This is faster version of Python hlda library. Due to some optimisations it can be up to 2 times faster.

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

The Nested Chinese Restaurant Process and Bayesian Nonparametric Inference of Topic Hierarchies

• sampler.py is the Gibbs sampler for hLDA inference, based on the implementation from Python hlda library having a fixed depth on the nCRP tree.

• See the original repository for examples of using this library.