OneVsRestLearningTInput, TBinary, TModel Class

Base learning algorithm for OneVsRestTModel, TInput multi-class classifiers.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearningOneVsRestLearningTInput, TBinary, TModel
      Accord.MachineLearningOneVsRestLearningTBinary, TModel
      Accord.MachineLearning.VectorMachines.LearningBaseMultilabelSupportVectorLearningTInput, TBinary, TKernel, TModel

Namespace: Accord.MachineLearning
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

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[SerializableAttribute]
public abstract class OneVsRestLearning<TInput, TBinary, TModel> : ParallelLearningBase, 
	ISupervisedLearning<TModel, TInput, int>, ISupervisedLearning<TModel, TInput, int[]>, 
	ISupervisedLearning<TModel, TInput, bool[]>
where TBinary : Object, IBinaryScoreClassifier<TInput>
where TModel : OneVsRest<TBinary, TInput>

<SerializableAttribute>
Public MustInherit Class OneVsRestLearning(Of TInput, TBinary As {Object, IBinaryScoreClassifier(Of TInput)}, TModel As OneVsRest(Of TBinary, TInput))
	Inherits ParallelLearningBase
	Implements ISupervisedLearning(Of TModel, TInput, Integer), 
	ISupervisedLearning(Of TModel, TInput, Integer()), ISupervisedLearning(Of TModel, TInput, Boolean())

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Type Parameters

TInput: The type for the samples handled by the classifier. Default is double[].
TBinary: The type for the inner binary classifiers used in the one-vs-rest approach.
TModel: The type of the model being learned.

The OneVsRestLearningTInput, TBinary, TModel type exposes the following members.

Constructors

	Name	Description
	OneVsRestLearningTInput, TBinary, TModel	Initializes a new instance of the OneVsRestLearningTInput, TBinary, TModel class.

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Properties

	Name	Description
	AggregateExceptions	Gets or sets a value indicating whether the entire training algorithm should stop in case an exception has been detected at just one of the inner binary learning problems. Default is true (execution will not be stopped).
	IsMultilabel	Gets or sets a value indicating whether the learning algorithm should generate multi-label (as opposed to multi-class) models. If left unspecified, the type of the model will be determined automatically depending on which overload of the Learn(TInput, Boolean, Double) method will be called first by the executing code.
	Learner	Gets or sets a function that takes a set of parameters and creates a learning algorithm for learning each of the binary inner classifiers needed by the one-vs-rest classification strategy.
	Model	Gets or sets the model being learned.
	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.)

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Methods

	Name	Description
	ConfigureTResult(FuncTResult)	Sets a callback function that takes a set of parameters and creates a learning algorithm for learning each of the binary inner classifiers needed by the one-vs-rest classification strategy. Calling this method sets the Learner property.
	ConfigureT, TResult(FuncT, TResult)	Sets a callback function that takes a set of parameters and creates a learning algorithm for learning each of the binary inner classifiers needed by the one-vs-rest classification strategy. Calling this method sets the Learner property.
	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(TInput, Boolean, Double)	Learns a model that can map the given inputs to the given outputs.
	Learn(TInput, Int32, Double)	Learns a model that can map the given inputs to the given outputs.
	Learn(TInput, Int32, Double)	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.)
	OnSubproblemFinished	Raises the [E:SubproblemFinished] event.
	OnSubproblemStarted	Raises the [E:SubproblemStarted] event.
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Events

	Name	Description
	SubproblemFinished	Occurs when the learning of a subproblem has finished.
	SubproblemStarted	Occurs when the learning of a subproblem has started.

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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.MachineLearning Namespace

Accord.MachineLearning.VectorMachines.LearningMultilabelSupportVectorLearningTKernel

OneVsRestLearningAddLanguageSpecificTextSet("LSTEC0356DE_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TInput, TBinary, TModelAddLanguageSpecificTextSet("LSTEC0356DE_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

OneVsRestLearningTInput, TBinary, TModel Class