[Numpy-discussion] numarray bug: dot product between 2x2 and 3x2x3 on Mac different from PC

[Sebastian Haase]

On Monday 17 July 2006 12:38, Travis Oliphant wrote:
> Sebastian Haase wrote:
> > On Monday 17 July 2006 12:10, Travis Oliphant wrote:
> >> Sebastian Haase wrote:
> >>> Traceback (most recent call last):
> >>>   File "<input>", line 1, in ?
> >>> TypeError: array cannot be safely cast to required type
> >>>
> >>>>>> dd=d.astype(N.float32)
> >>>>>> N.dot(dd,ccc)
> >>>
> >>> [[[ 1.  1.  1.]
> >>>   [ 1.  1.  1.]
> >>>   [ 1.  1.  1.]]
> >>>
> >>>  [[ 2.  2.  2.]
> >>>   [ 2.  2.  2.]
> >>>   [ 2.  2.  2.]]]
> >>>
> >>>
> >>>
> >>> The TypeError looks like a numpy bug !
> >>
> >> I don't see why this is a bug.  You are trying to coerce a 32-bit
> >> integer to a 32-bit float.  That is going to lose precision and so you
> >> get the error indicated.
> >>
> >> -Travis
> >
> > In numarray I do not get an error. Would the error go away if I had 64
> > bit float !?  It seems though that  having  ones and twos in an int array
> > should fit just fine into a float32 array !?
>
> This could be considered a bug in numarray.  It's force-casting the
> result.  That isn't the normal behavior of mixed-type functions.
>
> Also, the policy on type-casting is not to search the array to see if
> it's possible to do the conversion on every element (that would be slow
> on large arrays).   The policy is to base the decision only on the
> data-types themselves (i.e. whether it's *possible* to lose precision*).
>
> -Travis
>
>
>
> *There is one exception to this policy in NumPy: 64-bit integers are
> allowed to be cast to 64-bit doubles --- other-wise on you would get a
> lot of non-standard long-doubles showing up on 64-bit systems.  This
> policy was decided after discussion last year.

OK - understood.  Combining int32 with float64 proves to be less 
cumbersome ...

Any idea on my main question ?
What is the dot product of a 2x2 and 3x2x3 supposed to look like ?
Why are numarray and numpy giving different answers ??

Thanks,
Sebastian Haase