It seems that Packt Publishing is on a publishing spree on Machine Learning in Python. After Building Machine Learning Systems In Python for which I was technical reviewer, Packt published Learning Scikit-Learn In Python last November.
I had the opportunity from Packt Publishing to review the second edition of Numpy Beginner’s Guide. Many thanks to the publisher for this and let’s go to the review.
I’m please to announce a new version for scikits.optimization. The main focus of this iteration was to finish usual unconstrained optimization algorithms.
- Fixes on the Simplex state implementation
- Added several Quasi-Newton steps (BFGS, rank 1 update…)
The scikit can be installed with pip/easy_install or downloaded from PyPI
In the next version of scikits.optimization, I’ve added some Quasi-Newton steps. Before this version is released, I thought I would compare several methods of optimizing the Rosenbrock function.
Continue reading Comparison of optimization algorithms
It has been a while, too long for sure, since my last update on this scikit. I’m pleased to announce that some algorithms are finally fixed as well as some tests.
- Fixed Polytope/Simplex/Nelder-Mead
- Fixed the Quadratic Hessian helper class
Additional tutorials will be available in the next weeks.
A few months ago, I’ve posted a note on an overdrive. The main issue of this kind of non-linear filter is aliasing, a process that adds digital acoustic content. The best way to solve the issue is to oversample the input before processing the signal.
There are some effects that are simpler than other. Digital ones are generally easier than analog ones, and purely digital filter are also easier than digitally-transformed analog ones. Linear filters such as passband, cutband, … are easy to digitally design, chorus can be achieved through some spectral computations, delay and reverbation are computationnally expensive but easy to code.
It said that analog devices have a unique sound that digital devices cannot achieve. In fact, much is due to the simplications that occur when digitizing an analog device. One of the most blatant examples is the overdrive, which I took from Simulanalog.
Continue reading Electronic: Simulation of a simple overdrive
My last blog post on optimization helped me generate orthogonal sequences. Now, I will use those sequences to separate two signals. The basic use case is a linear system with two inputs, one output, and instead of recording the response of one input at a time, one plays both inputs simultaneously with specific sequences so that they can be separated in another process.
Continue reading Optimization scikit: separation of orthogonally convoluted signals
In my last post about optimization, I’ve derived my function analytically. Sometimes, it’s not as easy. Sometimes also, a simple gradient optimization is not enough.
scikits.optimization has a special class for handling numerical differentiation, and several tools for conjugate gradients.
Continue reading Optimization scikit: a conjugate-gradient optimization