Uses of Interface
de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel

Packages that use DifferentiableStatisticalModel

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.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. model probabilities or scores independent of the position within a sequence
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	Provides all TrainableStatisticalModels, which can be learned from a single DataSet.
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM

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

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

static GenDisMixClassifier[]

GenDisMixClassifier.create(GenDisMixClassifierParameterSet params, LogPrior prior, double[] weights, DifferentiableStatisticalModel[]... functions) This method creates an array of GenDisMixClassifiers by using the cross-product of the given DifferentiableStatisticalModels.

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

GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double[] beta, DifferentiableStatisticalModel... score)
The main constructor.

GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, double[] beta, DifferentiableStatisticalModel... score)
This constructor creates an instance and sets the value of the last (external) optimization.

GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double genBeta, double disBeta, double priorBeta, DifferentiableStatisticalModel... score)
This convenience constructor agglomerates the genBeta, disBeta, and priorBeta into an array and calls the main constructor.

GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, LearningPrinciple key, DifferentiableStatisticalModel... score)
This convenience constructor creates an array of weights for an elementary learning principle and calls the main constructor.

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

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior declared as DifferentiableStatisticalModel

protected DifferentiableStatisticalModel[]

SeparateLogPrior.funs The DifferentiableSequenceScores using the parameters that shall be penalized.

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

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

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 DifferentiableStatisticalModel

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 that return DifferentiableStatisticalModel

DifferentiableStatisticalModel	NormalizedDiffSM.getFunction() This method returns the internal function.
DifferentiableStatisticalModel	MappingDiffSM.getFunction() This method return the internal function.
static DifferentiableStatisticalModel	NormalizedDiffSM.getNormalizedVersion(DifferentiableStatisticalModel nsf, int starts) This method returns a normalized version of a DifferentiableStatisticalModel.

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

static MixtureDiffSM	DifferentiableStatisticalModelFactory.createMixtureModel(DifferentiableStatisticalModel[] models) This method allows to create a MixtureDiffSM that models a mixture of individual component DifferentiableStatisticalModels.
static StrandDiffSM	DifferentiableStatisticalModelFactory.createStrandModel(DifferentiableStatisticalModel model) This method allows to create a StrandDiffSM that allows to score binding sites on both strand of DNA.
static ExtendedZOOPSDiffSM	DifferentiableStatisticalModelFactory.createZOOPS(int length, DifferentiableStatisticalModel motif, HomogeneousDiffSM bg) This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a DataSet.
static DifferentiableStatisticalModel	NormalizedDiffSM.getNormalizedVersion(DifferentiableStatisticalModel nsf, int starts) This method returns a normalized version of a DifferentiableStatisticalModel.

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable with parameters of type DifferentiableStatisticalModel

IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel... functions)
This constructor creates an instance of an IndependentProductDiffSM from a given series of independent DifferentiableStatisticalModels.

IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel[] functions, int[] length)
This constructor creates an instance of an IndependentProductDiffSM from given series of independent DifferentiableStatisticalModels and lengths.

IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel[] functions, int[] index, int[] length, boolean[] reverse)
This is the main constructor.

MappingDiffSM(AlphabetContainer originalAlphabetContainer, DifferentiableStatisticalModel nsf, DiscreteAlphabetMapping... mapping)
The main constructor creating a MappingDiffSM.

NormalizedDiffSM(DifferentiableStatisticalModel nsf, int starts)
Creates a new instance using a given DifferentiableStatisticalModel.

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

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

class	BayesianNetworkDiffSM This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
class	MarkovModelDiffSM This class implements a AbstractDifferentiableStatisticalModel for an inhomogeneous Markov model.

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

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

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 DifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture

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

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.

Fields in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture declared as DifferentiableStatisticalModel

protected DifferentiableStatisticalModel[]

AbstractMixtureDiffSM.function This array contains the internal DifferentiableStatisticalModels that are used to determine the score.

Methods in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that return DifferentiableStatisticalModel

DifferentiableStatisticalModel[]	AbstractMixtureDiffSM.getDifferentiableStatisticalModels() Returns a deep copy of all internal used DifferentiableStatisticalModels.
DifferentiableStatisticalModel	AbstractMixtureDiffSM.getFunction(int index) This method returns a specific internal function.
DifferentiableStatisticalModel[]	AbstractMixtureDiffSM.getFunctions() This method returns an array of clones of the internal used functions.

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

protected void	AbstractMixtureDiffSM.cloneFunctions(DifferentiableStatisticalModel[] originalFunctions) This method clones the given array of functions and enables the user to do some post-processing.
static boolean	StrandDiffSM.isStrandModel(DifferentiableStatisticalModel nsf) Check whether a DifferentiableStatisticalModel is a StrandDiffSM.

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture with parameters of type DifferentiableStatisticalModel

AbstractMixtureDiffSM(int length, int starts, int dimension, boolean optimizeHidden, boolean plugIn, DifferentiableStatisticalModel... function)
This constructor creates a new AbstractMixtureDiffSM.

MixtureDiffSM(int starts, boolean plugIn, DifferentiableStatisticalModel... component)
This constructor creates a new MixtureDiffSM.

StrandDiffSM(DifferentiableStatisticalModel function, double forwardPartOfESS, int starts, boolean plugIn, StrandDiffSM.InitMethod initMethod)
This constructor creates a StrandDiffSM that optimizes the usage of each strand.

StrandDiffSM(DifferentiableStatisticalModel function, int starts, boolean plugIn, StrandDiffSM.InitMethod initMethod, double forward)
This constructor creates a StrandDiffSM that has a fixed frequency for the strand usage.

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

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

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.

Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif with parameters of type DifferentiableStatisticalModel

ExtendedZOOPSDiffSM(boolean type, int length, int starts, boolean plugIn, HomogeneousDiffSM bg, DifferentiableStatisticalModel[] motif, DurationDiffSM[] posPrior, boolean plugInBg)
This constructor creates an instance of ExtendedZOOPSDiffSM that allows to have one site of the specified motifs in a Sequence.

ExtendedZOOPSDiffSM(boolean type, int length, int starts, boolean plugIn, HomogeneousDiffSM bg, DifferentiableStatisticalModel motif, DurationDiffSM posPrior, boolean plugInBg)
This constructor creates an instance of ExtendedZOOPSDiffSM that is either an OOPS or a ZOOPS model depending on the chosen type.

Uses of DifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable

Fields in de.jstacs.sequenceScores.statisticalModels.trainable declared as DifferentiableStatisticalModel

protected DifferentiableStatisticalModel

DifferentiableStatisticalModelWrapperTrainSM.nsf The internally used DifferentiableStatisticalModel.

Methods in de.jstacs.sequenceScores.statisticalModels.trainable that return DifferentiableStatisticalModel

DifferentiableStatisticalModel

DifferentiableStatisticalModelWrapperTrainSM.getFunction() Returns a copy of the internally used DifferentiableStatisticalModel.

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable with parameters of type DifferentiableStatisticalModel

DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel nsf, int threads, byte algo, AbstractTerminationCondition tc, double lineps, double startD)
The main constructor that creates an instance with the user given parameters.

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

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

class

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