[SciPy-Dev] TestNorm.test_stable failure [was ANN: SciPy 0.11.0 release candidate 2]

[Ralf Gommers]

On Sat, Sep 22, 2012 at 9:31 PM, Rüdiger Kessel
<ruediger.kessel at gmail.com>wrote:

> Don't we talk about snrm2() for float32? So it should always return
> float32 and not float64, doesn't it?
>

Indeed. Maybe my message wasn't clear enough, but I did show float32 values:

>>> a = array(range(90000,100000), dtype=float32)/10.0
>>> linalg.norm(a)
950433.5
>>> np.linalg.norm(a)
950433.56



>
> Ruediger
>
> 2012/9/22 Ralf Gommers <ralf.gommers at gmail.com>:
> > 57711ac8b
> >
> >
> >
> > On Sat, Sep 22, 2012 at 6:14 PM, Rüdiger Kessel <
> ruediger.kessel at gmail.com>
> > wrote:
> >>
> >> Hi,
> >>
> >> test_basic.TestNorm.test_stable is very sensitive against the
> >> implementation of the basic BLAS routine snrm2() as I have explained
> >> before.
> >> 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
> > may work.
> >
> > Thanks,
> > Ralf
> >
> >
> >
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> >
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