Package | Description |
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dev.nm.stat.hmm | |
dev.nm.stat.hmm.discrete | |
dev.nm.stat.hmm.mixture |
Constructor and Description |
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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.
|
Modifier and Type | Class and Description |
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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.
|
Modifier and Type | Method and 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).
|
Modifier and Type | Class and 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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