Uses of Class
gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel

Packages that use HiddenMarkovModel

Package	Description
gov.sandia.cognition.learning.algorithm.hmm	Provides hidden Markov model (HMM) algorithms.

Uses of HiddenMarkovModel in gov.sandia.cognition.learning.algorithm.hmm

Subclasses of HiddenMarkovModel in gov.sandia.cognition.learning.algorithm.hmm

Modifier and Type	Class and Description
class	ParallelHiddenMarkovModel<ObservationType> A Hidden Markov Model with parallelized processing.

Fields in gov.sandia.cognition.learning.algorithm.hmm declared as HiddenMarkovModel

Modifier and Type	Field and Description
protected HiddenMarkovModel<ObservationType>	AbstractBaumWelchAlgorithm.initialGuess Initial guess for the iterations.
protected HiddenMarkovModel<ObservationType>	AbstractBaumWelchAlgorithm.result Result of the Baum-Welch Algorithm

Methods in gov.sandia.cognition.learning.algorithm.hmm that return HiddenMarkovModel

Modifier and Type	Method and Description
HiddenMarkovModel<ObservationType>	HiddenMarkovModel.clone()
static <ObservationType> HiddenMarkovModel<ObservationType>	HiddenMarkovModel.createRandom(java.util.Collection<? extends ProbabilityFunction<ObservationType>> distributions, java.util.Random random) Creates a Hidden Markov Model with the given probability function for each state, but sampling the columns of the transition matrix and the initial probability distributions from a diffuse Dirichlet.
static <ObservationType> HiddenMarkovModel<ObservationType>	HiddenMarkovModel.createRandom(int numStates, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> learner, java.util.Collection<? extends ObservationType> data, java.util.Random random) Creates a Hidden Markov Model with the same PMF/PDF for each state, but sampling the columns of the transition matrix and the initial probability distributions from a diffuse Dirichlet.
static <ObservationType> HiddenMarkovModel<ObservationType>	HiddenMarkovModel.createRandom(int numStates, ComputableDistribution<ObservationType> distribution, java.util.Random random) Creates a Hidden Markov Model with the same PMF/PDF for each state, but sampling the columns of the transition matrix and the initial probability distributions from a diffuse Dirichlet.
HiddenMarkovModel<ObservationType>	AbstractBaumWelchAlgorithm.getInitialGuess() Getter for initialGuess.
HiddenMarkovModel<ObservationType>	AbstractBaumWelchAlgorithm.getResult()
HiddenMarkovModel<ObservationType>	BaumWelchAlgorithm.learn(MultiCollection<ObservationType> data) Allows the algorithm to learn against multiple sequences of data.

Methods in gov.sandia.cognition.learning.algorithm.hmm with parameters of type HiddenMarkovModel

Modifier and Type	Method and Description
void	AbstractBaumWelchAlgorithm.setInitialGuess(HiddenMarkovModel<ObservationType> initialGuess) Setter for initialGuess.
protected double	BaumWelchAlgorithm.updateSequenceLogLikelihoods(HiddenMarkovModel<ObservationType> hmm) Updates the internal sequence likelihoods for the given HMM

Constructors in gov.sandia.cognition.learning.algorithm.hmm with parameters of type HiddenMarkovModel

Constructor and Description
AbstractBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities) Creates a new instance of AbstractBaumWelchAlgorithm
BaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities) Creates a new instance of BaumWelchAlgorithm
ParallelBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities) Creates a new instance of ParallelBaumWelchAlgorithm
ParallelHiddenMarkovModel(HiddenMarkovModel<ObservationType> other) \ Creates a new ParallelHiddenMarkovModel from another HiddenMarkovModel.