A Dissabte 18 Febrer 2006 20:49, Sasha va escriure:
>  PROPOSALS
>  ==========
> 
>  Here are three suggestions:
> 
>  1. Probably the most controversial question is what getitem should
>  return. I believe that most of the confusion comes from the fact that
>  the same syntax implements two different operations: indexing and
>  projection (for the lack of better name).  Using the analogy between
>  ndarrays and functions, indexing is just the application of the
>  function to its arguments and projection is the function projection
>  ((f, x) -> lambda (*args): f(x, *args)).
> 
>  The problem is that the same syntax results in different operations
>  depending on the rank of the array.
> 
>  Let
> 
>  >>> x = ones((2,2))
>  >>> y = ones(2)
> 
>  then x[1] is projection and type(x[1]) is ndarray, but y[1] is
>  indexing and type(y[1]) is int32.  Similarly, y[1,...] is indexing,
>  while x[1,...] is projection.
> 
>  I propose to change numpy rules so that if ellipsis is present inside
>  [], the operation is always projection and both y[1,...] and
>  x[1,1,...] return zero-rank arrays.  Note that I have previously
>  rejected Francesc's idea that x[...] and x[()] should have different
>  meaning for zero-rank arrays.  I was wrong.

+1 (if I want to be consequent ;-)

I guess that this would imply that:

In [19]: z=numpy.array(1)

In [20]: type(z[()])
Out[20]: <type 'int32scalar'> 

In [21]: type(z[...])
Out[21]: <type 'numpy.ndarray'> 

isn't it?

>  2. Another source of ambiguity is the various "reduce" operations such
>  as sum or max.  Using the previous example, type(x.sum(axis=0)) is
>  ndarray, but type(y.sum(axis=0)) is int32.  I propose two changes:
> 
>     a. Make x.sum(axis)  return ndarray unless axis is None, making
>  type(y.sum(axis=0)) is ndarray true in the example.
> 
>     b. Allow axis to be a sequence of ints and make
>  x.sum(axis=range(rank(x))) return rank-0 array according to the rule
>  2.a above.
> 
>     c. Make x.sum() raise an error for rank-0 arrays and scalars, but
>  allow x.sum(axis=()) to return x.  This will make numpy sum consistent
>  with the built-in sum that does not work on scalars.

Well, to say the truth, I've not a strong opinion on this one (this is
why I "mostly" supported your proposal ;-), but I think that if Travis
has reasons to oppose to it, we should listen to him.

>  3. This is a really small change currently
>  I propose to make shape=() valid in ndarray constructor.

+1

Cheers,

-- 
> 0,0<   Francesc Altet     http://www.carabos.com/
V   V   Cárabos Coop. V.   Enjoy Data
 "-"



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