{"id":31542534,"url":"https://github.com/neurodata/autogmm","last_synced_at":"2026-01-20T17:01:09.010Z","repository":{"id":309234468,"uuid":"1035559105","full_name":"neurodata/autogmm","owner":"neurodata","description":"Python package for automatic Gaussian mixture modeling","archived":false,"fork":false,"pushed_at":"2025-09-14T01:43:35.000Z","size":4346,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-10-04T12:00:27.483Z","etag":null,"topics":["gaussian-mixture-models","machine-learning","model-selection"],"latest_commit_sha":null,"homepage":"https://github.com/tingshanL/autogmm","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/neurodata.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","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":"2025-08-10T16:49:43.000Z","updated_at":"2025-09-15T06:24:46.000Z","dependencies_parsed_at":"2025-08-10T19:09:19.103Z","dependency_job_id":"41ce4b9e-9d4a-4232-ab93-551a25a6fc18","html_url":"https://github.com/neurodata/autogmm","commit_stats":null,"previous_names":["tingshanl/autogmm","neurodata/autogmm"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/neurodata/autogmm","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fautogmm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fautogmm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fautogmm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fautogmm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/neurodata","download_url":"https://codeload.github.com/neurodata/autogmm/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fautogmm/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28607624,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-20T16:10:39.856Z","status":"ssl_error","status_checked_at":"2026-01-20T16:10:39.493Z","response_time":117,"last_error":"SSL_read: 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":["gaussian-mixture-models","machine-learning","model-selection"],"created_at":"2025-10-04T11:59:01.626Z","updated_at":"2026-01-20T17:01:08.991Z","avatar_url":"https://github.com/neurodata.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AutoGMM\n\nAutomatic Gaussian Mixture Modeling in Python.\n\n### Install\n``` bash\npip install autogmm\n```\n\n### Quick start\n``` python\nfrom autogmm import AutoGMM\nfrom sklearn.datasets import make_blobs\n\nX, _ = make_blobs(\n                 n_samples=1000,\n                 centers=4,\n                 cluster_std=1.2,\n                 random_state=0\n)\n\nlabels = AutoGMM(\n                 min_components=1,\n                 max_components=10, # unknown K\n                 criterion=\"bic\",\n                 random_state=0\n).fit_predict(X)\n```\n\n\n### Features\n- Initializations: KMeans, Ward–Euclidean, Ward–Mahalanobis\n\n- EM with eigenvalue flooring and covariance constraints (spherical, diag, tied, full)\n\n- Model selection via BIC/AIC (unknown *K*)\n\n- Optional spectral front-end (ASE/LSE) for nonconvex structure\n\n- Parallel evaluation, clean API, reproducible scripts\n\n\n\n### Documentation\n- API \u0026 Guides: https://github.com/neurodata/autogmm/\n- Examples: [examples/](examples) (benchmarks; stress tests; runtime scaling)\n- Reproducibility: [scripts/reproduce.sh](scripts/reproduce.sh) (regenerates all figures with fixed seeds)\n\n### Legacy \u0026 Independence\nAutoGMM was originally developed in the [graspologic](https://github.com/graspologic-org/graspologic/) library.\nAs of v1.0, it is a standalone package with no dependency on graspologic.\n\n### Contributing\nIssues and PRs are welcome. See [CONTRIBUTING.md](CONTRIBUTING.md).\n\n### Citation\n``` bibtex\n@software{autogmm,\n  title   = {AutoGMM: Automatic Gaussian Mixture Modeling in Python},\n  author  = {Liu, Tingshan and Athey, Thomas L. and Pedigo, Benjamin D. and Vogelstein, Joshua T.},\n  year    = {2025},\n  url     = {https://github.com/neurodata/autogmm}\n}\n```\n\n### License\nBSD 3-Clause License. See [LICENSE](LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneurodata%2Fautogmm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneurodata%2Fautogmm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneurodata%2Fautogmm/lists"}