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 …

I recently added support for sparse matrices (as defined in scipy.sparse) in some classifiers of scikits.learn. In those classes, the fit method will perform the algorithm without converting to a dense representation and will also store parameters in an efficient format. Right now, the only classese that implements …

I often find myself debugging python C extensions from gdb, but usually some variables are hidden because aggressive optimizations that distutils sets by default. What I did not know, is that you can prevent those optimizations by passing flags -O0 -fno-inline to gcc in keyword extra_compile_args (note: this will only …

One of the best things of spending summer in Paris: its parcs (here, with friends @ Parc Montsouris).

If your numpy installation uses system-wide BLAS libraries (this will most likely be the case unless you installed it through prebuilt windows binaries), you can retrieve this information at compile time to link python modules to BLAS. The function get_info in numpy.distutils.system_info will return a dictionary that contains …

Today I released a new version of the scikits.learn library for machine learning. This new release includes the new libsvm bindings, Jake VanderPlas' BallTree algorithm for *fast* nearest neighbor queries in high dimension, etc. Here is the official announcement. As usual, it can be downloaded from sourceforge or from …

New job, new code, new city, new colleagues. Feels something like this:

I'm extremely glad that finally I am moving to Paris to work as part of the INRIA crew. I'll be working with Gael Varoquaux and his team in an extremely cool Python related project (more to come on this in the following weeks). Granada has been a great place for …

Background: DPLL is the algorithm behind SymPy's implementation of logic.inference.satisfiable After reading the original papers by Davis & Putnam [1], I managed to implement a more efficient version of the DPLL algorithm. It is 10x times faster on medium-sized problems (40 variables), and solves some wrong result bugs [2 …

Realmente mi ordenador no hace nada ... sólo le echo la culpa de todo