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Packages that use AbstractHMM | |
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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 |
Uses of AbstractHMM in de.jstacs.sequenceScores.statisticalModels.trainable.hmm |
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Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that return AbstractHMM | |
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AbstractHMM |
AbstractHMM.clone()
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static AbstractHMM |
HMMFactory.createErgodicHMM(HMMTrainingParameterSet pars,
int order,
double ess,
double selfTranistionPart,
double expectedSequenceLength,
Emission... emission)
This method creates an ergodic, i.e. a completely connected, HMM using the given emissions. |
static AbstractHMM |
HMMFactory.createProfileHMM(MaxHMMTrainingParameterSet trainingParameterSet,
double[][] initFromTo,
boolean likelihood,
int order,
int numLayers,
AlphabetContainer con,
double ess,
boolean conditionalMain,
boolean closeCircle,
double[][] conditionInitProbs,
boolean insertUniform)
Creates a new profile HMM for a given architecture and number of layers. |
static AbstractHMM |
HMMFactory.createProfileHMM(MaxHMMTrainingParameterSet trainingParameterSet,
double[][] initFromTo,
boolean likelihood,
int order,
int numLayers,
AlphabetContainer con,
double ess,
boolean conditionalMain,
int joiningStates,
double[][] conditionInitProbs,
boolean insertUniform)
Creates a new profile HMM for a given architecture and number of layers. |
static AbstractHMM |
HMMFactory.createProfileHMM(MaxHMMTrainingParameterSet trainingParameterSet,
HMMFactory.HMMType type,
boolean likelihood,
int order,
int numLayers,
AlphabetContainer con,
double ess,
boolean conditionalMain,
boolean closeCircle,
double[][] conditionInitProbs)
Creates a new profile HMM for a given architecture and number of layers. |
static AbstractHMM |
HMMFactory.createPseudoErgodicHMM(HMMTrainingParameterSet pars,
double ess,
double selfTranistionPart,
double finalTranistionPart,
AlphabetContainer con,
int numStates,
boolean insertUniform)
Creates an HMM with numStates+1 states, where numStates emitting build a clique and each of those states is connected to the absorbing silent final state. |
static AbstractHMM |
HMMFactory.createSunflowerHMM(HMMTrainingParameterSet pars,
AlphabetContainer con,
double ess,
int expectedSequenceLength,
boolean startCentral,
int... motifLength)
This method creates a first order sunflower HMM. |
static AbstractHMM |
HMMFactory.createSunflowerHMM(HMMTrainingParameterSet pars,
AlphabetContainer con,
double ess,
int expectedSequenceLength,
boolean startCentral,
PhyloTree[] t,
double[] motifProb,
int[] motifLength)
This method creates a first order sunflower HMM allowing phylogenetic emissions. |
Uses of AbstractHMM in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models |
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Subclasses of AbstractHMM in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | |
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class |
DifferentiableHigherOrderHMM
This class combines an HigherOrderHMM and a DifferentiableStatisticalModel by implementing some of the declared methods. |
class |
HigherOrderHMM
This class implements a higher order hidden Markov model. |
class |
SamplingHigherOrderHMM
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class |
SamplingPhyloHMM
This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree. |
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