machine learning - Output function for fminunc in Octave -

i trying implement regularized logistic regression algorithm, using fminunc() function in octave minimising cost function. advised, plot cost function function of iterations of fminunc() function. function call looks follows -

[theta, j, exit_flag] = ...     fminunc(@(t)(costfunctionreg(t, x, y, lambda)), initial_theta, options); 

with

options = optimset('gradobj', 'on', 'maxiter', 400, 'outputfcn',@showj_history); 

[showj-history intended output function; hope have set options parameter correctly].

but, can't find sources on internet highlighting how write output function, specifically, parameters passed fminunc(), returns (if in particular required fminunc()).

could please mention helpful links or assist me in writing output function.

i think can refer the source code. consider example:

1; function f = __rosenb (x)   # http://en.wikipedia.org/wiki/rosenbrock_function   n = length (x);   f = sumsq (1 - x(1:n-1)) + 100 * sumsq (x(2:n) - x(1:n-1).^2); endfunction  function bstop = showj_history(x, optv, state)     plot(optv.iter, optv.fval, 'x')     # setting bstop true stops optimization     bstop = false; endfunction  opt = optimset('outputfcn', @showj_history); figure() xlabel("iteration") ylabel("cost function") hold on [x, fval, info, out] = fminunc (@__rosenb, [5, -5], opt);