Index (adams-weka 0.4.0 API)

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absDev(int, Instances) - Static method in class weka.classifiers.trees.m5.Rule2: Returns the absolute deviation value of the supplied attribute index.
absErrTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Returns the tip text for this property
absoluteTipText() - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Returns the tip text for this property.
AbstainAttributePercentile - Class in weka.classifiers.meta: Only predict if attribute value within percentile range.
AbstainAttributePercentile() - Constructor for class weka.classifiers.meta.AbstainAttributePercentile
AbstainLeastMedianSq - Class in weka.classifiers.meta: Finds the base classifier with the best least median squared error.
AbstainLeastMedianSq() - Constructor for class weka.classifiers.meta.AbstainLeastMedianSq
AbstractAttributeCapabilities - Class in adams.flow.condition.bool: Ancestor for capabilities-based conditions.
AbstractAttributeCapabilities() - Constructor for class adams.flow.condition.bool.AbstractAttributeCapabilities
AbstractClustererPostProcessor - Class in adams.flow.transformer.wekaclusterer: Ancestor for post-processors for output that the WekaClusterer transformer produces.
AbstractClustererPostProcessor() - Constructor for class adams.flow.transformer.wekaclusterer.AbstractClustererPostProcessor
AbstractClusterMembershipPostProcessor - Class in adams.flow.transformer.wekaclusterer: Ancestor for post-processors that require a built clusterer and the dataset that was used to build the clusterer to be present in the model container.
AbstractClusterMembershipPostProcessor() - Constructor for class adams.flow.transformer.wekaclusterer.AbstractClusterMembershipPostProcessor
AbstractColumnFinder - Class in adams.data.weka.columnfinder: Ancestor for classes that find columns of interest in datasets.
AbstractColumnFinder() - Constructor for class adams.data.weka.columnfinder.AbstractColumnFinder
AbstractColumnFinderApplier - Class in weka.filters.unsupervised.attribute: Ancestor for filters that apply ColumnFinder schemes to the data.
AbstractColumnFinderApplier() - Constructor for class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier
AbstractColumnFinderWithCapabilities - Class in adams.data.weka.columnfinder: Ancestor for classes that find columns of interest in datasets.
AbstractColumnFinderWithCapabilities() - Constructor for class adams.data.weka.columnfinder.AbstractColumnFinderWithCapabilities
AbstractCrossvalidatedInstanceEvaluator<T extends AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer> - Class in adams.data.weka.evaluator: Ancestor for evalutors that use cross-validation for initialization.
AbstractCrossvalidatedInstanceEvaluator() - Constructor for class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator
AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer - Class in adams.data.weka.evaluator: Container for storing the evaluation results.
AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer(Instance) - Constructor for class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer: Initializes the container.
AbstractDatasetInstanceEvaluator - Class in adams.data.weka.evaluator: Ancestor for evaluators that need a data set for initialization.
AbstractDatasetInstanceEvaluator() - Constructor for class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator
AbstractErrorScaler - Class in adams.data.weka.predictions: Ancestor for classes that scale predictions.
AbstractErrorScaler() - Constructor for class adams.data.weka.predictions.AbstractErrorScaler
AbstractFilteredColumnFinder - Class in adams.data.weka.columnfinder: Ancestor for column finders that pre-filter the columns.
AbstractFilteredColumnFinder() - Constructor for class adams.data.weka.columnfinder.AbstractFilteredColumnFinder
AbstractFilteredRowFinder - Class in adams.data.weka.rowfinder: Ancestor for row finders that pre-filter the rows.
AbstractFilteredRowFinder() - Constructor for class adams.data.weka.rowfinder.AbstractFilteredRowFinder
AbstractGeneticAlgorithm - Class in adams.optimise.genetic: Base class for genetic algorithms.
AbstractGeneticAlgorithm() - Constructor for class adams.optimise.genetic.AbstractGeneticAlgorithm
AbstractGlobalWekaClassifierEvaluator - Class in adams.flow.transformer: Ancestor for classifier evaluators that make use of a global classifier.
AbstractGlobalWekaClassifierEvaluator() - Constructor for class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator
AbstractHashableInstance - Class in weka.core: Ancestor for instance classes that wraps around any WEKA Instance and allow them to be used in data structures that make use of on object's hash, like maps or hashtables.
AbstractHashableInstance(Instance) - Constructor for class weka.core.AbstractHashableInstance: Initializes the wrapper.
AbstractInstanceEvaluator - Class in adams.data.weka.evaluator: Ancestor for evaluators that evaluate weka.core.Instance objects.
AbstractInstanceEvaluator() - Constructor for class adams.data.weka.evaluator.AbstractInstanceEvaluator
AbstractInstanceGenerator<T extends DataContainer & ReportHandler> - Class in adams.data.instances: Abstract base class for schemes that turn temperature profiles into weka.core.Instance objects.
AbstractInstanceGenerator() - Constructor for class adams.data.instances.AbstractInstanceGenerator
AbstractInstanceGenerator<T extends DataContainer> - Class in adams.flow.transformer: Ancestor for transformers that turn data containers into WEKA Instance objects.
AbstractInstanceGenerator() - Constructor for class adams.flow.transformer.AbstractInstanceGenerator
AbstractInstanceInfoFrame - Class in adams.gui.visualization.weka: Ancestor for frames for displaying information on the displayed data, with some more domain-specific functionality.
AbstractInstanceInfoFrame() - Constructor for class adams.gui.visualization.weka.AbstractInstanceInfoFrame
AbstractLinearRegressionBased<T extends DataContainer> - Class in adams.data.baseline: Abstract ancestor for linear regression based baseline correction schemes.
AbstractLinearRegressionBased() - Constructor for class adams.data.baseline.AbstractLinearRegressionBased
AbstractMatchWekaInstanceAgainstHeader - Class in adams.data.conversion: Ancestor for classes that match Instance objects against Instances headers.
AbstractMatchWekaInstanceAgainstHeader() - Constructor for class adams.data.conversion.AbstractMatchWekaInstanceAgainstHeader
AbstractParameter - Class in weka.core.setupgenerator: Abstract container class for search parameters.
AbstractParameter() - Constructor for class weka.core.setupgenerator.AbstractParameter: default constructor.
AbstractParameterHandlingWekaMenuItemDefinition - Class in adams.gui.menu: Abstract menu item definition for Weka elements that also handle additional parameters.
AbstractParameterHandlingWekaMenuItemDefinition() - Constructor for class adams.gui.menu.AbstractParameterHandlingWekaMenuItemDefinition: Initializes the menu item with no owner.
AbstractParameterHandlingWekaMenuItemDefinition(AbstractApplicationFrame) - Constructor for class adams.gui.menu.AbstractParameterHandlingWekaMenuItemDefinition: Initializes the menu item.
AbstractProcessWekaInstanceWithModel<T> - Class in adams.flow.transformer: Ancestor for transformers that user models for processing Instance objects, e.g., classifiers making predictions.
AbstractProcessWekaInstanceWithModel() - Constructor for class adams.flow.transformer.AbstractProcessWekaInstanceWithModel
AbstractRowFinder - Class in adams.data.weka.rowfinder: Ancestor for classes that find rows of interest in datasets.
AbstractRowFinder() - Constructor for class adams.data.weka.rowfinder.AbstractRowFinder
AbstractRowFinderApplier - Class in weka.filters.unsupervised.instance: Ancestor for filters that apply RowFinder schemes to the data.
AbstractRowFinderApplier() - Constructor for class weka.filters.unsupervised.instance.AbstractRowFinderApplier
AbstractRowFinderWithCapabilities - Class in adams.data.weka.rowfinder: Ancestor for classes that find rows of interest in datasets.
AbstractRowFinderWithCapabilities() - Constructor for class adams.data.weka.rowfinder.AbstractRowFinderWithCapabilities
AbstractTrainableColumnFinder - Class in adams.data.weka.columnfinder: Ancestor for ColumnFinder algorithms that can be trained.
AbstractTrainableColumnFinder() - Constructor for class adams.data.weka.columnfinder.AbstractTrainableColumnFinder
AbstractTrainableRowFinder - Class in adams.data.weka.rowfinder: Ancestor for RowFinder algorithms that can be trained.
AbstractTrainableRowFinder() - Constructor for class adams.data.weka.rowfinder.AbstractTrainableRowFinder
AbstractWekaClassifierEvaluator - Class in adams.flow.transformer: Ancestor for transformers that evaluate classifiers.
AbstractWekaClassifierEvaluator() - Constructor for class adams.flow.transformer.AbstractWekaClassifierEvaluator
AbstractWEKAFitnessFunction - Class in adams.optimise.genetic.fitnessfunctions: Perform attribute selection using WEKA classification.
AbstractWEKAFitnessFunction() - Constructor for class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction
AbstractWEKAFitnessFunction.Measure - Enum in adams.optimise.genetic.fitnessfunctions: The measure to use for evaluating.
AbstractWekaInstanceAndWekaInstancesTransformer - Class in adams.flow.transformer: Transformer that processes weka.core.Instance, weka.core.Instances or adams.data.instance.Instance objects.
AbstractWekaInstanceAndWekaInstancesTransformer() - Constructor for class adams.flow.transformer.AbstractWekaInstanceAndWekaInstancesTransformer
AbstractWekaMenuItemDefinition - Class in adams.gui.menu: Abstract menu item menu item definitions for Weka elements.
AbstractWekaMenuItemDefinition() - Constructor for class adams.gui.menu.AbstractWekaMenuItemDefinition: Initializes the menu item with no owner.
AbstractWekaMenuItemDefinition(AbstractApplicationFrame) - Constructor for class adams.gui.menu.AbstractWekaMenuItemDefinition: Initializes the menu item.
AbstractWekaModelReader - Class in adams.flow.transformer: Ancestor for actors that deserialize models.
AbstractWekaModelReader() - Constructor for class adams.flow.transformer.AbstractWekaModelReader
AbstractWekaModelWriter - Class in adams.flow.sink: Ancestor for actors that serialize models.
AbstractWekaModelWriter() - Constructor for class adams.flow.sink.AbstractWekaModelWriter
AbstractWekaPredictionsTransformer - Class in adams.flow.transformer: Ancestor for transformers that convert the predictions stored in an Evaluation object into a different format.
AbstractWekaPredictionsTransformer() - Constructor for class adams.flow.transformer.AbstractWekaPredictionsTransformer
AbstractWekaSetupGenerator<T> - Class in adams.flow.source: Abstract ancestor for setup generator sources.
AbstractWekaSetupGenerator() - Constructor for class adams.flow.source.AbstractWekaSetupGenerator
accepts() - Method in class adams.data.conversion.AbstractMatchWekaInstanceAgainstHeader: Returns the class that is accepted as input.
accepts() - Method in class adams.data.conversion.AdamsInstanceToWekaInstance: Returns the class that is accepted as input.
accepts() - Method in class adams.data.conversion.ReportToWekaInstance: Returns the class that is accepted as input.
accepts() - Method in class adams.data.conversion.SpreadSheetToWekaInstances: Returns the class that is accepted as input.
accepts() - Method in class adams.data.conversion.WekaInstancesToSpreadSheet: Returns the class that is accepted as input.
accepts() - Method in class adams.data.conversion.WekaInstanceToAdamsInstance: Returns the class that is accepted as input.
accepts() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the class that is accepted as input.
accepts() - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.condition.bool.AdamsInstanceCapabilities: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.condition.bool.WekaCapabilities: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.condition.bool.WekaClassification: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.sink.AbstractWekaModelWriter: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.sink.WekaClassifierErrors: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.sink.WekaCostCurve: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.sink.WekaFileWriter: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.sink.WekaInstancesDisplay: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.sink.WekaInstanceViewer: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.sink.WekaROC: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.AbstractWekaInstanceAndWekaInstancesTransformer: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.AbstractWekaModelReader: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaAccumulatedError: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaAttributeIterator: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaClassifier: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaClassifierOptimizer: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaClusterer: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaEvaluationValues: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaExperiment: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaExtractArray: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaFileReader: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaGetInstanceValue: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaInstanceBuffer: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaInstancesInfo: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaInstancesStatistic: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaNewInstance: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaRandomSplit: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaRegexToRange: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaRelationName: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaSetInstanceValue: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaStoreInstance: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaSubsets: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaTestSetEvaluator: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaTextDirectoryReader: Returns the class that the consumer accepts.
accepts() - Method in class adams.flow.transformer.WekaTrainTestSetEvaluator: Returns the class that the consumer accepts.
acceptSelection() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Generates the indices.
adams.core.base - package adams.core.base
adams.core.management - package adams.core.management
adams.core.option - package adams.core.option
adams.data - package adams.data
adams.data.baseline - package adams.data.baseline
adams.data.conversion - package adams.data.conversion
adams.data.instance - package adams.data.instance
adams.data.instances - package adams.data.instances
adams.data.io.input - package adams.data.io.input
adams.data.utils - package adams.data.utils
adams.data.weka - package adams.data.weka
adams.data.weka.columnfinder - package adams.data.weka.columnfinder
adams.data.weka.evaluator - package adams.data.weka.evaluator
adams.data.weka.predictions - package adams.data.weka.predictions
adams.data.weka.rowfinder - package adams.data.weka.rowfinder
adams.env - package adams.env
adams.event - package adams.event
adams.flow.condition.bool - package adams.flow.condition.bool
adams.flow.container - package adams.flow.container
adams.flow.core - package adams.flow.core
adams.flow.sink - package adams.flow.sink
adams.flow.source - package adams.flow.source
adams.flow.template - package adams.flow.template
adams.flow.transformer - package adams.flow.transformer
adams.flow.transformer.wekaclusterer - package adams.flow.transformer.wekaclusterer
adams.genetic - package adams.genetic
adams.gui - package adams.gui
adams.gui.chooser - package adams.gui.chooser
adams.gui.goe - package adams.gui.goe
adams.gui.menu - package adams.gui.menu
adams.gui.tools.previewbrowser - package adams.gui.tools.previewbrowser
adams.gui.visualization.instance - package adams.gui.visualization.instance
adams.gui.visualization.weka - package adams.gui.visualization.weka
adams.ml - package adams.ml
adams.optimise - package adams.optimise
adams.optimise.genetic - package adams.optimise.genetic
adams.optimise.genetic.fitnessfunctions - package adams.optimise.genetic.fitnessfunctions
adams.tools - package adams.tools
AdamsInstanceCapabilities - Class in adams.flow.condition.bool: Filters adams.data.instance.Instance based on defined capabilities.
AdamsInstanceCapabilities() - Constructor for class adams.flow.condition.bool.AdamsInstanceCapabilities
AdamsInstanceToWekaInstance - Class in adams.data.conversion: Converts adams.data.instance.Instance objects into weka.core.Instance ones.
AdamsInstanceToWekaInstance() - Constructor for class adams.data.conversion.AdamsInstanceToWekaInstance
add(ArrayHistogram, Instance) - Method in class adams.gui.visualization.instance.HistogramFactory.Dialog: Adds a plot of the given instance.
add(ArrayHistogram, Instance, String) - Method in class adams.gui.visualization.instance.HistogramFactory.Dialog: Adds a plot of the given instance.
add(ArrayHistogram, Instance) - Method in class adams.gui.visualization.instance.HistogramFactory.Panel: Adds a plot of the given instance.
add(ArrayHistogram, Instance, String) - Method in class adams.gui.visualization.instance.HistogramFactory.Panel: Adds a plot of the given instance.
add(InstanceContainer) - Method in class adams.gui.visualization.instance.InstanceContainerManager: Adds the given container to the list.
add(String, int, double, double) - Method in class adams.optimise.genetic.PackDataDef
add(int, Performance) - Method in class weka.classifiers.meta.multisearch.PerformanceCache: adds the performance to the cache.
addChangeListener(ChangeListener) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Adds the change listener to the internal list.
addClassificationLabelTipText() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the tip text for this property.
addClassificationTipText() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the tip text for this property.
addDatabaseIDTipText() - Method in class adams.data.instances.AbstractInstanceGenerator: Returns the tip text for this property.
addDistributionTipText() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the tip text for this property.
addFitnessChangeListener(FitnessChangeListener) - Method in interface adams.event.FitnessChangeNotifier: Adds the given listener to its internal list of listeners.
addIndexTipText() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns the tip text for this property.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.FilteredSearch: Adds the given instance info.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.NewNNSearch: Adds the given instance info.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.PCANNSearch: Adds the given instance info.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.PLSNNSearch: Adds the given instance info.
addLabelIndexTipText() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns the tip text for this property.
addPerformance(Performance, int) - Method in class weka.classifiers.meta.MultiSearch: Adds the performance to the cache and the current list of performances.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.explorer.SqlPanel: Adds a PropertyChangeListener who will be notified of value changes.
addResult(String, Double) - Method in class adams.optimise.GeneticAlgorithm: Adds a result to the cache.
adjust(double) - Method in enum adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction.Measure: Adjusts the measure value for sorting: either multiplies it with -1 or 1.
ALGORITHM_CDF2_4 - Static variable in class weka.filters.unsupervised.attribute.FastWavelet: the type of algorithm: CDF2 4.
ALGORITHM_DAUBECHIES2 - Static variable in class weka.filters.unsupervised.attribute.FastWavelet: the type of algorithm: Daubechies2.
ALGORITHM_HAAR - Static variable in class weka.filters.unsupervised.attribute.FastWavelet: the type of algorithm: Haar.
ALGORITHM_SYMMLET8 - Static variable in class weka.filters.unsupervised.attribute.FastWavelet: the type of algorithm: Symmlet8.
algorithmTipText() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Returns the tip text for this property.
amountTipText() - Method in class weka.filters.unsupervised.instance.LatestRecords: Returns the tip text for this property.
antiAliasingEnabledTipText() - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Returns the tip text for this property.
apply(Instances, int[]) - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Applies the indices to the data.
apply(Instances, int[]) - Method in class weka.filters.unsupervised.attribute.DatasetCleaner: Applies the indices to the data.
apply(Instances, int[]) - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Applies the indices to the data.
apply(Instances, int[]) - Method in class weka.filters.unsupervised.instance.DatasetCleaner: Applies the indices to the data.
apply(Instances, int[]) - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Applies the indices to the data.
ArffUtils - Class in adams.data.weka: A helper class for ARFF related stuff.
ArffUtils() - Constructor for class adams.data.weka.ArffUtils
ArffViewer - Class in adams.gui.menu: Opens the ARFF viewer.
ArffViewer() - Constructor for class adams.gui.menu.ArffViewer: Initializes the menu item with no owner.
ArffViewer(AbstractApplicationFrame) - Constructor for class adams.gui.menu.ArffViewer: Initializes the menu item.
ARRAY_REGEX - Static variable in class adams.ml.WekaData
arrayToHashSet(int[]) - Static method in class adams.data.weka.columnfinder.AbstractColumnFinder: Turns the array into a hashset.
arrayToHashSet(int[]) - Static method in class adams.data.weka.rowfinder.AbstractRowFinder: Turns the array into a hashset.
assign(DataContainer<InstancePoint>) - Method in class adams.data.instance.Instance: Obtains the stored variables from the other data point, but not the actual data points.
assign(DataPoint) - Method in class adams.data.instance.InstancePoint: Obtains the stored variables from the other data point.
assign(InstanceContainer) - Method in class adams.gui.visualization.instance.InstanceContainer: Updates itself with the values from given container (the manager is excluded!).
assign(AbstractHashableInstance) - Method in class weka.core.AbstractHashableInstance: Assigns all the data, apart from wrapped instance, that the provided hashable instance provides.
assignIDs(int) - Method in class weka.classifiers.trees.m5.RuleNode2: Assigns a unique identifier to each node in the tree
ATT_CONSTANT - Static variable in class weka.classifiers.meta.LogTargetRegressor: Constant to add to attributes before logarithm is taken.
ATT_DATE - Static variable in class adams.core.base.AttributeTypeList: the type string for date attributes.
ATT_NOMINAL - Static variable in class adams.core.base.AttributeTypeList: the type string for nominal attributes.
ATT_NUMERIC - Static variable in class adams.core.base.AttributeTypeList: the type string for numeric attributes.
ATT_STRING - Static variable in class adams.core.base.AttributeTypeList: the type string for string attributes.
attribute(int) - Method in class weka.core.AbstractHashableInstance: Returns the attribute with the given index.
ATTRIBUTE_NAME - Static variable in class adams.flow.transformer.WekaInstanceEvaluator: the default name of the attribute with the evaluation value.
ATTRIBUTE_PREFIX - Static variable in class adams.flow.source.WekaNewInstances: the prefix for attributes (if nto specified explicitly).
attributeIndexTipText() - Method in class adams.data.weka.rowfinder.ByLabel: Returns the tip text for this property.
attributeIndexTipText() - Method in class adams.flow.transformer.WekaInstancesInfo: Returns the tip text for this property.
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.SpellChecker: Returns the tip text for this property.
attributeNamesTipText() - Method in class adams.flow.source.WekaNewInstances: Returns the tip text for this property.
attributeNameTipText() - Method in class adams.flow.transformer.WekaGetInstanceValue: Returns the tip text for this property.
attributeNameTipText() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Returns the tip text for this property.
attributeNameTipText() - Method in class weka.filters.unsupervised.instance.LatestRecords: Returns the tip text for this property.
attributeRangeTipText() - Method in class adams.data.weka.rowfinder.FilteredIQR: Returns the tip text for this property.
attributeRangeTipText() - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Returns the tip text for this property.
attributeRangeTipText() - Method in class weka.filters.unsupervised.attribute.SetMissingValue: Returns the tip text for this property.
AttributeSelection - Class in adams.optimise.genetic.fitnessfunctions: Perform attribute selection using WEKA classification.
AttributeSelection() - Constructor for class adams.optimise.genetic.fitnessfunctions.AttributeSelection
attributeSparse(int) - Method in class weka.core.AbstractHashableInstance: Returns the attribute with the given index in the sparse representation.
attributesTipText() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Returns the tip text for this property.
attributeTipText() - Method in class weka.classifiers.meta.AbstainAttributePercentile
AttributeTypeList - Class in adams.core.base: Wrapper for a comma-separated list of attribute types.
AttributeTypeList() - Constructor for class adams.core.base.AttributeTypeList: Initializes the list with length 0.
AttributeTypeList(String) - Constructor for class adams.core.base.AttributeTypeList: Initializes the object with the string to parse.
attributeTypesTipText() - Method in class adams.flow.source.WekaNewInstances: Returns the tip text for this property.
autoKeyGenerationTipText() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the tip text for this property.
AutoScaler - Class in adams.data.weka.predictions: Applies the specified numeric scaler to the data in case of a numeric class attribute, otherwise just passes on the data.
AutoScaler() - Constructor for class adams.data.weka.predictions.AutoScaler

B

BACKUP_ACCUMULATEDERROR - Static variable in class adams.flow.transformer.WekaAccumulatedError: the key for storing the current accumulated error in the backup.
BACKUP_ACTUALFOLDS - Static variable in class adams.flow.transformer.WekaCrossValidationSplit: the key for storing the actual folds in the backup.
BACKUP_ATTRIBUTESTOPROCESS - Static variable in class adams.flow.transformer.WekaMultiLabelSplitter: the key for storing the current attributes to process in the backup.
BACKUP_BUFFER - Static variable in class adams.flow.transformer.WekaInstanceBuffer: the key for storing the current buffer in the backup.
BACKUP_BUFFER - Static variable in class adams.flow.transformer.WekaInstanceDumper: the key for storing the buffer in the backup.
BACKUP_CLASSATTRIBUTES - Static variable in class adams.flow.transformer.WekaMultiLabelSplitter: the key for storing the current class attributes in the backup.
BACKUP_COUNTER - Static variable in class adams.flow.transformer.WekaInstanceDumper: the key for storing the counter in the backup.
BACKUP_CURRENTFOLD - Static variable in class adams.flow.transformer.WekaCrossValidationSplit: the key for storing the current fold in the backup.
BACKUP_HEADER - Static variable in class adams.flow.transformer.WekaInstanceDumper: the key for storing the header in the backup.
BACKUP_INCREMENTALCLASSIFIER - Static variable in class adams.flow.transformer.WekaClassifier: the key for storing the current incremental classifier in the backup.
BACKUP_INCREMENTALCLUSTERER - Static variable in class adams.flow.transformer.WekaClusterer: the key for storing the current incremental clusterer in the backup.
BACKUP_INITIALIZED - Static variable in class adams.flow.transformer.WekaFilter: the key for storing the current initialized state in the backup.
BACKUP_ITERATOR - Static variable in class adams.flow.transformer.WekaInstanceBuffer: the key for storing the current iterator in the backup.
BACKUP_MODEL - Static variable in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: the key for storing the current model in the backup.
BACKUP_NAMES - Static variable in class adams.flow.transformer.WekaAttributeIterator: the key for storing the names in the backup.
BACKUP_NUMPREDICTIONS - Static variable in class adams.flow.transformer.WekaAccumulatedError: the key for storing the current number of predictions in the backup.
BACKUP_PREDICTIONS - Static variable in class adams.flow.transformer.WekaAccumulatedError: the key for storing the current predictions in the backup.
BACKUP_SAVER - Static variable in class adams.flow.sink.WekaDatabaseWriter: the key for storing the current incremental clusterer in the backup.
BACKUP_SOURCE - Static variable in class adams.flow.transformer.WekaFileReader: the key for storing the current source in the backup.
BACKUP_STRUCTURE - Static variable in class adams.flow.transformer.WekaFileReader: the key for storing the current structure in the backup.
backupState() - Method in class adams.flow.sink.WekaDatabaseWriter: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.WekaAccumulatedError: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.WekaAttributeIterator: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.WekaClassifier: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.WekaClusterer: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.WekaFileReader: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.WekaFilter: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.WekaInstanceBuffer: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.WekaInstanceDumper: Backs up the current state of the actor before update the variables.
backupState() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Backs up the current state of the actor before update the variables.
baseObjectFileTipText() - Method in class weka.core.SetupGenerator: Returns the tip text for this property.
baseTipText() - Method in class weka.core.setupgenerator.MathParameter: Returns the tip text for this property.
batchFinished() - Method in class weka.filters.unsupervised.attribute.NormalizeAdaptive: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Signify that this batch of input to the filter is finished.
BayesNetEditor - Class in adams.gui.menu: Opens the BayesNet Editor.
BayesNetEditor() - Constructor for class adams.gui.menu.BayesNetEditor: Initializes the menu item with no owner.
BayesNetEditor(AbstractApplicationFrame) - Constructor for class adams.gui.menu.BayesNetEditor: Initializes the menu item.
beforeShow() - Method in class adams.gui.InstanceCompare: Hook method just before the dialog is made visible.
beforeShow() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Hook method just before the dialog is made visible.
bestRangeTipText() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the tip text for this property.
bestRangeTipText() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the tip text for this property.
binsPointTipText() - Method in class weka.core.SAXDistance: Returns the tip text for this property.
binsPointTipText() - Method in class weka.filters.unsupervised.attribute.SAX: Returns the tip text for this property.
bitsTipText() - Method in class adams.optimise.GeneticAlgorithm: Returns the tip text for this property.
block(boolean) - Method in class weka.classifiers.meta.MultiSearch: Helper method used for blocking.
borderTipText() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the tip text for this property.
BoundaryVisualizer - Class in adams.gui.menu: Displays data in the boundary visualizer.
BoundaryVisualizer() - Constructor for class adams.gui.menu.BoundaryVisualizer: Initializes the menu item with no owner.
BoundaryVisualizer(AbstractApplicationFrame) - Constructor for class adams.gui.menu.BoundaryVisualizer: Initializes the menu item.
bufferSizeTipText() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the tip text for this property.
build(String[], String) - Method in class adams.ml.WekaFilter
build(Dataset) - Method in class adams.ml.WekaFilter
build(Instance) - Method in class weka.classifiers.lazy.LWLSynchro: Builds the classifier.
buildandfilter(Dataset, String[]) - Method in class adams.ml.WekaFilter
buildandfilter(Dataset) - Method in class adams.ml.WekaFilter
buildandfilterP(Dataset, String[]) - Method in class adams.ml.WekaFilter
buildClassifier(Instances) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive
buildClassifier(Instances) - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.GPD: Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.PLSClassifierWeighted: builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.AbstainAttributePercentile: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.ClassificationViaRegressionD: Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Corr: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.FilteredClassifierExt: Build the classifier on the filtered data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.LeastMedianSq: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.LogTargetRegressor: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.MultiSearch: builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.PartitionedStacking: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.PeakTransformed: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RangeCheck: Build the classifier on the filtered data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.SubsetEnsemble: Stump method for building the classifiers
buildClassifier(Instances) - Method in class weka.classifiers.meta.SumTransformed: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.WeightedInstancesHandlerWrapper: Generates a classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.M5Base2: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.Rule2: Generates a single rule or m5 model tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.RuleNode2: Build this node (find an attribute and split point)
buildClassifier(Instances) - Method in class weka.classifiers.trees.RandomModelTrees
buildClassifier(Instances) - Method in class weka.classifiers.trees.RandomRegressionForest: builds the classifier
buildClassifier2(Instances) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Method for building the classifier.
buildClassifiers(Instances) - Method in class weka.classifiers.meta.PartitionedStacking: Does the actual construction of the base-classifiers.
buildClassifiers() - Method in class weka.classifiers.meta.SubsetEnsemble: Does the actual construction of the ensemble.
buildFilter(double, int) - Method in class weka.core.neighboursearch.PCANNSearch
buildFilter(int) - Method in class weka.core.neighboursearch.PLSNNSearch
buildRegressionTreeTipText() - Method in class weka.classifiers.trees.m5.M5Base2: Returns the tip text for this property
ByLabel - Class in adams.data.weka.rowfinder: Returns indices of rows which label match the regular expression.
ByLabel() - Constructor for class adams.data.weka.rowfinder.ByLabel
ByName - Class in adams.data.weka.columnfinder: Returns indices of attributes which names match the regular expression.
ByName() - Constructor for class adams.data.weka.columnfinder.ByName

C

calcAverageWidth(double[][]) - Static method in class adams.data.weka.evaluator.IntervalEstimatorBased: Calculates the average width of the intervals.
calcBreakPoints(int) - Static method in class adams.data.utils.SAXUtils: Calculate the break points for equal-frequency bins for a gaussian.
calcDistMatrix(double[]) - Static method in class adams.data.utils.SAXUtils: Calculate the distance matrix for use in the MINDIST function.
calcFitness() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Override the following function in sub-classes.
calcFitness() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Override the following function in sub-classes.
calcFitness() - Method in class adams.optimise.GeneticAlgorithm: Calculates the fitness of the population.
calcNewFitness() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: Calculates the new fitness.
calcNewFitness(FitnessFunction, int[]) - Method in class adams.optimise.GeneticAlgorithm: Calculates the new fitness.
calculateCenters(Instances, Clusterer, Instances) - Method in class adams.flow.transformer.wekaclusterer.ClusterCenters: Calculates the centers
calcWidth(double[]) - Static method in class adams.data.weka.evaluator.IntervalEstimatorBased: Calculates the width of the interval.
canPaint(Graphics) - Method in class adams.gui.visualization.instance.InstancePanel: Returns true if the paintlets can be executed.
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.ExperimentPanel: method gets called in case of a change event.
capabilitiesTipText() - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Returns the tip text for this property.
Capability - Enum in adams.flow.core: Enumeration of all capabilities.
cellPaddingTipText() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the tip text for this property.
cellSpacingTipText() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the tip text for this property.
centerClass(Instances) - Method in class weka.classifiers.trees.RandomRegressionForest: Centers the class value in the data.
charSetTipText() - Method in class adams.flow.transformer.WekaTextDirectoryReader: Returns the tip text for this property.
check(Instances) - Method in class adams.data.weka.columnfinder.AbstractColumnFinder: Checks the data.
check(Instances) - Method in class adams.data.weka.columnfinder.AbstractColumnFinderWithCapabilities: Checks the data.
check(Instances) - Method in class adams.data.weka.columnfinder.AbstractTrainableColumnFinder: Checks the data.
check(Instance) - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Peforms checks on the instance that is about to be evaluated.
check(Instances) - Method in class adams.data.weka.rowfinder.AbstractRowFinder: Checks the data.
check(Instances) - Method in class adams.data.weka.rowfinder.AbstractRowFinderWithCapabilities: Checks the data.
check(Instances) - Method in class adams.data.weka.rowfinder.AbstractTrainableRowFinder: Checks the data.
check(WekaModelContainer) - Method in class adams.flow.transformer.wekaclusterer.AbstractClustererPostProcessor: Checks the model container.
check(WekaModelContainer) - Method in class adams.flow.transformer.wekaclusterer.AbstractClusterMembershipPostProcessor: Checks the model container.
checkBest(Double, OptData, FitnessFunction, int) - Method in class adams.optimise.GeneticAlgorithm
checkHeader(T) - Method in class adams.data.instances.AbstractInstanceGenerator: Checks whether the number of waves are the same.
checkHeaderTipText() - Method in class adams.flow.transformer.WekaInstanceBuffer: Returns the tip text for this property.
checkHeaderTipText() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the tip text for this property.
checkInput(T) - Method in class adams.data.instances.AbstractInstanceGenerator: Checks the input profile.
checkRangeForInstance(Instance) - Method in class weka.classifiers.meta.RangeCheck: Checks the range for the instance.
checkRangeForInstance(Instance) - Method in interface weka.classifiers.RangeCheckClassifier: Checks the range for the instance.
checkSetup() - Method in class adams.data.instances.AbstractInstanceGenerator: Checks whether the setup is consistent.
choleskyDecomposition(double[][]) - Method in class weka.classifiers.functions.GPD
CLASS_CONSTANT - Static variable in class weka.classifiers.meta.LogTargetRegressor: Constant to add to class before logarithm is taken.
classAttribute() - Method in class weka.core.AbstractHashableInstance: Returns class attribute.
classDetailsTipText() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns the tip text for this property.
classificationEntryTipText() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the tip text for this property.
classificationLabelEntryTipText() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the tip text for this property.
ClassificationViaRegressionD - Class in weka.classifiers.meta: Class for doing classification using regression methods.
ClassificationViaRegressionD() - Constructor for class weka.classifiers.meta.ClassificationViaRegressionD: Default constructor.
classifierTipText() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Returns the tip text for this property.
classifierTipText() - Method in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: Returns the tip text for this property.
classifierTipText() - Method in class adams.flow.transformer.WekaClassifier: Returns the tip text for this property.
classifierTipText() - Method in class adams.flow.transformer.WekaClassifierOptimizer: Returns the tip text for this property.
classifierTipText() - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Returns the tip text for this property.
classifierTipText() - Method in class adams.flow.transformer.WekaTrainTestSetEvaluator: Returns the tip text for this property.
classifierTipText() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the tip text for this property.
classifierTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Returns the tip text for this property
classifyInstance(Instance) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.GPD: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.PLSClassifierWeighted: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.AbstainAttributePercentile: Returns the prediction.
classifyInstance(Instance) - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Returns the prediction.
classifyInstance(Instance) - Method in class weka.classifiers.meta.Corr: Returns the prediction.
classifyInstance(Instance) - Method in class weka.classifiers.meta.LeastMedianSq: Returns the prediction.
classifyInstance(Instance) - Method in class weka.classifiers.meta.LogTargetRegressor: Returns the prediction.
classifyInstance(Instance) - Method in class weka.classifiers.meta.PartitionedStacking: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.PeakTransformed: Returns the prediction.
classifyInstance(Instance) - Method in class weka.classifiers.meta.SubsetEnsemble: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.SumTransformed: Returns the prediction.
classifyInstance(Instance) - Method in class weka.classifiers.meta.WeightedInstancesHandlerWrapper: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.M5Base2: Calculates a prediction for an instance using a set of rules or an M5 model tree
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.Rule2: Calculates a prediction for an instance using this rule or M5 model tree
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.RuleNode2: Classify an instance using this node.
classifyInstance(Instance) - Method in class weka.classifiers.trees.RandomModelTrees: Calculates the class membership probabilities for the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.RandomRegressionForest: Calculates the class membership probabilities for the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.RandomRegressionForest.Node: classifies the given instance
classIndex() - Method in class weka.core.AbstractHashableInstance: Returns the class attribute's index.
classIndexTipText() - Method in class adams.flow.source.WekaNewInstances: Returns the tip text for this property.
classIndexTipText() - Method in class adams.flow.transformer.WekaClassSelector: Returns the tip text for this property.
classIndexTipText() - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Returns the tip text for this property.
classIndexTipText() - Method in class adams.flow.transformer.WekaEvaluationValues: Returns the tip text for this property.
classIndexTipText() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the tip text for this property.
classIndexTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Returns the tip text for this property
classIsMissing() - Method in class weka.core.AbstractHashableInstance: Tests if an instance's class is missing.
classLabelIndexTipText() - Method in class adams.flow.sink.WekaCostCurve: Returns the tip text for this property.
classLabelIndexTipText() - Method in class adams.flow.sink.WekaROC: Returns the tip text for this property.
classNameTipText() - Method in class adams.flow.source.WekaNewInstances: Returns the tip text for this property.
classNoiseTipText() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Returns the tip text for this property.
classValue() - Method in class weka.core.AbstractHashableInstance: Returns an instance's class value as a floating-point number.
cleanUp() - Method in class adams.data.instances.AbstractInstanceGenerator: Cleans up data structures, frees up memory.
cleanUp() - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Cleans up data structures, frees up memory.
cleanUp() - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Cleans up data structures, frees up memory.
cleanUp() - Method in class adams.flow.transformer.WekaClassifierRanker: Frees up memory in a "destructive" non-reversible way.
cleanUp() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: Cleans up data structures, frees up memory.
cleanUp() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: Cleans up data structures, frees up memory.
cleanUp() - Method in class adams.gui.visualization.instance.InstanceComparePanel: Cleans up data structures, frees up memory.
cleanUp() - Method in class adams.gui.visualization.instance.InstanceExplorer: Cleans up data structures, frees up memory.
cleanUp() - Method in class adams.gui.visualization.instance.InstancePanel: Cleans up data structures, frees up memory.
cleanUp() - Method in class adams.gui.visualization.instance.InstancePointHitDetector: Cleans up data structures, frees up memory.
cleanUp() - Method in class adams.optimise.GeneticAlgorithm: Cleans up data structures, frees up memory.
cleanUp() - Method in class adams.tools.CompareDatasets: Cleans up data structures, frees up memory.
cleanUpGUI() - Method in class adams.flow.sink.WekaClassifierErrors: Removes all graphical components.
cleanUpGUI() - Method in class adams.flow.sink.WekaCostCurve: Removes all graphical components.
cleanUpGUI() - Method in class adams.flow.sink.WekaInstancesDisplay: Removes all graphical components.
cleanUpGUI() - Method in class adams.flow.sink.WekaInstanceViewer: Removes all graphical components.
cleanUpGUI() - Method in class adams.flow.sink.WekaROC: Removes all graphical components.
clear() - Method in class adams.data.instance.Instance: Removes all the points and report and nulls the header reference.
clear() - Method in class adams.gui.visualization.instance.InstanceContainerManager: Clears the container list.
clearData() - Method in class adams.gui.visualization.instance.InstanceExplorer: Removes all the data.
clearPanel() - Method in class adams.flow.sink.WekaClassifierErrors: Clears the content of the panel.
clearPanel() - Method in class adams.flow.sink.WekaCostCurve: Clears the content of the panel.
clearPanel() - Method in class adams.flow.sink.WekaInstancesDisplay: Clears the content of the panel.
clearPanel() - Method in class adams.flow.sink.WekaInstanceViewer: Clears the content of the panel.
clearPanel() - Method in class adams.flow.sink.WekaROC: Clears the content of the panel.
clearResults() - Method in class adams.optimise.GeneticAlgorithm: Clears all currently stored results.
clone() - Method in class weka.core.setupgenerator.AbstractParameter: Returns a clone of itself.
clone() - Method in class weka.core.setupgenerator.Point: Returns a clone of itself.
close() - Method in class adams.gui.visualization.instance.InstanceComparePanel: closes the dialog/frame.
close() - Method in class adams.gui.visualization.instance.InstanceExplorer: closes the dialog/frame.
close() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Closes the dialog.
ClusterCenters - Class in adams.flow.transformer.wekaclusterer: Computes the cluster centers for the provided dataset.
ClusterCenters() - Constructor for class adams.flow.transformer.wekaclusterer.ClusterCenters
clustererTipText() - Method in class adams.flow.transformer.WekaClusterer: Returns the tip text for this property.
COLUMN_NAME - Static variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: the name column in the spreadsheet.
COLUMN_VALUE - Static variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: the value column in the spreadsheet.
ColumnFinder - Interface in adams.data.weka.columnfinder: Interface for classes that "find" columns of interest in datasets.
columnFinderTipText() - Method in class adams.data.weka.columnfinder.AbstractFilteredColumnFinder: Returns the tip text for this property.
columnFinderTipText() - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Returns the tip text for this property.
columnFinderTipText() - Method in class weka.filters.unsupervised.attribute.DatasetCleaner: Returns the tip text for this property.
columnTipText() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the tip text for this property.
combinationRuleTipText() - Method in class weka.classifiers.meta.SubsetEnsemble: Returns the tip text for this property.
combinationTipText() - Method in class adams.data.weka.columnfinder.MultiColumnFinder: Returns the tip text for this property.
combinationTipText() - Method in class adams.data.weka.rowfinder.MultiRowFinder: Returns the tip text for this property.
commentTipText() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns the tip text for this property.
compare(DataPoint, DataPoint) - Method in class adams.data.instance.InstancePointComparator: Compares its two arguments for order.
compare(Performance, Performance) - Method in class weka.classifiers.meta.multisearch.PerformanceComparator: Compares its two arguments for order.
CompareDatasets - Class in adams.tools: Compares two datasets, either row-by-row or using a row attribute listing a unique ID for matching the rows, outputting the correlation coefficient of the numeric attributes found in the ranges defined by the user.
In order to trim down the number of generated rows, a threshold can be specified.
CompareDatasets() - Constructor for class adams.tools.CompareDatasets
compareTo(WekaPredictionContainerToSpreadSheet.SortContainer) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet.SortContainer: Compares this object with the specified object for order.
compareTo(Object) - Method in class adams.data.instance.InstancePoint: Compares this object with the specified object for order.
compareTo(Object) - Method in class adams.data.instances.AbstractInstanceGenerator: Compares this object with the specified object for order.
compareTo(Object) - Method in class adams.data.weka.columnfinder.AbstractColumnFinder: Compares this object with the specified object for order.
compareTo(Object) - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer: Compares this object with the specified object for order.
compareTo(Object) - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Compares this object with the specified object for order.
compareTo(Object) - Method in class adams.data.weka.evaluator.IntervalEstimatorBased.SortedInterval: Compares this object with the specified object for order.
compareTo(Object) - Method in class adams.data.weka.predictions.AbstractErrorScaler: Compares this object with the specified object for order.
compareTo(Object) - Method in class adams.data.weka.rowfinder.AbstractRowFinder: Compares this object with the specified object for order.
compareTo(Object) - Method in class adams.flow.transformer.WekaAccumulatedError.SortablePrediction: Compares this object with the specified object for order.
compareTo(Container) - Method in class adams.gui.visualization.instance.InstanceContainer: Compares this object with the specified object for order.
compareTo(Object) - Method in class weka.core.neighboursearch.NewNNSearch.InstanceNode
comparisonFieldTipText() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the tip text for this property.
completedClassifier(int, boolean) - Method in class weka.classifiers.meta.SubsetEnsemble: Records the completion of the training of a single classifier.
completedEvaluation(Object, boolean) - Method in class weka.classifiers.meta.MultiSearch: Records the completion of the training of a single classifier.
complexityStatisticsTipText() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns the tip text for this property.
computeHashCode() - Method in class weka.core.AbstractHashableInstance: Computes the hashcode.
computeHashCode() - Method in class weka.core.HashableInstanceUsingString: Computes the hashcode.
computeHashCode() - Method in class weka.core.HashableInstanceUsingSum: Computes the hashcode.
computePercentage(double, double, double) - Static method in class weka.classifiers.RangeCheckHelper: Calculates the percentage of how much the value is outside the range.
computeThresholds(Instances) - Method in class weka.filters.unsupervised.attribute.InterquartileRangeSamp: computes the thresholds for outliers and extreme values
confidenceLevelTipText() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Returns the tip text for this property.
constructEnsemble(Instance) - Method in class weka.classifiers.meta.SubsetEnsemble: Constructs the ensemble.
copy() - Method in class weka.core.AbstractHashableInstance: This method produces a shallow copy of an object.
copy(Serializable) - Method in class weka.core.SetupGenerator: Returns a copy of the object.
copyGene(int, int) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Copies the values of one gene to another.
copyGene(int, int) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Copies the values of one gene to another.
Corr - Class in weka.classifiers.meta: Assume NO MISSING VALUES, all attributes must be NUMERIC (or 0/1 maybe ...).
Corr() - Constructor for class weka.classifiers.meta.Corr
correctData(T, double[]) - Method in class adams.data.baseline.AbstractLinearRegressionBased: Corrects the data with the given coefficients.
correctTipText() - Method in class weka.filters.unsupervised.attribute.SpellChecker: Returns the tip text for this property.
CorrelationMatrix - Class in weka.filters.unsupervised.attribute: Computes a matrix with the correlation coefficients between attributes.
CorrelationMatrix() - Constructor for class weka.filters.unsupervised.attribute.CorrelationMatrix
countVisible() - Method in class adams.gui.visualization.instance.InstanceContainerManager: Returns the number of visible containers.
createCustomEditor() - Method in class adams.gui.goe.WekaExperimentFileEditor: Gets the custom editor component.
createDataset(Evaluation) - Method in class adams.flow.sink.WekaClassifierErrors.DataGenerator: Generates a dataset, containing the predicted vs actual values.
createDefaultDataModel() - Method in class adams.gui.visualization.instance.InstanceTable: Creates an empty default model.
createDisplayPanel(Token) - Method in class adams.flow.sink.WekaClassifierErrors: Creates a new panel for the token.
createDisplayPanel(Token) - Method in class adams.flow.sink.WekaCostCurve: Creates a new panel for the token.
createDisplayPanel(Token) - Method in class adams.flow.sink.WekaInstancesDisplay: Creates a new panel for the token.
createDisplayPanel(Token) - Method in class adams.flow.sink.WekaInstanceViewer: Creates a new panel for the token.
createDisplayPanel(Token) - Method in class adams.flow.sink.WekaROC: Creates a new panel for the token.
createDummyDataset() - Method in class adams.flow.sink.WekaInstancesDisplay: Creates an empty dummy dataset.
createFilename(Instances) - Method in class adams.flow.transformer.WekaInstanceDumper: Generates the filename for the output.
createHeader(Instances) - Method in class adams.flow.transformer.WekaInstanceDumper: Turns the dataset header into the appropriate format.
createModel(InstanceContainerManager) - Method in class adams.gui.visualization.instance.InstanceContainerList: Creates a new model.
createOutputFormat(Instances) - Method in class adams.flow.transformer.wekaclusterer.ClusterCenters: Generates the output format (additional attribute for cluster index).
createPreview(Object) - Method in class adams.gui.tools.previewbrowser.GraphVisualizer: Creates the actual preview.
createPreview(File) - Method in class adams.gui.tools.previewbrowser.InstanceExplorerHandler: Creates the actual view.
createPreview(Object) - Method in class adams.gui.tools.previewbrowser.InterQuartileRangeViewer: Creates the actual preview.
createPreview(Object) - Method in class adams.gui.tools.previewbrowser.TreeVisualizer: Creates the actual preview.
createPreview(File) - Method in class adams.gui.tools.previewbrowser.WekaDatasetHandler: Creates the actual view.
createRelationName(boolean) - Method in class adams.flow.transformer.WekaCrossValidationSplit: Generates a relation name for the current fold.
createRow(Instance) - Method in class adams.flow.transformer.WekaInstanceDumper: Turns the row into the appropriate format.
crossValidate(int, int) - Method in class adams.ml.WekaClassifier
crossValidationSeedTipText() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the tip text for this property.
customizePopupMenu(MouseEvent, JPopupMenu) - Method in class adams.gui.visualization.instance.InstanceExplorer: Optional customizing of the menu that is about to be popped up.
customizePopupMenu(MouseEvent, JPopupMenu) - Method in class adams.gui.visualization.instance.InstancePanel: Optional customizing of the menu that is about to be popped up.
customLoaderTipText() - Method in class adams.flow.transformer.WekaFileReader: Returns the tip text for this property.
customPropsFileTipText() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the tip text for this property.
customPropsFileTipText() - Method in class adams.flow.source.WekaDatabaseReader: Returns the tip text for this property.
customSaverTipText() - Method in class adams.flow.sink.WekaFileWriter: Returns the tip text for this property.

D

dataChanged(DataChangeEvent) - Method in class adams.gui.visualization.instance.InstanceExplorer: Gets called if the data of the instance panel has changed.
dataGeneratorTipText() - Method in class adams.flow.source.WekaDataGenerator: Returns the tip text for this property.
DataRowToInstance(DataRow) - Method in class adams.ml.WekaData
dataset() - Method in class weka.core.AbstractHashableInstance: Returns the dataset this instance has access to.
dataset1TipText() - Method in class adams.tools.CompareDatasets: Returns the tip text for this property.
dataset2TipText() - Method in class adams.tools.CompareDatasets: Returns the tip text for this property.
DatasetCleaner - Class in weka.filters.unsupervised.attribute: Removes all columns from the data data that have been indentified.
DatasetCleaner() - Constructor for class weka.filters.unsupervised.attribute.DatasetCleaner
DatasetCleaner - Class in weka.filters.unsupervised.instance: Removes all rows from the data data that have been indentified.
DatasetCleaner() - Constructor for class weka.filters.unsupervised.instance.DatasetCleaner
DatasetFileChooserPanel - Class in adams.gui.chooser: A panel that contains a text field with the current file and a button for bringing up a ConverterFileChooser.
DatasetFileChooserPanel() - Constructor for class adams.gui.chooser.DatasetFileChooserPanel: Initializes the panel with no file.
DatasetFileChooserPanel(String) - Constructor for class adams.gui.chooser.DatasetFileChooserPanel: Initializes the panel with the given filename/directory.
DatasetFileChooserPanel(File) - Constructor for class adams.gui.chooser.DatasetFileChooserPanel: Initializes the panel with the given filename/directory.
DatasetLabeler - Class in weka.filters.unsupervised.instance: Adds an additional attribute to the dataset containing a label whether it was a match or not, i.e., whether the row finder selected a particular row or not.
DatasetLabeler() - Constructor for class weka.filters.unsupervised.instance.DatasetLabeler
datasetTipText() - Method in class adams.flow.transformer.WekaStoreInstance: Returns the tip text for this property.
datasetTipText() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the tip text for this property.
dataTypeTipText() - Method in class adams.flow.transformer.WekaInstancesStatistic: Returns the tip text for this property.
DEFAULT_AMOUNT - Static variable in class weka.filters.unsupervised.instance.LatestRecords: the default number of records to keep.
DEFAULT_ATTNAME - Static variable in class weka.filters.unsupervised.instance.LatestRecords: the default attribute name.
DEFAULT_CLASS - Static variable in class adams.flow.source.WekaNewInstances: the class attribute name (if not specified explicitly).
DEFAULT_DATE_FORMAT - Static variable in class adams.flow.source.WekaNewInstances: the default date format.
DEFAULT_NAME - Static variable in class weka.filters.unsupervised.instance.DatasetLabeler: the default name of the attribute.
defaultClassifierString() - Method in class weka.classifiers.lazy.LWLSynchro: Default classifier classname.
defaultClassifierString() - Method in class weka.classifiers.meta.ClassificationViaRegressionD: String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.Corr: String describing default classifier.
defaultClassifierString() - Method in class weka.classifiers.meta.MultiSearch: String describing default classifier.
defineOptions() - Method in class adams.data.baseline.AbstractLinearRegressionBased: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.conversion.MatchWekaInstanceAgainstFileHeader: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.conversion.MatchWekaInstanceAgainstStorageHeader: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.conversion.ReportToWekaInstance: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.conversion.WekaInstancesToSpreadSheet: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.instances.AbstractInstanceGenerator: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.columnfinder.AbstractFilteredColumnFinder: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.columnfinder.ByName: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.columnfinder.MultiColumnFinder: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.columnfinder.RowFilteredColumnFinder: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.evaluator.NamedSetup: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.predictions.AutoScaler: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.predictions.FixedSizeErrorScaler: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.predictions.NamedSetup: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.predictions.RelativeNumericErrorScaler: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.rowfinder.AbstractFilteredRowFinder: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.rowfinder.ByLabel: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.rowfinder.FilteredIQR: Adds options to the internal list of options.
defineOptions() - Method in class adams.data.weka.rowfinder.MultiRowFinder: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.condition.bool.WekaClassification: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.sink.WekaClassifierErrors: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.sink.WekaCostCurve: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.sink.WekaDatabaseWriter: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.sink.WekaExperimentGenerator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.sink.WekaFileWriter: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.sink.WekaInstanceViewer: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.sink.WekaROC: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.source.WekaDatabaseReader: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.source.WekaDataGenerator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.source.WekaNewInstances: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.template.InstanceDumperVariable: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.AbstractWekaClassifierEvaluator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.AbstractWekaModelReader: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaAccumulatedError: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaAttributeIterator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaClassifier: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaClassifierOptimizer: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaClassifierRanker: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaClassifying: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaClassSelector: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaClusterer: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaEvaluationSummary: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaEvaluationValues: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaExperiment: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaExtractArray: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaFileReader: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaFilter: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaGetInstanceValue: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaInstanceBuffer: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaInstanceDumper: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaInstancesInfo: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaInstancesMerge: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaInstancesStatistic: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaNewInstance: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaRandomSplit: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaRegexToRange: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaRenameRelation: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaSetInstanceValue: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaStoreInstance: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaSubsets: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaTestSetEvaluator: Adds options to the internal list of options.
defineOptions() - Method in class adams.flow.transformer.WekaTextDirectoryReader: Adds options to the internal list of options.
defineOptions() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Adds options to the internal list of options.
defineOptions() - Method in class adams.gui.InstanceCompare: Adds options to the internal list of options.
defineOptions() - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Adds options to the internal list of options.
defineOptions() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Adds options to the internal list of options.
defineOptions() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Adds options to the internal list of options.
defineOptions() - Method in class adams.optimise.GeneticAlgorithm: Adds options to the internal list of options.
defineOptions() - Method in class adams.tools.CompareDatasets: Adds options to the internal list of options.
deleteAttributeAt(int) - Method in class weka.core.AbstractHashableInstance: Deletes an attribute at the given position (0 to numAttributes() - 1).
derivativeOrderTipText() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns the tip text for this property.
destroy() - Method in class adams.data.instances.AbstractInstanceGenerator: Frees up memory in a "destructive" non-reversible way.
destroy() - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Frees up memory in a "destructive" non-reversible way.
determineAttributeName(Instances) - Method in class adams.flow.transformer.WekaInstanceEvaluator: Determines the name of the evaluation attribute.
determineBestInSpace(Space, Instances, int) - Method in class weka.classifiers.meta.MultiSearch: determines the best point for the given space, using CV with specified number of folds.
determineColumnNames(BaseString[], String, Instances) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns a vector with column names of the dataset, listed in "list".
determineOutputFormat(Instances) - Method in class weka.filters.FlowFilter: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.DatasetCleaner: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.DownSample: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.FastWavelet: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PAA: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.SAX: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.SetMissingValue: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.SpellChecker: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.DatasetCleaner: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.LatestRecords: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveInstancesWithMissingValue: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RowNorm: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Determines the output format based on the input format and returns this.
determineOutputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Sort: Determines the output format based on the input format and returns this.
devTipText() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Returns the tip text for this property.
difference(int, double, double) - Method in class weka.core.SAXDistance: Computes the difference between two given attribute values.
difference(int, double, double) - Method in class weka.core.WeightedEuclideanDistance: Computes the difference between two given attribute values.
dimensions() - Method in class weka.core.setupgenerator.Point: Returns the number of dimensions this points uses.
dimensions() - Method in class weka.core.setupgenerator.Space: Returns the number of dimensions of this space.
disableButtons(Container) - Method in class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog: Disables the buttons in the SimpleSetupPanel.
discardPredictionsTipText() - Method in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: Returns the tip text for this property.
discardPredictionsTipText() - Method in class adams.flow.transformer.WekaTestSetEvaluator: Returns the tip text for this property.
display(Token) - Method in class adams.flow.sink.WekaClassifierErrors: ClassifierErrorss the token (the panel and dialog have already been created at this stage).
display(Token) - Method in class adams.flow.sink.WekaCostCurve: Plots the token (the panel and dialog have already been created at this stage).
display(Token) - Method in class adams.flow.sink.WekaInstancesDisplay: Displays the token (the panel and dialog have already been created at this stage).
display(Token) - Method in class adams.flow.sink.WekaInstanceViewer: Displays the token (the panel and dialog have already been created at this stage).
display(Token) - Method in class adams.flow.sink.WekaROC: Plots the token (the panel and dialog have already been created at this stage).
displayDataset() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Displays the dataset in a separate window.
displayPanelRequiresScrollPane() - Method in class adams.flow.sink.WekaClassifierErrors: Returns whether the created display panel requires a scroll pane or not.
displayPanelRequiresScrollPane() - Method in class adams.flow.sink.WekaCostCurve: Returns whether the created display panel requires a scroll pane or not.
displayPanelRequiresScrollPane() - Method in class adams.flow.sink.WekaInstancesDisplay: Returns whether the created display panel requires a scroll pane or not.
displayPanelRequiresScrollPane() - Method in class adams.flow.sink.WekaInstanceViewer: Returns whether the created display panel requires a scroll pane or not.
displayPanelRequiresScrollPane() - Method in class adams.flow.sink.WekaROC: Returns whether the created display panel requires a scroll pane or not.
distance(Instance, Instance) - Method in class weka.core.SAXDistance: Calculates the distance between two instances.
distance(Instance, Instance, PerformanceStats) - Method in class weka.core.SAXDistance: Calculates the distance (or similarity) between two instances.
distance(Instance, Instance) - Method in class weka.core.WeightedEuclideanDistance: Calculates the distance between two instances.
distance(Instance, Instance, PerformanceStats) - Method in class weka.core.WeightedEuclideanDistance: Calculates the distance (or similarity) between two instances.
distance(Instance, Instance, double, PerformanceStats) - Method in class weka.core.WeightedEuclideanDistance
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.LWLSynchro: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.ClassificationViaRegressionD: Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.FilteredClassifierExt: Classifies a given instance after filtering.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.MultiSearch: Returns the distribution for the given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RangeCheck: Classifies a given instance after filtering.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.SubsetEnsemble: Predicts the class memberships for a given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.WeightedInstancesHandlerWrapper: Predicts the class memberships for a given instance.
distributionFormatTipText() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the tip text for this property.
distributionSortingTipText() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the tip text for this property.
doChoose() - Method in class adams.gui.chooser.DatasetFileChooserPanel: Performs the actual choosing of an object.
doConvert() - Method in class adams.data.conversion.AbstractMatchWekaInstanceAgainstHeader: Performs the actual conversion.
doConvert() - Method in class adams.data.conversion.AdamsInstanceToWekaInstance: Performs the actual conversion.
doConvert() - Method in class adams.data.conversion.ReportToWekaInstance: Performs the actual conversion.
doConvert() - Method in class adams.data.conversion.SpreadSheetToWekaInstances: Performs the actual conversion.
doConvert() - Method in class adams.data.conversion.WekaInstancesToSpreadSheet: Performs the actual conversion.
doConvert() - Method in class adams.data.conversion.WekaInstanceToAdamsInstance: Performs the actual conversion.
doConvert() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Performs the actual conversion.
doCrossovers() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Performs cross-over.
doCrossovers() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Performs cross-over.
doEvaluate(Instance) - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Performs the actual evaluation.
doEvaluate(Instance) - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Performs the actual evaluation.
doEvaluate(Instance) - Method in class adams.data.weka.evaluator.NamedSetup: Evaluates the instance using the named setup.
doEvaluate(Instance) - Method in class adams.data.weka.evaluator.PassThrough: Performs no real evaluation, just returns 1.0.
doEvaluate(AbstractActor, Token) - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Evaluates whether to executed the "then" or "else" branch.
doEvaluate(AbstractActor, Token) - Method in class adams.flow.condition.bool.AdamsInstanceCapabilities: Evaluates whether to executed the "then" or "else" branch.
doEvaluate(AbstractActor, Token) - Method in class adams.flow.condition.bool.WekaCapabilities: Evaluates whether to executed the "then" or "else" branch.
doEvaluate(AbstractActor, Token) - Method in class adams.flow.condition.bool.WekaClassification: Evaluates whether to executed the "then" or "else" branch.
doExecute() - Method in class adams.flow.sink.AbstractWekaModelWriter: Executes the flow item.
doExecute() - Method in class adams.flow.sink.WekaDatabaseWriter: Executes the flow item.
doExecute() - Method in class adams.flow.sink.WekaExperimentGenerator: Executes the flow item.
doExecute() - Method in class adams.flow.sink.WekaFileWriter: Executes the flow item.
doExecute() - Method in class adams.flow.sink.WekaInstancesDisplay: Executes the flow item.
doExecute() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Executes the flow item.
doExecute() - Method in class adams.flow.source.WekaDatabaseReader: Executes the flow item.
doExecute() - Method in class adams.flow.source.WekaDataGenerator: Executes the flow item.
doExecute() - Method in class adams.flow.source.WekaNewInstances: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.AbstractWekaModelReader: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaAccumulatedError: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaAttributeIterator: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaClassifier: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaClassifierOptimizer: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaClassifierRanker: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaClassSelector: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaClusterer: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaEvaluationSummary: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaEvaluationValues: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaExperiment: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaExtractArray: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaFileReader: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaFilter: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaGetInstanceValue: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaInstanceBuffer: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaInstanceDumper: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaInstancesInfo: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaInstancesMerge: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaInstancesStatistic: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaNewInstance: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaPredictionsToInstances: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaRandomSplit: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaRegexToRange: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaRelationName: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaRenameRelation: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaSetInstanceValue: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaStoreInstance: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaSubsets: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaTestSetEvaluator: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaTextDirectoryReader: Executes the flow item.
doExecute() - Method in class adams.flow.transformer.WekaTrainTestSetEvaluator: Executes the flow item.
doFindColumns(Instances) - Method in class adams.data.weka.columnfinder.AbstractColumnFinder: Returns the columns of interest in the dataset.
doFindColumns(Instances) - Method in class adams.data.weka.columnfinder.ByName: Returns the columns of interest in the dataset.
doFindColumns(Instances) - Method in class adams.data.weka.columnfinder.Invert: Returns the columns of interest in the dataset.
doFindColumns(Instances) - Method in class adams.data.weka.columnfinder.MultiColumnFinder: Returns the columns of interest in the dataset.
doFindColumns(Instances) - Method in class adams.data.weka.columnfinder.NullFinder: Returns the columns of interest in the dataset.
doFindColumns(Instances) - Method in class adams.data.weka.columnfinder.RowFilteredColumnFinder: Returns the columns of interest in the dataset.
doFindRows(Instances) - Method in class adams.data.weka.rowfinder.AbstractRowFinder: Returns the rows of interest in the dataset.
doFindRows(Instances) - Method in class adams.data.weka.rowfinder.ByLabel: Returns the rows of interest in the dataset.
doFindRows(Instances) - Method in class adams.data.weka.rowfinder.FilteredIQR: Returns the rows of interest in the dataset.
doFindRows(Instances) - Method in class adams.data.weka.rowfinder.Invert: Returns the rows of interest in the dataset.
doFindRows(Instances) - Method in class adams.data.weka.rowfinder.MultiRowFinder: Returns the rows of interest in the dataset.
doFindRows(Instances) - Method in class adams.data.weka.rowfinder.NullFinder: Returns the rows of interest in the dataset.
doGenerate() - Method in class adams.flow.template.InstanceDumperVariable: Generates the actor.
doMutations() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Performs mutations.
doMutations() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Performs mutations.
doMutations2() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Performs mutations.
doMutations2() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Performs mutations.
doPostProcess(WekaModelContainer) - Method in class adams.flow.transformer.wekaclusterer.AbstractClustererPostProcessor: Performs the actual post-processing.
doPostProcess(WekaModelContainer) - Method in class adams.flow.transformer.wekaclusterer.AbstractClusterMembershipPostProcessor: Performs the actual post-processing.
doPostProcess(WekaModelContainer) - Method in class adams.flow.transformer.wekaclusterer.PassThrough: Simply returns the container, no post-processing done.
doRegister() - Method in class adams.gui.goe.WekaEditorsRegistration: Performs the registration of the editors.
doRun() - Method in class adams.tools.CompareDatasets: Performs the comparison.
doTrainColumnFinder(Instances) - Method in class adams.data.weka.columnfinder.AbstractFilteredColumnFinder: Performs the actual training of the column finder with the specified dataset.
doTrainColumnFinder(Instances) - Method in class adams.data.weka.columnfinder.AbstractTrainableColumnFinder: Performs the actual training of the column finder with the specified dataset.
doTrainColumnFinder(Instances) - Method in class adams.data.weka.columnfinder.MultiColumnFinder: Performs the actual training of the column finder with the specified dataset.
doTrainColumnFinder(Instances) - Method in class adams.data.weka.columnfinder.RowFilteredColumnFinder: Performs the actual training of the column finder with the specified dataset.
doTrainRowFinder(Instances) - Method in class adams.data.weka.rowfinder.AbstractFilteredRowFinder: Performs the actual training of the row finder with the specified dataset.
doTrainRowFinder(Instances) - Method in class adams.data.weka.rowfinder.AbstractTrainableRowFinder: Performs the actual training of the row finder with the specified dataset.
doTrainRowFinder(Instances) - Method in class adams.data.weka.rowfinder.FilteredIQR: Performs the actual training of the row finder with the specified dataset.
doTrainRowFinder(Instances) - Method in class adams.data.weka.rowfinder.MultiRowFinder: Performs the actual training of the row finder with the specified dataset.
doubleToString(double) - Method in class adams.data.conversion.WekaInstancesToSpreadSheet: Turns the double into a string.
DownSample - Class in weka.filters.unsupervised.attribute: A simple filter that retains only every nth attribute.
DownSample() - Constructor for class weka.filters.unsupervised.attribute.DownSample
drawData(Graphics, Instance, Color, InstanceLinePaintlet.MarkerShape) - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Draws the data with the given color.

E

enumerateAttributes() - Method in class weka.core.AbstractHashableInstance: Returns an enumeration of all the attributes.
enumerateMeasures() - Method in class weka.classifiers.meta.MultiSearch: Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.m5.M5Base2: Returns an enumeration of the additional measure names
equalHeaders(Instance) - Method in class weka.core.AbstractHashableInstance: Tests if the headers of two instances are equivalent.
equalHeadersMsg(Instance) - Method in class weka.core.AbstractHashableInstance: Checks if the headers of two instances are equivalent.
equals(Object) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet.SortContainer: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class adams.data.instances.AbstractInstanceGenerator: Returns whether the two objects are the same.
equals(Object) - Method in class adams.data.weka.columnfinder.AbstractColumnFinder: Returns whether the two objects are the same.
equals(Object) - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer: Returns whether the two objects are the same.
equals(Object) - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Returns whether the two objects are the same.
equals(Object) - Method in class adams.data.weka.predictions.AbstractErrorScaler: Returns whether the two objects are the same.
equals(Object) - Method in class adams.data.weka.rowfinder.AbstractRowFinder: Returns whether the two objects are the same.
equals(Object) - Method in class weka.classifiers.meta.multisearch.PerformanceComparator: Indicates whether some other object is "equal to" this Comparator.
equals(Object) - Method in class weka.core.AbstractHashableInstance: Returns only true if the same class and the same hashcode.
equals(Object) - Method in class weka.core.setupgenerator.Point: Determines whether or not two points are equal.
equals(Object) - Method in class weka.core.setupgenerator.SpaceDimension: Tests itself against the provided dimension object.
errorScalerTipText() - Method in class adams.flow.sink.WekaClassifierErrors: Returns the tip text for this property.
errorTipText() - Method in class weka.classifiers.meta.AbstainLeastMedianSq
errorTipText() - Method in class weka.classifiers.meta.LeastMedianSq
evaluate(Instances, Instances) - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: Performs an evaluation on the given train and test set.
evaluate(Instance) - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Evaluates the given instance.
evaluate(Instances, Instances) - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Performs an evaluation on the given train and test set.
evaluate(OptData) - Method in class adams.optimise.genetic.fitnessfunctions.AttributeSelection
evaluate(Point<Object>) - Method in class weka.core.SetupGenerator: evalutes the expression for the current iteration.
evaluateExperiment(Experiment) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Evaluates the experiment.
EVALUATION_ACC - Static variable in class weka.classifiers.meta.multisearch.Performance: evaluation via: Accuracy.
EVALUATION_CC - Static variable in class weka.classifiers.meta.multisearch.Performance: evaluation via: Correlation coefficient.
EVALUATION_COMBINED - Static variable in class weka.classifiers.meta.multisearch.Performance: evaluation via: Combined = (1-CC) + RRSE + RAE.
EVALUATION_KAPPA - Static variable in class weka.classifiers.meta.multisearch.Performance: evaluation via: Kappa statistic.
EVALUATION_MAE - Static variable in class weka.classifiers.meta.multisearch.Performance: evaluation via: Mean absolute error.
EVALUATION_RAE - Static variable in class weka.classifiers.meta.multisearch.Performance: evaluation via: Relative absolute error.
EVALUATION_RMSE - Static variable in class weka.classifiers.meta.multisearch.Performance: evaluation via: Root mean squared error.
EVALUATION_RRSE - Static variable in class weka.classifiers.meta.multisearch.Performance: evaluation via: Root relative squared error.
EvaluationHelper - Class in adams.flow.core: A helper class for Evaluation related things.
EvaluationHelper() - Constructor for class adams.flow.core.EvaluationHelper
EvaluationStatistic - Enum in adams.flow.core: The enumeration for the comparison field.
evaluationTipText() - Method in class weka.classifiers.meta.MultiSearch: Returns the tip text for this property.
evaluationTypeTipText() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the tip text for this property.
evaluatorTipText() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Returns the tip text for this property.
excludeAttributes(Instances, int) - Method in class adams.flow.transformer.WekaInstancesMerge: Excludes attributes from the data.
excludedAttributesTipText() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns the tip text for this property.
Experimenter - Class in adams.gui.menu: Opens the WEKA Experimenter.
Experimenter() - Constructor for class adams.gui.menu.Experimenter: Initializes the menu item with no owner.
Experimenter(AbstractApplicationFrame) - Constructor for class adams.gui.menu.Experimenter: Initializes the menu item.
experimentFileTipText() - Method in class adams.flow.transformer.WekaExperiment: Returns the tip text for this property.
ExperimentPanel - Class in weka.gui.explorer: This panel allows the user to select and configure a classifier, set the attribute of the current dataset to be used as the class, and perform an Experiment (like in the Experimenter) with this Classifier/Dataset combination.
ExperimentPanel() - Constructor for class weka.gui.explorer.ExperimentPanel: Creates the Experiment panel.
ExperimentStatistic - Enum in adams.flow.core: The enumeration for the comparison field.
experimentTypeTipText() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the tip text for this property.
Explorer - Class in adams.gui.menu: Opens the WEKA Explorer.
Explorer() - Constructor for class adams.gui.menu.Explorer: Initializes the menu item with no owner.
Explorer(AbstractApplicationFrame) - Constructor for class adams.gui.menu.Explorer: Initializes the menu item.
expressionTipText() - Method in class weka.core.setupgenerator.MathParameter: Returns the tip text for this property.
extractDatabaseID(String) - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Extracts the database ID from a string in the comboxbox.

F

FastWavelet - Class in weka.filters.unsupervised.attribute: A filter for wavelet transformation using the JSci library's fast wavelet transform (FWT) algorithms.

For more information see:

(2009).
FastWavelet() - Constructor for class weka.filters.unsupervised.attribute.FastWavelet: default constructor.
favorZeroesTipText() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the tip text for this property.
favorZeroesTipText() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the tip text for this property.
fieldsTipText() - Method in class adams.data.conversion.ReportToWekaInstance: Returns the tip text for this property.
FILENAME - Static variable in class adams.gui.visualization.instance.InstanceComparePanel: the name of the props file.
FILENAME - Static variable in class adams.gui.visualization.instance.InstanceExplorer: the name of the props file.
filter(DataRow) - Method in class adams.ml.WekaFilter
filter(Dataset) - Method in class adams.ml.WekaFilter
filter(Instances) - Method in class weka.classifiers.meta.FilteredClassifierExt: Filters the dataset through the remove filter if necessary.
filter(Filter, Instance) - Method in class weka.classifiers.meta.FilteredClassifierExt: Filters the Instance through the specified filter.
filter(Instance) - Method in class weka.classifiers.meta.FilteredClassifierExt: Filters the Instance through the remove filter if necessary.
FILTER_NONE - Static variable in class weka.classifiers.functions.GaussianProcessesAdaptive: no filter
FILTER_NONE - Static variable in class weka.classifiers.functions.GaussianProcessesWeighted: no filter
FILTER_NONE - Static variable in class weka.classifiers.functions.GPD: no filter
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.GaussianProcessesAdaptive: normalizes the data
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.GaussianProcessesWeighted: normalizes the data
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.GPD: normalizes the data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.GaussianProcessesAdaptive: standardizes the data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.GaussianProcessesWeighted: standardizes the data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.GPD: standardizes the data
FilteredClassifierExt - Class in weka.classifiers.meta: Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
FilteredClassifierExt() - Constructor for class weka.classifiers.meta.FilteredClassifierExt
FilteredIQR - Class in adams.data.weka.rowfinder: Returns indices of rows that got identified as outliers/extreme values.
FilteredIQR() - Constructor for class adams.data.weka.rowfinder.FilteredIQR
FilteredSearch - Class in weka.core.neighboursearch: Class implementing the brute force search algorithm for nearest neighbour search, filtered using PLS.
FilteredSearch() - Constructor for class weka.core.neighboursearch.FilteredSearch: Constructor.
FilteredSearch(Instances) - Constructor for class weka.core.neighboursearch.FilteredSearch: Constructor that uses the supplied set of instances.
filterTipText() - Method in class adams.data.weka.rowfinder.FilteredIQR: Returns the tip text for this property.
filterTipText() - Method in class adams.flow.transformer.WekaFilter: Returns the tip text for this property.
filterTipText() - Method in class weka.classifiers.functions.PLSClassifierWeighted: Returns the tip text for this property
filterTipText() - Method in class weka.core.neighboursearch.FilteredSearch: Returns the tip text for this property
filterTipText() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Returns the tip text for this property.
filterTypeTipText() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.functions.GPD: Returns the tip text for this property
findArrays() - Method in class adams.ml.WekaData
findArrays(DataRow) - Method in class adams.ml.WekaData
findArrays(String) - Method in class adams.ml.WekaData
findBest(Instances) - Method in class weka.classifiers.meta.MultiSearch: returns the best point in the space.
findBestLeaf(double[], RuleNode2[]) - Method in class weka.classifiers.trees.m5.RuleNode2: Find the leaf with greatest coverage
findBestRegression() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Finds the best regression generated from m_samples random samples from the training data.
findBestRegression() - Method in class weka.classifiers.meta.LeastMedianSq: Finds the best regression generated from m_samples random samples from the training data.
findClosestAttributeIndex(Instance, double) - Method in class adams.gui.visualization.instance.InstancePointHitDetector: Returns the closest attribute index for the given x.
findClosestX(List<InstancePoint>, int) - Static method in class adams.data.instance.InstanceUtils: Returns the index in m_Points closest to the given x value.
findColumns(Instances) - Method in class adams.data.weka.columnfinder.AbstractColumnFinder: Returns the columns of interest in the dataset.
findColumns(Instances) - Method in interface adams.data.weka.columnfinder.ColumnFinder: Returns the columns of interest in the dataset.
findEnclosingAttributeIndices(Instance, double) - Method in class adams.gui.visualization.instance.InstancePointHitDetector: Determines the enclosing attribute indices for the given x value.
findEnclosingXs(List<InstancePoint>, int) - Static method in class adams.data.instance.InstanceUtils: Returns the indices of points in m_Points that enclose the given x value.
findersTipText() - Method in class adams.data.weka.columnfinder.MultiColumnFinder: Returns the tip text for this property.
findersTipText() - Method in class adams.data.weka.rowfinder.MultiRowFinder: Returns the tip text for this property.
findInstancePoint(Instance, int) - Method in class adams.gui.visualization.instance.InstancePointHitDetector: Returns the Instance point at the specified position.
findRandomSplit(Instances, Random, int) - Method in class weka.classifiers.trees.RandomRegressionForest.Node: determines a random split for the data, tries 10 pairs.
findResiduals() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Finds residuals (squared) for the current regression.
findResiduals() - Method in class weka.classifiers.meta.LeastMedianSq: Finds residuals (squared) for the current regression.
findRows(Instances) - Method in class adams.data.weka.rowfinder.AbstractRowFinder: Returns the rows of interest in the dataset.
findRows(Instances) - Method in interface adams.data.weka.rowfinder.RowFinder: Returns the rows of interest in the dataset.
findThreshold(Vector<T>) - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: Finds the threshold based on the collected data.
findThreshold() - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: Finds the user-defined threshold and sets other internal variables accordingly.
findThreshold() - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Finds the user-defined threshold and sets other internal variables accordingly.
findThreshold(Vector<IntervalEstimatorBased.SortedInterval>) - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Finds the threshold based on the collected data.
findTipText() - Method in class adams.flow.transformer.WekaRenameRelation: Returns the tip text for this property.
findX(List<InstancePoint>, InstancePoint) - Static method in class adams.data.instance.InstanceUtils: Returns the index in m_Points of the given sequence point.
findX(List<InstancePoint>, int) - Static method in class adams.data.instance.InstanceUtils: Returns the index in m_Points of the given x value.
fireSetupChanged() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Fires a ChangeEvent that the setup has changed.
firstAttributeRangeTipText() - Method in class adams.gui.InstanceCompare: Returns the tip text for this property.
firstDatasetTipText() - Method in class adams.gui.InstanceCompare: Returns the tip text for this property.
firstRowIndexTipText() - Method in class adams.gui.InstanceCompare: Returns the tip text for this property.
fitnessChanged(FitnessChangeEvent) - Method in interface adams.event.FitnessChangeListener: Gets called when the fitness of the genetic algorithm changed.
FitnessChangeEvent - Class in adams.event: Event that gets sent whenever the fitness of a genetic algorithm changed.
FitnessChangeEvent(MTAbstractGeneticAlgorithm, double) - Constructor for class adams.event.FitnessChangeEvent: Initializes the event.
FitnessChangeListener - Interface in adams.event: Interface for classes that listen to changes in the fitness of a genetic algorithm.
FitnessChangeNotifier - Interface in adams.event: Interface for genetic algorithms that notify other objects about changes of their fitness.
FixedClassifierErrors - Class in weka.gui.visualize.plugins: Displays the classifier errors using Weka panels, but with a fixed size of the error plots.
FixedClassifierErrors() - Constructor for class weka.gui.visualize.plugins.FixedClassifierErrors
FixedSizeErrorScaler - Class in adams.data.weka.predictions: Scales the errors to a fixed size.
FixedSizeErrorScaler() - Constructor for class adams.data.weka.predictions.FixedSizeErrorScaler
flowFileTipText() - Method in class weka.filters.FlowFilter: Returns the tip text for this property.
FlowFilter - Class in weka.filters: Processes the data with a flow.
FlowFilter() - Constructor for class weka.filters.FlowFilter
flowPauseStateChanged(FlowPauseStateEvent) - Method in class adams.flow.transformer.WekaClassifierRanker: Gets called when the pause state of the flow changes.
foldsTipText() - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: Returns the tip text for this property.
foldsTipText() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the tip text for this property.
foldsTipText() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the tip text for this property.
foldsTipText() - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Returns the tip text for this property.
foldsTipText() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Returns the tip text for this property.
foldsTipText() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the tip text for this property.
forCommandLine(String) - Static method in class adams.data.instances.AbstractInstanceGenerator: Instantiates the generator from the given commandline (i.e., classname and optional options).
forCommandLine(String) - Static method in class adams.data.weka.columnfinder.AbstractColumnFinder: Instantiates the column finder from the given commandline (i.e., classname and optional options).
forCommandLine(String) - Static method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Instantiates the evaluator from the given commandline (i.e., classname and optional options).
forCommandLine(String) - Static method in class adams.data.weka.predictions.AbstractErrorScaler: Instantiates the scaler from the given commandline (i.e., classname and optional options).
forCommandLine(String) - Static method in class adams.data.weka.rowfinder.AbstractRowFinder: Instantiates the row finder from the given commandline (i.e., classname and optional options).
forCommandLine(String) - Static method in class adams.genetic.MTAbstractGeneticAlgorithm: Instantiates the genetic algorithm from the given commandline (i.e., classname and optional options).
forCommandLine(String) - Static method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Instantiates the genetic algorithm from the given commandline (i.e., classname and optional options).
forName(String, String[]) - Static method in class adams.data.instances.AbstractInstanceGenerator: Instantiates the generator with the given options.
forName(String, String[]) - Static method in class adams.data.weka.columnfinder.AbstractColumnFinder: Instantiates the column finder with the given options.
forName(String, String[]) - Static method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Instantiates the evaluator with the given options.
forName(String, String[]) - Static method in class adams.data.weka.predictions.AbstractErrorScaler: Instantiates the scaler with the given options.
forName(String, String[]) - Static method in class adams.data.weka.rowfinder.AbstractRowFinder: Instantiates the row finder with the given options.
forName(String, String[]) - Static method in class adams.genetic.MTAbstractGeneticAlgorithm: Instantiates the genetic algorithm with the given options.
forName(String, String[]) - Static method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Instantiates the genetic algorithm with the given options.
fromArray(String[]) - Method in class adams.core.option.WekaCommandLineHandler: Generates an object from the commandline options.
fromBits(int) - Method in class adams.optimise.genetic.PackDataDef.DataInfo
fromCommandLine(String) - Method in class adams.core.option.WekaCommandLineHandler: Generates an object from the specified commandline.
fromCustomStringRepresentation(String) - Method in class adams.gui.goe.WekaExperimentFileEditor: Returns an object created from the custom string representation.

G

gammaTipText() - Method in class weka.classifiers.functions.GPD: Returns the tip text for this property
GaussianProcessesAdaptive - Class in weka.classifiers.functions: Implements Gaussian Processes for regression without hyperparameter-tuning.
GaussianProcessesAdaptive() - Constructor for class weka.classifiers.functions.GaussianProcessesAdaptive: the default constructor
GaussianProcessesWeighted - Class in weka.classifiers.functions: Implements Gaussian Processes for regression without hyperparameter-tuning.
GaussianProcessesWeighted() - Constructor for class weka.classifiers.functions.GaussianProcessesWeighted: the default constructor
generate(T) - Method in class adams.data.instances.AbstractInstanceGenerator: Returns the generated data, generates it if necessary.
generateAttributes(String) - Method in class adams.ml.WekaData: Try and guess at attribute type
generateHeader(T) - Method in class adams.data.instances.AbstractInstanceGenerator: Generates the header of the output data.
generateHeader(Instance) - Method in class adams.flow.transformer.WekaInstanceEvaluator: Generates the new header for the data.
generateHeader() - Method in class adams.ml.WekaData
generateLine(T, double[]) - Method in class adams.data.baseline.AbstractLinearRegressionBased: Generates fake data for the plotting the line.
generateLineTipText() - Method in class adams.data.baseline.AbstractLinearRegressionBased: Returns the tip text for this property.
generateNumPointsLabel(int) - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Generates the string for the label displaying the number of points that are currently selected.
generateOutput(T) - Method in class adams.data.instances.AbstractInstanceGenerator: Generates the actual data.
generateRulesTipText() - Method in class weka.classifiers.trees.m5.M5Base2: Returns the tip text for this property
generates() - Method in class adams.data.conversion.AbstractMatchWekaInstanceAgainstHeader: Returns the class that is generated as output.
generates() - Method in class adams.data.conversion.AdamsInstanceToWekaInstance: Returns the class that is generated as output.
generates() - Method in class adams.data.conversion.ReportToWekaInstance: Returns the class that is generated as output.
generates() - Method in class adams.data.conversion.SpreadSheetToWekaInstances: Returns the class that is generated as output.
generates() - Method in class adams.data.conversion.WekaInstancesToSpreadSheet: Returns the class that is generated as output.
generates() - Method in class adams.data.conversion.WekaInstanceToAdamsInstance: Returns the class that is generated as output.
generates() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the class that is generated as output.
generates() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Returns the class of objects that it generates.
generates() - Method in class adams.flow.source.WekaDatabaseReader: Returns the class of objects that it generates.
generates() - Method in class adams.flow.source.WekaDataGenerator: Returns the class of objects that it generates.
generates() - Method in class adams.flow.source.WekaNewInstances: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.AbstractWekaClassifierEvaluator: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.AbstractWekaInstanceAndWekaInstancesTransformer: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.AbstractWekaModelReader: Returns the class that the producer generates.
generates() - Method in class adams.flow.transformer.WekaAccumulatedError: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaAttributeIterator: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaClassifier: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaClassifierOptimizer: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaClassifying: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaClusterer: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaClustering: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaEvaluationValues: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaExperiment: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaExtractArray: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaFileReader: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaGetInstanceValue: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaInstanceBuffer: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaInstanceFileReader: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaInstancesInfo: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaInstancesStatistic: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaNewInstance: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaPredictionsToInstances: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaRandomSplit: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaRegexToRange: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaRelationName: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaSetInstanceValue: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaStoreInstance: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaSubsets: Returns the class of objects that it generates.
generates() - Method in class adams.flow.transformer.WekaTextDirectoryReader: Returns the class of objects that it generates.
generatorTipText() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Returns the tip text for this property.
GeneticAlgorithm - Class in adams.optimise: Morticia (GEX).
GeneticAlgorithm() - Constructor for class adams.optimise.GeneticAlgorithm: The default constructor.
GeneticAlgorithm.GAJob - Class in adams.optimise: Class for multithreading the ga.
GeneticAlgorithm.GAJob(GeneticAlgorithm, FitnessFunction, int[]) - Constructor for class adams.optimise.GeneticAlgorithm.GAJob: Constructor.
genRegression(Random) - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Generates a LinearRegression classifier from the current m_SubSample.
genRegression(Random) - Method in class weka.classifiers.meta.LeastMedianSq: Generates a LinearRegression classifier from the current m_SubSample.
get(String) - Method in class adams.optimise.genetic.PackData
get(String) - Method in class adams.optimise.genetic.PackDataDef
get(int, Point<Object>) - Method in class weka.classifiers.meta.multisearch.PerformanceCache: returns a cached performance object, null if not yet in the cache.
getAbsErr() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Gets the threshold for the max error when predicting a numeric class.
getAbsolute() - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Returns whether the absolute values of coefficients are returned.
getAbsoluteDifference() - Method in class adams.flow.transformer.WekaAccumulatedError.SortablePrediction: Returns the absolute difference between actual and predicted value.
getActionMethod(String) - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Returns the method associated with the specified action.
getActions() - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Returns the available actions to list.
getActualIndex(int) - Method in class weka.classifiers.meta.SubsetEnsemble: Returns the actual index in the data of the feature attribute.
getActualScheme() - Method in class adams.data.weka.evaluator.NamedSetup: Returns the named setup.
getActualScheme() - Method in class adams.data.weka.predictions.NamedSetup: Returns the named setup.
getAddClassification() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns whether to add the numeric classification (label index for nominal classes).
getAddClassificationLabel() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns whether to add the classification label (only for nominal classes).
getAddDatabaseID() - Method in class adams.data.instances.AbstractInstanceGenerator: Returns whether the database ID is added.
getAddDistribution() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns whether to add the class distribution (only for nominal classes).
getAddIndex() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns whether to add the dataset index number to the prefix.
getAdditionalAcceptedClasses() - Method in class adams.flow.sink.AbstractWekaModelWriter: Returns additional classes that are accepted as input.
getAdditionalAcceptedClasses() - Method in class adams.flow.sink.WekaModelWriter: Returns additional classes that are accepted as input (WEKA classifiers and clusterers).
getAdditionalAttributes() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the attributes indices of the original dataset to include in the reports.
getAdditionalErrorInformation() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: Returns additional information to be added to the error message.
getAdditionalFieldName(AbstractField) - Static method in class adams.data.weka.ArffUtils: Returns the name of an attribute for an additional field.
getAdditionalInformation() - Method in class adams.flow.transformer.wekaclusterer.AbstractClustererPostProcessor: Returns the additional information.
getAddLabelIndex() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns whether to show the error as well.
getAdjustToVisibleData() - Method in class adams.gui.visualization.instance.InstancePanel: Returns whether the display is adjusted to only the visible spectrums or all of them.
getAlgorithm() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Gets the type of algorithm to use.
getAllAttributes() - Method in class adams.ml.WekaData
getAllDatabaseIDs(int) - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Returns all database IDs.
getAllVisible() - Method in class adams.gui.visualization.instance.InstanceContainerManager: Returns (a copy of) all currently stored containers.
getAmount() - Method in class weka.filters.unsupervised.instance.LatestRecords: Returns the amount of records to keep.
getAttribute(int) - Method in class adams.gui.visualization.instance.InstanceTableModel: Returns the attribute for the given column.
getAttribute() - Method in class weka.classifiers.meta.AbstainAttributePercentile
getAttributeIndex() - Method in class adams.data.weka.rowfinder.ByLabel: Returns the index of the attribute to perform the matching on.
getAttributeIndex() - Method in class adams.flow.transformer.WekaInstancesInfo: Returns the attribute index to use for attribute specific information.
getAttributeIndex() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Returns the index of the attribute to use for indexing.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.SpellChecker: Returns the 1-based index of the attribute to process.
getAttributeName() - Method in class adams.flow.transformer.WekaGetInstanceValue: Returns the name of the attribute to get the value for.
getAttributeName() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Get the name of the attribute to be created.
getAttributeName() - Method in class weka.filters.unsupervised.instance.LatestRecords: Returns the name of the attribute containing the numeric database ID.
getAttributeNames() - Method in class adams.flow.source.WekaNewInstances: Returns the list of attribute names.
getAttributeRange() - Method in class adams.data.weka.rowfinder.FilteredIQR: Returns the attribute range to work on.
getAttributeRange() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Returns the current range of attributes.
getAttributeRange() - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Returns the range of attributes to compute the matrix for.
getAttributeRange() - Method in class weka.filters.unsupervised.attribute.SetMissingValue: Returns the range of attributes to compute the matrix for.
getAttributes() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Returns the range of attributes to create plot containers for.
getAttributeTypes() - Method in class adams.flow.source.WekaNewInstances: Returns the list of attribute types.
getAutoKeyGeneration() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns whether a primary key is automatically generated.
getAverageWidth() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased.SortedInterval: Returns the average width of the stored intervals.
getBase() - Method in class weka.core.setupgenerator.MathParameter: Get the value of the base.
getBaseObject() - Method in class weka.core.SetupGenerator: Returns the base object.
getBestClassifier() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: Returns the best classifier found.
getBestClassifier(Classifier, Classifier) - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: In case of GridSearch/MultiSearch the best setup is returned, otherwise the classifier itself.
getBestClassifier() - Method in class weka.classifiers.meta.MultiSearch: returns the best Classifier setup.
getBestRange() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the best range of attributes.
getBestRange() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the best range of attributes.
getBins() - Method in class weka.core.SAXDistance: Returns the nth point setting.
getBins() - Method in class weka.filters.unsupervised.attribute.SAX: Returns the nth point setting.
getBits() - Method in class adams.optimise.genetic.PackData
getBits(String) - Method in class adams.optimise.genetic.PackData
getBits() - Method in class adams.optimise.GeneticAlgorithm: Gets the number of bits.
getBorder() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the thickness of the border.
getBorderTitle() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Returns the title of the border.
getBufferSize() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the number of instances to buffer before writing them to disk.
getBuildRegressionTree() - Method in class weka.classifiers.trees.m5.M5Base2: Get the value of regressionTree.
getCanChangeClassInDialog(PropertyEditor) - Method in class adams.gui.goe.WekaGenericObjectEditorHandler: Returns whether the class can be changed in the dialog.
getCapabilities() - Method in class adams.data.weka.columnfinder.AbstractColumnFinderWithCapabilities: Returns the capabilities of this object.
getCapabilities() - Method in class adams.data.weka.predictions.AbstractErrorScaler: Returns the capabilities of this object.
getCapabilities() - Method in class adams.data.weka.predictions.AutoScaler: Returns the capabilities of this object.
getCapabilities() - Method in class adams.data.weka.predictions.FixedSizeErrorScaler: Returns the capabilities of this object.
getCapabilities() - Method in class adams.data.weka.predictions.NamedSetup: Returns the capabilities of this object.
getCapabilities() - Method in class adams.data.weka.predictions.RelativeNumericErrorScaler: Returns the capabilities of this object.
getCapabilities() - Method in class adams.data.weka.predictions.RoundErrorScaler: Returns the capabilities of this object.
getCapabilities() - Method in class adams.data.weka.rowfinder.AbstractRowFinderWithCapabilities: Returns the capabilities of this object.
getCapabilities() - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Returns the capabilities.
getCapabilities() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.GPD: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.PLSClassifierWeighted: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.AbstainAttributePercentile: Returns default capabilities of the base classifier.
getCapabilities() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Returns default capabilities of the base classifier.
getCapabilities() - Method in class weka.classifiers.meta.ClassificationViaRegressionD: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.FilteredClassifierExt: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.LeastMedianSq: Returns default capabilities of the base classifier.
getCapabilities() - Method in class weka.classifiers.meta.MultiSearch: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.PartitionedStacking: Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.
getCapabilities() - Method in class weka.classifiers.trees.m5.M5Base2: Returns default capabilities of the classifier, i.e., of LinearRegression.
getCapabilities() - Method in class weka.classifiers.trees.RandomModelTrees: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.RandomRegressionForest: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.filters.FlowFilter: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.DownSample: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NormalizeAdaptive: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.PAA: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValuesWithZero: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.SAX: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.SetMissingValue: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.SpellChecker: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.LatestRecords: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveInstancesWithMissingValue: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RowNorm: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.Sort: Returns the Capabilities of this filter.
getCaseIndex(AbstractActor, Token) - Method in class adams.flow.condition.bool.WekaClassification: Returns the index of the case that should get executed.
getCategory() - Method in class adams.gui.menu.AbstractWekaMenuItemDefinition: Returns the category of the menu item in which it should appear, i.e., the name of the menu.
getCellPadding() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the cell padding for the table.
getCellSpacing() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the cell spacing for the table.
getCharSet() - Method in class adams.flow.transformer.WekaTextDirectoryReader: Returns the character set in use.
getCheckHeader() - Method in class adams.flow.transformer.WekaInstanceBuffer: Returns whether the header gets checked or not.
getCheckHeader() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns whether the header gets checked or not.
getClassDetails() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns whether the class details are output as well.
getClassificationEntry() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the value for the 'Name' column for the numeric classification.
getClassificationLabelEntry() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the value for the 'Name' column for the classification label.
getClassifier() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Returns the classifier.
getClassifier() - Method in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: Returns the name of the global classifier in use.
getClassifier() - Method in class adams.flow.transformer.WekaClassifier: Returns the classifier in use.
getClassifier() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: Returns the classifier being used.
getClassifier() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the currently set classifier.
getClassifier() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Gets the classifier used by the filter.
getClassifierInstance() - Method in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: Returns an instance of the global classifier.
getClassifierSpec() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier.
getClassIndex() - Method in class adams.flow.source.WekaNewInstances: Returns the index of the class attribute.
getClassIndex() - Method in class adams.flow.transformer.WekaClassSelector: Returns the class index.
getClassIndex() - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Returns the current class label index (1-based).
getClassIndex() - Method in class adams.flow.transformer.WekaEvaluationValues: Returns the current class label index (1-based).
getClassIndex() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the current class index.
getClassIndex() - Method in class weka.filters.unsupervised.attribute.NormalizeAdaptive
getClassIndex() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Gets the attribute on which misclassifications are based.
getClassLabelIndex() - Method in class adams.flow.sink.WekaCostCurve: Returns the class label index (1-based index).
getClassLabelIndex() - Method in class adams.flow.sink.WekaROC: Returns the class label index (1-based index).
getClassName() - Method in class adams.flow.source.WekaNewInstances: Returns the name of the class attribute.
getClassNoise() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Get the value of noise for the class.
getClassPathAugmentation() - Method in class adams.core.management.WekaPackagesClassPathAugmenter: Returns the classpath parts (jars, directories) to add to the classpath.
getClassType(PropertyEditor) - Method in class adams.gui.goe.WekaGenericObjectEditorHandler: Returns the class type currently in use.
getClusterer() - Method in class adams.flow.transformer.WekaClusterer: Returns the clusterer in use.
getCoeffs() - Method in class weka.classifiers.meta.Corr
getColor() - Method in class adams.gui.visualization.instance.InstanceContainer: Returns the current color in use.
getColor(int) - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Returns the color for the data with the given index.
getColorProvider() - Method in class adams.gui.visualization.instance.InstanceContainerManager: Returns the color provider to use.
getColumn() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the list of fields that identify a column.
getColumnClass(int) - Method in class adams.gui.visualization.instance.InstanceTableModel: Returns the class for the column.
getColumnCount() - Method in class adams.gui.visualization.instance.InstanceTableModel: Returns the number of columns in the table.
getColumnFinder() - Method in class adams.data.weka.columnfinder.AbstractFilteredColumnFinder: Returns the column finder in use.
getColumnFinder() - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Returns the column finder used by the filter.
getColumnFinders() - Static method in class adams.data.weka.columnfinder.AbstractColumnFinder: Returns a list with classnames of column finders.
getColumnName(int) - Method in class adams.gui.visualization.instance.InstanceTableModel: Returns the name of the column.
getCombination() - Method in class adams.data.weka.columnfinder.MultiColumnFinder: Returns how the indices are combined.
getCombination() - Method in class adams.data.weka.rowfinder.MultiRowFinder: Returns how the indices are combined.
getCombinationRule() - Method in class weka.classifiers.meta.SubsetEnsemble: Gets the combination rule used
getCommandline(Object) - Method in class weka.classifiers.meta.MultiSearch: Returns the commandline of the given object.
getComment() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns the comment to output in the summary.
getComparator() - Method in class adams.data.instance.Instance: Returns the comparator in use.
getComparator() - Static method in class adams.data.instance.InstanceUtils: Returns the comparator used for finding X values.
getComparators(int) - Method in class weka.classifiers.trees.RandomModelTrees
getComparisonField() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the comparison field.
getComplexityStatistics() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns whether the complexity stats are output as well.
getConfidenceLevel() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Returns the confidence level.
getContainerKeys() - Method in class adams.flow.transformer.wekaclusterer.AbstractClustererPostProcessor: Returns the keys that the processor adds/modifies.
getContainerKeys() - Method in class adams.flow.transformer.wekaclusterer.AbstractClusterMembershipPostProcessor: Returns the keys that the processor adds/modifies.
getContainerKeys() - Method in class adams.flow.transformer.wekaclusterer.PassThrough: Returns the keys that the processor adds/modifies.
getContainerListPopupMenu(ContainerTable<InstanceContainerManager, InstanceContainer>, int) - Method in class adams.gui.visualization.instance.InstanceExplorer: Returns a popup menu for the table of the instance list.
getContainerListPopupMenu(ContainerTable<InstanceContainerManager, InstanceContainer>, int) - Method in class adams.gui.visualization.instance.InstancePanel: Returns a popup menu for the table of the spectrum list.
getContainerManager() - Method in class adams.gui.visualization.instance.InstanceExplorer: Returns the current container manager.
getContainerPaintlet() - Method in class adams.gui.visualization.instance.InstancePanel: Returns the paintlet used for painting the containers.
getContainerPanel() - Method in class adams.gui.visualization.instance.InstanceZoomOverviewPaintlet: Returns the panel to obtain plot and containers from.
getConversion() - Method in class adams.core.base.AttributeTypeList: Returns the conversion of the string before setting its value.
getCorrect() - Method in class weka.filters.unsupervised.attribute.SpellChecker: Returns the correct label.
getCorrelation(Instance, Instance) - Method in class adams.tools.CompareDatasets: Returns the correlation between the two rows.
getCrossValidationSeed() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the current seed value for cross-validation.
getCurrent() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the current file.
getCurrentAttributeRange() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the current range of attributes to use.
getCurrentClassIndex() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the currently selected class index.
getCurrentDirectory() - Method in class adams.gui.chooser.DatasetFileChooserPanel: Returns the current directory in use by the file chooser.
getCurrentDirectory() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the current directory in use by the file chooser.
getCurrentFitness() - Method in interface adams.event.FitnessChangeNotifier: Returns the best currently best fitness.
getCurrentSortIndex() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the currently selected sort index.
getCustomLoader() - Method in class adams.flow.transformer.WekaFileReader: Returns the custom loader in use.
getCustomPanel(PropertyEditor) - Method in class adams.gui.goe.WekaGenericObjectEditorHandler: Returns the custom panel of the editor.
getCustomPropsFile() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the custom properties file to use for initializing the database setup instead of WEKA's default one.
getCustomPropsFile() - Method in class adams.flow.source.WekaDatabaseReader: Returns the custom properties file to use for initializing the database setup instead of WEKA's default one.
getCustomSaver() - Method in class adams.flow.sink.WekaFileWriter: Returns the custom saver in use.
getData() - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Returns the data to use for training and so forth.
getData(Experiment) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Loads the experimental results.
getData() - Method in class adams.gui.visualization.instance.InstanceContainer: Returns the stored instance.
getData() - Method in class adams.gui.visualization.instance.InstanceContainerTableColumnNameGenerator: Returns the name of the column with the actual data in it.
getData() - Method in class adams.gui.visualization.instance.InstanceTable: Returns the underlying Instances object.
getData() - Method in class adams.gui.visualization.instance.InstanceTableModel: Returns the underlying data.
getDatabaseConnection() - Method in class adams.data.instances.AbstractInstanceGenerator: Returns the currently used database connection object, can be null.
getDatabaseConnection() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Returns the database connection to use.
getDatabaseID() - Method in class adams.gui.visualization.instance.InstanceContainer: Returns the database ID.
getDatabaseIDColumnIndex() - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Returns the index of the column with the database ID.
getDataDef() - Method in class adams.genetic.MTPackDataGeneticAlgorithm
getDataDef() - Method in class adams.optimise.genetic.fitnessfunctions.AttributeSelection
getDataDef() - Method in class adams.optimise.genetic.PackDataGeneticAlgorithm
getDataDef() - Method in class adams.optimise.GeneticAlgorithm
getDataGenerator() - Method in class adams.flow.source.WekaDataGenerator: Returns the data generator in use.
getDataset() - Method in class adams.flow.transformer.WekaStoreInstance: Returns the name of the dataset in internal storage to append to.
getDataset() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Returns the dataset to index.
getDataset() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Return the currently selected dataset.
getDataset() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the full dataset, can be null if none loaded.
getDataset() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the currently set filename of the dataset for cross-validation.
getDataset1() - Method in class adams.tools.CompareDatasets: Returns the first dataset for the comparison.
getDataset2() - Method in class adams.tools.CompareDatasets: Returns the second dataset for the comparison.
getDatasetHeader() - Method in class adams.data.conversion.AbstractMatchWekaInstanceAgainstHeader: Acquires the dataset header.
getDatasetHeader() - Method in class adams.data.conversion.MatchWekaInstanceAgainstFileHeader: Acquires the header.
getDatasetHeader() - Method in class adams.data.conversion.MatchWekaInstanceAgainstStorageHeader: Acquires the header.
getDatasetHeader() - Method in class adams.data.instance.Instance: Returns the header of the underlying dataset.
getDataSetups() - Method in class adams.genetic.MTPackDataGeneticAlgorithm
getDataSetups() - Method in class adams.optimise.genetic.PackDataGeneticAlgorithm
getDataSetups() - Method in class adams.optimise.GeneticAlgorithm
getDataType() - Method in class adams.flow.transformer.WekaInstancesStatistic: Returns what type of data to retrieve from the Instances object.
getDataWidth() - Method in class adams.gui.visualization.instance.InstanceContainerTableColumnNameGenerator: Returns the width of the data column.
getDBIDName() - Static method in class adams.data.weka.ArffUtils: Returns the name of the attribute containing the database ID.
getDebug() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Returns whether or not debugging output shouild be printed
getDebug() - Method in class weka.classifiers.meta.LeastMedianSq: Returns whether or not debugging output shouild be printed
getDefaultAttributeRange() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the default range of attributes to use.
getDefaultBorder() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the default border thickness of the table.
getDefaultCaseIndex(AbstractActor, Token) - Method in class adams.flow.condition.bool.WekaClassification: Returns the index of the default case.
getDefaultCellPadding() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the default cell padding for the table.
getDefaultCellSpacing() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the default cell spacing for the table.
getDefaultClassIndex() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the default class index in use.
getDefaultDatabaseConnection() - Method in class adams.data.instances.AbstractInstanceGenerator: Returns the default database connection.
getDefaultDatabaseConnection() - Method in class adams.gui.visualization.instance.InstancePanel: Returns the default database connection.
getDefaultEnumerateColNames() - Method in class weka.experiment.ResultMatrixMediaWiki: returns the default of whether column names are prefixed with the index.
getDefaultGenerator() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Returns the default generator.
getDefaultHeight() - Method in class adams.flow.sink.WekaClassifierErrors: Returns the default height for the dialog.
getDefaultHeight() - Method in class adams.flow.sink.WekaCostCurve: Returns the default height for the dialog.
getDefaultHeight() - Method in class adams.flow.sink.WekaInstancesDisplay: Returns the default height for the dialog.
getDefaultHeight() - Method in class adams.flow.sink.WekaInstanceViewer: Returns the default height for the dialog.
getDefaultHeight() - Method in class adams.flow.sink.WekaROC: Returns the default height for the dialog.
getDefaultInclueAttributes(int) - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the default for the specified attribute type.
getDefaultPackage() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Returns the default package of the types of setups to generate.
getDefaultParameters() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Returns the default parameters.
getDefaultParameters() - Method in class adams.flow.source.WekaClassifierGenerator: Returns the default parameters.
getDefaultParameters() - Method in class adams.flow.source.WekaClustererGenerator: Returns the default parameters.
getDefaultParameters() - Method in class adams.flow.source.WekaFilterGenerator: Returns the default parameters.
getDefaultPrintColNames() - Method in class weka.experiment.ResultMatrixMediaWiki: returns the default of whether column names or numbers instead are printed.
getDefaultReader() - Method in class adams.flow.transformer.WekaInstanceFileReader: Returns the default reader to use.
getDefaultRowNameWidth() - Method in class weka.experiment.ResultMatrixMediaWiki: returns the default width for the row names.
getDefaultSetup() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Returns the default setup.
getDefaultSetup() - Method in class adams.flow.source.WekaClassifierGenerator: Returns the default setup.
getDefaultSetup() - Method in class adams.flow.source.WekaClustererGenerator: Returns the default setup.
getDefaultSetup() - Method in class adams.flow.source.WekaFilterGenerator: Returns the default setup.
getDefaultSortIndex() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the default sort index in use.
getDefaultSuperClass() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Returns the default super class, the same as the type "T" when defining the generics.
getDefaultSuperClass() - Method in class adams.flow.source.WekaClassifierGenerator: Returns the default super class, the same as the type "T" when defining the generics.
getDefaultSuperClass() - Method in class adams.flow.source.WekaClustererGenerator: Returns the default super class, the same as the type "T" when defining the generics.
getDefaultSuperClass() - Method in class adams.flow.source.WekaFilterGenerator: Returns the default super class, the same as the type "T" when defining the generics.
getDefaultWidth() - Method in class adams.flow.sink.WekaClassifierErrors: Returns the default width for the dialog.
getDefaultWidth() - Method in class adams.flow.sink.WekaCostCurve: Returns the default width for the dialog.
getDefaultWidth() - Method in class adams.flow.sink.WekaInstancesDisplay: Returns the default width for the dialog.
getDefaultWidth() - Method in class adams.flow.sink.WekaInstanceViewer: Returns the default width for the dialog.
getDefaultWidth() - Method in class adams.flow.sink.WekaROC: Returns the default width for the dialog.
getDerivativeOrder() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns the order of the derivative.
getDesignVersion() - Method in class weka.gui.visualize.plugins.FixedClassifierErrors: Get the specific version of Weka the class is designed for.
getDesignVersion() - Method in class weka.gui.visualize.plugins.ThresholdCurves: Get the specific version of Weka the class is designed for.
getDev() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive
getDialog(Dialog, Dialog.ModalityType) - Static method in class adams.gui.visualization.instance.HistogramFactory: Returns a new dialog for displaying histograms.
getDialog(Frame, boolean) - Static method in class adams.gui.visualization.instance.HistogramFactory: Returns a new dialog for displaying displaying histograms.
getDialogTitle() - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Returns the title for the dialog.
getDimension(int) - Method in class weka.core.setupgenerator.Space: Returns the specified dimension.
getDiscardPredictions() - Method in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: Returns whether to discard the predictions in order to preserve memory.
getDiscardPredictions() - Method in class adams.flow.transformer.WekaTestSetEvaluator: Returns whether to discard the predictions in order to preserve memory.
getDisplay(InstanceContainer) - Method in class adams.gui.visualization.instance.InstanceContainerDisplayIDGenerator: Returns the display ID for the sequence.
getDisplayID() - Method in class adams.gui.visualization.instance.InstanceContainer: Returns the displayed container's ID.
getDisplayName() - Method in class weka.experiment.ResultMatrixMediaWiki: returns the name of the output format.
getDistances() - Method in class weka.core.neighboursearch.NewNNSearch: Returns the distances of the k nearest neighbours.
getDistributionFormat() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the format for the 'Name' column for the numeric classification.
getDistributionSorting() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the format for the 'Name' column for the numeric classification.
getError() - Method in class weka.classifiers.meta.AbstainLeastMedianSq
getError() - Method in class weka.classifiers.meta.LeastMedianSq
getErrorAtPct(int) - Method in class weka.classifiers.meta.AbstainLeastMedianSq: finds the median residual squared for the current regression.
getErrorAtPct(int) - Method in class weka.classifiers.meta.LeastMedianSq: finds the median residual squared for the current regression.
getErrorScaler() - Method in class adams.flow.sink.WekaClassifierErrors.DataGenerator: Returns the scaling scheme.
getErrorScaler() - Method in class adams.flow.sink.WekaClassifierErrors: Returns the scheme to use for scaling the errors.
getErrorScalers() - Static method in class adams.data.weka.predictions.AbstractErrorScaler: Returns a list with classnames of scalers.
getEvaluation() - Method in class adams.flow.sink.WekaClassifierErrors.DataGenerator: Returns the underlying Evaluation object.
getEvaluation() - Method in class weka.classifiers.meta.MultiSearch: Gets the criterion used for evaluating the classifier performance.
getEvaluation() - Method in class weka.classifiers.meta.multisearch.Performance: Returns the evaluation type.
getEvaluation() - Method in class weka.classifiers.meta.multisearch.PerformanceComparator: returns the performance measure that's used to compare the objects.
getEvaluationType() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the type of evaluation to perform.
getEvaluator() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Returns the evaluator to use.
getEvaluators() - Static method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Returns a list with classnames of evaluators.
getExcludeClass() - Method in class weka.core.AbstractHashableInstance: Returns whether the class is excluded from the hashcode computation.
getExcludedAttributes() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns the prefix separator string.
getExcludeWeight() - Method in class weka.core.AbstractHashableInstance: Returns whether the weight is excluded from the hashcode computation.
getExperiment() - Method in class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog: Returns the experiment.
getExperimentFile() - Method in class adams.flow.transformer.WekaExperiment: Returns the file the experiment is stored in.
getExperimentType() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the type of experiment to perform.
getExplorer() - Method in class weka.gui.explorer.ExperimentPanel: returns the parent Explorer frame.
getExplorer() - Method in class weka.gui.explorer.SqlPanel: returns the parent Explorer frame
getExpression() - Method in class weka.core.setupgenerator.MathParameter: Get the expression.
getExtensions() - Method in class adams.gui.tools.previewbrowser.InstanceExplorerHandler: Returns the list of extensions (without dot) that this handler can take care of.
getExtensions() - Method in class adams.gui.tools.previewbrowser.WekaDatasetHandler: Returns the list of extensions (without dot) that this handler can take care of.
getFavorZeroes() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns whether 0s are favored over 1s.
getFavorZeroes() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns whether 0s are favored over 1s.
getField() - Method in enum adams.flow.core.EvaluationStatistic: Returns the display string without the "numeric" or "nominal" in parentheses.
getField() - Method in enum adams.flow.core.ExperimentStatistic: Returns the display string without the "numeric" or "nominal" in parentheses.
getFieldName(AbstractField) - Static method in class adams.data.weka.ArffUtils: Returns the name of an attribute for a field.
getFields() - Method in class adams.data.conversion.ReportToWekaInstance: Returns the fields in use.
getFile() - Method in class adams.env.InstanceCompareDefinition: Returns the properties file name (no path) this definition is for.
getFile() - Method in class adams.env.InstanceExplorerDefinition: Returns the properties file name (no path) this definition is for.
getFilename() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the currently selected filename, "" if none selected.
getFilter() - Method in class adams.data.weka.rowfinder.FilteredIQR: Returns the IQR filter.
getFilter() - Method in class adams.flow.transformer.WekaFilter: Returns the filter in use.
getFilter() - Method in class weka.classifiers.functions.PLSClassifierWeighted: Get the PLS filter.
getFilter(int, int, boolean) - Method in class weka.classifiers.meta.SubsetEnsemble: Gets a filter for a particular index.
getFilter() - Method in class weka.core.neighboursearch.FilteredSearch: Gets the filter used.
getFilter() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Get the preprocessing filter.
getFilterSpec() - Method in class weka.core.neighboursearch.FilteredSearch: Gets the filter specification string, which contains the class name of the filter and any options to the filter
getFilterType() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.functions.GPD: Gets how the training data will be transformed.
getFind() - Method in class adams.flow.transformer.WekaRenameRelation: Returns the string to find.
getFinders() - Method in class adams.data.weka.columnfinder.MultiColumnFinder: Returns the column finders in use.
getFinders() - Method in class adams.data.weka.rowfinder.MultiRowFinder: Returns the row finders in use.
getFirstAttributeRange() - Method in class adams.gui.InstanceCompare: Returns the first attribute range.
getFirstAttributeRange() - Method in class adams.gui.visualization.instance.InstanceComparePanel: Returns the first attribute range.
getFirstDataset() - Method in class adams.gui.InstanceCompare: Returns the first dataset.
getFirstDataset() - Method in class adams.gui.visualization.instance.InstanceComparePanel: Returns the first dataset.
getFirstRowIndex() - Method in class adams.gui.InstanceCompare: Returns the first row index.
getFirstRowIndex() - Method in class adams.gui.visualization.instance.InstanceComparePanel: Returns the first row index.
getFitness() - Method in class adams.event.FitnessChangeEvent: Returns the fitness that triggered this event.
getFitness() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: Returns the fitness.
getFitness() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the current fitness values.
getFitness() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the current fitness values.
getFlowFile() - Method in class weka.filters.FlowFilter: Returns the flow to process the data with.
getFolds() - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: Returns the number of folds for cross-validation.
getFolds() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the number of folds to use (only CV).
getFolds() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the number of folds to use.
getFolds() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: Returns the number of folds.
getFolds() - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Returns the number of folds.
getFolds() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Returns the number of folds to generate.
getFolds() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the number of folds to use in cross-validation.
getFormatDescription() - Method in class adams.data.io.input.InstanceReader: Returns a string describing the format (used in the file chooser).
getFormatExtensions() - Method in class adams.data.io.input.InstanceReader: Returns the extension(s) of the format.
getGamma() - Method in class weka.classifiers.functions.GPD
getGene(int, int) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the value of the specified gene.
getGene(int, int) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the value of the specified gene.
getGenerateLine() - Method in class adams.data.baseline.AbstractLinearRegressionBased: Returns whether to return the line as fake data or the corrected data.
getGenerateRules() - Method in class weka.classifiers.trees.m5.M5Base2: get whether rules are being generated rather than a tree
getGenerator() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Returns the generator in use.
getGenerators() - Static method in class adams.data.instances.AbstractInstanceGenerator: Returns a list with classnames of generators.
getGenetic() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: Returns the algorithm this job belongs to.
getGeneticAlgorithm() - Method in class adams.event.FitnessChangeEvent: Returns the genetic algorithm that triggered the event.
getGeneticAlgorithms() - Static method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns a list with classnames of genetic algorithms.
getGeneticAlgorithms() - Static method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns a list with classnames of genetic algorithms.
getHeader() - Method in class adams.data.conversion.MatchWekaInstanceAgainstFileHeader: Returns the file to load the dataset header from.
getHeader() - Method in class adams.data.conversion.MatchWekaInstanceAgainstStorageHeader: Returns the name of the storage value representing the dataset header.
getIconName() - Method in class adams.gui.menu.AbstractWekaMenuItemDefinition: Returns the file name of the icon.
getIconName() - Method in class adams.gui.menu.BayesNetEditor: Returns the file name of the icon.
getIconName() - Method in class adams.gui.menu.InstanceCompare: Returns the file name of the icon.
getIconName() - Method in class adams.gui.menu.InstanceExplorer: Returns the file name of the icon.
getID() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Returns the ID to use for the returned instances.
getID() - Method in class adams.gui.visualization.instance.InstanceContainer: Returns the container's ID.
getID(int, Point<Object>) - Method in class weka.classifiers.meta.multisearch.PerformanceCache: returns the ID string for a cache item.
getIDName() - Static method in class adams.data.weka.ArffUtils: Returns the name of the attribute containing the ID of the data container.
getIncludeAttributes(int) - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns whether only numeric attributes should be used.
getIncludeClass() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Returns whether to include the class attribute in the comparison.
getIncludeClass() - Method in class weka.filters.unsupervised.instance.Sort: Returns whether to include the class attribute in the comparison.
getIncorrect() - Method in class weka.filters.unsupervised.attribute.SpellChecker: Returns the incorrect labels, blank-separated list.
getIncremental() - Method in class adams.flow.source.WekaDatabaseReader: Returns whether to output single Instance objects or just one Instances object.
getIndex() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet.SortContainer: Returns the index.
getIndex() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: Returns the index of the classifier in the actor's input array.
getIndex() - Method in class adams.flow.transformer.WekaExtractArray: Returns the type of extraction to perform.
getIndex() - Method in class adams.flow.transformer.WekaGetInstanceValue: Returns the 1-based index of the attribuate value to retrieve from the Instance.
getIndex() - Method in class adams.flow.transformer.WekaSetInstanceValue: Returns the 1-based attribute index to set in the Instance.
getIndex() - Method in class adams.flow.transformer.WekaSubsets: Returns the index of the attribute to split on.
getIndex() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Returns the index, generates it if necessary.
getIndices() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Returns the indices of the (actual) selected rows.
getInitializeOnce() - Method in class adams.flow.transformer.WekaFilter: Returns whether the filter gets initialized only with the first batch.
getInitialSetups() - Method in class adams.genetic.MTAbstractGeneticAlgorithm
getInitialSetups() - Method in class adams.genetic.MTPackDataGeneticAlgorithm
getInitialSetups() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm
getInitialSetups() - Method in class adams.optimise.genetic.PackDataGeneticAlgorithm
getInitialSpaceNumFolds() - Method in class weka.classifiers.meta.MultiSearch: Gets the number of CV folds for the initial space.
getInlineValue() - Method in class adams.gui.goe.WekaExperimentFileEditor: Returns the current value.
getInstance() - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer: Returns the stored Instance.
getInstanceClass() - Method in class adams.flow.transformer.WekaNewInstance: Returns the the class name of the Instance object to create.
getInstanceContainerList() - Method in class adams.gui.visualization.instance.InstanceExplorer: Returns the panel listing the instances.
getInstanceContainerList() - Method in class adams.gui.visualization.instance.InstancePanel: Returns the panel with the instance list.
getInstancePaintlet() - Method in class adams.gui.visualization.instance.InstancePanel: Returns the paintlet for painting the instance.
getInstancePanel() - Method in class adams.gui.visualization.instance.InstanceExplorer: Returns the panel for painting the instances.
getInstancePanel() - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Returns the sequence panel currently in use.
getInstances(T) - Method in class adams.data.baseline.AbstractLinearRegressionBased: Returns a dataset containing the x and y values.
getInstances() - Method in class adams.gui.visualization.instance.InstancePanel: Returns the currently visible instances.
getInstances() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the currently set dataset for cross-validation.
getInstancesActor() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Returns the global actor from which to retrieve Instances in case of AbstractDatasetInstanceEvaluator-derived evaluators.
getInstancesIndices() - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Gets ranges of instances selected.
getInt(int[], int, int) - Method in class adams.optimise.genetic.PackData
getInterval() - Method in class adams.flow.transformer.WekaInstanceBuffer: Returns the interval for outputting the Instances objects.
getIntervals() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased.SortedInterval: Returns the stored intervals.
getInverseTransform() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Gets whether to use the inverse transform.
getInvert() - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Returns whether the matching sense of the capabilities is inverted.
getInvert() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns whether to invert the matching sense.
getInvert() - Method in class adams.flow.transformer.WekaRegexToRange: Get invert match?
getInvert() - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Returns whether the invert the column indices.
getInvert() - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Returns whether the invert the row indices.
getInvert() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Get whether selection is inverted.
getInvertMatchingSense() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns whether to invert the matching sense.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Gets if selection is to be inverted.
getIqr() - Method in class adams.data.weka.rowfinder.FilteredIQR: Returns the iqr multiplier.
getIterations() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Gets the iterations use.
getJavaInitializationString() - Method in class adams.gui.goe.WekaExperimentFileEditor: Returns a representation of the current property value as java source.
getKeepExisting() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns whether the relation name is used as filename.
getKeepRelationName() - Method in class adams.flow.transformer.WekaFilter: Returns whether the filter doesn't change the relation name.
getKernel() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Gets the kernel to use.
getKernel() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Gets the kernel to use.
getKey() - Method in class adams.env.InstanceCompareDefinition: Returns the key this definition is for.
getKey() - Method in class adams.env.InstanceExplorerDefinition: Returns the key this definition is for.
getKeys() - Method in class adams.flow.source.WekaDatabaseReader: Returns the keys to use for identifying a single row (comma-separated list).
getKeySet() - Method in class adams.optimise.genetic.PackData
getLabel() - Method in class weka.core.setupgenerator.SpaceDimension: returns the label for the axis.
getLabelMatch() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Returns the label for the matching rows.
getLabelNonMatch() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Returns the label for the non-matching rows.
getList() - Method in class weka.core.setupgenerator.ListParameter: Get the blank-separated list of values.
getList() - Method in class weka.core.setupgenerator.SpaceDimension: Returns the list of values, null in case of a numeric dimension that is based on a mathematical function.
getLoader() - Method in class adams.gui.chooser.DatasetFileChooserPanel: Returns the current loader.
getLocation(Object) - Method in class weka.core.setupgenerator.SpaceDimension: returns the closest index for the given value in the dimension.
getLocations() - Method in class adams.flow.transformer.WekaInstancesStatistic: Returns the locations of the data (indices/regular expressions on attribute name).
getLocations(Point<Object>) - Method in class weka.core.setupgenerator.Space: Returns the locations for the given values in the various dimensions.
getLogFile() - Method in class weka.classifiers.meta.MultiSearch: Gets current log file.
getM5RootNode() - Method in class weka.classifiers.trees.m5.M5Base2
getM5RootNode() - Method in class weka.classifiers.trees.m5.Rule2
getMakeClassLast() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns whether to make the class attribute the last attribute.
getMarkerExtent() - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Returns the current marker extent (which is the width and height of the shape).
getMarkerShape(int) - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Determines the shape to paint around the data points, based on the index of the data.
getMax() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the maximum number of top-ranked classifiers to forward.
getMax() - Method in class weka.classifiers.trees.RandomModelTrees
getMax() - Method in class weka.core.setupgenerator.MathParameter: Get the value of the Maximum.
getMax() - Method in class weka.core.setupgenerator.SpaceDimension: returns the right border.
getMaximumAttributeNames() - Method in class weka.core.neighboursearch.PCANNSearch: Gets maximum number of attributes to include in transformed attribute names.
getMaxIterations() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Gets the maximum number of cleansing iterations performed
getMaxSize() - Method in class adams.data.weka.predictions.RelativeNumericErrorScaler: Returns the maximum size for the errors.
getMaxTrainTime() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the maximum number of seconds to perform training.
getMaxTrainTime() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the maximum number of seconds to perform training.
getMaxVal() - Method in class adams.optimise.genetic.PackDataDef.DataInfo
getMaxVersion() - Method in class weka.gui.visualize.plugins.FixedClassifierErrors: Get the maximum version of Weka, exclusive, the class is designed to work with.
getMaxVersion() - Method in class weka.gui.visualize.plugins.ThresholdCurves: Get the maximum version of Weka, exclusive, the class is designed to work with.
getMeanStdev(Instance) - Method in class weka.filters.unsupervised.attribute.PAA: Return an array where the 1st value is the mean, and the 2nd the standard deviation of the attribute values.
getMeanStdev(Instance) - Method in class weka.filters.unsupervised.attribute.SAX: Return an array where the 1st value is the mean, and the 2nd the standard deviation of the attribute values.
getMeasure() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the measure used for ranking the classifiers.
getMeasure() - Method in enum adams.flow.transformer.WekaClassifierRanker.Measure: Returns the associated measure.
getMeasure() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: Returns the measure for ranking.
getMeasure() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the current measure for evaluating the fitness.
getMeasure(String) - Method in class weka.classifiers.meta.MultiSearch: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.m5.M5Base2: Returns the value of the named measure
getMenuBar() - Method in class adams.gui.visualization.instance.InstanceComparePanel: Creates a menu bar (singleton per panel object).
getMenuBar() - Method in class adams.gui.visualization.instance.InstanceExplorer: Creates a menu bar (singleton per panel object).
getMetaLevelClassifier() - Method in class weka.classifiers.meta.PartitionedStacking: Returns the meta-level classifier.
getMiddle(double[]) - Method in class weka.core.SAXDistance: Returns value in the middle of the two parameter values.
getMiddle(double[]) - Method in class weka.core.WeightedEuclideanDistance: Returns value in the middle of the two parameter values.
getMin() - Method in class weka.classifiers.trees.RandomRegressionForest: Gets the current leaf threshold.
getMin() - Method in class weka.core.setupgenerator.MathParameter: Get the value of the minimum.
getMin() - Method in class weka.core.setupgenerator.SpaceDimension: returns the left border.
getMinNumInstances() - Method in class weka.classifiers.trees.m5.M5Base2: Get the minimum number of instances to allow at a leaf node
getMinNumInstances() - Method in class weka.classifiers.trees.m5.Rule2: Get the minimum number of instances to allow at a leaf node
getMinNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode2: Get the minimum number of instances to allow at a leaf node
getMinVersion() - Method in class weka.gui.visualize.plugins.FixedClassifierErrors: Get the minimum version of Weka, inclusive, the class is designed to work with.
getMinVersion() - Method in class weka.gui.visualize.plugins.ThresholdCurves: Get the minimum version of Weka, inclusive, the class is designed to work with.
getMissing() - Method in class adams.tools.CompareDatasets: Returns the first dataset for the comparison.
getModel(Report) - Static method in class adams.gui.visualization.instance.InstanceReportFactory: Returns a new model for the given report.
getModel() - Method in class weka.classifiers.trees.m5.RuleNode2: Get the linear model at this node
getModelActor() - Method in class adams.flow.condition.bool.WekaClassification: Returns the global actor to obtain the model from if model file is pointing to a directory.
getModelActor() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Returns the global actor to obtain the model from if model file is pointing to a directory.
getModelFile() - Method in class adams.flow.condition.bool.WekaClassification: Returns the file to load the model from.
getModelFile() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Returns the file to load the model from.
getN() - Method in class weka.core.SAXDistance: Returns the nth point setting.
getName() - Method in class adams.optimise.genetic.PackDataDef.DataInfo
getNextColor() - Method in class adams.gui.visualization.instance.InstanceContainerManager: Returns the next color in line.
getNoCheck() - Method in class adams.flow.transformer.WekaExperiment: Returns whether to avoid the check at setUp time whether the experiment file is present or not.
getNoise() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Get the value of noise.
getNoise() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Get the value of noise.
getNoise() - Method in class weka.classifiers.functions.GPD: Get the value of noise.
getNominal() - Method in class weka.filters.unsupervised.attribute.SAX: Gets whether output numeric or nominal values.
getNoteName(String) - Static method in class adams.data.weka.ArffUtils: Returns the name of an attribute for a note.
getNotes() - Method in class adams.data.instance.Instance: Returns the currently stored notes.
getNoUpdate() - Method in class weka.classifiers.lazy.LWLSynchro: Returns whether to suppress the update of the nearest-neighbor search algorithm when making predictions.
getNthPoint() - Method in class weka.filters.unsupervised.attribute.DownSample: Returns the nth point setting.
getNumattrs() - Method in class weka.classifiers.meta.Corr
getNumChrom() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: Returns the number of chromosomes.
getNumChrom() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the number of chromosomes to use.
getNumChrom() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the number of chromosomes to use.
getNumComponents() - Method in class weka.core.neighboursearch.PLSNNSearch
getNumDecimals() - Method in class adams.data.conversion.WekaInstancesToSpreadSheet: Returns the currently set number of decimals.
getNumExecutionSlots() - Method in class weka.classifiers.meta.MultiSearch: Get the number of execution slots (threads) to use for building the members of the ensemble.
getNumExecutionSlots() - Method in class weka.classifiers.meta.SubsetEnsemble: Get the number of execution slots (threads) to use for building the members of the ensemble.
getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Gets the number of cross-validation folds used by the filter.
getNumGenes() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the number of genes to use.
getNumGenes() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the number of genes to use.
getNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode2: Return the number of instances that reach this node.
getNumIterations() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the number of iterations to perform.
getNumIterations() - Method in class weka.classifiers.trees.RandomModelTrees: Gets the number of iterations.
getNumIterations() - Method in class weka.classifiers.trees.RandomRegressionForest: Gets the number of iterations
getNumPointsLeft() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns the number of points to the left of a data point.
getNumPointsRight() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns the number of points to the right of a data point.
getNumRandomFeatures() - Method in class weka.classifiers.meta.SubsetEnsemble: Returns the number of additional random features to use.
getNumRegressions() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: gets number of samples
getNumRegressions() - Method in class weka.classifiers.meta.LeastMedianSq: gets number of samples
getNumThreads() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the number of threads in use.
getNumZeroes() - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Returns the number of zeroes a row must have at least in order to be removed.
getOnTheFly() - Method in class adams.flow.condition.bool.WekaClassification: Returns whether the model file gets built on the fly and might not be present at start up time.
getOnTheFly() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Returns whether the model file gets built on the fly and might not be present at start up time.
getOperation() - Method in class adams.flow.transformer.WekaInstanceBuffer: Returns the way the buffer operates.
getOptimizer() - Method in class adams.flow.transformer.WekaClassifierOptimizer: Returns the optimizer in use.
getOptions(Object) - Method in class adams.core.option.WekaCommandLineHandler: Returns the commandline options (without classname) of the specified object.
getOptions() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.GPD: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.PLSClassifierWeighted: returns the options of the current setup
getOptions() - Method in class weka.classifiers.lazy.LWLSynchro: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.meta.AbstainAttributePercentile: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.meta.Corr: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.FilteredClassifierExt: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.LeastMedianSq: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.meta.MultiSearch: returns the options of the current setup.
getOptions() - Method in class weka.classifiers.meta.PartitionedStacking: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.SubsetEnsemble: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.trees.m5.M5Base2: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.trees.M5P2: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.trees.RandomModelTrees: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.RandomRegressionForest: Gets the current settings of the Classifier.
getOptions() - Method in class weka.core.neighboursearch.FilteredSearch
getOptions() - Method in class weka.core.neighboursearch.NewNNSearch: Gets the current settings.
getOptions() - Method in class weka.core.neighboursearch.PCANNSearch
getOptions() - Method in class weka.core.neighboursearch.PLSNNSearch
getOptions() - Method in class weka.core.SAXDistance: Gets the current settings of the filter.
getOptions() - Method in class weka.core.setupgenerator.AbstractParameter: returns the options of the current setup.
getOptions() - Method in class weka.core.SetupGenerator: returns the options of the current setup.
getOptions() - Method in class weka.core.setupgenerator.ListParameter: returns the options of the current setup.
getOptions() - Method in class weka.core.setupgenerator.MathParameter: returns the options of the current setup.
getOptions() - Method in class weka.experiment.ResultMatrixMediaWiki: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.filters.FlowFilter: returns the options of the current setup.
getOptions() - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: returns the options of the current setup.
getOptions() - Method in class weka.filters.unsupervised.attribute.DownSample: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.FastWavelet: returns the options of the current setup.
getOptions() - Method in class weka.filters.unsupervised.attribute.PAA: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.SAX: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.SetMissingValue: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.SpellChecker: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.unsupervised.instance.LatestRecords: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.Sort: Gets the current settings of the filter.
getOutput() - Method in class adams.flow.transformer.AbstractWekaClassifierEvaluator: Returns the prediction output generator in use.
getOutputArray() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Returns whether to output an array or a sequence of classifier setups.
getOutputBestSetup() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns whether to output the best setup for optimizers like GridSearch and MultiSearch.
getOutputBestSetup() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: Returns whether the best setup is output in case of optimizers like GridSearch/MultiSearch.
getOutputDirectory() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the currently set directory for the generated ARFF files.
getOutputFile() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the file to store the experiment in.
getOutputFile() - Method in class adams.tools.CompareDatasets: Returns the first dataset for the comparison.
getOutputFormat() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the output format in use for generating the output.
getOutputFormat() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the current output format.
getOutputHeader() - Method in class adams.data.instances.AbstractInstanceGenerator: Returns the current header.
getOutputHeader() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns whether to output the header of the result matrix as well.
getOutputInstance() - Method in class adams.flow.transformer.WekaClassifying: Returns whether Instance objects are output instead of PredictionContainer ones.
getOutputOnlyModel() - Method in class adams.flow.transformer.AbstractWekaModelReader: Returns whether only the model is output instead of the container.
getOutputPrefix() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the current output prefix.
getOutputRelationName() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns whether the relation name is output as well.
getOutputType() - Method in class adams.flow.transformer.WekaFileReader: Returns how to output the data.
getOverride() - Method in class adams.flow.transformer.WekaClassSelector: Returns whether any existing class index will be overriden or not.
getOwner() - Method in class adams.gui.visualization.instance.InstanceContainerManager: Returns the owning panel.
getOwner() - Method in class adams.gui.visualization.instance.InstancePointHitDetector: Returns the owner.
getPadding() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Gets the type of Padding to use.
getPaintMoment() - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Returns when this paintlet is to be executed.
getPanel() - Static method in class adams.gui.visualization.instance.HistogramFactory: Returns an instance of a new panel for displaying histograms.
getPanel(Report) - Static method in class adams.gui.visualization.instance.InstanceReportFactory: Returns a new panel for the given report.
getPanel(Vector<Container>) - Static method in class adams.gui.visualization.instance.InstanceReportFactory: Returns a new table for the given reports.
getPanelForReports(Vector) - Static method in class adams.gui.visualization.instance.InstanceReportFactory: Returns a new table for the given reports.
getParameters() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Returns the setup parameters.
getParameters() - Method in class weka.core.SetupGenerator: Returns the current parameters.
getPassword() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the database password.
getPassword() - Method in class adams.flow.source.WekaDatabaseReader: Returns the database password.
getPct() - Method in class weka.classifiers.meta.AbstainLeastMedianSq
getPct() - Method in class weka.classifiers.meta.LeastMedianSq
getPercentage() - Method in class adams.flow.transformer.WekaRandomSplit: Returns the percentage (0-1).
getPercentile() - Method in class weka.classifiers.meta.AbstainAttributePercentile
getPerformance() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: The generated performance.
getPerformance() - Method in class weka.classifiers.meta.multisearch.Performance: returns the performance measure.
getPerformance(int) - Method in class weka.classifiers.meta.multisearch.Performance: returns the performance measure.
getPlotData() - Method in class adams.flow.sink.WekaClassifierErrors.DataGenerator: Assembles and returns the plot.
getPlotInstances() - Method in class adams.flow.sink.WekaClassifierErrors.DataGenerator: Returns the generated dataset that is plotted.
getPlotName() - Method in class adams.flow.transformer.WekaAccumulatedError: Returns the current plot name.
getPLS() - Method in class weka.classifiers.trees.RandomRegressionForest: Gets the current number of PLS components to generate.
getPolynomialOrder() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns the polynominal order.
getPostProcessor() - Method in class adams.flow.transformer.WekaClusterer: Returns the post-processor in use.
getPrediction() - Method in class adams.flow.transformer.WekaAccumulatedError.SortablePrediction: Returns the stored prediction.
getPreFilter() - Method in class adams.data.weka.rowfinder.FilteredIQR: Returns the pre filter.
getPrefix() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns the optional prefix string.
getPrefixSeparator() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns the prefix separator string.
getPreprocessing() - Method in class weka.core.neighboursearch.PLSNNSearch: Gets the type of preprocessing to use
getPreserveOrder() - Method in class adams.flow.transformer.WekaRandomSplit: Returns whether to preserve order and suppress randomization.
getProbability() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet.SortContainer: Returns the probability.
getProperties() - Static method in class adams.gui.visualization.instance.InstanceComparePanel: Returns the properties that define the editor.
getProperties() - Static method in class adams.gui.visualization.instance.InstanceExplorer: Returns the properties that define the editor.
getProperty() - Method in class weka.core.setupgenerator.AbstractParameter: Get the property to update.
getQuery() - Method in class adams.flow.source.WekaDatabaseReader: Returns the query to execute.
getQuickInfo() - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Returns the quick info string to be displayed in the flow editor.
getQuickInfo() - Method in class adams.flow.condition.bool.WekaClassification: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.sink.WekaROC: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.source.WekaDatabaseReader: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.source.WekaDataGenerator: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.source.WekaNewInstances: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaAccumulatedError: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaAttributeIterator: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaClassifier: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaClassSelector: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaClusterer: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaEvaluationValues: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaExperiment: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaExtractArray: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaFileReader: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaFilter: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaGetInstanceValue: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaInstanceBuffer: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaInstancesInfo: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaInstancesStatistic: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaNewInstance: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaRandomSplit: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaRegexToRange: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaRenameRelation: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaSetInstanceValue: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaStoreInstance: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaSubsets: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaTestSetEvaluator: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaTextDirectoryReader: Returns a quick info about the actor, which will be displayed in the GUI.
getQuickInfo() - Method in class adams.flow.transformer.WekaTrainTestSetEvaluator: Returns a quick info about the actor, which will be displayed in the GUI.
getRandomize() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Returns whether to include the class attribute in the comparison.
getRandomSeed() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: get the seed for the random number generator
getRandomSeed() - Method in class weka.classifiers.meta.LeastMedianSq: get the seed for the random number generator
getRange() - Method in class adams.flow.transformer.WekaAttributeIterator: Returns the range of attributes to operate on.
getRange() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Returns the range of attributes to use.
getRange1() - Method in class adams.tools.CompareDatasets: Returns the range of attributes of the first dataset.
getRange2() - Method in class adams.tools.CompareDatasets: Returns the range of attributes of the second dataset.
getRanges() - Method in class weka.classifiers.meta.PartitionedStacking: Returns the attribute ranges for the base-classifiers.
getRegex() - Method in class adams.flow.transformer.WekaRegexToRange: Returns the regular expression for attribute matching.
getRegexName() - Method in class adams.flow.transformer.WekaClassSelector: Returns the regular expression for selecting the attributes.
getRegExp() - Method in class adams.data.weka.columnfinder.ByName: Returns the regular expression in use.
getRegExp() - Method in class adams.data.weka.rowfinder.ByLabel: Returns the regular expression in use.
getRegExp() - Method in class adams.flow.transformer.WekaAttributeIterator: Returns the regular expression for the name.
getRegExp() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns the regular expression to match the strings against.
getRegressionTree() - Method in class weka.classifiers.trees.m5.Rule2: Get the value of regressionTree.
getRegressionTree() - Method in class weka.classifiers.trees.m5.RuleNode2: Get the value of regressionTree.
getRelationName() - Method in class adams.flow.source.WekaNewInstances: Returns the name of the relation.
getRelationName() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Returns the pattern used for renaming the relation.
getRelativeWidths() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Returns whether the calculated widths are divided by the class value.
getRemoveAsString(int[]) - Method in class adams.optimise.genetic.fitnessfunctions.AttributeSelection: Generates a range string of attributes to keep (= one has to use the inverse matching sense with the Remove filter).
getRemoveAttributeIndices() - Method in class weka.classifiers.meta.FilteredClassifierExt: Returns the attributes indices that are removed before applying the actual filter.
getRemoveTrain() - Method in class weka.classifiers.meta.AbstainAttributePercentile
getReplace() - Method in class adams.flow.transformer.WekaRenameRelation: Returns the replacement string.
getReport() - Method in class adams.data.instance.Instance: Returns the report.
getResult(String) - Method in class adams.optimise.GeneticAlgorithm: Returns a value from the cache.
getResultFile() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the file to store the experimental results in.
getResultFormat() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the data format the results are stored in.
getRevision() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive
getRevision() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.PLSClassifierWeighted: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.LWLIntervalEstimator: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.LWLSynchro: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.AbstainAttributePercentile
getRevision() - Method in class weka.classifiers.meta.AbstainLeastMedianSq
getRevision() - Method in class weka.classifiers.meta.ClassificationViaRegressionD: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Corr
getRevision() - Method in class weka.classifiers.meta.FilteredClassifierExt: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.LeastMedianSq
getRevision() - Method in class weka.classifiers.meta.LogTargetRegressor
getRevision() - Method in class weka.classifiers.meta.MultiSearch: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.PartitionedStacking: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.PeakTransformed
getRevision() - Method in class weka.classifiers.meta.SubsetEnsemble: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.SumTransformed
getRevision() - Method in class weka.classifiers.trees.m5.Rule2: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.RuleNode2: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.M5P2: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.RandomModelTrees
getRevision() - Method in class weka.classifiers.trees.RandomRegressionForest
getRevision() - Method in class weka.core.neighboursearch.NewNNSearch: Returns the revision string.
getRevision() - Method in class weka.core.SAXDistance: Returns the revision string.
getRevision() - Method in class weka.core.WeightedEuclideanDistance: Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.DatasetCleaner: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.DownSample: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NormalizeAdaptive
getRevision() - Method in class weka.filters.unsupervised.attribute.PAA: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValuesWithZero: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.SAX: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.SetMissingValue: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.SpellChecker: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.DatasetCleaner: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.LatestRecords: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveInstancesWithMissingValue: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RowNorm: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.Sort: Returns the revision string.
getRidge() - Method in class adams.data.baseline.AbstractLinearRegressionBased: Returns the ridge parameter.
getRidge() - Method in class weka.classifiers.trees.RandomModelTrees
getRow() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the list of fields that identify a row.
getRow(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Returns the Instance for the row.
getRow(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Returns the row to display.
getRowAttribute1() - Method in class adams.tools.CompareDatasets: Returns the index of the attribute used for identifying rows to compare against each other (first dataset).
getRowAttribute2() - Method in class adams.tools.CompareDatasets: Returns the index of the attribute used for identifying rows to compare against each other (second dataset).
getRowCount() - Method in class adams.gui.visualization.instance.InstanceTableModel: Returns the number of rows.
getRowFinder() - Method in class adams.data.weka.columnfinder.RowFilteredColumnFinder: Returns the row finder in use.
getRowFinder() - Method in class adams.data.weka.rowfinder.AbstractFilteredRowFinder: Returns the row finder in use.
getRowFinder() - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Returns the row finder used by the filter.
getRowFinders() - Static method in class adams.data.weka.rowfinder.AbstractRowFinder: Returns a list with classnames of row finders.
getRowID(int) - Method in class adams.tools.CompareDatasets: Returns either the ID for the row, either the row index of the actual row attribute ID for that position.
getRowIndex() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Returns the currently selected.
getRows() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Returns a list of row IDs.
getRows() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Returns a list of row IDs.
getRuns() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the number of runs to perform.
getSampleSizePercent() - Method in class weka.classifiers.meta.MultiSearch: Gets the sample size for the initial space search.
getSaveInstances() - Method in class weka.classifiers.trees.M5P2: Get whether instance data is being save.
getSaver() - Method in class adams.gui.chooser.DatasetFileChooserPanel: Returns the current saver.
getScale() - Method in class weka.filters.unsupervised.attribute.NormalizeAdaptive
getScaler() - Method in class adams.data.weka.predictions.AutoScaler: Returns the scaler for numeric data.
getScriptingEngine() - Method in class adams.gui.visualization.instance.InstancePanel: Returns the current scripting engine, can be null.
getSearchParameters() - Method in class weka.classifiers.meta.MultiSearch: Returns the search parameters.
getSecondAttributeRange() - Method in class adams.gui.InstanceCompare: Returns the second attribute range.
getSecondAttributeRange() - Method in class adams.gui.visualization.instance.InstanceComparePanel: Returns the second attribute range.
getSecondDataset() - Method in class adams.gui.InstanceCompare: Returns the second dataset.
getSecondDataset() - Method in class adams.gui.visualization.instance.InstanceComparePanel: Returns the second dataset.
getSecondRowIndex() - Method in class adams.gui.InstanceCompare: Returns the second row index.
getSecondRowIndex() - Method in class adams.gui.visualization.instance.InstanceComparePanel: Returns the second row index.
getSeed() - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: Returns the seed value for cross-validation.
getSeed() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the seed value.
getSeed() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: Returns the seed value.
getSeed() - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Returns the seed value.
getSeed() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Returns the seed value.
getSeed() - Method in class adams.flow.transformer.WekaRandomSplit: Returns the seed value.
getSeed() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the current seed value.
getSeed() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the current seed value.
getSeed() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Gets the seed for the random number generations
getSelectedDatabaseID(int) - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Returns the database ID of the currently selected item.
getSelectedDatabaseIDs(int) - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Returns the database IDs of the currently selected items.
getSendToClasses() - Method in class adams.flow.sink.WekaInstancesDisplay: Returns the classes that the supporter generates.
getSendToClasses() - Method in class adams.gui.visualization.instance.InstanceExplorer: Returns the classes that the supporter generates.
getSendToItem(Class[]) - Method in class adams.flow.sink.WekaInstancesDisplay: Returns the object to send.
getSendToItem(Class[]) - Method in class adams.gui.visualization.instance.InstanceExplorer: Returns the object to send.
getSequenceManager() - Method in class adams.gui.visualization.instance.InstancePanel: Returns the current container manager.
getSetup() - Method in class adams.data.weka.evaluator.NamedSetup: Returns the setup name.
getSetup() - Method in class adams.data.weka.predictions.NamedSetup: Returns the setup name.
getSetup() - Method in class adams.flow.source.WekaClassifierGenerator: Returns the base classifier.
getSetup() - Method in class adams.flow.source.WekaClustererGenerator: Returns the base clusterer.
getSetup() - Method in class adams.flow.source.WekaFilterGenerator: Returns the base clusterer.
getSetupDialog(Dialog, Dialog.ModalityType) - Static method in class adams.gui.visualization.instance.HistogramFactory: Returns an instance of a setup dialog for displaying histograms.
getSetupDialog(Frame, boolean) - Static method in class adams.gui.visualization.instance.HistogramFactory: Returns an instance of a setup dialog for displaying histograms.
getShowDistribution() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns whether to show the class distribution as well.
getShowError() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns whether to show the error as well.
getShowProbability() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns whether to show the probability as well.
getShowWeight() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns whether to show the weight as well.
getSidePanel() - Method in class adams.gui.visualization.instance.InstanceExplorer: Returns the side panel.
getSignificance() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the current significance level (0-1).
getSize() - Method in class adams.data.weka.predictions.FixedSizeErrorScaler: Returns the size for the errors.
getSkipIdentical() - Method in class weka.core.neighboursearch.NewNNSearch: Gets whether if identical instances are skipped from the neighbourhood.
getSmoothing() - Method in class weka.classifiers.trees.m5.Rule2: Get whether or not smoothing has been turned on
getSpace() - Method in class weka.core.SetupGenerator: Returns the space currently in use.
getSparseFormat() - Method in class adams.flow.source.WekaDatabaseReader: Returns whether data is output in sparse format.
getSplitPercentage() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the split percentage (only train/test splits).
getStandardDeviation(Instance) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Gives the variance of the prediction at the given instance
getStandardDeviation(Instance) - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Gives the variance of the prediction at the given instance
getStatistic() - Method in class adams.flow.transformer.WekaInstancesStatistic: Returns the statistic in use.
getStatisticValue() - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Returns the value to extract.
getStatisticValues() - Method in class adams.flow.transformer.WekaEvaluationValues: Returns the values to extract.
getStep() - Method in class weka.core.setupgenerator.MathParameter: Get the value of the step size.
getStep() - Method in class weka.core.setupgenerator.SpaceDimension: returns the step size on the axis.
getStoreFilename() - Method in class adams.flow.transformer.WekaTextDirectoryReader: Returns whether the filename gets stored in extra attribute.
getSubsequentSpaceNumFolds() - Method in class weka.classifiers.meta.MultiSearch: Gets the number of CV folds for the sub-sequent sub-spaces.
getSubset(Instances) - Method in class weka.classifiers.meta.Corr
getSubset() - Method in class weka.classifiers.meta.Corr
getSwapRowsAndColumns() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns whether to swap rows and columns.
getTable(Report) - Static method in class adams.gui.visualization.instance.InstanceReportFactory: Returns a new table for the given report.
getTableEpilog() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the epilog for a table.
getTableModelClass() - Method in class adams.gui.visualization.instance.InstanceTable: Returns the class of the table model that the models need to be derived from.
getTableName() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the table name to store the data in.
getTableProlog() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns the prolog of a table.
getTabTitle() - Method in class weka.gui.explorer.ExperimentPanel: Returns the title for the tab in the Explorer.
getTabTitle() - Method in class weka.gui.explorer.SqlPanel: Returns the title for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.ExperimentPanel: Returns the tooltip for the tab in the Explorer.
getTabTitleToolTip() - Method in class weka.gui.explorer.SqlPanel: Returns the tooltip for the tab in the Explorer
getTechnicalInformation() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.GPD: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.ClassificationViaRegressionD: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.m5.M5Base2: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.SAXDistance: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.WeightedEuclideanDistance: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTest() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the name of the global actor to obtain the test set.
getTest() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: The test data.
getTestBase() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the index of the test base.
getTester() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the Tester in use.
getTester(Experiment, Instances) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Sets up the testing algorithm and returns it.
getTestset() - Method in class adams.flow.transformer.WekaTestSetEvaluator: Returns the name of the global classifier in use.
getThreshold() - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Returns the threshold.
getThreshold() - Method in class adams.tools.CompareDatasets: Returns the threshold for the correlation coefficient.
getThreshold() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Gets the threshold for the max error when predicting a numeric class.
getTipText() - Method in class adams.core.base.AttributeTypeList: Returns a tool tip for the GUI editor (ignored if null is returned).
getTitle() - Method in class adams.gui.menu.ArffViewer: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.BayesNetEditor: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.BoundaryVisualizer: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.Experimenter: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.Explorer: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.GraphVisualizer: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.InstanceCompare: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.InstanceExplorer: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.InstancesPlot: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.PackageManager: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.ROC: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.SqlViewer: Returns the title of the window (and text of menuitem).
getTitle() - Method in class adams.gui.menu.TreeVisualizer: Returns the title of the window (and text of menuitem).
getTitleNameColumn() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the title of the "Name" column, i.e., the first column.
getTitleValueColumn() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the title of the "Value" column, i.e., the first column.
getTrain() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the name of the global actor to obtain the training set.
getTrain() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: The training data.
getTrainingSet(int, int) - Method in class weka.classifiers.meta.SubsetEnsemble: Gets a training set for a particular index.
getTransformedInstances() - Method in class weka.core.neighboursearch.TransformNNSearch
getTrials() - Method in class weka.classifiers.trees.RandomModelTrees
getType() - Method in class adams.flow.transformer.WekaExtractArray: Returns the type of extraction to perform.
getType() - Method in class adams.flow.transformer.WekaInstancesInfo: Returns the type of information to generate.
getType(int) - Method in class weka.core.setupgenerator.Space: Returns the type of the dimension.
getType() - Method in class weka.core.setupgenerator.SpaceDimension: returns the tye of dimension.
getUndo() - Method in class adams.gui.visualization.instance.InstancePanel: Returns the current undo manager, can be null.
getUniqueID() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns the attribute (string/numeric) to use for uniquely identifying rows.
getUnpruned() - Method in class weka.classifiers.trees.m5.M5Base2: Get whether unpruned tree/rules are being generated
getUnpruned() - Method in class weka.classifiers.trees.m5.Rule2: Get whether unpruned tree/rules are being generated
getUpdateRelationName() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns whether to update the relation name with the new class attribute.
getURL() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the query to execute.
getURL() - Method in class adams.flow.source.WekaDatabaseReader: Returns the query to execute.
getUseCustomLoader() - Method in class adams.flow.transformer.WekaFileReader: Returns whether a custom loader is used or not.
getUseCustomSaver() - Method in class adams.flow.sink.WekaFileWriter: Returns whether a custom saver is used or not.
getUsePrefix() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns whether to use prefixes.
getUser() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the database user.
getUser() - Method in class adams.flow.source.WekaDatabaseReader: Returns the database user.
getUseRelationNameAsFilename() - Method in class adams.flow.sink.WekaFileWriter: Returns whether the relation name is used as filename.
getUseRelationNameAsFilename() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns whether the relation name is used as filename.
getUseRelationNameAsTable() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns whether to output single Instance objects or just one Instances object.
getUserMode() - Method in class adams.gui.menu.ArffViewer: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.BayesNetEditor: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.BoundaryVisualizer: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.Experimenter: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.Explorer: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.GraphVisualizer: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.InstanceCompare: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.InstanceExplorer: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.InstancesPlot: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.PackageManager: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.ROC: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.SqlViewer: Returns the user mode, which determines visibility as well.
getUserMode() - Method in class adams.gui.menu.TreeVisualizer: Returns the user mode, which determines visibility as well.
getUseRowAttribute() - Method in class adams.tools.CompareDatasets: Returns whether to use the row attribute or the order in the datasets for matching up the rows.
getUseTree() - Method in class weka.classifiers.trees.m5.Rule2: get whether an m5 tree is being used rather than rules
getUseUnsmoothed() - Method in class weka.classifiers.trees.m5.M5Base2: Get whether or not smoothing is being used
getValue(Evaluation, EvaluationStatistic, int) - Static method in class adams.flow.core.EvaluationHelper: Returns a statistical value from the evaluation object.
getValue() - Method in class adams.flow.transformer.WekaSetInstanceValue: Returns the value to set in the report.
getValue(PropertyEditor) - Method in class adams.gui.goe.WekaGenericObjectEditorHandler: Returns the value currently being edited.
getValue(int) - Method in class weka.core.setupgenerator.Point: Returns the value in the specified dimension.
getValue(int) - Method in class weka.core.setupgenerator.SpaceDimension: returns the value at the given point in the dimension.
getValueAt(int, int) - Method in class adams.gui.visualization.instance.InstanceTableModel: Returns the value at the given position.
getValues() - Method in class weka.classifiers.meta.MultiSearch: returns the parameter values that were found to work best.
getValues() - Method in class weka.classifiers.meta.multisearch.Performance: returns the values for this performance.
getValues(Point<Integer>) - Method in class weka.core.setupgenerator.Space: Returns the double values (or list values) for the given position.
getVariableName() - Method in class adams.flow.template.InstanceDumperVariable: Returns the variable name to generate the sub-flow for.
getVariance() - Method in class weka.classifiers.meta.AbstainLeastMedianSq
getVarianceCovered() - Method in class weka.core.neighboursearch.PCANNSearch: Gets the proportion of total variance to account for when retaining principal components.
getVisible(int) - Method in class adams.gui.visualization.instance.InstanceContainerManager: Returns the nth visible container.
getVisibleIndices() - Method in class adams.gui.visualization.instance.InstanceContainerManager: Returns the indices of all visible containers.
getVisualizeMenuItem(Instances) - Method in class weka.gui.visualize.plugins.FixedClassifierErrors: Get a JMenu or JMenuItem which contain action listeners that perform the visualization of the classifier errors.
getVisualizeMenuItem(FastVector, Attribute) - Method in class weka.gui.visualize.plugins.ThresholdCurves: Get a JMenu or JMenuItem which contain action listeners that perform the visualization, using some but not necessarily all of the data.
getWeights() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: Returns the current weights of the attributes.
getWeights(OptData) - Method in class adams.optimise.genetic.fitnessfunctions.AttributeSelection
getWindows() - Method in class weka.filters.unsupervised.attribute.PAA: Returns the nth point setting.
getWindows() - Method in class weka.filters.unsupervised.attribute.SAX: Returns the nth point setting.
getX() - Method in class adams.data.instance.InstancePoint: Returns the X value.
getY() - Method in class adams.data.instance.InstancePoint: Returns the Y value.
getZoomOverview() - Method in class adams.flow.sink.WekaInstanceViewer: Returns whether the zoom overview gets displayed.
globalInfo() - Method in class adams.data.baseline.AbstractLinearRegressionBased: Returns a string describing the object.
globalInfo() - Method in class adams.data.conversion.AdamsInstanceToWekaInstance: Returns a string describing the object.
globalInfo() - Method in class adams.data.conversion.MatchWekaInstanceAgainstFileHeader: Returns a string describing the object.
globalInfo() - Method in class adams.data.conversion.MatchWekaInstanceAgainstStorageHeader: Returns a string describing the object.
globalInfo() - Method in class adams.data.conversion.ReportToWekaInstance: Returns a string describing the object.
globalInfo() - Method in class adams.data.conversion.SpreadSheetToWekaInstances: Returns a string describing the object.
globalInfo() - Method in class adams.data.conversion.WekaInstancesToSpreadSheet: Returns a string describing the object.
globalInfo() - Method in class adams.data.conversion.WekaInstanceToAdamsInstance: Returns a string describing the object.
globalInfo() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns a string describing the object.
globalInfo() - Method in class adams.data.io.input.InstanceReader: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.columnfinder.ByName: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.columnfinder.Invert: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.columnfinder.MultiColumnFinder: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.columnfinder.NullFinder: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.columnfinder.RowFilteredColumnFinder: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.evaluator.NamedSetup: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.evaluator.PassThrough: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.predictions.AutoScaler: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.predictions.FixedSizeErrorScaler: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.predictions.NamedSetup: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.predictions.RelativeNumericErrorScaler: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.predictions.RoundErrorScaler: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.rowfinder.ByLabel: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.rowfinder.FilteredIQR: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.rowfinder.Invert: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.rowfinder.MultiRowFinder: Returns a string describing the object.
globalInfo() - Method in class adams.data.weka.rowfinder.NullFinder: Returns a string describing the object.
globalInfo() - Method in class adams.flow.condition.bool.AdamsInstanceCapabilities: Returns a string describing the object.
globalInfo() - Method in class adams.flow.condition.bool.WekaCapabilities: Returns a string describing the object.
globalInfo() - Method in class adams.flow.condition.bool.WekaClassification: Returns a string describing the object.
globalInfo() - Method in class adams.flow.sink.WekaClassifierErrors: Returns a string describing the object.
globalInfo() - Method in class adams.flow.sink.WekaCostCurve: Returns a string describing the object.
globalInfo() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns a string describing the object.
globalInfo() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.sink.WekaFileWriter: Returns a string describing the object.
globalInfo() - Method in class adams.flow.sink.WekaInstancesDisplay: Returns a string describing the object.
globalInfo() - Method in class adams.flow.sink.WekaInstanceViewer: Returns a string describing the object.
globalInfo() - Method in class adams.flow.sink.WekaModelWriter: Returns a string describing the object.
globalInfo() - Method in class adams.flow.sink.WekaROC: Returns a string describing the object.
globalInfo() - Method in class adams.flow.source.WekaClassifierGenerator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.source.WekaClustererGenerator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.source.WekaDatabaseReader: Returns a string describing the object.
globalInfo() - Method in class adams.flow.source.WekaDataGenerator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.source.WekaFilterGenerator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.source.WekaNewInstances: Returns a string describing the object.
globalInfo() - Method in class adams.flow.template.InstanceDumperVariable: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaAccumulatedError: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaAttributeIterator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaClassifier: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaClassifierOptimizer: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaClassifying: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaClassSelector: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.wekaclusterer.ClusterCenters: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaClusterer: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.wekaclusterer.PassThrough: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaClustering: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaEvaluationValues: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaExperiment: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaExtractArray: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaFileReader: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaFilter: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaGetInstanceValue: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaInstanceBuffer: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaInstanceFileReader: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaInstancesInfo: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaInstancesStatistic: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaModelReader: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaNewInstance: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaPredictionsToInstances: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaRandomSplit: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaRegexToRange: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaRelationName: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaRenameRelation: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaSetInstanceValue: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaStoreInstance: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaSubsets: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaTestSetEvaluator: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaTextDirectoryReader: Returns a string describing the object.
globalInfo() - Method in class adams.flow.transformer.WekaTrainTestSetEvaluator: Returns a string describing the object.
globalInfo() - Method in class adams.gui.tools.previewbrowser.GraphVisualizer: Returns a string describing the object.
globalInfo() - Method in class adams.gui.tools.previewbrowser.InstanceExplorerHandler: Returns a string describing the object.
globalInfo() - Method in class adams.gui.tools.previewbrowser.InterQuartileRangeViewer: Returns a string describing the object.
globalInfo() - Method in class adams.gui.tools.previewbrowser.TreeVisualizer: Returns a string describing the object.
globalInfo() - Method in class adams.gui.tools.previewbrowser.WekaDatasetHandler: Returns a string describing the object.
globalInfo() - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Returns a string describing the object.
globalInfo() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction
globalInfo() - Method in class adams.optimise.genetic.fitnessfunctions.AttributeSelection
globalInfo() - Method in class adams.optimise.GeneticAlgorithm: Returns a string describing the object.
globalInfo() - Method in class adams.tools.CompareDatasets: Returns a string describing the object.
globalInfo() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.GPD: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.PLSClassifierWeighted: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.AbstainAttributePercentile: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.meta.ClassificationViaRegressionD: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.Corr: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.meta.FilteredClassifierExt: Returns a string describing this classifier.
globalInfo() - Method in class weka.classifiers.meta.LeastMedianSq: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.meta.LogTargetRegressor: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.meta.MultiSearch: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.meta.PartitionedStacking: Returns a string describing this classifier.
globalInfo() - Method in class weka.classifiers.meta.PeakTransformed: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.meta.RangeCheck: Returns a string describing this classifier.
globalInfo() - Method in class weka.classifiers.meta.SubsetEnsemble: Returns a string describing the classifier.
globalInfo() - Method in class weka.classifiers.meta.SumTransformed: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.meta.WeightedInstancesHandlerWrapper: Returns a string describing the classifier.
globalInfo() - Method in class weka.classifiers.trees.m5.M5Base2: returns information about the classifier
globalInfo() - Method in class weka.classifiers.trees.RandomModelTrees: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.trees.RandomRegressionForest: Returns a string describing this classifier.
globalInfo() - Method in class weka.core.neighboursearch.NewNNSearch: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.SAXDistance: Returns a string describing this object.
globalInfo() - Method in class weka.core.setupgenerator.AbstractParameter: Returns a string describing the object.
globalInfo() - Method in class weka.core.SetupGenerator: Returns a string describing the object.
globalInfo() - Method in class weka.core.setupgenerator.ListParameter: Returns a string describing the object.
globalInfo() - Method in class weka.core.setupgenerator.MathParameter: Returns a string describing the object.
globalInfo() - Method in class weka.core.WeightedEuclideanDistance: Returns a string describing this object.
globalInfo() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns a string describing the matrix.
globalInfo() - Method in class weka.filters.FlowFilter: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.DatasetCleaner: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.DownSample: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.InterquartileRangeSamp: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NormalizeAdaptive: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.PAA: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValuesWithZero: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.SAX: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.SetMissingValue: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.SpellChecker: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.instance.DatasetCleaner: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.instance.LatestRecords: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveInstancesWithMissingValue: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.instance.RowNorm: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.Sort: Returns a string describing this classifier.
GPD - Class in weka.classifiers.functions: // this version: testbed for different solvers ...
GPD() - Constructor for class weka.classifiers.functions.GPD
graph(StringBuffer) - Method in class weka.classifiers.trees.m5.RuleNode2: Assign a unique identifier to each node in the tree and then calls graphTree
graph() - Method in class weka.classifiers.trees.M5P2: Return a dot style String describing the tree.
graphTree(StringBuffer) - Method in class weka.classifiers.trees.m5.RuleNode2: Return a dotty style string describing the tree
graphType() - Method in class weka.classifiers.trees.M5P2: Returns the type of graph this classifier represents.
GraphVisualizer - Class in adams.gui.menu: Displays data in the graph visualizer.
GraphVisualizer() - Constructor for class adams.gui.menu.GraphVisualizer: Initializes the menu item with no owner.
GraphVisualizer(AbstractApplicationFrame) - Constructor for class adams.gui.menu.GraphVisualizer: Initializes the menu item.
GraphVisualizer - Class in adams.gui.tools.previewbrowser: Displays BayesNet graphs.
GraphVisualizer() - Constructor for class adams.gui.tools.previewbrowser.GraphVisualizer

H

handles(Class) - Method in class adams.core.option.WekaCommandLineHandler: Checks whether the given class can be processed.
handles(Class) - Method in class adams.gui.goe.WekaGenericObjectEditorHandler: Checks whether the given class can be processed.
handles(Object) - Method in class adams.gui.tools.previewbrowser.GraphVisualizer: Returns whether viewer handles this object.
handles(Object) - Method in class adams.gui.tools.previewbrowser.InterQuartileRangeViewer: Returns whether viewer handles this object.
handles(Object) - Method in class adams.gui.tools.previewbrowser.TreeVisualizer: Returns whether viewer handles this object.
hasAttributeIndex() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Checks whether an attribute index has been set.
hasCustomPanel(PropertyEditor) - Method in class adams.gui.goe.WekaGenericObjectEditorHandler: Checks whether the editor supplies its own panel.
hasDataset() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Checks whether an existing file has been set.
hasDatasetHeader() - Method in class adams.data.instance.Instance: Returns whether a header of a dataset is available.
HashableInstanceUsingString - Class in weka.core: TODO: what this class does
HashableInstanceUsingString(Instance) - Constructor for class weka.core.HashableInstanceUsingString: Initializes the wrapper.
HashableInstanceUsingSum - Class in weka.core: Computes the hashcode as sum of the internal double values.
HashableInstanceUsingSum(Instance) - Constructor for class weka.core.HashableInstanceUsingSum: Initializes the wrapper.
hashCode() - Method in class adams.gui.visualization.instance.InstanceContainer: Returns the hashcode of the instance.
hashCode() - Method in class weka.core.AbstractHashableInstance: Returns the hashcode of this Instance, computes it if neccessary.
hasMissingValue() - Method in class weka.core.AbstractHashableInstance: Tests whether an instance has a missing value.
hasMoreData() - Method in class adams.data.io.input.InstanceReader: Returns whether there is more data available.
hasMoreZeroes(BitSet, BitSet) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: return if a has more zeroes than b.
hasMoreZeroes(BitSet, BitSet) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: return if a has more zeroes than b.
hasPendingOutput() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.source.WekaDatabaseReader: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.source.WekaDataGenerator: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.source.WekaNewInstances: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.transformer.WekaAccumulatedError: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.transformer.WekaAttributeIterator: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.transformer.WekaFileReader: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.transformer.WekaInstanceBuffer: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.transformer.WekaInstancesInfo: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Checks whether there is pending output to be collected after executing the flow item.
hasPendingOutput() - Method in class adams.flow.transformer.WekaSubsets: Checks whether there is pending output to be collected after executing the flow item.
hasRegistered() - Method in class adams.gui.goe.WekaEditorsRegistration: Returns whether registration already occurred.
hasReport() - Method in class adams.data.instance.Instance: Checks whether a report is present.
hasSendToItem(Class[]) - Method in class adams.gui.visualization.instance.InstanceExplorer: Checks whether something to send is available.
headerTipText() - Method in class adams.data.conversion.MatchWekaInstanceAgainstFileHeader: Returns the tip text for this property.
headerTipText() - Method in class adams.data.conversion.MatchWekaInstanceAgainstStorageHeader: Returns the tip text for this property.
HistogramFactory - Class in adams.gui.visualization.instance: A factory for histogram related objects.
HistogramFactory() - Constructor for class adams.gui.visualization.instance.HistogramFactory
HistogramFactory.Dialog - Class in adams.gui.visualization.instance: Dialog for displaying histograms generated from instances.
HistogramFactory.Dialog(Dialog, Dialog.ModalityType) - Constructor for class adams.gui.visualization.instance.HistogramFactory.Dialog: Initializes the dialog.
HistogramFactory.Dialog(Frame, boolean) - Constructor for class adams.gui.visualization.instance.HistogramFactory.Dialog: Initializes the dialog.
HistogramFactory.Panel - Class in adams.gui.visualization.instance: A panel for displaying a histogram based on the GC data of a instance.
HistogramFactory.Panel() - Constructor for class adams.gui.visualization.instance.HistogramFactory.Panel
HistogramFactory.SetupDialog - Class in adams.gui.visualization.instance: A dialog that queries the user about parameters for displaying histograms.
HistogramFactory.SetupDialog(Dialog, Dialog.ModalityType) - Constructor for class adams.gui.visualization.instance.HistogramFactory.SetupDialog: Initializes the dialog.
HistogramFactory.SetupDialog(Frame, boolean) - Constructor for class adams.gui.visualization.instance.HistogramFactory.SetupDialog: Initializes the dialog.

I

inc(Integer[], int[]) - Method in class weka.core.setupgenerator.Space

Increments the location array by 1.

includeClassTipText() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates

Returns the tip text for this property.

includeClassTipText() - Method in class weka.filters.unsupervised.instance.Sort

Returns the tip text for this property.

incorrectTipText() - Method in class weka.filters.unsupervised.attribute.SpellChecker

Returns the tip text for this property.

incrementalTipText() - Method in class adams.flow.source.WekaDatabaseReader

Returns the tip text for this property.

index(int) - Method in class weka.core.AbstractHashableInstance

Returns the index of the attribute stored at the given position in the sparse representation.

indexOf(String) - Method in class adams.gui.visualization.instance.InstanceContainerManager

Determines the index of the sequence with the specified ID.

indexTipText() - Method in class adams.flow.transformer.WekaExtractArray

Returns the tip text for this property.

indexTipText() - Method in class adams.flow.transformer.WekaGetInstanceValue

Returns the tip text for this property.

indexTipText() - Method in class adams.flow.transformer.WekaSetInstanceValue

Returns the tip text for this property.

indexTipText() - Method in class adams.flow.transformer.WekaSubsets

Returns the tip text for this property.

init(int, int) - Method in class adams.genetic.MTAbstractGeneticAlgorithm

Initializes the algorithm.

init(int) - Method in class adams.genetic.MTPackDataGeneticAlgorithm

init(int, int) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm

Initializes the algorithm.

init() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction

init(int) - Method in class adams.optimise.genetic.PackDataGeneticAlgorithm

initForDisplay() - Method in class adams.gui.goe.WekaExperimentFileEditor

Initializes the display of the value.

initFunction(double, double, double, String) - Method in class weka.core.setupgenerator.SpaceDimension

initializes the dimension (for numeric values).

initGUI() - Method in class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog

Sets up the GUI components.

initGUI() - Method in class adams.gui.InstanceCompare

Initializes the widgets.

initGUI() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel

Initializes the widgets.

initGUI() - Method in class adams.gui.visualization.instance.InstanceComparePanel

Initializes the widgets.

initGUI() - Method in class adams.gui.visualization.instance.InstanceExplorer

Initializes the widgets.

initGUI() - Method in class adams.gui.visualization.instance.InstancePanel

Initializes the GUI.

initGUI() - Method in class adams.gui.visualization.instance.LoadDatasetDialog

Initializes the GUI elements.

initGUI() - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame

Sets up the GUI components.

initGUI() - Method in class weka.gui.explorer.SqlPanel

initializes the GUI

initialize() - Method in class adams.data.instances.AbstractInstanceGenerator

Initializes the members.

initialize() - Method in class adams.data.weka.rowfinder.FilteredIQR

Initializes the members.

initialize() - Method in class adams.flow.sink.WekaCostCurve

Initializes the members.

initialize() - Method in class adams.flow.sink.WekaROC

Initializes the members.

initialize() - Method in class adams.flow.source.AbstractWekaSetupGenerator

Initializes the members.

initialize() - Method in class adams.flow.source.WekaNewInstances

Initializes the members.

initialize() - Method in class adams.flow.transformer.WekaAttributeIterator

Initializes the members.

initialize() - Method in class adams.flow.transformer.WekaClassifierRanker

Initializes the members.

initialize() - Method in class adams.flow.transformer.WekaClassSelector

Initializes the members.

initialize() - Method in class adams.flow.transformer.WekaExtractArray

Initializes the member variables.

initialize() - Method in class adams.flow.transformer.WekaGetInstanceValue

Initializes the members.

initialize() - Method in class adams.flow.transformer.WekaInstanceDumper

Initializes the members.

initialize() - Method in class adams.flow.transformer.WekaInstancesInfo

Initializes the members.

initialize() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator

Initializes the members.

initialize() - Method in class adams.flow.transformer.WekaSetInstanceValue

Initializes the members.

initialize() - Method in class adams.flow.transformer.WekaSubsets

Initializes the members.

initialize() - Method in class adams.genetic.MTAbstractGeneticAlgorithm

Initializes the members.

initialize() - Method in class adams.gui.chooser.DatasetFileChooserPanel

Initializes the members.

initialize() - Method in class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog

Initializes its members.

initialize() - Method in class adams.gui.InstanceCompare

For initializing members.

initialize() - Method in class adams.gui.menu.AbstractParameterHandlingWekaMenuItemDefinition

Initializes the members.

initialize() - Method in class adams.gui.menu.BoundaryVisualizer

Initializes members.

initialize() - Method in class adams.gui.menu.GraphVisualizer

Initializes members.

initialize() - Method in class adams.gui.menu.InstancesPlot

Initializes members.

initialize() - Method in class adams.gui.menu.ROC

Initializes members.

initialize() - Method in class adams.gui.menu.TreeVisualizer

Initializes members.

initialize() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer

Initializes the indexer, if necessary.

initialize() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel

Initializes the members.

initialize() - Method in class adams.gui.visualization.instance.InstanceComparePanel

Initializes the members.

initialize() - Method in class adams.gui.visualization.instance.InstanceContainer

Initializes members.

initialize() - Method in class adams.gui.visualization.instance.InstanceContainerModel

Initializes the members.

initialize() - Method in class adams.gui.visualization.instance.InstanceExplorer

Initializes the members.

initialize() - Method in class adams.gui.visualization.instance.InstancePanel

Initializes the members.

initialize() - Method in class adams.gui.visualization.instance.LoadDatasetDialog

Initializes the members.

initialize() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm

Initializes the members.

initialize() - Method in class adams.optimise.GeneticAlgorithm

Initializes the members.

initialize() - Method in class adams.tools.CompareDatasets

Initializes the members.

initialize() - Method in class weka.core.SAXDistance

initializes the ranges and the attributes being used.

initialize() - Method in class weka.core.SetupGenerator

Performs all the necessary initializations.

initialize() - Method in class weka.core.WeightedEuclideanDistance

initializes the ranges and the attributes being used.

initializeConverters(File) - Method in class adams.gui.chooser.DatasetFileChooserPanel

Initializes the converters.

initializeOnceTipText() - Method in class adams.flow.transformer.WekaFilter

Returns the tip text for this property.

initialSortNewTableModel() - Method in class adams.gui.visualization.instance.InstanceTable

Returns the initial setting of whether to sort new models.

initialSpaceNumFoldsTipText() - Method in class weka.classifiers.meta.MultiSearch

Returns the tip text for this property.

initialUseOptimalColumnWidths() - Method in class adams.gui.visualization.instance.InstanceTable

Returns the initial setting of whether to set optimal column widths.

initList(int, int, String[], String) - Method in class weka.core.setupgenerator.SpaceDimension

initializes the dimension (for list values).

initLookup() - Method in class adams.tools.CompareDatasets

Initializes the lookup table of indices for the second dataset, if necessary.

input(Instance) - Method in class weka.filters.unsupervised.attribute.NormalizeAdaptive

Input an instance for filtering.

input(Instance) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValuesWithZero

Input an instance for filtering.

input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel

Input an instance for filtering.

insertAttributeAt(int) - Method in class weka.core.AbstractHashableInstance

Inserts an attribute at the given position (0 to numAttributes()).

installLinearModels() - Method in class weka.classifiers.trees.m5.RuleNode2

Traverses the tree and installs linear models at each node.

installSmoothedModels() - Method in class weka.classifiers.trees.m5.RuleNode2

Instance - Class in adams.data.instance

Stores values from weka.core.Instance objects, with X being the attribute index (integer) and Y being the internal value (double).

Instance() - Constructor for class adams.data.instance.Instance

Initializes the sequence.

instanceClassTipText() - Method in class adams.flow.transformer.WekaNewInstance

Returns the tip text for this property.

InstanceCompare - Class in adams.gui

Stand-alone version of the Instance Compare utility.

InstanceCompare() - Constructor for class adams.gui.InstanceCompare

InstanceCompare - Class in adams.gui.menu

For comparing two datasets visually.

InstanceCompare() - Constructor for class adams.gui.menu.InstanceCompare

Initializes the menu item with no owner.

InstanceCompare(AbstractApplicationFrame) - Constructor for class adams.gui.menu.InstanceCompare

Initializes the menu item.

InstanceCompareDefinition - Class in adams.env

Definition for the InstanceCompare props file.

InstanceCompareDefinition() - Constructor for class adams.env.InstanceCompareDefinition

InstanceComparePanel - Class in adams.gui.visualization.instance

A tool for comparing two datasets visually.

InstanceComparePanel() - Constructor for class adams.gui.visualization.instance.InstanceComparePanel

InstanceComparePanel.DatasetIndexer - Class in adams.gui.visualization.instance

Helper class for indexing the rows of a dataset.

InstanceComparePanel.DatasetIndexer() - Constructor for class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer

Initializes the indexer.

InstanceComparePanel.DatasetPanel - Class in adams.gui.visualization.instance

Specialized panel for loading dataset and setting various parameters.

InstanceComparePanel.DatasetPanel(String, String) - Constructor for class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel

Initializes the panel.

InstanceContainer - Class in adams.gui.visualization.instance

A container class for a weka.core.Instance wrapped in a weka.core.Instance.

InstanceContainer(InstanceContainerManager, Instance) - Constructor for class adams.gui.visualization.instance.InstanceContainer

Initializes the container.

InstanceContainerDisplayIDGenerator - Class in adams.gui.visualization.instance

Class for generating display IDs for Instance objects (based on weka.core.Instance objects).

InstanceContainerDisplayIDGenerator() - Constructor for class adams.gui.visualization.instance.InstanceContainerDisplayIDGenerator

InstanceContainerList - Class in adams.gui.visualization.instance

A panel that lists Instances in a JTable.

InstanceContainerList() - Constructor for class adams.gui.visualization.instance.InstanceContainerList

InstanceContainerManager - Class in adams.gui.visualization.instance

A handler for the Instance containers.

InstanceContainerManager(ContainerListManager<InstanceContainerManager>) - Constructor for class adams.gui.visualization.instance.InstanceContainerManager

Initializes the manager.

InstanceContainerModel - Class in adams.gui.visualization.instance

A model for displaying the currently loaded Instance objects.

InstanceContainerModel(ContainerListManager<InstanceContainerManager>) - Constructor for class adams.gui.visualization.instance.InstanceContainerModel

Initializes the model.

InstanceContainerModel(InstanceContainerManager) - Constructor for class adams.gui.visualization.instance.InstanceContainerModel

Initializes the model.

InstanceContainerTableColumnNameGenerator - Class in adams.gui.visualization.instance

Abstract class for generating the column names of a table.

InstanceContainerTableColumnNameGenerator() - Constructor for class adams.gui.visualization.instance.InstanceContainerTableColumnNameGenerator

InstanceDumperVariable - Class in adams.flow.template

Generates a sub-flow that sets a variable for the adams.flow.transformer.WekaInstanceDumper transformer's outputPrefix property using a prefix based on the full flow name.

InstanceDumperVariable() - Constructor for class adams.flow.template.InstanceDumperVariable

InstanceExplorer - Class in adams.gui.menu

For displaying and filtering instances.

InstanceExplorer() - Constructor for class adams.gui.menu.InstanceExplorer

Initializes the menu item with no owner.

InstanceExplorer(AbstractApplicationFrame) - Constructor for class adams.gui.menu.InstanceExplorer

Initializes the menu item.

InstanceExplorer - Class in adams.gui.visualization.instance

A panel for exploring Instances visually.

InstanceExplorer() - Constructor for class adams.gui.visualization.instance.InstanceExplorer

InstanceExplorerDefinition - Class in adams.env

Definition for the InstanceExplorer props file.

InstanceExplorerDefinition() - Constructor for class adams.env.InstanceExplorerDefinition

InstanceExplorerHandler - Class in adams.gui.tools.previewbrowser

Displays the following WEKA dataset types in the Instance Explorer: csv,arff,arff.gz,xrff,xrff.gz

Valid options are:

InstanceExplorerHandler() - Constructor for class adams.gui.tools.previewbrowser.InstanceExplorerHandler

InstanceLinePaintlet - Class in adams.gui.visualization.instance

Paintlet for generating a line plot for Instance objects.

InstanceLinePaintlet() - Constructor for class adams.gui.visualization.instance.InstanceLinePaintlet

InstanceLinePaintlet.MarkerShape - Enum in adams.gui.visualization.instance

Enum for the marker shape to plot around the data points.

InstancePanel - Class in adams.gui.visualization.instance

A panel for displaying instances.

InstancePanel() - Constructor for class adams.gui.visualization.instance.InstancePanel

Initializes the panel.

InstancePanel(String) - Constructor for class adams.gui.visualization.instance.InstancePanel

Initializes the panel.

InstancePoint - Class in adams.data.instance

A 2-dimensional point (X: attribute index, Y: internal value).

InstancePoint() - Constructor for class adams.data.instance.InstancePoint

Initializes the point with no points and no ID.

InstancePoint(Integer, Double) - Constructor for class adams.data.instance.InstancePoint

Initializes the point with no ID.

InstancePointComparator - Class in adams.data.instance

A comparator for InstancePoint objects.

InstancePointComparator() - Constructor for class adams.data.instance.InstancePointComparator

The default constructor uses comparison by X in ascending manner.

InstancePointComparator(boolean, boolean) - Constructor for class adams.data.instance.InstancePointComparator

This constructor initializes the comparator either with comparison by X or by Y.

InstancePointHitDetector - Class in adams.gui.visualization.instance

Detects selections of instance points in the instance panel.

InstancePointHitDetector(InstancePanel) - Constructor for class adams.gui.visualization.instance.InstancePointHitDetector

Initializes the hit detector.

InstanceReader - Class in adams.data.io.input

Reads WEKA datasets in various formats.

InstanceReader() - Constructor for class adams.data.io.input.InstanceReader

InstanceReportFactory - Class in adams.gui.visualization.instance

A factory for GUI components for Instance-related reports.

InstanceReportFactory() - Constructor for class adams.gui.visualization.instance.InstanceReportFactory

InstanceReportFactory.Panel - Class in adams.gui.visualization.instance

A specialized panel that displays reports.

InstanceReportFactory.Panel() - Constructor for class adams.gui.visualization.instance.InstanceReportFactory.Panel

Initializes the tabbed pane with not reports.

InstanceReportFactory.Table - Class in adams.gui.visualization.instance

A specialized table for displaying a Report.

InstanceReportFactory.Table() - Constructor for class adams.gui.visualization.instance.InstanceReportFactory.Table

Initializes the table.

InstanceReportFactory.Table(Report) - Constructor for class adams.gui.visualization.instance.InstanceReportFactory.Table

Initializes the table.

InstanceReportFactory.Table(TableModel) - Constructor for class adams.gui.visualization.instance.InstanceReportFactory.Table

Initializes the table.

instancesActorTipText() - Method in class adams.flow.transformer.WekaInstanceEvaluator

Returns the tip text for this property.

instancesIndicesTipText() - Method in class weka.filters.unsupervised.instance.SafeRemoveRange

Returns the tip text for this property.

InstancesPlot - Class in adams.gui.menu

Displays plot of Instances.

InstancesPlot() - Constructor for class adams.gui.menu.InstancesPlot

Initializes the menu item with no owner.

InstancesPlot(AbstractApplicationFrame) - Constructor for class adams.gui.menu.InstancesPlot

Initializes the menu item.

instancesToDataset(Instances) - Method in class adams.ml.WekaData

instancesToDatasetNumericArray(Instances) - Method in class adams.ml.WekaData

InstanceTable - Class in adams.gui.visualization.instance

A specialized table for displaying an Instances object.

InstanceTable(Instances) - Constructor for class adams.gui.visualization.instance.InstanceTable

Initializes the table.

InstanceTableModel - Class in adams.gui.visualization.instance

A generic table model for displaying weka.core.Instances objects.

InstanceTableModel(Instances) - Constructor for class adams.gui.visualization.instance.InstanceTableModel

Initializes the model.

instanceToDataRow(Instance) - Method in class adams.ml.WekaData

InstanceUtils - Class in adams.data.instance

Utility class for instances.

InstanceUtils() - Constructor for class adams.data.instance.InstanceUtils

InstanceZoomOverviewPaintlet - Class in adams.gui.visualization.instance

Highlights the current zoom in the instance panel.

InstanceZoomOverviewPaintlet() - Constructor for class adams.gui.visualization.instance.InstanceZoomOverviewPaintlet

InstanceZoomOverviewPanel - Class in adams.gui.visualization.instance

Panel that shows the zoom in the TIC panel as overlay.

InstanceZoomOverviewPanel() - Constructor for class adams.gui.visualization.instance.InstanceZoomOverviewPanel

interpretePosition(Instances, String) - Method in class adams.data.instances.AbstractInstanceGenerator

Interpretes the position string based on the given dataset.

InterquartileRangeSamp - Class in weka.filters.unsupervised.attribute

A filter for detecting outliers and extreme values based on interquartile ranges.

InterquartileRangeSamp() - Constructor for class weka.filters.unsupervised.attribute.InterquartileRangeSamp

InterQuartileRangeViewer - Class in adams.gui.tools.previewbrowser

Displays internal values of the InterquartileRange filter.

InterQuartileRangeViewer() - Constructor for class adams.gui.tools.previewbrowser.InterQuartileRangeViewer

IntervalEstimatorBased - Class in adams.data.weka.evaluator

Uses a classifier that produces confidence intervals.

IntervalEstimatorBased() - Constructor for class adams.data.weka.evaluator.IntervalEstimatorBased

IntervalEstimatorBased.SortedInterval - Class in adams.data.weka.evaluator

Helper class for sorting the confidence intervals.

IntervalEstimatorBased.SortedInterval(Instance, double[][], boolean) - Constructor for class adams.data.weka.evaluator.IntervalEstimatorBased.SortedInterval

Initializes the intervals.

intervalTipText() - Method in class adams.flow.transformer.WekaInstanceBuffer

Returns the tip text for this property.

invalidateHashCode() - Method in class weka.core.AbstractHashableInstance

Invalidates the hash code.

inverse(Instances) - Method in class weka.filters.unsupervised.attribute.FastWavelet

Renames the attributes when using the inverse transform.

inverseTransformTipText() - Method in class weka.filters.unsupervised.attribute.FastWavelet

Returns the tip text for this property.

Invert - Class in adams.data.weka.columnfinder

Inverts the selected columns of the provided sub-column-filter.

Invert() - Constructor for class adams.data.weka.columnfinder.Invert

Invert - Class in adams.data.weka.rowfinder

Inverts the selected rows of the provided sub-row-filter.

Invert() - Constructor for class adams.data.weka.rowfinder.Invert

invertMatchingSenseTipText() - Method in class adams.flow.transformer.WekaInstancesMerge

Returns the tip text for this property.

invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.SafeRemoveRange

Returns the tip text for this property.

invertTipText() - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities

Returns the tip text for this property.

invertTipText() - Method in class adams.flow.transformer.WekaMultiLabelSplitter

Returns the tip text for this property.

invertTipText() - Method in class adams.flow.transformer.WekaRegexToRange

Returns the tip text for this property.

invertTipText() - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier

Returns the tip text for this property.

invertTipText() - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier

Returns the tip text for this property.

invertTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel

Returns the tip text for this property

iqrTipText() - Method in class adams.data.weka.rowfinder.FilteredIQR

Returns the tip text for this property.

isAntiAliasingEnabled() - Method in class adams.gui.visualization.instance.InstanceLinePaintlet

Returns whether anti-aliasing is used.

isCached(int, Point<Object>) - Method in class weka.classifiers.meta.multisearch.PerformanceCache

checks whether the point was already calculated once.

isColumnFinderTrained() - Method in class adams.data.weka.columnfinder.AbstractTrainableColumnFinder

Checks whether the column finder has been trained.

isColumnFinderTrained() - Method in interface adams.data.weka.columnfinder.TrainableColumnFinder

Checks whether the column finder has been trained.

isCompatible(Instance) - Method in class adams.data.conversion.AbstractMatchWekaInstanceAgainstHeader

Checks the instance against the header, whether they are compatible.

isHit(MouseEvent) - Method in class adams.gui.visualization.instance.InstancePointHitDetector

Checks for a hit.

isInlineEditingAvailable() - Method in class adams.gui.goe.WekaExperimentFileEditor

Checks whether inline editing is available.

isInlineValueValid(String) - Method in class adams.gui.goe.WekaExperimentFileEditor

Checks whether the value id valid.

isLeaf() - Method in class weka.classifiers.trees.m5.RuleNode2

Return true if this node is a leaf

isMarkersDisabled() - Method in class adams.gui.visualization.instance.InstanceLinePaintlet

Returns whether marker shapes are disabled.

isMissing(int) - Method in class weka.core.AbstractHashableInstance

Tests if a specific value is "missing".

isMissing(Attribute) - Method in class weka.core.AbstractHashableInstance

Tests if a specific value is "missing".

isMissingSparse(int) - Method in class weka.core.AbstractHashableInstance

Tests if a specific value is "missing" in the sparse representation.

isOnBorder(Point<?>) - Method in class weka.core.setupgenerator.Space

checks whether the given locations/values are on the border of the space.

isOnBorder(double) - Method in class weka.core.setupgenerator.SpaceDimension

checks whether the given value is on the border of the dimension.

isOnBorder(int) - Method in class weka.core.setupgenerator.SpaceDimension

checks whether the given location is on the border of the dimension.

isOutside(Instance, int, double, double) - Static method in class weka.classifiers.RangeCheckHelper

Performs a check for the given instance, whether it exceeds the range.

isPaused() - Method in class adams.flow.transformer.WekaClassifierRanker

Returns whether the object is currently paused.

isRemoveUsed() - Method in class weka.classifiers.meta.FilteredClassifierExt

Returns whether the Remove filter is used at all.

isRowFinderTrained() - Method in class adams.data.weka.rowfinder.AbstractTrainableRowFinder

Checks whether the row finder has been trained.

isRowFinderTrained() - Method in interface adams.data.weka.rowfinder.TrainableRowFinder

Checks whether the row finder has been trained.

isRunning() - Method in class adams.genetic.MTAbstractGeneticAlgorithm

Returns whether the algorithm is still running.

isRunning() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm

Returns whether the algorithm is still running.

isSearchMatch(SearchParameters, int) - Method in class adams.gui.visualization.instance.InstanceTableModel

Tests whether the search matches the specified row.

isSidePanelVisible() - Method in class adams.gui.visualization.instance.InstanceExplorer

Returns whether the side panel is visible or not.

isSingleton() - Method in class adams.gui.menu.ArffViewer

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.BayesNetEditor

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.BoundaryVisualizer

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.Experimenter

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.Explorer

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.GraphVisualizer

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.InstanceCompare

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.InstanceExplorer

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.InstancesPlot

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.PackageManager

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.ROC

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.SqlViewer

Whether the panel can only be displayed once.

isSingleton() - Method in class adams.gui.menu.TreeVisualizer

Whether the panel can only be displayed once.

isString() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer

Returns whether the sort index is nominal/string or numeric.

isString() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel

Returns whether the index values are nominal/string or numeric.

isUndoSupported() - Method in class adams.gui.visualization.instance.InstancePanel

Returns whether an Undo manager is currently available.

isUsingY() - Method in class adams.data.instance.InstancePointComparator

Returns whether Y or X number is used for ordering.

isValid(String) - Method in class adams.core.base.AttributeTypeList

Checks whether the string value is a valid presentation for this class.

isValid() - Method in class adams.flow.container.WekaClusteringContainer

Checks whether the setup of the container is valid.

isValid() - Method in class adams.flow.container.WekaModelContainer

Checks whether the setup of the container is valid.

isValid() - Method in class adams.flow.container.WekaPredictionContainer

Checks whether the setup of the container is valid.

isValid() - Method in class adams.flow.container.WekaTrainTestSetContainer

Checks whether the setup of the container is valid.

isValid(Instances) - Method in enum adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction.Measure

Checks whether the data can be used with this measure.

isVisible() - Method in class adams.gui.visualization.instance.InstanceContainer

Returns whether the instance is visible.

isVisible(int) - Method in class adams.gui.visualization.instance.InstanceContainerManager

Returns whether the container at the specified position is visible.

isZoomOverviewPanelVisible() - Method in class adams.gui.visualization.instance.InstanceExplorer

Returns whether the zoom overview panel is visible or not.

isZoomOverviewPanelVisible() - Method in class adams.gui.visualization.instance.InstancePanel

Returns whether the zoom overview panel is visible or not.

iterationsTipText() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm

Returns the tip text for this property.

J

joinOptions(String[]) - Method in class adams.core.option.WekaCommandLineHandler: Turns the option array back into a commandline.

K

keepExistingTipText() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the tip text for this property.
keepIndices(Instances) - Method in class weka.classifiers.meta.Corr
keepIndicesBasedOnCorrelation(Instances) - Method in class weka.classifiers.meta.Corr
keepRelationNameTipText() - Method in class adams.flow.transformer.WekaFilter: Returns the tip text for this property.
kernelTipText() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Returns the tip text for this property
KEY - Static variable in class adams.env.InstanceCompareDefinition: the key as constant.
KEY - Static variable in class adams.env.InstanceExplorerDefinition: the key as constant.
keysTipText() - Method in class adams.flow.source.WekaDatabaseReader: Returns the tip text for this property.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.FilteredSearch: Returns k nearest instances in the current neighbourhood to the supplied instance.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.NewNNSearch: Returns k nearest instances in the current neighbourhood to the supplied instance.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.PCANNSearch: Returns k nearest instances in the current neighbourhood to the supplied instance.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.PLSNNSearch: Returns k nearest instances in the current neighbourhood to the supplied instance.

L

LABEL_FALSE - Static variable in class adams.data.instances.AbstractInstanceGenerator: the "false" label for boolean data types.
LABEL_MATCH - Static variable in class weka.filters.unsupervised.instance.DatasetLabeler: the default label for a "match".
LABEL_NONMATCH - Static variable in class weka.filters.unsupervised.instance.DatasetLabeler: the default label for a "non-match".
LABEL_TRUE - Static variable in class adams.data.instances.AbstractInstanceGenerator: the "true" label for boolean data types.
labelMatchTipText() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Returns the tip text for this property.
labelNonMatchTipText() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Returns the tip text for this property.
LatestRecords - Class in weka.filters.unsupervised.instance: Retains the latest database records.
LatestRecords() - Constructor for class weka.filters.unsupervised.instance.LatestRecords
launch() - Method in class adams.gui.menu.ArffViewer: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.BayesNetEditor: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.BoundaryVisualizer: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.Experimenter: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.Explorer: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.GraphVisualizer: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.InstanceCompare: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.InstanceExplorer: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.InstancesPlot: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.PackageManager: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.ROC: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.SqlViewer: Launches the functionality of the menu item.
launch() - Method in class adams.gui.menu.TreeVisualizer: Launches the functionality of the menu item.
LeastMedianSq - Class in weka.classifiers.meta: Finds the base classifier with the best least median squared error.
LeastMedianSq() - Constructor for class weka.classifiers.meta.LeastMedianSq
LEFT - Static variable in class weka.classifiers.trees.m5.Rule2
leftNode() - Method in class weka.classifiers.trees.m5.RuleNode2: Get the left child of this node
listOptions() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.GPD: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.PLSClassifierWeighted: Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.LWLSynchro: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.AbstainAttributePercentile: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Corr: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.FilteredClassifierExt: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.LeastMedianSq: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MultiSearch: Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.PartitionedStacking: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.SubsetEnsemble: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.m5.M5Base2: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.trees.M5P2: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.trees.RandomModelTrees: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.RandomRegressionForest: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.FilteredSearch
listOptions() - Method in class weka.core.neighboursearch.NewNNSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.PCANNSearch
listOptions() - Method in class weka.core.neighboursearch.PLSNNSearch
listOptions() - Method in class weka.core.SAXDistance: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.setupgenerator.AbstractParameter: Gets an enumeration describing the available options.
listOptions() - Method in class weka.core.SetupGenerator: Gets an enumeration describing the available options.
listOptions() - Method in class weka.core.setupgenerator.ListParameter: Gets an enumeration describing the available options.
listOptions() - Method in class weka.core.setupgenerator.MathParameter: Gets an enumeration describing the available options.
listOptions() - Method in class weka.experiment.ResultMatrixMediaWiki: Returns an enumeration of all the available options..
listOptions() - Method in class weka.filters.FlowFilter: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.DownSample: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.FastWavelet: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PAA: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.SAX: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.SetMissingValue: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.SpellChecker: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.LatestRecords: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.Sort: Returns an enumeration describing the available options.
ListParameter - Class in weka.core.setupgenerator: Container class for search parameters.
ListParameter() - Constructor for class weka.core.setupgenerator.ListParameter
listPoints() - Method in class weka.core.setupgenerator.Space: returns a Vector with all points in the space.
listTipText() - Method in class weka.core.setupgenerator.ListParameter: Returns the tip text for this property.
loadArff(String, boolean) - Method in class adams.ml.WekaData
loadArff(String) - Method in class adams.ml.WekaData
loadData(Instances, Vector<InstanceContainer>) - Method in class adams.gui.visualization.instance.InstanceExplorer: Loads the given data into the container manager.
loadDataFromDatabase() - Method in class adams.gui.visualization.instance.InstanceExplorer: pops up SQL Viewer for SQL statement.
loadDataFromDisk() - Method in class adams.gui.visualization.instance.InstanceExplorer: pops up file dialog for loading dataset form disk.
loadDataFromDisk(File) - Method in class adams.gui.visualization.instance.InstanceExplorer: pops up file dialog for loading dataset form disk.
loadDataset() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Loads the dataset, if possible.
LoadDatasetDialog - Class in adams.gui.visualization.instance: A dialog for loading datasets from disk.
LoadDatasetDialog(Dialog) - Constructor for class adams.gui.visualization.instance.LoadDatasetDialog: Creates a modal dialog.
LoadDatasetDialog(Dialog, String) - Constructor for class adams.gui.visualization.instance.LoadDatasetDialog: Creates a modal dialog.
LoadDatasetDialog(Frame) - Constructor for class adams.gui.visualization.instance.LoadDatasetDialog: Creates a modal dialog.
LoadDatasetDialog(Frame, String) - Constructor for class adams.gui.visualization.instance.LoadDatasetDialog: Creates a modal dialog.
loadFile(boolean) - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Loads the file and displays the IDs.
loadPackageDirectory(File) - Method in class adams.core.management.WekaPackagesClassPathAugmenter: Processes a package directory.
locateMethod(String) - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Locates the method with the specified name (method is expected to take no parameters).
locationsTipText() - Method in class adams.flow.transformer.WekaInstancesStatistic: Returns the tip text for this property.
log(String) - Method in class weka.classifiers.meta.MultiSearch: prints the specified message to stdout if debug is on and can also dump the message to a log file.
log(String, boolean) - Method in class weka.classifiers.meta.MultiSearch: prints the specified message to stdout if debug is on and can also dump the message to a log file.
logFileTipText() - Method in class weka.classifiers.meta.MultiSearch: Returns the tip text for this property.
logPerformances(Space, Vector<Performance>, Tag) - Method in class weka.classifiers.meta.MultiSearch: generates a table string for all the performances in the space and returns that.
logPerformances(Space, Vector<Performance>) - Method in class weka.classifiers.meta.MultiSearch: aligns all performances in the space and prints those tables to the log file.
LogTargetRegressor - Class in weka.classifiers.meta: Takes logs of all numeric attributes in the data.
LogTargetRegressor() - Constructor for class weka.classifiers.meta.LogTargetRegressor
LWLIntervalEstimator - Class in weka.classifiers.lazy: Locally weighted learning.
LWLIntervalEstimator() - Constructor for class weka.classifiers.lazy.LWLIntervalEstimator
LWLSynchro - Class in weka.classifiers.lazy: Locally weighted learning.
LWLSynchro() - Constructor for class weka.classifiers.lazy.LWLSynchro: Initializes the classifier.

M

M5Base2 - Class in weka.classifiers.trees.m5: M5Base.
M5Base2() - Constructor for class weka.classifiers.trees.m5.M5Base2: Constructor
M5P2 - Class in weka.classifiers.trees: M5Base.
M5P2() - Constructor for class weka.classifiers.trees.M5P2: Creates a new M5P instance.
m_Absolute - Variable in class weka.filters.unsupervised.attribute.CorrelationMatrix: whether to return the absolute correlations.
m_ACC - Variable in class weka.classifiers.meta.multisearch.Performance: the Accuracy.
m_AccumulatdError - Variable in class adams.flow.transformer.WekaAccumulatedError: the accumulated error so far.
m_ActualCapabilities - Variable in class adams.flow.condition.bool.AbstractAttributeCapabilities: the capabilities object to use.
m_ActualFilter - Variable in class adams.data.weka.rowfinder.FilteredIQR: the actual IQR filter.
m_ActualFilter - Variable in class weka.classifiers.functions.PLSClassifierWeighted: the actual filter to use
m_ActualFolds - Variable in class adams.flow.transformer.WekaCrossValidationSplit: the actual number of folds to generate.
m_ActualScheme - Variable in class adams.data.weka.evaluator.NamedSetup: the actual scheme.
m_ActualScheme - Variable in class adams.data.weka.predictions.NamedSetup: the actual scheme.
m_AddClassification - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: whether to add the numeric classification to the output.
m_AddClassificationLabel - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: whether to add the classification label to the output (nominal classes only).
m_AddDatabaseID - Variable in class adams.data.instances.AbstractInstanceGenerator: whether to add the database ID.
m_AddDistribution - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: whether to add the distribution to the output (nominal classes only).
m_AddFilter - Variable in class weka.filters.unsupervised.instance.DatasetLabeler: the filter for adding the label attribute.
m_AddIndex - Variable in class adams.flow.transformer.WekaInstancesMerge: whether to add the index to the prefix.
m_AddLabelIndex - Variable in class adams.flow.transformer.AbstractWekaPredictionsTransformer: whether to prefix the labels with a 1-based index (only nominal classes).
m_AdjustToVisibleData - Variable in class adams.gui.visualization.instance.InstancePanel: whether to adjust to visible data or not.
m_af - Variable in class adams.ml.WekaData
m_Algorithm - Variable in class weka.filters.unsupervised.attribute.FastWavelet: the type of algorithm.
m_Alin - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: The parameters of the linear transforamtion realized by the filter on the class attribute
m_Alin - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: The parameters of the linear transforamtion realized by the filter on the class attribute
m_Alin - Variable in class weka.classifiers.functions.GPD: The parameters of the linear transforamtion realized by the filter on the class attribute
m_Amount - Variable in class weka.filters.unsupervised.instance.LatestRecords: the amount to keep (less than 1: percentage, otherwise absolute number).
m_AntiAliasingEnabled - Variable in class adams.gui.visualization.instance.InstanceLinePaintlet: whether anti-aliasing is enabled.
m_ArffPanel - Variable in class adams.flow.sink.WekaInstancesDisplay: the panel with the instances.
m_attnum - Variable in class weka.classifiers.meta.AbstainAttributePercentile
m_AttributeIndex - Variable in class adams.data.weka.rowfinder.ByLabel: the attribute index to work on.
m_AttributeIndex - Variable in class adams.flow.transformer.WekaInstancesInfo: the index of the attribute to get the information for.
m_AttributeIndex - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: the attribute to index.
m_AttributeIndex - Variable in class weka.filters.unsupervised.attribute.SpellChecker: the index of the attribute to work on.
m_AttributeName - Variable in class adams.flow.transformer.WekaGetInstanceValue: the name of the attribute to get the value from the Instance.
m_AttributeName - Variable in class adams.flow.transformer.WekaInstanceEvaluator: the attribute name of the evaluation object.
m_AttributeName - Variable in class weka.filters.unsupervised.instance.DatasetLabeler: the name of the attribute name to add.
m_AttributeName - Variable in class weka.filters.unsupervised.instance.LatestRecords: the name of the attribute that holds the numeric database ID.
m_AttributeNames - Variable in class adams.flow.source.WekaNewInstances: the comma-separated list of attribute names.
m_AttributeRange - Variable in class weka.filters.unsupervised.attribute.CorrelationMatrix: the range of attributes to work on.
m_AttributeRange - Variable in class weka.filters.unsupervised.attribute.SetMissingValue: the range of attributes to set to missing.
m_Attributes - Variable in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: the range of attributes to plot.
m_attributes - Variable in class adams.ml.WekaData: List of attributes to use in modeling.
m_AttributesToProcess - Variable in class adams.flow.transformer.WekaMultiLabelSplitter: the indices of the class attributes still to process.
m_AttributeTypes - Variable in class adams.flow.source.WekaNewInstances: the comma-separated list of attribute types.
m_AttType - Variable in class adams.flow.transformer.WekaInstancesMerge: the attribute type of the ID attribute.
m_Augmentations - Variable in class adams.core.management.WekaPackagesClassPathAugmenter: for storing the augmentations.
m_AutoKeyGeneration - Variable in class adams.flow.sink.WekaDatabaseWriter: whether to automatically generate a primary key.
m_AverageWidth - Variable in class adams.data.weka.evaluator.IntervalEstimatorBased.SortedInterval: the average width.
m_avg_target - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: The training data.
m_avg_target - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: The training data.
m_avg_target - Variable in class weka.classifiers.functions.GPD: The training data.
m_BackupModel - Variable in class weka.classifiers.meta.SubsetEnsemble: The backup classifier, in case no ensemble could be constructed at prediction time.
m_Base - Variable in class weka.core.setupgenerator.MathParameter: the base.
m_BaseObject - Variable in class weka.core.SetupGenerator: base object.
m_BestClassifier - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: the best classifier.
m_bestClassifier - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_bestClassifier - Variable in class weka.classifiers.meta.LeastMedianSq
m_BestClassifier - Variable in class weka.classifiers.meta.MultiSearch: the Classifier with the best setup.
m_bestMedian - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_bestMedian - Variable in class weka.classifiers.meta.LeastMedianSq
m_BestRange - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: stores the best range of attribtues.
m_BestRange - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: stores the best range of attribtues.
m_bins - Variable in class weka.core.SAXDistance: number of gaussian bins.
m_bins - Variable in class weka.filters.unsupervised.attribute.SAX: number of gaussian bins.
m_bits - Variable in class adams.optimise.genetic.PackDataDef.DataInfo
m_bits - Variable in class adams.optimise.GeneticAlgorithm
m_Blin - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive
m_Blin - Variable in class weka.classifiers.functions.GaussianProcessesWeighted
m_Blin - Variable in class weka.classifiers.functions.GPD
m_Border - Variable in class weka.experiment.ResultMatrixMediaWiki: the size of the border.
m_BorderTitle - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the border title.
m_bps - Variable in class weka.filters.unsupervised.attribute.SAX: breakpoints.
m_Buffer - Variable in class adams.flow.transformer.WekaInstanceBuffer: the currently buffered data.
m_Buffer - Variable in class adams.flow.transformer.WekaInstanceDumper: the buffer.
m_BufferSize - Variable in class adams.flow.transformer.WekaInstanceDumper: the size of the buffer.
m_ButtonCancel - Variable in class adams.gui.goe.WekaExperimentFileEditor: the Cancel button.
m_ButtonCancel - Variable in class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog: the Cancel button.
m_ButtonClose - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the Close button.
m_ButtonDataGo - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the button for displaying the instances.
m_ButtonDisplay - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the button for displaying the dataset.
m_ButtonEdit - Variable in class adams.gui.goe.WekaExperimentFileEditor: the button to bring up the dialog for editing the experiment.
m_ButtonLoad - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the Load button.
m_ButtonLoad - Variable in class weka.gui.explorer.SqlPanel: the Load button
m_ButtonOK - Variable in class adams.gui.goe.WekaExperimentFileEditor: the OK button.
m_ButtonOK - Variable in class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog: the OK button.
m_ButtonReload - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the button for reloading an existing file.
m_ButtonTextGo - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the button for displaying the instances.
m_C - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: The covariance matrix.
m_Cache - Variable in class weka.classifiers.meta.MultiSearch: the cache for points in the space that got calculated (raw points in space, not evaluated ones!).
m_Cache - Variable in class weka.classifiers.meta.multisearch.PerformanceCache: the cache for points in the space that got calculated.
m_Capabilities - Variable in class adams.flow.condition.bool.AbstractAttributeCapabilities: the class index.
m_CC - Variable in class weka.classifiers.meta.multisearch.Performance: the Correlation coefficient.
m_CellPadding - Variable in class weka.experiment.ResultMatrixMediaWiki: the cell padding.
m_CellSpacing - Variable in class weka.experiment.ResultMatrixMediaWiki: the cell spacing.
m_CEPanel - Variable in class weka.gui.explorer.ExperimentPanel: The panel showing the current classifier selection.
m_ChangeListeners - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the change listeners.
m_CharSet - Variable in class adams.flow.transformer.WekaTextDirectoryReader: the character set.
m_CheckBoxIncludeDateAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: whether to include date attributes.
m_CheckBoxIncludeNominalAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: whether to include numeric nominal.
m_CheckBoxIncludeNumericAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: whether to include numeric attributes.
m_CheckBoxIncludeRelationalAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: whether to include relational attributes.
m_CheckBoxIncludeStringAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: whether to include string attributes.
m_CheckHeader - Variable in class adams.flow.transformer.WekaInstanceBuffer: whether to check the header.
m_CheckHeader - Variable in class adams.flow.transformer.WekaInstanceDumper: whether to check the header.
m_checksTurnedOff - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class.
m_checksTurnedOff - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class.
m_checksTurnedOff - Variable in class weka.classifiers.functions.GPD: Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class.
m_chol - Variable in class weka.classifiers.functions.GPD
m_chrom_num - Variable in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: the number of chromosomes.
m_class - Variable in class adams.ml.WekaData: Target attribute.
m_ClassAttributes - Variable in class adams.flow.transformer.WekaMultiLabelSplitter: the indices of the class attributes.
m_ClassCombo - Variable in class weka.gui.explorer.ExperimentPanel: Lets the user select the class column.
m_ClassDetails - Variable in class adams.flow.transformer.WekaEvaluationSummary: whether to print the class details as well.
m_ClassificationEntry - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: the entry to use for the numeric classification in the spreadsheet.
m_ClassificationLabelEntry - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: the entry to use for the classification label in the spreadsheet.
m_Classifier - Variable in class adams.data.weka.evaluator.IntervalEstimatorBased: the IntervalEstimator to use.
m_Classifier - Variable in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: the name of the global weka classifier.
m_Classifier - Variable in class adams.flow.transformer.WekaClassifier: the weka classifier.
m_Classifier - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: the classifier to evaluate.
m_Classifier - Variable in class adams.ml.WekaClassifier
m_Classifier - Variable in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: the classifier to use if no serialized model is given.
m_ClassifierEditor - Variable in class weka.gui.explorer.ExperimentPanel: Lets the user configure the classifier.
m_Classifiers - Variable in class weka.classifiers.meta.SubsetEnsemble: the actual classifiers in use.
m_ClassIndex - Variable in class adams.flow.source.WekaNewInstances: the index for the class attribute, if any.
m_ClassIndex - Variable in class adams.flow.transformer.WekaClassSelector: the class index.
m_ClassIndex - Variable in class adams.flow.transformer.WekaEvaluationValuePicker: the index of the class label.
m_ClassIndex - Variable in class adams.flow.transformer.WekaEvaluationValues: the index of the class label.
m_ClassIndex - Variable in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: the class index.
m_classIndex - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: The class index from the training data
m_classIndex - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: The class index from the training data
m_classIndex - Variable in class weka.classifiers.functions.GPD: The class index from the training data
m_classIndex - Variable in class weka.classifiers.meta.Corr
m_classIndex - Variable in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: The attribute to treat as the class for purposes of cleansing.
m_ClassLabelIndex - Variable in class adams.flow.sink.WekaCostCurve: the class label index.
m_ClassLabelIndex - Variable in class adams.flow.sink.WekaROC: the class label index.
m_ClassName - Variable in class adams.flow.source.WekaNewInstances: the name for the class attribute, if any.
m_cleansingClassifier - Variable in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: The classifier used to do the cleansing
m_Clusterer - Variable in class adams.flow.transformer.WekaClusterer: the weka clusterer.
m_Coefficients - Variable in class weka.core.WeightedEuclideanDistance: Array for storing coefficients of linear regression.
m_Coefficients - Variable in class weka.filters.unsupervised.attribute.SavitzkyGolay: the calculated coefficients.
m_coeffs - Variable in class weka.classifiers.meta.Corr
m_Color - Variable in class adams.gui.visualization.instance.InstanceContainer: the associated color.
m_ColorProvider - Variable in class adams.gui.visualization.instance.InstanceContainerManager: the color provider for managing the colors.
m_Column - Variable in class adams.flow.transformer.WekaExperimentEvaluation: the column (= classifiers).
m_ColumnFinder - Variable in class adams.data.weka.columnfinder.AbstractFilteredColumnFinder: the ColumnFinder to apply.
m_ColumnFinder - Variable in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: The classifier template used to do the classification.
m_ColumnFinderTrained - Variable in class adams.data.weka.columnfinder.RowFilteredColumnFinder: whether the column finder was trained on the subset.
m_Combination - Variable in class adams.data.weka.columnfinder.MultiColumnFinder: how the indices are combined.
m_Combination - Variable in class adams.data.weka.rowfinder.MultiRowFinder: how the indices are combined.
m_CombinationRule - Variable in class weka.classifiers.meta.SubsetEnsemble: Combination Rule variable.
m_ComboBoxClass - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the class index.
m_ComboBoxClassModel - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the class index model.
m_ComboBoxData - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the combobox with all the datasets.
m_ComboBoxDataActions - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the combobox with all the actions.
m_ComboBoxRowAttribute - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the combobox with the attribute names.
m_ComboBoxRowAttributeModel - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the underlying model for the comboxbox.
m_ComboBoxSorting - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the sorting index.
m_ComboBoxSortingModel - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the soriting index model.
m_ComboBoxTextActions - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the combobox with all the actions.
m_ComboBoxTextInstances - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the combobox with all the instances.
m_Comment - Variable in class adams.flow.transformer.WekaEvaluationSummary: an optional comment to output.
m_Comparator - Static variable in class adams.data.instance.Instance: the default comparator.
m_Comparator - Static variable in class adams.data.instance.InstanceUtils: comparator for finding X values.
m_ComparisonField - Variable in class adams.flow.transformer.WekaExperimentEvaluation: the comparison field.
m_Completed - Variable in class weka.classifiers.meta.MultiSearch: The number of setups completed so far.
m_Completed - Variable in class weka.classifiers.meta.SubsetEnsemble: The number of classifiers completed so far
m_ComplexityStatistics - Variable in class adams.flow.transformer.WekaEvaluationSummary: whether to print the complexity statistics as well.
m_ConfidenceLevel - Variable in class adams.data.weka.evaluator.IntervalEstimatorBased: the confidence level.
m_Containers - Variable in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: the generated containers.
m_CoordinatesPaintlet - Variable in class adams.gui.visualization.instance.InstancePanel: paintlet for drawing the X-axis.
m_Correct - Variable in class weka.filters.unsupervised.attribute.SpellChecker: the correct spelling for the labels.
m_Counter - Variable in class adams.flow.transformer.WekaInstanceDumper: the counter for the filenames.
m_Counter - Variable in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: the counter for the X value of the containers.
m_CoverVariance - Variable in class weka.core.neighboursearch.PCANNSearch: the amount of varaince to cover in the original data when retaining the best n PC's.
m_CrossValidationSeed - Variable in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: the cross-validation seed.
m_cs - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_currentClassifier - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_currentClassifier - Variable in class weka.classifiers.meta.LeastMedianSq
m_CurrentFold - Variable in class adams.flow.transformer.WekaCrossValidationSplit: the current fold.
m_CustomLoader - Variable in class adams.flow.transformer.WekaFileReader: the custom loader.
m_CustomPropsFile - Variable in class adams.flow.sink.WekaDatabaseWriter: a custom properties file to use instead of default one.
m_CustomPropsFile - Variable in class adams.flow.source.WekaDatabaseReader: a custom properties file to use instead of default one.
m_CustomSaver - Variable in class adams.flow.sink.WekaFileWriter: the custom saver.
m_Data - Variable in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: the data set to use for training and other bits.
m_Data - Variable in class adams.flow.source.WekaDatabaseReader: the full data.
m_Data - Variable in class adams.flow.transformer.WekaCrossValidationSplit: the data to work with.
m_Data - Variable in class adams.gui.visualization.instance.InstanceTableModel: the underlying data.
m_data - Variable in class adams.optimise.genetic.PackData
m_data - Variable in class weka.classifiers.functions.GPD
m_Data - Variable in class weka.classifiers.meta.AbstainAttributePercentile
m_Data - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_Data - Variable in class weka.classifiers.meta.LeastMedianSq
m_Data - Variable in class weka.classifiers.meta.MultiSearch.EvaluationTask: the data to use for training.
m_Data - Variable in class weka.classifiers.meta.SubsetEnsemble: For holding the original training set temporarily.
m_data - Variable in class weka.classifiers.trees.RandomModelTrees
m_Data - Variable in class weka.classifiers.trees.RandomRegressionForest: the original header
m_Data - Variable in class weka.core.AbstractHashableInstance: the wrapped instance.
m_Data1 - Variable in class adams.tools.CompareDatasets: the current dataset 1.
m_Data2 - Variable in class adams.tools.CompareDatasets: the current dataset 2.
m_DatabaseConnection - Variable in class adams.data.instances.AbstractInstanceGenerator: the database connection.
m_DataGenerator - Variable in class adams.flow.source.WekaDataGenerator: the filter to apply.
m_Dataset - Variable in class adams.data.conversion.AbstractMatchWekaInstanceAgainstHeader: the header to match against.
m_Dataset - Variable in class adams.flow.transformer.WekaMultiLabelSplitter: the dataset to process.
m_Dataset - Variable in class adams.flow.transformer.WekaStoreInstance: the dataset to append to.
m_Dataset - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: the underlying dataset.
m_Dataset - Variable in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: the filename of the data to use for cross-validation.
m_Dataset1 - Variable in class adams.tools.CompareDatasets: the first dataset.
m_Dataset2 - Variable in class adams.tools.CompareDatasets: the second dataset.
m_DatasetHeader - Variable in class adams.data.instance.Instance: a reference to the dataset the data was obtained from.
m_DataType - Variable in class adams.flow.transformer.WekaInstancesStatistic: the type of data to get from the Instances object (rows or columns).
m_debug - Variable in class weka.classifiers.meta.AbstainAttributePercentile
m_debug - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_debug - Variable in class weka.classifiers.meta.LeastMedianSq
m_DefaultAttributeRange - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the default range.
m_DefaultClassIndex - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the default class index.
m_DefaultIncludeDateAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the default for date attributes.
m_DefaultIncludeNominalAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the default for nominal attributes.
m_DefaultIncludeNumericAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the default for numeric attributes.
m_DefaultIncludeRelationalAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the default for relational attributes.
m_DefaultIncludeStringAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the default for string attributes.
m_DefaultParameters - Variable in class weka.classifiers.meta.MultiSearch: the default parameters.
m_DefaultSortIndex - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the default sort index.
m_delta - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: Gaussian Noise Value.
m_delta - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: Gaussian Noise Value.
m_delta - Variable in class weka.classifiers.functions.GPD: Gaussian Noise Value.
m_deltaClass - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: Gaussian Noise Value for class.
m_DerivativeOrder - Variable in class weka.filters.unsupervised.attribute.SavitzkyGolay: the order of the derivative.
m_Dimensions - Variable in class weka.core.setupgenerator.Space: the dimensions.
m_DiscardPredictions - Variable in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: whether to discard predictions.
m_DiscardPredictions - Variable in class adams.flow.transformer.WekaTestSetEvaluator: whether to discard predictions.
m_Distances - Variable in class weka.core.neighboursearch.NewNNSearch: Array holding the distances of the nearest neighbours.
m_distMatrix - Variable in class weka.core.SAXDistance
m_DistributionFormat - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: the format to use for the distribution entries in the spreadsheet.
m_DistributionSorting - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: the type of sorting to apply to the distribution array (if applicable).
m_ErrorScaler - Variable in class adams.flow.sink.WekaClassifierErrors.DataGenerator: the scaler scheme to use.
m_ErrorScaler - Variable in class adams.flow.sink.WekaClassifierErrors: The scheme for scaling the errors.
m_errPct - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_errPct - Variable in class weka.classifiers.meta.LeastMedianSq
m_EvalCombo - Variable in class weka.gui.explorer.ExperimentPanel: The type of evaluation: cross-validation or random split.
m_Evaluation - Variable in class adams.flow.sink.WekaClassifierErrors.DataGenerator: the underlying Evaluation object.
m_Evaluation - Variable in class weka.classifiers.meta.MultiSearch.EvaluationTask: the type of evaluation.
m_Evaluation - Variable in class weka.classifiers.meta.MultiSearch: the type of evaluation.
m_Evaluation - Variable in class weka.classifiers.meta.multisearch.Performance: the evaluation type.
m_Evaluation - Variable in class weka.classifiers.meta.multisearch.PerformanceComparator: the performance measure to use for comparison.
m_EvaluationError - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: for storing evaluating errors.
m_EvaluationType - Variable in class adams.flow.sink.WekaExperimentGenerator: the type of evaluation.
m_Evaluator - Variable in class adams.flow.transformer.WekaInstanceEvaluator: the evaluator to use.
m_ExcludeClass - Variable in class weka.core.AbstractHashableInstance: whether to exclude the class from the hashcode.
m_ExcludedAttributes - Variable in class adams.flow.transformer.WekaInstancesMerge: regular expression for excluding attributes from the datasets.
m_ExcludeWeight - Variable in class weka.core.AbstractHashableInstance: whether to exclude the weight from the hashcode.
m_ExecutorPool - Variable in class weka.classifiers.meta.MultiSearch: Pool of threads to train models with.
m_ExecutorPool - Variable in class weka.classifiers.meta.SubsetEnsemble: Pool of threads to train models with
m_Experiment - Variable in class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog: the current experiment.
m_ExperimentFile - Variable in class adams.flow.transformer.WekaExperiment: the experiment file.
m_ExperimentType - Variable in class adams.flow.sink.WekaExperimentGenerator: the type of experiment.
m_Explorer - Variable in class weka.gui.explorer.ExperimentPanel: the parent frame.
m_Explorer - Variable in class weka.gui.explorer.SqlPanel: the parent frame
m_Expression - Variable in class weka.core.setupgenerator.MathParameter: The expression for the property.
m_Failed - Variable in class weka.classifiers.meta.MultiSearch: The number of setups that experienced a failure of some sort during construction.
m_Failed - Variable in class weka.classifiers.meta.SubsetEnsemble: The number of classifiers that experienced a failure of some sort during construction.
m_FavorZeroes - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: whether to favor 0s instead of 1s.
m_FavorZeroes - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: whether to favor 0s instead of 1s.
m_ff - Variable in class adams.optimise.GeneticAlgorithm.GAJob: fitness function.
m_Fields - Variable in class adams.data.conversion.ReportToWekaInstance: the fields to turn into an instance.
m_FileChooser - Variable in class adams.gui.chooser.DatasetFileChooserPanel: the filechooser for selecting the dataset.
m_FileChooser - Variable in class adams.gui.menu.BoundaryVisualizer: filechooser for BoundaryVisualizers.
m_FileChooser - Variable in class adams.gui.menu.GraphVisualizer: filechooser for GraphVisualizers.
m_FileChooser - Variable in class adams.gui.menu.InstancesPlot: filechooser for Plots.
m_FileChooser - Variable in class adams.gui.menu.ROC: filechooser for ROCs.
m_FileChooser - Variable in class adams.gui.menu.TreeVisualizer: filechooser for TreeVisualizers.
m_FilePanel - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: for selecting the dataset file.
m_Filter - Variable in class adams.data.weka.rowfinder.FilteredIQR: the IQR filter.
m_Filter - Variable in class adams.flow.transformer.WekaFilter: the filter to apply.
m_Filter - Variable in class adams.flow.transformer.WekaInstanceEvaluator: the filter that is used for generating the new data format.
m_Filter - Variable in class adams.ml.WekaFilter
m_Filter - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.functions.GPD: The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.functions.PLSClassifierWeighted: the PLS filter
m_Filter - Variable in class weka.core.neighboursearch.FilteredSearch: The filter
m_Filter - Variable in class weka.filters.unsupervised.attribute.FastWavelet: an optional filter for preprocessing of the data.
m_FilteredClassifier - Variable in class weka.classifiers.meta.MultiSearch: the filtered classifier to use, in case a filter is used.
m_filterType - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: Whether to normalize/standardize/neither
m_filterType - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: Whether to normalize/standardize/neither
m_filterType - Variable in class weka.classifiers.functions.GPD: Whether to normalize/standardize/neither
m_Find - Variable in class adams.flow.transformer.WekaRenameRelation: the string to find.
m_findArrays - Variable in class adams.ml.WekaData: regex for arrays
m_Finders - Variable in class adams.data.weka.columnfinder.MultiColumnFinder: the column finders to use.
m_Finders - Variable in class adams.data.weka.rowfinder.MultiRowFinder: the row finders to use.
m_FirstAttributeRange - Variable in class adams.gui.InstanceCompare: the first attribute range to use.
m_firstBatchFinished - Variable in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Have we processed the first batch (i.e.
m_FirstFile - Variable in class adams.gui.InstanceCompare: the first file to compare.
m_FirstRowIndex - Variable in class adams.gui.InstanceCompare: the index of the first attribute to use for matching rows.
m_Fitness - Variable in class adams.event.FitnessChangeEvent: the fitness that triggered this event.
m_fitness - Variable in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: the current fitness.
m_Fitness - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: the fitness of the genes.
m_Fitness - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: the fitness of the genes.
m_fitness - Variable in class adams.optimise.GeneticAlgorithm.GAJob
m_fitnessfn - Variable in class adams.optimise.GeneticAlgorithm
m_FlowFile - Variable in class weka.filters.FlowFilter: the flow file to process the data with.
m_Folds - Variable in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: the number of folds for cross-validation.
m_Folds - Variable in class adams.flow.sink.WekaExperimentGenerator: the number of folds to use (only cross-validation).
m_Folds - Variable in class adams.flow.transformer.WekaClassifierRanker: the number of folds to use in cross-validation.
m_Folds - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: the number of folds to use.
m_Folds - Variable in class adams.flow.transformer.WekaCrossValidationEvaluator: the number of folds.
m_Folds - Variable in class adams.flow.transformer.WekaCrossValidationSplit: the number of folds to generate.
m_Folds - Variable in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: the number of folds for cross-validation.
m_Folds - Variable in class weka.classifiers.meta.MultiSearch.EvaluationTask: the number of folds for cross-validation.
m_FoldsPercLabel - Variable in class weka.gui.explorer.ExperimentPanel: The label for either the number of folds or the percentage for the random split.
m_FoldsPercText - Variable in class weka.gui.explorer.ExperimentPanel: Either the number of folds or the percentage for the random split.
m_FullFilter - Variable in class adams.data.weka.rowfinder.FilteredIQR: the MultiFilter doing all the filtering.
m_ga - Variable in class adams.optimise.GeneticAlgorithm.GAJob: ga.
m_gamma - Variable in class weka.classifiers.functions.GPD
m_GenerateLine - Variable in class adams.data.baseline.AbstractLinearRegressionBased: whether to return the line as fake data or the corrected data.
m_Generator - Variable in class adams.flow.source.AbstractWekaSetupGenerator: the underlying setup generator.
m_Generator - Variable in class adams.flow.transformer.AbstractInstanceGenerator: the generator to use.
m_Generator - Variable in class weka.classifiers.meta.MultiSearch.EvaluationTask: the setup generator to use.
m_Generator - Variable in class weka.classifiers.meta.MultiSearch: for generating the search parameters.
m_Genes - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: the genes.
m_Genes - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: the genes.
m_genetic - Variable in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: the algorithm object this job belongs to.
m_GlobalSource - Variable in class adams.flow.transformer.WekaInstanceEvaluator: the global actor to use.
m_HashCode - Variable in class weka.core.AbstractHashableInstance: the current hashcode.
m_Header - Variable in class adams.data.conversion.MatchWekaInstanceAgainstFileHeader: the header to load from disk.
m_Header - Variable in class adams.data.conversion.MatchWekaInstanceAgainstStorageHeader: the header to load from storage.
m_Header - Variable in class adams.data.conversion.ReportToWekaInstance: the header to use.
m_Header - Variable in class adams.flow.transformer.WekaInstanceDumper: the header of the dataset.
m_Header - Variable in class adams.flow.transformer.WekaInstanceEvaluator: the new header.
m_header - Variable in class adams.ml.WekaData
m_Header - Variable in class weka.classifiers.meta.PeakTransformed: the header information of the transformed data.
m_Header - Variable in class weka.classifiers.meta.RangeCheck: the training header.
m_Header - Variable in class weka.classifiers.meta.SubsetEnsemble: The header of the training set.
m_Header - Variable in class weka.classifiers.meta.SumTransformed: the header information of the transformed data.
m_Helper - Variable in class adams.flow.transformer.WekaClassifierRanker: the helper class.
m_HistogramSetup - Variable in class adams.gui.visualization.instance.InstanceExplorer: the dialog for the histogram setup.
m_History - Variable in class weka.gui.explorer.ExperimentPanel: A panel controlling results viewing.
m_ID - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the ID to use for the returned instances.
m_IncludeClass - Variable in class weka.filters.unsupervised.instance.RemoveDuplicates: whether to take the class into account.
m_IncludeClass - Variable in class weka.filters.unsupervised.instance.Sort: whether to take the class into account.
m_Incorrect - Variable in class weka.filters.unsupervised.attribute.SpellChecker: the (misspelled) labels of the attribute to replace.
m_IncorrectCache - Variable in class weka.filters.unsupervised.attribute.SpellChecker: the hashset with the incorret labels (for faster access).
m_Incremental - Variable in class adams.flow.source.WekaDatabaseReader: whether to output the data row-by-row.
m_IncrementalClassifier - Variable in class adams.flow.transformer.WekaClassifier: the classifier to use when training incrementally.
m_IncrementalClusterer - Variable in class adams.flow.transformer.WekaClusterer: the clusterer used when training incrementally.
m_Index - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet.SortContainer: the distribution index (0-based).
m_Index - Variable in class adams.data.io.input.InstanceReader: the current index.
m_Index - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: the index in actor's input array.
m_Index - Variable in class adams.flow.transformer.WekaExtractArray: the index of the row/column to extract.
m_Index - Variable in class adams.flow.transformer.WekaGetInstanceValue: the index of the attribute to get the value from the Instance.
m_Index - Variable in class adams.flow.transformer.WekaSetInstanceValue: the attribute index to set in the Instance.
m_Index - Variable in class adams.flow.transformer.WekaSubsets: the attribute index to split on.
m_Index - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: the index.
m_Indexer - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the currently loaded dataset.
m_Indices - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the selected indices.
m_Indices - Variable in class weka.filters.unsupervised.attribute.CorrelationMatrix: the attribute indices to use.
m_Indices1 - Variable in class adams.tools.CompareDatasets: the indices for the first dataset.
m_Indices2 - Variable in class adams.tools.CompareDatasets: the indices for the second dataset.
m_init - Variable in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: initialised?
m_Initialized - Variable in class adams.data.weka.evaluator.AbstractInstanceEvaluator: whether the evaluator got initialized.
m_Initialized - Variable in class adams.flow.transformer.WekaFilter: whether the filter has been initialized.
m_Initialized - Variable in class weka.core.SetupGenerator: whether everything has been initialized.
m_InitializeOnce - Variable in class adams.flow.transformer.WekaFilter: whether to initialize filter only with the first batch.
m_InitialSpaceNumFolds - Variable in class weka.classifiers.meta.MultiSearch: number of cross-validation folds in the initial space.
m_Instance - Variable in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer: the Instance the container is for.
m_InstanceClass - Variable in class adams.flow.transformer.WekaNewInstance: the class of instance to create.
m_InstanceContainerList - Variable in class adams.gui.visualization.instance.InstancePanel: the instance ID list.
m_InstancePaintlet - Variable in class adams.gui.visualization.instance.InstancePanel: paintlet for drawing the graph.
m_InstancePanel - Variable in class adams.flow.sink.WekaInstanceViewer: the panel with the instances.
m_instancepct - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_instancepct - Variable in class weka.classifiers.meta.LeastMedianSq
m_InstancePointHitDetector - Variable in class adams.gui.visualization.instance.InstancePanel: the hit detector for the tooltip.
m_Instances - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the full dataset.
m_Instances - Variable in class adams.ml.WekaClassifier
m_Instances - Variable in class adams.ml.WekaFilter
m_Instances - Variable in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: the data to use for cross-validation.
m_Instances - Variable in class weka.gui.explorer.ExperimentPanel: The main set of instances we're playing with.
m_InstancesActor - Variable in class adams.flow.transformer.WekaInstanceEvaluator: the global actor to get the Instances from in case of AbstractDatasetInstanceEvaluator.
m_Interval - Variable in class adams.flow.transformer.WekaInstanceBuffer: the interval of when to output the Instances object.
m_Intervals - Variable in class adams.data.weka.evaluator.IntervalEstimatorBased.SortedInterval: the intervals.
m_InverseTransform - Variable in class weka.filters.unsupervised.attribute.FastWavelet: whether to perform inverse transformation.
m_Invert - Variable in class adams.flow.condition.bool.AbstractAttributeCapabilities: whether to invert the matching sense.
m_Invert - Variable in class adams.flow.transformer.WekaMultiLabelSplitter: whether to invert the matching sense.
m_Invert - Variable in class adams.flow.transformer.WekaRegexToRange: invert matching?
m_Invert - Variable in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Whether to invert the column indices.
m_Invert - Variable in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Whether to invert the row indices.
m_invertMatching - Variable in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Whether to invert the match so the correctly classified instances are discarded
m_InvertMatchingSense - Variable in class adams.flow.transformer.WekaInstancesMerge: whether to invert the matching sense for excluding attributes.
m_InvertSelection - Variable in class weka.filters.unsupervised.instance.SafeRemoveRange: whether to invert the selection.
m_IQR - Variable in class adams.data.weka.rowfinder.FilteredIQR: the maximum value of the attribute.
m_IsString - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: whether the attribute is numeric or string/nominal.
m_Iterations - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: number of iterations.
m_Iterator - Variable in class adams.flow.transformer.WekaInstanceBuffer: the iterator for broadcasting Instance objects.
m_JobRunner - Variable in class adams.flow.transformer.WekaClassifierRanker: the job runner for evaluating the setups.
m_k - Variable in class weka.classifiers.meta.Corr
m_Kappa - Variable in class weka.classifiers.meta.multisearch.Performance: the Kappa statistic.
m_KeepExisting - Variable in class adams.flow.transformer.WekaInstanceDumper: whether to keep existing output files when actor is called for the first time, in order to allow appending to files from multiple locations in flow.
m_KeepRelationName - Variable in class adams.flow.transformer.WekaFilter: whether to keep the incoming relation name.
m_kernel - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: Kernel to use *
m_kernel - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: Kernel to use
m_KernelIsLinear - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: whether the kernel is a linear one
m_KernelIsLinear - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: whether the kernel is a linear one
m_Keys - Variable in class adams.flow.source.WekaDatabaseReader: the keys that uniquely identify a single row.
m_L - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: (negative) covariance matrix in symmetric matrix representation
m_L - Variable in class weka.classifiers.functions.GPD
m_Label - Variable in class weka.core.setupgenerator.SpaceDimension: the label for the axis.
m_LabelAttributeRange - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the label for the range.
m_LabelMatch - Variable in class weka.filters.unsupervised.instance.DatasetLabeler: the label to use for a match.
m_LabelNonMatch - Variable in class weka.filters.unsupervised.instance.DatasetLabeler: the label to use for a nonmatch.
m_LabelQuery - Variable in class weka.gui.explorer.SqlPanel: displays the current query
m_LabelRowAttribute - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the label for the attribute names.
m_LabelRows - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the label for the combobox.
m_LabelTextNumPoints - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the number of data points.
m_LastNotificationTime - Variable in class adams.optimise.GeneticAlgorithm: the timestamp the last notification got sent.
m_left - Variable in class weka.classifiers.trees.m5.RuleNode2: left child node
m_Less - Variable in class weka.classifiers.trees.RandomRegressionForest.Node
m_LinearReg - Variable in class weka.classifiers.trees.RandomRegressionForest.Node
m_List - Variable in class weka.core.setupgenerator.ListParameter: the explicit list of values to use.
m_List - Variable in class weka.core.setupgenerator.SpaceDimension: the underlying list of values.
m_ListAdditionalAttributes - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the list of additional attribute values to store in the report.
m_ListAdditionalAttributesModel - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the model for the additional attributes.
m_ListRows - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the JLiast with the rows.
m_ListRowsModel - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the model for the combobox with the rows.
m_Loader - Variable in class adams.gui.chooser.DatasetFileChooserPanel: the current loader.
m_LoadFromDatabaseDialog - Variable in class adams.gui.visualization.instance.InstanceExplorer: the SQL viewer dialog.
m_LoadFromDiskDialog - Variable in class adams.gui.visualization.instance.InstanceExplorer: the dialog for loading datasets.
m_Locations - Variable in class adams.flow.transformer.WekaInstancesStatistic: the array of indices/regular expressions.
m_Log - Variable in class weka.gui.explorer.ExperimentPanel: The destination for log/status messages.
m_Log - Variable in class weka.gui.explorer.SqlPanel: The destination for log/status messages
m_LogFile - Variable in class weka.classifiers.meta.MultiSearch: the log file to use.
m_Lookup2 - Variable in class adams.tools.CompareDatasets: the lookup table of indices for the second dataset.
m_LR - Variable in class weka.core.WeightedEuclideanDistance
m_MAE - Variable in class weka.classifiers.meta.multisearch.Performance: the Mean absolute error.
m_MakeClassLast - Variable in class adams.flow.transformer.WekaMultiLabelSplitter: whether to move the class attribute to the end.
m_MarkerExtent - Variable in class adams.gui.visualization.instance.InstanceLinePaintlet: the maximum width/height of the shape to plot around the points (= data point marker), if there's enough space.
m_MarkersEnabled - Variable in class adams.gui.visualization.instance.InstanceLinePaintlet: indicates whether marker shapes are painted or not.
m_Max - Variable in class adams.flow.transformer.WekaClassifierRanker: the maximum number of top-ranked classifiers to forward.
m_max - Variable in class adams.optimise.genetic.PackDataDef.DataInfo
m_max - Variable in class weka.classifiers.meta.AbstainAttributePercentile
m_max - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_max - Variable in class weka.classifiers.trees.RandomModelTrees
m_Max - Variable in class weka.core.setupgenerator.MathParameter: the maximum.
m_Max - Variable in class weka.core.setupgenerator.SpaceDimension: the maximum on the axis.
m_MaxAttrsInName - Variable in class weka.core.neighboursearch.PCANNSearch: maximum number of attributes in the transformed attribute name.
m_MaxSize - Variable in class adams.data.weka.predictions.RelativeNumericErrorScaler: the maximum size.
m_MaxTrainTime - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: the maximum number of seconds to train.
m_MaxTrainTime - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: the maximum number of seconds to train.
m_MaxWidth - Variable in class adams.data.weka.evaluator.IntervalEstimatorBased: the maximum width allowed.
m_Mean - Variable in class weka.classifiers.trees.RandomRegressionForest: the mean
m_Measure - Variable in class adams.flow.transformer.WekaClassifierRanker: the measure for the evaluation.
m_Measure - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: the measure to use for ranking.
m_Measure - Variable in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: the measure to use for evaluating the fitness.
m_MenuBar - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the menu bar.
m_MenuBar - Variable in class adams.gui.visualization.instance.InstanceExplorer: the menu bar, if used.
m_MenuItemClearData - Variable in class adams.gui.visualization.instance.InstanceExplorer: the clear data menu item.
m_MenuItemLoadRecent - Variable in class adams.gui.visualization.instance.InstanceExplorer: the "load recent" submenu.
m_MenuItemLoadRecent1 - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the recent file menu item (first file).
m_MenuItemLoadRecent2 - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the recent file menu item (second file).
m_MenuItemReload - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the reload menu item.
m_MenuItemViewZoomOverview - Variable in class adams.gui.visualization.instance.InstanceExplorer: the toggle zoom overview menu item.
m_MenuView - Variable in class adams.gui.visualization.instance.InstanceExplorer: the menu item for view related stuff.
m_MetaLevelClassifier - Variable in class weka.classifiers.meta.PartitionedStacking: the meta-level classifier.
m_MetaLevelData - Variable in class weka.classifiers.meta.PartitionedStacking: the header for the meta-level data.
m_min - Variable in class adams.optimise.genetic.PackDataDef.DataInfo
m_min - Variable in class weka.classifiers.meta.AbstainAttributePercentile
m_min - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_Min - Variable in class weka.classifiers.trees.RandomRegressionForest: the minimum number of instances in subsets
m_Min - Variable in class weka.core.setupgenerator.MathParameter: the minimum.
m_Min - Variable in class weka.core.setupgenerator.SpaceDimension: the minimum on the axis.
m_minNumInstances - Variable in class weka.classifiers.trees.m5.M5Base2: The minimum number of instances to allow at a leaf node
m_MinWidth - Variable in class adams.data.weka.evaluator.IntervalEstimatorBased: the minimum width encountered.
m_MinZeroes - Variable in class weka.filters.unsupervised.instance.RemoveWithZeroes: the minimum number of zeroes that a row must have.
m_Missing - Variable in class adams.tools.CompareDatasets: the output file for missing tests (CSV format).
m_Missing - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: The filter used to get rid of missing values.
m_Missing - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: The filter used to get rid of missing values.
m_Missing - Variable in class weka.classifiers.functions.GPD: The filter used to get rid of missing values.
m_Model - Variable in class adams.flow.condition.bool.WekaClassification: the model that was loaded from the model file.
m_Model - Variable in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: the model that was loaded from the model file.
m_ModelActor - Variable in class adams.flow.condition.bool.WekaClassification: the global actor to get the model from.
m_ModelActor - Variable in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: the global actor to get the model from.
m_ModelFile - Variable in class adams.flow.condition.bool.WekaClassification: the serialized model to load.
m_ModelFile - Variable in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: the serialized model to load.
m_More - Variable in class weka.classifiers.trees.RandomRegressionForest.Node
m_myInstances - Variable in class weka.core.neighboursearch.PCANNSearch
m_myInstances - Variable in class weka.core.neighboursearch.TransformNNSearch: Transformed instances.
m_n - Variable in class weka.core.SAXDistance: pre-sax number of attributes.
m_name - Variable in class adams.optimise.genetic.PackDataDef.DataInfo
m_Names - Variable in class adams.flow.transformer.WekaAttributeIterator: the names of the attributes to output.
m_neighbours - Variable in class weka.core.neighboursearch.NewNNSearch
m_NextInstance - Variable in class adams.flow.source.WekaDatabaseReader: the next instance to output.
m_NoCheck - Variable in class adams.flow.transformer.WekaExperiment: whether not to check for experiment file to exist (e.g., when it generated on the fly).
m_node - Variable in class weka.classifiers.trees.RandomModelTrees
m_Node - Variable in class weka.classifiers.trees.RandomRegressionForest: the generated nodes
m_Nominalise - Variable in class adams.ml.WekaData
m_NominalToBinary - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: The filter used to make attributes numeric.
m_NominalToBinary - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: The filter used to make attributes numeric.
m_NominalToBinary - Variable in class weka.classifiers.functions.GPD: The filter used to make attributes numeric.
m_norm - Variable in class weka.core.SAXDistance: suid.
m_norm - Variable in class weka.core.WeightedEuclideanDistance
m_Notes - Variable in class adams.data.instance.Instance: the notes for the chromatogram.
m_NoUpdate - Variable in class weka.classifiers.lazy.LWLSynchro: whether to suppress the update of the nearest-neighbor search algorithm when making predictions.
m_NthPoint - Variable in class weka.filters.unsupervised.attribute.DownSample: the nth point.
m_NumChrom - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: number of chromosomes.
m_NumChrom - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: number of chromosomes.
m_NumDecimals - Variable in class adams.data.conversion.WekaInstancesToSpreadSheet: the number of decimals to display.
m_numericClassifyThreshold - Variable in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: The threshold for deciding when a numeric value is correctly classified
m_numericClassifyThresholdAbs - Variable in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: if Absolute error is less than this, then we're ok
m_NumExecutionSlots - Variable in class weka.classifiers.meta.MultiSearch: The number of threads to have executing at any one time.
m_NumExecutionSlots - Variable in class weka.classifiers.meta.SubsetEnsemble: The number of threads to have executing at any one time
m_NumGenes - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: number of genes per chromosome.
m_NumGenes - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: number of genes per chromosome.
m_numInstances - Variable in class weka.classifiers.trees.m5.RuleNode2: the number of instances reaching this node
m_NumIterations - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: number of iterations to perform.
m_numIterations - Variable in class weka.classifiers.trees.RandomModelTrees: The number of iterations.
m_NumIterations - Variable in class weka.classifiers.trees.RandomRegressionForest: The number of iterations.
m_NumNumericAttributes - Variable in class weka.filters.unsupervised.instance.RemoveWithZeroes: the number of numeric attributes in the dataset.
m_numOfCleansingIterations - Variable in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: The maximum number of cleansing iterations to perform (<1 = until fully cleansed)
m_numOfCrossValidationFolds - Variable in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: The number of cross validation folds to perform (<2 = no cross validation)
m_numParameters - Variable in class weka.classifiers.trees.m5.RuleNode2: the number of paramters in the chosen model for this node---either the subtree model or the linear model.
m_NumPointsLeft - Variable in class weka.filters.unsupervised.attribute.SavitzkyGolay: the number of points to the left of a data point.
m_NumPointsRight - Variable in class weka.filters.unsupervised.attribute.SavitzkyGolay: the number of points to the right of a data point.
m_NumPredictions - Variable in class adams.flow.transformer.WekaAccumulatedError: the number of overall predictions.
m_NumRandomFeatures - Variable in class weka.classifiers.meta.SubsetEnsemble: the number of random features to use (in addition to base attribute).
m_numreg - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_numreg - Variable in class weka.classifiers.meta.LeastMedianSq
m_NumSetups - Variable in class weka.classifiers.meta.MultiSearch: the number of setups to evaluate.
m_NumThreads - Variable in class adams.flow.transformer.WekaClassifierRanker: the number of threads to use for parallel execution.
m_NumTrain - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: The number of training instances
m_NumTrain - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: The number of training instances
m_NumTrain - Variable in class weka.classifiers.functions.GPD: The number of training instances
m_numTrials - Variable in class weka.classifiers.trees.RandomModelTrees
m_NumZeroes - Variable in class weka.filters.unsupervised.instance.RemoveWithZeroes: the number (or percentage) of zeroes that the row must contain to be removed.
m_ok - Variable in class weka.classifiers.meta.AbstainAttributePercentile
m_ok - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_OnTheFly - Variable in class adams.flow.condition.bool.WekaClassification: whether the model gets built on the fly and might not be present at the start.
m_OnTheFly - Variable in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: whether the model gets built on the fly and might not be present at the start.
m_Operation - Variable in class adams.flow.transformer.WekaInstanceBuffer: the way the buffer operates.
m_Optimizer - Variable in class adams.flow.transformer.WekaClassifierOptimizer: the classifier optimizer.
m_Output - Variable in class adams.flow.transformer.AbstractWekaClassifierEvaluator: for generating predictions output.
m_OutputArray - Variable in class adams.flow.source.AbstractWekaSetupGenerator: whether to output an array or a sequence of setups.
m_OutputBestSetup - Variable in class adams.flow.transformer.WekaClassifierRanker: whether to output the best setup in case of GridSearch/MultiSearch.
m_OutputBestSetup - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: whether to output the best classifier.
m_OutputBuffer - Variable in class adams.flow.transformer.AbstractWekaClassifierEvaluator: the buffer for the predictions.
m_OutputDirectory - Variable in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: the directory to store the generated ARFF files in.
m_OutputFile - Variable in class adams.flow.sink.WekaExperimentGenerator: the file to store the experiment in.
m_OutputFile - Variable in class adams.tools.CompareDatasets: the output file (CSV format).
m_OutputFormat - Variable in class adams.flow.transformer.WekaExperimentEvaluation: the output format.
m_OutputFormat - Variable in class adams.flow.transformer.WekaInstanceDumper: the output format.
m_OutputHeader - Variable in class adams.data.instances.AbstractInstanceGenerator: the generated header.
m_OutputHeader - Variable in class adams.flow.transformer.WekaExperimentEvaluation: whether to output the header.
m_OutputInstance - Variable in class adams.flow.transformer.WekaClassifying: whether to output weka.core.Instance objects or PredictionContainers.
m_OutputNominal - Variable in class weka.filters.unsupervised.attribute.SAX: If true output nominal, false output numeric .
m_OutputOnlyModel - Variable in class adams.flow.transformer.AbstractWekaModelReader: whether to only output the model.
m_OutputPrefix - Variable in class adams.flow.transformer.WekaInstanceDumper: the output prefix.
m_OutputRelationName - Variable in class adams.flow.transformer.WekaEvaluationSummary: whether to print the relation name of the dataset a well.
m_OutputToken - Variable in class adams.flow.source.WekaDataGenerator: the token to broadcast.
m_OutputToken - Variable in class adams.flow.source.WekaNewInstances: the token to broadcast.
m_OutputType - Variable in class adams.flow.transformer.WekaFileReader: how to output the data.
m_OutText - Variable in class weka.gui.explorer.ExperimentPanel: The output area for classification results.
m_Override - Variable in class adams.flow.transformer.WekaClassSelector: whether to override any set class attribute.
m_Owner - Variable in class adams.gui.visualization.instance.InstanceContainerManager: the owning panel.
m_Owner - Variable in class adams.gui.visualization.instance.InstancePointHitDetector: the owner of this detector.
m_Owner - Variable in class weka.classifiers.meta.MultiSearch.EvaluationTask: the owner.
m_packed - Variable in class adams.optimise.genetic.PackDataDef
m_Padding - Variable in class weka.filters.unsupervised.attribute.FastWavelet: the type of padding.
m_Panel - Variable in class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog: the setup panel.
m_PanelButtons - Variable in class weka.gui.explorer.SqlPanel: the panel for the buttons
m_PanelComboBoxData - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the panel for the combobox listing the datasets.
m_PanelCompare - Variable in class adams.gui.InstanceCompare: the panel for comparing the datasets.
m_PanelComparison - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the panel for displaying the two instances.
m_PanelData - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the panel holding the table and buttons for the dataset.
m_PanelDataAction - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the panel with buttons etc.
m_PanelDataset - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the panel for loading the file.
m_PanelDataset1 - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the panel for the first dataset.
m_PanelDataset2 - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the panel for the second dataset.
m_PanelDifference - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the panel with the difference.
m_PanelEditor - Variable in class adams.gui.goe.WekaExperimentFileEditor: the editor panel.
m_PanelFile - Variable in class adams.gui.goe.WekaExperimentFileEditor: the panel for selecting the experiment file.
m_PanelInstance - Variable in class adams.gui.visualization.instance.InstanceExplorer: the panel for displaying.
m_PanelTextAction - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the panel with buttons etc.
m_PanelTextAll - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the panel for the text tab.
m_PanelZoomOverview - Variable in class adams.gui.visualization.instance.InstancePanel: the zoom overview panel.
m_Parameters - Variable in class adams.gui.menu.AbstractParameterHandlingWekaMenuItemDefinition: the additional parameters.
m_Parameters - Variable in class weka.core.SetupGenerator: the parameters.
m_Password - Variable in class adams.flow.sink.WekaDatabaseWriter: the password for the user used for connecting.
m_Password - Variable in class adams.flow.source.WekaDatabaseReader: the password for the user used for connecting.
m_pca - Variable in class weka.core.neighboursearch.PCANNSearch: The neighbourhood of instances to find neighbours in.
m_pctile - Variable in class weka.classifiers.meta.AbstainAttributePercentile
m_pdd - Variable in class adams.genetic.MTPackDataGeneticAlgorithm
m_pdd - Variable in class adams.optimise.genetic.PackData
m_pdd - Variable in class adams.optimise.genetic.PackDataGeneticAlgorithm
m_pdd - Variable in class adams.optimise.GeneticAlgorithm
m_Percentage - Variable in class adams.flow.transformer.WekaRandomSplit: the percentage for the split (0-1).
m_Performance - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: the performance.
m_Performances - Variable in class weka.classifiers.meta.MultiSearch: for storing the performances.
m_PlotInstances - Variable in class adams.flow.sink.WekaClassifierErrors.DataGenerator: the underlying data.
m_PlotName - Variable in class adams.flow.transformer.WekaAccumulatedError: the name of the plot.
m_PlotShapes - Variable in class adams.flow.sink.WekaClassifierErrors.DataGenerator: for storing the plot shapes.
m_PlotSizes - Variable in class adams.flow.sink.WekaClassifierErrors.DataGenerator: for storing the plot sizes.
m_PLS - Variable in class weka.classifiers.trees.RandomRegressionForest: the number of components to use in PLS
m_PLSFilter - Variable in class weka.classifiers.trees.RandomRegressionForest: the PLS filter used internally
m_plsfilter - Variable in class weka.core.neighboursearch.PLSNNSearch: The neighbourhood of instances to find neighbours in.
m_PolynomialOrder - Variable in class weka.filters.unsupervised.attribute.SavitzkyGolay: the polynomial order.
m_PostProcessor - Variable in class adams.flow.transformer.WekaClusterer: the post-processor.
m_Prediction - Variable in class adams.flow.transformer.WekaAccumulatedError.SortablePrediction: the wrapped prediction.
m_Predictions - Variable in class adams.flow.transformer.WekaAccumulatedError: the sorted predictions.
m_PreFilter - Variable in class adams.data.weka.rowfinder.FilteredIQR: the filter to apply to the data first.
m_Prefix - Variable in class adams.flow.transformer.WekaInstancesMerge: the additional prefix name to use, apart from the index.
m_PrefixSeparator - Variable in class adams.flow.transformer.WekaInstancesMerge: the separator between index and actual attribute name.
m_Preprocessing - Variable in class weka.core.neighboursearch.PCANNSearch: the type of preprocessing
m_Preprocessing - Variable in class weka.core.neighboursearch.PLSNNSearch: the type of preprocessing
m_PreserveOrder - Variable in class adams.flow.transformer.WekaRandomSplit: whether to preserve the order.
m_Probability - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet.SortContainer: the probability.
m_Processed - Variable in class adams.flow.sink.WekaClassifierErrors.DataGenerator: whether the data has already been processed.
m_Properties - Static variable in class adams.gui.visualization.instance.InstanceComparePanel: the properties.
m_Properties - Static variable in class adams.gui.visualization.instance.InstanceExplorer: the properties.
m_Property - Variable in class weka.core.setupgenerator.AbstractParameter: the property to test.
m_Query - Variable in class adams.flow.source.WekaDatabaseReader: the query to execute.
m_Queue - Variable in class adams.flow.transformer.WekaInstancesInfo: the tokens to output.
m_Queue - Variable in class adams.flow.transformer.WekaSubsets: the generated subsets.
m_RAE - Variable in class weka.classifiers.meta.multisearch.Performance: the Relative absolute error.
m_Random - Variable in class adams.flow.transformer.WekaCrossValidationSplit: the random number generator to use for generating the folds.
m_Random - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: the random number generator.
m_Random - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: the random number generator.
m_random - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_random - Variable in class weka.classifiers.meta.LeastMedianSq
m_Randomize - Variable in class weka.filters.unsupervised.instance.RemoveDuplicates: whether to randomize the data after the removal.
m_randomseed - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_randomseed - Variable in class weka.classifiers.meta.LeastMedianSq
m_Range - Variable in class adams.data.weka.rowfinder.FilteredIQR: the attribute range to work on.
m_Range - Variable in class adams.flow.transformer.WekaAttributeIterator: the range of attributes to work on.
m_Range - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: the range of attributes to use.
m_Range - Variable in class weka.classifiers.meta.AbstainAttributePercentile
m_Range - Variable in class weka.filters.unsupervised.instance.SafeRemoveRange: Range of instances requested by the user.
m_Range1 - Variable in class adams.tools.CompareDatasets: the first range of attributes.
m_Range2 - Variable in class adams.tools.CompareDatasets: the second range of attributes.
m_Ranges - Variable in class weka.classifiers.meta.AbstainAttributePercentile: the ranges (attribute index <-> double[]).
m_Ranges - Variable in class weka.classifiers.meta.PartitionedStacking: the attribute ranges for the base-classifiers.
m_Ranges - Variable in class weka.classifiers.meta.RangeCheck: the ranges (attribute index <-> double[]).
m_RecentFilesHandler - Variable in class adams.gui.visualization.instance.InstanceExplorer: the recent files handler.
m_RecentFilesHandler1 - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the recent files handler.
m_RecentFilesHandler2 - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the recent files handler.
m_Regex - Variable in class adams.flow.transformer.WekaRegexToRange: regular expression used to determine attribute list.
m_regex - Variable in class adams.ml.WekaData.ArrayFinder
m_regex - Variable in class adams.ml.WekaFilter: regex for arrays
m_RegexName - Variable in class adams.flow.transformer.WekaClassSelector: the regular expression on the attribute for selecting the sub-set of attributes.
m_RegExp - Variable in class adams.data.weka.columnfinder.ByName: the regular expression to match the attribute names against.
m_RegExp - Variable in class adams.data.weka.rowfinder.ByLabel: the regular expression to match the labels against.
m_RegExp - Variable in class adams.flow.transformer.WekaAttributeIterator: the regular expression applied to the attribute names.
m_RegExp - Variable in class adams.flow.transformer.WekaMultiLabelSplitter: the regular expression that the class attribute names have to match.
m_Registered - Static variable in class adams.gui.goe.WekaEditorsRegistration: whether registration already occurred.
m_regressionTree - Variable in class weka.classifiers.trees.m5.M5Base2: Make a regression tree/rule instead of a model tree/rule
m_RelationName - Variable in class adams.flow.source.WekaNewInstances: the name for the relation, if any.
m_RelationName - Variable in class adams.flow.transformer.WekaCrossValidationSplit: the format of the relation names of the generated datasets.
m_RelativeWidths - Variable in class adams.data.weka.evaluator.IntervalEstimatorBased: whether to divide the calculated widths by the class value.
m_RelativeWidths - Variable in class adams.data.weka.evaluator.IntervalEstimatorBased.SortedInterval: whether the widths are relative.
m_Remove - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: the remove filter for trimming the range of attributes to return.
m_remove - Variable in class weka.classifiers.meta.Corr
m_Remove - Variable in class weka.classifiers.meta.FilteredClassifierExt: The additional remove filter.
m_Remove - Variable in class weka.classifiers.meta.PartitionedStacking: the filters for removing the unwanted attributes for the base classifiers.
m_Remove - Variable in class weka.filters.unsupervised.attribute.DatasetCleaner: the remove filter to use.
m_removetrain - Variable in class weka.classifiers.meta.AbstainAttributePercentile
m_Replace - Variable in class adams.flow.transformer.WekaRenameRelation: the string to replace with.
m_Report - Variable in class adams.data.instance.Instance: the automatically generated report.
m_Reports - Variable in class adams.gui.visualization.instance.InstanceExplorer: the reports.
m_ReportTable - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the table with the report.
m_Residuals - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_Residuals - Variable in class weka.classifiers.meta.LeastMedianSq
m_ResultFile - Variable in class adams.flow.sink.WekaExperimentGenerator: the file to store the result in.
m_ResultFormat - Variable in class adams.flow.sink.WekaExperimentGenerator: the result format.
m_Ridge - Variable in class adams.data.baseline.AbstractLinearRegressionBased: the ridge.
m_Ridge - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_Ridge - Variable in class weka.classifiers.meta.LeastMedianSq
m_ridge - Variable in class weka.classifiers.trees.RandomModelTrees
m_right - Variable in class weka.classifiers.trees.m5.RuleNode2: right child node
m_RMSE - Variable in class weka.classifiers.meta.multisearch.Performance: the Root mean squared error.
m_Row - Variable in class adams.flow.transformer.WekaExperimentEvaluation: the row (= datasets).
m_RowAttribute1 - Variable in class adams.tools.CompareDatasets: the optional attribute for matching up rows (dataset 1).
m_RowAttribute2 - Variable in class adams.tools.CompareDatasets: the optional attribute for matching up rows (dataset 2).
m_RowAttributeIsString - Variable in class adams.tools.CompareDatasets: whether the row attribute is a string/nominal attribute or not.
m_RowFinder - Variable in class adams.data.weka.columnfinder.RowFilteredColumnFinder: the RowFinder to use first.
m_RowFinder - Variable in class adams.data.weka.rowfinder.AbstractFilteredRowFinder: the RowFinder to apply.
m_RowFinder - Variable in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: The classifier template used to do the classification.
m_RowIndex - Variable in class adams.gui.visualization.instance.InstanceComparePanel: the internal index.
m_RRSE - Variable in class weka.classifiers.meta.multisearch.Performance: the Root relative squared error.
m_ruleSet - Variable in class weka.classifiers.trees.m5.M5Base2: the rule set
m_Running - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: whether the algorithm is still running.
m_Running - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: whether the algorithm is still running.
m_Runs - Variable in class adams.flow.sink.WekaExperimentGenerator: the number of runs to perform.
m_RunsSpinner - Variable in class weka.gui.explorer.ExperimentPanel: The spinner for the number of runs.
m_RunThread - Variable in class weka.gui.explorer.ExperimentPanel: A thread that classification runs in.
m_SampleSize - Variable in class weka.classifiers.meta.MultiSearch: the sample size to search the initial space with.
m_saveInstances - Variable in class weka.classifiers.trees.m5.M5Base2: Save instances at each node in an M5 tree for visualization purposes.
m_SaveOut - Variable in class weka.gui.explorer.ExperimentPanel: The buffer saving object for saving output.
m_Saver - Variable in class adams.flow.sink.WekaDatabaseWriter: the database saver.
m_Saver - Variable in class adams.gui.chooser.DatasetFileChooserPanel: the current saver.
m_scale - Variable in class adams.optimise.genetic.PackDataDef.DataInfo
m_scalefactor - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_scalefactor - Variable in class weka.classifiers.meta.LeastMedianSq
m_Scaler - Variable in class adams.data.weka.predictions.AutoScaler: the scaler to use for numeric classes.
m_SearchPanel - Variable in class adams.gui.visualization.instance.InstanceExplorer: for searching the fields in the reports.
m_SearchPanel - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the search panel.
m_SecondAttributeRange - Variable in class adams.gui.InstanceCompare: the second attribute range to use.
m_SecondFile - Variable in class adams.gui.InstanceCompare: the second file to compare.
m_SecondRowIndex - Variable in class adams.gui.InstanceCompare: the index of the second attribute to use for matching rows.
m_Seed - Variable in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: the random seed for cross-valiation.
m_Seed - Variable in class adams.flow.transformer.WekaClassifierRanker: the random seed to use.
m_Seed - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: the seed value to use.
m_Seed - Variable in class adams.flow.transformer.WekaCrossValidationEvaluator: the seed value.
m_Seed - Variable in class adams.flow.transformer.WekaCrossValidationSplit: the seed value.
m_Seed - Variable in class adams.flow.transformer.WekaRandomSplit: the seed value.
m_Seed - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: the seed value.
m_Seed - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: the seed value.
m_Seed - Variable in class weka.filters.unsupervised.instance.RemoveDuplicates: the seed value for the randomization.
m_Self - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the dialog itself.
m_Setup - Variable in class adams.data.weka.evaluator.NamedSetup: the name of the setup to load.
m_Setup - Variable in class adams.data.weka.predictions.NamedSetup: the name of the setup to load.
m_Setups - Variable in class adams.flow.source.AbstractWekaSetupGenerator: all the setups.
m_ShowDistribution - Variable in class adams.flow.transformer.AbstractWekaPredictionsTransformer: whether to output the class distribution (only nominal classes).
m_ShowError - Variable in class adams.flow.transformer.AbstractWekaPredictionsTransformer: whether to add an error colunm.
m_ShowProbability - Variable in class adams.flow.transformer.AbstractWekaPredictionsTransformer: whether to output the probability of the prediction (only nominal classes).
m_ShowWeight - Variable in class adams.flow.transformer.AbstractWekaPredictionsTransformer: whether to output the weight as well.
m_Significance - Variable in class adams.flow.transformer.WekaExperimentEvaluation: the significance.
m_Size - Variable in class adams.data.weka.predictions.FixedSizeErrorScaler: the size.
m_SkipIdentical - Variable in class weka.core.neighboursearch.NewNNSearch: Whether to skip instances from the neighbours that are identical to the query instance.
m_sort_packed - Variable in class adams.optimise.genetic.PackDataDef
m_Source - Variable in class adams.data.io.input.InstanceReader: the data source for reading.
m_Source - Variable in class adams.flow.source.WekaDatabaseReader: the actual loader for loading the data.
m_Source - Variable in class adams.flow.transformer.WekaFileReader: the actual loader for loading the data.
m_Space - Variable in class weka.classifiers.meta.MultiSearch: the parameter space.
m_Space - Variable in class weka.core.SetupGenerator: the parameter space to use for obtaining the setups from.
m_SparseFormat - Variable in class adams.flow.source.WekaDatabaseReader: whether to output data in sparse format.
m_SplitIndex - Variable in class weka.classifiers.trees.RandomRegressionForest.Node
m_SplitPercentage - Variable in class adams.flow.sink.WekaExperimentGenerator: the split-percentage to use (only train/test splits).
m_SplitValue - Variable in class weka.classifiers.trees.RandomRegressionForest.Node
m_SSR - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_SSR - Variable in class weka.classifiers.meta.LeastMedianSq
m_StartBut - Variable in class weka.gui.explorer.ExperimentPanel: Click to start running the experiment.
m_Statistic - Variable in class adams.flow.transformer.WekaInstancesStatistic: the statistic to generate.
m_StatisticValue - Variable in class adams.flow.transformer.WekaEvaluationValuePicker: the comparison field.
m_StatisticValues - Variable in class adams.flow.transformer.WekaEvaluationValues: the comparison fields.
m_StatusBar - Variable in class adams.gui.visualization.instance.InstanceComparePanel: for displaying error messages.
m_StatusBar - Variable in class adams.gui.visualization.instance.InstanceExplorer: the status bar.
m_Step - Variable in class weka.core.setupgenerator.MathParameter: the step.
m_Step - Variable in class weka.core.setupgenerator.SpaceDimension: the step size for the axis.
m_StopBut - Variable in class weka.gui.explorer.ExperimentPanel: Click to stop a running experiment.
m_StoredResults - Variable in class adams.optimise.GeneticAlgorithm: the cache for results.
m_StoreFilename - Variable in class adams.flow.transformer.WekaTextDirectoryReader: whether to store the filename as extra attribute.
m_Structure - Variable in class adams.data.io.input.InstanceReader: the current data structure.
m_Structure - Variable in class adams.flow.source.WekaDatabaseReader: the structure.
m_Structure - Variable in class adams.flow.transformer.WekaFileReader: the structure.
m_SubProcess - Variable in class weka.filters.FlowFilter: the flow for processing the data.
m_SubSample - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_SubSample - Variable in class weka.classifiers.meta.LeastMedianSq
m_SubsequentSpaceNumFolds - Variable in class weka.classifiers.meta.MultiSearch: number of cross-validation folds in the subsequent spaces.
m_subset - Variable in class weka.classifiers.meta.Corr
m_Support - Variable in class weka.gui.explorer.SqlPanel: Manages sending notifications to people when we change the set of working instances.
m_SwapRowsAndColumns - Variable in class adams.flow.transformer.WekaExperimentEvaluation: whether to swap rows and columns.
m_t - Variable in class weka.classifiers.functions.GaussianProcessesAdaptive: The vector of target values.
m_t - Variable in class weka.classifiers.functions.GaussianProcessesWeighted: The vector of target values.
m_t - Variable in class weka.classifiers.functions.GPD: The vector of target values.
m_TabbedPane - Variable in class adams.gui.visualization.instance.InstanceExplorer: the tabbed pane for the data to display.
m_TabbedPane - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the tabbed pane.
m_TabbedPane - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the tabbed pane for displaying the data.
m_TableData - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the table for displaying the instances.
m_TableData - Variable in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: the table with the dataset.
m_TableName - Variable in class adams.flow.sink.WekaDatabaseWriter: protected the name of the table to store the data in.
m_Test - Variable in class adams.flow.transformer.WekaClassifierRanker: the global actor to obtain the test dataset for train/test evaluation from.
m_Test - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: the test data to evaluate with (if folds less than 2).
m_TestBase - Variable in class adams.flow.transformer.WekaExperimentEvaluation: the test base.
m_Tester - Variable in class adams.flow.transformer.WekaExperimentEvaluation: the tester class to use.
m_TestLoader - Variable in class weka.gui.explorer.ExperimentPanel: The loader used to load the user-supplied test set (if any).
m_Testset - Variable in class adams.flow.transformer.WekaTestSetEvaluator: the name of the global trainset provider.
m_TextAttributeRange - Variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: the edit field for the range.
m_TextAttributeRange - Variable in class adams.gui.visualization.instance.LoadDatasetDialog: the text field for the attribute range.
m_Threshold - Variable in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: the percentage to the threshold.
m_Threshold - Variable in class adams.tools.CompareDatasets: the threshold for listing correlations.
m_TitleNameColumn - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: the title of the name column.
m_TitleValueColumn - Variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: the title of the value column.
m_ToolTipMaxColumns - Variable in class adams.gui.visualization.instance.InstancePanel: the maximum number of columns for the tooltip.
m_topOfTree - Variable in class weka.classifiers.trees.m5.Rule2: the top of the m5 tree for this rule
m_Train - Variable in class adams.flow.transformer.WekaClassifierRanker: the global actor to obtain the training dataset from.
m_Train - Variable in class adams.flow.transformer.WekaClassifierRanker.RankingJob: the train data to evaluate with.
m_Trained - Variable in class adams.data.weka.columnfinder.AbstractTrainableColumnFinder: whether the column finder was trained already.
m_Trained - Variable in class adams.data.weka.rowfinder.AbstractTrainableRowFinder: whether the row finder was trained already.
m_TrainStart - Variable in class adams.genetic.MTAbstractGeneticAlgorithm: the time when training commenced.
m_TrainStart - Variable in class adams.optimise.genetic.AbstractGeneticAlgorithm: the time when training commenced.
m_Type - Variable in class adams.flow.transformer.WekaExtractArray: the type of extraction.
m_Type - Variable in class adams.flow.transformer.WekaInstancesInfo: the type of information to generate.
m_Type - Variable in class weka.core.setupgenerator.SpaceDimension: the type of dimension.
m_Undo - Variable in class adams.gui.visualization.instance.InstancePanel: the undo manager.
m_UniformPerformance - Variable in class weka.classifiers.meta.MultiSearch: whether all performances in the space are the same.
m_UniqueID - Variable in class adams.flow.transformer.WekaInstancesMerge: the string or numeric attribute to use as unique identifier for rows.
m_UpdateRelationName - Variable in class adams.flow.transformer.WekaMultiLabelSplitter: whether to use the class attribute name as new relation name.
m_URL - Variable in class adams.flow.sink.WekaDatabaseWriter: the database URL to query.
m_URL - Variable in class adams.flow.source.WekaDatabaseReader: the database URL to query.
m_UseCustomLoader - Variable in class adams.flow.transformer.WekaFileReader: whether to use a custom converter.
m_UseCustomSaver - Variable in class adams.flow.sink.WekaFileWriter: whether to use a custom converter.
m_UsePrefix - Variable in class adams.flow.transformer.WekaInstancesMerge: whether to prefix the attribute names of each dataset with an index.
m_User - Variable in class adams.flow.sink.WekaDatabaseWriter: the database user to use for connecting.
m_User - Variable in class adams.flow.source.WekaDatabaseReader: the database user to use for connecting.
m_UseRelationNameAsFilename - Variable in class adams.flow.sink.WekaFileWriter: whether to use the relation name as filename.
m_UseRelationNameAsFilename - Variable in class adams.flow.transformer.WekaInstanceDumper: whether to use the relation name as filename.
m_UseRelationNameAsTable - Variable in class adams.flow.sink.WekaDatabaseWriter: whether to use the relation as table name.
m_UseRowAttribute - Variable in class adams.tools.CompareDatasets: whether to use the row attribute or not.
m_useUnpruned - Variable in class weka.classifiers.trees.m5.M5Base2: Do not prune tree/rules
m_UseY - Variable in class adams.data.instance.InstancePointComparator: whether to compare Y or X.
m_Value - Variable in class adams.flow.transformer.WekaSetInstanceValue: the value to set.
m_Values - Variable in class weka.classifiers.meta.MultiSearch.EvaluationTask: the setup.
m_Values - Variable in class weka.classifiers.meta.MultiSearch: the best values.
m_Values - Variable in class weka.classifiers.meta.multisearch.Performance: the values the filter/classifier were built with.
m_Values - Variable in class weka.core.setupgenerator.Point: the values in the various dimensions.
m_VariableName - Variable in class adams.flow.template.InstanceDumperVariable: the variable to update.
m_variance - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_Viewer - Variable in class weka.gui.explorer.SqlPanel: the SQL panel
m_Visible - Variable in class adams.gui.visualization.instance.InstanceContainer: whether the instance is visible.
m_VisualizePanel - Variable in class adams.flow.sink.WekaClassifierErrors: the Weka plot panel.
m_VisualizePanel - Variable in class adams.flow.sink.WekaCostCurve: the text area.
m_VisualizePanel - Variable in class adams.flow.sink.WekaROC: the text area.
m_weight - Variable in class weka.classifiers.meta.AbstainLeastMedianSq
m_weight - Variable in class weka.classifiers.meta.LeastMedianSq
m_weights - Variable in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: the weights of the chromosomes (0 = turned off, 1 = turned on).
m_weights - Variable in class adams.optimise.GeneticAlgorithm.GAJob: weights.
m_WekaData - Variable in class adams.ml.WekaClassifier
m_WekaData_in - Variable in class adams.ml.WekaFilter
m_WekaData_out - Variable in class adams.ml.WekaFilter
m_Width - Variable in class weka.core.setupgenerator.SpaceDimension: the number of points on the axis.
m_windows - Variable in class weka.filters.unsupervised.attribute.PAA: number of windows.
m_windows - Variable in class weka.filters.unsupervised.attribute.SAX: number of windows.
m_X - Variable in class adams.data.instance.InstancePoint: the X value.
m_Y - Variable in class adams.data.instance.InstancePoint: the Y value.
m_zerocount - Variable in class adams.optimise.GeneticAlgorithm
m_ZoomOverview - Variable in class adams.flow.sink.WekaInstanceViewer: whether to display the zoom overview.
main(String[]) - Static method in class adams.data.utils.SAXUtils: Runs the algorithm from commandline.
main(String[]) - Static method in class adams.gui.InstanceCompare: Starts the frame.
main(String[]) - Static method in class adams.ml.WekaClassifier
main(String[]) - Static method in class adams.ml.WekaData
main(String[]) - Static method in class adams.ml.WekaFilter
main(String[]) - Static method in class adams.optimise.GeneticAlgorithm
main(String[]) - Static method in class weka.classifiers.functions.GaussianProcessesAdaptive: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.GaussianProcessesWeighted: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.GPD: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.PLSClassifierWeighted: Main method for running this classifier from commandline.
main(String[]) - Static method in class weka.classifiers.lazy.LWLIntervalEstimator: Main method for executing this classifier.
main(String[]) - Static method in class weka.classifiers.lazy.LWLSynchro: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AbstainAttributePercentile: Main method for running this class.
main(String[]) - Static method in class weka.classifiers.meta.AbstainLeastMedianSq: Main method for running this class.
main(String[]) - Static method in class weka.classifiers.meta.ClassificationViaRegressionD: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Corr: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.FilteredClassifierExt: Main method for running this classifier.
main(String[]) - Static method in class weka.classifiers.meta.LeastMedianSq: Main method for running this class.
main(String[]) - Static method in class weka.classifiers.meta.LogTargetRegressor: Main method for running this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiSearch: Main method for running this classifier from commandline.
main(String[]) - Static method in class weka.classifiers.meta.PartitionedStacking: Main method for running this classifier.
main(String[]) - Static method in class weka.classifiers.meta.PeakTransformed: Main method for running this class.
main(String[]) - Static method in class weka.classifiers.meta.SubsetEnsemble: Main method for running this class from commandline.
main(String[]) - Static method in class weka.classifiers.meta.SumTransformed: Main method for running this class.
main(String[]) - Static method in class weka.classifiers.meta.WeightedInstancesHandlerWrapper: Main method for running this class.
main(String[]) - Static method in class weka.classifiers.trees.M5P2: Main method by which this class can be tested
main(String[]) - Static method in class weka.classifiers.trees.RandomModelTrees: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.RandomRegressionForest: Main method for testing this class.
main(String[]) - Static method in class weka.core.SetupGenerator: For testing only.
main(String[]) - Static method in class weka.experiment.ResultMatrixMediaWiki: for testing only.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.CorrelationMatrix: runs the filter with the given arguments.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.DownSample: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.FastWavelet: runs the filter with the given arguments.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.InterquartileRangeSamp: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NormalizeAdaptive: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PAA: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ReplaceMissingValuesWithZero: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.SAX: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.SetMissingValue: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.SpellChecker: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.LatestRecords: Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveDuplicates: Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveInstancesWithMissingValue: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RowNorm: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.SafeRemoveRange: Main method for running this filter from command-line.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Sort: Main method for running this filter.
main(String[]) - Static method in class weka.gui.explorer.ExperimentPanel: Tests out the Experiment panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.SqlPanel: For testing only.
makeClassLastTipText() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns the tip text for this property.
markersDisabledTipText() - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Returns the tip text for this property.
markersExtentTipText() - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Returns the tip text for this property.
match(Instance) - Method in class adams.data.conversion.AbstractMatchWekaInstanceAgainstHeader: Matches the input instance against the header.
match(String) - Method in class adams.flow.transformer.WekaRegexToRange: Return match, given invert status.
MatchWekaInstanceAgainstFileHeader - Class in adams.data.conversion: Matches an Instance against a dataset header loaded from a file, i.e., it automatically converts STRING attributes into NOMINAL ones and vice versa.
The file can be any format that WEKA recognizes.
MatchWekaInstanceAgainstFileHeader() - Constructor for class adams.data.conversion.MatchWekaInstanceAgainstFileHeader
MatchWekaInstanceAgainstStorageHeader - Class in adams.data.conversion: Matches an Instance against a dataset header from storage, i.e., it automatically converts STRING attributes into NOMINAL ones and vice versa.
MatchWekaInstanceAgainstStorageHeader() - Constructor for class adams.data.conversion.MatchWekaInstanceAgainstStorageHeader
MathParameter - Class in weka.core.setupgenerator: Container class for search parameters.
MathParameter() - Constructor for class weka.core.setupgenerator.MathParameter
MAX_DECIMAL - Static variable in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: the maximum number of decimals after the decimal point to use.
maxIterationsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Returns the tip text for this property
maxSizeTipText() - Method in class adams.data.weka.predictions.RelativeNumericErrorScaler: Returns the tip text for this property.
maxTipText() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the tip text for this property.
maxTipText() - Method in class weka.classifiers.trees.RandomModelTrees: Returns the tip text for this property.
maxTipText() - Method in class weka.core.setupgenerator.MathParameter: Returns the tip text for this property.
maxTrainTimeTipText() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the tip text for this property.
maxTrainTimeTipText() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the tip text for this property.
mayRemoveInstanceAfterFirstBatchDone() - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Derived filters may removed rows.
mayRemoveInstances() - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Method that returns whether the filter may remove instances after the first batch has been done.
mayRemoveInstances() - Method in class weka.filters.unsupervised.instance.DatasetCleaner: Method that returns whether the filter may remove instances after the first batch has been done.
mayRemoveInstances() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Method that returns whether the filter may remove instances after the first batch has been done.
means(Instances) - Method in class weka.classifiers.meta.Corr
measureNumRules() - Method in class weka.classifiers.trees.m5.M5Base2: return the number of rules
measureTipText() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the tip text for this property.
measureTipText() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the tip text for this property.
merge(Instances[], Instances[], HashSet) - Method in class adams.flow.transformer.WekaInstancesMerge: Merges the datasets based on the collected IDs.
mergeInstance(Instance) - Method in class weka.core.AbstractHashableInstance: Merges this instance with the given instance and returns the result.
metaLevelClassifierTipText() - Method in class weka.classifiers.meta.PartitionedStacking: Returns the tip text for this property.
minDist(double[], double[], double[][], int) - Static method in class adams.data.utils.SAXUtils: Calculate the distance between 2 SAX vectors.
minNumInstancesTipText() - Method in class weka.classifiers.trees.m5.M5Base2: Returns the tip text for this property
minTipText() - Method in class weka.classifiers.trees.RandomRegressionForest: Returns the tip text for this property
minTipText() - Method in class weka.core.setupgenerator.MathParameter: Returns the tip text for this property.
missingTipText() - Method in class adams.tools.CompareDatasets: Returns the tip text for this property.
mNumComponents - Variable in class weka.core.neighboursearch.PLSNNSearch
modelActorTipText() - Method in class adams.flow.condition.bool.WekaClassification: Returns the tip text for this property.
modelActorTipText() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Returns the tip text for this property.
modelFileTipText() - Method in class adams.flow.condition.bool.WekaClassification: Returns the tip text for this property.
modelFileTipText() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Returns the tip text for this property.
msq(Instances) - Method in class weka.classifiers.meta.Corr
MTAbstractGeneticAlgorithm - Class in adams.genetic: Base class for genetic algorithms.
MTAbstractGeneticAlgorithm() - Constructor for class adams.genetic.MTAbstractGeneticAlgorithm
MTAbstractGeneticAlgorithm.GeneticAlgorithmJob - Class in adams.genetic: A job class specific to genetic algorithms.
MTAbstractGeneticAlgorithm.GeneticAlgorithmJob(MTAbstractGeneticAlgorithm, int, int[]) - Constructor for class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: Initializes the job.
MTPackDataGeneticAlgorithm - Class in adams.genetic: ???
MTPackDataGeneticAlgorithm() - Constructor for class adams.genetic.MTPackDataGeneticAlgorithm
MultiColumnFinder - Class in adams.data.weka.columnfinder: Applies multiple column finding algorithms to the data.
The indices can be either joined or intersected.
MultiColumnFinder() - Constructor for class adams.data.weka.columnfinder.MultiColumnFinder
MultiColumnFinder.Combination - Enum in adams.data.weka.columnfinder: How combine the indices.
MultiRowFinder - Class in adams.data.weka.rowfinder: Applies multiple row finding algorithms to the data.
The indices can be either joined or intersected.
MultiRowFinder() - Constructor for class adams.data.weka.rowfinder.MultiRowFinder
MultiRowFinder.Combination - Enum in adams.data.weka.rowfinder: How combine the indices.
MultiSearch - Class in weka.classifiers.meta: Performs a search of an arbitrary number of parameters of a classifier and chooses the best pair found for the actual filtering and training.
The default MultiSearch is using the following FilteredClassifier setup:
- classifier: LinearRegression, searching for the "Ridge"
- filter: PLSFilter, searching for the "# of Components"
The properties being explored are totally up to the user, it can be a mix of classifier and filter properties, or only classifier ones or only filter ones.

Since the the MultiSearch classifier itself is used as the base object for the setups being generated, one has to prefix the properties with 'classifier.' (referring to MultiSearch's 'classifier' property).
E.g., if you have a FilteredClassifier selected as base classifier, sporting a PLSFilter and you want to explore the number of PLS components, then your property will be made up of the following components:
- classifier: referring to MultiSearch's classifier property
i.e., the FilteredClassifier.
- filter: referring to the FilteredClassifier's property (= PLSFilter)
- numComponents: the actual property of the PLSFilter that we want to modify
And assembled, the property looks like this:
classifier.filter.numComponents

The initial space is worked on with 2-fold CV to determine the values of the parameters for the selected type of evaluation (e.g., accuracy).
MultiSearch() - Constructor for class weka.classifiers.meta.MultiSearch: the default constructor.
MultiSearch.EvaluationTask - Class in weka.classifiers.meta: Helper class for evaluating a setup.
MultiSearch.EvaluationTask(MultiSearch, Instances, SetupGenerator, Point<Object>, int, int) - Constructor for class weka.classifiers.meta.MultiSearch.EvaluationTask: Initializes the task.

N

NamedSetup - Class in adams.data.weka.evaluator: Applies an evaluator that is referenced via its global setup name (see 'NamedSetups').
NamedSetup() - Constructor for class adams.data.weka.evaluator.NamedSetup
NamedSetup - Class in adams.data.weka.predictions: Applies a scaler that is referenced via its global setup name (see 'NamedSetups').
NamedSetup() - Constructor for class adams.data.weka.predictions.NamedSetup
names() - Method in class adams.flow.container.WekaClusteringContainer: Returns all value names that can be used (theoretically).
names() - Method in class adams.flow.container.WekaModelContainer: Returns all value names that can be used (theoretically).
names() - Method in class adams.flow.container.WekaPredictionContainer: Returns all value names that can be used (theoretically).
names() - Method in class adams.flow.container.WekaTrainTestSetContainer: Returns all value names that can be used (theoretically).
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.NewNNSearch: Returns the nearest instance in the current neighbourhood to the supplied instance.
newBest(double, OptData) - Method in class adams.optimise.genetic.fitnessfunctions.AttributeSelection: Callback for best measure so far
newComparator() - Method in class adams.data.instance.Instance: Returns the comparator in use.
newContainer(Comparable) - Method in class adams.gui.visualization.instance.InstanceContainerManager: Returns a new container containing the given payload.
newContainerManager() - Method in class adams.gui.visualization.instance.InstancePanel: Returns the container manager to use.
newExperiment() - Static method in class adams.gui.goe.WekaExperimentFileEditor: Generates a new (simple) experiment.
NewNNSearch - Class in weka.core.neighboursearch: Class implementing the brute force search algorithm for nearest neighbour search.
NewNNSearch() - Constructor for class weka.core.neighboursearch.NewNNSearch: Constructor.
NewNNSearch(Instances) - Constructor for class weka.core.neighboursearch.NewNNSearch: Constructor that uses the supplied set of instances.
NewNNSearch.InstanceNode - Class in weka.core.neighboursearch
NewNNSearch.InstanceNode(int, Double) - Constructor for class weka.core.neighboursearch.NewNNSearch.InstanceNode
newPanel() - Method in class adams.flow.sink.WekaClassifierErrors: Creates the panel to display in the dialog.
newPanel() - Method in class adams.flow.sink.WekaCostCurve: Creates the panel to display in the dialog.
newPanel() - Method in class adams.flow.sink.WekaInstancesDisplay: Creates the panel to display in the dialog.
newPanel() - Method in class adams.flow.sink.WekaInstanceViewer: Creates the panel to display in the dialog.
newPanel() - Method in class adams.flow.sink.WekaROC: Creates the panel to display in the dialog.
newPoint() - Method in class adams.data.instance.Instance: Returns a new instance of a sequence point.
newReport() - Method in class adams.data.instance.Instance: Creates an empty report.
newReportPanel() - Method in class adams.gui.visualization.instance.InstanceExplorer: Creates a new tabbed pane for the reports.
newTable(ReportFactory.Model) - Method in class adams.gui.visualization.instance.InstanceReportFactory.Panel: Returns a new table instance.
newZoomPaintlet() - Method in class adams.gui.visualization.instance.InstanceZoomOverviewPanel: Creates a new zoom paintlet.
next(int) - Method in class adams.tools.CompareDatasets: Returns the next row pair to compare.
nextByIndex(int) - Method in class adams.tools.CompareDatasets: Returns the next pair by simple index.
nextByRowAttribute(int) - Method in class adams.tools.CompareDatasets: Returns the next pair by using the value of the row attribute.
nextPowerOf2(int) - Static method in class weka.filters.unsupervised.attribute.FastWavelet: returns the next bigger number that's a power of 2.
NO_CLASS - Static variable in class adams.gui.visualization.instance.LoadDatasetDialog: the "no class" constant.
NO_SORTING - Static variable in class adams.gui.visualization.instance.LoadDatasetDialog: the "no sorting" constant.
noCheckTipText() - Method in class adams.flow.transformer.WekaExperiment: Returns the tip text for this property.
nodeToString() - Method in class weka.classifiers.trees.m5.RuleNode2: Returns a description of this node (debugging purposes)
noiseTipText() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Returns the tip text for this property
noiseTipText() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Returns the tip text for this property
noiseTipText() - Method in class weka.classifiers.functions.GPD: Returns the tip text for this property
nominalTipText() - Method in class weka.filters.unsupervised.attribute.SAX: Returns the tip text for this property.
NormalizeAdaptive - Class in weka.filters.unsupervised.attribute: Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
NormalizeAdaptive() - Constructor for class weka.filters.unsupervised.attribute.NormalizeAdaptive
notCoveredInstances() - Method in class weka.classifiers.trees.m5.Rule2: Get the instances not covered by this rule
notifyChangeListeners(ChangeEvent) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Sends the event to all change listeners.
noUpdateTipText() - Method in class weka.classifiers.lazy.LWLSynchro: Returns the tip text for this property.
nPointTipText() - Method in class weka.core.SAXDistance: Returns the tip text for this property.
nthPointTipText() - Method in class weka.filters.unsupervised.attribute.DownSample: Returns the tip text for this property.
NullFinder - Class in adams.data.weka.columnfinder: Dummy finder, does not find any columns.
NullFinder() - Constructor for class adams.data.weka.columnfinder.NullFinder
NullFinder - Class in adams.data.weka.rowfinder: Dummy finder, does not find any rows.
NullFinder() - Constructor for class adams.data.weka.rowfinder.NullFinder
numAttributes() - Method in class weka.core.AbstractHashableInstance: Returns the number of attributes.
numattrsTipText() - Method in class weka.classifiers.meta.Corr: Returns the tip text for this property.
numberOfLinearModels() - Method in class weka.classifiers.trees.m5.RuleNode2: Get the number of linear models in the tree
numChromTipText() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the tip text for this property.
numChromTipText() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the tip text for this property.
numClasses() - Method in class weka.core.AbstractHashableInstance: Returns the number of class labels.
numDecimalsTipText() - Method in class adams.data.conversion.WekaInstancesToSpreadSheet: Returns the tip text for this property.
numExecutionSlotsTipText() - Method in class weka.classifiers.meta.MultiSearch: Returns the tip text for this property.
numExecutionSlotsTipText() - Method in class weka.classifiers.meta.SubsetEnsemble: Returns the tip text for this property.
numFoldsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Returns the tip text for this property
numIterationsTipText() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the tip text for this property.
numIterationsTipText() - Method in class weka.classifiers.trees.RandomModelTrees: Returns the tip text for this property.
numIterationsTipText() - Method in class weka.classifiers.trees.RandomRegressionForest: Returns the tip text for this property
numLeaves(int) - Method in class weka.classifiers.trees.m5.RuleNode2: Sets the leaves' numbers
numPointsLeftTipText() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns the tip text for this property.
numPointsRightTipText() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Returns the tip text for this property.
numRandomFeaturesTipText() - Method in class weka.classifiers.meta.SubsetEnsemble: Returns the tip text for this property.
numRegressionsTipText() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Returns the tip text for this property
numRegressionsTipText() - Method in class weka.classifiers.meta.LeastMedianSq: Returns the tip text for this property
numThreadsTipText() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the tip text for this property.
numValues() - Method in class weka.core.AbstractHashableInstance: Returns the number of values present in a sparse representation.
numZeroesTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Returns the tip text for this property.

O

onTheFlyTipText() - Method in class adams.flow.condition.bool.WekaClassification: Returns the tip text for this property.
onTheFlyTipText() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Returns the tip text for this property.
operationTipText() - Method in class adams.flow.transformer.WekaInstanceBuffer: Returns the tip text for this property.
optimise(OptData, FitnessFunction) - Method in class adams.optimise.GeneticAlgorithm
optimizerTipText() - Method in class adams.flow.transformer.WekaClassifierOptimizer: Returns the tip text for this property.
output() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Returns the generated token.
output() - Method in class adams.flow.source.WekaDatabaseReader: Returns the generated token.
output() - Method in class adams.flow.source.WekaDataGenerator: Returns the generated token.
output() - Method in class adams.flow.source.WekaNewInstances: Returns the generated token.
output() - Method in class adams.flow.transformer.WekaAccumulatedError: Returns the generated token.
output() - Method in class adams.flow.transformer.WekaAttributeIterator: Returns the generated token.
output() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Returns the generated token.
output() - Method in class adams.flow.transformer.WekaFileReader: Returns the generated token.
output() - Method in class adams.flow.transformer.WekaInstanceBuffer: Returns the generated token.
output() - Method in class adams.flow.transformer.WekaInstancesInfo: Returns the generated token.
output() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Returns the generated token.
output() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns the generated token.
output() - Method in class adams.flow.transformer.WekaSubsets: Returns the generated token.
outputArrayTipText() - Method in class adams.flow.source.AbstractWekaSetupGenerator: Returns the tip text for this property.
outputBestSetupTipText() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the tip text for this property.
outputDirectoryTipText() - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Returns the tip text for this property.
outputFileTipText() - Method in class adams.flow.sink.AbstractWekaModelWriter: Returns the tip text for this property.
outputFileTipText() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the tip text for this property.
outputFileTipText() - Method in class adams.flow.sink.WekaFileWriter: Returns the tip text for this property.
outputFileTipText() - Method in class adams.tools.CompareDatasets: Returns the tip text for this property.
outputFormatTipText() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the tip text for this property.
outputFormatTipText() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the tip text for this property.
outputHeaderTipText() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the tip text for this property.
outputInstanceTipText() - Method in class adams.flow.transformer.WekaClassifying: Returns the tip text for this property.
outputOnlyModelTipText() - Method in class adams.flow.transformer.AbstractWekaModelReader: Returns the tip text for this property.
outputPrefixTipText() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the tip text for this property.
outputRelationNameTipText() - Method in class adams.flow.transformer.WekaEvaluationSummary: Returns the tip text for this property.
outputTipText() - Method in class adams.flow.transformer.AbstractWekaClassifierEvaluator: Returns the tip text for this property.
outputTypeTipText() - Method in class adams.flow.transformer.WekaFileReader: Returns the tip text for this property.
overrideTipText() - Method in class adams.flow.transformer.WekaClassSelector: Returns the tip text for this property.

P

PAA(double[], int) - Static method in class adams.data.utils.SAXUtils

Piecewise Aggregate Approximation.

PAA - Class in weka.filters.unsupervised.attribute

Valid options are:

PAA() - Constructor for class weka.filters.unsupervised.attribute.PAA

PackageManager - Class in adams.gui.menu

Opens the WEKA PackageManager.

PackageManager() - Constructor for class adams.gui.menu.PackageManager

Initializes the menu item with no owner.

PackageManager(AbstractApplicationFrame) - Constructor for class adams.gui.menu.PackageManager

Initializes the menu item.

PackData - Class in adams.optimise.genetic

???

PackData(PackDataDef) - Constructor for class adams.optimise.genetic.PackData

PackDataDef - Class in adams.optimise.genetic

???

PackDataDef() - Constructor for class adams.optimise.genetic.PackDataDef

PackDataDef.DataInfo - Class in adams.optimise.genetic

PackDataDef.DataInfo(String, int, double, double) - Constructor for class adams.optimise.genetic.PackDataDef.DataInfo

PackDataGeneticAlgorithm - Class in adams.optimise.genetic

???

PackDataGeneticAlgorithm() - Constructor for class adams.optimise.genetic.PackDataGeneticAlgorithm

pad(Instances) - Method in class weka.filters.unsupervised.attribute.FastWavelet

pads the data to conform to the necessary number of attributes.

PADDING_ZERO - Static variable in class weka.filters.unsupervised.attribute.FastWavelet

the type of padding: Zero padding.

paddingTipText() - Method in class weka.filters.unsupervised.attribute.FastWavelet

Returns the tip text for this property.

paintValue(Graphics, Rectangle) - Method in class adams.gui.goe.WekaExperimentFileEditor

Paints a representation of the current Object.

parametersFileTipText() - Method in class weka.core.SetupGenerator

Returns the tip text for this property.

parametersTipText() - Method in class adams.flow.source.AbstractWekaSetupGenerator

Returns the tip text for this property.

parentNode() - Method in class weka.classifiers.trees.m5.RuleNode2

Get the parent of this node

parse(String) - Method in class adams.data.instance.InstancePoint

Parses a string and instantiates a sequence point of it.

parse(String) - Method in enum adams.flow.core.EvaluationStatistic

Parses the given string and returns the associated enum.

parse(String) - Method in enum adams.flow.core.ExperimentStatistic

Parses the given string and returns the associated enum.

parse(String) - Method in enum adams.flow.transformer.WekaClassifierRanker.Measure

Parses the given string and returns the associated enum.

PartitionedStacking - Class in weka.classifiers.meta

Builds the base-classifiers on subsets of the data defined by ranges that correspond to the base-classifiers.

PartitionedStacking() - Constructor for class weka.classifiers.meta.PartitionedStacking

PassThrough - Class in adams.data.weka.evaluator

A dummy evaluator that OKs all data.

PassThrough() - Constructor for class adams.data.weka.evaluator.PassThrough

PassThrough - Class in adams.flow.transformer.wekaclusterer

Dummy post-processor that just returns the model container as it is.

PassThrough() - Constructor for class adams.flow.transformer.wekaclusterer.PassThrough

passwordTipText() - Method in class adams.flow.sink.WekaDatabaseWriter

Returns the tip text for this property.

passwordTipText() - Method in class adams.flow.source.WekaDatabaseReader

Returns the tip text for this property.

pauseExecution() - Method in class adams.flow.transformer.WekaClassifierRanker

Pauses the execution.

PCANNSearch - Class in weka.core.neighboursearch

Class implementing the brute force search algorithm for nearest neighbour search, filtered using PLS.

PCANNSearch() - Constructor for class weka.core.neighboursearch.PCANNSearch

Constructor.

PCANNSearch(Instances) - Constructor for class weka.core.neighboursearch.PCANNSearch

Constructor that uses the supplied set of instances.

pctTipText() - Method in class weka.classifiers.meta.AbstainLeastMedianSq

pctTipText() - Method in class weka.classifiers.meta.LeastMedianSq

PeakTransformed - Class in weka.classifiers.meta

Uses the maximum peak in the instances.

PeakTransformed() - Constructor for class weka.classifiers.meta.PeakTransformed

percentageTipText() - Method in class adams.flow.transformer.WekaRandomSplit

Returns the tip text for this property.

percentileTipText() - Method in class weka.classifiers.meta.AbstainAttributePercentile

Performance - Class in weka.classifiers.meta.multisearch

A helper class for storing the performance of values in the parameter space.

Performance(Point<Object>, Evaluation, int) - Constructor for class weka.classifiers.meta.multisearch.Performance

Initializes the performance container.

PerformanceCache - Class in weka.classifiers.meta.multisearch

Represents a simple cache for performance objects.

PerformanceCache() - Constructor for class weka.classifiers.meta.multisearch.PerformanceCache

PerformanceComparator - Class in weka.classifiers.meta.multisearch

A concrete Comparator for the Performance class.

PerformanceComparator(int) - Constructor for class weka.classifiers.meta.multisearch.PerformanceComparator

initializes the comparator with the given performance measure.

performComparison() - Method in class adams.gui.visualization.instance.InstanceComparePanel

Performs the comparison between the rows from the two datasets.

performPaint(Graphics, PaintEvent.PaintMoment) - Method in class adams.gui.visualization.instance.InstanceLinePaintlet

The paint routine of the paintlet.

performSetUpChecks(boolean) - Method in class adams.flow.sink.WekaFileWriter

Hook for performing setup checks -- used in setUp() and preExecute().

PLACEHOLDER_INDEX - Static variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet

the placeholder for the class index in the distribution format.

PLACEHOLDER_LABEL - Static variable in class adams.data.conversion.WekaPredictionContainerToSpreadSheet

the placeholder for the class label in the distribution format.

PLOT_SIZE - Static variable in class weka.gui.visualize.plugins.FixedClassifierErrors

fixed plot size.

plotNameTipText() - Method in class adams.flow.transformer.WekaAccumulatedError

Returns the tip text for this property.

PLSClassifierWeighted - Class in weka.classifiers.functions

A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.

PLSClassifierWeighted() - Constructor for class weka.classifiers.functions.PLSClassifierWeighted

PLSNNSearch - Class in weka.core.neighboursearch

Class implementing the brute force search algorithm for nearest neighbour search, filtered using PLS.

PLSNNSearch() - Constructor for class weka.core.neighboursearch.PLSNNSearch

Constructor.

PLSNNSearch(Instances) - Constructor for class weka.core.neighboursearch.PLSNNSearch

Constructor that uses the supplied set of instances.

PLSTipText() - Method in class weka.classifiers.trees.RandomRegressionForest

Returns the tip text for this property

Point<E> - Class in weka.core.setupgenerator

A multi-dimensional point.

Point(E[]) - Constructor for class weka.core.setupgenerator.Point

Initializes the point with the given values.

points() - Method in class weka.core.setupgenerator.Space

returns an Enumeration over all points.

polynomialOrderTipText() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay

Returns the tip text for this property.

postProcess(WekaModelContainer) - Method in class adams.flow.transformer.wekaclusterer.AbstractClustererPostProcessor

Post-processes the model container.

postProcessCheck() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob

Checks whether all post-conditions have been met.

postProcessCheck() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob

Checks whether all post-conditions have been met.

postProcessCheck() - Method in class adams.optimise.GeneticAlgorithm.GAJob

postProcessDistances(double[]) - Method in class weka.core.SAXDistance

Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue).

postProcessDistances(double[]) - Method in class weka.core.WeightedEuclideanDistance

Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue).

postProcessHeader(T) - Method in class adams.data.instances.AbstractInstanceGenerator

Adds IDs, notes, additional fields to header.

postProcessorTipText() - Method in class adams.flow.transformer.WekaClusterer

Returns the tip text for this property.

postProcessOutput(T, Instance) - Method in class adams.data.instances.AbstractInstanceGenerator

For adding IDs, notes, additional fields to the data.

postRun() - Method in class adams.genetic.MTAbstractGeneticAlgorithm

Further clean-ups in derived classes.

postRun() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm

Further clean-ups in derived classes.

postUpdate() - Method in class adams.gui.visualization.instance.InstancePanel

Hook method, called after the update was performed.

predictIntervals(Instance, double) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive

Predicts a confidence interval for the given instance and confidence level.

predictIntervals(Instance, double) - Method in class weka.classifiers.functions.GaussianProcessesWeighted

Predicts a confidence interval for the given instance and confidence level.

predictIntervals(Instance, double) - Method in class weka.classifiers.lazy.LWLIntervalEstimator

Returns an N * 2 array, where N is the number of prediction intervals.

predictIntervals(Instance, double) - Method in class weka.classifiers.meta.FilteredClassifierExt

Returns an N * 2 array, where N is the number of prediction intervals.

preFilterTipText() - Method in class adams.data.weka.rowfinder.FilteredIQR

Returns the tip text for this property.

prefix(int, StringBuffer) - Method in class weka.classifiers.trees.RandomRegressionForest.Node

generates the tree structure prefix

PREFIX_ADDITIONALFIELDS - Static variable in class adams.data.weka.ArffUtils

the prefix for additional fields.

PREFIX_NOTE - Static variable in class adams.data.weka.ArffUtils

the prefix for notes.

prefixAttributes(Instances, int) - Method in class adams.flow.transformer.WekaInstancesMerge

Prefixes the attributes.

prefixSeparatorTipText() - Method in class adams.flow.transformer.WekaInstancesMerge

Returns the tip text for this property.

prefixTipText() - Method in class adams.flow.transformer.WekaInstancesMerge

Returns the tip text for this property.

prepareData(Instances, int) - Method in class adams.flow.transformer.WekaInstancesMerge

Prepares the data, prefixing attributes, removing columns, etc, before merging it.

prepareUpdate() - Method in class adams.gui.visualization.instance.InstancePanel

Updates the axes with the min/max of the new data.

preProcessCheck() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob

Checks whether all pre-conditions have been met.

preProcessCheck() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob

Checks whether all pre-conditions have been met.

preProcessCheck() - Method in class adams.optimise.GeneticAlgorithm.GAJob

preRun() - Method in class adams.genetic.MTAbstractGeneticAlgorithm

Further initializations in derived classes.

preRun() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm

Further initializations in derived classes.

preRun() - Method in class adams.optimise.GeneticAlgorithm

Some more initializations.

preRun() - Method in class adams.tools.CompareDatasets

Before the actual run is executed.

preserveOrderTipText() - Method in class adams.flow.transformer.WekaRandomSplit

Returns the tip text for this property.

print(double[]) - Method in class adams.genetic.MTPackDataGeneticAlgorithm

print(double[]) - Method in class adams.optimise.genetic.PackDataGeneticAlgorithm

printAllModels() - Method in class weka.classifiers.trees.m5.RuleNode2

Print all the linear models at the learf (debugging purposes)

printBits(int[]) - Method in class adams.genetic.MTPackDataGeneticAlgorithm

printBits(int[]) - Method in class adams.optimise.genetic.PackDataGeneticAlgorithm

printLeafModels() - Method in class weka.classifiers.trees.m5.RuleNode2

print all leaf models

printNodeLinearModel() - Method in class weka.classifiers.trees.m5.RuleNode2

print the linear model at this node

process() - Method in class adams.flow.sink.WekaClassifierErrors.DataGenerator

Processes the data if necessary.

process() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob

Does the actual execution of the job.

process() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob

Does the actual execution of the job.

process() - Method in class adams.optimise.GeneticAlgorithm.GAJob

process(Instances) - Method in class weka.filters.FlowFilter

Processes the given data (may change the provided dataset) and returns the modified version.

process(Instances) - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier

Processes the given data (may change the provided dataset) and returns the modified version.

process(Instances) - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix

Processes the given data (may change the provided dataset) and returns the modified version.

process(Instance) - Method in class weka.filters.unsupervised.attribute.DownSample

processes the given instance (may change the provided instance) and returns the modified version.

process(Instances) - Method in class weka.filters.unsupervised.attribute.FastWavelet

Processes the given data (may change the provided dataset) and returns the modified version.

process(Instance) - Method in class weka.filters.unsupervised.attribute.PAA

processes the given instance (may change the provided instance) and returns the modified version.

process(Instance) - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay

processes the given instance (may change the provided instance) and returns the modified version.

process(Instance) - Method in class weka.filters.unsupervised.attribute.SAX

processes the given instance (may change the provided instance) and returns the modified version.

process(Instance) - Method in class weka.filters.unsupervised.attribute.SetMissingValue

processes the given instance (may change the provided instance) and returns the modified version.

process(Instance) - Method in class weka.filters.unsupervised.attribute.SpellChecker

processes the given instance (may change the provided instance) and returns the modified version.

process(Instances) - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier

Processes the given data (may change the provided dataset) and returns the modified version.

process(Instances) - Method in class weka.filters.unsupervised.instance.LatestRecords

Processes the given data (may change the provided dataset) and returns the modified version.

process(Instances) - Method in class weka.filters.unsupervised.instance.RemoveDuplicates

Processes the given data (may change the provided dataset) and returns the modified version.

process(Instances) - Method in class weka.filters.unsupervised.instance.RemoveInstancesWithMissingValue

Processes the given data (may change the provided dataset) and returns the modified version.

process(Instances) - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes

Processes the given data (may change the provided dataset) and returns the modified version.

process(Instance) - Method in class weka.filters.unsupervised.instance.RowNorm

processes the given instance (may change the provided instance) and returns the modified version.

process(Instances) - Method in class weka.filters.unsupervised.instance.SafeRemoveRange

Processes the given data (may change the provided dataset) and returns the modified version.

process(Instances) - Method in class weka.filters.unsupervised.instance.Sort

Processes the given data (may change the provided dataset) and returns the modified version.

processData(T) - Method in class adams.data.baseline.AbstractLinearRegressionBased

Performs the actual correcting.

processData(Instances) - Method in class weka.filters.unsupervised.attribute.FastWavelet

processes the instances using the HAAR/JSci algorithm.

processDatasetWithClusterer(Instances, Clusterer) - Method in class adams.flow.transformer.wekaclusterer.AbstractClusterMembershipPostProcessor

Performs some form of processing on the full dataset.

processDatasetWithClusterer(Instances, Clusterer) - Method in class adams.flow.transformer.wekaclusterer.ClusterCenters

Performs some form of processing on the full dataset.

processHit(MouseEvent, Object) - Method in class adams.gui.visualization.instance.InstancePointHitDetector

Performs the action when a hit is detected.

processInstance(Instance) - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel

Processes the instance and generates the output token.

processInstance(Instance) - Method in class adams.flow.transformer.WekaClassifying

Processes the instance and generates the output token.

processInstance(Instance) - Method in class adams.flow.transformer.WekaClustering

Processes the instance and generates the output token.

processTipText(PlotPanel, Point, String) - Method in class adams.gui.visualization.instance.InstancePanel

Processes the given tip text.

propertyTipText() - Method in class weka.core.setupgenerator.AbstractParameter

Returns the tip text for this property.

PROPS_FILTER - Static variable in class adams.genetic.MTAbstractGeneticAlgorithm

the key for a filter setup in the setup properties.

PROPS_FILTER - Static variable in class adams.optimise.genetic.AbstractGeneticAlgorithm

the key for a filter setup in the setup properties.

PROPS_MASK - Static variable in class adams.genetic.MTAbstractGeneticAlgorithm

the key for the mask in the setup properties.

PROPS_MASK - Static variable in class adams.optimise.genetic.AbstractGeneticAlgorithm

the key for the mask in the setup properties.

PROPS_RELATION - Static variable in class adams.genetic.MTAbstractGeneticAlgorithm

the key for the relation name in the generated properties file.

PROPS_RELATION - Static variable in class adams.optimise.genetic.AbstractGeneticAlgorithm

the key for the relation name in the generated properties file.

prune() - Method in class weka.classifiers.trees.m5.RuleNode2

Recursively prune the tree

pruneBackup() - Method in class adams.flow.sink.WekaDatabaseWriter

Removes entries from the backup.

pruneBackup() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel

Removes entries from the backup.

pruneBackup() - Method in class adams.flow.transformer.WekaAccumulatedError

Removes entries from the backup.

pruneBackup() - Method in class adams.flow.transformer.WekaClassifier

Removes entries from the backup.

pruneBackup() - Method in class adams.flow.transformer.WekaClusterer

Removes entries from the backup.

pruneBackup() - Method in class adams.flow.transformer.WekaCrossValidationSplit

Removes entries from the backup.

pruneBackup() - Method in class adams.flow.transformer.WekaFileReader

Removes entries from the backup.

pruneBackup() - Method in class adams.flow.transformer.WekaFilter

Removes entries from the backup.

pruneBackup() - Method in class adams.flow.transformer.WekaInstanceBuffer

Removes entries from the backup.

pruneBackup() - Method in class adams.flow.transformer.WekaInstanceDumper

Removes entries from the backup.

pruneBackup() - Method in class adams.flow.transformer.WekaMultiLabelSplitter

Removes entries from the backup.

putBits(int[]) - Method in class adams.optimise.genetic.PackData

Q

queryTipText() - Method in class adams.flow.source.WekaDatabaseReader: Returns the tip text for this property.

R

randomizeTipText() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Returns the tip text for this property.
RandomModelTrees - Class in weka.classifiers.trees
RandomModelTrees() - Constructor for class weka.classifiers.trees.RandomModelTrees
RandomRegressionForest - Class in weka.classifiers.trees: RandomRegressionForest: subtract mean and pls, then grow completely random trees (leaf: min ..
RandomRegressionForest() - Constructor for class weka.classifiers.trees.RandomRegressionForest
RandomRegressionForest.Node - Class in weka.classifiers.trees: TODO: description of class
RandomRegressionForest.Node(Instances, Random, int) - Constructor for class weka.classifiers.trees.RandomRegressionForest.Node: the constructor
randomSeedTipText() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Returns the tip text for this property
randomSeedTipText() - Method in class weka.classifiers.meta.LeastMedianSq: Returns the tip text for this property
range1TipText() - Method in class adams.tools.CompareDatasets: Returns the tip text for this property.
range2TipText() - Method in class adams.tools.CompareDatasets: Returns the tip text for this property.
RangeCheck - Class in weka.classifiers.meta: Keeps track of the ranges in case of numeric attributes.
RangeCheck() - Constructor for class weka.classifiers.meta.RangeCheck
RangeCheckClassifier - Interface in weka.classifiers: Interface for classifiers that allow checks whether data is outside the training range of the classifier.
RangeCheckHelper - Class in weka.classifiers: Helper class for generating range checks.
RangeCheckHelper() - Constructor for class weka.classifiers.RangeCheckHelper
rangesTipText() - Method in class weka.classifiers.meta.PartitionedStacking: Returns the tip text for this property.
rangeTipText() - Method in class adams.flow.transformer.WekaAttributeIterator: Returns the tip text for this property.
rbfKernel(double[], double[], double) - Method in class weka.classifiers.functions.GPD
readData() - Method in class adams.data.io.input.InstanceReader: Uses the named setup to read the data.
regexNameTipText() - Method in class adams.flow.transformer.WekaClassSelector: Returns the tip text for this property.
regExpTipText() - Method in class adams.data.weka.columnfinder.ByName: Returns the tip text for this property.
regExpTipText() - Method in class adams.data.weka.rowfinder.ByLabel: Returns the tip text for this property.
regExpTipText() - Method in class adams.flow.transformer.WekaAttributeIterator: Returns the tip text for this property.
regExpTipText() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns the tip text for this property.
regexTipText() - Method in class adams.flow.transformer.WekaRegexToRange: Returns the tip text for this property.
relationalValue(int) - Method in class weka.core.AbstractHashableInstance: Returns the relational value of a relational attribute.
relationalValue(Attribute) - Method in class weka.core.AbstractHashableInstance: Returns the relational value of a relational attribute.
relationNameTipText() - Method in class adams.flow.source.WekaNewInstances: Returns the tip text for this property.
relationNameTipText() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Returns the tip text for this property.
RelativeNumericErrorScaler - Class in adams.data.weka.predictions: Scales the errors for numeric class attributes.
RelativeNumericErrorScaler() - Constructor for class adams.data.weka.predictions.RelativeNumericErrorScaler
relativeWidthsTipText() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Returns the tip text for this property.
reload() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Reloads the currently loaded dataset.
reload() - Method in class adams.gui.visualization.instance.InstanceComparePanel: Reloads the datasets.
remove(int) - Method in class adams.gui.visualization.instance.InstanceContainerManager: Removes the container at the specified position.
removeAttributeIndicesTipText() - Method in class weka.classifiers.meta.FilteredClassifierExt: Returns the tip text for this property.
removeChangeListener(ChangeListener) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Removes the change listener from the internal list.
RemoveDuplicates - Class in weka.filters.unsupervised.instance: Removes all duplicate instances.
RemoveDuplicates() - Constructor for class weka.filters.unsupervised.instance.RemoveDuplicates
removeFitnessChangeListener(FitnessChangeListener) - Method in interface adams.event.FitnessChangeNotifier: Removes the given listener from its internal list of listeners.
RemoveInstancesWithMissingValue - Class in weka.filters.unsupervised.instance: Removes all instances that contain missing values.
RemoveInstancesWithMissingValue() - Constructor for class weka.filters.unsupervised.instance.RemoveInstancesWithMissingValue
RemoveMisclassifiedRel - Class in weka.filters.unsupervised.instance: A filter that removes instances which are incorrectly classified.
RemoveMisclassifiedRel() - Constructor for class weka.filters.unsupervised.instance.RemoveMisclassifiedRel
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.explorer.SqlPanel: Removes a PropertyChangeListener.
removeTrainTipText() - Method in class weka.classifiers.meta.AbstainAttributePercentile
RemoveWithZeroes - Class in weka.filters.unsupervised.instance: Removes all instances that contain at least the specified number (or percentage) of zeroes in numeric attributes.
RemoveWithZeroes() - Constructor for class weka.filters.unsupervised.instance.RemoveWithZeroes
replaceMissingValues(double[]) - Method in class weka.core.AbstractHashableInstance: Replaces all missing values in the instance with the values contained in the given array.
ReplaceMissingValuesWithZero - Class in weka.filters.unsupervised.attribute: Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
ReplaceMissingValuesWithZero() - Constructor for class weka.filters.unsupervised.attribute.ReplaceMissingValuesWithZero
replaceTipText() - Method in class adams.flow.transformer.WekaRenameRelation: Returns the tip text for this property.
REPORT_ADDITIONAL_PREFIX - Static variable in class adams.data.instance.Instance: the key prefix in the report for the additional attributes.
REPORT_CLASS - Static variable in class adams.data.instance.Instance: the key in the report for the class.
REPORT_DATASET - Static variable in class adams.data.instance.Instance: the key in the report for the dataset name.
REPORT_DB_ID - Static variable in class adams.data.instance.Instance: the key in the report for the database ID.
REPORT_DISPLAY_ID - Static variable in class adams.data.instance.Instance: the key in the report for the display ID.
REPORT_ID - Static variable in class adams.data.instance.Instance: the key in the report for the ID.
REPORT_ROW - Static variable in class adams.data.instance.Instance: the key in the report for the row in the dataset.
ReportToWekaInstance - Class in adams.data.conversion: Converts a report into a weka.core.Instance objects.
ReportToWekaInstance() - Constructor for class adams.data.conversion.ReportToWekaInstance
reset() - Method in class adams.data.conversion.AbstractMatchWekaInstanceAgainstHeader: Resets the converter.
reset() - Method in class adams.data.conversion.ReportToWekaInstance: Resets the scheme.
reset() - Method in class adams.data.instances.AbstractInstanceGenerator: Resets the generator (but does not clear the input data!).
reset() - Method in class adams.data.io.input.InstanceReader: Resets the filter.
reset() - Method in class adams.data.weka.columnfinder.AbstractTrainableColumnFinder: Resets the object, including the trained state.
reset() - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Resets the scheme.
reset() - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Resets the scheme.
reset() - Method in class adams.data.weka.evaluator.NamedSetup: Resets the filter.
reset() - Method in class adams.data.weka.predictions.NamedSetup: Resets the filter.
reset() - Method in class adams.data.weka.rowfinder.AbstractTrainableRowFinder: Resets the object, including the trained state.
reset() - Method in class adams.data.weka.rowfinder.FilteredIQR: Resets the object.
reset() - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Resets the scheme.
reset() - Method in class adams.flow.sink.WekaDatabaseWriter: Resets the actor.
reset() - Method in class adams.flow.source.WekaDatabaseReader: Resets the scheme.
reset() - Method in class adams.flow.transformer.AbstractWekaClassifierEvaluator: Resets the scheme.
reset() - Method in class adams.flow.transformer.WekaAccumulatedError: Resets the scheme.
reset() - Method in class adams.flow.transformer.WekaAttributeIterator: Resets the object.
reset() - Method in class adams.flow.transformer.WekaClassifier: Resets the scheme.
reset() - Method in class adams.flow.transformer.WekaClusterer: Resets the scheme.
reset() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Resets the scheme.
reset() - Method in class adams.flow.transformer.WekaFileReader: Resets the scheme.
reset() - Method in class adams.flow.transformer.WekaFilter: Resets the scheme.
reset() - Method in class adams.flow.transformer.WekaInstanceBuffer: Resets the scheme.
reset() - Method in class adams.flow.transformer.WekaInstanceDumper: Resets the scheme.
reset() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Resets the actor.
reset() - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Resets the actor.
reset() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Resets the scheme.
reset() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Resets the genetic algorihtm.
reset() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Invalidates the indexer.
reset() - Method in class adams.ml.WekaData.ArrayFinder
reset(String) - Method in class adams.ml.WekaData.ArrayFinder
reset() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Resets the genetic algorihtm.
reset() - Method in class adams.optimise.GeneticAlgorithm: Resets the genetic algorihtm.
reset() - Method in class weka.core.SetupGenerator: Resets the generation.
reset() - Method in class weka.filters.FlowFilter: Resets the filter.
reset() - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Resets the filter.
reset() - Method in class weka.filters.unsupervised.attribute.SpellChecker: resets the filter.
reset() - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Resets the filter.
resetMinMax(double, double) - Method in class adams.optimise.genetic.PackDataDef.DataInfo
restoreState(Hashtable<String, Object>) - Method in class adams.flow.sink.WekaDatabaseWriter: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.WekaAccumulatedError: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.WekaAttributeIterator: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.WekaClassifier: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.WekaClusterer: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.WekaCrossValidationSplit: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.WekaFileReader: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.WekaFilter: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.WekaInstanceBuffer: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.WekaInstanceDumper: Restores the state of the actor before the variables got updated.
restoreState(Hashtable<String, Object>) - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Restores the state of the actor before the variables got updated.
resultFileTipText() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the tip text for this property.
resultFormatTipText() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the tip text for this property.
ResultMatrixMediaWiki - Class in weka.experiment: Generates table output in MediaWiki format.
ResultMatrixMediaWiki() - Constructor for class weka.experiment.ResultMatrixMediaWiki: initializes the matrix as 1x1 matrix.
ResultMatrixMediaWiki(int, int) - Constructor for class weka.experiment.ResultMatrixMediaWiki: initializes the matrix with the given dimensions.
ResultMatrixMediaWiki(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixMediaWiki: initializes the matrix with the values from the given matrix.
resumeExecution() - Method in class adams.flow.transformer.WekaClassifierRanker: Resumes the execution.
returnLeaves(FastVector[]) - Method in class weka.classifiers.trees.m5.RuleNode2: Return a list containing all the leaves in the tree
ridgeTipText() - Method in class adams.data.baseline.AbstractLinearRegressionBased: Returns the tip text for this property.
ridgeTipText() - Method in class weka.classifiers.trees.RandomModelTrees: Returns the tip text for this property.
RIGHT - Static variable in class weka.classifiers.trees.m5.Rule2
rightNode() - Method in class weka.classifiers.trees.m5.RuleNode2: Get the right child of this node
ROC - Class in adams.gui.menu: Displays ROC curve data.
ROC() - Constructor for class adams.gui.menu.ROC: Initializes the menu item with no owner.
ROC(AbstractApplicationFrame) - Constructor for class adams.gui.menu.ROC: Initializes the menu item.
rootMeanSquaredError() - Method in class weka.classifiers.trees.m5.RuleNode2: Get the root mean squared error at this node
RoundErrorScaler - Class in adams.data.weka.predictions: Performs no scaling at all, just rounds the error to the next integer.
RoundErrorScaler() - Constructor for class adams.data.weka.predictions.RoundErrorScaler
rowAttribute1TipText() - Method in class adams.tools.CompareDatasets: Returns the tip text for this property.
rowAttribute2TipText() - Method in class adams.tools.CompareDatasets: Returns the tip text for this property.
RowFilteredColumnFinder - Class in adams.data.weka.columnfinder: This column finder first filters the rows before finding any columns on the subset of rows.
RowFilteredColumnFinder() - Constructor for class adams.data.weka.columnfinder.RowFilteredColumnFinder
RowFinder - Interface in adams.data.weka.rowfinder: Interface for classes that "find" rows of interest in datasets.
rowFinderTipText() - Method in class adams.data.weka.columnfinder.RowFilteredColumnFinder: Returns the tip text for this property.
rowFinderTipText() - Method in class adams.data.weka.rowfinder.AbstractFilteredRowFinder: Returns the tip text for this property.
rowFinderTipText() - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Returns the tip text for this property.
rowFinderTipText() - Method in class weka.filters.unsupervised.instance.DatasetCleaner: Returns the tip text for this property.
rowFinderTipText() - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Returns the tip text for this property.
RowNorm - Class in weka.filters.unsupervised.instance: Row wise normalization.
RowNorm() - Constructor for class weka.filters.unsupervised.instance.RowNorm
rowTipText() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the tip text for this property.
Rule2 - Class in weka.classifiers.trees.m5: Generates a single m5 tree or rule
Rule2() - Constructor for class weka.classifiers.trees.m5.Rule2: Constructor declaration
RuleNode2 - Class in weka.classifiers.trees.m5: Constructs a node for use in an m5 tree or rule
RuleNode2(double, double, RuleNode2) - Constructor for class weka.classifiers.trees.m5.RuleNode2: Creates a new RuleNode instance.
run() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Runs the genetic algorithm.
run() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Runs the genetic algorithm.
run() - Method in class weka.classifiers.meta.MultiSearch.EvaluationTask: Performs the evaluation.
runGeneticAlgorithm(Class, Class, String[]) - Static method in class adams.genetic.MTAbstractGeneticAlgorithm: Runs the genetic algorithm with the given options.
runGeneticAlgorithm(Class, Class, String[]) - Static method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Runs the genetic algorithm with the given options.
runsTipText() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the tip text for this property.

S

SafeRemoveRange - Class in weka.filters.unsupervised.instance: A filter that removes a given range of instances of a dataset.
Works just like weka.filters.unsupervised.instance.RemoveRange, but has a more robust handling of instance ranges.
SafeRemoveRange() - Constructor for class weka.filters.unsupervised.instance.SafeRemoveRange
sampleCorrs(Instances) - Method in class weka.classifiers.meta.Corr
sampleDevs(Instances, double[]) - Method in class weka.classifiers.meta.Corr
sampleSizePercentTipText() - Method in class weka.classifiers.meta.MultiSearch: Returns the tip text for this property.
saveBuffer(String) - Method in class weka.gui.explorer.ExperimentPanel: Save the currently selected experiment output to a file.
saveInstancesTipText() - Method in class weka.classifiers.trees.M5P2: Returns the tip text for this property
saveObject(Object) - Method in class weka.classifiers.meta.Corr
SavitzkyGolay - Class in weka.filters.unsupervised.attribute: A filter that applies Savitzky-Golay smoothing.
If a class attribute is present this will not be touched and moved to the end.

For more information see:

A.
SavitzkyGolay() - Constructor for class weka.filters.unsupervised.attribute.SavitzkyGolay
SAX - Class in weka.filters.unsupervised.attribute: A simple filter that retains only every nth attribute.
SAX() - Constructor for class weka.filters.unsupervised.attribute.SAX
SAXDistance - Class in weka.core: Implementing Euclidean distance (or similarity) function.

One object defines not one distance but the data model in which the distances between objects of that data model can be computed.

Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low.

For more information, see:

Wikipedia.
SAXDistance() - Constructor for class weka.core.SAXDistance: Constructs an Euclidean Distance object, Instances must be still set.
SAXDistance(Instances) - Constructor for class weka.core.SAXDistance: Constructs an Euclidean Distance object and automatically initializes the ranges.
SAXUtils - Class in adams.data.utils: A helper class for SAX
SAXUtils() - Constructor for class adams.data.utils.SAXUtils
scale(ArrayList) - Method in class adams.data.weka.predictions.AbstractErrorScaler: Scales the errors.
scale(ArrayList) - Method in class adams.data.weka.predictions.AutoScaler: Scales the errors.
scale(ArrayList) - Method in class adams.data.weka.predictions.FixedSizeErrorScaler: Scales the errors.
scale(ArrayList) - Method in class adams.data.weka.predictions.NamedSetup: Scales the errors.
scale(ArrayList) - Method in class adams.data.weka.predictions.RelativeNumericErrorScaler: Scales the errors.
scale(ArrayList) - Method in class adams.data.weka.predictions.RoundErrorScaler: Scales the errors.
scalerTipText() - Method in class adams.data.weka.predictions.AutoScaler: Returns the tip text for this property.
search() - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Performs a search in the fields.
searchParametersTipText() - Method in class weka.classifiers.meta.MultiSearch: Returns the tip text for this property.
secondAttributeRangeTipText() - Method in class adams.gui.InstanceCompare: Returns the tip text for this property.
secondDatasetTipText() - Method in class adams.gui.InstanceCompare: Returns the tip text for this property.
secondRowIndexTipText() - Method in class adams.gui.InstanceCompare: Returns the tip text for this property.
seedTipText() - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: Returns the tip text for this property.
seedTipText() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the tip text for this property.
seedTipText() - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Returns the tip text for this property.
seedTipText() - Method in class adams.flow.transformer.WekaCrossValidationSplit: Returns the tip text for this property.
seedTipText() - Method in class adams.flow.transformer.WekaRandomSplit: Returns the tip text for this property.
seedTipText() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Returns the tip text for this property.
seedTipText() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Returns the tip text for this property.
seedTipText() - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Returns the tip text for this property.
selectSubSample(Instances, Random) - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Produces a random sample from m_Data in m_SubSample.
selectSubSample(Instances, Random) - Method in class weka.classifiers.meta.LeastMedianSq: Produces a random sample from m_Data in m_SubSample.
SESSION_FILE - Static variable in class adams.gui.visualization.instance.InstanceExplorer: the file to store the recent files in.
SESSION_FILE_1 - Static variable in class adams.gui.visualization.instance.InstanceComparePanel: the file to store the recent files in (first file).
SESSION_FILE_2 - Static variable in class adams.gui.visualization.instance.InstanceComparePanel: the file to store the recent files in (second file).
set(Instance) - Method in class adams.data.instance.Instance: Clears the container and adds the data from the weka.core.Instance (internal values).
set(Instance, int, int[], Range, HashSet<Integer>) - Method in class adams.data.instance.Instance: Clears the container and adds the data from the weka.core.Instance (internal values).
set(String, double) - Method in class adams.optimise.genetic.PackData
setAbsErr(double) - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Sets the threshold for the max error when predicting a numeric class.
setAbsolute(boolean) - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Sets whether to return the absolute values of the coefficients.
setAddClassification(boolean) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Sets whether to add the numeric classification (label index for nominal classes).
setAddClassificationLabel(boolean) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Sets whether to add the classification label (only for nominal classes).
setAddDatabaseID(boolean) - Method in class adams.data.instances.AbstractInstanceGenerator: Sets whether the database ID is added to the data or not.
setAddDistribution(boolean) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Sets whether to add the class distribution (only for nominal classes).
setAddIndex(boolean) - Method in class adams.flow.transformer.WekaInstancesMerge: Sets whether to add the dataset index number to the prefix.
setAdditionalParameters(String[]) - Method in class adams.gui.menu.AbstractParameterHandlingWekaMenuItemDefinition: Sets the additional parameters.
setAddLabelIndex(boolean) - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Sets whether to prefix the labels with the index.
setAdjustToVisibleData(boolean) - Method in class adams.gui.visualization.instance.InstancePanel: Sets whether the display is adjusted to only the visible data or everything currently loaded.
setAlgorithm(SelectedTag) - Method in class weka.filters.unsupervised.attribute.FastWavelet: Sets the type of algorithm to use.
setAmount(double) - Method in class weka.filters.unsupervised.instance.LatestRecords: Sets the amount of records to keep (0,1]=percentage, (1,+inf)=absolute number.
setAntiAliasingEnabled(boolean) - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Sets whether to use anti-aliasing.
setArrayFinderFromInstancesHeader() - Method in class adams.ml.WekaData
setAttribute(String) - Method in class weka.classifiers.meta.AbstainAttributePercentile
setAttributeIndex(Index) - Method in class adams.data.weka.rowfinder.ByLabel: Sets the index of the attribute to perform the matching on.
setAttributeIndex(Index) - Method in class adams.flow.transformer.WekaInstancesInfo: Sets the attribute index to use for attribute-specific information.
setAttributeIndex(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Sets the index of the attribute to use for indexing.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.SpellChecker: Sets the attribute index (1-based) of the attribute to process.
setAttributeName(String) - Method in class adams.flow.transformer.WekaGetInstanceValue: Sets the name of the attribute to get the value for.
setAttributeName(String) - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Set the new attribute's name.
setAttributeName(String) - Method in class weka.filters.unsupervised.instance.LatestRecords: Sets the name of the attribute containing the numeric database ID.
setAttributeNames(BaseList) - Method in class adams.flow.source.WekaNewInstances: Sets the list of attribute names.
setAttributeRange(Range) - Method in class adams.data.weka.rowfinder.FilteredIQR: Sets the attribute range to work on.
setAttributeRange(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Sets the range of attributes.
setAttributeRange(String) - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Sets the range of attributes to compute the matrix for.
setAttributeRange(String) - Method in class weka.filters.unsupervised.attribute.SetMissingValue: Sets the range of attributes to compute the matrix for.
setAttributes(Range) - Method in class adams.flow.transformer.WekaInstanceStreamPlotGenerator: Sets the range of attributes to create plot containers for.
setAttributes(String[]) - Method in class adams.ml.WekaData
setAttributeTypes(AttributeTypeList) - Method in class adams.flow.source.WekaNewInstances: Sets the list of attribute types.
setAutoKeyGeneration(boolean) - Method in class adams.flow.sink.WekaDatabaseWriter: Sets whether to automatically generate a primary key.
setBase(double) - Method in class weka.core.setupgenerator.MathParameter: Set the value of the base.
setBaseObject(Serializable) - Method in class weka.core.SetupGenerator: Sets the base object, can be single object or array of objects.
setBestRange(Range) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Sets the best range of attributes.
setBestRange(String) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Sets the best range of attributes.
setBestRange(Range) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Sets the best range of attributes.
setBestRange(String) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Sets the best range of attributes.
setBins(int) - Method in class weka.core.SAXDistance: Sets the nth point setting.
setBins(int) - Method in class weka.filters.unsupervised.attribute.SAX: Sets the nth point setting.
setBits(int) - Method in class adams.optimise.GeneticAlgorithm: Bits per gene.
setBorder(int) - Method in class weka.experiment.ResultMatrixMediaWiki: Sets the thickness of the border.
setBorderTitle(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Sets the title of the border.
setBufferSize(int) - Method in class adams.flow.transformer.WekaInstanceDumper: Sets the number of instances to buffer before writing them to disk.
setBuildRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.M5Base2: Set the value of regressionTree.
setCanChangeClassInDialog(PropertyEditor, boolean) - Method in class adams.gui.goe.WekaGenericObjectEditorHandler: Sets whether the class can be changed in the dialog.
setCapabilities(Capability[]) - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Sets the capabilities.
setCellPadding(int) - Method in class weka.experiment.ResultMatrixMediaWiki: Sets the cell padding for the table.
setCellSpacing(int) - Method in class weka.experiment.ResultMatrixMediaWiki: Sets the cell spacing for the table.
setCharSet(String) - Method in class adams.flow.transformer.WekaTextDirectoryReader: Sets the character set to use.
setCheckHeader(boolean) - Method in class adams.flow.transformer.WekaInstanceBuffer: Sets whether to check the header or not.
setCheckHeader(boolean) - Method in class adams.flow.transformer.WekaInstanceDumper: Sets whether to check the header or not.
setClass(String) - Method in class adams.ml.WekaData
setClassDetails(boolean) - Method in class adams.flow.transformer.WekaEvaluationSummary: Sets whether the class details are output as well.
setClassificationEntry(String) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Sets the value for the 'Name' column for the numeric classification.
setClassificationLabelEntry(String) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Sets the value for the 'Name' column for the classification label.
setClassifier(Classifier) - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Sets the classifier to use, must implement weka.classifiers.IntervalEstimator.
setClassifier(GlobalActorReference) - Method in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: Sets the name of the global classifier to use.
setClassifier(Classifier) - Method in class adams.flow.transformer.WekaClassifier: Sets the classifier to use.
setClassifier(Classifier) - Method in class adams.ml.WekaClassifier
setClassifier(Classifier) - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Sets the classifier to use (if no serialized model is used).
setClassifier(Classifier) - Method in class weka.classifiers.lazy.LWLIntervalEstimator: Set the base learner, which must implement IntervalEstimator.
setClassifier(Classifier) - Method in class weka.classifiers.meta.MultiSearch: Set the base learner.
setClassifier(Classifier) - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Sets the classifier to classify instances with.
setClassIndex(String) - Method in class adams.flow.source.WekaNewInstances: Sets the index of the class attribute.
setClassIndex(Index) - Method in class adams.flow.transformer.WekaClassSelector: Sets the class index.
setClassIndex(int) - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Sets the class label index (1-based).
setClassIndex(int) - Method in class adams.flow.transformer.WekaEvaluationValues: Sets the class label index (1-based).
setClassIndex(String) - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Sets the class index.
setClassIndex(int) - Method in class weka.filters.unsupervised.attribute.NormalizeAdaptive
setClassIndex(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Sets the attribute on which misclassifications are based.
setClassLabelIndex(Index) - Method in class adams.flow.sink.WekaCostCurve: Sets the class label index (1-based index).
setClassLabelIndex(Index) - Method in class adams.flow.sink.WekaROC: Sets the class label index (1-based index).
setClassMissing() - Method in class weka.core.AbstractHashableInstance: Sets the class value of an instance to be "missing".
setClassName(String) - Method in class adams.flow.source.WekaNewInstances: Sets the name of the class attribute.
setClassNoise(double) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Set the level of Gaussian Noise for the class.
setClassType(PropertyEditor, Class) - Method in class adams.gui.goe.WekaGenericObjectEditorHandler: Sets the class type to use.
setClassValue(double) - Method in class weka.core.AbstractHashableInstance: Sets the class value of an instance to the given value (internal floating-point format).
setClassValue(String) - Method in class weka.core.AbstractHashableInstance: Sets the class value of an instance to the given value.
setClusterer(Clusterer) - Method in class adams.flow.transformer.WekaClusterer: Sets the clusterer to use.
setColor(Color) - Method in class adams.gui.visualization.instance.InstanceContainer: Sets the color to use.
setColorProvider(AbstractColorProvider) - Method in class adams.gui.visualization.instance.InstanceContainerManager: Sets the color provider to use.
setColumn(BaseString[]) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Sets list of fields that identify a column.
setColumnFinder(ColumnFinder) - Method in class adams.data.weka.columnfinder.AbstractFilteredColumnFinder: Sets the column finder to use.
setColumnFinder(ColumnFinder) - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Sets the column finder to use.
setCombination(MultiColumnFinder.Combination) - Method in class adams.data.weka.columnfinder.MultiColumnFinder: Sets how the indices are combined.
setCombination(MultiRowFinder.Combination) - Method in class adams.data.weka.rowfinder.MultiRowFinder: Sets how the indices are combined.
setCombinationRule(SelectedTag) - Method in class weka.classifiers.meta.SubsetEnsemble: Sets the combination rule to use.
setComment(BaseText) - Method in class adams.flow.transformer.WekaEvaluationSummary: Sets the comment to output in the summary.
setComparisonField(ExperimentStatistic) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Sets the comparison field.
setComplexityStatistics(boolean) - Method in class adams.flow.transformer.WekaEvaluationSummary: Sets whether to output complexity stats as well.
setConfidenceLevel(double) - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Sets the confidence level.
setContainerManager(InstanceContainerManager) - Method in class adams.gui.visualization.instance.InstanceExplorer: Sets the manager for handling the containers.
setCorrect(String) - Method in class weka.filters.unsupervised.attribute.SpellChecker: Sets the correct label.
setCrossValidationSeed(int) - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Sets the seed value to use for cross-validation.
setCurrent(File) - Method in class adams.gui.chooser.DatasetFileChooserPanel: Sets the current value.
setCurrent(File) - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Sets the current file.
setCurrentDirectory(File) - Method in class adams.gui.chooser.DatasetFileChooserPanel: Sets the current directory to use for the file chooser.
setCurrentDirectory(File) - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Sets the current directory to use for the file chooser.
setCustomLoader(AbstractFileLoader) - Method in class adams.flow.transformer.WekaFileReader: Sets the custom loader to use.
setCustomPropsFile(PlaceholderFile) - Method in class adams.flow.sink.WekaDatabaseWriter: Sets the custom properties file to use for initializing the database setup instead of WEKA's default one.
setCustomPropsFile(PlaceholderFile) - Method in class adams.flow.source.WekaDatabaseReader: Sets the custom properties file to use for initializing the database setup instead of WEKA's default one.
setCustomSaver(AbstractFileSaver) - Method in class adams.flow.sink.WekaFileWriter: Sets the custom saver to use.
setData(Instances) - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Sets the data set to use for training and so forth.
setData(Instance) - Method in class adams.gui.visualization.instance.InstanceContainer: Sets the instance.
setData(Instances) - Method in class adams.gui.visualization.instance.InstanceTable: Sets the Instances object to display.
setDatabaseConnection(AbstractDatabaseConnection) - Method in class adams.data.instances.AbstractInstanceGenerator: Sets the database connection object to use.
setDataGenerator(DataGenerator) - Method in class adams.flow.source.WekaDataGenerator: Sets the data generator to use.
setDataset(StorageName) - Method in class adams.flow.transformer.WekaStoreInstance: Sets the name of the dataset in internal storage to append to.
setDataset(Instances) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Sets the dataset to index.
setDataset(File) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Sets the dataset to use.
setDataset(WekaData) - Method in class adams.ml.WekaClassifier
setDataset(Dataset) - Method in class adams.ml.WekaClassifier
setDataset(PlaceholderFile) - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Sets the filename of the dataset to use for cross-validation.
setDataset(Instances) - Method in class weka.core.AbstractHashableInstance: Sets the reference to the dataset.
setDataset1(PlaceholderFile) - Method in class adams.tools.CompareDatasets: Sets the first dataset for the comparison.
setDataset2(PlaceholderFile) - Method in class adams.tools.CompareDatasets: Sets the second dataset for the comparison.
setDataType(WekaInstancesStatistic.DataType) - Method in class adams.flow.transformer.WekaInstancesStatistic: Sets what type of data to retrieve from the Instances object.
setDebug(boolean) - Method in class weka.classifiers.meta.AbstainLeastMedianSq: sets whether or not debugging output shouild be printed
setDebug(boolean) - Method in class weka.classifiers.meta.LeastMedianSq: sets whether or not debugging output shouild be printed
setDefaultAttributeRange(String) - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Sets the default range of attributes to use.
setDefaultClassIndex(String) - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Sets the default class index to use.
setDefaultIncludeAttributes(int, boolean) - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Sets the default for the specified type of attribute.
setDefaultSortIndex(String) - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Sets the default sort index to use.
setDerivativeOrder(int) - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Sets the order of the derivative.
setDev(double) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive
setDiscardPredictions(boolean) - Method in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: Sets whether to discard the predictions instead of collecting them for future use, in order to conserve memory.
setDiscardPredictions(boolean) - Method in class adams.flow.transformer.WekaTestSetEvaluator: Sets whether to discard the predictions instead of collecting them for future use, in order to conserve memory.
setDistributionFormat(String) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Sets the format for the 'Name' column for the class distribution.
setDistributionSorting(WekaPredictionContainerToSpreadSheet.Sorting) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Sets the sorting for the distribution array.
setError(int) - Method in class weka.classifiers.meta.AbstainLeastMedianSq
setError(int) - Method in class weka.classifiers.meta.LeastMedianSq
setErrorScaler(AbstractErrorScaler) - Method in class adams.flow.sink.WekaClassifierErrors: Sets the scheme for scaling the errors.
setEvaluation(SelectedTag) - Method in class weka.classifiers.meta.MultiSearch: Sets the criterion to use for evaluating the classifier performance.
setEvaluationType(WekaExperimentGenerator.EvaluationType) - Method in class adams.flow.sink.WekaExperimentGenerator: Sets the type of evaluation to perform.
setEvaluator(AbstractInstanceEvaluator) - Method in class adams.flow.transformer.WekaInstanceEvaluator: Sets the evaluator to use.
setExcludeClass(boolean) - Method in class weka.core.AbstractHashableInstance: Sets whether to exclude the class from the hashcode computation.
setExcludedAttributes(String) - Method in class adams.flow.transformer.WekaInstancesMerge: Sets the regular expression for excluding attributes.
setExcludeWeight(boolean) - Method in class weka.core.AbstractHashableInstance: Sets whether to exclude the weight from the hashcode computation.
setExperiment(Experiment) - Method in class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog: Sets the experiment to use.
setExperimentFile(WekaExperimentFile) - Method in class adams.flow.transformer.WekaExperiment: Sets the file the experiment is stored in.
setExperimentType(WekaExperimentGenerator.ExperimentType) - Method in class adams.flow.sink.WekaExperimentGenerator: Sets the type of experiment to perform.
setExplorer(Explorer) - Method in class weka.gui.explorer.ExperimentPanel: Sets the Explorer to use as parent frame (used for sending notifications about changes in the data).
setExplorer(Explorer) - Method in class weka.gui.explorer.SqlPanel: Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExpression(String) - Method in class weka.core.setupgenerator.MathParameter: Set the expression.
setFavorZeroes(boolean) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Sets whether 0s are favored over 1s.
setFavorZeroes(boolean) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Sets whether 0s are favored over 1s.
setFields(Field[]) - Method in class adams.data.conversion.ReportToWekaInstance: Sets the fields to use.
setFilter(Filter) - Method in class adams.data.weka.rowfinder.FilteredIQR: Sets the IQR filter.
setFilter(Filter) - Method in class adams.flow.transformer.WekaFilter: Sets the filter to use.
setFilter(Filter) - Method in class adams.ml.WekaFilter
setFilter(Filter) - Method in class weka.classifiers.functions.PLSClassifierWeighted: Set the PLS filter (only used for setup).
setFilter(Filter) - Method in class weka.core.neighboursearch.FilteredSearch: Sets the filter
setFilter(Filter) - Method in class weka.filters.unsupervised.attribute.FastWavelet: Set the preprocessing filter (only used for setup).
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.GPD: Sets how the training data will be transformed.
setFind(String) - Method in class adams.flow.transformer.WekaRenameRelation: Sets the string to find.
setFindArrays(Boolean) - Method in class adams.ml.WekaData
setFindArrays(String) - Method in class adams.ml.WekaData
setFindArrays(String) - Method in class adams.ml.WekaFilter
setFinders(ColumnFinder[]) - Method in class adams.data.weka.columnfinder.MultiColumnFinder: Sets the column finders to use.
setFinders(RowFinder[]) - Method in class adams.data.weka.rowfinder.MultiRowFinder: Sets the row finders to use.
setFirstAttributeRange(String) - Method in class adams.gui.InstanceCompare: Sets the first attribute range ('first' and 'last' can be used as well).
setFirstAttributeRange(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel: Sets the first attribute range ('first' and 'last' can be used as well).
setFirstDataset(PlaceholderFile) - Method in class adams.gui.InstanceCompare: Sets the first dataset.
setFirstDataset(File) - Method in class adams.gui.visualization.instance.InstanceComparePanel: Sets the first dataset.
setFirstRowIndex(String) - Method in class adams.gui.InstanceCompare: Sets the first row index ('first' and 'last' can be used as well).
setFirstRowIndex(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel: Sets the first row index ('first' and 'last' can be used as well).
setFlowFile(FlowFile) - Method in class weka.filters.FlowFilter: Sets the flow to process the data with.
setFolds(int) - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: Sets the number of folds for cross-validation.
setFolds(int) - Method in class adams.flow.sink.WekaExperimentGenerator: Sets the number of folds to use (only CV).
setFolds(int) - Method in class adams.flow.transformer.WekaClassifierRanker: Sets the number of folds to use.
setFolds(int) - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Sets the number of folds.
setFolds(int) - Method in class adams.flow.transformer.WekaCrossValidationSplit: Sets the number of folds to use.
setFolds(int) - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Sets the number of folds to use in cross-validation.
setGamma(double) - Method in class weka.classifiers.functions.GPD
setGene(int, int, int) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Sets the value of the specified gene.
setGene(int, int, boolean) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Sets the value of the specified gene.
setGene(int, int, int) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Sets the value of the specified gene.
setGene(int, int, boolean) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Sets the value of the specified gene.
setGenerateLine(boolean) - Method in class adams.data.baseline.AbstractLinearRegressionBased: Sets whether to return the line as fake data or the corrected data.
setGenerateRules(boolean) - Method in class weka.classifiers.trees.m5.M5Base2: Generate rules (decision list) rather than a tree
setGenerator(AbstractInstanceGenerator) - Method in class adams.flow.transformer.AbstractInstanceGenerator: Sets the generator to use.
setHeader(PlaceholderFile) - Method in class adams.data.conversion.MatchWekaInstanceAgainstFileHeader: Sets the file to load the dataset header from.
setHeader(StorageName) - Method in class adams.data.conversion.MatchWekaInstanceAgainstStorageHeader: Sets the name of the storage value representing the dataset header.
setID(String) - Method in class adams.data.instance.Instance: Sets the ID of the sequence.
setID(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Sets the ID to use for the returned instances.
setID(String) - Method in class adams.gui.visualization.instance.InstanceContainer: Sets the container's ID.
setIncludeAttributes(int, boolean) - Method in class adams.gui.visualization.instance.LoadDatasetDialog: Sets whether only numeric attributes should be used.
setIncludeClass(boolean) - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Sets whether to include the class attribute in the comparison.
setIncludeClass(boolean) - Method in class weka.filters.unsupervised.instance.Sort: Sets whether to include the class attribute in the comparison.
setIncorrect(String) - Method in class weka.filters.unsupervised.attribute.SpellChecker: Sets the incorrect labels, blank-separated list.
setIncremental(boolean) - Method in class adams.flow.source.WekaDatabaseReader: Sets whether to output single Instance objects or just one Instances object.
setIndex(Index) - Method in class adams.flow.transformer.WekaExtractArray: Sets the type of extraction to perform.
setIndex(Index) - Method in class adams.flow.transformer.WekaGetInstanceValue: Sets the 1-based index of the attribute value to retrieve from the Instance.
setIndex(Index) - Method in class adams.flow.transformer.WekaSetInstanceValue: Sets the 1-based attribute index to set in the Instance.
setIndex(Index) - Method in class adams.flow.transformer.WekaSubsets: Sets the index of the attribute to split on.
setInfoData(Vector<Instances>) - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Sets the underlying data.
setInitializeOnce(boolean) - Method in class adams.flow.transformer.WekaFilter: Sets whether the filter gets initialized only with the first batch.
setInitialSpaceNumFolds(int) - Method in class weka.classifiers.meta.MultiSearch: Sets the number of CV folds for the initial space.
setInlineValue(String) - Method in class adams.gui.goe.WekaExperimentFileEditor: Sets the value to use.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NormalizeAdaptive: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValuesWithZero: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Sets the format of the input instances.
setInstanceClass(String) - Method in class adams.flow.transformer.WekaNewInstance: Sets the class name of the Instance object to create.
setInstances(Instances) - Method in class adams.ml.WekaClassifier
setInstances(Instances) - Method in class adams.ml.WekaFilter
setInstances(Instances) - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Sets the data to use for cross-validation.
setInstances(Instances) - Method in class weka.core.neighboursearch.FilteredSearch: Sets the instances comprising the current neighbourhood.
setInstances(Instances) - Method in class weka.core.neighboursearch.NewNNSearch: Sets the instances comprising the current neighbourhood.
setInstances(Instances) - Method in class weka.core.neighboursearch.PCANNSearch: Sets the instances comprising the current neighbourhood.
setInstances(Instances) - Method in class weka.core.neighboursearch.PLSNNSearch: Sets the instances comprising the current neighbourhood.
setInstances(Instances) - Method in class weka.gui.explorer.ExperimentPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.SqlPanel: ignored
setInstancesActor(GlobalActorReference) - Method in class adams.flow.transformer.WekaInstanceEvaluator: Sets the global actor from which to retrieve Instances in case of AbstractDatasetInstanceEvaluator-derived evaluators.
setInstancesIndices(String) - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Sets the range of instances to be selected.
setInt(int[], int, int, int) - Method in class adams.optimise.genetic.PackData
setInterval(int) - Method in class adams.flow.transformer.WekaInstanceBuffer: Sets the interval for outputting the Instances objects.
setInverseTransform(boolean) - Method in class weka.filters.unsupervised.attribute.FastWavelet: Sets whether to use the inverse tranform.
setInvert(boolean) - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Sets whether to invert the matching sense of the capabilities.
setInvert(boolean) - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Sets whether to invert the matching sense.
setInvert(boolean) - Method in class adams.flow.transformer.WekaRegexToRange: Invert match?
setInvert(boolean) - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Set whether the invert the column indices.
setInvert(boolean) - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Set whether the invert the row indices.
setInvert(boolean) - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Set whether selection is inverted.
setInvertMatchingSense(boolean) - Method in class adams.flow.transformer.WekaInstancesMerge: Sets whether to invert the matching sense.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Sets if selection is to be inverted.
setIqr(double) - Method in class adams.data.weka.rowfinder.FilteredIQR: Sets the IQR multiplier.
setIterations(int) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Sets the iterations to use.
setKeepExisting(boolean) - Method in class adams.flow.transformer.WekaInstanceDumper: Sets whether to use the relation name as filename instead.
setKeepRelationName(boolean) - Method in class adams.flow.transformer.WekaFilter: Sets whether the filter doesn't change the relation name.
setKernel(Kernel) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Sets the kernel to use.
setKernel(Kernel) - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Sets the kernel to use.
setKeys(String) - Method in class adams.flow.source.WekaDatabaseReader: Sets the keys to use for identifying a single row (comma-separated list).
setLabelMatch(String) - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Sets the label for the matching rows.
setLabelNonMatch(String) - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Sets the label for the non-matching rows.
setList(String) - Method in class weka.core.setupgenerator.ListParameter: Set the blank-separated list of values.
setLocations(BaseString[]) - Method in class adams.flow.transformer.WekaInstancesStatistic: Sets the locations of the data (indices/regular expressions on attribute name).
setLog(Logger) - Method in class weka.gui.explorer.ExperimentPanel: Sets the Logger to receive informational messages.
setLog(Logger) - Method in class weka.gui.explorer.SqlPanel: Sets the Logger to receive informational messages
setLogFile(File) - Method in class weka.classifiers.meta.MultiSearch: Sets the log file to use.
setMakeClassLast(boolean) - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Sets whether to make the class attribute the last attribute.
setMarkerExtent(int) - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Sets the extent (width and height of the shape around the plotted point).
setMarkersDisabled(boolean) - Method in class adams.gui.visualization.instance.InstanceLinePaintlet: Sets whether to draw markers or not.
setMax(int) - Method in class adams.flow.transformer.WekaClassifierRanker: Sets the maximum number of top-ranked classifiers to forward.
setMax(int) - Method in class weka.classifiers.trees.RandomModelTrees
setMax(double) - Method in class weka.core.setupgenerator.MathParameter: Set the value of the Maximum.
setMaximumAttributeNames(int) - Method in class weka.core.neighboursearch.PCANNSearch: Sets maximum number of attributes to include in transformed attribute names.
setMaxIterations(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Sets the maximum number of cleansing iterations to perform - < 1 means go until fully cleansed
setMaxSize(int) - Method in class adams.data.weka.predictions.RelativeNumericErrorScaler: Sets the maximum size for the errors.
setMaxTrainTime(int) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Sets the maximum number of seconds to perform training.
setMaxTrainTime(int) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Sets the maximum number of seconds to perform training.
setMeasure(WekaClassifierRanker.Measure) - Method in class adams.flow.transformer.WekaClassifierRanker: Sets the measure to use for ranking the classifiers.
setMeasure(AbstractWEKAFitnessFunction.Measure) - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Sets the measure used for evaluating the fitness.
setMetaLevelClassifier(Classifier) - Method in class weka.classifiers.meta.PartitionedStacking: Sets the meta-level classifier.
setMin(int) - Method in class weka.classifiers.trees.RandomRegressionForest: Sets the leaf threshold.
setMin(double) - Method in class weka.core.setupgenerator.MathParameter: Set the value of the minimum.
setMinMax(String, double, double) - Method in class adams.optimise.genetic.PackDataDef
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.M5Base2: Set the minimum number of instances to allow at a leaf node
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.Rule2: Set the minumum number of instances to allow at a leaf node
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.RuleNode2: Set the minumum number of instances to allow at a leaf node
setMissing(PlaceholderFile) - Method in class adams.tools.CompareDatasets: Sets the first dataset for the comparison.
setMissing(int) - Method in class weka.core.AbstractHashableInstance: Sets a specific value to be "missing".
setMissing(Attribute) - Method in class weka.core.AbstractHashableInstance: Sets a specific value to be "missing".
SetMissingValue - Class in weka.filters.unsupervised.attribute: Attribute values in the given range are set to missing values.
NB: The class attribute is not excluded from this process.
SetMissingValue() - Constructor for class weka.filters.unsupervised.attribute.SetMissingValue
setModelActor(GlobalActorReference) - Method in class adams.flow.condition.bool.WekaClassification: Sets the global actor to obtain the model from if model file is pointing to a directory.
setModelActor(GlobalActorReference) - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Sets the global actor to obtain the model from if model file is pointing to a directory.
setModelFile(PlaceholderFile) - Method in class adams.flow.condition.bool.WekaClassification: Sets the file to load the model from.
setModelFile(PlaceholderFile) - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Sets the file to load the model from.
setN(int) - Method in class weka.core.SAXDistance: Sets the nth point setting.
setNoCheck(boolean) - Method in class adams.flow.transformer.WekaExperiment: Sets whether to avoid the check at setUp time whether the experiment file is present or not.
setNoise(double) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Set the level of Gaussian Noise.
setNoise(double) - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Set the level of Gaussian Noise.
setNoise(double) - Method in class weka.classifiers.functions.GPD: Set the level of Gaussian Noise.
setNominal(String, String[]) - Method in class adams.ml.WekaData
setNominal(boolean) - Method in class weka.filters.unsupervised.attribute.SAX: Sets whether to output nominal or numeric values.
setNominalFromDataset(String) - Method in class adams.ml.WekaData
setNoUpdate(boolean) - Method in class weka.classifiers.lazy.LWLSynchro: Sets whether to suppress updating the nearest-neighbor search algorithm when making predictions.
setNthPoint(int) - Method in class weka.filters.unsupervised.attribute.DownSample: Sets the nth point setting.
setNumattrs(int) - Method in class weka.classifiers.meta.Corr
setNumChrom(int) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Sets the number of chromosomes to use.
setNumChrom(int) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Sets the number of chromosomes to use.
setNumComponents(int) - Method in class weka.core.neighboursearch.PLSNNSearch
setNumDecimals(int) - Method in class adams.data.conversion.WekaInstancesToSpreadSheet: Sets the number of decimals to display.
setNumExecutionSlots(int) - Method in class weka.classifiers.meta.MultiSearch: Set the number of execution slots (threads) to use for building the members of the ensemble.
setNumExecutionSlots(int) - Method in class weka.classifiers.meta.SubsetEnsemble: Set the number of execution slots (threads) to use for building the members of the ensemble.
setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Sets the number of cross-validation folds to use - < 2 means no cross-validation.
setNumIterations(int) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Sets the number of iterations to perform.
setNumIterations(int) - Method in class weka.classifiers.trees.RandomModelTrees: Sets the number of iterations.
setNumIterations(int) - Method in class weka.classifiers.trees.RandomRegressionForest: Sets the number of iterations
setNumPointsLeft(int) - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Sets the number of points to the left of a data point.
setNumPointsRight(int) - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Sets the number of points to the right of a data point.
setNumRandomFeatures(int) - Method in class weka.classifiers.meta.SubsetEnsemble: Set the number of additional random features to use.
setNumRegressions(int) - Method in class weka.classifiers.meta.AbstainLeastMedianSq: sets number of samples
setNumRegressions(int) - Method in class weka.classifiers.meta.LeastMedianSq: sets number of samples
setNumThreads(int) - Method in class adams.flow.transformer.WekaClassifierRanker: Sets the number of threads to use.
setNumZeroes(double) - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Sets the number of zeroes a row must have at least in order to be removed.
setOnTheFly(boolean) - Method in class adams.flow.condition.bool.WekaClassification: Sets whether the model file gets built on the fly and might not be present at start up time.
setOnTheFly(boolean) - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Sets whether the model file gets built on the fly and might not be present at start up time.
setOperation(WekaInstanceBuffer.Operation) - Method in class adams.flow.transformer.WekaInstanceBuffer: Sets the way the buffer operates.
setOptimizer(Classifier) - Method in class adams.flow.transformer.WekaClassifierOptimizer: Sets the optimizer to use.
setOptions(Object, String[]) - Method in class adams.core.option.WekaCommandLineHandler: Sets the options of the specified object.
setOptions(String[]) - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.GPD: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.PLSClassifierWeighted: Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.lazy.LWLSynchro: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AbstainAttributePercentile: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.meta.Corr: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.FilteredClassifierExt: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.LeastMedianSq: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiSearch: Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.meta.PartitionedStacking: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.SubsetEnsemble: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.m5.M5Base2: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.M5P2: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.RandomModelTrees: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.RandomRegressionForest: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.FilteredSearch
setOptions(String[]) - Method in class weka.core.neighboursearch.NewNNSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.PCANNSearch
setOptions(String[]) - Method in class weka.core.neighboursearch.PLSNNSearch
setOptions(String[]) - Method in class weka.core.SAXDistance: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.core.setupgenerator.AbstractParameter: Parses the options for this object.
setOptions(String[]) - Method in class weka.core.setupgenerator.ListParameter: Parses the options for this object.
setOptions(String[]) - Method in class weka.core.setupgenerator.MathParameter: Parses the options for this object.
setOptions(String[]) - Method in class weka.core.SetupGenerator: Parses the options for this object.
setOptions(String[]) - Method in class weka.experiment.ResultMatrixMediaWiki: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.filters.FlowFilter: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AbstractColumnFinderApplier: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.CorrelationMatrix: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.DownSample: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.FastWavelet: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PAA: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.SAX: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.SetMissingValue: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.SpellChecker: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.DatasetLabeler: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.LatestRecords: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveWithZeroes: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.SafeRemoveRange: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Sort: Parses a given list of options.
setOutput(AbstractOutput) - Method in class adams.flow.transformer.AbstractWekaClassifierEvaluator: Sets the prediction output generator to use.
setOutputArray(boolean) - Method in class adams.flow.source.AbstractWekaSetupGenerator: Sets whether to output an array or a sequence of classifier setups.
setOutputBestSetup(boolean) - Method in class adams.flow.transformer.WekaClassifierRanker: Sets whether to output the best setup found for optimizers like GridSearch and MultiSearch.
setOutputDirectory(PlaceholderDirectory) - Method in class adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction: Sets the directory for the generated ARFF files.
setOutputFile(PlaceholderFile) - Method in class adams.flow.sink.WekaExperimentGenerator: Sets the file to store the experiment setup in (the extensions determines the type).
setOutputFile(PlaceholderFile) - Method in class adams.tools.CompareDatasets: Sets the first dataset for the comparison.
setOutputFormat(ResultMatrix) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Sets the output format to use for generating the output.
setOutputFormat(WekaInstanceDumper.OutputFormat) - Method in class adams.flow.transformer.WekaInstanceDumper: Sets the output format.
setOutputHeader(boolean) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Sets whether to output the header of the result matrix as well.
setOutputInstance(boolean) - Method in class adams.flow.transformer.WekaClassifying: Sets whether to output Instance objects instead of PredictionContainer ones.
setOutputOnlyModel(boolean) - Method in class adams.flow.transformer.AbstractWekaModelReader: Sets whether to output only the model instead of the container.
setOutputPrefix(PlaceholderFile) - Method in class adams.flow.transformer.WekaInstanceDumper: Sets the prefix for the output (path + partial filename).
setOutputRelationName(boolean) - Method in class adams.flow.transformer.WekaEvaluationSummary: Sets whether to output the relation name as well.
setOutputType(WekaFileReader.OutputType) - Method in class adams.flow.transformer.WekaFileReader: Sets how to output the data.
setOverride(boolean) - Method in class adams.flow.transformer.WekaClassSelector: Sets whether to override any existing class index or nor.
setPadding(SelectedTag) - Method in class weka.filters.unsupervised.attribute.FastWavelet: Sets the type of Padding to use.
setParameters(AbstractParameter[]) - Method in class adams.flow.source.AbstractWekaSetupGenerator: Sets the setup parameters.
setParameters(AbstractParameter[]) - Method in class weka.core.SetupGenerator: Sets the parameters to use as basis for the setups.
setPassword(BasePassword) - Method in class adams.flow.sink.WekaDatabaseWriter: Sets the database password.
setPassword(BasePassword) - Method in class adams.flow.source.WekaDatabaseReader: Sets the database password.
setPct(int) - Method in class weka.classifiers.meta.AbstainLeastMedianSq
setPct(int) - Method in class weka.classifiers.meta.LeastMedianSq
setPercentage(double) - Method in class adams.flow.transformer.WekaRandomSplit: Sets the percentage (0-1).
setPercentile(double) - Method in class weka.classifiers.meta.AbstainAttributePercentile
setPlotName(String) - Method in class adams.flow.transformer.WekaAccumulatedError: Sets the plot name.
setPLS(int) - Method in class weka.classifiers.trees.RandomRegressionForest: Sets the number of PLS components to generate.
setPolynomialOrder(int) - Method in class weka.filters.unsupervised.attribute.SavitzkyGolay: Sets the polynomial order.
setPostProcessor(AbstractClustererPostProcessor) - Method in class adams.flow.transformer.WekaClusterer: Sets the post-processor to use.
setPreFilter(Filter) - Method in class adams.data.weka.rowfinder.FilteredIQR: Sets the pre filter.
setPrefix(String) - Method in class adams.flow.transformer.WekaInstancesMerge: Sets the optional prefix string.
setPrefixSeparator(String) - Method in class adams.flow.transformer.WekaInstancesMerge: Sets the prefix separator string.
setPreprocessing(SelectedTag) - Method in class weka.core.neighboursearch.PCANNSearch: Sets the type of preprocessing to use
setPreprocessing(SelectedTag) - Method in class weka.core.neighboursearch.PLSNNSearch: Sets the type of preprocessing to use
setPreserveOrder(boolean) - Method in class adams.flow.transformer.WekaRandomSplit: Sets whether to preserve order and suppress randomization.
setProperty(String) - Method in class weka.core.setupgenerator.AbstractParameter: Set the property to update.
setQuery(SQLStatement) - Method in class adams.flow.source.WekaDatabaseReader: Sets the query to execute.
setRandomize(boolean) - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Sets whether to include the class attribute in the comparison.
setRandomSeed(long) - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Set the seed for the random number generator
setRandomSeed(long) - Method in class weka.classifiers.meta.LeastMedianSq: Set the seed for the random number generator
setRange(Range) - Method in class adams.flow.transformer.WekaAttributeIterator: Sets the range of attributes to operate on.
setRange(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetIndexer: Sets the range of attributes to use.
setRange1(Range) - Method in class adams.tools.CompareDatasets: Sets the range of attributes of the first dataset.
setRange2(Range) - Method in class adams.tools.CompareDatasets: Sets the range of attributes of the second dataset.
setRanges(Range[]) - Method in class weka.classifiers.meta.PartitionedStacking: Sets the attribute ranges for the base-classifiers.
setRegex(String) - Method in class adams.flow.transformer.WekaRegexToRange: Sets the regular expression for attribute matching.
setRegexName(BaseRegExp) - Method in class adams.flow.transformer.WekaClassSelector: Sets the regular expression for selecting the attributes.
setRegExp(BaseRegExp) - Method in class adams.data.weka.columnfinder.ByName: Sets the regular expression to use.
setRegExp(BaseRegExp) - Method in class adams.data.weka.rowfinder.ByLabel: Sets the regular expression to use.
setRegExp(BaseRegExp) - Method in class adams.flow.transformer.WekaAttributeIterator: Sets the regular expression for the names.
setRegExp(BaseRegExp) - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Sets the regular expression to match the strings against.
setRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.Rule2: Set the value of regressionTree.
setRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.RuleNode2: Set the value of regressionTree.
setRelationName(String) - Method in class adams.flow.source.WekaNewInstances: Sets the name of the relation.
setRelationName(String) - Method in class adams.flow.transformer.WekaCrossValidationSplit: Sets the pattern to use for renaming the relation.
setRelativeWidths(boolean) - Method in class adams.data.weka.evaluator.IntervalEstimatorBased: Sets whether to divide the calculated widths by the class value.
setRemoveAttributeIndices(String) - Method in class weka.classifiers.meta.FilteredClassifierExt: Sets the attribute indices to remove before applying the actual filter.
setRemoveTrain(boolean) - Method in class weka.classifiers.meta.AbstainAttributePercentile
setReplace(String) - Method in class adams.flow.transformer.WekaRenameRelation: Sets the replacement string.
setReport(Report) - Method in class adams.data.instance.Instance: Sets a new report.
setResultFile(PlaceholderFile) - Method in class adams.flow.sink.WekaExperimentGenerator: Sets the file to store the experimental results in.
setResultFormat(WekaExperimentGenerator.ResultFormat) - Method in class adams.flow.sink.WekaExperimentGenerator: Sets the data format the results are stored in.
setRidge(double) - Method in class adams.data.baseline.AbstractLinearRegressionBased: Sets the ridge parameter.
setRidge(double) - Method in class weka.classifiers.trees.RandomModelTrees
setRow(BaseString[]) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Sets the list of fields that identify a row.
setRowAttribute1(String) - Method in class adams.tools.CompareDatasets: Sets the index of the attribute used for identifying rows to compare against each other (first dataset).
setRowAttribute2(String) - Method in class adams.tools.CompareDatasets: Sets the index of the attribute used for identifying rows to compare against each other (second dataset).
setRowFinder(RowFinder) - Method in class adams.data.weka.columnfinder.RowFilteredColumnFinder: Sets the row finder to use.
setRowFinder(RowFinder) - Method in class adams.data.weka.rowfinder.AbstractFilteredRowFinder: Sets the row finder to use.
setRowFinder(RowFinder) - Method in class weka.filters.unsupervised.instance.AbstractRowFinderApplier: Sets the row finder to use.
setRowIndex(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Sets the index of the attribute to use for matching rows (only works if dataset already loaded).
setRuns(int) - Method in class adams.flow.sink.WekaExperimentGenerator: Sets the number of runs to perform.
setSampleSizePercent(double) - Method in class weka.classifiers.meta.MultiSearch: Sets the sample size for the initial space search.
setSaveInstances(boolean) - Method in class weka.classifiers.trees.m5.Rule2: Sets whether instances at each node in an M5 tree should be saved for visualization purposes.
setSaveInstances(boolean) - Method in class weka.classifiers.trees.m5.RuleNode2: Set whether to save instances for visualization purposes.
setSaveInstances(boolean) - Method in class weka.classifiers.trees.M5P2: Set whether to save instance data at each node in the tree for visualization purposes
setScale(double) - Method in class weka.filters.unsupervised.attribute.NormalizeAdaptive
setScaler(AbstractErrorScaler) - Method in class adams.data.weka.predictions.AutoScaler: Sets the scaler to use for numeric data.
setSearchParameters(AbstractParameter[]) - Method in class weka.classifiers.meta.MultiSearch: Sets the search parameters.
setSecondAttributeRange(String) - Method in class adams.gui.InstanceCompare: Sets the second attribute range ('second' and 'last' can be used as well).
setSecondAttributeRange(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel: Sets the second attribute range ('second' and 'last' can be used as well).
setSecondDataset(PlaceholderFile) - Method in class adams.gui.InstanceCompare: Sets the second dataset.
setSecondDataset(File) - Method in class adams.gui.visualization.instance.InstanceComparePanel: Sets the second dataset.
setSecondRowIndex(String) - Method in class adams.gui.InstanceCompare: Sets the second row index ('second' and 'last' can be used as well).
setSecondRowIndex(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel: Sets the second row index ('second' and 'last' can be used as well).
setSeed(int) - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator: Sets the seed value for cross-validation.
setSeed(long) - Method in class adams.flow.transformer.WekaClassifierRanker: Sets the seed value.
setSeed(long) - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Sets the seed value.
setSeed(long) - Method in class adams.flow.transformer.WekaCrossValidationSplit: Sets the seed value.
setSeed(long) - Method in class adams.flow.transformer.WekaRandomSplit: Sets the seed value.
setSeed(long) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Sets the seed value to use, resets the random number generator.
setSeed(int) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Sets the seed value to use, resets the random number generator.
setSeed(int) - Method in class weka.filters.unsupervised.instance.RemoveDuplicates: Set the seed for random number generation.
setSetup(NamedSetup) - Method in class adams.data.weka.evaluator.NamedSetup: Sets the setup name.
setSetup(NamedSetup) - Method in class adams.data.weka.predictions.NamedSetup: Sets the setup name.
setSetup(Classifier) - Method in class adams.flow.source.WekaClassifierGenerator: Sets the base classifier.
setSetup(Clusterer) - Method in class adams.flow.source.WekaClustererGenerator: Sets the base clusterer.
setSetup(Filter) - Method in class adams.flow.source.WekaFilterGenerator: Sets the base clusterer.
setShowDistribution(boolean) - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Sets whether to show the class distribution as well.
setShowError(boolean) - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Sets whether to show the error as well.
setShowProbability(boolean) - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Sets whether to show the probability of the prediction as well.
setShowWeight(boolean) - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Sets whether to show the weight as well.
setSidePanelVisible(boolean) - Method in class adams.gui.visualization.instance.InstanceExplorer: Whether to display the side panel or not.
setSignificance(double) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Sets the significance level (0-1).
setSize(int) - Method in class adams.data.weka.predictions.FixedSizeErrorScaler: Sets the size for the errors.
setSkipIdentical(boolean) - Method in class weka.core.neighboursearch.NewNNSearch: Sets the property to skip identical instances (with distance zero from the target) from the set of neighbours returned.
setSmoothing(boolean) - Method in class weka.classifiers.trees.m5.Rule2: Smooth predictions
setSpace(Space) - Method in class weka.core.SetupGenerator: Updates the space to use for the setup generation.
setSparseFormat(boolean) - Method in class adams.flow.source.WekaDatabaseReader: Sets whether to output the data in sparse format.
setSplitPercentage(double) - Method in class adams.flow.sink.WekaExperimentGenerator: Sets the split percentage (only train/test splits).
setStatistic(AbstractArrayStatistic) - Method in class adams.flow.transformer.WekaInstancesStatistic: Sets the statistic to use.
setStatisticValue(EvaluationStatistic) - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Sets the value to extract.
setStatisticValues(EvaluationStatistic[]) - Method in class adams.flow.transformer.WekaEvaluationValues: Sets the values to extract.
setStep(double) - Method in class weka.core.setupgenerator.MathParameter: Set the value of the step size.
setStoreFilename(boolean) - Method in class adams.flow.transformer.WekaTextDirectoryReader: Sets whether to store the filename in extra attribute.
setSubsequentSpaceNumFolds(int) - Method in class weka.classifiers.meta.MultiSearch: Sets the number of CV folds for the sub-sequent sub-spaces.
setSwapRowsAndColumns(boolean) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Sets whether to swap rows and columns.
setTableName(String) - Method in class adams.flow.sink.WekaDatabaseWriter: Sets the table name to store the data in.
setTest(GlobalActorReference) - Method in class adams.flow.transformer.WekaClassifierRanker: Sets the name of the global actor to obtain the test set.
setTestBase(int) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Sets the index of the test base.
setTester(Tester) - Method in class adams.flow.transformer.WekaExperimentEvaluation: Sets the Tester to use.
setTestset(GlobalActorReference) - Method in class adams.flow.transformer.WekaTestSetEvaluator: Sets the name of the global classifier to use.
setThreshold(double) - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Sets the threshold.
setThreshold(double) - Method in class adams.tools.CompareDatasets: Sets the threshold for the correlation coefficient.
setThreshold(double) - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Sets the threshold for the max error when predicting a numeric class.
setTitleNameColumn(String) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Sets the title of the "Name" column, i.e., the first column.
setTitleValueColumn(String) - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Sets the title of the "Value" column, i.e., the first column.
setTrain(GlobalActorReference) - Method in class adams.flow.transformer.WekaClassifierRanker: Sets the name of the global actor to obtain the training set.
setTrials(int) - Method in class weka.classifiers.trees.RandomModelTrees
setType(WekaExtractArray.ExtractionType) - Method in class adams.flow.transformer.WekaExtractArray: Sets the type of extraction to perform.
setType(WekaInstancesInfo.InfoType) - Method in class adams.flow.transformer.WekaInstancesInfo: Sets the type of information to generate.
setUndo(Undo) - Method in class adams.gui.visualization.instance.InstancePanel: Sets the undo manager to use, can be null if no undo-support wanted.
setUniqueID(String) - Method in class adams.flow.transformer.WekaInstancesMerge: Sets the attribute (string/numeric) to use for uniquely identifying rows.
setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.M5Base2: Use unpruned tree/rules
setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.Rule2: Use unpruned tree/rules
setUp() - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Performs necessary initializations before being able to evaluate.
setUp() - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Performs necessary initializations before being able to evaluate.
setUp(AbstractActor) - Method in class adams.flow.condition.bool.AbstractAttributeCapabilities: Configures the condition.
setUp(AbstractActor) - Method in class adams.flow.condition.bool.WekaClassification: Initializes the item for flow execution.
setUp() - Method in class adams.flow.sink.WekaExperimentGenerator: Initializes the item for flow execution.
setUp() - Method in class adams.flow.transformer.AbstractGlobalWekaClassifierEvaluator: Initializes the item for flow execution.
setUp() - Method in class adams.flow.transformer.AbstractInstanceGenerator: Initializes the item for flow execution.
setUp() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Initializes the item for flow execution.
setUp() - Method in class adams.flow.transformer.WekaClassifierRanker: Initializes the item for flow execution.
setUp() - Method in class adams.flow.transformer.WekaExperiment: Initializes the item for flow execution.
setUp() - Method in class adams.flow.transformer.WekaInstanceDumper: Initializes the item for flow execution.
setUp() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Initializes the item for flow execution.
setUp() - Method in class adams.flow.transformer.WekaStoreInstance: Initializes the item for flow execution.
setup(Serializable, Point<Object>) - Method in class weka.core.SetupGenerator: returns a fully configures object (a copy of the provided one).
setUpdateRelationName(boolean) - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Sets whether to update the relation name with the new class attribute.
setUpEvaluator() - Method in class adams.flow.transformer.WekaInstanceEvaluator: Initializes the evaluator.
SetupGenerator - Class in weka.core: Generates different setups of objects (e.g., classifiers or filters) based on parameter settings.
SetupGenerator() - Constructor for class weka.core.SetupGenerator: Default constructor.
setUpModel(AbstractActor) - Method in class adams.flow.condition.bool.WekaClassification: Loads the model from the model file.
setUpModel() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel: Loads the model from the model file.
setups() - Method in class weka.core.SetupGenerator: Returns an enumeration of all the setups.
setupTipText() - Method in class adams.data.weka.evaluator.NamedSetup: Returns the tip text for this property.
setupTipText() - Method in class adams.data.weka.predictions.NamedSetup: Returns the tip text for this property.
setupTipText() - Method in class adams.flow.source.WekaClassifierGenerator: Returns the tip text for this property.
setupTipText() - Method in class adams.flow.source.WekaClustererGenerator: Returns the tip text for this property.
setupTipText() - Method in class adams.flow.source.WekaFilterGenerator: Returns the tip text for this property.
setURL(String) - Method in class adams.flow.sink.WekaDatabaseWriter: Sets the database URL to query.
setURL(String) - Method in class adams.flow.source.WekaDatabaseReader: Sets the database URL to query.
setUseAllAttributes() - Method in class adams.ml.WekaData
setUseCustomLoader(boolean) - Method in class adams.flow.transformer.WekaFileReader: Sets whether to use a custom loader or not.
setUseCustomSaver(boolean) - Method in class adams.flow.sink.WekaFileWriter: Sets whether to use a custom saver or not.
setUsePrefix(boolean) - Method in class adams.flow.transformer.WekaInstancesMerge: Sets whether to use prefixes.
setUser(String) - Method in class adams.flow.sink.WekaDatabaseWriter: Sets the database user.
setUser(String) - Method in class adams.flow.source.WekaDatabaseReader: Sets the database user.
setUseRelationNameAsFilename(boolean) - Method in class adams.flow.sink.WekaFileWriter: Sets whether to use the relation name as filename instead.
setUseRelationNameAsFilename(boolean) - Method in class adams.flow.transformer.WekaInstanceDumper: Sets whether to use the relation name as filename instead.
setUseRelationNameAsTable(boolean) - Method in class adams.flow.sink.WekaDatabaseWriter: Sets whether to output single Instance objects or just one Instances object.
setUseTree(boolean) - Method in class weka.classifiers.trees.m5.Rule2: Use an m5 tree rather than generate rules
setUseUnsmoothed(boolean) - Method in class weka.classifiers.trees.m5.M5Base2: Use unsmoothed predictions
setValue(String) - Method in class adams.flow.transformer.WekaSetInstanceValue: Sets the value to set in the report.
setValue(PropertyEditor, Object) - Method in class adams.gui.goe.WekaGenericObjectEditorHandler: Sets the editor value.
setValue(int, double) - Method in class weka.core.AbstractHashableInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, String) - Method in class weka.core.AbstractHashableInstance: Sets a value of a nominal or string attribute to the given value.
setValue(Attribute, double) - Method in class weka.core.AbstractHashableInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(Attribute, String) - Method in class weka.core.AbstractHashableInstance: Sets a value of an nominal or string attribute to the given value.
setValue(Object, String, Object) - Method in class weka.core.SetupGenerator: tries to set the value as double, integer (just casts it to int!) or boolean (false if 0, otherwise true) in the object according to the specified path.
setValueSparse(int, double) - Method in class weka.core.AbstractHashableInstance: Sets a specific value in the instance to the given value (internal floating-point format), given an index into the sparse representation.
setVariableName(VariableName) - Method in class adams.flow.template.InstanceDumperVariable: Sets the variable name to generate the sub-flow for.
setVariance(double) - Method in class weka.classifiers.meta.AbstainLeastMedianSq
setVarianceCovered(double) - Method in class weka.core.neighboursearch.PCANNSearch: Sets the amount of variance to account for when retaining principal components.
setVisible(boolean) - Method in class adams.gui.visualization.instance.InstanceContainer: Sets the instance's visibility.
setVisible(int, boolean) - Method in class adams.gui.visualization.instance.InstanceContainerManager: Sets the specified container's visibility.
setWeight(double) - Method in class weka.core.AbstractHashableInstance: Sets the weight of an instance.
setWindows(int) - Method in class weka.filters.unsupervised.attribute.PAA: Sets the nth point setting.
setWindows(int) - Method in class weka.filters.unsupervised.attribute.SAX: Sets the nth point setting.
setX(Integer) - Method in class adams.data.instance.InstancePoint: Sets the X value.
setY(Double) - Method in class adams.data.instance.InstancePoint: Sets the Y value.
setZoomOverview(boolean) - Method in class adams.flow.sink.WekaInstanceViewer: Sets whether to display the zoom overview.
setZoomOverviewPanelVisible(boolean) - Method in class adams.gui.visualization.instance.InstanceExplorer: Sets the zoom overview panel visible or not.
setZoomOverviewPanelVisible(boolean) - Method in class adams.gui.visualization.instance.InstancePanel: Sets the zoom overview panel visible or hides it.
shallowCopy() - Method in class adams.data.instances.AbstractInstanceGenerator: Returns a shallow copy of itself, i.e., based on the commandline options.
shallowCopy(boolean) - Method in class adams.data.instances.AbstractInstanceGenerator: Returns a shallow copy of itself, i.e., based on the commandline options.
shallowCopy() - Method in class adams.data.weka.columnfinder.AbstractColumnFinder: Returns a shallow copy of itself, i.e., based on the commandline options.
shallowCopy(boolean) - Method in class adams.data.weka.columnfinder.AbstractColumnFinder: Returns a shallow copy of itself, i.e., based on the commandline options.
shallowCopy() - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Returns a shallow copy of itself, i.e., based on the commandline options.
shallowCopy(boolean) - Method in class adams.data.weka.evaluator.AbstractInstanceEvaluator: Returns a shallow copy of itself, i.e., based on the commandline options.
shallowCopy() - Method in class adams.data.weka.predictions.AbstractErrorScaler: Returns a shallow copy of itself, i.e., based on the commandline options.
shallowCopy(boolean) - Method in class adams.data.weka.predictions.AbstractErrorScaler: Returns a shallow copy of itself, i.e., based on the commandline options.
shallowCopy() - Method in class adams.data.weka.rowfinder.AbstractRowFinder: Returns a shallow copy of itself, i.e., based on the commandline options.
shallowCopy(boolean) - Method in class adams.data.weka.rowfinder.AbstractRowFinder: Returns a shallow copy of itself, i.e., based on the commandline options.
showDistributionTipText() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns the tip text for this property.
showErrorTipText() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns the tip text for this property.
showHistogram(Vector<InstanceContainer>) - Method in class adams.gui.visualization.instance.InstanceExplorer: Displays the histograms for the given instances.
showNotes(Vector<InstanceContainer>) - Method in class adams.gui.visualization.instance.InstanceExplorer: Displays the notes for the given chromatograms.
showPopup(String, int, int) - Method in class weka.gui.explorer.ExperimentPanel: Handles constructing a popup menu with visualization options.
showProbabilityTipText() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns the tip text for this property.
showStatistics(Vector<InformativeStatistic>) - Method in class adams.gui.visualization.instance.InstanceExplorer: Displays a dialog with the given statistics.
showStatus(String) - Method in class adams.gui.visualization.instance.InstanceComparePanel: Displays a message.
showStatus(String) - Method in class adams.gui.visualization.instance.InstanceExplorer: Displays a message.
showWeightTipText() - Method in class adams.flow.transformer.AbstractWekaPredictionsTransformer: Returns the tip text for this property.
significanceTipText() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the tip text for this property.
size() - Method in class adams.optimise.genetic.PackDataDef
size() - Method in class weka.core.setupgenerator.Space: Returns the size of the space.
sizeTipText() - Method in class adams.data.weka.predictions.FixedSizeErrorScaler: Returns the tip text for this property.
skipIdenticalTipText() - Method in class weka.core.neighboursearch.NewNNSearch: Returns the tip text for this property.
smoothingOriginal(double, double, double) - Static method in class weka.classifiers.trees.m5.RuleNode2: Applies the m5 smoothing procedure to a prediction
solveChol(double[][], double[]) - Method in class weka.classifiers.functions.GPD
sort() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Sorts genes and fitness arrays according to fitness.
sort() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Sorts genes and fitness arrays according to fitness.
Sort - Class in weka.filters.unsupervised.instance: Sorts the instances.
Sort() - Constructor for class weka.filters.unsupervised.instance.Sort
Space - Class in weka.core.setupgenerator: Represents a multidimensional value space.
Space(SpaceDimension[]) - Constructor for class weka.core.setupgenerator.Space: Initializes the space.
SpaceDimension - Class in weka.core.setupgenerator: Represents a single dimension in a multi-dimensional space.
SpaceDimension(AbstractParameter) - Constructor for class weka.core.setupgenerator.SpaceDimension: initializes the dimension (for numeric values).
SpaceDimension(double, double, double, String) - Constructor for class weka.core.setupgenerator.SpaceDimension: initializes the dimension (for numeric values).
SpaceDimension(int, int, String[], String) - Constructor for class weka.core.setupgenerator.SpaceDimension: initializes the dimension (for list values).
sparseFormatTipText() - Method in class adams.flow.source.WekaDatabaseReader: Returns the tip text for this property.
SpellChecker - Class in weka.filters.unsupervised.attribute: A simple filter that merges misspelled labels into a single correct one.
SpellChecker() - Constructor for class weka.filters.unsupervised.attribute.SpellChecker
split(Instances, double) - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Splits the dataset into two separate ones, according to the specified percentage (0-1).
split() - Method in class weka.classifiers.trees.m5.RuleNode2: Finds an attribute and split point for this node
splitAtt() - Method in class weka.classifiers.trees.m5.RuleNode2: Get the index of the splitting attribute for this node
splitOptions(String) - Method in class adams.core.option.WekaCommandLineHandler: Splits the commandline into an array.
splitPercentageTipText() - Method in class adams.flow.sink.WekaExperimentGenerator: Returns the tip text for this property.
splitVal() - Method in class weka.classifiers.trees.m5.RuleNode2: Get the split point for this node
SpreadSheetToWekaInstances - Class in adams.data.conversion: Generates a weke.core.Instances object from a SpreadSheet object.
SpreadSheetToWekaInstances() - Constructor for class adams.data.conversion.SpreadSheetToWekaInstances
sqDifference(int, double, double) - Method in class weka.core.SAXDistance: Returns the squared difference of two values of an attribute.
sqDifference(int, double, double) - Method in class weka.core.WeightedEuclideanDistance: Returns the squared difference of two values of an attribute.
SqlPanel - Class in weka.gui.explorer: A simple demonstration for extending the Explorer by another tab, in this case the SqlViewer (as an extra tab instead of only the button in the PreprocessPanel).
SqlPanel() - Constructor for class weka.gui.explorer.SqlPanel: initializes the panel
SqlViewer - Class in adams.gui.menu: Opens the SQL viewer.
SqlViewer() - Constructor for class adams.gui.menu.SqlViewer: Initializes the menu item with no owner.
SqlViewer(AbstractApplicationFrame) - Constructor for class adams.gui.menu.SqlViewer: Initializes the menu item.
squaredDistance(double[], double[]) - Method in class weka.classifiers.functions.GPD
startExecutorPool() - Method in class weka.classifiers.meta.MultiSearch: Start the pool of execution threads.
startExecutorPool() - Method in class weka.classifiers.meta.SubsetEnsemble: Start the pool of execution threads.
startExperiment() - Method in class weka.gui.explorer.ExperimentPanel: Starts running the currently configured classifier with the current settings in an experiment.
statisticTipText() - Method in class adams.flow.transformer.WekaInstancesStatistic: Returns the tip text for this property.
statisticValuesTipText() - Method in class adams.flow.transformer.WekaEvaluationValues: Returns the tip text for this property.
statisticValueTipText() - Method in class adams.flow.transformer.WekaEvaluationValuePicker: Returns the tip text for this property.
stdDev(int, Instances) - Static method in class weka.classifiers.trees.m5.Rule2: Returns the standard deviation value of the supplied attribute index.
stepTipText() - Method in class weka.core.setupgenerator.MathParameter: Returns the tip text for this property.
stop() - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Stops the execution of the algorithm.
stop() - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Stops the execution of the algorithm.
stopExecution() - Method in class adams.flow.transformer.WekaClassifierRanker: Stops the execution.
stopExecutorPool() - Method in class weka.classifiers.meta.MultiSearch: Stops the ppol of execution threads.
stopExperiment() - Method in class weka.gui.explorer.ExperimentPanel: Stops the currently running experiment (if any).
storeFilenameTipText() - Method in class adams.flow.transformer.WekaTextDirectoryReader: Returns the tip text for this property.
storeSetup(Instances, MTAbstractGeneticAlgorithm.GeneticAlgorithmJob) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Generates a Properties file that stores information on the setup of the genetic algorithm.
storeSetup(Instances) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Generates a Properties file that stores information on the setup of the genetic algorithm.
stringValue(int) - Method in class weka.core.AbstractHashableInstance: Returns the value of a nominal, string, date, or relational attribute for the instance as a string.
stringValue(Attribute) - Method in class weka.core.AbstractHashableInstance: Returns the value of a nominal, string, date, or relational attribute for the instance as a string.
subdimension(int, int) - Method in class weka.core.setupgenerator.SpaceDimension: returns a sub-dimension with the same type/step/list, but different borders.
subsequentSpaceNumFoldsTipText() - Method in class weka.classifiers.meta.MultiSearch: Returns the tip text for this property.
SubsetEnsemble - Class in weka.classifiers.meta: Generates an ensemble using the following approach:
- for each attribute apart from class attribute do:
* create new dataset with only this feature and the class attribute
* remove all instances that contain a missing value
* if no instances left in subset, don't build a classifier for this feature
* if at least 1 instance is left in subset, build base classifier with it
If no classifier gets built at all, use ZeroR as backup model, built on the full dataset.
In addition to the default feature for a subset, a number of random features can be added to the subset before the classifier is trained.
At prediction time, the Vote meta-classifier (using the pre-built classifiers) is used to determing the class probabilities or regression value.
SubsetEnsemble() - Constructor for class weka.classifiers.meta.SubsetEnsemble
subsetSizesOK(Instances, int) - Method in class weka.classifiers.trees.RandomRegressionForest.Node: tests whether the leaf threshold is OK
subspace(Point<Integer>) - Method in class weka.core.setupgenerator.Space: Returns a subspace around the given point, with just one more neighbor left and right on each dimension.
SumTransformed - Class in weka.classifiers.meta: Finds the base classifier with the best least median squared error.
SumTransformed() - Constructor for class weka.classifiers.meta.SumTransformed
sumXY(Instances) - Method in class weka.classifiers.meta.Corr
supplyText() - Method in class adams.flow.sink.WekaInstancesDisplay: Supplies the text.
supplyText() - Method in class adams.flow.sink.WekaInstanceViewer: Supplies the text.
supplyText(InstancePanel) - Static method in class adams.flow.sink.WekaInstanceViewer: Returns the displayed instances as ARFF.
swapRowsAndColumnsTipText() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the tip text for this property.

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tableNameTipText() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the tip text for this property.
TAGS_ALGORITHM - Static variable in class weka.filters.unsupervised.attribute.FastWavelet: the types of algorithm.
TAGS_EVALUATION - Static variable in class weka.classifiers.meta.MultiSearch: evaluation.
TAGS_FILTER - Static variable in class weka.classifiers.functions.GaussianProcessesAdaptive: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.functions.GaussianProcessesWeighted: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.functions.GPD: The filter to apply to the training data
TAGS_PADDING - Static variable in class weka.filters.unsupervised.attribute.FastWavelet: the types of padding.
TAGS_TYPE - Static variable in class weka.core.SetupGenerator: type of parameter.
testBaseTipText() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the tip text for this property.
testerTipText() - Method in class adams.flow.transformer.WekaExperimentEvaluation: Returns the tip text for this property.
testsetTipText() - Method in class adams.flow.transformer.WekaTestSetEvaluator: Returns the tip text for this property.
testTipText() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the tip text for this property.
ThresholdCurves - Class in weka.gui.visualize.plugins: Displays all the threshold curves (ROC) in a single plot.
ThresholdCurves() - Constructor for class weka.gui.visualize.plugins.ThresholdCurves
thresholdTipText() - Method in class adams.data.weka.evaluator.AbstractDatasetInstanceEvaluator: Returns the tip text for this property.
thresholdTipText() - Method in class adams.tools.CompareDatasets: Returns the tip text for this property.
thresholdTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassifiedRel: Returns the tip text for this property.
titleNameColumnTipText() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the tip text for this property.
titleValueColumnTipText() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet: Returns the tip text for this property.
toAdams(Capabilities.Capability) - Static method in enum adams.flow.core.Capability: Turns the WEKA capability into an ADAMS one.
toArray(Object) - Method in class adams.core.option.WekaCommandLineHandler: Generates an options array from the specified object.
toBits(double) - Method in class adams.optimise.genetic.PackDataDef.DataInfo
toCommandLine(Object) - Method in class adams.core.option.WekaCommandLineHandler: Generates a commandline from the specified object.
toCustomStringRepresentation(Object) - Method in class adams.gui.goe.WekaExperimentFileEditor: Returns a custom string representation of the object.
toDisplay() - Method in enum adams.flow.core.EvaluationStatistic: Returns the display string.
toDisplay() - Method in enum adams.flow.core.ExperimentStatistic: Returns the display string.
toDisplay() - Method in enum adams.flow.transformer.WekaClassifierRanker.Measure: Returns the display string.
toDoubleArray(Instance) - Static method in class adams.data.instance.InstanceUtils: Returns the points as double array.
toDoubleArray(List<InstancePoint>) - Static method in class adams.data.instance.InstanceUtils: Returns the points as double array.
toDoubleArray() - Method in class weka.core.AbstractHashableInstance: Returns the values of each attribute as an array of doubles.
toFile() - Method in class adams.data.WekaExperimentFile: Returns a file object.
toInstance() - Method in class adams.data.instance.Instance: Generates a weka instance, if a dataset header is available.
toInstances() - Method in class adams.ml.WekaData
toInstances(String[]) - Method in class adams.ml.WekaData
topOfTree() - Method in class weka.classifiers.trees.m5.Rule2: Returns the top of the tree.
toRaw() - Method in enum adams.flow.core.EvaluationStatistic: Returns the raw enum string.
toRaw() - Method in enum adams.flow.core.ExperimentStatistic: Returns the raw enum string.
toRaw() - Method in enum adams.flow.transformer.WekaClassifierRanker.Measure: Returns the raw enum string.
toSAX(double[], int, double[]) - Static method in class adams.data.utils.SAXUtils: Convert a row in original space into SAX labels.
toString() - Method in class adams.data.conversion.WekaPredictionContainerToSpreadSheet.SortContainer: Returns a short string description of the container.
toString() - Method in class adams.data.instance.InstancePoint: Returns a string representation of the point.
toString() - Method in class adams.data.weka.evaluator.AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer: Returns a string representation of the stored data.
toString() - Method in class adams.data.weka.evaluator.IntervalEstimatorBased.SortedInterval: Returns the intervals as string.
toString() - Method in class adams.data.weka.predictions.AbstractErrorScaler: Returns the commandline of this object.
toString() - Method in enum adams.flow.core.EvaluationStatistic: Returns the displays string.
toString(AbstractOption, Object) - Static method in enum adams.flow.core.EvaluationStatistic: Returns the enum as string.
toString() - Method in enum adams.flow.core.ExperimentStatistic: Returns the displays string.
toString(AbstractOption, Object) - Static method in enum adams.flow.core.ExperimentStatistic: Returns the enum as string.
toString() - Method in class adams.flow.transformer.WekaAccumulatedError.SortablePrediction: Returns a string representation of the wrapped prediction.
toString() - Method in enum adams.flow.transformer.WekaClassifierRanker.Measure: Returns the display string.
toString(AbstractOption, Object) - Static method in enum adams.flow.transformer.WekaClassifierRanker.Measure: Returns the enum as string.
toString() - Method in class adams.flow.transformer.WekaClassifierRanker.RankingJob: Returns a string representation of the job.
toString() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob: Returns a string representation of the job.
toString(AbstractOption, Object) - Static method in class adams.gui.goe.WekaExperimentFileEditor: Returns the file as string.
toString() - Method in class adams.gui.visualization.instance.InstanceContainer: Returns a short string representation of the container.
toString() - Method in class adams.optimise.genetic.PackData
toString() - Method in class adams.optimise.GeneticAlgorithm.GAJob
toString() - Method in class weka.classifiers.functions.GaussianProcessesAdaptive: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.GaussianProcessesWeighted: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.GPD: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.PLSClassifierWeighted: returns a string representation of the classifier
toString() - Method in class weka.classifiers.lazy.LWLSynchro: Returns a description of this classifier.
toString() - Method in class weka.classifiers.meta.AbstainAttributePercentile: Returns description of classifier.
toString() - Method in class weka.classifiers.meta.AbstainLeastMedianSq: Returns description of classifier.
toString() - Method in class weka.classifiers.meta.ClassificationViaRegressionD: Prints the classifiers.
toString() - Method in class weka.classifiers.meta.Corr: Returns description of classifier.
toString() - Method in class weka.classifiers.meta.LeastMedianSq: Returns description of classifier.
toString() - Method in class weka.classifiers.meta.LogTargetRegressor: Returns description of classifier.
toString() - Method in class weka.classifiers.meta.multisearch.Performance: returns a string representation of this performance object.
toString() - Method in class weka.classifiers.meta.multisearch.PerformanceCache: returns a string representation of the cache.
toString() - Method in class weka.classifiers.meta.MultiSearch: returns a string representation of the classifier.
toString() - Method in class weka.classifiers.meta.PeakTransformed: Returns description of classifier.
toString() - Method in class weka.classifiers.meta.RangeCheck: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.SubsetEnsemble: Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.meta.SumTransformed: Returns description of classifier.
toString() - Method in class weka.classifiers.meta.WeightedInstancesHandlerWrapper: Returns a string representation of the base classifier.
toString() - Method in class weka.classifiers.trees.m5.M5Base2: Returns a description of the classifier
toString() - Method in class weka.classifiers.trees.m5.Rule2: Return a description of the m5 tree or rule
toString() - Method in class weka.classifiers.trees.m5.RuleNode2: print the linear model at this node
toString() - Method in class weka.classifiers.trees.RandomModelTrees
toString(int, StringBuffer, List<String>) - Method in class weka.classifiers.trees.RandomRegressionForest.Node: Generates a string representation of the node.
toString() - Method in class weka.classifiers.trees.RandomRegressionForest: Returns description of the classifier.
toString() - Method in class weka.core.AbstractHashableInstance: Returns the value of the Instance's toString() method.
toString(int) - Method in class weka.core.AbstractHashableInstance: Returns the description of one value of the instance as a string.
toString(Attribute) - Method in class weka.core.AbstractHashableInstance: Returns the description of one value of the instance as a string.
toString(int, int) - Method in class weka.core.AbstractHashableInstance: Returns the description of one value of the instance as a string.
toString(Attribute, int) - Method in class weka.core.AbstractHashableInstance: Returns the description of one value of the instance as a string.
toString() - Method in class weka.core.setupgenerator.AbstractParameter: Returns a string representation of the search parameter.
toString() - Method in class weka.core.setupgenerator.ListParameter: Returns a string representation of the search parameter.
toString() - Method in class weka.core.setupgenerator.MathParameter: Returns a string representation of the search parameter.
toString() - Method in class weka.core.setupgenerator.Point: returns a string representation of the Point.
toString() - Method in class weka.core.setupgenerator.Space: Returns a string representation of the space.
toString() - Method in class weka.core.setupgenerator.SpaceDimension: Returns a string representation of the dimension.
toString() - Method in class weka.core.SetupGenerator: A string representation of the generator.
toStringHeader() - Method in class weka.experiment.ResultMatrixMediaWiki: returns the header of the matrix as a string.
toStringKey() - Method in class weka.experiment.ResultMatrixMediaWiki: returns a key for all the col names, for better readability if the names got cut off.
toStringMatrix() - Method in class weka.experiment.ResultMatrixMediaWiki: returns the matrix in CSV format.
toStringMaxDecimalDigits(int) - Method in class weka.core.AbstractHashableInstance: Returns the description of one instance with any numeric values printed at the supplied maximum number of decimal places.
toStringNoWeight() - Method in class weka.core.AbstractHashableInstance: Returns the description of one instance (without weight appended).
toStringNoWeight(int) - Method in class weka.core.AbstractHashableInstance: Returns the description of one instance (without weight appended).
toStringOLD() - Method in class weka.classifiers.trees.RandomModelTrees: Returns description of the bagged classifier.
toStringRanking() - Method in class weka.experiment.ResultMatrixMediaWiki: returns the ranking in a string representation.
toStringSummary() - Method in class weka.experiment.ResultMatrixMediaWiki: returns the summary as string.
toSummaryString() - Method in class weka.classifiers.meta.MultiSearch: Returns a string that summarizes the object.
toWeka(Capability) - Static method in enum adams.flow.core.Capability: Turns the ADAMS capability into a WEKA one.
TrainableColumnFinder - Interface in adams.data.weka.columnfinder: Interface for ColumnFinder algorithms that can be trained.
TrainableRowFinder - Interface in adams.data.weka.rowfinder: Interface for RowFinder algorithms that can be trained.
trainColumnFinder(Instances) - Method in class adams.data.weka.columnfinder.AbstractTrainableColumnFinder: Trains the column finder with the specified dataset.
trainColumnFinder(Instances) - Method in interface adams.data.weka.columnfinder.TrainableColumnFinder: Trains the column finder with the specified dataset.
trainRowFinder(Instances) - Method in class adams.data.weka.rowfinder.AbstractTrainableRowFinder: Trains the row finder with the specified dataset.
trainRowFinder(Instances) - Method in interface adams.data.weka.rowfinder.TrainableRowFinder: Trains the row finder with the specified dataset.
trainTipText() - Method in class adams.flow.transformer.WekaClassifierRanker: Returns the tip text for this property.
transform(Instance) - Method in class weka.classifiers.meta.LogTargetRegressor: Transform instance.
transform(Instance) - Method in class weka.core.SAXDistance
transform(Instance) - Method in class weka.core.WeightedEuclideanDistance
transformInstance(Instance) - Method in class weka.classifiers.meta.PeakTransformed: Just finds the maximum peak.
transformInstance(Instance) - Method in class weka.classifiers.meta.SumTransformed: Just sums up all the peaks.
transformInstance(Instance) - Method in class weka.core.neighboursearch.FilteredSearch
transformInstance(Instance) - Method in class weka.core.neighboursearch.PCANNSearch
transformInstance(Instance) - Method in class weka.core.neighboursearch.PLSNNSearch
transformInstance(Instance) - Method in class weka.core.neighboursearch.TransformNNSearch
transformInstances(Instances) - Method in class weka.core.neighboursearch.FilteredSearch
transformInstances(Instances) - Method in class weka.core.neighboursearch.PCANNSearch
transformInstances(Instances) - Method in class weka.core.neighboursearch.PLSNNSearch
TransformNNSearch - Class in weka.core.neighboursearch
TransformNNSearch() - Constructor for class weka.core.neighboursearch.TransformNNSearch
TransformNNSearch(Instances) - Constructor for class weka.core.neighboursearch.TransformNNSearch
treeToString(int) - Method in class weka.classifiers.trees.m5.RuleNode2: Recursively builds a textual description of the tree
TreeVisualizer - Class in adams.gui.menu: Displays data in the tree visualizer.
TreeVisualizer() - Constructor for class adams.gui.menu.TreeVisualizer: Initializes the menu item with no owner.
TreeVisualizer(AbstractApplicationFrame) - Constructor for class adams.gui.menu.TreeVisualizer: Initializes the menu item.
TreeVisualizer - Class in adams.gui.tools.previewbrowser: Displays trees in dot notation.
TreeVisualizer() - Constructor for class adams.gui.tools.previewbrowser.TreeVisualizer
trialsTipText() - Method in class weka.classifiers.trees.RandomModelTrees: Returns the tip text for this property.
trimIDs(List<String>) - Method in class adams.gui.visualization.instance.InstanceComparePanel: Removes the leading 0s.
turnIntoLeaf(Instances) - Method in class weka.classifiers.trees.RandomRegressionForest.Node: turns the node into a leaf
TYPE_FUNCTION - Static variable in class weka.core.SetupGenerator: type: mathematical function.
TYPE_LIST - Static variable in class weka.core.SetupGenerator: type: explicit, comma-separated list of values.
typeTipText() - Method in class adams.flow.transformer.WekaExtractArray: Returns the tip text for this property.
typeTipText() - Method in class adams.flow.transformer.WekaInstancesInfo: Returns the tip text for this property.
typeTipText() - Method in class weka.core.setupgenerator.AbstractParameter: Returns the tip text for this property.

U

uniqueIDTipText() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns the tip text for this property.
unprunedTipText() - Method in class weka.classifiers.trees.m5.M5Base2: Returns the tip text for this property
update(AbstractEnvironment) - Method in class adams.env.InstanceCompareDefinition: Updates the environment object with its definition for the props file (whether to add/replace/etc the values).
update(AbstractEnvironment) - Method in class adams.env.InstanceExplorerDefinition: Updates the environment object with its definition for the props file (whether to add/replace/etc the values).
update() - Method in class adams.gui.visualization.instance.InstanceComparePanel.DatasetPanel: Updates buttons, etc.
update(Instance) - Method in class weka.core.neighboursearch.FilteredSearch: Updates the LinearNNSearch to cater for the new added instance.
update(Instance) - Method in class weka.core.neighboursearch.NewNNSearch: Updates the LinearNNSearch to cater for the new added instance.
update(Instance) - Method in class weka.core.neighboursearch.PCANNSearch: Updates the LinearNNSearch to cater for the new added instance.
update(Instance) - Method in class weka.core.neighboursearch.PLSNNSearch: Updates the LinearNNSearch to cater for the new added instance.
updateCapabilitiesFilter(Capabilities) - Method in class weka.gui.explorer.ExperimentPanel: updates the capabilities filter of the GOE.
updateDataTable() - Method in class adams.gui.visualization.weka.AbstractInstanceInfoFrame: Updates the data in the data table.
updateDistance(double, double) - Method in class weka.core.SAXDistance: Updates the current distance calculated so far with the new difference between two attributes.
updateDistance(double, double) - Method in class weka.core.WeightedEuclideanDistance: Updates the current distance calculated so far with the new difference between two attributes.
updateHeader(Instances, MTAbstractGeneticAlgorithm.GeneticAlgorithmJob) - Method in class adams.genetic.MTAbstractGeneticAlgorithm: Creates a new dataset, with the setup as the new relation name.
updateHeader(Instances) - Method in class adams.optimise.genetic.AbstractGeneticAlgorithm: Creates a new dataset, with the setup as the new relation name.
updateIDs(Instances, HashSet) - Method in class adams.flow.transformer.WekaInstancesMerge: Updates the IDs in the hashset with the ones stored in the ID attribute of the provided dataset.
updateMenu() - Method in class adams.gui.visualization.instance.InstanceComparePanel: updates the enabled state of the menu items.
updateMenu() - Method in class adams.gui.visualization.instance.InstanceExplorer: updates the enabled state of the menu items.
updateOption(String[], String, String) - Method in class weka.classifiers.meta.MultiSearch: replaces the current option in the options array with a new value.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.source.WekaDatabaseReader: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.source.WekaDataGenerator: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.source.WekaNewInstances: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaClassSelector: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaCrossValidationEvaluator: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaCrossValidationSplit: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaFileReader: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaFilter: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaInstanceBuffer: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaInstancesMerge: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaRandomSplit: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaSubsets: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaTestSetEvaluator: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaTextDirectoryReader: Updates the provenance information in the provided container.
updateProvenance(ProvenanceContainer) - Method in class adams.flow.transformer.WekaTrainTestSetEvaluator: Updates the provenance information in the provided container.
updateRelationNameTipText() - Method in class adams.flow.transformer.WekaMultiLabelSplitter: Returns the tip text for this property.
updateRows() - Method in class adams.gui.visualization.instance.InstanceComparePanel: Updates the combobox with the rows.
URLTipText() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the tip text for this property.
URLTipText() - Method in class adams.flow.source.WekaDatabaseReader: Returns the tip text for this property.
useCustomLoaderTipText() - Method in class adams.flow.transformer.WekaFileReader: Returns the tip text for this property.
useCustomSaverTipText() - Method in class adams.flow.sink.WekaFileWriter: Returns the tip text for this property.
usePrefixTipText() - Method in class adams.flow.transformer.WekaInstancesMerge: Returns the tip text for this property.
useRelationNameAsFilenameTipText() - Method in class adams.flow.sink.WekaFileWriter: Returns the tip text for this property.
useRelationNameAsFilenameTipText() - Method in class adams.flow.transformer.WekaInstanceDumper: Returns the tip text for this property.
useRelationNameAsTableTipText() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the tip text for this property.
userTipText() - Method in class adams.flow.sink.WekaDatabaseWriter: Returns the tip text for this property.
userTipText() - Method in class adams.flow.source.WekaDatabaseReader: Returns the tip text for this property.
useUnsmoothedTipText() - Method in class weka.classifiers.trees.m5.M5Base2: Returns the tip text for this property

V

value(int) - Method in class weka.core.AbstractHashableInstance: Returns an instance's attribute value in internal format.
value(Attribute) - Method in class weka.core.AbstractHashableInstance: Returns an instance's attribute value in internal format.
VALUE_CLASSIFICATION - Static variable in class adams.flow.container.WekaPredictionContainer: the identifier for the Classification.
VALUE_CLASSIFICATION_LABEL - Static variable in class adams.flow.container.WekaPredictionContainer: the identifier for the Classification's label.
VALUE_CLUSTER - Static variable in class adams.flow.container.WekaClusteringContainer: the identifier for the Cluster.
VALUE_CLUSTERED_DATASET - Static variable in class adams.flow.transformer.wekaclusterer.AbstractClusterMembershipPostProcessor: the clustered full dataset.
VALUE_DATASET - Static variable in class adams.flow.container.WekaModelContainer: the identifier for the full dataset.
VALUE_DISTRIBUTION - Static variable in class adams.flow.container.WekaClusteringContainer: the identifier for the Distribution.
VALUE_DISTRIBUTION - Static variable in class adams.flow.container.WekaPredictionContainer: the identifier for the Distribution.
VALUE_FOLD_COUNT - Static variable in class adams.flow.container.WekaTrainTestSetContainer: the identifier for the fold count.
VALUE_FOLD_NUMBER - Static variable in class adams.flow.container.WekaTrainTestSetContainer: the identifier for the fold number.
VALUE_HEADER - Static variable in class adams.flow.container.WekaModelContainer: the identifier for the Header.
VALUE_INSTANCE - Static variable in class adams.flow.container.WekaClusteringContainer: the identifier for the Instance.
VALUE_INSTANCE - Static variable in class adams.flow.container.WekaPredictionContainer: the identifier for the Instance.
VALUE_LOGDENSITY - Static variable in class adams.flow.container.WekaClusteringContainer: the identifier for the LogDensity.
VALUE_LOGDENSITYPERCLUSTER - Static variable in class adams.flow.container.WekaClusteringContainer: the identifier for the LogDensityPerCluster.
VALUE_LOGJOINTDENSITIES - Static variable in class adams.flow.container.WekaClusteringContainer: the identifier for the LogJointDensities.
VALUE_MODEL - Static variable in class adams.flow.container.WekaModelContainer: the identifier for the Model.
VALUE_RANGECHECK - Static variable in class adams.flow.container.WekaPredictionContainer: the identifier for the Range check.
VALUE_SEED - Static variable in class adams.flow.container.WekaTrainTestSetContainer: the identifier for the random seed.
VALUE_TEST - Static variable in class adams.flow.container.WekaTrainTestSetContainer: the identifier for the test data.
VALUE_TRAIN - Static variable in class adams.flow.container.WekaTrainTestSetContainer: the identifier for the training data.
valueIsSmallerEqual(Instance, int, double) - Method in class weka.core.SAXDistance: Returns true if the value of the given dimension is smaller or equal the value to be compared with.
valueIsSmallerEqual(Instance, int, double) - Method in class weka.core.WeightedEuclideanDistance: Returns true if the value of the given dimension is smaller or equal the value to be compared with.
valueOf(String) - Static method in enum adams.data.conversion.WekaPredictionContainerToSpreadSheet.Sorting: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.data.weka.columnfinder.MultiColumnFinder.Combination: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.data.weka.rowfinder.MultiRowFinder.Combination: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.flow.core.Capability: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.flow.core.EvaluationStatistic: Returns the enum constant of this type with the specified name.
valueOf(AbstractOption, String) - Static method in enum adams.flow.core.EvaluationStatistic: Returns an enum generated from the string.
valueOf(String) - Static method in enum adams.flow.core.ExperimentStatistic: Returns the enum constant of this type with the specified name.
valueOf(AbstractOption, String) - Static method in enum adams.flow.core.ExperimentStatistic: Returns an enum generated from the string.
valueOf(String) - Static method in enum adams.flow.sink.WekaExperimentGenerator.EvaluationType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.flow.sink.WekaExperimentGenerator.ExperimentType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.flow.sink.WekaExperimentGenerator.ResultFormat: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.flow.transformer.WekaClassifierRanker.Measure: Returns the enum constant of this type with the specified name.
valueOf(AbstractOption, String) - Static method in enum adams.flow.transformer.WekaClassifierRanker.Measure: Returns an enum generated from the string.
valueOf(String) - Static method in enum adams.flow.transformer.WekaExtractArray.ExtractionType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.flow.transformer.WekaFileReader.OutputType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.flow.transformer.WekaInstanceBuffer.Operation: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.flow.transformer.WekaInstanceDumper.OutputFormat: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.flow.transformer.WekaInstancesInfo.InfoType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.flow.transformer.WekaInstancesStatistic.DataType: Returns the enum constant of this type with the specified name.
valueOf(AbstractOption, String) - Static method in class adams.gui.goe.WekaExperimentFileEditor: Returns a file generated from the string.
valueOf(String) - Static method in enum adams.gui.visualization.instance.InstanceLinePaintlet.MarkerShape: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction.Measure: Returns the enum constant of this type with the specified name.
values() - Static method in enum adams.data.conversion.WekaPredictionContainerToSpreadSheet.Sorting: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.data.weka.columnfinder.MultiColumnFinder.Combination: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.data.weka.rowfinder.MultiRowFinder.Combination: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.core.Capability: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.core.EvaluationStatistic: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.core.ExperimentStatistic: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.sink.WekaExperimentGenerator.EvaluationType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.sink.WekaExperimentGenerator.ExperimentType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.sink.WekaExperimentGenerator.ResultFormat: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.transformer.WekaClassifierRanker.Measure: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.transformer.WekaExtractArray.ExtractionType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.transformer.WekaFileReader.OutputType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.transformer.WekaInstanceBuffer.Operation: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.transformer.WekaInstanceDumper.OutputFormat: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.transformer.WekaInstancesInfo.InfoType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.flow.transformer.WekaInstancesStatistic.DataType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.gui.visualization.instance.InstanceLinePaintlet.MarkerShape: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum adams.optimise.genetic.fitnessfunctions.AbstractWEKAFitnessFunction.Measure: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Method in class weka.core.setupgenerator.Space: returns an Enumeration over all values.
valueSparse(int) - Method in class weka.core.AbstractHashableInstance: Returns an instance's attribute value in internal format, given an index in the sparse representation.
valueTipText() - Method in class adams.flow.transformer.WekaSetInstanceValue: Returns the tip text for this property.
variableNameTipText() - Method in class adams.flow.template.InstanceDumperVariable: Returns the tip text for this property.
varianceTipText() - Method in class weka.classifiers.meta.AbstainLeastMedianSq

W

weight() - Method in class weka.core.AbstractHashableInstance

Returns the instance's weight.

WeightedEuclideanDistance - Class in weka.core

Implementing Euclidean distance (or similarity) function.

One object defines not one distance but the data model in which the distances between objects of that data model can be computed.

Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low.

For more information, see:

Wikipedia.

WeightedEuclideanDistance() - Constructor for class weka.core.WeightedEuclideanDistance

Constructs an Euclidean Distance object, Instances must be still set.

WeightedEuclideanDistance(Instances) - Constructor for class weka.core.WeightedEuclideanDistance

Constructs an Euclidean Distance object and automatically initializes the ranges.

WeightedInstancesHandlerWrapper - Class in weka.classifiers.meta

A meta-classifier that implements the weka.core.WeightedInstancesHandler interface in order to enable all classifiers to be used in other meta-classifiers that require the base classifier to implem ent the WeightedInstancesHandler interface.

WeightedInstancesHandlerWrapper() - Constructor for class weka.classifiers.meta.WeightedInstancesHandlerWrapper

weightsToString() - Method in class adams.genetic.MTAbstractGeneticAlgorithm.GeneticAlgorithmJob

Turns the weights into a string representation.

weightsToString(int[]) - Method in class adams.optimise.GeneticAlgorithm

Turns the weights into a string representation.

weka.classifiers - package weka.classifiers

weka.classifiers.functions - package weka.classifiers.functions

weka.classifiers.lazy - package weka.classifiers.lazy

weka.classifiers.meta - package weka.classifiers.meta

weka.classifiers.meta.multisearch - package weka.classifiers.meta.multisearch

weka.classifiers.trees - package weka.classifiers.trees

weka.classifiers.trees.m5 - package weka.classifiers.trees.m5

weka.core - package weka.core

weka.core.neighboursearch - package weka.core.neighboursearch

weka.core.setupgenerator - package weka.core.setupgenerator

weka.experiment - package weka.experiment

weka.filters - package weka.filters

weka.filters.unsupervised.attribute - package weka.filters.unsupervised.attribute

weka.filters.unsupervised.instance - package weka.filters.unsupervised.instance

weka.gui.explorer - package weka.gui.explorer

weka.gui.visualize.plugins - package weka.gui.visualize.plugins

WekaAccumulatedError - Class in adams.flow.transformer

Generates plot containers from an evaluation object's predictions.

WekaAccumulatedError() - Constructor for class adams.flow.transformer.WekaAccumulatedError

WekaAccumulatedError.SortablePrediction - Class in adams.flow.transformer

Container for a classifier prediction, used for sorting.

WekaAccumulatedError.SortablePrediction(Prediction) - Constructor for class adams.flow.transformer.WekaAccumulatedError.SortablePrediction

Initializes the container.

WekaAttributeIterator - Class in adams.flow.transformer

Iterates through all attributes of a dataset and outputs the names.
The attributes can be limited with the range parameter and furthermore with the regular expression applied to the names.

WekaAttributeIterator() - Constructor for class adams.flow.transformer.WekaAttributeIterator

WekaCapabilities - Class in adams.flow.condition.bool

Filters weka.core.Instance and weka.core.Instances objects based on defined capabilities.

WekaCapabilities() - Constructor for class adams.flow.condition.bool.WekaCapabilities

WekaClassification - Class in adams.flow.condition.bool

Uses the index of the classification, i.e., the predicted label, as index of the switch

Valid options are:

WekaClassification() - Constructor for class adams.flow.condition.bool.WekaClassification

WekaClassifier - Class in adams.flow.transformer

Trains a classifier based on the incoming dataset and outputs the built classifier alongside the training header (in a model container).
Incremental training is performed, if the input are weka.core.Instance objects and the classifier implements weka.classifiers.UpdateableClassifier.
If the incoming token does not encapsulate a dataset or instance, then only a new instance of the classifier is sent around.

WekaClassifier() - Constructor for class adams.flow.transformer.WekaClassifier

WekaClassifier - Class in adams.ml

WekaClassifier() - Constructor for class adams.ml.WekaClassifier

WekaClassifierErrors - Class in adams.flow.sink

Actor for displaying classifier errors.

WekaClassifierErrors() - Constructor for class adams.flow.sink.WekaClassifierErrors

WekaClassifierErrors.DataGenerator - Class in adams.flow.sink

Helper class for generating visualization data.

WekaClassifierErrors.DataGenerator(Evaluation, AbstractErrorScaler) - Constructor for class adams.flow.sink.WekaClassifierErrors.DataGenerator

Initializes the generator.

WekaClassifierGenerator - Class in adams.flow.source

Generates multiple classifier setups.

WekaClassifierGenerator() - Constructor for class adams.flow.source.WekaClassifierGenerator

WekaClassifierOptimizer - Class in adams.flow.transformer

Evaluates a classifier optimizer on an incoming dataset.

WekaClassifierOptimizer() - Constructor for class adams.flow.transformer.WekaClassifierOptimizer

WekaClassifierRanker - Class in adams.flow.transformer

Performs a quick evaluation using cross-validation on a single dataset (or evaluation on a separate test set if the number of folds is less than 2) to rank the classifiers received on the input and forwarding the x best ones.

WekaClassifierRanker() - Constructor for class adams.flow.transformer.WekaClassifierRanker

WekaClassifierRanker.Measure - Enum in adams.flow.transformer

The performance measure to use.

WekaClassifierRanker.RankingJob - Class in adams.flow.transformer

A job class specific to ranking classifiers.

WekaClassifierRanker.RankingJob(Classifier, int, Instances, Instances, long, int, WekaClassifierRanker.Measure, boolean) - Constructor for class adams.flow.transformer.WekaClassifierRanker.RankingJob

Initializes the job.

WekaClassifying - Class in adams.flow.transformer

Uses a serialized model to perform predictions on the data being passed through.

WekaClassifying() - Constructor for class adams.flow.transformer.WekaClassifying

WekaClassSelector - Class in adams.flow.transformer

Sets the class index.

WekaClassSelector() - Constructor for class adams.flow.transformer.WekaClassSelector

WekaClusterer - Class in adams.flow.transformer

Trains a clusterer based on the incoming dataset and output the built clusterer alongside the training header (in a model container).
Incremental training is performed, if the input are weka.core.Instance objects and the clusterer implements weka.clusterers.UpdateableClusterer.
If the incoming token does not encapsulate a dataset, then only a new instance of the clusterer is sent around.

WekaClusterer() - Constructor for class adams.flow.transformer.WekaClusterer

WekaClustererGenerator - Class in adams.flow.source

Generates multiple clusterer setups.

WekaClustererGenerator() - Constructor for class adams.flow.source.WekaClustererGenerator

WekaClustering - Class in adams.flow.transformer

Uses a serialized model to cluster data being passed through.
The model can also be obtained from a global actor, if the model file is pointing to a directory.

WekaClustering() - Constructor for class adams.flow.transformer.WekaClustering

WekaClusteringContainer - Class in adams.flow.container

A container for clusterings made by a clusterer.

WekaClusteringContainer() - Constructor for class adams.flow.container.WekaClusteringContainer

Initializes the container.

WekaClusteringContainer(Instance, int, double[]) - Constructor for class adams.flow.container.WekaClusteringContainer

Initializes the container.

WekaClusteringContainer(Instance, int, double[], double, double[], double[]) - Constructor for class adams.flow.container.WekaClusteringContainer

Initializes the container.

WekaCommandLineHandler - Class in adams.core.option

Handles objects of classes that implement the weka.core.OptionHandler interface.

WekaCommandLineHandler() - Constructor for class adams.core.option.WekaCommandLineHandler

WekaCostCurve - Class in adams.flow.sink

Actor for displaying classifier errors.

WekaCostCurve() - Constructor for class adams.flow.sink.WekaCostCurve

WekaCrossValidationEvaluator - Class in adams.flow.transformer

Cross-validates a classifier on an incoming dataset.

WekaCrossValidationEvaluator() - Constructor for class adams.flow.transformer.WekaCrossValidationEvaluator

WekaCrossValidationSplit - Class in adams.flow.transformer

Generates train/test pairs like during a cross-validation run.

WekaCrossValidationSplit() - Constructor for class adams.flow.transformer.WekaCrossValidationSplit

WekaData - Class in adams.ml

WekaData() - Constructor for class adams.ml.WekaData

WekaData(Dataset) - Constructor for class adams.ml.WekaData

WekaData.ArrayFinder - Class in adams.ml

WekaData.ArrayFinder() - Constructor for class adams.ml.WekaData.ArrayFinder

WekaDatabaseReader - Class in adams.flow.source

Executes a query and returns the data either in batch or incremental mode.

WekaDatabaseReader() - Constructor for class adams.flow.source.WekaDatabaseReader

WekaDatabaseWriter - Class in adams.flow.sink

Actor for saving a weka.core.Instances object in a database.
The relation name of the incoming dataset can be used to replace the current filename (path and extension are kept).

WekaDatabaseWriter() - Constructor for class adams.flow.sink.WekaDatabaseWriter

WekaDataGenerator - Class in adams.flow.source

Generates artificial data using a Weka data generator.

WekaDataGenerator() - Constructor for class adams.flow.source.WekaDataGenerator

WekaDatasetHandler - Class in adams.gui.tools.previewbrowser

Displays the following WEKA dataset types: csv,arff,arff.gz,xrff,xrff.gz

Valid options are:

WekaDatasetHandler() - Constructor for class adams.gui.tools.previewbrowser.WekaDatasetHandler

WekaEditorsRegistration - Class in adams.gui.goe

Registers first the WEKA GenericObjectEditor editors and the ADAMS ones.

WekaEditorsRegistration() - Constructor for class adams.gui.goe.WekaEditorsRegistration

WekaEvaluationSummary - Class in adams.flow.transformer

Generates a summary string of the weka.classifiers.Evaluation objects that it receives.

WekaEvaluationSummary() - Constructor for class adams.flow.transformer.WekaEvaluationSummary

WekaEvaluationValuePicker - Class in adams.flow.transformer

Picks a specific value from an evaluation object.

WekaEvaluationValuePicker() - Constructor for class adams.flow.transformer.WekaEvaluationValuePicker

WekaEvaluationValues - Class in adams.flow.transformer

Generates a spreadsheet from statistics of an Evaluation object.

WekaEvaluationValues() - Constructor for class adams.flow.transformer.WekaEvaluationValues

WekaExperiment - Class in adams.flow.transformer

Represents a Weka experiment, stored in a file.

WekaExperiment() - Constructor for class adams.flow.transformer.WekaExperiment

WekaExperimentEvaluation - Class in adams.flow.transformer

Generates evaluation output of an experiment that was run previously.

WekaExperimentEvaluation() - Constructor for class adams.flow.transformer.WekaExperimentEvaluation

WekaExperimentFile - Class in adams.data

A dummy class for the GOE, for special handling of experiments.

WekaExperimentFile(File, String) - Constructor for class adams.data.WekaExperimentFile

Creates a new ExperimentFile instance from a parent abstract pathname and a child pathname string.

WekaExperimentFile(String) - Constructor for class adams.data.WekaExperimentFile

Creates a new ExperimentFile instance by converting the given pathname string into an abstract pathname.

WekaExperimentFile(String, String) - Constructor for class adams.data.WekaExperimentFile

Creates a new ExperimentFile instance from a parent pathname string and a child pathname string.

WekaExperimentFile(URI) - Constructor for class adams.data.WekaExperimentFile

Creates a new ExperimentFile instance by converting the given file: URI into an abstract pathname.

WekaExperimentFile(File) - Constructor for class adams.data.WekaExperimentFile

Creates a new ExperimentFile instance by using the given file.

WekaExperimentFileEditor - Class in adams.gui.goe

A PropertyEditor for WekaExperimentFile objects that lets the user select a file.

WekaExperimentFileEditor() - Constructor for class adams.gui.goe.WekaExperimentFileEditor

WekaExperimentFileEditor.SimpleSetupDialog - Class in adams.gui.goe

A dialog for displaying the simple setup of an experiment.

WekaExperimentFileEditor.SimpleSetupDialog(Frame) - Constructor for class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog

Initializes the dialog.

WekaExperimentFileEditor.SimpleSetupDialog(Dialog) - Constructor for class adams.gui.goe.WekaExperimentFileEditor.SimpleSetupDialog

Initializes the dialog.

WekaExperimentGenerator - Class in adams.flow.sink

Generates an experiment setup that can be used in conjunction with the Experiment transformer actor.

WekaExperimentGenerator() - Constructor for class adams.flow.sink.WekaExperimentGenerator

WekaExperimentGenerator.EvaluationType - Enum in adams.flow.sink

The evaluation type.

WekaExperimentGenerator.ExperimentType - Enum in adams.flow.sink

The experiment type.

WekaExperimentGenerator.ResultFormat - Enum in adams.flow.sink

The data format the experiment data is stored in.

WekaExtractArray - Class in adams.flow.transformer

Extracts a column or row of data from a weka.core.Instances or SpreadSheet object.
Only numeric columns can be returned.

WekaExtractArray() - Constructor for class adams.flow.transformer.WekaExtractArray

WekaExtractArray.ExtractionType - Enum in adams.flow.transformer

The type of extraction to perform.

WekaFileReader - Class in adams.flow.transformer

Reads any file format that Weka's converters can handle and returns the full dataset or single weka.core.Instance objects.

WekaFileReader() - Constructor for class adams.flow.transformer.WekaFileReader

WekaFileReader.OutputType - Enum in adams.flow.transformer

Defines how to output the data.

WekaFileWriter - Class in adams.flow.sink

Actor for saving a weka.core.Instances object as file.
The relation name of the incoming dataset can be used to replace the current filename (path and extension are kept).

WekaFileWriter() - Constructor for class adams.flow.sink.WekaFileWriter

WekaFilter - Class in adams.flow.transformer

Filters Instances/Instance objects using the specified filter.

WekaFilter() - Constructor for class adams.flow.transformer.WekaFilter

WekaFilter - Class in adams.ml

WekaFilter() - Constructor for class adams.ml.WekaFilter

WekaFilterGenerator - Class in adams.flow.source

Generates multiple filter setups.

WekaFilterGenerator() - Constructor for class adams.flow.source.WekaFilterGenerator

WekaGenericObjectEditorHandler - Class in adams.gui.goe

Handler for the WEKA GenericObjectEditor.

WekaGenericObjectEditorHandler() - Constructor for class adams.gui.goe.WekaGenericObjectEditorHandler

WekaGetInstanceValue - Class in adams.flow.transformer

Retrieves a value from a WEKA Instance object.
Notes:
- date and relational values are forwarded as strings
- missing values are output as '?' (without the single quotes)
- the 'attribute name' option overrides the 'index' option

Input/output:
- accepts:
   weka.core.Instance
- generates:
   java.lang.Double
   java.lang.String

Valid options are:

WekaGetInstanceValue() - Constructor for class adams.flow.transformer.WekaGetInstanceValue

WekaInstanceBuffer - Class in adams.flow.transformer

Can act in two different ways:
1.

WekaInstanceBuffer() - Constructor for class adams.flow.transformer.WekaInstanceBuffer

WekaInstanceBuffer.Operation - Enum in adams.flow.transformer

Defines how the buffer actor operates.

WekaInstanceDumper - Class in adams.flow.transformer

Dumps weka.core.Instance objects into an ARFF file.

WekaInstanceDumper() - Constructor for class adams.flow.transformer.WekaInstanceDumper

WekaInstanceDumper.OutputFormat - Enum in adams.flow.transformer

The format to output the data in.

WekaInstanceEvaluator - Class in adams.flow.transformer

Adds a new attribute to the data being passed through (normally 'evaluation') and sets the value to the evaluation value returned by the chosen evaluator scheme.

WekaInstanceEvaluator() - Constructor for class adams.flow.transformer.WekaInstanceEvaluator

WekaInstanceFileReader - Class in adams.flow.transformer

Loads a WEKA dataset from disk with a specified reader and passes on the adams.core.instance.Instance objects.

WekaInstanceFileReader() - Constructor for class adams.flow.transformer.WekaInstanceFileReader

WekaInstancesDisplay - Class in adams.flow.sink

Actor for displaying a weka.core.Instances object in table format.

WekaInstancesDisplay() - Constructor for class adams.flow.sink.WekaInstancesDisplay

WekaInstancesInfo - Class in adams.flow.transformer

Outputs statistics of a weka.core.Instances object.

WekaInstancesInfo() - Constructor for class adams.flow.transformer.WekaInstancesInfo

WekaInstancesInfo.InfoType - Enum in adams.flow.transformer

The type of information to generate.

WekaInstancesMerge - Class in adams.flow.transformer

Merges multiple datasets.
If no 'ID' attribute is named, then all datasets must contain the same number of rows.
Attributes can be excluded from ending up in the final dataset via a regular expression.

WekaInstancesMerge() - Constructor for class adams.flow.transformer.WekaInstancesMerge

WekaInstancesStatistic - Class in adams.flow.transformer

Generates statistics from a weka.core.Instances object.
The actor just uses the internal format (double array) and does not check whether the attributes are actually numeric.

WekaInstancesStatistic() - Constructor for class adams.flow.transformer.WekaInstancesStatistic

WekaInstancesStatistic.DataType - Enum in adams.flow.transformer

Defines what data to retrieve from an Instances object.

WekaInstancesToSpreadSheet - Class in adams.data.conversion

Generates a spreadsheet from a weka.core.Instances object.

WekaInstancesToSpreadSheet() - Constructor for class adams.data.conversion.WekaInstancesToSpreadSheet

WekaInstanceStreamPlotGenerator - Class in adams.flow.transformer

Generates plot containers from a range of attributes of the weka.core.Instance objects being passed through.
The generator merely uses the internal data representation for generating the Y value of the plot container.

WekaInstanceStreamPlotGenerator() - Constructor for class adams.flow.transformer.WekaInstanceStreamPlotGenerator

WekaInstanceToAdamsInstance - Class in adams.data.conversion

Converts weka.core.Instance objects into adams.data.instance.Instance ones.

WekaInstanceToAdamsInstance() - Constructor for class adams.data.conversion.WekaInstanceToAdamsInstance

WekaInstanceViewer - Class in adams.flow.sink

Actor for displaying adams.data.instance.Instance objects in a graphical way (using the internal format), like the 'Instance Explorer' tool.

WekaInstanceViewer() - Constructor for class adams.flow.sink.WekaInstanceViewer

WekaModelContainer - Class in adams.flow.container

A container for models (e.g., classifier or clusterer) and an optional header of a dataset.

WekaModelContainer() - Constructor for class adams.flow.container.WekaModelContainer

Initializes the container.

WekaModelContainer(Object) - Constructor for class adams.flow.container.WekaModelContainer

Initializes the container with no header.

WekaModelContainer(Object, Instances) - Constructor for class adams.flow.container.WekaModelContainer

Initializes the container with no header.

WekaModelContainer(Object, Instances, Instances) - Constructor for class adams.flow.container.WekaModelContainer

Initializes the container with no header.

WekaModelReader - Class in adams.flow.transformer

Actor for loading a model (classifier or clusterer).

WekaModelReader() - Constructor for class adams.flow.transformer.WekaModelReader

WekaModelWriter - Class in adams.flow.sink

Actor for saving a model (classifier or clusterer) alongside an optional header (i.e., weka.core.Instances object) as file.

WekaModelWriter() - Constructor for class adams.flow.sink.WekaModelWriter

WekaMultiLabelSplitter - Class in adams.flow.transformer

Splits a dataset containing multiple class attributes ('multi-label') into separate datasets with only a single class attribute.

WekaMultiLabelSplitter() - Constructor for class adams.flow.transformer.WekaMultiLabelSplitter

WekaNewInstance - Class in adams.flow.transformer

Creates a new weka.core.Instance-derived object, with all values marked as missing.
The class implementing the weka.core.Instance interface needs to have a constructor that takes the number of attributes as sole parameter.

WekaNewInstance() - Constructor for class adams.flow.transformer.WekaNewInstance

WekaNewInstances - Class in adams.flow.source

Generates an empty dataset, based on the attribute types and names specified.
Nominal attributes are generated with an empty set of labels.

WekaNewInstances() - Constructor for class adams.flow.source.WekaNewInstances

WekaPackagesClassPathAugmenter - Class in adams.core.management

Returns the classpath augmentations for all the installed WEKA packages.

WekaPackagesClassPathAugmenter() - Constructor for class adams.core.management.WekaPackagesClassPathAugmenter

WekaPredictionContainer - Class in adams.flow.container

A container for predictions made by a classifier.

WekaPredictionContainer() - Constructor for class adams.flow.container.WekaPredictionContainer

Initializes the container.

WekaPredictionContainer(Instance, double, double[]) - Constructor for class adams.flow.container.WekaPredictionContainer

Initializes the container.

WekaPredictionContainer(Instance, double, double[], String) - Constructor for class adams.flow.container.WekaPredictionContainer

Initializes the container.

WekaPredictionContainerToSpreadSheet - Class in adams.data.conversion

Turns a WEKA prediction container into a SpreadSheet object.

WekaPredictionContainerToSpreadSheet() - Constructor for class adams.data.conversion.WekaPredictionContainerToSpreadSheet

WekaPredictionContainerToSpreadSheet.SortContainer - Class in adams.data.conversion

Helper class for sorting the distribution.

WekaPredictionContainerToSpreadSheet.SortContainer(int, double) - Constructor for class adams.data.conversion.WekaPredictionContainerToSpreadSheet.SortContainer

Initializes the container.

WekaPredictionContainerToSpreadSheet.Sorting - Enum in adams.data.conversion

How to sort the distribution.

WekaPredictionsToInstances - Class in adams.flow.transformer

Generates weka.core.Instances from the predictions of an Evaluation object.

WekaPredictionsToInstances() - Constructor for class adams.flow.transformer.WekaPredictionsToInstances

WekaRandomSplit - Class in adams.flow.transformer

Splits a dataset into a training and test set according to a specified split percentage.

WekaRandomSplit() - Constructor for class adams.flow.transformer.WekaRandomSplit

WekaRegexToRange - Class in adams.flow.transformer

Produces a range string from a regular expression describing attributes.

WekaRegexToRange() - Constructor for class adams.flow.transformer.WekaRegexToRange

WekaRelationName - Class in adams.flow.transformer

Extracts the relation name of a weka.core.Instances or weka.core.Instance object.

WekaRelationName() - Constructor for class adams.flow.transformer.WekaRelationName

WekaRenameRelation - Class in adams.flow.transformer

Modifies relation names.

WekaRenameRelation() - Constructor for class adams.flow.transformer.WekaRenameRelation

WekaROC - Class in adams.flow.sink

Actor for displaying classifier errors.

WekaROC() - Constructor for class adams.flow.sink.WekaROC

WekaSetInstanceValue - Class in adams.flow.transformer

Sets a value in a WEKA Instance.
Notes:
- relational values cannot be set
- '?' (without single quotes) is interpreted as missing value

Input/output:
- accepts:
weka.core.Instance
- generates:
weka.core.Instance

Valid options are:

WekaSetInstanceValue() - Constructor for class adams.flow.transformer.WekaSetInstanceValue

WekaStoreInstance - Class in adams.flow.transformer

Appends the incoming weka.core.Instance to the dataset in storage.

WekaStoreInstance() - Constructor for class adams.flow.transformer.WekaStoreInstance

WekaSubsets - Class in adams.flow.transformer

Splits the dataset based on the unique values of the specified attribute: all rows with the same unique value form a subset.

WekaSubsets() - Constructor for class adams.flow.transformer.WekaSubsets

WekaTestSetEvaluator - Class in adams.flow.transformer

Evaluates a trained classifier (obtained from input) on the dataset obtained from the global actor.

WekaTestSetEvaluator() - Constructor for class adams.flow.transformer.WekaTestSetEvaluator

WekaTextDirectoryReader - Class in adams.flow.transformer

Loads all text files in a directory and uses the subdirectory names as class labels.

WekaTextDirectoryReader() - Constructor for class adams.flow.transformer.WekaTextDirectoryReader

WekaTrainTestSetContainer - Class in adams.flow.container

A container for storing train and test set.

WekaTrainTestSetContainer() - Constructor for class adams.flow.container.WekaTrainTestSetContainer

Initializes the container.

WekaTrainTestSetContainer(Instances, Instances) - Constructor for class adams.flow.container.WekaTrainTestSetContainer

Initializes the container.

WekaTrainTestSetContainer(Instances, Instances, Long) - Constructor for class adams.flow.container.WekaTrainTestSetContainer

Initializes the container.

WekaTrainTestSetContainer(Instances, Instances, Long, Integer, Integer) - Constructor for class adams.flow.container.WekaTrainTestSetContainer

Initializes the container.

WekaTrainTestSetEvaluator - Class in adams.flow.transformer

Trains a classifier on an incoming training dataset (from a container) and then evaluates it on the test set (also from a container).
The classifier setup being used in the evaluation is a global 'Classifier' actor.

WekaTrainTestSetEvaluator() - Constructor for class adams.flow.transformer.WekaTrainTestSetEvaluator

width() - Method in class weka.core.setupgenerator.SpaceDimension

returns the number of points on the axis (incl.

windowsPointTipText() - Method in class weka.filters.unsupervised.attribute.PAA

Returns the tip text for this property.

windowsPointTipText() - Method in class weka.filters.unsupervised.attribute.SAX

Returns the tip text for this property.

wrapUp() - Method in class adams.flow.sink.WekaDatabaseWriter

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.source.AbstractWekaSetupGenerator

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.source.WekaDatabaseReader

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.AbstractProcessWekaInstanceWithModel

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.AbstractWekaClassifierEvaluator

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaAccumulatedError

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaAttributeIterator

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaClassifier

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaCrossValidationSplit

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaFileReader

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaInstanceBuffer

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaInstanceDumper

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaInstanceEvaluator

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaInstancesInfo

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaMultiLabelSplitter

Cleans up after the execution has finished.

wrapUp() - Method in class adams.flow.transformer.WekaSubsets

Cleans up after the execution has finished.

writeToDisk(boolean) - Method in class adams.flow.transformer.WekaInstanceDumper

Writes the content of the buffer to disk.

Z

zoomOverviewTipText() - Method in class adams.flow.sink.WekaInstanceViewer: Returns the tip text for this property.

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