HiddenMarkovModelTDistribution Class 
Note: This API is now obsolete.
Namespace: Accord.Statistics.Models.Markov
[SerializableAttribute] [ObsoleteAttribute("Please use HiddenMarkovModel<TDistribution, TObservation> instead.")] public class HiddenMarkovModel<TDistribution> : BaseHiddenMarkovModel, IHiddenMarkovModel, ICloneable where TDistribution : IDistribution
The HiddenMarkovModelTDistribution type exposes the following members.
Name  Description  

HiddenMarkovModelTDistribution(Int32, TDistribution) 
Constructs a new Hidden Markov Model with arbitrarydensity state probabilities.
 
HiddenMarkovModelTDistribution(ITopology, TDistribution) 
Constructs a new Hidden Markov Model with arbitrarydensity state probabilities.
 
HiddenMarkovModelTDistribution(ITopology, TDistribution) 
Constructs a new Hidden Markov Model with arbitrarydensity state probabilities.
 
HiddenMarkovModelTDistribution(Double, TDistribution, Double, Boolean) 
Constructs a new Hidden Markov Model with arbitrarydensity state probabilities.

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 loginitial probabilities log(pi) for this model.
(Inherited from BaseHiddenMarkovModel.)  
LogTransitions 
Gets the logtransition matrix log(A) for this model.
(Inherited from BaseHiddenMarkovModel.)  
Probabilities  Obsolete.
Gets the loginitial 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 userdefined tag associated with this model.
(Inherited from BaseHiddenMarkovModel.)  
Transitions  Obsolete.
Gets the logtransition matrix log(A) for this model.
(Inherited from BaseHiddenMarkovModel.) 
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 loglikelihood 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.) 
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.) 