[Numpy-discussion] Bug in logaddexp2.reduce

Thu Apr 1 17:51:45 EDT 2010

On 1 April 2010 13:38, Charles R Harris <charlesr.harris at gmail.com> wrote:
>
>
> On Thu, Apr 1, 2010 at 8:37 AM, Charles R Harris <charlesr.harris at gmail.com>
> wrote:
>>
>>
>> On Thu, Apr 1, 2010 at 12:46 AM, Anne Archibald
>> <peridot.faceted at gmail.com> wrote:
>>>
>>> On 1 April 2010 02:24, Charles R Harris <charlesr.harris at gmail.com>
>>> wrote:
>>> >
>>> >
>>> > On Thu, Apr 1, 2010 at 12:04 AM, Anne Archibald
>>> > <peridot.faceted at gmail.com>
>>> > wrote:
>>> >>
>>> >> On 1 April 2010 01:59, Charles R Harris <charlesr.harris at gmail.com>
>>> >> wrote:
>>> >> >
>>> >> >
>>> >> > On Wed, Mar 31, 2010 at 11:46 PM, Anne Archibald
>>> >> > <peridot.faceted at gmail.com>
>>> >> > wrote:
>>> >> >>
>>> >> >> On 1 April 2010 01:40, Charles R Harris <charlesr.harris at gmail.com>
>>> >> >> wrote:
>>> >> >> >
>>> >> >> >
>>> >> >> > On Wed, Mar 31, 2010 at 11:25 PM, <josef.pktd at gmail.com> wrote:
>>> >> >> >>
>>> >> >> >> On Thu, Apr 1, 2010 at 1:22 AM,  <josef.pktd at gmail.com> wrote:
>>> >> >> >> > On Thu, Apr 1, 2010 at 1:17 AM, Charles R Harris
>>> >> >> >> > <charlesr.harris at gmail.com> wrote:
>>> >> >> >> >>
>>> >> >> >> >>
>>> >> >> >> >> On Wed, Mar 31, 2010 at 6:08 PM, <josef.pktd at gmail.com>
>>> >> >> >> >> wrote:
>>> >> >> >> >>>
>>> >> >> >> >>> On Wed, Mar 31, 2010 at 7:37 PM, Warren Weckesser
>>> >> >> >> >>> <warren.weckesser at enthought.com> wrote:
>>> >> >> >> >>> > T J wrote:
>>> >> >> >> >>> >> On Wed, Mar 31, 2010 at 1:21 PM, Charles R Harris
>>> >> >> >> >>> >> <charlesr.harris at gmail.com> wrote:
>>> >> >> >> >>> >>
>>> >> >> >> >>> >>> Looks like roundoff error.
>>> >> >> >> >>> >>>
>>> >> >> >> >>> >>>
>>> >> >> >> >>> >>
>>> >> >> >> >>> >> So this is "expected" behavior?
>>> >> >> >> >>> >>
>>> >> >> >> >>> >> In [1]: np.logaddexp2(-1.5849625007211563,
>>> >> >> >> >>> >> -53.584962500721154)
>>> >> >> >> >>> >> Out[1]: -1.5849625007211561
>>> >> >> >> >>> >>
>>> >> >> >> >>> >> In [2]: np.logaddexp2(-0.5849625007211563,
>>> >> >> >> >>> >> -53.584962500721154)
>>> >> >> >> >>> >> Out[2]: nan
>>> >> >> >> >>> >>
>>> >> >> >> >>> >
>>> >> >> >> >>> > Is any able to reproduce this?  I don't get 'nan' in
>>> >> >> >> >>> > either
>>> >> >> >> >>> > 1.4.0
>>> >> >> >> >>> > or
>>> >> >> >> >>> > 2.0.0.dev8313 (32 bit Mac OSX).  In an earlier email T J
>>> >> >> >> >>> > reported
>>> >> >> >> >>> > using
>>> >> >> >> >>> > 1.5.0.dev8106.
>>> >> >> >> >>>
>>> >> >> >> >>>
>>> >> >> >> >>>
>>> >> >> >> >>> >>> np.logaddexp2(-0.5849625007211563, -53.584962500721154)
>>> >> >> >> >>> nan
>>> >> >> >> >>> >>> np.logaddexp2(-1.5849625007211563, -53.584962500721154)
>>> >> >> >> >>> -1.5849625007211561
>>> >> >> >> >>>
>>> >> >> >> >>> >>> np.version.version
>>> >> >> >> >>> '1.4.0'
>>> >> >> >> >>>
>>> >> >> >> >>> WindowsXP 32
>>> >> >> >> >>>
>>> >> >> >> >>
>>> >> >> >> >> What compiler? Mingw?
>>> >> >> >> >
>>> >> >> >> > yes, mingw 3.4.5. , official binaries release 1.4.0 by David
>>> >> >> >>
>>> >> >> >> sse2 Pentium M
>>> >> >> >>
>>> >> >> >
>>> >> >> > Can you try the exp2/log2 functions with the problem data and see
>>> >> >> > if
>>> >> >> > something goes wrong?
>>> >> >>
>>> >> >> Works fine for me.
>>> >> >>
>>> >> >> If it helps clarify things, the difference between the two problem
>>> >> >> input values is exactly 53 (and that's what logaddexp2 does an exp2
>>> >> >> of); so I can provide a simpler example:
>>> >> >>
>>> >> >> In [23]: np.logaddexp2(0, -53)
>>> >> >> Out[23]: nan
>>> >> >>
>>> >> >> Of course, for me it fails in both orders.
>>> >> >>
>>> >> >
>>> >> > Ah, that's progress then ;) The effective number of bits in a double
>>> >> > is
>>> >> > 53
>>> >> > (52 + implicit bit). That shouldn't cause problems but it sure looks
>>> >> > suspicious.
>>> >>
>>> >> Indeed, that's what led me to the totally wrong suspicion that
>>> >> denormals have something to do with the problem. More data points:
>>> >>
>>> >> In [38]: np.logaddexp2(63.999, 0)
>>> >> Out[38]: nan
>>> >>
>>> >> In [39]: np.logaddexp2(64, 0)
>>> >> Out[39]: 64.0
>>> >>
>>> >> In [42]: np.logaddexp2(52.999, 0)
>>> >> Out[42]: 52.999000000000002
>>> >>
>>> >> In [43]: np.logaddexp2(53, 0)
>>> >> Out[43]: nan
>>> >>
>>> >> It looks to me like perhaps the NaNs are appearing when the smaller
>>> >> term affects only the "extra" bits provided by the FPU's internal
>>> >> larger-than-double representation. Some such issue would explain why
>>> >> the problem seems to be hardware- and compiler-dependent.
>>> >>
>>> >
>>> > Hmm, that 63.999 is kinda strange. Here is a bit of code that might
>>> > confuse
>>> > a compiler working with different size mantissas:
>>> >
>>> > @type@ npy_log2_1p at c@(@type@ x)
>>> > {
>>> >     @type@ u = 1 + x;
>>> >     if (u == 1) {
>>> >         return LOG2E*x;
>>> >     } else {
>>> >         return npy_log2 at c@(u) * x / (u - 1);
>>> >     }
>>> > }
>>> >
>>> > It might be that u != 1 does not imply u-1 != 0.
>>>
>>> That does indeed look highly suspicious. I'm not entirely sure how to
>>> work around it. GSL uses a volatile declaration:
>>>
>>> http://www.google.ca/codesearch/p?hl=en#p9nGS4eQGUI/gnu/gsl/gsl-1.8.tar.gz%7C8VCQSLJ5jR8/gsl-1.8/sys/log1p.c&q=log1p
>>> On the other hand boost declares itself defeated by optimizing
>>> compilers and uses a Taylor series:
>>>
>>> http://www.google.ca/codesearch/p?hl=en#sdP2GRSfgKo/dcplusplus/trunk/boost/boost/math/special_functions/log1p.hpp&q=log1p&sa=N&cd=7&ct=rc
>>> While R makes no mention of the corrected formula or optimizing
>>> compilers but takes the same approach, only with Chebyshev series:
>>>
>>> http://www.google.ca/codesearch/p?hl=en#gBBSWbwZmuk/src/base/R-2/R-2.3.1.tar.gz%7CVuh8XhRbUi8/R-2.3.1/src/nmath/log1p.c&q=log1p
>>>
>>> Since, at least on my machine, ordinary log1p appears to work fine, is
>>> there any reason not to have log2_1p call it and scale the result? Or
>>> does the compiler make a hash of our log1p too?
>>>
>>
>> Calling log1p and scaling looks like the right thing to do here. And our
>> log1p needs improvement.
>>
>
> Tinkering a bit, I think we should implement the auxiliary function f(p) =
> log((1+p)/(1 - p)), which is antisymmetric and has the expansion 2p*(1 +
> p^2/3 + p^4/5 + ...). The series in the parens is increasing, so it is easy
> to terminate. Note that for p = +/- 1 it goes over to the harmonic series,
> so convergence is slow near the ends, but they can be handled using normal
> logs. Given 1 + x = (1+p)/(1-p) and solving for p gives p = x/(2 + x), so
> when x ranges from -1/2 -> 1/2, p ranges from -1/3 -> 1/5, hence achieving
> double precision should involve no more than  about 17 terms. I think this
> is better than the expansion in R.

First I guess we should check which systems don't have log1p; if glibc
has it as an intrinsic, that should cover Linux (though I suppose we
should check its quality). Do Windows and Mac have log1p? For testing
I suppose we should make our own implementation somehow available even
on systems where it's unnecessary.

Power series are certainly easy, but would some of the other available
tricks - Chebyshev series or rational function approximations - be
better? I notice R uses Chebyshev series, although maybe that's just
because they have a good evaluator handy.

Anne

> Chuck
>
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