# Latent Dirichelet Allocation with Gibbs sampling

An LDA with Gibbs sampling implementation based on
"Parameter estimation for text analysis" by Gregor Heinrich.

## Latent Dirichelet Allocation with Gibbs sampling

An LDA with Gibbs sampling implementation based on
"Parameter estimation for text analysis" by Gregor Heinrich.

Copyright 2013 MIT Center for Civic Media
BSD 3-clause license, see LICENSE for info

## Dependencies

This library requires the following packages:
* numpy
* scipy

## How to use

For a detailed example see demos/demoldaem.py

First create a numpy array representing a term-document matrix with documents on dimension 0:

    corpus = numpy.array([[1,2,3], [4,5,6], [8,9,0], [9,8,7]])
    
Then create an LDA model, specifying the number of topics.  You can also specify the initial alpha (per-document Dirichlet parameter), eta (per-topic Dirichlet paramter).

    num_topics = 5
    alpha = 0.1
    eta = 0.1
    model = lda.LdaModel(corpus, num_topics, alpha, eta)
    
Then run a numer of EM iterations.

    model.em_iterate(25)

You can the query, for example, the expected per-topic word distributions (beta).

    beta = model.beta()
    print "Word distribution for topic 0: %s" % beta[0]

Add dependencies and usage to readme.
Add dependencies and usage to readme.