BaseBagOfWordsTModel, TInput, TClustering Class

Base class for Bag of Visual Words implementations.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearningBaseBagOfWordsTModel, TInput, TClustering
      Accord.MachineLearningBagOfWordsTInput
      Accord.MachineLearningBagOfWordsTInput, TClustering

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

Syntax

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[SerializableAttribute]
public class BaseBagOfWords<TModel, TInput, TClustering> : ParallelLearningBase, 
	IBagOfWords<TInput[]>, ITransform<TInput[], double[]>, 
	ICovariantTransform<TInput[], double[]>, ITransform, ITransform<TInput[], int[]>, 
	ICovariantTransform<TInput[], int[]>, IUnsupervisedLearning<TModel, TInput[], double[]>, 
	ITransform<TInput, Sparse<double>>, ICovariantTransform<TInput, Sparse<double>>
where TModel : BaseBagOfWords<TModel, TInput, TClustering>, ITransform<TInput[], int[]>, ITransform<TInput[], double[]>
where TClustering : Object, IUnsupervisedLearning<IClassifier<TInput, int>, TInput, int>

<SerializableAttribute>
Public Class BaseBagOfWords(Of TModel As {BaseBagOfWords(Of TModel, TInput, TClustering), ITransform(Of TInput(), Integer()), ITransform(Of TInput(), Double())}, TInput, TClustering As {Object, IUnsupervisedLearning(Of IClassifier(Of TInput, Integer), TInput, Integer)})
	Inherits ParallelLearningBase
	Implements IBagOfWords(Of TInput()), ITransform(Of TInput(), Double()), 
	ICovariantTransform(Of TInput(), Double()), ITransform, ITransform(Of TInput(), Integer()), 
	ICovariantTransform(Of TInput(), Integer()), IUnsupervisedLearning(Of TModel, TInput(), Double()), 
	ITransform(Of TInput, Sparse(Of Double)), ICovariantTransform(Of TInput, Sparse(Of Double))

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Type Parameters

TModel
TInput
TClustering

The BaseBagOfWordsTModel, TInput, TClustering type exposes the following members.

Constructors

	Name	Description
	BaseBagOfWordsTModel, TInput, TClustering	Constructs a new BagOfWords.

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Properties

	Name	Description
	Clustering	Gets the clustering algorithm used to create this model.
	NumberOfInputs	Gets the number of inputs accepted by the model.
	NumberOfOutputs	Gets the number of outputs generated by the model.
	NumberOfWords	Gets the number of words in this codebook.
	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
	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.)
	Init	Initializes this instance.
	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.)
	Transform(TInput)	Applies the transformation to an input, producing an associated output.
	Transform(TInput)	Applies the transformation to an input, producing an associated output.
	Transform(TInput)	Applies the transformation to a set of input vectors, producing an associated set of output vectors.
	Transform(TInput, SparseDouble)	Applies the transformation to a set of input vectors, producing an associated set of output vectors.
	Transform(TInput, Double)	Applies the transformation to a set of input vectors, producing an associated set of output vectors.
	Transform(TInput, Int32)	Applies the transformation to a set of input vectors, producing an associated set of output vectors.
	Transform(TInput, Double)	Applies the transformation to a set of input vectors, producing an associated set of output vectors.
	Transform(TInput, Int32)	Applies the transformation to a set of input vectors, producing an associated set of output vectors.

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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

BaseBagOfWordsAddLanguageSpecificTextSet("LSTF40FECF2_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TModel, TInput, TClusteringAddLanguageSpecificTextSet("LSTF40FECF2_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BaseBagOfWordsTModel, TInput, TClustering Class