On Wed, Dec 28, 2011 at 2:45 PM, Dag Sverre Seljebotn < d.s.seljeb...@astro.uio.no> wrote:
> On 12/28/2011 02:21 PM, Ralf Gommers wrote: > > > > > > On Wed, Dec 28, 2011 at 1:57 PM, Dag Sverre Seljebotn > > <d.s.seljeb...@astro.uio.no <mailto:d.s.seljeb...@astro.uio.no>> wrote: > > > > On 12/28/2011 01:52 PM, Dag Sverre Seljebotn wrote: > > > On 12/28/2011 09:33 AM, Ralf Gommers wrote: > > >> > > >> > > >> 2011/12/27 Jordi Gutiérrez Hermoso<jord...@octave.org > > <mailto:jord...@octave.org> > > >> <mailto:jord...@octave.org <mailto:jord...@octave.org>>> > > >> > > >> On 26 December 2011 14:56, Ralf > > Gommers<ralf.gomm...@googlemail.com <mailto: > ralf.gomm...@googlemail.com> > > >> <mailto:ralf.gomm...@googlemail.com > > <mailto:ralf.gomm...@googlemail.com>>> wrote: > > >> > > > >> > > > >> > On Mon, Dec 26, 2011 at 8:50 PM,<josef.p...@gmail.com > > <mailto:josef.p...@gmail.com> > > >> <mailto:josef.p...@gmail.com <mailto:josef.p...@gmail.com>>> > wrote: > > >> >> I have a hard time thinking through empty 2-dim arrays, and > > >> don't know > > >> >> what rules should apply. > > >> >> However, in my code I might want to catch these cases rather > > early > > >> >> than late and then having to work my way backwards to find > > out where > > >> >> the content disappeared. > > >> > > > >> > > > >> > Same here. Almost always, my empty arrays are either due to > bugs > > >> or they > > >> > signal that I do need to special-case something. Silent > passing > > >> through of > > >> > empty arrays to all numpy functions is not what I would want. > > >> > > >> I find it quite annoying to treat the empty set with special > > >> deference. "All of my great-grandkids live in Antarctica" > > should be > > >> true for me (I'm only 30 years old). If you decide that is > > not true > > >> for me, it leads to a bunch of other logical annoyances up > > there > > >> > > >> > > >> Guess you don't mean true/false, because it's neither. But I > > understand > > >> you want an empty array back instead of an error. > > >> > > >> Currently the problem is that when you do get that empty array > back, > > >> you'll then use that for something else and it will probably > still > > >> crash. Many numpy functions do not check for empty input and > > will still > > >> give exceptions. My impression is that you're better off > > handling these > > >> where you create the empty array, rather than in some random > > place later > > >> on. The alternative is to have consistent rules for empty > > arrays, and > > >> handle them explicitly in all functions. Can be done, but is of > > course a > > >> lot of work and has some overhead. > > > > > > Are you saying that the existence of other bugs means that this > bug > > > shouldn't be fixed? I just fail to see the relevance of these > > other bugs > > > to this discussion. > > > > > > See below. > > > > > For the record, I've encountered this bug many times myself and > it's > > > rather irritating, since it leads to more verbose code. > > > > > > It is useful whenever you want to return data that is a subset of > the > > > input data (since the selected subset can usually be zero-sized > > > sometimes -- remember, in computer science the only numbers are > 0, 1, > > > and "any number"). > > > > > > Here's one of the examples I've had. The Interpolative > Decomposition > > > decomposes a m-by-n matrix A of rank k as > > > > > > A = B C > > > > > > where B is an m-by-k matrix consisting of a subset of the columns > > of A, > > > and C is a k-by-n matrix. > > > > > > Now, if A is all zeros (which is often the case for me), then k > > is 0. I > > > would still like to create the m-by-0 matrix B by doing > > > > > > B = A[:, selected_columns] > > > > > > But now I have to do this instead: > > > > > > if len(selected_columns) == 0: > > > B = np.zeros((A.shape[0], 0), dtype=A.dtype) > > > else: > > > B = A[:, selected_columns] > > > > > > In this case, zero-sized B and C are of course perfectly valid and > > > useful results: > > > > > > In [2]: np.dot(np.ones((3,0)), np.ones((0, 5))) > > > Out[2]: > > > array([[ 0., 0., 0., 0., 0.], > > > [ 0., 0., 0., 0., 0.], > > > [ 0., 0., 0., 0., 0.]]) > > > > > > > And to answer the obvious question: Yes, this is a real usecase. It > is > > used for something similar to image compression, where sub-sections > of > > the images may well be all-zero and have zero rank (full story at > [1]). > > > > Thanks for the example. I was a little surprised that dot works. Then I > > read what wikipedia had to say about empty arrays. It mentions dot like > > you do, and that the determinant of the 0-by-0 matrix is 1. So I try: > > > > In [1]: a = np.zeros((0,0)) > > > > In [2]: a > > Out[2]: array([], shape=(0, 0), dtype=float64) > > > > In [3]: np.linalg.det(a) > > Parameter 4 to routine DGETRF was incorrect > > <segfault> > > :-) > > Well, a segfault is most certainly a bug, so this must be fixed one way > or the other way anyway, and returning 1 seems at least as good a > solution as raising an exception. Both solutions require an extra if-test. > > > > > Reading the above thread I understand Ralf's reasoning better, but > > really, relying on NumPy's buggy behaviour to discover bugs in user > code > > seems like the wrong approach. Tools should be dumb unless there are > > good reasons to make them smart. I'd be rather irritated about my > hammer > > if it refused to drive in nails that it decided where in the wrong > spot. > > > > > > The point is not that we shouldn't fix it, but that it's a waste of time > > to fix it in only one place. I remember fixing several functions to > > explicitly check for empty arrays and then returning an empty array or > > giving a sensible error. > > > > So can you answer my question: do you think it's worth the time and > > computational overhead to handle empty arrays in all functions? > > I'd hope the computational overhead is negligible? > If you have to check all array_like inputs in all functions, I wouldn't think so. > I do believe that handling this correctly everywhere is the right thing > to do and would improve overall code quality (as witnessed by the > segfault found above). > > Of course, likely nobody is ready to actually perform all that work. So > the right thing to do seems to be to state that places where NumPy does > not handle zero-size arrays is a bug, but not do anything about it until > somebody actually submits a patch. That means, ending this email > discussion by verifying that this is indeed a bug on Trac, and then wait > and see if anybody bothers to submit a patch. > Agreed. I've created http://projects.scipy.org/numpy/ticket/2007 Ralf
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