onlineldavb | Online variational Bayes for latent Dirichlet allocation

by blei-lab Python Version: Current License: GPL-3.0

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kandi X-RAY | onlineldavb Summary

onlineldavb is a Python library. onlineldavb has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However onlineldavb build file is not available. You can download it from GitHub.

online variational bayes for latent dirichlet allocation. matthew d. hoffman mdhoffma@cs.princeton.edu. (c) copyright 2010, matthew d. hoffman. this is free software, you can redistribute it and/or modify it under the terms of the gnu general public license. the gnu general public license does not permit this software to be redistributed in proprietary programs. this software is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. you should have received a copy of the gnu general public license along with this program; if not, write to the free software foundation, inc., 59 temple place, suite 330, boston, ma 02111-1307 usa. this python code implements the online variational bayes (vb) algorithm presented in the paper "online learning for latent dirichlet allocation" by matthew d. hoffman, david m. blei, and francis

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onlineldavb has a low active ecosystem.

It has 281 star(s) with 101 fork(s). There are 48 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 597 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of onlineldavb is current.

Quality

onlineldavb has 0 bugs and 54 code smells.

Security

onlineldavb has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

onlineldavb code analysis shows 0 unresolved vulnerabilities.

There are 4 security hotspots that need review.

License

onlineldavb is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

onlineldavb releases are not available. You will need to build from source code and install.

onlineldavb has no build file. You will be need to create the build yourself to build the component from source.

onlineldavb saves you 164 person hours of effort in developing the same functionality from scratch.

It has 407 lines of code, 22 functions and 5 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed onlineldavb and discovered the below as its top functions. This is intended to give you an instant insight into onlineldavb implemented functionality, and help decide if they suit your requirements.

Get random Wikipedia article .
Compute the approximation between two documents .
Parse a list of document ids .
Read data from file .
Read data from a stream .
Main function .
Generate random Wikipedia articles .
Parse a line of text .
Counts the number of tokens in a file .
r Dirichlet expectation .

Get all kandi verified functions for this library.

onlineldavb Key Features

No Key Features are available at this moment for onlineldavb.

onlineldavb Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install onlineldavb

You can download it from GitHub.
You can use onlineldavb like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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