errors.m

来自「一种新的人脸识别算法程序」· M 代码 · 共 55 行

function [area,roc] = errors(netans,answer,outfile)
% WBL 23 August 2002 Caculate true positive and false positive rates for all tresholds
%                    Returning the ROC and the area under its convex hull.

% $Revision: 1.6 $  $Date: 2002/10/31 11:24:12 $

%Inputs: netans two neurons
%        answer actual class (0 or 1)
%        outfile FID of a stream for output

% WBL 20 Sep calculate and save errors at Threshold=0.5
% WBL 16 Sep based on errors.m Caclculate complete ROC not just 11 sample points

[Qx,ntestsx] = size(answer);        %Q is junk

%use difference between both neurons as probability of 1
%then rescale for comparison with threshold Tx.
netconf = (0.5 + 0.5*( netans(2,:) - netans(1,:) ) );
%[netans; netconf]

[B,index] = sort(netconf);
roc = zeros(ntestsx,2);
for i=1:ntestsx-1
    tn = sum(~answer(index(1:i)));
    fn = i-tn;
    tp = sum(answer(index(i+1:ntestsx)));
    fp = (ntestsx-i)-tp;

    TP = tp/sum(answer);
    FP = fp/sum(~answer);
    roc(i,1) = TP;
    roc(i,2) = FP;
end

TX = 0.5;
AAx = netconf > TX;
%TX= median(netconf);
%AAx = netconf > median(netconf);
e  = sum(abs(AAx-answer))/ntestsx;
tn = sum(~answer & ~AAx);
fn = sum( answer & ~AAx);
fp = sum(~answer &  AAx);
tp = sum( answer &  AAx);
TP = tp/sum(answer);
FP = fp/sum(~answer);

fprintf('%f %f %f %3d %3d %3d %3d %f ',...
        TX,TP,FP,tn,fn,fp,tp,e);
fprintf(outfile,'%2f %f %f %3d %3d %3d %3d %f ',...
        TX,TP,FP,tn,fn,fp,tp,e);
 
area=auroc(roc,TP,FP);
fprintf('Area under ROC %f\n',area);