Compare commits

..

93 Commits

Author SHA1 Message Date
1881feb5e2 Merge pull request 'Fix #532 by making ShapeND a non-value class' (!522) from bug/defaultStridesCache into dev
Reviewed-on: kscience/kmath#522
Reviewed-by: Alexander Nozik <altavir@gmail.com>
2024-05-09 09:16:52 +03:00
Gleb Minaev
201887187d Make ShapeND a usual non-value class. Implement its equals and hashCode methods. Deprecate contentEquals and contentHashCode. 2024-05-08 21:59:49 +03:00
fc0393436f Document ShapeND.asArray() 2024-04-15 18:01:58 +03:00
9228e6019c Update Attributes version 2024-04-15 17:57:00 +03:00
edbf8c05be cleanup Minuit 2024-04-15 17:52:15 +03:00
SPC-code
f335d63659
Update docs/buffers.md
Co-authored-by: Gleb Minaev <43728100+lounres@users.noreply.github.com>
2024-04-15 17:42:52 +03:00
SPC-code
c696a22f62
Update kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegrator.kt
Co-authored-by: Gleb Minaev <43728100+lounres@users.noreply.github.com>
2024-04-15 17:41:52 +03:00
2fe04040c6 Fix AttributeBuilder inlining 2024-03-27 09:58:28 +03:00
255d4ba6b7 Dump API. Update readme 2024-03-27 09:51:23 +03:00
48b334a2b6 Singleton implementation for Attributes.EMPTY 2024-03-27 09:45:56 +03:00
ac851bea62 Change logic of AttributesBuilder. It no longer exposes the constructor 2024-03-27 09:18:46 +03:00
3b74968f9a Change logic of AttributesBuilder. It no longer exposes the constructor 2024-03-27 09:11:35 +03:00
214467d21c Reformat code 2024-03-27 09:11:12 +03:00
ecb5d28110 Attributes modify->modified 2024-03-27 08:51:56 +03:00
ec88d6be9e Remove unnecessary reification 2024-03-27 08:19:22 +03:00
461e5a7c54 Refactor names for AttributesBuilder behavior 2024-03-27 08:12:39 +03:00
1be6a5ca0e LinearSpace.compute -> LinearSpace.withComputedAttribute 2024-03-27 07:45:57 +03:00
a67bda8a33 Adjust build 2024-03-27 07:44:53 +03:00
efef5996e1 Remove contracts 2024-03-27 07:44:33 +03:00
69b59b43f4 Mark polymorphic attribute getters and setters as unstable 2024-03-27 07:43:54 +03:00
82196250f6 Remove unnecessary internal dependencies 2024-03-17 09:42:50 +03:00
86324a9219 Add RingBuffer reset and capacity 2024-03-17 09:29:15 +03:00
203a350650 Merge remote-tracking branch 'github/dev' into dev 2024-03-08 10:19:20 +03:00
b076a6573a Update versions 2024-03-08 10:18:32 +03:00
SPC-code
e7d8b94889
Update attributes-kt/src/commonMain/kotlin/space/kscience/attributes/SafeType.kt
Co-authored-by: Gleb Minaev <43728100+lounres@users.noreply.github.com>
2024-03-08 10:07:54 +03:00
5dea38eef8 Merge remote-tracking branch 'github/dev' into dev 2024-03-08 10:07:28 +03:00
SPC-code
dcf5b19d80
Update attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attributes.kt
Co-authored-by: Gleb Minaev <43728100+lounres@users.noreply.github.com>
2024-03-08 10:06:30 +03:00
11722db3c8 Add Attributes container equality 2024-03-08 10:04:37 +03:00
fcb7e2fa7d Reverse types for buffers and typealiases for geometry. 2024-02-22 21:03:58 +03:00
dba001eff3 Fix types in geometry algebras 2024-02-20 20:39:57 +03:00
49f0d1fe7d Fix types in geometry algebras 2024-02-20 19:35:00 +03:00
ad66a63ac2 Merge remote-tracking branch 'github/dev' into dev 2024-02-20 19:05:58 +03:00
32c5b3c10d Add publishing to attributes-kt 2024-02-20 18:38:21 +03:00
SPC-code
bc1b75f79e
Merge branch 'master' into dev 2024-02-18 15:10:51 +03:00
fd9da63ef9 Prepare for 0.4.0 release 2024-02-18 15:05:56 +03:00
024e2a1a4f Add .kotlin to gitignore 2024-02-18 14:26:47 +03:00
41a325d428 fix dot bug introduced in the last refactor. Add test for parallel linear algebra. 2024-02-18 14:22:20 +03:00
79642a869d LUP cleanup 2024-02-18 14:00:38 +03:00
fbee95ab8b LUP cleanup 2024-02-18 13:32:22 +03:00
10739e0d04 Performance fixes 2024-02-18 12:27:46 +03:00
f8e91c2402 Finishing fixes 2024-02-17 21:32:26 +03:00
7d88fb0166 Merge branch 'dev' into dev-0.4
# Conflicts:
#	kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/_generated.kt
2024-02-16 18:57:57 +03:00
ca9df8a167 Add more corner cases for complex power 2024-02-08 18:06:06 +03:00
9e3fd240b8 Update versions 2024-02-08 17:39:19 +03:00
a526dcc16b Merge branch 'dev' into dev-0.4
# Conflicts:
#	kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixFeatures.kt
#	kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/Featured.kt
#	kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/_generated.kt
#	kmath-memory/src/commonMain/kotlin/space/kscience/kmath/memory/MemoryBuffer.kt
#	kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationBuilder.kt
2024-02-07 21:53:49 +03:00
83d9e1f0af Merge remote-tracking branch 'github/dev' into dev 2024-02-07 21:42:49 +03:00
8a754ace19 Fixed GitHub #524 (Complex power of real-valued number 2024-02-07 21:18:47 +03:00
c6f6191ef1 Deprecate direct angle conversion 2024-01-28 18:15:33 +03:00
SPC-code
960a334b8e
Merge pull request #522 from SciProgCentre/copyright-upgrade
Update/Add copyright comments. Regenerate code for kmath-ejml.
2024-01-05 09:56:22 +03:00
Gleb Minaev
cc4159be67 Update/Add copyright comments. Regenerate code for kmath-ejml. 2024-01-05 01:50:27 +03:00
24b934eab7 Add Buffer.asList() 2023-11-22 14:32:56 +03:00
5c82a5e1fa 0.4 WIP 2023-11-18 22:29:59 +03:00
2f2f552648 0.4 WIP 2023-11-11 10:19:09 +03:00
2386ecba41 0.4 WIP 2023-11-04 11:49:31 +03:00
46eacbb750 0.4 WIP 2023-11-03 09:56:19 +03:00
ea887b8c72 0.4 WIP 2023-11-01 08:55:47 +03:00
544b8610e1 Merge branch 'dev' into dev-0.4
# Conflicts:
#	buildSrc/settings.gradle.kts
#	gradle.properties
#	gradle/wrapper/gradle-wrapper.properties
#	kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean2d/Circle2D.kt
#	kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean2d/Float32Space2D.kt
#	kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean2d/Float64Space2D.kt
#	kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/quaternionOperations.kt
2023-10-31 14:18:50 +03:00
a84f1e1500 Merge branch 'kotlin/1.9.20' into dev 2023-10-31 14:05:37 +03:00
328d45444c 1.9.20 finalization 2023-10-31 14:05:17 +03:00
1765f8cf8c Remove asPolynomial 2023-10-25 10:28:39 +03:00
bfb556b013 remove webpack and node version fixture 2023-10-03 19:33:39 +03:00
5129f29084 update geometry 2023-09-22 09:53:44 +03:00
56933ecff3 1.9.20-Beta2 2023-09-22 09:04:39 +03:00
12a02320ec Merge branch 'dev' into kotlin/1.9.20
# Conflicts:
#	build.gradle.kts
2023-09-22 08:33:40 +03:00
7a4e9e70f9 add some quaternion operations 2023-09-22 08:21:14 +03:00
23c0758ba6 Kotlin 1.9.20 2023-09-13 13:25:54 +03:00
dd3d38490a [WIP] refactor features to attributes 2023-09-13 09:00:56 +03:00
9da14089e0 Update integration to use Attributes 2023-08-14 10:06:23 +03:00
5196322b7a Update integration to use Attributes 2023-08-13 19:13:39 +03:00
eff70eb690 Refactor rotations. Add Rotation matrix to Euler conversion 2023-08-13 14:51:50 +03:00
67994d35d9 Merge branch 'dev' into dev-0.4
# Conflicts:
#	CHANGELOG.md
#	build.gradle.kts
#	examples/src/main/kotlin/space/kscience/kmath/operations/mixedNDOperations.kt
#	kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt
#	kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/Float64LinearSpace.kt
#	kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt
#	kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Float64FieldND.kt
#	kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean2d/Circle2D.kt
#	kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean2d/Float64Space2D.kt
#	kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean3d/Float64Space3D.kt
#	kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/vectorPrecision.kt
#	kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jTensorAlgebra.kt
#	kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowAlgebra.kt
#	kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
#	kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensorAlgebra.kt
#	kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorFieldOpsND.kt
2023-08-12 21:38:43 +03:00
efb853c1bc Refactor geometry 2023-08-12 13:16:18 +03:00
19bebfd1ed Finish naming change 2023-08-12 11:21:59 +03:00
62f1c59d73 Fix Median statistics. Update algebra naming. Add integer fields 2023-08-12 10:46:43 +03:00
976714475e levenbergMarquardt cleanup 2023-07-28 20:56:31 +03:00
1e2a8a40e5 levenbergMarquardt cleanup 2023-07-28 20:39:05 +03:00
cfac7ecffc Merge branch 'dev' into dev-0.4 2023-07-28 12:02:17 +03:00
14f0fa1a6f Merge remote-tracking branch 'space/dev' into dev 2023-07-18 11:15:27 +03:00
a3c65e5b17 [WIP] Features to Attributes refactoring 2023-07-18 11:14:23 +03:00
4abe25c188 [WIP] Features to Attributes refactoring 2023-07-18 10:13:36 +03:00
6da51b7794 [WIP] Features to Attributes refactoring 2023-07-09 15:51:50 +03:00
a001c74025 1.9.0-RC 2023-06-22 08:49:51 +03:00
d3893ab7e6 [WIP] moving from features to attributes 2023-06-20 19:45:21 +03:00
c0a7cff1d8 Merge branch 'dev' into dev-0.3.2
# Conflicts:
#	build.gradle.kts
#	gradle/wrapper/gradle-wrapper.properties
#	kmath-core/build.gradle.kts
#	kmath-tensors/build.gradle.kts
2023-06-11 09:10:31 +03:00
009f93adbb Add rotation coversion test for XYZ 2023-06-08 09:28:26 +03:00
1f6b7abf46 wasm test version 2023-06-07 15:16:58 +03:00
c940645e2e fix simja version 2023-06-06 17:43:38 +03:00
65c6962544 Update build tools 2023-05-26 16:46:18 +03:00
1316e6548e Remove vector type from polygon 2023-05-09 19:01:37 +03:00
40b3fa782c Merge remote-tracking branch 'space/master' into dev-0.3.2 2023-05-03 10:12:01 +03:00
f09371a3f9 Explicit mutability for StructureND builders 2023-04-22 09:13:06 +03:00
cdfddb7551 Explicit mutability for StructureND builders 2023-04-21 12:41:46 +03:00
875e32679b [WIP] geometry refactor 2023-04-12 11:39:28 +03:00
592 changed files with 8836 additions and 6621 deletions

1
.gitignore vendored
View File

@ -5,6 +5,7 @@ out/
.idea/
.vscode/
.fleet/
.kotlin/
# Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)

View File

@ -34,7 +34,7 @@ job("Publish") {
api.space().projects.automation.deployments.start(
project = api.projectIdentifier(),
targetIdentifier = TargetIdentifier.Key(projectName),
version = version+revisionSuffix,
version = version + revisionSuffix,
// automatically update deployment status based on the status of a job
syncWithAutomationJob = true
)

View File

@ -14,9 +14,51 @@
### Security
## 0.4.0-dev-3 - 2024-02-18
### Added
- Reification. Explicit `SafeType` for algebras.
- Integer division algebras.
- Float32 geometries.
- New Attributes-kt module that could be used as stand-alone. It declares. type-safe attributes containers.
- Explicit `mutableStructureND` builders for mutable structures.
- `Buffer.asList()` zero-copy transformation.
- Wasm support.
- Parallel implementation of `LinearSpace` for Float64
- Parallel buffer factories
### Changed
- Buffer copy removed from API (added as an extension).
- Default naming for algebra and buffers now uses IntXX/FloatXX notation instead of Java types.
- Remove unnecessary inlines in basic algebras.
- QuaternionField -> QuaternionAlgebra and does not implement `Field` anymore since it is non-commutative
- kmath-geometry is split into `euclidean2d` and `euclidean3d`
- Features replaced with Attributes.
- Transposed refactored.
- Kmath-memory is moved on top of core.
### Deprecated
- ND4J engine
### Removed
- `asPolynomial` function due to scope pollution
- Codegend for ejml (450 lines of codegen for 1000 lines of code is too much)
### Fixed
- Median statistics
- Complex power of negative real numbers
- Add proper mutability for MutableBufferND rows and columns
- Generic Float32 and Float64 vectors are used in geometry algebras.
## 0.3.1 - 2023-04-09
### Added
- Wasm support for `memory`, `core`, `complex` and `functions` modules.
- Generic builders for `BufferND` and `MutableBufferND`
- `NamedMatrix` - matrix with symbol-based indexing
@ -26,6 +68,8 @@
- Algebra now has an obligatory `bufferFactory` (#477).
### Changed
- Removed marker `Vector` type for geometry
- Geometry uses type-safe angles
- Tensor operations switched to prefix notation
- Row-wise and column-wise ND shapes in the core
@ -33,16 +77,19 @@
- Major refactor of tensors (only minor API changes)
- Kotlin 1.8.20
- `LazyStructure` `deffered` -> `async` to comply with coroutines code style
- Default `dot` operation in tensor algebra no longer support broadcasting. Instead `matmul` operation is added to `DoubleTensorAlgebra`.
- Default `dot` operation in tensor algebra no longer support broadcasting. Instead `matmul` operation is added
to `DoubleTensorAlgebra`.
- Multik went MPP
### Removed
- Trajectory moved to https://github.com/SciProgCentre/maps-kt
- Polynomials moved to https://github.com/SciProgCentre/kmath-polynomial
## 0.3.0
### Added
- `ScaleOperations` interface
- `Field` extends `ScaleOperations`
- Basic integration API
@ -67,6 +114,7 @@
- Compilation to TeX for MST: #254
### Changed
- Annotations moved to `space.kscience.kmath`
- Exponential operations merged with hyperbolic functions
- Space is replaced by Group. Space is reserved for vector spaces.
@ -100,9 +148,11 @@
- `UnivariateFunction` -> `Function1D`, `MultivariateFunction` -> `FunctionND`
### Deprecated
- Specialized `DoubleBufferAlgebra`
### Removed
- Nearest in Domain. To be implemented in geometry package.
- Number multiplication and division in main Algebra chain
- `contentEquals` from Buffer. It moved to the companion.
@ -113,12 +163,14 @@
- Algebra elements are completely removed. Use algebra contexts instead.
### Fixed
- Ring inherits RingOperations, not GroupOperations
- Univariate histogram filling
## 0.2.0
### Added
- `fun` annotation for SAM interfaces in library
- Explicit `public` visibility for all public APIs
- Better trigonometric and hyperbolic functions for `AutoDiffField` (https://github.com/mipt-npm/kmath/pull/140)
@ -138,6 +190,7 @@
- Basic Quaternion vector support in `kmath-complex`.
### Changed
- Package changed from `scientifik` to `space.kscience`
- Gradle version: 6.6 -> 6.8.2
- Minor exceptions refactor (throwing `IllegalArgumentException` by argument checks instead of `IllegalStateException`)
@ -162,6 +215,7 @@
- Add `out` projection to `Buffer` generic
### Removed
- `kmath-koma` module because it doesn't support Kotlin 1.4.
- Support of `legacy` JS backend (we will support only IR)
- `toGrid` method.
@ -170,20 +224,24 @@
- StructureND identity and equals
### Fixed
- `symbol` method in `MstExtendedField` (https://github.com/mipt-npm/kmath/pull/140)
## 0.1.4
### Added
- Functional Expressions API
- Mathematical Syntax Tree, its interpreter and API
- String to MST parser (https://github.com/mipt-npm/kmath/pull/120)
- MST to JVM bytecode translator (https://github.com/mipt-npm/kmath/pull/94)
- FloatBuffer (specialized MutableBuffer over FloatArray)
- FlaggedBuffer to associate primitive numbers buffer with flags (to mark values infinite or missing, etc.)
- Specialized builder functions for all primitive buffers like `IntBuffer(25) { it + 1 }` (https://github.com/mipt-npm/kmath/pull/125)
- Specialized builder functions for all primitive buffers
like `IntBuffer(25) { it + 1 }` (https://github.com/mipt-npm/kmath/pull/125)
- Interface `NumericAlgebra` where `number` operation is available to convert numbers to algebraic elements
- Inverse trigonometric functions support in ExtendedField (`asin`, `acos`, `atan`) (https://github.com/mipt-npm/kmath/pull/114)
- Inverse trigonometric functions support in
ExtendedField (`asin`, `acos`, `atan`) (https://github.com/mipt-npm/kmath/pull/114)
- New space extensions: `average` and `averageWith`
- Local coding conventions
- Geometric Domains API in `kmath-core`
@ -192,10 +250,12 @@
- Norm support for `Complex`
### Changed
- `readAsMemory` now has `throws IOException` in JVM signature.
- Several functions taking functional types were made `inline`.
- Several functions taking functional types now have `callsInPlace` contracts.
- BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor optimizations
- BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor
optimizations
- `power(T, Int)` extension function has preconditions and supports `Field<T>`
- Memory objects have more preconditions (overflow checking)
- `tg` function is renamed to `tan` (https://github.com/mipt-npm/kmath/pull/114)
@ -203,6 +263,7 @@
- Moved probability distributions to commons-rng and to `kmath-prob`
### Fixed
- Missing copy method in Memory implementation on JS (https://github.com/mipt-npm/kmath/pull/106)
- D3.dim value in `kmath-dimensions`
- Multiplication in integer rings in `kmath-core` (https://github.com/mipt-npm/kmath/pull/101)

View File

@ -2,7 +2,7 @@
[![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
![Gradle build](https://github.com/SciProgCentre/kmath/workflows/Gradle%20build/badge.svg)
[![Maven Central](https://img.shields.io/maven-central/v/space.kscience/kmath-core.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:%22space.kscience%22)
[![Space](https://img.shields.io/badge/dynamic/xml?color=orange&label=Space&query=//metadata/versioning/latest&url=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fspace%2Fkscience%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/spc/p/sci/maven/space/kscience/)
[![Space](https://img.shields.io/badge/dynamic/xml?color=orange&label=Space&query=//metadata/versioning/latest&url=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fspace%2Fkscience%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
# KMath
@ -11,18 +11,22 @@ analog to Python's NumPy library. Later we found that kotlin is much more flexib
architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
[Documentation site (**WIP**)](https://SciProgCentre.github.io/kmath/)
[Documentation site](https://SciProgCentre.github.io/kmath/)
## Publications and talks
* [A conceptual article about context-oriented design](https://proandroiddev.com/an-introduction-context-oriented-programming-in-kotlin-2e79d316b0a2)
* [Another article about context-oriented design](https://proandroiddev.com/diving-deeper-into-context-oriented-programming-in-kotlin-3ecb4ec38814)
* [ACAT 2019 conference paper](https://aip.scitation.org/doi/abs/10.1063/1.5130103)
* [A talk at KotlinConf 2019 about using kotlin for science](https://youtu.be/LI_5TZ7tnOE?si=4LknX41gl_YeUbIe)
* [A talk on architecture at Joker-2021 (in Russian)](https://youtu.be/1bZ2doHiRRM?si=9w953ro9yu98X_KJ)
* [The same talk in English](https://youtu.be/yP5DIc2fVwQ?si=louZzQ1dcXV6gP10)
* [A seminar on tensor API](https://youtu.be/0H99wUs0xTM?si=6c__04jrByFQtVpo)
# Goal
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native)
.
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS, Native and
Wasm).
* Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
* Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
@ -53,19 +57,21 @@ module definitions below. The module stability could have the following levels:
## Modules
### [benchmarks](benchmarks)
### [attributes-kt](attributes-kt)
> An API and basic implementation for arranging objects in a continuous memory block.
>
> **Maturity**: DEVELOPMENT
### [benchmarks](benchmarks)
>
> **Maturity**: EXPERIMENTAL
### [examples](examples)
>
>
> **Maturity**: EXPERIMENTAL
### [kmath-ast](kmath-ast)
>
>
> **Maturity**: EXPERIMENTAL
>
> **Features:**
@ -76,7 +82,7 @@ module definitions below. The module stability could have the following levels:
### [kmath-commons](kmath-commons)
>
> Commons math binding for kmath
>
> **Maturity**: EXPERIMENTAL
@ -105,21 +111,20 @@ 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
>
> **Features:**
@ -142,7 +147,7 @@ One can still use generic algebras though.
### [kmath-functions](kmath-functions)
>
> Functions, integration and interpolation
>
> **Maturity**: EXPERIMENTAL
>
@ -156,18 +161,16 @@ 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**: PROTOTYPE
> **Maturity**: DEPRECATED
>
> **Features:**
> - [jafama-double](kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/) : Double ExtendedField implementations based on Jafama
@ -175,11 +178,10 @@ One can still use generic algebras though.
### [kmath-jupyter](kmath-jupyter)
>
>
> **Maturity**: PROTOTYPE
### [kmath-kotlingrad](kmath-kotlingrad)
>
> Kotlin∇ integration module
>
> **Maturity**: EXPERIMENTAL
>
@ -194,14 +196,14 @@ One can still use generic algebras though.
> **Maturity**: DEVELOPMENT
### [kmath-multik](kmath-multik)
>
> JetBrains Multik connector
>
> **Maturity**: PROTOTYPE
### [kmath-nd4j](kmath-nd4j)
> ND4J NDStructure implementation and according NDAlgebra classes
>
>
> **Maturity**: EXPERIMENTAL
> **Maturity**: DEPRECATED
>
> **Features:**
> - [nd4jarraystructure](kmath-nd4j/#) : NDStructure wrapper for INDArray
@ -211,27 +213,24 @@ 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
>
> **Features:**
@ -241,13 +240,12 @@ One can still use generic algebras though.
### [kmath-viktor](kmath-viktor)
> Binding for https://github.com/JetBrains-Research/viktor
>
>
> **Maturity**: DEVELOPMENT
> **Maturity**: DEPRECATED
### [test-utils](test-utils)
>
>
> **Maturity**: EXPERIMENTAL
@ -256,22 +254,24 @@ One can still use generic algebras though.
KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
are delegated to platform-specific implementations even if they could be provided in the common module for performance
reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
reasons. Currently, Kotlin/JVM is the primary platform, however, Kotlin/Native and Kotlin/JS contributions and
feedback are also welcome.
## Performance
Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both
Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to
achieve both
performance and flexibility.
We expect to focus on creating convenient universal API first and then work on increasing performance for specific
We expect to focus on creating a convenient universal API first and then work on increasing performance for specific
cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be better than SciPy.
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
better than SciPy.
## Requirements
KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for
execution to get better performance.
KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend using GraalVM-CE or
Oracle GraalVM for execution to get better performance.
### Repositories
@ -291,10 +291,10 @@ dependencies {
}
```
Gradle `6.0+` is required for multiplatform artifacts.
## Contributing
The project requires a lot of additional work. The most important thing we need is a feedback about what features are
The project requires a lot of additional work. The most important thing we need is feedback about what features are
required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
marked with [waiting for a hero](https://github.com/SciProgCentre/kmath/labels/waiting%20for%20a%20hero) label.
marked
with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
label.

21
attributes-kt/README.md Normal file
View File

@ -0,0 +1,21 @@
# 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")
}
```

View File

@ -0,0 +1,104 @@
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;
}

View File

@ -0,0 +1,20 @@
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()
}

View File

@ -0,0 +1,29 @@
/*
* 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>>

View File

@ -0,0 +1,20 @@
/*
* 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>

View File

@ -0,0 +1,143 @@
/*
* 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)

View File

@ -0,0 +1,68 @@
/*
* 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()

View File

@ -0,0 +1,34 @@
/*
* 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)
}

View File

@ -0,0 +1,35 @@
/*
* 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>
}

View File

@ -0,0 +1,17 @@
/*
* 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

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -11,7 +11,7 @@ import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.math.sin
@ -84,7 +84,7 @@ class ExpressionsInterpretersBenchmark {
private val x by symbol
private const val times = 1_000_000
private val functional = DoubleField.expression {
private val functional = Float64Field.expression {
val x = bindSymbol(Symbol.x)
x * number(2.0) + 2.0 / x - 16.0 / sin(x)
}
@ -93,13 +93,14 @@ class ExpressionsInterpretersBenchmark {
x * 2.0 + number(2.0) / x - number(16.0) / sin(x)
}
private val mst = node.toExpression(DoubleField)
private val mst = node.toExpression(Float64Field)
@OptIn(UnstableKMathAPI::class)
private val wasm = node.wasmCompileToExpression(DoubleField)
private val estree = node.estreeCompileToExpression(DoubleField)
private val wasm = node.wasmCompileToExpression(Float64Field)
private val estree = node.estreeCompileToExpression(Float64Field)
private val raw = Expression<Double> { args ->
val x = args[x]!!
val x = args.getValue(x)
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -67,7 +67,7 @@ internal class BigIntBenchmark {
@Benchmark
fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmLargeNumber*kmLargeNumber)
blackhole.consume(kmLargeNumber * kmLargeNumber)
}
@Benchmark
@ -77,7 +77,7 @@ internal class BigIntBenchmark {
@Benchmark
fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(jvmLargeNumber*jvmLargeNumber)
blackhole.consume(jvmLargeNumber * jvmLargeNumber)
}
@Benchmark

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -14,7 +14,7 @@ import space.kscience.kmath.complex.ComplexField
import space.kscience.kmath.complex.complex
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.getDouble
import space.kscience.kmath.structures.permute
@ -33,7 +33,7 @@ internal class BufferBenchmark {
@Benchmark
fun doubleBufferReadWrite(blackhole: Blackhole) {
val buffer = DoubleBuffer(size) { it.toDouble() }
val buffer = Float64Buffer(size) { it.toDouble() }
var res = 0.0
(0 until size).forEach {
res += buffer[it]
@ -43,7 +43,7 @@ internal class BufferBenchmark {
@Benchmark
fun bufferViewReadWrite(blackhole: Blackhole) {
val buffer = DoubleBuffer(size) { it.toDouble() }.permute(reversedIndices)
val buffer = Float64Buffer(size) { it.toDouble() }.permute(reversedIndices)
var res = 0.0
(0 until size).forEach {
res += buffer[it]
@ -53,7 +53,7 @@ internal class BufferBenchmark {
@Benchmark
fun bufferViewReadWriteSpecialized(blackhole: Blackhole) {
val buffer = DoubleBuffer(size) { it.toDouble() }.permute(reversedIndices)
val buffer = Float64Buffer(size) { it.toDouble() }.permute(reversedIndices)
var res = 0.0
(0 until size).forEach {
res += buffer.getDouble(it)
@ -75,6 +75,6 @@ internal class BufferBenchmark {
private companion object {
private const val size = 100
private val reversedIndices = IntArray(size){it}.apply { reverse() }
private val reversedIndices = IntArray(size) { it }.apply { reverse() }
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -11,12 +11,11 @@ import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
import space.kscience.kmath.linear.Float64ParallelLinearSpace
import space.kscience.kmath.linear.invoke
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.tensorflow.produceWithTF
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.tensorAlgebra
import kotlin.random.Random
@ -27,10 +26,10 @@ internal class DotBenchmark {
const val dim = 1000
//creating invertible matrix
val matrix1 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
val matrix1 = Float64Field.linearSpace.buildMatrix(dim, dim) { _, _ ->
random.nextDouble()
}
val matrix2 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
val matrix2 = Float64Field.linearSpace.buildMatrix(dim, dim) { _, _ ->
random.nextDouble()
}
@ -45,7 +44,7 @@ internal class DotBenchmark {
@Benchmark
fun tfDot(blackhole: Blackhole) {
blackhole.consume(
DoubleField.produceWithTF {
Float64Field.produceWithTF {
matrix1 dot matrix1
}
)
@ -71,28 +70,24 @@ internal class DotBenchmark {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun tensorDot(blackhole: Blackhole) = with(DoubleField.tensorAlgebra) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun multikDot(blackhole: Blackhole) = with(multikAlgebra) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
fun tensorDot(blackhole: Blackhole) = with(Float64Field.tensorAlgebra) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
fun bufferedDot(blackhole: Blackhole) = with(Float64Field.linearSpace) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
fun parallelDot(blackhole: Blackhole) = with(Float64ParallelLinearSpace) {
blackhole.consume(matrix1 dot matrix2)
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -12,7 +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.DoubleField
import space.kscience.kmath.operations.Float64Field
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 = DoubleField.expression {
private val functional = Float64Field.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(DoubleField)
private val mst = node.toExpression(Float64Field)
private val asmPrimitive = node.compileToExpression(DoubleField)
private val asmPrimitive = node.compileToExpression(Float64Field)
private val xIdx = asmPrimitive.indexer.indexOf(x)
private val asmGeneric = node.compileToExpression(DoubleField as Algebra<Double>)
private val asmGeneric = node.compileToExpression(Float64Field as Algebra<Double>)
private val raw = Expression<Double> { args ->
val x = args[x]!!

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2023 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -24,7 +24,7 @@ internal class IntegrationBenchmark {
fun doubleIntegration(blackhole: Blackhole) {
val res = Double.algebra.gaussIntegrator.integrate(0.0..1.0, intervals = 1000) { x: Double ->
//sin(1 / x)
1/x
1 / x
}.value
blackhole.consume(res)
}
@ -33,7 +33,7 @@ internal class IntegrationBenchmark {
fun complexIntegration(blackhole: Blackhole) = with(Complex.algebra) {
val res = gaussIntegrator.integrate(0.0..1.0, intervals = 1000) { x: Double ->
// sin(1 / x) + i * cos(1 / x)
1/x - i/x
1 / x - i / x
}.value
blackhole.consume(res)
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -11,10 +11,8 @@ import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.jafama.JafamaDoubleField
import space.kscience.kmath.jafama.StrictJafamaDoubleField
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.random.Random
@State(Scope.Benchmark)
@ -26,7 +24,7 @@ internal class JafamaBenchmark {
@Benchmark
fun core(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
DoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
Float64Field { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
}
@Benchmark
@ -36,7 +34,6 @@ 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())) }
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -15,6 +15,7 @@ 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
@ -38,16 +39,19 @@ internal class MatrixInverseBenchmark {
}
@Benchmark
fun cmLUPInversion(blackhole: Blackhole) {
CMLinearSpace {
blackhole.consume(lupSolver().inverse(matrix))
}
fun kmathParallelLupInversion(blackhole: Blackhole) {
blackhole.consume(Double.algebra.linearSpace.parallel.lupSolver().inverse(matrix))
}
@Benchmark
fun ejmlInverse(blackhole: Blackhole) {
EjmlLinearSpaceDDRM {
blackhole.consume(matrix.toEjml().inverse())
}
fun cmLUPInversion(blackhole: Blackhole) = CMLinearSpace {
blackhole.consume(lupSolver().inverse(matrix))
}
@Benchmark
fun ejmlInverse(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
blackhole.consume(matrix.toEjml().inverted())
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -16,7 +16,7 @@ import org.jetbrains.kotlinx.multik.ndarray.data.DataType
import space.kscience.kmath.UnsafeKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd4j.nd4j
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.one
import space.kscience.kmath.tensors.core.tensorAlgebra
@ -25,6 +25,16 @@ import space.kscience.kmath.viktor.viktorAlgebra
@State(Scope.Benchmark)
internal class NDFieldBenchmark {
private companion object {
private const val dim = 1000
private const val n = 100
private val shape = ShapeND(dim, dim)
private val specializedField = Float64Field.ndAlgebra
private val genericField = BufferedFieldOpsND(Float64Field)
private val nd4jField = Float64Field.nd4j
private val viktorField = Float64Field.viktorAlgebra
}
@Benchmark
fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) {
var res: StructureND<Double> = one(shape)
@ -82,13 +92,5 @@ internal class NDFieldBenchmark {
// blackhole.consume(res)
// }
private companion object {
private const val dim = 1000
private const val n = 100
private val shape = ShapeND(dim, dim)
private val specializedField = DoubleField.ndAlgebra
private val genericField = BufferedFieldOpsND(DoubleField)
private val nd4jField = DoubleField.nd4j
private val viktorField = DoubleField.viktorAlgebra
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -12,7 +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.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.tensors.core.symEigJacobi
import space.kscience.kmath.tensors.core.symEigSvd
import space.kscience.kmath.tensors.core.tensorAlgebra
@ -24,7 +24,7 @@ internal class TensorAlgebraBenchmark {
private val random = Random(12224)
private const val dim = 30
private val matrix = DoubleField.linearSpace.matrix(dim, dim).symmetric { _, _ -> random.nextDouble() }
private val matrix = Float64Field.linearSpace.matrix(dim, dim).symmetric { _, _ -> random.nextDouble() }
}
@Benchmark

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -14,7 +14,7 @@ import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.viktor.ViktorFieldND
@State(Scope.Benchmark)
@ -52,7 +52,7 @@ internal class ViktorBenchmark {
private val shape = ShapeND(dim, dim)
// automatically build context most suited for given type.
private val doubleField = DoubleField.ndAlgebra
private val doubleField = Float64Field.ndAlgebra
private val viktorField = ViktorFieldND(dim, dim)
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -13,7 +13,7 @@ import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.viktor.ViktorFieldND
@State(Scope.Benchmark)
@ -52,7 +52,7 @@ internal class ViktorLogBenchmark {
private val shape = ShapeND(dim, dim)
// automatically build context most suited for given type.
private val doubleField = DoubleField.ndAlgebra
private val doubleField = Float64Field.ndAlgebra
private val viktorField = ViktorFieldND(dim, dim)
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,12 +1,13 @@
import space.kscience.gradle.isInDevelopment
import space.kscience.gradle.useApache2Licence
import space.kscience.gradle.useSPCTeam
plugins {
id("space.kscience.gradle.project")
id("org.jetbrains.kotlinx.kover") version "0.6.0"
id("org.jetbrains.kotlinx.kover") version "0.7.6"
}
val attributesVersion by extra("0.2.0")
allprojects {
repositories {
maven("https://repo.kotlin.link")
@ -15,7 +16,7 @@ allprojects {
}
group = "space.kscience"
version = "0.3.1"
version = "0.4.0"
}
subprojects {
@ -35,7 +36,7 @@ subprojects {
localDirectory.set(kotlinDir)
remoteUrl.set(
java.net.URL("https://github.com/SciProgCentre/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath")
uri("https://github.com/SciProgCentre/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath").toURL()
)
}
@ -64,17 +65,10 @@ ksciencePublish {
useApache2Licence()
useSPCTeam()
}
github("kmath", "SciProgCentre")
space(
if (isInDevelopment) {
"https://maven.pkg.jetbrains.space/spc/p/sci/dev"
} else {
"https://maven.pkg.jetbrains.space/spc/p/sci/maven"
}
)
repository("spc", "https://maven.sciprog.center/kscience")
sonatype("https://oss.sonatype.org")
}
apiValidation.nonPublicMarkers.add("space.kscience.kmath.UnstableKMathAPI")
val multikVersion by extra("0.2.0")
val multikVersion by extra("0.2.3")

View File

@ -17,15 +17,15 @@ val benchmarksVersion = spclibs.versions.kotlinx.benchmark.get()
dependencies {
api("space.kscience:gradle-tools:$toolsVersion")
//plugins form benchmarks
api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:0.4.7")
api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:$benchmarksVersion")
//api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
//to be used inside build-script only
//implementation(spclibs.kotlinx.serialization.json)
implementation("com.fasterxml.jackson.module:jackson-module-kotlin:2.14.+")
}
kotlin{
jvmToolchain{
kotlin {
jvmToolchain {
languageVersion.set(JavaLanguageVersion.of(11))
}
sourceSets.all {

View File

@ -2,10 +2,13 @@
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
rootProject.name = "kmath"
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 {
@ -19,6 +22,7 @@ dependencyResolutionManagement {
val toolsVersion: String = projectProperties["toolsVersion"].toString()
@Suppress("UnstableApiUsage")
repositories {
mavenLocal()
maven("https://repo.kotlin.link")

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -54,16 +54,17 @@ fun Project.addBenchmarkProperties() {
p.extensions.findByType(KScienceReadmeExtension::class.java)?.run {
benchmarksProject.extensions.findByType(BenchmarksExtension::class.java)?.configurations?.forEach { cfg ->
property("benchmark${cfg.name.replaceFirstChar { if (it.isLowerCase()) it.titlecase(Locale.getDefault()) else it.toString() }}") {
val launches = benchmarksProject.buildDir.resolve("reports/benchmarks/${cfg.name}")
val launches = benchmarksProject.layout.buildDirectory.dir("reports/benchmarks/${cfg.name}").get()
val resDirectory = launches.listFiles()?.maxByOrNull {
val resDirectory = launches.files().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: List<JmhReport> =
jsonMapper.readValue<List<JmhReport>>(resDirectory.resolve("jvm.json"))
buildString {
appendLine("<details>")
@ -76,16 +77,20 @@ 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

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

View File

@ -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.

View File

@ -1,20 +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.
@ -24,4 +31,5 @@ Use one-liners when they occupy single code window line both for functions and p
`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

@ -1,16 +1,19 @@
# 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:
@ -18,4 +21,4 @@ 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**
from commons-math. **TODO: add example**

View File

@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2022 KMath contributors.
- Copyright 2018-2024 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

Before

Width:  |  Height:  |  Size: 249 KiB

After

Width:  |  Height:  |  Size: 249 KiB

View File

@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2022 KMath contributors.
- Copyright 2018-2024 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

Before

Width:  |  Height:  |  Size: 19 KiB

After

Width:  |  Height:  |  Size: 19 KiB

View File

@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2022 KMath contributors.
- Copyright 2018-2024 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

Before

Width:  |  Height:  |  Size: 278 KiB

After

Width:  |  Height:  |  Size: 278 KiB

View File

@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2022 KMath contributors.
- Copyright 2018-2024 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

Before

Width:  |  Height:  |  Size: 118 KiB

After

Width:  |  Height:  |  Size: 118 KiB

View File

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

View File

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

View File

@ -1,27 +1,54 @@
# Polynomials and Rational Functions
KMath provides a way to work with uni- and multivariate polynomials and rational functions. It includes full support of arithmetic operations of integers, **constants** (elements of ring polynomials are build over), variables (for certain multivariate implementations), polynomials and rational functions encapsulated in so-called **polynomial space** and **rational function space** and some other utilities such as algebraic differentiation and substitution.
KMath provides a way to work with uni- and multivariate polynomials and rational functions. It includes full support of
arithmetic operations of integers, **constants** (elements of ring polynomials are build over), variables (for certain
multivariate implementations), polynomials and rational functions encapsulated in so-called **polynomial space** and *
*rational function space** and some other utilities such as algebraic differentiation and substitution.
## Concrete realizations
There are 3 approaches to represent polynomials:
1. For univariate polynomials one can represent and store polynomial as a list of coefficients for each power of the variable. I.e. polynomial $a_0 + \dots + a_n x^n $ can be represented as a finite sequence $(a_0; \dots; a_n)$. (Compare to sequential definition of polynomials.)
2. For multivariate polynomials one can represent and store polynomial as a matching (in programming it is called "map" or "dictionary", in math it is called [functional relation](https://en.wikipedia.org/wiki/Binary_relation#Special_types_of_binary_relations)) of each "**term signature**" (that describes what variables and in what powers appear in the term) with corresponding coefficient of the term. But there are 2 possible approaches of term signature representation:
1. One can number all the variables, so term signature can be represented as a sequence describing powers of the variables. I.e. signature of term $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for natural or zero $d_i $) can be represented as a finite sequence $(d_0; \dots; d_n)$.
2. One can represent variables as objects ("**labels**"), so term signature can be also represented as a matching of each appeared variable with its power in the term. I.e. signature of term $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for natural non-zero $d_i $) can be represented as a finite matching $(x_0 \to d_1; \dots; x_n \to d_n)$.
All that three approaches are implemented by "list", "numbered", and "labeled" versions of polynomials and polynomial spaces respectively. Whereas all rational functions are represented as fractions with corresponding polynomial numerator and denominator, and rational functions' spaces are implemented in the same way as usual field of rational numbers (or more precisely, as any field of fractions over integral domain) should be implemented.
1. For univariate polynomials one can represent and store polynomial as a list of coefficients for each power of the
variable. I.e. polynomial $a_0 + \dots + a_n x^n $ can be represented as a finite sequence $(a_0; \dots; a_n)$. (
Compare to sequential definition of polynomials.)
2. For multivariate polynomials one can represent and store polynomial as a matching (in programming it is called "map"
or "dictionary", in math it is
called [functional relation](https://en.wikipedia.org/wiki/Binary_relation#Special_types_of_binary_relations)) of
each "**term signature**" (that describes what variables and in what powers appear in the term) with corresponding
coefficient of the term. But there are 2 possible approaches of term signature representation:
1. One can number all the variables, so term signature can be represented as a sequence describing powers of the
variables. I.e. signature of term $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for natural or zero $d_i $) can be
represented as a finite sequence $(d_0; \dots; d_n)$.
2. One can represent variables as objects ("**labels**"), so term signature can be also represented as a matching of
each appeared variable with its power in the term. I.e. signature of term $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for
natural non-zero $d_i $) can be represented as a finite matching $(x_0 \to d_1; \dots; x_n \to d_n)$.
All that three approaches are implemented by "list", "numbered", and "labeled" versions of polynomials and polynomial
spaces respectively. Whereas all rational functions are represented as fractions with corresponding polynomial numerator
and denominator, and rational functions' spaces are implemented in the same way as usual field of rational numbers (or
more precisely, as any field of fractions over integral domain) should be implemented.
So here are a bit of details. Let `C` by type of constants. Then:
1. `ListPolynomial`, `ListPolynomialSpace`, `ListRationalFunction` and `ListRationalFunctionSpace` implement the first scenario. `ListPolynomial` stores polynomial $a_0 + \dots + a_n x^n $ as a coefficients list `listOf(a_0, ..., a_n)` (of type `List<C>`).
They also have variation `ScalableListPolynomialSpace` that replaces former polynomials and implements `ScaleOperations`.
2. `NumberedPolynomial`, `NumberedPolynomialSpace`, `NumberedRationalFunction` and `NumberedRationalFunctionSpace` implement second scenario. `NumberedPolynomial` stores polynomials as structures of type `Map<List<UInt>, C>`. Signatures are stored as `List<UInt>`. To prevent ambiguity signatures should not end with zeros.
3. `LabeledPolynomial`, `LabeledPolynomialSpace`, `LabeledRationalFunction` and `LabeledRationalFunctionSpace` implement third scenario using common `Symbol` as variable type. `LabeledPolynomial` stores polynomials as structures of type `Map<Map<Symbol, UInt>, C>`. Signatures are stored as `Map<Symbol, UInt>`. To prevent ambiguity each signature should not map any variable to zero.
1. `ListPolynomial`, `ListPolynomialSpace`, `ListRationalFunction` and `ListRationalFunctionSpace` implement the first
scenario. `ListPolynomial` stores polynomial $a_0 + \dots + a_n x^n $ as a coefficients
list `listOf(a_0, ..., a_n)` (of type `List<C>`).
They also have variation `ScalableListPolynomialSpace` that replaces former polynomials and
implements `ScaleOperations`.
2. `NumberedPolynomial`, `NumberedPolynomialSpace`, `NumberedRationalFunction` and `NumberedRationalFunctionSpace`
implement second scenario. `NumberedPolynomial` stores polynomials as structures of type `Map<List<UInt>, C>`.
Signatures are stored as `List<UInt>`. To prevent ambiguity signatures should not end with zeros.
3. `LabeledPolynomial`, `LabeledPolynomialSpace`, `LabeledRationalFunction` and `LabeledRationalFunctionSpace` implement
third scenario using common `Symbol` as variable type. `LabeledPolynomial` stores polynomials as structures of
type `Map<Map<Symbol, UInt>, C>`. Signatures are stored as `Map<Symbol, UInt>`. To prevent ambiguity each signature
should not map any variable to zero.
### Example: `ListPolynomial`
For example, polynomial $2 - 3x + x^2 $ (with `Int` coefficients) is represented
For example, polynomial $2 - 3x + x^2 $ (with `Int` coefficients) is represented
```kotlin
val polynomial: ListPolynomial<Int> = ListPolynomial(listOf(2, -3, 1))
// or
@ -29,6 +56,7 @@ val polynomial: ListPolynomial<Int> = ListPolynomial(2, -3, 1)
```
All algebraic operations can be used in corresponding space:
```kotlin
val computationResult = Int.algebra.listPolynomialSpace {
ListPolynomial(2, -3, 1) + ListPolynomial(0, 6) == ListPolynomial(2, 3, 1)
@ -41,7 +69,8 @@ For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functio
### Example: `NumberedPolynomial`
For example, polynomial $3 + 5 x_1 - 7 x_0^2 x_2 $ (with `Int` coefficients) is represented
For example, polynomial $3 + 5 x_1 - 7 x_0^2 x_2 $ (with `Int` coefficients) is represented
```kotlin
val polynomial: NumberedPolynomial<Int> = NumberedPolynomial(
mapOf(
@ -59,6 +88,7 @@ val polynomial: NumberedPolynomial<Int> = NumberedPolynomial(
```
All algebraic operations can be used in corresponding space:
```kotlin
val computationResult = Int.algebra.numberedPolynomialSpace {
NumberedPolynomial(
@ -83,7 +113,8 @@ For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functio
### Example: `LabeledPolynomial`
For example, polynomial $3 + 5 y - 7 x^2 z $ (with `Int` coefficients) is represented
For example, polynomial $3 + 5 y - 7 x^2 z $ (with `Int` coefficients) is represented
```kotlin
val polynomial: LabeledPolynomial<Int> = LabeledPolynomial(
mapOf(
@ -101,6 +132,7 @@ val polynomial: LabeledPolynomial<Int> = LabeledPolynomial(
```
All algebraic operations can be used in corresponding space:
```kotlin
val computationResult = Int.algebra.labeledPolynomialSpace {
LabeledPolynomial(
@ -150,23 +182,42 @@ classDiagram
PolynomialSpaceOfFractions <|-- MultivariatePolynomialSpaceOfFractions
```
There are implemented `Polynomial` and `RationalFunction` interfaces as abstractions of polynomials and rational functions respectively (although, there is not a lot of logic in them) and `PolynomialSpace` and `RationalFunctionSpace` (that implement `Ring` interface) as abstractions of polynomials' and rational functions' spaces respectively. More precisely, that means they allow to declare common logic of interaction with such objects and spaces:
There are implemented `Polynomial` and `RationalFunction` interfaces as abstractions of polynomials and rational
functions respectively (although, there is not a lot of logic in them) and `PolynomialSpace`
and `RationalFunctionSpace` (that implement `Ring` interface) as abstractions of polynomials' and rational functions'
spaces respectively. More precisely, that means they allow to declare common logic of interaction with such objects and
spaces:
- `Polynomial` does not provide any logic. It is marker interface.
- `RationalFunction` provides numerator and denominator of rational function and destructuring declaration for them.
- `PolynomialSpace` provides all possible arithmetic interactions of integers, constants (of type `C`), and polynomials (of type `P`) like addition, subtraction, multiplication, and some others and common properties like degree of polynomial.
- `RationalFunctionSpace` provides the same as `PolynomialSpace` but also for rational functions: all possible arithmetic interactions of integers, constants (of type `C`), polynomials (of type `P`), and rational functions (of type `R`) like addition, subtraction, multiplication, division (in some cases), and some others and common properties like degree of polynomial.
- `PolynomialSpace` provides all possible arithmetic interactions of integers, constants (of type `C`), and
polynomials (of type `P`) like addition, subtraction, multiplication, and some others and common properties like
degree of polynomial.
- `RationalFunctionSpace` provides the same as `PolynomialSpace` but also for rational functions: all possible
arithmetic interactions of integers, constants (of type `C`), polynomials (of type `P`), and rational functions (of
type `R`) like addition, subtraction, multiplication, division (in some cases), and some others and common properties
like degree of polynomial.
Then to add abstraction of similar behaviour with variables (in multivariate case) there are implemented `MultivariatePolynomialSpace` and `MultivariateRationalFunctionSpace`. They just include variables (of type `V`) in the interactions of the entities.
Then to add abstraction of similar behaviour with variables (in multivariate case) there are
implemented `MultivariatePolynomialSpace` and `MultivariateRationalFunctionSpace`. They just include variables (of
type `V`) in the interactions of the entities.
Also, to remove boilerplates there were provided helping subinterfaces and abstract subclasses:
- `PolynomialSpaceOverRing` allows to replace implementation of interactions of integers and constants with implementations from provided ring over constants (of type `A: Ring<C>`).
- `PolynomialSpaceOverRing` allows to replace implementation of interactions of integers and constants with
implementations from provided ring over constants (of type `A: Ring<C>`).
- `RationalFunctionSpaceOverRing` &mdash; the same but for `RationalFunctionSpace`.
- `RationalFunctionSpaceOverPolynomialSpace` &mdash; the same but "the inheritance" includes interactions with polynomials from provided `PolynomialSpace`.
- `PolynomialSpaceOfFractions` is actually abstract subclass of `RationalFunctionSpace` that implements all fractions boilerplates with provided (`protected`) constructor of rational functions by polynomial numerator and denominator.
- `MultivariateRationalFunctionSpaceOverMultivariatePolynomialSpace` and `MultivariatePolynomialSpaceOfFractions` &mdash; the same stories of operators inheritance and fractions boilerplates respectively but in multivariate case.
- `RationalFunctionSpaceOverPolynomialSpace` &mdash; the same but "the inheritance" includes interactions with
polynomials from provided `PolynomialSpace`.
- `PolynomialSpaceOfFractions` is actually abstract subclass of `RationalFunctionSpace` that implements all fractions
boilerplates with provided (`protected`) constructor of rational functions by polynomial numerator and denominator.
- `MultivariateRationalFunctionSpaceOverMultivariatePolynomialSpace` and `MultivariatePolynomialSpaceOfFractions`
&mdash; the same stories of operators inheritance and fractions boilerplates respectively but in multivariate case.
## Utilities
For all kinds of polynomials there are provided (implementation details depend on kind of polynomials) such common utilities as:
For all kinds of polynomials there are provided (implementation details depend on kind of polynomials) such common
utilities as:
1. differentiation and anti-differentiation,
2. substitution, invocation and functional representation.

View File

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

View File

@ -11,18 +11,22 @@ analog to Python's NumPy library. Later we found that kotlin is much more flexib
architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
[Documentation site (**WIP**)](https://SciProgCentre.github.io/kmath/)
[Documentation site](https://SciProgCentre.github.io/kmath/)
## Publications and talks
* [A conceptual article about context-oriented design](https://proandroiddev.com/an-introduction-context-oriented-programming-in-kotlin-2e79d316b0a2)
* [Another article about context-oriented design](https://proandroiddev.com/diving-deeper-into-context-oriented-programming-in-kotlin-3ecb4ec38814)
* [ACAT 2019 conference paper](https://aip.scitation.org/doi/abs/10.1063/1.5130103)
* [A talk at KotlinConf 2019 about using kotlin for science](https://youtu.be/LI_5TZ7tnOE?si=4LknX41gl_YeUbIe)
* [A talk on architecture at Joker-2021 (in Russian)](https://youtu.be/1bZ2doHiRRM?si=9w953ro9yu98X_KJ)
* [The same talk in English](https://youtu.be/yP5DIc2fVwQ?si=louZzQ1dcXV6gP10)
* [A seminar on tensor API](https://youtu.be/0H99wUs0xTM?si=6c__04jrByFQtVpo)
# Goal
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native)
.
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS, Native and
Wasm).
* Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
* Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
@ -59,22 +63,24 @@ ${modules}
KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
are delegated to platform-specific implementations even if they could be provided in the common module for performance
reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
reasons. Currently, Kotlin/JVM is the primary platform, however, Kotlin/Native and Kotlin/JS contributions and
feedback are also welcome.
## Performance
Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both
Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to
achieve both
performance and flexibility.
We expect to focus on creating convenient universal API first and then work on increasing performance for specific
We expect to focus on creating a convenient universal API first and then work on increasing performance for specific
cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be better than SciPy.
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
better than SciPy.
## Requirements
KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for
execution to get better performance.
KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend using GraalVM-CE or
Oracle GraalVM for execution to get better performance.
### Repositories
@ -94,11 +100,10 @@ dependencies {
}
```
Gradle `6.0+` is required for multiplatform artifacts.
## Contributing
The project requires a lot of additional work. The most important thing we need is a feedback about what features are
The project requires a lot of additional work. The most important thing we need is feedback about what features are
required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
marked with
[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.
marked
with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
label.

View File

@ -52,7 +52,7 @@ dependencies {
implementation("org.slf4j:slf4j-simple:1.7.32")
// plotting
implementation("space.kscience:plotlykt-server:0.5.0")
implementation("space.kscience:plotlykt-server:0.7.0")
}
kotlin {
@ -67,8 +67,8 @@ kotlin {
}
tasks.withType<KotlinJvmCompile> {
kotlinOptions {
freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xopt-in=kotlin.RequiresOptIn" + "-Xlambdas=indy"
compilerOptions {
freeCompilerArgs.addAll("-Xjvm-default=all", "-Xopt-in=kotlin.RequiresOptIn", "-Xlambdas=indy")
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -8,13 +8,13 @@ package space.kscience.kmath.ast
import space.kscience.kmath.asm.compileToExpression
import space.kscience.kmath.expressions.MstExtendedField
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
fun main() {
val expr = MstExtendedField {
x * 2.0 + number(2.0) / x - number(16.0) + asinh(x) / sin(x)
}.compileToExpression(DoubleField)
}.compileToExpression(Float64Field)
val m = DoubleArray(expr.indexer.symbols.size)
val xIdx = expr.indexer.indexOf(x)

View File

@ -1,16 +1,16 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.toExpression
import space.kscience.kmath.kotlingrad.toKotlingradExpression
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
/**
* In this example, *x<sup>2</sup> &minus; 4 x &minus; 44* function is differentiated with Kotlin, and the
@ -19,9 +19,9 @@ import space.kscience.kmath.operations.DoubleField
fun main() {
val actualDerivative = "x^2-4*x-44"
.parseMath()
.toKotlingradExpression(DoubleField)
.toKotlingradExpression(Float64Field)
.derivative(x)
val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField)
val expectedDerivative = "2*x-4".parseMath().toExpression(Float64Field)
check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -9,7 +9,7 @@ import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.expressions.toExpression
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.symja.toSymjaExpression
/**
@ -19,9 +19,9 @@ import space.kscience.kmath.symja.toSymjaExpression
fun main() {
val actualDerivative = "x^2-4*x-44"
.parseMath()
.toSymjaExpression(DoubleField)
.toSymjaExpression(Float64Field)
.derivative(x)
val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField)
val expectedDerivative = "2*x-4".parseMath().toExpression(Float64Field)
check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
}

View File

@ -1,11 +1,12 @@
/*
* Copyright 2018-2023 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.expressions
import space.kscience.kmath.UnstableKMathAPI
// Only kmath-core is needed.
// Let's declare some variables
@ -51,7 +52,7 @@ fun main() {
// >>> 0.0
// But in case you forgot to specify bound symbol's value, exception is thrown:
println( runCatching { someExpression(z to 4.0) } )
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,

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -13,10 +13,7 @@ import space.kscience.kmath.expressions.autodiff
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.operations.toList
import space.kscience.kmath.optimization.FunctionOptimizationTarget
import space.kscience.kmath.optimization.optimizeWith
import space.kscience.kmath.optimization.resultPoint
import space.kscience.kmath.optimization.resultValue
import space.kscience.kmath.optimization.*
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.real.DoubleVector
import space.kscience.kmath.real.map
@ -80,8 +77,9 @@ suspend fun main() {
val result = chi2.optimizeWith(
CMOptimizer,
mapOf(a to 1.5, b to 0.9, c to 1.0),
FunctionOptimizationTarget.MINIMIZE
)
) {
FunctionOptimizationTarget(OptimizationDirection.MINIMIZE)
}
//display a page with plot and numerical results
val page = Plotly.page {
@ -98,7 +96,7 @@ suspend fun main() {
scatter {
mode = ScatterMode.lines
x(x)
y(x.map { result.resultPoint[a]!! * it.pow(2) + result.resultPoint[b]!! * it + 1 })
y(x.map { result.result[a]!! * it.pow(2) + result.result[b]!! * it + 1 })
name = "fit"
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,6 +7,7 @@ package space.kscience.kmath.fit
import kotlinx.html.br
import kotlinx.html.h3
import space.kscience.attributes.Attributes
import space.kscience.kmath.data.XYErrorColumnarData
import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.expressions.Symbol
@ -64,7 +65,7 @@ suspend fun main() {
QowOptimizer,
Double.autodiff,
mapOf(a to 0.9, b to 1.2, c to 2.0, e to 1.0, d to 1.0, e to 0.0),
OptimizationParameters(a, b, c, d)
attributes = Attributes(OptimizationParameters, listOf(a, b, c, d))
) { arg ->
//bind variables to autodiff context
val a by binding
@ -94,13 +95,13 @@ suspend fun main() {
scatter {
mode = ScatterMode.lines
x(x)
y(x.map { result.model(result.startPoint + result.resultPoint + (Symbol.x to it)) })
y(x.map { result.model(result.startPoint + result.result + (Symbol.x to it)) })
name = "fit"
}
}
br()
h3 {
+"Fit result: ${result.resultPoint}"
+"Fit result: ${result.result}"
}
h3 {
+"Chi2/dof = ${result.chiSquaredOrNull!! / result.dof}"

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -13,7 +13,7 @@ import space.kscience.kmath.complex.algebra
import space.kscience.kmath.integration.gaussIntegrator
import space.kscience.kmath.integration.integrate
import space.kscience.kmath.integration.value
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import kotlin.math.pow
fun main() {
@ -21,7 +21,7 @@ fun main() {
val function: Function1D<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
//get the result of the integration
val result = DoubleField.gaussIntegrator.integrate(0.0..10.0, function = function)
val result = Float64Field.gaussIntegrator.integrate(0.0..10.0, function = function)
//the value is nullable because in some cases the integration could not succeed
println(result.value)

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,8 +7,7 @@ package space.kscience.kmath.functions
import space.kscience.kmath.interpolation.SplineInterpolator
import space.kscience.kmath.interpolation.interpolatePolynomials
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.operations.Float64Field
import space.kscience.plotly.Plotly
import space.kscience.plotly.UnstablePlotlyAPI
import space.kscience.plotly.makeFile
@ -24,11 +23,9 @@ fun main() {
x to sin(x)
}
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(
DoubleField, ::DoubleBuffer
).interpolatePolynomials(data)
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
val function = polynomial.asFunction(DoubleField, 0.0)
val function = polynomial.asFunction(Float64Field, 0.0)
val cmInterpolate = org.apache.commons.math3.analysis.interpolation.SplineInterpolator().interpolate(
data.map { it.first }.toDoubleArray(),

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,7 +7,7 @@ package space.kscience.kmath.functions
import space.kscience.kmath.interpolation.interpolatePolynomials
import space.kscience.kmath.interpolation.splineInterpolator
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
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> = DoubleField.splineInterpolator.interpolatePolynomials(xs, ys)
val polynomial: PiecewisePolynomial<Double> = Float64Field.splineInterpolator.interpolatePolynomials(xs, ys)
val polyFunction = polynomial.asFunction(DoubleField, 0.0)
val polyFunction = polynomial.asFunction(Float64Field, 0.0)
Plotly.plot {
scatter {

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,33 +1,28 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.linear
import space.kscience.kmath.operations.algebra
import kotlin.random.Random
import kotlin.time.ExperimentalTime
import kotlin.time.measureTime
@OptIn(ExperimentalTime::class)
fun main() {
fun main() = with(Float64ParallelLinearSpace) {
val random = Random(12224)
val dim = 1000
//creating invertible matrix
val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
val matrix1 = buildMatrix(dim, dim) { i, j ->
if (i <= j) random.nextDouble() else 0.0
}
val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
val matrix2 = buildMatrix(dim, dim) { i, j ->
if (i <= j) random.nextDouble() else 0.0
}
val time = measureTime {
with(Double.algebra.linearSpace) {
repeat(10) {
matrix1 dot matrix2
}
repeat(30) {
matrix1 dot matrix2
}
}

View File

@ -1,12 +1,12 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.linear
import space.kscience.kmath.real.*
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.Float64Buffer
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 DoubleBuffer(x.size) { i ->
return Float64Buffer(x.size) { i ->
val h = sigma[i] / 5
val dVector = DoubleBuffer(x.size) { if (it == i) h else 0.0 }
val dVector = Float64Buffer(x.size) { if (it == i) h else 0.0 }
val f1 = this(x + dVector / 2)
val f0 = this(x - dVector / 2)
(f1 - f0) / h

View File

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

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,21 +7,22 @@ package space.kscience.kmath.operations
import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.linear.matrix
import space.kscience.kmath.nd.DoubleBufferND
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.Float64BufferND
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.mutableStructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.viktor.ViktorStructureND
import space.kscience.kmath.viktor.viktorAlgebra
import kotlin.collections.component1
import kotlin.collections.component2
fun main() {
val viktorStructure: ViktorStructureND = DoubleField.viktorAlgebra.structureND(ShapeND(2, 2)) { (i, j) ->
val viktorStructure = Float64Field.viktorAlgebra.mutableStructureND(2, 2) { (i, j) ->
if (i == j) 2.0 else 0.0
}
val cmMatrix: Structure2D<Double> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
val res: DoubleBufferND = DoubleField.ndAlgebra {
val res: Float64BufferND = Float64Field.ndAlgebra {
exp(viktorStructure) + 2.0 * cmMatrix
}

View File

@ -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.kmath.series
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.operations.bufferAlgebra
import space.kscience.kmath.structures.asBuffer
fun main() = with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
val firstSequence: DoubleArray = doubleArrayOf(0.0, 2.0, 3.0, 1.0, 3.0, 0.1, 0.0, 1.0)
val secondSequence: DoubleArray = doubleArrayOf(1.0, 0.0, 3.0, 0.0, 0.0, 3.0, 2.0, 0.0, 2.0)
val seriesOne = firstSequence.asBuffer()
val seriesTwo = secondSequence.asBuffer()
val result = DoubleFieldOpsND.dynamicTimeWarping(seriesOne, seriesTwo)
println("Total penalty coefficient: ${result.totalCost}")
print("Alignment: ")
println(result.alignMatrix)
for ((i , j) in result.alignMatrix.indices) {
if (result.alignMatrix[i, j] > 0.0) {
print("[$i, $j] ")
}
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2023 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,3 +1,8 @@
/*
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.series
@ -13,7 +18,10 @@ import space.kscience.kmath.structures.slice
import space.kscience.plotly.*
import kotlin.math.PI
fun main() = with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
fun Double.Companion.seriesAlgebra() = Double.algebra.bufferAlgebra.seriesAlgebra()
fun main() = with(Double.seriesAlgebra()) {
fun Plot.plotSeries(name: String, buffer: Buffer<Double>) {
@ -36,10 +44,10 @@ fun main() = with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
Plotly.page {
h1 { +"This is my plot" }
p{
p {
+"Kolmogorov-smirnov test for s1 and s2: ${kmTest.value}"
}
plot{
plot {
plotSeries("s1", s1)
plotSeries("s2", s2)
plotSeries("s3", s3)

View File

@ -0,0 +1,50 @@
/*
* 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)
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -8,12 +8,12 @@
package space.kscience.kmath.structures
import space.kscience.kmath.complex.*
import space.kscience.kmath.linear.transpose
import space.kscience.kmath.linear.transposed
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.structureND
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import kotlin.system.measureTimeMillis
@ -21,7 +21,7 @@ fun main() {
val dim = 1000
val n = 1000
val realField = DoubleField.ndAlgebra(dim, dim)
val realField = Float64Field.ndAlgebra(dim, dim)
val complexField: ComplexFieldND = ComplexField.ndAlgebra(dim, dim)
val realTime = measureTimeMillis {
@ -60,7 +60,7 @@ fun complexExample() {
val sum = matrix + x + 1.0
//Represent the sum as 2d-structure and transpose
sum.as2D().transpose()
sum.as2D().transposed()
}
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -10,7 +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.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.viktor.ViktorFieldND
import kotlin.contracts.InvocationKind
@ -33,15 +33,15 @@ fun main() {
// specialized nd-field for Double. It works as generic Double field as well.
val doubleField = DoubleField.ndAlgebra
val doubleField = Float64Field.ndAlgebra
//A generic field. It should be used for objects, not primitives.
val genericField = BufferedFieldOpsND(DoubleField)
val genericField = BufferedFieldOpsND(Float64Field)
// Nd4j specialized field.
val nd4jField = DoubleField.nd4j
val nd4jField = Float64Field.nd4j
//viktor field
val viktorField = ViktorFieldND(dim, dim)
//parallel processing based on Java Streams
val parallelField = DoubleField.ndStreaming(dim, dim)
val parallelField = Float64Field.ndStreaming(dim, dim)
measureAndPrint("Boxing addition") {
genericField {

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -9,6 +9,7 @@ import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.NumbersAddOps
import java.util.*
import java.util.stream.IntStream
@ -17,12 +18,12 @@ 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, DoubleField>,
class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64Field>,
NumbersAddOps<StructureND<Double>>,
ExtendedField<StructureND<Double>> {
private val strides = ColumnStrides(shape)
override val elementAlgebra: DoubleField get() = DoubleField
override val elementAlgebra: Float64Field get() = Float64Field
override val zero: BufferND<Double> by lazy { structureND(shape) { zero } }
override val one: BufferND<Double> by lazy { structureND(shape) { one } }
@ -32,40 +33,52 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, DoubleF
}
@OptIn(PerformancePitfall::class)
private val StructureND<Double>.buffer: DoubleBuffer
private val StructureND<Double>.buffer: Float64Buffer
get() = when {
!shape.contentEquals(this@StreamDoubleFieldND.shape) -> throw ShapeMismatchException(
shape != this@StreamDoubleFieldND.shape -> throw ShapeMismatchException(
this@StreamDoubleFieldND.shape,
shape
)
this is BufferND && indices == this@StreamDoubleFieldND.strides -> this.buffer as DoubleBuffer
else -> DoubleBuffer(strides.linearSize) { offset -> get(strides.index(offset)) }
this is BufferND && indices == this@StreamDoubleFieldND.strides -> this.buffer as Float64Buffer
else -> Float64Buffer(strides.linearSize) { offset -> get(strides.index(offset)) }
}
override fun structureND(shape: ShapeND, initializer: DoubleField.(IntArray) -> Double): BufferND<Double> {
override fun structureND(shape: ShapeND, initializer: Float64Field.(IntArray) -> Double): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
DoubleField.initializer(index)
Float64Field.initializer(index)
}.toArray()
return BufferND(strides, array.asBuffer())
}
override fun mutableStructureND(
shape: ShapeND,
initializer: DoubleField.(IntArray) -> Double,
): MutableBufferND<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: DoubleField.(Double) -> Double,
transform: Float64Field.(Double) -> Double,
): BufferND<Double> {
val array = Arrays.stream(buffer.array).parallel().map { DoubleField.transform(it) }.toArray()
val array = Arrays.stream(buffer.array).parallel().map { Float64Field.transform(it) }.toArray()
return BufferND(strides, array.asBuffer())
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.mapIndexed(
transform: DoubleField.(index: IntArray, Double) -> Double,
transform: Float64Field.(index: IntArray, Double) -> Double,
): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
DoubleField.transform(
Float64Field.transform(
strides.index(offset),
buffer.array[offset]
)
@ -78,10 +91,10 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, DoubleF
override fun zip(
left: StructureND<Double>,
right: StructureND<Double>,
transform: DoubleField.(Double, Double) -> Double,
transform: Float64Field.(Double, Double) -> Double,
): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
DoubleField.transform(left.buffer.array[offset], right.buffer.array[offset])
Float64Field.transform(left.buffer.array[offset], right.buffer.array[offset])
}.toArray()
return BufferND(strides, array.asBuffer())
}
@ -111,4 +124,4 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, DoubleF
override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
}
fun DoubleField.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(ShapeND(shape))
fun Float64Field.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(ShapeND(shape))

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -16,7 +16,7 @@ import kotlin.system.measureTimeMillis
fun main() {
val n = 6000
val array = DoubleArray(n * n) { 1.0 }
val buffer = DoubleBuffer(array)
val buffer = Float64Buffer(array)
val strides = ColumnStrides(ShapeND(n, n))
val structure = BufferND(strides, buffer)

View File

@ -1,25 +1,23 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.mapToBuffer
import kotlin.system.measureTimeMillis
private inline fun <T, reified R : Any> BufferND<T>.mapToBufferND(
bufferFactory: BufferFactory<R> = BufferFactory.auto(),
bufferFactory: BufferFactory<R> = BufferFactory(),
crossinline block: (T) -> R,
): BufferND<R> = BufferND(indices, buffer.mapToBuffer(bufferFactory, block))
@Suppress("UNUSED_VARIABLE")
fun main() {
val n = 6000
val structure = StructureND.buffered(ShapeND(n, n), Buffer.Companion::auto) { 1.0 }
val structure = BufferND(n, n) { 1.0 }
structure.mapToBufferND { it + 1 } // warm-up
val time1 = measureTimeMillis { val res = structure.mapToBufferND { it + 1 } }
println("Structure mapping finished in $time1 millis")
@ -32,10 +30,10 @@ fun main() {
println("Array mapping finished in $time2 millis")
val buffer = DoubleBuffer(DoubleArray(n * n) { 1.0 })
val buffer = Float64Buffer(DoubleArray(n * n) { 1.0 })
val time3 = measureTimeMillis {
val target = DoubleBuffer(DoubleArray(n * n))
val target = Float64Buffer(DoubleArray(n * n))
val res = array.forEachIndexed { index, value ->
target[index] = value + 1
}

View File

@ -1,23 +1,23 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.buffer
import space.kscience.kmath.operations.bufferAlgebra
import space.kscience.kmath.operations.withSize
inline fun <reified R : Any> MutableBuffer.Companion.same(
n: Int,
value: R
): MutableBuffer<R> = auto(n) { value }
value: R,
): MutableBuffer<R> = MutableBuffer(n) { value }
fun main() {
with(DoubleField.bufferAlgebra.withSize(5)) {
with(Float64Field.bufferAlgebra.withSize(5)) {
println(number(2.0) + buffer(1, 2, 3, 4, 5))
}
}

View File

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

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -15,7 +15,7 @@ private fun DMatrixContext<Double, *>.simple() {
val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() }
//Dimension-safe addition
m1.transpose() + m2
m1.transposed() + m2
}
private object D5 : Dimension {

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2023 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -31,7 +31,7 @@ fun main() {
val exampleNumber = 1
var y_hat = funcDifficultForLm(t_example, p_example, exampleNumber)
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) {
@ -51,7 +51,8 @@ fun main() {
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,
val inputData = LMInput(
::funcDifficultForLm,
p_init.as2D(),
t,
y_dat,
@ -64,7 +65,8 @@ fun main() {
doubleArrayOf(opts[6], opts[7], opts[8]),
opts[9].toInt(),
10,
1)
1
)
val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
@ -76,7 +78,7 @@ fun main() {
println()
println("Y true and y received:")
var y_hat_after = funcDifficultForLm(t_example, result.resultParameters, exampleNumber)
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

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2023 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -18,7 +18,8 @@ import kotlin.math.roundToInt
fun main() {
val startedData = getStartDataForFuncEasy()
val inputData = LMInput(::funcEasyForLm,
val inputData = LMInput(
::funcEasyForLm,
DoubleTensorAlgebra.ones(ShapeND(intArrayOf(4, 1))).as2D(),
startedData.t,
startedData.y_dat,
@ -31,7 +32,8 @@ fun main() {
doubleArrayOf(startedData.opts[6], startedData.opts[7], startedData.opts[8]),
startedData.opts[9].toInt(),
10,
startedData.example_number)
startedData.example_number
)
val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
@ -43,7 +45,7 @@ fun main() {
println()
println("Y true and y received:")
var y_hat_after = funcDifficultForLm(startedData.t, result.resultParameters, startedData.example_number)
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

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2023 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -15,6 +15,7 @@ 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()
@ -30,7 +31,7 @@ fun main() {
val exampleNumber = 1
var y_hat = funcMiddleForLm(t_example, p_example, exampleNumber)
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) {
@ -49,7 +50,8 @@ fun main() {
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,
val inputData = LMInput(
::funcMiddleForLm,
p_init.as2D(),
t,
y_dat,
@ -62,7 +64,8 @@ fun main() {
doubleArrayOf(opts[6], opts[7], opts[8]),
opts[9].toInt(),
10,
1)
1
)
val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
@ -74,7 +77,7 @@ fun main() {
println()
var y_hat_after = funcMiddleForLm(t_example, result.resultParameters, exampleNumber)
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

View File

@ -1,23 +1,28 @@
/*
* Copyright 2018-2023 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
import kotlinx.coroutines.delay
import kotlinx.coroutines.flow.*
import space.kscience.kmath.nd.*
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
import kotlin.reflect.KFunction3
fun streamLm(lm_func: (MutableStructure2D<Double>, MutableStructure2D<Double>, Int) -> (MutableStructure2D<Double>),
startData: StartDataLm, launchFrequencyInMs: Long, numberOfLaunches: Int): Flow<MutableStructure2D<Double>> = flow{
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
@ -32,7 +37,8 @@ fun streamLm(lm_func: (MutableStructure2D<Double>, MutableStructure2D<Double>, I
var steps = numberOfLaunches
val isEndless = (steps <= 0)
val inputData = LMInput(lm_func,
val inputData = LMInput(
lm_func,
p_init,
t,
y_dat,
@ -45,7 +51,8 @@ fun streamLm(lm_func: (MutableStructure2D<Double>, MutableStructure2D<Double>, I
doubleArrayOf(opts[6], opts[7], opts[8]),
opts[9].toInt(),
10,
example_number)
example_number
)
while (isEndless || steps > 0) {
val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
@ -57,7 +64,7 @@ fun streamLm(lm_func: (MutableStructure2D<Double>, MutableStructure2D<Double>, I
}
}
fun generateNewYDat(y_dat: MutableStructure2D<Double>, delta: Double): MutableStructure2D<Double>{
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) {

View File

@ -1,18 +1,19 @@
/*
* Copyright 2018-2023 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
import space.kscience.kmath.nd.*
import space.kscience.kmath.tensors.LevenbergMarquardt.*
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(){
suspend fun main() {
val startData = getStartDataForFuncDifficult()
// Создание потока:
val lmFlow = streamLm(::funcDifficultForLm, startData, 0, 100)
val lmFlow = streamLm(::funcDifficultForLm, startData, 0, 100)
var initialTime = System.currentTimeMillis()
var lastTime: Long
val launches = mutableListOf<Long>()

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2023 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -18,7 +18,7 @@ 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 (
public data class StartDataLm(
var lm_matx_y_dat: MutableStructure2D<Double>,
var example_number: Int,
var p_init: MutableStructure2D<Double>,
@ -29,10 +29,14 @@ public data class StartDataLm (
var p_min: MutableStructure2D<Double>,
var p_max: MutableStructure2D<Double>,
var consts: MutableStructure2D<Double>,
var opts: DoubleArray
var opts: DoubleArray,
)
fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
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)))
@ -40,15 +44,13 @@ fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>,
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) {
} 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) {
} 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])
}
@ -56,32 +58,40 @@ fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>,
return y_hat.as2D()
}
fun funcMiddleForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
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)))
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
val mt = t.max()
for(i in 0 until p.shape.component1()){
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){
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> {
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)))
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 p.shape.component1()) {
y_hat = y_hat.plus((t.times(1.0 / mt)).times(p[i, 0]))
}
for(i in 0 until 4){
for (i in 0 until 4) {
y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, exampleNumber).asDoubleTensor()
}
@ -89,7 +99,7 @@ fun funcDifficultForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Doub
}
fun getStartDataForFuncDifficult(): StartDataLm {
fun getStartDataForFuncDifficult(): StartDataLm {
val NData = 200
var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
for (i in 0 until NData) {
@ -104,7 +114,7 @@ fun getStartDataForFuncDifficult(): StartDataLm {
val exampleNumber = 1
var y_hat = funcDifficultForLm(t_example, p_example, exampleNumber)
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) {
@ -129,7 +139,7 @@ fun getStartDataForFuncDifficult(): StartDataLm {
return StartDataLm(y_dat, 1, p_init, t, y_dat, weight, dp, p_min.as2D(), p_max.as2D(), consts, opts)
}
fun getStartDataForFuncMiddle(): StartDataLm {
fun getStartDataForFuncMiddle(): StartDataLm {
val NData = 100
var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
for (i in 0 until NData) {
@ -144,7 +154,7 @@ fun getStartDataForFuncMiddle(): StartDataLm {
val exampleNumber = 1
var y_hat = funcMiddleForLm(t_example, p_example, exampleNumber)
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) {

View File

@ -1,12 +1,11 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.contentEquals
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
@ -62,7 +61,7 @@ fun main() {
// figure out MSE of approximation
fun mse(yTrue: DoubleTensor, yPred: DoubleTensor): Double {
require(yTrue.shape.size == 1)
require(yTrue.shape contentEquals yPred.shape)
require(yTrue.shape == yPred.shape)
val diff = yTrue - yPred
return sqrt(diff.dot(diff)).value()

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,12 +1,11 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.contentEquals
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.*
@ -94,7 +93,7 @@ class Dense(
// simple accuracy equal to the proportion of correct answers
fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
check(yPred.shape contentEquals yTrue.shape)
check(yPred.shape == yTrue.shape)
val n = yPred.shape[0]
var correctCnt = 0
for (i in 0 until n) {

View File

@ -5,12 +5,10 @@
kotlin.code.style=official
kotlin.mpp.stability.nowarn=true
kotlin.native.ignoreDisabledTargets=true
org.gradle.configureondemand=true
org.gradle.jvmargs=-Xmx4096m
toolsVersion=0.14.8-kotlin-1.8.20
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

View File

@ -1,5 +1,5 @@
distributionBase=GRADLE_USER_HOME
distributionPath=wrapper/dists
distributionUrl=https\://services.gradle.org/distributions/gradle-8.1.1-bin.zip
distributionUrl=https\://services.gradle.org/distributions/gradle-8.7-bin.zip
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists

View File

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

View File

@ -2,7 +2,7 @@ plugins {
id("space.kscience.gradle.mpp")
}
kscience{
kscience {
jvm()
js()
native()
@ -22,7 +22,7 @@ kscience{
implementation(npm("js-base64", "3.6.1"))
}
dependencies(jvmMain){
dependencies(jvmMain) {
implementation("org.ow2.asm:asm-commons:9.2")
}

View File

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

View File

@ -1,10 +1,12 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.ast
import space.kscience.attributes.SafeType
import space.kscience.attributes.WithType
import space.kscience.kmath.expressions.Expression
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.Algebra
@ -15,7 +17,7 @@ import space.kscience.kmath.operations.NumericAlgebra
*
* @param T the type.
*/
public sealed interface TypedMst<T> {
public sealed interface TypedMst<T> : WithType<T> {
/**
* A node containing a unary operation.
*
@ -24,8 +26,13 @@ public sealed interface TypedMst<T> {
* @property function The function implementing this operation.
* @property value The argument of this operation.
*/
public class Unary<T>(public val operation: String, public val function: (T) -> T, public val value: TypedMst<T>) :
TypedMst<T> {
public class Unary<T>(
public val operation: String,
public val function: (T) -> T,
public val value: TypedMst<T>,
) : TypedMst<T> {
override val type: SafeType<T> get() = value.type
override fun equals(other: Any?): Boolean {
if (this === other) return true
if (other == null || this::class != other::class) return false
@ -59,6 +66,13 @@ public sealed interface TypedMst<T> {
public val left: TypedMst<T>,
public val right: TypedMst<T>,
) : TypedMst<T> {
init {
require(left.type == right.type) { "Left and right expressions must be of the same type" }
}
override val type: SafeType<T> get() = left.type
override fun equals(other: Any?): Boolean {
if (this === other) return true
if (other == null || this::class != other::class) return false
@ -89,7 +103,12 @@ public sealed interface TypedMst<T> {
* @property value The held value.
* @property number The number this value corresponds.
*/
public class Constant<T>(public val value: T, public val number: Number?) : TypedMst<T> {
public class Constant<T>(
override val type: SafeType<T>,
public val value: T,
public val number: Number?,
) : TypedMst<T> {
override fun equals(other: Any?): Boolean {
if (this === other) return true
if (other == null || this::class != other::class) return false
@ -114,7 +133,7 @@ public sealed interface TypedMst<T> {
* @param T the type.
* @property symbol The symbol of the variable.
*/
public class Variable<T>(public val symbol: Symbol) : TypedMst<T> {
public class Variable<T>(override val type: SafeType<T>, public val symbol: Symbol) : TypedMst<T> {
override fun equals(other: Any?): Boolean {
if (this === other) return true
if (other == null || this::class != other::class) return false
@ -167,6 +186,7 @@ public fun <T> TypedMst<T>.interpret(algebra: Algebra<T>, vararg arguments: Pair
/**
* Interpret this [TypedMst] node as expression.
*/
public fun <T : Any> TypedMst<T>.toExpression(algebra: Algebra<T>): Expression<T> = Expression { arguments ->
interpret(algebra, arguments)
}
public fun <T : Any> TypedMst<T>.toExpression(algebra: Algebra<T>): Expression<T> =
Expression(algebra.type) { arguments ->
interpret(algebra, arguments)
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -16,6 +16,7 @@ import space.kscience.kmath.operations.bindSymbolOrNull
*/
public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (this) {
is MST.Numeric -> TypedMst.Constant(
algebra.type,
(algebra as? NumericAlgebra<T>)?.number(value) ?: error("Numeric nodes are not supported by $algebra"),
value,
)
@ -27,7 +28,7 @@ public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (t
arg.value,
)
TypedMst.Constant(value, if (value is Number) value else null)
TypedMst.Constant(algebra.type, value, if (value is Number) value else null)
}
else -> TypedMst.Unary(operation, algebra.unaryOperationFunction(operation), arg)
@ -59,7 +60,7 @@ public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (t
)
}
TypedMst.Constant(value, if (value is Number) value else null)
TypedMst.Constant(algebra.type, value, if (value is Number) value else null)
}
algebra is NumericAlgebra && left is TypedMst.Constant && left.number != null -> TypedMst.Binary(
@ -84,8 +85,8 @@ public fun <T> MST.evaluateConstants(algebra: Algebra<T>): TypedMst<T> = when (t
val boundSymbol = algebra.bindSymbolOrNull(this)
if (boundSymbol != null)
TypedMst.Constant(boundSymbol, if (boundSymbol is Number) boundSymbol else null)
TypedMst.Constant(algebra.type, boundSymbol, if (boundSymbol is Number) boundSymbol else null)
else
TypedMst.Variable(this)
TypedMst.Variable(algebra.type, this)
}
}

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2022 KMath contributors.
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

Some files were not shown because too many files have changed in this diff Show More