Uses of Interface
de.jstacs.InstantiableFromParameterSet

Packages that use InstantiableFromParameterSet

Package	Description
de.jstacs	This package is the root package for the most and important packages.
de.jstacs.algorithms.optimization.termination	Provides classes for termination conditions that can be used in algorithms.
de.jstacs.data	Provides classes for the representation of data. The base classes to represent data are Alphabet and AlphabetContainer for representing alphabets, Sequence and its sub-classes to represent continuous and discrete sequences, and DataSet to represent data sets comprising a set of sequences.
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.io	Provides classes for reading data from and writing to a file and storing a number of datatypes, including all primitives, arrays of primitives, and Storables to an XML-representation.
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.sampling	This package contains many classes that can be used while a sampling.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures	Provides the facilities to learn the structure of a BayesianNetworkDiffSM.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures	Provides the facilities to learn the structure of a BayesianNetworkDiffSM as a Bayesian tree using a number of measures to define a rating of structures.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures	Provides the facilities to learn the structure of a BayesianNetworkDiffSM as a permuted Markov model using a number of measures to define a rating of structures.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous	This package contains various inhomogeneous models.
de.jstacs.utils	This package contains a bundle of useful classes and interfaces like ...

Uses of InstantiableFromParameterSet in de.jstacs

Methods in de.jstacs that return types with arguments of type InstantiableFromParameterSet

Modifier and Type	Method and Description
InstanceParameterSet<? extends InstantiableFromParameterSet>	InstantiableFromParameterSet.getCurrentParameterSet() Returns the InstanceParameterSet that has been used to instantiate the current instance of the implementing class.

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

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

Modifier and Type	Interface and Description
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 InstantiableFromParameterSet

Modifier and Type	Class and Description
class	AbsoluteValueCondition Deprecated. use of the absolute value condition is not recommended and it may be removed in future releases
class	AbstractTerminationCondition This class is the abstract super class of many TerminationConditions.
class	CombinedCondition This class allows to use many TerminationConditions at once.
class	IterationCondition This class will stop an optimization if the number of iteration reaches a given number.
class	MultipleIterationsCondition This TerminationCondition requires another provided TerminationCondition to fail a contiguous specified number of times before the optimization is terminated.
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., .
class	SmallGradientConditon This class implements a TerminationCondition that allows no further iteration in an optimization if the the gradient becomes small, i.e., .
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., .
class	TimeCondition This class implements a TerminationCondition that stops the optimization if the elapsed time in seconds is greater than a given value.

Uses of InstantiableFromParameterSet in de.jstacs.data

Classes in de.jstacs.data that implement InstantiableFromParameterSet

Modifier and Type	Class and Description
class	AlphabetContainer The container for Alphabets used in a Sequence, DataSet, AbstractTrainableStatisticalModel or ...

Uses of InstantiableFromParameterSet in de.jstacs.data.alphabets

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

Modifier and Type	Class and Description
class	Alphabet Class for a set of symbols, i.e.
class	ComplementableDiscreteAlphabet This abstract class indicates that an alphabet can be used to compute the complement.
class	ContinuousAlphabet Class for a continuous alphabet.
class	DiscreteAlphabet Class for an alphabet that consists of arbitrary Strings.
class	DNAAlphabet This class implements the discrete alphabet that is used for DNA.
class	DNAAlphabetContainer This class implements a singleton for an AlphabetContainer that can be used for DNA.
class	GenericComplementableDiscreteAlphabet This class implements an generic complementable discrete alphabet.
class	IUPACDNAAlphabet This class implements a discrete alphabet for the IUPAC DNA code.
class	ProteinAlphabet This class implements the discrete alphabet that is used for proteins (one letter code).

Uses of InstantiableFromParameterSet in de.jstacs.io

Methods in de.jstacs.io with type parameters of type InstantiableFromParameterSet

Modifier and Type	Method and Description
static <T extends InstantiableFromParameterSet> T	ParameterSetParser.getInstanceFromParameterSet(InstanceParameterSet<T> pars) Returns an instance of a subclass of InstantiableFromParameterSet that can be instantiated by the InstanceParameterSet pars.
static <T extends InstantiableFromParameterSet> T	ParameterSetParser.getInstanceFromParameterSet(ParameterSet pars, Class<T> instanceClass) Returns an instance of a subclass of InstantiableFromParameterSet that can be instantiated by the ParameterSet pars.

Uses of InstantiableFromParameterSet in de.jstacs.parameters

Classes in de.jstacs.parameters with type parameters of type InstantiableFromParameterSet

Modifier and Type	Class and Description
class	InstanceParameterSet<T extends InstantiableFromParameterSet> Container class for a set of Parameters that can be used to instantiate another class.
class	SequenceScoringParameterSet<T extends InstantiableFromParameterSet> Abstract class for a ParameterSet containing all parameters necessary to construct an Object that implements InstantiableFromParameterSet.

Uses of InstantiableFromParameterSet in de.jstacs.sampling

Classes in de.jstacs.sampling that implement InstantiableFromParameterSet

Modifier and Type	Class and Description
class	AbstractBurnInTest This abstract class implements some of the methods of BurnInTest to alleviate the implementation of efficient and new burn-in tests.
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).

Uses of InstantiableFromParameterSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement InstantiableFromParameterSet

Modifier and Type	Class and Description
class	BayesianNetworkDiffSM This class implements a scoring function that is a moral directed graphical model, i.e.
class	MarkovModelDiffSM This class implements a AbstractDifferentiableStatisticalModel for an inhomogeneous Markov model.

Uses of InstantiableFromParameterSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures that implement InstantiableFromParameterSet

Modifier and Type	Class and Description
class	InhomogeneousMarkov Class for a network structure of a BayesianNetworkDiffSM that is an inhomogeneous Markov model.
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 InstantiableFromParameterSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures that implement InstantiableFromParameterSet

Modifier and Type	Class and Description
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 BayesianNetworkDiffSM .
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 BayesianNetworkDiffSM .

Uses of InstantiableFromParameterSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures that implement InstantiableFromParameterSet

Modifier and Type	Class and Description
class	PMMExplainingAwayResidual Class for the network structure of a BayesianNetworkDiffSM that is a permuted Markov model based on the explaining away residual.
class	PMMMutualInformation Class for the network structure of a BayesianNetworkDiffSM that is a permuted Markov model based on mutual information.

Uses of InstantiableFromParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete that implement InstantiableFromParameterSet

Modifier and Type	Class and Description
class	DiscreteGraphicalTrainSM This is the main class for all discrete graphical models (DGM).

Uses of InstantiableFromParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that implement InstantiableFromParameterSet

Modifier and Type	Class and Description
class	HomogeneousMM This class implements homogeneous Markov models (hMM) of arbitrary order.
class	HomogeneousTrainSM This class implements homogeneous models of arbitrary order.

Uses of InstantiableFromParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that implement InstantiableFromParameterSet

Modifier and Type	Class and Description
class	BayesianNetworkTrainSM The class implements a Bayesian network ( StructureLearner.ModelType.BN ) of fixed order.
class	DAGTrainSM The abstract class for directed acyclic graphical models (DAGTrainSM).
class	FSDAGModelForGibbsSampling This is the class for a fixed structure directed acyclic graphical model (see FSDAGTrainSM) that can be used in a Gibbs sampling.
class	FSDAGTrainSM This class can be used for any discrete fixed structure directed acyclic graphical model ( FSDAGTrainSM).
class	FSMEManager This class can be used for any discrete fixed structure maximum entropy model (FSMEM).
class	InhomogeneousDGTrainSM This class is the main class for all inhomogeneous discrete graphical models (InhomogeneousDGTrainSM).
class	MEManager This class is the super class for all maximum entropy models

Uses of InstantiableFromParameterSet in de.jstacs.utils

Method parameters in de.jstacs.utils with type arguments of type InstantiableFromParameterSet

Modifier and Type	Method and Description
static LinkedList<Class<? extends InstanceParameterSet>>	SubclassFinder.getParameterSetFor(Class<? extends InstantiableFromParameterSet> clazz) Returns a LinkedList of the classes of the InstanceParameterSets that can be used to instantiate the sub-class of InstantiableFromParameterSet that is given by clazz