Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/JuliaGaussianProcesses/KernelFunctions.jl

Julia package for kernel functions for machine learning
https://github.com/JuliaGaussianProcesses/KernelFunctions.jl

gaussian-processes kernel-functions kernels machine-learning

Last synced: 14 days ago
JSON representation

Julia package for kernel functions for machine learning

Host: GitHub
URL: https://github.com/JuliaGaussianProcesses/KernelFunctions.jl
Owner: JuliaGaussianProcesses
License: mit
Created: 2019-05-24T13:53:47.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2024-07-28T01:16:19.000Z (4 months ago)
Last Synced: 2024-08-01T16:45:22.059Z (3 months ago)
Topics: gaussian-processes, kernel-functions, kernels, machine-learning
Language: Julia
Homepage: https://juliagaussianprocesses.github.io/KernelFunctions.jl/stable/
Size: 38 MB
Stars: 266
Watchers: 8
Forks: 32
Open Issues: 115
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

Awesome Lists containing this project

README

        # KernelFunctions.jl

[![CI](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/actions/workflows/ci.yml/badge.svg?branch=master)](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/actions/workflows/ci.yml?query=branch%3Amaster)

[![codecov](https://codecov.io/gh/JuliaGaussianProcesses/KernelFunctions.jl/branch/master/graph/badge.svg?token=rmDh3gb7hN)](https://codecov.io/gh/JuliaGaussianProcesses/KernelFunctions.jl)

[![Documentation (stable)](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliagaussianprocesses.github.io/KernelFunctions.jl/stable)

[![Documentation (latest)](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliagaussianprocesses.github.io/KernelFunctions.jl/dev)

[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)

[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)

[![DOI](https://zenodo.org/badge/188430419.svg)](https://zenodo.org/badge/latestdoi/188430419)

## Kernel functions for machine learning

**KernelFunctions.jl** is a general purpose [kernel](https://en.wikipedia.org/wiki/Positive-definite_kernel) package.

It provides a flexible framework for creating kernel functions and manipulating them, and an extensive collection of implementations.

The main goals of this package are:

- **Flexibility**: operations between kernels should be fluid and easy without breaking, with a user-friendly API.

- **Plug-and-play**: being model-agnostic; including the kernels before/after other steps should be straightforward. To interoperate well with generic packages for handling parameters like [ParameterHandling.jl](https://github.com/invenia/ParameterHandling.jl/) and FluxML's [Functors.jl](https://github.com/FluxML/Functors.jl/).

- **Automatic Differentiation compatibility**: all kernel functions which _ought_ to be differentiable using AD packages like [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) or [Zygote.jl](https://github.com/FluxML/Zygote.jl) _should_ be.

## Examples

```julia

x = range(-3.0, 3.0; length=100)

# A simple standardised squared-exponential / exponentiated-quadratic kernel.

k₁ = SqExponentialKernel()

K₁ = kernelmatrix(k₁, x)

# Set a function transformation on the data

k₂ = Matern32Kernel() ∘ FunctionTransform(sin)

K₂ = kernelmatrix(k₂, x)

# Set a matrix premultiplication on the data

k₃ = PolynomialKernel(; c=2.0, degree=2) ∘ LinearTransform(randn(4, 1))

K₃ = kernelmatrix(k₃, x)

# Add and sum kernels

k₄ = 0.5 * SqExponentialKernel() * LinearKernel(; c=0.5) + 0.4 * k₂

K₄ = kernelmatrix(k₄, x)

plot(

    heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;

    layout=(2, 2), title=["K₁" "K₂" "K₃" "K₄"],

)

```



  



## Related Work

This package replaces the now-defunct [MLKernels.jl](https://github.com/trthatcher/MLKernels.jl). It incorporates lots of excellent existing work from packages such as [GaussianProcesses.jl](https://github.com/STOR-i/GaussianProcesses.jl), and is used in downstream packages such as [AbstractGPs.jl](https://github.com/JuliaGaussianProcesses/AbstractGPs.jl), [ApproximateGPs.jl](https://github.com/JuliaGaussianProcesses/ApproximateGPs.jl), [Stheno.jl](https://github.com/willtebbutt/Stheno.jl), and [AugmentedGaussianProcesses.jl](https://github.com/theogf/AugmentedGaussianProcesses.jl).

See the JuliaGaussianProcesses [Github organisation](https://github.com/JuliaGaussianProcesses) and [website](https://juliagaussianprocesses.github.io/) for more information.

## Issues/Contributing

If you notice a problem or would like to contribute by adding more kernel functions or features please [submit an issue](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/issues), or open a PR (please see the [ColPrac](https://github.com/SciML/ColPrac) contribution guidelines).