Uses of Interface
de.jstacs.sequenceScores.SequenceScore

Packages that use SequenceScore

Package	Description
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	Provides all StatisticalModels, which can compute a proper (i.e., normalized) likelihood over the input space of sequences. StatisticalModels can be further differentiated into TrainableStatisticalModels, which can be learned from a single input DataSet, and DifferentiableStatisticalModels, which define a proper likelihood but can also compute gradients like DifferentiableSequenceScores.
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	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.discrete
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous	This package contains various inhomogeneous models.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared
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.models	The package provides different implementations of hidden Markov models based on AbstractHMM.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture	This package is the super package for any mixture model.
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif
de.jstacs.utils	This package contains a bundle of useful classes and interfaces like ...

Uses of SequenceScore in de.jstacs.sequenceScores

Subinterfaces of SequenceScore in de.jstacs.sequenceScores

Modifier and Type	Interface and Description
interface	QuickScanningSequenceScore Interface for SequenceScore that provide additional methods for computing scores of infix sequences and filtering infix sequences.

Methods in de.jstacs.sequenceScores that return SequenceScore

Modifier and Type	Method and Description
SequenceScore	SequenceScore.clone() Creates a clone (deep copy) of the current SequenceScore instance.

Uses of SequenceScore in de.jstacs.sequenceScores.differentiable

Subinterfaces of SequenceScore in de.jstacs.sequenceScores.differentiable

Modifier and Type	Interface and Description
interface	DifferentiableSequenceScore This interface is the main part of any ScoreClassifier.

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

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.

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

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

Modifier and Type	Class and Description
class	LogisticDiffSS This class implements a logistic function.

Uses of SequenceScore in de.jstacs.sequenceScores.statisticalModels

Subinterfaces of SequenceScore in de.jstacs.sequenceScores.statisticalModels

Modifier and Type	Interface and Description
interface	StatisticalModel This interface declares methods of a statistical model, i.e., a SequenceScore that defines a proper likelihood over the input Sequences.

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

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

Modifier and Type	Interface and Description
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 SequenceScore

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 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.

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

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

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.

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

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

Modifier and Type	Class and Description
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 SequenceScore in de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture

Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture that implement SequenceScore

Modifier and Type	Class and Description
class	LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder Class for a sparse local inhomogeneous mixture (Slim) model.

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

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

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.

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

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

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 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 SequenceScore in de.jstacs.sequenceScores.statisticalModels.trainable

Subinterfaces of SequenceScore in de.jstacs.sequenceScores.statisticalModels.trainable

Modifier and Type	Interface and Description
interface	TrainableStatisticalModel This interface defines all methods for a probabilistic model.

Classes in de.jstacs.sequenceScores.statisticalModels.trainable that implement SequenceScore

Modifier and Type	Class and Description
class	AbstractTrainableStatisticalModel Abstract class for a model for pattern recognition.
class	CompositeTrainSM This class is for modelling sequences by modelling the different positions of the each sequence by different models.
class	DifferentiableStatisticalModelWrapperTrainSM This model can be used to use a DifferentiableStatisticalModel as model.
class	PFMWrapperTrainSM A wrapper class for representing position weight matrices or position frequency matrices from databases as TrainableStatisticalModels.
class	UniformTrainSM This class represents a uniform model.
class	VariableLengthWrapperTrainSM This class allows to train any TrainableStatisticalModel on DataSets of Sequences with variable length if each individual length is at least getLength().

Uses of SequenceScore in de.jstacs.sequenceScores.statisticalModels.trainable.discrete

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete that implement SequenceScore

Modifier and Type	Class and Description
class	DiscreteGraphicalTrainSM This is the main class for all discrete graphical models (DGM).

Uses of SequenceScore in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that implement SequenceScore

Modifier and Type	Class and Description
class	HomogeneousMM This class implements homogeneous Markov models (hMM) of arbitrary order.
class	HomogeneousTrainSM This class implements homogeneous models of arbitrary order.

Uses of SequenceScore in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that implement SequenceScore

Modifier and Type	Class and Description
class	BayesianNetworkTrainSM The class implements a Bayesian network ( StructureLearner.ModelType.BN ) of fixed order.
class	DAGTrainSM The abstract class for directed acyclic graphical models (DAGTrainSM).
class	FSDAGModelForGibbsSampling This is the class for a fixed structure directed acyclic graphical model (see FSDAGTrainSM) that can be used in a Gibbs sampling.
class	FSDAGTrainSM This class can be used for any discrete fixed structure directed acyclic graphical model ( FSDAGTrainSM).
class	FSMEManager This class can be used for any discrete fixed structure maximum entropy model (FSMEM).
class	InhomogeneousDGTrainSM This class is the main class for all inhomogeneous discrete graphical models (InhomogeneousDGTrainSM).
class	MEManager This class is the super class for all maximum entropy models

Uses of SequenceScore in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared that implement SequenceScore

Modifier and Type	Class and Description
class	SharedStructureMixture This class handles a mixture of models with the same structure that is learned via EM.

Uses of SequenceScore in de.jstacs.sequenceScores.statisticalModels.trainable.hmm

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that implement SequenceScore

Modifier and Type	Class and Description
class	AbstractHMM This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs.

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

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

Modifier and Type	Class and Description
class	DifferentiableHigherOrderHMM This class combines an HigherOrderHMM and a DifferentiableStatisticalModel by implementing some of the declared methods.
class	HigherOrderHMM This class implements a higher order hidden Markov model.
class	SamplingHigherOrderHMM
class	SamplingPhyloHMM This class implements an (higher order) HMM that contains multi-dimensional emissions described by a phylogenetic tree.

Uses of SequenceScore in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that implement SequenceScore

Modifier and Type	Class and Description
class	AbstractMixtureTrainSM This is the abstract class for all kinds of mixture models.
class	MixtureTrainSM The class for a mixture model of any TrainableStatisticalModels.
class	StrandTrainSM This model handles sequences that can either lie on the forward strand or on the reverse complementary strand.

Uses of SequenceScore in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif

Classes in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif that implement SequenceScore

Modifier and Type	Class and Description
class	HiddenMotifMixture This is the main class that every generative motif discoverer should implement.
class	ZOOPSTrainSM This class enables the user to search for a single motif in a sequence.

Uses of SequenceScore in de.jstacs.utils

Methods in de.jstacs.utils with parameters of type SequenceScore

Modifier and Type	Method and Description
static Sequence	StatisticalModelTester.getMostProbableSequence(SequenceScore m, int length) Returns one most probable sequence for the discrete model m.