Documentation

Parameter set of the hidden Markov model. Direct use of the constructor is not recommended. Instead, call new or init.

new states outputs returns a model from the states and outputs. The initialStateDist and emissionDist are set to be uniform distributions. The transitionDist is specified as follows: with probability 1/2, move to the same state, otherwise, move to a random state (which might be the same state).

>>> new [1, 2 :: Int] ['C', 'D']
HMM {states = [1,2], outputs = "CD", initialStateDist = fromList [(0.5,1),(0.5,2)], transitionDist = [(fromList [(0.75,1),(0.25,2)],1),(fromList [(0.25,1),(0.75,2)],2)], emissionDist = [(fromList [(0.5,'C'),(0.5,'D')],1),(fromList [(0.5,'C'),(0.5,'D')],2)]}

init states outputs returns a random variable of the model with states and outputs, wherein parameters are sampled from uniform distributions.

model `withEmission` xs returns a model in which the emissionDist is updated by re-estimations using the observed outputs xs. The emissionDist is set to be normalized histograms each of which is calculated from segumentations of xs based on the Viterbi state path.

viterbi model xs performs the Viterbi algorithm using the observed outputs xs, and returns the most likely state path and its log likelihood.

baumWelch model xs iteratively performs the Baum-Welch algorithm using the observed outputs xs, and returns a list of updated models and their corresponding log likelihoods.

simulate model t generates a Markov process of length t using the model, and returns its state path and observed outputs.