{"id":28424927,"url":"https://github.com/bmwcarit/hmm-lib","last_synced_at":"2025-09-05T02:04:57.232Z","repository":{"id":31603727,"uuid":"35168637","full_name":"bmwcarit/hmm-lib","owner":"bmwcarit","description":"Library for time-inhomogeneous Hidden Markov Models","archived":false,"fork":false,"pushed_at":"2021-08-24T12:04:19.000Z","size":259,"stargazers_count":92,"open_issues_count":1,"forks_count":19,"subscribers_count":14,"default_branch":"master","last_synced_at":"2025-07-28T07:49:53.938Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bmwcarit.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":"2015-05-06T15:57:53.000Z","updated_at":"2025-07-24T16:26:22.000Z","dependencies_parsed_at":"2022-08-08T13:00:26.503Z","dependency_job_id":null,"html_url":"https://github.com/bmwcarit/hmm-lib","commit_stats":null,"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"purl":"pkg:github/bmwcarit/hmm-lib","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bmwcarit%2Fhmm-lib","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bmwcarit%2Fhmm-lib/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bmwcarit%2Fhmm-lib/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bmwcarit%2Fhmm-lib/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bmwcarit","download_url":"https://codeload.github.com/bmwcarit/hmm-lib/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bmwcarit%2Fhmm-lib/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273699712,"owners_count":25152286,"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","status":"online","status_checked_at":"2025-09-05T02:00:09.113Z","response_time":402,"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":[],"created_at":"2025-06-05T10:30:50.781Z","updated_at":"2025-09-05T02:04:57.217Z","avatar_url":"https://github.com/bmwcarit.png","language":"Java","funding_links":[],"categories":["人工智能"],"sub_categories":["机器学习"],"readme":"# Overview\n\nThis library implements Hidden Markov Models (HMM) for time-inhomogeneous Markov processes.\nThis means that, in contrast to many other HMM implementations, there can be different\nstates and a different transition matrix at each time step.\n\nThis library provides an implementation of\n* The Viterbi algorithm, which computes the most likely sequence of states.\n* The forward-backward algorithm, which computes the probability of all state candidates given\nthe entire sequence of observations. This process is also called smoothing.\n\n# Applications\n\nThis library was initially created for HMM-based map matching according to the paper\n\"NEWSON, Paul; KRUMM, John. Hidden Markov map matching through noise and sparseness.\nIn: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic\ninformation systems. ACM, 2009. S. 336-343.\"\n\n[Graphhopper](https://graphhopper.com/) [map matching](https://github.com/graphhopper/map-matching)\nis now using the hmm-lib for matching GPS positions to OpenStreetMap maps. \n\nThe [offline-map-matching](https://github.com/bmwcarit/offline-map-matching) project\ndemonstrates how to use the hmm-lib for map matching but does not provide integration to any\nparticular map.\n\nBesides map matching, the hmm-lib can also be used for other applications.\n\n# License\n\nThis library is licensed under the\n[Apache 2.0 license](http://www.apache.org/licenses/LICENSE-2.0.html).\n\n# Dependencies\n\nExcept for testing, there are no dependencies to other libraries.\n\n# Maven\n\nTo use this library, add the following to your pom.xml:\n\n```\n\u003cdependency\u003e\n  \u003cgroupId\u003ecom.bmw.hmm\u003c/groupId\u003e\n  \u003cartifactId\u003ehmm-lib\u003c/artifactId\u003e\n  \u003cversion\u003e1.1.0-SNAPSHOT\u003c/version\u003e\n\u003c/dependency\u003e\n```\n\nIf you want to use snapshots, add\n```\n\u003crepositories\u003e\n  ...\n  \u003crepository\u003e\n    \u003cid\u003ehmm-lib-snapshots\u003c/id\u003e\n    \u003curl\u003ehttps://raw.github.com/bmwcarit/hmm-lib/mvn-snapshots/\u003c/url\u003e\n    \u003csnapshots\u003e\n      \u003cenabled\u003etrue\u003c/enabled\u003e\n      \u003cupdatePolicy\u003ealways\u003c/updatePolicy\u003e\n    \u003c/snapshots\u003e\n  \u003c/repository\u003e\n\u003c/repositories\u003e\n```\n\n# Contribute\nContributions are welcome! For bug reports, please create an issue. \nFor code contributions (e.g. new features or bugfixes), please create a pull request.\n\n# Changes\n* 1.1.0:\n  * Add forward-backward algorithm, which performs smoothing on the hidden state variables.\n  * The Viterbi algorithm now optionally returns smoothing probabilities for the states of the\n    most likely sequence.   \n* 1.0.0:\n  * API redesign to allow calling the Viterbi algorithm iteratively. This gives the library user\n   increased flexibility and optimization opportunities when computing transition and observation\n   probabilities. Moreover, the new API enables better handling of HMM breaks.\n  * Add support for transition descriptors. For map matching, this allows retrieving the paths\n   between matched positions (the entire matched route) after computing the most likely sequence.\n  *  Reduce memory footprint from O(t\\*n²) to O(t\\*n) or even O(t) in many applications, where t is\n    the number of  time steps and n is the number of candidates per time step. \n* 0.2.0: Extend HmmProbabilities interface to include the observation\n* 0.1.0: Initial release\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbmwcarit%2Fhmm-lib","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbmwcarit%2Fhmm-lib","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbmwcarit%2Fhmm-lib/lists"}