On Mon, Nov 22, 2010 at 5:27 PM, Matthieu Brucher <matthieu.bruc...@gmail.com> wrote: > 2010/11/22 Gael Varoquaux <gael.varoqu...@normalesup.org>: >> On Mon, Nov 22, 2010 at 11:12:26PM +0100, Matthieu Brucher wrote: >>> It seems that a simplex is what you need. >> >> Ha! I am learning new fancy words. Now I can start looking clever. >> >>> > I realize that maybe I should rephrase my question to try and draw more >>> > out of the common wealth of knowledge on this mailing list: what do >>> > people suggest to tackle this problem? Guided by Matthieu's suggestion, I >>> > have started looking at Powell's algorithm, and given the introduction of >>> > www.damtp.cam.ac.uk/user/na/NA_papers/NA2007_03.pdf I am wondering >>> > whether I should not investigate it. Can people provide any insights on >>> > these problems. >> >>> Indeed, Powell may also a solution. A simplex is just what is closer >>> to what you hinted as an optimization algorithm. >> >> I'll do a bit more reading. >> >>> > PS: The reason I am looking at this optimization problem is that I got >>> > tired of looking at grid searches optimize the cross-validation score on >>> > my 3-parameter estimator (not in the scikit-learn, because it is way too >>> > specific to my problems). >> >>> Perhaps you may want to combine it with genetic algorithms. We also >>> kind of combine grid search with simplex-based optimizer with >>> simulated annealing in some of our global optimization problems, and I >>> think I'll try at one point to introduce genetic algorithms instead of >>> the grid search. >> >> Well, in the scikit, in the long run (it will take a little while) I'd >> like to expose other optimization methods then the GridSearchCV, so if >> you have code or advice to give us, we'd certainly be interested. >> >> Gael > > There is scikits.optimization partly in the externals :D But I don't > think they should be in scikits.learn directly. Of course, the scikit > may need access to some global optimization methods, but the most used > one is already there (the grid search). > Then for genetic algorithms, pyevolve is pretty much all you want (I > still have to check the multiprocessing part)
Is that license http://pyevolve.sourceforge.net/license.html BSD compatible ? Josef > > Matthieu > -- > Information System Engineer, Ph.D. > Blog: http://matt.eifelle.com > LinkedIn: http://www.linkedin.com/in/matthieubrucher > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion