[Numpy-discussion] Adding a 2D with a 1D array...

[Ruben Salvador]

I forgot...just in case:

rsalvador at 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 at 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 at 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
>>
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>>
>
>
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