Package | Description |
---|---|
de.jstacs.algorithms.alignment |
Provides classes for alignments.
|
de.jstacs.algorithms.alignment.cost |
Provides classes for cost functions used in alignments.
|
de.jstacs.classifiers |
This package provides the framework for any classifier.
|
de.jstacs.classifiers.differentiableSequenceScoreBased |
Provides the classes for
Classifier s that are based on SequenceScore s.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
AbstractScoreBasedClassifier s that are based on
SamplingDifferentiableStatisticalModel s
and that sample parameters using the Metropolis-Hastings algorithm. |
de.jstacs.classifiers.trainSMBased |
Provides the classes for
Classifier s that are based on TrainableStatisticalModel s. |
de.jstacs.data |
Provides classes for the representation of data.
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 DiscreteSequence s prepared for alphabets of different sizes, and ArbitrarySequence s that may
contain continuous values as well.As sub-package provides the facilities to annotate Sequence s. |
de.jstacs.data.sequences.annotation |
Provides the facilities to annotate
Sequence s using a number of pre-defined annotation types, or additional
implementations of the SequenceAnnotation class. |
de.jstacs.io |
Provides classes for reading data from and writing to a file and storing a number of datatypes, including all primitives, arrays of primitives, and
Storable s to an XML-representation. |
de.jstacs.motifDiscovery |
This package provides the framework including the interface for any de novo motif discoverer.
|
de.jstacs.sequenceScores |
Provides all
SequenceScore s, which can be used to score a Sequence , typically using some model assumptions. |
de.jstacs.sequenceScores.differentiable | |
de.jstacs.sequenceScores.differentiable.logistic | |
de.jstacs.sequenceScores.statisticalModels |
Provides all
StatisticalModel s, which can compute a proper (i.e., normalized) likelihood over the input space of sequences.StatisticalModel s can be further differentiated into TrainableStatisticalModel s,
which can be learned from a single input DataSet , and DifferentiableStatisticalModel s,
which define a proper likelihood but can also compute gradients like DifferentiableSequenceScore s. |
de.jstacs.sequenceScores.statisticalModels.differentiable |
Provides all
DifferentiableStatisticalModel s, which can compute the gradient with
respect to their parameters for a given input Sequence . |
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels |
Provides
DifferentiableStatisticalModel s that are directed graphical models. |
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous |
Provides
DifferentiableStatisticalModel s that are homogeneous, i.e. |
de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture | |
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture |
Provides
DifferentiableSequenceScore s that are mixtures of other DifferentiableSequenceScore s. |
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif | |
de.jstacs.sequenceScores.statisticalModels.trainable |
Provides all
TrainableStatisticalModel s, which can
be learned from a single DataSet . |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete | |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous | |
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous |
This package contains various inhomogeneous models.
|
de.jstacs.sequenceScores.statisticalModels.trainable.hmm |
The package provides all interfaces and classes for a hidden Markov model (HMM).
|
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models |
The package provides different implementations of hidden Markov models based on
AbstractHMM . |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states |
The package provides all interfaces and classes for states used in hidden Markov models.
|
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions | |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous | |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete | |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions |
The package provides all interfaces and classes for transitions used in hidden Markov models.
|
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.transitions.elements | |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
This package is the super package for any mixture model.
|
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif | |
de.jstacs.utils |
This package contains a bundle of useful classes and interfaces like ...
|
Modifier and Type | Field and Description |
---|---|
protected Sequence |
Alignment.s1
The first sequence
|
protected Sequence |
Alignment.s2
The second sequence
|
Modifier and Type | Method and Description |
---|---|
boolean |
Alignment.computeAlignment(Alignment.AlignmentType type,
Sequence s1,
int startS1,
int endS1,
Sequence s2,
int startS2,
int endS2)
Computes the alignment between
s1 and s2 starting from startS1 and startS2 until endS1 and endS2 , respectively. |
boolean |
Alignment.computeAlignment(Alignment.AlignmentType type,
Sequence s1,
int startS1,
int endS1,
Sequence s2,
int startS2,
int endS2)
Computes the alignment between
s1 and s2 starting from startS1 and startS2 until endS1 and endS2 , respectively. |
boolean |
Alignment.computeAlignment(Alignment.AlignmentType type,
Sequence s1,
Sequence s2)
Computes the alignment between
s1 and s2 . |
boolean |
Alignment.computeAlignment(Alignment.AlignmentType type,
Sequence s1,
Sequence s2)
Computes the alignment between
s1 and s2 . |
PairwiseStringAlignment |
Alignment.getAlignment(Alignment.AlignmentType type,
Sequence s1,
int startS1,
int endS1,
Sequence s2,
int startS2,
int endS2)
|
PairwiseStringAlignment |
Alignment.getAlignment(Alignment.AlignmentType type,
Sequence s1,
int startS1,
int endS1,
Sequence s2,
int startS2,
int endS2)
|
PairwiseStringAlignment |
Alignment.getAlignment(Alignment.AlignmentType type,
Sequence s1,
Sequence s2)
|
PairwiseStringAlignment |
Alignment.getAlignment(Alignment.AlignmentType type,
Sequence s1,
Sequence s2)
|
Modifier and Type | Method and Description |
---|---|
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) |
Modifier and Type | Method and Description |
---|---|
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)
|
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. |
Modifier and Type | Method and Description |
---|---|
protected double |
ScoreClassifier.getScore(Sequence seq,
int i,
boolean check) |
Modifier and Type | Method and Description |
---|---|
protected double |
SamplingScoreBasedClassifier.getScore(Sequence seq,
int cls,
boolean check) |
Modifier and Type | Method and Description |
---|---|
double[] |
TrainSMBasedClassifier.getLogLikelihoodRatio(Sequence seq)
Returns the log likelihood ratios along the sequence
seq for all
sliding windows of length AbstractClassifier.getLength() . |
protected double |
TrainSMBasedClassifier.getScore(Sequence seq,
int i,
boolean check) |
Modifier and Type | Method and Description |
---|---|
Sequence[] |
DataSet.getAllElements()
|
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 |
DataSet.ElementEnumerator.next() |
Sequence |
SequenceEnumeration.nextElement() |
Sequence<int[]> |
DiscreteSequenceEnumerator.nextElement() |
Sequence |
DataSetKMerEnumerator.nextElement() |
Sequence |
DataSet.ElementEnumerator.nextElement() |
Modifier and Type | Method and Description |
---|---|
Iterator<Sequence> |
DataSet.iterator() |
Modifier and Type | Method and Description |
---|---|
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. |
double |
DinucleotideProperty.getProperty(Sequence seq,
int start) |
ArbitrarySequence |
DinucleotideProperty.getPropertyAsSequence(Sequence original)
Computes this dinucleotide property for all overlapping dimers in
original
and returns the result as a Sequence of length original.getLength()-1 |
ArbitrarySequence |
DinucleotideProperty.getPropertyAsSequence(Sequence original,
DinucleotideProperty.Smoothing smoothing)
Computes this dinucleotide property for all overlapping dimers 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) |
Constructor and Description |
---|
DataSet(String annotation,
Sequence... seqs)
Creates a new
DataSet from an array of Sequence s 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
Sequence s sequences . |
Constructor and Description |
---|
DataSet(String annotation,
Collection<Sequence> seqs)
|
SequenceEnumeration(Collection<Sequence> sequences)
|
Modifier and Type | Class and Description |
---|---|
class |
ArbitraryFloatSequence
This class is for any continuous or hybrid sequence.
|
class |
ArbitrarySequence
This class is for any continuous or hybrid sequence.
|
class |
ByteSequence
This class is for sequences with the alphabet symbols encoded as
byte s and can therefore be used for discrete
AlphabetContainer s with alphabets that use only few symbols. |
class |
CyclicSequenceAdaptor<T>
This class is an adapter for sequence to mimic cyclic sequences.
|
class |
IntSequence
This class is for sequences with the alphabet symbols encoded as
int s and can therefore be used for discrete
AlphabetContainer s with alphabets that use a huge number of symbols. |
class |
MappedDiscreteSequence
This class allows to map a discrete
Sequence to an new Sequence using some DiscreteAlphabetMapping s. |
class |
MultiDimensionalArbitrarySequence
This class is for multidimensional arbitrary sequences.
|
class |
MultiDimensionalDiscreteSequence
This class is for multidimensional discrete sequences that can be used, for instance, for phylogenetic footprinting.
|
class |
MultiDimensionalSequence<T>
This class is for multidimensional sequences that can be used, for instance, for phylogenetic footprinting.
|
class |
PermutedSequence<T>
This class is for permuted sequences.
|
protected static class |
Sequence.CompositeSequence<T>
The class handles composite
Sequence s. |
static class |
Sequence.RecursiveSequence<T>
This is the main class for subsequences, composite sequences, ...
|
static class |
Sequence.SubSequence<T>
This class handles subsequences.
|
class |
ShortSequence
This class is for sequences with the alphabet symbols encoded as
shorts s and can therefore be used for discrete
AlphabetContainer s with alphabets that use many different symbols. |
class |
SimpleDiscreteSequence
This is the main class for any discrete sequence.
|
class |
SparseSequence
This class is an implementation for sequences on one alphabet with length 4.
|
Modifier and Type | Field and Description |
---|---|
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 . |
Modifier and Type | Method and Description |
---|---|
Sequence |
Sequence.annotate(boolean add,
SequenceAnnotation... annotation)
This method allows to append annotation to a
Sequence . |
Sequence |
Sequence.complement()
|
Sequence |
Sequence.complement(int start,
int end)
|
static Sequence |
Sequence.create(AlphabetContainer con,
SequenceAnnotation[] annotation,
String sequence,
String delim)
Creates a
Sequence from a String based on the given
AlphabetContainer using the given delimiter delim
and some annotation for the Sequence . |
static Sequence |
Sequence.create(AlphabetContainer con,
String sequence)
Creates a
Sequence from a String based on the given
AlphabetContainer using the standard delimiter for this
AlphabetContainer . |
static Sequence |
Sequence.create(AlphabetContainer con,
String sequence,
String delim)
Creates a
Sequence from a String based on the given
AlphabetContainer using the given delimiter delim . |
protected abstract Sequence |
Sequence.flatCloneWithoutAnnotation()
Works in analogy to
Object.clone() , but does not clone the
annotation. |
protected Sequence |
Sequence.CompositeSequence.flatCloneWithoutAnnotation() |
protected Sequence |
Sequence.SubSequence.flatCloneWithoutAnnotation() |
Sequence<T> |
Sequence.getCompositeSequence(AlphabetContainer abc,
int[] starts,
int[] lengths)
This method should be used if one wants to create a
DataSet of
Sequence.CompositeSequence s. |
Sequence |
Sequence.getCompositeSequence(int[] starts,
int[] lengths)
This is a very efficient way to create a
Sequence.CompositeSequence for
sequences with a simple AlphabetContainer . |
Sequence<T> |
Sequence.SubSequence.getOriginal()
Returns the original sequence, this sequence is a sub-sequence of.
|
Sequence |
MultiDimensionalSequence.getSequence(int index)
This method returns the internal sequence with index
index . |
Sequence |
Sequence.getSubSequence(AlphabetContainer abc,
int start)
This method should be used if one wants to create a
DataSet of
subsequences of defined length. |
Sequence |
Sequence.getSubSequence(AlphabetContainer abc,
int start,
int length)
This method should be used if one wants to create a
DataSet of
subsequences of defined length. |
Sequence |
Sequence.getSubSequence(int start)
This is a very efficient way to create a subsequence/suffix for
Sequence s with a simple AlphabetContainer . |
Sequence |
Sequence.getSubSequence(int start,
int length)
This is a very efficient way to create a subsequence of defined length
for
Sequence s with a simple AlphabetContainer . |
Sequence |
Sequence.reverse()
|
Sequence |
Sequence.reverse(int start,
int end)
|
Sequence |
Sequence.reverseComplement()
|
Sequence |
Sequence.reverseComplement(int start,
int end)
|
Sequence |
Sequence.SubSequence.reverseComplement(int start,
int end) |
Modifier and Type | Method and Description |
---|---|
int |
Sequence.compareTo(Sequence<T> s) |
int |
CyclicSequenceAdaptor.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)
Returns a new instance of a
MultiDimensionalSequence with given SequenceAnnotation s and given Sequence s. |
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.
|
Constructor and Description |
---|
CompositeSequence(AlphabetContainer abc,
Sequence<T> seq,
int[] starts,
int[] lengths)
This constructor should be used if one wants to create a
DataSet of Sequence.CompositeSequence s. |
CompositeSequence(Sequence seq,
int[] starts,
int[] lengths)
This is a very efficient way to create a
Sequence.CompositeSequence
for Sequence s with a simple AlphabetContainer . |
CyclicSequenceAdaptor(Sequence<T> seq)
Creates a new cyclic sequence of the length of the original sequence.
|
CyclicSequenceAdaptor(Sequence<T> seq,
int extLength)
Creates a new cyclic sequence of given virtual length (i.e., the length reported by
CyclicSequenceAdaptor.getLength() ). |
MultiDimensionalSequence(SequenceAnnotation[] seqAn,
Sequence... sequence)
This constructor creates an
MultiDimensionalSequence from a set of individual Sequence s. |
PermutedSequence(Sequence<T> seq)
Creates a new
PermutedSequence by shuffling the symbols of a
given Sequence . |
PermutedSequence(Sequence<T> seq,
int[] permutation)
Creates a new
PermutedSequence for a given permutation |
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 . |
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 . |
SubSequence(AlphabetContainer abc,
Sequence seq,
int start,
int length)
This constructor should be used if one wants to create a
DataSet of Sequence.SubSequence s of defined length. |
SubSequence(Sequence seq,
int start,
int length)
This is a very efficient way to create a
Sequence.SubSequence of
defined length for Sequence s with a simple
AlphabetContainer . |
Modifier and Type | Method and Description |
---|---|
Sequence |
ReferenceSequenceAnnotation.getReferenceSequence()
Returns the reference sequence.
|
Constructor and Description |
---|
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 . |
Modifier and Type | Method and Description |
---|---|
static Sequence[] |
XMLParser.extractSequencesWithTags(StringBuffer xml,
String tag)
Extracts a set of sequences from their XML representation.
|
Modifier and Type | Method and Description |
---|---|
static void |
XMLParser.appendSequencesWithTags(StringBuffer xml,
String tag,
Sequence... seqs)
|
Modifier and Type | Method and Description |
---|---|
static Sequence[] |
MutableMotifDiscovererToolbox.enumerate(DifferentiableSequenceScore[] funs,
int[] classIndex,
int[] motifIndex,
RecyclableSequenceEnumerator[] rse,
double weight,
DiffSSBasedOptimizableFunction opt,
OutputStream out)
This method allows to enumerate all possible seeds for a number of motifs in the
MutableMotifDiscoverer s of a specific classes. |
static Sequence |
MutableMotifDiscovererToolbox.enumerate(DifferentiableSequenceScore[] funs,
int classIndex,
int motifIndex,
RecyclableSequenceEnumerator rse,
double weight,
DiffSSBasedOptimizableFunction opt,
OutputStream out)
This method allows to enumerate all possible seeds for a motif in the
MutableMotifDiscoverer of a specific class. |
static Sequence[] |
KMereStatistic.getCommonString(DataSet data,
int motifLength,
boolean bothStrands)
This method returns an array of 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. |
Modifier and Type | Method and Description |
---|---|
static LinkedList<Sequence> |
KMereStatistic.getConservedPatterns(Hashtable<Sequence,BitSet[]> statistic,
int dataSetIndex,
int threshold)
This method returns a list of
Sequence s. |
static Hashtable<Sequence,BitSet[]> |
KMereStatistic.getKmereSequenceStatistic(int k,
boolean bothStrands,
int addIndex,
DataSet... data)
This method enables the user to get a statistic over all
k -mers
in the sequences. |
static Hashtable<Sequence,BitSet[]> |
KMereStatistic.merge(Hashtable<Sequence,BitSet[]> statistic,
int maximalMissmatch,
boolean bothStrands)
This method allows to merge the statistics of k-mers by allowing mismatches.
|
static Hashtable<Sequence,BitSet[]> |
KMereStatistic.removeBackground(Hashtable<Sequence,BitSet[]> statistic,
int fgIndex,
int bgIndex,
double fgWeight,
double bgWeight)
This method allows to remove those entries from the statistic that have a lower weighted foreground cardinality than the weighted background cardinality.
|
Modifier and Type | Method and Description |
---|---|
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 . |
Modifier and Type | Method and Description |
---|---|
static LinkedList<Sequence> |
KMereStatistic.getConservedPatterns(Hashtable<Sequence,BitSet[]> statistic,
int dataSetIndex,
int threshold)
This method returns a list of
Sequence s. |
static Pair<Sequence,BitSet[]>[] |
KMereStatistic.getKmereSequenceStatistic(boolean bothStrands,
int maxMismatch,
HashSet<Sequence> filter,
DataSet... data)
This method enables the user to get a statistic for a set of
k -mers. |
Pair<double[][],double[]> |
SignificantMotifOccurrencesFinder.getPWMAndPosDist(int motif,
DataSet data,
double[] weights,
double[] mean,
int addLeft,
int addRight,
LinkedList<Sequence> bs,
DoubleList bsWeight,
DoubleList bsScores)
Returns the position weight matrix and standard deviation of the position distribution using the given mean.
|
protected double[][] |
SignificantMotifOccurrencesFinder.getPWMAndPositions(int motif,
DataSet data,
double[] weights,
int addLeft,
int addRight,
int[][] positions,
double[][] pvals,
double[] mean,
double[] sd,
LinkedList<Sequence> bs,
DoubleList bsWeights,
DoubleList bsScores)
Returns the Position weight matrix (PWM) of the binding sites of motif
motif
in the data set data of the MotifDiscoverer of this SignificantMotifOccurrencesFinder
and fills with the positions of the binding sites within the sequences of data and the corresponding p-values into the corresponding arrays. |
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.
|
Modifier and Type | Method and Description |
---|---|
double |
SequenceScore.getLogScoreFor(Sequence seq)
Returns the logarithmic score for the
Sequence seq . |
double |
SequenceScore.getLogScoreFor(Sequence seq,
int start)
|
double |
SequenceScore.getLogScoreFor(Sequence seq,
int start,
int end)
|
Modifier and Type | Method and Description |
---|---|
double |
DifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq,
int start,
int end,
IntList indices,
DoubleList partialDer)
|
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)
|
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) |
Modifier and Type | Method and Description |
---|---|
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 |
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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) |
Modifier and Type | Method and Description |
---|---|
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)
|
double |
BNDiffSMParameterTree.getLocalScoreFor(Sequence seq,
int i)
Returns the score for the symbol at this
BNDiffSMParameterTree starting
from offset i |
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)
|
Modifier and Type | Method and Description |
---|---|
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) |
Modifier and Type | Method and Description |
---|---|
double |
LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder.getLogScoreAndPartialDerivation(Sequence seq,
int start,
IntList indices,
DoubleList partialDer) |
double |
LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder.getLogScoreFor(Sequence seq,
int start) |
Modifier and Type | Method and Description |
---|---|
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 . |
double[] |
AbstractMixtureDiffSM.getComponentScores(Sequence seq,
int start)
Return the scores for the individual components.
|
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) |
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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 |
PFMWrapperTrainSM.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) |
Modifier and Type | Method and Description |
---|---|
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 . |
Modifier and Type | Method and Description |
---|---|
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) |
Modifier and Type | Method and Description |
---|---|
void |
HomogeneousTrainSM.HomCondProb.addAll(Sequence seq,
double weight,
int start,
int prevIndex)
|
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) |
Modifier and Type | Method and Description |
---|---|
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) |
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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 . |
Modifier and Type | Method and Description |
---|---|
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) |
Modifier and Type | Method and Description |
---|---|
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)
|
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.
|
Modifier and Type | Field and Description |
---|---|
protected HashMap<Sequence,double[]> |
MultivariateGaussianEmission.gammas
Contains the emission sequences and corresponding gammas (state-posteriors) required for the estimation of the standard deviation.
|
Modifier and Type | Method and Description |
---|---|
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) |
Modifier and Type | Method and Description |
---|---|
protected static Sequence |
ReferenceSequenceDiscreteEmission.getReferenceSequence(Sequence seq)
Returns the reference sequence annotated to
seq . |
Modifier and Type | Method and Description |
---|---|
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.
|
protected int |
AbstractConditionalDiscreteEmission.getIndex(int seqPos,
Sequence seq)
Returns the index for position
seqPos in sequence seq . |
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) |
protected static Sequence |
ReferenceSequenceDiscreteEmission.getReferenceSequence(Sequence seq)
Returns the reference sequence annotated to
seq . |
Modifier and Type | Method and Description |
---|---|
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 . |
Modifier and Type | Field and Description |
---|---|
protected Hashtable<Sequence,double[]> |
DistanceBasedScaledTransitionElement.diagonalWeights
Contains the single epsilons of the diagonal elements required for estimating the self-transition probability.
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Modifier and Type | Method and Description |
---|---|
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) |
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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(int component,
Sequence s,
int start,
int end)
Returns the logarithmic probability for the sequence between start and end 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.
|
Modifier and Type | Method and Description |
---|---|
protected Sequence[] |
HiddenMotifMixture.emitDataSetUsingCurrentParameterSet(int n,
int... lengths)
Standard implementation throwing an
OperationNotSupportedException . |
Modifier and Type | Method and Description |
---|---|
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) |
Modifier and Type | Method and Description |
---|---|
static Sequence |
StatisticalModelTester.getMostProbableSequence(SequenceScore m,
int length)
Returns one most probable sequence for the discrete model
m . |
Modifier and Type | Method and Description |
---|---|
static Pair<IntList,ArrayList<Sequence>> |
LargeSequenceReader.readNextSequences(BufferedReader read,
StringBuffer lastHeader,
int minimumLength)
Returns the next chunk of input sequences.
|