Thanks, I was working on something similar, and this is very interesting, and much nicer written than mine. Although I haven't read all of it yet (it is quite dense), just comparing briefly to the Scikit-Learn implementation, (see: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/mixture/gaussian_mixture.py ) they use several possible covariance types spherical, full, diagonal, tied, I think based on this paper: https://www.cs.ubc.ca/~murphyk/Papers/learncg.pdf It seems your implementation is using only spherical type.
Also, I am wondering why you tested on simulated data - taken from normal distributions. Have you tried testing on real world datasets? Thanks, Jon -------------------------------------------- On Fri, 1/12/18, Pierre-Edouard PORTIER <[email protected]> wrote: Subject: [Jprogramming] JGMM, Mixture Model in J To: [email protected] Date: Friday, January 12, 2018, 10:10 PM Mixture models in J: http://peportier.me/blog/201801_JGMM/ <http://peportier.me/blog/201801_JGMM/> Enjoy your weekend, Pierre-Edouard ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm
