Fix parameters using fit function
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Andrea Calvi
el 17 de Dic. de 2015
Hi all,
This may be a dumb and easy question, but I'm having problems in understanding how to fix parametersin a multiparameter fit function. Going straight to the problem, i have a function which fits a 2D user inputted gaussian as follows:
function [gof,fittato] = fit_gaub(image,error,init)
a = init(1);
b = init(2);
c1 = init(3);
c2 = init(4);
t1 = init(5);
w1 = init(6);
w2 = init(7);
gaub = @(a,b,c1,c2,t1,w1,w2,x,y) a + b.*exp(-(((x-c1).*cosd(t1)+(y-c2).*sind(t1))/w1).^2-((-(x-c1).*sind(t1)+(y-c2).*cosd(t1))/w2).^2)./(pi.*w1.*w2);
lunghezza = numel(image);
z_vect = zeros(lunghezza,1);
k = 1;
for i=1:size(image,2)
for j=1:size(image,1)
r_fit(k) = j;
c_fit(k) = i;
z_vect(k) = image(j,i);
k = k+1;
end
end
weight(1:lunghezza)=error;
% a,b,c1,c2,t1,w1,w2
[fittato, gof] = fit([r_fit', c_fit'], z_vect,gaub,'Robust', 'Bisquare','Algorithm','Trust-Region','weights',weight...
,'StartPoint', init ...
,'Lower', [ -10 0 0 0 0 1 1 ]...
,'Upper', [ 100 10e12 200 50 15 100 100]);
In the framework of this function, how can I tell matlab to fix a parameter without playing with the contranints? I've found something about it only concerning the
lsqcurvefit
function, but I have no idea neither on how to use that function, nor what changes may that bring to my code(get same output from my function).
any help would be much appreciated,
Andrea Calvi
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Respuesta aceptada
jgg
el 17 de Dic. de 2015
Editada: jgg
el 17 de Dic. de 2015
I think I understand what you're trying to do. Your function is this thing:
gaub = @(a,b,c1,c2,t1,w1,w2,x,y) ...%bunch of function stuff
What you'd like to do is, say, fix b to be a particular value, say b = 3 then optimize your function, but you don't want to do this by saying 3< = b <= 3 in the constraints.
The simplest solution is to just refine your function and constaints:
b = 3;
gaub2 = @(a,c1,c2,t1,w1,w2,x,y) gaub(a,b,c1,c2,t1,w1,w2,x,y);
%now gaub2 is gaub, with b fixed at the value set.
Now, you just need to get rid of the b constraint, so in your optimization, set:
'Lower', [ -10 0 0 0 1 1 ]...
'Upper', [ 100 200 50 15 100 100]
by omitting the column of constraints associated with b. You can fiddle with this to be a little more robust, but I think this is the most straightforward way. (For example, to do it for all of your variables, you can set up a switch statement and have seven possible functions).
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Más respuestas (2)
gabodabo
el 19 de Mayo de 2019
Editada: gabodabo
el 19 de Mayo de 2019
I am nearly 4y late for the discussion, but a very simple way of fixing parameters in the 'fit' function is to put your value in the upper and lower limits. For example, for a fit to a function with four parameters, of which two are fixed:
opts.Lower = [ param1_fix param2_fix -Inf -Inf ];
opts.Upper = [ param1_fix param2_fix Inf Inf ];
[fitresult, gof] = fit( xData, yData, myfunc, opts);
Hope this helps!
Gabriel
0 comentarios
Andrea Calvi
el 18 de Dic. de 2015
4 comentarios
jgg
el 21 de Dic. de 2015
I don't think so; it's okay though. People should read through if they want to automate it, so it's all good.
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