{"id":19863442,"url":"https://github.com/sandialabs/fast_ticc","last_synced_at":"2026-06-09T08:32:34.062Z","repository":{"id":225033944,"uuid":"760707115","full_name":"sandialabs/fast_ticc","owner":"sandialabs","description":"Fast implementation of the TICC clustering algorithm from Hallac et al. 2017","archived":false,"fork":false,"pushed_at":"2026-05-27T03:04:20.000Z","size":2349,"stargazers_count":7,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-05-27T05:08:27.687Z","etag":null,"topics":["scr-2984","snl-data-analysis"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sandialabs.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","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":"2024-02-20T19:38:10.000Z","updated_at":"2026-05-27T03:04:24.000Z","dependencies_parsed_at":"2025-01-11T15:24:12.872Z","dependency_job_id":"5d47d981-6050-41ae-8d48-f500e8305f41","html_url":"https://github.com/sandialabs/fast_ticc","commit_stats":null,"previous_names":["sandialabs/fast_ticc"],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/sandialabs/fast_ticc","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Ffast_ticc","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Ffast_ticc/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Ffast_ticc/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Ffast_ticc/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sandialabs","download_url":"https://codeload.github.com/sandialabs/fast_ticc/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Ffast_ticc/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34098931,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-09T02:00:06.510Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["scr-2984","snl-data-analysis"],"created_at":"2024-11-12T15:14:40.678Z","updated_at":"2026-06-09T08:32:34.028Z","avatar_url":"https://github.com/sandialabs.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# fast_ticc: Toeplitz Inverse Covariance Clustering\n\nThis package contains a reimplementation of the algorithm described in\n\"Toeplitz Inverse Covariance-Based Clustering\" (hereinafter abbreviated\nTICC) by D. Hallac, S. Vare, S. Boyd, and J. Leskovec.  It improves on\nthe authors' reference implementation mainly by adding more documentation,\nmore test cases, and Numba-based parallelism / JIT compilation where\nappropriate.\n\nTICC's purpose is to segment multivariate time-series data into regions\nof similar behavior.  Here, \"behavior\" is defined by the covariance of the\ndifferent components of the time-series data in a window around the point\nbeing labeled.\n\n## Installing the Library\n\nOur implementation of TICC is available from [PyPI](https://pypi.org)\nand [conda-forge](https://conda-forge.org).  You can install it from\nthere with `pip install fast_ticc` and\n`conda install -c conda-forge fast_ticc` (for Anaconda users),\nrespectively.\n\nYou can install directly from a copy of this repository with the\nfollowing two commands:\n\n```bash\ncd src\npip install .\n```\n\n\n## Using the Library\n\nYour best resource for learning to use the library is\n[the documentation](https://fast-ticc.readthedocs.io).\nThere are also examples in this repository under the\n`src/examples/` directory.\n\n### The Briefest of Quick-Start Instructions\n\nStart with your data in an N x D NumPy array: one row per data point, one\ncolumn per variable.\n\nCall TICC to compute labels:\n\n```python\nticc_result = fast_ticc.ticc_compute_labels(my_data,\n                                            window_size,\n                                            num_clusters)\nlabels = ticc_result.point_labels\n```\n\nThe main function (`ticc_compute_labels`) returns a structure with the\ncomputed labels (in the `point_labels` field) and lots of information\ndescribing the clusters and how well they describe the data.  You are\nprobably most interested in the labels themselves, which are a list of\nintegers with one entry for each input data point.\n\n### Learning More\n\nFor further information, consult\n[the documentation](https://fast-ticc.readthedocs.io)\nor the original\n[TICC paper](https://web.stanford.edu/~boyd/papers/ticc.html).\n\n## Contributing\n\nWe welcome contributions!  Open a discussion or a pull request in this\nrepository and we'll talk.\n\n## Authors\n\nAndy Wilson, Daniel DeLayo, Renee Gooding, Jessica Jones, Kanad Khanna,\nNitin Sharan, and Jon Whetzel worked on this implementation.  Andy\nWilson was the chief author and is the maintainer.\n\n## License\n\nThis library is distributed under a 3-clause BSD license.  Full text is\navailable in LICENSE at the top level of the repository along with the\nlicense under which we use the original authors' implementation.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Ffast_ticc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsandialabs%2Ffast_ticc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Ffast_ticc/lists"}