ILikelihoodTaggerTInput Interface

Common interface for generative observation sequence taggers. A sequence tagger can predict the class label of each individual observation in a input sequence vector.

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

Syntax

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public interface ILikelihoodTagger<TInput> : ITransform<TInput[], double>, 
	ICovariantTransform<TInput[], double>, ITransform, IScoreTagger<TInput>, 
	ITagger<TInput>, IMultilabelClassifier<TInput[], int[]>, IClassifier<TInput[], int[]>, 
	IClassifier, ITransform<TInput[], int[]>, ICovariantTransform<TInput[], int[]>

Public Interface ILikelihoodTagger(Of TInput)
	Inherits ITransform(Of TInput(), Double), ICovariantTransform(Of TInput(), Double), 
	ITransform, IScoreTagger(Of TInput), ITagger(Of TInput), 
	IMultilabelClassifier(Of TInput(), Integer()), IClassifier(Of TInput(), Integer()), 
	IClassifier, ITransform(Of TInput(), Integer()), ICovariantTransform(Of TInput(), Integer())

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Type Parameters

TInput: The data type for the input data. Default is double[].

The ILikelihoodTaggerTInput type exposes the following members.

Properties

	Name	Description
	NumberOfClasses	Gets or sets the number of classes expected and recognized by the classifier. (Inherited from IClassifier.)
	NumberOfInputs	Gets or sets the number of inputs accepted by the model. (Inherited from ITransform.)
	NumberOfOutputs	Gets or sets the number of outputs generated by the model. (Inherited from ITransform.)

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Methods

	Name	Description
	Decide(TInput)	Computes a class-label decision for a given input. (Inherited from IClassifierTInput, TClasses.)
	Decide(TInput)	Computes class-label decisions for each vector in the given input. (Inherited from IClassifierTInput, TClasses.)
	Decide(TInput, TClasses)	Computes class-label decisions for each vector in the given input. (Inherited from IClassifierTInput, TClasses.)
	Decide(TInput, TClasses)	Computes class-label decisions for the given input. (Inherited from IMultilabelClassifierTInput, TClasses.)
	LogLikelihood(TInput)	Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
	LogLikelihood(TInput)	Predicts a the log-likelihood that the sequence vector has been generated by this log-likelihood tagger.
	LogLikelihood(TInput, Int32)	Predicts a the log-likelihood that the sequence vector has been generated by this log-likelihood tagger.
	LogLikelihood(TInput, Double)	Predicts a the log-likelihood that the sequence vector has been generated by this log-likelihood tagger.
	LogLikelihood(TInput, Int32)	Predicts a the log-likelihood that the sequence vector has been generated by this log-likelihood tagger.
	LogLikelihood(TInput, Int32, Double)	Predicts a the log-likelihood that the sequence vector has been generated by this log-likelihood tagger.
	LogLikelihoods(TInput)	Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	LogLikelihoods(TInput)	Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	LogLikelihoods(TInput, Double)	Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	LogLikelihoods(TInput, Int32)	Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	LogLikelihoods(TInput, Double)	Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	LogLikelihoods(TInput, Int32)	Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	LogLikelihoods(TInput, Int32, Double)	Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	LogLikelihoods(TInput, Int32, Double)	Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	Probabilities(TInput)	Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	Probabilities(TInput)	Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	Probabilities(TInput, Double)	Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	Probabilities(TInput, Int32)	Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	Probabilities(TInput, Double)	Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	Probabilities(TInput, Int32)	Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	Probabilities(TInput, Int32, Double)	Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	Probabilities(TInput, Int32, Double)	Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
	Probability(TInput)	Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
	Probability(TInput)	Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
	Probability(TInput, Int32)	Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
	Probability(TInput, Double)	Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
	Probability(TInput, Int32)	Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
	Probability(TInput, Int32, Double)	Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
	Scores(TInput)	Computes numerical scores measuring the association between each of the given sequence vectors and each possible class. (Inherited from IScoreTaggerTInput.)
	Scores(TInput)	Computes numerical scores measuring the association between each of the given sequences vectors and each possible class. (Inherited from IScoreTaggerTInput.)
	Scores(TInput, Double)	Computes numerical scores measuring the association between each of the given sequence vectors and each possible class. (Inherited from IScoreTaggerTInput.)
	Scores(TInput, Int32)	Computes numerical scores measuring the association between each of the given sequence vectors and each possible class. (Inherited from IScoreTaggerTInput.)
	Scores(TInput, Double)	Computes numerical scores measuring the association between each of the given sequences vectors and each possible class. (Inherited from IScoreTaggerTInput.)
	Scores(TInput, Int32)	Computes numerical scores measuring the association between each of the given sequences vectors and each possible class. (Inherited from IScoreTaggerTInput.)
	Scores(TInput, Int32, Double)	Computes numerical scores measuring the association between each of the given sequence vectors and each possible class. (Inherited from IScoreTaggerTInput.)
	Scores(TInput, Int32, Double)	Computes numerical scores measuring the association between each of the given sequences vectors and each possible class. (Inherited from IScoreTaggerTInput.)
	Transform(TInput)	Applies the transformation to an input, producing an associated output. (Inherited from ICovariantTransformTInput, TOutput.)
	Transform(TInput)	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from ICovariantTransformTInput, TOutput.)
	Transform(TInput, TOutput)	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from ITransformTInput, TOutput.)

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Reference

Accord.MachineLearning Namespace

ILikelihoodTaggerAddLanguageSpecificTextSet("LSTF1AAEBBB_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TInputAddLanguageSpecificTextSet("LSTF1AAEBBB_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Interface

Type Parameters

Reference

ILikelihoodTaggerTInput Interface