A B C D E F G H I J K L M N O P Q R S T U V W X Z

B

backward(BNDiffSMParameterTree[], int[][]) - Method in class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree

Computes the backward-part of the normalization constant starting from this BNDiffSMParameterTree.

backwardIntermediate - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM

Helper variable = only for internal use.

BasicHigherOrderTransition - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

This class implements the basic transition that allows to be trained using the viterbi or the Baum-Welch algorithm.

BasicHigherOrderTransition(boolean[], BasicHigherOrderTransition.AbstractTransitionElement...) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition

The main constructor.

BasicHigherOrderTransition(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.BasicHigherOrderTransition

The standard constructor for the interface Storable.

BasicHigherOrderTransition.AbstractTransitionElement - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

This class declares the probability distribution for a given context, i.e.

BasicPluginTransitionElement - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements

Basic transition element without random initialization of parameters.

BasicPluginTransitionElement(int[], int[], double[], double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicPluginTransitionElement

Constructor creating a new instance with given context, descendant states, and hyper parameters.

BasicPluginTransitionElement(int[], int[], double[], double[], double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicPluginTransitionElement

Constructor creating a new instance with given context, descendant states, and hyper parameters.

BasicPluginTransitionElement(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicPluginTransitionElement

The standard constructor for the interface Storable.

BasicTransitionElement - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements

This class implements the probability distribution for a given context, i.e.

BasicTransitionElement(int[], int[], double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicTransitionElement

This is the main constructor creating a new instance with given context, descendant states, and hyper parameters.

BasicTransitionElement(int[], int[], double[], double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicTransitionElement

This is the main constructor creating a new instance with given context, descendant states, and hyper parameters.

BasicTransitionElement(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements.BasicTransitionElement

The standard constructor for the interface Storable.

baumWelch(int, int, double, Sequence) - Method in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM

This method computes the likelihood and modifies the sufficient statistics according to the Baum-Welch algorithm.

BaumWelchParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training

This class implements an HMMTrainingParameterSet for the Baum-Welch training of an AbstractHMM.

BaumWelchParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.BaumWelchParameterSet

This is the empty constructor that can be used to fill the parameters after creation.

BaumWelchParameterSet(int, AbstractTerminationCondition, int) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.BaumWelchParameterSet

This constructor can be used to create an instance with specified parameters.

BaumWelchParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training.BaumWelchParameterSet

The standard constructor for the interface Storable.

BayesianNetworkDiffSM - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

This class implements a scoring function that is a moral directed graphical model, i.e.

BayesianNetworkDiffSM(AlphabetContainer, int, double, boolean, Measure) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM

Creates a new BayesianNetworkDiffSM that has neither been initialized nor trained.

BayesianNetworkDiffSM(BayesianNetworkDiffSMParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM

Creates a new BayesianNetworkDiffSM that has neither been initialized nor trained from a BayesianNetworkDiffSMParameterSet.

BayesianNetworkDiffSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSM

The standard constructor for the interface Storable.

BayesianNetworkDiffSMParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Class for the parameters of a BayesianNetworkDiffSM.

BayesianNetworkDiffSMParameterSet(AlphabetContainer, int, double, boolean, Measure) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSMParameterSet

Creates a new BayesianNetworkDiffSMParameterSet with pre-defined parameter values.

BayesianNetworkDiffSMParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSMParameterSet

Creates a new BayesianNetworkDiffSMParameterSet with empty parameter values.

BayesianNetworkDiffSMParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BayesianNetworkDiffSMParameterSet

Creates a new BayesianNetworkDiffSMParameterSet from its XML representation as defined by the Storable interface.

BayesianNetworkTrainSM - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

The class implements a Bayesian network ( StructureLearner.ModelType.BN ) of fixed order.

BayesianNetworkTrainSM(BayesianNetworkTrainSMParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM

Creates a new BayesianNetworkTrainSM from a given BayesianNetworkTrainSMParameterSet.

BayesianNetworkTrainSM(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.BayesianNetworkTrainSM

The standard constructor for the interface Storable.

BayesianNetworkTrainSMParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters

The ParameterSet for the class BayesianNetworkTrainSM.

BayesianNetworkTrainSMParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.BayesianNetworkTrainSMParameterSet

The standard constructor for the interface Storable.

BayesianNetworkTrainSMParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.BayesianNetworkTrainSMParameterSet

The simple constructor for an empty BayesianNetworkTrainSMParameterSet for a BayesianNetworkTrainSM.

BayesianNetworkTrainSMParameterSet(AlphabetContainer, int, double, String, StructureLearner.ModelType, byte, StructureLearner.LearningType) - Constructor for class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.BayesianNetworkTrainSMParameterSet

This is the constructor of a filled BayesianNetworkTrainSMParameterSet for a BayesianNetworkTrainSM.

best - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

This field contains the value of objective function of the best start of the training.

beta - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier

The weights that determine a point of the simplex that corresponds to the function that is optimized by this classifier.

beta - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LogGenDisMixFunction

The mixture parameters of the GenDisMix

BGIS - Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools

This constant can be used to specify that the model should use the blockwise iterative scaling for training.

BGIS_P - Static variable in class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEMTools

This constant can be used to specify that the model should use the blockwise iterative scaling for training.

bgMaxMarkovOrder - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.ZOOPSTrainSM

The order of the background model.

binarySearch(int, int, int) - Method in class de.jstacs.utils.IntList

Performs a binary search for element key by calling Arrays.binarySearch(int[], int, int, int) on the internal array.

BioJavaAdapter - Class in de.jstacs.data.bioJava

This class provides static methods to convert BioJava datatypes ( SequenceIterator, Sequence) to DataSets and vice versa.

BioJavaAdapter() - Constructor for class de.jstacs.data.bioJava.BioJavaAdapter

BNDiffSMParameter - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Class for the parameters of a BayesianNetworkDiffSM.

BNDiffSMParameter(int, byte, int, double, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter

Creates a new BNDiffSMParameter, that is BNDiffSMParameter no index in the list of BNDiffSMParameters of the BayesianNetworkDiffSM and responsible for symbol at position position and pseudo count pseudoCount.

BNDiffSMParameter(int, byte, int, int[][], double, boolean) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter

Creates a new BNDiffSMParameter, that is BNDiffSMParameter no index in the list of BNDiffSMParameters of the BayesianNetworkDiffSM and responsible for symbol at position position having context context and pseudocount pseudoCount.

BNDiffSMParameter(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameter

The standard constructor for the interface Storable.

BNDiffSMParameterTree - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Class for the tree that represents the context of a BNDiffSMParameter in a BayesianNetworkDiffSM.

BNDiffSMParameterTree(int, int[], AlphabetContainer, int, int[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree

Creates a new BNDiffSMParameterTree for the parameters at position pos using the parent positions in contextPoss.

BNDiffSMParameterTree(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.BNDiffSMParameterTree

Recreates a BNDiffSMParameterTree from its XML representation as returned by BNDiffSMParameterTree.toXML().

BNDiffSMParameterTree.TreeElement - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Class for the nodes of a BNDiffSMParameterTree

brentsMethod(OneDimensionalFunction, double, double, double, double, double) - Static method in class de.jstacs.algorithms.optimization.Optimizer

Approximates a minimum (not necessary the global) in the interval [lower,upper].

brentsMethod(OneDimensionalFunction, double, double, double, double) - Static method in class de.jstacs.algorithms.optimization.Optimizer

Approximates a minimum (not necessary the global) in the interval [lower,upper].

BTExplainingAwayResidual - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures

Structure learning Measure that computes a maximum spanning tree based on the explaining away residual and uses the resulting tree structure as structure of a Bayesian tree (special case of a Bayesian network) in a BayesianNetworkDiffSM .

BTExplainingAwayResidual(double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual

Creates a new explaining away residual Bayesian tree Measure.

BTExplainingAwayResidual(BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual

Creates a new BTExplainingAwayResidual from the corresponding InstanceParameterSet parameters.

BTExplainingAwayResidual(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual

The standard constructor for the interface Storable.

BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures

Class for the parameters of a BTExplainingAwayResidual structure Measure.

BTExplainingAwayResidualParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet

Creates a new BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet with empty parameter values.

BTExplainingAwayResidualParameterSet(double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet

Creates a new BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet with the parameter for the equivalent sample sizes set to ess.

BTExplainingAwayResidualParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet

The standard constructor for the interface Storable .

BTMutualInformation - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures

Structure learning Measure that computes a maximum spanning tree based on mutual information and uses the resulting tree structure as structure of a Bayesian tree (special case of a Bayesian network) in a BayesianNetworkDiffSM .

BTMutualInformation(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation

The standard constructor for the interface Storable.

BTMutualInformation(BTMutualInformation.DataSource, double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation

Creates a new mutual information Bayesian tree Measure.

BTMutualInformation(BTMutualInformation.BTMutualInformationParameterSet) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation

Creates a new BTMutualInformation from the corresponding InstanceParameterSet parameters.

BTMutualInformation.BTMutualInformationParameterSet - Class in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures

Class for the parameters of a BTMutualInformation structure Measure.

BTMutualInformation.DataSource - Enum in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures

Enum defining the possible sources of data to compute the mutual information.

BTMutualInformationParameterSet() - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet

Creates a new BTMutualInformation.BTMutualInformationParameterSet with empty parameter values.

BTMutualInformationParameterSet(BTMutualInformation.DataSource, double[]) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet

Creates a new BTMutualInformation.BTMutualInformationParameterSet with the parameter for the BTMutualInformation.DataSource set to clazz and the parameter for the equivalent sample sizes (ess) set to ess.

BTMutualInformationParameterSet(StringBuffer) - Constructor for class de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures.BTMutualInformation.BTMutualInformationParameterSet

The standard constructor for the interface Storable .

burnInLength - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier

The length of the burn-in phase as determined by SamplingScoreBasedClassifier.burnInTest

burnInTest - Variable in class de.jstacs.classifiers.differentiableSequenceScoreBased.sampling.SamplingScoreBasedClassifier

The BurnInTest, may be null for no test

BurnInTest - Interface in de.jstacs.sampling

This is the abstract super class for any test of the length of the burn-in phase.

burnInTest - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.SamplingHigherOrderHMM

This variable holds the BurnInTest used for training the model

burnInTest - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

The BurnInTest that is used to stop the sampling.

bwdMatrix - Variable in class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM

matrix for all backward-computed variables; bwdMatrix[l][c] = log P(x_{l+1},...,x_L | (s_{l-order+1},...,s_l)=c , parameter)

ByteSequence - Class in de.jstacs.data.sequences

This class is for sequences with the alphabet symbols encoded as bytes and can therefore be used for discrete AlphabetContainers with alphabets that use only few symbols.

ByteSequence(AlphabetContainer, byte[]) - Constructor for class de.jstacs.data.sequences.ByteSequence

Creates a new ByteSequence from an array of byte- encoded alphabet symbols.

ByteSequence(AlphabetContainer, String) - Constructor for class de.jstacs.data.sequences.ByteSequence

Creates a new ByteSequence from a String representation using the default delimiter.

ByteSequence(AlphabetContainer, SequenceAnnotation[], String, String) - Constructor for class de.jstacs.data.sequences.ByteSequence

Creates a new ByteSequence from a String representation using the delimiter delim.

ByteSequence(AlphabetContainer, SequenceAnnotation[], SymbolExtractor) - Constructor for class de.jstacs.data.sequences.ByteSequence

Creates a new ByteSequence from a SymbolExtractor.

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