Supprimer 'VANILLA_NeNMF.m'

VANILLA_NeNMF.m

@@ -1,150 +0,0 @@
-% Non-negative Matrix Factorization via Nesterov's Optimal Gradient Method.
-% NeNMF: Matlab Code for Efficient NMF Solver
-
-% Reference
-%  N. Guan, D. Tao, Z. Luo, and B. Yuan, "NeNMF: An Optimal Gradient Method
-%  for Non-negative Matrix Factorization", IEEE Transactions on Signal
-%  Processing, Vol. 60, No. 6, PP. 2882-2898, Jun. 2012. (DOI:
-%  10.1109/TSP.2012.2190406)
-
-%   Modified by:F. Yahaya
-%   Date: 06/09/2018
-%   Contact: farouk.yahaya@univ-littoral.fr
-
-
-% <Inputs>
-%        X : Input data matrix (m x n)
-%        r : Target low-rank
-%
-%        MAX_ITER : Maximum number of iterations. Default is 1,000.
-%        MIN_ITER : Minimum number of iterations. Default is 10.
-
-%        TOL : Stopping tolerance. Default is 1e-5. If you want to obtain a more accurate solution, decrease TOL and increase MAX_ITER at the same time.
-
-% <Outputs>
-%        W : Obtained basis matrix (m x r).
-%        H : Obtained coefficients matrix (r x n).
-%        T : CPU TIME.
-%        RRE: Relative reconstruction error in each iteration
-
-
-%        Tmax : CPU time in seconds.
-% Note: another file 'stop_rule.m' should be included under the same
-% directory as this code.
-
-
-
-
-function [W,H,RRE,T]=VANILLA_NeNMF(X,W, H,Tmax)
-MinIter=10;
-
-tol=1e-5;
-T=zeros(1,301);
-RRE=zeros(1,301);
-
-
-ITER_MAX=500;      % maximum inner iteration number (Default)
-ITER_MIN=10;        % minimum inner iteration number (Default)
-
-HVt=H*X'; HHt=H*H';
-WtV=W'*X; WtW=W'*W;
-
-GradW=W*HHt-HVt';
-GradH=WtW*H-WtV;
-
-init_delta=stop_rule([W',H],[GradW',GradH]);
-tolH=max(tol,1e-3)*init_delta;
-tolW=tolH;% Stopping tolerance
-
-
-W=W';
-k=1;
-tic
-RRE(k) = nmf_norm_fro( X, W', H);
-T(k) = 0;
-% main loop
-while(toc<= Tmax+0.05)
-      
-    % Optimize H with W fixed
-    [H,iterH]=NNLS(H,WtW,WtV,ITER_MIN,ITER_MAX,tolH);
-    
-    if iterH<=ITER_MIN
-        tolH=tolH/10;
-    end
-    
-    HHt=H*H';   HVt=H*X';
-    
-    % Optimize W with H fixed
-    [W,iterW,GradW]=NNLS(W,HHt,HVt,ITER_MIN,ITER_MAX,tolW);
-    if iterW<=ITER_MIN
-        tolW=tolW/10;
-    end
-    WtW=W*W'; WtV=W*X;
-    GradH=WtW*H-WtV;
-    %     HIS.niter=niter+iterH+iterW;
-    delta=stop_rule([W,H],[GradW,GradH]);
-    % Output running detials
-    
-    %     % Stopping condition
-    if (delta<=tol*init_delta && k>=MinIter)
-        break;
-    end
-
-    if toc-(k-1)*0.05>=0.05
-        k = k+1;
-        RRE(k) = nmf_norm_fro( X, W', H);
-        T(k) = toc;
-    end
-    
-end  %end of  loop
-
-
-W=W';
-return;
-
-function [H,iter,Grad]=NNLS(Z,WtW,WtV,iterMin,iterMax,tol)
-
-
-if ~issparse(WtW)
-    L=norm(WtW);	% Lipschitz constant
-else
-    L=norm(full(WtW));
-end
-H=Z;    % Initialization
-Grad=WtW*Z-WtV;     % Gradient
-alpha1=1;
-
-for iter=1:iterMax
-    H0=H;
-    H=max(Z-Grad/L,0);    % Calculate sequence 'Y'
-    alpha2=0.5*(1+sqrt(1+4*alpha1^2));
-    Z=H+((alpha1-1)/alpha2)*(H-H0);
-    alpha1=alpha2;
-    Grad=WtW*Z-WtV;
-    
-    % Stopping criteria
-    if iter>=iterMin
-        % Lin's stopping condition
-        pgn=stop_rule(Z,Grad);
-        if pgn<=tol
-            break;
-        end
-    end
-end
-
-Grad=WtW*H-WtV;
-
-return;
-function f = nmf_norm_fro(X, W, H)
-% Author : F. Yahaya
-% Date: 13/04/2018
-% Contact: farouk.yahaya@univ-littoral.fr
-% Goal: compute a normalized error reconstruction of the mixing matrix V
-% "Normalized" means that we divide the squared Frobenius norm of the error
-% by the squared Frobenius norm of the matrix V
-% Note: To express the error in dB, you have to compute 10*log10(f)
-%
-
-f = norm(X - W * H,'fro')^2/norm(X,'fro')^2;
-
-return;