BagOfAudioWords(TFeature) Class

System.Object
  Accord.MachineLearning.ParallelLearningBase
    Accord.MachineLearning.BaseBagOfWords<BagOfAudioWords<TFeature>, TFeature,Double[], IUnsupervisedLearning<IClassifier<Double[], Int32>,Double[], Int32>, IAudioFeatureExtractor<TFeature>, Signal>
      Accord.Audition.BaseBagOfAudioWords<BagOfAudioWords<TFeature>, TFeature,Double[], IUnsupervisedLearning<IClassifier<Double[], Int32>,Double[], Int32>, IAudioFeatureExtractor<TFeature>>
        Accord.Audition.BagOfAudioWords<TFeature>

[SerializableAttribute]
public class BagOfAudioWords<TFeature> : BaseBagOfAudioWords<BagOfAudioWords<TFeature>, TFeature, double[], IUnsupervisedLearning<IClassifier<double[], int>, double[], int>, IAudioFeatureExtractor<TFeature>>
where TFeature : Object, IFeatureDescriptor<double[]>

<SerializableAttribute>
Public Class BagOfAudioWords(Of TFeature As {Object, IFeatureDescriptor(Of Double())})
	Inherits BaseBagOfAudioWords(Of BagOfAudioWords(Of TFeature), TFeature, Double(), IUnsupervisedLearning(Of IClassifier(Of Double(), Integer), Double(), Integer), IAudioFeatureExtractor(Of TFeature))

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Type Parameters

TFeature: The IFeatureDescriptor< T> type to be used with this class, such as MelFrequencyCepstrumCoefficientDescriptor.

	Name	Description
	BagOfAudioWords<TFeature>(IAudioFeatureExtractor<TFeature>, IUnsupervisedLearning<IClassifier<Double[], Int32>,Double[], Int32>)	Constructs a new BagOfAudioWords.
	BagOfAudioWords<TFeature>(IAudioFeatureExtractor<TFeature>, Int32)	Constructs a new BagOfAudioWords.

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	Name	Description
	Clustering	Gets the clustering algorithm used to create this model. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Detector	Gets the feature extractor used to identify features in the input data. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	MaxDescriptorsPerInstance	Gets or sets the maximum number of descriptors per image that should be used to learn the codebook. Default is 0 (meaning to use all descriptors). (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	NumberOfDescriptors	Gets or sets the maximum number of descriptors that should be used to learn the codebook. Default is 0 (meaning to use all descriptors). (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	NumberOfInputs	Gets the number of inputs accepted by the model. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	NumberOfOutputs	Gets the number of outputs generated by the model. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	NumberOfWords	Gets the number of words in this codebook. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	ParallelOptions	Gets or sets the parallelization options for this algorithm. (Inherited from ParallelLearningBase.)
	Statistics	Gets statistics about the last codebook learned. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	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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	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.)
	For	Executes a parallel for using the feature detector in a thread-safe way. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	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. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	InnerLearn<T>	Generic learn method implementation that should work for any input type. This method is useful for re-using code between methods that accept Bitmap, BitmapData, UnmanagedImage, filenames as strings, etc. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Learn(String[],Double[])	Learns a model that can map the given inputs to the desired outputs. (Inherited from BaseBagOfAudioWords<TModel, TFeature, TPoint, TClustering, TExtractor>.)
	Learn(TFeature[],Double[])	Learns a model that can map the given inputs to the desired outputs. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Learn(TInput[],Double[])	Learns a model that can map the given inputs to the desired outputs. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	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(String)	Applies the transformation to an input, producing an associated output. (Inherited from BaseBagOfAudioWords<TModel, TFeature, TPoint, TClustering, TExtractor>.)
	Transform(String[])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfAudioWords<TModel, TFeature, TPoint, TClustering, TExtractor>.)
	Transform(List<TPoint>)	Applies the transformation to an input, producing an associated output. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Transform(TInput)	Applies the transformation to an input, producing an associated output. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Transform(TInput[])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Transform(String,Double[])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfAudioWords<TModel, TFeature, TPoint, TClustering, TExtractor>.)
	Transform(String,Int32[])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfAudioWords<TModel, TFeature, TPoint, TClustering, TExtractor>.)
	Transform(String[],Double[][])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfAudioWords<TModel, TFeature, TPoint, TClustering, TExtractor>.)
	Transform(String[],Int32[][])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfAudioWords<TModel, TFeature, TPoint, TClustering, TExtractor>.)
	Transform(IEnumerable<TPoint>,Double[])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Transform(IEnumerable<TPoint>,Int32[])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Transform(TInput,Double[])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Transform(TInput,Int32[])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Transform(TInput[],Double[][])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)
	Transform(TInput[],Int32[][])	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from BaseBagOfWords<TModel, TPoint, TFeature, TClustering, TExtractor, TInput>.)

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	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.)
	To<T>()	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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The bag-of-words (BoW) model can be used to extract finite length features from otherwise varying length representations. This class can uses any feature detector to determine a coded representation for a given Signal.

For a simpler, non-generic version of the Bag-of-Words model which defaults to the MFCC feature extractor, please see BagOfAudioWords.

Please see BagOfAudioWords.

Reference

Accord.Audition Namespace

Accord.Audition.BagOfAudioWords

BagOfAudioWords< AddLanguageSpecificTextSet("LST8AD1C8E2_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TFeature> AddLanguageSpecificTextSet("LST8AD1C8E2_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BagOfAudioWords< TFeature> Class