https://github.com/conradsnicta/armadillo-gmm

gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation
https://github.com/conradsnicta/armadillo-gmm

armadillo clustering clustering-algorithm cpp em-algorithm expectation-maximization gaussian-mixture-models gmm k-means k-means-clustering machine-learning mapreduce openmp statistics

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gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation

• Host: GitHub
• URL: https://github.com/conradsnicta/armadillo-gmm
• Owner: conradsnicta
• License: apache-2.0
• Created: 2017-08-02T08:44:12.000Z (over 7 years ago)
• Default Branch: main
• Last Pushed: 2022-07-04T03:44:44.000Z (over 2 years ago)
• Last Synced: 2023-07-19T14:45:38.208Z (over 1 year ago)
• Topics: armadillo, clustering, clustering-algorithm, cpp, em-algorithm, expectation-maximization, gaussian-mixture-models, gmm, k-means, k-means-clustering, machine-learning, mapreduce, openmp, statistics
• Language: C++
• Homepage:
• Size: 1000 Bytes
• Stars: 10
• Watchers: 2
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation

[![status](http://joss.theoj.org/papers/c7f0d840fec6ea426c07cf4a83f964f6/status.svg)](http://joss.theoj.org/papers/c7f0d840fec6ea426c07cf4a83f964f6)

* The *gmm_diag* and *gmm_full* classes are included in recent versions of the [Armadillo C++ library](https://gitlab.com/conradsnicta/armadillo-code).

* Documentation for the *gmm_diag* and *gmm_full* classes is available online: [http://arma.sourceforge.net/docs.html#gmm_diag](http://arma.sourceforge.net/docs.html#gmm_diag).

* The *gmm_diag* and *gmm_full* classes provide multi-threaded (parallelised) implementations of Gaussian mixture models (GMMs) and the associated k-means and Expectation-Maximisation (EM) training algorithms.

* The *gmm_diag* class is specifically tailored for diagonal covariance matrices (all entries outside the main diagonal in each covariance matrix are assumed to be zero), while the *gmm_full* class is tailored for full covariance matrices. The *gmm_diag* class is typically much faster to train and use than the *gmm_full* class, at the potential cost of some reduction in modelling accuracy.

* The interface for the *gmm_diag* and *gmm_full* classes allows the user full control over the parameters for GMM fitting, as well as easy and flexible access to the trained model. Specifically, the two classes contain functions for likelihood evaluation, vector quantisation, histogram generation, data synthesis, and parameter modification, in addition to training (learning) the GMM parameters via the EM algorithm. The classes use several techniques to improve numerical stability and promote convergence of EM based training, such as keeping as much as possible of the internal computations in the log domain, and ensuring the covariance matrices stay positive-definite.

* To achieve multi-threading, the k-means and EM training algorithms have been reformulated into a MapReduce-like framework and implemented with the aid of OpenMP pragma directives. As such, the EM algorithm runs much quicker on multi-core machines when OpenMP is enabled during compilation (use the ```-fopenmp``` option in GCC and clang compilers).