{"id":25890893,"url":"https://github.com/mikediessner/benchfuncs","last_synced_at":"2026-05-17T19:35:18.226Z","repository":{"id":153143908,"uuid":"522187591","full_name":"mikediessner/benchfuncs","owner":"mikediessner","description":"Benchmark functions to test optimisation algorithms.","archived":false,"fork":false,"pushed_at":"2022-12-20T20:57:17.000Z","size":24,"stargazers_count":1,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-24T17:54:31.435Z","etag":null,"topics":["benchmark-functions","numpy","optimization","test-functions"],"latest_commit_sha":null,"homepage":"","language":"Python","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/mikediessner.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}},"created_at":"2022-08-07T10:58:34.000Z","updated_at":"2023-04-14T08:15:40.000Z","dependencies_parsed_at":null,"dependency_job_id":"1d4742cb-c129-4673-b95c-f1313b3ea21a","html_url":"https://github.com/mikediessner/benchfuncs","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mikediessner/benchfuncs","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikediessner%2Fbenchfuncs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikediessner%2Fbenchfuncs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikediessner%2Fbenchfuncs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikediessner%2Fbenchfuncs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mikediessner","download_url":"https://codeload.github.com/mikediessner/benchfuncs/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikediessner%2Fbenchfuncs/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33151884,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-17T09:28:26.183Z","status":"ssl_error","status_checked_at":"2026-05-17T09:27:52.702Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 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":["benchmark-functions","numpy","optimization","test-functions"],"created_at":"2025-03-02T19:30:28.308Z","updated_at":"2026-05-17T19:35:18.202Z","avatar_url":"https://github.com/mikediessner.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# IN DEVELOPMENT: benchfuncs\n\nThe `benchfuncs` package offers a selection of benchmark functions that can be used to test optimisation algorithms. Below is an example that shows (a) how `bencfuncs` can be used to generate training points for modelling and machine learning and (b) how functions can be solved with an optimiser (in this case the L-BFGS-B solver from the `scipy.optimize`package.)\n\n```python\nfrom benchfuncs import Sphere\nimport numpy as np\nfrom scipy.optimize import minimize\n\n\n# define benchmark function\nfunc = Sphere(dims=4)\n\n# sample training data\nx = np.random.uniform(low=func.bounds[:, 0], high=func.bounds[:, 1], size=(3, func.dims))\n\n# get training data outpus\ny = func(x)\n\n# print results\nprint(\"Inputs: \", x)\nprint(\"Outputs: \", y)\n```\n\n```\nInputs:  [[-2.69264719  2.34062409 -0.16280928 -0.55581425]\n          [ 1.60939843  0.28119955 -0.47671281 -0.51286048]\n          [ 2.32721282  0.32551319 -1.25113608 -4.29779957]]\nOutputs:  [13.0643064   3.15951746 25.55830099]\n```\n\n```python\n# optimise benchmark function\nx0 = np.random.uniform(low=func.bounds[:, 0], high=func.bounds[:, 1], size=func.dims)\nresults = minimize(func, x0, method='L-BFGS-B', bounds=func.bounds)\n\n# compare solution with global minimum\nprint(f\"Solution: \\t Inputs: {results['x']} \\t Output: {results['fun']}\")\nprint(f\"Optimum: \\t Inputs: {func.optimum['inputs']} \\t Output: {func.optimum['output']}\")\n```\n\n```\nSolution:        Inputs: [-4.92781912e-09 -4.96113870e-09 -4.99729987e-09 -5.03493624e-09]      Output: 9.921988747713389e-17\nOptimum:         Inputs: [[0. 0. 0. 0.]]         Output: [0.]\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmikediessner%2Fbenchfuncs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmikediessner%2Fbenchfuncs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmikediessner%2Fbenchfuncs/lists"}