[SciPy-Dev] ANN: SciPy 0.8.0 beta 1

josef.pktd@gmai... josef.pktd@gmai...
Sat Jun 12 14:47:07 CDT 2010


On Sat, Jun 12, 2010 at 3:41 PM, Vincent Davis <vincent@vincentdavis.net> wrote:
> 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])

same here

>>> np.random.seed(987654321)
>>> np.random.rand(3)
array([ 0.07298833,  0.2160365 ,  0.46475349])
>>> np.random.rand(3)
array([ 0.62258994,  0.61838812,  0.42737911])

??

Josef
>
> 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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