John Salvatier wrote:
> I get the same result on 1.4.1
>
> On Thu, Jul 22, 2010 at 1:00 PM, Johann Hibschman
> <[email protected] <mailto:jhibschman%[email protected]>> wrote:
>
> I'm trying to understand numpy.subtract.reduce. The documentation
> doesn't seem to match the behavior. The documentation claims
>
> For a one-dimensional array, reduce produces results equivalent to:
>
> r = op.identity
> for i in xrange(len(A)):
> r = op(r,A[i])
> return r
>
> However, numpy.subtract.reduce([1,2,3]) gives me 1-2-3==-4, not
> 0-1-2-3==-6.
>
> Now, I'm on an older version (1.3.0), which might be the problem, but
> which is "correct" here, the code or the docs?
>
numpy.divide.reduce has the same "problem". If the docstring
is correct, then numpy.divide.reduce([2.0, 2.0]) should be
0.25, but
In [13]: np.divide.reduce([2.0, 2.0])
Out[13]: 1.0
Instead of
<identity> op <val0> op <val1> op ...
it appears to compute
<val0> op <val1> op ...
Warren
> Thanks,
> Johann
>
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