Uses of Class
de.jstacs.data.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.classifier	This package provides the framework for any classifier.
de.jstacs.classifier.modelBased	Provides the classes for Classifiers that are based on Models
de.jstacs.classifier.scoringFunctionBased	Provides the classes for Classifiers that are based on ScoringFunctions.
de.jstacs.classifier.scoringFunctionBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingScoringFunctions and that sample parameters using the Metropolis-Hastings algorithm.
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 Sequences using a number of pre-defined annotation types, or additional implementations of the SequenceAnnotation class
de.jstacs.models	Provides the interface Model and its abstract implementation AbstractModel, which is the super class of all other models.
de.jstacs.models.discrete
de.jstacs.models.discrete.homogeneous
de.jstacs.models.discrete.inhomogeneous	This package contains various inhomogeneous models.
de.jstacs.models.hmm	The package provides all interfaces and classes for a hidden Markov model (HMM).
de.jstacs.models.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM
de.jstacs.models.hmm.states	The package provides all interfaces and classes for states used in hidden Markov models.
de.jstacs.models.hmm.states.emissions
de.jstacs.models.hmm.states.emissions.continuous
de.jstacs.models.hmm.states.emissions.discrete
de.jstacs.models.hmm.transitions	The package provides all interfaces and classes for transitions used in hidden Markov models.
de.jstacs.models.hmm.transitions.elements
de.jstacs.models.mixture	This package is the super package for any mixture model.
de.jstacs.models.mixture.motif
de.jstacs.models.utils
de.jstacs.motifDiscovery	This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.scoringFunctions	Provides ScoringFunctions that can be used in a ScoreClassifier.
de.jstacs.scoringFunctions.directedGraphicalModels	Provides ScoringFunctions that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.homogeneous	Provides ScoringFunctions that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix	Provides ScoringFunctions that are mixtures of other ScoringFunctions.
de.jstacs.scoringFunctions.mix.motifSearch

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, Costs.Direction from)
double	SimpleCosts.getCostFor(Sequence s1, Sequence s2, int i, int j, Costs.Direction from)
double	MatrixCosts.getCostFor(Sequence s1, Sequence s2, int i, int j, Costs.Direction from)
double	MatrixCosts.getCostFor(Sequence s1, Sequence s2, int i, int j, Costs.Direction from)
double	Costs.getCostFor(Sequence s1, Sequence s2, int i, int j, Costs.Direction from) Returns the costs for the alignment if s1(i) and s2(j) coming from from.
double	Costs.getCostFor(Sequence s1, Sequence s2, int i, int j, Costs.Direction from) Returns the costs for the alignment if s1(i) and s2(j) coming from from.

Uses of Sequence in de.jstacs.classifier

Methods in de.jstacs.classifier 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, Sample bg) Returns the p-value for a Sequence candidate with respect to a given background Sample.
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.classifier.modelBased

Methods in de.jstacs.classifier.modelBased with parameters of type Sequence

protected double

ModelBasedClassifier.getScore(Sequence seq, int i, boolean check)

Uses of Sequence in de.jstacs.classifier.scoringFunctionBased

Methods in de.jstacs.classifier.scoringFunctionBased with parameters of type Sequence

protected double

ScoreClassifier.getScore(Sequence seq, int i, boolean check)

Uses of Sequence in de.jstacs.classifier.scoringFunctionBased.sampling

Methods in de.jstacs.classifier.scoringFunctionBased.sampling with parameters of type Sequence

protected double

SamplingScoreBasedClassifier.getScore(Sequence seq, int cls, boolean check)

Uses of Sequence in de.jstacs.data

Subclasses of Sequence in de.jstacs.data

protected static class	Sequence.CompositeSequence<T> The class handles composite Sequences.
static class	Sequence.RecursiveSequence<T> This is the main class for subsequences, composite sequences, ... .
protected static class	Sequence.SubSequence<T> This class handles subsequences.

Fields in de.jstacs.data declared as Sequence

protected Sequence<T>	Sequence.RecursiveSequence.content The internal sequence.
protected Sequence<T>	Sequence.rc The pointer to the reverse complement of the Sequence.

Methods in de.jstacs.data 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[]	Sample.getAllElements() Returns an array of Sequences containing all elements of this Sample.
Sequence<T>	Sequence.getCompositeSequence(AlphabetContainer abc, int[] starts, int[] lengths) This method should be used if one wants to create a Sample of Sequence.CompositeSequences.
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	Sample.getElementAt(int i) This method returns the element, i.e. the Sequence, with index i.
Sequence	Sample.WeightedSampleFactory.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	Sequence.getSubSequence(AlphabetContainer abc, int start) This method should be used if one wants to create a Sample 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 Sample of subsequences of defined length.
Sequence	Sequence.getSubSequence(int start) This is a very efficient way to create a subsequence/suffix for Sequences 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 Sequences with a simple AlphabetContainer.
Sequence	Sample.ElementEnumerator.next()
Sequence	SequenceEnumeration.nextElement()
Sequence	SampleKMerEnumerator.nextElement()
Sequence	Sample.ElementEnumerator.nextElement()
Sequence<int[]>	DiscreteSequenceEnumerator.nextElement()
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 that return types with arguments of type Sequence

Iterator<Sequence>

Sample.iterator()

Methods in de.jstacs.data 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.
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)
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 with parameters of type Sequence

Sample(String annotation, Sequence... seqs)
Creates a new Sample from an array of Sequences and a given annotation.

Sequence.CompositeSequence(AlphabetContainer abc, Sequence<T> seq, int[] starts, int[] lengths)
This constructor should be used if one wants to create a Sample of Sequence.CompositeSequences.

Sequence.CompositeSequence(Sequence seq, int[] starts, int[] lengths)
This is a very efficient way to create a Sequence.CompositeSequence for Sequences 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 Sample of Sequence.SubSequences 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 Sequences with a simple AlphabetContainer.

SequenceEnumeration(Sequence... sequences)
This constructor creates an instance based on the user-specified Sequences 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 Sequences sequences.

Uses of Sequence in de.jstacs.data.sequences

Subclasses of Sequence in de.jstacs.data.sequences

class	ArbitrarySequence This class is for any continuous or hybrid sequence.
class	ByteSequence This class is for sequences with the alphabet symbols encoded as bytes and can therefore be used for discrete AlphabetContainers with alphabets that use only few symbols.
class	DiscreteSequence This is the super class of discrete sequences.
class	IntSequence This class is for sequences with the alphabet symbols encoded as ints and can therefore be used for discrete AlphabetContainers 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 DiscreteAlphabetMappings.
class	MultiDimensionalDiscreteSequence This class is for multidimensional sequences that can be used, for instance, for phylogenetic footprinting.
class	PermutedSequence<T> This class is for permuted sequences.
class	ShortSequence This class is for sequences with the alphabet symbols encoded as shortss and can therefore be used for discrete AlphabetContainers 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.

Methods in de.jstacs.data.sequences that return Sequence

protected Sequence<int[]>	MultiDimensionalDiscreteSequence.flatCloneWithoutAnnotation()
protected Sequence	MappedDiscreteSequence.flatCloneWithoutAnnotation()

Constructors in de.jstacs.data.sequences with parameters of type Sequence

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

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.models

Methods in de.jstacs.models with parameters of type Sequence

double	Model.getLogProbFor(Sequence sequence) Returns the logarithm of the probability of the given sequence given the model.
double	AbstractModel.getLogProbFor(Sequence sequence)
double	Model.getLogProbFor(Sequence sequence, int startpos) Returns the logarithm of the probability of (a part of) the given sequence given the model.
double	AbstractModel.getLogProbFor(Sequence sequence, int startpos)
double	NormalizableScoringFunctionModel.getLogProbFor(Sequence sequence, int startpos, int endpos)
double	Model.getLogProbFor(Sequence sequence, int startpos, int endpos) Returns the logarithm of the probability of (a part of) the given sequence given the model.
double	CompositeModel.getLogProbFor(Sequence sequence, int startpos, int endpos)
double	AbstractModel.getLogProbFor(Sequence sequence, int startpos, int endpos)
double	Model.getProbFor(Sequence sequence) Returns the probability of the given sequence given the model.
double	AbstractModel.getProbFor(Sequence sequence)
double	Model.getProbFor(Sequence sequence, int startpos) Returns the probability of (a part of) the given sequence given the model.
double	AbstractModel.getProbFor(Sequence sequence, int startpos)
double	VariableLengthWrapperModel.getProbFor(Sequence sequence, int startpos, int endpos)
double	UniformModel.getProbFor(Sequence sequence, int startpos, int endpos)
double	NormalizableScoringFunctionModel.getProbFor(Sequence sequence, int startpos, int endpos)
double	Model.getProbFor(Sequence sequence, int startpos, int endpos) Returns the probability of (a part of) the given sequence given the model.
double	CompositeModel.getProbFor(Sequence sequence, int startpos, int endpos)

Uses of Sequence in de.jstacs.models.discrete

Methods in de.jstacs.models.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	DiscreteGraphicalModel.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.models.discrete.homogeneous

Methods in de.jstacs.models.discrete.homogeneous that return Sequence

protected abstract Sequence	HomogeneousModel.getRandomSequence(Random r, int length) This method creates a random Sequence from a trained homogeneous model.
protected Sequence	HomogeneousMM.getRandomSequence(Random r, int length)

Methods in de.jstacs.models.discrete.homogeneous with parameters of type Sequence

void	HomogeneousModel.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	HomogeneousModel.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	HomogeneousModel.getLogProbFor(Sequence sequence, int startpos, int endpos)
double	HomogeneousModel.getProbFor(Sequence sequence, int startpos, int endpos)
protected abstract double	HomogeneousModel.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)
protected abstract double	HomogeneousModel.probFor(Sequence sequence, int startpos, int endpos) This method computes the probability of the given Sequence in the given interval.
protected double	HomogeneousMM.probFor(Sequence sequence, int startpos, int endpos)
int	HomogeneousModel.HomCondProb.satisfiesSpecificConstraint(Sequence seq, int start)

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

Methods in de.jstacs.models.discrete.inhomogeneous with parameters of type Sequence

protected void	InhomogeneousDGM.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	DAGModel.getLogProbFor(Sequence sequence, int startpos, int endpos)
double	DAGModel.getProbFor(Sequence sequence, int startpos, int endpos)
int	InhConstraint.satisfiesSpecificConstraint(Sequence s, int start)

Uses of Sequence in de.jstacs.models.hmm

Methods in de.jstacs.models.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)
double	AbstractHMM.getLogProbFor(Sequence sequence, int startpos)
double	AbstractHMM.getLogProbFor(Sequence sequence, int startpos, int endpos)
abstract double	AbstractHMM.getLogProbForPath(IntList path, int startPos, Sequence seq)
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	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[][]	AbstractHMM.getLogStatePosteriorMatrixFor(int startPos, int endPos, Sequence seq) This method returns the log state posterior of all states for a sequence.
double	AbstractHMM.getProbFor(Sequence sequence)
double	AbstractHMM.getProbFor(Sequence sequence, int startpos)
double	AbstractHMM.getProbFor(Sequence sequence, int startpos, int endpos)
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.models.hmm.models

Methods in de.jstacs.models.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.getLogScore(Sequence seq)
double	DifferentiableHigherOrderHMM.getLogScore(Sequence seq, int start)
double	DifferentiableHigherOrderHMM.getLogScoreAndPartialDerivation(Sequence seq, int startPos, IntList indices, DoubleList partialDer)
double	DifferentiableHigherOrderHMM.getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)
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)
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.models.hmm.states

Methods in de.jstacs.models.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	SimpleState.getLogScoreFor(int startPos, int endPos, Sequence seq)

Uses of Sequence in de.jstacs.models.hmm.states.emissions

Methods in de.jstacs.models.hmm.states.emissions with parameters of type Sequence

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	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	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.models.hmm.states.emissions.continuous

Methods in de.jstacs.models.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.models.hmm.states.emissions.discrete

Methods in de.jstacs.models.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.models.hmm.transitions

Methods in de.jstacs.models.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	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.models.hmm.transitions.elements

Fields in de.jstacs.models.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.models.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.models.mixture

Methods in de.jstacs.models.mixture that return Sequence

protected Sequence[]	StrandModel.emitSampleUsingCurrentParameterSet(int n, int... lengths)
protected Sequence[]	MixtureModel.emitSampleUsingCurrentParameterSet(int n, int... lengths)
protected abstract Sequence[]	AbstractMixtureModel.emitSampleUsingCurrentParameterSet(int n, int... lengths) The method returns an array of sequences using the current parameter set.

Methods in de.jstacs.models.mixture with parameters of type Sequence

int	AbstractMixtureModel.getIndexOfMaximalComponentFor(Sequence s) Returns the index i of the component with P(i|s) maximal.
double	AbstractMixtureModel.getLogProbFor(int component, Sequence s) Returns the logarithmic probability for the sequence and the given component.
double	AbstractMixtureModel.getLogProbFor(Sequence sequence, int startpos, int endpos)
protected double	StrandModel.getLogProbUsingCurrentParameterSetFor(int component, Sequence s, int start, int end)
protected double	MixtureModel.getLogProbUsingCurrentParameterSetFor(int component, Sequence s, int start, int end)
protected abstract double	AbstractMixtureModel.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.
double	AbstractMixtureModel.getProbFor(Sequence sequence, int startpos, int endpos)

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

Methods in de.jstacs.models.mixture.motif that return Sequence

protected Sequence[]

HiddenMotifMixture.emitSampleUsingCurrentParameterSet(int n, int... lengths) Standard implementation throwing an OperationNotSupportedException.

Methods in de.jstacs.models.mixture.motif with parameters of type Sequence

protected double	SingleHiddenMotifMixture.getLogProbUsingCurrentParameterSetFor(int component, Sequence seq, int start, int end)
double[]	SingleHiddenMotifMixture.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)
double[]	SingleHiddenMotifMixture.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)

Uses of Sequence in de.jstacs.models.utils

Methods in de.jstacs.models.utils that return Sequence

static Sequence

ModelTester.getMostProbableSequence(Model m, int length) Returns one most probable sequence for the discrete model m.

Uses of Sequence in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery that return Sequence

static Sequence[]	MutableMotifDiscovererToolbox.enumerate(ScoringFunction[] funs, int[] classIndex, int[] motifIndex, RecyclableSequenceEnumerator[] rse, double weight, SFBasedOptimizableFunction opt, OutputStream out) This method allows to enumerate all possible seeds for a number of motifs in the MutableMotifDiscoverers of a specific classes.
static Sequence	MutableMotifDiscovererToolbox.enumerate(ScoringFunction[] funs, int classIndex, int motifIndex, RecyclableSequenceEnumerator rse, double weight, SFBasedOptimizableFunction 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(Sample 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 Sequences.
static Hashtable<Sequence,BitSet[]>	KMereStatistic.getKmereSequenceStatistic(int k, boolean bothStrands, int addIndex, Sample... 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 Sequences.
static Pair<Sequence,BitSet[]>[]	KMereStatistic.getKmereSequenceStatistic(boolean bothStrands, int maxMismatch, HashSet<Sequence> filter, Sample... 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.scoringFunctions

Methods in de.jstacs.scoringFunctions with parameters of type Sequence

int	MappingScoringFunction.getIndexOfMaximalComponentFor(Sequence sequence)
int	IndependentProductScoringFunction.getIndexOfMaximalComponentFor(Sequence sequence)
double	ScoringFunction.getLogScore(Sequence seq) Returns the logarithmic score for the Sequence seq.
double	AbstractNormalizableScoringFunction.getLogScore(Sequence seq) Returns the logarithmic score for a given Sequence.
double	UniformScoringFunction.getLogScore(Sequence seq, int start)
double	ScoringFunction.getLogScore(Sequence seq, int start) Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
double	NormalizedScoringFunction.getLogScore(Sequence seq, int start)
double	MRFScoringFunction.getLogScore(Sequence seq, int start)
double	MappingScoringFunction.getLogScore(Sequence seq, int start)
double	IndependentProductScoringFunction.getLogScore(Sequence seq, int start)
double	AbstractVariableLengthScoringFunction.getLogScore(Sequence seq, int start)
double	VariableLengthScoringFunction.getLogScore(Sequence seq, int start, int length) This method computes the logarithm of the score for a given subsequence.
double	CMMScoringFunction.getLogScore(Sequence seq, int start, int length)
double	VariableLengthScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList) This method computes the logarithm of the score and the partial derivations for a given subsequence.
double	CMMScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)
double	UniformScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList dList)
double	ScoringFunction.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	NormalizedScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
double	MRFScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
double	MappingScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
double	IndependentProductScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
double	AbstractVariableLengthScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList dList)
double	ScoringFunction.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	AbstractNormalizableScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)
double[]	MappingScoringFunction.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)
double[]	IndependentProductScoringFunction.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile dist)
StrandedLocatedSequenceAnnotationWithLength.Strand	NormalizedScoringFunction.getStrand(Sequence seq, int startPos) This method return the preferred StrandedLocatedSequenceAnnotationWithLength.Strand for a Sequence beginning at startPos.
double[]	MappingScoringFunction.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)
double[]	IndependentProductScoringFunction.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)

Uses of Sequence in de.jstacs.scoringFunctions.directedGraphicalModels

Methods in de.jstacs.scoringFunctions.directedGraphicalModels with parameters of type Sequence

void	ParameterTree.addCount(Sequence seq, int start, double count) Adds count to the parameter as returned by ParameterTree.getParameterFor(Sequence, int).
double	Parameter.doesApplyFor(Sequence seq) Indicates if the Sequence seq fulfills all requirements defined in the Parameter.context.
double	BayesianNetworkScoringFunction.getLogScore(Sequence seq, int start)
double	BayesianNetworkScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
Parameter	ParameterTree.getParameterFor(Sequence seq, int start) Returns the Parameter that is responsible for the suffix of sequence seq starting at position start.
double[]	BayesianNetworkScoringFunction.getPositionDependentKMerProb(Sequence kmer) Returns the probability of kmer for all possible positions in this BayesianNetworkScoringFunction starting at position kmer.getLength()-1.
double	ParameterTree.getProbFor(Sequence sequence) Returns the probability of Sequence sequence in this ParameterTree.

Uses of Sequence in de.jstacs.scoringFunctions.homogeneous

Methods in de.jstacs.scoringFunctions.homogeneous with parameters of type Sequence

double	UniformHomogeneousScoringFunction.getLogScore(Sequence seq, int start, int length)
double	HMMScoringFunction.getLogScore(Sequence seq, int start, int length)
double	HMM0ScoringFunction.getLogScore(Sequence seq, int start, int length)
double	UniformHomogeneousScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)
double	HMMScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)
double	HMM0ScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)

Uses of Sequence in de.jstacs.scoringFunctions.mix

Methods in de.jstacs.scoringFunctions.mix with parameters of type Sequence

protected void	StrandScoringFunction.fillComponentScores(Sequence seq, int start)
protected void	MixtureScoringFunction.fillComponentScores(Sequence seq, int start)
protected abstract void	AbstractMixtureScoringFunction.fillComponentScores(Sequence seq, int start) Fills the internal array AbstractMixtureScoringFunction.componentScore with the logarithmic scores of the components given a Sequence.
int	MixtureScoringFunction.getIndexOfMaximalComponentFor(Sequence sequence)
int	AbstractMixtureScoringFunction.getIndexOfMaximalComponentFor(Sequence seq, int start) Returns the index of the component that has the greatest impact on the complete score for a Sequence.
double	AbstractMixtureScoringFunction.getLogScore(Sequence seq, int start)
double	VariableLengthMixtureScoringFunction.getLogScore(Sequence seq, int start, int length)
double	VariableLengthMixtureScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList partialDer)
double	StrandScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
double	MixtureScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
double[]	AbstractMixtureScoringFunction.getProbsForComponent(Sequence seq) Returns the probabilities for each component given a Sequence.
double[]	MixtureScoringFunction.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)
StrandedLocatedSequenceAnnotationWithLength.Strand	StrandScoringFunction.getStrand(Sequence seq, int startPos) This method returns the preferred StrandedLocatedSequenceAnnotationWithLength.Strand for a given subsequence.
double[]	MixtureScoringFunction.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)

Uses of Sequence in de.jstacs.scoringFunctions.mix.motifSearch

Methods in de.jstacs.scoringFunctions.mix.motifSearch with parameters of type Sequence

protected int	HiddenMotifsMixture.fillComponentScoreOf(int i, Sequence seq, int start) This method fills an internal array with the partial scores.
protected void	HiddenMotifsMixture.fillComponentScores(Sequence seq, int start)
int	HiddenMotifsMixture.getIndexOfMaximalComponentFor(Sequence sequence)
double	PositionScoringFunction.getLogScore(Sequence seq, int start)
double	HiddenMotifsMixture.getLogScore(Sequence seq, int start)
double	PositionScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
double	HiddenMotifsMixture.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
double[]	HiddenMotifsMixture.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile dist)
double[]	HiddenMotifsMixture.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)
protected int[]	PositionScoringFunction.getValuesFromSequence(Sequence seq, int start) This method extracts the values form a sequence.