[SciPy-Dev] TestNorm.test_stable failure [was ANN: SciPy 0.11.0 release candidate 2]
Sat Sep 22 12:01:05 CDT 2012
On Sat, Sep 22, 2012 at 6:14 PM, Rüdiger Kessel
> test_basic.TestNorm.test_stable is very sensitive against the
> implementation of the basic BLAS routine snrm2() as I have explained
> Its good to tell implementations apart, but it is not very robust.
> A pure float32 implementation of snrm2() will give 10000.0 for norm(a).
> An internal double implementation with rounding to float32 at the end
> gives 10000.5 for norm(a).
> A mixed implementation with rounding to float32 before rescaling gives
> 10000.4990234375 for norm(a).
> I have not looked into the implementation in ATLAS. I just played with
> my python implementation to see if I can find an explanation.
> I think the problem here is that for matrix a some data was chosen
> where a pure float32 implementation of snrm2() will not give a precise
> result. So any implementation with gives something in the range from
> 10000.0 to 10000.5 need to be considered as correct.
> Maybe it would be better to chose data where rounded double and
> float32 implementation will give the same result like:
> a = array(range(90000,100000), dtype=float32)/10.0
> norm(a) should give 950434.0 for any implementation.
I think you mean 950433.5, which is what float64 gives (approximately). On
win32 both scipy and numpy give 950433.5, on OS X scipy gives 950433.5 and
numpy 950433.56. The original purpose of the test was to check that the
scipy version was more stable than the numpy one, so the input you suggest
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