{"id":13697230,"url":"https://github.com/davidandrzej/cvbLDA","last_synced_at":"2025-05-03T19:32:58.031Z","repository":{"id":1793468,"uuid":"2717403","full_name":"davidandrzej/cvbLDA","owner":"davidandrzej","description":"Collapsed variational Bayesian inference for LDA","archived":false,"fork":false,"pushed_at":"2011-11-05T21:39:18.000Z","size":100,"stargazers_count":21,"open_issues_count":0,"forks_count":5,"subscribers_count":4,"default_branch":"master","last_synced_at":"2024-08-03T18:21:51.370Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"jaredhanson/passport-openid","license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/davidandrzej.png","metadata":{"files":{"readme":"README","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}},"created_at":"2011-11-05T21:35:56.000Z","updated_at":"2023-06-03T09:11:50.000Z","dependencies_parsed_at":"2022-08-20T04:51:02.042Z","dependency_job_id":null,"html_url":"https://github.com/davidandrzej/cvbLDA","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/davidandrzej%2FcvbLDA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/davidandrzej%2FcvbLDA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/davidandrzej%2FcvbLDA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/davidandrzej%2FcvbLDA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/davidandrzej","download_url":"https://codeload.github.com/davidandrzej/cvbLDA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224373641,"owners_count":17300534,"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":"2024-08-02T18:00:54.250Z","updated_at":"2024-11-13T01:30:22.944Z","avatar_url":"https://github.com/davidandrzej.png","language":"C","funding_links":[],"categories":["Research Implementations"],"sub_categories":["Embedding based Topic Models"],"readme":"COLLAPSED VARIATIONAL BAYESIAN INFERENCE FOR\nLATENT DIRICHLET ALLOCATION (CVB-LDA)\nVersion 0.1\n\nDavid Andrzejewski (andrzeje@cs.wisc.edu)\nDepartment of Computer Sciences\nUniversity of Wisconsin-Madison, USA\n\nThis software implements Collapsed Variational Bayesian (CVB)\ninference [1] for the LDA model [2] of discrete count data.  The code\nis based on the Teh et al paper [1], but also uses some practical\nimplementation details kindly provided by the authors on the extremely\nhelpful topic-models mailing list:\n\nhttps://lists.cs.princeton.edu/mailman/listinfo/topic-models\n\nThe code implements the collapsed variational inference for LDA as a\nPython C extension module.\n\n\nDISCLAIMER\n\nImportantly, I wrote this code as an educational exercise for *my own\nbenefit only*. This software has no connection with [1] or its authors\nwhatsoever. Any errors contained herein are my own.\n\n\nBUILD/INSTALL\n\nBuilding this module requires installation of Python and NumPy, as\nwell as a C compiler.  From the command-line, do:\n\n% python setup.py install\n\n(Note that if NumPy or Python are installed to non-standard locations,\nyou may need to make the appropriate changes in setup.py)\n\nYou can then test the installation with:\n\n% python test/testCvbLDA.py -v\n\nThere is also a simple example scipt showing how to use the software:\n\n% python example.py\n\n\nLOCAL INSTALL\n\nIf you do not have write access to your Python installation directories,\nyou will need to tell setup.py to install this module somewhere else.\nFor example:\n\n% python setup.py install --prefix=~/local\n\nwill install the module under a subdirectory of your home directory called \n\"local\".\n\nIt may then be necessary to let Python know where that is by setting\nthe PYTHONPATH environment variable (e.g., in .bashrc or .cshrc).  For\nour example this might involve adding something like the line:\n\nsetenv PYTHONPATH ~/local/lib/python2.5/site-packages\n\n\nHOW TO USE\n\nThe commenting in the example.py script explains the meanings and\ntypes of all input and return arguments.  For more detailed examples\nof good/bad inputs, you can examine the tests in test/testCvbLDA.py.\n\n\nLICENSE\n\nThis software is open-source, released under the terms of the GNU\nGeneral Public License version 3, or any later version of the GPL (see\nLICENSE.txt).\n\n\nREFERENCES\n\n[1] \nA Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation \nTeh Y.W., Newman D., and Welling, M.\nAdvances in Neural Information Processing Systems (NIPS) 19, 2007.\n\n[2] \nLatent Dirichlet Allocation\nBlei, D. M., Ng, A. Y., and Jordan, M. I. \nJournal of Machine Learning Research (JMLR), 3, Mar. 2003, 993-1022.\n\n\nVERSION HISTORY\n0.1     Initial release\n0.1.1   Return (phi,theta,gamma) instead of just (phi,theta)\n        gamma is a length D List of T x Md NumPy arrays\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdavidandrzej%2FcvbLDA","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdavidandrzej%2FcvbLDA","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdavidandrzej%2FcvbLDA/lists"}