将matlab原始代码的ROC曲线张贴出来供大家学习

function [tpr,fpr,thresholds] = roc(targets,outputs)
%ROC Receiver operating characteristic.
%
%  The receiver operating characteristic is a metric used to check
%  the quality of classifiers. For each class of a classifier,
%  threshold values across the interval [0,1] are applied to
%  outputs. For each threshold, two values are calculated, the
%  True Positive Ratio (the number of outputs greater or equal
%  to the threshold, divided by the number of one targets),
%  and the False Positive Ratio (the number of outputs greater
%  then the threshold, divided by the number of zero targets).
%
%  For single class problems, [TPR,FPR,TH] = <a href="matlab:doc roc">roc</a>(T,Y) takes
%  a 1xQ target matrix T, where each element is either 1 or 0 indicating
%  class membership or non-menbership respectively, and 1xQ outputs Y of
%  values in the range [0,1].
%
%  It returns three 1xQ vectors: the true-positive/positive ratios TPR,
%  the false-positive/negative ratios FPR, and the thresholds associated
%  with each of those values TH.
%
%  For multi-class problems [TPR,FPR,TH] = <a href="matlab:doc roc">roc</a>(T,Y) takes
%  an SxQ target matrix T, where each column contains a single 1 value,
%  with all other elements 0. The row index of each 1 indicates which of S
%  categories that vector represents. It also takes an SxQ output matrix Y,
%  with values in the range [0,1]. The row indices of the largest elements in
%  each column of Y indicate the most likely class.
%
%  In the multi-class case, all three values returned are 1xS cell arrays,
%  so that TPR{i}, FPR{i} and TH{i} are the ratios and thresholds for the
%  ith class.
%
%  <a href="matlab:doc roc">roc</a>(T,Y) can also take a boolean row vector T, and row vector Y, in
%  which case two categories are represented by targets 1 and 0.
%
%  Here a network is trained to recognize iris flowers the ROC is
%  calculated and plotted.
%
%    [x,t] = <a href="matlab:doc iris_dataset">iris_dataset</a>;
%    net = <a href="matlab:doc patternnet">patternnet</a>(10);
%    net = <a href="matlab:doc train">train</a>(net,x,t);
%    y = net(x);
%    [tpr,fpr,th] = <a href="matlab:doc roc">roc</a>(t,y)
%    <a href="matlab:doc plotroc">plotroc</a>(t,y)
%
%  See also PLOTROC, CONFUSION

% Copyright 2007-2011 The MathWorks, Inc.

nnassert.minargs(nargin,2);
targets = nntype.data('format',targets,'Targets');
outputs = nntype.data('format',outputs,'Outputs');
% TOTO - nnassert_samesize({targets,outputs},{'Targets','Outputs'});
if size(targets,1) > 1
warning(message('nnet:roc:Arguments'));
end
targets = [targets{1,:}];
outputs = [outputs{1,:}];
numClasses = size(targets,1);

known = find(~isnan(sum(targets,1)));
targets = targets(:,known);
outputs = outputs(:,known);

if (numClasses == 1)
% 务必保证分类的标签targets是0与1,而且1是正样本,0是负样本
targets = [targets; 1-targets];
outputs = [outputs; 1-outputs-eps*(outputs==0.5)];
[tpr,fpr,thresholds] = roc(targets,outputs);
tpr = tpr{1};
fpr = fpr{1};
thresholds = thresholds{1};
return;
end

fpr = cell(1,numClasses);
tpr = cell(1,numClasses);
thresholds = cell(1,numClasses);

for i=1:numClasses
[tpr{i},fpr{i},thresholds{i}] = roc_one(targets(i,:),outputs(i,:));
end

%%
function [tpr,fpr,thresholds] = roc_one(targets,outputs)

numSamples = length(targets);
numPositiveTargets = sum(targets);
numNegativeTargets = numSamples-numPositiveTargets;

thresholds = unique([0 outputs 1]);
numThresholds = length(thresholds);

sortedPosTargetOutputs = sort(outputs(targets == 1));
numPosTargetOutputs = length(sortedPosTargetOutputs);
sortedNegTargetOutputs = sort(outputs(targets == 0));
numNegTargetOutputs = length(sortedNegTargetOutputs);

fpcount = zeros(1,numThresholds);
tpcount = zeros(1,numThresholds);

posInd = 1;
negInd = 1;
for i=1:numThresholds
threshold = thresholds(i);
while (posInd <= numPosTargetOutputs) && (sortedPosTargetOutputs(posInd) <= threshold)
posInd = posInd + 1;
end
tpcount(i) = numPosTargetOutputs + 1 - posInd;
while (negInd <= numNegTargetOutputs) && (sortedNegTargetOutputs(negInd) <= threshold)
negInd = negInd + 1;
end
fpcount(i) = numNegTargetOutputs + 1 - negInd;
end

tpr = fliplr(tpcount) ./ max(1,numPositiveTargets);
fpr = fliplr(fpcount) ./ max(1,numNegativeTargets);
thresholds = fliplr(thresholds);