Uses of Class
dev.nm.stat.hmm.HiddenMarkovModel
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Packages that use HiddenMarkovModel Package Description dev.nm.stat.hmm dev.nm.stat.hmm.discrete dev.nm.stat.hmm.mixture -
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Uses of HiddenMarkovModel in dev.nm.stat.hmm
Constructors in dev.nm.stat.hmm with parameters of type HiddenMarkovModel Constructor Description ForwardBackwardProcedure(HiddenMarkovModel model, double[] observations)
Constructs the forward and backward probability matrix calculator for an HMM model.ForwardBackwardProcedure(HiddenMarkovModel model, int[] observations)
Constructs the forward and backward probability matrix calculator for an HMM model.Viterbi(HiddenMarkovModel model)
Constructs an Viterbi algorithm for an HMM. -
Uses of HiddenMarkovModel in dev.nm.stat.hmm.discrete
Subclasses of HiddenMarkovModel in dev.nm.stat.hmm.discrete Modifier and Type Class Description class
BaumWelch
This implementation trains an HMM model by observations using the Baum–Welch algorithm.class
DiscreteHMM
This is the discrete hidden Markov model as defined by Rabiner.Methods in dev.nm.stat.hmm.discrete with parameters of type HiddenMarkovModel Modifier and Type Method Description static Vector[]
BaumWelch. gamma(HiddenMarkovModel model, int[] observations, Matrix[] xi)
Gets the (T-1 * N) γ matrix, where the (t, i)-th entry is γt(i).static Matrix[]
BaumWelch. xi(HiddenMarkovModel model, int[] observations, ForwardBackwardProcedure fb)
Gets the ξ matrices, where for 1 ≤ t ≤ T - 1, the t-th entry of ξ is an (N * N) matrix, for which the (i, j)-th entry is ξt(i, j). -
Uses of HiddenMarkovModel in dev.nm.stat.hmm.mixture
Subclasses of HiddenMarkovModel in dev.nm.stat.hmm.mixture Modifier and Type Class Description class
MixtureHMM
This is the mixture hidden Markov model (HMM).class
MixtureHMMEM
The EM algorithm is used to find the unknown parameters of a hidden Markov model (HMM) by making use of the forward-backward algorithm.
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