Julia package for kernel functions for machine learning
Author JuliaGaussianProcesses
115 Stars
Updated Last
1 Year Ago
Started In
May 2019


CI Coverage Status Documentation (stable) Documentation (latest) ColPrac: Contributor's Guide on Collaborative Practices for Community Packages Code Style: Blue

Kernel functions for machine learning

KernelFunctions.jl provide a flexible and complete framework for kernel functions, pretransforming the input data.

The aim is to make the API as model-agnostic as possible while still being user-friendly.


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)

    heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;
    layout=(2, 2), title=["K₁" "K₂" "K₃" "K₄"],

Packages goals (by priority)

  • Ensure AD Compatibility (already the case for Zygote, ForwardDiff)
  • Toeplitz Matrices compatibility

Directly inspired by the MLKernels package.


If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue, or open a PR (please see the ColPrac contribution guidelines).