Re: [Numpy-discussion] numerical gradient, Jacobian, and Hessian
alex wrote: On Mon, Apr 21, 2014 at 3:13 AM, Eelco Hoogendoorn hoogendoorn.ee...@gmail.com wrote: As far as I can tell, [Theano] is actually the only tensor/ndarray aware differentiator out there And AlgoPy, a tensor/ndarray aware arbitrary order automatic differentiator (https://pythonhosted.org/algopy/) I noticed julia seems to have a package ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numerical gradient, Jacobian, and Hessian
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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numerical gradient, Jacobian, and Hessian
On Mon, Apr 21, 2014 at 3:13 AM, Eelco Hoogendoorn hoogendoorn.ee...@gmail.com wrote: As far as I can tell, [Theano] is actually the only tensor/ndarray aware differentiator out there And AlgoPy, a tensor/ndarray aware arbitrary order automatic differentiator (https://pythonhosted.org/algopy/) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numerical gradient, Jacobian, and Hessian
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
