diff toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/mfa.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/mfa.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,153 @@
+% function [Lh,Ph,Mu,Pi,LL]=mfa(X,M,K,cyc,tol);
+% 
+% Maximum Likelihood Mixture of Factor Analysis using EM
+%
+% X - data matrix
+% M - number of mixtures (default 1)
+% K - number of factors in each mixture (default 2)
+% cyc - maximum number of cycles of EM (default 100)
+% tol - termination tolerance (prop change in likelihood) (default 0.0001)
+%
+% Lh - factor loadings 
+% Ph - diagonal uniquenesses matrix
+% Mu - mean vectors
+% Pi - priors
+% LL - log likelihood curve
+%
+% Iterates until a proportional change < tol in the log likelihood 
+% or cyc steps of EM 
+
+function [Lh, Ph,  Mu, Pi, LL] = mfa(X,M,K,cyc,tol)
+
+if nargin<5   tol=0.0001; end;
+if nargin<4   cyc=100; end;
+if nargin<3   K=2; end;
+if nargin<2   M=1; end;
+
+N=length(X(:,1));
+D=length(X(1,:));
+tiny=exp(-700);
+
+%rand('state',0);
+
+fprintf('\n');
+
+if (M==1)
+  [Lh,Ph,LL]=ffa(X,K,cyc,tol);
+  Mu=mean(X);
+  Pi=1;
+else
+  if N==1
+    mX = X;
+  else
+    mX=mean(X);
+  end
+  cX=cov(X);
+  scale=det(cX)^(1/D);
+  randn('state',0); 
+  Lh=randn(D*M,K)*sqrt(scale/K);
+  Ph=diag(cX)+tiny;
+  Pi=ones(M,1)/M;
+  %randn('state',0); 
+  Mu=randn(M,D)*sqrtm(cX)+ones(M,1)*mX;
+  oldMu=Mu;
+  I=eye(K);
+
+  lik=0;
+  LL=[];
+
+  H=zeros(N,M); 	% E(w|x) 
+  EZ=zeros(N*M,K);
+  EZZ=zeros(K*M,K);
+  XX=zeros(D*M,D);
+  s=zeros(M,1);
+  const=(2*pi)^(-D/2);
+  %%%%%%%%%%%%%%%%%%%%
+  for i=1:cyc;
+
+    %%%% E Step %%%%
+
+    Phi=1./Ph;
+    Phid=diag(Phi);
+    for k=1:M
+      Lht=Lh((k-1)*D+1:k*D,:);
+      LP=Phid*Lht;
+      MM=Phid-LP*inv(I+Lht'*LP)*LP';
+      dM=sqrt(det(MM));      	
+      Xk=(X-ones(N,1)*Mu(k,:)); 
+      XM=Xk*MM;
+      H(:,k)=const*Pi(k)*dM*exp(-0.5*rsum(XM.*Xk)); 	
+      EZ((k-1)*N+1:k*N,:)=XM*Lht;
+    end;
+    
+    Hsum=rsum(H);
+    oldlik=lik;
+    lik=sum(log(Hsum+(Hsum==0)*exp(-744)));
+
+    Hzero=(Hsum==0); Nz=sum(Hzero); 
+    H(Hzero,:)=tiny*ones(Nz,M)/M; 
+    Hsum(Hzero)=tiny*ones(Nz,1);
+    
+    H=rdiv(H,Hsum); 				
+    s=csum(H);
+    s=s+(s==0)*tiny;
+    s2=sum(s)+tiny;
+    
+    for k=1:M  
+      kD=(k-1)*D+1:k*D;
+      Lht=Lh(kD,:);
+      LP=Phid*Lht;
+      MM=Phid-LP*inv(I+Lht'*LP)*LP';
+      Xk=(X-ones(N,1)*Mu(k,:)); 
+      XX(kD,:)=rprod(Xk,H(:,k))'*Xk/s(k); 
+      beta=Lht'*MM;
+      EZZ((k-1)*K+1:k*K,:)=I-beta*Lht +beta*XX(kD,:)*beta'; 
+    end;
+
+    %%%% log likelihood %%%%
+
+    LL=[LL lik];
+    fprintf('cycle %g   \tlog likelihood %g ',i,lik);
+    
+    if (i<=2)
+      likbase=lik;
+    elseif (lik<oldlik) 
+      fprintf(' violation');
+    elseif ((lik-likbase)<(1 + tol)*(oldlik-likbase)||~isfinite(lik)) 
+      break;
+    end;
+
+    fprintf('\n');
+    
+    %%%% M Step %%%%
+    
+    % means and covariance structure
+    
+    Ph=zeros(D,1);
+    for k=1:M
+      kD=(k-1)*D+1:k*D;
+      kK=(k-1)*K+1:k*K;
+      kN=(k-1)*N+1:k*N;
+
+      T0=rprod(X,H(:,k));
+      T1=T0'*[EZ(kN,:) ones(N,1)];
+      XH=EZ(kN,:)'*H(:,k);
+      T2=inv([s(k)*EZZ(kK,:) XH; XH' s(k)]);
+      T3=T1*T2;
+      Lh(kD,:)=T3(:,1:K);
+      Mu(k,:)=T3(:,K+1)';
+      T4=diag(T0'*X-T3*T1')/s2;
+      Ph=Ph+T4.*(T4>0); 
+    end;
+
+    Phmin=exp(-700);
+    Ph=Ph.*(Ph>Phmin)+(Ph<=Phmin)*Phmin; % to avoid zero variances
+
+    % priors
+    Pi=s'/s2;
+    
+  end;
+  fprintf('\n');
+end;
+
+