CODEWITHSUNDEEP
matlabcurve-fittingoptional-parameters
Art
Art

Reputation: 1335

matlab: lsqcurvefit and parameters

I'm trying to do some fitting with lsqcurvefit. I have a function like that:

function F = cdf_3p_model(a,data)
F=1-((1-a(5)-a(6)).*(exp(-abs(data)./a(1)))+((1-a(4)-a(6)).*(exp(-abs(data)./a(2))))+((1-a(4)-a(5)).*(exp(-abs(data)./a(3)))));

and

function [a residual] = cdf_fit_3p(x,y)
a0 = [10 1 0.1 0.3 0.3 0.3];
lb = [0 0 0 0 0 0];
ub = [];
curvefitoptions = optimset('Display','final','MaxFunEvals',100000,'MaxIter',50000);
[a, residual] = fmincon(@cdf_3p_model,a0,x,y,lb,ub,curvefitoptions);
end

I set the initial parameters, ub, lb but how do I also declare that:

a(1) > a(2) > a(3)
a(5) + a(6) +a(7) = 1

Upvotes: 2

Views: 2432

Answers (3)

Ben Voigt
Ben Voigt

Reputation: 283684

You will have to calculate the parameters you "want" from a set of parameters that's more usable to MatLab.

For example, you can rewrite

a(1) > a(2) > a(3)
a(5) + a(6) + a(7) = 1

as

a(3) = p(1)
a(2) = p(1) + p(2)
a(1) = p(1) + p(2) + p(3)
a(4) = p(4)
a(5) = p(5)
a(6) = p(6)
a(7) = 1 - p(5) - p(6)

with

lb = [0 0 0 0 0 0]
ub = [Inf Inf Inf Inf 1 1]

Well, it's not perfect, because it allows a(7) as low as -1 instead of 0. But it includes your other constraints.

Upvotes: 0

Rasman
Rasman

Reputation: 5359

I think you have better chance using one of the minimization routines such as fmincon which allows you to specify constraints you might otherwise be unable to do. You can easily incorporate least-squares by taking the L2-norm of the difference between model and data

Upvotes: 3

Jim Clay
Jim Clay

Reputation: 1003

Normally I would say, "make clauses in your function that gives really terrible 'scores' when those conditions are not met." However, your conditions make the range of allowable parameters such a tiny, tiny subset of the range of possible numbers that I think you would cause lsqcurvefit to never converge if you do that. I would say lsqcurvefit is not the right solution for you.

Upvotes: 0

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