Now that version 0.2 of scikit.optimization is out, here is a tutorial on the gradient-free optimizer based on the simplex algorithm.
When the only thing you have is the cost function and when you don’t have dozens of parameters, the first thing that can be tried is a simplex algorithm.
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.
Changelog:
Additional tutorials will be available in the next weeks.
Old announces:
Yes, because Cover Trees are sometimes too slow. In fact, I asked myself this question, not for the build time, but for the search time if the data has a structure. Imagine, what would happen if your data was more a less a regular grid? When I tried that, starting with a point at (0,0), then (1,0)… the first node (0,0) had references to all the last points (9,9), (9,8)… And I figured, it would be slower than a tree search. So I decided to give kd-trees a shot for this kind of search on a regular grid.
I had to port a simplex/Nelder-Mead optimizer that I already have in Python in C++. As for the Python version, I tried to be as generic as possible but as efficient as possible, so the state is no longer a dictionary, but a simple structure.
I could have used the Numerical Recipes version, but the licence cost is not worth it, and the code is not generic enough, not explained enough. And also there are some design decisions that are questionable (one method = one responsibility).
How to explain the different kind of audio effects and how to understand what their use is? Although I learnt a lot by practice, there is sometimes the need for some theory and for experiments. I tried to find a book that matches these two points: good theory and proper practice. I’ve chosen this book, with tracks on a CD for experimentation. Was it really what I was looking for?
I’ve looked on github for a good C++ implementation of Cover Trees for nearest-neighbors search, but I didn’t find one. I may have overlooked some repositories, but in the end, implementing it myself wasn’t that difficult.
A few years ago, I mentioned that the registry pattern was my favorite pattern in Python. Well, it may also be my favorite C++ pattern.
I think I’ve said it already, I have a rock band. Currently, we are recording our first album, and while we used Traktion in the past, I’m considering moving to Sonar (it is continuously updated and has a great reputation, also I’ve played a few time in the past with its ancestor Cakewalk).
So let’s talk about the book’s comprehensive guide.
It’s been a while since I’ve started considering buying a real photo camera. And I’ve decided on a Canon 7D. As usual, the user guide covers the camera, but I thought it would be better to have a more complete guide to the 7D.
Just after the 0.1 release, I’ve worked to add some few tricks and fix a few bugs (see QtMosaic on Github). The most important change is a better image search.