A fellow researcher (read arun) in semantic parsing and sentiment analysis directed me towards the shogun toolbox. Amazing features; it implements a range of weighted kernels, SVM and HMMs. It has interfaces to python, matlab and R (in my order of preference). It also supports ascii, Json and xml data formats. The best part is its got about 600 examples (albeit not in pydoc and for dna data!!!); a few I read took me about 5 mins to understand. (5 mins, now to get someone more brilliant to explain everything else I need, R2-D2, scooby-doo)
So now if I am rapid prototyping some algorithm I can do a instant analysis for many SVM methods and also HMM classification in very little time. Now maybe I can prove my algorithms are seriously inefficient and need lots of work or they can kick ass with the best of methods. (I am rooting for the latter)
I know PyML is slow and trying to catch up, but if only they just used everything form shogun and wrote new wrappers in PyML form and their fantastic doc. (I do wish a lot!!)
Time to fire the infinite improb drive and head to my bed.
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