Uses of Interface
de.jstacs.Storable

Packages that use Storable

de.jstacs.algorithms.optimization.termination
de.jstacs.classifier	This package provides the framework for any classifier.
de.jstacs.classifier.assessment	This package allows to assess classifiers.
de.jstacs.classifier.modelBased	Provides the classes for Classifiers that are based on Models
de.jstacs.classifier.scoringFunctionBased	Provides the classes for Classifiers that are based on ScoringFunctions.
de.jstacs.classifier.scoringFunctionBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of NormalizableScoringFunctions by a unified generative-discriminative learning principle
de.jstacs.classifier.scoringFunctionBased.logPrior	Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.classifier.scoringFunctionBased.msp	Provides an implementation of a classifier that allows to train the parameters of a set of ScoringFunctions either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.classifier.scoringFunctionBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingScoringFunctions and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.data	Provides classes for the representation of data.
de.jstacs.data.alphabets	Provides classes for the representation of discrete and continuous alphabets, including a DNAAlphabet for the most common case of DNA-sequences
de.jstacs.data.sequences.annotation	Provides the facilities to annotate Sequences using a number of pre-defined annotation types, or additional implementations of the SequenceAnnotation class
de.jstacs.models	Provides the interface Model and its abstract implementation AbstractModel, which is the super class of all other models.
de.jstacs.models.discrete
de.jstacs.models.discrete.homogeneous
de.jstacs.models.discrete.homogeneous.parameters
de.jstacs.models.discrete.inhomogeneous	This package contains various inhomogeneous models.
de.jstacs.models.discrete.inhomogeneous.parameters
de.jstacs.models.discrete.inhomogeneous.shared
de.jstacs.models.hmm	The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.models.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM
de.jstacs.models.hmm.states.emissions
de.jstacs.models.hmm.states.emissions.continuous
de.jstacs.models.hmm.states.emissions.discrete
de.jstacs.models.hmm.training	The package provides all classes used to determine the training algorithm of a hidden Markov model
de.jstacs.models.hmm.transitions	The package provides all interfaces and classes for transitions used in hidden Markov models.
de.jstacs.models.hmm.transitions.elements
de.jstacs.models.mixture	This package is the super package for any mixture model.
de.jstacs.models.mixture.motif
de.jstacs.models.mixture.motif.positionprior
de.jstacs.models.phylo
de.jstacs.motifDiscovery	This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.motifDiscovery.history
de.jstacs.parameters	This package provides classes for parameters that establish a general convention for the description of parameters as defined in the Parameter-interface.
de.jstacs.parameters.validation	Provides classes for the validation of Parameter values
de.jstacs.results	This package provides classes for results and sets of results.
de.jstacs.sampling	This package contains many classes that can be used while a sampling.
de.jstacs.scoringFunctions	Provides ScoringFunctions that can be used in a ScoreClassifier.
de.jstacs.scoringFunctions.directedGraphicalModels	Provides ScoringFunctions that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures	Provides the facilities to learn the structure of a BayesianNetworkScoringFunction.
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures	Provides the facilities to learn the structure of a BayesianNetworkScoringFunction as a Bayesian tree using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures	Provides the facilities to learn the structure of a BayesianNetworkScoringFunction as a permuted Markov model using a number of measures to define a rating of structures
de.jstacs.scoringFunctions.homogeneous	Provides ScoringFunctions that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix	Provides ScoringFunctions that are mixtures of other ScoringFunctions.
de.jstacs.scoringFunctions.mix.motifSearch
de.jstacs.utils	This package contains a bundle of useful classes and interfaces like ...
de.jstacs.utils.galaxy

Uses of Storable in de.jstacs.algorithms.optimization.termination

Subinterfaces of Storable in de.jstacs.algorithms.optimization.termination

interface

TerminationCondition This interface can be used in any iterative algorithm for determining the end of the algorithm.

Classes in de.jstacs.algorithms.optimization.termination that implement Storable

class	AbsoluteValueCondition Deprecated. use of the absolute value condition is not recommended and it may be removed in future releases
static class	AbsoluteValueCondition.AbsoluteValueConditionParameterSet Deprecated. This class implements the parameter set for a AbsoluteValueCondition.
class	AbstractTerminationCondition This class is the abstract super class of many TerminationConditions.
static class	AbstractTerminationCondition.AbstractTerminationConditionParameterSet This class implements the super class of all parameter sets of instances from AbstractTerminationCondition.
class	CombinedCondition This class allows to use many TerminationConditions at once.
static class	CombinedCondition.CombinedConditionParameterSet This class implements the parameter set for a CombinedCondition.
class	IterationCondition This class will stop an optimization if the number of iteration reaches a given number.
static class	IterationCondition.IterationConditionParameterSet This class implements the parameter set for a IterationCondition.
class	SmallDifferenceOfFunctionEvaluationsCondition This class implements a TerminationCondition that stops an optimization if the difference of the current and the last function evaluations will be small, i.e., .
static class	SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet This class implements the parameter set for a SmallDifferenceOfFunctionEvaluationsCondition.
class	SmallGradientConditon This class implements a TerminationCondition that allows no further iteration in an optimization if the the gradient becomes small, i.e., .
static class	SmallGradientConditon.SmallGradientConditonParameterSet This class implements the parameter set for a SmallStepCondition.
class	SmallStepCondition This class implements a TerminationCondition that allows no further iteration in an optimization if the scalar product of the current and the last values of x will be small, i.e., .
static class	SmallStepCondition.SmallStepConditionParameterSet This class implements the parameter set for a SmallStepCondition.
class	TimeCondition This class implements a TerminationCondition that stops the optimization if the elapsed time in seconds is greater than a given value.
static class	TimeCondition.TimeConditionParameterSet This class implements the parameter set for a TimeCondition.

Uses of Storable in de.jstacs.classifier

Classes in de.jstacs.classifier that implement Storable

class	AbstractClassifier The super class for any classifier.
class	AbstractScoreBasedClassifier This class is the main class for all score based classifiers.
static class	AbstractScoreBasedClassifier.DoubleTableResult This class is for Results given as a table of double s.
class	ConfusionMatrix This class holds the confusion matrix of a classifier.
class	MappingClassifier This class allows the user to train the classifier on a given number of classes and to evaluate the classifier on a smaller number of classes by mapping classes together.
class	MeasureParameters This class holds the parameters for the evaluate-methods of a classifier.

Uses of Storable in de.jstacs.classifier.assessment

Classes in de.jstacs.classifier.assessment that implement Storable

class	ClassifierAssessmentAssessParameterSet This class is the superclass used by all ClassifierAssessmentAssessParameterSets.
class	KFoldCVAssessParameterSet This class implements a ClassifierAssessmentAssessParameterSet that must be used to call method assess( ... ) of a KFoldCrossValidation.
class	RepeatedHoldOutAssessParameterSet This class implements a ClassifierAssessmentAssessParameterSet that must be used to call method assess( ... ) of a RepeatedHoldOutExperiment.
class	RepeatedSubSamplingAssessParameterSet This class implements a ClassifierAssessmentAssessParameterSet that must be used to call method assess( ... ) of a RepeatedSubSamplingExperiment.
class	Sampled_RepeatedHoldOutAssessParameterSet This class implements a ClassifierAssessmentAssessParameterSet that must be used to call the method assess( ... ) of a Sampled_RepeatedHoldOutExperiment.

Uses of Storable in de.jstacs.classifier.modelBased

Classes in de.jstacs.classifier.modelBased that implement Storable

class

ModelBasedClassifier This class is the main class for all model based classifiers.

Uses of Storable in de.jstacs.classifier.scoringFunctionBased

Classes in de.jstacs.classifier.scoringFunctionBased that implement Storable

class	ScoreClassifier This abstract class implements the main functionality of a ScoringFunction based classifier.
class	ScoreClassifierParameterSet A set of Parameters for any ScoreClassifier.

Uses of Storable in de.jstacs.classifier.scoringFunctionBased.gendismix

Classes in de.jstacs.classifier.scoringFunctionBased.gendismix that implement Storable

class	GenDisMixClassifier This class implements a classifier the optimizes the following function The weights have to sum to 1.
class	GenDisMixClassifierParameterSet This class contains the parameters for the GenDisMixClassifier.

Uses of Storable in de.jstacs.classifier.scoringFunctionBased.logPrior

Classes in de.jstacs.classifier.scoringFunctionBased.logPrior that implement Storable

class	CompositeLogPrior This class implements a composite prior that can be used for NormalizableScoringFunction.
class	DoesNothingLogPrior This class defines a LogPrior that does not penalize any parameter.
class	LogPrior The abstract class for any log-prior used e.g. for maximum supervised posterior optimization.
class	SeparateGaussianLogPrior Class for a LogPrior that defines a Gaussian prior on the parameters of a set of NormalizableScoringFunctions and a set of class parameters.
class	SeparateLaplaceLogPrior Class for a LogPrior that defines a Laplace prior on the parameters of a set of NormalizableScoringFunctions and a set of class parameters.
class	SeparateLogPrior Abstract class for priors that penalize each parameter value independently and have some variances (and possible means) as hyperparameters.
class	SimpleGaussianSumLogPrior This class implements a prior that is a product of Gaussian distributions with mean 0 and equal variance for each parameter.

Uses of Storable in de.jstacs.classifier.scoringFunctionBased.msp

Classes in de.jstacs.classifier.scoringFunctionBased.msp that implement Storable

class

MSPClassifier This class implements a classifier that allows the training via MCL or MSP principle.

Uses of Storable in de.jstacs.classifier.scoringFunctionBased.sampling

Classes in de.jstacs.classifier.scoringFunctionBased.sampling that implement Storable

class	SamplingGenDisMixClassifier A classifier that samples its parameters from a LogGenDisMixFunction using the Metropolis-Hastings algorithm.
class	SamplingGenDisMixClassifierParameterSet ParameterSet to instantiate a SamplingGenDisMixClassifier.
class	SamplingScoreBasedClassifier A classifier that samples the parameters of SamplingScoringFunctions by the Metropolis-Hastings algorithm.
class	SamplingScoreBasedClassifierParameterSet ParameterSet to instantiate a SamplingScoreBasedClassifier.

Uses of Storable in de.jstacs.data

Classes in de.jstacs.data that implement Storable

class	Alphabet Class for a set of symbols, i.e. an Alphabet.
static class	Alphabet.AlphabetParameterSet The super class for the InstanceParameterSet of any Alphabet.
class	AlphabetContainer The container for Alphabets used in a Sequence, Sample, AbstractModel or ... .
class	AlphabetContainerParameterSet Class for the AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet of an AlphabetContainer.
static class	AlphabetContainerParameterSet.AlphabetArrayParameterSet Class for the parameters of an array of Alphabets of defined length.
static class	AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet Class for the parameter set of an array of Alphabets where each Alphabet may be used for one or more sections of positions.

Uses of Storable in de.jstacs.data.alphabets

Classes in de.jstacs.data.alphabets that implement Storable

class	ComplementableDiscreteAlphabet This abstract class indicates that an alphabet can be used to compute the complement.
class	ContinuousAlphabet Class for a continuous alphabet.
static class	ContinuousAlphabet.ContinuousAlphabetParameterSet Class for the ParameterSet of a ContinuousAlphabet.
class	DiscreteAlphabet Class for an alphabet that consists of arbitrary Strings.
static class	DiscreteAlphabet.DiscreteAlphabetParameterSet Class for the ParameterSet of a DiscreteAlphabet.
class	DiscreteAlphabetMapping This class implements the transformation of discrete values to other discrete values which define a DiscreteAlphabet.
class	DNAAlphabet This class implements the discrete alphabet that is used for DNA.
static class	DNAAlphabet.DNAAlphabetParameterSet The parameter set for a DNAAlphabet.
class	GenericComplementableDiscreteAlphabet This class implements an generic complementable discrete alphabet.
static class	GenericComplementableDiscreteAlphabet.GenericComplementableDiscreteAlphabetParameterSet This class is used as container for the parameters of a GenericComplementableDiscreteAlphabet.

Uses of Storable in de.jstacs.data.sequences.annotation

Classes in de.jstacs.data.sequences.annotation that implement Storable

class	CisRegulatoryModuleAnnotation Annotation for a cis-regulatory module as defined by a set of MotifAnnotations of the motifs in the module.
class	IntronAnnotation Annotation class for an intron as defined by a donor and an acceptor splice site.
class	LocatedSequenceAnnotation Class for a SequenceAnnotation that has a position on the sequence, e.g for transcription start sites or intron-exon junctions.
class	LocatedSequenceAnnotationWithLength Class for a SequenceAnnotation that has a position on the sequence and a length, e.g. for donor splice sites, exons or genes.
class	MotifAnnotation Class for a StrandedLocatedSequenceAnnotationWithLength that is a motif.
class	ReferenceSequenceAnnotation This class implements a SequenceAnnotation that contains a reference sequence.
class	SequenceAnnotation Class for a general annotation of a Sequence.
class	SinglePositionSequenceAnnotation Class for some annotations that consist mainly of one position on a sequence.
class	StrandedLocatedSequenceAnnotationWithLength Class for a SequenceAnnotation that has a position, a length and an orientation on the strand of a Sequence.

Uses of Storable in de.jstacs.models

Subinterfaces of Storable in de.jstacs.models

interface

Model This interface defines all methods for a probabilistic model.

Classes in de.jstacs.models that implement Storable

class	AbstractModel Abstract class for a model for pattern recognition.
class	CompositeModel This class is for modelling sequences by modelling the different positions of the each sequence by different models.
class	NormalizableScoringFunctionModel This model can be used to use a NormalizableScoringFunction as model.
class	UniformModel This class represents a uniform model.
class	VariableLengthWrapperModel This class allows to train any Model on Samples of Sequences with variable length if each individual length is at least Model.getLength().

Uses of Storable in de.jstacs.models.discrete

Classes in de.jstacs.models.discrete that implement Storable

class	Constraint The main class for all constraints on models.
class	DGMParameterSet The super ParameterSet for any parameter set of a DiscreteGraphicalModel.
class	DiscreteGraphicalModel This is the main class for all discrete graphical models (DGM).

Uses of Storable in de.jstacs.models.discrete.homogeneous

Classes in de.jstacs.models.discrete.homogeneous that implement Storable

class	HomogeneousMM This class implements homogeneous Markov models (hMM) of arbitrary order.
class	HomogeneousModel This class implements homogeneous models of arbitrary order.
protected class	HomogeneousModel.HomCondProb This class handles the (conditional) probabilities of a homogeneous model in a fast way.

Uses of Storable in de.jstacs.models.discrete.homogeneous.parameters

Classes in de.jstacs.models.discrete.homogeneous.parameters that implement Storable

class	HomMMParameterSet This class implements a container for all parameters of a homogeneous Markov model.
class	HomogeneousModelParameterSet This class implements a container for all parameters of any homogeneous model.

Uses of Storable in de.jstacs.models.discrete.inhomogeneous

Classes in de.jstacs.models.discrete.inhomogeneous that implement Storable

class	BayesianNetworkModel The class implements a Bayesian network ( StructureLearner.ModelType.BN ) of fixed order.
class	DAGModel The abstract class for directed acyclic graphical models (DAGModel).
class	FSDAGModel This class can be used for any discrete fixed structure directed acyclic graphical model ( FSDAGModel).
class	FSDAGModelForGibbsSampling This is the class for a fixed structure directed acyclic graphical model (see FSDAGModel) that can be used in a Gibbs sampling.
class	InhCondProb This class handles (conditional) probabilities of sequences for inhomogeneous models.
class	InhConstraint This class is the superclass for all inhomogeneous constraints.
class	InhomogeneousDGM This class is the main class for all inhomogeneous discrete graphical models (InhomogeneousDGM).
class	MEMConstraint This constraint can be used for any maximum entropy model (MEM) application.

Uses of Storable in de.jstacs.models.discrete.inhomogeneous.parameters

Classes in de.jstacs.models.discrete.inhomogeneous.parameters that implement Storable

class	BayesianNetworkModelParameterSet The ParameterSet for the class BayesianNetworkModel.
class	FSDAGModelForGibbsSamplingParameterSet The class for the parameters of a FSDAGModelForGibbsSampling.
class	FSDAGMParameterSet The class for the parameters of a FSDAGModel (fixed structure directed acyclic graphical model).
class	IDGMParameterSet This is the abstract container of parameters that is a root container for all inhomogeneous discrete graphical model parameter containers.

Uses of Storable in de.jstacs.models.discrete.inhomogeneous.shared

Classes in de.jstacs.models.discrete.inhomogeneous.shared that implement Storable

class	SharedStructureClassifier This class enables you to learn the structure on all classes of the classifier together.
class	SharedStructureMixture This class handles a mixture of models with the same structure that is learned via EM.

Uses of Storable in de.jstacs.models.hmm

Subinterfaces of Storable in de.jstacs.models.hmm

interface

Transition This interface declares the methods of the transition used in a hidden Markov model.

Classes in de.jstacs.models.hmm that implement Storable

class	AbstractHMM This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs.
class	HMMTrainingParameterSet This class implements an abstract ParameterSet that is used for the training of an AbstractHMM.

Uses of Storable in de.jstacs.models.hmm.models

Classes in de.jstacs.models.hmm.models that implement Storable

class	DifferentiableHigherOrderHMM This class combines an HigherOrderHMM and a NormalizableScoringFunction 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.

Uses of Storable in de.jstacs.models.hmm.states.emissions

Subinterfaces of Storable in de.jstacs.models.hmm.states.emissions

interface	DifferentiableEmission This interface declares all methods needed in an emission during a numerical optimization of HMM.
interface	Emission This interface declares all method for an emission of a state.
interface	SamplingEmission

Classes in de.jstacs.models.hmm.states.emissions that implement Storable

class	MixtureEmission This class implements a mixture of Emissions.
class	SilentEmission This class implements a silent emission which is used to create silent states.

Uses of Storable in de.jstacs.models.hmm.states.emissions.continuous

Classes in de.jstacs.models.hmm.states.emissions.continuous that implement Storable

class	GaussianEmission Emission for continuous values following a Gaussian distribution.
class	PluginGaussianEmission Basic Gaussian emission distribution without random initialization of parameters.

Uses of Storable in de.jstacs.models.hmm.states.emissions.discrete

Classes in de.jstacs.models.hmm.states.emissions.discrete that implement Storable

class	AbstractConditionalDiscreteEmission The abstract super class of discrete emissions.
class	DiscreteEmission This class implements a simple discrete emission without any condition.
class	PhyloDiscreteEmission Phylogenetic discrete emission This class uses a phylogenetic tree to describe multidimensional data It implements Felsensteins model for nucleotide substitution (F81)
class	ReferenceSequenceDiscreteEmission This class implements a discrete emission that depends on some ReferenceSequenceAnnotation at a certain reference position.

Uses of Storable in de.jstacs.models.hmm.training

Classes in de.jstacs.models.hmm.training that implement Storable

class	BaumWelchParameterSet This class implements an HMMTrainingParameterSet for the Baum-Welch training of an AbstractHMM.
class	MaxHMMTrainingParameterSet This class is the super class for any HMMTrainingParameterSet that is used for a maximizing training algorithm of a hidden Markov model.
class	NumericalHMMTrainingParameterSet This class implements an ParameterSet for numerical training of an AbstractHMM.
class	SamplingHMMTrainingParameterSet This class contains the parameters for training training an AbstractHMM using a sampling strategy.
class	ViterbiParameterSet This class implements an ParameterSet for the viterbi training of an AbstractHMM.

Uses of Storable in de.jstacs.models.hmm.transitions

Subinterfaces of Storable in de.jstacs.models.hmm.transitions

interface	DifferentiableTransition This class declares methods that allow for optimizing the parameters numerically using the Optimizer.
interface	SamplingTransition This interface declares all method used during a sampling.
interface	TrainableAndDifferentiableTransition This interface unifies the interfaces TrainableTransition and DifferentiableTransition.
interface	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.
interface	TransitionWithSufficientStatistic This interface defines method for reseting and filling an internal sufficient statistic.

Classes in de.jstacs.models.hmm.transitions that implement Storable

class	BasicHigherOrderTransition This class implements the basic transition that allows to be trained using the viterbi or the Baum-Welch algorithm.
static class	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.
class	HigherOrderTransition This class can be used in any AbstractHMM allowing to use gradient based or sampling training algorithm.

Uses of Storable in de.jstacs.models.hmm.transitions.elements

Classes in de.jstacs.models.hmm.transitions.elements that implement Storable

class	BasicPluginTransitionElement Basic transition element without random initialization of parameters.
class	BasicTransitionElement This class implements the probability distribution for a given context, i.e. it contains all possible transition and the corresponding probabilities for a given set of previously visited states.
class	DistanceBasedScaledTransitionElement Distance-based scaled transition element for an HMM with distance-scaled transition matrices (DSHMM).
class	ReferenceBasedTransitionElement This class implements transition elements that utilize a reference sequence to determine the transition probability.
class	ScaledTransitionElement Scaled transition element for an HMM with scaled transition matrices (SHMM).
class	TransitionElement This class implements an transition element implements method used for training via sampling or gradient based optimization approach.

Uses of Storable in de.jstacs.models.mixture

Classes in de.jstacs.models.mixture that implement Storable

class	AbstractMixtureModel This is the abstract class for all kinds of mixture models.
class	MixtureModel The class for a mixture model of any Models.
class	StrandModel This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

Uses of Storable in de.jstacs.models.mixture.motif

Classes in de.jstacs.models.mixture.motif that implement Storable

class	HiddenMotifMixture This is the main class that every generative motif discoverer should implement.
class	SingleHiddenMotifMixture This class enables the user to search for a single motif in a sequence.

Uses of Storable in de.jstacs.models.mixture.motif.positionprior

Classes in de.jstacs.models.mixture.motif.positionprior that implement Storable

class	GaussianLikePositionPrior This class implements a gaussian like discrete truncated prior.
class	PositionPrior This is the main class for any position prior that can be used in a motif discovery.
class	UniformPositionPrior This prior implements a uniform distribution for the start position.

Uses of Storable in de.jstacs.models.phylo

Classes in de.jstacs.models.phylo that implement Storable

class	PhyloNode This class implements a node in a PhyloTree A PhyloNode contains some basic informations of itself and the incoming edge Furthermore it contains a list of PhyloNodes that represent the children nodes
class	PhyloTree This class implements a simple (phylogenetic) tree.

Uses of Storable in de.jstacs.motifDiscovery

Subinterfaces of Storable in de.jstacs.motifDiscovery

interface	MotifDiscoverer This is the interface that any tool for de-novo motif discovery should implement.
interface	MutableMotifDiscoverer This is the interface that any tool for de-novo motif discovery should implement that allows any modify-operations like shift, shrink and expand.

Uses of Storable in de.jstacs.motifDiscovery.history

Subinterfaces of Storable in de.jstacs.motifDiscovery.history

interface

History This interface is used to manage the history of some process.

Classes in de.jstacs.motifDiscovery.history that implement Storable

class	CappedHistory This class combines a threshold on the number of steps which can be performed with any other History.
class	NoRevertHistory This class implements a history that allows operations, that are not a priorily forbidden and do not create a configuration that has already be considered.
class	RestrictedRepeatHistory This class implements a history that allows operations (i.e. a pair of int), that are not a priorily forbidden and that are done before less than a specified threshold.
class	SimpleHistory This class implements a simple history that has a limited memory that will be used cyclicly.

Uses of Storable in de.jstacs.parameters

Classes in de.jstacs.parameters that implement Storable

class	ArrayParameterSet Class for a ParameterSet that consists of a length-Parameter that defines the length of the array and an array of ParameterSetContainers of this length.
class	CollectionParameter Class for a collection parameter, i.e. a parameter that provides some collection of possible values the user can choose from.
class	EnumParameter This class implements a CollectionParameter based on an Enum.
class	ExpandableParameterSet A class for a ParameterSet that can be expanded by additional Parameters at runtime.
class	FileParameter Class for a Parameter that represents a local file.
static class	FileParameter.FileRepresentation Class that represents a file.
class	InstanceParameterSet Container class for a set of Parameters that can be used to instantiate another class.
class	MultiSelectionCollectionParameter Class for a Parameter that provides a collection of possible values.
class	Parameter Abstract class for a parameter that shall be used as the parameter of some method, constructor, etc.
class	ParameterSet (Container) class for a set of Parameters.
class	ParameterSetContainer Class for a ParameterSetContainer that contains a ParameterSet as value.
class	RangeParameter Class for a parameter wrapper that allows SimpleParameters to be set to a set of values.
class	SequenceScoringParameterSet Abstract class for a ParameterSet containing all parameters necessary to construct an Object that implements InstantiableFromParameterSet.
class	SimpleParameter Class for a "simple" parameter.
class	SimpleParameterSet Class for a ParameterSet that is constructed from an array of Parameters and thus does nothing in the method SimpleParameterSet.loadParameters().

Uses of Storable in de.jstacs.parameters.validation

Subinterfaces of Storable in de.jstacs.parameters.validation

interface	Constraint Interface for a constraint that must be fulfilled in a ConstraintValidator.
interface	ParameterValidator Interface for a parameter validator, i.e. a class that can validate some possible parameter values.

Classes in de.jstacs.parameters.validation that implement Storable

class	ConstraintValidator Class for a ParameterValidator that is based on Constraints.
class	NumberValidator<E extends Comparable<? extends Number>> Class that validates all subclasses of Number that implement Comparable (e.g.
class	ReferenceConstraint Class for a Constraint that defines a condition on one Parameter (the one containing the ConstraintValidator) with respect to another Parameter.
class	SimpleReferenceConstraint Class for a ReferenceConstraint that checks for "simple" conditions as defined in the interface Constraint.
class	SimpleStaticConstraint Class for a Constraint that checks values against static values using the comparison operators defined in the interface Constraint.
class	StorableValidator Class for a validator that validates instances and XML representations for the correct class types (e.g.

Constructor parameters in de.jstacs.parameters.validation with type arguments of type Storable

StorableValidator(Class<? extends Storable> clazz)
Creates a new StorableValidator for a subclass of Storable.

StorableValidator(Class<? extends Storable> clazz, boolean trained)
Creates a new StorableValidator for a subclass of AbstractModel or AbstractClassifier.

Uses of Storable in de.jstacs.results

Classes in de.jstacs.results that implement Storable

class	CategoricalResult A class for categorical results (i.e. non-numerical results) for primitives and Strings.
class	ImageResult A class for results that are images of the PNG format.
class	ListResult Class for a Result that contains a list or a matrix, respectively, of ResultSets.
class	MeanResultSet Class that computes the mean and the standard error of a series of NumericalResultSets.
class	NumericalResult Class for numerical Result values.
class	NumericalResultSet Class for a set of numerical result values, which are all of the type NumericalResult.
class	Result The abstract class for any result.
class	ResultSet Class for a set of Results which provides methods to access single Results in the set, to retrieve the number of Results in the set, to get a String representation or an XML representation of all the Results in the set.
class	SampleResult Result that contains a Sample.
class	SimpleResult Abstract class for a Result with a value of a primitive data type or String.
class	StorableResult Class for Results that are Storables.

Methods in de.jstacs.results that return Storable

Storable

StorableResult.getResultInstance() Returns the instance of the Storable that is the result of this StorableResult.

Constructors in de.jstacs.results with parameters of type Storable

StorableResult(String name, String comment, Storable object)
Creates a result for an XML representation of an object.

Uses of Storable in de.jstacs.sampling

Subinterfaces of Storable in de.jstacs.sampling

interface

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

Classes in de.jstacs.sampling that implement Storable

class	AbstractBurnInTest This abstract class implements some of the methods of BurnInTest to alleviate the implementation of efficient and new burn-in tests.
class	AbstractBurnInTestParameterSet Class for the parameters of a AbstractBurnInTest.
class	SimpleBurnInTest Deprecated.
class	VarianceRatioBurnInTest In this class the Variance-Ratio method of Gelman and Rubin is implemented to test the length of the burn-in phase of a multi-chain Gibbs Sampling (number of chains >2).
class	VarianceRatioBurnInTestParameterSet Class for the parameters of a VarianceRatioBurnInTest.

Uses of Storable in de.jstacs.scoringFunctions

Subinterfaces of Storable in de.jstacs.scoringFunctions

interface	NormalizableScoringFunction The interface for normalizable ScoringFunctions.
interface	SamplingScoringFunction Interface for NormalizableScoringFunctions that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.
interface	ScoringFunction This interface is the main part of any ScoreClassifier.
interface	VariableLengthScoringFunction This is an interface for all NormalizableScoringFunctions that allow to score subsequences of arbitrary length.

Classes in de.jstacs.scoringFunctions that implement Storable

class	AbstractNormalizableScoringFunction This class is the main part of any ScoreClassifier.
class	AbstractVariableLengthScoringFunction This abstract class implements some methods declared in NormalizableScoringFunction based on the declaration of methods in VariableLengthScoringFunction.
class	CMMScoringFunction This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
class	IndependentProductScoringFunction This class enables the user to model parts of a sequence independent of each other.
class	MappingScoringFunction This class implements a NormalizableScoringFunction that works on mapped Sequences.
class	MRFScoringFunction This class implements the scoring function for any MRF (Markov Random Field).
class	NormalizedScoringFunction This class makes an unnormalized NormalizableScoringFunction to a normalized NormalizableScoringFunction.
class	UniformScoringFunction This ScoringFunction does nothing.

Uses of Storable in de.jstacs.scoringFunctions.directedGraphicalModels

Classes in de.jstacs.scoringFunctions.directedGraphicalModels that implement Storable

class	BayesianNetworkScoringFunction This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
class	BayesianNetworkScoringFunctionParameterSet Class for the parameters of a BayesianNetworkScoringFunction.
class	MutableMarkovModelScoringFunction This class implements a AbstractNormalizableScoringFunction for an inhomogeneous Markov model.
class	ParameterTree Class for the tree that represents the context of a Parameter in a BayesianNetworkScoringFunction.
class	ParameterTree.TreeElement Class for the nodes of a ParameterTree

Uses of Storable in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures

Classes in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures that implement Storable

class	InhomogeneousMarkov Class for a network structure of a BayesianNetworkScoringFunction that is an inhomogeneous Markov model.
static class	InhomogeneousMarkov.InhomogeneousMarkovParameterSet Class for an InstanceParameterSet that defines the parameters of an InhomogeneousMarkov structure Measure.
class	Measure Class for structure measures that derive an optimal structure with respect to some criterion within a class of possible structures from data.

Uses of Storable in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures

Classes in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures that implement Storable

class	BTExplainingAwayResidual 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 BayesianNetworkScoringFunction .
static class	BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet Class for the parameters of a BTExplainingAwayResidual structure Measure.
class	BTMutualInformation 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 BayesianNetworkScoringFunction .
static class	BTMutualInformation.BTMutualInformationParameterSet Class for the parameters of a BTMutualInformation structure Measure.

Uses of Storable in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures

Classes in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures that implement Storable

class	PMMExplainingAwayResidual Class for the network structure of a BayesianNetworkScoringFunction that is a permuted Markov model based on the explaining away residual.
static class	PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet Class for the parameters of a PMMExplainingAwayResidual structure Measure.
class	PMMMutualInformation Class for the network structure of a BayesianNetworkScoringFunction that is a permuted Markov model based on mutual information.
static class	PMMMutualInformation.PMMMutualInformationParameterSet Class for the parameters of a PMMMutualInformation structure Measure.

Uses of Storable in de.jstacs.scoringFunctions.homogeneous

Classes in de.jstacs.scoringFunctions.homogeneous that implement Storable

class	HMM0ScoringFunction This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length.
class	HMMScoringFunction This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length.
class	HomogeneousScoringFunction This is the main class for all homogeneous ScoringFunctions.
class	UniformHomogeneousScoringFunction This scoring function does nothing.

Uses of Storable in de.jstacs.scoringFunctions.mix

Classes in de.jstacs.scoringFunctions.mix that implement Storable

class	AbstractMixtureScoringFunction This main abstract class for any mixture scoring function (e.g.
class	MixtureScoringFunction This class implements a real mixture model.
class	StrandScoringFunction This class enables the user to search on both strand.
class	VariableLengthMixtureScoringFunction This class implements a mixture of VariableLengthScoringFunction by extending MixtureScoringFunction and implementing the methods of VariableLengthScoringFunction.

Uses of Storable in de.jstacs.scoringFunctions.mix.motifSearch

Classes in de.jstacs.scoringFunctions.mix.motifSearch that implement Storable

class	DurationScoringFunction This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
class	HiddenMotifsMixture This class handles mixtures with at least one hidden motif.
class	MixtureDuration This class implements a mixture of DurationScoringFunctions.
class	PositionScoringFunction This class implements a position scoring function that enables the user to get a score without using a Sequence object.
class	SkewNormalLikeScoringFunction This class implements a skew normal like discrete truncated distribution.
class	UniformDurationScoringFunction This scoring function implements a uniform distribution for positions.

Uses of Storable in de.jstacs.utils

Classes in de.jstacs.utils that implement Storable

class

DoubleList A simple list of primitive type double.

Uses of Storable in de.jstacs.utils.galaxy

Classes in de.jstacs.utils.galaxy that implement Storable

static class	GalaxyAdaptor.FileResult Result for files that are results of some computation.
static class	GalaxyAdaptor.LinkedImageResult Class for an ImageResult that is linked to a file that can be downloaded.