[Numpy-discussion] __array_priority__ don't work for gt, lt, ... operator
Sebastian Berg
sebastian at sipsolutions.net
Fri May 10 16:03:09 EDT 2013
On Fri, 2013-05-10 at 15:35 -0400, Frédéric Bastien wrote:
> I'm trying to do it, but each time I want to test something, it takes
> a long time to rebuild numpy to test it. Is there a way to don't
> recompile everything for each test?
>
Are you using current master? It defaults to use
ENABLE_SEPARATE_COMPILATION enviroment variable, which, together with
ccache, makes most changes in numpy compile fast for me.
- Sebastian
> thanks
>
> Fred
>
>
>
> On Fri, May 10, 2013 at 1:34 PM, Charles R Harris
> <charlesr.harris at gmail.com> wrote:
>
>
> On Fri, May 10, 2013 at 10:08 AM, Frédéric Bastien
> <nouiz at nouiz.org> wrote:
> Hi,
>
>
> it popped again on the Theano mailing list that this
> don't work:
>
>
> np.arange(10) <= a_theano_vector.
>
>
> The reason is that __array_priority__ isn't respected
> for that class of operation.
>
>
>
> This page explain the problem and give a work around:
>
>
> http://stackoverflow.com/questions/14619449/how-can-i-override-comparisons-between-numpys-ndarray-and-my-type
>
>
> The work around is to make a python function that will
> decide witch version of the comparator to call and do
> the call. Then we tell NumPy to use that function
> instead of its current function with:
> np.set_numeric_ops(...)
>
> But if we do that, when we import theano, we will slow
> down all normal numpy comparison for the user, as when
> <= is execute, first there will be numpy c code
> executed, that will call the python function to decide
> witch version to do, then if it is 2 numpy ndarray, it
> will call again numpy c code.
>
>
> That isn't a good solution. We could do the same
> override in C, but then theano work the same when
> there isn't a c++ compiler. That isn't nice.
>
>
> What do you think of changing them to check for
> __array_priority__ before doing the comparison?
>
> This looks like an oversight and should be fixed.
>
> Chuck
>
>
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