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)
Logistic Normal topic model
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)
Requirements
Python 2.7 or Python 3.3+ is required. The following packages are also required:
futures (Python 2.7 only)
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.
Important links
Home page: https://github.com/ariddell/horizont/
Documentation: http://horizont.readthedocs.org
Repository: https://github.com/ariddell/horizont.git
License
horizont is licensed under Version 3.0 of the GNU General Public License. See LICENSE file for a text of the license or visit http://www.gnu.org/copyleft/gpl.html.
Project details
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