snmf.rar_jpg_matlab video_snmf

function snmf % V1=double(imread('E:\multal model\data\IMG_1089.JPG')); V=V1(:,:,1); rdim=500; alpha=0.2; showflag=1; fname='lfl'; % Check that we have non-negative data if min(V(:))<0, error('Negative values in data!'); end % Globally rescale data to avoid potential overflow/underflow V = V/max(V(:)); % Dimensions vdim = size(V,1); samples = size(V,2); % Create initial matrices W = abs(randn(vdim,rdim)); H = abs(randn(rdim,samples)); % Make sure W has unit sum columns! W = W./(ones(vdim,1)*sum(W,1)); % Initialize displays if showflag, figure(1); clf; % this will show the energies and sparsenesses figure(2); clf; % this will show the objective function drawnow; end % Calculate initial objective objhistory = sum(sum((V.*log(V./(W*H))) - V + W*H)) + alpha*sum(sum(H)); timestarted = clock; % Start iteration iter = 0; while 1, % Show progress fprintf('[%d]: %.5f \n',iter,objhistory(end)); % Save every once in a while if rem(iter,5)==0, elapsed = etime(clock,timestarted); fprintf('Saving...'); save(fname,'W','H','alpha','iter','objhistory','elapsed'); fprintf('Done!\n'); end % Show stats if showflag & (rem(iter,5)==0), figure(1); cursW = (sqrt(vdim)-(sum(W)./sqrt(sum(W.^2))))/(sqrt(vdim)-1); cursH = (sqrt(samples)-(sum(H')./sqrt(sum(H'.^2))))/(sqrt(samples)-1); subplot(3,1,1); bar(sqrt(sum(W.^2)).*sqrt(sum(H'.^2))); subplot(3,1,2); bar(cursW); subplot(3,1,3); bar(cursH); if iter>1, figure(2); plot(objhistory(2:end)); end drawnow; end % Update iteration count iter = iter+1; % Save old values Wold = W; Hold = H; % Update H with multiplicative step H = (H.*(W'*(V./(W*H))))/(1+alpha); % This update is the same as Lee and Seung's divergence step W = W.*((V./(W*H))*H')./(ones(vdim,1)*sum(H')); % Liu, Zheng, and Lu add this normalization step W = W./(ones(vdim,1)*sum(W)); % Calculate objective newobj = sum(sum((V.*log(V./(W*H))) - V + W*H)) + alpha*sum(sum(H)); objhistory = [objhistory newobj]; end end