HiddenMarkovModel Methods

Creates a new object that is a copy of the current instance.

Creates a discrete hidden Markov model using the generic interface.

Creates a discrete hidden Markov model using the generic interface.

Creates a discrete hidden Markov model using the generic interface.

Creates a discrete hidden Markov model using the generic interface.

Creates a discrete hidden Markov model using the generic interface.

Constructs a new Hidden Markov Model with discrete state probabilities.

Constructs a new Hidden Markov Model with discrete state probabilities.

Constructs a new Hidden Markov Model with discrete state probabilities.

Constructs a new Hidden Markov Model with discrete state probabilities.

Constructs a new discrete-density Hidden Markov Model.

Computes class-label decisions for the given input.

Computes class-label decisions for the given input.

Computes class-label decisions for the given input.

Computes class-label decisions for the given input.

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

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

Determines whether the specified object is equal to the current object.

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

Calculates the log-likelihood that this model has generated the given observation sequence along the given state path.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Generates a random vector of observations from the model.

Generates a random vector of observations from the model.

Serves as the default hash function.

Gets the Type of the current instance.

Loads a hidden Markov model from a stream.

Loads a hidden Markov model from a file.

Loads a hidden Markov model from a stream.

Loads a hidden Markov model from a file.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger along the given path of hidden states.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger along the given path of hidden states.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger along the given path of hidden states.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Creates a shallow copy of the current Object.

Calculates the probability of each hidden state for each observation in the observation vector.

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.

Predicts the next observation occurring after a given observation sequence.

Predicts the next observation occurring after a given observation sequence.

Predicts next observations occurring after a given observation sequence.

Predicts the next observation occurring after a given observation sequence.

Predicts next observations occurring after a given observation sequence.

Predicts the next observations occurring after a given observation sequence.

Predicts the next observations occurring after a given observation sequence.

Predicts the next observation occurring after a given observation sequence.

Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the log-likelihood for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the probabilities for each of the observations in the sequence vector assuming each of the possible states in the tagger model.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Predicts a the probability that the sequence vector has been generated by this log-likelihood tagger.

Saves the hidden Markov model to a stream.

Saves the hidden Markov model to a stream.

Computes numerical scores measuring the association between each of the given sequence vectors and each possible class.

Computes numerical scores measuring the association between each of the given sequences vectors and each possible class.

Computes numerical scores measuring the association between each of the given sequences vectors and each possible class.

Computes numerical scores measuring the association between each of the given sequence vectors and each possible class.

Computes numerical scores measuring the association between each of the given sequence vectors and each possible class.

Computes numerical scores measuring the association between each of the given sequences vectors and each possible class.

Computes numerical scores measuring the association between each of the given sequences vectors and each possible class.

Computes numerical scores measuring the association between each of the given sequence vectors and each possible class.

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

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

Returns a string that represents the current object.

Applies the transformation to an input, producing an associated output.

Applies the transformation to a set of input vectors, producing an associated set of output vectors.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

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.

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.