data-structure

HMMBOX, version 3.2, William Penny, Imperial College, Feb 1999 Matlab toolbox for Hidden Markov Mod

The routines use a common data structure 'hmm' with fields:

hmm.obsmodel		name of observation model
			'Gauss'		- Gaussian
			'GaussCom'	- Gaussian with common cov
			'AR'		- Autoregressive
			'LIKE'          - observations are likelihoods

hmm.train.obsupdate	update observation model (1 or 0)
	 .pupdate	update transition matrix (1 or 0)
	 .init		initialised (1 or 0)
	 .cyc		max number of cycles through data
	 .tol		termination tolerance of likelihood

hmm.data.Xtrain		training data
	.T		length of training sequence		
	.Xtest		testing data

hmm.K			number of hidden states
   .Pi 			initial state probability
   .P			state transition probabilities
   .LLtrain		training log likelihood
   .LLtest		testing log likelihood

hmm.gmmll		loglikelihood of gmm model used for initialisation
hmm.mix			gaussian mixture model trained on same data


For 'Gauss' and 'GaussCom' observation models we also have:

hmm.state(k).Mu		mean vector for state k
            .Cov	mean covariance matrix for state k
            .a		ar coefficients for state k
            .covtype    Covariance type (full/diag)

For 'AR' observation models we have:

hmm.state(k).p          order of AR model
hmm.state(k).a          parameter vector for AR model
hmm.state(k).v          noise variance for AR model

For 'LIKE' observation models, there are no extra parameters.