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Uses of SamplingDifferentiableStatisticalModel in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling |
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Fields in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling declared as SamplingDifferentiableStatisticalModel | |
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protected SamplingDifferentiableStatisticalModel[] |
SamplingScoreBasedClassifier.scoringFunctions
SamplingDifferentiableStatisticalModel s |
Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type SamplingDifferentiableStatisticalModel | |
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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 SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable that implement SamplingDifferentiableStatisticalModel | |
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class |
CyclicMarkovModelDiffSM
This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length. |
class |
UniformDiffSM
This DifferentiableStatisticalModel does nothing. |
Uses of SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels that implement SamplingDifferentiableStatisticalModel | |
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class |
MarkovModelDiffSM
This class implements a AbstractDifferentiableStatisticalModel for an inhomogeneous Markov model. |
Uses of SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous that implement SamplingDifferentiableStatisticalModel | |
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class |
HomogeneousDiffSM
This is the main class for all homogeneous DifferentiableSequenceScore s. |
class |
HomogeneousMM0DiffSM
This scoring function implements a homogeneous Markov model of order zero (hMM(0)) for a fixed sequence length. |
class |
HomogeneousMMDiffSM
This scoring function implements a homogeneous Markov model of arbitrary order for any sequence length. |
class |
UniformHomogeneousDiffSM
This scoring function does nothing. |
Uses of SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that implement SamplingDifferentiableStatisticalModel | |
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class |
AbstractMixtureDiffSM
This main abstract class for any mixture scoring function (e.g. |
class |
MixtureDiffSM
This class implements a real mixture model. |
class |
StrandDiffSM
This class enables the user to search on both strand. |
class |
VariableLengthMixtureDiffSM
This class implements a mixture of VariableLengthDiffSM by extending MixtureDiffSM and implementing the methods of VariableLengthDiffSM . |
Uses of SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif |
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Classes in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that implement SamplingDifferentiableStatisticalModel | |
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class |
ExtendedZOOPSDiffSM
This class handles mixtures with at least one hidden motif. |
Uses of SamplingDifferentiableStatisticalModel in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models |
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Classes in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that implement SamplingDifferentiableStatisticalModel | |
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class |
DifferentiableHigherOrderHMM
This class combines an HigherOrderHMM and a DifferentiableStatisticalModel by implementing some of the declared methods. |
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