📄 linceval.m
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function [lincOutput, recogRate, errorIndex1, errorIndex2, regOutput, regError]=lincEval(DS, coef)
% lincEval: Evaluation of linear classifier
% Usage: [lincOutput, recogRate, errorIndex1, errorIndex2, regOutput, regError]=lincEval(DS, coef)
% Roger Jang, 20041106
[dim, dataNum]=size(DS.input);
% Preapre A matrix for A*x=desired
regOutput=coef'*[DS.input; ones(1, dataNum)]; % Regression output
lincOutput=-ones(1, dataNum); % Classification output
lincOutput(regOutput>0)=1;
if isfield(DS, 'output')
desired=-ones(1, dataNum);
desired(DS.output>0)=1;
recogRate=sum(lincOutput==desired)/dataNum; % Classification recognition rate
errorIndex1=find(desired<0 & lincOutput>0); % - ===> +
errorIndex2=find(desired>0 & lincOutput<0); % + ===> -
regError=sum((regOutput-desired).^2); % Regression MSE
errorIndex1=find(desired<0 & lincOutput>0); % - ===> +
errorIndex2=find(desired>0 & lincOutput<0); % + ===> -
end
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