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

Abstract types and methods for Gaussian Processes.

Science Score: 46.0%

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Low similarity (12.3%) to scientific vocabulary

Keywords

gaussian-processes gps julia probabilistic-programming

Keywords from Contributors

symbolic-computing julialang automatic-differentiation differential-equations ode numerical sciml dde neural-ode dae

Last synced: 6 months ago · JSON representation

Repository

Abstract types and methods for Gaussian Processes.

Basic Info

Host: GitHub
Owner: JuliaGaussianProcesses
License: other
Language: Julia
Default Branch: main
Homepage: https://juliagaussianprocesses.github.io/AbstractGPs.jl/dev
Size: 112 MB

Statistics

Stars: 269
Watchers: 8
Forks: 25
Open Issues: 39
Releases: 67

Topics

gaussian-processes gps julia probabilistic-programming

Created almost 6 years ago · Last pushed 6 months ago

Metadata Files

Readme License

AbstractGPs

AbstractGPs.jl is a package that defines a low-level API for working with Gaussian processes (GPs), and basic functionality for working with them in the simplest cases. As such it is aimed more at developers and researchers who are interested in using it as a building block than end-users of GPs. You may want to go through the main API design documentation.

Installation

AbstractGPs is an officially registered Julia package, so the following will install AbstractGPs using the Julia's package manager:

julia ] add AbstractGPs

Example

```julia

Import packages.

using AbstractGPs, Plots

Generate toy synthetic data.

x = rand(10) y = sin.(x)

Define GP prior with Matern-3/2 kernel

f = GP(Matern32Kernel())

Finite projection of `f` at inputs `x`.

Added Gaussian noise with variance 0.001.

fx = f(x, 0.001)

Log marginal probability of `y` under `f` at `x`.

Quantity typically maximised to train hyperparameters.

logpdf(fx, y)

Exact posterior given `y`. This is another GP.

p_fx = posterior(fx, y)

Log marginal posterior predictive probability.

logpdf(p_fx(x), y)

Plot posterior.

scatter(x, y; label="Data") plot!(-0.5:0.001:1.5, p_fx; label="Posterior") ```

Related Julia packages

AbstractGPsMakie.jl - Plotting GPs with Makie.jl.
ApproximateGPs.jl - Approximate inference for GPs, both for sparse approximations and non-Gaussian likelihoods. Built on types which implement this package's APIs.
BayesianLinearRegressors.jl - Accelerated inference in GPs with a linear kernel. Built on types which implement this package's APIs.
GPLikelihoods.jl - Non-Gaussian likelihood functions to use with GPs.
KernelFunctions.jl - Kernel functions for machine learning.
Stheno.jl - Building probabilistic programmes involving GPs. Built on types which implement this package's APIs.
TemporalGPs.jl - Accelerated inference in GPs involving time. Built on types which implement this package's APIs.

Issues/Contributing

If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue.

Owner

Name: Gaussian Processes for Machine Learning in Julia
Login: JuliaGaussianProcesses
Kind: organization

Repositories: 14
Profile: https://github.com/JuliaGaussianProcesses

GitHub Events

Total

Fork event: 5
Create event: 25
Commit comment event: 6
Issues event: 8
Release event: 3
Watch event: 45
Delete event: 27
Member event: 3
Issue comment event: 49
Push event: 119
Pull request review comment event: 24
Pull request review event: 12
Pull request event: 49

Last Year

Fork event: 5
Create event: 25
Commit comment event: 6
Issues event: 8
Release event: 3
Watch event: 45
Delete event: 27
Member event: 3
Issue comment event: 49
Push event: 119
Pull request review comment event: 24
Pull request review event: 12
Pull request event: 49

Committers

Last synced: 9 months ago

All Time

Total Commits: 221
Total Committers: 20
Avg Commits per committer: 11.05
Development Distribution Score (DDS): 0.76

Past Year

Commits: 6
Committers: 4
Avg Commits per committer: 1.5
Development Distribution Score (DDS): 0.667

Top Committers

Name	Email	Commits
github-actions[bot]	4****]	53
David Widmann	d****n	47
willtebbutt	w**3@c**k	37
st--	s****-	32
Théo Galy-Fajou	t**u@g**m	18
Sharan Yalburgi	s**i@g**m	12
Simone Carlo Surace	5****e	5
Hong Ge	3****i	4
4aHxKzD	5****D	2
Jacob Vaverka	4****a	1
John Jackson	j**k@g**m	1
Lance (Weiqing) Xu	4****q	1
Nathanael Bosch	n**h@g**m	1
Niklas Schmitz	n**z@g**m	1
Ross Viljoen	r**s@v**k	1
Steffen Ridderbusch	s**n@r**k	1
Tom Wright	t**t@g**m	1
Vikram	v**n@g**m	1
andreaskoher	a**h@d**k	1
Šimon Soldát	3****m	1

Committer Domains (Top 20 + Academic)

dtu.dk: 1 robots.ox.ac.uk: 1 viljoen.co.uk: 1 cam.ac.uk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 53
Total pull requests: 156
Average time to close issues: about 1 year
Average time to close pull requests: 3 months
Total issue authors: 24
Total pull request authors: 17
Average comments per issue: 4.72
Average comments per pull request: 2.23
Merged pull requests: 85
Bot issues: 0
Bot pull requests: 94

Past Year

Issues: 4
Pull requests: 44
Average time to close issues: 26 days
Average time to close pull requests: 3 months
Issue authors: 2
Pull request authors: 6
Average comments per issue: 1.25
Average comments per pull request: 1.14
Merged pull requests: 21
Bot issues: 0
Bot pull requests: 33

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Top Authors

Issue Authors

theogf (8)
st-- (8)
willtebbutt (7)
simsurace (3)
vikram-s-narayan (3)
Crown421 (3)
filipporemonato (3)
yebai (2)
JuliaTagBot (1)
ancorso (1)
FelixBenning (1)
samuelbelko (1)
kaandocal (1)
jmbyars (1)
azev77 (1)

Pull Request Authors

github-actions[bot] (99)
st-- (18)
devmotion (10)
willtebbutt (10)
simsurace (7)
sharanry (4)
Crown421 (3)
lanceXwq (2)
jeetsuthar (2)
rossviljoen (2)
soldasim (2)
theogf (1)
Jmcjack (1)
jvaverka (1)
avik-pal (1)

Top Labels

Issue Labels

good first issue (4) documentation (1)

Pull Request Labels

help wanted (1)

Packages

Total packages: 1
Total downloads:
- julia 145 total

Total dependent packages: 23
Total dependent repositories: 0
Total versions: 67

juliahub.com: AbstractGPs

Abstract types and methods for Gaussian Processes.

Homepage: https://juliagaussianprocesses.github.io/AbstractGPs.jl/dev
Documentation: https://docs.juliahub.com/General/AbstractGPs/stable/
License: MIT
Latest release: 0.5.24
published 11 months ago

Versions: 67
Dependent Packages: 23
Dependent Repositories: 0
Downloads: 145 Total

Rankings

Dependent packages count: 3.6%

Stargazers count: 4.0%

Average: 6.3%

Forks count: 7.5%

Dependent repos count: 9.9%

Last synced: 6 months ago

Dependencies

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

Science Score: 46.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

AbstractGPs

Installation

Example

Import packages.

Generate toy synthetic data.

Define GP prior with Matern-3/2 kernel

Finite projection of f at inputs x.

Added Gaussian noise with variance 0.001.

Log marginal probability of y under f at x.

Quantity typically maximised to train hyperparameters.

Exact posterior given y. This is another GP.

Log marginal posterior predictive probability.

Plot posterior.

Related Julia packages

Issues/Contributing

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

juliahub.com: AbstractGPs

Rankings

Dependencies

Finite projection of `f` at inputs `x`.

Log marginal probability of `y` under `f` at `x`.

Exact posterior given `y`. This is another GP.