[Numpy-discussion] Slightly off-topic - accuracy of C exp function?
josef.pktd at gmail.com
josef.pktd at gmail.com
Sat Apr 26 22:03:05 EDT 2014
On Sat, Apr 26, 2014 at 8:31 PM, <josef.pktd at gmail.com> wrote:
>
>
>
> On Sat, Apr 26, 2014 at 8:05 PM, <josef.pktd at gmail.com> wrote:
>
>>
>>
>>
>> On Sat, Apr 26, 2014 at 6:37 PM, Matthew Brett <matthew.brett at gmail.com>wrote:
>>
>>> Hi,
>>>
>>> On Wed, Apr 23, 2014 at 11:59 AM, Matthew Brett <matthew.brett at gmail.com>
>>> wrote:
>>> > Hi,
>>> >
>>> > On Wed, Apr 23, 2014 at 1:43 AM, Nathaniel Smith <njs at pobox.com>
>>> wrote:
>>> >> On Wed, Apr 23, 2014 at 6:22 AM, Matthew Brett <
>>> matthew.brett at gmail.com> wrote:
>>> >>> Hi,
>>> >>>
>>> >>> I'm exploring Mingw-w64 for numpy building, and I've found it gives a
>>> >>> slightly different answer for 'exp' than - say - gcc on OSX.
>>> >>>
>>> >>> The difference is of the order of the eps value for the output number
>>> >>> (2 * eps for a result of ~2.0).
>>> >>>
>>> >>> Is accuracy somewhere specified for C functions like exp? Or is
>>> >>> accuracy left as an implementation detail for the C library author?
>>> >>
>>> >> C99 says (sec 5.2.4.2.2) that "The accuracy of the floating point
>>> >> operations ... and of the library functions in <math.h> and
>>> >> <complex.h> that return floating point results is implemenetation
>>> >> defined. The implementation may state that the accuracy is unknown."
>>> >> (This last sentence is basically saying that with regard to some
>>> >> higher up clauses that required all conforming implementations to
>>> >> document this stuff, saying "eh, who knows" counts as documenting it.
>>> >> Hooray for standards!)
>>> >>
>>> >> Presumably the accuracy in this case is a function of the C library
>>> >> anyway, not the compiler?
>>> >
>>> > Mingw-w64 implementation is in assembly:
>>> >
>>> >
>>> http://sourceforge.net/p/mingw-w64/code/HEAD/tree/trunk/mingw-w64-crt/math/exp.def.h
>>> >
>>> >> Numpy has its own implementations for a
>>> >> bunch of the math functions, and it's been unclear in the past whether
>>> >> numpy or the libc implementations were better in any particular case.
>>> >
>>> > I only investigated this particular value, in which case it looked as
>>> > though the OSX value was closer to the exact value (via sympy.mpmath)
>>> > - by ~1 unit-at-the-last-place. This was causing a divergence in the
>>> > powell optimization path and therefore a single scipy test failure. I
>>> > haven't investigated further - was wondering what investigation I
>>> > should do, more than running the numpy / scipy test suites.
>>>
>>> Investigating further, with this script:
>>>
>>> https://gist.github.com/matthew-brett/11301221
>>>
>>> The following are tests of np.exp accuracy for input values between 0
>>> and 10, for numpy 1.8.1.
>>>
>>> If np.exp(x) performs perfectly, it will return the nearest floating
>>> point value to the exact value of exp(x). If it does, this scores a
>>> zero for error in the tables below. If 'proportion of zeros' is 1 -
>>> then np.exp performs perfectly for all tested values of exp (as is the
>>> case for linux here).
>>>
>>> OSX 10.9
>>>
>>> Proportion of zeros: 0.99789
>>> Sum of error: 2.15021267458e-09
>>> Sum of squared error: 2.47149370032e-14
>>> Max / min error: 5.96046447754e-08 -2.98023223877e-08
>>> Sum of squared relative error: 5.22456992025e-30
>>> Max / min relative error: 2.19700100681e-16 -2.2098803255e-16
>>> eps: 2.22044604925e-16
>>> Proportion of relative err >= eps: 0.0
>>>
>>> Debian Jessie / Sid
>>>
>>> Proportion of zeros: 1.0
>>> Sum of error: 0.0
>>> Sum of squared error: 0.0
>>> Max / min error: 0.0 0.0
>>> Sum of squared relative error: 0.0
>>> Max / min relative error: 0.0 0.0
>>> eps: 2.22044604925e-16
>>> Proportion of relative err >= eps: 0.0
>>>
>>> Mingw-w64 Windows 7
>>>
>>> Proportion of zeros: 0.82089
>>> Sum of error: 8.08415331122e-07
>>> Sum of squared error: 2.90045099615e-12
>>> Max / min error: 5.96046447754e-08 -5.96046447754e-08
>>> Sum of squared relative error: 4.18466468175e-28
>>> Max / min relative error: 2.22041308226e-16 -2.22042100773e-16
>>> eps: 2.22044604925e-16
>>> Proportion of relative err >= eps: 0.0
>>>
>>> Take-home : exp implementation for mingw-w64 is exactly (floating
>>> point) correct 82% of the time, and one unit-at-the-last-place off for
>>> the rest [1]. OSX is off by 1 ULP only 0.2% of the time.
>>>
>>
>>
>> Windows 64 with MKL
>>
>> \WinPython-64bit-3.3.2.2\python-3.3.2.amd64>python
>> "E:\Josef\eclipsegworkspace\statsmodels-git\local_scripts\local_scripts\try_exp_error.py"
>> Proportion of zeros: 0.99793
>> Sum of error: -2.10546855506e-07
>> Sum of squared error: 3.33304327526e-14
>> Max / min error: 5.96046447754e-08 -5.96046447754e-08
>> Sum of squared relative error: 4.98420694339e-30
>> Max / min relative error: 2.20881302691e-16 -2.18321571939e-16
>> eps: 2.22044604925e-16
>> Proportion of relative err >= eps: 0.0
>>
>>
>> Windows 32 bit python with official MingW binaries
>>
>> Python 2.7.1 (r271:86832, Nov 27 2010, 18:30:46) [MSC v.1500 32 bit
>> (Intel)] on win32
>>
>> Proportion of zeros: 0.99464
>> Sum of error: -3.91621083118e-07
>> Sum of squared error: 9.2239247812e-14
>> Max / min error: 5.96046447754e-08 -5.96046447754e-08
>> Sum of squared relative error: 1.3334972729e-29
>> Max / min relative error: 2.21593462148e-16 -2.2098803255e-16
>> eps: 2.22044604925e-16
>> Proportion of relative err >= eps: 0.0
>>
>>
>>
>>>
>>> Is mingw-w64 accurate enough? Do we have any policy on this?
>>>
>>
>> I wouldn't worry about a missing or an extra eps in our applications, but
>> the competition is more accurate.
>>
>
>
> Just for comparison, I increased `until` to 300
> the proportion of zeros and relative error stays about the same for both
> MKL and your wheels
>
> absolute error are huge, the following is MKL
>
> Sum of error: -3.78802736366e+112
> Sum of squared error: 1.51136754049e+225
> Max / min error: 4.80981520952e+111 -3.84785216762e+112
>
> (I looked a lot at the behavior of exp in the hundreds recently :(
>
> As illustration why I don't care about one **relative** eps
> >>> np.finfo(np.double).eps + 10 == 10
> True
>
> https://github.com/scipy/scipy/pull/3547 and many others
>
another variation on the theme
with wheels
there is a positive bias, pretty large
>>> errors.sum()
8.0841533112163688e-07
but where did it go ?
>>> exact_exp.sum() - np.exp(vals).sum()
0.0
(Law of Large Numbers ? errors get swamped or cancel)
with MKL
smaller negative bias
>>> errors.sum()
-2.1054685550581098e-07
>>> exact_exp.sum() - np.exp(vals).sum()
0.0
Josef
>
>
> Josef
>
>
>>
>> Josef
>>
>>
>>>
>>> Cheers,
>>>
>>> Matthew
>>>
>>> [1] http://matthew-brett.github.io/pydagogue/floating_error.html
>>> _______________________________________________
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>
>>
>
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