{"id":18015506,"url":"https://github.com/wmkouw/sample-covariateshift","last_synced_at":"2026-01-18T01:35:26.587Z","repository":{"id":89538267,"uuid":"134880976","full_name":"wmkouw/sample-covariateshift","owner":"wmkouw","description":"Sample from synthetic covariate shift problem","archived":false,"fork":false,"pushed_at":"2018-05-27T17:50:01.000Z","size":5,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-04T15:17:09.366Z","etag":null,"topics":["covariate-shift","dataset","machine-learning","rejection-sampling"],"latest_commit_sha":null,"homepage":"","language":"Matlab","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/wmkouw.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":"2018-05-25T16:33:23.000Z","updated_at":"2019-05-22T10:30:01.000Z","dependencies_parsed_at":null,"dependency_job_id":"3749a15a-b968-4368-a028-f76a8d793b3c","html_url":"https://github.com/wmkouw/sample-covariateshift","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/wmkouw/sample-covariateshift","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wmkouw%2Fsample-covariateshift","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wmkouw%2Fsample-covariateshift/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wmkouw%2Fsample-covariateshift/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wmkouw%2Fsample-covariateshift/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wmkouw","download_url":"https://codeload.github.com/wmkouw/sample-covariateshift/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wmkouw%2Fsample-covariateshift/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28526552,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-18T00:39:45.795Z","status":"ssl_error","status_checked_at":"2026-01-18T00:39:39.467Z","response_time":85,"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":["covariate-shift","dataset","machine-learning","rejection-sampling"],"created_at":"2024-10-30T04:14:04.847Z","updated_at":"2026-01-18T01:35:26.542Z","avatar_url":"https://github.com/wmkouw.png","language":"Matlab","funding_links":[],"categories":[],"sub_categories":[],"readme":"# sample-covariateshift\n\nScripts to sample from class-conditional distributions, under covariate shift.\n\nTo be precise:\n\nAssume that there is a single sample space X, and a fixed number of classes Y. Let the _source domain_, referred to as p\u003csub\u003eS\u003c/sub\u003e(x,y), be one distribution over (X,Y) and the _target domain_ another, p\u003csub\u003eT\u003c/sub\u003e(x,y). In cases of covariate shift, the posterior distributions are equal in both domains; p\u003csub\u003eS\u003c/sub\u003e(y|x) = p\u003csub\u003eT\u003c/sub\u003e(y|x).\n\nDistributions:\n- p(y) is the distribution of the classes (equal in both domains).\n- p(y|x) is the posterio distribution (equal in both domains; hence _covariate shift_)\n- p\u003csub\u003eS\u003c/sub\u003e(x) is the source distribution of the data.\n- p\u003csub\u003eT\u003c/sub\u003e(x) is the target distribution of the data.\n\nThe scripts generate class-conditional distributions for each domain, p\u003csub\u003eS\u003c/sub\u003e(x|y) and p\u003csub\u003eT\u003c/sub\u003e(x|y), using Bayes' rule. Samples are drawn using a rejection sampler.\n\n\n## Usage\n\nHave a look at `example_call.m`\n\n## Questions\nQuestions, comments and bugs can be submitted in the [issues tracker](https://github.com/wmkouw/sample-covariateshift/issues).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwmkouw%2Fsample-covariateshift","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwmkouw%2Fsample-covariateshift","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwmkouw%2Fsample-covariateshift/lists"}