[Numpy-discussion] Floating Point Difference between numpy and numarray
Tue Sep 9 07:16:59 CDT 2008
Use 1./n instead of 1/n. If n is an integer, 1/n equals 0.
2008/9/9 Hanni Ali <email@example.com>:
> Hi Matthieu,
> Interesting example thanks. I can't however seem to get anything other than
> zero for the 100,000 to 1 sum.
> 2008/9/9 Matthieu Brucher <firstname.lastname@example.org>
>> > 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.
>> 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
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Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92
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