Uses of Interface
de.jstacs.scoringFunctions.NormalizableScoringFunction

Packages that use NormalizableScoringFunction

de.jstacs.classifier.scoringFunctionBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of NormalizableScoringFunctions by a unified generative-discriminative learning principle
de.jstacs.classifier.scoringFunctionBased.logPrior	Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors
de.jstacs.models	Provides the interface Model and its abstract implementation AbstractModel, which is the super class of all other models.
de.jstacs.models.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM
de.jstacs.scoringFunctions	Provides ScoringFunctions that can be used in a ScoreClassifier.
de.jstacs.scoringFunctions.directedGraphicalModels	Provides ScoringFunctions that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.homogeneous	Provides ScoringFunctions that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix	Provides ScoringFunctions that are mixtures of other ScoringFunctions.
de.jstacs.scoringFunctions.mix.motifSearch

Uses of NormalizableScoringFunction in de.jstacs.classifier.scoringFunctionBased.gendismix

Methods in de.jstacs.classifier.scoringFunctionBased.gendismix with parameters of type NormalizableScoringFunction

static GenDisMixClassifier[]

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

Constructors in de.jstacs.classifier.scoringFunctionBased.gendismix with parameters of type NormalizableScoringFunction

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

GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, double[] beta, NormalizableScoringFunction... 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, NormalizableScoringFunction... 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, NormalizableScoringFunction... score)
This convenience constructor creates an array of weights for an elementary learning principle and calls the main constructor.

Uses of NormalizableScoringFunction in de.jstacs.classifier.scoringFunctionBased.logPrior

Fields in de.jstacs.classifier.scoringFunctionBased.logPrior declared as NormalizableScoringFunction

protected NormalizableScoringFunction[]

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

Uses of NormalizableScoringFunction in de.jstacs.models

Fields in de.jstacs.models declared as NormalizableScoringFunction

protected NormalizableScoringFunction

NormalizableScoringFunctionModel.nsf The internally used NormalizableScoringFunction.

Methods in de.jstacs.models that return NormalizableScoringFunction

NormalizableScoringFunction

NormalizableScoringFunctionModel.getFunction() Returns a copy of the internally used NormalizableScoringFunction.

Constructors in de.jstacs.models with parameters of type NormalizableScoringFunction

NormalizableScoringFunctionModel(NormalizableScoringFunction 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 NormalizableScoringFunction in de.jstacs.models.hmm.models

Classes in de.jstacs.models.hmm.models that implement NormalizableScoringFunction

class

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

Uses of NormalizableScoringFunction in de.jstacs.scoringFunctions

Subinterfaces of NormalizableScoringFunction in de.jstacs.scoringFunctions

interface	SamplingScoringFunction Interface for NormalizableScoringFunctions that can be used for Metropolis-Hastings sampling in a SamplingScoreBasedClassifier.
interface	VariableLengthScoringFunction This is an interface for all NormalizableScoringFunctions that allow to score subsequences of arbitrary length.

Classes in de.jstacs.scoringFunctions that implement NormalizableScoringFunction

class	AbstractNormalizableScoringFunction This class is the main part of any ScoreClassifier.
class	AbstractVariableLengthScoringFunction This abstract class implements some methods declared in NormalizableScoringFunction based on the declaration of methods in VariableLengthScoringFunction.
class	CMMScoringFunction This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
class	IndependentProductScoringFunction This class enables the user to model parts of a sequence independent of each other.
class	MappingScoringFunction This class implements a NormalizableScoringFunction that works on mapped Sequences.
class	MRFScoringFunction This class implements the scoring function for any MRF (Markov Random Field).
class	NormalizedScoringFunction This class makes an unnormalized NormalizableScoringFunction to a normalized NormalizableScoringFunction.
class	UniformScoringFunction This ScoringFunction does nothing.

Methods in de.jstacs.scoringFunctions that return NormalizableScoringFunction

NormalizableScoringFunction	NormalizedScoringFunction.getFunction() This method returns the internal function.
NormalizableScoringFunction	MappingScoringFunction.getFunction() This method return the internal function.
NormalizableScoringFunction[]	IndependentProductScoringFunction.getFunctions() This method returns a deep copy of the internally used NormalizableScoringFunction.
static NormalizableScoringFunction	NormalizedScoringFunction.getNormalizedVersion(NormalizableScoringFunction nsf, int starts) This method returns a normalized version of a NormalizableScoringFunction.

Methods in de.jstacs.scoringFunctions with parameters of type NormalizableScoringFunction

static NormalizableScoringFunction	NormalizedScoringFunction.getNormalizedVersion(NormalizableScoringFunction nsf, int starts) This method returns a normalized version of a NormalizableScoringFunction.
static boolean	AbstractNormalizableScoringFunction.isNormalized(NormalizableScoringFunction... function) This method checks whether all given NormalizableScoringFunctions are normalized.

Constructors in de.jstacs.scoringFunctions with parameters of type NormalizableScoringFunction

IndependentProductScoringFunction(double ess, boolean plugIn, NormalizableScoringFunction... functions)
This constructor creates an instance of an IndependentProductScoringFunction from a given series of independent NormalizableScoringFunctions.

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

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

MappingScoringFunction(AlphabetContainer originalAlphabetContainer, NormalizableScoringFunction nsf, DiscreteAlphabetMapping... mapping)
The main constructor creating a MappingScoringFunction.

NormalizedScoringFunction(NormalizableScoringFunction nsf, int starts)
Creates a new instance using a given NormalizableScoringFunction.

Uses of NormalizableScoringFunction in de.jstacs.scoringFunctions.directedGraphicalModels

Classes in de.jstacs.scoringFunctions.directedGraphicalModels that implement NormalizableScoringFunction

class	BayesianNetworkScoringFunction This class implements a scoring function that is a moral directed graphical model, i.e. a moral Bayesian network.
class	MutableMarkovModelScoringFunction This class implements a AbstractNormalizableScoringFunction for an inhomogeneous Markov model.

Uses of NormalizableScoringFunction in de.jstacs.scoringFunctions.homogeneous

Classes in de.jstacs.scoringFunctions.homogeneous that implement NormalizableScoringFunction

class	HMM0ScoringFunction This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length.
class	HMMScoringFunction This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length.
class	HomogeneousScoringFunction This is the main class for all homogeneous ScoringFunctions.
class	UniformHomogeneousScoringFunction This scoring function does nothing.

Uses of NormalizableScoringFunction in de.jstacs.scoringFunctions.mix

Classes in de.jstacs.scoringFunctions.mix that implement NormalizableScoringFunction

class	AbstractMixtureScoringFunction This main abstract class for any mixture scoring function (e.g.
class	MixtureScoringFunction This class implements a real mixture model.
class	StrandScoringFunction This class enables the user to search on both strand.
class	VariableLengthMixtureScoringFunction This class implements a mixture of VariableLengthScoringFunction by extending MixtureScoringFunction and implementing the methods of VariableLengthScoringFunction.

Fields in de.jstacs.scoringFunctions.mix declared as NormalizableScoringFunction

protected NormalizableScoringFunction[]

AbstractMixtureScoringFunction.function This array contains the internal ScoringFunctions that are used to determine the score.

Methods in de.jstacs.scoringFunctions.mix that return NormalizableScoringFunction

NormalizableScoringFunction	AbstractMixtureScoringFunction.getFunction(int index) This method returns a specific internal function.
NormalizableScoringFunction[]	AbstractMixtureScoringFunction.getFunctions() This method returns an array of clones of the internal used functions.
NormalizableScoringFunction[]	AbstractMixtureScoringFunction.getScoringFunctions() Returns a deep copy of all internal used ScoringFunctions.

Methods in de.jstacs.scoringFunctions.mix with parameters of type NormalizableScoringFunction

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

Constructors in de.jstacs.scoringFunctions.mix with parameters of type NormalizableScoringFunction

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

MixtureScoringFunction(int starts, boolean plugIn, NormalizableScoringFunction... component)
This constructor creates a new MixtureScoringFunction.

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

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

Uses of NormalizableScoringFunction in de.jstacs.scoringFunctions.mix.motifSearch

Classes in de.jstacs.scoringFunctions.mix.motifSearch that implement NormalizableScoringFunction

class	DurationScoringFunction This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
class	HiddenMotifsMixture This class handles mixtures with at least one hidden motif.
class	MixtureDuration This class implements a mixture of DurationScoringFunctions.
class	PositionScoringFunction This class implements a position scoring function that enables the user to get a score without using a Sequence object.
class	SkewNormalLikeScoringFunction This class implements a skew normal like discrete truncated distribution.
class	UniformDurationScoringFunction This scoring function implements a uniform distribution for positions.

Constructors in de.jstacs.scoringFunctions.mix.motifSearch with parameters of type NormalizableScoringFunction

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

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