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## 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 `DifferentiableStatisticalModel`s 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 `DifferentiableStatisticalModel`s either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
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.

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 `DifferentiableStatisticalModel`s.

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 `DifferentiableSequenceScore`s 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 `DifferentiableSequenceScore`s 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.

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