HiddenMarkovModelTDistribution Class

Note: This API is now obsolete.

Obsolete. Please use HiddenMarkovModelTDistribution, TObservation instead.

Inheritance Hierarchy

SystemObject
Accord.Statistics.Models.MarkovBaseHiddenMarkovModel
Accord.Statistics.Models.MarkovHiddenMarkovModelTDistribution

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

Syntax

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[SerializableAttribute]
[ObsoleteAttribute("Please use HiddenMarkovModel<TDistribution, TObservation> instead.")]
public class HiddenMarkovModel<TDistribution> : BaseHiddenMarkovModel, 
	IHiddenMarkovModel, ICloneable
where TDistribution : IDistribution

<SerializableAttribute>
<ObsoleteAttribute("Please use HiddenMarkovModel<TDistribution, TObservation> instead.")>
Public Class HiddenMarkovModel(Of TDistribution As IDistribution)
	Inherits BaseHiddenMarkovModel
	Implements IHiddenMarkovModel, ICloneable

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Type Parameters

TDistribution

The HiddenMarkovModelTDistribution type exposes the following members.

Constructors

	Name	Description
	HiddenMarkovModelTDistribution(Int32, TDistribution)	Constructs a new Hidden Markov Model with arbitrary-density state probabilities.
	HiddenMarkovModelTDistribution(ITopology, TDistribution)	Constructs a new Hidden Markov Model with arbitrary-density state probabilities.
	HiddenMarkovModelTDistribution(ITopology, TDistribution)	Constructs a new Hidden Markov Model with arbitrary-density state probabilities.
	HiddenMarkovModelTDistribution(Double, TDistribution, Double, Boolean)	Constructs a new Hidden Markov Model with arbitrary-density state probabilities.

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Properties

	Name	Description
	Dimension	Gets the number of dimensions in the probability distributions for the states.
	Emissions	Gets the Emission matrix (B) for this model.
	LogInitial	Gets the log-initial probabilities log(pi) for this model. (Inherited from BaseHiddenMarkovModel.)
	LogTransitions	Gets the log-transition matrix log(A) for this model. (Inherited from BaseHiddenMarkovModel.)
	Probabilities	Obsolete. Gets the log-initial probabilities log(pi) for this model. (Inherited from BaseHiddenMarkovModel.)
	States	Gets the number of states of this model. (Inherited from BaseHiddenMarkovModel.)
	Tag	Gets or sets a user-defined tag associated with this model. (Inherited from BaseHiddenMarkovModel.)
	Transitions	Obsolete. Gets the log-transition matrix log(A) for this model. (Inherited from BaseHiddenMarkovModel.)

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Methods

	Name	Description
	Clone	Creates a new object that is a copy of the current instance.
	Decode(Array)	Calculates the most likely sequence of hidden states that produced the given observation sequence.
	Decode(Array, Double)	Calculates the most likely sequence of hidden states that produced the given observation sequence.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Evaluate(Array)	Calculates the likelihood that this model has generated the given sequence.
	Evaluate(Array, Int32)	Calculates the log-likelihood that this model has generated the given observation sequence along the given state path.
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	Generate(Int32)	Generates a random vector of observations from the model.
	Generate(Int32, Int32, Double)	Generates a random vector of observations from the model.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	Load(Stream)	Loads a hidden Markov model from a stream.
	Load(String)	Loads a hidden Markov model from a file.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Posterior(Array)	Calculates the probability of each hidden state for each observation in the observation vector.
	Posterior(Array, Int32)	Calculates the probability of each hidden state for each observation in the observation vector, and uses those probabilities to decode the most likely sequence of states for each observation in the sequence using the posterior decoding method. See remarks for details.
	Predict(Double)	Predicts the next observation occurring after a given observation sequence.
	Predict(Double)	Predicts the next observation occurring after a given observation sequence.
	Predict(Double, Double)	Predicts the next observation occurring after a given observation sequence.
	Predict(Double, Double)	Predicts the next observation occurring after a given observation sequence.
	Predict(Double, Int32, Double)	Predicts the next observations occurring after a given observation sequence.
	Predict(Double, Int32, Double)	Predicts the next observations occurring after a given observation sequence.
	PredictTUnivariate(Double, MixtureTUnivariate)	Predicts the next observation occurring after a given observation sequence.
	PredictTMultivariate(Double, MultivariateMixtureTMultivariate)	Predicts the next observation occurring after a given observation sequence.
	PredictTUnivariate(Double, Double, MixtureTUnivariate)	Predicts the next observation occurring after a given observation sequence.
	PredictTMultivariate(Double, Double, MultivariateMixtureTMultivariate)	Predicts the next observation occurring after a given observation sequence.
	Save(Stream)	Saves the hidden Markov model to a stream.
	Save(String)	Saves the hidden Markov model to a stream.
	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.Statistics.Models.Markov Namespace

HiddenMarkovModelAddLanguageSpecificTextSet("LSTBB630F6_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TDistributionAddLanguageSpecificTextSet("LSTBB630F6_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

HiddenMarkovModelTDistribution Class