This Python code (based on the implementation of ONLINE VARIATIONAL BAYES FOR LATENT DIRICHLET ALLOCATION by Matthew D. Hoffman) uses 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 Bach.

The algorithm uses stochastic optimization to maximize the variational objective function for the Latent Dirichlet Allocation (LDA) topic model. It only looks at a subset of the total corpus of documents (namely, text files whose content has been extracted from Web APIs documentation archives) each iteration, and thereby is able to find a locally optimal setting of the variational posterior over the topics more quickly than a batch VB algorithm could for large corpora.

##Files provided:

• onlineldavb.py: A package of functions for fitting LDA using stochastic optimization (by Matthew D. Hoffman).
• online_lda_wsdl.py: A Python script which uses online VB for LDA to analyze a bunch of text files whose content has been extracted from SOAP API descriptors (WSDLs). The script is based on Matthew D. Hoffman's 'onlinewikipedia.py'.
• printtopics.py: A Python script that displays the topics fit using the functions in onlineldavb.py and stores them as a text file in 'outcome/topics.txt' (based on the 'printtopics.py' script by Matthew D. Hoffman).
• printtopicdistributions.py: A Python script that displays the per-document topic distributions and stores them as a text file in 'outcome/per-document-topics.txt' (based on the 'printtopics.py' script by Matthew D. Hoffman).
• dictnostops.txt: A vocabulary of English words with the stop words removed.
• plot_topic_distributions.m: A simple octave script for displaying the per-document topic distribution of 12 text files analized by the algorithm (the files are selected at random).
• onlinewikipedia.py and wikirandom.py original scripts by Matthew D. Hoffman.
• README.md: This file.

You will need to have the numpy and scipy packages installed somewhere that Python can find them to use these scripts.

##Example:

python online_lda_wsdl.py path/to/the/folder/of/textFiles 
python printtopics.py dictnostops.txt parameters/lambda-<#>.dat
python printtopicdistributions.py parameters/gamma-all.dat

This would run the algorithm for a number of iterations which depends on the size of the corpus, display the (expected value under the variational posterior of the) topics fit by the algorithm and the per-document-distributions.