2014-08-08 16:37 GMT+02:00 Eelco Hoogendoorn <[email protected]>:
> Do it in pure numpy? How about copying the source of numdifftools? > Of course it is a solution. I was just wondering if it exist something similar in the numpy/scipy packages so I do not have to use a new third party library to do that. > What exactly is the obstacle to using numdifftools? There seem to be no > licensing issues. In my experience, its a crafty piece of work; and > calculating a hessian correctly, accounting for all kinds of nasty floating > point issues, is no walk in the park. Even if an analytical derivative > isn't too big a pain in the ass to implement, there is a good chance that > what numdifftools does is more numerically stable (though in all likelihood > much slower). > > The only good reason for a specialized solution I can think of is speed; > but be aware what you are trading it in for. If speed is your major concern > though, you really cant go wrong with Theano. > > > http://deeplearning.net/software/theano/library/gradient.html#theano.gradient.hessian > > Thanks, it seems that NumDiffTools is the way to go. > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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