All Packages Class Hierarchy
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E
- Edge class weka.gui.treevisualizer.Edge.
- This class is used in conjunction with the Node class to form a tree
structure.
- Edge(String, String, String).
Constructor for class weka.gui.treevisualizer.Edge
- This constructs an Edge with the specified label
and parent , child serial tags.
- editableProperties().
Method in class weka.gui.PropertySheetPanel
- Gets the number of editable properties for the current target.
- elementAt(int).
Method in class weka.core.FastVector
- Returns the element at the given position.
- elements().
Method in class weka.core.FastVector
- Returns an enumeration of this vector.
- elements(int).
Method in class weka.core.FastVector
- Returns an enumeration of this vector, skipping the
element with the given index.
- EM class weka.clusterers.EM.
- Simple EM (estimation maximisation) class.
- EM().
Constructor for class weka.clusterers.EM
- Constructor.
- empty().
Method in class weka.core.Queue
- Checks if queue is empty.
- EmptyAttributeFilter class weka.filters.EmptyAttributeFilter.
- Removes all attributes that do not contain more than one distinct
value.
- EmptyAttributeFilter().
Constructor for class weka.filters.EmptyAttributeFilter
-
- entropy(double[]).
Static method in class weka.core.ContingencyTables
- Computes the entropy of the given array.
- EntropyBasedSplitCrit class weka.classifiers.j48.EntropyBasedSplitCrit.
- "Abstract" class for computing splitting criteria
based on the entropy of a class distribution.
- EntropyBasedSplitCrit().
Constructor for class weka.classifiers.j48.EntropyBasedSplitCrit
-
- entropyConditionedOnColumns(double[][]).
Static method in class weka.core.ContingencyTables
- Computes conditional entropy of the rows given
the columns.
- entropyConditionedOnRows(double[][]).
Static method in class weka.core.ContingencyTables
- Computes conditional entropy of the columns given
the rows.
- entropyConditionedOnRows(double[][], double[][], double).
Static method in class weka.core.ContingencyTables
- Computes conditional entropy of the columns given the rows
of the test matrix with respect to the train matrix.
- entropyOverColumns(double[][]).
Static method in class weka.core.ContingencyTables
- Computes the columns' entropy for the given contingency table.
- entropyOverRows(double[][]).
Static method in class weka.core.ContingencyTables
- Computes the rows' entropy for the given contingency table.
- EntropySplitCrit class weka.classifiers.j48.EntropySplitCrit.
- Class for computing the entropy for a given distribution.
- EntropySplitCrit().
Constructor for class weka.classifiers.j48.EntropySplitCrit
-
- enumerateAttributes().
Method in class weka.core.Instances
- Returns an enumeration of all the attributes.
- enumerateAttributes().
Method in class weka.core.Instance
- Returns an enumeration of all the attributes.
- enumerateInstances().
Method in class weka.core.Instances
- Returns an enumeration of all instances in the dataset.
- enumerateMeasures().
Method in class weka.classifiers.DecisionTable
- Returns an enumeration of the additional measure names
- enumerateMeasures().
Method in class weka.classifiers.AttributeSelectedClassifier
- Returns an enumeration of the additional measure names
- enumerateMeasures().
Method in class weka.classifiers.AdditiveRegression
- Returns an enumeration of the additional measure names
- enumerateMeasures().
Method in class weka.classifiers.adtree.ADTree
- Returns an enumeration of the additional measure names.
- enumerateMeasures().
Method in class weka.classifiers.j48.J48
- Returns an enumeration of the additional measure names
- enumerateMeasures().
Method in class weka.classifiers.j48.PART
- Returns an enumeration of the additional measure names
- enumerateMeasures().
Method in class weka.classifiers.m5.M5Prime
- Returns an enumeration of the additional measure names
- enumerateMeasures().
Method in interface weka.core.AdditionalMeasureProducer
- Returns an enumeration of the measure names.
- enumerateMeasures().
Method in class weka.experiment.ClassifierSplitEvaluator
- Returns an enumeration of any additional measure names that might be
in the classifier
- enumerateMeasures().
Method in class weka.experiment.LearningRateResultProducer
- Returns an enumeration of any additional measure names that might be
in the result producer
- enumerateMeasures().
Method in class weka.experiment.CrossValidationResultProducer
- Returns an enumeration of any additional measure names that might be
in the SplitEvaluator
- enumerateMeasures().
Method in class weka.experiment.RegressionSplitEvaluator
- Returns an enumeration of any additional measure names that might be
in the classifier
- enumerateMeasures().
Method in class weka.experiment.RandomSplitResultProducer
- Returns an enumeration of any additional measure names that might be
in the SplitEvaluator
- enumerateMeasures().
Method in class weka.experiment.AveragingResultProducer
- Returns an enumeration of any additional measure names that might be
in the result producer
- enumerateMeasures().
Method in class weka.experiment.DatabaseResultProducer
- Returns an enumeration of any additional measure names that might be
in the result producer
- enumerateValues().
Method in class weka.core.Attribute
- Returns an enumeration of all the attribute's values if
the attribute is nominal or a string, null otherwise.
- EPSILON.
Static variable in interface weka.classifiers.kstar.KStarConstants
-
- eq(double, double).
Static method in class weka.core.Utils
- Tests if a is equal to b.
- eqDouble(double, double).
Static method in class weka.classifiers.m5.M5Utils
- Tests if two double values are equal to each other
- equalHeaders(Instance).
Method in class weka.core.Instance
- Tests if the headers of two instances are equivalent.
- equalHeaders(Instances).
Method in class weka.core.Instances
- Checks if two headers are equivalent.
- equals(Object).
Method in class weka.associations.ItemSet
- Tests if two item sets are equal.
- equals(Object).
Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
- Tests if two instances are equal
- equals(Object).
Method in class weka.classifiers.DecisionTable.hashKey
- Tests if two instances are equal
- equals(Object).
Method in class weka.classifiers.Evaluation
- Tests whether the current evaluation object is equal to another
evaluation object
- equals(Object).
Method in class weka.core.Attribute
- Tests if given attribute is equal to this attribute.
- equals(Object).
Method in class weka.core.SelectedTag
- Returns true if this SelectedTag equals another object
- equals(Object).
Method in class weka.core.SerializedObject
- Compares this object with another for equality.
- equalTo(Splitter).
Method in class weka.classifiers.adtree.Splitter
- Tests whether two splitters are equivalent.
- equalTo(Splitter).
Method in class weka.classifiers.adtree.TwoWayNominalSplit
- Tests whether two splitters are equivalent.
- equalTo(Splitter).
Method in class weka.classifiers.adtree.TwoWayNumericSplit
- Tests whether two splitters are equivalent.
- errms(StreamTokenizer, String).
Static method in class weka.core.converters.ConverterUtils
- Throws error message with line number and last token read.
- ERROR_EXHAUSTIVE.
Static variable in class weka.classifiers.MultiClassClassifier
-
- ERROR_NONE.
Static variable in class weka.classifiers.MultiClassClassifier
- The error correction modes
- ERROR_RANDOM.
Static variable in class weka.classifiers.MultiClassClassifier
-
- ERROR_SHAPE.
Static variable in class weka.gui.visualize.Plot2D
-
- error().
Method in class weka.classifiers.evaluation.NumericPrediction
- Calculates the prediction error.
- ErrorBasedMeritEvaluator interface weka.attributeSelection.ErrorBasedMeritEvaluator.
- Interface for evaluators that calculate the "merit" of attributes/subsets
as the error of a learning scheme
- errorCorrectionModeTipText().
Method in class weka.classifiers.MultiClassClassifier
-
- errorMsg(String).
Static method in class weka.classifiers.m5.M5Utils
- Prints error message and exits
- errorRate().
Method in class weka.classifiers.Evaluation
- Returns the estimated error rate or the root mean squared error
(if the class is numeric).
- errorRate().
Method in class weka.classifiers.evaluation.ConfusionMatrix
- Returns the estimated error rate.
- Errors class weka.classifiers.m5.Errors.
- Class for containing the evaluation results of a model
- errors(Instances).
Method in class weka.classifiers.m5.Function
- Evaluates a function
- errors(Instances, boolean).
Method in class weka.classifiers.m5.Node
- Evaluates a tree
- Errors(int, int).
Constructor for class weka.classifiers.m5.Errors
- Constructs an object which could contain the evaluation results of a model
- errorValue(boolean).
Method in class weka.classifiers.neural.NeuralConnection
- Call this to get the error value of this unit.
- errorValue(boolean).
Method in class weka.classifiers.neural.NeuralNode
- Call this to get the error value of this unit.
- errorValue(NeuralNode).
Method in class weka.classifiers.neural.LinearUnit
- This function calculates what the error value should be.
- errorValue(NeuralNode).
Method in class weka.classifiers.neural.SigmoidUnit
- This function calculates what the error value should be.
- errorValue(NeuralNode).
Method in interface weka.classifiers.neural.NeuralMethod
- This function calculates what the error value should be.
- Estimator interface weka.estimators.Estimator.
- Interface for probability estimators.
- EVAL_CROSS_VALIDATION.
Static variable in class weka.classifiers.ThresholdSelector
-
- EVAL_TRAINING_SET.
Static variable in class weka.classifiers.ThresholdSelector
-
- EVAL_TUNED_SPLIT.
Static variable in class weka.classifiers.ThresholdSelector
-
- evaluateAttribute(int).
Method in class weka.attributeSelection.AttributeEvaluator
- evaluates an individual attribute
- evaluateAttribute(int).
Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
- evaluates an individual attribute by measuring the symmetrical
uncertainty between it and the class.
- evaluateAttribute(int).
Method in class weka.attributeSelection.GainRatioAttributeEval
- evaluates an individual attribute by measuring the gain ratio
of the class given the attribute.
- evaluateAttribute(int).
Method in class weka.attributeSelection.ReliefFAttributeEval
- Evaluates an individual attribute using ReliefF's instance based approach.
- evaluateAttribute(int).
Method in class weka.attributeSelection.ChiSquaredAttributeEval
- evaluates an individual attribute by measuring its
chi-squared value.
- evaluateAttribute(int).
Method in class weka.attributeSelection.OneRAttributeEval
- evaluates an individual attribute by measuring the amount
of information gained about the class given the attribute.
- evaluateAttribute(int).
Method in class weka.attributeSelection.InfoGainAttributeEval
- evaluates an individual attribute by measuring the amount
of information gained about the class given the attribute.
- evaluateAttribute(int).
Method in class weka.attributeSelection.PrincipalComponents
- Evaluates the merit of a transformed attribute.
- evaluateClusterer(Clusterer, String[]).
Static method in class weka.clusterers.ClusterEvaluation
- Evaluates a clusterer with the options given in an array of
strings.
- evaluateClusterer(Instances).
Method in class weka.clusterers.ClusterEvaluation
- Evaluate the clusterer on a set of instances.
- evaluateModel(Classifier, Instances).
Method in class weka.classifiers.Evaluation
- Evaluates the classifier on a given set of instances.
- evaluateModel(Classifier, String[]).
Static method in class weka.classifiers.Evaluation
- Evaluates a classifier with the options given in an array of
strings.
- evaluateModel(String, String[]).
Static method in class weka.classifiers.Evaluation
- Evaluates a classifier with the options given in an array of
strings.
- evaluateModelOnce(Classifier, Instance).
Method in class weka.classifiers.Evaluation
- Evaluates the classifier on a single instance.
- evaluateModelOnce(double[], Instance).
Method in class weka.classifiers.Evaluation
- Evaluates the supplied distribution on a single instance.
- evaluateModelOnce(double, Instance).
Method in class weka.classifiers.Evaluation
- Evaluates the supplied prediction on a single instance.
- evaluateSubset(BitSet).
Method in class weka.attributeSelection.SubsetEvaluator
- evaluates a subset of attributes
- evaluateSubset(BitSet).
Method in class weka.attributeSelection.CfsSubsetEval
- evaluates a subset of attributes
- evaluateSubset(BitSet).
Method in class weka.attributeSelection.ConsistencySubsetEval
- Evaluates a subset of attributes
- evaluateSubset(BitSet).
Method in class weka.attributeSelection.ClassifierSubsetEval
- Evaluates a subset of attributes
- evaluateSubset(BitSet).
Method in class weka.attributeSelection.WrapperSubsetEval
- Evaluates a subset of attributes
- evaluateSubset(BitSet, Instance, boolean).
Method in class weka.attributeSelection.HoldOutSubsetEvaluator
- Evaluates a subset of attributes with respect to a single instance.
- evaluateSubset(BitSet, Instance, boolean).
Method in class weka.attributeSelection.ClassifierSubsetEval
- Evaluates a subset of attributes with respect to a single instance.
- evaluateSubset(BitSet, Instances).
Method in class weka.attributeSelection.HoldOutSubsetEvaluator
- Evaluates a subset of attributes with respect to a set of instances.
- evaluateSubset(BitSet, Instances).
Method in class weka.attributeSelection.ClassifierSubsetEval
- Evaluates a subset of attributes with respect to a set of instances.
- Evaluation class weka.classifiers.Evaluation.
- Class for evaluating machine learning models.
- Evaluation(Instances).
Constructor for class weka.classifiers.Evaluation
- Initializes all the counters for the evaluation.
- Evaluation(Instances, CostMatrix).
Constructor for class weka.classifiers.Evaluation
- Initializes all the counters for the evaluation and also takes a
cost matrix as parameter.
- evaluationModeTipText().
Method in class weka.classifiers.ThresholdSelector
-
- EvaluationUtils class weka.classifiers.evaluation.EvaluationUtils.
- Contains utility functions for generating lists of predictions in
various manners.
- EvaluationUtils().
Constructor for class weka.classifiers.evaluation.EvaluationUtils
-
- evaluatorTipText().
Method in class weka.classifiers.AttributeSelectedClassifier
- Returns the tip text for this property
- execute().
Method in interface weka.experiment.Task
- Execute this task.
- execute().
Method in class weka.experiment.RemoteExperimentSubTask
- Run the experiment
- execute(String).
Method in class weka.experiment.DatabaseUtils
- Executes a SQL query.
- executeTask(Task).
Method in class weka.experiment.RemoteEngine
- Takes a task object and queues it for execution
- executeTask(Task).
Method in interface weka.experiment.Compute
- Execute a task
- ExhaustiveSearch class weka.attributeSelection.ExhaustiveSearch.
- Class for performing an exhaustive search.
- ExhaustiveSearch().
Constructor for class weka.attributeSelection.ExhaustiveSearch
- Constructor
- EXP_INDEX_TABLE.
Static variable in class weka.experiment.DatabaseUtils
- The name of the table containing the index to experiments
- EXP_RESULT_COL.
Static variable in class weka.experiment.DatabaseUtils
- The name of the column containing the results table name
- EXP_RESULT_PREFIX.
Static variable in class weka.experiment.DatabaseUtils
- The prefix for result table names
- EXP_SETUP_COL.
Static variable in class weka.experiment.DatabaseUtils
- The name of the column containing the experiment setup (parameters)
- EXP_TYPE_COL.
Static variable in class weka.experiment.DatabaseUtils
- The name of the column containing the experiment type (ResultProducer)
- expectedCosts(double[]).
Method in class weka.classifiers.CostMatrix
- Calculates the expected misclassification cost for each possible
class value, given class probability estimates.
- expectedResultsPerAverageTipText().
Method in class weka.experiment.AveragingResultProducer
- Returns the tip text for this property
- Experiment class weka.experiment.Experiment.
- Holds all the necessary configuration information for a standard
type experiment.
- Experiment().
Constructor for class weka.experiment.Experiment
-
- Experimenter class weka.gui.experiment.Experimenter.
- The main class for the experiment environment.
- Experimenter(boolean).
Constructor for class weka.gui.experiment.Experimenter
- Creates the experiment environment gui with no initial experiment
- experimentIndexExists().
Method in class weka.experiment.DatabaseUtils
- Returns true if the experiment index exists.
- Explorer class weka.gui.explorer.Explorer.
- The main class for the Weka explorer.
- Explorer().
Constructor for class weka.gui.explorer.Explorer
- Creates the experiment environment gui with no initial experiment
- expressionTipText().
Method in class weka.filters.AttributeExpressionFilter
- Returns the tip text for this property
- ExtensionFileFilter class weka.gui.ExtensionFileFilter.
- Provides a file filter for FileChoosers that accepts or rejects files
based on their extension.
- ExtensionFileFilter(String, String).
Constructor for class weka.gui.ExtensionFileFilter
- Creates the ExtensionFileFilter
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
All Packages Class Hierarchy