Package | Description |
---|---|
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 |
Modifier and Type | Field and Description |
---|---|
protected BurnInTest |
SamplingScoreBasedClassifier.burnInTest
The
BurnInTest , may be null for no test |
Modifier and Type | Method and Description |
---|---|
protected double |
SamplingScoreBasedClassifier.sampleNSteps(Function function,
SamplingScoreBasedClassifier.DiffSMSamplingComponent component,
BurnInTest test,
int numSteps,
SamplingScoreBasedClassifier.SamplingScheme scheme)
Samples a predefined number of steps appended to the current sampling
|
Constructor and Description |
---|
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. |
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. |
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. |
Modifier and Type | Class and Description |
---|---|
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).
|
Modifier and Type | Method and Description |
---|---|
BurnInTest |
BurnInTest.clone()
Return a deep copy of this object.
|
Modifier and Type | Field and Description |
---|---|
protected BurnInTest |
SamplingHigherOrderHMM.burnInTest
This variable holds the BurnInTest used for training the model
|
Modifier and Type | Field and Description |
---|---|
protected BurnInTest |
AbstractMixtureTrainSM.burnInTest
The
BurnInTest that is used to stop the sampling. |
Constructor and Description |
---|
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 . |
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.
|
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 . |
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.
|
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 . |
StrandTrainSM(TrainableStatisticalModel model,
int starts,
double[] componentHyperParams,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and sampling the component
probabilities.
|
StrandTrainSM(TrainableStatisticalModel model,
int starts,
double forwardStrandProb,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and fixed component
probabilities.
|
Constructor and Description |
---|
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 . |
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 . |