I have changed derivsecant to derivsecant_jcalculus_ which is what I
think you wanted. I haven't tested anything. This addon is in the
hands of the users now.
Henry Rich
On 5/5/2020 5:33 PM, Jan-Pieter Jacobs wrote:
Hi!
Toying around with Gradiant Descent I ran into problems using
pderiv_jcalculus_ for second derivatives of a loss function.
It seems the problem lies in the case when the verb cannot be symbolically
derived:
for instance:
Ddot =: pderiv_jcalculus_
*: Ddot 2
2"0 NB. fine
! Ddot 2
0&(! (2 : 0) 2)
:
NB. Get function to use: u. or higher-order secant
if. n = 1 do. func =. u.@] else. func =. u. derivsecant (n-1) end.
NB. x must be an atom or conform to shape of y
if. 0=#@$x do. x =. ($y)$x end. NB. replicate atom
assert. x -:&$ y NB. shapes must agree
x =. x + 1e_7 * 0 = x NB. replace 0 by epsilon
newy =. y +"(#@$y) (,~$x) $ (#~ 1 j. #) ,x NB. array of moved points, each
an array with 1 nonzero
f0 =. x func y NB. the function at the initial point
((x func"(#@$y) newy) -"(#@$f0) f0) % x NB. evaluate function at moved
points, calc slope. x used only for higher orders
)
! Ddot 2] 3
|value error: derivsecant
| func=.u. derivsecant(n-1)
First derivative does work:
! Ddot 1]3
7.53671
It appears that adding _jcalculus_ to derivsecant (line 37 of the addon)
makes also ! Ddot 2 ]3 work, but I don't know whether it has any
unpredictable side effects.
Best regards,
Jan-Pieter
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