Uses of Class
de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior.LogPrior

Packages that use LogPrior

Package	Description
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for Classifiers that are based on SequenceScores. 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 DifferentiableStatisticalModels by a unified generative-discriminative learning principle.
de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior	Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors.
de.jstacs.classifiers.differentiableSequenceScoreBased.msp	Provides an implementation of a classifier that allows to train the parameters of a set of DifferentiableStatisticalModels either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL).
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, which can be learned from a single DataSet.

Uses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased declared as LogPrior

Modifier and Type	Field and Description
protected LogPrior	DiffSSBasedOptimizableFunction.prior The prior that is used in this function.

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased with parameters of type LogPrior

Constructor and Description
DiffSSBasedOptimizableFunction(int threads, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams) Creates an instance with the underlying infrastructure.

Uses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Fields in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix declared as LogPrior

Modifier and Type	Field and Description
protected LogPrior	GenDisMixClassifier.prior The prior that is used in this classifier.

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type LogPrior

Modifier and Type	Method and Description
static GenDisMixClassifier[]	GenDisMixClassifier.create(GenDisMixClassifierParameterSet params, LogPrior prior, double[] weights, DifferentiableStatisticalModel[]... functions) This method creates an array of GenDisMixClassifiers by using the cross-product of the given DifferentiableStatisticalModels.
void	GenDisMixClassifier.setPrior(LogPrior prior) This method set a new prior that should be used for optimization.

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type LogPrior

Constructor and Description
GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double[] beta, DifferentiableStatisticalModel... score) The main constructor.
GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, double[] beta, DifferentiableSequenceScore... score) This constructor creates an instance and sets the value of the last (external) optimization.
GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, double[] beta, DifferentiableStatisticalModel... score) This constructor creates an instance and sets the value of the last (external) optimization.
GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double genBeta, double disBeta, double priorBeta, DifferentiableStatisticalModel... score) This convenience constructor agglomerates the genBeta, disBeta, and priorBeta into an array and calls the main constructor.
GenDisMixClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, LearningPrinciple key, DifferentiableStatisticalModel... score) This convenience constructor creates an array of weights for an elementary learning principle and calls the main constructor.
LogGenDisMixFunction(int threads, DifferentiableSequenceScore[] score, DataSet[] data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams) The constructor for creating an instance that can be used in an Optimizer.
OneDataSetLogGenDisMixFunction(int threads, DifferentiableSequenceScore[] score, DataSet data, double[][] weights, LogPrior prior, double[] beta, boolean norm, boolean freeParams) The constructor for creating an instance that can be used in an Optimizer.

Uses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior

Subclasses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior

Modifier and Type	Class and Description
class	CompositeLogPrior This class implements a composite prior that can be used for DifferentiableStatisticalModel.
class	DoesNothingLogPrior This class defines a LogPrior that does not penalize any parameter.
class	SeparateGaussianLogPrior Class for a LogPrior that defines a Gaussian prior on the parameters of a set of DifferentiableStatisticalModels and a set of class parameters.
class	SeparateLaplaceLogPrior Class for a LogPrior that defines a Laplace prior on the parameters of a set of DifferentiableStatisticalModels and a set of class parameters.
class	SeparateLogPrior Abstract class for priors that penalize each parameter value independently and have some variances (and possible means) as hyperparameters.
class	SimpleGaussianSumLogPrior This class implements a prior that is a product of Gaussian distributions with mean 0 and equal variance for each parameter.

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.logPrior that return LogPrior

Modifier and Type	Method and Description
abstract LogPrior	LogPrior.getNewInstance() This method returns an empty new instance of the current prior.
LogPrior	DoesNothingLogPrior.getNewInstance()

Uses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased.msp

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.msp with parameters of type LogPrior

Constructor and Description
MSPClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, DifferentiableSequenceScore... score) The default constructor that creates a new MSPClassifier from a given parameter set, a prior and DifferentiableSequenceScores for the classes.
MSPClassifier(GenDisMixClassifierParameterSet params, LogPrior prior, double lastScore, DifferentiableSequenceScore... score) This constructor that creates a new MSPClassifier from a given parameter set, a prior and DifferentiableSequenceScores for the classes.

Uses of LogPrior in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type LogPrior

Constructor and Description
SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, LogPrior prior, double[] beta, SamplingDifferentiableStatisticalModel... scoringFunctions) Creates a new SamplingGenDisMixClassifier using the external parameters params, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, weights beta for the three components of the LogGenDisMixFunction, i.e., likelihood, conditional likelihood, and prior, and scoring functions that model the distribution for each of the classes.
SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet params, BurnInTest burnInTest, double[] classVariances, LogPrior prior, LearningPrinciple principle, SamplingDifferentiableStatisticalModel... scoringFunctions) Creates a new SamplingGenDisMixClassifier using the external parameters params, a burn-in test, a set of sampling variances for the different classes, a prior on the parameters, a learning principle, and scoring functions that model the distribution for each of the classes.

Uses of LogPrior in de.jstacs.sequenceScores.statisticalModels.trainable

Constructors in de.jstacs.sequenceScores.statisticalModels.trainable with parameters of type LogPrior

Constructor and Description
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.