HiddenMarkovClassifier Class 
Namespace: Accord.Statistics.Models.Markov
[SerializableAttribute] public class HiddenMarkovClassifier : BaseHiddenMarkovClassifier<HiddenMarkovModel, GeneralDiscreteDistribution, int>, IEnumerable, IHiddenMarkovClassifier
The HiddenMarkovClassifier type exposes the following members.
Name  Description  

HiddenMarkovClassifier(Int32, ITopology, Int32) 
Creates a new Sequence Classifier with the given number of classes.
 
HiddenMarkovClassifier(Int32, ITopology, Int32) 
Creates a new Sequence Classifier with the given number of classes.
 
HiddenMarkovClassifier(Int32, Int32, Int32) 
Creates a new Sequence Classifier with the given number of classes.
 
HiddenMarkovClassifier(Int32, ITopology, Int32, String) 
Creates a new Sequence Classifier with the given number of classes.
 
HiddenMarkovClassifier(Int32, ITopology, Int32, String) 
Creates a new Sequence Classifier with the given number of classes.
 
HiddenMarkovClassifier(Int32, Int32, Int32, String) 
Creates a new Sequence Classifier with the given number of classes.

Name  Description  

Classes 
Gets the number of classes which can be recognized by this classifier.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
Item 
Gets the Hidden Markov
Model implementation responsible for recognizing
each of the classes given the desired class label.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
Models 
Gets the collection of models specialized in each
class of the sequence classification problem.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
NumberOfInputs 
Gets the number of inputs accepted by the model.
(Inherited from TransformBaseTInput, TOutput.)  
NumberOfOutputs 
Gets the number of outputs generated by the model.
(Inherited from TransformBaseTInput, TOutput.)  
NumberOfSymbols 
Gets the number of symbols
recognizable by the models.
 
Priors 
Gets the prior distribution assumed for the classes.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
Sensitivity 
Gets or sets a value governing the rejection given by
a threshold model (if present). Increasing this value
will result in higher rejection rates. Default is 1.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
Symbols  Obsolete.
Obsolete. Please use NumberOfSymbols instead.
 
Threshold 
Gets or sets the threshold model.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.) 
Name  Description  

Compute(Int32)  Obsolete.
Computes the most likely class for a given sequence.
 
Compute(Int32, Double)  Obsolete.
Computes the most likely class for a given sequence.
 
Compute(Int32, Double)  Obsolete.
Computes the most likely class for a given sequence.
 
CreateGeneric 
Creates a new Sequence Classifier with the given number of classes.
 
CreateGeneric2 
Creates a new Sequence Classifier with the given number of classes.
 
Decide(TInput) 
Computes classlabel decisions for a given set of input vectors.
(Inherited from ClassifierBaseTInput, TClasses.)  
Decide(TObservation) 
Computes a classlabel decision for a given input.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
Decide(TInput, TClasses) 
Computes a classlabel decision for a given input.
(Inherited from ClassifierBaseTInput, TClasses.)  
Decide(TInput, Boolean) 
Computes classlabel decisions for the given input.
(Inherited from MulticlassClassifierBaseTInput.)  
Decide(TInput, Double) 
Computes classlabel decisions for the given input.
(Inherited from MulticlassClassifierBaseTInput.)  
Decide(TInput, Int32) 
Computes classlabel decisions for the given input.
(Inherited from MulticlassClassifierBaseTInput.)  
Decide(TInput, Double) 
Computes a classlabel decision for a given input.
(Inherited from MulticlassClassifierBaseTInput.)  
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.)  
GetEnumerator 
Returns an enumerator that iterates through the models in the classifier.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
Load(Stream)  Obsolete.
Loads a classifier from a stream.
 
Load(String)  Obsolete.
Loads a classifier from a file.
 
LoadTDistribution(Stream)  Obsolete.
Loads a classifier from a stream.
 
LoadTDistribution(String)  Obsolete.
Loads a classifier from a file.
 
LogLikelihood(TInput) 
Computes the loglikelihood that the given input
vectors belongs to each of the possible classes.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihood(TObservation) 
Computes the loglikelihood that the given input vector
belongs to its decided class.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
LogLikelihood(TInput, Int32) 
Predicts a class label vector for the given input vector, returning the
loglikelihood that the input vector belongs to its predicted class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihood(TInput, Double) 
Computes the loglikelihood that the given input
vectors belongs to each of the possible classes.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihood(TInput, Int32) 
Computes the loglikelihood that the given input vector
belongs to the specified classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihood(TInput, Int32) 
Predicts a class label for each input vector, returning the
loglikelihood that each vector belongs to its predicted class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihood(TObservation, Int32) 
Computes the loglikelihood that the given input vector
belongs to the specified classIndex.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
LogLikelihood(TObservation, Int32) 
Computes the loglikelihood that the given input vector
belongs to its decided class.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
LogLikelihood(Int32, Int32) 
Computes the loglikelihood of a set of sequences
belonging to their given respective classes according
to this classifier.
 
LogLikelihood(TInput, Int32, Double) 
Computes the loglikelihood that the given input vector
belongs to the specified classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihood(TInput, Int32, Double) 
Computes the loglikelihood that the given input vector
belongs to the specified classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihood(TInput, Int32, Double) 
Predicts a class label for each input vector, returning the
loglikelihood that each vector belongs to its predicted class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput) 
Computes the loglikelihood that the given input
vector belongs to each of the possible classes.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput) 
Computes the loglikelihood that the given input
vectors belongs to each of the possible classes.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Double) 
Computes the loglikelihood that the given input
vector belongs to each of the possible classes.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Int32) 
Predicts a class label vector for the given input vector, returning the
loglikelihoods of the input vector belonging to each possible class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Double) 
Computes the loglikelihood that the given input
vector belongs to each of the possible classes.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Int32) 
Computes the loglikelihood that the given input vector
belongs to the specified classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Int32) 
Predicts a class label vector for each input vector, returning the
loglikelihoods of the input vector belonging to each possible class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Int32, Double) 
Predicts a class label vector for the given input vector, returning the
loglikelihoods of the input vector belonging to each possible class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Int32, Double) 
Computes the loglikelihood that the given input vector
belongs to the specified classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TInput, Int32, Double) 
Predicts a class label vector for each input vector, returning the
loglikelihoods of the input vector belonging to each possible class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
LogLikelihoods(TObservation, Int32, Double) 
Predicts a class label vector for the given input vector, returning the
loglikelihoods of the input vector belonging to each possible class.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
Probabilities(TInput) 
Computes the probabilities that the given input
vector belongs to each of the possible classes.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probabilities(TInput) 
Computes the probabilities that the given input
vector belongs to each of the possible classes.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probabilities(TInput, Double) 
Computes the probabilities that the given input
vector belongs to each of the possible classes.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probabilities(TInput, Int32) 
Predicts a class label vector for the given input vector, returning the
probabilities of the input vector belonging to each possible class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probabilities(TInput, Double) 
Computes the probabilities that the given input
vector belongs to each of the possible classes.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probabilities(TInput, Int32) 
Predicts a class label vector for each input vector, returning the
probabilities of the input vector belonging to each possible class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probabilities(TInput, Int32, Double) 
Predicts a class label vector for the given input vector, returning the
probabilities of the input vector belonging to each possible class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probabilities(TInput, Int32, Double) 
Predicts a class label vector for each input vector, returning the
probabilities of the input vector belonging to each possible class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probabilities(TObservation, Int32, Double) 
Computes the probabilities that the given input
vector belongs to each of the possible classes.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
Probability(TInput) 
Predicts a class label for the given input vector, returning the
probability that the input vector belongs to its predicted class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probability(TObservation) 
Computes the likelihood that the given input vector
belongs to its decided class.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
Probability(TInput, Int32) 
Computes the probability that the given input vector
belongs to the specified classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probability(TInput, Int32) 
Predicts a class label for the given input vector, returning the
probability that the input vector belongs to its predicted class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probability(TInput, Double) 
Predicts a class label for the given input vector, returning the
probability that the input vector belongs to its predicted class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probability(TInput, Int32) 
Computes the probability that the given input vector
belongs to the specified classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probability(TInput, Int32) 
Computes the probability that the given input vector
belongs to the specified classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probability(TInput, Int32) 
Predicts a class label for each input vector, returning the
probability that each vector belongs to its predicted class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probability(TObservation, Int32) 
Computes the probability that the given input vector
belongs to its decided class.
(Inherited from BaseHiddenMarkovClassifierTModel, TDistribution, TObservation.)  
Probability(TInput, Int32, Double) 
Computes the probability that the given input vector
belongs to the specified classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probability(TInput, Int32, Double) 
Computes the probability that the given input vector
belongs to the specified classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Probability(TInput, Int32, Double) 
Predicts a class label for each input vector, returning the
probability that each vector belongs to its predicted class.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Save(Stream)  Obsolete.
Saves the classifier to a stream.
 
Save(String)  Obsolete.
Saves the classifier to a stream.
 
Score(TInput) 
Computes a numerical score measuring the association between
the given input vector and its most strongly
associated class (as predicted by the classifier).
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Score(TInput) 
Computes a numerical score measuring the association between
the given input vector and its most strongly
associated class (as predicted by the classifier).
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Score(TInput, Int32) 
Computes a numerical score measuring the association between
the given input vector and a given
classIndex.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Score(TInput, Int32) 
Predicts a class label for the input vector, returning a
numerical score measuring the strength of association of the
input vector to its most strongly related class.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Score(TInput, Double) 
Computes a numerical score measuring the association between
the given input vector and its most strongly
associated class (as predicted by the classifier).
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Score(TInput, Int32) 
Computes a numerical score measuring the association between
the given input vector and a given
classIndex.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Score(TInput, Int32) 
Computes a numerical score measuring the association between
the given input vector and a given
classIndex.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Score(TInput, Int32) 
Predicts a class label for each input vector, returning a
numerical score measuring the strength of association of the
input vector to the most strongly related class.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Score(TInput, Int32, Double) 
Computes a numerical score measuring the association between
the given input vector and a given
classIndex.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Score(TInput, Int32, Double) 
Computes a numerical score measuring the association between
the given input vector and a given
classIndex.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Score(TInput, Int32, Double) 
Predicts a class label for each input vector, returning a
numerical score measuring the strength of association of the
input vector to the most strongly related class.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Scores(TInput) 
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Scores(TInput) 
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Scores(TInput, Double) 
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Scores(TInput, Int32) 
Predicts a class label vector for the given input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Scores(TInput, Double) 
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Scores(TInput, Int32) 
Predicts a class label vector for each input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Scores(TInput, Int32, Double) 
Predicts a class label vector for the given input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
Scores(TInput, Int32, Double) 
Predicts a class label vector for each input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
(Inherited from MulticlassScoreClassifierBaseTInput.)  
ToMultilabel 
Views this instance as a multilabel generative classifier,
giving access to more advanced methods, such as the prediction
of onehot vectors.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
ToString  Returns a string that represents the current object. (Inherited from Object.)  
Transform(TInput) 
Applies the transformation to an input, producing an associated output.
(Inherited from ClassifierBaseTInput, TClasses.)  
Transform(TInput) 
Applies the transformation to a set of input vectors,
producing an associated set of output vectors.
(Inherited from TransformBaseTInput, TOutput.)  
Transform(TInput, TClasses) 
Applies the transformation to an input, producing an associated output.
(Inherited from ClassifierBaseTInput, TClasses.)  
Transform(TInput, Boolean) 
Applies the transformation to an input, producing an associated output.
(Inherited from MulticlassClassifierBaseTInput.)  
Transform(TInput, Int32) 
Applies the transformation to an input, producing an associated output.
(Inherited from MulticlassClassifierBaseTInput.)  
Transform(TInput, Boolean) 
Applies the transformation to an input, producing an associated output.
(Inherited from MulticlassClassifierBaseTInput.)  
Transform(TInput, Double) 
Applies the transformation to an input, producing an associated output.
(Inherited from MulticlassClassifierBaseTInput.)  
Transform(TInput, Int32) 
Applies the transformation to an input, producing an associated output.
(Inherited from MulticlassClassifierBaseTInput.)  
Transform(TInput, Double) 
Applies the transformation to an input, producing an associated output.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.)  
Transform(TInput, Double) 
Applies the transformation to an input, producing an associated output.
(Inherited from MulticlassLikelihoodClassifierBaseTInput.) 
Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
SetEqualsHiddenMarkovModel 
Compares two enumerables for set equality. Two
enumerables are set equal if they contain the
same elements, but not necessarily in the same
order.
(Defined by Matrix.)  
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.)  
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 Matrix.) 
This class uses a set of discrete hidden Markov models to classify sequences of integer symbols. Each model will try to learn and recognize each of the different output classes. For examples and details on how to learn such models, please take a look on the documentation for HiddenMarkovClassifierLearning.
For other type of sequences, such as discrete sequences (not necessarily symbols) or even continuous and multivariate variables, please see use the generic classifier counterpart HiddenMarkovClassifierTDistribution
Examples are available at the respective learning algorithm pages. For example, see HiddenMarkovClassifierLearning.