%
% Problem EPage 392
% 
% Examples 9.7
% 
% Written by:
% -- 
% John L. Weatherwax                2008-02-20
% 
% email: wax@alum.mit.edu
% 
% Please send comments and especially bug reports to the
% above email address.
% 
%-----

%
% use hold-one-out cross validation to estimate the probability of error 
% 1) using a product kernel density 
% 

%forge=forge(:,1); genuine=genuine(:,1); 
%forge=forge(:,2); genuine=genuine(:,2); 
%forge=forge(:,3); genuine=genuine(:,3); 
%forge=forge(:,4); genuine=genuine(:,4);

ncc = 0; 
[nc_f,d] = size(forge);
[nc_g,d] = size(genuine); 
n = nc_f + nc_g; 

% test on each GENUINE point
% 
p_f = cskernmd(genuine,forge);  % construct a PDF based on the FORGED data (evaluate on the genuine data)
for i=1:nc_g,                   % create a classifier WITHOUT the genuine point "i":
  genuine_train = genuine; 
  x = genuine(i,:);
  genuine_train(i,:)=[];
  p_g = cskernmd(x,genuine_train); 
  if( p_g > p_f(i) ) % <- genuine WINS
    ncc=ncc+1; 
  else               % <- genuine LOOSES ... do nothing 
    %
  end
end
% test on each FORGE point: 
%
p_g = cskernmd(forge,genuine);  % construct a PDF based on the GENUINE data (evaluate on the forged data)
for i=1:nc_f,                   % create a classifier WITHOUT the forge point "i":
  forge_train = forge; 
  x = forge(i,:);
  forge_train(i,:)=[];
  p_f = cskernmd(x,forge_train);
  if( p_f > p_g )    % <- forge WINS
    ncc=ncc+1; 
  else               % <- forge LOOSES ... do nothing 
    %
  end
end

pcc = ncc/n; 
fprintf('using the product kernel density estimate (for each class) we estimate pcc=%10.5f\n',pcc);