mailRe: r25529 - /branches/est_par_error/lib/dispersion/tsmfk01.py


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Posted by Troels Emtekær Linnet on September 02, 2014 - 10:58:
Hi Edward.

For the Jacobian to work with minfx, the parameter vector send to the
function should match 1:1 with the one to
chi2 function.

Best
Troels

2014-09-02 10:41 GMT+02:00 Edward d'Auvergne <edward@xxxxxxxxxxxxx>:
Hi Troels,

I just noticed a problem here.  For the Jacobian (as well as the
gradient and Hessian), you will need one element of the dR2eff/dr20a
gradient for each R20a parameter in the model.  Same for the
dR2eff/ddw gradient element.  You currently return a list of 3
elements, but I'm guessing that the parameter vector sent into the
target function is not simply [R20a, dw, k_AB].

Regards,

Edward

On 2 September 2014 10:29,  <tlinnet@xxxxxxxxxxxxx> wrote:
Author: tlinnet
Date: Tue Sep  2 10:29:46 2014
New Revision: 25529

URL: http://svn.gna.org/viewcvs/relax?rev=25529&view=rev
Log:
In the lib function of TSMFK01, returned the Jacobian as list of 
derivatives. This is for easier manipulation in target function.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR 
estimation from Jacobian and Co-variance matrix of dispersion models.

Modified:
    branches/est_par_error/lib/dispersion/tsmfk01.py

Modified: branches/est_par_error/lib/dispersion/tsmfk01.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/est_par_error/lib/dispersion/tsmfk01.py?rev=25529&r1=25528&r2=25529&view=diff
==============================================================================
--- branches/est_par_error/lib/dispersion/tsmfk01.py    (original)
+++ branches/est_par_error/lib/dispersion/tsmfk01.py    Tue Sep  2 
10:29:46 2014
@@ -142,6 +142,8 @@
     @type k_AB:             float
     @keyword tcp:           The tau_CPMG times (1 / 4.nu1).
     @type tcp:              numpy float array of rank [NE][NS][NM][NO][ND]
+    @return:                The Jacobian returned as list of derivatives. 
 This is for easier manipulation and possible back scaling from rad/s to 
normal units in relax.
+    @rtype:                 list of numpy arrays
     """

     # Get the partial derivatives.
@@ -149,7 +151,7 @@
     get_d_f_d_dw = d_f_d_dw(r20a=r20a, dw=dw, k_AB=k_AB, tcp=tcp)
     get_d_f_d_k_AB = d_f_d_k_AB(r20a=r20a, dw=dw, k_AB=k_AB, tcp=tcp)

-    return transpose(array( [get_d_f_d_r20a , get_d_f_d_dw, 
get_d_f_d_k_AB] ) )
+    return [get_d_f_d_r20a , get_d_f_d_dw, get_d_f_d_k_AB]


 def d_f_d_r20a(r20a=None, dw=None, k_AB=None, tcp=None):


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