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Packages that use BurnInTest | |
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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. |
de.jstacs.sampling | This package contains many classes that can be used while a sampling. |
de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models | The package provides different implementations of hidden Markov models based on AbstractHMM |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture | This package is the super package for any mixture model. |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
Uses of BurnInTest in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling |
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Fields in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling declared as BurnInTest | |
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protected BurnInTest |
SamplingScoreBasedClassifier.burnInTest
The BurnInTest , may be null for no test |
Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type BurnInTest | |
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protected double |
SamplingScoreBasedClassifier.sampleNSteps(DiffSSBasedOptimizableFunction function,
SamplingScoreBasedClassifier.DiffSMSamplingComponent component,
BurnInTest test,
int numSteps,
SamplingScoreBasedClassifier.SamplingScheme scheme)
Samples a predefined number of steps appended to the current sampling |
Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type BurnInTest | |
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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. |
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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. |
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SamplingScoreBasedClassifier(SamplingScoreBasedClassifierParameterSet params,
BurnInTest burnInTest,
double[] classVariances,
SamplingDifferentiableStatisticalModel... scoringFunctions)
Creates a new SamplingScoreBasedClassifier using the parameters in params ,
a specified BurnInTest (or null for no burn-in test), a set of sampling variances,
which may be different for each of the classes (in analogy to equivalent sample size for the Dirichlet distribution),
and set set of SamplingDifferentiableStatisticalModel s for each of the classes. |
Uses of BurnInTest in de.jstacs.sampling |
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Classes in de.jstacs.sampling that implement BurnInTest | |
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class |
AbstractBurnInTest
This abstract class implements some of the methods of BurnInTest to
alleviate the implementation of efficient and new burn-in tests. |
class |
SimpleBurnInTest
Deprecated. since this burn test ignore the data coming from the sampling, it might be problematic to use this test |
class |
VarianceRatioBurnInTest
In this class the Variance-Ratio method of Gelman and Rubin is implemented to test the length of the burn-in phase of a multi-chain Gibbs Sampling (number of chains >2). |
Methods in de.jstacs.sampling that return BurnInTest | |
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BurnInTest |
BurnInTest.clone()
Return a deep copy of this object. |
Uses of BurnInTest in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models |
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Fields in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models declared as BurnInTest | |
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protected BurnInTest |
SamplingHigherOrderHMM.burnInTest
This variable holds the BurnInTest used for training the model |
Uses of BurnInTest in de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
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Fields in de.jstacs.sequenceScores.statisticalModels.trainable.mixture declared as BurnInTest | |
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protected BurnInTest |
AbstractMixtureTrainSM.burnInTest
The BurnInTest that is used to stop the sampling. |
Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture with parameters of type BurnInTest | |
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AbstractMixtureTrainSM(int length,
TrainableStatisticalModel[] models,
boolean[] optimizeModel,
int dimension,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double[] weights,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new AbstractMixtureTrainSM . |
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MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
double[] weights,
int starts,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and fixed component probabilities. |
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MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double[] weights,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new MixtureTrainSM . |
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MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
int starts,
double[] componentHyperParams,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and sampling the component probabilities. |
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StrandTrainSM(TrainableStatisticalModel model,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double forwardStrandProb,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new StrandTrainSM . |
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StrandTrainSM(TrainableStatisticalModel model,
int starts,
double[] componentHyperParams,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and sampling the component probabilities. |
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StrandTrainSM(TrainableStatisticalModel model,
int starts,
double forwardStrandProb,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and fixed component probabilities. |
Uses of BurnInTest in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
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Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif with parameters of type BurnInTest | |
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HiddenMotifMixture(TrainableStatisticalModel[] models,
boolean[] optimzeArray,
int components,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double[] weights,
PositionPrior posPrior,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new HiddenMotifMixture . |
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ZOOPSTrainSM(TrainableStatisticalModel motif,
TrainableStatisticalModel bg,
boolean trainOnlyMotifModel,
int starts,
double[] componentHyperParams,
double[] weights,
PositionPrior posPrior,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new ZOOPSTrainSM . |
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