% function [lik,likv]=hmm_cl(X,T,K,Mu,Cov,P,Pi);% % Calculate Likelihood for Hidden Markov Model %% X - N x p data matrix% T - length of each sequence (N must evenly divide by T, default T=N)% K - number of states% Mu - mean vectors% Cov - output covariance matrix (full, tied across states)% P - state transition matrix% Pi - priors%% lik - log likelihood of X % likv - vector of log likelihoods of each sequence%% If 0 or 1 output arguments requested, lik is returned. If 2 output% arguments requested, [lik likv] is returned.%function [lik,likv]=hmm_cl(X,T,K,Mu,Cov,P,Pi);p=length(X(1,:));N=length(X(:,1));tiny=exp(-700);if (rem(N,T)~=0)  disp('Error: Data matrix length must be multiple of sequence length T');  return;end;N=N/T;alpha=zeros(T,K);B=zeros(T,K);      % P( output | s_i) 	k1=(2*pi)^(-p/2);Scale=zeros(T,1);likv=zeros(1,N);for n=1:N    B=zeros(T,K);   iCov=inv(Cov);        k2=k1/sqrt(det(Cov));  for i=1:T    for l=1:K      d= Mu(l,:)-X((n-1)*T+i,:);      B(i,l)=k2*exp(-0.5*d*iCov*d');    end;   end;     scale=zeros(T,1);  alpha(1,:)=Pi(:)'.*B(1,:);  scale(1)=sum(alpha(1,:));   alpha(1,:)=alpha(1,:)/(scale(1)+tiny);  for i=2:T    alpha(i,:)=(alpha(i-1,:)*P).*B(i,:);     scale(i)=sum(alpha(i,:));    alpha(i,:)=alpha(i,:)/(scale(i)+tiny);  end;  likv(n)=sum(log(scale+(scale==0)*tiny));  Scale=Scale+log(scale+(scale==0)*tiny);end;lik=sum(Scale);