How to fit curve using for loop?
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el 4 de Oct. de 2021
Comentada: Mathieu NOE
el 25 de Oct. de 2021
I have a 15 number of signals with data point 64 in each. I want to use loop to fit all the signals with fittype 'gauss2' and plot all the curves with thier fitting. I have written like this but it is showing eroor. Kindly suggest me to resolve this. Thank you.
figure;
for i1=1:64
for j1=1:15
recon1_f(j1)=fit(t(i1),recon_amp2_1(:,j1),'gauss2');
h2{i}=plot(recon1_f(j1),t,recon_amp2_1(:,j1));
ylim([0 0.05]);
end
end
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Respuesta aceptada
Mathieu NOE
el 4 de Oct. de 2021
hello again
in the mean time I created this example (only 5 loops) on dummy data
you can easily expand and adapt it to your own needs
NB you only need one for loop and not two as in your code
hope it helps
clearvars
clc
% dummy data
x1 = [0:1:20];
y1 = [0,0.004,0.008,0.024,0.054,0.112,0.33,0.508,0.712,0.926,1,0.874,0.602,0.404,0.252,0.146,0.074,0.036,0.018,0.004,0];
for ci = 1:5
% modify x and y range (dummy data generation)
x = x1*ci;
y = y1*ci^2 + 0.1*rand(size(y1));
% curve fit using fminsearch
f = @(a,b,c,x) a.*exp(-(x-b).^2 / c.^2);
obj_fun = @(params) norm(f(params(1), params(2), params(3),x)-y);
sol = fminsearch(obj_fun, [max(y),max(x)/2,max(x)/6]);
a_sol = sol(1);
b_sol = sol(2);
c_sol = sol(3);
xx = linspace(min(x),max(x),300);
y_fit = f(a_sol, b_sol,c_sol, xx);
yy = interp1(x,y, xx);
Rsquared = my_Rsquared_coeff(yy,y_fit); % correlation coefficient
figure(ci)
plot(xx, y_fit, '-',x,y, 'r .', 'MarkerSize', 40)
title(['Gaussian Fit / R² = ' num2str(Rsquared) ], 'FontSize', 15)
ylabel('Intensity (arb. unit)', 'FontSize', 14)
xlabel('x(nm)', 'FontSize', 14)
eqn = " y = "+a_sol+ " * exp(-(x - " +b_sol+")² / (" +c_sol+ ")²";
legend(eqn)
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function Rsquared = my_Rsquared_coeff(data,data_fit)
% R2 correlation coefficient computation
% The total sum of squares
sum_of_squares = sum((data-mean(data)).^2);
% The sum of squares of residuals, also called the residual sum of squares:
sum_of_squares_of_residuals = sum((data-data_fit).^2);
% definition of the coefficient of correlation is
Rsquared = 1 - sum_of_squares_of_residuals/sum_of_squares;
end
20 comentarios
Mathieu NOE
el 25 de Oct. de 2021
hello
seems to me the index i1 is not usedin the line
c(i,:,i2)=(data(p(i),:));
so It should be
c(i,i1,i2)=(data(p(i),:));
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