Description:

milk wraps libsvm in a Pythonic way (the models learned have weight arrays that are accessible from Python directly, the models are pickle()able, you can pass any Python function as a kernel,....)

It also supports k-means clustering with an implementation that is careful not to use too much memory (if your dataset fits into memory, milk can cluster it).

It does not have its own file format or in-memory format, which I consider a feature as it works on numpy arrays directly (or anything that is convertible to a numpy-array) without forcing you to copy memory around. For SVMs, you can even just use any datatype if you have your own kernel function.

Changes to previous version:

• no scipy.weave dependency
• flatter namespace
• faster kmeans
• affinity propagation (borrowed from scikits-learn & slightly improved to take less memory and time)
• pdist()
• more documentation

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: None

Tags: Svm, Supervised

Archive: download here

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