[Numpy-discussion] Different results from repeated calculation, part 2
Keith Goodman
kwgoodman@gmail....
Thu Aug 14 13:57:33 CDT 2008
On Thu, Aug 14, 2008 at 11:29 AM, Bruce Southey <bsouthey@gmail.com> wrote:
> Keith Goodman wrote:
>> I get slightly different results when I repeat a calculation.
>>
>> I've seen this problem before (it went away but has returned):
>>
>> http://projects.scipy.org/pipermail/numpy-discussion/2007-January/025724.html
>>
>> A unit test is attached. It contains three tests:
>>
>> In test1, I construct matrices x and y and then repeatedly calculate z
>> = calc(x,y). The result z is the same every time. So this test passes.
>>
>> In test2, I construct matrices x and y each time before calculating z
>> = calc(x,y). Sometimes z is slightly different. But the x's test to be
>> equal and so do the y's. This test fails (on Debian Lenny, Core 2 Duo,
>> with libatlas3gf-sse2 but not with libatlas3gf-sse).
>>
>> test3 is the same as test2 but I calculate z like this: z =
>> calc(100*x,y) / (100 * 100). This test passes.
>>
>> I get:
>>
>> ======================================================================
>> FAIL: repeatability #2
>> ----------------------------------------------------------------------
>> Traceback (most recent call last):
>> File "/home/[snip]/test/repeat_test.py", line 73, in test_repeat_2
>> self.assert_(result, msg)
>> AssertionError: Max difference = 2.04946e-16
>>
>> ----------------------------------------------------------------------
>>
>> Should a unit test like this be added to numpy?
>>
>> ------------------------------------------------------------------------
>>
>> _______________________________________________
>> Numpy-discussion mailing list
>> Numpy-discussion@scipy.org
>> http://projects.scipy.org/mailman/listinfo/numpy-discussion
> Hi,
> In the function 'test_repeat_2' you are redefining variables 'x and y'
> that were first defined using the setup function. (Also, you are not
> using the __init__ function.) I vaguely recall there are some quirks to
> Python classes with this, so does the problem go away with if you use
> 'a,b' instead of 'x, y'? (I suspect the answer is yes given test_repeat_3).
>
> Note that you should also test that 'x' and 'y' are same here as well
> (but these have been redefined...).
>
> Otherwise, can you please provide your OS (version), computer processor,
> Python version, numpy version, version of atlas (or similar) and
> compiler used?
>
> I went back and reread the thread but I could not see this information.
Here's a test that doesn't use classes and checks that x and y do not change:
http://projects.scipy.org/pipermail/numpy-discussion/attachments/20070127/52b3a51c/attachment.py
I'm using binaries from Debian Lenny:
$ uname -a
Linux jan 2.6.25-2-686 #1 SMP Fri Jul 18 17:46:56 UTC 2008 i686 GNU/Linux
$ python -V
Python 2.5.2
>> numpy.__version__
'1.1.0'
$ cat /proc/cpuinfo
processor : 0
vendor_id : GenuineIntel
cpu family : 6
model : 15
model name : Intel(R) Core(TM)2 CPU 6600 @ 2.40GHz
stepping : 6
cpu MHz : 2402.004
cache size : 4096 KB
physical id : 0
siblings : 2
core id : 0
cpu cores : 2
fdiv_bug : no
hlt_bug : no
f00f_bug : no
coma_bug : no
fpu : yes
fpu_exception : yes
cpuid level : 10
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov
pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe nx lm
constant_tsc arch_perfmon pebs bts pni monitor ds_cpl vmx est tm2
ssse3 cx16 xtpr lahf_lm
bogomips : 4807.45
clflush size : 64
processor : 1
vendor_id : GenuineIntel
cpu family : 6
model : 15
model name : Intel(R) Core(TM)2 CPU 6600 @ 2.40GHz
stepping : 6
cpu MHz : 2402.004
cache size : 4096 KB
physical id : 0
siblings : 2
core id : 1
cpu cores : 2
fdiv_bug : no
hlt_bug : no
f00f_bug : no
coma_bug : no
fpu : yes
fpu_exception : yes
cpuid level : 10
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov
pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe nx lm
constant_tsc arch_perfmon pebs bts pni monitor ds_cpl vmx est tm2
ssse3 cx16 xtpr lahf_lm
bogomips : 4750.69
clflush size : 64
More information about the Numpy-discussion
mailing list