| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.hmm |
Provides hidden Markov model (HMM) algorithms.
|
| Class and Description |
|---|
| AbstractBaumWelchAlgorithm
Partial implementation of the Baum-Welch algorithm.
|
| BaumWelchAlgorithm
Implements the Baum-Welch algorithm, also known as the "forward-backward
algorithm", the expectation-maximization algorithm, etc for
Hidden Markov Models (HMMs).
|
| HiddenMarkovModel
A discrete-state Hidden Markov Model (HMM) with either continuous
or discrete observations.
|
| MarkovChain
A Markov chain is a random process that has a finite number of states with
random transition probabilities between states at discrete time steps.
|
| ParallelBaumWelchAlgorithm.DistributionEstimatorTask
Re-estimates the PDF from the gammas.
|
| ParallelHiddenMarkovModel.ComputeTransitionsTask
Calls the computeTransitions method.
|
| ParallelHiddenMarkovModel.NormalizeTransitionTask
Calls the normalizeTransitionMatrix method.
|
| ParallelHiddenMarkovModel.ObservationLikelihoodTask
Calls the computeObservationLikelihoods() method.
|
| ParallelHiddenMarkovModel.StateObservationLikelihoodTask
Calls the computeStateObservationLikelihood() method.
|
| ParallelHiddenMarkovModel.ViterbiTask
Computes the most-likely "from state" for the given "destination state"
and the given deltas.
|