BaseMaximumLikelihoodLearningTModel, TDistribution, TObservation, TOptions Class

Base class for observable Markov model learning algorithms.

Inheritance Hierarchy

SystemObject
  Accord.Statistics.Models.Markov.LearningBaseMaximumLikelihoodLearningTModel, TDistribution, TObservation, TOptions
    Accord.Statistics.Models.Markov.LearningMaximumLikelihoodLearning
    Accord.Statistics.Models.Markov.LearningMaximumLikelihoodLearningTDistribution, 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 BaseMaximumLikelihoodLearning<TModel, TDistribution, TObservation, TOptions>
where TModel : HiddenMarkovModel<TDistribution, TObservation>
where TDistribution : Object, IFittableDistribution<TObservation>
where TOptions : IFittingOptions

Public MustInherit Class BaseMaximumLikelihoodLearning(Of TModel As HiddenMarkovModel(Of TDistribution, TObservation), TDistribution As {Object, IFittableDistribution(Of TObservation)}, TObservation, TOptions As IFittingOptions)

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Type Parameters

TModel
TDistribution
TObservation
TOptions

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

Constructors

	Name	Description
	BaseMaximumLikelihoodLearningTModel, TDistribution, TObservation, TOptions	Creates a new instance of the Maximum Likelihood learning algorithm.

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Properties

	Name	Description
	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.
	Model	Gets the model being trained.
	Token	Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.
	UseLaplaceRule	Gets or sets whether to use Laplace's rule of succession to avoid zero probabilities.
	UseWeights	Gets or sets whether the emission fitting algorithm should present weighted samples or simply the clustered samples to the density estimation methods.

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Methods

	Name	Description
	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.
	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.)
	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 given 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.)

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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

BaseMaximumLikelihoodLearningAddLanguageSpecificTextSet("LSTB49A121B_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TModel, TDistribution, TObservation, TOptionsAddLanguageSpecificTextSet("LSTB49A121B_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BaseMaximumLikelihoodLearningTModel, TDistribution, TObservation, TOptions Class