# [Numpy-discussion] Short circuiting the all() and any() methods/functions

Justin Peel jpscipy@gmail....
Mon Dec 20 16:32:28 CST 2010

```I'm using version 2.0.0.dev8716, which should be new enough I would
think.  Let me show you what makes me think that there isn't
short-circuiting going on.

I'll do two timeit's from the command line:

\$ python -m timeit -s 'import numpy as np; x = np.ones(200000)' 'x.all()'
100 loops, best of 3: 3.87 msec per loop
\$ python -m timeit -s 'import numpy as np; x = np.ones(200000); x[0] =
0' 'x.all()'
100 loops, best of 3: 2.76 msec per loop

You can try different sizes for the arrays if you like, but the ratio
of the times seems to hold pretty well. I would think that the second
statement would be much, much faster than the first. Instead, it is
only about 29% faster. I'm guessing that this speed isn't so much from
short-circuiting as that the logical AND operator is faster when the
first argument is 0 (the second argument doesn't need to be checked).
What do you think?

On Mon, Dec 20, 2010 at 2:12 PM, Charles R Harris
<charlesr.harris@gmail.com> wrote:
>
>
> On Mon, Dec 20, 2010 at 1:25 PM, Justin Peel <jpscipy@gmail.com> wrote:
>>
>> It has come to my attention that the all() and any() methods/functions
>> do not short circuit. It takes nearly as much time to call any() on an
>> array which has 1 as the first entry as it does to call it on an array
>> of the same size full of zeros.
>>
>> The cause of the problem is that all() and any() just call reduce()
>> with the appropriate operator. Is anyone opposed to changing the
>> implementations of these functions so that they short-circuit?
>>
>
> Recent version of reduce do short circuit. What version of numpy are you
> using?
>
> Chuck
>
>
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```