Daniel@0: % function [lik, likv]=mfa_cl(X,Lh,Ph,Mu,Pi);
Daniel@0: % 
Daniel@0: % Calculates log likelihoods of a data set under a mixture of factor
Daniel@0: % analysis model.
Daniel@0: %
Daniel@0: % X - data matrix
Daniel@0: % Lh - factor loadings 
Daniel@0: % Ph - diagonal uniquenesses matrix
Daniel@0: % Mu - mean vectors
Daniel@0: % Pi - priors
Daniel@0: %
Daniel@0: % lik - log likelihood of X 
Daniel@0: % likv - vector of log likelihoods
Daniel@0: % 
Daniel@0: % If 0 or 1 output arguments requested, lik is returned. If 2 output
Daniel@0: % arguments requested, [lik likv] is returned.
Daniel@0: 
Daniel@0: function [lik, likv]=mfa_cl(X,Lh,Ph,Mu,Pi);
Daniel@0: 
Daniel@0: N=length(X(:,1));
Daniel@0: D=length(X(1,:));
Daniel@0: K=length(Lh(1,:));
Daniel@0: M=length(Pi);
Daniel@0: 
Daniel@0: if (abs(sum(Pi)-1) > 1e-6) 
Daniel@0:   disp('ERROR: Pi should sum to 1');
Daniel@0:   return;
Daniel@0: elseif ((size(Lh) ~= [D*M K]) | (size(Ph) ~= [D 1]) | (size(Mu) ~= [M D]) ...
Daniel@0:   | (size(Pi) ~= [M 1] & size(Pi) ~= [1 M]))   
Daniel@0:   disp('ERROR in input matrix sizes');
Daniel@0:   return;
Daniel@0: end;  
Daniel@0: 
Daniel@0: tiny=exp(-744);
Daniel@0: const=(2*pi)^(-D/2);
Daniel@0: 
Daniel@0: I=eye(K);
Daniel@0: Phi=1./Ph;
Daniel@0: Phid=diag(Phi);
Daniel@0: for k=1:M  
Daniel@0:   Lht=Lh((k-1)*D+1:k*D,:);
Daniel@0:   LP=Phid*Lht;
Daniel@0:   MM=Phid-LP*inv(I+Lht'*LP)*LP';
Daniel@0:   dM=sqrt(det(MM));      	
Daniel@0:   Xk=(X-ones(N,1)*Mu(k,:)); 
Daniel@0:   XM=Xk*MM; 
Daniel@0:   H(:,k)=const*Pi(k)*dM*exp(-0.5*sum((XM.*Xk)'))'; 	
Daniel@0: end;
Daniel@0: 
Daniel@0: Hsum=rsum(H); 				
Daniel@0: 
Daniel@0: likv=log(Hsum+(Hsum==0)*tiny);
Daniel@0: lik=sum(likv);
Daniel@0: