Description:

This library implements the following algorithms and functions in C++ with python (2.7 and 3) and C++ interfaces:

• The variational Dirichlet process (VDP)
• The Bayesian Gaussian mixture model (BGMM)
• Gaussian latent Dirichlet allocation (LDA with Gaussian observations)
• Simultaneous Clustering Model (SCM) for Multinomial Documents, and Gaussian Observations
• Multiple-source Clustering Model (MCM) for clustering two observations, one of an image/document, and multiple of segments/words simultaneously
• And more clustering algorithms based on diagonal Gaussian, and Exponential distributions
• It is a template-based library, and so new algorithms can be defined by creating new distribution objects
• Various functions for evaluating means, standard deviations, covariance, primary Eigenvalues etc of data

Changes to previous version:

New maximum cluster argument for all algorithms. Also no more matlab interface since it seemed no one was using it, and I cannot support it any longer.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Mac

Data Formats: Numpy, Any Format Supported By Matlab, Eigen Matrix

Tags: Clustering, Gmm, Density Estimation, Approximate Inference, Mean Field, Gaussian Mixture Models, Latent Dirichlet Allocation, Hierarchical Models, Bayesian, Latent Variable Model, Mixture Model, Multi

Archive: download here

Other available revisons

Version	Changelog	Date
2.3	New maximum cluster argument for all algorithms. Also no more matlab interface since it seemed no one was using it, and I cannot support it any longer.	February 27, 2016, 00:36:01
2.2	Python 2 & 3 interface fixes and minor updates. Now uses Travis-CI as well.	January 24, 2016, 03:32:49
2.1	Initial Announcement on mloss.org.	October 31, 2014, 23:27:57

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