C# Класс Accord.Statistics.Models.Markov.Learning.BaumWelchLearningBase

Base class for implementations of the Baum-Welch learning algorithm.

Защищенные методы

Метод	Описание
BaumWelchLearningBase ( IHiddenMarkovModel model ) : System	Initializes a new instance of the BaumWelchLearningBase class.
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.
HasConverged ( double oldLikelihood, double newLikelihood, int currentIteration ) : bool	Checks if a model has converged given the likelihoods between two iterations of the Baum-Welch algorithm and a criteria for convergence.
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.
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.

Описание методов

BaumWelchLearningBase() защищенный Метод

Initializes a new instance of the BaumWelchLearningBase class.

protected BaumWelchLearningBase ( IHiddenMarkovModel model ) : System
model	IHiddenMarkovModel
Результат	System

ComputeForwardBackward() защищенный абстрактный Метод

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 &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.
Результат	void

ComputeKsi() защищенный абстрактный Метод

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 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.
Результат	void

HasConverged() защищенный Метод

Checks if a model has converged given the likelihoods between two iterations of the Baum-Welch algorithm and a criteria for convergence.

protected HasConverged ( double oldLikelihood, double newLikelihood, int currentIteration ) : bool
oldLikelihood	double
newLikelihood	double
currentIteration	int
Результат	bool

Run() защищенный Метод

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. ///
Результат	double

UpdateEmissions() защищенный абстрактный Метод

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 abstract UpdateEmissions ( ) : void
Результат	void