BaumWelchLearningTDistribution, TObservation, TOptions Class 
Namespace: Accord.Statistics.Models.Markov.Learning
public class BaumWelchLearning<TDistribution, TObservation, TOptions> : BaseBaumWelchLearningOptions<HiddenMarkovModel<TDistribution, TObservation>, TDistribution, TObservation, TOptions>, IConvergenceLearning where TDistribution : Object, IFittableDistribution<TObservation, TOptions> where TOptions : class, new(), IFittingOptions
The BaumWelchLearningTDistribution, TObservation, TOptions type exposes the following members.
Name  Description  

BaumWelchLearningTDistribution, TObservation, TOptions 
Initializes a new instance of the BaumWelchLearningTDistribution, TObservation, TOptions class.
 
BaumWelchLearningTDistribution, TObservation, TOptions(HiddenMarkovModelTDistribution, TObservation) 
Initializes a new instance of the BaumWelchLearningTDistribution, TObservation, TOptions class.

Name  Description  

Convergence 
Gets or sets convergence parameters.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
CurrentIteration 
Gets or sets the number of performed iterations.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
Emissions 
Gets or sets the function that initializes the emission
distributions in the hidden Markov Models.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
FittingOptions 
Gets or sets the distribution fitting options
to use when estimating distribution densities
during learning.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
HasConverged 
Gets or sets whether the algorithm has converged.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
Iterations  Obsolete.
Please use MaxIterations instead.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
LogGamma 
Gets the Gamma matrix of log probabilities created during
the last iteration of the BaumWelch learning algorithm.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
LogKsi 
Gets the Ksi matrix of log probabilities created during
the last iteration of the BaumWelch learning algorithm.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
LogLikelihood 
Gets the loglikelihood of the model at the last iteration.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
LogWeights 
Gets the sample weights in the last iteration of the
BaumWelch learning algorithm.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
MaxIterations 
Gets or sets the maximum number of iterations
performed by the learning algorithm.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
Model 
Gets or sets the model being trained.
(Inherited from BaseHiddenMarkovModelLearningTModel, TObservation.)  
NumberOfStates 
Gets or sets the number of states to be used when this
learning algorithm needs to create new models.
(Inherited from BaseHiddenMarkovModelLearningTModel, TObservation.)  
Observations 
Gets all observations as a single vector.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
ParallelOptions 
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.)  
Token 
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.)  
Tolerance 
Gets or sets the maximum change in the average loglikelihood
after an iteration of the algorithm used to detect convergence.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
Topology 
Gets or sets the state transition topology to be used when this learning
algorithm needs to create new models. Default is Forward.
(Inherited from BaseHiddenMarkovModelLearningTModel, TObservation.) 
Name  Description  

ComputeForwardBackward 
Computes the forward and backward probabilities matrices
for a given observation referenced by its index in the
input training data.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
ComputeKsi 
Computes the ksi matrix of probabilities for a given observation
referenced by its index in the input training data.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
Create 
Creates an instance of the model to be learned. Inheritors of this abstract
class must define this method so new models can be created from the training data.
(Overrides BaseHiddenMarkovModelLearningTModel, TObservationCreate(TObservation).)  
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.)  
Fit 
Fits one emission distribution. This method can be override in a
base class in order to implement special fitting options.
(Inherited from BaseBaumWelchLearningOptionsTModel, TDistribution, TObservation, TOptions.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
Learn 
Learns a model that can map the given inputs to the desired outputs.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.)  
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
ToString  Returns a string that represents the current object. (Inherited from Object.)  
UpdateEmissions 
Updates the emission probability matrix.
(Inherited from BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions.) 
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.) 