function [ w_i, data ] = prob_4_6(Niter)
% PROB_4_6 - 
% 
% Written by:
% -- 
% John L. Weatherwax                2005-08-04
% 
% email: wax@alum.mit.edu
% 
% Please send comments and especially bug reports to the
% above email address.
% 
%-----

data = [ 1 1 -1 0 2;
         0 0 1 2 0;
         -1 -1 1 1 0;
         4 0 1 2 1;
         -1 1 1 1 0;
         -1 -1 -1 1 0;
        -1 1 1 2 1 ];
class = [ 2 1 2 1 1 1 2 ];
M = length(class); 

d = 0.1;
w_i = [ 1 1 1 -1 -1 1 ];

%
% Code begins:
%---

% Append +1 to all data: 
data = [ data, ones(size(data,1),1) ]; 
% Negate the feature vector from class 2:
ind = find( class==2 ); 
data(ind,:) = -data(ind,:); clear ind;

w_history = zeros( Niter, length(w_i) ); 
for i=0:Niter+1,
  e_i = 1; 

  % Find the set J (the missclassified samples):
  [J] = find( data * (w_i.') < d );
  J

  % Update the weight vector (usin the many at a time algorithm):
  if( ~isempty(J) ) 
    w_ip1 = w_i; 
    for j=1:length(J), 
      w_ip1 = w_ip1 + e_i * data( J(j), : ); 
    end
   else
    w_ip1 = w_i; 
   end

  % Save w iterates:
  w_history( i+1, : ) = w_i; 
  
  % Prepare for next iteration:
  w_i = w_ip1; 
end

w_history(end-4:end,:)