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Gaussian Mixture Regression

Project description

Travis

Gaussian Mixture Models (GMMs) for clustering and regression in Python.

Source code repository: https://github.com/AlexanderFabisch/gmr

https://raw.githubusercontent.com/AlexanderFabisch/gmr/master/gmr.png

Example

Estimate GMM from samples and sample from GMM:

from gmr import GMM

gmm = GMM(n_components=3, random_state=random_state)
gmm.from_samples(X)
X_sampled = gmm.sample(100)

For more details, see:

help(gmr)

Installation

Install from PyPI:

sudo pip install gmr

or from source:

sudo python setup.py install

How Does It Compare to scikit-learn?

There is an implementation of Gaussian Mixture Models for clustering in scikit-learn as well. Regression could not be easily integrated in the interface of sklearn. That is the reason why I put the code in a separate repository. It is possible to initialize GMR from sklearn though:

from sklearn.mixture import GaussianMixture
from gmr import GMM
gmm_sklearn = GaussianMixture(n_components=3, covariance_type="diag")
gmm_sklearn.fit(X)
gmm = GMM(
    n_components=3, priors=gmm_sklearn.weights_, means=gmm_sklearn.means_,
    covariances=np.array([np.diag(c) for c in gmm_sklearn.covariances_]))

Original Publication(s)

The first publication that presents the GMR algorithm is

  1. Ghahramani, M. I. Jordan, “Supervised learning from incomplete data via an EM approach,” Advances in Neural Information Processing Systems 6, 1994, pp. 120-127, http://papers.nips.cc/paper/767-supervised-learning-from-incomplete-data-via-an-em-approach

but it does not use the term Gaussian Mixture Regression, which to my knowledge occurs first in

  1. Calinon, F. Guenter and A. Billard, “On Learning, Representing, and Generalizing a Task in a Humanoid Robot,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 2, 2007, pp. 286-298, doi: 10.1109/TSMCB.2006.886952.

A recent survey on various regression models including GMR is the following:

  1. Stulp, O. Sigaud, “Many regression algorithms, one unified model: A review,” in Neural Networks, vol. 69, 2015, pp. 60-79, doi: 10.1016/j.neunet.2015.05.005.

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Files for gmr, version 1.3
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