https://github.com/juliagaussianprocesses/easygps.jl

Easy automatic fitting of JuliaGP models
https://github.com/juliagaussianprocesses/easygps.jl

Last synced: 4 months ago
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Easy automatic fitting of JuliaGP models

• Host: GitHub
• URL: https://github.com/juliagaussianprocesses/easygps.jl
• Owner: JuliaGaussianProcesses
• License: mit
• Created: 2024-02-06T15:15:25.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2024-03-09T14:17:56.000Z (over 2 years ago)
• Last Synced: 2025-02-22T12:44:42.505Z (over 1 year ago)
• Language: Julia
• Homepage:
• Size: 2.21 MB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# EasyGPs

[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)

[![Docs: stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://JuliaGaussianProcesses.github.io/EasyGPs.jl/stable)

[![Docs: dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://JuliaGaussianProcesses.github.io/EasyGPs.jl/dev)

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[![Codecov](https://codecov.io/gh/JuliaGaussianProcesses/EasyGPs.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaGaussianProcesses/EasyGPs.jl/tree/master)

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EasyGPs.jl is a package that defines a high-level API for the JuliaGaussianProcesses

ecosystem. It handles model parameterization and training, allowing users to focus on the

data and results without being distracted by tedious and repetitive tasks.

> [!NOTE]  

> This is an experimental package and may undergo breaking changes.

## Usage

In order to fit a GP, define one according to the familiar AbstractGP.jl interface and

let EasyGPs.jl handle the rest. The entry point for this is `EasyGPs.fit` (not exported):

```julia

using EasyGPs

kernel = 1.0 * with_lengthscale(SEKernel(), 1.0)

gp = with_gaussian_noise(GP(0.0, kernel), 0.1)

x = 0:0.1:10

y = sin.(x) .+ 0.1 .* randn(length(x))

fitted_gp = EasyGPs.fit(gp, x, y)

```

Under the hood, this will recognize the parameters (mean, variance, lengthscale) of the `GP`

you defined and automatically construct a parameterized model. It will then choose a cost

function, optimizer, and AD backend, and determine the optimal parameters.