Andy,
Thanks for your quick reply. Yes, throwing some type of warning would
probably be good while the code is being revamped.
Best,
Aron
> On 10/18/2012 06:57 PM, Andreas Mueller wrote:
>
> 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 list
> > Scikit-learn-general@...
> > 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
