[SciPy-Dev] ANN: SciPy 0.8.0 beta 1

josef.pktd@gmai... josef.pktd@gmai...
Sat Jun 12 15:00:20 CDT 2010


On Sat, Jun 12, 2010 at 3:50 PM, Vincent Davis <vincent@vincentdavis.net> wrote:
> On Sat, Jun 12, 2010 at 1:47 PM,  <josef.pktd@gmail.com> wrote:
>> 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])
>>
>> ??
>
> Gets better, I just ran the test, I need to look above to see how this relates.
>
> FAIL: test_stats.test_kstest
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>  File "/Library/Frameworks/EPD64.framework/Versions/6.2/lib/python2.6/site-packages/nose/case.py",
> line 186, in runTest
>    self.test(*self.arg)
>  File "/Library/Frameworks/EPD64.framework/Versions/6.2/lib/python2.6/site-packages/scipy/stats/tests/test_stats.py",
> line 1228, in test_kstest
>    assert_almost_equal( D, 0.12464329735846891, 15)
>  File "/Library/Frameworks/EPD64.framework/Versions/6.2/lib/python2.6/site-packages/numpy/testing/utils.py",
> line 459, in assert_almost_equal
>    raise AssertionError(msg)
> AssertionError:
> Arrays are not almost equal
>  ACTUAL: 0.093893737596468518
>  DESIRED: 0.12464329735846891


can you check stats random numbers

>>> np.random.seed(987654321)
>>> stats.norm.rvs(size=3)
array([ 2.24655081, -0.64591822, -1.18357699])
>>> np.random.seed(987654321)
>>> np.random.randn(3)
array([ 2.24655081, -0.64591822, -1.18357699])

which numpy, scipy versions?

Josef

>
> Vincent
>
>>
>> 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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