[SciPy-Dev] ANN: SciPy 0.8.0 beta 1

Vincent Davis vincent@vincentdavis....
Sat Jun 12 14:02:54 CDT 2010


On Fri, Jun 11, 2010 at 6:41 PM,  <josef.pktd@gmail.com> wrote:
> On Fri, Jun 11, 2010 at 7:54 PM, Derek Homeier
> <derek@astro.physik.uni-goettingen.de> wrote:
>> Hi Josef,
>>
>>>> FAIL: test_stats.test_kstest
>>>> ----------------------------------------------------------------------
>>>> Traceback (most recent call last):
>>>>  File "/sw/lib/python2.6/site-packages/nose/case.py", line 186, in runTest
>>>>    self.test(*self.arg)
>>>>  File "/sw/lib/python2.6/site-packages/scipy/stats/tests/test_stats.py", line 1078, in test_kstest
>>>>    np.array((0.0072115233216310994, 0.98531158590396228)), 14)
>>>>  File "/sw/lib/python2.6/site-packages/numpy/testing/utils.py", line 441, in assert_almost_equal
>>>>    return assert_array_almost_equal(actual, desired, decimal, err_msg)
>>>>  File "/sw/lib/python2.6/site-packages/numpy/testing/utils.py", line 765, in assert_array_almost_equal
>>>>    header='Arrays are not almost equal')
>>>>  File "/sw/lib/python2.6/site-packages/numpy/testing/utils.py", line 609, in assert_array_compare
>>>>    raise AssertionError(msg)
>>>> AssertionError:
>>>> Arrays are not almost equal
>>>>
>>>> (mismatch 100.0%)
>>>>  x: array([ 0.007,  0.985])
>>>>  y: array([ 0.007,  0.985])
>>>
>>> maybe the precision (decimal 14) is too high for this test across platforms
>>>
>>> Could you check how large the difference is ?
>>>
>>> np.random.seed(987654321)
>>> x = stats.norm.rvs(loc=0.2, size=100)
>>> np.array(stats.kstest(x,'norm', alternative = 'greater')) -
>>>                np.array((0.0072115233216310994, 0.98531158590396228))
>>>
>>> (my line numbers differ, but this should be the right test given your numbers)
>>
>>
>> yes, just a decimal or two too high, if I got the numbers right:
>> # OS X 10.5 i386 / 10.6 x86_64:
>> array([  8.67361738e-18,   1.66533454e-15])
>>
>> # OS X 10.5 ppc:
>> array([  2.05955045e-13,  -7.16759985e-13])
>
> interesting that there are differences in the calculations, but for
> the test we can just reduce the precision to decimal=12 to avoid the
> test failure.

I must be doing something wrong here becuase I don't get anything
close that what you have above.
In [4]: np.random.seed(987654321)

In [5]: x = stats.norm.rvs(loc=0.2, size=100)

In [6]: r1 = np.array(stats.kstest(x,'norm', alternative = 'greater'))

In [7]: r2 = np.array((0.0072115233216310994, 0.98531158590396228))

In [8]: r1-r2
Out[8]: array([ 0.03704986, -0.32866092])

Vincent


>
> Thanks,
> Josef
>
>>
>> Cheers,
>>                                                Derek
>>
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