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/~murphyk/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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