Uses of Class
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifierParameterSet

Packages that use GenDisMixClassifierParameterSet

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.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.

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

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

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.

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

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.

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

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

MSPClassifier(GenDisMixClassifierParameterSet params, DifferentiableSequenceScore... score)
This convenience constructor creates an MSPClassifier that used MCL principle for training.

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 GenDisMixClassifierParameterSet in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type GenDisMixClassifierParameterSet

GenDisMixClassifier	SamplingGenDisMixClassifier.getClassifierForBestParameters(GenDisMixClassifierParameterSet params) Returns a standard, i.e., non-sampling, GenDisMixClassifier, where the parameters are set to those that yielded the maximum value of the objective functions among all sampled parameter values.
GenDisMixClassifier	SamplingGenDisMixClassifier.getClassifierForMeanParameters(GenDisMixClassifierParameterSet params, boolean testBurnIn, int minBurnInSteps) Returns a standard, i.e., non-sampling, GenDisMixClassifier, where the parameters are set to the mean values over all sampled parameter values in the stationary phase.