Uses of Class
org.apache.mahout.classifier.sequencelearning.hmm.HmmModel

Packages that use HmmModel

Package	Description
org.apache.mahout.classifier.sequencelearning.hmm

Uses of HmmModel in org.apache.mahout.classifier.sequencelearning.hmm

Methods in org.apache.mahout.classifier.sequencelearning.hmm that return HmmModel

Modifier and Type	Method and Description
HmmModel	HmmModel.clone() Get a copy of this model
static HmmModel	HmmTrainer.trainBaumWelch(HmmModel initialModel, int[] observedSequence, double epsilon, int maxIterations, boolean scaled) Iteratively train the parameters of the given initial model wrt the observed sequence using Baum-Welch training.
static HmmModel	HmmTrainer.trainSupervised(int nrOfHiddenStates, int nrOfOutputStates, int[] observedSequence, int[] hiddenSequence, double pseudoCount) Create an supervised initial estimate of an HMM Model based on a sequence of observed and hidden states.
static HmmModel	HmmTrainer.trainSupervisedSequence(int nrOfHiddenStates, int nrOfOutputStates, Collection<int[]> hiddenSequences, Collection<int[]> observedSequences, double pseudoCount) Create an supervised initial estimate of an HMM Model based on a number of sequences of observed and hidden states.
static HmmModel	HmmTrainer.trainViterbi(HmmModel initialModel, int[] observedSequence, double pseudoCount, double epsilon, int maxIterations, boolean scaled) Iteratively train the parameters of the given initial model wrt to the observed sequence using Viterbi training.
static HmmModel	HmmUtils.truncateModel(HmmModel model, double threshold) Method to reduce the size of an HMMmodel by converting the models DenseMatrix/DenseVectors to sparse implementations and setting every value < threshold to 0

Methods in org.apache.mahout.classifier.sequencelearning.hmm with parameters of type HmmModel

Modifier and Type	Method and Description
void	HmmModel.assign(HmmModel model) Assign the content of another HMM model to this one
static Matrix	HmmAlgorithms.backwardAlgorithm(HmmModel model, int[] observations, boolean scaled) External function to compute a matrix of beta factors
static int[]	HmmEvaluator.decode(HmmModel model, int[] observations, boolean scaled) Returns the most likely sequence of hidden states for the given model and observation
static List<String>	HmmUtils.decodeStateSequence(HmmModel model, int[] sequence, boolean observed, String defaultValue) Decodes a given collection of state IDs into the corresponding state names registered in a given model.
static int[]	HmmUtils.encodeStateSequence(HmmModel model, Collection<String> sequence, boolean observed, int defaultValue) Encodes a given collection of state names by the corresponding state IDs registered in a given model.
static Matrix	HmmAlgorithms.forwardAlgorithm(HmmModel model, int[] observations, boolean scaled) External function to compute a matrix of alpha factors
static Vector	HmmUtils.getCumulativeInitialProbabilities(HmmModel model) Compute the cumulative distribution of the initial hidden state probabilities for the given HMM model.
static Matrix	HmmUtils.getCumulativeOutputMatrix(HmmModel model) Compute the cumulative output probability matrix for the given HMM model.
static Matrix	HmmUtils.getCumulativeTransitionMatrix(HmmModel model) Compute the cumulative transition probability matrix for the given HMM model.
static double	HmmEvaluator.modelLikelihood(HmmModel model, int[] outputSequence, boolean scaled) Returns the likelihood that a given output sequence was produced by the given model.
static double	HmmEvaluator.modelLikelihood(HmmModel model, int[] outputSequence, Matrix beta, boolean scaled) Computes the likelihood that a given output sequence was computed by a given model.
static void	HmmUtils.normalizeModel(HmmModel model) Function used to normalize the probabilities of a given HMM model
static int[]	HmmEvaluator.predict(HmmModel model, int steps) Predict a sequence of steps output states for the given HMM model
static int[]	HmmEvaluator.predict(HmmModel model, int steps, long seed) Predict a sequence of steps output states for the given HMM model
static HmmModel	HmmTrainer.trainBaumWelch(HmmModel initialModel, int[] observedSequence, double epsilon, int maxIterations, boolean scaled) Iteratively train the parameters of the given initial model wrt the observed sequence using Baum-Welch training.
static HmmModel	HmmTrainer.trainViterbi(HmmModel initialModel, int[] observedSequence, double pseudoCount, double epsilon, int maxIterations, boolean scaled) Iteratively train the parameters of the given initial model wrt to the observed sequence using Viterbi training.
static HmmModel	HmmUtils.truncateModel(HmmModel model, double threshold) Method to reduce the size of an HMMmodel by converting the models DenseMatrix/DenseVectors to sparse implementations and setting every value < threshold to 0
static void	HmmUtils.validate(HmmModel model) Validates an HMM model set
static int[]	HmmAlgorithms.viterbiAlgorithm(HmmModel model, int[] observations, boolean scaled) Viterbi algorithm to compute the most likely hidden sequence for a given model and observed sequence