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.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.classifier.scoringFunctionBased.msp	Provides an implementation of a classifier that allows to train the parameters of a set of ScoringFunctions either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.models.hmm.models	The package provides different implementations of hidden Markov models based on AbstractHMM
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[][]	SFBasedOptimizableFunction.score These ScoringFunctions are used during the parallel computation.
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

abstract void

SFBasedOptimizableFunction.reset(ScoringFunction[] funs) This method allows to reset the internally used functions and the corresponding objects.

Constructors in de.jstacs.classifier.scoringFunctionBased with parameters of type ScoringFunction

ScoreClassifier(ScoreClassifierParameterSet params, double lastScore, ScoringFunction... score)
Creates a new ScoreClassifier from a given ScoreClassifierParameterSet and ScoringFunctions .

SFBasedOptimizableFunction(int threads, ScoringFunction[] score, Sample[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)
Creates an instance with the underlying infrastructure.

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

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

void	LogGenDisMixFunction.reset(ScoringFunction[] funs)

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

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

LogGenDisMixFunction(int threads, ScoringFunction[] score, Sample[] data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams)
The constructor for creating an instance that can be used in an Optimizer.

OneSampleLogGenDisMixFunction(int threads, ScoringFunction[] score, Sample data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams)
The constructor for creating an instance that can be used in an Optimizer.

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.classifier.scoringFunctionBased.msp

Constructors in de.jstacs.classifier.scoringFunctionBased.msp with parameters of type ScoringFunction

MSPClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, ScoringFunction... score)
This constructor that creates a new MSPClassifier from a given parameter set, a prior and ScoringFunctions for the classes.

MSPClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, ScoringFunction... score)
The default constructor that creates a new MSPClassifier from a given parameter set, a prior and ScoringFunctions for the classes.

MSPClassifier(GenDisMixClassifierParameterSet params, ScoringFunction... score)
This convenience constructor creates an MSPClassifier that used MCL principle for training.

Uses of ScoringFunction in de.jstacs.models.hmm.models

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

class

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

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, SFBasedOptimizableFunction opt, DifferentiableFunction neg, byte algorithm, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength, boolean maxPos) This method tries to make some heuristic step if at least one MutableMotifDiscovererToolbox.InitMethodForScoringFunction is a MutableMotifDiscoverer.
static Sequence[]	MutableMotifDiscovererToolbox.enumerate(ScoringFunction[] funs, int[] classIndex, int[] motifIndex, RecyclableSequenceEnumerator[] rse, double weight, SFBasedOptimizableFunction opt, OutputStream out) This method allows to enumerate all possible seeds for a number of motifs in the MutableMotifDiscoverers of a specific classes.
static Sequence	MutableMotifDiscovererToolbox.enumerate(ScoringFunction[] funs, int classIndex, int motifIndex, RecyclableSequenceEnumerator rse, double weight, SFBasedOptimizableFunction opt, OutputStream out) This method allows to enumerate all possible seeds for a motif in the MutableMotifDiscoverer of a specific class.
static boolean	MutableMotifDiscovererToolbox.findModification(int clazz, int motif, MutableMotifDiscoverer mmd, ScoringFunction[] score, Sample[] data, double[][] weights, SFBasedOptimizableFunction opt, DifferentiableFunction neg, byte algo, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, History hist, int minimalNewLength, boolean maxPos) This method tries to find a modification, i.e. shifting, shrinking, or expanding a motif, that is promising.
static ComparableElement<double[],Double>[]	MutableMotifDiscovererToolbox.getSortedInitialParameters(ScoringFunction[] funs, MutableMotifDiscovererToolbox.InitMethodForScoringFunction[] init, SFBasedOptimizableFunction opt, int n, OutputStream stream, int optimizationSteps) This method allows to initialize the MutableMotifDiscovererToolbox.InitMethodForScoringFunction using different MutableMotifDiscovererToolbox.InitMethodForScoringFunction.
static double[][]	MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs, SFBasedOptimizableFunction opt, byte algorithm, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength, OptimizableFunction.KindOfParameter plugIn, boolean maxPos) This method tries to optimize the problem at hand as good as possible.
static double[][]	MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs, SFBasedOptimizableFunction opt, byte algorithm, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History template, OptimizableFunction.KindOfParameter plugIn, boolean maxPos) 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.
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 ScoringFunction

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 ScoringFunction

ScoringFunction

ScoringFunction.clone() Creates a clone (deep copy) of the current ScoringFunction instance.

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

static int

AbstractNormalizableScoringFunction.getNumberOfStarts(ScoringFunction[] score) Returns the number of recommended starts in a numerical optimization.

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 AbstractNormalizableScoringFunction 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.
class	VariableLengthMixtureScoringFunction This class implements a mixture of VariableLengthScoringFunction by extending MixtureScoringFunction and implementing the methods of VariableLengthScoringFunction.

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	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.