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.motifDiscovery |
This package provides the framework including the interface for any de novo motif discoverer.
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de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous |
This package contains various inhomogeneous models.
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de.jstacs.sequenceScores.statisticalModels.trainable.hmm |
The package provides all interfaces and classes for a hidden Markov model (HMM).
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de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
This package is the super package for any mixture model.
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de.jstacs.utils |
This package contains a bundle of useful classes and interfaces like ...
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Modifier and Type | Field and Description |
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protected SafeOutputStream |
ScoreClassifier.sostream
This stream is used for comments, e.g.
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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 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 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.
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Modifier and Type | Field and Description |
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protected SafeOutputStream |
InhomogeneousDGTrainSM.sostream
This stream is used for comments, computation steps/results or any other
kind of output during the training, ...
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Modifier and Type | Method and Description |
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void |
MEM.train(SequenceIterator s,
byte algo,
TerminationCondition condition,
SafeOutputStream sostream)
This method approximates the distribution either analytically or numerically.
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Modifier and Type | Field and Description |
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protected SafeOutputStream |
AbstractHMM.sostream
This is the stream for writing information while training.
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Modifier and Type | Field and Description |
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protected SafeOutputStream |
AbstractMixtureTrainSM.sostream
This is the stream for writing information while training.
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Modifier and Type | Method and Description |
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static SafeOutputStream |
SafeOutputStream.getSafeOutputStream(OutputStream out)
This method returns an instance of
SafeOutputStream for a given OutputStream . |