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

Packages that use HmmModel
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
`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
`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

Copyright © 2008–2014 The Apache Software Foundation. All rights reserved.