Uses of Interface
com.rapidminer.example.ExampleSet

Packages that use ExampleSet

com.rapidminer.datatable	DataTables are the most important data container interface for RapidMiner which are used for all statistics and plotting purposes.
com.rapidminer.example	The data core classes of RapidMiner.
com.rapidminer.example.set	The available views (example sets) on the example tables.
com.rapidminer.example.table	The available example table implementations (data sources).
com.rapidminer.example.test	Test classes for classes in the example package.
com.rapidminer.generator	Provides feature generators.
com.rapidminer.gui	Provides the main GUI classes.
com.rapidminer.gui.dialog	This package contains all non-special dialogs of RapidMiner.
com.rapidminer.gui.graphs	This package contains plotting functionality for graphs and some graph plot implementations for common RapidMiner graphs.
com.rapidminer.gui.viewer	This package contain viewer classes for some standard data types like ExampleSets, DataTables etc.
com.rapidminer.operator	Provides operators for machine learning and data pre-processing.
com.rapidminer.operator.clustering	The base classes for clustering.
com.rapidminer.operator.clustering.clusterer	The operators for clustering.
com.rapidminer.operator.clustering.clusterer.soft	The operators and helper classes for soft clustering.
com.rapidminer.operator.features	Provides feature handling operators.
com.rapidminer.operator.features.aggregation	Provides operators for automatic feature aggregation.
com.rapidminer.operator.features.construction	Provides operators for automatic feature construction.
com.rapidminer.operator.features.selection	Provides operators for automatic feature selection.
com.rapidminer.operator.features.transformation	Provides operators for feature space transformations like PCA or ICA.
com.rapidminer.operator.features.weighting	Operators to weight features or determine feature relevance.
com.rapidminer.operator.generator	Provides operators for data generation.
com.rapidminer.operator.io	Operators to read data from files or write them into files.
com.rapidminer.operator.learner	Provides learning operators.
com.rapidminer.operator.learner.bayes	This package contains classes and operators for Naive Bayes learning.
com.rapidminer.operator.learner.functions	This package contains learners based on the concept of function approximation.
com.rapidminer.operator.learner.functions.kernel	Learning schemes which make use of kernel functions to transform the feature space, e.g. support vector machines.
com.rapidminer.operator.learner.functions.kernel.evosvm	Implementations of SVMs which makes use of general purpose optimization methods, e.g. evolutionary strategies or particle swarm optimization.
com.rapidminer.operator.learner.functions.kernel.hyperhyper	This package contains classes for the HyperHyper learner.
com.rapidminer.operator.learner.functions.kernel.jmysvm.examples	The package for data handling of the Java version of the support vector machine mySVM.
com.rapidminer.operator.learner.functions.kernel.jmysvm.svm	The main package for the Java version of the the regression and classification support vector machine mySVM.
com.rapidminer.operator.learner.functions.neuralnet	This package contains a neural net learner based on Joone.
com.rapidminer.operator.learner.igss	Provides classes for learning operator Iterating Generic Sequential Sampling.
com.rapidminer.operator.learner.igss.hypothesis	Provides the hypothesis classes for learning operator Iterating Generic Sequential Sampling.
com.rapidminer.operator.learner.lazy	Learning schemes which perform lazy learning.
com.rapidminer.operator.learner.meta	Meta learning schemes which uses other learning operators to increase the performance.
com.rapidminer.operator.learner.rules	Provides rule learners.
com.rapidminer.operator.learner.subgroups	Provides the major classes of a subgroup discovery algorithm.
com.rapidminer.operator.learner.tree	Provides decision tree learners.
com.rapidminer.operator.learner.weka	Operators which encapsulate the learning schemes provided by Weka.
com.rapidminer.operator.meta	Provides operators for experiment iteration, meta operators, and optimization.
com.rapidminer.operator.performance	Provides performance evaluating operators and performance criteria.
com.rapidminer.operator.postprocessing	Operators for post processing, usually used for models.
com.rapidminer.operator.preprocessing	Operators for preprocessing purposes.
com.rapidminer.operator.preprocessing.discretization	Contains discretization operators which can be used to transform numerical into nominal attributes.
com.rapidminer.operator.preprocessing.filter	Containing filter operators changing the input example set, e.g. by removing certain attributes or changing the data.
com.rapidminer.operator.preprocessing.filter.attributes	This package contains the attribute filter.
com.rapidminer.operator.preprocessing.join	This package contains the operators for joining and merging example sets.
com.rapidminer.operator.preprocessing.normalization	Preprocessing operators used for normalization.
com.rapidminer.operator.preprocessing.outlier	Operators for outlier detection.
com.rapidminer.operator.preprocessing.sampling	Preprocessing operators used for sampling.
com.rapidminer.operator.preprocessing.series	Containing preprocessing operators for (time) series handling.
com.rapidminer.operator.preprocessing.series.filter	Containing preprocessing operators for (time) series filtering.
com.rapidminer.operator.preprocessing.transformation	This package contains some simple operators for basic transformations like grouping, aggregation and pivotization.
com.rapidminer.operator.preprocessing.weighting	This package methods for the weighting of examples.
com.rapidminer.operator.validation	Operators for estimation of the performance which can be achieved by learning schemes (and other predictive operators).
com.rapidminer.operator.visualization	The operators in this package are used for visualization purposes.
com.rapidminer.operator.visualization.dependencies	The operators in this package are used for the calculation and the visualization of dependency matrices like those for correlations etc.
com.rapidminer.tools	Provides tools for RapidMiner like parsers for the input files.
com.rapidminer.tools.jdbc	Provides tools for database access via JDBC connections.
com.rapidminer.tools.math	Several tool classes for mathematical operations.
com.rapidminer.tools.math.function	The classes in this package represent basic functions which can, for example, be used as aggregation functions.
com.rapidminer.tools.math.kernels	This package contains several widely used kernel functions.
com.rapidminer.tools.math.matrix	Utitility classes for matrices.
com.rapidminer.tools.math.similarity	This package consists similariy and distance measures.
com.rapidminer.tools.math.similarity.divergences	This package consists of similariy functions based on divergences.
com.rapidminer.tools.math.similarity.mixed	This package consists of mixed similariy functions, i.e. those which can be used on both numerical and nominal dimensions.
com.rapidminer.tools.math.similarity.nominal	This package consists of similariy functions for nominal values.
com.rapidminer.tools.math.similarity.numerical	This package consists of similariy functions for numerical values only.

Uses of ExampleSet in com.rapidminer.datatable

Methods in com.rapidminer.datatable that return ExampleSet

static ExampleSet

DataTableExampleSetAdapter.createExampleSetFromDataTable(DataTable table)

Constructors in com.rapidminer.datatable with parameters of type ExampleSet

DataTableExampleSetAdapter(ExampleSet exampleSet, AttributeWeights weights)

Uses of ExampleSet in com.rapidminer.example

Methods in com.rapidminer.example that return ExampleSet

static ExampleSet	ExampleSetFactory.createExampleSet(double[][] data) Create a numerical example set from the given data matrix.
static ExampleSet	ExampleSetFactory.createExampleSet(double[][] data, double[] labels) Create a numerical example set from the given data matrix.
static ExampleSet	ExampleSetFactory.createExampleSet(double[][] data, int classColumn) Create a numerical example set from the given data matrix.
static ExampleSet	ExampleSetFactory.createExampleSet(java.lang.Object[][] data) Create a mixed-type example set from the given data matrix.
static ExampleSet	ExampleSetFactory.createExampleSet(java.lang.Object[][] data, int classColumn) Create a numerical example set from the given data matrix.
static ExampleSet	ExampleSetFactory.createExampleSet(java.lang.Object[][] data, java.lang.Object[] labels) Create a numerical example set from the given data matrix.
static ExampleSet	Tools.getLinearSubsetCopy(ExampleSet exampleSet, int size, int offset) Returns a new example set based on a fresh memory example table sampled from the given set.
static ExampleSet	Tools.getShuffledSubsetCopy(ExampleSet exampleSet, int size, RandomGenerator randomGenerator) Returns a new example set based on a fresh memory example table sampled from the given set.

Methods in com.rapidminer.example with parameters of type ExampleSet

static void	Tools.checkAndCreateIds(ExampleSet es) The example set has to have ids.
static ExampleFormatter	ExampleFormatter.compile(java.lang.String formatString, ExampleSet exampleSet, int fractionDigits, boolean quoteWhitespace) Factory method that compiles a format string and creates an instance of ExampleFormatter.
static boolean	Tools.containsValueType(ExampleSet exampleSet, int valueType)
static Attribute[]	Tools.createRegularAttributeArray(ExampleSet exampleSet)
static Attribute	Tools.createSpecialAttribute(ExampleSet exampleSet, java.lang.String name, int valueType)
static Attribute	Tools.createWeightAttribute(ExampleSet exampleSet)
static void	Tools.fillTableWithRandomValues(ExampleTable exampleTable, ExampleSet baseSet, RandomGenerator random) After creation of a new MemoryExampleTable with given size all values are Double.NaN.
void	AttributeParser.generateAll(LoggingHandler logging, ExampleSet exampleSet, java.io.InputStream in) Parses all lines.
static java.lang.String[]	Tools.getAllAttributeNames(ExampleSet exampleSet)
static ExampleSet	Tools.getLinearSubsetCopy(ExampleSet exampleSet, int size, int offset) Returns a new example set based on a fresh memory example table sampled from the given set.
static Attribute[]	Tools.getRandomCompatibleAttributes(ExampleSet exampleSet, FeatureGenerator generator, java.lang.String[] functions, java.util.Random random)
static java.lang.String[]	Tools.getRegularAttributeConstructions(ExampleSet exampleSet)
static java.lang.String[]	Tools.getRegularAttributeNames(ExampleSet exampleSet)
static ExampleSet	Tools.getShuffledSubsetCopy(ExampleSet exampleSet, int size, RandomGenerator randomGenerator) Returns a new example set based on a fresh memory example table sampled from the given set.
static void	Tools.hasNominalLabels(ExampleSet es) The example set has to have nominal labels.
static void	Tools.isIdTagged(ExampleSet es) The example set has to be tagged with ids.
static void	Tools.isLabelled(ExampleSet es) The example set has to contain labels.
static void	Tools.isNonEmpty(ExampleSet es) The example set has to contain at least one example.
static void	Tools.onlyNominalAttributes(ExampleSet es, java.lang.String task) The attributes all have to be nominal or binary.
static void	Tools.onlyNonMissingValues(ExampleSet exampleSet, java.lang.String task) The data set is not allowed to contain missing values.
static void	Tools.onlyNumericalAttributes(ExampleSet es, java.lang.String task) The attributes all have to be numerical.
static void	Tools.replaceValue(ExampleSet exampleSet, Attribute attribute, double oldValue, double newValue) Replaces the given real value by the new one.
static void	Tools.replaceValue(ExampleSet exampleSet, Attribute attribute, java.lang.String oldValue, java.lang.String newValue) Replaces the given value by the new one.

Constructors in com.rapidminer.example with parameters of type ExampleSet

AttributeWeights(ExampleSet exampleSet)
Creates a new attribute weights object containing a weight of 1 for each of the given input attributes.

Example(DataRow data, ExampleSet parentExampleSet)
Creates a new Example that uses the data stored in a DataRow.

ExampleFormatter.ValueCommand(char command, java.lang.String[] arguments, ExampleSet exampleSet, int fractionDigits, boolean quoteWhitespace)

FastExample2SparseTransform(ExampleSet es)
Returns for a table giving the equivalence between the positions of the Attributes in the ExampleTable and the number of the regular Attributes in the ExampleSet.

Uses of ExampleSet in com.rapidminer.example.set

Classes in com.rapidminer.example.set that implement ExampleSet

class	AbstractExampleSet Implements wrapper methods of abstract example set.
class	AttributeSelectionExampleSet An implementation of ExampleSet that is only a fixed view on a selection of attributes of the parent example set.
class	AttributeWeightedExampleSet An implementation of ExampleSet that allows the weighting of the attributes.
class	ConditionedExampleSet Hides Examples that do not fulfill a given Condition.
class	HeaderExampleSet This example set is a clone of the attributes without reference to any data.
class	MappedExampleSet This example set uses a mapping of indices to access the examples provided by the parent example set.
class	ModelViewExampleSet This is a generic example set (view on the view stack of the data) which can be used to apply any preprocessing model and create a view from it.
class	NonSpecialAttributesExampleSet This example set treats all special attributes as regular attributes.
class	RemappedExampleSet This example set uses the mapping given by another example set and "remaps" on the fly the nominal values according to the given set.
class	ReplaceMissingExampleSet An implementation of ExampleSet that allows the replacement of missing values on the fly.
class	SimilarityExampleSet This similarity based example set is used for the operator ExampleSet2SimilarityExampleSet.
class	SimpleExampleSet A simple implementation of ExampleSet containing a list of attributes and a special attribute map.
class	SingleExampleExampleSet This view can be used to wrap a single example.
class	SortedExampleSet This example set uses a mapping of indices to access the examples provided by the parent example set.
class	SplittedExampleSet An example set that can be split into subsets by using a Partition.

Methods in com.rapidminer.example.set with parameters of type ExampleSet

static int[]	MappedExampleSet.createBootstrappingMapping(ExampleSet exampleSet, int size, java.util.Random random) Creates a new mapping for the given example set by sampling with replacement.
static Condition	ConditionedExampleSet.createCondition(java.lang.String name, ExampleSet exampleSet, java.lang.String parameterString) Checks if the given name is the short name of a known condition and creates it.
static int[]	MappedExampleSet.createWeightedBootstrappingMapping(ExampleSet exampleSet, int size, java.util.Random random)
static SplittedExampleSet	SplittedExampleSet.splitByAttribute(ExampleSet exampleSet, Attribute attribute) Works only for nominal and integer attributes.
static SplittedExampleSet	SplittedExampleSet.splitByAttribute(ExampleSet exampleSet, Attribute attribute, double value) Works only for real-value attributes.

Constructors in com.rapidminer.example.set with parameters of type ExampleSet

AcceptAllCondition(ExampleSet exampleSet, java.lang.String parameterString)
Throws an exception since this condition does not support parameter string.

AttributeSelectionExampleSet(ExampleSet exampleSet, boolean[] selectionMask)
Constructs a new AttributeSelectionExampleSet.

AttributesExampleReader(java.util.Iterator<Example> parent, ExampleSet exampleSet)
Creates a simple example reader.

AttributeValueFilter(ExampleSet exampleSet, java.lang.String parameterString)
Constructs an AttributeValueFilter for a given ExampleSet from a parameter string

AttributeValueFilterSingleCondition(ExampleSet exampleSet, java.lang.String parameterString)
Constructs an AttributeValueFilter for a given ExampleSet from a parameter string

AttributeWeightedExampleSet(ExampleSet exampleSet)
Constructs a new AttributeWeightedExampleSet.

AttributeWeightedExampleSet(ExampleSet exampleSet, AttributeWeights weights)
Constructs a new AttributeWeightedExampleSet.

AttributeWeightedExampleSet(ExampleSet exampleSet, AttributeWeights weights, double defaultWeight)
Constructs a new AttributeWeightedExampleSet.

ConditionedExampleSet(ExampleSet parent, Condition condition)
Creates a new example which used only examples fulfilling the given condition.

ConditionedExampleSet(ExampleSet parent, Condition condition, boolean inverted)
Creates a new example which used only examples fulfilling the given condition.

CorrectPredictionCondition(ExampleSet exampleSet, java.lang.String parameterString)
Throws an exception since this condition does not support parameter string.

HeaderExampleSet(ExampleSet parent)

IndexBasedExampleSetReader(ExampleSet parent)

MappedExampleSet(ExampleSet parent, int[] mapping)
Constructs an example set based on the given mapping.

MappedExampleSet(ExampleSet parent, int[] mapping, boolean useMappedExamples)
Constructs an example set based on the given mapping.

MappedExampleSet(ExampleSet parent, int[] mapping, boolean useMappedExamples, boolean sort)
Constructs an example set based on the given mapping.

MissingAttributesCondition(ExampleSet exampleSet, java.lang.String parameterString)
Throws an exception since this condition does not support parameter string.

MissingLabelsCondition(ExampleSet exampleSet, java.lang.String parameterString)
Throws an exception since a parameter string is not allowed for this condition.

ModelViewExampleSet(ExampleSet parent, ViewModel model)

NoMissingAttributesCondition(ExampleSet exampleSet, java.lang.String parameterString)
Throws an exception since this condition does not support parameter string.

NoMissingAttributeValueCondition(ExampleSet exampleSet, java.lang.String parameterString)

NoMissingLabelsCondition(ExampleSet exampleSet, java.lang.String parameterString)
Throws an exception since a parameter string is not allowed for this condition.

NonSpecialAttributesExampleSet(ExampleSet exampleSet)

RemappedExampleSet(ExampleSet parentSet, ExampleSet _mappingSet)

ReplaceMissingExampleSet(ExampleSet exampleSet)

ReplaceMissingExampleSet(ExampleSet exampleSet, java.util.Map<java.lang.String,java.lang.Double> replacementMap)

SimilarityExampleSet(ExampleSet parent, DistanceMeasure measure)

SimpleExampleReader(DataRowReader drr, ExampleSet exampleSet)
Creates a simple example reader.

SingleExampleExampleSet(ExampleSet exampleSet, Example example)

SortedExampleReader(ExampleSet parent)
Constructs a new mapped example reader.

SortedExampleSet(ExampleSet parent, Attribute sortingAttribute, int sortingDirection)

SortedExampleSet(ExampleSet parent, int[] mapping)
Constructs an example set based on the given sort mapping.

SplittedExampleSet(ExampleSet exampleSet, double[] splitRatios, int samplingType, int seed)
Creates an example set that is splitted into n subsets with the given sampling type.

SplittedExampleSet(ExampleSet exampleSet, double splitRatio, int samplingType, int seed)
Creates an example set that is splitted into two subsets using the given sampling type.

SplittedExampleSet(ExampleSet exampleSet, int numberOfSubsets, int samplingType, int seed)
Creates an example set that is splitted into numberOfSubsets parts with the given sampling type.

SplittedExampleSet(ExampleSet exampleSet, Partition partition)
Constructs a SplittedExampleSet with the given partition.

StratifiedPartitionBuilder(ExampleSet exampleSet, int seed)

WrongPredictionCondition(ExampleSet exampleSet, java.lang.String parameterString)
Throws an exception since this condition does not support parameter string.

Uses of ExampleSet in com.rapidminer.example.table

Methods in com.rapidminer.example.table that return ExampleSet

ExampleSet	ExampleTable.createExampleSet() Returns a new example set with all attributes switched on.
ExampleSet	AbstractExampleTable.createExampleSet() Returns a new example set with all attributes switched on.
ExampleSet	ExampleTable.createExampleSet(Attribute labelAttribute) Returns a new example set with all attributes switched on.
ExampleSet	AbstractExampleTable.createExampleSet(Attribute labelAttribute) Returns a new example set with all attributes switched on.
ExampleSet	ExampleTable.createExampleSet(Attribute labelAttribute, Attribute weightAttribute, Attribute idAttribute) Returns a new example set with all attributes switched on.
ExampleSet	AbstractExampleTable.createExampleSet(Attribute labelAttribute, Attribute weightAttribute, Attribute idAttribute) Returns a new example set with all attributes switched on.
ExampleSet	ExampleTable.createExampleSet(AttributeSet attributeSet) Returns a new example set with all attributes of the given attribute set.
ExampleSet	AbstractExampleTable.createExampleSet(AttributeSet attributeSet) Returns a new example set with all attributes of the given attribute set.
ExampleSet	ExampleTable.createExampleSet(java.util.Iterator<AttributeRole> newSpecialAttributes)
ExampleSet	AbstractExampleTable.createExampleSet(java.util.Iterator<AttributeRole> newSpecialAttributes) Returns a new example set with all attributes switched on.
ExampleSet	ExampleTable.createExampleSet(java.util.Map<Attribute,java.lang.String> specialAttributes) Returns a new example set with all attributes switched on.
ExampleSet	AbstractExampleTable.createExampleSet(java.util.Map<Attribute,java.lang.String> specialAttributes) Returns a new example set with all attributes switched on.

Constructors in com.rapidminer.example.table with parameters of type ExampleSet

RandomDataRowReader(ExampleSet baseExampleSet, Attribute[] attributes, int size)

RandomExampleTable(ExampleSet baseExampleSet, java.util.List<Attribute> attributes, int size)

Uses of ExampleSet in com.rapidminer.example.test

Methods in com.rapidminer.example.test with parameters of type ExampleSet

static Attribute

ExampleTestTools.createPredictedLabel(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.generator

Methods in com.rapidminer.generator with parameters of type ExampleSet

void	SinusFactory.generateSinusFunctions(ExampleSet exampleSet, java.util.List<AttributePeak> attributes, java.util.Random random) Generates a new sinus function attribute for all given attribute peaks.
java.util.List<AttributePeak>	SinusFactory.getAttributePeaks(ExampleSet exampleSet, Attribute first, Attribute second) Calculates the fourier transformation from the first attribute on the second and delivers the maxPeaks highest peaks.
java.util.List<Attribute[]>	SingularNumericalGenerator.getInputCandidates(ExampleSet exampleSet, java.lang.String[] functions) Returns all compatible input attribute arrays for this generator from the given example set as list.
abstract java.util.List<Attribute[]>	FeatureGenerator.getInputCandidates(ExampleSet exampleSet, java.lang.String[] functions) Returns all compatible input attribute arrays for this generator from the given example set as list.
java.util.List<Attribute[]>	ConstantGenerator.getInputCandidates(ExampleSet exampleSet, java.lang.String[] functions) Returns all compatible input attribute arrays for this generator from the given example set as list.
java.util.List<Attribute[]>	BinaryNumericalGenerator.getInputCandidates(ExampleSet exampleSet, java.lang.String[] functions) Returns all compatible input attribute arrays for this generator from the given example set as list.
static FeatureGenerator	FeatureGenerator.selectGenerator(ExampleSet exampleSet, java.util.List generators, java.lang.String[] functions, RandomGenerator random) Randomly selects a generator from the generator list.

Uses of ExampleSet in com.rapidminer.gui

Constructors in com.rapidminer.gui with parameters of type ExampleSet

ExampleVisualizer(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.gui.dialog

Constructors in com.rapidminer.gui.dialog with parameters of type ExampleSet

IndividualSelector(ExampleSet exampleSet, Population population)

IndividualSelector(ExampleSet exampleSet, Population population, boolean modal)

IndividualSelector(java.awt.Frame owner, ExampleSet exampleSet, Population population, int width, int height, boolean modal)

Uses of ExampleSet in com.rapidminer.gui.graphs

Constructors in com.rapidminer.gui.graphs with parameters of type ExampleSet

SimilarityGraphCreator(DistanceMeasure measure, ExampleSet exampleSet)

TransitionGraphCreator(TransitionGraph transitionGraph, ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.gui.viewer

Methods in com.rapidminer.gui.viewer with parameters of type ExampleSet

void	MetaDataViewerTable.setExampleSet(ExampleSet exampleSet)
void	MetaDataViewer.setExampleSet(ExampleSet exampleSet)
void	DataViewerTable.setExampleSet(ExampleSet exampleSet)
void	DataViewer.setExampleSet(ExampleSet exampleSet)

Constructors in com.rapidminer.gui.viewer with parameters of type ExampleSet

DataViewer(ExampleSet exampleSet, boolean providedFilter)

DataViewerTableModel(ExampleSet exampleSet)

MetaDataViewer(ExampleSet exampleSet, boolean showOptions)

MetaDataViewerTableModel(ExampleSet exampleSet)

SimilarityKDistanceVisualization(DistanceMeasure measure, ExampleSet exampleSet)

SimilarityTable(DistanceMeasure measure, ExampleSet exampleSet)

SimilarityTableModel(DistanceMeasure similarity, ExampleSet exampleSet)

SimilarityVisualization(SimilarityMeasure sim, ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator

Methods in com.rapidminer.operator that return ExampleSet

ExampleSet	Model.apply(ExampleSet testSet) Applies the model on the given example set.
ExampleSet	GroupedModel.apply(ExampleSet exampleSet) Applies all models.
abstract ExampleSet	AbstractExampleSetProcessing.apply(ExampleSet exampleSet) Delegate for the apply method.

Methods in com.rapidminer.operator with parameters of type ExampleSet

ExampleSet	Model.apply(ExampleSet testSet) Applies the model on the given example set.
ExampleSet	GroupedModel.apply(ExampleSet exampleSet) Applies all models.
abstract ExampleSet	AbstractExampleSetProcessing.apply(ExampleSet exampleSet) Delegate for the apply method.
Attributes	ViewModel.getTargetAttributes(ExampleSet viewParent) This method has to return a legal Attributes object containing every Attribute, the view should contain
void	Model.updateModel(ExampleSet updateExampleSet) Updates the model according to the given example set.
void	GroupedModel.updateModel(ExampleSet updateExampleSet) Updates the model if the classifier is updatable.
void	AbstractModel.updateModel(ExampleSet updateExampleSet) This default implementation throws an UserError.

Constructors in com.rapidminer.operator with parameters of type ExampleSet

AbstractModel(ExampleSet exampleSet)
Created a new model which was built on the given example set.

Uses of ExampleSet in com.rapidminer.operator.clustering

Methods in com.rapidminer.operator.clustering that return ExampleSet

ExampleSet

WekaClusterModel.apply(ExampleSet exampleSet)

Methods in com.rapidminer.operator.clustering with parameters of type ExampleSet

ExampleSet	WekaClusterModel.apply(ExampleSet exampleSet)
void	ClusterModel.checkCapabilities(ExampleSet exampleSet)
void	CentroidClusterModel.checkCapabilities(ExampleSet exampleSet)
int[]	ClusterModel.getClusterAssignments(ExampleSet exampleSet) This method returns an array with the indices or the cluster for all examples in the set.
int[]	CentroidClusterModel.getClusterAssignments(ExampleSet exampleSet)
void	ClusterModel.setClusterAssignments(int[] clusterId, ExampleSet exampleSet)

Constructors in com.rapidminer.operator.clustering with parameters of type ExampleSet

WekaClusterModel(ExampleSet exampleSet, weka.clusterers.Clusterer clusterer)

Uses of ExampleSet in com.rapidminer.operator.clustering.clusterer

Methods in com.rapidminer.operator.clustering.clusterer with parameters of type ExampleSet

ClusterModel	SVClustering.generateClusterModel(ExampleSet exampleSet)
ClusterModel	RandomClustering.generateClusterModel(ExampleSet exampleSet)
ClusterModel	KMedoids.generateClusterModel(ExampleSet exampleSet)
ClusterModel	KMeans.generateClusterModel(ExampleSet exampleSet)
ClusterModel	KernelKMeans.generateClusterModel(ExampleSet exampleSet)
ClusterModel	GenericWekaClustererAdaptor.generateClusterModel(ExampleSet exampleSet)
ClusterModel	DBScan.generateClusterModel(ExampleSet exampleSet)
abstract ClusterModel	AbstractClusterer.generateClusterModel(ExampleSet exampleSet) Generates a cluster model from an example set.
protected java.util.LinkedList<java.lang.Integer>	SVClustering.getNeighbours(ExampleSet exampleSet, Example centroid, int centroidIndex, int[] assignments, SVClusteringAlgorithm clustering)

Constructors in com.rapidminer.operator.clustering.clusterer with parameters of type ExampleSet

SVCExampleSet(ExampleSet exampleSet, boolean scale)

SVCExampleSet(ExampleSet exampleSet, java.util.Map<java.lang.Integer,SVMExamples.MeanVariance> meanVariances)

Uses of ExampleSet in com.rapidminer.operator.clustering.clusterer.soft

Methods in com.rapidminer.operator.clustering.clusterer.soft with parameters of type ExampleSet

ClusterModel	EMClusterer.createClusterModel(ExampleSet exampleSet)
protected void	EMClusterer.expectationCorrelated(ExampleSet exampleSet, int k, double[][] exampleInClusterProbability, FlatFuzzyClusterModel oldResult)
protected void	EMClusterer.expectationNonCorrelated(ExampleSet exampleSet, int k, double[][] exampleInClusterProbability, FlatFuzzyClusterModel oldResult)
ClusterModel	EMClusterer.generateClusterModel(ExampleSet exampleSet)
protected void	EMClusterer.maximization(ExampleSet exampleSet, int k, double[][] exampleInClusterProbability, FlatFuzzyClusterModel result)

Uses of ExampleSet in com.rapidminer.operator.features

Methods in com.rapidminer.operator.features that return ExampleSet

static ExampleSet

FeatureOperator.createCleanClone(ExampleSet exampleSet, double[] weights)

Methods in com.rapidminer.operator.features with parameters of type ExampleSet

static ExampleSet	FeatureOperator.createCleanClone(ExampleSet exampleSet, double[] weights)
abstract Population	FeatureOperator.createInitialPopulation(ExampleSet es) Create an initial population.
protected PopulationEvaluator	FeatureOperator.getPopulationEvaluator(ExampleSet exampleSet)
abstract java.util.List<PopulationOperator>	FeatureOperator.getPostEvaluationPopulationOperators(ExampleSet input) Must return a list of PopulationOperators.
abstract java.util.List<PopulationOperator>	FeatureOperator.getPreEvaluationPopulationOperators(ExampleSet input) Must return a list of PopulationOperators.

Constructors in com.rapidminer.operator.features with parameters of type ExampleSet

PopulationPlotter(ExampleSet exampleSet)
Creates plotter panel which is repainted every generation.

PopulationPlotter(ExampleSet exampleSet, int plotGenerations, boolean setDrawRange, boolean drawDominated)
Creates plotter panel which is repainted each plotGenerations generations.

SimplePopulationEvaluator(FeatureOperator operator, IOContainer input, ExampleSet originalSet)

Uses of ExampleSet in com.rapidminer.operator.features.aggregation

Methods in com.rapidminer.operator.features.aggregation that return ExampleSet

ExampleSet

AggregationIndividual.createExampleSet(ExampleSet originalExampleSet, Attribute[] allAttributes, FeatureGenerator generator)

Methods in com.rapidminer.operator.features.aggregation with parameters of type ExampleSet

ExampleSet	AggregationIndividual.createExampleSet(ExampleSet originalExampleSet, Attribute[] allAttributes, FeatureGenerator generator)
void	EvolutionaryFeatureAggregation.evaluate(java.util.List population, ExampleSet originalExampleSet) Creates example sets from all individuals and invoke the inner operators in order to estimate the performance.

Constructors in com.rapidminer.operator.features.aggregation with parameters of type ExampleSet

AggregationPopulationPlotter(ExampleSet originalExampleSet, Attribute[] allAttributes, FeatureGenerator generator)
Creates plotter panel which is repainted every generation.

Uses of ExampleSet in com.rapidminer.operator.features.construction

Methods in com.rapidminer.operator.features.construction that return ExampleSet

ExampleSet	ProductGenerationOperator.apply(ExampleSet exampleSet)
ExampleSet	LinearCombinationOperator.apply(ExampleSet exampleSet)
ExampleSet	GaussFeatureConstructionOperator.apply(ExampleSet exampleSet)
ExampleSet	FeatureGenerationOperator.apply(ExampleSet exampleSet)
ExampleSet	ConditionedFeatureGeneration.apply(ExampleSet exampleSet)
ExampleSet	CompleteFeatureGenerationOperator.apply(ExampleSet exampleSet)
ExampleSet	AttributeConstruction.apply(ExampleSet exampleSet)
ExampleSet	AttributeAggregationOperator.apply(ExampleSet exampleSet)

Methods in com.rapidminer.operator.features.construction with parameters of type ExampleSet

ExampleSet	ProductGenerationOperator.apply(ExampleSet exampleSet)
ExampleSet	LinearCombinationOperator.apply(ExampleSet exampleSet)
ExampleSet	GaussFeatureConstructionOperator.apply(ExampleSet exampleSet)
ExampleSet	FeatureGenerationOperator.apply(ExampleSet exampleSet)
ExampleSet	ConditionedFeatureGeneration.apply(ExampleSet exampleSet)
ExampleSet	CompleteFeatureGenerationOperator.apply(ExampleSet exampleSet)
ExampleSet	AttributeConstruction.apply(ExampleSet exampleSet)
ExampleSet	AttributeAggregationOperator.apply(ExampleSet exampleSet)
ExampleSetBasedPopulation	YAGGA.createInitialPopulation(ExampleSet es) Creates a initial population.
abstract ExampleSetBasedPopulation	ExampleSetBasedFeatureOperator.createInitialPopulation(ExampleSet es) Create an initial population.
ExampleSetBasedPopulation	AbstractGeneratingGeneticAlgorithm.createInitialPopulation(ExampleSet es) Sets up a population of given size and creates ExampleSets with randomly selected attributes (the probability to be switched on is controlled by pInitialize).
protected ExampleSetBasedPopulationOperator	AbstractGeneratingGeneticAlgorithm.getCrossoverPopulationOperator(ExampleSet exampleSet) Returns an UnbalancedCrossover.
protected ExampleSetBasedPopulationOperator	YAGGA.getGeneratingPopulationOperator(ExampleSet exampleSet) Since the mutation of YAGGA also creates new attributes this method returns null.
protected ExampleSetBasedPopulationOperator	GeneratingGeneticAlgorithm.getGeneratingPopulationOperator(ExampleSet eSet) Returns a specialized mutation, i.e. a AttributeGenerator
protected abstract ExampleSetBasedPopulationOperator	AbstractGeneratingGeneticAlgorithm.getGeneratingPopulationOperator(ExampleSet exampleSet) Returns a specialized generating mutation, e.g. a AttributeGenerator.
protected ExampleSetBasedPopulationOperator	YAGGA2.getMutationPopulationOperator(ExampleSet exampleSet)
protected ExampleSetBasedPopulationOperator	YAGGA.getMutationPopulationOperator(ExampleSet eSet) Returns the generating mutation PopulationOperator.
protected ExampleSetBasedPopulationOperator	GeneratingGeneticAlgorithm.getMutationPopulationOperator(ExampleSet eSet) Returns an operator that performs the mutation.
protected ExampleSetBasedPopulationOperator	FourierGGA.getMutationPopulationOperator(ExampleSet eSet) Returns the generating mutation PopulationOperator.
protected ExampleSetBasedPopulationOperator	DirectedGGA.getMutationPopulationOperator(ExampleSet eSet) Returns the DirectedGeneratingMutation.
protected abstract ExampleSetBasedPopulationOperator	AbstractGeneratingGeneticAlgorithm.getMutationPopulationOperator(ExampleSet example) Returns an operator that performs the mutation.
abstract java.util.List<ExampleSetBasedPopulationOperator>	ExampleSetBasedFeatureOperator.getPostEvaluationPopulationOperators(ExampleSet input) Must return a list of PopulationOperators.
java.util.List<ExampleSetBasedPopulationOperator>	AbstractGeneratingGeneticAlgorithm.getPostEvaluationPopulationOperators(ExampleSet input) Returns the list with post eval pop ops.
protected java.util.List<ExampleSetBasedPopulationOperator>	AbstractGeneratingGeneticAlgorithm.getPostProcessingPopulationOperators(ExampleSet input) Returns an empty list.
abstract java.util.List<ExampleSetBasedPopulationOperator>	ExampleSetBasedFeatureOperator.getPreEvaluationPopulationOperators(ExampleSet input) Must return a list of PopulationOperators.
java.util.List<ExampleSetBasedPopulationOperator>	AbstractGeneratingGeneticAlgorithm.getPreEvaluationPopulationOperators(ExampleSet input) Returns the list with pre eval pop ops.
protected java.util.List<ExampleSetBasedPopulationOperator>	YAGGA2.getPreProcessingPopulationOperators(ExampleSet eSet)
protected java.util.List<ExampleSetBasedPopulationOperator>	FourierGGA.getPreProcessingPopulationOperators(ExampleSet eSet)
protected java.util.List<ExampleSetBasedPopulationOperator>	AGA.getPreProcessingPopulationOperators(ExampleSet input)
protected java.util.List<ExampleSetBasedPopulationOperator>	AbstractGeneratingGeneticAlgorithm.getPreProcessingPopulationOperators(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.features.selection

Methods in com.rapidminer.operator.features.selection that return ExampleSet

ExampleSet	RemoveUselessFeatures.apply(ExampleSet exampleSet)
ExampleSet	RemoveCorrelatedFeatures.apply(ExampleSet exampleSet)
ExampleSet	RandomSelection.apply(ExampleSet exampleSet)

Methods in com.rapidminer.operator.features.selection with parameters of type ExampleSet

ExampleSet	RemoveUselessFeatures.apply(ExampleSet exampleSet)
ExampleSet	RemoveCorrelatedFeatures.apply(ExampleSet exampleSet)
ExampleSet	RandomSelection.apply(ExampleSet exampleSet)
Population	WeightGuidedSelectionOperator.createInitialPopulation(ExampleSet es) Returns an example set containing only the feature with the biggest weight.
Population	GeneticAlgorithm.createInitialPopulation(ExampleSet es) Sets up a population of given size and creates ExampleSets with randomly selected attributes (the probability to be switched on is controlled by pInitialize).
Population	FeatureSelectionOperator.createInitialPopulation(ExampleSet es) May es have n features.
Population	BruteForceSelection.createInitialPopulation(ExampleSet es)
protected PopulationOperator	GeneticAlgorithm.getCrossoverPopulationOperator(ExampleSet eSet) Returns an operator that performs crossover.
protected abstract PopulationOperator	AbstractGeneticAlgorithm.getCrossoverPopulationOperator(ExampleSet example) Returns an operator that performs crossover.
protected PopulationOperator	GeneticAlgorithm.getMutationPopulationOperator(ExampleSet eSet) Returns an operator that performs the mutation.
protected abstract PopulationOperator	AbstractGeneticAlgorithm.getMutationPopulationOperator(ExampleSet example) Returns an operator that performs the mutation.
java.util.List<PopulationOperator>	WeightGuidedSelectionOperator.getPostEvaluationPopulationOperators(ExampleSet input) Returns an empty list.
java.util.List<PopulationOperator>	FeatureSelectionOperator.getPostEvaluationPopulationOperators(ExampleSet input) empty list
java.util.List<PopulationOperator>	BruteForceSelection.getPostEvaluationPopulationOperators(ExampleSet input) Returns an empty list if the parameter debug_output is set to false.
java.util.List<PopulationOperator>	AbstractGeneticAlgorithm.getPostEvaluationPopulationOperators(ExampleSet input) Returns the list with post eval pop ops.
protected java.util.List<PopulationOperator>	AbstractGeneticAlgorithm.getPostProcessingPopulationOperators(ExampleSet input) Returns an empty list.
java.util.List<PopulationOperator>	WeightGuidedSelectionOperator.getPreEvaluationPopulationOperators(ExampleSet input) The operators add the feature with the next highest weight.
java.util.List<PopulationOperator>	FeatureSelectionOperator.getPreEvaluationPopulationOperators(ExampleSet input) The operators performs two steps: forward selection/backward elimination kick out all but the keep_best individuals remove redundant individuals
java.util.List<PopulationOperator>	BruteForceSelection.getPreEvaluationPopulationOperators(ExampleSet input) Does nothing.
java.util.List<PopulationOperator>	AbstractGeneticAlgorithm.getPreEvaluationPopulationOperators(ExampleSet input) Returns the list with pre eval pop ops.
protected java.util.List<PopulationOperator>	AbstractGeneticAlgorithm.getPreProcessingPopulationOperators(ExampleSet input) Returns an empty list.

Uses of ExampleSet in com.rapidminer.operator.features.transformation

Methods in com.rapidminer.operator.features.transformation that return ExampleSet

ExampleSet	SOMDimensionalityReductionModel.apply(ExampleSet exampleSet)
ExampleSet	PrincipalComponentsTransformation.apply(ExampleSet exampleSet)
ExampleSet	PCAModel.apply(ExampleSet exampleSet)
ExampleSet	KernelPCAModel.apply(ExampleSet exampleSet)
ExampleSet	GHAModel.apply(ExampleSet exampleSet)
ExampleSet	FourierTransform.apply(ExampleSet exampleSet)
ExampleSet	FastICAModel.apply(ExampleSet testSet)
ExampleSet	DimensionalityReducerModel.apply(ExampleSet es)

Methods in com.rapidminer.operator.features.transformation with parameters of type ExampleSet

ExampleSet	SOMDimensionalityReductionModel.apply(ExampleSet exampleSet)
ExampleSet	PrincipalComponentsTransformation.apply(ExampleSet exampleSet)
ExampleSet	PCAModel.apply(ExampleSet exampleSet)
ExampleSet	KernelPCAModel.apply(ExampleSet exampleSet)
ExampleSet	GHAModel.apply(ExampleSet exampleSet)
ExampleSet	FourierTransform.apply(ExampleSet exampleSet)
ExampleSet	FastICAModel.apply(ExampleSet testSet)
ExampleSet	DimensionalityReducerModel.apply(ExampleSet es)
protected Jama.Matrix	SVDReduction.callMatrixMethod(ExampleSet es, int dimensions, Jama.Matrix in)
protected abstract Jama.Matrix	JamaDimensionalityReduction.callMatrixMethod(ExampleSet es, int dimension, Jama.Matrix in)
protected double[][]	JamaDimensionalityReduction.dimensionalityReduction(ExampleSet es, int dimensions)
protected abstract double[][]	DimensionalityReducer.dimensionalityReduction(ExampleSet es, int dimensions) Perform the actual dimensionality reduction.

Constructors in com.rapidminer.operator.features.transformation with parameters of type ExampleSet

DimensionalityReducerModel(ExampleSet exampleSet, double[][] p, int dimensions)

FastICAModel(ExampleSet exampleSet, int numberOfComponents, double[] means, boolean rowNorm, Jama.Matrix K, Jama.Matrix W, Jama.Matrix A)

GHAModel(ExampleSet eSet, double[] eigenvalues, double[][] weights, double[] mean)

KernelPCAModel(ExampleSet exampleSet)

KernelPCAModel(ExampleSet exampleSet, double[] means, Jama.Matrix eigenVectors, java.util.ArrayList<double[]> exampleValues, Kernel kernel)

PCAModel(ExampleSet eSet, double[] eigenvalues, double[][] eigenvectors)

SOMDimensionalityReductionModel(ExampleSet exampleSet, KohonenNet net, int dimensions)

Uses of ExampleSet in com.rapidminer.operator.features.weighting

Methods in com.rapidminer.operator.features.weighting with parameters of type ExampleSet

AttributeWeights	SymmetricalUncertaintyOperator.calculateWeights(ExampleSet exampleSet)
AttributeWeights	SVMWeighting.calculateWeights(ExampleSet exampleSet)
AttributeWeights	StandardDeviationWeighting.calculateWeights(ExampleSet exampleSet)
AttributeWeights	ReliefWeighting.calculateWeights(ExampleSet inputSet)
AttributeWeights	PCAWeighting.calculateWeights(ExampleSet exampleSet)
AttributeWeights	OneRErrorWeighting.calculateWeights(ExampleSet exampleSet)
AttributeWeights	NameBasedWeighting.calculateWeights(ExampleSet exampleSet)
AttributeWeights	GenericWekaAttributeWeighting.calculateWeights(ExampleSet exampleSet)
AttributeWeights	CorrelationWeighting.calculateWeights(ExampleSet exampleSet)
AttributeWeights	CorpusBasedFeatureWeighting.calculateWeights(ExampleSet es)
AttributeWeights	ChiSquaredWeighting.calculateWeights(ExampleSet exampleSet)
abstract AttributeWeights	AbstractWeighting.calculateWeights(ExampleSet exampleSet)
AttributeWeights	AbstractEntropyWeighting.calculateWeights(ExampleSet exampleSet)
Population	ForwardWeighting.createInitialPopulation(ExampleSet es)
Population	EvolutionaryWeighting.createInitialPopulation(ExampleSet exampleSet)
Population	BackwardWeighting.createInitialPopulation(ExampleSet es)
PopulationOperator	EvolutionaryWeighting.getCrossoverPopulationOperator(ExampleSet eSet)
PopulationOperator	EvolutionaryWeighting.getMutationPopulationOperator(ExampleSet eSet)
java.util.List<PopulationOperator>	FeatureWeighting.getPostEvaluationPopulationOperators(ExampleSet eSet)
protected java.util.List<PopulationOperator>	EvolutionaryWeighting.getPostProcessingPopulationOperators(ExampleSet eSet)
java.util.List<PopulationOperator>	FeatureWeighting.getPreEvaluationPopulationOperators(ExampleSet eSet)

Uses of ExampleSet in com.rapidminer.operator.generator

Methods in com.rapidminer.operator.generator that return ExampleSet

ExampleSet	UpSellingExampleSetGenerator.createExampleSet()
ExampleSet	TransfersExampleSetGenerator.createExampleSet()
ExampleSet	TeamProfitExampleSetGenerator.createExampleSet()
ExampleSet	SalesExampleSetGenerator.createExampleSet()
ExampleSet	NominalExampleSetGenerator.createExampleSet()
ExampleSet	MultipleLabelGenerator.createExampleSet()
ExampleSet	MassiveDataGenerator.createExampleSet()
ExampleSet	ExampleSetGenerator.createExampleSet()
ExampleSet	DirectMailingExampleSetGenerator.createExampleSet()
ExampleSet	ChurnReductionExampleSetGenerator.createExampleSet()

Uses of ExampleSet in com.rapidminer.operator.io

Methods in com.rapidminer.operator.io that return ExampleSet

ExampleSet	XrffExampleSource.createExampleSet()
ExampleSet	URLExampleSource.createExampleSet()
ExampleSet	SparseFormatExampleSource.createExampleSet()
ExampleSet	SimpleExampleSource.createExampleSet()
ExampleSet	ResultSetExampleSource.createExampleSet()
ExampleSet	ExcelExampleSource.createExampleSet()
ExampleSet	ExampleSource.createExampleSet()
ExampleSet	DatabaseExampleSource.createExampleSet()
ExampleSet	CachedDatabaseExampleSource.createExampleSet()
ExampleSet	C45ExampleSource.createExampleSet()
ExampleSet	BytewiseExampleSource.createExampleSet()
ExampleSet	ArffExampleSource.createExampleSet()
ExampleSet	AccessExampleSource.createExampleSet()
abstract ExampleSet	AbstractExampleSource.createExampleSet() Creates (or reads) the ExampleSet that will be returned by AbstractReader.apply().
static ExampleSet	ResultSetExampleSource.createExampleSet(ExampleTable table, Operator operator)
ExampleSet	AbstractExampleSource.read()
protected ExampleSet	StataExampleSource.readStream(java.io.InputStream inputStream, DataRowFactory dataRowFactory)
protected ExampleSet	SPSSExampleSource.readStream(java.io.InputStream inputStream, DataRowFactory dataRowFactory)
protected ExampleSet	DasyLabDataReader.readStream(java.io.InputStream inputStream, DataRowFactory dataRowFactory)
protected abstract ExampleSet	BytewiseExampleSource.readStream(java.io.InputStream inputStream, DataRowFactory dataRowFactory) Reads the given file and constructs an example set from the read data.
ExampleSet	XrffExampleSetWriter.write(ExampleSet exampleSet)
ExampleSet	ExcelExampleSetWriter.write(ExampleSet exampleSet)
ExampleSet	ExampleSetWriter.write(ExampleSet eSet)
ExampleSet	DatabaseExampleSetWriter.write(ExampleSet exampleSet)
ExampleSet	CSVExampleSetWriter.write(ExampleSet exampleSet)
ExampleSet	ArffExampleSetWriter.write(ExampleSet exampleSet)

Methods in com.rapidminer.operator.io with parameters of type ExampleSet

ExampleSet	XrffExampleSetWriter.write(ExampleSet exampleSet)
ExampleSet	ExcelExampleSetWriter.write(ExampleSet exampleSet)
ExampleSet	ExampleSetWriter.write(ExampleSet eSet)
ExampleSet	DatabaseExampleSetWriter.write(ExampleSet exampleSet)
ExampleSet	CSVExampleSetWriter.write(ExampleSet exampleSet)
ExampleSet	ArffExampleSetWriter.write(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.learner

Methods in com.rapidminer.operator.learner that return ExampleSet

ExampleSet	PredictionModel.apply(ExampleSet exampleSet) Applies the model by creating a predicted label attribute and setting the predicted label values.
ExampleSet	SimplePredictionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all examples and applies the model to them.
ExampleSet	SimpleBinaryPredictionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all examples and applies the model to them.
abstract ExampleSet	PredictionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Subclasses should iterate through the given example set and set the prediction for each example.

Methods in com.rapidminer.operator.learner with parameters of type ExampleSet

ExampleSet	PredictionModel.apply(ExampleSet exampleSet) Applies the model by creating a predicted label attribute and setting the predicted label values.
protected void	PredictionModel.checkCompatibility(ExampleSet exampleSet) This method is invoked before the model is actually applied.
void	CapabilityCheck.checkLearnerCapabilities(Operator learningOperator, ExampleSet exampleSet) Checks if this learner can be used for the given example set, i.e. if it has sufficient capabilities.
static void	PredictionModel.copyPredictedLabel(ExampleSet source, ExampleSet destination) Copies the predicted label from the source example set to the destination example set.
static Attribute	PredictionModel.createPredictedLabel(ExampleSet exampleSet, Attribute label) Creates a predicted label for the given example set based on the label attribute defined for this prediction model.
AttributeWeights	Learner.getWeights(ExampleSet eSet) Most learners should throw an exception if they are not able to calculate attribute weights.
AttributeWeights	AbstractLearner.getWeights(ExampleSet exampleSet) Returns the calculated weight vectors.
Model	Learner.learn(ExampleSet exampleSet) Trains a model.
ExampleSet	SimplePredictionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all examples and applies the model to them.
ExampleSet	SimpleBinaryPredictionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all examples and applies the model to them.
abstract ExampleSet	PredictionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Subclasses should iterate through the given example set and set the prediction for each example.
static void	PredictionModel.removePredictedLabel(ExampleSet exampleSet) Helper method in order to lower memory consumption.
static void	PredictionModel.removePredictedLabel(ExampleSet exampleSet, boolean removePredictionFromTable, boolean removeConfidencesFromTable) Helper method in order to lower memory consumption.

Constructors in com.rapidminer.operator.learner with parameters of type ExampleSet

PredictionModel(ExampleSet trainingExampleSet)
Created a new prediction model which was built on the given example set.

SimpleBinaryPredictionModel(ExampleSet exampleSet, double threshold)

SimplePredictionModel(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.learner.bayes

Methods in com.rapidminer.operator.learner.bayes that return ExampleSet

ExampleSet	SimpleDistributionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	KernelDistributionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Perform predictions based on the distribution properties.
abstract ExampleSet	DistributionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	SimpleDistributionModel.performPredictionOld(ExampleSet exampleSet, Attribute predictedLabel) Perform predictions based on the distribution properties.

Methods in com.rapidminer.operator.learner.bayes with parameters of type ExampleSet

protected Jama.Matrix[]	RegularizedDiscriminantAnalysis.getInverseCovarianceMatrices(ExampleSet exampleSet, java.lang.String[] labels)
protected Jama.Matrix[]	QuadraticDiscriminantAnalysis.getInverseCovarianceMatrices(ExampleSet exampleSet, java.lang.String[] labels)
protected Jama.Matrix[]	LinearDiscriminantAnalysis.getInverseCovarianceMatrices(ExampleSet exampleSet, java.lang.String[] labels)
protected Jama.Matrix[]	LinearDiscriminantAnalysis.getMeanVectors(ExampleSet exampleSet, int numberOfAttributes, java.lang.String[] labels)
protected DiscriminantModel	RegularizedDiscriminantAnalysis.getModel(ExampleSet exampleSet, java.lang.String[] labels, Jama.Matrix[] meanVectors, Jama.Matrix[] inverseCovariances, double[] aprioriProbabilities)
protected DiscriminantModel	QuadraticDiscriminantAnalysis.getModel(ExampleSet exampleSet, java.lang.String[] labels, Jama.Matrix[] meanVectors, Jama.Matrix[] inverseCovariances, double[] aprioriProbabilities)
protected DiscriminantModel	LinearDiscriminantAnalysis.getModel(ExampleSet exampleSet, java.lang.String[] labels, Jama.Matrix[] meanVectors, Jama.Matrix[] inverseCovariances, double[] aprioriProbabilities)
Model	NaiveBayes.learn(ExampleSet exampleSet)
Model	LinearDiscriminantAnalysis.learn(ExampleSet exampleSet)
Model	KernelNaiveBayes.learn(ExampleSet exampleSet)
ExampleSet	SimpleDistributionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	KernelDistributionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Perform predictions based on the distribution properties.
abstract ExampleSet	DistributionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	SimpleDistributionModel.performPredictionOld(ExampleSet exampleSet, Attribute predictedLabel) Perform predictions based on the distribution properties.
void	SimpleDistributionModel.updateModel(ExampleSet exampleSet) Updates the model by counting the occurances of classes and attribute values in combination with the class values.
void	KernelDistributionModel.updateModel(ExampleSet exampleSet) Updates the model by counting the occurances of classes and attribute values in combination with the class values.

Constructors in com.rapidminer.operator.learner.bayes with parameters of type ExampleSet

DiscriminantModel(ExampleSet exampleSet, java.lang.String[] labels, Jama.Matrix[] meanVectors, Jama.Matrix[] inverseCovariances, double[] aprioriProbabilities, double alpha)

DistributionModel(ExampleSet exampleSet)

KernelDistributionModel(ExampleSet exampleSet, boolean laplaceCorrectionEnabled, int estimationMode, int bandwidthSelectionMode, double bandwidth, int numberOfKernels, int gridSize)

SimpleDistributionModel(ExampleSet exampleSet)

SimpleDistributionModel(ExampleSet exampleSet, boolean laplaceCorrectionEnabled)

Uses of ExampleSet in com.rapidminer.operator.learner.functions

Methods in com.rapidminer.operator.learner.functions that return ExampleSet

ExampleSet	VectorRegressionModel.apply(ExampleSet exampleSet)
ExampleSet	LinearRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	FastMarginModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)

Methods in com.rapidminer.operator.learner.functions with parameters of type ExampleSet

ExampleSet	VectorRegressionModel.apply(ExampleSet exampleSet)
Model	VectorLinearRegression.learn(ExampleSet exampleSet)
Model	PolynomialRegression.learn(ExampleSet exampleSet)
Model	Perceptron.learn(ExampleSet exampleSet)
Model	LogisticRegression.learn(ExampleSet exampleSet)
Model	LinearRegression.learn(ExampleSet exampleSet)
Model	FastLargeMargin.learn(ExampleSet exampleSet) Learns a new SVM model with the LibSVM package.
ExampleSet	LinearRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	FastMarginModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)

Constructors in com.rapidminer.operator.learner.functions with parameters of type ExampleSet

FastMarginModel(ExampleSet headerSet, liblinear.Model linearModel, boolean useBias)

HyperplaneModel(ExampleSet exampleSet)

HyperplaneModel(ExampleSet exampleSet, java.lang.String classNegative, java.lang.String classPositive)

HyperplaneModel(ExampleSet exampleSet, java.lang.String classNegative, java.lang.String classPositive, Kernel kernel)

LinearRegressionModel(ExampleSet exampleSet, boolean[] selectedAttributes, double[] coefficients, boolean useIntercept, java.lang.String firstClassName, java.lang.String secondClassName)

LogisticRegressionModel(ExampleSet exampleSet, double[] beta, double[] variance, boolean interceptAdded)

LogisticRegressionOptimization(ExampleSet exampleSet, boolean addIntercept, int initType, int maxIterations, int generationsWithoutImprovement, int popSize, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showConvergencePlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary optimization.

PolynomialRegressionModel(ExampleSet exampleSet, double[][] coefficients, double[][] degrees, double offset)

VectorRegressionModel(ExampleSet exampleSet, java.lang.String[] labelNames, Jama.Matrix coefficients, boolean useIntercept)

Uses of ExampleSet in com.rapidminer.operator.learner.functions.kernel

Methods in com.rapidminer.operator.learner.functions.kernel that return ExampleSet

ExampleSet	RVMModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	LibSVMModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	KernelLogisticRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predLabel) Applies the model to each example of the example set.
ExampleSet	GPModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	AbstractMySVMModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute)

Methods in com.rapidminer.operator.learner.functions.kernel with parameters of type ExampleSet

SVMInterface	MyKLRLearner.createSVM(Attribute label, Kernel kernel, SVMExamples sVMExamples, ExampleSet rapidMinerExamples)
SVMInterface	JMySVMLearner.createSVM(Attribute label, Kernel kernel, SVMExamples sVMExamples, ExampleSet rapidMinerExamples)
abstract SVMInterface	AbstractMySVMLearner.createSVM(Attribute label, Kernel kernel, SVMExamples svmExamples, ExampleSet rapidMinerExamples) Creates a new SVM according to the given label.
AbstractMySVMModel	MyKLRLearner.createSVMModel(ExampleSet exampleSet, SVMExamples sVMExamples, Kernel kernel, int kernelType)
AbstractMySVMModel	JMySVMLearner.createSVMModel(ExampleSet exampleSet, SVMExamples sVMExamples, Kernel kernel, int kernelType)
abstract AbstractMySVMModel	AbstractMySVMLearner.createSVMModel(ExampleSet exampleSet, SVMExamples svmExamples, Kernel kernel, int kernelType) Creates a new SVM model from the given data.
AttributeWeights	AbstractMySVMLearner.getWeights(ExampleSet exampleSet) Returns the weights for all features.
Model	RVMLearner.learn(ExampleSet exampleSet)
Model	LibSVMLearner.learn(ExampleSet exampleSet) Learns a new SVM model with the LibSVM package.
Model	KernelLogisticRegression.learn(ExampleSet exampleSet)
Model	GPLearner.learn(ExampleSet exampleSet)
Model	AbstractMySVMLearner.learn(ExampleSet exampleSet)
ExampleSet	RVMModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	LibSVMModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	KernelLogisticRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predLabel) Applies the model to each example of the example set.
ExampleSet	GPModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	AbstractMySVMModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute)

Constructors in com.rapidminer.operator.learner.functions.kernel with parameters of type ExampleSet

AbstractMySVMModel(ExampleSet exampleSet, SVMExamples model, Kernel kernel, int kernelType)

GPModel(ExampleSet exampleSet, Model model)

JMySVMModel(ExampleSet exampleSet, SVMExamples model, Kernel kernel, int kernelType)

KernelLogisticRegressionModel(ExampleSet exampleSet, java.util.List<SupportVector> supportVectors, Kernel kernel, double bias)
Creates a classification model.

KernelLogisticRegressionOptimization(ExampleSet exampleSet, Kernel kernel, double c, int initType, int maxIterations, int generationsWithoutImprovement, int popSize, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showConvergencePlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

KernelModel(ExampleSet exampleSet)

LibSVMModel(ExampleSet exampleSet, libsvm.svm_model model, int numberOfAttributes, boolean confidenceForMultiClass)

MyKLRModel(ExampleSet exampleSet, SVMExamples model, Kernel kernel, int kernelType)

RVMModel(ExampleSet exampleSet, Model model)

Uses of ExampleSet in com.rapidminer.operator.learner.functions.kernel.evosvm

Methods in com.rapidminer.operator.learner.functions.kernel.evosvm that return ExampleSet

ExampleSet

EvoSVMModel.performPrediction(ExampleSet exampleSet, Attribute predLabel) Applies the model to each example of the example set.

Methods in com.rapidminer.operator.learner.functions.kernel.evosvm with parameters of type ExampleSet

static double[]	EvoSVM.determineMax(double _c, Kernel kernel, ExampleSet exampleSet, int selectionType, int arraySize)
Model	PSOSVM.learn(ExampleSet exampleSet) Learns and returns a model.
Model	EvoSVM.learn(ExampleSet exampleSet) Learns and returns a model.
ExampleSet	EvoSVMModel.performPrediction(ExampleSet exampleSet, Attribute predLabel) Applies the model to each example of the example set.

Constructors in com.rapidminer.operator.learner.functions.kernel.evosvm with parameters of type ExampleSet

ClassificationEvoOptimization(ExampleSet exampleSet, Kernel kernel, double c, int initType, int maxIterations, int generationsWithoutImprovement, int popSize, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showConvergencePlot, boolean showPopulationPlot, ExampleSet holdOutSet, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

EvoSVMModel(ExampleSet exampleSet, java.util.List<SupportVector> supportVectors, Kernel kernel, double bias)
Creates a classification model.

PSOSVMOptimization(ExampleSet exampleSet, Kernel kernel, double c, int maxIterations, int generationsWithoutImprovement, int popSize, double inertiaWeight, double localWeight, double globalWeight, boolean dynamicInertiaWeight, boolean showPlot, RandomGenerator random)
Creates a new evolutionary SVM optimization.

RegressionEvoOptimization(ExampleSet exampleSet, Kernel kernel, double c, double epsilon, int initType, int maxIterations, int generationsWithoutImprovement, int popSize, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showConvergencePlot, boolean showPopulationPlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

Uses of ExampleSet in com.rapidminer.operator.learner.functions.kernel.hyperhyper

Methods in com.rapidminer.operator.learner.functions.kernel.hyperhyper that return ExampleSet

ExampleSet

HyperModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)

Methods in com.rapidminer.operator.learner.functions.kernel.hyperhyper with parameters of type ExampleSet

Model	HyperHyper.learn(ExampleSet exampleSet)
ExampleSet	HyperModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)

Constructors in com.rapidminer.operator.learner.functions.kernel.hyperhyper with parameters of type ExampleSet

HyperModel(ExampleSet trainingExampleSet, double bias, double[] w, double[] x1, double[] x2)

Uses of ExampleSet in com.rapidminer.operator.learner.functions.kernel.jmysvm.examples

Constructors in com.rapidminer.operator.learner.functions.kernel.jmysvm.examples with parameters of type ExampleSet

SVMExamples(ExampleSet exampleSet, Attribute labelAttribute, boolean scale)

SVMExamples(ExampleSet exampleSet, Attribute labelAttribute, java.util.Map<java.lang.Integer,SVMExamples.MeanVariance> meanVariances)
Creates a fresh example set of the given size from the RapidMiner example reader.

Uses of ExampleSet in com.rapidminer.operator.learner.functions.kernel.jmysvm.svm

Constructors in com.rapidminer.operator.learner.functions.kernel.jmysvm.svm with parameters of type ExampleSet

SVM(Operator paramOperator, Kernel new_kernel, SVMExamples new_examples, ExampleSet rapidMinerExamples, RandomGenerator randomGenerator)
class constructor.

SVMpattern(Operator paramOperator, Kernel kernel, SVMExamples sVMExamples, ExampleSet rapidMinerExamples, RandomGenerator randomGenerator)

SVMregression(Operator paramOperator, Kernel kernel, SVMExamples sVMExamples, ExampleSet rapidMinerExamples, RandomGenerator randomGenerator)

Uses of ExampleSet in com.rapidminer.operator.learner.functions.neuralnet

Methods in com.rapidminer.operator.learner.functions.neuralnet that return ExampleSet

ExampleSet	SimpleNeuralNetModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	NeuralNetModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	ImprovedNeuralNetModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)

Methods in com.rapidminer.operator.learner.functions.neuralnet with parameters of type ExampleSet

Model	SimpleNeuralNetLearner.learn(ExampleSet exampleSet)
Model	NeuralNetLearner.learn(ExampleSet exampleSet) Learns and returns a model.
Model	ImprovedNeuralNetLearner.learn(ExampleSet exampleSet)
ExampleSet	SimpleNeuralNetModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	NeuralNetModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	ImprovedNeuralNetModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
void	NeuralNetLearner.train(ExampleSet exampleSet)
void	ImprovedNeuralNetModel.train(ExampleSet exampleSet, java.util.List<java.lang.String[]> hiddenLayers, int maxCycles, double maxError, double learningRate, double momentum, boolean decay, boolean shuffle, boolean normalize, RandomGenerator randomGenerator)

Constructors in com.rapidminer.operator.learner.functions.neuralnet with parameters of type ExampleSet

ImprovedNeuralNetModel(ExampleSet trainingExampleSet)

NeuralNetModel(ExampleSet exampleSet, org.joone.net.NeuralNet neuralNet, int numberOfInputAttributes, double minLabel, double maxLabel)

SimpleNeuralNetModel(ExampleSet trainingExampleSet, org.encog.neural.networks.BasicNetwork network, double[] attributeMin, double[] attributeMax, double labelMin, double labelMax)

Uses of ExampleSet in com.rapidminer.operator.learner.igss

Methods in com.rapidminer.operator.learner.igss with parameters of type ExampleSet

static double	IGSSResult.calculateDiversity(ExampleSet exampleSet, java.util.LinkedList theResults) Calculates the diversity in the predictions of the results for the given example set.
static double[]	IGSSResult.getPriors(ExampleSet exampleSet) Returns the default probability of the given example set.
java.util.LinkedList<Result>	IteratingGSS.gss(ExampleSet exampleSet, java.util.LinkedList<Hypothesis> hypothesisList, double delta, double epsilon) Returns the n best hypothesis with maximum error epsilon with confidence 1-delta.
boolean	IteratingGSS.isUseful(Result current, java.util.LinkedList<Result> otherResults, int criterion, ExampleSet exampleSet, int min_model_number) Test if the model is useful according to the given criterion.
Model	IteratingGSS.learn(ExampleSet exampleSet)
ContingencyMatrix	IteratingGSS.reweight(ExampleSet exampleSet, Model model, boolean normalize) Reweights the examples according to knowledge based sampling.

Constructors in com.rapidminer.operator.learner.igss with parameters of type ExampleSet

IGSSResult(ExampleSet eSet)

Uses of ExampleSet in com.rapidminer.operator.learner.igss.hypothesis

Methods in com.rapidminer.operator.learner.igss.hypothesis that return ExampleSet

ExampleSet

GSSModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all examples and applies the model to them.

Methods in com.rapidminer.operator.learner.igss.hypothesis with parameters of type ExampleSet

ExampleSet

GSSModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all examples and applies the model to them.

Constructors in com.rapidminer.operator.learner.igss.hypothesis with parameters of type ExampleSet

GSSModel(ExampleSet exampleSet, Hypothesis hypothesis, double[] confidences)
Creates a new GSSModel.

Uses of ExampleSet in com.rapidminer.operator.learner.lazy

Methods in com.rapidminer.operator.learner.lazy that return ExampleSet

ExampleSet	KNNRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	KNNClassificationModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	DefaultModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) Iterates over all examples and applies the model to them.
ExampleSet	AttributeBasedVotingModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute)

Methods in com.rapidminer.operator.learner.lazy with parameters of type ExampleSet

Model	KNNLearner.learn(ExampleSet exampleSet)
Model	DefaultLearner.learn(ExampleSet exampleSet)
Model	AttributeBasedVotingLearner.learn(ExampleSet exampleSet)
ExampleSet	KNNRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	KNNClassificationModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	DefaultModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) Iterates over all examples and applies the model to them.
ExampleSet	AttributeBasedVotingModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute)

Constructors in com.rapidminer.operator.learner.lazy with parameters of type ExampleSet

AttributeBasedVotingModel(ExampleSet exampleSet, double majorityVote)

DefaultModel(ExampleSet exampleSet, double value)
Can be used to create a default model for regression tasks.

DefaultModel(ExampleSet exampleSet, double value, double[] confidences)
Can be used to create a default model for classification tasks (confidence values should not be null in this case).

KNNClassificationModel(ExampleSet trainingSet, GeometricDataCollection<java.lang.Integer> samples, int k, boolean weightByDistance)

KNNRegressionModel(ExampleSet trainingSet, GeometricDataCollection<java.lang.Double> samples, int k, boolean weightByDistance)

Uses of ExampleSet in com.rapidminer.operator.learner.meta

Methods in com.rapidminer.operator.learner.meta that return ExampleSet

ExampleSet	TransformedRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) Iterates over all examples and applies this model.
ExampleSet	ThresholdModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	StackingModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	SDEnsemble.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) Iterates over all models and returns the class with maximum likelihood.
ExampleSet	RelativeRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	MultiModelByRegression.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) Iterates over all classes of the label and applies one model for each class.
ExampleSet	MultiModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all classes of the label and applies one model for each class.
ExampleSet	MetaCostModel.performPrediction(ExampleSet originalExampleSet, Attribute predictedLabel)
ExampleSet	Binary2MultiClassModel.performPrediction(ExampleSet originalExampleSet, Attribute predictedLabel) Chooses the right evaluation procedure depending on classificationType.
ExampleSet	BayBoostModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all models and returns the class with maximum likelihood.
ExampleSet	BaggingModel.performPrediction(ExampleSet origExampleSet, Attribute predictedLabel) Iterates over all models and averages confidences.
ExampleSet	AdditiveRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	AdaBoostModel.performPrediction(ExampleSet origExampleSet, Attribute predictedLabel) Iterates over all models and returns the class with maximum likelihood.

Methods in com.rapidminer.operator.learner.meta with parameters of type ExampleSet

protected Model	AbstractMetaLearner.applyInnerLearner(ExampleSet exampleSet) This is a convenience method to apply the inner operators and return the model which must be output of the last operator.
protected Attribute	SDEnsemble.createPredictedLabel(ExampleSet exampleSet) Creates a predicted label with the given name.
AttributeWeights	AbstractMetaLearner.getWeights(ExampleSet exampleSet) Returns the calculated weight vectors.
Model	Tree2RuleConverter.learn(ExampleSet exampleSet)
Model	RelativeRegression.learn(ExampleSet exampleSet)
Model	MetaCost.learn(ExampleSet inputSet)
Model	CostBasedThresholdLearner.learn(ExampleSet exampleSet)
Model	ClassificationByRegression.learn(ExampleSet inputSet)
Model	Binary2MultiClassLearner.learn(ExampleSet inputSet)
Model	BayesianBoosting.learn(ExampleSet exampleSet) Constructs a Model repeatedly running a weak learner, reweighting the training example set accordingly, and combining the hypothesis using the available weighted performance values.
Model	BayBoostStream.learn(ExampleSet exampleSet) Constructs a Model repeatedly running a weak learner, reweighting the training example set accordingly, and combining the hypothesis using the available weighted performance values.
Model	Bagging.learn(ExampleSet exampleSet) Constructs a Model repeatedly running a base learner on subsamples.
Model	AdditiveRegression.learn(ExampleSet exampleSet)
Model	AdaBoost.learn(ExampleSet exampleSet) Constructs a Model repeatedly running a weak learner, reweighting the training example set accordingly, and combining the hypothesis using the available weighted performance values.
Model	AbstractStacking.learn(ExampleSet exampleSet)
ExampleSet	TransformedRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) Iterates over all examples and applies this model.
ExampleSet	ThresholdModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	StackingModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	SDEnsemble.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) Iterates over all models and returns the class with maximum likelihood.
ExampleSet	RelativeRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	MultiModelByRegression.performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) Iterates over all classes of the label and applies one model for each class.
ExampleSet	MultiModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all classes of the label and applies one model for each class.
ExampleSet	MetaCostModel.performPrediction(ExampleSet originalExampleSet, Attribute predictedLabel)
ExampleSet	Binary2MultiClassModel.performPrediction(ExampleSet originalExampleSet, Attribute predictedLabel) Chooses the right evaluation procedure depending on classificationType.
ExampleSet	BayBoostModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all models and returns the class with maximum likelihood.
ExampleSet	BaggingModel.performPrediction(ExampleSet origExampleSet, Attribute predictedLabel) Iterates over all models and averages confidences.
ExampleSet	AdditiveRegressionModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
ExampleSet	AdaBoostModel.performPrediction(ExampleSet origExampleSet, Attribute predictedLabel) Iterates over all models and returns the class with maximum likelihood.
protected double[]	BayesianBoosting.prepareWeights(ExampleSet exampleSet) Creates a weight attribute if not yet done.
protected void	BayBoostStream.prepareWeights(ExampleSet exampleSet)
protected double	AdaBoost.prepareWeights(ExampleSet exampleSet) Creates a weight attribute if not yet done.
double	AdaBoostPerformanceMeasures.reweightExamples(ExampleSet exampleSet) This method reweights the example set with respect to the performance measures.
static double	WeightedPerformanceMeasures.reweightExamples(ExampleSet exampleSet, ContingencyMatrix cm, boolean allowMarginalSkews) Helper method of the BayesianBoosting operator This method reweights the example set with respect to the WeightedPerformanceMeasures object.
boolean	SDReweightMeasures.reweightExamples(ExampleSet exampleSet, int posIndex, int coveredSubset) Overwrites method from super class.
protected double	BayesianBoosting.reweightExamples(WeightedPerformanceMeasures wp, ExampleSet exampleSet) This method reweights the example set with respect to the WeightedPerformanceMeasures object.
protected Model	BayesianBoosting.trainBaseModel(ExampleSet exampleSet) Runs the "embedded" learner on the example set and retuns a model.

Constructors in com.rapidminer.operator.learner.meta with parameters of type ExampleSet

AdaBoostModel(ExampleSet exampleSet, java.util.List<Model> models, java.util.List<java.lang.Double> weights)

AdaBoostPerformanceMeasures(ExampleSet exampleSet)

AdditiveRegressionModel(ExampleSet exampleSet, Model defaultModel, Model[] residualModels, double shrinkage)

BaggingModel(ExampleSet exampleSet, java.util.List<Model> models)

BayBoostModel(ExampleSet exampleSet, java.util.List<BayBoostBaseModelInfo> modelInfos, double[] priors)

Binary2MultiClassModel(ExampleSet exampleSet, Model[] models, int classificationType, java.util.LinkedList<java.lang.String> modelNames)

Binary2MultiClassModel(ExampleSet exampleSet, Model[] models, int classificationType, java.lang.String[][] codeMatrix)

MetaCostModel(ExampleSet exampleSet, Model[] models, double[][] costMatrix)

MultiModel(ExampleSet exampleSet, Model[] models)

MultiModelByRegression(ExampleSet exampleSet, Model[] models)

RelativeRegressionModel(ExampleSet trainingExampleSet, Model baseModel, java.lang.String relativeAttributeName)

SDEnsemble(ExampleSet exampleSet, java.util.List modelInfo, double[] priors, short combinationMethod)

SDReweightMeasures(ExampleSet e)

SimpleVoteModel(ExampleSet exampleSet, java.util.List<SimplePredictionModel> baseModels)

StackingModel(ExampleSet exampleSet, java.lang.String modelName, java.util.List<Model> baseModels, Model stackingModel, boolean useAllAttributes)

ThresholdModel(ExampleSet exampleSet, Model innerModel, double[] thresholds)

TransformedRegressionModel(ExampleSet exampleSet, int method, double[] rank, Model model, boolean zscale, double mean, double stddev, boolean interpolate)

WeightedPerformanceMeasures(ExampleSet exampleSet)
Constructor.

Uses of ExampleSet in com.rapidminer.operator.learner.rules

Methods in com.rapidminer.operator.learner.rules that return ExampleSet

ExampleSet	Rule.getCovered(ExampleSet exampleSet)
ExampleSet	Rule.removeCovered(ExampleSet exampleSet)

Methods in com.rapidminer.operator.learner.rules with parameters of type ExampleSet

java.util.Collection<ConjunctiveRuleModel>	ConjunctiveRuleModel.getAllRefinedRules(ExampleSet exampleSet) A refinement method that - when applied sytematically during learning - generates all rules for nominal attributes and a boolean target exactly once.
double[]	InfoGainCriterion.getBenefit(ExampleSet coveredSet, ExampleSet uncoveredSet, java.lang.String labelName)
double[]	Criterion.getBenefit(ExampleSet coveredSet, ExampleSet uncoveredSet, java.lang.String labelName)
double[]	AccuracyCriterion.getBenefit(ExampleSet coveredSet, ExampleSet uncoveredSet, java.lang.String labelName)
Split	NumericalSplitter.getBestSplit(ExampleSet inputSet, Attribute attribute, java.lang.String labelName)
SplitCondition	TermDetermination.getBestTerm(ExampleSet exampleSet, java.lang.String labelName)
protected double[]	BestRuleInduction.getCounts(ConjunctiveRuleModel rule, ExampleSet exampleSet)
ExampleSet	Rule.getCovered(ExampleSet exampleSet)
protected int	ConjunctiveRuleModel.getFirstUnusedAttribute(ExampleSet exampleSet, Attribute[] allAttributes) Helper method of getAllRefinedRules.
boolean	Rule.isPure(ExampleSet exampleSet, double pureness)
Model	SingleRuleLearner.learn(ExampleSet inputSet)
Model	SimpleRuleLearner.learn(ExampleSet exampleSet)
Model	RuleLearner.learn(ExampleSet exampleSet)
Model	BestRuleInduction.learn(ExampleSet exampleSet)
void	Criterion.reinitOnlineCounting(ExampleSet exampleSet)
void	AbstractCriterion.reinitOnlineCounting(ExampleSet exampleSet)
ExampleSet	Rule.removeCovered(ExampleSet exampleSet)

Constructors in com.rapidminer.operator.learner.rules with parameters of type ExampleSet

ConjunctiveRuleModel(ExampleSet exampleSet, int predictedLabel)
Constructor to create an empty rule that makes a default prediction

ConjunctiveRuleModel(ExampleSet exampleSet, int predictedLabel, int positives, int negatives)
Constructor to create an empty rule that makes a default prediction

RuleModel(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.learner.subgroups

Methods in com.rapidminer.operator.learner.subgroups with parameters of type ExampleSet

Model

SubgroupDiscovery.learn(ExampleSet exampleSet)

Constructors in com.rapidminer.operator.learner.subgroups with parameters of type ExampleSet

RuleSet(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.learner.tree

Methods in com.rapidminer.operator.learner.tree that return ExampleSet

ExampleSet	Tree.getTrainingSet()
ExampleSet	SplitPreprocessing.preprocess(ExampleSet exampleSet) Will be invoked before each new split.
ExampleSet	RandomSubsetPreprocessing.preprocess(ExampleSet inputSet)

Methods in com.rapidminer.operator.learner.tree with parameters of type ExampleSet

protected void	TreeBuilder.buildTree(Tree current, ExampleSet exampleSet, int depth)
protected java.util.Vector<Benefit>	TreeBuilder.calculateAllBenefits(ExampleSet trainingSet)
Benefit	TreeBuilder.calculateBenefit(ExampleSet trainingSet, Attribute attribute) This method calculates the benefit of the given attribute.
protected Benefit	CHAIDLearner.calculateBenefit(ExampleSet trainingSet, Attribute attribute) This method calculates the benefit of the given attribute.
void	LeafCreator.changeTreeToLeaf(Tree node, ExampleSet exampleSet)
void	DecisionTreeLeafCreator.changeTreeToLeaf(Tree node, ExampleSet exampleSet)
protected double[]	MultiCriterionDecisionStumps.computePriors(ExampleSet exampleSet)
double	NumericalSplitter.getBestSplit(ExampleSet inputSet, Attribute attribute)
double[]	FrequencyCalculator.getLabelWeights(ExampleSet exampleSet) Returns an array of size of the number of different label values.
double	InfoGainCriterion.getNominalBenefit(ExampleSet exampleSet, Attribute attribute)
double	GiniIndexCriterion.getNominalBenefit(ExampleSet exampleSet, Attribute attribute)
double	GainRatioCriterion.getNominalBenefit(ExampleSet exampleSet, Attribute attribute)
double	Criterion.getNominalBenefit(ExampleSet exampleSet, Attribute attribute)
double	AccuracyCriterion.getNominalBenefit(ExampleSet exampleSet, Attribute attribute)
double[][]	FrequencyCalculator.getNominalWeightCounts(ExampleSet exampleSet, Attribute attribute)
double	InfoGainCriterion.getNumericalBenefit(ExampleSet exampleSet, Attribute attribute, double splitValue)
double	GiniIndexCriterion.getNumericalBenefit(ExampleSet exampleSet, Attribute attribute, double splitValue)
double	GainRatioCriterion.getNumericalBenefit(ExampleSet exampleSet, Attribute attribute, double splitValue)
double	Criterion.getNumericalBenefit(ExampleSet exampleSet, Attribute attribute, double splitValue)
double	AccuracyCriterion.getNumericalBenefit(ExampleSet exampleSet, Attribute attribute, double splitValue)
double[][]	FrequencyCalculator.getNumericalWeightCounts(ExampleSet exampleSet, Attribute attribute, double splitValue)
java.util.List<Terminator>	RelevanceTreeLearner.getTerminationCriteria(ExampleSet exampleSet)
java.util.List<Terminator>	ID3NumericalLearner.getTerminationCriteria(ExampleSet exampleSet)
java.util.List<Terminator>	ID3Learner.getTerminationCriteria(ExampleSet exampleSet)
java.util.List<Terminator>	DecisionTreeLearner.getTerminationCriteria(ExampleSet exampleSet)
java.util.List<Terminator>	DecisionStumpLearner.getTerminationCriteria(ExampleSet exampleSet)
abstract java.util.List<Terminator>	AbstractTreeLearner.getTerminationCriteria(ExampleSet exampleSet) Returns all termination criteria.
protected TreeBuilder	ID3NumericalLearner.getTreeBuilder(ExampleSet exampleSet)
protected TreeBuilder	ID3Learner.getTreeBuilder(ExampleSet exampleSet)
protected TreeBuilder	DecisionTreeLearner.getTreeBuilder(ExampleSet exampleSet)
protected TreeBuilder	DecisionStumpLearner.getTreeBuilder(ExampleSet exampleSet)
protected abstract TreeBuilder	AbstractTreeLearner.getTreeBuilder(ExampleSet exampleSet)
Model	RelevanceTreeLearner.learn(ExampleSet exampleSet)
Model	RandomForestLearner.learn(ExampleSet exampleSet)
Model	MultiwayDecisionTree.learn(ExampleSet exampleSet)
Model	MultiCriterionDecisionStumps.learn(ExampleSet exampleSet)
Model	AbstractTreeLearner.learn(ExampleSet eSet)
Tree	TreeBuilder.learnTree(ExampleSet exampleSet)
ExampleSet	SplitPreprocessing.preprocess(ExampleSet exampleSet) Will be invoked before each new split.
ExampleSet	RandomSubsetPreprocessing.preprocess(ExampleSet inputSet)
double	PessimisticPruner.prunedLabel(ExampleSet exampleSet)
protected boolean	TreeBuilder.shouldStop(ExampleSet exampleSet, int depth)
boolean	Terminator.shouldStop(ExampleSet exampleSet, int depth)
boolean	SingleLabelTermination.shouldStop(ExampleSet exampleSet, int depth)
boolean	NoAttributeLeftTermination.shouldStop(ExampleSet exampleSet, int depth)
boolean	MinSizeTermination.shouldStop(ExampleSet exampleSet, int depth)
boolean	MaxDepthTermination.shouldStop(ExampleSet exampleSet, int depth)
boolean	EmptyTermination.shouldStop(ExampleSet exampleSet, int depth)
void	Criterion.startIncrementalCalculation(ExampleSet exampleSet)
void	AbstractCriterion.startIncrementalCalculation(ExampleSet exampleSet)

Constructors in com.rapidminer.operator.learner.tree with parameters of type ExampleSet

MultiCriterionDecisionStumps.DecisionStumpModel(Attribute attribute, double testValue, ExampleSet exampleSet, boolean prediction, boolean includeNaNs)

Tree(ExampleSet trainingSet)

TreeModel(ExampleSet exampleSet, Tree root)

Uses of ExampleSet in com.rapidminer.operator.learner.weka

Methods in com.rapidminer.operator.learner.weka that return ExampleSet

ExampleSet

WekaClassifier.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)

Methods in com.rapidminer.operator.learner.weka with parameters of type ExampleSet

AttributeWeights	GenericWekaMetaLearner.getWeights(ExampleSet exampleSet) Returns the calculated weight vectors.
AttributeWeights	GenericWekaEnsembleLearner.getWeights(ExampleSet exampleSet) Returns the calculated weight vectors.
Model	GenericWekaMetaLearner.learn(ExampleSet exampleSet)
Model	GenericWekaLearner.learn(ExampleSet exampleSet)
Model	GenericWekaEnsembleLearner.learn(ExampleSet exampleSet)
ExampleSet	WekaClassifier.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)
void	WekaClassifier.updateModel(ExampleSet updateExampleSet) Updates the model if the classifier is updatable.

Constructors in com.rapidminer.operator.learner.weka with parameters of type ExampleSet

WekaClassifier(ExampleSet exampleSet, java.lang.String name, weka.classifiers.Classifier classifier)

Uses of ExampleSet in com.rapidminer.operator.meta

Methods in com.rapidminer.operator.meta with parameters of type ExampleSet

protected SplittedExampleSet	RatioSplitChain.createSplittedExampleSet(ExampleSet inputSet)
protected abstract SplittedExampleSet	AbstractSplitChain.createSplittedExampleSet(ExampleSet exampleSet) Creates the splitted example for this operator.
protected SplittedExampleSet	AbsoluteSplitChain.createSplittedExampleSet(ExampleSet inputSet)

Uses of ExampleSet in com.rapidminer.operator.performance

Methods in com.rapidminer.operator.performance with parameters of type ExampleSet

protected void	UserBasedPerformanceEvaluator.checkCompatibility(ExampleSet exampleSet) Does nothing.
protected void	SimplePerformanceEvaluator.checkCompatibility(ExampleSet exampleSet) Does nothing.
protected void	RegressionPerformanceEvaluator.checkCompatibility(ExampleSet exampleSet)
protected void	PolynominalClassificationPerformanceEvaluator.checkCompatibility(ExampleSet exampleSet)
protected void	PerformanceEvaluator.checkCompatibility(ExampleSet exampleSet) Does nothing.
protected void	ForecastingPerformanceEvaluator.checkCompatibility(ExampleSet exampleSet)
protected void	BinominalClassificationPerformanceEvaluator.checkCompatibility(ExampleSet exampleSet)
protected abstract void	AbstractPerformanceEvaluator.checkCompatibility(ExampleSet exampleSet) Performs a check if this operator can be used for this type of exampel set at all.
static void	AbstractPerformanceEvaluator.evaluate(AbstractPerformanceEvaluator evaluator, ExampleSet testSet, PerformanceVector performanceCriteria, java.util.List<PerformanceCriterion> givenCriteria, boolean skipUndefinedLabels, boolean useExampleWeights) Static version of AbstractPerformanceEvaluator.evaluate(ExampleSet,PerformanceVector).
PerformanceVector	Data2Performance.evaluate(ExampleSet exampleSet)
PerformanceVector	AttributeCounter.evaluate(ExampleSet exampleSet)
abstract PerformanceVector	AbstractExampleSetEvaluator.evaluate(ExampleSet exampleSet) Implements the evaluation.
protected PerformanceVector	AbstractPerformanceEvaluator.evaluate(ExampleSet testSet, PerformanceVector inputPerformance) Evaluates the given test set.
protected void	SimplePerformanceEvaluator.init(ExampleSet testSet) Uses this example set in order to create appropriate criteria.
protected void	AbstractPerformanceEvaluator.init(ExampleSet exampleSet) This method will be invoked before the actual calculation is started.
static double[]	RankStatistics.rank(ExampleSet eSet, Attribute att, Attribute mappingAtt) Calculates ranks for an attribute.
static double[]	RankStatistics.rank(ExampleSet eSet, Attribute att, Attribute mappingAtt, double fuzz) Calculates ranks for an attribute.
static double	RankStatistics.rho(ExampleSet eSet, Attribute a, Attribute b) Calculates the Spearman rank correlation between two attributes.
static double	RankStatistics.rho(ExampleSet eSet, Attribute a, Attribute b, double f) Calculates the Spearman rank correlation between two attributes.
void	MeasuredPerformance.startCounting(ExampleSet set) Deprecated. Please use the other start counting method directly
void	WeightedMultiClassPerformance.startCounting(ExampleSet eSet, boolean useExampleWeights) Initializes the criterion and sets the label.
void	SoftMarginLoss.startCounting(ExampleSet exampleSet, boolean useExampleWeights) Calculates the margin.
void	SimpleCriterion.startCounting(ExampleSet eset, boolean useExampleWeights)
void	RootRelativeSquaredError.startCounting(ExampleSet exampleSet, boolean useExampleWeights)
void	RankCorrelation.startCounting(ExampleSet eSet, boolean useExampleWeights) Computes whichever of rho and tau was requested.
void	PredictionTrendAccuracy.startCounting(ExampleSet eSet, boolean useExampleWeights)
void	PredictionAverage.startCounting(ExampleSet set, boolean useExampleWeights)
void	NormalizedAbsoluteError.startCounting(ExampleSet exampleSet, boolean useExampleWeights)
void	MultiClassificationPerformance.startCounting(ExampleSet eSet, boolean useExampleWeights) Initializes the criterion and sets the label.
void	MeasuredPerformance.startCounting(ExampleSet set, boolean useExampleWeights) Initializes the criterion.
void	MDLCriterion.startCounting(ExampleSet eSet, boolean useExampleWeights)
void	Margin.startCounting(ExampleSet exampleSet, boolean useExampleWeights) Calculates the margin.
void	LogisticLoss.startCounting(ExampleSet exampleSet, boolean useExampleWeights) Calculates the margin.
void	CrossEntropy.startCounting(ExampleSet exampleSet, boolean useExampleWeights) Calculates the margin.
void	CorrelationCriterion.startCounting(ExampleSet eset, boolean useExampleWeights)
void	BinaryClassificationPerformance.startCounting(ExampleSet eSet, boolean useExampleWeights)
void	AreaUnderCurve.startCounting(ExampleSet exampleSet, boolean useExampleWeights) Calculates the AUC.
static double	RankStatistics.tau_b(ExampleSet eSet, Attribute a, Attribute b) Computes Kendall's tau-b rank correlation statistic, ignoring examples containing missing values.
static double	RankStatistics.tau_b(ExampleSet eSet, Attribute a, Attribute b, double fuzz) Computes Kendall's tau-b rank correlation statistic, ignoring examples containing missing values, with approximate comparisons.

Uses of ExampleSet in com.rapidminer.operator.postprocessing

Methods in com.rapidminer.operator.postprocessing that return ExampleSet

ExampleSet	WindowExamples2OriginalData.apply(ExampleSet exampleSet)
ExampleSet	SimpleUncertainPredictionsTransformation.apply(ExampleSet exampleSet)
ExampleSet	PlattScalingModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)

Methods in com.rapidminer.operator.postprocessing with parameters of type ExampleSet

ExampleSet	WindowExamples2OriginalData.apply(ExampleSet exampleSet)
ExampleSet	SimpleUncertainPredictionsTransformation.apply(ExampleSet exampleSet)
static PlattParameters	PlattScaling.computeParameters(ExampleSet exampleSet, Attribute label) Implementation of Platt' scaling algorithm as found in [Platt, 1999].
ExampleSet	PlattScalingModel.performPrediction(ExampleSet exampleSet, Attribute predictedLabel)

Constructors in com.rapidminer.operator.postprocessing with parameters of type ExampleSet

PlattScalingModel(ExampleSet exampleSet, Model model, PlattParameters parameters)

Uses of ExampleSet in com.rapidminer.operator.preprocessing

Methods in com.rapidminer.operator.preprocessing that return ExampleSet

ExampleSet	UseRowAsAttributeNames.apply(ExampleSet exampleSet)
ExampleSet	PreprocessingModel.apply(ExampleSet exampleSet)
ExampleSet	Obfuscator.apply(ExampleSet exampleSet)
ExampleSet	NoiseOperator.apply(ExampleSet exampleSet)
ExampleSet	MaterializeDataInMemory.apply(ExampleSet exampleSet)
ExampleSet	IdTagging.apply(ExampleSet eSet)
ExampleSet	GuessValueTypes.apply(ExampleSet exampleSet)
ExampleSet	ExampleSetTranspose.apply(ExampleSet exampleSet)
ExampleSet	Deobfuscator.apply(ExampleSet exampleSet)
abstract ExampleSet	PreprocessingModel.applyOnData(ExampleSet exampleSet)

Methods in com.rapidminer.operator.preprocessing with parameters of type ExampleSet

ExampleSet	UseRowAsAttributeNames.apply(ExampleSet exampleSet)
ExampleSet	PreprocessingModel.apply(ExampleSet exampleSet)
ExampleSet	Obfuscator.apply(ExampleSet exampleSet)
ExampleSet	NoiseOperator.apply(ExampleSet exampleSet)
ExampleSet	MaterializeDataInMemory.apply(ExampleSet exampleSet)
ExampleSet	IdTagging.apply(ExampleSet eSet)
ExampleSet	GuessValueTypes.apply(ExampleSet exampleSet)
ExampleSet	ExampleSetTranspose.apply(ExampleSet exampleSet)
ExampleSet	Deobfuscator.apply(ExampleSet exampleSet)
abstract ExampleSet	PreprocessingModel.applyOnData(ExampleSet exampleSet)
abstract Model	PreprocessingOperator.createPreprocessingModel(ExampleSet exampleSet)

Constructors in com.rapidminer.operator.preprocessing with parameters of type ExampleSet

PreprocessingModel(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.preprocessing.discretization

Methods in com.rapidminer.operator.preprocessing.discretization that return ExampleSet

ExampleSet

DiscretizationModel.applyOnData(ExampleSet exampleSet)

Methods in com.rapidminer.operator.preprocessing.discretization with parameters of type ExampleSet

ExampleSet	DiscretizationModel.applyOnData(ExampleSet exampleSet)
Model	UserBasedDiscretization.createPreprocessingModel(ExampleSet exampleSet)
Model	MinMaxBinDiscretization.createPreprocessingModel(ExampleSet exampleSet)
Model	MinimalEntropyDiscretization.createPreprocessingModel(ExampleSet exampleSet)
Model	FrequencyDiscretization.createPreprocessingModel(ExampleSet exampleSet)
Model	BinDiscretization.createPreprocessingModel(ExampleSet exampleSet)
Model	AbsoluteDiscretization.createPreprocessingModel(ExampleSet exampleSet)
Attributes	DiscretizationModel.getTargetAttributes(ExampleSet parentSet)

Constructors in com.rapidminer.operator.preprocessing.discretization with parameters of type ExampleSet

DiscretizationModel(ExampleSet exampleSet)

DiscretizationModel(ExampleSet exampleSet, boolean removeUseless)

Uses of ExampleSet in com.rapidminer.operator.preprocessing.filter

Methods in com.rapidminer.operator.preprocessing.filter that return ExampleSet

ExampleSet	ValueReplenishment.apply(ExampleSet eSet) Iterates over all examples and all attributes makes callbacks to ValueReplenishment.getReplenishmentValue(int, ExampleSet, Attribute, double, String) if ValueReplenishment.replenishValue(double) returns true.
ExampleSet	TFIDFFilter.apply(ExampleSet exampleSet)
ExampleSet	String2Nominal.apply(ExampleSet exampleSet)
ExampleSet	Sorting.apply(ExampleSet exampleSet)
ExampleSet	SetData.apply(ExampleSet exampleSet)
ExampleSet	RemoveDuplicates.apply(ExampleSet exampleSet)
ExampleSet	Real2Integer.apply(ExampleSet exampleSet)
ExampleSet	PermutationOperator.apply(ExampleSet exampleSet)
ExampleSet	NumericToNominal.apply(ExampleSet exampleSet)
ExampleSet	Numerical2Real.apply(ExampleSet exampleSet)
ExampleSet	NominalNumbers2Numerical.apply(ExampleSet exampleSet)
ExampleSet	Nominal2String.apply(ExampleSet exampleSet)
ExampleSet	Nominal2Date.apply(ExampleSet exampleSet)
ExampleSet	MissingValueReplenishmentView.apply(ExampleSet exampleSet)
ExampleSet	MergeNominalValues.apply(ExampleSet exampleSet)
ExampleSet	InternalBinominalRemapping.apply(ExampleSet exampleSet)
ExampleSet	FeatureRangeRemoval.apply(ExampleSet exampleSet)
ExampleSet	FeatureNameFilter.apply(ExampleSet exampleSet)
ExampleSet	FeatureFilter.apply(ExampleSet eSet) Applies filtering of features by looping through all features and checking switchOffFeature().
ExampleSet	ExchangeAttributeRoles.apply(ExampleSet exampleSet)
ExampleSet	ExampleRangeFilter.apply(ExampleSet exampleSet)
ExampleSet	ExampleFilter.apply(ExampleSet inputSet)
ExampleSet	DateAdjust.apply(ExampleSet exampleSet)
ExampleSet	Date2Numerical.apply(ExampleSet exampleSet)
ExampleSet	Date2Nominal.apply(ExampleSet exampleSet)
ExampleSet	Construction2Names.apply(ExampleSet exampleSet)
ExampleSet	ChangeAttributeType.apply(ExampleSet exampleSet)
ExampleSet	ChangeAttributeRole.apply(ExampleSet exampleSet)
ExampleSet	ChangeAttributeNamesReplace.apply(ExampleSet exampleSet)
ExampleSet	ChangeAttributeNames2Generic.apply(ExampleSet exampleSet)
ExampleSet	ChangeAttributeName.apply(ExampleSet exampleSet)
ExampleSet	AttributeValueTrim.apply(ExampleSet exampleSet)
ExampleSet	AttributeValueSubstring.apply(ExampleSet exampleSet)
ExampleSet	AttributeValueSplit.apply(ExampleSet exampleSet)
ExampleSet	AttributeValueReplace.apply(ExampleSet exampleSet)
ExampleSet	AttributeValueMapper.apply(ExampleSet exampleSet)
ExampleSet	AttributeMerge.apply(ExampleSet exampleSet)
ExampleSet	AttributeCopy.apply(ExampleSet exampleSet)
ExampleSet	AttributeAdd.apply(ExampleSet exampleSet)
ExampleSet	AddNominalValue.apply(ExampleSet exampleSet)
ExampleSet	AbsoluteValueFilter.apply(ExampleSet exampleSet)
ExampleSet	NominalToBinominalModel.applyOnData(ExampleSet exampleSet)
ExampleSet	Dictionary.applyOnData(ExampleSet exampleSet)

Methods in com.rapidminer.operator.preprocessing.filter with parameters of type ExampleSet

ExampleSet	ValueReplenishment.apply(ExampleSet eSet) Iterates over all examples and all attributes makes callbacks to ValueReplenishment.getReplenishmentValue(int, ExampleSet, Attribute, double, String) if ValueReplenishment.replenishValue(double) returns true.
ExampleSet	TFIDFFilter.apply(ExampleSet exampleSet)
ExampleSet	String2Nominal.apply(ExampleSet exampleSet)
ExampleSet	Sorting.apply(ExampleSet exampleSet)
ExampleSet	SetData.apply(ExampleSet exampleSet)
ExampleSet	RemoveDuplicates.apply(ExampleSet exampleSet)
ExampleSet	Real2Integer.apply(ExampleSet exampleSet)
ExampleSet	PermutationOperator.apply(ExampleSet exampleSet)
ExampleSet	NumericToNominal.apply(ExampleSet exampleSet)
ExampleSet	Numerical2Real.apply(ExampleSet exampleSet)
ExampleSet	NominalNumbers2Numerical.apply(ExampleSet exampleSet)
ExampleSet	Nominal2String.apply(ExampleSet exampleSet)
ExampleSet	Nominal2Date.apply(ExampleSet exampleSet)
ExampleSet	MissingValueReplenishmentView.apply(ExampleSet exampleSet)
ExampleSet	MergeNominalValues.apply(ExampleSet exampleSet)
ExampleSet	InternalBinominalRemapping.apply(ExampleSet exampleSet)
ExampleSet	FeatureRangeRemoval.apply(ExampleSet exampleSet)
ExampleSet	FeatureNameFilter.apply(ExampleSet exampleSet)
ExampleSet	FeatureFilter.apply(ExampleSet eSet) Applies filtering of features by looping through all features and checking switchOffFeature().
ExampleSet	ExchangeAttributeRoles.apply(ExampleSet exampleSet)
ExampleSet	ExampleRangeFilter.apply(ExampleSet exampleSet)
ExampleSet	ExampleFilter.apply(ExampleSet inputSet)
ExampleSet	DateAdjust.apply(ExampleSet exampleSet)
ExampleSet	Date2Numerical.apply(ExampleSet exampleSet)
ExampleSet	Date2Nominal.apply(ExampleSet exampleSet)
ExampleSet	Construction2Names.apply(ExampleSet exampleSet)
ExampleSet	ChangeAttributeType.apply(ExampleSet exampleSet)
ExampleSet	ChangeAttributeRole.apply(ExampleSet exampleSet)
ExampleSet	ChangeAttributeNamesReplace.apply(ExampleSet exampleSet)
ExampleSet	ChangeAttributeNames2Generic.apply(ExampleSet exampleSet)
ExampleSet	ChangeAttributeName.apply(ExampleSet exampleSet)
ExampleSet	AttributeValueTrim.apply(ExampleSet exampleSet)
ExampleSet	AttributeValueSubstring.apply(ExampleSet exampleSet)
ExampleSet	AttributeValueSplit.apply(ExampleSet exampleSet)
ExampleSet	AttributeValueReplace.apply(ExampleSet exampleSet)
ExampleSet	AttributeValueMapper.apply(ExampleSet exampleSet)
ExampleSet	AttributeMerge.apply(ExampleSet exampleSet)
ExampleSet	AttributeCopy.apply(ExampleSet exampleSet)
ExampleSet	AttributeAdd.apply(ExampleSet exampleSet)
ExampleSet	AddNominalValue.apply(ExampleSet exampleSet)
ExampleSet	AbsoluteValueFilter.apply(ExampleSet exampleSet)
ExampleSet	NominalToBinominalModel.applyOnData(ExampleSet exampleSet)
ExampleSet	Dictionary.applyOnData(ExampleSet exampleSet)
Model	NominalToNumeric.createPreprocessingModel(ExampleSet exampleSet)
Model	NominalToBinominal.createPreprocessingModel(ExampleSet exampleSet)
Model	ExampleSetToDictionary.createPreprocessingModel(ExampleSet exampleSet)
Attribute[]	MissingValueImputation.getOrderedAttributes(ExampleSet exampleSet, int order, boolean ascending)
abstract double	ValueReplenishment.getReplenishmentValue(int functionIndex, ExampleSet baseExampleSet, Attribute attribute, double currentValue, java.lang.String valueString) Returns the value of the replenishment function with the given index.
double	MissingValueReplenishment.getReplenishmentValue(int functionIndex, ExampleSet exampleSet, Attribute attribute, double currentValue, java.lang.String valueString)
double	InfiniteValueReplenishment.getReplenishmentValue(int functionIndex, ExampleSet exampleSet, Attribute attribute, double currentValue, java.lang.String valueString) Replaces the values
Attributes	NominalToBinominalModel.getTargetAttributes(ExampleSet applySet)
Attributes	Dictionary.getTargetAttributes(ExampleSet viewParent)

Constructors in com.rapidminer.operator.preprocessing.filter with parameters of type ExampleSet

Dictionary(boolean regexp, ExampleSet exampleSet, java.util.List<java.lang.String[]> replacements, boolean toLowerCase)

NominalToBinominalModel(ExampleSet exampleSet, boolean translateBinominals, boolean useOnlyUnderscoreInNames)

Uses of ExampleSet in com.rapidminer.operator.preprocessing.filter.attributes

Methods in com.rapidminer.operator.preprocessing.filter.attributes that return ExampleSet

ExampleSet

AttributeFilter.apply(ExampleSet exampleSet)

Methods in com.rapidminer.operator.preprocessing.filter.attributes with parameters of type ExampleSet

ExampleSet

AttributeFilter.apply(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.preprocessing.join

Methods in com.rapidminer.operator.preprocessing.join with parameters of type ExampleSet

protected MemoryExampleTable	ExampleSetJoin.joinData(ExampleSet leftExampleSet, ExampleSet rightExampleSet, java.util.List<AbstractExampleSetJoin.AttributeSource> originalAttributeSources, java.util.List<Attribute> unionAttributeList)
protected MemoryExampleTable	ExampleSetCartesian.joinData(ExampleSet es1, ExampleSet es2, java.util.List<AbstractExampleSetJoin.AttributeSource> originalAttributeSources, java.util.List<Attribute> unionAttributeList) Joins the data WITHOUT a WHERE criteria.
protected abstract MemoryExampleTable	AbstractExampleSetJoin.joinData(ExampleSet es1, ExampleSet es2, java.util.List<AbstractExampleSetJoin.AttributeSource> originalAttributeSources, java.util.List<Attribute> unionAttributeList)

Uses of ExampleSet in com.rapidminer.operator.preprocessing.normalization

Methods in com.rapidminer.operator.preprocessing.normalization that return ExampleSet

ExampleSet	ZTransformationModel.applyOnData(ExampleSet exampleSet) Performs the transformation.
ExampleSet	ProportionNormalizationModel.applyOnData(ExampleSet exampleSet) Performs the transformation.
ExampleSet	MinMaxNormalizationModel.applyOnData(ExampleSet exampleSet) Performs the transformation.

Methods in com.rapidminer.operator.preprocessing.normalization with parameters of type ExampleSet

ExampleSet	ZTransformationModel.applyOnData(ExampleSet exampleSet) Performs the transformation.
ExampleSet	ProportionNormalizationModel.applyOnData(ExampleSet exampleSet) Performs the transformation.
ExampleSet	MinMaxNormalizationModel.applyOnData(ExampleSet exampleSet) Performs the transformation.
Model	Normalization.createPreprocessingModel(ExampleSet exampleSet) Depending on the parameter value of "standardize" this method creates either a ZTransformationModel, MinMaxNormalizationModel or PercentageNormalizationModel.
Attributes	ZTransformationModel.getTargetAttributes(ExampleSet viewParent)
Attributes	ProportionNormalizationModel.getTargetAttributes(ExampleSet viewParent)
Attributes	MinMaxNormalizationModel.getTargetAttributes(ExampleSet viewParent)

Constructors in com.rapidminer.operator.preprocessing.normalization with parameters of type ExampleSet

MinMaxNormalizationModel(ExampleSet exampleSet, double min, double max, java.util.HashMap<java.lang.String,Tupel<java.lang.Double,java.lang.Double>> attributeRanges)
Create a new normalization model.

ProportionNormalizationModel(ExampleSet exampleSet, java.util.HashMap<java.lang.String,java.lang.Double> attributeSums)
Create a new normalization model.

ZTransformationModel(ExampleSet exampleSet, java.util.HashMap<java.lang.String,Tupel<java.lang.Double,java.lang.Double>> attributeMeanVarianceMap)

Uses of ExampleSet in com.rapidminer.operator.preprocessing.outlier

Methods in com.rapidminer.operator.preprocessing.outlier that return ExampleSet

ExampleSet	LOFOutlierOperator.apply(ExampleSet eSet) This method implements the main functionality of the Operator but can be considered as a sort of wrapper to pass the RapidMiner operator chain data deeper into the SearchSpace class, so do not expect a lot of things happening here.
ExampleSet	DKNOutlierOperator.apply(ExampleSet eSet) This method implements the main functionality of the Operator but can be considered as a sort of wrapper to pass the RapidMiner operator chain data deeper into the search space class, so do not expect a lot of things happening here.
ExampleSet	DBOutlierOperator.apply(ExampleSet eSet) This method implements the main functionality of the Operator but can be considered as a sort of wrapper to pass the RapidMiner operator chain data deeper into the SearchSpace class, so do not expect a lot of things happening here.

Methods in com.rapidminer.operator.preprocessing.outlier with parameters of type ExampleSet

ExampleSet	LOFOutlierOperator.apply(ExampleSet eSet) This method implements the main functionality of the Operator but can be considered as a sort of wrapper to pass the RapidMiner operator chain data deeper into the SearchSpace class, so do not expect a lot of things happening here.
ExampleSet	DKNOutlierOperator.apply(ExampleSet eSet) This method implements the main functionality of the Operator but can be considered as a sort of wrapper to pass the RapidMiner operator chain data deeper into the search space class, so do not expect a lot of things happening here.
ExampleSet	DBOutlierOperator.apply(ExampleSet eSet) This method implements the main functionality of the Operator but can be considered as a sort of wrapper to pass the RapidMiner operator chain data deeper into the SearchSpace class, so do not expect a lot of things happening here.

Uses of ExampleSet in com.rapidminer.operator.preprocessing.sampling

Methods in com.rapidminer.operator.preprocessing.sampling that return ExampleSet

ExampleSet	SimpleSampling.apply(ExampleSet exampleSet)
ExampleSet	ModelBasedSampling.apply(ExampleSet exampleSet)
ExampleSet	KennardStoneSampling.apply(ExampleSet exampleSet)
ExampleSet	AbstractStratifiedSampling.apply(ExampleSet exampleSet)
ExampleSet	AbstractBootstrapping.apply(ExampleSet exampleSet)
ExampleSet	AbsoluteSampling.apply(ExampleSet exampleSet)

Methods in com.rapidminer.operator.preprocessing.sampling with parameters of type ExampleSet

ExampleSet	SimpleSampling.apply(ExampleSet exampleSet)
ExampleSet	ModelBasedSampling.apply(ExampleSet exampleSet)
ExampleSet	KennardStoneSampling.apply(ExampleSet exampleSet)
ExampleSet	AbstractStratifiedSampling.apply(ExampleSet exampleSet)
ExampleSet	AbstractBootstrapping.apply(ExampleSet exampleSet)
ExampleSet	AbsoluteSampling.apply(ExampleSet exampleSet)
int[]	WeightedBootstrapping.createMapping(ExampleSet exampleSet, int size, java.util.Random random)
int[]	Bootstrapping.createMapping(ExampleSet exampleSet, int size, java.util.Random random)
abstract int[]	AbstractBootstrapping.createMapping(ExampleSet exampleSet, int size, java.util.Random random)
double	RatioStratifiedSampling.getRatio(ExampleSet exampleSet)
abstract double	AbstractStratifiedSampling.getRatio(ExampleSet exampleSet) This method should return the ratio used for stratifiedSampling
double	AbsoluteStratifiedSampling.getRatio(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.preprocessing.series

Methods in com.rapidminer.operator.preprocessing.series that return ExampleSet

ExampleSet	WindowExamples2ModelingData.apply(ExampleSet exampleSet)
ExampleSet	SingleAttributes2ValueSeries.apply(ExampleSet exampleSet)
ExampleSet	Series2WindowExamples.apply(ExampleSet exampleSet)
ExampleSet	LabelTrend2Classification.apply(ExampleSet exampleSet)
ExampleSet	FillDataGaps.apply(ExampleSet inputSet)
ExampleSet	EnsureMonotonicity.apply(ExampleSet exampleSet)

Methods in com.rapidminer.operator.preprocessing.series with parameters of type ExampleSet

ExampleSet	WindowExamples2ModelingData.apply(ExampleSet exampleSet)
ExampleSet	SingleAttributes2ValueSeries.apply(ExampleSet exampleSet)
ExampleSet	Series2WindowExamples.apply(ExampleSet exampleSet)
ExampleSet	LabelTrend2Classification.apply(ExampleSet exampleSet)
ExampleSet	FillDataGaps.apply(ExampleSet inputSet)
ExampleSet	EnsureMonotonicity.apply(ExampleSet exampleSet)
Attribute	UnivariateSeries2WindowExamples.createLabel(ExampleSet exampleSet, int representation)
abstract Attribute	Series2WindowExamples.createLabel(ExampleSet exampleSet, int representation) Subclasses have to override this method.
Attribute	MultivariateSeries2WindowExamples.createLabel(ExampleSet exampleSet, int representation)
void	UnivariateSeries2WindowExamples.fillSeriesExampleTable(MemoryExampleTable table, ExampleSet exampleSet, Attribute idAttribute, int representation, int windowWidth, int stepSize, int horizon)
abstract void	Series2WindowExamples.fillSeriesExampleTable(MemoryExampleTable table, ExampleSet exampleSet, Attribute idAttribute, int representation, int width, int stepSize, int horizon) The given label attribute might be null.
void	MultivariateSeries2WindowExamples.fillSeriesExampleTable(MemoryExampleTable table, ExampleSet exampleSet, Attribute idAttribute, int representation, int windowWidth, int stepSize, int horizon)
int[]	UnivariateSeries2WindowExamples.getValueTypes(ExampleSet exampleSet, int representation, int windowWidth)
abstract int[]	Series2WindowExamples.getValueTypes(ExampleSet exampleSet, int representation, int windowWidth)
int[]	MultivariateSeries2WindowExamples.getValueTypes(ExampleSet exampleSet, int representation, int windowWidth)
void	UnivariateSeries2WindowExamples.performChecks(ExampleSet exampleSet, int representation, int windowWidth, int stepSize, int horizon)
abstract void	Series2WindowExamples.performChecks(ExampleSet exampleSet, int representation, int width, int stepSize, int horizon)
void	MultivariateSeries2WindowExamples.performChecks(ExampleSet exampleSet, int representation, int windowWidth, int stepSize, int horizon)

Uses of ExampleSet in com.rapidminer.operator.preprocessing.series.filter

Methods in com.rapidminer.operator.preprocessing.series.filter that return ExampleSet

ExampleSet	SeriesMissingValueReplenishment.apply(ExampleSet exampleSet)
ExampleSet	MovingAverage.apply(ExampleSet exampleSet)
ExampleSet	IndexSeries.apply(ExampleSet exampleSet)
ExampleSet	ExponentialSmoothing.apply(ExampleSet exampleSet)
ExampleSet	DifferentiateSeries.apply(ExampleSet exampleSet)
ExampleSet	CumulateSeries.apply(ExampleSet exampleSet)

Methods in com.rapidminer.operator.preprocessing.series.filter with parameters of type ExampleSet

ExampleSet	SeriesMissingValueReplenishment.apply(ExampleSet exampleSet)
ExampleSet	MovingAverage.apply(ExampleSet exampleSet)
ExampleSet	IndexSeries.apply(ExampleSet exampleSet)
ExampleSet	ExponentialSmoothing.apply(ExampleSet exampleSet)
ExampleSet	DifferentiateSeries.apply(ExampleSet exampleSet)
ExampleSet	CumulateSeries.apply(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.preprocessing.transformation

Methods in com.rapidminer.operator.preprocessing.transformation that return ExampleSet

ExampleSet

AggregationOperator.apply(ExampleSet exampleSet)

Methods in com.rapidminer.operator.preprocessing.transformation with parameters of type ExampleSet

ExampleSet

AggregationOperator.apply(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.preprocessing.weighting

Methods in com.rapidminer.operator.preprocessing.weighting that return ExampleSet

ExampleSet

EqualLabelWeighting.apply(ExampleSet exampleSet)

Methods in com.rapidminer.operator.preprocessing.weighting with parameters of type ExampleSet

ExampleSet

EqualLabelWeighting.apply(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.operator.validation

Methods in com.rapidminer.operator.validation with parameters of type ExampleSet

protected int[]	WeightedBootstrappingValidation.createMapping(ExampleSet exampleSet, int size, java.util.Random random)
protected int[]	BootstrappingValidation.createMapping(ExampleSet exampleSet, int size, java.util.Random random)
protected abstract int[]	AbstractBootstrappingValidation.createMapping(ExampleSet exampleSet, int size, java.util.Random random)
IOObject[]	XValidation.estimatePerformance(ExampleSet inputSet)
abstract IOObject[]	ValidationChain.estimatePerformance(ExampleSet inputSet) This is the main method of the validation chain and must be implemented to estimate a performance of inner operators on the given example set.
IOObject[]	SlidingWindowValidation.estimatePerformance(ExampleSet inputSet)
IOObject[]	RandomSplitValidationChain.estimatePerformance(ExampleSet inputSet)
IOObject[]	FixedSplitValidationChain.estimatePerformance(ExampleSet inputSet)
IOObject[]	BatchXValidation.estimatePerformance(ExampleSet inputSet)
IOObject[]	BatchSlidingWindowValidation.estimatePerformance(ExampleSet inputSet)
IOObject[]	AbstractBootstrappingValidation.estimatePerformance(ExampleSet inputSet)
protected IOContainer	ValidationChain.evaluate(ExampleSet testSet) Applies the applier and evaluator (= second encapsulated inner operator).
PerformanceVector	ConsistencyFeatureSetEvaluator.evaluate(ExampleSet exampleSet)
PerformanceVector	CFSFeatureSetEvaluator.evaluate(ExampleSet exampleSet)
IOContainer	ValidationChain.evaluate(ExampleSet testSet, IOContainer learnResult) Applies the applier and evaluator (= second encapsulated inner operator).
protected IOContainer	ValidationChain.learn(ExampleSet trainingSet) Applies the learner (= first encapsulated inner operator).

Uses of ExampleSet in com.rapidminer.operator.visualization

Methods in com.rapidminer.operator.visualization with parameters of type ExampleSet

void	DataStatistics.addInfo(ExampleSet exampleSet, Attribute attribute)
void	SOMModelPlotter.setExampleSet(ExampleSet exampleSet)

Constructors in com.rapidminer.operator.visualization with parameters of type ExampleSet

SOMModelPlotter(ExampleSet exampleSet, Model model)

Uses of ExampleSet in com.rapidminer.operator.visualization.dependencies

Methods in com.rapidminer.operator.visualization.dependencies that return ExampleSet

protected ExampleSet	MutualInformationMatrixOperator.performPreprocessing(ExampleSet eSet) This preprocessing discretizes the input example set by a view.
protected ExampleSet	AbstractPairwiseMatrixOperator.performPreprocessing(ExampleSet exampleSet) This default implementation does nothing.

Methods in com.rapidminer.operator.visualization.dependencies with parameters of type ExampleSet

double	MutualInformationMatrixOperator.getMatrixValue(ExampleSet exampleSet, Attribute firstAttribute, Attribute secondAttribute) Calculates the mutual information for both attributes.
abstract double	AbstractPairwiseMatrixOperator.getMatrixValue(ExampleSet exampleSet, Attribute firstAttribute, Attribute secondAttribute)
protected ExampleSet	MutualInformationMatrixOperator.performPreprocessing(ExampleSet eSet) This preprocessing discretizes the input example set by a view.
protected ExampleSet	AbstractPairwiseMatrixOperator.performPreprocessing(ExampleSet exampleSet) This default implementation does nothing.

Constructors in com.rapidminer.operator.visualization.dependencies with parameters of type ExampleSet

NumericalMatrix(java.lang.String name, ExampleSet exampleSet, boolean symmetrical)

Uses of ExampleSet in com.rapidminer.tools

Methods in com.rapidminer.tools that return ExampleSet

static ExampleSet	WekaTools.toRapidMinerExampleSet(weka.core.Instances instances) Invokes toRapidMinerExampleSet(instances, null, DataRowFactory.TYPE_DOUBLE_ARRAY).
static ExampleSet	WekaTools.toRapidMinerExampleSet(weka.core.Instances instances, java.lang.String attributeNamePrefix) Invokes toRapidMinerExampleSet(instances, attributeNamePrefix, DataRowFactory.TYPE_DOUBLE_ARRAY).
static ExampleSet	WekaTools.toRapidMinerExampleSet(weka.core.Instances instances, java.lang.String attributeNamePrefix, int datamanagement) Creates a RapidMiner example set from Weka instances.

Methods in com.rapidminer.tools with parameters of type ExampleSet

static weka.core.Instances

WekaTools.toWekaInstances(ExampleSet exampleSet, java.lang.String name, int taskType) Creates Weka instances with the given name from the given example set.

Constructors in com.rapidminer.tools with parameters of type ExampleSet

WekaInstancesAdaptor(java.lang.String name, ExampleSet exampleSet, int taskType)
Creates a new Instances object based on the given example set.

Uses of ExampleSet in com.rapidminer.tools.jdbc

Methods in com.rapidminer.tools.jdbc with parameters of type ExampleSet

void	DatabaseHandler.createTable(ExampleSet exampleSet, java.lang.String tableName, int overwriteMode, boolean firstAttempt, int defaultVarcharLength) Creates a new table in this connection and fills it with the provided data.

Uses of ExampleSet in com.rapidminer.tools.math

Methods in com.rapidminer.tools.math with parameters of type ExampleSet

static double	MathFunctions.correlation(ExampleSet exampleSet, Attribute firstAttribute, Attribute secondAttribute, boolean squared) This method calculates the correlation between two (numerical) attributes of an example set.
java.util.List<double[]>	LiftDataGenerator.createLiftDataList(ExampleSet exampleSet) Creates a list of ROC data poings from the given example set.
ROCData	ROCDataGenerator.createROCData(ExampleSet exampleSet, boolean useExampleWeights) Creates a list of ROC data points from the given example set.
Complex[]	FastFourierTransform.getFourierTransform(ExampleSet exampleSet, Attribute source, Attribute target) Builds the fourier transform from the values of the first attribute onto the second.

Uses of ExampleSet in com.rapidminer.tools.math.function

Methods in com.rapidminer.tools.math.function with parameters of type ExampleSet

void	ExpressionParser.addAttribute(ExampleSet exampleSet, java.lang.String name, java.lang.String function) Iterates over the ExampleSet, interprets attributes as variables, evaluates the function and creates a new attribute with the given name that takes the expression's value.

Uses of ExampleSet in com.rapidminer.tools.math.kernels

Methods in com.rapidminer.tools.math.kernels with parameters of type ExampleSet

void	Kernel.init(ExampleSet exampleSet) Calculates all distances and store them in a matrix to speed up optimization.

Constructors in com.rapidminer.tools.math.kernels with parameters of type ExampleSet

FullCache(ExampleSet exampleSet, Kernel kernel)

Uses of ExampleSet in com.rapidminer.tools.math.matrix

Methods in com.rapidminer.tools.math.matrix with parameters of type ExampleSet

static Jama.Matrix

CovarianceMatrix.getCovarianceMatrix(ExampleSet exampleSet) Transforms the example set into a double matrix (doubling the amount of used memory) and invokes CovarianceMatrix.getCovarianceMatrix(double[][]).

Uses of ExampleSet in com.rapidminer.tools.math.similarity

Methods in com.rapidminer.tools.math.similarity with parameters of type ExampleSet

static DistanceMeasure	DistanceMeasures.createDivergence(ParameterHandler parameterHandler, ExampleSet exampleSet, IOContainer ioContainer)
static DistanceMeasure	DistanceMeasures.createMeasure(ParameterHandler parameterHandler, ExampleSet exampleSet, IOContainer ioContainer)
static DistanceMeasure	DistanceMeasures.createMixedMeasure(ParameterHandler parameterHandler, ExampleSet exampleSet, IOContainer ioContainer)
static DistanceMeasure	DistanceMeasures.createNominalMeasure(ParameterHandler parameterHandler, ExampleSet exampleSet, IOContainer ioContainer)
static DistanceMeasure	DistanceMeasures.createNumericalMeasure(ParameterHandler parameterHandler, ExampleSet exampleSet, IOContainer ioContainer)
abstract void	DistanceMeasure.init(ExampleSet exampleSet) Before using a similarity measure, it is needed to initialise.
void	DistanceMeasure.init(ExampleSet exampleSet, ParameterHandler parameterHandler) Before using a similarity measure, it is needed to initialise.
void	DistanceMeasure.init(ExampleSet exampleSet, ParameterHandler parameterHandler, IOContainer ioContainer) Before using a similarity measure, it is needed to initialize.

Uses of ExampleSet in com.rapidminer.tools.math.similarity.divergences

Methods in com.rapidminer.tools.math.similarity.divergences with parameters of type ExampleSet

void	SquaredLoss.init(ExampleSet exampleSet)
void	SquaredEuclideanDistance.init(ExampleSet exampleSet)
void	MahalanobisDistance.init(ExampleSet exampleSet)
void	LogisticLoss.init(ExampleSet exampleSet)
void	LogarithmicLoss.init(ExampleSet exampleSet)
void	KLDivergence.init(ExampleSet exampleSet)
void	ItakuraSaitoDistance.init(ExampleSet exampleSet)
void	GeneralizedIDivergence.init(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.tools.math.similarity.mixed

Methods in com.rapidminer.tools.math.similarity.mixed with parameters of type ExampleSet

void	MixedEuclideanDistance.init(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.tools.math.similarity.nominal

Methods in com.rapidminer.tools.math.similarity.nominal with parameters of type ExampleSet

void	NominalDistance.init(ExampleSet exampleSet)
void	AbstractNominalSimilarity.init(ExampleSet exampleSet)

Uses of ExampleSet in com.rapidminer.tools.math.similarity.numerical

Methods in com.rapidminer.tools.math.similarity.numerical with parameters of type ExampleSet

void	OverlapNumericalSimilarity.init(ExampleSet exampleSet)
void	MaxProductSimilarity.init(ExampleSet exampleSet)
void	ManhattanDistance.init(ExampleSet exampleSet)
void	KernelEuclideanDistance.init(ExampleSet exampleSet)
void	JaccardNumericalSimilarity.init(ExampleSet exampleSet)
void	InnerProductSimilarity.init(ExampleSet exampleSet)
void	EuclideanDistance.init(ExampleSet exampleSet)
void	DTWDistance.init(ExampleSet exampleSet)
void	DiceNumericalSimilarity.init(ExampleSet exampleSet)
void	CosineSimilarity.init(ExampleSet exampleSet)
void	CorrelationSimilarity.init(ExampleSet exampleSet)
void	ChebychevNumericalDistance.init(ExampleSet exampleSet)
void	CamberraNumericalDistance.init(ExampleSet exampleSet)