在网上看到的程序,粘贴在自己的博客中,以备以后用后用到了好查找:

function [auc, curve] = ROC(score, target, Lp, Ln)

% This function is to calculat the ordinats of points of ROC curve and the area

% under ROC curve(AUC).

% This program was described in Fawcett's paper "ROC Graphs: notes and practical

% considerations for researchers".

%

% Output:

% curve: N*3 matrix.

% the 1st column is FP

% the 2nd column is TP

% the 3rd column is score

% note: the last row, etc.the last point is [1,1,0]. if output of this

% function is applied to roc_av.m to calculate average curve of roc,

% it should be delete

% auc: scale number, area under ROC curve.

%

% Input parameters:

% score: output of classifier. high socre denote the pattern is more likely

% to be POSITIVE pattern.

% target: classlabel of each pattern.

% Lp: label of POSITIVE pattern.

% Ln: label of NEGATIVE pattern.

%

%

% QingRen (qingren_ny#126.com)

% 2006-7-20

%

len = length(score); % number of patterns

if len ~= length(target)

error('The length of tow input vectors should be equal\n');

end

P = 0; % number of Positive pattern

N = 0; % number of Negative pattern

for i = 1:len

if target(i) == Lp

P = P + 1;

elseif target(i) == Ln

N = N + 1;

else

error('Wrong target value');

end

end

% sort "L" in decending order by scores

score = score(:);

target = target(:);

L = [score target];

L = sortrows(L,1);

index = len:-1:1;

index = index'; %'

L = L(index,:);

fp = 0; fp_pre = 0; % number of False Positive pattern

tp = 0; tp_pre = 0; % number of True Positive pattern.

score_pre = -10000;

curve = [];

auc = 0;

for i = 1:len

if L(i,1) ~= score_pre

curve = [curve; [fp/N, tp/P, L(i,1)]];

auc = auc + trapezoid(fp, fp_pre, tp, tp_pre);

score_pre = L(i,1);

fp_pre = fp;

tp_pre = tp;

end

if L(i,2) == Lp

tp = tp + 1;

else

fp = fp + 1;

end

end

curve = [curve; [1,1,0]]

auc = auc / P / N;

auc = auc + trapezoid(1, fp_pre/N, 1, tp_pre/P)

% calculat the area of trapezoid

function area = trapezoid(x1,x2,y1,y2)

a = abs(x1-x2);

b = abs(y1+y2);

area = a * b / 2;在求得AUC和curve后, 用

plot(curve(:,1),curve(:,2))即可得到ROC曲线