Uses of Package
be.ac.ulg.montefiore.run.jahmm

Packages that use be.ac.ulg.montefiore.run.jahmm
be.ac.ulg.montefiore.run.jahmm This package is an Hidden Markov Model implementation. 
be.ac.ulg.montefiore.run.jahmm.io This package holds classes that read and write Hidden Markov Model-related objects. 
be.ac.ulg.montefiore.run.jahmm.learn This package holds Hidden Markov Model-related learning algorithms. 
be.ac.ulg.montefiore.run.jahmm.toolbox This package holds Hidden Markov Model-related tool algorithms. 
 

Classes in be.ac.ulg.montefiore.run.jahmm used by be.ac.ulg.montefiore.run.jahmm
Centroid
          The centroid (basically, the mean) of a cluster.
CentroidFactory
          Creates a centroid for type .
ForwardBackwardCalculator
          This class can be used to compute the probability of a given observations sequence for a given HMM.
ForwardBackwardCalculator.Computation
          Flags used to explain how the observation sequence probability should be computed (either forward, using the alpha array, or backward, using the beta array).
Hmm
          Main Hmm class; it implements an Hidden Markov Model.
Observation
          Observations generated by a Markovian process.
ObservationDiscrete
          This class implements observations whose values are taken out of a finite set implemented as an enumeration.
ObservationInteger
          This class holds an integer observation.
ObservationReal
          This class implements observations made of a real value.
ObservationVector
          This class holds an Observation described by a vector of reals.
Opdf
          Objects implementing this interface represent an observation probability (distribution) function.
OpdfDiscrete
          This class implements a distribution over a finite set of elements.
OpdfFactory
          Classes implementing this interface are able to generate observation probability distribution functions.
OpdfGaussian
          This class represents a (monovariate) gaussian distribution function.
OpdfGaussianMixture
          This class implements a mixture of monovariate gaussian distributions.
OpdfInteger
          This class represents a distribution of a finite number of positive integer observations.
OpdfMultiGaussian
          This class represents a multivariate gaussian distribution function.
 

Classes in be.ac.ulg.montefiore.run.jahmm used by be.ac.ulg.montefiore.run.jahmm.io
Hmm
          Main Hmm class; it implements an Hidden Markov Model.
Observation
          Observations generated by a Markovian process.
ObservationInteger
          This class holds an integer observation.
ObservationReal
          This class implements observations made of a real value.
ObservationVector
          This class holds an Observation described by a vector of reals.
Opdf
          Objects implementing this interface represent an observation probability (distribution) function.
OpdfGaussian
          This class represents a (monovariate) gaussian distribution function.
OpdfGaussianMixture
          This class implements a mixture of monovariate gaussian distributions.
OpdfInteger
          This class represents a distribution of a finite number of positive integer observations.
OpdfMultiGaussian
          This class represents a multivariate gaussian distribution function.
 

Classes in be.ac.ulg.montefiore.run.jahmm used by be.ac.ulg.montefiore.run.jahmm.learn
CentroidFactory
          Creates a centroid for type .
ForwardBackwardCalculator
          This class can be used to compute the probability of a given observations sequence for a given HMM.
Hmm
          Main Hmm class; it implements an Hidden Markov Model.
Observation
          Observations generated by a Markovian process.
Opdf
          Objects implementing this interface represent an observation probability (distribution) function.
OpdfFactory
          Classes implementing this interface are able to generate observation probability distribution functions.
 

Classes in be.ac.ulg.montefiore.run.jahmm used by be.ac.ulg.montefiore.run.jahmm.toolbox
Hmm
          Main Hmm class; it implements an Hidden Markov Model.
Observation
          Observations generated by a Markovian process.
 



Copyright © 2004,2005 Jean-Marc François.