On 8/25/07, Matthieu Brucher <[EMAIL PROTECTED]> wrote: > > > 2007/8/24, mark <[EMAIL PROTECTED]>: > > There may be multiple nan-s, but what Chris did is simply create one > > with the same nan's > > > > >>> a = N.array((1,2,3,N.nan)) > > >>> b = N.array((1,2,3,N.nan)) > > > > I think these should be the same. > > Can anybody give me a good reason why they shouldn't, because it could > > confuse a lot of people? > > > > Thanks, Mark > > > > It's the IEEE norm for flotting point numbers. You can have sevaral > different NaN, although in this case, they are the same kind. > Even if they are the same kind, the norm tells that NaN != NaN. > AFAIK, this is the definition of Nan, eg on a system which FPU is IEEE compatible, a number is x is NAN iff x != x. A Nan is defined at the binary level as having the exponent to 1 everywhere, and any non zero value in the mantissa:
http://en.wikipedia.org/wiki/NaN Personaly, I would simply compare the non Nan numbers if Nan is a possible outcome of the operation. Checking at the binary level may make sense, but it really depends on the cases. David _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
