On Mon, Feb 17, 2014 at 4:49 AM, Dave Hirschfeld <novi...@gmail.com> wrote: > alex <argriffi <at> ncsu.edu> writes: > >> >> Hello list, >> >> Here's another idea resurrection from numpy github comments that I've >> been advised could be posted here for re-discussion. >> >> The proposal would be to make np.linalg.svd more like scipy.linalg.svd >> with respect to input checking. The argument against the change is >> raw speed; if you know that you will never feed non-finite input to >> svd, then np.linalg.svd is a bit faster than scipy.linalg.svd. An >> argument for the change could be to avoid issues reported on github >> like crashes, hangs, spurious non-convergence exceptions, etc. from >> the undefined behavior of svd of non-finite input. >> >> """ >> [...] the following numpy code hangs until I `kill -9` it. >> >> ``` >> $ python runtests.py --shell >> $ python >> Python 2.7.5+ >> [GCC 4.8.1] on linux2 >> >>> import numpy as np >> >>> np.__version__ >> '1.9.0.dev-e3f0f53' >> >>> A = np.array([[1e3, 0], [0, 1]]) >> >>> B = np.array([[1e300, 0], [0, 1]]) >> >>> C = np.array([[1e3000, 0], [0, 1]]) >> >>> np.linalg.svd(A) >> (array([[ 1., 0.], >> [ 0., 1.]]), array([ 1000., 1.]), array([[ 1., 0.], >> [ 0., 1.]])) >> >>> np.linalg.svd(B) >> (array([[ 1., 0.], >> [ 0., 1.]]), array([ 1.00000000e+300, 1.00000000e+000]), >> array([[ 1., 0.], >> [ 0., 1.]])) >> >>> np.linalg.svd(C) >> [hangs forever] >> ``` >> """ >> >> Alex >> > > I'm -1 on checking finiteness - if there's one place you usually want > maximum performance it's linear algebra operations. > > It certainly shouldn't crash or hang though and for me at least it doesn't - > it returns NaN
btw when I use the python/numpy/openblas packaged for ubuntu, I also get NaN. The infinite loop appears when I build numpy letting it use its lapack lite. I don't know which LAPACK Josef uses to get the weird behavior he observes "13% cpu usage for a hanging process". This is consistent with the scipy svd docstring describing its check_finite flag, where it warns "Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs." I think this caveat also applies to most numpy linalg functions that connect more or less directly to lapack. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
