NaiveBayesLearningBaseTModel, TDistribution, TInput, TOptions Class

Base class for Naive Bayes learning algorithms.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearning.BayesNaiveBayesLearningBaseTModel, TDistribution, TInput, TOptions
    Accord.MachineLearning.BayesNaiveBayesLearningTDistribution
    Accord.MachineLearning.BayesNaiveBayesLearningTDistribution, TOptions
    Accord.MachineLearning.BayesNaiveBayesLearningTDistribution, TOptions, TInput
    Accord.MachineLearning.BayesNaiveBayesLearningBaseTModel, TDistribution, TInput, TOptions, TInnerOptions

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

Syntax

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public abstract class NaiveBayesLearningBase<TModel, TDistribution, TInput, TOptions> : ISupervisedLearning<TModel, TInput[], double[]>, 
	ISupervisedLearning<TModel, TInput[], int>, IParallel, ISupportsCancellation
where TModel : NaiveBayes<TDistribution, TInput>
where TDistribution : Object, IFittableDistribution<TInput>, IUnivariateDistribution<TInput>, IUnivariateDistribution
where TOptions : new(), IndependentOptions

Public MustInherit Class NaiveBayesLearningBase(Of TModel As NaiveBayes(Of TDistribution, TInput), TDistribution As {Object, IFittableDistribution(Of TInput), IUnivariateDistribution(Of TInput), IUnivariateDistribution}, TInput, TOptions As {New, IndependentOptions})
	Implements ISupervisedLearning(Of TModel, TInput(), Double()), 
	ISupervisedLearning(Of TModel, TInput(), Integer), IParallel, 
	ISupportsCancellation

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Type Parameters

TModel: The type for the Naive Bayes model to be learned.
TDistribution: The univariate distribution to be used as components in the Naive Bayes distribution.
TInput: The type for the samples modeled by the distribution.
TOptions: The fitting options for the independent distribution.

The NaiveBayesLearningBaseTModel, TDistribution, TInput, TOptions type exposes the following members.

Constructors

	Name	Description
	NaiveBayesLearningBaseTModel, TDistribution, TInput, TOptions	Constructs a new Naïve Bayes learning algorithm.

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Properties

	Name	Description
	Distribution	Gets or sets the distribution creation function. This function can be used to specify how the initial distributions of the model should be created. By default, this function attempts to call the empty constructor of the distribution using Activator.CreateInstance().
	Empirical	Gets or sets whether the class priors should be estimated from the data.
	Model	Gets or sets the model being learned.
	Options	Gets or sets the fitting options to use when estimating the class-specific distributions.
	ParallelOptions	Gets or sets the parallelization options for this algorithm.
	Token	Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.

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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.)
	Fit	Fits one of the distributions in the naive bayes model.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	Learn(TInput, Double, 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.)
	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.MachineLearning.Bayes Namespace

NaiveBayesLearningBaseAddLanguageSpecificTextSet("LSTB6BAA246_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TModel, TDistribution, TInput, TOptionsAddLanguageSpecificTextSet("LSTB6BAA246_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

NaiveBayesLearningBaseTModel, TDistribution, TInput, TOptions Class