blei-lab/lda-c

Stars
166
Rank 227,748 (Top 5 %)
Language
C
License
GNU Lesser Genera...
Created about 10 years ago
Updated over 8 years ago

blei-lab/lda-c

blei-lab

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

This is a C implementation of variational EM for latent Dirichlet allocation (LDA), a topic model for text or other discrete data.

Latent Dirichlet allocation

This is a C implementation of variational EM for latent Dirichlet allocation (LDA), a topic model for text or other discrete data. LDA allows you to analyze of corpus, and extract the topics that combined to form its documents. For example, click here to see the topics estimated from a small corpus of Associated Press documents. LDA is fully described in Blei et al. (2003).

This code contains:

an implementation of variational inference for the per-document topic proportions and per-word topic assignments
a variational EM procedure for estimating the topics and exchangeable Dirichlet hyperparameter

Readme

View the readme.txt and fork or clone the repository.

Sample data

2246 documents from the Associated Press download.

Top 20 words from 100 topics estimated from the AP corpus pdf.

Bug fixes and updates

To learn about bug-fixes, updates, and discuss LDA and related techniques, please join the topic-models mailing list, topic-models [at] lists.cs.princeton.edu.

To join, click here.

Other implementations on the web

There are several other implementations of LDA on the web:

edward

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

Jupyter Notebook

onlineldavb

Online variational Bayes for latent Dirichlet allocation (LDA)

dtm

This implements topics that change over time (Dynamic Topic Models) and a model of how individual documents predict that change.

hdp

Hierarchical Dirichlet processes. Topic models where the data determine the number of topics. This implements Gibbs sampling.

ctr

Collaborative modeling for recommendation. Implements variational inference for a collaborative topic models. These models recommend items to users based on item content and other users' ratings.

online-hdp

Online inference for the Hierarchical Dirichlet Process. Fits hierarchical Dirichlet process topic models to massive data. The algorithm determines the number of topics.

causal-text-embeddings

Software and data for "Using Text Embeddings for Causal Inference"

deconfounder_tutorial

Jupyter Notebook

hlda

This implements hierarchical latent Dirichlet allocation, a topic model that finds a hierarchy of topics. The structure of the hierarchy is determined by the data.

publications

The pdf and LaTeX for each paper (and sometimes the code and data used to generate the figures).

class-slda

Implements supervised topic models with a categorical response.

variational-smc

Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)

deep-exponential-families

Deep exponential families (DEFs)

DynamicPoissonFactorization

Dynamic version of Poisson Factorization (dPF). dPF captures the changing interest of users and the evolution of items over time according to user-item ratings.

turbotopics

Turbo topics find significant multiword phrases in topics.

ars-reparameterization

Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)

Jupyter Notebook

zero-inflated-embedding

Code for the icml paper "zero inflated exponential family embedding"

context-selection-embedding

Context Selection for Embedding Models

ctm-c

This implements variational inference for the correlated topic model.

deconfounder_public

Jupyter Notebook

treeffuser

Treeffuser is an easy-to-use package for probabilistic prediction on tabular data with tree-based diffusion models.

Jupyter Notebook

factorial-network-models

Discussion of Durante et al for JSM 2017. Includes factorial network model generalization.

Jupyter Notebook

markovian-score-climbing

diln

This implements the discrete infinite logistic normal, a Bayesian nonparametric topic model that finds correlated topics.

poisson-influence-factorization

Jupyter Notebook

Riken_tutorial

Jupyter Notebook

circuitry