I also get the same issue with prod()
On Mon, Jan 24, 2011 at 10:23 AM, Warren Weckesser <
warren.weckes...@enthought.com> wrote:
> I see the same "randomness", but at a different array size:
>
> In [23]: numpy.__version__
> Out[23]: '1.4.0'
>
> In [24]: import numexpr
>
> In [25]: numexpr.__version__
> Out[25]: '1.4.1'
>
> In [26]: x = zeros(8192)+0.01
>
> In [27]: print evaluate('sum(x, axis=0)')
> 72.97
>
> In [28]: print evaluate('sum(x, axis=0)')
> 66.92
>
> In [29]: print evaluate('sum(x, axis=0)')
> 67.9
>
> In [30]: x = zeros(8193)+0.01
>
> In [31]: print evaluate('sum(x, axis=0)')
> 72.63
>
> In [32]: print evaluate('sum(x, axis=0)')
> 71.74
>
> In [33]: print evaluate('sum(x, axis=0)')
> 81.93
>
> In [34]: x = zeros(8191)+0.01
>
> In [35]: print evaluate('sum(x, axis=0)')
> 81.91
>
> In [36]: print evaluate('sum(x, axis=0)')
> 81.91
>
>
> Warren
>
>
>
> On Mon, Jan 24, 2011 at 12:19 PM, John Salvatier <
> jsalv...@u.washington.edu> wrote:
>
>> Forgot to mention that I am using numexpr 1.4.1 and numpy 1.5.1
>>
>>
>> On Mon, Jan 24, 2011 at 9:47 AM, John Salvatier <
>> jsalv...@u.washington.edu> wrote:
>>
>>> Hello,
>>>
>>> I have discovered a strange bug with numexpr. numexpr.evaluate gives
>>> randomized results on arrays larger than 2047 elements. The following
>>> program demonstrates this:
>>>
>>> from numpy import *
>>> from numexpr import evaluate
>>>
>>> def func(x):
>>>
>>> return evaluate("sum(x, axis = 0)")
>>>
>>>
>>> x = zeros(2048)+.01
>>>
>>> print evaluate("sum(x, axis = 0)")
>>> print evaluate("sum(x, axis = 0)")
>>>
>>> For me this prints different results each time, for example:
>>>
>>> 11.67
>>> 14.84
>>>
>>> If we set the size to 2047 I get consistent results.
>>>
>>> 20.47
>>> 20.47
>>>
>>> Interestingly, if I do not add .01 to x, it consistently sums to 0.
>>
>>
>>
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>>
>>
>
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