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Packages that use DiffSSBasedOptimizableFunction | |
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de.jstacs.classifiers.differentiableSequenceScoreBased | Provides the classes for Classifier s that are based on SequenceScore s. |
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix | Provides an implementation of a classifier that allows to train the parameters of a set of
DifferentiableStatisticalModel s by
a unified generative-discriminative learning principle |
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling | Provides the classes for AbstractScoreBasedClassifier s that are based on
SamplingDifferentiableStatisticalModel s
and that sample parameters using the Metropolis-Hastings algorithm. |
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer |
Uses of DiffSSBasedOptimizableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased |
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Methods in de.jstacs.classifiers.differentiableSequenceScoreBased that return DiffSSBasedOptimizableFunction | |
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protected abstract DiffSSBasedOptimizableFunction |
ScoreClassifier.getFunction(DataSet[] data,
double[][] weights)
Returns the function that should be optimized. |
Uses of DiffSSBasedOptimizableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix |
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Subclasses of DiffSSBasedOptimizableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix | |
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class |
LogGenDisMixFunction
This class implements the the following function ![]() |
class |
OneDataSetLogGenDisMixFunction
This class implements the the following function ![]() ![]() ![]() |
Uses of DiffSSBasedOptimizableFunction in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling |
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Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling that return DiffSSBasedOptimizableFunction | |
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protected abstract DiffSSBasedOptimizableFunction |
SamplingScoreBasedClassifier.getFunction(DataSet[] data,
double[][] weights)
Returns the function that should be sampled from. |
protected DiffSSBasedOptimizableFunction |
SamplingGenDisMixClassifier.getFunction(DataSet[] data,
double[][] weights)
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Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type DiffSSBasedOptimizableFunction | |
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protected double |
SamplingScoreBasedClassifier.doOneSamplingStep(DiffSSBasedOptimizableFunction function,
SamplingScoreBasedClassifier.SamplingScheme scheme,
double previousValue)
Performs one sampling step, i.e., one sampling of all parameter values. |
protected void |
SamplingScoreBasedClassifier.sample(SamplingScoreBasedClassifier.DiffSMSamplingComponent sfsc,
DiffSSBasedOptimizableFunction function)
Samples as many steps as needed to get into the stationary phase according to SamplingScoreBasedClassifier.burnInTest and then samples the number of
stationary steps as set in SamplingScoreBasedClassifier.params . |
protected double |
SamplingScoreBasedClassifier.sampleNSteps(DiffSSBasedOptimizableFunction function,
SamplingScoreBasedClassifier.DiffSMSamplingComponent component,
BurnInTest test,
int numSteps,
SamplingScoreBasedClassifier.SamplingScheme scheme)
Samples a predefined number of steps appended to the current sampling |
Uses of DiffSSBasedOptimizableFunction in de.jstacs.motifDiscovery |
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Methods in de.jstacs.motifDiscovery with parameters of type DiffSSBasedOptimizableFunction | |
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static boolean |
MutableMotifDiscovererToolbox.doHeuristicSteps(DifferentiableSequenceScore[] funs,
DataSet[] data,
double[][] weights,
DiffSSBasedOptimizableFunction 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 DifferentiableSequenceScore is a MutableMotifDiscoverer . |
static Sequence[] |
MutableMotifDiscovererToolbox.enumerate(DifferentiableSequenceScore[] funs,
int[] classIndex,
int[] motifIndex,
RecyclableSequenceEnumerator[] rse,
double weight,
DiffSSBasedOptimizableFunction opt,
OutputStream out)
This method allows to enumerate all possible seeds for a number of motifs in the MutableMotifDiscoverer s of a specific classes. |
static Sequence |
MutableMotifDiscovererToolbox.enumerate(DifferentiableSequenceScore[] funs,
int classIndex,
int motifIndex,
RecyclableSequenceEnumerator rse,
double weight,
DiffSSBasedOptimizableFunction 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,
DifferentiableSequenceScore[] score,
DataSet[] data,
double[][] weights,
DiffSSBasedOptimizableFunction 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(DifferentiableSequenceScore[] funs,
MutableMotifDiscovererToolbox.InitMethodForDiffSM[] init,
DiffSSBasedOptimizableFunction opt,
int n,
OutputStream stream,
int optimizationSteps)
This method allows to initialize the DifferentiableSequenceScore using different MutableMotifDiscovererToolbox.InitMethodForDiffSM . |
static double[][] |
MutableMotifDiscovererToolbox.optimize(DifferentiableSequenceScore[] funs,
DiffSSBasedOptimizableFunction opt,
byte algorithm,
AbstractTerminationCondition condition,
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(DifferentiableSequenceScore[] funs,
DiffSSBasedOptimizableFunction opt,
byte algorithm,
AbstractTerminationCondition condition,
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. |
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