** Note: this post contains a fair amount of LaTeX, if you don't
visualize the math correctly come to its original location **

In machine learning it is common to formulate the classification task
as a minimization problem over a given loss function. Given data input
data $(x_1, ..., x_n)$ and associated labels …

Besides performing a line-by-line analysis of memory consumption,
`memory_profiler`

exposes some functions that allow to retrieve the memory consumption
of a function in real-time, allowing e.g. to visualize the memory
consumption of a given function over time.

The function to be used is `memory_usage`

. The first argument
specifies what …

SciPy contains two methods to compute the singular value decomposition (SVD) of a matrix: `scipy.linalg.svd`

and `scipy.sparse.linalg.svds`

. In this post I'll compare both methods for the task of computing the full SVD of a large dense matrix.

The first method, `scipy.linalg.svd`

, is perhaps …

This tutorial introduces the concept of pairwise preference used in most ranking problems. I'll use scikit-learn and for learning and matplotlib for visualization.

In the ranking setting, training data consists of lists of items with some order specified between items in each list. This order is typically induced by giving …

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 …