Skip to content
forked from ZiyangS/IGMM

infinite Gaussian Mixture Model(Dirichlet Process Gaussian Mixture Model) by gibbs sampling. Multivaraite Gaussian with full covariance and diagonal covariance.

Notifications You must be signed in to change notification settings

stephenjlee/IGMM

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

16 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

IGMM

infinite Gaussian Mixture Model(Dirichlet Process Gaussian Mixture Model) by gibbs sampling.

Multivaraite Gaussian Distribution with full covariance and diagonal covariance matrix. Implementing IGMM with full covariance will use gibbs sampling. If there is diagonal covariance matrix, it can become the product of lots of Univariate Guassian distribution. In this condition, I update algorithms and change the formulas about Sj which are eq 6 - 9 in (Rasmussen 2000).

Command line: implementing by full covariance(if not assign args, default is full) : python main.py -i datasets/MVN_3components_full_cov.csv

implementing by full covariance : python main.py -c diagonal -i datasets/MVN_4components_diagonal_cov.csv

implementing by diagonal covariance : python main.py -c diagonal -i datasets/MVN_4components_diagonal_cov.csv

About

infinite Gaussian Mixture Model(Dirichlet Process Gaussian Mixture Model) by gibbs sampling. Multivaraite Gaussian with full covariance and diagonal covariance.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%