Hi Shishir,
this duplication was due to the fact that the previous ProbsbilisticPCA
class
inherited from PCA and hence the documentation was almost the same.
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/pca.py#L459
This class is now deprecated, I hope the new documentation is less
ambiguous.
If you still see issues room for improvement don't hesitate to share your
insights.
Cheers,
Denis
On Tue, Sep 24, 2013 at 3:56 PM, Shishir Pandey <[email protected]>wrote:
> On 24-09-2013 18:03, Denis-Alexander Engemann wrote:
>
> Hi Shishir,
>
> please note that the ProbabilisiticPCA got recently refactored which
> lead to API changes and improved documentation + examples.
>
> https://github.com/scikit-learn/scikit-learn/pull/2404
>
> Did you take these changes into account? If not it would be great to
> know which exmample / reduplicate documentation you witnessed.
>
> You can think of it as an additional PCA option to obtain a
> probabilistic score when predicting the model fit on unseen data and to
> estimate the data covariance.
> The PCA components themselves will be the same in both variants. In other
> words you can just apply it as you would regular PCA + additional arguments
> + extended application.
>
> HTH,
> Denis
>
> Also see:
> Tipping, Michael E., and Christopher M. Bishop. "Probabilistic principal
> component analysis." *Journal of the Royal Statistical Society: Series B
> (Statistical Methodology)* 61.3 (1999): 611-622.
>
> Hi Denis
>
> I hadn't seen these documents. I was talking about the documentation of
> the stable version of scikit learn.
>
>
> http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.ProbabilisticPCA.html#sklearn.decomposition.ProbabilisticPCA
>
>
> http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
>
> Thanks for the reference to Tipping's paper.
>
> --
> sp
>
>
>
> ------------------------------------------------------------------------------
> October Webinars: Code for Performance
> Free Intel webinars can help you accelerate application performance.
> Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most
> from
> the latest Intel processors and coprocessors. See abstracts and register >
> http://pubads.g.doubleclick.net/gampad/clk?id=60133471&iu=/4140/ostg.clktrk
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
------------------------------------------------------------------------------
October Webinars: Code for Performance
Free Intel webinars can help you accelerate application performance.
Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most from
the latest Intel processors and coprocessors. See abstracts and register >
http://pubads.g.doubleclick.net/gampad/clk?id=60133471&iu=/4140/ostg.clktrk
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
