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 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.

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