Hi guys,

I'm trying to learn the principles of artificial intelligence. I just finished coding up a belief net and a corresponding EM algorithm that learns the CPTs for latent nodes using training data (if you're unfamiliar with the lingo, you're probably going :eek: right now). While the program (written in Python, of course) works and is pretty quick, it is also pretty unstructured - I had difficulty figuring out how to best represent probability computations, so parts of the algorithm look kind of awkward.

Are there any good, complete AI/machine learning packages in Python - are there any which incorporate Bayesian belief nets? Any which represent the belief net, the update procedure, and EM algorithms to find the parameters at latent nodes? How about BioPython? Since I'm a computational biology guy, that would be right up my alley, but I'm not sure how complete the BioPython package is. How about Orange - how easily does it lend itself to bioinformatics-type applications? Does anyone here have any experience with this stuff? If not, I guess I'll start plumbing it myself, but I'd appreciate it if anyone could save me some time :cheesy:.

Here's a link to BioPython's homepage, if anyone is interested in learning:


And here is Orange:



Thanks G-do for informing us over the interesting medical work you are doing! Orange sounds like Python based data mining module, things that should be explored. Slovenia is just like Austria, very nice with much history and great people.

BioPython does simplify things a good deal, doesn't it? :cheesy: If I get a free moment, I might do a BioPython/Orange code snippet with some basic biology background, the goal of which would be to describe how to code up a simple research program.

Have a look at the scikit-learn. While it does not have any code for belief nets, it has many state of the art algorithms that can be used for machine learning with Python.

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