On Tue, Jul 19, 2011 at 6:10 PM, Pauli Virtanen <p...@iki.fi> wrote:
> k = m - 0.5
>
> does here the same thing as
>
> k = np.empty_like(m)
> np.subtract(m, 0.5, out=k)
>
> The memory allocation (empty_like and the subsequent deallocation)
> costs essentially nothing, and there are no temporaries or copying
> in `subtract`.
As verification:
>>> import timeit
>>> import numpy as np
>>> t=timeit.Timer('k = m - 0.5', setup='import numpy as np;m =
>>> np.ones([8092,8092],float)')
>>> np.mean(t.repeat(repeat=10, number=1))
0.53904647827148433
>>> t=timeit.Timer('k = np.empty_like(m);np.subtract(m, 0.5, out=k)',
>>> setup='import numpy as np;m = np.ones([8092,8092],float)')
>>> np.mean(t.repeat(repeat=10, number=1))
0.54006035327911373
The trivial difference is expected as extra python parsing overhead, I think.
Which leads me to apologize, since in my previous post I clearly meant
to type "m -= 0.5", not "m =- 0.5", which is *quite* a different
operation... Carlos, and Lutz, please take heed. :) In fact, as Lutz
pointed out, that example was not at all what I intended to show
anyway.
So, just to demonstrate how it was wrong:
>>> t=timeit.Timer('m =- 0.5', setup='import numpy as np;m =
>>> np.ones([8092,8092],float)')
>>> np.mean(t.repeat(repeat=10, number=1))
0.058299207687377931
>>> t=timeit.Timer('m -= 0.5', setup='import numpy as np;m =
>>> np.ones([8092,8092],float)')
>>> np.mean(t.repeat(repeat=10, number=1))
0.28192551136016847
>>> t=timeit.Timer('np.subtract(m, 0.5, m)', setup='import numpy as np;m =
>>> np.ones([8092,8092],float)')
>>> np.mean(t.repeat(repeat=10, number=1))
0.27014491558074949
>>> t=timeit.Timer('np.subtract(m, 0.5, k)', setup='import numpy as np;m =
>>> np.ones([8092,8092],float); k = np.empty_like(m)')
>>> np.mean(t.repeat(repeat=10, number=1))
0.54962997436523442
Perhaps the difference in the last two simply comes down to cache
effects (having to iterate over two different large memory blocks,
rather than one)?
-Chad
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
