Ridge coefficients for multiple values of the regularization parameter can be elegantly computed by updating the thin SVD decomposition of the design matrix:

import numpy as np
from scipy import linalg
def ridge(A, b, alphas):
    """
    Return coefficients for regularized least squares

         min ||A x - b||^2 + alpha ||x||^2 …

I haven't worked in the manifold module since last time, yet thanks to Jake VanderPlas there are some cool features I can talk about. First of, the ARPACK backend is finally working and gives factor one speedup over the lobcpg + PyAMG approach. The key is to use ARPACK's shift-invert mode …

The manifold module in scikit-learn is slowly progressing: the locally linear embedding implementation was finally merged along with some documentation. At about the same time but in a different timezone, Jake VanderPlas began coding other manifold learning methods and back in Paris Olivier Grisel made my digits example a lot …

I decided to test my new Locally Linear Embedding (LLE) implementation against a real dataset. At first I didn't think this would turn out very well, since LLE seems to be somewhat fragile, yielding largely different results for small differences in parameters such as number of neighbors or tolerance, but …

I've been working lately in improving the low-level API of the libsvm bindings in scikit-learn. The goal is to provide an API that encourages an efficient use of these libraries for expert users. These are methods that have lower overhead than the object-oriented interface as they are closer to the …

Today got merged some changes I made to function scipy.linalg.get_blas_funcs(). The main enhacement is that get_blas_funcs() now also accepts a single string as input parameter and a dtype, so that fetching the BLAS function for a specific type becomes more natural. For example, fetching the gemm routine for …

I've been working for some time on implementing a locally linear embedding algorithm for the upcoming manifold module in scikit-learn. While several implementations of this algorithm exist in Python, as far as I know none of them is able to use a sparse eigensolver in the last step of the …

The guys behind pythonxy have been kind enough to add the latest scikit-learn as an additional plugin for their distribution. Having scikit-learn being in both pythonxy and EPD will hopefully make it easier to use for Windows users. For now I will continue to make windows precompiled binaries, but pythonxy …

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 …