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1 function [falseAlarmRate, detectionRate, area, th] = plotROC(confidence, testClass, col, varargin)
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2 % You pass the scores and the classes, and the function returns the false
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3 % alarm rate and the detection rate for different points across the ROC.
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4 %
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5 % [faR, dR] = plotROC(score, class)
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6 %
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7 % faR (false alarm rate) is uniformly sampled from 0 to 1
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8 % dR (detection rate) is computed using the scores.
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9 %
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10 % class = 0 => target absent
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11 % class = 1 => target present
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12 %
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13 % score is the output of the detector, or any other measure of detection.
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14 % There is no plot unless you add a third parameter that is the color of
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15 % the graph. For instance:
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16 % [faR, dR] = plotROC(score, class, 'r')
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17 %
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18 % faR, dR are size 1x1250
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19
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20 if nargin < 3, col = []; end
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21 [scale01] = process_options(varargin, 'scale01', 1);
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22
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23 S = rand('state');
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24 rand('state',0);
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25 confidence = confidence + rand(size(confidence))*10^(-10);
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26 rand('state',S)
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27
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28 ndxAbs = find(testClass==0); % absent
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29 ndxPres = find(testClass==1); % present
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30
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31 [th, j] = sort(confidence(ndxAbs));
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32 th = th(fix(linspace(1, length(th), 1250)));
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33
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34 cAbs = confidence(ndxAbs);
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35 cPres = confidence(ndxPres);
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36 for t=1:length(th)
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37 if length(ndxPres) == 0
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38 detectionRate(t) = 0;
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39 else
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40 detectionRate(t) = sum(cPres>=th(t)) / length(ndxPres);
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41 end
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42 if length(ndxAbs) == 0
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43 falseAlarmRate(t) = 0;
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44 else
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45 falseAlarmRate(t) = sum(cAbs>=th(t)) / length(ndxAbs);
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46 end
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47
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48 %detectionRate(t) = sum(confidence(ndxPres)>=th(t)) / length(ndxPres);
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49 %falseAlarmRate(t) = sum(confidence(ndxAbs)>=th(t)) / length(ndxAbs);
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50 %detections(t) = sum(confidence(ndxPres)>=th(t));
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51 %falseAlarms(t) = sum(confidence(ndxAbs)>=th(t));
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52 end
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53
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54 area = sum(abs(falseAlarmRate(2:end) - falseAlarmRate(1:end-1)) .* detectionRate(2:end));
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55
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56 if ~isempty(col)
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57 h=plot(falseAlarmRate, detectionRate, [col '-']);
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58 %set(h, 'linewidth', 2);
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59 e = 0.05;
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60 if scale01
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61 axis([0-e 1+e 0-e 1+e])
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62 else
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63 % zoom in on the top left corner
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64 axis([0-e 0.5+e 0.5-e 1+e])
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65 end
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66 grid on
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67 ylabel('detection rate')
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68 xlabel('false alarm rate')
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69 end
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