{"id":19120457,"url":"https://github.com/juliagaussianprocesses/inducingpoints.jl","last_synced_at":"2025-10-26T16:17:24.822Z","repository":{"id":45176147,"uuid":"282531878","full_name":"JuliaGaussianProcesses/InducingPoints.jl","owner":"JuliaGaussianProcesses","description":"Package for different inducing points selection methods","archived":false,"fork":false,"pushed_at":"2023-05-03T15:09:49.000Z","size":536,"stargazers_count":8,"open_issues_count":4,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-01-20T18:57:29.960Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Julia","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/JuliaGaussianProcesses.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-07-25T22:01:24.000Z","updated_at":"2024-06-11T08:19:30.000Z","dependencies_parsed_at":"2023-02-10T18:50:16.093Z","dependency_job_id":null,"html_url":"https://github.com/JuliaGaussianProcesses/InducingPoints.jl","commit_stats":null,"previous_names":[],"tags_count":20,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JuliaGaussianProcesses%2FInducingPoints.jl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JuliaGaussianProcesses%2FInducingPoints.jl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JuliaGaussianProcesses%2FInducingPoints.jl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JuliaGaussianProcesses%2FInducingPoints.jl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JuliaGaussianProcesses","download_url":"https://codeload.github.com/JuliaGaussianProcesses/InducingPoints.jl/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240177055,"owners_count":19760308,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-09T05:14:11.203Z","updated_at":"2025-10-26T16:17:19.796Z","avatar_url":"https://github.com/JuliaGaussianProcesses.png","language":"Julia","funding_links":[],"categories":[],"sub_categories":[],"readme":"# InducingPoints\n\n[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://JuliaGaussianProcesses.github.io/InducingPoints.jl/stable)\n[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://JuliaGaussianProcesses.github.io/InducingPoints.jl/dev)\n![BuildStatus](https://github.com/JuliaGaussianProcesses/InducingPoints.jl/workflows/CI/badge.svg)\n[![Coverage](https://coveralls.io/repos/github/JuliaGaussianProcesses/InducingPoints.jl/badge.svg?branch=master)](https://coveralls.io/github/JuliaGaussianProcesses/InducingPoints.jl?branch=master)\n\nA package for selecting inducing points for sparse GPs\n\nThis package provide a collection of inducing point location selection algorithms, both offline and online.\n\n## Offline algorithms\n\n\nOffline algorithms are meant to be run once over the data before training begins.\nHere is an example where we use the k-means algorithm\n```julia\nusing InducingPoints\nX = [rand(5) for _ in 1:100]\nalg = KMeansAlg(10) # Create the kmeans algorithm\nZ = inducingpoints(alg, X) # Returns a vector of vector of size 10 \n```\nwill return 10 inducing points selected as clusters by the k-means algorithm\n\nNote that it is possible to pass data as a matrix as well following the convention of [KernelFunctions.jl](https://juliagaussianprocesses.github.io/KernelFunctions.jl/dev/userguide/#Creating-a-Kernel-Matrix)\n```julia\nX = rand(5 , 1000)\nalg = KMeansAlg(10, Euclidean()) # We can also use different metrics\nZ = inducingpoints(alg, X) # This still returns a vector of vector of size 10 \n```\n\n## Online algorithms\n\nOnline algorithms needs two API, a first one to create the initial vector of inducing points and another one to update it with new data.\nFor example following [this work](https://drive.google.com/file/d/1IPTUBfY_b2WElTWBIVU4lrbHcXnbTWdB/view)\n\n```julia\nalg = OIPS(200) # We expect 200 inducing points\nkernel = SqExponential()\nX = [rand(5) for _ in 1:100] # We have some initial data\nZ = inducingpoints(alg, X; kernel=kernel) # We create an initial vector\nX_new = [rand(5) for _ in 1:50] # We get some new data\nupdateZ!(Z, alg, X_new; kernel=kernel) # Points will be acordingly added (or removed!)\n```\n\nNote that `Z` is directly changed in place.\n\n## Notes\n\nMake sure to check each algorithm docs independently, they will give you more details on what arguments they need and what they do!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjuliagaussianprocesses%2Finducingpoints.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjuliagaussianprocesses%2Finducingpoints.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjuliagaussianprocesses%2Finducingpoints.jl/lists"}