qrj2d.m

矩阵实验室的内容

function [Y,B,varargout]=QRJ2D(X,varargin)
%QR based Jacbi-like JD; This function minimizes the cost 
%J_{2}=\sum{i=1}^{N} \|C_{i}-B^{-1}diag(BC_{i}B^{T})B^{-T}\|_{F}^{2}
%where \{C_{i}\}_{i=1}^{N} is a set of N, n\times n symmetric matrices 
%and B the joint diagonalizer sought.
%
%
%Standard usage: [Y,B]=QRJ2D(X), 
%Here X is a large matrix of size n\times nN which contains the 
%matrices to be jointly diagonalized such that X=[C1,C2,...,CN], 
%Y contains the jointly diagonalized version of the input 
%matrices, and B is the found diagonalizer.
%
%
%More controlled usage:[X,B,S,BB]=QRJ2D(X,'mode',ERR or ITER,RBALANCE): 
%
%'mode'='B' or 'E' or 'N':  In the 'B' mode the stopping criteria at each 
%                           step is max(max(abs(LU-I))) which measures 
%                           how much the diagonalizer B has changed
%                           after a sweep. In the 'E' mode 
%                           the stopping criterion is the difference between 
%                           the values of the cost function J2 in two consequtive 
%                           updates.In the 'N' mode the stopping criterion is 
%                           the number of sweeps over L and U phases. 
%
%ERR: In the 'B' mode it specifies the stopping value for the change in B i.e. max(max(abs(LU-I))).
%The default value for ERR in this mode and other modes including standard usage 
%is ERR=10^-5. In implementation of the algorithm in order to account 
%for dpendence of accuracy on the dimension n ERR is multiplied 
%by n the size of matrices for JD. In the 'E' mode ERR specifies the stopping value
%for the relative change of J_{2} in two consequetive sweeps. 
%In the 'B' or 'E' mode or the standard mode
%if the change in B or relative change in J2 does not reach ERR after the default number of 
%iterations (=200) then the program aborts and itreturns the current computed variables.
%
%ITER: Number of iterations in the 'N' mode  
%
%%RBALANCE: if given it is the period for row balancing after each sweep.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Outputs:
%Y= the diagonalized set of matrices
%B=the found joint diagonalizer
%S=a structure containing some information about the run program:
%          S.Iterations: number of iterations
%          S.LUerror: the LU error after each sweep
%          S.J2error: the J2 error after each sweep
%          S.J2RelativeError:the relative J2 error after each sweep
%BB=a three dimensional array containing the joint diagonalizer after each sweep
%Note: S and BB are not required as output in the function call
%
%This algorithm is based on a paper presented in ICA2006 conference and published in Springer LNCS
%Bijan Afsari, ''Simple LU and QR based Non-Orthogonal Matrix Joint Diagonalization''
%%Coded by Bijan Afsari. Please forward any questions and problem to [e-mail]
%v.1.1
%Acknowledgements: Some data structures and implementation ideas in this code are inspired from the code for JADE
%written by J.F. Cardoso and from the code FFDIAG written by Andreas Ziehe and Pavel Laskov
%Disclaimer: This code is to be used only for non-commercial research purposes and the author does not
%accept any reponsibility about its performance or fauilure

[n,m]=size(X);N=m/n;

BB=[];
%defaulat values
ERR=1*10^-5;RBALANCE=3;ITER=200;
%%%
MODE='B';
if nargin==0, display('you must enter the data'); B=eye(n); return; end;
if nargin==1, Err=ERR;Rbalance=RBALANCE;end;
if nargin> 1, MODE=upper(varargin{1});
   switch MODE
   case {'B'} 
      ERR=varargin{2}; mflag='D'; if ERR >= 1, disp('Error value should be much smaller than unity');B=[];S=[]; return; end;
   case ('E')
      ERR=varargin{2};mflag='E'; if ERR >=1, disp('Error value should be much smaller than unity'); B=[];S=[];return;end;
   case ('N');mflag='N'; ITER=varargin{2}; ERR=0; if ITER <= 1, disp('Number of itternations should be higher than one');B=[];S=[];return;end;
   end
end;
if nargin==4, RBALANCE=varargin{3}; if ceil(RBALANCE)~=RBALANCE | RBALANCE<1, disp('RBALANCE should be a positive integer');B=[];S=[];return;end;end;
JJ=[];EERR=[]; EERRJ2=[];  
X1=X;
B=eye(n,n);Binv=eye(n);
J=0;

for t=1:N
   J=J+norm(X1(:,(t-1)*n+1:t*n)-diag(diag(X(:,(t-1)*n+1:t*n))),'fro')^2;
end
JJ=[JJ,J];

%err=10^-3;
%the following part implements a sweep 
%%%%%%%%%%%%%%%%%%%%%%%%%%
err=ERR*n+1;
if MODE=='B', ERR=ERR*n;end,
   k=0;
   while err>ERR & k<ITER
      k=k+1;      
      L=eye(n);%Linv=eye(n);
      U=eye(n);%Uinv=eye(n);
      Dinv=eye(n);

      
for i=2:n,
   for j=1:i-1,
      G=[-X(i,[i:n:m])+X(j,[j:n:m]);-2*X(i,[j:n:m])];
      [U1,D1,V1]=svd(G*G');
      v=U1(:,1);
      tetha=1/2*atan(v(2)/v(1));
      c=cos(tetha);
      s=sin(tetha);
      h1=c*X(:,[j:n:m])-s*X(:,[i:n:m]);
      h2=c*X(:,[i:n:m])+s*X(:,[j:n:m]);
      X(:,[j:n:m])=h1;
      X(:,[i:n:m])=h2;
      h1=c*X(j,:)-s*X(i,:);
      h2=s*X(j,:)+c*X(i,:);
      X(j,:)=h1;
      X(i,:)=h2;
      %h1=c*U(:,j)+s*U(:,i);
      %h2=-s*U(:,j)+c*U(:,i);    
      h1=c*U(j,:)-s*U(i,:);
      h2=s*U(j,:)+c*U(i,:);
      U(j,:)=h1;
      U(i,:)=h2;
    
    end;%end for i
 end;%end for j

for j=1:n
   %for j=i+1:n
   for i=j+1:n
      
      xjj=X(j,j:n:m);
      xij=X(i,j:n:m);
      coff=[sum(xjj.^2),sum(xjj.*xij),sum(xij.^2)];
      %r=roots([4*coff(1),2*coff(2),coff(1)+2*coff(3),2*coff(2)]);
      r=roots([4*coff(1),6*coff(2),coff(1)+2*coff(3),1*coff(2)]);
      if ~isreal(r) a=r(find(imag(r)==0)); else coff4=[2*coff(1),4*coff(2),coff(1)+2*coff(3),2*coff(2),coff(3)];
         coff4=repmat(coff4,3,1);
         rr=[r.^4,r.^3,r.^2,r.^1,r.^0];
         [mm,ii]=min(diag(coff4*rr'));a=r(ii);
      end
      
      
      %coorelation quefficient
      %a=-(X(i,cindex)*X(j,cindex)')/(norm(X(i,cindex))*norm(X(j,cindex)));
      %a=tanh(a);
      %if abs(a)>1, a=sign(a)*1; end;
      X(i,:)=a*X(j,:)+X(i,:);
      I=i:n:m;
      J=j:n:m;
      X(:,I)=reshape(X(i,:),n,N);
      X(i,I)=X(i,I)+a*X(i,J);
      L(i,:)=L(i,:)+a*L(j,:);
   end%end loop over j
end

      
      
      B=L*U*B;%Binv=Binv*Uinv*Linv;
      %err=norm(L*U-eye(n,n),'fro');
      err=max(max(abs(L*U-eye(n))));EERR=[EERR,err];
      if rem(k,RBALANCE)==0
         d=sum(abs(X')); 
         D=diag(1./d*N); Dinv=diag(d*N);
         J=0;
         for t=1:N
            X(:,(t-1)*n+1:t*n)=D*X(:,(t-1)*n+1:t*n)*D;
         end;
         B=D*B; %Binv=Binv*Dinv;
      end
      BB(:,:,k)=B;
      J=0;
      Binv=inv(B);
      for t=1:N
         J=J+norm(X1(:,(t-1)*n+1:t*n)-Binv*diag(diag(X(:,(t-1)*n+1:t*n)))*Binv','fro')^2;
      end
      JJ=[JJ,J];
      if MODE=='E', err=abs(JJ(end-1)-JJ(end))/JJ(end-1);EERRJ2=[EERRJ2,err];end
   end
   Y=X;
   S=struct('iterations',k,'LUerror',EERR,'J2error',JJ,'J2RelativeError',EERRJ2);varargout{1}=S;varargout{2}=BB;