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Packages that use LogPrior | |
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de.jstacs.classifier.scoringFunctionBased.cll | Provides the implementation of the log conditional likelihood as an OptimizableFunction and a classifier that uses log conditional likelihood or supervised posterior
to learn the parameters of a set of ScoringFunctions |
de.jstacs.classifier.scoringFunctionBased.logPrior | Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors |
Uses of LogPrior in de.jstacs.classifier.scoringFunctionBased.cll |
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Fields in de.jstacs.classifier.scoringFunctionBased.cll declared as LogPrior | |
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protected LogPrior |
CLLClassifier.prior
The prior that is used in this instance. |
Methods in de.jstacs.classifier.scoringFunctionBased.cll with parameters of type LogPrior | |
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static CLLClassifier[] |
CLLClassifier.create(CLLClassifierParameterSet params,
LogPrior prior,
ScoringFunction[]... functions)
This method creates an array of CLLClassifier by using the cross-product of the given ScoringFunctions. |
void |
CLLClassifier.setPrior(LogPrior prior)
This method set a new prior that should be used for optimization. |
Constructors in de.jstacs.classifier.scoringFunctionBased.cll with parameters of type LogPrior | |
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CLLClassifier(CLLClassifierParameterSet params,
LogPrior prior,
ScoringFunction... score)
The default constructor. |
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NormConditionalLogLikelihood(ScoringFunction[] score,
Sample[] data,
double[][] weights,
LogPrior prior,
boolean norm,
boolean freeParams)
The constructor creates an instance using the given prior. |
Uses of LogPrior in de.jstacs.classifier.scoringFunctionBased.logPrior |
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Subclasses of LogPrior in de.jstacs.classifier.scoringFunctionBased.logPrior | |
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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 NormalizableScoringFunction s and a set of class-parameters. |
class |
SeparateLaplaceLogPrior
Class for a LogPrior that defines a Laplace-prior on the parameters of a set of NormalizableScoringFunction s and a set of class-parameters. |
class |
SeparateLogPrior
Abstract class for priors that penalize each parameter value independently and have some variance (and possible mean) as hyper-parameters. |
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.classifier.scoringFunctionBased.logPrior that return LogPrior | |
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abstract LogPrior |
LogPrior.getNewInstance()
This method returns an empty new instance of the current prior. |
LogPrior |
DoesNothingLogPrior.getNewInstance()
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