将matlab原始代码的ROC曲线张贴出来供大家学习

对roc曲线的matlab原始代码贴出来，给出target的制作标准

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'));endtargets = [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;endfpr = 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;endtpr = fliplr(tpcount) ./ max(1,numPositiveTargets);fpr = fliplr(fpcount) ./ max(1,numNegativeTargets);thresholds = fliplr(thresholds);