On Tue, Jan 12, 2010 at 12:31, Sebastian Walter <sebastian.wal...@gmail.com> wrote: > On Tue, Jan 12, 2010 at 7:09 PM, Robert Kern <robert.k...@gmail.com> wrote: >> On Tue, Jan 12, 2010 at 12:05, Sebastian Walter >> <sebastian.wal...@gmail.com> wrote: >>> Hello, >>> I have a question about the augmented assignment statements *=, +=, etc. >>> Apparently, the casting of types is not working correctly. Is this >>> known resp. intended behavior of numpy? >> >> Augmented assignment modifies numpy arrays in-place, so the usual >> casting rules for assignment into an array apply. Namely, the array >> being assigned into keeps its dtype. > > what are the usual casting rules?
For assignment into an array, the array keeps its dtype and the data being assigned into it will be cast to that dtype. > How does numpy know how to cast an object to a float? For a general object, numpy will call its __float__ method. >> If you do not want in-place modification, do not use augmented assignment. > > Normally, I'd be perfectly fine with that. > However, this particular problem occurs when you try to automatically > differentiate an algorithm by using an Algorithmic Differentiation > (AD) tool. > E.g. given a function > > x = numpy.ones(2) > def f(x): > a = numpy.ones(2) > a *= x > return numpy.sum(a) > > one would use an AD tool as follows: > x = numpy.array([adouble(1.), adouble(1.)]) > y = f(x) > > but since the casting from object to float is not possible the > computed gradient \nabla_x f(x) will be wrong. Sorry, but that's just a limitation of the AD approach. There are all kinds of numpy constructions that AD can't handle. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
