Class HMMExponentialClassifier

  • All Implemented Interfaces:
    java.lang.Cloneable, Actor, Executable, FiringsRecordable, Initializable, TypedActor, Changeable, Debuggable, DebugListener, Derivable, Instantiable, ModelErrorHandler, MoMLExportable, Moveable, Nameable

    public class HMMExponentialClassifier
    extends ObservationClassifier

    This actor performs Maximum-Likelihood classification of the partially-observed Bayesian Network models. ClassifyObservations is designed to work with ExpectationMaximization, which provides the Maximum-Likelihood model parameters from which the observations are assumed to be drawn. The output is an integer array of labels, representing the maximum-likelihood hidden state sequence of the given model.

    The user provides a set of parameter estimates as inputs to the model, and The mean is a double array input containing the mean estimate and sigma is a double array input containing standard deviation estimate of each mixture component. If the modelType is HMM, then an additional input, transitionMatrix is provided, which is an estimate of the transition matrix governing the Markovian process representing the hidden state evolution. The prior input is an estimate of the prior state distribution.

    Since:
    Ptolemy II 10.0
    Version:
    $Id$
    Author:
    Ilge Akkaya
    Pt.AcceptedRating:
    Pt.ProposedRating:
    Red (ilgea)