HiddenMarkovClassifierTDistribution, TObservation Class

Arbitrary-density Hidden Markov Model Set for Sequence Classification.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningTransformBaseTObservation, Int32
    Accord.MachineLearningClassifierBaseTObservation, Int32
      Accord.MachineLearningMulticlassClassifierBaseTObservation
        Accord.MachineLearningMulticlassScoreClassifierBaseTObservation
          Accord.MachineLearningMulticlassLikelihoodClassifierBaseTObservation
            Accord.Statistics.Models.MarkovBaseHiddenMarkovClassifierHiddenMarkovModelTDistribution, TObservation, TDistribution, TObservation
              Accord.Statistics.Models.MarkovHiddenMarkovClassifierTDistribution, TObservation

Namespace: Accord.Statistics.Models.Markov
Assembly: Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0

Syntax

Copy

[SerializableAttribute]
public class HiddenMarkovClassifier<TDistribution, TObservation> : BaseHiddenMarkovClassifier<HiddenMarkovModel<TDistribution, TObservation>, TDistribution, TObservation>, 
	IEnumerable
where TDistribution : Object, IDistribution<TObservation>

<SerializableAttribute>
Public Class HiddenMarkovClassifier(Of TDistribution As {Object, IDistribution(Of TObservation)}, TObservation)
	Inherits BaseHiddenMarkovClassifier(Of HiddenMarkovModel(Of TDistribution, TObservation), TDistribution, TObservation)
	Implements IEnumerable

Request Example View Source

Type Parameters

TDistribution
TObservation

The HiddenMarkovClassifierTDistribution, TObservation type exposes the following members.

Constructors

	Name	Description
	HiddenMarkovClassifierTDistribution, TObservation(Int32)	Creates a new Sequence Classifier with the given number of classes.
	HiddenMarkovClassifierTDistribution, TObservation(HiddenMarkovModelTDistribution, TObservation)	Creates a new Sequence Classifier with the given number of classes.
	HiddenMarkovClassifierTDistribution, TObservation(Int32, ITopology, FuncInt32, Int32, TDistribution)	Creates a new Sequence Classifier with the given number of classes.
	HiddenMarkovClassifierTDistribution, TObservation(Int32, ITopology, TDistribution)	Creates a new Sequence Classifier with the given number of classes.
	HiddenMarkovClassifierTDistribution, TObservation(Int32, ITopology, TDistribution)	Creates a new Sequence Classifier with the given number of classes.
	HiddenMarkovClassifierTDistribution, TObservation(Int32, ITopology, TDistribution)	Creates a new Sequence Classifier with the given number of classes.
	HiddenMarkovClassifierTDistribution, TObservation(Int32, ITopology, TDistribution)	Creates a new Sequence Classifier with the given number of classes.
	HiddenMarkovClassifierTDistribution, TObservation(Int32, Int32, FuncInt32, Int32, TDistribution)	Creates a new Sequence Classifier with the given number of classes.
	HiddenMarkovClassifierTDistribution, TObservation(Int32, Int32, TDistribution)	Creates a new Sequence Classifier with the given number of classes.
	HiddenMarkovClassifierTDistribution, TObservation(Int32, ITopology, TDistribution, String)	Creates a new Sequence Classifier with the given number of classes.
	HiddenMarkovClassifierTDistribution, TObservation(Int32, ITopology, TDistribution, String)	Creates a new Sequence Classifier with the given number of classes.

Top

Properties

	Name	Description
	Classes	Obsolete. Gets the number of classes which can be recognized by this classifier. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	Item	Gets the Hidden Markov Model implementation responsible for recognizing each of the classes given the desired class label. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	Models	Gets the collection of models specialized in each class of the sequence classification problem. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	NumberOfClasses	Gets the number of classes expected and recognized by the classifier. (Inherited from ClassifierBaseTInput, TClasses.)
	NumberOfInputs	Gets the number of inputs accepted by the model. (Inherited from TransformBaseTInput, TOutput.)
	NumberOfOutputs	Gets the number of outputs generated by the model. (Inherited from TransformBaseTInput, TOutput.)
	Priors	Gets the prior distribution assumed for the classes. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	Sensitivity	Gets or sets a value governing the rejection given by a threshold model (if present). Increasing this value will result in higher rejection rates. Default is 1. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	Threshold	Gets or sets the threshold model. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)

Top

Methods

	Name	Description
	Decide(TInput)	Computes class-label decisions for a given set of input vectors. (Inherited from ClassifierBaseTInput, TClasses.)
	Decide(TObservation)	Computes a class-label decision for a given input. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	Decide(TInput, TClasses)	Computes a class-label decision for a given input. (Inherited from ClassifierBaseTInput, TClasses.)
	Decide(TInput, Boolean)	Computes class-label decisions for the given input. (Inherited from MulticlassClassifierBaseTInput.)
	Decide(TInput, Double)	Computes class-label decisions for the given input. (Inherited from MulticlassClassifierBaseTInput.)
	Decide(TInput, Int32)	Computes class-label decisions for the given input. (Inherited from MulticlassClassifierBaseTInput.)
	Decide(TInput, Double)	Computes a class-label decision for a given input. (Inherited from MulticlassClassifierBaseTInput.)
	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.)
	GetEnumerator	Returns an enumerator that iterates through the models in the classifier. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	LogLikelihood(TInput)	Computes the log-likelihood that the given input vectors belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TObservation)	Computes the log-likelihood that the given input vector belongs to its decided class. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	LogLikelihood(TInput, Int32)	Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Double)	Computes the log-likelihood that the given input vectors belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32)	Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TObservation, Int32)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	LogLikelihood(TObservation, Int32)	Computes the log-likelihood that the given input vector belongs to its decided class. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	LogLikelihood(TInput, Int32, Double)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32, Double)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihood(TInput, Int32, Double)	Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput)	Computes the log-likelihood that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput)	Computes the log-likelihood that the given input vectors belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Double)	Computes the log-likelihood that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32)	Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Double)	Computes the log-likelihood that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32)	Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32, Double)	Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32, Double)	Computes the log-likelihood that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TInput, Int32, Double)	Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	LogLikelihoods(TObservation, Int32, Double)	Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Probabilities(TInput)	Computes the probabilities that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput)	Computes the probabilities that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Double)	Computes the probabilities that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Int32)	Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Double)	Computes the probabilities that the given input vector belongs to each of the possible classes. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Int32)	Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Int32, Double)	Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TInput, Int32, Double)	Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probabilities(TObservation, Int32, Double)	Computes the probabilities that the given input vector belongs to each of the possible classes. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	Probability(TInput)	Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TObservation)	Computes the likelihood that the given input vector belongs to its decided class. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	Probability(TInput, Int32)	Computes the probability that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32)	Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Double)	Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32)	Computes the probability that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32)	Computes the probability that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32)	Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TObservation, Int32)	Computes the probability that the given input vector belongs to its decided class. (Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)
	Probability(TInput, Int32, Double)	Computes the probability that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32, Double)	Computes the probability that the given input vector belongs to the specified classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Probability(TInput, Int32, Double)	Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Score(TInput)	Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier). (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput)	Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier). (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32)	Computes a numerical score measuring the association between the given input vector and a given classIndex. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Score(TInput, Int32)	Predicts a class label for the input vector, returning a numerical score measuring the strength of association of the input vector to its most strongly related class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Double)	Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier). (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32)	Computes a numerical score measuring the association between the given input vector and a given classIndex. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32)	Computes a numerical score measuring the association between the given input vector and a given classIndex. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32)	Predicts a class label for each input vector, returning a numerical score measuring the strength of association of the input vector to the most strongly related class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32, Double)	Computes a numerical score measuring the association between the given input vector and a given classIndex. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32, Double)	Computes a numerical score measuring the association between the given input vector and a given classIndex. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Score(TInput, Int32, Double)	Predicts a class label for each input vector, returning a numerical score measuring the strength of association of the input vector to the most strongly related class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput)	Computes a numerical score measuring the association between the given input vector and each class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput)	Computes a numerical score measuring the association between the given input vector and each class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Double)	Computes a numerical score measuring the association between the given input vector and each class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Int32)	Predicts a class label vector for the given input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Double)	Computes a numerical score measuring the association between the given input vector and each class. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Int32)	Predicts a class label vector for each input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Int32, Double)	Predicts a class label vector for the given input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes. (Inherited from MulticlassScoreClassifierBaseTInput.)
	Scores(TInput, Int32, Double)	Predicts a class label vector for each input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes. (Inherited from MulticlassScoreClassifierBaseTInput.)
	ToMulticlass	Views this instance as a multi-class generative classifier. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	ToMultilabel	Views this instance as a multi-label generative classifier, giving access to more advanced methods, such as the prediction of one-hot vectors. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)
	Transform(TInput)	Applies the transformation to an input, producing an associated output. (Inherited from ClassifierBaseTInput, TClasses.)
	Transform(TInput)	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from TransformBaseTInput, TOutput.)
	Transform(TInput, TClasses)	Applies the transformation to an input, producing an associated output. (Inherited from ClassifierBaseTInput, TClasses.)
	Transform(TInput, Boolean)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Int32)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Boolean)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Double)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Int32)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassClassifierBaseTInput.)
	Transform(TInput, Double)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)
	Transform(TInput, Double)	Applies the transformation to an input, producing an associated output. (Inherited from MulticlassLikelihoodClassifierBaseTInput.)

Top

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.)
	SetEqualsHiddenMarkovModelTDistribution, TObservation	Compares two enumerables for set equality. Two enumerables are set equal if they contain the same elements, but not necessarily in the same order. (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.)

Top

Remarks

This class uses a set of density hidden Markov models to classify sequences of real (double-precision floating point) numbers or arrays of those numbers. Each model will try to learn and recognize each of the different output classes. For examples and details on how to learn such models, please take a look on the documentation for HiddenMarkovClassifierLearningTDistribution.

For the discrete version of this classifier, please see its non-generic counterpart HiddenMarkovClassifier.

Examples

Examples are available at the respective learning algorithm pages. For example, see HiddenMarkovClassifierLearningTDistribution, TObservation.

Reference

Accord.Statistics.Models.Markov Namespace

Accord.Statistics.Models.Markov.LearningHiddenMarkovClassifierLearningTDistribution

Accord.Statistics.Models.MarkovHiddenMarkovClassifier

HiddenMarkovClassifierAddLanguageSpecificTextSet("LST4BEE7078_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TDistribution, TObservationAddLanguageSpecificTextSet("LST4BEE7078_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

HiddenMarkovClassifierTDistribution, TObservation Class