npls.m

多维数据分析,有nPLS,PARAFAC,TURKER等

%            If const is not defined, no constraints are used.
%            For no constraints in a threeway problem const = [0 0 0]
%
% OldLoad    If initial guess of the loadings is available. OldLoad should be
%            given a cell array where OldLoad{1}=A,OldLoad{2}=B etc.
%
% FixMode    FixMode is a binary vector of same sixe as DimX. If 
%            FixMode(i) = 1 => Mode i is fixed (requires old values given)
%            FixMode(i) = 0 => Mode i is not fixed hence estimated
%            Ex.: FixMode = [0 1 1] find the scores of a data set given the loadings.
%            When some modes are fixed, the numbering of the components will 
%            also be fixed. Normally components are sorted according to variance
%            as in PCA, but this will not be performed if some modes are fixed.
%
% Weights    If a matrix of the same size as X is given, weighted regression
%            is performed using the weights in the matrix Weights. 
%
% ---------------------OUTPUT---------------------
%
% Factors    PARAFAC estimate of loadings in one matrix. For a 3 component
%            solution to a 4 x 3 x 3 array the loadings A, B & C will be
%            stored in a 3 element cell vector:
%            Factors{1}=A,
%            Factors{2}=B
%            Factors{3}=C
%            etc.
%
%            Use FAC2LET.M for converting to "normal" output.
%
% it         Number of iterations used. Can be helpful for checking if the algorithm
%            has converged or simply hit the maximal number of iterations (default 2500).
%
% err        The fit of the model = the sum of squares of errors (not including missing
%            elements).
%
% Corcondia  Core consistency test. Should ideally be 100%. If significantly below
%            100% the model is not valid
%
%
%
% OTHER STUFF
%  
%  Missing values are handled by expectation maximization only. Set all 
%  missing values to NaN
%
%  COMMAND LINE (SHORT)
%
%  Factors = parafac(X,Fac);
%

% Copyright, 1998 - 
% This M-file and the code in it belongs to the holder of the
% copyrights and is made public under the following constraints:
% It must not be changed or modified and code cannot be added.
% The file must be regarded as read-only. Furthermore, the
% code can not be made part of anything but the 'N-way Toolbox'.
% In case of doubt, contact the holder of the copyrights.
%
% Rasmus Bro
% Chemometrics Group, Food Technology
% Department of Food and Dairy Science
% Royal Veterinary and Agricultutal University
% Rolighedsvej 30, DK-1958 Frederiksberg, Denmark
% Phone  [phone]
% Fax    [phone]
% E-mail [e-mail]

% $ Version 1.03 $ Date 1. October   1998 $ Not compiled $ Changed sign-convention because of problems with centered data
% $ Version 1.04 $ Date 18. February 1999 $ Not compiled $ Removed auxiliary line
% $ Version 1.06 $ Date 1. December  1999 $ Not compiled $ Fixed bug in low fit error handling
% $ Version 1.07 $ Date 17. January  2000 $ Not compiled $ Fixed bug in nnls handling so that the algorithm is not stopped until nonnegative appear
% $ Version 1.08 $ Date 21. January  2000 $ Not compiled $ Changed init DTLD so that primarily negative loadings are reflected if possible
% $ Version 1.09 $ Date 30. May 2000 $ Not compiled $ changed name noptioPF to noptiopf
% $ Version 2.00 $ May 2001 $ Changed to array notation $ RB $ Not compiled $
% $ Version 2.001 $ June 2001 $ Fixed error in weighted regression $ RB $ Not compiled $

NumbIteraInitia=20;

if nargin==0
   disp(' ')
   disp(' ')
   disp(' THE PARAFAC MODEL')
   disp(' ')
   disp(' Type <<help parafac>> for more info')
   disp('  ')
   disp(' [Factors,it,err,Corcondia] = parafac(X,Fac,Options,const,OldLoad,FixMode,Weights);')
   disp(' or short')
   disp(' Factors = parafac(X,Fac);')
   disp(' ')
   disp(' Options=[Crit Init Plot NotUsed ShowFit MaxIt]')
   disp(' ')
   disp(' ')
   disp(' EXAMPLE:')
   disp(' To fit a four-component PARAFAC model to X of size 6 x 2 x 200 x 3 type')
   disp(' Factors=parafac(X,4)')
   disp(' and to obtain the scores and loadings from the output type')
   disp(' [A,B,C,D]=fac2let(Factors);')
   return
elseif nargin<2
   error(' The inputs X, and Fac must be given')
end

DimX = size(X);
X = reshape(X,DimX(1),prod(DimX(2:end)));

if nargin<3
   load noptiopf
   OptionsDefault=Options;
else
   % Call the current Options OptionsHere and load default to use if some of the current settings should be default
   Options=Options(:);
   I=length(Options);
   if I==0
      Options=zeros(8,1);
   end
   I=length(Options);
   if I<8
      Options=[Options;zeros(8-I,1)];
   end
   OptionsHere=Options;
   load noptiopf
   OptionsDefault=Options;
   Options=OptionsHere;
end

if ~exist('OldLoad')==1
   OldLoad=0;
elseif length(OldLoad)==0
   OldLoad=0;
end

% Convergence criteria
if Options(1,1)==0
   Options(1,1)=OptionsDefault(1,1);
end
crit=Options(1);

% Initialization
if ~any(Options(2))
   Options(2)=OptionsDefault(2);
end
Init=Options(2);

% Interim plotting
Plt=Options(3,1);
if ~any([0 1 2]==Plt)
   error(' Options(3,1) - Plotting - not set correct; must be 0,1, or 2')
end

if Options(5,1)==0
   Options(5,1)=OptionsDefault(5,1);
end
showfit=Options(5,1);
if isnan(showfit)
   showfit=-1;
end
if showfit<-1|round(showfit)~=showfit
   error(' Options(5,1) - How often to show fit - not set correct; must be positive integer or -1')
end

if Options(6,1)==0
   Options(6,1)=OptionsDefault(6,1);
   maxit=Options(6,1);
elseif Options(6)>0&round(Options(6))==Options(6)
   maxit=Options(6,1);
else
   error(' Options(6,1) - Maximal number of iterations - not set correct; must be positive integer')
end

ShowPhi=0; % Counter. Tuckers congruence coef/Multiple cosine/UUC shown every ShowPhiWhen'th time the fit is shown
ShowPhiWhen=10;
MissConvCrit=1e-4; % Convergence criterion for estimates of missing values
NumberOfInc=0; % Counter for indicating the number of iterations that increased the fit. ALS algorithms ALLWAYS decrease the fit, but using outside knowledge in some sense (approximate equality or iteratively reweighting might cause the algorithm to diverge

% INITIALIZE 
if showfit~=-1
   disp(' ') 
   disp(' PRELIMINARY')
   disp(' ')
end
ord=length(DimX);

if showfit~=-1
   disp([' A ',num2str(Fac),'-component model will be fitted'])
end

if exist('const')~=1
   const=zeros(size(DimX));
elseif length(const)~=ord
   const=zeros(size(DimX));
   if showfit~=-1
      disp(' Constraints are not given properly')
   end
end

if showfit~=-1
   for i=1:ord
      if const(i)==0
         disp([' No constraints on mode ',num2str(i)])
      elseif const(i)==1
         disp([' Orthogonality on mode ',num2str(i)])
      elseif const(i)==2
         disp([' Nonnegativity on mode ',num2str(i)])
      elseif const(i)==3
         disp([' Unimodality on mode ',num2str(i)])
      end
   end
end

% Check if orthogonality required on all modes
if max(max(const))==1
   if min(min(const))==1,disp(' ')
      disp(' Not possible to orthogonalize all modes in this implementation.')
      error(' Contact the authors for further information')
   end
end

if exist('FixMode')==1
   if length(FixMode)~=ord
      FixMode = zeros(1,ord);
   end
else
   FixMode = zeros(1,ord);
end

if showfit~=-1
   if any(FixMode)
      disp([' The loadings of mode : ',num2str(find(FixMode(:)')),' are fixed']) 
   end
end
if exist('Weights')~=1
   Weights=[];
end

% Display convergence criterion
if showfit~=-1
   disp([' The convergence criterion is ',num2str(crit)]) 
end

% Define loading as one ((r1*r2*r3*...*r7)*Fac x 1) vector [A(:);B(:);C(:);...].
% The i'th loading goes from lidx(i,1) to lidx(i,2)
lidx=[1 DimX(1)*Fac];
for i=2:ord
   lidx=[lidx;[lidx(i-1,2)+1 sum(DimX(1:i))*Fac]];
end

% Check if weighted regression required
if size(Weights,1)==size(X,1)&prod(size(Weights))/size(X,1)==size(X,2)
    Weights = reshape(Weights,size(Weights,1),prod(size(Weights))/size(X,1));
   if showfit~=-1
      disp(' Given weights will be used for weighted regression')
   end
   DoWeight=1;
else
   if showfit~=-1
      disp(' No weights given')
   end
   DoWeight=0;
end

% Make idx matrices if missing values
if any(isnan(X(:)))
   MissMeth=1;
else
   MissMeth=0;
end
if MissMeth
   id=sparse(find(isnan(X)));
   idmiss2=sparse(find(~isnan(X)));
   if showfit~=-1
      disp([' ', num2str(100*(length(id)/prod(DimX))),'% missing values']);
      disp(' Expectation maximization will be used for handling missing values')
   end
   SSX=sum(sum(X(idmiss2).^2)); % To be used for evaluating the %var explained
   % If weighting to zero should be used
   % Replace missing with mean values or model estimates initially
   if length(OldLoad)==sum(DimX)*Fac
      model=nmodel(OldLoad);
      model = reshape(model,DimX);
      X(id)=model(id);
   else
      meanX=mean(X(find(~isnan(X))));
      meanX=mean(meanX);
      X(id)=meanX*ones(size(id));
   end
else
   if showfit~=-1
      disp(' No missing values')
   end
   SSX=sum(sum(X.^2)); % To be used for evaluating the %var explained
end

% Check if weighting is tried used together with unimodality or orthogonality
if any(const==3)|any(const==1)
   if DoWeight==1
      disp(' ')
      disp(' Weighting is not possible together with unimodality and orthogonality.')
      disp(' It can be done using majorization, but has not been implemented here')
      disp(' Please contact the authors for further information')
      error
   end
end

% Acceleration
acc=-5;     
do_acc=1;   % Do acceleration every do_acc'th time
acc_pow=2;  % Extrapolate to the iteration^(1/acc_pow) ahead
acc_fail=0; % Indicate how many times acceleration have failed 
max_fail=4; % Increase acc_pow with one after max_fail failure
if showfit~=-1
   disp(' Line-search acceleration scheme initialized')
end

% Find initial guesses for the loadings if no initial values are given

% Use old loadings
if length(OldLoad)==ord % Use old values
   if showfit~=-1
      disp(' Using old values for initialization')
   end
   Factors=OldLoad;
   % Use DTLD
elseif Init==0
   if min(DimX)>1&ord==3&MissMeth==0
      if showfit~=-1
         disp(' Using direct trilinear decomposition for initialization')
      end
      [A,B,C]=dtld(reshape(X,DimX),Fac);
      A=real(A);B=real(B);C=real(C);
      % Check for signs and reflect if appropriate
      for f=1:Fac
         if sign(sum(A(:,f)))<0
            if sign(sum(B(:,f)))<0
               B(:,f)=-B(:,f);
               A(:,f)=-A(:,f);
            elseif sign(sum(C(:,f)))<0
               C(:,f)=-C(:,f);
               A(:,f)=-A(:,f);
            end
         end
         if sign(sum(B(:,f)))<0
            if sign(sum(C(:,f)))<0
               C(:,f)=-C(:,f);
               B(:,f)=-B(:,f);
            end
         end
      end
      Factors{1}=A;Factors{2}=B;Factors{3}=C;

   else
      if showfit~=-1
         disp(' Using singular values for initialization')
      end
      Factors=ini(X,Fac,2);
   end
   
   % Use SVD 
elseif Init==1
   if showfit~=-1
      disp(' Using singular values for initialization')
   end
   Factors=ini(X,Fac,2);
   
   % Use random (orthogonal)
elseif Init==2
   if showfit~=-1
      disp(' Using orthogonal random for initialization')
   end
   Factors=ini(X,Fac,1);
   
elseif Init==3
   error(' Initialization option set to three has been changed to 10')
   
   % Use several small ones of the above
elseif Init==10
   if showfit~=-1
      disp(' Using several small runs for initialization')
   end
   Opt=Options;
   Opt(5) = NaN;
   Opt(6) = NumbIteraInitia;
   Opt(2) = 0;
   ERR=[];
   [Factors,it,err] = parafac(X,Fac,Opt,const,[],[],Weights);
   ERR = [ERR;err];
   Opt(2) = 1;
   [F,it,Err] = parafac(X,Fac,Opt,const,[],[],Weights);
   ERR=[ERR;Err];
   if Err<err
      Factors=F;
      err=Err;
   end
   Opt(2)=2;
   for rep=1:3
      [F,it,Err]=parafac(X,Fac,Opt,const,[],[],Weights);
      ERR=[ERR;Err];
      if Err<err
         Factors=F;
         err=Err;
      end
   end
   if showfit~=-1
      disp(' ')
      disp(' Obtained fit-values')
      disp([' Method   Fit'])
      disp([' DTLD     ',num2str(ERR(1))])
      disp([' SVD      ',num2str(ERR(2))])
      disp([' RandOrth ',num2str(ERR(3))])
      disp([' RandOrth ',num2str(ERR(4))])
      disp([' RandOrth ',num2str(ERR(5))])
   end
else
   error(' Problem in PARAFAC initialization - Not set correct')
end
% Convert to old format
ff = [];
for f=1:length(Factors)
 ff=[ff;Factors{f}(:)];
end
Factors = ff;

% ALTERNATING LEAST SQUARES
err=SSX;
f=2*crit;
it=0;
connew=2;conold=1; % for missing values
ConstraintsNotRight = 0; % Just to ensure that iterations are not stopped if constraints are not yet fully imposed

if showfit~=-1
   disp(' ')
   disp(' Sum-of-Squares   Iterations  Explained')
   disp(' of residuals                 variation')
end

while ((f>crit) | (norm(connew-conold)/norm(conold)>MissConvCrit) | ConstraintsNotRight) & it<maxit
   conold=connew; % for missing values
   it=it+1;
   acc=acc+1; 
   if acc==do_acc;
      Load_o1=Factors;
   end
   if acc==do_acc+1;
      acc=0;Load_o2=Factors;
      Factors=Load_o1+(Load_o2-Load_o1)*(it^(1/acc_pow));
      % Convert to new format
      clear ff,id1 = 0;
      for i = 1:length(DimX) 
         id2 = sum(DimX(1:i).*Fac);ff{i} = reshape(Factors(id1+1:id2),DimX(i),Fac);id1 = id2;
      end
      model=nmodel(ff);
      model = reshape(model,DimX(1),prod(DimX(2:end)));
      
      if MissMeth
         connew=model(id);
         errX=X-model;
         if DoWeight==0
            nerr=sum(sum(errX(idmiss2).^2));
         else
            nerr=sum(sum((Weights(idmiss2).*errX(idmiss2)).^2));
         end
      else
         if DoWeight==0
            nerr=sum(sum((X-model).^2));
         else
            nerr=sum(sum((X.*Weights-model.*Weights).^2));
         end
      end
      if nerr>err
         acc_fail=acc_fail+1;
         Factors=Load_o2;
         if acc_fail==max_fail,
            acc_pow=acc_pow+1+1;
            acc_fail=0;
            if showfit~=-1
               disp(' Reducing acceleration');
            end
         end
      else
         if MissMeth
            X(id)=model(id);
         end
      end
   end
   
   
   if DoWeight==0
      for ii=ord:-1:1
         if ii==ord;
            i=1;
         else
            i=ii+1;
         end
         idd=[i+1:ord 1:i-1];
         l_idx2=lidx(idd,:);
         dimx=DimX(idd);
         if ~FixMode(i)
            L1=reshape(Factors(l_idx2(1,1):l_idx2(1,2)),dimx(1),Fac);
            if ord>2
               L2=reshape(Factors(l_idx2(2,1):l_idx2(2,2)),dimx(2),Fac);
               Z=kr(L2,L1);
            else
               Z = L1;
            end
            for j=3:ord-1