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Packages that use ListResult | |
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de.jstacs.classifiers.assessment | This package allows to assess classifiers. |
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer |
de.jstacs.results | This package provides classes for results and sets of results. |
Uses of ListResult in de.jstacs.classifiers.assessment |
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Methods in de.jstacs.classifiers.assessment that return ListResult | |
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ListResult |
ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp,
ClassifierAssessmentAssessParameterSet assessPS,
DataSet... s)
Assesses the contained classifiers. |
ListResult |
ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp,
ClassifierAssessmentAssessParameterSet assessPS,
ProgressUpdater pU,
DataSet... s)
Assesses the contained classifiers. |
ListResult |
ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp,
ClassifierAssessmentAssessParameterSet assessPS,
ProgressUpdater pU,
DataSet[][]... s)
Assesses the contained classifiers. |
ListResult |
KFoldCrossValidation.assessWithPredefinedSplits(NumericalPerformanceMeasureParameterSet mp,
ClassifierAssessmentAssessParameterSet caaps,
ProgressUpdater pU,
DataSet[]... splitData)
This method implements a k-fold crossvalidation on previously split data. |
Uses of ListResult in de.jstacs.motifDiscovery |
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Methods in de.jstacs.motifDiscovery that return ListResult | |
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static ListResult |
MotifDiscoveryAssessment.assess(DataSet truth,
DataSet prediction,
int maxDiff)
This method computes the nucleotide and site measures. |
Uses of ListResult in de.jstacs.results |
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Methods in de.jstacs.results that return ListResult | |
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ListResult |
ListResult.sort(String columnName)
This method enables you to sort the entries of this container by a specified column. |
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