Ohh wow, so scikit-learn IS part of GSoC!?! I am so happy to know that! :)
regards,
shankar.
On Sun, Mar 18, 2012 at 6:20 PM, Andreas Mueller
<[email protected]>wrote:
> Hi Shankar.
> > Thank you very much for offering to mentor my project! But sadly, it
> > looks like sklearn is not in the GSoC organization list :(...
> >
> Scikit-learn is not on the organzation list but it is part of GSoC
> through the Python Software Foundation..
> > To answer your question about how i'll go about implementing the first
> > case:
> >
> > The first thing to decide would be how to represent the DAG. For that,
> > i could either use something like py_graph, or roll my own, like so:
> >
> > dag = {'A': ['B', 'C'],
> > 'B': ['C', 'D']}
> >
> > dag represents a graph with nodes A, B, C, D and edges from A to B,C
> > and from B to C,D.
> >
> > Once we have that, we will next need to represent factors. Factors are
> > similar to conditional probability distributions, except that they
> > don't normalize to unity. Inference operations on bayes nets are done
> > by manipulating factors. Each factor has a scope: for example, phi(X1,
> > X2, X3) has a scope of X1, X2, X3. phi(X1=x1, X2=x2, X3=x3) = y means
> > that the factor phi attains value y when X1, X2, X3 are assigned the
> > values x1, x2, x3.
> >
> > I'll design a custom class that will hold the scope of the factors,
> > and have a mechanism to set/get the factor values for each assignment
> > of the variables in it's scope.
> >
> > Finally, i'll implement methods for factor multiplication and
> > marginalization. Once these are done, we can now do inference
> > operations on our DAG.
> >
>
> That sounds like a good approach in general. The reason I was asking is
> that this involves a lot of custom data structures,
> that we tried to avoid in Scikit-learn.
> Also your approach would be very close to what pymc does (I think), so
> maybe your implementation would
> better fit in their framework.
>
> This is just my personal impression, though, and I don't know what the
> others think.
>
> Cheers,
> Andy
>
>
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