{"id":34046368,"url":"https://github.com/deftasparagusanaconda/gapprox","last_synced_at":"2026-04-07T11:31:34.319Z","repository":{"id":279987380,"uuid":"940665578","full_name":"deftasparagusanaconda/gapprox","owner":"deftasparagusanaconda","description":"python toolkit to find the approximate function of a graph","archived":false,"fork":false,"pushed_at":"2026-02-14T06:39:46.000Z","size":1240,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-02-14T23:15:21.507Z","etag":null,"topics":["approximation","graph","regression"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/deftasparagusanaconda.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-02-28T15:20:54.000Z","updated_at":"2026-02-14T06:39:49.000Z","dependencies_parsed_at":"2025-02-28T20:47:06.824Z","dependency_job_id":"c0705247-c078-4030-b41f-1c8a3675b575","html_url":"https://github.com/deftasparagusanaconda/gapprox","commit_stats":null,"previous_names":["deftasparagusanaconda/graph-approximator","deftasparagusanaconda/graphapproximator","deftasparagusanaconda/hana","deftasparagusanaconda/gapprox"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/deftasparagusanaconda/gapprox","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deftasparagusanaconda%2Fgapprox","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deftasparagusanaconda%2Fgapprox/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deftasparagusanaconda%2Fgapprox/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deftasparagusanaconda%2Fgapprox/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/deftasparagusanaconda","download_url":"https://codeload.github.com/deftasparagusanaconda/gapprox/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deftasparagusanaconda%2Fgapprox/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31511557,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-07T03:10:19.677Z","status":"ssl_error","status_checked_at":"2026-04-07T03:10:13.982Z","response_time":105,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["approximation","graph","regression"],"created_at":"2025-12-13T23:47:06.148Z","updated_at":"2026-04-07T11:31:34.313Z","avatar_url":"https://github.com/deftasparagusanaconda.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[📖 manual][manual] | [🔍 examples][examples] | [📜 license][license] | [💡suggest silly ideas!][contact]  \n\n# gapprox\na python toolkit to find the approximate function of any [graph][graph]  \ninstead of \"find the graph of the function\", youre flipping it: \"find the function of the graph\"\n\n## 💾 installation\nget it from PyPI: \n```shell\npip install gapprox\n```\n\nor install the latest from GitHub:\n```shell\ngit clone https://github.com/deftasparagusanaconda/gapprox\ncd gapprox\npip install .\n```\n\nfor better performance, install with all optional dependencies:\n```shell\npip install gapprox[all]\n```\n\n## 📗 basic guide\n```python\nimport gapprox as ga\n\nf = ga.fit([1, 2, 4, 6, 3], [1, 2, 5, 5, 2])\n\nprint(f)\nprint(f(2.5))\n```\n\n`.fit()` selects the best approximation method and returns an [Expression][advanced guide]  \nyou can print the Function `print(f)` or call it like a function `f(2.5)`  \n\ngapprox can do a lot more. check out more examples [here!][examples] or read the [manual][manual]\n\n## 🚀 roadmap\n+ DAG/expression trees  \n+ multi-objective analysis \n+ complex numbers  \n+ parametric function support  \n+ multiple-input multiple-output\n- [pareto front](https://en.wikipedia.org/wiki/Pareto_front) presentation\n- web app  \n- symbolic regression  \n- n-dimensional plotters  \n- surface approximation  \n- [many-to-many][relation types] relation approximation  \n- point density evaluators  \n- hypersonic blasters 🚀\n\n~~in the far far future, ga will support multiple-input multiple-output approximation. for m inputs and n outputs, it runs n approximations of m-dimensional [manifolds][manifold] separately  \neffectively, this turns it into a general-purpose prediction library, analogous to AI  \ncurrently, ga only supports single-input single-output [many-to-one][relation types] functions. see [roadmap][roadmap] for details  ~~\n\ngapprox now supports any general mathematical expression as long as it is representable on a directed acyclic graph. you may go ham on tensorial input/output\n\n## 📔 you read all that?!?\n\nthis project is still budding 🌱 if you'd like to change something, add something, or suggest ideas—[come say hi!][contact]\n\nwith love, and a passion for maths ~  \n\\- [daa][contact] 🌸\n\n[examples]: https://github.com/deftasparagusanaconda/gapprox/tree/main/examples/  \n[manual]: https://github.com/deftasparagusanaconda/gapprox/tree/main/documentation/manual.md  \n[license]: https://github.com/deftasparagusanaconda/gapprox/tree/main/LICENSE  \n[contact]: https://discordapp.com/users/608255432859058177\n\n[graph]: https://en.wikipedia.org/wiki/Graph_of_a_function  \n[function]: https://en.wikipedia.org/wiki/Function_(mathematics)\n[functional analysis]: https://en.wikipedia.org/wiki/Functional_analysis\n[approximation]: https://en.wikipedia.org/wiki/Approximation_theory\n[manifold]: https://en.wikipedia.org/wiki/Manifold\n[smoothness]: https://en.wikipedia.org/wiki/Smoothness\n[parsing]: https://en.wikipedia.org/wiki/Parsing\n[sampling]: https://en.wikipedia.org/wiki/Sampling_(statistics)\n[interpolation]: https://en.wikipedia.org/wiki/Interpolation\n[optimization]: https://en.wikipedia.org/wiki/Mathematical_optimization\n[iterative method]: https://en.wikipedia.org/wiki/Iterative_method\n[expression]: https://en.wikipedia.org/wiki/Expression_(mathematics)\n[error analysis]: https://en.wikipedia.org/wiki/Error_analysis_(mathematics)\n[relation types]: https://en.wikipedia.org/wiki/Relation_(mathematics)#Combinations_of_properties\n[object in cs]: https://en.wikipedia.org/wiki/Object_(computer_science)\n[advanced guide]: https://github.com/deftasparagusanaconda/gapprox/blob/main/documentation/manual.md#-advanced-guide-\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeftasparagusanaconda%2Fgapprox","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeftasparagusanaconda%2Fgapprox","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeftasparagusanaconda%2Fgapprox/lists"}