[Scilab-users] Problem using datafit()

[Federico Miyara]

Dear all,

I have a problem using datafit() to adjust the parameters of a model. 
The script below has four parts. The first one actually is a synthesis 
of the data I'm working with. It just creates the data (logf0, Te) to model.

The second part introduces the function g(x, p) to model the data.

Part three creates the "gap" function required for datafit(). It does so 
in two alternative ways selected with the variable option. If option = 
1, the definition of the gap function is given inside the body of that 
function. If option = 2, it uses the model function previously created.

Finally, part 4 addresses the fitting procedure.

My problem is that with option = 1, it works fine, but when option = 2 
it halts with the following error:

    --> [P, err] = datafit(G, Z, po)
    at line     8 of function G
    at line    16 of function costf
    at line   -12 of function optim
    at line   243 of function datafit ( C:\Program
    Files\scilab-6.1.1\modules\optimization\macros\datafit.sci line 255 )

    Invalid index.
    --> toc
      ans  =
        0.6467990

The normal time to do the full fitting is about 40 s. The time of the 
aborted process is 0.64 s, long enough to have done some iterations, 
which is baffling. I tried to see inside the datafit() source but 
frankly I don't understand what may be happening.

May be somebody with more experience in the use of this function can 
give a hint on what's going on...

Regards,

Federico Miyara

// 1 Data to approximate (Te = F(logf0))
logf  =  linspace(2.3025851,  9.9010847,  7)
Telogf  =  [  0.0446706   0.0225414   0.0147556   0.0055733   0.010994  ...
            0.0189552   0.0277157]
d   =      [-0.0338451  -0.0073327  -0.0076891  -0.0021065   0.0072043  ...
            0.0049894   0.0124498]
logf0  =  log(10*10.^(0:0.005:3.3))
Te  =  interp(logf0,  logf,  Telogf,  d);            
            
// 2 Analytic model
function  y=g(x, p)
    y  =  p(1)  +  p(2)*abs(x).^p(3)  -  p(4)*exp(-p(5)*x.^2);
endfunction

// 3 Function to minimize
// (Gap or difference between data and model)
function  e=G(p, z)
    option  =  1
    select  option
    case  1
       gg  =  p(1)  +  p(2)*abs(z(1)).^p(3)  -  p(4)*exp(-p(5)*z(1).^2);
       e  =  z(2)  -  gg;
    case  2
       e  =  z(2)  -  g(z(1),  p)
    end
endfunction

// 4 Parameter algorithm adjustment
Z  =  [logf0  -  6.2515185;  Te];  
po  =  [0.005   -0.00013   4   0.01   1.7]
tic
[P,  err]  =  datafit(G,  Z,  po)
toc



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