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See:
Description
Interface Summary | |
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DifferentiableState | This interface declares a method that allows to evaluate the gradient which is essential for numerical optimization. |
SamplingState | |
State | This interface declares the methods of any state used in a hidden Markov model. |
TrainableState | This class implements method that allows to fill a statistic, which is used to estimate the parameters of a state during, for instance, the Baum-Welch training. |
Class Summary | |
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SimpleDifferentiableState | This class implements a State based on Emission that allows to reuse Emission s for different State s. |
SimpleSamplingState | This class implements a state that can be used for a HMM that obtains its parameters from sampling. |
SimpleState | This class implements a State based on Emission that allows to reuse Emission s for different State s. |
The package provides all interfaces and classes for states used in hidden Markov models. Since states can share the same emission, the emissions build a subpackage.
State
,
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions
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