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Packages that use HmmModel | |
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org.apache.mahout.classifier.sequencelearning.hmm |
Uses of HmmModel in org.apache.mahout.classifier.sequencelearning.hmm |
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Methods in org.apache.mahout.classifier.sequencelearning.hmm that return HmmModel | |
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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 | |
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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 |
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