On Thu, Oct 18, 2012 at 1:57 PM, Aron Culotta <[email protected]> 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]]
Do I read this correctly that you are fitting a mixture with 5 components to 8 data points? Josef > > 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 list > [email protected] > https://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 [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
