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Packages that use GenDisMixClassifierParameterSet | |
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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 |
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Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type GenDisMixClassifierParameterSet | |
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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 | |
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GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double[] beta,
DifferentiableStatisticalModel... score)
The main constructor. |
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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. |
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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. |
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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. |
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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 |
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Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.msp with parameters of type GenDisMixClassifierParameterSet | |
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MSPClassifier(GenDisMixClassifierParameterSet params,
DifferentiableSequenceScore... score)
This convenience constructor creates an MSPClassifier that used MCL principle for training. |
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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. |
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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 |
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Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type GenDisMixClassifierParameterSet | |
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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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