{"id":23500470,"url":"https://github.com/gully/bivariate_practice","last_synced_at":"2025-04-25T08:59:57.136Z","repository":{"id":12568586,"uuid":"15239069","full_name":"gully/bivariate_practice","owner":"gully","description":"This is bivariate practice from the astroML textbook chapter 3.0 figures","archived":false,"fork":false,"pushed_at":"2013-12-26T06:41:30.000Z","size":220,"stargazers_count":0,"open_issues_count":1,"forks_count":2,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-02-16T16:25:30.643Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gully.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2013-12-16T22:32:07.000Z","updated_at":"2013-12-26T06:41:31.000Z","dependencies_parsed_at":"2022-09-23T08:02:32.677Z","dependency_job_id":null,"html_url":"https://github.com/gully/bivariate_practice","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gully%2Fbivariate_practice","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gully%2Fbivariate_practice/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gully%2Fbivariate_practice/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gully%2Fbivariate_practice/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gully","download_url":"https://codeload.github.com/gully/bivariate_practice/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250573347,"owners_count":21452345,"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":"2024-12-25T06:44:23.111Z","updated_at":"2025-04-24T05:53:21.281Z","avatar_url":"https://github.com/gully.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"bivariate_practice\n==================\n\nDecember 20, 2013\ngully; Austin, TX\n\nThis is bivariate practice from the astroML textbook chapter 3.0 figures\nThe original code is located at:\n\nhttp://www.astroml.org/book_figures/chapter3/fig_robust_pca.html\n\nThe file fig_robust_pca.py is the original.\n\nThe file fig_robust_pca_gull.py is an edit by gully.\n\nThe key idea was that I explored the dependence of the robust and non-robust fitting techniques by trying contamination fraction from 0.001 to 0.70.  The original example only looked at 5% and 15% outliers.\n\nHope you enjoyed the example, and that you can get it to run.\n\nBest wishes,\n-gully\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgully%2Fbivariate_practice","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgully%2Fbivariate_practice","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgully%2Fbivariate_practice/lists"}