related recent issue: https://github.com/numpy/numpy/issues/4638
and pandas is now explicitly specifying the accumulator to avoid this
problem: https://github.com/pydata/pandas/pull/6954/files
pandas also implemented the Welfords method for rolling_var in 0.14.0, see
here: https://github.com/pydat
Probably a number of scipy places as well
import numpy
import scipy.stats
print numpy.__version__
print scipy.__version__
for s in range(16777214, 16777944):
if scipy.stats.nanmean(numpy.ones((s, 1), numpy.float32))[0]!=1:
print '\nbroke', s, scipy.stats.nanmean(numpy.ones((s, 1),
import numpy
print numpy.__version__
for s in range(1864100, 1864200):
if numpy.ones((s, 9), numpy.float32).sum()!= s*9:
print '\nbroke', s
break
else:
print '\r',s,
C:\temp>python np_sum.py
1.8.0b2
1864135
broke 1864136
import numpy
print numpy.__version__
for s
On Thu, Jul 24, 2014 at 12:59 PM, Charles R Harris <
charlesr.har...@gmail.com> wrote:
>
>
>
> On Thu, Jul 24, 2014 at 8:27 AM, Jaime Fernández del Río <
> jaime.f...@gmail.com> wrote:
>
>> On Thu, Jul 24, 2014 at 4:56 AM, Julian Taylor <
>> jtaylor.deb...@googlemail.com> wrote:
>>
>>> In practice
Le 24/07/2014 12:55, Thomas Unterthiner a écrit :
> I don't agree. The problem is that I expect `mean` to do something
> reasonable. The documentation mentions that the results can be
> "inaccurate", which is a huge understatement: the results can be utterly
> wrong. That is not reasonable. At the
On Thu, Jul 24, 2014 at 8:27 AM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> On Thu, Jul 24, 2014 at 4:56 AM, Julian Taylor <
> jtaylor.deb...@googlemail.com> wrote:
>
>> In practice one of the better methods is pairwise summation that is
>> pretty much as fast as a naive summation b
On 7/24/2014 5:59 AM, Eelco Hoogendoorn wrote to Thomas:
> np.mean isn't broken; your understanding of floating point number is.
This comment seems to conflate separate issues:
the desirable return type, and the computational algorithm.
It is certainly possible to compute a mean of float32
doing
On Thu, Jul 24, 2014 at 4:56 AM, Julian Taylor <
jtaylor.deb...@googlemail.com> wrote:
> In practice one of the better methods is pairwise summation that is
> pretty much as fast as a naive summation but has an accuracy of
> O(logN) ulp.
> This is the method numpy 1.9 will use this method by defau
On Thu, Jul 24, 2014 at 1:33 PM, Fabien wrote:
> Hi all,
>
> On 24.07.2014 11:59, Eelco Hoogendoorn wrote:
>> np.mean isn't broken; your understanding of floating point number is.
>
> I am quite new to python, and this problem is discussed over and over
> for other languages too. However, numpy's
Hi all,
On 24.07.2014 11:59, Eelco Hoogendoorn wrote:
> np.mean isn't broken; your understanding of floating point number is.
I am quite new to python, and this problem is discussed over and over
for other languages too. However, numpy's summation problem appears with
relatively small arrays al
I don't agree. The problem is that I expect `mean` to do something
reasonable. The documentation mentions that the results can be
"inaccurate", which is a huge understatement: the results can be utterly
wrong. That is not reasonable. At the very least, a warning should be
issued in cases where
Arguably, this isn't a problem of numpy, but of programmers being trained
to think of floating point numbers as 'real' numbers, rather than just a
finite number of states with a funny distribution over the number line.
np.mean isn't broken; your understanding of floating point number is.
What you
Hi!
The following is a known "bug" since at least 2010 [1]:
import numpy as np
X = np.ones((5, 1024), np.float32)
print X.mean()
>>> 0.32768
I ran into this for the first time today as part of a larger program. I
was very surprised by this, and spent over an hour lookin
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