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

Base class for implementations of the Baum-Welch learning algorithm. This class cannot be instantiated.
Inheritance: BaseHiddenMarkovModelLearning, IUnsupervisedLearning, IConvergenceLearning
Afficher le fichier Open project: accord-net/framework

Méthodes protégées

Méthode Description
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.

Private Methods

Méthode Description
run ( Array observations ) : double

Method Details

BaseBaumWelchLearning() protected méthode

Initializes a new instance of the BaseBaumWelchLearning class.
protected BaseBaumWelchLearning ( IHiddenMarkovModel model ) : System
model IHiddenMarkovModel
Résultat System

ComputeForwardBackward() protected abstract méthode

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.
Résultat void

ComputeKsi() protected abstract méthode

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.
Résultat void

Run() protected 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.
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. ///
Résultat double

Run() protected 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.
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.
Résultat double