Mallet Extension

In Mallet package, it only contains two topic Models--LDA and Hierachical LDA. So I tried to implement some useful topic modeling methods on it.
Model: * Hierarchical Dirichlet Process with Gibbs Sampling. (in HDP folder) * Inference part for hLDA. (in hLDA folder) Usage: 1. This is an extension for Mallet, so you need to have Mallet's source code first. 2. put HDP.java, HDPInferencer.java and HierarchicalLDAInferencer.java in src/cc/mallet/topics folder. 3. If you are going to run HDP, make sure you include knowceans package in your project. 4. run HDPTest.java or hLDATest.java will give you a demo for a small dataset in data folder. References: * Mallet * knowceans * HDP paper * "Implementing the HDP with minimum code complexity", Gregor Heinrich

Scikit-learn Extension

Note: This extension is merged in scikit-learn 0.17 version. Model: * online LDA with variational inference. (In LDA folder) Usage: 1. Make sure numpy, scipy, and scikit-learn are installed. 2. run python test in lda folder for unit test 3. The onlineLDA model is in lda.py. 4. For a quick exmaple, runpython ldaexample.py online will fit a 10 topics model with 20 NewsGroup dataset. online means we use online update(or partialfit method). Change online to batch will fit the model with batch update(or fit method). Reference: * Scikit-learn * onlineLDA * "Online Learning for Latent Dirichlet Allocation", Matthew D. Hoffman, David M. Blei, Francis Bach Others: * Another HDP implementation can be found it my bnp repository. It also follows scikit-learn API and is optimized with cython.