Skip to main content

Dynamic topic models

Project description

horizont implements a number of topic models. Conventions from scikit-learn are followed.

The following models are implemented using Gibbs sampling.

  • Latent Dirichlet allocation (Blei et al., 2003; Pritchard et al., 2000)
  • (Coming soon) Logistic normal topic model
  • (Coming soon) Dynamic topic model (Blei and Lafferty, 2006)

Getting started

horizont.LDA implements latent Dirichlet allocation (LDA) using Gibbs sampling. The interface follows conventions in scikit-learn.

>>> import numpy as np
>>> from horizont import LDA
>>> X = np.array([[1,1], [2, 1], [3, 1], [4, 1], [5, 8], [6, 1]])
>>> model = LDA(n_topics=2, random_state=0, n_iter=100)
>>> doc_topic = model.fit_transform(X)  # estimate of document-topic distributions
>>> model.components_  # estimate of topic-word distributions


Python 2.7 or Python 3.3+ is required. The following packages are also required:

GSL is required for random number generation inside the Pólya-Gamma random variate generator. On Debian-based sytems, GSL may be installed with the command sudo apt-get install libgsl0-dev. horizont looks for GSL headers and libraries in /usr/include and /usr/lib/ respectively.

Cython is needed if compiling from source.


horizont is licensed under Version 3.0 of the GNU General Public License. See LICENSE file for a text of the license or visit

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

horizont-0.0.5.tar.gz (1.3 MB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page