[scilab-Users] datafit

[Collette yann]

Hello,

Adding such constraints is rather simple. You just have to modify the 
parameters in datafit. Here is an example:

[parameters_opti,err] = datafit(G,RESU', 'b', [0 0]', [1e6 1e6]', 
parameters_init)

the 'b', binf, bsup thing add bounds constraints on the parameters.

YC

paul.carrico at free.fr a écrit :
> Thanks for this (so quick) answer.
>
> Am I wrong to say that is possible in SCILAB to surround the parameters ?  .... I mean I would like to impose the 2 optimized parameters to be strictly positive (and/or to be in-between [0,a value].
>
> PC
>
>
> ----- Mail Original -----
> De: "Collette yann" <yann.collette at scilab.org>
> À: users at lists.scilab.org
> Envoyé: Lundi 15 Mars 2010 16h13:32 GMT +01:00 Amsterdam / Berlin / Berne / Rome / Stockholm / Vienne
> Objet: Re: [scilab-Users] datafit
>
> paul.carrico at free.fr a écrit :
>   
>> All,
>>
>> Does somebody have an example of using datafit procedure (fitting experimental data) ?
>>
>> As I said in a previous post( with no answer yet), the tests I made failed in using it.
>>
>> Thanks in advance
>>
>> PC
>>     
> Hello,
>
> I noticed 2 mistakes:
> - the prototype of the G function, you must switch "parameters" and 
> "measured"
> - the orientation of the RESU dataset, you must transpose it.
>
> I attach the corrected script.
>
> Yann
>
> RESU = [
> //1,0;
> 0.99,-0.021732882;
> 0.98,-0.033976269;
> 0.97,-0.036566652;
> 0.96,-0.06467633;
> 0.95,-0.072344532;
> 0.94,-0.101610874;
> 0.93,-0.169098626;
> 0.92,-0.220626222;
> 0.91,-0.288628719;
> 0.9,-0.34254837;
> 0.89,-0.420396144;
> 0.88,-0.479622221;
> 0.87,-0.53212632;
> 0.86,-0.572882097;
> 0.85,-0.630563936;
> 0.84,-0.686731816;
> 0.83,-0.728819877;
> 0.8,-0.859928729;
> 0.79,-0.908981002;
> 0.78,-0.956519316;
> 0.77,-0.979228701;
> 0.76,-1.024041888;
> 0.75,-1.067341117;
> 0.74,-1.103524737;
> 0.73,-1.118452373;
> 0.72,-1.152183381;
> 0.71,-1.225398439;
> 0.7,-1.239932446;
> 0.69,-1.295343347;
> 0.68,-1.297160097;
> 0.67,-1.359232418;
> 0.66,-1.398898144;
> 0.65,-1.456428587;
> 0.64,-1.492158021;
> 0.63,-1.506994819;
> 0.62,-1.605099364;
> 0.61,-1.653091866;
> 0.6,-1.67141077;
> 0.59,-1.741809865;
> 0.58,-1.76672963;
> 0.57,-1.829468093;
> 0.56,-1.890632037;
> 0.55,-1.931811723;
> 0.54,-2.060195449;
> 0.53,-2.094259527;
> 0.52,-2.141343653;
> 0.51,-2.246866597;
> 0.5,-2.308787522;
> ];
> [Line_number,columne_number]=size(RESU);
>
> // Function to fit with 2 parameters C10 & C01
> function sigma = mooney_rivlin(lambda,parameters)
>   C10 = parameters(1)
>   C01 = parameters(2)
>   sigma  = 2 * ( lambda^2 - (1 / lambda) ) * ( C10 + (C01 / lambda) )
> endfunction  
>
>
> // function descriptor
> function e = G(parameters,measured)
>   lambda_measured = measured(1)
>   sigma_measured = measured(2)
>   e = sigma_measured - mooney_rivlin(lambda_measured,parameters) 
> endfunction
>
> // +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> // measured(2) = measured sigma
> // measured(1) = measured lambda
> // deff = (measured SIGMA - calculated SIGMA)
> // deff('[e]=G(param,measured)','e=measured(2)- 2*(measured(1)^2 - 1/measured(1))*(param(1) + param(2)/measured(1))')
>
>
> // Solve the problem
> parameters_init = [1 ; 0]
> [parameters_opti,err] = datafit(G,RESU',parameters_init)
>
>
> // Drawing of the experimental data & the fitted curve
> sigma_calc = zeros(Line_number,1);
> for i = 1 : Line_number
>   sigma_calc(i,1) = mooney_rivlin(RESU(i,1),parameters_opti);
> end;
>
> clf(0)
> plot2d(RESU(:,1),RESU(:,2),style=-2)          // attention bien le placer apr�s sinon la l�gende n'apparait pas
> plot2d(RESU(:,1), sigma_calc, style = 2)
> legend(["Experimental data", "fitted curve"],2);
> xtitle("Mooney-Rivlin fitted curve")
>