function [FP,FN]=roc(dfce,y)% ROC Computes Receive Operator Characteristic.%% Synopsis:%  [FP,FN]=roc(dfce,y)%  % Description:%  It computes false positive rate FP and false negative rate FN%  with rescpect to the shift of the bias of given decision function.%  The values of the decision function are given in dfce and y %  contains true labels (number 1 and/or 2). The vectors dfce and y %  must be of the same length. %  The bias is shifted from min(dfce) to max(dfce). %% Input:%  dfce [1 x num_data] Values of decision function returned by %   a classifier.%  y [1 x num_data] True labels.%% Output:%  FP [1 x num_data] False positive rate.%  FN [1 x num_data] False negative rate.%% Example:%  data = load('riply_trn');%  model = fld(data);%  [y_pred,dfce] = linclass(data.X,model);%  [FP,FN] = roc(dfce,data.y);%  figure; hold on; plot(FP,FN);%  xlabel('false positives'); %  ylabel('false negatives');%% See also %  CERROR%% (c) Statistical Pattern Recognition Toolbox, (C) 1999-2003,% Written by Vojtech Franc and Vaclav Hlavac,% <a href="http://www.cvut.cz">Czech Technical University Prague</a>,% <a href="http://www.feld.cvut.cz">Faculty of Electrical engineering</a>,% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>% Modifications:% 17-may-2004, VF% 6-June-2003, VF% 24-Feb-2003, VFnum_data=length(dfce);n1=length(find(y==1));n2=length(find(y==2));[dfce,inx]=sort(dfce);y = y(inx);FP=zeros(1,num_data);FN=zeros(1,num_data);wrong1=0;wrong2=n2;for i=1:num_data,  if y(i) == 1,    wrong1=wrong1+1;  else    wrong2=wrong2-1;  end    FP(i)=wrong2/n2;  FN(i)=wrong1/n1;endreturn;