function [J] = artifical_neural_net_genetic_fn(x, x_sample, f_sample)
%
% The function called by the general genetic algorithm code
%
% Inputs: 
%   x is a matrix of size [ N_pop x N_vars ] (the genetic population)
%   x_sample: the "x" sampling 
%   f_sample: = f(x_sample)
%   
% Written by:
% -- 
% John L. Weatherwax                2006-08-28
% 
% email: wax@alum.mit.edu
% 
% Please send comments and especially bug reports to the
% above email address.
% 
%-----

[ N_pop, N_vars ] = size(x);

logsig = @(x) ( 1 ./ ( 1 + exp(-x) ) );

J = zeros(N_pop,1);
for pi=1:N_pop,
  
  % Extract out the various NN weights: 
  W  = x(pi,:);
  W1 = W(1:5); % weights in the first hidden layer
  b1 = W(6:11); % bias of in the first hidden layer b_1,b_2,...,b_5, and b_6
  W2 = W(12:16); % weights in the second hidden layer
  
  % Compute the output of the NN at each input x value:
  out = zeros(1,length(x_sample));
  for ii=1:length(x_sample)
    xin = ( x_sample(ii) - 3 )/2.; % scale inputs so they are [ -1, +1 ]
    a1 = logsig( W1*xin + b1(1:5) );
    a2 = logsig( a1(:)' * W2(:) + b1(6) );
    out(ii) = a2;
  end
  
  % compute the norm between the truth and what we found when we used our NN:
  J(pi) = mean( (f_sample(:) - out(:)).^2 );
  
end