Uses of Class
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM

Packages that use AbstractHMM

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that return AbstractHMM

AbstractHMM	AbstractHMM.clone()
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

Subclasses of AbstractHMM in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

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
class	SamplingPhyloHMM This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree.