The following algorithm computes the Least squares solution || Ax -
b|| subject to the equality constrain Bx = d. It's a classic algorithm
that can be implemented only using a QR decomposition and a least
squares solver. This implementation uses numpy and scipy. It makes use
of the new linalg.solve_triangular function …

Profiling Python extensions has not been a pleasant experience for me,
so I made my own package to do the job. Existing alternatives were
either hard to use, forcing you to recompile with custom flags like
gprofile or desperately slow like valgrind/callgrind. The package I'll
talk about is called …

Yesterday was the scikit-learn coding sprint in Paris. It was great to
meet with old developers (Vincent Michel) and new ones: some of whom I
was already familiar with from the mailing list while others came just
to say hi and get familiar with the code. It was really great …

One thing I'd really like to see done in this Friday's scikit-learn
sprint is to have full support for Python 3. There's a branch were
the hard word has been done (porting C extensions, automatic 2to3
conversion, etc.), although joblib still has some bugs and no one has
attempted to …

**Update: a fast and stable norm was added to scipy.linalg in August
2011 and will be available in scipy 0.10** Last week I discussed with
Gael how we should compute the euclidean norm of a vector a using
SciPy. Two approaches suggest themselves, either calling
scipy.linalg.norm …

I was last weekend in FOSDEM presenting scikits.learn (here are
the slides I used at the Data Analytics Devroom). Kudos to Olivier
Grisel and all the people who organized such a fun and authentic
meeting!

Latest release of scikits.learn comes with an awesome collection of
examples. These are some of my favorites:

Based on the work of libsvm-dense by Ming-Wei Chang, Hsuan-Tien Lin,
Ming-Hen Tsai, Chia-Hua Ho and Hsiang-Fu Yu I patched the libsvm
distribution shipped with scikits.learn to allow setting weights for
individual instances. The motivation behind this is to be able force a
classifier to focus its attention in …

Highlights for this release: * New stochastic
gradient descent module by Peter Prettenhofer * Improved svm
module: memory efficiency, automatic class weights. * Wrap for
liblinear's Multi-class SVC (option multi_class in LinearSVC) * New
features and performance improvements of text feature extraction. *
Improved sparse matrix support, both in main classes (GridSearch) as in
sparse …

scikits.learn.svm now uses LibSVM-dense instead of LibSVM for
some support vector machine related algorithms when input is a dense
matrix. As a result most of the copies associated with argument passing
are avoided, giving 50% less memory footprint and several times less
than the python bindings that ship …