Uses of Interface
de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore

Packages that use DifferentiableSequenceScore

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.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 DifferentiableStatisticalModels 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 DifferentiableStatisticalModels, which can compute the gradient with respect to their parameters for a given input Sequence.
de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels	Provides DifferentiableStatisticalModels that are directed graphical models.
de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous	Provides DifferentiableStatisticalModels that are homogeneous, i.e.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture	Provides DifferentiableSequenceScores that are mixtures of other DifferentiableSequenceScores.
de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM.

Uses of DifferentiableSequenceScore in de.jstacs.classifiers

Methods in de.jstacs.classifiers with parameters of type DifferentiableSequenceScore

static AbstractClassifier

ClassifierFactory.createClassifier(DifferentiableSequenceScore... models) Creates a classifier that is based on at least two DifferentiableSequenceScores.

Uses of DifferentiableSequenceScore in de.jstacs.classifiers.differentiableSequenceScoreBased

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased declared as DifferentiableSequenceScore

protected DifferentiableSequenceScore[]	ScoreClassifier.score The internally used scoring functions.
protected DifferentiableSequenceScore[][]	DiffSSBasedOptimizableFunction.score These DifferentiableSequenceScores are used during the parallel computation.

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased that return DifferentiableSequenceScore

DifferentiableSequenceScore	ScoreClassifier.getDifferentiableSequenceScore(int i) Returns the internally used DifferentiableSequenceScore with index i.
DifferentiableSequenceScore[]	ScoreClassifier.getDifferentiableSequenceScores() Returns all internally used DifferentiableSequenceScores in the internal order.

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased with parameters of type DifferentiableSequenceScore

abstract void

DiffSSBasedOptimizableFunction.reset(DifferentiableSequenceScore[] funs) This method allows to reset the internally used functions and the corresponding objects.

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased with parameters of type DifferentiableSequenceScore

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 DifferentiableSequenceScores .

Uses of DifferentiableSequenceScore in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type DifferentiableSequenceScore

void	LogGenDisMixFunction.reset(DifferentiableSequenceScore[] funs)

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type DifferentiableSequenceScore

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.

Uses of DifferentiableSequenceScore in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior with parameters of type DifferentiableSequenceScore

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 DifferentiableSequenceScores funs.
void	CompositeLogPrior.set(boolean freeParameters, DifferentiableSequenceScore... funs)

Uses of DifferentiableSequenceScore in de.jstacs.classifiers.differentiableSequenceScoreBased.msp

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.msp with parameters of type DifferentiableSequenceScore

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 DifferentiableSequenceScores 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 DifferentiableSequenceScores for the classes.

Uses of DifferentiableSequenceScore in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery with parameters of type DifferentiableSequenceScore

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 MutableMotifDiscoverers of a specific classes.
static Sequence	MutableMotifDiscovererToolbox.enumerate(DifferentiableSequenceScore[] funs, int classIndex, int motifIndex, RecyclableSequenceEnumerator rse, double weight, DiffSSBasedOptimizableFunction opt, OutputStream out) This method allows to enumerate all possible seeds for a motif in the MutableMotifDiscoverer of a specific class.
static 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.

Uses of DifferentiableSequenceScore in de.jstacs.sequenceScores.differentiable

Classes in de.jstacs.sequenceScores.differentiable that implement DifferentiableSequenceScore

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.

Fields in de.jstacs.sequenceScores.differentiable declared as DifferentiableSequenceScore

protected DifferentiableSequenceScore[]

IndependentProductDiffSS.score The internally used DifferentiableSequenceScores.

Methods in de.jstacs.sequenceScores.differentiable that return DifferentiableSequenceScore

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.

Methods in de.jstacs.sequenceScores.differentiable with parameters of type DifferentiableSequenceScore

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.

Constructors in de.jstacs.sequenceScores.differentiable with parameters of type DifferentiableSequenceScore

IndependentProductDiffSS(boolean plugIn, DifferentiableSequenceScore... functions)
This constructor creates an instance of an IndependentProductDiffSS from a given series of independent DifferentiableSequenceScores.

IndependentProductDiffSS(boolean plugIn, DifferentiableSequenceScore[] functions, int[] length)
This constructor creates an instance of an IndependentProductDiffSS from given series of independent DifferentiableSequenceScores and lengths.

IndependentProductDiffSS(boolean plugIn, DifferentiableSequenceScore[] functions, int[] index, int[] length, boolean[] reverse)
This is the main constructor.

MultiDimensionalSequenceWrapperDiffSS(DifferentiableSequenceScore function)
The main constructor.

Uses of DifferentiableSequenceScore in de.jstacs.sequenceScores.differentiable.logistic

Classes in de.jstacs.sequenceScores.differentiable.logistic that implement DifferentiableSequenceScore

class

LogisticDiffSS This class implements a logistic function.

Uses of DifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable

Subinterfaces of DifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable

interface	DifferentiableStatisticalModel The interface for normalizable DifferentiableSequenceScores.
interface	SamplingDifferentiableStatisticalModel Interface for DifferentiableStatisticalModels that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.
interface	VariableLengthDiffSM This is an interface for all DifferentiableStatisticalModels that allow to score subsequences of arbitrary length.

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable that implement DifferentiableSequenceScore

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 Sequences.
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.

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable with parameters of type DifferentiableSequenceScore

static boolean

AbstractDifferentiableStatisticalModel.isNormalized(DifferentiableSequenceScore... function) This method checks whether all given DifferentiableStatisticalModels are normalized.

Uses of DifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement DifferentiableSequenceScore

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.

Uses of DifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous that implement DifferentiableSequenceScore

class	HomogeneousDiffSM This is the main class for all homogeneous DifferentiableSequenceScores.
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.

Uses of DifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that implement DifferentiableSequenceScore

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.

Uses of DifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that implement DifferentiableSequenceScore

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 DurationDiffSMs.
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.

Uses of DifferentiableSequenceScore in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that implement DifferentiableSequenceScore

class

DifferentiableHigherOrderHMM This class combines an HigherOrderHMM and a DifferentiableStatisticalModel by implementing some of the declared methods.