[Numpy-discussion] Adding a 2D with a 1D array...
Ruben Salvador
rsalvador.wk@gmail....
Wed Sep 9 07:42:43 CDT 2009
I forgot...just in case:
rsalvador@cactus:~$ python --version
Python 2.5.2
python-scipy: version 0.6.0
On Wed, Sep 9, 2009 at 2:36 PM, Ruben Salvador <rsalvador.wk@gmail.com>wrote:
> Your results are what I expected...but. This code is called from my main
> program, and what I have in there (output array already created for both
> cases) is:
>
> print "lambd", lambd
> print "np.shape(a)", np.shape(a)
> print "np.shape(r)", np.shape(r)
> print "np.shape(offspr)", np.shape(offspr)
> t = clock()
> for i in range(lambd):
> offspr[i] = r[i] + a[i]
> t1 = clock() - t
> print "For loop time ==> %.8f seconds" % t1
> t2 = clock()
> offspr = r + a[:,None]
> t3 = clock() - t2
> print "Pythonic time ==> %.8f seconds" % t3
>
> The results I obtain are:
>
> lambd 80000
> np.shape(a) (80000,)
> np.shape(r) (80000, 26)
> np.shape(offspr) (80000, 26)
> For loop time ==> 0.34528804 seconds
> Pythonic time ==> 0.35956192 seconds
>
> Maybe I'm not measuring properly, so, how should I do it?
>
> On Wed, Sep 9, 2009 at 1:20 PM, Citi, Luca <lciti@essex.ac.uk> wrote:
>
>> I am sorry but it doesn't make much sense.
>> How do you measure the performance?
>> Are you sure you include the creation of the "c" output array in the time
>> spent (which is outside the for loop but should be considered anyway)?
>>
>> Here are my results...
>>
>> In [84]: a = np.random.rand(8,26)
>>
>> In [85]: b = np.random.rand(8)
>>
>> In [86]: def o(a,b):
>> ....: c = np.empty_like(a)
>> ....: for i in range(len(a)):
>> ....: c[i] = a[i] + b[i]
>> ....: return c
>> ....:
>>
>> In [87]: d = a + b[:,None]
>>
>> In [88]: (d == o(a,b)).all()
>> Out[88]: True
>>
>> In [89]: %timeit o(a,b)
>> %ti10000 loops, best of 3: 36.8 µs per loop
>>
>> In [90]: %timeit d = a + b[:,None]
>> 100000 loops, best of 3: 5.17 µs per loop
>>
>> In [91]: a = np.random.rand(80000,26)
>>
>> In [92]: b = np.random.rand(80000)
>>
>> In [93]: %timeit o(a,b)
>> %ti10 loops, best of 3: 287 ms per loop
>>
>> In [94]: %timeit d = a + b[:,None]
>> 100 loops, best of 3: 15.4 ms per loop
>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.scipy.org/pipermail/numpy-discussion/attachments/20090909/39e75ce2/attachment.html
More information about the NumPy-Discussion
mailing list