Uses of Interface
de.jstacs.scoringFunctions.NormalizableScoringFunction

Packages that use NormalizableScoringFunction
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.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.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.scoringFunctions
 

Classes in de.jstacs.scoringFunctions that implement NormalizableScoringFunction
 class AbstractNormalizableScoringFunction
          This class is the main part of any ScoreClassifier.
 class IndependentProductScoringFunction
          This class enables the user to model parts of a sequence independent of each other.
 class MRFScoringFunction
          This class implements the scoring function for any MRF (Markov Random Field).
 class NormalizedScoringFunction
          This class makes an unnormalized ScoringFunction to a normalized ScoringFunction.
 class UniformScoringFunction
          This ScoringFunction does nothing.
 class VariableLengthScoringFunction
          This is the main class for all ScoringFunctions that allow to score subsequences of arbitrary length.
 

Methods in de.jstacs.scoringFunctions that return NormalizableScoringFunction
 NormalizableScoringFunction NormalizedScoringFunction.getFunction()
          This method returns the internal function.
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(NormalizableScoringFunction... functions)
          This constructor creates an instance of an IndependentProductScoringFunction from a given series of independent NormalizableScoringFunctions.
IndependentProductScoringFunction(NormalizableScoringFunction[] functions, int[] length)
          This constructor creates an instance of an IndependentProductScoringFunction from given series of independent NormalizableScoringFunctions and lengths.
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 NormalizableScoringFunction 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.
 

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.
 

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