John Salvatier wrote: > I get the same result on 1.4.1 > > On Thu, Jul 22, 2010 at 1:00 PM, Johann Hibschman > <jhibschman+nu...@gmail.com <mailto:jhibschman%2bnu...@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 > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org> > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > ------------------------------------------------------------------------ > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion