function [s, count, M]=hop_test(W,x,update)
% function [s, count, M]=hop_test(W,x,update)
%
% s - output state vector
% count - number of cyckes until stable state is reached
% M - matrix containing the intermediate network states
% W - weight matrix
% x - probe vector
% update - how often to view the network
%
% Hugh Pasika 1997
% initialize a few variables
[r c]=size(x); s_prev=x(:); N=r*c; count=0; M=zeros(120,1);
% the next 5 lines determine if interim plotting is to be done
if update > 0,
plot='on ';
else
plot='off'; update=1; % set update to prevent divide by zero error later
end
% the upper limit in the loop is arbitrary (you'll never reach it)
while count < 1000,
ind=1; clear ch;
% store the network state
count=count+1;
M(:,count)=s_prev;
% vector will declare which neurons want to change
for j=1:120,
nv(j)=sgn(sum(W(j,:)'.*s_prev));
if abs(nv(j)-s_prev(j)) > 0, ch(ind)=j; ind=ind+1; end;
end
% now, do any neurons want to change? if no, break out of the loop
if ind==1, break; end
% update one neuron
r_ind=ceil(rand(1)*length(ch)); % select one neuron for updating
s_prev(ch(r_ind))=s_prev(ch(r_ind))*(-1); % update it
% the next 6 lines are more plotting directives to show intermediate stages
if (plot == 'on ') & floor(count/update) == (count/update) ;
hop_plotdig(s_prev,12,10,'Current State');
fprintf(1,'\nThe current iteration is: %4.0f \n',count);
fprintf(1,'Hit any key to continue. \n')
pause
end
end
% more plotting directives
if (plot == 'on '),
hop_plotdig(s_prev,12,10,'Final State');
fprintf(1,'\nTraining stopped after %4.0f iterations.\n',count);
end
% storage of stable state
M(:,count)=s_prev;
s=reshape(s_prev,r,c);
