Could you please comment on why a Python machine learning developer should use your code and not, say, Shogun or scikits.learn? In what use cases would your code be preferable, or dispreferred?
How does your k-means implementation compare to that of scipy's?
Why not push your code as modules into scikits.learn? Their library is designed to be many loosely coupled components.
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.
How does your k-means implementation compare to that of scipy's?
Why not push your code as modules into scikits.learn? Their library is designed to be many loosely coupled components.