Andy,
I wrote Python code for Newton's method logistic regression and a plot of
the hyperplane. Is this something the GSoC project would be interested in
or is it too low level?
Anne Dwyer
On Fri, Mar 22, 2013 at 6:58 AM, Andreas Mueller
<amuel...@ais.uni-bonn.de>wrote:
> Hi Ricardo.
> I think you forgot to mention what [1] and [2] are.
> What is the difference between a relative neighborhood graph and a
> neighborhood graph?
>
> To me that sounds a bit to special purpose for the moment.
> We need Logistic Regression first (which might also be a good GSoC
> project)!
>
> Just my opinion though ;)
>
> Cheers,
> Andy
>
>
>
> On 03/22/2013 06:49 AM, Ricardo Corral C. wrote:
>
> Ok, this is a brief description of what I'm interested in.
>
> Recently, I faced a problem of evaluating the quality of a method to
> obtain features from protein structures.
> I adopted the approach given in [1] to measure separability of my
> classes independently of my capacity of make good predictions.
> This is basically a hypothesis testing of whether or not the
> distribution of classes over feature vectors is somewhat random.
> This test is made over the construction of a Relative Neighbourhood
> Graph, which is O(n^3), thus, so prohibitive for practical use.
> There is an efficient method for constructing RNG on the plane
> described in [2] O(n*log(n)), but O(n^2) for a higher d dimension (in
> fact O(n^2*f(d)) with f(d) <= (2*sqrt(d) +2)^d...).
>
> Actually, I have the test implemented, and I'm refining a speedup of
> RNG construction based on the Half-Space Proximal (HSP) graph. This is
> O(n^2log(n)), and there is no dependence of dimension other than time
> consumed in calculating distances.
>
> This is made by doing RNG test over edges in HSP (attached images for
> clarify this).
>
> Could this be of interest for sklearn users? And if so, be considered for
> GSoC?
>
>
> On Thu, Mar 21, 2013 at 12:02 PM, Andreas Mueller<amuel...@ais.uni-bonn.de>
> <amuel...@ais.uni-bonn.de> wrote:
>
> On 03/21/2013 06:56 PM, Ricardo Corral C. wrote:
>
> I would like to contribute with an idea different from those listed.
> Is this the place to describe my proposal?
>
>
>
> I think posting it on the mailing list (at least a short description)
> would be a good start.
> Also starting to contribute ;)
>
> ------------------------------------------------------------------------------
> 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_d2d_mar
> _______________________________________________
> Scikit-learn-general mailing
> listScikit-learn-general@lists.sourceforge.nethttps://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_d2d_mar
>
>
>
> _______________________________________________
> Scikit-learn-general mailing
> listScikit-learn-general@lists.sourceforge.nethttps://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_d2d_mar
> _______________________________________________
> Scikit-learn-general mailing list
> Scikit-learn-general@lists.sourceforge.net
> 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_d2d_mar
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
