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? How does numpy know how to cast an object to a float? > > 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. > > -- > 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 > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
