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ÈçÌ⣬½«ÒÔϳÌÐò½øÐÐת»» ÒòΪÕâ¸ö³ÌÐòÔÚÎÒµÄmatlab³ÌÐòÖжà´Îµ÷Ó㬺ܺÄʱ¡£ Çó¸ßÊÖ½â´ð»òÖ¸µã¡£ л¹ý£¡ function [A]=OMPerr(D,X,errorGoal)%#codegen % ======================================================== % Sparse coding of a group of signals based on a given dictionary and specified representation % error to get. % input arguments: D - the dictionary % X - the signals to represent % errorGoal - the maximal allowed representation error % output arguments: A - sparse coefficient matrix. % ======================================================== [n,P]=size(X); [n,K]=size(D); E2 = errorGoal^2*n; maxNumCoef = n/2; A = zeros(size(D,2),size(X,2)); % h=waitbar(0,'OMP on each example ...'); for k=1:1 ,% waitbar(k/P); a=[]; x=X(:,k); residual=x; indx = []; a = []; currResNorm2 = sum(residual.^2); j = 0; while currResNorm2>E2 && j < maxNumCoef, j = j+1; proj=D'*residual; pos=find(abs(proj)==max(abs(proj))); pos=pos(1); indx(j)=pos; a=pinv(D(:,indx(1:j)))*x; residual=x-D(:,indx(1:j))*a; currResNorm2 = sum(residual.^2); end; if (~isempty(indx)) A(indx,k)=a; end end; % close(h); return;[ À´×Ô¿ÆÑмÒ×å ¿ØÖÆ&ͼÏñ¼Ò×å ] |
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