Package | Description |
---|---|
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.gendismix |
Provides an implementation of a classifier that allows to train the parameters of a set of
DifferentiableStatisticalModel s by
a unified generative-discriminative learning principle. |
de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior |
Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors.
|
de.jstacs.classifiers.differentiableSequenceScoreBased.msp |
Provides an implementation of a classifier that allows to train the parameters of a set of
DifferentiableStatisticalModel s either
by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL). |
de.jstacs.motifDiscovery |
This package provides the framework including the interface for any de novo motif discoverer.
|
de.jstacs.sequenceScores.differentiable | |
de.jstacs.sequenceScores.differentiable.logistic | |
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.hmm.models |
The package provides different implementations of hidden Markov models based on
AbstractHMM . |
Modifier and Type | Method and Description |
---|---|
static AbstractClassifier |
ClassifierFactory.createClassifier(DifferentiableSequenceScore... models)
Creates a classifier that is based on at least two
DifferentiableSequenceScore s. |
Modifier and Type | Field and Description |
---|---|
protected DifferentiableSequenceScore[] |
ScoreClassifier.score
The internally used scoring functions.
|
protected DifferentiableSequenceScore[][] |
DiffSSBasedOptimizableFunction.score
These
DifferentiableSequenceScore s are used during the parallel computation. |
Modifier and Type | Method and Description |
---|---|
DifferentiableSequenceScore |
ScoreClassifier.getDifferentiableSequenceScore(int i)
Returns the internally used
DifferentiableSequenceScore with index
i . |
DifferentiableSequenceScore[] |
ScoreClassifier.getDifferentiableSequenceScores()
Returns all internally used
DifferentiableSequenceScore s in the internal
order. |
Modifier and Type | Method and Description |
---|---|
abstract void |
DiffSSBasedOptimizableFunction.reset(DifferentiableSequenceScore[] funs)
This method allows to reset the internally used functions and the corresponding objects.
|
Constructor and Description |
---|
DiffSSBasedOptimizableFunction(int threads,
DifferentiableSequenceScore[] score,
DataSet[] data,
double[][] weights,
LogPrior prior,
boolean norm,
boolean freeParams)
Creates an instance with the underlying infrastructure.
|
ScoreClassifier(ScoreClassifierParameterSet params,
double lastScore,
DifferentiableSequenceScore... score)
Creates a new
ScoreClassifier from a given
ScoreClassifierParameterSet and DifferentiableSequenceScore s . |
Modifier and Type | Method and Description |
---|---|
void |
LogGenDisMixFunction.reset(DifferentiableSequenceScore[] funs) |
Constructor and Description |
---|
GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double lastScore,
double[] beta,
DifferentiableSequenceScore... score)
This constructor creates an instance and sets the value of the last (external) optimization.
|
LogGenDisMixFunction(int threads,
DifferentiableSequenceScore[] score,
DataSet[] data,
double[][] weights,
LogPrior prior,
double[] beta,
boolean norm,
boolean freeParams)
The constructor for creating an instance that can be used in an
Optimizer . |
OneDataSetLogGenDisMixFunction(int threads,
DifferentiableSequenceScore[] score,
DataSet data,
double[][] weights,
LogPrior prior,
double[] beta,
boolean norm,
boolean freeParams)
The constructor for creating an instance that can be used in an
Optimizer . |
Modifier and Type | Method and Description |
---|---|
void |
SeparateLogPrior.set(boolean freeParameters,
DifferentiableSequenceScore... funs) |
void |
LogPrior.set(boolean freeParameters,
DifferentiableSequenceScore... funs)
Resets all pre-computed values to their initial values using the
DifferentiableSequenceScore s funs . |
void |
CompositeLogPrior.set(boolean freeParameters,
DifferentiableSequenceScore... funs) |
Constructor and Description |
---|
MSPClassifier(GenDisMixClassifierParameterSet params,
DifferentiableSequenceScore... score)
This convenience constructor creates an
MSPClassifier that used MCL principle for training. |
MSPClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
DifferentiableSequenceScore... score)
The default constructor that creates a new
MSPClassifier from a
given parameter set, a prior and DifferentiableSequenceScore s for the
classes. |
MSPClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double lastScore,
DifferentiableSequenceScore... score)
This constructor that creates a new
MSPClassifier from a
given parameter set, a prior and DifferentiableSequenceScore s for the
classes. |
Modifier and Type | Method and Description |
---|---|
static History[][] |
MutableMotifDiscovererToolbox.createHistoryArray(DifferentiableSequenceScore[] funs,
History template)
This method creates a History-array that can be used in an optimization.
|
static int[][] |
MutableMotifDiscovererToolbox.createMinimalNewLengthArray(DifferentiableSequenceScore[] funs)
This method creates a minimalNewLength-array that can be used in an optimization.
|
static boolean |
MutableMotifDiscovererToolbox.doHeuristicSteps(DifferentiableSequenceScore[] funs,
DataSet[] data,
double[][] weights,
DiffSSBasedOptimizableFunction opt,
DifferentiableFunction neg,
byte algorithm,
double linEps,
StartDistanceForecaster startDistance,
SafeOutputStream out,
boolean breakOnChanged,
History[][] hist,
int[][] minimalNewLength,
boolean maxPos)
This method tries to make some heuristic step if at least one
DifferentiableSequenceScore is a MutableMotifDiscoverer . |
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 boolean |
MutableMotifDiscovererToolbox.findModification(int clazz,
int motif,
MutableMotifDiscoverer mmd,
DifferentiableSequenceScore[] score,
DataSet[] data,
double[][] weights,
DiffSSBasedOptimizableFunction opt,
DifferentiableFunction neg,
byte algo,
double linEps,
StartDistanceForecaster startDistance,
SafeOutputStream out,
History hist,
int minimalNewLength,
boolean maxPos)
This method tries to find a modification, i.e.
|
static ComparableElement<double[],Double>[] |
MutableMotifDiscovererToolbox.getSortedInitialParameters(DifferentiableSequenceScore[] funs,
MutableMotifDiscovererToolbox.InitMethodForDiffSM[] init,
DiffSSBasedOptimizableFunction opt,
int n,
OutputStream stream,
int optimizationSteps)
This method allows to initialize the
DifferentiableSequenceScore using different MutableMotifDiscovererToolbox.InitMethodForDiffSM . |
static double[][] |
MutableMotifDiscovererToolbox.optimize(DifferentiableSequenceScore[] funs,
DiffSSBasedOptimizableFunction opt,
byte algorithm,
AbstractTerminationCondition condition,
double linEps,
StartDistanceForecaster startDistance,
SafeOutputStream out,
boolean breakOnChanged,
History[][] hist,
int[][] minimalNewLength,
OptimizableFunction.KindOfParameter plugIn,
boolean maxPos)
This method tries to optimize the problem at hand as good as possible.
|
static double[][] |
MutableMotifDiscovererToolbox.optimize(DifferentiableSequenceScore[] funs,
DiffSSBasedOptimizableFunction opt,
byte algorithm,
AbstractTerminationCondition condition,
double linEps,
StartDistanceForecaster startDistance,
SafeOutputStream out,
boolean breakOnChanged,
History template,
OptimizableFunction.KindOfParameter plugIn,
boolean maxPos)
This method tries to optimize the problem at hand as good as possible.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDifferentiableSequenceScore
This class is the main part of any
ScoreClassifier . |
class |
IndependentProductDiffSS
This class enables the user to model parts of a sequence independent of each
other.
|
class |
MultiDimensionalSequenceWrapperDiffSS
This class implements a simple wrapper for multidimensional sequences.
|
class |
UniformDiffSS
This
DifferentiableSequenceScore does nothing. |
Modifier and Type | Field and Description |
---|---|
protected DifferentiableSequenceScore[] |
IndependentProductDiffSS.score
The internally used
DifferentiableSequenceScore s. |
Modifier and Type | Method and Description |
---|---|
DifferentiableSequenceScore |
DifferentiableSequenceScore.clone()
Creates a clone (deep copy) of the current
DifferentiableSequenceScore
instance. |
DifferentiableSequenceScore[] |
IndependentProductDiffSS.getFunctions()
This method returns a deep copy of the internally used
DifferentiableSequenceScore . |
Modifier and Type | Method and Description |
---|---|
protected static int[] |
IndependentProductDiffSS.getLengthArray(DifferentiableSequenceScore... function)
This method provides an array of lengths that can be used for instance as
IndependentProductDiffSS.partialLength . |
static int |
AbstractDifferentiableSequenceScore.getNumberOfStarts(DifferentiableSequenceScore[] score)
Returns the number of recommended starts in a numerical optimization.
|
Constructor and Description |
---|
IndependentProductDiffSS(boolean plugIn,
DifferentiableSequenceScore... functions)
This constructor creates an instance of an
IndependentProductDiffSS from a given series of
independent DifferentiableSequenceScore s. |
IndependentProductDiffSS(boolean plugIn,
DifferentiableSequenceScore[] functions,
int[] length)
This constructor creates an instance of an
IndependentProductDiffSS from given series of
independent DifferentiableSequenceScore s and lengths. |
IndependentProductDiffSS(boolean plugIn,
DifferentiableSequenceScore[] functions,
int[] index,
int[] length,
boolean[] reverse)
This is the main constructor.
|
MultiDimensionalSequenceWrapperDiffSS(DifferentiableSequenceScore function)
The main constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
LogisticDiffSS
This class implements a logistic function.
|
Modifier and Type | Interface and Description |
---|---|
interface |
DifferentiableStatisticalModel
The interface for normalizable
DifferentiableSequenceScore s. |
interface |
SamplingDifferentiableStatisticalModel
Interface for
DifferentiableStatisticalModel s that can be used for
Metropolis-Hastings sampling in a SamplingScoreBasedClassifier . |
interface |
VariableLengthDiffSM
This is an interface for all
DifferentiableStatisticalModel s that allow to score
subsequences of arbitrary length. |
Modifier and Type | Class and Description |
---|---|
class |
AbstractDifferentiableStatisticalModel
This class is the main part of any
ScoreClassifier . |
class |
AbstractVariableLengthDiffSM
This abstract class implements some methods declared in
DifferentiableStatisticalModel based on the declaration
of methods in VariableLengthDiffSM . |
class |
CyclicMarkovModelDiffSM
This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
|
class |
IndependentProductDiffSM
This class enables the user to model parts of a sequence independent of each
other.
|
class |
MappingDiffSM
This class implements a
DifferentiableStatisticalModel that works on
mapped Sequence s. |
class |
MarkovRandomFieldDiffSM
This class implements the scoring function for any MRF (Markov Random Field).
|
class |
NormalizedDiffSM
This class makes an unnormalized
DifferentiableStatisticalModel to a normalized DifferentiableStatisticalModel . |
class |
UniformDiffSM
This
DifferentiableStatisticalModel does nothing. |
Modifier and Type | Method and Description |
---|---|
static boolean |
AbstractDifferentiableStatisticalModel.isNormalized(DifferentiableSequenceScore... function)
This method checks whether all given
DifferentiableStatisticalModel s
are normalized. |
Modifier and Type | Class and Description |
---|---|
class |
BayesianNetworkDiffSM
This class implements a scoring function that is a moral directed graphical
model, i.e.
|
class |
MarkovModelDiffSM
This class implements a
AbstractDifferentiableStatisticalModel for an inhomogeneous Markov model. |
Modifier and Type | Class and Description |
---|---|
class |
HomogeneousDiffSM
This is the main class for all homogeneous
DifferentiableSequenceScore s. |
class |
HomogeneousMM0DiffSM
This scoring function implements a homogeneous Markov model of order zero
(hMM(0)) for a fixed sequence length.
|
class |
HomogeneousMMDiffSM
This scoring function implements a homogeneous Markov model of arbitrary
order for any sequence length.
|
class |
UniformHomogeneousDiffSM
This scoring function does nothing.
|
Modifier and Type | Class and Description |
---|---|
class |
LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
Class for a sparse local inhomogeneous mixture (Slim) model.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMixtureDiffSM
This main abstract class for any mixture scoring function (e.g.
|
class |
MixtureDiffSM
This class implements a real mixture model.
|
class |
StrandDiffSM
This class enables the user to search on both strand.
|
class |
VariableLengthMixtureDiffSM
This class implements a mixture of
VariableLengthDiffSM by extending MixtureDiffSM and implementing the methods of VariableLengthDiffSM . |
Modifier and Type | Class and Description |
---|---|
class |
DurationDiffSM
This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
|
class |
ExtendedZOOPSDiffSM
This class handles mixtures with at least one hidden motif.
|
class |
MixtureDurationDiffSM
This class implements a mixture of
DurationDiffSM s. |
class |
PositionDiffSM
This class implements a position scoring function that enables the user to get a score without using a Sequence
object.
|
class |
SkewNormalLikeDurationDiffSM
This class implements a skew normal like discrete truncated distribution.
|
class |
UniformDurationDiffSM
This scoring function implements a uniform distribution for positions.
|
Modifier and Type | Class and Description |
---|---|
class |
DifferentiableHigherOrderHMM
This class combines an
HigherOrderHMM and a DifferentiableStatisticalModel by implementing some of the declared methods. |