I was going to suggest numdifftools; its a very capable package in my experience. Indeed it would be nice to have it integrated into scipy.
Also, in case trying to calculate a numerical gradient is a case of 'the math getting too bothersome' rather than no closed form gradient actually existing: Theano may be your best bet; I have very good experiences with it as well. As far as I can tell, it is actually the only tensor/ndarray aware differentiator out there (maple and mathematica don't appear to support this) On Sun, Apr 20, 2014 at 4:55 PM, Alan G Isaac <alan.is...@gmail.com> wrote: > Awhile back there were good signs that SciPy > would end up with a `diff` module: > https://github.com/scipy/scipy/issues/2035 > Is this still moving forward? > > It would certainly be nice for SciPy to have intuitive > numerical gradients, Jacobians, and Hessians. The last > two are I think missing altogether. The first exists > as scipy.optimize.approx_fprime. > > `approx_fprime` seems to work fine, but I suggest it > has the following drawbacks: > - it is hard to find (e.g., try doing a Google search > on "scipy gradient" or "scipy numerical gradient" > - related, it is in the wrong location (scipy.optimize) > - the signature is odd: (x,f,dx) instead of (f,x,dx) > (This matters for ease of recall and for teaching.) > > In any case, as I understand it, the author's of numdifftools > http://code.google.com/p/numdifftools/ > expressed willingness to have their code moved into SciPy. > This seems like an excellent way forward. > There was talk of making this a summer of code project, > but that seems to have sputtered. > > Alan Isaac > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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