On Tue, May 08, 2012 at 02:19:31PM -0400, Yaroslav Halchenko wrote: > is there a point where generic numpy.random gets explicitly seeded > upon sklearn import?
No, and I don't think that this is desirable: it would be a weird side effect of importing the scikit. It might be interesting to seed the global RNG in the tests, but I have found such an approach inefficient, as the order in which the tests can be executed is not reproducible. > I guess then such global seeding would be of great help ;) Don't think that it is related to RNGs. > since it happened on my laptop I guess I have no chance but to bombard > it with testing while trying to reproduce... but for that I better also > first provide that deterministic seeding, otherwise it might be of no > use :-/ In theory I agree, and we have been fighting a lot with these issue. In practice I am against global seeding, as it has gotten me in troubles, and all it means is that we need to be even more careful with RNGs. For the specific situation of ICA, there are indeed some unseeded RNGs, which I am going to fix right now. G ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
