first commit with current lessons

09-29/eigenfaces/eigenfaces_classify.m

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+function [matched_individual,bestmatchdistance]=eigenfaces_classify(test,training,n);
+%classifies using n principal components, closest match
+[w, h, nExpressions, nIndividuals]=size(training);
+X=reshape(training,[w*h,nIndividuals*nExpressions]);
+avg=mean(X,2);
+Xd=bsxfun(@minus,X,avg);
+[U,S,V]=svd(Xd,0);
+Xt=reshape(test,w*h,numel(test)/(w*h));
+Xtd=bsxfun(@minus,Xt,avg);
+scores=U(:,1:n)'*Xtd;
+trainingscores=U(:,1:n)'*Xd;
+%normalize scores and samples
+%scores=bsxfun(@rdivide,scores,sqrt(sum(abs(scores).^2)));
+%trainingscores=bsxfun(@rdivide,trainingscores,sqrt(sum(abs(trainingscores).^2)));
+%cosine similarity
+%C=scores'*trainingscores;
+%[bestmatchdistance bestmatchindex]=max(C,[],2);
+%matched_individual=ceil(bestmatchindex/nExpressions);
+
+%Euclidean distance
+distanceMatrix=nan(size(scores,2),size(trainingscores,2));
+for i=1:size(scores,2)
+    for j=1:size(trainingscores,2)
+        distanceMatrix(i,j)=norm(scores(:,i)-trainingscores(:,j));
+    end
+end
+[bestmatchdistance bestmatchindex]=min(distanceMatrix,[],2);
+matched_individual=ceil(bestmatchindex/nExpressions);
+if numel(test)==w*h
+   subplot(1,2,1);
+   imagesc(test);
+   colormap(gray);
+   subplot(1,2,2);
+   imagesc(reshape(X(:,bestmatchindex),[w,h]));
+   colormap(gray);
+   disp('best match distance=');
+   disp(bestmatchdistance);
+end