Hello, looks like a conflict with internals or bug, changing the function name of "g" will solve the problem.
Best regards J.K. st 8. 9. 2021 o 0:18 Federico Miyara <[email protected]> napísal(a): > > 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 modelfunction 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) > endendfunction > // 4 Parameter algorithm adjustmentZ = [logf0 - 6.2515185; Te]; po = [0.005 > -0.00013 4 0.01 1.7]tic[P, err] = datafit(G, Z, po)toc > > > > <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> > Libre > de virus. www.avast.com > <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> > <#m_2079245045849932327_DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> > _______________________________________________ > users mailing list > [email protected] > http://lists.scilab.org/mailman/listinfo/users >
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