On Tue, Jan 12, 2010 at 7:38 PM, Robert Kern <robert.k...@gmail.com> wrote: > 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.
1) the object does not have a __float__ method. 2) I've now implemented the __float__ method (to raise an error). However, it doesn't get called. All objects are casted to 1. > >>> 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 > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
