All, This means that all gmm related implementation are not correct ?
i checked and analyze the gaussiamhmm it looks good so far. regarding the gmmhmm , do we have an issue related to the aron message ? Didier --- Original Message --- From: Andreas Mueller <amuel...@ais.uni-bonn.de> Sent: October 18, 2012 10/18/12 To: scikit-learn-general@lists.sourceforge.net Subject: Re: [Scikit-learn-general] sklearn.mixture.DPGMM: Unexpected results Hi Aron. I think this might be an instance of this bug: https://github.com/scikit-learn/scikit-learn/issues/393 Unfortunately this part of the scikit is in a very bad state. Sorry for making you wonder. I have been thinking about putting in a user warning earlier today. What do others think? This seems to be a serious issue that has been around for way to long! Best, Andy On 10/18/2012 06:57 PM, Aron Culotta wrote: The results I get from DPGMM are not what I expect. E.g.: >>> import sklearn.mixture >>> sklearn.__version__ '0.12-git' >>> data = >>> [[1.1],[0.9],[1.0],[1.2],[1.0], [6.0],[6.1],[6.1]] >>> m = >>> sklearn.mixture.DPGMM(n_components=5, n_iter=1000, alpha=1) >>> >>> m.fit(data) DPGMM(alpha=1, covariance_type='diag', init_params='wmc', >>> min_covar=None, n_components=5, n_iter=1000, params='wmc', >>> random_state=<mtrand.RandomState object at 0x108a3f168>, thresh=0.01, >>> verbose=False) >>> m.converged_ True >>> m.weights_ array([ 0.2, 0.2, 0.2, >>> 0.2, 0.2]) >>> m.means_ array([[ 0.62019109], [ 1.16867356], [ >>> 0.55713292], [ 0.36860511], [ 0.17886128]]) I expected the result to be >>> more similar to the vanilla GMM; that is, two gaussians (around values 1 >>> and 6), with non-uniform weights (like [ 0.625, 0.375]). I expected the >>> "unused" gaussians to have weights near zero. Am I using the model incorrectly? I've also tried changing alpha without any luck. I've also tried a different data in a smaller range with no luck: [[0.1], [0.2], [0.15], [0.112], [0.13], [0.8], [0.85], [0.79]] Thanks, Aron ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today:http://p.sf.net/sfu/appdyn_sfd2d_oct _______________________________________________ Scikit-learn-general mailing listScikit-learn-general@lists.sourceforge.nethttps://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_sfd2d_oct _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
