2014-08-08 16:37 GMT+02:00 Eelco Hoogendoorn :
> 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 e
2014-08-08 11:51 GMT+02:00 Jose Gomez-Dans :
> Your function looks fairly simple to differentiate by hand, but if you
> have access to the gradient (or you estimate it numerically using
> scipy...), this function might do the job:
>
> def hessian ( x, the_func, epsilon=1e-8):
> """Numerical ap
Do it in pure numpy? How about copying the source of numdifftools?
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, i
Your function looks fairly simple to differentiate by hand, but if you have
access to the gradient (or you estimate it numerically using scipy...),
this function might do the job:
def hessian ( x, the_func, epsilon=1e-8):
"""Numerical approximation to the Hessian
Parameters
---
Hi all,
I am trying to calculate a Hessian. I am using numdifftools for this (
https://pypi.python.org/pypi/Numdifftools).
My question is, is it possible to make it using pure numpy?.
The actual code is like this:
*import numdifftools as nd*
*import numpy as np*
*def log_likelihood(params):*
