Uses of Class
de.jstacs.results.NumericalResultSet

Packages that use NumericalResultSet

de.jstacs.classifiers	This package provides the framework for any classifier.
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for Classifiers that are based on SequenceScores. It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior.
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifiers.performanceMeasures	This package provides the implementations of performance measures that can be used to assess any classifier.
de.jstacs.classifiers.trainSMBased	Provides the classes for Classifiers that are based on TrainableStatisticalModels.
de.jstacs.results	This package provides classes for results and sets of results.
de.jstacs.sequenceScores	Provides all SequenceScores, which can be used to score a Sequence, typically using some model assumptions.
de.jstacs.sequenceScores.differentiable
de.jstacs.sequenceScores.statisticalModels.trainable	Provides all TrainableStatisticalModels, which can be learned from a single DataSet.
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous	This package contains various inhomogeneous models.
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.

Uses of NumericalResultSet in de.jstacs.classifiers

Methods in de.jstacs.classifiers that return NumericalResultSet

NumericalResultSet	MappingClassifier.getNumericalCharacteristics()
abstract NumericalResultSet	AbstractClassifier.getNumericalCharacteristics() Returns the subset of numerical values that are also returned by AbstractClassifier.getCharacteristics().

Uses of NumericalResultSet in de.jstacs.classifiers.differentiableSequenceScoreBased

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased that return NumericalResultSet

NumericalResultSet

ScoreClassifier.getNumericalCharacteristics()

Uses of NumericalResultSet in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling that return NumericalResultSet

NumericalResultSet

SamplingScoreBasedClassifier.getNumericalCharacteristics()

Uses of NumericalResultSet in de.jstacs.classifiers.performanceMeasures

Methods in de.jstacs.classifiers.performanceMeasures that return NumericalResultSet

NumericalResultSet	NumericalPerformanceMeasure.compute(double[][][] classSpecificScores) This method allows to compute the performance measure of given class specific scores.
NumericalResultSet	ClassificationRate.compute(double[][][] classSpecificScores)
NumericalResultSet	AucROC.compute(double[][][] classSpecificScores)
NumericalResultSet	AucPR.compute(double[][][] classSpecificScores)
NumericalResultSet	AbstractNumericalTwoClassPerformanceMeasure.compute(double[][][] classSpecificScores)
NumericalResultSet	NumericalPerformanceMeasure.compute(double[][][] classSpecificScores, double[][] weights) This method allows to compute the performance measure of given class specific scores.
NumericalResultSet	ClassificationRate.compute(double[][][] classSpecificScores, double[][] weights)
NumericalResultSet	AucROC.compute(double[][][] classSpecificScores, double[][] weights)
NumericalResultSet	AucPR.compute(double[][][] classSpecificScores, double[][] weights)
NumericalResultSet	AbstractNumericalTwoClassPerformanceMeasure.compute(double[][][] classSpecificScores, double[][] weights)
NumericalResultSet	NumericalPerformanceMeasure.compute(double[] sortedScoresClass0, double[] sortedScoresClass1) This method allows to compute the performance measure of given sorted score ratios.
NumericalResultSet	ClassificationRate.compute(double[] sortedScoresClass0, double[] sortedScoresClass1)
NumericalResultSet	AucROC.compute(double[] sortedScoresClass0, double[] sortedScoresClass1)
NumericalResultSet	AucPR.compute(double[] sortedScoresClass0, double[] sortedScoresClass1)
NumericalResultSet	AbstractNumericalTwoClassPerformanceMeasure.compute(double[] sortedScoresClass0, double[] sortedScoresClass1)
NumericalResultSet	SensitivityForFixedSpecificity.compute(double[] sortedScoresClass0, double[] weightClass0, double[] sortedScoresClass1, double[] weightClass1)
NumericalResultSet	PositivePredictiveValueForFixedSensitivity.compute(double[] sortedScoresClass0, double[] weightsClass0, double[] sortedScoresClass1, double[] weightsClass1)
NumericalResultSet	NumericalPerformanceMeasure.compute(double[] sortedScoresClass0, double[] weightsClass0, double[] sortedScoresClass1, double[] weightsClass1) This method allows to compute the performance measure of given sorted score ratios.
NumericalResultSet	MaximumNumericalTwoClassMeasure.compute(double[] sortedScoresClass0, double[] weightsClass0, double[] sortedScoresClass1, double[] weightsClass1)
NumericalResultSet	FalsePositiveRateForFixedSensitivity.compute(double[] sortedScoresClass0, double[] weightsClass0, double[] sortedScoresClass1, double[] weightsClass1)
NumericalResultSet	ClassificationRate.compute(double[] sortedScoresClass0, double[] weightsClass0, double[] sortedScoresClass1, double[] weightsClass1)
NumericalResultSet	AucROC.compute(double[] sortedScoresClass0, double[] weightsClass0, double[] sortedScoresClass1, double[] weightsClass1)
NumericalResultSet	AucPR.compute(double[] sortedScoresClass0, double[] weightsClass0, double[] sortedScoresClass1, double[] weightsClass1)
abstract NumericalResultSet	AbstractNumericalTwoClassPerformanceMeasure.compute(double[] sortedScoresClass0, double[] weightClass0, double[] sortedScoresClass1, double[] weightsClass1)

Uses of NumericalResultSet in de.jstacs.classifiers.trainSMBased

Methods in de.jstacs.classifiers.trainSMBased that return NumericalResultSet

NumericalResultSet

TrainSMBasedClassifier.getNumericalCharacteristics()

Uses of NumericalResultSet in de.jstacs.results

Subclasses of NumericalResultSet in de.jstacs.results

class

MeanResultSet Class that computes the mean and the standard error of a series of NumericalResultSets.

Methods in de.jstacs.results that return NumericalResultSet

NumericalResultSet

MeanResultSet.getStatistics() Returns the means and (if possible the) standard errors of the results in this MeanResultSet as a new NumericalResultSet.

Methods in de.jstacs.results with parameters of type NumericalResultSet

void	MeanResultSet.addResults(NumericalResultSet... rs) Adds NumericalResultSets to this MeanResultSet.

Uses of NumericalResultSet in de.jstacs.sequenceScores

Methods in de.jstacs.sequenceScores that return NumericalResultSet

NumericalResultSet

SequenceScore.getNumericalCharacteristics() Returns the subset of numerical values that are also returned by SequenceScore.getCharacteristics().

Uses of NumericalResultSet in de.jstacs.sequenceScores.differentiable

Methods in de.jstacs.sequenceScores.differentiable that return NumericalResultSet

NumericalResultSet

AbstractDifferentiableSequenceScore.getNumericalCharacteristics()

Uses of NumericalResultSet in de.jstacs.sequenceScores.statisticalModels.trainable

Methods in de.jstacs.sequenceScores.statisticalModels.trainable that return NumericalResultSet

NumericalResultSet	VariableLengthWrapperTrainSM.getNumericalCharacteristics()
NumericalResultSet	UniformTrainSM.getNumericalCharacteristics()
NumericalResultSet	DifferentiableStatisticalModelWrapperTrainSM.getNumericalCharacteristics()
NumericalResultSet	CompositeTrainSM.getNumericalCharacteristics()

Uses of NumericalResultSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that return NumericalResultSet

NumericalResultSet

HomogeneousTrainSM.getNumericalCharacteristics()

Uses of NumericalResultSet in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that return NumericalResultSet

NumericalResultSet	MEManager.getNumericalCharacteristics()
NumericalResultSet	DAGTrainSM.getNumericalCharacteristics()

Uses of NumericalResultSet in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that return NumericalResultSet

NumericalResultSet

HigherOrderHMM.getNumericalCharacteristics()

Uses of NumericalResultSet in de.jstacs.sequenceScores.statisticalModels.trainable.mixture

Methods in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that return NumericalResultSet

NumericalResultSet

AbstractMixtureTrainSM.getNumericalCharacteristics()