Hi Bernard, Looks like you're on to something - two other people have raised this discrepancy before: https://github.com/numpy/numpy/issues/2588. Unfortunately, when it comes to resolving the discrepancy one of the previous comments takes the opposite view. Namely, that the docstring is correct and the code is wrong.
Do different domains use different conventions here? Are there some references to back up one stance or another? But all else being equal, I'm guessing there'll be far more appetite for updating the documentation than the code. Regards, Richard Hattersley On 7 October 2013 22:09, Bernhard Spinnler <bernhard.spinn...@gmx.net>wrote: > The numpy.correlate documentation says: > > correlate(a, v) = z[k] = sum_n a[n] * conj(v[n+k]) > > In [1]: a = [1, 2] > > In [2]: v = [2, 1j] > > In [3]: z = correlate(a, v, 'full') > > In [4]: z > Out[4]: array([ 0.-1.j, 2.-2.j, 4.+0.j]) > > However, according to the documentation, z should be > > z[-1] = a[1] * conj(v[0]) = 4.+0.j > z[0] = a[0] * conj(v[0]) + a[1] * conj(v[1]) = 2.-2.j > z[1] = a[0] * conj(v[1]) = 0.-1.j > > which is the time reversed version of what correlate() calculates. > > IMHO, the correlate() code is correct. The correct formula in the docs > (which is also the correlation formula in standard text books) should be > > z[k] = sum_n a[n+k] * conj(v[n]) > > Cheers, > Bernhard > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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