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## Uses of Class
de.jstacs.data.sequences.Sequence

Packages that use Sequence
de.jstacs.algorithms.alignment | Provides classes for alignments
de.jstacs.algorithms.alignment.cost | Provides classes for cost functions used in alignments
de.jstacs.classifiers | This package provides the framework for any classifier.
de.jstacs.classifiers.differentiableSequenceScoreBased | Provides the classes for `Classifier`s that are based on `SequenceScore`s.
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.trainSMBased | Provides the classes for `Classifier`s that are based on `TrainableStatisticalModel`s
de.jstacs.data | Provides classes for the representation of data.
de.jstacs.data.sequences | Provides classes for representing sequences.
de.jstacs.data.sequences.annotation | Provides the facilities to annotate `Sequence`s using a number of pre-defined annotation types, or additional implementations of the `SequenceAnnotation` class
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.sequenceScores | Provides all `SequenceScore`s, which can be used to score a `Sequence`, typically using some model assumptions.
de.jstacs.sequenceScores.differentiable |
de.jstacs.sequenceScores.differentiable.logistic |
de.jstacs.sequenceScores.statisticalModels | Provides all `StatisticalModel`s, which can compute a proper (i.e., normalized) likelihood over the input space of sequences.
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.directedGraphicalModels | Provides `DifferentiableStatisticalModel`s that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous | Provides `DifferentiableStatisticalModel`s that are homogeneous, i.e. model probabilities or scores independent of the position within a 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.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.hmm.states | The package provides all interfaces and classes for states used in hidden Markov models.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions | The package provides all interfaces and classes for transitions used in hidden Markov models.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture | This package is the super package for any mixture model.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
de.jstacs.utils | This package contains a bundle of useful classes and interfaces like ...

Uses of Sequence in de.jstacs.algorithms.alignment

Methods in de.jstacs.algorithms.alignment with parameters of type Sequence
`PairwiseStringAlignment` | `Alignment.getAlignment(Sequence s1, int startS1, int endS1, Sequence s2, int startS2, int endS2)` Computes and returns the alignment of `s1` and `s2` (`Alignment.Alignment(AlignmentType, Costs)`).
`PairwiseStringAlignment` | `Alignment.getAlignment(Sequence s1, int startS1, int endS1, Sequence s2, int startS2, int endS2)` Computes and returns the alignment of `s1` and `s2` (`Alignment.Alignment(AlignmentType, Costs)`).
`PairwiseStringAlignment` | `Alignment.getAlignment(Sequence s1, Sequence s2)` Computes and returns the alignment of `s1` and `s2` (`Alignment.Alignment(AlignmentType, Costs)`).
`PairwiseStringAlignment` | `Alignment.getAlignment(Sequence s1, Sequence s2)` Computes and returns the alignment of `s1` and `s2` (`Alignment.Alignment(AlignmentType, Costs)`).

Uses of Sequence in de.jstacs.algorithms.alignment.cost

Methods in de.jstacs.algorithms.alignment.cost with parameters of type Sequence
`double` | `SimpleCosts.getCostFor(Sequence s1, Sequence s2, int i, int j)`
`double` | `SimpleCosts.getCostFor(Sequence s1, Sequence s2, int i, int j)`
`double` | `MatrixCosts.getCostFor(Sequence s1, Sequence s2, int i, int j)`
`double` | `MatrixCosts.getCostFor(Sequence s1, Sequence s2, int i, int j)`
`double` | `Costs.getCostFor(Sequence s1, Sequence s2, int i, int j)` Returns the costs for the alignment of `s1(i)` and `s2(j)`.
`double` | `Costs.getCostFor(Sequence s1, Sequence s2, int i, int j)` Returns the costs for the alignment of `s1(i)` and `s2(j)`.
`double` | `AffineCosts.getCostFor(Sequence s1, Sequence s2, int i, int j)`
`double` | `AffineCosts.getCostFor(Sequence s1, Sequence s2, int i, int j)`

Uses of Sequence in de.jstacs.classifiers

Methods in de.jstacs.classifiers with parameters of type Sequence
`protected void` | `AbstractScoreBasedClassifier.check(Sequence seq)` This method checks if the given `Sequence` can be used.
`byte` | `AbstractScoreBasedClassifier.classify(Sequence seq)`
`abstract byte` | `AbstractClassifier.classify(Sequence seq)` This method classifies a sequence and returns the index `i` of the class to which the sequence is assigned with `0 < i < getNumberOfClasses()`.
`protected byte` | `AbstractScoreBasedClassifier.classify(Sequence seq, boolean check)` This method classifies a `Sequence`.
`double` | `AbstractScoreBasedClassifier.getPValue(Sequence candidate, DataSet bg)` Returns the p-value for a `Sequence` `candidate` with respect to a given background `DataSet`.
`double` | `AbstractScoreBasedClassifier.getScore(Sequence seq, int i)` This method returns the score for a given `Sequence` and a given class.
`protected double` | `MappingClassifier.getScore(Sequence seq, int i, boolean check)`
`protected abstract double` | `AbstractScoreBasedClassifier.getScore(Sequence seq, int i, boolean check)` This method returns the score for a given `Sequence` and a given class.

Uses of Sequence in de.jstacs.classifiers.differentiableSequenceScoreBased

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased with parameters of type Sequence
`protected double` | `ScoreClassifier.getScore(Sequence seq, int i, boolean check)`

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

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type Sequence
`protected double` | `SamplingScoreBasedClassifier.getScore(Sequence seq, int cls, boolean check)`

Uses of Sequence in de.jstacs.classifiers.trainSMBased

Methods in de.jstacs.classifiers.trainSMBased with parameters of type Sequence
`protected double` | `TrainSMBasedClassifier.getScore(Sequence seq, int i, boolean check)`

Uses of Sequence in de.jstacs.data

Methods in de.jstacs.data that return Sequence
`Sequence[]` | `DataSet.getAllElements()` Returns an array of `Sequence`s containing all elements of this `DataSet`.
`Sequence` | `DataSet.getElementAt(int i)` This method returns the element, i.e. the `Sequence`, with index `i`.
`Sequence` | `DataSet.WeightedDataSetFactory.getElementAt(int index)` Returns the `Sequence` with index `index`.
`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`
`Sequence` | `DinucleotideProperty.getPropertyAsSequence(Sequence original, DinucleotideProperty.Smoothing smoothing)` Computes this dinucleotide property for all overlapping twomers in `original`, smoothes the result using `smoothing`, and returns the smoothed property as a `Sequence`.
`Sequence` | `DataSet.ElementEnumerator.next()`
`Sequence` | `SequenceEnumeration.nextElement()`
`Sequence<int[]>` | `DiscreteSequenceEnumerator.nextElement()`
`Sequence` | `DataSetKMerEnumerator.nextElement()`
`Sequence` | `DataSet.ElementEnumerator.nextElement()`

Methods in de.jstacs.data that return types with arguments of type Sequence
`Iterator<Sequence>` | `DataSet.iterator()`

Methods in de.jstacs.data with parameters of type Sequence
`double[]` | `DinucleotideProperty.getProperty(Sequence original)` Computes this dinucleotide property for all overlapping twomers in `original` and returns the result as a `double` array of length `original.getLength()-1`
`double[]` | `DinucleotideProperty.getProperty(Sequence original, DinucleotideProperty.Smoothing smoothing)` Computes this dinucleotide property for all overlapping twomers in `original`, smoothes the result using `smoothing`, and returns the smoothed property as a `double` array.
`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`
`Sequence` | `DinucleotideProperty.getPropertyAsSequence(Sequence original, DinucleotideProperty.Smoothing smoothing)` Computes this dinucleotide property for all overlapping twomers in `original`, smoothes the result using `smoothing`, and returns the smoothed property as a `Sequence`.
`static ImageResult` | `DinucleotideProperty.getPropertyImage(Sequence original, DinucleotideProperty prop, DinucleotideProperty.Smoothing smoothing, REnvironment re, int xLeft, String pltOptions, int width, int height)`

Constructors in de.jstacs.data with parameters of type Sequence
`DataSet(String annotation, Sequence... seqs)` Creates a new `DataSet` from an array of `Sequence`s and a given annotation.
`SequenceEnumeration(Sequence... sequences)` This constructor creates an instance based on the user-specified `Sequence`s `sequences`.

Constructor parameters in de.jstacs.data with type arguments of type Sequence
`SequenceEnumeration(Collection<Sequence> sequences)` This constructor creates an instance based on the user-specified `Collection` of `Sequence`s `sequences`.

Uses of Sequence in de.jstacs.data.sequences

Subclasses of Sequence in de.jstacs.data.sequences
`class` | `ArbitraryFloatSequence` This class is for any continuous or hybrid sequence.
`class` | `ArbitrarySequence` This class is for any continuous or hybrid sequence.
`class` | `ByteSequence` This class is for sequences with the alphabet symbols encoded as `byte`s and can therefore be used for discrete `AlphabetContainer`s with alphabets that use only few symbols.
`class` | `IntSequence` This class is for sequences with the alphabet symbols encoded as `int`s and can therefore be used for discrete `AlphabetContainer`s with alphabets that use a huge number of symbols.
`class` | `MappedDiscreteSequence` This class allows to map a discrete `Sequence` to an new `Sequence` using some `DiscreteAlphabetMapping`s.
`class` | `MultiDimensionalArbitrarySequence` This class is for multidimensional arbitrary sequences.
`class` | `MultiDimensionalDiscreteSequence` This class is for multidimensional discrete sequences that can be used, for instance, for phylogenetic footprinting.
`class` | `MultiDimensionalSequence<T>` This class is for multidimensional sequences that can be used, for instance, for phylogenetic footprinting.
`class` | `PermutedSequence<T>` This class is for permuted sequences.
`protected static class` | `Sequence.CompositeSequence<T>` The class handles composite `Sequence`s.
`static class` | `Sequence.RecursiveSequence<T>` This is the main class for subsequences, composite sequences, ... .
`protected static class` | `Sequence.SubSequence<T>` This class handles subsequences.
`class` | `ShortSequence` This class is for sequences with the alphabet symbols encoded as `shorts`s and can therefore be used for discrete `AlphabetContainer`s with alphabets that use many different symbols.
`class` | `SimpleDiscreteSequence` This is the main class for any discrete sequence.
`class` | `SparseSequence` This class is an implementation for sequences on one alphabet with length 4.

Fields in de.jstacs.data.sequences declared as Sequence
`protected Sequence<T>` | `Sequence.RecursiveSequence.content` The internal sequence.
`protected Sequence[]` | `MultiDimensionalSequence.content` The internally used sequences.
`protected Sequence<T>` | `Sequence.rc` The pointer to the reverse complement of the `Sequence`.

Methods in de.jstacs.data.sequences that return Sequence
`Sequence` | `Sequence.annotate(boolean add, SequenceAnnotation... annotation)` This method allows to append annotation to a `Sequence`.
`Sequence` | `Sequence.complement()` This method returns a new instance of `Sequence` containing the complementary current `Sequence`.
`Sequence` | `Sequence.complement(int start, int end)` This method returns a new instance of `Sequence` containing a part of the complementary current `Sequence`.
`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`.
`protected abstract Sequence` | `Sequence.flatCloneWithoutAnnotation()` Works in analogy to `Object.clone()`, but does not clone the annotation.
`protected Sequence` | `Sequence.CompositeSequence.flatCloneWithoutAnnotation()`
`protected Sequence` | `Sequence.SubSequence.flatCloneWithoutAnnotation()`
`Sequence<T>` | `Sequence.getCompositeSequence(AlphabetContainer abc, int[] starts, int[] lengths)` This method should be used if one wants to create a `DataSet` of `Sequence.CompositeSequence`s.
`Sequence` | `Sequence.getCompositeSequence(int[] starts, int[] lengths)` This is a very efficient way to create a `Sequence.CompositeSequence` for sequences with a simple `AlphabetContainer`.
`Sequence` | `MultiDimensionalSequence.getSequence(int index)` This method returns the internal sequence with index `index`.
`Sequence` | `Sequence.getSubSequence(AlphabetContainer abc, int start)` This method should be used if one wants to create a `DataSet` of subsequences of defined length.
`Sequence` | `Sequence.getSubSequence(AlphabetContainer abc, int start, int length)` This method should be used if one wants to create a `DataSet` of subsequences of defined length.
`Sequence` | `Sequence.getSubSequence(int start)` This is a very efficient way to create a subsequence/suffix for `Sequence`s with a simple `AlphabetContainer`.
`Sequence` | `Sequence.getSubSequence(int start, int length)` This is a very efficient way to create a subsequence of defined length for `Sequence`s with a simple `AlphabetContainer`.
`Sequence` | `Sequence.reverse()` This method returns a new instance of `Sequence` containing the reverse current `Sequence`.
`Sequence` | `Sequence.reverse(int start, int end)` This method returns a new instance of `Sequence` containing a part of the reverse current `Sequence`.
`Sequence` | `Sequence.reverseComplement()` This method returns a new instance of `Sequence` containing the reverse complementary current `Sequence`.
`Sequence` | `Sequence.reverseComplement(int start, int end)` This method returns a new instance of `Sequence` containing a reverse part of the complementary current `Sequence`.
`Sequence` | `Sequence.SubSequence.reverseComplement(int start, int end)`

Methods in de.jstacs.data.sequences with parameters of type Sequence
`int` | `Sequence.compareTo(Sequence<T> s)`
`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 with parameters of type Sequence
`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
`Sequence.CompositeSequence(AlphabetContainer abc, Sequence<T> seq, int[] starts, int[] lengths)` This constructor should be used if one wants to create a `DataSet` of `Sequence.CompositeSequence`s.
`Sequence.CompositeSequence(Sequence seq, int[] starts, int[] lengths)` This is a very efficient way to create a `Sequence.CompositeSequence` for `Sequence`s with a simple `AlphabetContainer`.
`Sequence.RecursiveSequence(AlphabetContainer alphabet, Sequence<T> seq)` Creates a new `Sequence.RecursiveSequence` on the `Sequence` `seq` with the `AlphabetContainer` `alphabet` using the annotation of the given `Sequence`.
`Sequence.RecursiveSequence(AlphabetContainer alphabet, SequenceAnnotation[] annotation, Sequence<T> seq)` Creates a new `Sequence.RecursiveSequence` on the `Sequence` `seq` with the `AlphabetContainer` `alphabet` and the annotation `annotation`.
`Sequence.SubSequence(AlphabetContainer abc, Sequence seq, int start, int length)` This constructor should be used if one wants to create a `DataSet` of `Sequence.SubSequence`s of defined length.
`Sequence.SubSequence(Sequence seq, int start, int length)` This is a very efficient way to create a `Sequence.SubSequence` of defined length for `Sequence`s with a simple `AlphabetContainer`.

Uses of Sequence in de.jstacs.data.sequences.annotation

Methods in de.jstacs.data.sequences.annotation that return Sequence
`Sequence` | `ReferenceSequenceAnnotation.getReferenceSequence()` Returns the reference sequence.

Constructors in de.jstacs.data.sequences.annotation with parameters of type Sequence
`ReferenceSequenceAnnotation(String identifier, Sequence ref, Result... results)` Creates a new `ReferenceSequenceAnnotation` with identifier `identifier`, reference sequence `ref`, and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as a `Result` `result`.

Uses of Sequence in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery that return Sequence
`static Sequence[]` | `MutableMotifDiscovererToolbox.enumerate(DifferentiableSequenceScore[] funs, int[] classIndex, int[] motifIndex, RecyclableSequenceEnumerator[] rse, double weight, DiffSSBasedOptimizableFunction opt, OutputStream out)` This method allows to enumerate all possible seeds for a number of motifs in the `MutableMotifDiscoverer`s of a specific classes.
`static Sequence` | `MutableMotifDiscovererToolbox.enumerate(DifferentiableSequenceScore[] funs, int classIndex, int motifIndex, RecyclableSequenceEnumerator rse, double weight, DiffSSBasedOptimizableFunction opt, OutputStream out)` This method allows to enumerate all possible seeds for a motif in the `MutableMotifDiscoverer` of a specific class.
`static Sequence[]` | `KMereStatistic.getCommonString(DataSet data, int motifLength, boolean bothStrands)` This method returns an array of strings of length `motifLength` so that each String is contained in all sequences of the sample respectively in the sample and the reverse complementary sample.

Methods in de.jstacs.motifDiscovery that return types with arguments of type Sequence
`static LinkedList<Sequence>` | `KMereStatistic.getConservedPatterns(Hashtable<Sequence,BitSet[]> statistic, int dataSetIndex, int threshold)` This method returns a list of `Sequence`s.
`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.
`static Hashtable<Sequence,BitSet[]>` | `KMereStatistic.removeBackground(Hashtable<Sequence,BitSet[]> statistic, int fgIndex, int bgIndex, double fgWeight, double bgWeight)` This method allows to remove those entries from the statistic that have a lower weighted foreground cardinality than the weighted background cardinality.

Methods in de.jstacs.motifDiscovery with parameters of type Sequence
`MotifAnnotation[]` | `SignificantMotifOccurrencesFinder.findSignificantMotifOccurrences(int motif, Sequence seq, int start)` This method finds the significant motif occurrences in the sequence.
`MotifAnnotation[]` | `SignificantMotifOccurrencesFinder.findSignificantMotifOccurrences(int motif, Sequence seq, int addMax, int start)` This method finds the significant motif occurrences in the sequence.
`int` | `MotifDiscoverer.getIndexOfMaximalComponentFor(Sequence sequence)` Returns the index of the component with the maximal score for the sequence `sequence`.
`double[]` | `MotifDiscoverer.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)` Returns the profile of the scores for component `component` and motif `motif` at all possible start positions of the motif in the sequence `sequence` beginning at `startpos`.
`double[][]` | `KMereStatistic.getSmoothedProfile(int window, Sequence... seq)` This method returns an array of smoothed profiles.
`double[]` | `MotifDiscoverer.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)` This method returns the probabilities of the strand orientations for a given subsequence if it is considered as site of the motif model in a specific component.
`static ImageResult` | `MotifDiscovererToolBox.plot(MotifDiscoverer motifDisc, int component, int motif, Sequence sequence, int startpos, REnvironment r, int width, int height, MotifDiscoverer.KindOfProfile kind)` This method creates a simple plot of the profile of scores for a sequence and a start position.
`static ImageResult` | `MotifDiscovererToolBox.plotAndAnnotate(MotifDiscoverer motifDisc, int component, int motif, Sequence sequence, int startpos, REnvironment r, int width, int height, double yMin, double yMax, double threshold, MotifDiscoverer.KindOfProfile kind)` This method creates a plot of the profile of scores for a sequence and a start position and annotates bindings sites in the plot that have a higher score than `threshold`.

Method parameters in de.jstacs.motifDiscovery with type arguments of type Sequence
`static LinkedList<Sequence>` | `KMereStatistic.getConservedPatterns(Hashtable<Sequence,BitSet[]> statistic, int dataSetIndex, int threshold)` This method returns a list of `Sequence`s.
`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.merge(Hashtable<Sequence,BitSet[]> statistic, int maximalMissmatch, boolean bothStrands)` This method allows to merge the statistics of k-mers by allowing mismatches.
`static Hashtable<Sequence,BitSet[]>` | `KMereStatistic.removeBackground(Hashtable<Sequence,BitSet[]> statistic, int fgIndex, int bgIndex, double fgWeight, double bgWeight)` This method allows to remove those entries from the statistic that have a lower weighted foreground cardinality than the weighted background cardinality.

Uses of Sequence in de.jstacs.sequenceScores

Methods in de.jstacs.sequenceScores with parameters of type Sequence
`double` | `SequenceScore.getLogScoreFor(Sequence seq)` Returns the logarithmic score for the `Sequence` `seq`.
`double` | `SequenceScore.getLogScoreFor(Sequence seq, int start)` Returns the logarithmic score for the `Sequence` `seq` beginning at position `start` in the `Sequence`.
`double` | `SequenceScore.getLogScoreFor(Sequence seq, int start, int end)` Returns the logarithmic score for the `Sequence` `seq` beginning at position `start` in the `Sequence`.

Uses of Sequence in de.jstacs.sequenceScores.differentiable

Methods in de.jstacs.sequenceScores.differentiable with parameters of type Sequence
`double` | `DifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` beginning at position `start` in the `Sequence` and fills lists with the indices and the partial derivations.
`double` | `AbstractDifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq, int startpos, int endpos, IntList indices, DoubleList partialDer)`
`double` | `UniformDiffSS.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList dList)`
`double` | `IndependentProductDiffSS.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `DifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` beginning at position `start` in the `Sequence` and fills lists with the indices and the partial derivations.
`double` | `DifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` `seq` and fills lists with the indices and the partial derivations.
`double` | `AbstractDifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)`
`double` | `AbstractDifferentiableSequenceScore.getLogScoreFor(Sequence seq)`
`double` | `UniformDiffSS.getLogScoreFor(Sequence seq, int start)`
`double` | `IndependentProductDiffSS.getLogScoreFor(Sequence seq, int start)`
`double` | `AbstractDifferentiableSequenceScore.getLogScoreFor(Sequence seq, int startpos, int endpos)`

Uses of Sequence in de.jstacs.sequenceScores.differentiable.logistic

Methods in de.jstacs.sequenceScores.differentiable.logistic with parameters of type Sequence
`double` | `LogisticDiffSS.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `LogisticDiffSS.getLogScoreFor(Sequence seq, int start)`
`double` | `ProductConstraint.getValue(Sequence seq, int start)`
`double` | `LogisticConstraint.getValue(Sequence seq, int start)` This method returns the value f(seq) used in `LogisticDiffSS`

Uses of Sequence in de.jstacs.sequenceScores.statisticalModels

Methods in de.jstacs.sequenceScores.statisticalModels with parameters of type Sequence
`double` | `StatisticalModel.getLogProbFor(Sequence sequence)` Returns the logarithm of the probability of the given sequence given the model.
`double` | `StatisticalModel.getLogProbFor(Sequence sequence, int startpos)` Returns the logarithm of the probability of (a part of) the given sequence given the model.
`double` | `StatisticalModel.getLogProbFor(Sequence sequence, int startpos, int endpos)` Returns the logarithm of the probability of (a part of) the given sequence given the model.

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

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable with parameters of type Sequence
`int` | `MappingDiffSM.getIndexOfMaximalComponentFor(Sequence sequence)`
`int` | `IndependentProductDiffSM.getIndexOfMaximalComponentFor(Sequence sequence)`
`double` | `UniformDiffSM.getLogProbFor(Sequence sequence)`
`double` | `IndependentProductDiffSM.getLogProbFor(Sequence sequence)`
`double` | `AbstractDifferentiableStatisticalModel.getLogProbFor(Sequence sequence)`
`double` | `UniformDiffSM.getLogProbFor(Sequence sequence, int startpos)`
`double` | `IndependentProductDiffSM.getLogProbFor(Sequence sequence, int startpos)`
`double` | `AbstractDifferentiableStatisticalModel.getLogProbFor(Sequence sequence, int startpos)`
`double` | `UniformDiffSM.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `IndependentProductDiffSM.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `AbstractDifferentiableStatisticalModel.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `VariableLengthDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int startpos, int endpos, IntList indices, DoubleList partialDer)`
`double` | `CyclicMarkovModelDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList dList)`
`abstract double` | `AbstractVariableLengthDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int startpos, int endpos, IntList indices, DoubleList partialDer)`
`double` | `UniformDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList dList)`
`double` | `NormalizedDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `MultiDimensionalSequenceWrapperDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `MarkovRandomFieldDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `MappingDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `AbstractVariableLengthDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList dList)`
`double` | `UniformDiffSM.getLogScoreFor(Sequence seq, int start)`
`double` | `NormalizedDiffSM.getLogScoreFor(Sequence seq, int start)`
`double` | `MultiDimensionalSequenceWrapperDiffSM.getLogScoreFor(Sequence seq, int start)`
`double` | `MarkovRandomFieldDiffSM.getLogScoreFor(Sequence seq, int start)`
`double` | `MappingDiffSM.getLogScoreFor(Sequence seq, int start)`
`double` | `AbstractVariableLengthDiffSM.getLogScoreFor(Sequence seq, int start)`
`double` | `VariableLengthDiffSM.getLogScoreFor(Sequence seq, int startpos, int endpos)`
`double` | `CyclicMarkovModelDiffSM.getLogScoreFor(Sequence seq, int start, int end)`
`abstract double` | `AbstractVariableLengthDiffSM.getLogScoreFor(Sequence seq, int startpos, int endpos)`
`double[]` | `MappingDiffSM.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)`
`double[]` | `IndependentProductDiffSM.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile dist)`
`StrandedLocatedSequenceAnnotationWithLength.Strand` | `NormalizedDiffSM.getStrand(Sequence seq, int startPos)` This method return the preferred `StrandedLocatedSequenceAnnotationWithLength.Strand` for a `Sequence` beginning at `startPos`.
`double[]` | `MappingDiffSM.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)`
`double[]` | `IndependentProductDiffSM.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)`

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

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels with parameters of type Sequence
`void` | `BNDiffSMParameterTree.addCount(Sequence seq, int start, double count)` Adds `count` to the parameter as returned by `BNDiffSMParameterTree.getParameterFor(Sequence, int)`.
`double` | `BNDiffSMParameter.doesApplyFor(Sequence seq)` Indicates if the `Sequence` `seq` fulfills all requirements defined in the `BNDiffSMParameter.context`.
`double` | `BayesianNetworkDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `BayesianNetworkDiffSM.getLogScoreFor(Sequence seq, int start)`
`BNDiffSMParameter` | `BNDiffSMParameterTree.getParameterFor(Sequence seq, int start)` Returns the `BNDiffSMParameter` that is responsible for the suffix of sequence `seq` starting at position `start`.
`double[]` | `BayesianNetworkDiffSM.getPositionDependentKMerProb(Sequence kmer)` Returns the probability of `kmer` for all possible positions in this `BayesianNetworkDiffSM` starting at position `kmer.getLength()-1`.``
`double` | `BNDiffSMParameterTree.getProbFor(Sequence sequence)` Returns the probability of `Sequence` `sequence` in this `BNDiffSMParameterTree`.

Uses of Sequence in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous with parameters of type Sequence
`double` | `UniformHomogeneousDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList dList)`
`double` | `HomogeneousMMDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList dList)`
`double` | `HomogeneousMM0DiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList dList)`
`double` | `UniformHomogeneousDiffSM.getLogScoreFor(Sequence seq, int start, int end)`
`double` | `HomogeneousMMDiffSM.getLogScoreFor(Sequence seq, int start, int end)`
`double` | `HomogeneousMM0DiffSM.getLogScoreFor(Sequence seq, int start, int end)`

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

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture with parameters of type Sequence
`protected void` | `StrandDiffSM.fillComponentScores(Sequence seq, int start)`
`protected void` | `MixtureDiffSM.fillComponentScores(Sequence seq, int start)`
`protected abstract void` | `AbstractMixtureDiffSM.fillComponentScores(Sequence seq, int start)` Fills the internal array `AbstractMixtureDiffSM.componentScore` with the logarithmic scores of the components given a `Sequence`.
`int` | `MixtureDiffSM.getIndexOfMaximalComponentFor(Sequence sequence)`
`int` | `AbstractMixtureDiffSM.getIndexOfMaximalComponentFor(Sequence seq, int start)` Returns the index of the component that has the greatest impact on the complete score for a `Sequence`.
`double` | `VariableLengthMixtureDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, int end, IntList indices, DoubleList partialDer)`
`double` | `StrandDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `MixtureDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `AbstractMixtureDiffSM.getLogScoreFor(Sequence seq, int start)`
`double` | `VariableLengthMixtureDiffSM.getLogScoreFor(Sequence seq, int start, int end)`
`double[]` | `AbstractMixtureDiffSM.getProbsForComponent(Sequence seq)` Returns the probabilities for each component given a `Sequence`.
`double[]` | `MixtureDiffSM.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)`
`StrandedLocatedSequenceAnnotationWithLength.Strand` | `StrandDiffSM.getStrand(Sequence seq, int startPos)` This method returns the preferred `StrandedLocatedSequenceAnnotationWithLength.Strand` for a given subsequence.
`double[]` | `MixtureDiffSM.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)`

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

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif with parameters of type Sequence
`protected int` | `ExtendedZOOPSDiffSM.fillComponentScoreOf(int i, Sequence seq, int start)` This method fills an internal array with the partial scores.
`protected void` | `ExtendedZOOPSDiffSM.fillComponentScores(Sequence seq, int start)`
`int` | `ExtendedZOOPSDiffSM.getIndexOfMaximalComponentFor(Sequence sequence)`
`double` | `PositionDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `ExtendedZOOPSDiffSM.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `PositionDiffSM.getLogScoreFor(Sequence seq, int start)`
`double` | `ExtendedZOOPSDiffSM.getLogScoreFor(Sequence seq, int start)`
`double[]` | `ExtendedZOOPSDiffSM.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile dist)`
`double[]` | `ExtendedZOOPSDiffSM.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)`
`protected int[]` | `PositionDiffSM.getValuesFromSequence(Sequence seq, int start)` This method extracts the values form a sequence.

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable with parameters of type Sequence
`protected void` | `AbstractTrainableStatisticalModel.check(Sequence sequence, int startpos, int endpos)` This method checks all parameters before a probability can be computed for a sequence.
`double` | `AbstractTrainableStatisticalModel.getLogProbFor(Sequence sequence)`
`double` | `AbstractTrainableStatisticalModel.getLogProbFor(Sequence sequence, int startpos)`
`double` | `VariableLengthWrapperTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `UniformTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `DifferentiableStatisticalModelWrapperTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `CompositeTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `AbstractTrainableStatisticalModel.getLogScoreFor(Sequence sequence)`
`double` | `AbstractTrainableStatisticalModel.getLogScoreFor(Sequence sequence, int startpos)`
`double` | `AbstractTrainableStatisticalModel.getLogScoreFor(Sequence sequence, int startpos, int endpos)`

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete with parameters of type Sequence
`void` | `Constraint.add(Sequence seq, int start, double weight)` This method determines the specific constraint that is fulfilled by the `Sequence` `seq` and adds the `weight` to the specific counter.
`protected void` | `DiscreteGraphicalTrainSM.check(Sequence sequence, int startpos, int endpos)` Checks some conditions on a `Sequence`.
`double` | `Constraint.getFreq(Sequence seq, int start)` This method determines the specific constraint that is fulfilled by the `Sequence` `seq` beginning at position `start`.
`abstract int` | `Constraint.satisfiesSpecificConstraint(Sequence seq, int start)` This method returns the index of the specific constraint that is fulfilled by the `Sequence` `seq` beginning at position `start`.

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that return Sequence
`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)`

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous with parameters of type Sequence
`void` | `HomogeneousTrainSM.HomCondProb.addAll(Sequence seq, double weight, int start, int prevIndex)` Adds the given `weight` to the counts corresponding to the `Sequence` `seq` from `start` to the end of the `Sequence`.
`protected void` | `HomogeneousTrainSM.check(Sequence sequence, int startpos, int endpos)` Checks some constraints, these are in general conditions on the `AlphabetContainer` of a (sub)`Sequence` between `startpos` und `endpos`.
`double` | `HomogeneousTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`protected abstract double` | `HomogeneousTrainSM.logProbFor(Sequence sequence, int startpos, int endpos)` This method computes the logarithm of the probability of the given `Sequence` in the given interval.
`protected double` | `HomogeneousMM.logProbFor(Sequence sequence, int startpos, int endpos)`
`int` | `HomogeneousTrainSM.HomCondProb.satisfiesSpecificConstraint(Sequence seq, int start)`

Uses of Sequence in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous with parameters of type Sequence
`protected void` | `InhomogeneousDGTrainSM.check(Sequence sequence, int startpos, int endpos)`
`double` | `InhCondProb.getLnFreq(Sequence s, int start)` Returns the logarithm of the relative frequency (=probability) with the position in the distribution given by the index of the specific constraint that is fulfilled by the `Sequence` `s` beginning at `start`.
`double` | `DAGTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`int` | `InhConstraint.satisfiesSpecificConstraint(Sequence s, int start)`

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm with parameters of type Sequence
`protected abstract void` | `AbstractHMM.fillBwdMatrix(int startPos, int endPos, Sequence seq)` This method fills the backward-matrix for a given sequence.
`protected abstract void` | `AbstractHMM.fillFwdMatrix(int startPos, int endPos, Sequence seq)` This method fills the forward-matrix for a given sequence.
`protected abstract void` | `AbstractHMM.fillLogStatePosteriorMatrix(double[][] statePosterior, int startPos, int endPos, Sequence seq, boolean silentZero)` This method fills the log state posterior of Sequence `seq` in a given matrix.
`double` | `AbstractHMM.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`abstract double` | `AbstractHMM.getLogProbForPath(IntList path, int startPos, Sequence seq)`
`double[][]` | `AbstractHMM.getLogStatePosteriorMatrixFor(int startPos, int endPos, Sequence seq)` This method returns the log state posterior of all states for a sequence.
`double[][]` | `AbstractHMM.getStatePosteriorMatrixFor(Sequence seq)` This method returns the log state posterior of all states for a sequence.
`abstract Pair<IntList,Double>` | `AbstractHMM.getViterbiPathFor(int startPos, int endPos, Sequence seq)`
`Pair<IntList,Double>` | `AbstractHMM.getViterbiPathFor(Sequence seq)`
`protected double` | `AbstractHMM.logProb(int startpos, int endpos, Sequence sequence)` This method computes the logarithm of the probability of the corresponding subsequences.

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models with parameters of type Sequence
`protected double` | `HigherOrderHMM.baumWelch(int startPos, int endPos, double weight, Sequence seq)` This method computes the likelihood and modifies the sufficient statistics according to the Baum-Welch algorithm.
`protected void` | `HigherOrderHMM.fillBwdMatrix(int startPos, int endPos, Sequence seq)`
`protected void` | `HigherOrderHMM.fillBwdOrViterbiMatrix(HigherOrderHMM.Type t, int startPos, int endPos, double weight, Sequence seq)` This method computes the entries of the backward or the viterbi matrix.
`protected void` | `HigherOrderHMM.fillFwdMatrix(int startPos, int endPos, Sequence seq)`
`protected void` | `HigherOrderHMM.fillLogStatePosteriorMatrix(double[][] statePosterior, int startPos, int endPos, Sequence seq, boolean silentZero)`
`double` | `SamplingHigherOrderHMM.getLogProbForPath(IntList path, int startPos, Sequence seq)`
`double` | `HigherOrderHMM.getLogProbForPath(IntList path, int startPos, Sequence seq)`
`double` | `DifferentiableHigherOrderHMM.getLogScoreAndPartialDerivation(Sequence seq, int startPos, int endPos, IntList indices, DoubleList partialDer)`
`double` | `DifferentiableHigherOrderHMM.getLogScoreAndPartialDerivation(Sequence seq, int startPos, IntList indices, DoubleList partialDer)`
`double` | `DifferentiableHigherOrderHMM.getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)`
`double` | `DifferentiableHigherOrderHMM.getLogScoreFor(Sequence seq)`
`double` | `DifferentiableHigherOrderHMM.getLogScoreFor(Sequence seq, int start)`
`double` | `DifferentiableHigherOrderHMM.getLogScoreFor(Sequence seq, int start, int end)`
`double[][]` | `SamplingHigherOrderHMM.getLogStatePosteriorMatrixFor(int startPos, int endPos, Sequence seq)`
`Pair<IntList,Double>` | `SamplingHigherOrderHMM.getViterbiPath(int startPos, int endPos, Sequence seq, SamplingHigherOrderHMM.ViterbiComputation compute)` This method returns a viterbi path that is the optimum for the choosen ViterbiComputation method
`Pair<IntList,Double>` | `SamplingHigherOrderHMM.getViterbiPathFor(int startPos, int endPos, Sequence seq)`
`Pair<IntList,Double>` | `HigherOrderHMM.getViterbiPathFor(int startPos, int endPos, Sequence seq)`
`protected double` | `SamplingHigherOrderHMM.gibbsSampling(int startPos, int endPos, double weight, Sequence seq)` This method implements a sampling step in the sampling procedure
`protected double` | `SamplingHigherOrderHMM.logProb(int startpos, int endpos, Sequence sequence)`
`protected double` | `DifferentiableHigherOrderHMM.logProb(int startpos, int endpos, Sequence sequence)`
`void` | `HigherOrderHMM.samplePath(IntList path, int startPos, int endPos, Sequence seq)` This method samples a valid path for the given sequence `seq` using the internal parameters.
`protected double` | `HigherOrderHMM.viterbi(IntList path, int startPos, int endPos, double weight, Sequence seq)` This method computes the viterbi score of a given sequence `seq`.

Uses of Sequence in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states with parameters of type Sequence
`void` | `TrainableState.addToStatistic(int startPos, int endPos, double weight, Sequence seq)` This method allows to add a certain `weight` to the sufficient statistic of the parameters that are used for scoring the specific subsequence(s).
`void` | `SimpleState.addToStatistic(int startPos, int endPos, double weight, Sequence seq)`
`double` | `SimpleDifferentiableState.getLogScoreAndPartialDerivation(int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`
`double` | `DifferentiableState.getLogScoreAndPartialDerivation(int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)` This method allows to compute the logarithm of the score and the gradient for the given subsequences.
`double` | `State.getLogScoreFor(int startPos, int endPos, Sequence seq)` This method returns the logarithm of the score for a given sequence with given start and end position.
`double` | `SimpleState.getLogScoreFor(int startPos, int endPos, Sequence seq)`

Uses of Sequence in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions with parameters of type Sequence
`void` | `UniformEmission.addToStatistic(boolean forward, int startPos, int endPos, double weight, Sequence seq)`
`void` | `SilentEmission.addToStatistic(boolean forward, int startPos, int endPos, double weight, Sequence seq)`
`void` | `MixtureEmission.addToStatistic(boolean forward, int startPos, int endPos, double weight, Sequence seq)`
`void` | `Emission.addToStatistic(boolean forward, int startPos, int endPos, double weight, Sequence seq)` This method adds the `weight` to the internal sufficient statistic.
`double` | `UniformEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`
`double` | `SilentEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`
`double` | `DifferentiableEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)` Returns the logarithmic score for a `Sequence` beginning at position `start` in the `Sequence` and fills lists with the indices and the partial derivations.
`double` | `UniformEmission.getLogProbFor(boolean forward, int startPos, int endPos, Sequence seq)`
`double` | `SilentEmission.getLogProbFor(boolean forward, int startPos, int endPos, Sequence seq)`
`double` | `MixtureEmission.getLogProbFor(boolean forward, int startPos, int endPos, Sequence seq)`
`double` | `Emission.getLogProbFor(boolean forward, int startPos, int endPos, Sequence seq)` This method computes the logarithm of the likelihood.

Uses of Sequence in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous with parameters of type Sequence
`void` | `GaussianEmission.addToStatistic(boolean forward, int startPos, int endPos, double weight, Sequence seq)`
`double` | `GaussianEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`
`double` | `GaussianEmission.getLogProbFor(boolean forward, int startPos, int endPos, Sequence seq)`

Uses of Sequence in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete with parameters of type Sequence
`void` | `PhyloDiscreteEmission.addToStatistic(boolean forward, int startPos, int endPos, double weight, Sequence seq)`
`void` | `AbstractConditionalDiscreteEmission.addToStatistic(boolean forward, int startPos, int endPos, double weight, Sequence seq)`
`protected int` | `ReferenceSequenceDiscreteEmission.getConditionIndex(boolean forward, int seqPos, Sequence seq)`
`protected int` | `DiscreteEmission.getConditionIndex(boolean forward, int seqPos, Sequence seq)`
`protected abstract int` | `AbstractConditionalDiscreteEmission.getConditionIndex(boolean forward, int seqPos, Sequence seq)` This method returns an index encoding the condition.
`double` | `PhyloDiscreteEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`
`double` | `AbstractConditionalDiscreteEmission.getLogProbAndPartialDerivationFor(boolean forward, int startPos, int endPos, IntList indices, DoubleList partDer, Sequence seq)`
`double` | `PhyloDiscreteEmission.getLogProbFor(boolean forward, int startPos, int endPos, Sequence seq)`
`double` | `AbstractConditionalDiscreteEmission.getLogProbFor(boolean forward, int startPos, int endPos, Sequence seq)`

Uses of Sequence in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions with parameters of type Sequence
`void` | `BasicHigherOrderTransition.AbstractTransitionElement.addToStatistic(int childIdx, double weight, Sequence sequence, int sequencePosition)` This method adds a given weight to the sufficient statistic for the parameters.
`void` | `TransitionWithSufficientStatistic.addToStatistic(int layer, int index, int childIdx, double weight, Sequence sequence, int sequencePosition)` This method allows to add a certain `weight` to the sufficient statistic of a specific transition.
`void` | `BasicHigherOrderTransition.addToStatistic(int layer, int index, int childIdx, double weight, Sequence sequence, int sequencePosition)`
`double` | `HigherOrderTransition.getLogScoreAndPartialDerivation(int layer, int index, int childIdx, IntList indices, DoubleList partDer, Sequence sequence, int sequencePosition)`
`double` | `DifferentiableTransition.getLogScoreAndPartialDerivation(int layer, int index, int childIdx, IntList indices, DoubleList partDer, Sequence sequence, int sequencePosition)` This method allows to compute the logarithm of the score and the gradient for a specific transition.
`double` | `Transition.getLogScoreFor(int layer, int index, int childIdx, Sequence sequence, int sequencePosition)` This method returns the logarithm of the score for the transition.
`double` | `BasicHigherOrderTransition.getLogScoreFor(int layer, int index, int childIdx, Sequence sequence, int sequencePosition)`
`double` | `BasicHigherOrderTransition.AbstractTransitionElement.getLogScoreFor(int index, Sequence sequence, int sequencePosition)` This method returns the score for the transition from the current context to the state with index `index`.

Uses of Sequence in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements

Fields in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements with type parameters of type Sequence
`protected Hashtable<Sequence,double[]>` | `DistanceBasedScaledTransitionElement.diagonalWeights` Contains the single epsilons of the diagonal elements required for estimating the self-transition probability.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements with parameters of type Sequence
`void` | `ScaledTransitionElement.addToStatistic(int childIdx, double weight, Sequence sequence, int sequencePosition)`
`void` | `DistanceBasedScaledTransitionElement.addToStatistic(int childIdx, double weight, Sequence sequence, int sequencePosition)`
`protected int` | `ScaledTransitionElement.getIndex(int pos, Sequence seq)` Returns the index of the transition matrix used for the transition from `pos - 1` to `pos` in sequences `seq`.
`protected double` | `DistanceBasedScaledTransitionElement.getIndex(int pos, Sequence seq)` Returns the distance integrated into the transition from `pos - 1` to `pos` in sequences `seq`.
`double` | `TransitionElement.getLogScoreAndPartialDerivation(int childIdx, IntList indices, DoubleList partialDer, Sequence sequence, int sequencePosition)` Returns the logarithmic score and fills lists with the indices and the partial derivations.
`double` | `ScaledTransitionElement.getLogScoreFor(int state, Sequence sequence, int sequencePosition)`
`double` | `DistanceBasedScaledTransitionElement.getLogScoreFor(int state, Sequence sequence, int sequencePosition)`

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

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that return Sequence
`protected Sequence[]` | `StrandTrainSM.emitDataSetUsingCurrentParameterSet(int n, int... lengths)`
`protected Sequence[]` | `MixtureTrainSM.emitDataSetUsingCurrentParameterSet(int n, int... lengths)`
`protected abstract Sequence[]` | `AbstractMixtureTrainSM.emitDataSetUsingCurrentParameterSet(int n, int... lengths)` The method returns an array of sequences using the current parameter set.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture with parameters of type Sequence
`int` | `AbstractMixtureTrainSM.getIndexOfMaximalComponentFor(Sequence s)` Returns the index `i` of the component with `P(i|s)`maximal.``
`double` | `AbstractMixtureTrainSM.getLogProbFor(int component, Sequence s)` Returns the logarithmic probability for the sequence and the given component.
`double` | `AbstractMixtureTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`protected double` | `StrandTrainSM.getLogProbUsingCurrentParameterSetFor(int component, Sequence s, int start, int end)`
`protected double` | `MixtureTrainSM.getLogProbUsingCurrentParameterSetFor(int component, Sequence s, int start, int end)`
`protected abstract double` | `AbstractMixtureTrainSM.getLogProbUsingCurrentParameterSetFor(int component, Sequence s, int start, int end)` Returns the logarithmic probability for the sequence and the given component using the current parameter set.

Uses of Sequence in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif that return Sequence
`protected Sequence[]` | `HiddenMotifMixture.emitDataSetUsingCurrentParameterSet(int n, int... lengths)` Standard implementation throwing an `OperationNotSupportedException`.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type Sequence
`protected double` | `ZOOPSTrainSM.getLogProbUsingCurrentParameterSetFor(int component, Sequence seq, int start, int end)`
`double[]` | `ZOOPSTrainSM.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)`
`double[]` | `ZOOPSTrainSM.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)`

Uses of Sequence in de.jstacs.utils

Methods in de.jstacs.utils that return Sequence
`static Sequence` | `StatisticalModelTester.getMostProbableSequence(SequenceScore m, int length)` Returns one most probable sequence for the discrete model `m`.

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