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## Uses of Interface
de.jstacs.InstantiableFromParameterSet

Packages that use InstantiableFromParameterSet
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.
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 `Storable`s 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 `DifferentiableStatisticalModel`s 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
`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
`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 `TerminationCondition`s.
`class` | `CombinedCondition` This class allows to use many `TerminationCondition`s 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., .
`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
`class` | `AlphabetContainer` The container for `Alphabet`s used in a `Sequence`, `DataSet`, `AbstractTrainableStatisticalModel` or ... .

Uses of InstantiableFromParameterSet in de.jstacs.data.alphabets

Classes in de.jstacs.data.alphabets that implement InstantiableFromParameterSet
`class` | `Alphabet` Class for a set of symbols, i.e. an `Alphabet`.
`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 `String`s.
`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` | `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
`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
`class` | `InstanceParameterSet<T extends InstantiableFromParameterSet>` Container class for a set of `Parameter`s 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
`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
`class` | `BayesianNetworkDiffSM` This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
`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
`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
`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
`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
`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
`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
`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` | `InhomogeneousDGTrainSM` This class is the main class for all inhomogeneous discrete graphical models (`InhomogeneousDGTrainSM`).

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 `InstanceParameterSet`s that can be used to instantiate the sub-class of `InstantiableFromParameterSet` that is given by `clazz`

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