On Tue, Oct 11, 2011 at 12:17 PM, Matthew Brett <matthew.br...@gmail.com>wrote:
> Hi,
>
> While struggling with floating point precision, I ran into this:
>
> In [52]: a = 2**54+3
>
> In [53]: a
> Out[53]: 18014398509481987L
>
> In [54]: np.float128(a)
> Out[54]: 18014398509481988.0
>
> In [55]: np.float128(a)-1
> Out[55]: 18014398509481987.0
>
> The line above tells us that float128 can exactly represent 2**54+3,
> but the line above that says that np.float128(2**54+3) rounds upwards
> as if it were a float64:
>
> In [59]: np.float64(a)
> Out[59]: 18014398509481988.0
>
> In [60]: np.float64(a)-1
> Out[60]: 18014398509481988.0
>
> Similarly:
>
> In [66]: np.float128('1e308')
> Out[66]: 1.000000000000000011e+308
>
> In [67]: np.float128('1e309')
> Out[67]: inf
>
> Is it possible that float64 is being used somewhere in float128 casting?
>
>
The problem is probably in specifying the values. Python doesn't support
long double and I expect python integers to be converted to doubles, then
cast to long double. The only way to get around this is probably using
string representations of the numbers, and I don't know how
well/consistently numpy does that at the moment. If it calls python to do
the job, then double is probably what is returned. It doesn't help on my
system:
In [1]: float128("18014398509481987.0")
Out[1]: 18014398509481988.0
Chuck
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
