Minimize using fminsearch?

The question is this

Use fminsearch to minimize the function you wrote above, using an initial guess of your choosing. Use the resulting c1 and c2 to evaluate f(x) at the values of x from exercise 2, storing the result as y. Plot the data and your curve (using x and y) to verify the fit. Once verified, save the first 10 elements of y

My code is:

objFunc = @(c) ( errFunc(c) );
C = fminsearch(objFunc, [1.0;1.0]);

for j=1:n
    y(j) = C(1)*sin(x(j)+C(2));
end

What am i doing wrong??? i'm sure my errFunc is correct...

[Geoff Hayes]

Brady - what is your errFunc? And what makes you think that you are doing something wrong? If you are observing an error, then copy and paste the full error message to your question.

[Brady Retzlaff]

if true
  % code
end

my effFunc is as follows...

function [e] = errFunc(c)

data2x =

data2y =

e = 0.0;

M=length(data2x);

for j=1:M

      e = e + (c(1)*sin(data2x(j)+c(2)) - data2y(j))^2;

end

end

I know that it's incorrect because when i plot it next to the data im trying to fit, the graph is not even close... but im not getting an error

[Star Strider]

You need to use fminsearch to minimise the sum of squares difference between your objective function (which is a function only of your parameters and your independent variable) and your dependent variable. I usually do this with two functions, the objective function and the sum-of-squares cost function, and minimise the cost function.