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## Uses of Class
de.jstacs.data.WrongAlphabetException

Packages that use WrongAlphabetException
de.jstacs.classifiers.assessment | This package allows to assess classifiers.
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.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.sequenceScores.differentiable |
de.jstacs.sequenceScores.statisticalModels.differentiable | Provides all `DifferentiableStatisticalModel`s, which can compute the gradient with respect to their parameters for a given input `Sequence`.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture | Provides `DifferentiableSequenceScore`s that are mixtures of other `DifferentiableSequenceScore`s.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif |
de.jstacs.sequenceScores.statisticalModels.trainable | Provides all `TrainableStatisticalModel`s, which can be learned from a single `DataSet`.
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.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm | The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on `AbstractHMM`
de.jstacs.sequenceScores.statisticalModels.trainable.mixture | This package is the super package for any mixture model.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |

Uses of WrongAlphabetException in de.jstacs.classifiers.assessment

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

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

Uses of WrongAlphabetException in de.jstacs.data

Methods in de.jstacs.data that throw WrongAlphabetException
`static DataSet` | `DataSet.diff(DataSet data, DataSet... samples)` This method computes the difference between the `DataSet` `data` and the `DataSet`s `samples`.
`double` | `AlphabetContainer.getCode(int pos, String sym)` Returns the encoded symbol for `sym` of the `Alphabet` of position `pos` of this `AlphabetContainer`.
`Sequence` | `DinucleotideProperty.getPropertyAsSequence(Sequence original)` Computes this dinucleotide property for all overlapping twomers in `original` and returns the result as a `Sequence` of length `original.getLength()-1`

Constructors in de.jstacs.data that throw WrongAlphabetException
`DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort, DataSet... data)` Creates a new `DataSet.WeightedDataSetFactory` on the given `DataSet`(s) with `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
`DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort, DataSet[] data, double[][] weights, int length)` Creates a new `DataSet.WeightedDataSetFactory` on the given array of `DataSet`s and an array of `weights` with a given `length` and `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
`DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort, DataSet data, double[] weights)` Creates a new `DataSet.WeightedDataSetFactory` on the given `DataSet` and an array of `weights` with `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
`DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort, DataSet data, double[] weights, int length)` Creates a new `DataSet.WeightedDataSetFactory` on the given `DataSet` and an array of `weights` with a given `length` and `DataSet.WeightedDataSetFactory.SortOperation` `sort`.
`DataSet(AlphabetContainer abc, AbstractStringExtractor se)` Creates a new `DataSet` from a `StringExtractor` using the given `AlphabetContainer`.
`DataSet(AlphabetContainer abc, AbstractStringExtractor se, int subsequenceLength)` Creates a new `DataSet` from a `StringExtractor` using the given `AlphabetContainer` and all overlapping windows of length `subsequenceLength`.
`DataSet(AlphabetContainer abc, AbstractStringExtractor se, String delim)` Creates a new `DataSet` from a `StringExtractor` using the given `AlphabetContainer` and a delimiter `delim`.
`DataSet(AlphabetContainer abc, AbstractStringExtractor se, String delim, int subsequenceLength)` Creates a new `DataSet` from a `StringExtractor` using the given `AlphabetContainer`, the given delimiter `delim` and all overlapping windows of length `subsequenceLength`.
`DataSet(String annotation, Sequence... seqs)` Creates a new `DataSet` from an array of `Sequence`s and a given annotation.
`DNADataSet(String fName)` Creates a new sample of DNA sequence from a FASTA file with file name `fName`.
`DNADataSet(String fName, char ignore)` Creates a new sample of DNA sequence from a file with file name `fName`.
`DNADataSet(String fName, char ignore, SequenceAnnotationParser parser)` Creates a new sample of DNA sequence from a file with file name `fName` using the given `parser`.

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.dataSetToSequenceIterator(DataSet sample, boolean flat)` Creates a `SequenceIterator` from the `DataSet` `sample` preserving as much annotation as possible.

Uses of WrongAlphabetException in de.jstacs.data.sequences

Methods in de.jstacs.data.sequences that throw WrongAlphabetException
`static Sequence` | `Sequence.create(AlphabetContainer con, SequenceAnnotation[] annotation, String sequence, String delim)` Creates a `Sequence` from a `String` based on the given `AlphabetContainer` using the given delimiter `delim` and some `annotation` for the `Sequence`.
`static Sequence` | `Sequence.create(AlphabetContainer con, String sequence)` Creates a `Sequence` 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` from a `String` based on the given `AlphabetContainer` using the given delimiter `delim`.
`static DataSet` | `SparseSequence.getDataSet(AlphabetContainer con, AbstractStringExtractor... se)` This method allows to create a `DataSet` containing `SparseSequence`s.
`static DataSet` | `ArbitraryFloatSequence.getDataSet(AlphabetContainer con, AbstractStringExtractor... se)` This method allows to create a `DataSet` containing `ArbitraryFloatSequence`s.
`static DataSet` | `SparseSequence.getDataSet(AlphabetContainer con, String filename)` This method allows to create a `DataSet` containing `SparseSequence`s using a file name.
`static DataSet` | `ArbitraryFloatSequence.getDataSet(AlphabetContainer con, String filename)` This method allows to create a `DataSet` containing `ArbitraryFloatSequence`s using a file name.
`static DataSet` | `SparseSequence.getDataSet(AlphabetContainer con, String filename, SequenceAnnotationParser parser)` This method allows to create a `DataSet` containing `SparseSequence`s using a file name.
`static DataSet` | `ArbitraryFloatSequence.getDataSet(AlphabetContainer con, String filename, SequenceAnnotationParser parser)` This method allows to create a `DataSet` containing `ArbitraryFloatSequence`s using a file name.
`int` | `Sequence.getHammingDistance(Sequence seq)` This method returns the Hamming distance between the current `Sequence` and `seq`.
`protected abstract MultiDimensionalSequence<T>` | `MultiDimensionalSequence.getInstance(SequenceAnnotation[] seqAn, Sequence... seqs)`
`protected MultiDimensionalDiscreteSequence` | `MultiDimensionalDiscreteSequence.getInstance(SequenceAnnotation[] seqAn, Sequence... seqs)`
`protected MultiDimensionalArbitrarySequence` | `MultiDimensionalArbitrarySequence.getInstance(SequenceAnnotation[] seqAn, Sequence... seqs)`
`boolean` | `Sequence.matches(int maxHammingDistance, Sequence shortSequence)` This method allows to answer the question whether there is a similar pattern find a match with a given maximal number of mismatches.

Constructors in de.jstacs.data.sequences that throw WrongAlphabetException
`ArbitraryFloatSequence(AlphabetContainer alphabetContainer, float[] content)` Creates a new `ArbitraryFloatSequence` from an array of `float`-encoded alphabet symbols.
`ArbitraryFloatSequence(AlphabetContainer alphabetContainer, SequenceAnnotation[] annotation, String sequence, String delim)` Creates a new `ArbitraryFloatSequence` from a `String` representation using the delimiter `delim`.
`ArbitraryFloatSequence(AlphabetContainer alphabetContainer, SequenceAnnotation[] annotation, SymbolExtractor extractor)` Creates a new `ArbitraryFloatSequence` from a `SymbolExtractor`.
`ArbitraryFloatSequence(AlphabetContainer alphabetContainer, String sequence)` Creates a new `ArbitraryFloatSequence` from a `String` representation using the default delimiter.
`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.
`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.
`MappedDiscreteSequence(AlphabetContainer originalAlphabetContainer, SequenceAnnotation[] seqAn, DiscreteAlphabetMapping... transformation)` This method allows to create an empty `MappedDiscreteSequence`.
`MappedDiscreteSequence(SimpleDiscreteSequence original, DiscreteAlphabetMapping... transformation)` This method allows to create a `MappedDiscreteSequence` from a given `Sequence` and some given `DiscreteAlphabetMapping`s.
`MultiDimensionalArbitrarySequence(SequenceAnnotation[] seqAn, ArbitrarySequence... sequence)` This constructor creates an `MultiDimensionalDiscreteSequence` from a set of individual `Sequence`s.
`MultiDimensionalDiscreteSequence(SequenceAnnotation[] seqAn, SimpleDiscreteSequence... sequence)` This constructor creates an `MultiDimensionalDiscreteSequence` from a set of individual `Sequence`s.
`MultiDimensionalSequence(SequenceAnnotation[] seqAn, Sequence... sequence)` This constructor creates an `MultiDimensionalSequence` from a set of individual `Sequence`s.
`PermutedSequence(Sequence<T> seq)` Creates a new `PermutedSequence` by shuffling the symbols of a given `Sequence`.
`PermutedSequence(Sequence<T> seq, int[] permutation)` Creates a new `PermutedSequence` for a given permutation
`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.
`SimpleDiscreteSequence(AlphabetContainer container, SequenceAnnotation[] annotation)` This constructor creates a new `SimpleDiscreteSequence` with the `AlphabetContainer` `container` and the annotation `annotation` but without the content.
`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.motifDiscovery

Methods in de.jstacs.motifDiscovery that throw WrongAlphabetException
`static Pair<Sequence,BitSet[]>[]` | `KMereStatistic.getKmereSequenceStatistic(boolean bothStrands, int maxMismatch, HashSet<Sequence> filter, DataSet... data)` This method enables the user to get a statistic for a set of `k`-mers.
`static Hashtable<Sequence,BitSet[]>` | `KMereStatistic.getKmereSequenceStatistic(int k, boolean bothStrands, int addIndex, DataSet... data)` This method enables the user to get a statistic over all `k`-mers in the sequences.
`static Hashtable<Sequence,BitSet[]>` | `KMereStatistic.merge(Hashtable<Sequence,BitSet[]> statistic, int maximalMissmatch, boolean bothStrands)` This method allows to merge the statistics of k-mers by allowing mismatches.

Uses of WrongAlphabetException in de.jstacs.sequenceScores.differentiable

Constructors in de.jstacs.sequenceScores.differentiable that throw WrongAlphabetException
`IndependentProductDiffSS(boolean plugIn, DifferentiableSequenceScore... functions)` This constructor creates an instance of an `IndependentProductDiffSS` from a given series of independent `DifferentiableSequenceScore`s.
`IndependentProductDiffSS(boolean plugIn, DifferentiableSequenceScore[] functions, int[] length)` This constructor creates an instance of an `IndependentProductDiffSS` from given series of independent `DifferentiableSequenceScore`s and lengths.
`IndependentProductDiffSS(boolean plugIn, DifferentiableSequenceScore[] functions, int[] index, int[] length, boolean[] reverse)` This is the main constructor.

Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.differentiable

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable that throw WrongAlphabetException
`static StrandDiffSM` | `DifferentiableStatisticalModelFactory.createStrandModel(DifferentiableStatisticalModel model)` This method allows to create a `StrandDiffSM` that allows to score binding sites on both strand of DNA.

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable that throw WrongAlphabetException
`IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel... functions)` This constructor creates an instance of an `IndependentProductDiffSM` from a given series of independent `DifferentiableStatisticalModel`s.
`IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel[] functions, int[] length)` This constructor creates an instance of an `IndependentProductDiffSM` from given series of independent `DifferentiableStatisticalModel`s and lengths.
`IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel[] functions, int[] index, int[] length, boolean[] reverse)` This is the main constructor.
`MappingDiffSM(AlphabetContainer originalAlphabetContainer, DifferentiableStatisticalModel nsf, DiscreteAlphabetMapping... mapping)` The main constructor creating a `MappingDiffSM`.

Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that throw WrongAlphabetException
`StrandDiffSM(DifferentiableStatisticalModel function, double forwardPartOfESS, int starts, boolean plugIn, StrandDiffSM.InitMethod initMethod)` This constructor creates a StrandDiffSM that optimizes the usage of each strand.
`StrandDiffSM(DifferentiableStatisticalModel function, int starts, boolean plugIn, StrandDiffSM.InitMethod initMethod, double forward)` This constructor creates a StrandDiffSM that has a fixed frequency for the strand usage.

Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that throw WrongAlphabetException
`MixtureDurationDiffSM(int starts, DurationDiffSM... function)` The main constructor of a `MixtureDurationDiffSM`.

Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable

Methods in de.jstacs.sequenceScores.statisticalModels.trainable that throw WrongAlphabetException
`double` | `UniformTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)`

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable that throw WrongAlphabetException
`CompositeTrainSM(AlphabetContainer alphabets, int[] assignment, TrainableStatisticalModel... models)` Creates a new `CompositeTrainSM`.

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

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

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that throw WrongAlphabetException
`DataSet` | `HomogeneousTrainSM.emitDataSet(int no, int... length)` Creates a `DataSet` of a given number of `Sequence`s from a trained homogeneous model.
`protected abstract Sequence` | `HomogeneousTrainSM.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.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that throw WrongAlphabetException
`SymmetricTensor` | `StructureLearner.getTensor(DataSet 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.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

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

Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that throw WrongAlphabetException
`AbstractHMM(HMMTrainingParameterSet trainingParameterSet, String[] name, int[] emissionIdx, boolean[] forward, Emission[] emission)` This is the main constructor for an HMM.

Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that throw WrongAlphabetException
`SamplingPhyloHMM(SamplingHMMTrainingParameterSet trainingParameterSet, String[] name, int[] emissionIdx, boolean[] forward, PhyloDiscreteEmission[] emission, TransitionElement... te)` This is the main constructor for a hidden markov model with phylogenetic emission(s) This model can be trained using a metropolis hastings algorithm

Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that throw WrongAlphabetException
`AbstractMixtureTrainSM(int length, TrainableStatisticalModel[] models, boolean[] optimizeModel, int dimension, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `AbstractMixtureTrainSM`.
`MixtureTrainSM(int length, TrainableStatisticalModel[] models, double[] weights, int starts, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)` Creates an instance using EM and fixed component probabilities.
`MixtureTrainSM(int length, TrainableStatisticalModel[] models, double[] weights, int starts, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and fixed component probabilities.
`MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `MixtureTrainSM`.
`MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, double[] componentHyperParams, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)` Creates an instance using EM and estimating the component probabilities.
`MixtureTrainSM(int length, TrainableStatisticalModel[] models, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and sampling the component probabilities.
`StrandTrainSM(TrainableStatisticalModel model, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double forwardStrandProb, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `StrandTrainSM`.
`StrandTrainSM(TrainableStatisticalModel model, int starts, double[] componentHyperParams, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)` Creates an instance using EM and estimating the component probabilities.
`StrandTrainSM(TrainableStatisticalModel model, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and sampling the component probabilities.
`StrandTrainSM(TrainableStatisticalModel model, int starts, double forwardStrandProb, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization)` Creates an instance using EM and fixed component probabilities.
`StrandTrainSM(TrainableStatisticalModel 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.sequenceScores.statisticalModels.trainable.mixture.motif

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

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