{"id":13570905,"url":"https://github.com/davidhallac/TICC","last_synced_at":"2025-04-04T07:32:24.276Z","repository":{"id":44545895,"uuid":"80052158","full_name":"davidhallac/TICC","owner":"davidhallac","description":null,"archived":false,"fork":false,"pushed_at":"2020-06-14T19:43:50.000Z","size":2441,"stargazers_count":446,"open_issues_count":23,"forks_count":161,"subscribers_count":35,"default_branch":"master","last_synced_at":"2024-04-23T15:02:48.435Z","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":"bsd-2-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/davidhallac.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}},"created_at":"2017-01-25T19:59:10.000Z","updated_at":"2024-04-21T07:08:58.000Z","dependencies_parsed_at":"2022-07-19T04:02:20.354Z","dependency_job_id":null,"html_url":"https://github.com/davidhallac/TICC","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/davidhallac%2FTICC","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/davidhallac%2FTICC/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/davidhallac%2FTICC/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/davidhallac%2FTICC/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/davidhallac","download_url":"https://codeload.github.com/davidhallac/TICC/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247076228,"owners_count":20879638,"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":"2024-08-01T14:00:56.369Z","updated_at":"2025-04-04T07:32:19.261Z","avatar_url":"https://github.com/davidhallac.png","language":"Python","readme":"# TICC\nTICC is a python solver for efficiently segmenting and clustering a multivariate time series. It takes as input a T-by-n data matrix, a regularization parameter `lambda` and smoothness parameter `beta`, the window size `w` and the number of clusters `k`.  TICC breaks the T timestamps into segments where each segment belongs to one of the `k` clusters. The total number of segments is affected by the smoothness parameter `beta`. It does so by running an EM algorithm where TICC alternately assigns points to clusters using a dynamic programming algorithm and updates the cluster parameters by solving a Toeplitz Inverse Covariance Estimation problem. \n\nFor details about the method and implementation see the paper [1].\n\n## Download \u0026 Setup\nDownload the source code, by running in the terminal:\n```\ngit clone https://github.com/davidhallac/TICC.git\n```\n\n\n## Using TICC\nThe `TICC`-constructor takes the following parameters:\n\n* `window_size`: the size of the sliding window\n* `number_of_clusters`: the number of underlying clusters 'k'\n* `lambda_parameter`: sparsity of the Markov Random Field (MRF) for each of the clusters. The sparsity of the inverse covariance matrix of each cluster.\n* `beta`: The switching penalty used in the TICC algorithm. Same as the beta parameter described in the paper. \n* `maxIters`: the maximum iterations of the TICC algorithm before convergence. Default value is 100.\n* `threshold`: convergence threshold\n* `write_out_file`: Boolean. Flag indicating if the computed inverse covariances for each of the clusters should be saved.\n* `prefix_string`: Location of the folder to which you want to save the outputs.\n\n\nThe `TICC.fit(input_file)`-function runs the TICC algorithm on a specific dataset to learn the model parameters.\n\n* `input_file`: Location of the data matrix of size T-by-n.\n\nAn array of cluster assignments for each time point is returned in the form of a dictionary with keys being the `cluster_id` (from `0` to `k-1`) and the values being the cluster MRFs.\n\n\n## Example Usage\n\nSee `example.py`.\n\n\n## References\n[1] D. Hallac, S. Vare, S. Boyd, and J. Leskovec [Toeplitz Inverse Covariance-Based Clustering of\nMultivariate Time Series Data](http://stanford.edu/~hallac/TICC.pdf) Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 215--223\n","funding_links":[],"categories":["📦 Packages"],"sub_categories":["Python"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdavidhallac%2FTICC","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdavidhallac%2FTICC","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdavidhallac%2FTICC/lists"}