A colleague of mine ran into this this morning, and I've fixed the upstream bug in NumPy, but I thought I'd let you guys know in case you want to put in a workaround.
Basically, if you copy.copy or copy.deepcopy a LinearSVC object (and probably some other objects where the underlying code relies on Fortran arrays) things don't go so well when you try to predict() with the new object. The reason is that as of last few stables and this morning's master, at any rate, NumPy does this: >>> copy.deepcopy(numpy.zeros((2, 2), order='F')).flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False I've fixed it in https://github.com/numpy/numpy/pull/2699 A workaround would be to check flags in predict() and do the necessary reshuffle if the flags have been botched, but that's kind of a maintenance headache. David ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_sfd2d_oct _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
