Kotlin mathematics extensions library

Last update: Dec 31, 2022

Overview

KMath

Could be pronounced as key-math. The Kotlin Mathematics library was initially intended as a Kotlin-based analog to Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior architecture designs. In contrast to numpy and scipy it is modular and has a lightweight core. The numpy-like experience could be achieved with kmath-for-real extension module.

Documentation site (WIP)

Publications and talks

Goal

Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native) .
Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
Provide bindings and wrappers with those abstractions for popular optimized platform libraries.

Non-goals

Be like NumPy. It was the idea at the beginning, but we decided that we can do better in API.
Provide the best performance out of the box. We have specialized libraries for that. Need only API wrappers for them.
Cover all cases as immediately and in one bundle. We will modularize everything and add new features gradually.
Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like for Double in the core. For that we will have specialization modules like kmath-for-real, which will give better experience for those, who want to work with specific types.

Features and stability

KMath is a modular library. Different modules provide different features with different API stability guarantees. All core modules are released with the same version, but with different API change policy. The features are described in module definitions below. The module stability could have the following levels:

PROTOTYPE. On this level there are no compatibility guarantees. All methods and classes form those modules could break any moment. You can still use it, but be sure to fix the specific version.
EXPERIMENTAL. The general API is decided, but some changes could be made. Volatile API is marked with @UnstableKmathAPI or other stability warning annotations.
DEVELOPMENT. API breaking generally follows semantic versioning ideology. There could be changes in minor versions, but not in patch versions. API is protected with binary-compatibility-validator tool.
STABLE. The API stabilized. Breaking changes are allowed only in major releases.

Modules

benchmarks

Maturity: EXPERIMENTAL

examples

Maturity: EXPERIMENTAL

kmath-ast

Maturity: EXPERIMENTAL

Features:

expression-language : Expression language and its parser

mst-jvm-codegen : Dynamic MST to JVM bytecode compiler

mst-js-codegen : Dynamic MST to JS compiler

rendering : Extendable MST rendering

kmath-commons

Maturity: EXPERIMENTAL

kmath-complex

Complex numbers and quaternions.

Maturity: PROTOTYPE

Features:

complex : Complex Numbers

quaternion : Quaternions

kmath-core

Core classes, algebra definitions, basic linear algebra

Maturity: DEVELOPMENT

Features:

algebras : Algebraic structures like rings, spaces and fields.

nd : Many-dimensional structures and operations on them.

linear : Basic linear algebra operations (sums, products, etc.), backed by the Space API. Advanced linear algebra operations like matrix inversion and LU decomposition.

buffers : One-dimensional structure

expressions : By writing a single mathematical expression once, users will be able to apply different types of objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high performance calculations to code generation.

domains : Domains

autodiff : Automatic differentiation

kmath-coroutines

Maturity: EXPERIMENTAL

kmath-dimensions

Maturity: PROTOTYPE

kmath-ejml

Maturity: PROTOTYPE

Features:

ejml-vector : Point implementations.

ejml-matrix : Matrix implementation.

ejml-linear-space : LinearSpace implementations.

kmath-for-real

Extension module that should be used to achieve numpy-like behavior. All operations are specialized to work with Double numbers without declaring algebraic contexts. One can still use generic algebras though.

Maturity: EXPERIMENTAL

Features:

DoubleVector : Numpy-like operations for Buffers/Points

DoubleMatrix : Numpy-like operations for 2d real structures

grids : Uniform grid generators

kmath-functions

Maturity: EXPERIMENTAL

Features:

piecewise : Piecewise functions.

polynomials : Polynomial functions.

linear interpolation : Linear XY interpolator.

spline interpolation : Cubic spline XY interpolator.

integration : Univariate and multivariate quadratures

kmath-geometry

Maturity: PROTOTYPE

kmath-histograms

Maturity: PROTOTYPE

kmath-jafama

Maturity: PROTOTYPE

Features:

jafama-double : Double ExtendedField implementations based on Jafama

kmath-jupyter

Maturity: PROTOTYPE

kmath-kotlingrad

Maturity: EXPERIMENTAL

Features:

differentiable-mst-expression : MST based DifferentiableExpression.

scalars-adapters : Conversions between Kotlin∇'s SFun and MST

kmath-memory

An API and basic implementation for arranging objects in a continuous memory block.

Maturity: DEVELOPMENT

kmath-nd4j

Maturity: EXPERIMENTAL

Features:

nd4jarraystructure : NDStructure wrapper for INDArray

nd4jarrayrings : Rings over Nd4jArrayStructure of Int and Long

nd4jarrayfields : Fields over Nd4jArrayStructure of Float and Double

kmath-stat

Maturity: EXPERIMENTAL

kmath-symja

Maturity: PROTOTYPE

kmath-tensors

Maturity: PROTOTYPE

Features:

tensor algebra : Basic linear algebra operations on tensors (plus, dot, etc.)

tensor algebra with broadcasting : Basic linear algebra operations implemented with broadcasting.

linear algebra operations : Advanced linear algebra operations like LU decomposition, SVD, etc.

kmath-viktor

Maturity: DEVELOPMENT

Multi-platform support

KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the common source sets and implemented there wherever it is possible. In some cases, features are delegated to platform-specific implementations even if they could be provided in the common module for performance reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and feedback are also welcome.

Performance

Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both performance and flexibility.

We expect to focus on creating convenient universal API first and then work on increasing performance for specific cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be better than SciPy.

Requirements

KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for execution to get better performance.

Repositories

Release and development artifacts are accessible from mipt-npm Space repository https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven (see documentation of Kotlin Multiplatform for more details). The repository could be reached through repo.kotlin.link proxy:

repositories {
    maven("https://repo.kotlin.link")
}

dependencies {
    api("space.kscience:kmath-core:0.3.0-dev-14")
    // api("space.kscience:kmath-core-jvm:0.3.0-dev-14") for jvm-specific version
}

Gradle 6.0+ is required for multiplatform artifacts.

Contributing

The project requires a lot of additional work. The most important thing we need is a feedback about what features are required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues marked with waiting for a hero label.

Comments

Integration with Jafama

To external contributors:

Notify us if you are going to try to implement this feature to receive our contacts (and maybe guest access to our Space) and extensive explanations of what does this problem means.
feature performance

opened by CommanderTvis 20
Feature: Polynomials and rational functions
This is implementation of different kinds of polynomials and rational functions. It should close issues #462, #336 See also: #39, #446,

Comment: files systematisation

Each kind of polynomials and corresponding polynomial spaces are placed in corresponding file named after type of polynomials.

The same goes for rational functions: each kind of rational functions and corresponding rational functions' spaces are placed in corresponding file named after type of polynomials.

Utilities for each kind of polynomials and rational functions are placed in file named after corresponding type of polynomials/rational functions with additional Util suffix.

Constructors (and building DSLs) for each kind of polynomials and rational functions are placed in file named after corresponding type with additional Constructors suffix.

Abstract entities are places in Polynomial.kt and RationalFunction.kt.

Problems and questions

Is it worth to change Map<Symbol, UInt> to android.util.ArrayMap<Symbo, UInt> or something similar? This class is designed to be more memory efficient for small numbers of entries which is exactly our use case.

What is better way to implement interoperability with integers? As fact, additive (abelian) groups has natural product operation with integers: product of element c and integer n is just:

sum of n copies of c if n is positive,

sum of -n copies of -c if n is negative (or negate of sum of -n copies of c, which is the same),

zero (neutral element) of the group if n is zero.

The same goes for multiplicative groups and power. (As a matter of fact, rings do not provide multiplicative (abelian) groups, but multiplicative (abelian) monoids, so the power can be defined not for integers, but for non-negative integers.) In context of rings we also have addition of ring members and integers: for real, sum of element c and integer n is just c + n * one (where one is unit of the ring). And multiplication and exponentiation are very helpful and frequently used.

For example, when you calculate derivative of polynomial you multiply coefficients of terms to corresponding degrees (which are positive integers), so to implement derivative you have to implement multiplication yourself (see algebraicStub.kt) or require ability to convert integer to ring member (see Polynomial<C>.derivative that preserves former logic). Both variants are bad: manual multiplication usually slower then multiplication that could be provided by corresponding ring, and requiring convertibility (by ScaleOperations or even NumericAlgebra because there are no alternatives) makes it impossible to use it in common rings (say hi to rational numbers again).

In my opinion, the best way is to declare the interactions in GroupOps/Group (for multiplication) and Ring (for exponentiation that is already declared and addition) interfaces and implement them in all current algebraic structures. (It's very easy, even is a bit of boilerplate.) It's even inconsistent in some way that exponentiation is declared in Ring, but multiplication by Int/UInt (which is the same operation not in multiplicative group/monoid, but in additive group/monoid) is not.

So the trivial question here: what should I do here?

Maybe, it is better to move first argument ring of utility functions (substitute, derivative, etc.) to context (when context receivers will be available), isn't it?

Is there need in other functionality? For example, string representation: it is already implemented in my other repositories, it remains only to copy it with minimal modification.

Should RFs spaces be implemented as field? If yes, then what division operations should be implemented? (For example, what should c / c return? And when field over C is provided?) And how to implement current Number-oriented functions of Field?

Should cases for A (the type of provided ring of constants) being Field be considered as well? I mean that function operator fun Polynomial<C>.div(other: C): Polynomial<C> makes sense only in context of of field over C. And it looks helpful. So should I implement something like this? (But of course, I'll be able to implement something like this when context receivers will be released. For now I can sketch it, comment it and add TODO mark to it.)

TODO list

[ ] Write optimized algorithms for substitution.

[ ] Anything else? (Look at inline TODOs.)

feature
opened by lounres 19
release() for DirectByteBuffer

as you come upon DirectByteBuffers, see

https://stackoverflow.com/a/8462690

re: https://github.com/mipt-npm/kmath/blob/e52cfcaafe7eb1149170838d9910e54851ba1a92/kmath-memory/src/jvmMain/kotlin/scientifik/memory/ByteBufferMemory.kt#L88
discussion

opened by jnorthrup 18
Integration with kotlingrad

https://github.com/breandan/kotlingrad

The idea is to connect kotlingrad automatic differentiation done in kotlngrad via MST expressions. One should be able to transform MST expressions to kotlingrad node-graph and back again. This way we will be able to perform autodif on any algebra. The integration possibly will require some changes in kotlingrad API.

cc @breandan
feature

opened by altavir 12
Initial geometry projections implemetations
Added Projections support into geometry package along with Shift/LinearMap transformations

Also tried to enhance Vector/VectorSpace API with parametrization:

from one side tried to make interfaces generic enough so it will be easy to add a custom implementation (if the default implementation is not enough) and still use all existing infrastructure;

from the other hand tried not to introduce too much overhead to not harm performance;

added Dimension parameter to Vector/VectorSpace to allow operations only on vectors of the same size;

added Vector's type to VectorSpace class to allow custom implementation that can leverage some internals of particular implementation of Vector's class;

provided default implementation to generic EuclideanSpace in D dimension over some field;

provided an implementation of EuclideanSpace in D dimension over Field.

Covered code with basic tests.

feature
opened by knok16 12
Bug in cast to Structure2D
There is a probable bug in cast of NDStructure to Structure2D with as2D() function. In that case class uses swapped rowNum and colNum values because of the reversed shape, so in fact you store row number in colNum and vice versa. https://github.com/mipt-npm/kmath/blob/e52cfcaafe7eb1149170838d9910e54851ba1a92/kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Structure2D.kt#L7-L8 Since rowNum and colNum values are swapped, there is also another probable bug in get method (swapped requested column and row indices again) https://github.com/mipt-npm/kmath/blob/e52cfcaafe7eb1149170838d9910e54851ba1a92/kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Structure2D.kt#L18-L20 For example, this makes dot operation for the structures work incorrectly. A simple example that shows an error:

val firstMatrix = BufferNDStructure(DefaultStrides(intArrayOf(3, 2)), ListBuffer(6) { 1.0 }) val secondMatrix = BufferNDStructure(DefaultStrides(intArrayOf(5, 3)), ListBuffer(15) { 1.0 }) val product = MatrixContext.real.run { firstMatrix.as2D() dot secondMatrix.as2D() }

My possible fix - link
bug
opened by cupertank 10
Slowness with Neural Network Example

Slowness with neural network usage example

I started to port over some existing machine learning from scratch demos to use kmath. This particular neural network demo I made using stochastic hill climbing is extremely slow, much slower than just using vanilla Kotlin.

I'm open to feedback and learning if I'm doing something wrong...

https://github.com/thomasnield/numky/blob/master/src/test/kotlin/org/nield/numky/NeuralNetworkSimulatedAnnealing.kt
performance

opened by thomasnield 8

Support for Probability Distributions

Hi, Is there any way to include these components? Url apache commons math

distribution normal
distribution t-student
distribution chi-square
distribution F-fisher

for example

f.cumulativeProbability(x)
f.probability(x)
f.inverseCumulativeProbability(p)

//    example t-student distribution

TDistribution t = new TDistribution(29);
double lowerTail = t.cumulativeProbability(-2.656);     // P(T(29) <= -2.656)
double upperTail = 1.0 - t.cumulativeProbability(2.75); // P(T(29) >= 2.75)

feature

opened by Moriand 7

Set up continuous integration

It looks like the build artifacts are outdated. TeamCity offers free builds for OSS, as well as Travis CI or CircleCI. It is possible to autobuild on a per-commit basis, and publish releases to a custom Maven repository. Please let me know if you need any help setting this up.
discussion

opened by breandan 7
Karatsuba added, 2 bugs are fixed
Karatsuba was added and increased benchmarks results.

The bug that was caused by abs(Int.MIN_VALUE) == Int.MIN_VALUE was fixed.

The equals function now behaves with instances of other types as all other objects do.
opened by zhelenskiy 6
Support for BigInteger

Secure multi-party computation (SMPC) and related mechanisms work with numbers that exceed the maximum values of Kotlin primitive types.

By adding support to BigInteger, components using KMath could be used in SMPC setups.

An interesting field where this could be applied is to training and inference of machine learning algorithms where the data is never shared.
feature

opened by mccorby 6
Pull request quality gates
There is already a bunch of nice features from KMath contributors. But since the code is usually sophisticated, some community design process needs to be enforced in order to make them play together. So here are some rules.

Any contribution with new classes/features must have test coverage of algorithmic part (not necessary 100% code coverage).

Any feature contribution must include a set of examples (goes into examples project), short feature description (goes into readme/feature section of appropriate module build file) and a design document (goes into docs directory). The design document is made to explain the design decisions made for the specific features (look at how KEEP works). Designing mathematics library is hard and we want all decisions to be consistent. The discussion in Kotlin Slack is welcome.

Any changes made to stable maturity modules must be ABI compatible between major versions and development maturity means ABI compatibility between minor versions. If your change is breaking it should be moved to a separate module or to the next release branch.

discussion documentation
opened by altavir 2
Add functions to compute Union & Intersection Area of two rectangles
Feature Request

Use Case

When doing object detection using deep learning, we have to do what is known as non-max supression. To do so we need to compute the IoU (Intersection over Union) of two rectangles (compute area of the union and the area of the intersection of the two rectangles). Adding it to KMath would allow us to make use of it for the IoU computation without implementing the area computation part from scratch. Even though it is not very hard to do, I think it could be a nice addition to KMath since its usage can be much broader than just for deep learning, area computation is pretty generic in maths.

Proposal

A function/method similar to getIntersectionArea(Rect1, Rect2)

A function/method similar to getUnionArea(Rect1, Rect2) or

A single function getIoU(Rect1, Rect2) Ideally the solution should be mockable so that library users have more freedom.

feature
opened by AlexandreBrown 4

Releases(v0.3.0)

v0.3.0(Apr 12, 2022)
Added

ScaleOperations interface

Field extends ScaleOperations

Basic integration API

Basic MPP distributions and samplers

bindSymbolOrNull

Blocking chains and Statistics

Multiplatform integration

Integration for any Field element

Extended operations for ND4J fields

Jupyter Notebook integration module (kmath-jupyter)

@PerformancePitfall annotation to mark possibly slow API

Unified architecture for Integration and Optimization using features.

BigInt operation performance improvement and fixes by @zhelenskiy (#328)

Integration between MST and Symja IExpr

Complex power

Separate methods for UInt, Int and Number powers. NaN safety.

Tensorflow prototype

ValueAndErrorField

MST compilation to WASM: #286

Jafama integration: #176

contentEquals with tolerance: #364

Compilation to TeX for MST: #254

Changed

Exponential operations merged with hyperbolic functions

Space is replaced by Group. Space is reserved for vector spaces.

VectorSpace is now a vector space

Buffer factories for primitives moved to MutableBuffer.Companion

Rename NDStructure and NDAlgebra to StructureND and AlgebraND respectively

Real -> Double

DataSets are moved from functions to core

Redesign advanced Chain API

Redesign MST. Remove MstExpression.

Move MST to core

Separated benchmarks and examples

Rewrite kmath-ejml without ejml-simple artifact, support sparse matrices

Promote stability of kmath-ast and kmath-kotlingrad to EXPERIMENTAL.

ColumnarData returns nullable column

MST is made sealed interface

Replace MST.Symbolic by Symbol, Symbol now implements MST

Remove Any restriction on polynomials

Add out variance to type parameters of StructureND and its implementations where possible

Rename DifferentiableMstExpression to KotlingradExpression

FeatureSet now accepts only Feature. It is possible to override keys and use interfaces.

Use Symbol factory function instead of StringSymbol

New discoverability pattern: <Type>.algebra.<nd/etc>

Adjusted commons-math API for linear solvers to match conventions.

Buffer algebra does not require size anymore

Operations -> Ops

Default Buffer and ND algebras are now Ops and lack neutral elements (0, 1) as well as algebra-level shapes.

Tensor algebra takes read-only structures as input and inherits AlgebraND

UnivariateDistribution renamed to Distribution1D

Rework of histograms.

UnivariateFunction -> Function1D, MultivariateFunction -> FunctionND

Deprecated

Specialized DoubleBufferAlgebra

Removed

Nearest in Domain. To be implemented in geometry package.

Number multiplication and division in main Algebra chain

contentEquals from Buffer. It moved to the companion.

MSTExpression

Expression algebra builders

Complex and Quaternion no longer are elements.

Second generic from DifferentiableExpression

Algebra elements are completely removed. Use algebra contexts instead.

Fixed

Ring inherits RingOperations, not GroupOperations

Univariate histogram filling

Source code(tar.gz)
Source code(zip)
v0.3.0-dev-18(Feb 13, 2022)
Added

ScaleOperations interface

Field extends ScaleOperations

Basic integration API

Basic MPP distributions and samplers

bindSymbolOrNull

Blocking chains and Statistics

Multiplatform integration

Integration for any Field element

Extended operations for ND4J fields

Jupyter Notebook integration module (kmath-jupyter)

@PerformancePitfall annotation to mark possibly slow API

Unified architecture for Integration and Optimization using features.

BigInt operation performance improvement and fixes by @zhelenskiy (#328)

Integration between MST and Symja IExpr

Complex power

Separate methods for UInt, Int and Number powers. NaN safety.

Tensorflow prototype

Changed

Exponential operations merged with hyperbolic functions

Space is replaced by Group. Space is reserved for vector spaces.

VectorSpace is now a vector space

Buffer factories for primitives moved to MutableBuffer.Companion

Rename NDStructure and NDAlgebra to StructureND and AlgebraND respectively

Real -> Double

DataSets are moved from functions to core

Redesign advanced Chain API

Redesign MST. Remove MstExpression.

Move MST to core

Separated benchmarks and examples

Rewrite kmath-ejml without ejml-simple artifact, support sparse matrices

Promote stability of kmath-ast and kmath-kotlingrad to EXPERIMENTAL.

ColumnarData returns nullable column

MST is made sealed interface

Replace MST.Symbolic by Symbol, Symbol now implements MST

Remove Any restriction on polynomials

Add out variance to type parameters of StructureND and its implementations where possible

Rename DifferentiableMstExpression to KotlingradExpression

FeatureSet now accepts only Feature. It is possible to override keys and use interfaces.

Use Symbol factory function instead of StringSymbol

New discoverability pattern: <Type>.algebra.<nd/etc>

Adjusted commons-math API for linear solvers to match conventions.

Buffer algebra does not require size anymore

Operations -> Ops

Default Buffer and ND algebras are now Ops and lack neutral elements (0, 1) as well as algebra-level shapes.

Tensor algebra takes read-only structures as input and inherits AlgebraND

Deprecated

Specialized DoubleBufferAlgebra

Removed

Nearest in Domain. To be implemented in geometry package.

Number multiplication and division in main Algebra chain

contentEquals from Buffer. It moved to the companion.

MSTExpression

Expression algebra builders

Complex and Quaternion no longer are elements.

Second generic from DifferentiableExpression

Algebra elements are completely removed. Use algebra contexts instead.

Fixed

Ring inherits RingOperations, not GroupOperations

Univariate histogram filling

Source code(tar.gz)
Source code(zip)
v0.3.0-dev-8(May 8, 2021)
Added

ScaleOperations interface

Field extends ScaleOperations

Basic integration API

Basic MPP distributions and samplers

bindSymbolOrNull

Blocking chains and Statistics

Multiplatform integration

Integration for any Field element

Extended operations for ND4J fields

Jupyter Notebook integration module (kmath-jupyter)

Changed

Exponential operations merged with hyperbolic functions

Space is replaced by Group. Space is reserved for vector spaces.

VectorSpace is now a vector space

Buffer factories for primitives moved to MutableBuffer.Companion

NDStructure and NDAlgebra to StructureND and AlgebraND respectively

Real -> Double

DataSets are moved from functions to core

Redesign advanced Chain API

Redesign MST. Remove MSTExpression.

Move MST to core

Separated benchmarks and examples

Rewritten EJML module without ejml-simple

Deprecated

Removed

Nearest in Domain. To be implemented in geometry package.

Number multiplication and division in main Algebra chain

contentEquals from Buffer. It moved to the companion.

MSTExpression

Expression algebra builders

Comples and Quaternion no longer are elements.

Fixed

Ring inherits RingOperations, not GroupOperations

Source code(tar.gz)
Source code(zip)
v0.2.0(Feb 21, 2021)
[0.2.0]

Added

fun annotation for SAM interfaces in library

Explicit public visibility for all public APIs

Better trigonometric and hyperbolic functions for AutoDiffField (https://github.com/mipt-npm/kmath/pull/140)

Automatic README generation for features (#139)

Native support for memory, core and dimensions

kmath-ejml to supply EJML SimpleMatrix wrapper (https://github.com/mipt-npm/kmath/pull/136)

A separate Symbol entity, which is used for global unbound symbol.

A Symbol indexing scope.

Basic optimization API for Commons-math.

Chi squared optimization for array-like data in CM

Fitting utility object in prob/stat

ND4J support module submitting NDStructure and NDAlgebra over INDArray

Coroutine-deterministic Monte-Carlo scope with a random number generator

Some minor utilities to kmath-for-real

Generic operation result parameter to MatrixContext

New MatrixFeature interfaces for matrix decompositions

Basic Quaternion vector support in kmath-complex.

Changed

Package changed from scientifik to space.kscience

Gradle version: 6.6 -> 6.8.2

Minor exceptions refactor (throwing IllegalArgumentException by argument checks instead of IllegalStateException)

Polynomial secondary constructor made function

Kotlin version: 1.3.72 -> 1.4.30

kmath-ast doesn't depend on heavy kotlin-reflect library

Full autodiff refactoring based on Symbol

kmath-prob renamed to kmath-stat

Grid generators moved to kmath-for-real

Use Point<Double> instead of specialized type in kmath-for-real

Optimized dot product for buffer matrices moved to kmath-for-real

EjmlMatrix context is an object

Matrix LUP inverse renamed to inverseWithLup

NumericAlgebra moved outside of regular algebra chain (Ring no longer implements it).

Features moved to NDStructure and became transparent.

Capitalization of LUP in many names changed to Lup.

Refactored NDStructure algebra to be more simple, preferring under-the-hood conversion to explicit NDStructure types

Refactor histograms. They are marked as prototype

Complex and related features moved to a separate module kmath-complex

Refactor AlgebraElement

symbol method in Algebra renamed to bindSymbol to avoid ambiguity

Add out projection to Buffer generic

Deprecated

Removed

kmath-koma module because it doesn't support Kotlin 1.4.

Support of legacy JS backend (we will support only IR)

toGrid method.

Public visibility of BufferAccessor2D

Real class

Fixed

symbol method in MstExtendedField (https://github.com/mipt-npm/kmath/pull/140)

Source code(tar.gz)
Source code(zip)
v0.2.0-dev-5(Jan 20, 2021)
Added

fun annotation for SAM interfaces in library

Explicit public visibility for all public APIs

Better trigonometric and hyperbolic functions for AutoDiffField (https://github.com/mipt-npm/kmath/pull/140)

Automatic README generation for features (#139)

Native support for memory, core and dimensions

kmath-ejml to supply EJML SimpleMatrix wrapper (https://github.com/mipt-npm/kmath/pull/136)

A separate Symbol entity, which is used for global unbound symbol.

A Symbol indexing scope.

Basic optimization API for Commons-math.

Chi squared optimization for array-like data in CM

Fitting utility object in prob/stat

ND4J support module submitting NDStructure and NDAlgebra over INDArray

Coroutine-deterministic Monte-Carlo scope with a random number generator

Some minor utilities to kmath-for-real

Generic operation result parameter to MatrixContext

New MatrixFeature interfaces for matrix decompositions

Changed

Package changed from scientifik to kscience.kmath

Gradle version: 6.6 -> 6.8

Minor exceptions refactor (throwing IllegalArgumentException by argument checks instead of IllegalStateException)

Polynomial secondary constructor made function

Kotlin version: 1.3.72 -> 1.4.21

kmath-ast doesn't depend on heavy kotlin-reflect library

Full autodiff refactoring based on Symbol

kmath-prob renamed to kmath-stat

Grid generators moved to kmath-for-real

Use Point<Double> instead of specialized type in kmath-for-real

Optimized dot product for buffer matrices moved to kmath-for-real

EjmlMatrix context is an object

Matrix LUP inverse renamed to inverseWithLUP

NumericAlgebra moved outside of regular algebra chain (Ring no longer implements it).

Features moved to NDStructure and became transparent.

Deprecated

Removed

kmath-koma module because it doesn't support Kotlin 1.4.

Support of legacy JS backend (we will support only IR)

toGrid method.

Public visibility of BufferAccessor2D

Fixed

symbol method in MstExtendedField (https://github.com/mipt-npm/kmath/pull/140)

Security
Source code(tar.gz)
Source code(zip)
v0.2.0-dev-2(Sep 27, 2020)
Added

fun annotation for SAM interfaces in library

Explicit public visibility for all public APIs

Better trigonometric and hyperbolic functions for AutoDiffField (https://github.com/mipt-npm/kmath/pull/140).

Automatic README generation for features (#139)

Changed

Package changed from scientifik to kscience.kmath.

Gradle version: 6.6 -> 6.6.1

Minor exceptions refactor (throwing IllegalArgumentException by argument checks instead of IllegalStateException)

Polynomial secondary constructor made function.

Kotlin version: 1.3.72 -> 1.4.20-M1

Deprecated

Removed

kmath-koma module because it doesn't support Kotlin 1.4.

Fixed

symbol method in MstExtendedField (https://github.com/mipt-npm/kmath/pull/140)

Source code(tar.gz)
Source code(zip)
v0.1.4(Sep 14, 2020)
Added

Functional Expressions API

Mathematical Syntax Tree, its interpreter and API

String to MST parser (https://github.com/mipt-npm/kmath/pull/120)

MST to JVM bytecode translator (https://github.com/mipt-npm/kmath/pull/94)

FloatBuffer (specialized MutableBuffer over FloatArray)

FlaggedBuffer to associate primitive numbers buffer with flags (to mark values infinite or missing, etc.)

Specialized builder functions for all primitive buffers like IntBuffer(25) { it + 1 } (https://github.com/mipt-npm/kmath/pull/125)

Interface NumericAlgebra where number operation is available to convert numbers to algebraic elements

Inverse trigonometric functions support in ExtendedField (asin, acos, atan) (https://github.com/mipt-npm/kmath/pull/114)

New space extensions: average and averageWith

Local coding conventions

Geometric Domains API in kmath-core

Blocking chains in kmath-coroutines

Full hyperbolic functions support and default implementations within ExtendedField

Norm support for Complex

Changed

readAsMemory now has throws IOException in JVM signature.

Several functions taking functional types were made inline.

Several functions taking functional types now have callsInPlace contracts.

BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor optimizations

power(T, Int) extension function has preconditions and supports Field<T>

Memory objects have more preconditions (overflow checking)

tg function is renamed to tan (https://github.com/mipt-npm/kmath/pull/114)

Gradle version: 6.3 -> 6.6

Moved probability distributions to commons-rng and to kmath-prob

Fixed

Missing copy method in Memory implementation on JS (https://github.com/mipt-npm/kmath/pull/106)

D3.dim value in kmath-dimensions

Multiplication in integer rings in kmath-core (https://github.com/mipt-npm/kmath/pull/101)

Commons RNG compatibility (https://github.com/mipt-npm/kmath/issues/93)

Multiplication of BigInt by scalar

Source code(tar.gz)
Source code(zip)
0.1.4-dev-8(Jun 27, 2020)
A lot of refactoring

kmath-ast module with advanced expression generation and compilation.

Source code(tar.gz)
Source code(zip)
0.1.4-dev-4(Apr 30, 2020)
Experimental type-safe dimensions

Viktor integration

Kmath-for-real module for those who want simple access to real (Double) world without writing contexts

Interpolation API

BigInteger

Basic statistics

Source code(tar.gz)
Source code(zip)
0.1.3(Jun 26, 2019)
A lot of minor fixes.

Initial work on disributions.

Source code(tar.gz)
Source code(zip)
0.1.2(May 31, 2019)
Refactored build. Proper bintray artifacts.

Streaming processing with Flow API.

Vectors are deprecated. To be removed in next release. Use context operations instead.

Source code(tar.gz)
Source code(zip)
0.1.0(Feb 22, 2019)
Features

Algebra - Context-based operations on different primitives and structures.

NDStructures

Linear algebra - Matrices, operations and linear equations solving. To be moved to separate module. Currently supports basic api and multiple library back-ends.

Histograms - Multidimensional histogram calculation and operations.

Expressions

Commons math integration

Koma integration

Source code(tar.gz)
Source code(zip)
0.1.0-dev(Feb 21, 2019)
Features

Algebra - Context-based operations on different primitives and structures.

NDStructures

Linear algebra - Matrices, operations and linear equations solving. To be moved to separate module. Currently supports basic api and multiple library back-ends.

Histograms - Multidimensional histogram calculation and operations.

Expressions

Commons math integration

Koma integration

Source code(tar.gz)
Source code(zip)
0.0.2-dev-1(Dec 11, 2018)

Lazy n-dimensional structures
Source code(tar.gz)
Source code(zip)