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 Classifiers that are based on SequenceScores. It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior.
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifiers.trainSMBased	Provides the classes for Classifiers that are based on TrainableStatisticalModels.
de.jstacs.data	Provides classes for the representation of data. The base classes to represent data are Alphabet and AlphabetContainer for representing alphabets, Sequence and its sub-classes to represent continuous and discrete sequences, and DataSet to represent data sets comprising a set of sequences.
de.jstacs.data.sequences	Provides classes for representing sequences. The implementations of sequences currently include DiscreteSequences prepared for alphabets of different sizes, and ArbitrarySequences that may contain continuous values as well. As sub-package provides the facilities to annotate Sequences.
de.jstacs.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.motifDiscovery	This package provides the framework including the interface for any de novo motif discoverer.
de.jstacs.sequenceScores	Provides all SequenceScores, 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 StatisticalModels, which can compute a proper (i.e., normalized) likelihood over the input space of sequences. StatisticalModels can be further differentiated into TrainableStatisticalModels, which can be learned from a single input DataSet, and DifferentiableStatisticalModels, which define a proper likelihood but can also compute gradients like DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable	Provides all DifferentiableStatisticalModels, which can compute the gradient with respect to their parameters for a given input Sequence.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous	Provides DifferentiableStatisticalModels that are homogeneous, i.e.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture	Provides DifferentiableSequenceScores that are mixtures of other DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, which can be learned from a single DataSet.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous	This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.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 Sequences containing all elements of this DataSet.
Sequence	DataSet.getElementAt(int i) This method returns the element, i.e.
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 Sequences and a given annotation.
This constructor is specially designed for the method StatisticalModel.emitDataSet(int, int...)

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

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

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	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 bytes and can therefore be used for discrete AlphabetContainers with alphabets that use only few symbols.
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	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 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.
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.

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.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	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 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	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 Sequences.

PermutedSequence(Sequence<T> seq)
Creates a new PermutedSequence by shuffling the symbols of a given Sequence.

PermutedSequence(Sequence<T> seq, int[] permutation)
Creates a new PermutedSequence for a given permutation

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.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 DataSet 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.

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 MutableMotifDiscoverers 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 sequences of length motifLength so that each string is contained in all sequences of the data set, more precisely in the data set or the reverse complementary data set.

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, 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 Sequences.
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	MultiDimensionalSequenceWrapperDiffSS.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)
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	MultiDimensionalSequenceWrapperDiffSS.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	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	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	MEManager.getLogProbFor(Sequence sequence, int startpos, int endpos)
double	DAGTrainSM.getLogProbFor(Sequence sequence, int startpos, int endpos)
double	MEM.getLogScoreFor(Sequence seq, int start) Returns the logarithmic score for the sequence beginning at start.
double	MEM.getScoreFor(Sequence seq, int start) Returns the score for the sequence beginning at start.
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

Fields in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous with type parameters of type Sequence

protected HashMap<Sequence,double[]>

MultivariateGaussianEmission.gammas Contains the emission sequences and corresponding gammas (state-posteriors) required for the estimation of the standard deviation.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous with parameters of type Sequence

void	MultivariateGaussianEmission.addToStatistic(boolean forward, int startPos, int endPos, double weight, Sequence seq)
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	MultivariateGaussianEmission.getLogProbFor(boolean forward, int startPos, int endPos, 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.