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: 3 months ago
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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 (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2025-02-03T18:11:18.000Z (4 months ago)
• Last Synced: 2025-02-06T09:57:33.360Z (4 months ago)
• Topics: gaussian-processes, kernel-functions, kernels, machine-learning
• Language: Julia
• Homepage: https://juliagaussianprocesses.github.io/KernelFunctions.jl/stable/
• Size: 21.5 MB
• Stars: 268
• Watchers: 8
• Forks: 34
• Open Issues: 114
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# KernelFunctions.jl

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## 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).