% A demonstration of the HMM software using the 'Likelihood' observation% model. There are K=2 time series where EACH TIME SERIES IS THE % LIKELIHOOD OF THE DATA GIVEN THAT STATE - in effect there is % no observation model; the likelihood of each data point is simply% set to the value of each data point.clearload demlikefigureplot(pp_t(:,1));title('Original data - series 1');disp('The plot shows the likelihood of data given state/class 1');disp(' ');disp('Press a key to train up an HMM');disp(' ');pauseXseries=pp_t;hmm=struct('K',2);disp(' ');% I don't initialise, I have to do it manuallyhmm.obsmodel='LIKE';hmm.train.obsupdate=0;hmm.train.init=1;   % We've already initialisedhmm.priors=struct('Dir2d_alpha',ones(hmm.K),'Dir_alpha',ones(1,hmm.K));T=size(Xseries,1);% Train HMMhmm=hmmtrain(Xseries,T,hmm);[block,LL]=hmmdecode(Xseries,T,hmm);        % Find most likely hidden state sequence using Viterbi methodfigureplot(block(1).q_star);axis([0 800 0 3]);title('Viterbi decoding');disp('State transition matrix is:');hmm.Pdisp('The Viterbi decoding plot shows that the time series');disp('has been correctly partitioned.');