I forgot...just in case: rsalva...@cactus:~$ python --version Python 2.5.2
python-scipy: version 0.6.0 On Wed, Sep 9, 2009 at 2:36 PM, Ruben Salvador <[email protected]>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 <[email protected]> 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 >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > >
_______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
