C# 클래스 Accord.Statistics.Models.Markov.Learning.BaseBaumWelchLearning

Base class for implementations of the Baum-Welch learning algorithm. This class cannot be instantiated.

상속: BaseHiddenMarkovModelLearning, IUnsupervisedLearning, IConvergenceLearning

보호된 메소드들

메소드	설명
BaseBaumWelchLearning ( IHiddenMarkovModel model ) : System	Initializes a new instance of the BaseBaumWelchLearning class.
ComputeForwardBackward ( int index, double lnFwd, double lnBwd ) : 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 lnFwd, double lnBwd ) : void	Computes the ksi matrix of probabilities for a given observation referenced by its index in the input training data.
Run ( Array observations ) : 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.
Run ( Array observations, double weights ) : 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.

비공개 메소드들

메소드	설명
run ( Array observations ) : double

메소드 상세

BaseBaumWelchLearning() 보호된 메소드

Initializes a new instance of the BaseBaumWelchLearning class.

protected BaseBaumWelchLearning ( IHiddenMarkovModel model ) : System
model	IHiddenMarkovModel
리턴	System

ComputeForwardBackward() 보호된 추상적인 메소드

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

protected abstract ComputeForwardBackward ( int index, double lnFwd, double lnBwd ) : void
index	int	The index of the observation in the input training data.
lnFwd	double	Returns the computed forward probabilities matrix.
lnBwd	double	Returns the computed backward probabilities matrix.
리턴	void

ComputeKsi() 보호된 추상적인 메소드

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

protected abstract ComputeKsi ( int index, double lnFwd, double lnBwd ) : void
index	int	The index of the observation in the input training data.
lnFwd	double	The matrix of forward probabilities for the observation.
lnBwd	double	The matrix of backward probabilities for the observation.
리턴	void

Run() 보호된 메소드

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.

protected Run ( Array observations ) : double
observations	System.Array	/// The sequences of univariate or multivariate observations used to train the model. /// Can be either of type double[] (for the univariate case) or double[][] for the /// multivariate case. ///
리턴	double

Run() 보호된 메소드

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

protected Run ( Array observations, double weights ) : double
observations	System.Array	/// The sequences of univariate or multivariate observations used to train the model. /// Can be either of type double[] (for the univariate case) or double[][] for the /// multivariate case.
weights	double	/// The weight associated with each sequence.
리턴	double