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Uses of Interface
de.jstacs.InstantiableFromParameterSet

Packages that use InstantiableFromParameterSet
de.jstacs.algorithms.optimization.termination   
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.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.models.discrete   
de.jstacs.models.discrete.homogeneous   
de.jstacs.models.discrete.inhomogeneous This package contains various inhomogeneous models. 
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.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.utils This package contains a bundle of useful classes and interfaces like ... 
 

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

Subinterfaces of InstantiableFromParameterSet 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 InstantiableFromParameterSet
 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 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., $|f(\underline{x}_{i-1}) - f(\underline{x}_i)| < \epsilon$.
 class SmallGradientConditon
          This class implements a TerminationCondition that allows no further iteration in an optimization if the the gradient becomes small, i.e., $\sum_i \left|\frac{\partial f(\underline{x})}{\partial x_i}\right| < \epsilon$.
 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., $(\underline{x}_i-\underline{x}_{i-1})^T (\underline{x}_i-\underline{x}_{i-1}) < \epsilon$.
 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
 class Alphabet
          Class for a set of symbols, i.e. an Alphabet.
 class AlphabetContainer
          The container for Alphabets used in a Sequence, Sample, AbstractModel or ... .
 

Uses of InstantiableFromParameterSet in de.jstacs.data.alphabets
 

Classes in de.jstacs.data.alphabets that implement InstantiableFromParameterSet
 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 GenericComplementableDiscreteAlphabet
          This class implements an generic complementable discrete alphabet.
 

Uses of InstantiableFromParameterSet in de.jstacs.io
 

Methods in de.jstacs.io that return InstantiableFromParameterSet
static InstantiableFromParameterSet ParameterSetParser.getInstanceFromParameterSet(InstanceParameterSet pars)
          Returns an instance of a subclass of InstantiableFromParameterSet that can be instantiated by the InstanceParameterSet pars.
static InstantiableFromParameterSet ParameterSetParser.getInstanceFromParameterSet(ParameterSet pars, Class instanceClass)
          Returns an instance of a subclass of InstantiableFromParameterSet that can be instantiated by the ParameterSet pars.
 

Uses of InstantiableFromParameterSet in de.jstacs.models.discrete
 

Classes in de.jstacs.models.discrete that implement InstantiableFromParameterSet
 class DiscreteGraphicalModel
          This is the main class for all discrete graphical models (DGM).
 

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

Classes in de.jstacs.models.discrete.homogeneous that implement InstantiableFromParameterSet
 class HomogeneousMM
          This class implements homogeneous Markov models (hMM) of arbitrary order.
 class HomogeneousModel
          This class implements homogeneous models of arbitrary order.
 

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

Classes in de.jstacs.models.discrete.inhomogeneous that implement InstantiableFromParameterSet
 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 InhomogeneousDGM
          This class is the main class for all inhomogeneous discrete graphical models (InhomogeneousDGM).
 

Uses of InstantiableFromParameterSet in de.jstacs.parameters
 

Methods in de.jstacs.parameters that return InstantiableFromParameterSet
 InstantiableFromParameterSet InstanceParameterSet.getInstance()
          Returns a new instance of the class of InstanceParameterSet.getInstanceClass() that was created using this ParameterSet.
 

Uses of InstantiableFromParameterSet in de.jstacs.sampling
 

Classes in de.jstacs.sampling that implement InstantiableFromParameterSet
 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.scoringFunctions.directedGraphicalModels
 

Classes in de.jstacs.scoringFunctions.directedGraphicalModels that implement InstantiableFromParameterSet
 class BayesianNetworkScoringFunction
          This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
 class MutableMarkovModelScoringFunction
          This class implements a AbstractNormalizableScoringFunction for an inhomogeneous Markov model.
 

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

Classes in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures that implement InstantiableFromParameterSet
 class InhomogeneousMarkov
          Class for a network structure of a BayesianNetworkScoringFunction 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.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures
 

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

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

Classes in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures that implement InstantiableFromParameterSet
 class PMMExplainingAwayResidual
          Class for the network structure of a BayesianNetworkScoringFunction that is a permuted Markov model based on the explaining away residual.
 class PMMMutualInformation
          Class for the network structure of a BayesianNetworkScoringFunction that is a permuted Markov model based on mutual information.
 

Uses of InstantiableFromParameterSet in de.jstacs.utils
 

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

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