John Salvatier wrote:
I get the same result on 1.4.1
On Thu, Jul 22, 2010 at 1:00 PM, Johann Hibschman <jhibschman+numpy@gmail.com <mailto:jhibschman%2Bnumpy@gmail.com>> 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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