HN user
Yeah, I think we were writing at the same time.
Thanks for the tips. I know that the PCA implementation is very naïve, but I never put as much work into it as into some of the other parts of the library.
You could have complained to me. I put up some simple examples in the github:
https://github.com/luispedro/milk
The main documentation for the package is actually at:
Why would you use my code instead of others? None of the packages covers all of machine learning, so it depends on what you're looking for. I focus mainly on supervised learning and kmeans. I want my algorithms to be as scalable as possible too.
Other projects have other priorities/functionality.
I like my interfaces better too, but I might be biased by being so much more familiar with them.
"""How does your k-means implementation compare to that of scipy's?"""
I think that my code is faster: http://bit.ly/e8VOXy and it is probably more scalable.
Scikits.learn has more functionality in certain aspects. So, if you need those, use it. I started milk when there was no scikits.learn. The interfaces are different and they work together here.
A new version was just released: http://freshmeat.net/projects/milk/releases/329513
(I'm the author of this package).