[Numpy-discussion] casting bug
Charles سمير Doutriaux
doutriaux1@llnl....
Wed May 27 10:14:53 CDT 2009
Thanks Robert,
I thought it was something like that but couldn't figure it out.
C.
On May 26, 2009, at 4:50 PM, Robert Kern wrote:
> 2009/5/26 Charles سمير Doutriaux <doutriaux1@llnl.gov>:
>> Hi there,
>>
>> One of our users just found a bug in numpy that has to do with
>> casting.
>>
>> Consider the attached example.
>>
>> The difference at the end should be 0 (zero) everywhere.
>>
>> But it's not by default.
>>
>> Casting the data to 'float64' at reading and assiging to the arrays
>> works
>> Defining the arrays "at creation time" as "float32" works
>>
>> But simply reading in the data (flaot32) and putting them into the
>> default
>> arrays (float64) leads to differences!
>
> That is probably not a good characterization of the code. You are
> doing a summation of squares of floats two different ways. The data is
> stored in the file with dtype=float32. You are reading in the data a
> "row" at a time (with the name "tmp" for future reference). You are
> keeping an accumulator ("ex2" with dtype=float64) and storing each row
> into an array shaped like the whole data in the file ("a" with
> dtype=float64).
>
> You calculate the sum of squares one way by "ex2 = ex2 + power(tmp,
> 2.0)". In this case, the square is computed, then stuffed back into a
> float32 intermediate result, *then* added to "ex2".
>
> You calculate the sum of squares the other way by "numpy.sum(power(a,
> 2.0))". In this case, the square is computed in full float64
> precision, then added up.
>
> The difference between the two results is just that of using a low
> precision intermediate for the square. You have fairly large inputs so
> you are losing a good number of digits by using a float32 intermediate
> before the summation.
>
> --
> Robert Kern
>
> "I have come to believe that the whole world is an enigma, a harmless
> enigma that is made terrible by our own mad attempt to interpret it as
> though it had an underlying truth."
> -- Umberto Eco
> _______________________________________________
> Numpy-discussion mailing list
> Numpy-discussion@scipy.org
> http:// mail.scipy.org/mailman/listinfo/numpy-discussion
More information about the Numpy-discussion
mailing list