On Sun, Jul 17, 2016 at 02:41:33PM +0200, A.Brozi wrote: > Hello > > I'm puzzling over some strange behavior of raising a matrix to a matrix > power: > > If I create two matrices (containing integers): > a = np.array([2]) > and > b = np.array([-1]) > the operation a**b produces: > array([0], dtype=int32)
What result did you expect? 2**-1 as an int32 cannot be 0.5, as that's a float. I'm not really sure about the rules that numpy uses for coercing from one type to another, but I'm not surprised by this result. I don't know if it is documented anywhere, but it seems like the sort of thing numpy would do. Here's another similar example: py> np.array([0])**-1 __main__:1: RuntimeWarning: divide by zero encountered in power __main__:1: RuntimeWarning: invalid value encountered in power array([-2147483648]) > The result of division b/a is correct: > array([-0.5]) You only get that result in Python 3, or Python 2 with "from __future__ import division". Without it, / behaves the same as // (integer division), for example: py> b//a array([-1]) py> b/a array([-1]) But once I run the future import, the behaviour changes: py> from __future__ import division py> b/a array([-0.5]) > I wonder if it's a bug or a feature. > And if it's a feature, then what is it useful for? My guess is that the only reason for it is that it keeps the code simple, which allows numpy to be fast. -- Steve _______________________________________________ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: https://mail.python.org/mailman/listinfo/tutor
