Uses of Class de.jstacs.parameters.ParameterSet

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Uses of Class
de.jstacs.parameters.ParameterSet

Packages that use ParameterSet
de.jstacs.algorithms.optimization.termination	Provides classes for termination conditions that can be used in algorithms
de.jstacs.classifiers.assessment	This package allows to assess classifiers.
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for `Classifier`s that are based on `SequenceScore`s.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s by a unified generative-discriminative learning principle
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingDifferentiableStatisticalModel`s and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifiers.performanceMeasures	This package provides the implementations of performance measures that can be used to assess any classifier
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.parameters
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training	The package provides all classes used to determine the training algorithm of a hidden Markov model
de.jstacs.utils	This package contains a bundle of useful classes and interfaces like ...
de.jstacs.utils.galaxy

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

Subclasses of ParameterSet in de.jstacs.algorithms.optimization.termination
`static class`	`AbsoluteValueCondition.AbsoluteValueConditionParameterSet` Deprecated. This class implements the parameter set for a `AbsoluteValueCondition`.
`static class`	`AbstractTerminationCondition.AbstractTerminationConditionParameterSet` This class implements the super class of all parameter sets of instances from `AbstractTerminationCondition`.
`static class`	`CombinedCondition.CombinedConditionParameterSet` This class implements the parameter set for a `CombinedCondition`.
`static class`	`IterationCondition.IterationConditionParameterSet` This class implements the parameter set for a `IterationCondition`.
`static class`	`SmallDifferenceOfFunctionEvaluationsCondition.SmallDifferenceOfFunctionEvaluationsConditionParameterSet` This class implements the parameter set for a `SmallDifferenceOfFunctionEvaluationsCondition`.
`static class`	`SmallGradientConditon.SmallGradientConditonParameterSet` This class implements the parameter set for a `SmallStepCondition`.
`static class`	`SmallStepCondition.SmallStepConditionParameterSet` This class implements the parameter set for a `SmallStepCondition`.
`static class`	`TimeCondition.TimeConditionParameterSet` This class implements the parameter set for a `TimeCondition`.

Uses of ParameterSet in de.jstacs.classifiers.assessment

Subclasses of ParameterSet in de.jstacs.classifiers.assessment
`class`	`ClassifierAssessmentAssessParameterSet` This class is the superclass used by all `ClassifierAssessmentAssessParameterSet`s.
`class`	`KFoldCrossValidationAssessParameterSet` 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 ParameterSet in de.jstacs.classifiers.differentiableSequenceScoreBased

Subclasses of ParameterSet in de.jstacs.classifiers.differentiableSequenceScoreBased
`class`	`ScoreClassifierParameterSet` A set of `Parameter`s for any `ScoreClassifier`.

Uses of ParameterSet in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Subclasses of ParameterSet in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
`class`	`GenDisMixClassifierParameterSet` This class contains the parameters for the `GenDisMixClassifier`.

Uses of ParameterSet in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Subclasses of ParameterSet in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
`class`	`SamplingGenDisMixClassifierParameterSet` `ParameterSet` to instantiate a `SamplingGenDisMixClassifier`.
`class`	`SamplingScoreBasedClassifierParameterSet` `ParameterSet` to instantiate a `SamplingScoreBasedClassifier`.

Uses of ParameterSet in de.jstacs.classifiers.performanceMeasures

Subclasses of ParameterSet in de.jstacs.classifiers.performanceMeasures
`class`	`AbstractPerformanceMeasure` This class is the abstract super class of any performance measure used to evaluate an `AbstractClassifier`.
`class`	`AucPR` This class implements the area under curve of the precision-recall curve.
`class`	`AucROC` This class implements the area under curve of the Receiver Operating Characteristics curve.
`class`	`ClassificationRate` This class implements the classification rate, i.e.
`class`	`ConfusionMatrix` This class implements the performance measure confusion matrix.
`class`	`FalsePositiveRateForFixedSensitivity` This class implements the false positive rate for a fixed sensitivity.
`class`	`MaximumCorrelationCoefficient` This class implements the maximum of the correlation coefficient $\frac{ TPTN - FNFP }{ \sqrt{ (TP+FN)(TN+FP)(TP+FP)*(TN+FN) } }$ .
`class`	`MaximumFMeasure` Computes the maximum of the general F-measure given a positive real parameter $\beta$ .
`class`	`MaximumNumericalTwoClassMeasure` This class prepares everything for an easy implementation of a maximum of any numerical performance measure.
`class`	`NumericalPerformanceMeasureParameterSet` This class implements a container for `NumericalPerformanceMeasure`s that can be used, for instance, in an repeated assessment, (cf.
`class`	`PerformanceMeasureParameterSet` This class implements a container of `AbstractPerformanceMeasure`s that can be used in `AbstractClassifier.evaluate(PerformanceMeasureParameterSet, boolean, de.jstacs.data.DataSet...)`.
`class`	`PositivePredictiveValueForFixedSensitivity` This class implements the positive predictive value for a fixed sensitivity.
`class`	`PRCurve` This class implements the precision-recall curve and its area under the curve.
`class`	`ROCCurve` This class implements the Receiver Operating Characteristics curve and the area under the curve.
`class`	`SensitivityForFixedSpecificity` This class implements the sensitivity for a fixed specificity.
`class`	`TwoClassAbstractPerformanceMeasure` This class is the abstract super class of any performance measure that can only be computed for binary classifiers.

Uses of ParameterSet in de.jstacs.data

Subclasses of ParameterSet in de.jstacs.data
`static class`	`AlphabetContainer.AbstractAlphabetContainerParameterSet<T extends AlphabetContainer>` This class is the super class of any `InstanceParameterSet` for `AlphabetContainer`.
`class`	`AlphabetContainerParameterSet` Class for the `AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet` of an `AlphabetContainer`.
`static class`	`AlphabetContainerParameterSet.AlphabetArrayParameterSet` Class for the parameters of an array of `Alphabet`s of defined length.
`static class`	`AlphabetContainerParameterSet.SectionDefinedAlphabetParameterSet` Class for the parameter set of an array of `Alphabet`s where each `Alphabet` may be used for one or more sections of positions.

Uses of ParameterSet in de.jstacs.data.alphabets

Subclasses of ParameterSet in de.jstacs.data.alphabets
`static class`	`Alphabet.AlphabetParameterSet<T extends Alphabet>` The super class for the `InstanceParameterSet` of any `Alphabet`.
`static class`	`ContinuousAlphabet.ContinuousAlphabetParameterSet` Class for the `ParameterSet` of a `ContinuousAlphabet`.
`static class`	`DiscreteAlphabet.DiscreteAlphabetParameterSet` Class for the `ParameterSet` of a `DiscreteAlphabet`.
`static class`	`DNAAlphabet.DNAAlphabetParameterSet` The parameter set for a `DNAAlphabet`.
`static class`	`DNAAlphabetContainer.DNAAlphabetContainerParameterSet` This class implements a singleton for the `ParameterSet` of a `DNAAlphabetContainer`.
`static class`	`GenericComplementableDiscreteAlphabet.GenericComplementableDiscreteAlphabetParameterSet` This class is used as container for the parameters of a `GenericComplementableDiscreteAlphabet`.
`static class`	`ProteinAlphabet.ProteinAlphabetParameterSet` The parameter set for a `ProteinAlphabet`.

Uses of ParameterSet in de.jstacs.io

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

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 ParameterSet in de.jstacs.parameters

Subclasses of ParameterSet in de.jstacs.parameters
`class`	`ArrayParameterSet` Class for a `ParameterSet` that consists of a length-`Parameter` that defines the length of the array and an array of `ParameterSetContainer`s of this length.
`class`	`ExpandableParameterSet` A class for a `ParameterSet` that can be expanded by additional `Parameter`s at runtime.
`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`.
`class`	`SimpleParameterSet` Class for a `ParameterSet` that is constructed from an array of `Parameter`s.

Fields in de.jstacs.parameters declared as ParameterSet
`protected ParameterSet`	`AbstractSelectionParameter.parameters` The internal `ParameterSet` that holds the possible values
`protected ParameterSet`	`Parameter.parent` If this `Parameter` is enclosed in a `ParameterSet`, this variable holds a reference to that `ParameterSet`.
`protected ParameterSet`	`ExpandableParameterSet.template` The template for each `ParameterSet`

Methods in de.jstacs.parameters that return ParameterSet
`ParameterSet`	`ParameterSet.clone()` Creates a full clone (deep copy) of this `ParameterSet`.
`ParameterSet`	`AbstractSelectionParameter.getParametersInCollection()` Returns the possible values in this collection.
`ParameterSet`	`Parameter.getParent()` Returns a reference to the `ParameterSet` enclosing this `Parameter`.
`ParameterSet`	`ParameterSetContainer.getValue()`

Methods in de.jstacs.parameters with parameters of type ParameterSet
`static String`	`ParameterSet.getComment(ParameterSet p)` Returns a comment for the `ParameterSet`.
`static String`	`ParameterSet.getName(ParameterSet p)` Returns a name for the `ParameterSet`.
`boolean`	`ParameterSet.isComparable(ParameterSet p)` This method checks whether the given `ParameterSet` is comparable to the current instance, i.e. whether the `Class` and the number of parameters are identical, and the individual `Parameter`s are comparable.
`boolean`	`ExpandableParameterSet.replaceContentWith(ParameterSet[] paramSetArray)` First removes all previous added `ParameterSetContainer`s and afterwards adds all given `ParameterSet`s (in the given order) enclosed in new `ParameterSetContainer`s.
`void`	`Parameter.setParent(ParameterSet parent)` Sets the reference of the enclosing `ParameterSet` of this `Parameter` to `parent`.

Method parameters in de.jstacs.parameters with type arguments of type ParameterSet
`static String`	`ParameterSet.getComment(Class<? extends ParameterSet> c)` Returns a comment for the class.
`static String`	`ParameterSet.getName(Class<? extends ParameterSet> c)` Returns a name for the class.

Constructors in de.jstacs.parameters with parameters of type ParameterSet
`AbstractSelectionParameter(String name, String comment, boolean required, ParameterSet... values)` Constructor for a `AbstractSelectionParameter` from an array of `ParameterSet`s.
`ArrayParameterSet(ParameterSet template, String nameTemplate, String commentTemplate)` Creates a new `ArrayParameterSet` from a `Class` that can be instantiated using this `ArrayParameterSet` and templates for the `ParameterSet` in each element of the array, the name and the comment that are displayed for the `ParameterSetContainer`s enclosing the `ParameterSet`s.
`ArrayParameterSet(ParameterSet template, String nameTemplate, String commentTemplate, String lengthName, String lengthComment, NumberValidator<Integer> allowedLengths)` Creates a new `ArrayParameterSet` from a `Class` that can be instantiated using this `ArrayParameterSet` and templates for the `ParameterSet` in each element of the array, the name and the comment that are displayed for the `ParameterSetContainer`s enclosing the `ParameterSet`s.
`ExpandableParameterSet(ParameterSet[] templateAndContent, String nameTemplate, String commentTemplate)` Creates a new `ExpandableParameterSet` from a `ParameterSet` -array.
`ExpandableParameterSet(ParameterSet template, String nameTemplate, String commentTemplate)` Creates a new `ExpandableParameterSet` from a `Class` that can be instantiated using this `ExpandableParameterSet` and templates for the `ParameterSet` in each element of the array, the name and the comment that are displayed for the `ParameterSetContainer`s enclosing the `ParameterSet`s.
`ExpandableParameterSet(ParameterSet template, String nameTemplate, String commentTemplate, int initCount)` Creates a new `ExpandableParameterSet` from a `Class` that can be instantiated using this `ExpandableParameterSet` and templates for the `ParameterSet` in each element of the array, the name and the comment that are displayed for the `ParameterSetContainer`s enclosing the `ParameterSet`s.
`MultiSelectionParameter(String name, String comment, boolean required, ParameterSet... values)` Creates a new `MultiSelectionParameter` from an array of `ParameterSet`s.
`ParameterSetContainer(ParameterSet p)` Creates an new `ParameterSetContainer` out of a `ParameterSet`.
`ParameterSetContainer(String name, String comment, ParameterSet content)` Creates an new `ParameterSetContainer` out of a `ParameterSet`.
`ParameterSetTagger(String[] tags, ParameterSet... sets)` The constructor creates an new instance by collecting and tagging all parameters of the `ParameterSet`s.
`SelectionParameter(String name, String comment, boolean required, ParameterSet... values)` Constructor for a `SelectionParameter` from an array of `ParameterSet`s.

Constructor parameters in de.jstacs.parameters with type arguments of type ParameterSet
`ParameterSetContainer(Class<? extends ParameterSet> contentClazz)` Creates an new `ParameterSetContainer` out of the class of a `ParameterSet`.
`ParameterSetContainer(String name, String comment, Class<? extends ParameterSet> contentClazz)` Creates an new `ParameterSetContainer` out of the class of a `ParameterSet`.

Uses of ParameterSet in de.jstacs.sampling

Subclasses of ParameterSet in de.jstacs.sampling
`class`	`AbstractBurnInTestParameterSet` Class for the parameters of a `AbstractBurnInTest`.
`class`	`VarianceRatioBurnInTestParameterSet` Class for the parameters of a `VarianceRatioBurnInTest`.

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

Subclasses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels
`class`	`BayesianNetworkDiffSMParameterSet` Class for the parameters of a `BayesianNetworkDiffSM`.

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

Subclasses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures
`static class`	`InhomogeneousMarkov.InhomogeneousMarkovParameterSet` Class for an `InstanceParameterSet` that defines the parameters of an `InhomogeneousMarkov` structure `Measure`.
`static class`	`Measure.MeasureParameterSet` This class is the super class of any `ParameterSet` that can be used to instantiate a `Measure`.

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

Subclasses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures
`static class`	`BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet` Class for the parameters of a `BTExplainingAwayResidual` structure `Measure`.
`static class`	`BTMutualInformation.BTMutualInformationParameterSet` Class for the parameters of a `BTMutualInformation` structure `Measure`.

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

Subclasses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures
`static class`	`PMMExplainingAwayResidual.PMMExplainingAwayResidualParameterSet` Class for the parameters of a `PMMExplainingAwayResidual` structure `Measure`.
`static class`	`PMMMutualInformation.PMMMutualInformationParameterSet` Class for the parameters of a `PMMMutualInformation` structure `Measure`.

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

Subclasses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete
`class`	`DGTrainSMParameterSet<T extends DiscreteGraphicalTrainSM>` The super `ParameterSet` for any parameter set of a `DiscreteGraphicalTrainSM`.

Uses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters

Subclasses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters
`class`	`HomMMParameterSet` This class implements a container for all parameters of a homogeneous Markov model.
`class`	`HomogeneousTrainSMParameterSet` This class implements a container for all parameters of any homogeneous model.

Uses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters

Subclasses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters
`class`	`BayesianNetworkTrainSMParameterSet` The `ParameterSet` for the class `BayesianNetworkTrainSM`.
`class`	`FSDAGModelForGibbsSamplingParameterSet` The class for the parameters of a `FSDAGModelForGibbsSampling`.
`class`	`FSDAGTrainSMParameterSet` The class for the parameters of a `FSDAGTrainSM` (fixed structure directed acyclic graphical model).
`class`	`IDGTrainSMParameterSet` This is the abstract container of parameters that is a root container for all inhomogeneous discrete graphical model parameter containers.

Uses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training

Subclasses of ParameterSet in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training
`class`	`BaumWelchParameterSet` This class implements an `HMMTrainingParameterSet` for the Baum-Welch training of an `AbstractHMM`.
`class`	`HMMTrainingParameterSet` This class implements an abstract `ParameterSet` that is used for the 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`	`MultiThreadedTrainingParameterSet` This class is the super class for any `MaxHMMTrainingParameterSet` that is used for a multi-threaded 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 ParameterSet in de.jstacs.utils

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

static



<T> SelectionParameter

SubclassFinder.getSelectionParameter(Class<? extends ParameterSet> clazz,
                      String startPackage,
                      String name,
                      String comment,
                      boolean required)

This method creates an SelectionParameter that contains InstanceParameterSet for each possible class.

Uses of ParameterSet in de.jstacs.utils.galaxy

Constructors in de.jstacs.utils.galaxy with parameters of type ParameterSet
`GalaxyAdaptor(ParameterSet parameters, String toolname, String description, String version, String command)` Creates a new `GalaxyAdaptor` from a given `ParameterSet` containing all parameters that are necessary for a program is shall be included in a Galaxy installation.