal typealias LinkError1 = Error - -internal typealias RuntimeError1 = Error diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/internal/WasmBuilder.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/internal/WasmBuilder.kt deleted file mode 100644 index aacb62f36..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/internal/WasmBuilder.kt +++ /dev/null @@ -1,157 +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. - */ - -package space.kscience.kmath.wasm.internal - -import space.kscience.kmath.ast.TypedMst -import space.kscience.kmath.expressions.* -import space.kscience.kmath.internal.binaryen.* -import space.kscience.kmath.internal.webassembly.Instance -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import space.kscience.kmath.internal.binaryen.Module as BinaryenModule -import space.kscience.kmath.internal.webassembly.Module as WasmModule - -private val spreader = eval("(obj, args) => obj(...args)") - -@OptIn(UnstableKMathAPI::class) -@Suppress("UnsafeCastFromDynamic") -internal sealed class WasmBuilder>( - protected val binaryenType: Type, - protected val algebra: Algebra, - protected val target: TypedMst, -) { - protected val keys: MutableList = mutableListOf() - protected lateinit var ctx: BinaryenModule - - abstract val instance: E - - protected val executable = run { - val c = WasmModule(with(createModule()) { - ctx = this - val expr = visit(target) - - addFunction( - "executable", - createType(Array(keys.size) { binaryenType }), - binaryenType, - arrayOf(), - expr - ) - - setOptimizeLevel(3) - optimizeFunction("executable") - addFunctionExport("executable", "executable") - val res = emitBinary() - dispose() - res - }) - - Instance(c, js("{}")).exports.executable - } - - protected abstract fun visitNumber(number: Number): ExpressionRef - - protected open fun visitVariable(node: TypedMst.Variable): ExpressionRef { - var idx = keys.indexOf(node.symbol) - - if (idx == -1) { - keys += node.symbol - idx = keys.lastIndex - } - - return ctx.local.get(idx, binaryenType) - } - - protected open fun visitUnary(node: TypedMst.Unary): ExpressionRef = - error("Unary operation ${node.operation} not defined in $this") - - protected open fun visitBinary(mst: TypedMst.Binary): ExpressionRef = - error("Binary operation ${mst.operation} not defined in $this") - - protected open fun createModule(): BinaryenModule = js("new \$module\$binaryen.Module()") - - protected fun visit(node: TypedMst): ExpressionRef = when (node) { - is TypedMst.Constant -> visitNumber( - node.number ?: error("Object constants are not supported by pritimive ASM builder"), - ) - - is TypedMst.Variable -> visitVariable(node) - is TypedMst.Unary -> visitUnary(node) - is TypedMst.Binary -> visitBinary(node) - } -} - -@UnstableKMathAPI -internal class DoubleWasmBuilder(target: TypedMst) : - WasmBuilder(f64, DoubleField, target) { - override val instance by lazy { - object : DoubleExpression { - override val indexer = SimpleSymbolIndexer(keys) - - override fun invoke(arguments: DoubleArray) = spreader(executable, arguments).unsafeCast() - } - } - - override fun createModule() = readBinary(f64StandardFunctions) - - override fun visitNumber(number: Number) = ctx.f64.const(number.toDouble()) - - override fun visitUnary(node: TypedMst.Unary): ExpressionRef = when (node.operation) { - GroupOps.MINUS_OPERATION -> ctx.f64.neg(visit(node.value)) - GroupOps.PLUS_OPERATION -> visit(node.value) - PowerOperations.SQRT_OPERATION -> ctx.f64.sqrt(visit(node.value)) - TrigonometricOperations.SIN_OPERATION -> ctx.call("sin", arrayOf(visit(node.value)), f64) - TrigonometricOperations.COS_OPERATION -> ctx.call("cos", arrayOf(visit(node.value)), f64) - TrigonometricOperations.TAN_OPERATION -> ctx.call("tan", arrayOf(visit(node.value)), f64) - TrigonometricOperations.ASIN_OPERATION -> ctx.call("asin", arrayOf(visit(node.value)), f64) - TrigonometricOperations.ACOS_OPERATION -> ctx.call("acos", arrayOf(visit(node.value)), f64) - TrigonometricOperations.ATAN_OPERATION -> ctx.call("atan", arrayOf(visit(node.value)), f64) - ExponentialOperations.SINH_OPERATION -> ctx.call("sinh", arrayOf(visit(node.value)), f64) - ExponentialOperations.COSH_OPERATION -> ctx.call("cosh", arrayOf(visit(node.value)), f64) - ExponentialOperations.TANH_OPERATION -> ctx.call("tanh", arrayOf(visit(node.value)), f64) - ExponentialOperations.ASINH_OPERATION -> ctx.call("asinh", arrayOf(visit(node.value)), f64) - ExponentialOperations.ACOSH_OPERATION -> ctx.call("acosh", arrayOf(visit(node.value)), f64) - ExponentialOperations.ATANH_OPERATION -> ctx.call("atanh", arrayOf(visit(node.value)), f64) - ExponentialOperations.EXP_OPERATION -> ctx.call("exp", arrayOf(visit(node.value)), f64) - ExponentialOperations.LN_OPERATION -> ctx.call("log", arrayOf(visit(node.value)), f64) - else -> super.visitUnary(node) - } - - override fun visitBinary(mst: TypedMst.Binary): ExpressionRef = when (mst.operation) { - GroupOps.PLUS_OPERATION -> ctx.f64.add(visit(mst.left), visit(mst.right)) - GroupOps.MINUS_OPERATION -> ctx.f64.sub(visit(mst.left), visit(mst.right)) - RingOps.TIMES_OPERATION -> ctx.f64.mul(visit(mst.left), visit(mst.right)) - FieldOps.DIV_OPERATION -> ctx.f64.div(visit(mst.left), visit(mst.right)) - PowerOperations.POW_OPERATION -> ctx.call("pow", arrayOf(visit(mst.left), visit(mst.right)), f64) - else -> super.visitBinary(mst) - } -} - -@UnstableKMathAPI -internal class IntWasmBuilder(target: TypedMst) : WasmBuilder(i32, IntRing, target) { - override val instance by lazy { - object : IntExpression { - override val indexer = SimpleSymbolIndexer(keys) - - override fun invoke(arguments: IntArray) = spreader(executable, arguments).unsafeCast() - } - } - - override fun visitNumber(number: Number) = ctx.i32.const(number.toInt()) - - override fun visitUnary(node: TypedMst.Unary): ExpressionRef = when (node.operation) { - GroupOps.MINUS_OPERATION -> ctx.i32.sub(ctx.i32.const(0), visit(node.value)) - GroupOps.PLUS_OPERATION -> visit(node.value) - else -> super.visitUnary(node) - } - - override fun visitBinary(mst: TypedMst.Binary): ExpressionRef = when (mst.operation) { - GroupOps.PLUS_OPERATION -> ctx.i32.add(visit(mst.left), visit(mst.right)) - GroupOps.MINUS_OPERATION -> ctx.i32.sub(visit(mst.left), visit(mst.right)) - RingOps.TIMES_OPERATION -> ctx.i32.mul(visit(mst.left), visit(mst.right)) - else -> super.visitBinary(mst) - } -} diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/internal/f64StandardFunctions.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/internal/f64StandardFunctions.kt deleted file mode 100644 index 21a88b5d0..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/internal/f64StandardFunctions.kt +++ /dev/null @@ -1,13 +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. - */ - -package space.kscience.kmath.wasm.internal - -import space.kscience.kmath.internal.base64.toUint8Array - -internal val f64StandardFunctions by lazy { toUint8Array(B) } - -private const val B = - 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diff --git a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/wasm.kt b/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/wasm.kt deleted file mode 100644 index f9540f9db..000000000 --- a/kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/wasm.kt +++ /dev/null @@ -1,90 +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. - */ - -@file:Suppress("UNUSED_PARAMETER") - -package space.kscience.kmath.wasm - -import space.kscience.kmath.ast.TypedMst -import space.kscience.kmath.ast.evaluateConstants -import space.kscience.kmath.expressions.* -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.IntRing -import space.kscience.kmath.wasm.internal.DoubleWasmBuilder -import space.kscience.kmath.wasm.internal.IntWasmBuilder - -/** - * Create a compiled expression with given [MST] and given [algebra]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compileToExpression(algebra: IntRing): IntExpression { - val typed = evaluateConstants(algebra) - - return if (typed is TypedMst.Constant) object : IntExpression { - override val indexer = SimpleSymbolIndexer(emptyList()) - - override fun invoke(arguments: IntArray): Int = typed.value - } else - IntWasmBuilder(typed).instance -} - -/** - * Compile given MST to expression and evaluate it against [arguments]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compile(algebra: IntRing, arguments: Map): Int = - compileToExpression(algebra)(arguments) - - -/** - * Compile given MST to expression and evaluate it against [arguments]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compile(algebra: IntRing, vararg arguments: Pair): Int = - compileToExpression(algebra)(*arguments) - -/** - * Create a compiled expression with given [MST] and given [algebra]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compileToExpression(algebra: DoubleField): Expression { - val typed = evaluateConstants(algebra) - - return if (typed is TypedMst.Constant) object : DoubleExpression { - override val indexer = SimpleSymbolIndexer(emptyList()) - - override fun invoke(arguments: DoubleArray): Double = typed.value - } else - DoubleWasmBuilder(typed).instance -} - - -/** - * Compile given MST to expression and evaluate it against [arguments]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compile(algebra: DoubleField, arguments: Map): Double = - compileToExpression(algebra)(arguments) - - -/** - * Compile given MST to expression and evaluate it against [arguments]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compile(algebra: DoubleField, vararg arguments: Pair): Double = - compileToExpression(algebra)(*arguments) diff --git a/kmath-ast/src/jsTest/kotlin/space/kscience/kmath/ast/utils.kt b/kmath-ast/src/jsTest/kotlin/space/kscience/kmath/ast/utils.kt deleted file mode 100644 index 0d896c6f6..000000000 --- a/kmath-ast/src/jsTest/kotlin/space/kscience/kmath/ast/utils.kt +++ /dev/null @@ -1,42 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.expressions.Expression -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.IntRing -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract -import space.kscience.kmath.estree.compile as estreeCompile -import space.kscience.kmath.estree.compileToExpression as estreeCompileToExpression -import space.kscience.kmath.wasm.compile as wasmCompile -import space.kscience.kmath.wasm.compileToExpression as wasmCompileToExpression - -private object WasmCompilerTestContext : CompilerTestContext { - override fun MST.compileToExpression(algebra: IntRing): Expression = wasmCompileToExpression(algebra) - override fun MST.compile(algebra: IntRing, arguments: Map): Int = wasmCompile(algebra, arguments) - override fun MST.compileToExpression(algebra: DoubleField): Expression = wasmCompileToExpression(algebra) - - override fun MST.compile(algebra: DoubleField, arguments: Map): Double = - wasmCompile(algebra, arguments) -} - -private object ESTreeCompilerTestContext : CompilerTestContext { - override fun MST.compileToExpression(algebra: IntRing): Expression = estreeCompileToExpression(algebra) - override fun MST.compile(algebra: IntRing, arguments: Map): Int = estreeCompile(algebra, arguments) - override fun MST.compileToExpression(algebra: DoubleField): Expression = estreeCompileToExpression(algebra) - - override fun MST.compile(algebra: DoubleField, arguments: Map): Double = - estreeCompile(algebra, arguments) -} - -internal actual inline fun runCompilerTest(action: CompilerTestContext.() -> Unit) { - contract { callsInPlace(action, InvocationKind.AT_LEAST_ONCE) } - action(WasmCompilerTestContext) - action(ESTreeCompilerTestContext) -} diff --git a/kmath-ast/src/jsTest/kotlin/space/kscience/kmath/wasm/TestWasmSpecific.kt b/kmath-ast/src/jsTest/kotlin/space/kscience/kmath/wasm/TestWasmSpecific.kt deleted file mode 100644 index 8ae5fcb36..000000000 --- a/kmath-ast/src/jsTest/kotlin/space/kscience/kmath/wasm/TestWasmSpecific.kt +++ /dev/null @@ -1,52 +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. - */ - -package space.kscience.kmath.wasm - -import space.kscience.kmath.expressions.MstExtendedField -import space.kscience.kmath.expressions.MstRing -import space.kscience.kmath.expressions.invoke -import space.kscience.kmath.expressions.symbol -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.IntRing -import space.kscience.kmath.operations.invoke -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class TestWasmSpecific { - @Test - fun int() { - val res = MstRing { number(100000000) + number(10000000) }.compile(IntRing) - assertEquals(110000000, res) - } - - @Test - fun real() { - val res = MstExtendedField { number(100000000) + number(2).pow(10) }.compile(DoubleField) - assertEquals(100001024.0, res) - } - - @Test - fun argsPassing() { - val res = MstExtendedField { y + x.pow(10) }.compile( - DoubleField, - x to 2.0, - y to 100000000.0, - ) - - assertEquals(100001024.0, res) - } - - @Test - fun powFunction() { - val expr = MstExtendedField { x.pow(1.0 / 6.0) }.compileToExpression(DoubleField) - assertEquals(0.9730585187140817, expr(x to 0.8488554755054833)) - } - - private companion object { - private val x by symbol - private val y by symbol - } -} diff --git a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt b/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt deleted file mode 100644 index 73b9c97a7..000000000 --- a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt +++ /dev/null @@ -1,195 +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. - */ - -@file:Suppress("UNUSED_PARAMETER") - -package space.kscience.kmath.asm - -import space.kscience.kmath.asm.internal.* -import space.kscience.kmath.ast.TypedMst -import space.kscience.kmath.ast.evaluateConstants -import space.kscience.kmath.expressions.* -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Algebra -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.IntRing -import space.kscience.kmath.operations.LongRing - -/** - * Compiles given MST to an Expression using AST compiler. - * - * @param type the target type. - * @param algebra the target algebra. - * @return the compiled expression. - * @author Alexander Nozik - */ -@OptIn(UnstableKMathAPI::class) -@PublishedApi -internal fun MST.compileWith(type: Class, algebra: Algebra): Expression { - val typed = evaluateConstants(algebra) - if (typed is TypedMst.Constant) return Expression { typed.value } - - fun GenericAsmBuilder.variablesVisitor(node: TypedMst): Unit = when (node) { - is TypedMst.Unary -> variablesVisitor(node.value) - - is TypedMst.Binary -> { - variablesVisitor(node.left) - variablesVisitor(node.right) - } - - is TypedMst.Variable -> prepareVariable(node.symbol) - is TypedMst.Constant -> Unit - } - - fun GenericAsmBuilder.expressionVisitor(node: TypedMst): Unit = when (node) { - is TypedMst.Constant -> if (node.number != null) - loadNumberConstant(node.number) - else - loadObjectConstant(node.value) - - is TypedMst.Variable -> loadVariable(node.symbol) - is TypedMst.Unary -> buildCall(node.function) { expressionVisitor(node.value) } - - is TypedMst.Binary -> buildCall(node.function) { - expressionVisitor(node.left) - expressionVisitor(node.right) - } - } - - return GenericAsmBuilder( - type, - buildName("${typed.hashCode()}_${type.simpleName}"), - { variablesVisitor(typed) }, - { expressionVisitor(typed) }, - ).instance -} - -/** - * Create a compiled expression with given [MST] and given [algebra]. - */ -public inline fun MST.compileToExpression(algebra: Algebra): Expression = - compileWith(T::class.java, algebra) - -/** - * Compile given MST to expression and evaluate it against [arguments] - */ -public inline fun MST.compile(algebra: Algebra, arguments: Map): T = - compileToExpression(algebra)(arguments) - -/** - * Compile given MST to expression and evaluate it against [arguments] - */ -public inline fun MST.compile(algebra: Algebra, vararg arguments: Pair): T = - compileToExpression(algebra)(*arguments) - - -/** - * Create a compiled expression with given [MST] and given [algebra]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compileToExpression(algebra: IntRing): IntExpression { - val typed = evaluateConstants(algebra) - - return if (typed is TypedMst.Constant) object : IntExpression { - override val indexer = SimpleSymbolIndexer(emptyList()) - - override fun invoke(arguments: IntArray): Int = typed.value - } else - IntAsmBuilder(typed).instance -} - -/** - * Compile given MST to expression and evaluate it against [arguments]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compile(algebra: IntRing, arguments: Map): Int = - compileToExpression(algebra)(arguments) - -/** - * Compile given MST to expression and evaluate it against [arguments]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compile(algebra: IntRing, vararg arguments: Pair): Int = - compileToExpression(algebra)(*arguments) - - -/** - * Create a compiled expression with given [MST] and given [algebra]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compileToExpression(algebra: LongRing): LongExpression { - val typed = evaluateConstants(algebra) - - return if (typed is TypedMst.Constant) object : LongExpression { - override val indexer = SimpleSymbolIndexer(emptyList()) - - override fun invoke(arguments: LongArray): Long = typed.value - } else - LongAsmBuilder(typed).instance -} - -/** - * Compile given MST to expression and evaluate it against [arguments]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compile(algebra: LongRing, arguments: Map): Long = - compileToExpression(algebra)(arguments) - - -/** - * Compile given MST to expression and evaluate it against [arguments]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compile(algebra: LongRing, vararg arguments: Pair): Long = - compileToExpression(algebra)(*arguments) - - -/** - * Create a compiled expression with given [MST] and given [algebra]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compileToExpression(algebra: DoubleField): DoubleExpression { - val typed = evaluateConstants(algebra) - - return if (typed is TypedMst.Constant) object : DoubleExpression { - override val indexer = SimpleSymbolIndexer(emptyList()) - - override fun invoke(arguments: DoubleArray): Double = typed.value - } else - DoubleAsmBuilder(typed).instance -} - - -/** - * Compile given MST to expression and evaluate it against [arguments]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compile(algebra: DoubleField, arguments: Map): Double = - compileToExpression(algebra)(arguments) - -/** - * Compile given MST to expression and evaluate it against [arguments]. - * - * @author Iaroslav Postovalov - */ -@UnstableKMathAPI -public fun MST.compile(algebra: DoubleField, vararg arguments: Pair): Double = - compileToExpression(algebra)(*arguments) diff --git a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/AsmBuilder.kt b/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/AsmBuilder.kt deleted file mode 100644 index a85079fc8..000000000 --- a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/AsmBuilder.kt +++ /dev/null @@ -1,53 +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. - */ - -package space.kscience.kmath.asm.internal - -import org.objectweb.asm.Type -import org.objectweb.asm.Type.getObjectType -import space.kscience.kmath.expressions.Expression - -internal abstract class AsmBuilder { - /** - * Internal classloader with alias to define class from byte array. - */ - class ByteArrayClassLoader(parent: ClassLoader) : ClassLoader(parent) { - fun defineClass(name: String?, b: ByteArray): Class<*> = defineClass(name, b, 0, b.size) - } - - protected val classLoader = ByteArrayClassLoader(javaClass.classLoader) - - protected companion object { - /** - * ASM type for [Expression]. - */ - val EXPRESSION_TYPE: Type = getObjectType("space/kscience/kmath/expressions/Expression") - - /** - * ASM type for [java.util.Map]. - */ - val MAP_TYPE: Type = getObjectType("java/util/Map") - - /** - * ASM type for [java.lang.Object]. - */ - val OBJECT_TYPE: Type = getObjectType("java/lang/Object") - - /** - * ASM type for [java.lang.String]. - */ - val STRING_TYPE: Type = getObjectType("java/lang/String") - - /** - * ASM type for MapIntrinsics. - */ - val MAP_INTRINSICS_TYPE: Type = getObjectType("space/kscience/kmath/asm/internal/MapIntrinsics") - - /** - * ASM Type for [space.kscience.kmath.expressions.Symbol]. - */ - val SYMBOL_TYPE: Type = getObjectType("space/kscience/kmath/expressions/Symbol") - } -} diff --git a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/GenericAsmBuilder.kt b/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/GenericAsmBuilder.kt deleted file mode 100644 index 6cf3d8721..000000000 --- a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/GenericAsmBuilder.kt +++ /dev/null @@ -1,325 +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. - */ - -package space.kscience.kmath.asm.internal - -import org.objectweb.asm.* -import org.objectweb.asm.Opcodes.* -import org.objectweb.asm.Type.* -import org.objectweb.asm.commons.InstructionAdapter -import space.kscience.kmath.expressions.* -import java.lang.invoke.MethodHandles -import java.lang.invoke.MethodType -import java.nio.file.Paths -import java.util.stream.Collectors.toMap -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract -import kotlin.io.path.writeBytes - -/** - * ASM Builder is a structure that abstracts building a class designated to unwrap [MST] to plain Java expression. - * This class uses [ClassLoader] for loading the generated class, then it is able to instantiate the new class. - * - * @property T the type of AsmExpression to unwrap. - * @property className the unique class name of new loaded class. - * @property expressionResultCallback the function to apply to this object when generating expression value. - * @author Iaroslav Postovalov - */ -internal class GenericAsmBuilder( - classOfT: Class<*>, - private val className: String, - private val variablesPrepareCallback: GenericAsmBuilder.() -> Unit, - private val expressionResultCallback: GenericAsmBuilder.() -> Unit, -) : AsmBuilder() { - /** - * ASM type for [T]. - */ - private val tType: Type = classOfT.asm - - /** - * ASM type for new class. - */ - private val classType: Type = getObjectType(className.replace(oldChar = '.', newChar = '/')) - - /** - * List of constants to provide to the subclass. - */ - private val constants = mutableListOf() - - /** - * Method visitor of `invoke` method of the subclass. - */ - private lateinit var invokeMethodVisitor: InstructionAdapter - - /** - * Local variables indices are indices of symbols in this list. - */ - private val argumentsLocals = mutableListOf() - - /** - * Subclasses, loads and instantiates [Expression] for given parameters. - * - * The built instance is cached. - */ - @Suppress("UNCHECKED_CAST", "UNUSED_VARIABLE") - val instance: Expression by lazy { - val hasConstants: Boolean - - val classWriter = ClassWriter(ClassWriter.COMPUTE_FRAMES) { - visit( - V1_8, - ACC_PUBLIC or ACC_FINAL or ACC_SUPER, - classType.internalName, - "${OBJECT_TYPE.descriptor}L${EXPRESSION_TYPE.internalName}<${tType.descriptor}>;", - OBJECT_TYPE.internalName, - arrayOf(EXPRESSION_TYPE.internalName), - ) - - visitMethod( - ACC_PUBLIC, - "invoke", - getMethodDescriptor(tType, MAP_TYPE), - "(L${MAP_TYPE.internalName}<${SYMBOL_TYPE.descriptor}+${tType.descriptor}>;)${tType.descriptor}", - null, - ).instructionAdapter { - invokeMethodVisitor = this - visitCode() - val preparingVariables = label() - variablesPrepareCallback() - val expressionResult = label() - expressionResultCallback() - areturn(tType) - val end = label() - - visitLocalVariable( - "this", - classType.descriptor, - null, - preparingVariables, - end, - 0, - ) - - visitLocalVariable( - "arguments", - MAP_TYPE.descriptor, - "L${MAP_TYPE.internalName}<${SYMBOL_TYPE.descriptor}+${tType.descriptor}>;", - preparingVariables, - end, - 1, - ) - - visitMaxs(0, 0) - visitEnd() - } - - visitMethod( - ACC_PUBLIC or ACC_BRIDGE or ACC_SYNTHETIC, - "invoke", - getMethodDescriptor(OBJECT_TYPE, MAP_TYPE), - null, - null, - ).instructionAdapter { - visitCode() - val start = label() - load(0, OBJECT_TYPE) - load(1, MAP_TYPE) - invokevirtual(classType.internalName, "invoke", getMethodDescriptor(tType, MAP_TYPE), false) - areturn(tType) - val end = label() - - visitLocalVariable( - "this", - classType.descriptor, - null, - start, - end, - 0, - ) - - visitMaxs(0, 0) - visitEnd() - } - - hasConstants = constants.isNotEmpty() - - if (hasConstants) - visitField( - access = ACC_PRIVATE or ACC_FINAL, - name = "constants", - descriptor = OBJECT_ARRAY_TYPE.descriptor, - signature = null, - value = null, - block = FieldVisitor::visitEnd, - ) - - visitMethod( - ACC_PUBLIC or ACC_SYNTHETIC, - "", - getMethodDescriptor(VOID_TYPE, *OBJECT_ARRAY_TYPE.wrapToArrayIf { hasConstants }), - null, - null, - ).instructionAdapter { - val l0 = label() - load(0, classType) - invokespecial(OBJECT_TYPE.internalName, "", getMethodDescriptor(VOID_TYPE), false) - label() - load(0, classType) - - if (hasConstants) { - label() - load(0, classType) - load(1, OBJECT_ARRAY_TYPE) - putfield(classType.internalName, "constants", OBJECT_ARRAY_TYPE.descriptor) - } - - label() - areturn(VOID_TYPE) - val l4 = label() - visitLocalVariable("this", classType.descriptor, null, l0, l4, 0) - - if (hasConstants) - visitLocalVariable("constants", OBJECT_ARRAY_TYPE.descriptor, null, l0, l4, 1) - - visitMaxs(0, 0) - visitEnd() - } - - visitEnd() - } - - val binary = classWriter.toByteArray() - val cls = classLoader.defineClass(className, binary) - - if (System.getProperty("space.kscience.kmath.ast.dump.generated.classes") == "1") - Paths.get("${className.split('.').last()}.class").writeBytes(binary) - - val l = MethodHandles.publicLookup() - - (if (hasConstants) - l.findConstructor(cls, MethodType.methodType(Void.TYPE, Array::class.java))(constants.toTypedArray()) - else - l.findConstructor(cls, MethodType.methodType(Void.TYPE))()) as Expression - } - - /** - * Loads [java.lang.Object] constant from constants. - */ - fun loadObjectConstant(value: Any, type: Type = tType): Unit = invokeMethodVisitor.run { - val idx = if (value in constants) constants.indexOf(value) else constants.also { it += value }.lastIndex - load(0, classType) - getfield(classType.internalName, "constants", OBJECT_ARRAY_TYPE.descriptor) - iconst(idx) - aload(OBJECT_TYPE) - if (type != OBJECT_TYPE) checkcast(type) - } - - /** - * Either loads a numeric constant [value] from the class's constants field or boxes a primitive - * constant from the constant pool. - */ - fun loadNumberConstant(value: Number) { - val boxed = value.javaClass.asm - val primitive = BOXED_TO_PRIMITIVES[boxed] - - if (primitive != null) { - when (primitive) { - BYTE_TYPE -> invokeMethodVisitor.iconst(value.toInt()) - DOUBLE_TYPE -> invokeMethodVisitor.dconst(value.toDouble()) - FLOAT_TYPE -> invokeMethodVisitor.fconst(value.toFloat()) - LONG_TYPE -> invokeMethodVisitor.lconst(value.toLong()) - INT_TYPE -> invokeMethodVisitor.iconst(value.toInt()) - SHORT_TYPE -> invokeMethodVisitor.iconst(value.toInt()) - } - - val r = boxed - - invokeMethodVisitor.invokestatic( - r.internalName, - "valueOf", - getMethodDescriptor(r, primitive), - false, - ) - - return - } - - loadObjectConstant(value, boxed) - } - - /** - * Stores value variable [name] into a local. Should be called within [variablesPrepareCallback] before using - * [loadVariable]. - */ - fun prepareVariable(name: Symbol): Unit = invokeMethodVisitor.run { - if (name in argumentsLocals) return@run - load(1, MAP_TYPE) - aconst(name.identity) - - invokestatic( - MAP_INTRINSICS_TYPE.internalName, - "getOrFail", - getMethodDescriptor(OBJECT_TYPE, MAP_TYPE, STRING_TYPE), - false, - ) - - checkcast(tType) - var idx = argumentsLocals.indexOf(name) - - if (idx == -1) { - argumentsLocals += name - idx = argumentsLocals.lastIndex - } - - store(2 + idx, tType) - } - - /** - * Loads a variable [name] from arguments [Map] parameter of [Expression.invoke]. The variable should be stored - * with [prepareVariable] first. - */ - fun loadVariable(name: Symbol): Unit = invokeMethodVisitor.load(2 + argumentsLocals.indexOf(name), tType) - - inline fun buildCall(function: Function, parameters: GenericAsmBuilder.() -> Unit) { - contract { callsInPlace(parameters, InvocationKind.EXACTLY_ONCE) } - val `interface` = function.javaClass.interfaces.first { Function::class.java in it.interfaces } - - val arity = `interface`.methods.find { it.name == "invoke" }?.parameterCount - ?: error("Provided function object doesn't contain invoke method") - - val type = getType(`interface`) - loadObjectConstant(function, type) - parameters(this) - - invokeMethodVisitor.invokeinterface( - type.internalName, - "invoke", - getMethodDescriptor(OBJECT_TYPE, *Array(arity) { OBJECT_TYPE }), - ) - - invokeMethodVisitor.checkcast(tType) - } - - private companion object { - /** - * Maps JVM primitive numbers boxed ASM types to their primitive ASM types. - */ - private val BOXED_TO_PRIMITIVES: Map by lazy { - hashMapOf( - Byte::class.java.asm to BYTE_TYPE, - Short::class.java.asm to SHORT_TYPE, - Integer::class.java.asm to INT_TYPE, - Long::class.java.asm to LONG_TYPE, - Float::class.java.asm to FLOAT_TYPE, - Double::class.java.asm to DOUBLE_TYPE, - ) - } - - /** - * ASM type for array of [java.lang.Object]. - */ - val OBJECT_ARRAY_TYPE: Type = getType("[Ljava/lang/Object;") - } -} diff --git a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/PrimitiveAsmBuilder.kt b/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/PrimitiveAsmBuilder.kt deleted file mode 100644 index 01bad83e5..000000000 --- a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/PrimitiveAsmBuilder.kt +++ /dev/null @@ -1,516 +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. - */ - -package space.kscience.kmath.asm.internal - -import org.objectweb.asm.ClassWriter -import org.objectweb.asm.FieldVisitor -import org.objectweb.asm.Opcodes.* -import org.objectweb.asm.Type -import org.objectweb.asm.Type.* -import org.objectweb.asm.commons.InstructionAdapter -import space.kscience.kmath.ast.TypedMst -import space.kscience.kmath.expressions.* -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import java.lang.invoke.MethodHandles -import java.lang.invoke.MethodType -import java.nio.file.Paths -import kotlin.io.path.writeBytes - -@UnstableKMathAPI -internal sealed class PrimitiveAsmBuilder>( - protected val algebra: NumericAlgebra, - classOfT: Class<*>, - protected val classOfTPrimitive: Class<*>, - expressionParent: Class, - protected val target: TypedMst, -) : AsmBuilder() { - private val className: String = buildName("${target.hashCode()}_${classOfT.simpleName}") - - /** - * ASM type for [tType]. - */ - private val tType: Type = classOfT.asm - - /** - * ASM type for [classOfTPrimitive]. - */ - protected val tTypePrimitive: Type = classOfTPrimitive.asm - - /** - * ASM type for array of [classOfTPrimitive]. - */ - protected val tTypePrimitiveArray: Type = getType("[" + classOfTPrimitive.asm.descriptor) - - /** - * ASM type for expression parent. - */ - private val expressionParentType = expressionParent.asm - - /** - * ASM type for new class. - */ - private val classType: Type = getObjectType(className.replace(oldChar = '.', newChar = '/')) - - /** - * Method visitor of `invoke` method of the subclass. - */ - protected lateinit var invokeMethodVisitor: InstructionAdapter - - /** - * Indexer for arguments in [target]. - */ - private val argumentsIndexer = mutableListOf() - - /** - * Subclasses, loads and instantiates [Expression] for given parameters. - * - * The built instance is cached. - */ - @Suppress("UNCHECKED_CAST") - val instance: E by lazy { - val classWriter = ClassWriter(ClassWriter.COMPUTE_FRAMES) { - visit( - V1_8, - ACC_PUBLIC or ACC_FINAL or ACC_SUPER, - classType.internalName, - "${OBJECT_TYPE.descriptor}${expressionParentType.descriptor}", - OBJECT_TYPE.internalName, - arrayOf(expressionParentType.internalName), - ) - - visitField( - access = ACC_PRIVATE or ACC_FINAL, - name = "indexer", - descriptor = SYMBOL_INDEXER_TYPE.descriptor, - signature = null, - value = null, - block = FieldVisitor::visitEnd, - ) - visitMethod( - ACC_PUBLIC, - "getIndexer", - getMethodDescriptor(SYMBOL_INDEXER_TYPE), - null, - null, - ).instructionAdapter { - visitCode() - val start = label() - load(0, classType) - getfield(classType.internalName, "indexer", SYMBOL_INDEXER_TYPE.descriptor) - areturn(SYMBOL_INDEXER_TYPE) - val end = label() - - visitLocalVariable( - "this", - classType.descriptor, - null, - start, - end, - 0, - ) - - visitMaxs(0, 0) - visitEnd() - } - - visitMethod( - ACC_PUBLIC, - "invoke", - getMethodDescriptor(tTypePrimitive, tTypePrimitiveArray), - null, - null, - ).instructionAdapter { - invokeMethodVisitor = this - visitCode() - val start = label() - visitVariables(target, arrayMode = true) - visitExpression(target) - areturn(tTypePrimitive) - val end = label() - - visitLocalVariable( - "this", - classType.descriptor, - null, - start, - end, - 0, - ) - - visitLocalVariable( - "arguments", - tTypePrimitiveArray.descriptor, - null, - start, - end, - 1, - ) - - visitMaxs(0, 0) - visitEnd() - } - - visitMethod( - ACC_PUBLIC or ACC_FINAL, - "invoke", - getMethodDescriptor(tType, MAP_TYPE), - "(L${MAP_TYPE.internalName}<${SYMBOL_TYPE.descriptor}+${tType.descriptor}>;)${tType.descriptor}", - null, - ).instructionAdapter { - invokeMethodVisitor = this - visitCode() - val start = label() - visitVariables(target, arrayMode = false) - visitExpression(target) - box() - areturn(tType) - val end = label() - - visitLocalVariable( - "this", - classType.descriptor, - null, - start, - end, - 0, - ) - - visitLocalVariable( - "arguments", - MAP_TYPE.descriptor, - "L${MAP_TYPE.internalName}<${SYMBOL_TYPE.descriptor}+${tType.descriptor}>;", - start, - end, - 1, - ) - - visitMaxs(0, 0) - visitEnd() - } - - visitMethod( - ACC_PUBLIC or ACC_FINAL or ACC_BRIDGE or ACC_SYNTHETIC, - "invoke", - getMethodDescriptor(OBJECT_TYPE, MAP_TYPE), - null, - null, - ).instructionAdapter { - visitCode() - val start = label() - load(0, OBJECT_TYPE) - load(1, MAP_TYPE) - invokevirtual(classType.internalName, "invoke", getMethodDescriptor(tType, MAP_TYPE), false) - areturn(tType) - val end = label() - - visitLocalVariable( - "this", - classType.descriptor, - null, - start, - end, - 0, - ) - - visitMaxs(0, 0) - visitEnd() - } - - visitMethod( - ACC_PUBLIC or ACC_SYNTHETIC, - "", - getMethodDescriptor(VOID_TYPE, SYMBOL_INDEXER_TYPE), - null, - null, - ).instructionAdapter { - val start = label() - load(0, classType) - invokespecial(OBJECT_TYPE.internalName, "", getMethodDescriptor(VOID_TYPE), false) - load(0, classType) - load(1, SYMBOL_INDEXER_TYPE) - putfield(classType.internalName, "indexer", SYMBOL_INDEXER_TYPE.descriptor) - areturn(VOID_TYPE) - val end = label() - visitLocalVariable("this", classType.descriptor, null, start, end, 0) - visitLocalVariable("indexer", SYMBOL_INDEXER_TYPE.descriptor, null, start, end, 1) - visitMaxs(0, 0) - visitEnd() - } - - visitEnd() - } - - val binary = classWriter.toByteArray() - val cls = classLoader.defineClass(className, binary) - - if (System.getProperty("space.kscience.kmath.ast.dump.generated.classes") == "1") - Paths.get("${className.split('.').last()}.class").writeBytes(binary) - - MethodHandles - .publicLookup() - .findConstructor(cls, MethodType.methodType(Void.TYPE, SymbolIndexer::class.java)) - .invoke(SimpleSymbolIndexer(argumentsIndexer)) as E - } - - /** - * Loads a numeric constant [value] from the class's constants. - */ - protected fun loadNumberConstant(value: Number) { - when (tTypePrimitive) { - BYTE_TYPE -> invokeMethodVisitor.iconst(value.toInt()) - DOUBLE_TYPE -> invokeMethodVisitor.dconst(value.toDouble()) - FLOAT_TYPE -> invokeMethodVisitor.fconst(value.toFloat()) - LONG_TYPE -> invokeMethodVisitor.lconst(value.toLong()) - INT_TYPE -> invokeMethodVisitor.iconst(value.toInt()) - SHORT_TYPE -> invokeMethodVisitor.iconst(value.toInt()) - } - } - - /** - * Stores value variable [name] into a local. Should be called before using [loadVariable]. Should be called only - * once for a variable. - */ - protected fun prepareVariable(name: Symbol, arrayMode: Boolean): Unit = invokeMethodVisitor.run { - var argumentIndex = argumentsIndexer.indexOf(name) - - if (argumentIndex == -1) { - argumentsIndexer += name - argumentIndex = argumentsIndexer.lastIndex - } - - val localIndex = 2 + argumentIndex * tTypePrimitive.size - - if (arrayMode) { - load(1, tTypePrimitiveArray) - iconst(argumentIndex) - aload(tTypePrimitive) - store(localIndex, tTypePrimitive) - } else { - load(1, MAP_TYPE) - aconst(name.identity) - - invokestatic( - MAP_INTRINSICS_TYPE.internalName, - "getOrFail", - getMethodDescriptor(OBJECT_TYPE, MAP_TYPE, STRING_TYPE), - false, - ) - - checkcast(tType) - unbox() - store(localIndex, tTypePrimitive) - } - } - - /** - * Loads a variable [name] from arguments [Map] parameter of [Expression.invoke]. The variable should be stored - * with [prepareVariable] first. - */ - protected fun loadVariable(name: Symbol) { - val argumentIndex = argumentsIndexer.indexOf(name) - val localIndex = 2 + argumentIndex * tTypePrimitive.size - invokeMethodVisitor.load(localIndex, tTypePrimitive) - } - - private fun unbox() = invokeMethodVisitor.run { - invokevirtual( - NUMBER_TYPE.internalName, - "${classOfTPrimitive.simpleName}Value", - getMethodDescriptor(tTypePrimitive), - false - ) - } - - private fun box() = invokeMethodVisitor.run { - invokestatic(tType.internalName, "valueOf", getMethodDescriptor(tType, tTypePrimitive), false) - } - - private fun visitVariables( - node: TypedMst, - arrayMode: Boolean, - alreadyLoaded: MutableList = mutableListOf() - ): Unit = when (node) { - is TypedMst.Variable -> if (node.symbol !in alreadyLoaded) { - alreadyLoaded += node.symbol - prepareVariable(node.symbol, arrayMode) - } else Unit - - is TypedMst.Unary -> visitVariables(node.value, arrayMode, alreadyLoaded) - - is TypedMst.Binary -> { - visitVariables(node.left, arrayMode, alreadyLoaded) - visitVariables(node.right, arrayMode, alreadyLoaded) - } - - is TypedMst.Constant -> Unit - } - - private fun visitExpression(node: TypedMst): Unit = when (node) { - is TypedMst.Variable -> loadVariable(node.symbol) - - is TypedMst.Constant -> loadNumberConstant( - node.number ?: error("Object constants are not supported by pritimive ASM builder"), - ) - - is TypedMst.Unary -> visitUnary(node) - is TypedMst.Binary -> visitBinary(node) - } - - protected open fun visitUnary(node: TypedMst.Unary) = visitExpression(node.value) - - protected open fun visitBinary(node: TypedMst.Binary) { - visitExpression(node.left) - visitExpression(node.right) - } - - protected companion object { - /** - * ASM type for [java.lang.Number]. - */ - val NUMBER_TYPE: Type = getObjectType("java/lang/Number") - - /** - * ASM type for [SymbolIndexer]. - */ - val SYMBOL_INDEXER_TYPE: Type = getObjectType("space/kscience/kmath/expressions/SymbolIndexer") - } -} - -@UnstableKMathAPI -internal class DoubleAsmBuilder(target: TypedMst) : PrimitiveAsmBuilder( - DoubleField, - java.lang.Double::class.java, - java.lang.Double.TYPE, - DoubleExpression::class.java, - target, -) { - private fun buildUnaryJavaMathCall(name: String) = invokeMethodVisitor.invokestatic( - MATH_TYPE.internalName, - name, - getMethodDescriptor(tTypePrimitive, tTypePrimitive), - false, - ) - - @Suppress("SameParameterValue") - private fun buildBinaryJavaMathCall(name: String) = invokeMethodVisitor.invokestatic( - MATH_TYPE.internalName, - name, - getMethodDescriptor(tTypePrimitive, tTypePrimitive, tTypePrimitive), - false, - ) - - private fun buildUnaryKotlinMathCall(name: String) = invokeMethodVisitor.invokestatic( - MATH_KT_TYPE.internalName, - name, - getMethodDescriptor(tTypePrimitive, tTypePrimitive), - false, - ) - - override fun visitUnary(node: TypedMst.Unary) { - super.visitUnary(node) - - when (node.operation) { - GroupOps.MINUS_OPERATION -> invokeMethodVisitor.visitInsn(DNEG) - GroupOps.PLUS_OPERATION -> Unit - PowerOperations.SQRT_OPERATION -> buildUnaryJavaMathCall("sqrt") - TrigonometricOperations.SIN_OPERATION -> buildUnaryJavaMathCall("sin") - TrigonometricOperations.COS_OPERATION -> buildUnaryJavaMathCall("cos") - TrigonometricOperations.TAN_OPERATION -> buildUnaryJavaMathCall("tan") - TrigonometricOperations.ASIN_OPERATION -> buildUnaryJavaMathCall("asin") - TrigonometricOperations.ACOS_OPERATION -> buildUnaryJavaMathCall("acos") - TrigonometricOperations.ATAN_OPERATION -> buildUnaryJavaMathCall("atan") - ExponentialOperations.SINH_OPERATION -> buildUnaryJavaMathCall("sqrt") - ExponentialOperations.COSH_OPERATION -> buildUnaryJavaMathCall("cosh") - ExponentialOperations.TANH_OPERATION -> buildUnaryJavaMathCall("tanh") - ExponentialOperations.ASINH_OPERATION -> buildUnaryKotlinMathCall("asinh") - ExponentialOperations.ACOSH_OPERATION -> buildUnaryKotlinMathCall("acosh") - ExponentialOperations.ATANH_OPERATION -> buildUnaryKotlinMathCall("atanh") - ExponentialOperations.EXP_OPERATION -> buildUnaryJavaMathCall("exp") - ExponentialOperations.LN_OPERATION -> buildUnaryJavaMathCall("log") - else -> super.visitUnary(node) - } - } - - override fun visitBinary(node: TypedMst.Binary) { - super.visitBinary(node) - - when (node.operation) { - GroupOps.PLUS_OPERATION -> invokeMethodVisitor.visitInsn(DADD) - GroupOps.MINUS_OPERATION -> invokeMethodVisitor.visitInsn(DSUB) - RingOps.TIMES_OPERATION -> invokeMethodVisitor.visitInsn(DMUL) - FieldOps.DIV_OPERATION -> invokeMethodVisitor.visitInsn(DDIV) - PowerOperations.POW_OPERATION -> buildBinaryJavaMathCall("pow") - else -> super.visitBinary(node) - } - } - - private companion object { - val MATH_TYPE: Type = getObjectType("java/lang/Math") - val MATH_KT_TYPE: Type = getObjectType("kotlin/math/MathKt") - } -} - -@UnstableKMathAPI -internal class IntAsmBuilder(target: TypedMst) : - PrimitiveAsmBuilder( - IntRing, - Integer::class.java, - Integer.TYPE, - IntExpression::class.java, - target - ) { - override fun visitUnary(node: TypedMst.Unary) { - super.visitUnary(node) - - when (node.operation) { - GroupOps.MINUS_OPERATION -> invokeMethodVisitor.visitInsn(INEG) - GroupOps.PLUS_OPERATION -> Unit - else -> super.visitUnary(node) - } - } - - override fun visitBinary(node: TypedMst.Binary) { - super.visitBinary(node) - - when (node.operation) { - GroupOps.PLUS_OPERATION -> invokeMethodVisitor.visitInsn(IADD) - GroupOps.MINUS_OPERATION -> invokeMethodVisitor.visitInsn(ISUB) - RingOps.TIMES_OPERATION -> invokeMethodVisitor.visitInsn(IMUL) - else -> super.visitBinary(node) - } - } -} - -@UnstableKMathAPI -internal class LongAsmBuilder(target: TypedMst) : PrimitiveAsmBuilder( - LongRing, - java.lang.Long::class.java, - java.lang.Long.TYPE, - LongExpression::class.java, - target, -) { - override fun visitUnary(node: TypedMst.Unary) { - super.visitUnary(node) - - when (node.operation) { - GroupOps.MINUS_OPERATION -> invokeMethodVisitor.visitInsn(LNEG) - GroupOps.PLUS_OPERATION -> Unit - else -> super.visitUnary(node) - } - } - - override fun visitBinary(node: TypedMst.Binary) { - super.visitBinary(node) - - when (node.operation) { - GroupOps.PLUS_OPERATION -> invokeMethodVisitor.visitInsn(LADD) - GroupOps.MINUS_OPERATION -> invokeMethodVisitor.visitInsn(LSUB) - RingOps.TIMES_OPERATION -> invokeMethodVisitor.visitInsn(LMUL) - else -> super.visitBinary(node) - } - } -} diff --git a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/codegenUtils.kt b/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/codegenUtils.kt deleted file mode 100644 index 9e880f4fc..000000000 --- a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/codegenUtils.kt +++ /dev/null @@ -1,98 +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. - */ - -package space.kscience.kmath.asm.internal - -import org.objectweb.asm.* -import org.objectweb.asm.commons.InstructionAdapter -import space.kscience.kmath.expressions.Expression -import space.kscience.kmath.expressions.MST -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract - -/** - * Returns ASM [Type] for given [Class]. - * - * @author Iaroslav Postovalov - */ -internal inline val Class<*>.asm: Type - get() = Type.getType(this) - -/** - * Returns singleton array with this value if the [predicate] is true, returns empty array otherwise. - * - * @author Iaroslav Postovalov - */ -internal inline fun T.wrapToArrayIf(predicate: (T) -> Boolean): Array { - contract { callsInPlace(predicate, InvocationKind.EXACTLY_ONCE) } - return if (predicate(this)) arrayOf(this) else emptyArray() -} - -/** - * Creates an [InstructionAdapter] from this [MethodVisitor]. - * - * @author Iaroslav Postovalov - */ -private fun MethodVisitor.instructionAdapter(): InstructionAdapter = InstructionAdapter(this) - -/** - * Creates an [InstructionAdapter] from this [MethodVisitor] and applies [block] to it. - * - * @author Iaroslav Postovalov - */ -internal inline fun MethodVisitor.instructionAdapter(block: InstructionAdapter.() -> Unit): InstructionAdapter { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return instructionAdapter().apply(block) -} - -/** - * Constructs a [Label], then applies it to this visitor. - * - * @author Iaroslav Postovalov - */ -internal fun MethodVisitor.label(): Label = Label().also(::visitLabel) - -/** - * Creates a class name for [Expression] based with appending [marker] to reduce collisions. - * - * These methods help to avoid collisions of class name to prevent loading several classes with the same name. If there - * is a colliding class, change [collision] parameter or leave it `0` to check existing classes recursively. - * - * @author Iaroslav Postovalov - */ -internal tailrec fun buildName(marker: String, collision: Int = 0): String { - val name = "space.kscience.kmath.asm.generated.CompiledExpression_${marker}_$collision" - - try { - Class.forName(name) - } catch (ignored: ClassNotFoundException) { - return name - } - - return buildName(marker, collision + 1) -} - -@Suppress("FunctionName") -internal inline fun ClassWriter(flags: Int, block: ClassWriter.() -> Unit): ClassWriter { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return ClassWriter(flags).apply(block) -} - -/** - * Invokes [visitField] and applies [block] to the [FieldVisitor]. - * - * @author Iaroslav Postovalov - */ -internal inline fun ClassWriter.visitField( - access: Int, - name: String, - descriptor: String, - signature: String?, - value: Any?, - block: FieldVisitor.() -> Unit, -): FieldVisitor { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return visitField(access, name, descriptor, signature, value).apply(block) -} diff --git a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/mapIntrinsics.kt b/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/mapIntrinsics.kt deleted file mode 100644 index 3a5ef74f7..000000000 --- a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/mapIntrinsics.kt +++ /dev/null @@ -1,18 +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. - */ - -@file:JvmName("MapIntrinsics") - -package space.kscience.kmath.asm.internal - -import space.kscience.kmath.expressions.Symbol - -/** - * Gets value with given [key] or throws [NoSuchElementException] whenever it is not present. - * - * @author Iaroslav Postovalov - */ -@Suppress("unused") -internal fun Map.getOrFail(key: String): V = getValue(Symbol(key)) diff --git a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt b/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt deleted file mode 100644 index 556adbe7d..000000000 --- a/kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt +++ /dev/null @@ -1,9 +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. - */ - -package space.kscience.kmath.ast.rendering - -internal actual fun Double.multiplatformToString(): String = toString() -internal actual fun Float.multiplatformToString(): String = toString() diff --git a/kmath-ast/src/jvmTest/kotlin/space/kscience/kmath/ast/utils.kt b/kmath-ast/src/jvmTest/kotlin/space/kscience/kmath/ast/utils.kt deleted file mode 100644 index 47f1cc476..000000000 --- a/kmath-ast/src/jvmTest/kotlin/space/kscience/kmath/ast/utils.kt +++ /dev/null @@ -1,47 +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. - */ - -package space.kscience.kmath.ast - -import space.kscience.kmath.expressions.Expression -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.operations.Algebra -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.IntRing -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract -import space.kscience.kmath.asm.compile as asmCompile -import space.kscience.kmath.asm.compileToExpression as asmCompileToExpression - -private object GenericAsmCompilerTestContext : CompilerTestContext { - override fun MST.compileToExpression(algebra: IntRing): Expression = - asmCompileToExpression(algebra as Algebra) - - override fun MST.compile(algebra: IntRing, arguments: Map): Int = - asmCompile(algebra as Algebra, arguments) - - override fun MST.compileToExpression(algebra: DoubleField): Expression = - asmCompileToExpression(algebra as Algebra) - - override fun MST.compile(algebra: DoubleField, arguments: Map): Double = - asmCompile(algebra as Algebra, arguments) -} - -private object PrimitiveAsmCompilerTestContext : CompilerTestContext { - override fun MST.compileToExpression(algebra: IntRing): Expression = asmCompileToExpression(algebra) - override fun MST.compile(algebra: IntRing, arguments: Map): Int = asmCompile(algebra, arguments) - override fun MST.compileToExpression(algebra: DoubleField): Expression = asmCompileToExpression(algebra) - - override fun MST.compile(algebra: DoubleField, arguments: Map): Double = - asmCompile(algebra, arguments) -} - - -internal actual inline fun runCompilerTest(action: CompilerTestContext.() -> Unit) { - contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) } - action(GenericAsmCompilerTestContext) - action(PrimitiveAsmCompilerTestContext) -} diff --git a/kmath-commons/README.md b/kmath-commons/README.md deleted file mode 100644 index a17a39205..000000000 --- a/kmath-commons/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-commons - -Commons math binding for kmath - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-commons:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-commons:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-commons:0.3.0-dev-20") -} -``` diff --git a/kmath-commons/build.gradle.kts b/kmath-commons/build.gradle.kts deleted file mode 100644 index 96c17a215..000000000 --- a/kmath-commons/build.gradle.kts +++ /dev/null @@ -1,20 +0,0 @@ -plugins { - kotlin("jvm") - id("ru.mipt.npm.gradle.common") -} - -description = "Commons math binding for kmath" - -dependencies { - api(project(":kmath-core")) - api(project(":kmath-complex")) - api(project(":kmath-coroutines")) - api(project(":kmath-optimization")) - api(project(":kmath-stat")) - api(project(":kmath-functions")) - api("org.apache.commons:commons-math3:3.6.1") -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL -} \ No newline at end of file diff --git a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/expressions/DerivativeStructureExpression.kt b/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/expressions/DerivativeStructureExpression.kt deleted file mode 100644 index 82694d95a..000000000 --- a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/expressions/DerivativeStructureExpression.kt +++ /dev/null @@ -1,132 +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. - */ - -package space.kscience.kmath.commons.expressions - -import org.apache.commons.math3.analysis.differentiation.DerivativeStructure -import space.kscience.kmath.expressions.* -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.ExtendedField -import space.kscience.kmath.operations.NumbersAddOps - -/** - * A field over commons-math [DerivativeStructure]. - * - * @property order The derivation order. - * @param bindings The map of bindings values. All bindings are considered free parameters - */ -@OptIn(UnstableKMathAPI::class) -public class DerivativeStructureField( - public val order: Int, - bindings: Map, -) : ExtendedField, ExpressionAlgebra, - NumbersAddOps { - public val numberOfVariables: Int = bindings.size - - override val zero: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order) } - override val one: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order, 1.0) } - - override fun number(value: Number): DerivativeStructure = const(value.toDouble()) - - /** - * A class implementing both [DerivativeStructure] and [Symbol]. - */ - public inner class DerivativeStructureSymbol( - size: Int, - index: Int, - symbol: Symbol, - value: Double, - ) : DerivativeStructure(size, order, index, value), Symbol { - override val identity: String = symbol.identity - override fun toString(): String = identity - override fun equals(other: Any?): Boolean = this.identity == (other as? Symbol)?.identity - override fun hashCode(): Int = identity.hashCode() - } - - /** - * Identity-based symbol bindings map - */ - private val variables: Map = bindings.entries.mapIndexed { index, (key, value) -> - key.identity to DerivativeStructureSymbol(numberOfVariables, index, key, value) - }.toMap() - - override fun const(value: Double): DerivativeStructure = DerivativeStructure(numberOfVariables, order, value) - - override fun bindSymbolOrNull(value: String): DerivativeStructureSymbol? = variables[value] - override fun bindSymbol(value: String): DerivativeStructureSymbol = variables.getValue(value) - - public fun bindSymbolOrNull(symbol: Symbol): DerivativeStructureSymbol? = variables[symbol.identity] - public fun bindSymbol(symbol: Symbol): DerivativeStructureSymbol = variables.getValue(symbol.identity) - - public fun DerivativeStructure.derivative(symbols: List): Double { - require(symbols.size <= order) { "The order of derivative ${symbols.size} exceeds computed order $order" } - val ordersCount = symbols.map { it.identity }.groupBy { it }.mapValues { it.value.size } - return getPartialDerivative(*variables.keys.map { ordersCount[it] ?: 0 }.toIntArray()) - } - - public fun DerivativeStructure.derivative(vararg symbols: Symbol): Double = derivative(symbols.toList()) - - override fun DerivativeStructure.unaryMinus(): DerivativeStructure = negate() - - override fun add(left: DerivativeStructure, right: DerivativeStructure): DerivativeStructure = left.add(right) - - override fun scale(a: DerivativeStructure, value: Double): DerivativeStructure = a.multiply(value) - - override fun multiply(left: DerivativeStructure, right: DerivativeStructure): DerivativeStructure = left.multiply(right) - override fun divide(left: DerivativeStructure, right: DerivativeStructure): DerivativeStructure = left.divide(right) - override fun sin(arg: DerivativeStructure): DerivativeStructure = arg.sin() - override fun cos(arg: DerivativeStructure): DerivativeStructure = arg.cos() - override fun tan(arg: DerivativeStructure): DerivativeStructure = arg.tan() - override fun asin(arg: DerivativeStructure): DerivativeStructure = arg.asin() - override fun acos(arg: DerivativeStructure): DerivativeStructure = arg.acos() - override fun atan(arg: DerivativeStructure): DerivativeStructure = arg.atan() - override fun sinh(arg: DerivativeStructure): DerivativeStructure = arg.sinh() - override fun cosh(arg: DerivativeStructure): DerivativeStructure = arg.cosh() - override fun tanh(arg: DerivativeStructure): DerivativeStructure = arg.tanh() - override fun asinh(arg: DerivativeStructure): DerivativeStructure = arg.asinh() - override fun acosh(arg: DerivativeStructure): DerivativeStructure = arg.acosh() - override fun atanh(arg: DerivativeStructure): DerivativeStructure = arg.atanh() - - override fun power(arg: DerivativeStructure, pow: Number): DerivativeStructure = when (pow) { - is Double -> arg.pow(pow) - is Int -> arg.pow(pow) - else -> arg.pow(pow.toDouble()) - } - - public fun power(arg: DerivativeStructure, pow: DerivativeStructure): DerivativeStructure = arg.pow(pow) - override fun exp(arg: DerivativeStructure): DerivativeStructure = arg.exp() - override fun ln(arg: DerivativeStructure): DerivativeStructure = arg.log() - - override operator fun DerivativeStructure.plus(other: Number): DerivativeStructure = add(other.toDouble()) - override operator fun DerivativeStructure.minus(other: Number): DerivativeStructure = subtract(other.toDouble()) - override operator fun Number.plus(other: DerivativeStructure): DerivativeStructure = other + this - override operator fun Number.minus(other: DerivativeStructure): DerivativeStructure = other - this -} - -/** - * Auto-diff processor based on Commons-math [DerivativeStructure] - */ -public object DSProcessor : AutoDiffProcessor { - override fun differentiate( - function: DerivativeStructureField.() -> DerivativeStructure, - ): DerivativeStructureExpression = DerivativeStructureExpression(function) -} - -/** - * A constructs that creates a derivative structure with required order on-demand - */ -public class DerivativeStructureExpression( - public val function: DerivativeStructureField.() -> DerivativeStructure, -) : DifferentiableExpression { - override operator fun invoke(arguments: Map): Double = - DerivativeStructureField(0, arguments).function().value - - /** - * Get the derivative expression with given orders - */ - override fun derivativeOrNull(symbols: List): Expression = Expression { arguments -> - with(DerivativeStructureField(symbols.size, arguments)) { function().derivative(symbols) } - } -} diff --git a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/integration/CMGaussRuleIntegrator.kt b/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/integration/CMGaussRuleIntegrator.kt deleted file mode 100644 index e0a2f4931..000000000 --- a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/integration/CMGaussRuleIntegrator.kt +++ /dev/null @@ -1,83 +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. - */ -package space.kscience.kmath.commons.integration - -import org.apache.commons.math3.analysis.integration.gauss.GaussIntegrator -import org.apache.commons.math3.analysis.integration.gauss.GaussIntegratorFactory -import space.kscience.kmath.integration.* - -/** - * A simple one-pass integrator based on Gauss rule - */ -public class CMGaussRuleIntegrator( - private val numpoints: Int, - private var type: GaussRule = GaussRule.LEGANDRE, -) : UnivariateIntegrator { - - override fun process(integrand: UnivariateIntegrand): UnivariateIntegrand { - val range = integrand.getFeature()?.range - ?: error("Integration range is not provided") - val integrator: GaussIntegrator = getIntegrator(range) - //TODO check performance - val res: Double = integrator.integrate(integrand.function) - return integrand + IntegrandValue(res) + IntegrandCallsPerformed(integrand.calls + numpoints) - } - - private fun getIntegrator(range: ClosedRange): GaussIntegrator { - return when (type) { - GaussRule.LEGANDRE -> factory.legendre( - numpoints, - range.start, - range.endInclusive - ) - GaussRule.LEGANDREHP -> factory.legendreHighPrecision( - numpoints, - range.start, - range.endInclusive - ) - GaussRule.UNIFORM -> GaussIntegrator( - getUniformRule( - range.start, - range.endInclusive, - numpoints - ) - ) - } - } - - private fun getUniformRule( - min: Double, - max: Double, - numPoints: Int, - ): org.apache.commons.math3.util.Pair { - assert(numPoints > 2) - val points = DoubleArray(numPoints) - val weights = DoubleArray(numPoints) - val step = (max - min) / (numPoints - 1) - points[0] = min - for (i in 1 until numPoints) { - points[i] = points[i - 1] + step - weights[i] = step - } - return org.apache.commons.math3.util.Pair(points, weights) - } - - public enum class GaussRule { - UNIFORM, LEGANDRE, LEGANDREHP - } - - public companion object { - private val factory: GaussIntegratorFactory = GaussIntegratorFactory() - - public fun integrate( - range: ClosedRange, - numPoints: Int = 100, - type: GaussRule = GaussRule.LEGANDRE, - function: (Double) -> Double, - ): Double = CMGaussRuleIntegrator(numPoints, type).process( - UnivariateIntegrand(function, IntegrationRange(range)) - ).value - } -} \ No newline at end of file diff --git a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/integration/CMIntegrator.kt b/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/integration/CMIntegrator.kt deleted file mode 100644 index 257429fa7..000000000 --- a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/integration/CMIntegrator.kt +++ /dev/null @@ -1,88 +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. - */ - -package space.kscience.kmath.commons.integration - -import org.apache.commons.math3.analysis.integration.IterativeLegendreGaussIntegrator -import org.apache.commons.math3.analysis.integration.SimpsonIntegrator -import space.kscience.kmath.integration.* -import space.kscience.kmath.misc.UnstableKMathAPI - -/** - * Integration wrapper for Common-maths UnivariateIntegrator - */ -public class CMIntegrator( - private val defaultMaxCalls: Int = 200, - public val integratorBuilder: (Integrand) -> org.apache.commons.math3.analysis.integration.UnivariateIntegrator, -) : UnivariateIntegrator { - - override fun process(integrand: UnivariateIntegrand): UnivariateIntegrand { - val integrator = integratorBuilder(integrand) - val maxCalls = integrand.getFeature()?.maxCalls ?: defaultMaxCalls - val remainingCalls = maxCalls - integrand.calls - val range = integrand.getFeature()?.range - ?: error("Integration range is not provided") - val res = integrator.integrate(remainingCalls, integrand.function, range.start, range.endInclusive) - - return integrand + - IntegrandValue(res) + - IntegrandAbsoluteAccuracy(integrator.absoluteAccuracy) + - IntegrandRelativeAccuracy(integrator.relativeAccuracy) + - IntegrandCallsPerformed(integrator.evaluations + integrand.calls) - } - - - public companion object { - /** - * Create a Simpson integrator based on [SimpsonIntegrator] - */ - public fun simpson(defaultMaxCalls: Int = 200): CMIntegrator = CMIntegrator(defaultMaxCalls) { integrand -> - val absoluteAccuracy = integrand.getFeature()?.accuracy - ?: SimpsonIntegrator.DEFAULT_ABSOLUTE_ACCURACY - val relativeAccuracy = integrand.getFeature()?.accuracy - ?: SimpsonIntegrator.DEFAULT_ABSOLUTE_ACCURACY - val iterations = integrand.getFeature()?.range - ?: SimpsonIntegrator.DEFAULT_MIN_ITERATIONS_COUNT..SimpsonIntegrator.SIMPSON_MAX_ITERATIONS_COUNT - - - SimpsonIntegrator(relativeAccuracy, absoluteAccuracy, iterations.first, iterations.last) - } - - /** - * Create a Gauss-Legandre integrator based on [IterativeLegendreGaussIntegrator] - */ - public fun legandre(numPoints: Int, defaultMaxCalls: Int = numPoints * 5): CMIntegrator = - CMIntegrator(defaultMaxCalls) { integrand -> - val absoluteAccuracy = integrand.getFeature()?.accuracy - ?: IterativeLegendreGaussIntegrator.DEFAULT_ABSOLUTE_ACCURACY - val relativeAccuracy = integrand.getFeature()?.accuracy - ?: IterativeLegendreGaussIntegrator.DEFAULT_ABSOLUTE_ACCURACY - val iterations = integrand.getFeature()?.range - ?: IterativeLegendreGaussIntegrator.DEFAULT_MIN_ITERATIONS_COUNT..IterativeLegendreGaussIntegrator.DEFAULT_MAX_ITERATIONS_COUNT - - IterativeLegendreGaussIntegrator( - numPoints, - relativeAccuracy, - absoluteAccuracy, - iterations.first, - iterations.last - ) - } - } -} - -@UnstableKMathAPI -public var MutableList.targetAbsoluteAccuracy: Double? - get() = filterIsInstance().lastOrNull()?.accuracy - set(value) { - value?.let { add(IntegrandAbsoluteAccuracy(value)) } - } - -@UnstableKMathAPI -public var MutableList.targetRelativeAccuracy: Double? - get() = filterIsInstance().lastOrNull()?.accuracy - set(value) { - value?.let { add(IntegrandRelativeAccuracy(value)) } - } \ No newline at end of file diff --git a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt b/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt deleted file mode 100644 index aa7e0a638..000000000 --- a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt +++ /dev/null @@ -1,150 +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. - */ - -package space.kscience.kmath.commons.linear - -import org.apache.commons.math3.linear.* -import space.kscience.kmath.linear.* -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.StructureFeature -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.reflect.KClass -import kotlin.reflect.cast - -public class CMMatrix(public val origin: RealMatrix) : Matrix { - override val rowNum: Int get() = origin.rowDimension - override val colNum: Int get() = origin.columnDimension - - override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j) -} - -@JvmInline -public value class CMVector(public val origin: RealVector) : Point { - override val size: Int get() = origin.dimension - - override operator fun get(index: Int): Double = origin.getEntry(index) - - override operator fun iterator(): Iterator = origin.toArray().iterator() - - override fun toString(): String = Buffer.toString(this) -} - -public fun RealVector.toPoint(): CMVector = CMVector(this) - -public object CMLinearSpace : LinearSpace { - override val elementAlgebra: DoubleField get() = DoubleField - - override fun buildMatrix( - rows: Int, - columns: Int, - initializer: DoubleField.(i: Int, j: Int) -> Double, - ): CMMatrix { - val array = Array(rows) { i -> DoubleArray(columns) { j -> DoubleField.initializer(i, j) } } - return CMMatrix(Array2DRowRealMatrix(array)) - } - - @OptIn(UnstableKMathAPI::class) - public fun Matrix.toCM(): CMMatrix = when (val matrix = origin) { - is CMMatrix -> matrix - else -> { - //TODO add feature analysis - val array = Array(rowNum) { i -> DoubleArray(colNum) { j -> get(i, j) } } - Array2DRowRealMatrix(array).wrap() - } - } - - public fun Point.toCM(): CMVector = if (this is CMVector) this else { - val array = DoubleArray(size) { this[it] } - ArrayRealVector(array).wrap() - } - - internal fun RealMatrix.wrap(): CMMatrix = CMMatrix(this) - internal fun RealVector.wrap(): CMVector = CMVector(this) - - override fun buildVector(size: Int, initializer: DoubleField.(Int) -> Double): Point = - ArrayRealVector(DoubleArray(size) { DoubleField.initializer(it) }).wrap() - - override fun Matrix.plus(other: Matrix): CMMatrix = - toCM().origin.add(other.toCM().origin).wrap() - - override fun Point.plus(other: Point): CMVector = - toCM().origin.add(other.toCM().origin).wrap() - - override fun Point.minus(other: Point): CMVector = - toCM().origin.subtract(other.toCM().origin).wrap() - - override fun Matrix.dot(other: Matrix): CMMatrix = - toCM().origin.multiply(other.toCM().origin).wrap() - - override fun Matrix.dot(vector: Point): CMVector = - toCM().origin.preMultiply(vector.toCM().origin).wrap() - - override operator fun Matrix.minus(other: Matrix): CMMatrix = - toCM().origin.subtract(other.toCM().origin).wrap() - - override operator fun Matrix.times(value: Double): CMMatrix = - toCM().origin.scalarMultiply(value).wrap() - - override fun Double.times(m: Matrix): CMMatrix = - m * this - - override fun Point.times(value: Double): CMVector = - toCM().origin.mapMultiply(value).wrap() - - override fun Double.times(v: Point): CMVector = - v * this - - @UnstableKMathAPI - override fun computeFeature(structure: Matrix, type: KClass): F? { - //Return the feature if it is intrinsic to the structure - structure.getFeature(type)?.let { return it } - - val origin = structure.toCM().origin - - return when (type) { - DiagonalFeature::class -> if (origin is DiagonalMatrix) DiagonalFeature else null - - DeterminantFeature::class, LupDecompositionFeature::class -> object : - DeterminantFeature, - LupDecompositionFeature { - private val lup by lazy { LUDecomposition(origin) } - override val determinant: Double by lazy { lup.determinant } - override val l: Matrix by lazy> { CMMatrix(lup.l).withFeature(LFeature) } - override val u: Matrix by lazy> { CMMatrix(lup.u).withFeature(UFeature) } - override val p: Matrix by lazy { CMMatrix(lup.p) } - } - - CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature { - override val l: Matrix by lazy> { - val cholesky = CholeskyDecomposition(origin) - CMMatrix(cholesky.l).withFeature(LFeature) - } - } - - QRDecompositionFeature::class -> object : QRDecompositionFeature { - private val qr by lazy { QRDecomposition(origin) } - override val q: Matrix by lazy> { CMMatrix(qr.q).withFeature(OrthogonalFeature) } - override val r: Matrix by lazy> { CMMatrix(qr.r).withFeature(UFeature) } - } - - SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature { - private val sv by lazy { SingularValueDecomposition(origin) } - override val u: Matrix by lazy { CMMatrix(sv.u) } - override val s: Matrix by lazy { CMMatrix(sv.s) } - override val v: Matrix by lazy { CMMatrix(sv.v) } - override val singularValues: Point by lazy { DoubleBuffer(sv.singularValues) } - } - else -> null - }?.let(type::cast) - } -} - -public operator fun CMMatrix.plus(other: CMMatrix): CMMatrix = CMMatrix(origin.add(other.origin)) - -public operator fun CMMatrix.minus(other: CMMatrix): CMMatrix = CMMatrix(origin.subtract(other.origin)) - -public infix fun CMMatrix.dot(other: CMMatrix): CMMatrix = CMMatrix(origin.multiply(other.origin)) diff --git a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMSolver.kt b/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMSolver.kt deleted file mode 100644 index 9bb5deffd..000000000 --- a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMSolver.kt +++ /dev/null @@ -1,58 +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. - */ - -package space.kscience.kmath.commons.linear - -import org.apache.commons.math3.linear.* -import space.kscience.kmath.linear.LinearSolver -import space.kscience.kmath.linear.Matrix -import space.kscience.kmath.linear.Point - -public enum class CMDecomposition { - LUP, - QR, - RRQR, - EIGEN, - CHOLESKY -} - -private fun CMLinearSpace.solver( - a: Matrix, - decomposition: CMDecomposition = CMDecomposition.LUP, -): DecompositionSolver = when (decomposition) { - CMDecomposition.LUP -> LUDecomposition(a.toCM().origin).solver - CMDecomposition.RRQR -> RRQRDecomposition(a.toCM().origin).solver - CMDecomposition.QR -> QRDecomposition(a.toCM().origin).solver - CMDecomposition.EIGEN -> EigenDecomposition(a.toCM().origin).solver - CMDecomposition.CHOLESKY -> CholeskyDecomposition(a.toCM().origin).solver -} - -public fun CMLinearSpace.solve( - a: Matrix, - b: Matrix, - decomposition: CMDecomposition = CMDecomposition.LUP, -): CMMatrix = solver(a, decomposition).solve(b.toCM().origin).wrap() - -public fun CMLinearSpace.solve( - a: Matrix, - b: Point, - decomposition: CMDecomposition = CMDecomposition.LUP, -): CMVector = solver(a, decomposition).solve(b.toCM().origin).toPoint() - -public fun CMLinearSpace.inverse( - a: Matrix, - decomposition: CMDecomposition = CMDecomposition.LUP, -): CMMatrix = solver(a, decomposition).inverse.wrap() - - -public fun CMLinearSpace.solver(decomposition: CMDecomposition): LinearSolver = object : LinearSolver { - override fun solve(a: Matrix, b: Matrix): Matrix = solver(a, decomposition).solve(b.toCM().origin).wrap() - - override fun solve(a: Matrix, b: Point): Point = solver(a, decomposition).solve(b.toCM().origin).toPoint() - - override fun inverse(matrix: Matrix): Matrix = solver(matrix, decomposition).inverse.wrap() -} - -public fun CMLinearSpace.lupSolver(): LinearSolver = solver((CMDecomposition.LUP)) \ No newline at end of file diff --git a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/optimization/CMOptimizer.kt b/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/optimization/CMOptimizer.kt deleted file mode 100644 index 11eb6fba8..000000000 --- a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/optimization/CMOptimizer.kt +++ /dev/null @@ -1,145 +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. - */ -@file:OptIn(UnstableKMathAPI::class) -package space.kscience.kmath.commons.optimization - -import org.apache.commons.math3.optim.* -import org.apache.commons.math3.optim.nonlinear.scalar.GoalType -import org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer -import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction -import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunctionGradient -import org.apache.commons.math3.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer -import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.NelderMeadSimplex -import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.expressions.SymbolIndexer -import space.kscience.kmath.expressions.derivative -import space.kscience.kmath.expressions.withSymbols -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.misc.log -import space.kscience.kmath.optimization.* -import kotlin.collections.set -import kotlin.reflect.KClass - -public operator fun PointValuePair.component1(): DoubleArray = point -public operator fun PointValuePair.component2(): Double = value - -public class CMOptimizerEngine(public val optimizerBuilder: () -> MultivariateOptimizer) : OptimizationFeature { - override fun toString(): String = "CMOptimizer($optimizerBuilder)" -} - -/** - * Specify a Commons-maths optimization engine - */ -public fun FunctionOptimizationBuilder.cmEngine(optimizerBuilder: () -> MultivariateOptimizer) { - addFeature(CMOptimizerEngine(optimizerBuilder)) -} - -public class CMOptimizerData(public val data: List OptimizationData>) : OptimizationFeature { - public constructor(vararg data: (SymbolIndexer.() -> OptimizationData)) : this(data.toList()) - - override fun toString(): String = "CMOptimizerData($data)" -} - -/** - * Specify Commons-maths optimization data. - */ -public fun FunctionOptimizationBuilder.cmOptimizationData(data: SymbolIndexer.() -> OptimizationData) { - updateFeature { - val newData = (it?.data ?: emptyList()) + data - CMOptimizerData(newData) - } -} - -public fun FunctionOptimizationBuilder.simplexSteps(vararg steps: Pair) { - //TODO use convergence checker from features - cmEngine { SimplexOptimizer(CMOptimizer.defaultConvergenceChecker) } - cmOptimizationData { NelderMeadSimplex(mapOf(*steps).toDoubleArray()) } -} - -@OptIn(UnstableKMathAPI::class) -public object CMOptimizer : Optimizer> { - - public const val DEFAULT_RELATIVE_TOLERANCE: Double = 1e-4 - public const val DEFAULT_ABSOLUTE_TOLERANCE: Double = 1e-4 - public const val DEFAULT_MAX_ITER: Int = 1000 - - public val defaultConvergenceChecker: SimpleValueChecker = SimpleValueChecker( - DEFAULT_RELATIVE_TOLERANCE, - DEFAULT_ABSOLUTE_TOLERANCE, - DEFAULT_MAX_ITER - ) - - - override suspend fun optimize( - problem: FunctionOptimization, - ): FunctionOptimization { - val startPoint = problem.startPoint - - val parameters = problem.getFeature()?.symbols - ?: problem.getFeature>()?.point?.keys - ?: startPoint.keys - - - withSymbols(parameters) { - val convergenceChecker: ConvergenceChecker = SimpleValueChecker( - DEFAULT_RELATIVE_TOLERANCE, - DEFAULT_ABSOLUTE_TOLERANCE, - DEFAULT_MAX_ITER - ) - - val cmOptimizer: MultivariateOptimizer = problem.getFeature()?.optimizerBuilder?.invoke() - ?: NonLinearConjugateGradientOptimizer( - NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES, - convergenceChecker - ) - - val optimizationData: HashMap, OptimizationData> = HashMap() - - fun addOptimizationData(data: OptimizationData) { - optimizationData[data::class] = data - } - - addOptimizationData(MaxEval.unlimited()) - addOptimizationData(InitialGuess(startPoint.toDoubleArray())) - - //fun exportOptimizationData(): List = optimizationData.values.toList() - - val objectiveFunction = ObjectiveFunction { - val args = startPoint + it.toMap() - val res = problem.expression(args) - res - } - addOptimizationData(objectiveFunction) - - val gradientFunction = ObjectiveFunctionGradient { - val args = startPoint + it.toMap() - val res = DoubleArray(symbols.size) { index -> - problem.expression.derivative(symbols[index])(args) - } - res - } - addOptimizationData(gradientFunction) - - val logger = problem.getFeature() - - for (feature in problem.features) { - when (feature) { - is CMOptimizerData -> feature.data.forEach { dataBuilder -> - addOptimizationData(dataBuilder()) - } - is FunctionOptimizationTarget -> when (feature) { - FunctionOptimizationTarget.MAXIMIZE -> addOptimizationData(GoalType.MAXIMIZE) - FunctionOptimizationTarget.MINIMIZE -> addOptimizationData(GoalType.MINIMIZE) - } - else -> logger?.log { "The feature $feature is unused in optimization" } - } - } - - val (point, value) = cmOptimizer.optimize(*optimizationData.values.toTypedArray()) - return problem.withFeatures(OptimizationResult(point.toMap()), OptimizationValue(value)) - } - } -} diff --git a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/random/CMRandomGeneratorWrapper.kt b/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/random/CMRandomGeneratorWrapper.kt deleted file mode 100644 index 194be6002..000000000 --- a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/random/CMRandomGeneratorWrapper.kt +++ /dev/null @@ -1,44 +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. - */ - -package space.kscience.kmath.commons.random - -import kotlinx.coroutines.runBlocking -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.samplers.GaussianSampler -import space.kscience.kmath.misc.toIntExact -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.stat.next - -public class CMRandomGeneratorWrapper( - public val factory: (IntArray) -> RandomGenerator, -) : org.apache.commons.math3.random.RandomGenerator { - private var generator: RandomGenerator = factory(intArrayOf()) - - override fun nextBoolean(): Boolean = generator.nextBoolean() - override fun nextFloat(): Float = generator.nextDouble().toFloat() - - override fun setSeed(seed: Int) { - generator = factory(intArrayOf(seed)) - } - - override fun setSeed(seed: IntArray) { - generator = factory(seed) - } - - override fun setSeed(seed: Long) { - setSeed(seed.toIntExact()) - } - - override fun nextBytes(bytes: ByteArray) { - generator.fillBytes(bytes) - } - - override fun nextInt(): Int = generator.nextInt() - override fun nextInt(n: Int): Int = generator.nextInt(n) - override fun nextGaussian(): Double = runBlocking { GaussianSampler(0.0, 1.0).next(generator) } - override fun nextDouble(): Double = generator.nextDouble() - override fun nextLong(): Long = generator.nextLong() -} diff --git a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/transform/Transformations.kt b/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/transform/Transformations.kt deleted file mode 100644 index 40168971e..000000000 --- a/kmath-commons/src/main/kotlin/space/kscience/kmath/commons/transform/Transformations.kt +++ /dev/null @@ -1,115 +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. - */ - -package space.kscience.kmath.commons.transform - -import kotlinx.coroutines.FlowPreview -import kotlinx.coroutines.flow.Flow -import kotlinx.coroutines.flow.map -import org.apache.commons.math3.transform.* -import space.kscience.kmath.complex.Complex -import space.kscience.kmath.operations.SuspendBufferTransform -import space.kscience.kmath.streaming.chunked -import space.kscience.kmath.streaming.spread -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.VirtualBuffer -import space.kscience.kmath.structures.asBuffer - - -/** - * Streaming and buffer transformations - */ -public object Transformations { - private fun Buffer.toArray(): Array = - Array(size) { org.apache.commons.math3.complex.Complex(get(it).re, get(it).im) } - - private fun Buffer.asArray() = if (this is DoubleBuffer) { - array - } else { - DoubleArray(size) { i -> get(i) } - } - - /** - * Create a virtual buffer on top of array - */ - private fun Array.asBuffer() = VirtualBuffer(size) { - val value = get(it) - Complex(value.real, value.imaginary) - } - - public fun fourier( - normalization: DftNormalization = DftNormalization.STANDARD, - direction: TransformType = TransformType.FORWARD, - ): SuspendBufferTransform = { - FastFourierTransformer(normalization).transform(it.toArray(), direction).asBuffer() - } - - public fun realFourier( - normalization: DftNormalization = DftNormalization.STANDARD, - direction: TransformType = TransformType.FORWARD, - ): SuspendBufferTransform = { - FastFourierTransformer(normalization).transform(it.asArray(), direction).asBuffer() - } - - public fun sine( - normalization: DstNormalization = DstNormalization.STANDARD_DST_I, - direction: TransformType = TransformType.FORWARD, - ): SuspendBufferTransform = { - FastSineTransformer(normalization).transform(it.asArray(), direction).asBuffer() - } - - public fun cosine( - normalization: DctNormalization = DctNormalization.STANDARD_DCT_I, - direction: TransformType = TransformType.FORWARD, - ): SuspendBufferTransform = { - FastCosineTransformer(normalization).transform(it.asArray(), direction).asBuffer() - } - - public fun hadamard( - direction: TransformType = TransformType.FORWARD, - ): SuspendBufferTransform = { - FastHadamardTransformer().transform(it.asArray(), direction).asBuffer() - } -} - -/** - * Process given [Flow] with commons-math fft transformation - */ -@FlowPreview -public fun Flow>.FFT( - normalization: DftNormalization = DftNormalization.STANDARD, - direction: TransformType = TransformType.FORWARD, -): Flow> { - val transform = Transformations.fourier(normalization, direction) - return map { transform(it) } -} - -@FlowPreview -@JvmName("realFFT") -public fun Flow>.FFT( - normalization: DftNormalization = DftNormalization.STANDARD, - direction: TransformType = TransformType.FORWARD, -): Flow> { - val transform = Transformations.realFourier(normalization, direction) - return map(transform) -} - -/** - * Process a continuous flow of real numbers in FFT splitting it in chunks of [bufferSize]. - */ -@FlowPreview -@JvmName("realFFT") -public fun Flow.FFT( - bufferSize: Int = Int.MAX_VALUE, - normalization: DftNormalization = DftNormalization.STANDARD, - direction: TransformType = TransformType.FORWARD, -): Flow = chunked(bufferSize).FFT(normalization, direction).spread() - -/** - * Map a complex flow into real flow by taking real part of each number - */ -@FlowPreview -public fun Flow.real(): Flow = map { it.re } diff --git a/kmath-commons/src/test/kotlin/space/kscience/kmath/commons/expressions/DerivativeStructureExpressionTest.kt b/kmath-commons/src/test/kotlin/space/kscience/kmath/commons/expressions/DerivativeStructureExpressionTest.kt deleted file mode 100644 index 56252ab34..000000000 --- a/kmath-commons/src/test/kotlin/space/kscience/kmath/commons/expressions/DerivativeStructureExpressionTest.kt +++ /dev/null @@ -1,55 +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. - */ - -package space.kscience.kmath.commons.expressions - -import space.kscience.kmath.expressions.* -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertFails - -internal inline fun diff( - order: Int, - vararg parameters: Pair, - block: DerivativeStructureField.() -> Unit, -) { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - DerivativeStructureField(order, mapOf(*parameters)).run(block) -} - -internal class AutoDiffTest { - private val x by symbol - private val y by symbol - - @Test - fun derivativeStructureFieldTest() { - diff(2, x to 1.0, y to 1.0) { - val x = bindSymbol(x)//by binding() - val y = bindSymbol("y") - val z = x * (-sin(x * y) + y) + 2.0 - println(z.derivative(x)) - println(z.derivative(y, x)) - assertEquals(z.derivative(x, y), z.derivative(y, x)) - // check improper order cause failure - assertFails { z.derivative(x, x, y) } - } - } - - @Test - fun autoDifTest() { - val f = DerivativeStructureExpression { - val x by binding - val y by binding - x.pow(2) + 2 * x * y + y.pow(2) + 1 - } - - assertEquals(10.0, f(x to 1.0, y to 2.0)) - assertEquals(6.0, f.derivative(x)(x to 1.0, y to 2.0)) - assertEquals(2.0, f.derivative(x, x)(x to 1.234, y to -2.0)) - assertEquals(2.0, f.derivative(x, y)(x to 1.0, y to 2.0)) - } -} diff --git a/kmath-commons/src/test/kotlin/space/kscience/kmath/commons/integration/IntegrationTest.kt b/kmath-commons/src/test/kotlin/space/kscience/kmath/commons/integration/IntegrationTest.kt deleted file mode 100644 index c5573fef1..000000000 --- a/kmath-commons/src/test/kotlin/space/kscience/kmath/commons/integration/IntegrationTest.kt +++ /dev/null @@ -1,36 +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. - */ - -package space.kscience.kmath.commons.integration - -import org.junit.jupiter.api.Test -import space.kscience.kmath.integration.integrate -import space.kscience.kmath.integration.value -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleField.sin -import kotlin.math.PI -import kotlin.math.abs -import kotlin.test.assertTrue - -@UnstableKMathAPI -internal class IntegrationTest { - private val function: (Double) -> Double = { sin(it) } - - @Test - fun simpson() { - val res = CMIntegrator.simpson().integrate(0.0..2 * PI, function = function).value - assertTrue { abs(res) < 1e-3 } - } - - @Test - fun customSimpson() { - val res = CMIntegrator.simpson().integrate(0.0..PI, { - targetRelativeAccuracy = 1e-4 - targetAbsoluteAccuracy = 1e-4 - }, function).value - assertTrue { abs(res - 2) < 1e-3 } - assertTrue { abs(res - 2) > 1e-12 } - } -} \ No newline at end of file diff --git a/kmath-commons/src/test/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt b/kmath-commons/src/test/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt deleted file mode 100644 index 0977dc247..000000000 --- a/kmath-commons/src/test/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt +++ /dev/null @@ -1,79 +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. - */ - -package space.kscience.kmath.commons.optimization - -import kotlinx.coroutines.runBlocking -import space.kscience.kmath.commons.expressions.DSProcessor -import space.kscience.kmath.commons.expressions.DerivativeStructureExpression -import space.kscience.kmath.distributions.NormalDistribution -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.expressions.Symbol.Companion.y -import space.kscience.kmath.expressions.chiSquaredExpression -import space.kscience.kmath.expressions.symbol -import space.kscience.kmath.operations.map -import space.kscience.kmath.optimization.* -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.asBuffer -import kotlin.math.pow -import kotlin.test.Test - -internal class OptimizeTest { - val normal = DerivativeStructureExpression { - exp(-bindSymbol(x).pow(2) / 2) + exp(-bindSymbol(y).pow(2) / 2) - } - - @Test - fun testGradientOptimization() = runBlocking { - val result = normal.optimizeWith(CMOptimizer, x to 1.0, y to 1.0) - println(result.resultPoint) - println(result.resultValue) - } - - @Test - fun testSimplexOptimization() = runBlocking { - val result = normal.optimizeWith(CMOptimizer, x to 1.0, y to 1.0) { - simplexSteps(x to 2.0, y to 0.5) - //this sets simplex optimizer - } - - println(result.resultPoint) - println(result.resultValue) - } - - @Test - fun testCmFit() = runBlocking { - val a by symbol - val b by symbol - val c by symbol - - val sigma = 1.0 - val generator = NormalDistribution(0.0, sigma) - val chain = generator.sample(RandomGenerator.default(112667)) - val x = (1..100).map(Int::toDouble).asBuffer() - - val y = x.map { - it.pow(2) + it + 1 + chain.next() - } - - val yErr = DoubleBuffer(x.size) { sigma } - - val chi2 = DSProcessor.chiSquaredExpression( - x, y, yErr - ) { arg -> - val cWithDefault = bindSymbolOrNull(c) ?: one - bindSymbol(a) * arg.pow(2) + bindSymbol(b) * arg + cWithDefault - } - - val result: FunctionOptimization = chi2.optimizeWith( - CMOptimizer, - mapOf(a to 1.5, b to 0.9, c to 1.0), - FunctionOptimizationTarget.MINIMIZE - ) - println(result) - println("Chi2/dof = ${result.resultValue / (x.size - 3)}") - } -} diff --git a/kmath-complex/README.md b/kmath-complex/README.md deleted file mode 100644 index b55ce0fea..000000000 --- a/kmath-complex/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# Module kmath-complex - -Complex and hypercomplex number systems in KMath. - - - [complex](src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt) : Complex Numbers - - [quaternion](src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions - - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-complex:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-complex:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-complex:0.3.0-dev-20") -} -``` diff --git a/kmath-complex/build.gradle.kts b/kmath-complex/build.gradle.kts deleted file mode 100644 index ea74df646..000000000 --- a/kmath-complex/build.gradle.kts +++ /dev/null @@ -1,31 +0,0 @@ -plugins { - kotlin("multiplatform") - id("ru.mipt.npm.gradle.common") - id("ru.mipt.npm.gradle.native") -} - -kotlin.sourceSets { - commonMain { - dependencies { - api(project(":kmath-core")) - } - } -} - -readme { - description = "Complex numbers and quaternions." - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE - propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md")) - - feature( - id = "complex", - description = "Complex Numbers", - ref = "src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt" - ) - - feature( - id = "quaternion", - description = "Quaternions", - ref = "src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt" - ) -} diff --git a/kmath-complex/docs/README-TEMPLATE.md b/kmath-complex/docs/README-TEMPLATE.md deleted file mode 100644 index 106d4aff1..000000000 --- a/kmath-complex/docs/README-TEMPLATE.md +++ /dev/null @@ -1,7 +0,0 @@ -# Module kmath-complex - -Complex and hypercomplex number systems in KMath. - -${features} - -${artifact} diff --git a/kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt b/kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt deleted file mode 100644 index 8f73005cf..000000000 --- a/kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt +++ /dev/null @@ -1,238 +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. - */ - -package space.kscience.kmath.complex - -import space.kscience.kmath.memory.MemoryReader -import space.kscience.kmath.memory.MemorySpec -import space.kscience.kmath.memory.MemoryWriter -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.MemoryBuffer -import space.kscience.kmath.structures.MutableBuffer -import space.kscience.kmath.structures.MutableMemoryBuffer -import kotlin.math.* - -/** - * This complex's conjugate. - */ -public val Complex.conjugate: Complex - get() = Complex(re, -im) - -/** - * This complex's reciprocal. - */ -public val Complex.reciprocal: Complex - get() { - val scale = re * re + im * im - return Complex(re / scale, -im / scale) - } - -/** - * Absolute value of complex number. - */ -public val Complex.r: Double - get() = sqrt(re * re + im * im) - -/** - * An angle between vector represented by complex number and X axis. - */ -public val Complex.theta: Double - get() = atan2(im, re) - -private val PI_DIV_2 = Complex(PI / 2, 0) - -/** - * A field of [Complex]. - */ -@OptIn(UnstableKMathAPI::class) -public object ComplexField : - ExtendedField, - Norm, - NumbersAddOps, - ScaleOperations { - - override val zero: Complex = 0.0.toComplex() - override val one: Complex = 1.0.toComplex() - - override fun bindSymbolOrNull(value: String): Complex? = if (value == "i") i else null - - override fun binaryOperationFunction(operation: String): (left: Complex, right: Complex) -> Complex = - when (operation) { - PowerOperations.POW_OPERATION -> ComplexField::power - else -> super.binaryOperationFunction(operation) - } - - /** - * The imaginary unit. - */ - public val i: Complex by lazy { Complex(0.0, 1.0) } - - - override fun number(value: Number): Complex = Complex(value.toDouble(), 0.0) - - override fun scale(a: Complex, value: Double): Complex = Complex(a.re * value, a.im * value) - - override fun add(left: Complex, right: Complex): Complex = Complex(left.re + right.re, left.im + right.im) - override fun negate(arg: Complex): Complex = Complex(-arg.re, -arg.im) - - override fun multiply(left: Complex, right: Complex): Complex = - Complex(left.re * right.re - left.im * right.im, left.re * right.im + left.im * right.re) - - override fun divide(left: Complex, right: Complex): Complex = when { - abs(right.im) < abs(right.re) -> { - val wr = right.im / right.re - val wd = right.re + wr * right.im - - if (wd.isNaN() || wd == 0.0) - throw ArithmeticException("Division by zero or infinity") - else - Complex((left.re + left.im * wr) / wd, (left.im - left.re * wr) / wd) - } - - right.im == 0.0 -> throw ArithmeticException("Division by zero") - - else -> { - val wr = right.re / right.im - val wd = right.im + wr * right.re - - if (wd.isNaN() || wd == 0.0) - throw ArithmeticException("Division by zero or infinity") - else - Complex((left.re * wr + left.im) / wd, (left.im * wr - left.re) / wd) - } - } - - override operator fun Complex.div(k: Number): Complex = Complex(re / k.toDouble(), im / k.toDouble()) - - override fun sin(arg: Complex): Complex = i * (exp(-i * arg) - exp(i * arg)) / 2.0 - override fun cos(arg: Complex): Complex = (exp(-i * arg) + exp(i * arg)) / 2.0 - - override fun tan(arg: Complex): Complex { - val e1 = exp(-i * arg) - val e2 = exp(i * arg) - return i * (e1 - e2) / (e1 + e2) - } - - override fun asin(arg: Complex): Complex = -i * ln(sqrt(1 - (arg * arg)) + i * arg) - override fun acos(arg: Complex): Complex = PI_DIV_2 + i * ln(sqrt(1 - (arg * arg)) + i * arg) - - override fun atan(arg: Complex): Complex { - val iArg = i * arg - return i * (ln(1 - iArg) - ln(1 + iArg)) / 2 - } - - override fun power(arg: Complex, pow: Number): Complex = if (arg.im == 0.0) { - arg.re.pow(pow.toDouble()).toComplex() - } else { - exp(pow * ln(arg)) - } - - public fun power(arg: Complex, pow: Complex): Complex = exp(pow * ln(arg)) - - - override fun exp(arg: Complex): Complex = exp(arg.re) * (cos(arg.im) + i * sin(arg.im)) - - override fun ln(arg: Complex): Complex = ln(arg.r) + i * atan2(arg.im, arg.re) - - /** - * Adds complex number to real one. - * - * @receiver the augend. - * @param c the addend. - * @return the sum. - */ - public operator fun Double.plus(c: Complex): Complex = add(this.toComplex(), c) - - /** - * Subtracts complex number from real one. - * - * @receiver the minuend. - * @param c the subtrahend. - * @return the difference. - */ - public operator fun Double.minus(c: Complex): Complex = add(this.toComplex(), -c) - - /** - * Adds real number to complex one. - * - * @receiver the augend. - * @param d the addend. - * @return the sum. - */ - public operator fun Complex.plus(d: Double): Complex = d + this - - /** - * Subtracts real number from complex one. - * - * @receiver the minuend. - * @param d the subtrahend. - * @return the difference. - */ - public operator fun Complex.minus(d: Double): Complex = add(this, -d.toComplex()) - - /** - * Multiplies real number by complex one. - * - * @receiver the multiplier. - * @param c the multiplicand. - * @receiver the product. - */ - public operator fun Double.times(c: Complex): Complex = Complex(c.re * this, c.im * this) - - override fun norm(arg: Complex): Complex = sqrt(arg.conjugate * arg) -} - -/** - * Represents `double`-based complex number. - * - * @property re The real part. - * @property im The imaginary part. - */ -@OptIn(UnstableKMathAPI::class) -public data class Complex(val re: Double, val im: Double) { - public constructor(re: Number, im: Number) : this(re.toDouble(), im.toDouble()) - public constructor(re: Number) : this(re.toDouble(), 0.0) - - override fun toString(): String = "($re + i * $im)" - - public companion object : MemorySpec { - override val objectSize: Int - get() = 16 - - override fun MemoryReader.read(offset: Int): Complex = - Complex(readDouble(offset), readDouble(offset + 8)) - - override fun MemoryWriter.write(offset: Int, value: Complex) { - writeDouble(offset, value.re) - writeDouble(offset + 8, value.im) - } - } -} - -public val Complex.Companion.algebra: ComplexField get() = ComplexField - -/** - * Creates a complex number with real part equal to this real. - * - * @receiver the real part. - * @return the new complex number. - */ -public fun Number.toComplex(): Complex = Complex(this) - -/** - * Creates a new buffer of complex numbers with the specified [size], where each element is calculated by calling the - * specified [init] function. - */ -public inline fun Buffer.Companion.complex(size: Int, init: (Int) -> Complex): Buffer = - MemoryBuffer.create(Complex, size, init) - -/** - * Creates a new buffer of complex numbers with the specified [size], where each element is calculated by calling the - * specified [init] function. - */ -public inline fun MutableBuffer.Companion.complex(size: Int, init: (Int) -> Complex): MutableBuffer = - MutableMemoryBuffer.create(Complex, size, init) diff --git a/kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/ComplexFieldND.kt b/kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/ComplexFieldND.kt deleted file mode 100644 index 46d4b7c5c..000000000 --- a/kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/ComplexFieldND.kt +++ /dev/null @@ -1,85 +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. - */ - -package space.kscience.kmath.complex - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.* -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.Buffer -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract - - -/** - * An optimized nd-field for complex numbers - */ -@OptIn(UnstableKMathAPI::class) -public sealed class ComplexFieldOpsND : BufferedFieldOpsND(ComplexField.bufferAlgebra), - ScaleOperations>, ExtendedFieldOps>, PowerOperations> { - - override fun StructureND.toBufferND(): BufferND = when (this) { - is BufferND -> this - else -> { - val indexer = indexerBuilder(shape) - BufferND(indexer, Buffer.complex(indexer.linearSize) { offset -> get(indexer.index(offset)) }) - } - } - - //TODO do specialization - - override fun scale(a: StructureND, value: Double): BufferND = - mapInline(a.toBufferND()) { it * value } - - override fun exp(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { exp(it) } - override fun ln(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { ln(it) } - - override fun sin(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { sin(it) } - override fun cos(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { cos(it) } - override fun tan(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { tan(it) } - override fun asin(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { asin(it) } - override fun acos(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { acos(it) } - override fun atan(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { atan(it) } - - override fun sinh(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { sinh(it) } - override fun cosh(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { cosh(it) } - override fun tanh(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { tanh(it) } - override fun asinh(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { asinh(it) } - override fun acosh(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { acosh(it) } - override fun atanh(arg: StructureND): BufferND = mapInline(arg.toBufferND()) { atanh(it) } - - override fun power(arg: StructureND, pow: Number): StructureND = - mapInline(arg.toBufferND()) { power(it,pow) } - - public companion object : ComplexFieldOpsND() -} - -@UnstableKMathAPI -public val ComplexField.bufferAlgebra: BufferFieldOps - get() = bufferAlgebra(Buffer.Companion::complex) - - -@OptIn(UnstableKMathAPI::class) -public class ComplexFieldND(override val shape: Shape) : - ComplexFieldOpsND(), FieldND, - NumbersAddOps> { - - override fun number(value: Number): BufferND { - val d = value.toDouble() // minimize conversions - return structureND(shape) { d.toComplex() } - } -} - -public val ComplexField.ndAlgebra: ComplexFieldOpsND get() = ComplexFieldOpsND - -public fun ComplexField.ndAlgebra(vararg shape: Int): ComplexFieldND = ComplexFieldND(shape) - -/** - * Produce a context for n-dimensional operations inside this real field - */ -public inline fun ComplexField.withNdAlgebra(vararg shape: Int, action: ComplexFieldND.() -> R): R { - contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) } - return ComplexFieldND(shape).action() -} diff --git a/kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt b/kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt deleted file mode 100644 index 545f3041b..000000000 --- a/kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt +++ /dev/null @@ -1,274 +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. - */ - -package space.kscience.kmath.complex - -import space.kscience.kmath.memory.MemoryReader -import space.kscience.kmath.memory.MemorySpec -import space.kscience.kmath.memory.MemoryWriter -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.MemoryBuffer -import space.kscience.kmath.structures.MutableBuffer -import space.kscience.kmath.structures.MutableMemoryBuffer -import kotlin.math.* - -/** - * This quaternion's conjugate. - */ -public val Quaternion.conjugate: Quaternion - get() = QuaternionField { z - x * i - y * j - z * k } - -/** - * This quaternion's reciprocal. - */ -public val Quaternion.reciprocal: Quaternion - get() { - QuaternionField { - val n = norm(this@reciprocal) - return conjugate / (n * n) - } - } - -/** - * Absolute value of the quaternion. - */ -public val Quaternion.r: Double - get() = sqrt(w * w + x * x + y * y + z * z) - -/** - * A field of [Quaternion]. - */ -@OptIn(UnstableKMathAPI::class) -public object QuaternionField : Field, Norm, PowerOperations, - ExponentialOperations, NumbersAddOps, ScaleOperations { - override val zero: Quaternion = 0.toQuaternion() - override val one: Quaternion = 1.toQuaternion() - - /** - * The `i` quaternion unit. - */ - public val i: Quaternion = Quaternion(0, 1) - - /** - * The `j` quaternion unit. - */ - public val j: Quaternion = Quaternion(0, 0, 1) - - /** - * The `k` quaternion unit. - */ - public val k: Quaternion = Quaternion(0, 0, 0, 1) - - override fun add(left: Quaternion, right: Quaternion): Quaternion = - Quaternion(left.w + right.w, left.x + right.x, left.y + right.y, left.z + right.z) - - override fun scale(a: Quaternion, value: Double): Quaternion = - Quaternion(a.w * value, a.x * value, a.y * value, a.z * value) - - override fun multiply(left: Quaternion, right: Quaternion): Quaternion = Quaternion( - left.w * right.w - left.x * right.x - left.y * right.y - left.z * right.z, - left.w * right.x + left.x * right.w + left.y * right.z - left.z * right.y, - left.w * right.y - left.x * right.z + left.y * right.w + left.z * right.x, - left.w * right.z + left.x * right.y - left.y * right.x + left.z * right.w, - ) - - override fun divide(left: Quaternion, right: Quaternion): Quaternion { - val s = right.w * right.w + right.x * right.x + right.y * right.y + right.z * right.z - - return Quaternion( - (right.w * left.w + right.x * left.x + right.y * left.y + right.z * left.z) / s, - (right.w * left.x - right.x * left.w - right.y * left.z + right.z * left.y) / s, - (right.w * left.y + right.x * left.z - right.y * left.w - right.z * left.x) / s, - (right.w * left.z - right.x * left.y + right.y * left.x - right.z * left.w) / s, - ) - } - - override fun power(arg: Quaternion, pow: Number): Quaternion { - if (pow is Int) return pwr(arg, pow) - if (floor(pow.toDouble()) == pow.toDouble()) return pwr(arg, pow.toInt()) - return exp(pow * ln(arg)) - } - - private fun pwr(x: Quaternion, a: Int): Quaternion = when { - a < 0 -> -(pwr(x, -a)) - a == 0 -> one - a == 1 -> x - a == 2 -> pwr2(x) - a == 3 -> pwr3(x) - a == 4 -> pwr4(x) - - else -> { - val x4 = pwr4(x) - var y = x4 - repeat((1 until a / 4).count()) { y *= x4 } - if (a % 4 == 3) y *= pwr3(x) - if (a % 4 == 2) y *= pwr2(x) - if (a % 4 == 1) y *= x - y - } - } - - private fun pwr2(x: Quaternion): Quaternion { - val aa = 2 * x.w - return Quaternion(x.w * x.w - (x.x * x.x + x.y * x.y + x.z * x.z), aa * x.x, aa * x.y, aa * x.z) - } - - private fun pwr3(x: Quaternion): Quaternion { - val a2 = x.w * x.w - val n1 = x.x * x.x + x.y * x.y + x.z * x.z - val n2 = 3.0 * a2 - n1 - return Quaternion(x.w * (a2 - 3 * n1), x.x * n2, x.y * n2, x.z * n2) - } - - private fun pwr4(x: Quaternion): Quaternion { - val a2 = x.w * x.w - val n1 = x.x * x.x + x.y * x.y + x.z * x.z - val n2 = 4 * x.w * (a2 - n1) - return Quaternion(a2 * a2 - 6 * a2 * n1 + n1 * n1, x.x * n2, x.y * n2, x.z * n2) - } - - override fun exp(arg: Quaternion): Quaternion { - val un = arg.x * arg.x + arg.y * arg.y + arg.z * arg.z - if (un == 0.0) return exp(arg.w).toQuaternion() - val n1 = sqrt(un) - val ea = exp(arg.w) - val n2 = ea * sin(n1) / n1 - return Quaternion(ea * cos(n1), n2 * arg.x, n2 * arg.y, n2 * arg.z) - } - - override fun ln(arg: Quaternion): Quaternion { - val nu2 = arg.x * arg.x + arg.y * arg.y + arg.z * arg.z - - if (nu2 == 0.0) - return if (arg.w > 0) - Quaternion(ln(arg.w), 0, 0, 0) - else { - val l = ComplexField { ln(arg.w.toComplex()) } - Quaternion(l.re, l.im, 0, 0) - } - - val a = arg.w - check(nu2 > 0) - val n = sqrt(a * a + nu2) - val th = acos(a / n) / sqrt(nu2) - return Quaternion(ln(n), th * arg.x, th * arg.y, th * arg.z) - } - - override operator fun Number.plus(other: Quaternion): Quaternion = Quaternion(toDouble() + other.w, other.x, other.y, other.z) - - override operator fun Number.minus(other: Quaternion): Quaternion = - Quaternion(toDouble() - other.w, -other.x, -other.y, -other.z) - - override operator fun Quaternion.plus(other: Number): Quaternion = Quaternion(w + other.toDouble(), x, y, z) - override operator fun Quaternion.minus(other: Number): Quaternion = Quaternion(w - other.toDouble(), x, y, z) - - override operator fun Number.times(arg: Quaternion): Quaternion = - Quaternion(toDouble() * arg.w, toDouble() * arg.x, toDouble() * arg.y, toDouble() * arg.z) - - override fun negate(arg: Quaternion): Quaternion = Quaternion(-arg.w, -arg.x, -arg.y, -arg.z) - override fun norm(arg: Quaternion): Quaternion = sqrt(arg.conjugate * arg) - - override fun bindSymbolOrNull(value: String): Quaternion? = when (value) { - "i" -> i - "j" -> j - "k" -> k - else -> null - } - - override fun number(value: Number): Quaternion = value.toQuaternion() - - override fun sinh(arg: Quaternion): Quaternion = (exp(arg) - exp(-arg)) / 2.0 - override fun cosh(arg: Quaternion): Quaternion = (exp(arg) + exp(-arg)) / 2.0 - override fun tanh(arg: Quaternion): Quaternion = (exp(arg) - exp(-arg)) / (exp(-arg) + exp(arg)) - override fun asinh(arg: Quaternion): Quaternion = ln(sqrt(arg * arg + one) + arg) - override fun acosh(arg: Quaternion): Quaternion = ln(arg + sqrt((arg - one) * (arg + one))) - override fun atanh(arg: Quaternion): Quaternion = (ln(arg + one) - ln(one - arg)) / 2.0 -} - -/** - * Represents `double`-based quaternion. - * - * @property w The first component. - * @property x The second component. - * @property y The third component. - * @property z The fourth component. - */ -@OptIn(UnstableKMathAPI::class) -public data class Quaternion( - val w: Double, val x: Double, val y: Double, val z: Double, -) { - public constructor(w: Number, x: Number, y: Number, z: Number) : this( - w.toDouble(), - x.toDouble(), - y.toDouble(), - z.toDouble(), - ) - - public constructor(w: Number, x: Number, y: Number) : this(w.toDouble(), x.toDouble(), y.toDouble(), 0.0) - public constructor(w: Number, x: Number) : this(w.toDouble(), x.toDouble(), 0.0, 0.0) - public constructor(w: Number) : this(w.toDouble(), 0.0, 0.0, 0.0) - public constructor(wx: Complex, yz: Complex) : this(wx.re, wx.im, yz.re, yz.im) - public constructor(wx: Complex) : this(wx.re, wx.im, 0, 0) - - init { - require(!w.isNaN()) { "w-component of quaternion is not-a-number" } - require(!x.isNaN()) { "x-component of quaternion is not-a-number" } - require(!y.isNaN()) { "x-component of quaternion is not-a-number" } - require(!z.isNaN()) { "x-component of quaternion is not-a-number" } - } - - /** - * Returns a string representation of this quaternion. - */ - override fun toString(): String = "($w + $x * i + $y * j + $z * k)" - - public companion object : MemorySpec { - override val objectSize: Int - get() = 32 - - override fun MemoryReader.read(offset: Int): Quaternion = - Quaternion(readDouble(offset), readDouble(offset + 8), readDouble(offset + 16), readDouble(offset + 24)) - - override fun MemoryWriter.write(offset: Int, value: Quaternion) { - writeDouble(offset, value.w) - writeDouble(offset + 8, value.x) - writeDouble(offset + 16, value.y) - writeDouble(offset + 24, value.z) - } - } -} - -/** - * Creates a quaternion with real part equal to this real. - * - * @receiver the real part. - * @return a new quaternion. - */ -public fun Number.toQuaternion(): Quaternion = Quaternion(this) - -/** - * Creates a quaternion with `w`-component equal to `re`-component of given complex and `x`-component equal to - * `im`-component of given complex. - * - * @receiver the complex number. - * @return a new quaternion. - */ -public fun Complex.toQuaternion(): Quaternion = Quaternion(this) - -/** - * Creates a new buffer of quaternions with the specified [size], where each element is calculated by calling the - * specified [init] function. - */ -public inline fun Buffer.Companion.quaternion(size: Int, init: (Int) -> Quaternion): Buffer = - MemoryBuffer.create(Quaternion, size, init) - -/** - * Creates a new buffer of quaternions with the specified [size], where each element is calculated by calling the - * specified [init] function. - */ -public inline fun MutableBuffer.Companion.quaternion(size: Int, init: (Int) -> Quaternion): MutableBuffer = - MutableMemoryBuffer.create(Quaternion, size, init) diff --git a/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ComplexBufferSpecTest.kt b/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ComplexBufferSpecTest.kt deleted file mode 100644 index 17a077ea7..000000000 --- a/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ComplexBufferSpecTest.kt +++ /dev/null @@ -1,18 +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. - */ - -package space.kscience.kmath.complex - -import space.kscience.kmath.structures.Buffer -import kotlin.test.Test -import kotlin.test.assertEquals - -class ComplexBufferSpecTest { - @Test - fun testComplexBuffer() { - val buffer = Buffer.complex(20) { Complex(it.toDouble(), -it.toDouble()) } - assertEquals(Complex(5.0, -5.0), buffer[5]) - } -} \ No newline at end of file diff --git a/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ComplexFieldTest.kt b/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ComplexFieldTest.kt deleted file mode 100644 index cbaaa815b..000000000 --- a/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ComplexFieldTest.kt +++ /dev/null @@ -1,81 +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. - */ - -package space.kscience.kmath.complex - -import space.kscience.kmath.operations.invoke -import kotlin.math.PI -import kotlin.math.abs -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue - -internal class ComplexFieldTest { - // TODO make verifier classes available in this source set - // @Test - // fun verify() = ComplexField { FieldVerifier(this, 42.0 * i, 66.0 + 28 * i, 2.0 + 0 * i, 5).verify() } - - @Test - fun testAddition() { - assertEquals(Complex(42, 42), ComplexField { Complex(16, 16) + Complex(26, 26) }) - assertEquals(Complex(42, 16), ComplexField { Complex(16, 16) + 26 }) - assertEquals(Complex(42, 16), ComplexField { 26 + Complex(16, 16) }) - } - - @Test - fun testSubtraction() { - assertEquals(Complex(42, 42), ComplexField { Complex(86, 55) - Complex(44, 13) }) - assertEquals(Complex(42, 56), ComplexField { Complex(86, 56) - 44 }) - assertEquals(Complex(42, 56), ComplexField { 86 - Complex(44, -56) }) - } - - @Test - fun testMultiplication() { - assertEquals(Complex(42, 42), ComplexField { Complex(4.2, 0) * Complex(10, 10) }) - assertEquals(Complex(42, 21), ComplexField { Complex(4.2, 2.1) * 10 }) - assertEquals(Complex(42, 21), ComplexField { 10 * Complex(4.2, 2.1) }) - } - - @Test - fun testDivision() { - assertEquals(Complex(42, 42), ComplexField { Complex(0, 168) / Complex(2, 2) }) - assertEquals(Complex(42, 56), ComplexField { Complex(86, 56) - 44 }) - assertEquals(Complex(42, 56), ComplexField { 86 - Complex(44, -56) }) - } - - @Test - fun testSine() { - assertEquals(ComplexField { i * sinh(one) }, ComplexField { sin(i) }) - assertEquals(ComplexField { i * sinh(PI.toComplex()) }, ComplexField { sin(i * PI.toComplex()) }) - } - - @Test - fun testInverseSine() { - assertEquals(Complex(0, -0.0), ComplexField { asin(zero) }) - assertTrue(abs(ComplexField { i * asinh(one) }.r - ComplexField { asin(i) }.r) < 0.000000000000001) - } - - @Test - fun testInverseHyperbolicSine() { - assertEquals( - ComplexField { i * PI.toComplex() / 2 }, - ComplexField { asinh(i) }) - } - - @Test - fun testPower() { - assertEquals(ComplexField.zero, ComplexField { zero pow 2 }) - assertEquals(ComplexField.zero, ComplexField { zero pow 2 }) - - assertEquals( - ComplexField { i * 8 }.let { it.im.toInt() to it.re.toInt() }, - ComplexField { Complex(2, 2) pow 2 }.let { it.im.toInt() to it.re.toInt() }) - } - - @Test - fun testNorm() { - assertEquals(2.toComplex(), ComplexField { norm(2 * i) }) - } -} diff --git a/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ComplexTest.kt b/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ComplexTest.kt deleted file mode 100644 index 7ad7f883d..000000000 --- a/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ComplexTest.kt +++ /dev/null @@ -1,36 +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. - */ - -package space.kscience.kmath.complex - -import space.kscience.kmath.operations.invoke -import kotlin.math.sqrt -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue - -internal class ComplexTest { - @Test - fun conjugate() = ComplexField { assertEquals(Complex(0, 42), Complex(0, -42).conjugate) } - - @Test - fun reciprocal() = ComplexField { assertTrue((Complex(0.5, -0.0) - 2.toComplex().reciprocal).r < 1e-10) } - - @Test - fun r() = ComplexField { assertEquals(sqrt(2.0), (i + 1.0.toComplex()).r) } - - @Test - fun theta() = assertEquals(0.0, 1.toComplex().theta) - - @Test - fun toComplex() { - assertEquals(Complex(42), 42.toComplex()) - assertEquals(Complex(42.0), 42.0.toComplex()) - assertEquals(Complex(42f), 42f.toComplex()) - assertEquals(Complex(42.0), 42.0.toComplex()) - assertEquals(Complex(42.toByte()), 42.toByte().toComplex()) - assertEquals(Complex(42.toShort()), 42.toShort().toComplex()) - } -} diff --git a/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ExpressionFieldForComplexTest.kt b/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ExpressionFieldForComplexTest.kt deleted file mode 100644 index 4279471d4..000000000 --- a/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/ExpressionFieldForComplexTest.kt +++ /dev/null @@ -1,27 +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. - */ - -package space.kscience.kmath.complex - -import space.kscience.kmath.expressions.FunctionalExpressionField -import space.kscience.kmath.expressions.invoke -import space.kscience.kmath.expressions.symbol -import space.kscience.kmath.operations.bindSymbol -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class ExpressionFieldForComplexTest { - val x by symbol - - @Test - fun testComplex() { - val expression = FunctionalExpressionField(ComplexField).run { - val x = bindSymbol(x) - x * x + 2 * x + one - } - - assertEquals(expression(x to Complex(1.0, 0.0)), Complex(4.0, 0.0)) - } -} diff --git a/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/QuaternionFieldTest.kt b/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/QuaternionFieldTest.kt deleted file mode 100644 index 6784f3516..000000000 --- a/kmath-complex/src/commonTest/kotlin/space/kscience/kmath/complex/QuaternionFieldTest.kt +++ /dev/null @@ -1,50 +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. - */ - -package space.kscience.kmath.complex - -import space.kscience.kmath.operations.invoke -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class QuaternionFieldTest { - @Test - fun testAddition() { - assertEquals(Quaternion(42, 42), QuaternionField { Quaternion(16, 16) + Quaternion(26, 26) }) - assertEquals(Quaternion(42, 16), QuaternionField { Quaternion(16, 16) + 26 }) - assertEquals(Quaternion(42, 16), QuaternionField { 26 + Quaternion(16, 16) }) - } - -// @Test -// fun testSubtraction() { -// assertEquals(Quaternion(42, 42), QuaternionField { Quaternion(86, 55) - Quaternion(44, 13) }) -// assertEquals(Quaternion(42, 56), QuaternionField { Quaternion(86, 56) - 44 }) -// assertEquals(Quaternion(42, 56), QuaternionField { 86 - Quaternion(44, -56) }) -// } - - @Test - fun testMultiplication() { - assertEquals(Quaternion(42, 42), QuaternionField { Quaternion(4.2, 0) * Quaternion(10, 10) }) - assertEquals(Quaternion(42, 21), QuaternionField { Quaternion(4.2, 2.1) * 10 }) - assertEquals(Quaternion(42, 21), QuaternionField { 10 * Quaternion(4.2, 2.1) }) - } - -// @Test -// fun testDivision() { -// assertEquals(Quaternion(42, 42), QuaternionField { Quaternion(0, 168) / Quaternion(2, 2) }) -// assertEquals(Quaternion(42, 56), QuaternionField { Quaternion(86, 56) - 44 }) -// assertEquals(Quaternion(42, 56) , QuaternionField { 86 - Quaternion(44, -56) }) -// } - - @Test - fun testPower() { - assertEquals(QuaternionField.zero, QuaternionField { zero pow 2 }) - assertEquals(QuaternionField.zero, QuaternionField { zero pow 2 }) - - assertEquals( - QuaternionField { i * 8 }.let { it.x.toInt() to it.w.toInt() }, - QuaternionField { Quaternion(2, 2) pow 2 }.let { it.x.toInt() to it.w.toInt() }) - } -} diff --git a/kmath-core/build.gradle.kts b/kmath-core/build.gradle.kts index 4a35a54fb..d812a3bc7 100644 --- a/kmath-core/build.gradle.kts +++ b/kmath-core/build.gradle.kts @@ -15,60 +15,6 @@ kotlin.sourceSets { commonMain { dependencies { - api(project(":kmath-memory")) } } } - -//generateUml { -// classTree { -// -// } -//} - -readme { - description = "Core classes, algebra definitions, basic linear algebra" - maturity = ru.mipt.npm.gradle.Maturity.DEVELOPMENT - propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md")) - - feature( - id = "algebras", - ref = "src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt", - ) { "Algebraic structures like rings, spaces and fields." } - - feature( - id = "nd", - ref = "src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt", - ) { "Many-dimensional structures and operations on them." } - - feature( - id = "linear", - ref = "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." } - - feature( - id = "buffers", - ref = "src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt", - ) { "One-dimensional structure" } - - feature( - id = "expressions", - ref = "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. - """.trimIndent() - } - - feature( - id = "domains", - ref = "src/commonMain/kotlin/space/kscience/kmath/domains", - ) { "Domains" } - - feature( - id = "autodiff", - ref = "src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt" - ) { "Automatic differentiation" } -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/ColumnarData.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/ColumnarData.kt deleted file mode 100644 index e06b774fd..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/ColumnarData.kt +++ /dev/null @@ -1,47 +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. - */ - -package space.kscience.kmath.data - -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.Structure2D -import space.kscience.kmath.structures.Buffer - -/** - * A column-based data set with all columns of the same size (not necessary fixed in time). - * The column could be retrieved by a [get] operation. - */ -@UnstableKMathAPI -public interface ColumnarData { - public val size: Int - - /** - * Provide a column by symbol or null if column with given symbol is not defined - */ - public operator fun get(symbol: Symbol): Buffer? -} - -@UnstableKMathAPI -public val ColumnarData<*>.indices: IntRange get() = 0 until size - -/** - * A zero-copy method to represent a [Structure2D] as a two-column x-y data. - * There could more than two columns in the structure. - */ -@OptIn(PerformancePitfall::class) -@UnstableKMathAPI -public fun Structure2D.asColumnarData(mapping: Map): ColumnarData { - require(shape[1] >= mapping.maxOf { it.value }) { "Column index out of bounds" } - return object : ColumnarData { - override val size: Int get() = shape[0] - override fun get(symbol: Symbol): Buffer { - val index = mapping[symbol] ?: error("No column mapping for symbol $symbol") - return columns[index] - } - } -} - diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYColumnarData.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYColumnarData.kt deleted file mode 100644 index 2fce772cc..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYColumnarData.kt +++ /dev/null @@ -1,85 +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. - */ - -package space.kscience.kmath.data - -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.Structure2D -import space.kscience.kmath.structures.Buffer -import kotlin.math.max - -/** - * The buffer of X values. - */ -@UnstableKMathAPI -public interface XYColumnarData : ColumnarData { - /** - * The buffer of X values - */ - public val x: Buffer - - /** - * The buffer of Y values. - */ - public val y: Buffer - - override fun get(symbol: Symbol): Buffer? = when (symbol) { - Symbol.x -> x - Symbol.y -> y - else -> null - } - - public companion object{ - @UnstableKMathAPI - public fun of(x: Buffer, y: Buffer): XYColumnarData { - require(x.size == y.size) { "Buffer size mismatch. x buffer size is ${x.size}, y buffer size is ${y.size}" } - return object : XYColumnarData { - override val size: Int = x.size - override val x: Buffer = x - override val y: Buffer = y - } - } - } -} - - -/** - * Represent a [ColumnarData] as an [XYColumnarData]. The presence or respective columns is checked on creation. - */ -@UnstableKMathAPI -public fun ColumnarData.asXYData( - xSymbol: Symbol, - ySymbol: Symbol, -): XYColumnarData = object : XYColumnarData { - init { - requireNotNull(this@asXYData[xSymbol]){"The column with name $xSymbol is not present in $this"} - requireNotNull(this@asXYData[ySymbol]){"The column with name $ySymbol is not present in $this"} - } - override val size: Int get() = this@asXYData.size - override val x: Buffer get() = this@asXYData[xSymbol]!! - override val y: Buffer get() = this@asXYData[ySymbol]!! - override fun get(symbol: Symbol): Buffer? = when (symbol) { - Symbol.x -> x - Symbol.y -> y - else -> this@asXYData.get(symbol) - } -} - -/** - * A zero-copy method to represent a [Structure2D] as a two-column x-y data. - * There could more than two columns in the structure. - */ -@OptIn(PerformancePitfall::class) -@UnstableKMathAPI -public fun Structure2D.asXYData(xIndex: Int = 0, yIndex: Int = 1): XYColumnarData { - require(shape[1] >= max(xIndex, yIndex)) { "Column index out of bounds" } - return object : XYColumnarData { - override val size: Int get() = this@asXYData.shape[0] - override val x: Buffer get() = columns[xIndex] - override val y: Buffer get() = columns[yIndex] - } -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYErrorColumnarData.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYErrorColumnarData.kt deleted file mode 100644 index 8ddd6406f..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYErrorColumnarData.kt +++ /dev/null @@ -1,44 +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. - */ - -package space.kscience.kmath.data - -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.structures.Buffer - - -/** - * A [ColumnarData] with additional [Symbol.yError] column for an [Symbol.y] error - * Inherits [XYColumnarData]. - */ -@UnstableKMathAPI -public interface XYErrorColumnarData : XYColumnarData { - public val yErr: Buffer - - override fun get(symbol: Symbol): Buffer = when (symbol) { - Symbol.x -> x - Symbol.y -> y - Symbol.yError -> yErr - else -> error("A column for symbol $symbol not found") - } - - public companion object { - public fun of( - x: Buffer, y: Buffer, yErr: Buffer - ): XYErrorColumnarData { - require(x.size == y.size) { "Buffer size mismatch. x buffer size is ${x.size}, y buffer size is ${y.size}" } - require(y.size == yErr.size) { "Buffer size mismatch. y buffer size is ${x.size}, yErr buffer size is ${y.size}" } - - return object : XYErrorColumnarData { - override val size: Int = x.size - override val x: Buffer = x - override val y: Buffer = y - override val yErr: Buffer = yErr - } - } - } -} - diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYZColumnarData.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYZColumnarData.kt deleted file mode 100644 index e99ae0698..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYZColumnarData.kt +++ /dev/null @@ -1,26 +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. - */ - -package space.kscience.kmath.data - -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.structures.Buffer - -/** - * A [ColumnarData] with guaranteed [x], [y] and [z] columns designated by corresponding symbols. - * Inherits [XYColumnarData]. - */ -@UnstableKMathAPI -public interface XYZColumnarData : XYColumnarData { - public val z: Buffer - - override fun get(symbol: Symbol): Buffer? = when (symbol) { - Symbol.x -> x - Symbol.y -> y - Symbol.z -> z - else -> null - } -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/Domain.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/Domain.kt deleted file mode 100644 index b5a84cf6c..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/Domain.kt +++ /dev/null @@ -1,25 +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. - */ - -package space.kscience.kmath.domains - -import space.kscience.kmath.linear.Point - -/** - * A simple geometric domain. - * - * @param T the type of element of this domain. - */ -public interface Domain { - /** - * Checks if the specified point is contained in this domain. - */ - public operator fun contains(point: Point): Boolean - - /** - * Number of hyperspace dimensions. - */ - public val dimension: Int -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/DoubleDomain.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/DoubleDomain.kt deleted file mode 100644 index ee1bebde0..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/DoubleDomain.kt +++ /dev/null @@ -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. - */ -package space.kscience.kmath.domains - -import space.kscience.kmath.misc.UnstableKMathAPI - -/** - * n-dimensional volume - * - * @author Alexander Nozik - */ -@UnstableKMathAPI -public interface DoubleDomain : Domain { - /** - * Global lower edge - * @param num axis number - */ - public fun getLowerBound(num: Int): Double - - /** - * Global upper edge - * @param num axis number - */ - public fun getUpperBound(num: Int): Double - - /** - * Hyper volume - * @return - */ - public fun volume(): Double -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/HyperSquareDomain.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/HyperSquareDomain.kt deleted file mode 100644 index bd5514623..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/HyperSquareDomain.kt +++ /dev/null @@ -1,40 +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. - */ -package space.kscience.kmath.domains - -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.indices - -/** - * - * HyperSquareDomain class. - * - * @author Alexander Nozik - */ -@UnstableKMathAPI -public class HyperSquareDomain(private val lower: Buffer, private val upper: Buffer) : DoubleDomain { - override val dimension: Int get() = lower.size - - override operator fun contains(point: Point): Boolean = point.indices.all { i -> - point[i] in lower[i]..upper[i] - } - - override fun getLowerBound(num: Int): Double = lower[num] - - override fun getUpperBound(num: Int): Double = upper[num] - - override fun volume(): Double { - var res = 1.0 - - for (i in 0 until dimension) { - if (lower[i].isInfinite() || upper[i].isInfinite()) return Double.POSITIVE_INFINITY - if (upper[i] > lower[i]) res *= upper[i] - lower[i] - } - - return res - } -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnconstrainedDomain.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnconstrainedDomain.kt deleted file mode 100644 index 32a5fc56c..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnconstrainedDomain.kt +++ /dev/null @@ -1,19 +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. - */ -package space.kscience.kmath.domains - -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI - -@UnstableKMathAPI -public class UnconstrainedDomain(override val dimension: Int) : DoubleDomain { - override operator fun contains(point: Point): Boolean = true - - override fun getLowerBound(num: Int): Double = Double.NEGATIVE_INFINITY - - override fun getUpperBound(num: Int): Double = Double.POSITIVE_INFINITY - - override fun volume(): Double = Double.POSITIVE_INFINITY -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnivariateDomain.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnivariateDomain.kt deleted file mode 100644 index 9020ef8cb..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnivariateDomain.kt +++ /dev/null @@ -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. - */ - -package space.kscience.kmath.domains - -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI - -@UnstableKMathAPI -public class UnivariateDomain(public val range: ClosedFloatingPointRange) : DoubleDomain { - override val dimension: Int get() = 1 - - public operator fun contains(d: Double): Boolean = range.contains(d) - - override operator fun contains(point: Point): Boolean { - require(point.size == 0) - return contains(point[0]) - } - - override fun getLowerBound(num: Int): Double { - require(num == 0) - return range.start - } - - override fun getUpperBound(num: Int): Double { - require(num == 0) - return range.endInclusive - } - - override fun volume(): Double = range.endInclusive - range.start -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/DifferentiableExpression.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/DifferentiableExpression.kt deleted file mode 100644 index 12b7df0ea..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/DifferentiableExpression.kt +++ /dev/null @@ -1,75 +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. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.operations.Algebra - -/** - * Represents expression, which structure can be differentiated. - * - * @param T the type this expression takes as argument and returns. - */ -public interface DifferentiableExpression : Expression { - /** - * Differentiates this expression by ordered collection of [symbols]. - * - * @param symbols the symbols. - * @return the derivative or `null`. - */ - public fun derivativeOrNull(symbols: List): Expression? -} - -public fun DifferentiableExpression.derivative(symbols: List): Expression = - derivativeOrNull(symbols) ?: error("Derivative by symbols $symbols not provided") - -public fun DifferentiableExpression.derivative(vararg symbols: Symbol): Expression = - derivative(symbols.toList()) - -public fun DifferentiableExpression.derivative(name: String): Expression = - derivative(StringSymbol(name)) - -/** - * A special type of [DifferentiableExpression] which returns typed expressions as derivatives. - * - * @param R the type of expression this expression can be differentiated to. - */ -public interface SpecialDifferentiableExpression> : DifferentiableExpression { - override fun derivativeOrNull(symbols: List): R? -} - -public fun > SpecialDifferentiableExpression.derivative(symbols: List): R = - derivativeOrNull(symbols) ?: error("Derivative by symbols $symbols not provided") - -public fun > SpecialDifferentiableExpression.derivative(vararg symbols: Symbol): R = - derivative(symbols.toList()) - -public fun > SpecialDifferentiableExpression.derivative(name: String): R = - derivative(StringSymbol(name)) - -/** - * A [DifferentiableExpression] that defines only first derivatives - */ -public abstract class FirstDerivativeExpression : DifferentiableExpression { - /** - * Returns first derivative of this expression by given [symbol]. - */ - public abstract fun derivativeOrNull(symbol: Symbol): Expression? - - public final override fun derivativeOrNull(symbols: List): Expression? { - val dSymbol = symbols.firstOrNull() ?: return null - return derivativeOrNull(dSymbol) - } -} - -/** - * A factory that converts an expression in autodiff variables to a [DifferentiableExpression] - * @param T type of the constants for the expression - * @param I type of the actual expression state - * @param A type of expression algebra - */ -public fun interface AutoDiffProcessor> { - public fun differentiate(function: A.() -> I): DifferentiableExpression -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Expression.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Expression.kt deleted file mode 100644 index 5ba32f190..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Expression.kt +++ /dev/null @@ -1,225 +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. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Algebra -import kotlin.jvm.JvmName -import kotlin.properties.ReadOnlyProperty - -/** - * An elementary function that could be invoked on a map of arguments. - * - * @param T the type this expression takes as argument and returns. - */ -public fun interface Expression { - /** - * Calls this expression from arguments. - * - * @param arguments the map of arguments. - * @return the value. - */ - public operator fun invoke(arguments: Map): T -} - -/** - * Specialization of [Expression] for [Double] allowing better performance because of using array. - */ -@UnstableKMathAPI -public interface DoubleExpression : Expression { - /** - * The indexer of this expression's arguments that should be used to build array for [invoke]. - * - * Implementations must fulfil the following requirement: for any argument symbol `x` and its value `y`, - * `indexer.indexOf(x) == arguments.indexOf(y)` if `arguments` is the array passed to [invoke]. - */ - public val indexer: SymbolIndexer - - public override operator fun invoke(arguments: Map): Double = - this(DoubleArray(indexer.symbols.size) { arguments.getValue(indexer.symbols[it]) }) - - /** - * Calls this expression from arguments. - * - * @param arguments the array of arguments. - * @return the value. - */ - public operator fun invoke(arguments: DoubleArray): Double -} - -/** - * Specialization of [Expression] for [Int] allowing better performance because of using array. - */ -@UnstableKMathAPI -public interface IntExpression : Expression { - /** - * The indexer of this expression's arguments that should be used to build array for [invoke]. - * - * Implementations must fulfil the following requirement: for any argument symbol `x` and its value `y`, - * `indexer.indexOf(x) == arguments.indexOf(y)` if `arguments` is the array passed to [invoke]. - */ - public val indexer: SymbolIndexer - - public override operator fun invoke(arguments: Map): Int = - this(IntArray(indexer.symbols.size) { arguments.getValue(indexer.symbols[it]) }) - - /** - * Calls this expression from arguments. - * - * @param arguments the array of arguments. - * @return the value. - */ - public operator fun invoke(arguments: IntArray): Int -} - -/** - * Specialization of [Expression] for [Long] allowing better performance because of using array. - */ -@UnstableKMathAPI -public interface LongExpression : Expression { - /** - * The indexer of this expression's arguments that should be used to build array for [invoke]. - * - * Implementations must fulfil the following requirement: for any argument symbol `x` and its value `y`, - * `indexer.indexOf(x) == arguments.indexOf(y)` if `arguments` is the array passed to [invoke]. - */ - public val indexer: SymbolIndexer - - public override operator fun invoke(arguments: Map): Long = - this(LongArray(indexer.symbols.size) { arguments.getValue(indexer.symbols[it]) }) - - /** - * Calls this expression from arguments. - * - * @param arguments the array of arguments. - * @return the value. - */ - public operator fun invoke(arguments: LongArray): Long -} - -/** - * Calls this expression without providing any arguments. - * - * @return a value. - */ -public operator fun Expression.invoke(): T = this(emptyMap()) - -/** - * Calls this expression from arguments. - * - * @param pairs the pairs of arguments to values. - * @return a value. - */ -@JvmName("callBySymbol") -public operator fun Expression.invoke(vararg pairs: Pair): T = this( - when (pairs.size) { - 0 -> emptyMap() - 1 -> mapOf(pairs[0]) - else -> hashMapOf(*pairs) - } -) - -/** - * Calls this expression from arguments. - * - * @param pairs the pairs of arguments' names to value. - * @return a value. - */ -@JvmName("callByString") -public operator fun Expression.invoke(vararg pairs: Pair): T = this( - when (pairs.size) { - 0 -> emptyMap() - - 1 -> { - val (k, v) = pairs[0] - mapOf(StringSymbol(k) to v) - } - - else -> hashMapOf(*Array>(pairs.size) { - val (k, v) = pairs[it] - StringSymbol(k) to v - }) - } -) - -private val EMPTY_DOUBLE_ARRAY = DoubleArray(0) - -/** - * Calls this expression without providing any arguments. - * - * @return a value. - */ -@UnstableKMathAPI -public operator fun DoubleExpression.invoke(): Double = this(EMPTY_DOUBLE_ARRAY) - -/** - * Calls this expression from arguments. - * - * @param pairs the pairs of arguments to values. - * @return a value. - */ -@UnstableKMathAPI -public operator fun DoubleExpression.invoke(vararg arguments: Double): Double = this(arguments) - -private val EMPTY_INT_ARRAY = IntArray(0) - -/** - * Calls this expression without providing any arguments. - * - * @return a value. - */ -@UnstableKMathAPI -public operator fun IntExpression.invoke(): Int = this(EMPTY_INT_ARRAY) - -/** - * Calls this expression from arguments. - * - * @param pairs the pairs of arguments to values. - * @return a value. - */ -@UnstableKMathAPI -public operator fun IntExpression.invoke(vararg arguments: Int): Int = this(arguments) - -private val EMPTY_LONG_ARRAY = LongArray(0) - -/** - * Calls this expression without providing any arguments. - * - * @return a value. - */ -@UnstableKMathAPI -public operator fun LongExpression.invoke(): Long = this(EMPTY_LONG_ARRAY) - -/** - * Calls this expression from arguments. - * - * @param pairs the pairs of arguments to values. - * @return a value. - */ -@UnstableKMathAPI -public operator fun LongExpression.invoke(vararg arguments: Long): Long = this(arguments) - -/** - * A context for expression construction - * - * @param T type of the constants for the expression - * @param E type of the actual expression state - */ -public interface ExpressionAlgebra : Algebra { - - /** - * A constant expression that does not depend on arguments. - */ - public fun const(value: T): E -} - -/** - * Bind a symbol by name inside the [ExpressionAlgebra] - */ -public val ExpressionAlgebra.binding: ReadOnlyProperty - get() = ReadOnlyProperty { _, property -> - bindSymbol(property.name) ?: error("A variable with name ${property.name} does not exist") - } diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt deleted file mode 100644 index 86be1b3dc..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt +++ /dev/null @@ -1,196 +0,0 @@ -/* - * Copyright 2018-2021 KMath contributors. - * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.operations.* -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract - -/** - * A context class for [Expression] construction. - * - * @param algebra The algebra to provide for Expressions built. - */ -public abstract class FunctionalExpressionAlgebra>( - public val algebra: A, -) : ExpressionAlgebra> { - /** - * Builds an Expression of constant expression that does not depend on arguments. - */ - override fun const(value: T): Expression = Expression { value } - - /** - * Builds an Expression to access a variable. - */ - override fun bindSymbolOrNull(value: String): Expression? = Expression { arguments -> - algebra.bindSymbolOrNull(value) - ?: arguments[StringSymbol(value)] - ?: error("Symbol '$value' is not supported in $this") - } - - override fun binaryOperationFunction(operation: String): (left: Expression, right: Expression) -> Expression = - { left, right -> - Expression { arguments -> - algebra.binaryOperationFunction(operation)(left(arguments), right(arguments)) - } - } - - override fun unaryOperationFunction(operation: String): (arg: Expression) -> Expression = { arg -> - Expression { arguments -> algebra.unaryOperation(operation, arg(arguments)) } - } -} - -/** - * A context class for [Expression] construction for [Ring] algebras. - */ -public open class FunctionalExpressionGroup>( - algebra: A, -) : FunctionalExpressionAlgebra(algebra), Group> { - override val zero: Expression get() = const(algebra.zero) - - override fun negate(arg: Expression): Expression = - unaryOperation(GroupOps.MINUS_OPERATION, arg) - - /** - * Builds an Expression of addition of two another expressions. - */ - override fun add(left: Expression, right: Expression): Expression = - binaryOperation(GroupOps.PLUS_OPERATION, left, right) - -// /** -// * Builds an Expression of multiplication of expression by number. -// */ -// override fun multiply(a: Expression, k: Number): Expression = Expression { arguments -> -// algebra.multiply(a.invoke(arguments), k) -// } - - public operator fun Expression.plus(arg: T): Expression = this + const(arg) - public operator fun Expression.minus(arg: T): Expression = this - const(arg) - public operator fun T.plus(arg: Expression): Expression = arg + this - public operator fun T.minus(arg: Expression): Expression = arg - this - - override fun unaryOperationFunction(operation: String): (arg: Expression) -> Expression = - super.unaryOperationFunction(operation) - - override fun binaryOperationFunction(operation: String): (left: Expression, right: Expression) -> Expression = - super.binaryOperationFunction(operation) - -} - -public open class FunctionalExpressionRing>( - algebra: A, -) : FunctionalExpressionGroup(algebra), Ring> { - override val one: Expression get() = const(algebra.one) - - /** - * Builds an Expression of multiplication of two expressions. - */ - override fun multiply(left: Expression, right: Expression): Expression = - binaryOperationFunction(RingOps.TIMES_OPERATION)(left, right) - - public operator fun Expression.times(arg: T): Expression = this * const(arg) - public operator fun T.times(arg: Expression): Expression = arg * this - - override fun unaryOperationFunction(operation: String): (arg: Expression) -> Expression = - super.unaryOperationFunction(operation) - - override fun binaryOperationFunction(operation: String): (left: Expression, right: Expression) -> Expression = - super.binaryOperationFunction(operation) -} - -public open class FunctionalExpressionField>( - algebra: A, -) : FunctionalExpressionRing(algebra), Field>, ScaleOperations> { - /** - * Builds an Expression of division an expression by another one. - */ - override fun divide(left: Expression, right: Expression): Expression = - binaryOperationFunction(FieldOps.DIV_OPERATION)(left, right) - - public operator fun Expression.div(arg: T): Expression = this / const(arg) - public operator fun T.div(arg: Expression): Expression = arg / this - - override fun unaryOperationFunction(operation: String): (arg: Expression) -> Expression = - super.unaryOperationFunction(operation) - - override fun binaryOperationFunction(operation: String): (left: Expression, right: Expression) -> Expression = - super.binaryOperationFunction(operation) - - override fun scale(a: Expression, value: Double): Expression = algebra { - Expression { args -> a(args) * value } - } - - override fun bindSymbolOrNull(value: String): Expression? = - super.bindSymbolOrNull(value) -} - -public open class FunctionalExpressionExtendedField>( - algebra: A, -) : FunctionalExpressionField(algebra), ExtendedField> { - override fun number(value: Number): Expression = const(algebra.number(value)) - - override fun sqrt(arg: Expression): Expression = - unaryOperationFunction(PowerOperations.SQRT_OPERATION)(arg) - - override fun sin(arg: Expression): Expression = - unaryOperationFunction(TrigonometricOperations.SIN_OPERATION)(arg) - - override fun cos(arg: Expression): Expression = - unaryOperationFunction(TrigonometricOperations.COS_OPERATION)(arg) - - override fun asin(arg: Expression): Expression = - unaryOperationFunction(TrigonometricOperations.ASIN_OPERATION)(arg) - - override fun acos(arg: Expression): Expression = - unaryOperationFunction(TrigonometricOperations.ACOS_OPERATION)(arg) - - override fun atan(arg: Expression): Expression = - unaryOperationFunction(TrigonometricOperations.ATAN_OPERATION)(arg) - - override fun power(arg: Expression, pow: Number): Expression = - binaryOperationFunction(PowerOperations.POW_OPERATION)(arg, number(pow)) - - override fun exp(arg: Expression): Expression = - unaryOperationFunction(ExponentialOperations.EXP_OPERATION)(arg) - - override fun ln(arg: Expression): Expression = - unaryOperationFunction(ExponentialOperations.LN_OPERATION)(arg) - - override fun unaryOperationFunction(operation: String): (arg: Expression) -> Expression = - super.unaryOperationFunction(operation) - - override fun binaryOperationFunction(operation: String): (left: Expression, right: Expression) -> Expression = - super.binaryOperationFunction(operation) -} - -public inline fun > A.expressionInGroup( - block: FunctionalExpressionGroup.() -> Expression, -): Expression { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return FunctionalExpressionGroup(this).block() -} - -public inline fun > A.expressionInRing( - block: FunctionalExpressionRing.() -> Expression, -): Expression { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return FunctionalExpressionRing(this).block() -} - -public inline fun > A.expressionInField( - block: FunctionalExpressionField.() -> Expression, -): Expression { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return FunctionalExpressionField(this).block() -} - -public inline fun > A.expressionInExtendedField( - block: FunctionalExpressionExtendedField.() -> Expression, -): Expression = FunctionalExpressionExtendedField(this).block() - -public inline fun DoubleField.expression( - block: FunctionalExpressionExtendedField.() -> Expression, -): Expression = FunctionalExpressionExtendedField(this).block() diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/MST.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/MST.kt deleted file mode 100644 index 18226119b..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/MST.kt +++ /dev/null @@ -1,111 +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. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.operations.Algebra -import space.kscience.kmath.operations.NumericAlgebra -import space.kscience.kmath.operations.bindSymbolOrNull - -/** - * A Mathematical Syntax Tree (MST) node for mathematical expressions. - * - * @author Alexander Nozik - */ -public sealed interface MST { - - /** - * A node containing a numeric value or scalar. - * - * @property value the value of this number. - */ - public data class Numeric(val value: Number) : MST - - /** - * A node containing a unary operation. - * - * @property operation the identifier of operation. - * @property value the argument of this operation. - */ - public data class Unary(val operation: String, val value: MST) : MST - - /** - * A node containing binary operation. - * - * @property operation the identifier of operation. - * @property left the left operand. - * @property right the right operand. - */ - public data class Binary(val operation: String, val left: MST, val right: MST) : MST -} - -// TODO add a function with named arguments - - -/** - * Interprets the [MST] node with this [Algebra] and optional [arguments] - */ -public fun MST.interpret(algebra: Algebra, arguments: Map): T = when (this) { - is MST.Numeric -> (algebra as NumericAlgebra?)?.number(value) - ?: error("Numeric nodes are not supported by $algebra") - - is Symbol -> algebra.bindSymbolOrNull(this) ?: arguments.getValue(this) - - is MST.Unary -> when { - algebra is NumericAlgebra && this.value is MST.Numeric -> algebra.unaryOperation( - this.operation, - algebra.number(this.value.value), - ) - else -> algebra.unaryOperationFunction(this.operation)(this.value.interpret(algebra, arguments)) - } - - is MST.Binary -> when { - algebra is NumericAlgebra && this.left is MST.Numeric && this.right is MST.Numeric -> algebra.binaryOperation( - this.operation, - algebra.number(this.left.value), - algebra.number(this.right.value), - ) - - algebra is NumericAlgebra && this.left is MST.Numeric -> algebra.leftSideNumberOperation( - this.operation, - this.left.value, - this.right.interpret(algebra, arguments), - ) - - algebra is NumericAlgebra && this.right is MST.Numeric -> algebra.rightSideNumberOperation( - this.operation, - left.interpret(algebra, arguments), - right.value, - ) - - else -> algebra.binaryOperation( - this.operation, - this.left.interpret(algebra, arguments), - this.right.interpret(algebra, arguments), - ) - } -} - -/** - * Interprets the [MST] node with this [Algebra] and optional [arguments] - * - * @receiver the node to evaluate. - * @param algebra the algebra that provides operations. - * @return the value of expression. - */ -public fun MST.interpret(algebra: Algebra, vararg arguments: Pair): T = interpret( - algebra, - when (arguments.size) { - 0 -> emptyMap() - 1 -> mapOf(arguments[0]) - else -> hashMapOf(*arguments) - }, -) - -/** - * Interpret this [MST] as expression. - */ -public fun MST.toExpression(algebra: Algebra): Expression = - Expression { arguments -> interpret(algebra, arguments) } diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/MstAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/MstAlgebra.kt deleted file mode 100644 index a1f65e1f1..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/MstAlgebra.kt +++ /dev/null @@ -1,177 +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. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* - -/** - * [Algebra] over [MST] nodes. - */ -public object MstNumericAlgebra : NumericAlgebra { - override fun number(value: Number): MST.Numeric = MST.Numeric(value) - override fun bindSymbolOrNull(value: String): Symbol = StringSymbol(value) - override fun bindSymbol(value: String): Symbol = bindSymbolOrNull(value) - - override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary = - { arg -> MST.Unary(operation, arg) } - - override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary = - { left, right -> MST.Binary(operation, left, right) } -} - -/** - * [Group] over [MST] nodes. - */ -public object MstGroup : Group, NumericAlgebra, ScaleOperations { - override val zero: MST.Numeric = number(0.0) - - override fun number(value: Number): MST.Numeric = MstNumericAlgebra.number(value) - override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value) - override fun add(left: MST, right: MST): MST.Binary = binaryOperationFunction(GroupOps.PLUS_OPERATION)(left, right) - - override fun negate(arg: MST): MST.Unary = - unaryOperationFunction(GroupOps.MINUS_OPERATION)(arg) - - override operator fun MST.minus(arg: MST): MST.Binary = - binaryOperationFunction(GroupOps.MINUS_OPERATION)(this, arg) - - override fun scale(a: MST, value: Double): MST.Binary = - binaryOperationFunction(RingOps.TIMES_OPERATION)(a, number(value)) - - override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary = - MstNumericAlgebra.binaryOperationFunction(operation) - - override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary = - MstNumericAlgebra.unaryOperationFunction(operation) -} - -/** - * [Ring] over [MST] nodes. - */ -@Suppress("OVERRIDE_BY_INLINE") -@OptIn(UnstableKMathAPI::class) -public object MstRing : Ring, NumbersAddOps, ScaleOperations { - override inline val zero: MST.Numeric get() = MstGroup.zero - override val one: MST.Numeric = number(1.0) - - override fun number(value: Number): MST.Numeric = MstGroup.number(value) - override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value) - override fun add(left: MST, right: MST): MST.Binary = MstGroup.add(left, right) - - override fun scale(a: MST, value: Double): MST.Binary = - MstGroup.binaryOperationFunction(RingOps.TIMES_OPERATION)(a, MstGroup.number(value)) - - override fun multiply(left: MST, right: MST): MST.Binary = - binaryOperationFunction(RingOps.TIMES_OPERATION)(left, right) - - override fun negate(arg: MST): MST.Unary = MstGroup.negate(arg) - override operator fun MST.minus(arg: MST): MST.Binary = MstGroup { this@minus - arg } - - override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary = - MstGroup.binaryOperationFunction(operation) - - override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary = - MstNumericAlgebra.unaryOperationFunction(operation) -} - -/** - * [Field] over [MST] nodes. - */ -@Suppress("OVERRIDE_BY_INLINE") -@OptIn(UnstableKMathAPI::class) -public object MstField : Field, NumbersAddOps, ScaleOperations { - override inline val zero: MST.Numeric get() = MstRing.zero - override inline val one: MST.Numeric get() = MstRing.one - - override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value) - override fun number(value: Number): MST.Numeric = MstRing.number(value) - override fun add(left: MST, right: MST): MST.Binary = MstRing.add(left, right) - - override fun scale(a: MST, value: Double): MST.Binary = - MstGroup.binaryOperationFunction(RingOps.TIMES_OPERATION)(a, MstGroup.number(value)) - - override fun multiply(left: MST, right: MST): MST.Binary = MstRing.multiply(left, right) - override fun divide(left: MST, right: MST): MST.Binary = - binaryOperationFunction(FieldOps.DIV_OPERATION)(left, right) - - override fun negate(arg: MST): MST.Unary = MstRing.negate(arg) - override operator fun MST.minus(arg: MST): MST.Binary = MstRing { this@minus - arg } - - override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary = - MstRing.binaryOperationFunction(operation) - - override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary = - MstRing.unaryOperationFunction(operation) -} - -/** - * [ExtendedField] over [MST] nodes. - */ -@Suppress("OVERRIDE_BY_INLINE") -public object MstExtendedField : ExtendedField, NumericAlgebra { - override inline val zero: MST.Numeric get() = MstField.zero - override inline val one: MST.Numeric get() = MstField.one - - override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value) - override fun number(value: Number): MST.Numeric = MstRing.number(value) - override fun sin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.SIN_OPERATION)(arg) - override fun cos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.COS_OPERATION)(arg) - override fun tan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.TAN_OPERATION)(arg) - override fun asin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ASIN_OPERATION)(arg) - override fun acos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ACOS_OPERATION)(arg) - override fun atan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ATAN_OPERATION)(arg) - override fun sinh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.SINH_OPERATION)(arg) - override fun cosh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.COSH_OPERATION)(arg) - override fun tanh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.TANH_OPERATION)(arg) - override fun asinh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ASINH_OPERATION)(arg) - override fun acosh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ACOSH_OPERATION)(arg) - override fun atanh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ATANH_OPERATION)(arg) - override fun add(left: MST, right: MST): MST.Binary = MstField.add(left, right) - override fun sqrt(arg: MST): MST = unaryOperationFunction(PowerOperations.SQRT_OPERATION)(arg) - - override fun scale(a: MST, value: Double): MST = - binaryOperation(GroupOps.PLUS_OPERATION, a, number(value)) - - override fun multiply(left: MST, right: MST): MST.Binary = MstField.multiply(left, right) - override fun divide(left: MST, right: MST): MST.Binary = MstField.divide(left, right) - override fun negate(arg: MST): MST.Unary = MstField.negate(arg) - override operator fun MST.minus(arg: MST): MST.Binary = MstField { this@minus - arg } - - override fun power(arg: MST, pow: Number): MST.Binary = - binaryOperationFunction(PowerOperations.POW_OPERATION)(arg, number(pow)) - - override fun exp(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.EXP_OPERATION)(arg) - override fun ln(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.LN_OPERATION)(arg) - - override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary = - MstField.binaryOperationFunction(operation) - - override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary = - MstField.unaryOperationFunction(operation) -} - -/** - * Logic algebra for [MST] - */ -@UnstableKMathAPI -public object MstLogicAlgebra : LogicAlgebra { - override fun bindSymbolOrNull(value: String): MST = super.bindSymbolOrNull(value) ?: StringSymbol(value) - - override fun const(boolean: Boolean): Symbol = if (boolean) { - LogicAlgebra.TRUE - } else { - LogicAlgebra.FALSE - } - - override fun MST.not(): MST = MST.Unary(Boolean::not.name, this) - - override fun MST.and(other: MST): MST = MST.Binary(Boolean::and.name, this, other) - - override fun MST.or(other: MST): MST = MST.Binary(Boolean::or.name, this, other) - - override fun MST.xor(other: MST): MST = MST.Binary(Boolean::xor.name, this, other) -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt deleted file mode 100644 index 15cfac1c5..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt +++ /dev/null @@ -1,415 +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. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.asBuffer -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract - -/* - * Implementation of backward-mode automatic differentiation. - * Initial gist by Roman Elizarov: https://gist.github.com/elizarov/1ad3a8583e88cb6ea7a0ad09bb591d3d - */ - - -public open class AutoDiffValue(public val value: T) - -/** - * Represents result of [simpleAutoDiff] call. - * - * @param T the non-nullable type of value. - * @param value the value of result. - * @property simpleAutoDiff The mapping of differentiated variables to their derivatives. - * @property context The field over [T]. - */ -public class DerivationResult( - public val value: T, - private val derivativeValues: Map, - public val context: Field, -) { - /** - * Returns derivative of [variable] or returns [Ring.zero] in [context]. - */ - public fun derivative(variable: Symbol): T = derivativeValues[variable.identity] ?: context.zero - - /** - * Computes the divergence. - */ - public fun div(): T = context { sum(derivativeValues.values) } -} - -/** - * Computes the gradient for variables in given order. - */ -public fun DerivationResult.grad(vararg variables: Symbol): Point { - check(variables.isNotEmpty()) { "Variable order is not provided for gradient construction" } - return variables.map(::derivative).asBuffer() -} - -/** - * Represents field in context of which functions can be derived. - */ -@OptIn(UnstableKMathAPI::class) -public open class SimpleAutoDiffField>( - public val context: F, - bindings: Map, -) : Field>, ExpressionAlgebra>, NumbersAddOps> { - override val zero: AutoDiffValue get() = const(context.zero) - override val one: AutoDiffValue get() = const(context.one) - - // this stack contains pairs of blocks and values to apply them to - private var stack: Array = arrayOfNulls(8) - private var sp: Int = 0 - private val derivatives: MutableMap, T> = hashMapOf() - - private val bindings: Map> = bindings.entries.associate { - it.key.identity to AutoDiffVariableWithDerivative(it.key.identity, it.value, context.zero) - } - - /** - * Differentiable variable with value and derivative of differentiation ([simpleAutoDiff]) result - * with respect to this variable. - * - * @param T the non-nullable type of value. - * @property value The value of this variable. - */ - private class AutoDiffVariableWithDerivative( - override val identity: String, - value: T, - var d: T, - ) : AutoDiffValue(value), Symbol { - override fun toString(): String = identity - override fun equals(other: Any?): Boolean = this.identity == (other as? Symbol)?.identity - override fun hashCode(): Int = identity.hashCode() - } - - override fun bindSymbolOrNull(value: String): AutoDiffValue? = bindings[value] - - private fun getDerivative(variable: AutoDiffValue): T = - (variable as? AutoDiffVariableWithDerivative)?.d ?: derivatives[variable] ?: context.zero - - private fun setDerivative(variable: AutoDiffValue, value: T) { - if (variable is AutoDiffVariableWithDerivative) variable.d = value else derivatives[variable] = value - } - - @Suppress("UNCHECKED_CAST") - private fun runBackwardPass() { - while (sp > 0) { - val value = stack[--sp] - val block = stack[--sp] as F.(Any?) -> Unit - context.block(value) - } - } - - override fun const(value: T): AutoDiffValue = AutoDiffValue(value) - - override fun number(value: Number): AutoDiffValue = const { one * value } - - /** - * A variable accessing inner state of derivatives. - * Use this value in inner builders to avoid creating additional derivative bindings. - */ - public var AutoDiffValue.d: T - get() = getDerivative(this) - set(value) = setDerivative(this, value) - - /** - * Performs update of derivative after the rest of the formula in the back-pass. - * - * For example, implementation of `sin` function is: - * - * ``` - * fun AD.sin(x: Variable): Variable = derive(Variable(sin(x.x)) { z -> // call derive with function result - * x.d += z.d * cos(x.x) // update derivative using chain rule and derivative of the function - * } - * ``` - */ - @Suppress("UNCHECKED_CAST") - public fun derive(value: R, block: F.(R) -> Unit): R { - // save block to stack for backward pass - if (sp >= stack.size) stack = stack.copyOf(stack.size * 2) - stack[sp++] = block - stack[sp++] = value - return value - } - - - internal fun differentiate(function: SimpleAutoDiffField.() -> AutoDiffValue): DerivationResult { - val result = function() - result.d = context.one // computing derivative w.r.t result - runBackwardPass() - return DerivationResult(result.value, bindings.mapValues { it.value.d }, context) - } - -// // Overloads for Double constants -// -// override operator fun Number.plus(b: AutoDiffValue): AutoDiffValue = -// derive(const { this@plus.toDouble() * one + b.value }) { z -> -// b.d += z.d -// } -// -// override operator fun AutoDiffValue.plus(b: Number): AutoDiffValue = b.plus(this) -// -// override operator fun Number.minus(b: AutoDiffValue): AutoDiffValue = -// derive(const { this@minus.toDouble() * one - b.value }) { z -> b.d -= z.d } -// -// override operator fun AutoDiffValue.minus(b: Number): AutoDiffValue = -// derive(const { this@minus.value - one * b.toDouble() }) { z -> d += z.d } - - - override fun negate(arg: AutoDiffValue): AutoDiffValue = - derive(const { -arg.value }) { z -> arg.d -= z.d } - - // Basic math (+, -, *, /) - - override fun add(left: AutoDiffValue, right: AutoDiffValue): AutoDiffValue = - derive(const { left.value + right.value }) { z -> - left.d += z.d - right.d += z.d - } - - override fun multiply(left: AutoDiffValue, right: AutoDiffValue): AutoDiffValue = - derive(const { left.value * right.value }) { z -> - left.d += z.d * right.value - right.d += z.d * left.value - } - - override fun divide(left: AutoDiffValue, right: AutoDiffValue): AutoDiffValue = - derive(const { left.value / right.value }) { z -> - left.d += z.d / right.value - right.d -= z.d * left.value / (right.value * right.value) - } - - override fun scale(a: AutoDiffValue, value: Double): AutoDiffValue = - derive(const { value * a.value }) { z -> - a.d += z.d * value - } -} - -public inline fun > SimpleAutoDiffField.const(block: F.() -> T): AutoDiffValue { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return const(context.block()) -} - - -/** - * Runs differentiation and establishes [SimpleAutoDiffField] context inside the block of code. - * - * The partial derivatives are placed in argument `d` variable - * - * Example: - * ``` - * val x by symbol // define variable(s) and their values - * val y = DoubleField.withAutoDiff() { sqr(x) + 5 * x + 3 } // write formulate in deriv context - * assertEquals(17.0, y.x) // the value of result (y) - * assertEquals(9.0, x.d) // dy/dx - * ``` - * - * @param body the action in [SimpleAutoDiffField] context returning [AutoDiffValue] to differentiate with respect to. - * @return the result of differentiation. - */ -public fun > F.simpleAutoDiff( - bindings: Map, - body: SimpleAutoDiffField.() -> AutoDiffValue, -): DerivationResult { - contract { callsInPlace(body, InvocationKind.EXACTLY_ONCE) } - - return SimpleAutoDiffField(this, bindings).differentiate(body) -} - -public fun > F.simpleAutoDiff( - vararg bindings: Pair, - body: SimpleAutoDiffField.() -> AutoDiffValue, -): DerivationResult = simpleAutoDiff(bindings.toMap(), body) - - -/** - * A constructs that creates a derivative structure with required order on-demand - */ -public class SimpleAutoDiffExpression>( - public val field: F, - public val function: SimpleAutoDiffField.() -> AutoDiffValue, -) : FirstDerivativeExpression() { - override operator fun invoke(arguments: Map): T { - //val bindings = arguments.entries.map { it.key.bind(it.value) } - return SimpleAutoDiffField(field, arguments).function().value - } - - override fun derivativeOrNull(symbol: Symbol): Expression = Expression { arguments -> - //val bindings = arguments.entries.map { it.key.bind(it.value) } - val derivationResult = SimpleAutoDiffField(field, arguments).differentiate(function) - derivationResult.derivative(symbol) - } -} - -/** - * Generate [AutoDiffProcessor] for [SimpleAutoDiffExpression] - */ -public fun > simpleAutoDiff( - field: F, -): AutoDiffProcessor, SimpleAutoDiffField> = - AutoDiffProcessor { function -> - SimpleAutoDiffExpression(field, function) - } - -// Extensions for differentiation of various basic mathematical functions - -// x ^ 2 -public fun > SimpleAutoDiffField.sqr(x: AutoDiffValue): AutoDiffValue = - derive(const { x.value * x.value }) { z -> x.d += z.d * 2.0 * x.value } - -// x ^ 1/2 -public fun > SimpleAutoDiffField.sqrt(x: AutoDiffValue): AutoDiffValue = - derive(const { sqrt(x.value) }) { z -> x.d += z.d / 2.0 / z.value } - -// x ^ y (const) -public fun > SimpleAutoDiffField.pow( - x: AutoDiffValue, - y: Double, -): AutoDiffValue = derive(const { x.value.pow(y) }) { z -> - x.d += z.d * y * x.value.pow(y - 1) -} - -public fun > SimpleAutoDiffField.pow( - x: AutoDiffValue, - y: Int, -): AutoDiffValue = pow(x, y.toDouble()) - -// exp(x) -public fun > SimpleAutoDiffField.exp(x: AutoDiffValue): AutoDiffValue = - derive(const { exp(x.value) }) { z -> x.d += z.d * z.value } - -// ln(x) -public fun > SimpleAutoDiffField.ln(x: AutoDiffValue): AutoDiffValue = - derive(const { ln(x.value) }) { z -> x.d += z.d / x.value } - -// x ^ y (any) -public fun > SimpleAutoDiffField.pow( - x: AutoDiffValue, - y: AutoDiffValue, -): AutoDiffValue = - exp(y * ln(x)) - -// sin(x) -public fun > SimpleAutoDiffField.sin(x: AutoDiffValue): AutoDiffValue = - derive(const { sin(x.value) }) { z -> x.d += z.d * cos(x.value) } - -// cos(x) -public fun > SimpleAutoDiffField.cos(x: AutoDiffValue): AutoDiffValue = - derive(const { cos(x.value) }) { z -> x.d -= z.d * sin(x.value) } - -public fun > SimpleAutoDiffField.tan(x: AutoDiffValue): AutoDiffValue = - derive(const { tan(x.value) }) { z -> - val c = cos(x.value) - x.d += z.d / (c * c) - } - -public fun > SimpleAutoDiffField.asin(x: AutoDiffValue): AutoDiffValue = - derive(const { asin(x.value) }) { z -> x.d += z.d / sqrt(one - x.value * x.value) } - -public fun > SimpleAutoDiffField.acos(x: AutoDiffValue): AutoDiffValue = - derive(const { acos(x.value) }) { z -> x.d -= z.d / sqrt(one - x.value * x.value) } - -public fun > SimpleAutoDiffField.atan(x: AutoDiffValue): AutoDiffValue = - derive(const { atan(x.value) }) { z -> x.d += z.d / (one + x.value * x.value) } - -public fun > SimpleAutoDiffField.sinh(x: AutoDiffValue): AutoDiffValue = - derive(const { sinh(x.value) }) { z -> x.d += z.d * cosh(x.value) } - -public fun > SimpleAutoDiffField.cosh(x: AutoDiffValue): AutoDiffValue = - derive(const { cosh(x.value) }) { z -> x.d += z.d * sinh(x.value) } - -public fun > SimpleAutoDiffField.tanh(x: AutoDiffValue): AutoDiffValue = - derive(const { tanh(x.value) }) { z -> - val c = cosh(x.value) - x.d += z.d / (c * c) - } - -public fun > SimpleAutoDiffField.asinh(x: AutoDiffValue): AutoDiffValue = - derive(const { asinh(x.value) }) { z -> x.d += z.d / sqrt(one + x.value * x.value) } - -public fun > SimpleAutoDiffField.acosh(x: AutoDiffValue): AutoDiffValue = - derive(const { acosh(x.value) }) { z -> x.d += z.d / (sqrt((x.value - one) * (x.value + one))) } - -public fun > SimpleAutoDiffField.atanh(x: AutoDiffValue): AutoDiffValue = - derive(const { atanh(x.value) }) { z -> x.d += z.d / (one - x.value * x.value) } - -public class SimpleAutoDiffExtendedField>( - context: F, - bindings: Map, -) : ExtendedField>, ScaleOperations>, SimpleAutoDiffField(context, bindings) { - - override fun number(value: Number): AutoDiffValue = const { number(value) } - - override fun scale(a: AutoDiffValue, value: Double): AutoDiffValue = a * number(value) - - // x ^ 2 - public fun sqr(x: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).sqr(x) - - // x ^ 1/2 - override fun sqrt(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).sqrt(arg) - - // x ^ y (const) - override fun power(arg: AutoDiffValue, pow: Number): AutoDiffValue = - (this as SimpleAutoDiffField).pow(arg, pow.toDouble()) - - // exp(x) - override fun exp(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).exp(arg) - - // ln(x) - override fun ln(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).ln(arg) - - // x ^ y (any) - public fun pow( - x: AutoDiffValue, - y: AutoDiffValue, - ): AutoDiffValue = exp(y * ln(x)) - - // sin(x) - override fun sin(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).sin(arg) - - // cos(x) - override fun cos(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).cos(arg) - - override fun tan(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).tan(arg) - - override fun asin(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).asin(arg) - - override fun acos(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).acos(arg) - - override fun atan(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).atan(arg) - - override fun sinh(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).sinh(arg) - - override fun cosh(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).cosh(arg) - - override fun tanh(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).tanh(arg) - - override fun asinh(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).asinh(arg) - - override fun acosh(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).acosh(arg) - - override fun atanh(arg: AutoDiffValue): AutoDiffValue = - (this as SimpleAutoDiffField).atanh(arg) -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Symbol.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Symbol.kt deleted file mode 100644 index 8ab2bec31..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Symbol.kt +++ /dev/null @@ -1,73 +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. - */ - -package space.kscience.kmath.expressions - -import kotlin.jvm.JvmInline -import kotlin.properties.ReadOnlyProperty - -/** - * A marker interface for a symbol. A symbol must have an identity with equality relation based on it. - * Other properties are to store additional, transient data only. - */ -public interface Symbol : MST { - /** - * Identity object for the symbol. Two symbols with the same identity are considered to be the same symbol. - */ - public val identity: String - - public companion object { - public val x: Symbol = Symbol("x") - public val xError: Symbol = Symbol("x.error") - public val y: Symbol = Symbol("y") - public val yError: Symbol = Symbol("y.error") - public val z: Symbol = Symbol("z") - public val zError: Symbol = Symbol("z.error") - } -} - -/** - * A [Symbol] with a [String] identity - */ -@JvmInline -internal value class StringSymbol(override val identity: String) : Symbol { - override fun toString(): String = identity -} - -/** - * Create s Symbols with a string identity - */ -public fun Symbol(identity: String): Symbol = StringSymbol(identity) - -/** - * A delegate to create a symbol with a string identity in this scope - */ -public val symbol: ReadOnlyProperty = ReadOnlyProperty { _, property -> - StringSymbol(property.name) -} - -/** - * Ger a value from a [String]-keyed map by a [Symbol] - */ -public operator fun Map.get(symbol: Symbol): T? = get(symbol.identity) - -/** - * Set a value of [String]-keyed map by a [Symbol] - */ -public operator fun MutableMap.set(symbol: Symbol, value: T) { - set(symbol.identity, value) -} - -/** - * Get a value from a [Symbol]-keyed map by a [String] - */ -public operator fun Map.get(string: String): T? = get(StringSymbol(string)) - -/** - * Set a value of [String]-keyed map by a [Symbol] - */ -public operator fun MutableMap.set(string: String, value: T) { - set(StringSymbol(string), value) -} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SymbolIndexer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SymbolIndexer.kt deleted file mode 100644 index bf37e9615..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SymbolIndexer.kt +++ /dev/null @@ -1,89 +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. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.Structure2D -import space.kscience.kmath.structures.BufferFactory -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract -import kotlin.jvm.JvmInline - -/** - * An environment to easy transform indexed variables to symbols and back. - * TODO requires multi-receivers to be beautiful - */ -@UnstableKMathAPI -public interface SymbolIndexer { - public val symbols: List - public fun indexOf(symbol: Symbol): Int = symbols.indexOf(symbol) - - public operator fun List.get(symbol: Symbol): T { - require(size == symbols.size) { "The input list size for indexer should be ${symbols.size} but $size found" } - return get(this@SymbolIndexer.indexOf(symbol)) - } - - public operator fun Array.get(symbol: Symbol): T { - require(size == symbols.size) { "The input array size for indexer should be ${symbols.size} but $size found" } - return get(this@SymbolIndexer.indexOf(symbol)) - } - - public operator fun DoubleArray.get(symbol: Symbol): Double { - require(size == symbols.size) { "The input array size for indexer should be ${symbols.size} but $size found" } - return get(indexOf(symbol)) - } - - public operator fun Point.get(symbol: Symbol): T { - require(size == symbols.size) { "The input buffer size for indexer should be ${symbols.size} but $size found" } - return get(indexOf(symbol)) - } - - public fun DoubleArray.toMap(): Map { - require(size == symbols.size) { "The input array size for indexer should be ${symbols.size} but $size found" } - return symbols.indices.associate { symbols[it] to get(it) } - } - - public fun Point.toMap(): Map { - require(size == symbols.size) { "The input array size for indexer should be ${symbols.size} but $size found" } - return symbols.indices.associate { symbols[it] to get(it) } - } - - public operator fun Structure2D.get(rowSymbol: Symbol, columnSymbol: Symbol): T = - get(indexOf(rowSymbol), indexOf(columnSymbol)) - - - public fun Map.toList(): List = symbols.map { getValue(it) } - - public fun Map.toPoint(bufferFactory: BufferFactory): Point = - bufferFactory(symbols.size) { getValue(symbols[it]) } - - public fun Map.toPoint(): DoubleBuffer = - DoubleBuffer(symbols.size) { getValue(symbols[it]) } - - - public fun Map.toDoubleArray(): DoubleArray = DoubleArray(symbols.size) { getValue(symbols[it]) } -} - -@UnstableKMathAPI -@JvmInline -public value class SimpleSymbolIndexer(override val symbols: List) : SymbolIndexer - -/** - * Execute the block with symbol indexer based on given symbol order - */ -@UnstableKMathAPI -public inline fun withSymbols(vararg symbols: Symbol, block: SymbolIndexer.() -> R): R { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return with(SimpleSymbolIndexer(symbols.toList()), block) -} - -@UnstableKMathAPI -public inline fun withSymbols(symbols: Collection, block: SymbolIndexer.() -> R): R { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return with(SimpleSymbolIndexer(symbols.toList()), block) -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/specialExpressions.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/specialExpressions.kt deleted file mode 100644 index 907ce4004..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/specialExpressions.kt +++ /dev/null @@ -1,53 +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. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.operations.ExtendedField -import space.kscience.kmath.operations.asIterable -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.indices -import kotlin.jvm.JvmName - -/** - * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic - * differentiation. - * - * **WARNING** All elements of [yErr] must be positive. - */ -@JvmName("genericChiSquaredExpression") -public fun , I : Any, A> AutoDiffProcessor.chiSquaredExpression( - x: Buffer, - y: Buffer, - yErr: Buffer, - model: A.(I) -> I, -): DifferentiableExpression where A : ExtendedField, A : ExpressionAlgebra { - require(x.size == y.size) { "X and y buffers should be of the same size" } - require(y.size == yErr.size) { "Y and yErr buffer should of the same size" } - - return differentiate { - var sum = zero - - x.indices.forEach { - val xValue = const(x[it]) - val yValue = const(y[it]) - val yErrValue = const(yErr[it]) - val modelValue = model(xValue) - sum += ((yValue - modelValue) / yErrValue).pow(2) - } - - sum - } -} - -public fun AutoDiffProcessor.chiSquaredExpression( - x: Buffer, - y: Buffer, - yErr: Buffer, - model: A.(I) -> I, -): DifferentiableExpression where A : ExtendedField, A : ExpressionAlgebra { - require(yErr.asIterable().all { it > 0.0 }) { "All errors must be strictly positive" } - return chiSquaredExpression(x, y, yErr, model) -} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferedLinearSpace.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferedLinearSpace.kt deleted file mode 100644 index 36cbd9064..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferedLinearSpace.kt +++ /dev/null @@ -1,95 +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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.BufferedRingOpsND -import space.kscience.kmath.nd.as2D -import space.kscience.kmath.nd.asND -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.BufferFactory -import space.kscience.kmath.structures.VirtualBuffer -import space.kscience.kmath.structures.indices - - -public class BufferedLinearSpace>( - private val bufferAlgebra: BufferAlgebra -) : LinearSpace { - override val elementAlgebra: A get() = bufferAlgebra.elementAlgebra - - private val ndAlgebra = BufferedRingOpsND(bufferAlgebra) - - override fun buildMatrix(rows: Int, columns: Int, initializer: A.(i: Int, j: Int) -> T): Matrix = - ndAlgebra.structureND(intArrayOf(rows, columns)) { (i, j) -> elementAlgebra.initializer(i, j) }.as2D() - - override fun buildVector(size: Int, initializer: A.(Int) -> T): Point = - bufferAlgebra.buffer(size) { elementAlgebra.initializer(it) } - - @OptIn(PerformancePitfall::class) - override fun Matrix.unaryMinus(): Matrix = ndAlgebra { - asND().map { -it }.as2D() - } - - override fun Matrix.plus(other: Matrix): Matrix = ndAlgebra { - require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" } - asND().plus(other.asND()).as2D() - } - - override fun Matrix.minus(other: Matrix): Matrix = ndAlgebra { - require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" } - asND().minus(other.asND()).as2D() - } - - private fun Buffer.linearize() = if (this is VirtualBuffer) { - buildVector(size) { get(it) } - } else { - this - } - - @OptIn(PerformancePitfall::class) - override fun Matrix.dot(other: Matrix): Matrix { - require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" } - return elementAlgebra { - val rows = this@dot.rows.map { it.linearize() } - val columns = other.columns.map { it.linearize() } - buildMatrix(rowNum, other.colNum) { i, j -> - val r = rows[i] - val c = columns[j] - var res = zero - for (l in r.indices) { - res += r[l] * c[l] - } - res - } - } - } - - @OptIn(PerformancePitfall::class) - override fun Matrix.dot(vector: Point): Point { - require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" } - return elementAlgebra { - val rows = this@dot.rows.map { it.linearize() } - buildVector(rowNum) { i -> - val r = rows[i] - var res = zero - for (j in r.indices) { - res += r[j] * vector[j] - } - res - } - } - } - - @OptIn(PerformancePitfall::class) - override fun Matrix.times(value: T): Matrix = ndAlgebra { - asND().map { it * value }.as2D() - } -} - - -public fun > A.linearSpace(bufferFactory: BufferFactory): BufferedLinearSpace = - BufferedLinearSpace(BufferRingOps(this, bufferFactory)) diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/DoubleLinearSpace.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/DoubleLinearSpace.kt deleted file mode 100644 index 4e6debc60..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/DoubleLinearSpace.kt +++ /dev/null @@ -1,110 +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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.DoubleFieldOpsND -import space.kscience.kmath.nd.as2D -import space.kscience.kmath.nd.asND -import space.kscience.kmath.operations.DoubleBufferOps -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.DoubleBuffer - -public object DoubleLinearSpace : LinearSpace { - - override val elementAlgebra: DoubleField get() = DoubleField - - override fun buildMatrix( - rows: Int, - columns: Int, - initializer: DoubleField.(i: Int, j: Int) -> Double - ): Matrix = DoubleFieldOpsND.structureND(intArrayOf(rows, columns)) { (i, j) -> - DoubleField.initializer(i, j) - }.as2D() - - override fun buildVector(size: Int, initializer: DoubleField.(Int) -> Double): DoubleBuffer = - DoubleBuffer(size) { DoubleField.initializer(it) } - - override fun Matrix.unaryMinus(): Matrix = DoubleFieldOpsND { - asND().map { -it }.as2D() - } - - override fun Matrix.plus(other: Matrix): Matrix = DoubleFieldOpsND { - require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" } - asND().plus(other.asND()).as2D() - } - - override fun Matrix.minus(other: Matrix): Matrix = DoubleFieldOpsND { - require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" } - asND().minus(other.asND()).as2D() - } - - // Create a continuous in-memory representation of this vector for better memory layout handling - private fun Buffer.linearize() = if (this is DoubleBuffer) { - this.array - } else { - DoubleArray(size) { get(it) } - } - - @OptIn(PerformancePitfall::class) - override fun Matrix.dot(other: Matrix): Matrix { - require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" } - val rows = this@dot.rows.map { it.linearize() } - val columns = other.columns.map { it.linearize() } - return buildMatrix(rowNum, other.colNum) { i, j -> - val r = rows[i] - val c = columns[j] - var res = 0.0 - for (l in r.indices) { - res += r[l] * c[l] - } - res - } - } - - @OptIn(PerformancePitfall::class) - override fun Matrix.dot(vector: Point): DoubleBuffer { - require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" } - val rows = this@dot.rows.map { it.linearize() } - return DoubleBuffer(rowNum) { i -> - val r = rows[i] - var res = 0.0 - for (j in r.indices) { - res += r[j] * vector[j] - } - res - } - - } - - override fun Matrix.times(value: Double): Matrix = DoubleFieldOpsND { - asND().map { it * value }.as2D() - } - - public override fun Point.plus(other: Point): DoubleBuffer = DoubleBufferOps.run { - this@plus + other - } - - public override fun Point.minus(other: Point): DoubleBuffer = DoubleBufferOps.run { - this@minus - other - } - - public override fun Point.times(value: Double): DoubleBuffer = DoubleBufferOps.run { - scale(this@times, value) - } - - public operator fun Point.div(value: Double): DoubleBuffer = DoubleBufferOps.run { - scale(this@div, 1.0 / value) - } - - public override fun Double.times(v: Point): DoubleBuffer = v * this - - -} - -public val DoubleField.linearSpace: DoubleLinearSpace get() = DoubleLinearSpace diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSolver.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSolver.kt deleted file mode 100644 index fae9e7c91..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSolver.kt +++ /dev/null @@ -1,30 +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. - */ - -package space.kscience.kmath.linear - -/** - * A group of methods to solve for *X* in equation *X = A−1 · B*, where *A* and *B* are - * matrices or vectors. - * - * @param T the type of items. - */ -public interface LinearSolver { - /** - * Solve a dot x = b matrix equation and return x - */ - public fun solve(a: Matrix, b: Matrix): Matrix - - /** - * Solve a dot x = b vector equation and return b - */ - public fun solve(a: Matrix, b: Point): Point = solve(a, b.asMatrix()).asVector() - - /** - * Get inverse of a matrix - */ - public fun inverse(matrix: Matrix): Matrix -} - diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt deleted file mode 100644 index 34a1ef036..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt +++ /dev/null @@ -1,231 +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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.MutableStructure2D -import space.kscience.kmath.nd.Structure2D -import space.kscience.kmath.nd.StructureFeature -import space.kscience.kmath.nd.as1D -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.BufferFactory -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.reflect.KClass - -/** - * Alias for [Structure2D] with more familiar name. - * - * @param T the type of items. - */ -public typealias Matrix = Structure2D -public typealias MutableMatrix = MutableStructure2D - -/** - * Alias or using [Buffer] as a point/vector in a many-dimensional space. - * - * @param T the type of elements contained in the buffer. - */ -public typealias Point = Buffer - -/** - * Basic operations on matrices and vectors. - * - * @param T the type of items in the matrices. - * @param A the type of ring over [T]. - */ -public interface LinearSpace> { - public val elementAlgebra: A - - /** - * Produces a matrix with this context and given dimensions. - */ - public fun buildMatrix(rows: Int, columns: Int, initializer: A.(i: Int, j: Int) -> T): Matrix - - /** - * Produces a point compatible with matrix space (and possibly optimized for it). - */ - public fun buildVector(size: Int, initializer: A.(Int) -> T): Point - - public operator fun Matrix.unaryMinus(): Matrix = buildMatrix(rowNum, colNum) { i, j -> - -get(i, j) - } - - public operator fun Point.unaryMinus(): Point = buildVector(size) { - -get(it) - } - - /** - * Matrix sum - */ - public operator fun Matrix.plus(other: Matrix): Matrix = buildMatrix(rowNum, colNum) { i, j -> - get(i, j) + other[i, j] - } - - - /** - * Vector sum - */ - public operator fun Point.plus(other: Point): Point = buildVector(size) { - get(it) + other[it] - } - - /** - * Matrix subtraction - */ - public operator fun Matrix.minus(other: Matrix): Matrix = buildMatrix(rowNum, colNum) { i, j -> - get(i, j) - other[i, j] - } - - /** - * Vector subtraction - */ - public operator fun Point.minus(other: Point): Point = buildVector(size) { - get(it) - other[it] - } - - - /** - * Computes the dot product of this matrix and another one. - * - * @receiver the multiplicand. - * @param other the multiplier. - * @return the dot product. - */ - public infix fun Matrix.dot(other: Matrix): Matrix { - require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" } - return elementAlgebra { - buildMatrix(rowNum, other.colNum) { i, j -> - var res = zero - for (l in 0 until colNum) { - res += this@dot[i, l] * other[l, j] - } - res - } - } - } - - /** - * Computes the dot product of this matrix and a vector. - * - * @receiver the multiplicand. - * @param vector the multiplier. - * @return the dot product. - */ - public infix fun Matrix.dot(vector: Point): Point { - require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" } - return elementAlgebra { - buildVector(rowNum) { i -> - var res = one - for (j in 0 until colNum) { - res += this@dot[i, j] * vector[j] - } - res - } - } - } - - /** - * Multiplies a matrix by its element. - * - * @receiver the multiplicand. - * @param value the multiplier. - * @receiver the product. - */ - public operator fun Matrix.times(value: T): Matrix = - buildMatrix(rowNum, colNum) { i, j -> get(i, j) * value } - - /** - * Multiplies an element by a matrix of it. - * - * @receiver the multiplicand. - * @param m the multiplier. - * @receiver the product. - */ - public operator fun T.times(m: Matrix): Matrix = m * this - - /** - * Multiplies a vector by its element. - * - * @receiver the multiplicand. - * @param value the multiplier. - * @receiver the product. - */ - public operator fun Point.times(value: T): Point = - buildVector(size) { i -> get(i) * value } - - /** - * Multiplies an element by a vector of it. - * - * @receiver the multiplicand. - * @param v the multiplier. - * @receiver the product. - */ - public operator fun T.times(v: Point): Point = v * this - - /** - * Compute a feature of the structure in this scope. Structure features take precedence other context features. - * - * @param F the type of feature. - * @param structure the structure. - * @param type the [KClass] instance of [F]. - * @return a feature object or `null` if it isn't present. - */ - @UnstableKMathAPI - public fun computeFeature(structure: Matrix, type: KClass): F? = - structure.getFeature(type) - - public companion object { - - /** - * A structured matrix with custom buffer - */ - public fun > buffered( - algebra: A, - bufferFactory: BufferFactory = Buffer.Companion::boxing, - ): LinearSpace = BufferedLinearSpace(BufferRingOps(algebra, bufferFactory)) - - @Deprecated("use DoubleField.linearSpace") - public val double: LinearSpace = buffered(DoubleField, ::DoubleBuffer) - - /** - * Automatic buffered matrix, unboxed if it is possible - */ - public inline fun > auto(ring: A): LinearSpace = - buffered(ring, Buffer.Companion::auto) - } -} - -/** - * Get a feature of the structure in this scope. Structure features take precedence other context features. - * - * @param T the type of items in the matrices. - * @param F the type of feature. - * @return a feature object or `null` if it isn't present. - */ -@UnstableKMathAPI -public inline fun LinearSpace.computeFeature(structure: Matrix): F? = - computeFeature(structure, F::class) - - -public inline operator fun , R> LS.invoke(block: LS.() -> R): R = run(block) - - -/** - * Convert matrix to vector if it is possible. - */ -public fun Matrix.asVector(): Point = - if (this.colNum == 1) as1D() - else error("Can't convert matrix with more than one column to vector") - -/** - * Creates an n × 1 [VirtualMatrix], where n is the size of the given buffer. - * - * @param T the type of elements contained in the buffer. - * @receiver a buffer. - * @return the new matrix. - */ -public fun Point.asMatrix(): VirtualMatrix = VirtualMatrix(size, 1) { i, _ -> get(i) } \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt deleted file mode 100644 index fb57f2343..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt +++ /dev/null @@ -1,230 +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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.BufferAccessor2D -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.MutableBuffer -import space.kscience.kmath.structures.MutableBufferFactory - -/** - * Common implementation of [LupDecompositionFeature]. - */ -public class LupDecomposition( - public val context: LinearSpace, - public val elementContext: Field, - public val lu: Matrix, - public val pivot: IntArray, - private val even: Boolean, -) : LupDecompositionFeature, DeterminantFeature { - /** - * Returns the matrix L of the decomposition. - * - * L is a lower-triangular matrix with [Ring.one] in diagonal - */ - override val l: Matrix = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j -> - when { - j < i -> lu[i, j] - j == i -> elementContext.one - else -> elementContext.zero - } - }.withFeature(LFeature) - - - /** - * Returns the matrix U of the decomposition. - * - * U is an upper-triangular matrix including the diagonal - */ - override val u: Matrix = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j -> - if (j >= i) lu[i, j] else elementContext.zero - }.withFeature(UFeature) - - /** - * Returns the P rows permutation matrix. - * - * P is a sparse matrix with exactly one element set to [Ring.one] in - * each row and each column, all other elements being set to [Ring.zero]. - */ - override val p: Matrix = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j -> - if (j == pivot[i]) elementContext.one else elementContext.zero - } - - /** - * Return the determinant of the matrix - * @return determinant of the matrix - */ - override val determinant: T by lazy { - elementContext { (0 until lu.shape[0]).fold(if (even) one else -one) { value, i -> value * lu[i, i] } } - } - -} - -@PublishedApi -internal fun > LinearSpace>.abs(value: T): T = - if (value > elementAlgebra.zero) value else elementAlgebra { -value } - -/** - * Create a lup decomposition of generic matrix. - */ -public fun > LinearSpace>.lup( - factory: MutableBufferFactory, - matrix: Matrix, - checkSingular: (T) -> Boolean, -): LupDecomposition { - require(matrix.rowNum == matrix.colNum) { "LU decomposition supports only square matrices" } - val m = matrix.colNum - val pivot = IntArray(matrix.rowNum) - - //TODO just waits for multi-receivers - BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run { - elementAlgebra { - val lu = create(matrix) - - // Initialize permutation array and parity - for (row in 0 until m) pivot[row] = row - var even = true - - // Initialize permutation array and parity - for (row in 0 until m) pivot[row] = row - - // Loop over columns - for (col in 0 until m) { - // upper - for (row in 0 until col) { - val luRow = lu.row(row) - var sum = luRow[col] - for (i in 0 until row) sum -= luRow[i] * lu[i, col] - luRow[col] = sum - } - - // lower - var max = col // permutation row - var largest = -one - - for (row in col until m) { - val luRow = lu.row(row) - var sum = luRow[col] - for (i in 0 until col) sum -= luRow[i] * lu[i, col] - luRow[col] = sum - - // maintain the best permutation choice - if (abs(sum) > largest) { - largest = abs(sum) - max = row - } - } - - // Singularity check - check(!checkSingular(abs(lu[max, col]))) { "The matrix is singular" } - - // Pivot if necessary - if (max != col) { - val luMax = lu.row(max) - val luCol = lu.row(col) - - for (i in 0 until m) { - val tmp = luMax[i] - luMax[i] = luCol[i] - luCol[i] = tmp - } - - val temp = pivot[max] - pivot[max] = pivot[col] - pivot[col] = temp - even = !even - } - - // Divide the lower elements by the "winning" diagonal elt. - val luDiag = lu[col, col] - for (row in col + 1 until m) lu[row, col] /= luDiag - } - - return LupDecomposition(this@lup, elementAlgebra, lu.collect(), pivot, even) - } - } -} - -public inline fun > LinearSpace>.lup( - matrix: Matrix, - noinline checkSingular: (T) -> Boolean, -): LupDecomposition = lup(MutableBuffer.Companion::auto, matrix, checkSingular) - -public fun LinearSpace.lup( - matrix: Matrix, - singularityThreshold: Double = 1e-11, -): LupDecomposition = - lup(::DoubleBuffer, matrix) { it < singularityThreshold } - -internal fun LupDecomposition.solve( - factory: MutableBufferFactory, - matrix: Matrix, -): Matrix { - require(matrix.rowNum == pivot.size) { "Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}" } - - BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run { - elementContext { - // Apply permutations to b - val bp = create { _, _ -> zero } - - for (row in pivot.indices) { - val bpRow = bp.row(row) - val pRow = pivot[row] - for (col in 0 until matrix.colNum) bpRow[col] = matrix[pRow, col] - } - - // Solve LY = b - for (col in pivot.indices) { - val bpCol = bp.row(col) - - for (i in col + 1 until pivot.size) { - val bpI = bp.row(i) - val luICol = lu[i, col] - for (j in 0 until matrix.colNum) { - bpI[j] -= bpCol[j] * luICol - } - } - } - - // Solve UX = Y - for (col in pivot.size - 1 downTo 0) { - val bpCol = bp.row(col) - val luDiag = lu[col, col] - for (j in 0 until matrix.colNum) bpCol[j] /= luDiag - - for (i in 0 until col) { - val bpI = bp.row(i) - val luICol = lu[i, col] - for (j in 0 until matrix.colNum) bpI[j] -= bpCol[j] * luICol - } - } - - return context.buildMatrix(pivot.size, matrix.colNum) { i, j -> bp[i, j] } - } - } -} - -/** - * Produce a generic solver based on LUP decomposition - */ -@OptIn(UnstableKMathAPI::class) -public fun , F : Field> LinearSpace.lupSolver( - bufferFactory: MutableBufferFactory, - singularityCheck: (T) -> Boolean, -): LinearSolver = object : LinearSolver { - override fun solve(a: Matrix, b: Matrix): Matrix { - // Use existing decomposition if it is provided by matrix - val decomposition = computeFeature(a) ?: lup(bufferFactory, a, singularityCheck) - return decomposition.solve(bufferFactory, b) - } - - override fun inverse(matrix: Matrix): Matrix = solve(matrix, one(matrix.rowNum, matrix.colNum)) -} - -public fun LinearSpace.lupSolver(singularityThreshold: Double = 1e-11): LinearSolver = - lupSolver(::DoubleBuffer) { it < singularityThreshold } diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixBuilder.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixBuilder.kt deleted file mode 100644 index 029612bc5..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixBuilder.kt +++ /dev/null @@ -1,77 +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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Ring -import space.kscience.kmath.structures.BufferAccessor2D -import space.kscience.kmath.structures.MutableBuffer - -public class MatrixBuilder>( - public val linearSpace: LinearSpace, - public val rows: Int, - public val columns: Int, -) { - public operator fun invoke(vararg elements: T): Matrix { - require(rows * columns == elements.size) { "The number of elements ${elements.size} is not equal $rows * $columns" } - return linearSpace.buildMatrix(rows, columns) { i, j -> elements[i * columns + j] } - } - - //TODO add specific matrix builder functions like diagonal, etc -} - -/** - * Create a matrix builder with given number of rows and columns - */ -@UnstableKMathAPI -public fun > LinearSpace.matrix(rows: Int, columns: Int): MatrixBuilder = - MatrixBuilder(this, rows, columns) - -@UnstableKMathAPI -public fun LinearSpace>.vector(vararg elements: T): Point { - return buildVector(elements.size) { elements[it] } -} - -public inline fun LinearSpace>.row( - size: Int, - crossinline builder: (Int) -> T, -): Matrix = buildMatrix(1, size) { _, j -> builder(j) } - -public fun LinearSpace>.row(vararg values: T): Matrix = row(values.size, values::get) - -public inline fun LinearSpace>.column( - size: Int, - crossinline builder: (Int) -> T, -): Matrix = buildMatrix(size, 1) { i, _ -> builder(i) } - -public fun LinearSpace>.column(vararg values: T): Matrix = column(values.size, values::get) - -public object SymmetricMatrixFeature : MatrixFeature - -/** - * Naive implementation of a symmetric matrix builder, that adds a [SymmetricMatrixFeature] tag. The resulting matrix contains - * full `size^2` number of elements, but caches elements during calls to save [builder] calls. [builder] is always called in the - * upper triangle region meaning that `i <= j` - */ -public fun > MatrixBuilder.symmetric( - builder: (i: Int, j: Int) -> T, -): Matrix { - require(columns == rows) { "In order to build symmetric matrix, number of rows $rows should be equal to number of columns $columns" } - return with(BufferAccessor2D(rows, rows, MutableBuffer.Companion::boxing)) { - val cache = factory(rows * rows) { null } - linearSpace.buildMatrix(rows, rows) { i, j -> - val cached = cache[i, j] - if (cached == null) { - val value = if (i <= j) builder(i, j) else builder(j, i) - cache[i, j] = value - cache[j, i] = value - value - } else { - cached - } - }.withFeature(SymmetricMatrixFeature) - } -} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixFeatures.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixFeatures.kt deleted file mode 100644 index b70e9d8a9..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixFeatures.kt +++ /dev/null @@ -1,182 +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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.nd.StructureFeature - -/** - * A marker interface representing some properties of matrices or additional transformations of them. Features are used - * to optimize matrix operations performance in some cases or retrieve the APIs. - */ -public interface MatrixFeature: StructureFeature - -/** - * Matrices with this feature are considered to have only diagonal non-null elements. - */ -public interface DiagonalFeature : MatrixFeature { - public companion object : DiagonalFeature -} - -/** - * Matrices with this feature have all zero elements. - */ -public object ZeroFeature : DiagonalFeature - -/** - * Matrices with this feature have unit elements on diagonal and zero elements in all other places. - */ -public object UnitFeature : DiagonalFeature - -/** - * Matrices with this feature can be inverted: *[inverse] = a−1* where *a* is the owning matrix. - * - * @param T the type of matrices' items. - */ -public interface InverseMatrixFeature : MatrixFeature { - /** - * The inverse matrix of the matrix that owns this feature. - */ - public val inverse: Matrix -} - -/** - * Matrices with this feature can compute their determinant. - * - * @param T the type of matrices' items. - */ -public interface DeterminantFeature : MatrixFeature { - /** - * The determinant of the matrix that owns this feature. - */ - public val determinant: T -} - -/** - * Produces a [DeterminantFeature] where the [DeterminantFeature.determinant] is [determinant]. - * - * @param determinant the value of determinant. - * @return a new [DeterminantFeature]. - */ -@Suppress("FunctionName") -public fun DeterminantFeature(determinant: T): DeterminantFeature = object : DeterminantFeature { - override val determinant: T = determinant -} - -/** - * Matrices with this feature are lower triangular ones. - */ -public object LFeature : MatrixFeature - -/** - * Matrices with this feature are upper triangular ones. - */ -public object UFeature : MatrixFeature - -/** - * Matrices with this feature support LU factorization: *a = [l] · [u]* where *a* is the owning matrix. - * - * @param T the type of matrices' items. - */ -public interface LUDecompositionFeature : MatrixFeature { - /** - * The lower triangular matrix in this decomposition. It may have [LFeature]. - */ - public val l: Matrix - - /** - * The upper triangular matrix in this decomposition. It may have [UFeature]. - */ - public val u: Matrix -} - -/** - * Matrices with this feature support LU factorization with partial pivoting: *[p] · a = [l] · [u]* where - * *a* is the owning matrix. - * - * @param T the type of matrices' items. - */ -public interface LupDecompositionFeature : MatrixFeature { - /** - * The lower triangular matrix in this decomposition. It may have [LFeature]. - */ - public val l: Matrix - - /** - * The upper triangular matrix in this decomposition. It may have [UFeature]. - */ - public val u: Matrix - - /** - * The permutation matrix in this decomposition. - */ - public val p: Matrix -} - -/** - * Matrices with this feature are orthogonal ones: *a · aT = u* where *a* is the owning matrix, *u* - * is the unit matrix ([UnitFeature]). - */ -public object OrthogonalFeature : MatrixFeature - -/** - * Matrices with this feature support QR factorization: *a = [q] · [r]* where *a* is the owning matrix. - * - * @param T the type of matrices' items. - */ -public interface QRDecompositionFeature : MatrixFeature { - /** - * The orthogonal matrix in this decomposition. It may have [OrthogonalFeature]. - */ - public val q: Matrix - - /** - * The upper triangular matrix in this decomposition. It may have [UFeature]. - */ - public val r: Matrix -} - -/** - * Matrices with this feature support Cholesky factorization: *a = [l] · [l]H* where *a* is the - * owning matrix. - * - * @param T the type of matrices' items. - */ -public interface CholeskyDecompositionFeature : MatrixFeature { - /** - * The triangular matrix in this decomposition. It may have either [UFeature] or [LFeature]. - */ - public val l: Matrix -} - -/** - * Matrices with this feature support SVD: *a = [u] · [s] · [v]H* where *a* is the owning - * matrix. - * - * @param T the type of matrices' items. - */ -public interface SingularValueDecompositionFeature : MatrixFeature { - /** - * The matrix in this decomposition. It is unitary, and it consists from left singular vectors. - */ - public val u: Matrix - - /** - * The matrix in this decomposition. Its main diagonal elements are singular values. - */ - public val s: Matrix - - /** - * The matrix in this decomposition. It is unitary, and it consists from right singular vectors. - */ - public val v: Matrix - - /** - * The buffer of singular values of this SVD. - */ - public val singularValues: Point -} - -//TODO add sparse matrix feature diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixWrapper.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixWrapper.kt deleted file mode 100644 index b1812f49d..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixWrapper.kt +++ /dev/null @@ -1,94 +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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.misc.FeatureSet -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.StructureFeature -import space.kscience.kmath.operations.Ring -import kotlin.reflect.KClass - -/** - * A [Matrix] that holds [MatrixFeature] objects. - * - * @param T the type of items. - */ -public class MatrixWrapper internal constructor( - public val origin: Matrix, - public val features: FeatureSet, -) : Matrix by origin { - - /** - * Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the - * criteria. - */ - @Suppress("UNCHECKED_CAST") - override fun getFeature(type: KClass): F? = - features.getFeature(type) ?: origin.getFeature(type) - - override fun toString(): String = "MatrixWrapper(matrix=$origin, features=$features)" -} - -/** - * Return the original matrix. If this is a wrapper, return its origin. If not, this matrix. - * Origin does not necessary store all features. - */ -@UnstableKMathAPI -public val Matrix.origin: Matrix - get() = (this as? MatrixWrapper)?.origin ?: this - -/** - * Add a single feature to a [Matrix] - */ -public fun Matrix.withFeature(newFeature: MatrixFeature): MatrixWrapper = if (this is MatrixWrapper) { - MatrixWrapper(origin, features.with(newFeature)) -} else { - MatrixWrapper(this, FeatureSet.of(newFeature)) -} - -@Deprecated("To be replaced by withFeature") -public operator fun Matrix.plus(newFeature: MatrixFeature): MatrixWrapper = withFeature(newFeature) - -/** - * Add a collection of features to a [Matrix] - */ -public fun Matrix.withFeatures(newFeatures: Iterable): MatrixWrapper = - if (this is MatrixWrapper) { - MatrixWrapper(origin, features.with(newFeatures)) - } else { - MatrixWrapper(this, FeatureSet.of(newFeatures)) - } - -/** - * Diagonal matrix of ones. The matrix is virtual no actual matrix is created. - */ -public fun LinearSpace>.one( - rows: Int, - columns: Int, -): Matrix = VirtualMatrix(rows, columns) { i, j -> - if (i == j) elementAlgebra.one else elementAlgebra.zero -}.withFeature(UnitFeature) - - -/** - * A virtual matrix of zeroes - */ -public fun LinearSpace>.zero( - rows: Int, - columns: Int, -): Matrix = VirtualMatrix(rows, columns) { _, _ -> - elementAlgebra.zero -}.withFeature(ZeroFeature) - -public class TransposedFeature(public val original: Matrix) : MatrixFeature - -/** - * Create a virtual transposed matrix without copying anything. `A.transpose().transpose() === A` - */ -@Suppress("UNCHECKED_CAST") -@OptIn(UnstableKMathAPI::class) -public fun Matrix.transpose(): Matrix = getFeature(TransposedFeature::class)?.original as? Matrix - ?: VirtualMatrix(colNum, rowNum) { i, j -> get(j, i) }.withFeature(TransposedFeature(this)) diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/VirtualMatrix.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/VirtualMatrix.kt deleted file mode 100644 index fb2b1e547..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/VirtualMatrix.kt +++ /dev/null @@ -1,25 +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. - */ - -package space.kscience.kmath.linear - -/** - * The matrix where each element is evaluated each time when is being accessed. - * - * @property generator the function that provides elements. - */ -public class VirtualMatrix( - override val rowNum: Int, - override val colNum: Int, - public val generator: (i: Int, j: Int) -> T, -) : Matrix { - - override val shape: IntArray get() = intArrayOf(rowNum, colNum) - - override operator fun get(i: Int, j: Int): T = generator(i, j) -} - -public fun MatrixBuilder.virtual(generator: (i: Int, j: Int) -> T): VirtualMatrix = - VirtualMatrix(rows, columns, generator) diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/Featured.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/Featured.kt deleted file mode 100644 index 29b7caec6..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/Featured.kt +++ /dev/null @@ -1,61 +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. - */ - -package space.kscience.kmath.misc - -import kotlin.jvm.JvmInline -import kotlin.reflect.KClass - -/** - * A entity that contains a set of features defined by their types - */ -public interface Featured { - public fun getFeature(type: FeatureKey): T? -} - -public typealias FeatureKey = KClass - -public interface Feature> { - - /** - * A key used for extraction - */ - @Suppress("UNCHECKED_CAST") - public val key: FeatureKey - get() = this::class as FeatureKey -} - -/** - * A container for a set of features - */ -@JvmInline -public value class FeatureSet> private constructor(public val features: Map, F>) : Featured { - @Suppress("UNCHECKED_CAST") - override fun getFeature(type: FeatureKey): T? = features[type]?.let { it as T } - - public inline fun getFeature(): T? = getFeature(T::class) - - public fun with(feature: T, type: FeatureKey = feature.key): FeatureSet = - FeatureSet(features + (type to feature)) - - public fun with(other: FeatureSet): FeatureSet = FeatureSet(features + other.features) - - public fun with(vararg otherFeatures: F): FeatureSet = - FeatureSet(features + otherFeatures.associateBy { it.key }) - - public fun with(otherFeatures: Iterable): FeatureSet = - FeatureSet(features + otherFeatures.associateBy { it.key }) - - public operator fun iterator(): Iterator = features.values.iterator() - - override fun toString(): String = features.values.joinToString(prefix = "[ ", postfix = " ]") - - - public companion object { - public fun > of(vararg features: F): FeatureSet = FeatureSet(features.associateBy { it.key }) - public fun > of(features: Iterable): FeatureSet = - FeatureSet(features.associateBy { it.key }) - } -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/annotations.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/annotations.kt deleted file mode 100644 index 7c612b6a9..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/annotations.kt +++ /dev/null @@ -1,31 +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. - */ - -package space.kscience.kmath.misc - -/** - * Marks declarations that are still experimental in the KMath APIs, which means that the design of the corresponding - * declarations has open issues that may (or may not) lead to their changes in the future. Roughly speaking, there is - * a chance of those declarations will be deprecated in the near future or the semantics of their behavior may change - * in some way that may break some code. - */ -@MustBeDocumented -@Retention(value = AnnotationRetention.BINARY) -@RequiresOptIn("This API is unstable and could change in future", RequiresOptIn.Level.WARNING) -public annotation class UnstableKMathAPI - -/** - * Marks API that could cause performance problems. The code marked by this API is unnecessary slow but could cause - * slow-down in some cases. Refer to the documentation and benchmark it to be sure. - */ -@MustBeDocumented -@Retention(value = AnnotationRetention.BINARY) -@RequiresOptIn( - "Refer to the documentation to use this API in performance-critical code", - RequiresOptIn.Level.WARNING, -) -public annotation class PerformancePitfall( - val message: String = "Potential performance problem" -) diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/cumulative.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/cumulative.kt deleted file mode 100644 index ee7f1d8be..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/cumulative.kt +++ /dev/null @@ -1,79 +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. - */ - -package space.kscience.kmath.misc - -import space.kscience.kmath.operations.Ring -import space.kscience.kmath.operations.invoke -import kotlin.jvm.JvmName - -/** - * Generic cumulative operation on iterator. - * - * @param T the type of initial iterable. - * @param R the type of resulting iterable. - * @param initial lazy evaluated. - */ -public inline fun Iterator.cumulative(initial: R, crossinline operation: (R, T) -> R): Iterator = - object : Iterator { - var state: R = initial - - override fun hasNext(): Boolean = this@cumulative.hasNext() - - override fun next(): R { - state = operation(state, this@cumulative.next()) - return state - } - } - -public inline fun Iterable.cumulative(initial: R, crossinline operation: (R, T) -> R): Iterable = - Iterable { this@cumulative.iterator().cumulative(initial, operation) } - -public inline fun Sequence.cumulative(initial: R, crossinline operation: (R, T) -> R): Sequence = - Sequence { this@cumulative.iterator().cumulative(initial, operation) } - -public inline fun List.cumulative(initial: R, crossinline operation: (R, T) -> R): List = - iterator().cumulative(initial, operation).asSequence().toList() - -//Cumulative sum - -/** - * Cumulative sum with custom space - */ -public fun Iterable.cumulativeSum(group: Ring): Iterable = - group { cumulative(zero) { element: T, sum: T -> sum + element } } - -@JvmName("cumulativeSumOfDouble") -public fun Iterable.cumulativeSum(): Iterable = cumulative(0.0) { element, sum -> sum + element } - -@JvmName("cumulativeSumOfInt") -public fun Iterable.cumulativeSum(): Iterable = cumulative(0) { element, sum -> sum + element } - -@JvmName("cumulativeSumOfLong") -public fun Iterable.cumulativeSum(): Iterable = cumulative(0L) { element, sum -> sum + element } - -public fun Sequence.cumulativeSum(group: Ring): Sequence = - group { cumulative(zero) { element: T, sum: T -> sum + element } } - -@JvmName("cumulativeSumOfDouble") -public fun Sequence.cumulativeSum(): Sequence = cumulative(0.0) { element, sum -> sum + element } - -@JvmName("cumulativeSumOfInt") -public fun Sequence.cumulativeSum(): Sequence = cumulative(0) { element, sum -> sum + element } - -@JvmName("cumulativeSumOfLong") -public fun Sequence.cumulativeSum(): Sequence = cumulative(0L) { element, sum -> sum + element } - -public fun List.cumulativeSum(group: Ring): List = - group { cumulative(zero) { element: T, sum: T -> sum + element } } - -@JvmName("cumulativeSumOfDouble") -public fun List.cumulativeSum(): List = cumulative(0.0) { element, sum -> sum + element } - -@JvmName("cumulativeSumOfInt") -public fun List.cumulativeSum(): List = cumulative(0) { element, sum -> sum + element } - -@JvmName("cumulativeSumOfLong") -public fun List.cumulativeSum(): List = cumulative(0L) { element, sum -> sum + element } diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/logging.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/logging.kt deleted file mode 100644 index 9dfc564c3..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/logging.kt +++ /dev/null @@ -1,22 +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. - */ - -package space.kscience.kmath.misc - -import space.kscience.kmath.misc.Loggable.Companion.INFO - -public fun interface Loggable { - public fun log(tag: String, block: () -> String) - - public companion object { - public const val INFO: String = "INFO" - - public val console: Loggable = Loggable { tag, block -> - println("[$tag] ${block()}") - } - } -} - -public fun Loggable.log(block: () -> String): Unit = log(INFO, block) \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/numbers.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/numbers.kt deleted file mode 100644 index f879a06d5..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/numbers.kt +++ /dev/null @@ -1,8 +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. - */ - -package space.kscience.kmath.misc - -public expect fun Long.toIntExact(): Int diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/sorting.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/sorting.kt deleted file mode 100644 index a144e49b4..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/sorting.kt +++ /dev/null @@ -1,55 +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 file. - */ - -package space.kscience.kmath.misc - -import kotlin.comparisons.* -import space.kscience.kmath.structures.Buffer - -/** - * Return a new list filled with buffer indices. Indice order is defined by sorting associated buffer value. - * This feature allows to sort buffer values without reordering its content. - * - * @return List of buffer indices, sorted by associated value. - */ -@PerformancePitfall -@UnstableKMathAPI -public fun > Buffer.permSort() : IntArray = _permSortWith(compareBy { get(it) }) - -@PerformancePitfall -@UnstableKMathAPI -public fun > Buffer.permSortDescending() : IntArray = _permSortWith(compareByDescending { get(it) }) - -@PerformancePitfall -@UnstableKMathAPI -public fun > Buffer.permSortBy(selector: (V) -> C) : IntArray = _permSortWith(compareBy { selector(get(it)) }) - -@PerformancePitfall -@UnstableKMathAPI -public fun > Buffer.permSortByDescending(selector: (V) -> C) : IntArray = _permSortWith(compareByDescending { selector(get(it)) }) - -@PerformancePitfall -@UnstableKMathAPI -public fun Buffer.permSortWith(comparator : Comparator) : IntArray = _permSortWith { i1, i2 -> comparator.compare(get(i1), get(i2)) } - -@PerformancePitfall -@UnstableKMathAPI -private fun Buffer._permSortWith(comparator : Comparator) : IntArray { - if (size < 2) return IntArray(size) - - /* TODO: optimisation : keep a constant big array of indices (Ex: from 0 to 4096), then create indice - * arrays more efficiently by copying subpart of cached one. For bigger needs, we could copy entire - * cached array, then fill remaining indices manually. Not done for now, because: - * 1. doing it right would require some statistics about common used buffer sizes. - * 2. Some benchmark would be needed to ensure it would really provide better performance - */ - val packedIndices = IntArray(size) { idx -> idx } - - /* TODO: find an efficient way to sort in-place instead, and return directly the IntArray. - * Not done for now, because no standard utility is provided yet. An open issue exists for this. - * See: https://youtrack.jetbrains.com/issue/KT-37860 - */ - return packedIndices.sortedWith(comparator).toIntArray() -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/AlgebraND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/AlgebraND.kt deleted file mode 100644 index a071c1eb3..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/AlgebraND.kt +++ /dev/null @@ -1,275 +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. - */ - -package space.kscience.kmath.nd - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import kotlin.reflect.KClass - -/** - * An exception is thrown when the expected and actual shape of NDArray differ. - * - * @property expected the expected shape. - * @property actual the actual shape. - */ -public class ShapeMismatchException(public val expected: IntArray, public val actual: IntArray) : - RuntimeException("Shape ${actual.contentToString()} doesn't fit in expected shape ${expected.contentToString()}.") - -public typealias Shape = IntArray - -public fun Shape(shapeFirst: Int, vararg shapeRest: Int): Shape = intArrayOf(shapeFirst, *shapeRest) - -public interface WithShape { - public val shape: Shape - - public val indices: ShapeIndexer get() = DefaultStrides(shape) -} - -/** - * The base interface for all ND-algebra implementations. - * - * @param T the type of ND-structure element. - * @param C the type of the element context. - */ -public interface AlgebraND>: Algebra> { - /** - * The algebra over elements of ND structure. - */ - public val elementAlgebra: C - - /** - * Produces a new [StructureND] using given initializer function. - */ - public fun structureND(shape: Shape, initializer: C.(IntArray) -> T): StructureND - - /** - * Maps elements from one structure to another one by applying [transform] to them. - */ - @PerformancePitfall("Very slow on remote execution algebras") - public fun StructureND.map(transform: C.(T) -> T): StructureND = structureND(shape) { index -> - elementAlgebra.transform(get(index)) - } - - /** - * Maps elements from one structure to another one by applying [transform] to them alongside with their indices. - */ - @PerformancePitfall("Very slow on remote execution algebras") - public fun StructureND.mapIndexed(transform: C.(index: IntArray, T) -> T): StructureND = - structureND(shape) { index -> - elementAlgebra.transform(index, get(index)) - } - - /** - * Combines two structures into one. - */ - @PerformancePitfall("Very slow on remote execution algebras") - public fun zip(left: StructureND, right: StructureND, transform: C.(T, T) -> T): StructureND { - require(left.shape.contentEquals(right.shape)) { - "Expected left and right of the same shape, but left - ${left.shape} and right - ${right.shape}" - } - return structureND(left.shape) { index -> - elementAlgebra.transform(left[index], right[index]) - } - } - - /** - * Element-wise invocation of function working on [T] on a [StructureND]. - */ - @PerformancePitfall - public operator fun Function1.invoke(structure: StructureND): StructureND = - structure.map { value -> this@invoke(value) } - - /** - * Get a feature of the structure in this scope. Structure features take precedence other context features. - * - * @param F the type of feature. - * @param structure the structure. - * @param type the [KClass] instance of [F]. - * @return a feature object or `null` if it isn't present. - */ - @UnstableKMathAPI - public fun getFeature(structure: StructureND, type: KClass): F? = - structure.getFeature(type) - - public companion object -} - -/** - * Get a feature of the structure in this scope. Structure features take precedence other context features. - * - * @param T the type of items in the matrices. - * @param F the type of feature. - * @return a feature object or `null` if it isn't present. - */ -@UnstableKMathAPI -public inline fun AlgebraND.getFeature(structure: StructureND): F? = - getFeature(structure, F::class) - -/** - * Space of [StructureND]. - * - * @param T the type of the element contained in ND structure. - * @param A the type of group over structure elements. - */ -public interface GroupOpsND> : GroupOps>, AlgebraND { - /** - * Element-wise addition. - * - * @param left the augend. - * @param right the addend. - * @return the sum. - */ - @OptIn(PerformancePitfall::class) - override fun add(left: StructureND, right: StructureND): StructureND = - zip(left, right) { aValue, bValue -> add(aValue, bValue) } - - // TODO move to extensions after KEEP-176 - - /** - * Adds an ND structure to an element of it. - * - * @receiver the augend. - * @param arg the addend. - * @return the sum. - */ - @OptIn(PerformancePitfall::class) - public operator fun StructureND.plus(arg: T): StructureND = this.map { value -> add(arg, value) } - - /** - * Subtracts an element from ND structure of it. - * - * @receiver the dividend. - * @param arg the divisor. - * @return the quotient. - */ - @OptIn(PerformancePitfall::class) - public operator fun StructureND.minus(arg: T): StructureND = this.map { value -> add(arg, -value) } - - /** - * Adds an element to ND structure of it. - * - * @receiver the augend. - * @param arg the addend. - * @return the sum. - */ - @OptIn(PerformancePitfall::class) - public operator fun T.plus(arg: StructureND): StructureND = arg.map { value -> add(this@plus, value) } - - /** - * Subtracts an ND structure from an element of it. - * - * @receiver the dividend. - * @param arg the divisor. - * @return the quotient. - */ - @OptIn(PerformancePitfall::class) - public operator fun T.minus(arg: StructureND): StructureND = arg.map { value -> add(-this@minus, value) } - - public companion object -} - -public interface GroupND> : Group>, GroupOpsND, WithShape { - override val zero: StructureND get() = structureND(shape) { elementAlgebra.zero } -} - -/** - * Ring of [StructureND]. - * - * @param T the type of the element contained in ND structure. - * @param A the type of ring over structure elements. - */ -public interface RingOpsND> : RingOps>, GroupOpsND { - /** - * Element-wise multiplication. - * - * @param left the multiplicand. - * @param right the multiplier. - * @return the product. - */ - @OptIn(PerformancePitfall::class) - override fun multiply(left: StructureND, right: StructureND): StructureND = - zip(left, right) { aValue, bValue -> multiply(aValue, bValue) } - - //TODO move to extensions after KEEP-176 - - /** - * Multiplies an ND structure by an element of it. - * - * @receiver the multiplicand. - * @param arg the multiplier. - * @return the product. - */ - @OptIn(PerformancePitfall::class) - public operator fun StructureND.times(arg: T): StructureND = this.map { value -> multiply(arg, value) } - - /** - * Multiplies an element by a ND structure of it. - * - * @receiver the multiplicand. - * @param arg the multiplier. - * @return the product. - */ - @OptIn(PerformancePitfall::class) - public operator fun T.times(arg: StructureND): StructureND = arg.map { value -> multiply(this@times, value) } - - public companion object -} - -public interface RingND> : Ring>, RingOpsND, GroupND, WithShape { - override val one: StructureND get() = structureND(shape) { elementAlgebra.one } -} - - -/** - * Field of [StructureND]. - * - * @param T the type of the element contained in ND structure. - * @param A the type field over structure elements. - */ -public interface FieldOpsND> : - FieldOps>, - RingOpsND, - ScaleOperations> { - /** - * Element-wise division. - * - * @param left the dividend. - * @param right the divisor. - * @return the quotient. - */ - @OptIn(PerformancePitfall::class) - override fun divide(left: StructureND, right: StructureND): StructureND = - zip(left, right) { aValue, bValue -> divide(aValue, bValue) } - - //TODO move to extensions after https://github.com/Kotlin/KEEP/blob/master/proposals/context-receivers.md - /** - * Divides an ND structure by an element of it. - * - * @receiver the dividend. - * @param arg the divisor. - * @return the quotient. - */ - @OptIn(PerformancePitfall::class) - public operator fun StructureND.div(arg: T): StructureND = this.map { value -> divide(arg, value) } - - /** - * Divides an element by an ND structure of it. - * - * @receiver the dividend. - * @param arg the divisor. - * @return the quotient. - */ - @OptIn(PerformancePitfall::class) - public operator fun T.div(arg: StructureND): StructureND = arg.map { divide(it, this@div) } - - @OptIn(PerformancePitfall::class) - override fun scale(a: StructureND, value: Double): StructureND = a.map { scale(it, value) } -} - -public interface FieldND> : Field>, FieldOpsND, RingND, WithShape { - override val one: StructureND get() = structureND(shape) { elementAlgebra.one } -} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferAlgebraND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferAlgebraND.kt deleted file mode 100644 index 80c4c28ac..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferAlgebraND.kt +++ /dev/null @@ -1,199 +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. - */ - -@file:OptIn(UnstableKMathAPI::class) - -package space.kscience.kmath.nd - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.BufferFactory - -public interface BufferAlgebraND> : AlgebraND { - public val indexerBuilder: (IntArray) -> ShapeIndexer - public val bufferAlgebra: BufferAlgebra - override val elementAlgebra: A get() = bufferAlgebra.elementAlgebra - - override fun structureND(shape: Shape, initializer: A.(IntArray) -> T): BufferND { - val indexer = indexerBuilder(shape) - return BufferND( - indexer, - bufferAlgebra.buffer(indexer.linearSize) { offset -> - elementAlgebra.initializer(indexer.index(offset)) - } - ) - } - - public fun StructureND.toBufferND(): BufferND = when (this) { - is BufferND -> this - else -> { - val indexer = indexerBuilder(shape) - BufferND(indexer, bufferAlgebra.buffer(indexer.linearSize) { offset -> get(indexer.index(offset)) }) - } - } - - @PerformancePitfall - override fun StructureND.map(transform: A.(T) -> T): BufferND = mapInline(toBufferND(), transform) - - @PerformancePitfall - override fun StructureND.mapIndexed(transform: A.(index: IntArray, T) -> T): BufferND = - mapIndexedInline(toBufferND(), transform) - - @PerformancePitfall - override fun zip(left: StructureND, right: StructureND, transform: A.(T, T) -> T): BufferND = - zipInline(left.toBufferND(), right.toBufferND(), transform) - - public companion object { - public val defaultIndexerBuilder: (IntArray) -> ShapeIndexer = DefaultStrides.Companion::invoke - } -} - -public inline fun > BufferAlgebraND.mapInline( - arg: BufferND, - crossinline transform: A.(T) -> T, -): BufferND { - val indexes = arg.indices - val buffer = arg.buffer - return BufferND( - indexes, - bufferAlgebra.run { - bufferFactory(buffer.size) { elementAlgebra.transform(buffer[it]) } - } - ) -} - -internal inline fun > BufferAlgebraND.mapIndexedInline( - arg: BufferND, - crossinline transform: A.(index: IntArray, arg: T) -> T, -): BufferND { - val indexes = arg.indices - val buffer = arg.buffer - return BufferND( - indexes, - bufferAlgebra.run { - bufferFactory(buffer.size) { elementAlgebra.transform(indexes.index(it), buffer[it]) } - } - ) -} - -internal inline fun > BufferAlgebraND.zipInline( - l: BufferND, - r: BufferND, - crossinline block: A.(l: T, r: T) -> T, -): BufferND { - require(l.indices == r.indices) { "Zip requires the same shapes, but found ${l.shape} on the left and ${r.shape} on the right" } - val indexes = l.indices - val lbuffer = l.buffer - val rbuffer = r.buffer - return BufferND( - indexes, - bufferAlgebra.run { - bufferFactory(lbuffer.size) { elementAlgebra.block(lbuffer[it], rbuffer[it]) } - } - ) -} - -@OptIn(PerformancePitfall::class) -public open class BufferedGroupNDOps>( - override val bufferAlgebra: BufferAlgebra, - override val indexerBuilder: (IntArray) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder, -) : GroupOpsND, BufferAlgebraND { - override fun negate(arg: StructureND): StructureND = arg.map { -it } -} - -public open class BufferedRingOpsND>( - bufferAlgebra: BufferAlgebra, - indexerBuilder: (IntArray) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder, -) : BufferedGroupNDOps(bufferAlgebra, indexerBuilder), RingOpsND - -public open class BufferedFieldOpsND>( - bufferAlgebra: BufferAlgebra, - indexerBuilder: (IntArray) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder, -) : BufferedRingOpsND(bufferAlgebra, indexerBuilder), FieldOpsND { - - public constructor( - elementAlgebra: A, - bufferFactory: BufferFactory, - indexerBuilder: (IntArray) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder, - ) : this(BufferFieldOps(elementAlgebra, bufferFactory), indexerBuilder) - - @OptIn(PerformancePitfall::class) - override fun scale(a: StructureND, value: Double): StructureND = a.map { it * value } -} - -public val > BufferAlgebra.nd: BufferedGroupNDOps get() = BufferedGroupNDOps(this) -public val > BufferAlgebra.nd: BufferedRingOpsND get() = BufferedRingOpsND(this) -public val > BufferAlgebra.nd: BufferedFieldOpsND get() = BufferedFieldOpsND(this) - - -public fun > BufferAlgebraND.structureND( - vararg shape: Int, - initializer: A.(IntArray) -> T, -): BufferND = structureND(shape, initializer) - -public fun , A> A.structureND( - initializer: EA.(IntArray) -> T, -): BufferND where A : BufferAlgebraND, A : WithShape = structureND(shape, initializer) - -//// group factories -//public fun > A.ndAlgebra( -// bufferAlgebra: BufferAlgebra, -// vararg shape: Int, -//): BufferedGroupNDOps = BufferedGroupNDOps(bufferAlgebra) -// -//@JvmName("withNdGroup") -//public inline fun , R> A.withNdAlgebra( -// noinline bufferFactory: BufferFactory, -// vararg shape: Int, -// action: BufferedGroupNDOps.() -> R, -//): R { -// contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) } -// return ndAlgebra(bufferFactory, *shape).run(action) -//} - -////ring factories -//public fun > A.ndAlgebra( -// bufferFactory: BufferFactory, -// vararg shape: Int, -//): BufferedRingNDOps = BufferedRingNDOps(shape, this, bufferFactory) -// -//@JvmName("withNdRing") -//public inline fun , R> A.withNdAlgebra( -// noinline bufferFactory: BufferFactory, -// vararg shape: Int, -// action: BufferedRingNDOps.() -> R, -//): R { -// contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) } -// return ndAlgebra(bufferFactory, *shape).run(action) -//} -// -////field factories -//public fun > A.ndAlgebra( -// bufferFactory: BufferFactory, -// vararg shape: Int, -//): BufferedFieldNDOps = BufferedFieldNDOps(shape, this, bufferFactory) -// -///** -// * Create a [FieldND] for this [Field] inferring proper buffer factory from the type -// */ -//@UnstableKMathAPI -//@Suppress("UNCHECKED_CAST") -//public inline fun > A.autoNdAlgebra( -// vararg shape: Int, -//): FieldND = when (this) { -// DoubleField -> DoubleFieldND(shape) as FieldND -// else -> BufferedFieldNDOps(shape, this, Buffer.Companion::auto) -//} -// -//@JvmName("withNdField") -//public inline fun , R> A.withNdAlgebra( -// noinline bufferFactory: BufferFactory, -// vararg shape: Int, -// action: BufferedFieldNDOps.() -> R, -//): R { -// contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) } -// return ndAlgebra(bufferFactory, *shape).run(action) -//} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt deleted file mode 100644 index 539499794..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt +++ /dev/null @@ -1,76 +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. - */ - -package space.kscience.kmath.nd - -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.BufferFactory -import space.kscience.kmath.structures.MutableBuffer -import space.kscience.kmath.structures.MutableBufferFactory - -/** - * Represents [StructureND] over [Buffer]. - * - * @param T the type of items. - * @param indices The strides to access elements of [Buffer] by linear indices. - * @param buffer The underlying buffer. - */ -public open class BufferND( - override val indices: ShapeIndexer, - public open val buffer: Buffer, -) : StructureND { - - override operator fun get(index: IntArray): T = buffer[indices.offset(index)] - - override val shape: IntArray get() = indices.shape - - override fun toString(): String = StructureND.toString(this) -} - -/** - * Transform structure to a new structure using provided [BufferFactory] and optimizing if argument is [BufferND] - */ -public inline fun StructureND.mapToBuffer( - factory: BufferFactory = Buffer.Companion::auto, - crossinline transform: (T) -> R, -): BufferND { - return if (this is BufferND) - BufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) }) - else { - val strides = DefaultStrides(shape) - BufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) }) - } -} - -/** - * Represents [MutableStructureND] over [MutableBuffer]. - * - * @param T the type of items. - * @param strides The strides to access elements of [MutableBuffer] by linear indices. - * @param buffer The underlying buffer. - */ -public class MutableBufferND( - strides: ShapeIndexer, - override val buffer: MutableBuffer, -) : MutableStructureND, BufferND(strides, buffer) { - override fun set(index: IntArray, value: T) { - buffer[indices.offset(index)] = value - } -} - -/** - * Transform structure to a new structure using provided [MutableBufferFactory] and optimizing if argument is [MutableBufferND] - */ -public inline fun MutableStructureND.mapToMutableBuffer( - factory: MutableBufferFactory = MutableBuffer.Companion::auto, - crossinline transform: (T) -> R, -): MutableBufferND { - return if (this is MutableBufferND) - MutableBufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) }) - else { - val strides = DefaultStrides(shape) - MutableBufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) }) - } -} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/DoubleFieldND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/DoubleFieldND.kt deleted file mode 100644 index 5dd1eb101..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/DoubleFieldND.kt +++ /dev/null @@ -1,221 +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. - */ - -package space.kscience.kmath.nd - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract -import kotlin.math.pow as kpow - -public class DoubleBufferND( - indexes: ShapeIndexer, - override val buffer: DoubleBuffer, -) : BufferND(indexes, buffer) - - -public sealed class DoubleFieldOpsND : BufferedFieldOpsND(DoubleField.bufferAlgebra), - ScaleOperations>, ExtendedFieldOps> { - - override fun StructureND.toBufferND(): DoubleBufferND = when (this) { - is DoubleBufferND -> this - else -> { - val indexer = indexerBuilder(shape) - DoubleBufferND(indexer, DoubleBuffer(indexer.linearSize) { offset -> get(indexer.index(offset)) }) - } - } - - protected inline fun mapInline( - arg: DoubleBufferND, - transform: (Double) -> Double, - ): DoubleBufferND { - val indexes = arg.indices - val array = arg.buffer.array - return DoubleBufferND(indexes, DoubleBuffer(indexes.linearSize) { transform(array[it]) }) - } - - private inline fun zipInline( - l: DoubleBufferND, - r: DoubleBufferND, - block: (l: Double, r: Double) -> Double, - ): DoubleBufferND { - require(l.indices == r.indices) { "Zip requires the same shapes, but found ${l.shape} on the left and ${r.shape} on the right" } - val indexes = l.indices - val lArray = l.buffer.array - val rArray = r.buffer.array - return DoubleBufferND(indexes, DoubleBuffer(indexes.linearSize) { block(lArray[it], rArray[it]) }) - } - - @OptIn(PerformancePitfall::class) - override fun StructureND.map(transform: DoubleField.(Double) -> Double): BufferND = - mapInline(toBufferND()) { DoubleField.transform(it) } - - - @OptIn(PerformancePitfall::class) - override fun zip( - left: StructureND, - right: StructureND, - transform: DoubleField.(Double, Double) -> Double, - ): BufferND = zipInline(left.toBufferND(), right.toBufferND()) { l, r -> DoubleField.transform(l, r) } - - override fun structureND(shape: Shape, initializer: DoubleField.(IntArray) -> Double): DoubleBufferND { - val indexer = indexerBuilder(shape) - return DoubleBufferND( - indexer, - DoubleBuffer(indexer.linearSize) { offset -> - elementAlgebra.initializer(indexer.index(offset)) - } - ) - } - - override fun add(left: StructureND, right: StructureND): DoubleBufferND = - zipInline(left.toBufferND(), right.toBufferND()) { l, r -> l + r } - - override fun multiply(left: StructureND, right: StructureND): DoubleBufferND = - zipInline(left.toBufferND(), right.toBufferND()) { l, r -> l * r } - - override fun negate(arg:StructureND): DoubleBufferND = mapInline(arg.toBufferND()) { -it } - - override fun StructureND.div(arg: StructureND): DoubleBufferND = - zipInline(toBufferND(), arg.toBufferND()) { l, r -> l / r } - - override fun divide(left: StructureND, right: StructureND): DoubleBufferND = - zipInline(left.toBufferND(), right.toBufferND()) { l: Double, r: Double -> l / r } - - override fun StructureND.div(arg: Double): DoubleBufferND = - mapInline(toBufferND()) { it / arg } - - override fun Double.div(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { this / it } - - override fun StructureND.plus(arg: StructureND): DoubleBufferND = - zipInline(toBufferND(), arg.toBufferND()) { l: Double, r: Double -> l + r } - - override fun StructureND.minus(arg: StructureND): DoubleBufferND = - zipInline(toBufferND(), arg.toBufferND()) { l: Double, r: Double -> l - r } - - override fun StructureND.times(arg: StructureND): DoubleBufferND = - zipInline(toBufferND(), arg.toBufferND()) { l: Double, r: Double -> l * r } - - override fun StructureND.times(k: Number): DoubleBufferND = - mapInline(toBufferND()) { it * k.toDouble() } - - override fun StructureND.div(k: Number): DoubleBufferND = - mapInline(toBufferND()) { it / k.toDouble() } - - override fun Number.times(arg: StructureND): DoubleBufferND = arg * this - - override fun StructureND.plus(arg: Double): DoubleBufferND = mapInline(toBufferND()) { it + arg } - - override fun StructureND.minus(arg: Double): StructureND = mapInline(toBufferND()) { it - arg } - - override fun Double.plus(arg: StructureND): StructureND = arg + this - - override fun Double.minus(arg: StructureND): StructureND = mapInline(arg.toBufferND()) { this - it } - - override fun scale(a: StructureND, value: Double): DoubleBufferND = - mapInline(a.toBufferND()) { it * value } - - override fun exp(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.exp(it) } - - override fun ln(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.ln(it) } - - override fun sin(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.sin(it) } - - override fun cos(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.cos(it) } - - override fun tan(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.tan(it) } - - override fun asin(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.asin(it) } - - override fun acos(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.acos(it) } - - override fun atan(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.atan(it) } - - override fun sinh(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.sinh(it) } - - override fun cosh(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.cosh(it) } - - override fun tanh(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.tanh(it) } - - override fun asinh(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.asinh(it) } - - override fun acosh(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.acosh(it) } - - override fun atanh(arg: StructureND): DoubleBufferND = - mapInline(arg.toBufferND()) { kotlin.math.atanh(it) } - - public companion object : DoubleFieldOpsND() -} - -@OptIn(UnstableKMathAPI::class) -public class DoubleFieldND(override val shape: Shape) : - DoubleFieldOpsND(), FieldND, NumbersAddOps>, - ExtendedField> { - - override fun power(arg: StructureND, pow: UInt): DoubleBufferND = mapInline(arg.toBufferND()) { - it.kpow(pow.toInt()) - } - - override fun power(arg: StructureND, pow: Int): DoubleBufferND = mapInline(arg.toBufferND()) { - it.kpow(pow) - } - - override fun power(arg: StructureND, pow: Number): DoubleBufferND = if(pow.isInteger()){ - power(arg, pow.toInt()) - } else { - val dpow = pow.toDouble() - mapInline(arg.toBufferND()) { - if (it < 0) throw IllegalArgumentException("Can't raise negative $it to a fractional power") - else it.kpow(dpow) - } - } - - override fun sinh(arg: StructureND): DoubleBufferND = super.sinh(arg) - - override fun cosh(arg: StructureND): DoubleBufferND = super.cosh(arg) - - override fun tanh(arg: StructureND): DoubleBufferND = super.tan(arg) - - override fun asinh(arg: StructureND): DoubleBufferND = super.asinh(arg) - - override fun acosh(arg: StructureND): DoubleBufferND = super.acosh(arg) - - override fun atanh(arg: StructureND): DoubleBufferND = super.atanh(arg) - - override fun number(value: Number): DoubleBufferND { - val d = value.toDouble() // minimize conversions - return structureND(shape) { d } - } -} - -public val DoubleField.ndAlgebra: DoubleFieldOpsND get() = DoubleFieldOpsND - -public fun DoubleField.ndAlgebra(vararg shape: Int): DoubleFieldND = DoubleFieldND(shape) - -/** - * Produce a context for n-dimensional operations inside this real field - */ -@UnstableKMathAPI -public inline fun DoubleField.withNdAlgebra(vararg shape: Int, action: DoubleFieldND.() -> R): R { - contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) } - return DoubleFieldND(shape).run(action) -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShapeIndexer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShapeIndexer.kt deleted file mode 100644 index c6ff79587..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShapeIndexer.kt +++ /dev/null @@ -1,121 +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. - */ - -package space.kscience.kmath.nd - -import kotlin.native.concurrent.ThreadLocal - -/** - * A converter from linear index to multivariate index - */ -public interface ShapeIndexer: Iterable{ - public val shape: Shape - - /** - * Get linear index from multidimensional index - */ - public fun offset(index: IntArray): Int - - /** - * Get multidimensional from linear - */ - public fun index(offset: Int): IntArray - - /** - * The size of linear buffer to accommodate all elements of ND-structure corresponding to strides - */ - public val linearSize: Int - - // TODO introduce a fast way to calculate index of the next element? - - /** - * Iterate over ND indices in a natural order - */ - public fun asSequence(): Sequence - - override fun iterator(): Iterator = asSequence().iterator() - - override fun equals(other: Any?): Boolean - override fun hashCode(): Int -} - -/** - * Linear transformation of indexes - */ -public abstract class Strides: ShapeIndexer { - /** - * Array strides - */ - public abstract val strides: IntArray - - public override fun offset(index: IntArray): Int = index.mapIndexed { i, value -> - if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${this.shape[i]})") - value * strides[i] - }.sum() - - // TODO introduce a fast way to calculate index of the next element? - - /** - * Iterate over ND indices in a natural order - */ - public override fun asSequence(): Sequence = (0 until linearSize).asSequence().map(::index) -} - -/** - * Simple implementation of [Strides]. - */ -public class DefaultStrides private constructor(override val shape: IntArray) : Strides() { - override val linearSize: Int get() = strides[shape.size] - - /** - * Strides for memory access - */ - override val strides: IntArray by lazy { - sequence { - var current = 1 - yield(1) - - shape.forEach { - current *= it - yield(current) - } - }.toList().toIntArray() - } - - override fun index(offset: Int): IntArray { - val res = IntArray(shape.size) - var current = offset - var strideIndex = strides.size - 2 - - while (strideIndex >= 0) { - res[strideIndex] = (current / strides[strideIndex]) - current %= strides[strideIndex] - strideIndex-- - } - - return res - } - - override fun equals(other: Any?): Boolean { - if (this === other) return true - if (other !is DefaultStrides) return false - if (!shape.contentEquals(other.shape)) return false - return true - } - - override fun hashCode(): Int = shape.contentHashCode() - - - public companion object { - /** - * Cached builder for default strides - */ - public operator fun invoke(shape: IntArray): Strides = - defaultStridesCache.getOrPut(shape) { DefaultStrides(shape) } - } -} - -@ThreadLocal -private val defaultStridesCache = HashMap() \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShortRingND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShortRingND.kt deleted file mode 100644 index 8152adaa5..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShortRingND.kt +++ /dev/null @@ -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. - */ - -package space.kscience.kmath.nd - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.NumbersAddOps -import space.kscience.kmath.operations.ShortRing -import space.kscience.kmath.operations.bufferAlgebra -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract - -public sealed class ShortRingOpsND : BufferedRingOpsND(ShortRing.bufferAlgebra) { - public companion object : ShortRingOpsND() -} - -@OptIn(UnstableKMathAPI::class) -public class ShortRingND( - override val shape: Shape -) : ShortRingOpsND(), RingND, NumbersAddOps> { - - override fun number(value: Number): BufferND { - val d = value.toShort() // minimize conversions - return structureND(shape) { d } - } -} - -public inline fun ShortRing.withNdAlgebra(vararg shape: Int, action: ShortRingND.() -> R): R { - contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) } - return ShortRingND(shape).run(action) -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt deleted file mode 100644 index 4ccb15eef..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt +++ /dev/null @@ -1,144 +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. - */ - -package space.kscience.kmath.nd - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.operations.asSequence -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.MutableBuffer -import space.kscience.kmath.structures.asMutableBuffer -import kotlin.jvm.JvmInline - -/** - * A structure that is guaranteed to be one-dimensional - */ -public interface Structure1D : StructureND, Buffer { - override val dimension: Int get() = 1 - - override operator fun get(index: IntArray): T { - require(index.size == 1) { "Index dimension mismatch. Expected 1 but found ${index.size}" } - return get(index[0]) - } - - override operator fun iterator(): Iterator = (0 until size).asSequence().map(::get).iterator() - - public companion object -} - -/** - * A mutable structure that is guaranteed to be one-dimensional - */ -public interface MutableStructure1D : Structure1D, MutableStructureND, MutableBuffer { - override operator fun set(index: IntArray, value: T) { - require(index.size == 1) { "Index dimension mismatch. Expected 1 but found ${index.size}" } - set(index[0], value) - } -} - -/** - * A 1D wrapper for nd-structure - */ -@JvmInline -private value class Structure1DWrapper(val structure: StructureND) : Structure1D { - override val shape: IntArray get() = structure.shape - override val size: Int get() = structure.shape[0] - - override operator fun get(index: Int): T = structure[index] - - @PerformancePitfall - override fun elements(): Sequence> = structure.elements() -} - -/** - * A 1D wrapper for a mutable nd-structure - */ -private class MutableStructure1DWrapper(val structure: MutableStructureND) : MutableStructure1D { - override val shape: IntArray get() = structure.shape - override val size: Int get() = structure.shape[0] - - @PerformancePitfall - override fun elements(): Sequence> = structure.elements() - - override fun get(index: Int): T = structure[index] - override fun set(index: Int, value: T) { - structure[intArrayOf(index)] = value - } - - @OptIn(PerformancePitfall::class) - override fun copy(): MutableBuffer = structure - .elements() - .map(Pair::second) - .toMutableList() - .asMutableBuffer() - - override fun toString(): String = Buffer.toString(this) -} - - -/** - * A structure wrapper for buffer - */ -@JvmInline -private value class Buffer1DWrapper(val buffer: Buffer) : Structure1D { - override val shape: IntArray get() = intArrayOf(buffer.size) - override val size: Int get() = buffer.size - - @PerformancePitfall - override fun elements(): Sequence> = buffer.asSequence().mapIndexed { index, value -> - intArrayOf(index) to value - } - - override operator fun get(index: Int): T = buffer[index] -} - -internal class MutableBuffer1DWrapper(val buffer: MutableBuffer) : MutableStructure1D { - override val shape: IntArray get() = intArrayOf(buffer.size) - override val size: Int get() = buffer.size - - @PerformancePitfall - override fun elements(): Sequence> = buffer.asSequence().mapIndexed { index, value -> - intArrayOf(index) to value - } - - override operator fun get(index: Int): T = buffer[index] - override fun set(index: Int, value: T) { - buffer[index] = value - } - - override fun copy(): MutableBuffer = buffer.copy() - - override fun toString(): String = Buffer.toString(this) -} - -/** - * Represent a [StructureND] as [Structure1D]. Throw error in case of dimension mismatch. - */ -public fun StructureND.as1D(): Structure1D = this as? Structure1D ?: if (shape.size == 1) { - when (this) { - is BufferND -> Buffer1DWrapper(this.buffer) - else -> Structure1DWrapper(this) - } -} else error("Can't create 1d-structure from ${shape.size}d-structure") - -public fun MutableStructureND.as1D(): MutableStructure1D = - this as? MutableStructure1D ?: if (shape.size == 1) { - MutableStructure1DWrapper(this) - } else error("Can't create 1d-structure from ${shape.size}d-structure") - -/** - * Represent this buffer as 1D structure - */ -public fun Buffer.asND(): Structure1D = Buffer1DWrapper(this) - -/** - * Expose inner buffer of this [Structure1D] if possible - */ -internal fun Structure1D.asND(): Buffer = when { - this is Buffer1DWrapper -> buffer - this is Structure1DWrapper && structure is BufferND -> structure.buffer - else -> this -} - diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt deleted file mode 100644 index cf8559869..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt +++ /dev/null @@ -1,169 +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. - */ - -package space.kscience.kmath.nd - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.MutableListBuffer -import space.kscience.kmath.structures.VirtualBuffer -import kotlin.jvm.JvmInline -import kotlin.reflect.KClass - -/** - * A structure that is guaranteed to be two-dimensional. - * - * @param T the type of items. - */ -public interface Structure2D : StructureND { - /** - * The number of rows in this structure. - */ - public val rowNum: Int - - /** - * The number of columns in this structure. - */ - public val colNum: Int - - override val shape: IntArray get() = intArrayOf(rowNum, colNum) - - /** - * The buffer of rows of this structure. It gets elements from the structure dynamically. - */ - @PerformancePitfall - public val rows: List> - get() = List(rowNum) { i -> VirtualBuffer(colNum) { j -> get(i, j) } } - - /** - * The buffer of columns of this structure. It gets elements from the structure dynamically. - */ - @PerformancePitfall - public val columns: List> - get() = List(colNum) { j -> VirtualBuffer(rowNum) { i -> get(i, j) } } - - /** - * Retrieves an element from the structure by two indices. - * - * @param i the first index. - * @param j the second index. - * @return an element. - */ - public operator fun get(i: Int, j: Int): T - - override operator fun get(index: IntArray): T { - require(index.size == 2) { "Index dimension mismatch. Expected 2 but found ${index.size}" } - return get(index[0], index[1]) - } - - @PerformancePitfall - override fun elements(): Sequence> = sequence { - for (i in 0 until rowNum) - for (j in 0 until colNum) yield(intArrayOf(i, j) to get(i, j)) - } - - public companion object -} - -/** - * Represents mutable [Structure2D]. - */ -public interface MutableStructure2D : Structure2D, MutableStructureND { - /** - * Inserts an item at the specified indices. - * - * @param i the first index. - * @param j the second index. - * @param value the value. - */ - public operator fun set(i: Int, j: Int, value: T) - - /** - * The buffer of rows of this structure. It gets elements from the structure dynamically. - */ - @PerformancePitfall - override val rows: List> - get() = List(rowNum) { i -> MutableBuffer1DWrapper(MutableListBuffer(colNum) { j -> get(i, j) }) } - - /** - * The buffer of columns of this structure. It gets elements from the structure dynamically. - */ - @PerformancePitfall - override val columns: List> - get() = List(colNum) { j -> MutableBuffer1DWrapper(MutableListBuffer(rowNum) { i -> get(i, j) }) } -} - -/** - * A 2D wrapper for nd-structure - */ -@JvmInline -private value class Structure2DWrapper(val structure: StructureND) : Structure2D { - override val shape: Shape get() = structure.shape - - override val rowNum: Int get() = shape[0] - override val colNum: Int get() = shape[1] - - override operator fun get(i: Int, j: Int): T = structure[i, j] - - override fun getFeature(type: KClass): F? = structure.getFeature(type) - - @PerformancePitfall - override fun elements(): Sequence> = structure.elements() -} - -/** - * A 2D wrapper for a mutable nd-structure - */ -private class MutableStructure2DWrapper(val structure: MutableStructureND) : MutableStructure2D { - override val shape: Shape get() = structure.shape - - override val rowNum: Int get() = shape[0] - override val colNum: Int get() = shape[1] - - override operator fun get(i: Int, j: Int): T = structure[i, j] - - override fun set(index: IntArray, value: T) { - structure[index] = value - } - - override operator fun set(i: Int, j: Int, value: T) { - structure[intArrayOf(i, j)] = value - } - - @PerformancePitfall - override fun elements(): Sequence> = structure.elements() - - override fun equals(other: Any?): Boolean = false - - override fun hashCode(): Int = 0 -} - -/** - * Represents a [StructureND] as [Structure2D]. Throws runtime error in case of dimension mismatch. - */ -public fun StructureND.as2D(): Structure2D = this as? Structure2D ?: when (shape.size) { - 2 -> Structure2DWrapper(this) - else -> error("Can't create 2d-structure from ${shape.size}d-structure") -} - -/** - * Represents a [StructureND] as [Structure2D]. Throws runtime error in case of dimension mismatch. - */ -public fun MutableStructureND.as2D(): MutableStructure2D = - this as? MutableStructure2D ?: when (shape.size) { - 2 -> MutableStructure2DWrapper(this) - else -> error("Can't create 2d-structure from ${shape.size}d-structure") - } - -/** - * Expose inner [StructureND] if possible - */ -internal fun Structure2D.asND(): StructureND = - if (this is Structure2DWrapper) structure - else this - -internal fun MutableStructure2D.asND(): MutableStructureND = - if (this is MutableStructure2DWrapper) structure else this - diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt deleted file mode 100644 index d948cf36f..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt +++ /dev/null @@ -1,242 +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. - */ - -package space.kscience.kmath.nd - -import space.kscience.kmath.linear.LinearSpace -import space.kscience.kmath.misc.Feature -import space.kscience.kmath.misc.Featured -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.operations.Ring -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.BufferFactory -import kotlin.jvm.JvmName -import kotlin.math.abs -import kotlin.reflect.KClass - -public interface StructureFeature : Feature - -/** - * Represents n-dimensional structure i.e., multidimensional container of items of the same type and size. The number - * of dimensions and items in an array is defined by its shape, which is a sequence of non-negative integers that - * specify the sizes of each dimension. - * - * StructureND is in general identity-free. [StructureND.contentEquals] should be used in tests to compare contents. - * - * @param T the type of items. - */ -public interface StructureND : Featured, WithShape { - /** - * The shape of structure i.e., non-empty sequence of non-negative integers that specify sizes of dimensions of - * this structure. - */ - override val shape: Shape - - /** - * The count of dimensions in this structure. It should be equal to size of [shape]. - */ - public val dimension: Int get() = shape.size - - /** - * Returns the value at the specified indices. - * - * @param index the indices. - * @return the value. - */ - public operator fun get(index: IntArray): T - - /** - * Returns the sequence of all the elements associated by their indices. - * - * @return the lazy sequence of pairs of indices to values. - */ - @PerformancePitfall - public fun elements(): Sequence> = indices.asSequence().map { it to get(it) } - - /** - * Feature is some additional structure information that allows to access it special properties or hints. - * If the feature is not present, `null` is returned. - */ - override fun getFeature(type: KClass): F? = null - - public companion object { - /** - * Indicates whether some [StructureND] is equal to another one. - */ - @PerformancePitfall - public fun contentEquals(st1: StructureND, st2: StructureND): Boolean { - if (st1 === st2) return true - - // fast comparison of buffers if possible - if (st1 is BufferND && st2 is BufferND && st1.indices == st2.indices) - return Buffer.contentEquals(st1.buffer, st2.buffer) - - //element by element comparison if it could not be avoided - return st1.elements().all { (index, value) -> value == st2[index] } - } - - @PerformancePitfall - public fun contentEquals( - st1: StructureND, - st2: StructureND, - tolerance: Double = 1e-11 - ): Boolean { - if (st1 === st2) return true - - // fast comparison of buffers if possible - if (st1 is BufferND && st2 is BufferND && st1.indices == st2.indices) - return Buffer.contentEquals(st1.buffer, st2.buffer) - - //element by element comparison if it could not be avoided - return st1.elements().all { (index, value) -> abs(value - st2[index]) < tolerance } - } - - /** - * Debug output to string - */ - public fun toString(structure: StructureND<*>): String { - val bufferRepr: String = when (structure.shape.size) { - 1 -> (0 until structure.shape[0]).map { structure[it] } - .joinToString(prefix = "[", postfix = "]", separator = ", ") - 2 -> (0 until structure.shape[0]).joinToString(prefix = "[", postfix = "]", separator = ", ") { i -> - (0 until structure.shape[1]).joinToString(prefix = "[", postfix = "]", separator = ", ") { j -> - structure[i, j].toString() - } - } - else -> "..." - } - val className = structure::class.simpleName ?: "StructureND" - - return "$className(shape=${structure.shape.contentToString()}, buffer=$bufferRepr)" - } - - /** - * Creates a NDStructure with explicit buffer factory. - * - * Strides should be reused if possible. - */ - public fun buffered( - strides: Strides, - bufferFactory: BufferFactory = Buffer.Companion::boxing, - initializer: (IntArray) -> T, - ): BufferND = BufferND(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) }) - - /** - * Inline create NDStructure with non-boxing buffer implementation if it is possible - */ - public inline fun auto( - strides: Strides, - crossinline initializer: (IntArray) -> T, - ): BufferND = BufferND(strides, Buffer.auto(strides.linearSize) { i -> initializer(strides.index(i)) }) - - public inline fun auto( - type: KClass, - strides: Strides, - crossinline initializer: (IntArray) -> T, - ): BufferND = BufferND(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) }) - - public fun buffered( - shape: IntArray, - bufferFactory: BufferFactory = Buffer.Companion::boxing, - initializer: (IntArray) -> T, - ): BufferND = buffered(DefaultStrides(shape), bufferFactory, initializer) - - public inline fun auto( - shape: IntArray, - crossinline initializer: (IntArray) -> T, - ): BufferND = auto(DefaultStrides(shape), initializer) - - @JvmName("autoVarArg") - public inline fun auto( - vararg shape: Int, - crossinline initializer: (IntArray) -> T, - ): BufferND = - auto(DefaultStrides(shape), initializer) - - public inline fun auto( - type: KClass, - vararg shape: Int, - crossinline initializer: (IntArray) -> T, - ): BufferND = auto(type, DefaultStrides(shape), initializer) - } -} - -/** - * Indicates whether some [StructureND] is equal to another one. - */ -@PerformancePitfall -public fun > AlgebraND>.contentEquals( - st1: StructureND, - st2: StructureND, -): Boolean = StructureND.contentEquals(st1, st2) - -/** - * Indicates whether some [StructureND] is equal to another one. - */ -@PerformancePitfall -public fun > LinearSpace>.contentEquals( - st1: StructureND, - st2: StructureND, -): Boolean = StructureND.contentEquals(st1, st2) - -/** - * Indicates whether some [StructureND] is equal to another one with [absoluteTolerance]. - */ -@PerformancePitfall -public fun > GroupOpsND>.contentEquals( - st1: StructureND, - st2: StructureND, - absoluteTolerance: T, -): Boolean = st1.elements().all { (index, value) -> elementAlgebra { (value - st2[index]) } < absoluteTolerance } - -/** - * Indicates whether some [StructureND] is equal to another one with [absoluteTolerance]. - */ -@PerformancePitfall -public fun > LinearSpace>.contentEquals( - st1: StructureND, - st2: StructureND, - absoluteTolerance: T, -): Boolean = st1.elements().all { (index, value) -> elementAlgebra { (value - st2[index]) } < absoluteTolerance } - -/** - * Returns the value at the specified indices. - * - * @param index the indices. - * @return the value. - */ -public operator fun StructureND.get(vararg index: Int): T = get(index) - -//@UnstableKMathAPI -//public inline fun StructureND<*>.getFeature(): T? = getFeature(T::class) - -/** - * Represents mutable [StructureND]. - */ -public interface MutableStructureND : StructureND { - /** - * Inserts an item at the specified indices. - * - * @param index the indices. - * @param value the value. - */ - public operator fun set(index: IntArray, value: T) -} - -/** - * Transform a structure element-by element in place. - */ -@OptIn(PerformancePitfall::class) -public inline fun MutableStructureND.mapInPlace(action: (index: IntArray, t: T) -> T): Unit = - elements().forEach { (index, oldValue) -> this[index] = action(index, oldValue) } - -public inline fun StructureND.zip( - struct: StructureND, - crossinline block: (T, T) -> T, -): StructureND { - require(shape.contentEquals(struct.shape)) { "Shape mismatch in structure combination" } - return StructureND.auto(shape) { block(this[it], struct[it]) } -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/algebraNDExtentions.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/algebraNDExtentions.kt deleted file mode 100644 index 53f946fbd..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/algebraNDExtentions.kt +++ /dev/null @@ -1,34 +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. - */ - -package space.kscience.kmath.nd - -import space.kscience.kmath.operations.Algebra -import space.kscience.kmath.operations.Group -import space.kscience.kmath.operations.Ring -import kotlin.jvm.JvmName - - -public fun > AlgebraND.structureND( - shapeFirst: Int, - vararg shapeRest: Int, - initializer: A.(IntArray) -> T -): StructureND = structureND(Shape(shapeFirst, *shapeRest), initializer) - -public fun > AlgebraND.zero(shape: Shape): StructureND = structureND(shape) { zero } - -@JvmName("zeroVarArg") -public fun > AlgebraND.zero( - shapeFirst: Int, - vararg shapeRest: Int, -): StructureND = structureND(shapeFirst, *shapeRest) { zero } - -public fun > AlgebraND.one(shape: Shape): StructureND = structureND(shape) { one } - -@JvmName("oneVarArg") -public fun > AlgebraND.one( - shapeFirst: Int, - vararg shapeRest: Int, -): StructureND = structureND(shapeFirst, *shapeRest) { one } \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt index b66f67228..b52d7a41b 100644 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt +++ b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt @@ -5,365 +5,9 @@ package space.kscience.kmath.operations -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Ring.Companion.optimizedPower - -/** - * Represents an algebraic structure. - * - * @param T the type of element of this structure. - */ -public interface Algebra { - /** - * Wraps a raw string to [T] object. This method is designed for three purposes: - * - * 1. Mathematical constants (`e`, `pi`). - * 1. Variables for expression-like contexts (`a`, `b`, `c`…). - * 1. Literals (`{1, 2}`, (`(3; 4)`)). - * - * If algebra can't parse the string, then this method must throw [kotlin.IllegalStateException]. - * - * @param value the raw string. - * @return an object or `null` if symbol could not be bound to the context. - */ - public fun bindSymbolOrNull(value: String): T? = null - - /** - * The same as [bindSymbolOrNull] but throws an error if symbol could not be bound - */ - public fun bindSymbol(value: String): T = - bindSymbolOrNull(value) ?: error("Symbol '$value' is not supported in $this") - - /** - * Dynamically dispatches a unary operation with the certain name. - * - * Implementations must fulfil the following requirements: - * - * 1. If operation is not defined in the structure, then the function throws [kotlin.IllegalStateException]. - * 1. Equivalence to [unaryOperation]: for any `a` and `b`, `unaryOperationFunction(a)(b) == unaryOperation(a, b)`. - * - * @param operation the name of operation. - * @return an operation. - */ - public fun unaryOperationFunction(operation: String): (arg: T) -> T = - error("Unary operation $operation not defined in $this") - - /** - * Dynamically invokes a unary operation with the certain name. - * - * Implementations must fulfil the following requirements: - * - * 1. If operation is not defined in the structure, then the function throws [kotlin.IllegalStateException]. - * 1. Equivalence to [unaryOperationFunction]: i.e., for any `a` and `b`, - * `unaryOperationFunction(a)(b) == unaryOperation(a, b)`. - * - * @param operation the name of operation. - * @param arg the argument of operation. - * @return a result of operation. - */ - public fun unaryOperation(operation: String, arg: T): T = unaryOperationFunction(operation)(arg) - - /** - * Dynamically dispatches a binary operation with the certain name. - * - * Implementations must fulfil the following requirements: - * - * 1. If operation is not defined in the structure, then the function throws [kotlin.IllegalStateException]. - * 1. Equivalence to [binaryOperation]: for any `a`, `b`, and `c`, - * `binaryOperationFunction(a)(b, c) == binaryOperation(a, b, c)`. - * - * @param operation the name of operation. - * @return an operation. - */ - public fun binaryOperationFunction(operation: String): (left: T, right: T) -> T = - error("Binary operation '$operation' not defined in $this") - - /** - * Dynamically invokes a binary operation with the certain name. - * - * Implementations must fulfil the following requirements: - * - * 1. If operation is not defined in the structure, then the function throws [kotlin.IllegalStateException]. - * 1. Equivalence to [binaryOperationFunction]: for any `a`, `b`, and `c`, - * `binaryOperationFunction(a)(b, c) == binaryOperation(a, b, c)`. - * - * @param operation the name of operation. - * @param left the first argument of operation. - * @param right the second argument of operation. - * @return a result of operation. - */ - public fun binaryOperation(operation: String, left: T, right: T): T = - binaryOperationFunction(operation)(left, right) - - /** - * Export an algebra element, so it could be accessed even after algebra scope is closed. - * This method must be used on algebras where data is stored externally or any local algebra state is used. - * By default (if not overridden), exports the object itself. - */ - @UnstableKMathAPI - public fun export(arg: T): T = arg -} - -public fun Algebra.bindSymbolOrNull(symbol: Symbol): T? = bindSymbolOrNull(symbol.identity) - -public fun Algebra.bindSymbol(symbol: Symbol): T = bindSymbol(symbol.identity) - -/** - * Call a block with an [Algebra] as receiver. - */ -// TODO add contract when KT-32313 is fixed -public inline operator fun , R> A.invoke(block: A.() -> R): R = run(block) - -/** - * Represents group without neutral element (also known as inverse semigroup) i.e., algebraic structure with - * associative, binary operation [add]. - * - * @param T the type of element of this semispace. - */ -public interface GroupOps : Algebra { - /** - * Addition of two elements. - * - * @param left the augend. - * @param right the addend. - * @return the sum. - */ - public fun add(left: T, right: T): T - - /** - * The negation of this element. - * - * @param arg the element. - * @return the additive inverse of this value. - */ +public interface GroupOps { public fun negate(arg: T): T - - /** - * Addition of two elements. - * - * @receiver the augend. - * @param arg the addend. - * @return the sum. - */ - public operator fun T.plus(arg: T): T = add(this, arg) - - /** - * Subtraction of two elements. - * - * @receiver the minuend. - * @param arg the subtrahend. - * @return the difference. - */ - public operator fun T.minus(arg: T): T = add(this, -arg) - - override fun unaryOperationFunction(operation: String): (arg: T) -> T = when (operation) { - PLUS_OPERATION -> { arg -> +arg } - MINUS_OPERATION -> ::negate - else -> super.unaryOperationFunction(operation) - } - - override fun binaryOperationFunction(operation: String): (left: T, right: T) -> T = when (operation) { - PLUS_OPERATION -> ::add - MINUS_OPERATION -> { left, right -> left - right } - else -> super.binaryOperationFunction(operation) - } - - public companion object { - /** - * The identifier of addition and unary positive operator. - */ - public const val PLUS_OPERATION: String = "+" - - /** - * The identifier of subtraction and unary negative operator. - */ - public const val MINUS_OPERATION: String = "-" - } } -/** - * The negation of this element. - * - * @receiver the element. - * @return the additive inverse of this value. - */ context(GroupOps) public operator fun T.unaryMinus(): T = negate(this) - -/** - * Returns this value. - * - * @receiver this value. - * @return this value. - */ -context(GroupOps) -public operator fun T.unaryPlus(): T = this - -/** - * Represents group i.e., algebraic structure with associative, binary operation [add]. - * - * @param T the type of element of this semispace. - */ -public interface Group : GroupOps { - /** - * The neutral element of addition. - */ - public val zero: T -} - -/** - * Represents ring without multiplicative and additive identities i.e., algebraic structure with - * associative, binary, commutative operation [add] and associative, operation [multiply] distributive over [add]. - * - * @param T the type of element of this semiring. - */ -public interface RingOps : GroupOps { - /** - * Multiplies two elements. - * - * @param left the multiplier. - * @param right the multiplicand. - */ - public fun multiply(left: T, right: T): T - - /** - * Multiplies this element by scalar. - * - * @receiver the multiplier. - * @param arg the multiplicand. - */ - public operator fun T.times(arg: T): T = multiply(this, arg) - - override fun binaryOperationFunction(operation: String): (left: T, right: T) -> T = when (operation) { - TIMES_OPERATION -> ::multiply - else -> super.binaryOperationFunction(operation) - } - - public companion object { - /** - * The identifier of multiplication. - */ - public const val TIMES_OPERATION: String = "*" - } -} - -/** - * Represents ring i.e., algebraic structure with two associative binary operations called "addition" and - * "multiplication" and their neutral elements. - * - * @param T the type of element of this ring. - */ -public interface Ring : Group, RingOps { - /** - * The neutral element of multiplication - */ - public val one: T - - /** - * Raises [arg] to the integer power [pow]. - */ - public fun power(arg: T, pow: UInt): T = optimizedPower(arg, pow) - - public companion object { - /** - * Raises [arg] to the non-negative integer power [exponent]. - * - * Special case: 0 ^ 0 is 1. - * - * @receiver the algebra to provide multiplication. - * @param arg the base. - * @param exponent the exponent. - * @return the base raised to the power. - * @author Evgeniy Zhelenskiy - */ - internal fun Ring.optimizedPower(arg: T, exponent: UInt): T = when { - arg == zero && exponent > 0U -> zero - arg == one -> arg - arg == -one -> powWithoutOptimization(arg, exponent % 2U) - else -> powWithoutOptimization(arg, exponent) - } - - private fun Ring.powWithoutOptimization(base: T, exponent: UInt): T = when (exponent) { - 0U -> one - 1U -> base - else -> { - val pre = powWithoutOptimization(base, exponent shr 1).let { it * it } - if (exponent and 1U == 0U) pre else pre * base - } - } - } -} - -/** - * Represents field without multiplicative and additive identities i.e., algebraic structure with associative, binary, - * commutative operations [add] and [multiply]; binary operation [divide] as multiplication of left operand by - * reciprocal of right one. - * - * @param T the type of element of this semifield. - */ -public interface FieldOps : RingOps { - /** - * Division of two elements. - * - * @param left the dividend. - * @param right the divisor. - * @return the quotient. - */ - public fun divide(left: T, right: T): T - - /** - * Division of two elements. - * - * @receiver the dividend. - * @param arg the divisor. - * @return the quotient. - */ - public operator fun T.div(arg: T): T = divide(this, arg) - - override fun binaryOperationFunction(operation: String): (left: T, right: T) -> T = when (operation) { - DIV_OPERATION -> ::divide - else -> super.binaryOperationFunction(operation) - } - - public companion object { - /** - * The identifier of division. - */ - public const val DIV_OPERATION: String = "/" - } -} - -/** - * Represents field i.e., algebraic structure with three operations: associative, commutative addition and - * multiplication, and division. **This interface differs from the eponymous mathematical definition: fields in KMath - * also support associative multiplication by scalar.** - * - * @param T the type of element of this field. - */ -public interface Field : Ring, FieldOps, ScaleOperations, NumericAlgebra { - override fun number(value: Number): T = scale(one, value.toDouble()) - - public fun power(arg: T, pow: Int): T = optimizedPower(arg, pow) - - public companion object { - /** - * Raises [arg] to the integer power [exponent]. - * - * Special case: 0 ^ 0 is 1. - * - * @receiver the algebra to provide multiplication and division. - * @param arg the base. - * @param exponent the exponent. - * @return the base raised to the power. - * @author Iaroslav Postovalov, Evgeniy Zhelenskiy - */ - private fun Field.optimizedPower(arg: T, exponent: Int): T = when { - exponent < 0 -> one / (this as Ring).optimizedPower( - arg, - if (exponent == Int.MIN_VALUE) Int.MAX_VALUE.toUInt().inc() else (-exponent).toUInt() - ) - else -> (this as Ring).optimizedPower(arg, exponent.toUInt()) - } - } -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BigInt.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BigInt.kt deleted file mode 100644 index b4b39dfc0..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BigInt.kt +++ /dev/null @@ -1,546 +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. - */ - -package space.kscience.kmath.operations - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.BufferedRingOpsND -import space.kscience.kmath.operations.BigInt.Companion.BASE -import space.kscience.kmath.operations.BigInt.Companion.BASE_SIZE -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.MutableBuffer -import kotlin.math.log2 -import kotlin.math.max -import kotlin.math.min -import kotlin.math.sign - -private typealias Magnitude = UIntArray -private typealias TBase = ULong - -/** - * Kotlin Multiplatform implementation of Big Integer numbers (KBigInteger). - * - * @author Robert Drynkin - * @author Peter Klimai - */ -@OptIn(UnstableKMathAPI::class) -public object BigIntField : Field, NumbersAddOps, ScaleOperations { - override val zero: BigInt = BigInt.ZERO - override val one: BigInt = BigInt.ONE - - override fun number(value: Number): BigInt = value.toLong().toBigInt() - - @Suppress("EXTENSION_SHADOWED_BY_MEMBER") - override fun negate(arg: BigInt): BigInt = -arg - override fun add(left: BigInt, right: BigInt): BigInt = left.plus(right) - override fun scale(a: BigInt, value: Double): BigInt = a.times(number(value)) - override fun multiply(left: BigInt, right: BigInt): BigInt = left.times(right) - override fun divide(left: BigInt, right: BigInt): BigInt = left.div(right) - - public operator fun String.unaryPlus(): BigInt = this.parseBigInteger() ?: error("Can't parse $this as big integer") - public operator fun String.unaryMinus(): BigInt = - -(this.parseBigInteger() ?: error("Can't parse $this as big integer")) -} - -public class BigInt internal constructor( - private val sign: Byte, - private val magnitude: Magnitude, -) : Comparable { - override fun compareTo(other: BigInt): Int = when { - (sign == 0.toByte()) and (other.sign == 0.toByte()) -> 0 - sign < other.sign -> -1 - sign > other.sign -> 1 - else -> sign * compareMagnitudes(magnitude, other.magnitude) - } - - override fun equals(other: Any?): Boolean = other is BigInt && compareTo(other) == 0 - - override fun hashCode(): Int = magnitude.hashCode() + sign - - public fun abs(): BigInt = if (sign == 0.toByte()) this else BigInt(1, magnitude) - - public operator fun unaryMinus(): BigInt = - if (this.sign == 0.toByte()) this else BigInt((-this.sign).toByte(), this.magnitude) - - public operator fun plus(b: BigInt): BigInt = when { - b.sign == 0.toByte() -> this - sign == 0.toByte() -> b - this == -b -> ZERO - sign == b.sign -> BigInt(sign, addMagnitudes(magnitude, b.magnitude)) - - else -> { - val comp = compareMagnitudes(magnitude, b.magnitude) - - if (comp == 1) - BigInt(sign, subtractMagnitudes(magnitude, b.magnitude)) - else - BigInt((-sign).toByte(), subtractMagnitudes(b.magnitude, magnitude)) - } - } - - public operator fun minus(b: BigInt): BigInt = this + (-b) - - public operator fun times(b: BigInt): BigInt = when { - this.sign == 0.toByte() -> ZERO - b.sign == 0.toByte() -> ZERO - b.magnitude.size == 1 -> this * b.magnitude[0] * b.sign.toInt() - this.magnitude.size == 1 -> b * this.magnitude[0] * this.sign.toInt() - else -> BigInt((this.sign * b.sign).toByte(), multiplyMagnitudes(this.magnitude, b.magnitude)) - } - - public operator fun times(other: UInt): BigInt = when { - sign == 0.toByte() -> ZERO - other == 0U -> ZERO - other == 1U -> this - else -> BigInt(sign, multiplyMagnitudeByUInt(magnitude, other)) - } - - public fun pow(exponent: UInt): BigInt = BigIntField.power(this, exponent) - - public operator fun times(other: Int): BigInt = when { - other > 0 -> this * kotlin.math.abs(other).toUInt() - other != Int.MIN_VALUE -> -this * kotlin.math.abs(other).toUInt() - else -> times(other.toBigInt()) - } - - public operator fun div(other: UInt): BigInt = BigInt(this.sign, divideMagnitudeByUInt(this.magnitude, other)) - - public operator fun div(other: Int): BigInt = BigInt( - (this.sign * other.sign).toByte(), - divideMagnitudeByUInt(this.magnitude, kotlin.math.abs(other).toUInt()) - ) - - private fun division(other: BigInt): Pair { - // Long division algorithm: - // https://en.wikipedia.org/wiki/Division_algorithm#Integer_division_(unsigned)_with_remainder - // TODO: Implement more effective algorithm - var q = ZERO - var r = ZERO - - val bitSize = - (BASE_SIZE * (this.magnitude.size - 1) + log2(this.magnitude.lastOrNull()?.toFloat() ?: (0f + 1))).toInt() - - for (i in bitSize downTo 0) { - r = r shl 1 - r = r or ((abs(this) shr i) and ONE) - - if (r >= abs(other)) { - r -= abs(other) - q += (ONE shl i) - } - } - - return Pair(BigInt((this.sign * other.sign).toByte(), q.magnitude), r) - } - - public operator fun div(other: BigInt): BigInt = division(other).first - - public infix fun shl(i: Int): BigInt { - if (this == ZERO) return ZERO - if (i == 0) return this - val fullShifts = i / BASE_SIZE + 1 - val relShift = i % BASE_SIZE - val shiftLeft = { x: UInt -> if (relShift >= 32) 0U else x shl relShift } - val shiftRight = { x: UInt -> if (BASE_SIZE - relShift >= 32) 0U else x shr (BASE_SIZE - relShift) } - val newMagnitude = Magnitude(magnitude.size + fullShifts) - - for (j in magnitude.indices) { - newMagnitude[j + fullShifts - 1] = shiftLeft(this.magnitude[j]) - - if (j != 0) - newMagnitude[j + fullShifts - 1] = newMagnitude[j + fullShifts - 1] or shiftRight(this.magnitude[j - 1]) - } - - newMagnitude[magnitude.size + fullShifts - 1] = shiftRight(magnitude.last()) - return BigInt(this.sign, stripLeadingZeros(newMagnitude)) - } - - public infix fun shr(i: Int): BigInt { - if (this == ZERO) return ZERO - if (i == 0) return this - val fullShifts = i / BASE_SIZE - val relShift = i % BASE_SIZE - val shiftRight = { x: UInt -> if (relShift >= 32) 0U else x shr relShift } - val shiftLeft = { x: UInt -> if (BASE_SIZE - relShift >= 32) 0U else x shl (BASE_SIZE - relShift) } - if (this.magnitude.size - fullShifts <= 0) return ZERO - val newMagnitude: Magnitude = Magnitude(magnitude.size - fullShifts) - - for (j in fullShifts until magnitude.size) { - newMagnitude[j - fullShifts] = shiftRight(magnitude[j]) - - if (j != magnitude.size - 1) - newMagnitude[j - fullShifts] = newMagnitude[j - fullShifts] or shiftLeft(magnitude[j + 1]) - } - - return BigInt(this.sign, stripLeadingZeros(newMagnitude)) - } - - public infix fun or(other: BigInt): BigInt { - if (this == ZERO) return other - if (other == ZERO) return this - val resSize = max(magnitude.size, other.magnitude.size) - val newMagnitude: Magnitude = Magnitude(resSize) - - for (i in 0 until resSize) { - if (i < magnitude.size) newMagnitude[i] = newMagnitude[i] or magnitude[i] - if (i < other.magnitude.size) newMagnitude[i] = newMagnitude[i] or other.magnitude[i] - } - - return BigInt(1, stripLeadingZeros(newMagnitude)) - } - - public infix fun and(other: BigInt): BigInt { - if ((this == ZERO) or (other == ZERO)) return ZERO - val resSize = min(this.magnitude.size, other.magnitude.size) - val newMagnitude: Magnitude = Magnitude(resSize) - for (i in 0 until resSize) newMagnitude[i] = this.magnitude[i] and other.magnitude[i] - return BigInt(1, stripLeadingZeros(newMagnitude)) - } - - public operator fun rem(other: Int): Int { - val res = this - (this / other) * other - return if (res == ZERO) 0 else res.sign * res.magnitude[0].toInt() - } - - public operator fun rem(other: BigInt): BigInt = this - (this / other) * other - - public fun modPow(exponent: BigInt, m: BigInt): BigInt = when { - exponent == ZERO -> ONE - exponent % 2 == 1 -> (this * modPow(exponent - ONE, m)) % m - - else -> { - val sqRoot = modPow(exponent / 2, m) - (sqRoot * sqRoot) % m - } - } - - override fun toString(): String { - if (this.sign == 0.toByte()) { - return "0x0" - } - var res: String = if (this.sign == (-1).toByte()) "-0x" else "0x" - var numberStarted = false - - for (i in this.magnitude.size - 1 downTo 0) { - for (j in BASE_SIZE / 4 - 1 downTo 0) { - val curByte = (this.magnitude[i] shr 4 * j) and 0xfU - if (numberStarted or (curByte != 0U)) { - numberStarted = true - res += hexMapping[curByte] - } - } - } - - return res - } - - public companion object { - public const val BASE: ULong = 0xffffffffUL - public const val BASE_SIZE: Int = 32 - public val ZERO: BigInt = BigInt(0, uintArrayOf()) - public val ONE: BigInt = BigInt(1, uintArrayOf(1u)) - private const val KARATSUBA_THRESHOLD = 80 - - private val hexMapping: HashMap = hashMapOf( - 0U to "0", 1U to "1", 2U to "2", 3U to "3", - 4U to "4", 5U to "5", 6U to "6", 7U to "7", - 8U to "8", 9U to "9", 10U to "a", 11U to "b", - 12U to "c", 13U to "d", 14U to "e", 15U to "f" - ) - - private fun compareMagnitudes(mag1: Magnitude, mag2: Magnitude): Int { - return when { - mag1.size > mag2.size -> 1 - mag1.size < mag2.size -> -1 - - else -> { - for (i in mag1.size - 1 downTo 0) return when { - mag1[i] > mag2[i] -> 1 - mag1[i] < mag2[i] -> -1 - else -> continue - } - - 0 - } - } - } - - private fun addMagnitudes(mag1: Magnitude, mag2: Magnitude): Magnitude { - val resultLength = max(mag1.size, mag2.size) + 1 - val result = Magnitude(resultLength) - var carry = 0uL - - for (i in 0 until resultLength - 1) { - val res = when { - i >= mag1.size -> mag2[i].toULong() + carry - i >= mag2.size -> mag1[i].toULong() + carry - else -> mag1[i].toULong() + mag2[i].toULong() + carry - } - - result[i] = (res and BASE).toUInt() - carry = res shr BASE_SIZE - } - - result[resultLength - 1] = carry.toUInt() - return stripLeadingZeros(result) - } - - private fun subtractMagnitudes(mag1: Magnitude, mag2: Magnitude): Magnitude { - val resultLength = mag1.size - val result = Magnitude(resultLength) - var carry = 0L - - for (i in 0 until resultLength) { - var res = - if (i < mag2.size) mag1[i].toLong() - mag2[i].toLong() - carry - else mag1[i].toLong() - carry - - carry = if (res < 0) 1 else 0 - res += carry * (BASE + 1UL).toLong() - - result[i] = res.toUInt() - } - - return stripLeadingZeros(result) - } - - private fun multiplyMagnitudeByUInt(mag: Magnitude, x: UInt): Magnitude { - val resultLength = mag.size + 1 - val result = Magnitude(resultLength) - var carry = 0uL - - for (i in mag.indices) { - val cur = carry + mag[i].toULong() * x.toULong() - result[i] = (cur and BASE).toUInt() - carry = cur shr BASE_SIZE - } - - result[resultLength - 1] = (carry and BASE).toUInt() - - return stripLeadingZeros(result) - } - - internal fun multiplyMagnitudes(mag1: Magnitude, mag2: Magnitude): Magnitude = when { - mag1.size + mag2.size < KARATSUBA_THRESHOLD || mag1.isEmpty() || mag2.isEmpty() -> - naiveMultiplyMagnitudes(mag1, mag2) - // TODO implement Fourier - else -> karatsubaMultiplyMagnitudes(mag1, mag2) - } - - internal fun naiveMultiplyMagnitudes(mag1: Magnitude, mag2: Magnitude): Magnitude { - val resultLength = mag1.size + mag2.size - val result = Magnitude(resultLength) - - for (i in mag1.indices) { - var carry = 0uL - - for (j in mag2.indices) { - val cur: ULong = result[i + j].toULong() + mag1[i].toULong() * mag2[j].toULong() + carry - result[i + j] = (cur and BASE).toUInt() - carry = cur shr BASE_SIZE - } - - result[i + mag2.size] = (carry and BASE).toUInt() - } - - return stripLeadingZeros(result) - } - - internal fun karatsubaMultiplyMagnitudes(mag1: Magnitude, mag2: Magnitude): Magnitude { - //https://en.wikipedia.org/wiki/Karatsuba_algorithm - val halfSize = min(mag1.size, mag2.size) / 2 - val x0 = mag1.sliceArray(0 until halfSize).toBigInt(1) - val x1 = mag1.sliceArray(halfSize until mag1.size).toBigInt(1) - val y0 = mag2.sliceArray(0 until halfSize).toBigInt(1) - val y1 = mag2.sliceArray(halfSize until mag2.size).toBigInt(1) - - val z0 = x0 * y0 - val z2 = x1 * y1 - val z1 = (x0 - x1) * (y1 - y0) + z0 + z2 - - return (z2.shl(2 * halfSize * BASE_SIZE) + z1.shl(halfSize * BASE_SIZE) + z0).magnitude - } - - private fun divideMagnitudeByUInt(mag: Magnitude, x: UInt): Magnitude { - val resultLength = mag.size - val result = Magnitude(resultLength) - var carry = 0uL - - for (i in mag.size - 1 downTo 0) { - val cur: ULong = mag[i].toULong() + (carry shl BASE_SIZE) - result[i] = (cur / x).toUInt() - carry = cur % x - } - - return stripLeadingZeros(result) - } - } -} - -private fun stripLeadingZeros(mag: Magnitude): Magnitude { - if (mag.isEmpty() || mag.last() != 0U) return mag - var resSize = mag.size - 1 - - while (mag[resSize] == 0U) { - if (resSize == 0) break - resSize -= 1 - } - - return mag.sliceArray(IntRange(0, resSize)) -} - -/** - * Returns the absolute value of the given value [x]. - */ -public fun abs(x: BigInt): BigInt = x.abs() - -/** - * Convert this [Int] to [BigInt] - */ -public fun Int.toBigInt(): BigInt = BigInt(sign.toByte(), uintArrayOf(kotlin.math.abs(this).toUInt())) - -/** - * Convert this [Long] to [BigInt] - */ -public fun Long.toBigInt(): BigInt = BigInt( - sign.toByte(), - stripLeadingZeros( - uintArrayOf( - (kotlin.math.abs(this).toULong() and BASE).toUInt(), - ((kotlin.math.abs(this).toULong() shr BASE_SIZE) and BASE).toUInt() - ) - ) -) - -/** - * Convert UInt to [BigInt] - */ -public fun UInt.toBigInt(): BigInt = BigInt(1, uintArrayOf(this)) - -/** - * Convert ULong to [BigInt] - */ -public fun ULong.toBigInt(): BigInt = BigInt( - 1, - stripLeadingZeros( - uintArrayOf( - (this and BASE).toUInt(), - ((this shr BASE_SIZE) and BASE).toUInt() - ) - ) -) - -/** - * Create a [BigInt] with this array of magnitudes with protective copy - */ -public fun UIntArray.toBigInt(sign: Byte): BigInt { - require(sign != 0.toByte() || !isNotEmpty()) - return BigInt(sign, copyOf()) -} - -/** - * Returns `null` if a valid number cannot be read from a string - */ -public fun String.parseBigInteger(): BigInt? { - if (isEmpty()) return null - val sign: Int - - val positivePartIndex = when (this[0]) { - '+' -> { - sign = +1 - 1 - } - '-' -> { - sign = -1 - 1 - } - else -> { - sign = +1 - 0 - } - } - - var isEmpty = true - - return if (this.startsWith("0X", startIndex = positivePartIndex, ignoreCase = true)) { - // hex representation - - val uInts = ArrayList(length).apply { add(0U) } - var offset = 0 - fun addDigit(value: UInt) { - uInts[uInts.lastIndex] += value shl offset - offset += 4 - if (offset == 32) { - uInts.add(0U) - offset = 0 - } - } - - for (index in lastIndex downTo positivePartIndex + 2) { - when (val ch = this[index]) { - '_' -> continue - in '0'..'9' -> addDigit((ch - '0').toUInt()) - in 'A'..'F' -> addDigit((ch - 'A').toUInt() + 10U) - in 'a'..'f' -> addDigit((ch - 'a').toUInt() + 10U) - else -> return null - } - isEmpty = false - } - - while (uInts.isNotEmpty() && uInts.last() == 0U) - uInts.removeLast() - - if (isEmpty) null else BigInt(sign.toByte(), uInts.toUIntArray()) - } else { - // decimal representation - - val positivePart = buildList(length) { - for (index in positivePartIndex until length) - when (val a = this@parseBigInteger[index]) { - '_' -> continue - in '0'..'9' -> add(a) - else -> return null - } - } - - val offset = positivePart.size % 9 - isEmpty = offset == 0 - - fun parseUInt(fromIndex: Int, toIndex: Int): UInt? { - var res = 0U - for (i in fromIndex until toIndex) { - res = res * 10U + (positivePart[i].digitToIntOrNull()?.toUInt() ?: return null) - } - return res - } - - var res = parseUInt(0, offset)?.toBigInt() ?: return null - - for (index in offset..positivePart.lastIndex step 9) { - isEmpty = false - res = res * 1_000_000_000U + (parseUInt(index, index + 9) ?: return null).toBigInt() - } - if (isEmpty) null else res * sign - } -} - -public val BigInt.algebra: BigIntField get() = BigIntField - -@Deprecated("Use BigInt::buffer") -public inline fun Buffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): Buffer = - boxing(size, initializer) - -public inline fun BigInt.Companion.buffer(size: Int, initializer: (Int) -> BigInt): Buffer = - Buffer.boxing(size, initializer) - -@Deprecated("Use BigInt::mutableBuffer") -public inline fun MutableBuffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): MutableBuffer = - boxing(size, initializer) - -public inline fun BigInt.mutableBuffer(size: Int, initializer: (Int) -> BigInt): Buffer = - Buffer.boxing(size, initializer) - -public val BigIntField.nd: BufferedRingOpsND - get() = BufferedRingOpsND(BufferRingOps(BigIntField, BigInt::buffer)) diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BufferAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BufferAlgebra.kt deleted file mode 100644 index 3764484ef..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BufferAlgebra.kt +++ /dev/null @@ -1,196 +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. - */ - -package space.kscience.kmath.operations - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.BufferFactory -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.ShortBuffer - -public interface WithSize { - public val size: Int -} - -/** - * An algebra over [Buffer] - */ -public interface BufferAlgebra> : Algebra> { - public val elementAlgebra: A - public val bufferFactory: BufferFactory - - public fun buffer(size: Int, vararg elements: T): Buffer { - require(elements.size == size) { "Expected $size elements but found ${elements.size}" } - return bufferFactory(size) { elements[it] } - } - - //TODO move to multi-receiver inline extension - public fun Buffer.map(block: A.(T) -> T): Buffer = mapInline(this, block) - - public fun Buffer.mapIndexed(block: A.(index: Int, arg: T) -> T): Buffer = mapIndexedInline(this, block) - - public fun Buffer.zip(other: Buffer, block: A.(left: T, right: T) -> T): Buffer = - zipInline(this, other, block) - - override fun unaryOperationFunction(operation: String): (arg: Buffer) -> Buffer { - val operationFunction = elementAlgebra.unaryOperationFunction(operation) - return { arg -> bufferFactory(arg.size) { operationFunction(arg[it]) } } - } - - override fun binaryOperationFunction(operation: String): (left: Buffer, right: Buffer) -> Buffer { - val operationFunction = elementAlgebra.binaryOperationFunction(operation) - return { left, right -> - bufferFactory(left.size) { operationFunction(left[it], right[it]) } - } - } -} - -/** - * Inline map - */ -private inline fun > BufferAlgebra.mapInline( - buffer: Buffer, - crossinline block: A.(T) -> T -): Buffer = bufferFactory(buffer.size) { elementAlgebra.block(buffer[it]) } - -/** - * Inline map - */ -private inline fun > BufferAlgebra.mapIndexedInline( - buffer: Buffer, - crossinline block: A.(index: Int, arg: T) -> T -): Buffer = bufferFactory(buffer.size) { elementAlgebra.block(it, buffer[it]) } - -/** - * Inline zip - */ -private inline fun > BufferAlgebra.zipInline( - l: Buffer, - r: Buffer, - crossinline block: A.(l: T, r: T) -> T -): Buffer { - require(l.size == r.size) { "Incompatible buffer sizes. left: ${l.size}, right: ${r.size}" } - return bufferFactory(l.size) { elementAlgebra.block(l[it], r[it]) } -} - -public fun BufferAlgebra.buffer(size: Int, initializer: (Int) -> T): Buffer { - return bufferFactory(size, initializer) -} - -public fun A.buffer(initializer: (Int) -> T): Buffer where A : BufferAlgebra, A : WithSize { - return bufferFactory(size, initializer) -} - -public fun > BufferAlgebra.sin(arg: Buffer): Buffer = - mapInline(arg) { sin(it) } - -public fun > BufferAlgebra.cos(arg: Buffer): Buffer = - mapInline(arg) { cos(it) } - -public fun > BufferAlgebra.tan(arg: Buffer): Buffer = - mapInline(arg) { tan(it) } - -public fun > BufferAlgebra.asin(arg: Buffer): Buffer = - mapInline(arg) { asin(it) } - -public fun > BufferAlgebra.acos(arg: Buffer): Buffer = - mapInline(arg) { acos(it) } - -public fun > BufferAlgebra.atan(arg: Buffer): Buffer = - mapInline(arg) { atan(it) } - -public fun > BufferAlgebra.exp(arg: Buffer): Buffer = - mapInline(arg) { exp(it) } - -public fun > BufferAlgebra.ln(arg: Buffer): Buffer = - mapInline(arg) { ln(it) } - -public fun > BufferAlgebra.sinh(arg: Buffer): Buffer = - mapInline(arg) { sinh(it) } - -public fun > BufferAlgebra.cosh(arg: Buffer): Buffer = - mapInline(arg) { cosh(it) } - -public fun > BufferAlgebra.tanh(arg: Buffer): Buffer = - mapInline(arg) { tanh(it) } - -public fun > BufferAlgebra.asinh(arg: Buffer): Buffer = - mapInline(arg) { asinh(it) } - -public fun > BufferAlgebra.acosh(arg: Buffer): Buffer = - mapInline(arg) { acosh(it) } - -public fun > BufferAlgebra.atanh(arg: Buffer): Buffer = - mapInline(arg) { atanh(it) } - -public fun > BufferAlgebra.pow(arg: Buffer, pow: Number): Buffer = - mapInline(arg) {it.pow(pow) } - - -public open class BufferRingOps>( - override val elementAlgebra: A, - override val bufferFactory: BufferFactory, -) : BufferAlgebra, RingOps>{ - - override fun add(left: Buffer, right: Buffer): Buffer = zipInline(left, right) { l, r -> l + r } - override fun multiply(left: Buffer, right: Buffer): Buffer = zipInline(left, right) { l, r -> l * r } - override fun negate(arg: Buffer): Buffer = arg.map { negate(it) } - - override fun unaryOperationFunction(operation: String): (arg: Buffer) -> Buffer = - super.unaryOperationFunction(operation) - - override fun binaryOperationFunction(operation: String): (left: Buffer, right: Buffer) -> Buffer = - super.binaryOperationFunction(operation) -} - -public val ShortRing.bufferAlgebra: BufferRingOps - get() = BufferRingOps(ShortRing, ::ShortBuffer) - -public open class BufferFieldOps>( - elementAlgebra: A, - bufferFactory: BufferFactory, -) : BufferRingOps(elementAlgebra, bufferFactory), BufferAlgebra, FieldOps>, ScaleOperations> { - - override fun add(left: Buffer, right: Buffer): Buffer = zipInline(left, right) { l, r -> l + r } - override fun multiply(left: Buffer, right: Buffer): Buffer = zipInline(left, right) { l, r -> l * r } - override fun divide(left: Buffer, right: Buffer): Buffer = zipInline(left, right) { l, r -> l / r } - - override fun scale(a: Buffer, value: Double): Buffer = a.map { scale(it, value) } - override fun negate(arg: Buffer): Buffer = arg.map { -it } - - override fun binaryOperationFunction(operation: String): (left: Buffer, right: Buffer) -> Buffer = - super.binaryOperationFunction(operation) -} - -public class BufferField>( - elementAlgebra: A, - bufferFactory: BufferFactory, - override val size: Int -) : BufferFieldOps(elementAlgebra, bufferFactory), Field>, WithSize { - - override val zero: Buffer = bufferFactory(size) { elementAlgebra.zero } - override val one: Buffer = bufferFactory(size) { elementAlgebra.one } -} - -/** - * Generate full buffer field from given buffer operations - */ -public fun > BufferFieldOps.withSize(size: Int): BufferField = - BufferField(elementAlgebra, bufferFactory, size) - -//Double buffer specialization - -public fun BufferField.buffer(vararg elements: Number): Buffer { - require(elements.size == size) { "Expected $size elements but found ${elements.size}" } - return bufferFactory(size) { elements[it].toDouble() } -} - -public fun > A.bufferAlgebra(bufferFactory: BufferFactory): BufferFieldOps = - BufferFieldOps(this, bufferFactory) - -public val DoubleField.bufferAlgebra: BufferFieldOps - get() = BufferFieldOps(DoubleField, ::DoubleBuffer) - diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/DoubleBufferField.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/DoubleBufferField.kt deleted file mode 100644 index f2f7326aa..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/DoubleBufferField.kt +++ /dev/null @@ -1,144 +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. - */ - -package space.kscience.kmath.operations - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleField.pow -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.DoubleBuffer - -/** - * [ExtendedField] over [DoubleBuffer]. - * - * @property size the size of buffers to operate on. - */ -public class DoubleBufferField(public val size: Int) : ExtendedField>, DoubleBufferOps() { - override val zero: Buffer by lazy { DoubleBuffer(size) { 0.0 } } - override val one: Buffer by lazy { DoubleBuffer(size) { 1.0 } } - - override fun sinh(arg: Buffer): DoubleBuffer = super.sinh(arg) - - override fun cosh(arg: Buffer): DoubleBuffer = super.cosh(arg) - - override fun tanh(arg: Buffer): DoubleBuffer = super.tanh(arg) - - override fun asinh(arg: Buffer): DoubleBuffer = super.asinh(arg) - - override fun acosh(arg: Buffer): DoubleBuffer = super.acosh(arg) - - override fun atanh(arg: Buffer): DoubleBuffer = super.atanh(arg) - - override fun power(arg: Buffer, pow: Number): DoubleBuffer = if (pow.isInteger()) { - arg.mapInline { it.pow(pow.toInt()) } - } else { - arg.mapInline { - if(it<0) throw IllegalArgumentException("Negative argument $it could not be raised to the fractional power") - it.pow(pow.toDouble()) - } - } - - override fun unaryOperationFunction(operation: String): (arg: Buffer) -> Buffer = - super.unaryOperationFunction(operation) - - // override fun number(value: Number): Buffer = DoubleBuffer(size) { value.toDouble() } -// -// override fun Buffer.unaryMinus(): Buffer = DoubleBufferOperations.run { -// -this@unaryMinus -// } -// -// override fun add(a: Buffer, b: Buffer): DoubleBuffer { -// require(a.size == size) { "The buffer size ${a.size} does not match context size $size" } -// return DoubleBufferOperations.add(a, b) -// } -// - -// -// override fun multiply(a: Buffer, b: Buffer): DoubleBuffer { -// require(a.size == size) { "The buffer size ${a.size} does not match context size $size" } -// return DoubleBufferOperations.multiply(a, b) -// } -// -// override fun divide(a: Buffer, b: Buffer): DoubleBuffer { -// require(a.size == size) { "The buffer size ${a.size} does not match context size $size" } -// return DoubleBufferOperations.divide(a, b) -// } -// -// override fun sin(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.sin(arg) -// } -// -// override fun cos(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.cos(arg) -// } -// -// override fun tan(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.tan(arg) -// } -// -// override fun asin(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.asin(arg) -// } -// -// override fun acos(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.acos(arg) -// } -// -// override fun atan(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.atan(arg) -// } -// -// override fun sinh(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.sinh(arg) -// } -// -// override fun cosh(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.cosh(arg) -// } -// -// override fun tanh(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.tanh(arg) -// } -// -// override fun asinh(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.asinh(arg) -// } -// -// override fun acosh(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.acosh(arg) -// } -// -// override fun atanh(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.atanh(arg) -// } -// -// override fun power(arg: Buffer, pow: Number): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.power(arg, pow) -// } -// -// override fun exp(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.exp(arg) -// } -// -// override fun ln(arg: Buffer): DoubleBuffer { -// require(arg.size == size) { "The buffer size ${arg.size} does not match context size $size" } -// return DoubleBufferOperations.ln(arg) -// } - -} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/DoubleBufferOps.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/DoubleBufferOps.kt deleted file mode 100644 index c1bd1daa3..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/DoubleBufferOps.kt +++ /dev/null @@ -1,151 +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. - */ - -package space.kscience.kmath.operations - -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.BufferFactory -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.asBuffer - -import kotlin.math.* - -/** - * [ExtendedFieldOps] over [DoubleBuffer]. - */ -public abstract class DoubleBufferOps : BufferAlgebra, ExtendedFieldOps>, - Norm, Double> { - - override val elementAlgebra: DoubleField get() = DoubleField - override val bufferFactory: BufferFactory get() = ::DoubleBuffer - - override fun Buffer.map(block: DoubleField.(Double) -> Double): DoubleBuffer = - mapInline { DoubleField.block(it) } - - override fun unaryOperationFunction(operation: String): (arg: Buffer) -> Buffer = - super.unaryOperationFunction(operation) - - override fun binaryOperationFunction(operation: String): (left: Buffer, right: Buffer) -> Buffer = - super.binaryOperationFunction(operation) - - override fun negate(arg: Buffer): DoubleBuffer = arg.mapInline { -it } - - override fun add(left: Buffer, right: Buffer): DoubleBuffer { - require(right.size == left.size) { - "The size of the first buffer ${left.size} should be the same as for second one: ${right.size} " - } - - return if (left is DoubleBuffer && right is DoubleBuffer) { - val aArray = left.array - val bArray = right.array - DoubleBuffer(DoubleArray(left.size) { aArray[it] + bArray[it] }) - } else DoubleBuffer(DoubleArray(left.size) { left[it] + right[it] }) - } - - override fun Buffer.plus(arg: Buffer): DoubleBuffer = add(this, arg) - - override fun Buffer.minus(arg: Buffer): DoubleBuffer { - require(arg.size == this.size) { - "The size of the first buffer ${this.size} should be the same as for second one: ${arg.size} " - } - - return if (this is DoubleBuffer && arg is DoubleBuffer) { - val aArray = this.array - val bArray = arg.array - DoubleBuffer(DoubleArray(this.size) { aArray[it] - bArray[it] }) - } else DoubleBuffer(DoubleArray(this.size) { this[it] - arg[it] }) - } - - // -// override fun multiply(a: Buffer, k: Number): RealBuffer { -// val kValue = k.toDouble() -// -// return if (a is RealBuffer) { -// val aArray = a.array -// RealBuffer(DoubleArray(a.size) { aArray[it] * kValue }) -// } else RealBuffer(DoubleArray(a.size) { a[it] * kValue }) -// } -// -// override fun divide(a: Buffer, k: Number): RealBuffer { -// val kValue = k.toDouble() -// -// return if (a is RealBuffer) { -// val aArray = a.array -// RealBuffer(DoubleArray(a.size) { aArray[it] / kValue }) -// } else RealBuffer(DoubleArray(a.size) { a[it] / kValue }) -// } - - override fun multiply(left: Buffer, right: Buffer): DoubleBuffer { - require(right.size == left.size) { - "The size of the first buffer ${left.size} should be the same as for second one: ${right.size} " - } - - return if (left is DoubleBuffer && right is DoubleBuffer) { - val aArray = left.array - val bArray = right.array - DoubleBuffer(DoubleArray(left.size) { aArray[it] * bArray[it] }) - } else DoubleBuffer(DoubleArray(left.size) { left[it] * right[it] }) - } - - override fun divide(left: Buffer, right: Buffer): DoubleBuffer { - require(right.size == left.size) { - "The size of the first buffer ${left.size} should be the same as for second one: ${right.size} " - } - - return if (left is DoubleBuffer && right is DoubleBuffer) { - val aArray = left.array - val bArray = right.array - DoubleBuffer(DoubleArray(left.size) { aArray[it] / bArray[it] }) - } else DoubleBuffer(DoubleArray(left.size) { left[it] / right[it] }) - } - - override fun sin(arg: Buffer): DoubleBuffer = arg.mapInline(::sin) - - override fun cos(arg: Buffer): DoubleBuffer = arg.mapInline(::cos) - - override fun tan(arg: Buffer): DoubleBuffer = arg.mapInline(::tan) - - override fun asin(arg: Buffer): DoubleBuffer = arg.mapInline(::asin) - - override fun acos(arg: Buffer): DoubleBuffer = arg.mapInline(::acos) - - override fun atan(arg: Buffer): DoubleBuffer = arg.mapInline(::atan) - - override fun sinh(arg: Buffer): DoubleBuffer = arg.mapInline(::sinh) - - override fun cosh(arg: Buffer): DoubleBuffer = arg.mapInline(::cosh) - - override fun tanh(arg: Buffer): DoubleBuffer = arg.mapInline(::tanh) - - override fun asinh(arg: Buffer): DoubleBuffer = arg.mapInline(::asinh) - - override fun acosh(arg: Buffer): DoubleBuffer = arg.mapInline(::acosh) - - override fun atanh(arg: Buffer): DoubleBuffer = arg.mapInline(::atanh) - - override fun exp(arg: Buffer): DoubleBuffer = arg.mapInline(::exp) - - override fun ln(arg: Buffer): DoubleBuffer = arg.mapInline(::ln) - - override fun norm(arg: Buffer): Double = DoubleL2Norm.norm(arg) - - override fun scale(a: Buffer, value: Double): DoubleBuffer = a.mapInline { it * value } - - public companion object : DoubleBufferOps() { - public inline fun Buffer.mapInline(block: (Double) -> Double): DoubleBuffer = - if (this is DoubleBuffer) { - DoubleArray(size) { block(array[it]) }.asBuffer() - } else { - DoubleArray(size) { block(get(it)) }.asBuffer() - } - } -} - -public object DoubleL2Norm : Norm, Double> { - override fun norm(arg: Point): Double = sqrt(arg.fold(0.0) { acc: Double, d: Double -> acc + d.pow(2) }) -} - diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/LogicAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/LogicAlgebra.kt deleted file mode 100644 index 9037525e1..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/LogicAlgebra.kt +++ /dev/null @@ -1,85 +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. - */ - -package space.kscience.kmath.operations - -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.expressions.symbol -import space.kscience.kmath.misc.UnstableKMathAPI - -/** - * An algebra for generic boolean logic - */ -@UnstableKMathAPI -public interface LogicAlgebra : Algebra { - - /** - * Represent constant [Boolean] as [T] - */ - public fun const(boolean: Boolean): T - - override fun bindSymbolOrNull(value: String): T? = value.lowercase().toBooleanStrictOrNull()?.let(::const) - - override fun unaryOperation(operation: String, arg: T): T = when (operation) { - Boolean::not.name -> arg.not() - else -> super.unaryOperation(operation, arg) - } - - override fun unaryOperationFunction(operation: String): (arg: T) -> T = { unaryOperation(operation, it) } - - override fun binaryOperation(operation: String, left: T, right: T): T = when (operation) { - Boolean::and.name -> left.and(right) - Boolean::or.name -> left.or(right) - else -> super.binaryOperation(operation, left, right) - } - - override fun binaryOperationFunction(operation: String): (left: T, right: T) -> T = { l, r -> - binaryOperation(operation, l, r) - } - - /** - * Logic 'not' - */ - public operator fun T.not(): T - - /** - * Logic 'and' - */ - public infix fun T.and(other: T): T - - /** - * Logic 'or' - */ - public infix fun T.or(other: T): T - - /** - * Logic 'xor' - */ - public infix fun T.xor(other: T): T - - - public companion object { - public val TRUE: Symbol by symbol - public val FALSE: Symbol by symbol - } -} - -/** - * An implementation of [LogicAlgebra] for primitive booleans - */ -@UnstableKMathAPI -@Suppress("EXTENSION_SHADOWED_BY_MEMBER") -public object BooleanAlgebra : LogicAlgebra { - - override fun const(boolean: Boolean): Boolean = boolean - - override fun Boolean.not(): Boolean = !this - - override fun Boolean.and(other: Boolean): Boolean = this && other - - override fun Boolean.or(other: Boolean): Boolean = this || other - - override fun Boolean.xor(other: Boolean): Boolean = this xor other -} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/NumericAlgebra.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/NumericAlgebra.kt deleted file mode 100644 index d0405c705..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/NumericAlgebra.kt +++ /dev/null @@ -1,187 +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. - */ - -package space.kscience.kmath.operations - -import space.kscience.kmath.misc.UnstableKMathAPI -import kotlin.math.E -import kotlin.math.PI - -/** - * An algebraic structure where elements can have numeric representation. - * - * @param T the type of element of this structure. - */ -public interface NumericAlgebra : Algebra { - /** - * Wraps a number to [T] object. - * - * @param value the number to wrap. - * @return an object. - */ - public fun number(value: Number): T - - /** - * Dynamically dispatches a binary operation with the certain name with numeric first argument. - * - * Implementations must fulfil the following requirements: - * - * 1. If operation is not defined in the structure, then function throws [kotlin.IllegalStateException]. - * 1. Equivalence to [leftSideNumberOperation]: for any `a`, `b`, and `c`, - * `leftSideNumberOperationFunction(a)(b, c) == leftSideNumberOperation(a, b)`. - * - * @param operation the name of operation. - * @return an operation. - */ - public fun leftSideNumberOperationFunction(operation: String): (left: Number, right: T) -> T = - { l, r -> binaryOperationFunction(operation)(number(l), r) } - - /** - * Dynamically invokes a binary operation with the certain name with numeric first argument. - * - * Implementations must fulfil the following requirements: - * - * 1. If operation is not defined in the structure, then the function throws [kotlin.IllegalStateException]. - * 1. Equivalence to [leftSideNumberOperation]: for any `a`, `b`, and `c`, - * `leftSideNumberOperationFunction(a)(b, c) == leftSideNumberOperation(a, b, c)`. - * - * @param operation the name of operation. - * @param left the first argument of operation. - * @param right the second argument of operation. - * @return a result of operation. - */ - public fun leftSideNumberOperation(operation: String, left: Number, right: T): T = - leftSideNumberOperationFunction(operation)(left, right) - - /** - * Dynamically dispatches a binary operation with the certain name with numeric first argument. - * - * Implementations must fulfil the following requirements: - * - * 1. If operation is not defined in the structure, then the function throws [kotlin.IllegalStateException]. - * 1. Equivalence to [rightSideNumberOperation]: for any `a`, `b`, and `c`, - * `rightSideNumberOperationFunction(a)(b, c) == leftSideNumberOperation(a, b, c)`. - * - * @param operation the name of operation. - * @return an operation. - */ - public fun rightSideNumberOperationFunction(operation: String): (left: T, right: Number) -> T = - { l, r -> binaryOperationFunction(operation)(l, number(r)) } - - /** - * Dynamically invokes a binary operation with the certain name with numeric second argument. - * - * Implementations must fulfil the following requirements: - * - * 1. If operation is not defined in the structure, then the function throws [kotlin.IllegalStateException]. - * 1. Equivalence to [rightSideNumberOperationFunction]: for any `a`, `b`, and `c`, - * `rightSideNumberOperationFunction(a)(b, c) == rightSideNumberOperation(a, b, c)`. - * - * @param operation the name of operation. - * @param left the first argument of operation. - * @param right the second argument of operation. - * @return a result of operation. - */ - public fun rightSideNumberOperation(operation: String, left: T, right: Number): T = - rightSideNumberOperationFunction(operation)(left, right) - - override fun bindSymbolOrNull(value: String): T? = when (value) { - "pi" -> number(PI) - "e" -> number(E) - else -> super.bindSymbolOrNull(value) - } -} - -/** - * The π mathematical constant. - */ -public val NumericAlgebra.pi: T get() = bindSymbolOrNull("pi") ?: number(PI) - -/** - * The *e* mathematical constant. - */ -public val NumericAlgebra.e: T get() = number(E) - -/** - * Scale by scalar operations - */ -public interface ScaleOperations : Algebra { - /** - * Scaling an element by a scalar. - * - * @param a the multiplier. - * @param value the multiplicand. - * @return the produce. - */ - public fun scale(a: T, value: Double): T - - /** - * Multiplication of this element by a scalar. - * - * @receiver the multiplier. - * @param k the multiplicand. - * @return the product. - */ - public operator fun T.times(k: Number): T = scale(this, k.toDouble()) - - /** - * Division of this element by scalar. - * - * @receiver the dividend. - * @param k the divisor. - * @return the quotient. - */ - public operator fun T.div(k: Number): T = scale(this, 1.0 / k.toDouble()) - - /** - * Multiplication of this number by element. - * - * @receiver the multiplier. - * @param arg the multiplicand. - * @return the product. - */ - public operator fun Number.times(arg: T): T = arg * this -} - -/** - * A combination of [NumericAlgebra] and [Ring] that adds intrinsic simple operations on numbers like `T+1` - * TODO to be removed and replaced by extensions after multiple receivers are there - */ -@UnstableKMathAPI -public interface NumbersAddOps : RingOps, NumericAlgebra { - /** - * Addition of element and scalar. - * - * @receiver the augend. - * @param other the addend. - */ - public operator fun T.plus(other: Number): T = this + number(other) - - /** - * Addition of scalar and element. - * - * @receiver the augend. - * @param other the addend. - */ - public operator fun Number.plus(other: T): T = other + this - - /** - * Subtraction of element from number. - * - * @receiver the minuend. - * @param other the subtrahend. - * @receiver the difference. - */ - public operator fun T.minus(other: Number): T = this - number(other) - - /** - * Subtraction of number from element. - * - * @receiver the minuend. - * @param other the subtrahend. - * @receiver the difference. - */ - public operator fun Number.minus(other: T): T = -other + this -} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/OptionalOperations.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/OptionalOperations.kt deleted file mode 100644 index 709506fc4..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/OptionalOperations.kt +++ /dev/null @@ -1,220 +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. - */ - -package space.kscience.kmath.operations - -/** - * A container for trigonometric operations for specific type. - * - * @param T the type of element of this structure. - */ -public interface TrigonometricOperations : Algebra { - /** - * Computes the sine of [arg]. - */ - public fun sin(arg: T): T - - /** - * Computes the cosine of [arg]. - */ - public fun cos(arg: T): T - - /** - * Computes the tangent of [arg]. - */ - public fun tan(arg: T): T - - /** - * Computes the inverse sine of [arg]. - */ - public fun asin(arg: T): T - - /** - * Computes the inverse cosine of [arg]. - */ - public fun acos(arg: T): T - - /** - * Computes the inverse tangent of [arg]. - */ - public fun atan(arg: T): T - - public companion object { - /** - * The identifier of sine. - */ - public const val SIN_OPERATION: String = "sin" - - /** - * The identifier of cosine. - */ - public const val COS_OPERATION: String = "cos" - - /** - * The identifier of tangent. - */ - public const val TAN_OPERATION: String = "tan" - - /** - * The identifier of inverse sine. - */ - public const val ASIN_OPERATION: String = "asin" - - /** - * The identifier of inverse cosine. - */ - public const val ACOS_OPERATION: String = "acos" - - /** - * The identifier of inverse tangent. - */ - public const val ATAN_OPERATION: String = "atan" - } -} - -/** - * Check if number is an integer from platform point of view - */ -public expect fun Number.isInteger(): Boolean - -/** - * A context extension to include power operations based on exponentiation. - * - * @param T the type of element of this structure. - */ -public interface PowerOperations : FieldOps { - - /** - * Raises [arg] to a power if possible (negative number could not be raised to a fractional power). - * Throws [IllegalArgumentException] if not possible. - */ - public fun power(arg: T, pow: Number): T - - /** - * Computes the square root of the value [arg]. - */ - public fun sqrt(arg: T): T = power(arg, 0.5) - - /** - * Raises this value to the power [pow]. - */ - public infix fun T.pow(pow: Number): T = power(this, pow) - - public companion object { - /** - * The identifier of exponentiation. - */ - public const val POW_OPERATION: String = "pow" - - /** - * The identifier of square root. - */ - public const val SQRT_OPERATION: String = "sqrt" - } -} - - -/** - * A container for operations related to `exp` and `ln` functions. - * - * @param T the type of element of this structure. - */ -public interface ExponentialOperations : Algebra { - /** - * Computes Euler's number `e` raised to the power of the value [arg]. - */ - public fun exp(arg: T): T - - /** - * Computes the natural logarithm (base `e`) of the value [arg]. - */ - public fun ln(arg: T): T - - /** - * Computes the hyperbolic sine of [arg]. - */ - public fun sinh(arg: T): T - - /** - * Computes the hyperbolic cosine of [arg]. - */ - public fun cosh(arg: T): T - - /** - * Computes the hyperbolic tangent of [arg]. - */ - public fun tanh(arg: T): T - - /** - * Computes the inverse hyperbolic sine of [arg]. - */ - public fun asinh(arg: T): T - - /** - * Computes the inverse hyperbolic cosine of [arg]. - */ - public fun acosh(arg: T): T - - /** - * Computes the inverse hyperbolic tangent of [arg]. - */ - public fun atanh(arg: T): T - - public companion object { - /** - * The identifier of exponential function. - */ - public const val EXP_OPERATION: String = "exp" - - /** - * The identifier of natural logarithm. - */ - public const val LN_OPERATION: String = "ln" - - /** - * The identifier of hyperbolic sine. - */ - public const val SINH_OPERATION: String = "sinh" - - /** - * The identifier of hyperbolic cosine. - */ - public const val COSH_OPERATION: String = "cosh" - - /** - * The identifier of hyperbolic tangent. - */ - public const val TANH_OPERATION: String = "tanh" - - /** - * The identifier of inverse hyperbolic sine. - */ - public const val ASINH_OPERATION: String = "asinh" - - /** - * The identifier of inverse hyperbolic cosine. - */ - public const val ACOSH_OPERATION: String = "acosh" - - /** - * The identifier of inverse hyperbolic tangent. - */ - public const val ATANH_OPERATION: String = "atanh" - } -} - -/** - * A container for norm functional on element. - * - * @param T the type of element having norm defined. - * @param R the type of norm. - */ -public interface Norm { - /** - * Computes the norm of [arg] (i.e., absolute value or vector length). - */ - public fun norm(arg: T): R -} - diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/algebraExtensions.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/algebraExtensions.kt deleted file mode 100644 index 539440de9..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/algebraExtensions.kt +++ /dev/null @@ -1,98 +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. - */ - -package space.kscience.kmath.operations - -/** - * Returns the sum of all elements in the iterable in this [Group]. - * - * @receiver the algebra that provides addition. - * @param data the iterable to sum up. - * @return the sum. - */ -public fun Group.sum(data: Iterable): T = data.fold(zero) { left, right -> - add(left, right) -} - -//TODO replace by sumOf with multi-receivers - -/** - * Returns the sum of all elements in the sequence in this [Group]. - * - * @receiver the algebra that provides addition. - * @param data the sequence to sum up. - * @return the sum. - */ -public fun Group.sum(data: Sequence): T = data.fold(zero) { left, right -> - add(left, right) -} - -/** - * Returns an average value of elements in the iterable in this [Group]. - * - * @receiver the algebra that provides addition and division. - * @param data the iterable to find average. - * @return the average value. - * @author Iaroslav Postovalov - */ -public fun S.average(data: Iterable): T where S : Group, S : ScaleOperations = - sum(data) / data.count() - -/** - * Returns an average value of elements in the sequence in this [Group]. - * - * @receiver the algebra that provides addition and division. - * @param data the sequence to find average. - * @return the average value. - * @author Iaroslav Postovalov - */ -public fun S.average(data: Sequence): T where S : Group, S : ScaleOperations = - sum(data) / data.count() - -/** - * Absolute of the comparable [value] - */ -public fun > Group.abs(value: T): T = if (value > zero) value else -value - -/** - * Returns the sum of all elements in the iterable in provided space. - * - * @receiver the collection to sum up. - * @param group the algebra that provides addition. - * @return the sum. - */ -public fun Iterable.sumWith(group: Group): T = group.sum(this) - -/** - * Returns the sum of all elements in the sequence in provided space. - * - * @receiver the collection to sum up. - * @param group the algebra that provides addition. - * @return the sum. - */ -public fun Sequence.sumWith(group: Group): T = group.sum(this) - -/** - * Returns an average value of elements in the iterable in this [Group]. - * - * @receiver the iterable to find average. - * @param space the algebra that provides addition and division. - * @return the average value. - * @author Iaroslav Postovalov - */ -public fun Iterable.averageWith(space: S): T where S : Group, S : ScaleOperations = - space.average(this) - -/** - * Returns an average value of elements in the sequence in this [Group]. - * - * @receiver the sequence to find average. - * @param space the algebra that provides addition and division. - * @return the average value. - * @author Iaroslav Postovalov - */ -public fun Sequence.averageWith(space: S): T where S : Group, S : ScaleOperations = - space.average(this) - diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/bufferOperation.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/bufferOperation.kt deleted file mode 100644 index 31b0c2841..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/bufferOperation.kt +++ /dev/null @@ -1,105 +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. - */ - -package space.kscience.kmath.operations - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.structures.* - -/** - * Typealias for buffer transformations. - */ -public typealias BufferTransform = (Buffer) -> Buffer - -/** - * Typealias for buffer transformations with suspend function. - */ -public typealias SuspendBufferTransform = suspend (Buffer) -> Buffer - - -/** - * Creates a sequence that returns all elements from this [Buffer]. - */ -public fun Buffer.asSequence(): Sequence = Sequence(::iterator) - -/** - * Creates an iterable that returns all elements from this [Buffer]. - */ -public fun Buffer.asIterable(): Iterable = Iterable(::iterator) - -/** - * Returns a new [List] containing all elements of this buffer. - */ -public fun Buffer.toList(): List = when (this) { - is ArrayBuffer -> array.toList() - is ListBuffer -> list.toList() - is MutableListBuffer -> list.toList() - else -> asSequence().toList() -} - -/** - * Returns a new [MutableList] filled with all elements of this buffer. - * **NOTE:** this method uses a protective copy, so it should not be used in performance-critical code. - */ -@UnstableKMathAPI -public fun Buffer.toMutableList(): MutableList = when (this) { - is ArrayBuffer -> array.toMutableList() - is ListBuffer -> list.toMutableList() - is MutableListBuffer -> list.toMutableList() - else -> MutableList(size, ::get) -} - -/** - * Returns a new [Array] containing all elements of this buffer. - * **NOTE:** this method uses a protective copy, so it should not be used in performance-critical code. - */ -@UnstableKMathAPI -public inline fun Buffer.toTypedArray(): Array = Array(size, ::get) - -/** - * Create a new buffer from this one with the given mapping function and using [Buffer.Companion.auto] buffer factory. - */ -public inline fun Buffer.map(block: (T) -> R): Buffer = - Buffer.auto(size) { block(get(it)) } - -/** - * Create a new buffer from this one with the given mapping function. - * Provided [bufferFactory] is used to construct the new buffer. - */ -public inline fun Buffer.map( - bufferFactory: BufferFactory, - crossinline block: (T) -> R, -): Buffer = bufferFactory(size) { block(get(it)) } - -/** - * Create a new buffer from this one with the given indexed mapping function. - * Provided [BufferFactory] is used to construct the new buffer. - */ -public inline fun Buffer.mapIndexed( - bufferFactory: BufferFactory = Buffer.Companion::auto, - crossinline block: (index: Int, value: T) -> R, -): Buffer = bufferFactory(size) { block(it, get(it)) } - -/** - * Fold given buffer according to [operation] - */ -public inline fun Buffer.fold(initial: R, operation: (acc: R, T) -> R): R { - var accumulator = initial - for (index in this.indices) accumulator = operation(accumulator, get(index)) - return accumulator -} - -/** - * Zip two buffers using given [transform]. - */ -@UnstableKMathAPI -public inline fun Buffer.zip( - other: Buffer, - bufferFactory: BufferFactory = Buffer.Companion::auto, - crossinline transform: (T1, T2) -> R, -): Buffer { - require(size == other.size) { "Buffer size mismatch in zip: expected $size but found ${other.size}" } - return bufferFactory(size) { transform(get(it), other[it]) } -} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/numbers.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/numbers.kt deleted file mode 100644 index 3376a166b..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/numbers.kt +++ /dev/null @@ -1,273 +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. - */ - -package space.kscience.kmath.operations - -import kotlin.math.pow as kpow - -/** - * Advanced Number-like semifield that implements basic operations. - */ -public interface ExtendedFieldOps : - FieldOps, - TrigonometricOperations, - ExponentialOperations, - ScaleOperations { - override fun tan(arg: T): T = sin(arg) / cos(arg) - override fun tanh(arg: T): T = sinh(arg) / cosh(arg) - - override fun unaryOperationFunction(operation: String): (arg: T) -> T = when (operation) { - TrigonometricOperations.COS_OPERATION -> ::cos - TrigonometricOperations.SIN_OPERATION -> ::sin - TrigonometricOperations.TAN_OPERATION -> ::tan - TrigonometricOperations.ACOS_OPERATION -> ::acos - TrigonometricOperations.ASIN_OPERATION -> ::asin - TrigonometricOperations.ATAN_OPERATION -> ::atan - ExponentialOperations.EXP_OPERATION -> ::exp - ExponentialOperations.LN_OPERATION -> ::ln - ExponentialOperations.COSH_OPERATION -> ::cosh - ExponentialOperations.SINH_OPERATION -> ::sinh - ExponentialOperations.TANH_OPERATION -> ::tanh - ExponentialOperations.ACOSH_OPERATION -> ::acosh - ExponentialOperations.ASINH_OPERATION -> ::asinh - ExponentialOperations.ATANH_OPERATION -> ::atanh - else -> super.unaryOperationFunction(operation) - } -} - -/** - * Advanced Number-like field that implements basic operations. - */ -public interface ExtendedField : ExtendedFieldOps, Field, PowerOperations, NumericAlgebra { - override fun sinh(arg: T): T = (exp(arg) - exp(-arg)) / 2.0 - override fun cosh(arg: T): T = (exp(arg) + exp(-arg)) / 2.0 - override fun tanh(arg: T): T = (exp(arg) - exp(-arg)) / (exp(-arg) + exp(arg)) - override fun asinh(arg: T): T = ln(sqrt(arg * arg + one) + arg) - override fun acosh(arg: T): T = ln(arg + sqrt((arg - one) * (arg + one))) - override fun atanh(arg: T): T = (ln(arg + one) - ln(one - arg)) / 2.0 - - override fun unaryOperationFunction(operation: String): (arg: T) -> T { - return if (operation == PowerOperations.SQRT_OPERATION) ::sqrt - else super.unaryOperationFunction(operation) - } - - override fun rightSideNumberOperationFunction(operation: String): (left: T, right: Number) -> T = - when (operation) { - PowerOperations.POW_OPERATION -> ::power - else -> super.rightSideNumberOperationFunction(operation) - } -} - -/** - * A field for [Double] without boxing. Does not produce appropriate field element. - */ -@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE") -public object DoubleField : ExtendedField, Norm, ScaleOperations { - override inline val zero: Double get() = 0.0 - override inline val one: Double get() = 1.0 - - override fun number(value: Number): Double = value.toDouble() - - override fun binaryOperationFunction(operation: String): (left: Double, right: Double) -> Double = - when (operation) { - PowerOperations.POW_OPERATION -> { l, r -> l.kpow(r) } - else -> super.binaryOperationFunction(operation) - } - - override inline fun add(left: Double, right: Double): Double = left + right - override inline fun negate(arg: Double): Double = -arg - - override inline fun multiply(left: Double, right: Double): Double = left * right - override inline fun divide(left: Double, right: Double): Double = left / right - - override inline fun scale(a: Double, value: Double): Double = a * value - - override inline fun sin(arg: Double): Double = kotlin.math.sin(arg) - override inline fun cos(arg: Double): Double = kotlin.math.cos(arg) - override inline fun tan(arg: Double): Double = kotlin.math.tan(arg) - override inline fun acos(arg: Double): Double = kotlin.math.acos(arg) - override inline fun asin(arg: Double): Double = kotlin.math.asin(arg) - override inline fun atan(arg: Double): Double = kotlin.math.atan(arg) - - override inline fun sinh(arg: Double): Double = kotlin.math.sinh(arg) - override inline fun cosh(arg: Double): Double = kotlin.math.cosh(arg) - override inline fun tanh(arg: Double): Double = kotlin.math.tanh(arg) - override inline fun asinh(arg: Double): Double = kotlin.math.asinh(arg) - override inline fun acosh(arg: Double): Double = kotlin.math.acosh(arg) - override inline fun atanh(arg: Double): Double = kotlin.math.atanh(arg) - - override fun sqrt(arg: Double): Double = kotlin.math.sqrt(arg) - override fun power(arg: Double, pow: Number): Double = when { - pow.isInteger() -> arg.kpow(pow.toInt()) - arg < 0 -> throw IllegalArgumentException("Can't raise negative $arg to a fractional power $pow") - else -> arg.kpow(pow.toDouble()) - } - - override inline fun exp(arg: Double): Double = kotlin.math.exp(arg) - override inline fun ln(arg: Double): Double = kotlin.math.ln(arg) - - override inline fun norm(arg: Double): Double = abs(arg) - - override inline fun Double.plus(arg: Double): Double = this + arg - override inline fun Double.minus(arg: Double): Double = this - arg - override inline fun Double.times(arg: Double): Double = this * arg - override inline fun Double.div(arg: Double): Double = this / arg -} - -public val Double.Companion.algebra: DoubleField get() = DoubleField - -/** - * A field for [Float] without boxing. Does not produce appropriate field element. - */ -@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE") -public object FloatField : ExtendedField, Norm { - override inline val zero: Float get() = 0.0f - override inline val one: Float get() = 1.0f - - override fun number(value: Number): Float = value.toFloat() - - override fun binaryOperationFunction(operation: String): (left: Float, right: Float) -> Float = - when (operation) { - PowerOperations.POW_OPERATION -> { l, r -> l.kpow(r) } - else -> super.binaryOperationFunction(operation) - } - - override inline fun add(left: Float, right: Float): Float = left + right - override inline fun negate(arg: Float): Float = -arg - - override inline fun scale(a: Float, value: Double): Float = a * value.toFloat() - - override inline fun multiply(left: Float, right: Float): Float = left * right - - override inline fun divide(left: Float, right: Float): Float = left / right - - override inline fun sin(arg: Float): Float = kotlin.math.sin(arg) - override inline fun cos(arg: Float): Float = kotlin.math.cos(arg) - override inline fun tan(arg: Float): Float = kotlin.math.tan(arg) - override inline fun acos(arg: Float): Float = kotlin.math.acos(arg) - override inline fun asin(arg: Float): Float = kotlin.math.asin(arg) - override inline fun atan(arg: Float): Float = kotlin.math.atan(arg) - - override inline fun sinh(arg: Float): Float = kotlin.math.sinh(arg) - override inline fun cosh(arg: Float): Float = kotlin.math.cosh(arg) - override inline fun tanh(arg: Float): Float = kotlin.math.tanh(arg) - override inline fun asinh(arg: Float): Float = kotlin.math.asinh(arg) - override inline fun acosh(arg: Float): Float = kotlin.math.acosh(arg) - override inline fun atanh(arg: Float): Float = kotlin.math.atanh(arg) - - override inline fun sqrt(arg: Float): Float = kotlin.math.sqrt(arg) - override inline fun power(arg: Float, pow: Number): Float = arg.kpow(pow.toFloat()) - - override inline fun exp(arg: Float): Float = kotlin.math.exp(arg) - override inline fun ln(arg: Float): Float = kotlin.math.ln(arg) - - override inline fun norm(arg: Float): Float = abs(arg) - - override inline fun Float.plus(arg: Float): Float = this + arg - override inline fun Float.minus(arg: Float): Float = this - arg - override inline fun Float.times(arg: Float): Float = this * arg - override inline fun Float.div(arg: Float): Float = this / arg -} - -public val Float.Companion.algebra: FloatField get() = FloatField - -/** - * A field for [Int] without boxing. Does not produce corresponding ring element. - */ -@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE") -public object IntRing : Ring, Norm, NumericAlgebra { - override inline val zero: Int - get() = 0 - - override inline val one: Int - get() = 1 - - override fun number(value: Number): Int = value.toInt() - override inline fun add(left: Int, right: Int): Int = left + right - override inline fun negate(arg: Int): Int = -arg - - override inline fun multiply(left: Int, right: Int): Int = left * right - override inline fun norm(arg: Int): Int = abs(arg) - - override inline fun Int.plus(arg: Int): Int = this + arg - override inline fun Int.minus(arg: Int): Int = this - arg - override inline fun Int.times(arg: Int): Int = this * arg -} - -public val Int.Companion.algebra: IntRing get() = IntRing - -/** - * A field for [Short] without boxing. Does not produce appropriate ring element. - */ -@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE") -public object ShortRing : Ring, Norm, NumericAlgebra { - override inline val zero: Short - get() = 0 - - override inline val one: Short - get() = 1 - - override fun number(value: Number): Short = value.toShort() - override inline fun add(left: Short, right: Short): Short = (left + right).toShort() - override inline fun negate(arg: Short): Short = (-arg).toShort() - - override inline fun multiply(left: Short, right: Short): Short = (left * right).toShort() - override inline fun norm(arg: Short): Short = if (arg > 0) arg else (-arg).toShort() - - override inline fun Short.plus(arg: Short): Short = (this + arg).toShort() - override inline fun Short.minus(arg: Short): Short = (this - arg).toShort() - override inline fun Short.times(arg: Short): Short = (this * arg).toShort() -} - -public val Short.Companion.algebra: ShortRing get() = ShortRing - -/** - * A field for [Byte] without boxing. Does not produce appropriate ring element. - */ -@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE") -public object ByteRing : Ring, Norm, NumericAlgebra { - override inline val zero: Byte - get() = 0 - - override inline val one: Byte - get() = 1 - - override fun number(value: Number): Byte = value.toByte() - override inline fun add(left: Byte, right: Byte): Byte = (left + right).toByte() - override inline fun negate(arg: Byte): Byte = (-arg).toByte() - override inline fun multiply(left: Byte, right: Byte): Byte = (left * right).toByte() - override inline fun norm(arg: Byte): Byte = if (arg > 0) arg else (-arg).toByte() - - override inline fun Byte.plus(arg: Byte): Byte = (this + arg).toByte() - override inline fun Byte.minus(arg: Byte): Byte = (this - arg).toByte() - override inline fun Byte.times(arg: Byte): Byte = (this * arg).toByte() -} - -public val Byte.Companion.algebra: ByteRing get() = ByteRing - -/** - * A field for [Double] without boxing. Does not produce appropriate ring element. - */ -@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE") -public object LongRing : Ring, Norm, NumericAlgebra { - override inline val zero: Long - get() = 0L - - override inline val one: Long - get() = 1L - - override fun number(value: Number): Long = value.toLong() - override inline fun add(left: Long, right: Long): Long = left + right - override inline fun negate(arg: Long): Long = (-arg) - - override inline fun multiply(left: Long, right: Long): Long = left * right - override inline fun norm(arg: Long): Long = abs(arg) - - override inline fun Long.plus(arg: Long): Long = (this + arg) - override inline fun Long.minus(arg: Long): Long = (this - arg) - override inline fun Long.times(arg: Long): Long = (this * arg) -} - -public val Long.Companion.algebra: LongRing get() = LongRing diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ArrayBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ArrayBuffer.kt deleted file mode 100644 index 3528b0460..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ArrayBuffer.kt +++ /dev/null @@ -1,34 +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. - */ - -package space.kscience.kmath.structures - -/** - * [MutableBuffer] implementation over [Array]. - * - * @param T the type of elements contained in the buffer. - * @property array The underlying array. - */ -public class ArrayBuffer(internal val array: Array) : MutableBuffer { - // Can't inline because array is invariant - override val size: Int get() = array.size - - override operator fun get(index: Int): T = array[index] - - override operator fun set(index: Int, value: T) { - array[index] = value - } - - override operator fun iterator(): Iterator = array.iterator() - override fun copy(): MutableBuffer = ArrayBuffer(array.copyOf()) - - override fun toString(): String = Buffer.toString(this) -} - - -/** - * Returns an [ArrayBuffer] that wraps the original array. - */ -public fun Array.asBuffer(): ArrayBuffer = ArrayBuffer(this) \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt deleted file mode 100644 index 58c6d5ded..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt +++ /dev/null @@ -1,143 +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. - */ - -package space.kscience.kmath.structures - -import space.kscience.kmath.operations.asSequence -import kotlin.jvm.JvmInline -import kotlin.reflect.KClass - -/** - * Function that produces [Buffer] from its size and function that supplies values. - * - * @param T the type of buffer. - */ -public typealias BufferFactory = (Int, (Int) -> T) -> Buffer - -/** - * Function that produces [MutableBuffer] from its size and function that supplies values. - * - * @param T the type of buffer. - */ -public typealias MutableBufferFactory = (Int, (Int) -> T) -> MutableBuffer - -/** - * A generic read-only random-access structure for both primitives and objects. - * - * [Buffer] is in general identity-free. [Buffer.contentEquals] should be used for content equality checks. - * - * @param T the type of elements contained in the buffer. - */ -public interface Buffer { - /** - * The size of this buffer. - */ - public val size: Int - - /** - * Gets element at given index. - */ - public operator fun get(index: Int): T - - /** - * Iterates over all elements. - */ - public operator fun iterator(): Iterator - - override fun toString(): String - - public companion object { - - public fun toString(buffer: Buffer<*>): String = - buffer.asSequence().joinToString(prefix = "[", separator = ", ", postfix = "]") - - /** - * Check the element-by-element match of content of two buffers. - */ - public fun contentEquals(first: Buffer, second: Buffer): Boolean { - if (first.size != second.size) return false - for (i in first.indices) { - if (first[i] != second[i]) return false - } - return true - } - - /** - * Creates a [ListBuffer] of given type [T] with given [size]. Each element is calculated by calling the - * specified [initializer] function. - */ - public inline fun boxing(size: Int, initializer: (Int) -> T): Buffer = - List(size, initializer).asBuffer() - - /** - * Creates a [Buffer] of given [type]. If the type is primitive, specialized buffers are used ([IntBuffer], - * [DoubleBuffer], etc.), [ListBuffer] is returned otherwise. - * - * The [size] is specified, and each element is calculated by calling the specified [initializer] function. - */ - @Suppress("UNCHECKED_CAST") - public inline fun auto(type: KClass, size: Int, initializer: (Int) -> T): Buffer = - when (type) { - Double::class -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer - Short::class -> MutableBuffer.short(size) { initializer(it) as Short } as Buffer - Int::class -> MutableBuffer.int(size) { initializer(it) as Int } as Buffer - Long::class -> MutableBuffer.long(size) { initializer(it) as Long } as Buffer - Float::class -> MutableBuffer.float(size) { initializer(it) as Float } as Buffer - else -> boxing(size, initializer) - } - - /** - * Creates a [Buffer] of given type [T]. If the type is primitive, specialized buffers are used ([IntBuffer], - * [DoubleBuffer], etc.), [ListBuffer] is returned otherwise. - * - * The [size] is specified, and each element is calculated by calling the specified [initializer] function. - */ - @Suppress("UNCHECKED_CAST") - public inline fun auto(size: Int, initializer: (Int) -> T): Buffer = - auto(T::class, size, initializer) - } -} - -/** - * Returns an [IntRange] of the valid indices for this [Buffer]. - */ -public val Buffer<*>.indices: IntRange get() = 0 until size - -/** - * Immutable wrapper for [MutableBuffer]. - * - * @param T the type of elements contained in the buffer. - * @property buffer The underlying buffer. - */ -@JvmInline -public value class ReadOnlyBuffer(public val buffer: MutableBuffer) : Buffer { - override val size: Int get() = buffer.size - - override operator fun get(index: Int): T = buffer[index] - - override operator fun iterator(): Iterator = buffer.iterator() -} - -/** - * A buffer with content calculated on-demand. The calculated content is not stored, so it is recalculated on each call. - * Useful when one needs single element from the buffer. - * - * @param T the type of elements provided by the buffer. - */ -public class VirtualBuffer(override val size: Int, private val generator: (Int) -> T) : Buffer { - override operator fun get(index: Int): T { - if (index < 0 || index >= size) throw IndexOutOfBoundsException("Expected index from 0 to ${size - 1}, but found $index") - return generator(index) - } - - override operator fun iterator(): Iterator = (0 until size).asSequence().map(generator).iterator() - - override fun toString(): String = Buffer.toString(this) -} - -/** - * Convert this buffer to read-only buffer. - */ -public fun Buffer.asReadOnly(): Buffer = if (this is MutableBuffer) ReadOnlyBuffer(this) else this \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/BufferAccessor2D.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/BufferAccessor2D.kt deleted file mode 100644 index 4d04a5235..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/BufferAccessor2D.kt +++ /dev/null @@ -1,60 +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. - */ - -package space.kscience.kmath.structures - -import space.kscience.kmath.nd.DefaultStrides -import space.kscience.kmath.nd.Structure2D -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.nd.as2D - -/** - * A context that allows to operate on a [MutableBuffer] as on 2d array - */ -internal class BufferAccessor2D( - val rowNum: Int, - val colNum: Int, - val factory: MutableBufferFactory, -) { - operator fun Buffer.get(i: Int, j: Int): T = get(i * colNum + j) - - operator fun MutableBuffer.set(i: Int, j: Int, value: T) { - set(i * colNum + j, value) - } - - inline fun create(crossinline init: (i: Int, j: Int) -> T): MutableBuffer = - factory(rowNum * colNum) { offset -> init(offset / colNum, offset % colNum) } - - fun create(mat: Structure2D): MutableBuffer = create { i, j -> mat[i, j] } - - //TODO optimize wrapper - fun MutableBuffer.collect(): Structure2D = StructureND.buffered( - DefaultStrides(intArrayOf(rowNum, colNum)), - factory - ) { (i, j) -> - get(i, j) - }.as2D() - - inner class Row(val buffer: MutableBuffer, val rowIndex: Int) : MutableBuffer { - override val size: Int get() = colNum - - override operator fun get(index: Int): T = buffer[rowIndex, index] - - override operator fun set(index: Int, value: T) { - buffer[rowIndex, index] = value - } - - override fun copy(): MutableBuffer = factory(colNum) { get(it) } - override operator fun iterator(): Iterator = (0 until colNum).map(::get).iterator() - - override fun toString(): String = Buffer.toString(this) - - } - - /** - * Get row - */ - fun MutableBuffer.row(i: Int): Row = Row(this, i) -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/DoubleBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/DoubleBuffer.kt deleted file mode 100644 index f4388a477..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/DoubleBuffer.kt +++ /dev/null @@ -1,69 +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. - */ - -package space.kscience.kmath.structures - -import kotlin.jvm.JvmInline - -/** - * Specialized [MutableBuffer] implementation over [DoubleArray]. - * - * @property array the underlying array. - */ -@JvmInline -public value class DoubleBuffer(public val array: DoubleArray) : MutableBuffer { - override val size: Int get() = array.size - - override operator fun get(index: Int): Double = array[index] - - override operator fun set(index: Int, value: Double) { - array[index] = value - } - - override operator fun iterator(): DoubleIterator = array.iterator() - - override fun copy(): DoubleBuffer = DoubleBuffer(array.copyOf()) - - override fun toString(): String = Buffer.toString(this) - - public companion object{ - public fun zero(size: Int): DoubleBuffer = DoubleArray(size).asBuffer() - } -} - -/** - * Creates a new [DoubleBuffer] with the specified [size], where each element is calculated by calling the specified - * [init] function. - * - * The function [init] is called for each array element sequentially starting from the first one. - * It should return the value for a buffer element given its index. - */ -public inline fun DoubleBuffer(size: Int, init: (Int) -> Double): DoubleBuffer = DoubleBuffer(DoubleArray(size) { init(it) }) - -/** - * Returns a new [DoubleBuffer] of given elements. - */ -public fun DoubleBuffer(vararg doubles: Double): DoubleBuffer = DoubleBuffer(doubles) - -/** - * Simplified [DoubleBuffer] to array comparison - */ -public fun DoubleBuffer.contentEquals(vararg doubles: Double): Boolean = array.contentEquals(doubles) - -/** - * Returns a new [DoubleArray] containing all the elements of this [Buffer]. - */ -public fun Buffer.toDoubleArray(): DoubleArray = when (this) { - is DoubleBuffer -> array.copyOf() - else -> DoubleArray(size, ::get) -} - -/** - * Returns [DoubleBuffer] over this array. - * - * @receiver the array. - * @return the new buffer. - */ -public fun DoubleArray.asBuffer(): DoubleBuffer = DoubleBuffer(this) diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FlaggedBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FlaggedBuffer.kt deleted file mode 100644 index b3c537280..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FlaggedBuffer.kt +++ /dev/null @@ -1,82 +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. - */ - -package space.kscience.kmath.structures - -import kotlin.experimental.and - -/** - * Represents flags to supply additional info about values of buffer. - * - * @property mask bit mask value of this flag. - */ -public enum class ValueFlag(public val mask: Byte) { - /** - * Reports the value is NaN. - */ - NAN(0b0000_0001), - - /** - * Reports the value doesn't present in the buffer (when the type of value doesn't support `null`). - */ - MISSING(0b0000_0010), - - /** - * Reports the value is negative infinity. - */ - NEGATIVE_INFINITY(0b0000_0100), - - /** - * Reports the value is positive infinity - */ - POSITIVE_INFINITY(0b0000_1000) -} - -/** - * A buffer with flagged values. - */ -public interface FlaggedBuffer : Buffer { - public fun getFlag(index: Int): Byte -} - -/** - * The value is valid if all flags are down - */ -public fun FlaggedBuffer<*>.isValid(index: Int): Boolean = getFlag(index) != 0.toByte() - -public fun FlaggedBuffer<*>.hasFlag(index: Int, flag: ValueFlag): Boolean = (getFlag(index) and flag.mask) != 0.toByte() - -public fun FlaggedBuffer<*>.isMissing(index: Int): Boolean = hasFlag(index, ValueFlag.MISSING) - -/** - * A [Double] buffer that supports flags for each value like `NaN` or Missing. - */ -public class FlaggedDoubleBuffer( - public val values: DoubleArray, - public val flags: ByteArray -) : FlaggedBuffer, Buffer { - init { - require(values.size == flags.size) { "Values and flags must have the same dimensions" } - } - - override fun getFlag(index: Int): Byte = flags[index] - - override val size: Int get() = values.size - - override operator fun get(index: Int): Double? = if (isValid(index)) values[index] else null - - override operator fun iterator(): Iterator = values.indices.asSequence().map { - if (isValid(it)) values[it] else null - }.iterator() - - override fun toString(): String = Buffer.toString(this) -} - -public inline fun FlaggedDoubleBuffer.forEachValid(block: (Double) -> Unit) { - indices - .asSequence() - .filter(::isValid) - .forEach { block(values[it]) } -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt deleted file mode 100644 index e7e98fc71..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FloatBuffer.kt +++ /dev/null @@ -1,60 +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. - */ - -package space.kscience.kmath.structures - -import kotlin.jvm.JvmInline - -/** - * Specialized [MutableBuffer] implementation over [FloatArray]. - * - * @property array the underlying array. - * @author Iaroslav Postovalov - */ -@JvmInline -public value class FloatBuffer(public val array: FloatArray) : MutableBuffer { - override val size: Int get() = array.size - - override operator fun get(index: Int): Float = array[index] - - override operator fun set(index: Int, value: Float) { - array[index] = value - } - - override operator fun iterator(): FloatIterator = array.iterator() - - override fun copy(): MutableBuffer = - FloatBuffer(array.copyOf()) -} - -/** - * Creates a new [FloatBuffer] with the specified [size], where each element is calculated by calling the specified - * [init] function. - * - * The function [init] is called for each array element sequentially starting from the first one. - * It should return the value for a buffer element given its index. - */ -public inline fun FloatBuffer(size: Int, init: (Int) -> Float): FloatBuffer = FloatBuffer(FloatArray(size) { init(it) }) - -/** - * Returns a new [FloatBuffer] of given elements. - */ -public fun FloatBuffer(vararg floats: Float): FloatBuffer = FloatBuffer(floats) - -/** - * Returns a new [FloatArray] containing all the elements of this [Buffer]. - */ -public fun Buffer.toFloatArray(): FloatArray = when (this) { - is FloatBuffer -> array.copyOf() - else -> FloatArray(size, ::get) -} - -/** - * Returns [FloatBuffer] over this array. - * - * @receiver the array. - * @return the new buffer. - */ -public fun FloatArray.asBuffer(): FloatBuffer = FloatBuffer(this) diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt deleted file mode 100644 index 35b722e2b..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/IntBuffer.kt +++ /dev/null @@ -1,59 +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. - */ - -package space.kscience.kmath.structures - -import kotlin.jvm.JvmInline - -/** - * Specialized [MutableBuffer] implementation over [IntArray]. - * - * @property array the underlying array. - */ -@JvmInline -public value class IntBuffer(public val array: IntArray) : MutableBuffer { - override val size: Int get() = array.size - - override operator fun get(index: Int): Int = array[index] - - override operator fun set(index: Int, value: Int) { - array[index] = value - } - - override operator fun iterator(): IntIterator = array.iterator() - - override fun copy(): MutableBuffer = - IntBuffer(array.copyOf()) -} - -/** - * Creates a new [IntBuffer] with the specified [size], where each element is calculated by calling the specified - * [init] function. - * - * The function [init] is called for each array element sequentially starting from the first one. - * It should return the value for a buffer element given its index. - */ -public inline fun IntBuffer(size: Int, init: (Int) -> Int): IntBuffer = IntBuffer(IntArray(size) { init(it) }) - -/** - * Returns a new [IntBuffer] of given elements. - */ -public fun IntBuffer(vararg ints: Int): IntBuffer = IntBuffer(ints) - -/** - * Returns a new [IntArray] containing all the elements of this [Buffer]. - */ -public fun Buffer.toIntArray(): IntArray = when (this) { - is IntBuffer -> array.copyOf() - else -> IntArray(size, ::get) -} - -/** - * Returns [IntBuffer] over this array. - * - * @receiver the array. - * @return the new buffer. - */ -public fun IntArray.asBuffer(): IntBuffer = IntBuffer(this) diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ListBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ListBuffer.kt deleted file mode 100644 index 65d9dc77d..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ListBuffer.kt +++ /dev/null @@ -1,60 +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. - */ - -package space.kscience.kmath.structures - -import kotlin.jvm.JvmInline - -/** - * [Buffer] implementation over [List]. - * - * @param T the type of elements contained in the buffer. - * @property list The underlying list. - */ -public class ListBuffer(public val list: List) : Buffer { - - public constructor(size: Int, initializer: (Int) -> T) : this(List(size, initializer)) - - override val size: Int get() = list.size - - override operator fun get(index: Int): T = list[index] - override operator fun iterator(): Iterator = list.iterator() - - override fun toString(): String = Buffer.toString(this) -} - - -/** - * Returns an [ListBuffer] that wraps the original list. - */ -public fun List.asBuffer(): ListBuffer = ListBuffer(this) - -/** - * [MutableBuffer] implementation over [MutableList]. - * - * @param T the type of elements contained in the buffer. - * @property list The underlying list. - */ -@JvmInline -public value class MutableListBuffer(public val list: MutableList) : MutableBuffer { - - public constructor(size: Int, initializer: (Int) -> T) : this(MutableList(size, initializer)) - - override val size: Int get() = list.size - - override operator fun get(index: Int): T = list[index] - - override operator fun set(index: Int, value: T) { - list[index] = value - } - - override operator fun iterator(): Iterator = list.iterator() - override fun copy(): MutableBuffer = MutableListBuffer(ArrayList(list)) -} - -/** - * Returns an [MutableListBuffer] that wraps the original list. - */ -public fun MutableList.asMutableBuffer(): MutableListBuffer = MutableListBuffer(this) diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt deleted file mode 100644 index c69f4646d..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/LongBuffer.kt +++ /dev/null @@ -1,59 +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. - */ - -package space.kscience.kmath.structures - -import kotlin.jvm.JvmInline - -/** - * Specialized [MutableBuffer] implementation over [LongArray]. - * - * @property array the underlying array. - */ -@JvmInline -public value class LongBuffer(public val array: LongArray) : MutableBuffer { - override val size: Int get() = array.size - - override operator fun get(index: Int): Long = array[index] - - override operator fun set(index: Int, value: Long) { - array[index] = value - } - - override operator fun iterator(): LongIterator = array.iterator() - - override fun copy(): MutableBuffer = - LongBuffer(array.copyOf()) -} - -/** - * Creates a new [LongBuffer] with the specified [size], where each element is calculated by calling the specified - * [init] function. - * - * The function [init] is called for each array element sequentially starting from the first one. - * It should return the value for a buffer element given its index. - */ -public inline fun LongBuffer(size: Int, init: (Int) -> Long): LongBuffer = LongBuffer(LongArray(size) { init(it) }) - -/** - * Returns a new [LongBuffer] of given elements. - */ -public fun LongBuffer(vararg longs: Long): LongBuffer = LongBuffer(longs) - -/** - * Returns a new [LongArray] containing all the elements of this [Buffer]. - */ -public fun Buffer.toLongArray(): LongArray = when (this) { - is LongBuffer -> array.copyOf() - else -> LongArray(size, ::get) -} - -/** - * Returns [LongBuffer] over this array. - * - * @receiver the array. - * @return the new buffer. - */ -public fun LongArray.asBuffer(): LongBuffer = LongBuffer(this) diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/MemoryBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/MemoryBuffer.kt deleted file mode 100644 index 1dadaf7d4..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/MemoryBuffer.kt +++ /dev/null @@ -1,68 +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. - */ - -package space.kscience.kmath.structures - -import space.kscience.kmath.memory.* - -/** - * A non-boxing buffer over [Memory] object. - * - * @param T the type of elements contained in the buffer. - * @property memory the underlying memory segment. - * @property spec the spec of [T] type. - */ -public open class MemoryBuffer(protected val memory: Memory, protected val spec: MemorySpec) : Buffer { - override val size: Int get() = memory.size / spec.objectSize - - private val reader: MemoryReader = memory.reader() - - override operator fun get(index: Int): T = reader.read(spec, spec.objectSize * index) - override operator fun iterator(): Iterator = (0 until size).asSequence().map { get(it) }.iterator() - - override fun toString(): String = Buffer.toString(this) - - public companion object { - public fun create(spec: MemorySpec, size: Int): MemoryBuffer = - MemoryBuffer(Memory.allocate(size * spec.objectSize), spec) - - public inline fun create( - spec: MemorySpec, - size: Int, - initializer: (Int) -> T, - ): MemoryBuffer = MutableMemoryBuffer(Memory.allocate(size * spec.objectSize), spec).also { buffer -> - (0 until size).forEach { buffer[it] = initializer(it) } - } - } -} - -/** - * A mutable non-boxing buffer over [Memory] object. - * - * @param T the type of elements contained in the buffer. - * @property memory the underlying memory segment. - * @property spec the spec of [T] type. - */ -public class MutableMemoryBuffer(memory: Memory, spec: MemorySpec) : MemoryBuffer(memory, spec), - MutableBuffer { - - private val writer: MemoryWriter = memory.writer() - - override operator fun set(index: Int, value: T): Unit = writer.write(spec, spec.objectSize * index, value) - override fun copy(): MutableBuffer = MutableMemoryBuffer(memory.copy(), spec) - - public companion object { - public fun create(spec: MemorySpec, size: Int): MutableMemoryBuffer = - MutableMemoryBuffer(Memory.allocate(size * spec.objectSize), spec) - - public inline fun create( - spec: MemorySpec, - size: Int, - initializer: (Int) -> T, - ): MutableMemoryBuffer = MutableMemoryBuffer(Memory.allocate(size * spec.objectSize), spec).also { buffer -> - (0 until size).forEach { buffer[it] = initializer(it) } - } - } -} diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/MutableBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/MutableBuffer.kt deleted file mode 100644 index 429c1a64b..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/MutableBuffer.kt +++ /dev/null @@ -1,97 +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. - */ - -package space.kscience.kmath.structures - -import kotlin.reflect.KClass - -/** - * A generic mutable random-access structure for both primitives and objects. - * - * @param T the type of elements contained in the buffer. - */ -public interface MutableBuffer : Buffer { - /** - * Sets the array element at the specified [index] to the specified [value]. - */ - public operator fun set(index: Int, value: T) - - /** - * Returns a shallow copy of the buffer. - */ - public fun copy(): MutableBuffer - - public companion object { - /** - * Creates a [DoubleBuffer] with the specified [size], where each element is calculated by calling the specified - * [initializer] function. - */ - public inline fun double(size: Int, initializer: (Int) -> Double): DoubleBuffer = - DoubleBuffer(size, initializer) - - /** - * Creates a [ShortBuffer] with the specified [size], where each element is calculated by calling the specified - * [initializer] function. - */ - public inline fun short(size: Int, initializer: (Int) -> Short): ShortBuffer = - ShortBuffer(size, initializer) - - /** - * Creates a [IntBuffer] with the specified [size], where each element is calculated by calling the specified - * [initializer] function. - */ - public inline fun int(size: Int, initializer: (Int) -> Int): IntBuffer = - IntBuffer(size, initializer) - - /** - * Creates a [LongBuffer] with the specified [size], where each element is calculated by calling the specified - * [initializer] function. - */ - public inline fun long(size: Int, initializer: (Int) -> Long): LongBuffer = - LongBuffer(size, initializer) - - - /** - * Creates a [FloatBuffer] with the specified [size], where each element is calculated by calling the specified - * [initializer] function. - */ - public inline fun float(size: Int, initializer: (Int) -> Float): FloatBuffer = - FloatBuffer(size, initializer) - - - /** - * Create a boxing mutable buffer of given type - */ - public inline fun boxing(size: Int, initializer: (Int) -> T): MutableBuffer = - MutableListBuffer(MutableList(size, initializer)) - - /** - * Creates a [MutableBuffer] of given [type]. If the type is primitive, specialized buffers are used - * ([IntBuffer], [DoubleBuffer], etc.), [ListBuffer] is returned otherwise. - * - * The [size] is specified, and each element is calculated by calling the specified [initializer] function. - */ - @Suppress("UNCHECKED_CAST") - public inline fun auto(type: KClass, size: Int, initializer: (Int) -> T): MutableBuffer = - when (type) { - Double::class -> double(size) { initializer(it) as Double } as MutableBuffer - Short::class -> short(size) { initializer(it) as Short } as MutableBuffer - Int::class -> int(size) { initializer(it) as Int } as MutableBuffer - Float::class -> float(size) { initializer(it) as Float } as MutableBuffer - Long::class -> long(size) { initializer(it) as Long } as MutableBuffer - else -> boxing(size, initializer) - } - - /** - * Creates a [MutableBuffer] of given type [T]. If the type is primitive, specialized buffers are used - * ([IntBuffer], [DoubleBuffer], etc.), [ListBuffer] is returned otherwise. - * - * The [size] is specified, and each element is calculated by calling the specified [initializer] function. - */ - @Suppress("UNCHECKED_CAST") - public inline fun auto(size: Int, initializer: (Int) -> T): MutableBuffer = - auto(T::class, size, initializer) - } -} \ No newline at end of file diff --git a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ShortBuffer.kt b/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ShortBuffer.kt deleted file mode 100644 index 20691511b..000000000 --- a/kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ShortBuffer.kt +++ /dev/null @@ -1,57 +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. - */ - -package space.kscience.kmath.structures - -import kotlin.jvm.JvmInline - -/** - * Specialized [MutableBuffer] implementation over [ShortArray]. - * - * @property array the underlying array. - */ -@JvmInline -public value class ShortBuffer(public val array: ShortArray) : MutableBuffer { - override val size: Int get() = array.size - - override operator fun get(index: Int): Short = array[index] - - override operator fun set(index: Int, value: Short) { - array[index] = value - } - - override operator fun iterator(): ShortIterator = array.iterator() - override fun copy(): MutableBuffer = ShortBuffer(array.copyOf()) -} - -/** - * Creates a new [ShortBuffer] with the specified [size], where each element is calculated by calling the specified - * [init] function. - * - * The function [init] is called for each array element sequentially starting from the first one. - * It should return the value for a buffer element given its index. - */ -public inline fun ShortBuffer(size: Int, init: (Int) -> Short): ShortBuffer = ShortBuffer(ShortArray(size) { init(it) }) - -/** - * Returns a new [ShortBuffer] of given elements. - */ -public fun ShortBuffer(vararg shorts: Short): ShortBuffer = ShortBuffer(shorts) - -/** - * Returns a new [ShortArray] containing all the elements of this [Buffer]. - */ -public fun Buffer.toShortArray(): ShortArray = when (this) { - is ShortBuffer -> array.copyOf() - else -> ShortArray(size, ::get) -} - -/** - * Returns [ShortBuffer] over this array. - * - * @receiver the array. - * @return the new buffer. - */ -public fun ShortArray.asBuffer(): ShortBuffer = ShortBuffer(this) diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/ExpressionFieldTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/ExpressionFieldTest.kt deleted file mode 100644 index 80c5943cf..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/ExpressionFieldTest.kt +++ /dev/null @@ -1,48 +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. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.operations.DoubleField -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertFails - -class ExpressionFieldTest { - val x by symbol - - @Test - fun testExpression() { - val expression = with(FunctionalExpressionField(DoubleField)) { - val x by binding - x * x + 2 * x + one - } - - assertEquals(expression(x to 1.0), 4.0) - assertFails { expression() } - } - - @Test - fun separateContext() { - fun FunctionalExpressionField.expression(): Expression { - val x by binding - return x * x + 2 * x + one - } - - val expression = FunctionalExpressionField(DoubleField).expression() - assertEquals(expression(x to 1.0), 4.0) - } - - @Test - fun valueExpression() { - val expressionBuilder: FunctionalExpressionField.() -> Expression = { - val x by binding - x * x + 2 * x + one - } - - val expression = FunctionalExpressionField(DoubleField).expressionBuilder() - assertEquals(expression(x to 1.0), 4.0) - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/InterpretTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/InterpretTest.kt deleted file mode 100644 index 156334b2e..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/InterpretTest.kt +++ /dev/null @@ -1,36 +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. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.BooleanAlgebra -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.invoke -import kotlin.test.Test -import kotlin.test.assertEquals - - -internal class InterpretTest { - @Test - fun interpretation() { - val expr = MstField { - x * 2.0 + number(2.0) / x - 16.0 - }.toExpression(DoubleField) - assertEquals(-10.69, expr(x to 2.2), 0.02) - } - - @Test - @UnstableKMathAPI - fun booleanAlgebra() { - val expr = MstLogicAlgebra { - x and const(true) - }.toExpression(BooleanAlgebra) - - assertEquals(true, expr(x to true)) - assertEquals(false, expr(x to false)) - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/SimpleAutoDiffTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/SimpleAutoDiffTest.kt deleted file mode 100644 index 201890933..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/SimpleAutoDiffTest.kt +++ /dev/null @@ -1,292 +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. - */ - -package space.kscience.kmath.expressions - -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.bindSymbol -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.asBuffer -import kotlin.math.E -import kotlin.math.PI -import kotlin.math.pow -import kotlin.math.sqrt -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue - -internal class SimpleAutoDiffTest { - - fun dx( - xBinding: Pair, - body: SimpleAutoDiffField.(x: AutoDiffValue) -> AutoDiffValue, - ): DerivationResult = DoubleField.simpleAutoDiff(xBinding) { body(bindSymbol(xBinding.first)) } - - fun dxy( - xBinding: Pair, - yBinding: Pair, - body: SimpleAutoDiffField.(x: AutoDiffValue, y: AutoDiffValue) -> AutoDiffValue, - ): DerivationResult = DoubleField.simpleAutoDiff(xBinding, yBinding) { - body(bindSymbol(xBinding.first), bindSymbol(yBinding.first)) - } - - fun diff(block: SimpleAutoDiffField.() -> AutoDiffValue): SimpleAutoDiffExpression { - return SimpleAutoDiffExpression(DoubleField, block) - } - - val x by symbol - val y by symbol - val z by symbol - - @Test - fun testPlusX2() { - val y = DoubleField.simpleAutoDiff(x to 3.0) { - // diff w.r.t this x at 3 - val x = bindSymbol(x) - x + x - } - assertEquals(6.0, y.value) // y = x + x = 6 - assertEquals(2.0, y.derivative(x)) // dy/dx = 2 - } - - @Test - fun testPlusX2Expr() { - val expr = diff { - val x = bindSymbol(x) - x + x - } - assertEquals(6.0, expr(x to 3.0)) // y = x + x = 6 - assertEquals(2.0, expr.derivative(x)(x to 3.0)) // dy/dx = 2 - } - - - @Test - fun testPlus() { - // two variables - val z = DoubleField.simpleAutoDiff(x to 2.0, y to 3.0) { - val x = bindSymbol(x) - val y = bindSymbol(y) - x + y - } - assertEquals(5.0, z.value) // z = x + y = 5 - assertEquals(1.0, z.derivative(x)) // dz/dx = 1 - assertEquals(1.0, z.derivative(y)) // dz/dy = 1 - } - - @Test - fun testMinus() { - // two variables - val z = DoubleField.simpleAutoDiff(x to 7.0, y to 3.0) { - val x = bindSymbol(x) - val y = bindSymbol(y) - - x - y - } - assertEquals(4.0, z.value) // z = x - y = 4 - assertEquals(1.0, z.derivative(x)) // dz/dx = 1 - assertEquals(-1.0, z.derivative(y)) // dz/dy = -1 - } - - @Test - fun testMulX2() { - val y = dx(x to 3.0) { x -> - // diff w.r.t this x at 3 - x * x - } - assertEquals(9.0, y.value) // y = x * x = 9 - assertEquals(6.0, y.derivative(x)) // dy/dx = 2 * x = 7 - } - - @Test - fun testSqr() { - val y = dx(x to 3.0) { x -> sqr(x) } - assertEquals(9.0, y.value) // y = x ^ 2 = 9 - assertEquals(6.0, y.derivative(x)) // dy/dx = 2 * x = 7 - } - - @Test - fun testSqrSqr() { - val y = dx(x to 2.0) { x -> sqr(sqr(x)) } - assertEquals(16.0, y.value) // y = x ^ 4 = 16 - assertEquals(32.0, y.derivative(x)) // dy/dx = 4 * x^3 = 32 - } - - @Test - fun testX3() { - val y = dx(x to 2.0) { x -> - // diff w.r.t this x at 2 - x * x * x - } - assertEquals(8.0, y.value) // y = x * x * x = 8 - assertEquals(12.0, y.derivative(x)) // dy/dx = 3 * x * x = 12 - } - - @Test - fun testDiv() { - val z = dxy(x to 5.0, y to 2.0) { x, y -> - x / y - } - assertEquals(2.5, z.value) // z = x / y = 2.5 - assertEquals(0.5, z.derivative(x)) // dz/dx = 1 / y = 0.5 - assertEquals(-1.25, z.derivative(y)) // dz/dy = -x / y^2 = -1.25 - } - - @Test - fun testPow3() { - val y = dx(x to 2.0) { x -> - // diff w.r.t this x at 2 - pow(x, 3) - } - assertEquals(8.0, y.value) // y = x ^ 3 = 8 - assertEquals(12.0, y.derivative(x)) // dy/dx = 3 * x ^ 2 = 12 - } - - @Test - fun testPowFull() { - val z = dxy(x to 2.0, y to 3.0) { x, y -> - pow(x, y) - } - assertApprox(8.0, z.value) // z = x ^ y = 8 - assertApprox(12.0, z.derivative(x)) // dz/dx = y * x ^ (y - 1) = 12 - assertApprox(8.0 * kotlin.math.ln(2.0), z.derivative(y)) // dz/dy = x ^ y * ln(x) - } - - @Test - fun testFromPaper() { - val y = dx(x to 3.0) { x -> 2 * x + x * x * x } - assertEquals(33.0, y.value) // y = 2 * x + x * x * x = 33 - assertEquals(29.0, y.derivative(x)) // dy/dx = 2 + 3 * x * x = 29 - } - - @Test - fun testInnerVariable() { - val y = dx(x to 1.0) { x -> - const(1.0) * x - } - assertEquals(1.0, y.value) // y = x ^ n = 1 - assertEquals(1.0, y.derivative(x)) // dy/dx = n * x ^ (n - 1) = n - 1 - } - - @Test - fun testLongChain() { - val n = 10_000 - val y = dx(x to 1.0) { x -> - var res = const(1.0) - for (i in 1..n) res *= x - res - } - assertEquals(1.0, y.value) // y = x ^ n = 1 - assertEquals(n.toDouble(), y.derivative(x)) // dy/dx = n * x ^ (n - 1) = n - 1 - } - - @Test - fun testExample() { - val y = dx(x to 2.0) { x -> sqr(x) + 5 * x + 3 } - assertEquals(17.0, y.value) // the value of result (y) - assertEquals(9.0, y.derivative(x)) // dy/dx - } - - @Test - fun testSqrt() { - val y = dx(x to 16.0) { x -> sqrt(x) } - assertEquals(4.0, y.value) // y = x ^ 1/2 = 4 - assertEquals(1.0 / 8, y.derivative(x)) // dy/dx = 1/2 / x ^ 1/4 = 1/8 - } - - @Test - fun testSin() { - val y = dx(x to PI / 6.0) { x -> sin(x) } - assertApprox(0.5, y.value) // y = sin(PI/6) = 0.5 - assertApprox(sqrt(3.0) / 2, y.derivative(x)) // dy/dx = cos(pi/6) = sqrt(3)/2 - } - - @Test - fun testCos() { - val y = dx(x to PI / 6) { x -> cos(x) } - assertApprox(sqrt(3.0) / 2, y.value) //y = cos(pi/6) = sqrt(3)/2 - assertApprox(-0.5, y.derivative(x)) // dy/dx = -sin(pi/6) = -0.5 - } - - @Test - fun testTan() { - val y = dx(x to PI / 6) { x -> tan(x) } - assertApprox(1.0 / sqrt(3.0), y.value) // y = tan(pi/6) = 1/sqrt(3) - assertApprox(4.0 / 3.0, y.derivative(x)) // dy/dx = sec(pi/6)^2 = 4/3 - } - - @Test - fun testAsin() { - val y = dx(x to PI / 6) { x -> asin(x) } - assertApprox(kotlin.math.asin(PI / 6.0), y.value) // y = asin(pi/6) - assertApprox(6.0 / sqrt(36 - PI * PI), y.derivative(x)) // dy/dx = 6/sqrt(36-pi^2) - } - - @Test - fun testAcos() { - val y = dx(x to PI / 6) { x -> acos(x) } - assertApprox(kotlin.math.acos(PI / 6.0), y.value) // y = acos(pi/6) - assertApprox(-6.0 / sqrt(36.0 - PI * PI), y.derivative(x)) // dy/dx = -6/sqrt(36-pi^2) - } - - @Test - fun testAtan() { - val y = dx(x to PI / 6) { x -> atan(x) } - assertApprox(kotlin.math.atan(PI / 6.0), y.value) // y = atan(pi/6) - assertApprox(36.0 / (36.0 + PI * PI), y.derivative(x)) // dy/dx = 36/(36+pi^2) - } - - @Test - fun testSinh() { - val y = dx(x to 0.0) { x -> sinh(x) } - assertApprox(kotlin.math.sinh(0.0), y.value) // y = sinh(0) - assertApprox(kotlin.math.cosh(0.0), y.derivative(x)) // dy/dx = cosh(0) - } - - @Test - fun testCosh() { - val y = dx(x to 0.0) { x -> cosh(x) } - assertApprox(1.0, y.value) //y = cosh(0) - assertApprox(0.0, y.derivative(x)) // dy/dx = sinh(0) - } - - @Test - fun testTanh() { - val y = dx(x to 1.0) { x -> tanh(x) } - assertApprox((E * E - 1) / (E * E + 1), y.value) // y = tanh(pi/6) - assertApprox(1.0 / kotlin.math.cosh(1.0).pow(2), y.derivative(x)) // dy/dx = sech(pi/6)^2 - } - - @Test - fun testAsinh() { - val y = dx(x to PI / 6) { x -> asinh(x) } - assertApprox(kotlin.math.asinh(PI / 6.0), y.value) // y = asinh(pi/6) - assertApprox(6.0 / sqrt(36 + PI * PI), y.derivative(x)) // dy/dx = 6/sqrt(pi^2+36) - } - - @Test - fun testAcosh() { - val y = dx(x to PI / 6) { x -> acosh(x) } - assertApprox(kotlin.math.acosh(PI / 6.0), y.value) // y = acosh(pi/6) - assertApprox(-6.0 / sqrt(36.0 - PI * PI), y.derivative(x)) // dy/dx = -6/sqrt(36-pi^2) - } - - @Test - fun testAtanh() { - val y = dx(x to PI / 6) { x -> atanh(x) } - assertApprox(kotlin.math.atanh(PI / 6.0), y.value) // y = atanh(pi/6) - assertApprox(-36.0 / (PI * PI - 36.0), y.derivative(x)) // dy/dx = -36/(pi^2-36) - } - - @Test - fun testDivGrad() { - val res = dxy(x to 1.0, y to 2.0) { x, y -> x * x + y * y } - assertEquals(6.0, res.div()) - assertTrue(Buffer.contentEquals(res.grad(x, y), doubleArrayOf(2.0, 4.0).asBuffer())) - } - - private fun assertApprox(a: Double, b: Double) { - if ((a - b) > 1e-10) assertEquals(a, b) - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/DoubleLUSolverTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/DoubleLUSolverTest.kt deleted file mode 100644 index 70e010f2e..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/DoubleLUSolverTest.kt +++ /dev/null @@ -1,62 +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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.algebra -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue - -@OptIn(PerformancePitfall::class) -fun assertMatrixEquals(expected: StructureND, actual: StructureND) { - assertTrue { StructureND.contentEquals(expected, actual) } -} - -@UnstableKMathAPI -class DoubleLUSolverTest { - - @Test - fun testInvertOne() = Double.algebra.linearSpace.run{ - val matrix = one(2, 2) - val inverted = lupSolver().inverse(matrix) - assertMatrixEquals(matrix, inverted) - } - - @Test - fun testDecomposition() = Double.algebra.linearSpace.run { - val matrix = matrix(2, 2)( - 3.0, 1.0, - 2.0, 3.0 - ) - - val lup = lup(matrix) - - //Check determinant - assertEquals(7.0, lup.determinant) - - assertMatrixEquals(lup.p dot matrix, lup.l dot lup.u) - } - - @Test - fun testInvert() = Double.algebra.linearSpace.run{ - val matrix = matrix(2, 2)( - 3.0, 1.0, - 1.0, 3.0 - ) - - val inverted = lupSolver().inverse(matrix) - - val expected = matrix(2, 2)( - 0.375, -0.125, - -0.125, 0.375 - ) - - assertMatrixEquals(expected, inverted) - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt deleted file mode 100644 index 25d187bf0..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt +++ /dev/null @@ -1,76 +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. - */ - -package space.kscience.kmath.linear - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.nd.as2D -import space.kscience.kmath.operations.algebra -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue - -@UnstableKMathAPI -@OptIn(PerformancePitfall::class) -@Suppress("UNUSED_VARIABLE") -class MatrixTest { - - @Test - fun testTranspose() = Double.algebra.linearSpace.run { - val matrix = one(3, 3) - val transposed = matrix.transpose() - assertTrue { StructureND.contentEquals(matrix, transposed) } - } - - @Test - fun testBuilder() = Double.algebra.linearSpace.run { - val matrix = matrix(2, 3)( - 1.0, 0.0, 0.0, - 0.0, 1.0, 2.0 - ) - - assertEquals(2.0, matrix[1, 2]) - } - - @Test - fun testMatrixExtension() = Double.algebra.linearSpace.run { - val transitionMatrix: Matrix = VirtualMatrix(6, 6) { row, col -> - when { - col == 0 -> .50 - row + 1 == col -> .50 - row == 5 && col == 5 -> 1.0 - else -> 0.0 - } - } - - infix fun Matrix.pow(power: Int): Matrix { - var res = this - repeat(power - 1) { - res = res dot this@pow - } - return res - } - - val toTenthPower = transitionMatrix pow 10 - } - - @Test - fun test2DDot() = Double.algebra.linearSpace.run { - val firstMatrix = StructureND.auto(2, 3) { (i, j) -> (i + j).toDouble() }.as2D() - val secondMatrix = StructureND.auto(3, 2) { (i, j) -> (i + j).toDouble() }.as2D() - -// val firstMatrix = produce(2, 3) { i, j -> (i + j).toDouble() } -// val secondMatrix = produce(3, 2) { i, j -> (i + j).toDouble() } - val result = firstMatrix dot secondMatrix - assertEquals(2, result.rowNum) - assertEquals(2, result.colNum) - assertEquals(8.0, result[0, 1]) - assertEquals(8.0, result[1, 0]) - assertEquals(14.0, result[1, 1]) - - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/misc/CumulativeKtTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/misc/CumulativeKtTest.kt deleted file mode 100644 index e5f3f337f..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/misc/CumulativeKtTest.kt +++ /dev/null @@ -1,18 +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. - */ - -package space.kscience.kmath.misc - -import kotlin.test.Test -import kotlin.test.assertEquals - -class CumulativeKtTest { - @Test - fun testCumulativeSum() { - val initial = listOf(-1.0, 2.0, 1.0, 1.0) - val cumulative = initial.cumulativeSum() - assertEquals(listOf(-1.0, 1.0, 2.0, 3.0), cumulative) - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/misc/PermSortTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/misc/PermSortTest.kt deleted file mode 100644 index 0a2bb9138..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/misc/PermSortTest.kt +++ /dev/null @@ -1,103 +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 file. - */ - -package space.kscience.kmath.misc - -import space.kscience.kmath.misc.PermSortTest.Platform.* -import kotlin.random.Random -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue - -import space.kscience.kmath.structures.IntBuffer -import space.kscience.kmath.structures.asBuffer -import kotlin.test.assertContentEquals - -class PermSortTest { - - private enum class Platform { - ANDROID, JVM, JS, NATIVE, WASM - } - - private val platforms = Platform.values().asBuffer() - - /** - * Permutation on empty buffer should immediately return an empty array. - */ - @Test - fun testOnEmptyBuffer() { - val emptyBuffer = IntBuffer(0) {it} - var permutations = emptyBuffer.permSort() - assertTrue(permutations.isEmpty(), "permutation on an empty buffer should return an empty result") - permutations = emptyBuffer.permSortDescending() - assertTrue(permutations.isEmpty(), "permutation on an empty buffer should return an empty result") - } - - @Test - fun testOnSingleValueBuffer() { - testPermutation(1) - } - - @Test - fun testOnSomeValues() { - testPermutation(10) - } - - @Test - fun testPermSortBy() { - val permutations = platforms.permSortBy { it.name } - val expected = listOf(ANDROID, JS, JVM, NATIVE, WASM) - assertContentEquals(expected, permutations.map { platforms[it] }, "Ascending PermSort by name") - } - - @Test - fun testPermSortByDescending() { - val permutations = platforms.permSortByDescending { it.name } - val expected = listOf(WASM, NATIVE, JVM, JS, ANDROID) - assertContentEquals(expected, permutations.map { platforms[it] }, "Descending PermSort by name") - } - - @Test - fun testPermSortWith() { - var permutations = platforms.permSortWith { p1, p2 -> p1.name.length.compareTo(p2.name.length) } - val expected = listOf(JS, JVM, WASM, NATIVE, ANDROID) - assertContentEquals(expected, permutations.map { platforms[it] }, "PermSort using custom ascending comparator") - - permutations = platforms.permSortWith(compareByDescending { it.name.length }) - assertContentEquals(expected.reversed(), permutations.map { platforms[it] }, "PermSort using custom descending comparator") - } - - private fun testPermutation(bufferSize: Int) { - - val seed = Random.nextLong() - println("Test randomization seed: $seed") - - val buffer = Random(seed).buffer(bufferSize) - val indices = buffer.permSort() - - assertEquals(bufferSize, indices.size) - // Ensure no doublon is present in indices - assertEquals(indices.toSet().size, indices.size) - - for (i in 0 until (bufferSize-1)) { - val current = buffer[indices[i]] - val next = buffer[indices[i+1]] - assertTrue(current <= next, "Permutation indices not properly sorted") - } - - val descIndices = buffer.permSortDescending() - assertEquals(bufferSize, descIndices.size) - // Ensure no doublon is present in indices - assertEquals(descIndices.toSet().size, descIndices.size) - - for (i in 0 until (bufferSize-1)) { - val current = buffer[descIndices[i]] - val next = buffer[descIndices[i+1]] - assertTrue(current >= next, "Permutation indices not properly sorted in descending order") - } - } - - private fun Random.buffer(size : Int) = IntBuffer(size) { nextInt() } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntAlgebraTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntAlgebraTest.kt deleted file mode 100644 index 0527f5252..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntAlgebraTest.kt +++ /dev/null @@ -1,72 +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. - */ - -package space.kscience.kmath.operations - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.testutils.RingVerifier -import kotlin.math.pow -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class BigIntAlgebraTest { - @Test - fun verify() = BigIntField { RingVerifier(this, +"42", +"10", +"-12", 10).verify() } - - @Test - fun testKBigIntegerRingSum() { - val res = BigIntField { - 1_000L.toBigInt() * 1_000L.toBigInt() - } - assertEquals(res, 1_000_000.toBigInt()) - } - - @UnstableKMathAPI - @Test - fun testKBigIntegerRingPow() { - for (num in -5..5) - for (exponent in 0U..10U) - assertEquals( - num.toDouble().pow(exponent.toInt()).toLong().toBigInt(), - num.toBigInt().pow(exponent), - "$num ^ $exponent" - ) - } - - @Test - fun testKBigIntegerRingSum_100_000_000__100_000_000() { - BigIntField { - val sum = +"100_000_000" + +"100_000_000" - assertEquals(sum, "200_000_000".parseBigInteger()) - } - } - - @Test - fun test_mul_3__4() { - BigIntField { - val prod = +"0x3000_0000_0000" * +"0x4000_0000_0000_0000_0000" - assertEquals(prod, "0xc00_0000_0000_0000_0000_0000_0000_0000".parseBigInteger()) - } - } - - @Test - fun test_div_big_1() { - BigIntField { - val res = +"1_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000" / - +"555_000_444_000_333_000_222_000_111_000_999_001" - assertEquals(res, +"1801800360360432432518919022699") - } - } - - @Test - fun test_rem_big_1() { - BigIntField { - val res = +"1_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000_000" % - +"555_000_444_000_333_000_222_000_111_000_999_001" - assertEquals(res, +"324121220440768000291647788404676301") - } - } - -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntConstructorTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntConstructorTest.kt deleted file mode 100644 index eec3dc3bf..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntConstructorTest.kt +++ /dev/null @@ -1,31 +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. - */ - -package space.kscience.kmath.operations - -import kotlin.test.Test -import kotlin.test.assertEquals - -class BigIntConstructorTest { - @Test - fun testConstructorZero() { - assertEquals(0.toBigInt(), uintArrayOf().toBigInt(0)) - } - - @Test - fun testConstructor8() { - assertEquals( - 8.toBigInt(), - uintArrayOf(8U).toBigInt(1) - ) - } - - @Test - fun testConstructor_0xffffffffaL() { - val x = (-0xffffffffaL).toBigInt() - val y = uintArrayOf(0xfffffffaU, 0xfU).toBigInt(-1) - assertEquals(x, y) - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntConversionsTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntConversionsTest.kt deleted file mode 100644 index 85f368f3e..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntConversionsTest.kt +++ /dev/null @@ -1,76 +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. - */ - -package space.kscience.kmath.operations - -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertNull - -@kotlin.ExperimentalUnsignedTypes -class BigIntConversionsTest { - - @Test - fun testEmptyString() { - assertNull("".parseBigInteger()) - assertNull("+".parseBigInteger()) - assertNull("-".parseBigInteger()) - - assertNull("0x".parseBigInteger()) - assertNull("+0x".parseBigInteger()) - assertNull("-0x".parseBigInteger()) - - - assertNull("_".parseBigInteger()) - assertNull("+_".parseBigInteger()) - assertNull("-_".parseBigInteger()) - - assertNull("0x_".parseBigInteger()) - assertNull("+0x_".parseBigInteger()) - assertNull("-0x_".parseBigInteger()) - } - - @Test - fun testToString0x10() { - val x = 0x10.toBigInt() - assertEquals("0x10", x.toString()) - } - - @Test - fun testUnderscores() { - assertEquals("0x10", "0x_1_0_".parseBigInteger().toString()) - assertEquals("0xa", "_1_0_".parseBigInteger().toString()) - } - - @Test - fun testToString0x17ffffffd() { - val x = 0x17ffffffdL.toBigInt() - assertEquals("0x17ffffffd", x.toString()) - } - - @Test - fun testToString_0x17ead2ffffd() { - val x = (-0x17ead2ffffdL).toBigInt() - assertEquals("-0x17ead2ffffd", x.toString()) - } - - @Test - fun testToString_0x17ead2ffffd11223344() { - val x = uintArrayOf(0x11223344U, 0xad2ffffdU, 0x17eU).toBigInt(-1) - assertEquals("-0x17ead2ffffd11223344", x.toString()) - } - - @Test - fun testFromString_0x17ead2ffffd11223344() { - val x = "0x17ead2ffffd11223344".parseBigInteger() - assertEquals("0x17ead2ffffd11223344", x.toString()) - } - - @Test - fun testFromString_7059135710711894913860() { - val x = "-7059135710711894913860".parseBigInteger() - assertEquals("-0x17ead2ffffd11223344", x.toString()) - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntOperationsTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntOperationsTest.kt deleted file mode 100644 index 26d6af224..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/BigIntOperationsTest.kt +++ /dev/null @@ -1,409 +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. - */ - -package space.kscience.kmath.operations - -import kotlin.random.Random -import kotlin.random.nextUInt -import kotlin.test.Test -import kotlin.test.assertContentEquals -import kotlin.test.assertEquals -import kotlin.test.assertFalse - -@kotlin.ExperimentalUnsignedTypes -class BigIntOperationsTest { - @Test - fun testPlus_1_1() { - val x = 1.toBigInt() - val y = 1.toBigInt() - - val res = x + y - val sum = 2.toBigInt() - - assertEquals(sum, res) - } - - @Test - fun testPlusBigNumbers() { - val x = 0x7fffffff.toBigInt() - val y = 0x7fffffff.toBigInt() - val z = 0x7fffffff.toBigInt() - - val res = x + y + z - val sum = uintArrayOf(0x7ffffffdU, 0x1U).toBigInt(1) - - assertEquals(sum, res) - } - - @Test - fun testUnaryMinus() { - val x = 1234.toBigInt() - val y = (-1234).toBigInt() - assertEquals(-x, y) - } - - @Test - fun testMinus_2_1() { - val x = 2.toBigInt() - val y = 1.toBigInt() - - val res = x - y - val sum = 1.toBigInt() - - assertEquals(sum, res) - } - - @Test - fun testMinus__2_1() { - val x = (-2).toBigInt() - val y = 1.toBigInt() - - val res = x - y - val sum = (-3).toBigInt() - - assertEquals(sum, res) - } - - @Test - fun testMinus___2_1() { - val x = (-2).toBigInt() - val y = 1.toBigInt() - - val res = -x - y - val sum = 1.toBigInt() - - assertEquals(sum, res) - } - - @Test - fun testMinusBigNumbers() { - val x = 12345.toBigInt() - val y = 0xffffffffaL.toBigInt() - - val res = x - y - val sum = (-0xfffffcfc1L).toBigInt() - - assertEquals(sum, res) - } - - @Test - fun testMultiply_2_3() { - val x = 2.toBigInt() - val y = 3.toBigInt() - - val res = x * y - val prod = 6.toBigInt() - - assertEquals(prod, res) - } - - @Test - fun testMultiply__2_3() { - val x = (-2).toBigInt() - val y = 3.toBigInt() - - val res = x * y - val prod = (-6).toBigInt() - - assertEquals(prod, res) - } - - @Test - fun testMultiply_0xfff123_0xfff456() { - val x = 0xfff123.toBigInt() - val y = 0xfff456.toBigInt() - - val res = x * y - val prod = 0xffe579ad5dc2L.toBigInt() - - assertEquals(prod, res) - } - - @Test - fun testMultiplyUInt_0xfff123_0xfff456() { - val x = 0xfff123.toBigInt() - val y = 0xfff456U - - val res = x * y - val prod = 0xffe579ad5dc2L.toBigInt() - - assertEquals(prod, res) - } - - @Test - fun testMultiplyInt_0xfff123__0xfff456() { - val x = 0xfff123.toBigInt() - val y = -0xfff456 - - val res = x * y - val prod = (-0xffe579ad5dc2L).toBigInt() - - assertEquals(prod, res) - } - - @Test - fun testMultiply_0xffffffff_0xffffffff() { - val x = 0xffffffffL.toBigInt() - val y = 0xffffffffL.toBigInt() - - val res = x * y - val prod = 0xfffffffe00000001UL.toBigInt() - - assertEquals(prod, res) - } - - @Test - fun testKaratsuba() { - val random = Random(2222) - val x = uintArrayOf(12U, 345U) - val y = uintArrayOf(6U, 789U) - assertContentEquals(BigInt.naiveMultiplyMagnitudes(x, y), BigInt.karatsubaMultiplyMagnitudes(x, y)) - val x1 = UIntArray(Random.nextInt(100, 1000)) { random.nextUInt() } - val y1 = UIntArray(Random.nextInt(100, 1000)) { random.nextUInt() } - assertContentEquals(BigInt.naiveMultiplyMagnitudes(x1, y1), BigInt.karatsubaMultiplyMagnitudes(x1, y1)) - } - - @Test - fun test_shr_20() { - val x = 20.toBigInt() - assertEquals(10.toBigInt(), x shr 1) - } - - @Test - fun test_shl_20() { - val x = 20.toBigInt() - assertEquals(40.toBigInt(), x shl 1) - } - - @Test - fun test_shl_1_0() { - assertEquals( - BigInt.ONE, - BigInt.ONE shl 0 - ) - } - - @Test - fun test_shl_1_32() { - assertEquals( - 0x100000000UL.toBigInt(), - BigInt.ONE shl 32 - ) - } - - @Test - fun test_shl_1_33() { - assertEquals( - 0x200000000UL.toBigInt(), - BigInt.ONE shl 33 - ) - } - - @Test - fun test_shr_1_33_33() { - assertEquals( - BigInt.ONE, - (BigInt.ONE shl 33) shr 33 - ) - } - - @Test - fun test_shr_1_32() { - assertEquals( - BigInt.ZERO, - BigInt.ONE shr 32 - ) - } - - @Test - fun test_and_123_456() { - val x = 123.toBigInt() - val y = 456.toBigInt() - assertEquals(72.toBigInt(), x and y) - } - - @Test - fun test_or_123_456() { - val x = 123.toBigInt() - val y = 456.toBigInt() - assertEquals(507.toBigInt(), x or y) - } - - @Test - fun test_asd() { - assertEquals( - BigInt.ONE, - BigInt.ZERO or ((20.toBigInt() shr 4) and BigInt.ONE) - ) - } - - @Test - fun test_square_0x11223344U_0xad2ffffdU_0x17eU() { - val num = - uintArrayOf(0x11223344U, 0xad2ffffdU, 0x17eU).toBigInt(-1) - println(num) - val res = num * num - assertEquals( - res, - uintArrayOf(0xb0542a10U, 0xbbd85bc8U, 0x2a1fa515U, 0x5069e03bU, 0x23c09U).toBigInt(1) - ) - } - - @Test - fun testDivision_6_3() { - val x = 6.toBigInt() - val y = 3U - - val res = x / y - val div = 2.toBigInt() - - assertEquals(div, res) - } - - @Test - fun testBigDivision_6_3() { - val x = 6.toBigInt() - val y = 3.toBigInt() - - val res = x / y - val div = 2.toBigInt() - - assertEquals(div, res) - } - - @Test - fun testDivision_20__3() { - val x = 20.toBigInt() - val y = -3 - - val res = x / y - val div = (-6).toBigInt() - - assertEquals(div, res) - } - - @Test - fun testBigDivision_20__3() { - val x = 20.toBigInt() - val y = (-3).toBigInt() - - val res = x / y - val div = (-6).toBigInt() - - assertEquals(div, res) - } - - @Test - fun testDivision_0xfffffffe00000001_0xffffffff() { - val x = 0xfffffffe00000001UL.toBigInt() - val y = 0xffffffffU - - val res = x / y - val div = 0xffffffffL.toBigInt() - - assertEquals(div, res) - } - - @Test - fun testBigDivision_0xfffffffeabcdef01UL_0xfffffffeabc() { - val res = 0xfffffffeabcdef01UL.toBigInt() / 0xfffffffeabc.toBigInt() - assertEquals(res, 0x100000.toBigInt()) - } - - @Test - fun testBigDivision_0xfffffffe00000001_0xffffffff() { - val x = 0xfffffffe00000001UL.toBigInt() - val y = 0xffffffffU.toBigInt() - - val res = x / y - val div = 0xffffffffL.toBigInt() - - assertEquals(div, res) - } - - @Test - fun testMod_20_3() { - val x = 20.toBigInt() - val y = 3 - - val res = x % y - val mod = 2 - - assertEquals(mod, res) - } - - @Test - fun testBigMod_20_3() { - val x = 20.toBigInt() - val y = 3.toBigInt() - - val res = x % y - val mod = 2.toBigInt() - - assertEquals(mod, res) - } - - @Test - fun testMod_0xfffffffe00000001_12345() { - val x = 0xfffffffe00000001UL.toBigInt() - val y = 12345 - - val res = x % y - val mod = 1980 - - assertEquals(mod, res) - } - - @Test - fun testBigMod_0xfffffffe00000001_12345() { - val x = 0xfffffffe00000001UL.toBigInt() - val y = 12345.toBigInt() - - val res = x % y - val mod = 1980.toBigInt() - - assertEquals(mod, res) - } - - @Test - fun testModPow_3_10_17() { - val x = 3.toBigInt() - val exp = 10.toBigInt() - val mod = 17.toBigInt() - - val res = 8.toBigInt() - - return assertEquals(res, x.modPow(exp, mod)) - } - - @Test - fun testModPowBigNumbers() { - val x = 0xfffffffeabcdef01UL.toBigInt() - val exp = 2.toBigInt() - val mod = 0xfffffffeabcUL.toBigInt() - - val res = 0xc2253cde01.toBigInt() - - return assertEquals(res, x.modPow(exp, mod)) - } - - @Test - fun testModBigNumbers() { - val x = 0xfffffffeabcdef01UL.toBigInt() - val mod = 0xfffffffeabcUL.toBigInt() - - val res = 0xdef01.toBigInt() - - return assertEquals(res, x % mod) - } - - @Test - fun testNotEqualsOtherTypeInstanceButButNotFails() = assertFalse(0.toBigInt().equals("")) - - @Test - fun testIntAbsOverflow() { - assertEquals((-Int.MAX_VALUE.toLong().toBigInt() - 1.toBigInt()) * 2, 2.toBigInt() * Int.MIN_VALUE) - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/DoubleFieldTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/DoubleFieldTest.kt deleted file mode 100644 index 76171fedd..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/operations/DoubleFieldTest.kt +++ /dev/null @@ -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. - */ - -package space.kscience.kmath.operations - -import space.kscience.kmath.testutils.FieldVerifier -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class DoubleFieldTest { - @Test - fun verify() = FieldVerifier(DoubleField, 42.0, 66.0, 2.0, 5).verify() - - @Test - fun testSqrt() { - val sqrt = DoubleField { sqrt(25 * one) } - assertEquals(5.0, sqrt) - } - - @Test - fun testPow() = DoubleField { - val num = 5 * one - assertEquals(5.0, power(num, 1), 0.01) - assertEquals(25.0, power(num, 2), 0.01) - assertEquals(1.0, power(num, 0), 0.01) - assertEquals(0.2, power(num, -1), 0.01) - assertEquals(0.04, power(num, -2), 0.01) - assertEquals(0.0, power(num, Int.MIN_VALUE), 0.01) - assertEquals(1.0, power(zero, 0), 0.01) - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/NDFieldTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/NDFieldTest.kt deleted file mode 100644 index b7b89d107..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/NDFieldTest.kt +++ /dev/null @@ -1,28 +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. - */ - -package space.kscience.kmath.structures - -import space.kscience.kmath.nd.get -import space.kscience.kmath.nd.ndAlgebra -import space.kscience.kmath.nd.structureND -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.testutils.FieldVerifier -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class NDFieldTest { - @Test - fun verify() { - (DoubleField.ndAlgebra(12, 32)) { FieldVerifier(this, one + 3, one - 23, one * 12, 6.66) } - } - - @Test - fun testStrides() { - val ndArray = DoubleField.ndAlgebra.structureND(10, 10) { (it[0] + it[1]).toDouble() } - assertEquals(ndArray[5, 5], 10.0) - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/NumberNDFieldTest.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/NumberNDFieldTest.kt deleted file mode 100644 index d33eb5112..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/NumberNDFieldTest.kt +++ /dev/null @@ -1,92 +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. - */ - -package space.kscience.kmath.structures - -import space.kscience.kmath.linear.linearSpace -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.* -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.Norm -import space.kscience.kmath.operations.algebra -import space.kscience.kmath.operations.invoke -import kotlin.math.abs -import kotlin.math.pow -import kotlin.test.Test -import kotlin.test.assertEquals - -@Suppress("UNUSED_VARIABLE") -class NumberNDFieldTest { - val algebra = DoubleField.ndAlgebra - val array1 = algebra.structureND(3, 3) { (i, j) -> (i + j).toDouble() } - val array2 = algebra.structureND(3, 3) { (i, j) -> (i - j).toDouble() } - - @Test - fun testSum() { - algebra { - val sum = array1 + array2 - assertEquals(4.0, sum[2, 2]) - } - } - - @Test - fun testProduct() { - algebra { - val product = array1 * array2 - assertEquals(0.0, product[2, 2]) - } - } - - @Test - fun testGeneration() = Double.algebra.linearSpace.run { - - val array = buildMatrix(3, 3) { i, j -> - (i * 10 + j).toDouble() - } - - for (i in 0..2) { - for (j in 0..2) { - val expected = (i * 10 + j).toDouble() - assertEquals(expected, array[i, j], "Error at index [$i, $j]") - } - } - } - - @Test - fun testExternalFunction() { - algebra { - val function: (Double) -> Double = { x -> x.pow(2) + 2 * x + 1 } - val result = function(array1) + 1.0 - assertEquals(10.0, result[1, 1]) - } - } - - @Test - fun testLibraryFunction() { - algebra { - val abs: (Double) -> Double = ::abs - val result = abs(array2) - assertEquals(2.0, result[0, 2]) - } - } - - @Test - fun combineTest() { - val division = array1.zip(array2, Double::div) - } - - object L2Norm : Norm, Double> { - @OptIn(PerformancePitfall::class) - override fun norm(arg: StructureND): Double = - kotlin.math.sqrt(arg.elements().sumOf { it.second.toDouble() }) - } - - @Test - fun testInternalContext() { - algebra { - (DoubleField.ndAlgebra(*array1.shape)) { with(L2Norm) { 1 + norm(array1) + exp(array2) } } - } - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/AlgebraicVerifier.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/AlgebraicVerifier.kt deleted file mode 100644 index ddd8fc3ea..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/AlgebraicVerifier.kt +++ /dev/null @@ -1,14 +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. - */ - -package space.kscience.kmath.testutils - -import space.kscience.kmath.operations.Algebra - -internal interface AlgebraicVerifier where A : Algebra { - val algebra: A - - fun verify() -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/FieldVerifier.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/FieldVerifier.kt deleted file mode 100644 index 20a7b6a72..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/FieldVerifier.kt +++ /dev/null @@ -1,32 +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. - */ - -package space.kscience.kmath.testutils - -import space.kscience.kmath.operations.Field -import space.kscience.kmath.operations.invoke -import kotlin.test.assertEquals -import kotlin.test.assertNotEquals - -internal class FieldVerifier>( - algebra: A, a: T, b: T, c: T, x: Number, -) : RingVerifier(algebra, a, b, c, x) { - - override fun verify() { - super.verify() - - algebra { - assertEquals(a + b, b + a, "Addition in $algebra is not commutative.") - assertEquals(a * b, b * a, "Multiplication in $algebra is not commutative.") - assertNotEquals(a / b, b / a, "Division in $algebra is not anti-commutative.") - assertNotEquals((a / b) / c, a / (b / c), "Division in $algebra is associative.") - assertEquals((a + b) / c, (a / c) + (b / c), "Division in $algebra is not right-distributive.") - assertEquals(a, a / one, "$one in $algebra is not neutral division element.") - assertEquals(one, one / a * a, "$algebra does not provide single reciprocal element.") - assertEquals(zero / a, zero, "$zero in $algebra is not left neutral element for division.") - assertEquals(-one, a / (-a), "Division by sign reversal element in $algebra does not give ${-one}.") - } - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/RingVerifier.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/RingVerifier.kt deleted file mode 100644 index daf18834a..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/RingVerifier.kt +++ /dev/null @@ -1,35 +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. - */ - -package space.kscience.kmath.testutils - -import space.kscience.kmath.operations.Ring -import space.kscience.kmath.operations.ScaleOperations -import space.kscience.kmath.operations.invoke -import kotlin.test.assertEquals - -internal open class RingVerifier(algebra: A, a: T, b: T, c: T, x: Number) : - SpaceVerifier(algebra, a, b, c, x) where A : Ring, A : ScaleOperations { - - override fun verify() { - super.verify() - - algebra { - assertEquals(a + b, b + a, "Addition in $algebra is not commutative.") - assertEquals(a * b * c, a * (b * c), "Multiplication in $algebra is not associative.") - assertEquals(c * (a + b), (c * a) + (c * b), "Multiplication in $algebra is not distributive.") - assertEquals(a * one, one * a, "$one in $algebra is not a neutral multiplication element.") - assertEquals(a, one * a, "$one in $algebra is not a neutral multiplication element.") - assertEquals(a, a * one, "$one in $algebra is not a neutral multiplication element.") - assertEquals(a, one * a, "$one in $algebra is not a neutral multiplication element.") - assertEquals(a, a * one * one, "Multiplication by $one in $algebra is not idempotent.") - assertEquals(a, a * one * one * one, "Multiplication by $one in $algebra is not idempotent.") - assertEquals(a, a * one * one * one * one, "Multiplication by $one in $algebra is not idempotent.") - assertEquals(zero, a * zero, "Multiplication by $zero in $algebra doesn't give $zero.") - assertEquals(zero, zero * a, "Multiplication by $zero in $algebra doesn't give $zero.") - assertEquals(a * zero, a * zero, "Multiplication by $zero in $algebra doesn't give $zero.") - } - } -} diff --git a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/SpaceVerifier.kt b/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/SpaceVerifier.kt deleted file mode 100644 index 951197fc6..000000000 --- a/kmath-core/src/commonTest/kotlin/space/kscience/kmath/testutils/SpaceVerifier.kt +++ /dev/null @@ -1,37 +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. - */ - -package space.kscience.kmath.testutils - -import space.kscience.kmath.operations.Ring -import space.kscience.kmath.operations.ScaleOperations -import space.kscience.kmath.operations.invoke -import kotlin.test.assertEquals -import kotlin.test.assertNotEquals - -internal open class SpaceVerifier( - override val algebra: S, - val a: T, - val b: T, - val c: T, - val x: Number, -) : AlgebraicVerifier> where S : Ring, S : ScaleOperations { - override fun verify() { - algebra { - assertEquals(a + b + c, a + (b + c), "Addition in $algebra is not associative.") - assertEquals(x * (a + b), x * a + x * b, "Addition in $algebra is not distributive.") - assertEquals((a + b) * x, a * x + b * x, "Addition in $algebra is not distributive.") - assertEquals(a + zero, zero + a, "$zero in $algebra is not a neutral addition element.") - assertEquals(a, a + zero, "$zero in $algebra is not a neutral addition element.") - assertEquals(a, zero + a, "$zero in $algebra is not a neutral addition element.") - assertEquals(a - b, -(b - a), "Subtraction in $algebra is not anti-commutative.") - assertNotEquals(a - b - c, a - (b - c), "Subtraction in $algebra is associative.") - assertEquals(x * (a - b), x * a - x * b, "Subtraction in $algebra is not distributive.") - assertEquals(a, a - zero, "$zero in $algebra is not a neutral addition element.") - assertEquals(a * x, x * a, "Multiplication by scalar in $algebra is not commutative.") - assertEquals(x * (a + b), (x * a) + (x * b), "Multiplication by scalar in $algebra is not distributive.") - } - } -} diff --git a/kmath-core/src/jsMain/kotlin/space/kscience/kmath/misc/numbers.kt b/kmath-core/src/jsMain/kotlin/space/kscience/kmath/misc/numbers.kt deleted file mode 100644 index 68a3c995b..000000000 --- a/kmath-core/src/jsMain/kotlin/space/kscience/kmath/misc/numbers.kt +++ /dev/null @@ -1,12 +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. - */ - -package space.kscience.kmath.misc - -public actual fun Long.toIntExact(): Int { - val i = toInt() - if (i.toLong() == this) throw ArithmeticException("integer overflow") - return i -} diff --git a/kmath-core/src/jsMain/kotlin/space/kscience/kmath/operations/isInteger.kt b/kmath-core/src/jsMain/kotlin/space/kscience/kmath/operations/isInteger.kt deleted file mode 100644 index 24b81322e..000000000 --- a/kmath-core/src/jsMain/kotlin/space/kscience/kmath/operations/isInteger.kt +++ /dev/null @@ -1,11 +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. - */ - -package space.kscience.kmath.operations - -/** - * Check if number is an integer - */ -public actual fun Number.isInteger(): Boolean = js("Number").isInteger(this) as Boolean \ No newline at end of file diff --git a/kmath-core/src/jvmMain/kotlin/space/kscience/kmath/misc/numbersJVM.kt b/kmath-core/src/jvmMain/kotlin/space/kscience/kmath/misc/numbersJVM.kt deleted file mode 100644 index 5ba0dbc9b..000000000 --- a/kmath-core/src/jvmMain/kotlin/space/kscience/kmath/misc/numbersJVM.kt +++ /dev/null @@ -1,8 +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. - */ - -package space.kscience.kmath.misc - -public actual fun Long.toIntExact(): Int = Math.toIntExact(this) diff --git a/kmath-core/src/jvmMain/kotlin/space/kscience/kmath/operations/BigNumbers.kt b/kmath-core/src/jvmMain/kotlin/space/kscience/kmath/operations/BigNumbers.kt deleted file mode 100644 index 8a59adcef..000000000 --- a/kmath-core/src/jvmMain/kotlin/space/kscience/kmath/operations/BigNumbers.kt +++ /dev/null @@ -1,61 +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. - */ - -package space.kscience.kmath.operations - -import java.math.BigDecimal -import java.math.BigInteger -import java.math.MathContext - -/** - * A field over [BigInteger]. - */ -public object JBigIntegerField : Ring, NumericAlgebra { - override val zero: BigInteger get() = BigInteger.ZERO - - override val one: BigInteger get() = BigInteger.ONE - - override fun number(value: Number): BigInteger = BigInteger.valueOf(value.toLong()) - override fun add(left: BigInteger, right: BigInteger): BigInteger = left.add(right) - override fun negate(arg: BigInteger): BigInteger = arg.negate() - - override operator fun BigInteger.minus(arg: BigInteger): BigInteger = subtract(arg) - override fun multiply(left: BigInteger, right: BigInteger): BigInteger = left.multiply(right) -} - -/** - * An abstract field over [BigDecimal]. - * - * @property mathContext the [MathContext] to use. - */ -public abstract class JBigDecimalFieldBase internal constructor( - private val mathContext: MathContext = MathContext.DECIMAL64, -) : Field, PowerOperations, NumericAlgebra, ScaleOperations { - override val zero: BigDecimal - get() = BigDecimal.ZERO - - override val one: BigDecimal - get() = BigDecimal.ONE - - override fun add(left: BigDecimal, right: BigDecimal): BigDecimal = left.add(right) - override fun negate(arg: BigDecimal): BigDecimal = arg.negate(mathContext) - override operator fun BigDecimal.minus(arg: BigDecimal): BigDecimal = subtract(arg) - override fun number(value: Number): BigDecimal = BigDecimal.valueOf(value.toDouble()) - - override fun scale(a: BigDecimal, value: Double): BigDecimal = - a.multiply(value.toBigDecimal(mathContext), mathContext) - - override fun multiply(left: BigDecimal, right: BigDecimal): BigDecimal = left.multiply(right, mathContext) - override fun divide(left: BigDecimal, right: BigDecimal): BigDecimal = left.divide(right, mathContext) - override fun power(arg: BigDecimal, pow: Number): BigDecimal = arg.pow(pow.toInt(), mathContext) - override fun sqrt(arg: BigDecimal): BigDecimal = arg.sqrt(mathContext) -} - -/** - * A field over [BigDecimal]. - */ -public class JBigDecimalField(mathContext: MathContext = MathContext.DECIMAL64) : JBigDecimalFieldBase(mathContext) { - public companion object : JBigDecimalFieldBase() -} diff --git a/kmath-core/src/jvmMain/kotlin/space/kscience/kmath/operations/isInteger.kt b/kmath-core/src/jvmMain/kotlin/space/kscience/kmath/operations/isInteger.kt deleted file mode 100644 index b2f9b957b..000000000 --- a/kmath-core/src/jvmMain/kotlin/space/kscience/kmath/operations/isInteger.kt +++ /dev/null @@ -1,11 +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. - */ - -package space.kscience.kmath.operations - -/** - * Check if number is an integer - */ -public actual fun Number.isInteger(): Boolean = (this is Int) || (this is Long) || (this is Short) || (this.toDouble() % 1 == 0.0) \ No newline at end of file diff --git a/kmath-core/src/nativeMain/kotlin/space/kscience/kmath/misc/numbers.kt b/kmath-core/src/nativeMain/kotlin/space/kscience/kmath/misc/numbers.kt deleted file mode 100644 index 68a3c995b..000000000 --- a/kmath-core/src/nativeMain/kotlin/space/kscience/kmath/misc/numbers.kt +++ /dev/null @@ -1,12 +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. - */ - -package space.kscience.kmath.misc - -public actual fun Long.toIntExact(): Int { - val i = toInt() - if (i.toLong() == this) throw ArithmeticException("integer overflow") - return i -} diff --git a/kmath-core/src/nativeMain/kotlin/space/kscience/kmath/operations/isInteger.kt b/kmath-core/src/nativeMain/kotlin/space/kscience/kmath/operations/isInteger.kt deleted file mode 100644 index b2f9b957b..000000000 --- a/kmath-core/src/nativeMain/kotlin/space/kscience/kmath/operations/isInteger.kt +++ /dev/null @@ -1,11 +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. - */ - -package space.kscience.kmath.operations - -/** - * Check if number is an integer - */ -public actual fun Number.isInteger(): Boolean = (this is Int) || (this is Long) || (this is Short) || (this.toDouble() % 1 == 0.0) \ No newline at end of file diff --git a/kmath-coroutines/README.md b/kmath-coroutines/README.md deleted file mode 100644 index 0d83a6c60..000000000 --- a/kmath-coroutines/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-coroutines - - - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-coroutines:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-coroutines:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-coroutines:0.3.0-dev-20") -} -``` diff --git a/kmath-coroutines/build.gradle.kts b/kmath-coroutines/build.gradle.kts deleted file mode 100644 index aa30c412b..000000000 --- a/kmath-coroutines/build.gradle.kts +++ /dev/null @@ -1,27 +0,0 @@ -plugins { - kotlin("multiplatform") - id("ru.mipt.npm.gradle.common") - id("ru.mipt.npm.gradle.native") -} - -kotlin.sourceSets { - all { - with(languageSettings) { - optIn("kotlinx.coroutines.InternalCoroutinesApi") - optIn("kotlinx.coroutines.ExperimentalCoroutinesApi") - optIn("kotlinx.coroutines.FlowPreview") - } - } - - commonMain { - dependencies { - api(project(":kmath-core")) - api(project(":kmath-complex")) - api("org.jetbrains.kotlinx:kotlinx-coroutines-core:${ru.mipt.npm.gradle.KScienceVersions.coroutinesVersion}") - } - } -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL -} \ No newline at end of file diff --git a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingChain.kt b/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingChain.kt deleted file mode 100644 index 87aebff61..000000000 --- a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingChain.kt +++ /dev/null @@ -1,55 +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. - */ - -package space.kscience.kmath.chains - -import space.kscience.kmath.structures.Buffer - - -public interface BufferChain : Chain { - public suspend fun nextBuffer(size: Int): Buffer - override suspend fun fork(): BufferChain -} - -/** - * A chain with blocking generator that could be used without suspension - */ -public interface BlockingChain : Chain { - /** - * Get the next value without concurrency support. Not guaranteed to be thread safe. - */ - public fun nextBlocking(): T - - override suspend fun next(): T = nextBlocking() - - override suspend fun fork(): BlockingChain -} - - -public interface BlockingBufferChain : BlockingChain, BufferChain { - - public fun nextBufferBlocking(size: Int): Buffer - - override fun nextBlocking(): T = nextBufferBlocking(1)[0] - - override suspend fun nextBuffer(size: Int): Buffer = nextBufferBlocking(size) - - override suspend fun fork(): BlockingBufferChain -} - - -public suspend inline fun Chain.nextBuffer(size: Int): Buffer = if (this is BufferChain) { - nextBuffer(size) -} else { - Buffer.auto(size) { next() } -} - -public inline fun BlockingChain.nextBufferBlocking( - size: Int, -): Buffer = if (this is BlockingBufferChain) { - nextBufferBlocking(size) -} else { - Buffer.auto(size) { nextBlocking() } -} \ No newline at end of file diff --git a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingDoubleChain.kt b/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingDoubleChain.kt deleted file mode 100644 index 25e20291e..000000000 --- a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingDoubleChain.kt +++ /dev/null @@ -1,32 +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. - */ - -package space.kscience.kmath.chains - -import space.kscience.kmath.structures.DoubleBuffer - -/** - * Chunked, specialized chain for double values, which supports blocking [nextBlocking] operation - */ -public interface BlockingDoubleChain : BlockingBufferChain { - - /** - * Returns an [DoubleArray] chunk of [size] values of [next]. - */ - override fun nextBufferBlocking(size: Int): DoubleBuffer - - override suspend fun fork(): BlockingDoubleChain - - public companion object -} - -public fun BlockingDoubleChain.map(transform: (Double) -> Double): BlockingDoubleChain = object : BlockingDoubleChain { - override fun nextBufferBlocking(size: Int): DoubleBuffer { - val block = this@map.nextBufferBlocking(size) - return DoubleBuffer(size) { transform(block[it]) } - } - - override suspend fun fork(): BlockingDoubleChain = this@map.fork().map(transform) -} diff --git a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingIntChain.kt b/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingIntChain.kt deleted file mode 100644 index ac0327d0b..000000000 --- a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingIntChain.kt +++ /dev/null @@ -1,17 +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. - */ - -package space.kscience.kmath.chains - -import space.kscience.kmath.structures.IntBuffer - -/** - * Performance optimized chain for integer values - */ -public interface BlockingIntChain : BlockingBufferChain { - override fun nextBufferBlocking(size: Int): IntBuffer - - override suspend fun fork(): BlockingIntChain -} \ No newline at end of file diff --git a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/Chain.kt b/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/Chain.kt deleted file mode 100644 index 994255e38..000000000 --- a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/Chain.kt +++ /dev/null @@ -1,152 +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. - */ - -package space.kscience.kmath.chains - -import kotlinx.coroutines.flow.Flow -import kotlinx.coroutines.flow.FlowCollector -import kotlinx.coroutines.flow.flow -import kotlinx.coroutines.sync.Mutex -import kotlinx.coroutines.sync.withLock -import space.kscience.kmath.misc.UnstableKMathAPI - -/** - * A not-necessary-Markov chain of some type - * @param T the chain element type - */ -public interface Chain : Flow { - /** - * Generate next value, changing state if needed - */ - public suspend fun next(): T - - /** - * Create a copy of current chain state. Consuming resulting chain does not affect initial chain. - */ - public suspend fun fork(): Chain - - override suspend fun collect(collector: FlowCollector): Unit = - flow { while (true) emit(next()) }.collect(collector) - - public companion object -} - -public fun Iterator.asChain(): Chain = SimpleChain { next() } -public fun Sequence.asChain(): Chain = iterator().asChain() - -/** - * A simple chain of independent tokens. [fork] returns the same chain. - */ -public class SimpleChain(private val gen: suspend () -> R) : Chain { - override suspend fun next(): R = gen() - override suspend fun fork(): Chain = this -} - -/** - * A stateless Markov chain - */ -public class MarkovChain(private val seed: suspend () -> R, private val gen: suspend (R) -> R) : Chain { - private val mutex: Mutex = Mutex() - private var value: R? = null - - public fun value(): R? = value - - override suspend fun next(): R = mutex.withLock { - val newValue = gen(value ?: seed()) - value = newValue - newValue - } - - override suspend fun fork(): Chain = MarkovChain(seed = { value ?: seed() }, gen = gen) -} - -/** - * A chain with possibly mutable state. The state must not be changed outside the chain. Two chins should never share - * the state. - * - * @param S the state of the chain. - * @param forkState the function to copy current state without modifying it. - */ -public class StatefulChain( - private val state: S, - private val seed: S.() -> R, - private val forkState: ((S) -> S), - private val gen: suspend S.(R) -> R, -) : Chain { - private val mutex: Mutex = Mutex() - private var value: R? = null - - public fun value(): R? = value - - override suspend fun next(): R = mutex.withLock { - val newValue = state.gen(value ?: state.seed()) - value = newValue - newValue - } - - override suspend fun fork(): Chain = StatefulChain(forkState(state), seed, forkState, gen) -} - -/** - * A chain that repeats the same value - */ -public class ConstantChain(public val value: T) : Chain { - override suspend fun next(): T = value - override suspend fun fork(): Chain = this -} - -/** - * Map the chain result using suspended transformation. Initial chain result can no longer be safely consumed - * since mapped chain consumes tokens. Accepts regular transformation function. - */ -public fun Chain.map(func: suspend (T) -> R): Chain = object : Chain { - override suspend fun next(): R = func(this@map.next()) - override suspend fun fork(): Chain = this@map.fork().map(func) -} - -/** - * [block] must be a pure function or at least not use external random variables, otherwise fork could be broken - */ -public fun Chain.filter(block: (T) -> Boolean): Chain = object : Chain { - override suspend fun next(): T { - var next: T - - do next = this@filter.next() - while (!block(next)) - - return next - } - - override suspend fun fork(): Chain = this@filter.fork().filter(block) -} - -/** - * Map the whole chain - */ -@UnstableKMathAPI -public fun Chain.combine(mapper: suspend (Chain) -> R): Chain = object : Chain { - override suspend fun next(): R = mapper(this@combine) - override suspend fun fork(): Chain = this@combine.fork().combine(mapper) -} - -@UnstableKMathAPI -public fun Chain.combineWithState( - state: S, - stateFork: (S) -> S, - mapper: suspend S.(Chain) -> R, -): Chain = object : Chain { - override suspend fun next(): R = state.mapper(this@combineWithState) - - override suspend fun fork(): Chain = - this@combineWithState.fork().combineWithState(stateFork(state), stateFork, mapper) -} - -/** - * Zip two chains together using given transformation - */ -public fun Chain.zip(other: Chain, block: suspend (T, U) -> R): Chain = object : Chain { - override suspend fun next(): R = block(this@zip.next(), other.next()) - override suspend fun fork(): Chain = this@zip.fork().zip(other.fork(), block) -} diff --git a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/flowExtra.kt b/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/flowExtra.kt deleted file mode 100644 index 7bf54d50f..000000000 --- a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/flowExtra.kt +++ /dev/null @@ -1,31 +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. - */ - -package space.kscience.kmath.chains - -import kotlinx.coroutines.ExperimentalCoroutinesApi -import kotlinx.coroutines.flow.Flow -import kotlinx.coroutines.flow.map -import kotlinx.coroutines.flow.runningReduce -import kotlinx.coroutines.flow.scan -import space.kscience.kmath.operations.GroupOps -import space.kscience.kmath.operations.Ring -import space.kscience.kmath.operations.ScaleOperations -import space.kscience.kmath.operations.invoke - -public fun Flow.cumulativeSum(group: GroupOps): Flow = - group { runningReduce { sum, element -> sum + element } } - -@ExperimentalCoroutinesApi -public fun Flow.mean(space: S): Flow where S : Ring, S : ScaleOperations = space { - data class Accumulator(var sum: T, var num: Int) - - scan(Accumulator(zero, 0)) { sum, element -> - sum.apply { - this.sum += element - this.num += 1 - } - }.map { it.sum / it.num } -} diff --git a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/coroutines/coroutinesExtra.kt b/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/coroutines/coroutinesExtra.kt deleted file mode 100644 index 1f17efe49..000000000 --- a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/coroutines/coroutinesExtra.kt +++ /dev/null @@ -1,91 +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. - */ - -package space.kscience.kmath.coroutines - -import kotlinx.coroutines.* -import kotlinx.coroutines.channels.produce -import kotlinx.coroutines.flow.* - -public val Dispatchers.Math: CoroutineDispatcher - get() = Default - -/** - * An imitator of [Deferred] which holds a suspended function block and dispatcher - */ -@PublishedApi -internal class LazyDeferred(val dispatcher: CoroutineDispatcher, val block: suspend CoroutineScope.() -> T) { - private var deferred: Deferred? = null - - fun start(scope: CoroutineScope) { - if (deferred == null) deferred = scope.async(dispatcher, block = block) - } - - suspend fun await(): T = deferred?.await() ?: error("Coroutine not started") -} - -public class AsyncFlow @PublishedApi internal constructor( - @PublishedApi internal val deferredFlow: Flow>, -) : Flow { - override suspend fun collect(collector: FlowCollector): Unit = - deferredFlow.collect { collector.emit((it.await())) } -} - -public inline fun Flow.async( - dispatcher: CoroutineDispatcher = Dispatchers.Default, - crossinline block: suspend CoroutineScope.(T) -> R, -): AsyncFlow { - val flow = map { LazyDeferred(dispatcher) { block(it) } } - return AsyncFlow(flow) -} - -public inline fun AsyncFlow.map(crossinline action: (T) -> R): AsyncFlow = - AsyncFlow(deferredFlow.map { input -> - //TODO add function composition - LazyDeferred(input.dispatcher) { - input.start(this) - action(input.await()) - } - }) - -public suspend fun AsyncFlow.collect(concurrency: Int, collector: FlowCollector) { - require(concurrency >= 1) { "Buffer size should be more than 1, but was $concurrency" } - - coroutineScope { - //Starting up to N deferred coroutines ahead of time - val channel = produce(capacity = concurrency - 1) { - deferredFlow.collect { value -> - value.start(this@coroutineScope) - send(value) - } - } - - (channel as Job).invokeOnCompletion { - if (it is CancellationException && it.cause == null) cancel() - } - - for (element in channel) { - collector.emit(element.await()) - } - - val producer = channel as Job - if (producer.isCancelled) { - producer.join() - //throw producer.getCancellationException() - } - } -} - -public suspend inline fun AsyncFlow.collect( - concurrency: Int, - crossinline action: suspend (value: T) -> Unit, -): Unit = collect(concurrency, object : FlowCollector { - override suspend fun emit(value: T): Unit = action(value) -}) - -public inline fun Flow.mapParallel( - dispatcher: CoroutineDispatcher = Dispatchers.Default, - crossinline transform: suspend (T) -> R, -): Flow = flatMapMerge { value -> flow { emit(transform(value)) } }.flowOn(dispatcher) diff --git a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/streaming/BufferFlow.kt b/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/streaming/BufferFlow.kt deleted file mode 100644 index 4d4493aa4..000000000 --- a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/streaming/BufferFlow.kt +++ /dev/null @@ -1,89 +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. - */ - -package space.kscience.kmath.streaming - -import kotlinx.coroutines.FlowPreview -import kotlinx.coroutines.flow.* -import space.kscience.kmath.chains.BlockingDoubleChain -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.BufferFactory -import space.kscience.kmath.structures.DoubleBuffer - -/** - * Create a [Flow] from buffer - */ -public fun Buffer.asFlow(): Flow = iterator().asFlow() - -/** - * Flat map a [Flow] of [Buffer] into continuous [Flow] of elements - */ -@FlowPreview -public fun Flow>.spread(): Flow = flatMapConcat { it.asFlow() } - -/** - * Collect incoming flow into fixed size chunks - */ -public fun Flow.chunked(bufferSize: Int, bufferFactory: BufferFactory): Flow> = flow { - require(bufferSize > 0) { "Resulting chunk size must be more than zero" } - val list = ArrayList(bufferSize) - var counter = 0 - - this@chunked.collect { element -> - list += element - counter++ - - if (counter == bufferSize) { - val buffer = bufferFactory(bufferSize) { list[it] } - emit(buffer) - list.clear() - counter = 0 - } - } - - if (counter > 0) emit(bufferFactory(counter) { list[it] }) -} - -/** - * Specialized flow chunker for real buffer - */ -public fun Flow.chunked(bufferSize: Int): Flow = flow { - require(bufferSize > 0) { "Resulting chunk size must be more than zero" } - - if (this@chunked is BlockingDoubleChain) { - // performance optimization for blocking primitive chain - while (true) emit(nextBufferBlocking(bufferSize)) - } else { - val array = DoubleArray(bufferSize) - var counter = 0 - - this@chunked.collect { element -> - array[counter] = element - counter++ - - if (counter == bufferSize) { - val buffer = DoubleBuffer(array) - emit(buffer) - counter = 0 - } - } - - if (counter > 0) emit(DoubleBuffer(counter) { array[it] }) - } -} - -/** - * Map a flow to a moving window buffer. The window step is one. - * To get different steps, one could use skip operation. - */ -public fun Flow.windowed(window: Int): Flow> = flow { - require(window > 1) { "Window size must be more than one" } - val ringBuffer = RingBuffer.boxing(window) - - this@windowed.collect { element -> - ringBuffer.push(element) - emit(ringBuffer.snapshot()) - } -} diff --git a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/streaming/RingBuffer.kt b/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/streaming/RingBuffer.kt deleted file mode 100644 index abe1c9df9..000000000 --- a/kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/streaming/RingBuffer.kt +++ /dev/null @@ -1,88 +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. - */ - -package space.kscience.kmath.streaming - -import kotlinx.coroutines.sync.Mutex -import kotlinx.coroutines.sync.withLock -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.MutableBuffer -import space.kscience.kmath.structures.VirtualBuffer - -/** - * Thread-safe ring buffer - */ -@Suppress("UNCHECKED_CAST") -public class RingBuffer( - private val buffer: MutableBuffer, - private var startIndex: Int = 0, - size: Int = 0, -) : Buffer { - private val mutex: Mutex = Mutex() - - override var size: Int = size - private set - - override operator fun get(index: Int): T { - require(index >= 0) { "Index must be positive" } - require(index < size) { "Index $index is out of circular buffer size $size" } - return buffer[startIndex.forward(index)] as T - } - - public fun isFull(): Boolean = size == buffer.size - - /** - * Iterator could provide wrong results if buffer is changed in initialization (iteration is safe) - */ - override operator fun iterator(): Iterator = object : AbstractIterator() { - private var count = size - private var index = startIndex - val copy = buffer.copy() - - override fun computeNext() { - if (count == 0) done() else { - setNext(copy[index] as T) - index = index.forward(1) - count-- - } - } - } - - /** - * A safe snapshot operation - */ - public suspend fun snapshot(): Buffer = mutex.withLock { - val copy = buffer.copy() - VirtualBuffer(size) { i -> copy[startIndex.forward(i)] as T } - } - - public suspend fun push(element: T) { - mutex.withLock { - buffer[startIndex.forward(size)] = element - if (isFull()) startIndex++ else size++ - } - } - - - @Suppress("NOTHING_TO_INLINE") - private inline fun Int.forward(n: Int): Int = (this + n) % (buffer.size) - - override fun toString(): String = Buffer.toString(this) - - public companion object { - public inline fun build(size: Int, empty: T): RingBuffer { - val buffer = MutableBuffer.auto(size) { empty } as MutableBuffer - return RingBuffer(buffer) - } - - /** - * Slow yet universal buffer - */ - public fun boxing(size: Int): RingBuffer { - val buffer: MutableBuffer = MutableBuffer.boxing(size) { null } - return RingBuffer(buffer) - } - } -} diff --git a/kmath-coroutines/src/jvmMain/kotlin/space/kscience/kmath/chains/ChainExt.kt b/kmath-coroutines/src/jvmMain/kotlin/space/kscience/kmath/chains/ChainExt.kt deleted file mode 100644 index dd6e39071..000000000 --- a/kmath-coroutines/src/jvmMain/kotlin/space/kscience/kmath/chains/ChainExt.kt +++ /dev/null @@ -1,21 +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. - */ - -package space.kscience.kmath.chains - -import kotlinx.coroutines.runBlocking - -/** - * Represent a chain as regular iterator (uses blocking calls) - */ -public operator fun Chain.iterator(): Iterator = object : Iterator { - override fun hasNext(): Boolean = true - override fun next(): R = runBlocking { next() } -} - -/** - * Represent a chain as a sequence - */ -public fun Chain.asSequence(): Sequence = Sequence { this@asSequence.iterator() } diff --git a/kmath-coroutines/src/jvmMain/kotlin/space/kscience/kmath/structures/LazyStructureND.kt b/kmath-coroutines/src/jvmMain/kotlin/space/kscience/kmath/structures/LazyStructureND.kt deleted file mode 100644 index ac9eb773a..000000000 --- a/kmath-coroutines/src/jvmMain/kotlin/space/kscience/kmath/structures/LazyStructureND.kt +++ /dev/null @@ -1,53 +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. - */ - -package space.kscience.kmath.structures - -import kotlinx.coroutines.* -import space.kscience.kmath.coroutines.Math -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.DefaultStrides -import space.kscience.kmath.nd.StructureND - -public class LazyStructureND( - public val scope: CoroutineScope, - override val shape: IntArray, - public val function: suspend (IntArray) -> T, -) : StructureND { - private val cache: MutableMap> = HashMap() - - public fun deferred(index: IntArray): Deferred = cache.getOrPut(index) { - scope.async(context = Dispatchers.Math) { function(index) } - } - - public suspend fun await(index: IntArray): T = deferred(index).await() - override operator fun get(index: IntArray): T = runBlocking { deferred(index).await() } - - @OptIn(PerformancePitfall::class) - override fun elements(): Sequence> { - val strides = DefaultStrides(shape) - val res = runBlocking { strides.asSequence().toList().map { index -> index to await(index) } } - return res.asSequence() - } -} - -public fun StructureND.deferred(index: IntArray): Deferred = - if (this is LazyStructureND) deferred(index) else CompletableDeferred(get(index)) - -public suspend fun StructureND.await(index: IntArray): T = - if (this is LazyStructureND) await(index) else get(index) - -/** - * PENDING would benefit from KEEP-176 - */ -public inline fun StructureND.mapAsyncIndexed( - scope: CoroutineScope, - crossinline function: suspend (T, index: IntArray) -> R, -): LazyStructureND = LazyStructureND(scope, shape) { index -> function(get(index), index) } - -public inline fun StructureND.mapAsync( - scope: CoroutineScope, - crossinline function: suspend (T) -> R, -): LazyStructureND = LazyStructureND(scope, shape) { index -> function(get(index)) } diff --git a/kmath-coroutines/src/jvmTest/kotlin/space/kscience/kmath/streaming/BufferFlowTest.kt b/kmath-coroutines/src/jvmTest/kotlin/space/kscience/kmath/streaming/BufferFlowTest.kt deleted file mode 100644 index 9b67f7253..000000000 --- a/kmath-coroutines/src/jvmTest/kotlin/space/kscience/kmath/streaming/BufferFlowTest.kt +++ /dev/null @@ -1,55 +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. - */ - -package space.kscience.kmath.streaming - -import kotlinx.coroutines.* -import kotlinx.coroutines.flow.asFlow -import kotlinx.coroutines.flow.collect -import org.junit.jupiter.api.Timeout -import space.kscience.kmath.coroutines.async -import space.kscience.kmath.coroutines.collect -import space.kscience.kmath.coroutines.mapParallel -import java.util.concurrent.Executors -import kotlin.test.Test - - -@ExperimentalCoroutinesApi -@InternalCoroutinesApi -@FlowPreview -internal class BufferFlowTest { - val dispatcher: CoroutineDispatcher = Executors.newFixedThreadPool(4).asCoroutineDispatcher() - - @Test - @Timeout(2000) - fun map() { - runBlocking { - (1..20).asFlow().mapParallel(dispatcher) { - println("Started $it on ${Thread.currentThread().name}") - @Suppress("BlockingMethodInNonBlockingContext") - Thread.sleep(200) - it - }.collect { - println("Completed $it on ${Thread.currentThread().name}") - } - } - } - - @Test - @Timeout(2000) - fun async() { - runBlocking { - (1..20).asFlow().async(dispatcher) { - println("Started $it on ${Thread.currentThread().name}") - @Suppress("BlockingMethodInNonBlockingContext") - Thread.sleep(200) - it - }.collect(4) { - println("Completed $it on ${Thread.currentThread().name}") - } - } - } - -} \ No newline at end of file diff --git a/kmath-coroutines/src/jvmTest/kotlin/space/kscience/kmath/streaming/RingBufferTest.kt b/kmath-coroutines/src/jvmTest/kotlin/space/kscience/kmath/streaming/RingBufferTest.kt deleted file mode 100644 index 305b97e5d..000000000 --- a/kmath-coroutines/src/jvmTest/kotlin/space/kscience/kmath/streaming/RingBufferTest.kt +++ /dev/null @@ -1,43 +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. - */ - -package space.kscience.kmath.streaming - -import kotlinx.coroutines.flow.* -import kotlinx.coroutines.runBlocking -import space.kscience.kmath.operations.asSequence -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class RingBufferTest { - @Test - fun push() { - val buffer = RingBuffer.build(20, Double.NaN) - runBlocking { - for (i in 1..30) { - buffer.push(i.toDouble()) - } - assertEquals(410.0, buffer.asSequence().sum()) - } - } - - @Test - fun windowed() { - val flow = flow { - var i = 0 - while (true) emit(i++) - } - - val windowed = flow.windowed(10) - - runBlocking { - @Suppress("UNUSED_VARIABLE") val first = windowed.take(1).single() - val res = windowed.take(15).map { it.asSequence().average() }.toList() - assertEquals(0.0, res[0]) - assertEquals(4.5, res[9]) - assertEquals(9.5, res[14]) - } - } -} \ No newline at end of file diff --git a/kmath-dimensions/README.md b/kmath-dimensions/README.md deleted file mode 100644 index 52097cf40..000000000 --- a/kmath-dimensions/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-dimensions - -A proof of concept module for adding type-safe dimensions to structures - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-dimensions:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-dimensions:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-dimensions:0.3.0-dev-20") -} -``` diff --git a/kmath-dimensions/build.gradle.kts b/kmath-dimensions/build.gradle.kts deleted file mode 100644 index 885f3c227..000000000 --- a/kmath-dimensions/build.gradle.kts +++ /dev/null @@ -1,25 +0,0 @@ -plugins { - kotlin("multiplatform") - id("ru.mipt.npm.gradle.common") - id("ru.mipt.npm.gradle.native") -} - -description = "A proof of concept module for adding type-safe dimensions to structures" - -kotlin.sourceSets { - commonMain { - dependencies { - api(project(":kmath-core")) - } - } - - jvmMain { - dependencies { - api(kotlin("reflect")) - } - } -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE -} diff --git a/kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt b/kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt deleted file mode 100644 index e57c22834..000000000 --- a/kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt +++ /dev/null @@ -1,54 +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. - */ - -package space.kscience.kmath.dimensions - -import kotlin.reflect.KClass - -/** - * Represents a quantity of dimensions in certain structure. **This interface must be implemented only by objects.** - * - * @property dim The number of dimensions. - */ -public interface Dimension { - public val dim: Int - - public companion object -} - -public fun KClass.dim(): Int = Dimension.resolve(this).dim - -public expect fun Dimension.Companion.resolve(type: KClass): D - -/** - * Finds or creates [Dimension] with [Dimension.dim] equal to [dim]. - */ -public expect fun Dimension.Companion.of(dim: Int): Dimension - -/** - * Finds [Dimension.dim] of given type [D]. - */ -public inline fun Dimension.Companion.dim(): Int = D::class.dim() - -/** - * Type representing 1 dimension. - */ -public object D1 : Dimension { - override val dim: Int get() = 1 -} - -/** - * Type representing 2 dimensions. - */ -public object D2 : Dimension { - override val dim: Int get() = 2 -} - -/** - * Type representing 3 dimensions. - */ -public object D3 : Dimension { - override val dim: Int get() = 3 -} diff --git a/kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Wrappers.kt b/kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Wrappers.kt deleted file mode 100644 index f04536f04..000000000 --- a/kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Wrappers.kt +++ /dev/null @@ -1,168 +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. - */ - -package space.kscience.kmath.dimensions - -import space.kscience.kmath.linear.* -import space.kscience.kmath.nd.Structure2D -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.Ring -import space.kscience.kmath.operations.algebra -import kotlin.jvm.JvmInline - -/** - * A matrix with compile-time controlled dimension - */ -public interface DMatrix : Structure2D { - public companion object { - /** - * Coerces a regular matrix to a matrix with type-safe dimensions and throws an error if coercion failed - */ - public inline fun coerce(structure: Structure2D): DMatrix { - require(structure.rowNum == Dimension.dim()) { - "Row number mismatch: expected ${Dimension.dim()} but found ${structure.rowNum}" - } - - require(structure.colNum == Dimension.dim()) { - "Column number mismatch: expected ${Dimension.dim()} but found ${structure.colNum}" - } - - return DMatrixWrapper(structure) - } - - /** - * The same as [DMatrix.coerce] but without dimension checks. Use with caution. - */ - public fun coerceUnsafe(structure: Structure2D): DMatrix = - DMatrixWrapper(structure) - } -} - -/** - * An inline wrapper for a Matrix - */ -@JvmInline -public value class DMatrixWrapper( - private val structure: Structure2D, -) : DMatrix { - override val shape: IntArray get() = structure.shape - override val rowNum: Int get() = shape[0] - override val colNum: Int get() = shape[1] - override operator fun get(i: Int, j: Int): T = structure[i, j] -} - -/** - * Dimension-safe point - */ -public interface DPoint : Point { - public companion object { - public inline fun coerce(point: Point): DPoint { - require(point.size == Dimension.dim()) { - "Vector dimension mismatch: expected ${Dimension.dim()}, but found ${point.size}" - } - - return DPointWrapper(point) - } - - public fun coerceUnsafe(point: Point): DPoint = DPointWrapper(point) - } -} - -/** - * Dimension-safe point wrapper - */ -@JvmInline -public value class DPointWrapper(public val point: Point) : - DPoint { - override val size: Int get() = point.size - - override operator fun get(index: Int): T = point[index] - - override operator fun iterator(): Iterator = point.iterator() -} - - -/** - * Basic operations on dimension-safe matrices. Operates on [Matrix] - */ -@JvmInline -public value class DMatrixContext>(public val context: LinearSpace) { - public inline fun Matrix.coerce(): DMatrix { - require(rowNum == Dimension.dim()) { - "Row number mismatch: expected ${Dimension.dim()} but found $rowNum" - } - - require(colNum == Dimension.dim()) { - "Column number mismatch: expected ${Dimension.dim()} but found $colNum" - } - - return DMatrix.coerceUnsafe(this) - } - - /** - * Produce a matrix with this context and given dimensions - */ - public inline fun produce( - noinline initializer: A.(i: Int, j: Int) -> T, - ): DMatrix { - val rows = Dimension.dim() - val cols = Dimension.dim() - return context.buildMatrix(rows, cols, initializer).coerce() - } - - public inline fun point(noinline initializer: A.(Int) -> T): DPoint { - val size = Dimension.dim() - - return DPoint.coerceUnsafe( - context.buildVector( - size, - initializer - ) - ) - } - - public inline infix fun DMatrix.dot( - other: DMatrix, - ): DMatrix = context.run { this@dot dot other }.coerce() - - public inline infix fun DMatrix.dot(vector: DPoint): DPoint = - DPoint.coerceUnsafe(context.run { this@dot dot vector }) - - public inline operator fun DMatrix.times(value: T): DMatrix = - context.run { this@times.times(value) }.coerce() - - public inline operator fun T.times(m: DMatrix): DMatrix = - m * this - - public inline operator fun DMatrix.plus(other: DMatrix): DMatrix = - context.run { this@plus + other }.coerce() - - public inline operator fun DMatrix.minus(other: DMatrix): DMatrix = - context.run { this@minus + other }.coerce() - - public inline operator fun DMatrix.unaryMinus(): DMatrix = - context.run { this@unaryMinus.unaryMinus() }.coerce() - - public inline fun DMatrix.transpose(): DMatrix = - context.run { (this@transpose as Matrix).transpose() }.coerce() - - public companion object { - public val real: DMatrixContext = DMatrixContext(Double.algebra.linearSpace) - } -} - - -/** - * A square unit matrix - */ -public inline fun DMatrixContext.one(): DMatrix = - produce { i, j -> - if (i == j) 1.0 else 0.0 - } - -public inline fun DMatrixContext.zero(): DMatrix = - produce { _, _ -> - 0.0 - } diff --git a/kmath-dimensions/src/commonTest/kotlin/space/kscience/dimensions/DMatrixContextTest.kt b/kmath-dimensions/src/commonTest/kotlin/space/kscience/dimensions/DMatrixContextTest.kt deleted file mode 100644 index 59260fe73..000000000 --- a/kmath-dimensions/src/commonTest/kotlin/space/kscience/dimensions/DMatrixContextTest.kt +++ /dev/null @@ -1,36 +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. - */ - -package space.kscience.dimensions - -import space.kscience.kmath.dimensions.D2 -import space.kscience.kmath.dimensions.D3 -import space.kscience.kmath.dimensions.DMatrixContext -import space.kscience.kmath.dimensions.one -import kotlin.test.Test - -@Suppress("UNUSED_VARIABLE") -internal class DMatrixContextTest { - @Test - fun testDimensionSafeMatrix() { - val res = with(DMatrixContext.real) { - val m = produce { i, j -> (i + j).toDouble() } - - //The dimension of `one()` is inferred from type - (m + one()) - } - } - - @Test - fun testTypeCheck() { - val res = with(DMatrixContext.real) { - val m1 = produce { i, j -> (i + j).toDouble() } - val m2 = produce { i, j -> (i + j).toDouble() } - - //Dimension-safe addition - m1.transpose() + m2 - } - } -} diff --git a/kmath-dimensions/src/jsMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt b/kmath-dimensions/src/jsMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt deleted file mode 100644 index 610e8b4c0..000000000 --- a/kmath-dimensions/src/jsMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt +++ /dev/null @@ -1,23 +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. - */ - -package space.kscience.kmath.dimensions - -import kotlin.reflect.KClass - -private val dimensionMap: MutableMap = hashMapOf(1 to D1, 2 to D2, 3 to D3) - -@Suppress("UNCHECKED_CAST") -public actual fun Dimension.Companion.resolve(type: KClass): D = dimensionMap - .entries - .map(MutableMap.MutableEntry::value) - .find { it::class == type } as? D - ?: error("Can't resolve dimension $type") - -public actual fun Dimension.Companion.of(dim: Int): Dimension = dimensionMap.getOrPut(dim) { - object : Dimension { - override val dim: Int get() = dim - } -} diff --git a/kmath-dimensions/src/jvmMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt b/kmath-dimensions/src/jvmMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt deleted file mode 100644 index e6d8b3b35..000000000 --- a/kmath-dimensions/src/jvmMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt +++ /dev/null @@ -1,23 +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. - */ - -@file:JvmName("DimensionJVM") - -package space.kscience.kmath.dimensions - -import kotlin.reflect.KClass - -public actual fun Dimension.Companion.resolve(type: KClass): D = - type.objectInstance ?: error("No object instance for dimension class") - -public actual fun Dimension.Companion.of(dim: Int): Dimension = when (dim) { - 1 -> D1 - 2 -> D2 - 3 -> D3 - - else -> object : Dimension { - override val dim: Int get() = dim - } -} diff --git a/kmath-dimensions/src/nativeMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt b/kmath-dimensions/src/nativeMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt deleted file mode 100644 index 64edbe935..000000000 --- a/kmath-dimensions/src/nativeMain/kotlin/space/kscience/kmath/dimensions/Dimension.kt +++ /dev/null @@ -1,25 +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. - */ - -package space.kscience.kmath.dimensions - -import kotlin.native.concurrent.ThreadLocal -import kotlin.reflect.KClass - -@ThreadLocal -private val dimensionMap: MutableMap = hashMapOf(1 to D1, 2 to D2, 3 to D3) - -@Suppress("UNCHECKED_CAST") -public actual fun Dimension.Companion.resolve(type: KClass): D = dimensionMap - .entries - .map(MutableMap.MutableEntry::value) - .find { it::class == type } as? D - ?: error("Can't resolve dimension $type") - -public actual fun Dimension.Companion.of(dim: Int): Dimension = dimensionMap.getOrPut(dim) { - object : Dimension { - override val dim: Int get() = dim - } -} diff --git a/kmath-ejml/README.md b/kmath-ejml/README.md deleted file mode 100644 index 3fe6d9e1a..000000000 --- a/kmath-ejml/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# Module kmath-ejml - -EJML based linear algebra implementation. - - - [ejml-vector](src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt) : Point implementations. - - [ejml-matrix](src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt) : Matrix implementation. - - [ejml-linear-space](src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : LinearSpace implementations. - - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-ejml:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-ejml:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-ejml:0.3.0-dev-20") -} -``` diff --git a/kmath-ejml/build.gradle.kts b/kmath-ejml/build.gradle.kts deleted file mode 100644 index 727d21e3a..000000000 --- a/kmath-ejml/build.gradle.kts +++ /dev/null @@ -1,42 +0,0 @@ -import space.kscience.kmath.ejml.codegen.ejmlCodegen - -plugins { - kotlin("jvm") - id("ru.mipt.npm.gradle.common") -} - -dependencies { - api("org.ejml:ejml-ddense:0.41") - api("org.ejml:ejml-fdense:0.41") - api("org.ejml:ejml-dsparse:0.41") - api("org.ejml:ejml-fsparse:0.41") - api(project(":kmath-core")) -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE - propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md")) - - feature( - id = "ejml-vector", - ref = "src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt" - ) { "Point implementations." } - - feature( - id = "ejml-matrix", - ref = "src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt" - ) { "Matrix implementation." } - - feature( - id = "ejml-linear-space", - ref = "src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt" - ) { "LinearSpace implementations." } -} - -kotlin.sourceSets.main { - val codegen by tasks.creating { - ejmlCodegen(kotlin.srcDirs.first().absolutePath + "/space/kscience/kmath/ejml/_generated.kt") - } - - kotlin.srcDirs(files().builtBy(codegen)) -} diff --git a/kmath-ejml/docs/README-TEMPLATE.md b/kmath-ejml/docs/README-TEMPLATE.md deleted file mode 100644 index 27fcedd65..000000000 --- a/kmath-ejml/docs/README-TEMPLATE.md +++ /dev/null @@ -1,7 +0,0 @@ -# Module kmath-ejml - -EJML based linear algebra implementation. - -${features} - -${artifact} diff --git a/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt b/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt deleted file mode 100644 index 32030dfe3..000000000 --- a/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt +++ /dev/null @@ -1,47 +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. - */ - -package space.kscience.kmath.ejml - -import space.kscience.kmath.linear.InverseMatrixFeature -import space.kscience.kmath.linear.LinearSpace -import space.kscience.kmath.linear.Matrix -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.Structure2D -import space.kscience.kmath.operations.Ring - -/** - * [LinearSpace] implementation specialized for a certain EJML type. - * - * @param T the type of items in the matrices. - * @param A the element context type. - * @param M the EJML matrix type. - * @author Iaroslav Postovalov - */ -public abstract class EjmlLinearSpace, out M : org.ejml.data.Matrix> : LinearSpace { - /** - * Converts this matrix to EJML one. - */ - public abstract fun Matrix.toEjml(): EjmlMatrix - - /** - * Converts this vector to EJML one. - */ - public abstract fun Point.toEjml(): EjmlVector - - public abstract override fun buildMatrix( - rows: Int, - columns: Int, - initializer: A.(i: Int, j: Int) -> T, - ): EjmlMatrix - - public abstract override fun buildVector(size: Int, initializer: A.(Int) -> T): EjmlVector - - @Suppress("UNCHECKED_CAST") - @UnstableKMathAPI - public fun EjmlMatrix.inverse(): Structure2D = - computeFeature(this, InverseMatrixFeature::class)?.inverse as Structure2D -} diff --git a/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt b/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt deleted file mode 100644 index 27fd3fc53..000000000 --- a/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt +++ /dev/null @@ -1,22 +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. - */ - -package space.kscience.kmath.ejml - -import org.ejml.data.Matrix -import space.kscience.kmath.nd.Structure2D - -/** - * [space.kscience.kmath.linear.Matrix] implementation based on EJML [Matrix]. - * - * @param T the type of elements contained in the buffer. - * @param M the type of EJML matrix. - * @property origin The underlying EJML matrix. - * @author Iaroslav Postovalov - */ -public abstract class EjmlMatrix(public open val origin: M) : Structure2D { - override val rowNum: Int get() = origin.numRows - override val colNum: Int get() = origin.numCols -} diff --git a/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt b/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt deleted file mode 100644 index 37995c27e..000000000 --- a/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt +++ /dev/null @@ -1,35 +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. - */ - -package space.kscience.kmath.ejml - -import org.ejml.data.Matrix -import space.kscience.kmath.linear.Point - -/** - * [Point] implementation based on EJML [Matrix]. - * - * @param T the type of elements contained in the buffer. - * @param M the type of EJML matrix. - * @property origin The underlying matrix, must have only one row. - * @author Iaroslav Postovalov - */ -public abstract class EjmlVector(public open val origin: M) : Point { - override val size: Int - get() = origin.numCols - - override operator fun iterator(): Iterator = object : Iterator { - private var cursor: Int = 0 - - override fun next(): T { - cursor += 1 - return this@EjmlVector[cursor - 1] - } - - override fun hasNext(): Boolean = cursor < origin.numCols * origin.numRows - } - - override fun toString(): String = "EjmlVector(origin=$origin)" -} diff --git a/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/_generated.kt b/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/_generated.kt deleted file mode 100644 index dce739dc2..000000000 --- a/kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/_generated.kt +++ /dev/null @@ -1,995 +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 file. - */ - -/* This file is generated with buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt */ - -package space.kscience.kmath.ejml - -import org.ejml.data.* -import org.ejml.dense.row.CommonOps_DDRM -import org.ejml.dense.row.CommonOps_FDRM -import org.ejml.dense.row.factory.DecompositionFactory_DDRM -import org.ejml.dense.row.factory.DecompositionFactory_FDRM -import org.ejml.sparse.FillReducing -import org.ejml.sparse.csc.CommonOps_DSCC -import org.ejml.sparse.csc.CommonOps_FSCC -import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC -import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC -import org.ejml.sparse.csc.factory.LinearSolverFactory_DSCC -import org.ejml.sparse.csc.factory.LinearSolverFactory_FSCC -import space.kscience.kmath.linear.* -import space.kscience.kmath.linear.Matrix -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.StructureFeature -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.FloatField -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.FloatBuffer -import kotlin.reflect.KClass -import kotlin.reflect.cast - -/** - * [EjmlVector] specialization for [Double]. - */ -public class EjmlDoubleVector(override val origin: M) : EjmlVector(origin) { - init { - require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" } - } - - override operator fun get(index: Int): Double = origin[0, index] -} - -/** - * [EjmlVector] specialization for [Float]. - */ -public class EjmlFloatVector(override val origin: M) : EjmlVector(origin) { - init { - require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" } - } - - override operator fun get(index: Int): Float = origin[0, index] -} - -/** - * [EjmlMatrix] specialization for [Double]. - */ -public class EjmlDoubleMatrix(override val origin: M) : EjmlMatrix(origin) { - override operator fun get(i: Int, j: Int): Double = origin[i, j] -} - -/** - * [EjmlMatrix] specialization for [Float]. - */ -public class EjmlFloatMatrix(override val origin: M) : EjmlMatrix(origin) { - override operator fun get(i: Int, j: Int): Float = origin[i, j] -} - -/** - * [EjmlLinearSpace] implementation based on [CommonOps_DDRM], [DecompositionFactory_DDRM] operations and - * [DMatrixRMaj] matrices. - */ -public object EjmlLinearSpaceDDRM : EjmlLinearSpace() { - /** - * The [DoubleField] reference. - */ - override val elementAlgebra: DoubleField get() = DoubleField - - @Suppress("UNCHECKED_CAST") - override fun Matrix.toEjml(): EjmlDoubleMatrix = when { - this is EjmlDoubleMatrix<*> && origin is DMatrixRMaj -> this as EjmlDoubleMatrix - else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) } - } - - @Suppress("UNCHECKED_CAST") - override fun Point.toEjml(): EjmlDoubleVector = when { - this is EjmlDoubleVector<*> && origin is DMatrixRMaj -> this as EjmlDoubleVector - else -> EjmlDoubleVector(DMatrixRMaj(size, 1).also { - (0 until it.numRows).forEach { row -> it[row, 0] = get(row) } - }) - } - - override fun buildMatrix( - rows: Int, - columns: Int, - initializer: DoubleField.(i: Int, j: Int) -> Double, - ): EjmlDoubleMatrix = DMatrixRMaj(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: DoubleField.(Int) -> Double, - ): EjmlDoubleVector = EjmlDoubleVector(DMatrixRMaj(size, 1).also { - (0 until it.numRows).forEach { row -> it[row, 0] = elementAlgebra.initializer(row) } - }) - - private fun T.wrapMatrix() = EjmlDoubleMatrix(this) - private fun T.wrapVector() = EjmlDoubleVector(this) - - override fun Matrix.unaryMinus(): Matrix = this * elementAlgebra { -one } - - override fun Matrix.dot(other: Matrix): EjmlDoubleMatrix { - val out = DMatrixRMaj(1, 1) - CommonOps_DDRM.mult(toEjml().origin, other.toEjml().origin, out) - return out.wrapMatrix() - } - - override fun Matrix.dot(vector: Point): EjmlDoubleVector { - val out = DMatrixRMaj(1, 1) - CommonOps_DDRM.mult(toEjml().origin, vector.toEjml().origin, out) - return out.wrapVector() - } - - override operator fun Matrix.minus(other: Matrix): EjmlDoubleMatrix { - val out = DMatrixRMaj(1, 1) - - CommonOps_DDRM.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra { -one }, - other.toEjml().origin, - out, - ) - - return out.wrapMatrix() - } - - override operator fun Matrix.times(value: Double): EjmlDoubleMatrix { - val res = DMatrixRMaj(1, 1) - CommonOps_DDRM.scale(value, toEjml().origin, res) - return res.wrapMatrix() - } - - override fun Point.unaryMinus(): EjmlDoubleVector { - val res = DMatrixRMaj(1, 1) - CommonOps_DDRM.changeSign(toEjml().origin, res) - return res.wrapVector() - } - - override fun Matrix.plus(other: Matrix): EjmlDoubleMatrix { - val out = DMatrixRMaj(1, 1) - - CommonOps_DDRM.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra.one, - other.toEjml().origin, - out, - ) - - return out.wrapMatrix() - } - - override fun Point.plus(other: Point): EjmlDoubleVector { - val out = DMatrixRMaj(1, 1) - - CommonOps_DDRM.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra.one, - other.toEjml().origin, - out, - ) - - return out.wrapVector() - } - - override fun Point.minus(other: Point): EjmlDoubleVector { - val out = DMatrixRMaj(1, 1) - - CommonOps_DDRM.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra { -one }, - other.toEjml().origin, - out, - ) - - return out.wrapVector() - } - - override fun Double.times(m: Matrix): EjmlDoubleMatrix = m * this - - override fun Point.times(value: Double): EjmlDoubleVector { - val res = DMatrixRMaj(1, 1) - CommonOps_DDRM.scale(value, toEjml().origin, res) - return res.wrapVector() - } - - override fun Double.times(v: Point): EjmlDoubleVector = v * this - - @UnstableKMathAPI - override fun computeFeature(structure: Matrix, type: KClass): F? { - structure.getFeature(type)?.let { return it } - val origin = structure.toEjml().origin - - return when (type) { - InverseMatrixFeature::class -> object : InverseMatrixFeature { - override val inverse: Matrix by lazy { - val res = origin.copy() - CommonOps_DDRM.invert(res) - res.wrapMatrix() - } - } - - DeterminantFeature::class -> object : DeterminantFeature { - override val determinant: Double by lazy { CommonOps_DDRM.det(origin) } - } - - SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature { - private val svd by lazy { - DecompositionFactory_DDRM.svd(origin.numRows, origin.numCols, true, true, false) - .apply { decompose(origin.copy()) } - } - - override val u: Matrix by lazy { svd.getU(null, false).wrapMatrix() } - override val s: Matrix by lazy { svd.getW(null).wrapMatrix() } - override val v: Matrix by lazy { svd.getV(null, false).wrapMatrix() } - override val singularValues: Point by lazy { DoubleBuffer(svd.singularValues) } - } - - QRDecompositionFeature::class -> object : QRDecompositionFeature { - private val qr by lazy { - DecompositionFactory_DDRM.qr().apply { decompose(origin.copy()) } - } - - override val q: Matrix by lazy { - qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature) - } - - override val r: Matrix by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) } - } - - CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature { - override val l: Matrix by lazy { - val cholesky = - DecompositionFactory_DDRM.chol(structure.rowNum, true).apply { decompose(origin.copy()) } - - cholesky.getT(null).wrapMatrix().withFeature(LFeature) - } - } - - LupDecompositionFeature::class -> object : LupDecompositionFeature { - private val lup by lazy { - DecompositionFactory_DDRM.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) } - } - - override val l: Matrix by lazy { - lup.getLower(null).wrapMatrix().withFeature(LFeature) - } - - override val u: Matrix by lazy { - lup.getUpper(null).wrapMatrix().withFeature(UFeature) - } - - override val p: Matrix by lazy { lup.getRowPivot(null).wrapMatrix() } - } - - else -> null - }?.let(type::cast) - } - - /** - * Solves for *x* in the following equation: *x = [a] -1 · [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, b: Matrix): EjmlDoubleMatrix { - val res = DMatrixRMaj(1, 1) - CommonOps_DDRM.solve(DMatrixRMaj(a.toEjml().origin), DMatrixRMaj(b.toEjml().origin), res) - return res.wrapMatrix() - } - - /** - * Solves for *x* in the following equation: *x = [a] -1 · [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, b: Point): EjmlDoubleVector { - val res = DMatrixRMaj(1, 1) - CommonOps_DDRM.solve(DMatrixRMaj(a.toEjml().origin), DMatrixRMaj(b.toEjml().origin), res) - return EjmlDoubleVector(res) - } -} - -/** - * [EjmlLinearSpace] implementation based on [CommonOps_FDRM], [DecompositionFactory_FDRM] operations and - * [FMatrixRMaj] matrices. - */ -public object EjmlLinearSpaceFDRM : EjmlLinearSpace() { - /** - * The [FloatField] reference. - */ - override val elementAlgebra: FloatField get() = FloatField - - @Suppress("UNCHECKED_CAST") - override fun Matrix.toEjml(): EjmlFloatMatrix = when { - this is EjmlFloatMatrix<*> && origin is FMatrixRMaj -> this as EjmlFloatMatrix - else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) } - } - - @Suppress("UNCHECKED_CAST") - override fun Point.toEjml(): EjmlFloatVector = when { - this is EjmlFloatVector<*> && origin is FMatrixRMaj -> this as EjmlFloatVector - else -> EjmlFloatVector(FMatrixRMaj(size, 1).also { - (0 until it.numRows).forEach { row -> it[row, 0] = get(row) } - }) - } - - override fun buildMatrix( - rows: Int, - columns: Int, - initializer: FloatField.(i: Int, j: Int) -> Float, - ): EjmlFloatMatrix = FMatrixRMaj(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: FloatField.(Int) -> Float, - ): EjmlFloatVector = EjmlFloatVector(FMatrixRMaj(size, 1).also { - (0 until it.numRows).forEach { row -> it[row, 0] = elementAlgebra.initializer(row) } - }) - - private fun T.wrapMatrix() = EjmlFloatMatrix(this) - private fun T.wrapVector() = EjmlFloatVector(this) - - override fun Matrix.unaryMinus(): Matrix = this * elementAlgebra { -one } - - override fun Matrix.dot(other: Matrix): EjmlFloatMatrix { - val out = FMatrixRMaj(1, 1) - CommonOps_FDRM.mult(toEjml().origin, other.toEjml().origin, out) - return out.wrapMatrix() - } - - override fun Matrix.dot(vector: Point): EjmlFloatVector { - val out = FMatrixRMaj(1, 1) - CommonOps_FDRM.mult(toEjml().origin, vector.toEjml().origin, out) - return out.wrapVector() - } - - override operator fun Matrix.minus(other: Matrix): EjmlFloatMatrix { - val out = FMatrixRMaj(1, 1) - - CommonOps_FDRM.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra { -one }, - other.toEjml().origin, - out, - ) - - return out.wrapMatrix() - } - - override operator fun Matrix.times(value: Float): EjmlFloatMatrix { - val res = FMatrixRMaj(1, 1) - CommonOps_FDRM.scale(value, toEjml().origin, res) - return res.wrapMatrix() - } - - override fun Point.unaryMinus(): EjmlFloatVector { - val res = FMatrixRMaj(1, 1) - CommonOps_FDRM.changeSign(toEjml().origin, res) - return res.wrapVector() - } - - override fun Matrix.plus(other: Matrix): EjmlFloatMatrix { - val out = FMatrixRMaj(1, 1) - - CommonOps_FDRM.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra.one, - other.toEjml().origin, - out, - ) - - return out.wrapMatrix() - } - - override fun Point.plus(other: Point): EjmlFloatVector { - val out = FMatrixRMaj(1, 1) - - CommonOps_FDRM.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra.one, - other.toEjml().origin, - out, - ) - - return out.wrapVector() - } - - override fun Point.minus(other: Point): EjmlFloatVector { - val out = FMatrixRMaj(1, 1) - - CommonOps_FDRM.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra { -one }, - other.toEjml().origin, - out, - ) - - return out.wrapVector() - } - - override fun Float.times(m: Matrix): EjmlFloatMatrix = m * this - - override fun Point.times(value: Float): EjmlFloatVector { - val res = FMatrixRMaj(1, 1) - CommonOps_FDRM.scale(value, toEjml().origin, res) - return res.wrapVector() - } - - override fun Float.times(v: Point): EjmlFloatVector = v * this - - @UnstableKMathAPI - override fun computeFeature(structure: Matrix, type: KClass): F? { - structure.getFeature(type)?.let { return it } - val origin = structure.toEjml().origin - - return when (type) { - InverseMatrixFeature::class -> object : InverseMatrixFeature { - override val inverse: Matrix by lazy { - val res = origin.copy() - CommonOps_FDRM.invert(res) - res.wrapMatrix() - } - } - - DeterminantFeature::class -> object : DeterminantFeature { - override val determinant: Float by lazy { CommonOps_FDRM.det(origin) } - } - - SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature { - private val svd by lazy { - DecompositionFactory_FDRM.svd(origin.numRows, origin.numCols, true, true, false) - .apply { decompose(origin.copy()) } - } - - override val u: Matrix by lazy { svd.getU(null, false).wrapMatrix() } - override val s: Matrix by lazy { svd.getW(null).wrapMatrix() } - override val v: Matrix by lazy { svd.getV(null, false).wrapMatrix() } - override val singularValues: Point by lazy { FloatBuffer(svd.singularValues) } - } - - QRDecompositionFeature::class -> object : QRDecompositionFeature { - private val qr by lazy { - DecompositionFactory_FDRM.qr().apply { decompose(origin.copy()) } - } - - override val q: Matrix by lazy { - qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature) - } - - override val r: Matrix by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) } - } - - CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature { - override val l: Matrix by lazy { - val cholesky = - DecompositionFactory_FDRM.chol(structure.rowNum, true).apply { decompose(origin.copy()) } - - cholesky.getT(null).wrapMatrix().withFeature(LFeature) - } - } - - LupDecompositionFeature::class -> object : LupDecompositionFeature { - private val lup by lazy { - DecompositionFactory_FDRM.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) } - } - - override val l: Matrix by lazy { - lup.getLower(null).wrapMatrix().withFeature(LFeature) - } - - override val u: Matrix by lazy { - lup.getUpper(null).wrapMatrix().withFeature(UFeature) - } - - override val p: Matrix by lazy { lup.getRowPivot(null).wrapMatrix() } - } - - else -> null - }?.let(type::cast) - } - - /** - * Solves for *x* in the following equation: *x = [a] -1 · [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, b: Matrix): EjmlFloatMatrix { - val res = FMatrixRMaj(1, 1) - CommonOps_FDRM.solve(FMatrixRMaj(a.toEjml().origin), FMatrixRMaj(b.toEjml().origin), res) - return res.wrapMatrix() - } - - /** - * Solves for *x* in the following equation: *x = [a] -1 · [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, b: Point): EjmlFloatVector { - val res = FMatrixRMaj(1, 1) - CommonOps_FDRM.solve(FMatrixRMaj(a.toEjml().origin), FMatrixRMaj(b.toEjml().origin), res) - return EjmlFloatVector(res) - } -} - -/** - * [EjmlLinearSpace] implementation based on [CommonOps_DSCC], [DecompositionFactory_DSCC] operations and - * [DMatrixSparseCSC] matrices. - */ -public object EjmlLinearSpaceDSCC : EjmlLinearSpace() { - /** - * The [DoubleField] reference. - */ - override val elementAlgebra: DoubleField get() = DoubleField - - @Suppress("UNCHECKED_CAST") - override fun Matrix.toEjml(): EjmlDoubleMatrix = when { - this is EjmlDoubleMatrix<*> && origin is DMatrixSparseCSC -> this as EjmlDoubleMatrix - else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) } - } - - @Suppress("UNCHECKED_CAST") - override fun Point.toEjml(): EjmlDoubleVector = when { - this is EjmlDoubleVector<*> && origin is DMatrixSparseCSC -> this as EjmlDoubleVector - else -> EjmlDoubleVector(DMatrixSparseCSC(size, 1).also { - (0 until it.numRows).forEach { row -> it[row, 0] = get(row) } - }) - } - - override fun buildMatrix( - rows: Int, - columns: Int, - initializer: DoubleField.(i: Int, j: Int) -> Double, - ): EjmlDoubleMatrix = DMatrixSparseCSC(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: DoubleField.(Int) -> Double, - ): EjmlDoubleVector = EjmlDoubleVector(DMatrixSparseCSC(size, 1).also { - (0 until it.numRows).forEach { row -> it[row, 0] = elementAlgebra.initializer(row) } - }) - - private fun T.wrapMatrix() = EjmlDoubleMatrix(this) - private fun T.wrapVector() = EjmlDoubleVector(this) - - override fun Matrix.unaryMinus(): Matrix = this * elementAlgebra { -one } - - override fun Matrix.dot(other: Matrix): EjmlDoubleMatrix { - val out = DMatrixSparseCSC(1, 1) - CommonOps_DSCC.mult(toEjml().origin, other.toEjml().origin, out) - return out.wrapMatrix() - } - - override fun Matrix.dot(vector: Point): EjmlDoubleVector { - val out = DMatrixSparseCSC(1, 1) - CommonOps_DSCC.mult(toEjml().origin, vector.toEjml().origin, out) - return out.wrapVector() - } - - override operator fun Matrix.minus(other: Matrix): EjmlDoubleMatrix { - val out = DMatrixSparseCSC(1, 1) - - CommonOps_DSCC.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra { -one }, - other.toEjml().origin, - out, - null, - null, - ) - - return out.wrapMatrix() - } - - override operator fun Matrix.times(value: Double): EjmlDoubleMatrix { - val res = DMatrixSparseCSC(1, 1) - CommonOps_DSCC.scale(value, toEjml().origin, res) - return res.wrapMatrix() - } - - override fun Point.unaryMinus(): EjmlDoubleVector { - val res = DMatrixSparseCSC(1, 1) - CommonOps_DSCC.changeSign(toEjml().origin, res) - return res.wrapVector() - } - - override fun Matrix.plus(other: Matrix): EjmlDoubleMatrix { - val out = DMatrixSparseCSC(1, 1) - - CommonOps_DSCC.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra.one, - other.toEjml().origin, - out, - null, - null, - ) - - return out.wrapMatrix() - } - - override fun Point.plus(other: Point): EjmlDoubleVector { - val out = DMatrixSparseCSC(1, 1) - - CommonOps_DSCC.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra.one, - other.toEjml().origin, - out, - null, - null, - ) - - return out.wrapVector() - } - - override fun Point.minus(other: Point): EjmlDoubleVector { - val out = DMatrixSparseCSC(1, 1) - - CommonOps_DSCC.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra { -one }, - other.toEjml().origin, - out, - null, - null, - ) - - return out.wrapVector() - } - - override fun Double.times(m: Matrix): EjmlDoubleMatrix = m * this - - override fun Point.times(value: Double): EjmlDoubleVector { - val res = DMatrixSparseCSC(1, 1) - CommonOps_DSCC.scale(value, toEjml().origin, res) - return res.wrapVector() - } - - override fun Double.times(v: Point): EjmlDoubleVector = v * this - - @UnstableKMathAPI - override fun computeFeature(structure: Matrix, type: KClass): F? { - structure.getFeature(type)?.let { return it } - val origin = structure.toEjml().origin - - return when (type) { - QRDecompositionFeature::class -> object : QRDecompositionFeature { - private val qr by lazy { - DecompositionFactory_DSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) } - } - - override val q: Matrix by lazy { - qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature) - } - - override val r: Matrix by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) } - } - - CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature { - override val l: Matrix by lazy { - val cholesky = - DecompositionFactory_DSCC.cholesky().apply { decompose(origin.copy()) } - - (cholesky.getT(null) as DMatrix).wrapMatrix().withFeature(LFeature) - } - } - - LUDecompositionFeature::class, DeterminantFeature::class, InverseMatrixFeature::class -> object : - LUDecompositionFeature, DeterminantFeature, InverseMatrixFeature { - private val lu by lazy { - DecompositionFactory_DSCC.lu(FillReducing.NONE).apply { decompose(origin.copy()) } - } - - override val l: Matrix by lazy { - lu.getLower(null).wrapMatrix().withFeature(LFeature) - } - - override val u: Matrix by lazy { - lu.getUpper(null).wrapMatrix().withFeature(UFeature) - } - - override val inverse: Matrix by lazy { - var a = origin - val inverse = DMatrixRMaj(1, 1) - val solver = LinearSolverFactory_DSCC.lu(FillReducing.NONE) - if (solver.modifiesA()) a = a.copy() - val i = CommonOps_DDRM.identity(a.numRows) - solver.solve(i, inverse) - inverse.wrapMatrix() - } - - override val determinant: Double by lazy { elementAlgebra.number(lu.computeDeterminant().real) } - } - - else -> null - }?.let(type::cast) - } - - /** - * Solves for *x* in the following equation: *x = [a] -1 · [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, b: Matrix): EjmlDoubleMatrix { - val res = DMatrixSparseCSC(1, 1) - CommonOps_DSCC.solve(DMatrixSparseCSC(a.toEjml().origin), DMatrixSparseCSC(b.toEjml().origin), res) - return res.wrapMatrix() - } - - /** - * Solves for *x* in the following equation: *x = [a] -1 · [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, b: Point): EjmlDoubleVector { - val res = DMatrixSparseCSC(1, 1) - CommonOps_DSCC.solve(DMatrixSparseCSC(a.toEjml().origin), DMatrixSparseCSC(b.toEjml().origin), res) - return EjmlDoubleVector(res) - } -} - -/** - * [EjmlLinearSpace] implementation based on [CommonOps_FSCC], [DecompositionFactory_FSCC] operations and - * [FMatrixSparseCSC] matrices. - */ -public object EjmlLinearSpaceFSCC : EjmlLinearSpace() { - /** - * The [FloatField] reference. - */ - override val elementAlgebra: FloatField get() = FloatField - - @Suppress("UNCHECKED_CAST") - override fun Matrix.toEjml(): EjmlFloatMatrix = when { - this is EjmlFloatMatrix<*> && origin is FMatrixSparseCSC -> this as EjmlFloatMatrix - else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) } - } - - @Suppress("UNCHECKED_CAST") - override fun Point.toEjml(): EjmlFloatVector = when { - this is EjmlFloatVector<*> && origin is FMatrixSparseCSC -> this as EjmlFloatVector - else -> EjmlFloatVector(FMatrixSparseCSC(size, 1).also { - (0 until it.numRows).forEach { row -> it[row, 0] = get(row) } - }) - } - - override fun buildMatrix( - rows: Int, - columns: Int, - initializer: FloatField.(i: Int, j: Int) -> Float, - ): EjmlFloatMatrix = FMatrixSparseCSC(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: FloatField.(Int) -> Float, - ): EjmlFloatVector = EjmlFloatVector(FMatrixSparseCSC(size, 1).also { - (0 until it.numRows).forEach { row -> it[row, 0] = elementAlgebra.initializer(row) } - }) - - private fun T.wrapMatrix() = EjmlFloatMatrix(this) - private fun T.wrapVector() = EjmlFloatVector(this) - - override fun Matrix.unaryMinus(): Matrix = this * elementAlgebra { -one } - - override fun Matrix.dot(other: Matrix): EjmlFloatMatrix { - val out = FMatrixSparseCSC(1, 1) - CommonOps_FSCC.mult(toEjml().origin, other.toEjml().origin, out) - return out.wrapMatrix() - } - - override fun Matrix.dot(vector: Point): EjmlFloatVector { - val out = FMatrixSparseCSC(1, 1) - CommonOps_FSCC.mult(toEjml().origin, vector.toEjml().origin, out) - return out.wrapVector() - } - - override operator fun Matrix.minus(other: Matrix): EjmlFloatMatrix { - val out = FMatrixSparseCSC(1, 1) - - CommonOps_FSCC.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra { -one }, - other.toEjml().origin, - out, - null, - null, - ) - - return out.wrapMatrix() - } - - override operator fun Matrix.times(value: Float): EjmlFloatMatrix { - val res = FMatrixSparseCSC(1, 1) - CommonOps_FSCC.scale(value, toEjml().origin, res) - return res.wrapMatrix() - } - - override fun Point.unaryMinus(): EjmlFloatVector { - val res = FMatrixSparseCSC(1, 1) - CommonOps_FSCC.changeSign(toEjml().origin, res) - return res.wrapVector() - } - - override fun Matrix.plus(other: Matrix): EjmlFloatMatrix { - val out = FMatrixSparseCSC(1, 1) - - CommonOps_FSCC.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra.one, - other.toEjml().origin, - out, - null, - null, - ) - - return out.wrapMatrix() - } - - override fun Point.plus(other: Point): EjmlFloatVector { - val out = FMatrixSparseCSC(1, 1) - - CommonOps_FSCC.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra.one, - other.toEjml().origin, - out, - null, - null, - ) - - return out.wrapVector() - } - - override fun Point.minus(other: Point): EjmlFloatVector { - val out = FMatrixSparseCSC(1, 1) - - CommonOps_FSCC.add( - elementAlgebra.one, - toEjml().origin, - elementAlgebra { -one }, - other.toEjml().origin, - out, - null, - null, - ) - - return out.wrapVector() - } - - override fun Float.times(m: Matrix): EjmlFloatMatrix = m * this - - override fun Point.times(value: Float): EjmlFloatVector { - val res = FMatrixSparseCSC(1, 1) - CommonOps_FSCC.scale(value, toEjml().origin, res) - return res.wrapVector() - } - - override fun Float.times(v: Point): EjmlFloatVector = v * this - - @UnstableKMathAPI - override fun computeFeature(structure: Matrix, type: KClass): F? { - structure.getFeature(type)?.let { return it } - val origin = structure.toEjml().origin - - return when (type) { - QRDecompositionFeature::class -> object : QRDecompositionFeature { - private val qr by lazy { - DecompositionFactory_FSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) } - } - - override val q: Matrix by lazy { - qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature) - } - - override val r: Matrix by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) } - } - - CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature { - override val l: Matrix by lazy { - val cholesky = - DecompositionFactory_FSCC.cholesky().apply { decompose(origin.copy()) } - - (cholesky.getT(null) as FMatrix).wrapMatrix().withFeature(LFeature) - } - } - - LUDecompositionFeature::class, DeterminantFeature::class, InverseMatrixFeature::class -> object : - LUDecompositionFeature, DeterminantFeature, InverseMatrixFeature { - private val lu by lazy { - DecompositionFactory_FSCC.lu(FillReducing.NONE).apply { decompose(origin.copy()) } - } - - override val l: Matrix by lazy { - lu.getLower(null).wrapMatrix().withFeature(LFeature) - } - - override val u: Matrix by lazy { - lu.getUpper(null).wrapMatrix().withFeature(UFeature) - } - - override val inverse: Matrix by lazy { - var a = origin - val inverse = FMatrixRMaj(1, 1) - val solver = LinearSolverFactory_FSCC.lu(FillReducing.NONE) - if (solver.modifiesA()) a = a.copy() - val i = CommonOps_FDRM.identity(a.numRows) - solver.solve(i, inverse) - inverse.wrapMatrix() - } - - override val determinant: Float by lazy { elementAlgebra.number(lu.computeDeterminant().real) } - } - - else -> null - }?.let(type::cast) - } - - /** - * Solves for *x* in the following equation: *x = [a] -1 · [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, b: Matrix): EjmlFloatMatrix { - val res = FMatrixSparseCSC(1, 1) - CommonOps_FSCC.solve(FMatrixSparseCSC(a.toEjml().origin), FMatrixSparseCSC(b.toEjml().origin), res) - return res.wrapMatrix() - } - - /** - * Solves for *x* in the following equation: *x = [a] -1 · [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, b: Point): EjmlFloatVector { - val res = FMatrixSparseCSC(1, 1) - CommonOps_FSCC.solve(FMatrixSparseCSC(a.toEjml().origin), FMatrixSparseCSC(b.toEjml().origin), res) - return EjmlFloatVector(res) - } -} - diff --git a/kmath-ejml/src/test/kotlin/space/kscience/kmath/ejml/EjmlMatrixTest.kt b/kmath-ejml/src/test/kotlin/space/kscience/kmath/ejml/EjmlMatrixTest.kt deleted file mode 100644 index 209bb5b27..000000000 --- a/kmath-ejml/src/test/kotlin/space/kscience/kmath/ejml/EjmlMatrixTest.kt +++ /dev/null @@ -1,104 +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. - */ - -package space.kscience.kmath.ejml - -import org.ejml.data.DMatrixRMaj -import org.ejml.dense.row.CommonOps_DDRM -import org.ejml.dense.row.RandomMatrices_DDRM -import org.ejml.dense.row.factory.DecompositionFactory_DDRM -import space.kscience.kmath.linear.* -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.algebra -import kotlin.random.Random -import kotlin.random.asJavaRandom -import kotlin.test.* - -@OptIn(PerformancePitfall::class) -fun assertMatrixEquals(expected: StructureND, actual: StructureND) { - assertTrue { StructureND.contentEquals(expected, actual) } -} - -internal class EjmlMatrixTest { - private val random = Random(0) - - private val randomMatrix: DMatrixRMaj - get() { - val s = random.nextInt(2, 100) - val d = DMatrixRMaj(s, s) - RandomMatrices_DDRM.fillUniform(d, random.asJavaRandom()) - return d - } - - @Test - fun rowNum() { - val m = randomMatrix - assertEquals(m.numRows, EjmlDoubleMatrix(m).rowNum) - } - - @Test - fun colNum() { - val m = randomMatrix - assertEquals(m.numCols, EjmlDoubleMatrix(m).rowNum) - } - - @Test - fun shape() { - val m = randomMatrix - val w = EjmlDoubleMatrix(m) - assertContentEquals(intArrayOf(m.numRows, m.numCols), w.shape) - } - - @OptIn(UnstableKMathAPI::class) - @Test - fun features() { - val m = randomMatrix - val w = EjmlDoubleMatrix(m) - val det: DeterminantFeature = EjmlLinearSpaceDDRM.computeFeature(w) ?: fail() - assertEquals(CommonOps_DDRM.det(m), det.determinant) - val lup: LupDecompositionFeature = EjmlLinearSpaceDDRM.computeFeature(w) ?: fail() - - val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows, m.numCols) - .also { it.decompose(m.copy()) } - - assertMatrixEquals(EjmlDoubleMatrix(ludecompositionF64.getLower(null)), lup.l) - assertMatrixEquals(EjmlDoubleMatrix(ludecompositionF64.getUpper(null)), lup.u) - assertMatrixEquals(EjmlDoubleMatrix(ludecompositionF64.getRowPivot(null)), lup.p) - } - - @Test - fun get() { - val m = randomMatrix - assertEquals(m[0, 0], EjmlDoubleMatrix(m)[0, 0]) - } - - @Test - fun origin() { - val m = randomMatrix - assertSame(m, EjmlDoubleMatrix(m).origin) - } - - @Test - fun inverse() = EjmlLinearSpaceDDRM { - val random = Random(1224) - val dim = 20 - - val space = Double.algebra.linearSpace - - //creating invertible matrix - val u = space.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 } - val l = space.buildMatrix(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 } - val matrix = space { l dot u } - val inverted = matrix.toEjml().inverse() - - val res = matrix dot inverted - - println(StructureND.toString(res)) - - assertTrue { StructureND.contentEquals(one(dim, dim), res, 1e-3) } - } -} diff --git a/kmath-ejml/src/test/kotlin/space/kscience/kmath/ejml/EjmlVectorTest.kt b/kmath-ejml/src/test/kotlin/space/kscience/kmath/ejml/EjmlVectorTest.kt deleted file mode 100644 index 9592bfa6c..000000000 --- a/kmath-ejml/src/test/kotlin/space/kscience/kmath/ejml/EjmlVectorTest.kt +++ /dev/null @@ -1,57 +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. - */ - -package space.kscience.kmath.ejml - -import org.ejml.data.DMatrixRMaj -import org.ejml.dense.row.RandomMatrices_DDRM -import kotlin.random.Random -import kotlin.random.asJavaRandom -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertSame - -internal class EjmlVectorTest { - private val random = Random(0) - - private val randomMatrix: DMatrixRMaj - get() { - val d = DMatrixRMaj(1, random.nextInt(2, 100)) - RandomMatrices_DDRM.fillUniform(d, random.asJavaRandom()) - return d - } - - @Test - fun size() { - val m = randomMatrix - val w = EjmlDoubleVector(m) - assertEquals(m.numCols, w.size) - } - - @Test - fun get() { - val m = randomMatrix - val w = EjmlDoubleVector(m) - assertEquals(m[0, 0], w[0]) - } - - @Test - fun iterator() { - val m = randomMatrix - val w = EjmlDoubleVector(m) - - assertEquals( - m.iterator(true, 0, 0, 0, m.numCols - 1).asSequence().toList(), - w.iterator().asSequence().toList() - ) - } - - @Test - fun origin() { - val m = randomMatrix - val w = EjmlDoubleVector(m) - assertSame(m, w.origin) - } -} diff --git a/kmath-for-real/README.md b/kmath-for-real/README.md deleted file mode 100644 index 197190dcd..000000000 --- a/kmath-for-real/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# Module kmath-for-real - -Specialization of KMath APIs for Double numbers. - - - [DoubleVector](src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt) : Numpy-like operations for Buffers/Points - - [DoubleMatrix](src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt) : Numpy-like operations for 2d real structures - - [grids](src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt) : Uniform grid generators - - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-for-real:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-for-real:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-for-real:0.3.0-dev-20") -} -``` diff --git a/kmath-for-real/build.gradle.kts b/kmath-for-real/build.gradle.kts deleted file mode 100644 index 4cccaef5c..000000000 --- a/kmath-for-real/build.gradle.kts +++ /dev/null @@ -1,39 +0,0 @@ -plugins { - kotlin("multiplatform") - id("ru.mipt.npm.gradle.common") - id("ru.mipt.npm.gradle.native") -} - -kotlin.sourceSets.commonMain { - dependencies { - api(project(":kmath-core")) - } -} - -readme { - description = """ - Extension module that should be used to achieve numpy-like behavior. - All operations are specialized to work with `Double` numbers without declaring algebraic contexts. - One can still use generic algebras though. - """.trimIndent() - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL - propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md")) - - feature( - id = "DoubleVector", - description = "Numpy-like operations for Buffers/Points", - ref = "src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt" - ) - - feature( - id = "DoubleMatrix", - description = "Numpy-like operations for 2d real structures", - ref = "src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt" - ) - - feature( - id = "grids", - description = "Uniform grid generators", - ref = "src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt" - ) -} diff --git a/kmath-for-real/docs/README-TEMPLATE.md b/kmath-for-real/docs/README-TEMPLATE.md deleted file mode 100644 index c2ef25aa7..000000000 --- a/kmath-for-real/docs/README-TEMPLATE.md +++ /dev/null @@ -1,7 +0,0 @@ -# Module kmath-for-real - -Specialization of KMath APIs for Double numbers. - -${features} - -${artifact} diff --git a/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt b/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt deleted file mode 100644 index 7b9740c35..000000000 --- a/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt +++ /dev/null @@ -1,106 +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. - */ - -package space.kscience.kmath.real - -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleL2Norm -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.MutableBuffer.Companion.double -import space.kscience.kmath.structures.asBuffer -import space.kscience.kmath.structures.indices -import kotlin.math.pow - -public typealias DoubleVector = Point - -@Suppress("FunctionName") -public fun DoubleVector(vararg doubles: Double): DoubleVector = doubles.asBuffer() - -/** - * Fill the vector with given [size] with given [value] - */ -@UnstableKMathAPI -public fun Buffer.Companion.same(size: Int, value: Number): DoubleVector = double(size) { value.toDouble() } - -// Transformation methods - -public inline fun DoubleVector.map(transform: (Double) -> Double): DoubleVector = - double(size) { transform(get(it)) } - -public inline fun DoubleVector.mapIndexed(transform: (index: Int, value: Double) -> Double): DoubleVector = - double(size) { transform(it, get(it)) } - -public operator fun DoubleVector.plus(other: DoubleVector): DoubleVector { - require(size == other.size) { "Vector size $size expected but ${other.size} found" } - return mapIndexed { index, value -> value + other[index] } -} - -public operator fun DoubleVector.plus(number: Number): DoubleVector = map { it + number.toDouble() } - -public operator fun Number.plus(vector: DoubleVector): DoubleVector = vector + this - -public operator fun DoubleVector.unaryMinus(): Buffer = map { -it } - -public operator fun DoubleVector.minus(other: DoubleVector): DoubleVector { - require(size == other.size) { "Vector size $size expected but ${other.size} found" } - return mapIndexed { index, value -> value - other[index] } -} - -public operator fun DoubleVector.minus(number: Number): DoubleVector = map { it - number.toDouble() } - -public operator fun Number.minus(vector: DoubleVector): DoubleVector = vector.map { toDouble() - it } - -public operator fun DoubleVector.times(other: DoubleVector): DoubleVector { - require(size == other.size) { "Vector size $size expected but ${other.size} found" } - return mapIndexed { index, value -> value * other[index] } -} - -public operator fun DoubleVector.times(number: Number): DoubleVector = map { it * number.toDouble() } - -public operator fun Number.times(vector: DoubleVector): DoubleVector = vector * this - -public operator fun DoubleVector.div(other: DoubleVector): DoubleVector { - require(size == other.size) { "Vector size $size expected but ${other.size} found" } - return mapIndexed { index, value -> value / other[index] } -} - -public operator fun DoubleVector.div(number: Number): DoubleVector = map { it / number.toDouble() } - -public operator fun Number.div(vector: DoubleVector): DoubleVector = vector.map { toDouble() / it } - -//extended operations - -public fun DoubleVector.pow(p: Double): DoubleVector = map { it.pow(p) } - -public fun DoubleVector.pow(p: Int): DoubleVector = map { it.pow(p) } - -public fun exp(vector: DoubleVector): DoubleVector = vector.map { kotlin.math.exp(it) } - -public fun sqrt(vector: DoubleVector): DoubleVector = vector.map { kotlin.math.sqrt(it) } - -public fun DoubleVector.square(): DoubleVector = map { it.pow(2) } - -public fun sin(vector: DoubleVector): DoubleVector = vector.map { kotlin.math.sin(it) } - -public fun cos(vector: DoubleVector): DoubleVector = vector.map { kotlin.math.cos(it) } - -public fun tan(vector: DoubleVector): DoubleVector = vector.map { kotlin.math.tan(it) } - -public fun ln(vector: DoubleVector): DoubleVector = vector.map { kotlin.math.ln(it) } - -public fun log10(vector: DoubleVector): DoubleVector = vector.map { kotlin.math.log10(it) } - -// reductions methods - -public fun DoubleVector.sum(): Double { - var res = 0.0 - for (i in indices) { - res += get(i) - } - return res -} - -public val DoubleVector.norm: Double get() = DoubleL2Norm.norm(this) \ No newline at end of file diff --git a/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/RealMatrix.kt b/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/RealMatrix.kt deleted file mode 100644 index 671e272ab..000000000 --- a/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/RealMatrix.kt +++ /dev/null @@ -1,188 +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. - */ - -@file:OptIn(PerformancePitfall::class) -@file:Suppress("unused") - -package space.kscience.kmath.real - -import space.kscience.kmath.linear.* -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.algebra -import space.kscience.kmath.operations.asIterable -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.math.pow - -/* - * Functions for convenient "numpy-like" operations with Double matrices. - * - * Initial implementation of these functions is taken from: - * https://github.com/thomasnield/numky/blob/master/src/main/kotlin/org/nield/numky/linear/DoubleOperators.kt - * - */ - -/* - * Functions that help create a real (Double) matrix - */ - -public typealias RealMatrix = Matrix - -public fun realMatrix(rowNum: Int, colNum: Int, initializer: DoubleField.(i: Int, j: Int) -> Double): RealMatrix = - Double.algebra.linearSpace.buildMatrix(rowNum, colNum, initializer) - -@OptIn(UnstableKMathAPI::class) -public fun realMatrix(rowNum: Int, colNum: Int): MatrixBuilder = - Double.algebra.linearSpace.matrix(rowNum, colNum) - -public fun Array.toMatrix(): RealMatrix { - return Double.algebra.linearSpace.buildMatrix(size, this[0].size) { row, col -> this@toMatrix[row][col] } -} - -public fun Sequence.toMatrix(): RealMatrix = toList().let { - Double.algebra.linearSpace.buildMatrix(it.size, it[0].size) { row, col -> it[row][col] } -} - -public fun RealMatrix.repeatStackVertical(n: Int): RealMatrix = - VirtualMatrix(rowNum * n, colNum) { row, col -> - get(if (row == 0) 0 else row % rowNum, col) - } - -/* - * Operations for matrix and real number - */ - -public operator fun RealMatrix.times(double: Double): RealMatrix = - Double.algebra.linearSpace.buildMatrix(rowNum, colNum) { row, col -> - get(row, col) * double - } - -public operator fun RealMatrix.plus(double: Double): RealMatrix = - Double.algebra.linearSpace.buildMatrix(rowNum, colNum) { row, col -> - get(row, col) + double - } - -public operator fun RealMatrix.minus(double: Double): RealMatrix = - Double.algebra.linearSpace.buildMatrix(rowNum, colNum) { row, col -> - get(row, col) - double - } - -public operator fun RealMatrix.div(double: Double): RealMatrix = - Double.algebra.linearSpace.buildMatrix(rowNum, colNum) { row, col -> - get(row, col) / double - } - -public operator fun Double.times(matrix: RealMatrix): RealMatrix = - Double.algebra.linearSpace.buildMatrix(matrix.rowNum, matrix.colNum) { row, col -> - this@times * matrix[row, col] - } - -public operator fun Double.plus(matrix: RealMatrix): RealMatrix = - Double.algebra.linearSpace.buildMatrix(matrix.rowNum, matrix.colNum) { row, col -> - this@plus + matrix[row, col] - } - -public operator fun Double.minus(matrix: RealMatrix): RealMatrix = - Double.algebra.linearSpace.buildMatrix(matrix.rowNum, matrix.colNum) { row, col -> - this@minus - matrix[row, col] - } - -// TODO: does this operation make sense? Should it be 'this/matrix[row, col]'? -//operator fun Double.div(matrix: RealMatrix) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { -// row, col -> matrix[row, col] / this -//} - -/* - * Operations on two matrices (per-element!) - */ - -@UnstableKMathAPI -public operator fun RealMatrix.times(other: RealMatrix): RealMatrix = - Double.algebra.linearSpace.buildMatrix(rowNum, colNum) { row, col -> this@times[row, col] * other[row, col] } - -public operator fun RealMatrix.plus(other: RealMatrix): RealMatrix = - Double.algebra.linearSpace.run { this@plus + other } - -public operator fun RealMatrix.minus(other: RealMatrix): RealMatrix = - Double.algebra.linearSpace.buildMatrix(rowNum, colNum) { row, col -> this@minus[row, col] - other[row, col] } - -/* - * Operations on columns - */ - -public inline fun RealMatrix.appendColumn(crossinline mapper: (Buffer) -> Double): RealMatrix = - Double.algebra.linearSpace.buildMatrix(rowNum, colNum + 1) { row, col -> - if (col < colNum) - get(row, col) - else - mapper(rows[row]) - } - -public fun RealMatrix.extractColumns(columnRange: IntRange): RealMatrix = - Double.algebra.linearSpace.buildMatrix(rowNum, columnRange.count()) { row, col -> - this@extractColumns[row, columnRange.first + col] - } - -public fun RealMatrix.extractColumn(columnIndex: Int): RealMatrix = - extractColumns(columnIndex..columnIndex) - -public fun RealMatrix.sumByColumn(): DoubleBuffer = DoubleBuffer(colNum) { j -> - columns[j].asIterable().sum() -} - -public fun RealMatrix.minByColumn(): DoubleBuffer = DoubleBuffer(colNum) { j -> - columns[j].asIterable().minOrNull() ?: error("Cannot produce min on empty column") -} - -public fun RealMatrix.maxByColumn(): DoubleBuffer = DoubleBuffer(colNum) { j -> - columns[j].asIterable().maxOrNull() ?: error("Cannot produce min on empty column") -} - -public fun RealMatrix.averageByColumn(): DoubleBuffer = DoubleBuffer(colNum) { j -> - columns[j].asIterable().average() -} - -/* - * Operations processing all elements - */ - -public fun RealMatrix.sum(): Double = elements().map { (_, value) -> value }.sum() -public fun RealMatrix.min(): Double? = elements().map { (_, value) -> value }.minOrNull() -public fun RealMatrix.max(): Double? = elements().map { (_, value) -> value }.maxOrNull() -public fun RealMatrix.average(): Double = elements().map { (_, value) -> value }.average() - -public inline fun RealMatrix.map(crossinline transform: (Double) -> Double): RealMatrix = - Double.algebra.linearSpace.buildMatrix(rowNum, colNum) { i, j -> - transform(get(i, j)) - } - -/** - * Inverse a square real matrix using LUP decomposition - */ -public fun RealMatrix.inverseWithLup(): RealMatrix = Double.algebra.linearSpace.lupSolver().inverse(this) - -//extended operations - -public fun RealMatrix.pow(p: Double): RealMatrix = map { it.pow(p) } - -public fun RealMatrix.pow(p: Int): RealMatrix = map { it.pow(p) } - -public fun exp(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.exp(it) } - -public fun sqrt(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.sqrt(it) } - -public fun RealMatrix.square(): RealMatrix = map { it.pow(2) } - -public fun sin(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.sin(it) } - -public fun cos(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.cos(it) } - -public fun tan(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.tan(it) } - -public fun ln(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.ln(it) } - -public fun log10(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.log10(it) } diff --git a/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/dot.kt b/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/dot.kt deleted file mode 100644 index dc5c58be0..000000000 --- a/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/dot.kt +++ /dev/null @@ -1,18 +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. - */ - -package space.kscience.kmath.real - -import space.kscience.kmath.linear.Matrix -import space.kscience.kmath.linear.linearSpace -import space.kscience.kmath.operations.algebra - - -/** - * Optimized dot product for real matrices - */ -public infix fun Matrix.dot(other: Matrix): Matrix = Double.algebra.linearSpace.run { - this@dot dot other -} \ No newline at end of file diff --git a/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/grids.kt b/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/grids.kt deleted file mode 100644 index 1926ef02c..000000000 --- a/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/grids.kt +++ /dev/null @@ -1,54 +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. - */ - -package space.kscience.kmath.real - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.math.floor - -public val ClosedFloatingPointRange.length: Double get() = endInclusive - start - -/** - * Create a Buffer-based grid with equally distributed [numberOfPoints] points. The range could be increasing or decreasing. - * If range has a zero size, then the buffer consisting of [numberOfPoints] equal values is returned. - */ -public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange, numberOfPoints: Int): DoubleBuffer { - require(numberOfPoints >= 2) { "Number of points in grid must be more than 1" } - val normalizedRange = when { - range.endInclusive > range.start -> range - range.endInclusive < range.start -> range.endInclusive..range.start - else -> return DoubleBuffer(numberOfPoints) { range.start } - } - val step = normalizedRange.length / (numberOfPoints - 1) - return DoubleBuffer(numberOfPoints) { normalizedRange.start + step * it } -} - -/** - * Create a Buffer-based grid with equally distributed points with a fixed [step]. The range could be increasing or decreasing. - * If the step is larger than the range size, single point is returned. - */ -public fun Buffer.Companion.withFixedStep(range: ClosedFloatingPointRange, step: Double): DoubleBuffer { - require(step > 0) { "The grid step must be positive" } - val normalizedRange = when { - range.endInclusive > range.start -> range - range.endInclusive < range.start -> range.endInclusive..range.start - else -> return DoubleBuffer(range.start) - } - val numberOfPoints = floor(normalizedRange.length / step).toInt() + 1 - return DoubleBuffer(numberOfPoints) { normalizedRange.start + step * it } -} - -/** - * Convert double range to sequence. - * - * If the step is positive, then the sequence starts with the lower boundary and increments by [step] until current value is lower than upper boundary. - * The boundary itself is unnecessarily included. - * - * If step is negative, the same goes from upper boundary downwards - */ -@UnstableKMathAPI -public infix fun ClosedFloatingPointRange.step(step: Double): DoubleBuffer = Buffer.withFixedStep(this, step) \ No newline at end of file diff --git a/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/realND.kt b/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/realND.kt deleted file mode 100644 index 52362f4b4..000000000 --- a/kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/realND.kt +++ /dev/null @@ -1,36 +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. - */ - -package space.kscience.kmath.real - -import space.kscience.kmath.nd.BufferND -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.structures.DoubleBuffer - -/** - * Map one [BufferND] using function without indices. - */ -public inline fun BufferND.mapInline(crossinline transform: DoubleField.(Double) -> Double): BufferND { - val array = DoubleArray(indices.linearSize) { offset -> DoubleField.transform(buffer[offset]) } - return BufferND(indices, DoubleBuffer(array)) -} - -/** - * Element by element application of any operation on elements to the whole array. Just like in numpy. - */ -public operator fun Function1.invoke(elementND: BufferND): BufferND = - elementND.mapInline { this@invoke(it) } - -/* plus and minus */ - -/** - * Summation operation for [BufferND] and single element - */ -public operator fun BufferND.plus(arg: Double): BufferND = mapInline { it + arg } - -/** - * Subtraction operation between [BufferND] and single element - */ -public operator fun BufferND.minus(arg: Double): BufferND = mapInline { it - arg } \ No newline at end of file diff --git a/kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleMatrixTest.kt b/kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleMatrixTest.kt deleted file mode 100644 index 4a0e8de1d..000000000 --- a/kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleMatrixTest.kt +++ /dev/null @@ -1,183 +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. - */ - -package space.kscience.kmath.real - -import space.kscience.kmath.linear.linearSpace -import space.kscience.kmath.linear.matrix -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.algebra -import space.kscience.kmath.structures.contentEquals -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue - -@OptIn(PerformancePitfall::class) -fun assertMatrixEquals(expected: StructureND, actual: StructureND) { - assertTrue { StructureND.contentEquals(expected, actual) } -} - -@UnstableKMathAPI -internal class DoubleMatrixTest { - @Test - fun testSum() { - val m = realMatrix(10, 10) { i, j -> (i + j).toDouble() } - assertEquals(m.sum(), 900.0) - assertEquals(m.average(), 9.0) - } - - @Test - fun testSequenceToMatrix() { - val m = Sequence { - listOf( - DoubleArray(10) { 10.0 }, - DoubleArray(10) { 20.0 }, - DoubleArray(10) { 30.0 }).iterator() - }.toMatrix() - assertEquals(m.sum(), 20.0 * 30) - } - - @Test - fun testRepeatStackVertical() { - val matrix1 = realMatrix(2, 3)( - 1.0, 0.0, 0.0, - 0.0, 1.0, 2.0 - ) - val matrix2 = realMatrix(6, 3)( - 1.0, 0.0, 0.0, - 0.0, 1.0, 2.0, - 1.0, 0.0, 0.0, - 0.0, 1.0, 2.0, - 1.0, 0.0, 0.0, - 0.0, 1.0, 2.0 - ) - assertMatrixEquals(matrix2, matrix1.repeatStackVertical(3)) - } - - @Test - fun testMatrixAndDouble() = Double.algebra.linearSpace.run { - val matrix1 = realMatrix(2, 3)( - 1.0, 0.0, 3.0, - 4.0, 6.0, 2.0 - ) - val matrix2 = (matrix1 * 2.5 + 1.0 - 2.0) / 2.0 - val expectedResult = matrix(2, 3)( - 0.75, -0.5, 3.25, - 4.5, 7.0, 2.0 - ) - assertMatrixEquals(matrix2, expectedResult) - } - - @Test - fun testDoubleAndMatrix() { - val matrix1 = realMatrix(2, 3)( - 1.0, 0.0, 3.0, - 4.0, 6.0, 2.0 - ) - val matrix2 = 20.0 - (10.0 + (5.0 * matrix1)) - //val matrix2 = 10.0 + (5.0 * matrix1) - val expectedResult = realMatrix(2, 3)( - 5.0, 10.0, -5.0, - -10.0, -20.0, 0.0 - ) - assertMatrixEquals(matrix2, expectedResult) - } - - @Test - fun testSquareAndPower() { - val matrix1 = realMatrix(2, 3)( - -1.0, 0.0, 3.0, - 4.0, -6.0, -2.0 - ) - val matrix2 = realMatrix(2, 3)( - 1.0, 0.0, 9.0, - 16.0, 36.0, 4.0 - ) - val matrix3 = realMatrix(2, 3)( - -1.0, 0.0, 27.0, - 64.0, -216.0, -8.0 - ) - assertMatrixEquals(matrix1.square(), matrix2) - assertMatrixEquals(matrix1.pow(3), matrix3) - } - - @OptIn(UnstableKMathAPI::class) - @Test - fun testTwoMatrixOperations() { - val matrix1 = realMatrix(2, 3)( - -1.0, 0.0, 3.0, - 4.0, -6.0, 7.0 - ) - val matrix2 = realMatrix(2, 3)( - 1.0, 0.0, 3.0, - 4.0, 6.0, -2.0 - ) - val result = matrix1 * matrix2 + matrix1 - matrix2 - val expectedResult = realMatrix(2, 3)( - -3.0, 0.0, 9.0, - 16.0, -48.0, -5.0 - ) - assertMatrixEquals(result, expectedResult) - } - - @Test - fun testColumnOperations() { - val matrix1 = realMatrix(2, 4)( - -1.0, 0.0, 3.0, 15.0, - 4.0, -6.0, 7.0, -11.0 - ) - val matrix2 = realMatrix(2, 5)( - -1.0, 0.0, 3.0, 15.0, -1.0, - 4.0, -6.0, 7.0, -11.0, 4.0 - ) - val col1 = realMatrix(2, 1)(0.0, -6.0) - val cols1to2 = realMatrix(2, 2)( - 0.0, 3.0, - -6.0, 7.0 - ) - - assertMatrixEquals(matrix1.appendColumn { it[0] }, matrix2) - assertMatrixEquals(matrix1.extractColumn(1), col1) - assertMatrixEquals(matrix1.extractColumns(1..2), cols1to2) - //equals should never be called on buffers - assertTrue { - matrix1.sumByColumn().contentEquals(3.0, -6.0, 10.0, 4.0) - } //assertEquals(matrix1.sumByColumn(), DoubleBuffer(3.0, -6.0, 10.0, 4.0)) - assertTrue { - matrix1.minByColumn().contentEquals(-1.0, -6.0, 3.0, -11.0) - } //assertEquals(matrix1.minByColumn(), DoubleBuffer(-1.0, -6.0, 3.0, -11.0)) - assertTrue { - matrix1.maxByColumn().contentEquals(4.0, 0.0, 7.0, 15.0) - } //assertEquals(matrix1.maxByColumn(), DoubleBuffer(4.0, 0.0, 7.0, 15.0)) - assertTrue { - matrix1.averageByColumn().contentEquals(1.5, -3.0, 5.0, 2.0) - } //assertEquals(matrix1.averageByColumn(), DoubleBuffer(1.5, -3.0, 5.0, 2.0)) - } - - @Test - fun testAllElementOperations() = Double.algebra.linearSpace.run { - val matrix1 = matrix(2, 4)( - -1.0, 0.0, 3.0, 15.0, - 4.0, -6.0, 7.0, -11.0 - ) - assertEquals(matrix1.sum(), 11.0) - assertEquals(matrix1.min(), -11.0) - assertEquals(matrix1.max(), 15.0) - assertEquals(matrix1.average(), 1.375) - } - -// fun printMatrix(m: Matrix) { -// for (row in 0 until m.shape[0]) { -// for (col in 0 until m.shape[1]) { -// print(m[row, col]) -// print(" ") -// } -// println() -// } -// } - -} diff --git a/kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleVectorTest.kt b/kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleVectorTest.kt deleted file mode 100644 index e77b96c12..000000000 --- a/kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleVectorTest.kt +++ /dev/null @@ -1,42 +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. - */ - -package space.kscience.kmath.real - -import space.kscience.kmath.linear.asMatrix -import space.kscience.kmath.linear.linearSpace -import space.kscience.kmath.linear.transpose -import space.kscience.kmath.operations.algebra -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class DoubleVectorTest { - @Test - fun testSum() { - val vector1 = DoubleBuffer(5) { it.toDouble() } - val vector2 = DoubleBuffer(5) { 5 - it.toDouble() } - val sum = vector1 + vector2 - assertEquals(5.0, sum[2]) - } - - @Test - fun testVectorToMatrix() { - val vector = DoubleBuffer(5) { it.toDouble() } - val matrix = vector.asMatrix() - assertEquals(4.0, matrix[4, 0]) - } - - @Test - fun testDot() = Double.algebra.linearSpace.run { - val vector1 = DoubleBuffer(5) { it.toDouble() } - val vector2 = DoubleBuffer(5) { 5 - it.toDouble() } - val matrix1 = vector1.asMatrix() - val matrix2 = vector2.asMatrix().transpose() - val product = matrix1 dot matrix2 - assertEquals(5.0, product[1, 0]) - assertEquals(6.0, product[2, 2]) - } -} diff --git a/kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/GridTest.kt b/kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/GridTest.kt deleted file mode 100644 index 8fed8d10e..000000000 --- a/kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/GridTest.kt +++ /dev/null @@ -1,34 +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. - */ - -package space.kscience.kmath.real - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.real.DoubleVector -import space.kscience.kmath.real.minus -import space.kscience.kmath.real.norm -import space.kscience.kmath.real.step -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue - -@UnstableKMathAPI -class GridTest { - @Test - fun testStepGrid() { - val grid = 0.0..1.0 step 0.2 - assertEquals(6, grid.size) - assertTrue { (grid - DoubleVector(0.0, 0.2, 0.4, 0.6, 0.8, 1.0)).norm < 1e-4 } - } - - @Test - fun testIterateGrid(){ - var res = 0.0 - for(d in 0.0..1.0 step 0.2){ - res = d - } - assertEquals(1.0, res) - } -} \ No newline at end of file diff --git a/kmath-for-real/src/jvmMain/kotlin/.gitkeep b/kmath-for-real/src/jvmMain/kotlin/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/kmath-functions/README.md b/kmath-functions/README.md deleted file mode 100644 index 6379ad0b5..000000000 --- a/kmath-functions/README.md +++ /dev/null @@ -1,37 +0,0 @@ -# Module kmath-functions - -Functions and interpolations. - - - [piecewise](src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt) : Piecewise functions. - - [polynomials](src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt) : Polynomial functions. - - [linear interpolation](src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt) : Linear XY interpolator. - - [spline interpolation](src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt) : Cubic spline XY interpolator. - - [integration](#) : Univariate and multivariate quadratures - - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-functions:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-functions:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-functions:0.3.0-dev-20") -} -``` diff --git a/kmath-functions/build.gradle.kts b/kmath-functions/build.gradle.kts deleted file mode 100644 index fadbac091..000000000 --- a/kmath-functions/build.gradle.kts +++ /dev/null @@ -1,34 +0,0 @@ -plugins { - kotlin("multiplatform") - id("ru.mipt.npm.gradle.common") - id("ru.mipt.npm.gradle.native") -} - -description = "Functions, integration and interpolation" - -kotlin.sourceSets.commonMain { - dependencies { - api(project(":kmath-core")) - } -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL - propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md")) - - feature("piecewise", "src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt") { - "Piecewise functions." - } - feature("polynomials", "src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt") { - "Polynomial functions." - } - feature("linear interpolation", "src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt") { - "Linear XY interpolator." - } - feature("spline interpolation", "src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt") { - "Cubic spline XY interpolator." - } - feature("integration") { - "Univariate and multivariate quadratures" - } -} diff --git a/kmath-functions/docs/README-TEMPLATE.md b/kmath-functions/docs/README-TEMPLATE.md deleted file mode 100644 index 2e163eee5..000000000 --- a/kmath-functions/docs/README-TEMPLATE.md +++ /dev/null @@ -1,7 +0,0 @@ -# Module kmath-functions - -Functions and interpolations. - -${features} - -${artifact} diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt deleted file mode 100644 index 16af7f555..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt +++ /dev/null @@ -1,132 +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. - */ - -package space.kscience.kmath.functions - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.operations.Ring - -/** - * Represents piecewise-defined function. - * - * @param T the piece key type. - * @param R the sub-function type. - */ -public fun interface Piecewise { - /** - * Returns the appropriate sub-function for given piece key. - */ - public fun findPiece(arg: T): R? -} - -/** - * Represents piecewise-defined function where all the sub-functions are polynomials. - * - * @property pieces An ordered list of range-polynomial pairs. The list does not in general guarantee that there are no - * "holes" in it. - */ -public interface PiecewisePolynomial> : Piecewise> { - public val pieces: Collection, Polynomial>> - - override fun findPiece(arg: T): Polynomial? -} - -/** - * A generic piecewise without constraints on how pieces are placed - */ -@PerformancePitfall("findPiece method of resulting piecewise is slow") -public fun > PiecewisePolynomial( - pieces: Collection, Polynomial>>, -): PiecewisePolynomial = object : PiecewisePolynomial { - override val pieces: Collection, Polynomial>> = pieces - - override fun findPiece(arg: T): Polynomial? = pieces.firstOrNull { arg in it.first }?.second -} - -/** - * An optimized piecewise that uses not separate pieces, but a range separated by delimiters. - * The pieces search is logarithmic. - */ -private class OrderedPiecewisePolynomial>( - override val pieces: List, Polynomial>>, -) : PiecewisePolynomial { - - override fun findPiece(arg: T): Polynomial? { - val index = pieces.binarySearch { (range, _) -> - when { - arg >= range.endInclusive -> -1 - arg < range.start -> +1 - else -> 0 - } - } - return if (index < 0) null else pieces[index].second - } - -} - -/** - * A [Piecewise] builder where all the pieces are ordered by the [Comparable] type instances. - * - * @param T the comparable piece key type. - * @param delimiter the initial piecewise separator - */ -public class PiecewiseBuilder>(delimiter: T) { - private val delimiters: MutableList = arrayListOf(delimiter) - private val pieces: MutableList> = arrayListOf() - - /** - * Dynamically adds a piece to the right side (beyond maximum argument value of previous piece) - * - * @param right new rightmost position. If is less than current rightmost position, an error is thrown. - * @param piece the sub-function. - */ - public fun putRight(right: T, piece: Polynomial) { - require(right > delimiters.last()) { "New delimiter should be to the right of old one" } - delimiters += right - pieces += piece - } - - /** - * Dynamically adds a piece to the left side (beyond maximum argument value of previous piece) - * - * @param left the new leftmost position. If is less than current rightmost position, an error is thrown. - * @param piece the sub-function. - */ - public fun putLeft(left: T, piece: Polynomial) { - require(left < delimiters.first()) { "New delimiter should be to the left of old one" } - delimiters.add(0, left) - pieces.add(0, piece) - } - - public fun build(): PiecewisePolynomial = OrderedPiecewisePolynomial(delimiters.zipWithNext { l, r -> - l..r - }.zip(pieces)) -} - -/** - * A builder for [PiecewisePolynomial] - */ -public fun > PiecewisePolynomial( - startingPoint: T, - builder: PiecewiseBuilder.() -> Unit, -): PiecewisePolynomial = PiecewiseBuilder(startingPoint).apply(builder).build() - -/** - * Return a value of polynomial function with given [ring] a given [arg] or null if argument is outside piecewise - * definition. - */ -public fun , C : Ring> PiecewisePolynomial.value(ring: C, arg: T): T? = - findPiece(arg)?.value(ring, arg) - -/** - * Convert this polynomial to a function returning nullable value (null if argument is outside piecewise range). - */ -public fun , C : Ring> PiecewisePolynomial.asFunction(ring: C): (T) -> T? = { value(ring, it) } - -/** - * Convert this polynomial to a function using [defaultValue] for arguments outside the piecewise range. - */ -public fun , C : Ring> PiecewisePolynomial.asFunction(ring: C, defaultValue: T): (T) -> T = - { value(ring, it) ?: defaultValue } diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt deleted file mode 100644 index f782496f3..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt +++ /dev/null @@ -1,132 +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. - */ - -package space.kscience.kmath.functions - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract -import kotlin.math.max -import kotlin.math.pow - -/** - * Polynomial coefficients model without fixation on specific context they are applied to. - * - * @param coefficients constant is the leftmost coefficient. - */ -public class Polynomial(public val coefficients: List) { - override fun toString(): String = "Polynomial$coefficients" -} - -/** - * Returns a [Polynomial] instance with given [coefficients]. - */ -@Suppress("FunctionName") -public fun Polynomial(vararg coefficients: T): Polynomial = Polynomial(coefficients.toList()) - -/** - * Evaluates the value of the given double polynomial for given double argument. - */ -public fun Polynomial.value(arg: Double): Double = coefficients.reduceIndexed { index, acc, c -> - acc + c * arg.pow(index) -} - -/** - * Evaluates the value of the given polynomial for given argument. - * https://en.wikipedia.org/wiki/Horner%27s_method - */ -public fun > Polynomial.value(ring: C, arg: T): T = ring { - if (coefficients.isEmpty()) return@ring zero - var result: T = coefficients.last() - for (j in coefficients.size - 2 downTo 0) { - result = (arg * result) + coefficients[j] - } - return result -} - -/** - * Represent the polynomial as a regular context-less function. - */ -public fun > Polynomial.asFunction(ring: C): (T) -> T = { value(ring, it) } - -/** - * Create a polynomial witch represents differentiated version of this polynomial - */ -@UnstableKMathAPI -public fun Polynomial.differentiate( - algebra: A, -): Polynomial where A : Ring, A : NumericAlgebra = algebra { - Polynomial(coefficients.drop(1).mapIndexed { index, t -> number(index) * t }) -} - -/** - * Create a polynomial witch represents indefinite integral version of this polynomial - */ -@UnstableKMathAPI -public fun Polynomial.integrate( - algebra: A, -): Polynomial where A : Field, A : NumericAlgebra = algebra { - val integratedCoefficients = buildList(coefficients.size + 1) { - add(zero) - coefficients.forEachIndexed { index, t -> add(t / (number(index) + one)) } - } - Polynomial(integratedCoefficients) -} - -/** - * Compute a definite integral of a given polynomial in a [range] - */ -@UnstableKMathAPI -public fun > Polynomial.integrate( - algebra: Field, - range: ClosedRange, -): T = algebra { - val integral = integrate(algebra) - integral.value(algebra, range.endInclusive) - integral.value(algebra, range.start) -} - -/** - * Space of polynomials. - * - * @param T the type of operated polynomials. - * @param C the intersection of [Ring] of [T] and [ScaleOperations] of [T]. - * @param ring the [C] instance. - */ -public class PolynomialSpace( - private val ring: C, -) : Group>, ScaleOperations> where C : Ring, C : ScaleOperations { - override val zero: Polynomial = Polynomial(emptyList()) - - override fun negate(arg: Polynomial): Polynomial = ring { - Polynomial(arg.coefficients.map { -it }) - } - - override fun add(left: Polynomial, right: Polynomial): Polynomial { - val dim = max(left.coefficients.size, right.coefficients.size) - - return ring { - Polynomial(List(dim) { index -> - left.coefficients.getOrElse(index) { zero } + right.coefficients.getOrElse(index) { zero } - }) - } - } - - override fun scale(a: Polynomial, value: Double): Polynomial = - ring { Polynomial(List(a.coefficients.size) { index -> a.coefficients[index] * value }) } - - /** - * Evaluates the polynomial for the given value [arg]. - */ - public operator fun Polynomial.invoke(arg: T): T = value(ring, arg) - - public fun Polynomial.asFunction(): (T) -> T = asFunction(ring) - -} - -public inline fun C.polynomial(block: PolynomialSpace.() -> R): R where C : Ring, C : ScaleOperations { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return PolynomialSpace(this).block() -} diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/functionTypes.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/functionTypes.kt deleted file mode 100644 index 88b24c756..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/functionTypes.kt +++ /dev/null @@ -1,12 +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. - */ - -package space.kscience.kmath.functions - -import space.kscience.kmath.structures.Buffer - -public typealias UnivariateFunction = (T) -> T - -public typealias MultivariateFunction = (Buffer) -> T \ No newline at end of file diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegrator.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegrator.kt deleted file mode 100644 index 9785d7744..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegrator.kt +++ /dev/null @@ -1,110 +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. - */ -package space.kscience.kmath.integration - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Field -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.asBuffer -import space.kscience.kmath.structures.indices - -/** - * A simple one-pass integrator based on Gauss rule - * Following integrand features are accepted: - * - * * [GaussIntegratorRuleFactory]—a factory for computing the Gauss integration rule. By default, uses - * [GaussLegendreRuleFactory]. - * * [IntegrationRange]—the univariate range of integration. By default, uses `0..1` interval. - * * [IntegrandMaxCalls]—the maximum number of function calls during integration. For non-iterative rules, always - * uses the maximum number of points. By default, uses 10 points. - * * [UnivariateIntegrandRanges]—set of ranges and number of points per range. Defaults to given - * [IntegrationRange] and [IntegrandMaxCalls]. - */ -public class GaussIntegrator( - public val algebra: Field, -) : UnivariateIntegrator { - - private fun buildRule(integrand: UnivariateIntegrand): Pair, Buffer> { - val factory = integrand.getFeature() ?: GaussLegendreRuleFactory - val predefinedRanges = integrand.getFeature() - if (predefinedRanges == null || predefinedRanges.ranges.isEmpty()) { - val numPoints = integrand.getFeature()?.maxCalls ?: 100 - val range = integrand.getFeature()?.range ?: 0.0..1.0 - return factory.build(numPoints, range) - } else { - val ranges = predefinedRanges.ranges - var counter = 0 - val length = ranges.sumOf { it.second } - val pointsArray = DoubleArray(length) - val weightsArray = DoubleArray(length) - - for (range in ranges) { - val rule = factory.build(range.second, range.first) - repeat(rule.first.size) { i -> - pointsArray[counter] = rule.first[i] - weightsArray[counter] = rule.second[i] - counter++ - } - - } - return pointsArray.asBuffer() to weightsArray.asBuffer() - } - } - - override fun process(integrand: UnivariateIntegrand): UnivariateIntegrand = with(algebra) { - val f = integrand.function - val (points, weights) = buildRule(integrand) - var res = zero - var c = zero - for (i in points.indices) { - val x = points[i] - val weight = weights[i] - val y: T = weight * f(x) - c - val t = res + y - c = t - res - y - res = t - } - return integrand + IntegrandValue(res) + IntegrandCallsPerformed(integrand.calls + points.size) - } - - public companion object -} - -/** - * Create a Gauss integrator for this field. By default, uses Legendre rule to compute points and weights. - * Custom rules could be provided by [GaussIntegratorRuleFactory] feature. - * @see [GaussIntegrator] - */ -public val Field.gaussIntegrator: GaussIntegrator get() = GaussIntegrator(this) - - -/** - * Integrate using [intervals] segments with Gauss-Legendre rule of [order] order. - */ -@UnstableKMathAPI -public fun GaussIntegrator.integrate( - range: ClosedRange, - order: Int = 10, - intervals: Int = 10, - vararg features: IntegrandFeature, - function: (Double) -> T, -): UnivariateIntegrand { - require(range.endInclusive > range.start) { "The range upper bound should be higher than lower bound" } - require(order > 1) { "The order of polynomial must be more than 1" } - require(intervals > 0) { "Number of intervals must be positive" } - val rangeSize = (range.endInclusive - range.start) / intervals - val ranges = UnivariateIntegrandRanges( - (0 until intervals).map { i -> (range.start + rangeSize * i)..(range.start + rangeSize * (i + 1)) to order } - ) - return process( - UnivariateIntegrand( - function, - IntegrationRange(range), - GaussLegendreRuleFactory, - ranges, - *features - ) - ) -} \ No newline at end of file diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegratorRuleFactory.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegratorRuleFactory.kt deleted file mode 100644 index 778d85e66..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegratorRuleFactory.kt +++ /dev/null @@ -1,169 +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. - */ - -package space.kscience.kmath.integration - -import space.kscience.kmath.operations.map -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.asBuffer -import kotlin.jvm.Synchronized -import kotlin.math.ulp -import kotlin.native.concurrent.ThreadLocal - -public interface GaussIntegratorRuleFactory : IntegrandFeature { - public fun build(numPoints: Int): Pair, Buffer> - - public companion object { - public fun double(numPoints: Int, range: ClosedRange): Pair, Buffer> = - GaussLegendreRuleFactory.build(numPoints, range) - } -} - -/** - * Create an integration rule by scaling existing normalized rule - * - */ -public fun GaussIntegratorRuleFactory.build( - numPoints: Int, - range: ClosedRange, -): Pair, Buffer> { - val normalized = build(numPoints) - val length = range.endInclusive - range.start - - val points = normalized.first.map(::DoubleBuffer) { - range.start + length / 2 + length / 2 * it - } - - val weights = normalized.second.map(::DoubleBuffer) { - it * length / 2 - } - - return points to weights - -} - - -/** - * Gauss integrator rule based ont Legendre polynomials. All rules are normalized to - * - * The code is based on [Apache Commons Math source code version 3.6.1](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/analysis/integration/gauss/LegendreRuleFactory.html) - * - */ -@ThreadLocal -public object GaussLegendreRuleFactory : GaussIntegratorRuleFactory { - - private val cache = HashMap, Buffer>>() - - @Synchronized - private fun getOrBuildRule(numPoints: Int): Pair, Buffer> = - cache.getOrPut(numPoints) { buildRule(numPoints) } - - - private fun buildRule(numPoints: Int): Pair, Buffer> { - if (numPoints == 1) { - // Break recursion. - return Pair( - DoubleBuffer(0.0), - DoubleBuffer(0.0) - ) - } - - // Get previous rule. - // If it has not been computed, yet it will trigger a recursive call - // to this method. - val previousPoints: Buffer = getOrBuildRule(numPoints - 1).first - - // Compute next rule. - val points = DoubleArray(numPoints) - val weights = DoubleArray(numPoints) - - // Find i-th root of P[n+1] by bracketing. - val iMax = numPoints / 2 - for (i in 0 until iMax) { - // Lower-bound of the interval. - var a: Double = if (i == 0) -1.0 else previousPoints[i - 1] - // Upper-bound of the interval. - var b: Double = if (iMax == 1) 1.0 else previousPoints[i] - // P[j-1](a) - var pma = 1.0 - // P[j](a) - var pa = a - // P[j-1](b) - var pmb = 1.0 - // P[j](b) - var pb = b - for (j in 1 until numPoints) { - val twoJP1 = 2 * j + 1 - val jP1 = j + 1 - // P[j+1](a) - val ppa = (twoJP1 * a * pa - j * pma) / jP1 - // P[j+1](b) - val ppb = (twoJP1 * b * pb - j * pmb) / jP1 - pma = pa - pa = ppa - pmb = pb - pb = ppb - } - // Now pa = P[n+1](a), and pma = P[n](a) (same holds for b). - // Middle of the interval. - var c = 0.5 * (a + b) - // P[j-1](c) - var pmc = 1.0 - // P[j](c) - var pc = c - var done = false - while (!done) { - done = b - a <= c.ulp - pmc = 1.0 - pc = c - for (j in 1 until numPoints) { - // P[j+1](c) - val ppc = ((2 * j + 1) * c * pc - j * pmc) / (j + 1) - pmc = pc - pc = ppc - } - // Now pc = P[n+1](c) and pmc = P[n](c). - if (!done) { - if (pa * pc <= 0) { - b = c - } else { - a = c - pa = pc - } - c = 0.5 * (a + b) - } - } - val d = numPoints * (pmc - c * pc) - val w = 2 * (1 - c * c) / (d * d) - points[i] = c - weights[i] = w - val idx = numPoints - i - 1 - points[idx] = -c - weights[idx] = w - } - // If "numPoints" is odd, 0 is a root. - // Note: as written, the test for oddness will work for negative - // integers too (although it is unnecessary here), preventing - // a FindBugs warning. - if (numPoints % 2 != 0) { - var pmc = 1.0 - var j = 1 - while (j < numPoints) { - pmc = -j * pmc / (j + 1) - j += 2 - } - val d = numPoints * pmc - val w = 2 / (d * d) - points[iMax] = 0.0 - weights[iMax] = w - } - return Pair(points.asBuffer(), weights.asBuffer()) - } - - override fun build(numPoints: Int): Pair, Buffer> = getOrBuildRule(numPoints) - - override fun toString(): String = "GaussLegendreRule" -} \ No newline at end of file diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/Integrand.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/Integrand.kt deleted file mode 100644 index 05e2e5c55..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/Integrand.kt +++ /dev/null @@ -1,48 +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. - */ - -package space.kscience.kmath.integration - -import space.kscience.kmath.misc.Feature -import space.kscience.kmath.misc.FeatureSet -import space.kscience.kmath.misc.Featured -import kotlin.reflect.KClass - -public interface IntegrandFeature : Feature { - override fun toString(): String -} - -public interface Integrand : Featured { - public val features: FeatureSet - override fun getFeature(type: KClass): T? = features.getFeature(type) -} - -public inline fun Integrand.getFeature(): T? = getFeature(T::class) - -public class IntegrandValue(public val value: T) : IntegrandFeature { - override fun toString(): String = "Value($value)" -} - -public class IntegrandRelativeAccuracy(public val accuracy: Double) : IntegrandFeature { - override fun toString(): String = "TargetRelativeAccuracy($accuracy)" -} - -public class IntegrandAbsoluteAccuracy(public val accuracy: Double) : IntegrandFeature { - override fun toString(): String = "TargetAbsoluteAccuracy($accuracy)" -} - -public class IntegrandCallsPerformed(public val calls: Int) : IntegrandFeature { - override fun toString(): String = "Calls($calls)" -} - -public val Integrand.calls: Int get() = getFeature()?.calls ?: 0 - -public class IntegrandMaxCalls(public val maxCalls: Int) : IntegrandFeature { - override fun toString(): String = "MaxCalls($maxCalls)" -} - -public class IntegrandIterationsRange(public val range: IntRange) : IntegrandFeature { - override fun toString(): String = "Iterations(${range.first}..${range.last})" -} diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/Integrator.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/Integrator.kt deleted file mode 100644 index 1cf15b42f..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/Integrator.kt +++ /dev/null @@ -1,16 +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. - */ - -package space.kscience.kmath.integration - -/** - * A general interface for all integrators. - */ -public interface Integrator { - /** - * Runs one integration pass and return a new [Integrand] with a new set of features. - */ - public fun process(integrand: I): I -} diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/MultivariateIntegrand.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/MultivariateIntegrand.kt deleted file mode 100644 index 96b81aaa6..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/MultivariateIntegrand.kt +++ /dev/null @@ -1,26 +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. - */ - -package space.kscience.kmath.integration - -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.FeatureSet - -public class MultivariateIntegrand internal constructor( - override val features: FeatureSet, - public val function: (Point) -> T, -) : Integrand { - - public operator fun plus(feature: F): MultivariateIntegrand = - MultivariateIntegrand(features.with(feature), function) -} - -@Suppress("FunctionName") -public fun MultivariateIntegrand( - vararg features: IntegrandFeature, - function: (Point) -> T, -): MultivariateIntegrand = MultivariateIntegrand(FeatureSet.of(*features), function) - -public val MultivariateIntegrand.value: T? get() = getFeature>()?.value diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SimpsonIntegrator.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SimpsonIntegrator.kt deleted file mode 100644 index 7815757aa..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SimpsonIntegrator.kt +++ /dev/null @@ -1,108 +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. - */ - -package space.kscience.kmath.integration - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.Field -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.operations.sum - -/** - * Use double pass Simpson rule integration with a fixed number of points. - * Requires [UnivariateIntegrandRanges] or [IntegrationRange] and [IntegrandMaxCalls]. - * * [IntegrationRange]—the univariate range of integration. By default, uses `0..1` interval. - * * [IntegrandMaxCalls]—the maximum number of function calls during integration. For non-iterative rules, always - * uses the maximum number of points. By default, uses 10 points. - */ -@UnstableKMathAPI -public class SimpsonIntegrator( - public val algebra: Field, -) : UnivariateIntegrator { - - private fun integrateRange( - integrand: UnivariateIntegrand, range: ClosedRange, numPoints: Int, - ): T = algebra { - val h: Double = (range.endInclusive - range.start) / (numPoints - 1) - val values: List = List(numPoints) { i -> - integrand.function(range.start + i * h) - }// equally distributed point - - //TODO don't use list, reassign values instead - fun simpson(index: Int) = h / 3 * (values[index - 1] + 4 * values[index] + values[index + 1]) - - var res = zero - res += simpson(1) / 1.5 //border points with 1.5 factor - for (i in 2 until (values.size - 2)) { - //each half-interval is computed twice, therefore /2 - res += simpson(i) / 2 - } - res += simpson(values.size - 2) / 1.5 //border points with 1.5 factor - return res - } - - override fun process(integrand: UnivariateIntegrand): UnivariateIntegrand { - val ranges = integrand.getFeature() - return if (ranges != null) { - val res = algebra.sum(ranges.ranges.map { integrateRange(integrand, it.first, it.second) }) - integrand + IntegrandValue(res) + IntegrandCallsPerformed(integrand.calls + ranges.ranges.sumOf { it.second }) - } else { - val numPoints = integrand.getFeature()?.maxCalls ?: 100 - require(numPoints >= 4) { "Simpson integrator requires at least 4 nodes" } - val range = integrand.getFeature()?.range ?: 0.0..1.0 - val res = integrateRange(integrand, range, numPoints) - integrand + IntegrandValue(res) + IntegrandCallsPerformed(integrand.calls + numPoints) - } - } -} - -@UnstableKMathAPI -public val Field.simpsonIntegrator: SimpsonIntegrator get() = SimpsonIntegrator(this) - -/** - * Use double pass Simpson rule integration with a fixed number of points. - * Requires [UnivariateIntegrandRanges] or [IntegrationRange] and [IntegrandMaxCalls]. - * * [IntegrationRange]—the univariate range of integration. By default, uses `0.0..1.0` interval. - * * [IntegrandMaxCalls]—the maximum number of function calls during integration. For non-iterative rules, always uses - * the maximum number of points. By default, uses 10 points. - */ -public object DoubleSimpsonIntegrator : UnivariateIntegrator { - private fun integrateRange( - integrand: UnivariateIntegrand, range: ClosedRange, numPoints: Int, - ): Double { - val h: Double = (range.endInclusive - range.start) / (numPoints - 1) - val values = DoubleArray(numPoints) { i -> - integrand.function(range.start + i * h) - }// equally distributed point - - fun simpson(index: Int) = h / 3 * (values[index - 1] + 4 * values[index] + values[index + 1]) - - var res = 0.0 - res += simpson(1) / 1.5 //border points with 1.5 factor - for (i in 2 until (values.size - 2)) { - //each half-interval is computed twice, therefore /2 - res += simpson(i) / 2 - } - res += simpson(values.size - 2) / 1.5 //border points with 1.5 factor - return res - } - - override fun process(integrand: UnivariateIntegrand): UnivariateIntegrand { - val ranges = integrand.getFeature() - return if (ranges != null) { - val res = ranges.ranges.sumOf { integrateRange(integrand, it.first, it.second) } - integrand + IntegrandValue(res) + IntegrandCallsPerformed(integrand.calls + ranges.ranges.sumOf { it.second }) - } else { - val numPoints = integrand.getFeature()?.maxCalls ?: 100 - require(numPoints >= 4) { "Simpson integrator requires at least 4 nodes" } - val range = integrand.getFeature()?.range ?: 0.0..1.0 - val res = integrateRange(integrand, range, numPoints) - integrand + IntegrandValue(res) + IntegrandCallsPerformed(integrand.calls + numPoints) - } - } -} - -public val DoubleField.simpsonIntegrator: DoubleSimpsonIntegrator get() = DoubleSimpsonIntegrator \ No newline at end of file diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SplineIntegrator.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SplineIntegrator.kt deleted file mode 100644 index eb88d9ae0..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SplineIntegrator.kt +++ /dev/null @@ -1,106 +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. - */ - -package space.kscience.kmath.integration - -import space.kscience.kmath.functions.PiecewisePolynomial -import space.kscience.kmath.functions.integrate -import space.kscience.kmath.interpolation.PolynomialInterpolator -import space.kscience.kmath.interpolation.SplineInterpolator -import space.kscience.kmath.interpolation.interpolatePolynomials -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.MutableBufferFactory - -/** - * Compute analytical indefinite integral of this [PiecewisePolynomial], keeping all intervals intact - */ -@OptIn(PerformancePitfall::class) -@UnstableKMathAPI -public fun > PiecewisePolynomial.integrate(algebra: Field): PiecewisePolynomial = - PiecewisePolynomial(pieces.map { it.first to it.second.integrate(algebra) }) - -/** - * Compute definite integral of given [PiecewisePolynomial] piece by piece in a given [range] - * Requires [UnivariateIntegrationNodes] or [IntegrationRange] and [IntegrandMaxCalls] - * - * TODO use context receiver for algebra - */ -@UnstableKMathAPI -public fun > PiecewisePolynomial.integrate( - algebra: Field, range: ClosedRange, -): T = algebra.sum( - pieces.map { (region, poly) -> - val intersectedRange = maxOf(range.start, region.start)..minOf(range.endInclusive, region.endInclusive) - //Check if polynomial range is not used - if (intersectedRange.start == intersectedRange.endInclusive) algebra.zero - else poly.integrate(algebra, intersectedRange) - } -) - -/** - * A generic spline-interpolation-based analytic integration - * * [IntegrationRange]—the univariate range of integration. By default, uses `0..1` interval. - * * [IntegrandMaxCalls]—the maximum number of function calls during integration. For non-iterative rules, always uses - * the maximum number of points. By default, uses 10 points. - */ -@UnstableKMathAPI -public class SplineIntegrator>( - public val algebra: Field, - public val bufferFactory: MutableBufferFactory, -) : UnivariateIntegrator { - override fun process(integrand: UnivariateIntegrand): UnivariateIntegrand = algebra { - val range = integrand.getFeature()?.range ?: 0.0..1.0 - - val interpolator: PolynomialInterpolator = SplineInterpolator(algebra, bufferFactory) - - val nodes: Buffer = integrand.getFeature()?.nodes ?: run { - val numPoints = integrand.getFeature()?.maxCalls ?: 100 - val step = (range.endInclusive - range.start) / (numPoints - 1) - DoubleBuffer(numPoints) { i -> range.start + i * step } - } - - val values = nodes.map(bufferFactory) { integrand.function(it) } - val polynomials = interpolator.interpolatePolynomials( - nodes.map(bufferFactory) { number(it) }, - values - ) - val res = polynomials.integrate(algebra, number(range.start)..number(range.endInclusive)) - integrand + IntegrandValue(res) + IntegrandCallsPerformed(integrand.calls + nodes.size) - } -} - -/** - * A simplified double-based spline-interpolation-based analytic integration - * * [IntegrationRange]—the univariate range of integration. By default, uses `0.0..1.0` interval. - * * [IntegrandMaxCalls]—the maximum number of function calls during integration. For non-iterative rules, always - * uses the maximum number of points. By default, uses 10 points. - */ -@UnstableKMathAPI -public object DoubleSplineIntegrator : UnivariateIntegrator { - override fun process(integrand: UnivariateIntegrand): UnivariateIntegrand { - val range = integrand.getFeature()?.range ?: 0.0..1.0 - val interpolator: PolynomialInterpolator = SplineInterpolator(DoubleField, ::DoubleBuffer) - - val nodes: Buffer = integrand.getFeature()?.nodes ?: run { - val numPoints = integrand.getFeature()?.maxCalls ?: 100 - val step = (range.endInclusive - range.start) / (numPoints - 1) - DoubleBuffer(numPoints) { i -> range.start + i * step } - } - - val values = nodes.map { integrand.function(it) } - val polynomials = interpolator.interpolatePolynomials(nodes, values) - val res = polynomials.integrate(DoubleField, range) - return integrand + IntegrandValue(res) + IntegrandCallsPerformed(integrand.calls + nodes.size) - } -} - -@Suppress("unused") -@UnstableKMathAPI -public inline val DoubleField.splineIntegrator: UnivariateIntegrator - get() = DoubleSplineIntegrator \ No newline at end of file diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/UnivariateIntegrand.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/UnivariateIntegrand.kt deleted file mode 100644 index 6fd75e6e6..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/UnivariateIntegrand.kt +++ /dev/null @@ -1,102 +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. - */ - -package space.kscience.kmath.integration - -import space.kscience.kmath.misc.FeatureSet -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.DoubleBuffer - -public class UnivariateIntegrand internal constructor( - override val features: FeatureSet, - public val function: (Double) -> T, -) : Integrand { - public operator fun plus(feature: F): UnivariateIntegrand = - UnivariateIntegrand(features.with(feature), function) -} - -@Suppress("FunctionName") -public fun UnivariateIntegrand( - function: (Double) -> T, - vararg features: IntegrandFeature, -): UnivariateIntegrand = UnivariateIntegrand(FeatureSet.of(*features), function) - -public typealias UnivariateIntegrator = Integrator> - -public class IntegrationRange(public val range: ClosedRange) : IntegrandFeature { - override fun toString(): String = "Range(${range.start}..${range.endInclusive})" -} - -/** - * Set of univariate integration ranges. First components correspond to the ranges themselves, second components to - * number of integration nodes per range. - */ -public class UnivariateIntegrandRanges(public val ranges: List, Int>>) : IntegrandFeature { - public constructor(vararg pairs: Pair, Int>) : this(pairs.toList()) - - override fun toString(): String { - val rangesString = ranges.joinToString(separator = ",") { (range, points) -> - "${range.start}..${range.endInclusive} : $points" - } - return "UnivariateRanges($rangesString)" - } -} - -public class UnivariateIntegrationNodes(public val nodes: Buffer) : IntegrandFeature { - public constructor(vararg nodes: Double) : this(DoubleBuffer(nodes)) - - override fun toString(): String = "UnivariateNodes($nodes)" -} - - -/** - * Value of the integrand if it is present or null - */ -public val UnivariateIntegrand.valueOrNull: T? get() = getFeature>()?.value - -/** - * Value of the integrand or error - */ -public val UnivariateIntegrand.value: T get() = valueOrNull ?: error("No value in the integrand") - -/** - * A shortcut method to integrate a [function] with additional [features]. Range must be provided in features. - * The [function] is placed in the end position to allow passing a lambda. - */ -@UnstableKMathAPI -public fun UnivariateIntegrator.integrate( - vararg features: IntegrandFeature, - function: (Double) -> T, -): UnivariateIntegrand = process(UnivariateIntegrand(function, *features)) - -/** - * A shortcut method to integrate a [function] in [range] with additional [features]. - * The [function] is placed in the end position to allow passing a lambda. - */ -@UnstableKMathAPI -public fun UnivariateIntegrator.integrate( - range: ClosedRange, - vararg features: IntegrandFeature, - function: (Double) -> T, -): UnivariateIntegrand = process(UnivariateIntegrand(function, IntegrationRange(range), *features)) - -/** - * A shortcut method to integrate a [function] in [range] with additional features. - * The [function] is placed in the end position to allow passing a lambda. - */ -@UnstableKMathAPI -public fun UnivariateIntegrator.integrate( - range: ClosedRange, - featureBuilder: MutableList.() -> Unit = {}, - function: (Double) -> T, -): UnivariateIntegrand { - //TODO use dedicated feature builder class instead or add extensions to MutableList - val features = buildList { - featureBuilder() - add(IntegrationRange(range)) - } - return process(UnivariateIntegrand(function, *features.toTypedArray())) -} diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/Interpolator.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/Interpolator.kt deleted file mode 100644 index 2266092a3..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/Interpolator.kt +++ /dev/null @@ -1,92 +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. - */ - -@file:OptIn(UnstableKMathAPI::class) - -package space.kscience.kmath.interpolation - -import space.kscience.kmath.data.XYColumnarData -import space.kscience.kmath.functions.PiecewisePolynomial -import space.kscience.kmath.functions.asFunction -import space.kscience.kmath.functions.value -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Ring -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.asBuffer - -/** - * And interpolator for data with x column type [X], y column type [Y]. - */ -public fun interface Interpolator { - public fun interpolate(points: XYColumnarData): (X) -> Y -} - -/** - * And interpolator returning [PiecewisePolynomial] function - */ -public interface PolynomialInterpolator> : Interpolator { - public val algebra: Ring - - public fun getDefaultValue(): T = error("Out of bounds") - - public fun interpolatePolynomials(points: XYColumnarData): PiecewisePolynomial - - override fun interpolate(points: XYColumnarData): (T) -> T = { x -> - interpolatePolynomials(points).value(algebra, x) ?: getDefaultValue() - } -} - - -public fun > PolynomialInterpolator.interpolatePolynomials( - x: Buffer, - y: Buffer, -): PiecewisePolynomial { - val pointSet = XYColumnarData.of(x, y) - return interpolatePolynomials(pointSet) -} - -public fun > PolynomialInterpolator.interpolatePolynomials( - data: Map, -): PiecewisePolynomial { - val pointSet = XYColumnarData.of(data.keys.toList().asBuffer(), data.values.toList().asBuffer()) - return interpolatePolynomials(pointSet) -} - -public fun > PolynomialInterpolator.interpolatePolynomials( - data: List>, -): PiecewisePolynomial { - val pointSet = XYColumnarData.of(data.map { it.first }.asBuffer(), data.map { it.second }.asBuffer()) - return interpolatePolynomials(pointSet) -} - -public fun > PolynomialInterpolator.interpolate( - x: Buffer, - y: Buffer, -): (T) -> T? = interpolatePolynomials(x, y).asFunction(algebra) - -public fun > PolynomialInterpolator.interpolate( - data: Map, -): (T) -> T? = interpolatePolynomials(data).asFunction(algebra) - -public fun > PolynomialInterpolator.interpolate( - data: List>, -): (T) -> T? = interpolatePolynomials(data).asFunction(algebra) - - -public fun > PolynomialInterpolator.interpolate( - x: Buffer, - y: Buffer, - defaultValue: T, -): (T) -> T = interpolatePolynomials(x, y).asFunction(algebra, defaultValue) - -public fun > PolynomialInterpolator.interpolate( - data: Map, - defaultValue: T, -): (T) -> T = interpolatePolynomials(data).asFunction(algebra, defaultValue) - -public fun > PolynomialInterpolator.interpolate( - data: List>, - defaultValue: T, -): (T) -> T = interpolatePolynomials(data).asFunction(algebra, defaultValue) \ No newline at end of file diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt deleted file mode 100644 index 34d7bcf41..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt +++ /dev/null @@ -1,43 +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. - */ - -package space.kscience.kmath.interpolation - -import space.kscience.kmath.data.XYColumnarData -import space.kscience.kmath.functions.PiecewisePolynomial -import space.kscience.kmath.functions.Polynomial -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Field -import space.kscience.kmath.operations.invoke - -@OptIn(UnstableKMathAPI::class) -internal fun > insureSorted(points: XYColumnarData<*, T, *>) { - for (i in 0 until points.size - 1) - require(points.x[i + 1] > points.x[i]) { "Input data is not sorted at index $i" } -} - -/** - * Reference JVM implementation: https://github.com/apache/commons-math/blob/master/src/main/java/org/apache/commons/math4/analysis/interpolation/LinearInterpolator.java - */ -public class LinearInterpolator>(override val algebra: Field) : PolynomialInterpolator { - - @OptIn(UnstableKMathAPI::class) - override fun interpolatePolynomials(points: XYColumnarData): PiecewisePolynomial = algebra { - require(points.size > 0) { "Point array should not be empty" } - insureSorted(points) - - PiecewisePolynomial(points.x[0]) { - for (i in 0 until points.size - 1) { - val slope = (points.y[i + 1] - points.y[i]) / (points.x[i + 1] - points.x[i]) - val const = points.y[i] - slope * points.x[i] - val polynomial = Polynomial(const, slope) - putRight(points.x[i + 1], polynomial) - } - } - } -} - -public val > Field.linearInterpolator: LinearInterpolator - get() = LinearInterpolator(this) diff --git a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt b/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt deleted file mode 100644 index afcb33bd4..000000000 --- a/kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt +++ /dev/null @@ -1,83 +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. - */ - -package space.kscience.kmath.interpolation - -import space.kscience.kmath.data.XYColumnarData -import space.kscience.kmath.functions.PiecewisePolynomial -import space.kscience.kmath.functions.Polynomial -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.Field -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.MutableBufferFactory - -/** - * Generic spline interpolator. Not recommended for performance critical places, use platform-specific and type - * specific ones. - * - * Based on - * https://github.com/apache/commons-math/blob/eb57d6d457002a0bb5336d789a3381a24599affe/src/main/java/org/apache/commons/math4/analysis/interpolation/SplineInterpolator.java - */ -public class SplineInterpolator>( - override val algebra: Field, - public val bufferFactory: MutableBufferFactory, -) : PolynomialInterpolator { - //TODO possibly optimize zeroed buffers - - @OptIn(UnstableKMathAPI::class) - override fun interpolatePolynomials(points: XYColumnarData): PiecewisePolynomial = algebra { - require(points.size >= 3) { "Can't use spline interpolator with less than 3 points" } - insureSorted(points) - // Number of intervals. The number of data points is n + 1. - val n = points.size - 1 - // Differences between knot points - val h = bufferFactory(n) { i -> points.x[i + 1] - points.x[i] } - val mu = bufferFactory(n) { zero } - val z = bufferFactory(n + 1) { zero } - - for (i in 1 until n) { - val g = 2.0 * (points.x[i + 1] - points.x[i - 1]) - h[i - 1] * mu[i - 1] - mu[i] = h[i] / g - z[i] = - ((points.y[i + 1] * h[i - 1] - points.y[i] * (points.x[i + 1] - points.x[i - 1]) + points.y[i - 1] * h[i]) * 3.0 / - (h[i - 1] * h[i]) - h[i - 1] * z[i - 1]) / g - } - - // cubic spline coefficients -- b is linear, c quadratic, d is cubic (original y's are constants) - - PiecewisePolynomial(points.x[points.size - 1]) { - var cOld = zero - - for (j in n - 1 downTo 0) { - val c = z[j] - mu[j] * cOld - val a = points.y[j] - val b = (points.y[j + 1] - points.y[j]) / h[j] - h[j] * (cOld + 2.0 * c) / 3.0 - val d = (cOld - c) / (3.0 * h[j]) - val x0 = points.x[j] - val x02 = x0 * x0 - val x03 = x02 * x0 - //Shift coefficients to represent absolute polynomial instead of one with an offset - val polynomial = Polynomial( - a - b * x0 + c * x02 - d * x03, - b - 2 * c * x0 + 3 * d * x02, - c - 3 * d * x0, - d - ) - cOld = c - putLeft(x0, polynomial) - } - } - } -} - - -public fun > Field.splineInterpolator( - bufferFactory: MutableBufferFactory, -): SplineInterpolator = SplineInterpolator(this, bufferFactory) - -public val DoubleField.splineInterpolator: SplineInterpolator - get() = SplineInterpolator(this, ::DoubleBuffer) \ No newline at end of file diff --git a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/functions/PolynomialTest.kt b/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/functions/PolynomialTest.kt deleted file mode 100644 index 05c16d17e..000000000 --- a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/functions/PolynomialTest.kt +++ /dev/null @@ -1,17 +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. - */ - -package space.kscience.kmath.functions - -import kotlin.test.Test -import kotlin.test.assertEquals - -class PolynomialTest { - @Test - fun testIntegration() { - val polynomial = Polynomial(1.0, -2.0, 1.0) - assertEquals(0.0, polynomial.value(1.0), 0.001) - } -} \ No newline at end of file diff --git a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/integration/GaussIntegralTest.kt b/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/integration/GaussIntegralTest.kt deleted file mode 100644 index 9f48a15ea..000000000 --- a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/integration/GaussIntegralTest.kt +++ /dev/null @@ -1,38 +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. - */ - -package space.kscience.kmath.integration - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleField -import kotlin.math.PI -import kotlin.math.sin -import kotlin.test.Test -import kotlin.test.assertEquals - -@OptIn(UnstableKMathAPI::class) -class GaussIntegralTest { - @Test - fun gaussSin() { - val res = DoubleField.gaussIntegrator.integrate(0.0..2 * PI) { x -> - sin(x) - } - assertEquals(0.0, res.value, 1e-2) - } - - @Test - fun gaussUniform() { - val res = DoubleField.gaussIntegrator.integrate(35.0..100.0) { x -> - if(x in 30.0..50.0){ - 1.0 - } else { - 0.0 - } - } - assertEquals(15.0, res.value, 0.5) - } - - -} \ No newline at end of file diff --git a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/integration/SimpsonIntegralTest.kt b/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/integration/SimpsonIntegralTest.kt deleted file mode 100644 index 9f2d71554..000000000 --- a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/integration/SimpsonIntegralTest.kt +++ /dev/null @@ -1,36 +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. - */ - -package space.kscience.kmath.integration - -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleField -import kotlin.math.PI -import kotlin.math.sin -import kotlin.test.Test -import kotlin.test.assertEquals - -@OptIn(UnstableKMathAPI::class) -class SimpsonIntegralTest { - @Test - fun gaussSin() { - val res = DoubleField.simpsonIntegrator.integrate(0.0..2 * PI, IntegrandMaxCalls(5)) { x -> - sin(x) - } - assertEquals(0.0, res.value, 1e-2) - } - - @Test - fun gaussUniform() { - val res = DoubleField.simpsonIntegrator.integrate(35.0..100.0, IntegrandMaxCalls(20)) { x -> - if (x in 30.0..50.0) { - 1.0 - } else { - 0.0 - } - } - assertEquals(15.0, res.value, 0.5) - } -} \ No newline at end of file diff --git a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/integration/SplineIntegralTest.kt b/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/integration/SplineIntegralTest.kt deleted file mode 100644 index afeba0be4..000000000 --- a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/integration/SplineIntegralTest.kt +++ /dev/null @@ -1,48 +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. - */ - -package space.kscience.kmath.integration - -import space.kscience.kmath.functions.Polynomial -import space.kscience.kmath.functions.integrate -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleField -import kotlin.math.PI -import kotlin.math.sin -import kotlin.test.Test -import kotlin.test.assertEquals - -@OptIn(UnstableKMathAPI::class) -class SplineIntegralTest { - - @Test - fun integratePolynomial(){ - val polynomial = Polynomial(1.0, 2.0, 3.0) - val integral = polynomial.integrate(DoubleField,1.0..2.0) - assertEquals(11.0, integral, 0.001) - } - - @Test - fun gaussSin() { - val res = DoubleField.splineIntegrator.integrate(0.0..2 * PI, IntegrandMaxCalls(5)) { x -> - sin(x) - } - assertEquals(0.0, res.value, 1e-2) - } - - @Test - fun gaussUniform() { - val res = DoubleField.splineIntegrator.integrate(35.0..100.0, IntegrandMaxCalls(20)) { x -> - if(x in 30.0..50.0){ - 1.0 - } else { - 0.0 - } - } - assertEquals(15.0, res.value, 0.5) - } - - -} \ No newline at end of file diff --git a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/LinearInterpolatorTest.kt b/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/LinearInterpolatorTest.kt deleted file mode 100644 index 1143036d4..000000000 --- a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/LinearInterpolatorTest.kt +++ /dev/null @@ -1,29 +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. - */ - -package space.kscience.kmath.interpolation - -import space.kscience.kmath.operations.DoubleField -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class LinearInterpolatorTest { - @Test - fun testInterpolation() { - val data = listOf( - 0.0 to 0.0, - 1.0 to 1.0, - 2.0 to 3.0, - 3.0 to 4.0 - ) - - //val polynomial: PiecewisePolynomial = DoubleField.linearInterpolator.interpolatePolynomials(data) - val function = DoubleField.linearInterpolator.interpolate(data) - assertEquals(null, function(-1.0)) - assertEquals(0.5, function(0.5)) - assertEquals(2.0, function(1.5)) - assertEquals(3.0, function(2.0)) - } -} diff --git a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/SplineInterpolatorTest.kt b/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/SplineInterpolatorTest.kt deleted file mode 100644 index 4c7d816d4..000000000 --- a/kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/SplineInterpolatorTest.kt +++ /dev/null @@ -1,31 +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. - */ - -package space.kscience.kmath.interpolation - -import space.kscience.kmath.operations.DoubleField -import kotlin.math.PI -import kotlin.math.sin -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class SplineInterpolatorTest { - @Test - fun testInterpolation() { - val data = (0..10).map { - val x = it.toDouble() / 5 * PI - x to sin(x) - } - - //val polynomial: PiecewisePolynomial = DoubleField.splineInterpolator.interpolatePolynomials(data) - - val function = DoubleField.splineInterpolator.interpolate(data, Double.NaN) - - assertEquals(Double.NaN, function(-1.0)) - assertEquals(sin(0.5), function(0.5), 0.1) - assertEquals(sin(1.5), function(1.5), 0.1) - assertEquals(sin(2.0), function(2.0), 0.1) - } -} diff --git a/kmath-geometry/README.md b/kmath-geometry/README.md deleted file mode 100644 index 6b8fb25a0..000000000 --- a/kmath-geometry/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-geometry - - - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-geometry:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-geometry:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-geometry:0.3.0-dev-20") -} -``` diff --git a/kmath-geometry/build.gradle.kts b/kmath-geometry/build.gradle.kts deleted file mode 100644 index 7eb814683..000000000 --- a/kmath-geometry/build.gradle.kts +++ /dev/null @@ -1,15 +0,0 @@ -plugins { - kotlin("multiplatform") - id("ru.mipt.npm.gradle.common") - id("ru.mipt.npm.gradle.native") -} - -kotlin.sourceSets.commonMain { - dependencies { - api(project(":kmath-core")) - } -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE -} diff --git a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Euclidean2DSpace.kt b/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Euclidean2DSpace.kt deleted file mode 100644 index afe1d0ea5..000000000 --- a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Euclidean2DSpace.kt +++ /dev/null @@ -1,53 +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. - */ - -package space.kscience.kmath.geometry - -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.ScaleOperations -import space.kscience.kmath.operations.invoke -import kotlin.math.sqrt - -@OptIn(UnstableKMathAPI::class) -public interface Vector2D : Point, Vector { - public val x: Double - public val y: Double - override val size: Int get() = 2 - - override operator fun get(index: Int): Double = when (index) { - 0 -> x - 1 -> y - else -> error("Accessing outside of point bounds") - } - - override operator fun iterator(): Iterator = listOf(x, y).iterator() -} - -public val Vector2D.r: Double - get() = Euclidean2DSpace { norm() } - -@Suppress("FunctionName") -public fun Vector2D(x: Double, y: Double): Vector2D = Vector2DImpl(x, y) - -private data class Vector2DImpl( - override val x: Double, - override val y: Double, -) : Vector2D - -/** - * 2D Euclidean space - */ -public object Euclidean2DSpace : GeometrySpace, ScaleOperations { - override val zero: Vector2D by lazy { Vector2D(0.0, 0.0) } - - public fun Vector2D.norm(): Double = sqrt(x * x + y * y) - override fun negate(arg: Vector2D): Vector2D = Vector2D(-arg.x, -arg.y) - - override fun Vector2D.distanceTo(other: Vector2D): Double = (this - other).norm() - override fun add(left: Vector2D, right: Vector2D): Vector2D = Vector2D(left.x + right.x, left.y + right.y) - override fun scale(a: Vector2D, value: Double): Vector2D = Vector2D(a.x * value, a.y * value) - override fun Vector2D.dot(other: Vector2D): Double = x * other.x + y * other.y -} diff --git a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Euclidean3DSpace.kt b/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Euclidean3DSpace.kt deleted file mode 100644 index e12563b46..000000000 --- a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Euclidean3DSpace.kt +++ /dev/null @@ -1,58 +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. - */ - -package space.kscience.kmath.geometry - -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.ScaleOperations -import space.kscience.kmath.operations.invoke -import kotlin.math.sqrt - -@OptIn(UnstableKMathAPI::class) -public interface Vector3D : Point, Vector { - public val x: Double - public val y: Double - public val z: Double - override val size: Int get() = 3 - - override operator fun get(index: Int): Double = when (index) { - 0 -> x - 1 -> y - 2 -> z - else -> error("Accessing outside of point bounds") - } - - override operator fun iterator(): Iterator = listOf(x, y, z).iterator() -} - -@Suppress("FunctionName") -public fun Vector3D(x: Double, y: Double, z: Double): Vector3D = Vector3DImpl(x, y, z) - -public val Vector3D.r: Double get() = Euclidean3DSpace { norm() } - -private data class Vector3DImpl( - override val x: Double, - override val y: Double, - override val z: Double, -) : Vector3D - -public object Euclidean3DSpace : GeometrySpace, ScaleOperations { - override val zero: Vector3D by lazy { Vector3D(0.0, 0.0, 0.0) } - - public fun Vector3D.norm(): Double = sqrt(x * x + y * y + z * z) - override fun Vector3D.unaryMinus(): Vector3D = Vector3D(-x, -y, -z) - - override fun Vector3D.distanceTo(other: Vector3D): Double = (this - other).norm() - - override fun add(left: Vector3D, right: Vector3D): Vector3D = - Vector3D(left.x + right.x, left.y + right.y, left.z + right.z) - - override fun scale(a: Vector3D, value: Double): Vector3D = - Vector3D(a.x * value, a.y * value, a.z * value) - - override fun Vector3D.dot(other: Vector3D): Double = - x * other.x + y * other.y + z * other.z -} diff --git a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/GeometrySpace.kt b/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/GeometrySpace.kt deleted file mode 100644 index d4245c744..000000000 --- a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/GeometrySpace.kt +++ /dev/null @@ -1,23 +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. - */ - -package space.kscience.kmath.geometry - -import space.kscience.kmath.operations.Group -import space.kscience.kmath.operations.ScaleOperations - -public interface Vector - -public interface GeometrySpace : Group, ScaleOperations { - /** - * L2 distance - */ - public fun V.distanceTo(other: V): Double - - /** - * Scalar product - */ - public infix fun V.dot(other: V): Double -} \ No newline at end of file diff --git a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Line.kt b/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Line.kt deleted file mode 100644 index 8c6ccb55e..000000000 --- a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Line.kt +++ /dev/null @@ -1,11 +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. - */ - -package space.kscience.kmath.geometry - -public data class Line(val base: V, val direction: V) - -public typealias Line2D = Line -public typealias Line3D = Line diff --git a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Projections.kt b/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Projections.kt deleted file mode 100644 index 5e299f450..000000000 --- a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Projections.kt +++ /dev/null @@ -1,25 +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. - */ - -package space.kscience.kmath.geometry - -/** - * Project vector onto a line. - * @param vector to project - * @param line line to which vector should be projected - */ -public fun GeometrySpace.projectToLine(vector: V, line: Line): V = with(line) { - base + (direction dot (vector - base)) / (direction dot direction) * direction -} - -/** - * Project vector onto a hyperplane, which is defined by a normal and base. - * In 2D case it is the projection to a line, in 3d case it is the one to a plane. - * @param vector to project - * @param normal normal (perpendicular) vector to a hyper-plane to which vector should be projected - * @param base point belonging to a hyper-plane to which vector should be projected - */ -public fun GeometrySpace.projectAlong(vector: V, normal: V, base: V): V = - vector + normal * ((base - vector) dot normal) / (normal dot normal) diff --git a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/ReferenceFrame.kt b/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/ReferenceFrame.kt deleted file mode 100644 index a7a28b596..000000000 --- a/kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/ReferenceFrame.kt +++ /dev/null @@ -1,8 +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. - */ - -package space.kscience.kmath.geometry - -public interface ReferenceFrame diff --git a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Euclidean2DSpaceTest.kt b/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Euclidean2DSpaceTest.kt deleted file mode 100644 index 6b5f474bc..000000000 --- a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Euclidean2DSpaceTest.kt +++ /dev/null @@ -1,67 +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. - */ - -package space.kscience.kmath.geometry - -import kotlin.math.sqrt -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class Euclidean2DSpaceTest { - @Test - fun zero() { - assertVectorEquals(Vector2D(0.0, 0.0), Euclidean2DSpace.zero) - } - - @Test - fun norm() { - with(Euclidean2DSpace) { - assertEquals(0.0, zero.norm()) - assertEquals(1.0, Vector2D(1.0, 0.0).norm()) - assertEquals(sqrt(2.0), Vector2D(1.0, 1.0).norm()) - assertEquals(sqrt(5.002001), Vector2D(-2.0, 1.001).norm()) - } - } - - @Test - fun dotProduct() { - with(Euclidean2DSpace) { - assertEquals(0.0, zero dot zero) - assertEquals(0.0, zero dot Vector2D(1.0, 0.0)) - assertEquals(0.0, Vector2D(-2.0, 0.001) dot zero) - assertEquals(0.0, Vector2D(1.0, 0.0) dot Vector2D(0.0, 1.0)) - - assertEquals(1.0, Vector2D(1.0, 0.0) dot Vector2D(1.0, 0.0)) - assertEquals(-2.0, Vector2D(0.0, 1.0) dot Vector2D(1.0, -2.0)) - assertEquals(2.0, Vector2D(1.0, 1.0) dot Vector2D(1.0, 1.0)) - assertEquals(4.001001, Vector2D(-2.0, 1.001) dot Vector2D(-2.0, 0.001)) - - assertEquals(-4.998, Vector2D(1.0, 2.0) dot Vector2D(-5.0, 0.001)) - } - } - - @Test - fun add() { - with(Euclidean2DSpace) { - assertVectorEquals( - Vector2D(-2.0, 0.001), - Vector2D(-2.0, 0.001) + zero - ) - assertVectorEquals( - Vector2D(-3.0, 3.001), - Vector2D(2.0, 3.0) + Vector2D(-5.0, 0.001) - ) - } - } - - @Test - fun multiply() { - with(Euclidean2DSpace) { - assertVectorEquals(Vector2D(-4.0, 0.0), Vector2D(-2.0, 0.0) * 2) - assertVectorEquals(Vector2D(4.0, 0.0), Vector2D(-2.0, 0.0) * -2) - assertVectorEquals(Vector2D(300.0, 0.0003), Vector2D(100.0, 0.0001) * 3) - } - } -} diff --git a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Euclidean3DSpaceTest.kt b/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Euclidean3DSpaceTest.kt deleted file mode 100644 index 0bc91e77e..000000000 --- a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Euclidean3DSpaceTest.kt +++ /dev/null @@ -1,79 +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. - */ - -package space.kscience.kmath.geometry - -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class Euclidean3DSpaceTest { - @Test - fun zero() { - assertVectorEquals(Vector3D(0.0, 0.0, 0.0), Euclidean3DSpace.zero) - } - - @Test - fun distance() { - with(Euclidean3DSpace) { - assertEquals(0.0, zero.distanceTo(zero)) - assertEquals(1.0, zero.distanceTo(Vector3D(1.0, 0.0, 0.0))) - assertEquals(kotlin.math.sqrt(5.000001), Vector3D(1.0, -2.0, 0.001).distanceTo(zero)) - assertEquals(0.0, Vector3D(1.0, -2.0, 0.001).distanceTo(Vector3D(1.0, -2.0, 0.001))) - assertEquals(0.0, Vector3D(1.0, 0.0, 0.0).distanceTo(Vector3D(1.0, 0.0, 0.0))) - assertEquals(kotlin.math.sqrt(2.0), Vector3D(1.0, 0.0, 0.0).distanceTo(Vector3D(1.0, 1.0, 1.0))) - assertEquals(3.1622778182822584, Vector3D(0.0, 1.0, 0.0).distanceTo(Vector3D(1.0, -2.0, 0.001))) - assertEquals(0.0, Vector3D(1.0, -2.0, 0.001).distanceTo(Vector3D(1.0, -2.0, 0.001))) - assertEquals(9.695050335093676, Vector3D(1.0, 2.0, 3.0).distanceTo(Vector3D(7.0, -5.0, 0.001))) - } - } - - @Test - fun norm() { - with(Euclidean3DSpace) { - assertEquals(0.0, zero.norm()) - assertEquals(1.0, Vector3D(1.0, 0.0, 0.0).norm()) - assertEquals(kotlin.math.sqrt(3.0), Vector3D(1.0, 1.0, 1.0).norm()) - assertEquals(kotlin.math.sqrt(5.000001), Vector3D(1.0, -2.0, 0.001).norm()) - } - } - - @Test - fun dotProduct() { - with(Euclidean3DSpace) { - assertEquals(0.0, zero dot zero) - assertEquals(0.0, zero dot Vector3D(1.0, 0.0, 0.0)) - assertEquals(0.0, Vector3D(1.0, -2.0, 0.001) dot zero) - - assertEquals(1.0, Vector3D(1.0, 0.0, 0.0) dot Vector3D(1.0, 0.0, 0.0)) - assertEquals(1.0, Vector3D(1.0, 0.0, 0.0) dot Vector3D(1.0, 1.0, 1.0)) - assertEquals(-2.0, Vector3D(0.0, 1.0, 0.0) dot Vector3D(1.0, -2.0, 0.001)) - assertEquals(3.0, Vector3D(1.0, 1.0, 1.0) dot Vector3D(1.0, 1.0, 1.0)) - assertEquals(5.000001, Vector3D(1.0, -2.0, 0.001) dot Vector3D(1.0, -2.0, 0.001)) - - assertEquals(-2.997, Vector3D(1.0, 2.0, 3.0) dot Vector3D(7.0, -5.0, 0.001)) - } - } - - @Test - fun add() { - with(Euclidean3DSpace) { - assertVectorEquals( - Vector3D(1.0, -2.0, 0.001), - Vector3D(1.0, -2.0, 0.001) + zero - ) - assertVectorEquals( - Vector3D(8.0, -3.0, 3.001), - Vector3D(1.0, 2.0, 3.0) + Vector3D(7.0, -5.0, 0.001) - ) - } - } - - @Test - fun multiply() { - with(Euclidean3DSpace) { - assertVectorEquals(Vector3D(2.0, -4.0, 0.0), Vector3D(1.0, -2.0, 0.0) * 2) - } - } -} diff --git a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/ProjectionAlongTest.kt b/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/ProjectionAlongTest.kt deleted file mode 100644 index dfb65a57c..000000000 --- a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/ProjectionAlongTest.kt +++ /dev/null @@ -1,66 +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. - */ - -package space.kscience.kmath.geometry - -import kotlin.test.Test -import kotlin.test.assertTrue - -internal class ProjectionAlongTest { - @Test - fun projectionIntoYEqualsX() { - with(Euclidean2DSpace) { - val normal = Vector2D(-2.0, 2.0) - val base = Vector2D(2.3, 2.3) - - assertVectorEquals(zero, projectAlong(zero, normal, base)) - - grid(-10.0..10.0, -10.0..10.0, 0.15).forEach { (x, y) -> - val d = (y - x) / 2.0 - assertVectorEquals(Vector2D(x + d, y - d), projectAlong(Vector2D(x, y), normal, base)) - } - } - } - - @Test - fun projectionOntoLine() { - with(Euclidean2DSpace) { - val a = 5.0 - val b = -3.0 - val c = -15.0 - val normal = Vector2D(-5.0, 3.0) - val base = Vector2D(3.0, 0.0) - - grid(-10.0..10.0, -10.0..10.0, 0.15).forEach { (x, y) -> - val xProj = (b * (b * x - a * y) - a * c) / (a * a + b * b) - val yProj = (a * (-b * x + a * y) - b * c) / (a * a + b * b) - assertVectorEquals(Vector2D(xProj, yProj), projectAlong(Vector2D(x, y), normal, base)) - } - } - } - - @Test - fun projectOntoPlane() { - val normal = Vector3D(1.0, 3.5, 0.07) - val base = Vector3D(2.0, -0.0037, 11.1111) - - with(Euclidean3DSpace) { - val testDomain = (-10.0..10.0).generateList(0.43) - for (x in testDomain) { - for (y in testDomain) { - for (z in testDomain) { - val v = Vector3D(x, y, z) - val result = projectAlong(v, normal, base) - - // assert that result is on plane - assertTrue(isOrthogonal(result - base, normal)) - // assert that PV vector is collinear to normal vector - assertTrue(isCollinear(v - result, normal)) - } - } - } - } - } -} diff --git a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/ProjectionOntoLineTest.kt b/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/ProjectionOntoLineTest.kt deleted file mode 100644 index 076025110..000000000 --- a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/ProjectionOntoLineTest.kt +++ /dev/null @@ -1,88 +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. - */ - -package space.kscience.kmath.geometry - -import kotlin.test.Test -import kotlin.test.assertTrue - -internal class ProjectionOntoLineTest { - @Test - fun projectionIntoOx() { - with(Euclidean2DSpace) { - val ox = Line(zero, Vector2D(1.0, 0.0)) - - grid(-10.0..10.0, -10.0..10.0, 0.15).forEach { (x, y) -> - assertVectorEquals(Vector2D(x, 0.0), projectToLine(Vector2D(x, y), ox)) - } - } - } - - @Test - fun projectionIntoOy() { - with(Euclidean2DSpace) { - val line = Line(zero, Vector2D(0.0, 1.0)) - - grid(-10.0..10.0, -10.0..10.0, 0.15).forEach { (x, y) -> - assertVectorEquals(Vector2D(0.0, y), projectToLine(Vector2D(x, y), line)) - } - } - } - - @Test - fun projectionIntoYEqualsX() { - with(Euclidean2DSpace) { - val line = Line(zero, Vector2D(1.0, 1.0)) - - assertVectorEquals(zero, projectToLine(zero, line)) - - grid(-10.0..10.0, -10.0..10.0, 0.15).forEach { (x, y) -> - val d = (y - x) / 2.0 - assertVectorEquals(Vector2D(x + d, y - d), projectToLine(Vector2D(x, y), line)) - } - } - } - - @Test - fun projectionOntoLine2d() { - with(Euclidean2DSpace) { - val a = 5.0 - val b = -3.0 - val c = -15.0 - val line = Line(Vector2D(3.0, 0.0), Vector2D(3.0, 5.0)) - - grid(-10.0..10.0, -10.0..10.0, 0.15).forEach { (x, y) -> - val xProj = (b * (b * x - a * y) - a * c) / (a * a + b * b) - val yProj = (a * (-b * x + a * y) - b * c) / (a * a + b * b) - assertVectorEquals(Vector2D(xProj, yProj), projectToLine(Vector2D(x, y), line)) - } - } - } - - @Test - fun projectionOntoLine3d() { - val line = Line3D( - base = Vector3D(1.0, 3.5, 0.07), - direction = Vector3D(2.0, -0.0037, 11.1111) - ) - - with(Euclidean3DSpace) { - val testDomain = (-10.0..10.0).generateList(0.43) - for (x in testDomain) { - for (y in testDomain) { - for (z in testDomain) { - val v = Vector3D(x, y, z) - val result = projectToLine(v, line) - - // assert that result is on line - assertTrue(isCollinear(result - line.base, line.direction)) - // assert that PV vector is orthogonal to direction vector - assertTrue(isOrthogonal(v - result, line.direction)) - } - } - } - } - } -} diff --git a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Vector2DTest.kt b/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Vector2DTest.kt deleted file mode 100644 index 5e45b4870..000000000 --- a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Vector2DTest.kt +++ /dev/null @@ -1,40 +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. - */ - -package space.kscience.kmath.geometry - -import space.kscience.kmath.operations.toList -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class Vector2DTest { - private val vector = Vector2D(1.0, -7.999) - - @Test - fun size() { - assertEquals(2, vector.size) - } - - @Test - fun get() { - assertEquals(1.0, vector[0]) - assertEquals(-7.999, vector[1]) - } - - @Test - fun iterator() { - assertEquals(listOf(1.0, -7.999), vector.toList()) - } - - @Test - fun x() { - assertEquals(1.0, vector.x) - } - - @Test - fun y() { - assertEquals(-7.999, vector.y) - } -} diff --git a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Vector3DTest.kt b/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Vector3DTest.kt deleted file mode 100644 index 55bab4775..000000000 --- a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/Vector3DTest.kt +++ /dev/null @@ -1,46 +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. - */ - -package space.kscience.kmath.geometry - -import space.kscience.kmath.operations.toList -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class Vector3DTest { - private val vector = Vector3D(1.0, -7.999, 0.001) - - @Test - fun size() { - assertEquals(3, vector.size) - } - - @Test - fun get() { - assertEquals(1.0, vector[0]) - assertEquals(-7.999, vector[1]) - assertEquals(0.001, vector[2]) - } - - @Test - fun iterator() { - assertEquals(listOf(1.0, -7.999, 0.001), vector.toList()) - } - - @Test - fun x() { - assertEquals(1.0, vector.x) - } - - @Test - fun y() { - assertEquals(-7.999, vector.y) - } - - @Test - fun z() { - assertEquals(0.001, vector.z) - } -} diff --git a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/testUtils.kt b/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/testUtils.kt deleted file mode 100644 index 0f957529d..000000000 --- a/kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/testUtils.kt +++ /dev/null @@ -1,46 +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. - */ - -package space.kscience.kmath.geometry - -import kotlin.math.abs -import kotlin.test.assertEquals - -fun ClosedRange.generateList(step: Double): List = generateSequence(start) { previous -> - if (previous == Double.POSITIVE_INFINITY) return@generateSequence null - val next = previous + step - if (next > endInclusive) null else next -}.toList() - -fun grid( - xRange: ClosedRange, - yRange: ClosedRange, - step: Double -): List> { - val xs = xRange.generateList(step) - val ys = yRange.generateList(step) - - return xs.flatMap { x -> ys.map { y -> x to y } } -} - -fun assertVectorEquals(expected: Vector2D, actual: Vector2D, absoluteTolerance: Double = 1e-6) { - assertEquals(expected.x, actual.x, absoluteTolerance) - assertEquals(expected.y, actual.y, absoluteTolerance) -} - -fun assertVectorEquals(expected: Vector3D, actual: Vector3D, absoluteTolerance: Double = 1e-6) { - assertEquals(expected.x, actual.x, absoluteTolerance) - assertEquals(expected.y, actual.y, absoluteTolerance) - assertEquals(expected.z, actual.z, absoluteTolerance) -} - -fun GeometrySpace.isCollinear(a: V, b: V, absoluteTolerance: Double = 1e-6): Boolean { - val aDist = a.distanceTo(zero) - val bDist = b.distanceTo(zero) - return abs(aDist) < absoluteTolerance || abs(bDist) < absoluteTolerance || abs(abs((a dot b) / (aDist * bDist)) - 1) < absoluteTolerance -} - -fun GeometrySpace.isOrthogonal(a: V, b: V, absoluteTolerance: Double = 1e-6): Boolean = - abs(a dot b) < absoluteTolerance diff --git a/kmath-histograms/README.md b/kmath-histograms/README.md deleted file mode 100644 index 0b8a632d6..000000000 --- a/kmath-histograms/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-histograms - - - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-histograms:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-histograms:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-histograms:0.3.0-dev-20") -} -``` diff --git a/kmath-histograms/build.gradle.kts b/kmath-histograms/build.gradle.kts deleted file mode 100644 index 7e511faa0..000000000 --- a/kmath-histograms/build.gradle.kts +++ /dev/null @@ -1,26 +0,0 @@ -plugins { - kotlin("multiplatform") - id("ru.mipt.npm.gradle.common") - id("ru.mipt.npm.gradle.native") -} - -kscience { - useAtomic() -} - -kotlin.sourceSets { - commonMain { - dependencies { - api(project(":kmath-core")) - } - } - commonTest { - dependencies { - implementation(project(":kmath-for-real")) - } - } -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE -} diff --git a/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Counter.kt b/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Counter.kt deleted file mode 100644 index 291284444..000000000 --- a/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Counter.kt +++ /dev/null @@ -1,75 +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. - */ - -package space.kscience.kmath.histogram - -import kotlinx.atomicfu.atomic -import kotlinx.atomicfu.getAndUpdate -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.Group - -/** - * Common representation for atomic counters - */ -public interface Counter { - public fun add(delta: T) - public val value: T - - public companion object { - public fun double(): ObjectCounter = ObjectCounter(DoubleField) - } -} - -public class IntCounter : Counter { - private val innerValue = atomic(0) - - override fun add(delta: Int) { - innerValue += delta - } - - override val value: Int get() = innerValue.value -} - -public operator fun IntCounter.inc(): IntCounter { - add(1) - return this -} - -public operator fun IntCounter.dec(): IntCounter { - add(-1) - return this -} - -public class LongCounter : Counter { - private val innerValue = atomic(0L) - - override fun add(delta: Long) { - innerValue += delta - } - - override val value: Long get() = innerValue.value -} - -public operator fun LongCounter.inc(): LongCounter { - add(1L) - return this -} - -public operator fun LongCounter.dec(): LongCounter { - add(-1L) - return this -} - -public class ObjectCounter(private val group: Group) : Counter { - private val innerValue = atomic(group.zero) - - override fun add(delta: T) { - innerValue.getAndUpdate { group.run { it + delta } } - } - - override val value: T get() = innerValue.value -} - - diff --git a/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/DoubleHistogramSpace.kt b/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/DoubleHistogramSpace.kt deleted file mode 100644 index 61d0b9f33..000000000 --- a/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/DoubleHistogramSpace.kt +++ /dev/null @@ -1,130 +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. - */ - -package space.kscience.kmath.histogram - -import space.kscience.kmath.domains.Domain -import space.kscience.kmath.domains.HyperSquareDomain -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.* -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.structures.* -import kotlin.math.floor - -public class DoubleHistogramSpace( - private val lower: Buffer, - private val upper: Buffer, - private val binNums: IntArray = IntArray(lower.size) { 20 }, -) : IndexedHistogramSpace { - - init { - // argument checks - require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." } - require(lower.size == binNums.size) { "Dimension mismatch in bin count." } - require(!lower.indices.any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" } - } - - public val dimension: Int get() = lower.size - - override val shape: IntArray = IntArray(binNums.size) { binNums[it] + 2 } - override val histogramValueSpace: DoubleFieldND = DoubleField.ndAlgebra(*shape) - - private val binSize = DoubleBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] } - - /** - * Get internal [StructureND] bin index for given axis - */ - private fun getIndex(axis: Int, value: Double): Int = when { - value >= upper[axis] -> binNums[axis] + 1 // overflow - value < lower[axis] -> 0 // underflow - else -> floor((value - lower[axis]) / binSize[axis]).toInt() - } - - override fun getIndex(point: Buffer): IntArray = IntArray(dimension) { - getIndex(it, point[it]) - } - - @OptIn(UnstableKMathAPI::class) - override fun getDomain(index: IntArray): Domain { - val lowerBoundary = index.mapIndexed { axis, i -> - when (i) { - 0 -> Double.NEGATIVE_INFINITY - shape[axis] - 1 -> upper[axis] - else -> lower[axis] + (i.toDouble()) * binSize[axis] - } - }.asBuffer() - - val upperBoundary = index.mapIndexed { axis, i -> - when (i) { - 0 -> lower[axis] - shape[axis] - 1 -> Double.POSITIVE_INFINITY - else -> lower[axis] + (i.toDouble() + 1) * binSize[axis] - } - }.asBuffer() - - return HyperSquareDomain(lowerBoundary, upperBoundary) - } - - - override fun produceBin(index: IntArray, value: Double): Bin { - val domain = getDomain(index) - return DomainBin(domain, value) - } - - override fun produce(builder: HistogramBuilder.() -> Unit): IndexedHistogram { - val ndCounter = StructureND.auto(shape) { Counter.double() } - val hBuilder = HistogramBuilder { point, value -> - val index = getIndex(point) - ndCounter[index].add(value.toDouble()) - } - hBuilder.apply(builder) - val values: BufferND = ndCounter.mapToBuffer { it.value } - return IndexedHistogram(this, values) - } - - override fun IndexedHistogram.unaryMinus(): IndexedHistogram = this * (-1) - - public companion object { - /** - * Use it like - * ``` - *FastHistogram.fromRanges( - * (-1.0..1.0), - * (-1.0..1.0) - *) - *``` - */ - public fun fromRanges(vararg ranges: ClosedFloatingPointRange): DoubleHistogramSpace = DoubleHistogramSpace( - ranges.map(ClosedFloatingPointRange::start).asBuffer(), - ranges.map(ClosedFloatingPointRange::endInclusive).asBuffer() - ) - - /** - * Use it like - * ``` - *FastHistogram.fromRanges( - * (-1.0..1.0) to 50, - * (-1.0..1.0) to 32 - *) - *``` - */ - public fun fromRanges(vararg ranges: Pair, Int>): DoubleHistogramSpace = - DoubleHistogramSpace( - ListBuffer( - ranges - .map(Pair, Int>::first) - .map(ClosedFloatingPointRange::start) - ), - - ListBuffer( - ranges - .map(Pair, Int>::first) - .map(ClosedFloatingPointRange::endInclusive) - ), - - ranges.map(Pair, Int>::second).toIntArray() - ) - } -} \ No newline at end of file diff --git a/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Histogram.kt b/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Histogram.kt deleted file mode 100644 index 4e803fc63..000000000 --- a/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Histogram.kt +++ /dev/null @@ -1,60 +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. - */ - -package space.kscience.kmath.histogram - -import space.kscience.kmath.domains.Domain -import space.kscience.kmath.linear.Point -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.asBuffer - -/** - * The binned data element. Could be a histogram bin with a number of counts or an artificial construct. - */ -public interface Bin : Domain { - /** - * The value of this bin. - */ - public val value: Number -} - -public interface Histogram> { - /** - * Find existing bin, corresponding to given coordinates - */ - public operator fun get(point: Point): B? - - /** - * Dimension of the histogram - */ - public val dimension: Int - - public val bins: Iterable -} - -public fun interface HistogramBuilder { - - /** - * Increment appropriate bin - */ - public fun putValue(point: Point, value: Number) - -} - -public fun > HistogramBuilder.put(point: Point): Unit = putValue(point, 1.0) - -public fun HistogramBuilder.put(vararg point: T): Unit = put(point.asBuffer()) - -public fun HistogramBuilder.put(vararg point: Number): Unit = - put(DoubleBuffer(point.map { it.toDouble() }.toDoubleArray())) - -public fun HistogramBuilder.put(vararg point: Double): Unit = put(DoubleBuffer(point)) -public fun HistogramBuilder.fill(sequence: Iterable>): Unit = sequence.forEach { put(it) } - -/** - * Pass a sequence builder into histogram - */ -public fun HistogramBuilder.fill(block: suspend SequenceScope>.() -> Unit): Unit = - fill(sequence(block).asIterable()) diff --git a/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/IndexedHistogramSpace.kt b/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/IndexedHistogramSpace.kt deleted file mode 100644 index 9275c1c5e..000000000 --- a/kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/IndexedHistogramSpace.kt +++ /dev/null @@ -1,82 +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. - */ - -package space.kscience.kmath.histogram - -import space.kscience.kmath.domains.Domain -import space.kscience.kmath.linear.Point -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.DefaultStrides -import space.kscience.kmath.nd.FieldND -import space.kscience.kmath.nd.Shape -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.Group -import space.kscience.kmath.operations.ScaleOperations -import space.kscience.kmath.operations.invoke - -/** - * A simple histogram bin based on domain - */ -public data class DomainBin>( - public val domain: Domain, - override val value: Number, -) : Bin, Domain by domain - -@OptIn(UnstableKMathAPI::class) -public class IndexedHistogram, V : Any>( - public val context: IndexedHistogramSpace, - public val values: StructureND, -) : Histogram> { - - override fun get(point: Point): Bin? { - val index = context.getIndex(point) ?: return null - return context.produceBin(index, values[index]) - } - - override val dimension: Int get() = context.shape.size - - override val bins: Iterable> - get() = DefaultStrides(context.shape).asSequence().map { - context.produceBin(it, values[it]) - }.asIterable() -} - -/** - * A space for producing histograms with values in a NDStructure - */ -public interface IndexedHistogramSpace, V : Any> - : Group>, ScaleOperations> { - //public val valueSpace: Space - public val shape: Shape - public val histogramValueSpace: FieldND //= NDAlgebra.space(valueSpace, Buffer.Companion::boxing, *shape), - - /** - * Resolve index of the bin including given [point] - */ - public fun getIndex(point: Point): IntArray? - - /** - * Get a bin domain represented by given index - */ - public fun getDomain(index: IntArray): Domain? - - public fun produceBin(index: IntArray, value: V): Bin - - public fun produce(builder: HistogramBuilder.() -> Unit): IndexedHistogram - - override fun add(left: IndexedHistogram, right: IndexedHistogram): IndexedHistogram { - require(left.context == this) { "Can't operate on a histogram produced by external space" } - require(right.context == this) { "Can't operate on a histogram produced by external space" } - return IndexedHistogram(this, histogramValueSpace { left.values + right.values }) - } - - override fun scale(a: IndexedHistogram, value: Double): IndexedHistogram { - require(a.context == this) { "Can't operate on a histogram produced by external space" } - return IndexedHistogram(this, histogramValueSpace { a.values * value }) - } - - override val zero: IndexedHistogram get() = produce { } -} - diff --git a/kmath-histograms/src/commonTest/kotlin/space/kscience/kmath/histogram/MultivariateHistogramTest.kt b/kmath-histograms/src/commonTest/kotlin/space/kscience/kmath/histogram/MultivariateHistogramTest.kt deleted file mode 100644 index 923cc98de..000000000 --- a/kmath-histograms/src/commonTest/kotlin/space/kscience/kmath/histogram/MultivariateHistogramTest.kt +++ /dev/null @@ -1,83 +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. - */ - -package space.kscience.kmath.histogram - -import space.kscience.kmath.nd.DefaultStrides -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.real.DoubleVector -import kotlin.random.Random -import kotlin.test.* - -internal class MultivariateHistogramTest { - @Test - fun testSinglePutHistogram() { - val hSpace = DoubleHistogramSpace.fromRanges( - (-1.0..1.0), - (-1.0..1.0) - ) - val histogram = hSpace.produce { - put(0.55, 0.55) - } - val bin = histogram.bins.find { it.value.toInt() > 0 } ?: fail() - assertTrue { bin.contains(DoubleVector(0.55, 0.55)) } - assertTrue { bin.contains(DoubleVector(0.6, 0.5)) } - assertFalse { bin.contains(DoubleVector(-0.55, 0.55)) } - } - - @Test - fun testSequentialPut() { - val hSpace = DoubleHistogramSpace.fromRanges( - (-1.0..1.0), - (-1.0..1.0), - (-1.0..1.0) - ) - val random = Random(1234) - - fun nextDouble() = random.nextDouble(-1.0, 1.0) - - val n = 10000 - val histogram = hSpace.produce { - repeat(n) { - put(nextDouble(), nextDouble(), nextDouble()) - } - } - assertEquals(n, histogram.bins.sumOf { it.value.toInt() }) - } - - @Test - fun testHistogramAlgebra() { - DoubleHistogramSpace.fromRanges( - (-1.0..1.0), - (-1.0..1.0), - (-1.0..1.0) - ).invoke { - val random = Random(1234) - - fun nextDouble() = random.nextDouble(-1.0, 1.0) - val n = 10000 - val histogram1 = produce { - repeat(n) { - put(nextDouble(), nextDouble(), nextDouble()) - } - } - val histogram2 = produce { - repeat(n) { - put(nextDouble(), nextDouble(), nextDouble()) - } - } - val res = histogram1 - histogram2 - assertTrue { - DefaultStrides(shape).asSequence().all { index -> - res.values[index] <= histogram1.values[index] - } - } - assertTrue { - res.bins.count() >= histogram1.bins.count() - } - assertEquals(0.0, res.bins.sumOf { it.value.toDouble() }) - } - } -} \ No newline at end of file diff --git a/kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/TreeHistogramSpace.kt b/kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/TreeHistogramSpace.kt deleted file mode 100644 index 0853615e6..000000000 --- a/kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/TreeHistogramSpace.kt +++ /dev/null @@ -1,171 +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. - */ - -package space.kscience.kmath.histogram - -import space.kscience.kmath.domains.UnivariateDomain -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.Group -import space.kscience.kmath.operations.ScaleOperations -import space.kscience.kmath.structures.Buffer -import java.util.* -import kotlin.math.abs -import kotlin.math.floor -import kotlin.math.sqrt - -private fun > TreeMap.getBin(value: Double): B? { - // check ceiling entry and return it if it is what needed - val ceil = ceilingEntry(value)?.value - if (ceil != null && value in ceil) return ceil - //check floor entry - val floor = floorEntry(value)?.value - if (floor != null && value in floor) return floor - //neither is valid, not found - return null -} - -@UnstableKMathAPI -public class TreeHistogram( - private val binMap: TreeMap, -) : UnivariateHistogram { - override fun get(value: Double): UnivariateBin? = binMap.getBin(value) - override val dimension: Int get() = 1 - override val bins: Collection get() = binMap.values -} - -@OptIn(UnstableKMathAPI::class) -@PublishedApi -internal class TreeHistogramBuilder(val binFactory: (Double) -> UnivariateDomain) : UnivariateHistogramBuilder { - - internal class BinCounter(val domain: UnivariateDomain, val counter: Counter = Counter.double()) : - ClosedFloatingPointRange by domain.range - - private val bins: TreeMap = TreeMap() - - fun get(value: Double): BinCounter? = bins.getBin(value) - - fun createBin(value: Double): BinCounter { - val binDefinition = binFactory(value) - val newBin = BinCounter(binDefinition) - synchronized(this) { bins[binDefinition.center] = newBin } - return newBin - } - - /** - * Thread safe put operation - */ - override fun putValue(at: Double, value: Double) { - (get(at) ?: createBin(at)).apply { - counter.add(value) - } - } - - override fun putValue(point: Buffer, value: Number) { - require(point.size == 1) { "Only points with single value could be used in univariate histogram" } - putValue(point[0], value.toDouble()) - } - - fun build(): TreeHistogram { - val map = bins.mapValuesTo(TreeMap()) { (_, binCounter) -> - val count = binCounter.counter.value - UnivariateBin(binCounter.domain, count, sqrt(count)) - } - return TreeHistogram(map) - } -} - -/** - * A space for univariate histograms with variable bin borders based on a tree map - */ -@UnstableKMathAPI -public class TreeHistogramSpace( - @PublishedApi internal val binFactory: (Double) -> UnivariateDomain, -) : Group, ScaleOperations { - - public inline fun fill(block: UnivariateHistogramBuilder.() -> Unit): UnivariateHistogram = - TreeHistogramBuilder(binFactory).apply(block).build() - - override fun add( - left: UnivariateHistogram, - right: UnivariateHistogram, - ): UnivariateHistogram { -// require(a.context == this) { "Histogram $a does not belong to this context" } -// require(b.context == this) { "Histogram $b does not belong to this context" } - val bins = TreeMap().apply { - (left.bins.map { it.domain } union right.bins.map { it.domain }).forEach { def -> - put( - def.center, - UnivariateBin( - def, - value = (left[def.center]?.value ?: 0.0) + (right[def.center]?.value ?: 0.0), - standardDeviation = (left[def.center]?.standardDeviation - ?: 0.0) + (right[def.center]?.standardDeviation ?: 0.0) - ) - ) - } - } - return TreeHistogram(bins) - } - - override fun scale(a: UnivariateHistogram, value: Double): UnivariateHistogram { - val bins = TreeMap().apply { - a.bins.forEach { bin -> - put( - bin.domain.center, - UnivariateBin( - bin.domain, - value = bin.value * value, - standardDeviation = abs(bin.standardDeviation * value) - ) - ) - } - } - - return TreeHistogram(bins) - } - - override fun UnivariateHistogram.unaryMinus(): UnivariateHistogram = this * (-1) - - override val zero: UnivariateHistogram by lazy { fill { } } - - public companion object { - /** - * Build and fill a [UnivariateHistogram]. Returns a read-only histogram. - */ - public fun uniform( - binSize: Double, - start: Double = 0.0, - ): TreeHistogramSpace = TreeHistogramSpace { value -> - val center = start + binSize * floor((value - start) / binSize + 0.5) - UnivariateDomain((center - binSize / 2)..(center + binSize / 2)) - } - - /** - * Create a histogram with custom cell borders - */ - public fun custom(borders: DoubleArray): TreeHistogramSpace { - val sorted = borders.sortedArray() - - return TreeHistogramSpace { value -> - when { - value < sorted.first() -> UnivariateDomain( - Double.NEGATIVE_INFINITY..sorted.first() - ) - - value > sorted.last() -> UnivariateDomain( - sorted.last()..Double.POSITIVE_INFINITY - ) - - else -> { - val index = sorted.indices.first { value > sorted[it] } - val left = sorted[index] - val right = sorted[index + 1] - UnivariateDomain(left..right) - } - } - } - } - } -} \ No newline at end of file diff --git a/kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/UnivariateHistogram.kt b/kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/UnivariateHistogram.kt deleted file mode 100644 index ac0576a8e..000000000 --- a/kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/UnivariateHistogram.kt +++ /dev/null @@ -1,79 +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. - */ - -package space.kscience.kmath.histogram - -import space.kscience.kmath.domains.UnivariateDomain -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.asSequence -import space.kscience.kmath.structures.Buffer - - -@UnstableKMathAPI -public val UnivariateDomain.center: Double - get() = (range.endInclusive + range.start) / 2 - -/** - * A univariate bin based on a range - * - * @property value The value of histogram including weighting - * @property standardDeviation Standard deviation of the bin value. Zero or negative if not applicable - */ -@UnstableKMathAPI -public class UnivariateBin( - public val domain: UnivariateDomain, - override val value: Double, - public val standardDeviation: Double, -) : Bin, ClosedFloatingPointRange by domain.range { - - override val dimension: Int get() = 1 - - override fun contains(point: Buffer): Boolean = point.size == 1 && contains(point[0]) -} - -@OptIn(UnstableKMathAPI::class) -public interface UnivariateHistogram : Histogram { - public operator fun get(value: Double): UnivariateBin? - override operator fun get(point: Buffer): UnivariateBin? = get(point[0]) - - public companion object { - /** - * Build and fill a [UnivariateHistogram]. Returns a read-only histogram. - */ - public inline fun uniform( - binSize: Double, - start: Double = 0.0, - builder: UnivariateHistogramBuilder.() -> Unit, - ): UnivariateHistogram = TreeHistogramSpace.uniform(binSize, start).fill(builder) - - /** - * Build and fill a histogram with custom borders. Returns a read-only histogram. - */ - public inline fun custom( - borders: DoubleArray, - builder: UnivariateHistogramBuilder.() -> Unit, - ): UnivariateHistogram = TreeHistogramSpace.custom(borders).fill(builder) - - } -} - -@UnstableKMathAPI -public interface UnivariateHistogramBuilder : HistogramBuilder { - /** - * Thread safe put operation - */ - public fun putValue(at: Double, value: Double = 1.0) - - override fun putValue(point: Buffer, value: Number) -} - -@UnstableKMathAPI -public fun UnivariateHistogramBuilder.fill(items: Iterable): Unit = items.forEach(this::putValue) - -@UnstableKMathAPI -public fun UnivariateHistogramBuilder.fill(array: DoubleArray): Unit = array.forEach(this::putValue) - -@UnstableKMathAPI -public fun UnivariateHistogramBuilder.fill(buffer: Buffer): Unit = buffer.asSequence().forEach(this::putValue) \ No newline at end of file diff --git a/kmath-histograms/src/jvmTest/kotlin/space/kscience/kmath/histogram/TreeHistogramTest.kt b/kmath-histograms/src/jvmTest/kotlin/space/kscience/kmath/histogram/TreeHistogramTest.kt deleted file mode 100644 index 28a1b03cb..000000000 --- a/kmath-histograms/src/jvmTest/kotlin/space/kscience/kmath/histogram/TreeHistogramTest.kt +++ /dev/null @@ -1,24 +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. - */ - -package space.kscience.kmath.histogram - -import org.junit.jupiter.api.Test -import kotlin.random.Random -import kotlin.test.assertTrue - -class TreeHistogramTest { - - @Test - fun normalFill() { - val histogram = UnivariateHistogram.uniform(0.1) { - repeat(100_000) { - putValue(Random.nextDouble()) - } - } - - assertTrue { histogram.bins.count() > 10 } - } -} \ No newline at end of file diff --git a/kmath-jafama/README.md b/kmath-jafama/README.md deleted file mode 100644 index a274b3d88..000000000 --- a/kmath-jafama/README.md +++ /dev/null @@ -1,55 +0,0 @@ -# Module kmath-jafama - -Integration with [Jafama](https://github.com/jeffhain/jafama). - - - [jafama-double](src/main/kotlin/space/kscience/kmath/jafama/) : Double ExtendedField implementations based on Jafama - - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-jafama:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-jafama:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-jafama:0.3.0-dev-20") -} -``` - -## Example usage - -All the `DoubleField` uses can be replaced with `JafamaDoubleField` or `StrictJafamaDoubleField`. - -```kotlin -import space.kscience.kmath.jafama.* -import space.kscience.kmath.operations.* - -fun main() { - val a = 2.0 - val b = StrictJafamaDoubleField { exp(a) } - println(JafamaDoubleField { b + a }) - println(StrictJafamaDoubleField { ln(b) }) -} -``` - -## Performance - -According to KMath benchmarks on GraalVM, Jafama functions are slower than JDK math; however, there are indications that on Hotspot Jafama is a bit faster. - -> **Can't find appropriate benchmark data. Try generating readme files after running benchmarks**. diff --git a/kmath-jafama/build.gradle.kts b/kmath-jafama/build.gradle.kts deleted file mode 100644 index 925a2bc60..000000000 --- a/kmath-jafama/build.gradle.kts +++ /dev/null @@ -1,27 +0,0 @@ -plugins { - id("ru.mipt.npm.gradle.jvm") -} - -description = "Jafama integration module" - -dependencies { - api(project(":kmath-core")) - api("net.jafama:jafama:2.3.2") -} - -repositories { - mavenCentral() -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE - propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md")) - - feature("jafama-double", "src/main/kotlin/space/kscience/kmath/jafama/") { - "Double ExtendedField implementations based on Jafama" - } -} - -kotlin.sourceSets.all { - languageSettings.optIn("space.kscience.kmath.misc.UnstableKMathAPI") -} diff --git a/kmath-jafama/docs/README-TEMPLATE.md b/kmath-jafama/docs/README-TEMPLATE.md deleted file mode 100644 index 54348467b..000000000 --- a/kmath-jafama/docs/README-TEMPLATE.md +++ /dev/null @@ -1,29 +0,0 @@ -# Module kmath-jafama - -Integration with [Jafama](https://github.com/jeffhain/jafama). - -${features} - -${artifact} - -## Example usage - -All the `DoubleField` uses can be replaced with `JafamaDoubleField` or `StrictJafamaDoubleField`. - -```kotlin -import space.kscience.kmath.jafama.* -import space.kscience.kmath.operations.* - -fun main() { - val a = 2.0 - val b = StrictJafamaDoubleField { exp(a) } - println(JafamaDoubleField { b + a }) - println(StrictJafamaDoubleField { ln(b) }) -} -``` - -## Performance - -According to KMath benchmarks on GraalVM, Jafama functions are slower than JDK math; however, there are indications that on Hotspot Jafama is a bit faster. - -${benchmarkJafamaDouble} diff --git a/kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/KMathJafama.kt b/kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/KMathJafama.kt deleted file mode 100644 index 888b549e1..000000000 --- a/kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/KMathJafama.kt +++ /dev/null @@ -1,115 +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. - */ - -package space.kscience.kmath.jafama - -import net.jafama.FastMath -import net.jafama.StrictFastMath -import space.kscience.kmath.operations.ExtendedField -import space.kscience.kmath.operations.Norm -import space.kscience.kmath.operations.PowerOperations -import space.kscience.kmath.operations.ScaleOperations - -/** - * A field for [Double] (using FastMath) without boxing. Does not produce appropriate field element. - */ -@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE") -public object JafamaDoubleField : ExtendedField, Norm, ScaleOperations { - override inline val zero: Double get() = 0.0 - override inline val one: Double get() = 1.0 - - override inline fun number(value: Number): Double = value.toDouble() - - override fun binaryOperationFunction(operation: String): (left: Double, right: Double) -> Double = - when (operation) { - PowerOperations.POW_OPERATION -> ::power - else -> super.binaryOperationFunction(operation) - } - - override inline fun add(left: Double, right: Double): Double = left + right - - override inline fun multiply(left: Double, right: Double): Double = left * right - override inline fun divide(left: Double, right: Double): Double = left / right - - override inline fun scale(a: Double, value: Double): Double = a * value - - override inline fun sin(arg: Double): Double = FastMath.sin(arg) - override inline fun cos(arg: Double): Double = FastMath.cos(arg) - override inline fun tan(arg: Double): Double = FastMath.tan(arg) - override inline fun acos(arg: Double): Double = FastMath.acos(arg) - override inline fun asin(arg: Double): Double = FastMath.asin(arg) - override inline fun atan(arg: Double): Double = FastMath.atan(arg) - - override inline fun sinh(arg: Double): Double = FastMath.sinh(arg) - override inline fun cosh(arg: Double): Double = FastMath.cosh(arg) - override inline fun tanh(arg: Double): Double = FastMath.tanh(arg) - override inline fun asinh(arg: Double): Double = FastMath.asinh(arg) - override inline fun acosh(arg: Double): Double = FastMath.acosh(arg) - override inline fun atanh(arg: Double): Double = FastMath.atanh(arg) - - override inline fun sqrt(arg: Double): Double = FastMath.sqrt(arg) - override inline fun power(arg: Double, pow: Number): Double = FastMath.pow(arg, pow.toDouble()) - override inline fun exp(arg: Double): Double = FastMath.exp(arg) - override inline fun ln(arg: Double): Double = FastMath.log(arg) - - override inline fun norm(arg: Double): Double = FastMath.abs(arg) - - override inline fun Double.unaryMinus(): Double = -this - override inline fun Double.plus(arg: Double): Double = this + arg - override inline fun Double.minus(arg: Double): Double = this - arg - override inline fun Double.times(arg: Double): Double = this * arg - override inline fun Double.div(arg: Double): Double = this / arg -} - -/** - * A field for [Double] (using StrictMath) without boxing. Does not produce appropriate field element. - */ -@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE") -public object StrictJafamaDoubleField : ExtendedField, Norm, ScaleOperations { - override inline val zero: Double get() = 0.0 - override inline val one: Double get() = 1.0 - - override inline fun number(value: Number): Double = value.toDouble() - - override fun binaryOperationFunction(operation: String): (left: Double, right: Double) -> Double = - when (operation) { - PowerOperations.POW_OPERATION -> ::power - else -> super.binaryOperationFunction(operation) - } - - override inline fun add(left: Double, right: Double): Double = left + right - override inline fun negate(arg: Double): Double = -arg - - override inline fun multiply(left: Double, right: Double): Double = left * right - override inline fun divide(left: Double, right: Double): Double = left / right - - override inline fun scale(a: Double, value: Double): Double = a * value - - override inline fun sin(arg: Double): Double = StrictFastMath.sin(arg) - override inline fun cos(arg: Double): Double = StrictFastMath.cos(arg) - override inline fun tan(arg: Double): Double = StrictFastMath.tan(arg) - override inline fun acos(arg: Double): Double = StrictFastMath.acos(arg) - override inline fun asin(arg: Double): Double = StrictFastMath.asin(arg) - override inline fun atan(arg: Double): Double = StrictFastMath.atan(arg) - - override inline fun sinh(arg: Double): Double = StrictFastMath.sinh(arg) - override inline fun cosh(arg: Double): Double = StrictFastMath.cosh(arg) - override inline fun tanh(arg: Double): Double = StrictFastMath.tanh(arg) - override inline fun asinh(arg: Double): Double = StrictFastMath.asinh(arg) - override inline fun acosh(arg: Double): Double = StrictFastMath.acosh(arg) - override inline fun atanh(arg: Double): Double = StrictFastMath.atanh(arg) - - override inline fun sqrt(arg: Double): Double = StrictFastMath.sqrt(arg) - override inline fun power(arg: Double, pow: Number): Double = StrictFastMath.pow(arg, pow.toDouble()) - override inline fun exp(arg: Double): Double = StrictFastMath.exp(arg) - override inline fun ln(arg: Double): Double = StrictFastMath.log(arg) - - override inline fun norm(arg: Double): Double = StrictFastMath.abs(arg) - - override inline fun Double.plus(arg: Double): Double = this + arg - override inline fun Double.minus(arg: Double): Double = this - arg - override inline fun Double.times(arg: Double): Double = this * arg - override inline fun Double.div(arg: Double): Double = this / arg -} diff --git a/kmath-jupyter/README.md b/kmath-jupyter/README.md deleted file mode 100644 index 8014b969d..000000000 --- a/kmath-jupyter/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-jupyter - - - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-jupyter:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-jupyter:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-jupyter:0.3.0-dev-20") -} -``` diff --git a/kmath-jupyter/build.gradle.kts b/kmath-jupyter/build.gradle.kts deleted file mode 100644 index ca1a5485f..000000000 --- a/kmath-jupyter/build.gradle.kts +++ /dev/null @@ -1,26 +0,0 @@ -plugins { - id("ru.mipt.npm.gradle.jvm") - kotlin("jupyter.api") -} - -dependencies { - api(project(":kmath-ast")) - api(project(":kmath-complex")) - api(project(":kmath-for-real")) -} - -kscience { - useHtml() -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE -} - -kotlin.sourceSets.all { - languageSettings.optIn("space.kscience.kmath.misc.UnstableKMathAPI") -} - -tasks.processJupyterApiResources { - libraryProducers = listOf("space.kscience.kmath.jupyter.KMathJupyter") -} diff --git a/kmath-jupyter/src/main/kotlin/space/kscience/kmath/jupyter/KMathJupyter.kt b/kmath-jupyter/src/main/kotlin/space/kscience/kmath/jupyter/KMathJupyter.kt deleted file mode 100644 index 504ad693e..000000000 --- a/kmath-jupyter/src/main/kotlin/space/kscience/kmath/jupyter/KMathJupyter.kt +++ /dev/null @@ -1,142 +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. - */ - -package space.kscience.kmath.jupyter - -import kotlinx.html.Unsafe -import kotlinx.html.div -import kotlinx.html.stream.createHTML -import kotlinx.html.unsafe -import org.jetbrains.kotlinx.jupyter.api.DisplayResult -import org.jetbrains.kotlinx.jupyter.api.HTML -import org.jetbrains.kotlinx.jupyter.api.libraries.JupyterIntegration -import space.kscience.kmath.ast.rendering.FeaturedMathRendererWithPostProcess -import space.kscience.kmath.ast.rendering.MathMLSyntaxRenderer -import space.kscience.kmath.ast.rendering.renderWithStringBuilder -import space.kscience.kmath.complex.Complex -import space.kscience.kmath.complex.Quaternion -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.MstRing -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.Structure2D -import space.kscience.kmath.operations.asSequence -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.structures.Buffer - -/** - * A function for conversion of number to MST for pretty print - */ -public fun Number.toMst(): MST.Numeric = MST.Numeric(this) - -internal class KMathJupyter : JupyterIntegration() { - private val mathRender = FeaturedMathRendererWithPostProcess.Default - private val syntaxRender = MathMLSyntaxRenderer - - private fun MST.toDisplayResult(): DisplayResult = HTML(createHTML().div { - unsafe { - +syntaxRender.renderWithStringBuilder(mathRender.render(this@toDisplayResult)) - } - }) - - private fun Unsafe.appendCellValue(it: Any?) { - when (it) { - is Number -> { - val s = StringBuilder() - syntaxRender.renderPart(mathRender.render(MST.Numeric(it)), s) - +s.toString() - } - is MST -> { - val s = StringBuilder() - syntaxRender.renderPart(mathRender.render(it), s) - +s.toString() - } - else -> { - +"" - +it.toString() - +"" - } - } - } - - @OptIn(PerformancePitfall::class) - override fun Builder.onLoaded() { - import( - "space.kscience.kmath.ast.*", - "space.kscience.kmath.ast.rendering.*", - "space.kscience.kmath.structures.*", - "space.kscience.kmath.operations.*", - "space.kscience.kmath.expressions.*", - "space.kscience.kmath.nd.*", - "space.kscience.kmath.misc.*", - "space.kscience.kmath.real.*", - ) - - import("space.kscience.kmath.jupyter.toMst") - - render { it.toDisplayResult() } - //render { MST.Numeric(it).toDisplayResult() } - - render> { structure -> - HTML(createHTML().div { - unsafe { - +"" - +"" - +"" - +"" - structure.rows.forEach { row -> - +"" - row.asSequence().forEach { - +"" - appendCellValue(it) - +"" - } - +"" - } - +"" - +"" - +"" - +"" - } - }) - } - - render> { buffer -> - HTML(createHTML().div { - unsafe { - +"" - +"" - +"" - +"" - buffer.asSequence().forEach { - +"" - +"" - appendCellValue(it) - +"" - +"" - } - +"" - +"" - +"" - +"" - } - }) - } - - render { - MstRing { - number(it.re) + number(it.im) * bindSymbol("i") - }.toDisplayResult() - } - - render { - MstRing { - number(it.w) + - number(it.x) * bindSymbol("i") + - number(it.x) * bindSymbol("j") + - number(it.x) * bindSymbol("k") - }.toDisplayResult() - } - } -} diff --git a/kmath-kotlingrad/README.md b/kmath-kotlingrad/README.md deleted file mode 100644 index 08c913090..000000000 --- a/kmath-kotlingrad/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# Module kmath-kotlingrad - -[Kotlin∇](https://github.com/breandan/kotlingrad) integration module. - - - [differentiable-mst-expression](src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt) : MST based DifferentiableExpression. - - [scalars-adapters](src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt) : Conversions between Kotlin∇'s SFun and MST - - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-kotlingrad:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-kotlingrad:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-kotlingrad:0.3.0-dev-20") -} -``` diff --git a/kmath-kotlingrad/build.gradle.kts b/kmath-kotlingrad/build.gradle.kts deleted file mode 100644 index 2f6d41bbb..000000000 --- a/kmath-kotlingrad/build.gradle.kts +++ /dev/null @@ -1,37 +0,0 @@ -plugins { - kotlin("jvm") - id("ru.mipt.npm.gradle.common") -} - -kotlin.sourceSets - .filter { it.name.contains("test", true) } - .map(org.jetbrains.kotlin.gradle.plugin.KotlinSourceSet::languageSettings) - .forEach { it.optIn("space.kscience.kmath.misc.UnstableKMathAPI") } - -description = "Kotlin∇ integration module" - -dependencies { - api("ai.hypergraph:kaliningraph:0.1.9") - api("ai.hypergraph:kotlingrad:0.4.7") - api(project(":kmath-core")) - testImplementation(project(":kmath-ast")) -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL - propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md")) - - feature( - "differentiable-mst-expression", - "src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt", - ) { - "MST based DifferentiableExpression." - } - - feature( - "scalars-adapters", - "src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt", - ) { - "Conversions between Kotlin∇'s SFun and MST" - } -} diff --git a/kmath-kotlingrad/docs/README-TEMPLATE.md b/kmath-kotlingrad/docs/README-TEMPLATE.md deleted file mode 100644 index bc99bdf5f..000000000 --- a/kmath-kotlingrad/docs/README-TEMPLATE.md +++ /dev/null @@ -1,7 +0,0 @@ -# Module kmath-kotlingrad - -[Kotlin∇](https://github.com/breandan/kotlingrad) integration module. - -${features} - -${artifact} diff --git a/kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KMathNumber.kt b/kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KMathNumber.kt deleted file mode 100644 index f7858e172..000000000 --- a/kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KMathNumber.kt +++ /dev/null @@ -1,30 +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. - */ - -package space.kscience.kmath.kotlingrad - -import ai.hypergraph.kotlingrad.api.SConst -import space.kscience.kmath.operations.NumericAlgebra - -/** - * Implements [SConst] by delegating its functionality to [NumericAlgebra]. - * - * @param T The type of number. - * @param A The [NumericAlgebra] over [T]. - * @property algebra The algebra. - * @property value The value of this number. - */ -public class KMathNumber(public val algebra: A, override val value: T) : - SConst>(value) where T : Number, A : NumericAlgebra { - /** - * Returns a string representation of the [value]. - */ - override fun toString(): String = value.toString() - - /** - * Wraps [Number] to [KMathNumber]. - */ - override fun wrap(number: Number): KMathNumber = KMathNumber(algebra, algebra.number(number)) -} diff --git a/kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt b/kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt deleted file mode 100644 index 31b8a9286..000000000 --- a/kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt +++ /dev/null @@ -1,56 +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. - */ - -package space.kscience.kmath.kotlingrad - -import ai.hypergraph.kotlingrad.api.SFun -import ai.hypergraph.kotlingrad.api.SVar -import space.kscience.kmath.expressions.* -import space.kscience.kmath.operations.NumericAlgebra - -/** - * Represents [MST] based [DifferentiableExpression] relying on [Kotlin∇](https://github.com/breandan/kotlingrad). - * - * The principle of this API is converting the [mst] to an [SFun], differentiating it with Kotlin∇, then converting - * [SFun] back to [MST]. - * - * @param T The type of number. - * @param A The [NumericAlgebra] of [T]. - * @property algebra The [A] instance. - * @property mst The [MST] node. - */ -public class KotlingradExpression>( - public val algebra: A, - public val mst: MST, -) : SpecialDifferentiableExpression> { - override fun invoke(arguments: Map): T = mst.interpret(algebra, arguments) - - override fun derivativeOrNull( - symbols: List, - ): KotlingradExpression = KotlingradExpression( - algebra, - symbols.map(Symbol::identity) - .map(MstNumericAlgebra::bindSymbol) - .map>>(Symbol::toSVar) - .fold(mst.toSFun(), SFun>::d) - .toMst(), - ) -} - -/** - * A diff processor using [MST] to Kotlingrad converter - */ -public class KotlingradProcessor>( - public val algebra: A, -) : AutoDiffProcessor { - override fun differentiate(function: MstExtendedField.() -> MST): DifferentiableExpression = - MstExtendedField.function().toKotlingradExpression(algebra) -} - -/** - * Wraps this [MST] into [KotlingradExpression] in the context of [algebra]. - */ -public fun > MST.toKotlingradExpression(algebra: A): KotlingradExpression = - KotlingradExpression(algebra, this) diff --git a/kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt b/kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt deleted file mode 100644 index 37ce40cab..000000000 --- a/kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt +++ /dev/null @@ -1,134 +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. - */ - -package space.kscience.kmath.kotlingrad - -import ai.hypergraph.kotlingrad.api.* -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.MstExtendedField -import space.kscience.kmath.expressions.MstExtendedField.unaryMinus -import space.kscience.kmath.expressions.MstNumericAlgebra -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.operations.* - -/** - * Maps [SVar] to [Symbol] directly. - * - * @receiver The variable. - * @returnAa node. - */ -public fun > SVar.toMst(): Symbol = MstNumericAlgebra.bindSymbol(name) - -/** - * Maps [SVar] to [MST.Numeric] directly. - * - * @receiver The constant. - * @return A node. - */ -public fun > SConst.toMst(): MST.Numeric = MstNumericAlgebra.number(doubleValue) - -/** - * Maps [SFun] objects to [MST]. Some unsupported operations like [Derivative] are bound and converted then. - * [Power] operation is limited to constant right-hand side arguments. - * - * Detailed mapping is: - * - * - [SVar] -> [MstExtendedField.bindSymbol]; - * - [SConst] -> [MstExtendedField.number]; - * - [Sum] -> [MstExtendedField.add]; - * - [Prod] -> [MstExtendedField.multiply]; - * - [Power] -> [MstExtendedField.power] (limited to constant exponents only); - * - [Negative] -> [MstExtendedField.unaryMinus]; - * - [Log] -> [MstExtendedField.ln] (left) / [MstExtendedField.ln] (right); - * - [Sine] -> [MstExtendedField.sin]; - * - [Cosine] -> [MstExtendedField.cos]; - * - [Tangent] -> [MstExtendedField.tan]; - * - [DProd] is vector operation, and it is requested to be evaluated; - * - [SComposition] is also requested to be evaluated eagerly; - * - [VSumAll] is requested to be evaluated; - * - [Derivative] is requested to be evaluated. - * - * @receiver The scalar function. - * @return A node. - */ -public fun > SFun.toMst(): MST = MstExtendedField { - when (this@toMst) { - is SVar -> toMst() - is SConst -> toMst() - is Sum -> left.toMst() + right.toMst() - is Prod -> left.toMst() * right.toMst() - is Power -> left.toMst() pow ((right as? SConst<*>)?.doubleValue ?: (right() as SConst<*>).doubleValue) - is Negative -> -input.toMst() - is Log -> ln(left.toMst()) / ln(right.toMst()) - is Sine -> sin(input.toMst()) - is Cosine -> cos(input.toMst()) - is Tangent -> tan(input.toMst()) - is DProd -> this@toMst().toMst() - is SComposition -> this@toMst().toMst() - is VSumAll -> this@toMst().toMst() - is Derivative -> this@toMst().toMst() - } -} - -/** - * Maps [MST.Numeric] to [SConst] directly. - * - * @receiver The node. - * @return A new constant. - */ -public fun > MST.Numeric.toSConst(): SConst = SConst(value) - -/** - * Maps [Symbol] to [SVar] directly. - * - * @receiver The node. - * @return A new variable. - */ -internal fun > Symbol.toSVar(): SVar = SVar(identity) - -/** - * Maps [MST] objects to [SFun]. Unsupported operations throw [IllegalStateException]. - * - * Detailed mapping is: - * - * - [MST.Numeric] -> [SConst]; - * - [Symbol] -> [SVar]; - * - [MST.Unary] -> [Negative], [Sine], [Cosine], [Tangent], [Power], [Log]; - * - [MST.Binary] -> [Sum], [Prod], [Power]. - * - * @receiver The node. - * @return A scalar function. - */ -public fun > MST.toSFun(): SFun = when (this) { - is MST.Numeric -> toSConst() - is Symbol -> toSVar() - - is MST.Unary -> when (operation) { - GroupOps.PLUS_OPERATION -> +value.toSFun() - GroupOps.MINUS_OPERATION -> -value.toSFun() - TrigonometricOperations.SIN_OPERATION -> sin(value.toSFun()) - TrigonometricOperations.COS_OPERATION -> cos(value.toSFun()) - TrigonometricOperations.TAN_OPERATION -> tan(value.toSFun()) - PowerOperations.SQRT_OPERATION -> sqrt(value.toSFun()) - ExponentialOperations.EXP_OPERATION -> exp(value.toSFun()) - ExponentialOperations.LN_OPERATION -> value.toSFun().ln() - ExponentialOperations.SINH_OPERATION -> MstExtendedField { (exp(value) - exp(-value)) / 2.0 }.toSFun() - ExponentialOperations.COSH_OPERATION -> MstExtendedField { (exp(value) + exp(-value)) / 2.0 }.toSFun() - ExponentialOperations.TANH_OPERATION -> MstExtendedField { (exp(value) - exp(-value)) / (exp(-value) + exp(value)) }.toSFun() - ExponentialOperations.ASINH_OPERATION -> MstExtendedField { ln(sqrt(value * value + one) + value) }.toSFun() - ExponentialOperations.ACOSH_OPERATION -> MstExtendedField { ln(value + sqrt((value - one) * (value + one))) }.toSFun() - ExponentialOperations.ATANH_OPERATION -> MstExtendedField { (ln(value + one) - ln(one - value)) / 2.0 }.toSFun() - else -> error("Unary operation $operation not defined in $this") - } - - is MST.Binary -> when (operation) { - GroupOps.PLUS_OPERATION -> left.toSFun() + right.toSFun() - GroupOps.MINUS_OPERATION -> left.toSFun() - right.toSFun() - RingOps.TIMES_OPERATION -> left.toSFun() * right.toSFun() - FieldOps.DIV_OPERATION -> left.toSFun() / right.toSFun() - PowerOperations.POW_OPERATION -> left.toSFun() pow (right as MST.Numeric).toSConst() - else -> error("Binary operation $operation not defined in $this") - } -} diff --git a/kmath-kotlingrad/src/test/kotlin/space/kscience/kmath/kotlingrad/AdaptingTests.kt b/kmath-kotlingrad/src/test/kotlin/space/kscience/kmath/kotlingrad/AdaptingTests.kt deleted file mode 100644 index 570d7dd7f..000000000 --- a/kmath-kotlingrad/src/test/kotlin/space/kscience/kmath/kotlingrad/AdaptingTests.kt +++ /dev/null @@ -1,67 +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. - */ - -package space.kscience.kmath.kotlingrad - -import ai.hypergraph.kotlingrad.api.* -import space.kscience.kmath.asm.compileToExpression -import space.kscience.kmath.ast.parseMath -import space.kscience.kmath.expressions.MstNumericAlgebra -import space.kscience.kmath.expressions.Symbol.Companion.x -import space.kscience.kmath.expressions.invoke -import space.kscience.kmath.operations.DoubleField -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue -import kotlin.test.fail - -internal class AdaptingTests { - @Test - fun symbol() { - assertEquals(x.identity, x.toSVar>().name) - val c2 = "kitten".parseMath().toSFun>() - if (c2 is SVar<*>) assertTrue(c2.name == "kitten") else fail() - } - - @Test - fun number() { - val c1 = MstNumericAlgebra.number(12354324) - assertTrue(c1.toSConst().doubleValue == 12354324.0) - val c2 = "0.234".parseMath().toSFun>() - if (c2 is SConst<*>) assertTrue(c2.doubleValue == 0.234) else fail() - val c3 = "1e-3".parseMath().toSFun>() - if (c3 is SConst<*>) assertEquals(0.001, c3.value) else fail() - } - - @Test - fun simpleFunctionShape() { - val linear = "2*x+16".parseMath().toSFun>() - if (linear !is Sum<*>) fail() - if (linear.left !is Prod<*>) fail() - if (linear.right !is SConst<*>) fail() - } - - @Test - fun simpleFunctionDerivative() { - val xSVar = x.toSVar>() - val quadratic = "x^2-4*x-44".parseMath().toSFun>() - val actualDerivative = quadratic.d(xSVar).toMst().compileToExpression(DoubleField) - val expectedDerivative = "2*x-4".parseMath().compileToExpression(DoubleField) - assertEquals(actualDerivative(x to 123.0), expectedDerivative(x to 123.0)) - } - - @Test - fun moreComplexDerivative() { - val xSVar = x.toSVar>() - val composition = "-sqrt(sin(x^2)-cos(x)^2-16*x)".parseMath().toSFun>() - val actualDerivative = composition.d(xSVar).toMst().compileToExpression(DoubleField) - - val expectedDerivative = "-(2*x*cos(x^2)+2*sin(x)*cos(x)-16)/(2*sqrt(sin(x^2)-16*x-cos(x)^2))" - .parseMath() - .compileToExpression(DoubleField) - - assertEquals(actualDerivative(x to -0.1), expectedDerivative(x to -0.1)) - } -} diff --git a/kmath-memory/README.md b/kmath-memory/README.md deleted file mode 100644 index d37087a66..000000000 --- a/kmath-memory/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-memory - - - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-memory:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-memory:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-memory:0.3.0-dev-20") -} -``` diff --git a/kmath-memory/api/kmath-memory.api b/kmath-memory/api/kmath-memory.api deleted file mode 100644 index 9c9641461..000000000 --- a/kmath-memory/api/kmath-memory.api +++ /dev/null @@ -1,72 +0,0 @@ -public final class space/kscience/kmath/memory/ByteBufferMemory : space/kscience/kmath/memory/Memory { - public fun (Ljava/nio/ByteBuffer;II)V - public synthetic fun (Ljava/nio/ByteBuffer;IIILkotlin/jvm/internal/DefaultConstructorMarker;)V - public fun copy ()Lspace/kscience/kmath/memory/Memory; - public final fun getBuffer ()Ljava/nio/ByteBuffer; - public fun getSize ()I - public final fun getStartOffset ()I - public fun reader ()Lspace/kscience/kmath/memory/MemoryReader; - public fun view (II)Lspace/kscience/kmath/memory/Memory; - public fun writer ()Lspace/kscience/kmath/memory/MemoryWriter; -} - -public final class space/kscience/kmath/memory/ByteBufferMemoryKt { - public static final fun allocate (Lspace/kscience/kmath/memory/Memory$Companion;I)Lspace/kscience/kmath/memory/Memory; - public static final fun asMemory (Ljava/nio/ByteBuffer;II)Lspace/kscience/kmath/memory/Memory; - public static synthetic fun asMemory$default (Ljava/nio/ByteBuffer;IIILjava/lang/Object;)Lspace/kscience/kmath/memory/Memory; - public static final fun readAsMemory (Ljava/nio/file/Path;JJLkotlin/jvm/functions/Function1;)Ljava/lang/Object; - public static synthetic fun readAsMemory$default (Ljava/nio/file/Path;JJLkotlin/jvm/functions/Function1;ILjava/lang/Object;)Ljava/lang/Object; - public static final fun wrap (Lspace/kscience/kmath/memory/Memory$Companion;[B)Lspace/kscience/kmath/memory/Memory; -} - -public abstract interface class space/kscience/kmath/memory/Memory { - public static final field Companion Lspace/kscience/kmath/memory/Memory$Companion; - public abstract fun copy ()Lspace/kscience/kmath/memory/Memory; - public abstract fun getSize ()I - public abstract fun reader ()Lspace/kscience/kmath/memory/MemoryReader; - public abstract fun view (II)Lspace/kscience/kmath/memory/Memory; - public abstract fun writer ()Lspace/kscience/kmath/memory/MemoryWriter; -} - -public final class space/kscience/kmath/memory/Memory$Companion { -} - -public final class space/kscience/kmath/memory/MemoryKt { - public static final fun read (Lspace/kscience/kmath/memory/Memory;Lkotlin/jvm/functions/Function1;)Ljava/lang/Object; - public static final fun write (Lspace/kscience/kmath/memory/Memory;Lkotlin/jvm/functions/Function1;)V -} - -public abstract interface class space/kscience/kmath/memory/MemoryReader { - public abstract fun getMemory ()Lspace/kscience/kmath/memory/Memory; - public abstract fun readByte (I)B - public abstract fun readDouble (I)D - public abstract fun readFloat (I)F - public abstract fun readInt (I)I - public abstract fun readLong (I)J - public abstract fun readShort (I)S - public abstract fun release ()V -} - -public abstract interface class space/kscience/kmath/memory/MemorySpec { - public abstract fun getObjectSize ()I - public abstract fun read (Lspace/kscience/kmath/memory/MemoryReader;I)Ljava/lang/Object; - public abstract fun write (Lspace/kscience/kmath/memory/MemoryWriter;ILjava/lang/Object;)V -} - -public final class space/kscience/kmath/memory/MemorySpecKt { - public static final fun read (Lspace/kscience/kmath/memory/MemoryReader;Lspace/kscience/kmath/memory/MemorySpec;I)Ljava/lang/Object; - public static final fun write (Lspace/kscience/kmath/memory/MemoryWriter;Lspace/kscience/kmath/memory/MemorySpec;ILjava/lang/Object;)V - public static final fun writeArray (Lspace/kscience/kmath/memory/MemoryWriter;Lspace/kscience/kmath/memory/MemorySpec;I[Ljava/lang/Object;)V -} - -public abstract interface class space/kscience/kmath/memory/MemoryWriter { - public abstract fun getMemory ()Lspace/kscience/kmath/memory/Memory; - public abstract fun release ()V - public abstract fun writeByte (IB)V - public abstract fun writeDouble (ID)V - public abstract fun writeFloat (IF)V - public abstract fun writeInt (II)V - public abstract fun writeLong (IJ)V - public abstract fun writeShort (IS)V -} - diff --git a/kmath-memory/build.gradle.kts b/kmath-memory/build.gradle.kts deleted file mode 100644 index 4478e5b80..000000000 --- a/kmath-memory/build.gradle.kts +++ /dev/null @@ -1,12 +0,0 @@ -plugins { - kotlin("multiplatform") - id("ru.mipt.npm.gradle.common") - id("ru.mipt.npm.gradle.native") -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.DEVELOPMENT - description = """ - An API and basic implementation for arranging objects in a continuous memory block. - """.trimIndent() -} diff --git a/kmath-memory/src/commonMain/kotlin/space/kscience/kmath/memory/Memory.kt b/kmath-memory/src/commonMain/kotlin/space/kscience/kmath/memory/Memory.kt deleted file mode 100644 index e8e51e9e2..000000000 --- a/kmath-memory/src/commonMain/kotlin/space/kscience/kmath/memory/Memory.kt +++ /dev/null @@ -1,161 +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. - */ - -package space.kscience.kmath.memory - -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract - -/** - * Represents a display of certain memory structure. - */ -public interface Memory { - /** - * The length of this memory in bytes. - */ - public val size: Int - - /** - * Get a projection of this memory (it reflects the changes in the parent memory block). - */ - public fun view(offset: Int, length: Int): Memory - - /** - * Creates an independent copy of this memory. - */ - public fun copy(): Memory - - /** - * Gets or creates a reader of this memory. - */ - public fun reader(): MemoryReader - - /** - * Gets or creates a writer of this memory. - */ - public fun writer(): MemoryWriter - - public companion object -} - -/** - * The interface to read primitive types in this memory. - */ -public interface MemoryReader { - /** - * The underlying memory. - */ - public val memory: Memory - - /** - * Reads [Double] at certain [offset]. - */ - public fun readDouble(offset: Int): Double - - /** - * Reads [Float] at certain [offset]. - */ - public fun readFloat(offset: Int): Float - - /** - * Reads [Byte] at certain [offset]. - */ - public fun readByte(offset: Int): Byte - - /** - * Reads [Short] at certain [offset]. - */ - public fun readShort(offset: Int): Short - - /** - * Reads [Int] at certain [offset]. - */ - public fun readInt(offset: Int): Int - - /** - * Reads [Long] at certain [offset]. - */ - public fun readLong(offset: Int): Long - - /** - * Disposes this reader if needed. - */ - public fun release() -} - -/** - * Uses the memory for read then releases the reader. - */ -public inline fun Memory.read(block: MemoryReader.() -> R): R { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - val reader = reader() - val result = reader.block() - reader.release() - return result -} - -/** - * The interface to write primitive types into this memory. - */ -public interface MemoryWriter { - /** - * The underlying memory. - */ - public val memory: Memory - - /** - * Writes [Double] at certain [offset]. - */ - public fun writeDouble(offset: Int, value: Double) - - /** - * Writes [Float] at certain [offset]. - */ - public fun writeFloat(offset: Int, value: Float) - - /** - * Writes [Byte] at certain [offset]. - */ - public fun writeByte(offset: Int, value: Byte) - - /** - * Writes [Short] at certain [offset]. - */ - public fun writeShort(offset: Int, value: Short) - - /** - * Writes [Int] at certain [offset]. - */ - public fun writeInt(offset: Int, value: Int) - - /** - * Writes [Long] at certain [offset]. - */ - public fun writeLong(offset: Int, value: Long) - - /** - * Disposes this writer if needed. - */ - public fun release() -} - -/** - * Uses the memory for write then releases the writer. - */ -public inline fun Memory.write(block: MemoryWriter.() -> Unit) { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - writer().apply(block).release() -} - -/** - * Allocates the most effective platform-specific memory. - */ -public expect fun Memory.Companion.allocate(length: Int): Memory - -/** - * Wraps a [Memory] around existing [ByteArray]. This operation is unsafe since the array is not copied - * and could be mutated independently of the resulting [Memory]. - */ -public expect fun Memory.Companion.wrap(array: ByteArray): Memory diff --git a/kmath-memory/src/commonMain/kotlin/space/kscience/kmath/memory/MemorySpec.kt b/kmath-memory/src/commonMain/kotlin/space/kscience/kmath/memory/MemorySpec.kt deleted file mode 100644 index 1ee1cf4e2..000000000 --- a/kmath-memory/src/commonMain/kotlin/space/kscience/kmath/memory/MemorySpec.kt +++ /dev/null @@ -1,55 +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. - */ - -package space.kscience.kmath.memory - -/** - * A specification to read or write custom objects with fixed size in bytes. - * - * @param T the type of object this spec manages. - */ -public interface MemorySpec { - /** - * Size of [T] in bytes after serialization. - */ - public val objectSize: Int - - /** - * Reads the object starting from [offset]. - */ - public fun MemoryReader.read(offset: Int): T - - // TODO consider thread safety - - /** - * Writes the object [value] starting from [offset]. - */ - public fun MemoryWriter.write(offset: Int, value: T) -} - -/** - * Reads the object with [spec] starting from [offset]. - */ -public fun MemoryReader.read(spec: MemorySpec, offset: Int): T = with(spec) { read(offset) } - -/** - * Writes the object [value] with [spec] starting from [offset]. - */ -public fun MemoryWriter.write(spec: MemorySpec, offset: Int, value: T): Unit = - with(spec) { write(offset, value) } - -/** - * Reads array of [size] objects mapped by [spec] at certain [offset]. - */ -public inline fun MemoryReader.readArray(spec: MemorySpec, offset: Int, size: Int): Array = - Array(size) { i -> with(spec) { read(offset + i * objectSize) } } - -/** - * Writes [array] of objects mapped by [spec] at certain [offset]. - */ -public fun MemoryWriter.writeArray(spec: MemorySpec, offset: Int, array: Array): Unit = - with(spec) { array.indices.forEach { i -> write(offset + i * objectSize, array[i]) } } - -// TODO It is possible to add elastic MemorySpec with unknown object size diff --git a/kmath-memory/src/jsMain/kotlin/space/kscience/kmath/memory/DataViewMemory.kt b/kmath-memory/src/jsMain/kotlin/space/kscience/kmath/memory/DataViewMemory.kt deleted file mode 100644 index 6153743fc..000000000 --- a/kmath-memory/src/jsMain/kotlin/space/kscience/kmath/memory/DataViewMemory.kt +++ /dev/null @@ -1,103 +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. - */ - -package space.kscience.kmath.memory - -import org.khronos.webgl.ArrayBuffer -import org.khronos.webgl.DataView -import org.khronos.webgl.Int8Array - -private class DataViewMemory(val view: DataView) : Memory { - override val size: Int get() = view.byteLength - - override fun view(offset: Int, length: Int): Memory { - require(offset >= 0) { "offset shouldn't be negative: $offset" } - require(length >= 0) { "length shouldn't be negative: $length" } - require(offset + length <= size) { "Can't view memory outside the parent region." } - - if (offset + length > size) - throw IndexOutOfBoundsException("offset + length > size: $offset + $length > $size") - - return DataViewMemory(DataView(view.buffer, view.byteOffset + offset, length)) - } - - override fun copy(): Memory = DataViewMemory(DataView(view.buffer.slice(0))) - - private val reader: MemoryReader = object : MemoryReader { - override val memory: Memory get() = this@DataViewMemory - - override fun readDouble(offset: Int): Double = view.getFloat64(offset, false) - - override fun readFloat(offset: Int): Float = view.getFloat32(offset, false) - - override fun readByte(offset: Int): Byte = view.getInt8(offset) - - override fun readShort(offset: Int): Short = view.getInt16(offset, false) - - override fun readInt(offset: Int): Int = view.getInt32(offset, false) - - override fun readLong(offset: Int): Long = - view.getInt32(offset, false).toLong() shl 32 or view.getInt32(offset + 4, false).toLong() - - override fun release() { - // does nothing on JS - } - } - - override fun reader(): MemoryReader = reader - - private val writer: MemoryWriter = object : MemoryWriter { - override val memory: Memory get() = this@DataViewMemory - - override fun writeDouble(offset: Int, value: Double) { - view.setFloat64(offset, value, false) - } - - override fun writeFloat(offset: Int, value: Float) { - view.setFloat32(offset, value, false) - } - - override fun writeByte(offset: Int, value: Byte) { - view.setInt8(offset, value) - } - - override fun writeShort(offset: Int, value: Short) { - view.setUint16(offset, value, false) - } - - override fun writeInt(offset: Int, value: Int) { - view.setInt32(offset, value, false) - } - - override fun writeLong(offset: Int, value: Long) { - view.setInt32(offset, (value shr 32).toInt(), littleEndian = false) - view.setInt32(offset + 4, (value and 0xffffffffL).toInt(), littleEndian = false) - } - - override fun release() { - // does nothing on JS - } - } - - override fun writer(): MemoryWriter = writer - -} - -/** - * Allocates memory based on a [DataView]. - */ -public actual fun Memory.Companion.allocate(length: Int): Memory { - val buffer = ArrayBuffer(length) - return DataViewMemory(DataView(buffer, 0, length)) -} - -/** - * Wraps a [Memory] around existing [ByteArray]. This operation is unsafe since the array is not copied - * and could be mutated independently of the resulting [Memory]. - */ -public actual fun Memory.Companion.wrap(array: ByteArray): Memory { - @Suppress("CAST_NEVER_SUCCEEDS") val int8Array = array as Int8Array - return DataViewMemory(DataView(int8Array.buffer, int8Array.byteOffset, int8Array.length)) -} diff --git a/kmath-memory/src/jvmMain/kotlin/space/kscience/kmath/memory/ByteBufferMemory.kt b/kmath-memory/src/jvmMain/kotlin/space/kscience/kmath/memory/ByteBufferMemory.kt deleted file mode 100644 index aef68fd80..000000000 --- a/kmath-memory/src/jvmMain/kotlin/space/kscience/kmath/memory/ByteBufferMemory.kt +++ /dev/null @@ -1,132 +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. - */ - -package space.kscience.kmath.memory - -import java.io.IOException -import java.nio.ByteBuffer -import java.nio.channels.FileChannel -import java.nio.file.Files -import java.nio.file.Path -import java.nio.file.StandardOpenOption -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract - -@PublishedApi -internal class ByteBufferMemory( - val buffer: ByteBuffer, - val startOffset: Int = 0, - override val size: Int = buffer.limit(), -) : Memory { - @Suppress("NOTHING_TO_INLINE") - private inline fun position(o: Int): Int = startOffset + o - - override fun view(offset: Int, length: Int): Memory { - require(offset >= 0) { "offset shouldn't be negative: $offset" } - require(length >= 0) { "length shouldn't be negative: $length" } - require(offset + length <= size) { "Can't view memory outside the parent region." } - return ByteBufferMemory(buffer, position(offset), length) - } - - override fun copy(): Memory { - val copy = ByteBuffer.allocate(buffer.capacity()) - buffer.rewind() - copy.put(buffer) - copy.flip() - return ByteBufferMemory(copy) - } - - private val reader: MemoryReader = object : MemoryReader { - override val memory: Memory get() = this@ByteBufferMemory - - override fun readDouble(offset: Int) = buffer.getDouble(position(offset)) - - override fun readFloat(offset: Int) = buffer.getFloat(position(offset)) - - override fun readByte(offset: Int) = buffer.get(position(offset)) - - override fun readShort(offset: Int) = buffer.getShort(position(offset)) - - override fun readInt(offset: Int) = buffer.getInt(position(offset)) - - override fun readLong(offset: Int) = buffer.getLong(position(offset)) - - override fun release() { - // does nothing on JVM - } - } - - override fun reader(): MemoryReader = reader - - private val writer: MemoryWriter = object : MemoryWriter { - override val memory: Memory get() = this@ByteBufferMemory - - override fun writeDouble(offset: Int, value: Double) { - buffer.putDouble(position(offset), value) - } - - override fun writeFloat(offset: Int, value: Float) { - buffer.putFloat(position(offset), value) - } - - override fun writeByte(offset: Int, value: Byte) { - buffer.put(position(offset), value) - } - - override fun writeShort(offset: Int, value: Short) { - buffer.putShort(position(offset), value) - } - - override fun writeInt(offset: Int, value: Int) { - buffer.putInt(position(offset), value) - } - - override fun writeLong(offset: Int, value: Long) { - buffer.putLong(position(offset), value) - } - - override fun release() { - // does nothing on JVM - } - } - - override fun writer(): MemoryWriter = writer -} - -/** - * Allocates memory based on a [ByteBuffer]. - */ -public actual fun Memory.Companion.allocate(length: Int): Memory = - ByteBufferMemory(checkNotNull(ByteBuffer.allocate(length))) - -/** - * Wraps a [Memory] around existing [ByteArray]. This operation is unsafe since the array is not copied - * and could be mutated independently of the resulting [Memory]. - */ -public actual fun Memory.Companion.wrap(array: ByteArray): Memory = - ByteBufferMemory(checkNotNull(ByteBuffer.wrap(array))) - -/** - * Wraps this [ByteBuffer] to [Memory] object. - * - * @receiver the byte buffer. - * @param startOffset the start offset. - * @param size the size of memory to map. - * @return the [Memory] object. - */ -public fun ByteBuffer.asMemory(startOffset: Int = 0, size: Int = limit()): Memory = - ByteBufferMemory(this, startOffset, size) - -/** - * Uses direct memory-mapped buffer from file to read something and close it afterwards. - */ -@Throws(IOException::class) -public inline fun Path.readAsMemory(position: Long = 0, size: Long = Files.size(this), block: Memory.() -> R): R { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - - return FileChannel - .open(this, StandardOpenOption.READ) - .use { ByteBufferMemory(it.map(FileChannel.MapMode.READ_ONLY, position, size)).block() } -} diff --git a/kmath-memory/src/nativeMain/kotlin/space/kscience/kmath/memory/NativeMemory.kt b/kmath-memory/src/nativeMain/kotlin/space/kscience/kmath/memory/NativeMemory.kt deleted file mode 100644 index 5146d9689..000000000 --- a/kmath-memory/src/nativeMain/kotlin/space/kscience/kmath/memory/NativeMemory.kt +++ /dev/null @@ -1,98 +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. - */ - -package space.kscience.kmath.memory - -@PublishedApi -internal class NativeMemory( - val array: ByteArray, - val startOffset: Int = 0, - override val size: Int = array.size, -) : Memory { - @Suppress("NOTHING_TO_INLINE") - private inline fun position(o: Int): Int = startOffset + o - - override fun view(offset: Int, length: Int): Memory { - require(offset >= 0) { "offset shouldn't be negative: $offset" } - require(length >= 0) { "length shouldn't be negative: $length" } - require(offset + length <= size) { "Can't view memory outside the parent region." } - return NativeMemory(array, position(offset), length) - } - - override fun copy(): Memory { - val copy = array.copyOfRange(startOffset, startOffset + size) - return NativeMemory(copy) - } - - private val reader: MemoryReader = object : MemoryReader { - override val memory: Memory get() = this@NativeMemory - - override fun readDouble(offset: Int) = array.getDoubleAt(position(offset)) - - override fun readFloat(offset: Int) = array.getFloatAt(position(offset)) - - override fun readByte(offset: Int) = array[position(offset)] - - override fun readShort(offset: Int) = array.getShortAt(position(offset)) - - override fun readInt(offset: Int) = array.getIntAt(position(offset)) - - override fun readLong(offset: Int) = array.getLongAt(position(offset)) - - override fun release() { - // does nothing on JVM - } - } - - override fun reader(): MemoryReader = reader - - private val writer: MemoryWriter = object : MemoryWriter { - override val memory: Memory get() = this@NativeMemory - - override fun writeDouble(offset: Int, value: Double) { - array.setDoubleAt(position(offset), value) - } - - override fun writeFloat(offset: Int, value: Float) { - array.setFloatAt(position(offset), value) - } - - override fun writeByte(offset: Int, value: Byte) { - array[position(offset)] = value - } - - override fun writeShort(offset: Int, value: Short) { - array.setShortAt(position(offset), value) - } - - override fun writeInt(offset: Int, value: Int) { - array.setIntAt(position(offset), value) - } - - override fun writeLong(offset: Int, value: Long) { - array.setLongAt(position(offset), value) - } - - override fun release() { - // does nothing on JVM - } - } - - override fun writer(): MemoryWriter = writer -} - -/** - * Wraps a [Memory] around existing [ByteArray]. This operation is unsafe since the array is not copied - * and could be mutated independently of the resulting [Memory]. - */ -public actual fun Memory.Companion.wrap(array: ByteArray): Memory = NativeMemory(array) - -/** - * Allocates the most effective platform-specific memory. - */ -public actual fun Memory.Companion.allocate(length: Int): Memory { - val array = ByteArray(length) - return NativeMemory(array) -} \ No newline at end of file diff --git a/kmath-multik/README.md b/kmath-multik/README.md deleted file mode 100644 index 167445f52..000000000 --- a/kmath-multik/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-multik - -JetBrains Multik connector - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-multik:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-multik:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-multik:0.3.0-dev-20") -} -``` diff --git a/kmath-multik/build.gradle.kts b/kmath-multik/build.gradle.kts deleted file mode 100644 index df2292f2e..000000000 --- a/kmath-multik/build.gradle.kts +++ /dev/null @@ -1,14 +0,0 @@ -plugins { - id("ru.mipt.npm.gradle.jvm") -} - -description = "JetBrains Multik connector" - -dependencies { - api(project(":kmath-tensors")) - api("org.jetbrains.kotlinx:multik-default:0.1.0") -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE -} \ No newline at end of file diff --git a/kmath-multik/src/main/kotlin/space/kscience/kmath/multik/MultikDoubleAlgebra.kt b/kmath-multik/src/main/kotlin/space/kscience/kmath/multik/MultikDoubleAlgebra.kt deleted file mode 100644 index 1dc318517..000000000 --- a/kmath-multik/src/main/kotlin/space/kscience/kmath/multik/MultikDoubleAlgebra.kt +++ /dev/null @@ -1,54 +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. - */ - -package space.kscience.kmath.multik - -import org.jetbrains.kotlinx.multik.ndarray.data.DataType -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.ExponentialOperations -import space.kscience.kmath.operations.TrigonometricOperations - -public object MultikDoubleAlgebra : MultikDivisionTensorAlgebra(), - TrigonometricOperations>, ExponentialOperations> { - override val elementAlgebra: DoubleField get() = DoubleField - override val type: DataType get() = DataType.DoubleDataType - - override fun sin(arg: StructureND): MultikTensor = multikMath.mathEx.sin(arg.asMultik().array).wrap() - - override fun cos(arg: StructureND): MultikTensor = multikMath.mathEx.cos(arg.asMultik().array).wrap() - - override fun tan(arg: StructureND): MultikTensor = sin(arg) / cos(arg) - - override fun asin(arg: StructureND): MultikTensor = arg.map { asin(it) } - - override fun acos(arg: StructureND): MultikTensor = arg.map { acos(it) } - - override fun atan(arg: StructureND): MultikTensor = arg.map { atan(it) } - - override fun exp(arg: StructureND): MultikTensor = multikMath.mathEx.exp(arg.asMultik().array).wrap() - - override fun ln(arg: StructureND): MultikTensor = multikMath.mathEx.log(arg.asMultik().array).wrap() - - override fun sinh(arg: StructureND): MultikTensor = (exp(arg) - exp(-arg)) / 2.0 - - override fun cosh(arg: StructureND): MultikTensor = (exp(arg) + exp(-arg)) / 2.0 - - override fun tanh(arg: StructureND): MultikTensor { - val expPlus = exp(arg) - val expMinus = exp(-arg) - return (expPlus - expMinus) / (expPlus + expMinus) - } - - override fun asinh(arg: StructureND): MultikTensor = arg.map { asinh(it) } - - override fun acosh(arg: StructureND): MultikTensor = arg.map { acosh(it) } - - override fun atanh(arg: StructureND): MultikTensor = arg.map { atanh(it) } -} - -public val Double.Companion.multikAlgebra: MultikTensorAlgebra get() = MultikDoubleAlgebra -public val DoubleField.multikAlgebra: MultikTensorAlgebra get() = MultikDoubleAlgebra - diff --git a/kmath-multik/src/main/kotlin/space/kscience/kmath/multik/MultikTensorAlgebra.kt b/kmath-multik/src/main/kotlin/space/kscience/kmath/multik/MultikTensorAlgebra.kt deleted file mode 100644 index 89b5c5116..000000000 --- a/kmath-multik/src/main/kotlin/space/kscience/kmath/multik/MultikTensorAlgebra.kt +++ /dev/null @@ -1,342 +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. - */ - -@file:Suppress("unused") - -package space.kscience.kmath.multik - -import org.jetbrains.kotlinx.multik.api.* -import org.jetbrains.kotlinx.multik.api.linalg.LinAlg -import org.jetbrains.kotlinx.multik.api.math.Math -import org.jetbrains.kotlinx.multik.ndarray.data.* -import org.jetbrains.kotlinx.multik.ndarray.operations.* -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.DefaultStrides -import space.kscience.kmath.nd.Shape -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.nd.mapInPlace -import space.kscience.kmath.operations.* -import space.kscience.kmath.tensors.api.Tensor -import space.kscience.kmath.tensors.api.TensorAlgebra -import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra - -@JvmInline -public value class MultikTensor(public val array: MutableMultiArray) : Tensor { - override val shape: Shape get() = array.shape - - override fun get(index: IntArray): T = array[index] - - @PerformancePitfall - override fun elements(): Sequence> = - array.multiIndices.iterator().asSequence().map { it to get(it) } - - override fun set(index: IntArray, value: T) { - array[index] = value - } -} - -private fun MultiArray.asD1Array(): D1Array { - if (this is NDArray) - return this.asD1Array() - else throw ClassCastException("Cannot cast MultiArray to NDArray.") -} - - -private fun MultiArray.asD2Array(): D2Array { - if (this is NDArray) - return this.asD2Array() - else throw ClassCastException("Cannot cast MultiArray to NDArray.") -} - -public abstract class MultikTensorAlgebra> : TensorAlgebra - where T : Number, T : Comparable { - - public abstract val type: DataType - - protected val multikMath: Math = mk.math - protected val multikLinAl: LinAlg = mk.linalg - protected val multikStat: Statistics = mk.stat - - - override fun structureND(shape: Shape, initializer: A.(IntArray) -> T): MultikTensor { - val strides = DefaultStrides(shape) - val memoryView = initMemoryView(strides.linearSize, type) - strides.asSequence().forEachIndexed { linearIndex, tensorIndex -> - memoryView[linearIndex] = elementAlgebra.initializer(tensorIndex) - } - return MultikTensor(NDArray(memoryView, shape = shape, dim = DN(shape.size))) - } - - @OptIn(PerformancePitfall::class) - override fun StructureND.map(transform: A.(T) -> T): MultikTensor = if (this is MultikTensor) { - val data = initMemoryView(array.size, type) - var count = 0 - for (el in array) data[count++] = elementAlgebra.transform(el) - NDArray(data, shape = shape, dim = array.dim).wrap() - } else { - structureND(shape) { index -> - transform(get(index)) - } - } - - @OptIn(PerformancePitfall::class) - override fun StructureND.mapIndexed(transform: A.(index: IntArray, T) -> T): MultikTensor = - if (this is MultikTensor) { - val array = asMultik().array - val data = initMemoryView(array.size, type) - val indexIter = array.multiIndices.iterator() - var index = 0 - for (item in array) { - if (indexIter.hasNext()) { - data[index++] = elementAlgebra.transform(indexIter.next(), item) - } else { - throw ArithmeticException("Index overflow has happened.") - } - } - NDArray(data, shape = array.shape, dim = array.dim).wrap() - } else { - structureND(shape) { index -> - transform(index, get(index)) - } - } - - @OptIn(PerformancePitfall::class) - override fun zip(left: StructureND, right: StructureND, transform: A.(T, T) -> T): MultikTensor { - require(left.shape.contentEquals(right.shape)) { "ND array shape mismatch" } //TODO replace by ShapeMismatchException - val leftArray = left.asMultik().array - val rightArray = right.asMultik().array - val data = initMemoryView(leftArray.size, type) - var counter = 0 - val leftIterator = leftArray.iterator() - val rightIterator = rightArray.iterator() - //iterating them together - while (leftIterator.hasNext()) { - data[counter++] = elementAlgebra.transform(leftIterator.next(), rightIterator.next()) - } - return NDArray(data, shape = leftArray.shape, dim = leftArray.dim).wrap() - } - - /** - * Convert a tensor to [MultikTensor] if necessary. If tensor is converted, changes on the resulting tensor - * are not reflected back onto the source - */ - public fun StructureND.asMultik(): MultikTensor = if (this is MultikTensor) { - this - } else { - val res = mk.zeros(shape, type).asDNArray() - for (index in res.multiIndices) { - res[index] = this[index] - } - res.wrap() - } - - public fun MutableMultiArray.wrap(): MultikTensor = MultikTensor(this.asDNArray()) - - override fun StructureND.valueOrNull(): T? = if (shape contentEquals intArrayOf(1)) { - get(intArrayOf(0)) - } else null - - override fun T.plus(arg: StructureND): MultikTensor = - arg.plus(this) - - override fun StructureND.plus(arg: T): MultikTensor = - asMultik().array.deepCopy().apply { plusAssign(arg) }.wrap() - - override fun StructureND.plus(arg: StructureND): MultikTensor = - asMultik().array.plus(arg.asMultik().array).wrap() - - override fun Tensor.plusAssign(value: T) { - if (this is MultikTensor) { - array.plusAssign(value) - } else { - mapInPlace { _, t -> elementAlgebra.add(t, value) } - } - } - - override fun Tensor.plusAssign(arg: StructureND) { - if (this is MultikTensor) { - array.plusAssign(arg.asMultik().array) - } else { - mapInPlace { index, t -> elementAlgebra.add(t, arg[index]) } - } - } - - override fun T.minus(arg: StructureND): MultikTensor = (-(arg.asMultik().array - this)).wrap() - - override fun StructureND.minus(arg: T): MultikTensor = - asMultik().array.deepCopy().apply { minusAssign(arg) }.wrap() - - override fun StructureND.minus(arg: StructureND): MultikTensor = - asMultik().array.minus(arg.asMultik().array).wrap() - - override fun Tensor.minusAssign(value: T) { - if (this is MultikTensor) { - array.minusAssign(value) - } else { - mapInPlace { _, t -> elementAlgebra.run { t - value } } - } - } - - override fun Tensor.minusAssign(arg: StructureND) { - if (this is MultikTensor) { - array.minusAssign(arg.asMultik().array) - } else { - mapInPlace { index, t -> elementAlgebra.run { t - arg[index] } } - } - } - - override fun T.times(arg: StructureND): MultikTensor = - arg.asMultik().array.deepCopy().apply { timesAssign(this@times) }.wrap() - - override fun StructureND.times(arg: T): Tensor = - asMultik().array.deepCopy().apply { timesAssign(arg) }.wrap() - - override fun StructureND.times(arg: StructureND): MultikTensor = - asMultik().array.times(arg.asMultik().array).wrap() - - override fun Tensor.timesAssign(value: T) { - if (this is MultikTensor) { - array.timesAssign(value) - } else { - mapInPlace { _, t -> elementAlgebra.multiply(t, value) } - } - } - - override fun Tensor.timesAssign(arg: StructureND) { - if (this is MultikTensor) { - array.timesAssign(arg.asMultik().array) - } else { - mapInPlace { index, t -> elementAlgebra.multiply(t, arg[index]) } - } - } - - override fun negate(arg: StructureND): MultikTensor = arg.asMultik().array.unaryMinus().wrap() - - override fun Tensor.get(i: Int): MultikTensor = asMultik().array.mutableView(i).wrap() - - override fun Tensor.transpose(i: Int, j: Int): MultikTensor = asMultik().array.transpose(i, j).wrap() - - override fun Tensor.view(shape: IntArray): MultikTensor { - require(shape.all { it > 0 }) - require(shape.fold(1, Int::times) == this.shape.size) { - "Cannot reshape array of size ${this.shape.size} into a new shape ${ - shape.joinToString( - prefix = "(", - postfix = ")" - ) - }" - } - - val mt = asMultik().array - return if (mt.shape.contentEquals(shape)) { - mt - } else { - NDArray(mt.data, mt.offset, shape, dim = DN(shape.size), base = mt.base ?: mt) - }.wrap() - } - - override fun Tensor.viewAs(other: StructureND): MultikTensor = view(other.shape) - - override fun StructureND.dot(other: StructureND): MultikTensor = - if (this.shape.size == 1 && other.shape.size == 1) { - Multik.ndarrayOf( - multikLinAl.linAlgEx.dotVV(asMultik().array.asD1Array(), other.asMultik().array.asD1Array()) - ).wrap() - } else if (this.shape.size == 2 && other.shape.size == 2) { - multikLinAl.linAlgEx.dotMM(asMultik().array.asD2Array(), other.asMultik().array.asD2Array()).wrap() - } else if (this.shape.size == 2 && other.shape.size == 1) { - multikLinAl.linAlgEx.dotMV(asMultik().array.asD2Array(), other.asMultik().array.asD1Array()).wrap() - } else { - TODO("Not implemented for broadcasting") - } - - override fun diagonalEmbedding(diagonalEntries: Tensor, offset: Int, dim1: Int, dim2: Int): MultikTensor { - TODO("Diagonal embedding not implemented") - } - - override fun StructureND.sum(): T = asMultik().array.reduceMultiIndexed { _: IntArray, acc: T, t: T -> - elementAlgebra.add(acc, t) - } - - override fun StructureND.sum(dim: Int, keepDim: Boolean): MultikTensor { - TODO("Not yet implemented") - } - - override fun StructureND.min(): T? = asMultik().array.min() - - override fun StructureND.min(dim: Int, keepDim: Boolean): Tensor { - TODO("Not yet implemented") - } - - override fun StructureND.max(): T? = asMultik().array.max() - - override fun StructureND.max(dim: Int, keepDim: Boolean): Tensor { - TODO("Not yet implemented") - } - - override fun StructureND.argMax(dim: Int, keepDim: Boolean): Tensor { - TODO("Not yet implemented") - } -} - -public abstract class MultikDivisionTensorAlgebra> - : MultikTensorAlgebra(), TensorPartialDivisionAlgebra where T : Number, T : Comparable { - - override fun T.div(arg: StructureND): MultikTensor = arg.map { elementAlgebra.divide(this@div, it) } - - override fun StructureND.div(arg: T): MultikTensor = - asMultik().array.deepCopy().apply { divAssign(arg) }.wrap() - - override fun StructureND.div(arg: StructureND): MultikTensor = - asMultik().array.div(arg.asMultik().array).wrap() - - override fun Tensor.divAssign(value: T) { - if (this is MultikTensor) { - array.divAssign(value) - } else { - mapInPlace { _, t -> elementAlgebra.divide(t, value) } - } - } - - override fun Tensor.divAssign(arg: StructureND) { - if (this is MultikTensor) { - array.divAssign(arg.asMultik().array) - } else { - mapInPlace { index, t -> elementAlgebra.divide(t, arg[index]) } - } - } -} - -public object MultikFloatAlgebra : MultikDivisionTensorAlgebra() { - override val elementAlgebra: FloatField get() = FloatField - override val type: DataType get() = DataType.FloatDataType -} - -public val Float.Companion.multikAlgebra: MultikTensorAlgebra get() = MultikFloatAlgebra -public val FloatField.multikAlgebra: MultikTensorAlgebra get() = MultikFloatAlgebra - -public object MultikShortAlgebra : MultikTensorAlgebra() { - override val elementAlgebra: ShortRing get() = ShortRing - override val type: DataType get() = DataType.ShortDataType -} - -public val Short.Companion.multikAlgebra: MultikTensorAlgebra get() = MultikShortAlgebra -public val ShortRing.multikAlgebra: MultikTensorAlgebra get() = MultikShortAlgebra - -public object MultikIntAlgebra : MultikTensorAlgebra() { - override val elementAlgebra: IntRing get() = IntRing - override val type: DataType get() = DataType.IntDataType -} - -public val Int.Companion.multikAlgebra: MultikTensorAlgebra get() = MultikIntAlgebra -public val IntRing.multikAlgebra: MultikTensorAlgebra get() = MultikIntAlgebra - -public object MultikLongAlgebra : MultikTensorAlgebra() { - override val elementAlgebra: LongRing get() = LongRing - override val type: DataType get() = DataType.LongDataType -} - -public val Long.Companion.multikAlgebra: MultikTensorAlgebra get() = MultikLongAlgebra -public val LongRing.multikAlgebra: MultikTensorAlgebra get() = MultikLongAlgebra \ No newline at end of file diff --git a/kmath-multik/src/test/kotlin/space/kscience/kmath/multik/MultikNDTest.kt b/kmath-multik/src/test/kotlin/space/kscience/kmath/multik/MultikNDTest.kt deleted file mode 100644 index 72c43d8e6..000000000 --- a/kmath-multik/src/test/kotlin/space/kscience/kmath/multik/MultikNDTest.kt +++ /dev/null @@ -1,43 +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. - */ - -package space.kscience.kmath.multik - -import org.junit.jupiter.api.Test -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.nd.one -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.tensors.core.DoubleTensorAlgebra -import space.kscience.kmath.tensors.core.tensorAlgebra -import kotlin.test.assertTrue - -internal class MultikNDTest { - @Test - fun basicAlgebra(): Unit = DoubleField.multikAlgebra{ - one(2,2) + 1.0 - } - - @Test - fun dotResult(){ - val dim = 100 - - val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224) - val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225) - - val multikResult = with(DoubleField.multikAlgebra){ - tensor1 dot tensor2 - } - - val defaultResult = with(DoubleField.tensorAlgebra){ - tensor1 dot tensor2 - } - - assertTrue { - StructureND.contentEquals(multikResult, defaultResult) - } - - } -} \ No newline at end of file diff --git a/kmath-nd4j/README.md b/kmath-nd4j/README.md deleted file mode 100644 index 225de18e9..000000000 --- a/kmath-nd4j/README.md +++ /dev/null @@ -1,71 +0,0 @@ -# Module kmath-nd4j - -ND4J based implementations of KMath abstractions. - - - [nd4jarraystructure](#) : NDStructure wrapper for INDArray - - [nd4jarrayrings](#) : Rings over Nd4jArrayStructure of Int and Long - - [nd4jarrayfields](#) : Fields over Nd4jArrayStructure of Float and Double - - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-nd4j:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-nd4j:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-nd4j:0.3.0-dev-20") -} -``` - -## Examples - -NDStructure wrapper for INDArray: - -```kotlin -import org.nd4j.linalg.factory.* -import scientifik.kmath.nd4j.* -import scientifik.kmath.structures.* - -val array = Nd4j.ones(2, 2).asDoubleStructure() -println(array[0, 0]) // 1.0 -array[intArrayOf(0, 0)] = 24.0 -println(array[0, 0]) // 24.0 -``` - -Fast element-wise and in-place arithmetics for INDArray: - -```kotlin -import org.nd4j.linalg.factory.* -import scientifik.kmath.nd4j.* -import scientifik.kmath.operations.* - -val field = DoubleNd4jArrayField(intArrayOf(2, 2)) -val array = Nd4j.rand(2, 2).asDoubleStructure() - -val res = field { - (25.0 / array + 20) * 4 -} - -println(res.ndArray) -// [[ 250.6449, 428.5840], -// [ 269.7913, 202.2077]] -``` - -Contributed by [Iaroslav Postovalov](https://github.com/CommanderTvis). diff --git a/kmath-nd4j/build.gradle.kts b/kmath-nd4j/build.gradle.kts deleted file mode 100644 index 09264501f..000000000 --- a/kmath-nd4j/build.gradle.kts +++ /dev/null @@ -1,21 +0,0 @@ -plugins { - kotlin("jvm") - id("ru.mipt.npm.gradle.common") -} - -description = "ND4J NDStructure implementation and according NDAlgebra classes" - -dependencies { - api(project(":kmath-tensors")) - api("org.nd4j:nd4j-api:1.0.0-M1") - testImplementation("org.nd4j:nd4j-native-platform:1.0.0-M1") - testImplementation("org.slf4j:slf4j-simple:1.7.32") -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL - propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md")) - feature(id = "nd4jarraystructure") { "NDStructure wrapper for INDArray" } - feature(id = "nd4jarrayrings") { "Rings over Nd4jArrayStructure of Int and Long" } - feature(id = "nd4jarrayfields") { "Fields over Nd4jArrayStructure of Float and Double" } -} diff --git a/kmath-nd4j/docs/README-TEMPLATE.md b/kmath-nd4j/docs/README-TEMPLATE.md deleted file mode 100644 index 5f325cab5..000000000 --- a/kmath-nd4j/docs/README-TEMPLATE.md +++ /dev/null @@ -1,43 +0,0 @@ -# Module kmath-nd4j - -ND4J based implementations of KMath abstractions. - -${features} - -${artifact} - -## Examples - -NDStructure wrapper for INDArray: - -```kotlin -import org.nd4j.linalg.factory.* -import scientifik.kmath.nd4j.* -import scientifik.kmath.structures.* - -val array = Nd4j.ones(2, 2).asDoubleStructure() -println(array[0, 0]) // 1.0 -array[intArrayOf(0, 0)] = 24.0 -println(array[0, 0]) // 24.0 -``` - -Fast element-wise and in-place arithmetics for INDArray: - -```kotlin -import org.nd4j.linalg.factory.* -import scientifik.kmath.nd4j.* -import scientifik.kmath.operations.* - -val field = DoubleNd4jArrayField(intArrayOf(2, 2)) -val array = Nd4j.rand(2, 2).asDoubleStructure() - -val res = field { - (25.0 / array + 20) * 4 -} - -println(res.ndArray) -// [[ 250.6449, 428.5840], -// [ 269.7913, 202.2077]] -``` - -Contributed by [Iaroslav Postovalov](https://github.com/CommanderTvis). diff --git a/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayAlgebra.kt b/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayAlgebra.kt deleted file mode 100644 index e29a3f467..000000000 --- a/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayAlgebra.kt +++ /dev/null @@ -1,314 +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. - */ - -package space.kscience.kmath.nd4j - -import org.nd4j.linalg.api.ndarray.INDArray -import org.nd4j.linalg.api.ops.impl.transforms.strict.ACosh -import org.nd4j.linalg.api.ops.impl.transforms.strict.ASinh -import org.nd4j.linalg.factory.Nd4j -import org.nd4j.linalg.ops.transforms.Transforms -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.* -import space.kscience.kmath.operations.* - -/** - * Represents [AlgebraND] over [Nd4jArrayAlgebra]. - * - * @param T the type of ND-structure element. - * @param C the type of the element context. - */ -public sealed interface Nd4jArrayAlgebra> : AlgebraND { - /** - * Wraps [INDArray] to [Nd4jArrayStructure]. - */ - public fun INDArray.wrap(): Nd4jArrayStructure - - /** - * Unwraps to or get [INDArray] from [StructureND]. - */ - public val StructureND.ndArray: INDArray - - override fun structureND(shape: Shape, initializer: C.(IntArray) -> T): Nd4jArrayStructure { - val struct = Nd4j.create(*shape)!!.wrap() - struct.indicesIterator().forEach { struct[it] = elementAlgebra.initializer(it) } - return struct - } - - @OptIn(PerformancePitfall::class) - override fun StructureND.map(transform: C.(T) -> T): Nd4jArrayStructure { - val newStruct = ndArray.dup().wrap() - newStruct.elements().forEach { (idx, value) -> newStruct[idx] = elementAlgebra.transform(value) } - return newStruct - } - - override fun StructureND.mapIndexed( - transform: C.(index: IntArray, T) -> T, - ): Nd4jArrayStructure { - val new = Nd4j.create(*shape).wrap() - new.indicesIterator().forEach { idx -> new[idx] = elementAlgebra.transform(idx, this[idx]) } - return new - } - - override fun zip( - left: StructureND, - right: StructureND, - transform: C.(T, T) -> T, - ): Nd4jArrayStructure { - require(left.shape.contentEquals(right.shape)) { "Can't zip tow structures of shape ${left.shape} and ${right.shape}" } - val new = Nd4j.create(*left.shape).wrap() - new.indicesIterator().forEach { idx -> new[idx] = elementAlgebra.transform(left[idx], right[idx]) } - return new - } -} - -/** - * Represents [GroupND] over [Nd4jArrayStructure]. - * - * @param T the type of the element contained in ND structure. - * @param S the type of space of structure elements. - */ -public sealed interface Nd4jArrayGroupOps> : GroupOpsND, Nd4jArrayAlgebra { - - override fun add(left: StructureND, right: StructureND): Nd4jArrayStructure = - left.ndArray.add(right.ndArray).wrap() - - override operator fun StructureND.minus(arg: StructureND): Nd4jArrayStructure = - ndArray.sub(arg.ndArray).wrap() - - override operator fun StructureND.unaryMinus(): Nd4jArrayStructure = - ndArray.neg().wrap() - - public fun multiply(a: StructureND, k: Number): Nd4jArrayStructure = - a.ndArray.mul(k).wrap() -} - -/** - * Represents [RingND] over [Nd4jArrayStructure]. - * - * @param T the type of the element contained in ND structure. - * @param R the type of ring of structure elements. - */ -@OptIn(UnstableKMathAPI::class) -public sealed interface Nd4jArrayRingOps> : RingOpsND, Nd4jArrayGroupOps { - - override fun multiply(left: StructureND, right: StructureND): Nd4jArrayStructure = - left.ndArray.mul(right.ndArray).wrap() -// -// override operator fun Nd4jArrayStructure.minus(b: Number): Nd4jArrayStructure { -// check(this) -// return ndArray.sub(b).wrap() -// } -// -// override operator fun Nd4jArrayStructure.plus(b: Number): Nd4jArrayStructure { -// check(this) -// return ndArray.add(b).wrap() -// } -// -// override operator fun Number.minus(b: Nd4jArrayStructure): Nd4jArrayStructure { -// check(b) -// return b.ndArray.rsub(this).wrap() -// } - - public companion object { - /** - * Creates a most suitable implementation of [RingND] using reified class. - */ - @Suppress("UNCHECKED_CAST") - public inline fun auto(): Nd4jArrayRingOps> = when { - T::class == Int::class -> IntRing.nd4j as Nd4jArrayRingOps> - else -> throw UnsupportedOperationException("This factory method only supports Long type.") - } - } -} - -/** - * Represents [FieldND] over [Nd4jArrayStructure]. - * - * @param T the type of the element contained in ND structure. - * @param F the type field of structure elements. - */ -public sealed interface Nd4jArrayField> : FieldOpsND, Nd4jArrayRingOps { - - override fun divide(left: StructureND, right: StructureND): Nd4jArrayStructure = - left.ndArray.div(right.ndArray).wrap() - - public operator fun Number.div(b: StructureND): Nd4jArrayStructure = b.ndArray.rdiv(this).wrap() - - public companion object { - /** - * Creates a most suitable implementation of [FieldND] using reified class. - */ - @Suppress("UNCHECKED_CAST") - public inline fun auto(): Nd4jArrayField> = when { - T::class == Float::class -> FloatField.nd4j as Nd4jArrayField> - T::class == Double::class -> DoubleField.nd4j as Nd4jArrayField> - else -> throw UnsupportedOperationException("This factory method only supports Float and Double types.") - } - } -} - -/** - * Represents intersection of [ExtendedField] and [Field] over [Nd4jArrayStructure]. - */ -public sealed interface Nd4jArrayExtendedFieldOps> : - ExtendedFieldOps>, Nd4jArrayField, PowerOperations> { - - override fun sin(arg: StructureND): StructureND = Transforms.sin(arg.ndArray).wrap() - override fun cos(arg: StructureND): StructureND = Transforms.cos(arg.ndArray).wrap() - override fun asin(arg: StructureND): StructureND = Transforms.asin(arg.ndArray).wrap() - override fun acos(arg: StructureND): StructureND = Transforms.acos(arg.ndArray).wrap() - override fun atan(arg: StructureND): StructureND = Transforms.atan(arg.ndArray).wrap() - - override fun power(arg: StructureND, pow: Number): StructureND = - Transforms.pow(arg.ndArray, pow).wrap() - - override fun exp(arg: StructureND): StructureND = Transforms.exp(arg.ndArray).wrap() - override fun ln(arg: StructureND): StructureND = Transforms.log(arg.ndArray).wrap() - override fun sqrt(arg: StructureND): StructureND = Transforms.sqrt(arg.ndArray).wrap() - override fun sinh(arg: StructureND): StructureND = Transforms.sinh(arg.ndArray).wrap() - override fun cosh(arg: StructureND): StructureND = Transforms.cosh(arg.ndArray).wrap() - override fun tanh(arg: StructureND): StructureND = Transforms.tanh(arg.ndArray).wrap() - - override fun asinh(arg: StructureND): StructureND = - Nd4j.getExecutioner().exec(ASinh(arg.ndArray, arg.ndArray.ulike())).wrap() - - override fun acosh(arg: StructureND): StructureND = - Nd4j.getExecutioner().exec(ACosh(arg.ndArray, arg.ndArray.ulike())).wrap() - - override fun atanh(arg: StructureND): StructureND = Transforms.atanh(arg.ndArray).wrap() -} - -/** - * Represents [FieldND] over [Nd4jArrayDoubleStructure]. - */ -public open class DoubleNd4jArrayFieldOps : Nd4jArrayExtendedFieldOps { - override val elementAlgebra: DoubleField get() = DoubleField - - override fun INDArray.wrap(): Nd4jArrayStructure = asDoubleStructure() - - @OptIn(PerformancePitfall::class) - override val StructureND.ndArray: INDArray - get() = when (this) { - is Nd4jArrayStructure -> ndArray - else -> Nd4j.zeros(*shape).also { - elements().forEach { (idx, value) -> it.putScalar(idx, value) } - } - } - - override fun scale(a: StructureND, value: Double): Nd4jArrayStructure = a.ndArray.mul(value).wrap() - - override operator fun StructureND.div(arg: Double): Nd4jArrayStructure = ndArray.div(arg).wrap() - - override operator fun StructureND.plus(arg: Double): Nd4jArrayStructure = ndArray.add(arg).wrap() - - override operator fun StructureND.minus(arg: Double): Nd4jArrayStructure = ndArray.sub(arg).wrap() - - override operator fun StructureND.times(arg: Double): Nd4jArrayStructure = ndArray.mul(arg).wrap() - - override operator fun Double.div(arg: StructureND): Nd4jArrayStructure = - arg.ndArray.rdiv(this).wrap() - - override operator fun Double.minus(arg: StructureND): Nd4jArrayStructure = - arg.ndArray.rsub(this).wrap() - - public companion object : DoubleNd4jArrayFieldOps() -} - -public val DoubleField.nd4j: DoubleNd4jArrayFieldOps get() = DoubleNd4jArrayFieldOps - -public class DoubleNd4jArrayField(override val shape: Shape) : DoubleNd4jArrayFieldOps(), FieldND - -public fun DoubleField.nd4j(shapeFirst: Int, vararg shapeRest: Int): DoubleNd4jArrayField = - DoubleNd4jArrayField(intArrayOf(shapeFirst, * shapeRest)) - - -/** - * Represents [FieldND] over [Nd4jArrayStructure] of [Float]. - */ -public open class FloatNd4jArrayFieldOps : Nd4jArrayExtendedFieldOps { - override val elementAlgebra: FloatField get() = FloatField - - override fun INDArray.wrap(): Nd4jArrayStructure = asFloatStructure() - - @OptIn(PerformancePitfall::class) - override val StructureND.ndArray: INDArray - get() = when (this) { - is Nd4jArrayStructure -> ndArray - else -> Nd4j.zeros(*shape).also { - elements().forEach { (idx, value) -> it.putScalar(idx, value) } - } - } - - override fun scale(a: StructureND, value: Double): StructureND = - a.ndArray.mul(value).wrap() - - override operator fun StructureND.div(arg: Float): Nd4jArrayStructure = - ndArray.div(arg).wrap() - - override operator fun StructureND.plus(arg: Float): Nd4jArrayStructure = - ndArray.add(arg).wrap() - - override operator fun StructureND.minus(arg: Float): Nd4jArrayStructure = - ndArray.sub(arg).wrap() - - override operator fun StructureND.times(arg: Float): Nd4jArrayStructure = - ndArray.mul(arg).wrap() - - override operator fun Float.div(arg: StructureND): Nd4jArrayStructure = - arg.ndArray.rdiv(this).wrap() - - override operator fun Float.minus(arg: StructureND): Nd4jArrayStructure = - arg.ndArray.rsub(this).wrap() - - public companion object : FloatNd4jArrayFieldOps() -} - -public class FloatNd4jArrayField(override val shape: Shape) : FloatNd4jArrayFieldOps(), RingND - -public val FloatField.nd4j: FloatNd4jArrayFieldOps get() = FloatNd4jArrayFieldOps - -public fun FloatField.nd4j(shapeFirst: Int, vararg shapeRest: Int): FloatNd4jArrayField = - FloatNd4jArrayField(intArrayOf(shapeFirst, * shapeRest)) - -/** - * Represents [RingND] over [Nd4jArrayIntStructure]. - */ -public open class IntNd4jArrayRingOps : Nd4jArrayRingOps { - override val elementAlgebra: IntRing get() = IntRing - - override fun INDArray.wrap(): Nd4jArrayStructure = asIntStructure() - - @OptIn(PerformancePitfall::class) - override val StructureND.ndArray: INDArray - get() = when (this) { - is Nd4jArrayStructure -> ndArray - else -> Nd4j.zeros(*shape).also { - elements().forEach { (idx, value) -> it.putScalar(idx, value) } - } - } - - override operator fun StructureND.plus(arg: Int): Nd4jArrayStructure = - ndArray.add(arg).wrap() - - override operator fun StructureND.minus(arg: Int): Nd4jArrayStructure = - ndArray.sub(arg).wrap() - - override operator fun StructureND.times(arg: Int): Nd4jArrayStructure = - ndArray.mul(arg).wrap() - - override operator fun Int.minus(arg: StructureND): Nd4jArrayStructure = - arg.ndArray.rsub(this).wrap() - - public companion object : IntNd4jArrayRingOps() -} - -public val IntRing.nd4j: IntNd4jArrayRingOps get() = IntNd4jArrayRingOps - -public class IntNd4jArrayRing(override val shape: Shape) : IntNd4jArrayRingOps(), RingND - -public fun IntRing.nd4j(shapeFirst: Int, vararg shapeRest: Int): IntNd4jArrayRing = - IntNd4jArrayRing(intArrayOf(shapeFirst, * shapeRest)) \ No newline at end of file diff --git a/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayIterator.kt b/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayIterator.kt deleted file mode 100644 index d4cad8996..000000000 --- a/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayIterator.kt +++ /dev/null @@ -1,61 +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. - */ - -package space.kscience.kmath.nd4j - -import org.nd4j.linalg.api.ndarray.INDArray -import org.nd4j.linalg.api.shape.Shape - -private class Nd4jArrayIndicesIterator(private val iterateOver: INDArray) : Iterator { - private var i: Int = 0 - - override fun hasNext(): Boolean = i < iterateOver.length() - - override fun next(): IntArray { - val la = if (iterateOver.ordering() == 'c') - Shape.ind2subC(iterateOver, i++.toLong())!! - else - Shape.ind2sub(iterateOver, i++.toLong())!! - - return la.toIntArray() - } -} - -internal fun INDArray.indicesIterator(): Iterator = Nd4jArrayIndicesIterator(this) - -private sealed class Nd4jArrayIteratorBase(protected val iterateOver: INDArray) : Iterator> { - private var i: Int = 0 - - final override fun hasNext(): Boolean = i < iterateOver.length() - - abstract fun getSingle(indices: LongArray): T - - final override fun next(): Pair { - val la = if (iterateOver.ordering() == 'c') - Shape.ind2subC(iterateOver, i++.toLong())!! - else - Shape.ind2sub(iterateOver, i++.toLong())!! - - return la.toIntArray() to getSingle(la) - } -} - -private class Nd4jArrayDoubleIterator(iterateOver: INDArray) : Nd4jArrayIteratorBase(iterateOver) { - override fun getSingle(indices: LongArray): Double = iterateOver.getDouble(*indices) -} - -internal fun INDArray.realIterator(): Iterator> = Nd4jArrayDoubleIterator(this) - -private class Nd4jArrayIntIterator(iterateOver: INDArray) : Nd4jArrayIteratorBase(iterateOver) { - override fun getSingle(indices: LongArray) = iterateOver.getInt(*indices.toIntArray()) -} - -internal fun INDArray.intIterator(): Iterator> = Nd4jArrayIntIterator(this) - -private class Nd4jArrayFloatIterator(iterateOver: INDArray) : Nd4jArrayIteratorBase(iterateOver) { - override fun getSingle(indices: LongArray) = iterateOver.getFloat(*indices) -} - -internal fun INDArray.floatIterator(): Iterator> = Nd4jArrayFloatIterator(this) diff --git a/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructure.kt b/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructure.kt deleted file mode 100644 index 2a0fdc86c..000000000 --- a/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructure.kt +++ /dev/null @@ -1,65 +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. - */ - -package space.kscience.kmath.nd4j - -import org.nd4j.linalg.api.ndarray.INDArray -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.MutableStructureND -import space.kscience.kmath.nd.StructureND - -/** - * Represents a [StructureND] wrapping an [INDArray] object. - * - * @param T the type of items. - */ -public sealed class Nd4jArrayStructure : MutableStructureND { - /** - * The wrapped [INDArray]. Since KMath uses [Int] indexes, assuming the size of [INDArray] is less or equal to - * [Int.MAX_VALUE]. - */ - public abstract val ndArray: INDArray - - override val shape: IntArray get() = ndArray.shape().toIntArray() - - internal abstract fun elementsIterator(): Iterator> - internal fun indicesIterator(): Iterator = ndArray.indicesIterator() - - @PerformancePitfall - override fun elements(): Sequence> = Sequence(::elementsIterator) -} - -private data class Nd4jArrayIntStructure(override val ndArray: INDArray) : Nd4jArrayStructure() { - override fun elementsIterator(): Iterator> = ndArray.intIterator() - override fun get(index: IntArray): Int = ndArray.getInt(*index) - override fun set(index: IntArray, value: Int): Unit = run { ndArray.putScalar(index, value) } -} - -/** - * Wraps this [INDArray] to [Nd4jArrayStructure]. - */ -public fun INDArray.asIntStructure(): Nd4jArrayStructure = Nd4jArrayIntStructure(this) - -private data class Nd4jArrayDoubleStructure(override val ndArray: INDArray) : Nd4jArrayStructure() { - override fun elementsIterator(): Iterator> = ndArray.realIterator() - override fun get(index: IntArray): Double = ndArray.getDouble(*index) - override fun set(index: IntArray, value: Double): Unit = run { ndArray.putScalar(index, value) } -} - -/** - * Wraps this [INDArray] to [Nd4jArrayStructure]. - */ -public fun INDArray.asDoubleStructure(): Nd4jArrayStructure = Nd4jArrayDoubleStructure(this) - -private data class Nd4jArrayFloatStructure(override val ndArray: INDArray) : Nd4jArrayStructure() { - override fun elementsIterator(): Iterator> = ndArray.floatIterator() - override fun get(index: IntArray): Float = ndArray.getFloat(*index) - override fun set(index: IntArray, value: Float): Unit = run { ndArray.putScalar(index, value) } -} - -/** - * Wraps this [INDArray] to [Nd4jArrayStructure]. - */ -public fun INDArray.asFloatStructure(): Nd4jArrayStructure = Nd4jArrayFloatStructure(this) diff --git a/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jTensorAlgebra.kt b/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jTensorAlgebra.kt deleted file mode 100644 index ceb384f0d..000000000 --- a/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jTensorAlgebra.kt +++ /dev/null @@ -1,208 +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. - */ - -package space.kscience.kmath.nd4j - -import org.nd4j.linalg.api.ndarray.INDArray -import org.nd4j.linalg.api.ops.impl.summarystats.Variance -import org.nd4j.linalg.api.ops.impl.transforms.strict.ACosh -import org.nd4j.linalg.api.ops.impl.transforms.strict.ASinh -import org.nd4j.linalg.factory.Nd4j -import org.nd4j.linalg.factory.ops.NDBase -import org.nd4j.linalg.ops.transforms.Transforms -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.DefaultStrides -import space.kscience.kmath.nd.Shape -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.Field -import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra -import space.kscience.kmath.tensors.api.Tensor -import space.kscience.kmath.tensors.api.TensorAlgebra -import space.kscience.kmath.tensors.core.DoubleTensorAlgebra - -/** - * ND4J based [TensorAlgebra] implementation. - */ -public sealed interface Nd4jTensorAlgebra> : AnalyticTensorAlgebra { - - /** - * Wraps [INDArray] to [Nd4jArrayStructure]. - */ - public fun INDArray.wrap(): Nd4jArrayStructure - - /** - * Unwraps to or gets [INDArray] from [StructureND]. - */ - public val StructureND.ndArray: INDArray - - override fun structureND(shape: Shape, initializer: A.(IntArray) -> T): Nd4jArrayStructure - - override fun StructureND.map(transform: A.(T) -> T): Nd4jArrayStructure = - structureND(shape) { index -> elementAlgebra.transform(get(index)) } - - override fun StructureND.mapIndexed(transform: A.(index: IntArray, T) -> T): Nd4jArrayStructure = - structureND(shape) { index -> elementAlgebra.transform(index, get(index)) } - - override fun zip(left: StructureND, right: StructureND, transform: A.(T, T) -> T): Nd4jArrayStructure { - require(left.shape.contentEquals(right.shape)) - return structureND(left.shape) { index -> elementAlgebra.transform(left[index], right[index]) } - } - - override fun T.plus(arg: StructureND): Nd4jArrayStructure = arg.ndArray.add(this).wrap() - override fun StructureND.plus(arg: T): Nd4jArrayStructure = ndArray.add(arg).wrap() - - override fun StructureND.plus(arg: StructureND): Nd4jArrayStructure = ndArray.add(arg.ndArray).wrap() - - override fun Tensor.plusAssign(value: T) { - ndArray.addi(value) - } - - override fun Tensor.plusAssign(arg: StructureND) { - ndArray.addi(arg.ndArray) - } - - override fun T.minus(arg: StructureND): Nd4jArrayStructure = arg.ndArray.rsub(this).wrap() - override fun StructureND.minus(arg: T): Nd4jArrayStructure = ndArray.sub(arg).wrap() - override fun StructureND.minus(arg: StructureND): Nd4jArrayStructure = ndArray.sub(arg.ndArray).wrap() - - override fun Tensor.minusAssign(value: T) { - ndArray.rsubi(value) - } - - override fun Tensor.minusAssign(arg: StructureND) { - ndArray.subi(arg.ndArray) - } - - override fun T.times(arg: StructureND): Nd4jArrayStructure = arg.ndArray.mul(this).wrap() - - override fun StructureND.times(arg: T): Nd4jArrayStructure = - ndArray.mul(arg).wrap() - - override fun StructureND.times(arg: StructureND): Nd4jArrayStructure = ndArray.mul(arg.ndArray).wrap() - - override fun Tensor.timesAssign(value: T) { - ndArray.muli(value) - } - - override fun Tensor.timesAssign(arg: StructureND) { - ndArray.mmuli(arg.ndArray) - } - - override fun StructureND.unaryMinus(): Nd4jArrayStructure = ndArray.neg().wrap() - override fun Tensor.get(i: Int): Nd4jArrayStructure = ndArray.slice(i.toLong()).wrap() - override fun Tensor.transpose(i: Int, j: Int): Nd4jArrayStructure = ndArray.swapAxes(i, j).wrap() - override fun StructureND.dot(other: StructureND): Nd4jArrayStructure = ndArray.mmul(other.ndArray).wrap() - - override fun StructureND.min(dim: Int, keepDim: Boolean): Nd4jArrayStructure = - ndArray.min(keepDim, dim).wrap() - - override fun StructureND.sum(dim: Int, keepDim: Boolean): Nd4jArrayStructure = - ndArray.sum(keepDim, dim).wrap() - - override fun StructureND.max(dim: Int, keepDim: Boolean): Nd4jArrayStructure = - ndArray.max(keepDim, dim).wrap() - - override fun Tensor.view(shape: IntArray): Nd4jArrayStructure = ndArray.reshape(shape).wrap() - override fun Tensor.viewAs(other: StructureND): Nd4jArrayStructure = view(other.shape) - - override fun StructureND.argMax(dim: Int, keepDim: Boolean): Tensor = - ndBase.get().argmax(ndArray, keepDim, dim).asIntStructure() - - override fun StructureND.mean(dim: Int, keepDim: Boolean): Nd4jArrayStructure = - ndArray.mean(keepDim, dim).wrap() - - override fun StructureND.exp(): Nd4jArrayStructure = Transforms.exp(ndArray).wrap() - override fun StructureND.ln(): Nd4jArrayStructure = Transforms.log(ndArray).wrap() - override fun StructureND.sqrt(): Nd4jArrayStructure = Transforms.sqrt(ndArray).wrap() - override fun StructureND.cos(): Nd4jArrayStructure = Transforms.cos(ndArray).wrap() - override fun StructureND.acos(): Nd4jArrayStructure = Transforms.acos(ndArray).wrap() - override fun StructureND.cosh(): Nd4jArrayStructure = Transforms.cosh(ndArray).wrap() - - override fun StructureND.acosh(): Nd4jArrayStructure = - Nd4j.getExecutioner().exec(ACosh(ndArray, ndArray.ulike())).wrap() - - override fun StructureND.sin(): Nd4jArrayStructure = Transforms.sin(ndArray).wrap() - override fun StructureND.asin(): Nd4jArrayStructure = Transforms.asin(ndArray).wrap() - override fun StructureND.sinh(): Tensor = Transforms.sinh(ndArray).wrap() - - override fun StructureND.asinh(): Nd4jArrayStructure = - Nd4j.getExecutioner().exec(ASinh(ndArray, ndArray.ulike())).wrap() - - override fun StructureND.tan(): Nd4jArrayStructure = Transforms.tan(ndArray).wrap() - override fun StructureND.atan(): Nd4jArrayStructure = Transforms.atan(ndArray).wrap() - override fun StructureND.tanh(): Nd4jArrayStructure = Transforms.tanh(ndArray).wrap() - override fun StructureND.atanh(): Nd4jArrayStructure = Transforms.atanh(ndArray).wrap() - override fun StructureND.ceil(): Nd4jArrayStructure = Transforms.ceil(ndArray).wrap() - override fun StructureND.floor(): Nd4jArrayStructure = Transforms.floor(ndArray).wrap() - override fun StructureND.std(dim: Int, keepDim: Boolean): Nd4jArrayStructure = - ndArray.std(true, keepDim, dim).wrap() - - override fun T.div(arg: StructureND): Nd4jArrayStructure = arg.ndArray.rdiv(this).wrap() - override fun StructureND.div(arg: T): Nd4jArrayStructure = ndArray.div(arg).wrap() - override fun StructureND.div(arg: StructureND): Nd4jArrayStructure = ndArray.div(arg.ndArray).wrap() - - override fun Tensor.divAssign(value: T) { - ndArray.divi(value) - } - - override fun Tensor.divAssign(arg: StructureND) { - ndArray.divi(arg.ndArray) - } - - override fun StructureND.variance(dim: Int, keepDim: Boolean): Nd4jArrayStructure = - Nd4j.getExecutioner().exec(Variance(ndArray, true, true, dim)).wrap() - - private companion object { - private val ndBase: ThreadLocal = ThreadLocal.withInitial(::NDBase) - } -} - -/** - * [Double] specialization of [Nd4jTensorAlgebra]. - */ -public object DoubleNd4jTensorAlgebra : Nd4jTensorAlgebra { - - override val elementAlgebra: DoubleField get() = DoubleField - - override fun INDArray.wrap(): Nd4jArrayStructure = asDoubleStructure() - - override fun structureND(shape: Shape, initializer: DoubleField.(IntArray) -> Double): Nd4jArrayStructure { - val array: INDArray = Nd4j.zeros(*shape) - val indices = DefaultStrides(shape) - indices.asSequence().forEach { index -> - array.putScalar(index, elementAlgebra.initializer(index)) - } - return array.wrap() - } - - - @OptIn(PerformancePitfall::class) - override val StructureND.ndArray: INDArray - get() = when (this) { - is Nd4jArrayStructure -> ndArray - else -> Nd4j.zeros(*shape).also { - elements().forEach { (idx, value) -> it.putScalar(idx, value) } - } - } - - override fun StructureND.valueOrNull(): Double? = - if (shape contentEquals intArrayOf(1)) ndArray.getDouble(0) else null - - // TODO rewrite - override fun diagonalEmbedding( - diagonalEntries: Tensor, - offset: Int, - dim1: Int, - dim2: Int, - ): Tensor = DoubleTensorAlgebra.diagonalEmbedding(diagonalEntries, offset, dim1, dim2) - - override fun StructureND.sum(): Double = ndArray.sumNumber().toDouble() - override fun StructureND.min(): Double = ndArray.minNumber().toDouble() - override fun StructureND.max(): Double = ndArray.maxNumber().toDouble() - override fun StructureND.mean(): Double = ndArray.meanNumber().toDouble() - override fun StructureND.std(): Double = ndArray.stdNumber().toDouble() - override fun StructureND.variance(): Double = ndArray.varNumber().toDouble() -} diff --git a/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/arrays.kt b/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/arrays.kt deleted file mode 100644 index 3ca756600..000000000 --- a/kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/arrays.kt +++ /dev/null @@ -1,10 +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. - */ - -package space.kscience.kmath.nd4j - -import space.kscience.kmath.misc.toIntExact - -internal fun LongArray.toIntArray(): IntArray = IntArray(size) { this[it].toIntExact() } diff --git a/kmath-nd4j/src/test/kotlin/space/kscience/kmath/nd4j/Nd4jArrayAlgebraTest.kt b/kmath-nd4j/src/test/kotlin/space/kscience/kmath/nd4j/Nd4jArrayAlgebraTest.kt deleted file mode 100644 index 9d30c2027..000000000 --- a/kmath-nd4j/src/test/kotlin/space/kscience/kmath/nd4j/Nd4jArrayAlgebraTest.kt +++ /dev/null @@ -1,68 +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. - */ - -package space.kscience.kmath.nd4j - -import org.nd4j.linalg.factory.Nd4j -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.nd.one -import space.kscience.kmath.nd.structureND -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.IntRing -import space.kscience.kmath.operations.invoke -import kotlin.math.PI -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue -import kotlin.test.fail - -@OptIn(PerformancePitfall::class) -internal class Nd4jArrayAlgebraTest { - @Test - fun testProduce() { - val res = DoubleField.nd4j.structureND(2, 2) { it.sum().toDouble() } - val expected = (Nd4j.create(2, 2) ?: fail()).asDoubleStructure() - expected[intArrayOf(0, 0)] = 0.0 - expected[intArrayOf(0, 1)] = 1.0 - expected[intArrayOf(1, 0)] = 1.0 - expected[intArrayOf(1, 1)] = 2.0 - assertEquals(expected, res) - } - - @Test - fun testMap() { - val res = IntRing.nd4j { - one(2, 2).map { it + it * 2 } - } - val expected = (Nd4j.create(2, 2) ?: fail()).asIntStructure() - expected[intArrayOf(0, 0)] = 3 - expected[intArrayOf(0, 1)] = 3 - expected[intArrayOf(1, 0)] = 3 - expected[intArrayOf(1, 1)] = 3 - assertEquals(expected, res) - } - - @Test - fun testAdd() { - val res = IntRing.nd4j { one(2, 2) + 25 } - val expected = (Nd4j.create(2, 2) ?: fail()).asIntStructure() - expected[intArrayOf(0, 0)] = 26 - expected[intArrayOf(0, 1)] = 26 - expected[intArrayOf(1, 0)] = 26 - expected[intArrayOf(1, 1)] = 26 - assertEquals(expected, res) - } - - @Test - fun testSin() = DoubleField.nd4j{ - val initial = structureND(2, 2) { (i, j) -> if (i == j) PI / 2 else 0.0 } - val transformed = sin(initial) - val expected = structureND(2, 2) { (i, j) -> if (i == j) 1.0 else 0.0 } - - println(transformed) - assertTrue { StructureND.contentEquals(transformed, expected) } - } -} diff --git a/kmath-nd4j/src/test/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructureTest.kt b/kmath-nd4j/src/test/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructureTest.kt deleted file mode 100644 index 30d01338f..000000000 --- a/kmath-nd4j/src/test/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructureTest.kt +++ /dev/null @@ -1,79 +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. - */ - -package space.kscience.kmath.nd4j - -import org.nd4j.linalg.factory.Nd4j -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.get -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertNotEquals -import kotlin.test.fail - -internal class Nd4jArrayStructureTest { - @OptIn(PerformancePitfall::class) - @Test - fun testElements() { - val nd = Nd4j.create(doubleArrayOf(1.0, 2.0, 3.0))!! - val struct = nd.asDoubleStructure() - val res = struct.elements().map(Pair::second).toList() - assertEquals(listOf(1.0, 2.0, 3.0), res) - } - - @Test - fun testShape() { - val nd = Nd4j.rand(10, 2, 3, 6) ?: fail() - val struct = nd.asDoubleStructure() - assertEquals(intArrayOf(10, 2, 3, 6).toList(), struct.shape.toList()) - } - - @Test - fun testEquals() { - val nd1 = Nd4j.create(doubleArrayOf(1.0, 2.0, 3.0)) ?: fail() - val struct1 = nd1.asDoubleStructure() - assertEquals(struct1, struct1) - assertNotEquals(struct1 as Any?, null) - val nd2 = Nd4j.create(doubleArrayOf(1.0, 2.0, 3.0)) ?: fail() - val struct2 = nd2.asDoubleStructure() - assertEquals(struct1, struct2) - assertEquals(struct2, struct1) - val nd3 = Nd4j.create(doubleArrayOf(1.0, 2.0, 3.0)) ?: fail() - val struct3 = nd3.asDoubleStructure() - assertEquals(struct2, struct3) - assertEquals(struct1, struct3) - } - - @Test - fun testHashCode() { - val nd1 = Nd4j.create(doubleArrayOf(1.0, 2.0, 3.0)) ?: fail() - val struct1 = nd1.asDoubleStructure() - val nd2 = Nd4j.create(doubleArrayOf(1.0, 2.0, 3.0)) ?: fail() - val struct2 = nd2.asDoubleStructure() - assertEquals(struct1.hashCode(), struct2.hashCode()) - } - - @Test - fun testDimension() { - val nd = Nd4j.rand(8, 16, 3, 7, 1)!! - val struct = nd.asFloatStructure() - assertEquals(5, struct.dimension) - } - - @Test - fun testGet() { - val nd = Nd4j.rand(10, 2, 3, 6) ?: fail() - val struct = nd.asIntStructure() - assertEquals(nd.getInt(0, 0, 0, 0), struct[0, 0, 0, 0]) - } - - @Test - fun testSet() { - val nd = Nd4j.rand(17, 12, 4, 8)!! - val struct = nd.asIntStructure() - struct[intArrayOf(1, 2, 3, 4)] = 777 - assertEquals(777, struct[1, 2, 3, 4]) - } -} diff --git a/kmath-optimization/README.md b/kmath-optimization/README.md deleted file mode 100644 index 137975c90..000000000 --- a/kmath-optimization/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-optimization - - - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-optimization:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-optimization:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-optimization:0.3.0-dev-20") -} -``` diff --git a/kmath-optimization/build.gradle.kts b/kmath-optimization/build.gradle.kts deleted file mode 100644 index b920b9267..000000000 --- a/kmath-optimization/build.gradle.kts +++ /dev/null @@ -1,24 +0,0 @@ -plugins { - id("ru.mipt.npm.gradle.mpp") - id("ru.mipt.npm.gradle.native") -} - -kscience { - useAtomic() -} - -kotlin.sourceSets { - all { - languageSettings.optIn("space.kscience.kmath.misc.UnstableKMathAPI") - } - - commonMain { - dependencies { - api(project(":kmath-coroutines")) - } - } -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL -} diff --git a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/FunctionOptimization.kt b/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/FunctionOptimization.kt deleted file mode 100644 index 02602b068..000000000 --- a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/FunctionOptimization.kt +++ /dev/null @@ -1,71 +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. - */ - -package space.kscience.kmath.optimization - -import space.kscience.kmath.expressions.DifferentiableExpression -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.FeatureSet - -public class OptimizationValue(public val value: T) : OptimizationFeature { - override fun toString(): String = "Value($value)" -} - -public enum class FunctionOptimizationTarget : OptimizationFeature { - MAXIMIZE, - MINIMIZE -} - -public class FunctionOptimization( - override val features: FeatureSet, - public val expression: DifferentiableExpression, -) : OptimizationProblem { - - - override fun equals(other: Any?): Boolean { - if (this === other) return true - if (other == null || this::class != other::class) return false - - other as FunctionOptimization<*> - - if (features != other.features) return false - if (expression != other.expression) return false - - return true - } - - override fun hashCode(): Int { - var result = features.hashCode() - result = 31 * result + expression.hashCode() - return result - } - - override fun toString(): String = "FunctionOptimization(features=$features)" -} - -public fun FunctionOptimization.withFeatures( - vararg newFeature: OptimizationFeature, -): FunctionOptimization = FunctionOptimization( - features.with(*newFeature), - expression, -) - -/** - * Optimizes differentiable expression using specific [optimizer] form given [startingPoint]. - */ -public suspend fun DifferentiableExpression.optimizeWith( - optimizer: Optimizer>, - startingPoint: Map, - vararg features: OptimizationFeature, -): FunctionOptimization { - val problem = FunctionOptimization(FeatureSet.of(OptimizationStartPoint(startingPoint), *features), this) - return optimizer.optimize(problem) -} - -public val FunctionOptimization.resultValueOrNull: T? - get() = getFeature>()?.point?.let { expression(it) } - -public val FunctionOptimization.resultValue: T - get() = resultValueOrNull ?: error("Result is not present in $this") \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationBuilder.kt b/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationBuilder.kt deleted file mode 100644 index 416d0195d..000000000 --- a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationBuilder.kt +++ /dev/null @@ -1,93 +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. - */ - -package space.kscience.kmath.optimization - -import space.kscience.kmath.data.XYColumnarData -import space.kscience.kmath.expressions.DifferentiableExpression -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.FeatureSet - -public abstract class OptimizationBuilder> { - public val features: MutableList = ArrayList() - - public fun addFeature(feature: OptimizationFeature) { - features.add(feature) - } - - public inline fun updateFeature(update: (T?) -> T) { - val existing = features.find { it.key == T::class } as? T - val new = update(existing) - if (existing != null) { - features.remove(existing) - } - addFeature(new) - } - - public abstract fun build(): R -} - -public fun OptimizationBuilder.startAt(startingPoint: Map) { - addFeature(OptimizationStartPoint(startingPoint)) -} - -public class FunctionOptimizationBuilder( - private val expression: DifferentiableExpression, -) : OptimizationBuilder>() { - override fun build(): FunctionOptimization = FunctionOptimization(FeatureSet.of(features), expression) -} - -public fun FunctionOptimization( - expression: DifferentiableExpression, - builder: FunctionOptimizationBuilder.() -> Unit, -): FunctionOptimization = FunctionOptimizationBuilder(expression).apply(builder).build() - -public suspend fun DifferentiableExpression.optimizeWith( - optimizer: Optimizer>, - startingPoint: Map, - builder: FunctionOptimizationBuilder.() -> Unit = {}, -): FunctionOptimization { - val problem = FunctionOptimization(this) { - startAt(startingPoint) - builder() - } - return optimizer.optimize(problem) -} - -public suspend fun DifferentiableExpression.optimizeWith( - optimizer: Optimizer>, - vararg startingPoint: Pair, - builder: FunctionOptimizationBuilder.() -> Unit = {}, -): FunctionOptimization { - val problem = FunctionOptimization(this) { - startAt(mapOf(*startingPoint)) - builder() - } - return optimizer.optimize(problem) -} - - -public class XYOptimizationBuilder( - public val data: XYColumnarData, - public val model: DifferentiableExpression, -) : OptimizationBuilder() { - - public var pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY - public var pointWeight: PointWeight = PointWeight.byYSigma - - override fun build(): XYFit = XYFit( - data, - model, - FeatureSet.of(features), - pointToCurveDistance, - pointWeight - ) -} - -public fun XYOptimization( - data: XYColumnarData, - model: DifferentiableExpression, - builder: XYOptimizationBuilder.() -> Unit, -): XYFit = XYOptimizationBuilder(data, model).apply(builder).build() \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationProblem.kt b/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationProblem.kt deleted file mode 100644 index b42be4035..000000000 --- a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationProblem.kt +++ /dev/null @@ -1,66 +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. - */ - -package space.kscience.kmath.optimization - -import space.kscience.kmath.expressions.DifferentiableExpression -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.linear.Matrix -import space.kscience.kmath.misc.* -import kotlin.reflect.KClass - -public interface OptimizationFeature : Feature { - // enforce toString override - override fun toString(): String -} - -public interface OptimizationProblem : Featured { - public val features: FeatureSet - override fun getFeature(type: KClass): F? = features.getFeature(type) -} - -public inline fun OptimizationProblem<*>.getFeature(): F? = getFeature(F::class) - -public open class OptimizationStartPoint(public val point: Map) : OptimizationFeature { - override fun toString(): String = "StartPoint($point)" -} - - -public interface OptimizationPrior : OptimizationFeature, DifferentiableExpression { - override val key: FeatureKey get() = OptimizationPrior::class -} - -public class OptimizationCovariance(public val covariance: Matrix) : OptimizationFeature { - override fun toString(): String = "Covariance($covariance)" -} - -/** - * Get the starting point for optimization. Throws error if not defined. - */ -public val OptimizationProblem.startPoint: Map - get() = getFeature>()?.point - ?: error("Starting point not defined in $this") - -public open class OptimizationResult(public val point: Map) : OptimizationFeature { - override fun toString(): String = "Result($point)" -} - -public val OptimizationProblem.resultPointOrNull: Map? - get() = getFeature>()?.point - -public val OptimizationProblem.resultPoint: Map - get() = resultPointOrNull ?: error("Result is not present in $this") - -public class OptimizationLog(private val loggable: Loggable) : Loggable by loggable, OptimizationFeature { - override fun toString(): String = "Log($loggable)" -} - -public class OptimizationParameters(public val symbols: List) : OptimizationFeature { - public constructor(vararg symbols: Symbol) : this(listOf(*symbols)) - - override fun toString(): String = "Parameters($symbols)" -} - - diff --git a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/Optimizer.kt b/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/Optimizer.kt deleted file mode 100644 index 78385a99b..000000000 --- a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/Optimizer.kt +++ /dev/null @@ -1,10 +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. - */ - -package space.kscience.kmath.optimization - -public interface Optimizer> { - public suspend fun optimize(problem: P): P -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/QowOptimizer.kt b/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/QowOptimizer.kt deleted file mode 100644 index babbaf6cd..000000000 --- a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/QowOptimizer.kt +++ /dev/null @@ -1,266 +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. - */ - -package space.kscience.kmath.optimization - -import space.kscience.kmath.expressions.DifferentiableExpression -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.expressions.SymbolIndexer -import space.kscience.kmath.expressions.derivative -import space.kscience.kmath.linear.* -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.misc.log -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.DoubleL2Norm -import space.kscience.kmath.operations.algebra -import space.kscience.kmath.structures.DoubleBuffer - - -public class QowRuns(public val runs: Int) : OptimizationFeature { - init { - require(runs >= 1) { "Number of runs must be more than zero" } - } - - override fun toString(): String = "QowRuns(runs=$runs)" -} - - -/** - * An optimizer based onf Fyodor Tkachev's quasi-optimal weights method. - * See [the article](http://arxiv.org/abs/physics/0604127). - */ -@UnstableKMathAPI -public object QowOptimizer : Optimizer { - - private val linearSpace: LinearSpace = Double.algebra.linearSpace - private val solver: LinearSolver = linearSpace.lupSolver() - - @OptIn(UnstableKMathAPI::class) - private class QoWeight( - val problem: XYFit, - val parameters: Map, - ) : Map by parameters, SymbolIndexer { - override val symbols: List = parameters.keys.toList() - - val data get() = problem.data - - /** - * Derivatives of the spectrum over parameters. First index in the point number, second one - index of parameter - */ - val derivs: Matrix by lazy { - linearSpace.buildMatrix(problem.data.size, symbols.size) { d, s -> - problem.distance(d).derivative(symbols[s])(parameters) - } - } - - /** - * Array of dispersions in each point - */ - val dispersion: Point by lazy { - DoubleBuffer(problem.data.size) { d -> - 1.0/problem.weight(d).invoke(parameters) - } - } - - val prior: DifferentiableExpression? get() = problem.getFeature>() - - override fun toString(): String = parameters.toString() - } - - /** - * The signed distance from the model to the [d]-th point of data. - */ - private fun QoWeight.distance(d: Int, parameters: Map): Double = problem.distance(d)(parameters) - - - /** - * The derivative of [distance] - */ - private fun QoWeight.distanceDerivative(symbol: Symbol, d: Int, parameters: Map): Double = - problem.distance(d).derivative(symbol)(parameters) - - /** - * Теоретическая ковариация весовых функций. - * - * D(\phi)=E(\phi_k(\theta_0) \phi_l(\theta_0))= disDeriv_k * disDeriv_l /sigma^2 - */ - private fun QoWeight.covarF(): Matrix = - linearSpace.matrix(size, size).symmetric { s1, s2 -> - (0 until data.size).sumOf { d -> derivs[d, s1] * derivs[d, s2] / dispersion[d] } - } - - /** - * Экспериментальная ковариация весов. Формула (22) из - * http://arxiv.org/abs/physics/0604127 - */ - private fun QoWeight.covarFExp(theta: Map): Matrix = - with(linearSpace) { - /* - * Важно! Если не делать предварителього вычисления этих производных, то - * количество вызывов функции будет dim^2 вместо dim Первый индекс - - * номер точки, второй - номер переменной, по которой берется производная - */ - val eqvalues = linearSpace.buildMatrix(data.size, size) { d, s -> - distance(d, theta) * derivs[d, s] / dispersion[d] - } - - buildMatrix(size, size) { s1, s2 -> - (0 until data.size).sumOf { d -> eqvalues[d, s2] * eqvalues[d, s1] } - } - } - - /** - * Equation derivatives for Newton run - */ - private fun QoWeight.getEqDerivValues( - theta: Map = parameters, - ): Matrix = with(linearSpace) { - //Возвращает производную k-того Eq по l-тому параметру - //val res = Array(fitDim) { DoubleArray(fitDim) } - val sderiv = buildMatrix(data.size, size) { d, s -> - distanceDerivative(symbols[s], d, theta) - } - - buildMatrix(size, size) { s1, s2 -> - val base = (0 until data.size).sumOf { d -> - require(dispersion[d] > 0) - sderiv[d, s2] * derivs[d, s1] / dispersion[d] - } - prior?.let { prior -> - //Check if this one is correct - val pi = prior(theta) - val deriv1 = prior.derivative(symbols[s1])(theta) - val deriv2 = prior.derivative(symbols[s2])(theta) - base + deriv1 * deriv2 / pi / pi - } ?: base - } - } - - - /** - * Значения уравнений метода квазиоптимальных весов - */ - private fun QoWeight.getEqValues(theta: Map = this): Point { - val distances = DoubleBuffer(data.size) { d -> distance(d, theta) } - - return DoubleBuffer(size) { s -> - val base = (0 until data.size).sumOf { d -> distances[d] * derivs[d, s] / dispersion[d] } - //Поправка на априорную вероятность - prior?.let { prior -> - base - prior.derivative(symbols[s])(theta) / prior(theta) - } ?: base - } - } - - - private fun QoWeight.newtonianStep( - theta: Map, - eqvalues: Point, - ): QoWeight = linearSpace { - with(this@newtonianStep) { - val start = theta.toPoint() - val invJacob = solver.inverse(this@newtonianStep.getEqDerivValues(theta)) - - val step = invJacob.dot(eqvalues) - return QoWeight(problem, theta + (start - step).toMap()) - } - } - - private fun QoWeight.newtonianRun( - maxSteps: Int = 100, - tolerance: Double = 0.0, - fast: Boolean = false, - ): QoWeight { - - val logger = problem.getFeature() - - var dis: Double //discrepancy value - // Working with the full set of parameters - var par = problem.startPoint - - logger?.log { "Starting newtonian iteration from: \n\t$par" } - - var eqvalues = getEqValues(par) //Values of the weight functions - - dis = DoubleL2Norm.norm(eqvalues) // discrepancy - logger?.log { "Starting discrepancy is $dis" } - var i = 0 - var flag = false - while (!flag) { - i++ - logger?.log { "Starting step number $i" } - - val currentSolution = if (fast) { - //Берет значения матрицы в той точке, где считается вес - newtonianStep(this, eqvalues) - } else { - //Берет значения матрицы в точке par - newtonianStep(par, eqvalues) - } - // здесь должен стоять учет границ параметров - logger?.log { "Parameter values after step are: \n\t$currentSolution" } - - eqvalues = getEqValues(currentSolution) - val currentDis = DoubleL2Norm.norm(eqvalues)// невязка после шага - - logger?.log { "The discrepancy after step is: $currentDis." } - - if (currentDis >= dis && i > 1) { - //дополнительно проверяем, чтобы был сделан хотя бы один шаг - flag = true - logger?.log { "The discrepancy does not decrease. Stopping iteration." } - } else { - par = currentSolution - dis = currentDis - } - if (i >= maxSteps) { - flag = true - logger?.log { "Maximum number of iterations reached. Stopping iteration." } - } - if (dis <= tolerance) { - flag = true - logger?.log { "Tolerance threshold is reached. Stopping iteration." } - } - } - - return QoWeight(problem, par) - } - - private fun QoWeight.covariance(): Matrix { - val logger = problem.getFeature() - - logger?.log { - """ - Starting errors estimation using quasioptimal weights method. The starting weight is: - ${problem.startPoint} - """.trimIndent() - } - - val covar = solver.inverse(getEqDerivValues()) - //TODO fix eigenvalues check -// val decomposition = EigenDecomposition(covar.matrix) -// var valid = true -// for (lambda in decomposition.realEigenvalues) { -// if (lambda <= 0) { -// logger?.log { "The covariance matrix is not positive defined. Error estimation is not valid" } -// valid = false -// } -// } - return covar - } - - override suspend fun optimize(problem: XYFit): XYFit { - val qowRuns = problem.getFeature()?.runs ?: 2 - - - var qow = QoWeight(problem, problem.startPoint) - var res = qow.newtonianRun() - repeat(qowRuns - 1) { - qow = QoWeight(problem, res.parameters) - res = qow.newtonianRun() - } - return res.problem.withFeature(OptimizationResult(res.parameters)) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/XYFit.kt b/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/XYFit.kt deleted file mode 100644 index 07fea3126..000000000 --- a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/XYFit.kt +++ /dev/null @@ -1,146 +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. - */ -@file:OptIn(UnstableKMathAPI::class) - -package space.kscience.kmath.optimization - -import space.kscience.kmath.data.XYColumnarData -import space.kscience.kmath.data.indices -import space.kscience.kmath.expressions.* -import space.kscience.kmath.misc.FeatureSet -import space.kscience.kmath.misc.Loggable -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.ExtendedField -import space.kscience.kmath.operations.bindSymbol -import kotlin.math.pow - -/** - * Specify the way to compute distance from point to the curve as DifferentiableExpression - */ -public interface PointToCurveDistance : OptimizationFeature { - public fun distance(problem: XYFit, index: Int): DifferentiableExpression - - public companion object { - public val byY: PointToCurveDistance = object : PointToCurveDistance { - override fun distance(problem: XYFit, index: Int): DifferentiableExpression { - val x = problem.data.x[index] - val y = problem.data.y[index] - - return object : DifferentiableExpression { - override fun derivativeOrNull( - symbols: List - ): Expression? = problem.model.derivativeOrNull(symbols)?.let { derivExpression -> - Expression { arguments -> - derivExpression.invoke(arguments + (Symbol.x to x)) - } - } - - override fun invoke(arguments: Map): Double = - problem.model(arguments + (Symbol.x to x)) - y - } - } - - override fun toString(): String = "PointToCurveDistanceByY" - } - } -} - -/** - * Compute a wight of the point. The more the weight, the more impact this point will have on the fit. - * By default, uses Dispersion^-1 - */ -public interface PointWeight : OptimizationFeature { - public fun weight(problem: XYFit, index: Int): DifferentiableExpression - - public companion object { - public fun bySigma(sigmaSymbol: Symbol): PointWeight = object : PointWeight { - override fun weight(problem: XYFit, index: Int): DifferentiableExpression = - object : DifferentiableExpression { - override fun invoke(arguments: Map): Double { - return problem.data[sigmaSymbol]?.get(index)?.pow(-2) ?: 1.0 - } - - override fun derivativeOrNull(symbols: List): Expression = Expression { 0.0 } - } - - override fun toString(): String = "PointWeightBySigma($sigmaSymbol)" - - } - - public val byYSigma: PointWeight = bySigma(Symbol.yError) - } -} - -/** - * A fit problem for X-Y-Yerr data. Also known as "least-squares" problem. - */ -public class XYFit( - public val data: XYColumnarData, - public val model: DifferentiableExpression, - override val features: FeatureSet, - internal val pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY, - internal val pointWeight: PointWeight = PointWeight.byYSigma, - public val xSymbol: Symbol = Symbol.x, -) : OptimizationProblem { - public fun distance(index: Int): DifferentiableExpression = pointToCurveDistance.distance(this, index) - - public fun weight(index: Int): DifferentiableExpression = pointWeight.weight(this, index) -} - -public fun XYFit.withFeature(vararg features: OptimizationFeature): XYFit { - return XYFit(data, model, this.features.with(*features), pointToCurveDistance, pointWeight) -} - -/** - * Fit given dta with - */ -public suspend fun XYColumnarData.fitWith( - optimizer: Optimizer, - processor: AutoDiffProcessor, - startingPoint: Map, - vararg features: OptimizationFeature = emptyArray(), - xSymbol: Symbol = Symbol.x, - pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY, - pointWeight: PointWeight = PointWeight.byYSigma, - model: A.(I) -> I -): XYFit where A : ExtendedField, A : ExpressionAlgebra { - val modelExpression = processor.differentiate { - val x = bindSymbol(xSymbol) - model(x) - } - - var actualFeatures = FeatureSet.of(*features, OptimizationStartPoint(startingPoint)) - - if (actualFeatures.getFeature() == null) { - actualFeatures = actualFeatures.with(OptimizationLog(Loggable.console)) - } - val problem = XYFit( - this, - modelExpression, - actualFeatures, - pointToCurveDistance, - pointWeight, - xSymbol - ) - return optimizer.optimize(problem) -} - -/** - * Compute chi squared value for completed fit. Return null for incomplete fit - */ -public val XYFit.chiSquaredOrNull: Double? get() { - val result = resultPointOrNull ?: return null - - return data.indices.sumOf { index-> - - val x = data.x[index] - val y = data.y[index] - val yErr = data[Symbol.yError]?.get(index) ?: 1.0 - - val mu = model.invoke(result + (xSymbol to x) ) - - ((y - mu)/yErr).pow(2) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/logLikelihood.kt b/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/logLikelihood.kt deleted file mode 100644 index b4cb2f1cf..000000000 --- a/kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/logLikelihood.kt +++ /dev/null @@ -1,66 +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. - */ - -package space.kscience.kmath.optimization - -import space.kscience.kmath.data.XYColumnarData -import space.kscience.kmath.data.indices -import space.kscience.kmath.expressions.DifferentiableExpression -import space.kscience.kmath.expressions.Expression -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.expressions.derivative -import space.kscience.kmath.misc.UnstableKMathAPI -import kotlin.math.PI -import kotlin.math.ln -import kotlin.math.pow -import kotlin.math.sqrt - - -private val oneOver2Pi = 1.0 / sqrt(2 * PI) - -@UnstableKMathAPI -internal fun XYFit.logLikelihood(): DifferentiableExpression = object : DifferentiableExpression { - override fun derivativeOrNull(symbols: List): Expression = Expression { arguments -> - data.indices.sumOf { index -> - val d = distance(index)(arguments) - val weight = weight(index)(arguments) - val weightDerivative = weight(index).derivative(symbols)(arguments) - - // -1 / (sqrt(2 PI) * sigma) + 2 (x-mu)/ 2 sigma^2 * d mu/ d theta - (x-mu)^2 / 2 * d w/ d theta - return@sumOf -oneOver2Pi * sqrt(weight) + //offset derivative - d * model.derivative(symbols)(arguments) * weight - //model derivative - d.pow(2) * weightDerivative / 2 //weight derivative - } - } - - override fun invoke(arguments: Map): Double { - return data.indices.sumOf { index -> - val d = distance(index)(arguments) - val weight = weight(index)(arguments) - //1/sqrt(2 PI sigma^2) - (x-mu)^2/ (2 * sigma^2) - oneOver2Pi * ln(weight) - d.pow(2) * weight - } / 2 - } - -} - -/** - * Optimize given XY (least squares) [problem] using this function [Optimizer]. - * The problem is treated as maximum likelihood problem and is done via maximizing logarithmic likelihood, respecting - * possible weight dependency on the model and parameters. - */ -@UnstableKMathAPI -public suspend fun Optimizer>.maximumLogLikelihood(problem: XYFit): XYFit { - val functionOptimization = FunctionOptimization(problem.features, problem.logLikelihood()) - val result = optimize(functionOptimization.withFeatures(FunctionOptimizationTarget.MAXIMIZE)) - return XYFit(problem.data, problem.model, result.features) -} - -@UnstableKMathAPI -public suspend fun Optimizer>.maximumLogLikelihood( - data: XYColumnarData, - model: DifferentiableExpression, - builder: XYOptimizationBuilder.() -> Unit, -): XYFit = maximumLogLikelihood(XYOptimization(data, model, builder)) diff --git a/kmath-optimization/src/commonMain/tmp/QowFit.kt b/kmath-optimization/src/commonMain/tmp/QowFit.kt deleted file mode 100644 index c78aef401..000000000 --- a/kmath-optimization/src/commonMain/tmp/QowFit.kt +++ /dev/null @@ -1,372 +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. - */ - -package space.kscience.kmath.optimization.qow - -import space.kscience.kmath.data.ColumnarData -import space.kscience.kmath.data.XYErrorColumnarData -import space.kscience.kmath.expressions.* -import space.kscience.kmath.linear.* -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.Field -import space.kscience.kmath.optimization.OptimizationFeature -import space.kscience.kmath.optimization.OptimizationProblemFactory -import space.kscience.kmath.optimization.OptimizationResult -import space.kscience.kmath.optimization.XYOptimization -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.DoubleL2Norm -import kotlin.math.pow - - -private typealias ParamSet = Map - -@OptIn(UnstableKMathAPI::class) -public class QowFit( - override val symbols: List, - private val space: LinearSpace, - private val solver: LinearSolver, -) : XYOptimization, SymbolIndexer { - - private var logger: FitLogger? = null - - private var startingPoint: Map = TODO() - private var covariance: Matrix? = TODO() - private val prior: DifferentiableExpression>? = TODO() - private var data: XYErrorColumnarData = TODO() - private var model: DifferentiableExpression> = TODO() - - private val features = HashSet() - - override fun update(result: OptimizationResult) { - TODO("Not yet implemented") - } - - override val algebra: Field - get() = TODO("Not yet implemented") - - override fun data( - dataSet: ColumnarData, - xSymbol: Symbol, - ySymbol: Symbol, - xErrSymbol: Symbol?, - yErrSymbol: Symbol?, - ) { - TODO("Not yet implemented") - } - - override fun model(model: (Double) -> DifferentiableExpression) { - TODO("Not yet implemented") - } - - private var x: Symbol = Symbol.x - - /** - * The signed distance from the model to the [i]-th point of data. - */ - private fun distance(i: Int, parameters: Map): Double = - model(parameters + (x to data.x[i])) - data.y[i] - - - /** - * The derivative of [distance] - * TODO use expressions instead - */ - private fun distanceDerivative(symbol: Symbol, i: Int, parameters: Map): Double = - model.derivative(symbol)(parameters + (x to data.x[i])) - - /** - * The dispersion of [i]-th data point - */ - private fun getDispersion(i: Int, parameters: Map): Double = data.yErr[i].pow(2) - - private fun getCovariance(weight: QoWeight): Matrix = solver.inverse(getEqDerivValues(weight)) - - /** - * Теоретическая ковариация весовых функций. - * - * D(\phi)=E(\phi_k(\theta_0) \phi_l(\theta_0))= disDeriv_k * disDeriv_l /sigma^2 - */ - private fun covarF(weight: QoWeight): Matrix = space.buildSymmetricMatrix(symbols.size) { k, l -> - (0 until data.size).sumOf { i -> weight.derivs[k, i] * weight.derivs[l, i] / weight.dispersion[i] } - } - - /** - * Экспериментальная ковариация весов. Формула (22) из - * http://arxiv.org/abs/physics/0604127 - * - * @param source - * @param set - * @param fitPars - * @param weight - * @return - */ - private fun covarFExp(weight: QoWeight, theta: Map): Matrix = space.run { - /* - * Важно! Если не делать предварителього вычисления этих производных, то - * количество вызывов функции будет dim^2 вместо dim Первый индекс - - * номер точки, второй - номер переменной, по которой берется производная - */ - val eqvalues = buildMatrix(data.size, symbols.size) { i, l -> - distance(i, theta) * weight.derivs[l, i] / weight.dispersion[i] - } - - buildMatrix(symbols.size, symbols.size) { k, l -> - (0 until data.size).sumOf { i -> eqvalues[i, l] * eqvalues[i, k] } - } - } - - /** - * производные уравнений для метода Ньютона - * - * @param source - * @param set - * @param fitPars - * @param weight - * @return - */ - private fun getEqDerivValues( - weight: QoWeight, theta: Map = weight.theta, - ): Matrix = space.run { - val fitDim = symbols.size - //Возвращает производную k-того Eq по l-тому параметру - val res = Array(fitDim) { DoubleArray(fitDim) } - val sderiv = buildMatrix(data.size, symbols.size) { i, l -> - distanceDerivative(symbols[l], i, theta) - } - - buildMatrix(symbols.size, symbols.size) { k, l -> - val base = (0 until data.size).sumOf { i -> - require(weight.dispersion[i] > 0) - sderiv[i, l] * weight.derivs[k, i] / weight.dispersion[i] - } - prior?.let { prior -> - //Check if this one is correct - val pi = prior(theta) - val deriv1 = prior.derivative(symbols[k])(theta) - val deriv2 = prior.derivative(symbols[l])(theta) - base + deriv1 * deriv2 / pi / pi - } ?: base - } - } - - - /** - * Значения уравнений метода квазиоптимальных весов - * - * @param source - * @param set - * @param fitPars - * @param weight - * @return - */ - private fun getEqValues(weight: QoWeight, theta: Map = weight.theta): Point { - val distances = DoubleBuffer(data.size) { i -> distance(i, theta) } - - return DoubleBuffer(symbols.size) { k -> - val base = (0 until data.size).sumOf { i -> distances[i] * weight.derivs[k, i] / weight.dispersion[i] } - //Поправка на априорную вероятность - prior?.let { prior -> - base - prior.derivative(symbols[k])(theta) / prior(theta) - } ?: base - } - } - - - /** - * The state of QOW fitter - * Created by Alexander Nozik on 17-Oct-16. - */ - private inner class QoWeight( - val theta: Map, - ) { - - init { - require(data.size > 0) { "The state does not contain data" } - } - - /** - * Derivatives of the spectrum over parameters. First index in the point number, second one - index of parameter - */ - val derivs: Matrix by lazy { - space.buildMatrix(data.size, symbols.size) { i, k -> - distanceDerivative(symbols[k], i, theta) - } - } - - /** - * Array of dispersions in each point - */ - val dispersion: Point by lazy { - DoubleBuffer(data.size) { i -> getDispersion(i, theta) } - } - - } - - private fun newtonianStep( - weight: QoWeight, - par: Map, - eqvalues: Point, - ): Map = space.run { - val start = par.toPoint() - val invJacob = solver.inverse(getEqDerivValues(weight, par)) - - val step = invJacob.dot(eqvalues) - return par + (start - step).toMap() - } - - private fun newtonianRun( - weight: QoWeight, - maxSteps: Int = 100, - tolerance: Double = 0.0, - fast: Boolean = false, - ): ParamSet { - - var dis: Double//норма невязки - // Для удобства работаем всегда с полным набором параметров - var par = startingPoint - - logger?.log { "Starting newtonian iteration from: \n\t$par" } - - var eqvalues = getEqValues(weight, par)//значения функций - - dis = DoubleL2Norm.norm(eqvalues)// невязка - logger?.log { "Starting discrepancy is $dis" } - var i = 0 - var flag = false - while (!flag) { - i++ - logger?.log { "Starting step number $i" } - - val currentSolution = if (fast) { - //Берет значения матрицы в той точке, где считается вес - newtonianStep(weight, weight.theta, eqvalues) - } else { - //Берет значения матрицы в точке par - newtonianStep(weight, par, eqvalues) - } - // здесь должен стоять учет границ параметров - logger?.log { "Parameter values after step are: \n\t$currentSolution" } - - eqvalues = getEqValues(weight, currentSolution) - val currentDis = DoubleL2Norm.norm(eqvalues)// невязка после шага - - logger?.log { "The discrepancy after step is: $currentDis." } - - if (currentDis >= dis && i > 1) { - //дополнительно проверяем, чтобы был сделан хотя бы один шаг - flag = true - logger?.log { "The discrepancy does not decrease. Stopping iteration." } - } else { - par = currentSolution - dis = currentDis - } - if (i >= maxSteps) { - flag = true - logger?.log { "Maximum number of iterations reached. Stopping iteration." } - } - if (dis <= tolerance) { - flag = true - logger?.log { "Tolerance threshold is reached. Stopping iteration." } - } - } - - return par - } - - -// -// override fun run(state: FitState, parentLog: History?, meta: Meta): FitResult { -// val log = Chronicle("QOW", parentLog) -// val action = meta.getString(FIT_STAGE_TYPE, TASK_RUN) -// log.report("QOW fit engine started task '{}'", action) -// return when (action) { -// TASK_SINGLE -> makeRun(state, log, meta) -// TASK_COVARIANCE -> generateErrors(state, log, meta) -// TASK_RUN -> { -// var res = makeRun(state, log, meta) -// res = makeRun(res.optState().get(), log, meta) -// generateErrors(res.optState().get(), log, meta) -// } -// else -> throw IllegalArgumentException("Unknown task") -// } -// } - -// private fun makeRun(state: FitState, log: History, meta: Meta): FitResult { -// /*Инициализация объектов, задание исходных значений*/ -// log.report("Starting fit using quasioptimal weights method.") -// -// val fitPars = getFitPars(state, meta) -// -// val curWeight = QoWeight(state, fitPars, state.parameters) -// -// // вычисляем вес в allPar. Потом можно будет попробовать ручное задание веса -// log.report("The starting weight is: \n\t{}", -// MathUtils.toString(curWeight.theta)) -// -// //Стартовая точка такая же как и параметр веса -// /*Фитирование*/ -// val res = newtonianRun(state, curWeight, log, meta) -// -// /*Генерация результата*/ -// -// return FitResult.build(state.edit().setPars(res).build(), *fitPars) -// } - - /** - * generateErrors. - */ - private fun generateErrors(): Matrix { - logger?.log { """ - Starting errors estimation using quasioptimal weights method. The starting weight is: - ${curWeight.theta} - """.trimIndent()} - val curWeight = QoWeight(startingPoint) - - val covar = getCovariance(curWeight) - - val decomposition = EigenDecomposition(covar.matrix) - var valid = true - for (lambda in decomposition.realEigenvalues) { - if (lambda <= 0) { - log.report("The covariance matrix is not positive defined. Error estimation is not valid") - valid = false - } - } - } - - - override suspend fun optimize(): OptimizationResult { - val curWeight = QoWeight(startingPoint) - logger?.log { - """ - Starting fit using quasioptimal weights method. The starting weight is: - ${curWeight.theta} - """.trimIndent() - } - val res = newtonianRun(curWeight) - } - - - companion object : OptimizationProblemFactory { - override fun build(symbols: List): QowFit { - TODO("Not yet implemented") - } - - - /** - * Constant `QOW_ENGINE_NAME="QOW"` - */ - const val QOW_ENGINE_NAME = "QOW" - - /** - * Constant `QOW_METHOD_FAST="fast"` - */ - const val QOW_METHOD_FAST = "fast" - - - } -} - diff --git a/kmath-optimization/src/commonMain/tmp/minuit/AnalyticalGradientCalculator.kt b/kmath-optimization/src/commonMain/tmp/minuit/AnalyticalGradientCalculator.kt deleted file mode 100644 index 912fa22eb..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/AnalyticalGradientCalculator.kt +++ /dev/null @@ -1,61 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction - -/** - * - * @version $Id$ - */ -internal class AnalyticalGradientCalculator(fcn: MultiFunction?, state: MnUserTransformation, checkGradient: Boolean) : - GradientCalculator { - private val function: MultiFunction? - private val theCheckGradient: Boolean - private val theTransformation: MnUserTransformation - fun checkGradient(): Boolean { - return theCheckGradient - } - - /** {@inheritDoc} */ - fun gradient(par: MinimumParameters): FunctionGradient { -// double[] grad = theGradCalc.gradientValue(theTransformation.andThen(par.vec()).data()); - val point: DoubleArray = theTransformation.transform(par.vec()).toArray() - require(!(function.getDimension() !== theTransformation.parameters().size())) { "Invalid parameter size" } - val v: RealVector = ArrayRealVector(par.vec().getDimension()) - for (i in 0 until par.vec().getDimension()) { - val ext: Int = theTransformation.extOfInt(i) - if (theTransformation.parameter(ext).hasLimits()) { - val dd: Double = theTransformation.dInt2Ext(i, par.vec().getEntry(i)) - v.setEntry(i, dd * function.derivValue(ext, point)) - } else { - v.setEntry(i, function.derivValue(ext, point)) - } - } - return FunctionGradient(v) - } - - /** {@inheritDoc} */ - fun gradient(par: MinimumParameters, grad: FunctionGradient?): FunctionGradient { - return gradient(par) - } - - init { - function = fcn - theTransformation = state - theCheckGradient = checkGradient - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/CombinedMinimizer.kt b/kmath-optimization/src/commonMain/tmp/minuit/CombinedMinimizer.kt deleted file mode 100644 index 9363492ad..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/CombinedMinimizer.kt +++ /dev/null @@ -1,32 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -internal class CombinedMinimizer : ModularFunctionMinimizer() { - private val theMinBuilder: CombinedMinimumBuilder = CombinedMinimumBuilder() - private val theMinSeedGen: MnSeedGenerator = MnSeedGenerator() - override fun builder(): MinimumBuilder { - return theMinBuilder - } - - override fun seedGenerator(): MinimumSeedGenerator { - return theMinSeedGen - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/CombinedMinimumBuilder.kt b/kmath-optimization/src/commonMain/tmp/minuit/CombinedMinimumBuilder.kt deleted file mode 100644 index 8c5452575..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/CombinedMinimumBuilder.kt +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import space.kscience.kmath.optimization.minuit.MINUITPlugin -import space.kscience.kmath.optimization.minuit.MinimumSeed - -/** - * - * @version $Id$ - */ -internal class CombinedMinimumBuilder : MinimumBuilder { - private val theSimplexMinimizer: SimplexMinimizer = SimplexMinimizer() - private val theVMMinimizer: VariableMetricMinimizer = VariableMetricMinimizer() - - /** {@inheritDoc} */ - override fun minimum( - fcn: MnFcn?, - gc: GradientCalculator?, - seed: MinimumSeed?, - strategy: MnStrategy?, - maxfcn: Int, - toler: Double - ): FunctionMinimum { - val min: FunctionMinimum = theVMMinimizer.minimize(fcn!!, gc, seed, strategy, maxfcn, toler) - if (!min.isValid()) { - MINUITPlugin.logStatic("CombinedMinimumBuilder: migrad method fails, will try with simplex method first.") - val str = MnStrategy(2) - val min1: FunctionMinimum = theSimplexMinimizer.minimize(fcn, gc, seed, str, maxfcn, toler) - if (!min1.isValid()) { - MINUITPlugin.logStatic("CombinedMinimumBuilder: both migrad and simplex method fail.") - return min1 - } - val seed1: MinimumSeed = theVMMinimizer.seedGenerator().generate(fcn, gc, min1.userState(), str) - val min2: FunctionMinimum = theVMMinimizer.minimize(fcn, gc, seed1, str, maxfcn, toler) - if (!min2.isValid()) { - MINUITPlugin.logStatic("CombinedMinimumBuilder: both migrad and method fails also at 2nd attempt.") - MINUITPlugin.logStatic("CombinedMinimumBuilder: return simplex minimum.") - return min1 - } - return min2 - } - return min - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/ContoursError.kt b/kmath-optimization/src/commonMain/tmp/minuit/ContoursError.kt deleted file mode 100644 index 214d94c80..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/ContoursError.kt +++ /dev/null @@ -1,150 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * ContoursError class. - * - * @author Darksnake - * @version $Id$ - */ -class ContoursError internal constructor( - private val theParX: Int, - private val theParY: Int, - points: List, - xmnos: MinosError, - ymnos: MinosError, - nfcn: Int -) { - private val theNFcn: Int - private val thePoints: List = points - private val theXMinos: MinosError - private val theYMinos: MinosError - - /** - * - * nfcn. - * - * @return a int. - */ - fun nfcn(): Int { - return theNFcn - } - - /** - * - * points. - * - * @return a [List] object. - */ - fun points(): List { - return thePoints - } - - /** - * {@inheritDoc} - */ - override fun toString(): String { - return MnPrint.toString(this) - } - - /** - * - * xMinosError. - * - * @return a [hep.dataforge.MINUIT.MinosError] object. - */ - fun xMinosError(): MinosError { - return theXMinos - } - - /** - * - * xRange. - * - * @return - */ - fun xRange(): Range { - return theXMinos.range() - } - - /** - * - * xmin. - * - * @return a double. - */ - fun xmin(): Double { - return theXMinos.min() - } - - /** - * - * xpar. - * - * @return a int. - */ - fun xpar(): Int { - return theParX - } - - /** - * - * yMinosError. - * - * @return a [hep.dataforge.MINUIT.MinosError] object. - */ - fun yMinosError(): MinosError { - return theYMinos - } - - /** - * - * yRange. - * - * @return - */ - fun yRange(): Range { - return theYMinos.range() - } - - /** - * - * ymin. - * - * @return a double. - */ - fun ymin(): Double { - return theYMinos.min() - } - - /** - * - * ypar. - * - * @return a int. - */ - fun ypar(): Int { - return theParY - } - - init { - theXMinos = xmnos - theYMinos = ymnos - theNFcn = nfcn - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/DavidonErrorUpdator.kt b/kmath-optimization/src/commonMain/tmp/minuit/DavidonErrorUpdator.kt deleted file mode 100644 index 9eb2443e4..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/DavidonErrorUpdator.kt +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.RealVector -import ru.inr.mass.minuit.* - -/** - * - * @version $Id$ - */ -internal class DavidonErrorUpdator : MinimumErrorUpdator { - /** {@inheritDoc} */ - fun update(s0: MinimumState, p1: MinimumParameters, g1: FunctionGradient): MinimumError { - val V0: MnAlgebraicSymMatrix = s0.error().invHessian() - val dx: RealVector = MnUtils.sub(p1.vec(), s0.vec()) - val dg: RealVector = MnUtils.sub(g1.getGradient(), s0.gradient().getGradient()) - val delgam: Double = MnUtils.innerProduct(dx, dg) - val gvg: Double = MnUtils.similarity(dg, V0) - val vg: RealVector = MnUtils.mul(V0, dg) - var Vupd: MnAlgebraicSymMatrix = - MnUtils.sub(MnUtils.div(MnUtils.outerProduct(dx), delgam), MnUtils.div(MnUtils.outerProduct(vg), gvg)) - if (delgam > gvg) { - Vupd = MnUtils.add(Vupd, - MnUtils.mul(MnUtils.outerProduct(MnUtils.sub(MnUtils.div(dx, delgam), MnUtils.div(vg, gvg))), gvg)) - } - val sum_upd: Double = MnUtils.absoluteSumOfElements(Vupd) - Vupd = MnUtils.add(Vupd, V0) - val dcov: Double = 0.5 * (s0.error().dcovar() + sum_upd / MnUtils.absoluteSumOfElements(Vupd)) - return MinimumError(Vupd, dcov) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/FunctionGradient.kt b/kmath-optimization/src/commonMain/tmp/minuit/FunctionGradient.kt deleted file mode 100644 index a0866d916..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/FunctionGradient.kt +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector - -/** - * - * @version $Id$ - */ -class FunctionGradient { - private var theAnalytical = false - private var theG2ndDerivative: RealVector - private var theGStepSize: RealVector - private var theGradient: RealVector - private var theValid = false - - constructor(n: Int) { - theGradient = ArrayRealVector(n) - theG2ndDerivative = ArrayRealVector(n) - theGStepSize = ArrayRealVector(n) - } - - constructor(grd: RealVector) { - theGradient = grd - theG2ndDerivative = ArrayRealVector(grd.getDimension()) - theGStepSize = ArrayRealVector(grd.getDimension()) - theValid = true - theAnalytical = true - } - - constructor(grd: RealVector, g2: RealVector, gstep: RealVector) { - theGradient = grd - theG2ndDerivative = g2 - theGStepSize = gstep - theValid = true - theAnalytical = false - } - - fun getGradient(): RealVector { - return theGradient - } - - fun getGradientDerivative(): RealVector { - return theG2ndDerivative - } - - fun getStep(): RealVector { - return theGStepSize - } - - fun isAnalytical(): Boolean { - return theAnalytical - } - - fun isValid(): Boolean { - return theValid - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/FunctionMinimum.kt b/kmath-optimization/src/commonMain/tmp/minuit/FunctionMinimum.kt deleted file mode 100644 index e43523291..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/FunctionMinimum.kt +++ /dev/null @@ -1,260 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.minuit.* -import space.kscience.kmath.optimization.minuit.MinimumSeed - -/** - * Result of the minimization. - * - * - * The FunctionMinimum is the output of the minimizers and contains the - * minimization result. The methods - * - * * userState(), - * * userParameters() and - * * userCovariance() - * - * are provided. These can be used as new input to a new minimization after some - * manipulation. The parameters and/or the FunctionMinimum can be printed using - * the toString() method or the MnPrint class. - * - * @author Darksnake - */ -class FunctionMinimum { - private var theAboveMaxEdm = false - private var theErrorDef: Double - private var theReachedCallLimit = false - private var theSeed: MinimumSeed - private var theStates: MutableList - private var theUserState: MnUserParameterState - - internal constructor(seed: MinimumSeed, up: Double) { - theSeed = seed - theStates = ArrayList() - theStates.add(MinimumState(seed.parameters(), - seed.error(), - seed.gradient(), - seed.parameters().fval(), - seed.nfcn())) - theErrorDef = up - theUserState = MnUserParameterState() - } - - internal constructor(seed: MinimumSeed, states: MutableList, up: Double) { - theSeed = seed - theStates = states - theErrorDef = up - theUserState = MnUserParameterState() - } - - internal constructor(seed: MinimumSeed, states: MutableList, up: Double, x: MnReachedCallLimit?) { - theSeed = seed - theStates = states - theErrorDef = up - theReachedCallLimit = true - theUserState = MnUserParameterState() - } - - internal constructor(seed: MinimumSeed, states: MutableList, up: Double, x: MnAboveMaxEdm?) { - theSeed = seed - theStates = states - theErrorDef = up - theAboveMaxEdm = true - theReachedCallLimit = false - theUserState = MnUserParameterState() - } - - // why not - fun add(state: MinimumState) { - theStates.add(state) - } - - /** - * returns the expected vertical distance to the minimum (EDM) - * - * @return a double. - */ - fun edm(): Double { - return lastState().edm() - } - - fun error(): MinimumError { - return lastState().error() - } - - /** - * - * - * errorDef. - * - * @return a double. - */ - fun errorDef(): Double { - return theErrorDef - } - - /** - * Returns the function value at the minimum. - * - * @return a double. - */ - fun fval(): Double { - return lastState().fval() - } - - fun grad(): FunctionGradient { - return lastState().gradient() - } - - fun hasAccurateCovar(): Boolean { - return state().error().isAccurate() - } - - fun hasCovariance(): Boolean { - return state().error().isAvailable() - } - - fun hasMadePosDefCovar(): Boolean { - return state().error().isMadePosDef() - } - - fun hasPosDefCovar(): Boolean { - return state().error().isPosDef() - } - - fun hasReachedCallLimit(): Boolean { - return theReachedCallLimit - } - - fun hasValidCovariance(): Boolean { - return state().error().isValid() - } - - fun hasValidParameters(): Boolean { - return state().parameters().isValid() - } - - fun hesseFailed(): Boolean { - return state().error().hesseFailed() - } - - fun isAboveMaxEdm(): Boolean { - return theAboveMaxEdm - } - - /** - * In general, if this returns true, the minimizer did find a - * minimum without running into troubles. However, in some cases a minimum - * cannot be found, then the return value will be false. - * Reasons for the minimization to fail are - * - * * the number of allowed function calls has been exhausted - * * the minimizer could not improve the values of the parameters (and - * knowing that it has not converged yet) - * * a problem with the calculation of the covariance matrix - * - * Additional methods for the analysis of the state at the minimum are - * provided. - * - * @return a boolean. - */ - fun isValid(): Boolean { - return state().isValid() && !isAboveMaxEdm() && !hasReachedCallLimit() - } - - private fun lastState(): MinimumState { - return theStates[theStates.size - 1] - } - // forward interface of last state - /** - * returns the total number of function calls during the minimization. - * - * @return a int. - */ - fun nfcn(): Int { - return lastState().nfcn() - } - - fun parameters(): MinimumParameters { - return lastState().parameters() - } - - fun seed(): MinimumSeed { - return theSeed - } - - fun state(): MinimumState { - return lastState() - } - - fun states(): List { - return theStates - } - - /** - * {@inheritDoc} - * - * @return - */ - override fun toString(): String { - return MnPrint.toString(this) - } - - /** - * - * - * userCovariance. - * - * @return a [hep.dataforge.MINUIT.MnUserCovariance] object. - */ - fun userCovariance(): MnUserCovariance { - if (!theUserState.isValid()) { - theUserState = MnUserParameterState(state(), errorDef(), seed().trafo()) - } - return theUserState.covariance() - } - - /** - * - * - * userParameters. - * - * @return a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - fun userParameters(): MnUserParameters { - if (!theUserState.isValid()) { - theUserState = MnUserParameterState(state(), errorDef(), seed().trafo()) - } - return theUserState.parameters() - } - - /** - * user representation of state at minimum - * - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun userState(): MnUserParameterState { - if (!theUserState.isValid()) { - theUserState = MnUserParameterState(state(), errorDef(), seed().trafo()) - } - return theUserState - } - - internal class MnAboveMaxEdm - internal class MnReachedCallLimit -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/GradientCalculator.kt b/kmath-optimization/src/commonMain/tmp/minuit/GradientCalculator.kt deleted file mode 100644 index 379de1b6d..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/GradientCalculator.kt +++ /dev/null @@ -1,41 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -interface GradientCalculator { - /** - * - * gradient. - * - * @param par a [hep.dataforge.MINUIT.MinimumParameters] object. - * @return a [hep.dataforge.MINUIT.FunctionGradient] object. - */ - fun gradient(par: MinimumParameters?): FunctionGradient - - /** - * - * gradient. - * - * @param par a [hep.dataforge.MINUIT.MinimumParameters] object. - * @param grad a [hep.dataforge.MINUIT.FunctionGradient] object. - * @return a [hep.dataforge.MINUIT.FunctionGradient] object. - */ - fun gradient(par: MinimumParameters?, grad: FunctionGradient?): FunctionGradient -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/HessianGradientCalculator.kt b/kmath-optimization/src/commonMain/tmp/minuit/HessianGradientCalculator.kt deleted file mode 100644 index 150d192f9..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/HessianGradientCalculator.kt +++ /dev/null @@ -1,137 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector -import ru.inr.mass.minuit.* - -/** - * - * @version $Id$ - */ -internal class HessianGradientCalculator(fcn: MnFcn, par: MnUserTransformation, stra: MnStrategy) : GradientCalculator { - private val theFcn: MnFcn = fcn - private val theStrategy: MnStrategy - private val theTransformation: MnUserTransformation - fun deltaGradient(par: MinimumParameters, gradient: FunctionGradient): Pair { - require(par.isValid()) { "parameters are invalid" } - val x: RealVector = par.vec().copy() - val grd: RealVector = gradient.getGradient().copy() - val g2: RealVector = gradient.getGradientDerivative() - val gstep: RealVector = gradient.getStep() - val fcnmin: Double = par.fval() - // std::cout<<"fval: "< optstp) { - d = optstp - } - if (d < dmin) { - d = dmin - } - var chgold = 10000.0 - var dgmin = 0.0 - var grdold = 0.0 - var grdnew = 0.0 - for (j in 0 until ncycle()) { - x.setEntry(i, xtf + d) - val fs1: Double = theFcn.value(x) - x.setEntry(i, xtf - d) - val fs2: Double = theFcn.value(x) - x.setEntry(i, xtf) - // double sag = 0.5*(fs1+fs2-2.*fcnmin); - grdold = grd.getEntry(i) - grdnew = (fs1 - fs2) / (2.0 * d) - dgmin = precision().eps() * (abs(fs1) + abs(fs2)) / d - if (abs(grdnew) < precision().eps()) { - break - } - val change: Double = abs((grdold - grdnew) / grdnew) - if (change > chgold && j > 1) { - break - } - chgold = change - grd.setEntry(i, grdnew) - if (change < 0.05) { - break - } - if (abs(grdold - grdnew) < dgmin) { - break - } - if (d < dmin) { - break - } - d *= 0.2 - } - dgrd.setEntry(i, max(dgmin, abs(grdold - grdnew))) - } - return Pair(FunctionGradient(grd, g2, gstep), dgrd) - } - - fun fcn(): MnFcn { - return theFcn - } - - fun gradTolerance(): Double { - return strategy().gradientTolerance() - } - - /** {@inheritDoc} */ - fun gradient(par: MinimumParameters): FunctionGradient { - val gc = InitialGradientCalculator(theFcn, theTransformation, theStrategy) - val gra: FunctionGradient = gc.gradient(par) - return gradient(par, gra) - } - - /** {@inheritDoc} */ - fun gradient(par: MinimumParameters, gradient: FunctionGradient): FunctionGradient { - return deltaGradient(par, gradient).getFirst() - } - - fun ncycle(): Int { - return strategy().hessianGradientNCycles() - } - - fun precision(): MnMachinePrecision { - return theTransformation.precision() - } - - fun stepTolerance(): Double { - return strategy().gradientStepTolerance() - } - - fun strategy(): MnStrategy { - return theStrategy - } - - fun trafo(): MnUserTransformation { - return theTransformation - } - - init { - theTransformation = par - theStrategy = stra - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/InitialGradientCalculator.kt b/kmath-optimization/src/commonMain/tmp/minuit/InitialGradientCalculator.kt deleted file mode 100644 index 794556414..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/InitialGradientCalculator.kt +++ /dev/null @@ -1,116 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector -import ru.inr.mass.minuit.* - -/** - * Calculating derivatives via finite differences - * @version $Id$ - */ -internal class InitialGradientCalculator(fcn: MnFcn, par: MnUserTransformation, stra: MnStrategy) { - private val theFcn: MnFcn = fcn - private val theStrategy: MnStrategy - private val theTransformation: MnUserTransformation - fun fcn(): MnFcn { - return theFcn - } - - fun gradTolerance(): Double { - return strategy().gradientTolerance() - } - - fun gradient(par: MinimumParameters): FunctionGradient { - require(par.isValid()) { "Parameters are invalid" } - val n: Int = trafo().variableParameters() - require(n == par.vec().getDimension()) { "Parameters have invalid size" } - val gr: RealVector = ArrayRealVector(n) - val gr2: RealVector = ArrayRealVector(n) - val gst: RealVector = ArrayRealVector(n) - - // initial starting values - for (i in 0 until n) { - val exOfIn: Int = trafo().extOfInt(i) - val `var`: Double = par.vec().getEntry(i) //parameter value - val werr: Double = trafo().parameter(exOfIn).error() //parameter error - val sav: Double = trafo().int2ext(i, `var`) //value after transformation - var sav2 = sav + werr //value after transfomation + error - if (trafo().parameter(exOfIn).hasLimits()) { - if (trafo().parameter(exOfIn).hasUpperLimit() - && sav2 > trafo().parameter(exOfIn).upperLimit() - ) { - sav2 = trafo().parameter(exOfIn).upperLimit() - } - } - var var2: Double = trafo().ext2int(exOfIn, sav2) - val vplu = var2 - `var` - sav2 = sav - werr - if (trafo().parameter(exOfIn).hasLimits()) { - if (trafo().parameter(exOfIn).hasLowerLimit() - && sav2 < trafo().parameter(exOfIn).lowerLimit() - ) { - sav2 = trafo().parameter(exOfIn).lowerLimit() - } - } - var2 = trafo().ext2int(exOfIn, sav2) - val vmin = var2 - `var` - val dirin: Double = 0.5 * (abs(vplu) + abs(vmin)) - val g2: Double = 2.0 * theFcn.errorDef() / (dirin * dirin) - val gsmin: Double = 8.0 * precision().eps2() * (abs(`var`) + precision().eps2()) - var gstep: Double = max(gsmin, 0.1 * dirin) - val grd = g2 * dirin - if (trafo().parameter(exOfIn).hasLimits()) { - if (gstep > 0.5) { - gstep = 0.5 - } - } - gr.setEntry(i, grd) - gr2.setEntry(i, g2) - gst.setEntry(i, gstep) - } - return FunctionGradient(gr, gr2, gst) - } - - fun gradient(par: MinimumParameters, gra: FunctionGradient?): FunctionGradient { - return gradient(par) - } - - fun ncycle(): Int { - return strategy().gradientNCycles() - } - - fun precision(): MnMachinePrecision { - return theTransformation.precision() - } - - fun stepTolerance(): Double { - return strategy().gradientStepTolerance() - } - - fun strategy(): MnStrategy { - return theStrategy - } - - fun trafo(): MnUserTransformation { - return theTransformation - } - - init { - theTransformation = par - theStrategy = stra - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MINOSResult.kt b/kmath-optimization/src/commonMain/tmp/minuit/MINOSResult.kt deleted file mode 100644 index c33994648..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MINOSResult.kt +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package space.kscience.kmath.optimization.minuit - - -/** - * Контейнер для несимметричных оценок и доверительных интервалов - * - * @author Darksnake - * @version $Id: $Id - */ -class MINOSResult -/** - * - * Constructor for MINOSResult. - * - * @param list an array of [String] objects. - */(private val names: Array, private val errl: DoubleArray?, private val errp: DoubleArray?) : - IntervalEstimate { - fun getNames(): NameList { - return NameList(names) - } - - fun getInterval(parName: String?): Pair { - val index: Int = getNames().getNumberByName(parName) - return Pair(ValueFactory.of(errl!![index]), ValueFactory.of(errp!![index])) - } - - val cL: Double - get() = 0.68 - - /** {@inheritDoc} */ - fun print(out: PrintWriter) { - if (errl != null || errp != null) { - out.println() - out.println("Assymetrical errors:") - out.println() - out.println("Name\tLower\tUpper") - for (i in 0 until getNames().size()) { - out.print(getNames().get(i)) - out.print("\t") - if (errl != null) { - out.print(errl[i]) - } else { - out.print("---") - } - out.print("\t") - if (errp != null) { - out.print(errp[i]) - } else { - out.print("---") - } - out.println() - } - } - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MINUITFitter.kt b/kmath-optimization/src/commonMain/tmp/minuit/MINUITFitter.kt deleted file mode 100644 index a26321249..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MINUITFitter.kt +++ /dev/null @@ -1,205 +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. - */ -package space.kscience.kmath.optimization.minuit - -import ru.inr.mass.minuit.* - -/** - * - * - * MINUITFitter class. - * - * @author Darksnake - * @version $Id: $Id - */ -class MINUITFitter : Fitter { - fun run(state: FitState, parentLog: History?, meta: Meta): FitResult { - val log = Chronicle("MINUIT", parentLog) - val action: String = meta.getString("action", TASK_RUN) - log.report("MINUIT fit engine started action '{}'", action) - return when (action) { - TASK_COVARIANCE -> runHesse(state, log, meta) - TASK_SINGLE, TASK_RUN -> runFit(state, log, meta) - else -> throw IllegalArgumentException("Unknown task") - } - } - - @NotNull - fun getName(): String { - return MINUIT_ENGINE_NAME - } - - /** - * - * - * runHesse. - * - * @param state a [hep.dataforge.stat.fit.FitState] object. - * @param log - * @return a [FitResult] object. - */ - fun runHesse(state: FitState, log: History, meta: Meta?): FitResult { - val strategy: Int - strategy = Global.INSTANCE.getInt("MINUIT_STRATEGY", 2) - log.report("Generating errors using MnHesse 2-nd order gradient calculator.") - val fcn: MultiFunction - val fitPars: Array = Fitter.Companion.getFitPars(state, meta) - val pars: ParamSet = state.getParameters() - fcn = MINUITUtils.getFcn(state, pars, fitPars) - val hesse = MnHesse(strategy) - val mnState: MnUserParameterState = hesse.calculate(fcn, MINUITUtils.getFitParameters(pars, fitPars)) - val allPars: ParamSet = pars.copy() - for (fitPar in fitPars) { - allPars.setParValue(fitPar, mnState.value(fitPar)) - allPars.setParError(fitPar, mnState.error(fitPar)) - } - val newState: FitState.Builder = state.edit() - newState.setPars(allPars) - if (mnState.hasCovariance()) { - val mnCov: MnUserCovariance = mnState.covariance() - var j: Int - val cov = Array(mnState.variableParameters()) { DoubleArray(mnState.variableParameters()) } - for (i in 0 until mnState.variableParameters()) { - j = 0 - while (j < mnState.variableParameters()) { - cov[i][j] = mnCov.get(i, j) - j++ - } - } - newState.setCovariance(NamedMatrix(fitPars, cov), true) - } - return FitResult.build(newState.build(), fitPars) - } - - fun runFit(state: FitState, log: History, meta: Meta): FitResult { - val minuit: MnApplication - log.report("Starting fit using Minuit.") - val strategy: Int - strategy = Global.INSTANCE.getInt("MINUIT_STRATEGY", 2) - var force: Boolean - force = Global.INSTANCE.getBoolean("FORCE_DERIVS", false) - val fitPars: Array = Fitter.Companion.getFitPars(state, meta) - for (fitPar in fitPars) { - if (!state.modelProvidesDerivs(fitPar)) { - force = true - log.reportError("Model does not provide derivatives for parameter '{}'", fitPar) - } - } - if (force) { - log.report("Using MINUIT gradient calculator.") - } - val fcn: MultiFunction - val pars: ParamSet = state.getParameters().copy() - fcn = MINUITUtils.getFcn(state, pars, fitPars) - val method: String = meta.getString("method", MINUIT_MIGRAD) - when (method) { - MINUIT_MINOS, MINUIT_MINIMIZE -> minuit = - MnMinimize(fcn, MINUITUtils.getFitParameters(pars, fitPars), strategy) - MINUIT_SIMPLEX -> minuit = MnSimplex(fcn, MINUITUtils.getFitParameters(pars, fitPars), strategy) - else -> minuit = MnMigrad(fcn, MINUITUtils.getFitParameters(pars, fitPars), strategy) - } - if (force) { - minuit.setUseAnalyticalDerivatives(false) - log.report("Forced to use MINUIT internal derivative calculator!") - } - -// minuit.setUseAnalyticalDerivatives(true); - val minimum: FunctionMinimum - val maxSteps: Int = meta.getInt("iterations", -1) - val tolerance: Double = meta.getDouble("tolerance", -1) - minimum = if (maxSteps > 0) { - if (tolerance > 0) { - minuit.minimize(maxSteps, tolerance) - } else { - minuit.minimize(maxSteps) - } - } else { - minuit.minimize() - } - if (!minimum.isValid()) { - log.report("Minimization failed!") - } - log.report("MINUIT run completed in {} function calls.", minimum.nfcn()) - - /* - * Генерация результата - */ - val allPars: ParamSet = pars.copy() - for (fitPar in fitPars) { - allPars.setParValue(fitPar, minimum.userParameters().value(fitPar)) - allPars.setParError(fitPar, minimum.userParameters().error(fitPar)) - } - val newState: FitState.Builder = state.edit() - newState.setPars(allPars) - var valid: Boolean = minimum.isValid() - if (minimum.userCovariance().nrow() > 0) { - var j: Int - val cov = Array(minuit.variableParameters()) { DoubleArray(minuit.variableParameters()) } - if (cov[0].length == 1) { - cov[0][0] = minimum.userParameters().error(0) * minimum.userParameters().error(0) - } else { - for (i in 0 until minuit.variableParameters()) { - j = 0 - while (j < minuit.variableParameters()) { - cov[i][j] = minimum.userCovariance().get(i, j) - j++ - } - } - } - newState.setCovariance(NamedMatrix(fitPars, cov), true) - } - if (method == MINUIT_MINOS) { - log.report("Starting MINOS procedure for precise error estimation.") - val minos = MnMinos(fcn, minimum, strategy) - var mnError: MinosError - val errl = DoubleArray(fitPars.size) - val errp = DoubleArray(fitPars.size) - for (i in fitPars.indices) { - mnError = minos.minos(i) - if (mnError.isValid()) { - errl[i] = mnError.lower() - errp[i] = mnError.upper() - } else { - valid = false - } - } - val minosErrors = MINOSResult(fitPars, errl, errp) - newState.setInterval(minosErrors) - } - return FitResult.build(newState.build(), valid, fitPars) - } - - companion object { - /** - * Constant `MINUIT_MIGRAD="MIGRAD"` - */ - const val MINUIT_MIGRAD = "MIGRAD" - - /** - * Constant `MINUIT_MINIMIZE="MINIMIZE"` - */ - const val MINUIT_MINIMIZE = "MINIMIZE" - - /** - * Constant `MINUIT_SIMPLEX="SIMPLEX"` - */ - const val MINUIT_SIMPLEX = "SIMPLEX" - - /** - * Constant `MINUIT_MINOS="MINOS"` - */ - const val MINUIT_MINOS = "MINOS" //MINOS errors - - /** - * Constant `MINUIT_HESSE="HESSE"` - */ - const val MINUIT_HESSE = "HESSE" //HESSE errors - - /** - * Constant `MINUIT_ENGINE_NAME="MINUIT"` - */ - const val MINUIT_ENGINE_NAME = "MINUIT" - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MINUITPlugin.kt b/kmath-optimization/src/commonMain/tmp/minuit/MINUITPlugin.kt deleted file mode 100644 index 7eaefd9d2..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MINUITPlugin.kt +++ /dev/null @@ -1,86 +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. - */ -package space.kscience.kmath.optimization.minuit - -import hep.dataforge.context.* - -/** - * Мэнеджер для MINUITа. Пока не играет никакой активной роли кроме ведения - * внутреннего лога. - * - * @author Darksnake - * @version $Id: $Id - */ -@PluginDef(group = "hep.dataforge", - name = "MINUIT", - dependsOn = ["hep.dataforge:fitting"], - info = "The MINUIT fitter engine for DataForge fitting") -class MINUITPlugin : BasicPlugin() { - fun attach(@NotNull context: Context?) { - super.attach(context) - clearStaticLog() - } - - @Provides(Fitter.FITTER_TARGET) - fun getFitter(fitterName: String): Fitter? { - return if (fitterName == "MINUIT") { - MINUITFitter() - } else { - null - } - } - - @ProvidesNames(Fitter.FITTER_TARGET) - fun listFitters(): List { - return listOf("MINUIT") - } - - fun detach() { - clearStaticLog() - super.detach() - } - - class Factory : PluginFactory() { - fun build(meta: Meta?): Plugin { - return MINUITPlugin() - } - - fun getType(): java.lang.Class { - return MINUITPlugin::class.java - } - } - - companion object { - /** - * Constant `staticLog` - */ - private val staticLog: Chronicle? = Chronicle("MINUIT-STATIC", Global.INSTANCE.getHistory()) - - /** - * - * - * clearStaticLog. - */ - fun clearStaticLog() { - staticLog.clear() - } - - /** - * - * - * logStatic. - * - * @param str a [String] object. - * @param pars a [Object] object. - */ - fun logStatic(str: String?, vararg pars: Any?) { - checkNotNull(staticLog) { "MINUIT log is not initialized." } - staticLog.report(str, pars) - LoggerFactory.getLogger("MINUIT").info(String.format(str, *pars)) - // Out.out.printf(str,pars); -// Out.out.println(); - } - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MINUITUtils.kt b/kmath-optimization/src/commonMain/tmp/minuit/MINUITUtils.kt deleted file mode 100644 index 44c70cb42..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MINUITUtils.kt +++ /dev/null @@ -1,121 +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. - */ -package space.kscience.kmath.optimization.minuit - -import hep.dataforge.MINUIT.FunctionMinimum - -internal object MINUITUtils { - fun getFcn(source: FitState, allPar: ParamSet, fitPars: Array): MultiFunction { - return MnFunc(source, allPar, fitPars) - } - - fun getFitParameters(set: ParamSet, fitPars: Array): MnUserParameters { - val pars = MnUserParameters() - var i: Int - var par: Param - i = 0 - while (i < fitPars.size) { - par = set.getByName(fitPars[i]) - pars.add(fitPars[i], par.getValue(), par.getErr()) - if (par.getLowerBound() > Double.NEGATIVE_INFINITY && par.getUpperBound() < Double.POSITIVE_INFINITY) { - pars.setLimits(i, par.getLowerBound(), par.getUpperBound()) - } else if (par.getLowerBound() > Double.NEGATIVE_INFINITY) { - pars.setLowerLimit(i, par.getLowerBound()) - } else if (par.getUpperBound() < Double.POSITIVE_INFINITY) { - pars.setUpperLimit(i, par.getUpperBound()) - } - i++ - } - return pars - } - - fun getValueSet(allPar: ParamSet, names: Array, values: DoubleArray): ParamSet { - assert(values.size == names.size) - assert(allPar.getNames().contains(names)) - val vector: ParamSet = allPar.copy() - for (i in values.indices) { - vector.setParValue(names[i], values[i]) - } - return vector - } - - fun isValidArray(ar: DoubleArray): Boolean { - for (i in ar.indices) { - if (java.lang.Double.isNaN(ar[i])) { - return false - } - } - return true - } - - /** - * - * - * printMINUITResult. - * - * @param out a [PrintWriter] object. - * @param minimum a [hep.dataforge.MINUIT.FunctionMinimum] object. - */ - fun printMINUITResult(out: PrintWriter, minimum: FunctionMinimum?) { - out.println() - out.println("***MINUIT INTERNAL FIT INFORMATION***") - out.println() - MnPrint.print(out, minimum) - out.println() - out.println("***END OF MINUIT INTERNAL FIT INFORMATION***") - out.println() - } - - internal class MnFunc(source: FitState, allPar: ParamSet, fitPars: Array) : MultiFunction { - var source: FitState - var allPar: ParamSet - var fitPars: Array - fun value(doubles: DoubleArray): Double { - assert(isValidArray(doubles)) - assert(doubles.size == fitPars.size) - return -2 * source.getLogProb(getValueSet(allPar, fitPars, doubles)) - // source.getChi2(getValueSet(allPar, fitPars, doubles)); - } - - @Throws(NotDefinedException::class) - fun derivValue(n: Int, doubles: DoubleArray): Double { - assert(isValidArray(doubles)) - assert(doubles.size == getDimension()) - val set: ParamSet = getValueSet(allPar, fitPars, doubles) - -// double res; -// double d, s, deriv; -// -// res = 0; -// for (int i = 0; i < source.getDataNum(); i++) { -// d = source.getDis(i, set); -// s = source.getDispersion(i, set); -// if (source.modelProvidesDerivs(fitPars[n])) { -// deriv = source.getDisDeriv(fitPars[n], i, set); -// } else { -// throw new NotDefinedException(); -// // Такого не должно быть, поскольку мы где-то наверху должы были проверить, что производные все есть. -// } -// res += 2 * d * deriv / s; -// } - return -2 * source.getLogProbDeriv(fitPars[n], set) - } - - fun getDimension(): Int { - return fitPars.size - } - - fun providesDeriv(n: Int): Boolean { - return source.modelProvidesDerivs(fitPars[n]) - } - - init { - this.source = source - this.allPar = allPar - this.fitPars = fitPars - assert(source.getModel().getNames().contains(fitPars)) - } - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MinimumBuilder.kt b/kmath-optimization/src/commonMain/tmp/minuit/MinimumBuilder.kt deleted file mode 100644 index 7d918c339..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MinimumBuilder.kt +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import space.kscience.kmath.optimization.minuit.MinimumSeed - -/** - * - * @version $Id$ - */ -interface MinimumBuilder { - /** - * - * minimum. - * - * @param fcn a [hep.dataforge.MINUIT.MnFcn] object. - * @param gc a [hep.dataforge.MINUIT.GradientCalculator] object. - * @param seed a [hep.dataforge.MINUIT.MinimumSeed] object. - * @param strategy a [hep.dataforge.MINUIT.MnStrategy] object. - * @param maxfcn a int. - * @param toler a double. - * @return a [hep.dataforge.MINUIT.FunctionMinimum] object. - */ - fun minimum( - fcn: MnFcn?, - gc: GradientCalculator?, - seed: MinimumSeed?, - strategy: MnStrategy?, - maxfcn: Int, - toler: Double - ): FunctionMinimum -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MinimumError.kt b/kmath-optimization/src/commonMain/tmp/minuit/MinimumError.kt deleted file mode 100644 index 6993b9e6d..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MinimumError.kt +++ /dev/null @@ -1,155 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import space.kscience.kmath.optimization.minuit.MINUITPlugin - -/** - * MinimumError keeps the inverse 2nd derivative (inverse Hessian) used for - * calculating the parameter step size (-V*g) and for the covariance update - * (ErrorUpdator). The covariance matrix is equal to twice the inverse Hessian. - * - * @version $Id$ - */ -class MinimumError { - private var theAvailable = false - private var theDCovar: Double - private var theHesseFailed = false - private var theInvertFailed = false - private var theMadePosDef = false - private var theMatrix: MnAlgebraicSymMatrix - private var thePosDef = false - private var theValid = false - - constructor(n: Int) { - theMatrix = MnAlgebraicSymMatrix(n) - theDCovar = 1.0 - } - - constructor(mat: MnAlgebraicSymMatrix, dcov: Double) { - theMatrix = mat - theDCovar = dcov - theValid = true - thePosDef = true - theAvailable = true - } - - constructor(mat: MnAlgebraicSymMatrix, x: MnHesseFailed?) { - theMatrix = mat - theDCovar = 1.0 - theValid = false - thePosDef = false - theMadePosDef = false - theHesseFailed = true - theInvertFailed = false - theAvailable = true - } - - constructor(mat: MnAlgebraicSymMatrix, x: MnMadePosDef?) { - theMatrix = mat - theDCovar = 1.0 - theValid = false - thePosDef = false - theMadePosDef = true - theHesseFailed = false - theInvertFailed = false - theAvailable = true - } - - constructor(mat: MnAlgebraicSymMatrix, x: MnInvertFailed?) { - theMatrix = mat - theDCovar = 1.0 - theValid = false - thePosDef = true - theMadePosDef = false - theHesseFailed = false - theInvertFailed = true - theAvailable = true - } - - constructor(mat: MnAlgebraicSymMatrix, x: MnNotPosDef?) { - theMatrix = mat - theDCovar = 1.0 - theValid = false - thePosDef = false - theMadePosDef = false - theHesseFailed = false - theInvertFailed = false - theAvailable = true - } - - fun dcovar(): Double { - return theDCovar - } - - fun hesseFailed(): Boolean { - return theHesseFailed - } - - fun hessian(): MnAlgebraicSymMatrix { - return try { - val tmp: MnAlgebraicSymMatrix = theMatrix.copy() - tmp.invert() - tmp - } catch (x: SingularMatrixException) { - MINUITPlugin.logStatic("BasicMinimumError inversion fails; return diagonal matrix.") - val tmp = MnAlgebraicSymMatrix(theMatrix.nrow()) - var i = 0 - while (i < theMatrix.nrow()) { - tmp[i, i] = 1.0 / theMatrix[i, i] - i++ - } - tmp - } - } - - fun invHessian(): MnAlgebraicSymMatrix { - return theMatrix - } - - fun invertFailed(): Boolean { - return theInvertFailed - } - - fun isAccurate(): Boolean { - return theDCovar < 0.1 - } - - fun isAvailable(): Boolean { - return theAvailable - } - - fun isMadePosDef(): Boolean { - return theMadePosDef - } - - fun isPosDef(): Boolean { - return thePosDef - } - - fun isValid(): Boolean { - return theValid - } - - fun matrix(): MnAlgebraicSymMatrix { - return MnUtils.mul(theMatrix, 2) - } - - internal class MnHesseFailed - internal class MnInvertFailed - internal class MnMadePosDef - internal class MnNotPosDef -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MinimumErrorUpdator.kt b/kmath-optimization/src/commonMain/tmp/minuit/MinimumErrorUpdator.kt deleted file mode 100644 index 6022aa5b7..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MinimumErrorUpdator.kt +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -internal interface MinimumErrorUpdator { - /** - * - * update. - * - * @param state a [hep.dataforge.MINUIT.MinimumState] object. - * @param par a [hep.dataforge.MINUIT.MinimumParameters] object. - * @param grad a [hep.dataforge.MINUIT.FunctionGradient] object. - * @return a [hep.dataforge.MINUIT.MinimumError] object. - */ - fun update(state: MinimumState?, par: MinimumParameters?, grad: FunctionGradient?): MinimumError? -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MinimumParameters.kt b/kmath-optimization/src/commonMain/tmp/minuit/MinimumParameters.kt deleted file mode 100644 index bed13ea0b..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MinimumParameters.kt +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector - -/** - * - * @version $Id$ - */ -class MinimumParameters { - private var theFVal = 0.0 - private var theHasStep = false - private var theParameters: RealVector - private var theStepSize: RealVector - private var theValid = false - - constructor(n: Int) { - theParameters = ArrayRealVector(n) - theStepSize = ArrayRealVector(n) - } - - constructor(avec: RealVector, fval: Double) { - theParameters = avec - theStepSize = ArrayRealVector(avec.getDimension()) - theFVal = fval - theValid = true - } - - constructor(avec: RealVector, dirin: RealVector, fval: Double) { - theParameters = avec - theStepSize = dirin - theFVal = fval - theValid = true - theHasStep = true - } - - fun dirin(): RealVector { - return theStepSize - } - - fun fval(): Double { - return theFVal - } - - fun hasStepSize(): Boolean { - return theHasStep - } - - fun isValid(): Boolean { - return theValid - } - - fun vec(): RealVector { - return theParameters - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MinimumSeed.kt b/kmath-optimization/src/commonMain/tmp/minuit/MinimumSeed.kt deleted file mode 100644 index 53a78da75..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MinimumSeed.kt +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package space.kscience.kmath.optimization.minuit - -import ru.inr.mass.minuit.* - -/** - * - * @version $Id$ - */ -class MinimumSeed(state: MinimumState, trafo: MnUserTransformation) { - private val theState: MinimumState = state - private val theTrafo: MnUserTransformation = trafo - private val theValid: Boolean = true - val edm: Double get() = state().edm() - - fun error(): MinimumError { - return state().error() - } - - fun fval(): Double { - return state().fval() - } - - fun gradient(): FunctionGradient { - return state().gradient() - } - - fun isValid(): Boolean { - return theValid - } - - fun nfcn(): Int { - return state().nfcn() - } - - fun parameters(): MinimumParameters { - return state().parameters() - } - - fun precision(): MnMachinePrecision { - return theTrafo.precision() - } - - fun state(): MinimumState { - return theState - } - - fun trafo(): MnUserTransformation { - return theTrafo - } - -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MinimumSeedGenerator.kt b/kmath-optimization/src/commonMain/tmp/minuit/MinimumSeedGenerator.kt deleted file mode 100644 index e152559b5..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MinimumSeedGenerator.kt +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import space.kscience.kmath.optimization.minuit.MinimumSeed - -/** - * base class for seed generators (starting values); the seed generator prepares - * initial starting values from the input (MnUserParameterState) for the - * minimization; - * - * @version $Id$ - */ -interface MinimumSeedGenerator { - /** - * - * generate. - * - * @param fcn a [hep.dataforge.MINUIT.MnFcn] object. - * @param calc a [hep.dataforge.MINUIT.GradientCalculator] object. - * @param user a [hep.dataforge.MINUIT.MnUserParameterState] object. - * @param stra a [hep.dataforge.MINUIT.MnStrategy] object. - * @return a [hep.dataforge.MINUIT.MinimumSeed] object. - */ - fun generate(fcn: MnFcn?, calc: GradientCalculator?, user: MnUserParameterState?, stra: MnStrategy?): MinimumSeed -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MinimumState.kt b/kmath-optimization/src/commonMain/tmp/minuit/MinimumState.kt deleted file mode 100644 index 9f63e0e1f..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MinimumState.kt +++ /dev/null @@ -1,104 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.RealVector - -/** - * MinimumState keeps the information (position, gradient, 2nd deriv, etc) after - * one minimization step (usually in MinimumBuilder). - * - * @version $Id$ - */ -class MinimumState { - private var theEDM = 0.0 - private var theError: MinimumError - private var theGradient: FunctionGradient - private var theNFcn = 0 - private var theParameters: MinimumParameters - - constructor(n: Int) { - theParameters = MinimumParameters(n) - theError = MinimumError(n) - theGradient = FunctionGradient(n) - } - - constructor(states: MinimumParameters, err: MinimumError, grad: FunctionGradient, edm: Double, nfcn: Int) { - theParameters = states - theError = err - theGradient = grad - theEDM = edm - theNFcn = nfcn - } - - constructor(states: MinimumParameters, edm: Double, nfcn: Int) { - theParameters = states - theError = MinimumError(states.vec().getDimension()) - theGradient = FunctionGradient(states.vec().getDimension()) - theEDM = edm - theNFcn = nfcn - } - - fun edm(): Double { - return theEDM - } - - fun error(): MinimumError { - return theError - } - - fun fval(): Double { - return theParameters.fval() - } - - fun gradient(): FunctionGradient { - return theGradient - } - - fun hasCovariance(): Boolean { - return theError.isAvailable() - } - - fun hasParameters(): Boolean { - return theParameters.isValid() - } - - fun isValid(): Boolean { - return if (hasParameters() && hasCovariance()) { - parameters().isValid() && error().isValid() - } else if (hasParameters()) { - parameters().isValid() - } else { - false - } - } - - fun nfcn(): Int { - return theNFcn - } - - fun parameters(): MinimumParameters { - return theParameters - } - - fun size(): Int { - return theParameters.vec().getDimension() - } - - fun vec(): RealVector { - return theParameters.vec() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MinosError.kt b/kmath-optimization/src/commonMain/tmp/minuit/MinosError.kt deleted file mode 100644 index c7cf10523..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MinosError.kt +++ /dev/null @@ -1,219 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * MinosError class. - * - * @author Darksnake - * @version $Id$ - */ -class MinosError { - private var theLower: MnCross - private var theMinValue = 0.0 - private var theParameter = 0 - private var theUpper: MnCross - - internal constructor() { - theUpper = MnCross() - theLower = MnCross() - } - - internal constructor(par: Int, min: Double, low: MnCross, up: MnCross) { - theParameter = par - theMinValue = min - theUpper = up - theLower = low - } - - /** - * - * atLowerLimit. - * - * @return a boolean. - */ - fun atLowerLimit(): Boolean { - return theLower.atLimit() - } - - /** - * - * atLowerMaxFcn. - * - * @return a boolean. - */ - fun atLowerMaxFcn(): Boolean { - return theLower.atMaxFcn() - } - - /** - * - * atUpperLimit. - * - * @return a boolean. - */ - fun atUpperLimit(): Boolean { - return theUpper.atLimit() - } - - /** - * - * atUpperMaxFcn. - * - * @return a boolean. - */ - fun atUpperMaxFcn(): Boolean { - return theUpper.atMaxFcn() - } - - /** - * - * isValid. - * - * @return a boolean. - */ - fun isValid(): Boolean { - return theLower.isValid() && theUpper.isValid() - } - - /** - * - * lower. - * - * @return a double. - */ - fun lower(): Double { - return -1.0 * lowerState().error(parameter()) * (1.0 + theLower.value()) - } - - /** - * - * lowerNewMin. - * - * @return a boolean. - */ - fun lowerNewMin(): Boolean { - return theLower.newMinimum() - } - - /** - * - * lowerState. - * - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun lowerState(): MnUserParameterState { - return theLower.state() - } - - /** - * - * lowerValid. - * - * @return a boolean. - */ - fun lowerValid(): Boolean { - return theLower.isValid() - } - - /** - * - * min. - * - * @return a double. - */ - fun min(): Double { - return theMinValue - } - - /** - * - * nfcn. - * - * @return a int. - */ - fun nfcn(): Int { - return theUpper.nfcn() + theLower.nfcn() - } - - /** - * - * parameter. - * - * @return a int. - */ - fun parameter(): Int { - return theParameter - } - - /** - * - * range. - * - * @return - */ - fun range(): Range { - return Range(lower(), upper()) - } - - /** - * {@inheritDoc} - */ - override fun toString(): String { - return MnPrint.toString(this) - } - - /** - * - * upper. - * - * @return a double. - */ - fun upper(): Double { - return upperState().error(parameter()) * (1.0 + theUpper.value()) - } - - /** - * - * upperNewMin. - * - * @return a boolean. - */ - fun upperNewMin(): Boolean { - return theUpper.newMinimum() - } - - /** - * - * upperState. - * - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun upperState(): MnUserParameterState { - return theUpper.state() - } - - /** - * - * upperValid. - * - * @return a boolean. - */ - fun upperValid(): Boolean { - return theUpper.isValid() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MinuitParameter.kt b/kmath-optimization/src/commonMain/tmp/minuit/MinuitParameter.kt deleted file mode 100644 index ff6834df4..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MinuitParameter.kt +++ /dev/null @@ -1,314 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -class MinuitParameter { - private var theConst = false - private var theError = 0.0 - private var theFix = false - private var theLoLimValid = false - private var theLoLimit = 0.0 - private var theName: String - private var theNum: Int - private var theUpLimValid = false - private var theUpLimit = 0.0 - private var theValue: Double - - /** - * constructor for constant parameter - * - * @param num a int. - * @param name a [String] object. - * @param val a double. - */ - constructor(num: Int, name: String, `val`: Double) { - theNum = num - theValue = `val` - theConst = true - theName = name - } - - /** - * constructor for standard parameter - * - * @param num a int. - * @param name a [String] object. - * @param val a double. - * @param err a double. - */ - constructor(num: Int, name: String, `val`: Double, err: Double) { - theNum = num - theValue = `val` - theError = err - theName = name - } - - /** - * constructor for limited parameter - * - * @param num a int. - * @param name a [String] object. - * @param val a double. - * @param err a double. - * @param min a double. - * @param max a double. - */ - constructor(num: Int, name: String, `val`: Double, err: Double, min: Double, max: Double) { - theNum = num - theValue = `val` - theError = err - theLoLimit = min - theUpLimit = max - theLoLimValid = true - theUpLimValid = true - require(min != max) { "min == max" } - if (min > max) { - theLoLimit = max - theUpLimit = min - } - theName = name - } - - private constructor(other: MinuitParameter) { - theNum = other.theNum - theName = other.theName - theValue = other.theValue - theError = other.theError - theConst = other.theConst - theFix = other.theFix - theLoLimit = other.theLoLimit - theUpLimit = other.theUpLimit - theLoLimValid = other.theLoLimValid - theUpLimValid = other.theUpLimValid - } - - /** - * - * copy. - * - * @return a [hep.dataforge.MINUIT.MinuitParameter] object. - */ - fun copy(): MinuitParameter { - return MinuitParameter(this) - } - - /** - * - * error. - * - * @return a double. - */ - fun error(): Double { - return theError - } - - /** - * - * fix. - */ - fun fix() { - theFix = true - } - - /** - * - * hasLimits. - * - * @return a boolean. - */ - fun hasLimits(): Boolean { - return theLoLimValid || theUpLimValid - } - - /** - * - * hasLowerLimit. - * - * @return a boolean. - */ - fun hasLowerLimit(): Boolean { - return theLoLimValid - } - - /** - * - * hasUpperLimit. - * - * @return a boolean. - */ - fun hasUpperLimit(): Boolean { - return theUpLimValid - } - //state of parameter (fixed/const/limited) - /** - * - * isConst. - * - * @return a boolean. - */ - fun isConst(): Boolean { - return theConst - } - - /** - * - * isFixed. - * - * @return a boolean. - */ - fun isFixed(): Boolean { - return theFix - } - - /** - * - * lowerLimit. - * - * @return a double. - */ - fun lowerLimit(): Double { - return theLoLimit - } - - /** - * - * name. - * - * @return a [String] object. - */ - fun name(): String { - return theName - } - //access methods - /** - * - * number. - * - * @return a int. - */ - fun number(): Int { - return theNum - } - - /** - * - * release. - */ - fun release() { - theFix = false - } - - /** - * - * removeLimits. - */ - fun removeLimits() { - theLoLimit = 0.0 - theUpLimit = 0.0 - theLoLimValid = false - theUpLimValid = false - } - - /** - * - * setError. - * - * @param err a double. - */ - fun setError(err: Double) { - theError = err - theConst = false - } - - /** - * - * setLimits. - * - * @param low a double. - * @param up a double. - */ - fun setLimits(low: Double, up: Double) { - require(low != up) { "min == max" } - theLoLimit = low - theUpLimit = up - theLoLimValid = true - theUpLimValid = true - if (low > up) { - theLoLimit = up - theUpLimit = low - } - } - - /** - * - * setLowerLimit. - * - * @param low a double. - */ - fun setLowerLimit(low: Double) { - theLoLimit = low - theUpLimit = 0.0 - theLoLimValid = true - theUpLimValid = false - } - - /** - * - * setUpperLimit. - * - * @param up a double. - */ - fun setUpperLimit(up: Double) { - theLoLimit = 0.0 - theUpLimit = up - theLoLimValid = false - theUpLimValid = true - } - //interaction - /** - * - * setValue. - * - * @param val a double. - */ - fun setValue(`val`: Double) { - theValue = `val` - } - - /** - * - * upperLimit. - * - * @return a double. - */ - fun upperLimit(): Double { - return theUpLimit - } - - /** - * - * value. - * - * @return a double. - */ - fun value(): Double { - return theValue - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnAlgebraicSymMatrix.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnAlgebraicSymMatrix.kt deleted file mode 100644 index 4b75858e1..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnAlgebraicSymMatrix.kt +++ /dev/null @@ -1,458 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector - -/** - * - * @version $Id$ - */ -class MnAlgebraicSymMatrix(n: Int) { - private val theData: DoubleArray - private val theNRow: Int - private val theSize: Int - - /** - * - * copy. - * - * @return a [hep.dataforge.MINUIT.MnAlgebraicSymMatrix] object. - */ - fun copy(): MnAlgebraicSymMatrix { - val copy = MnAlgebraicSymMatrix(theNRow) - java.lang.System.arraycopy(theData, 0, copy.theData, 0, theSize) - return copy - } - - fun data(): DoubleArray { - return theData - } - - fun eigenvalues(): ArrayRealVector { - val nrow = theNRow - val tmp = DoubleArray((nrow + 1) * (nrow + 1)) - val work = DoubleArray(1 + 2 * nrow) - for (i in 0 until nrow) { - for (j in 0..i) { - tmp[1 + i + (1 + j) * nrow] = get(i, j) - tmp[(1 + i) * nrow + (1 + j)] = get(i, j) - } - } - val info = mneigen(tmp, nrow, nrow, work.size, work, 1e-6) - if (info != 0) { - throw EigenvaluesException() - } - val result = ArrayRealVector(nrow) - for (i in 0 until nrow) { - result.setEntry(i, work[1 + i]) - } - return result - } - - operator fun get(row: Int, col: Int): Double { - if (row >= theNRow || col >= theNRow) { - throw ArrayIndexOutOfBoundsException() - } - return theData[theIndex(row, col)] - } - - @Throws(SingularMatrixException::class) - fun invert() { - if (theSize == 1) { - val tmp = theData[0] - if (tmp <= 0.0) { - throw SingularMatrixException() - } - theData[0] = 1.0 / tmp - } else { - val nrow = theNRow - val s = DoubleArray(nrow) - val q = DoubleArray(nrow) - val pp = DoubleArray(nrow) - for (i in 0 until nrow) { - val si = theData[theIndex(i, i)] - if (si < 0.0) { - throw SingularMatrixException() - } - s[i] = 1.0 / sqrt(si) - } - for (i in 0 until nrow) { - for (j in i until nrow) { - theData[theIndex(i, j)] *= s[i] * s[j] - } - } - for (i in 0 until nrow) { - var k = i - if (theData[theIndex(k, k)] == 0.0) { - throw SingularMatrixException() - } - q[k] = 1.0 / theData[theIndex(k, k)] - pp[k] = 1.0 - theData[theIndex(k, k)] = 0.0 - val kp1 = k + 1 - if (k != 0) { - for (j in 0 until k) { - val index = theIndex(j, k) - pp[j] = theData[index] - q[j] = theData[index] * q[k] - theData[index] = 0.0 - } - } - if (k != nrow - 1) { - for (j in kp1 until nrow) { - val index = theIndex(k, j) - pp[j] = theData[index] - q[j] = -theData[index] * q[k] - theData[index] = 0.0 - } - } - for (j in 0 until nrow) { - k = j - while (k < nrow) { - theData[theIndex(j, k)] += pp[j] * q[k] - k++ - } - } - } - for (j in 0 until nrow) { - for (k in j until nrow) { - theData[theIndex(j, k)] *= s[j] * s[k] - } - } - } - } - - fun ncol(): Int { - return nrow() - } - - fun nrow(): Int { - return theNRow - } - - operator fun set(row: Int, col: Int, value: Double) { - if (row >= theNRow || col >= theNRow) { - throw ArrayIndexOutOfBoundsException() - } - theData[theIndex(row, col)] = value - } - - fun size(): Int { - return theSize - } - - private fun theIndex(row: Int, col: Int): Int { - return if (row > col) { - col + row * (row + 1) / 2 - } else { - row + col * (col + 1) / 2 - } - } - - /** {@inheritDoc} */ - override fun toString(): String { - return MnPrint.toString(this) - } /* mneig_ */ - - private inner class EigenvaluesException : RuntimeException() - companion object { - private fun mneigen(a: DoubleArray, ndima: Int, n: Int, mits: Int, work: DoubleArray, precis: Double): Int { - - /* System generated locals */ - var i__2: Int - var i__3: Int - - /* Local variables */ - var b: Double - var c__: Double - var f: Double - var h__: Double - var i__: Int - var j: Int - var k: Int - var l: Int - var m = 0 - var r__: Double - var s: Double - var i0: Int - var i1: Int - var j1: Int - var m1: Int - var hh: Double - var gl: Double - var pr: Double - var pt: Double - - /* PRECIS is the machine precision EPSMAC */ - /* Parameter adjustments */ - val a_dim1: Int = ndima - val a_offset: Int = 1 + a_dim1 * 1 - - /* Function Body */ - var ifault = 1 - i__ = n - var i__1: Int = n - i1 = 2 - while (i1 <= i__1) { - l = i__ - 2 - f = a[i__ + (i__ - 1) * a_dim1] - gl = 0.0 - if (l >= 1) { - i__2 = l - k = 1 - while (k <= i__2) { - - /* Computing 2nd power */ - val r__1 = a[i__ + k * a_dim1] - gl += r__1 * r__1 - ++k - } - } - /* Computing 2nd power */h__ = gl + f * f - if (gl <= 1e-35) { - work[i__] = 0.0 - work[n + i__] = f - } else { - ++l - gl = sqrt(h__) - if (f >= 0.0) { - gl = -gl - } - work[n + i__] = gl - h__ -= f * gl - a[i__ + (i__ - 1) * a_dim1] = f - gl - f = 0.0 - i__2 = l - j = 1 - while (j <= i__2) { - a[j + i__ * a_dim1] = a[i__ + j * a_dim1] / h__ - gl = 0.0 - i__3 = j - k = 1 - while (k <= i__3) { - gl += a[j + k * a_dim1] * a[i__ + k * a_dim1] - ++k - } - if (j < l) { - j1 = j + 1 - i__3 = l - k = j1 - while (k <= i__3) { - gl += a[k + j * a_dim1] * a[i__ + k * a_dim1] - ++k - } - } - work[n + j] = gl / h__ - f += gl * a[j + i__ * a_dim1] - ++j - } - hh = f / (h__ + h__) - i__2 = l - j = 1 - while (j <= i__2) { - f = a[i__ + j * a_dim1] - gl = work[n + j] - hh * f - work[n + j] = gl - i__3 = j - k = 1 - while (k <= i__3) { - a[j + k * a_dim1] = a[j + k * a_dim1] - f * work[n + k] - (gl - * a[i__ + k * a_dim1]) - ++k - } - ++j - } - work[i__] = h__ - } - --i__ - ++i1 - } - work[1] = 0.0 - work[n + 1] = 0.0 - i__1 = n - i__ = 1 - while (i__ <= i__1) { - l = i__ - 1 - if (work[i__] != 0.0 && l != 0) { - i__3 = l - j = 1 - while (j <= i__3) { - gl = 0.0 - i__2 = l - k = 1 - while (k <= i__2) { - gl += a[i__ + k * a_dim1] * a[k + j * a_dim1] - ++k - } - i__2 = l - k = 1 - while (k <= i__2) { - a[k + j * a_dim1] -= gl * a[k + i__ * a_dim1] - ++k - } - ++j - } - } - work[i__] = a[i__ + i__ * a_dim1] - a[i__ + i__ * a_dim1] = 1.0 - if (l != 0) { - i__2 = l - j = 1 - while (j <= i__2) { - a[i__ + j * a_dim1] = 0.0 - a[j + i__ * a_dim1] = 0.0 - ++j - } - } - ++i__ - } - val n1: Int = n - 1 - i__1 = n - i__ = 2 - while (i__ <= i__1) { - i0 = n + i__ - 1 - work[i0] = work[i0 + 1] - ++i__ - } - work[n + n] = 0.0 - b = 0.0 - f = 0.0 - i__1 = n - l = 1 - while (l <= i__1) { - j = 0 - h__ = precis * (abs(work[l]) + abs(work[n + l])) - if (b < h__) { - b = h__ - } - i__2 = n - m1 = l - while (m1 <= i__2) { - m = m1 - if (abs(work[n + m]) <= b) { - break - } - ++m1 - } - if (m != l) { - while (true) { - if (j == mits) { - return ifault - } - ++j - pt = (work[l + 1] - work[l]) / (work[n + l] * 2.0) - r__ = sqrt(pt * pt + 1.0) - pr = pt + r__ - if (pt < 0.0) { - pr = pt - r__ - } - h__ = work[l] - work[n + l] / pr - i__2 = n - i__ = l - while (i__ <= i__2) { - work[i__] -= h__ - ++i__ - } - f += h__ - pt = work[m] - c__ = 1.0 - s = 0.0 - m1 = m - 1 - i__ = m - i__2 = m1 - i1 = l - while (i1 <= i__2) { - j = i__ - --i__ - gl = c__ * work[n + i__] - h__ = c__ * pt - if (abs(pt) < abs(work[n + i__])) { - c__ = pt / work[n + i__] - r__ = sqrt(c__ * c__ + 1.0) - work[n + j] = s * work[n + i__] * r__ - s = 1.0 / r__ - c__ /= r__ - } else { - c__ = work[n + i__] / pt - r__ = sqrt(c__ * c__ + 1.0) - work[n + j] = s * pt * r__ - s = c__ / r__ - c__ = 1.0 / r__ - } - pt = c__ * work[i__] - s * gl - work[j] = h__ + s * (c__ * gl + s * work[i__]) - i__3 = n - k = 1 - while (k <= i__3) { - h__ = a[k + j * a_dim1] - a[k + j * a_dim1] = s * a[k + i__ * a_dim1] + c__ * h__ - a[k + i__ * a_dim1] = c__ * a[k + i__ * a_dim1] - s * h__ - ++k - } - ++i1 - } - work[n + l] = s * pt - work[l] = c__ * pt - if (abs(work[n + l]) <= b) { - break - } - } - } - work[l] += f - ++l - } - i__1 = n1 - i__ = 1 - while (i__ <= i__1) { - k = i__ - pt = work[i__] - i1 = i__ + 1 - i__3 = n - j = i1 - while (j <= i__3) { - if (work[j] < pt) { - k = j - pt = work[j] - } - ++j - } - if (k != i__) { - work[k] = work[i__] - work[i__] = pt - i__3 = n - j = 1 - while (j <= i__3) { - pt = a[j + i__ * a_dim1] - a[j + i__ * a_dim1] = a[j + k * a_dim1] - a[j + k * a_dim1] = pt - ++j - } - } - ++i__ - } - ifault = 0 - return ifault - } /* mneig_ */ - } - - init { - require(n >= 0) { "Invalid matrix size: $n" } - theSize = n * (n + 1) / 2 - theNRow = n - theData = DoubleArray(theSize) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnApplication.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnApplication.kt deleted file mode 100644 index 025eea4ae..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnApplication.kt +++ /dev/null @@ -1,554 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction -import ru.inr.mass.minuit.* - -/** - * Base class for minimizers. - * - * @version $Id$ - * @author Darksnake - */ -abstract class MnApplication { - /* package protected */ - var checkAnalyticalDerivatives: Boolean - - /* package protected */ /* package protected */ - var theErrorDef = 1.0 /* package protected */ - var theFCN: MultiFunction? - - /* package protected */ /* package protected */ - var theNumCall /* package protected */ = 0 - var theState: MnUserParameterState - - /* package protected */ - var theStrategy: MnStrategy - - /* package protected */ - var useAnalyticalDerivatives: Boolean - - /* package protected */ - internal constructor(fcn: MultiFunction?, state: MnUserParameterState, stra: MnStrategy) { - theFCN = fcn - theState = state - theStrategy = stra - checkAnalyticalDerivatives = true - useAnalyticalDerivatives = true - } - - internal constructor(fcn: MultiFunction?, state: MnUserParameterState, stra: MnStrategy, nfcn: Int) { - theFCN = fcn - theState = state - theStrategy = stra - theNumCall = nfcn - checkAnalyticalDerivatives = true - useAnalyticalDerivatives = true - } - - /** - * - * MultiFunction. - * - * @return a [MultiFunction] object. - */ - fun MultiFunction(): MultiFunction? { - return theFCN - } - - /** - * add free parameter - * - * @param err a double. - * @param val a double. - * @param name a [String] object. - */ - fun add(name: String, `val`: Double, err: Double) { - theState.add(name, `val`, err) - } - - /** - * add limited parameter - * - * @param up a double. - * @param low a double. - * @param name a [String] object. - * @param val a double. - * @param err a double. - */ - fun add(name: String, `val`: Double, err: Double, low: Double, up: Double) { - theState.add(name, `val`, err, low, up) - } - - /** - * add const parameter - * - * @param name a [String] object. - * @param val a double. - */ - fun add(name: String, `val`: Double) { - theState.add(name, `val`) - } - - /** - * - * checkAnalyticalDerivatives. - * - * @return a boolean. - */ - fun checkAnalyticalDerivatives(): Boolean { - return checkAnalyticalDerivatives - } - - /** - * - * covariance. - * - * @return a [hep.dataforge.MINUIT.MnUserCovariance] object. - */ - fun covariance(): MnUserCovariance { - return theState.covariance() - } - - /** - * - * error. - * - * @param index a int. - * @return a double. - */ - fun error(index: Int): Double { - return theState.error(index) - } - - /** - * - * error. - * - * @param name a [String] object. - * @return a double. - */ - fun error(name: String?): Double { - return theState.error(name) - } - - /** - * - * errorDef. - * - * @return a double. - */ - fun errorDef(): Double { - return theErrorDef - } - - /** - * - * errors. - * - * @return an array of double. - */ - fun errors(): DoubleArray { - return theState.errors() - } - - fun ext2int(i: Int, value: Double): Double { - return theState.ext2int(i, value) - } - - fun extOfInt(i: Int): Int { - return theState.extOfInt(i) - } - //interaction via external number of parameter - /** - * - * fix. - * - * @param index a int. - */ - fun fix(index: Int) { - theState.fix(index) - } - //interaction via name of parameter - /** - * - * fix. - * - * @param name a [String] object. - */ - fun fix(name: String?) { - theState.fix(name) - } - - /** - * convert name into external number of parameter - * - * @param name a [String] object. - * @return a int. - */ - fun index(name: String?): Int { - return theState.index(name) - } - - // transformation internal <-> external - fun int2ext(i: Int, value: Double): Double { - return theState.int2ext(i, value) - } - - fun intOfExt(i: Int): Int { - return theState.intOfExt(i) - } - - /** - * - * minimize. - * - * @return a [hep.dataforge.MINUIT.FunctionMinimum] object. - */ - fun minimize(): FunctionMinimum { - return minimize(DEFAULT_MAXFCN) - } - - /** - * - * minimize. - * - * @param maxfcn a int. - * @return a [hep.dataforge.MINUIT.FunctionMinimum] object. - */ - fun minimize(maxfcn: Int): FunctionMinimum { - return minimize(maxfcn, DEFAULT_TOLER) - } - - /** - * Causes minimization of the FCN and returns the result in form of a - * FunctionMinimum. - * - * @param maxfcn specifies the (approximate) maximum number of function - * calls after which the calculation will be stopped even if it has not yet - * converged. - * @param toler specifies the required tolerance on the function value at - * the minimum. The default tolerance value is 0.1, and the minimization - * will stop when the estimated vertical distance to the minimum (EDM) is - * less than 0:001*tolerance*errorDef - * @return a [hep.dataforge.MINUIT.FunctionMinimum] object. - */ - fun minimize(maxfcn: Int, toler: Double): FunctionMinimum { - var maxfcn = maxfcn - check(theState.isValid()) { "Invalid state" } - val npar = variableParameters() - if (maxfcn == 0) { - maxfcn = 200 + 100 * npar + 5 * npar * npar - } - val min: FunctionMinimum = minimizer().minimize(theFCN, - theState, - theStrategy, - maxfcn, - toler, - theErrorDef, - useAnalyticalDerivatives, - checkAnalyticalDerivatives) - theNumCall += min.nfcn() - theState = min.userState() - return min - } - - abstract fun minimizer(): ModularFunctionMinimizer - - // facade: forward interface of MnUserParameters and MnUserTransformation - fun minuitParameters(): List { - return theState.minuitParameters() - } - - /** - * convert external number into name of parameter - * - * @param index a int. - * @return a [String] object. - */ - fun name(index: Int): String { - return theState.name(index) - } - - /** - * - * numOfCalls. - * - * @return a int. - */ - fun numOfCalls(): Int { - return theNumCall - } - - /** - * access to single parameter - * @param i - * @return - */ - fun parameter(i: Int): MinuitParameter { - return theState.parameter(i) - } - - /** - * - * parameters. - * - * @return a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - fun parameters(): MnUserParameters { - return theState.parameters() - } - - /** - * access to parameters and errors in column-wise representation - * - * @return an array of double. - */ - fun params(): DoubleArray { - return theState.params() - } - - /** - * - * precision. - * - * @return a [hep.dataforge.MINUIT.MnMachinePrecision] object. - */ - fun precision(): MnMachinePrecision { - return theState.precision() - } - - /** - * - * release. - * - * @param index a int. - */ - fun release(index: Int) { - theState.release(index) - } - - /** - * - * release. - * - * @param name a [String] object. - */ - fun release(name: String?) { - theState.release(name) - } - - /** - * - * removeLimits. - * - * @param index a int. - */ - fun removeLimits(index: Int) { - theState.removeLimits(index) - } - - /** - * - * removeLimits. - * - * @param name a [String] object. - */ - fun removeLimits(name: String?) { - theState.removeLimits(name) - } - - /** - * Minuit does a check of the user gradient at the beginning, if this is not - * wanted the set this to "false". - * - * @param check a boolean. - */ - fun setCheckAnalyticalDerivatives(check: Boolean) { - checkAnalyticalDerivatives = check - } - - /** - * - * setError. - * - * @param index a int. - * @param err a double. - */ - fun setError(index: Int, err: Double) { - theState.setError(index, err) - } - - /** - * - * setError. - * - * @param name a [String] object. - * @param err a double. - */ - fun setError(name: String?, err: Double) { - theState.setError(name, err) - } - - /** - * errorDef() is the error definition of the function. E.g. is 1 if function - * is Chi2 and 0.5 if function is -logLikelihood. If the user wants instead - * the 2-sigma errors, errorDef() = 4, as Chi2(x+n*sigma) = Chi2(x) + n*n. - * - * @param errorDef a double. - */ - fun setErrorDef(errorDef: Double) { - theErrorDef = errorDef - } - - /** - * - * setLimits. - * - * @param index a int. - * @param low a double. - * @param up a double. - */ - fun setLimits(index: Int, low: Double, up: Double) { - theState.setLimits(index, low, up) - } - - /** - * - * setLimits. - * - * @param name a [String] object. - * @param low a double. - * @param up a double. - */ - fun setLimits(name: String?, low: Double, up: Double) { - theState.setLimits(name, low, up) - } - - /** - * - * setPrecision. - * - * @param prec a double. - */ - fun setPrecision(prec: Double) { - theState.setPrecision(prec) - } - - /** - * By default if the function to be minimized implements MultiFunction then - * the analytical gradient provided by the function will be used. Set this - * to - * false to disable this behaviour and force numerical - * calculation of the gradient. - * - * @param use a boolean. - */ - fun setUseAnalyticalDerivatives(use: Boolean) { - useAnalyticalDerivatives = use - } - - /** - * - * setValue. - * - * @param index a int. - * @param val a double. - */ - fun setValue(index: Int, `val`: Double) { - theState.setValue(index, `val`) - } - - /** - * - * setValue. - * - * @param name a [String] object. - * @param val a double. - */ - fun setValue(name: String?, `val`: Double) { - theState.setValue(name, `val`) - } - - /** - * - * state. - * - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun state(): MnUserParameterState { - return theState - } - - /** - * - * strategy. - * - * @return a [hep.dataforge.MINUIT.MnStrategy] object. - */ - fun strategy(): MnStrategy { - return theStrategy - } - - /** - * - * useAnalyticalDerivaties. - * - * @return a boolean. - */ - fun useAnalyticalDerivaties(): Boolean { - return useAnalyticalDerivatives - } - - /** - * - * value. - * - * @param index a int. - * @return a double. - */ - fun value(index: Int): Double { - return theState.value(index) - } - - /** - * - * value. - * - * @param name a [String] object. - * @return a double. - */ - fun value(name: String?): Double { - return theState.value(name) - } - - /** - * - * variableParameters. - * - * @return a int. - */ - fun variableParameters(): Int { - return theState.variableParameters() - } - - companion object { - var DEFAULT_MAXFCN = 0 - var DEFAULT_STRATEGY = 1 - var DEFAULT_TOLER = 0.1 - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnContours.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnContours.kt deleted file mode 100644 index 1b700f4e2..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnContours.kt +++ /dev/null @@ -1,283 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction -import ru.inr.mass.minuit.* - -/** - * API class for Contours error analysis (2-dim errors). Minimization has to be - * done before and minimum must be valid. Possibility to ask only for the points - * or the points and associated Minos errors. - * - * @version $Id$ - * @author Darksnake - */ -class MnContours(fcn: MultiFunction?, min: FunctionMinimum?, stra: MnStrategy?) { - private var theFCN: MultiFunction? = null - private var theMinimum: FunctionMinimum? = null - private var theStrategy: MnStrategy? = null - - /** - * construct from FCN + minimum - * - * @param fcn a [MultiFunction] object. - * @param min a [hep.dataforge.MINUIT.FunctionMinimum] object. - */ - constructor(fcn: MultiFunction?, min: FunctionMinimum?) : this(fcn, min, MnApplication.DEFAULT_STRATEGY) - - /** - * construct from FCN + minimum + strategy - * - * @param stra a int. - * @param min a [hep.dataforge.MINUIT.FunctionMinimum] object. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, min: FunctionMinimum?, stra: Int) : this(fcn, min, MnStrategy(stra)) - - /** - * - * contour. - * - * @param px a int. - * @param py a int. - * @return a [hep.dataforge.MINUIT.ContoursError] object. - */ - fun contour(px: Int, py: Int): ContoursError { - return contour(px, py, 1.0) - } - - /** - * - * contour. - * - * @param px a int. - * @param py a int. - * @param errDef a double. - * @return a [hep.dataforge.MINUIT.ContoursError] object. - */ - fun contour(px: Int, py: Int, errDef: Double): ContoursError { - return contour(px, py, errDef, 20) - } - - /** - * Causes a CONTOURS error analysis and returns the result in form of - * ContoursError. As a by-product ContoursError keeps the MinosError - * information of parameters parx and pary. The result ContoursError can be - * easily printed using MnPrint or toString(). - * - * @param npoints a int. - * @param px a int. - * @param py a int. - * @param errDef a double. - * @return a [hep.dataforge.MINUIT.ContoursError] object. - */ - fun contour(px: Int, py: Int, errDef: Double, npoints: Int): ContoursError { - var errDef = errDef - errDef *= theMinimum!!.errorDef() - assert(npoints > 3) - val maxcalls: Int = 100 * (npoints + 5) * (theMinimum!!.userState().variableParameters() + 1) - var nfcn = 0 - val result: MutableList = java.util.ArrayList(npoints) - val states: List = java.util.ArrayList() - val toler = 0.05 - - //get first four points - val minos = MnMinos(theFCN, theMinimum, theStrategy) - val valx: Double = theMinimum!!.userState().value(px) - val valy: Double = theMinimum!!.userState().value(py) - val mex: MinosError = minos.minos(px, errDef) - nfcn += mex.nfcn() - if (!mex.isValid()) { - MINUITPlugin.logStatic("MnContours is unable to find first two points.") - return ContoursError(px, py, result, mex, mex, nfcn) - } - val ex: Range = mex.range() - val mey: MinosError = minos.minos(py, errDef) - nfcn += mey.nfcn() - if (!mey.isValid()) { - MINUITPlugin.logStatic("MnContours is unable to find second two points.") - return ContoursError(px, py, result, mex, mey, nfcn) - } - val ey: Range = mey.range() - val migrad = MnMigrad(theFCN, - theMinimum!!.userState().copy(), - MnStrategy(max(0, theStrategy!!.strategy() - 1))) - migrad.fix(px) - migrad.setValue(px, valx + ex.getSecond()) - val exy_up: FunctionMinimum = migrad.minimize() - nfcn += exy_up.nfcn() - if (!exy_up.isValid()) { - MINUITPlugin.logStatic("MnContours is unable to find upper y value for x parameter $px.") - return ContoursError(px, py, result, mex, mey, nfcn) - } - migrad.setValue(px, valx + ex.getFirst()) - val exy_lo: FunctionMinimum = migrad.minimize() - nfcn += exy_lo.nfcn() - if (!exy_lo.isValid()) { - MINUITPlugin.logStatic("MnContours is unable to find lower y value for x parameter $px.") - return ContoursError(px, py, result, mex, mey, nfcn) - } - val migrad1 = MnMigrad(theFCN, - theMinimum!!.userState().copy(), - MnStrategy(max(0, theStrategy!!.strategy() - 1))) - migrad1.fix(py) - migrad1.setValue(py, valy + ey.getSecond()) - val eyx_up: FunctionMinimum = migrad1.minimize() - nfcn += eyx_up.nfcn() - if (!eyx_up.isValid()) { - MINUITPlugin.logStatic("MnContours is unable to find upper x value for y parameter $py.") - return ContoursError(px, py, result, mex, mey, nfcn) - } - migrad1.setValue(py, valy + ey.getFirst()) - val eyx_lo: FunctionMinimum = migrad1.minimize() - nfcn += eyx_lo.nfcn() - if (!eyx_lo.isValid()) { - MINUITPlugin.logStatic("MnContours is unable to find lower x value for y parameter $py.") - return ContoursError(px, py, result, mex, mey, nfcn) - } - val scalx: Double = 1.0 / (ex.getSecond() - ex.getFirst()) - val scaly: Double = 1.0 / (ey.getSecond() - ey.getFirst()) - result.add(Range(valx + ex.getFirst(), exy_lo.userState().value(py))) - result.add(Range(eyx_lo.userState().value(px), valy + ey.getFirst())) - result.add(Range(valx + ex.getSecond(), exy_up.userState().value(py))) - result.add(Range(eyx_up.userState().value(px), valy + ey.getSecond())) - val upar: MnUserParameterState = theMinimum!!.userState().copy() - upar.fix(px) - upar.fix(py) - val par = intArrayOf(px, py) - val cross = MnFunctionCross(theFCN, upar, theMinimum!!.fval(), theStrategy, errDef) - for (i in 4 until npoints) { - var idist1: Range = result[result.size - 1] - var idist2: Range = result[0] - var pos2 = 0 - val distx: Double = idist1.getFirst() - idist2.getFirst() - val disty: Double = idist1.getSecond() - idist2.getSecond() - var bigdis = scalx * scalx * distx * distx + scaly * scaly * disty * disty - for (j in 0 until result.size - 1) { - val ipair: Range = result[j] - val distx2: Double = ipair.getFirst() - result[j + 1].getFirst() - val disty2: Double = ipair.getSecond() - result[j + 1].getSecond() - val dist = scalx * scalx * distx2 * distx2 + scaly * scaly * disty2 * disty2 - if (dist > bigdis) { - bigdis = dist - idist1 = ipair - idist2 = result[j + 1] - pos2 = j + 1 - } - } - val a1 = 0.5 - val a2 = 0.5 - var sca = 1.0 - while (true) { - if (nfcn > maxcalls) { - MINUITPlugin.logStatic("MnContours: maximum number of function calls exhausted.") - return ContoursError(px, py, result, mex, mey, nfcn) - } - val xmidcr: Double = a1 * idist1.getFirst() + a2 * idist2.getFirst() - val ymidcr: Double = a1 * idist1.getSecond() + a2 * idist2.getSecond() - val xdir: Double = idist2.getSecond() - idist1.getSecond() - val ydir: Double = idist1.getFirst() - idist2.getFirst() - val scalfac: Double = - sca * max(abs(xdir * scalx), abs(ydir * scaly)) - val xdircr = xdir / scalfac - val ydircr = ydir / scalfac - val pmid = doubleArrayOf(xmidcr, ymidcr) - val pdir = doubleArrayOf(xdircr, ydircr) - val opt: MnCross = cross.cross(par, pmid, pdir, toler, maxcalls) - nfcn += opt.nfcn() - if (opt.isValid()) { - val aopt: Double = opt.value() - if (pos2 == 0) { - result.add(Range(xmidcr + aopt * xdircr, ymidcr + aopt * ydircr)) - } else { - result.add(pos2, Range(xmidcr + aopt * xdircr, ymidcr + aopt * ydircr)) - } - break - } - if (sca < 0.0) { - MINUITPlugin.logStatic("MnContours is unable to find point " + (i + 1) + " on contour.") - MINUITPlugin.logStatic("MnContours finds only $i points.") - return ContoursError(px, py, result, mex, mey, nfcn) - } - sca = -1.0 - } - } - return ContoursError(px, py, result, mex, mey, nfcn) - } - - /** - * - * points. - * - * @param px a int. - * @param py a int. - * @return a [List] object. - */ - fun points(px: Int, py: Int): List { - return points(px, py, 1.0) - } - - /** - * - * points. - * - * @param px a int. - * @param py a int. - * @param errDef a double. - * @return a [List] object. - */ - fun points(px: Int, py: Int, errDef: Double): List { - return points(px, py, errDef, 20) - } - - /** - * Calculates one function contour of FCN with respect to parameters parx - * and pary. The return value is a list of (x,y) points. FCN minimized - * always with respect to all other n - 2 variable parameters (if any). - * MINUITPlugin will try to find n points on the contour (default 20). To - * calculate more than one contour, the user needs to set the error - * definition in its FCN to the appropriate value for the desired confidence - * level and call this method for each contour. - * - * @param npoints a int. - * @param px a int. - * @param py a int. - * @param errDef a double. - * @return a [List] object. - */ - fun points(px: Int, py: Int, errDef: Double, npoints: Int): List { - val cont: ContoursError = contour(px, py, errDef, npoints) - return cont.points() - } - - fun strategy(): MnStrategy? { - return theStrategy - } - - /** - * construct from FCN + minimum + strategy - * - * @param stra a [hep.dataforge.MINUIT.MnStrategy] object. - * @param min a [hep.dataforge.MINUIT.FunctionMinimum] object. - * @param fcn a [MultiFunction] object. - */ - init { - theFCN = fcn - theMinimum = min - theStrategy = stra - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnCovarianceSqueeze.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnCovarianceSqueeze.kt deleted file mode 100644 index 7614a93b0..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnCovarianceSqueeze.kt +++ /dev/null @@ -1,113 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import space.kscience.kmath.optimization.minuit.MINUITPlugin - -/** - * - * @version $Id$ - */ -internal object MnCovarianceSqueeze { - fun squeeze(cov: MnUserCovariance, n: Int): MnUserCovariance { - assert(cov.nrow() > 0) - assert(n < cov.nrow()) - val hess = MnAlgebraicSymMatrix(cov.nrow()) - for (i in 0 until cov.nrow()) { - for (j in i until cov.nrow()) { - hess[i, j] = cov[i, j] - } - } - try { - hess.invert() - } catch (x: SingularMatrixException) { - MINUITPlugin.logStatic("MnUserCovariance inversion failed; return diagonal matrix;") - val result = MnUserCovariance(cov.nrow() - 1) - var i = 0 - var j = 0 - while (i < cov.nrow()) { - if (i == n) { - i++ - continue - } - result[j, j] = cov[i, i] - j++ - i++ - } - return result - } - val squeezed: MnAlgebraicSymMatrix = squeeze(hess, n) - try { - squeezed.invert() - } catch (x: SingularMatrixException) { - MINUITPlugin.logStatic("MnUserCovariance back-inversion failed; return diagonal matrix;") - val result = MnUserCovariance(squeezed.nrow()) - var i = 0 - while (i < squeezed.nrow()) { - result[i, i] = 1.0 / squeezed[i, i] - i++ - } - return result - } - return MnUserCovariance(squeezed.data(), squeezed.nrow()) - } - - fun squeeze(err: MinimumError, n: Int): MinimumError { - val hess: MnAlgebraicSymMatrix = err.hessian() - val squeezed: MnAlgebraicSymMatrix = squeeze(hess, n) - try { - squeezed.invert() - } catch (x: SingularMatrixException) { - MINUITPlugin.logStatic("MnCovarianceSqueeze: MinimumError inversion fails; return diagonal matrix.") - val tmp = MnAlgebraicSymMatrix(squeezed.nrow()) - var i = 0 - while (i < squeezed.nrow()) { - tmp[i, i] = 1.0 / squeezed[i, i] - i++ - } - return MinimumError(tmp, MnInvertFailed()) - } - return MinimumError(squeezed, err.dcovar()) - } - - fun squeeze(hess: MnAlgebraicSymMatrix, n: Int): MnAlgebraicSymMatrix { - assert(hess.nrow() > 0) - assert(n < hess.nrow()) - val hs = MnAlgebraicSymMatrix(hess.nrow() - 1) - var i = 0 - var j = 0 - while (i < hess.nrow()) { - if (i == n) { - i++ - continue - } - var k = i - var l = j - while (k < hess.nrow()) { - if (k == n) { - k++ - continue - } - hs[j, l] = hess[i, k] - l++ - k++ - } - j++ - i++ - } - return hs - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnCross.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnCross.kt deleted file mode 100644 index f1487b106..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnCross.kt +++ /dev/null @@ -1,99 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * MnCross class. - * - * @version $Id$ - * @author Darksnake - */ -class MnCross { - private var theLimset = false - private var theMaxFcn = false - private var theNFcn = 0 - private var theNewMin = false - private var theState: MnUserParameterState - private var theValid = false - private var theValue = 0.0 - - internal constructor() { - theState = MnUserParameterState() - } - - internal constructor(nfcn: Int) { - theState = MnUserParameterState() - theNFcn = nfcn - } - - internal constructor(value: Double, state: MnUserParameterState, nfcn: Int) { - theValue = value - theState = state - theNFcn = nfcn - theValid = true - } - - internal constructor(state: MnUserParameterState, nfcn: Int, x: CrossParLimit?) { - theState = state - theNFcn = nfcn - theLimset = true - } - - internal constructor(state: MnUserParameterState, nfcn: Int, x: CrossFcnLimit?) { - theState = state - theNFcn = nfcn - theMaxFcn = true - } - - internal constructor(state: MnUserParameterState, nfcn: Int, x: CrossNewMin?) { - theState = state - theNFcn = nfcn - theNewMin = true - } - - fun atLimit(): Boolean { - return theLimset - } - - fun atMaxFcn(): Boolean { - return theMaxFcn - } - - fun isValid(): Boolean { - return theValid - } - - fun newMinimum(): Boolean { - return theNewMin - } - - fun nfcn(): Int { - return theNFcn - } - - fun state(): MnUserParameterState { - return theState - } - - fun value(): Double { - return theValue - } - - internal class CrossFcnLimit - internal class CrossNewMin - internal class CrossParLimit -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnEigen.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnEigen.kt deleted file mode 100644 index d7aade0c9..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnEigen.kt +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.RealVector - -/** - * Calculates and the eigenvalues of the user covariance matrix - * MnUserCovariance. - * - * @version $Id$ - * @author Darksnake - */ -object MnEigen { - /* Calculate eigenvalues of the covariance matrix. - * Will perform the calculation of the eigenvalues of the covariance matrix - * and return the result in the form of a double array. - * The eigenvalues are ordered from the smallest to the largest eigenvalue. - */ - /** - * - * eigenvalues. - * - * @param covar a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @return an array of double. - */ - fun eigenvalues(covar: MnUserCovariance): DoubleArray { - val cov = MnAlgebraicSymMatrix(covar.nrow()) - for (i in 0 until covar.nrow()) { - for (j in i until covar.nrow()) { - cov[i, j] = covar[i, j] - } - } - val eigen: RealVector = cov.eigenvalues() - return eigen.toArray() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnFcn.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnFcn.kt deleted file mode 100644 index b11f71035..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnFcn.kt +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction - -/** - * Функция, которая помнит количество вызовов себя и ErrorDef - * @version $Id$ - */ -class MnFcn(fcn: MultiFunction?, errorDef: Double) { - private val theErrorDef: Double - private val theFCN: MultiFunction? - protected var theNumCall: Int - fun errorDef(): Double { - return theErrorDef - } - - fun fcn(): MultiFunction? { - return theFCN - } - - fun numOfCalls(): Int { - return theNumCall - } - - fun value(v: RealVector): Double { - theNumCall++ - return theFCN.value(v.toArray()) - } - - init { - theFCN = fcn - theNumCall = 0 - theErrorDef = errorDef - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnFunctionCross.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnFunctionCross.kt deleted file mode 100644 index a05590e53..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnFunctionCross.kt +++ /dev/null @@ -1,369 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction -import ru.inr.mass.minuit.* -import kotlin.math.* - -/** - * - * @version $Id$ - */ -internal class MnFunctionCross( - fcn: MultiFunction?, - state: MnUserParameterState, - fval: Double, - stra: MnStrategy?, - errorDef: Double -) { - private val theErrorDef: Double - private val theFCN: MultiFunction? - private val theFval: Double - private val theState: MnUserParameterState - private val theStrategy: MnStrategy? - fun cross(par: IntArray, pmid: DoubleArray, pdir: DoubleArray, tlr: Double, maxcalls: Int): MnCross { - val npar = par.size - var nfcn = 0 - val prec: MnMachinePrecision = theState.precision() - val tlf = tlr * theErrorDef - var tla = tlr - val maxitr = 15 - var ipt = 0 - val aminsv = theFval - val aim = aminsv + theErrorDef - var aopt = 0.0 - var limset = false - val alsb = DoubleArray(3) - val flsb = DoubleArray(3) - val up = theErrorDef - var aulim = 100.0 - for (i in par.indices) { - val kex = par[i] - if (theState.parameter(kex).hasLimits()) { - val zmid = pmid[i] - val zdir = pdir[i] - if (abs(zdir) < theState.precision().eps()) { - continue - } - if (zdir > 0.0 && theState.parameter(kex).hasUpperLimit()) { - val zlim: Double = theState.parameter(kex).upperLimit() - aulim = min(aulim, (zlim - zmid) / zdir) - } else if (zdir < 0.0 && theState.parameter(kex).hasLowerLimit()) { - val zlim: Double = theState.parameter(kex).lowerLimit() - aulim = min(aulim, (zlim - zmid) / zdir) - } - } - } - if (aulim < aopt + tla) { - limset = true - } - val migrad = MnMigrad(theFCN, theState, MnStrategy(max(0, theStrategy!!.strategy() - 1))) - for (i in 0 until npar) { - migrad.setValue(par[i], pmid[i]) - } - val min0: FunctionMinimum = migrad.minimize(maxcalls, tlr) - nfcn += min0.nfcn() - if (min0.hasReachedCallLimit()) { - return MnCross(min0.userState(), nfcn, MnCross.CrossFcnLimit()) - } - if (!min0.isValid()) { - return MnCross(nfcn) - } - if (limset && min0.fval() < aim) { - return MnCross(min0.userState(), nfcn, MnCross.CrossParLimit()) - } - ipt++ - alsb[0] = 0.0 - flsb[0] = min0.fval() - flsb[0] = max(flsb[0], aminsv + 0.1 * up) - aopt = sqrt(up / (flsb[0] - aminsv)) - 1.0 - if (abs(flsb[0] - aim) < tlf) { - return MnCross(aopt, min0.userState(), nfcn) - } - if (aopt > 1.0) { - aopt = 1.0 - } - if (aopt < -0.5) { - aopt = -0.5 - } - limset = false - if (aopt > aulim) { - aopt = aulim - limset = true - } - for (i in 0 until npar) { - migrad.setValue(par[i], pmid[i] + aopt * pdir[i]) - } - var min1: FunctionMinimum = migrad.minimize(maxcalls, tlr) - nfcn += min1.nfcn() - if (min1.hasReachedCallLimit()) { - return MnCross(min1.userState(), nfcn, MnCross.CrossFcnLimit()) - } - if (!min1.isValid()) { - return MnCross(nfcn) - } - if (limset && min1.fval() < aim) { - return MnCross(min1.userState(), nfcn, MnCross.CrossParLimit()) - } - ipt++ - alsb[1] = aopt - flsb[1] = min1.fval() - var dfda = (flsb[1] - flsb[0]) / (alsb[1] - alsb[0]) - var ecarmn = 0.0 - var ecarmx = 0.0 - var ibest = 0 - var iworst = 0 - var noless = 0 - var min2: FunctionMinimum? = null - L300@ while (true) { - if (dfda < 0.0) { - val maxlk = maxitr - ipt - for (it in 0 until maxlk) { - alsb[0] = alsb[1] - flsb[0] = flsb[1] - aopt = alsb[0] + 0.2 * it - limset = false - if (aopt > aulim) { - aopt = aulim - limset = true - } - for (i in 0 until npar) { - migrad.setValue(par[i], pmid[i] + aopt * pdir[i]) - } - min1 = migrad.minimize(maxcalls, tlr) - nfcn += min1.nfcn() - if (min1.hasReachedCallLimit()) { - return MnCross(min1.userState(), nfcn, MnCross.CrossFcnLimit()) - } - if (!min1.isValid()) { - return MnCross(nfcn) - } - if (limset && min1.fval() < aim) { - return MnCross(min1.userState(), nfcn, MnCross.CrossParLimit()) - } - ipt++ - alsb[1] = aopt - flsb[1] = min1.fval() - dfda = (flsb[1] - flsb[0]) / (alsb[1] - alsb[0]) - if (dfda > 0.0) { - break - } - } - if (ipt > maxitr) { - return MnCross(nfcn) - } - } - L460@ while (true) { - aopt = alsb[1] + (aim - flsb[1]) / dfda - val fdist: Double = - min(abs(aim - flsb[0]), abs(aim - flsb[1])) - val adist: Double = - min(abs(aopt - alsb[0]), abs(aopt - alsb[1])) - tla = tlr - if (abs(aopt) > 1.0) { - tla = tlr * abs(aopt) - } - if (adist < tla && fdist < tlf) { - return MnCross(aopt, min1.userState(), nfcn) - } - if (ipt > maxitr) { - return MnCross(nfcn) - } - val bmin: Double = min(alsb[0], alsb[1]) - 1.0 - if (aopt < bmin) { - aopt = bmin - } - val bmax: Double = max(alsb[0], alsb[1]) + 1.0 - if (aopt > bmax) { - aopt = bmax - } - limset = false - if (aopt > aulim) { - aopt = aulim - limset = true - } - for (i in 0 until npar) { - migrad.setValue(par[i], pmid[i] + aopt * pdir[i]) - } - min2 = migrad.minimize(maxcalls, tlr) - nfcn += min2.nfcn() - if (min2.hasReachedCallLimit()) { - return MnCross(min2.userState(), nfcn, CrossFcnLimit()) - } - if (!min2.isValid()) { - return MnCross(nfcn) - } - if (limset && min2.fval() < aim) { - return MnCross(min2.userState(), nfcn, MnCross.CrossParLimit()) - } - ipt++ - alsb[2] = aopt - flsb[2] = min2.fval() - ecarmn = abs(flsb[2] - aim) - ecarmx = 0.0 - ibest = 2 - iworst = 0 - noless = 0 - for (i in 0..2) { - val ecart: Double = abs(flsb[i] - aim) - if (ecart > ecarmx) { - ecarmx = ecart - iworst = i - } - if (ecart < ecarmn) { - ecarmn = ecart - ibest = i - } - if (flsb[i] < aim) { - noless++ - } - } - if (noless == 1 || noless == 2) { - break@L300 - } - if (noless == 0 && ibest != 2) { - return MnCross(nfcn) - } - if (noless == 3 && ibest != 2) { - alsb[1] = alsb[2] - flsb[1] = flsb[2] - continue@L300 - } - flsb[iworst] = flsb[2] - alsb[iworst] = alsb[2] - dfda = (flsb[1] - flsb[0]) / (alsb[1] - alsb[0]) - } - } - do { - val parbol: MnParabola = MnParabolaFactory.create(MnParabolaPoint(alsb[0], flsb[0]), - MnParabolaPoint(alsb[1], flsb[1]), - MnParabolaPoint( - alsb[2], flsb[2])) - val coeff1: Double = parbol.c() - val coeff2: Double = parbol.b() - val coeff3: Double = parbol.a() - val determ = coeff2 * coeff2 - 4.0 * coeff3 * (coeff1 - aim) - if (determ < prec.eps()) { - return MnCross(nfcn) - } - val rt: Double = sqrt(determ) - val x1 = (-coeff2 + rt) / (2.0 * coeff3) - val x2 = (-coeff2 - rt) / (2.0 * coeff3) - val s1 = coeff2 + 2.0 * x1 * coeff3 - val s2 = coeff2 + 2.0 * x2 * coeff3 - if (s1 * s2 > 0.0) { - MINUITPlugin.logStatic("MnFunctionCross problem 1") - } - aopt = x1 - var slope = s1 - if (s2 > 0.0) { - aopt = x2 - slope = s2 - } - tla = tlr - if (abs(aopt) > 1.0) { - tla = tlr * abs(aopt) - } - if (abs(aopt - alsb[ibest]) < tla && abs(flsb[ibest] - aim) < tlf) { - return MnCross(aopt, min2!!.userState(), nfcn) - } - var ileft = 3 - var iright = 3 - var iout = 3 - ibest = 0 - ecarmx = 0.0 - ecarmn = abs(aim - flsb[0]) - for (i in 0..2) { - val ecart: Double = abs(flsb[i] - aim) - if (ecart < ecarmn) { - ecarmn = ecart - ibest = i - } - if (ecart > ecarmx) { - ecarmx = ecart - } - if (flsb[i] > aim) { - if (iright == 3) { - iright = i - } else if (flsb[i] > flsb[iright]) { - iout = i - } else { - iout = iright - iright = i - } - } else if (ileft == 3) { - ileft = i - } else if (flsb[i] < flsb[ileft]) { - iout = i - } else { - iout = ileft - ileft = i - } - } - if (ecarmx > 10.0 * abs(flsb[iout] - aim)) { - aopt = 0.5 * (aopt + 0.5 * (alsb[iright] + alsb[ileft])) - } - var smalla = 0.1 * tla - if (slope * smalla > tlf) { - smalla = tlf / slope - } - val aleft = alsb[ileft] + smalla - val aright = alsb[iright] - smalla - if (aopt < aleft) { - aopt = aleft - } - if (aopt > aright) { - aopt = aright - } - if (aleft > aright) { - aopt = 0.5 * (aleft + aright) - } - limset = false - if (aopt > aulim) { - aopt = aulim - limset = true - } - for (i in 0 until npar) { - migrad.setValue(par[i], pmid[i] + aopt * pdir[i]) - } - min2 = migrad.minimize(maxcalls, tlr) - nfcn += min2.nfcn() - if (min2.hasReachedCallLimit()) { - return MnCross(min2.userState(), nfcn, CrossFcnLimit()) - } - if (!min2.isValid()) { - return MnCross(nfcn) - } - if (limset && min2.fval() < aim) { - return MnCross(min2.userState(), nfcn, CrossParLimit()) - } - ipt++ - alsb[iout] = aopt - flsb[iout] = min2.fval() - ibest = iout - } while (ipt < maxitr) - return MnCross(nfcn) - } - - init { - theFCN = fcn - theState = state - theFval = fval - theStrategy = stra - theErrorDef = errorDef - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnGlobalCorrelationCoeff.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnGlobalCorrelationCoeff.kt deleted file mode 100644 index 939dd7fa0..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnGlobalCorrelationCoeff.kt +++ /dev/null @@ -1,79 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.SingularMatrixException - -/** - * - * MnGlobalCorrelationCoeff class. - * - * @version $Id$ - * @author Darksnake - */ -class MnGlobalCorrelationCoeff { - private var theGlobalCC: DoubleArray - private var theValid = false - - internal constructor() { - theGlobalCC = DoubleArray(0) - } - - internal constructor(cov: MnAlgebraicSymMatrix) { - try { - val inv: MnAlgebraicSymMatrix = cov.copy() - inv.invert() - theGlobalCC = DoubleArray(cov.nrow()) - for (i in 0 until cov.nrow()) { - val denom: Double = inv[i, i] * cov[i, i] - if (denom < 1.0 && denom > 0.0) { - theGlobalCC[i] = 0 - } else { - theGlobalCC[i] = sqrt(1.0 - 1.0 / denom) - } - } - theValid = true - } catch (x: SingularMatrixException) { - theValid = false - theGlobalCC = DoubleArray(0) - } - } - - /** - * - * globalCC. - * - * @return an array of double. - */ - fun globalCC(): DoubleArray { - return theGlobalCC - } - - /** - * - * isValid. - * - * @return a boolean. - */ - fun isValid(): Boolean { - return theValid - } - - /** {@inheritDoc} */ - override fun toString(): String { - return MnPrint.toString(this) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnHesse.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnHesse.kt deleted file mode 100644 index 3bb6c4551..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnHesse.kt +++ /dev/null @@ -1,371 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction -import ru.inr.mass.minuit.* - -/** - * With MnHesse the user can instructs MINUITPlugin to calculate, by finite - * differences, the Hessian or error matrix. That is, it calculates the full - * matrix of second derivatives of the function with respect to the currently - * variable parameters, and inverts it. - * - * @version $Id$ - * @author Darksnake - */ -class MnHesse { - private var theStrategy: MnStrategy - - /** - * default constructor with default strategy - */ - constructor() { - theStrategy = MnStrategy(1) - } - - /** - * constructor with user-defined strategy level - * - * @param stra a int. - */ - constructor(stra: Int) { - theStrategy = MnStrategy(stra) - } - - /** - * conctructor with specific strategy - * - * @param stra a [hep.dataforge.MINUIT.MnStrategy] object. - */ - constructor(stra: MnStrategy) { - theStrategy = stra - } - /// - /// low-level API - /// - /** - * - * calculate. - * - * @param fcn a [MultiFunction] object. - * @param par an array of double. - * @param err an array of double. - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun calculate(fcn: MultiFunction?, par: DoubleArray, err: DoubleArray): MnUserParameterState { - return calculate(fcn, par, err, 0) - } - - /** - * FCN + parameters + errors - * - * @param maxcalls a int. - * @param fcn a [MultiFunction] object. - * @param par an array of double. - * @param err an array of double. - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun calculate(fcn: MultiFunction?, par: DoubleArray, err: DoubleArray, maxcalls: Int): MnUserParameterState { - return calculate(fcn, MnUserParameterState(par, err), maxcalls) - } - - /** - * - * calculate. - * - * @param fcn a [MultiFunction] object. - * @param par an array of double. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun calculate(fcn: MultiFunction?, par: DoubleArray, cov: MnUserCovariance): MnUserParameterState { - return calculate(fcn, par, cov, 0) - } - - /** - * FCN + parameters + MnUserCovariance - * - * @param maxcalls a int. - * @param fcn a [MultiFunction] object. - * @param par an array of double. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun calculate(fcn: MultiFunction?, par: DoubleArray, cov: MnUserCovariance, maxcalls: Int): MnUserParameterState { - return calculate(fcn, MnUserParameterState(par, cov), maxcalls) - } - /// - /// high-level API - /// - /** - * - * calculate. - * - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun calculate(fcn: MultiFunction?, par: MnUserParameters): MnUserParameterState { - return calculate(fcn, par, 0) - } - - /** - * FCN + MnUserParameters - * - * @param maxcalls a int. - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun calculate(fcn: MultiFunction?, par: MnUserParameters, maxcalls: Int): MnUserParameterState { - return calculate(fcn, MnUserParameterState(par), maxcalls) - } - - /** - * - * calculate. - * - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun calculate(fcn: MultiFunction?, par: MnUserParameters, cov: MnUserCovariance?): MnUserParameterState { - return calculate(fcn, par, 0) - } - - /** - * FCN + MnUserParameters + MnUserCovariance - * - * @param maxcalls a int. - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun calculate( - fcn: MultiFunction?, - par: MnUserParameters, - cov: MnUserCovariance, - maxcalls: Int - ): MnUserParameterState { - return calculate(fcn, MnUserParameterState(par, cov), maxcalls) - } - - /** - * FCN + MnUserParameterState - * - * @param maxcalls a int. - * @param fcn a [MultiFunction] object. - * @param state a [hep.dataforge.MINUIT.MnUserParameterState] object. - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun calculate(fcn: MultiFunction?, state: MnUserParameterState, maxcalls: Int): MnUserParameterState { - val errDef = 1.0 // FixMe! - val n: Int = state.variableParameters() - val mfcn = MnUserFcn(fcn, errDef, state.getTransformation()) - val x: RealVector = ArrayRealVector(n) - for (i in 0 until n) { - x.setEntry(i, state.intParameters()[i]) - } - val amin: Double = mfcn.value(x) - val gc = Numerical2PGradientCalculator(mfcn, state.getTransformation(), theStrategy) - val par = MinimumParameters(x, amin) - val gra: FunctionGradient = gc.gradient(par) - val tmp: MinimumState = calculate(mfcn, - MinimumState(par, MinimumError(MnAlgebraicSymMatrix(n), 1.0), gra, state.edm(), state.nfcn()), - state.getTransformation(), - maxcalls) - return MnUserParameterState(tmp, errDef, state.getTransformation()) - } - - /// - /// internal interface - /// - fun calculate(mfcn: MnFcn, st: MinimumState, trafo: MnUserTransformation, maxcalls: Int): MinimumState { - var maxcalls = maxcalls - val prec: MnMachinePrecision = trafo.precision() - // make sure starting at the right place - val amin: Double = mfcn.value(st.vec()) - val aimsag: Double = sqrt(prec.eps2()) * (abs(amin) + mfcn.errorDef()) - - // diagonal elements first - val n: Int = st.parameters().vec().getDimension() - if (maxcalls == 0) { - maxcalls = 200 + 100 * n + 5 * n * n - } - var vhmat = MnAlgebraicSymMatrix(n) - var g2: RealVector = st.gradient().getGradientDerivative().copy() - var gst: RealVector = st.gradient().getStep().copy() - var grd: RealVector = st.gradient().getGradient().copy() - var dirin: RealVector = st.gradient().getStep().copy() - val yy: RealVector = ArrayRealVector(n) - if (st.gradient().isAnalytical()) { - val igc = InitialGradientCalculator(mfcn, trafo, theStrategy) - val tmp: FunctionGradient = igc.gradient(st.parameters()) - gst = tmp.getStep().copy() - dirin = tmp.getStep().copy() - g2 = tmp.getGradientDerivative().copy() - } - return try { - val x: RealVector = st.parameters().vec().copy() - for (i in 0 until n) { - val xtf: Double = x.getEntry(i) - val dmin: Double = 8.0 * prec.eps2() * (abs(xtf) + prec.eps2()) - var d: Double = abs(gst.getEntry(i)) - if (d < dmin) { - d = dmin - } - for (icyc in 0 until ncycles()) { - var sag = 0.0 - var fs1 = 0.0 - var fs2 = 0.0 - var multpy = 0 - while (multpy < 5) { - x.setEntry(i, xtf + d) - fs1 = mfcn.value(x) - x.setEntry(i, xtf - d) - fs2 = mfcn.value(x) - x.setEntry(i, xtf) - sag = 0.5 * (fs1 + fs2 - 2.0 * amin) - if (sag > prec.eps2()) { - break - } - if (trafo.parameter(i).hasLimits()) { - if (d > 0.5) { - throw MnHesseFailedException("MnHesse: 2nd derivative zero for parameter") - } - d *= 10.0 - if (d > 0.5) { - d = 0.51 - } - multpy++ - continue - } - d *= 10.0 - multpy++ - } - if (multpy >= 5) { - throw MnHesseFailedException("MnHesse: 2nd derivative zero for parameter") - } - val g2bfor: Double = g2.getEntry(i) - g2.setEntry(i, 2.0 * sag / (d * d)) - grd.setEntry(i, (fs1 - fs2) / (2.0 * d)) - gst.setEntry(i, d) - dirin.setEntry(i, d) - yy.setEntry(i, fs1) - val dlast = d - d = sqrt(2.0 * aimsag / abs(g2.getEntry(i))) - if (trafo.parameter(i).hasLimits()) { - d = min(0.5, d) - } - if (d < dmin) { - d = dmin - } - - // see if converged - if (abs((d - dlast) / d) < tolerstp()) { - break - } - if (abs((g2.getEntry(i) - g2bfor) / g2.getEntry(i)) < tolerg2()) { - break - } - d = min(d, 10.0 * dlast) - d = max(d, 0.1 * dlast) - } - vhmat[i, i] = g2.getEntry(i) - if (mfcn.numOfCalls() - st.nfcn() > maxcalls) { - throw MnHesseFailedException("MnHesse: maximum number of allowed function calls exhausted.") - } - } - if (theStrategy.strategy() > 0) { - // refine first derivative - val hgc = HessianGradientCalculator(mfcn, trafo, theStrategy) - val gr: FunctionGradient = hgc.gradient(st.parameters(), FunctionGradient(grd, g2, gst)) - grd = gr.getGradient() - } - - //off-diagonal elements - for (i in 0 until n) { - x.setEntry(i, x.getEntry(i) + dirin.getEntry(i)) - for (j in i + 1 until n) { - x.setEntry(j, x.getEntry(j) + dirin.getEntry(j)) - val fs1: Double = mfcn.value(x) - val elem: Double = - (fs1 + amin - yy.getEntry(i) - yy.getEntry(j)) / (dirin.getEntry(i) * dirin.getEntry(j)) - vhmat[i, j] = elem - x.setEntry(j, x.getEntry(j) - dirin.getEntry(j)) - } - x.setEntry(i, x.getEntry(i) - dirin.getEntry(i)) - } - - //verify if matrix pos-def (still 2nd derivative) - val tmp: MinimumError = MnPosDef.test(MinimumError(vhmat, 1.0), prec) - vhmat = tmp.invHessian() - try { - vhmat.invert() - } catch (xx: SingularMatrixException) { - throw MnHesseFailedException("MnHesse: matrix inversion fails!") - } - val gr = FunctionGradient(grd, g2, gst) - if (tmp.isMadePosDef()) { - MINUITPlugin.logStatic("MnHesse: matrix is invalid!") - MINUITPlugin.logStatic("MnHesse: matrix is not pos. def.!") - MINUITPlugin.logStatic("MnHesse: matrix was forced pos. def.") - return MinimumState(st.parameters(), - MinimumError(vhmat, MnMadePosDef()), - gr, - st.edm(), - mfcn.numOfCalls()) - } - - //calculate edm - val err = MinimumError(vhmat, 0.0) - val edm: Double = VariableMetricEDMEstimator().estimate(gr, err) - MinimumState(st.parameters(), err, gr, edm, mfcn.numOfCalls()) - } catch (x: MnHesseFailedException) { - MINUITPlugin.logStatic(x.message) - MINUITPlugin.logStatic("MnHesse fails and will return diagonal matrix ") - var j = 0 - while (j < n) { - val tmp = if (g2.getEntry(j) < prec.eps2()) 1.0 else 1.0 / g2.getEntry(j) - vhmat[j, j] = if (tmp < prec.eps2()) 1.0 else tmp - j++ - } - MinimumState(st.parameters(), - MinimumError(vhmat, MnHesseFailed()), - st.gradient(), - st.edm(), - st.nfcn() + mfcn.numOfCalls()) - } - } - - /// forward interface of MnStrategy - fun ncycles(): Int { - return theStrategy.hessianNCycles() - } - - fun tolerg2(): Double { - return theStrategy.hessianG2Tolerance() - } - - fun tolerstp(): Double { - return theStrategy.hessianStepTolerance() - } - - private inner class MnHesseFailedException(message: String?) : java.lang.Exception(message) -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnLineSearch.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnLineSearch.kt deleted file mode 100644 index 7b1171d3c..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnLineSearch.kt +++ /dev/null @@ -1,204 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.RealVector -import ru.inr.mass.minuit.* - -/** - * - * @version $Id$ - */ -internal object MnLineSearch { - fun search( - fcn: MnFcn, - st: MinimumParameters, - step: RealVector, - gdel: Double, - prec: MnMachinePrecision - ): MnParabolaPoint { - var overal = 1000.0 - var undral = -100.0 - val toler = 0.05 - var slamin = 0.0 - val slambg = 5.0 - val alpha = 2.0 - val maxiter = 12 - var niter = 0 - for (i in 0 until step.getDimension()) { - if (abs(step.getEntry(i)) < prec.eps()) { - continue - } - val ratio: Double = abs(st.vec().getEntry(i) / step.getEntry(i)) - if (abs(slamin) < prec.eps()) { - slamin = ratio - } - if (ratio < slamin) { - slamin = ratio - } - } - if (abs(slamin) < prec.eps()) { - slamin = prec.eps() - } - slamin *= prec.eps2() - val F0: Double = st.fval() - val F1: Double = fcn.value(MnUtils.add(st.vec(), step)) - var fvmin: Double = st.fval() - var xvmin = 0.0 - if (F1 < F0) { - fvmin = F1 - xvmin = 1.0 - } - var toler8 = toler - var slamax = slambg - var flast = F1 - var slam = 1.0 - var iterate = false - var p0 = MnParabolaPoint(0.0, F0) - var p1 = MnParabolaPoint(slam, flast) - var F2 = 0.0 - do { - // cut toler8 as function goes up - iterate = false - val pb: MnParabola = MnParabolaFactory.create(p0, gdel, p1) - var denom = 2.0 * (flast - F0 - gdel * slam) / (slam * slam) - if (abs(denom) < prec.eps()) { - denom = -0.1 * gdel - slam = 1.0 - } - if (abs(denom) > prec.eps()) { - slam = -gdel / denom - } - if (slam < 0.0) { - slam = slamax - } - if (slam > slamax) { - slam = slamax - } - if (slam < toler8) { - slam = toler8 - } - if (slam < slamin) { - return MnParabolaPoint(xvmin, fvmin) - } - if (abs(slam - 1.0) < toler8 && p1.y() < p0.y()) { - return MnParabolaPoint(xvmin, fvmin) - } - if (abs(slam - 1.0) < toler8) { - slam = 1.0 + toler8 - } - F2 = fcn.value(MnUtils.add(st.vec(), MnUtils.mul(step, slam))) - if (F2 < fvmin) { - fvmin = F2 - xvmin = slam - } - if (p0.y() - prec.eps() < fvmin && fvmin < p0.y() + prec.eps()) { - iterate = true - flast = F2 - toler8 = toler * slam - overal = slam - toler8 - slamax = overal - p1 = MnParabolaPoint(slam, flast) - niter++ - } - } while (iterate && niter < maxiter) - if (niter >= maxiter) { - // exhausted max number of iterations - return MnParabolaPoint(xvmin, fvmin) - } - var p2 = MnParabolaPoint(slam, F2) - do { - slamax = max(slamax, alpha * abs(xvmin)) - val pb: MnParabola = MnParabolaFactory.create(p0, p1, p2) - if (pb.a() < prec.eps2()) { - val slopem: Double = 2.0 * pb.a() * xvmin + pb.b() - slam = if (slopem < 0.0) { - xvmin + slamax - } else { - xvmin - slamax - } - } else { - slam = pb.min() - if (slam > xvmin + slamax) { - slam = xvmin + slamax - } - if (slam < xvmin - slamax) { - slam = xvmin - slamax - } - } - if (slam > 0.0) { - if (slam > overal) { - slam = overal - } - } else { - if (slam < undral) { - slam = undral - } - } - var F3 = 0.0 - do { - iterate = false - val toler9: Double = max(toler8, abs(toler8 * slam)) - // min. of parabola at one point - if (abs(p0.x() - slam) < toler9 || abs(p1.x() - slam) < toler9 || abs( - p2.x() - slam) < toler9 - ) { - return MnParabolaPoint(xvmin, fvmin) - } - F3 = fcn.value(MnUtils.add(st.vec(), MnUtils.mul(step, slam))) - // if latest point worse than all three previous, cut step - if (F3 > p0.y() && F3 > p1.y() && F3 > p2.y()) { - if (slam > xvmin) { - overal = min(overal, slam - toler8) - } - if (slam < xvmin) { - undral = max(undral, slam + toler8) - } - slam = 0.5 * (slam + xvmin) - iterate = true - niter++ - } - } while (iterate && niter < maxiter) - if (niter >= maxiter) { - // exhausted max number of iterations - return MnParabolaPoint(xvmin, fvmin) - } - - // find worst previous point out of three and replace - val p3 = MnParabolaPoint(slam, F3) - if (p0.y() > p1.y() && p0.y() > p2.y()) { - p0 = p3 - } else if (p1.y() > p0.y() && p1.y() > p2.y()) { - p1 = p3 - } else { - p2 = p3 - } - if (F3 < fvmin) { - fvmin = F3 - xvmin = slam - } else { - if (slam > xvmin) { - overal = min(overal, slam - toler8) - } - if (slam < xvmin) { - undral = max(undral, slam + toler8) - } - } - niter++ - } while (niter < maxiter) - return MnParabolaPoint(xvmin, fvmin) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnMachinePrecision.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnMachinePrecision.kt deleted file mode 100644 index 161ee0c0a..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnMachinePrecision.kt +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * Determines the relative floating point arithmetic precision. The - * setPrecision() method can be used to override Minuit's own determination, - * when the user knows that the {FCN} function value is not calculated to the - * nominal machine accuracy. - * - * @version $Id$ - * @author Darksnake - */ -class MnMachinePrecision internal constructor() { - private var theEpsMa2 = 0.0 - private var theEpsMac = 0.0 - - /** - * eps returns the smallest possible number so that 1.+eps > 1. - * @return - */ - fun eps(): Double { - return theEpsMac - } - - /** - * eps2 returns 2*sqrt(eps) - * @return - */ - fun eps2(): Double { - return theEpsMa2 - } - - /** - * override Minuit's own determination - * - * @param prec a double. - */ - fun setPrecision(prec: Double) { - theEpsMac = prec - theEpsMa2 = 2.0 * sqrt(theEpsMac) - } - - init { - setPrecision(4.0E-7) - var epstry = 0.5 - val one = 1.0 - for (i in 0..99) { - epstry *= 0.5 - val epsp1 = one + epstry - val epsbak = epsp1 - one - if (epsbak < epstry) { - setPrecision(8.0 * epstry) - break - } - } - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnMigrad.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnMigrad.kt deleted file mode 100644 index 22616a1a6..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnMigrad.kt +++ /dev/null @@ -1,136 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction - -/** - * MnMigrad provides minimization of the function by the method of MIGRAD, the - * most efficient and complete single method, recommended for general functions, - * and the functionality for parameters interaction. It also retains the result - * from the last minimization in case the user may want to do subsequent - * minimization steps with parameter interactions in between the minimization - * requests. The minimization produces as a by-product the error matrix of the - * parameters, which is usually reliable unless warning messages are produced. - * - * @version $Id$ - * @author Darksnake - */ -class MnMigrad -/** - * construct from MultiFunction + MnUserParameterState + MnStrategy - * - * @param str a [hep.dataforge.MINUIT.MnStrategy] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameterState] object. - * @param fcn a [MultiFunction] object. - */ - (fcn: MultiFunction?, par: MnUserParameterState, str: MnStrategy) : MnApplication(fcn, par, str) { - private val theMinimizer: VariableMetricMinimizer = VariableMetricMinimizer() - - /** - * construct from MultiFunction + double[] for parameters and errors - * with default strategy - * - * @param err an array of double. - * @param par an array of double. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, err: DoubleArray) : this(fcn, par, err, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + double[] for parameters and errors - * - * @param stra a int. - * @param err an array of double. - * @param fcn a [MultiFunction] object. - * @param par an array of double. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, err: DoubleArray, stra: Int) : this(fcn, - MnUserParameterState(par, err), - MnStrategy(stra)) - - /** - * construct from MultiFunction + double[] for parameters and - * MnUserCovariance with default strategy - * - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param par an array of double. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, cov: MnUserCovariance) : this(fcn, par, cov, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + double[] for parameters and - * MnUserCovariance - * - * @param stra a int. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param fcn a [MultiFunction] object. - * @param par an array of double. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, cov: MnUserCovariance, stra: Int) : this(fcn, - MnUserParameterState(par, cov), - MnStrategy(stra)) - - /** - * construct from MultiFunction + MnUserParameters with default - * strategy - * - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters) : this(fcn, par, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + MnUserParameters - * - * @param stra a int. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, stra: Int) : this(fcn, - MnUserParameterState(par), - MnStrategy(stra)) - - /** - * construct from MultiFunction + MnUserParameters + MnUserCovariance - * with default strategy - * - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, cov: MnUserCovariance) : this(fcn, - par, - cov, - DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + MnUserParameters + MnUserCovariance - * - * @param stra a int. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, cov: MnUserCovariance, stra: Int) : this(fcn, - MnUserParameterState(par, cov), - MnStrategy(stra)) - - override fun minimizer(): ModularFunctionMinimizer { - return theMinimizer - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnMinimize.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnMinimize.kt deleted file mode 100644 index ea14a5453..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnMinimize.kt +++ /dev/null @@ -1,133 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction - -/** - * Causes minimization of the function by the method of MIGRAD, as does the - * MnMigrad class, but switches to the SIMPLEX method if MIGRAD fails to - * converge. Constructor arguments, methods arguments and names of methods are - * the same as for MnMigrad or MnSimplex. - * - * @version $Id$ - * @author Darksnake - */ -class MnMinimize -/** - * construct from MultiFunction + MnUserParameterState + MnStrategy - * - * @param str a [hep.dataforge.MINUIT.MnStrategy] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameterState] object. - * @param fcn a [MultiFunction] object. - */ - (fcn: MultiFunction?, par: MnUserParameterState, str: MnStrategy) : MnApplication(fcn, par, str) { - private val theMinimizer: CombinedMinimizer = CombinedMinimizer() - - /** - * construct from MultiFunction + double[] for parameters and errors - * with default strategy - * - * @param err an array of double. - * @param par an array of double. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, err: DoubleArray) : this(fcn, par, err, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + double[] for parameters and errors - * - * @param stra a int. - * @param err an array of double. - * @param fcn a [MultiFunction] object. - * @param par an array of double. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, err: DoubleArray, stra: Int) : this(fcn, - MnUserParameterState(par, err), - MnStrategy(stra)) - - /** - * construct from MultiFunction + double[] for parameters and - * MnUserCovariance with default strategy - * - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param par an array of double. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, cov: MnUserCovariance) : this(fcn, par, cov, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + double[] for parameters and - * MnUserCovariance - * - * @param stra a int. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param fcn a [MultiFunction] object. - * @param par an array of double. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, cov: MnUserCovariance, stra: Int) : this(fcn, - MnUserParameterState(par, cov), - MnStrategy(stra)) - - /** - * construct from MultiFunction + MnUserParameters with default - * strategy - * - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters) : this(fcn, par, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + MnUserParameters - * - * @param stra a int. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, stra: Int) : this(fcn, - MnUserParameterState(par), - MnStrategy(stra)) - - /** - * construct from MultiFunction + MnUserParameters + MnUserCovariance - * with default strategy - * - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, cov: MnUserCovariance) : this(fcn, - par, - cov, - DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + MnUserParameters + MnUserCovariance - * - * @param stra a int. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, cov: MnUserCovariance, stra: Int) : this(fcn, - MnUserParameterState(par, cov), - MnStrategy(stra)) - - override fun minimizer(): ModularFunctionMinimizer { - return theMinimizer - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnMinos.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnMinos.kt deleted file mode 100644 index d49379b3b..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnMinos.kt +++ /dev/null @@ -1,379 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction -import ru.inr.mass.minuit.* -import kotlin.jvm.JvmOverloads - -/** - * API class for Minos error analysis (asymmetric errors). Minimization has to - * be done before and minimum must be valid; possibility to ask only for one - * side of the Minos error; - * - * @version $Id$ - * @author Darksnake - */ -class MnMinos(fcn: MultiFunction?, min: FunctionMinimum?, stra: MnStrategy?) { - private var theFCN: MultiFunction? = null - private var theMinimum: FunctionMinimum? = null - private var theStrategy: MnStrategy? = null - - /** - * construct from FCN + minimum - * - * @param fcn a [MultiFunction] object. - * @param min a [hep.dataforge.MINUIT.FunctionMinimum] object. - */ - constructor(fcn: MultiFunction?, min: FunctionMinimum?) : this(fcn, min, MnApplication.DEFAULT_STRATEGY) - - /** - * construct from FCN + minimum + strategy - * - * @param stra a int. - * @param min a [hep.dataforge.MINUIT.FunctionMinimum] object. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, min: FunctionMinimum?, stra: Int) : this(fcn, min, MnStrategy(stra)) - // public MnMinos(MultiFunction fcn, MnUserParameterState state, double errDef, MnStrategy stra) { - // theFCN = fcn; - // theStrategy = stra; - // - // MinimumState minState = null; - // - // MnUserTransformation transformation = state.getTransformation(); - // - // MinimumSeed seed = new MinimumSeed(minState, transformation); - // - // theMinimum = new FunctionMinimum(seed,errDef); - // } - /** - * - * loval. - * - * @param par a int. - * @return a [hep.dataforge.MINUIT.MnCross] object. - */ - fun loval(par: Int): MnCross { - return loval(par, 1.0) - } - - /** - * - * loval. - * - * @param par a int. - * @param errDef a double. - * @return a [hep.dataforge.MINUIT.MnCross] object. - */ - fun loval(par: Int, errDef: Double): MnCross { - return loval(par, errDef, MnApplication.DEFAULT_MAXFCN) - } - - /** - * - * loval. - * - * @param par a int. - * @param errDef a double. - * @param maxcalls a int. - * @return a [hep.dataforge.MINUIT.MnCross] object. - */ - fun loval(par: Int, errDef: Double, maxcalls: Int): MnCross { - var errDef = errDef - var maxcalls = maxcalls - errDef *= theMinimum!!.errorDef() - assert(theMinimum!!.isValid()) - assert(!theMinimum!!.userState().parameter(par).isFixed()) - assert(!theMinimum!!.userState().parameter(par).isConst()) - if (maxcalls == 0) { - val nvar: Int = theMinimum!!.userState().variableParameters() - maxcalls = 2 * (nvar + 1) * (200 + 100 * nvar + 5 * nvar * nvar) - } - val para = intArrayOf(par) - val upar: MnUserParameterState = theMinimum!!.userState().copy() - val err: Double = upar.error(par) - val `val`: Double = upar.value(par) - err - val xmid = doubleArrayOf(`val`) - val xdir = doubleArrayOf(-err) - val ind: Int = upar.intOfExt(par) - val m: MnAlgebraicSymMatrix = theMinimum!!.error().matrix() - val xunit: Double = sqrt(errDef / err) - for (i in 0 until m.nrow()) { - if (i == ind) { - continue - } - val xdev: Double = xunit * m[ind, i] - val ext: Int = upar.extOfInt(i) - upar.setValue(ext, upar.value(ext) - xdev) - } - upar.fix(par) - upar.setValue(par, `val`) - val toler = 0.1 - val cross = MnFunctionCross(theFCN, upar, theMinimum!!.fval(), theStrategy, errDef) - val aopt: MnCross = cross.cross(para, xmid, xdir, toler, maxcalls) - if (aopt.atLimit()) { - MINUITPlugin.logStatic("MnMinos parameter $par is at lower limit.") - } - if (aopt.atMaxFcn()) { - MINUITPlugin.logStatic("MnMinos maximum number of function calls exceeded for parameter $par") - } - if (aopt.newMinimum()) { - MINUITPlugin.logStatic("MnMinos new minimum found while looking for parameter $par") - } - if (!aopt.isValid()) { - MINUITPlugin.logStatic("MnMinos could not find lower value for parameter $par.") - } - return aopt - } - /** - * calculate one side (negative or positive error) of the parameter - * - * @param maxcalls a int. - * @param par a int. - * @param errDef a double. - * @return a double. - */ - /** - * - * lower. - * - * @param par a int. - * @param errDef a double. - * @return a double. - */ - /** - * - * lower. - * - * @param par a int. - * @return a double. - */ - @JvmOverloads - fun lower(par: Int, errDef: Double = 1.0, maxcalls: Int = MnApplication.DEFAULT_MAXFCN): Double { - val upar: MnUserParameterState = theMinimum!!.userState() - val err: Double = theMinimum!!.userState().error(par) - val aopt: MnCross = loval(par, errDef, maxcalls) - return if (aopt.isValid()) -1.0 * err * (1.0 + aopt.value()) else if (aopt.atLimit()) upar.parameter(par) - .lowerLimit() else upar.value(par) - } - - /** - * - * minos. - * - * @param par a int. - * @return a [hep.dataforge.MINUIT.MinosError] object. - */ - fun minos(par: Int): MinosError { - return minos(par, 1.0) - } - - /** - * - * minos. - * - * @param par a int. - * @param errDef a double. - * @return a [hep.dataforge.MINUIT.MinosError] object. - */ - fun minos(par: Int, errDef: Double): MinosError { - return minos(par, errDef, MnApplication.DEFAULT_MAXFCN) - } - - /** - * Causes a MINOS error analysis to be performed on the parameter whose - * number is specified. MINOS errors may be expensive to calculate, but are - * very reliable since they take account of non-linearities in the problem - * as well as parameter correlations, and are in general asymmetric. - * - * @param maxcalls Specifies the (approximate) maximum number of function - * calls per parameter requested, after which the calculation will be - * stopped for that parameter. - * @param errDef a double. - * @param par a int. - * @return a [hep.dataforge.MINUIT.MinosError] object. - */ - fun minos(par: Int, errDef: Double, maxcalls: Int): MinosError { - assert(theMinimum!!.isValid()) - assert(!theMinimum!!.userState().parameter(par).isFixed()) - assert(!theMinimum!!.userState().parameter(par).isConst()) - val up: MnCross = upval(par, errDef, maxcalls) - val lo: MnCross = loval(par, errDef, maxcalls) - return MinosError(par, theMinimum!!.userState().value(par), lo, up) - } - - /** - * - * range. - * - * @param par a int. - * @return - */ - fun range(par: Int): Range { - return range(par, 1.0) - } - - /** - * - * range. - * - * @param par a int. - * @param errDef a double. - * @return - */ - fun range(par: Int, errDef: Double): Range { - return range(par, errDef, MnApplication.DEFAULT_MAXFCN) - } - - /** - * Causes a MINOS error analysis for external parameter n. - * - * @param maxcalls a int. - * @param errDef a double. - * @return The lower and upper bounds of parameter - * @param par a int. - */ - fun range(par: Int, errDef: Double, maxcalls: Int): Range { - val mnerr: MinosError = minos(par, errDef, maxcalls) - return mnerr.range() - } - /** - * - * upper. - * - * @param par a int. - * @param errDef a double. - * @param maxcalls a int. - * @return a double. - */ - /** - * - * upper. - * - * @param par a int. - * @param errDef a double. - * @return a double. - */ - /** - * - * upper. - * - * @param par a int. - * @return a double. - */ - @JvmOverloads - fun upper(par: Int, errDef: Double = 1.0, maxcalls: Int = MnApplication.DEFAULT_MAXFCN): Double { - val upar: MnUserParameterState = theMinimum!!.userState() - val err: Double = theMinimum!!.userState().error(par) - val aopt: MnCross = upval(par, errDef, maxcalls) - return if (aopt.isValid()) err * (1.0 + aopt.value()) else if (aopt.atLimit()) upar.parameter(par) - .upperLimit() else upar.value(par) - } - - /** - * - * upval. - * - * @param par a int. - * @return a [hep.dataforge.MINUIT.MnCross] object. - */ - fun upval(par: Int): MnCross { - return upval(par, 1.0) - } - - /** - * - * upval. - * - * @param par a int. - * @param errDef a double. - * @return a [hep.dataforge.MINUIT.MnCross] object. - */ - fun upval(par: Int, errDef: Double): MnCross { - return upval(par, errDef, MnApplication.DEFAULT_MAXFCN) - } - - /** - * - * upval. - * - * @param par a int. - * @param errDef a double. - * @param maxcalls a int. - * @return a [hep.dataforge.MINUIT.MnCross] object. - */ - fun upval(par: Int, errDef: Double, maxcalls: Int): MnCross { - var errDef = errDef - var maxcalls = maxcalls - errDef *= theMinimum!!.errorDef() - assert(theMinimum!!.isValid()) - assert(!theMinimum!!.userState().parameter(par).isFixed()) - assert(!theMinimum!!.userState().parameter(par).isConst()) - if (maxcalls == 0) { - val nvar: Int = theMinimum!!.userState().variableParameters() - maxcalls = 2 * (nvar + 1) * (200 + 100 * nvar + 5 * nvar * nvar) - } - val para = intArrayOf(par) - val upar: MnUserParameterState = theMinimum!!.userState().copy() - val err: Double = upar.error(par) - val `val`: Double = upar.value(par) + err - val xmid = doubleArrayOf(`val`) - val xdir = doubleArrayOf(err) - val ind: Int = upar.intOfExt(par) - val m: MnAlgebraicSymMatrix = theMinimum!!.error().matrix() - val xunit: Double = sqrt(errDef / err) - for (i in 0 until m.nrow()) { - if (i == ind) { - continue - } - val xdev: Double = xunit * m[ind, i] - val ext: Int = upar.extOfInt(i) - upar.setValue(ext, upar.value(ext) + xdev) - } - upar.fix(par) - upar.setValue(par, `val`) - val toler = 0.1 - val cross = MnFunctionCross(theFCN, upar, theMinimum!!.fval(), theStrategy, errDef) - val aopt: MnCross = cross.cross(para, xmid, xdir, toler, maxcalls) - if (aopt.atLimit()) { - MINUITPlugin.logStatic("MnMinos parameter $par is at upper limit.") - } - if (aopt.atMaxFcn()) { - MINUITPlugin.logStatic("MnMinos maximum number of function calls exceeded for parameter $par") - } - if (aopt.newMinimum()) { - MINUITPlugin.logStatic("MnMinos new minimum found while looking for parameter $par") - } - if (!aopt.isValid()) { - MINUITPlugin.logStatic("MnMinos could not find upper value for parameter $par.") - } - return aopt - } - - /** - * construct from FCN + minimum + strategy - * - * @param stra a [hep.dataforge.MINUIT.MnStrategy] object. - * @param min a [hep.dataforge.MINUIT.FunctionMinimum] object. - * @param fcn a [MultiFunction] object. - */ - init { - theFCN = fcn - theMinimum = min - theStrategy = stra - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnParabola.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnParabola.kt deleted file mode 100644 index a0a56dedd..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnParabola.kt +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * parabola = a*xx + b*x + c - * - * @version $Id$ - */ -internal class MnParabola(private val theA: Double, private val theB: Double, private val theC: Double) { - fun a(): Double { - return theA - } - - fun b(): Double { - return theB - } - - fun c(): Double { - return theC - } - - fun min(): Double { - return -theB / (2.0 * theA) - } - - fun x_neg(y: Double): Double { - return -sqrt(y / theA + min() * min() - theC / theA) + min() - } - - fun x_pos(y: Double): Double { - return sqrt(y / theA + min() * min() - theC / theA) + min() - } - - fun y(x: Double): Double { - return theA * x * x + theB * x + theC - } - - fun ymin(): Double { - return -theB * theB / (4.0 * theA) + theC - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnParabolaFactory.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnParabolaFactory.kt deleted file mode 100644 index f45d2b9c9..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnParabolaFactory.kt +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -internal object MnParabolaFactory { - fun create(p1: MnParabolaPoint, p2: MnParabolaPoint, p3: MnParabolaPoint): MnParabola { - var x1: Double = p1.x() - var x2: Double = p2.x() - var x3: Double = p3.x() - val dx12 = x1 - x2 - val dx13 = x1 - x3 - val dx23 = x2 - x3 - val xm = (x1 + x2 + x3) / 3.0 - x1 -= xm - x2 -= xm - x3 -= xm - val y1: Double = p1.y() - val y2: Double = p2.y() - val y3: Double = p3.y() - val a = y1 / (dx12 * dx13) - y2 / (dx12 * dx23) + y3 / (dx13 * dx23) - var b = -y1 * (x2 + x3) / (dx12 * dx13) + y2 * (x1 + x3) / (dx12 * dx23) - y3 * (x1 + x2) / (dx13 * dx23) - var c = y1 - a * x1 * x1 - b * x1 - c += xm * (xm * a - b) - b -= 2.0 * xm * a - return MnParabola(a, b, c) - } - - fun create(p1: MnParabolaPoint, dxdy1: Double, p2: MnParabolaPoint): MnParabola { - val x1: Double = p1.x() - val xx1 = x1 * x1 - val x2: Double = p2.x() - val xx2 = x2 * x2 - val y1: Double = p1.y() - val y12: Double = p1.y() - p2.y() - val det = xx1 - xx2 - 2.0 * x1 * (x1 - x2) - val a = -(y12 + (x2 - x1) * dxdy1) / det - val b = -(-2.0 * x1 * y12 + (xx1 - xx2) * dxdy1) / det - val c = y1 - a * xx1 - b * x1 - return MnParabola(a, b, c) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnParabolaPoint.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnParabolaPoint.kt deleted file mode 100644 index 858e010e6..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnParabolaPoint.kt +++ /dev/null @@ -1,30 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -internal class MnParabolaPoint(private val theX: Double, private val theY: Double) { - fun x(): Double { - return theX - } - - fun y(): Double { - return theY - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnParameterScan.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnParameterScan.kt deleted file mode 100644 index 7791c20e8..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnParameterScan.kt +++ /dev/null @@ -1,113 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction - -/** - * Scans the values of FCN as a function of one parameter and retains the best - * function and parameter values found - * - * @version $Id$ - */ -internal class MnParameterScan { - private var theAmin: Double - private var theFCN: MultiFunction? - private var theParameters: MnUserParameters - - constructor(fcn: MultiFunction, par: MnUserParameters) { - theFCN = fcn - theParameters = par - theAmin = fcn.value(par.params()) - } - - constructor(fcn: MultiFunction?, par: MnUserParameters, fval: Double) { - theFCN = fcn - theParameters = par - theAmin = fval - } - - fun fval(): Double { - return theAmin - } - - fun parameters(): MnUserParameters { - return theParameters - } - - fun scan(par: Int): List { - return scan(par, 41) - } - - fun scan(par: Int, maxsteps: Int): List { - return scan(par, maxsteps, 0.0, 0.0) - } - - /** - * returns pairs of (x,y) points, x=parameter value, y=function value of FCN - * @param high - * @return - */ - fun scan(par: Int, maxsteps: Int, low: Double, high: Double): List { - var maxsteps = maxsteps - var low = low - var high = high - if (maxsteps > 101) { - maxsteps = 101 - } - val result: MutableList = java.util.ArrayList(maxsteps + 1) - val params: DoubleArray = theParameters.params() - result.add(Range(params[par], theAmin)) - if (low > high) { - return result - } - if (maxsteps < 2) { - return result - } - if (low == 0.0 && high == 0.0) { - low = params[par] - 2.0 * theParameters.error(par) - high = params[par] + 2.0 * theParameters.error(par) - } - if (low == 0.0 && high == 0.0 && theParameters.parameter(par).hasLimits()) { - if (theParameters.parameter(par).hasLowerLimit()) { - low = theParameters.parameter(par).lowerLimit() - } - if (theParameters.parameter(par).hasUpperLimit()) { - high = theParameters.parameter(par).upperLimit() - } - } - if (theParameters.parameter(par).hasLimits()) { - if (theParameters.parameter(par).hasLowerLimit()) { - low = max(low, theParameters.parameter(par).lowerLimit()) - } - if (theParameters.parameter(par).hasUpperLimit()) { - high = min(high, theParameters.parameter(par).upperLimit()) - } - } - val x0 = low - val stp = (high - low) / (maxsteps - 1.0) - for (i in 0 until maxsteps) { - params[par] = x0 + i.toDouble() * stp - val fval: Double = theFCN.value(params) - if (fval < theAmin) { - theParameters.setValue(par, params[par]) - theAmin = fval - } - result.add(Range(params[par], fval)) - } - return result - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnPlot.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnPlot.kt deleted file mode 100644 index 656dd8d35..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnPlot.kt +++ /dev/null @@ -1,438 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import java.lang.StringBuffer -import kotlin.jvm.JvmOverloads - -/** - * MnPlot produces a text-screen graphical output of (x,y) points. E.g. from - * Scan or Contours. - * - * @version $Id$ - * @author Darksnake - */ -class MnPlot @JvmOverloads constructor(private val thePageWidth: Int = 80, private val thePageLength: Int = 30) { - private var bh = 0.0 - private var bl = 0.0 - private var bwid = 0.0 - private var nb = 0 - fun length(): Int { - return thePageLength - } - - private fun mnbins(a1: Double, a2: Double, naa: Int) { - - //*-*-*-*-*-*-*-*-*-*-*Compute reasonable histogram intervals*-*-*-*-*-*-*-*-* - //*-* ====================================== - //*-* Function TO DETERMINE REASONABLE HISTOGRAM INTERVALS - //*-* GIVEN ABSOLUTE UPPER AND LOWER BOUNDS A1 AND A2 - //*-* AND DESIRED MAXIMUM NUMBER OF BINS NAA - //*-* PROGRAM MAKES REASONABLE BINNING FROM BL TO BH OF WIDTH BWID - //*-* F. JAMES, AUGUST, 1974 , stolen for Minuit, 1988 - //*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* - - /* Local variables */ - var awid: Double - var ah: Double - var sigfig: Double - var sigrnd: Double - var alb: Double - var kwid: Int - var lwid: Int - var na = 0 - var log_: Int - val al: Double = if (a1 < a2) a1 else a2 - ah = if (a1 > a2) a1 else a2 - if (al == ah) { - ah = al + 1 - } - - //*-*- IF NAA .EQ. -1 , PROGRAM USES BWID INPUT FROM CALLING ROUTINE - var skip = naa == -1 && bwid > 0 - if (!skip) { - na = naa - 1 - if (na < 1) { - na = 1 - } - } - while (true) { - if (!skip) { - //*-*- GET NOMINAL BIN WIDTH IN EXPON FORM - awid = (ah - al) / na.toDouble() - log_ = log10(awid) - if (awid <= 1) { - --log_ - } - sigfig = awid * pow(10.0, -log_.toDouble()) - //*-*- ROUND MANTISSA UP TO 2, 2.5, 5, OR 10 - if (sigfig <= 2) { - sigrnd = 2.0 - } else if (sigfig <= 2.5) { - sigrnd = 2.5 - } else if (sigfig <= 5) { - sigrnd = 5.0 - } else { - sigrnd = 1.0 - ++log_ - } - bwid = sigrnd * pow(10.0, log_.toDouble()) - } - alb = al / bwid - lwid = alb.toInt() - if (alb < 0) { - --lwid - } - bl = bwid * lwid.toDouble() - alb = ah / bwid + 1 - kwid = alb.toInt() - if (alb < 0) { - --kwid - } - bh = bwid * kwid.toDouble() - nb = kwid - lwid - if (naa <= 5) { - if (naa == -1) { - return - } - //*-*- REQUEST FOR ONE BIN IS DIFFICULT CASE - if (naa > 1 || nb == 1) { - return - } - bwid *= 2.0 - nb = 1 - return - } - if (nb shl 1 != naa) { - return - } - ++na - skip = false - continue - } - } - - private fun mnplot(xpt: DoubleArray, ypt: DoubleArray, chpt: StringBuffer, nxypt: Int, npagwd: Int, npagln: Int) { - //*-*-*-*Plots points in array xypt onto one page with labelled axes*-*-*-*-* - //*-* =========================================================== - //*-* NXYPT is the number of points to be plotted - //*-* XPT(I) = x-coord. of ith point - //*-* YPT(I) = y-coord. of ith point - //*-* CHPT(I) = character to be plotted at this position - //*-* the input point arrays XPT, YPT, CHPT are destroyed. - //*-* - //*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* - - /* Local variables */ - var xmin: Double - var xmax: Double - var ymax: Double - var savx: Double - var savy: Double - var yprt: Double - var xbest: Double - var ybest: Double - val xvalus = DoubleArray(12) - val any: Double - val iten: Int - var j: Int - var k: Int - var maxnx: Int - var maxny: Int - var iquit: Int - var ni: Int - var linodd: Int - var ibk: Int - var isp1: Int - var ks: Int - var ix: Int - var overpr: Boolean - val cline = StringBuffer(npagwd) - for (ii in 0 until npagwd) { - cline.append(' ') - } - var chsav: Char - val chbest: Char - - /* Function Body */ - //*-* Computing MIN - maxnx = if (npagwd - 20 < 100) npagwd - 20 else 100 - if (maxnx < 10) { - maxnx = 10 - } - maxny = npagln - if (maxny < 10) { - maxny = 10 - } - if (nxypt <= 1) { - return - } - xbest = xpt[0] - ybest = ypt[0] - chbest = chpt.get(0) - //*-*- order the points by decreasing y - val km1: Int = nxypt - 1 - var i: Int = 1 - while (i <= km1) { - iquit = 0 - ni = nxypt - i - j = 1 - while (j <= ni) { - if (ypt[j - 1] > ypt[j]) { - ++j - continue - } - savx = xpt[j - 1] - xpt[j - 1] = xpt[j] - xpt[j] = savx - savy = ypt[j - 1] - ypt[j - 1] = ypt[j] - ypt[j] = savy - chsav = chpt.get(j - 1) - chpt.setCharAt(j - 1, chpt.get(j)) - chpt.setCharAt(j, chsav) - iquit = 1 - ++j - } - if (iquit == 0) { - break - } - ++i - } - //*-*- find extreme values - xmax = xpt[0] - xmin = xmax - i = 1 - while (i <= nxypt) { - if (xpt[i - 1] > xmax) { - xmax = xpt[i - 1] - } - if (xpt[i - 1] < xmin) { - xmin = xpt[i - 1] - } - ++i - } - val dxx: Double = (xmax - xmin) * .001 - xmax += dxx - xmin -= dxx - mnbins(xmin, xmax, maxnx) - xmin = bl - xmax = bh - var nx: Int = nb - val bwidx: Double = bwid - ymax = ypt[0] - var ymin: Double = ypt[nxypt - 1] - if (ymax == ymin) { - ymax = ymin + 1 - } - val dyy: Double = (ymax - ymin) * .001 - ymax += dyy - ymin -= dyy - mnbins(ymin, ymax, maxny) - ymin = bl - ymax = bh - var ny: Int = nb - val bwidy: Double = bwid - any = ny.toDouble() - //*-*- if first point is blank, it is an 'origin' - if (chbest != ' ') { - xbest = (xmax + xmin) * .5 - ybest = (ymax + ymin) * .5 - } - //*-*- find scale constants - val ax: Double = 1 / bwidx - val ay: Double = 1 / bwidy - val bx: Double = -ax * xmin + 2 - val by: Double = -ay * ymin - 2 - //*-*- convert points to grid positions - i = 1 - while (i <= nxypt) { - xpt[i - 1] = ax * xpt[i - 1] + bx - ypt[i - 1] = any - ay * ypt[i - 1] - by - ++i - } - val nxbest: Int = (ax * xbest + bx).toInt() - val nybest: Int = (any - ay * ybest - by).toInt() - //*-*- print the points - ny += 2 - nx += 2 - isp1 = 1 - linodd = 1 - overpr = false - i = 1 - while (i <= ny) { - ibk = 1 - while (ibk <= nx) { - cline.setCharAt(ibk - 1, ' ') - ++ibk - } - // cline.setCharAt(nx,'\0'); - // cline.setCharAt(nx+1,'\0'); - cline.setCharAt(0, '.') - cline.setCharAt(nx - 1, '.') - cline.setCharAt(nxbest - 1, '.') - if (i == 1 || i == nybest || i == ny) { - j = 1 - while (j <= nx) { - cline.setCharAt(j - 1, '.') - ++j - } - } - yprt = ymax - (i - 1.0) * bwidy - var isplset = false - if (isp1 <= nxypt) { - //*-*- find the points to be plotted on this line - k = isp1 - while (k <= nxypt) { - ks = ypt[k - 1].toInt() - if (ks > i) { - isp1 = k - isplset = true - break - } - ix = xpt[k - 1].toInt() - if (cline.get(ix - 1) != '.' && cline.get(ix - 1) != ' ') { - if (cline.get(ix - 1) == chpt.get(k - 1)) { - ++k - continue - } - overpr = true - //*-*- OVERPR is true if one or more positions contains more than - //*-*- one point - cline.setCharAt(ix - 1, '&') - ++k - continue - } - cline.setCharAt(ix - 1, chpt.get(k - 1)) - ++k - } - if (!isplset) { - isp1 = nxypt + 1 - } - } - if (linodd != 1 && i != ny) { - linodd = 1 - java.lang.System.out.printf(" %s", cline.substring(0, 60)) - } else { - java.lang.System.out.printf(" %14.7g ..%s", yprt, cline.substring(0, 60)) - linodd = 0 - } - println() - ++i - } - //*-*- print labels on x-axis every ten columns - ibk = 1 - while (ibk <= nx) { - cline.setCharAt(ibk - 1, ' ') - if (ibk % 10 == 1) { - cline.setCharAt(ibk - 1, '/') - } - ++ibk - } - java.lang.System.out.printf(" %s", cline) - java.lang.System.out.printf("\n") - ibk = 1 - while (ibk <= 12) { - xvalus[ibk - 1] = xmin + (ibk - 1.0) * 10 * bwidx - ++ibk - } - java.lang.System.out.printf(" ") - iten = (nx + 9) / 10 - ibk = 1 - while (ibk <= iten) { - java.lang.System.out.printf(" %9.4g", xvalus[ibk - 1]) - ++ibk - } - java.lang.System.out.printf("\n") - if (overpr) { - val chmess = " Overprint character is &" - java.lang.System.out.printf(" ONE COLUMN=%13.7g%s", bwidx, chmess) - } else { - val chmess = " " - java.lang.System.out.printf(" ONE COLUMN=%13.7g%s", bwidx, chmess) - } - println() - } - - /** - * - * plot. - * - * @param points a [List] object. - */ - fun plot(points: List) { - val x = DoubleArray(points.size) - val y = DoubleArray(points.size) - val chpt = StringBuffer(points.size) - for ((i, ipoint) in points.withIndex()) { - x[i] = ipoint.getFirst() - y[i] = ipoint.getSecond() - chpt.append('*') - } - mnplot(x, y, chpt, points.size, width(), length()) - } - - /** - * - * plot. - * - * @param xmin a double. - * @param ymin a double. - * @param points a [List] object. - */ - fun plot(xmin: Double, ymin: Double, points: List) { - val x = DoubleArray(points.size + 2) - x[0] = xmin - x[1] = xmin - val y = DoubleArray(points.size + 2) - y[0] = ymin - y[1] = ymin - val chpt = StringBuffer(points.size + 2) - chpt.append(' ') - chpt.append('X') - var i = 2 - for (ipoint in points) { - x[i] = ipoint.getFirst() - y[i] = ipoint.getSecond() - chpt.append('*') - i++ - } - mnplot(x, y, chpt, points.size + 2, width(), length()) - } - - fun width(): Int { - return thePageWidth - } - /** - * - * Constructor for MnPlot. - * - * @param thePageWidth a int. - * @param thePageLength a int. - */ - /** - * - * Constructor for MnPlot. - */ - init { - if (thePageWidth > 120) { - thePageWidth = 120 - } - if (thePageLength > 56) { - thePageLength = 56 - } - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnPosDef.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnPosDef.kt deleted file mode 100644 index f94e387d9..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnPosDef.kt +++ /dev/null @@ -1,89 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import space.kscience.kmath.optimization.minuit.MINUITPlugin - -/** - * - * @version $Id$ - */ -internal object MnPosDef { - fun test(st: MinimumState, prec: MnMachinePrecision): MinimumState { - val err: MinimumError = test(st.error(), prec) - return MinimumState(st.parameters(), err, st.gradient(), st.edm(), st.nfcn()) - } - - fun test(e: MinimumError, prec: MnMachinePrecision): MinimumError { - val err: MnAlgebraicSymMatrix = e.invHessian().copy() - if (err.size() === 1 && err[0, 0] < prec.eps()) { - err[0, 0] = 1.0 - return MinimumError(err, MnMadePosDef()) - } - if (err.size() === 1 && err[0, 0] > prec.eps()) { - return e - } - // std::cout<<"MnPosDef init matrix= "< 0.0) { - os.printf(" limited || %10g", ipar.value()) - if (abs(ipar.value() - ipar.lowerLimit()) < par.precision().eps2()) { - os.print("* ") - atLoLim = true - } - if (abs(ipar.value() - ipar.upperLimit()) < par.precision().eps2()) { - os.print("**") - atHiLim = true - } - os.printf(" || %10g\n", ipar.error()) - } else { - os.printf(" free || %10g || no\n", ipar.value()) - } - } else { - if (ipar.error() > 0.0) { - os.printf(" free || %10g || %10g\n", ipar.value(), ipar.error()) - } else { - os.printf(" free || %10g || no\n", ipar.value()) - } - } - } - os.println() - if (atLoLim) { - os.print("* parameter is at lower limit") - } - if (atHiLim) { - os.print("** parameter is at upper limit") - } - os.println() - } - - /** - * - * print. - * - * @param os a [PrintWriter] object. - * @param matrix a [hep.dataforge.MINUIT.MnUserCovariance] object. - */ - fun print(os: PrintWriter, matrix: MnUserCovariance) { - os.println() - os.println("MnUserCovariance: ") - run { - os.println() - val n: Int = matrix.nrow() - for (i in 0 until n) { - for (j in 0 until n) { - os.printf("%10g ", matrix[i, j]) - } - os.println() - } - } - os.println() - os.println("MnUserCovariance parameter correlations: ") - run { - os.println() - val n: Int = matrix.nrow() - for (i in 0 until n) { - val di: Double = matrix[i, i] - for (j in 0 until n) { - val dj: Double = matrix[j, j] - os.printf("%g ", matrix[i, j] / sqrt(abs(di * dj))) - } - os.println() - } - } - } - - /** - * - * print. - * - * @param os a [PrintWriter] object. - * @param coeff a [hep.dataforge.MINUIT.MnGlobalCorrelationCoeff] object. - */ - fun print(os: PrintWriter, coeff: MnGlobalCorrelationCoeff) { - os.println() - os.println("MnGlobalCorrelationCoeff: ") - run { - os.println() - for (i in 0 until coeff.globalCC().length) { - os.printf("%g\n", coeff.globalCC()[i]) - } - } - } - - /** - * - * print. - * - * @param os a [PrintWriter] object. - * @param state a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun print(os: PrintWriter, state: MnUserParameterState) { - os.println() - if (!state.isValid()) { - os.println() - os.println("WARNING: MnUserParameterState is not valid.") - os.println() - } - os.println("# of function calls: " + state.nfcn()) - os.println("function value: " + state.fval()) - os.println("expected distance to the minimum (edm): " + state.edm()) - os.println("external parameters: " + state.parameters()) - if (state.hasCovariance()) { - os.println("covariance matrix: " + state.covariance()) - } - if (state.hasGlobalCC()) { - os.println("global correlation coefficients : " + state.globalCC()) - } - if (!state.isValid()) { - os.println("WARNING: MnUserParameterState is not valid.") - } - os.println() - } - - /** - * - * print. - * - * @param os a [PrintWriter] object. - * @param me a [hep.dataforge.MINUIT.MinosError] object. - */ - fun print(os: PrintWriter, me: MinosError) { - os.println() - os.printf("Minos # of function calls: %d\n", me.nfcn()) - if (!me.isValid()) { - os.println("Minos error is not valid.") - } - if (!me.lowerValid()) { - os.println("lower Minos error is not valid.") - } - if (!me.upperValid()) { - os.println("upper Minos error is not valid.") - } - if (me.atLowerLimit()) { - os.println("Minos error is lower limit of parameter " + me.parameter()) - } - if (me.atUpperLimit()) { - os.println("Minos error is upper limit of parameter " + me.parameter()) - } - if (me.atLowerMaxFcn()) { - os.println("Minos number of function calls for lower error exhausted.") - } - if (me.atUpperMaxFcn()) { - os.println("Minos number of function calls for upper error exhausted.") - } - if (me.lowerNewMin()) { - os.println("Minos found a new minimum in negative direction.") - os.println(me.lowerState()) - } - if (me.upperNewMin()) { - os.println("Minos found a new minimum in positive direction.") - os.println(me.upperState()) - } - os.println("# ext. || name || value@min || negative || positive ") - os.printf("%4d||%10s||%10g||%10g||%10g\n", - me.parameter(), - me.lowerState().name(me.parameter()), - me.min(), - me.lower(), - me.upper()) - os.println() - } - - /** - * - * print. - * - * @param os a [PrintWriter] object. - * @param ce a [hep.dataforge.MINUIT.ContoursError] object. - */ - fun print(os: PrintWriter, ce: ContoursError) { - os.println() - os.println("Contours # of function calls: " + ce.nfcn()) - os.println("MinosError in x: ") - os.println(ce.xMinosError()) - os.println("MinosError in y: ") - os.println(ce.yMinosError()) - val plot = MnPlot() - plot.plot(ce.xmin(), ce.ymin(), ce.points()) - for ((i, ipoint) in ce.points().withIndex()) { - os.printf("%d %10g %10g\n", i, ipoint.getFirst(), ipoint.getSecond()) - } - os.println() - } - - fun toString(x: RealVector): String { - val writer: java.io.StringWriter = java.io.StringWriter() - PrintWriter(writer).use { pw -> print(pw, x) } - return writer.toString() - } - - fun toString(x: MnAlgebraicSymMatrix?): String { - val writer: java.io.StringWriter = java.io.StringWriter() - PrintWriter(writer).use { pw -> print(pw, x) } - return writer.toString() - } - - fun toString(min: FunctionMinimum?): String { - val writer: java.io.StringWriter = java.io.StringWriter() - PrintWriter(writer).use { pw -> print(pw, min) } - return writer.toString() - } - - fun toString(x: MinimumState?): String { - val writer: java.io.StringWriter = java.io.StringWriter() - PrintWriter(writer).use { pw -> print(pw, x) } - return writer.toString() - } - - fun toString(x: MnUserParameters?): String { - val writer: java.io.StringWriter = java.io.StringWriter() - PrintWriter(writer).use { pw -> print(pw, x) } - return writer.toString() - } - - fun toString(x: MnUserCovariance?): String { - val writer: java.io.StringWriter = java.io.StringWriter() - PrintWriter(writer).use { pw -> print(pw, x) } - return writer.toString() - } - - fun toString(x: MnGlobalCorrelationCoeff?): String { - val writer: java.io.StringWriter = java.io.StringWriter() - PrintWriter(writer).use { pw -> print(pw, x) } - return writer.toString() - } - - fun toString(x: MnUserParameterState?): String { - val writer: java.io.StringWriter = java.io.StringWriter() - PrintWriter(writer).use { pw -> print(pw, x) } - return writer.toString() - } - - fun toString(x: MinosError?): String { - val writer: java.io.StringWriter = java.io.StringWriter() - PrintWriter(writer).use { pw -> print(pw, x) } - return writer.toString() - } - - fun toString(x: ContoursError?): String { - val writer: java.io.StringWriter = java.io.StringWriter() - PrintWriter(writer).use { pw -> print(pw, x) } - return writer.toString() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnScan.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnScan.kt deleted file mode 100644 index 63e565b4f..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnScan.kt +++ /dev/null @@ -1,181 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction -import ru.inr.mass.minuit.* - -/** - * MnScan scans the value of the user function by varying one parameter. It is - * sometimes useful for debugging the user function or finding a reasonable - * starting point. - * construct from MultiFunction + MnUserParameterState + MnStrategy - * - * @param str a [hep.dataforge.MINUIT.MnStrategy] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameterState] object. - * @param fcn a [MultiFunction] object. - * @version $Id$ - * @author Darksnake - */ -class MnScan(fcn: MultiFunction?, par: MnUserParameterState, str: MnStrategy) : MnApplication(fcn, par, str) { - private val theMinimizer: ScanMinimizer = ScanMinimizer() - - /** - * construct from MultiFunction + double[] for parameters and errors - * with default strategy - * - * @param err an array of double. - * @param par an array of double. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, err: DoubleArray) : this(fcn, par, err, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + double[] for parameters and errors - * - * @param stra a int. - * @param err an array of double. - * @param fcn a [MultiFunction] object. - * @param par an array of double. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, err: DoubleArray, stra: Int) : this(fcn, - MnUserParameterState(par, err), - MnStrategy(stra)) - - /** - * construct from MultiFunction + double[] for parameters and - * MnUserCovariance with default strategy - * - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param par an array of double. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, cov: MnUserCovariance) : this(fcn, par, cov, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + double[] for parameters and - * MnUserCovariance - * - * @param stra a int. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param fcn a [MultiFunction] object. - * @param par an array of double. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, cov: MnUserCovariance, stra: Int) : this(fcn, - MnUserParameterState(par, cov), - MnStrategy(stra)) - - /** - * construct from MultiFunction + MnUserParameters with default - * strategy - * - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters) : this(fcn, par, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + MnUserParameters - * - * @param stra a int. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, stra: Int) : this(fcn, - MnUserParameterState(par), - MnStrategy(stra)) - - /** - * construct from MultiFunction + MnUserParameters + MnUserCovariance - * with default strategy - * - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, cov: MnUserCovariance) : this(fcn, - par, - cov, - DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + MnUserParameters + MnUserCovariance - * - * @param stra a int. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, cov: MnUserCovariance, stra: Int) : this(fcn, - MnUserParameterState(par, cov), - MnStrategy(stra)) - - override fun minimizer(): ModularFunctionMinimizer { - return theMinimizer - } - - /** - * - * scan. - * - * @param par a int. - * @return a [List] object. - */ - fun scan(par: Int): List { - return scan(par, 41) - } - - /** - * - * scan. - * - * @param par a int. - * @param maxsteps a int. - * @return a [List] object. - */ - fun scan(par: Int, maxsteps: Int): List { - return scan(par, maxsteps, 0.0, 0.0) - } - - /** - * Scans the value of the user function by varying parameter number par, - * leaving all other parameters fixed at the current value. If par is not - * specified, all variable parameters are scanned in sequence. The number of - * points npoints in the scan is 40 by default, and cannot exceed 100. The - * range of the scan is by default 2 standard deviations on each side of the - * current best value, but can be specified as from low to high. After each - * scan, if a new minimum is found, the best parameter values are retained - * as start values for future scans or minimizations. The curve resulting - * from each scan can be plotted on the output terminal using MnPlot in - * order to show the approximate behaviour of the function. - * - * @param high a double. - * @param par a int. - * @param maxsteps a int. - * @param low a double. - * @return a [List] object. - */ - fun scan(par: Int, maxsteps: Int, low: Double, high: Double): List { - val scan = MnParameterScan(theFCN, theState.parameters()) - var amin: Double = scan.fval() - val result: List = scan.scan(par, maxsteps, low, high) - if (scan.fval() < amin) { - theState.setValue(par, scan.parameters().value(par)) - amin = scan.fval() - } - return result - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnSeedGenerator.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnSeedGenerator.kt deleted file mode 100644 index a42edf4f1..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnSeedGenerator.kt +++ /dev/null @@ -1,108 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import space.kscience.kmath.optimization.minuit.MINUITPlugin -import ru.inr.mass.minuit.* -import space.kscience.kmath.optimization.minuit.MinimumSeed - -/** - * - * @version $Id$ - */ -internal class MnSeedGenerator : MinimumSeedGenerator { - /** {@inheritDoc} */ - fun generate(fcn: MnFcn, gc: GradientCalculator, st: MnUserParameterState, stra: MnStrategy): MinimumSeed { - val n: Int = st.variableParameters() - val prec: MnMachinePrecision = st.precision() - - // initial starting values - val x: RealVector = ArrayRealVector(n) - for (i in 0 until n) { - x.setEntry(i, st.intParameters()[i]) - } - val fcnmin: Double = fcn.value(x) - val pa = MinimumParameters(x, fcnmin) - val dgrad: FunctionGradient - if (gc is AnalyticalGradientCalculator) { - val igc = InitialGradientCalculator(fcn, st.getTransformation(), stra) - val tmp: FunctionGradient = igc.gradient(pa) - val grd: FunctionGradient = gc.gradient(pa) - dgrad = FunctionGradient(grd.getGradient(), tmp.getGradientDerivative(), tmp.getStep()) - if (gc.checkGradient()) { - val good = true - val hgc = HessianGradientCalculator(fcn, st.getTransformation(), MnStrategy(2)) - val hgrd: Pair = hgc.deltaGradient(pa, dgrad) - for (i in 0 until n) { - val provided: Double = grd.getGradient().getEntry(i) - val calculated: Double = hgrd.getFirst().getGradient().getEntry(i) - val delta: Double = hgrd.getSecond().getEntry(i) - if (abs(calculated - provided) > delta) { - MINUITPlugin.logStatic("" - + "gradient discrepancy of external parameter \"%d\" " - + "(internal parameter \"%d\") too large. Expected: \"%f\", provided: \"%f\"", - st.getTransformation().extOfInt(i), i, provided, calculated) - -// -// MINUITPlugin.logStatic("gradient discrepancy of external parameter " -// + st.getTransformation().extOfInt(i) -// + " (internal parameter " + i + ") too large."); -// good = false; - } - } - if (!good) { - MINUITPlugin.logStatic("Minuit does not accept user specified gradient.") - // assert(good); - } - } - } else { - dgrad = gc.gradient(pa) - } - val mat = MnAlgebraicSymMatrix(n) - var dcovar = 1.0 - if (st.hasCovariance()) { - for (i in 0 until n) { - for (j in i until n) { - mat[i, j] = st.intCovariance()[i, j] - } - } - dcovar = 0.0 - } else { - for (i in 0 until n) { - mat[i, i] = if (abs(dgrad.getGradientDerivative() - .getEntry(i)) > prec.eps2() - ) 1.0 / dgrad.getGradientDerivative().getEntry(i) else 1.0 - } - } - val err = MinimumError(mat, dcovar) - val edm: Double = VariableMetricEDMEstimator().estimate(dgrad, err) - var state = MinimumState(pa, err, dgrad, edm, fcn.numOfCalls()) - if (NegativeG2LineSearch.hasNegativeG2(dgrad, prec)) { - state = if (gc is AnalyticalGradientCalculator) { - val ngc = Numerical2PGradientCalculator(fcn, st.getTransformation(), stra) - NegativeG2LineSearch.search(fcn, state, ngc, prec) - } else { - NegativeG2LineSearch.search(fcn, state, gc, prec) - } - } - if (stra.strategy() === 2 && !st.hasCovariance()) { - //calculate full 2nd derivative - val tmp: MinimumState = MnHesse(stra).calculate(fcn, state, st.getTransformation(), 0) - return MinimumSeed(tmp, st.getTransformation()) - } - return MinimumSeed(state, st.getTransformation()) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnSimplex.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnSimplex.kt deleted file mode 100644 index b00745f26..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnSimplex.kt +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction -import ru.inr.mass.minuit.* - -/** - * SIMPLEX is a function minimization method using the simplex method of Nelder - * and Mead. MnSimplex provides minimization of the function by the method of - * SIMPLEX and the functionality for parameters interaction. It also retains the - * result from the last minimization in case the user may want to do subsequent - * minimization steps with parameter interactions in between the minimization - * requests. As SIMPLEX is a stepping method it does not produce a covariance - * matrix. - * - * @version $Id$ - * @author Darksnake - */ -class MnSimplex -/** - * construct from MultiFunction + MnUserParameterState + MnStrategy - * - * @param str a [hep.dataforge.MINUIT.MnStrategy] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameterState] object. - * @param fcn a [MultiFunction] object. - */ - (fcn: MultiFunction?, par: MnUserParameterState, str: MnStrategy) : MnApplication(fcn, par, str) { - private val theMinimizer: SimplexMinimizer = SimplexMinimizer() - - /** - * construct from MultiFunction + double[] for parameters and errors - * with default strategy - * - * @param err an array of double. - * @param par an array of double. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, err: DoubleArray) : this(fcn, par, err, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + double[] for parameters and errors - * - * @param stra a int. - * @param err an array of double. - * @param fcn a [MultiFunction] object. - * @param par an array of double. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, err: DoubleArray, stra: Int) : this(fcn, - MnUserParameterState(par, err), - MnStrategy(stra)) - - /** - * construct from MultiFunction + double[] for parameters and - * MnUserCovariance with default strategy - * - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param par an array of double. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, cov: MnUserCovariance) : this(fcn, par, cov, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + double[] for parameters and - * MnUserCovariance - * - * @param stra a int. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param fcn a [MultiFunction] object. - * @param par an array of double. - */ - constructor(fcn: MultiFunction?, par: DoubleArray, cov: MnUserCovariance, stra: Int) : this(fcn, - MnUserParameterState(par, cov), - MnStrategy(stra)) - - /** - * construct from MultiFunction + MnUserParameters with default - * strategy - * - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters) : this(fcn, par, DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + MnUserParameters - * - * @param stra a int. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, stra: Int) : this(fcn, - MnUserParameterState(par), - MnStrategy(stra)) - - /** - * construct from MultiFunction + MnUserParameters + MnUserCovariance - * with default strategy - * - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - * @param fcn a [MultiFunction] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, cov: MnUserCovariance) : this(fcn, - par, - cov, - DEFAULT_STRATEGY) - - /** - * construct from MultiFunction + MnUserParameters + MnUserCovariance - * - * @param stra a int. - * @param cov a [hep.dataforge.MINUIT.MnUserCovariance] object. - * @param fcn a [MultiFunction] object. - * @param par a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - constructor(fcn: MultiFunction?, par: MnUserParameters, cov: MnUserCovariance, stra: Int) : this(fcn, - MnUserParameterState(par, cov), - MnStrategy(stra)) - - /** {@inheritDoc} */ - override fun minimizer(): ModularFunctionMinimizer { - return theMinimizer - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnStrategy.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnStrategy.kt deleted file mode 100644 index 31b894665..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnStrategy.kt +++ /dev/null @@ -1,310 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * API class for defining three levels of strategies: low (0), medium (1), high - * (2). - * - * - * At many places in the analysis of the FCN (the user provided function), - * MINUIT must decide whether to be safe and waste a few function calls - * in order to know where it is, or to be fast and attempt to get the - * requested results with the fewest possible calls at a certain risk of not - * obtaining the precision desired by the user. In order to allow the user to - * infuence these decisions, the MnStrategy class allows the user to control - * different settings. MnStrategy can be instantiated with three different - * minimization quality levels for low (0), medium (1) and high (2) quality. - * Default settings for iteration cycles and tolerances are initialized then. - * - * - * The default setting is set for medium quality. Value 0 (low) indicates to - * MINUIT that it should economize function calls; it is intended for cases - * where there are many variable parameters and/or the function takes a long - * time to calculate and/or the user is not interested in very precise values - * for parameter errors. On the other hand, value 2 (high) indicates that MINUIT - * is allowed to waste function calls in order to be sure that all values are - * precise; it is it is intended for cases where the function is evaluated in a - * relatively short time and/or where the parameter errors must be calculated - * reliably. - * - * In addition all constants set in MnStrategy can be changed individually by - * the user, e.g. the number of iteration cycles in the numerical gradient. - * - * - * - * - * Acts on: Migrad (behavioural), Minos (lowers strategy by 1 for Minos-own - * minimization), Hesse (iterations), Numerical2PDerivative (iterations) - * - * @author Darksnake - * @version $Id$ - */ -class MnStrategy { - private var theGradNCyc = 0 - private var theGradTlr = 0.0 - private var theGradTlrStp = 0.0 - private var theHessGradNCyc = 0 - - //default strategy - private var theHessNCyc = 0 - private var theHessTlrG2 = 0.0 - private var theHessTlrStp = 0.0 - private var theStrategy = 0 - - /** - * Creates a MnStrategy object with the default strategy (medium) - */ - constructor() { - setMediumStrategy() - } - //user defined strategy (0, 1, >=2) - /** - * Creates a MnStrategy object with the user specified strategy. - * - * @param stra The use defined strategy, 0=low, 1 medium, 2=high. - */ - constructor(stra: Int) { - if (stra == 0) { - setLowStrategy() - } else if (stra == 1) { - setMediumStrategy() - } else { - setHighStrategy() - } - } - - /** - * - * gradientNCycles. - * - * @return a int. - */ - fun gradientNCycles(): Int { - return theGradNCyc - } - - /** - * - * gradientStepTolerance. - * - * @return a double. - */ - fun gradientStepTolerance(): Double { - return theGradTlrStp - } - - /** - * - * gradientTolerance. - * - * @return a double. - */ - fun gradientTolerance(): Double { - return theGradTlr - } - - /** - * - * hessianG2Tolerance. - * - * @return a double. - */ - fun hessianG2Tolerance(): Double { - return theHessTlrG2 - } - - /** - * - * hessianGradientNCycles. - * - * @return a int. - */ - fun hessianGradientNCycles(): Int { - return theHessGradNCyc - } - - /** - * - * hessianNCycles. - * - * @return a int. - */ - fun hessianNCycles(): Int { - return theHessNCyc - } - - /** - * - * hessianStepTolerance. - * - * @return a double. - */ - fun hessianStepTolerance(): Double { - return theHessTlrStp - } - - /** - * - * isHigh. - * - * @return a boolean. - */ - fun isHigh(): Boolean { - return theStrategy >= 2 - } - - /** - * - * isLow. - * - * @return a boolean. - */ - fun isLow(): Boolean { - return theStrategy <= 0 - } - - /** - * - * isMedium. - * - * @return a boolean. - */ - fun isMedium(): Boolean { - return theStrategy == 1 - } - - /** - * - * setGradientNCycles. - * - * @param n a int. - */ - fun setGradientNCycles(n: Int) { - theGradNCyc = n - } - - /** - * - * setGradientStepTolerance. - * - * @param stp a double. - */ - fun setGradientStepTolerance(stp: Double) { - theGradTlrStp = stp - } - - /** - * - * setGradientTolerance. - * - * @param toler a double. - */ - fun setGradientTolerance(toler: Double) { - theGradTlr = toler - } - - /** - * - * setHessianG2Tolerance. - * - * @param toler a double. - */ - fun setHessianG2Tolerance(toler: Double) { - theHessTlrG2 = toler - } - - /** - * - * setHessianGradientNCycles. - * - * @param n a int. - */ - fun setHessianGradientNCycles(n: Int) { - theHessGradNCyc = n - } - - /** - * - * setHessianNCycles. - * - * @param n a int. - */ - fun setHessianNCycles(n: Int) { - theHessNCyc = n - } - - /** - * - * setHessianStepTolerance. - * - * @param stp a double. - */ - fun setHessianStepTolerance(stp: Double) { - theHessTlrStp = stp - } - - fun setHighStrategy() { - theStrategy = 2 - setGradientNCycles(5) - setGradientStepTolerance(0.1) - setGradientTolerance(0.02) - setHessianNCycles(7) - setHessianStepTolerance(0.1) - setHessianG2Tolerance(0.02) - setHessianGradientNCycles(6) - } - - /** - * - * setLowStrategy. - */ - fun setLowStrategy() { - theStrategy = 0 - setGradientNCycles(2) - setGradientStepTolerance(0.5) - setGradientTolerance(0.1) - setHessianNCycles(3) - setHessianStepTolerance(0.5) - setHessianG2Tolerance(0.1) - setHessianGradientNCycles(1) - } - - /** - * - * setMediumStrategy. - */ - fun setMediumStrategy() { - theStrategy = 1 - setGradientNCycles(3) - setGradientStepTolerance(0.3) - setGradientTolerance(0.05) - setHessianNCycles(5) - setHessianStepTolerance(0.3) - setHessianG2Tolerance(0.05) - setHessianGradientNCycles(2) - } - - /** - * - * strategy. - * - * @return a int. - */ - fun strategy(): Int { - return theStrategy - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnUserCovariance.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnUserCovariance.kt deleted file mode 100644 index 297588f8e..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnUserCovariance.kt +++ /dev/null @@ -1,147 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * MnUserCovariance is the external covariance matrix designed for the - * interaction of the user. The result of the minimization (internal covariance - * matrix) is converted into the user representable format. It can also be used - * as input prior to the minimization. The size of the covariance matrix is - * according to the number of variable parameters (free and limited). - * - * @version $Id$ - * @author Darksnake - */ -class MnUserCovariance { - private var theData: DoubleArray - private var theNRow: Int - - private constructor(other: MnUserCovariance) { - theData = other.theData.clone() - theNRow = other.theNRow - } - - internal constructor() { - theData = DoubleArray(0) - theNRow = 0 - } - - /* - * covariance matrix is stored in upper triangular packed storage format, - * e.g. the elements in the array are arranged like - * {a(0,0), a(0,1), a(1,1), a(0,2), a(1,2), a(2,2), ...}, - * the size is nrow*(nrow+1)/2. - */ - internal constructor(data: DoubleArray, nrow: Int) { - require(data.size == nrow * (nrow + 1) / 2) { "Inconsistent arguments" } - theData = data - theNRow = nrow - } - - /** - * - * Constructor for MnUserCovariance. - * - * @param nrow a int. - */ - constructor(nrow: Int) { - theData = DoubleArray(nrow * (nrow + 1) / 2) - theNRow = nrow - } - - /** - * - * copy. - * - * @return a [hep.dataforge.MINUIT.MnUserCovariance] object. - */ - fun copy(): MnUserCovariance { - return MnUserCovariance(this) - } - - fun data(): DoubleArray { - return theData - } - - /** - * - * get. - * - * @param row a int. - * @param col a int. - * @return a double. - */ - operator fun get(row: Int, col: Int): Double { - require(!(row >= theNRow || col >= theNRow)) - return if (row > col) { - theData[col + row * (row + 1) / 2] - } else { - theData[row + col * (col + 1) / 2] - } - } - - /** - * - * ncol. - * - * @return a int. - */ - fun ncol(): Int { - return theNRow - } - - /** - * - * nrow. - * - * @return a int. - */ - fun nrow(): Int { - return theNRow - } - - fun scale(f: Double) { - for (i in theData.indices) { - theData[i] *= f - } - } - - /** - * - * set. - * - * @param row a int. - * @param col a int. - * @param value a double. - */ - operator fun set(row: Int, col: Int, value: Double) { - require(!(row >= theNRow || col >= theNRow)) - if (row > col) { - theData[col + row * (row + 1) / 2] = value - } else { - theData[row + col * (col + 1) / 2] = value - } - } - - fun size(): Int { - return theData.size - } - - /** {@inheritDoc} */ - override fun toString(): String { - return MnPrint.toString(this) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnUserFcn.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnUserFcn.kt deleted file mode 100644 index 8198a41ab..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnUserFcn.kt +++ /dev/null @@ -1,30 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction - -/** - * - * @version $Id$ - */ -internal class MnUserFcn(fcn: MultiFunction?, errDef: Double, trafo: MnUserTransformation) : MnFcn(fcn, errDef) { - private val theTransform: MnUserTransformation = trafo - override fun value(v: RealVector): Double { - return super.value(theTransform.transform(v)) - } - -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnUserParameterState.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnUserParameterState.kt deleted file mode 100644 index e80dd60a1..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnUserParameterState.kt +++ /dev/null @@ -1,756 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.minuit.* - -/** - * The class MnUserParameterState contains the MnUserParameters and the - * MnUserCovariance. It can be created on input by the user, or by MINUIT itself - * as user representable format of the result of the minimization. - * - * @version $Id$ - * @author Darksnake - */ -class MnUserParameterState { - private var theCovariance: MnUserCovariance - private var theCovarianceValid = false - private var theEDM = 0.0 - private var theFVal = 0.0 - private var theGCCValid = false - private var theGlobalCC: MnGlobalCorrelationCoeff? = null - private var theIntCovariance: MnUserCovariance - private var theIntParameters: MutableList - private var theNFcn = 0 - private var theParameters: MnUserParameters - private var theValid: Boolean - - internal constructor() { - theValid = false - theCovarianceValid = false - theParameters = MnUserParameters() - theCovariance = MnUserCovariance() - theIntParameters = java.util.ArrayList() - theIntCovariance = MnUserCovariance() - } - - private constructor(other: MnUserParameterState) { - theValid = other.theValid - theCovarianceValid = other.theCovarianceValid - theGCCValid = other.theGCCValid - theFVal = other.theFVal - theEDM = other.theEDM - theNFcn = other.theNFcn - theParameters = other.theParameters.copy() - theCovariance = other.theCovariance - theGlobalCC = other.theGlobalCC - theIntParameters = java.util.ArrayList(other.theIntParameters) - theIntCovariance = other.theIntCovariance.copy() - } - - /** - * construct from user parameters (before minimization) - * @param par - * @param err - */ - internal constructor(par: DoubleArray, err: DoubleArray) { - theValid = true - theParameters = MnUserParameters(par, err) - theCovariance = MnUserCovariance() - theGlobalCC = MnGlobalCorrelationCoeff() - theIntParameters = java.util.ArrayList(par.size) - for (i in par.indices) { - theIntParameters.add(par[i]) - } - theIntCovariance = MnUserCovariance() - } - - internal constructor(par: MnUserParameters) { - theValid = true - theParameters = par - theCovariance = MnUserCovariance() - theGlobalCC = MnGlobalCorrelationCoeff() - theIntParameters = java.util.ArrayList(par.variableParameters()) - theIntCovariance = MnUserCovariance() - val i = 0 - for (ipar in par.parameters()) { - if (ipar.isConst() || ipar.isFixed()) { - continue - } - if (ipar.hasLimits()) { - theIntParameters.add(ext2int(ipar.number(), ipar.value())) - } else { - theIntParameters.add(ipar.value()) - } - } - } - - /** - * construct from user parameters + covariance (before minimization) - * @param nrow - * @param cov - */ - internal constructor(par: DoubleArray, cov: DoubleArray, nrow: Int) { - theValid = true - theCovarianceValid = true - theCovariance = MnUserCovariance(cov, nrow) - theGlobalCC = MnGlobalCorrelationCoeff() - theIntParameters = java.util.ArrayList(par.size) - theIntCovariance = MnUserCovariance(cov, nrow) - val err = DoubleArray(par.size) - for (i in par.indices) { - assert(theCovariance[i, i] > 0.0) - err[i] = sqrt(theCovariance[i, i]) - theIntParameters.add(par[i]) - } - theParameters = MnUserParameters(par, err) - assert(theCovariance.nrow() === variableParameters()) - } - - internal constructor(par: DoubleArray, cov: MnUserCovariance) { - theValid = true - theCovarianceValid = true - theCovariance = cov - theGlobalCC = MnGlobalCorrelationCoeff() - theIntParameters = java.util.ArrayList(par.size) - theIntCovariance = cov.copy() - require(!(theCovariance.nrow() !== variableParameters())) { "Bad covariance size" } - val err = DoubleArray(par.size) - for (i in par.indices) { - require(theCovariance[i, i] > 0.0) { "Bad covariance" } - err[i] = sqrt(theCovariance[i, i]) - theIntParameters.add(par[i]) - } - theParameters = MnUserParameters(par, err) - } - - internal constructor(par: MnUserParameters, cov: MnUserCovariance) { - theValid = true - theCovarianceValid = true - theParameters = par - theCovariance = cov - theGlobalCC = MnGlobalCorrelationCoeff() - theIntParameters = java.util.ArrayList() - theIntCovariance = cov.copy() - theIntCovariance.scale(0.5) - val i = 0 - for (ipar in par.parameters()) { - if (ipar.isConst() || ipar.isFixed()) { - continue - } - if (ipar.hasLimits()) { - theIntParameters.add(ext2int(ipar.number(), ipar.value())) - } else { - theIntParameters.add(ipar.value()) - } - } - assert(theCovariance.nrow() === variableParameters()) - } - - /** - * construct from internal parameters (after minimization) - * @param trafo - * @param up - */ - internal constructor(st: MinimumState, up: Double, trafo: MnUserTransformation) { - theValid = st.isValid() - theCovarianceValid = false - theGCCValid = false - theFVal = st.fval() - theEDM = st.edm() - theNFcn = st.nfcn() - theParameters = MnUserParameters() - theCovariance = MnUserCovariance() - theGlobalCC = MnGlobalCorrelationCoeff() - theIntParameters = java.util.ArrayList() - theIntCovariance = MnUserCovariance() - for (ipar in trafo.parameters()) { - if (ipar.isConst()) { - add(ipar.name(), ipar.value()) - } else if (ipar.isFixed()) { - add(ipar.name(), ipar.value(), ipar.error()) - if (ipar.hasLimits()) { - if (ipar.hasLowerLimit() && ipar.hasUpperLimit()) { - setLimits(ipar.name(), ipar.lowerLimit(), ipar.upperLimit()) - } else if (ipar.hasLowerLimit() && !ipar.hasUpperLimit()) { - setLowerLimit(ipar.name(), ipar.lowerLimit()) - } else { - setUpperLimit(ipar.name(), ipar.upperLimit()) - } - } - fix(ipar.name()) - } else if (ipar.hasLimits()) { - val i: Int = trafo.intOfExt(ipar.number()) - val err: Double = if (st.hasCovariance()) sqrt(2.0 * up * st.error().invHessian()[i, i]) else st.parameters().dirin().getEntry(i) - add(ipar.name(), - trafo.int2ext(i, st.vec().getEntry(i)), - trafo.int2extError(i, st.vec().getEntry(i), err)) - if (ipar.hasLowerLimit() && ipar.hasUpperLimit()) { - setLimits(ipar.name(), ipar.lowerLimit(), ipar.upperLimit()) - } else if (ipar.hasLowerLimit() && !ipar.hasUpperLimit()) { - setLowerLimit(ipar.name(), ipar.lowerLimit()) - } else { - setUpperLimit(ipar.name(), ipar.upperLimit()) - } - } else { - val i: Int = trafo.intOfExt(ipar.number()) - val err: Double = if (st.hasCovariance()) sqrt(2.0 * up * st.error().invHessian()[i, i]) else st.parameters().dirin().getEntry(i) - add(ipar.name(), st.vec().getEntry(i), err) - } - } - theCovarianceValid = st.error().isValid() - if (theCovarianceValid) { - theCovariance = trafo.int2extCovariance(st.vec(), st.error().invHessian()) - theIntCovariance = MnUserCovariance(st.error().invHessian().data().clone(), st.error().invHessian().nrow()) - theCovariance.scale(2.0 * up) - theGlobalCC = MnGlobalCorrelationCoeff(st.error().invHessian()) - theGCCValid = true - assert(theCovariance.nrow() === variableParameters()) - } - } - - /** - * add free parameter name, value, error - * - * @param err a double. - * @param val a double. - * @param name a [String] object. - */ - fun add(name: String, `val`: Double, err: Double) { - theParameters.add(name, `val`, err) - theIntParameters.add(`val`) - theCovarianceValid = false - theGCCValid = false - theValid = true - } - - /** - * add limited parameter name, value, lower bound, upper bound - * - * @param name a [String] object. - * @param val a double. - * @param low a double. - * @param err a double. - * @param up a double. - */ - fun add(name: String, `val`: Double, err: Double, low: Double, up: Double) { - theParameters.add(name, `val`, err, low, up) - theCovarianceValid = false - theIntParameters.add(ext2int(index(name), `val`)) - theGCCValid = false - theValid = true - } - - /** - * add const parameter name, value - * - * @param name a [String] object. - * @param val a double. - */ - fun add(name: String, `val`: Double) { - theParameters.add(name, `val`) - theValid = true - } - - /** - * - * copy. - * - * @return a [hep.dataforge.MINUIT.MnUserParameterState] object. - */ - fun copy(): MnUserParameterState { - return MnUserParameterState(this) - } - - /** - * Covariance matrix in the external representation - * - * @return a [hep.dataforge.MINUIT.MnUserCovariance] object. - */ - fun covariance(): MnUserCovariance { - return theCovariance - } - - /** - * Returns the expected vertival distance to the minimum (EDM) - * - * @return a double. - */ - fun edm(): Double { - return theEDM - } - - /** - * - * error. - * - * @param index a int. - * @return a double. - */ - fun error(index: Int): Double { - return theParameters.error(index) - } - - /** - * - * error. - * - * @param name a [String] object. - * @return a double. - */ - fun error(name: String?): Double { - return error(index(name)) - } - - /** - * - * errors. - * - * @return an array of double. - */ - fun errors(): DoubleArray { - return theParameters.errors() - } - - fun ext2int(i: Int, `val`: Double): Double { - return theParameters.trafo().ext2int(i, `val`) - } - - /** - * - * extOfInt. - * - * @param internal a int. - * @return a int. - */ - fun extOfInt(internal: Int): Int { - return theParameters.trafo().extOfInt(internal) - } - /// interaction via external number of parameter - /** - * - * fix. - * - * @param e a int. - */ - fun fix(e: Int) { - val i = intOfExt(e) - if (theCovarianceValid) { - theCovariance = MnCovarianceSqueeze.squeeze(theCovariance, i) - theIntCovariance = MnCovarianceSqueeze.squeeze(theIntCovariance, i) - } - theIntParameters.removeAt(i) - theParameters.fix(e) - theGCCValid = false - } - /// interaction via name of parameter - /** - * - * fix. - * - * @param name a [String] object. - */ - fun fix(name: String?) { - fix(index(name)) - } - - /** - * returns the function value at the minimum - * - * @return a double. - */ - fun fval(): Double { - return theFVal - } - - /** - * transformation internal <-> external - * @return - */ - fun getTransformation(): MnUserTransformation { - return theParameters.trafo() - } - - fun globalCC(): MnGlobalCorrelationCoeff? { - return theGlobalCC - } - - /** - * Returns - * true if the the state has a valid covariance, - * false otherwise. - * - * @return a boolean. - */ - fun hasCovariance(): Boolean { - return theCovarianceValid - } - - /** - * - * hasGlobalCC. - * - * @return a boolean. - */ - fun hasGlobalCC(): Boolean { - return theGCCValid - } - - /** - * convert name into external number of parameter - * - * @param name a [String] object. - * @return a int. - */ - fun index(name: String?): Int { - return theParameters.index(name) - } - - // transformation internal <-> external - fun int2ext(i: Int, `val`: Double): Double { - return theParameters.trafo().int2ext(i, `val`) - } - - fun intCovariance(): MnUserCovariance { - return theIntCovariance - } - - fun intOfExt(ext: Int): Int { - return theParameters.trafo().intOfExt(ext) - } - - /** - * Minuit internal representation - * @return - */ - fun intParameters(): List { - return theIntParameters - } - - /** - * Returns - * true if the the state is valid, - * false if not - * - * @return a boolean. - */ - fun isValid(): Boolean { - return theValid - } - - // facade: forward interface of MnUserParameters and MnUserTransformation - fun minuitParameters(): List { - return theParameters.parameters() - } - - /** - * convert external number into name of parameter - * - * @param index a int. - * @return a [String] object. - */ - fun name(index: Int): String { - return theParameters.name(index) - } - - /** - * Returns the number of function calls during the minimization. - * - * @return a int. - */ - fun nfcn(): Int { - return theNFcn - } - - fun parameter(i: Int): MinuitParameter { - return theParameters.parameter(i) - } - - //user external representation - fun parameters(): MnUserParameters { - return theParameters - } - - /** - * access to parameters and errors in column-wise representation - * - * @return an array of double. - */ - fun params(): DoubleArray { - return theParameters.params() - } - - /** - * - * precision. - * - * @return a [hep.dataforge.MINUIT.MnMachinePrecision] object. - */ - fun precision(): MnMachinePrecision { - return theParameters.precision() - } - - /** - * - * release. - * - * @param e a int. - */ - fun release(e: Int) { - theParameters.release(e) - theCovarianceValid = false - theGCCValid = false - val i = intOfExt(e) - if (parameter(e).hasLimits()) { - theIntParameters.add(i, ext2int(e, parameter(e).value())) - } else { - theIntParameters.add(i, parameter(e).value()) - } - } - - /** - * - * release. - * - * @param name a [String] object. - */ - fun release(name: String?) { - release(index(name)) - } - - /** - * - * removeLimits. - * - * @param e a int. - */ - fun removeLimits(e: Int) { - theParameters.removeLimits(e) - theCovarianceValid = false - theGCCValid = false - if (!parameter(e).isFixed() && !parameter(e).isConst()) { - theIntParameters[intOfExt(e)] = value(e) - } - } - - /** - * - * removeLimits. - * - * @param name a [String] object. - */ - fun removeLimits(name: String?) { - removeLimits(index(name)) - } - - /** - * - * setError. - * - * @param e a int. - * @param err a double. - * @param err a double. - */ - fun setError(e: Int, err: Double) { - theParameters.setError(e, err) - } - - /** - * - * setError. - * - * @param name a [String] object. - * @param err a double. - */ - fun setError(name: String?, err: Double) { - setError(index(name), err) - } - - /** - * - * setLimits. - * - * @param e a int. - * @param low a double. - * @param up a double. - */ - fun setLimits(e: Int, low: Double, up: Double) { - theParameters.setLimits(e, low, up) - theCovarianceValid = false - theGCCValid = false - if (!parameter(e).isFixed() && !parameter(e).isConst()) { - val i = intOfExt(e) - if (low < theIntParameters[i] && theIntParameters[i] < up) { - theIntParameters[i] = ext2int(e, theIntParameters[i]) - } else { - theIntParameters[i] = ext2int(e, 0.5 * (low + up)) - } - } - } - - /** - * - * setLimits. - * - * @param name a [String] object. - * @param low a double. - * @param up a double. - */ - fun setLimits(name: String?, low: Double, up: Double) { - setLimits(index(name), low, up) - } - - /** - * - * setLowerLimit. - * - * @param e a int. - * @param low a double. - */ - fun setLowerLimit(e: Int, low: Double) { - theParameters.setLowerLimit(e, low) - theCovarianceValid = false - theGCCValid = false - if (!parameter(e).isFixed() && !parameter(e).isConst()) { - val i = intOfExt(e) - if (low < theIntParameters[i]) { - theIntParameters[i] = ext2int(e, theIntParameters[i]) - } else { - theIntParameters[i] = ext2int(e, low + 0.5 * abs(low + 1.0)) - } - } - } - - /** - * - * setLowerLimit. - * - * @param name a [String] object. - * @param low a double. - */ - fun setLowerLimit(name: String?, low: Double) { - setLowerLimit(index(name), low) - } - - /** - * - * setPrecision. - * - * @param eps a double. - */ - fun setPrecision(eps: Double) { - theParameters.setPrecision(eps) - } - - /** - * - * setUpperLimit. - * - * @param e a int. - * @param up a double. - */ - fun setUpperLimit(e: Int, up: Double) { - theParameters.setUpperLimit(e, up) - theCovarianceValid = false - theGCCValid = false - if (!parameter(e).isFixed() && !parameter(e).isConst()) { - val i = intOfExt(e) - if (theIntParameters[i] < up) { - theIntParameters[i] = ext2int(e, theIntParameters[i]) - } else { - theIntParameters[i] = ext2int(e, up - 0.5 * abs(up + 1.0)) - } - } - } - - /** - * - * setUpperLimit. - * - * @param name a [String] object. - * @param up a double. - */ - fun setUpperLimit(name: String?, up: Double) { - setUpperLimit(index(name), up) - } - - /** - * - * setValue. - * - * @param e a int. - * @param val a double. - */ - fun setValue(e: Int, `val`: Double) { - theParameters.setValue(e, `val`) - if (!parameter(e).isFixed() && !parameter(e).isConst()) { - val i = intOfExt(e) - if (parameter(e).hasLimits()) { - theIntParameters[i] = ext2int(e, `val`) - } else { - theIntParameters[i] = `val` - } - } - } - - /** - * - * setValue. - * - * @param name a [String] object. - * @param val a double. - */ - fun setValue(name: String?, `val`: Double) { - setValue(index(name), `val`) - } - - /** {@inheritDoc} */ - override fun toString(): String { - return MnPrint.toString(this) - } - - /** - * - * value. - * - * @param index a int. - * @return a double. - */ - fun value(index: Int): Double { - return theParameters.value(index) - } - - /** - * - * value. - * - * @param name a [String] object. - * @return a double. - */ - fun value(name: String?): Double { - return value(index(name)) - } - - /** - * - * variableParameters. - * - * @return a int. - */ - fun variableParameters(): Int { - return theParameters.variableParameters() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnUserParameters.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnUserParameters.kt deleted file mode 100644 index 9bac54b25..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnUserParameters.kt +++ /dev/null @@ -1,402 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * API class for the user interaction with the parameters. Serves as input to - * the minimizer as well as output from it; users can interact: fix/release - * parameters, set values and errors, etc.; parameters can be accessed via their - * parameter number or via their user-specified name. - * - * @version $Id$ - * @author Darksnake - */ -class MnUserParameters { - private var theTransformation: MnUserTransformation - - /** - * Creates a new instance of MnUserParameters - */ - constructor() { - theTransformation = MnUserTransformation() - } - - /** - * - * Constructor for MnUserParameters. - * - * @param par an array of double. - * @param err an array of double. - */ - constructor(par: DoubleArray, err: DoubleArray) { - theTransformation = MnUserTransformation(par, err) - } - - private constructor(other: MnUserParameters) { - theTransformation = other.theTransformation.copy() - } - - /** - * Add free parameter name, value, error - * - * - * When adding parameters, MINUIT assigns indices to each parameter which - * will be the same as in the double[] in the - * MultiFunction.valueOf(). That means the first parameter the user - * adds gets index 0, the second index 1, and so on. When calculating the - * function value inside FCN, MINUIT will call - * MultiFunction.valueOf() with the elements at their respective - * positions. - * - * @param err a double. - * @param val a double. - * @param name a [String] object. - */ - fun add(name: String, `val`: Double, err: Double) { - theTransformation.add(name, `val`, err) - } - - /** - * Add limited parameter name, value, lower bound, upper bound - * - * @param up a double. - * @param low a double. - * @param name a [String] object. - * @param val a double. - * @param err a double. - */ - fun add(name: String, `val`: Double, err: Double, low: Double, up: Double) { - theTransformation.add(name, `val`, err, low, up) - } - - /** - * Add const parameter name, value - * - * @param name a [String] object. - * @param val a double. - */ - fun add(name: String, `val`: Double) { - theTransformation.add(name, `val`) - } - - /** - * - * copy. - * - * @return a [hep.dataforge.MINUIT.MnUserParameters] object. - */ - fun copy(): MnUserParameters { - return MnUserParameters(this) - } - - /** - * - * error. - * - * @param index a int. - * @return a double. - */ - fun error(index: Int): Double { - return theTransformation.error(index) - } - - /** - * - * error. - * - * @param name a [String] object. - * @return a double. - */ - fun error(name: String?): Double { - return theTransformation.error(name) - } - - fun errors(): DoubleArray { - return theTransformation.errors() - } - /// interaction via external number of parameter - /** - * Fixes the specified parameter (so that the minimizer will no longer vary - * it) - * - * @param index a int. - */ - fun fix(index: Int) { - theTransformation.fix(index) - } - /// interaction via name of parameter - /** - * Fixes the specified parameter (so that the minimizer will no longer vary - * it) - * - * @param name a [String] object. - */ - fun fix(name: String?) { - theTransformation.fix(name) - } - - /** - * convert name into external number of parameter - * @param name - * @return - */ - fun index(name: String?): Int { - return theTransformation.index(name) - } - - /** - * convert external number into name of parameter - * @param index - * @return - */ - fun name(index: Int): String { - return theTransformation.name(index) - } - - /** - * access to single parameter - * @param index - * @return - */ - fun parameter(index: Int): MinuitParameter { - return theTransformation.parameter(index) - } - - /** - * access to parameters (row-wise) - * @return - */ - fun parameters(): List { - return theTransformation.parameters() - } - - /** - * access to parameters and errors in column-wise representation - * @return - */ - fun params(): DoubleArray { - return theTransformation.params() - } - - /** - * - * precision. - * - * @return a [hep.dataforge.MINUIT.MnMachinePrecision] object. - */ - fun precision(): MnMachinePrecision { - return theTransformation.precision() - } - - /** - * Releases the specified parameter (so that the minimizer can vary it) - * - * @param index a int. - */ - fun release(index: Int) { - theTransformation.release(index) - } - - /** - * Releases the specified parameter (so that the minimizer can vary it) - * - * @param name a [String] object. - */ - fun release(name: String?) { - theTransformation.release(name) - } - - /** - * - * removeLimits. - * - * @param index a int. - */ - fun removeLimits(index: Int) { - theTransformation.removeLimits(index) - } - - /** - * - * removeLimits. - * - * @param name a [String] object. - */ - fun removeLimits(name: String?) { - theTransformation.removeLimits(name) - } - - /** - * - * setError. - * - * @param index a int. - * @param err a double. - */ - fun setError(index: Int, err: Double) { - theTransformation.setError(index, err) - } - - /** - * - * setError. - * - * @param name a [String] object. - * @param err a double. - */ - fun setError(name: String?, err: Double) { - theTransformation.setError(name, err) - } - - /** - * Set the lower and upper bound on the specified variable. - * - * @param up a double. - * @param low a double. - * @param index a int. - */ - fun setLimits(index: Int, low: Double, up: Double) { - theTransformation.setLimits(index, low, up) - } - - /** - * Set the lower and upper bound on the specified variable. - * - * @param up a double. - * @param low a double. - * @param name a [String] object. - */ - fun setLimits(name: String?, low: Double, up: Double) { - theTransformation.setLimits(name, low, up) - } - - /** - * - * setLowerLimit. - * - * @param index a int. - * @param low a double. - */ - fun setLowerLimit(index: Int, low: Double) { - theTransformation.setLowerLimit(index, low) - } - - /** - * - * setLowerLimit. - * - * @param name a [String] object. - * @param low a double. - */ - fun setLowerLimit(name: String?, low: Double) { - theTransformation.setLowerLimit(name, low) - } - - /** - * - * setPrecision. - * - * @param eps a double. - */ - fun setPrecision(eps: Double) { - theTransformation.setPrecision(eps) - } - - /** - * - * setUpperLimit. - * - * @param index a int. - * @param up a double. - */ - fun setUpperLimit(index: Int, up: Double) { - theTransformation.setUpperLimit(index, up) - } - - /** - * - * setUpperLimit. - * - * @param name a [String] object. - * @param up a double. - */ - fun setUpperLimit(name: String?, up: Double) { - theTransformation.setUpperLimit(name, up) - } - - /** - * Set the value of parameter. The parameter in question may be variable, - * fixed, or constant, but must be defined. - * - * @param index a int. - * @param val a double. - */ - fun setValue(index: Int, `val`: Double) { - theTransformation.setValue(index, `val`) - } - - /** - * Set the value of parameter. The parameter in question may be variable, - * fixed, or constant, but must be defined. - * - * @param name a [String] object. - * @param val a double. - */ - fun setValue(name: String?, `val`: Double) { - theTransformation.setValue(name, `val`) - } - - /** {@inheritDoc} */ - override fun toString(): String { - return MnPrint.toString(this) - } - - fun trafo(): MnUserTransformation { - return theTransformation - } - - /** - * - * value. - * - * @param index a int. - * @return a double. - */ - fun value(index: Int): Double { - return theTransformation.value(index) - } - - /** - * - * value. - * - * @param name a [String] object. - * @return a double. - */ - fun value(name: String?): Double { - return theTransformation.value(name) - } - - /** - * - * variableParameters. - * - * @return a int. - */ - fun variableParameters(): Int { - return theTransformation.variableParameters() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnUserTransformation.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnUserTransformation.kt deleted file mode 100644 index 1066ac2da..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnUserTransformation.kt +++ /dev/null @@ -1,390 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector - -/** - * knows how to andThen between user specified parameters (external) and - * internal parameters used for minimization - * - * Жуткий октопус, который занимается преобразованием внешних параметров во внутренние - * TODO по возможности отказаться от использования этого монстра - * @version $Id$ - */ -class MnUserTransformation { - private val nameMap: MutableMap = HashMap() - private var theCache: MutableList - private var theExtOfInt: MutableList - private var theParameters: MutableList - private var thePrecision: MnMachinePrecision - - constructor() { - thePrecision = MnMachinePrecision() - theParameters = java.util.ArrayList() - theExtOfInt = java.util.ArrayList() - theCache = java.util.ArrayList(0) - } - - private constructor(other: MnUserTransformation) { - thePrecision = other.thePrecision - theParameters = java.util.ArrayList(other.theParameters.size) - for (par in other.theParameters) { - theParameters.add(par.copy()) - } - theExtOfInt = java.util.ArrayList(other.theExtOfInt) - theCache = java.util.ArrayList(other.theCache) - } - - constructor(par: DoubleArray, err: DoubleArray) { - thePrecision = MnMachinePrecision() - theParameters = java.util.ArrayList(par.size) - theExtOfInt = java.util.ArrayList(par.size) - theCache = java.util.ArrayList(par.size) - for (i in par.indices) { - add("p$i", par[i], err[i]) - } - } - - /** - * add free parameter - * @param err - * @param val - */ - fun add(name: String, `val`: Double, err: Double) { - require(!nameMap.containsKey(name)) { "duplicate name: $name" } - nameMap[name] = theParameters.size - theExtOfInt.add(theParameters.size) - theCache.add(`val`) - theParameters.add(MinuitParameter(theParameters.size, name, `val`, err)) - } - - /** - * add limited parameter - * @param up - * @param low - */ - fun add(name: String, `val`: Double, err: Double, low: Double, up: Double) { - require(!nameMap.containsKey(name)) { "duplicate name: $name" } - nameMap[name] = theParameters.size - theExtOfInt.add(theParameters.size) - theCache.add(`val`) - theParameters.add(MinuitParameter(theParameters.size, name, `val`, err, low, up)) - } - - /** - * add parameter - * @param name - * @param val - */ - fun add(name: String, `val`: Double) { - require(!nameMap.containsKey(name)) { "duplicate name: $name" } - nameMap[name] = theParameters.size - theCache.add(`val`) - theParameters.add(MinuitParameter(theParameters.size, name, `val`)) - } - - /** - * - * copy. - * - * @return a [hep.dataforge.MINUIT.MnUserTransformation] object. - */ - fun copy(): MnUserTransformation { - return MnUserTransformation(this) - } - - fun dInt2Ext(i: Int, `val`: Double): Double { - var dd = 1.0 - val parm: MinuitParameter = theParameters[theExtOfInt[i]] - if (parm.hasLimits()) { - dd = if (parm.hasUpperLimit() && parm.hasLowerLimit()) { - theDoubleLimTrafo.dInt2Ext(`val`, - parm.upperLimit(), - parm.lowerLimit()) - } else if (parm.hasUpperLimit() && !parm.hasLowerLimit()) { - theUpperLimTrafo.dInt2Ext(`val`, parm.upperLimit()) - } else { - theLowerLimTrafo.dInt2Ext(`val`, parm.lowerLimit()) - } - } - return dd - } - - fun error(index: Int): Double { - return theParameters[index].error() - } - - fun error(name: String?): Double { - return error(index(name)) - } - - fun errors(): DoubleArray { - val result = DoubleArray(theParameters.size) - var i = 0 - for (parameter in theParameters) { - result[i++] = parameter.error() - } - return result - } - - fun ext2int(i: Int, `val`: Double): Double { - val parm: MinuitParameter = theParameters[i] - return if (parm.hasLimits()) { - if (parm.hasUpperLimit() && parm.hasLowerLimit()) { - theDoubleLimTrafo.ext2int(`val`, - parm.upperLimit(), - parm.lowerLimit(), - precision()) - } else if (parm.hasUpperLimit() && !parm.hasLowerLimit()) { - theUpperLimTrafo.ext2int(`val`, - parm.upperLimit(), - precision()) - } else { - theLowerLimTrafo.ext2int(`val`, - parm.lowerLimit(), - precision()) - } - } else `val` - } - - fun extOfInt(internal: Int): Int { - return theExtOfInt[internal] - } - - /** - * interaction via external number of parameter - * @param index - */ - fun fix(index: Int) { - val iind = intOfExt(index) - theExtOfInt.removeAt(iind) - theParameters[index].fix() - } - - /** - * interaction via name of parameter - * @param name - */ - fun fix(name: String?) { - fix(index(name)) - } - - /** - * convert name into external number of parameter - * @param name - * @return - */ - fun index(name: String?): Int { - return nameMap[name]!! - } - - fun int2ext(i: Int, `val`: Double): Double { - val parm: MinuitParameter = theParameters[theExtOfInt[i]] - return if (parm.hasLimits()) { - if (parm.hasUpperLimit() && parm.hasLowerLimit()) { - theDoubleLimTrafo.int2ext(`val`, - parm.upperLimit(), - parm.lowerLimit()) - } else if (parm.hasUpperLimit() && !parm.hasLowerLimit()) { - theUpperLimTrafo.int2ext(`val`, parm.upperLimit()) - } else { - theLowerLimTrafo.int2ext(`val`, parm.lowerLimit()) - } - } else `val` - } - - fun int2extCovariance(vec: RealVector, cov: MnAlgebraicSymMatrix): MnUserCovariance { - val result = MnUserCovariance(cov.nrow()) - for (i in 0 until vec.getDimension()) { - var dxdi = 1.0 - if (theParameters[theExtOfInt[i]].hasLimits()) { - dxdi = dInt2Ext(i, vec.getEntry(i)) - } - for (j in i until vec.getDimension()) { - var dxdj = 1.0 - if (theParameters[theExtOfInt[j]].hasLimits()) { - dxdj = dInt2Ext(j, vec.getEntry(j)) - } - result[i, j] = dxdi * cov[i, j] * dxdj - } - } - return result - } - - fun int2extError(i: Int, `val`: Double, err: Double): Double { - var dx = err - val parm: MinuitParameter = theParameters[theExtOfInt[i]] - if (parm.hasLimits()) { - val ui = int2ext(i, `val`) - var du1 = int2ext(i, `val` + dx) - ui - val du2 = int2ext(i, `val` - dx) - ui - if (parm.hasUpperLimit() && parm.hasLowerLimit()) { - if (dx > 1.0) { - du1 = parm.upperLimit() - parm.lowerLimit() - } - dx = 0.5 * (abs(du1) + abs(du2)) - } else { - dx = 0.5 * (abs(du1) + abs(du2)) - } - } - return dx - } - - fun intOfExt(ext: Int): Int { - for (iind in theExtOfInt.indices) { - if (ext == theExtOfInt[iind]) { - return iind - } - } - throw IllegalArgumentException("ext=$ext") - } - - /** - * convert external number into name of parameter - * @param index - * @return - */ - fun name(index: Int): String { - return theParameters[index].name() - } - - /** - * access to single parameter - * @param index - * @return - */ - fun parameter(index: Int): MinuitParameter { - return theParameters[index] - } - - fun parameters(): List { - return theParameters - } - - //access to parameters and errors in column-wise representation - fun params(): DoubleArray { - val result = DoubleArray(theParameters.size) - var i = 0 - for (parameter in theParameters) { - result[i++] = parameter.value() - } - return result - } - - fun precision(): MnMachinePrecision { - return thePrecision - } - - fun release(index: Int) { - require(!theExtOfInt.contains(index)) { "index=$index" } - theExtOfInt.add(index) - Collections.sort(theExtOfInt) - theParameters[index].release() - } - - fun release(name: String?) { - release(index(name)) - } - - fun removeLimits(index: Int) { - theParameters[index].removeLimits() - } - - fun removeLimits(name: String?) { - removeLimits(index(name)) - } - - fun setError(index: Int, err: Double) { - theParameters[index].setError(err) - } - - fun setError(name: String?, err: Double) { - setError(index(name), err) - } - - fun setLimits(index: Int, low: Double, up: Double) { - theParameters[index].setLimits(low, up) - } - - fun setLimits(name: String?, low: Double, up: Double) { - setLimits(index(name), low, up) - } - - fun setLowerLimit(index: Int, low: Double) { - theParameters[index].setLowerLimit(low) - } - - fun setLowerLimit(name: String?, low: Double) { - setLowerLimit(index(name), low) - } - - fun setPrecision(eps: Double) { - thePrecision.setPrecision(eps) - } - - fun setUpperLimit(index: Int, up: Double) { - theParameters[index].setUpperLimit(up) - } - - fun setUpperLimit(name: String?, up: Double) { - setUpperLimit(index(name), up) - } - - fun setValue(index: Int, `val`: Double) { - theParameters[index].setValue(`val`) - theCache[index] = `val` - } - - fun setValue(name: String?, `val`: Double) { - setValue(index(name), `val`) - } - - fun transform(pstates: RealVector): ArrayRealVector { - // FixMe: Worry about efficiency here - val result = ArrayRealVector(theCache.size) - for (i in 0 until result.getDimension()) { - result.setEntry(i, theCache[i]) - } - for (i in 0 until pstates.getDimension()) { - if (theParameters[theExtOfInt[i]].hasLimits()) { - result.setEntry(theExtOfInt[i], int2ext(i, pstates.getEntry(i))) - } else { - result.setEntry(theExtOfInt[i], pstates.getEntry(i)) - } - } - return result - } - - //forwarded interface - fun value(index: Int): Double { - return theParameters[index].value() - } - - fun value(name: String?): Double { - return value(index(name)) - } - - fun variableParameters(): Int { - return theExtOfInt.size - } - - companion object { - private val theDoubleLimTrafo: SinParameterTransformation = SinParameterTransformation() - private val theLowerLimTrafo: SqrtLowParameterTransformation = SqrtLowParameterTransformation() - private val theUpperLimTrafo: SqrtUpParameterTransformation = SqrtUpParameterTransformation() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/MnUtils.kt b/kmath-optimization/src/commonMain/tmp/minuit/MnUtils.kt deleted file mode 100644 index d9f3e1bd5..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/MnUtils.kt +++ /dev/null @@ -1,147 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector - -/** - * Utilities for operating on vectors and matrices - * - * @version $Id$ - */ -internal object MnUtils { - fun absoluteSumOfElements(m: MnAlgebraicSymMatrix): Double { - val data: DoubleArray = m.data() - var result = 0.0 - for (i in data.indices) { - result += abs(data[i]) - } - return result - } - - fun add(v1: RealVector, v2: RealVector?): RealVector { - return v1.add(v2) - } - - fun add(m1: MnAlgebraicSymMatrix, m2: MnAlgebraicSymMatrix): MnAlgebraicSymMatrix { - require(!(m1.size() !== m2.size())) { "Incompatible matrices" } - val result: MnAlgebraicSymMatrix = m1.copy() - val a: DoubleArray = result.data() - val b: DoubleArray = m2.data() - for (i in a.indices) { - a[i] += b[i] - } - return result - } - - fun div(m: MnAlgebraicSymMatrix?, scale: Double): MnAlgebraicSymMatrix { - return mul(m, 1 / scale) - } - - fun div(m: RealVector?, scale: Double): RealVector { - return mul(m, 1 / scale) - } - - fun innerProduct(v1: RealVector, v2: RealVector): Double { - require(!(v1.getDimension() !== v2.getDimension())) { "Incompatible vectors" } - var total = 0.0 - for (i in 0 until v1.getDimension()) { - total += v1.getEntry(i) * v2.getEntry(i) - } - return total - } - - fun mul(v1: RealVector, scale: Double): RealVector { - return v1.mapMultiply(scale) - } - - fun mul(m1: MnAlgebraicSymMatrix, scale: Double): MnAlgebraicSymMatrix { - val result: MnAlgebraicSymMatrix = m1.copy() - val a: DoubleArray = result.data() - for (i in a.indices) { - a[i] *= scale - } - return result - } - - fun mul(m1: MnAlgebraicSymMatrix, v1: RealVector): ArrayRealVector { - require(!(m1.nrow() !== v1.getDimension())) { "Incompatible arguments" } - val result = ArrayRealVector(m1.nrow()) - for (i in 0 until result.getDimension()) { - var total = 0.0 - for (k in 0 until result.getDimension()) { - total += m1[i, k] * v1.getEntry(k) - } - result.setEntry(i, total) - } - return result - } - - fun mul(m1: MnAlgebraicSymMatrix, m2: MnAlgebraicSymMatrix): MnAlgebraicSymMatrix { - require(!(m1.size() !== m2.size())) { "Incompatible matrices" } - val n: Int = m1.nrow() - val result = MnAlgebraicSymMatrix(n) - for (i in 0 until n) { - for (j in 0..i) { - var total = 0.0 - for (k in 0 until n) { - total += m1[i, k] * m2[k, j] - } - result[i, j] = total - } - } - return result - } - - fun outerProduct(v2: RealVector): MnAlgebraicSymMatrix { - // Fixme: check this. I am assuming this is just an outer-product of vector - // with itself. - val n: Int = v2.getDimension() - val result = MnAlgebraicSymMatrix(n) - val data: DoubleArray = v2.toArray() - for (i in 0 until n) { - for (j in 0..i) { - result[i, j] = data[i] * data[j] - } - } - return result - } - - fun similarity(avec: RealVector, mat: MnAlgebraicSymMatrix): Double { - val n: Int = avec.getDimension() - val tmp: RealVector = mul(mat, avec) - var result = 0.0 - for (i in 0 until n) { - result += tmp.getEntry(i) * avec.getEntry(i) - } - return result - } - - fun sub(v1: RealVector, v2: RealVector?): RealVector { - return v1.subtract(v2) - } - - fun sub(m1: MnAlgebraicSymMatrix, m2: MnAlgebraicSymMatrix): MnAlgebraicSymMatrix { - require(!(m1.size() !== m2.size())) { "Incompatible matrices" } - val result: MnAlgebraicSymMatrix = m1.copy() - val a: DoubleArray = result.data() - val b: DoubleArray = m2.data() - for (i in a.indices) { - a[i] -= b[i] - } - return result - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/ModularFunctionMinimizer.kt b/kmath-optimization/src/commonMain/tmp/minuit/ModularFunctionMinimizer.kt deleted file mode 100644 index 84130d24f..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/ModularFunctionMinimizer.kt +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import ru.inr.mass.maths.MultiFunction -import ru.inr.mass.minuit.* -import space.kscience.kmath.optimization.minuit.MinimumSeed - -/** - * - * @version $Id$ - */ -abstract class ModularFunctionMinimizer { - abstract fun builder(): MinimumBuilder - fun minimize( - fcn: MultiFunction?, - st: MnUserParameterState, - strategy: MnStrategy, - maxfcn: Int, - toler: Double, - errorDef: Double, - useAnalyticalGradient: Boolean, - checkGradient: Boolean - ): FunctionMinimum { - var maxfcn = maxfcn - val mfcn = MnUserFcn(fcn, errorDef, st.getTransformation()) - val gc: GradientCalculator - var providesAllDerivs = true - /* - * Проверяем в явном виде, что все аналитические производные присутствуют - * TODO сделать возможность того, что часть производных задается аналитически, а часть численно - */for (i in 0 until fcn.getDimension()) { - if (!fcn.providesDeriv(i)) providesAllDerivs = false - } - gc = if (providesAllDerivs && useAnalyticalGradient) { - AnalyticalGradientCalculator(fcn, st.getTransformation(), checkGradient) - } else { - Numerical2PGradientCalculator(mfcn, st.getTransformation(), strategy) - } - val npar: Int = st.variableParameters() - if (maxfcn == 0) { - maxfcn = 200 + 100 * npar + 5 * npar * npar - } - val mnseeds: MinimumSeed = seedGenerator().generate(mfcn, gc, st, strategy) - return minimize(mfcn, gc, mnseeds, strategy, maxfcn, toler) - } - - fun minimize( - mfcn: MnFcn, - gc: GradientCalculator?, - seed: MinimumSeed?, - strategy: MnStrategy?, - maxfcn: Int, - toler: Double - ): FunctionMinimum { - return builder().minimum(mfcn, gc, seed, strategy, maxfcn, toler * mfcn.errorDef()) - } - - abstract fun seedGenerator(): MinimumSeedGenerator -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/NegativeG2LineSearch.kt b/kmath-optimization/src/commonMain/tmp/minuit/NegativeG2LineSearch.kt deleted file mode 100644 index 2e9ce5813..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/NegativeG2LineSearch.kt +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector -import ru.inr.mass.minuit.* - -/** - * In case that one of the components of the second derivative g2 calculated by - * the numerical gradient calculator is negative, a 1dim line search in the - * direction of that component is done in order to find a better position where - * g2 is again positive. - * - * @version $Id$ - */ -internal object NegativeG2LineSearch { - fun hasNegativeG2(grad: FunctionGradient, prec: MnMachinePrecision): Boolean { - for (i in 0 until grad.getGradient().getDimension()) { - if (grad.getGradientDerivative().getEntry(i) < prec.eps2()) { - return true - } - } - return false - } - - fun search(fcn: MnFcn, st: MinimumState, gc: GradientCalculator, prec: MnMachinePrecision): MinimumState { - val negG2 = hasNegativeG2(st.gradient(), prec) - if (!negG2) { - return st - } - val n: Int = st.parameters().vec().getDimension() - var dgrad: FunctionGradient = st.gradient() - var pa: MinimumParameters = st.parameters() - var iterate = false - var iter = 0 - do { - iterate = false - for (i in 0 until n) { - if (dgrad.getGradientDerivative().getEntry(i) < prec.eps2()) { - // do line search if second derivative negative - var step: RealVector = ArrayRealVector(n) - step.setEntry(i, dgrad.getStep().getEntry(i) * dgrad.getGradient().getEntry(i)) - if (abs(dgrad.getGradient().getEntry(i)) > prec.eps2()) { - step.setEntry(i, - step.getEntry(i) * (-1.0 / abs(dgrad.getGradient().getEntry(i)))) - } - val gdel: Double = step.getEntry(i) * dgrad.getGradient().getEntry(i) - val pp: MnParabolaPoint = MnLineSearch.search(fcn, pa, step, gdel, prec) - step = MnUtils.mul(step, pp.x()) - pa = MinimumParameters(MnUtils.add(pa.vec(), step), pp.y()) - dgrad = gc.gradient(pa, dgrad) - iterate = true - break - } - } - } while (iter++ < 2 * n && iterate) - val mat = MnAlgebraicSymMatrix(n) - for (i in 0 until n) { - mat[i, i] = if (abs(dgrad.getGradientDerivative() - .getEntry(i)) > prec.eps2() - ) 1.0 / dgrad.getGradientDerivative().getEntry(i) else 1.0 - } - val err = MinimumError(mat, 1.0) - val edm: Double = VariableMetricEDMEstimator().estimate(dgrad, err) - return MinimumState(pa, err, dgrad, edm, fcn.numOfCalls()) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/Numerical2PGradientCalculator.kt b/kmath-optimization/src/commonMain/tmp/minuit/Numerical2PGradientCalculator.kt deleted file mode 100644 index efa1d57af..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/Numerical2PGradientCalculator.kt +++ /dev/null @@ -1,122 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.RealVector -import ru.inr.mass.minuit.* - -/** - * - * @version $Id$ - */ -internal class Numerical2PGradientCalculator(fcn: MnFcn, par: MnUserTransformation, stra: MnStrategy) : - GradientCalculator { - private val theFcn: MnFcn = fcn - private val theStrategy: MnStrategy - private val theTransformation: MnUserTransformation - fun fcn(): MnFcn { - return theFcn - } - - fun gradTolerance(): Double { - return strategy().gradientTolerance() - } - - /** {@inheritDoc} */ - fun gradient(par: MinimumParameters): FunctionGradient { - val gc = InitialGradientCalculator(theFcn, theTransformation, theStrategy) - val gra: FunctionGradient = gc.gradient(par) - return gradient(par, gra) - } - - /** {@inheritDoc} */ - fun gradient(par: MinimumParameters, gradient: FunctionGradient): FunctionGradient { - require(par.isValid()) { "Parameters are invalid" } - val x: RealVector = par.vec().copy() - val fcnmin: Double = par.fval() - val dfmin: Double = 8.0 * precision().eps2() * (abs(fcnmin) + theFcn.errorDef()) - val vrysml: Double = 8.0 * precision().eps() * precision().eps() - val n: Int = x.getDimension() - val grd: RealVector = gradient.getGradient().copy() - val g2: RealVector = gradient.getGradientDerivative().copy() - val gstep: RealVector = gradient.getStep().copy() - for (i in 0 until n) { - val xtf: Double = x.getEntry(i) - val epspri: Double = precision().eps2() + abs(grd.getEntry(i) * precision().eps2()) - var stepb4 = 0.0 - for (j in 0 until ncycle()) { - val optstp: Double = sqrt(dfmin / (abs(g2.getEntry(i)) + epspri)) - var step: Double = max(optstp, abs(0.1 * gstep.getEntry(i))) - if (trafo().parameter(trafo().extOfInt(i)).hasLimits()) { - if (step > 0.5) { - step = 0.5 - } - } - val stpmax: Double = 10.0 * abs(gstep.getEntry(i)) - if (step > stpmax) { - step = stpmax - } - val stpmin: Double = - max(vrysml, 8.0 * abs(precision().eps2() * x.getEntry(i))) - if (step < stpmin) { - step = stpmin - } - if (abs((step - stepb4) / step) < stepTolerance()) { - break - } - gstep.setEntry(i, step) - stepb4 = step - x.setEntry(i, xtf + step) - val fs1: Double = theFcn.value(x) - x.setEntry(i, xtf - step) - val fs2: Double = theFcn.value(x) - x.setEntry(i, xtf) - val grdb4: Double = grd.getEntry(i) - grd.setEntry(i, 0.5 * (fs1 - fs2) / step) - g2.setEntry(i, (fs1 + fs2 - 2.0 * fcnmin) / step / step) - if (abs(grdb4 - grd.getEntry(i)) / (abs(grd.getEntry(i)) + dfmin / step) < gradTolerance()) { - break - } - } - } - return FunctionGradient(grd, g2, gstep) - } - - fun ncycle(): Int { - return strategy().gradientNCycles() - } - - fun precision(): MnMachinePrecision { - return theTransformation.precision() - } - - fun stepTolerance(): Double { - return strategy().gradientStepTolerance() - } - - fun strategy(): MnStrategy { - return theStrategy - } - - fun trafo(): MnUserTransformation { - return theTransformation - } - - init { - theTransformation = par - theStrategy = stra - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/ScanBuilder.kt b/kmath-optimization/src/commonMain/tmp/minuit/ScanBuilder.kt deleted file mode 100644 index 57f910a26..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/ScanBuilder.kt +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector -import ru.inr.mass.minuit.* -import space.kscience.kmath.optimization.minuit.MinimumSeed - -/** - * Performs a minimization using the simplex method of Nelder and Mead (ref. - * Comp. J. 7, 308 (1965)). - * - * @version $Id$ - */ -internal class ScanBuilder : MinimumBuilder { - /** {@inheritDoc} */ - fun minimum( - mfcn: MnFcn, - gc: GradientCalculator?, - seed: MinimumSeed, - stra: MnStrategy?, - maxfcn: Int, - toler: Double - ): FunctionMinimum { - val x: RealVector = seed.parameters().vec().copy() - val upst = MnUserParameterState(seed.state(), mfcn.errorDef(), seed.trafo()) - val scan = MnParameterScan(mfcn.fcn(), upst.parameters(), seed.fval()) - var amin: Double = scan.fval() - val n: Int = seed.trafo().variableParameters() - val dirin: RealVector = ArrayRealVector(n) - for (i in 0 until n) { - val ext: Int = seed.trafo().extOfInt(i) - scan.scan(ext) - if (scan.fval() < amin) { - amin = scan.fval() - x.setEntry(i, seed.trafo().ext2int(ext, scan.parameters().value(ext))) - } - dirin.setEntry(i, sqrt(2.0 * mfcn.errorDef() * seed.error().invHessian()[i, i])) - } - val mp = MinimumParameters(x, dirin, amin) - val st = MinimumState(mp, 0.0, mfcn.numOfCalls()) - val states: MutableList = java.util.ArrayList(1) - states.add(st) - return FunctionMinimum(seed, states, mfcn.errorDef()) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/ScanMinimizer.kt b/kmath-optimization/src/commonMain/tmp/minuit/ScanMinimizer.kt deleted file mode 100644 index e39a49c0d..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/ScanMinimizer.kt +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -internal class ScanMinimizer : ModularFunctionMinimizer() { - private val theBuilder: ScanBuilder - private val theSeedGenerator: SimplexSeedGenerator = SimplexSeedGenerator() - override fun builder(): MinimumBuilder { - return theBuilder - } - - override fun seedGenerator(): MinimumSeedGenerator { - return theSeedGenerator - } - - init { - theBuilder = ScanBuilder() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/SimplexBuilder.kt b/kmath-optimization/src/commonMain/tmp/minuit/SimplexBuilder.kt deleted file mode 100644 index 0b10155ff..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/SimplexBuilder.kt +++ /dev/null @@ -1,180 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import space.kscience.kmath.optimization.minuit.MINUITPlugin -import ru.inr.mass.minuit.* -import space.kscience.kmath.optimization.minuit.MinimumSeed - -/** - * - * @version $Id$ - */ -internal class SimplexBuilder : MinimumBuilder { - /** {@inheritDoc} */ - fun minimum( - mfcn: MnFcn, - gc: GradientCalculator?, - seed: MinimumSeed, - strategy: MnStrategy?, - maxfcn: Int, - minedm: Double - ): FunctionMinimum { - val prec: MnMachinePrecision = seed.precision() - val x: RealVector = seed.parameters().vec().copy() - val step: RealVector = MnUtils.mul(seed.gradient().getStep(), 10.0) - val n: Int = x.getDimension() - val wg = 1.0 / n - val alpha = 1.0 - val beta = 0.5 - val gamma = 2.0 - val rhomin = 4.0 - val rhomax = 8.0 - val rho1 = 1.0 + alpha - val rho2 = 1.0 + alpha * gamma - val simpl: MutableList> = java.util.ArrayList>(n + 1) - simpl.add(Pair(seed.fval(), x.copy())) - var jl = 0 - var jh = 0 - var amin: Double = seed.fval() - var aming: Double = seed.fval() - for (i in 0 until n) { - val dmin: Double = 8.0 * prec.eps2() * (abs(x.getEntry(i)) + prec.eps2()) - if (step.getEntry(i) < dmin) { - step.setEntry(i, dmin) - } - x.setEntry(i, x.getEntry(i) + step.getEntry(i)) - val tmp: Double = mfcn.value(x) - if (tmp < amin) { - amin = tmp - jl = i + 1 - } - if (tmp > aming) { - aming = tmp - jh = i + 1 - } - simpl.add(Pair(tmp, x.copy())) - x.setEntry(i, x.getEntry(i) - step.getEntry(i)) - } - val simplex = SimplexParameters(simpl, jh, jl) - do { - amin = simplex[jl].getFirst() - jl = simplex.jl() - jh = simplex.jh() - var pbar: RealVector = ArrayRealVector(n) - for (i in 0 until n + 1) { - if (i == jh) { - continue - } - pbar = MnUtils.add(pbar, MnUtils.mul(simplex[i].getSecond(), wg)) - } - val pstar: RealVector = - MnUtils.sub(MnUtils.mul(pbar, 1.0 + alpha), MnUtils.mul(simplex[jh].getSecond(), alpha)) - val ystar: Double = mfcn.value(pstar) - if (ystar > amin) { - if (ystar < simplex[jh].getFirst()) { - simplex.update(ystar, pstar) - if (jh != simplex.jh()) { - continue - } - } - val pstst: RealVector = - MnUtils.add(MnUtils.mul(simplex[jh].getSecond(), beta), MnUtils.mul(pbar, 1.0 - beta)) - val ystst: Double = mfcn.value(pstst) - if (ystst > simplex[jh].getFirst()) { - break - } - simplex.update(ystst, pstst) - continue - } - var pstst: RealVector = MnUtils.add(MnUtils.mul(pstar, gamma), MnUtils.mul(pbar, 1.0 - gamma)) - var ystst: Double = mfcn.value(pstst) - val y1: Double = (ystar - simplex[jh].getFirst()) * rho2 - val y2: Double = (ystst - simplex[jh].getFirst()) * rho1 - var rho = 0.5 * (rho2 * y1 - rho1 * y2) / (y1 - y2) - if (rho < rhomin) { - if (ystst < simplex[jl].getFirst()) { - simplex.update(ystst, pstst) - } else { - simplex.update(ystar, pstar) - } - continue - } - if (rho > rhomax) { - rho = rhomax - } - val prho: RealVector = - MnUtils.add(MnUtils.mul(pbar, rho), MnUtils.mul(simplex[jh].getSecond(), 1.0 - rho)) - val yrho: Double = mfcn.value(prho) - if (yrho < simplex[jl].getFirst() && yrho < ystst) { - simplex.update(yrho, prho) - continue - } - if (ystst < simplex[jl].getFirst()) { - simplex.update(ystst, pstst) - continue - } - if (yrho > simplex[jl].getFirst()) { - if (ystst < simplex[jl].getFirst()) { - simplex.update(ystst, pstst) - } else { - simplex.update(ystar, pstar) - } - continue - } - if (ystar > simplex[jh].getFirst()) { - pstst = MnUtils.add(MnUtils.mul(simplex[jh].getSecond(), beta), MnUtils.mul(pbar, 1 - beta)) - ystst = mfcn.value(pstst) - if (ystst > simplex[jh].getFirst()) { - break - } - simplex.update(ystst, pstst) - } - } while (simplex.edm() > minedm && mfcn.numOfCalls() < maxfcn) - amin = simplex[jl].getFirst() - jl = simplex.jl() - jh = simplex.jh() - var pbar: RealVector = ArrayRealVector(n) - for (i in 0 until n + 1) { - if (i == jh) { - continue - } - pbar = MnUtils.add(pbar, MnUtils.mul(simplex[i].getSecond(), wg)) - } - var ybar: Double = mfcn.value(pbar) - if (ybar < amin) { - simplex.update(ybar, pbar) - } else { - pbar = simplex[jl].getSecond() - ybar = simplex[jl].getFirst() - } - var dirin: RealVector = simplex.dirin() - // scale to sigmas on parameters werr^2 = dirin^2 * (up/edm) - dirin = MnUtils.mul(dirin, sqrt(mfcn.errorDef() / simplex.edm())) - val st = MinimumState(MinimumParameters(pbar, dirin, ybar), simplex.edm(), mfcn.numOfCalls()) - val states: MutableList = java.util.ArrayList(1) - states.add(st) - if (mfcn.numOfCalls() > maxfcn) { - MINUITPlugin.logStatic("Simplex did not converge, #fcn calls exhausted.") - return FunctionMinimum(seed, states, mfcn.errorDef(), MnReachedCallLimit()) - } - if (simplex.edm() > minedm) { - MINUITPlugin.logStatic("Simplex did not converge, edm > minedm.") - return FunctionMinimum(seed, states, mfcn.errorDef(), MnAboveMaxEdm()) - } - return FunctionMinimum(seed, states, mfcn.errorDef()) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/SimplexMinimizer.kt b/kmath-optimization/src/commonMain/tmp/minuit/SimplexMinimizer.kt deleted file mode 100644 index f4bbcc320..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/SimplexMinimizer.kt +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -internal class SimplexMinimizer : ModularFunctionMinimizer() { - private val theBuilder: SimplexBuilder - private val theSeedGenerator: SimplexSeedGenerator = SimplexSeedGenerator() - - /** {@inheritDoc} */ - override fun builder(): MinimumBuilder { - return theBuilder - } - - /** {@inheritDoc} */ - override fun seedGenerator(): MinimumSeedGenerator { - return theSeedGenerator - } - - /** - * - * Constructor for SimplexMinimizer. - */ - init { - theBuilder = SimplexBuilder() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/SimplexParameters.kt b/kmath-optimization/src/commonMain/tmp/minuit/SimplexParameters.kt deleted file mode 100644 index fef6e2010..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/SimplexParameters.kt +++ /dev/null @@ -1,85 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector - -/** - * - * @version $Id$ - */ -internal class SimplexParameters(simpl: MutableList>, jh: Int, jl: Int) { - private var theJHigh: Int - private var theJLow: Int - private val theSimplexParameters: MutableList> - fun dirin(): ArrayRealVector { - val dirin = ArrayRealVector(theSimplexParameters.size - 1) - for (i in 0 until theSimplexParameters.size - 1) { - var pbig: Double = theSimplexParameters[0].getSecond().getEntry(i) - var plit = pbig - for (theSimplexParameter in theSimplexParameters) { - if (theSimplexParameter.getSecond().getEntry(i) < plit) { - plit = theSimplexParameter.getSecond().getEntry(i) - } - if (theSimplexParameter.getSecond().getEntry(i) > pbig) { - pbig = theSimplexParameter.getSecond().getEntry(i) - } - } - dirin.setEntry(i, pbig - plit) - } - return dirin - } - - fun edm(): Double { - return theSimplexParameters[jh()].getFirst() - theSimplexParameters[jl()].getFirst() - } - - operator fun get(i: Int): Pair { - return theSimplexParameters[i] - } - - fun jh(): Int { - return theJHigh - } - - fun jl(): Int { - return theJLow - } - - fun simplex(): List> { - return theSimplexParameters - } - - fun update(y: Double, p: RealVector?) { - theSimplexParameters.set(jh(), Pair(y, p)) - if (y < theSimplexParameters[jl()].getFirst()) { - theJLow = jh() - } - var jh = 0 - for (i in 1 until theSimplexParameters.size) { - if (theSimplexParameters[i].getFirst() > theSimplexParameters[jh].getFirst()) { - jh = i - } - } - theJHigh = jh - } - - init { - theSimplexParameters = simpl - theJHigh = jh - theJLow = jl - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/SimplexSeedGenerator.kt b/kmath-optimization/src/commonMain/tmp/minuit/SimplexSeedGenerator.kt deleted file mode 100644 index 577545fc3..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/SimplexSeedGenerator.kt +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import org.apache.commons.math3.linear.ArrayRealVector -import ru.inr.mass.minuit.* -import space.kscience.kmath.optimization.minuit.MinimumSeed - -/** - * - * @version $Id$ - */ -internal class SimplexSeedGenerator : MinimumSeedGenerator { - /** {@inheritDoc} */ - fun generate(fcn: MnFcn, gc: GradientCalculator?, st: MnUserParameterState, stra: MnStrategy): MinimumSeed { - val n: Int = st.variableParameters() - val prec: MnMachinePrecision = st.precision() - - // initial starting values - val x: RealVector = ArrayRealVector(n) - for (i in 0 until n) { - x.setEntry(i, st.intParameters()[i]) - } - val fcnmin: Double = fcn.value(x) - val pa = MinimumParameters(x, fcnmin) - val igc = InitialGradientCalculator(fcn, st.getTransformation(), stra) - val dgrad: FunctionGradient = igc.gradient(pa) - val mat = MnAlgebraicSymMatrix(n) - val dcovar = 1.0 - for (i in 0 until n) { - mat[i, i] = if (abs(dgrad.getGradientDerivative() - .getEntry(i)) > prec.eps2() - ) 1.0 / dgrad.getGradientDerivative().getEntry(i) else 1.0 - } - val err = MinimumError(mat, dcovar) - val edm: Double = VariableMetricEDMEstimator().estimate(dgrad, err) - val state = MinimumState(pa, err, dgrad, edm, fcn.numOfCalls()) - return MinimumSeed(state, st.getTransformation()) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/SinParameterTransformation.kt b/kmath-optimization/src/commonMain/tmp/minuit/SinParameterTransformation.kt deleted file mode 100644 index 821addef7..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/SinParameterTransformation.kt +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -internal class SinParameterTransformation { - fun dInt2Ext(value: Double, upper: Double, lower: Double): Double { - return 0.5 * abs((upper - lower) * cos(value)) - } - - fun ext2int(value: Double, upper: Double, lower: Double, prec: MnMachinePrecision): Double { - val piby2: Double = 2.0 * atan(1.0) - val distnn: Double = 8.0 * sqrt(prec.eps2()) - val vlimhi = piby2 - distnn - val vlimlo = -piby2 + distnn - val yy = 2.0 * (value - lower) / (upper - lower) - 1.0 - val yy2 = yy * yy - return if (yy2 > 1.0 - prec.eps2()) { - if (yy < 0.0) { - vlimlo - } else { - vlimhi - } - } else { - asin(yy) - } - } - - fun int2ext(value: Double, upper: Double, lower: Double): Double { - return lower + 0.5 * (upper - lower) * (sin(value) + 1.0) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/SqrtLowParameterTransformation.kt b/kmath-optimization/src/commonMain/tmp/minuit/SqrtLowParameterTransformation.kt deleted file mode 100644 index 444b63847..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/SqrtLowParameterTransformation.kt +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -internal class SqrtLowParameterTransformation { - // derivative of transformation from internal to external - fun dInt2Ext(value: Double, lower: Double): Double { - return value / sqrt(value * value + 1.0) - } - - // transformation from external to internal - fun ext2int(value: Double, lower: Double, prec: MnMachinePrecision): Double { - val yy = value - lower + 1.0 - val yy2 = yy * yy - return if (yy2 < 1.0 + prec.eps2()) { - 8 * sqrt(prec.eps2()) - } else { - sqrt(yy2 - 1) - } - } - - // transformation from internal to external - fun int2ext(value: Double, lower: Double): Double { - return lower - 1.0 + sqrt(value * value + 1.0) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/SqrtUpParameterTransformation.kt b/kmath-optimization/src/commonMain/tmp/minuit/SqrtUpParameterTransformation.kt deleted file mode 100644 index 5774848bd..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/SqrtUpParameterTransformation.kt +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -internal class SqrtUpParameterTransformation { - // derivative of transformation from internal to external - fun dInt2Ext(value: Double, upper: Double): Double { - return -value / sqrt(value * value + 1.0) - } - - // transformation from external to internal - fun ext2int(value: Double, upper: Double, prec: MnMachinePrecision): Double { - val yy = upper - value + 1.0 - val yy2 = yy * yy - return if (yy2 < 1.0 + prec.eps2()) { - 8 * sqrt(prec.eps2()) - } else { - sqrt(yy2 - 1) - } - } - - // transformation from internal to external - fun int2ext(value: Double, upper: Double): Double { - return upper + 1.0 - sqrt(value * value + 1.0) - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/VariableMetricBuilder.kt b/kmath-optimization/src/commonMain/tmp/minuit/VariableMetricBuilder.kt deleted file mode 100644 index edc6783b6..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/VariableMetricBuilder.kt +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -import space.kscience.kmath.optimization.minuit.MINUITPlugin -import ru.inr.mass.minuit.* -import space.kscience.kmath.optimization.minuit.MinimumSeed - -/** - * - * @version $Id$ - */ -internal class VariableMetricBuilder : MinimumBuilder { - private val theErrorUpdator: DavidonErrorUpdator - private val theEstimator: VariableMetricEDMEstimator = VariableMetricEDMEstimator() - fun errorUpdator(): DavidonErrorUpdator { - return theErrorUpdator - } - - fun estimator(): VariableMetricEDMEstimator { - return theEstimator - } - - /** {@inheritDoc} */ - fun minimum( - fcn: MnFcn, - gc: GradientCalculator, - seed: MinimumSeed, - strategy: MnStrategy, - maxfcn: Int, - edmval: Double - ): FunctionMinimum { - val min: FunctionMinimum = minimum(fcn, gc, seed, maxfcn, edmval) - if (strategy.strategy() === 2 || strategy.strategy() === 1 && min.error().dcovar() > 0.05) { - val st: MinimumState = MnHesse(strategy).calculate(fcn, min.state(), min.seed().trafo(), 0) - min.add(st) - } - if (!min.isValid()) { - MINUITPlugin.logStatic("FunctionMinimum is invalid.") - } - return min - } - - fun minimum(fcn: MnFcn, gc: GradientCalculator, seed: MinimumSeed, maxfcn: Int, edmval: Double): FunctionMinimum { - var edmval = edmval - edmval *= 0.0001 - if (seed.parameters().vec().getDimension() === 0) { - return FunctionMinimum(seed, fcn.errorDef()) - } - val prec: MnMachinePrecision = seed.precision() - val result: MutableList = java.util.ArrayList(8) - var edm: Double = seed.state().edm() - if (edm < 0.0) { - MINUITPlugin.logStatic("VariableMetricBuilder: initial matrix not pos.def.") - if (seed.error().isPosDef()) { - throw RuntimeException("Something is wrong!") - } - return FunctionMinimum(seed, fcn.errorDef()) - } - result.add(seed.state()) - - // iterate until edm is small enough or max # of iterations reached - edm *= 1.0 + 3.0 * seed.error().dcovar() - var step: RealVector // = new ArrayRealVector(seed.gradient().getGradient().getDimension()); - do { - var s0: MinimumState = result[result.size - 1] - step = MnUtils.mul(MnUtils.mul(s0.error().invHessian(), s0.gradient().getGradient()), -1) - var gdel: Double = MnUtils.innerProduct(step, s0.gradient().getGradient()) - if (gdel > 0.0) { - MINUITPlugin.logStatic("VariableMetricBuilder: matrix not pos.def.") - MINUITPlugin.logStatic("gdel > 0: $gdel") - s0 = MnPosDef.test(s0, prec) - step = MnUtils.mul(MnUtils.mul(s0.error().invHessian(), s0.gradient().getGradient()), -1) - gdel = MnUtils.innerProduct(step, s0.gradient().getGradient()) - MINUITPlugin.logStatic("gdel: $gdel") - if (gdel > 0.0) { - result.add(s0) - return FunctionMinimum(seed, result, fcn.errorDef()) - } - } - val pp: MnParabolaPoint = MnLineSearch.search(fcn, s0.parameters(), step, gdel, prec) - if (abs(pp.y() - s0.fval()) < prec.eps()) { - MINUITPlugin.logStatic("VariableMetricBuilder: no improvement") - break //no improvement - } - val p = MinimumParameters(MnUtils.add(s0.vec(), MnUtils.mul(step, pp.x())), pp.y()) - val g: FunctionGradient = gc.gradient(p, s0.gradient()) - edm = estimator().estimate(g, s0.error()) - if (edm < 0.0) { - MINUITPlugin.logStatic("VariableMetricBuilder: matrix not pos.def.") - MINUITPlugin.logStatic("edm < 0") - s0 = MnPosDef.test(s0, prec) - edm = estimator().estimate(g, s0.error()) - if (edm < 0.0) { - result.add(s0) - return FunctionMinimum(seed, result, fcn.errorDef()) - } - } - val e: MinimumError = errorUpdator().update(s0, p, g) - result.add(MinimumState(p, e, g, edm, fcn.numOfCalls())) - // result[0] = MinimumState(p, e, g, edm, fcn.numOfCalls()); - edm *= 1.0 + 3.0 * e.dcovar() - } while (edm > edmval && fcn.numOfCalls() < maxfcn) - if (fcn.numOfCalls() >= maxfcn) { - MINUITPlugin.logStatic("VariableMetricBuilder: call limit exceeded.") - return FunctionMinimum(seed, result, fcn.errorDef(), MnReachedCallLimit()) - } - return if (edm > edmval) { - if (edm < abs(prec.eps2() * result[result.size - 1].fval())) { - MINUITPlugin.logStatic("VariableMetricBuilder: machine accuracy limits further improvement.") - FunctionMinimum(seed, result, fcn.errorDef()) - } else if (edm < 10.0 * edmval) { - FunctionMinimum(seed, result, fcn.errorDef()) - } else { - MINUITPlugin.logStatic("VariableMetricBuilder: finishes without convergence.") - MINUITPlugin.logStatic("VariableMetricBuilder: edm= $edm requested: $edmval") - FunctionMinimum(seed, result, fcn.errorDef(), MnAboveMaxEdm()) - } - } else FunctionMinimum(seed, result, fcn.errorDef()) - } - - init { - theErrorUpdator = DavidonErrorUpdator() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/VariableMetricEDMEstimator.kt b/kmath-optimization/src/commonMain/tmp/minuit/VariableMetricEDMEstimator.kt deleted file mode 100644 index 8fca4e6ee..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/VariableMetricEDMEstimator.kt +++ /dev/null @@ -1,31 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @author tonyj - * @version $Id$ - */ -internal class VariableMetricEDMEstimator { - fun estimate(g: FunctionGradient, e: MinimumError): Double { - if (e.invHessian().size() === 1) { - return 0.5 * g.getGradient().getEntry(0) * g.getGradient().getEntry(0) * e.invHessian()[0, 0] - } - val rho: Double = MnUtils.similarity(g.getGradient(), e.invHessian()) - return 0.5 * rho - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/VariableMetricMinimizer.kt b/kmath-optimization/src/commonMain/tmp/minuit/VariableMetricMinimizer.kt deleted file mode 100644 index 2a13a5fff..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/VariableMetricMinimizer.kt +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - -/** - * - * @version $Id$ - */ -internal class VariableMetricMinimizer : ModularFunctionMinimizer() { - private val theMinBuilder: VariableMetricBuilder - private val theMinSeedGen: MnSeedGenerator = MnSeedGenerator() - - /** {@inheritDoc} */ - override fun builder(): MinimumBuilder { - return theMinBuilder - } - - /** {@inheritDoc} */ - override fun seedGenerator(): MinimumSeedGenerator { - return theMinSeedGen - } - - /** - * - * Constructor for VariableMetricMinimizer. - */ - init { - theMinBuilder = VariableMetricBuilder() - } -} \ No newline at end of file diff --git a/kmath-optimization/src/commonMain/tmp/minuit/package-info.kt b/kmath-optimization/src/commonMain/tmp/minuit/package-info.kt deleted file mode 100644 index 22779da86..000000000 --- a/kmath-optimization/src/commonMain/tmp/minuit/package-info.kt +++ /dev/null @@ -1,17 +0,0 @@ -/* - * Copyright 2015 Alexander Nozik. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package ru.inr.mass.minuit - diff --git a/kmath-stat/README.md b/kmath-stat/README.md deleted file mode 100644 index 762e61237..000000000 --- a/kmath-stat/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-stat - - - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-stat:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-stat:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-stat:0.3.0-dev-20") -} -``` diff --git a/kmath-stat/build.gradle.kts b/kmath-stat/build.gradle.kts deleted file mode 100644 index 41a1666f8..000000000 --- a/kmath-stat/build.gradle.kts +++ /dev/null @@ -1,27 +0,0 @@ -plugins { - id("ru.mipt.npm.gradle.mpp") - id("ru.mipt.npm.gradle.native") -} - -kscience { - useAtomic() -} - -kotlin.sourceSets { - commonMain { - dependencies { - api(project(":kmath-coroutines")) - } - } - - jvmMain { - dependencies { - api("org.apache.commons:commons-rng-sampling:1.3") - api("org.apache.commons:commons-rng-simple:1.3") - } - } -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL -} \ No newline at end of file diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/Distribution.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/Distribution.kt deleted file mode 100644 index 3d3f95f8f..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/Distribution.kt +++ /dev/null @@ -1,43 +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. - */ - -package space.kscience.kmath.distributions - -import space.kscience.kmath.chains.Chain -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.stat.Sampler - -/** - * A distribution of typed objects. - */ -public interface Distribution : Sampler { - /** - * A probability value for given argument [arg]. - * For continuous distributions returns PDF - */ - public fun probability(arg: T): Double - - override fun sample(generator: RandomGenerator): Chain - - /** - * An empty companion. Distribution factories should be written as its extensions. - */ - public companion object -} - -public interface UnivariateDistribution> : Distribution { - /** - * Cumulative distribution for ordered parameter (CDF) - */ - public fun cumulative(arg: T): Double -} - -/** - * Compute probability integral in an interval - */ -public fun > UnivariateDistribution.integral(from: T, to: T): Double { - require(to > from) - return cumulative(to) - cumulative(from) -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/FactorizedDistribution.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/FactorizedDistribution.kt deleted file mode 100644 index 1218f13c5..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/FactorizedDistribution.kt +++ /dev/null @@ -1,49 +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. - */ - -package space.kscience.kmath.distributions - -import space.kscience.kmath.chains.Chain -import space.kscience.kmath.chains.SimpleChain -import space.kscience.kmath.stat.RandomGenerator - -/** - * A multivariate distribution that takes a map of parameters. - */ -public interface NamedDistribution : Distribution> - -/** - * A multivariate distribution that has independent distributions for separate axis. - */ -public class FactorizedDistribution(public val distributions: Collection>) : - NamedDistribution { - override fun probability(arg: Map): Double = - distributions.fold(1.0) { acc, dist -> acc * dist.probability(arg) } - - override fun sample(generator: RandomGenerator): Chain> { - val chains = distributions.map { it.sample(generator) } - return SimpleChain { chains.fold(emptyMap()) { acc, chain -> acc + chain.next() } } - } -} - -public class NamedDistributionWrapper(public val name: String, public val distribution: Distribution) : - NamedDistribution { - override fun probability(arg: Map): Double = distribution.probability( - arg[name] ?: error("Argument with name $name not found in input parameters") - ) - - override fun sample(generator: RandomGenerator): Chain> { - val chain = distribution.sample(generator) - return SimpleChain { mapOf(name to chain.next()) } - } -} - -public class DistributionBuilder { - private val distributions = ArrayList>() - - public infix fun String.to(distribution: Distribution) { - distributions += NamedDistributionWrapper(this, distribution) - } -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/NormalDistribution.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/NormalDistribution.kt deleted file mode 100644 index 66e041f05..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/NormalDistribution.kt +++ /dev/null @@ -1,45 +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. - */ - -package space.kscience.kmath.distributions - -import space.kscience.kmath.chains.Chain -import space.kscience.kmath.internal.InternalErf -import space.kscience.kmath.samplers.GaussianSampler -import space.kscience.kmath.samplers.NormalizedGaussianSampler -import space.kscience.kmath.samplers.ZigguratNormalizedGaussianSampler -import space.kscience.kmath.stat.RandomGenerator -import kotlin.math.* - -/** - * Implements [UnivariateDistribution] for the normal (gaussian) distribution. - */ -public class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution { - public constructor( - mean: Double, - standardDeviation: Double, - normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler, - ) : this(GaussianSampler(mean, standardDeviation, normalized)) - - override fun probability(arg: Double): Double { - val x1 = (arg - sampler.mean) / sampler.standardDeviation - return exp(-0.5 * x1 * x1 - (ln(sampler.standardDeviation) + 0.5 * ln(2 * PI))) - } - - override fun sample(generator: RandomGenerator): Chain = sampler.sample(generator) - - override fun cumulative(arg: Double): Double { - val dev = arg - sampler.mean - - return when { - abs(dev) > 40 * sampler.standardDeviation -> if (dev < 0) 0.0 else 1.0 - else -> 0.5 * InternalErf.erfc(-dev / (sampler.standardDeviation * SQRT2)) - } - } - - private companion object { - private val SQRT2 = sqrt(2.0) - } -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/internal/InternalErf.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/internal/InternalErf.kt deleted file mode 100644 index 25668446c..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/internal/InternalErf.kt +++ /dev/null @@ -1,20 +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. - */ - -package space.kscience.kmath.internal - -import kotlin.math.abs - -/** - * Based on Commons Math implementation. - * See [https://commons.apache.org/proper/commons-math/javadocs/api-3.3/org/apache/commons/math3/special/Erf.html]. - */ -internal object InternalErf { - fun erfc(x: Double): Double { - if (abs(x) > 40) return if (x > 0) 0.0 else 2.0 - val ret = InternalGamma.regularizedGammaQ(0.5, x * x, 10000) - return if (x < 0) 2 - ret else ret - } -} \ No newline at end of file diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/internal/InternalGamma.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/internal/InternalGamma.kt deleted file mode 100644 index 63db1c56f..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/internal/InternalGamma.kt +++ /dev/null @@ -1,243 +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. - */ - -package space.kscience.kmath.internal - -import kotlin.math.* - -private abstract class ContinuedFraction protected constructor() { - protected abstract fun getA(n: Int, x: Double): Double - protected abstract fun getB(n: Int, x: Double): Double - - fun evaluate(x: Double, maxIterations: Int): Double { - val small = 1e-50 - var hPrev = getA(0, x) - if (hPrev == 0.0 || abs(0.0 - hPrev) <= small) hPrev = small - var n = 1 - var dPrev = 0.0 - var cPrev = hPrev - var hN = hPrev - - while (n < maxIterations) { - val a = getA(n, x) - val b = getB(n, x) - var dN = a + b * dPrev - if (dN == 0.0 || abs(0.0 - dN) <= small) dN = small - var cN = a + b / cPrev - if (cN == 0.0 || abs(0.0 - cN) <= small) cN = small - dN = 1 / dN - val deltaN = cN * dN - hN = hPrev * deltaN - check(!hN.isInfinite()) { "hN is infinite" } - check(!hN.isNaN()) { "hN is NaN" } - if (abs(deltaN - 1.0) < 10e-9) break - dPrev = dN - cPrev = cN - hPrev = hN - n++ - } - - check(n < maxIterations) { "n is more than maxIterations" } - return hN - } -} - -internal object InternalGamma { - const val LANCZOS_G = 607.0 / 128.0 - - private val LANCZOS = doubleArrayOf( - 0.99999999999999709182, - 57.156235665862923517, - -59.597960355475491248, - 14.136097974741747174, - -0.49191381609762019978, - .33994649984811888699e-4, - .46523628927048575665e-4, - -.98374475304879564677e-4, - .15808870322491248884e-3, - -.21026444172410488319e-3, - .21743961811521264320e-3, - -.16431810653676389022e-3, - .84418223983852743293e-4, - -.26190838401581408670e-4, - .36899182659531622704e-5 - ) - - private val HALF_LOG_2_PI = 0.5 * ln(2.0 * PI) - private const val INV_GAMMA1P_M1_A0 = .611609510448141581788E-08 - private const val INV_GAMMA1P_M1_A1 = .624730830116465516210E-08 - private const val INV_GAMMA1P_M1_B1 = .203610414066806987300E+00 - private const val INV_GAMMA1P_M1_B2 = .266205348428949217746E-01 - private const val INV_GAMMA1P_M1_B3 = .493944979382446875238E-03 - private const val INV_GAMMA1P_M1_B4 = -.851419432440314906588E-05 - private const val INV_GAMMA1P_M1_B5 = -.643045481779353022248E-05 - private const val INV_GAMMA1P_M1_B6 = .992641840672773722196E-06 - private const val INV_GAMMA1P_M1_B7 = -.607761895722825260739E-07 - private const val INV_GAMMA1P_M1_B8 = .195755836614639731882E-09 - private const val INV_GAMMA1P_M1_P0 = .6116095104481415817861E-08 - private const val INV_GAMMA1P_M1_P1 = .6871674113067198736152E-08 - private const val INV_GAMMA1P_M1_P2 = .6820161668496170657918E-09 - private const val INV_GAMMA1P_M1_P3 = .4686843322948848031080E-10 - private const val INV_GAMMA1P_M1_P4 = .1572833027710446286995E-11 - private const val INV_GAMMA1P_M1_P5 = -.1249441572276366213222E-12 - private const val INV_GAMMA1P_M1_P6 = .4343529937408594255178E-14 - private const val INV_GAMMA1P_M1_Q1 = .3056961078365221025009E+00 - private const val INV_GAMMA1P_M1_Q2 = .5464213086042296536016E-01 - private const val INV_GAMMA1P_M1_Q3 = .4956830093825887312020E-02 - private const val INV_GAMMA1P_M1_Q4 = .2692369466186361192876E-03 - private const val INV_GAMMA1P_M1_C = -.422784335098467139393487909917598E+00 - private const val INV_GAMMA1P_M1_C0 = .577215664901532860606512090082402E+00 - private const val INV_GAMMA1P_M1_C1 = -.655878071520253881077019515145390E+00 - private const val INV_GAMMA1P_M1_C2 = -.420026350340952355290039348754298E-01 - private const val INV_GAMMA1P_M1_C3 = .166538611382291489501700795102105E+00 - private const val INV_GAMMA1P_M1_C4 = -.421977345555443367482083012891874E-01 - private const val INV_GAMMA1P_M1_C5 = -.962197152787697356211492167234820E-02 - private const val INV_GAMMA1P_M1_C6 = .721894324666309954239501034044657E-02 - private const val INV_GAMMA1P_M1_C7 = -.116516759185906511211397108401839E-02 - private const val INV_GAMMA1P_M1_C8 = -.215241674114950972815729963053648E-03 - private const val INV_GAMMA1P_M1_C9 = .128050282388116186153198626328164E-03 - private const val INV_GAMMA1P_M1_C10 = -.201348547807882386556893914210218E-04 - private const val INV_GAMMA1P_M1_C11 = -.125049348214267065734535947383309E-05 - private const val INV_GAMMA1P_M1_C12 = .113302723198169588237412962033074E-05 - private const val INV_GAMMA1P_M1_C13 = -.205633841697760710345015413002057E-06 - - fun logGamma(x: Double): Double = when { - x.isNaN() || x <= 0.0 -> Double.NaN - x < 0.5 -> logGamma1p(x) - ln(x) - x <= 2.5 -> logGamma1p(x - 0.5 - 0.5) - - x <= 8.0 -> { - val n = floor(x - 1.5).toInt() - val prod = (1..n).fold(1.0) { prod, i -> prod * (x - i) } - logGamma1p(x - (n + 1)) + ln(prod) - } - - else -> { - val tmp = x + LANCZOS_G + .5 - (x + .5) * ln(tmp) - tmp + HALF_LOG_2_PI + ln(lanczos(x) / x) - } - } - - private fun regularizedGammaP( - a: Double, - x: Double, - maxIterations: Int = Int.MAX_VALUE - ): Double = when { - a.isNaN() || x.isNaN() || a <= 0.0 || x < 0.0 -> Double.NaN - x == 0.0 -> 0.0 - x >= a + 1 -> 1.0 - regularizedGammaQ(a, x, maxIterations) - - else -> { - // calculate series - var n = 0.0 // current element index - var an = 1.0 / a // n-th element in the series - var sum = an // partial sum - - while (abs(an / sum) > 10e-15 && n < maxIterations && sum < Double.POSITIVE_INFINITY) { - // compute next element in the series - n += 1.0 - an *= x / (a + n) - - // update partial sum - sum += an - } - - when { - n >= maxIterations -> error("Maximal iterations is exceeded $maxIterations") - sum.isInfinite() -> 1.0 - else -> exp(-x + a * ln(x) - logGamma(a)) * sum - } - } - } - - fun regularizedGammaQ( - a: Double, - x: Double, - maxIterations: Int = Int.MAX_VALUE - ): Double = when { - a.isNaN() || x.isNaN() || a <= 0.0 || x < 0.0 -> Double.NaN - x == 0.0 -> 1.0 - x < a + 1.0 -> 1.0 - regularizedGammaP(a, x, maxIterations) - - else -> 1.0 / object : ContinuedFraction() { - override fun getA(n: Int, x: Double): Double = 2.0 * n + 1.0 - a + x - override fun getB(n: Int, x: Double): Double = n * (a - n) - }.evaluate(x, maxIterations) * exp(-x + a * ln(x) - logGamma(a)) - } - - private fun lanczos(x: Double): Double = - (LANCZOS.size - 1 downTo 1).sumOf { LANCZOS[it] / (x + it) } + LANCZOS[0] - - private fun invGamma1pm1(x: Double): Double { - require(x >= -0.5) - require(x <= 1.5) - val ret: Double - val t = if (x <= 0.5) x else x - 0.5 - 0.5 - - if (t < 0.0) { - val a = INV_GAMMA1P_M1_A0 + t * INV_GAMMA1P_M1_A1 - var b = INV_GAMMA1P_M1_B8 - b = INV_GAMMA1P_M1_B7 + t * b - b = INV_GAMMA1P_M1_B6 + t * b - b = INV_GAMMA1P_M1_B5 + t * b - b = INV_GAMMA1P_M1_B4 + t * b - b = INV_GAMMA1P_M1_B3 + t * b - b = INV_GAMMA1P_M1_B2 + t * b - b = INV_GAMMA1P_M1_B1 + t * b - b = 1.0 + t * b - var c = INV_GAMMA1P_M1_C13 + t * (a / b) - c = INV_GAMMA1P_M1_C12 + t * c - c = INV_GAMMA1P_M1_C11 + t * c - c = INV_GAMMA1P_M1_C10 + t * c - c = INV_GAMMA1P_M1_C9 + t * c - c = INV_GAMMA1P_M1_C8 + t * c - c = INV_GAMMA1P_M1_C7 + t * c - c = INV_GAMMA1P_M1_C6 + t * c - c = INV_GAMMA1P_M1_C5 + t * c - c = INV_GAMMA1P_M1_C4 + t * c - c = INV_GAMMA1P_M1_C3 + t * c - c = INV_GAMMA1P_M1_C2 + t * c - c = INV_GAMMA1P_M1_C1 + t * c - c = INV_GAMMA1P_M1_C + t * c - ret = (if (x > 0.5) t * c / x else x * (c + 0.5 + 0.5)) - } else { - var p = INV_GAMMA1P_M1_P6 - p = INV_GAMMA1P_M1_P5 + t * p - p = INV_GAMMA1P_M1_P4 + t * p - p = INV_GAMMA1P_M1_P3 + t * p - p = INV_GAMMA1P_M1_P2 + t * p - p = INV_GAMMA1P_M1_P1 + t * p - p = INV_GAMMA1P_M1_P0 + t * p - var q = INV_GAMMA1P_M1_Q4 - q = INV_GAMMA1P_M1_Q3 + t * q - q = INV_GAMMA1P_M1_Q2 + t * q - q = INV_GAMMA1P_M1_Q1 + t * q - q = 1.0 + t * q - var c = INV_GAMMA1P_M1_C13 + p / q * t - c = INV_GAMMA1P_M1_C12 + t * c - c = INV_GAMMA1P_M1_C11 + t * c - c = INV_GAMMA1P_M1_C10 + t * c - c = INV_GAMMA1P_M1_C9 + t * c - c = INV_GAMMA1P_M1_C8 + t * c - c = INV_GAMMA1P_M1_C7 + t * c - c = INV_GAMMA1P_M1_C6 + t * c - c = INV_GAMMA1P_M1_C5 + t * c - c = INV_GAMMA1P_M1_C4 + t * c - c = INV_GAMMA1P_M1_C3 + t * c - c = INV_GAMMA1P_M1_C2 + t * c - c = INV_GAMMA1P_M1_C1 + t * c - c = INV_GAMMA1P_M1_C0 + t * c - ret = (if (x > 0.5) t / x * (c - 0.5 - 0.5) else x * c) - } - - return ret - } - - private fun logGamma1p(x: Double): Double { - require(x >= -0.5) - require(x <= 1.5) - return -ln1p(invGamma1pm1(x)) - } -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/internal/InternalUtils.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/internal/InternalUtils.kt deleted file mode 100644 index 3997a77b3..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/internal/InternalUtils.kt +++ /dev/null @@ -1,75 +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. - */ - -package space.kscience.kmath.internal - -import kotlin.math.ln -import kotlin.math.min - -internal object InternalUtils { - private val FACTORIALS = longArrayOf( - 1L, 1L, 2L, - 6L, 24L, 120L, - 720L, 5040L, 40320L, - 362880L, 3628800L, 39916800L, - 479001600L, 6227020800L, 87178291200L, - 1307674368000L, 20922789888000L, 355687428096000L, - 6402373705728000L, 121645100408832000L, 2432902008176640000L - ) - - private const val BEGIN_LOG_FACTORIALS = 2 - - fun factorial(n: Int): Long = FACTORIALS[n] - - fun validateProbabilities(probabilities: DoubleArray?): Double { - require(!(probabilities == null || probabilities.isEmpty())) { "Probabilities must not be empty." } - - val sumProb = probabilities.sumOf { prob -> - require(!(prob < 0 || prob.isInfinite() || prob.isNaN())) { "Invalid probability: $prob" } - prob - } - - require(!(sumProb.isInfinite() || sumProb <= 0)) { "Invalid sum of probabilities: $sumProb" } - return sumProb - } - - class FactorialLog private constructor(numValues: Int, cache: DoubleArray?) { - private val logFactorials: DoubleArray = DoubleArray(numValues) - - init { - val endCopy: Int - - if (cache != null && cache.size > BEGIN_LOG_FACTORIALS) { - // Copy available values. - endCopy = min(cache.size, numValues) - - cache.copyInto( - logFactorials, - BEGIN_LOG_FACTORIALS, - BEGIN_LOG_FACTORIALS, endCopy - ) - } else - // All values to be computed - endCopy = BEGIN_LOG_FACTORIALS - - // Compute remaining values. - (endCopy until numValues).forEach { i -> - if (i < FACTORIALS.size) - logFactorials[i] = ln(FACTORIALS[i].toDouble()) - else - logFactorials[i] = logFactorials[i - 1] + ln(i.toDouble()) - } - } - - fun value(n: Int): Double { - if (n < logFactorials.size) return logFactorials[n] - return if (n < FACTORIALS.size) ln(FACTORIALS[n].toDouble()) else InternalGamma.logGamma(n + 1.0) - } - - companion object { - fun create(): FactorialLog = FactorialLog(0, null) - } - } -} \ No newline at end of file diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/AhrensDieterExponentialSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/AhrensDieterExponentialSampler.kt deleted file mode 100644 index 77d29981f..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/AhrensDieterExponentialSampler.kt +++ /dev/null @@ -1,77 +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. - */ - -package space.kscience.kmath.samplers - -import space.kscience.kmath.chains.BlockingDoubleChain -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.stat.Sampler -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.math.ln -import kotlin.math.pow - -/** - * Sampling from an [exponential distribution](http://mathworld.wolfram.com/ExponentialDistribution.html). - * - * Based on Commons RNG implementation. - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.html]. - */ -public class AhrensDieterExponentialSampler(public val mean: Double) : Sampler { - - init { - require(mean > 0) { "mean is not strictly positive: $mean" } - } - - override fun sample(generator: RandomGenerator): BlockingDoubleChain = object : BlockingDoubleChain { - override fun nextBlocking(): Double { - // Step 1: - var a = 0.0 - var u = generator.nextDouble() - - // Step 2 and 3: - while (u < 0.5) { - a += EXPONENTIAL_SA_QI[0] - u *= 2.0 - } - - // Step 4 (now u >= 0.5): - u += u - 1 - // Step 5: - if (u <= EXPONENTIAL_SA_QI[0]) return mean * (a + u) - // Step 6: - var i = 0 // Should be 1, be we iterate before it in while using 0. - var u2 = generator.nextDouble() - var umin = u2 - - // Step 7 and 8: - do { - ++i - u2 = generator.nextDouble() - if (u2 < umin) umin = u2 - // Step 8: - } while (u > EXPONENTIAL_SA_QI[i]) // Ensured to exit since EXPONENTIAL_SA_QI[MAX] = 1. - - return mean * (a + umin * EXPONENTIAL_SA_QI[0]) - } - - override fun nextBufferBlocking(size: Int): DoubleBuffer = DoubleBuffer(size) { nextBlocking() } - - override suspend fun fork(): BlockingDoubleChain = sample(generator.fork()) - } - - public companion object { - private val EXPONENTIAL_SA_QI by lazy { - val ln2 = ln(2.0) - var qi = 0.0 - - DoubleArray(16) { i -> - qi += ln2.pow(i + 1.0) / space.kscience.kmath.internal.InternalUtils.factorial(i + 1) - qi - } - } - } - -} - diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/AhrensDieterMarsagliaTsangGammaSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/AhrensDieterMarsagliaTsangGammaSampler.kt deleted file mode 100644 index 993215d41..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/AhrensDieterMarsagliaTsangGammaSampler.kt +++ /dev/null @@ -1,125 +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. - */ - -package space.kscience.kmath.samplers - -import space.kscience.kmath.chains.Chain -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.stat.Sampler -import space.kscience.kmath.stat.chain -import space.kscience.kmath.stat.next -import kotlin.math.* - -/** - * Sampling from the [gamma distribution](http://mathworld.wolfram.com/GammaDistribution.html). - * * For 0 < alpha < 1: - * Ahrens, J. H. and Dieter, U., Computer methods for sampling from gamma, beta, Poisson and binomial distributions, Computing, 12, 223-246, 1974. - * * For alpha >= 1: - * Marsaglia and Tsang, A Simple Method for Generating Gamma Variables. ACM Transactions on Mathematical Software, Volume 26 Issue 3, September, 2000. - * - * Based on Commons RNG implementation. - * - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.html]. - */ -public class AhrensDieterMarsagliaTsangGammaSampler private constructor( - alpha: Double, - theta: Double -) : Sampler { - private val delegate: BaseGammaSampler = - if (alpha < 1) AhrensDieterGammaSampler(alpha, theta) else MarsagliaTsangGammaSampler(alpha, theta) - - private abstract class BaseGammaSampler internal constructor( - protected val alpha: Double, - protected val theta: Double - ) : Sampler { - init { - require(alpha > 0) { "alpha is not strictly positive: $alpha" } - require(theta > 0) { "theta is not strictly positive: $theta" } - } - - override fun toString(): String = "Ahrens-Dieter-Marsaglia-Tsang Gamma deviate" - } - - private class AhrensDieterGammaSampler(alpha: Double, theta: Double) : - BaseGammaSampler(alpha, theta) { - private val oneOverAlpha: Double = 1.0 / alpha - private val bGSOptim: Double = 1.0 + alpha / E - - override fun sample(generator: RandomGenerator): Chain = generator.chain { - var x: Double - - // [1]: p. 228, Algorithm GS. - while (true) { - // Step 1: - val u = generator.nextDouble() - val p = bGSOptim * u - - if (p <= 1) { - // Step 2: - x = p.pow(oneOverAlpha) - val u2 = generator.nextDouble() - - if (u2 > exp(-x)) // Reject. - continue - - break - } - - // Step 3: - x = -ln((bGSOptim - p) * oneOverAlpha) - val u2: Double = generator.nextDouble() - if (u2 <= x.pow(alpha - 1.0)) break - // Reject and continue. - } - - x * theta - } - } - - private class MarsagliaTsangGammaSampler(alpha: Double, theta: Double) : - BaseGammaSampler(alpha, theta) { - private val dOptim: Double - private val cOptim: Double - private val gaussian: NormalizedGaussianSampler - - init { - gaussian = ZigguratNormalizedGaussianSampler - dOptim = alpha - ONE_THIRD - cOptim = ONE_THIRD / sqrt(dOptim) - } - - override fun sample(generator: RandomGenerator): Chain = generator.chain { - var v: Double - - while (true) { - val x = gaussian.next(generator) - val oPcTx = 1 + cOptim * x - v = oPcTx * oPcTx * oPcTx - if (v <= 0) continue - val x2 = x * x - val u = generator.nextDouble() - // Squeeze. - if (u < 1 - 0.0331 * x2 * x2) break - if (ln(u) < 0.5 * x2 + dOptim * (1 - v + ln(v))) break - } - - theta * dOptim * v - } - - companion object { - private const val ONE_THIRD = 1.0 / 3.0 - } - } - - override fun sample(generator: RandomGenerator): Chain = delegate.sample(generator) - override fun toString(): String = delegate.toString() - - public companion object { - public fun of( - alpha: Double, - theta: Double - ): Sampler = AhrensDieterMarsagliaTsangGammaSampler(alpha, theta) - } -} \ No newline at end of file diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/AliasMethodDiscreteSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/AliasMethodDiscreteSampler.kt deleted file mode 100644 index 5390a2e09..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/AliasMethodDiscreteSampler.kt +++ /dev/null @@ -1,293 +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. - */ - -package space.kscience.kmath.samplers - -import space.kscience.kmath.chains.Chain -import space.kscience.kmath.internal.InternalUtils -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.stat.Sampler -import space.kscience.kmath.stat.chain -import kotlin.math.ceil -import kotlin.math.max -import kotlin.math.min - -/** - * Distribution sampler that uses the Alias method. It can be used to sample from n values each with an associated - * probability. This implementation is based on the detailed explanation of the alias method by Keith Schartz and - * implements Vose's algorithm. - * - * Vose, M.D., A linear algorithm for generating random numbers with a given distribution, IEEE Transactions on - * Software Engineering, 17, 972-975, 1991. The algorithm will sample values in O(1) time after a pre-processing step - * of O(n) time. - * - * The alias tables are constructed using fraction probabilities with an assumed denominator of 253. In the generic - * case sampling uses UniformRandomProvider.nextInt(int) and the upper 53-bits from UniformRandomProvider.nextLong(). - * - * Zero padding the input probabilities can be used to make more sampling more efficient. Any zero entry will always be - * aliased removing the requirement to compute a long. Increased sampling speed comes at the cost of increased storage - * space. The algorithm requires approximately 12 bytes of storage per input probability, that is n * 12 for size n. - * Zero-padding only requires 4 bytes of storage per padded value as the probability is known to be zero. - * - * An optimisation is performed for small table sizes that are a power of 2. In this case the sampling uses 1 or 2 - * calls from UniformRandomProvider.nextInt() to generate up to 64-bits for creation of an 11-bit index and 53-bits - * for the long. This optimisation requires a generator with a high cycle length for the lower order bits. - * - * Larger table sizes that are a power of 2 will benefit from fast algorithms for UniformRandomProvider.nextInt(int) - * that exploit the power of 2. - * - * Based on Commons RNG implementation. - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.html]. - */ -public open class AliasMethodDiscreteSampler private constructor( - // Deliberate direct storage of input arrays - protected val probability: LongArray, - protected val alias: IntArray, -) : Sampler { - - private class SmallTableAliasMethodDiscreteSampler( - probability: LongArray, - alias: IntArray, - ) : AliasMethodDiscreteSampler(probability, alias) { - // Assume the table size is a power of 2 and create the mask - private val mask: Int = alias.size - 1 - - override fun sample(generator: RandomGenerator): Chain = generator.chain { - val bits = generator.nextInt() - // Isolate lower bits - val j = bits and mask - - // Optimisation for zero-padded input tables - if (j >= probability.size) - // No probability must use the alias - return@chain alias[j] - - // Create a uniform random deviate as a long. - // This replicates functionality from the o.a.c.rng.core.utils.NumberFactory.makeLong - val longBits = generator.nextInt().toLong() shl 32 or (bits.toLong() and hex_ffffffff) - // Choose between the two. Use a 53-bit long for the probability. - if (longBits ushr 11 < probability[j]) j else alias[j] - } - - private companion object { - private const val hex_ffffffff = 4294967295L - } - } - - override fun sample(generator: RandomGenerator): Chain = generator.chain { - // This implements the algorithm in accordance with Vose (1991): - // v = uniform() in [0, 1) - // j = uniform(n) in [0, n) - // if v < prob[j] then - // return j - // else - // return alias[j] - val j = generator.nextInt(alias.size) - - // Optimisation for zero-padded input tables - // No probability must use the alias - if (j >= probability.size) return@chain alias[j] - - // Note: We could check the probability before computing a deviate. - // p(j) == 0 => alias[j] - // p(j) == 1 => j - // However it is assumed these edge cases are rare: - // - // The probability table will be 1 for approximately 1/n samples i.e., only the - // last unpaired probability. This is only worth checking for when the table size (n) - // is small. But in that case the user should zero-pad the table for performance. - // - // The probability table will be 0 when an input probability was zero. We - // will assume this is also rare if modelling a discrete distribution where - // all samples are possible. The edge case for zero-padded tables is handled above. - - // Choose between the two. Use a 53-bit long for the probability. - if (generator.nextLong() ushr 11 < probability[j]) j else alias[j] - } - - override fun toString(): String = "Alias method" - - public companion object { - private const val DEFAULT_ALPHA = 0 - private const val ZERO = 0.0 - private const val ONE_AS_NUMERATOR = 1L shl 53 - private const val CONVERT_TO_NUMERATOR: Double = ONE_AS_NUMERATOR.toDouble() - private const val MAX_SMALL_POWER_2_SIZE = 1 shl 11 - - private fun fillRemainingIndices(length: Int, indices: IntArray, small: Int): Int { - var updatedSmall = small - (length until indices.size).forEach { i -> indices[updatedSmall++] = i } - return updatedSmall - } - - private fun findLastNonZeroIndex(probabilities: DoubleArray): Int { - // No bounds check is performed when decrementing as the array contains at least one - // value above zero. - var nonZeroIndex = probabilities.size - 1 - while (probabilities[nonZeroIndex] == ZERO) nonZeroIndex-- - return nonZeroIndex - } - - private fun computeSize(length: Int, alpha: Int): Int { - // If No padding - if (alpha < 0) return length - // Use the number of leading zeros function to find the next power of 2, - // i.e. ceil(log2(x)) - var pow2 = 32 - numberOfLeadingZeros(length - 1) - // Increase by the alpha. Clip this to limit to a positive integer (2^30) - pow2 = min(30, pow2 + alpha) - // Use max to handle a length above the highest possible power of 2 - return max(length, 1 shl pow2) - } - - private fun fillTable( - probability: LongArray, - alias: IntArray, - indices: IntArray, - start: Int, - end: Int, - ) = (start until end).forEach { i -> - val index = indices[i] - probability[index] = ONE_AS_NUMERATOR - alias[index] = index - } - - private fun isSmallPowerOf2(n: Int): Boolean = n <= MAX_SMALL_POWER_2_SIZE && n and n - 1 == 0 - - private fun numberOfLeadingZeros(i: Int): Int { - var mutI = i - if (mutI <= 0) return if (mutI == 0) 32 else 0 - var n = 31 - - if (mutI >= 1 shl 16) { - n -= 16 - mutI = mutI ushr 16 - } - - if (mutI >= 1 shl 8) { - n -= 8 - mutI = mutI ushr 8 - } - - if (mutI >= 1 shl 4) { - n -= 4 - mutI = mutI ushr 4 - } - - if (mutI >= 1 shl 2) { - n -= 2 - mutI = mutI ushr 2 - } - - return n - (mutI ushr 1) - } - } - - @Suppress("FunctionName") - public fun AliasMethodDiscreteSampler( - probabilities: DoubleArray, - alpha: Int = DEFAULT_ALPHA, - ): Sampler { - // The Alias method balances N categories with counts around the mean into N sections, - // each allocated 'mean' observations. - // - // Consider 4 categories with counts 6,3,2,1. The histogram can be balanced into a - // 2D array as 4 sections with a height of the mean: - // - // 6 - // 6 - // 6 - // 63 => 6366 -- - // 632 6326 |-- mean - // 6321 6321 -- - // - // section abcd - // - // Each section is divided as: - // a: 6=1/1 - // b: 3=1/1 - // c: 2=2/3; 6=1/3 (6 is the alias) - // d: 1=1/3; 6=2/3 (6 is the alias) - // - // The sample is obtained by randomly selecting a section, then choosing, which category - // from the pair based on a uniform random deviate. - val sumProb = InternalUtils.validateProbabilities(probabilities) - // Allow zero-padding - val n = computeSize(probabilities.size, alpha) - // Partition into small and large by splitting on the average. - val mean = sumProb / n - // The cardinality of smallSize + largeSize = n. - // So fill the same array from either end. - val indices = IntArray(n) - var large = n - var small = 0 - - probabilities.indices.forEach { i -> - if (probabilities[i] >= mean) indices[--large] = i else indices[small++] = i - } - - small = fillRemainingIndices(probabilities.size, indices, small) - // This may be smaller than the input length if the probabilities were already padded. - val nonZeroIndex = findLastNonZeroIndex(probabilities) - // The probabilities are modified so use a copy. - // Note: probabilities are required only up to last nonZeroIndex - val remainingProbabilities = probabilities.copyOf(nonZeroIndex + 1) - // Allocate the final tables. - // Probability table may be truncated (when zero padded). - // The alias table is full length. - val probability = LongArray(remainingProbabilities.size) - val alias = IntArray(n) - - // This loop uses each large in turn to fill the alias table for small probabilities that - // do not reach the requirement to fill an entire section alone (i.e., p < mean). - // Since the sum of the small should be less than the sum of the large it should use up - // all the small first. However, floating point round-off can result in - // misclassification of items as small or large. The Vose algorithm handles this using - // a while loop conditioned on the size of both sets and a subsequent loop to use - // unpaired items. - while (large != n && small != 0) { - // Index of the small and the large probabilities. - val j = indices[--small] - val k = indices[large++] - - // Optimisation for zero-padded input: - // p(j) = 0 above the last nonZeroIndex - if (j > nonZeroIndex) - // The entire amount for the section is taken from the alias. - remainingProbabilities[k] -= mean - else { - val pj = remainingProbabilities[j] - // Item j is a small probability that is below the mean. - // Compute the weight of the section for item j: pj / mean. - // This is scaled by 2^53 and the ceiling function used to round-up - // the probability to a numerator of a fraction in the range [1,2^53]. - // Ceiling ensures non-zero values. - probability[j] = ceil(CONVERT_TO_NUMERATOR * (pj / mean)).toLong() - // The remaining amount for the section is taken from the alias. - // Effectively: probabilities[k] -= (mean - pj) - remainingProbabilities[k] += pj - mean - } - - // If not j then the alias is k - alias[j] = k - - // Add the remaining probability from large to the appropriate list. - if (remainingProbabilities[k] >= mean) indices[--large] = k else indices[small++] = k - } - - // Final loop conditions to consume unpaired items. - // Note: The large set should never be non-empty but this can occur due to round-off - // error so consume from both. - fillTable(probability, alias, indices, 0, small) - fillTable(probability, alias, indices, large, n) - - // Change the algorithm for small power of 2 sized tables - return if (isSmallPowerOf2(n)) { - SmallTableAliasMethodDiscreteSampler(probability, alias) - } else { - AliasMethodDiscreteSampler(probability, alias) - } - } -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/BoxMullerSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/BoxMullerSampler.kt deleted file mode 100644 index b3c014553..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/BoxMullerSampler.kt +++ /dev/null @@ -1,57 +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. - */ - -package space.kscience.kmath.samplers - -import space.kscience.kmath.chains.BlockingDoubleChain -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.math.* - -/** - * [Box-Muller algorithm](https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform) for sampling from a Gaussian - * distribution. - * - * Based on Commons RNG implementation. - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.html]. - */ - -public object BoxMullerSampler : NormalizedGaussianSampler { - override fun sample(generator: RandomGenerator): BlockingDoubleChain = object : BlockingDoubleChain { - var state = Double.NaN - - override fun nextBufferBlocking(size: Int): DoubleBuffer { - val xs = generator.nextDoubleBuffer(size) - val ys = generator.nextDoubleBuffer(size) - - return DoubleBuffer(size) { index -> - if (state.isNaN()) { - // Generate a pair of Gaussian numbers. - val x = xs[index] - val y = ys[index] - val alpha = 2 * PI * x - val r = sqrt(-2 * ln(y)) - - // Keep second element of the pair for next invocation. - state = r * sin(alpha) - - // Return the first element of the generated pair. - r * cos(alpha) - } else { - // Use the second element of the pair (generated at the - // previous invocation). - state.also { - // Both elements of the pair have been used. - state = Double.NaN - } - } - } - } - - - override suspend fun fork(): BlockingDoubleChain = sample(generator.fork()) - } - -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/GaussianSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/GaussianSampler.kt deleted file mode 100644 index 9219df43e..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/GaussianSampler.kt +++ /dev/null @@ -1,38 +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. - */ - -package space.kscience.kmath.samplers - -import space.kscience.kmath.chains.BlockingDoubleChain -import space.kscience.kmath.chains.map -import space.kscience.kmath.stat.RandomGenerator - -/** - * Sampling from a Gaussian distribution with given mean and standard deviation. - * - * Based on Commons RNG implementation. - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/GaussianSampler.html]. - * - * @property mean the mean of the distribution. - * @property standardDeviation the variance of the distribution. - */ -public class GaussianSampler( - public val mean: Double, - public val standardDeviation: Double, - private val normalized: NormalizedGaussianSampler = BoxMullerSampler -) : BlockingDoubleSampler { - - init { - require(standardDeviation > 0.0) { "standard deviation is not strictly positive: $standardDeviation" } - } - - override fun sample(generator: RandomGenerator): BlockingDoubleChain = normalized - .sample(generator) - .map { standardDeviation * it + mean } - - override fun toString(): String = "N($mean, $standardDeviation)" - - public companion object -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/KempSmallMeanPoissonSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/KempSmallMeanPoissonSampler.kt deleted file mode 100644 index 0105731c4..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/KempSmallMeanPoissonSampler.kt +++ /dev/null @@ -1,72 +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. - */ - -package space.kscience.kmath.samplers - -import space.kscience.kmath.chains.BlockingIntChain -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.stat.Sampler -import space.kscience.kmath.structures.IntBuffer -import kotlin.math.exp - -/** - * Sampler for the Poisson distribution. - * * Kemp, A, W, (1981) Efficient Generation of Logarithmically Distributed Pseudo-Random Variables. Journal of the Royal Statistical Society. Vol. 30, No. 3, pp. 249-253. - * This sampler is suitable for mean < 40. For large means, LargeMeanPoissonSampler should be used instead. - * - * Note: The algorithm uses a recurrence relation to compute the Poisson probability and a rolling summation for the cumulative probability. When the mean is large the initial probability (Math.exp(-mean)) is zero and an exception is raised by the constructor. - * - * Sampling uses 1 call to UniformRandomProvider.nextDouble(). This method provides an alternative to the SmallMeanPoissonSampler for slow generators of double. - * - * Based on Commons RNG implementation. - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.html]. - */ -public class KempSmallMeanPoissonSampler internal constructor( - private val p0: Double, - private val mean: Double, -) : Sampler { - override fun sample(generator: RandomGenerator): BlockingIntChain = object : BlockingIntChain { - override fun nextBlocking(): Int { - //TODO move to nextBufferBlocking - // Note on the algorithm: - // - X is the unknown sample deviate (the output of the algorithm) - // - x is the current value from the distribution - // - p is the probability of the current value x, p(X=x) - // - u is effectively the cumulative probability that the sample X - // is equal or above the current value x, p(X>=x) - // So if p(X>=x) > p(X=x) the sample must be above x, otherwise it is x - var u = generator.nextDouble() - var x = 0 - var p = p0 - - while (u > p) { - u -= p - // Compute the next probability using a recurrence relation. - // p(x+1) = p(x) * mean / (x+1) - p *= mean / ++x - // The algorithm listed in Kemp (1981) does not check that the rolling probability - // is positive. This check is added to ensure no errors when the limit of the summation - // 1 - sum(p(x)) is above 0 due to cumulative error in floating point arithmetic. - if (p == 0.0) return x - } - - return x - } - - override fun nextBufferBlocking(size: Int): IntBuffer = IntBuffer(size) { nextBlocking() } - - override suspend fun fork(): BlockingIntChain = sample(generator.fork()) - } - - override fun toString(): String = "Kemp Small Mean Poisson deviate" -} - -public fun KempSmallMeanPoissonSampler(mean: Double): KempSmallMeanPoissonSampler { - require(mean > 0) { "Mean is not strictly positive: $mean" } - val p0 = exp(-mean) - // Probability must be positive. As mean increases, p(0) decreases. - require(p0 > 0) { "No probability for mean: $mean" } - return KempSmallMeanPoissonSampler(p0, mean) -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/MarsagliaNormalizedGaussianSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/MarsagliaNormalizedGaussianSampler.kt deleted file mode 100644 index 0a68e5c88..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/MarsagliaNormalizedGaussianSampler.kt +++ /dev/null @@ -1,66 +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. - */ - -package space.kscience.kmath.samplers - -import space.kscience.kmath.chains.BlockingDoubleChain -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.math.ln -import kotlin.math.sqrt - -/** - * [Marsaglia polar method](https://en.wikipedia.org/wiki/Marsaglia_polar_method) for sampling from a Gaussian - * distribution with mean 0 and standard deviation 1. This is a variation of the algorithm implemented in - * [BoxMullerNormalizedGaussianSampler]. - * - * Based on Commons RNG implementation. - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.html] - */ -public object MarsagliaNormalizedGaussianSampler : NormalizedGaussianSampler { - - override fun sample(generator: RandomGenerator): BlockingDoubleChain = object : BlockingDoubleChain { - var nextGaussian = Double.NaN - - override fun nextBlocking(): Double { - return if (nextGaussian.isNaN()) { - val alpha: Double - var x: Double - - // Rejection scheme for selecting a pair that lies within the unit circle. - while (true) { - // Generate a pair of numbers within [-1 , 1). - x = 2.0 * generator.nextDouble() - 1.0 - val y = 2.0 * generator.nextDouble() - 1.0 - val r2 = x * x + y * y - - if (r2 < 1 && r2 > 0) { - // Pair (x, y) is within unit circle. - alpha = sqrt(-2 * ln(r2) / r2) - // Keep second element of the pair for next invocation. - nextGaussian = alpha * y - // Return the first element of the generated pair. - break - } - // Pair is not within the unit circle: Generate another one. - } - - // Return the first element of the generated pair. - alpha * x - } else { - // Use the second element of the pair (generated at the - // previous invocation). - val r = nextGaussian - // Both elements of the pair have been used. - nextGaussian = Double.NaN - r - } - } - - override fun nextBufferBlocking(size: Int): DoubleBuffer = DoubleBuffer(size) { nextBlocking() } - - override suspend fun fork(): BlockingDoubleChain = sample(generator.fork()) - } -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/NormalizedGaussianSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/NormalizedGaussianSampler.kt deleted file mode 100644 index 83f87e832..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/NormalizedGaussianSampler.kt +++ /dev/null @@ -1,23 +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. - */ - -package space.kscience.kmath.samplers - -import space.kscience.kmath.chains.BlockingDoubleChain -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.stat.Sampler - -public interface BlockingDoubleSampler: Sampler{ - override fun sample(generator: RandomGenerator): BlockingDoubleChain -} - - -/** - * Marker interface for a sampler that generates values from an N(0,1) - * [Gaussian distribution](https://en.wikipedia.org/wiki/Normal_distribution). - */ -public fun interface NormalizedGaussianSampler : BlockingDoubleSampler{ - public companion object -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/PoissonSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/PoissonSampler.kt deleted file mode 100644 index f0f94900e..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/PoissonSampler.kt +++ /dev/null @@ -1,209 +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. - */ - -package space.kscience.kmath.samplers - -import space.kscience.kmath.chains.BlockingIntChain -import space.kscience.kmath.internal.InternalUtils -import space.kscience.kmath.misc.toIntExact -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.stat.Sampler -import space.kscience.kmath.structures.IntBuffer -import kotlin.math.* - - -private const val PIVOT = 40.0 - -/** - * Sampler for the Poisson distribution. - * * For small means, a Poisson process is simulated using uniform deviates, as described in - * Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Chapter 3.4.1.F.3 - * Important integer-valued distributions: The Poisson distribution. Addison Wesley. - * The Poisson process (and hence, the returned value) is bounded by 1000 * mean. - * * For large means, we use the rejection algorithm described in - * Devroye, Luc. (1981). The Computer Generation of Poisson Random Variables Computing vol. 26 pp. 197-207. - * - * Based on Commons RNG implementation. - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/PoissonSampler.html]. - */ -@Suppress("FunctionName") -public fun PoissonSampler(mean: Double): Sampler { - return if (mean < PIVOT) SmallMeanPoissonSampler(mean) else LargeMeanPoissonSampler(mean) -} - -/** - * Sampler for the Poisson distribution. - * * For small means, a Poisson process is simulated using uniform deviates, as described in - * Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Chapter 3.4.1.F.3 Important - * integer-valued distributions: The Poisson distribution. Addison Wesley. - * * The Poisson process (and hence, the returned value) is bounded by 1000 * mean. - * This sampler is suitable for mean < 40. For large means, [LargeMeanPoissonSampler] should be used instead. - * - * Based on Commons RNG implementation. - * - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.html]. - */ -public class SmallMeanPoissonSampler(public val mean: Double) : Sampler { - - init { - require(mean > 0) { "mean is not strictly positive: $mean" } - } - - private val p0: Double = exp(-mean) - - private val limit: Int = if (p0 > 0) { - ceil(1000 * mean) - } else { - throw IllegalArgumentException("No p(x=0) probability for mean: $mean") - }.toInt() - - override fun sample(generator: RandomGenerator): BlockingIntChain = object : BlockingIntChain { - override fun nextBlocking(): Int { - var n = 0 - var r = 1.0 - - while (n < limit) { - r *= generator.nextDouble() - if (r >= p0) n++ else break - } - - return n - } - - override fun nextBufferBlocking(size: Int): IntBuffer = IntBuffer(size) { nextBlocking() } - - override suspend fun fork(): BlockingIntChain = sample(generator.fork()) - } - - override fun toString(): String = "Small Mean Poisson deviate" -} - - -/** - * Sampler for the Poisson distribution. - * - For large means, we use the rejection algorithm described in - * Devroye, Luc. (1981).The Computer Generation of Poisson Random Variables - * Computing vol. 26 pp. 197-207. - * - * This sampler is suitable for mean >= 40. - * - * Based on Commons RNG implementation. - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.html]. - */ -public class LargeMeanPoissonSampler(public val mean: Double) : Sampler { - - init { - require(mean >= 1) { "mean is not >= 1: $mean" } - // The algorithm is not valid if Math.floor(mean) is not an integer. - require(mean <= MAX_MEAN) { "mean $mean > $MAX_MEAN" } - } - - private val factorialLog: InternalUtils.FactorialLog = NO_CACHE_FACTORIAL_LOG - private val lambda: Double = floor(mean) - private val logLambda: Double = ln(lambda) - private val logLambdaFactorial: Double = getFactorialLog(lambda.toInt()) - private val delta: Double = sqrt(lambda * ln(32 * lambda / PI + 1)) - private val halfDelta: Double = delta / 2 - private val twolpd: Double = 2 * lambda + delta - private val c1: Double = 1 / (8 * lambda) - private val a1: Double = sqrt(PI * twolpd) * exp(c1) - private val a2: Double = twolpd / delta * exp(-delta * (1 + delta) / twolpd) - private val aSum: Double = a1 + a2 + 1 - private val p1: Double = a1 / aSum - private val p2: Double = a2 / aSum - - override fun sample(generator: RandomGenerator): BlockingIntChain = object : BlockingIntChain { - override fun nextBlocking(): Int { - val exponential = AhrensDieterExponentialSampler(1.0).sample(generator) - val gaussian = ZigguratNormalizedGaussianSampler.sample(generator) - - val smallMeanPoissonSampler = if (mean - lambda < Double.MIN_VALUE) { - null - } else { - KempSmallMeanPoissonSampler(mean - lambda).sample(generator) - } - - val y2 = smallMeanPoissonSampler?.nextBlocking() ?: 0 - var x: Double - var y: Double - var v: Double - var a: Int - var t: Double - var qr: Double - var qa: Double - - while (true) { - // Step 1: - val u = generator.nextDouble() - - if (u <= p1) { - // Step 2: - val n = gaussian.nextBlocking() - x = n * sqrt(lambda + halfDelta) - 0.5 - if (x > delta || x < -lambda) continue - y = if (x < 0) floor(x) else ceil(x) - val e = exponential.nextBlocking() - v = -e - 0.5 * n * n + c1 - } else { - // Step 3: - if (u > p1 + p2) { - y = lambda - break - } - - x = delta + twolpd / delta * exponential.nextBlocking() - y = ceil(x) - v = -exponential.nextBlocking() - delta * (x + 1) / twolpd - } - - // The Squeeze Principle - // Step 4.1: - a = if (x < 0) 1 else 0 - t = y * (y + 1) / (2 * lambda) - - // Step 4.2 - if (v < -t && a == 0) { - y += lambda - break - } - - // Step 4.3: - qr = t * ((2 * y + 1) / (6 * lambda) - 1) - qa = qr - t * t / (3 * (lambda + a * (y + 1))) - - // Step 4.4: - if (v < qa) { - y += lambda - break - } - - // Step 4.5: - if (v > qr) continue - - // Step 4.6: - if (v < y * logLambda - getFactorialLog((y + lambda).toInt()) + logLambdaFactorial) { - y += lambda - break - } - } - - return min(y2 + y.toLong(), Int.MAX_VALUE.toLong()).toIntExact() - } - - override fun nextBufferBlocking(size: Int): IntBuffer = IntBuffer(size) { nextBlocking() } - - override suspend fun fork(): BlockingIntChain = sample(generator.fork()) - } - - private fun getFactorialLog(n: Int): Double = factorialLog.value(n) - override fun toString(): String = "Large Mean Poisson deviate" - - public companion object { - private const val MAX_MEAN: Double = 0.5 * Int.MAX_VALUE - private val NO_CACHE_FACTORIAL_LOG: InternalUtils.FactorialLog = InternalUtils.FactorialLog.create() - } -} - - diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/ZigguratNormalizedGaussianSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/ZigguratNormalizedGaussianSampler.kt deleted file mode 100644 index b534fdc14..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/ZigguratNormalizedGaussianSampler.kt +++ /dev/null @@ -1,94 +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. - */ - -package space.kscience.kmath.samplers - -import space.kscience.kmath.chains.BlockingDoubleChain -import space.kscience.kmath.misc.toIntExact -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.math.* - -/** - * [Marsaglia and Tsang "Ziggurat"](https://en.wikipedia.org/wiki/Ziggurat_algorithm) method for sampling from a - * Gaussian distribution with mean 0 and standard deviation 1. The algorithm is explained in this paper and this - * implementation has been adapted from the C code provided therein. - * - * Based on Commons RNG implementation. - * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.html]. - */ -public object ZigguratNormalizedGaussianSampler : NormalizedGaussianSampler { - - private const val R: Double = 3.442619855899 - private const val ONE_OVER_R: Double = 1 / R - private const val V: Double = 9.91256303526217e-3 - private val MAX: Double = 2.0.pow(63.0) - private val ONE_OVER_MAX: Double = 1.0 / MAX - private const val LEN: Int = 128 - private const val LAST: Int = LEN - 1 - private val K: LongArray = LongArray(LEN) - private val W: DoubleArray = DoubleArray(LEN) - private val F: DoubleArray = DoubleArray(LEN) - - init { - // Filling the tables. - var d = R - var t = d - var fd = gauss(d) - val q = V / fd - K[0] = (d / q * MAX).toLong() - K[1] = 0 - W[0] = q * ONE_OVER_MAX - W[LAST] = d * ONE_OVER_MAX - F[0] = 1.0 - F[LAST] = fd - - (LAST - 1 downTo 1).forEach { i -> - d = sqrt(-2 * ln(V / d + fd)) - fd = gauss(d) - K[i + 1] = (d / t * MAX).toLong() - t = d - F[i] = fd - W[i] = d * ONE_OVER_MAX - } - } - - private fun gauss(x: Double): Double = exp(-0.5 * x * x) - - private fun sampleOne(generator: RandomGenerator): Double { - val j = generator.nextLong() - val i = (j and LAST.toLong()).toInt() - return if (abs(j) < K[i]) j * W[i] else fix(generator, j, i) - } - - override fun sample(generator: RandomGenerator): BlockingDoubleChain = object : BlockingDoubleChain { - override fun nextBufferBlocking(size: Int): DoubleBuffer = DoubleBuffer(size) { sampleOne(generator) } - - override suspend fun fork(): BlockingDoubleChain = sample(generator.fork()) - } - - - private fun fix(generator: RandomGenerator, hz: Long, iz: Int): Double { - var x = hz * W[iz] - - return when { - iz == 0 -> { - var y: Double - - do { - y = -ln(generator.nextDouble()) - x = -ln(generator.nextDouble()) * ONE_OVER_R - } while (y + y < x * x) - - val out = R + x - if (hz > 0) out else -out - } - - F[iz] + generator.nextDouble() * (F[iz - 1] - F[iz]) < gauss(x) -> x - else -> sampleOne(generator) - } - } - -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/MCScope.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/MCScope.kt deleted file mode 100644 index 0e06fa162..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/MCScope.kt +++ /dev/null @@ -1,69 +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. - */ - -package space.kscience.kmath.stat - -import kotlinx.coroutines.* -import kotlin.contracts.InvocationKind -import kotlin.contracts.contract -import kotlin.coroutines.CoroutineContext -import kotlin.coroutines.EmptyCoroutineContext -import kotlin.coroutines.coroutineContext - -/** - * A scope for a Monte-Carlo computations or multi-coroutine random number generation. - * The scope preserves the order of random generator calls as long as all concurrency calls is done via [launch] and [async] - * functions. - */ -public class MCScope( - public val coroutineContext: CoroutineContext, - public val random: RandomGenerator, -) - -/** - * Launches a supervised Monte-Carlo scope - */ -public suspend inline fun mcScope(generator: RandomGenerator, block: MCScope.() -> T): T { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return MCScope(coroutineContext, generator).block() -} - -/** - * Launch mc scope with a given seed - */ -public suspend inline fun mcScope(seed: Long, block: MCScope.() -> T): T { - contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) } - return mcScope(RandomGenerator.default(seed), block) -} - -/** - * Specialized launch for [MCScope]. Behaves the same way as regular [CoroutineScope.launch], but also stores the generator fork. - * The method itself is not thread safe. - */ -public inline fun MCScope.launch( - context: CoroutineContext = EmptyCoroutineContext, - start: CoroutineStart = CoroutineStart.DEFAULT, - crossinline block: suspend MCScope.() -> Unit, -): Job { - val newRandom = random.fork() - return CoroutineScope(coroutineContext).launch(context, start) { - MCScope(coroutineContext, newRandom).block() - } -} - -/** - * Specialized async for [MCScope]. Behaves the same way as regular [CoroutineScope.async], but also stores the generator fork. - * The method itself is not thread safe. - */ -public inline fun MCScope.async( - context: CoroutineContext = EmptyCoroutineContext, - start: CoroutineStart = CoroutineStart.DEFAULT, - crossinline block: suspend MCScope.() -> T, -): Deferred { - val newRandom = random.fork() - return CoroutineScope(coroutineContext).async(context, start) { - MCScope(coroutineContext, newRandom).block() - } -} \ No newline at end of file diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Mean.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Mean.kt deleted file mode 100644 index aff7d03d9..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Mean.kt +++ /dev/null @@ -1,65 +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. - */ - -package space.kscience.kmath.stat - -import space.kscience.kmath.operations.* -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.indices - -/** - * Arithmetic mean - */ -public class Mean( - private val group: Ring, - private val division: (sum: T, count: Int) -> T, -) : ComposableStatistic, T>, BlockingStatistic { - - override fun evaluateBlocking(data: Buffer): T = group { - var res = zero - for (i in data.indices) { - res += data[i] - } - division(res, data.size) - } - - override suspend fun evaluate(data: Buffer): T = super.evaluate(data) - - override suspend fun computeIntermediate(data: Buffer): Pair = group { - var res = zero - for (i in data.indices) { - res += data[i] - } - res to data.size - } - - override suspend fun composeIntermediate(first: Pair, second: Pair): Pair = - group { first.first + second.first } to (first.second + second.second) - - override suspend fun toResult(intermediate: Pair): T = group { - division(intermediate.first, intermediate.second) - } - - public companion object { - @Deprecated("Use Double.mean instead") - public val double: Mean = Mean(DoubleField) { sum, count -> sum / count } - @Deprecated("Use Int.mean instead") - public val int: Mean = Mean(IntRing) { sum, count -> sum / count } - @Deprecated("Use Long.mean instead") - public val long: Mean = Mean(LongRing) { sum, count -> sum / count } - - public fun evaluate(buffer: Buffer): Double = Double.mean.evaluateBlocking(buffer) - public fun evaluate(buffer: Buffer): Int = Int.mean.evaluateBlocking(buffer) - public fun evaluate(buffer: Buffer): Long = Long.mean.evaluateBlocking(buffer) - } -} - - -//TODO replace with optimized version which respects overflow -public val Double.Companion.mean: Mean get() = Mean(DoubleField) { sum, count -> sum / count } -public val Int.Companion.mean: Mean get() = Mean(IntRing) { sum, count -> sum / count } -public val Long.Companion.mean: Mean get() = Mean(LongRing) { sum, count -> sum / count } - - diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Median.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Median.kt deleted file mode 100644 index c587277f9..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Median.kt +++ /dev/null @@ -1,21 +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. - */ - -package space.kscience.kmath.stat - -import space.kscience.kmath.operations.asSequence -import space.kscience.kmath.structures.Buffer - -/** - * Non-composable median - */ -public class Median(private val comparator: Comparator) : BlockingStatistic { - override fun evaluateBlocking(data: Buffer): T = - data.asSequence().sortedWith(comparator).toList()[data.size / 2] //TODO check if this is correct - - public companion object { - public val real: Median = Median { a: Double, b: Double -> a.compareTo(b) } - } -} \ No newline at end of file diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/RandomChain.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/RandomChain.kt deleted file mode 100644 index d4bc36b5b..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/RandomChain.kt +++ /dev/null @@ -1,39 +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. - */ - -package space.kscience.kmath.stat - -import space.kscience.kmath.chains.BlockingDoubleChain -import space.kscience.kmath.chains.Chain -import space.kscience.kmath.structures.DoubleBuffer - -/** - * A possibly stateful chain producing random values. - * - * @property generator the underlying [RandomGenerator] instance. - */ -public class RandomChain( - public val generator: RandomGenerator, - private val gen: suspend RandomGenerator.() -> R, -) : Chain { - override suspend fun next(): R = generator.gen() - override suspend fun fork(): Chain = RandomChain(generator.fork(), gen) -} - -/** - * Create a generic random chain with provided [generator] - */ -public fun RandomGenerator.chain(generator: suspend RandomGenerator.() -> R): RandomChain = RandomChain(this, generator) - -/** - * A type-specific double chunk random chain - */ -public class UniformDoubleChain(public val generator: RandomGenerator) : BlockingDoubleChain { - override fun nextBufferBlocking(size: Int): DoubleBuffer = generator.nextDoubleBuffer(size) - override suspend fun nextBuffer(size: Int): DoubleBuffer = nextBufferBlocking(size) - - override suspend fun fork(): UniformDoubleChain = UniformDoubleChain(generator.fork()) -} - diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/RandomGenerator.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/RandomGenerator.kt deleted file mode 100644 index f280a78aa..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/RandomGenerator.kt +++ /dev/null @@ -1,113 +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. - */ - -package space.kscience.kmath.stat - -import space.kscience.kmath.structures.DoubleBuffer -import kotlin.random.Random - -/** - * An interface that is implemented by random number generator algorithms. - */ -public interface RandomGenerator { - /** - * Gets the next random [Boolean] value. - */ - public fun nextBoolean(): Boolean - - /** - * Gets the next random [Double] value uniformly distributed between 0 (inclusive) and 1 (exclusive). - */ - public fun nextDouble(): Double - - /** - * A chunk of doubles of given [size]. - */ - public fun nextDoubleBuffer(size: Int): DoubleBuffer = DoubleBuffer(size) { nextDouble() } - - /** - * Gets the next random `Int` from the random number generator. - * - * Generates an `Int` random value uniformly distributed between [Int.MIN_VALUE] and [Int.MAX_VALUE] (inclusive). - */ - public fun nextInt(): Int - - /** - * Gets the next random non-negative `Int` from the random number generator less than the specified [until] bound. - * - * Generates an `Int` random value uniformly distributed between `0` (inclusive) and the specified [until] bound - * (exclusive). - */ - public fun nextInt(until: Int): Int - - /** - * Gets the next random `Long` from the random number generator. - * - * Generates a `Long` random value uniformly distributed between [Long.MIN_VALUE] and [Long.MAX_VALUE] (inclusive). - */ - public fun nextLong(): Long - - /** - * Gets the next random non-negative `Long` from the random number generator less than the specified [until] bound. - * - * Generates a `Long` random value uniformly distributed between `0` (inclusive) and the specified [until] bound (exclusive). - */ - public fun nextLong(until: Long): Long - - /** - * Fills a subrange with the specified byte [array] starting from [fromIndex] inclusive and ending [toIndex] exclusive - * with random bytes. - * - * @return [array] with the subrange filled with random bytes. - */ - public fun fillBytes(array: ByteArray, fromIndex: Int = 0, toIndex: Int = array.size) - - /** - * Creates a byte array of the specified [size], filled with random bytes. - */ - public fun nextBytes(size: Int): ByteArray = ByteArray(size).also { fillBytes(it) } - - /** - * Create a new generator that is independent of current generator (operations on new generator do not affect this one - * and vise versa). The statistical properties of new generator should be the same as for this one. - * For pseudo-random generator, the fork is keeping the same sequence of numbers for given call order for each run. - * - * The thread safety of this operation is not guaranteed since it could affect the state of the generator. - */ - public fun fork(): RandomGenerator - - public companion object { - /** - * The [DefaultGenerator] instance. - */ - public val default: DefaultGenerator by lazy(::DefaultGenerator) - - /** - * Returns [DefaultGenerator] of given [seed]. - */ - public fun default(seed: Long): DefaultGenerator = DefaultGenerator(Random(seed)) - } -} - -/** - * Implements [RandomGenerator] by delegating all operations to [Random]. - * - * @property random the underlying [Random] object. - */ -public class DefaultGenerator(public val random: Random = Random) : RandomGenerator { - override fun nextBoolean(): Boolean = random.nextBoolean() - override fun nextDouble(): Double = random.nextDouble() - override fun nextInt(): Int = random.nextInt() - override fun nextInt(until: Int): Int = random.nextInt(until) - override fun nextLong(): Long = random.nextLong() - override fun nextLong(until: Long): Long = random.nextLong(until) - - override fun fillBytes(array: ByteArray, fromIndex: Int, toIndex: Int) { - random.nextBytes(array, fromIndex, toIndex) - } - - override fun nextBytes(size: Int): ByteArray = random.nextBytes(size) - override fun fork(): RandomGenerator = RandomGenerator.default(random.nextLong()) -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Sampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Sampler.kt deleted file mode 100644 index 0dd121f3b..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Sampler.kt +++ /dev/null @@ -1,69 +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. - */ - -package space.kscience.kmath.stat - -import kotlinx.coroutines.flow.first -import space.kscience.kmath.chains.Chain -import space.kscience.kmath.chains.combine -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.BufferFactory -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.IntBuffer -import kotlin.jvm.JvmName - -/** - * Sampler that generates chains of values of type [T]. - */ -public fun interface Sampler { - /** - * Generates a chain of samples. - * - * @param generator the randomness provider. - * @return the new chain. - */ - public fun sample(generator: RandomGenerator): Chain -} - -/** - * Sample a bunch of values - */ -public fun Sampler.sampleBuffer( - generator: RandomGenerator, - size: Int, - bufferFactory: BufferFactory = Buffer.Companion::boxing, -): Chain> { - require(size > 1) - //creating temporary storage once - val tmp = ArrayList(size) - - return sample(generator).combine { chain -> - //clear list from previous run - tmp.clear() - //Fill list - repeat(size) { tmp.add(chain.next()) } - //return new buffer with elements from tmp - bufferFactory(size) { tmp[it] } - } -} - -/** - * Samples one value from this [Sampler]. - */ -public suspend fun Sampler.next(generator: RandomGenerator): T = sample(generator).first() - -/** - * Generates [size] real samples and chunks them into some buffers. - */ -@JvmName("sampleRealBuffer") -public fun Sampler.sampleBuffer(generator: RandomGenerator, size: Int): Chain> = - sampleBuffer(generator, size, ::DoubleBuffer) - -/** - * Generates [size] integer samples and chunks them into some buffers. - */ -@JvmName("sampleIntBuffer") -public fun Sampler.sampleBuffer(generator: RandomGenerator, size: Int): Chain> = - sampleBuffer(generator, size, ::IntBuffer) diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/SamplerAlgebra.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/SamplerAlgebra.kt deleted file mode 100644 index ddf0ea622..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/SamplerAlgebra.kt +++ /dev/null @@ -1,55 +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. - */ - -package space.kscience.kmath.stat - -import space.kscience.kmath.chains.Chain -import space.kscience.kmath.chains.ConstantChain -import space.kscience.kmath.chains.map -import space.kscience.kmath.chains.zip -import space.kscience.kmath.operations.Group -import space.kscience.kmath.operations.ScaleOperations -import space.kscience.kmath.operations.invoke - -/** - * Implements [Sampler] by sampling only certain [value]. - * - * @property value the value to sample. - */ -public class ConstantSampler(public val value: T) : Sampler { - override fun sample(generator: RandomGenerator): Chain = ConstantChain(value) -} - -/** - * Implements [Sampler] by delegating sampling to value of [chainBuilder]. - * - * @property chainBuilder the provider of [Chain]. - */ -public class BasicSampler(public val chainBuilder: (RandomGenerator) -> Chain) : Sampler { - override fun sample(generator: RandomGenerator): Chain = chainBuilder(generator) -} - -/** - * A space of samplers. Allows performing simple operations on distributions. - * - * @property algebra the space to provide addition and scalar multiplication for [T]. - */ -public class SamplerSpace(public val algebra: S) : Group>, - ScaleOperations> where S : Group, S : ScaleOperations { - - override val zero: Sampler = ConstantSampler(algebra.zero) - - override fun add(left: Sampler, right: Sampler): Sampler = BasicSampler { generator -> - left.sample(generator).zip(right.sample(generator)) { aValue, bValue -> algebra { aValue + bValue } } - } - - override fun scale(a: Sampler, value: Double): Sampler = BasicSampler { generator -> - a.sample(generator).map { a -> - algebra { a * value } - } - } - - override fun negate(arg: Sampler): Sampler = scale(arg, -1.0) -} diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Statistic.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Statistic.kt deleted file mode 100644 index 43cd5b402..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Statistic.kt +++ /dev/null @@ -1,80 +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. - */ - -package space.kscience.kmath.stat - -import kotlinx.coroutines.CoroutineDispatcher -import kotlinx.coroutines.Dispatchers -import kotlinx.coroutines.ExperimentalCoroutinesApi -import kotlinx.coroutines.flow.Flow -import kotlinx.coroutines.flow.map -import kotlinx.coroutines.flow.runningReduce -import space.kscience.kmath.coroutines.mapParallel -import space.kscience.kmath.structures.Buffer - -/** - * A function, that transforms a buffer of random quantities to some resulting value - */ -public fun interface Statistic { - public suspend fun evaluate(data: Buffer): R -} - -public suspend operator fun Statistic.invoke(data: Buffer): R = evaluate(data) - -/** - * A statistic that is computed in a synchronous blocking mode - */ -public fun interface BlockingStatistic : Statistic { - public fun evaluateBlocking(data: Buffer): R - - override suspend fun evaluate(data: Buffer): R = evaluateBlocking(data) -} - -public operator fun BlockingStatistic.invoke(data: Buffer): R = evaluateBlocking(data) - -/** - * A statistic tha could be computed separately on different blocks of data and then composed - * - * @param T the source type. - * @param I the intermediate block type. - * @param R the result type. - */ -public interface ComposableStatistic : Statistic { - //compute statistic on a single block - public suspend fun computeIntermediate(data: Buffer): I - - //Compose two blocks - public suspend fun composeIntermediate(first: I, second: I): I - - //Transform block to result - public suspend fun toResult(intermediate: I): R - - override suspend fun evaluate(data: Buffer): R = toResult(computeIntermediate(data)) -} - -/** - * Flow intermediate state of the [ComposableStatistic] - */ -@OptIn(ExperimentalCoroutinesApi::class) -private fun ComposableStatistic.flowIntermediate( - flow: Flow>, - dispatcher: CoroutineDispatcher = Dispatchers.Default, -): Flow = flow - .mapParallel(dispatcher) { computeIntermediate(it) } - .runningReduce(::composeIntermediate) - - -/** - * Perform a streaming statistical analysis on a chunked data. The computation of inner representation is done in parallel - * if [dispatcher] allows it. - * - * The resulting flow contains values that include the whole previous statistics, not only the last chunk. - */ -@OptIn(ExperimentalCoroutinesApi::class) -public fun ComposableStatistic.flow( - flow: Flow>, - dispatcher: CoroutineDispatcher = Dispatchers.Default, -): Flow = flowIntermediate(flow, dispatcher).map(::toResult) - diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/UniformDistribution.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/UniformDistribution.kt deleted file mode 100644 index 970a3aab5..000000000 --- a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/UniformDistribution.kt +++ /dev/null @@ -1,29 +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. - */ - -package space.kscience.kmath.stat - -import space.kscience.kmath.chains.Chain -import space.kscience.kmath.chains.SimpleChain -import space.kscience.kmath.distributions.Distribution -import space.kscience.kmath.distributions.UnivariateDistribution - -public class UniformDistribution(public val range: ClosedFloatingPointRange) : UnivariateDistribution { - private val length: Double = range.endInclusive - range.start - - override fun probability(arg: Double): Double = if (arg in range) 1.0 / length else 0.0 - - override fun sample(generator: RandomGenerator): Chain = - SimpleChain { range.start + generator.nextDouble() * length } - - override fun cumulative(arg: Double): Double = when { - arg < range.start -> 0.0 - arg >= range.endInclusive -> 1.0 - else -> (arg - range.start) / length - } -} - -public fun Distribution.Companion.uniform(range: ClosedFloatingPointRange): UniformDistribution = - UniformDistribution(range) diff --git a/kmath-stat/src/jvmMain/kotlin/space/kscience/kmath/stat/RandomSourceGenerator.kt b/kmath-stat/src/jvmMain/kotlin/space/kscience/kmath/stat/RandomSourceGenerator.kt deleted file mode 100644 index 202a1c8dd..000000000 --- a/kmath-stat/src/jvmMain/kotlin/space/kscience/kmath/stat/RandomSourceGenerator.kt +++ /dev/null @@ -1,135 +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. - */ - -package space.kscience.kmath.stat - -import org.apache.commons.rng.UniformRandomProvider -import org.apache.commons.rng.simple.RandomSource - -/** - * Implements [RandomGenerator] by delegating all operations to [RandomSource]. - * - * @property source the underlying [RandomSource] object. - */ -public class RandomSourceGenerator internal constructor(public val source: RandomSource, seed: Long?) : RandomGenerator { - internal val random: UniformRandomProvider = seed?.let { RandomSource.create(source, seed) } - ?: RandomSource.create(source) - - override fun nextBoolean(): Boolean = random.nextBoolean() - override fun nextDouble(): Double = random.nextDouble() - override fun nextInt(): Int = random.nextInt() - override fun nextInt(until: Int): Int = random.nextInt(until) - override fun nextLong(): Long = random.nextLong() - override fun nextLong(until: Long): Long = random.nextLong(until) - - override fun fillBytes(array: ByteArray, fromIndex: Int, toIndex: Int) { - require(toIndex > fromIndex) - random.nextBytes(array, fromIndex, toIndex - fromIndex) - } - - override fun fork(): RandomGenerator = RandomSourceGenerator(source, nextLong()) -} - -/** - * Implements [UniformRandomProvider] by delegating all operations to [RandomGenerator]. - * - * @property generator the underlying [RandomGenerator] object. - */ -public class RandomGeneratorProvider(public val generator: RandomGenerator) : UniformRandomProvider { - /** - * Generates a [Boolean] value. - * - * @return the next random value. - */ - override fun nextBoolean(): Boolean = generator.nextBoolean() - - /** - * Generates a [Float] value between 0 and 1. - * - * @return the next random value between 0 and 1. - */ - override fun nextFloat(): Float = generator.nextDouble().toFloat() - - /** - * Generates [Byte] values and places them into a user-supplied array. - * - * The number of random bytes produced is equal to the length of the byte array. - * - * @param bytes byte array in which to put the random bytes. - */ - override fun nextBytes(bytes: ByteArray): Unit = generator.fillBytes(bytes) - - /** - * Generates [Byte] values and places them into a user-supplied array. - * - * The array is filled with bytes extracted from random integers. This implies that the number of random bytes - * generated may be larger than the length of the byte array. - * - * @param bytes the array in which to put the generated bytes. - * @param start the index at which to start inserting the generated bytes. - * @param len the number of bytes to insert. - */ - override fun nextBytes(bytes: ByteArray, start: Int, len: Int) { - generator.fillBytes(bytes, start, start + len) - } - - /** - * Generates an [Int] value. - * - * @return the next random value. - */ - override fun nextInt(): Int = generator.nextInt() - - /** - * Generates an [Int] value between 0 (inclusive) and the specified value (exclusive). - * - * @param n the bound on the random number to be returned. Must be positive. - * @return a random integer between 0 (inclusive) and [n] (exclusive). - */ - override fun nextInt(n: Int): Int = generator.nextInt(n) - - /** - * Generates a [Double] value between 0 and 1. - * - * @return the next random value between 0 and 1. - */ - override fun nextDouble(): Double = generator.nextDouble() - - /** - * Generates a [Long] value. - * - * @return the next random value. - */ - override fun nextLong(): Long = generator.nextLong() - - /** - * Generates a [Long] value between 0 (inclusive) and the specified value (exclusive). - * - * @param n Bound on the random number to be returned. Must be positive. - * @return a random long value between 0 (inclusive) and [n] (exclusive). - */ - override fun nextLong(n: Long): Long = generator.nextLong(n) -} - -/** - * Represent this [RandomGenerator] as commons-rng [UniformRandomProvider] preserving and mirroring its current state. - * Getting new value from one of those changes the state of another. - */ -public fun RandomGenerator.asUniformRandomProvider(): UniformRandomProvider = if (this is RandomSourceGenerator) - random -else - RandomGeneratorProvider(this) - -/** - * Returns [RandomSourceGenerator] with given [RandomSource] and [seed]. - */ -public fun RandomGenerator.Companion.fromSource(source: RandomSource, seed: Long? = null): RandomSourceGenerator = - RandomSourceGenerator(source, seed) - -/** - * Returns [RandomSourceGenerator] with [RandomSource.MT] algorithm and given [seed]. - */ -public fun RandomGenerator.Companion.mersenneTwister(seed: Long? = null): RandomSourceGenerator = - fromSource(RandomSource.MT, seed) diff --git a/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/CommonsDistributionsTest.kt b/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/CommonsDistributionsTest.kt deleted file mode 100644 index 19c01e099..000000000 --- a/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/CommonsDistributionsTest.kt +++ /dev/null @@ -1,29 +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. - */ - -package space.kscience.kmath.stat - -import kotlinx.coroutines.runBlocking -import org.junit.jupiter.api.Assertions -import org.junit.jupiter.api.Test -import space.kscience.kmath.samplers.GaussianSampler - -internal class CommonsDistributionsTest { - @Test - fun testNormalDistributionSuspend() = runBlocking { - val distribution = GaussianSampler(7.0, 2.0) - val generator = RandomGenerator.default(1) - val sample = distribution.sample(generator).nextBuffer(1000) - Assertions.assertEquals(7.0, Mean.evaluate(sample), 0.2) - } - - @Test - fun testNormalDistributionBlocking() { - val distribution = GaussianSampler(7.0, 2.0) - val generator = RandomGenerator.default(1) - val sample = distribution.sample(generator).nextBufferBlocking(1000) - Assertions.assertEquals(7.0, Mean.evaluate(sample), 0.2) - } -} diff --git a/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/MCScopeTest.kt b/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/MCScopeTest.kt deleted file mode 100644 index 075d7f3e5..000000000 --- a/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/MCScopeTest.kt +++ /dev/null @@ -1,90 +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. - */ - -package space.kscience.kmath.stat - -import kotlinx.coroutines.* -import java.util.* -import kotlin.test.Test -import kotlin.test.assertEquals - -data class RandomResult(val branch: String, val order: Int, val value: Int) - -typealias ATest = suspend CoroutineScope.() -> Set - -class MCScopeTest { - val simpleTest: ATest = { - mcScope(1111) { - val res = Collections.synchronizedSet(HashSet()) - - launch { - //println(random) - repeat(10) { - delay(10) - res.add(RandomResult("first", it, random.nextInt())) - } - launch { - //empty fork - } - } - - launch { - //println(random) - repeat(10) { - delay(10) - res.add(RandomResult("second", it, random.nextInt())) - } - } - - - res - } - } - - val testWithJoin: ATest = { - mcScope(1111) { - val res = Collections.synchronizedSet(HashSet()) - - val job = launch { - repeat(10) { - delay(10) - res.add(RandomResult("first", it, random.nextInt())) - } - } - launch { - repeat(10) { - delay(10) - if (it == 4) job.join() - res.add(RandomResult("second", it, random.nextInt())) - } - } - - res - } - } - - - @OptIn(ObsoleteCoroutinesApi::class) - fun compareResult(test: ATest) { - val res1 = runBlocking(Dispatchers.Default) { test() } - val res2 = runBlocking(newSingleThreadContext("test")) { test() } - assertEquals( - res1.find { it.branch == "first" && it.order == 7 }?.value, - res2.find { it.branch == "first" && it.order == 7 }?.value - ) - assertEquals(res1, res2) - } - - @Test - fun testParallel() { - compareResult(simpleTest) - } - - - @Test - fun testConditionalJoin() { - compareResult(testWithJoin) - } -} \ No newline at end of file diff --git a/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/SamplerTest.kt b/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/SamplerTest.kt deleted file mode 100644 index 1dbbf591b..000000000 --- a/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/SamplerTest.kt +++ /dev/null @@ -1,19 +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. - */ - -package space.kscience.kmath.stat - -import kotlinx.coroutines.runBlocking -import kotlin.test.Test - -class SamplerTest { - - @Test - fun bufferSamplerTest() { - val sampler = Sampler { it.chain { nextDouble() } } - val data = sampler.sampleBuffer(RandomGenerator.default, 100) - runBlocking { println(data.next()) } - } -} \ No newline at end of file diff --git a/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/StatisticTest.kt b/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/StatisticTest.kt deleted file mode 100644 index 9eb84899c..000000000 --- a/kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/StatisticTest.kt +++ /dev/null @@ -1,50 +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. - */ - -package space.kscience.kmath.stat - -import kotlinx.coroutines.flow.first -import kotlinx.coroutines.flow.last -import kotlinx.coroutines.flow.take -import kotlinx.coroutines.runBlocking -import space.kscience.kmath.streaming.chunked -import kotlin.test.Test -import kotlin.test.assertEquals - -internal class StatisticTest { - //create a random number generator. - val generator = RandomGenerator.default(1) - - //Create a stateless chain from generator. - val data = generator.chain { nextDouble() } - - //Convert a chain to Flow and break it into chunks. - val chunked = data.chunked(1000) - - @Test - fun singleBlockingMean() { - val first = runBlocking { chunked.first()} - val res = Double.mean(first) - assertEquals(0.5,res, 1e-1) - } - - @Test - fun singleSuspendMean() = runBlocking { - val first = runBlocking { chunked.first()} - val res = Double.mean(first) - assertEquals(0.5,res, 1e-1) - } - - @Test - fun parallelMean() = runBlocking { - val average = Double.mean - .flow(chunked) //create a flow from evaluated results - .take(100) // Take 100 data chunks from the source and accumulate them - .last() //get 1e5 data samples average - - assertEquals(0.5,average, 1e-2) - } - -} diff --git a/kmath-symja/README.md b/kmath-symja/README.md deleted file mode 100644 index e3c717cff..000000000 --- a/kmath-symja/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-symja - -Symja integration module - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-symja:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-symja:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-symja:0.3.0-dev-20") -} -``` diff --git a/kmath-symja/build.gradle.kts b/kmath-symja/build.gradle.kts deleted file mode 100644 index 65c329d52..000000000 --- a/kmath-symja/build.gradle.kts +++ /dev/null @@ -1,42 +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. - */ - -plugins { - kotlin("jvm") - id("ru.mipt.npm.gradle.common") -} - -description = "Symja integration module" - -dependencies { - api("org.matheclipse:matheclipse-core:2.0.0-SNAPSHOT") { - // Incorrect transitive dependencies - exclude("org.apfloat", "apfloat") - exclude("org.hipparchus", "hipparchus-clustering") - exclude("org.hipparchus", "hipparchus-core") - exclude("org.hipparchus", "hipparchus-fft") - exclude("org.hipparchus", "hipparchus-fitting") - exclude("org.hipparchus", "hipparchus-ode") - exclude("org.hipparchus", "hipparchus-optim") - exclude("org.hipparchus", "hipparchus-stat") - } - - // Replaces for incorrect transitive dependencies - api("org.apfloat:apfloat:1.10.0") - api("org.hipparchus:hipparchus-clustering:1.8") - api("org.hipparchus:hipparchus-core:1.8") - api("org.hipparchus:hipparchus-fft:1.8") - api("org.hipparchus:hipparchus-fitting:1.8") - api("org.hipparchus:hipparchus-ode:1.8") - api("org.hipparchus:hipparchus-optim:1.8") - api("org.hipparchus:hipparchus-stat:1.8") - - api(project(":kmath-core")) - testImplementation("org.slf4j:slf4j-simple:1.7.31") -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE -} diff --git a/kmath-symja/src/main/kotlin/space/kscience/kmath/symja/SymjaExpression.kt b/kmath-symja/src/main/kotlin/space/kscience/kmath/symja/SymjaExpression.kt deleted file mode 100644 index 3067b5efb..000000000 --- a/kmath-symja/src/main/kotlin/space/kscience/kmath/symja/SymjaExpression.kt +++ /dev/null @@ -1,46 +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. - */ - -package space.kscience.kmath.symja - -import org.matheclipse.core.eval.ExprEvaluator -import org.matheclipse.core.expression.F -import space.kscience.kmath.expressions.SpecialDifferentiableExpression -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.expressions.interpret -import space.kscience.kmath.operations.NumericAlgebra - -/** - * Represents [MST] based [space.kscience.kmath.expressions.DifferentiableExpression] relying on - * [Symja](https://github.com/axkr/symja_android_library). - * - * The principle of this API is converting the [mst] to an [org.matheclipse.core.interfaces.IExpr], differentiating it - * with Symja's [F.D], then converting [org.matheclipse.core.interfaces.IExpr] back to [MST]. - * - * @param T The type of number. - * @param A The [NumericAlgebra] of [T]. - * @property algebra The [A] instance. - * @property mst The [MST] node. - */ -public class SymjaExpression>( - public val algebra: A, - public val mst: MST, - public val evaluator: ExprEvaluator = DEFAULT_EVALUATOR, -) : SpecialDifferentiableExpression> { - override fun invoke(arguments: Map): T = mst.interpret(algebra, arguments) - - override fun derivativeOrNull(symbols: List): SymjaExpression = SymjaExpression( - algebra, - symbols.map(Symbol::toIExpr).fold(mst.toIExpr(), F::D).toMst(evaluator), - evaluator, - ) -} - -/** - * Wraps this [MST] into [SymjaExpression] in the context of [algebra]. - */ -public fun > MST.toSymjaExpression(algebra: A): SymjaExpression = - SymjaExpression(algebra, this) diff --git a/kmath-symja/src/main/kotlin/space/kscience/kmath/symja/adapters.kt b/kmath-symja/src/main/kotlin/space/kscience/kmath/symja/adapters.kt deleted file mode 100644 index 30c37c799..000000000 --- a/kmath-symja/src/main/kotlin/space/kscience/kmath/symja/adapters.kt +++ /dev/null @@ -1,95 +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. - */ - -package space.kscience.kmath.symja - -import org.matheclipse.core.eval.ExprEvaluator -import org.matheclipse.core.expression.ComplexNum -import org.matheclipse.core.expression.F -import org.matheclipse.core.interfaces.IExpr -import org.matheclipse.core.interfaces.ISymbol -import space.kscience.kmath.expressions.MST -import space.kscience.kmath.expressions.MstExtendedField -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.operations.* - -internal val DEFAULT_EVALUATOR = ExprEvaluator(false, 100) - -/** - * Matches the given [IExpr] instance to appropriate [MST] node or evaluates it with [evaluator]. - */ -public fun IExpr.toMst(evaluator: ExprEvaluator = DEFAULT_EVALUATOR): MST = MstExtendedField { - when { - isPlus -> first().toMst(evaluator) + second().toMst(evaluator) - isSin -> sin(first().toMst(evaluator)) - isSinh -> sinh(first().toMst(evaluator)) - isCos -> cos(first().toMst(evaluator)) - isCosh -> cosh(first().toMst(evaluator)) - isTan -> tan(first().toMst(evaluator)) - isTanh -> tanh(first().toMst(evaluator)) - isArcSin -> asin(first().toMst(evaluator)) - isArcCos -> acos(first().toMst(evaluator)) - isArcTan -> atan(first().toMst(evaluator)) - isArcTanh -> atanh(first().toMst(evaluator)) - isE -> bindSymbol("e") - isPi -> bindSymbol("pi") - isTimes -> first().toMst(evaluator) * second().toMst(evaluator) - isOne -> one - isZero -> zero - isImaginaryUnit -> bindSymbol("i") - isMinusOne -> -one - this@toMst is ISymbol -> bindSymbol(symbolName) - isPower -> power(first().toMst(evaluator), evaluator.evalf(second())) - isExp -> exp(first().toMst(evaluator)) - isNumber -> number(evaluator.evalf(this@toMst)) - this@toMst === F.NIL -> error("NIL cannot be converted to MST") - else -> evaluator.eval(this@toMst.toString()).toMst(evaluator) - } -} - -/** - * Matches the given [MST] instance to appropriate [IExpr] node, only standard operations and symbols (which are - * present in, say, [MstExtendedField]) are supported. - */ -public fun MST.toIExpr(): IExpr = when (this) { - is MST.Numeric -> F.symjify(value) - - is Symbol -> when (identity) { - "e" -> F.E - "pi" -> F.Pi - "i" -> ComplexNum.I - else -> F.Dummy(identity) - } - - is MST.Unary -> when (operation) { - GroupOps.PLUS_OPERATION -> value.toIExpr() - GroupOps.MINUS_OPERATION -> F.Negate(value.toIExpr()) - TrigonometricOperations.SIN_OPERATION -> F.Sin(value.toIExpr()) - TrigonometricOperations.COS_OPERATION -> F.Cos(value.toIExpr()) - TrigonometricOperations.TAN_OPERATION -> F.Tan(value.toIExpr()) - TrigonometricOperations.ASIN_OPERATION -> F.ArcSin(value.toIExpr()) - TrigonometricOperations.ACOS_OPERATION -> F.ArcCos(value.toIExpr()) - TrigonometricOperations.ATAN_OPERATION -> F.ArcTan(value.toIExpr()) - ExponentialOperations.SINH_OPERATION -> F.Sinh(value.toIExpr()) - ExponentialOperations.COSH_OPERATION -> F.Cosh(value.toIExpr()) - ExponentialOperations.TANH_OPERATION -> F.Tanh(value.toIExpr()) - ExponentialOperations.ASINH_OPERATION -> F.ArcSinh(value.toIExpr()) - ExponentialOperations.ACOSH_OPERATION -> F.ArcCosh(value.toIExpr()) - ExponentialOperations.ATANH_OPERATION -> F.ArcTanh(value.toIExpr()) - PowerOperations.SQRT_OPERATION -> F.Sqrt(value.toIExpr()) - ExponentialOperations.EXP_OPERATION -> F.Exp(value.toIExpr()) - ExponentialOperations.LN_OPERATION -> F.Log(value.toIExpr()) - else -> error("Unary operation $operation not defined in $this") - } - - is MST.Binary -> when (operation) { - GroupOps.PLUS_OPERATION -> left.toIExpr() + right.toIExpr() - GroupOps.MINUS_OPERATION -> left.toIExpr() - right.toIExpr() - RingOps.TIMES_OPERATION -> left.toIExpr() * right.toIExpr() - FieldOps.DIV_OPERATION -> F.Divide(left.toIExpr(), right.toIExpr()) - PowerOperations.POW_OPERATION -> F.Power(left.toIExpr(), F.symjify((right as MST.Numeric).value)) - else -> error("Binary operation $operation not defined in $this") - } -} diff --git a/kmath-tensorflow/README.md b/kmath-tensorflow/README.md deleted file mode 100644 index f1dfa0202..000000000 --- a/kmath-tensorflow/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-tensorflow - -Google tensorflow connector - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-tensorflow:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-tensorflow:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-tensorflow:0.3.0-dev-20") -} -``` diff --git a/kmath-tensorflow/build.gradle.kts b/kmath-tensorflow/build.gradle.kts deleted file mode 100644 index 9380a7308..000000000 --- a/kmath-tensorflow/build.gradle.kts +++ /dev/null @@ -1,15 +0,0 @@ -plugins { - id("ru.mipt.npm.gradle.jvm") -} - -description = "Google tensorflow connector" - -dependencies { - api(project(":kmath-tensors")) - api("org.tensorflow:tensorflow-core-api:0.4.0") - testImplementation("org.tensorflow:tensorflow-core-platform:0.4.0") -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE -} \ No newline at end of file diff --git a/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowAlgebra.kt b/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowAlgebra.kt deleted file mode 100644 index ecfd8d098..000000000 --- a/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowAlgebra.kt +++ /dev/null @@ -1,94 +0,0 @@ -package space.kscience.kmath.tensorflow - -import org.tensorflow.Graph -import org.tensorflow.Output -import org.tensorflow.ndarray.NdArray -import org.tensorflow.op.core.Constant -import org.tensorflow.types.TFloat64 -import space.kscience.kmath.expressions.Symbol -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.DefaultStrides -import space.kscience.kmath.nd.Shape -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.PowerOperations - -public class DoubleTensorFlowOutput( - graph: Graph, - output: Output, -) : TensorFlowOutput(graph, output) { - - override fun org.tensorflow.Tensor.actualizeTensor(): NdArray = this as TFloat64 - -} - -public class DoubleTensorFlowAlgebra internal constructor( - graph: Graph, -) : TensorFlowAlgebra(graph), PowerOperations> { - - override val elementAlgebra: DoubleField get() = DoubleField - - override fun structureND( - shape: Shape, - initializer: DoubleField.(IntArray) -> Double, - ): StructureND { - val res = TFloat64.tensorOf(org.tensorflow.ndarray.Shape.of(*shape.toLongArray())) { array -> - DefaultStrides(shape).forEach { index -> - array.setDouble(elementAlgebra.initializer(index), *index.toLongArray()) - } - } - return DoubleTensorFlowOutput(graph, ops.constant(res).asOutput()) - } - - override fun StructureND.asTensorFlow(): TensorFlowOutput = - if (this is TensorFlowOutput && output.type() == TFloat64::class.java) { - @Suppress("UNCHECKED_CAST") - this as TensorFlowOutput - } else { - val res = TFloat64.tensorOf(org.tensorflow.ndarray.Shape.of(*shape.toLongArray())) { array -> - @OptIn(PerformancePitfall::class) - elements().forEach { (index, value) -> - array.setDouble(value, *index.toLongArray()) - } - } - DoubleTensorFlowOutput(graph, ops.constant(res).asOutput()) - } - - override fun Output.wrap(): TensorFlowOutput = DoubleTensorFlowOutput(graph, this) - - override fun const(value: Double): Constant = ops.constant(value) - - override fun divide( - left: StructureND, - right: StructureND, - ): TensorFlowOutput = left.operate(right) { l, r -> - ops.math.div(l, r) - } - - override fun power(arg: StructureND, pow: Number): TensorFlowOutput = - arg.operate { ops.math.pow(it, const(pow.toDouble())) } -} - -/** - * Compute a tensor with TensorFlow in a single run. - * - * The resulting tensor is available outside of scope - */ -public fun DoubleField.produceWithTF( - block: DoubleTensorFlowAlgebra.() -> StructureND, -): StructureND = Graph().use { graph -> - val scope = DoubleTensorFlowAlgebra(graph) - scope.export(scope.block()) -} - -/** - * Compute several outputs with TensorFlow in a single run. - * - * The resulting tensors are available outside of scope - */ -public fun DoubleField.produceMapWithTF( - block: DoubleTensorFlowAlgebra.() -> Map>, -): Map> = Graph().use { graph -> - val scope = DoubleTensorFlowAlgebra(graph) - scope.block().mapValues { scope.export(it.value) } -} \ No newline at end of file diff --git a/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/IntTensorFlowAlgebra.kt b/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/IntTensorFlowAlgebra.kt deleted file mode 100644 index 084a445e0..000000000 --- a/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/IntTensorFlowAlgebra.kt +++ /dev/null @@ -1,21 +0,0 @@ -package space.kscience.kmath.tensorflow - -import org.tensorflow.Graph -import org.tensorflow.Output -import org.tensorflow.ndarray.NdArray -import org.tensorflow.types.TInt32 -import org.tensorflow.types.TInt64 - -public class IntTensorFlowOutput( - graph: Graph, - output: Output, -) : TensorFlowOutput(graph, output) { - override fun org.tensorflow.Tensor.actualizeTensor(): NdArray = this as TInt32 -} - -public class LongTensorFlowOutput( - graph: Graph, - output: Output, -) : TensorFlowOutput(graph, output) { - override fun org.tensorflow.Tensor.actualizeTensor(): NdArray = this as TInt64 -} \ No newline at end of file diff --git a/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt b/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt deleted file mode 100644 index 18f4c7472..000000000 --- a/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt +++ /dev/null @@ -1,260 +0,0 @@ -package space.kscience.kmath.tensorflow - - -import org.tensorflow.Graph -import org.tensorflow.Operand -import org.tensorflow.Output -import org.tensorflow.Session -import org.tensorflow.ndarray.NdArray -import org.tensorflow.op.Ops -import org.tensorflow.op.core.Constant -import org.tensorflow.op.core.Max -import org.tensorflow.op.core.Min -import org.tensorflow.op.core.Sum -import org.tensorflow.types.TInt32 -import org.tensorflow.types.family.TNumber -import org.tensorflow.types.family.TType -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.Shape -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.Ring -import space.kscience.kmath.tensors.api.Tensor -import space.kscience.kmath.tensors.api.TensorAlgebra - -internal fun IntArray.toLongArray() = LongArray(size) { get(it).toLong() } -internal fun LongArray.toIntArray() = IntArray(size) { get(it).toInt() } - -internal val NdArray.scalar: T get() = getObject() - - -public sealed interface TensorFlowTensor : Tensor - -/** - * Static (eager) in-memory TensorFlow tensor - */ -@JvmInline -public value class TensorFlowArray(public val tensor: NdArray) : Tensor { - override val shape: Shape get() = tensor.shape().asArray().toIntArray() - - override fun get(index: IntArray): T = tensor.getObject(*index.toLongArray()) - - //TODO implement native element sequence - - override fun set(index: IntArray, value: T) { - tensor.setObject(value, *index.toLongArray()) - } -} - -/** - * Lazy graph-based TensorFlow tensor. The tensor is actualized on call. - * - * If the tensor is used for intermediate operations, actualizing it could impact performance. - */ -public abstract class TensorFlowOutput( - protected val graph: Graph, - output: Output, -) : TensorFlowTensor { - - public var output: Output = output - internal set - - override val shape: Shape get() = output.shape().asArray().toIntArray() - - protected abstract fun org.tensorflow.Tensor.actualizeTensor(): NdArray - - internal val actualTensor by lazy { - Session(graph).use { session -> - TensorFlowArray(session.runner().fetch(output).run().first().actualizeTensor()) - } - } - - override fun get(index: IntArray): T = actualTensor[index] - - @PerformancePitfall - override fun elements(): Sequence> = actualTensor.elements() - - override fun set(index: IntArray, value: T) { - actualTensor[index] = value - } - -} - - -public abstract class TensorFlowAlgebra> internal constructor( - protected val graph: Graph, -) : TensorAlgebra { - - public val ops: Ops by lazy { Ops.create(graph) } - - protected abstract fun StructureND.asTensorFlow(): TensorFlowOutput - - protected abstract fun Output.wrap(): TensorFlowOutput - - protected abstract fun const(value: T): Constant - - override fun StructureND.valueOrNull(): T? = if (shape contentEquals intArrayOf(1)) - get(Shape(0)) else null - - /** - * Perform binary lazy operation on tensor. Both arguments are implicitly converted - */ - public fun StructureND.operate( - other: StructureND, - operation: (left: Operand, right: Operand) -> Operand, - ): TensorFlowOutput { - val left = asTensorFlow().output - val right = other.asTensorFlow().output - return operation(left, right).asOutput().wrap() - } - - public fun T.operate( - other: StructureND, - operation: (left: Operand, right: Operand) -> Operand, - ): TensorFlowOutput { - val left = const(this) - val right = other.asTensorFlow().output - return operation(left, right).asOutput().wrap() - } - - public fun StructureND.operate( - value: T, - operation: (left: Operand, right: Operand) -> Operand, - ): TensorFlowOutput { - val left = asTensorFlow().output - val right = const(value) - return operation(left, right).asOutput().wrap() - } - - public fun Tensor.operateInPlace( - other: StructureND, - operation: (left: Operand, right: Operand) -> Operand, - ): Unit { - val origin = asTensorFlow() - val left = origin.output - val right = other.asTensorFlow().output - origin.output = operation(left, right).asOutput() - } - - public fun Tensor.operateInPlace( - value: T, - operation: (left: Operand, right: Operand) -> Operand, - ): Unit { - val origin = asTensorFlow() - val left = origin.output - val right = const(value) - origin.output = operation(left, right).asOutput() - } - - public fun StructureND.operate(operation: (Operand) -> Operand): TensorFlowOutput = - operation(asTensorFlow().output).asOutput().wrap() - - override fun T.plus(arg: StructureND): TensorFlowOutput = operate(arg, ops.math::add) - - override fun StructureND.plus(arg: T): TensorFlowOutput = operate(arg, ops.math::add) - - override fun StructureND.plus(arg: StructureND): TensorFlowOutput = operate(arg, ops.math::add) - - override fun Tensor.plusAssign(value: T): Unit = operateInPlace(value, ops.math::add) - - override fun Tensor.plusAssign(arg: StructureND): Unit = operateInPlace(arg, ops.math::add) - - override fun StructureND.minus(arg: T): TensorFlowOutput = operate(arg, ops.math::sub) - - override fun StructureND.minus(arg: StructureND): TensorFlowOutput = operate(arg, ops.math::sub) - - override fun T.minus(arg: StructureND): Tensor = operate(arg, ops.math::sub) - - override fun Tensor.minusAssign(value: T): Unit = operateInPlace(value, ops.math::sub) - - override fun Tensor.minusAssign(arg: StructureND): Unit = operateInPlace(arg, ops.math::sub) - - override fun T.times(arg: StructureND): TensorFlowOutput = operate(arg, ops.math::mul) - - override fun StructureND.times(arg: T): TensorFlowOutput = operate(arg, ops.math::mul) - - override fun StructureND.times(arg: StructureND): TensorFlowOutput = operate(arg, ops.math::mul) - - override fun Tensor.timesAssign(value: T): Unit = operateInPlace(value, ops.math::mul) - - override fun Tensor.timesAssign(arg: StructureND): Unit = operateInPlace(arg, ops.math::mul) - - override fun negate(arg: StructureND): TensorFlowOutput = arg.operate(ops.math::neg) - - override fun Tensor.get(i: Int): Tensor = operate { - TODO("Not yet implemented") - } - - override fun Tensor.transpose(i: Int, j: Int): Tensor = operate { - ops.linalg.transpose(it, ops.constant(intArrayOf(i, j))) - } - - override fun Tensor.view(shape: IntArray): Tensor = operate { - ops.reshape(it, ops.constant(shape)) - } - - override fun Tensor.viewAs(other: StructureND): Tensor = operate(other) { l, r -> - ops.reshape(l, ops.shape(r)) - } - - override fun StructureND.dot(other: StructureND): TensorFlowOutput = operate(other) { l, r -> - ops.linalg.matMul( - if (l.shape().numDimensions() == 1) ops.expandDims(l, ops.constant(0)) else l, - if (r.shape().numDimensions() == 1) ops.expandDims(r, ops.constant(-1)) else r - ) - } - - override fun diagonalEmbedding( - diagonalEntries: Tensor, - offset: Int, - dim1: Int, - dim2: Int, - ): TensorFlowOutput = diagonalEntries.operate { - TODO("Not yet implemented") - } - - override fun StructureND.sum(): T = operate { - ops.sum(it, ops.constant(intArrayOf())) - }.value() - - override fun StructureND.sum(dim: Int, keepDim: Boolean): TensorFlowOutput = operate { - ops.sum(it, ops.constant(dim), Sum.keepDims(keepDim)) - } - - override fun StructureND.min(): T = operate { - ops.min(it, ops.constant(intArrayOf())) - }.value() - - override fun StructureND.min(dim: Int, keepDim: Boolean): Tensor = operate { - ops.min(it, ops.constant(dim), Min.keepDims(keepDim)) - } - - override fun StructureND.max(): T = operate { - ops.max(it, ops.constant(intArrayOf())) - }.value() - - override fun StructureND.max(dim: Int, keepDim: Boolean): Tensor = operate { - ops.max(it, ops.constant(dim), Max.keepDims(keepDim)) - } - - override fun StructureND.argMax(dim: Int, keepDim: Boolean): Tensor = IntTensorFlowOutput( - graph, - ops.math.argMax(asTensorFlow().output, ops.constant(dim), TInt32::class.java).output() - ).actualTensor - -// private val symbolCache = HashMap>() -// -// override fun bindSymbolOrNull(value: String): TensorFlowOutput? { -// return symbolCache.getOrPut(value){ops.var} -// } -// -// public fun StructureND.grad( -// -// )= operate { ops.gradients() } - - @OptIn(UnstableKMathAPI::class) - override fun export(arg: StructureND): StructureND = - if (arg is TensorFlowOutput) arg.actualTensor else arg -} - -//TODO add TensorFlow expressions \ No newline at end of file diff --git a/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/tfOperations.kt b/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/tfOperations.kt deleted file mode 100644 index f67c333ce..000000000 --- a/kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/tfOperations.kt +++ /dev/null @@ -1,23 +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. - */ - -package space.kscience.kmath.tensorflow - -import org.tensorflow.types.family.TNumber -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.Ring -import space.kscience.kmath.operations.TrigonometricOperations - -// - -// TODO add other operations - -public fun TensorFlowAlgebra.sin( - arg: StructureND, -): TensorFlowOutput where A : TrigonometricOperations, A : Ring = arg.operate { ops.math.sin(it) } - -public fun TensorFlowAlgebra.cos( - arg: StructureND, -): TensorFlowOutput where A : TrigonometricOperations, A : Ring = arg.operate { ops.math.cos(it) } \ No newline at end of file diff --git a/kmath-tensorflow/src/test/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowOps.kt b/kmath-tensorflow/src/test/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowOps.kt deleted file mode 100644 index 308469eed..000000000 --- a/kmath-tensorflow/src/test/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowOps.kt +++ /dev/null @@ -1,47 +0,0 @@ -package space.kscience.kmath.tensorflow - -import org.junit.jupiter.api.Test -import space.kscience.kmath.nd.get -import space.kscience.kmath.nd.structureND -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.tensors.core.DoubleTensorAlgebra -import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.sum -import kotlin.test.assertEquals - -class DoubleTensorFlowOps { - @Test - fun basicOps() { - val res = DoubleField.produceWithTF { - val initial = structureND(2, 2) { 1.0 } - - initial + (initial * 2.0) - } - //println(StructureND.toString(res)) - assertEquals(3.0, res[0, 0]) - } - - @Test - fun dot(){ - val dim = 1000 - - val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224) - val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225) - - DoubleField.produceWithTF { - tensor1 dot tensor2 - }.sum() - } - - @Test - fun extensionOps(){ - val res = DoubleField.produceWithTF { - val i = structureND(2, 2) { 0.5 } - - sin(i).pow(2) + cos(i).pow(2) - } - - assertEquals(1.0, res[0,0],0.01) - } - - -} \ No newline at end of file diff --git a/kmath-tensors/README.md b/kmath-tensors/README.md deleted file mode 100644 index 3d7dfd1fb..000000000 --- a/kmath-tensors/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# Module kmath-tensors - -Common linear algebra operations on tensors. - - - [tensor algebra](src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic linear algebra operations on tensors (plus, dot, etc.) - - [tensor algebra with broadcasting](src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt) : Basic linear algebra operations implemented with broadcasting. - - [linear algebra operations](src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Advanced linear algebra operations like LU decomposition, SVD, etc. - - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-tensors:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-tensors:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-tensors:0.3.0-dev-20") -} -``` diff --git a/kmath-tensors/build.gradle.kts b/kmath-tensors/build.gradle.kts deleted file mode 100644 index 66316d21d..000000000 --- a/kmath-tensors/build.gradle.kts +++ /dev/null @@ -1,42 +0,0 @@ -plugins { - kotlin("multiplatform") - id("ru.mipt.npm.gradle.common") - id("ru.mipt.npm.gradle.native") -} - -kotlin.sourceSets { - all { - languageSettings.optIn("space.kscience.kmath.misc.UnstableKMathAPI") - } - - filter { it.name.contains("test", true) } - .map(org.jetbrains.kotlin.gradle.plugin.KotlinSourceSet::languageSettings) - .forEach { it.optIn("space.kscience.kmath.misc.PerformancePitfall") } - - commonMain { - dependencies { - api(project(":kmath-core")) - api(project(":kmath-stat")) - } - } -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE - propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md")) - - feature( - id = "tensor algebra", - ref = "src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt" - ) { "Basic linear algebra operations on tensors (plus, dot, etc.)" } - - feature( - id = "tensor algebra with broadcasting", - ref = "src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt" - ) { "Basic linear algebra operations implemented with broadcasting." } - - feature( - id = "linear algebra operations", - ref = "src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt" - ) { "Advanced linear algebra operations like LU decomposition, SVD, etc." } -} diff --git a/kmath-tensors/docs/README-TEMPLATE.md b/kmath-tensors/docs/README-TEMPLATE.md deleted file mode 100644 index 5fd968afd..000000000 --- a/kmath-tensors/docs/README-TEMPLATE.md +++ /dev/null @@ -1,7 +0,0 @@ -# Module kmath-tensors - -Common linear algebra operations on tensors. - -${features} - -${artifact} diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt deleted file mode 100644 index 3ed34ae5e..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt +++ /dev/null @@ -1,149 +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. - */ - -package space.kscience.kmath.tensors.api - -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.ExtendedFieldOps -import space.kscience.kmath.operations.Field - - -/** - * Analytic operations on [Tensor]. - * - * @param T the type of items closed under analytic functions in the tensors. - */ -public interface AnalyticTensorAlgebra> : - TensorPartialDivisionAlgebra, ExtendedFieldOps> { - - /** - * @return the mean of all elements in the input tensor. - */ - public fun StructureND.mean(): T - - /** - * Returns the mean of each row of the input tensor in the given dimension [dim]. - * - * If [keepDim] is true, the output tensor is of the same size as - * input except in the dimension [dim] where it is of size 1. - * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension. - * - * @param dim the dimension to reduce. - * @param keepDim whether the output tensor has [dim] retained or not. - * @return the mean of each row of the input tensor in the given dimension [dim]. - */ - public fun StructureND.mean(dim: Int, keepDim: Boolean): Tensor - - /** - * @return the standard deviation of all elements in the input tensor. - */ - public fun StructureND.std(): T - - /** - * Returns the standard deviation of each row of the input tensor in the given dimension [dim]. - * - * If [keepDim] is true, the output tensor is of the same size as - * input except in the dimension [dim] where it is of size 1. - * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension. - * - * @param dim the dimension to reduce. - * @param keepDim whether the output tensor has [dim] retained or not. - * @return the standard deviation of each row of the input tensor in the given dimension [dim]. - */ - public fun StructureND.std(dim: Int, keepDim: Boolean): Tensor - - /** - * @return the variance of all elements in the input tensor. - */ - public fun StructureND.variance(): T - - /** - * Returns the variance of each row of the input tensor in the given dimension [dim]. - * - * If [keepDim] is true, the output tensor is of the same size as - * input except in the dimension [dim] where it is of size 1. - * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension. - * - * @param dim the dimension to reduce. - * @param keepDim whether the output tensor has [dim] retained or not. - * @return the variance of each row of the input tensor in the given dimension [dim]. - */ - public fun StructureND.variance(dim: Int, keepDim: Boolean): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.exp.html - public fun StructureND.exp(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.log.html - public fun StructureND.ln(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.sqrt.html - public fun StructureND.sqrt(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.acos.html#torch.cos - public fun StructureND.cos(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.acos.html#torch.acos - public fun StructureND.acos(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.cosh - public fun StructureND.cosh(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.acosh - public fun StructureND.acosh(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sin - public fun StructureND.sin(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asin - public fun StructureND.asin(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sinh - public fun StructureND.sinh(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asinh - public fun StructureND.asinh(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.atan.html#torch.tan - public fun StructureND.tan(): Tensor - - //https://pytorch.org/docs/stable/generated/torch.atan.html#torch.atan - public fun StructureND.atan(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.tanh - public fun StructureND.tanh(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.atanh - public fun StructureND.atanh(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.ceil.html#torch.ceil - public fun StructureND.ceil(): Tensor - - //For information: https://pytorch.org/docs/stable/generated/torch.floor.html#torch.floor - public fun StructureND.floor(): Tensor - - override fun sin(arg: StructureND): StructureND = arg.sin() - - override fun cos(arg: StructureND): StructureND = arg.cos() - - override fun asin(arg: StructureND): StructureND = arg.asin() - - override fun acos(arg: StructureND): StructureND = arg.acos() - - override fun atan(arg: StructureND): StructureND = arg.atan() - - override fun exp(arg: StructureND): StructureND = arg.exp() - - override fun ln(arg: StructureND): StructureND = arg.ln() - - override fun sinh(arg: StructureND): StructureND = arg.sinh() - - override fun cosh(arg: StructureND): StructureND = arg.cosh() - - override fun asinh(arg: StructureND): StructureND = arg.asinh() - - override fun acosh(arg: StructureND): StructureND = arg.acosh() - - override fun atanh(arg: StructureND): StructureND = arg.atanh() -} \ No newline at end of file diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt deleted file mode 100644 index 0bddc3f9c..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt +++ /dev/null @@ -1,106 +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. - */ - -package space.kscience.kmath.tensors.api - -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.Field - -/** - * Common linear algebra operations. Operates on [Tensor]. - * - * @param T the type of items closed under division in the tensors. - */ -public interface LinearOpsTensorAlgebra> : TensorPartialDivisionAlgebra { - - /** - * Computes the determinant of a square matrix input, or of each square matrix in a batched input. - * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.det - * - * @return the determinant. - */ - public fun StructureND.det(): Tensor - - /** - * Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input. - * Given a square matrix `A`, return the matrix `AInv` satisfying - * `A dot AInv == AInv dot A == eye(a.shape[0])`. - * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv - * - * @return the multiplicative inverse of a matrix. - */ - public fun StructureND.inv(): Tensor - - /** - * Cholesky decomposition. - * - * Computes the Cholesky decomposition of a Hermitian (or symmetric for real-valued matrices) - * positive-definite matrix or the Cholesky decompositions for a batch of such matrices. - * Each decomposition has the form: - * Given a tensor `input`, return the tensor `L` satisfying `input = L dot LH`, - * where `L` is a lower-triangular matrix and `LH` is the conjugate transpose of `L`, - * which is just a transpose for the case of real-valued input matrices. - * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.cholesky - * - * @receiver the `input`. - * @return the batch of `L` matrices. - */ - public fun StructureND.cholesky(): Tensor - - /** - * QR decomposition. - * - * Computes the QR decomposition of a matrix or a batch of matrices, and returns a pair `Q to R` of tensors. - * Given a tensor `input`, return tensors `Q to R` satisfying `input == Q dot R`, - * with `Q` being an orthogonal matrix or batch of orthogonal matrices - * and `R` being an upper triangular matrix or batch of upper triangular matrices. - * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr - * - * @receiver the `input`. - * @return pair of `Q` and `R` tensors. - */ - public fun StructureND.qr(): Pair, Tensor> - - /** - * LUP decomposition - * - * Computes the LUP decomposition of a matrix or a batch of matrices. - * Given a tensor `input`, return tensors (P, L, U) satisfying `P dot input = L dot U`, - * with `P` being a permutation matrix or batch of matrices, - * `L` being a lower triangular matrix or batch of matrices, - * `U` being an upper triangular matrix or batch of matrices. - * - * @receiver the `input`. - * @return triple of P, L and U tensors - */ - public fun StructureND.lu(): Triple, Tensor, Tensor> - - /** - * Singular Value Decomposition. - * - * Computes the singular value decomposition of either a matrix or batch of matrices `input`. - * The singular value decomposition is represented as a triple `Triple(U, S, V)`, - * such that `input = U dot diagonalEmbedding(S) dot VH`, - * where `VH` is the conjugate transpose of V. - * If `input` is a batch of tensors, then `U`, `S`, and `VH` are also batched with the same batch dimensions as - * `input`. - * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd - * - * @receiver the `input`. - * @return triple `Triple(U, S, V)`. - */ - public fun StructureND.svd(): Triple, Tensor, Tensor> - - /** - * Returns eigenvalues and eigenvectors of a real symmetric matrix `input` or a batch of real symmetric matrices, - * represented by a pair `eigenvalues to eigenvectors`. - * For more information: https://pytorch.org/docs/stable/generated/torch.symeig.html - * - * @receiver the `input`. - * @return a pair `eigenvalues to eigenvectors` - */ - public fun StructureND.symEig(): Pair, Tensor> - -} diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/Tensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/Tensor.kt deleted file mode 100644 index 482bb5244..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/Tensor.kt +++ /dev/null @@ -1,10 +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. - */ - -package space.kscience.kmath.tensors.api - -import space.kscience.kmath.nd.MutableStructureND - -public typealias Tensor = MutableStructureND diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt deleted file mode 100644 index 26c21c7a9..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt +++ /dev/null @@ -1,332 +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. - */ - -package space.kscience.kmath.tensors.api - -import space.kscience.kmath.nd.RingOpsND -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.Ring - -/** - * Algebra over a ring on [Tensor]. - * For more information: https://proofwiki.org/wiki/Definition:Algebra_over_Ring - * - * @param T the type of items in the tensors. - */ -public interface TensorAlgebra> : RingOpsND { - /** - * Returns a single tensor value of unit dimension if tensor shape equals to [1]. - * - * @return a nullable value of a potentially scalar tensor. - */ - public fun StructureND.valueOrNull(): T? - - /** - * Returns a single tensor value of unit dimension. The tensor shape must be equal to [1]. - * - * @return the value of a scalar tensor. - */ - public fun StructureND.value(): T = - valueOrNull() ?: throw IllegalArgumentException("Inconsistent value for tensor of with $shape shape") - - /** - * Each element of the tensor [arg] is added to this value. - * The resulting tensor is returned. - * - * @param arg tensor to be added. - * @return the sum of this value and tensor [arg]. - */ - override operator fun T.plus(arg: StructureND): Tensor - - /** - * Adds the scalar [arg] to each element of this tensor and returns a new resulting tensor. - * - * @param arg the number to be added to each element of this tensor. - * @return the sum of this tensor and [arg]. - */ - override operator fun StructureND.plus(arg: T): Tensor - - /** - * Each element of the tensor [arg] is added to each element of this tensor. - * The resulting tensor is returned. - * - * @param arg tensor to be added. - * @return the sum of this tensor and [arg]. - */ - override operator fun StructureND.plus(arg: StructureND): Tensor - - /** - * Adds the scalar [value] to each element of this tensor. - * - * @param value the number to be added to each element of this tensor. - */ - public operator fun Tensor.plusAssign(value: T) - - /** - * Each element of the tensor [arg] is added to each element of this tensor. - * - * @param arg tensor to be added. - */ - public operator fun Tensor.plusAssign(arg: StructureND) - - /** - * Each element of the tensor [arg] is subtracted from this value. - * The resulting tensor is returned. - * - * @param arg tensor to be subtracted. - * @return the difference between this value and tensor [arg]. - */ - override operator fun T.minus(arg: StructureND): Tensor - - /** - * Subtracts the scalar [arg] from each element of this tensor and returns a new resulting tensor. - * - * @param arg the number to be subtracted from each element of this tensor. - * @return the difference between this tensor and [arg]. - */ - override operator fun StructureND.minus(arg: T): Tensor - - /** - * Each element of the tensor [arg] is subtracted from each element of this tensor. - * The resulting tensor is returned. - * - * @param arg tensor to be subtracted. - * @return the difference between this tensor and [arg]. - */ - override operator fun StructureND.minus(arg: StructureND): Tensor - - /** - * Subtracts the scalar [value] from each element of this tensor. - * - * @param value the number to be subtracted from each element of this tensor. - */ - public operator fun Tensor.minusAssign(value: T) - - /** - * Each element of the tensor [arg] is subtracted from each element of this tensor. - * - * @param arg tensor to be subtracted. - */ - public operator fun Tensor.minusAssign(arg: StructureND) - - - /** - * Each element of the tensor [arg] is multiplied by this value. - * The resulting tensor is returned. - * - * @param arg tensor to be multiplied. - * @return the product of this value and tensor [arg]. - */ - override operator fun T.times(arg: StructureND): Tensor - - /** - * Multiplies the scalar [arg] by each element of this tensor and returns a new resulting tensor. - * - * @param arg the number to be multiplied by each element of this tensor. - * @return the product of this tensor and [arg]. - */ - override operator fun StructureND.times(arg: T): Tensor - - /** - * Each element of the tensor [arg] is multiplied by each element of this tensor. - * The resulting tensor is returned. - * - * @param arg tensor to be multiplied. - * @return the product of this tensor and [arg]. - */ - override operator fun StructureND.times(arg: StructureND): Tensor - - /** - * Multiplies the scalar [value] by each element of this tensor. - * - * @param value the number to be multiplied by each element of this tensor. - */ - public operator fun Tensor.timesAssign(value: T) - - /** - * Each element of the tensor [arg] is multiplied by each element of this tensor. - * - * @param arg tensor to be multiplied. - */ - public operator fun Tensor.timesAssign(arg: StructureND) - - /** - * Returns the tensor at index i - * For more information: https://pytorch.org/cppdocs/notes/tensor_indexing.html - * - * @param i index of the extractable tensor - * @return subtensor of the original tensor with index [i] - */ - public operator fun Tensor.get(i: Int): Tensor - - /** - * Returns a tensor that is a transposed version of this tensor. The given dimensions [i] and [j] are swapped. - * For more information: https://pytorch.org/docs/stable/generated/torch.transpose.html - * - * @param i the first dimension to be transposed - * @param j the second dimension to be transposed - * @return transposed tensor - */ - public fun Tensor.transpose(i: Int = -2, j: Int = -1): Tensor - - /** - * Returns a new tensor with the same data as the self tensor but of a different shape. - * The returned tensor shares the same data and must have the same number of elements, but may have a different size - * For more information: https://pytorch.org/docs/stable/tensor_view.html - * - * @param shape the desired size - * @return tensor with new shape - */ - public fun Tensor.view(shape: IntArray): Tensor - - /** - * View this tensor as the same size as [other]. - * `this.viewAs(other)` is equivalent to `this.view(other.shape)`. - * For more information: https://pytorch.org/cppdocs/notes/tensor_indexing.html - * - * @param other the result tensor has the same size as other. - * @return the result tensor with the same size as other. - */ - public fun Tensor.viewAs(other: StructureND): Tensor - - /** - * Matrix product of two tensors. - * - * The behavior depends on the dimensionality of the tensors as follows: - * 1. If both tensors are 1-dimensional, the dot product (scalar) is returned. - * - * 2. If both arguments are 2-dimensional, the matrix-matrix product is returned. - * - * 3. If the first argument is 1-dimensional and the second argument is 2-dimensional, - * a 1 is prepended to its dimension for the purpose of the matrix multiply. - * After the matrix multiply, depending on the implementation the prepended dimension might be removed. - * - * 4. If the first argument is 2-dimensional and the second argument is 1-dimensional, - * the matrix-vector product is returned. - * - * 5. If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), - * then a batched matrix multiply is returned. If the first argument is 1-dimensional, - * a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after. - * If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix - * multiple and removed after. - * The non-matrix (i.e., batch) dimensions are broadcast (and thus must be broadcastable). - * For example, if `input` is a (j × 1 × n × n) tensor and `other` is a - * (k × n × n) tensor, out will be a (j × k × n × n) tensor. - * - * For more information: https://pytorch.org/docs/stable/generated/torch.matmul.html - * - * @param other tensor to be multiplied. - * @return a mathematical product of two tensors. - */ - public infix fun StructureND.dot(other: StructureND): Tensor - - /** - * Creates a tensor whose diagonals of certain 2D planes (specified by [dim1] and [dim2]) - * are filled by [diagonalEntries]. - * To facilitate creating batched diagonal matrices, - * the 2D planes formed by the last two dimensions of the returned tensor are chosen by default. - * - * The argument [offset] controls, which diagonal to consider: - * 1. If [offset] = 0, it is the main diagonal. - * 1. If [offset] > 0, it is above the main diagonal. - * 1. If [offset] < 0, it is below the main diagonal. - * - * The size of the new matrix will be calculated - * to make the specified diagonal of the size of the last input dimension. - * For more information: https://pytorch.org/docs/stable/generated/torch.diag_embed.html - * - * @param diagonalEntries the input tensor. Must be at least 1-dimensional. - * @param offset which diagonal to consider. Default: 0 (main diagonal). - * @param dim1 first dimension with respect to which to take diagonal. Default: -2. - * @param dim2 second dimension with respect to which to take diagonal. Default: -1. - * - * @return tensor whose diagonals of certain 2D planes (specified by [dim1] and [dim2]) - * are filled by [diagonalEntries] - */ - public fun diagonalEmbedding( - diagonalEntries: Tensor, - offset: Int = 0, - dim1: Int = -2, - dim2: Int = -1, - ): Tensor - - /** - * @return the sum of all elements in the input tensor. - */ - public fun StructureND.sum(): T - - /** - * Returns the sum of each row of the input tensor in the given dimension [dim]. - * - * If [keepDim] is true, the output tensor is of the same size as - * input except in the dimension [dim] where it is of size 1. - * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension. - * - * @param dim the dimension to reduce. - * @param keepDim whether the output tensor has [dim] retained or not. - * @return the sum of each row of the input tensor in the given dimension [dim]. - */ - public fun StructureND.sum(dim: Int, keepDim: Boolean): Tensor - - /** - * @return the minimum value of all elements in the input tensor or null if there are no values - */ - public fun StructureND.min(): T? - - /** - * Returns the minimum value of each row of the input tensor in the given dimension [dim]. - * - * If [keepDim] is true, the output tensor is of the same size as - * input except in the dimension [dim] where it is of size 1. - * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension. - * - * @param dim the dimension to reduce. - * @param keepDim whether the output tensor has [dim] retained or not. - * @return the minimum value of each row of the input tensor in the given dimension [dim]. - */ - public fun StructureND.min(dim: Int, keepDim: Boolean): Tensor - - /** - * Returns the maximum value of all elements in the input tensor or null if there are no values - */ - public fun StructureND.max(): T? - - /** - * Returns the maximum value of each row of the input tensor in the given dimension [dim]. - * - * If [keepDim] is true, the output tensor is of the same size as - * input except in the dimension [dim] where it is of size 1. - * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension. - * - * @param dim the dimension to reduce. - * @param keepDim whether the output tensor has [dim] retained or not. - * @return the maximum value of each row of the input tensor in the given dimension [dim]. - */ - public fun StructureND.max(dim: Int, keepDim: Boolean): Tensor - - /** - * Returns the index of maximum value of each row of the input tensor in the given dimension [dim]. - * - * If [keepDim] is true, the output tensor is of the same size as - * input except in the dimension [dim] where it is of size 1. - * Otherwise, [dim] is squeezed, resulting in the output tensor having 1 fewer dimension. - * - * @param dim the dimension to reduce. - * @param keepDim whether the output tensor has [dim] retained or not. - * @return the index of maximum value of each row of the input tensor in the given dimension [dim]. - */ - public fun StructureND.argMax(dim: Int, keepDim: Boolean): Tensor - - override fun add(left: StructureND, right: StructureND): Tensor = left + right - - /** - * Numerical negative, element-wise. - * - * @return tensor negation of the original tensor. - */ - override fun negate(arg: StructureND): Tensor - - override fun multiply(left: StructureND, right: StructureND): Tensor = left * right -} diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt deleted file mode 100644 index 9c492cda1..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt +++ /dev/null @@ -1,61 +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. - */ - -package space.kscience.kmath.tensors.api - -import space.kscience.kmath.nd.FieldOpsND -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.operations.Field - -/** - * Algebra over a field with partial division on [Tensor]. - * For more information: https://proofwiki.org/wiki/Definition:Division_Algebra - * - * @param T the type of items closed under division in the tensors. - */ -public interface TensorPartialDivisionAlgebra> : TensorAlgebra, FieldOpsND { - - /** - * Each element of the tensor [arg] is divided by this value. - * The resulting tensor is returned. - * - * @param arg tensor to divide by. - * @return the division of this value by the tensor [arg]. - */ - override operator fun T.div(arg: StructureND): Tensor - - /** - * Divide by the scalar [arg] each element of this tensor returns a new resulting tensor. - * - * @param arg the number to divide by each element of this tensor. - * @return the division of this tensor by the [arg]. - */ - override operator fun StructureND.div(arg: T): Tensor - - /** - * Each element of the tensor [arg] is divided by each element of this tensor. - * The resulting tensor is returned. - * - * @param arg tensor to be divided by. - * @return the division of this tensor by [arg]. - */ - override operator fun StructureND.div(arg: StructureND): Tensor - - override fun divide(left: StructureND, right: StructureND): StructureND = left.div(right) - - /** - * Divides by the scalar [value] each element of this tensor. - * - * @param value the number to divide by each element of this tensor. - */ - public operator fun Tensor.divAssign(value: T) - - /** - * Each element of this tensor is divided by each element of the [arg] tensor. - * - * @param arg tensor to be divided by. - */ - public operator fun Tensor.divAssign(arg: StructureND) -} diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt deleted file mode 100644 index e412ab5bb..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt +++ /dev/null @@ -1,107 +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. - */ - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.tensors.api.Tensor -import space.kscience.kmath.tensors.core.internal.array -import space.kscience.kmath.tensors.core.internal.broadcastTensors -import space.kscience.kmath.tensors.core.internal.broadcastTo -import space.kscience.kmath.tensors.core.internal.tensor - -/** - * Basic linear algebra operations implemented with broadcasting. - * For more information: https://pytorch.org/docs/stable/notes/broadcasting.html - */ - -@PerformancePitfall -public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() { - - override fun StructureND.plus(arg: StructureND): DoubleTensor { - val broadcast = broadcastTensors(tensor, arg.tensor) - val newThis = broadcast[0] - val newOther = broadcast[1] - val resBuffer = DoubleArray(newThis.indices.linearSize) { i -> - newThis.mutableBuffer.array()[i] + newOther.mutableBuffer.array()[i] - } - return DoubleTensor(newThis.shape, resBuffer) - } - - override fun Tensor.plusAssign(arg: StructureND) { - val newOther = broadcastTo(arg.tensor, tensor.shape) - for (i in 0 until tensor.indices.linearSize) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] += - newOther.mutableBuffer.array()[tensor.bufferStart + i] - } - } - - override fun StructureND.minus(arg: StructureND): DoubleTensor { - val broadcast = broadcastTensors(tensor, arg.tensor) - val newThis = broadcast[0] - val newOther = broadcast[1] - val resBuffer = DoubleArray(newThis.indices.linearSize) { i -> - newThis.mutableBuffer.array()[i] - newOther.mutableBuffer.array()[i] - } - return DoubleTensor(newThis.shape, resBuffer) - } - - override fun Tensor.minusAssign(arg: StructureND) { - val newOther = broadcastTo(arg.tensor, tensor.shape) - for (i in 0 until tensor.indices.linearSize) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] -= - newOther.mutableBuffer.array()[tensor.bufferStart + i] - } - } - - override fun StructureND.times(arg: StructureND): DoubleTensor { - val broadcast = broadcastTensors(tensor, arg.tensor) - val newThis = broadcast[0] - val newOther = broadcast[1] - val resBuffer = DoubleArray(newThis.indices.linearSize) { i -> - newThis.mutableBuffer.array()[newThis.bufferStart + i] * - newOther.mutableBuffer.array()[newOther.bufferStart + i] - } - return DoubleTensor(newThis.shape, resBuffer) - } - - override fun Tensor.timesAssign(arg: StructureND) { - val newOther = broadcastTo(arg.tensor, tensor.shape) - for (i in 0 until tensor.indices.linearSize) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] *= - newOther.mutableBuffer.array()[tensor.bufferStart + i] - } - } - - override fun StructureND.div(arg: StructureND): DoubleTensor { - val broadcast = broadcastTensors(tensor, arg.tensor) - val newThis = broadcast[0] - val newOther = broadcast[1] - val resBuffer = DoubleArray(newThis.indices.linearSize) { i -> - newThis.mutableBuffer.array()[newOther.bufferStart + i] / - newOther.mutableBuffer.array()[newOther.bufferStart + i] - } - return DoubleTensor(newThis.shape, resBuffer) - } - - override fun Tensor.divAssign(arg: StructureND) { - val newOther = broadcastTo(arg.tensor, tensor.shape) - for (i in 0 until tensor.indices.linearSize) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] /= - newOther.mutableBuffer.array()[tensor.bufferStart + i] - } - } -} - - -/** - * Compute a value using broadcast double tensor algebra - */ -@UnstableKMathAPI -@PerformancePitfall -public fun DoubleTensorAlgebra.withBroadcast(block: BroadcastDoubleTensorAlgebra.() -> R): R = - BroadcastDoubleTensorAlgebra.block() \ No newline at end of file diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt deleted file mode 100644 index 73a89502c..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt +++ /dev/null @@ -1,43 +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. - */ - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.Strides -import space.kscience.kmath.structures.MutableBuffer -import space.kscience.kmath.tensors.api.Tensor - -/** - * Represents [Tensor] over a [MutableBuffer] intended to be used through [DoubleTensor] and [IntTensor] - */ -public open class BufferedTensor internal constructor( - override val shape: IntArray, - @PublishedApi internal val mutableBuffer: MutableBuffer, - @PublishedApi internal val bufferStart: Int, -) : Tensor { - - /** - * Buffer strides based on [TensorLinearStructure] implementation - */ - override val indices: Strides get() = TensorLinearStructure(shape) - - /** - * Number of elements in tensor - */ - public val numElements: Int - get() = indices.linearSize - - override fun get(index: IntArray): T = mutableBuffer[bufferStart + indices.offset(index)] - - override fun set(index: IntArray, value: T) { - mutableBuffer[bufferStart + indices.offset(index)] = value - } - - @PerformancePitfall - override fun elements(): Sequence> = indices.asSequence().map { - it to get(it) - } -} diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt deleted file mode 100644 index 8e5116336..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt +++ /dev/null @@ -1,20 +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. - */ - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.tensors.core.internal.toPrettyString - -/** - * Default [BufferedTensor] implementation for [Double] values - */ -public class DoubleTensor @PublishedApi internal constructor( - shape: IntArray, - buffer: DoubleArray, - offset: Int = 0 -) : BufferedTensor(shape, DoubleBuffer(buffer), offset) { - override fun toString(): String = toPrettyString() -} diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt deleted file mode 100644 index e9dc34748..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt +++ /dev/null @@ -1,1144 +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. - */ - - -@file:OptIn(PerformancePitfall::class) - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.* -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.structures.MutableBuffer -import space.kscience.kmath.structures.indices -import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra -import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra -import space.kscience.kmath.tensors.api.Tensor -import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra -import space.kscience.kmath.tensors.core.internal.* -import kotlin.math.* - -/** - * Implementation of basic operations over double tensors and basic algebra operations on them. - */ -public open class DoubleTensorAlgebra : - TensorPartialDivisionAlgebra, - AnalyticTensorAlgebra, - LinearOpsTensorAlgebra { - - public companion object : DoubleTensorAlgebra() - - override val elementAlgebra: DoubleField - get() = DoubleField - - - /** - * Applies the [transform] function to each element of the tensor and returns the resulting modified tensor. - * - * @param transform the function to be applied to each element of the tensor. - * @return the resulting tensor after applying the function. - */ - @PerformancePitfall - @Suppress("OVERRIDE_BY_INLINE") - final override inline fun StructureND.map(transform: DoubleField.(Double) -> Double): DoubleTensor { - val tensor = this.tensor - //TODO remove additional copy - val sourceArray = tensor.copyArray() - val array = DoubleArray(tensor.numElements) { DoubleField.transform(sourceArray[it]) } - return DoubleTensor( - tensor.shape, - array, - tensor.bufferStart - ) - } - - @PerformancePitfall - @Suppress("OVERRIDE_BY_INLINE") - final override inline fun StructureND.mapIndexed(transform: DoubleField.(index: IntArray, Double) -> Double): DoubleTensor { - val tensor = this.tensor - //TODO remove additional copy - val sourceArray = tensor.copyArray() - val array = DoubleArray(tensor.numElements) { DoubleField.transform(tensor.indices.index(it), sourceArray[it]) } - return DoubleTensor( - tensor.shape, - array, - tensor.bufferStart - ) - } - - @PerformancePitfall - override fun zip( - left: StructureND, - right: StructureND, - transform: DoubleField.(Double, Double) -> Double, - ): DoubleTensor { - require(left.shape.contentEquals(right.shape)) { - "The shapes in zip are not equal: left - ${left.shape}, right - ${right.shape}" - } - val leftTensor = left.tensor - val leftArray = leftTensor.copyArray() - val rightTensor = right.tensor - val rightArray = rightTensor.copyArray() - val array = DoubleArray(leftTensor.numElements) { DoubleField.transform(leftArray[it], rightArray[it]) } - return DoubleTensor( - leftTensor.shape, - array - ) - } - - override fun StructureND.valueOrNull(): Double? = if (tensor.shape contentEquals intArrayOf(1)) - tensor.mutableBuffer.array()[tensor.bufferStart] else null - - override fun StructureND.value(): Double = valueOrNull() - ?: throw IllegalArgumentException("The tensor shape is $shape, but value method is allowed only for shape [1]") - - /** - * Constructs a tensor with the specified shape and data. - * - * @param shape the desired shape for the tensor. - * @param buffer one-dimensional data array. - * @return tensor with the [shape] shape and [buffer] data. - */ - public fun fromArray(shape: IntArray, buffer: DoubleArray): DoubleTensor { - checkEmptyShape(shape) - checkEmptyDoubleBuffer(buffer) - checkBufferShapeConsistency(shape, buffer) - return DoubleTensor(shape, buffer, 0) - } - - /** - * Constructs a tensor with the specified shape and initializer. - * - * @param shape the desired shape for the tensor. - * @param initializer mapping tensor indices to values. - * @return tensor with the [shape] shape and data generated by the [initializer]. - */ - override fun structureND(shape: IntArray, initializer: DoubleField.(IntArray) -> Double): DoubleTensor = fromArray( - shape, - TensorLinearStructure(shape).asSequence().map { DoubleField.initializer(it) }.toMutableList().toDoubleArray() - ) - - override operator fun Tensor.get(i: Int): DoubleTensor { - val lastShape = tensor.shape.drop(1).toIntArray() - val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1) - val newStart = newShape.reduce(Int::times) * i + tensor.bufferStart - return DoubleTensor(newShape, tensor.mutableBuffer.array(), newStart) - } - - /** - * Creates a tensor of a given shape and fills all elements with a given value. - * - * @param value the value to fill the output tensor with. - * @param shape array of integers defining the shape of the output tensor. - * @return tensor with the [shape] shape and filled with [value]. - */ - public fun full(value: Double, shape: IntArray): DoubleTensor { - checkEmptyShape(shape) - val buffer = DoubleArray(shape.reduce(Int::times)) { value } - return DoubleTensor(shape, buffer) - } - - /** - * Returns a tensor with the same shape as `input` filled with [value]. - * - * @param value the value to fill the output tensor with. - * @return tensor with the `input` tensor shape and filled with [value]. - */ - public fun Tensor.fullLike(value: Double): DoubleTensor { - val shape = tensor.shape - val buffer = DoubleArray(tensor.numElements) { value } - return DoubleTensor(shape, buffer) - } - - /** - * Returns a tensor filled with the scalar value `0.0`, with the shape defined by the variable argument [shape]. - * - * @param shape array of integers defining the shape of the output tensor. - * @return tensor filled with the scalar value `0.0`, with the [shape] shape. - */ - public fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape) - - /** - * Returns a tensor filled with the scalar value `0.0`, with the same shape as a given array. - * - * @return tensor filled with the scalar value `0.0`, with the same shape as `input` tensor. - */ - public fun StructureND.zeroesLike(): DoubleTensor = tensor.fullLike(0.0) - - /** - * Returns a tensor filled with the scalar value `1.0`, with the shape defined by the variable argument [shape]. - * - * @param shape array of integers defining the shape of the output tensor. - * @return tensor filled with the scalar value `1.0`, with the [shape] shape. - */ - public fun ones(shape: IntArray): DoubleTensor = full(1.0, shape) - - /** - * Returns a tensor filled with the scalar value `1.0`, with the same shape as a given array. - * - * @return tensor filled with the scalar value `1.0`, with the same shape as `input` tensor. - */ - public fun Tensor.onesLike(): DoubleTensor = tensor.fullLike(1.0) - - /** - * Returns a 2D tensor with shape ([n], [n]), with ones on the diagonal and zeros elsewhere. - * - * @param n the number of rows and columns - * @return a 2-D tensor with ones on the diagonal and zeros elsewhere. - */ - public fun eye(n: Int): DoubleTensor { - val shape = intArrayOf(n, n) - val buffer = DoubleArray(n * n) { 0.0 } - val res = DoubleTensor(shape, buffer) - for (i in 0 until n) { - res[intArrayOf(i, i)] = 1.0 - } - return res - } - - /** - * Return a copy of the tensor. - * - * @return a copy of the `input` tensor with a copied buffer. - */ - public fun StructureND.copy(): DoubleTensor = - DoubleTensor(tensor.shape, tensor.mutableBuffer.array().copyOf(), tensor.bufferStart) - - override fun Double.plus(arg: StructureND): DoubleTensor { - val resBuffer = DoubleArray(arg.tensor.numElements) { i -> - arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] + this - } - return DoubleTensor(arg.shape, resBuffer) - } - - override fun StructureND.plus(arg: Double): DoubleTensor = arg + tensor - - override fun StructureND.plus(arg: StructureND): DoubleTensor { - checkShapesCompatible(tensor, arg.tensor) - val resBuffer = DoubleArray(tensor.numElements) { i -> - tensor.mutableBuffer.array()[i] + arg.tensor.mutableBuffer.array()[i] - } - return DoubleTensor(tensor.shape, resBuffer) - } - - override fun Tensor.plusAssign(value: Double) { - for (i in 0 until tensor.numElements) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] += value - } - } - - override fun Tensor.plusAssign(arg: StructureND) { - checkShapesCompatible(tensor, arg.tensor) - for (i in 0 until tensor.numElements) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] += - arg.tensor.mutableBuffer.array()[tensor.bufferStart + i] - } - } - - override fun Double.minus(arg: StructureND): DoubleTensor { - val resBuffer = DoubleArray(arg.tensor.numElements) { i -> - this - arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] - } - return DoubleTensor(arg.shape, resBuffer) - } - - override fun StructureND.minus(arg: Double): DoubleTensor { - val resBuffer = DoubleArray(tensor.numElements) { i -> - tensor.mutableBuffer.array()[tensor.bufferStart + i] - arg - } - return DoubleTensor(tensor.shape, resBuffer) - } - - override fun StructureND.minus(arg: StructureND): DoubleTensor { - checkShapesCompatible(tensor, arg) - val resBuffer = DoubleArray(tensor.numElements) { i -> - tensor.mutableBuffer.array()[i] - arg.tensor.mutableBuffer.array()[i] - } - return DoubleTensor(tensor.shape, resBuffer) - } - - override fun Tensor.minusAssign(value: Double) { - for (i in 0 until tensor.numElements) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] -= value - } - } - - override fun Tensor.minusAssign(arg: StructureND) { - checkShapesCompatible(tensor, arg) - for (i in 0 until tensor.numElements) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] -= - arg.tensor.mutableBuffer.array()[tensor.bufferStart + i] - } - } - - override fun Double.times(arg: StructureND): DoubleTensor { - val resBuffer = DoubleArray(arg.tensor.numElements) { i -> - arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] * this - } - return DoubleTensor(arg.shape, resBuffer) - } - - override fun StructureND.times(arg: Double): DoubleTensor = arg * tensor - - override fun StructureND.times(arg: StructureND): DoubleTensor { - checkShapesCompatible(tensor, arg) - val resBuffer = DoubleArray(tensor.numElements) { i -> - tensor.mutableBuffer.array()[tensor.bufferStart + i] * - arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] - } - return DoubleTensor(tensor.shape, resBuffer) - } - - override fun Tensor.timesAssign(value: Double) { - for (i in 0 until tensor.numElements) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] *= value - } - } - - override fun Tensor.timesAssign(arg: StructureND) { - checkShapesCompatible(tensor, arg) - for (i in 0 until tensor.numElements) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] *= - arg.tensor.mutableBuffer.array()[tensor.bufferStart + i] - } - } - - override fun Double.div(arg: StructureND): DoubleTensor { - val resBuffer = DoubleArray(arg.tensor.numElements) { i -> - this / arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] - } - return DoubleTensor(arg.shape, resBuffer) - } - - override fun StructureND.div(arg: Double): DoubleTensor { - val resBuffer = DoubleArray(tensor.numElements) { i -> - tensor.mutableBuffer.array()[tensor.bufferStart + i] / arg - } - return DoubleTensor(shape, resBuffer) - } - - override fun StructureND.div(arg: StructureND): DoubleTensor { - checkShapesCompatible(tensor, arg) - val resBuffer = DoubleArray(tensor.numElements) { i -> - tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] / - arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] - } - return DoubleTensor(tensor.shape, resBuffer) - } - - override fun Tensor.divAssign(value: Double) { - for (i in 0 until tensor.numElements) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] /= value - } - } - - override fun Tensor.divAssign(arg: StructureND) { - checkShapesCompatible(tensor, arg) - for (i in 0 until tensor.numElements) { - tensor.mutableBuffer.array()[tensor.bufferStart + i] /= - arg.tensor.mutableBuffer.array()[tensor.bufferStart + i] - } - } - - override fun StructureND.unaryMinus(): DoubleTensor { - val resBuffer = DoubleArray(tensor.numElements) { i -> - tensor.mutableBuffer.array()[tensor.bufferStart + i].unaryMinus() - } - return DoubleTensor(tensor.shape, resBuffer) - } - - override fun Tensor.transpose(i: Int, j: Int): DoubleTensor { - val ii = tensor.minusIndex(i) - val jj = tensor.minusIndex(j) - checkTranspose(tensor.dimension, ii, jj) - val n = tensor.numElements - val resBuffer = DoubleArray(n) - - val resShape = tensor.shape.copyOf() - resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] } - - val resTensor = DoubleTensor(resShape, resBuffer) - - for (offset in 0 until n) { - val oldMultiIndex = tensor.indices.index(offset) - val newMultiIndex = oldMultiIndex.copyOf() - newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] } - - val linearIndex = resTensor.indices.offset(newMultiIndex) - resTensor.mutableBuffer.array()[linearIndex] = - tensor.mutableBuffer.array()[tensor.bufferStart + offset] - } - return resTensor - } - - override fun Tensor.view(shape: IntArray): DoubleTensor { - checkView(tensor, shape) - return DoubleTensor(shape, tensor.mutableBuffer.array(), tensor.bufferStart) - } - - override fun Tensor.viewAs(other: StructureND): DoubleTensor = - tensor.view(other.shape) - - override infix fun StructureND.dot(other: StructureND): DoubleTensor { - if (tensor.shape.size == 1 && other.shape.size == 1) { - return DoubleTensor(intArrayOf(1), doubleArrayOf(tensor.times(other).tensor.mutableBuffer.array().sum())) - } - - var newThis = tensor.copy() - var newOther = other.copy() - - var penultimateDim = false - var lastDim = false - if (tensor.shape.size == 1) { - penultimateDim = true - newThis = tensor.view(intArrayOf(1) + tensor.shape) - } - if (other.shape.size == 1) { - lastDim = true - newOther = other.tensor.view(other.shape + intArrayOf(1)) - } - - val broadcastTensors = broadcastOuterTensors(newThis.tensor, newOther.tensor) - newThis = broadcastTensors[0] - newOther = broadcastTensors[1] - - val l = newThis.shape[newThis.shape.size - 2] - val m1 = newThis.shape[newThis.shape.size - 1] - val m2 = newOther.shape[newOther.shape.size - 2] - val n = newOther.shape[newOther.shape.size - 1] - check(m1 == m2) { - "Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)" - } - - val resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last()) - val resSize = resShape.reduce { acc, i -> acc * i } - val resTensor = DoubleTensor(resShape, DoubleArray(resSize)) - - for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) { - val (a, b) = ab - dotTo(a, b, res, l, m1, n) - } - - return if (penultimateDim) { - resTensor.view(resTensor.shape.dropLast(2).toIntArray() + intArrayOf(resTensor.shape.last())) - } else if (lastDim) { - resTensor.view(resTensor.shape.dropLast(1).toIntArray()) - } else { - resTensor - } - } - - override fun diagonalEmbedding( - diagonalEntries: Tensor, - offset: Int, - dim1: Int, - dim2: Int, - ): DoubleTensor { - val n = diagonalEntries.shape.size - val d1 = minusIndexFrom(n + 1, dim1) - val d2 = minusIndexFrom(n + 1, dim2) - - check(d1 != d2) { - "Diagonal dimensions cannot be identical $d1, $d2" - } - check(d1 <= n && d2 <= n) { - "Dimension out of range" - } - - var lessDim = d1 - var greaterDim = d2 - var realOffset = offset - if (lessDim > greaterDim) { - realOffset *= -1 - lessDim = greaterDim.also { greaterDim = lessDim } - } - - val resShape = diagonalEntries.shape.slice(0 until lessDim).toIntArray() + - intArrayOf(diagonalEntries.shape[n - 1] + abs(realOffset)) + - diagonalEntries.shape.slice(lessDim until greaterDim - 1).toIntArray() + - intArrayOf(diagonalEntries.shape[n - 1] + abs(realOffset)) + - diagonalEntries.shape.slice(greaterDim - 1 until n - 1).toIntArray() - val resTensor = zeros(resShape) - - for (i in 0 until diagonalEntries.tensor.numElements) { - val multiIndex = diagonalEntries.tensor.indices.index(i) - - var offset1 = 0 - var offset2 = abs(realOffset) - if (realOffset < 0) { - offset1 = offset2.also { offset2 = offset1 } - } - val diagonalMultiIndex = multiIndex.slice(0 until lessDim).toIntArray() + - intArrayOf(multiIndex[n - 1] + offset1) + - multiIndex.slice(lessDim until greaterDim - 1).toIntArray() + - intArrayOf(multiIndex[n - 1] + offset2) + - multiIndex.slice(greaterDim - 1 until n - 1).toIntArray() - - resTensor[diagonalMultiIndex] = diagonalEntries[multiIndex] - } - - return resTensor.tensor - } - - /** - * Compares element-wise two tensors with a specified precision. - * - * @param other the tensor to compare with `input` tensor. - * @param epsilon permissible error when comparing two Double values. - * @return true if two tensors have the same shape and elements, false otherwise. - */ - public fun Tensor.eq(other: Tensor, epsilon: Double): Boolean = - tensor.eq(other) { x, y -> abs(x - y) < epsilon } - - /** - * Compares element-wise two tensors. - * Comparison of two Double values occurs with `1e-5` precision. - * - * @param other the tensor to compare with `input` tensor. - * @return true if two tensors have the same shape and elements, false otherwise. - */ - public infix fun Tensor.eq(other: Tensor): Boolean = tensor.eq(other, 1e-5) - - private fun Tensor.eq( - other: Tensor, - eqFunction: (Double, Double) -> Boolean, - ): Boolean { - checkShapesCompatible(tensor, other) - val n = tensor.numElements - if (n != other.tensor.numElements) { - return false - } - for (i in 0 until n) { - if (!eqFunction( - tensor.mutableBuffer[tensor.bufferStart + i], - other.tensor.mutableBuffer[other.tensor.bufferStart + i] - ) - ) { - return false - } - } - return true - } - - /** - * Returns a tensor of random numbers drawn from normal distributions with `0.0` mean and `1.0` standard deviation. - * - * @param shape the desired shape for the output tensor. - * @param seed the random seed of the pseudo-random number generator. - * @return tensor of a given shape filled with numbers from the normal distribution - * with `0.0` mean and `1.0` standard deviation. - */ - public fun randomNormal(shape: IntArray, seed: Long = 0): DoubleTensor = - DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed)) - - /** - * Returns a tensor with the same shape as `input` of random numbers drawn from normal distributions - * with `0.0` mean and `1.0` standard deviation. - * - * @receiver the `input`. - * @param seed the random seed of the pseudo-random number generator. - * @return a tensor with the same shape as `input` filled with numbers from the normal distribution - * with `0.0` mean and `1.0` standard deviation. - */ - public fun Tensor.randomNormalLike(seed: Long = 0): DoubleTensor = - DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed)) - - /** - * Concatenates a sequence of tensors with equal shapes along the first dimension. - * - * @param tensors the [List] of tensors with same shapes to concatenate - * @return tensor with concatenation result - */ - public fun stack(tensors: List>): DoubleTensor { - check(tensors.isNotEmpty()) { "List must have at least 1 element" } - val shape = tensors[0].shape - check(tensors.all { it.shape contentEquals shape }) { "Tensors must have same shapes" } - val resShape = intArrayOf(tensors.size) + shape - val resBuffer = tensors.flatMap { - it.tensor.mutableBuffer.array().drop(it.tensor.bufferStart).take(it.tensor.numElements) - }.toDoubleArray() - return DoubleTensor(resShape, resBuffer, 0) - } - - /** - * Builds tensor from rows of the input tensor. - * - * @param indices the [IntArray] of 1-dimensional indices - * @return tensor with rows corresponding to row by [indices] - */ - public fun Tensor.rowsByIndices(indices: IntArray): DoubleTensor = stack(indices.map { this[it] }) - - private inline fun StructureND.fold(foldFunction: (DoubleArray) -> Double): Double = - foldFunction(tensor.copyArray()) - - private inline fun StructureND.foldDim( - dim: Int, - keepDim: Boolean, - foldFunction: (DoubleArray) -> R, - ): BufferedTensor { - check(dim < dimension) { "Dimension $dim out of range $dimension" } - val resShape = if (keepDim) { - shape.take(dim).toIntArray() + intArrayOf(1) + shape.takeLast(dimension - dim - 1).toIntArray() - } else { - shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray() - } - val resNumElements = resShape.reduce(Int::times) - val init = foldFunction(DoubleArray(1) { 0.0 }) - val resTensor = BufferedTensor(resShape, - MutableBuffer.auto(resNumElements) { init }, 0) - for (index in resTensor.indices) { - val prefix = index.take(dim).toIntArray() - val suffix = index.takeLast(dimension - dim - 1).toIntArray() - resTensor[index] = foldFunction(DoubleArray(shape[dim]) { i -> - tensor[prefix + intArrayOf(i) + suffix] - }) - } - return resTensor - } - - override fun StructureND.sum(): Double = tensor.fold { it.sum() } - - override fun StructureND.sum(dim: Int, keepDim: Boolean): DoubleTensor = - foldDim(dim, keepDim) { x -> x.sum() }.toDoubleTensor() - - override fun StructureND.min(): Double = this.fold { it.minOrNull()!! } - - override fun StructureND.min(dim: Int, keepDim: Boolean): DoubleTensor = - foldDim(dim, keepDim) { x -> x.minOrNull()!! }.toDoubleTensor() - - override fun StructureND.max(): Double = this.fold { it.maxOrNull()!! } - - override fun StructureND.max(dim: Int, keepDim: Boolean): DoubleTensor = - foldDim(dim, keepDim) { x -> x.maxOrNull()!! }.toDoubleTensor() - - - override fun StructureND.argMax(dim: Int, keepDim: Boolean): IntTensor = - foldDim(dim, keepDim) { x -> - x.withIndex().maxByOrNull { it.value }?.index!! - }.toIntTensor() - - - override fun StructureND.mean(): Double = this.fold { it.sum() / tensor.numElements } - - override fun StructureND.mean(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(dim, keepDim) { arr -> - check(dim < dimension) { "Dimension $dim out of range $dimension" } - arr.sum() / shape[dim] - }.toDoubleTensor() - - override fun StructureND.std(): Double = fold { arr -> - val mean = arr.sum() / tensor.numElements - sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)) - } - - override fun StructureND.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim( - dim, - keepDim - ) { arr -> - check(dim < dimension) { "Dimension $dim out of range $dimension" } - val mean = arr.sum() / shape[dim] - sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)) - }.toDoubleTensor() - - override fun StructureND.variance(): Double = fold { arr -> - val mean = arr.sum() / tensor.numElements - arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1) - } - - override fun StructureND.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim( - dim, - keepDim - ) { arr -> - check(dim < dimension) { "Dimension $dim out of range $dimension" } - val mean = arr.sum() / shape[dim] - arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1) - }.toDoubleTensor() - - private fun cov(x: DoubleTensor, y: DoubleTensor): Double { - val n = x.shape[0] - return ((x - x.mean()) * (y - y.mean())).mean() * n / (n - 1) - } - - /** - * Returns the covariance matrix `M` of given vectors. - * - * `M[i, j]` contains covariance of `i`-th and `j`-th given vectors - * - * @param tensors the [List] of 1-dimensional tensors with same shape - * @return `M`. - */ - public fun cov(tensors: List>): DoubleTensor { - check(tensors.isNotEmpty()) { "List must have at least 1 element" } - val n = tensors.size - val m = tensors[0].shape[0] - check(tensors.all { it.shape contentEquals intArrayOf(m) }) { "Tensors must have same shapes" } - val resTensor = DoubleTensor( - intArrayOf(n, n), - DoubleArray(n * n) { 0.0 } - ) - for (i in 0 until n) { - for (j in 0 until n) { - resTensor[intArrayOf(i, j)] = cov(tensors[i].tensor, tensors[j].tensor) - } - } - return resTensor - } - - override fun StructureND.exp(): DoubleTensor = tensor.map { exp(it) } - - override fun StructureND.ln(): DoubleTensor = tensor.map { ln(it) } - - override fun StructureND.sqrt(): DoubleTensor = tensor.map { sqrt(it) } - - override fun StructureND.cos(): DoubleTensor = tensor.map { cos(it) } - - override fun StructureND.acos(): DoubleTensor = tensor.map { acos(it) } - - override fun StructureND.cosh(): DoubleTensor = tensor.map { cosh(it) } - - override fun StructureND.acosh(): DoubleTensor = tensor.map { acosh(it) } - - override fun StructureND.sin(): DoubleTensor = tensor.map { sin(it) } - - override fun StructureND.asin(): DoubleTensor = tensor.map { asin(it) } - - override fun StructureND.sinh(): DoubleTensor = tensor.map { sinh(it) } - - override fun StructureND.asinh(): DoubleTensor = tensor.map { asinh(it) } - - override fun StructureND.tan(): DoubleTensor = tensor.map { tan(it) } - - override fun StructureND.atan(): DoubleTensor = tensor.map { atan(it) } - - override fun StructureND.tanh(): DoubleTensor = tensor.map { tanh(it) } - - override fun StructureND.atanh(): DoubleTensor = tensor.map { atanh(it) } - - override fun StructureND.ceil(): DoubleTensor = tensor.map { ceil(it) } - - override fun StructureND.floor(): DoubleTensor = tensor.map { floor(it) } - - override fun StructureND.inv(): DoubleTensor = invLU(1e-9) - - override fun StructureND.det(): DoubleTensor = detLU(1e-9) - - /** - * Computes the LU factorization of a matrix or batches of matrices `input`. - * Returns a tuple containing the LU factorization and pivots of `input`. - * - * @param epsilon permissible error when comparing the determinant of a matrix with zero - * @return pair of `factorization` and `pivots`. - * The `factorization` has the shape ``(*, m, n)``, where``(*, m, n)`` is the shape of the `input` tensor. - * The `pivots` has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows. - */ - public fun StructureND.luFactor(epsilon: Double): Pair = - computeLU(tensor, epsilon) - ?: throw IllegalArgumentException("Tensor contains matrices which are singular at precision $epsilon") - - /** - * Computes the LU factorization of a matrix or batches of matrices `input`. - * Returns a tuple containing the LU factorization and pivots of `input`. - * Uses an error of ``1e-9`` when calculating whether a matrix is degenerate. - * - * @return pair of `factorization` and `pivots`. - * The `factorization` has the shape ``(*, m, n)``, where``(*, m, n)`` is the shape of the `input` tensor. - * The `pivots` has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows. - */ - public fun StructureND.luFactor(): Pair = luFactor(1e-9) - - /** - * Unpacks the data and pivots from a LU factorization of a tensor. - * Given a tensor [luTensor], return tensors `Triple(P, L, U)` satisfying `P dot luTensor = L dot U`, - * with `P` being a permutation matrix or batch of matrices, - * `L` being a lower triangular matrix or batch of matrices, - * `U` being an upper triangular matrix or batch of matrices. - * - * @param luTensor the packed LU factorization data - * @param pivotsTensor the packed LU factorization pivots - * @return triple of `P`, `L` and `U` tensors - */ - public fun luPivot( - luTensor: StructureND, - pivotsTensor: Tensor, - ): Triple { - checkSquareMatrix(luTensor.shape) - check( - luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() || - luTensor.shape.last() == pivotsTensor.shape.last() - 1 - ) { "Inappropriate shapes of input tensors" } - - val n = luTensor.shape.last() - val pTensor = luTensor.zeroesLike() - pTensor - .matrixSequence() - .zip(pivotsTensor.tensor.vectorSequence()) - .forEach { (p, pivot) -> pivInit(p.as2D(), pivot.as1D(), n) } - - val lTensor = luTensor.zeroesLike() - val uTensor = luTensor.zeroesLike() - - lTensor.matrixSequence() - .zip(uTensor.matrixSequence()) - .zip(luTensor.tensor.matrixSequence()) - .forEach { (pairLU, lu) -> - val (l, u) = pairLU - luPivotHelper(l.as2D(), u.as2D(), lu.as2D(), n) - } - - return Triple(pTensor, lTensor, uTensor) - } - - /** - * QR decomposition. - * - * Computes the QR decomposition of a matrix or a batch of matrices, and returns a pair `Q to R` of tensors. - * Given a tensor `input`, return tensors `Q to R` satisfying `input == Q dot R`, - * with `Q` being an orthogonal matrix or batch of orthogonal matrices - * and `R` being an upper triangular matrix or batch of upper triangular matrices. - * - * @receiver the `input`. - * @param epsilon the permissible error when comparing tensors for equality. - * Used when checking the positive definiteness of the input matrix or matrices. - * @return a pair of `Q` and `R` tensors. - */ - public fun StructureND.cholesky(epsilon: Double): DoubleTensor { - checkSquareMatrix(shape) - checkPositiveDefinite(tensor, epsilon) - - val n = shape.last() - val lTensor = zeroesLike() - - for ((a, l) in tensor.matrixSequence().zip(lTensor.matrixSequence())) - for (i in 0 until n) choleskyHelper(a.as2D(), l.as2D(), n) - - return lTensor - } - - override fun StructureND.cholesky(): DoubleTensor = cholesky(1e-6) - - override fun StructureND.qr(): Pair { - checkSquareMatrix(shape) - val qTensor = zeroesLike() - val rTensor = zeroesLike() - tensor.matrixSequence() - .zip( - (qTensor.matrixSequence() - .zip(rTensor.matrixSequence())) - ).forEach { (matrix, qr) -> - val (q, r) = qr - qrHelper(matrix.asTensor(), q.asTensor(), r.as2D()) - } - - return qTensor to rTensor - } - - override fun StructureND.svd(): Triple = - svd(epsilon = 1e-10) - - /** - * Singular Value Decomposition. - * - * Computes the singular value decomposition of either a matrix or batch of matrices `input`. - * The singular value decomposition is represented as a triple `Triple(U, S, V)`, - * such that `input == U dot diagonalEmbedding(S) dot V.transpose()`. - * If `input` is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as `input. - * - * @receiver the `input`. - * @param epsilon permissible error when calculating the dot product of vectors - * i.e., the precision with which the cosine approaches 1 in an iterative algorithm. - * @return a triple `Triple(U, S, V)`. - */ - public fun StructureND.svd(epsilon: Double): Triple { - val size = tensor.dimension - val commonShape = tensor.shape.sliceArray(0 until size - 2) - val (n, m) = tensor.shape.sliceArray(size - 2 until size) - val uTensor = zeros(commonShape + intArrayOf(min(n, m), n)) - val sTensor = zeros(commonShape + intArrayOf(min(n, m))) - val vTensor = zeros(commonShape + intArrayOf(min(n, m), m)) - - val matrices = tensor.matrices - val uTensors = uTensor.matrices - val sTensorVectors = sTensor.vectors - val vTensors = vTensor.matrices - - for (index in matrices.indices) { - val matrix = matrices[index] - val usv = Triple( - uTensors[index], - sTensorVectors[index], - vTensors[index] - ) - val matrixSize = matrix.shape.reduce { acc, i -> acc * i } - val curMatrix = DoubleTensor( - matrix.shape, - matrix.mutableBuffer.array() - .slice(matrix.bufferStart until matrix.bufferStart + matrixSize) - .toDoubleArray() - ) - svdHelper(curMatrix, usv, m, n, epsilon) - } - - return Triple(uTensor.transpose(), sTensor, vTensor.transpose()) - } - - override fun StructureND.symEig(): Pair = symEigJacobi(maxIteration = 50, epsilon = 1e-15) - - /** - * Returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices, - * represented by a pair `eigenvalues to eigenvectors`. - * - * @param epsilon the permissible error when comparing tensors for equality - * and when the cosine approaches 1 in the SVD algorithm. - * @return a pair `eigenvalues to eigenvectors`. - */ - public fun StructureND.symEigSvd(epsilon: Double): Pair { - checkSymmetric(tensor, epsilon) - - fun MutableStructure2D.cleanSym(n: Int) { - for (i in 0 until n) { - for (j in 0 until n) { - if (i == j) { - this[i, j] = sign(this[i, j]) - } else { - this[i, j] = 0.0 - } - } - } - } - - val (u, s, v) = tensor.svd(epsilon) - val shp = s.shape + intArrayOf(1) - val utv = u.transpose() dot v - val n = s.shape.last() - for (matrix in utv.matrixSequence()) { - matrix.as2D().cleanSym(n) - } - - val eig = (utv dot s.view(shp)).view(s.shape) - return eig to v - } - - public fun StructureND.symEigJacobi(maxIteration: Int, epsilon: Double): Pair { - checkSymmetric(tensor, epsilon) - - val size = this.dimension - val eigenvectors = zeros(this.shape) - val eigenvalues = zeros(this.shape.sliceArray(0 until size - 1)) - - var eigenvalueStart = 0 - var eigenvectorStart = 0 - for (matrix in tensor.matrixSequence()) { - val matrix2D = matrix.as2D() - val (d, v) = matrix2D.jacobiHelper(maxIteration, epsilon) - - for (i in 0 until matrix2D.rowNum) { - for (j in 0 until matrix2D.colNum) { - eigenvectors.mutableBuffer.array()[eigenvectorStart + i * matrix2D.rowNum + j] = v[i, j] - } - } - - for (i in 0 until matrix2D.rowNum) { - eigenvalues.mutableBuffer.array()[eigenvalueStart + i] = d[i] - } - - eigenvalueStart += this.shape.last() - eigenvectorStart += this.shape.last() * this.shape.last() - } - - return eigenvalues to eigenvectors - } - - private fun MutableStructure2D.jacobiHelper( - maxIteration: Int, - epsilon: Double - ): Pair, Structure2D> { - val n = this.shape[0] - val A_ = this.copy() - val V = eye(n) - val D = DoubleTensor(intArrayOf(n), (0 until this.rowNum).map { this[it, it] }.toDoubleArray()).as1D() - val B = DoubleTensor(intArrayOf(n), (0 until this.rowNum).map { this[it, it] }.toDoubleArray()).as1D() - val Z = zeros(intArrayOf(n)).as1D() - - // assume that buffered tensor is square matrix - operator fun BufferedTensor.get(i: Int, j: Int): Double { - return this.mutableBuffer.array()[bufferStart + i * this.shape[0] + j] - } - - operator fun BufferedTensor.set(i: Int, j: Int, value: Double) { - this.mutableBuffer.array()[bufferStart + i * this.shape[0] + j] = value - } - - fun maxOffDiagonal(matrix: BufferedTensor): Double { - var maxOffDiagonalElement = 0.0 - for (i in 0 until n - 1) { - for (j in i + 1 until n) { - maxOffDiagonalElement = max(maxOffDiagonalElement, abs(matrix[i, j])) - } - } - return maxOffDiagonalElement - } - - fun rotate(a: BufferedTensor, s: Double, tau: Double, i: Int, j: Int, k: Int, l: Int) { - val g = a[i, j] - val h = a[k, l] - a[i, j] = g - s * (h + g * tau) - a[k, l] = h + s * (g - h * tau) - } - - fun jacobiIteration( - a: BufferedTensor, - v: BufferedTensor, - d: MutableStructure1D, - z: MutableStructure1D, - ) { - for (ip in 0 until n - 1) { - for (iq in ip + 1 until n) { - val g = 100.0 * abs(a[ip, iq]) - - if (g <= epsilon * abs(d[ip]) && g <= epsilon * abs(d[iq])) { - a[ip, iq] = 0.0 - continue - } - - var h = d[iq] - d[ip] - val t = when { - g <= epsilon * abs(h) -> (a[ip, iq]) / h - else -> { - val theta = 0.5 * h / (a[ip, iq]) - val denominator = abs(theta) + sqrt(1.0 + theta * theta) - if (theta < 0.0) -1.0 / denominator else 1.0 / denominator - } - } - - val c = 1.0 / sqrt(1 + t * t) - val s = t * c - val tau = s / (1.0 + c) - h = t * a[ip, iq] - z[ip] -= h - z[iq] += h - d[ip] -= h - d[iq] += h - a[ip, iq] = 0.0 - - for (j in 0 until ip) { - rotate(a, s, tau, j, ip, j, iq) - } - for (j in (ip + 1) until iq) { - rotate(a, s, tau, ip, j, j, iq) - } - for (j in (iq + 1) until n) { - rotate(a, s, tau, ip, j, iq, j) - } - for (j in 0 until n) { - rotate(v, s, tau, j, ip, j, iq) - } - } - } - } - - fun updateDiagonal( - d: MutableStructure1D, - z: MutableStructure1D, - b: MutableStructure1D, - ) { - for (ip in 0 until d.size) { - b[ip] += z[ip] - d[ip] = b[ip] - z[ip] = 0.0 - } - } - - var sm = maxOffDiagonal(A_) - for (iteration in 0 until maxIteration) { - if (sm < epsilon) { - break - } - - jacobiIteration(A_, V, D, Z) - updateDiagonal(D, Z, B) - sm = maxOffDiagonal(A_) - } - - // TODO sort eigenvalues - return D to V.as2D() - } - - /** - * Computes the determinant of a square matrix input, or of each square matrix in a batched input - * using LU factorization algorithm. - * - * @param epsilon the error in the LU algorithm—permissible error when comparing the determinant of a matrix - * with zero. - * @return the determinant. - */ - public fun StructureND.detLU(epsilon: Double = 1e-9): DoubleTensor { - checkSquareMatrix(tensor.shape) - val luTensor = tensor.copy() - val pivotsTensor = tensor.setUpPivots() - - val n = shape.size - - val detTensorShape = IntArray(n - 1) { i -> shape[i] } - detTensorShape[n - 2] = 1 - val resBuffer = DoubleArray(detTensorShape.reduce(Int::times)) { 0.0 } - - val detTensor = DoubleTensor( - detTensorShape, - resBuffer - ) - - luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).forEachIndexed { index, (lu, pivots) -> - resBuffer[index] = if (luHelper(lu.as2D(), pivots.as1D(), epsilon)) - 0.0 else luMatrixDet(lu.as2D(), pivots.as1D()) - } - - return detTensor - } - - /** - * Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input - * using LU factorization algorithm. - * Given a square matrix `a`, return the matrix `aInv` satisfying - * `a dot aInv == aInv dot a == eye(a.shape[0])`. - * - * @param epsilon error in the LU algorithm—permissible error when comparing the determinant of a matrix with zero - * @return the multiplicative inverse of a matrix. - */ - public fun StructureND.invLU(epsilon: Double = 1e-9): DoubleTensor { - val (luTensor, pivotsTensor) = luFactor(epsilon) - val invTensor = luTensor.zeroesLike() - - val seq = luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence()) - for ((luP, invMatrix) in seq) { - val (lu, pivots) = luP - luMatrixInv(lu.as2D(), pivots.as1D(), invMatrix.as2D()) - } - - return invTensor - } - - /** - * LUP decomposition. - * - * Computes the LUP decomposition of a matrix or a batch of matrices. - * Given a tensor `input`, return tensors `Triple(P, L, U)` satisfying `P dot input == L dot U`, - * with `P` being a permutation matrix or batch of matrices, - * `L` being a lower triangular matrix or batch of matrices, - * `U` being an upper triangular matrix or batch of matrices. - * - * @param epsilon permissible error when comparing the determinant of a matrix with zero. - * @return triple of `P`, `L` and `U` tensors. - */ - public fun StructureND.lu(epsilon: Double = 1e-9): Triple { - val (lu, pivots) = tensor.luFactor(epsilon) - return luPivot(lu, pivots) - } - - override fun StructureND.lu(): Triple = lu(1e-9) -} - -public val Double.Companion.tensorAlgebra: DoubleTensorAlgebra.Companion get() = DoubleTensorAlgebra -public val DoubleField.tensorAlgebra: DoubleTensorAlgebra.Companion get() = DoubleTensorAlgebra - - diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensor.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensor.kt deleted file mode 100644 index e3d7c3d35..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensor.kt +++ /dev/null @@ -1,21 +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. - */ - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.structures.IntBuffer -import space.kscience.kmath.tensors.core.internal.array - -/** - * Default [BufferedTensor] implementation for [Int] values - */ -public class IntTensor internal constructor( - shape: IntArray, - buffer: IntArray, - offset: Int = 0 -) : BufferedTensor(shape, IntBuffer(buffer), offset){ - public fun asDouble() : DoubleTensor = - DoubleTensor(shape, mutableBuffer.array().map{ it.toDouble()}.toDoubleArray(), bufferStart) -} diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt deleted file mode 100644 index 19eefc2f8..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt +++ /dev/null @@ -1,74 +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. - */ - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.nd.Strides -import kotlin.math.max - -/** - * This [Strides] implementation follows the last dimension first convention - * For more information: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html - * - * @param shape the shape of the tensor. - */ -public class TensorLinearStructure(override val shape: IntArray) : Strides() { - override val strides: IntArray - get() = stridesFromShape(shape) - - override fun index(offset: Int): IntArray = - indexFromOffset(offset, strides, shape.size) - - override val linearSize: Int - get() = shape.reduce(Int::times) - - override fun equals(other: Any?): Boolean { - if (this === other) return true - if (other == null || this::class != other::class) return false - - other as TensorLinearStructure - - if (!shape.contentEquals(other.shape)) return false - - return true - } - - override fun hashCode(): Int { - return shape.contentHashCode() - } - - public companion object { - - public fun stridesFromShape(shape: IntArray): IntArray { - val nDim = shape.size - val res = IntArray(nDim) - if (nDim == 0) - return res - - var current = nDim - 1 - res[current] = 1 - - while (current > 0) { - res[current - 1] = max(1, shape[current]) * res[current] - current-- - } - return res - } - - public fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray { - val res = IntArray(nDim) - var current = offset - var strideIndex = 0 - - while (strideIndex < nDim) { - res[strideIndex] = (current / strides[strideIndex]) - current %= strides[strideIndex] - strideIndex++ - } - return res - } - } - -} \ No newline at end of file diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/broadcastUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/broadcastUtils.kt deleted file mode 100644 index 9d37423e5..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/broadcastUtils.kt +++ /dev/null @@ -1,146 +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. - */ - -package space.kscience.kmath.tensors.core.internal - -import space.kscience.kmath.tensors.core.DoubleTensor -import kotlin.math.max - -internal fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: DoubleTensor, linearSize: Int) { - for (linearIndex in 0 until linearSize) { - val totalMultiIndex = resTensor.indices.index(linearIndex) - val curMultiIndex = tensor.shape.copyOf() - - val offset = totalMultiIndex.size - curMultiIndex.size - - for (i in curMultiIndex.indices) { - if (curMultiIndex[i] != 1) { - curMultiIndex[i] = totalMultiIndex[i + offset] - } else { - curMultiIndex[i] = 0 - } - } - - val curLinearIndex = tensor.indices.offset(curMultiIndex) - resTensor.mutableBuffer.array()[linearIndex] = - tensor.mutableBuffer.array()[tensor.bufferStart + curLinearIndex] - } -} - -internal fun broadcastShapes(vararg shapes: IntArray): IntArray { - var totalDim = 0 - for (shape in shapes) { - totalDim = max(totalDim, shape.size) - } - - val totalShape = IntArray(totalDim) { 0 } - for (shape in shapes) { - for (i in shape.indices) { - val curDim = shape[i] - val offset = totalDim - shape.size - totalShape[i + offset] = max(totalShape[i + offset], curDim) - } - } - - for (shape in shapes) { - for (i in shape.indices) { - val curDim = shape[i] - val offset = totalDim - shape.size - check(curDim == 1 || totalShape[i + offset] == curDim) { - "Shapes are not compatible and cannot be broadcast" - } - } - } - - return totalShape -} - -internal fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor { - require(tensor.shape.size <= newShape.size) { - "Tensor is not compatible with the new shape" - } - - val n = newShape.reduce { acc, i -> acc * i } - val resTensor = DoubleTensor(newShape, DoubleArray(n)) - - for (i in tensor.shape.indices) { - val curDim = tensor.shape[i] - val offset = newShape.size - tensor.shape.size - check(curDim == 1 || newShape[i + offset] == curDim) { - "Tensor is not compatible with the new shape and cannot be broadcast" - } - } - - multiIndexBroadCasting(tensor, resTensor, n) - return resTensor -} - -internal fun broadcastTensors(vararg tensors: DoubleTensor): List { - val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray()) - val n = totalShape.reduce { acc, i -> acc * i } - - return tensors.map { tensor -> - val resTensor = DoubleTensor(totalShape, DoubleArray(n)) - multiIndexBroadCasting(tensor, resTensor, n) - resTensor - } -} - -internal fun broadcastOuterTensors(vararg tensors: DoubleTensor): List { - val onlyTwoDims = tensors.asSequence().onEach { - require(it.shape.size >= 2) { - "Tensors must have at least 2 dimensions" - } - }.any { it.shape.size != 2 } - - if (!onlyTwoDims) { - return tensors.asList() - } - - val totalShape = broadcastShapes(*(tensors.map { it.shape.sliceArray(0..it.shape.size - 3) }).toTypedArray()) - val n = totalShape.reduce { acc, i -> acc * i } - - return buildList { - for (tensor in tensors) { - val matrixShape = tensor.shape.sliceArray(tensor.shape.size - 2 until tensor.shape.size).copyOf() - val matrixSize = matrixShape[0] * matrixShape[1] - val matrix = DoubleTensor(matrixShape, DoubleArray(matrixSize)) - - val outerTensor = DoubleTensor(totalShape, DoubleArray(n)) - val resTensor = DoubleTensor(totalShape + matrixShape, DoubleArray(n * matrixSize)) - - for (linearIndex in 0 until n) { - val totalMultiIndex = outerTensor.indices.index(linearIndex) - var curMultiIndex = tensor.shape.sliceArray(0..tensor.shape.size - 3).copyOf() - curMultiIndex = IntArray(totalMultiIndex.size - curMultiIndex.size) { 1 } + curMultiIndex - - val newTensor = DoubleTensor(curMultiIndex + matrixShape, tensor.mutableBuffer.array()) - - for (i in curMultiIndex.indices) { - if (curMultiIndex[i] != 1) { - curMultiIndex[i] = totalMultiIndex[i] - } else { - curMultiIndex[i] = 0 - } - } - - for (i in 0 until matrixSize) { - val curLinearIndex = newTensor.indices.offset( - curMultiIndex + - matrix.indices.index(i) - ) - val newLinearIndex = resTensor.indices.offset( - totalMultiIndex + - matrix.indices.index(i) - ) - - resTensor.mutableBuffer.array()[resTensor.bufferStart + newLinearIndex] = - newTensor.mutableBuffer.array()[newTensor.bufferStart + curLinearIndex] - } - } - add(resTensor) - } - } -} \ No newline at end of file diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt deleted file mode 100644 index a2d445b45..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt +++ /dev/null @@ -1,65 +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. - */ - -package space.kscience.kmath.tensors.core.internal - -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.tensors.api.Tensor -import space.kscience.kmath.tensors.core.DoubleTensor -import space.kscience.kmath.tensors.core.DoubleTensorAlgebra - - -internal fun checkEmptyShape(shape: IntArray) = - check(shape.isNotEmpty()) { - "Illegal empty shape provided" - } - -internal fun checkEmptyDoubleBuffer(buffer: DoubleArray) = - check(buffer.isNotEmpty()) { - "Illegal empty buffer provided" - } - -internal fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray) = - check(buffer.size == shape.reduce(Int::times)) { - "Inconsistent shape ${shape.toList()} for buffer of size ${buffer.size} provided" - } - -internal fun checkShapesCompatible(a: StructureND, b: StructureND) = - check(a.shape contentEquals b.shape) { - "Incompatible shapes ${a.shape.toList()} and ${b.shape.toList()} " - } - -internal fun checkTranspose(dim: Int, i: Int, j: Int) = - check((i < dim) and (j < dim)) { - "Cannot transpose $i to $j for a tensor of dim $dim" - } - -internal fun checkView(a: Tensor, shape: IntArray) = - check(a.shape.reduce(Int::times) == shape.reduce(Int::times)) - -internal fun checkSquareMatrix(shape: IntArray) { - val n = shape.size - check(n >= 2) { - "Expected tensor with 2 or more dimensions, got size $n instead" - } - check(shape[n - 1] == shape[n - 2]) { - "Tensor must be batches of square matrices, but they are ${shape[n - 1]} by ${shape[n - 1]} matrices" - } -} - -internal fun DoubleTensorAlgebra.checkSymmetric( - tensor: Tensor, epsilon: Double = 1e-6 -) = - check(tensor.eq(tensor.transpose(), epsilon)) { - "Tensor is not symmetric about the last 2 dimensions at precision $epsilon" - } - -internal fun DoubleTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor, epsilon: Double = 1e-6) { - checkSymmetric(tensor, epsilon) - for (mat in tensor.matrixSequence()) - check(mat.asTensor().detLU().value() > 0.0) { - "Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}" - } -} \ No newline at end of file diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt deleted file mode 100644 index aba6167ce..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt +++ /dev/null @@ -1,351 +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. - */ - -package space.kscience.kmath.tensors.core.internal - -import space.kscience.kmath.nd.MutableStructure1D -import space.kscience.kmath.nd.MutableStructure2D -import space.kscience.kmath.nd.as1D -import space.kscience.kmath.nd.as2D -import space.kscience.kmath.operations.asSequence -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.structures.VirtualBuffer -import space.kscience.kmath.tensors.core.BufferedTensor -import space.kscience.kmath.tensors.core.DoubleTensor -import space.kscience.kmath.tensors.core.DoubleTensorAlgebra -import space.kscience.kmath.tensors.core.IntTensor -import kotlin.math.abs -import kotlin.math.min -import kotlin.math.sqrt - -internal val BufferedTensor.vectors: VirtualBuffer> - get() { - val n = shape.size - val vectorOffset = shape[n - 1] - val vectorShape = intArrayOf(shape.last()) - - return VirtualBuffer(numElements / vectorOffset) { index -> - val offset = index * vectorOffset - BufferedTensor(vectorShape, mutableBuffer, bufferStart + offset) - } - } - - -internal fun BufferedTensor.vectorSequence(): Sequence> = vectors.asSequence() - -/** - * A random access alternative to [matrixSequence] - */ -internal val BufferedTensor.matrices: VirtualBuffer> - get() { - val n = shape.size - check(n >= 2) { "Expected tensor with 2 or more dimensions, got size $n" } - val matrixOffset = shape[n - 1] * shape[n - 2] - val matrixShape = intArrayOf(shape[n - 2], shape[n - 1]) - - return VirtualBuffer(numElements / matrixOffset) { index -> - val offset = index * matrixOffset - BufferedTensor(matrixShape, mutableBuffer, bufferStart + offset) - } - } - -internal fun BufferedTensor.matrixSequence(): Sequence> = matrices.asSequence() - -internal fun dotTo( - a: BufferedTensor, - b: BufferedTensor, - res: BufferedTensor, - l: Int, m: Int, n: Int, -) { - val aStart = a.bufferStart - val bStart = b.bufferStart - val resStart = res.bufferStart - - val aBuffer = a.mutableBuffer - val bBuffer = b.mutableBuffer - val resBuffer = res.mutableBuffer - - for (i in 0 until l) { - for (j in 0 until n) { - var curr = 0.0 - for (k in 0 until m) { - curr += aBuffer[aStart + i * m + k] * bBuffer[bStart + k * n + j] - } - resBuffer[resStart + i * n + j] = curr - } - } -} - -internal fun luHelper( - lu: MutableStructure2D, - pivots: MutableStructure1D, - epsilon: Double, -): Boolean { - - val m = lu.rowNum - - for (row in 0..m) pivots[row] = row - - for (i in 0 until m) { - var maxVal = 0.0 - var maxInd = i - - for (k in i until m) { - val absA = abs(lu[k, i]) - if (absA > maxVal) { - maxVal = absA - maxInd = k - } - } - - if (abs(maxVal) < epsilon) - return true // matrix is singular - - if (maxInd != i) { - - val j = pivots[i] - pivots[i] = pivots[maxInd] - pivots[maxInd] = j - - for (k in 0 until m) { - val tmp = lu[i, k] - lu[i, k] = lu[maxInd, k] - lu[maxInd, k] = tmp - } - - pivots[m] += 1 - - } - - for (j in i + 1 until m) { - lu[j, i] /= lu[i, i] - for (k in i + 1 until m) { - lu[j, k] -= lu[j, i] * lu[i, k] - } - } - } - return false -} - -internal fun BufferedTensor.setUpPivots(): IntTensor { - val n = this.shape.size - val m = this.shape.last() - val pivotsShape = IntArray(n - 1) { i -> this.shape[i] } - pivotsShape[n - 2] = m + 1 - - return IntTensor( - pivotsShape, - IntArray(pivotsShape.reduce(Int::times)) { 0 } - ) -} - -internal fun DoubleTensorAlgebra.computeLU( - tensor: DoubleTensor, - epsilon: Double, -): Pair? { - - checkSquareMatrix(tensor.shape) - val luTensor = tensor.copy() - val pivotsTensor = tensor.setUpPivots() - - for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence())) - if (luHelper(lu.as2D(), pivots.as1D(), epsilon)) - return null - - return Pair(luTensor, pivotsTensor) -} - -internal fun pivInit( - p: MutableStructure2D, - pivot: MutableStructure1D, - n: Int, -) { - for (i in 0 until n) { - p[i, pivot[i]] = 1.0 - } -} - -internal fun luPivotHelper( - l: MutableStructure2D, - u: MutableStructure2D, - lu: MutableStructure2D, - n: Int, -) { - for (i in 0 until n) { - for (j in 0 until n) { - if (i == j) { - l[i, j] = 1.0 - } - if (j < i) { - l[i, j] = lu[i, j] - } - if (j >= i) { - u[i, j] = lu[i, j] - } - } - } -} - -internal fun choleskyHelper( - a: MutableStructure2D, - l: MutableStructure2D, - n: Int, -) { - for (i in 0 until n) { - for (j in 0 until i) { - var h = a[i, j] - for (k in 0 until j) { - h -= l[i, k] * l[j, k] - } - l[i, j] = h / l[j, j] - } - var h = a[i, i] - for (j in 0 until i) { - h -= l[i, j] * l[i, j] - } - l[i, i] = sqrt(h) - } -} - -internal fun luMatrixDet(lu: MutableStructure2D, pivots: MutableStructure1D): Double { - if (lu[0, 0] == 0.0) { - return 0.0 - } - val m = lu.shape[0] - val sign = if ((pivots[m] - m) % 2 == 0) 1.0 else -1.0 - return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right } -} - -internal fun luMatrixInv( - lu: MutableStructure2D, - pivots: MutableStructure1D, - invMatrix: MutableStructure2D, -) { - val m = lu.shape[0] - - for (j in 0 until m) { - for (i in 0 until m) { - if (pivots[i] == j) { - invMatrix[i, j] = 1.0 - } - - for (k in 0 until i) { - invMatrix[i, j] -= lu[i, k] * invMatrix[k, j] - } - } - - for (i in m - 1 downTo 0) { - for (k in i + 1 until m) { - invMatrix[i, j] -= lu[i, k] * invMatrix[k, j] - } - invMatrix[i, j] /= lu[i, i] - } - } -} - -internal fun DoubleTensorAlgebra.qrHelper( - matrix: DoubleTensor, - q: DoubleTensor, - r: MutableStructure2D, -) { - checkSquareMatrix(matrix.shape) - val n = matrix.shape[0] - val qM = q.as2D() - val matrixT = matrix.transpose(0, 1) - val qT = q.transpose(0, 1) - - for (j in 0 until n) { - val v = matrixT[j] - val vv = v.as1D() - if (j > 0) { - for (i in 0 until j) { - r[i, j] = (qT[i] dot matrixT[j]).value() - for (k in 0 until n) { - val qTi = qT[i].as1D() - vv[k] = vv[k] - r[i, j] * qTi[k] - } - } - } - r[j, j] = DoubleTensorAlgebra { (v dot v).sqrt().value() } - for (i in 0 until n) { - qM[i, j] = vv[i] / r[j, j] - } - } -} - -internal fun DoubleTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10): DoubleTensor { - val (n, m) = a.shape - var v: DoubleTensor - val b: DoubleTensor - if (n > m) { - b = a.transpose(0, 1).dot(a) - v = DoubleTensor(intArrayOf(m), getRandomUnitVector(m, 0)) - } else { - b = a.dot(a.transpose(0, 1)) - v = DoubleTensor(intArrayOf(n), getRandomUnitVector(n, 0)) - } - - var lastV: DoubleTensor - while (true) { - lastV = v - v = b.dot(lastV) - val norm = DoubleTensorAlgebra { (v dot v).sqrt().value() } - v = v.times(1.0 / norm) - if (abs(v.dot(lastV).value()) > 1 - epsilon) { - return v - } - } -} - -internal fun DoubleTensorAlgebra.svdHelper( - matrix: DoubleTensor, - USV: Triple, BufferedTensor, BufferedTensor>, - m: Int, n: Int, epsilon: Double, -) { - val res = ArrayList>(0) - val (matrixU, matrixS, matrixV) = USV - - for (k in 0 until min(n, m)) { - var a = matrix.copy() - for ((singularValue, u, v) in res.slice(0 until k)) { - val outerProduct = DoubleArray(u.shape[0] * v.shape[0]) - for (i in 0 until u.shape[0]) { - for (j in 0 until v.shape[0]) { - outerProduct[i * v.shape[0] + j] = u[i].value() * v[j].value() - } - } - a = a - singularValue.times(DoubleTensor(intArrayOf(u.shape[0], v.shape[0]), outerProduct)) - } - var v: DoubleTensor - var u: DoubleTensor - var norm: Double - if (n > m) { - v = svd1d(a, epsilon) - u = matrix.dot(v) - norm = DoubleTensorAlgebra { (u dot u).sqrt().value() } - u = u.times(1.0 / norm) - } else { - u = svd1d(a, epsilon) - v = matrix.transpose(0, 1).dot(u) - norm = DoubleTensorAlgebra { (v dot v).sqrt().value() } - v = v.times(1.0 / norm) - } - - res.add(Triple(norm, u, v)) - } - - val s = res.map { it.first }.toDoubleArray() - val uBuffer = res.map { it.second }.flatMap { it.mutableBuffer.array().toList() }.toDoubleArray() - val vBuffer = res.map { it.third }.flatMap { it.mutableBuffer.array().toList() }.toDoubleArray() - for (i in uBuffer.indices) { - matrixU.mutableBuffer.array()[matrixU.bufferStart + i] = uBuffer[i] - } - for (i in s.indices) { - matrixS.mutableBuffer.array()[matrixS.bufferStart + i] = s[i] - } - for (i in vBuffer.indices) { - matrixV.mutableBuffer.array()[matrixV.bufferStart + i] = vBuffer[i] - } -} diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/tensorCastsUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/tensorCastsUtils.kt deleted file mode 100644 index a5cdb2f47..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/tensorCastsUtils.kt +++ /dev/null @@ -1,50 +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. - */ - -package space.kscience.kmath.tensors.core.internal - -import space.kscience.kmath.nd.MutableBufferND -import space.kscience.kmath.nd.StructureND -import space.kscience.kmath.structures.asMutableBuffer -import space.kscience.kmath.tensors.api.Tensor -import space.kscience.kmath.tensors.core.BufferedTensor -import space.kscience.kmath.tensors.core.DoubleTensor -import space.kscience.kmath.tensors.core.IntTensor -import space.kscience.kmath.tensors.core.TensorLinearStructure - -internal fun BufferedTensor.asTensor(): IntTensor = - IntTensor(this.shape, this.mutableBuffer.array(), this.bufferStart) - -internal fun BufferedTensor.asTensor(): DoubleTensor = - DoubleTensor(this.shape, this.mutableBuffer.array(), this.bufferStart) - -internal fun StructureND.copyToBufferedTensor(): BufferedTensor = - BufferedTensor( - this.shape, - TensorLinearStructure(this.shape).asSequence().map(this::get).toMutableList().asMutableBuffer(), 0 - ) - -internal fun StructureND.toBufferedTensor(): BufferedTensor = when (this) { - is BufferedTensor -> this - is MutableBufferND -> if (this.indices == TensorLinearStructure(this.shape)) { - BufferedTensor(this.shape, this.buffer, 0) - } else { - this.copyToBufferedTensor() - } - else -> this.copyToBufferedTensor() -} - -@PublishedApi -internal val StructureND.tensor: DoubleTensor - get() = when (this) { - is DoubleTensor -> this - else -> this.toBufferedTensor().asTensor() - } - -internal val Tensor.tensor: IntTensor - get() = when (this) { - is IntTensor -> this - else -> this.toBufferedTensor().asTensor() - } diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/utils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/utils.kt deleted file mode 100644 index 85cc91b1d..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/utils.kt +++ /dev/null @@ -1,126 +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. - */ - -package space.kscience.kmath.tensors.core.internal - -import space.kscience.kmath.nd.as1D -import space.kscience.kmath.operations.toMutableList -import space.kscience.kmath.samplers.GaussianSampler -import space.kscience.kmath.stat.RandomGenerator -import space.kscience.kmath.structures.* -import space.kscience.kmath.tensors.core.BufferedTensor -import space.kscience.kmath.tensors.core.DoubleTensor -import kotlin.math.* - -/** - * Returns a reference to [IntArray] containing all the elements of this [Buffer] or copy the data. - */ -internal fun Buffer.array(): IntArray = when (this) { - is IntBuffer -> array - else -> this.toIntArray() -} - -/** - * Returns a reference to [DoubleArray] containing all the elements of this [Buffer] or copy the data. - */ -@PublishedApi -internal fun Buffer.array(): DoubleArray = when (this) { - is DoubleBuffer -> array - else -> this.toDoubleArray() -} - -internal fun getRandomNormals(n: Int, seed: Long): DoubleArray { - val distribution = GaussianSampler(0.0, 1.0) - val generator = RandomGenerator.default(seed) - return distribution.sample(generator).nextBufferBlocking(n).toDoubleArray() -} - -internal fun getRandomUnitVector(n: Int, seed: Long): DoubleArray { - val unnorm = getRandomNormals(n, seed) - val norm = sqrt(unnorm.sumOf { it * it }) - return unnorm.map { it / norm }.toDoubleArray() -} - -internal fun minusIndexFrom(n: Int, i: Int): Int = if (i >= 0) i else { - val ii = n + i - check(ii >= 0) { - "Out of bound index $i for tensor of dim $n" - } - ii -} - -internal fun BufferedTensor.minusIndex(i: Int): Int = minusIndexFrom(this.dimension, i) - -internal fun format(value: Double, digits: Int = 4): String = buildString { - val res = buildString { - val ten = 10.0 - val approxOrder = if (value == 0.0) 0 else ceil(log10(abs(value))).toInt() - val order = if ( - ((value % ten) == 0.0) || - (value == 1.0) || - ((1 / value) % ten == 0.0) - ) approxOrder else approxOrder - 1 - val lead = value / ten.pow(order) - if (value >= 0.0) append(' ') - append(round(lead * ten.pow(digits)) / ten.pow(digits)) - when { - order == 0 -> Unit - order > 0 -> { - append("e+") - append(order) - } - else -> { - append('e') - append(order) - } - } - } - val fLength = digits + 6 - append(res) - repeat(fLength - res.length) { append(' ') } -} - -internal fun DoubleTensor.toPrettyString(): String = buildString { - var offset = 0 - val shape = this@toPrettyString.shape - val linearStructure = this@toPrettyString.indices - val vectorSize = shape.last() - append("DoubleTensor(\n") - var charOffset = 3 - for (vector in vectorSequence()) { - repeat(charOffset) { append(' ') } - val index = linearStructure.index(offset) - for (ind in index.reversed()) { - if (ind != 0) { - break - } - append('[') - charOffset += 1 - } - - val values = vector.as1D().toMutableList().map(::format) - - values.joinTo(this, separator = ", ") - - append(']') - charOffset -= 1 - - index.reversed().zip(shape.reversed()).drop(1).forEach { (ind, maxInd) -> - if (ind != maxInd - 1) { - return@forEach - } - append(']') - charOffset -= 1 - } - - offset += vectorSize - if (this@toPrettyString.numElements == offset) { - break - } - - append(",\n") - } - append("\n)") -} diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorAlgebraExtensions.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorAlgebraExtensions.kt deleted file mode 100644 index d8e8df31e..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorAlgebraExtensions.kt +++ /dev/null @@ -1,19 +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. - */ - -@file:OptIn(PerformancePitfall::class) - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.Shape -import kotlin.jvm.JvmName - -@JvmName("varArgOne") -public fun DoubleTensorAlgebra.one(vararg shape: Int): DoubleTensor = ones(intArrayOf(*shape)) -public fun DoubleTensorAlgebra.one(shape: Shape): DoubleTensor = ones(shape) -@JvmName("varArgZero") -public fun DoubleTensorAlgebra.zero(vararg shape: Int): DoubleTensor = zeros(intArrayOf(*shape)) -public fun DoubleTensorAlgebra.zero(shape: Shape): DoubleTensor = zeros(shape) \ No newline at end of file diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt deleted file mode 100644 index 5dc8114dd..000000000 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorCasts.kt +++ /dev/null @@ -1,38 +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. - */ - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.tensors.api.Tensor -import space.kscience.kmath.tensors.core.internal.tensor - -/** - * Casts [Tensor] of [Double] to [DoubleTensor] - */ -public fun Tensor.toDoubleTensor(): DoubleTensor = this.tensor - -/** - * Casts [Tensor] of [Int] to [IntTensor] - */ -public fun Tensor.toIntTensor(): IntTensor = this.tensor - -/** - * Returns a copy-protected [DoubleArray] of tensor elements - */ -public fun DoubleTensor.copyArray(): DoubleArray { - //TODO use ArrayCopy - return DoubleArray(numElements) { i -> - mutableBuffer[bufferStart + i] - } -} - -/** - * Returns a copy-protected [IntArray] of tensor elements - */ -public fun IntTensor.copyArray(): IntArray { - return IntArray(numElements) { i -> - mutableBuffer[bufferStart + i] - } -} diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt deleted file mode 100644 index 6788ae792..000000000 --- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt +++ /dev/null @@ -1,110 +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. - */ - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.tensors.core.internal.* -import kotlin.test.Test -import kotlin.test.assertTrue - -internal class TestBroadcasting { - - @Test - fun testBroadcastShapes() = DoubleTensorAlgebra { - assertTrue( - broadcastShapes( - intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1) - ) contentEquals intArrayOf(1, 2, 3) - ) - - assertTrue( - broadcastShapes( - intArrayOf(6, 7), intArrayOf(5, 6, 1), intArrayOf(7), intArrayOf(5, 1, 7) - ) contentEquals intArrayOf(5, 6, 7) - ) - } - - @Test - fun testBroadcastTo() = DoubleTensorAlgebra { - val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0)) - - val res = broadcastTo(tensor2, tensor1.shape) - assertTrue(res.shape contentEquals intArrayOf(2, 3)) - assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0)) - } - - @Test - fun testBroadcastTensors() = DoubleTensorAlgebra { - val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0)) - val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0)) - - val res = broadcastTensors(tensor1, tensor2, tensor3) - - assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3)) - assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3)) - assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3)) - - assertTrue(res[0].mutableBuffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - assertTrue(res[1].mutableBuffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0)) - assertTrue(res[2].mutableBuffer.array() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0)) - } - - @Test - fun testBroadcastOuterTensors() = DoubleTensorAlgebra { - val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0)) - val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0)) - - val res = broadcastOuterTensors(tensor1, tensor2, tensor3) - - assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3)) - assertTrue(res[1].shape contentEquals intArrayOf(1, 1, 3)) - assertTrue(res[2].shape contentEquals intArrayOf(1, 1, 1)) - - assertTrue(res[0].mutableBuffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - assertTrue(res[1].mutableBuffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0)) - assertTrue(res[2].mutableBuffer.array() contentEquals doubleArrayOf(500.0)) - } - - @Test - fun testBroadcastOuterTensorsShapes() = DoubleTensorAlgebra { - val tensor1 = fromArray(intArrayOf(2, 1, 3, 2, 3), DoubleArray(2 * 1 * 3 * 2 * 3) {0.0}) - val tensor2 = fromArray(intArrayOf(4, 2, 5, 1, 3, 3), DoubleArray(4 * 2 * 5 * 1 * 3 * 3) {0.0}) - val tensor3 = fromArray(intArrayOf(1, 1), doubleArrayOf(500.0)) - - val res = broadcastOuterTensors(tensor1, tensor2, tensor3) - - assertTrue(res[0].shape contentEquals intArrayOf(4, 2, 5, 3, 2, 3)) - assertTrue(res[1].shape contentEquals intArrayOf(4, 2, 5, 3, 3, 3)) - assertTrue(res[2].shape contentEquals intArrayOf(4, 2, 5, 3, 1, 1)) - } - - @Test - fun testMinusTensor() = BroadcastDoubleTensorAlgebra.invoke { - val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0)) - val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0)) - - val tensor21 = tensor2 - tensor1 - val tensor31 = tensor3 - tensor1 - val tensor32 = tensor3 - tensor2 - - assertTrue(tensor21.shape contentEquals intArrayOf(2, 3)) - assertTrue(tensor21.mutableBuffer.array() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0)) - - assertTrue(tensor31.shape contentEquals intArrayOf(1, 2, 3)) - assertTrue( - tensor31.mutableBuffer.array() - contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0) - ) - - assertTrue(tensor32.shape contentEquals intArrayOf(1, 1, 3)) - assertTrue(tensor32.mutableBuffer.array() contentEquals doubleArrayOf(490.0, 480.0, 470.0)) - } - -} diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt deleted file mode 100644 index 1e21379b4..000000000 --- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt +++ /dev/null @@ -1,163 +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. - */ - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.operations.invoke -import kotlin.math.* -import kotlin.test.Test -import kotlin.test.assertTrue - -internal class TestDoubleAnalyticTensorAlgebra { - - val shape = intArrayOf(2, 1, 3, 2) - val buffer = doubleArrayOf( - 27.1, 20.0, 19.84, - 23.123, 3.0, 2.0, - - 3.23, 133.7, 25.3, - 100.3, 11.0, 12.012 - ) - val tensor = DoubleTensor(shape, buffer) - - fun DoubleArray.fmap(transform: (Double) -> Double): DoubleArray { - return this.map(transform).toDoubleArray() - } - - fun expectedTensor(transform: (Double) -> Double): DoubleTensor { - return DoubleTensor(shape, buffer.fmap(transform)) - } - - @Test - fun testExp() = DoubleTensorAlgebra { - assertTrue { tensor.exp() eq expectedTensor(::exp) } - } - - @Test - fun testLog() = DoubleTensorAlgebra { - assertTrue { tensor.ln() eq expectedTensor(::ln) } - } - - @Test - fun testSqrt() = DoubleTensorAlgebra { - assertTrue { tensor.sqrt() eq expectedTensor(::sqrt) } - } - - @Test - fun testCos() = DoubleTensorAlgebra { - assertTrue { tensor.cos() eq expectedTensor(::cos) } - } - - - @Test - fun testCosh() = DoubleTensorAlgebra { - assertTrue { tensor.cosh() eq expectedTensor(::cosh) } - } - - @Test - fun testAcosh() = DoubleTensorAlgebra { - assertTrue { tensor.acosh() eq expectedTensor(::acosh) } - } - - @Test - fun testSin() = DoubleTensorAlgebra { - assertTrue { tensor.sin() eq expectedTensor(::sin) } - } - - @Test - fun testSinh() = DoubleTensorAlgebra { - assertTrue { tensor.sinh() eq expectedTensor(::sinh) } - } - - @Test - fun testAsinh() = DoubleTensorAlgebra { - assertTrue { tensor.asinh() eq expectedTensor(::asinh) } - } - - @Test - fun testTan() = DoubleTensorAlgebra { - assertTrue { tensor.tan() eq expectedTensor(::tan) } - } - - @Test - fun testAtan() = DoubleTensorAlgebra { - assertTrue { tensor.atan() eq expectedTensor(::atan) } - } - - @Test - fun testTanh() = DoubleTensorAlgebra { - assertTrue { tensor.tanh() eq expectedTensor(::tanh) } - } - - @Test - fun testCeil() = DoubleTensorAlgebra { - assertTrue { tensor.ceil() eq expectedTensor(::ceil) } - } - - @Test - fun testFloor() = DoubleTensorAlgebra { - assertTrue { tensor.floor() eq expectedTensor(::floor) } - } - - val shape2 = intArrayOf(2, 2) - val buffer2 = doubleArrayOf( - 1.0, 2.0, - -3.0, 4.0 - ) - val tensor2 = DoubleTensor(shape2, buffer2) - - @Test - fun testMin() = DoubleTensorAlgebra { - assertTrue { tensor2.min() == -3.0 } - assertTrue { tensor2.min(0, true) eq fromArray( - intArrayOf(1, 2), - doubleArrayOf(-3.0, 2.0) - )} - assertTrue { tensor2.min(1, false) eq fromArray( - intArrayOf(2), - doubleArrayOf(1.0, -3.0) - )} - } - - @Test - fun testMax() = DoubleTensorAlgebra { - assertTrue { tensor2.max() == 4.0 } - assertTrue { tensor2.max(0, true) eq fromArray( - intArrayOf(1, 2), - doubleArrayOf(1.0, 4.0) - )} - assertTrue { tensor2.max(1, false) eq fromArray( - intArrayOf(2), - doubleArrayOf(2.0, 4.0) - )} - } - - @Test - fun testSum() = DoubleTensorAlgebra { - assertTrue { tensor2.sum() == 4.0 } - assertTrue { tensor2.sum(0, true) eq fromArray( - intArrayOf(1, 2), - doubleArrayOf(-2.0, 6.0) - )} - assertTrue { tensor2.sum(1, false) eq fromArray( - intArrayOf(2), - doubleArrayOf(3.0, 1.0) - )} - } - - @Test - fun testMean() = DoubleTensorAlgebra { - assertTrue { tensor2.mean() == 1.0 } - assertTrue { tensor2.mean(0, true) eq fromArray( - intArrayOf(1, 2), - doubleArrayOf(-1.0, 3.0) - )} - assertTrue { tensor2.mean(1, false) eq fromArray( - intArrayOf(2), - doubleArrayOf(1.5, 0.5) - )} - } - -} diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt deleted file mode 100644 index e025d4b71..000000000 --- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt +++ /dev/null @@ -1,201 +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. - */ - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.tensors.core.internal.array -import space.kscience.kmath.tensors.core.internal.svd1d -import kotlin.math.abs -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue - -internal class TestDoubleLinearOpsTensorAlgebra { - - @Test - fun testDetLU() = DoubleTensorAlgebra { - val tensor = fromArray( - intArrayOf(2, 2, 2), - doubleArrayOf( - 1.0, 3.0, - 1.0, 2.0, - 1.5, 1.0, - 10.0, 2.0 - ) - ) - - val expectedTensor = fromArray( - intArrayOf(2, 1), - doubleArrayOf( - -1.0, - -7.0 - ) - ) - val detTensor = tensor.detLU() - - assertTrue(detTensor.eq(expectedTensor)) - - } - - @Test - fun testDet() = DoubleTensorAlgebra { - val expectedValue = 0.019827417 - val m = fromArray( - intArrayOf(3, 3), doubleArrayOf( - 2.1843, 1.4391, -0.4845, - 1.4391, 1.7772, 0.4055, - -0.4845, 0.4055, 0.7519 - ) - ) - - assertTrue { abs(m.det().value() - expectedValue) < 1e-5 } - } - - @Test - fun testDetSingle() = DoubleTensorAlgebra { - val expectedValue = 48.151623 - val m = fromArray( - intArrayOf(1, 1), doubleArrayOf( - expectedValue - ) - ) - - assertTrue { abs(m.det().value() - expectedValue) < 1e-5 } - } - - @Test - fun testInvLU() = DoubleTensorAlgebra { - val tensor = fromArray( - intArrayOf(2, 2, 2), - doubleArrayOf( - 1.0, 0.0, - 0.0, 2.0, - 1.0, 1.0, - 1.0, 0.0 - ) - ) - - val expectedTensor = fromArray( - intArrayOf(2, 2, 2), doubleArrayOf( - 1.0, 0.0, - 0.0, 0.5, - 0.0, 1.0, - 1.0, -1.0 - ) - ) - - val invTensor = tensor.invLU() - assertTrue(invTensor.eq(expectedTensor)) - } - - @Test - fun testScalarProduct() = DoubleTensorAlgebra { - val a = fromArray(intArrayOf(3), doubleArrayOf(1.8, 2.5, 6.8)) - val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4)) - assertEquals(a.dot(b).value(), 59.92) - } - - @Test - fun testQR() = DoubleTensorAlgebra { - val shape = intArrayOf(2, 2, 2) - val buffer = doubleArrayOf( - 1.0, 3.0, - 1.0, 2.0, - 1.5, 1.0, - 10.0, 2.0 - ) - - val tensor = fromArray(shape, buffer) - - val (q, r) = tensor.qr() - - assertTrue { q.shape contentEquals shape } - assertTrue { r.shape contentEquals shape } - - assertTrue((q dot r).eq(tensor)) - - } - - @Test - fun testLU() = DoubleTensorAlgebra { - val shape = intArrayOf(2, 2, 2) - val buffer = doubleArrayOf( - 1.0, 3.0, - 1.0, 2.0, - 1.5, 1.0, - 10.0, 2.0 - ) - val tensor = fromArray(shape, buffer) - - val (p, l, u) = tensor.lu() - - assertTrue { p.shape contentEquals shape } - assertTrue { l.shape contentEquals shape } - assertTrue { u.shape contentEquals shape } - - assertTrue((p dot tensor).eq(l dot u)) - } - - @Test - fun testCholesky() = DoubleTensorAlgebra { - val tensor = randomNormal(intArrayOf(2, 5, 5), 0) - val sigma = (tensor dot tensor.transpose()) + diagonalEmbedding( - fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 }) - ) - val low = sigma.cholesky() - val sigmChol = low dot low.transpose() - assertTrue(sigma.eq(sigmChol)) - } - - @Test - fun testSVD1D() = DoubleTensorAlgebra { - val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - - val res = svd1d(tensor2) - - assertTrue(res.shape contentEquals intArrayOf(2)) - assertTrue { abs(abs(res.mutableBuffer.array()[res.bufferStart]) - 0.386) < 0.01 } - assertTrue { abs(abs(res.mutableBuffer.array()[res.bufferStart + 1]) - 0.922) < 0.01 } - } - - @Test - fun testSVD() = DoubleTensorAlgebra{ - testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))) - testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0))) - } - - @Test - fun testBatchedSVD() = DoubleTensorAlgebra { - val tensor = randomNormal(intArrayOf(2, 5, 3), 0) - val (tensorU, tensorS, tensorV) = tensor.svd() - val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose()) - assertTrue(tensor.eq(tensorSVD)) - } - - @Test - fun testBatchedSymEig() = DoubleTensorAlgebra { - val tensor = randomNormal(shape = intArrayOf(2, 3, 3), 0) - val tensorSigma = tensor + tensor.transpose() - val (tensorS, tensorV) = tensorSigma.symEig() - val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose()) - assertTrue(tensorSigma.eq(tensorSigmaCalc)) - } - - -} - - -private fun DoubleTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10) { - val svd = tensor.svd() - - val tensorSVD = svd.first - .dot( - diagonalEmbedding(svd.second) - .dot(svd.third.transpose()) - ) - - assertTrue(tensor.eq(tensorSVD, epsilon)) -} diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt deleted file mode 100644 index d808637c7..000000000 --- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt +++ /dev/null @@ -1,96 +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. - */ - -package space.kscience.kmath.tensors.core - -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.DefaultStrides -import space.kscience.kmath.nd.MutableBufferND -import space.kscience.kmath.nd.as1D -import space.kscience.kmath.nd.as2D -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.structures.DoubleBuffer -import space.kscience.kmath.structures.toDoubleArray -import space.kscience.kmath.tensors.core.internal.array -import space.kscience.kmath.tensors.core.internal.asTensor -import space.kscience.kmath.tensors.core.internal.matrixSequence -import space.kscience.kmath.tensors.core.internal.toBufferedTensor -import kotlin.test.Test -import kotlin.test.assertEquals -import kotlin.test.assertTrue - -internal class TestDoubleTensor { - - @Test - fun testValue() = DoubleTensorAlgebra { - val value = 12.5 - val tensor = fromArray(intArrayOf(1), doubleArrayOf(value)) - assertEquals(tensor.value(), value) - } - - @OptIn(PerformancePitfall::class) - @Test - fun testStrides() = DoubleTensorAlgebra { - val tensor = fromArray(intArrayOf(2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4)) - assertEquals(tensor[intArrayOf(0, 1)], 5.8) - assertTrue( - tensor.elements().map { it.second }.toList().toDoubleArray() contentEquals tensor.mutableBuffer.toDoubleArray() - ) - } - - @Test - fun testGet() = DoubleTensorAlgebra { - val tensor = fromArray(intArrayOf(1, 2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4)) - val matrix = tensor[0].as2D() - assertEquals(matrix[0, 1], 5.8) - - val vector = tensor[0][1].as1D() - assertEquals(vector[0], 58.4) - - matrix[0, 1] = 77.89 - assertEquals(tensor[intArrayOf(0, 0, 1)], 77.89) - - vector[0] = 109.56 - assertEquals(tensor[intArrayOf(0, 1, 0)], 109.56) - - tensor.matrixSequence().forEach { - val a = it.asTensor() - val secondRow = a[1].as1D() - val secondColumn = a.transpose(0, 1)[1].as1D() - assertEquals(secondColumn[0], 77.89) - assertEquals(secondRow[1], secondColumn[1]) - } - } - - @Test - fun testNoBufferProtocol() { - - // create buffer - val doubleArray = DoubleBuffer(doubleArrayOf(1.0, 2.0, 3.0)) - - // create ND buffers, no data is copied - val ndArray = MutableBufferND(DefaultStrides(intArrayOf(3)), doubleArray) - - // map to tensors - val bufferedTensorArray = ndArray.toBufferedTensor() // strides are flipped so data copied - val tensorArray = bufferedTensorArray.asTensor() // data not contiguous so copied again - - val tensorArrayPublic = ndArray.toDoubleTensor() // public API, data copied twice - val sharedTensorArray = tensorArrayPublic.toDoubleTensor() // no data copied by matching type - - assertTrue(tensorArray.mutableBuffer.array() contentEquals sharedTensorArray.mutableBuffer.array()) - - tensorArray[intArrayOf(0)] = 55.9 - assertEquals(tensorArrayPublic[intArrayOf(0)], 1.0) - - tensorArrayPublic[intArrayOf(0)] = 55.9 - assertEquals(sharedTensorArray[intArrayOf(0)], 55.9) - assertEquals(bufferedTensorArray[intArrayOf(0)], 1.0) - - bufferedTensorArray[intArrayOf(0)] = 55.9 - assertEquals(ndArray[intArrayOf(0)], 1.0) - - } -} diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt deleted file mode 100644 index 205ae2fee..000000000 --- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt +++ /dev/null @@ -1,181 +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. - */ - -package space.kscience.kmath.tensors.core - - -import space.kscience.kmath.operations.invoke -import space.kscience.kmath.tensors.core.internal.array -import kotlin.test.Test -import kotlin.test.assertFalse -import kotlin.test.assertTrue - -internal class TestDoubleTensorAlgebra { - - @Test - fun testDoublePlus() = DoubleTensorAlgebra { - val tensor = fromArray(intArrayOf(2), doubleArrayOf(1.0, 2.0)) - val res = 10.0 + tensor - assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(11.0, 12.0)) - } - - @Test - fun testDoubleDiv() = DoubleTensorAlgebra { - val tensor = fromArray(intArrayOf(2), doubleArrayOf(2.0, 4.0)) - val res = 2.0/tensor - assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 0.5)) - } - - @Test - fun testDivDouble() = DoubleTensorAlgebra { - val tensor = fromArray(intArrayOf(2), doubleArrayOf(10.0, 5.0)) - val res = tensor / 2.5 - assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(4.0, 2.0)) - } - - @Test - fun testTranspose1x1() = DoubleTensorAlgebra { - val tensor = fromArray(intArrayOf(1), doubleArrayOf(0.0)) - val res = tensor.transpose(0, 0) - - assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(0.0)) - assertTrue(res.shape contentEquals intArrayOf(1)) - } - - @Test - fun testTranspose3x2() = DoubleTensorAlgebra { - val tensor = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val res = tensor.transpose(1, 0) - - assertTrue(res.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0)) - assertTrue(res.shape contentEquals intArrayOf(2, 3)) - } - - @Test - fun testTranspose1x2x3() = DoubleTensorAlgebra { - val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val res01 = tensor.transpose(0, 1) - val res02 = tensor.transpose(-3, 2) - val res12 = tensor.transpose() - - assertTrue(res01.shape contentEquals intArrayOf(2, 1, 3)) - assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1)) - assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2)) - - assertTrue(res01.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - assertTrue(res02.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0)) - assertTrue(res12.mutableBuffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0)) - } - - @Test - fun testLinearStructure() = DoubleTensorAlgebra { - val shape = intArrayOf(3) - val tensorA = full(value = -4.5, shape = shape) - val tensorB = full(value = 10.9, shape = shape) - val tensorC = full(value = 789.3, shape = shape) - val tensorD = full(value = -72.9, shape = shape) - val tensorE = full(value = 553.1, shape = shape) - val result = 15.8 * tensorA - 1.5 * tensorB * (-tensorD) + 0.02 * tensorC / tensorE - 39.4 - - val expected = fromArray( - shape, - (1..3).map { - 15.8 * (-4.5) - 1.5 * 10.9 * 72.9 + 0.02 * 789.3 / 553.1 - 39.4 - }.toDoubleArray() - ) - - val assignResult = zeros(shape) - tensorA *= 15.8 - tensorB *= 1.5 - tensorB *= -tensorD - tensorC *= 0.02 - tensorC /= tensorE - assignResult += tensorA - assignResult -= tensorB - assignResult += tensorC - assignResult += -39.4 - - assertTrue(expected.mutableBuffer.array() contentEquals result.mutableBuffer.array()) - assertTrue(expected.mutableBuffer.array() contentEquals assignResult.mutableBuffer.array()) - } - - @Test - fun testDot() = DoubleTensorAlgebra { - val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val tensor11 = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val tensor2 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0)) - val tensor3 = fromArray(intArrayOf(1, 1, 3), doubleArrayOf(-1.0, -2.0, -3.0)) - val tensor4 = fromArray(intArrayOf(2, 3, 3), (1..18).map { it.toDouble() }.toDoubleArray()) - val tensor5 = fromArray(intArrayOf(2, 3, 3), (1..18).map { 1 + it.toDouble() }.toDoubleArray()) - - val res12 = tensor1.dot(tensor2) - assertTrue(res12.mutableBuffer.array() contentEquals doubleArrayOf(140.0, 320.0)) - assertTrue(res12.shape contentEquals intArrayOf(2)) - - val res32 = tensor3.dot(tensor2) - assertTrue(res32.mutableBuffer.array() contentEquals doubleArrayOf(-140.0)) - assertTrue(res32.shape contentEquals intArrayOf(1, 1)) - - val res22 = tensor2.dot(tensor2) - assertTrue(res22.mutableBuffer.array() contentEquals doubleArrayOf(1400.0)) - assertTrue(res22.shape contentEquals intArrayOf(1)) - - val res11 = tensor1.dot(tensor11) - assertTrue(res11.mutableBuffer.array() contentEquals doubleArrayOf(22.0, 28.0, 49.0, 64.0)) - assertTrue(res11.shape contentEquals intArrayOf(2, 2)) - - val res45 = tensor4.dot(tensor5) - assertTrue(res45.mutableBuffer.array() contentEquals doubleArrayOf( - 36.0, 42.0, 48.0, 81.0, 96.0, 111.0, 126.0, 150.0, 174.0, - 468.0, 501.0, 534.0, 594.0, 636.0, 678.0, 720.0, 771.0, 822.0 - )) - assertTrue(res45.shape contentEquals intArrayOf(2, 3, 3)) - } - - @Test - fun testDiagonalEmbedding() = DoubleTensorAlgebra { - val tensor1 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0)) - val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val tensor3 = zeros(intArrayOf(2, 3, 4, 5)) - - assertTrue(diagonalEmbedding(tensor3, 0, 3, 4).shape contentEquals - intArrayOf(2, 3, 4, 5, 5)) - assertTrue(diagonalEmbedding(tensor3, 1, 3, 4).shape contentEquals - intArrayOf(2, 3, 4, 6, 6)) - assertTrue(diagonalEmbedding(tensor3, 2, 0, 3).shape contentEquals - intArrayOf(7, 2, 3, 7, 4)) - - val diagonal1 = diagonalEmbedding(tensor1, 0, 1, 0) - assertTrue(diagonal1.shape contentEquals intArrayOf(3, 3)) - assertTrue(diagonal1.mutableBuffer.array() contentEquals - doubleArrayOf(10.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 30.0)) - - val diagonal1Offset = diagonalEmbedding(tensor1, 1, 1, 0) - assertTrue(diagonal1Offset.shape contentEquals intArrayOf(4, 4)) - assertTrue(diagonal1Offset.mutableBuffer.array() contentEquals - doubleArrayOf(0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 0.0, 30.0, 0.0)) - - val diagonal2 = diagonalEmbedding(tensor2, 1, 0, 2) - assertTrue(diagonal2.shape contentEquals intArrayOf(4, 2, 4)) - assertTrue(diagonal2.mutableBuffer.array() contentEquals - doubleArrayOf( - 0.0, 1.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, - 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 5.0, 0.0, - 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 6.0, - 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)) - } - - @Test - fun testEq() = DoubleTensorAlgebra { - val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)) - val tensor3 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0)) - - assertTrue(tensor1 eq tensor1) - assertTrue(tensor1 eq tensor2) - assertFalse(tensor1.eq(tensor3)) - - } -} diff --git a/kmath-viktor/README.md b/kmath-viktor/README.md deleted file mode 100644 index 229d4dcd4..000000000 --- a/kmath-viktor/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Module kmath-viktor - -Binding for https://github.com/JetBrains-Research/viktor - -## Usage - -## Artifact: - -The Maven coordinates of this project are `space.kscience:kmath-viktor:0.3.0-dev-20`. - -**Gradle Groovy:** -```groovy -repositories { - maven { url 'https://repo.kotlin.link' } - mavenCentral() -} - -dependencies { - implementation 'space.kscience:kmath-viktor:0.3.0-dev-20' -} -``` -**Gradle Kotlin DSL:** -```kotlin -repositories { - maven("https://repo.kotlin.link") - mavenCentral() -} - -dependencies { - implementation("space.kscience:kmath-viktor:0.3.0-dev-20") -} -``` diff --git a/kmath-viktor/api/kmath-viktor.api b/kmath-viktor/api/kmath-viktor.api deleted file mode 100644 index 59882627b..000000000 --- a/kmath-viktor/api/kmath-viktor.api +++ /dev/null @@ -1,83 +0,0 @@ -public final class space/kscience/kmath/viktor/ViktorBuffer : space/kscience/kmath/structures/MutableBuffer { - public fun (Lorg/jetbrains/bio/viktor/F64FlatArray;)V - public fun copy ()Lspace/kscience/kmath/structures/MutableBuffer; - public fun get (I)Ljava/lang/Double; - public synthetic fun get (I)Ljava/lang/Object; - public final fun getFlatArray ()Lorg/jetbrains/bio/viktor/F64FlatArray; - public fun getSize ()I - public fun iterator ()Ljava/util/Iterator; - public fun set (ID)V - public synthetic fun set (ILjava/lang/Object;)V -} - -public final class space/kscience/kmath/viktor/ViktorFieldND : space/kscience/kmath/nd/FieldND, space/kscience/kmath/operations/ExtendedField, space/kscience/kmath/operations/NumbersAddOperations, space/kscience/kmath/operations/ScaleOperations { - public fun ([I)V - public synthetic fun acos (Ljava/lang/Object;)Ljava/lang/Object; - public fun acos (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun add (Ljava/lang/Object;Ljava/lang/Object;)Ljava/lang/Object; - public synthetic fun add (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/nd/StructureND; - public fun add (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun asin (Ljava/lang/Object;)Ljava/lang/Object; - public fun asin (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun atan (Ljava/lang/Object;)Ljava/lang/Object; - public fun atan (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun combine (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function3;)Lspace/kscience/kmath/nd/StructureND; - public fun combine (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function3;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun cos (Ljava/lang/Object;)Ljava/lang/Object; - public fun cos (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun exp (Ljava/lang/Object;)Ljava/lang/Object; - public fun exp (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun getElementContext ()Lspace/kscience/kmath/operations/Algebra; - public fun getElementContext ()Lspace/kscience/kmath/operations/DoubleField; - public final fun getF64Buffer (Lspace/kscience/kmath/nd/StructureND;)Lorg/jetbrains/bio/viktor/F64Array; - public synthetic fun getOne ()Ljava/lang/Object; - public fun getOne ()Lspace/kscience/kmath/viktor/ViktorStructureND; - public fun getShape ()[I - public synthetic fun getZero ()Ljava/lang/Object; - public fun getZero ()Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun ln (Ljava/lang/Object;)Ljava/lang/Object; - public fun ln (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun map (Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function2;)Lspace/kscience/kmath/nd/StructureND; - public fun map (Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function2;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun mapIndexed (Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function3;)Lspace/kscience/kmath/nd/StructureND; - public fun mapIndexed (Lspace/kscience/kmath/nd/StructureND;Lkotlin/jvm/functions/Function3;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun minus (Ljava/lang/Object;Ljava/lang/Object;)Ljava/lang/Object; - public fun minus (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun number (Ljava/lang/Number;)Ljava/lang/Object; - public fun number (Ljava/lang/Number;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun plus (Ljava/lang/Object;Ljava/lang/Object;)Ljava/lang/Object; - public fun plus (Lspace/kscience/kmath/nd/StructureND;D)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun plus (Lspace/kscience/kmath/nd/StructureND;Ljava/lang/Object;)Lspace/kscience/kmath/nd/StructureND; - public fun plus (Lspace/kscience/kmath/nd/StructureND;Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun power (Ljava/lang/Object;Ljava/lang/Number;)Ljava/lang/Object; - public fun power (Lspace/kscience/kmath/nd/StructureND;Ljava/lang/Number;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun produce (Lkotlin/jvm/functions/Function2;)Lspace/kscience/kmath/nd/StructureND; - public fun produce (Lkotlin/jvm/functions/Function2;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun scale (Ljava/lang/Object;D)Ljava/lang/Object; - public fun scale (Lspace/kscience/kmath/nd/StructureND;D)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun sin (Ljava/lang/Object;)Ljava/lang/Object; - public fun sin (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun tan (Ljava/lang/Object;)Ljava/lang/Object; - public fun tan (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun times (Ljava/lang/Object;Ljava/lang/Number;)Ljava/lang/Object; - public fun times (Lspace/kscience/kmath/nd/StructureND;Ljava/lang/Number;)Lspace/kscience/kmath/viktor/ViktorStructureND; - public synthetic fun unaryMinus (Ljava/lang/Object;)Ljava/lang/Object; - public fun unaryMinus (Lspace/kscience/kmath/nd/StructureND;)Lspace/kscience/kmath/nd/StructureND; -} - -public final class space/kscience/kmath/viktor/ViktorStructureND : space/kscience/kmath/nd/MutableStructureND { - public fun (Lorg/jetbrains/bio/viktor/F64Array;)V - public fun elements ()Lkotlin/sequences/Sequence; - public fun get ([I)Ljava/lang/Double; - public synthetic fun get ([I)Ljava/lang/Object; - public final fun getF64Buffer ()Lorg/jetbrains/bio/viktor/F64Array; - public fun getShape ()[I - public fun set ([ID)V - public synthetic fun set ([ILjava/lang/Object;)V -} - -public final class space/kscience/kmath/viktor/ViktorStructureNDKt { - public static final fun ViktorNDField ([I)Lspace/kscience/kmath/viktor/ViktorFieldND; - public static final fun asStructure (Lorg/jetbrains/bio/viktor/F64Array;)Lspace/kscience/kmath/viktor/ViktorStructureND; -} - diff --git a/kmath-viktor/build.gradle.kts b/kmath-viktor/build.gradle.kts deleted file mode 100644 index 2e932b441..000000000 --- a/kmath-viktor/build.gradle.kts +++ /dev/null @@ -1,15 +0,0 @@ -plugins { - kotlin("jvm") - id("ru.mipt.npm.gradle.common") -} - -description = "Binding for https://github.com/JetBrains-Research/viktor" - -dependencies { - api(project(":kmath-core")) - api("org.jetbrains.bio:viktor:1.1.0") -} - -readme { - maturity = ru.mipt.npm.gradle.Maturity.DEVELOPMENT -} diff --git a/kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorBuffer.kt b/kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorBuffer.kt deleted file mode 100644 index 4eedcb5ee..000000000 --- a/kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorBuffer.kt +++ /dev/null @@ -1,28 +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. - */ - -package space.kscience.kmath.viktor - -import org.jetbrains.bio.viktor.F64FlatArray -import space.kscience.kmath.structures.Buffer -import space.kscience.kmath.structures.MutableBuffer - -@Suppress("NOTHING_TO_INLINE", "OVERRIDE_BY_INLINE") -@JvmInline -public value class ViktorBuffer(public val flatArray: F64FlatArray) : MutableBuffer { - override val size: Int - get() = flatArray.size - - override inline fun get(index: Int): Double = flatArray[index] - - override inline fun set(index: Int, value: Double) { - flatArray[index] = value - } - - override fun copy(): MutableBuffer = ViktorBuffer(flatArray.copy().flatten()) - override operator fun iterator(): Iterator = flatArray.data.iterator() - - override fun toString(): String = Buffer.toString(this) -} diff --git a/kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorFieldOpsND.kt b/kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorFieldOpsND.kt deleted file mode 100644 index 1d4d6cebd..000000000 --- a/kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorFieldOpsND.kt +++ /dev/null @@ -1,132 +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. - */ - -@file:OptIn(PerformancePitfall::class) - -package space.kscience.kmath.viktor - -import org.jetbrains.bio.viktor.F64Array -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.misc.UnstableKMathAPI -import space.kscience.kmath.nd.* -import space.kscience.kmath.operations.DoubleField -import space.kscience.kmath.operations.ExtendedFieldOps -import space.kscience.kmath.operations.NumbersAddOps -import space.kscience.kmath.operations.PowerOperations - -@OptIn(UnstableKMathAPI::class, PerformancePitfall::class) -@Suppress("OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE") -public open class ViktorFieldOpsND : - FieldOpsND, - ExtendedFieldOps>, - PowerOperations> { - - public val StructureND.f64Buffer: F64Array - get() = when (this) { - is ViktorStructureND -> this.f64Buffer - else -> structureND(shape) { this@f64Buffer[it] }.f64Buffer - } - - override val elementAlgebra: DoubleField get() = DoubleField - - override fun structureND(shape: IntArray, initializer: DoubleField.(IntArray) -> Double): ViktorStructureND = - F64Array(*shape).apply { - DefaultStrides(shape).asSequence().forEach { index -> - set(value = DoubleField.initializer(index), indices = index) - } - }.asStructure() - - override fun StructureND.unaryMinus(): StructureND = -1 * this - - @PerformancePitfall - override fun StructureND.map(transform: DoubleField.(Double) -> Double): ViktorStructureND = - F64Array(*shape).apply { - DefaultStrides(shape).asSequence().forEach { index -> - set(value = DoubleField.transform(this@map[index]), indices = index) - } - }.asStructure() - - @PerformancePitfall - override fun StructureND.mapIndexed( - transform: DoubleField.(index: IntArray, Double) -> Double, - ): ViktorStructureND = F64Array(*shape).apply { - DefaultStrides(shape).asSequence().forEach { index -> - set(value = DoubleField.transform(index, this@mapIndexed[index]), indices = index) - } - }.asStructure() - - @PerformancePitfall - override fun zip( - left: StructureND, - right: StructureND, - transform: DoubleField.(Double, Double) -> Double, - ): ViktorStructureND { - require(left.shape.contentEquals(right.shape)) - return F64Array(*left.shape).apply { - DefaultStrides(left.shape).asSequence().forEach { index -> - set(value = DoubleField.transform(left[index], right[index]), indices = index) - } - }.asStructure() - } - - override fun add(left: StructureND, right: StructureND): ViktorStructureND = - (left.f64Buffer + right.f64Buffer).asStructure() - - override fun scale(a: StructureND, value: Double): ViktorStructureND = - (a.f64Buffer * value).asStructure() - - override fun StructureND.plus(arg: StructureND): ViktorStructureND = - (f64Buffer + arg.f64Buffer).asStructure() - - override fun StructureND.minus(arg: StructureND): ViktorStructureND = - (f64Buffer - arg.f64Buffer).asStructure() - - override fun StructureND.times(k: Number): ViktorStructureND = - (f64Buffer * k.toDouble()).asStructure() - - override fun StructureND.plus(arg: Double): ViktorStructureND = - (f64Buffer.plus(arg)).asStructure() - - override fun sin(arg: StructureND): ViktorStructureND = arg.map { sin(it) } - override fun cos(arg: StructureND): ViktorStructureND = arg.map { cos(it) } - override fun tan(arg: StructureND): ViktorStructureND = arg.map { tan(it) } - override fun asin(arg: StructureND): ViktorStructureND = arg.map { asin(it) } - override fun acos(arg: StructureND): ViktorStructureND = arg.map { acos(it) } - override fun atan(arg: StructureND): ViktorStructureND = arg.map { atan(it) } - - override fun power(arg: StructureND, pow: Number): ViktorStructureND = arg.map { it.pow(pow) } - - override fun exp(arg: StructureND): ViktorStructureND = arg.f64Buffer.exp().asStructure() - - override fun ln(arg: StructureND): ViktorStructureND = arg.f64Buffer.log().asStructure() - - override fun sinh(arg: StructureND): ViktorStructureND = arg.map { sinh(it) } - - override fun cosh(arg: StructureND): ViktorStructureND = arg.map { cosh(it) } - - override fun asinh(arg: StructureND): ViktorStructureND = arg.map { asinh(it) } - - override fun acosh(arg: StructureND): ViktorStructureND = arg.map { acosh(it) } - - override fun atanh(arg: StructureND): ViktorStructureND = arg.map { atanh(it) } - - public companion object : ViktorFieldOpsND() -} - -public val DoubleField.viktorAlgebra: ViktorFieldOpsND get() = ViktorFieldOpsND - -public open class ViktorFieldND( - override val shape: Shape, -) : ViktorFieldOpsND(), FieldND, NumbersAddOps> { - override val zero: ViktorStructureND by lazy { F64Array.full(init = 0.0, shape = shape).asStructure() } - override val one: ViktorStructureND by lazy { F64Array.full(init = 1.0, shape = shape).asStructure() } - - override fun number(value: Number): ViktorStructureND = - F64Array.full(init = value.toDouble(), shape = shape).asStructure() -} - -public fun DoubleField.viktorAlgebra(vararg shape: Int): ViktorFieldND = ViktorFieldND(shape) - -public fun ViktorFieldND(vararg shape: Int): ViktorFieldND = ViktorFieldND(shape) \ No newline at end of file diff --git a/kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorStructureND.kt b/kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorStructureND.kt deleted file mode 100644 index 25ca3a10e..000000000 --- a/kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorStructureND.kt +++ /dev/null @@ -1,30 +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. - */ - -package space.kscience.kmath.viktor - -import org.jetbrains.bio.viktor.F64Array -import space.kscience.kmath.misc.PerformancePitfall -import space.kscience.kmath.nd.DefaultStrides -import space.kscience.kmath.nd.MutableStructureND - -@Suppress("OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE") -public class ViktorStructureND(public val f64Buffer: F64Array) : MutableStructureND { - override val shape: IntArray get() = f64Buffer.shape - - override inline fun get(index: IntArray): Double = f64Buffer.get(*index) - - override inline fun set(index: IntArray, value: Double) { - f64Buffer.set(*index, value = value) - } - - @PerformancePitfall - override fun elements(): Sequence> = - DefaultStrides(shape).asSequence().map { it to get(it) } -} - -public fun F64Array.asStructure(): ViktorStructureND = ViktorStructureND(this) - - diff --git a/settings.gradle.kts b/settings.gradle.kts index b3c275810..c8b62227c 100644 --- a/settings.gradle.kts +++ b/settings.gradle.kts @@ -2,29 +2,5 @@ rootProject.name = "kmath" enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS") include( - ":kmath-memory", - ":kmath-complex", ":kmath-core", - ":kmath-coroutines", - ":kmath-functions", - ":kmath-histograms", - ":kmath-commons", - ":kmath-viktor", - ":kmath-multik", - ":kmath-tensorflow", - ":kmath-optimization", - ":kmath-stat", - ":kmath-nd4j", - ":kmath-dimensions", - ":kmath-for-real", - ":kmath-geometry", - ":kmath-ast", - ":kmath-ejml", - ":kmath-kotlingrad", - ":kmath-tensors", - ":kmath-jupyter", - ":kmath-symja", - ":kmath-jafama", - ":examples", - ":benchmarks", )