BaseHiddenGradientOptimizationLearningTData, TOptimizer Class

Base class for Hidden Conditional Random Fields learning algorithms based on gradient optimization algorithms.

Inheritance Hierarchy

SystemObject
  Accord.Statistics.Models.Fields.LearningBaseHiddenConditionalRandomFieldLearningTData
    Accord.Statistics.Models.Fields.LearningBaseHiddenGradientOptimizationLearningTData, TOptimizer
      Accord.Statistics.Models.Fields.LearningHiddenConjugateGradientLearningT
      Accord.Statistics.Models.Fields.LearningHiddenQuasiNewtonLearningT

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

Syntax

Copy

public abstract class BaseHiddenGradientOptimizationLearning<TData, TOptimizer> : BaseHiddenConditionalRandomFieldLearning<TData>, 
	ISupervisedLearning<HiddenConditionalRandomField<TData>, TData[], int>, IParallel, 
	ISupportsCancellation, IHiddenConditionalRandomFieldLearning<TData>, IDisposable
where TOptimizer : IGradientOptimizationMethod, ISupportsCancellation

Public MustInherit Class BaseHiddenGradientOptimizationLearning(Of TData, TOptimizer As {IGradientOptimizationMethod, ISupportsCancellation})
	Inherits BaseHiddenConditionalRandomFieldLearning(Of TData)
	Implements ISupervisedLearning(Of HiddenConditionalRandomField(Of TData), TData(), Integer), 
	IParallel, ISupportsCancellation, IHiddenConditionalRandomFieldLearning(Of TData), IDisposable

Request Example View Source

Type Parameters

TData
TOptimizer

The BaseHiddenGradientOptimizationLearningTData, TOptimizer type exposes the following members.

Constructors

	Name	Description
	BaseHiddenGradientOptimizationLearningTData, TOptimizer	Constructs a new L-BFGS learning algorithm.

Top

Properties

	Name	Description
	Function	Gets or sets the potential function to be used if this learning algorithm needs to create a new HiddenConditionalRandomFieldT. (Inherited from BaseHiddenConditionalRandomFieldLearningT.)
	HasConverged	Gets or sets whether the algorithm has converged.
	MaxIterations	Gets or sets the maximum number of iterations performed by the learning algorithm.
	Model	Gets or sets the model being trained. (Inherited from BaseHiddenConditionalRandomFieldLearningT.)
	Optimizer	Gets the optimization algorithm being used.
	ParallelOptions	Gets or sets the parallelization options for this algorithm.
	Regularization	Gets or sets the amount of the parameter weights which should be included in the objective function. Default is 0 (do not include regularization).
	Token	Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running. (Inherited from BaseHiddenConditionalRandomFieldLearningT.)
	Tolerance	Gets or sets the tolerance value used to determine whether the algorithm has converged.

Top

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. (Inherited from BaseHiddenConditionalRandomFieldLearningT.)
	CreateOptimizer	Inheritors of this class should create the optimization algorithm in this method, using the current MaxIterations and Tolerance settings.
	Dispose	Performs application-defined tasks associated with freeing, releasing, or resetting unmanaged resources.
	Dispose(Boolean)	Releases unmanaged and - optionally - managed resources
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Releases unmanaged resources and performs other cleanup operations before the HiddenQuasiNewtonLearningT is reclaimed by garbage collection. (Overrides ObjectFinalize.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	InnerRun	Runs the learning algorithm. (Overrides BaseHiddenConditionalRandomFieldLearningTInnerRun(T, Int32).)
	Learn	Learns a model that can map the given inputs to the given outputs. (Inherited from BaseHiddenConditionalRandomFieldLearningT.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Run(TData, Int32)	Online learning is not supported.
	Run(TData, Int32)	Obsolete. Runs the learning algorithm with the specified input training observations and corresponding output labels.
	RunEpoch	Obsolete. Online learning is not supported.
	ToString	Returns a string that represents the current object. (Inherited from Object.)

Top

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.)

Top

Reference

Accord.Statistics.Models.Fields.Learning Namespace

BaseHiddenGradientOptimizationLearningAddLanguageSpecificTextSet("LSTA35377D9_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TData, TOptimizerAddLanguageSpecificTextSet("LSTA35377D9_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BaseHiddenGradientOptimizationLearningTData, TOptimizer Class