function [Tps, Fps] = ROC(scores, labels)
 
%% Sort Labels and Scores by Scores
sl = [scores; labels];
[d1 d2] = sort(sl(1,:));
 
sorted_sl = sl(:,d2);
s_scores = sorted_sl(1,:);
s_labels = round(sorted_sl(2,:));
 
%% Constants
counts = histc(s_labels, unique(s_labels));
 
Tps = zeros(1, size(s_labels,2) + 1);
Fps = zeros(1,  size(s_labels,2) + 1);
 
negCount = counts(1);
posCount = counts(2);
 
%% Shift threshold to find the ROC
for thresIdx = 1:(size(s_scores,2)+1)
 
    % for each Threshold Index
    tpCount = 0;
    fpCount = 0;
 
    for i = [1:size(s_scores,2)]
 
        if (i >= thresIdx)           % We think it is positive
            if (s_labels(i) == 1)   % Right!
                tpCount = tpCount + 1;
            else                    % Wrong!
                fpCount = fpCount + 1;
            end
        end
 
    end
 
    Tps(thresIdx) = tpCount/posCount;
    Fps(thresIdx) = fpCount/negCount;
 
end
 
%% Draw the Curve
 
% Sort [Tps;Fps]
x = Tps;
y = Fps;
 
% Interplotion to draw spline line
count = 100;
dist = (x(1) - x(size(x,2)))/100;
xx = [x(1):-dist:x(size(x,2))];
 
% In order to get the interpolations, we remove all the unique numbers
[d1 d2] = unique(x);
uni_x = x(1,d2);
uni_y = y(1,d2);
yy = spline(uni_x,uni_y,xx);
 
% No value should exceed 1
yy = min(yy, 1);
 
plot(x,y,'x',xx,yy);

>> scores = rand(1,20)*100
 
scores =
 
  Columns 1 through 7
 
   43.8744   38.1558   76.5517   79.5200   18.6873   48.9764   44.5586
 
  Columns 8 through 14
 
   64.6313   70.9365   75.4687   27.6025   67.9703   65.5098   16.2612
 
  Columns 15 through 20
 
   11.8998   49.8364   95.9744   34.0386   58.5268   22.3812
 
>> labels = round(rand(1,20))
 
labels =
 
  Columns 1 through 12
 
     1     0     1     1     1     1     1     0     0     0     1     0
 
  Columns 13 through 20
 
     1     0     1     0     0     0     1     0
 
>> ROC(scores,labels);