| Package | Description |
|---|---|
| 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.gendismix |
Provides an implementation of a classifier that allows to train the parameters of a set of
DifferentiableStatisticalModels by
a unified generative-discriminative learning principle. |
| 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.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.alphabets |
Provides classes for the representation of discrete and continuous alphabets, including a
DNAAlphabet for the most common case of DNA-sequences. |
| de.jstacs.data.bioJava |
Provides an adapter between the representation of data in BioJava and the representation used in Jstacs.
|
| 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.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
Storables to an XML-representation. |
| de.jstacs.parameters |
This package provides classes for parameters that establish a general convention for the description of parameters
as defined in the
Parameter-interface. |
| 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.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.localMixture | |
| 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.homogeneous.parameters | |
| de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous |
This package contains various inhomogeneous models.
|
| de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters | |
| 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.states.emissions | |
| de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.continuous | |
| de.jstacs.sequenceScores.statisticalModels.trainable.hmm.states.emissions.discrete | |
| de.jstacs.utils |
This package contains a bundle of useful classes and interfaces like ...
|
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
AbstractClassifier.getAlphabetContainer()
This method returns the container of alphabets that is used in the
classifier.
|
| Constructor and Description |
|---|
AbstractClassifier(AlphabetContainer abc)
The constructor for a homogeneous classifier.
|
AbstractClassifier(AlphabetContainer abc,
int length)
The constructor for an inhomogeneous classifier.
|
AbstractScoreBasedClassifier(AlphabetContainer abc,
int classes)
The constructor for a homogeneous classifier.
|
AbstractScoreBasedClassifier(AlphabetContainer abc,
int classes,
double classWeight)
The constructor for a homogeneous classifier.
|
AbstractScoreBasedClassifier(AlphabetContainer abc,
int length,
int classes)
The constructor for an inhomogeneous classifier.
|
AbstractScoreBasedClassifier(AlphabetContainer abc,
int length,
int classes,
double classWeight)
The constructor for an inhomogeneous classifier.
|
| Constructor and Description |
|---|
ScoreClassifierParameterSet(Class<? extends ScoreClassifier> instanceClass,
AlphabetContainer alphabet,
int length,
byte algo,
AbstractTerminationCondition tc,
double lineps,
double startD,
boolean free,
OptimizableFunction.KindOfParameter kind)
The constructor for a simple, instantiated parameter set.
|
ScoreClassifierParameterSet(Class<? extends ScoreClassifier> instanceClass,
AlphabetContainer alphabet,
int length,
byte algo,
double eps,
double lineps,
double startD,
boolean free,
OptimizableFunction.KindOfParameter kind)
The constructor for a simple, instantiated parameter set.
|
| Constructor and Description |
|---|
GenDisMixClassifierParameterSet(AlphabetContainer alphabet,
int length,
byte algo,
double eps,
double lineps,
double startD,
boolean free,
OptimizableFunction.KindOfParameter kind,
boolean norm,
int threads)
The default constructor that constructs a new
GenDisMixClassifierParameterSet. |
GenDisMixClassifierParameterSet(Class<? extends ScoreClassifier> instanceClass,
AlphabetContainer alphabet,
int length,
byte algo,
double eps,
double lineps,
double startD,
boolean free,
OptimizableFunction.KindOfParameter kind,
boolean norm,
int threads)
The default constructor that constructs a new
GenDisMixClassifierParameterSet. |
| Constructor and Description |
|---|
SamplingGenDisMixClassifierParameterSet(AlphabetContainer alphabet,
int length,
int numStarts,
int testSamplings,
int stationarySamplings,
String outfilePrefix,
int threads)
Create a new
SamplingGenDisMixClassifierParameterSet with a grouped sampling scheme, sampling all parameters
(and not only the free ones), and adaption of the variance. |
SamplingGenDisMixClassifierParameterSet(AlphabetContainer alphabet,
int length,
int numStarts,
SamplingScoreBasedClassifier.SamplingScheme scheme,
int testSamplings,
int stationarySamplings,
boolean freeParameters,
boolean adaptVariance,
String outfilePrefix,
int threads)
Create a new
SamplingGenDisMixClassifierParameterSet. |
SamplingGenDisMixClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier> instanceClass,
AlphabetContainer alphabet,
int length,
int numStarts,
SamplingScoreBasedClassifier.SamplingScheme scheme,
int testSamplings,
int stationarySamplings,
boolean freeParameters,
boolean adaptVariance,
String outfilePrefix,
int threads)
Create a new
SamplingGenDisMixClassifierParameterSet. |
SamplingScoreBasedClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier> instanceClass,
AlphabetContainer alphabet,
int length,
int numStarts,
int testSamplings,
int stationarySamplings,
String outfilePrefix)
Create a new
SamplingScoreBasedClassifierParameterSet with a grouped sampling scheme, sampling all parameters
(and not only the free ones), and adaption of the variance. |
SamplingScoreBasedClassifierParameterSet(Class<? extends SamplingScoreBasedClassifier> instanceClass,
AlphabetContainer alphabet,
int length,
int numStarts,
SamplingScoreBasedClassifier.SamplingScheme scheme,
int testSamplings,
int stationarySamplings,
boolean freeParameters,
boolean adaptVariance,
String outfilePrefix)
Create a new
SamplingScoreBasedClassifierParameterSet. |
| Modifier and Type | Class and Description |
|---|---|
static class |
AlphabetContainer.AbstractAlphabetContainerParameterSet<T extends AlphabetContainer>
This class is the super class of any
InstanceParameterSet for AlphabetContainer. |
| Modifier and Type | Field and Description |
|---|---|
static AlphabetContainer |
DinucleotideProperty.continuousAlphabet |
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
DataSet.getAlphabetContainer()
Returns the
AlphabetContainer of this DataSet. |
AlphabetContainer |
AlphabetContainer.getCompositeContainer(int[] start,
int[] length)
Returns an
AlphabetContainer of Alphabets e.g. |
static AlphabetContainer |
AlphabetContainer.getSimplifiedAlphabetContainer(Alphabet[] abc,
int[] assignment)
This method creates a new
AlphabetContainer that uses as less as
possible Alphabets to describe the container. |
AlphabetContainer |
AlphabetContainer.getSubContainer(int start,
int length)
Returns a sub-container with the
Alphabets for the positions
starting at start and with length length. |
static AlphabetContainer |
AlphabetContainer.insertAlphabet(AlphabetContainer aC,
Alphabet a,
boolean[] useNewAlphabet)
This method may be used to construct a new
AlphabetContainer by
incorporating additional Alphabets into an existing
AlphabetContainer. |
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer.AbstractAlphabetContainerParameterSet<? extends AlphabetContainer> |
AlphabetContainer.getCurrentParameterSet() |
| Modifier and Type | Method and Description |
|---|---|
boolean |
AlphabetContainer.checkConsistency(AlphabetContainer abc)
Checks if this
AlphabetContainer is consistent consistent with
another AlphabetContainer. |
int |
AlphabetContainer.compareTo(AlphabetContainer abc) |
static AlphabetContainer |
AlphabetContainer.insertAlphabet(AlphabetContainer aC,
Alphabet a,
boolean[] useNewAlphabet)
This method may be used to construct a new
AlphabetContainer by
incorporating additional Alphabets into an existing
AlphabetContainer. |
| Constructor and Description |
|---|
AlphabetContainer(AlphabetContainer[] cons,
int[] lengths)
Creates an new sparse
AlphabetContainer based on given
AlphabetContainers. |
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. |
DiscreteSequenceEnumerator(AlphabetContainer con,
int length,
boolean sparse)
Creates a new
DiscreteSequenceEnumerator from a given
AlphabetContainer and a length. |
| Constructor and Description |
|---|
AlphabetContainerParameterSet(Class<? extends AlphabetContainer> instanceClass,
Alphabet... alphabets)
/**
Creates a new
AlphabetContainerParameterSet from an array of
Alphabets for a given sub-class of AlphabetContainer. |
| Modifier and Type | Class and Description |
|---|---|
class |
DNAAlphabetContainer
This class implements a singleton for an
AlphabetContainer that can be used for DNA. |
| Modifier and Type | Method and Description |
|---|---|
static DataSet |
BioJavaAdapter.sequenceIteratorToDataSet(SequenceIterator it,
FeatureFilter filter,
AlphabetContainer con)
This method creates a new
DataSet from a SequenceIterator. |
| Modifier and Type | Field and Description |
|---|---|
protected AlphabetContainer |
Sequence.alphabetCon
The underlying alphabets.
|
protected AlphabetContainer |
MappedDiscreteSequence.originalAlphabetContainer
The original
AlphabetContainer. |
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
Sequence.getAlphabetContainer()
Return the alphabets, i.e.
|
static AlphabetContainer |
MappedDiscreteSequence.getNewAlphabetContainer(AlphabetContainer original,
DiscreteAlphabetMapping... transformation)
This method allows to create a new
AlphabetContainer given an old AlphabetContainer and some DiscreteAlphabetMappings. |
| Modifier and Type | Method and Description |
|---|---|
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. |
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. |
static DataSet |
SparseSequence.getDataSet(AlphabetContainer con,
AbstractStringExtractor... se)
This method allows to create a
DataSet containing SparseSequences. |
static DataSet |
ArbitraryFloatSequence.getDataSet(AlphabetContainer con,
AbstractStringExtractor... se)
This method allows to create a
DataSet containing ArbitraryFloatSequences. |
static DataSet |
SparseSequence.getDataSet(AlphabetContainer con,
String filename)
This method allows to create a
DataSet containing SparseSequences using
a file name. |
static DataSet |
ArbitraryFloatSequence.getDataSet(AlphabetContainer con,
String filename)
This method allows to create a
DataSet containing ArbitraryFloatSequences using
a file name. |
static DataSet |
SparseSequence.getDataSet(AlphabetContainer con,
String filename,
SequenceAnnotationParser parser)
This method allows to create a
DataSet containing SparseSequences using
a file name. |
static DataSet |
ArbitraryFloatSequence.getDataSet(AlphabetContainer con,
String filename,
SequenceAnnotationParser parser)
This method allows to create a
DataSet containing ArbitraryFloatSequences using
a file name. |
static AlphabetContainer |
MappedDiscreteSequence.getNewAlphabetContainer(AlphabetContainer original,
DiscreteAlphabetMapping... transformation)
This method allows to create a new
AlphabetContainer given an old AlphabetContainer and some DiscreteAlphabetMappings. |
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. |
| Constructor and Description |
|---|
ArbitraryFloatSequence(AlphabetContainer alphabetContainer,
float[] content)
Creates a new
ArbitraryFloatSequence from an array of
float-encoded alphabet symbols. |
ArbitraryFloatSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
String sequence,
String delim)
|
ArbitraryFloatSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
SymbolExtractor extractor)
Creates a new
ArbitraryFloatSequence from a SymbolExtractor. |
ArbitraryFloatSequence(AlphabetContainer alphabetContainer,
String sequence)
Creates a new
ArbitraryFloatSequence from a String
representation using the default delimiter. |
ArbitrarySequence(AlphabetContainer alphabetContainer,
double... content)
Creates a new
ArbitrarySequence from an array of
double-encoded alphabet symbols. |
ArbitrarySequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
String sequence,
String delim)
|
ArbitrarySequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
SymbolExtractor extractor)
Creates a new
ArbitrarySequence from a SymbolExtractor. |
ArbitrarySequence(AlphabetContainer alphabetContainer,
String sequence)
Creates a new
ArbitrarySequence from a String
representation using the default delimiter. |
ByteSequence(AlphabetContainer alphabetContainer,
byte[] content)
Creates a new
ByteSequence from an array of byte-
encoded alphabet symbols. |
ByteSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
String sequence,
String delim)
|
ByteSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
SymbolExtractor extractor)
Creates a new
ByteSequence from a SymbolExtractor. |
ByteSequence(AlphabetContainer alphabetContainer,
String sequence)
Creates a new
ByteSequence from a String representation
using the default delimiter. |
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. |
IntSequence(AlphabetContainer alphabetContainer,
int... content)
Creates a new
IntSequence from an array of int-
encoded alphabet symbols. |
IntSequence(AlphabetContainer alphabetContainer,
int[] content,
int start,
int length)
Creates a new
IntSequence from a part of the array of
int- encoded alphabet symbols. |
IntSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
String sequence,
String delim)
|
IntSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
SymbolExtractor extractor)
Creates a new
IntSequence from a SymbolExtractor. |
IntSequence(AlphabetContainer alphabetContainer,
String sequence)
Creates a new
IntSequence from a String representation
using the default delimiter. |
MappedDiscreteSequence(AlphabetContainer originalAlphabetContainer,
SequenceAnnotation[] seqAn,
DiscreteAlphabetMapping... transformation)
This method allows to create an empty
MappedDiscreteSequence. |
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. |
Sequence(AlphabetContainer container,
SequenceAnnotation[] annotation)
Creates a new
Sequence with the given AlphabetContainer
and the given annotation, but without the content. |
ShortSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
String sequence,
String delim)
|
ShortSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
SymbolExtractor extractor)
Creates a new
ShortSequence from a SymbolExtractor. |
ShortSequence(AlphabetContainer alphabetContainer,
short[] content)
Creates a new
ShortSequence from an array of short-
encoded alphabet symbols. |
ShortSequence(AlphabetContainer alphabetContainer,
String sequence)
Creates a new
ShortSequence from a String representation
using the default delimiter. |
SimpleDiscreteSequence(AlphabetContainer container,
SequenceAnnotation[] annotation)
This constructor creates a new
SimpleDiscreteSequence with the
AlphabetContainer container and the annotation
annotation but without the content. |
SparseSequence(AlphabetContainer alphCon,
String seq)
Creates a new
SparseSequence from a String
representation. |
SparseSequence(AlphabetContainer alphCon,
SymbolExtractor se)
Creates a new
SparseSequence from a SymbolExtractor. |
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. |
| Constructor and Description |
|---|
ReferenceSequenceAnnotationParser(String key,
AlphabetContainer alphabet,
String keyValueDelimiter,
String annotationDelimiter)
Creates a new
ReferenceSequenceAnnotationParser with user-specified delimiters. |
ReferenceSequenceAnnotationParser(String key,
AlphabetContainer alphabet,
String keyValueDelimiter,
String annotationDelimiter,
String delim)
Creates a new
ReferenceSequenceAnnotationParser with user-specified delimiters. |
| Modifier and Type | Method and Description |
|---|---|
static int |
SymbolExtractor.filter(String inFile,
char ignore,
AlphabetContainer con,
int minLength,
String outFile)
This method allows the user to filter the content of a
File using a given AlphabetContainer and a
minimal sequence length. |
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
SequenceScoringParameterSet.getAlphabetContainer()
Returns the
AlphabetContainer of the current instance. |
| Constructor and Description |
|---|
SequenceScoringParameterSet(Class<T> instanceClass,
AlphabetContainer alphabet)
Constructs a
SequenceScoringParameterSet for an object that can
handle sequences of variable length and with the
AlphabetContainer alphabet. |
SequenceScoringParameterSet(Class<T> instanceClass,
AlphabetContainer alphabet,
int length,
boolean variableLength)
Constructs a
SequenceScoringParameterSet from an
AlphabetContainer and the length of a sequence. |
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
SequenceScore.getAlphabetContainer()
Returns the container of alphabets that were used when constructing the instance.
|
| Modifier and Type | Field and Description |
|---|---|
protected AlphabetContainer |
AbstractDifferentiableSequenceScore.alphabets
The
AlphabetContainer of this AbstractDifferentiableSequenceScore
. |
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
AbstractDifferentiableSequenceScore.getAlphabetContainer() |
| Constructor and Description |
|---|
AbstractDifferentiableSequenceScore(AlphabetContainer alphabets,
int length)
The main constructor.
|
UniformDiffSS(AlphabetContainer alphabets,
int length)
This is the main constructor that creates an instance of a
UniformDiffSS that models each sequence uniformly. |
| Constructor and Description |
|---|
LogisticDiffSS(AlphabetContainer con,
int length,
LogisticConstraint... constraint)
This is the main constructor to create
LogisticDiffSS instance. |
| Modifier and Type | Method and Description |
|---|---|
static HomogeneousMMDiffSM |
DifferentiableStatisticalModelFactory.createHomogeneousMarkovModel(AlphabetContainer con,
double ess,
int order,
int priorLength)
This method returns a homogeneous Markov model with user-specified order.
|
static BayesianNetworkDiffSM |
DifferentiableStatisticalModelFactory.createInhomogeneousMarkovModel(AlphabetContainer con,
int length,
double ess,
int order)
This method returns a inhomogeneous Markov model (IMM) with user-specified order.
|
static MarkovRandomFieldDiffSM |
DifferentiableStatisticalModelFactory.createMarkovRandomField(AlphabetContainer con,
int length,
String constraintType)
This method allows to create a
MarkovRandomFieldDiffSM of the specified length and with the given constraint type. |
static BayesianNetworkDiffSM |
DifferentiableStatisticalModelFactory.createPWM(AlphabetContainer con,
int length,
double ess)
This method returns a position weight matrix (PWM).
|
| Constructor and Description |
|---|
AbstractDifferentiableStatisticalModel(AlphabetContainer alphabets,
int length)
The main constructor.
|
AbstractVariableLengthDiffSM(AlphabetContainer alphabets)
This is the main constructor that creates an instance of a
VariableLengthDiffSM that models sequences of arbitrary
length. |
AbstractVariableLengthDiffSM(AlphabetContainer alphabets,
int length)
This is the main constructor that creates an instance of a
VariableLengthDiffSM that models sequences of a given
length. |
CyclicMarkovModelDiffSM(AlphabetContainer alphabets,
double[] frameHyper,
double[][][] hyper,
boolean plugIn,
boolean optimize,
int starts,
int initFrame)
This constructor allows to create an instance with specific hyper-parameters for all conditional distributions.
|
CyclicMarkovModelDiffSM(AlphabetContainer alphabets,
int order,
int period,
double classEss,
double[] sumOfHyperParams,
boolean plugIn,
boolean optimize,
int starts,
int initFrame)
The main constructor.
|
MappingDiffSM(AlphabetContainer originalAlphabetContainer,
DifferentiableStatisticalModel nsf,
DiscreteAlphabetMapping... mapping)
The main constructor creating a
MappingDiffSM. |
MarkovRandomFieldDiffSM(AlphabetContainer alphabets,
int length,
double ess,
String constr)
This is the main constructor that creates an instance of a
MarkovRandomFieldDiffSM. |
MarkovRandomFieldDiffSM(AlphabetContainer alphabets,
int length,
String constr)
This constructor creates an instance of a
MarkovRandomFieldDiffSM with
equivalent sample size (ess) 0. |
UniformDiffSM(AlphabetContainer alphabets,
int length,
double ess)
This is the main constructor that creates an instance of a
UniformDiffSM that models each sequence uniformly. |
| Constructor and Description |
|---|
BayesianNetworkDiffSM(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
Measure structureMeasure)
Creates a new
BayesianNetworkDiffSM that has neither
been initialized nor trained. |
BayesianNetworkDiffSMParameterSet(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
Measure structureMeasure)
Creates a new
BayesianNetworkDiffSMParameterSet with
pre-defined parameter values. |
BNDiffSMParameterTree(int pos,
int[] contextPoss,
AlphabetContainer alphabet,
int firstParent,
int[] firstChildren)
Creates a new
BNDiffSMParameterTree for the parameters at position
pos using the parent positions in contextPoss. |
MarkovModelDiffSM(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
InhomogeneousMarkov structureMeasure)
This constructor creates an instance without any prior for the modeled length.
|
MarkovModelDiffSM(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
InhomogeneousMarkov structureMeasure,
DurationDiffSM lengthPenalty)
This constructor creates an instance with an prior for the modeled length.
|
MarkovModelDiffSM(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
int order,
DurationDiffSM lengthPenalty)
This constructor creates an instance with an prior for the modeled length.
|
| Constructor and Description |
|---|
HomogeneousDiffSM(AlphabetContainer alphabets)
This is the main constructor that creates an instance of a
HomogeneousDiffSM that models sequences of arbitrary
length. |
HomogeneousDiffSM(AlphabetContainer alphabets,
int length)
This is the main constructor that creates an instance of a
HomogeneousDiffSM that models sequences of a given
length. |
HomogeneousMM0DiffSM(AlphabetContainer alphabets,
int length,
double ess,
boolean plugIn,
boolean optimize)
The main constructor that creates an instance of a homogeneous Markov
model of order 0.
|
HomogeneousMMDiffSM(AlphabetContainer alphabets,
int order,
double classEss,
double[][] hyperParams,
boolean plugIn,
boolean optimize,
int starts)
This is the main constructor that creates an instance of a homogeneous
Markov model of arbitrary order with given hyper-parameters for the prior.
|
HomogeneousMMDiffSM(AlphabetContainer alphabets,
int order,
double classEss,
double[] sumOfHyperParams,
boolean plugIn,
boolean optimize,
int starts)
This is the main constructor that creates an instance of a homogeneous
Markov model of arbitrary order.
|
HomogeneousMMDiffSM(AlphabetContainer alphabets,
int order,
double classEss,
int length)
This is a convenience constructor for creating an instance of a homogeneous
Markov model of arbitrary order.
|
UniformHomogeneousDiffSM(AlphabetContainer alphabets,
double ess)
This is the main constructor that creates an instance of a
UniformHomogeneousDiffSM that models each sequence
uniformly. |
| Constructor and Description |
|---|
LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder(AlphabetContainer alphabets,
int length,
int order,
int distance,
double ess,
double q,
LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder.PriorType t)
Creates a new Slim model with given number of components and maximum distance.
|
| Constructor and Description |
|---|
PositionDiffSM(AlphabetContainer con,
int length)
This constructor allows create instance with more than one dimension.
|
| Modifier and Type | Field and Description |
|---|---|
protected AlphabetContainer |
AbstractTrainableStatisticalModel.alphabets
The underlying alphabets
|
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
AbstractTrainableStatisticalModel.getAlphabetContainer() |
| Modifier and Type | Method and Description |
|---|---|
static BayesianNetworkTrainSM |
TrainableStatisticalModelFactory.createBayesianNetworkModel(AlphabetContainer con,
int length,
double ess,
byte order)
This method returns a Bayesian network model (BN) with user-specified order.
|
static HomogeneousMM |
TrainableStatisticalModelFactory.createHomogeneousMarkovModel(AlphabetContainer con,
double ess,
byte order)
This method returns a homogeneous Markov model with user-specified order.
|
static FSDAGTrainSM |
TrainableStatisticalModelFactory.createInhomogeneousMarkovModel(AlphabetContainer con,
int length,
double ess,
byte order)
This method returns a inhomogeneous Markov model (IMM) with user-specified order.
|
static BayesianNetworkTrainSM |
TrainableStatisticalModelFactory.createPermutedMarkovModel(AlphabetContainer con,
int length,
double ess,
byte order)
This method returns a permuted Markov model (PMM) with user-specified order.
|
static FSDAGTrainSM |
TrainableStatisticalModelFactory.createPWM(AlphabetContainer con,
int length,
double ess)
This method returns a position weight matrix (PWM).
|
| Constructor and Description |
|---|
AbstractTrainableStatisticalModel(AlphabetContainer alphabets,
int length)
|
CompositeTrainSM(AlphabetContainer alphabets,
int[] assignment,
TrainableStatisticalModel... models)
Creates a new
CompositeTrainSM. |
PFMWrapperTrainSM(AlphabetContainer alphabets,
String name,
double[][] pssm)
Creates a new wrapper for a given position frequency matrix.
|
PFMWrapperTrainSM(AlphabetContainer alphabets,
String name,
double[][] pfm,
double ess)
Creates a new wrapper for a given position frequency matrix.
|
UniformTrainSM(AlphabetContainer alphabet)
Creates a new
UniformTrainSM using a given AlphabetContainer. |
| Modifier and Type | Method and Description |
|---|---|
static double |
ConstraintManager.countInhomogeneous(AlphabetContainer alphabets,
int length,
DataSet data,
double[] weights,
boolean reset,
Constraint... constr)
Fills the (inhomogeneous)
constr with the weighted absolute frequency of the DataSet
data and computes the frequencies will not be computed. |
abstract String |
Constraint.getDescription(AlphabetContainer con,
int i)
Returns the decoded symbol for the encoded symbol
i. |
String |
Constraint.getFreqInfo(AlphabetContainer con,
NumberFormat nf)
Returns an information about the stored frequencies.
|
| Constructor and Description |
|---|
DGTrainSMParameterSet(Class<T> instanceClass,
AlphabetContainer alphabet,
double ess,
String description)
The constructor for models that can handle variable lengths.
|
DGTrainSMParameterSet(Class<T> instanceClass,
AlphabetContainer alphabet,
int length,
double ess,
String description)
The constructor for models that can handle only sequences of fixed length
given by
length. |
| Modifier and Type | Method and Description |
|---|---|
String |
HomogeneousTrainSM.HomCondProb.getDescription(AlphabetContainer con,
int i) |
| Constructor and Description |
|---|
HomMMParameterSet(AlphabetContainer alphabet,
double ess,
String description,
byte order)
Creates a new
HomMMParameterSet with AlphabetContainer,
ess (equivalent sample size), description and order
of the homogeneous Markov model. |
HomogeneousTrainSMParameterSet(Class<? extends HomogeneousTrainSM> instanceClass,
AlphabetContainer alphabet,
double ess,
String description,
byte order)
Creates a new
HomogeneousTrainSMParameterSet with
AlphabetContainer, ess (equivalent sample
size), description and order of the homogeneous Markov model. |
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
StructureLearner.getAlphabetContainer()
This method returns the
AlphabetContainer of the
StructureLearner. |
| Modifier and Type | Method and Description |
|---|---|
String |
InhConstraint.getDescription(AlphabetContainer con,
int i) |
String |
InhCondProb.getDescription(AlphabetContainer con,
int i) |
| Constructor and Description |
|---|
StructureLearner(AlphabetContainer con,
int length)
Creates a
StructureLearner with equivalent sample
size (ess) = 0. |
StructureLearner(AlphabetContainer con,
int length,
double ess)
Creates a new
StructureLearner for a given
AlphabetContainer, a given length and a given equivalent
sample size (ess). |
| Constructor and Description |
|---|
BayesianNetworkTrainSMParameterSet(AlphabetContainer alphabet,
int length,
double ess,
String description,
StructureLearner.ModelType model,
byte order,
StructureLearner.LearningType method)
This is the constructor of a filled
BayesianNetworkTrainSMParameterSet for a
BayesianNetworkTrainSM. |
FSDAGModelForGibbsSamplingParameterSet(AlphabetContainer alphabet,
int length,
double ess,
String description,
String graph)
This is the constructor that creates a filled parameter set.
|
FSDAGTrainSMParameterSet(AlphabetContainer alphabet,
int length,
double ess,
String description,
String graph)
This constructor creates an
FSDAGTrainSMParameterSet instance. |
FSDAGTrainSMParameterSet(Class<? extends FSDAGTrainSM> clazz,
AlphabetContainer alphabet,
int length,
double ess,
String description,
String graph)
This constructor creates an
FSDAGTrainSMParameterSet instance for the
specified class. |
FSMEMParameterSet(AlphabetContainer alphabet,
int length,
double ess,
String description,
ConstraintManager.Decomposition decomposition,
boolean reduce,
byte algorithm,
double epsilon,
String constraints)
The fast constructor.
|
IDGTrainSMParameterSet(Class<? extends InhomogeneousDGTrainSM> instanceClass,
AlphabetContainer alphabet,
int length,
double ess,
String description)
This constructor creates an
IDGTrainSMParameterSet instance for the
specified class. |
MEManagerParameterSet(Class<? extends MEManager> instanceClass,
AlphabetContainer alphabet,
int length,
double ess,
String description,
ConstraintManager.Decomposition decomposition,
boolean reduce,
byte algorithm,
double epsilon)
The fast constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static AbstractHMM |
HMMFactory.createProfileHMM(MaxHMMTrainingParameterSet trainingParameterSet,
double[][] initFromTo,
boolean likelihood,
int order,
int numLayers,
AlphabetContainer con,
double ess,
boolean conditionalMain,
boolean closeCircle,
double[][] conditionInitProbs,
boolean insertUniform)
Creates a new profile HMM for a given architecture and number of layers.
|
static AbstractHMM |
HMMFactory.createProfileHMM(MaxHMMTrainingParameterSet trainingParameterSet,
double[][] initFromTo,
boolean likelihood,
int order,
int numLayers,
AlphabetContainer con,
double ess,
boolean conditionalMain,
int joiningStates,
double[][] conditionInitProbs,
boolean insertUniform)
Creates a new profile HMM for a given architecture and number of layers.
|
static AbstractHMM |
HMMFactory.createProfileHMM(MaxHMMTrainingParameterSet trainingParameterSet,
HMMFactory.HMMType type,
boolean likelihood,
int order,
int numLayers,
AlphabetContainer con,
double ess,
boolean conditionalMain,
boolean closeCircle,
double[][] conditionInitProbs)
Creates a new profile HMM for a given architecture and number of layers.
|
static AbstractHMM |
HMMFactory.createPseudoErgodicHMM(HMMTrainingParameterSet pars,
double ess,
double selfTranistionPart,
double finalTranistionPart,
AlphabetContainer con,
int numStates,
boolean insertUniform)
Creates an HMM with
numStates+1 states, where numStates emitting build a clique and each of those states is connected to the absorbing silent final state. |
static AbstractHMM |
HMMFactory.createSunflowerHMM(HMMTrainingParameterSet pars,
AlphabetContainer con,
double ess,
int expectedSequenceLength,
boolean startCentral,
int... motifLength)
This method creates a first order sunflower HMM.
|
static AbstractHMM |
HMMFactory.createSunflowerHMM(HMMTrainingParameterSet pars,
AlphabetContainer con,
double ess,
int expectedSequenceLength,
boolean startCentral,
PhyloTree[] t,
double[] motifProb,
int[] motifLength)
This method creates a first order sunflower HMM allowing phylogenetic emissions.
|
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
UniformEmission.getAlphabetContainer() |
AlphabetContainer |
SilentEmission.getAlphabetContainer() |
AlphabetContainer |
MixtureEmission.getAlphabetContainer() |
AlphabetContainer |
Emission.getAlphabetContainer()
This method returns the
AlphabetContainer of this emission. |
| Constructor and Description |
|---|
UniformEmission(AlphabetContainer con)
The main constructor for a
UniformEmission. |
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
MultivariateGaussianEmission.getAlphabetContainer() |
AlphabetContainer |
GaussianEmission.getAlphabetContainer() |
| Constructor and Description |
|---|
GaussianEmission(AlphabetContainer con)
Creates a
GaussianEmission which can be used for maximum likelihood. |
GaussianEmission(AlphabetContainer con,
double ess,
double priorMu,
double priorAlpha,
double priorBeta,
boolean transformed)
Creates a
GaussianEmission with normal-gamma prior by directly defining the hyper-parameters of the prior. |
GaussianEmission(double ess,
AlphabetContainer con,
double priorMu,
double expectedPrecision,
double sdPrecision,
boolean transformed)
Creates a
GaussianEmission with normal-gamma prior by defining the expected precision and the expected standard deviation of the precision, i.e. |
| Modifier and Type | Field and Description |
|---|---|
protected AlphabetContainer |
AbstractConditionalDiscreteEmission.con
The alphabet of the emissions
|
| Modifier and Type | Method and Description |
|---|---|
AlphabetContainer |
AbstractConditionalDiscreteEmission.getAlphabetContainer() |
| Constructor and Description |
|---|
AbstractConditionalDiscreteEmission(AlphabetContainer con,
double[][] hyperParams)
This is a simple constructor for a
AbstractConditionalDiscreteEmission defining the individual hyper parameters. |
AbstractConditionalDiscreteEmission(AlphabetContainer con,
double[][] hyperParams,
double[][] initHyperParams)
This constructor creates a
AbstractConditionalDiscreteEmission defining the individual hyper parameters for the
prior used during training and initialization. |
AbstractConditionalDiscreteEmission(AlphabetContainer con,
int numberOfConditions,
double ess)
This is a simple constructor for a
AbstractConditionalDiscreteEmission based on the equivalent sample size. |
DiscreteEmission(AlphabetContainer con,
double ess)
This is a simple constructor for a
DiscreteEmission based on the equivalent sample size. |
DiscreteEmission(AlphabetContainer con,
double[] hyperParams)
This is a simple constructor for a
DiscreteEmission defining the individual hyper parameters. |
PhyloDiscreteEmission(AlphabetContainer con,
double[] hyperParams,
PhyloTree t)
This is a simple constructor for a
DiscreteEmission defining the individual hyper parameters. |
PhyloDiscreteEmission(AlphabetContainer con,
double ess,
PhyloTree t)
This is a simple constructor for a
PhyloDiscreteEmission based on the equivalent sample size. |
ReferenceSequenceDiscreteEmission(AlphabetContainer con,
AlphabetContainer refCon,
int refIdx,
double ess)
This is a simple constructor for a
ReferenceSequenceDiscreteEmission based on the equivalent sample size. |
ReferenceSequenceDiscreteEmission(AlphabetContainer con,
AlphabetContainer refCon,
int refIdx,
double[][] hyperParams)
This constructor creates a
ReferenceSequenceDiscreteEmission defining the individual hyper parameters. |
ReferenceSequenceDiscreteEmission(AlphabetContainer con,
AlphabetContainer refCon,
int refIdx,
double[][] hyperParams,
double[][] initHyperParams)
This constructor creates a
ReferenceSequenceDiscreteEmission defining the individual hyper parameters. |
ReferenceSequenceDiscreteEmission(AlphabetContainer con,
AlphabetContainer refCon,
int refIdx,
double ess,
double[][] initHyperParams)
This is a simple constructor for a
ReferenceSequenceDiscreteEmission based on the equivalent sample size. |
| Modifier and Type | Method and Description |
|---|---|
static String |
PFMComparator.getConsensus(AlphabetContainer con,
double[][] pfm)
This method extracts the
The method does not use any degenerated IUPAC code.
|