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

Packages that use HiddenMarkovModel
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
 class ParallelHiddenMarkovModel<ObservationType>
          A Hidden Markov Model with parallelized processing.
 

Fields in gov.sandia.cognition.learning.algorithm.hmm declared as HiddenMarkovModel
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
 HiddenMarkovModel<ObservationType> HiddenMarkovModel.clone()
           
static
<ObservationType>
HiddenMarkovModel<ObservationType>
HiddenMarkovModel.createRandom(Collection<? extends ProbabilityFunction<ObservationType>> distributions, 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<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> learner, Collection<? extends ObservationType> data, 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, 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
 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
AbstractBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities)
          Creates a new instance of AbstractBaumWelchAlgorithm
BaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities)
          Creates a new instance of BaumWelchAlgorithm
ParallelBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<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.