Problem finding minimum of a function

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snipsnap333
snipsnap333 2018-11-27
评论： snipsnap333 2018-11-27
Hello,
I have a function f = (x,n,mu1,mu2,sigma1,sigma2,prop2), which is a mixed Gumbel distribution. I want to estimate the parameters mu1,mu2,sigma1,sigma2,prop2 dependend on my collected data x and n. I'm trying to find the values of the parameters which minimize L = sum(log(f = (x,n,mu1,mu2,sigma1,sigma2,prop2))) using fminunc or fminsearch.
When i try to run my code there is an error:
Not enough input arguments.
Error in max_like>myfunc (line 25)
L = sum(log(f(x_dat,n_dat,mu1,mu2,sigma1,sigma2,prop2)));
Error in fminsearch (line 200)
fv(:,1) = funfcn(x,varargin{:});
Error in max_like (line 10)
x = fminsearch(@myfunc,[25, 35, 5, 5, 0.5]);
When I only call the function myfunc it works fine. It has something to do with evaluating the function f() inside myfunc(). I also tried using mle(), but this doesnt work with two variables. I know using global variables isn't a good practise, but this function will be implemented into a GUI, where I have private properties for the whole program and I don't need global variables there.
Thanks for your help in advance.
global x_dat;
x_dat = [12;13;14.5;15];
global n_dat;
n_dat = [2;2;5;5];
%x = fminunc(@myfunc,[16, 12, 5, 5, 0.5]);
x = fminsearch(@myfunc,[16, 12, 5, 5, 0.5]);
function L = myfunc(mu1,mu2,sigma1,sigma2,prop2)
global x_dat
global n_dat
F1 = @(x,mu1,sigma1)(exp(-exp(-(x-mu1)/sigma1)));
F2 = @(x,mu2,sigma2)(exp(-exp(-(x-mu2)/sigma2)));
f1 = @(x,mu1,sigma1) ((1/sigma1)*exp(-((x-mu1)/sigma1 + exp(-(x-mu1)/sigma1)))).* (exp(-exp(-(x-mu1)/sigma1)));
f2 = @(x,mu2,sigma2) ((1/sigma2)*exp(-((x-mu2)/sigma2 + exp(-(x-mu2)/sigma2)))).* (exp(-exp(-(x-mu2)/sigma2)));
f_mix = @(x,mu1,mu2,sigma1,sigma2,prop2)(1-prop2)*f1(x,mu1,sigma1)+ prop2*f2(x,mu2,sigma2);
f = @(x,n,mu1,mu2,sigma1,sigma2,prop2)(n.*((1-prop2).*F1(x,mu1,sigma1) + prop2.*F2(x,mu2,sigma2)).^(n-1)).*f_mix(x,mu1,mu2,sigma1,sigma2,prop2);
L = sum(log(f(x_dat,n_dat,mu1,mu2,sigma1,sigma2,prop2)));
end

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采纳的回答

Stephan
Stephan 2018-11-27
编辑：Stephan 2018-11-27
Hi,
try this:
[x, fval] = curvefit_gumble
function [x,fval] = curvefit_gumble
x_dat = [12;13;14.5;15];
n_dat = [2;2;5;5];
oldopts = optimset('fminsearch');
options = optimset(oldopts,'MaxFunEvals',250,'MaxIter',250,'Display','Off');
[x,fval] = fminsearch(@myfunc,[16, 12, 5, 5, 0.5],options);
function L = myfunc(x)
mu1 = x(1);
mu2 = x(2);
sigma1 = x(3);
sigma2 = x(4);
prop2 = x(5);
F1 = @(x,mu1,sigma1)(exp(-exp(-(x-mu1)/sigma1)));
F2 = @(x,mu2,sigma2)(exp(-exp(-(x-mu2)/sigma2)));
f1 = @(x,mu1,sigma1) ((1/sigma1)*exp(-((x-mu1)/sigma1 + exp(-(x-mu1)/sigma1)))).* (exp(-exp(-(x-mu1)/sigma1)));
f2 = @(x,mu2,sigma2) ((1/sigma2)*exp(-((x-mu2)/sigma2 + exp(-(x-mu2)/sigma2)))).* (exp(-exp(-(x-mu2)/sigma2)));
f_mix = @(x,mu1,mu2,sigma1,sigma2,prop2)(1-prop2)*f1(x,mu1,sigma1)+ prop2*f2(x,mu2,sigma2);
f = @(x,n,mu1,mu2,sigma1,sigma2,prop2)(n.*((1-prop2).*F1(x,mu1,sigma1) + prop2.*F2(x,mu2,sigma2)).^(n-1)).*f_mix(x,mu1,mu2,sigma1,sigma2,prop2);
L = sum(log(f(x_dat,n_dat,mu1,mu2,sigma1,sigma2,prop2)));
end
end
Results:
Exiting: Maximum number of iterations has been exceeded
- increase MaxIter option.
Current function value: -47.742019
x =
23.3458 13.8903 5.3200 6.4483 0.1364
fval =
-47.7420
Increasing MaxFunEvals and MaxIter does not change the result.

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