You're right, I got the same behavior. Interesting. On Mon, Jan 24, 2011 at 11:35 AM, Warren Weckesser < warren.weckes...@enthought.com> wrote:
> > > On Mon, Jan 24, 2011 at 1:13 PM, John Salvatier <jsalv...@u.washington.edu > > wrote: > >> Looks like this is related to issue 41 ( >> http://code.google.com/p/numexpr/issues/detail?id=41&can=1). > > > > That might not be the same issue. > > You can fix the "randomness" by setting the number of threads to 1, as in > input [6] here: > > In [1]: import numexpr as ne > > In [2]: x = zeros(8192)+0.01 > > In [3]: ne.evaluate('sum(x, axis=0)') > Out[3]: array(71.119999999999479) > > In [4]: ne.evaluate('sum(x, axis=0)') > Out[4]: array(81.920000000005004) > > In [5]: ne.evaluate('sum(x, axis=0)') > Out[5]: array(68.379999999998077) > > In [6]: ne.set_num_threads(1) > > In [7]: ne.evaluate('sum(x, axis=0)') > Out[7]: array(81.920000000005004) > > In [8]: ne.evaluate('sum(x, axis=0)') > Out[8]: array(81.920000000005004) > > In [9]: ne.evaluate('sum(x, axis=0)') > Out[9]: array(81.920000000005004) > > > Warren > > > >> >> >> On Mon, Jan 24, 2011 at 10:29 AM, John Salvatier < >> jsalv...@u.washington.edu> wrote: >> >>> 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. >>>>> >>>>> >>>>> >>>>> _______________________________________________ >>>>> NumPy-Discussion mailing list >>>>> NumPy-Discussion@scipy.org >>>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>>>> >>>>> >>>> >>>> _______________________________________________ >>>> NumPy-Discussion mailing list >>>> NumPy-Discussion@scipy.org >>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>>> >>>> >>> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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