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615 changed files with 8996 additions and 20820 deletions

3
.github/CODEOWNERS vendored
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@ -1,3 +0,0 @@
@altavir
/kmath-trajectory @ESchouten

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@ -7,18 +7,26 @@ on:
jobs:
build:
runs-on: windows-latest
timeout-minutes: 20
strategy:
matrix:
os: [ macOS-latest, windows-latest ]
runs-on: ${{matrix.os}}
timeout-minutes: 40
steps:
- uses: actions/checkout@v3
- uses: actions/setup-java@v3.5.1
- uses: actions/checkout@v3.0.0
- uses: actions/setup-java@v3.0.0
with:
java-version: '11'
distribution: 'liberica'
cache: 'gradle'
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
- name: Gradle Build
uses: gradle/gradle-build-action@v2.4.2
- uses: gradle/gradle-build-action@v2.1.5
with:
arguments: test jvmTest
arguments: build

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@ -22,7 +22,7 @@ jobs:
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
- uses: gradle/gradle-build-action@v2.4.2
- uses: gradle/gradle-build-action@v2.1.5
with:
arguments: dokkaHtmlMultiModule --no-parallel
- uses: JamesIves/github-pages-deploy-action@v4.3.0

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@ -15,7 +15,7 @@ jobs:
runs-on: ${{matrix.os}}
steps:
- uses: actions/checkout@v3.0.0
- uses: actions/setup-java@v3.10.0
- uses: actions/setup-java@v3.0.0
with:
java-version: 11
distribution: liberica
@ -26,25 +26,26 @@ jobs:
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
- uses: gradle/wrapper-validation-action@v1.0.4
- name: Publish Windows Artifacts
if: matrix.os == 'windows-latest'
uses: gradle/gradle-build-action@v2.4.2
uses: gradle/gradle-build-action@v2.1.5
with:
arguments: |
publishAllPublicationsToSpaceRepository
-Ppublishing.targets=all
releaseAll
-Ppublishing.enabled=true
-Ppublishing.sonatype=false
-Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
-Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}
- name: Publish Mac Artifacts
if: matrix.os == 'macOS-latest'
uses: gradle/gradle-build-action@v2.4.2
uses: gradle/gradle-build-action@v2.1.5
with:
arguments: |
publishMacosX64PublicationToSpaceRepository
publishMacosArm64PublicationToSpaceRepository
publishIosX64PublicationToSpaceRepository
publishIosArm64PublicationToSpaceRepository
publishIosSimulatorArm64PublicationToSpaceRepository
-Ppublishing.targets=all
releaseMacosX64
releaseIosArm64
releaseIosX64
-Ppublishing.enabled=true
-Ppublishing.sonatype=false
-Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
-Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}

8
.gitignore vendored
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@ -3,11 +3,10 @@ build/
out/
.idea/
.vscode/
.fleet/
.kotlin/
.vscode/
# Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)
!gradle-wrapper.jar
@ -20,5 +19,4 @@ out/
!/.idea/copyright/
!/.idea/scopes/
/gradle/yarn.lock
/kotlin-js-store/yarn.lock

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

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

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@ -1,48 +1,3 @@
import kotlin.io.path.readText
val projectName = "kmath"
job("Build") {
//Perform only jvm tests
gradlew("spc.registry.jetbrains.space/p/sci/containers/kotlin-ci:1.0.3", "test", "jvmTest")
gradlew("openjdk:11", "build")
}
job("Publish") {
startOn {
gitPush { enabled = false }
}
container("spc.registry.jetbrains.space/p/sci/containers/kotlin-ci:1.0.3") {
env["SPACE_USER"] = "{{ project:space_user }}"
env["SPACE_TOKEN"] = "{{ project:space_token }}"
kotlinScript { api ->
val spaceUser = System.getenv("SPACE_USER")
val spaceToken = System.getenv("SPACE_TOKEN")
// write the version to the build directory
api.gradlew("version")
//read the version from build file
val version = java.nio.file.Path.of("build/project-version.txt").readText()
val revisionSuffix = if (version.endsWith("SNAPSHOT")) {
"-" + api.gitRevision().take(7)
} else {
""
}
api.space().projects.automation.deployments.start(
project = api.projectIdentifier(),
targetIdentifier = TargetIdentifier.Key(projectName),
version = version + revisionSuffix,
// automatically update deployment status based on the status of a job
syncWithAutomationJob = true
)
api.gradlew(
"publishAllPublicationsToSpaceRepository",
"-Ppublishing.space.user=\"$spaceUser\"",
"-Ppublishing.space.token=\"$spaceToken\"",
)
}
}
}

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@ -1,10 +1,11 @@
# KMath
## Unreleased
## [Unreleased]
### Added
### Changed
- Kotlin 1.7
- `LazyStructure` `deffered` -> `async` to comply with coroutines code style
### Deprecated
@ -14,82 +15,8 @@
### Security
## 0.4.0-dev-3 - 2024-02-18
## [0.3.0]
### Added
- Reification. Explicit `SafeType` for algebras.
- Integer division algebras.
- Float32 geometries.
- New Attributes-kt module that could be used as stand-alone. It declares. type-safe attributes containers.
- Explicit `mutableStructureND` builders for mutable structures.
- `Buffer.asList()` zero-copy transformation.
- Wasm support.
- Parallel implementation of `LinearSpace` for Float64
- Parallel buffer factories
### Changed
- Buffer copy removed from API (added as an extension).
- Default naming for algebra and buffers now uses IntXX/FloatXX notation instead of Java types.
- Remove unnecessary inlines in basic algebras.
- QuaternionField -> QuaternionAlgebra and does not implement `Field` anymore since it is non-commutative
- kmath-geometry is split into `euclidean2d` and `euclidean3d`
- Features replaced with Attributes.
- Transposed refactored.
- Kmath-memory is moved on top of core.
### Deprecated
- ND4J engine
### Removed
- `asPolynomial` function due to scope pollution
- Codegend for ejml (450 lines of codegen for 1000 lines of code is too much)
### Fixed
- Median statistics
- Complex power of negative real numbers
- Add proper mutability for MutableBufferND rows and columns
- Generic Float32 and Float64 vectors are used in geometry algebras.
## 0.3.1 - 2023-04-09
### Added
- Wasm support for `memory`, `core`, `complex` and `functions` modules.
- Generic builders for `BufferND` and `MutableBufferND`
- `NamedMatrix` - matrix with symbol-based indexing
- `Expression` with default arguments
- Type-aliases for numbers like `Float64`
- Autodiff for generic algebra elements in core!
- Algebra now has an obligatory `bufferFactory` (#477).
### Changed
- Removed marker `Vector` type for geometry
- Geometry uses type-safe angles
- Tensor operations switched to prefix notation
- Row-wise and column-wise ND shapes in the core
- Shape is read-only
- Major refactor of tensors (only minor API changes)
- Kotlin 1.8.20
- `LazyStructure` `deffered` -> `async` to comply with coroutines code style
- Default `dot` operation in tensor algebra no longer support broadcasting. Instead `matmul` operation is added
to `DoubleTensorAlgebra`.
- Multik went MPP
### Removed
- Trajectory moved to https://github.com/SciProgCentre/maps-kt
- Polynomials moved to https://github.com/SciProgCentre/kmath-polynomial
## 0.3.0
### Added
- `ScaleOperations` interface
- `Field` extends `ScaleOperations`
- Basic integration API
@ -113,9 +40,8 @@
- `contentEquals` with tolerance: #364
- Compilation to TeX for MST: #254
### Changed
- Annotations moved to `space.kscience.kmath`
### Changed
- Exponential operations merged with hyperbolic functions
- Space is replaced by Group. Space is reserved for vector spaces.
- VectorSpace is now a vector space
@ -147,12 +73,12 @@
- Rework of histograms.
- `UnivariateFunction` -> `Function1D`, `MultivariateFunction` -> `FunctionND`
### Deprecated
### Deprecated
- Specialized `DoubleBufferAlgebra`
### Removed
### 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.
@ -162,15 +88,16 @@
- Second generic from DifferentiableExpression
- Algebra elements are completely removed. Use algebra contexts instead.
### Fixed
### Fixed
- Ring inherits RingOperations, not GroupOperations
- Univariate histogram filling
## 0.2.0
### 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)
@ -189,8 +116,8 @@
- New `MatrixFeature` interfaces for matrix decompositions
- Basic Quaternion vector support in `kmath-complex`.
### Changed
### 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`)
@ -214,8 +141,8 @@
- `symbol` method in `Algebra` renamed to `bindSymbol` to avoid ambiguity
- Add `out` projection to `Buffer` generic
### Removed
### Removed
- `kmath-koma` module because it doesn't support Kotlin 1.4.
- Support of `legacy` JS backend (we will support only IR)
- `toGrid` method.
@ -223,25 +150,21 @@
- `Real` class
- StructureND identity and equals
### Fixed
### Fixed
- `symbol` method in `MstExtendedField` (https://github.com/mipt-npm/kmath/pull/140)
## 0.1.4
## [0.1.4]
### Added
- Functional Expressions API
- Mathematical Syntax Tree, its interpreter and API
- String to MST parser (https://github.com/mipt-npm/kmath/pull/120)
- MST to JVM bytecode translator (https://github.com/mipt-npm/kmath/pull/94)
- FloatBuffer (specialized MutableBuffer over FloatArray)
- FlaggedBuffer to associate primitive numbers buffer with flags (to mark values infinite or missing, etc.)
- Specialized builder functions for all primitive buffers
like `IntBuffer(25) { it + 1 }` (https://github.com/mipt-npm/kmath/pull/125)
- Specialized builder functions for all primitive buffers like `IntBuffer(25) { it + 1 }` (https://github.com/mipt-npm/kmath/pull/125)
- Interface `NumericAlgebra` where `number` operation is available to convert numbers to algebraic elements
- Inverse trigonometric functions support in
ExtendedField (`asin`, `acos`, `atan`) (https://github.com/mipt-npm/kmath/pull/114)
- Inverse trigonometric functions support in ExtendedField (`asin`, `acos`, `atan`) (https://github.com/mipt-npm/kmath/pull/114)
- New space extensions: `average` and `averageWith`
- Local coding conventions
- Geometric Domains API in `kmath-core`
@ -249,23 +172,22 @@
- Full hyperbolic functions support and default implementations within `ExtendedField`
- Norm support for `Complex`
### Changed
### Changed
- `readAsMemory` now has `throws IOException` in JVM signature.
- Several functions taking functional types were made `inline`.
- Several functions taking functional types now have `callsInPlace` contracts.
- BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor
optimizations
- BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor optimizations
- `power(T, Int)` extension function has preconditions and supports `Field<T>`
- Memory objects have more preconditions (overflow checking)
- `tg` function is renamed to `tan` (https://github.com/mipt-npm/kmath/pull/114)
- Gradle version: 6.3 -> 6.6
- Moved probability distributions to commons-rng and to `kmath-prob`
### Fixed
### 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
- Multiplication of BigInt by scalar

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@ -1,6 +1,6 @@
[![JetBrains Research](https://jb.gg/badges/research.svg)](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)
[![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
![Gradle build](https://github.com/SciProgCentre/kmath/workflows/Gradle%20build/badge.svg)
![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/)
@ -11,22 +11,18 @@ analog to Python's NumPy library. Later we found that kotlin is much more flexib
architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
[Documentation site](https://SciProgCentre.github.io/kmath/)
[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)
* [A talk at KotlinConf 2019 about using kotlin for science](https://youtu.be/LI_5TZ7tnOE?si=4LknX41gl_YeUbIe)
* [A talk on architecture at Joker-2021 (in Russian)](https://youtu.be/1bZ2doHiRRM?si=9w953ro9yu98X_KJ)
* [The same talk in English](https://youtu.be/yP5DIc2fVwQ?si=louZzQ1dcXV6gP10)
* [A seminar on tensor API](https://youtu.be/0H99wUs0xTM?si=6c__04jrByFQtVpo)
# Goal
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS, Native and
Wasm).
* Provide 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.
@ -48,7 +44,7 @@ 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.
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.
@ -57,20 +53,18 @@ module definitions below. The module stability could have the following levels:
## Modules
### [attributes-kt](attributes-kt)
> An API and basic implementation for arranging objects in a continuous memory block.
>
> **Maturity**: DEVELOPMENT
### [benchmarks](benchmarks)
>
>
> **Maturity**: EXPERIMENTAL
### [examples](examples)
>
>
> **Maturity**: EXPERIMENTAL
### [kmath-ast](kmath-ast)
>
>
> **Maturity**: EXPERIMENTAL
>
@ -82,7 +76,7 @@ module definitions below. The module stability could have the following levels:
### [kmath-commons](kmath-commons)
> Commons math binding for kmath
>
>
> **Maturity**: EXPERIMENTAL
@ -92,8 +86,8 @@ module definitions below. The module stability could have the following levels:
> **Maturity**: PROTOTYPE
>
> **Features:**
> - [complex](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt) : Complex numbers operations
> - [quaternion](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions and their composition
> - [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)
@ -111,19 +105,20 @@ objects to the expression by providing a context. Expressions can be used for a
performance calculations to code generation.
> - [domains](kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
> - [autodiff](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
> - [Parallel linear algebra](kmath-core/#) : Parallel implementation for `LinearAlgebra`
### [kmath-coroutines](kmath-coroutines)
>
>
> **Maturity**: EXPERIMENTAL
### [kmath-dimensions](kmath-dimensions)
> A proof of concept module for adding type-safe dimensions to structures
>
>
> **Maturity**: PROTOTYPE
### [kmath-ejml](kmath-ejml)
>
>
> **Maturity**: PROTOTYPE
>
@ -147,7 +142,7 @@ One can still use generic algebras though.
### [kmath-functions](kmath-functions)
> Functions, integration and interpolation
>
>
> **Maturity**: EXPERIMENTAL
>
@ -160,28 +155,31 @@ One can still use generic algebras though.
### [kmath-geometry](kmath-geometry)
>
>
> **Maturity**: PROTOTYPE
### [kmath-histograms](kmath-histograms)
>
>
> **Maturity**: PROTOTYPE
### [kmath-jafama](kmath-jafama)
> Jafama integration module
>
>
> **Maturity**: DEPRECATED
> **Maturity**: PROTOTYPE
>
> **Features:**
> - [jafama-double](kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/) : Double ExtendedField implementations based on Jafama
### [kmath-jupyter](kmath-jupyter)
>
>
> **Maturity**: PROTOTYPE
### [kmath-kotlingrad](kmath-kotlingrad)
> Kotlin∇ integration module
>
>
> **Maturity**: EXPERIMENTAL
>
@ -196,14 +194,14 @@ One can still use generic algebras though.
> **Maturity**: DEVELOPMENT
### [kmath-multik](kmath-multik)
> JetBrains Multik connector
>
>
> **Maturity**: PROTOTYPE
### [kmath-nd4j](kmath-nd4j)
> ND4J NDStructure implementation and according NDAlgebra classes
>
>
> **Maturity**: DEPRECATED
> **Maturity**: EXPERIMENTAL
>
> **Features:**
> - [nd4jarraystructure](kmath-nd4j/#) : NDStructure wrapper for INDArray
@ -212,24 +210,27 @@ One can still use generic algebras though.
### [kmath-optimization](kmath-optimization)
>
>
> **Maturity**: EXPERIMENTAL
### [kmath-stat](kmath-stat)
>
>
> **Maturity**: EXPERIMENTAL
### [kmath-symja](kmath-symja)
> Symja integration module
>
>
> **Maturity**: PROTOTYPE
### [kmath-tensorflow](kmath-tensorflow)
> Google tensorflow connector
>
>
> **Maturity**: PROTOTYPE
### [kmath-tensors](kmath-tensors)
>
>
> **Maturity**: PROTOTYPE
>
@ -240,13 +241,9 @@ One can still use generic algebras though.
### [kmath-viktor](kmath-viktor)
> Binding for https://github.com/JetBrains-Research/viktor
>
>
> **Maturity**: DEPRECATED
### [test-utils](test-utils)
>
> **Maturity**: EXPERIMENTAL
> **Maturity**: DEVELOPMENT
## Multi-platform support
@ -254,24 +251,23 @@ One can still use generic algebras though.
KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
are delegated to platform-specific implementations even if they could be provided in the common module for performance
reasons. Currently, Kotlin/JVM is the primary platform, however, Kotlin/Native and Kotlin/JS contributions and
reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
feedback are also welcome.
## Performance
Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to
achieve both
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 a convenient universal API first and then work on increasing performance for specific
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 using GraalVM-CE or
Oracle GraalVM for execution to get better performance.
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
@ -291,10 +287,11 @@ dependencies {
}
```
Gradle `6.0+` is required for multiplatform artifacts.
## Contributing
The project requires a lot of additional work. The most important thing we need is feedback about what features are
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 [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
label.
marked with
[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.

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

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

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

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

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

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

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

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

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

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

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@ -1,11 +1,10 @@
@file:Suppress("UNUSED_VARIABLE")
import org.jetbrains.kotlin.gradle.tasks.KotlinJvmCompile
import space.kscience.kmath.benchmarks.addBenchmarkProperties
plugins {
kotlin("multiplatform")
alias(spclibs.plugins.kotlin.plugin.allopen)
kotlin("plugin.allopen")
id("org.jetbrains.kotlinx.benchmark")
}
@ -16,8 +15,6 @@ repositories {
mavenCentral()
}
val multikVersion: String by rootProject.extra
kotlin {
jvm()
@ -29,9 +26,6 @@ kotlin {
all {
languageSettings {
progressiveMode = true
optIn("kotlin.contracts.ExperimentalContracts")
optIn("kotlin.ExperimentalUnsignedTypes")
optIn("space.kscience.kmath.UnstableKMathAPI")
}
}
@ -45,9 +39,7 @@ kotlin {
implementation(project(":kmath-dimensions"))
implementation(project(":kmath-for-real"))
implementation(project(":kmath-tensors"))
implementation(project(":kmath-multik"))
implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
implementation(spclibs.kotlinx.benchmark.runtime)
implementation("org.jetbrains.kotlinx:kotlinx-benchmark-runtime:0.4.2")
}
}
@ -59,6 +51,7 @@ kotlin {
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")
@ -145,10 +138,12 @@ benchmark {
commonConfiguration()
include("ViktorLogBenchmark")
}
}
configurations.register("integration") {
commonConfiguration()
include("IntegrationBenchmark")
// Fix kotlinx-benchmarks bug
afterEvaluate {
val jvmBenchmarkJar by tasks.getting(org.gradle.jvm.tasks.Jar::class) {
duplicatesStrategy = DuplicatesStrategy.EXCLUDE
}
}
@ -156,11 +151,11 @@ kotlin.sourceSets.all {
with(languageSettings) {
optIn("kotlin.contracts.ExperimentalContracts")
optIn("kotlin.ExperimentalUnsignedTypes")
optIn("space.kscience.kmath.UnstableKMathAPI")
optIn("space.kscience.kmath.misc.UnstableKMathAPI")
}
}
tasks.withType<KotlinJvmCompile> {
tasks.withType<org.jetbrains.kotlin.gradle.dsl.KotlinJvmCompile> {
kotlinOptions {
jvmTarget = "11"
freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xlambdas=indy"
@ -168,7 +163,7 @@ tasks.withType<KotlinJvmCompile> {
}
readme {
maturity = space.kscience.gradle.Maturity.EXPERIMENTAL
maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
}
addBenchmarkProperties()

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -9,9 +9,9 @@ import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.math.sin
@ -84,7 +84,7 @@ class ExpressionsInterpretersBenchmark {
private val x by symbol
private const val times = 1_000_000
private val functional = Float64Field.expression {
private val functional = DoubleField.expression {
val x = bindSymbol(Symbol.x)
x * number(2.0) + 2.0 / x - 16.0 / sin(x)
}
@ -93,14 +93,12 @@ class ExpressionsInterpretersBenchmark {
x * 2.0 + number(2.0) / x - number(16.0) / sin(x)
}
private val mst = node.toExpression(Float64Field)
@OptIn(UnstableKMathAPI::class)
private val wasm = node.wasmCompileToExpression(Float64Field)
private val estree = node.estreeCompileToExpression(Float64Field)
private val mst = node.toExpression(DoubleField)
private val wasm = node.wasmCompileToExpression(DoubleField)
private val estree = node.estreeCompileToExpression(DoubleField)
private val raw = Expression<Double> { args ->
val x = args.getValue(x)
val x = args[x]!!
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}
}

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -10,7 +10,7 @@ import kotlinx.benchmark.Blackhole
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.BigIntField
import space.kscience.kmath.operations.JBigIntegerField
import space.kscience.kmath.operations.invoke
@ -67,7 +67,7 @@ internal class BigIntBenchmark {
@Benchmark
fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmLargeNumber * kmLargeNumber)
blackhole.consume(kmLargeNumber*kmLargeNumber)
}
@Benchmark
@ -77,7 +77,7 @@ internal class BigIntBenchmark {
@Benchmark
fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(jvmLargeNumber * jvmLargeNumber)
blackhole.consume(jvmLargeNumber*jvmLargeNumber)
}
@Benchmark

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@ -1,80 +1,39 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.complex.ComplexField
import space.kscience.kmath.complex.complex
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.getDouble
import space.kscience.kmath.structures.permute
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.MutableBuffer
@State(Scope.Benchmark)
internal class BufferBenchmark {
@Benchmark
fun doubleArrayReadWrite(blackhole: Blackhole) {
val buffer = DoubleArray(size) { it.toDouble() }
var res = 0.0
fun genericDoubleBufferReadWrite() {
val buffer = DoubleBuffer(size) { it.toDouble() }
(0 until size).forEach {
res += buffer[it]
buffer[it]
}
blackhole.consume(res)
}
@Benchmark
fun doubleBufferReadWrite(blackhole: Blackhole) {
val buffer = Float64Buffer(size) { it.toDouble() }
var res = 0.0
(0 until size).forEach {
res += buffer[it]
}
blackhole.consume(res)
}
fun complexBufferReadWrite() {
val buffer = MutableBuffer.complex(size / 2) { Complex(it.toDouble(), -it.toDouble()) }
@Benchmark
fun bufferViewReadWrite(blackhole: Blackhole) {
val buffer = Float64Buffer(size) { it.toDouble() }.permute(reversedIndices)
var res = 0.0
(0 until size).forEach {
res += buffer[it]
}
blackhole.consume(res)
}
@Benchmark
fun bufferViewReadWriteSpecialized(blackhole: Blackhole) {
val buffer = Float64Buffer(size) { it.toDouble() }.permute(reversedIndices)
var res = 0.0
(0 until size).forEach {
res += buffer.getDouble(it)
}
blackhole.consume(res)
}
@Benchmark
fun complexBufferReadWrite(blackhole: Blackhole) = ComplexField {
val buffer = Buffer.complex(size / 2) { Complex(it.toDouble(), -it.toDouble()) }
var res = zero
(0 until size / 2).forEach {
res += buffer[it]
buffer[it]
}
blackhole.consume(res)
}
private companion object {
private const val size = 100
private val reversedIndices = IntArray(size) { it }.apply { reverse() }
}
}

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -11,11 +11,14 @@ import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
import space.kscience.kmath.linear.Float64ParallelLinearSpace
import space.kscience.kmath.linear.invoke
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.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
@ -26,10 +29,10 @@ internal class DotBenchmark {
const val dim = 1000
//creating invertible matrix
val matrix1 = Float64Field.linearSpace.buildMatrix(dim, dim) { _, _ ->
val matrix1 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
random.nextDouble()
}
val matrix2 = Float64Field.linearSpace.buildMatrix(dim, dim) { _, _ ->
val matrix2 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
random.nextDouble()
}
@ -44,7 +47,7 @@ internal class DotBenchmark {
@Benchmark
fun tfDot(blackhole: Blackhole) {
blackhole.consume(
Float64Field.produceWithTF {
DoubleField.produceWithTF {
matrix1 dot matrix1
}
)
@ -71,23 +74,27 @@ internal class DotBenchmark {
}
@Benchmark
fun multikDot(blackhole: Blackhole) = with(multikAlgebra) {
fun tensorDot(blackhole: Blackhole) = with(DoubleField.tensorAlgebra) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun tensorDot(blackhole: Blackhole) = with(Float64Field.tensorAlgebra) {
fun multikDot(blackhole: Blackhole) = with(DoubleField.multikAlgebra) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun bufferedDot(blackhole: Blackhole) = with(Float64Field.linearSpace) {
fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace(Buffer.Companion::auto)) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun parallelDot(blackhole: Blackhole) = with(Float64ParallelLinearSpace) {
fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
blackhole.consume(matrix1 dot matrix2)
}
}

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -12,7 +12,7 @@ import kotlinx.benchmark.State
import space.kscience.kmath.asm.compileToExpression
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.math.sin
@ -100,7 +100,7 @@ internal class ExpressionsInterpretersBenchmark {
private val x by symbol
private const val times = 1_000_000
private val functional = Float64Field.expression {
private val functional = DoubleField.expression {
val x = bindSymbol(Symbol.x)
x * number(2.0) + 2.0 / x - 16.0 / sin(x)
}
@ -109,12 +109,12 @@ internal class ExpressionsInterpretersBenchmark {
x * 2.0 + number(2.0) / x - number(16.0) / sin(x)
}
private val mst = node.toExpression(Float64Field)
private val mst = node.toExpression(DoubleField)
private val asmPrimitive = node.compileToExpression(Float64Field)
private val asmPrimitive = node.compileToExpression(DoubleField)
private val xIdx = asmPrimitive.indexer.indexOf(x)
private val asmGeneric = node.compileToExpression(Float64Field as Algebra<Double>)
private val asmGeneric = node.compileToExpression(DoubleField as Algebra<Double>)
private val raw = Expression<Double> { args ->
val x = args[x]!!

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

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -11,8 +11,10 @@ import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.jafama.JafamaDoubleField
import space.kscience.kmath.jafama.StrictJafamaDoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.random.Random
@State(Scope.Benchmark)
@ -24,7 +26,7 @@ internal class JafamaBenchmark {
@Benchmark
fun core(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
Float64Field { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
DoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
}
@Benchmark
@ -34,6 +36,7 @@ internal class JafamaBenchmark {
}
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())) }
}

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -15,7 +15,6 @@ import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
import space.kscience.kmath.linear.invoke
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.linear.lupSolver
import space.kscience.kmath.linear.parallel
import space.kscience.kmath.operations.algebra
import kotlin.random.Random
@ -39,19 +38,16 @@ internal class MatrixInverseBenchmark {
}
@Benchmark
fun kmathParallelLupInversion(blackhole: Blackhole) {
blackhole.consume(Double.algebra.linearSpace.parallel.lupSolver().inverse(matrix))
fun cmLUPInversion(blackhole: Blackhole) {
CMLinearSpace {
blackhole.consume(lupSolver().inverse(matrix))
}
}
@Benchmark
fun cmLUPInversion(blackhole: Blackhole) = CMLinearSpace {
blackhole.consume(lupSolver().inverse(matrix))
fun ejmlInverse(blackhole: Blackhole) {
EjmlLinearSpaceDDRM {
blackhole.consume(matrix.toEjml().inverse())
}
}
@Benchmark
fun ejmlInverse(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
blackhole.consume(matrix.toEjml().inverted())
}
}

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -13,10 +13,14 @@ import org.jetbrains.kotlinx.multik.api.Multik
import org.jetbrains.kotlinx.multik.api.ones
import org.jetbrains.kotlinx.multik.ndarray.data.DN
import org.jetbrains.kotlinx.multik.ndarray.data.DataType
import space.kscience.kmath.UnsafeKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.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.Float64Field
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
@ -24,15 +28,11 @@ import space.kscience.kmath.viktor.viktorAlgebra
@State(Scope.Benchmark)
internal class NDFieldBenchmark {
private companion object {
private const val dim = 1000
private const val n = 100
private val shape = ShapeND(dim, dim)
private val specializedField = Float64Field.ndAlgebra
private val genericField = BufferedFieldOpsND(Float64Field)
private val nd4jField = Float64Field.nd4j
private val viktorField = Float64Field.viktorAlgebra
@Benchmark
fun autoFieldAdd(blackhole: Blackhole) = with(autoField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
@ -50,7 +50,7 @@ internal class NDFieldBenchmark {
}
@Benchmark
fun multikAdd(blackhole: Blackhole) = with(multikAlgebra) {
fun multikAdd(blackhole: Blackhole) = with(multikField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
@ -77,10 +77,9 @@ internal class NDFieldBenchmark {
blackhole.consume(res)
}
@OptIn(UnsafeKMathAPI::class)
@Benchmark
fun multikInPlaceAdd(blackhole: Blackhole) = with(multikAlgebra) {
val res = Multik.ones<Double, DN>(shape.asArray(), DataType.DoubleDataType).wrap()
fun multikInPlaceAdd(blackhole: Blackhole) = with(DoubleField.multikAlgebra) {
val res = Multik.ones<Double, DN>(shape, DataType.DoubleDataType).wrap()
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@ -92,5 +91,15 @@ internal class NDFieldBenchmark {
// 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
}
}

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -12,9 +12,7 @@ import kotlinx.benchmark.State
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.linear.matrix
import space.kscience.kmath.linear.symmetric
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.tensors.core.symEigJacobi
import space.kscience.kmath.tensors.core.symEigSvd
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.tensors.core.tensorAlgebra
import kotlin.random.Random
@ -24,16 +22,16 @@ internal class TensorAlgebraBenchmark {
private val random = Random(12224)
private const val dim = 30
private val matrix = Float64Field.linearSpace.matrix(dim, dim).symmetric { _, _ -> random.nextDouble() }
private val matrix = DoubleField.linearSpace.matrix(dim, dim).symmetric { _, _ -> random.nextDouble() }
}
@Benchmark
fun tensorSymEigSvd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
blackhole.consume(symEigSvd(matrix, 1e-10))
blackhole.consume(matrix.symEigSvd(1e-10))
}
@Benchmark
fun tensorSymEigJacobi(blackhole: Blackhole) = with(Double.tensorAlgebra) {
blackhole.consume(symEigJacobi(matrix, 50, 1e-10))
blackhole.consume(matrix.symEigJacobi(50, 1e-10))
}
}

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -10,19 +10,25 @@ import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.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<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
}
@Benchmark
fun doubleFieldAddition(blackhole: Blackhole) {
with(doubleField) {
fun realFieldAddition(blackhole: Blackhole) {
with(realField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
@ -49,10 +55,11 @@ internal class ViktorBenchmark {
private companion object {
private const val dim = 1000
private const val n = 100
private val shape = ShapeND(dim, dim)
private val shape = Shape(dim, dim)
// automatically build context most suited for given type.
private val doubleField = Float64Field.ndAlgebra
private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto)
private val realField = DoubleField.ndAlgebra
private val viktorField = ViktorFieldND(dim, dim)
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -10,17 +10,19 @@ import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.BufferedFieldOpsND
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.Float64Field
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(doubleField) {
with(realField) {
val fortyTwo = structureND(shape) { 42.0 }
var res = one(shape)
repeat(n) { res = ln(fortyTwo) }
@ -49,10 +51,11 @@ internal class ViktorLogBenchmark {
private companion object {
private const val dim = 1000
private const val n = 100
private val shape = ShapeND(dim, dim)
private val shape = Shape(dim, dim)
// automatically build context most suited for given type.
private val doubleField = Float64Field.ndAlgebra
private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto)
private val realField = DoubleField.ndAlgebra
private val viktorField = ViktorFieldND(dim, dim)
}
}

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

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@ -1,13 +1,8 @@
import space.kscience.gradle.useApache2Licence
import space.kscience.gradle.useSPCTeam
plugins {
id("space.kscience.gradle.project")
id("org.jetbrains.kotlinx.kover") version "0.7.6"
id("ru.mipt.npm.gradle.project")
id("org.jetbrains.kotlinx.kover") version "0.5.0"
}
val attributesVersion by extra("0.2.0")
allprojects {
repositories {
maven("https://repo.kotlin.link")
@ -16,7 +11,7 @@ allprojects {
}
group = "space.kscience"
version = "0.4.0"
version = "0.3.1-dev-1"
}
subprojects {
@ -36,7 +31,7 @@ subprojects {
localDirectory.set(kotlinDir)
remoteUrl.set(
uri("https://github.com/SciProgCentre/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath").toURL()
java.net.URL("https://github.com/mipt-npm/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath")
)
}
@ -56,19 +51,26 @@ subprojects {
}
}
}
plugins.withId("org.jetbrains.kotlin.multiplatform") {
configure<org.jetbrains.kotlin.gradle.dsl.KotlinMultiplatformExtension> {
sourceSets {
val commonTest by getting {
dependencies {
implementation(projects.testUtils)
}
}
}
}
}
}
readme.readmeTemplate = file("docs/templates/README-TEMPLATE.md")
ksciencePublish {
pom("https://github.com/SciProgCentre/kmath") {
useApache2Licence()
useSPCTeam()
}
repository("spc", "https://maven.sciprog.center/kscience")
sonatype("https://oss.sonatype.org")
github("kmath", addToRelease = false)
space()
sonatype()
}
apiValidation.nonPublicMarkers.add("space.kscience.kmath.UnstableKMathAPI")
val multikVersion by extra("0.2.3")
apiValidation.nonPublicMarkers.add("space.kscience.kmath.misc.UnstableKMathAPI")

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@ -1,8 +1,12 @@
plugins {
kotlin("jvm") version "1.7.0"
`kotlin-dsl`
`version-catalog`
alias(npmlibs.plugins.kotlin.plugin.serialization)
}
java.targetCompatibility = JavaVersion.VERSION_11
repositories {
mavenLocal()
maven("https://repo.kotlin.link")
@ -10,25 +14,20 @@ repositories {
gradlePluginPortal()
}
val toolsVersion = spclibs.versions.tools.get()
val kotlinVersion = spclibs.versions.kotlin.asProvider().get()
val benchmarksVersion = spclibs.versions.kotlinx.benchmark.get()
val toolsVersion = npmlibs.versions.tools.get()
val kotlinVersion = npmlibs.versions.kotlin.asProvider().get()
val benchmarksVersion = npmlibs.versions.kotlinx.benchmark.get()
dependencies {
api("space.kscience:gradle-tools:$toolsVersion")
api("ru.mipt.npm:gradle-tools:$toolsVersion")
api(npmlibs.atomicfu.gradle)
//plugins form benchmarks
api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:$benchmarksVersion")
//api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
//to be used inside build-script only
//implementation(spclibs.kotlinx.serialization.json)
implementation("com.fasterxml.jackson.module:jackson-module-kotlin:2.14.+")
implementation(npmlibs.kotlinx.serialization.json)
}
kotlin {
jvmToolchain {
languageVersion.set(JavaLanguageVersion.of(11))
}
sourceSets.all {
languageSettings.optIn("kotlin.OptIn")
}
kotlin.sourceSets.all {
languageSettings.optIn("kotlin.OptIn")
}

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@ -5,10 +5,6 @@
enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
plugins {
id("org.gradle.toolchains.foojay-resolver-convention") version "0.8.0"
}
dependencyResolutionManagement {
val projectProperties = java.util.Properties()
file("../gradle.properties").inputStream().use {
@ -22,7 +18,6 @@ dependencyResolutionManagement {
val toolsVersion: String = projectProperties["toolsVersion"].toString()
@Suppress("UnstableApiUsage")
repositories {
mavenLocal()
maven("https://repo.kotlin.link")
@ -31,8 +26,8 @@ dependencyResolutionManagement {
}
versionCatalogs {
create("spclibs") {
from("space.kscience:version-catalog:$toolsVersion")
create("npmlibs") {
from("ru.mipt.npm:version-catalog:$toolsVersion")
}
}
}

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

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

View File

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

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@ -17,4 +17,4 @@ own `MemoryBuffer.create()` factory).
## Buffer performance
One should avoid using default boxing buffer wherever it is possible. Try to use primitive buffers or memory buffers
instead.
instead .

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@ -1,35 +1,27 @@
# Coding Conventions
Generally, KMath code follows
general [Kotlin coding conventions](https://kotlinlang.org/docs/reference/coding-conventions.html), but with a number of
small changes and clarifications.
Generally, KMath code follows general [Kotlin coding conventions](https://kotlinlang.org/docs/reference/coding-conventions.html), but with a number of small changes and clarifications.
## Utility Class Naming
Filename should coincide with a name of one of the classes contained in the file or start with small letter and describe
its contents.
Filename should coincide with a name of one of the classes contained in the file or start with small letter and describe its contents.
The code convention [here](https://kotlinlang.org/docs/reference/coding-conventions.html#source-file-names) says that
file names should start with a capital letter even if file does not contain classes. Yet starting utility classes and
aggregators with a small letter seems to be a good way to visually separate those files.
The code convention [here](https://kotlinlang.org/docs/reference/coding-conventions.html#source-file-names) says that file names should start with a capital letter even if file does not contain classes. Yet starting utility classes and aggregators with a small letter seems to be a good way to visually separate those files.
This convention could be changed in future in a non-breaking way.
## Private Variable Naming
Private variables' names may start with underscore `_` for of the private mutable variable is shadowed by the public
read-only value with the same meaning.
Private variables' names may start with underscore `_` for of the private mutable variable is shadowed by the public read-only value with the same meaning.
This rule does not permit underscores in names, but it is sometimes useful to "underscore" the fact that public and
private versions draw up the same entity. It is allowed only for private variables.
This rule does not permit underscores in names, but it is sometimes useful to "underscore" the fact that public and private versions draw up the same entity. It is allowed only for private variables.
This convention could be changed in future in a non-breaking way.
## Functions and Properties One-liners
Use one-liners when they occupy single code window line both for functions and properties with getters like
`val b: String get() = "fff"`. The same should be performed with multiline expressions when they could be
Use one-liners when they occupy single code window line both for functions and properties with getters like
`val b: String get() = "fff"`. The same should be performed with multiline expressions when they could be
cleanly separated.
There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook
one-lines seem to better show that the property or function is easily calculated.
There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook one-lines seem to better show that the property or function is easily calculated.

View File

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

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@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2024 KMath contributors.
- Copyright 2018-2021 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

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@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2024 KMath contributors.
- Copyright 2018-2021 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

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@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2024 KMath contributors.
- Copyright 2018-2021 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

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@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2024 KMath contributors.
- Copyright 2018-2021 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

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@ -1,12 +1,8 @@
## Basic linear algebra layout
KMath support for linear algebra organized in a context-oriented way, which means that operations are in most cases
declared in context classes, and are not the members of classes that store data. This allows more flexible approach to
maintain multiple back-ends. The new operations added as extensions to contexts instead of being member functions of
data structures.
KMath support for linear algebra organized in a context-oriented way, which means that operations are in most cases declared in context classes, and are not the members of classes that store data. This allows more flexible approach to maintain multiple back-ends. The new operations added as extensions to contexts instead of being member functions of data structures.
The main context for linear algebra over matrices and vectors is `LinearSpace`, which defines addition and dot products
of matrices and vectors:
The main context for linear algebra over matrices and vectors is `LinearSpace`, which defines addition and dot products of matrices and vectors:
```kotlin
import space.kscience.kmath.linear.*
@ -32,5 +28,4 @@ LinearSpace.Companion.real {
## Backends overview
### EJML
### Commons Math

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@ -8,7 +8,6 @@ One of the most sought after features of mathematical libraries is the high-perf
structures. In `kmath` performance depends on which particular context was used for operation.
Let us consider following contexts:
```kotlin
// automatically build context most suited for given type.
val autoField = NDField.auto(DoubleField, dim, dim)
@ -17,7 +16,6 @@ Let us consider following contexts:
//A generic boxing field. It should be used for objects, not primitives.
val genericField = NDField.buffered(DoubleField, dim, dim)
```
Now let us perform several tests and see, which implementation is best suited for each case:
## Test case
@ -26,9 +24,7 @@ To test performance we will take 2d-structures with `dim = 1000` and add a struc
to it `n = 1000` times.
## Specialized
The code to run this looks like:
```kotlin
specializedField.run {
var res: NDBuffer<Double> = one
@ -37,16 +33,13 @@ The code to run this looks like:
}
}
```
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
@ -55,16 +48,13 @@ Let's do the same with automatic field inference:
}
}
```
Ths speed of this operation is approximately the same as for specialized case since `NDField.auto` just
returns the same `RealNDField` in this case. Of course, it is usually better to use specialized method to be sure.
## Lazy
Lazy field does not produce a structure when asked, instead it generates an empty structure and fills it on-demand
using coroutines to parallelize computations.
When one calls
```kotlin
lazyField.run {
var res = one
@ -73,14 +63,12 @@ When one calls
}
}
```
The result will be calculated almost immediately but the result will be empty. To get the full result
structure one needs to call all its elements. In this case computation overhead will be huge. So this field never
should be used if one expects to use the full result structure. Though if one wants only small fraction, it could
save a lot of time.
This field still could be used with reasonable performance if call code is changed:
```kotlin
lazyField.run {
val res = one.map {
@ -94,13 +82,10 @@ This field still could be used with reasonable performance if call code is chang
res.elements().forEach { it.second }
}
```
In this case it completes in about `4x-5x` time due to boxing.
## Boxing
The boxing field produced by
```kotlin
genericField.run {
var res: NDBuffer<Double> = one
@ -109,22 +94,18 @@ The boxing field produced by
}
}
```
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.
@ -133,7 +114,6 @@ via extension function.
Usually it is bad idea to compare the direct numerical operation performance in different languages, but it hard to
work completely without frame of reference. In this case, simple numpy code:
```python
import numpy as np
@ -141,9 +121,7 @@ res = np.ones((1000,1000))
for i in range(1000):
res = res + 1.0
```
gives the completion time of about `1.1x`, which means that specialized kotlin code in fact is working faster (I think
it is
gives the completion time of about `1.1x`, which means that specialized kotlin code in fact is working faster (I think it is
because better memory management). Of course if one writes `res += 1.0`, the performance will be different,
but it would be different case, because numpy overrides `+=` with in-place operations. In-place operations are
available in `kmath` with `MutableNDStructure` but there is no field for it (one can still work with mapping

View File

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

View File

@ -3,7 +3,17 @@
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")

View File

@ -1,6 +1,6 @@
[![JetBrains Research](https://jb.gg/badges/research.svg)](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)
[![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
![Gradle build](https://github.com/SciProgCentre/kmath/workflows/Gradle%20build/badge.svg)
![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/)
@ -11,22 +11,18 @@ analog to Python's NumPy library. Later we found that kotlin is much more flexib
architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
[Documentation site](https://SciProgCentre.github.io/kmath/)
[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)
* [A talk at KotlinConf 2019 about using kotlin for science](https://youtu.be/LI_5TZ7tnOE?si=4LknX41gl_YeUbIe)
* [A talk on architecture at Joker-2021 (in Russian)](https://youtu.be/1bZ2doHiRRM?si=9w953ro9yu98X_KJ)
* [The same talk in English](https://youtu.be/yP5DIc2fVwQ?si=louZzQ1dcXV6gP10)
* [A seminar on tensor API](https://youtu.be/0H99wUs0xTM?si=6c__04jrByFQtVpo)
# Goal
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS, Native and
Wasm).
* Provide 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.
@ -48,7 +44,7 @@ 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.
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.
@ -63,24 +59,23 @@ ${modules}
KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
are delegated to platform-specific implementations even if they could be provided in the common module for performance
reasons. Currently, Kotlin/JVM is the primary platform, however, Kotlin/Native and Kotlin/JS contributions and
reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
feedback are also welcome.
## Performance
Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to
achieve both
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 a convenient universal API first and then work on increasing performance for specific
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 using GraalVM-CE or
Oracle GraalVM for execution to get better performance.
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
@ -100,10 +95,11 @@ dependencies {
}
```
Gradle `6.0+` is required for multiplatform artifacts.
## Contributing
The project requires a lot of additional work. The most important thing we need is feedback about what features are
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 [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
label.
marked with
[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.

View File

@ -1,5 +1,3 @@
import org.jetbrains.kotlin.gradle.tasks.KotlinJvmCompile
plugins {
kotlin("jvm")
}
@ -10,8 +8,6 @@ repositories {
maven("https://maven.pkg.jetbrains.space/kotlin/p/kotlin/kotlin-js-wrappers")
}
val multikVersion: String by rootProject.extra
dependencies {
implementation(project(":kmath-ast"))
implementation(project(":kmath-kotlingrad"))
@ -19,7 +15,6 @@ dependencies {
implementation(project(":kmath-coroutines"))
implementation(project(":kmath-commons"))
implementation(project(":kmath-complex"))
implementation(project(":kmath-functions"))
implementation(project(":kmath-optimization"))
implementation(project(":kmath-stat"))
implementation(project(":kmath-viktor"))
@ -33,10 +28,7 @@ dependencies {
implementation(project(":kmath-jafama"))
//multik
implementation(project(":kmath-multik"))
implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
//datetime
implementation("org.jetbrains.kotlinx:kotlinx-datetime:0.4.0")
implementation("org.nd4j:nd4j-native:1.0.0-beta7")
@ -50,28 +42,29 @@ dependencies {
// } 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:0.7.0")
implementation("space.kscience:plotlykt-server:0.5.0")
}
kotlin {
jvmToolchain(11)
sourceSets.all {
languageSettings {
optIn("kotlin.contracts.ExperimentalContracts")
optIn("kotlin.ExperimentalUnsignedTypes")
optIn("space.kscience.kmath.UnstableKMathAPI")
}
kotlin.sourceSets.all {
with(languageSettings) {
optIn("kotlin.contracts.ExperimentalContracts")
optIn("kotlin.ExperimentalUnsignedTypes")
optIn("space.kscience.kmath.misc.UnstableKMathAPI")
}
}
tasks.withType<KotlinJvmCompile> {
compilerOptions {
freeCompilerArgs.addAll("-Xjvm-default=all", "-Xopt-in=kotlin.RequiresOptIn", "-Xlambdas=indy")
tasks.withType<org.jetbrains.kotlin.gradle.dsl.KotlinJvmCompile> {
kotlinOptions {
jvmTarget = "11"
freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xopt-in=kotlin.RequiresOptIn" + "-Xlambdas=indy"
}
}
readme {
maturity = space.kscience.gradle.Maturity.EXPERIMENTAL
maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
}

View File

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

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -8,13 +8,13 @@ package space.kscience.kmath.ast
import space.kscience.kmath.asm.compileToExpression
import space.kscience.kmath.expressions.MstExtendedField
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.operations.Float64Field
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(Float64Field)
}.compileToExpression(DoubleField)
val m = DoubleArray(expr.indexer.symbols.size)
val xIdx = expr.indexer.indexOf(x)

View File

@ -1,16 +1,16 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.toExpression
import space.kscience.kmath.kotlingrad.toKotlingradExpression
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.DoubleField
/**
* In this example, *x<sup>2</sup> &minus; 4 x &minus; 44* function is differentiated with Kotlin, and the
@ -19,9 +19,9 @@ import space.kscience.kmath.operations.Float64Field
fun main() {
val actualDerivative = "x^2-4*x-44"
.parseMath()
.toKotlingradExpression(Float64Field)
.toKotlingradExpression(DoubleField)
.derivative(x)
val expectedDerivative = "2*x-4".parseMath().toExpression(Float64Field)
val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField)
check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -9,7 +9,7 @@ import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.expressions.toExpression
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.symja.toSymjaExpression
/**
@ -19,9 +19,9 @@ import space.kscience.kmath.symja.toSymjaExpression
fun main() {
val actualDerivative = "x^2-4*x-44"
.parseMath()
.toSymjaExpression(Float64Field)
.toSymjaExpression(DoubleField)
.derivative(x)
val expectedDerivative = "2*x-4".parseMath().toExpression(Float64Field)
val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField)
check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
}

View File

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

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,18 +7,21 @@ 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.autodiff
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.*
import space.kscience.kmath.random.RandomGenerator
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.chiSquaredExpression
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.plotly.*
import space.kscience.plotly.models.ScatterMode
import space.kscience.plotly.models.TraceValues
@ -64,7 +67,7 @@ suspend fun main() {
val yErr = y.map { sqrt(it) }//RealVector.same(x.size, sigma)
// compute differentiable chi^2 sum for given model ax^2 + bx + c
val chi2 = Double.autodiff.chiSquaredExpression(x, y, yErr) { arg ->
val chi2 = DSProcessor.chiSquaredExpression(x, y, yErr) { arg ->
//bind variables to autodiff context
val a = bindSymbol(a)
val b = bindSymbol(b)
@ -77,9 +80,8 @@ suspend fun main() {
val result = chi2.optimizeWith(
CMOptimizer,
mapOf(a to 1.5, b to 0.9, c to 1.0),
) {
FunctionOptimizationTarget(OptimizationDirection.MINIMIZE)
}
FunctionOptimizationTarget.MINIMIZE
)
//display a page with plot and numerical results
val page = Plotly.page {
@ -96,7 +98,7 @@ suspend fun main() {
scatter {
mode = ScatterMode.lines
x(x)
y(x.map { result.result[a]!! * it.pow(2) + result.result[b]!! * it + 1 })
y(x.map { result.resultPoint[a]!! * it.pow(2) + result.resultPoint[b]!! * it + 1 })
name = "fit"
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,19 +7,21 @@ package space.kscience.kmath.fit
import kotlinx.html.br
import kotlinx.html.h3
import space.kscience.attributes.Attributes
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.autodiff
import space.kscience.kmath.expressions.binding
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.operations.toList
import space.kscience.kmath.optimization.*
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.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
@ -30,8 +32,6 @@ import kotlin.math.sqrt
private val a by symbol
private val b by symbol
private val c by symbol
private val d by symbol
private val e by symbol
/**
@ -63,23 +63,17 @@ suspend fun main() {
val result = XYErrorColumnarData.of(x, y, yErr).fitWith(
QowOptimizer,
Double.autodiff,
mapOf(a to 0.9, b to 1.2, c to 2.0, e to 1.0, d to 1.0, e to 0.0),
attributes = Attributes(OptimizationParameters, listOf(a, b, c, d))
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
val d by binding
val e by binding
a * arg.pow(2) + b * arg + c + d * arg.pow(3) + e / arg
a * arg.pow(2) + b * arg + c
}
println("Resulting chi2/dof: ${result.chiSquaredOrNull}/${result.dof}")
//display a page with plot and numerical results
val page = Plotly.page {
plot {
@ -95,16 +89,16 @@ suspend fun main() {
scatter {
mode = ScatterMode.lines
x(x)
y(x.map { result.model(result.startPoint + result.result + (Symbol.x to it)) })
y(x.map { result.model(result.resultPoint + (Symbol.x to it)) })
name = "fit"
}
}
br()
h3 {
+"Fit result: ${result.result}"
+"Fit result: ${result.resultPoint}"
}
h3 {
+"Chi2/dof = ${result.chiSquaredOrNull!! / result.dof}"
+"Chi2/dof = ${result.chiSquaredOrNull!! / (x.size - 3)}"
}
}

View File

@ -1,19 +1,14 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.functions
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.complex.ComplexField
import space.kscience.kmath.complex.ComplexField.div
import space.kscience.kmath.complex.ComplexField.minus
import space.kscience.kmath.complex.algebra
import space.kscience.kmath.integration.gaussIntegrator
import space.kscience.kmath.integration.integrate
import space.kscience.kmath.integration.value
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.DoubleField
import kotlin.math.pow
fun main() {
@ -21,16 +16,8 @@ fun main() {
val function: Function1D<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
//get the result of the integration
val result = Float64Field.gaussIntegrator.integrate(0.0..10.0, function = function)
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)
repeat(100000) {
Complex.algebra.gaussIntegrator.integrate(0.0..1.0, intervals = 1000) { x: Double ->
// sin(1 / x) + i * cos(1 / x)
1 / x - ComplexField.i / x
}.value
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,7 +7,8 @@ package space.kscience.kmath.functions
import space.kscience.kmath.interpolation.SplineInterpolator
import space.kscience.kmath.interpolation.interpolatePolynomials
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.plotly.Plotly
import space.kscience.plotly.UnstablePlotlyAPI
import space.kscience.plotly.makeFile
@ -23,9 +24,11 @@ fun main() {
x to sin(x)
}
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(
DoubleField, ::DoubleBuffer
).interpolatePolynomials(data)
val function = polynomial.asFunction(Float64Field, 0.0)
val function = polynomial.asFunction(DoubleField, 0.0)
val cmInterpolate = org.apache.commons.math3.analysis.interpolation.SplineInterpolator().interpolate(
data.map { it.first }.toDoubleArray(),

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,7 +7,7 @@ package space.kscience.kmath.functions
import space.kscience.kmath.interpolation.interpolatePolynomials
import space.kscience.kmath.interpolation.splineInterpolator
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.real.map
import space.kscience.kmath.real.step
import space.kscience.plotly.Plotly
@ -28,9 +28,9 @@ fun main() {
val xs = 0.0..100.0 step 0.5
val ys = xs.map(function)
val polynomial: PiecewisePolynomial<Double> = Float64Field.splineInterpolator.interpolatePolynomials(xs, ys)
val polynomial: PiecewisePolynomial<Double> = DoubleField.splineInterpolator.interpolatePolynomials(xs, ys)
val polyFunction = polynomial.asFunction(Float64Field, 0.0)
val polyFunction = polynomial.asFunction(DoubleField, 0.0)
Plotly.plot {
scatter {

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -12,21 +12,23 @@ import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.structureND
import space.kscience.kmath.nd.withNdAlgebra
import space.kscience.kmath.operations.algebra
import kotlin.math.pow
import space.kscience.kmath.operations.invoke
fun main(): Unit = Double.algebra.withNdAlgebra(2, 2) {
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
//Produce a diagonal StructureND
fun diagonal(v: Double) = structureND { (i, j) ->
if (i == j) v else 0.0
}
//Define a function in a nd space
val function: (Double) -> StructureND<Double> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
//get the result of the integration
val result = gaussIntegrator.integrate(0.0..10.0, function = function)
//the value is nullable because in some cases the integration could not succeed
println(result.value)
}
//Define a function in a nd space
val function: (Double) -> StructureND<Double> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
//get the result of the integration
val result = gaussIntegrator.integrate(0.0..10.0, function = function)
//the value is nullable because in some cases the integration could not succeed
println(result.value)
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,28 +1,31 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.linear
import space.kscience.kmath.operations.algebra
import kotlin.random.Random
import kotlin.time.measureTime
import kotlin.system.measureTimeMillis
fun main() = with(Float64ParallelLinearSpace) {
fun main() {
val random = Random(12224)
val dim = 1000
//creating invertible matrix
val matrix1 = buildMatrix(dim, dim) { i, j ->
val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
if (i <= j) random.nextDouble() else 0.0
}
val matrix2 = buildMatrix(dim, dim) { i, j ->
val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
if (i <= j) random.nextDouble() else 0.0
}
val time = measureTime {
repeat(30) {
matrix1 dot matrix2
val time = measureTimeMillis {
with(Double.algebra.linearSpace) {
repeat(10) {
matrix1 dot matrix2
}
}
}

View File

@ -1,12 +1,12 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.linear
import space.kscience.kmath.real.*
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.DoubleBuffer
fun main() {
val x0 = DoubleVector(0.0, 0.0, 0.0)
@ -19,9 +19,9 @@ fun main() {
fun ((Point<Double>) -> Double).grad(x: Point<Double>): Point<Double> {
require(x.size == x0.size)
return Float64Buffer(x.size) { i ->
return DoubleBuffer(x.size) { i ->
val h = sigma[i] / 5
val dVector = Float64Buffer(x.size) { if (it == i) h else 0.0 }
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

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

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,6 +7,7 @@ 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
@ -17,7 +18,7 @@ fun main() = Complex.algebra {
println(complex * 8 - 5 * i)
//flat buffer
val buffer = with(bufferAlgebra) {
val buffer = with(bufferAlgebra){
buffer(8) { Complex(it, -it) }.map { Complex(it.im, it.re) }
}
println(buffer)
@ -29,7 +30,7 @@ fun main() = Complex.algebra {
println(element)
// 1d element operation
val result: StructureND<Complex> = ndAlgebra {
val result: StructureND<Complex> = ndAlgebra{
val a = structureND(8) { (it) -> i * it - it.toDouble() }
val b = 3
val c = Complex(1.0, 1.0)

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,22 +7,21 @@ package space.kscience.kmath.operations
import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.linear.matrix
import space.kscience.kmath.nd.Float64BufferND
import space.kscience.kmath.nd.DoubleBufferND
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.mutableStructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.viktor.ViktorStructureND
import space.kscience.kmath.viktor.viktorAlgebra
import kotlin.collections.component1
import kotlin.collections.component2
fun main() {
val viktorStructure = Float64Field.viktorAlgebra.mutableStructureND(2, 2) { (i, j) ->
val viktorStructure: ViktorStructureND = DoubleField.viktorAlgebra.structureND(Shape(2, 2)) { (i, j) ->
if (i == j) 2.0 else 0.0
}
val cmMatrix: Structure2D<Double> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
val res: Float64BufferND = Float64Field.ndAlgebra {
val res: DoubleBufferND = DoubleField.ndAlgebra {
exp(viktorStructure) + 2.0 * cmMatrix
}

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

View File

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

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

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -10,7 +10,6 @@ import kotlinx.coroutines.async
import kotlinx.coroutines.runBlocking
import org.apache.commons.rng.sampling.distribution.BoxMullerNormalizedGaussianSampler
import org.apache.commons.rng.simple.RandomSource
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.samplers.GaussianSampler
import java.time.Duration
import java.time.Instant
@ -36,7 +35,7 @@ private suspend fun runKMathChained(): Duration {
return Duration.between(startTime, Instant.now())
}
private fun runCMDirect(): Duration {
private fun runApacheDirect(): Duration {
val rng = RandomSource.create(RandomSource.MT, 123L)
val sampler = CMGaussianSampler.of(
@ -65,7 +64,7 @@ private fun runCMDirect(): Duration {
* Comparing chain sampling performance with direct sampling performance
*/
fun main(): Unit = runBlocking(Dispatchers.Default) {
val directJob = async { runCMDirect() }
val directJob = async { runApacheDirect() }
val chainJob = async { runKMathChained() }
println("KMath Chained: ${chainJob.await()}")
println("Apache Direct: ${directJob.await()}")

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -9,7 +9,6 @@ import kotlinx.coroutines.runBlocking
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.chains.combineWithState
import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.random.RandomGenerator
private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -8,12 +8,12 @@
package space.kscience.kmath.structures
import space.kscience.kmath.complex.*
import space.kscience.kmath.linear.transposed
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.Float64Field
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import kotlin.system.measureTimeMillis
@ -21,7 +21,7 @@ fun main() {
val dim = 1000
val n = 1000
val realField = Float64Field.ndAlgebra(dim, dim)
val realField = DoubleField.ndAlgebra(dim, dim)
val complexField: ComplexFieldND = ComplexField.ndAlgebra(dim, dim)
val realTime = measureTimeMillis {
@ -60,7 +60,7 @@ fun complexExample() {
val sum = matrix + x + 1.0
//Represent the sum as 2d-structure and transpose
sum.as2D().transposed()
sum.as2D().transpose()
}
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -10,7 +10,7 @@ import kotlinx.coroutines.GlobalScope
import org.nd4j.linalg.factory.Nd4j
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd4j.nd4j
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.viktor.ViktorFieldND
import kotlin.contracts.InvocationKind
@ -29,29 +29,31 @@ fun main() {
Nd4j.zeros(0)
val dim = 1000
val n = 1000
val shape = ShapeND(dim, dim)
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 doubleField = Float64Field.ndAlgebra
//A generic field. It should be used for objects, not primitives.
val genericField = BufferedFieldOpsND(Float64Field)
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 = Float64Field.nd4j
val nd4jField = DoubleField.nd4j
//viktor field
val viktorField = ViktorFieldND(dim, dim)
//parallel processing based on Java Streams
val parallelField = Float64Field.ndStreaming(dim, dim)
val parallelField = DoubleField.ndStreaming(dim, dim)
measureAndPrint("Boxing addition") {
genericField {
boxingField {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Specialized addition") {
doubleField {
realField {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
@ -78,8 +80,15 @@ fun main() {
}
}
measureAndPrint("Automatic field addition") {
autoField {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Lazy addition") {
val res = doubleField.one(shape).mapAsync(GlobalScope) {
val res = realField.one(shape).mapAsync(GlobalScope) {
var c = 0.0
repeat(n) {
c += 1.0

View File

@ -1,15 +1,13 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.NumbersAddOps
import java.util.*
import java.util.stream.IntStream
@ -18,12 +16,12 @@ import java.util.stream.IntStream
* A demonstration implementation of NDField over Real using Java [java.util.stream.DoubleStream] for parallel
* execution.
*/
class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64Field>,
class StreamDoubleFieldND(override val shape: IntArray) : FieldND<Double, DoubleField>,
NumbersAddOps<StructureND<Double>>,
ExtendedField<StructureND<Double>> {
private val strides = ColumnStrides(shape)
override val elementAlgebra: Float64Field get() = Float64Field
private val strides = DefaultStrides(shape)
override val elementAlgebra: DoubleField get() = DoubleField
override val zero: BufferND<Double> by lazy { structureND(shape) { zero } }
override val one: BufferND<Double> by lazy { structureND(shape) { one } }
@ -32,53 +30,37 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
return structureND(shape) { d }
}
@OptIn(PerformancePitfall::class)
private val StructureND<Double>.buffer: Float64Buffer
private val StructureND<Double>.buffer: DoubleBuffer
get() = when {
shape != this@StreamDoubleFieldND.shape -> throw ShapeMismatchException(
!shape.contentEquals(this@StreamDoubleFieldND.shape) -> throw ShapeMismatchException(
this@StreamDoubleFieldND.shape,
shape
)
this is BufferND && indices == this@StreamDoubleFieldND.strides -> this.buffer as Float64Buffer
else -> Float64Buffer(strides.linearSize) { offset -> get(strides.index(offset)) }
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: ShapeND, initializer: Float64Field.(IntArray) -> Double): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
Float64Field.initializer(index)
}.toArray()
return BufferND(strides, array.asBuffer())
}
override fun mutableStructureND(
shape: ShapeND,
initializer: DoubleField.(IntArray) -> Double,
): MutableBufferND<Double> {
override fun structureND(shape: Shape, initializer: DoubleField.(IntArray) -> Double): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
DoubleField.initializer(index)
}.toArray()
return MutableBufferND(strides, array.asBuffer())
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.map(
transform: Float64Field.(Double) -> Double,
): BufferND<Double> {
val array = Arrays.stream(buffer.array).parallel().map { Float64Field.transform(it) }.toArray()
return BufferND(strides, array.asBuffer())
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.map(
transform: DoubleField.(Double) -> Double,
): BufferND<Double> {
val array = Arrays.stream(buffer.array).parallel().map { DoubleField.transform(it) }.toArray()
return BufferND(strides, array.asBuffer())
}
override fun StructureND<Double>.mapIndexed(
transform: Float64Field.(index: IntArray, Double) -> Double,
transform: DoubleField.(index: IntArray, Double) -> Double,
): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
Float64Field.transform(
DoubleField.transform(
strides.index(offset),
buffer.array[offset]
)
@ -87,14 +69,13 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
return BufferND(strides, array.asBuffer())
}
@OptIn(PerformancePitfall::class)
override fun zip(
left: StructureND<Double>,
right: StructureND<Double>,
transform: Float64Field.(Double, Double) -> Double,
transform: DoubleField.(Double, Double) -> Double,
): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
Float64Field.transform(left.buffer.array[offset], right.buffer.array[offset])
DoubleField.transform(left.buffer.array[offset], right.buffer.array[offset])
}.toArray()
return BufferND(strides, array.asBuffer())
}
@ -124,4 +105,4 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
}
fun Float64Field.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(ShapeND(shape))
fun DoubleField.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(shape)

View File

@ -1,23 +1,20 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.nd.ColumnStrides
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.DefaultStrides
import kotlin.system.measureTimeMillis
@Suppress("ASSIGNED_BUT_NEVER_ACCESSED_VARIABLE")
@OptIn(PerformancePitfall::class)
fun main() {
val n = 6000
val array = DoubleArray(n * n) { 1.0 }
val buffer = Float64Buffer(array)
val strides = ColumnStrides(ShapeND(n, n))
val buffer = DoubleBuffer(array)
val strides = DefaultStrides(intArrayOf(n, n))
val structure = BufferND(strides, buffer)
measureTimeMillis {

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@ -1,25 +1,20 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.operations.mapToBuffer
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.mapToBuffer
import kotlin.system.measureTimeMillis
private inline fun <T, reified R : Any> BufferND<T>.mapToBufferND(
bufferFactory: BufferFactory<R> = BufferFactory(),
crossinline block: (T) -> R,
): BufferND<R> = BufferND(indices, buffer.mapToBuffer(bufferFactory, block))
@Suppress("UNUSED_VARIABLE")
fun main() {
val n = 6000
val structure = BufferND(n, n) { 1.0 }
structure.mapToBufferND { it + 1 } // warm-up
val time1 = measureTimeMillis { val res = structure.mapToBufferND { it + 1 } }
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 }
@ -30,10 +25,10 @@ fun main() {
println("Array mapping finished in $time2 millis")
val buffer = Float64Buffer(DoubleArray(n * n) { 1.0 })
val buffer = DoubleBuffer(DoubleArray(n * n) { 1.0 })
val time3 = measureTimeMillis {
val target = Float64Buffer(DoubleArray(n * n))
val target = DoubleBuffer(DoubleArray(n * n))
val res = array.forEachIndexed { index, value ->
target[index] = value + 1
}

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@ -1,23 +1,23 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.operations.Float64Field
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 <reified R : Any> MutableBuffer.Companion.same(
n: Int,
value: R,
): MutableBuffer<R> = MutableBuffer(n) { value }
value: R
): MutableBuffer<R> = auto(n) { value }
fun main() {
with(Float64Field.bufferAlgebra.withSize(5)) {
with(DoubleField.bufferAlgebra.withSize(5)) {
println(number(2.0) + buffer(1, 2, 3, 4, 5))
}
}

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

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -15,7 +15,7 @@ private fun DMatrixContext<Double, *>.simple() {
val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() }
//Dimension-safe addition
m1.transposed() + m2
m1.transpose() + m2
}
private object D5 : Dimension {

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

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

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

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

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

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/*
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors.LevenbergMarquardt
import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.component1
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.max
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.plus
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.pow
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.times
import space.kscience.kmath.tensors.core.asDoubleTensor
public data class StartDataLm(
var lm_matx_y_dat: MutableStructure2D<Double>,
var example_number: Int,
var p_init: MutableStructure2D<Double>,
var t: MutableStructure2D<Double>,
var y_dat: MutableStructure2D<Double>,
var weight: Double,
var dp: MutableStructure2D<Double>,
var p_min: MutableStructure2D<Double>,
var p_max: MutableStructure2D<Double>,
var consts: MutableStructure2D<Double>,
var opts: DoubleArray,
)
fun funcEasyForLm(
t: MutableStructure2D<Double>,
p: MutableStructure2D<Double>,
exampleNumber: Int,
): MutableStructure2D<Double> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
if (exampleNumber == 1) {
y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0]))).times(p[0, 0]) + t.times(p[2, 0]).times(
DoubleTensorAlgebra.exp((t.times(-1.0 / p[3, 0])))
)
} else if (exampleNumber == 2) {
val mt = t.max()
y_hat = (t.times(1.0 / mt)).times(p[0, 0]) +
(t.times(1.0 / mt)).pow(2).times(p[1, 0]) +
(t.times(1.0 / mt)).pow(3).times(p[2, 0]) +
(t.times(1.0 / mt)).pow(4).times(p[3, 0])
} else if (exampleNumber == 3) {
y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0])))
.times(p[0, 0]) + DoubleTensorAlgebra.sin((t.times(1.0 / p[3, 0]))).times(p[2, 0])
}
return y_hat.as2D()
}
fun funcMiddleForLm(
t: MutableStructure2D<Double>,
p: MutableStructure2D<Double>,
exampleNumber: Int,
): MutableStructure2D<Double> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
val mt = t.max()
for (i in 0 until p.shape.component1()) {
y_hat += (t.times(1.0 / mt)).times(p[i, 0])
}
for (i in 0 until 5) {
y_hat = funcEasyForLm(y_hat.as2D(), p, exampleNumber).asDoubleTensor()
}
return y_hat.as2D()
}
fun funcDifficultForLm(
t: MutableStructure2D<Double>,
p: MutableStructure2D<Double>,
exampleNumber: Int,
): MutableStructure2D<Double> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
val mt = t.max()
for (i in 0 until p.shape.component1()) {
y_hat = y_hat.plus((t.times(1.0 / mt)).times(p[i, 0]))
}
for (i in 0 until 4) {
y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, exampleNumber).asDoubleTensor()
}
return y_hat.as2D()
}
fun getStartDataForFuncDifficult(): StartDataLm {
val NData = 200
var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
for (i in 0 until NData) {
t_example[i, 0] = t_example[i, 0] * (i + 1) - 104
}
val Nparams = 15
var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
for (i in 0 until Nparams) {
p_example[i, 0] = p_example[i, 0] + i - 25
}
val exampleNumber = 1
var y_hat = funcDifficultForLm(t_example, p_example, exampleNumber)
var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
for (i in 0 until Nparams) {
p_init[i, 0] = (p_example[i, 0] + 0.9)
}
var t = t_example
val y_dat = y_hat
val weight = 1.0 / Nparams * 1.0 - 0.085
val dp = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
).as2D()
var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / -50.0)
val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / 50.0)
val consts = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
).as2D()
val opts = doubleArrayOf(3.0, 10000.0, 1e-2, 1e-3, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
return StartDataLm(y_dat, 1, p_init, t, y_dat, weight, dp, p_min.as2D(), p_max.as2D(), consts, opts)
}
fun getStartDataForFuncMiddle(): StartDataLm {
val NData = 100
var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
for (i in 0 until NData) {
t_example[i, 0] = t_example[i, 0] * (i + 1)
}
val Nparams = 20
var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
for (i in 0 until Nparams) {
p_example[i, 0] = p_example[i, 0] + i - 25
}
val exampleNumber = 1
var y_hat = funcMiddleForLm(t_example, p_example, exampleNumber)
var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
for (i in 0 until Nparams) {
p_init[i, 0] = (p_example[i, 0] + 10.0)
}
var t = t_example
val y_dat = y_hat
val weight = 1.0
val dp = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
).as2D()
var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / -50.0)
val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / 50.0)
val consts = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
).as2D()
val opts = doubleArrayOf(3.0, 10000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
var example_number = 1
return StartDataLm(y_dat, example_number, p_init, t, y_dat, weight, dp, p_min.as2D(), p_max.as2D(), consts, opts)
}
fun getStartDataForFuncEasy(): StartDataLm {
val lm_matx_y_dat = doubleArrayOf(
19.6594, 18.6096, 17.6792, 17.2747, 16.3065, 17.1458, 16.0467, 16.7023, 15.7809, 15.9807,
14.7620, 15.1128, 16.0973, 15.1934, 15.8636, 15.4763, 15.6860, 15.1895, 15.3495, 16.6054,
16.2247, 15.9854, 16.1421, 17.0960, 16.7769, 17.1997, 17.2767, 17.5882, 17.5378, 16.7894,
17.7648, 18.2512, 18.1581, 16.7037, 17.8475, 17.9081, 18.3067, 17.9632, 18.2817, 19.1427,
18.8130, 18.5658, 18.0056, 18.4607, 18.5918, 18.2544, 18.3731, 18.7511, 19.3181, 17.3066,
17.9632, 19.0513, 18.7528, 18.2928, 18.5967, 17.8567, 17.7859, 18.4016, 18.9423, 18.4959,
17.8000, 18.4251, 17.7829, 17.4645, 17.5221, 17.3517, 17.4637, 17.7563, 16.8471, 17.4558,
17.7447, 17.1487, 17.3183, 16.8312, 17.7551, 17.0942, 15.6093, 16.4163, 15.3755, 16.6725,
16.2332, 16.2316, 16.2236, 16.5361, 15.3721, 15.3347, 15.5815, 15.6319, 14.4538, 14.6044,
14.7665, 13.3718, 15.0587, 13.8320, 14.7873, 13.6824, 14.2579, 14.2154, 13.5818, 13.8157
)
var example_number = 1
val p_init = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(4, 1)), doubleArrayOf(5.0, 2.0, 0.2, 10.0)
).as2D()
var t = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(100, 1))).as2D()
for (i in 0 until 100) {
t[i, 0] = t[i, 0] * (i + 1)
}
val y_dat = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(100, 1)), lm_matx_y_dat
).as2D()
val weight = 4.0
val dp = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
).as2D()
val p_min = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(4, 1)), doubleArrayOf(-50.0, -20.0, -2.0, -100.0)
).as2D()
val p_max = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(4, 1)), doubleArrayOf(50.0, 20.0, 2.0, 100.0)
).as2D()
val consts = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
).as2D()
val opts = doubleArrayOf(3.0, 100.0, 1e-3, 1e-3, 1e-1, 1e-1, 1e-2, 11.0, 9.0, 1.0)
return StartDataLm(y_dat, example_number, p_init, t, y_dat, weight, dp, p_min, p_max, consts, opts)
}

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/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.randomNormal
import space.kscience.kmath.tensors.core.randomNormalLike
import kotlin.math.abs
// OLS estimator using SVD
@ -23,10 +21,10 @@ fun main() {
DoubleTensorAlgebra {
// take coefficient vector from normal distribution
val alpha = randomNormal(
ShapeND(5),
intArrayOf(5),
randSeed
) + fromArray(
ShapeND(5),
intArrayOf(5),
doubleArrayOf(1.0, 2.5, 3.4, 5.0, 10.1)
)
@ -34,37 +32,35 @@ fun main() {
// also take sample of size 20 from normal distribution for x
val x = randomNormal(
ShapeND(20, 5),
intArrayOf(20, 5),
randSeed
)
// calculate y and add gaussian noise (N(0, 0.05))
val y = x dot alpha
y += randomNormalLike(y, randSeed) * 0.05
y += y.randomNormalLike(randSeed) * 0.05
// now restore the coefficient vector with OSL estimator with SVD
val (u, singValues, v) = svd(x)
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$singValues")
// inverse Sigma matrix can be restored from singular values with diagonalEmbedding function
val sigma = diagonalEmbedding(singValues.map { if (abs(it) < 1e-3) 0.0 else 1.0 / it })
val sigma = diagonalEmbedding(singValues.map{ if (abs(it) < 1e-3) 0.0 else 1.0/it })
val alphaOLS = v dot sigma dot u.transposed() dot y
println(
"Estimated alpha:\n" +
"$alphaOLS"
)
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 == yPred.shape)
require(yTrue.shape contentEquals yPred.shape)
val diff = yTrue - yPred
return sqrt(diff.dot(diff)).value()
return diff.dot(diff).sqrt().value()
}
println("MSE: ${mse(alpha, alphaOLS)}")

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@ -1,12 +1,12 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.tensors.core.*
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
// simple PCA
@ -16,49 +16,49 @@ fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with
// assume x is range from 0 until 10
val x = fromArray(
ShapeND(10),
intArrayOf(10),
DoubleArray(10) { it.toDouble() }
)
// take y dependent on x with noise
val y = 2.0 * x + (3.0 + randomNormalLike(x, seed) * 1.5)
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)).transposed()
val dataset = stack(listOf(x, y)).transpose()
// normalize both x and y
val xMean = mean(x)
val yMean = mean(y)
val xMean = x.mean()
val yMean = y.mean()
val xStd = std(x)
val yStd = std(y)
val xStd = x.std()
val yStd = y.std()
val xScaled: DoubleTensor = (x - xMean) / xStd
val yScaled: DoubleTensor = (y - yMean) / yStd
val xScaled = (x - xMean) / xStd
val yScaled = (y - yMean) / yStd
// save means ans standard deviations for further recovery
val mean = fromArray(
ShapeND(2),
intArrayOf(2),
doubleArrayOf(xMean, yMean)
)
println("Means:\n$mean")
val std = fromArray(
ShapeND(2),
intArrayOf(2),
doubleArrayOf(xStd, yStd)
)
println("Standard deviations:\n$std")
// calculate the covariance matrix of scaled x and y
val covMatrix = covariance(listOf(xScaled.asDoubleTensor1D(), yScaled.asDoubleTensor1D()))
val covMatrix = cov(listOf(xScaled, yScaled))
println("Covariance matrix:\n$covMatrix")
// and find out eigenvector of it
val (_, evecs) = symEig(covMatrix)
val v = evecs.getTensor(0)
val (_, evecs) = covMatrix.symEig()
val v = evecs[0]
println("Eigenvector:\n$v")
// reduce dimension of dataset
@ -68,7 +68,7 @@ fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with
// we can restore original data from reduced data;
// for example, find 7th element of dataset.
val n = 7
val restored = (datasetReduced.getTensor(n) dot v.view(ShapeND(1, 2))) * std + mean
println("Original value:\n${dataset.getTensor(n)}")
val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
println("Original value:\n${dataset[n]}")
println("Restored value:\n$restored")
}

View File

@ -1,12 +1,10 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.tensors.core.randomNormal
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
@ -15,17 +13,17 @@ import space.kscience.kmath.tensors.core.withBroadcast
fun main() = Double.tensorAlgebra.withBroadcast { // work in context with broadcast methods
// take dataset of 5-element vectors from normal distribution
val dataset = randomNormal(ShapeND(100, 5)) * 1.5 // all elements from N(0, 1.5)
val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
dataset += fromArray(
ShapeND(5),
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 = mean(dataset, 0, false)
val std = std(dataset, 0, false)
val mean = dataset.mean(0, false)
val std = dataset.std(0, false)
println("Mean:\n$mean")
println("Standard deviation:\n$std")
@ -37,8 +35,8 @@ fun main() = Double.tensorAlgebra.withBroadcast { // work in context with broad
// now we can scale dataset with mean normalization
val datasetScaled = (dataset - mean) / std
// find out mean and standardDiviation of scaled dataset
// find out mean and std of scaled dataset
println("Mean of scaled:\n${mean(datasetScaled, 0, false)}")
println("Mean of scaled:\n${std(datasetScaled, 0, false)}")
println("Mean of scaled:\n${datasetScaled.mean(0, false)}")
println("Mean of scaled:\n${datasetScaled.std(0, false)}")
}

View File

@ -1,11 +1,10 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
@ -16,13 +15,13 @@ fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear
// set true value of x
val trueX = fromArray(
ShapeND(4),
intArrayOf(4),
doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
)
// and A matrix
val a = fromArray(
ShapeND(4, 4),
intArrayOf(4, 4),
doubleArrayOf(
0.5, 10.5, 4.5, 1.0,
8.5, 0.9, 12.8, 0.1,
@ -41,7 +40,7 @@ fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear
// solve `Ax = b` system using LUP decomposition
// get P, L, U such that PA = LU
val (p, l, u) = lu(a)
val (p, l, u) = a.lu()
// check P is permutation matrix
println("P:\n$p")
@ -65,9 +64,9 @@ fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear
// this function returns solution x of a system lx = b, l should be lower triangular
fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
val n = l.shape[0]
val x = zeros(ShapeND(n))
val x = zeros(intArrayOf(n))
for (i in 0 until n) {
x[intArrayOf(i)] = (b[intArrayOf(i)] - l.getTensor(i).dot(x).value()) / l[intArrayOf(i, i)]
x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / l[intArrayOf(i, i)]
}
return x
}
@ -76,7 +75,7 @@ fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear
// solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
// create it by placing ones on side diagonal
val revMat = zeroesLike(u)
val revMat = u.zeroesLike()
val n = revMat.shape[0]
for (i in 0 until n) {
revMat[intArrayOf(i, n - 1 - i)] = 1.0

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,14 +7,11 @@ package space.kscience.kmath.tensors
import org.jetbrains.kotlinx.multik.api.Multik
import org.jetbrains.kotlinx.multik.api.ndarray
import org.jetbrains.kotlinx.multik.default.DefaultEngine
import space.kscience.kmath.multik.MultikDoubleAlgebra
import space.kscience.kmath.multik.multikAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
val multikAlgebra = MultikDoubleAlgebra(DefaultEngine())
fun main(): Unit = with(multikAlgebra) {
fun main(): Unit = with(DoubleField.multikAlgebra) {
val a = Multik.ndarray(intArrayOf(1, 2, 3)).asType<Double>().wrap()
val b = Multik.ndarray(doubleArrayOf(1.0, 2.0, 3.0)).wrap()
one(a.shape) - a + b * 3.0

View File

@ -1,14 +1,15 @@
/*
* Copyright 2018-2024 KMath contributors.
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.*
import 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
@ -47,7 +48,7 @@ fun reluDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
class ReLU : Activation(::relu, ::reluDer)
fun sigmoid(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
1.0 / (1.0 + exp((-x)))
1.0 / (1.0 + (-x).exp())
}
fun sigmoidDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
@ -66,22 +67,22 @@ class Dense(
private val weights: DoubleTensor = DoubleTensorAlgebra {
randomNormal(
ShapeND(inputUnits, outputUnits),
intArrayOf(inputUnits, outputUnits),
seed
) * sqrt(2.0 / (inputUnits + outputUnits))
}
private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(ShapeND(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.transposed()
val gradInput = outputError dot weights.transpose()
val gradW = input.transposed() dot outputError
val gradBias = mean(structureND = outputError, dim = 0, keepDim = false) * input.shape[0].toDouble()
val gradW = input.transpose() dot outputError
val gradBias = outputError.mean(dim = 0, keepDim = false) * input.shape[0].toDouble()
weights -= learningRate * gradW
bias -= learningRate * gradBias
@ -93,7 +94,7 @@ class Dense(
// simple accuracy equal to the proportion of correct answers
fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
check(yPred.shape == yTrue.shape)
check(yPred.shape contentEquals yTrue.shape)
val n = yPred.shape[0]
var correctCnt = 0
for (i in 0 until n) {
@ -105,16 +106,17 @@ fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
}
// neural network class
@OptIn(ExperimentalStdlibApi::class)
class NeuralNetwork(private val layers: List<Layer>) {
private fun softMaxLoss(yPred: DoubleTensor, yTrue: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
val onesForAnswers = zeroesLike(yPred)
yTrue.source.asIterable().forEachIndexed { index, labelDouble ->
val onesForAnswers = yPred.zeroesLike()
yTrue.copyArray().forEachIndexed { index, labelDouble ->
val label = labelDouble.toInt()
onesForAnswers[intArrayOf(index, label)] = 1.0
}
val softmaxValue = exp(yPred) / exp(yPred).sum(dim = 1, keepDim = true)
val softmaxValue = yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
(-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
}
@ -161,7 +163,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
for ((xBatch, yBatch) in iterBatch(xTrain, yTrain)) {
train(xBatch, yBatch)
}
println("Accuracy:${accuracy(yTrain, predict(xTrain).argMax(1, true).toDoubleTensor())}")
println("Accuracy:${accuracy(yTrain, predict(xTrain).argMax(1, true).asDouble())}")
}
}
@ -172,6 +174,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
}
@OptIn(ExperimentalStdlibApi::class)
fun main() = BroadcastDoubleTensorAlgebra {
val features = 5
val sampleSize = 250
@ -179,19 +182,19 @@ fun main() = BroadcastDoubleTensorAlgebra {
//val testSize = sampleSize - trainSize
// take sample of features from normal distribution
val x = randomNormal(ShapeND(sampleSize, features), seed) * 2.5
val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
x += fromArray(
ShapeND(5),
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(
ShapeND(sampleSize, 1),
intArrayOf(sampleSize, 1),
DoubleArray(sampleSize) { i ->
if (x.getTensor(i).sum() > 0.0) {
if (x[i].sum() > 0.0) {
1.0
} else {
0.0
@ -227,7 +230,7 @@ fun main() = BroadcastDoubleTensorAlgebra {
val prediction = model.predict(xTest)
// process raw prediction via argMax
val predictionLabels = prediction.argMax(1, true).toDoubleTensor()
val predictionLabels = prediction.argMax(1, true).asDouble()
// find out accuracy
val acc = accuracy(yTest, predictionLabels)

View File

@ -3,12 +3,13 @@
# Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
#
kotlin.code.style=official
kotlin.jupyter.add.scanner=false
kotlin.mpp.stability.nowarn=true
kotlin.native.ignoreDisabledTargets=true
//kotlin.incremental.js.ir=true
org.gradle.configureondemand=true
org.gradle.jvmargs=-Xmx4096m
org.gradle.parallel=true
org.gradle.workers.max=4
toolsVersion=0.15.2-kotlin-1.9.22
#kotlin.experimental.tryK2=true
#kscience.wasm.disabled=true
org.gradle.jvmargs=-Xmx4096m
toolsVersion=0.11.7-kotlin-1.7.0

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