Package | Description |
---|---|
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.motifDiscovery |
This package provides the framework including the interface for any de novo motif discoverer.
|
de.jstacs.sequenceScores.differentiable | |
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif | |
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.
|
Modifier and Type | Method and Description |
---|---|
int |
DataSet.getNumberOfElementsWithLength(int len)
Returns the number of overlapping elements that can be extracted.
|
double |
DataSet.getNumberOfElementsWithLength(int len,
double[] weights)
Returns the weighted number of overlapping elements that can be extracted.
|
Pair<DataSet,double[]> |
DataSet.resize(double[] weights,
int subsequenceLength)
Returns modified version of this data set with adjusted subsequence length.
|
static Pair<DataSet,double[]> |
DataSet.union(DataSet[] s,
double[][] weights,
boolean[] in)
|
Constructor and Description |
---|
DataSet(AlphabetContainer abc,
AbstractStringExtractor se)
|
DataSet(AlphabetContainer abc,
AbstractStringExtractor se,
int subsequenceLength)
Creates a new
DataSet from a StringExtractor
using the given AlphabetContainer and all overlapping windows of
length subsequenceLength . |
DataSet(AlphabetContainer abc,
AbstractStringExtractor se,
String delim)
Creates a new
DataSet from a StringExtractor
using the given AlphabetContainer and a delimiter
delim . |
DataSet(AlphabetContainer abc,
AbstractStringExtractor se,
String delim,
int subsequenceLength)
Creates a new
DataSet from a StringExtractor
using the given AlphabetContainer , the given delimiter
delim and all overlapping windows of length
subsequenceLength . |
DataSet(AlphabetContainer abc,
AbstractStringExtractor se,
String delim,
int subsequenceLength,
double percentage)
Creates a new
DataSet from a StringExtractor
using the given AlphabetContainer , the given delimiter
delim and all overlapping windows of length
subsequenceLength . |
DataSet(DataSet s,
int subsequenceLength)
|
DNADataSet(String fName)
Creates a new data set of DNA sequence from a FASTA file with file name
fName . |
DNADataSet(String fName,
char ignore)
Creates a new data set of DNA sequence from a file with file name
fName . |
DNADataSet(String fName,
char ignore,
SequenceAnnotationParser parser)
Creates a new data set of DNA sequence from a file with file name
fName using the given parser . |
WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort,
DataSet... data)
Creates a new
DataSet.WeightedDataSetFactory on the given
DataSet (s) with DataSet.WeightedDataSetFactory.SortOperation sort . |
WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort,
DataSet[] data,
double[][] weights,
int length)
Creates a new
DataSet.WeightedDataSetFactory on the given array of
DataSet s and an array of weights with a given
length and DataSet.WeightedDataSetFactory.SortOperation sort . |
WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort,
DataSet data,
double[] weights)
Creates a new
DataSet.WeightedDataSetFactory on the given
DataSet and an array of weights with
DataSet.WeightedDataSetFactory.SortOperation sort . |
WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort,
DataSet data,
double[] weights,
int length)
Creates a new
DataSet.WeightedDataSetFactory on the given
DataSet and an array of weights with a given
length and DataSet.WeightedDataSetFactory.SortOperation sort . |
Modifier and Type | Method and Description |
---|---|
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) |
Constructor and Description |
---|
MultiDimensionalArbitrarySequence(SequenceAnnotation[] seqAn,
ArbitrarySequence... sequence)
This constructor creates an
MultiDimensionalDiscreteSequence from a set of individual Sequence s. |
MultiDimensionalDiscreteSequence(SequenceAnnotation[] seqAn,
SimpleDiscreteSequence... sequence)
This constructor creates an
MultiDimensionalDiscreteSequence from a set of individual Sequence s. |
MultiDimensionalSequence(SequenceAnnotation[] seqAn,
Sequence... sequence)
This constructor creates an
MultiDimensionalSequence from a set of individual Sequence s. |
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
double |
AbstractDifferentiableSequenceScore.getLogScoreAndPartialDerivation(Sequence seq,
int startpos,
int endpos,
IntList indices,
DoubleList partialDer) |
double |
AbstractDifferentiableSequenceScore.getLogScoreFor(Sequence seq,
int startpos,
int endpos) |
Modifier and Type | Method and Description |
---|---|
double[] |
ExtendedZOOPSDiffSM.getProfileOfScoresFor(int component,
int motif,
Sequence sequence,
int startpos,
MotifDiscoverer.KindOfProfile dist) |
Modifier and Type | Method and Description |
---|---|
protected void |
HigherOrderHMM.fillFwdMatrix(int startPos,
int endPos,
Sequence seq) |
Modifier and Type | Method and Description |
---|---|
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) |