The final GSOC post

Since I had delayed the last post, so I am combining two of my blog posts and am going to use this blog post to give you an update of all that has happened in this GSOC (special emphasis on the stuff of the last 4 weeks).

So first question is, where am I right now? In Mumbai. In my college. A stupid answer !
I have finished all the weekly goals of the GSOC project apart from a few things :-

1) I wanted to implement atleast one the optimal triangulation algorithms (using the research paper). The particular algorithm which I had to implement was decided pretty late and I didn't find time to implement it towards the end and hence that is still pending. I hope to do it soon.

2) Alpha - expansion algorithm is due. I have written the code, but there seems to be bug. I will debug it and push the code for alpha-beta sampling soon.

Also, some code reviews are pending and hence I am expecting some work on the code improvement part. But other than that, the work which I was officially expected to do in GSOC is over. 

Now that I am done with my status, let me talk a little bit about what happened since my last blog post (since the virtual time of my blog post, if you may). So for those who had read my last blog post, I was slightly stuck with integrating the factor product code for cython. However, I realized soon that cython could easily take in the python objects directly (duh) and so I easily modified the factor product to work seamlessly with extra data that is stored in factors (this extra data is the assignment of the eliminate variables (the variables which were eliminated while maximizing) corresponding to every combinations of the existing variables). 

Anyway, once I was done with that, the next task was the implementation of specific special-setting inference algorithms. One of them was the "Graph-cut algorithm for MAP in pairwise binary MRFs with submodular potentials ". This particular algorithm requires a max-flow-min-cut algorithm and I was really surprised to see that networkx didn't have a function which returns the exact cut-edges which I needed for this algorithm. I could have used external python libraries for this but this would have created an extra dependency just for one function. So we (I and mentors) decided that it would be best to implement the max-flow algorithm on my own which could then be used for this algorithm. So I implemented this and then implemented the algorithm.

However, I was slightly busy for a week after this with resume submission deadlines and so on in the institute and after that came back to the GSOC work. I implemented the gibbs sampling algorithm first and then went to alpha-expansion algorithm. While implementing I suddenly realized that alpha-expansion algorithm might not be too helpful for practical purposes because there are just too many constraints (All the variables must have equal cardinality. Anyway, I implemented it and have not pushed it yet, as there are a few issues to resolve in this function.

Once I was done with that, I spent some time with code review, examples, test cases and documentation (Well, this is the part that I didn't like. I mean while writing examples, I just had to copy stuff from the examples, change the format etc and it was one of the most boring things which i did as a part of gsoc (which kind of tells you how interesting GSOC was for me, in general ). I don't know if there is a better way of doing this but copy-pasting examples, removing "self." and formatting it doesn't look like the kind of work which doesn't have alternatives in the CS world). I had tried to find out more about it, but couldn't find better methods. If you know of something, please tell me too.

I plan to write another blog about the stuff which I learned from GSOC. However, I think it is best written after a week or so of reflection. :)

That's all for now.
 Navin