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Packages that use de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions | |
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de.jstacs.sequenceScores.statisticalModels.trainable.hmm | The package provides all interfaces and classes for a hidden Markov model (HMM). |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on AbstractHMM |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions | The package provides all interfaces and classes for transitions used in hidden Markov models. |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements |
Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions used by de.jstacs.sequenceScores.statisticalModels.trainable.hmm | |
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BasicHigherOrderTransition.AbstractTransitionElement
This class declares the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set offset previously visited states. |
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Transition
This interface declares the methods of the transition used in a hidden Markov model. |
Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions used by de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | |
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BasicHigherOrderTransition.AbstractTransitionElement
This class declares the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set offset previously visited states. |
Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions used by de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions | |
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BasicHigherOrderTransition
This class implements the basic transition that allows to be trained using the viterbi or the Baum-Welch algorithm. |
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BasicHigherOrderTransition.AbstractTransitionElement
This class declares the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set offset previously visited states. |
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DifferentiableTransition
This class declares methods that allow for optimizing the parameters numerically using the Optimizer . |
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HigherOrderTransition
This class can be used in any AbstractHMM allowing to use gradient based or sampling training algorithm. |
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SamplingTransition
This interface declares all method used during a sampling. |
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TrainableTransition
This class declares methods that allow for estimating the parameters from a sufficient statistic, as for instance done in the (modified) Baum-Welch algorithm, viterbi training, or Gibbs sampling. |
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Transition
This interface declares the methods of the transition used in a hidden Markov model. |
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TransitionWithSufficientStatistic
This interface defines method for reseting and filling an internal sufficient statistic. |
Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions used by de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements | |
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BasicHigherOrderTransition.AbstractTransitionElement
This class declares the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set offset previously visited states. |
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