Hi Matthieu, Interesting example thanks. I can't however seem to get anything other than zero for the 100,000 to 1 sum.
Cheers, Hanni 2008/9/9 Matthieu Brucher <[EMAIL PROTECTED]> > > I now have a distinct dislike of float values (it'll probably wear off > over > > time), how can the sum of 100,000 numbers be anything other than the sum > of > > those numbers. I know the reasoning, as highlighted by the couple of > other > > e-mails we have had, but I feel the default should probably lean towards > > accuracy than speed. 2.0+2.0=4.0 and 2.0+2.0.....=200,000.0 not > 2array.sum() > > != 200,000... > > In that case, we should not use doubles, but long double or even > better, the real numbers themselves. Which would mean that > computations would be very very very slow. > Numpy leans somehow towards accuracy. If you want more accuracy > (because even with double, you can hit the limit very fast), use > another type. > > You said : > how can the sum of 100,000 numbers be anything other than the sum of > > those numbers > > This will always be a problem. With doubles, try to sum 1/n > (1...100000), you'll be surprized. And then do sum 1/n (100000...1) > with float values, and here the result should be better than when > using doubles. Numerical issues in scientific computing are tricky. > There is no single answer, it depends on your problem. > > Matthieu > -- > French PhD student > Information System Engineer > Website: http://matthieu-brucher.developpez.com/ > Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92 > LinkedIn: http://www.linkedin.com/in/matthieubrucher > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://projects.scipy.org/mailman/listinfo/numpy-discussion >
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