Package | Description |
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de.jstacs.classifiers.differentiableSequenceScoreBased |
Provides the classes for
Classifier s that are based on SequenceScore s.It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior. |
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.
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Modifier and Type | Method and Description |
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protected abstract DiffSSBasedOptimizableFunction |
ScoreClassifier.getFunction(DataSet[] data,
double[][] weights)
Returns the function that should be optimized.
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Modifier and Type | Class and Description |
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class |
LogGenDisMixFunction
This class implements the the following function
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class |
OneDataSetLogGenDisMixFunction
This class implements the the following function
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Modifier and Type | Method and Description |
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protected abstract DiffSSBasedOptimizableFunction |
SamplingScoreBasedClassifier.getFunction(DataSet[] data,
double[][] weights)
Returns the function that should be sampled from.
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protected DiffSSBasedOptimizableFunction |
SamplingGenDisMixClassifier.getFunction(DataSet[] data,
double[][] weights) |
Modifier and Type | Method and Description |
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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.
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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
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Modifier and Type | Method and Description |
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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.
|
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.
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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.
|