{"id":13697190,"url":"https://github.com/blei-lab/onlineldavb","last_synced_at":"2025-04-07T17:07:44.870Z","repository":{"id":22687703,"uuid":"26031456","full_name":"blei-lab/onlineldavb","owner":"blei-lab","description":"Online variational Bayes for latent Dirichlet allocation (LDA)","archived":false,"fork":false,"pushed_at":"2021-05-21T15:20:53.000Z","size":51,"stargazers_count":304,"open_issues_count":4,"forks_count":101,"subscribers_count":50,"default_branch":"master","last_synced_at":"2025-03-31T16:21:42.377Z","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":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/blei-lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"COPYING","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2014-10-31T20:06:27.000Z","updated_at":"2025-03-01T23:44:29.000Z","dependencies_parsed_at":"2022-08-17T16:30:53.333Z","dependency_job_id":null,"html_url":"https://github.com/blei-lab/onlineldavb","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/blei-lab%2Fonlineldavb","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/blei-lab%2Fonlineldavb/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/blei-lab%2Fonlineldavb/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/blei-lab%2Fonlineldavb/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/blei-lab","download_url":"https://codeload.github.com/blei-lab/onlineldavb/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247694875,"owners_count":20980733,"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":"2024-08-02T18:00:53.749Z","updated_at":"2025-04-07T17:07:44.853Z","avatar_url":"https://github.com/blei-lab.png","language":"Python","funding_links":[],"categories":["Research Implementations"],"sub_categories":["Embedding based Topic Models"],"readme":"ONLINE VARIATIONAL BAYES FOR LATENT DIRICHLET ALLOCATION\n\nMatthew D. Hoffman\nmdhoffma@cs.princeton.edu\n\n(C) Copyright 2010, Matthew D. Hoffman\n\nThis is free software, you can redistribute it and/or modify it under\nthe terms of the GNU General Public License.\n\nThe GNU General Public License does not permit this software to be\nredistributed in proprietary programs.\n\nThis software is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n\nYou should have received a copy of the GNU General Public License\nalong with this program; if not, write to the Free Software\nFoundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307\nUSA\n\n------------------------------------------------------------------------\n\nThis Python code implements the online Variational Bayes (VB)\nalgorithm presented in the paper \"Online Learning for Latent Dirichlet\nAllocation\" by Matthew D. Hoffman, David M. Blei, and Francis Bach,\nto be presented at NIPS 2010.\n\nThe algorithm uses stochastic optimization to maximize the variational\nobjective function for the Latent Dirichlet Allocation (LDA) topic model.\nIt only looks at a subset of the total corpus of documents each\niteration, and thereby is able to find a locally optimal setting of\nthe variational posterior over the topics more quickly than a batch\nVB algorithm could for large corpora.\n\n\nFiles provided:\n* onlineldavb.py: A package of functions for fitting LDA using stochastic\n    optimization.\n* onlinewikipedia.py: An example Python script that uses the functions in\n    onlineldavb.py to fit a set of topics to the documents in Wikipedia.\n* wikirandom.py: A package of functions for downloading randomly chosen\n    Wikipedia articles.\n* printtopics.py: A Python script that displays the topics fit using the\n    functions in onlineldavb.py.\n* dictnostops.txt: A vocabulary of English words with the stop words removed.\n* readme.txt: This file.\n* COPYING: A copy of the GNU public license version 3.\n\nYou will need to have the numpy and scipy packages installed somewhere\nthat Python can find them to use these scripts.\n\n\nExample:\npython onlinewikipedia.py 101\npython printtopics.py dictnostops.txt lambda-100.dat\n\nThis would run the algorithm for 101 iterations, and display the\n(expected value under the variational posterior of the) topics fit by\nthe algorithm. (Note that the algorithm will not have fully converged\nafter 101 iterations---this is just to give an idea of how to use the\ncode.)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblei-lab%2Fonlineldavb","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fblei-lab%2Fonlineldavb","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblei-lab%2Fonlineldavb/lists"}