Hi Didier,
I have found that the normal GMM learnt using EM is fine:
http://scikit-learn.org/stable/modules/mixture.html#gmm-classifier
It's the variational and Dirichlet versions that are incorrect!
Martin
On 18 October 2012 19:28, didier vila <[email protected]> wrote:
> 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 <[email protected]>
> Sent: October 18, 2012 10/18/12
> To: [email protected]
> 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
>
>
>
>
>
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