Dear all, I published a new post <http://xuewei4d.github.io/2015/06/05/gsoc-week2-vbgmm-and-gmm-api.html> in my blog for the second week. Basically, I have done the derivation of VBGMM and working on cleaning GMM API.
Thanks, Wei On Thu, May 28, 2015 at 12:12 PM, Andreas Mueller <t3k...@gmail.com> wrote: > Hi Wei Xue. > > I think 1) sounds like a good idea. > For 3) I think we should deprecate params. Deprecating doesn't mean > changing users' behavior. It means giving them time to adjust. > > For 4) I am unsure. > > The bottom of the user guide here: > http://scikit-learn.org/dev/modules/mixture.html > has a link to the derivation here: > http://scikit-learn.org/dev/modules/dp-derivation.html > > Cheers, > Andy > > > > On 05/27/2015 07:08 PM, Wei Xue wrote: > > Hi Olivier, Loïc, Andreas and group, > > I have been thinking over the API convention for GMM. The discussion on > issue #2473 <https://github.com/scikit-learn/scikit-learn/issues/2473>, > #4062 <https://github.com/scikit-learn/scikit-learn/issues/4062> points > out the inconsistency on ``score_ sample``, ``score``. So I changed and > made a new API interface of some functions in the ipython notebook > <http://nbviewer.ipython.org/gist/xuewei4d/de5492d0320eed561b78/GMM_API.ipynb?flush_cache=true>. > In summary, > > 1) create a density mixin class, which contains ``score`` and > ``density``, > > 2) make ``score_sample`` return only the log probability of each data > instance, > > 3) I am not sure we should deprecate ``params='wmc'``. @Andreas pointed > out that ``params`` would cause strange estimation of GMM, but it is not > good to change users' behavior. > > 4) Rename GMM, VBGMM and DPGMM to GaussianMixture, VBGaussianMixture, > and DPGaussianMixture? (DirichletProcessGaussianMixture is quite lengthy) > So any comment? And do you like to discuss on a github issue or here? > > I don't quite understand how the current implementation of DPGMM and > VBGMM works now, couldn't find any doc about the current implementation of > DPGMM at all. But I have been working on derivation of VBGMM for a while, > and have written 4 pdf pages full of equations. I think there will be 10 > pages for all four kinds of covariance matrix. Upon I finish that, I will > upload it to my blog. > > > Thanks, > Wei Xue > > > > On Tue, May 19, 2015 at 11:07 AM, Andreas Mueller <t3k...@gmail.com> > wrote: > >> Hey Wei Xue. >> Thanks for posting the blog post! >> I think you are right, for diag and tied you can just use gamma >> distributions, which makes everything easier. >> Oliver and Loic, it would be great if you found the time to comment on >> the blog-post and future direction! >> >> Thanks! >> Andy >> >> >> On 05/18/2015 04:04 PM, Wei Xue wrote: >> >> Dear Olivier, Loic and group, >> >> I feel very excited to be selected as a GSoC student this year. Thank >> you very much. >> >> Following the timeline in my proposal, I have published the first post >> <http://xuewei4d.github.io/gsoc/2015/05/08/gsoc-prelude.html> >> introducing this project i.e., 'Improve GMM module'. >> >> My first step is to derive the updating functions for VBGMM for four >> types of covariance matrix, namely, sphere, diag, tied, and full. Following >> PRML chapter 10 variational inference, I have verified the updating >> functions 10.60-10.67 using Gaussian-Wishart distribution as an >> approximation distribution. The derivation involving Wishart distribution >> is cumbersome. :| >> >> I am currently trying to get equations for other three types of >> covariance types, 'sphere', 'diag', 'tied' in VBGMM. After digging into the >> Wishart distribution, I think for 'full' covariance, the approximate >> distribution is Gaussian-Wishart distribution, but for 'sphere' and 'diag' >> covariance, it is not. In this case, the multivariate Gaussian distribution >> could be decomposed into the production of several univariate Gaussian >> distribution. Therefore, we should use multiple Gaussian-Gamma distribution >> for approximation. Working on that. Also I am going to start thinking of >> API convention for all three models. Among the issues related API I listed >> in my proposal, I think 4429 >> <https://github.com/scikit-learn/scikit-learn/issues/4429> and 4062 >> <https://github.com/scikit-learn/scikit-learn/issues/4062> need more >> discussion. >> >> To answer a common question 'what is a good outcome?', I would like to >> say that, in priority order, the three models should 1) be implemented >> correctly (in math), 2) have clean APIs, 3) pass test cases (especially >> for the last two models), 4) be benchmarked and have speed tuning with >> respect to existing implementation. >> >> Any comment is welcome. >> >> BTW, I will keep this thread for all the following work. >> >> Cheers, >> Wei Xue >> >> >> >> ------------------------------------------------------------------------------ >> One dashboard for servers and applications across Physical-Virtual-Cloud >> Widest out-of-the-box monitoring support with 50+ applications >> Performance metrics, stats and reports that give you Actionable Insights >> Deep dive visibility with transaction tracing using APM >> Insight.http://ad.doubleclick.net/ddm/clk/290420510;117567292;y >> >> >> >> _______________________________________________ >> Scikit-learn-general mailing >> listScikit-learn-general@lists.sourceforge.nethttps://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >> >> >> ------------------------------------------------------------------------------ >> One dashboard for servers and applications across Physical-Virtual-Cloud >> Widest out-of-the-box monitoring support with 50+ applications >> Performance metrics, stats and reports that give you Actionable Insights >> Deep dive visibility with transaction tracing using APM Insight. >> http://ad.doubleclick.net/ddm/clk/290420510;117567292;y >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> > >
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