Why am I getting the error "not enough input arguments"?

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I have this code. Even though I entered all 4 input arguments necessary, I'm still getting the error "not enough input arguments". Any help will be appriciated!
function [Mtrue, H0true] = fit_disL(z, M, H0, dL)
[Mtrue, H0true] = fminsearch(@error_disL, [M, H0])
function E = error_disL(M, H0)
E = 1/length(z)*sum(((disL(z, M, H0)).^2 - dL.^2).^0.5);
end
end
This is the error message:
Not enough input arguments.
Error in fit_disL/error_disL (line 4)
E = 1/length(z)*sum(((disL(z, M, H0)).^2 - dL.^2).^0.5);
Error in fminsearch (line 201)
fv(:,1) = funfcn(x,varargin{:});
Error in fit_disL (line 2)
[Mtrue, H0true] = fminsearch(@error_disL, [M, H0])
  2 Comments
Noya Linder
Noya Linder on 17 Jun 2022
Well, are you familier with the concept of curve fitting using the LSQ method? I'm trying to minimize the error, which is represented by the function error_disL(). This means I'm trying to get the values for M and H0 which will result in the smallest error. Anyway, I solved the problem thanks to some nicer comments that explained it. Thanks anyway :)

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Accepted Answer

Voss
Voss on 17 Jun 2022
The error message is actually saying there aren't enough inputs to the function error_disL (not fit_disL). That happens because error_disL is defined to take 2 inputs, M and H0, but fminsearch is giving it 1 input, which is effectively the vector [M H0].
To fix this, you can redefine error_disL to take 1 input, a vector containing both M and H0 like fminsearch will give it:
function [Mtrue, H0true] = fit_disL(z, M, H0, dL)
[Mtrue, H0true] = fminsearch(@error_disL, [M, H0])
function E = error_disL(MH0)
M = MH0(1);
H0 = MH0(2);
E = 1/length(z)*sum(((disL(z, M, H0)).^2 - dL.^2).^0.5);
end
end

More Answers (1)

Jan
Jan on 17 Jun 2022
Edited: Jan on 17 Jun 2022
The function handle provided to fminsearch must accept 1 input argument. fminsearch cannot guess, that H0 should be provided also.
You have defined error_disL as a nested function. Then it shares the value of H0 with the enclosing function. So this should work:
function [Mtrue, H0true] = fit_disL(z, M, H0, dL)
[Mtrue, H0true] = fminsearch(@error_disL, [M, H0])
function E = error_disL(x)
M = x(1);
H0 = x(2);
E = 1 / length(z) * sum(sqrt(disL(z, M, H0).^2 - dL.^2));
end
end
Alternatively:
function [Mtrue, H0true] = fit_disL(z, M, H0, dL)
fcn = @(x) error_disL(x, z, dL);
[Mtrue, H0true] = fminsearch(fcn, [M, H0]);
end
function E = error_disL(x, z, dL)
M = x(1);
H0 = x(2);
E = 1 / length(z) * sum(sqrt(disL(z, M, H0).^2 - dL.^2));
end
I've replaced the expensive ^0.5 by the cheaper sqrt().

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