Uses of Interface
de.jstacs.scoringFunctions.ScoringFunction

Packages that use ScoringFunction
de.jstacs.classifier.scoringFunctionBased Provides the classes for Classifiers that are based on ScoringFunctions. 
de.jstacs.classifier.scoringFunctionBased.cll Provides the implementation of the log conditional likelihood as an OptimizableFunction and a classifier that uses log conditional likelihood or supervised posterior to learn the parameters of a set of ScoringFunctions 
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.motifDiscovery This package provides the framework including the interface for any de novo motif discoverer 
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 ScoringFunction in de.jstacs.classifier.scoringFunctionBased
 

Fields in de.jstacs.classifier.scoringFunctionBased declared as ScoringFunction
protected  ScoringFunction[] ScoreClassifier.score
          The internally used scoring functions.
 

Methods in de.jstacs.classifier.scoringFunctionBased that return ScoringFunction
 ScoringFunction ScoreClassifier.getScoringFunction(int i)
          Returns the internally used ScoringFunction with index i.
 ScoringFunction[] ScoreClassifier.getScoringFunctions()
          Returns all internally used ScoringFunctions in the internal order.
 

Methods in de.jstacs.classifier.scoringFunctionBased with parameters of type ScoringFunction
protected  int AbstractOptimizableFunction.getNumberOfStarts(ScoringFunction[] score)
          Returns the number of recommended starts.
abstract  void OptimizableFunction.reset(ScoringFunction[] score)
          Resets the ScoringFunctions and all pre-computed values.
 

Constructors in de.jstacs.classifier.scoringFunctionBased with parameters of type ScoringFunction
ScoreClassifier(ScoreClassifierParameterSet params, ScoringFunction... score)
          Creates a new ScoreClassifier from a given ScoreClassifierParameterSet and ScoringFunctions .
 

Uses of ScoringFunction in de.jstacs.classifier.scoringFunctionBased.cll
 

Methods in de.jstacs.classifier.scoringFunctionBased.cll with parameters of type ScoringFunction
static CLLClassifier[] CLLClassifier.create(CLLClassifierParameterSet params, LogPrior prior, ScoringFunction[]... functions)
          This method creates an array of CLLClassifiers by using the cross-product of the given ScoringFunctions.
 void NormConditionalLogLikelihood.reset(ScoringFunction[] funs)
           
 

Constructors in de.jstacs.classifier.scoringFunctionBased.cll with parameters of type ScoringFunction
CLLClassifier(CLLClassifierParameterSet params, LogPrior prior, ScoringFunction... score)
          The default constructor that creates a new CLLClassifier from a given parameter set, a prior and ScoringFunctions for the classes.
CLLClassifier(CLLClassifierParameterSet params, ScoringFunction... score)
          The default constructor that creates a new CLLClassifier from a given parameter set and ScoringFunctions for the classes.
NormConditionalLogLikelihood(ScoringFunction[] score, Sample[] data, double[][] weights, boolean norm, boolean freeParams)
          The constructor creates an instance of the NormConditionalLogLikelihood.
NormConditionalLogLikelihood(ScoringFunction[] score, Sample[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)
          The constructor creates an instance of the NormConditionalLogLikelihood using the given prior.
 

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

Methods in de.jstacs.classifier.scoringFunctionBased.logPrior with parameters of type ScoringFunction
 void SeparateLogPrior.set(boolean freeParameters, ScoringFunction... funs)
           
 void LogPrior.set(boolean freeParameters, ScoringFunction... funs)
          Resets all pre-computed values to their initial values using the ScoringFunctions funs.
 void CompositeLogPrior.set(boolean freeParameters, ScoringFunction... funs)
           
 

Uses of ScoringFunction in de.jstacs.motifDiscovery
 

Methods in de.jstacs.motifDiscovery with parameters of type ScoringFunction
static History[][] MutableMotifDiscovererToolbox.createHistoryArray(ScoringFunction[] funs, History template)
          This method creates a History-array that can be used in an optimization.
static int[][] MutableMotifDiscovererToolbox.createMinimalNewLengthArray(ScoringFunction[] funs)
          This method creates a minimalNewLength-array that can be used in an optimization.
static boolean MutableMotifDiscovererToolbox.doHeuristicSteps(ScoringFunction[] funs, Sample[] data, double[][] weights, OptimizableFunction opt, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength)
          This method tries to make some heuristic step if at least one MutableMotifDiscovererToolbox.InitMethodForScoringFunction is a MutableMotifDiscoverer.
static Sequence MutableMotifDiscovererToolbox.enumerate(Sample[] data, ScoringFunction[] funs, int classIndex, int motifIndex, double weight, OptimizableFunction opt, OutputStream out)
          This method allows to enumerate all possible seeds for a motif in the HiddenMotifsMixture of a specific class.
static ComparableElement<double[],Double>[] MutableMotifDiscovererToolbox.getSortedInitialParameters(Sample[] data, ScoringFunction[] funs, MutableMotifDiscovererToolbox.InitMethodForScoringFunction[] init, OptimizableFunction opt, int n, SafeOutputStream stream)
          This method allows to initialize the MutableMotifDiscovererToolbox.InitMethodForScoringFunction using different MutableMotifDiscovererToolbox.InitMethodForScoringFunction.
static double[][] MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs, OptimizableFunction opt, byte algorithm, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength, OptimizableFunction.KindOfParameter plugIn)
          This method tries to optimize the problem at hand as good as possible.
static double[][] MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs, OptimizableFunction opt, byte algorithm, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History template, OptimizableFunction.KindOfParameter plugIn)
          This method tries to optimize the problem at hand as good as possible.
 

Uses of ScoringFunction in de.jstacs.scoringFunctions
 

Subinterfaces of ScoringFunction in de.jstacs.scoringFunctions
 interface NormalizableScoringFunction
          The interface for normalizable ScoringFunctions.
 

Classes in de.jstacs.scoringFunctions that implement ScoringFunction
 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 ScoringFunction
 ScoringFunction ScoringFunction.clone()
          Creates a clone (deep copy) of the current ScoringFunction instance.
 

Uses of ScoringFunction in de.jstacs.scoringFunctions.directedGraphicalModels
 

Classes in de.jstacs.scoringFunctions.directedGraphicalModels that implement ScoringFunction
 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 ScoringFunction in de.jstacs.scoringFunctions.homogeneous
 

Classes in de.jstacs.scoringFunctions.homogeneous that implement ScoringFunction
 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 ScoringFunction in de.jstacs.scoringFunctions.mix
 

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

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

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