[SciPy-Dev] ANN: SciPy 0.8.0 beta 1

Vincent Davis vincent@vincentdavis....
Sat Jun 12 14:41:14 CDT 2010


On Sat, Jun 12, 2010 at 1:37 PM,  <josef.pktd@gmail.com> wrote:
> On Sat, Jun 12, 2010 at 3:28 PM, Vincent Davis <vincent@vincentdavis.net> wrote:
>> On Sat, Jun 12, 2010 at 1:22 PM,  <josef.pktd@gmail.com> wrote:
>>> On Sat, Jun 12, 2010 at 3:02 PM, Vincent Davis <vincent@vincentdavis.net> wrote:
>>>> 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])
>>>
>>>>>> np.random.seed(987654321)
>>>>>> xrvs = stats.norm.rvs(loc=0.2, size=100)
>>>>>> r1 = np.array(stats.kstest(xrvs,'norm', alternative = 'greater'))
>>>>>> r2 = np.array((0.0072115233216310994, 0.98531158590396228))
>>>>>> r1-r2
>>> array([  8.67361738e-18,   1.66533454e-15])
>>>
>>> Can you check mean and var to see if you have the same random  numbers?
>>>
>>>>>> xrvs.mean()
>>> 0.20830662128271851
>>>>>> xrvs.var()
>>> 1.1210385272356511
>>
>> In [11]: x.mean()
>> Out[11]: 0.054996065027031464
>>
>> In [12]: x.var()
>> Out[12]: 0.92731406990162746
>
> looks like you have different random numbers
>
>>
>> I am cheating and using the enthought distribution, I just click install.
>> How do I run all of the tests for scipy or numpy when they are already
>> installed?
>
> scipy.stats.test()
> .test() works for scipy and every subpackage
>
> is ipython messing with the RandomState ?

In [19]: np.random.seed(987654321)

In [20]: np.random.rand(3)
Out[20]: array([ 0.07298833,  0.2160365 ,  0.46475349])

In [21]: np.random.rand(3)
Out[21]: array([ 0.62258994,  0.61838812,  0.42737911])

In [22]: np.random.seed(987654321)

In [23]: np.random.rand(3)
Out[23]: array([ 0.07298833,  0.2160365 ,  0.46475349])

Vincent

>
> Josef
>
>>
>> Vincent
>>
>>>
>>> otherwise I have no clue, (but I guess your scipy.stats tests pass)
>>>
>>> Josef
>>>
>>>
>>>>
>>>> Vincent
>>>>
>>>>
>>>>>
>>>>> Thanks,
>>>>> Josef
>>>>>
>>>>>>
>>>>>> Cheers,
>>>>>>                                                Derek
>>>>>>
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