Ya i remember the discussion. Was just confirming if there's any change of
plan.
Thanks for clarifying and the suggestion. I will ask on Pandas. There is
stuff on timeseries already in Pandas, and HMM's might fall somewhat nearby.
On Fri, Mar 22, 2013 at 7:12 PM, Andreas Mueller
<amuel...@ais.uni-bonn.de>wrote:
> Hi Nipun.
> We discussed this and basically think structured learning is off-topic for
> sklearn at the moment.
> I am building a structured learning library, but it is still changing
> quite a bit.
>
> It is not so clear to me what happens with the HMMs.
> And I guess we should decide that soon.
> I think throwing them out would probably be the best idea.
> But then there should be a new place for them to go to.
> So I think we should keep them until we find a new place.
> Pandas might be a good idea?
>
> Or starting a new lib. But that is a lot of work.
>
> Maybe ask on the Pandas mailing list?
>
> Cheers,
> Andy
>
>
> On 03/22/2013 02:11 PM, nipun batra wrote:
>
> Might be a bit off topic. Is Structured Learning still not a priority for
> sklearn?
> I would have ideally liked to have put my development code in sklearn for
> HMM's (since what i need would goes beyond what is currently implemented in
> sklearn). I have started porting Murphy's HMM
> toolbox<http://www.cs.ubc.ca/%7Emurphyk/Software/HMM/hmm.html>.
>
> Apart from the standard Discrete and Gaussian HMM, i would be developing
> atleast the following:
>
> 1. Factorial HMM
> 2. Multiple input/output HMM
> 3. Conditional HMM
> 4. Semi HMM
>
> Started committing on Github only since a couple of days
> here<https://github.com/nipunreddevil/PyHMM>.
> Would like to see it one day merge in the main branch! Being a grad student
> it's best not to overcommit, but i plan to do major chunk of this
> implementation for my thesis work during the summer break.
>
> Just wanted to know if someone is willing to lend a helping hand.
> On Fri, Mar 22, 2013 at 5:53 PM, Andreas Mueller <
> amuel...@ais.uni-bonn.de> wrote:
>
>> Hi Anne.
>> Thanks for the offer.
>> I'm not sure we want a newtons method implementation. There is on in
>> liblinear. but that is one-vs-rest.
>> If we start reimplementing parts of liblinear, we might open pandoras box
>> ;)
>> In principal I could imagine a "MultinomialLogisticRegression" estimator.
>> The speed should be comparable with LinearSVC, though,
>> which might not be that easy.
>>
>> Currently, an SGD implementation would be great.
>>
>> Cheers,
>> Andy
>>
>>
>> On 03/22/2013 01:17 PM, Anne Dwyer wrote:
>>
>> 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 ;)
>>>
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