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HiddenMarkovModel Methods

The HiddenMarkovModel type exposes the following members.

Methods
  NameDescription
Public methodClone
Creates a new object that is a copy of the current instance.
(Overrides HiddenMarkovModelTDistribution, TObservationClone.)
Public methodStatic memberCreateDiscrete(Int32, Int32)
Creates a discrete hidden Markov model using the generic interface.
Public methodStatic memberCreateDiscrete(ITopology, Int32)
Creates a discrete hidden Markov model using the generic interface.
Public methodStatic memberCreateDiscrete(Int32, Int32, Boolean)
Creates a discrete hidden Markov model using the generic interface.
Public methodStatic memberCreateDiscrete(ITopology, Int32, Boolean)
Creates a discrete hidden Markov model using the generic interface.
Public methodStatic memberCreateDiscrete(Double, Double, Double, Boolean)
Creates a discrete hidden Markov model using the generic interface.
Public methodStatic memberCreateGeneric(Int32, Int32) Obsolete.
Constructs a new Hidden Markov Model with discrete state probabilities.
Public methodStatic memberCreateGeneric(ITopology, Int32) Obsolete.
Constructs a new Hidden Markov Model with discrete state probabilities.
Public methodStatic memberCreateGeneric(Int32, Int32, Boolean) Obsolete.
Constructs a new Hidden Markov Model with discrete state probabilities.
Public methodStatic memberCreateGeneric(ITopology, Int32, Boolean) Obsolete.
Constructs a new Hidden Markov Model with discrete state probabilities.
Public methodStatic memberCreateGeneric(Double, Double, Double, Boolean) Obsolete.
Constructs a new discrete-density Hidden Markov Model.
Public methodDecide(TInput)
Computes class-label decisions for the given input.
(Inherited from TaggerBaseTInput.)
Public methodDecide(TInput)
Computes class-label decisions for the given input.
(Inherited from TaggerBaseTInput.)
Public methodDecide(TObservation, Int32)
Computes class-label decisions for the given input.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodDecide(TObservation, Int32)
Computes class-label decisions for the given input.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodDecode(TObservation) Obsolete.
Calculates the most likely sequence of hidden states that produced the given observation sequence.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodDecode(TObservation, Double) Obsolete.
Calculates the most likely sequence of hidden states that produced the given observation sequence.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Public methodEvaluate(TObservation) Obsolete.
Calculates the likelihood that this model has generated the given sequence.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodEvaluate(TObservation, Int32) Obsolete.
Calculates the log-likelihood that this model has generated the given observation sequence along the given state path.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGenerate(Int32)
Generates a random vector of observations from the model.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodGenerate(Int32, Int32, Double)
Generates a random vector of observations from the model.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodStatic memberLoad(Stream) Obsolete.
Loads a hidden Markov model from a stream.
Public methodStatic memberLoad(String) Obsolete.
Loads a hidden Markov model from a file.
Public methodStatic memberLoadTDistribution(Stream) Obsolete.
Loads a hidden Markov model from a stream.
Public methodStatic memberLoadTDistribution(String) Obsolete.
Loads a hidden Markov model from a file.
Public methodLogLikelihood(TInput)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodLogLikelihood(TInput)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodLogLikelihood(TInput, Int32)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodLogLikelihood(TInput, Int32)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodLogLikelihood(TObservation, Int32)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger along the given path of hidden states.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodLogLikelihood(TObservation, Double)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodLogLikelihood(TObservation, Int32)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger along the given path of hidden states.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodLogLikelihood(TObservation, Int32, Double)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger along the given path of hidden states.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodLogLikelihood(TObservation, Int32, Double)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodLogLikelihoods(TInput)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodLogLikelihoods(TInput)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodLogLikelihoods(TInput, Int32)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodLogLikelihoods(TInput, Double)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodLogLikelihoods(TInput, Int32)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodLogLikelihoods(TObservation, Double)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodLogLikelihoods(TInput, Int32, Double)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodLogLikelihoods(TObservation, Int32, Double)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodPosterior(TObservation) Obsolete.
Calculates the probability of each hidden state for each observation in the observation vector.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodPosterior(TObservation, Int32) Obsolete.
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.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodCode examplePredict(TObservation)
Predicts the next observation occurring after a given observation sequence.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodCode examplePredict(Int32, Double)
Predicts the next observation occurring after a given observation sequence.
Public methodCode examplePredict(Int32, Int32)
Predicts next observations occurring after a given observation sequence.
(Overrides HiddenMarkovModelTDistribution, TObservationPredict(TObservation, Int32).)
Public methodCode examplePredict(TObservation, Double)
Predicts the next observation occurring after a given observation sequence.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodCode examplePredict(Int32, Int32, Double)
Predicts next observations occurring after a given observation sequence.
Public methodCode examplePredict(TObservation, Int32, Double)
Predicts the next observations occurring after a given observation sequence.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodCode examplePredict(Int32, Int32, Double, Double)
Predicts the next observations occurring after a given observation sequence.
Public methodCode examplePredictTMultivariate(TObservation, Double, MultivariateMixtureTMultivariate)
Predicts the next observation occurring after a given observation sequence.
(Inherited from HiddenMarkovModelTDistribution, TObservation.)
Public methodProbabilities(TInput)
Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbabilities(TInput)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbabilities(TInput, Double)
Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbabilities(TInput, Int32)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbabilities(TInput, Double)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbabilities(TInput, Int32)
Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbabilities(TInput, Int32, Double)
Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbabilities(TInput, Int32, Double)
Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbability(TInput)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbability(TInput)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbability(TInput, Int32)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbability(TInput, Double)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbability(TInput, Int32)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodProbability(TInput, Int32, Double)
Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodSave(Stream) Obsolete.
Saves the hidden Markov model to a stream.
Public methodSave(String) Obsolete.
Saves the hidden Markov model to a stream.
Public methodScores(TInput)
Computes numerical scores measuring the association between each of the given sequence vectors and each possible class.
(Inherited from ScoreTaggerBaseTInput.)
Public methodScores(TInput)
Computes numerical scores measuring the association between each of the given sequences vectors and each possible class.
(Inherited from ScoreTaggerBaseTInput.)
Public methodScores(TInput, Double)
Computes numerical scores measuring the association between each of the given sequences vectors and each possible class.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodScores(TInput, Double)
Computes numerical scores measuring the association between each of the given sequence vectors and each possible class.
(Inherited from ScoreTaggerBaseTInput.)
Public methodScores(TInput, Int32)
Computes numerical scores measuring the association between each of the given sequence vectors and each possible class.
(Inherited from ScoreTaggerBaseTInput.)
Public methodScores(TInput, Int32)
Computes numerical scores measuring the association between each of the given sequences vectors and each possible class.
(Inherited from ScoreTaggerBaseTInput.)
Public methodScores(TInput, Int32, Double)
Computes numerical scores measuring the association between each of the given sequences vectors and each possible class.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodScores(TInput, Int32, Double)
Computes numerical scores measuring the association between each of the given sequence vectors and each possible class.
(Inherited from ScoreTaggerBaseTInput.)
Public methodToContinuousModel Obsolete.
Public methodToGenericModel
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
Public methodTransform(TInput)
Applies the transformation to an input, producing an associated output.
(Inherited from TaggerBaseTInput.)
Public methodTransform(TInput)
Applies the transformation to a set of input vectors, producing an associated set of output vectors.
(Inherited from TransformBaseTInput, TOutput.)
Public methodTransform(TInput, Double)
Applies the transformation to an input, producing an associated output.
(Inherited from LikelihoodTaggerBaseTInput.)
Public methodTransform(TInput, TOutput)
Applies the transformation to an input, producing an associated output.
(Inherited from TransformBaseTInput, TOutput.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodTo(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.)
Public Extension MethodToTOverloaded.
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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See Also