C# Class Accord.Statistics.Models.Markov.Learning.ContinuousBaumWelchLearning

Baum-Welch learning algorithm for continuous density Hidden Markov Models.

Inheritance: BaumWelchLearningBase, IUnsupervisedLearning

Afficher le fichier Open project: atosorigin/Kinect Class Usage Examples

Méthodes publiques

Méthode	Description
ContinuousBaumWelchLearning ( ContinuousHiddenMarkovModel model ) : System	Creates a new instance of the Baum-Welch learning algorithm.
Run ( ) : double	Runs the Baum-Welch learning algorithm for hidden Markov models. Learning problem. Given some training observation sequences O = {o1, o2, ..., oK} and general structure of HMM (numbers of hidden and visible states), determine HMM parameters M = (A, B, pi) that best fit training data.

Méthodes protégées

Méthode	Description
ComputeForwardBackward ( int index, double &fwd, double &bwd, double &scaling ) : void	Computes the forward and backward probabilities matrices for a given observation referenced by its index in the input training data.
ComputeKsi ( int index, double fwd, double bwd, double scaling ) : void	Computes the ksi matrix of probabilities for a given observation referenced by its index in the input training data.
UpdateEmissions ( ) : void	Updates the emission probability matrix. Implementations of this method should use the observations in the training data and the Gamma probability matrix to update the probability distributions of symbol emissions.

Private Methods

Méthode	Description
convert ( Array array, int dimension ) : double[][]	Converts a univariate or multivariate array of observations into a two-dimensional jagged array.

Method Details

ComputeForwardBackward() protected méthode

Computes the forward and backward probabilities matrices for a given observation referenced by its index in the input training data.

protected ComputeForwardBackward ( int index, double &fwd, double &bwd, double &scaling ) : void
index	int	The index of the observation in the input training data.
fwd	double	Returns the computed forward probabilities matrix.
bwd	double	Returns the computed backward probabilities matrix.
scaling	double	Returns the scaling parameters used during calculations.
Résultat	void

ComputeKsi() protected méthode

Computes the ksi matrix of probabilities for a given observation referenced by its index in the input training data.

protected ComputeKsi ( int index, double fwd, double bwd, double scaling ) : void
index	int	The index of the observation in the input training data.
fwd	double	The matrix of forward probabilities for the observation.
bwd	double	The matrix of backward probabilities for the observation.
scaling	double	The scaling vector computed in previous calculations.
Résultat	void

ContinuousBaumWelchLearning() public méthode

Creates a new instance of the Baum-Welch learning algorithm.

public ContinuousBaumWelchLearning ( ContinuousHiddenMarkovModel model ) : System
model	ContinuousHiddenMarkovModel
Résultat	System

Run() public méthode

Runs the Baum-Welch learning algorithm for hidden Markov models.

Learning problem. Given some training observation sequences O = {o1, o2, ..., oK} and general structure of HMM (numbers of hidden and visible states), determine HMM parameters M = (A, B, pi) that best fit training data.

public Run ( ) : double
Résultat	double

UpdateEmissions() protected méthode

Updates the emission probability matrix.

Implementations of this method should use the observations in the training data and the Gamma probability matrix to update the probability distributions of symbol emissions.

protected UpdateEmissions ( ) : void
Résultat	void