wolffd@0: function [falseAlarmRate, detectionRate, area, th] = plotROC(confidence, testClass, col, varargin)
wolffd@0: % You pass the scores and the classes, and the function returns the false
wolffd@0: % alarm rate and the detection rate for different points across the ROC.
wolffd@0: %
wolffd@0: % [faR, dR] = plotROC(score, class)
wolffd@0: %
wolffd@0: %  faR (false alarm rate) is uniformly sampled from 0 to 1
wolffd@0: %  dR (detection rate) is computed using the scores.
wolffd@0: %
wolffd@0: % class = 0 => target absent
wolffd@0: % class = 1 => target present
wolffd@0: %
wolffd@0: % score is the output of the detector, or any other measure of detection.
wolffd@0: % There is no plot unless you add a third parameter that is the color of
wolffd@0: % the graph. For instance:
wolffd@0: % [faR, dR] = plotROC(score, class, 'r')
wolffd@0: %
wolffd@0: % faR, dR are size 1x1250
wolffd@0: 
wolffd@0: if nargin < 3, col = []; end
wolffd@0: [scale01] = process_options(varargin, 'scale01', 1);
wolffd@0: 
wolffd@0: S = rand('state');
wolffd@0: rand('state',0);
wolffd@0: confidence = confidence + rand(size(confidence))*10^(-10);
wolffd@0: rand('state',S)
wolffd@0: 
wolffd@0: ndxAbs = find(testClass==0); % absent
wolffd@0: ndxPres = find(testClass==1); % present
wolffd@0: 
wolffd@0: [th, j] = sort(confidence(ndxAbs));
wolffd@0: th = th(fix(linspace(1, length(th), 1250))); 
wolffd@0: 
wolffd@0: cAbs = confidence(ndxAbs);
wolffd@0: cPres = confidence(ndxPres);
wolffd@0: for t=1:length(th)
wolffd@0:   if length(ndxPres) == 0
wolffd@0:     detectionRate(t) = 0;
wolffd@0:   else
wolffd@0:     detectionRate(t)  = sum(cPres>=th(t)) / length(ndxPres);
wolffd@0:   end
wolffd@0:   if length(ndxAbs) == 0
wolffd@0:     falseAlarmRate(t) = 0;
wolffd@0:   else
wolffd@0:     falseAlarmRate(t) = sum(cAbs>=th(t)) / length(ndxAbs);
wolffd@0:   end
wolffd@0:   
wolffd@0:   %detectionRate(t)  = sum(confidence(ndxPres)>=th(t)) / length(ndxPres);
wolffd@0:   %falseAlarmRate(t) = sum(confidence(ndxAbs)>=th(t)) / length(ndxAbs);
wolffd@0:   %detections(t)     = sum(confidence(ndxPres)>=th(t));
wolffd@0:   %falseAlarms(t)    = sum(confidence(ndxAbs)>=th(t));
wolffd@0: end
wolffd@0: 
wolffd@0: area = sum(abs(falseAlarmRate(2:end) - falseAlarmRate(1:end-1)) .* detectionRate(2:end));
wolffd@0: 
wolffd@0: if ~isempty(col)
wolffd@0:     h=plot(falseAlarmRate, detectionRate, [col '-']);
wolffd@0:     %set(h, 'linewidth', 2);
wolffd@0:     e = 0.05;
wolffd@0:     if scale01
wolffd@0:       axis([0-e 1+e 0-e 1+e])
wolffd@0:     else
wolffd@0:       % zoom in on the top left corner
wolffd@0:       axis([0-e 0.5+e 0.5-e 1+e])
wolffd@0:     end
wolffd@0:     grid on
wolffd@0:     ylabel('detection rate')
wolffd@0:     xlabel('false alarm rate')
wolffd@0: end