Merge branch 'JuliaGaussianProcesses:master' into master

Author: pitmonticone

.github/PULL_REQUEST_TEMPLATE/pull_request_template.md

@@ -0,0 +1,2 @@
+
+New? Check CONTRIBUTING.md!

.gitignore

@@ -1,7 +1,5 @@
 *.json
 *.cov
-/Manifest.toml
-/test/Manifest.toml
+Manifest.toml
 coverage/
 .DS_store
-benchmark/Manifest.toml

CONTRIBUTING.md

@@ -0,0 +1,35 @@
+# Contribution guidelines
+
+We follow the [ColPrac guide for collaborative practices](https://colprac.sciml.ai/). New contributors should make sure to read the [first section](https://github.com/SciML/ColPrac#colprac-contributors-guide-on-collaborative-practices-for-community-packages) of that guide, but could also read the [Further Guidance](https://github.com/SciML/ColPrac#colprac-further-guidance) section if interested.
+
+
+# Workflows
+
+
+## Creating a new [pull request](https://docs.github.com/en/github/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests)
+
+### Bumping the version number
+
+When contributing a PR, bump the version number (defined by `version = "..."` at the top of the base `Project.toml`) accordingly (as explained by the [guidance on the versioning scheme](https://colprac.sciml.ai/#incrementing-the-package-version) in the ColPrac guide).
+If unsure about what the new version should be, please just open the PR anyway -- existing contributors will provide a suggestion.
+
+### Running tests locally
+
+Firstly, run `using Pkg; Pkg.develop("KernelFunctions")` at the Julia REPL, and navigate to `~/.julia/dev/KernelFunctions`.
+
+Running `make test` will now run the entire test suite.
+These tests can take a long time to run, so it's often a good idea to simply comment out the blocks of tests not required in `test/runtests.jl`.
+Test files are paired 1-1 with source files, so if you're modifying code in `src/foo.jl`, you should only need to run the tests in `test/foo.jl` during development.
+
+### Code formatting
+
+Run `make format` before pushing your changes.
+
+
+### How to make a new release (for organization members)
+
+We use [JuliaRegistrator](https://github.com/JuliaRegistries/Registrator.jl#via-the-github-app):
+
+On Github, go to the commit that you want to register (this should normally be the commit of a squash-merged pull request on the `master` branch that has bumped the version number) and add a comment saying `@JuliaRegistrator register`.
+
+The next release will then be processed automatically. KernelFunctions.jl does use TagBot so you can ignore the message about tagging. Note that it may take around 20 minutes until the actual release appears and you can edit the release notes if needed.

Makefile

@@ -0,0 +1,16 @@
+JULIA=$(shell which julia)
+
+.PHONY: help format test
+
+help:
+	@echo "The following make targets are available:"
+	@echo "	format			auto-format code"
+	@echo "	test			run all tests"
+
+format:
+	@if [ "$(JULIA)" = "" ]; then echo 'Julia not found; run `make JULIA=/path/to/julia format`'; exit 1; fi
+	$(JULIA) -e 'using Pkg; Pkg.activate(; temp=true); Pkg.add("JuliaFormatter"); using JuliaFormatter; format(".")'
+
+test:
+	@if [ "$(JULIA)" = "" ]; then echo 'Julia not found; run `make JULIA=/path/to/julia test`'; exit 1; fi
+	$(JULIA) --project=. -e 'using Pkg; Pkg.test()'

Project.toml

@@ -1,6 +1,6 @@
 name = "KernelFunctions"
 uuid = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
-version = "0.10.33"
+version = "0.10.38"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
@@ -15,6 +15,7 @@ LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 TensorCore = "62fd8b95-f654-4bbd-a8a5-9c27f68ccd50"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

README.md

@@ -9,14 +9,14 @@
 [![DOI](https://zenodo.org/badge/188430419.svg)](https://zenodo.org/badge/latestdoi/188430419)
 
 
-
 ## Kernel functions for machine learning
 
-KernelFunctions.jl provides a flexible framework for defining kernel functions, and an extensive collection of implementations.
-
-The aim is to make the API as model-agnostic as possible while still being user-friendly, and 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/).
-
-Where appropriate, kernels are AD-compatible.
+**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
 
@@ -50,9 +50,9 @@ plot(
 
 ## Related Work
 
-Directly inspired by the [MLKernels](https://github.com/trthatcher/MLKernels.jl) package.
+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 related packages.
+See the JuliaGaussianProcesses [Github organisation](https://github.com/JuliaGaussianProcesses) and [website](https://juliagaussianprocesses.github.io/) for more information.
 
 ## Issues/Contributing