Package | Description |
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de.jstacs.algorithms.optimization.termination |
Provides classes for termination conditions that can be used in algorithms.
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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 |
Provides all
TrainableStatisticalModel s, which can
be learned from a single DataSet . |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training |
The package provides all classes used to determine the training algorithm of a hidden Markov model.
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Modifier and Type | Class and Description |
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class |
AbsoluteValueCondition
Deprecated.
use of the absolute value condition is not recommended and it may be removed in future releases
|
class |
CombinedCondition
This class allows to use many
TerminationCondition s at once. |
class |
IterationCondition
This class will stop an optimization if the number of iteration reaches a given number.
|
class |
MultipleIterationsCondition
This
TerminationCondition requires another provided TerminationCondition to fail a contiguous specified number of times
before the optimization is terminated. |
class |
SmallDifferenceOfFunctionEvaluationsCondition
This class implements a
TerminationCondition that stops an optimization
if the difference of the current and the last function evaluations will be small, i.e.,
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class |
SmallGradientConditon
This class implements a
TerminationCondition that allows no further iteration in an optimization if the
the gradient becomes small, i.e.,
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class |
SmallStepCondition
This class implements a
TerminationCondition that allows no further iteration in an optimization if the
scalar product of the current and the last values of x will be small, i.e.,
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class |
TimeCondition
This class implements a
TerminationCondition that stops the optimization if the elapsed time in seconds is
greater than a given value. |
Modifier and Type | Method and Description |
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AbstractTerminationCondition |
AbstractTerminationCondition.clone() |
Constructor and Description |
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CombinedCondition(int threshold,
AbstractTerminationCondition... condition)
This constructor creates an instance that allows to use many
TerminationCondition s at once. |
CombinedConditionParameterSet(int threshold,
AbstractTerminationCondition[] condition)
This constructor creates a filled instance of a parameters set.
|
MultipleIterationsCondition(int threshold,
AbstractTerminationCondition condition)
This constructor creates an instance that stops the optimization if provided termination condition
fails a contiguously a specified number of times.
|
MultipleIterationsConditionParameterSet(int threshold,
AbstractTerminationCondition condition)
This constructor creates a filled instance of the parameter set.
|
Constructor and Description |
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AbstractTerminationConditionParameterSet(Class<? extends AbstractTerminationCondition> instanceClass)
Constructs an
AbstractTerminationCondition.AbstractTerminationConditionParameterSet from the class that can be
instantiated using this AbstractTerminationCondition.AbstractTerminationConditionParameterSet . |
Modifier and Type | Method and Description |
---|---|
AbstractTerminationCondition |
ScoreClassifierParameterSet.getTerminantionCondition()
This method returns the
AbstractTerminationCondition for stopping the training, e.g., if the
difference of the scores between two iterations is smaller than a given
threshold the training is stopped. |
Constructor and Description |
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ScoreClassifierParameterSet(Class<? extends ScoreClassifier> instanceClass,
AlphabetContainer alphabet,
int length,
byte algo,
AbstractTerminationCondition tc,
double lineps,
double startD,
boolean free,
OptimizableFunction.KindOfParameter kind)
The constructor for a simple, instantiated parameter set.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Constructor and Description |
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DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel nsf,
int threads,
byte algo,
AbstractTerminationCondition tc,
double lineps,
double startD)
The main constructor that creates an instance with the user given parameters and
CompositeLogPrior . |
DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel nsf,
int threads,
byte algo,
AbstractTerminationCondition tc,
double lineps,
double startD,
LogPrior prior)
Constructor that creates an instance with the user given parameters.
|
Modifier and Type | Method and Description |
---|---|
AbstractTerminationCondition |
MaxHMMTrainingParameterSet.getTerminationCondition()
This method returns the
AbstractTerminationCondition for stopping the training, e.g., if the
difference of the scores between two iterations is smaller than a given
threshold the training is stopped. |
Constructor and Description |
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BaumWelchParameterSet(int starts,
AbstractTerminationCondition tc,
int threads)
This constructor can be used to create an instance with specified parameters.
|
MaxHMMTrainingParameterSet(int starts,
AbstractTerminationCondition tc)
This constructor can be used to create an instance with specified parameters.
|
MultiThreadedTrainingParameterSet(int starts,
AbstractTerminationCondition tc,
int threads)
This constructor can be used to create an instance with specified parameters.
|
NumericalHMMTrainingParameterSet(int starts,
AbstractTerminationCondition tc,
int threads,
byte algorithm,
double lineEps,
double startDist)
This constructor can be used to create an instance with specified parameters.
|
ViterbiParameterSet(int starts,
AbstractTerminationCondition tc,
int threads)
This constructor can be used to create an instance with specified parameters.
|