Metadata-Version: 2.1
Name: hlda
Version: 0.3
Summary: Gibbs sampler for the Hierarchical Latent Dirichlet Allocation topic model. This is based on the hLDA implementation from Mallet, having a fixed depth on the nCRP tree.
Home-page: https://github.com/joewandy/hlda
Author: Joe Wandy
Author-email: joe.wandy@glasgow.ac.uk
License: UNKNOWN
Description: Hierarchical Latent Dirichlet Allocation
        ----------------------------------------
        
        Hierarchical Latent Dirichlet Allocation (hLDA) addresses the problem of learning topic hierarchies from data. The model relies on a non-parametric prior called the nested Chinese restaurant process, which allows for arbitrarily large branching factors and readily accommodates growing
        data collections. The hLDA model combines this prior with a likelihood that is based on a hierarchical variant of latent Dirichlet allocation.
        
        [Hierarchical Topic Models and the Nested Chinese Restaurant Process](http://www.cs.columbia.edu/~blei/papers/BleiGriffithsJordanTenenbaum2003.pdf)
        
        [The Nested Chinese Restaurant Process and Bayesian Nonparametric Inference of Topic Hierarchies](http://cocosci.berkeley.edu/tom/papers/ncrp.pdf)
        
        Implementation
        --------------
        
        - [hlda/sampler.py](hlda/sampler.py) is the Gibbs sampler for hLDA inference, based on the implementation from [Mallet](http://mallet.cs.umass.edu/topics.php) having a fixed depth on the nCRP tree.
        
        
        Installation
        ------------
        
        - Simply use `pip install hlda` to install the package.
        - An example notebook that infers the hierarchical topics on the BBC Insight corpus can be found in [notebooks/bbc_test.ipynb](notebooks/bbc_test.ipynb).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
