{"id":19863594,"url":"https://github.com/sandialabs/chmmpp","last_synced_at":"2025-02-28T23:16:20.793Z","repository":{"id":243094704,"uuid":"810939159","full_name":"sandialabs/chmmpp","owner":"sandialabs","description":"Analysis of multivariate time series data to detect patterns using a Hidden Markov Model (HMM).","archived":false,"fork":false,"pushed_at":"2024-07-22T22:29:38.000Z","size":449,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-01-11T15:24:26.217Z","etag":null,"topics":["scr-3025","snl-data-analysis","snl-science-libs"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sandialabs.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-06-05T16:28:11.000Z","updated_at":"2024-08-01T12:43:28.000Z","dependencies_parsed_at":"2024-07-08T20:28:07.335Z","dependency_job_id":"0f0ea36f-99e4-489f-a67f-249b0d2ef992","html_url":"https://github.com/sandialabs/chmmpp","commit_stats":null,"previous_names":["sandialabs/chmmpp"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fchmmpp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fchmmpp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fchmmpp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fchmmpp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sandialabs","download_url":"https://codeload.github.com/sandialabs/chmmpp/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241272627,"owners_count":19937091,"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":["scr-3025","snl-data-analysis","snl-science-libs"],"created_at":"2024-11-12T15:15:17.934Z","updated_at":"2025-02-28T23:16:20.765Z","avatar_url":"https://github.com/sandialabs.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# chmmpp\n\nThe chmmpp software supports the analysis of multivariate time series\ndata to detect patterns using a Hidden Markov Model (HMM).\n\nMany applications involve the detection and characterization of hidden or\nlatent states in a complex system, using observable states and variables.\nThe chmmpp software supports inference of latent states integrating\nboth (1) a HMM and (2) application-specific constraints that reflect\nknown relationships amongst hidden states. For example, HMMs have been\nwidely used in natural language processing to tag the part of speech\nof words in a sentence (e.g. noun, verb, adjective, etc.). But in many\napplications there are known relationships that need to be enforced,\nsuch as the fact that a simple English sentence must contain at least\none noun and exactly one verb.\n\nThe chmmpp software supports both application-specific and generic methods\nfor constrained inference.  This includes a framework for customized\nViterbi methods, constrained inference of hidden states with A-star and\ninteger programming methods, and various contraint-informed methods for\nlearning HMM model parameters. A focus of chmmpp is support for generic\nmethods that enable the agile expression of complex sets of constraints\nthat naturally arise in many real-world applications. Optimization\nconstraints can be expressed in chmmpp directly in C++ or using the\ncoek modeling framework. A variety of commercial and open source source\noptimization solvers can be used to ensure that maximum likelihood\nsolutions are found for hidden states.\n\n\n\n\n\n## Setup\n\nTo install the library create a directory called build, navigate to this directory, and run cmake ..\n\nA libary will be created in build/library and executables of the examples will be found in build/examples  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fchmmpp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsandialabs%2Fchmmpp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fchmmpp/lists"}