David, I'm confused about your reply. I don't think Ruben was only asking why you'd ever get non-zero error after the forward and inverse transform, but why his implementation using lists gives zero error but using arrays he gets something of order 1e-15.
On Mon, Apr 20, 2009 at 9:47 AM, David Cournapeau <da...@ar.media.kyoto-u.ac.jp> wrote: >> I'm using Scipy/Numpy to do image wavelet transforms via the lifting >> scheme. I grabbed some code implementing the transforms with Python >> lists (float type). This code works perfectly, but slow for my needs >> (I'll be doing some genetic algorithms to evolve coefficients of the >> filters and the forward and inverse transform will be done many >> times). It's just implemented by looping in the lists and making >> computations this way. Reconstructed image after doing a forward and >> inverse transform is perfect, this is, original and reconstructed >> images difference is 0. >> >> With Scipy/Numpy float arrays slicing this code is much faster as you >> know. But the reconstructed image is not perfect. The image difference >> maximum and minimum values returns: >> maximum difference => 3.5527136788e-15 >> minimum difference => -3.5527136788e-15 >> >> Is this behavior expected? > > Yes, it is expected, it is inherent to how floating point works. By > default, the precision for floating point array is double precision, for > which, in normal settings, a = a + 1e-17. I don't think it's expected in this sense. The question is why the exact same sequence of arithmetic ops on lists yields zero error but on arrays yields 3.6e-15 error. This doesn't seem to be about lists not showing full precision of the values, because the differences are even zero when extracted from the lists. >> Because it seems sooo weird to me. > > It shouldn't :) The usual answer is that you should read this: > > http://docs.sun.com/app/docs/doc/800-7895 This doesn't help! This is a python question, methinks. Best, Rob _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
