My newest project is a Python library for monitoring memory consumption
of arbitrary process, and one of its most useful features is the
line-by-line analysis of memory usage for Python code. I wrote a basic
prototype six months ago after being surprised by the lack of related
tools. I wanted …

A little experiment to see what low rank approximation looks like. These
are the best rank-k approximations (in the Frobenius norm) to the a
natural image for increasing values of k and an original image of rank
512.

Python code can be found here. GIF animation made
using ImageMagic's convert …

In scipy's development version there's a new function closely related to
the QR-decomposition of a matrix and to the least-squares solution of
a linear system. What this function does is to compute the
QR-decomposition of a matrix and then multiply the resulting orthogonal
factor by another arbitrary matrix. In pseudocode …

Last week we released a new version of scikit-learn. The Changelog is
particularly impressive, yet personally this release is important for
other reasons. This will probably be my last release as a paid engineer.
I'm starting a PhD next month, and although I plan to continue
contributing to the project …

I've been working lately in improving the scikit-learn example gallery
to show also a small thumbnail of the plotted result. Here is what the
gallery looks like now:

And the real thing should be already displayed in the development-documentation. The next thing is to add a static image to those …

Today's coding sprint was a bit more crowded, with some
notable scipy hackers such as Ralph Gommers, Stefan van der Walt,
David Cournapeau or Fernando Perez from Ipython joining in. On
what got done: - We merged Jake's new BallTree code. This is a pure
Cython implementation of a nearest-neighbor …

As a warm-up for the upcoming EuroScipy-conference, some of the
scikit-learn developers decided to gather and work together for a
couple of days. Today was the first day and there was only a handfull of
us, as the real kickoff is expected tomorrow. Some interesting coding
happened, although most of …

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 …