C# Класс Accord.Statistics.Models.Markov.HiddenMarkovModel

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Открытые методы

Метод	Описание
Decode ( int observations, bool logarithm, double &probability ) : int[]	Calculates the most likely sequence of hidden states that produced the given observation sequence. Decoding problem. Given the HMM M = (A, B, pi) and the observation sequence O = {o1,o2, ..., oK}, calculate the most likely sequence of hidden states Si that produced this observation sequence O. This can be computed efficiently using the Viterbi algorithm.
Decode ( int observations, double &probability ) : int[]	Calculates the most likely sequence of hidden states that produced the given observation sequence. Decoding problem. Given the HMM M = (A, B, pi) and the observation sequence O = {o1,o2, ..., oK}, calculate the most likely sequence of hidden states Si that produced this observation sequence O. This can be computed efficiently using the Viterbi algorithm.
Evaluate ( int observations ) : double	Calculates the probability that this model has generated the given sequence. Evaluation problem. Given the HMM M = (A, B, pi) and the observation sequence O = {o1, o2, ..., oK}, calculate the probability that model M has generated sequence O. This can be computed efficiently using the either the Viterbi or the Forward algorithms.
Evaluate ( int observations, bool logarithm ) : double	Calculates the probability that this model has generated the given sequence. Evaluation problem. Given the HMM M = (A, B, pi) and the observation sequence O = {o1, o2, ..., oK}, calculate the probability that model M has generated sequence O. This can be computed efficiently using the either the Viterbi or the Forward algorithms.
HiddenMarkovModel ( ITopology topology, double emissions ) : System	Constructs a new Hidden Markov Model.
HiddenMarkovModel ( ITopology topology, int symbols ) : System	Constructs a new Hidden Markov Model.
HiddenMarkovModel ( double transitions, double emissions, double initial ) : System	Constructs a new Hidden Markov Model.
HiddenMarkovModel ( int states, int symbols ) : System	Constructs a new Hidden Markov Model.
Predict ( int observations, double &probabilities ) : int	Predicts the next observation occurring after a given observation sequence.
Predict ( int observations, int next ) : int[]	Predicts next observations occurring after a given observation sequence.
Predict ( int observations, int next, bool logarithm, double &probability, double &probabilities ) : int[]	Predicts the next observations occurring after a given observation sequence.
Predict ( int observations, int next, double &probability ) : int[]	Predicts next observations occurring after a given observation sequence.
ToContinuousModel ( ) : ContinuousHiddenMarkovModel	Converts this Discrete density Hidden Markov Model into a Continuous density model.

Приватные методы

Метод	Описание
IHiddenMarkovModel ( Array sequence ) : double
IHiddenMarkovModel ( Array sequence, bool logarithm ) : double
IHiddenMarkovModel ( Array sequence, bool logarithm, double &probability ) : int[]
IHiddenMarkovModel ( Array sequence, double &probability ) : int[]

Описание методов

Decode() публичный Метод

Calculates the most likely sequence of hidden states that produced the given observation sequence.

Decoding problem. Given the HMM M = (A, B, pi) and the observation sequence O = {o1,o2, ..., oK}, calculate the most likely sequence of hidden states Si that produced this observation sequence O. This can be computed efficiently using the Viterbi algorithm.

public Decode ( int observations, bool logarithm, double &probability ) : int[]
observations	int	A sequence of observations.
logarithm	bool	True to return the log-likelihood, false to return /// the likelihood. Default is false (default is to return the likelihood).
probability	double	The state optimized probability.
Результат	int[]

Decode() публичный Метод

Calculates the most likely sequence of hidden states that produced the given observation sequence.

public Decode ( int observations, double &probability ) : int[]
observations	int	A sequence of observations.
probability	double	The state optimized probability.
Результат	int[]

Evaluate() публичный Метод

Calculates the probability that this model has generated the given sequence.

Evaluation problem. Given the HMM M = (A, B, pi) and the observation sequence O = {o1, o2, ..., oK}, calculate the probability that model M has generated sequence O. This can be computed efficiently using the either the Viterbi or the Forward algorithms.

public Evaluate ( int observations ) : double
observations	int	/// A sequence of observations. ///
Результат	double

Evaluate() публичный Метод

Calculates the probability that this model has generated the given sequence.

public Evaluate ( int observations, bool logarithm ) : double
observations	int	/// A sequence of observations. ///
logarithm	bool	/// True to return the log-likelihood, false to return /// the likelihood. Default is false. ///
Результат	double

HiddenMarkovModel() публичный Метод

Constructs a new Hidden Markov Model.

public HiddenMarkovModel ( ITopology topology, double emissions ) : System
topology	ITopology	/// A object specifying the initial values of the matrix of transition /// probabilities A and initial state probabilities pi to be used by this model. ///
emissions	double	The emissions matrix B for this model.
Результат	System

HiddenMarkovModel() публичный Метод

Constructs a new Hidden Markov Model.

public HiddenMarkovModel ( ITopology topology, int symbols ) : System
topology	ITopology	/// A object specifying the initial values of the matrix of transition /// probabilities A and initial state probabilities pi to be used by this model. ///
symbols	int	The number of output symbols used for this model.
Результат	System

HiddenMarkovModel() публичный Метод

Constructs a new Hidden Markov Model.

public HiddenMarkovModel ( double transitions, double emissions, double initial ) : System
transitions	double	The transitions matrix A for this model.
emissions	double	The emissions matrix B for this model.
initial	double	The initial state probabilities for this model.
Результат	System

HiddenMarkovModel() публичный Метод

Constructs a new Hidden Markov Model.

public HiddenMarkovModel ( int states, int symbols ) : System
states	int	The number of states for this model.
symbols	int	The number of output symbols used for this model.
Результат	System

Predict() публичный Метод

Predicts the next observation occurring after a given observation sequence.

public Predict ( int observations, double &probabilities ) : int
observations	int
probabilities	double
Результат	int

Predict() публичный Метод

Predicts next observations occurring after a given observation sequence.

public Predict ( int observations, int next ) : int[]
observations	int
next	int
Результат	int[]

Predict() публичный Метод

Predicts the next observations occurring after a given observation sequence.

public Predict ( int observations, int next, bool logarithm, double &probability, double &probabilities ) : int[]
observations	int
next	int
logarithm	bool
probability	double
probabilities	double
Результат	int[]

Predict() публичный Метод

Predicts next observations occurring after a given observation sequence.

public Predict ( int observations, int next, double &probability ) : int[]
observations	int
next	int
probability	double
Результат	int[]

ToContinuousModel() публичный Метод

Converts this Discrete density Hidden Markov Model into a Continuous density model.

public ToContinuousModel ( ) : ContinuousHiddenMarkovModel
Результат	ContinuousHiddenMarkovModel