BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions Class

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

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.Statistics.Models.Markov.LearningBaseHiddenMarkovModelLearningTModel, TObservation
      Accord.Statistics.Models.Markov.LearningBaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions
        Accord.Statistics.Models.Markov.LearningBaseBaumWelchLearningOptionsTModel, TDistribution, TObservation, TOptions
        Accord.Statistics.Models.Markov.LearningBaumWelchLearningTDistribution, TObservation

Namespace: Accord.Statistics.Models.Markov.Learning
Assembly: Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0

Syntax

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public abstract class BaseBaumWelchLearning<TModel, TDistribution, TObservation, TOptions> : BaseHiddenMarkovModelLearning<TModel, TObservation>, 
	IUnsupervisedLearning<TModel, TObservation[], int[]>, IConvergenceLearning
where TModel : HiddenMarkovModel<TDistribution, TObservation>
where TDistribution : Object, IFittableDistribution<TObservation>
where TOptions : class, IFittingOptions

Public MustInherit Class BaseBaumWelchLearning(Of TModel As HiddenMarkovModel(Of TDistribution, TObservation), TDistribution As {Object, IFittableDistribution(Of TObservation)}, TObservation, TOptions As {Class, IFittingOptions})
	Inherits BaseHiddenMarkovModelLearning(Of TModel, TObservation)
	Implements IUnsupervisedLearning(Of TModel, TObservation(), Integer()), 
	IConvergenceLearning

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Type Parameters

TModel
TDistribution
TObservation
TOptions

The BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions type exposes the following members.

Constructors

	Name	Description
	BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions	Creates a new instance of the Baum-Welch learning algorithm.
	BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions(TModel)	Creates a new instance of the Baum-Welch learning algorithm.

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Properties

	Name	Description
	Convergence	Gets or sets convergence parameters.
	CurrentIteration	Gets or sets the number of performed iterations.
	Emissions	Gets or sets the function that initializes the emission distributions in the hidden Markov Models.
	FittingOptions	Gets or sets the distribution fitting options to use when estimating distribution densities during learning.
	HasConverged	Gets or sets whether the algorithm has converged.
	Iterations	Obsolete. Please use MaxIterations instead.
	LogGamma	Gets the Gamma matrix of log probabilities created during the last iteration of the Baum-Welch learning algorithm.
	LogKsi	Gets the Ksi matrix of log probabilities created during the last iteration of the Baum-Welch learning algorithm.
	LogLikelihood	Gets the log-likelihood of the model at the last iteration.
	LogWeights	Gets the sample weights in the last iteration of the Baum-Welch learning algorithm.
	MaxIterations	Gets or sets the maximum number of iterations performed by the learning algorithm.
	Model	Gets or sets the model being trained. (Inherited from BaseHiddenMarkovModelLearningTModel, TObservation.)
	NumberOfStates	Gets or sets the number of states to be used when this learning algorithm needs to create new models. (Inherited from BaseHiddenMarkovModelLearningTModel, TObservation.)
	Observations	Gets all observations as a single vector.
	ParallelOptions	Gets or sets the parallelization options for this algorithm. (Inherited from ParallelLearningBase.)
	Token	Gets or sets a cancellation token that can be used to cancel the algorithm while it is running. (Inherited from ParallelLearningBase.)
	Tolerance	Gets or sets the maximum change in the average log-likelihood after an iteration of the algorithm used to detect convergence.
	Topology	Gets or sets the state transition topology to be used when this learning algorithm needs to create new models. Default is Forward. (Inherited from BaseHiddenMarkovModelLearningTModel, TObservation.)

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Methods

	Name	Description
	ComputeForwardBackward	Computes the forward and backward probabilities matrices for a given observation referenced by its index in the input training data.
	ComputeKsi	Computes the ksi matrix of probabilities for a given observation referenced by its index in the input training data.
	Create	Creates an instance of the model to be learned. Inheritors of this abstract class must define this method so new models can be created from the training data. (Inherited from BaseHiddenMarkovModelLearningTModel, TObservation.)
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	Fit	Fits one emission distribution. This method can be override in a base class in order to implement special fitting options.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	Learn	Learns a model that can map the given inputs to the desired outputs.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)
	UpdateEmissions	Updates the emission probability matrix.

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Reference

Accord.Statistics.Models.Markov.Learning Namespace

BaseBaumWelchLearningAddLanguageSpecificTextSet("LSTB5793B3D_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TModel, TDistribution, TObservation, TOptionsAddLanguageSpecificTextSet("LSTB5793B3D_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions Class