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## Uses of Class
de.jstacs.WrongAlphabetException

Packages that use WrongAlphabetException
de.jstacs.classifier.assessment | This package allows to assess classifiers.
de.jstacs.classifier.scoringFunctionBased | Provides the classes for `Classifier`s that are based on `ScoringFunction`s.
de.jstacs.classifier.scoringFunctionBased.cll | Provides the implementation of the log conditional likelihood as an `OptimizableFunction` and a classifier that uses log conditional likelihood or supervised posterior to learn the parameters of a set of `ScoringFunctions`
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.bioJava | Provides an adapter between the representation of data in BioJava and the representation used in Jstacs.
de.jstacs.data.sequences | Provides classes for representing sequences.
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.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.models.discrete.inhomogeneous.shared |
de.jstacs.models.mixture | This package is the super package for any mixture model.
de.jstacs.models.mixture.motif |
de.jstacs.scoringFunctions.mix | Provides `ScoringFunction`s that are mixtures of other `ScoringFunction`s.

Uses of WrongAlphabetException in de.jstacs.classifier.assessment

Methods in de.jstacs.classifier.assessment that throw WrongAlphabetException
`ListResult` | `ClassifierAssessment.assess(MeasureParameters mp, ClassifierAssessmentAssessParameterSet assessPS, ProgressUpdater pU, Sample... s)` Assesses the contained classifiers.
`ListResult` | `ClassifierAssessment.assess(MeasureParameters mp, ClassifierAssessmentAssessParameterSet assessPS, ProgressUpdater pU, Sample[][]... s)` Assesses the contained classifiers.
`ListResult` | `ClassifierAssessment.assess(MeasureParameters mp, ClassifierAssessmentAssessParameterSet assessPS, Sample... s)` Assesses the contained classifiers.
`protected void` | `ClassifierAssessment.prepareAssessment(Sample... s)` Prepares an assessment.

Constructors in de.jstacs.classifier.assessment that throw WrongAlphabetException
`ClassifierAssessment(AbstractClassifier... aCs)` Creates a new `ClassifierAssessment` from a set of `AbstractClassifier`s.
`ClassifierAssessment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, Model[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and, in addition, classifiers that will be constructed using the given `AbstractModel`s.
`ClassifierAssessment(AbstractClassifier[] aCs, Model[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `ClassifierAssessment` from an array of `AbstractClassifier`s and a two-dimensional array of `Model` s, which are combined to additional classifiers.
`ClassifierAssessment(boolean buildClassifiersByCrossProduct, Model[]... aMs)` Creates a new `ClassifierAssessment` from a set of `Model`s.
`KFoldCrossValidation(AbstractClassifier... aCs)` Creates a new `KFoldCrossValidation` from a set of `AbstractClassifier`s.
`KFoldCrossValidation(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, Model[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `AbstractModel`s by a `KFoldCrossValidation` .
`KFoldCrossValidation(AbstractClassifier[] aCs, Model[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `KFoldCrossValidation` from an array of `AbstractClassifier`s and a two-dimensional array of `Model` s, which are combined to additional classifiers.
`KFoldCrossValidation(boolean buildClassifiersByCrossProduct, Model[]... aMs)` Creates a new `KFoldCrossValidation` from a set of `Model`s.
`RepeatedHoldOutExperiment(AbstractClassifier... aCs)` Creates a new `RepeatedHoldOutExperiment` from a set of `AbstractClassifier`s.
`RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, Model[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `AbstractModel`s by a `RepeatedHoldOutExperiment`.
`RepeatedHoldOutExperiment(AbstractClassifier[] aCs, Model[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `RepeatedHoldOutExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `Model` s, which are combined to additional classifiers.
`RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct, Model[]... aMs)` Creates a new `RepeatedHoldOutExperiment` from a set of `Model`s.
`RepeatedSubSamplingExperiment(AbstractClassifier... aCs)` Creates a new `RepeatedSubSamplingExperiment` from a set of `AbstractClassifier`s.
`RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, Model[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `AbstractModel`s by a `RepeatedSubSamplingExperiment`.
`RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, Model[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `RepeatedSubSamplingExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `Model` s, which are combined to additional classifiers.
`RepeatedSubSamplingExperiment(boolean buildClassifiersByCrossProduct, Model[]... aMs)` Creates a new `RepeatedSubSamplingExperiment` from a set of `Model`s.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier... aCs)` Creates a new `Sampled_RepeatedHoldOutExperiment` from a set of `AbstractClassifier`s.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, Model[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `AbstractModel`s by a `Sampled_RepeatedHoldOutExperiment`.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, Model[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `Sampled_RepeatedHoldOutExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `Model` s, which are combined to additional classifiers.
`Sampled_RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct, Model[]... aMs)` Creates a new `Sampled_RepeatedHoldOutExperiment` from a set of `Model`s.

Uses of WrongAlphabetException in de.jstacs.classifier.scoringFunctionBased

Constructors in de.jstacs.classifier.scoringFunctionBased that throw WrongAlphabetException
`AbstractOptimizableFunction(Sample[] data, double[][] weights, boolean norm, boolean freeParams)` The constructor creates an instance using the given weighted data.

Uses of WrongAlphabetException in de.jstacs.classifier.scoringFunctionBased.cll

Constructors in de.jstacs.classifier.scoringFunctionBased.cll that throw WrongAlphabetException
`NormConditionalLogLikelihood(ScoringFunction[] score, Sample[] data, double[][] weights, boolean norm, boolean freeParams)` The constructor creates an instance of the `NormConditionalLogLikelihood`.
`NormConditionalLogLikelihood(ScoringFunction[] score, Sample[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)` The constructor creates an instance of the `NormConditionalLogLikelihood` using the given prior.

Uses of WrongAlphabetException in de.jstacs.data

Methods in de.jstacs.data that throw WrongAlphabetException
`static Sequence` | `Sequence.create(AlphabetContainer con, SequenceAnnotation[] annotation, String sequence, String delim)` Creates a `Sequence.CompositeSequence` from a `String` based on the given `AlphabetContainer` using the given delimiter `delim` and some `annotation` for the `Sequence.CompositeSequence`.
`static Sequence` | `Sequence.create(AlphabetContainer con, String sequence)` Creates a `Sequence.CompositeSequence` from a `String` based on the given `AlphabetContainer` using the standard delimiter for this `AlphabetContainer`.
`static Sequence` | `Sequence.create(AlphabetContainer con, String sequence, String delim)` Creates a `Sequence.CompositeSequence` from a `String` based on the given `AlphabetContainer` using the given delimiter `delim`.
`double` | `AlphabetContainer.getCode(int pos, String sym)` Returns the encoded symbol for `sym` of the `Alphabet` of position `pos` of this `AlphabetContainer`.

Constructors in de.jstacs.data that throw WrongAlphabetException
`Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample... data)` Creates a new `Sample.WeightedSampleFactory` on the given `Sample`(s) with `Sample.WeightedSampleFactory.SortOperation` `sort`.
`Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample[] data, double[][] weights, int length)` Creates a new `Sample.WeightedSampleFactory` on the given array of `Sample`s and an array of `weights` with a given `length` and `Sample.WeightedSampleFactory.SortOperation` `sort`.
`Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample data, double[] weights)` Creates a new `Sample.WeightedSampleFactory` on the given `Sample` and an array of `weights` with `Sample.WeightedSampleFactory.SortOperation` `sort`.
`Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort, Sample data, double[] weights, int length)` Creates a new `Sample.WeightedSampleFactory` on the given `Sample` and an array of `weights` with a given `length` and `Sample.WeightedSampleFactory.SortOperation` `sort`.
`Sample(AlphabetContainer abc, AbstractStringExtractor se)` Creates a new `Sample` from a `StringExtractor` using the given `AlphabetContainer`.
`Sample(AlphabetContainer abc, AbstractStringExtractor se, int subsequenceLength)` Creates a new `Sample` from a `StringExtractor` using the given `AlphabetContainer` and all overlapping windows of length `subsequenceLength`.
`Sample(AlphabetContainer abc, AbstractStringExtractor se, String delim)` Creates a new `Sample` from a `StringExtractor` using the given `AlphabetContainer` and a delimiter `delim`.
`Sample(AlphabetContainer abc, AbstractStringExtractor se, String delim, int subsequenceLength)` Creates a new `Sample` from a `StringExtractor` using the given `AlphabetContainer`, the given delimiter `delim` and all overlapping windows of length `subsequenceLength`.

Uses of WrongAlphabetException in de.jstacs.data.alphabets

Methods in de.jstacs.data.alphabets that throw WrongAlphabetException
`int` | `DiscreteAlphabet.getCode(String symbol)` Returns the code of a given symbol.

Uses of WrongAlphabetException in de.jstacs.data.bioJava

Methods in de.jstacs.data.bioJava that throw WrongAlphabetException
`static SequenceIterator` | `BioJavaAdapter.sampleToSequenceIterator(Sample sample, boolean flat)` Creates a `SequenceIterator` from the `Sample` `sample` preserving as much annotation as possible.

Uses of WrongAlphabetException in de.jstacs.data.sequences

Constructors in de.jstacs.data.sequences that throw WrongAlphabetException
`ArbitrarySequence(AlphabetContainer alphabetContainer, double[] content)` Creates a new `ArbitrarySequence` from an array of `double`-encoded alphabet symbols.
`ArbitrarySequence(AlphabetContainer alphabetContainer, SequenceAnnotation[] annotation, String sequence, String delim)` Creates a new `ArbitrarySequence` from a `String` representation using the delimiter `delim`.
`ArbitrarySequence(AlphabetContainer alphabetContainer, SequenceAnnotation[] annotation, SymbolExtractor extractor)` Creates a new `ArbitrarySequence` from a `SymbolExtractor`.
`ArbitrarySequence(AlphabetContainer alphabetContainer, String sequence)` Creates a new `ArbitrarySequence` from a `String` representation using the default delimiter.
`ByteSequence(AlphabetContainer alphabetContainer, byte[] content)` Creates a new `ByteSequence` from an array of `byte`- encoded alphabet symbols.
`ByteSequence(AlphabetContainer alphabetContainer, SequenceAnnotation[] annotation, String sequence, String delim)` Creates a new `ByteSequence` from a `String` representation using the delimiter `delim`.
`ByteSequence(AlphabetContainer alphabetContainer, SequenceAnnotation[] annotation, SymbolExtractor extractor)` Creates a new `ByteSequence` from a `SymbolExtractor`.
`ByteSequence(AlphabetContainer alphabetContainer, String sequence)` Creates a new `ByteSequence` from a `String` representation using the default delimiter.
`DiscreteSequence(AlphabetContainer container, SequenceAnnotation[] annotation)` This constructor creates a new `DiscreteSequence` with the `AlphabetContainer` `container` and the annotation `annotation` but without the content.
`IntSequence(AlphabetContainer alphabetContainer, int... content)` Creates a new `IntSequence` from an array of `int`- encoded alphabet symbols.
`IntSequence(AlphabetContainer alphabetContainer, int[] content, int start, int length)` Creates a new `IntSequence` from a part of the array of `int`- encoded alphabet symbols.
`IntSequence(AlphabetContainer alphabetContainer, SequenceAnnotation[] annotation, String sequence, String delim)` Creates a new `IntSequence` from a `String` representation using the delimiter `delim`.
`IntSequence(AlphabetContainer alphabetContainer, SequenceAnnotation[] annotation, SymbolExtractor extractor)` Creates a new `IntSequence` from a `SymbolExtractor`.
`IntSequence(AlphabetContainer alphabetContainer, String sequence)` Creates a new `IntSequence` from a `String` representation using the default delimiter.
`PermutedSequence(Sequence seq)` Creates a new `PermutedSequence` by shuffling the symbols of a given `Sequence`.
`ShortSequence(AlphabetContainer alphabetContainer, SequenceAnnotation[] annotation, String sequence, String delim)` Creates a new `ShortSequence` from a `String` representation using the delimiter `delim`.
`ShortSequence(AlphabetContainer alphabetContainer, SequenceAnnotation[] annotation, SymbolExtractor extractor)` Creates a new `ShortSequence` from a `SymbolExtractor`.
`ShortSequence(AlphabetContainer alphabetContainer, short[] content)` Creates a new `ShortSequence` from an array of `short`- encoded alphabet symbols.
`ShortSequence(AlphabetContainer alphabetContainer, String sequence)` Creates a new `ShortSequence` from a `String` representation using the default delimiter.
`SparseSequence(AlphabetContainer alphCon, String seq)` Creates a new `SparseSequence` from a `String` representation.
`SparseSequence(AlphabetContainer alphCon, SymbolExtractor se)` Creates a new `SparseSequence` from a `SymbolExtractor`.

Uses of WrongAlphabetException in de.jstacs.models

Methods in de.jstacs.models that throw WrongAlphabetException
`double` | `UniformModel.getProbFor(Sequence sequence, int startpos, int endpos)`

Constructors in de.jstacs.models that throw WrongAlphabetException
`CompositeModel(AlphabetContainer alphabets, int[] assignment, Model... models)` Creates a new `CompositeModel`.

Uses of WrongAlphabetException in de.jstacs.models.discrete

Methods in de.jstacs.models.discrete that throw WrongAlphabetException
`static double` | `ConstraintManager.countInhomogeneous(AlphabetContainer alphabets, int length, Sample data, double[] weights, boolean reset, Constraint... constr)` Fills the (inhomogeneous) `Constraint` `constr` with the weighted absolute frequencies of the `Sample` `data`.

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

Methods in de.jstacs.models.discrete.homogeneous that throw WrongAlphabetException
`Sample` | `HomogeneousModel.emitSample(int no, int... length)` Creates a `Sample` of a given number of `Sequence`s from a trained homogeneous model.
`protected abstract Sequence` | `HomogeneousModel.getRandomSequence(Random r, int length)` This method creates a random `Sequence` from a trained homogeneous model.
`protected Sequence` | `HomogeneousMM.getRandomSequence(Random r, int length)`

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

Methods in de.jstacs.models.discrete.inhomogeneous that throw WrongAlphabetException
`SymmetricTensor` | `StructureLearner.getTensor(Sample data, double[] weights, byte order, StructureLearner.LearningType method)` This method can be used to compute a `Tensor` that can be used to determine the optimal structure.

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

Constructors in de.jstacs.models.discrete.inhomogeneous.shared that throw WrongAlphabetException
`SharedStructureMixture(FSDAGModel[] m, StructureLearner.ModelType model, byte order, int starts, boolean estimateComponentProbs, double[] weights, double alpha, double epsilon)` Creates a new `SharedStructureMixture` instance with all relevant values.
`SharedStructureMixture(FSDAGModel[] m, StructureLearner.ModelType model, byte order, int starts, double[] weights, double alpha, double epsilon)` Creates a new `SharedStructureMixture` instance with fixed component weights.
`SharedStructureMixture(FSDAGModel[] m, StructureLearner.ModelType model, byte order, int starts, double alpha, double epsilon)` Creates a new `SharedStructureMixture` instance which estimates the component probabilities/weights.

Uses of WrongAlphabetException in de.jstacs.models.mixture

Constructors in de.jstacs.models.mixture that throw WrongAlphabetException
`AbstractMixtureModel(int length, Model[] models, boolean[] optimizeModel, int dimension, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureModel.Algorithm algorithm, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `AbstractMixtureModel`.
`MixtureModel(int length, Model[] models, double[] weights, int starts, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization)` Creates an instance using EM and fixed component probabilities.
`MixtureModel(int length, Model[] models, double[] weights, int starts, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and fixed component probabilities.
`MixtureModel(int length, Model[] models, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureModel.Algorithm algorithm, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `MixtureModel`.
`MixtureModel(int length, Model[] models, int starts, double[] componentHyperParams, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization)` Creates an instance using EM and estimating the component probabilities.
`MixtureModel(int length, Model[] models, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and sampling the component probabilities.
`StrandModel(Model model, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double forwardStrandProb, AbstractMixtureModel.Algorithm algorithm, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `StrandModel`.
`StrandModel(Model model, int starts, double[] componentHyperParams, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization)` Creates an instance using EM and estimating the component probabilities.
`StrandModel(Model model, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and sampling the component probabilities.
`StrandModel(Model model, int starts, double forwardStrandProb, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization)` Creates an instance using EM and fixed component probabilities.
`StrandModel(Model model, int starts, double forwardStrandProb, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and fixed component probabilities.

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

Constructors in de.jstacs.models.mixture.motif that throw WrongAlphabetException
`HiddenMotifMixture(Model[] models, boolean[] optimzeArray, int components, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, PositionPrior posPrior, AbstractMixtureModel.Algorithm algorithm, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `HiddenMotifMixture`.
`SingleHiddenMotifMixture(Model motif, Model bg, boolean trainOnlyMotifModel, int starts, double[] componentHyperParams, double[] weights, PositionPrior posPrior, AbstractMixtureModel.Algorithm algorithm, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `SingleHiddenMotifMixture`.
`SingleHiddenMotifMixture(Model motif, Model bg, boolean trainOnlyMotifModel, int starts, double[] componentHyperParams, PositionPrior posPrior, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization)` Creates a new `SingleHiddenMotifMixture` using EM and estimating the probability for finding a motif.
`SingleHiddenMotifMixture(Model motif, Model bg, boolean trainOnlyMotifModel, int starts, double motifProb, PositionPrior posPrior, double alpha, double eps, AbstractMixtureModel.Parameterization parametrization)` Creates a new `SingleHiddenMotifMixture` using EM and fixed probability for finding a motif.

Uses of WrongAlphabetException in de.jstacs.scoringFunctions.mix

Constructors in de.jstacs.scoringFunctions.mix that throw WrongAlphabetException
`StrandScoringFunction(NormalizableScoringFunction function, double forwardPartOfESS, int starts, boolean plugIn, StrandScoringFunction.InitMethod initMethod)` This constructor creates a StrandScoringFunction that optimizes the usage of each strand.
`StrandScoringFunction(NormalizableScoringFunction function, int starts, boolean plugIn, StrandScoringFunction.InitMethod initMethod, double forward)` This constructor creates a StrandScoringFunction that has a fixed frequency for the strand usage.

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