GMM clustering algorithm with equal weight and shared diagonal covariance

Question

I'm looking for a Gaussian mixture model clustering algorithm that would allow me to set equal component weights and shared diagonal covariances. I need to analyze a set of data and I don't have the time to try to write the code myself.

Answer 1

In python you can use scikit's GMM. It's easy to do, see the doc:

http://scikit-learn.sourceforge.net/dev/modules/generated/sklearn.mixture.GMM.html

Re your specific needs:

thegmm = GMM(cvtype='tied', params='mc')
thegmm.fit(mydata)

Meaning:

• shared diagonal covariances: use covariance_type='tied' in the constructor
• equal component weights: use params='mc' in the constructor (rather than the default 'wmc' which lets weights update).

Actually, I'm not sure if 'tied' implies diagonal covariances. It looks like you can choose 'tied' or 'diagonal' but not both, according to the doc. Anyone confirm?

Answer 2

Looks like the standard Matlab GMM tool will work, set the 'CovType' option to diagonal and the 'SharedCov' option to true