[SciPy-dev] Suppressing of numpy __mul__, __div__ etc
Charles R Harris
charlesr.harris@gmail....
Thu Dec 17 17:35:46 CST 2009
On Thu, Dec 17, 2009 at 2:27 PM, James Bergstra <james.bergstra@gmail.com>wrote:
> I develop another symbolic-over-numpy package called theano, and
> somehow we avoid this problem.
>
> In [1]: import theano
>
> In [2]: import numpy
>
> In [3]: numpy.ones(4) * theano.tensor.dmatrix()
> Out[3]: Elemwise{mul,no_inplace}.0
>
> In [4]: theano.tensor.dmatrix() * theano.tensor.dmatrix()
> Out[4]: Elemwise{mul,no_inplace}.0
>
> In [5]: theano.tensor.dmatrix() * numpy.ones(4)
> Out[5]: Elemwise{mul,no_inplace}.0
>
>
> The dmatrix() function returns an instance of the TensorVariable class
> defined in this file:
> http://trac-hg.assembla.com/theano/browser/theano/tensor/basic.py#L901
>
> I think the only thing we added for numpy was __array_priority__ =
> 1000, which has already been suggested here. I'm confused by why this
> thread goes on.
>
>
Hmm,
That does seem to work. I wonder if it is intended or just fortuitous, the
documentation says:
The __array_priority__ attribute
>
> __array_priority__
> This attribute allows simple but flexible determination of which sub- type
> should be considered “primary” when an operation involving two or more
> sub-types arises. In operations where different sub-types are being used,
> the sub-type with the largest __array_priority__ attribute will determine
> the sub-type of the output(s). If two sub- types have the same
> __array_priority__ then the sub-type of the first argument determines
> the output. The default __array_priority__ attribute returns a value of 0.0
> for the base ndarray type and 1.0 for a sub-type. This attribute can also be
> defined by objects that are not sub-types of the ndarray and can be used to
> determine which __array_wrap__ method should be called for the return
> output.
>
Which doesn't seem directly applicable. Perhaps the documentation is wrong,
the last sentence is a bit confusing.
Chuck
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