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Packages that use ScoringFunction | |
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de.jstacs.classifier.scoringFunctionBased | Provides the classes for Classifier s that are based on ScoringFunction s. |
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 ScoringFunction s that can be used in a ScoreClassifier . |
de.jstacs.scoringFunctions.directedGraphicalModels | Provides ScoringFunction s that are equivalent to directed graphical models. |
de.jstacs.scoringFunctions.homogeneous | Provides ScoringFunction s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence |
de.jstacs.scoringFunctions.mix | Provides ScoringFunction s that are mixtures of other ScoringFunction s. |
de.jstacs.scoringFunctions.mix.motifSearch |
Uses of ScoringFunction in de.jstacs.classifier.scoringFunctionBased |
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Fields in de.jstacs.classifier.scoringFunctionBased declared as ScoringFunction | |
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protected ScoringFunction[] |
ScoreClassifier.score
The internally used scoring functions. |
Methods in de.jstacs.classifier.scoringFunctionBased that return ScoringFunction | |
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ScoringFunction |
ScoreClassifier.getScoringFunction(int i)
Returns the internally used ScoringFunction with index
i . |
ScoringFunction[] |
ScoreClassifier.getScoringFunctions()
Returns all internally used ScoringFunction s in the internal
order. |
Methods in de.jstacs.classifier.scoringFunctionBased with parameters of type ScoringFunction | |
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protected int |
AbstractOptimizableFunction.getNumberOfStarts(ScoringFunction[] score)
Returns the number of recommended starts. |
abstract void |
OptimizableFunction.reset(ScoringFunction[] score)
Resets the ScoringFunction s and all pre-computed values. |
Constructors in de.jstacs.classifier.scoringFunctionBased with parameters of type ScoringFunction | |
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ScoreClassifier(ScoreClassifierParameterSet params,
ScoringFunction... score)
Creates a new ScoreClassifier from a given
ScoreClassifierParameterSet and ScoringFunction s . |
Uses of ScoringFunction in de.jstacs.classifier.scoringFunctionBased.cll |
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Methods in de.jstacs.classifier.scoringFunctionBased.cll with parameters of type ScoringFunction | |
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static CLLClassifier[] |
CLLClassifier.create(CLLClassifierParameterSet params,
LogPrior prior,
ScoringFunction[]... functions)
This method creates an array of CLLClassifier s by using the
cross-product of the given ScoringFunction s. |
void |
NormConditionalLogLikelihood.reset(ScoringFunction[] funs)
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Constructors in de.jstacs.classifier.scoringFunctionBased.cll with parameters of type ScoringFunction | |
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CLLClassifier(CLLClassifierParameterSet params,
LogPrior prior,
ScoringFunction... score)
The default constructor that creates a new CLLClassifier from a
given parameter set, a prior and ScoringFunction s for the
classes. |
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CLLClassifier(CLLClassifierParameterSet params,
ScoringFunction... score)
The default constructor that creates a new CLLClassifier from a
given parameter set and ScoringFunction s for the classes. |
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NormConditionalLogLikelihood(ScoringFunction[] score,
Sample[] data,
double[][] weights,
boolean norm,
boolean freeParams)
The constructor creates an instance of the NormConditionalLogLikelihood . |
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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 |
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Methods in de.jstacs.classifier.scoringFunctionBased.logPrior with parameters of type ScoringFunction | |
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void |
SeparateLogPrior.set(boolean freeParameters,
ScoringFunction... funs)
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void |
LogPrior.set(boolean freeParameters,
ScoringFunction... funs)
Resets all pre-computed values to their initial values using the ScoringFunction s funs . |
void |
CompositeLogPrior.set(boolean freeParameters,
ScoringFunction... funs)
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Uses of ScoringFunction in de.jstacs.motifDiscovery |
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Methods in de.jstacs.motifDiscovery with parameters of type ScoringFunction | |
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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 |
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Subinterfaces of ScoringFunction in de.jstacs.scoringFunctions | |
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interface |
NormalizableScoringFunction
The interface for normalizable ScoringFunction s. |
Classes in de.jstacs.scoringFunctions that implement ScoringFunction | |
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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 ScoringFunction s that allow to score
subsequences of arbitrary length. |
Methods in de.jstacs.scoringFunctions that return ScoringFunction | |
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ScoringFunction |
ScoringFunction.clone()
Creates a clone (deep copy) of the current ScoringFunction
instance. |
Uses of ScoringFunction in de.jstacs.scoringFunctions.directedGraphicalModels |
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Classes in de.jstacs.scoringFunctions.directedGraphicalModels that implement ScoringFunction | |
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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 |
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Classes in de.jstacs.scoringFunctions.homogeneous that implement ScoringFunction | |
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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 ScoringFunction s. |
class |
UniformHomogeneousScoringFunction
This scoring function does nothing. |
Uses of ScoringFunction in de.jstacs.scoringFunctions.mix |
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Classes in de.jstacs.scoringFunctions.mix that implement ScoringFunction | |
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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 |
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Classes in de.jstacs.scoringFunctions.mix.motifSearch that implement ScoringFunction | |
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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. |
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