Uses of Class
de.jstacs.data.WrongAlphabetException

Packages that use WrongAlphabetException

de.jstacs.classifiers.assessment	This package allows to assess classifiers. It contains the class ClassifierAssessment that is used as a super-class of all implemented methodologies of an assessment to assess classifiers.
de.jstacs.data	Provides classes for the representation of data. The base classes to represent data are Alphabet and AlphabetContainer for representing alphabets, Sequence and its sub-classes to represent continuous and discrete sequences, and DataSet to represent data sets comprising a set of sequences.
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. The implementations of sequences currently include DiscreteSequences prepared for alphabets of different sizes, and ArbitrarySequences that may contain continuous values as well. As sub-package provides the facilities to annotate 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 DifferentiableStatisticalModels, which can compute the gradient with respect to their parameters for a given input Sequence.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture	Provides DifferentiableSequenceScores that are mixtures of other DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, 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, T assessPS, DataSet... s) Assesses the contained classifiers.
ListResult	ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, T assessPS, ProgressUpdater pU, DataSet[] s) Assesses the contained classifiers.
ListResult	ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, T assessPS, ProgressUpdater pU, DataSet[][]... s) Assesses the contained classifiers.
ListResult	ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, T assessPS, ProgressUpdater pU, DataSet[] s, double[][] weights) Assesses the contained classifiers.
protected void	ClassifierAssessment.prepareAssessment(boolean storeAll, 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 AbstractClassifiers.

ClassifierAssessment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and, in addition, classifiers that will be constructed using the given TrainableStatisticalModels.

ClassifierAssessment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new ClassifierAssessment from an array of AbstractClassifiers 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 TrainableStatisticalModels.

KFoldCrossValidation(AbstractClassifier... aCs)
Creates a new KFoldCrossValidation from a set of AbstractClassifiers.

KFoldCrossValidation(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a KFoldCrossValidation .

KFoldCrossValidation(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new KFoldCrossValidation from an array of AbstractClassifiers 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 TrainableStatisticalModels.

RepeatedHoldOutExperiment(AbstractClassifier... aCs)
Creates a new RepeatedHoldOutExperiment from a set of AbstractClassifiers.

RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a RepeatedHoldOutExperiment.

RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new RepeatedHoldOutExperiment from an array of AbstractClassifiers 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 TrainableStatisticalModels.

RepeatedSubSamplingExperiment(AbstractClassifier... aCs)
Creates a new RepeatedSubSamplingExperiment from a set of AbstractClassifiers.

RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a RepeatedSubSamplingExperiment.

RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new RepeatedSubSamplingExperiment from an array of AbstractClassifiers 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 TrainableStatisticalModels.

Sampled_RepeatedHoldOutExperiment(AbstractClassifier... aCs)
Creates a new Sampled_RepeatedHoldOutExperiment from a set of AbstractClassifiers.

Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a Sampled_RepeatedHoldOutExperiment.

Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new Sampled_RepeatedHoldOutExperiment from an array of AbstractClassifiers 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 TrainableStatisticalModels.

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 DataSets 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 DataSets 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, Collection<Sequence> seqs)
Creates a new DataSet from a Collection of Sequences and a given annotation.

DataSet(String annotation, Sequence... seqs)
Creates a new DataSet from an array of Sequences and a given annotation.
This constructor is specially designed for the method StatisticalModel.emitDataSet(int, int...)

DNADataSet(String fName)
Creates a new data set of DNA sequence from a FASTA file with file name fName.

DNADataSet(String fName, char ignore)
Creates a new data set of DNA sequence from a file with file name fName.

DNADataSet(String fName, char ignore, SequenceAnnotationParser parser)
Creates a new data set 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	DNAAlphabet.getCode(String symbol)
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 SparseSequences.
static DataSet	ArbitraryFloatSequence.getDataSet(AlphabetContainer con, AbstractStringExtractor... se) This method allows to create a DataSet containing ArbitraryFloatSequences.
static DataSet	SparseSequence.getDataSet(AlphabetContainer con, String filename) This method allows to create a DataSet containing SparseSequences using a file name.
static DataSet	ArbitraryFloatSequence.getDataSet(AlphabetContainer con, String filename) This method allows to create a DataSet containing ArbitraryFloatSequences using a file name.
static DataSet	SparseSequence.getDataSet(AlphabetContainer con, String filename, SequenceAnnotationParser parser) This method allows to create a DataSet containing SparseSequences using a file name.
static DataSet	ArbitraryFloatSequence.getDataSet(AlphabetContainer con, String filename, SequenceAnnotationParser parser) This method allows to create a DataSet containing ArbitraryFloatSequences 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 DiscreteAlphabetMappings.

MultiDimensionalArbitrarySequence(SequenceAnnotation[] seqAn, ArbitrarySequence... sequence)
This constructor creates an MultiDimensionalDiscreteSequence from a set of individual Sequences.

MultiDimensionalDiscreteSequence(SequenceAnnotation[] seqAn, SimpleDiscreteSequence... sequence)
This constructor creates an MultiDimensionalDiscreteSequence from a set of individual Sequences.

MultiDimensionalSequence(SequenceAnnotation[] seqAn, Sequence... sequence)
This constructor creates an MultiDimensionalSequence from a set of individual Sequences.

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 DifferentiableSequenceScores.

IndependentProductDiffSS(boolean plugIn, DifferentiableSequenceScore[] functions, int[] length)
This constructor creates an instance of an IndependentProductDiffSS from given series of independent DifferentiableSequenceScores 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 DifferentiableStatisticalModels.

IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel[] functions, int[] length)
This constructor creates an instance of an IndependentProductDiffSM from given series of independent DifferentiableStatisticalModels 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) constr with the weighted absolute frequency of the DataSet data and computes the frequencies will not be computed.

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 Sequences 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.