All Classes Interface Summary Class Summary Enum Summary Annotation Types Summary
Class |
Description |
AArffLoader |
Safe version of the ArffLoader , always retaining string values.
|
AArffLoader.AArffReader |
|
AbsolutePredictionErrorComparator |
Comparator for predictions using the (absolute) prediction error (sorting increasingly).
|
AbstainAttributePercentile |
Only predict if attribute value within percentile range.
|
AbstainAverage |
Average base classifiers, abstain if difference outside thresholds
Valid options are:
|
AbstainAverageWithClassifierWeights |
Average base classifiers, abstain if difference outside thresholds
Valid options are:
|
AbstainingCascade |
The specified classifiers represent a cascade: if the first one abstains, the second is used (and so on), otherwise the prediction is returned.
If all classifiers prior to the last one abstained then the prediction of the last one is returned.
|
AbstainingClassifier |
Interface for classifiers that may support abstaining.
|
AbstainingClassifierWrapper |
Wraps an abstaining classifier and allows turning on/of abstaining.
|
AbstainingLWL |
LWL variant that supports abstaining if the base classifier is able to.
|
AbstainLeastMedianSq |
Finds the base classifier with the best least median squared error.
|
AbstainMinimumProbability |
Abstains if the probability of the chosen class label is below the specified threshold.
|
AbstainVote |
Finds the base classifier with the best least median squared error.
|
AbstractAdamsExperimentIO<T extends AbstractExperiment> |
Ancestor for classes that handle loading/saving of experiments.
|
AbstractAdamsExperimentReader |
Ancestor for readers for ADAMS Experiments.
|
AbstractAdamsExperimentRunner<T extends AbstractExperiment> |
Ancestor for classes that handle running a copy of the experiment
in a separate thread.
|
AbstractAdamsExperimentWriter |
Ancestor for ADAMS Experiment writers.
|
AbstractAdamsSetupPanel |
Ancestor for setup panels for ADAMS experiments.
|
AbstractAdditionalExplorerPanel |
Wrapper class for additional panels to be displayed in the Explorer.
|
AbstractAnalysisPanel |
Ancestor for panels that analysis experimental results.
|
AbstractAssociatorEvaluation |
Ancestor for associator evaluation setups.
|
AbstractAttributeCapabilities |
Ancestor for capabilities-based conditions.
|
AbstractAttributeSelectionEvaluation |
Ancestor for attribute selection evaluation setups.
|
AbstractCallableWekaClassifierEvaluator |
Ancestor for classifier evaluators that make use of a callable classifier.
|
AbstractCallableWekaClustererEvaluator |
Ancestor for clusterer evaluators that make use of a callable clusterer.
|
AbstractClassAttributeHeuristic |
Ancestor for heuristics that determine the class attribute for a dataset.
|
AbstractClassifierBasedGeneticAlgorithm |
Ancestor for genetic algorithms that evaluate classifiers.
|
AbstractClassifierBasedGeneticAlgorithm.ClassifierBasedGeneticAlgorithmJob<T extends AbstractClassifierBasedGeneticAlgorithm> |
Job class for algorithms with datasets.
|
AbstractClassifierBasedGeneticAlgorithmWithSecondEvaluation |
Ancestor for genetic algorithms that offer a second evaluation using
a different seed value.
|
AbstractClassifierBasedGeneticAlgorithmWithSecondEvaluation.ClassifierBasedGeneticAlgorithmWithSecondEvaluationJob<T extends AbstractClassifierBasedGeneticAlgorithmWithSecondEvaluation> |
Job class for algorithms with datasets.
|
AbstractClassifierBasedGeneticAlgorithmWizard |
Ancestor for optimizing datasets (attribute selection) using a genetic algorithm.
|
AbstractClassifierBasedGeneticAlgorithmWizard.PerformancePlot |
For plotting the performance of the genetic algorithm.
|
AbstractClassifierEvaluation |
Ancestor for classifier evaluation setups.
|
AbstractClassifierSetupProcessor |
Ancestor for schemes that preprocess classifier arrays.
|
AbstractClustererEvaluation |
Ancestor for clusterer evaluation setups.
|
AbstractClustererPostProcessor |
Ancestor for post-processors for output that the WekaClusterer transformer
produces.
|
AbstractClusterMembershipPostProcessor |
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.
|
AbstractColumnFinder |
Ancestor for classes that find columns of interest in datasets.
|
AbstractColumnFinderApplier |
Ancestor for filters that apply ColumnFinder schemes to the data.
|
AbstractColumnFinderWithCapabilities |
Ancestor for classes that find columns of interest in datasets.
|
AbstractCommunicationProcessor |
Ancestor for classes processing the communication to/fro Pyro proxy models.
|
AbstractCrossvalidatedInstanceEvaluator<T extends AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer> |
Ancestor for evalutors that use cross-validation for initialization.
|
AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer |
Container for storing the evaluation results.
|
AbstractDataContainer |
Ancestor for data containers.
|
AbstractDataPreparation |
Ancestor for classes that prepare data for the SocketFacade
classifier.
|
AbstractDatasetInstanceEvaluator |
Ancestor for evaluators that need a data set for initialization.
|
AbstractDetrend |
Ancestor for schemes that perform detrend.
|
AbstractEditableDataTableAction |
|
AbstractErrorScaler |
Ancestor for classes that scale predictions.
|
AbstractEvaluation<T extends AbstractInvestigatorTab,R extends AbstractResultItem> |
Ancestor for evaluation setups.
|
AbstractExperiment |
Ancestor for simple experiments.
|
AbstractExperiment.AbstractExperimentJob<T extends AbstractExperiment> |
For evaluating a single classifier/dataset combination.
|
AbstractExperimenterPanel |
Ancestor for panels in the experimenter.
|
AbstractExperimentIO<T> |
Ancestor for classes that handle loading/saving of experiments.
|
AbstractExperimentRunner<T> |
Ancestor for classes that handle running a copy of the experiment
in a separate thread.
|
AbstractExperimentSetup |
Ancestor for experiment setups.
|
AbstractExplorerPanelHandler |
Ancestor for handlers for specific Explorer panels.
|
AbstractFilteredColumnFinder |
Ancestor for column finders that pre-filter the columns.
|
AbstractFilteredRowFinder |
Ancestor for row finders that pre-filter the rows.
|
AbstractFinalModelGenerator |
.
|
AbstractGeneticAlgorithm |
Base class for genetic algorithms.
|
AbstractGeneticDoubleMatrixDiscoveryHandler |
Ancestor for genetic discovery handlers that handle matrix properties.
|
AbstractHashableInstance |
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.
|
AbstractHistoryPopupMenuItem<H extends adams.gui.core.AbstractNamedHistoryPanel,T extends AbstractInvestigatorTab> |
Ancestor for classes that add menu items to the history popup menu.
|
AbstractHistoryPopupMenuItem |
Ancestor for classes that add menu items to the history popup menu.
|
AbstractHistoryPopupMenuItem |
Ancestor for classes that add menu items to the history popup menu.
|
AbstractHistoryPopupMenuItem |
Ancestor for classes that add menu items to the history popup menu.
|
AbstractHistoryPopupMenuItem |
Ancestor for classes that add menu items to the history popup menu.
|
AbstractInstanceEvaluator |
Ancestor for evaluators that evaluate weka.core.Instance objects.
|
AbstractInstanceGenerator<T extends adams.data.container.DataContainer & adams.data.report.ReportHandler> |
Abstract base class for schemes that turn temperature profiles into
weka.core.Instance objects.
|
AbstractInstanceGenerator<T extends adams.data.container.DataContainer> |
Ancestor for transformers that turn data containers into WEKA Instance
objects.
|
AbstractInstanceInfoFrame |
Ancestor for frames for displaying information on the displayed data, with
some more domain-specific functionality.
|
AbstractInstancePaintlet |
Ancestor for Instance paintlets.
|
AbstractInstancePanelUpdater |
Ancestor for classes that determine when to update the instance panel,
i.e., repaint all of it.
|
AbstractInstancesAnalysis |
Ancestor for data analysis classes.
|
AbstractInstancesIndexedSplitsRunsGenerator |
Ancestor for generators that process Instances objects.
|
AbstractInvestigatorTab |
Ancestor for tabs in the Investigator.
|
AbstractInvestigatorTab.SerializationOption |
options for serialization.
|
AbstractInvestigatorTabWithDataTable |
Ancestor for tabs that have the data table on top.
|
AbstractInvestigatorTabWithEditableDataTable |
Ancestor for tabs with modifiable data table.
|
AbstractLinearRegressionBased<T extends adams.data.container.DataContainer> |
Abstract ancestor for linear regression based baseline correction schemes.
|
AbstractMatchWekaInstanceAgainstHeader |
Ancestor for classes that match Instance objects against Instances headers.
|
AbstractMerge |
AbstractMultiClassPLS |
Ancestor for schemes that predict multiple classes.
|
AbstractMultiplicativeScatterCorrection |
Ancestor for correction schemes.
|
AbstractMultiRowProcessorPlugin |
Ancestor for MultiRowProcessor plugins.
|
AbstractNestableResultItem |
Container for a data to be stored in result history that can also store
nested result items.
|
AbstractNumericClassPostProcessor |
Ancestor for numeric class post-processors.
|
AbstractOutputGenerator<T extends AbstractResultItem> |
Ancestor for output generators.
|
AbstractOutputGenerator |
Ancestor for output generators using t.
|
AbstractOutputGenerator |
Ancestor for output generators using t.
|
AbstractOutputGenerator |
Ancestor for output generators using the data from the per-fold pane.
|
AbstractOutputGenerator |
Ancestor for output generators using t.
|
AbstractOutputGenerator |
Ancestor for output generators using the data from the per-fold pane.
|
AbstractOutputGeneratorWithSeparateFoldsSupport<T extends JComponent> |
Ancestor for output generators that can generate output for separate folds
just using the Evaluation objects.
|
AbstractOutputPanel |
Ancestor for panels that allow the user to configure ResultListener s.
|
AbstractOutputPanelWithPopupMenu<T extends adams.gui.chooser.BaseFileChooser> |
Ancestor for output panels that can save the displayed output to a file.
|
AbstractPanelWithFile<T extends adams.gui.chooser.AbstractChooserPanel> |
Ancestor for panels that allow the user to select a file.
|
AbstractParameterHandlingWekaMenuItemDefinition |
Abstract menu item definition for Weka elements that also handle additional
parameters.
|
AbstractPerFoldPopupMenuItem |
Ancestor for classes that add menu items to the per-fold popup menu.
|
AbstractPlotColumn |
Ancestor for plugins that plot a column.
|
AbstractPlotRow |
Ancestor for plugins that plot a row.
|
AbstractPlotSelectedRows |
Ancestor for plugins that plot rows.
|
AbstractPLS |
Ancestor for PLS implementations.
|
AbstractPLSAttributeEval |
Ancestor for PLS attribute evaluators
|
AbstractPLSAttributeEval.LoadingsCalculations |
|
AbstractProcessCell |
Ancestor for plugins that process a cell.
|
AbstractProcessColumn |
Ancestor for plugins that process a column.
|
AbstractProcessRow |
Ancestor for plugins that process a row.
|
AbstractProcessSelectedRows |
Ancestor for plugins that process a row.
|
AbstractProcessWekaInstanceWithModel<T> |
Ancestor for transformers that user models for processing Instance objects,
e.g., classifiers making predictions.
|
AbstractRangeBasedSelectionProcessor |
Ancestor for processors that work on a range of attributes.
|
AbstractRelationNameHeuristic |
Ancestor for heuristics that determine the relation name for a dataset.
|
AbstractResultItem |
Container for a data to be stored in result history.
|
AbstractResultsHandler |
Ancestor for classes that store the results from an experiment run.
|
AbstractResultsPanel |
Ancestor for displaying the results of an analysis.
|
AbstractRowFinder |
Ancestor for classes that find rows of interest in datasets.
|
AbstractRowFinderApplier |
Ancestor for filters that apply RowFinder schemes to the data.
|
AbstractRowFinderWithCapabilities |
Ancestor for classes that find rows of interest in datasets.
|
AbstractRowSelection |
Ancestor for row selection schemes.
|
AbstractSelectedAttributesAction |
Ancestor for actions on ther checked attributes in the PreprocessTab .
|
AbstractSelectionProcessor |
Ancestor for row selection processors.
|
AbstractSetupOptionPanel |
|
AbstractSetupPanel<T> |
Ancestor for setup panels.
|
AbstractSimpleClassifier |
Ancestor for classifiers using ADAMS option handling.
|
AbstractSimpleOptionHandler |
Ancestor for Weka classes that use the ADAMS option handling framework.
|
AbstractSimpleRegressionMeasure |
Computes the mean error.
|
AbstractSingleClassPLS |
Ancestor for schemes that predict a single class.
|
AbstractSource |
Ancestor for additional "source" actions in the main menu.
|
AbstractSplitGenerator |
Ancestor for helper classes that generates dataset splits.
|
AbstractSplitter |
Parent class for different methods of splitting a dataset into
smaller datasets.
|
AbstractTokenCleaner |
Ancestor for cleaning tokens.
|
AbstractTrainableColumnFinder |
|
AbstractTrainableRowFinder |
Ancestor for RowFinder algorithms that can be trained.
|
AbstractWekaClassifierEvaluator |
Ancestor for transformers that evaluate classifiers.
|
AbstractWekaEnsembleGenerator |
Ancestor for schemes that generate ensembles.
|
AbstractWekaEvaluationPostProcessor |
Ancestor for classes that post-process Evaluation objects.
|
AbstractWekaExperimentIO<T extends weka.experiment.Experiment> |
Ancestor for classes that handle loading/saving of experiments.
|
AbstractWekaExperimentRunner<T extends weka.experiment.Experiment> |
Ancestor for classes that handle running a copy of the experiment
in a separate thread.
|
AbstractWEKAFitnessFunction |
Perform attribute selection using WEKA classification.
|
AbstractWEKAFitnessFunction.Measure |
The measure to use for evaluating.
|
AbstractWekaMenuItemDefinition |
Abstract menu item menu item definitions for Weka elements.
|
AbstractWekaModelReader |
Ancestor for actors that deserialize models.
|
AbstractWekaModelWriter |
Ancestor for actors that serialize models.
|
AbstractWekaPackageManagerAction |
Ancestor for package manager actions.
|
AbstractWekaPackageManagerAction |
Ancestor for package manager actions.
|
AbstractWekaPackageManagerAction |
Ancestor for package manager actions.
|
AbstractWekaPredictionsTransformer |
Ancestor for transformers that convert the predictions stored in an
Evaluation object into a different format.
|
AbstractWekaRepeatedCrossValidationOutput<T> |
Ancestor for classes that generate output from repeated cross-validations.
|
AbstractWekaSetupGenerator<T> |
Abstract ancestor for setup generator sources.
|
AbstractWekaSetupPanel |
Ancestor for setup panels for Weka experiments.
|
AbstractWekaSpreadSheetReader |
Ancestor for WEKA file format readers.
|
AbstractWekaSpreadSheetWriter |
Ancestor for WEKA file format readers.
|
AccumulatedLWLWeights |
Generates an LWL-like dataset for each instance of the data from the first batch and accumulate these weights.
|
AdamsExperimentFileChooser |
A specialized JFileChooser that lists all available file Readers and Writers
for ADAMS Experiments.
|
AdamsExperimentRunner<T extends AbstractExperiment> |
Ancestor for classes that handle running a copy of the experiment
in a separate thread.
|
AdamsHelper |
Helper class to make Weka GUI more ADAMS-like.
|
AdamsInstanceCapabilities |
Filters adams.data.instance.Instance based on defined capabilities.
|
AdamsInstanceToWekaInstance |
Converts adams.data.instance.Instance objects into weka.core.Instance ones.
|
AddCluster |
Just adds the predicted cluster (or distribution) to the original dataset.
Stored in container under: Clustered dataset
|
AdditionalExplorerPanel |
Interface for classes that supply additional Explorer panels.
|
Adjust |
Whether to adjust the plot to the loaded or visible data.
|
AggregateEvaluations |
Allows the aggregation of Evaluation objects.
|
AlignDataset |
Aligns the dataset(s) passing through to the reference dataset.
Makes use of the following other filters internally:
- weka.filters.unsupervised.attribute.AnyToString
- weka.filters.unsupervised.instance.RemoveWithLabels
Valid options are:
|
AllFinder |
Dummy finder, finds all columns.
|
AllFinder |
Dummy finder, finds all rows.
|
AnalysisPanel |
The analysis panel.
|
AndrewsCurves |
Generates Andrews Curves from array data.
César Ignacio GarcÃa Osorio, Colin Fyfe (2003).
|
AnyToString |
Turns the selected range of attributes into string ones.
|
Append |
Appends the selected datasets into single dataset (one-after-the-other).
|
AppendDatasets |
For appending datasets into single dataset.
|
AppendDatasetsPanel |
Wizard panel that allows appending datasets (one after the other).
|
ArffOutputPanel |
Stores the results in an ARFF file.
|
ArffSpreadSheetReader |
Reads WEKA datasets in ARFF format and turns them into spreadsheets.
|
ArffSpreadSheetWriter |
Writes a spreadsheet in ARFF file format.
|
ArffUtils |
A helper class for ARFF related stuff.
|
ArffViewer |
Opens the ARFF viewer.
|
ArrayStatistic |
Allows the calculation of row statistics.
|
AssociateTab |
Tab for associators.
|
AssociateTab.HistoryPanel |
Customized history panel.
|
AssociationsHandler |
Manages the AssociationsPanel .
|
AttributeIndex |
Uses the supplied attribute index to select the class attribute.
|
AttributeIndex |
Uses the name of the specified attribute as relation name.
|
AttributeSelection |
Perform attribute selection using WEKA classification.
|
AttributeSelectionHandler |
Manages the AttributeSelectionPanel .
|
AttributeSelectionPanel |
Creates a panel that displays the attributes contained in a set of instances,
letting the user toggle whether each attribute is selected or not (eg: so
that unselected attributes can be removed before classification).
|
AttributeSelectionPanel.AttributeTableModel |
A table model that looks at the names of attributes and maintains a list of
attributes that have been "selected".
|
AttributeSelectionTab |
Tab for attribute selection.
|
AttributeSelectionTab.HistoryPanel |
Customized history panel.
|
AttributeStatistics |
Displays statistics for the selected attribute.
|
AttributeSummaryPanel |
This panel displays summary statistics about an attribute: name, type
number/% of missing/unique values, number of distinct values.
|
AttributeSummaryPanel.AttributeInfoPanel |
Panel with labels displaying some basic info.
|
AttributeSummaryPanel.StatisticsTable |
Displays other stats in a table.
|
AttributeSummaryTransferFilter |
Filter which trains another filter to summarise a sub-set of the data's attributes.
|
AttributeTypeList |
Wrapper for a comma-separated list of attribute types.
|
AttributeValueCellRenderer |
Handles the background colors for missing values differently than the
DefaultTableCellRenderer.
|
AttributeVisualizationPanel |
Creates a panel that shows a visualization of an attribute in a dataset.
|
AutoScaler |
Applies the specified numeric scaler to the data in case of a numeric class attribute, otherwise just passes on the data.
|
Average |
Computes the average of the numeric attributes defined in the range.
|
AverageSilhouetteCoefficient |
Computes the average Silhouette coefficient for the clusters.
|
BasicAdamsSetupPanel |
Basic interface for setting up an ADAMS experiment.
|
BasicWekaSetupPanel |
Basic interface for setting up a Weka experiment.
|
BatchFilterDatasets |
For batch filtering datasets using a single filter setup (files get output
into a different directory).
|
BatchFilterDatasetsPanel |
Wizard panel that allows appending datasets (one after the other).
|
BayesNetEditor |
Opens the BayesNet Editor.
|
BestBinnedNumericClassRandomSplitGenerator |
Picks the best binning algorithm from the provided ones.
|
Bias |
Computes the bias (mean error) for regression models.
|
BinnableInstances |
Helper class for binning instances.
|
BinnableInstances.ClassValueBinValueExtractor |
Uses the class value as bin value.
|
BinnableInstances.GroupedClassValueBinValueExtractor |
Uses the class value of the first instance in the group as bin value.
|
BinnableInstances.NumericClassGroupExtractor |
Group extractor for numeric class attributes
(using string representation of values).
|
BinnableInstances.StringAttributeGroupExtractor |
Group extractor for string attributes.
|
BinnedNumericClassCrossValidationFoldGenerator |
Helper class for generating cross-validation folds.
|
BinnedNumericClassRandomSplitGenerator |
Generates random splits of datasets with numeric classes using a binning algorithm.
|
Binning |
Allows to perform binning of the values from a column or row.
|
BoundaryVisualizer |
Displays data in the boundary visualizer.
|
BoxPlotTab |
Visualizes the selected dataset as box plot.
|
BuildModel |
Builds a model and serializes it to a file.
|
BuildModel |
Builds a model and serializes it to a file.
|
ByExactName |
Returns indices of columns which names match the exact name.
|
ByExactName |
Returns indices of columns which names match the exact name.
|
ByLabel |
Returns the indices of rows which attributes labels match the provided regular expression.
|
ByName |
The first attribute name that matches the regular expression is used as class attribute.
|
ByName |
Returns indices of attributes which names match the regular expression.
|
ByNumericRange |
Returns indices of rows which numeric value match the min/max.
|
ByNumericValue |
Returns indices of rows which numeric value match the min/max.
|
Capability |
Enumeration of all capabilities.
|
CenterStatistic |
Enumeration of available center statistics.
|
ChangeAttributeWeight |
Allows the user to change the weight of the selected attribute.
|
ChangeInstanceWeights |
Allows the user to change the weight of the selected attribute.
|
Class |
Column finder which finds the class column (if one is set).
|
ClassAttribute |
Uses the class attribute name.
|
ClassesToClusters |
Tries to map the clusters of the built clusterer to the class labels in
the dataset.
|
ClassificationViaPLS |
Performs ClassificationViaRegression using PLSClassifierWeightedWithLoadings as base classifier, allowing access to the PLS matrices.
|
ClassificationViaRegressionD |
Class for doing classification using regression methods.
|
ClassifierCascade |
Generates a classifier cascade, with each deeper level of classifiers being built on the input data and either the class distributions (nominal class) or classification (numeric class) of the classifiers of the previous level in the cascade.
The build process is stopped when either the maximum number of levels is reached, the termination criterion is satisfied or no further improvement is achieved.
In case of a level performing worse than the prior one, the build process is terminated immediately and the current level discarded.
|
ClassifierCascade.Combination |
Defines how to combine the predictions of the final layer and turn it into
actual predictions.
|
ClassifierCascade.ThresholdCheck |
Defines how to check the threshold.
|
ClassifierErrors |
Generates classifier errors plot.
|
ClassifierHandler |
Manages the ClassifierPanel .
|
ClassifierPanel |
Panel for listing datasets.
|
ClassifyTab |
Tab for classification.
|
ClassifyTab.HistoryPanel |
Customized history panel.
|
ClassRangeBasedClassifierErrors |
Displays the classifier errors using Weka panels, but with a sizes adjusted
to the class range.
|
Clipboard |
Parses content on the clipboard.
|
ClusterAssignments |
Displays the cluster assignments.
|
ClusterCenters |
Computes the cluster centers for the provided dataset.
|
ClusterCounts |
Creates an overview of how many instances get clustered into each cluster.
Stored in container under: Clustered dataset
|
ClustererHandler |
Manages the ClustererPanel .
|
ClusterStatistics |
Computes cluster statistics (min/max/mean/stdev) for the provided dataset.
|
ClusterTab |
Tab for clustering.
|
ClusterTab.HistoryPanel |
Customized history panel.
|
ColumnFinder |
Interface for classes that "find" columns of interest in datasets.
|
ColumnSplitter |
Splits a dataset in two based on the columns selected by a column-finder.
|
ColumnStatistic |
Allows the calculation of column statistics.
|
CompareDatasets |
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.
|
CompareModels |
Compares the predictions of two models.
|
CompareTab |
For comparing datasets.
|
Compatibility |
Checks the compatibility of the selected datasets.
|
ComponentContentPanel |
Panel for exporting the graphical component as image.
|
ConfusionMatrix |
Displays the confusion matrix.
|
Consensus |
Outputs predictions only if the ensemble agrees.
|
ConsensusOrVote |
If the required minimum number of classifiers of the ensemble agree on a label, then this label is predicted.
|
Constant |
Column finder that finds a constant set of columns.
|
Constant |
Row finder that finds a constant set of rows.
|
ConvertToDate |
Converts the selected string attributes to date ones.
|
ConvertToNominal |
Converts the selected attributes to nominal ones.
|
ConvertToString |
Converts the selected attributes to string ones.
|
Copy |
Copies the selected dataset.
|
CopySetup |
Simply copies the classifier setup to the clipboard.
|
Corr |
Assume NO MISSING VALUES, all attributes must be NUMERIC (or 0/1 maybe ...).
|
CorrelationMatrix |
Computes a matrix with the correlation coefficients between attributes.
|
CostCurve |
Displays Cost curve data.
|
CostCurvePanel |
Displays cost curve data.
|
CrossValidation |
Performs cross-validation.
|
CrossValidation |
Performs cross-validation.
|
CrossValidation |
Performs cross-validation.
|
CrossValidationExperiment |
Performs cross-validation.
|
CrossValidationExperiment.CrossValidationExperimentJob |
Performs cross-validation on the classifier/data combination.
|
CrossValidationFoldGenerator |
Interface for generating cross-validation folds.
|
CrossValidationHelper |
Helper class for cross-validation.
|
CrossValidationSetup |
Setup for a cross-validation experiment.
|
CsvOutputPanel |
Stores the results in a CSV file.
|
CustomOutputPanel |
Allows the user to configure any ResultListener .
|
DarkLord |
For optimizing datasets (attribute selection) using genetic algorithm.
|
DarkLord |
DarkLord.DarkLordJob |
A job class specific to The Dark Lord.
|
Database |
For loading data from a database.
|
DatabaseContainer |
Dataset loaded from database.
|
DataCellView |
Wrapper for single cell values in a Instance object.
|
DataContainer |
Interface for data containers.
|
DataContainerList |
For managing the data containers.
|
DataGenerator |
For generating data using a data generator.
|
DataGeneratorContainer |
Dataset generated by datagenerator.
|
DataQueryTab |
Allows the execution of an SQL-like query to manipulate datasets.
|
DatasetCleaner |
Removes all columns from the data data that have been indentified.
|
DatasetCleaner |
Removes all rows from the data data that have been indentified.
|
DatasetCompatibility |
For checking compatibility of datasets.
|
DatasetCompatibilityPanel |
Compares the headers of a number of datasets and outputs the results.
|
DatasetFileChooserPanel |
A panel that contains a text field with the current file and a
button for bringing up a ConverterFileChooser.
|
DatasetHelper |
Helper class for dealing with datasets.
|
DatasetLabeler |
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.
|
DatasetPanel |
Panel for listing datasets.
|
DataSort |
Allows sorting the data using multiple columns.
|
DataTab |
Lists the currently loaded datasets.
|
DataTable |
Specialized table with custom cell editors for the class.
|
DataTableModel |
Model for displaying the loaded data.
|
DataTableWithButtons |
Specialized table with buttons.
|
DefaultAdamsExperimentIO |
Default IO handler for experiments.
|
DefaultAnalysisPanel |
Default panel for analyzing results from experiments.
|
DefaultAnalysisPanel.HistoryPanel |
Customized history panel.
|
DefaultCrossValidationFoldGenerator |
Helper class for generating cross-validation folds.
|
DefaultHandler |
Dummy handler, in case no other handler was located for an explorer panel.
|
DefaultRandomSplitGenerator |
Generates random splits of datasets.
|
DefaultWekaExperimentIO |
Default IO handler for experiments.
|
DefaultWekaExperimentRunner |
A class that handles running a copy of the experiment
in a separate thread.
|
Detrend |
Performs Detrend, using the specified correction scheme.
|
Dice |
Sørensen–Dice coefficient:
https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
|
DIPLS |
DownSample |
A simple filter that retains only every nth attribute.
|
EditWekaASEvaluator |
Lets the user edit the Weka attribute selection evaluator.
|
EditWekaASSearch |
Lets the user edit the Weka attribute selection search.
|
EditWekaClassifier |
Lets the user edit the Weka classifier.
|
EditWekaClusterer |
Lets the user edit the Weka clusterer.
|
EditWekaDataGenerator |
Lets the user edit the Weka data generator.
|
EditWekaFilter |
Lets the user edit the Weka filter.
|
EditWekaStreamableFilter |
Lets the user edit the Weka filter.
|
EncloseClassifier |
For enclosing classifiers in SingleClassifierEnhancer wrappers.
|
EncloseClusterer |
For enclosing clusterers in SingleClustererEnhancer wrappers.
|
EquiDistance |
A filter for interpolating the numeric attributes.Using the same number of points as are currently present in the input will have no effect.
|
EquiDistance.AttributeSelection |
Defines how the attributes are selected.
|
EvaluationHelper |
A helper class for Evaluation related things.
|
EvaluationStatistic |
The enumeration for the comparison field.
|
Experimenter |
Opens the WEKA Experimenter.
|
ExperimenterEntryPanel |
Allows the display of multiple Experimenter panels.
|
ExperimenterPanel |
The Experimenter panel.
|
ExperimentHandler |
|
ExperimentPanel |
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.
|
ExperimentStatistic |
The enumeration for the comparison field.
|
ExperimentTab |
Tab for running experiment on selected dataset/classifier.
|
ExperimentTab.HistoryPanel |
Customized history panel.
|
ExperimentWithCustomizableRelationNames |
Interface for experiments that allow customizing the relation names
of the datasets.
|
Explorer |
Opens the WEKA Explorer.
|
ExplorerEntryPanel |
Allows the display of multiple Explorer panels.
|
ExplorerExt |
An extended Explorer interface using menus instead of buttons, as well
as remembering recent files.
|
ExtExperiment |
Extended version of the Weka Experiment class.
|
FakeClassifier |
Fake classifier that requires no dataset for training and just outputs random values within the specified bounds.
Fake build and prediction times can be set as well.
|
Fallback |
In case the base classifier fails to make predictions, uses fallback one.
|
FastICA |
Performs independent components analysis and allows access to components and sources.
|
FastWavelet |
A filter for wavelet transformation using the JSci library's fast wavelet transform (FWT) algorithms.
For more information see:
(2009).
|
FFT |
A filter that transforms the data with Fast Fourier Transform.
Pads with zeroes.
For more information see:
Mark Hale (2009).
|
FileContainer |
File-based dataset.
|
FileName |
Suggests the file name (without extension) as the relation name.
|
FileResultsHandler |
Writes the experiment results to a file.
|
FilteredClassifierExt |
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
|
FilteredFilter |
First applies the pre-filter to the data and the generated data is fed into the main filter.
|
FilteredIQR |
Returns indices of rows that got identified as outliers/extreme values.
|
FilteredSearch |
Class implementing the brute force search algorithm for nearest neighbour search, filtered using PLS.
|
FixedClassifierErrors |
Displays the classifier errors using Weka panels, but with a fixed size of
the error plots.
|
FixedClassifierErrorsPlot |
Displays the classifier errors using an ADAMS plot with fixed size crosses.
|
FixedSizeErrorScaler |
Scales the errors to a fixed size.
|
FlowFilter |
Processes the data with a flow.
|
FourInOnePlot |
Generates the 4-in-1 plot: normal plot, histogram, residuals vs fit and vs order.
|
FromPredictions |
Loads predictions from a spreadsheet for evaluation.
|
FromPredictions |
Encapsulates predictions from a spreadsheet.
|
FusionJsonCommunicationProcessor |
Turns Instances/Instance into fusion JSON.
|
GaussianProcessesAdaptive |
Implements Gaussian Processes for regression without hyperparameter-tuning.
|
GaussianProcessesNoWeights |
* Implements Gaussian processes for regression without hyperparameter-tuning.
|
GaussianProcessesWeighted |
Implements Gaussian Processes for regression without hyperparameter-tuning.
|
GenericDoubleResolution |
Generic handler for double properties (using resolution).
|
GenericFloatResolution |
Generic handler for float properties (using resolution).
|
GenericInteger |
Generic handler for integer properties.
|
GenericPLSMatrixAccess |
For classes that allow access to PLS matrices.
|
GenericString |
Generic handler for string properties.
|
GeneticAlgorithm |
Morticia (GEX).
|
GeneticAlgorithm |
Applies the specified genetic algorithm to the training data and uses the best setup for the final model.
|
GeneticAlgorithm.GAJob |
Class for multithreading the ga.
|
GPD |
Implements Gaussian Processes for regression without hyperparameter-tuning, with an inline RBF kernel.
For more information see
David J.C.
|
GPDGamma |
GPD gamma handler.
|
GPDNoise |
GPD noise handler.
|
GraphHelper |
Helper class for graphs.
|
GraphSource |
Displays the source of a weka.core.Drawable graph.
|
GraphSource |
Displays the source code of the graph (dot or XML BIF).
|
GraphSource |
Displays the source code of the graph (dot or XML BIF).
|
GraphVisualizer |
Displays data in the graph visualizer.
|
GraphVisualizer |
Displays BayesNet graphs.
|
GroupedBinnedNumericClassCrossValidationFoldGenerator |
Helper class for generating cross-validation folds.
|
GroupedBinnedNumericClassRandomSplitGenerator |
Generates random splits of datasets with numeric classes using a binning algorithm.
|
GroupedCrossValidationFoldGenerator |
Helper class for generating cross-validation folds.
|
GroupedCrossValidationFoldGeneratorUsingNumericClassValues |
Helper class for generating cross-validation folds.
Uses the string representation of the numeric class values as grouping.
|
GroupedRandomSplitGenerator |
Generates random splits of datasets, making sure that groups of instances
stay together (identified via a regexp).
|
GroupExpression |
Identifies groups in strings using regular expressions.
|
HashableInstanceUsingString |
TODO: what this class does
|
HashableInstanceUsingSum |
Computes the hashcode as sum of the internal double values.
|
Hermione |
For optimizing datasets (parameter selection) using genetic algorithm.
|
Hermione |
Hermione.
|
Hermione.HermioneJob |
A job class specific to Hermione.
|
HighLowSplit |
Uses base classifier to get guess, then get prediction from either lo/hi classifier
Valid options are:
|
HighLowSplitSingleClassifier |
Uses base classifier to get guess, then get prediction from either lo/hi classifier
Valid options are:
|
Histogram |
Generates histograms from the visible containers.
|
Histogram |
Allows to generate a histogram from a column or row.
|
HistogramFactory |
A factory for histogram related objects.
|
HistogramFactory.Dialog |
Dialog for displaying histograms generated from instances.
|
HistogramFactory.Panel |
A panel for displaying a histogram based on the GC data of a instance.
|
HistogramFactory.SetupDialog |
A dialog that queries the user about parameters for displaying histograms.
|
IndependentComponentsTab |
Visualizes the ICA components/sources and ICA space calculated from the selected
dataset.
|
IndexedSplitsRunsEvaluation |
Performs the evaluation according to the provided indexed splits.
|
IndividualRows |
Just selects each row by itself.
|
InputSmearing |
Extended version of weka.classifiers.meta.Bagging, which allows input smearing of numeric attributes.
Class for bagging a classifier to reduce variance.
|
InputSmearing |
InstallFromFile |
Action that installs packages from files.
|
InstallFromURL |
Action that installs packages from URLs.
|
InstallOfficial |
Action that installs official packages via their name and (optional) version.
|
InstallPackage |
Action that installs the incoming package.
|
Instance |
Stores values from weka.core.Instance objects, with X being the
attribute index (integer) and Y being the internal value (double).
|
InstanceComparator |
For comparing instance objects.
|
InstanceCompare |
Stand-alone version of the Instance Compare utility.
|
InstanceCompare |
For comparing two datasets visually.
|
InstanceCompareDefinition |
Definition for the InstanceCompare props file.
|
InstanceComparePanel |
A tool for comparing two datasets visually.
|
InstanceComparePanel.DatasetIndexer |
Helper class for indexing the rows of a dataset.
|
InstanceComparePanel.DatasetPanel |
Specialized panel for loading dataset and setting various parameters.
|
InstanceContainer |
A container class for a weka.core.Instance wrapped in a
weka.core.Instance.
|
InstanceContainerDisplayIDGenerator |
Class for generating display IDs for Instance objects (based on
weka.core.Instance objects).
|
InstanceContainerList |
A panel that lists Instances in a JTable.
|
InstanceContainerManager |
A handler for the Instance containers.
|
InstanceContainerModel |
A model for displaying the currently loaded Instance objects.
|
InstanceContainerTableColumnNameGenerator |
Abstract class for generating the column names of a table.
|
InstanceDumperVariable |
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.
|
InstanceExplorer |
For displaying and filtering instances.
|
InstanceExplorer |
A panel for exploring Instances visually.
|
InstanceExplorerDefinition |
Definition for the InstanceExplorer props file.
|
InstanceExplorerHandler |
Displays the following WEKA dataset types in the Instance Explorer: csv,arff,arff.gz,xrff,xrff.gz
Valid options are:
|
InstanceGeneratorWithAdditionalFields |
Generators with additional fields.
|
InstanceGeneratorWithFields |
Generators with fields.
|
InstanceGrouping |
Groups rows in a dataset using a regular expression on a nominal or string
attribute.
|
InstanceLinePaintlet |
Paintlet for generating a line plot for Instance objects.
|
InstanceLinePaintlet.MarkerShape |
Enum for the marker shape to plot around the data points.
|
InstancePanel |
A panel for displaying instances.
|
InstancePoint |
A 2-dimensional point (X: attribute index, Y: internal value).
|
InstancePointComparator |
A comparator for InstancePoint objects.
|
InstancePointHitDetector |
Detects selections of instance points in the instance panel.
|
InstanceReader |
Reads WEKA datasets in various formats.
|
InstanceReportFactory |
A factory for GUI components for Instance-related reports.
|
InstanceReportFactory.Panel |
A specialized panel that displays reports.
|
InstanceReportFactory.Table |
A specialized table for displaying a Report.
|
InstancesColumnComboBox |
ComboBox that lists the attribute names of the associated Instances in
alphabetical order and when the user selects one, ensures that this
column is visible.
|
InstancesColumnComboBox.ColumnContainer |
Container for storing column name and
|
InstancesCrossValidationFoldGenerator |
Split generator that generates folds for cross-validation for Instances objects.
|
InstancesGroupedCrossValidationFoldGenerator |
Split generator that generates folds for cross-validation for Instances objects.
|
InstancesGroupedRandomSplitGenerator |
Random split generator that works on Instances objects (groups instances).
|
InstancesHeaderRow |
Header row for an Instances object.
|
InstancesIndexedSplitsRunsCompatibility |
Performs compatibility tests between indexed splits configurations and Weka Instances objects.
|
InstancesIndexedSplitsRunsEvaluation |
Evaluates the specified classifier on the indexed splits runs applied to the incoming data.
|
InstancesIndexedSplitsRunsGenerator |
Indicator interface for generators that process Instances objects.
|
InstancesIndexedSplitsRunsPredictions |
Trains the referenced classifier on the training splits and generates predictions for the test splits.
|
InstancesPanel |
Panel displaying an Instances table.
|
InstancesPlot |
Displays plot of Instances.
|
InstancesRandomSplitGenerator |
Random split generator that works on Instances objects.
|
InstancesSortDefinitionPanel |
Represents a single sorting definition.
|
InstancesSortPanel |
Panel that allows users to sort instances over an arbitrary number
of columns.
|
InstancesSortSetupEvent |
|
InstancesSortSetupEvent.EventType |
The type of event.
|
InstancesSortSetupListener |
|
InstancesSummaryPanel |
This panel just displays relation name, number of instances, and number of
attributes.
|
InstancesTable |
Table for displaying Instances objects.
|
InstancesTableModel |
The model for the Instances.
|
InstancesTablePopupMenuItem |
Ancestor for menu items of popups for the InstancesTable.
|
InstancesTablePopupMenuItemHelper |
Helper class for constructing popup menus for the InstancesTable.
|
InstancesTablePopupMenuItemHelper.TableState |
Container object for the table state used by the popup menu items.
|
InstancesView |
Provides a view of an Instances object.
|
InstancesView |
Presents a view of an Instances object.
|
InstancesViewCreator |
Interface for classes that generate Weka Instances views.
|
InstancesViewSupporter |
Interface for classes that support Weka Instances views.
|
InstanceTab |
Visualizes the selected dataset like the instance explorer.
|
InstanceTable |
A specialized table for displaying an Instances object.
|
InstanceTableModel |
A generic table model for displaying weka.core.Instances objects.
|
InstanceUtils |
Utility class for instances.
|
InstanceView |
Wrapper around an Instance object.
|
InstanceZoomOverviewPaintlet |
Highlights the current zoom in the instance panel.
|
InstanceZoomOverviewPanel |
Panel that shows the zoom in the TIC panel as overlay.
|
InterquartileRangeSamp |
A sampling filter for detecting outliers and extreme values based on interquartile ranges.
|
InterquartileRangeSamp.IQRs |
Container class for the IQR values.
|
InterQuartileRangeViewer |
Displays internal values of the InterquartileRange filter.
|
IntervalEstimatorBased |
Uses a classifier that produces confidence intervals.
|
IntervalEstimatorBased.SortedInterval |
Helper class for sorting the confidence intervals.
|
Invert |
Inverts the selected columns of the provided sub-column-filter.
|
Invert |
Inverts the selected rows of the provided sub-row-filter.
|
InvertInstancesColumnFinder |
Encloses a Instances ColumnFinder in Invert.
|
InvertInstancesRowFinder |
Encloses a Instances RowFinder in Invert.
|
InvestigatorAsNewDataset |
Allows the user to add the selected rows as new dataset in the Investigator.
|
InvestigatorJob |
|
InvestigatorManagerPanel |
Manages multiple sessions of the Investigator.
|
InvestigatorPanel |
The main panel for the Investigator.
|
InvestigatorTabbedPane |
Tabbed pane for managing the tabs of the Investigator.
|
InvestigatorTabJob |
|
InvestigatorTabRunnableJob |
|
InvestigatorWorkspaceHelper |
Helper class for Weka Investigator workspaces.
|
InvestigatorWorkspaceList |
Lists the sessions.
|
JdbcOutputPanel |
Stores the results in a JDBC database.
|
JFreeChart |
Allows to perform a simple plot of a column or row.
|
JoinAttributes |
A simple filter that joins several attributes into a single STRING one, with a user defined string acting as 'glue'.
|
JoinOnID |
Joins the datasets by concatenating rows that share a unique ID.
|
JsonAdamsExperimentReader |
Reads ADAMS Experiments in JSON format.
|
JsonAdamsExperimentWriter |
Writes ADAMS experiments in JSON format.
|
JSONSpreadSheetReader |
Reads WEKA datasets in JSON format and turns them into spreadsheets.
|
JSONSpreadSheetWriter |
Writes a spreadsheet in JSON file format.
|
KeepRange |
Keeps only the range of rows, in the order specified.
|
KennardStone |
Applies the Kennard-Stone algorithm to the dataset.
Each row has the pre-filter (eg PLS) applied before performing the search.
|
KernelPLS |
LastAttribute |
Simply chooses the last attribute as class attribute.
|
LatestRecords |
Retains the latest database records.
|
LeanMultiScheme |
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
|
LeastMedianSq |
Finds the base classifier with the best least median squared error.
|
LeaveOneOutByValueGenerator |
Generates train/test split pairs using the unique values from the specified attribute.
|
LegacyClassifierErrors |
Generates classifier errors plot (legacy Weka output).
|
LegacyCostBenefitAnalysis |
Generates cost benefit analysis (legacy Weka output).
|
LegacyCostCurve |
Generates cost curve (legacy Weka output).
|
LegacyGraphVisualizer |
Displays the graph that the model generated (legacy Weka output).
|
LegacyMarginCurve |
Generates margin curve (legacy Weka output).
|
LegacyThresholdCurve |
Generates margin curve (legacy Weka output).
|
LegacyTreeVisualizer |
Displays the tree that the model generated (legacy Weka output).
|
LegacyTreeVisualizer |
Displays the tree that the model generated (legacy Weka output).
|
LibSVMSpreadSheetReader |
Reads WEKA datasets in LibSVM format and turns them into spreadsheets.
|
LibSVMSpreadSheetWriter |
Writes a spreadsheet in LibSVM file format.
|
LinearRegressionAttributeEval |
Uses the coefficients of LinearRegressionJ to determine the importance of the attributes
(attribute selection turned off, no elimination of collinear attributes).
|
LinearRegressionJ |
Class for using linear regression for prediction.
|
ListPackages |
Lists the packages.
|
ListPackages.ListType |
The type of list to generate.
|
ListPackages.OutputFormat |
How to output the packages.
|
LoadDatasetDialog |
A dialog for loading datasets from disk.
|
LogClassRegressor |
Takes log of the class attribute in the data.
|
LogPanel |
The log panel.
|
LogTab |
Just displays the log messages.
|
LogTargetRegressor |
Takes logs of all numeric attributes in the data.
|
LogTransform |
Transforms all numeric attributes in the specified range using a log-transform.
The class attribute is omitted.
If a value less or equal to zero is encountered, a missing value is output.
|
LowerStatistic |
Enumeration of lower bound statistics to compute.
|
LWLDatasetBuilder |
Class for building LWL-style weighted datasets.
|
LWLDatasetBuilder.LWLContainer |
the container with the weighted dataset, distances, indices.
|
LWLIntervalEstimator |
Locally weighted learning.
|
LWLSynchro |
Locally weighted learning.
|
LWLSynchroPrefilter |
Locally weighted learning.
|
M5Base2 |
M5Base.
|
M5P2 |
M5Base.
|
MakeCompatibleDatasets |
For making compatible ARFF datasets.
|
MapToWekaInstance |
Converts a map into a Weka Instance, using the provided storage object (Instances) as template.
|
MarginCurve |
Displays a margin curve.
|
MatchWekaInstanceAgainstFileHeader |
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.
|
MatchWekaInstanceAgainstStorageHeader |
Matches an Instance against a dataset header from storage, i.e., it automatically converts STRING attributes into NOMINAL ones and vice versa.
|
MathExpressionClassifier |
Simple classifier that uses a pre-defined formula that can make use of attribute values using their names.
Grammar:
expr_list ::= '=' expr_list expr_part | expr_part ;
expr_part ::= expr ;
expr ::= ( expr )
# data types
| number
| string
| boolean
| date
# constants
| true
| false
| pi
| e
| now()
| today()
# negating numeric value
| -expr
# comparisons
| expr < expr
| expr <= expr
| expr > expr
| expr >= expr
| expr = expr
| expr != expr (or: expr <> expr)
# boolean operations
| ! expr (or: not expr)
| expr & expr (or: expr and expr)
| expr | expr (or: expr or expr)
| if[else] ( expr , expr (if true) , expr (if false) )
| ifmissing ( variable , expr (default value if variable is missing) )
| isNaN ( expr )
# arithmetics
| expr + expr
| expr - expr
| expr * expr
| expr / expr
| expr ^ expr (power of)
| expr % expr (modulo)
;
# numeric functions
| abs ( expr )
| sqrt ( expr )
| cbrt ( expr )
| log ( expr )
| log10 ( expr )
| exp ( expr )
| sin ( expr )
| sinh ( expr )
| cos ( expr )
| cosh ( expr )
| tan ( expr )
| tanh ( expr )
| atan ( expr )
| atan2 ( exprY , exprX )
| hypot ( exprX , exprY )
| signum ( expr )
| rint ( expr )
| floor ( expr )
| pow[er] ( expr , expr )
| ceil ( expr )
| min ( expr1 , expr2 )
| max ( expr1 , expr2 )
| year ( expr )
| month ( expr )
| day ( expr )
| hour ( expr )
| minute ( expr )
| second ( expr )
| weekday ( expr )
| weeknum ( expr )
# string functions
| substr ( expr , start [, end] )
| left ( expr , len )
| mid ( expr , start , len )
| right ( expr , len )
| rept ( expr , count )
| concatenate ( expr1 , expr2 [, expr3-5] )
| lower[case] ( expr )
| upper[case] ( expr )
| trim ( expr )
| matches ( expr , regexp )
| trim ( expr )
| len[gth] ( str )
| find ( search , expr [, pos] )
| replace ( str , pos , len , newstr )
| substitute ( str , find , replace [, occurrences] )
;
Notes:
- Variables are either all upper case letters (e.g., "ABC") or any character apart from "]" enclosed by "[" and "]" (e.g., "[Hello World]").
- 'start' and 'end' for function 'substr' are indices that start at 1.
- Index 'end' for function 'substr' is excluded (like Java's 'String.substring(int,int)' method)
- Line comments start with '#'.
- Semi-colons (';') or commas (',') can be used as separator in the formulas,
e.g., 'pow(2,2)' is equivalent to 'pow(2;2)'
- dates have to be of format 'yyyy-MM-dd' or 'yyyy-MM-dd HH:mm:ss'
- times have to be of format 'HH:mm:ss' or 'yyyy-MM-dd HH:mm:ss'
- the characters in square brackets in function names are optional:
e.g.
|
MatlabSpreadSheetReader |
Reads WEKA datasets in ARFF format and turns them into spreadsheets.
|
MatlabSpreadSheetWriter |
Writes a spreadsheet in ARFF file format.
|
MatrixHelper |
Helper class for the matrix-algorithm library.
|
MatrixHelper |
Some matrix operations.
|
MatrixTab |
Visualizes the selected dataset as matrix plot.
|
Mean |
Performs the correction using simply the mean.
|
Measure |
The measure to use for evaluating.
|
MemoryContainer |
Dataset exists only in memory.
|
MenuItemComparator |
Comparator for sorting the menu items for the history panel.
|
MenuItemComparator |
Comparator for sorting the menu items for the per-fold pane.
|
Merge |
Merges the selected datasets (side-by-side).
|
MergeDatasets |
For merging datasets (side-by-side) into single dataset.
|
MergeManyAttributes |
Merges two or more attributes, offers various strategies if values differ or not present.
Uses the common subsequence (either from start or end) of the attributes as name of the merged attribute, otherwise the concatenation of them (separated by '-').
|
MergeTwoAttributes |
Merges two attributes, offers various strategies if values differ or not present.
Uses the common subsequence (either from start or end) of the two attributes as name of the merged attribute, otherwise the concatenation of the both (separated by '-').
|
MetaPartitionedMultiFilter |
With each specified filter, a regular expression is associated that defines the range of attributes to apply the filter to.
|
MinMaxLimits |
Allows to influence the handling of lower/upper limits of the built classifier when making predictions.
The following types of handling are available: AS_IS, MANUAL, CLASS_RANGE
Details on the types:
- AS_IS: prediction does not get changed
- MANUAL: applies the manual limit, ie at most this limit is output
- CLASS_RANGE: applies the percentage leeway to the class attribute range of the training set to determine the actual limit value.
|
MinMaxLimits.LimitHandling |
Determines the type of handling for the limit
|
ModelOutput |
Outputs the model if available.
|
ModelOutput |
Outputs the model if available.
|
ModelOutput |
Outputs the model if available.
|
ModelOutputHandler |
Interface for classes that allow user to decide whether to output a
model in string representation or not.
|
MonitoringDataContainer |
Interface for data containers that monitor their source for changes.
|
MSLE |
Computes the mean squared log error (MSLE) for regression models.
|
MultiAttributeSummaryPanel |
Can display one or more instances of AttributeSummaryPanel class.
|
MultiAttributeVisualizationPanel |
Can display one or more instances of AttributeVisualizationPanel class.
|
MultiClassifiersCombinerModels |
Generates a MultipleClassifiersCombiner meta-classifier from the incoming pre-built classifier models.
|
MultiCleaner |
Combines multiple cleaners, applies them sequentially.
|
MultiClustererPostProcessor |
Applies the specified post-processors sequentially.
|
MultiColumnFinder |
Applies multiple column finding algorithms to the data.
The indices can be either joined or intersected.
|
MultiColumnFinder.Combination |
How combine the indices.
|
MultiExperimenter |
Extended interface for the WEKA Experimenter, allowing for an arbitrary
number of Experimenter panels.
|
MultiExplorer |
Opens the (multi-version of the) WEKA Explorer.
|
MultiExplorer |
Extended interface for the WEKA Explorer, allowing for an arbitrary
number of Explorer panels.
|
MultiLevelSplitGenerator |
Generates splits based on groups extracted via regular expressions.
|
MultiplicativeScatterCorrection |
Performs Multiplicative Scatter Correction, using the specified correction scheme.
|
MultiPLS |
For each Y that gets identified by the regular expression for Y attributes, the specified PLS (partial least squares) algorithm gets applied to the X attributes identified by the corresponding regular expression.
|
MultiPostProcessor |
Applies the specified post-processors sequentially to the input data and combines their output.
|
MultiRowFinder |
Applies multiple row finding algorithms to the data.
The indices can be either joined or intersected.
|
MultiRowFinder.Combination |
How combine the indices.
|
MultiRowProcessor |
Uses the specified row selection scheme to identify groups of rows in the data coming through and then applies the selected row processor to these subsets.
|
MultiTokenizer |
Combines the tokens of several tokenizers, skipping duplicate tokens.
|
NestedAdamsExperimentReader |
Reads ADAMS Experiments in nested format.
|
NestedAdamsExperimentWriter |
Writes ADAMS experiments in nested format.
|
NewNNSearch |
Class implementing the brute force search algorithm for nearest neighbour search.
|
NIPALS |
NoChange |
Simply returns the current relation name.
|
NoClassAttribute |
Never returns a class attribute.
|
NominalToNumeric |
Converts a nominal attribute into a numeric one.
|
NominalToNumeric.ConversionType |
Enumeration of conversion types.
|
NormalizeAdaptive |
Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
|
NormalizeDuplicateChars |
Replaces all duplicate characters with a single one.
|
Null |
Does not generate a final model.
|
NullCommunicationProcessor |
Dummy, does nothing.
|
NullFinder |
Dummy finder, does not find any columns.
|
NullFinder |
Dummy finder, does not find any rows.
|
NumericErrorScalerWithReference |
Scales the errors for numeric class attributes, using an user-specified error as reference point for a specified size.
|
OPLS |
OuterProductAnalysis |
Performs Outer Product Analysis (OPA).
For more information, see:
Fabricio S.Terra, Raphael A.Viscarra Rossel, Jose A.M.Dematte (2019).
|
OutputPanel |
Allows the user to select the output type, e.g., ARFF file or JDBC database.
|
OutputPrefixType |
Defines what kind of prefix to use for outputting data and setups.
|
OutputTabbedPane |
Tabbed pane for the output.
|
OutputType |
Defines what to output during a genetic algorithm run.
|
PAA |
Valid options are:
|
PackageManager |
Opens the WEKA PackageManager.
|
PackData |
???
|
PackDataDef |
???
|
PackDataDef.DataInfo |
|
PackDataGeneticAlgorithm |
???
|
PackDataGeneticAlgorithm |
???
|
PackDataInitialSetupsProvider<T extends PackDataGeneticAlgorithm> |
PartialLeastSquaresTab |
Visualizes the PLS loadings and PLS space calculated from the selected
dataset.
|
PartitionedMultiFilter2 |
A filter that applies filters on subsets of
attributes and assembles the output into a new dataset.
|
PartitionedStacking |
Builds the base-classifiers on subsets of the data defined by ranges that correspond to the base-classifiers.
|
PassThrough |
A dummy evaluator that OKs all data.
|
PassThrough |
Simply returns the same setups.
|
PassThrough |
Dummy post-processor that just returns the model container as it is.
|
PassThrough |
Does nothing, just passes through the input data.
|
PassThrough |
Dummy, never removes a token.
|
PassThrough |
Just passes through the data.
|
PCA |
Performs principal components analysis and allows access to loadings and scores.
|
PCANNSearch |
Class implementing the brute force search algorithm for nearest neighbour search, filtered using PLS.
|
PeakTransformed |
Uses the maximum peak in the instances.
|
PerFoldMultiPagePane |
Specialized multi-page pane for managing per-fold data.
|
PlainTextResultsPanel |
Displays the results in plain text.
|
PlotAttributeVsAttribute |
Allows the user to select a dataset and plot attribute vs attribute (selected by user).
|
PlotColumn |
Interface for plugins that plot a column.
|
PlotRow |
Interface for plugins that plot a row.
|
PlotSelectedRows |
Interface for plugins that plot selected rows.
|
PLS |
Performs partial least squares analysis and allows access to loadings and scores.
|
PLS |
Applies the specified partial least squares (PLS) algorithm to the data.
|
PLS1 |
Implementation of PLS1 algorithm.
For more information see:
Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
|
PLS1AttributeEval |
Uses the first component of PLS1 to determine the importance of the attributes
(defaults: no preprocessing, missing values not replaced, and 20 components)
|
PLSClassifierWeighted |
A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
|
PLSClassifierWeightedWithLoadings |
A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
Allows access to the PLS matrices in case the filter is a PLSFilterWithLoadings instance.
|
PLSFilterExtended |
Class contains changes to the Weka's PLSFilter in order to
have simpls work with multiple y attributes.
|
PLSFilterNumComponents |
SavitzkyGolay numPoints handler.
|
PLSFilterWithLoadings |
Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data.
Allows access to the internal matrices.
For more information see:
Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
|
PLSMatrixAccess |
For classes that allow access to PLS matrices.
|
PLSNNSearch |
Class implementing the brute force search algorithm for nearest neighbour search, filtered using PLS.
|
PLSWeighted |
A wrapper classifier for the PLS filter, utilizing the filter's ability to perform predictions.
|
PreCleanedTokenizer |
Allows the cleaning of tokens before actual tokenization.
|
PredictionEccentricity |
Generates classifier prediction eccentricity.
|
PredictionHelper |
Helper class for dealing with predictions from result items.
|
Predictions |
Generates statistics for predictions from repeated cross-validation runs.
|
Predictions |
Displays the predictions.
|
Predictions |
Generates statistics for predictions from repeated cross-validation runs.
|
PredictionTrend |
Generates a 'prediction trend' for classifier errors: sorts the
predictions on the actual value and plots actual and predicted side-by-side.
|
PredictionType |
The type of prediction to perform.
|
PredictionUtils |
Helper class for predictions from repeated cross-validation runs.
|
PreprocessHandler |
Manages the PreprocessPanel .
|
PreprocessingType |
The preprocessing type.
|
PreprocessTab |
Preprocessing tab.
|
PrincipalComponentsJ |
* Performs a principal components analysis and transformation of the data.
* Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data -- default 0.95 (95%).
* Based on code of the attribute selection scheme 'PrincipalComponents' by Mark Hall and Gabi Schmidberger.
|
PrincipalComponentsTab |
Visualizes the PCA loadings and PCA space calculated from the selected
dataset.
|
PRM |
ProcessCell |
Interface for plugins that process a cell.
|
ProcessColumn |
Interface for plugins that process a column.
|
ProcessRow |
Interface for plugins that process a row.
|
ProcessSelectedRows |
Interface for plugins that processes selected rows.
|
PublicPrincipalComponents |
Class that is identical to the Principal components class except it contains a public method to get the coefficients
from the principal components model
|
PullUpClassifier |
For pulling up classifiers from SingleClassifierEnhancer wrappers.
|
PullUpClusterer |
For pulling up clusterers from SingleClustererEnhancer wrappers.
|
PullUpInstancesColumnFinder |
Pulls up the base Instances ColumnFinder from a filtered ColumnFinder.
|
PullUpInstancesRowFinder |
Pulls up the base Instances RowFinder from a filtered RowFinder.
|
PyroProxy |
Proxy for a python model using Pyro4 for communication.
|
PyroProxyObject |
Interface for classes that make use of Pyro4.
|
Randomize |
Randomizes the selected dataset.
|
RandomModelTrees |
RandomRegressionForest |
RandomRegressionForest: subtract mean and pls, then grow completely random trees (leaf: min ..
|
RandomSplitGenerator |
Interface for generators of random splits of datasets.
|
RandomSubset |
Creates a random subset from a dataset and inserts it as a new dataset.
|
RangeBased |
Performs the correction using slopes/intercepts calculated for the defined ranges.
|
RangeBased |
Performs the correction using slopes/intercepts calculated for the defined ranges.
|
RangeCheck |
Keeps track of the ranges in case of numeric attributes.
|
RangeCheckClassifier |
Interface for classifiers that allow checks whether data is outside
the training range of the classifier.
|
RangeCheckHelper |
Helper class for generating range checks.
|
ReducedData |
Generates the reduced dataset.
|
ReevaluateModel |
Re-evaluates a serialized model.
|
ReevaluateModel |
Re-evaluates a serialized model.
|
RefreshCache |
Refreshes the package cache.
|
RelativeNumericErrorScaler |
Scales the errors for numeric class attributes.
|
RemoteWekaExperimentIO |
IO handler for remote experiments.
|
RemoteWekaExperimentRunner |
A class that handles running a copy of the experiment
in a separate thread.
|
Remove |
Removes the selected attributes.
|
RemoveDuplicateIDs |
Removes rows with IDs that occur multiple times.
Also skips rows with missing ID.
|
RemoveDuplicates |
Removes all duplicate instances.
|
RemoveInstancesWithMissingValue |
Removes all instances that contain missing values.
|
RemoveMisclassifiedAbs |
A filter that removes instances which are incorrectly classified.
|
RemoveMisclassifiedRel |
A filter that removes instances which are incorrectly classified.
|
RemoveNonWordCharTokens |
Removes tokens that contain non-word characters.
|
RemoveOutliers |
Cross-validates the specified classifier on the incoming data and applies the outlier detector to the actual vs predicted data to remove the outliers.
NB: only works on full dataset, not instance by instance.
|
RemoveTestInstances |
Removes all instances of the provided test set from the data passing through.
Requires an attribute in the data that uniquely identifies instances across datasets.
|
RemoveTestSet |
Removes the test instances from one dataset in another.
|
RemoveWithLabels |
Allows the user to remove nominal labels via a regular expression.
|
RemoveWithWeights |
Removes instances with weights outside the defined limits.
|
RemoveWithZeroes |
Removes all instances that contain at least the specified number (or percentage) of zeroes in numeric attributes.
|
RemoveWorst |
Removes the worst predictions, which are considered outliers that
detract from the actual model performance.
|
RemoveWorstStdDev |
Removes the worst predictions, which are considered outliers that
detract from the actual model performance.
|
Rename |
Renames the selected dataset.
|
Rename |
Renames the selected attribute.
|
ReorderAttributes |
Allows the user to reorder the attributes.
|
RepeatedCrossValidation |
Performs repeated cross-validation.
|
ReplaceMissingValuesWithZero |
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
|
ReportColorInstancePaintlet |
Paintlet for generating a line plot using the color stored in the report.
|
ReportToWekaInstance |
Converts a report into a weka.core.Instance objects.
|
ResettableExperiment |
Interface for experiments that can clear any prior results.
|
ResidualsVsFitted |
Plots the residuals vs the fitted values (= predictions).
|
ResidualsVsPredictor |
Plots the residuals vs the predictor.
|
ResultItem |
Container for an evaluation, model, training set header.
|
ResultItem |
Container for an attribute selection, evaluator and search method.
|
ResultItem |
Container for an evaluation, model, training set header.
|
ResultItem |
Container for an evaluation, model, training set header.
|
ResultItem |
Container for an experiment run.
|
ResultMatrixAdamsCSV |
Generates the matrix in ADAMS CSV ('comma-separated values') format.
|
ResultMatrixMediaWiki |
Generates table output in MediaWiki format.
|
Revert |
Reverts the selected dataset (if possible).
|
ROC |
Displays ROC curve data.
|
RoundErrorScaler |
Performs no scaling at all, just rounds the error to the next integer.
|
RowFilteredColumnFinder |
This column finder first filters the rows before finding any columns on
the subset of rows.
|
RowFinder |
Interface for classes that "find" rows of interest in datasets.
|
RowNorm |
Row wise normalization.
|
RowSplitter |
Splits a dataset in two based on the rows selected by the row-finder.
|
RowStatistic |
Allows the calculation of row statistics.
|
RowSum |
Sums up all numeric values in a row and replaces them with it.
|
RPD |
Computes the RPD (Ratio of Performance to Deviation) for regression models:
RPD = SD / RMSE
https://www.academia.edu/4303409/Why_you_dont_need_to_use_RPD
|
RSquared |
Computes the R^2 for regression models.
|
Rule2 |
Generates a single m5 tree or rule
|
RuleNode2 |
Constructs a node for use in an m5 tree or rule
|
Rules |
Outputs the rules if available AssociationRulesProducer .
|
RunInformation |
Generates run information.
|
RunInformation |
Generates run information.
|
RunInformation |
Generates run information.
|
RunInformation |
Generates run information.
|
RunInformation |
Generates run information.
|
RunInformationHelper |
Helper class for run information.
|
SafeRemoveRange |
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.
|
SamplePlot |
Generates plot containers with statistics derived for each sample across the cross-validation runs.
|
SamplePlot |
Generates a plot with statistics derived for each sample across the cross-validation runs.
|
Save |
Saves the selected data.
|
SaveAs |
Allows the saving of an instance container.
|
SaveGraph |
Allows user to save graph (eg generated by BayesNet) as file.
|
SaveIndexedSplitsRuns |
Saves the indexed splits runs generated from the selected data.
|
SaveTree |
Saves a tree in dotty notation as file.
|
SaveVisible |
Allows the user to save the visible containers as ARFF.
|
SavitzkyGolay |
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.
|
SavitzkyGolay2 |
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.
|
SavitzkyGolay2NumPoints |
SavitzkyGolay numPoints handler.
|
SAX |
A simple filter that retains only every nth attribute.
|
SAXDistance |
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.
|
SAXKMeans |
SimpleKMeans adapted for SAX.
|
Scale |
Scales all numeric attributes between the specified min/max.
|
ScatterPlotTab |
For plotting attributes against each other.
|
SDR |
Computes the SDR (Standard Deviation of Residuals) for regression models.
|
SerializedAdamsExperimentReader |
Reads serialized ADAMS Experiments.
|
SerializedAdamsExperimentWriter |
Writes serialized ADAMS experiments.
|
SerializedFilter |
Processes the data with a the (trained) filter deserialized from the specified file.
|
SetMissingValue |
Attribute values in the given range are set to missing values.
NB: The class attribute is not excluded from this process.
|
Simple |
Just merges the datasets side by side.
|
Simple |
Simply builds the classifier on the full dataset.
|
Simple |
Simple preparation scheme, using JSON with the actual data in CSV format.
|
Simple.SimpleRowSetIterator |
Enumeration class which just returns the concatenation of the source
data rows in order.
|
SimpleArffLoader |
A simple ARFF loader, only supports batch loading.
|
SimpleArffSaver |
Writes the Instances to an ARFF file in batch mode.
|
SimpleDetrend |
Performs Detrend, using the specified correction scheme.
|
SimpleInstanceLinePaintlet |
Paintlet for generating a line plot for Instance objects (no markers).
|
SimpleInstancePanelUpdater |
Updates the flow after the specified number of tokens have been processed.
|
SimpleJsonCommunicationProcessor |
Turns Instances/Instance into simple JSON.
|
SimpleLinearRegressionIntervalEstimator |
Learns a simple linear regression model.
|
SimpleLinearRegressionWithAccess |
Learns a simple linear regression model.
|
SimplePlot |
Allows to perform a simple plot of a column or row.
|
SimpleSubRange |
Generates an Evaluation object based on the actual class values that fall within the specified interval ranges.
|
SIMPLS |
Implementation of SIMPLS algorithm.
Available matrices: W, B
For more information see:
Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
|
SIMPLSAttributeEval |
Uses the first component of SIMPLS to determine the importance of the attributes
(defaults: no preprocessing, missing values not replaced, and 20 components)
|
SIMPLSMatrixFilter |
Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data.
Allows access to the internal matrices.
For more information see:
Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
|
SIMPLSMatrixFilterFromGeneticString |
Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data.
Allows access to the internal matrices.
For more information see:
Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
|
SIMPLSWeightsMatrix |
SIMPLS pls internal weights handler.
|
SocketFacade |
Uses sockets to communicate with a process for training and
making predictions.
|
Sort |
Sorts the instances.
|
SortOnAttribute |
Sorts the instances on a particular attribute.
|
SourceCode |
Outputs source code from the model (if classifier implements Sourcable ).
|
SparsePLS |
SpellChecker |
A simple filter that merges misspelled labels into a single correct one.
|
Split |
Creates train/test splits from a dataset and inserts these as new datasets.
|
SplitGenerator |
Interface for helper classes that generate dataset splits.
|
SpreadSheet |
For loading ADAMS spreadsheets.
|
SpreadSheetContainer |
SpreadSheet-based dataset.
|
SpreadSheetLoader |
Loads a CSV file using an ADAMS spreadsheet reader and converts it into an Instances object.
|
SpreadSheetSaver |
Writes the Instances to a spreadsheet file using the specified ADAMS spreadsheet writer.
|
SpreadSheetToWekaInstances |
Generates a weka.core.Instances object from a SpreadSheet object.
If there are too many unique lables for a NOMINAL attribute, it gets turned into a STRING attribute (see 'maxLabels' property).
|
SqlPanel |
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).
|
SqlViewer |
Opens the SQL viewer.
|
Statistics |
Generates mean/stddev for the specified statistics.
|
Statistics |
Generates statistics for repeated cross-validation runs.
|
StoppableEvaluation |
Specializes Evaluation class that can stop its evaluation processes better.
|
StoppableEvaluation |
Extended Evaluation class that can stop its evaluation processes better.
|
StringToDate |
Parses the selected range of string attributes using the specified format and turns them into date ones.
|
SubRange |
Generates an Evaluation object based on the actual class values that fall within the specified interval ranges.
|
SubRangeEvaluation |
Generates a fake evaluation using only predictions with an actual class value
that fits in the specified sub-range.
|
SubRangeEvaluation |
Generates a fake evaluation using only predictions with an actual class value
that fits in the specified sub-range.
|
SubsetEnsemble |
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.
|
SumTransformed |
Finds the base classifier with the best least median squared error.
|
Supplementary |
Outputs the supplementary data if available.
|
SuppressModelOutput |
Meta-classifier that enables the user to suppress the model output.
Useful for ensembles, since their output can be extremely long.
|
SVMLightSpreadSheetReader |
Reads WEKA datasets in ARFF format and turns them into spreadsheets.
|
SVMLightSpreadSheetWriter |
Writes a spreadsheet in SVMLight file format.
|
SwapPLS |
Swaps one PLS filter for another.
|
TableContentPanel |
Panel for exporting the table as spreadsheet.
|
TableResultsPanel |
Displays the results in a table.
|
TestingHelper |
Helper class for evaluating models on test data.
|
TestingHelper.TestingUpdateListener |
The interface for objects that listen for testing updates.
|
TextDirectory |
Uses the TextDirectoryLoader to load text documents.
|
TextDirectoryLoaderContainer |
Dataset generated by TextDirectoryLoader.
|
TextOutput |
Generates textual output.
|
TextStatistics |
Generates basic text statistic.
|
TextStatistics |
Generates basic text statistic.
|
TextStatistics |
Generates basic text statistic.
|
TextualContentPanel |
Panel for exporting the textual component as text.
|
ThreadSafeClassifier |
Indicator interface for thread-safe classifiers.
|
ThreadSafeClassifierWrapper |
Wraps an abstaining classifier and allows turning on/of abstaining.
|
ThresholdCurves |
Displays all the threshold curves (ROC) in a single plot.
|
ThresholdedBinaryClassification |
Meta classifier for binary classification problems that allows to specify a minimum probability threshold for one of the labels.
|
TokenCleaner |
Interface for token cleaners.
|
Train |
Builds an associator on a dataset.
|
Train |
Performs attribute selection on the train data.
|
TrainableColumnFinder |
|
TrainableRowFinder |
Interface for RowFinder algorithms that can be trained.
|
TrainTestSet |
Uses dedicated train/test sets.
|
TrainTestSet |
Uses dedicated train/test sets.
|
TrainTestSplit |
Uses a (random) percentage split to generate train/test.
|
TrainTestSplit |
Uses a (random) percentage split to generate train/test.
|
TrainTestSplitExperiment |
Performs train-test splits.
|
TrainTestSplitExperiment.TrainTestSplitExperimentJob |
|
TrainTestSplitSetup |
Setup for a train/test-split experiment.
|
TrainValidateTestSet |
Uses dedicated train/validate/test sets.
|
TransformNNSearch |
|
TreeGraphML |
Displays the GraphML source code of the tree graph.
|
TreeVisualizer |
Displays data in the tree visualizer.
|
TreeVisualizer |
Displays trees in dot notation.
|
TreeVisualizer |
Displays the tree that the model generated.
|
Uninstall |
Action that removes installed packages.
|
UpperStatistic |
Enumeration of upper statistics to compute.
|
UseAsClass |
Uses the selected attribute as class attribute.
|
VCPLS |
Veto |
If the specified label is predicted by the required minimum number of classifiers of the ensemble, then this label is predicted.
|
ViewAsTable |
Views the selected instance as table.
|
ViewCell |
For viewing the cell content.
|
Viewport |
Allows the user to perform operations on the instances visible in the current
viewport.
|
VotedFolds |
Generates a Vote meta-classifier from the models from the cross-validation folds.
|
VotedImbalance |
Generates an ensemble using the following approach:
- do x times:
* create new dataset, resampled with specified bias
* build base classifier with it
If no classifier gets built at all, use ZeroR as backup model, built on the full dataset.
At prediction time, the Vote meta-classifier (using the pre-built classifiers) is used to determining the class probabilities or regression value.
Instead of just using a fixed number of resampled models, you can also specify thresholds (= probability that the minority class does not meet) with associated number of resampled models to use.
|
VotedModels |
Generates a Vote meta-classifier from the incoming pre-built classifier models.
|
VotedPairs |
Generates an array of classifiers that contains the original ones, but also
all possible classifier pairs encapsulated in the Vote meta-classifier.
|
VotedPairs.VotingType |
How the voting is done.
|
WeightedEuclideanDistance |
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.
|
WeightedEuclideanDistanceRidge |
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.
|
WeightedInstancesHandlerWrapper |
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.
|
WeightsBasedResample |
Normalizes all instance weights and drops the ones that fall below the specified threshold, but at most the specified percentage.
Of the left over instances, the smallest weight, e.g., 0.2, represents one instance, which translates a weight of 1.0 to five instances.
|
Weka |
Generates features in spreadsheet format.
|
WekaAccumulatedError |
Generates plot containers from an evaluation object's predictions.
|
WekaAccumulatedError.SortablePrediction |
Container for a classifier prediction, used for sorting.
|
WekaAggregateEvaluations |
Aggregates incoming weka.classifiers.Evaluation objects and forwards the current aggregated state.
|
WekaAssociatorContainer |
Container for associators and their rules.
|
WekaAssociatorSetup |
Outputs an instance of the specified associator.
|
WekaAttributeIndex |
Extended Index class that can use an attribute name to determine an
index of a attribute as well.
|
WekaAttributeIndexEditor |
|
WekaAttributeIndexParsing |
For parsing WekaAttributeIndex options.
|
WekaAttributeIterator |
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.
Instead of outputting the names, it is also possible to output the 1-based indices.
|
WekaAttributeRange |
Extended Range class that also allows attribute names for specifying
attribute positions (names are case-insensitive, just like placeholders for
'first', 'second', etc).
|
WekaAttributeRangeEditor |
|
WekaAttributeRangeParsing |
For parsing WekaAttributeRange options.
|
WekaAttributeSelection |
Performs attribute selection on the incoming data.
In case of input in form of a class adams.flow.container.WekaTrainTestSetContainer object, the train and test sets stored in the container are being used.
NB: In case of cross-validation no reduced or transformed data can get generated!
Input/output:
- accepts:
weka.core.Instances
adams.flow.container.WekaTrainTestSetContainer
- generates:
adams.flow.container.WekaAttributeSelectionContainer
Container information:
- adams.flow.container.WekaTrainTestSetContainer: Train, Test, Seed, FoldNumber, FoldCount, Train original indices, Test original indices
- adams.flow.container.WekaAttributeSelectionContainer: Train, Reduced, Transformed, Test, Test reduced, Test transformed, Evaluation, Statistics, Selected attributes, Seed, FoldCount
|
WekaAttributeSelectionContainer |
A container for storing results from attribute selection.
|
WekaAttributeSelectionSummary |
Outputs a summary string of the attribute selection.
|
WekaAttributeSummary |
Displays an attribute summary.
|
WekaBootstrapping |
Performs bootstrapping on the incoming evaluation and outputs a spreadsheet where each row represents the results from bootstrapping sub-sample.
|
WekaBootstrapping.ErrorCalculation |
how to calculate the error.
|
WekaCapabilities |
Filters weka.core.Instance and weka.core.Instances objects based on defined capabilities.
|
WekaCapabilitiesToInstances |
Turns a weka.core.Capabilities object into a Weka dataset filled with random data that is compatible with these capabilities.
|
WekaCapabilitiesToSpreadSheet |
Turns a weka.core.Capabilities object into a spreadsheet, listing all individual capabilities and whether they are supported.
|
WekaChooseAttributes |
Lets the user select attributes interactively to use down the track.
Internally, a weka.filters.unsupervised.attribute.Remove WEKA filter is constructed from the selection, to remove the attributes that the user didn't select.
|
WekaClassification |
Uses the index of the classification, i.e., the predicted label, as index of the switch
|
WekaClassificationModel |
Classification model for Weka classifiers.
|
WekaClassifier |
Wraps around a Weka classifier that handles nominal classes (= classification).
|
WekaClassifierErrors |
Actor for displaying classifier errors.
|
WekaClassifierErrors.DataGenerator |
Helper class for generating visualization data.
|
WekaClassifierGenerator |
Generates multiple classifier setups.
|
WekaClassifierInfo |
Outputs information of a trained weka.classifiers.Classifier object.
|
WekaClassifierInfo.InfoType |
The type of information to generate.
|
WekaClassifierModelLoader |
Manages classifier models.
|
WekaClassifierOptimizer |
Evaluates a classifier optimizer on an incoming dataset.
|
WekaClassifierRanker |
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.Measure |
The performance measure to use.
|
WekaClassifierRanker.RankingJob |
A job class specific to ranking classifiers.
|
WekaClassifierSetup |
Outputs an instance of the specified classifier.
|
WekaClassifierSetupProcessor |
Applies the specified processor to the incoming array of classifiers, e.g., for generating new or filtered setups.
|
WekaClassifying |
Uses a serialized model to perform predictions on the data being passed through.
The following order is used to obtain the model (when using AUTO):
1.
|
WekaClassSelector |
Sets the class index.
|
WekaClusterAssignments |
Outputs the cluster assignments from the evaluation.
|
WekaClusterer |
Wraps around a Weka clusterer.
|
WekaClustererGenerator |
Generates multiple clusterer setups.
|
WekaClustererInfo |
Outputs information of a trained weka.clusterers.Clusterer object.
|
WekaClustererInfo.InfoType |
The type of information to generate.
|
WekaClustererModelLoader |
Manages clusterer models.
|
WekaClustererPostProcessor |
Applies the specified post-processor to the cluster container (adams.flow.container.WekaModelContainer)
See also:
adams.flow.transformer.WekaTrainClusterer
Input/output:
- accepts:
adams.flow.container.WekaModelContainer
- generates:
adams.flow.container.WekaModelContainer
Container information:
- adams.flow.container.WekaModelContainer: Model, Header, Dataset
|
WekaClustererSetup |
Outputs an instance of the specified clusterer.
|
WekaClusterEvaluationContainer |
A container for ClusterEvaluation objects, with optional trained model.
|
WekaClusterEvaluationSummary |
Generates a summary string of the weka.clusterers.ClusterEvaluation objects that it receives.
|
WekaClustering |
Uses a serialized model to cluster data being passed through.
The following order is used to obtain the model (when using AUTO):
1.
|
WekaClusteringContainer |
A container for clusterings made by a clusterer.
|
WekaClusteringModel |
Clustering model for Weka classifiers.
|
WekaCommandLineHandler |
Handles objects of classes that implement the weka.core.OptionHandler
interface.
|
WekaCommandToCode |
Applies a commandline converter to the incoming commandline to generate code.
Uses the following project:
https://github.com/fracpete/command-to-code-weka-package
|
WekaCommandToCode |
For turning Weka commandline strings into code.
|
WekaConverter |
Helper class for converting data to and fro Weka.
|
WekaCostBenefitAnalysis |
Actor for displaying a cost benefit analysis dialog.
|
WekaCostCurve |
Actor for displaying a cost curve.
|
WekaCrossValidationClustererEvaluator |
Cross-validates a clusterer on an incoming dataset.
|
WekaCrossValidationEvaluator |
Cross-validates a classifier on an incoming dataset.
|
WekaCrossValidationExecution |
Performs cross-validation, either single or multi-threaded.
|
WekaCrossValidationJob |
For evaluation of a single train/test fold in parallel.
|
WekaCrossValidationSplit |
Generates train/test pairs like during a cross-validation run.
|
WekaDatabaseReader |
Executes a query and returns the data either in batch or incremental mode.
|
WekaDatabaseWriter |
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).
|
WekaDataGenerator |
Generates artificial data using a Weka data generator.
|
WekaDatasetHandler |
Displays the following WEKA dataset types: csv,arff,arff.gz,xrff,xrff.gz
Valid options are:
|
WekaDatasetsMerge |
Merges 2 or more datasets into a single dataset, under a selectable merge method.
|
WekaDatasetSplit |
Splits the incoming dataset into sub-sets using the specified splitter.
|
WekaDrawableToString |
Extracts the string representation of a weka.core.Drawable object, e.g., the tree representation of a decision tree or the graph of a BayesNet.
|
WekaEditorsRegistration |
Registers first the WEKA GenericObjectEditor editors and the ADAMS ones.
|
WekaEditorsRegistration.AccessibleGenericObjectEditor |
Subclass of GenericObjectEditor to get access to the
class hierarchies.
|
WekaEditorsRegistration.AccessiblePluginManager |
For getting access to protected members in the package manager.
|
WekaEnsembleGenerator |
Uses the specified generator to create ensembles from the incoming data.
|
WekaEvaluation |
Provides further insight into an Evaluation object.
|
WekaEvaluationContainer |
A container for Evaluation objects, with optional trained model.
|
WekaEvaluationInfo |
Outputs information about a Weka weka.classifiers.Evaluation object.
|
WekaEvaluationInfo.InfoType |
The type of information to output.
|
WekaEvaluationPostProcessor |
Applies the specified post-processor to the incoming Evaluation data.
|
WekaEvaluationSummary |
Generates a summary string of the weka.classifiers.Evaluation objects that it receives.
|
WekaEvaluationToCostCurve |
Generates cost-curve data from a WEKA Evaluation object.
|
WekaEvaluationToMarginCurve |
Generates margin-curve data from a WEKA Evaluation object.
|
WekaEvaluationToThresholdCurve |
Generates threshold-curve data from a WEKA Evaluation object.
|
WekaEvaluationValuePicker |
Picks a specific value from an evaluation object.
|
WekaEvaluationValues |
Generates a spreadsheet from statistics of an Evaluation object.
|
WekaExperiment |
Represents a Weka experiment, stored in a file.
|
WekaExperimentContainer |
Container for Weka experiment results.
|
WekaExperimenterPreferencesPanel |
Preferences for the WEKA Experimenter.
|
WekaExperimentEvaluation |
Generates evaluation output of an experiment that was run previously.
|
WekaExperimentExecution |
Executes an experiment.
|
WekaExperimentFile |
A dummy class for the GOE, for special handling of experiments.
|
WekaExperimentFileEditor |
A PropertyEditor for WekaExperimentFile objects that lets the user select a file.
|
WekaExperimentFileEditor.SimpleSetupDialog |
A dialog for displaying the simple setup of an experiment.
|
WekaExperimentFileParsing |
For parsing WekaExperimentFile options.
|
WekaExperimentFileReader |
Loads an experiment file.
|
WekaExperimentFileWriter |
Saves an experiment file.
|
WekaExperimentGenerator |
Generates an experiment setup that can be used in conjunction with the Experiment transformer actor.
|
WekaExperimentGenerator.EvaluationType |
The evaluation type.
|
WekaExperimentGenerator.ExperimentType |
The experiment type.
|
WekaExperimentGenerator.ResultFormat |
The data format the experiment data is stored in.
|
WekaExplorerPreferencesPanel |
Preferences for the WEKA Explorer.
|
WekaExtractArray |
Extracts a column or row of data from a weka.core.Instances or SpreadSheet object.
Only numeric columns can be returned.
|
WekaExtractArray.ExtractionType |
The type of extraction to perform.
|
WekaExtractPLSMatrix |
Transformer that allows the extraction of internal PLS filter/classifier matrices, forwarding them as spreadsheets.
|
WekaExtractPLSMatrix.MatrixType |
The type of PLS matrix to extract (either PLS1 or SIMPLS ones will
be available).
|
WekaFileChooser |
A specialized JFileChooser that lists all available file Readers and Writers
for Weka file formats.
|
WekaFileReader |
Reads any file format that Weka's converters can handle and returns the full dataset or single weka.core.Instance objects.
|
WekaFileReader.OutputType |
Defines how to output the data.
|
WekaFileWriter |
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).
|
WekaFilter |
Applies a Weka filter to the data.
|
WekaFilter |
Filters Instances/Instance objects using the specified filter.
When re-using a trained filter, ensure that 'initializeOnce' is checked.
The following order is used to obtain the model (when using AUTO):
1.
|
WekaFilter.BatchFilterJob |
|
WekaFilterContainer |
A container for filters and the filtered data.
|
WekaFilterGenerator |
Generates multiple filter setups.
|
WekaFilterModelLoader |
Model loader for Weka filters.
|
WekaGenericArrayEditorDialog |
Displays a GenericArrayEditor.
|
WekaGenericArrayEditorPanel |
A panel that contains text field with the current setup of the array
and a button for bringing up the GenericArrayEditor.
|
WekaGenericObjectEditorDialog |
Displays a GenericObjectEditor.
|
WekaGenericObjectEditorHandler |
Handler for the WEKA GenericObjectEditor.
|
WekaGenericObjectEditorPanel |
A panel that contains text field with the current setup of the object
and a button for bringing up the GenericObjectEditor.
|
WekaGenericObjectEditorPopupMenu |
Generic GOE popup menu, for copy/paste, etc.
|
WekaGenericPLSMatrixAccess |
Transformer that allows the extraction of internal PLS filter/classifier matrices, forwarding them as spreadsheets.
See the respective PLS implementation for details on available matrix names (derived from: weka.filters.supervised.attribute.pls.AbstractPLS)
Input/output:
- accepts:
weka.classifiers.Classifier
weka.filters.Filter
weka.core.GenericPLSMatrixAccess
adams.flow.container.WekaModelContainer
- generates:
adams.data.spreadsheet.SpreadSheet
Container information:
- adams.flow.container.WekaModelContainer: Model, Header, Dataset
|
WekaGeneticAlgorithm |
Applies the genetic algorithm to the incoming dataset.
Forwards the best setup(s) after the algorithm finishes.
A callable sink can be specified for receiving intermediate performance results.
|
WekaGeneticAlgorithmContainer |
A container for genetic algorithms output (setup, measure, fitness).
|
WekaGeneticAlgorithmInitializationContainer |
A container for initializing genetic algorithms.
|
WekaGeneticAlgorithmInitializer |
Populates a adams.flow.container.WekaGeneticAlgorithmInitializationContainer container from the data obtained from the incoming setup (in properties format, can be gzip compressed).
|
WekaGeneticHelper |
Helper for Weka classes.
|
WekaGetCapabilities |
Retrieves the capabilities of a weka.core.CapabilitiesHandler (eg filter or classifier) and forwards them.
|
WekaGetInstancesValue |
Retrieves a value from a WEKA Instances object.
Notes:
- date and relational values are forwarded as strings
- missing values are output as '?' (without the single quotes)
Input/output:
- accepts:
weka.core.Instances
- generates:
java.lang.Double
java.lang.String
|
WekaGetInstanceValue |
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:
|
WekaGOEValueDefinition |
Definition for generic WEKA GOE objects.
|
WekaGraphVisualizer |
Displays BayesNet graphs in XML or BIF notation
Either displays the contents of a file or an object that implements weka.core.Drawable and generates a BayesNet graph.
|
WekaHomeEnvironmentModifier |
Sets a custom WEKA_HOME environment variable inside the project's home directory.
|
WekaInstanceBuffer |
Can act in two different ways:
1.
|
WekaInstanceBuffer.Operation |
Defines how the buffer actor operates.
|
WekaInstanceContainer |
Encapsulates a Instance object.
|
WekaInstanceDumper |
Dumps weka.core.Instance objects into an ARFF file.
|
WekaInstanceDumper.OutputFormat |
The format to output the data in.
|
WekaInstanceEvaluator |
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.
|
WekaInstanceFileReader |
Loads a WEKA dataset from disk with a specified reader and passes on the adams.core.instance.Instance objects.
|
WekaInstances |
Provides further insight into Instance and Instances objects.
|
WekaInstancesAppend |
Creates one large dataset by appending all one after the other.
|
WekaInstancesDisplay |
Actor for displaying a weka.core.Instances object in table format.
|
WekaInstancesExporter |
Exports Weka Instances/Instance objects.
|
WekaInstancesHistogramRanges |
Outputs the ranges generated by adams.data.statistics.ArrayHistogram using the incoming weka.core.Instances object.
The actor just uses the internal format (double array) and does not check whether the attributes are actually numeric.
|
WekaInstancesInfo |
Outputs statistics of a weka.core.Instances object.
FULL_ATTRIBUTE and FULL_CLASS output a spreadsheet with detailed attribute statistics.
|
WekaInstancesInfo.InfoType |
The type of information to generate.
|
WekaInstancesMerge |
Merges multiple datasets, either from file or using Instances/Instance objects.
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.
|
WekaInstancesPlot |
Actor for plotting one attribute vs another.
|
WekaInstancesRenderer |
Renders Weka Instances/Instance objects.
|
WekaInstancesStatistic |
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.
|
WekaInstancesStatisticDataType |
Defines what data to retrieve from an Instances object.
|
WekaInstancesToSpreadSheet |
Generates a spreadsheet from a weka.core.Instances object.
|
WekaInstanceStreamPlotGenerator |
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.
|
WekaInstanceToAdamsInstance |
Converts weka.core.Instance objects into adams.data.instance.Instance ones.
|
WekaInstanceToMap |
Turns the Weka Instance into a Map, with the attribute names the keys.
|
WekaInstanceViewer |
Actor for displaying adams.data.instance.Instance objects in a graphical way (using the internal format), like the 'Instance Explorer' tool.
|
WekaInvestigator |
Opens the WEKA Investigator.
|
WekaInvestigatorDataEvent |
|
WekaInvestigatorDataListener |
Interface for classes that get notified about changes in the data in
an InvestigatorPanel .
|
WekaInvestigatorDefinition |
Definition for the Weka Investigator props file.
|
WekaInvestigatorPreferencesPanel |
Preferences for the WEKA Investigator.
|
WekaInvestigatorShortcutsDefinition |
Definition for the Weka Investigator shortcuts props file.
|
WekaLabelIndex |
Extended Index class that can use a label name to determine an
index of a label as well.
|
WekaLabelIndexEditor |
|
WekaLabelIndexParsing |
For parsing WekaLabelIndex options.
|
WekaLabelRange |
Extended Range class that also allows attribute names for specifying
attribute positions (names are case-insensitive, just like placeholders for
'first', 'second', etc).
|
WekaLabelRangeEditor |
|
WekaLabelRangeParsing |
For parsing WekaLabelRange options.
|
WekaMarginCurve |
Actor for displaying margin errors.
|
WekaMergeInstancesActor |
Interface for transformers that merge Weka Instances.
|
WekaModelContainer |
A container for models (e.g., classifier or clusterer) and an optional
header of a dataset.
|
WekaModelReader |
Actor for loading a model (classifier or clusterer).
|
WekaModelWriter |
Actor for saving a model (classifier or clusterer) alongside an optional header (i.e., weka.core.Instances object) as file.
|
WekaMultiExperimenter |
Opens the WEKA Multi-Experimenter.
|
WekaMultiLabelSplitter |
Splits a dataset containing multiple class attributes ('multi-label') into separate datasets with only a single class attribute.
|
WekaNearestNeighborSearch |
Outputs the specified number of nearest neighbors for the incoming Weka Instance.
The data used for the nearest neighbor search is either obtained from storage.
|
WekaNearestNeighborSearchContainer |
A container for nearest neighbor search (instance and neighborhood).
|
WekaNewExperiment |
Generates a new ADAMS experiment setup.
|
WekaNewInstance |
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.
|
WekaNewInstances |
Generates an empty dataset, based on the attribute types and names specified.
Nominal attributes are generated with an empty set of labels.
|
WekaOptionHandlerHelpGenerator |
Help generator for OptionHandler .
|
WekaOptionsConversionPanel |
Helper panel that turns Weka commandline strings into quoted strings
suitable to be placed into code.
|
WekaOptionUtils |
Helper class for option parsing.
|
WekaPackageManagerAction |
Executes the specified action and forwards the generated output.
|
WekaPackageManagerAction |
Executes the specified action and forwards the generated output.
|
WekaPackageManagerAction |
Applies the selected Weka Package Manager action to the incoming data and forwards the generated output.
|
WekaPackagesClassPathAugmenter |
Returns the classpath augmentations for all the installed WEKA packages.
|
WekaPackageToMap |
Turns the Weka Package into a Map.
|
WekaPackageUtils |
Utility functions for Weka packages.
|
WekaPluginManagerExtensions |
Enables further extensions through Weka's PluginManager.
|
WekaPredictionContainer |
A container for predictions made by a classifier.
|
WekaPredictionContainerToSpreadSheet |
Turns a WEKA prediction container into a SpreadSheet object.
|
WekaPredictionContainerToSpreadSheet.SortContainer |
Helper class for sorting the distribution.
|
WekaPredictionContainerToSpreadSheet.Sorting |
How to sort the distribution.
|
WekaPredictionsToInstances |
Generates weka.core.Instances from the predictions of an Evaluation object.
|
WekaPredictionsToSpreadSheet |
Generates a SpreadSheet object from the predictions of an Evaluation object.
See also:
adams.flow.transformer.WekaSpreadSheetToPredictions
Input/output:
- accepts:
weka.classifiers.Evaluation
adams.flow.container.WekaEvaluationContainer
- generates:
adams.data.spreadsheet.SpreadSheet
Container information:
- adams.flow.container.WekaEvaluationContainer: Evaluation, Model, Prediction output, Original indices
|
WekaPrincipalComponents |
Performs principal components analysis on the incoming data and outputs the loadings and the transformed data as spreadsheet array.
Automatically filters out attributes that cannot be handled by PCA.
|
WekaPropertySheetPanelPage |
Wizard page that use a PropertySheetPanel for displaying
the properties of an object.
|
WekaPropertySheetPanelPage.CustomPropertySheetPanel |
Allowing better access to property sheet panel.
|
WekaPropertyValueConverter |
Handler for WEKA classes.
|
WekaRandomSplit |
Splits a dataset into a training and test set according to a specified split percentage.
|
WekaRegexToRange |
Produces a range string from a regular expression describing attributes.
|
WekaRegressionModel |
Regression model for Weka classifiers.
|
WekaRegressor |
Wraps around a Weka classifier that handles numeric classes (= regression).
|
WekaRelationName |
Deprecated. |
WekaRenameRelation |
Modifies relation names.
|
WekaReorderAttributesToReference |
Reorders the attributes of the Instance/Instances passing through according to the provided reference dataset (callable actor or reference file).
This ensures that the generated data always has the same structure as the reference dataset.
|
WekaRepeatedCrossValidationEvaluator |
Performs repeated cross-validation a classifier on an incoming dataset.
|
WekaRepeatedCrossValidationOutput |
Generates output from the incoming repeated cross-validation data.
|
WekaSelectDataset |
Pops up a file chooser dialog, prompting the user to select one or more datasets.
|
WekaSelectDatasetPage |
Wizard page that allows the user to select a Weka dataset.
|
WekaSelectMultipleDatasetsPage |
Wizard page that allows the user to select multiple datasets.
|
WekaSelectObjects |
Allows the user to select an arbitrary number of Weka objects from the specified class hierarchy using the GenericObjectArray.
|
WekaSetInstancesValue |
Sets a value in a WEKA Instances object.
Notes:
- relational values cannot be set
- '?' (without single quotes) is interpreted as missing value
Input/output:
- accepts:
weka.core.Instances
- generates:
weka.core.Instances
|
WekaSetInstanceValue |
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:
|
WekaSimpleCLI |
Opens the WEKA SimpleCLI.
|
WekaSplitGenerator |
WekaSpreadSheetToPredictions |
Turns the predictions stored in the incoming spreadsheet (actual and predicted) into a Weka weka.classifiers.Evaluation object.
For recreating the predictions of a nominal class, the class distributions must be present in the spreadsheet as well.
See also:
adams.flow.transformer.WekaPredictionsToSpreadSheet
Input/output:
- accepts:
adams.data.spreadsheet.SpreadSheet
- generates:
weka.classifiers.Evaluation
|
WekaStoreInstance |
Appends the incoming weka.core.Instance to the dataset in storage.
|
WekaStreamEvaluator |
Evaluates an incremental classifier on a data stream using prequential evaluation (first evaluate, then train).
|
WekaStreamFilter |
Filters Instance objects using the specified filter.
|
WekaSubsets |
Splits the dataset based on the unique values of the specified attribute: all rows with the same unique value form a subset.
|
WekaSystemProperties |
Sets some Weka-specific system properties to improve performance.
|
WekaTestSetClustererEvaluator |
Evaluates a trained clusterer (obtained from input) on the dataset obtained from the callable actor.
If a class attribute is set, a classes-to-clusters evaluation is performed automatically
Input/output:
- accepts:
weka.clusterers.Clusterer
adams.flow.container.WekaModelContainer
- generates:
adams.flow.container.WekaClusterEvaluationContainer
Container information:
- adams.flow.container.WekaModelContainer: Model, Header, Dataset
- adams.flow.container.WekaClusterEvaluationContainer: Evaluation, Model, Log-likelohood
|
WekaTestSetEvaluator |
Evaluates a trained classifier (obtained from input) on the dataset obtained from the callable actor.
|
WekaTestSetEvaluator.EvaluateJob |
|
WekaTextDirectoryReader |
Loads all text files in a directory and uses the subdirectory names as class labels.
|
WekaThresholdCurve |
Actor for displaying threshold curves, like ROC or precision/recall.
|
WekaThresholdCurve.AttributeName |
The type of the fields.
|
WekaTrainAssociator |
Trains an associator based on the incoming dataset and outputs the built associator alongside the training header and rules (in a model container)..
|
WekaTrainAssociator.TrainJob |
|
WekaTrainClassifier |
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.
|
WekaTrainClassifier.BatchTrainJob |
|
WekaTrainClusterer |
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.
|
WekaTrainClusterer.BatchTrainJob |
|
WekaTrainTestSetClustererEvaluator |
Trains a clusterer on an incoming training dataset (from a container) and then evaluates it on the test set (also from a container).
The clusterer setup being used in the evaluation is a callable 'Clusterer' actor.
If a class attribute is set, a classes-to-clusters evaluation is performed automatically
Input/output:
- accepts:
adams.flow.container.WekaTrainTestSetContainer
- generates:
adams.flow.container.WekaClusterEvaluationContainer
Container information:
- adams.flow.container.WekaTrainTestSetContainer: Train, Test, Seed, FoldNumber, FoldCount
- adams.flow.container.WekaClusterEvaluationContainer: Evaluation, Model, Log-likelohood
|
WekaTrainTestSetContainer |
A container for storing train and test set.
|
WekaTrainTestSetEvaluator |
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 callable 'Classifier' actor.
|
WekaTrainTestSetEvaluator.EvaluateJob |
|
WekaTreeVisualizer |
Displays trees in dot notation.
|
WekaUnorderedAttributeRange |
Extended UnorderedRange class that also allows attribute names for specifying
attribute positions (names are case-insensitive, just like placeholders for
'first', 'second', etc).
|
WekaUnorderedAttributeRangeEditor |
|
WekaUnorderedAttributeRangeParsing |
For parsing WekaUnorderedAttributeRange options.
|
Workbench |
Opens the WEKA Workbench.
|
WorkspaceHelper |
Helper class for loading/saving workspaces.
|
XGBoost |
Classifier implementing XGBoost.
|
XGBoost.BoosterType |
The available types of booster.
|
XGBoost.FeatureSelector |
Available feature selectors.
|
XGBoost.GrowPolicy |
Available grow policy settings.
|
XGBoost.NormaliseType |
Available normalisation-type settings.
|
XGBoost.Objective |
The set of possible learning objectives.
|
XGBoost.ParamValueProvider |
Provides a value suitable as a proxy for the XGBoost parameter system.
|
XGBoost.Predictor |
Available predictors.
|
XGBoost.ProcessType |
Available process-type settings.
|
XGBoost.SampleType |
Available sample-type settings.
|
XGBoost.TreeMethod |
Possible tree-method settings.
|
XGBoost.Updater |
Available updaters.
|
XGBoost.Verbosity |
The possible verbosity levels.
|
XGBoost.XGBoostParameter |
Marks a field as participating in the XGBoost parameter system.
|
XrffSpreadSheetReader |
Reads WEKA datasets in ARFF format and turns them into spreadsheets.
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XrffSpreadSheetWriter |
Writes a spreadsheet in XRFF file format.
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YGradientEPO |
Applies the External Parameter Orthogonalization (EPO) algorithm to the data.
For more information see:
http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Multivariate_Filtering#External_Parameter_Orthogonalization_.28EPO.29
Valid options are:
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YGradientGLSW |
Applies the Generalized Least Squares Weighting (GLSW) algorithm to the data.
For more information see:
http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Multivariate_Filtering#Y-Gradient_GLSW
Valid options are:
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