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

M

M(Instances) - Method in class weka.clusterers.EM
The M step of the EM algorithm.
M5Base - class weka.classifiers.trees.m5.M5Base.
M5Base.
M5Base() - Constructor for class weka.classifiers.trees.m5.M5Base
Constructor
M5P - class weka.classifiers.trees.M5P.
M5P.
M5P() - Constructor for class weka.classifiers.trees.M5P
Creates a new M5P instance.
M5Rules - class weka.classifiers.rules.M5Rules.
Generates a decision list for regression problems using separate-and-conquer.
M5Rules() - Constructor for class weka.classifiers.rules.M5Rules
 
MACHEP - Static variable in class weka.core.Statistics
Some constants
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
 
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.MetaCost
 
MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
 
MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.MetaCost
 
MAXGAM - Static variable in class weka.core.Statistics
 
MAXLOG - Static variable in class weka.core.Statistics
 
MAX_DL_SURPLUS - Static variable in class weka.classifiers.rules.JRip
The limit of description length surplus in ruleset generation
MAX_ERROR - Static variable in class weka.estimators.KernelEstimator
Maximum percentage error permitted in probability calculations
MAX_FAILURES - Static variable in class weka.experiment.RemoteExperiment
allow at most 3 failures on a host before it is removed from the list of usable hosts
MAX_FAILURES - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
 
MAX_NUM_RESAMPLING_ITERATIONS - Static variable in class weka.classifiers.meta.AdaBoostM1
Max num iterations tried to find classifier with non-zero error.
MAX_PRECISION - Static variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel
 
MAX_PRECISION - Static variable in class weka.gui.visualize.VisualizeUtils
Default maximum precision for the display of numeric values
MAX_SHAPES - Static variable in class weka.gui.visualize.Plot2D
 
MDL - Static variable in interface weka.classifiers.bayes.Scoreable
 
MDL_THEORY_WEIGHT - Variable in class weka.classifiers.rules.RuleStats
The theory weight in the MDL calculation
MEMORY - Static variable in class weka.associations.Tertius
 
METHOD_1_AGAINST_1 - Static variable in class weka.classifiers.meta.MultiClassClassifier
 
METHOD_1_AGAINST_ALL - Static variable in class weka.classifiers.meta.MultiClassClassifier
The error correction modes
METHOD_ERROR_EXHAUSTIVE - Static variable in class weka.classifiers.meta.MultiClassClassifier
 
METHOD_ERROR_RANDOM - Static variable in class weka.classifiers.meta.MultiClassClassifier
 
MINLOG - Static variable in class weka.core.Statistics
 
MIN_RECORD_SIZE - Static variable in class weka.classifiers.bayes.ADNode
 
MIN_SF_PROB - Static variable in class weka.classifiers.Evaluation
The minimum probablility accepted from an estimator to avoid taking log(0) in Sf calculations.
MIN_VALUE - Static variable in class weka.classifiers.meta.ThresholdSelector
The minimum value for the criterion.
MISSING_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
MISSING_VALUE - Static variable in interface weka.classifiers.evaluation.Prediction
Constant representing a missing value.
MISSING_VALUE - Static variable in class weka.core.Instance
Constant representing a missing value.
MODEL_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClassifierPanel
The filename extension that should be used for model files
MODEL_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClustererPanel
The filename extension that should be used for model files
MOVING - Static variable in class weka.gui.beans.KnowledgeFlow
 
M_AVERAGE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
M_DELETE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
Missing value handling mode
M_MAXDIFF - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
M_NORMAL - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
MahalanobisEstimator - class weka.estimators.MahalanobisEstimator.
Simple probability estimator that places a single normal distribution over the observed values.
MahalanobisEstimator(Matrix, double, double) - Constructor for class weka.estimators.MahalanobisEstimator
Constructor
MakeADTree(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.ADNode
create sub tree
MakeADTree(Instances) - Static method in class weka.classifiers.bayes.ADNode
create AD tree from set of instances
MakeDecList - class weka.classifiers.rules.part.MakeDecList.
Class for handling a decision list.
MakeDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
Constructor for unpruned dec list.
MakeDecList(ModelSelection, double, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
Constructor for dec list pruned using C4.5 pruning.
MakeDecList(ModelSelection, int, int, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
Constructor for dec list pruned using hold-out pruning.
MakeDensityBasedClusterer - class weka.clusterers.MakeDensityBasedClusterer.
Class for wrapping a Clusterer to make it return a distribution and density.
MakeDensityBasedClusterer() - Constructor for class weka.clusterers.MakeDensityBasedClusterer
Default constructor.
MakeDensityBasedClusterer(Clusterer) - Constructor for class weka.clusterers.MakeDensityBasedClusterer
Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.
MakeIndicator - class weka.filters.unsupervised.attribute.MakeIndicator.
Creates a new dataset with a boolean attribute replacing a nominal attribute.
MakeIndicator() - Constructor for class weka.filters.unsupervised.attribute.MakeIndicator
 
MakeVaryNode(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.ADNode
create sub tree
MarginCurve - class weka.classifiers.evaluation.MarginCurve.
Generates points illustrating the prediction margin.
MarginCurve() - Constructor for class weka.classifiers.evaluation.MarginCurve
 
Matchable - interface weka.core.Matchable.
Interface to something that can be matched with tree matching algorithms.
Maths - class weka.classifiers.functions.pace.Maths.
Class for some utility mathematical or statistical functions.
Maths() - Constructor for class weka.classifiers.functions.pace.Maths
 
Matrix - class weka.classifiers.functions.pace.Matrix.
Jama = Java Matrix class.
Matrix(int, int) - Constructor for class weka.classifiers.functions.pace.Matrix
Construct an m-by-n matrix of zeros.
Matrix(int, int, double) - Constructor for class weka.classifiers.functions.pace.Matrix
Construct an m-by-n constant matrix.
Matrix(double[][]) - Constructor for class weka.classifiers.functions.pace.Matrix
Construct a matrix from a 2-D array.
Matrix(double[][], int, int) - Constructor for class weka.classifiers.functions.pace.Matrix
Construct a matrix quickly without checking arguments.
Matrix(double[], int) - Constructor for class weka.classifiers.functions.pace.Matrix
Construct a matrix from a one-dimensional packed array
Matrix - class weka.core.Matrix.
Class for performing operations on a matrix of floating-point values.
Matrix(int, int) - Constructor for class weka.core.Matrix
Constructs a matrix and initializes it with default values.
Matrix(double[][]) - Constructor for class weka.core.Matrix
Constructs a matrix using a given array.
Matrix(Reader) - Constructor for class weka.core.Matrix
Reads a matrix from a reader.
MatrixPanel - class weka.gui.visualize.MatrixPanel.
This panel displays a plot matrix of the user selected attributes of a given data set.
MatrixPanel() - Constructor for class weka.gui.visualize.MatrixPanel
Constructor
MatrixPanel.Plot - class weka.gui.visualize.MatrixPanel.Plot.
Internal class responsible for displaying the actual matrix Requires the internal data fields of the parent class to be properly initialized before being created
MatrixPanel.Plot() - Constructor for class weka.gui.visualize.MatrixPanel.Plot
Constructor
MaxParentSetSize(int) - Method in class weka.classifiers.bayes.ParentSet
reserve memory for parent set
MergeTwoValues - class weka.filters.unsupervised.attribute.MergeTwoValues.
Merges two values of a nominal attribute.
MergeTwoValues() - Constructor for class weka.filters.unsupervised.attribute.MergeTwoValues
 
MetaCost - class weka.classifiers.meta.MetaCost.
This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos (1999).
MetaCost() - Constructor for class weka.classifiers.meta.MetaCost
 
MixtureDistribution - class weka.classifiers.functions.pace.MixtureDistribution.
Abtract class for manipulating mixture distributions.
MixtureDistribution() - Constructor for class weka.classifiers.functions.pace.MixtureDistribution
 
ModelSelection - class weka.classifiers.trees.j48.ModelSelection.
Abstract class for model selection criteria.
ModelSelection() - Constructor for class weka.classifiers.trees.j48.ModelSelection
 
MultiBoostAB - class weka.classifiers.meta.MultiBoostAB.
Class for boosting a classifier using the MultiBoosting method.
MultiBoostAB() - Constructor for class weka.classifiers.meta.MultiBoostAB
 
MultiClassClassifier - class weka.classifiers.meta.MultiClassClassifier.
Class for handling multi-class datasets with 2-class distribution classifiers.
MultiClassClassifier() - Constructor for class weka.classifiers.meta.MultiClassClassifier
 
MultiClassClassifier.Code - class weka.classifiers.meta.MultiClassClassifier.Code.
Interface for the code constructors
MultiClassClassifier.Code() - Constructor for class weka.classifiers.meta.MultiClassClassifier.Code
 
MultiClassClassifier.ExhaustiveCode - class weka.classifiers.meta.MultiClassClassifier.ExhaustiveCode.
Constructs an exhaustive code assignment
MultiClassClassifier.ExhaustiveCode(int) - Constructor for class weka.classifiers.meta.MultiClassClassifier.ExhaustiveCode
 
MultiClassClassifier.RandomCode - class weka.classifiers.meta.MultiClassClassifier.RandomCode.
Constructs a random code assignment
MultiClassClassifier.RandomCode(int, int, Instances) - Constructor for class weka.classifiers.meta.MultiClassClassifier.RandomCode
 
MultiClassClassifier.StandardCode - class weka.classifiers.meta.MultiClassClassifier.StandardCode.
Constructs a code with no error correction
MultiClassClassifier.StandardCode(int) - Constructor for class weka.classifiers.meta.MultiClassClassifier.StandardCode
 
MultiScheme - class weka.classifiers.meta.MultiScheme.
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
MultiScheme() - Constructor for class weka.classifiers.meta.MultiScheme
 
MultilayerPerceptron - class weka.classifiers.functions.MultilayerPerceptron.
A Classifier that uses backpropagation to classify instances.
MultilayerPerceptron() - Constructor for class weka.classifiers.functions.MultilayerPerceptron
The constructor.
MultilayerPerceptron.ControlPanel - class weka.classifiers.functions.MultilayerPerceptron.ControlPanel.
This provides the basic controls for working with the neuralnetwork
MultilayerPerceptron.ControlPanel() - Constructor for class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
The constructor.
MultilayerPerceptron.NeuralEnd - class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd.
This inner class is used to connect the nodes in the network up to the data that they are classifying, Note that objects of this class are only suitable to go on the attribute side or class side of the network and not both.
MultilayerPerceptron.NeuralEnd(String) - Constructor for class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
 
MultilayerPerceptron.NodePanel - class weka.classifiers.functions.MultilayerPerceptron.NodePanel.
Inner class used to draw the nodes onto.
MultilayerPerceptron.NodePanel() - Constructor for class weka.classifiers.functions.MultilayerPerceptron.NodePanel
The constructor.
MultipleClassifiersCombiner - class weka.classifiers.MultipleClassifiersCombiner.
Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.
MultipleClassifiersCombiner() - Constructor for class weka.classifiers.MultipleClassifiersCombiner
 
m - Variable in class weka.classifiers.functions.pace.Matrix
Row and column dimensions.
m_ADNodes - Variable in class weka.classifiers.bayes.VaryNode
list of ADNode children
m_ADTree - Variable in class weka.classifiers.bayes.BayesNet
 
m_AEEPanel - Variable in class weka.gui.explorer.AttributeSelectionPanel
The panel showing the current attribute evaluation method
m_ALF - Variable in class weka.core.Optimization
 
m_ASEPanel - Variable in class weka.gui.explorer.AttributeSelectionPanel
The panel showing the current search method
m_ASEval - Variable in class weka.attributeSelection.ForwardSelection
 
m_ASEval - Variable in class weka.attributeSelection.RaceSearch
the attribute evaluator to generate the initial ranking when doing a rank race
m_ASEval - Variable in class weka.attributeSelection.RankSearch
the attribute evaluator to use for generating the ranking
m_ASEvaluator - Variable in class weka.attributeSelection.AttributeSelection
the attribute/subset evaluator
m_ASEvaluator - Variable in class weka.filters.supervised.attribute.AttributeSelection
the attribute evaluator to use
m_ASSearch - Variable in class weka.filters.supervised.attribute.AttributeSelection
the search method if any
m_AccuG - Variable in class weka.classifiers.rules.Ridor.RidorRule
The accurate and covered data of this rule in the growing data
m_ActiveTasks - Variable in class weka.gui.WekaTaskMonitor
The number of running weka threads
m_Actual - Variable in class weka.classifiers.evaluation.NominalPrediction
The actual class value
m_Actual - Variable in class weka.classifiers.evaluation.NumericPrediction
The actual class value
m_ActualCount - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of train instances with no missing attribute values
m_AddAtEnd - Variable in class weka.classifiers.meta.CVParameterSelection.CVParameter
True if the parameter should be added at the end of the argument list
m_AddBut - Variable in class weka.gui.GenericArrayEditor
Click to add the current object configuration to the array
m_AddBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to add an algorithm
m_AddBut - Variable in class weka.gui.experiment.DatasetListPanel
Click to add a dataset
m_AdditionalMeasures - Variable in class weka.experiment.AveragingResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.ClassifierSplitEvaluator
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.CrossValidationResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.DatabaseResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.Experiment
Method names of additional measures of objects contained in the custom property iterator.
m_AdditionalMeasures - Variable in class weka.experiment.LearningRateResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.RandomSplitResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.RegressionSplitEvaluator
The names of any additional measures to look for in SplitEvaluators
m_Additions - Variable in class weka.classifiers.functions.VotedPerceptron
The training instances added to the perceptron
m_AdjustWeights - Variable in class weka.filters.supervised.instance.SpreadSubsample
True if instance weights will be adjusted to maintain total weight per class.
m_AdvanceDataSetFirst - Variable in class weka.experiment.Experiment
If true an experiment will advance the current data set befor any custom itererator
m_AdvancedSetupRBut - Variable in class weka.gui.experiment.SetupModePanel
The button for choosing advanced setup mode
m_AlgorithmListModel - Variable in class weka.gui.experiment.AlgorithmListPanel
The list model used
m_AlgorithmListPanel - Variable in class weka.gui.experiment.SimpleSetupPanel
The panel for configuring selected algorithms
m_Alin - Variable in class weka.classifiers.functions.SMOreg
The parameters of the linear transforamtion realized by the filter on the class attribute
m_AllWeightsOne - Variable in class weka.estimators.KKConditionalEstimator
Whether we can optimise the kernel summation
m_AllWeightsOne - Variable in class weka.estimators.KernelEstimator
Whether we can optimise the kernel summation
m_AllWeightsOne - Variable in class weka.estimators.NNConditionalEstimator
Whether we can optimise the kernel summation
m_Alpha - Variable in class weka.classifiers.functions.Winnow
The promotion coefficient
m_AnalysisResults - Variable in class weka.classifiers.CheckClassifier
The results of the analysis as a string
m_Antds - Variable in class weka.classifiers.rules.ConjunctiveRule
The vector of antecedents of this rule
m_Antds - Variable in class weka.classifiers.rules.JRip.RipperRule
The vector of antecedents of this rule
m_Antds - Variable in class weka.classifiers.rules.Ridor.RidorRule
The vector of antecedents of this rule
m_ApplyFilterBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to apply filters and save the results
m_ArffFilter - Variable in class weka.gui.SetInstancesPanel
Filter to ensure only arff files are selected
m_ArffFilter - Variable in class weka.gui.experiment.DatasetListPanel
A filter to ensure only arff files get selected
m_ArffFilter - Variable in class weka.gui.experiment.ResultsPanel
Filter to ensure only arff files are selected for result files
m_ArffFilter - Variable in class weka.gui.explorer.PreprocessPanel
Filter to ensure only arff files are selected
m_ArffFilter - Variable in class weka.gui.visualize.VisualizePanel
Filter to ensure only arff files are selected
m_ArrayEditor - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Allows editing of the custom property values
m_ArtSize - Variable in class weka.classifiers.meta.Decorate
Amount of artificial/random instances to use - specified as a fraction of the training data size.
m_AssociationPanel - Variable in class weka.gui.explorer.Explorer
Label for a panel that still need to be implemented
m_AssociatorEditor - Variable in class weka.gui.explorer.AssociationsPanel
Lets the user configure the associator
m_AttFilter - Variable in class weka.classifiers.functions.Logistic
 
m_AttIndex - Variable in class weka.classifiers.trees.DecisionStump
The attribute used for classification.
m_AttIndex - Variable in class weka.datagenerators.Test
 
m_AttIndex - Variable in class weka.filters.unsupervised.attribute.AddNoise
The attribute's index setting.
m_AttIndex - Variable in class weka.filters.unsupervised.attribute.MakeIndicator
The attribute's index setting.
m_AttIndex - Variable in class weka.filters.unsupervised.attribute.MergeTwoValues
The attribute's index setting.
m_AttIndex - Variable in class weka.filters.unsupervised.attribute.StringToNominal
The attribute's index setting.
m_AttIndex - Variable in class weka.filters.unsupervised.attribute.SwapValues
The attribute's index setting.
m_AttIndex - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
The attribute's index setting.
m_AttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
the array attribute indexes
m_AttList_Irr - Variable in class weka.datagenerators.RDG1
 
m_AttPanel - Variable in class weka.gui.explorer.PreprocessPanel
Panel to let the user toggle attributes
m_AttSummaryPanel - Variable in class weka.gui.explorer.PreprocessPanel
Displays summary stats on the selected attribute
m_AttValues - Variable in class weka.core.Instance
The instance's attribute values.
m_AttVisualizePanel - Variable in class weka.gui.explorer.PreprocessPanel
The visualization of the attribute values
m_AttrIndex - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The index of the nominal attribute in the test and train instances
m_AttrIndex - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The index of the attribute in the test and train instances
m_Attribute - Variable in class weka.classifiers.trees.Id3
Attribute used for splitting.
m_Attribute - Variable in class weka.classifiers.trees.REPTree.Tree
The attribute to split on.
m_Attribute - Variable in class weka.classifiers.trees.RandomTree
The attribute to split on.
m_AttributeEvaluatorEditor - Variable in class weka.gui.explorer.AttributeSelectionPanel
Lets the user configure the attribute evaluator
m_AttributeNameLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the name of the relation
m_AttributeSearchEditor - Variable in class weka.gui.explorer.AttributeSelectionPanel
Lets the user configure the search method
m_AttributeSelection - Variable in class weka.classifiers.functions.LinearRegression
The current attribute selection method
m_AttributeSelection - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The attribute selection object
m_AttributeSelectionPanel - Variable in class weka.gui.explorer.Explorer
Label for a panel that still need to be implemented
m_AttributeStats - Variable in class weka.classifiers.meta.Decorate
Attribute statistics - used for generating artificial examples.
m_AttributeStats - Variable in class weka.gui.AttributeSummaryPanel
Cached stats on the attributes we've summarized so far
m_AttributeType - Variable in class weka.filters.unsupervised.attribute.Add
Record the type of attribute to insert
m_AttributeTypeLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the type of attribute
m_AttributeTypes - Variable in class weka.experiment.InstancesResultListener
Stores the attribute types for each column
m_Attributes - Variable in class weka.core.Instances
The attribute information.
m_AverageProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Average probability of test attribute transforming into train attribute
m_AverageProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Average probability of test attribute transforming into train attribute
m_BETA - Variable in class weka.core.Optimization
 
m_Backup - Variable in class weka.gui.GenericObjectEditor
Holds a copy of the current object that can be reverted to if the user decides to cancel
m_BagSizePercent - Variable in class weka.classifiers.meta.Bagging
The size of each bag sample, as a percentage of the training size
m_BagSizePercent - Variable in class weka.classifiers.meta.MetaCost
The size of each bag sample, as a percentage of the training size
m_Balanced - Variable in class weka.classifiers.functions.Winnow
Use the balanced variant?
m_Base - Variable in class weka.datagenerators.BIRCHCluster.GridVector
 
m_BaseClassifier - Variable in class weka.attributeSelection.WrapperSubsetEval
holds the base classifier object
m_BaseFormat - Variable in class weka.classifiers.meta.Stacking
Format for base data
m_BestClassifierOptions - Variable in class weka.classifiers.meta.CVParameterSelection
The set of all classifier options as determined by cross-validation
m_BestPerformance - Variable in class weka.classifiers.meta.CVParameterSelection
The cross-validated performance of the best options
m_BestThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
The threshold that lead to the best performance
m_BestValue - Variable in class weka.classifiers.meta.ThresholdSelector
The best value that has been observed
m_Beta - Variable in class weka.classifiers.functions.Winnow
The demotion coefficient
m_Betas - Variable in class weka.classifiers.meta.AdaBoostM1
Array for storing the weights for the votes.
m_Bias - Variable in class weka.classifiers.BVDecompose
The calculated bias (squared)
m_BiasToUniformClass - Variable in class weka.filters.supervised.instance.Resample
The degree of bias towards uniform (nominal) class distribution
m_Binarize - Variable in class weka.attributeSelection.ChiSquaredAttributeEval
Just binarize numeric attributes
m_Binarize - Variable in class weka.attributeSelection.InfoGainAttributeEval
Just binarize numeric attributes
m_BlendFactor - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
default sphere of influence blend setting
m_BlendFactor - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
default sphere of influence blend setting
m_BlendMethod - Variable in class weka.classifiers.lazy.KStar
0 = use specified blend, 1 = entropic blend setting
m_BlendMethod - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
B_SPHERE = use specified blend, B_ENTROPY = entropic blend setting
m_BlendMethod - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
0 = use specified blend, 1 = entropic blend setting
m_Blin - Variable in class weka.classifiers.functions.SMOreg
 
m_BoundsFile - Variable in class weka.classifiers.misc.FLR
 
m_BrowseDestinationButton - Variable in class weka.gui.experiment.SimpleSetupPanel
Button for browsing destination files
m_C - Variable in class weka.classifiers.functions.SMO
The complexity parameter.
m_C - Variable in class weka.classifiers.functions.SMOreg
The complexity parameter
m_CEPanel - Variable in class weka.gui.explorer.AssociationsPanel
The panel showing the current associator selection
m_CEPanel - Variable in class weka.gui.explorer.ClassifierPanel
The panel showing the current classifier selection
m_CF - Variable in class weka.classifiers.rules.PART
Confidence level
m_CF - Variable in class weka.classifiers.trees.J48
Confidence level
m_CF - Variable in class weka.classifiers.trees.j48.C45PruneableClassifierTree
The confidence factor for pruning.
m_CLPanel - Variable in class weka.gui.explorer.ClustererPanel
The panel showing the current clusterer selection
m_CVBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to set evaluation mode to cross-validation
m_CVBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to cross-validation
m_CVFolds - Variable in class weka.classifiers.rules.DecisionTable
Number of folds for cross validating feature sets
m_CVLab - Variable in class weka.gui.explorer.AttributeSelectionPanel
Label by where the cv folds are entered
m_CVLab - Variable in class weka.gui.explorer.ClassifierPanel
Label by where the cv folds are entered
m_CVParams - Variable in class weka.classifiers.meta.CVParameterSelection
The set of parameters to cross-validate over
m_CVText - Variable in class weka.gui.explorer.AttributeSelectionPanel
The field where the cv folds are entered
m_CVText - Variable in class weka.gui.explorer.ClassifierPanel
The field where the cv folds are entered
m_Cache - Variable in class weka.classifiers.lazy.KStar
A custom data structure for caching distinct attribute values and their scale factor or stop parameter.
m_Cache - Variable in class weka.classifiers.lazy.kstar.KStarCache
cache table
m_Cache - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
A cache for storing attribute values and their corresponding stop parameters
m_Cache - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
A cache for storing attribute values and their corresponding scale parameters
m_Cache - Variable in class weka.experiment.DatabaseResultListener
Stores the cached values
m_CacheKey - Variable in class weka.experiment.DatabaseResultListener
Stores the key for which the cache is valid
m_CacheKeyIndex - Variable in class weka.experiment.DatabaseResultListener
Stores the index of the key column holding the cache key data
m_CacheKeyName - Variable in class weka.experiment.DatabaseResultListener
Holds the name of the key field to cache upon, or null if no caching
m_CalcOutOfBag - Variable in class weka.classifiers.meta.Bagging
Whether to calculate the out of bag error
m_CalculateStdDevs - Variable in class weka.experiment.AveragingResultProducer
True if standard deviation fields should be produced
m_CancelBut - Variable in class weka.gui.ListSelectorDialog
Click to cancel the property selection
m_CancelBut - Variable in class weka.gui.PropertySelectorDialog
Click to cancel the property selection
m_CapacityIncrement - Variable in class weka.core.FastVector
The capacity increment
m_CapacityMultiplier - Variable in class weka.core.FastVector
The capacity multiplier.
m_Center - Variable in class weka.datagenerators.BIRCHCluster.Cluster
 
m_CheckErr - Variable in class weka.classifiers.rules.JRip
Whether check the error rate >= 0.5 in stopping criteria
m_ChiSquareds - Variable in class weka.attributeSelection.ChiSquaredAttributeEval
The chi-squared value for each attribute
m_ChildPropertySheet - Variable in class weka.gui.GenericObjectEditor.GOEPanel
The component that performs classifier customization
m_Class - Variable in class weka.classifiers.rules.JRip
The class attribute of the data
m_Class - Variable in class weka.classifiers.rules.Ridor.RidorRule
The internal representation of the class label to be predicted
m_Class - Variable in class weka.classifiers.rules.Ridor
The class attribute of the data
m_Class - Variable in class weka.classifiers.rules.ZeroR
The class attribute.
m_Class - Variable in class weka.filters.unsupervised.attribute.NumericTransform
Class containing transformation method.
m_ClassAttribute - Variable in class weka.classifiers.meta.LogitBoost
The actual class attribute (for getting class names)
m_ClassAttribute - Variable in class weka.classifiers.meta.MultiClassClassifier
Internal copy of the class attribute for output purposes
m_ClassAttribute - Variable in class weka.classifiers.meta.OrdinalClassClassifier
Internal copy of the class attribute for output purposes
m_ClassAttribute - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The actual class attribute (for getting class names)
m_ClassAttribute - Variable in class weka.classifiers.rules.ConjunctiveRule
The class attribute of the data
m_ClassAttribute - Variable in class weka.classifiers.rules.Ridor.RidorRule
The class attribute of the data
m_ClassAttribute - Variable in class weka.classifiers.trees.Id3
Class attribute of dataset.
m_ClassAttribute - Variable in class weka.filters.supervised.attribute.ClassOrder
Class attribute of the data
m_ClassCombo - Variable in class weka.gui.explorer.AttributeSelectionPanel
Lets the user select the class column
m_ClassCombo - Variable in class weka.gui.explorer.ClassifierPanel
Lets the user select the class column
m_ClassCombo - Variable in class weka.gui.explorer.ClustererPanel
Lets the user select the class column for classes to clusters based evaluation
m_ClassCounts - Variable in class weka.classifiers.bayes.AODE
The number of times each class value occurs in the dataset
m_ClassCounts - Variable in class weka.filters.supervised.attribute.ClassOrder
This class can provide the class distribution in the sorted order as side effect
m_ClassDistribution - Variable in class weka.classifiers.bayes.NaiveBayes
The class estimator.
m_ClassFilters - Variable in class weka.classifiers.meta.ClassificationViaRegression
The filters used to transform the class.
m_ClassFilters - Variable in class weka.classifiers.meta.MultiClassClassifier
The filters used to transform the class.
m_ClassFilters - Variable in class weka.classifiers.meta.OrdinalClassClassifier
The filters used to transform the class.
m_ClassFirst - Variable in class weka.experiment.Experiment
True if the class attribute is the first attribute for all datasets involved in this experiment.
m_ClassFirst - Variable in class weka.gui.experiment.Experimenter
True if the class attribute is the first attribute for all datasets involved in this experiment.
m_ClassFlag - Variable in class weka.datagenerators.ClusterGenerator
 
m_ClassIndex - Variable in class weka.classifiers.BVDecompose
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.BVDecomposeSegCVSub
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.bayes.AODE
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.functions.LinearRegression
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.functions.Logistic
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.functions.PaceRegression
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.lazy.LBR.Indexes
the Class Index for the data set
m_ClassIndex - Variable in class weka.classifiers.misc.HyperPipes
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.misc.VFI
The index of the class attribute
m_ClassIndex - Variable in class weka.core.Instances
The class attribute's index
m_ClassIndex - Variable in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
Storing the class index
m_ClassIsNominal - Variable in class weka.classifiers.Evaluation
Is the class nominal or numeric?
m_ClassIsOrdinal - Variable in class weka.classifiers.meta.TreeBasedMultiClassClassifier
Whether class is ordinal
m_ClassMean - Variable in class weka.classifiers.functions.LinearRegression
The mean of the class attribute
m_ClassMeans - Variable in class weka.classifiers.meta.RegressionByDiscretization
The mean values for each Discretized class interval.
m_ClassMode - Variable in class weka.classifiers.meta.ThresholdSelector
Method to determine which class to optimize for
m_ClassNameLabel - Variable in class weka.gui.GenericObjectEditor.GOEPanel
The name of the current class
m_ClassNames - Variable in class weka.classifiers.Evaluation
The names of the classes.
m_ClassNames - Variable in class weka.classifiers.evaluation.ConfusionMatrix
Stores the names of the classes
m_ClassOrder - Variable in class weka.filters.supervised.attribute.ClassOrder
The class order to be sorted
m_ClassPriors - Variable in class weka.classifiers.Evaluation
The prior probabilities of the classes
m_ClassPriorsSum - Variable in class weka.classifiers.Evaluation
The sum of counts for priors
m_ClassProbs - Variable in class weka.classifiers.trees.REPTree.Tree
Class probabilities from the training data in the nominal case.
m_ClassProbs - Variable in class weka.classifiers.trees.RandomTree
Class probabilities from the training data.
m_ClassStdDev - Variable in class weka.classifiers.functions.LinearRegression
The standard deviations of the class attribute
m_ClassType - Variable in class weka.classifiers.lazy.IBk
The class attribute type
m_ClassType - Variable in class weka.classifiers.lazy.KStar
The class attribute type
m_ClassType - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The class attribute type
m_ClassType - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The class attribute type
m_ClassType - Variable in class weka.gui.GenericObjectEditor
The Class of objects being edited
m_ClassValue - Variable in class weka.classifiers.rules.NNge.Exemplar
class of the Exemplar
m_ClassValue - Variable in class weka.classifiers.rules.ZeroR
The class value 0R predicts.
m_ClassValue - Variable in class weka.classifiers.trees.Id3
Class value if node is leaf.
m_ClassValue - Variable in class weka.datagenerators.RDG1.RuleList
 
m_ClassesToClustersBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to classes to clusters based evaluation
m_Classifier - Variable in class weka.attributeSelection.ClassifierSubsetEval
holds the classifier to use for error estimates
m_Classifier - Variable in class weka.classifiers.BVDecompose
An instantiated base classifier used for getting and testing options.
m_Classifier - Variable in class weka.classifiers.BVDecomposeSegCVSub
An instantiated base classifier used for getting and testing options.
m_Classifier - Variable in class weka.classifiers.CheckClassifier
The classifier to be examined
m_Classifier - Variable in class weka.classifiers.SingleClassifierEnhancer
The base classifier to use
m_Classifier - Variable in class weka.classifiers.meta.AdditiveRegression
Base classifier.
m_Classifier - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The classifier
m_Classifier - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The classifier
m_Classifier - Variable in class weka.classifiers.meta.Decorate
The model base classifier to use.
m_Classifier - Variable in class weka.classifiers.meta.FilteredClassifier
The classifier
m_Classifier - Variable in class weka.classifiers.meta.MultiClassClassifier
The class name of the base classifier.
m_Classifier - Variable in class weka.classifiers.meta.MultiScheme
The classifier that had the best performance on training data.
m_Classifier - Variable in class weka.classifiers.meta.OrdinalClassClassifier
The class name of the base classifier.
m_Classifier - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The model base classifier to use
m_Classifier - Variable in class weka.classifiers.meta.ThresholdSelector
The generated base classifier
m_Classifier - Variable in class weka.classifiers.meta.TreeBasedMultiClassClassifier
The classifier at this node.
m_Classifier - Variable in class weka.experiment.ClassifierSplitEvaluator
The classifier used for evaluation
m_Classifier - Variable in class weka.experiment.RegressionSplitEvaluator
The classifier used for evaluation
m_Classifier - Variable in class weka.gui.beans.Classifier
 
m_ClassifierEditor - Variable in class weka.gui.beans.ClassifierCustomizer
 
m_ClassifierEditor - Variable in class weka.gui.experiment.AlgorithmListPanel
Lets the user configure the classifier
m_ClassifierEditor - Variable in class weka.gui.explorer.ClassifierPanel
Lets the user configure the classifier
m_ClassifierIndex - Variable in class weka.classifiers.meta.MultiScheme
The index into the vector for the selected scheme
m_ClassifierOptions - Variable in class weka.classifiers.BVDecompose
The options to be passed to the base classifier.
m_ClassifierOptions - Variable in class weka.classifiers.BVDecomposeSegCVSub
The options to be passed to the base classifier.
m_ClassifierOptions - Variable in class weka.classifiers.CheckClassifier
The options to be passed to the base classifier.
m_ClassifierOptions - Variable in class weka.classifiers.meta.CVParameterSelection
The base classifier options (not including those being set by cross-validation)
m_ClassifierOptions - Variable in class weka.experiment.ClassifierSplitEvaluator
The classifier options (if any)
m_ClassifierOptions - Variable in class weka.experiment.RegressionSplitEvaluator
The classifier options (if any)
m_ClassifierPanel - Variable in class weka.gui.explorer.Explorer
The panel for running classifiers
m_ClassifierVersion - Variable in class weka.experiment.ClassifierSplitEvaluator
The classifier version
m_ClassifierVersion - Variable in class weka.experiment.RegressionSplitEvaluator
The classifier version
m_Classifiers - Variable in class weka.classifiers.IteratedSingleClassifierEnhancer
Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.MultipleClassifiersCombiner
Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.meta.ClassificationViaRegression
The classifiers.
m_Classifiers - Variable in class weka.classifiers.meta.LogitBoost
Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.meta.MultiClassClassifier
The classifiers.
m_Classifiers - Variable in class weka.classifiers.meta.OrdinalClassClassifier
The classifiers.
m_ClassifyIterations - Variable in class weka.classifiers.BVDecomposeSegCVSub
The number of times an instance is classified
m_Clear - Variable in class weka.gui.streams.InstanceTable
 
m_Clear - Variable in class weka.gui.streams.InstanceViewer
 
m_ClusterCentroids - Variable in class weka.clusterers.FarthestFirst
holds the cluster centroids
m_ClusterCentroids - Variable in class weka.clusterers.SimpleKMeans
holds the cluster centroids
m_ClusterList - Variable in class weka.datagenerators.BIRCHCluster
 
m_ClusterStdDevs - Variable in class weka.clusterers.SimpleKMeans
Holds the standard deviations of attributes in each cluster
m_Clusterer - Variable in class weka.clusterers.ClusterEvaluation
the clusterer
m_Clusterer - Variable in class weka.filters.unsupervised.attribute.AddCluster
The clusterer used to do the cleansing
m_ClustererEditor - Variable in class weka.gui.explorer.ClustererPanel
Lets the user configure the clusterer
m_ClustererPanel - Variable in class weka.gui.explorer.Explorer
Label for a panel that still need to be implemented
m_Cnsqt - Variable in class weka.classifiers.rules.ConjunctiveRule
The consequent of this rule
m_Codebits - Variable in class weka.classifiers.meta.MultiClassClassifier.Code
Subclasses must allocate and fill these.
m_Coefficients - Variable in class weka.classifiers.functions.LinearRegression
Array for storing coefficients of linear regression.
m_Coefficients - Variable in class weka.classifiers.functions.PaceRegression
Array for storing coefficients of linear regression.
m_Colors - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_ColourChangeListeners - Variable in class weka.gui.visualize.ClassPanel
An optional list of listeners who want to know when a colour changes.
m_ColourCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the attribute to use for colouring
m_Cols - Variable in class weka.filters.unsupervised.attribute.NumericTransform
Stores which columns to transform.
m_CommandArgs - Variable in class weka.gui.SimpleCLI.ClassRunner
Stores the command line arguments to pass to the main method
m_CommandHistory - Variable in class weka.gui.SimpleCLI
The history of commands entered interactively
m_Committee - Variable in class weka.classifiers.meta.Decorate
Vector of classifiers that make up the committee/ensemble.
m_CompareCombo - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select which performance measure to analyze
m_CompareModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_CompareCombo
m_ComputeRandomCols - Variable in class weka.classifiers.lazy.KStar
Flag turning on and off the computation of random class colomns
m_CondMean - Variable in class weka.estimators.NNConditionalEstimator
Current Conditional mean
m_CondValues - Variable in class weka.estimators.KKConditionalEstimator
Vector containing all of the conditioning values seen
m_CondValues - Variable in class weka.estimators.NNConditionalEstimator
Vector containing all of the conditioning values seen
m_CondiCounts - Variable in class weka.classifiers.bayes.AODE
3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts
m_ConfigureBut - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Click to select the property to iterate over
m_ConfigureListener - Variable in class weka.gui.experiment.ResultsPanel
An actionlisteners that updates ttest settings
m_ConfusionMatrix - Variable in class weka.classifiers.Evaluation
Array for storing the confusion matrix.
m_Connection - Variable in class weka.experiment.DatabaseUtils
The database connection
m_Consequent - Variable in class weka.classifiers.rules.JRip.RipperRule
The internal representation of the class label to be predicted
m_ConstDelta - Variable in class weka.estimators.MahalanobisEstimator
The difference between the conditioning value and the conditioning mean
m_Contents - Variable in class weka.core.Queue.QueueNode
The nodes contents
m_Converter - Variable in class weka.filters.supervised.attribute.ClassOrder
The 1-1 converting table from the original class values to the new values
m_CopyCols - Variable in class weka.filters.unsupervised.attribute.Copy
Stores which columns to copy
m_Correct - Variable in class weka.classifiers.Evaluation
The weight of all correctly classified instances.
m_CostFile - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The name of the cost file, for command line options
m_CostFile - Variable in class weka.classifiers.meta.MetaCost
The name of the cost file, for command line options
m_CostMatrix - Variable in class weka.classifiers.Evaluation
The cost matrix (if given).
m_CostMatrix - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The cost matrix
m_CostMatrix - Variable in class weka.classifiers.meta.MetaCost
The cost matrix
m_CostMatrixEditor - Variable in class weka.gui.explorer.ClassifierPanel
The cost matrix editor for evaluation costs
m_Count - Variable in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
The total number of entries in the hash table.
m_Count - Variable in class weka.gui.streams.InstanceCounter
 
m_Count - Variable in class weka.gui.streams.InstanceSavePanel
 
m_CountFieldName - Variable in class weka.experiment.AveragingResultProducer
The name of the field that will contain the number of results averaged over.
m_Count_Lab - Variable in class weka.gui.streams.InstanceCounter
 
m_Counter - Variable in class weka.core.FastVector.FastVectorEnumeration
The counter.
m_Counts - Variable in class weka.classifiers.bayes.DiscreteEstimatorBayes
Hold the counts
m_Counts - Variable in class weka.classifiers.bayes.NaiveBayesSimple
All the counts for nominal attributes.
m_Counts - Variable in class weka.classifiers.lazy.LBR
All the counts for nominal attributes.
m_Counts - Variable in class weka.classifiers.rules.ZeroR
The number of instances in each class (null if class numeric).
m_Counts - Variable in class weka.estimators.DiscreteEstimator
Hold the counts
m_Covariance - Variable in class weka.estimators.NNConditionalEstimator
Current covariance matrix
m_CovarianceInverse - Variable in class weka.estimators.MahalanobisEstimator
The inverse of the covariance matrix
m_Cover - Variable in class weka.classifiers.rules.Ridor
Statistics of the data
m_CoverG - Variable in class weka.classifiers.rules.Ridor.RidorRule
The accurate and covered data of this rule in the growing data
m_CoverP - Variable in class weka.classifiers.rules.Ridor.RidorRule
The sum of weights of the data covered by this rule in the pruning data
m_CreateSparseData - Variable in class weka.experiment.InstanceQuery
Determines whether sparse data is created
m_CrossValidate - Variable in class weka.classifiers.lazy.IBk
Whether to select k by cross validation
m_Current - Variable in class weka.gui.HierarchyPropertyParser
Keep track of the current node when traversing the tree
m_CurrentInstances - Variable in class weka.experiment.Experiment
The dataset currently being used
m_CurrentProperty - Variable in class weka.experiment.Experiment
The custom property value that has actually been set
m_CurrentSize - Variable in class weka.experiment.LearningRateResultProducer
The current dataset size during stepping
m_CurrentVis - Variable in class weka.gui.explorer.ClassifierPanel
The current visualization object
m_CurrentVis - Variable in class weka.gui.explorer.ClustererPanel
The current visualization object
m_CutPoints - Variable in class weka.filters.supervised.attribute.Discretize
Store the current cutpoints
m_CutPoints - Variable in class weka.filters.unsupervised.attribute.Discretize
Store the current cutpoints
m_DP - Variable in class weka.gui.beans.Loader.LoadThread
 
m_Data - Variable in class weka.classifiers.functions.LeastMedSq
 
m_Data - Variable in class weka.classifiers.functions.Logistic
The data saved as a matrix
m_Data - Variable in class weka.classifiers.meta.TreeBasedMultiClassClassifier
 
m_Data - Variable in class weka.classifiers.rules.RuleStats
The data on which the stats calculation is based
m_DataFileName - Variable in class weka.classifiers.BVDecompose
The name of the data file used for the decomposition
m_DataFileName - Variable in class weka.classifiers.BVDecomposeSegCVSub
The name of the data file used for the decomposition
m_DatabaseQueryEditor - Variable in class weka.gui.explorer.PreprocessPanel
Lets the user enter a DB query
m_DatabaseURL - Variable in class weka.experiment.DatabaseUtils
Database URL
m_Dataset - Variable in class weka.core.Instance
The dataset the instance has access to.
m_Dataset - Variable in class weka.core.converters.SerializedInstancesLoader
Holds the structure (header) of the data set.
m_Dataset - Variable in class weka.datagenerators.Test
 
m_Dataset - Variable in class weka.experiment.PairedTTester.Dataset
 
m_DatasetFormat - Variable in class weka.datagenerators.BIRCHCluster
 
m_DatasetFormat - Variable in class weka.datagenerators.RDG1
 
m_DatasetKeyBut - Variable in class weka.gui.experiment.ResultsPanel
Click to edit the columns used to determine the scheme
m_DatasetKeyColumns - Variable in class weka.experiment.PairedTTester
An array containing the indexes of just the selected columns
m_DatasetKeyColumnsRange - Variable in class weka.experiment.PairedTTester
The range of columns that specify a unique "dataset" (eg: scheme plus configuration)
m_DatasetKeyLabel - Variable in class weka.gui.experiment.ResultsPanel
Displays the currently selected column names for the scheme & options
m_DatasetKeyList - Variable in class weka.gui.experiment.ResultsPanel
Displays the list of selected columns determining the scheme
m_DatasetKeyModel - Variable in class weka.gui.experiment.ResultsPanel
Stores the list of attributes for selecting the scheme columns
m_DatasetListPanel - Variable in class weka.gui.experiment.SetupPanel
The panel for configuring selected datasets
m_DatasetListPanel - Variable in class weka.gui.experiment.SimpleSetupPanel
The panel for configuring selected datasets
m_DatasetModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_DatasetCombo
m_DatasetNumber - Variable in class weka.experiment.Experiment
The current dataset number when the experiment is running
m_DatasetSpecifiers - Variable in class weka.experiment.PairedTTester
The list of dataset specifiers
m_Datasets - Variable in class weka.experiment.Experiment
An array of dataset files
m_Datasets - Variable in class weka.experiment.PairedTTester.Resultset
 
m_DateFormat - Variable in class weka.core.Attribute
Date format specification for date attributes
m_DbaseURLLab - Variable in class weka.gui.DatabaseConnectionDialog
 
m_DbaseURLText - Variable in class weka.gui.DatabaseConnectionDialog
 
m_Debug - Variable in class weka.classifiers.BVDecompose
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.BVDecomposeSegCVSub
Debugging mode, gives extra output if true.
m_Debug - Variable in class weka.classifiers.CheckClassifier
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.Classifier
Whether the classifier is run in debug mode.
m_Debug - Variable in class weka.classifiers.bayes.AODE
If true, outputs debugging info
m_Debug - Variable in class weka.classifiers.functions.Logistic
Debugging output
m_Debug - Variable in class weka.classifiers.functions.PaceRegression
True if debug output will be printed
m_Debug - Variable in class weka.classifiers.meta.Decorate
Set to true to get debugging output.
m_Debug - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Whether to output debug messages
m_Debug - Variable in class weka.classifiers.rules.JRip
Whether in a debug mode
m_Debug - Variable in class weka.classifiers.trees.RandomTree
Debug info
m_Debug - Static variable in class weka.core.Optimization
 
m_Debug - Variable in class weka.datagenerators.BIRCHCluster
 
m_Debug - Variable in class weka.datagenerators.ClusterGenerator
 
m_Debug - Variable in class weka.datagenerators.Generator
 
m_Debug - Variable in class weka.datagenerators.RDG1
 
m_Debug - Variable in class weka.experiment.DatabaseResultListener
True if debugging output should be printed
m_Debug - Variable in class weka.experiment.DatabaseUtils
True if debugging output should be printed
m_Debug - Variable in class weka.filters.Filter
Debugging mode
m_Debug - Variable in class weka.filters.unsupervised.attribute.AddExpression
If true, makes the attribute name equal to the postfix parse of the expression
m_Debug - Variable in class weka.gui.streams.InstanceCounter
 
m_Debug - Variable in class weka.gui.streams.InstanceLoader
 
m_Debug - Variable in class weka.gui.streams.InstanceTable
 
m_Debug - Variable in class weka.gui.streams.InstanceViewer
 
m_DecisionList - Variable in class weka.datagenerators.RDG1
 
m_DefDstr - Variable in class weka.classifiers.rules.ConjunctiveRule
The default rule distribution of the data not covered
m_DefaultColors - Variable in class weka.gui.visualize.AttributePanel
default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.ClassPanel
default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.Plot2D
default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.VisualizePanel
default colours for colouring discrete class
m_DefaultCols - Variable in class weka.filters.unsupervised.attribute.Discretize
The default columns to discretize
m_Del - Static variable in class weka.classifiers.functions.SMO
Precision constant for updating sets
m_Del - Static variable in class weka.classifiers.functions.SMOreg
Precision constant for updating sets
m_DeleteBut - Variable in class weka.gui.GenericArrayEditor
Click this to delete the selected array values
m_DeleteBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to remove the selected dataset from the list
m_DeleteBut - Variable in class weka.gui.experiment.DatasetListPanel
Click to remove the selected dataset from the list
m_DeleteBut - Variable in class weka.gui.experiment.HostListPanel
Click to remove the selected host from the list
m_DeltaCols - Variable in class weka.filters.unsupervised.attribute.FirstOrder
Stores which columns to take differences between
m_Depth - Variable in class weka.gui.HierarchyPropertyParser
The depth of the tree
m_Description - Variable in class weka.core.Option
What does this option do?
m_Description - Variable in class weka.gui.ExtensionFileFilter
The text description of the types of files accepted
m_DesignatedClass - Variable in class weka.classifiers.meta.ThresholdSelector
Designated class value, determined during building
m_DesiredSize - Variable in class weka.classifiers.meta.Decorate
The desired ensemble size.
m_DesiredWeightOfInstancesPerInterval - Variable in class weka.filters.unsupervised.attribute.Discretize
The desired weight of instances per bin
m_DestFileChooser - Variable in class weka.gui.experiment.SimpleSetupPanel
The file chooser for selecting result destinations
m_Determinant - Variable in class weka.estimators.MahalanobisEstimator
The determinant of the covariance matrix
m_Devs - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The standard deviations for numeric attributes.
m_Dictionary - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
Contains a mapping of valid words to attribute indexes
m_Disc - Variable in class weka.classifiers.bayes.NaiveBayes
The discretization filter.
m_DiscretizeCols - Variable in class weka.filters.supervised.attribute.Discretize
Stores which columns to Discretize
m_DiscretizeCols - Variable in class weka.filters.unsupervised.attribute.Discretize
Stores which columns to Discretize
m_Discretizer - Variable in class weka.classifiers.meta.RegressionByDiscretization
The discretization filter.
m_DistMult - Variable in class weka.datagenerators.BIRCHCluster
 
m_Distance - Variable in class weka.classifiers.lazy.IBk.NeighborNode
The distance from the current instance to this neighbor
m_DistanceWeighting - Variable in class weka.classifiers.lazy.IBk
Whether the neighbours should be distance-weighted
m_Distances - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The set of disctances from the test attribute to the set of train attributes
m_DistinctLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the number of distinct values
m_DistributeExperimentPanel - Variable in class weka.gui.experiment.SetupPanel
The panel for enabling a distributed experiment
m_Distribution - Variable in class weka.classifiers.evaluation.NominalPrediction
The predicted probabilities
m_Distribution - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Distribution of the attribute value in the train dataset
m_Distribution - Variable in class weka.classifiers.trees.DecisionStump
The distribution of class values or the means in each subset.
m_Distribution - Variable in class weka.classifiers.trees.Id3
Class distribution if node is leaf.
m_Distribution - Variable in class weka.classifiers.trees.REPTree.Tree
The (unnormalized) class distribution in the nominal case.
m_Distribution - Variable in class weka.classifiers.trees.RandomTree
The class distribution from the training data.
m_DistributionSpread - Variable in class weka.filters.supervised.instance.SpreadSubsample
True if the first batch has been done
m_Distributions - Variable in class weka.classifiers.bayes.BayesNet
The attribute estimators containing CPTs.
m_Distributions - Variable in class weka.classifiers.bayes.NaiveBayes
The attribute estimators.
m_Distributions - Variable in class weka.classifiers.rules.JRip
The predicted class distribution
m_Distributions - Variable in class weka.classifiers.rules.RuleStats
The class distributions predicted by each rule
m_DontNormalize - Variable in class weka.classifiers.lazy.IBk
True if normalization is turned off
m_Editor - Variable in class weka.gui.PropertyDialog
The property editor
m_Editor - Variable in class weka.gui.PropertyPanel
The property editor
m_Editor - Variable in class weka.gui.PropertyText
The property editor
m_Editor - Variable in class weka.gui.PropertyValueSelector
The property editor
m_EditorClass - Variable in class weka.gui.GenericArrayEditor.EditorListCellRenderer
The class of the property editor for array objects
m_EditorComponent - Variable in class weka.gui.GenericObjectEditor
The GUI component for editing values, created when needed
m_EditorComponent - Variable in class weka.gui.PropertyDialog
The custom editor component
m_Editors - Variable in class weka.gui.PropertySheetPanel
Holds property editors of the object
m_ElementClass - Variable in class weka.gui.GenericArrayEditor
The class of objects allowed in the array
m_ElementEditor - Variable in class weka.gui.GenericArrayEditor
The property editor for the class we are editing
m_ElementList - Variable in class weka.gui.GenericArrayEditor
The list component displaying current values
m_Elements - Variable in class weka.core.Matrix
The values of the matrix
m_EliminateColinearAttributes - Variable in class weka.classifiers.functions.LinearRegression
Try to eliminate correlated attributes?
m_Enabled - Variable in class weka.gui.GenericObjectEditor
True if the GUI component is needed
m_Epsilon - Static variable in class weka.core.Optimization
 
m_Err - Variable in class weka.classifiers.rules.Ridor
Statistics of the data
m_ErrRedirector - Variable in class weka.gui.SimpleCLI
The thread that sends output from m_POE to the output box
m_Error - Variable in class weka.classifiers.BVDecompose
The error rate
m_Error - Variable in class weka.classifiers.BVDecomposeSegCVSub
The error rate
m_ErrorEstimator - Variable in class weka.classifiers.Evaluation
Numeric class error estimator for scheme
m_ErrorFlags - Variable in class weka.classifiers.lazy.LBR
 
m_Errors - Variable in class weka.classifiers.lazy.LBR
 
m_Estimators - Variable in class weka.estimators.DDConditionalEstimator
Hold the sub-estimators
m_Estimators - Variable in class weka.estimators.DKConditionalEstimator
Hold the sub-estimators
m_Estimators - Variable in class weka.estimators.DNConditionalEstimator
Hold the sub-estimators
m_Estimators - Variable in class weka.estimators.KDConditionalEstimator
Hold the sub-estimators
m_Estimators - Variable in class weka.estimators.NDConditionalEstimator
Hold the sub-estimators
m_EvalMode - Variable in class weka.classifiers.meta.ThresholdSelector
The evaluation mode
m_EvalWRTCostsBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to evaluate w.r.t a cost matrix
m_Evaluation - Variable in class weka.attributeSelection.ClassifierSubsetEval
holds the evaluation object to use for evaluating the classifier
m_Evaluation - Variable in class weka.attributeSelection.WrapperSubsetEval
holds an evaluation object
m_Evaluator - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The attribute evaluator to use
m_ExecutionStatus - Variable in class weka.experiment.TaskStatusInfo
Holds current execution status.
m_Exemplars - Variable in class weka.classifiers.rules.NNge
The list of Exemplars
m_ExemplarsByClass - Variable in class weka.classifiers.rules.NNge
The lists of Exemplars by class
m_Exp - Variable in class weka.gui.experiment.AlgorithmListPanel
The experiment to set the algorithm list of
m_Exp - Variable in class weka.gui.experiment.DatasetListPanel
The experiment to set the dataset list of
m_Exp - Variable in class weka.gui.experiment.DistributeExperimentPanel
The experiment to configure.
m_Exp - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
The experiment this all applies to
m_Exp - Variable in class weka.gui.experiment.HostListPanel
The remote experiment to set the host list of
m_Exp - Variable in class weka.gui.experiment.ResultsPanel
An experiment (used for identifying a result source) -- optional
m_Exp - Variable in class weka.gui.experiment.RunNumberPanel
The experiment being configured
m_Exp - Variable in class weka.gui.experiment.RunPanel
The experiment to run
m_Exp - Variable in class weka.gui.experiment.SetupPanel
The experiment being configured
m_Exp - Variable in class weka.gui.experiment.SimpleSetupPanel
The experiment being configured
m_ExpClassificationRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing classification experiment
m_ExpCopy - Variable in class weka.gui.experiment.RunPanel.ExperimentRunner
 
m_ExpFilter - Variable in class weka.gui.experiment.SetupPanel
A filter to ensure only experiment files get shown in the chooser
m_ExpFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
A filter to ensure only experiment files get shown in the chooser
m_ExpRegressionRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing regression experiment
m_ExpectedResultsPerAverage - Variable in class weka.experiment.AveragingResultProducer
The number of results expected to average over for each run
m_ExperimentParameterLabel - Variable in class weka.gui.experiment.SimpleSetupPanel
Label for parameter field
m_ExperimentParameterTField - Variable in class weka.gui.experiment.SimpleSetupPanel
Input field for experiment parameter
m_ExperimentTypeCBox - Variable in class weka.gui.experiment.SimpleSetupPanel
Combo box for choosing experiment type
m_ExperimenterBut - Variable in class weka.gui.GUIChooser
Click to open the Explorer
m_ExperimenterFrame - Variable in class weka.gui.GUIChooser
The frame containing the experiment interface
m_ExplorerBut - Variable in class weka.gui.GUIChooser
Click to open the Explorer
m_ExplorerFrame - Variable in class weka.gui.GUIChooser
The frame containing the explorer interface
m_Exponent - Variable in class weka.classifiers.functions.VotedPerceptron
The exponent
m_Extension - Variable in class weka.gui.ExtensionFileFilter
The filename extension of accepted files
m_FalseNeg - Variable in class weka.classifiers.evaluation.TwoClassStats
Pos predicted as neg
m_FalsePos - Variable in class weka.classifiers.evaluation.TwoClassStats
Neg predicted as pos
m_File - Variable in class weka.core.converters.ArffLoader
 
m_FileChooser - Variable in class weka.gui.FileEditor
The file chooser used for selecting files
m_FileChooser - Variable in class weka.gui.GenericObjectEditor.GOEPanel
The filechooser for opening and saving object files
m_FileChooser - Variable in class weka.gui.SetInstancesPanel
The file chooser for selecting arff files
m_FileChooser - Variable in class weka.gui.beans.KnowledgeFlow
The file chooser for selecting layout files
m_FileChooser - Variable in class weka.gui.experiment.DatasetListPanel
The file chooser component
m_FileChooser - Variable in class weka.gui.experiment.ResultsPanel
The file chooser for selecting result files
m_FileChooser - Variable in class weka.gui.experiment.SetupPanel
The file chooser for selecting experiments
m_FileChooser - Variable in class weka.gui.experiment.SimpleSetupPanel
The file chooser for selecting experiments
m_FileChooser - Variable in class weka.gui.explorer.ClassifierPanel
The file chooser for selecting model files
m_FileChooser - Variable in class weka.gui.explorer.ClustererPanel
The file chooser for selecting model files
m_FileChooser - Variable in class weka.gui.explorer.PreprocessPanel
The file chooser for selecting arff files
m_FileChooser - Variable in class weka.gui.visualize.VisualizePanel
file chooser for saving instances
m_FileNameTex - Variable in class weka.gui.streams.InstanceLoader
 
m_FillWithMissing - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
True if missing values should be used rather than removing instances where the translated value is not known (due to border effects).
m_Filter - Variable in class weka.classifiers.functions.SMO
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.functions.SMOreg
The filter used to standardize/normalize all values.
m_Filter - Variable in class weka.classifiers.meta.FilteredClassifier
The filter
m_Filter - Variable in class weka.classifiers.meta.TreeBasedMultiClassClassifier
The filter used at this node
m_Filter - Variable in class weka.classifiers.rules.JRip
The filter used to randomize the class order
m_Filter - Variable in class weka.gui.beans.Filter
The filter to use.
m_FilterEditor - Variable in class weka.gui.explorer.PreprocessPanel
Lets the user configure the filter
m_FilterOptions - Variable in class weka.filters.supervised.attribute.AttributeSelection
holds a copy of the full set of valid options passed to the filter
m_FilterPanel - Variable in class weka.gui.explorer.PreprocessPanel
Filter configuration
m_Filtered - Variable in class weka.classifiers.rules.RuleStats
The set of instances filtered by the ruleset
m_FilteredInstances - Variable in class weka.classifiers.meta.FilteredClassifier
The instance structure of the filtered instances
m_FindNumBins - Variable in class weka.filters.unsupervised.attribute.Discretize
Find the number of bins using cross-validated entropy.
m_Finished - Variable in class weka.experiment.Experiment
True if the experiment has finished running
m_First - Variable in class weka.classifiers.lazy.IBk.NeighborList
The first node in the list
m_First - Variable in class weka.gui.LogPanel
An indicator for whether text has been output yet
m_FirstBatchDone - Variable in class weka.filters.supervised.instance.Resample
True if the first batch has been done
m_FirstBatchDone - Variable in class weka.filters.supervised.instance.SpreadSubsample
True if the first batch has been done
m_FirstBatchDone - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
True if the first batch has been done
m_FirstBatchDone - Variable in class weka.filters.unsupervised.instance.Resample
True if the first batch has been done
m_FirstIndex - Variable in class weka.filters.unsupervised.attribute.MergeTwoValues
The first value's index setting.
m_FirstIndex - Variable in class weka.filters.unsupervised.attribute.SwapValues
The first value's index setting.
m_FirstSuccessor - Variable in class weka.classifiers.meta.TreeBasedMultiClassClassifier
The first successor
m_Fold - Variable in class weka.filters.supervised.instance.StratifiedRemoveFolds
Fold to output
m_Fold - Variable in class weka.filters.unsupervised.instance.RemoveFolds
Fold to output
m_FoldColumn - Variable in class weka.experiment.PairedTTester
The option setting for the fold number column (-1 means none)
m_Folds - Variable in class weka.classifiers.rules.ConjunctiveRule
The number of folds to split data into Grow and Prune for REP
m_Folds - Variable in class weka.classifiers.rules.JRip
The number of folds to split data into Grow and Prune for IREP
m_Folds - Variable in class weka.classifiers.rules.Ridor
The number of folds to split data into Grow and Prune for IREP
m_Format - Variable in class weka.datagenerators.ClusterGenerator
 
m_Format - Variable in class weka.datagenerators.Generator
 
m_FramedOutput - Variable in class weka.gui.ResultHistoryPanel
A Hashtable mapping names to output text components
m_Frequencies - Variable in class weka.classifiers.bayes.AODE
The frequency of each attribute value for the dataset
m_FromDBaseBut - Variable in class weka.gui.experiment.ResultsPanel
Click to load results from a database
m_FromExpBut - Variable in class weka.gui.experiment.ResultsPanel
Click to get results from the destination given in the experiment
m_FromFileBut - Variable in class weka.gui.experiment.ResultsPanel
Click to load results from a file
m_FromLab - Variable in class weka.gui.experiment.ResultsPanel
Displays a message about the current result set
m_GeneratorPropertyPanel - Variable in class weka.gui.experiment.SetupPanel
The panel that configures iteration on custom resultproducer property
m_GlobalBlend - Variable in class weka.classifiers.lazy.KStar
default sphere of influence blend setting
m_GridSize - Variable in class weka.datagenerators.BIRCHCluster
 
m_GridVector - Variable in class weka.datagenerators.BIRCHCluster.GridVector
 
m_GridWidth - Variable in class weka.datagenerators.BIRCHCluster
 
m_HandleRightClicks - Variable in class weka.gui.ResultHistoryPanel
Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer
m_HasCustomPanel - Variable in class weka.gui.PropertyPanel
Whether the editor has provided its own panel
m_HasZeropoint - Variable in class weka.core.Attribute
Whether the attribute has a zeropoint.
m_Hashtable - Variable in class weka.core.Attribute
Mapping of values to indices (if nominal or string).
m_Head - Variable in class weka.core.Queue
Store a reference to the head of the queue
m_HelpBut - Variable in class weka.gui.PropertySheetPanel
Button to pop up the full help text in a separate frame
m_HelpFrame - Variable in class weka.gui.PropertySheetPanel
Help frame
m_HelpText - Variable in class weka.gui.PropertySheetPanel
StringBuffer containing help text for the object being edited
m_HighThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
The upper threshold used as the basis of correction
m_History - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Stores the historical instances to copy values between
m_History - Variable in class weka.gui.experiment.ResultsPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.AssociationsPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.AttributeSelectionPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.ClassifierPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.ClustererPanel
A panel controlling results viewing
m_HistoryPos - Variable in class weka.gui.SimpleCLI
The current position in the command history
m_HoldOutDist - Variable in class weka.classifiers.trees.REPTree.Tree
Class distribution of hold-out set at node in the nominal case.
m_HoldOutError - Variable in class weka.classifiers.trees.REPTree.Tree
The hold-out error of the node.
m_HorizontalPad - Variable in class weka.gui.visualize.ClassPanel
The amount of space to leave either side of the legend
m_HostField - Variable in class weka.gui.experiment.HostListPanel
The field with which to enter host names
m_HostName - Variable in class weka.experiment.RemoteEngine
The name of the host that this engine is started on
m_HyperPipes - Variable in class weka.classifiers.misc.HyperPipes
Stores the HyperPipe for each class
m_I0 - Variable in class weka.classifiers.functions.SMO.BinarySMO
The five different sets used by the algorithm.
m_I0 - Variable in class weka.classifiers.functions.SMOreg
The five different sets used by the algorithm.
m_I1 - Variable in class weka.classifiers.functions.SMO.BinarySMO
 
m_I1 - Variable in class weka.classifiers.functions.SMOreg
 
m_I2 - Variable in class weka.classifiers.functions.SMO.BinarySMO
 
m_I2 - Variable in class weka.classifiers.functions.SMOreg
 
m_I3 - Variable in class weka.classifiers.functions.SMO.BinarySMO
 
m_I3 - Variable in class weka.classifiers.functions.SMOreg
 
m_I4 - Variable in class weka.classifiers.functions.SMO.BinarySMO
 
m_ID - Variable in class weka.core.Tag
The ID
m_ID - Variable in class weka.gui.streams.InstanceEvent
 
m_IDFTransform - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
True if word frequencies should be transformed into fij*log(numOfDocs/numOfDocsWithWordi)
m_IOThread - Variable in class weka.gui.SetInstancesPanel
The thread we do loading in
m_IOThread - Variable in class weka.gui.explorer.PreprocessPanel
A thread for loading/saving instances from a file or URL
m_IP - Variable in class weka.gui.streams.InstanceLoader.LoadThread
 
m_IRclass - Variable in class weka.experiment.ClassifierSplitEvaluator
Class index for information retrieval statistics (default 0)
m_IgnoreAttributesRange - Variable in class weka.filters.unsupervised.attribute.AddCluster
Range of attributes to ignore
m_IgnoreClass - Variable in class weka.filters.unsupervised.attribute.PotentialClassIgnorer
True if the class is to be unset
m_IncludeAll - Variable in class weka.gui.AttributeSelectionPanel
Press to select all attributes
m_Incorrect - Variable in class weka.classifiers.Evaluation
The weight of all incorrectly classified instances.
m_IncrementalIndex - Variable in class weka.core.converters.SerializedInstancesLoader
The current index position for incremental reading
m_Index - Variable in class weka.core.Attribute
The attribute's index.
m_IndexString - Variable in class weka.core.SingleIndex
Record the string representation of the number
m_Indices - Variable in class weka.core.SparseInstance
The index of the attribute associated with each stored value.
m_Indices - Variable in class weka.filters.supervised.attribute.NominalToBinary
The sorted indices of the attribute values.
m_Indices - Variable in class weka.filters.unsupervised.attribute.NominalToBinary
The sorted indices of the attribute values.
m_IndicesBuffer - Variable in class weka.core.Instances
Buffer of indices for sparse instance
m_Info - Variable in class weka.classifiers.trees.REPTree.Tree
The header information (for printing the tree).
m_Info - Variable in class weka.classifiers.trees.RandomTree
The header information.
m_InfoGains - Variable in class weka.attributeSelection.InfoGainAttributeEval
The info gain for each attribute
m_InitFlag - Variable in class weka.classifiers.lazy.KStar
Flag turning on and off the initialisation of config variables
m_InitOptions - Variable in class weka.classifiers.meta.CVParameterSelection
The set of all options at initialization time.
m_InnerActionListener - Variable in class weka.gui.GenericArrayEditor
Listens to buttons being pressed and taking the appropriate action
m_InnerSelectionListener - Variable in class weka.gui.GenericArrayEditor
Listens to list items being selected and takes appropriate action
m_Input - Variable in class weka.gui.SimpleCLI.ReaderToTextArea
The reader being monitored
m_Input - Variable in class weka.gui.SimpleCLI
The command input area
m_InputFormat - Variable in class weka.filters.Filter
The input format for instances
m_InputFormat - Variable in class weka.gui.streams.InstanceJoiner
The input format for instances
m_InputOrder - Variable in class weka.datagenerators.BIRCHCluster
 
m_InputStringAtts - Variable in class weka.filters.Filter
Indices of string attributes in the input format
m_InputStringIndex - Variable in class weka.filters.unsupervised.attribute.Copy
Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated
m_InputStringIndex - Variable in class weka.filters.unsupervised.attribute.Remove
Contains an index of string attributes in the input format that will survive the filtering process
m_Insert - Variable in class weka.filters.unsupervised.attribute.Add
The location to insert the new attribute
m_InstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
the array instance indexes
m_InstNum - Variable in class weka.datagenerators.BIRCHCluster.Cluster
 
m_InstPerClass - Variable in class weka.classifiers.meta.Grading
InstPerClass
m_InstSummaryPanel - Variable in class weka.gui.explorer.PreprocessPanel
Displays simple stats on the working instances
m_Instance - Variable in class weka.classifiers.lazy.IBk.NeighborNode
The neighbor instance
m_InstanceInfo - Variable in class weka.gui.visualize.Plot2D
For popping up text info on data points
m_InstanceInfoText - Variable in class weka.gui.visualize.Plot2D
 
m_InstanceQuery - Variable in class weka.gui.experiment.ResultsPanel
Does any database querying for us
m_InstanceRange - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
The number of instances forward to translate values between.
m_InstanceTable - Variable in class weka.gui.streams.InstanceTable
 
m_Instances - Variable in class weka.attributeSelection.ForwardSelection
 
m_Instances - Variable in class weka.attributeSelection.RaceSearch
 
m_Instances - Variable in class weka.attributeSelection.RankSearch
the training instances
m_Instances - Variable in class weka.classifiers.bayes.ADNode
list of Instance children (either m_Instances or m_VaryNodes is instantiated)
m_Instances - Variable in class weka.classifiers.bayes.AODE
The dataset
m_Instances - Variable in class weka.classifiers.bayes.BayesNet
The dataset header for the purposes of printing out a semi-intelligible model
m_Instances - Variable in class weka.classifiers.bayes.NaiveBayes
The dataset header for the purposes of printing out a semi-intelligible model
m_Instances - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The instances used for training.
m_Instances - Variable in class weka.classifiers.lazy.LBR
The set of instances used for current training.
m_Instances - Variable in class weka.classifiers.misc.HyperPipes
The structure of the training data
m_Instances - Variable in class weka.classifiers.misc.VFI
The training data
m_Instances - Variable in class weka.classifiers.trees.DecisionStump
The instances used for training.
m_Instances - Variable in class weka.core.Instances
The instances.
m_Instances - Variable in class weka.experiment.AveragingResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.CrossValidationResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.DatabaseResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.InstancesResultListener
Stores the instances created so far, before assigning to a header
m_Instances - Variable in class weka.experiment.LearningRateResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.PairedTTester
The set of instances we will analyse
m_Instances - Variable in class weka.experiment.RandomSplitResultProducer
The dataset of interest
m_Instances - Variable in class weka.gui.AttributeListPanel.AttributeTableModel
The instances who's attribute structure we are reporting
m_Instances - Variable in class weka.gui.AttributeSelectionPanel.AttributeTableModel
The instances who's attribute structure we are reporting
m_Instances - Variable in class weka.gui.AttributeSummaryPanel
The instances we're playing with
m_Instances - Variable in class weka.gui.InstancesSummaryPanel
The instances we're playing with
m_Instances - Variable in class weka.gui.SetInstancesPanel
The current set of instances loaded
m_Instances - Variable in class weka.gui.experiment.ResultsPanel
The instances we're extracting results from
m_Instances - Variable in class weka.gui.explorer.AssociationsPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.AttributeSelectionPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.ClassifierPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.ClustererPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.PreprocessPanel
The working instances
m_Instances - Variable in class weka.gui.streams.InstanceTable
 
m_Instances - Variable in class weka.gui.visualize.ClassPanel
Instances being plotted
m_Inverse - Variable in class weka.filters.supervised.instance.StratifiedRemoveFolds
Indicates if inverse of selection is to be output.
m_Inverse - Variable in class weka.filters.unsupervised.instance.RemoveFolds
Indicates if inverse of selection is to be output.
m_Inverse - Variable in class weka.filters.unsupervised.instance.RemovePercentage
Indicates if inverse of selection is to be output.
m_Invert - Variable in class weka.core.Range
Whether matching should be inverted
m_Invert - Variable in class weka.gui.AttributeSelectionPanel
Press to invert the current selection
m_IsAddition - Variable in class weka.classifiers.functions.VotedPerceptron
Addition or subtraction?
m_IsAllErr - Variable in class weka.classifiers.rules.Ridor
Whether use error rate on all the data
m_IsAveragable - Variable in class weka.core.Attribute
Whether the attribute is averagable.
m_IsExclude - Variable in class weka.classifiers.rules.ConjunctiveRule
Whether to use exlusive expressions for nominal attributes
m_IsMajority - Variable in class weka.classifiers.rules.Ridor
Whether use majority class as default class
m_IsRegular - Variable in class weka.core.Attribute
Whether the attribute is regular.
m_IsZeroStep - Variable in class weka.core.Optimization
 
m_Iterations - Variable in class weka.clusterers.SimpleKMeans
Keep track of the number of iterations completed before convergence
m_JRand - Variable in class weka.gui.visualize.Plot2D
random values for perterbing the data points
m_Jitter - Variable in class weka.gui.visualize.VisualizePanel
The jitter slider
m_JitterLab - Variable in class weka.gui.visualize.VisualizePanel
Label for the jitter slider
m_JitterVal - Variable in class weka.gui.visualize.Plot2D
the level of jitter
m_K - Variable in class weka.classifiers.functions.VotedPerceptron
The actual number of alterations
m_KValue - Variable in class weka.classifiers.trees.RandomForest
Final number of features that were considered in last build.
m_KValue - Variable in class weka.classifiers.trees.RandomTree
The number of attributes considered for a split.
m_KWBias - Variable in class weka.classifiers.BVDecomposeSegCVSub
The calculated Kohavi & Wolpert bias (squared)
m_KWSigma - Variable in class weka.classifiers.BVDecomposeSegCVSub
The calculated Kohavi & Wolpert sigma
m_KWVariance - Variable in class weka.classifiers.BVDecomposeSegCVSub
The calculated Kohavi & Wolpert variance
m_KeyFieldName - Variable in class weka.experiment.AveragingResultProducer
The name of the key field to average over
m_KeyIndex - Variable in class weka.experiment.AveragingResultProducer
The index of the field to average over in the resultproducers key
m_Keys - Variable in class weka.experiment.AveragingResultProducer
Collects the keys from a single run
m_Knn - Variable in class weka.attributeSelection.ReliefFAttributeEval
The number of nearest hits/misses
m_KnowledgeFlowBut - Variable in class weka.gui.GUIChooser
Click to open the KnowledgeFlow
m_KnowledgeFlowFrame - Variable in class weka.gui.GUIChooser
The frame containing the knowledge flow interface
m_LL - Variable in class weka.classifiers.functions.Logistic
Log-likelihood of the searched model
m_Label - Variable in class weka.gui.GenericArrayEditor
The label for when we can't edit that type
m_Labels - Variable in class weka.filters.unsupervised.attribute.Add
The list of labels for nominal attribute
m_Labels - Variable in class weka.gui.PropertySheetPanel
The labels for each property
m_Lambda - Variable in class weka.estimators.PoissonEstimator
The average number of times an event occurs in an interval.
m_Last - Variable in class weka.classifiers.lazy.IBk.NeighborList
The last node in the list
m_LastURL - Variable in class weka.gui.SetInstancesPanel
Stores the last URL that instances were loaded from
m_LastURL - Variable in class weka.gui.explorer.PreprocessPanel
Stores the last URL that instances were loaded from
m_Length - Variable in class weka.classifiers.lazy.IBk.NeighborList
The number of nodes to attempt to maintain in the list
m_Limit - Variable in class weka.classifiers.bayes.AODE
An att's frequency must be this value or more to be a superParent
m_List - Variable in class weka.gui.ListSelectorDialog
The list component
m_List - Variable in class weka.gui.ResultHistoryPanel
The list component
m_List - Variable in class weka.gui.experiment.AlgorithmListPanel
The component displaying the algorithm list
m_List - Variable in class weka.gui.experiment.DatasetListPanel
The component displaying the dataset list
m_List - Variable in class weka.gui.experiment.HostListPanel
The component displaying the host list
m_ListModel - Variable in class weka.gui.GenericArrayEditor
The defaultlistmodel holding our data
m_Listeners - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Listeners who want to be notified about editing status of this panel
m_Listeners - Variable in class weka.gui.streams.InstanceLoader
 
m_Listeners - Variable in class weka.gui.visualize.AttributePanel
The list of things listening to this panel
m_LoadFactor - Variable in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
The load factor for the hashtable.
m_LoadThread - Variable in class weka.gui.experiment.ResultsPanel
A thread to load results instances from a file or database
m_Loader - Variable in class weka.gui.beans.Loader
Loader
m_LoaderEditor - Variable in class weka.gui.beans.LoaderCustomizer
 
m_LoaderThread - Variable in class weka.gui.streams.InstanceLoader
 
m_Log - Variable in class weka.gui.SaveBuffer
The Logger to send messages to
m_Log - Variable in class weka.gui.experiment.RunPanel
 
m_Log - Variable in class weka.gui.explorer.AssociationsPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.AttributeSelectionPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.ClassifierPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.ClustererPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.PreprocessPanel
The message logger
m_Log - Variable in class weka.gui.visualize.VisualizePanel
the logger
m_LogPanel - Variable in class weka.gui.explorer.Explorer
The panel for log and status messages
m_LogText - Variable in class weka.gui.LogPanel
Displays the log messages
m_LowThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
The lower threshold used as the basis of correction
m_Lower - Variable in class weka.classifiers.meta.CVParameterSelection.CVParameter
Lower bound for the CV search
m_LowerBound - Variable in class weka.core.Attribute
The attribute's lower numeric bound.
m_LowerBoundIsOpen - Variable in class weka.core.Attribute
Whether the lower bound is open.
m_LowerSize - Variable in class weka.experiment.LearningRateResultProducer
The minimum number of instances to use.
m_LowerText - Variable in class weka.gui.experiment.RunNumberPanel
Configures the lower run number
m_Ls - Variable in class weka.associations.Apriori
The set of all sets of itemsets L.
m_MAXITS - Variable in class weka.core.Optimization
 
m_MI - Variable in class weka.classifiers.rules.NNge
 
m_MI_MaxArray - Variable in class weka.classifiers.rules.NNge
 
m_MI_MinArray - Variable in class weka.classifiers.rules.NNge
 
m_MI_NumAttrClassInter - Variable in class weka.classifiers.rules.NNge
MUTUAL INFORMATION'S DATAS
m_MI_NumAttrClassValue - Variable in class weka.classifiers.rules.NNge
 
m_MI_NumAttrInter - Variable in class weka.classifiers.rules.NNge
 
m_MI_NumAttrValue - Variable in class weka.classifiers.rules.NNge
 
m_MI_NumClass - Variable in class weka.classifiers.rules.NNge
 
m_MI_NumInst - Variable in class weka.classifiers.rules.NNge
 
m_MainMethod - Variable in class weka.gui.SimpleCLI.ClassRunner
Stores the main method to call
m_MakeBinary - Variable in class weka.filters.supervised.attribute.Discretize
Output binary attributes for discretized attributes.
m_MakeBinary - Variable in class weka.filters.unsupervised.attribute.Discretize
Output binary attributes for discretized attributes.
m_MarginCounts - Variable in class weka.classifiers.Evaluation
Cumulative margin distribution
m_MatchMissingValues - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
True if missing values should count as a match
m_MatrixSource - Variable in class weka.classifiers.meta.CostSensitiveClassifier
Indicates the current cost matrix source
m_MatrixSource - Variable in class weka.classifiers.meta.MetaCost
Indicates the current cost matrix source
m_Max - Variable in class weka.classifiers.lazy.IBk
The maximum values for numeric attributes.
m_Max - Variable in class weka.classifiers.lazy.LWL
The maximum values for numeric attributes.
m_Max - Variable in class weka.clusterers.FarthestFirst
attribute max values
m_Max - Variable in class weka.clusterers.SimpleKMeans
attribute max values
m_Max - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
The maximum values for numeric attributes.
m_MaxArray - Variable in class weka.classifiers.lazy.IB1
The maximum values for numeric attributes.
m_MaxArray - Variable in class weka.classifiers.rules.NNge
The maximum values for numeric attributes.
m_MaxArray - Variable in class weka.filters.unsupervised.attribute.Normalize
The maximum values for numeric attributes.
m_MaxBorder - Variable in class weka.classifiers.rules.NNge.Exemplar
The max borders of the rectangle for numeric attributes
m_MaxCount - Variable in class weka.filters.supervised.instance.SpreadSubsample
The maximum count of any class
m_MaxDepth - Variable in class weka.classifiers.trees.REPTree
Upper bound on the tree depth
m_MaxInstNum - Variable in class weka.datagenerators.BIRCHCluster
 
m_MaxIts - Variable in class weka.classifiers.functions.Logistic
The maximum number of iterations.
m_MaxK - Variable in class weka.classifiers.functions.VotedPerceptron
The maximum number of alterations to the perceptron
m_MaxRadius - Variable in class weka.datagenerators.BIRCHCluster
 
m_MaxRuleSize - Variable in class weka.datagenerators.RDG1
 
m_MaxSize - Variable in class weka.attributeSelection.BestFirst.LinkedList2
 
m_Mean - Variable in class weka.estimators.NormalEstimator
The current mean
m_MeanSquared - Variable in class weka.classifiers.lazy.IBk
Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks
m_Means - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The means for numeric attributes.
m_Means - Variable in class weka.classifiers.functions.LinearRegression
The attributes means
m_Means - Variable in class weka.filters.unsupervised.attribute.Standardize
The means
m_MetaClassifier - Variable in class weka.classifiers.meta.Stacking
The meta classifier
m_MetaClassifiers - Variable in class weka.classifiers.meta.Grading
The meta classifiers, one for each base classifier.
m_MetaClassifiers - Variable in class weka.classifiers.meta.StackingC
The meta classifiers (one for each class, like in ClassificationViaRegression)
m_MetaFormat - Variable in class weka.classifiers.meta.Stacking
Format for meta data
m_Metadata - Variable in class weka.core.Attribute
The attribute's metadata.
m_Method - Variable in class weka.classifiers.meta.MultiClassClassifier
The multiclass method to use
m_Method - Variable in class weka.filters.unsupervised.attribute.NumericTransform
Transformation method.
m_Methods - Variable in class weka.gui.PropertySheetPanel
Holds the methods of the target
m_Min - Variable in class weka.classifiers.lazy.IBk
The minimum values for numeric attributes.
m_Min - Variable in class weka.classifiers.lazy.LWL
The minimum values for numeric attributes.
m_Min - Variable in class weka.clusterers.FarthestFirst
attribute min values
m_Min - Variable in class weka.clusterers.SimpleKMeans
attribute min values
m_Min - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
The minimum values for numeric attributes.
m_MinArray - Variable in class weka.classifiers.lazy.IB1
The minimum values for numeric attributes.
m_MinArray - Variable in class weka.classifiers.rules.NNge
The minimum values for numeric attributes.
m_MinArray - Variable in class weka.filters.unsupervised.attribute.Normalize
The minimum values for numeric attributes.
m_MinBorder - Variable in class weka.classifiers.rules.NNge.Exemplar
The min borders of the rectangle for numeric attributes
m_MinInstNum - Variable in class weka.datagenerators.BIRCHCluster
 
m_MinNo - Variable in class weka.classifiers.rules.ConjunctiveRule
The minimal number of instance weights within a split
m_MinNo - Variable in class weka.classifiers.rules.JRip
The minimal number of instance weights within a split
m_MinNo - Variable in class weka.classifiers.rules.Ridor
The minimal number of instance weights within a split
m_MinNum - Variable in class weka.classifiers.trees.REPTree
The minimum number of instances per leaf.
m_MinNum - Variable in class weka.classifiers.trees.RandomTree
Minimum number of instances for leaf.
m_MinRadius - Variable in class weka.datagenerators.BIRCHCluster
 
m_MinRuleSize - Variable in class weka.datagenerators.RDG1
 
m_MinVarianceProp - Variable in class weka.classifiers.trees.REPTree
The minimum proportion of the total variance (over all the data) required for split.
m_MinimizeExpectedCost - Variable in class weka.classifiers.meta.CostSensitiveClassifier
True if the costs should be used by selecting the minimum expected cost (false means weight training data by the costs)
m_Missing - Variable in class weka.classifiers.functions.SMO
The filter used to get rid of missing values.
m_Missing - Variable in class weka.classifiers.functions.SMOreg
The filter used to get rid of missing values.
m_MissingClass - Variable in class weka.classifiers.Evaluation
The weight of all instances that had no class assigned to them.
m_MissingFilter - Variable in class weka.classifiers.functions.LeastMedSq
 
m_MissingFilter - Variable in class weka.classifiers.functions.LinearRegression
The filter for removing missing values.
m_MissingLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the number of missing values
m_MissingMode - Variable in class weka.classifiers.lazy.KStar
missing value treatment
m_MissingMode - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
missing value treatment
m_MissingMode - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
missing value treatment
m_MissingProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Probability of test attribute transforming into train attribute with missing value
m_MissingProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Probability of test attribute transforming into train attribute with missing value
m_MissingVector - Variable in class weka.classifiers.rules.NNge
Values to use for missing value
m_Mistakes - Variable in class weka.classifiers.functions.Winnow
Accumulated mistake count (for statistics)
m_Model - Variable in class weka.classifiers.functions.PaceRegression
The model used
m_Model - Variable in class weka.gui.AttributeListPanel
The table model containing attribute names
m_Model - Variable in class weka.gui.AttributeSelectionPanel
The table model containingn attribute names and selection status
m_Model - Variable in class weka.gui.ResultHistoryPanel
The list model
m_ModelFilter - Variable in class weka.gui.explorer.ClassifierPanel
Filter to ensure only model files are selected
m_ModelFilter - Variable in class weka.gui.explorer.ClustererPanel
Filter to ensure only model files are selected
m_ModesAndMeans - Variable in class weka.filters.unsupervised.attribute.ReplaceMissingValues
The modes and means
m_ModifyHeader - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Modify header for nominal attributes?
m_MonitorLabel - Variable in class weka.gui.WekaTaskMonitor
The label for displaying info
m_MoreOptions - Variable in class weka.gui.explorer.ClassifierPanel
Button for further output/visualize options
m_NCV - Variable in class weka.classifiers.lazy.LBR
 
m_NNge - Variable in class weka.classifiers.rules.NNge.Exemplar
The NNge which owns this Exemplar
m_Name - Variable in class weka.core.Attribute
The attribute's name.
m_Name - Variable in class weka.core.Option
What's the option's name?
m_Name - Variable in class weka.filters.unsupervised.attribute.Add
The name for the new attribute
m_NegativeCount - Variable in class weka.classifiers.rules.NNge.Exemplar
Number of incorrect prediction for this examplar
m_NewBatch - Variable in class weka.filters.Filter
Record whether the filter is at the start of a batch
m_NewBut - Variable in class weka.gui.experiment.SetupPanel
Click to create a new experiment with default settings
m_NewBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Click to create a new experiment with default settings
m_Next - Variable in class weka.classifiers.lazy.IBk.NeighborNode
A link to the next neighbor instance
m_Next - Variable in class weka.core.Queue.QueueNode
The next node in the queue
m_NoPruning - Variable in class weka.classifiers.trees.REPTree
Don't prune
m_NoiseRate - Variable in class weka.datagenerators.BIRCHCluster
 
m_NominalBounds - Variable in class weka.classifiers.misc.HyperPipes.HyperPipe
Contains the nominal bounds of all instances in the HyperPipe
m_NominalIndexes - Variable in class weka.experiment.InstancesResultListener
For lookup of indices given a string value for each nominal attribute
m_NominalMapping - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
If m_ModifyHeader, stores a mapping from old to new indexes
m_NominalStrings - Variable in class weka.experiment.InstancesResultListener
Contains strings seen so far for each nominal attribute
m_NominalToBinary - Variable in class weka.classifiers.functions.Logistic
The filter used to make attributes numeric.
m_NominalToBinary - Variable in class weka.classifiers.functions.SMO
The filter used to make attributes numeric.
m_NominalToBinary - Variable in class weka.classifiers.functions.SMOreg
The filter used to make attributes numeric.
m_NominalToBinary - Variable in class weka.classifiers.functions.SimpleLogistic
Filter for converting nominal attributes to binary ones
m_NominalToBinary - Variable in class weka.classifiers.functions.VotedPerceptron
The filter used to make attributes numeric.
m_NominalToBinary - Variable in class weka.classifiers.functions.Winnow
The filter used to make attributes numeric.
m_Not - Variable in class weka.datagenerators.Test
 
m_Notes - Variable in class weka.experiment.Experiment
User notes about the experiment
m_NotesText - Variable in class weka.gui.experiment.SetupPanel
Area for user notes Default of 5 rows
m_NotesText - Variable in class weka.gui.experiment.SimpleSetupPanel
Area for user notes Default of 5 rows
m_NumAntds - Variable in class weka.classifiers.rules.ConjunctiveRule
The number of antecedents in pre-pruning
m_NumArguments - Variable in class weka.core.Option
How many arguments does it take?
m_NumAttValues - Variable in class weka.classifiers.bayes.AODE
The number of values for each attribute
m_NumAttemptsOfGene - Variable in class weka.classifiers.rules.NNge
The number of try for generalisation
m_NumAttributes - Variable in class weka.classifiers.bayes.AODE
The number of attributes in dataset, including class
m_NumAttributes - Variable in class weka.classifiers.lazy.KStar
The number of attributes
m_NumAttributes - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The number of attributes
m_NumAttributes - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of attributes
m_NumAttributes - Variable in class weka.classifiers.meta.CVParameterSelection
The number of attributes in the data
m_NumAttributes - Variable in class weka.core.SparseInstance
The maximum number of values that can be stored.
m_NumAttributes - Variable in class weka.datagenerators.ClusterGenerator
 
m_NumAttributes - Variable in class weka.datagenerators.Generator
 
m_NumAttributesLab - Variable in class weka.gui.InstancesSummaryPanel
Displays the number of attributes
m_NumAttributesUsed - Variable in class weka.classifiers.lazy.IBk
The number of attributes the contribute to a prediction
m_NumAtts - Variable in class weka.classifiers.lazy.LBR.Indexes
the number of attributes indexed
m_NumAttsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
the number of attributes "in use" or set to a the original value (true or false)
m_NumBins - Variable in class weka.classifiers.meta.RegressionByDiscretization
The number of discretization intervals.
m_NumBins - Variable in class weka.filters.unsupervised.attribute.Discretize
The number of bins to divide the attribute into
m_NumClasses - Variable in class weka.classifiers.Evaluation
The number of classes.
m_NumClasses - Variable in class weka.classifiers.bayes.AODE
The number of classes
m_NumClasses - Variable in class weka.classifiers.bayes.BayesNet
The number of classes
m_NumClasses - Variable in class weka.classifiers.bayes.NaiveBayes
The number of classes (or 1 for numeric class)
m_NumClasses - Variable in class weka.classifiers.functions.Logistic
The number of the class labels
m_NumClasses - Variable in class weka.classifiers.lazy.IBk
The number of class values (or 1 if predicting numeric)
m_NumClasses - Variable in class weka.classifiers.lazy.KStar
The number of class values
m_NumClasses - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The number of class values
m_NumClasses - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of class values
m_NumClasses - Variable in class weka.classifiers.meta.AdaBoostM1
The number of classes
m_NumClasses - Variable in class weka.classifiers.meta.LogitBoost
The number of classes
m_NumClasses - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The number of classes
m_NumClasses - Variable in class weka.classifiers.misc.VFI
The number of classes
m_NumClasses - Variable in class weka.classifiers.rules.ConjunctiveRule
Number of classes in the training data
m_NumClasses - Variable in class weka.datagenerators.Generator
 
m_NumClusters - Variable in class weka.clusterers.FarthestFirst
number of clusters to generate
m_NumClusters - Variable in class weka.clusterers.SimpleKMeans
number of clusters to generate
m_NumClusters - Variable in class weka.datagenerators.ClusterGenerator
 
m_NumCycles - Variable in class weka.datagenerators.BIRCHCluster
 
m_NumEditable - Variable in class weka.gui.PropertySheetPanel
A count of the number of properties we have an editor for
m_NumExamples - Variable in class weka.datagenerators.Generator
 
m_NumExamplesAct - Variable in class weka.datagenerators.ClusterGenerator
 
m_NumExamplesAct - Variable in class weka.datagenerators.Generator
 
m_NumFoldersMI - Variable in class weka.classifiers.rules.NNge
The number of folder for the Mutual Information
m_NumFolds - Variable in class weka.classifiers.Evaluation
The number of folds for a cross-validation.
m_NumFolds - Variable in class weka.classifiers.meta.CVParameterSelection
The number of folds used in cross-validation
m_NumFolds - Variable in class weka.classifiers.meta.LogitBoost
The number of folds for the internal cross-validation.
m_NumFolds - Variable in class weka.classifiers.meta.Stacking
Set the number of folds for the cross-validation
m_NumFolds - Variable in class weka.classifiers.trees.REPTree
Number of folds for reduced error pruning.
m_NumFolds - Variable in class weka.experiment.CrossValidationResultProducer
The number of folds in the cross-validation
m_NumFolds - Variable in class weka.filters.supervised.instance.StratifiedRemoveFolds
Number of folds to split dataset into
m_NumFolds - Variable in class weka.filters.unsupervised.instance.RemoveFolds
Number of folds to split dataset into
m_NumGenerated - Variable in class weka.classifiers.meta.LogitBoost
The number of successfully generated base classifiers.
m_NumInstances - Variable in class weka.classifiers.bayes.AODE
The number of instances in the dataset
m_NumInstances - Variable in class weka.classifiers.lazy.KStar
The number of instances in the dataset
m_NumInstances - Variable in class weka.classifiers.lazy.LBR.Indexes
the number of instances indexed
m_NumInstances - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The number of instances in the dataset
m_NumInstances - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of instances in the dataset
m_NumInstancesLab - Variable in class weka.gui.InstancesSummaryPanel
Displays the number of instances
m_NumInstsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
the number of instances "in use" or set to a the original value (true or false)
m_NumIrrelevant - Variable in class weka.datagenerators.RDG1
 
m_NumIterations - Variable in class weka.classifiers.IteratedSingleClassifierEnhancer
The number of iterations.
m_NumIterations - Variable in class weka.classifiers.functions.VotedPerceptron
The number of iterations
m_NumIterations - Variable in class weka.classifiers.meta.Decorate
The maximum number of Decorate iterations to run.
m_NumIterations - Variable in class weka.classifiers.meta.MetaCost
The number of iterations.
m_NumIterationsPerformed - Variable in class weka.classifiers.meta.AdaBoostM1
The number of successfully generated base classifiers.
m_NumNumeric - Variable in class weka.datagenerators.RDG1
 
m_NumPredictors - Variable in class weka.classifiers.functions.Logistic
The number of attributes in the model
m_NumRuns - Variable in class weka.classifiers.meta.LogitBoost
The number of runs for the internal cross-validation.
m_NumSeqAttsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
the number of sequential attributes "in use" or set to a the original value (true or false)
m_NumSeqInstsSet - Variable in class weka.classifiers.lazy.LBR.Indexes
the number of sequential instances "in use" or set to a the original value (true or false)
m_NumSubCmtys - Variable in class weka.classifiers.meta.MultiBoostAB
The number of sub-committees to use
m_NumTrainClassVals - Variable in class weka.classifiers.Evaluation
Number of non-missing class training instances seen
m_NumValues - Variable in class weka.estimators.KKConditionalEstimator
Number of values stored in m_Weights, m_CondValues, and m_Values so far
m_NumValues - Variable in class weka.estimators.KernelEstimator
Number of values stored in m_Weights and m_Values so far
m_NumValues - Variable in class weka.estimators.PoissonEstimator
The number of values seen
m_NumXValFolds - Variable in class weka.classifiers.meta.MultiScheme
Number of folds to use for cross validation (0 means use training error for selection)
m_NumXValFolds - Variable in class weka.classifiers.meta.ThresholdSelector
The number of folds used in cross-validation
m_Number - Variable in class weka.classifiers.lazy.LBR
 
m_NumberOfInstances - Variable in class weka.classifiers.lazy.LBR
 
m_NumberOfRepetitionsTField - Variable in class weka.gui.experiment.SimpleSetupPanel
Input field for number of repetitions
m_Numeric - Variable in class weka.filters.supervised.attribute.NominalToBinary
Are the new attributes going to be nominal or numeric ones?
m_Numeric - Variable in class weka.filters.unsupervised.attribute.MakeIndicator
Make boolean attribute numeric.
m_Numeric - Variable in class weka.filters.unsupervised.attribute.NominalToBinary
Are the new attributes going to be nominal or numeric ones?
m_NumericBounds - Variable in class weka.classifiers.misc.HyperPipes.HyperPipe
Contains the numeric bounds of all instances in the HyperPipe
m_NumericClassData - Variable in class weka.classifiers.meta.LogitBoost
Dummy dataset with a numeric class
m_NumericClassData - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Dummy dataset with a numeric class
m_Object - Variable in class weka.gui.GenericObjectEditor
The object being configured
m_ObjectNames - Variable in class weka.gui.GenericObjectEditor
The model containing the list of names to select from
m_ObjectPropertyPanel - Variable in class weka.gui.GenericObjectEditor
The property panel created for the objects
m_Objects - Variable in class weka.core.FastVector
The array of objects.
m_Objs - Variable in class weka.gui.ResultHistoryPanel
A hashtable mapping names to arbitrary objects
m_Offset - Variable in class weka.classifiers.meta.LogitBoost
The value by which the actual target value for the true class is offset.
m_OnDemandDirectory - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The directory used when loading cost files on demand, null indicates current directory
m_OnDemandDirectory - Variable in class weka.classifiers.meta.MetaCost
The directory used when loading cost files on demand, null indicates current directory
m_OnDemandDirectory - Variable in class weka.experiment.CostSensitiveClassifierSplitEvaluator
The directory used when loading cost files on demand, null indicates current directory
m_OpenBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
Open object from disk
m_OpenBut - Variable in class weka.gui.experiment.SetupPanel
Click to load an experiment
m_OpenBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Click to load an experiment
m_OpenDBBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to load base instances from a Database
m_OpenFileBut - Variable in class weka.gui.SetInstancesPanel
Click to open instances from a file
m_OpenFileBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to load base instances from a file
m_OpenURLBut - Variable in class weka.gui.SetInstancesPanel
Click to open instances from a URL
m_OpenURLBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to load base instances from a URL
m_Optimizations - Variable in class weka.classifiers.rules.JRip
Runs of optimizations
m_OrderAlgorithmsFirstRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing algorithms first in order of execution
m_OrderDatasetsFirstRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing datasets first in order of execution
m_Ordering - Variable in class weka.core.Attribute
The attribute's ordering.
m_Out - Variable in class weka.experiment.CSVResultListener
The destination for results (typically connected to the output file)
m_OutOfBagError - Variable in class weka.classifiers.meta.Bagging
The out of bag error that has been calculated
m_OutRedirector - Variable in class weka.gui.SimpleCLI
The thread that sends output from m_POO to the output box
m_OutText - Variable in class weka.gui.experiment.ResultsPanel
Displays the output of tests
m_OutText - Variable in class weka.gui.explorer.AssociationsPanel
The output area for associations
m_OutText - Variable in class weka.gui.explorer.AttributeSelectionPanel
The output area for attribute selection results
m_OutText - Variable in class weka.gui.explorer.ClassifierPanel
The output area for classification results
m_OutText - Variable in class weka.gui.explorer.ClustererPanel
The output area for classification results
m_Output - Variable in class weka.datagenerators.ClusterGenerator
 
m_Output - Variable in class weka.datagenerators.Generator
 
m_Output - Variable in class weka.gui.SimpleCLI.ReaderToTextArea
The output text component
m_OutputArea - Variable in class weka.gui.SimpleCLI
The output area canvas added to the frame
m_OutputConfusionBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output a confusion matrix
m_OutputCounts - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
True if output instances should contain word frequency rather than boolean 0 or 1.
m_OutputEntropyBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output entropy statistics
m_OutputFile - Variable in class weka.experiment.CSVResultListener
The destination output file, null sends to System.out
m_OutputFile - Variable in class weka.experiment.CrossValidationResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.RandomSplitResultProducer
The destination output file/directory for raw output
m_OutputFormat - Variable in class weka.filters.Filter
The output format for instances
m_OutputFormatDefined - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Keeps track of output format if it is defined or not
m_OutputInstance - Variable in class weka.gui.streams.InstanceJoiner
The current output instance
m_OutputInstance - Variable in class weka.gui.streams.InstanceLoader
 
m_OutputInstances - Variable in class weka.gui.streams.InstanceLoader
 
m_OutputModelBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output the model built from the training data
m_OutputPerClassBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output true/false positives, precision/recall for each class
m_OutputPredictionsTextBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output text predictions
m_OutputQueue - Variable in class weka.filters.Filter
The output instance queue
m_OutputStringAtts - Variable in class weka.filters.Filter
Indices of string attributes in the output format
m_OutputTex - Variable in class weka.gui.streams.InstanceViewer
 
m_P - Variable in class weka.classifiers.BVDecomposeSegCVSub
Proportion of instances common between any two training sets.
m_PD - Variable in class weka.gui.PropertyPanel
The currently displayed property dialog, if any
m_PD - Variable in class weka.gui.experiment.AlgorithmListPanel
The currently displayed property dialog, if any
m_POE - Variable in class weka.gui.SimpleCLI
The new output stream for System.err
m_POO - Variable in class weka.gui.SimpleCLI
The new output stream for System.out
m_Par - Variable in class weka.classifiers.functions.Logistic
The coefficients (optimized parameters) of the model
m_ParamChar - Variable in class weka.classifiers.meta.CVParameterSelection.CVParameter
Char used to identify the option of interest
m_ParamValue - Variable in class weka.classifiers.meta.CVParameterSelection.CVParameter
The parameter value with the best performance
m_ParentSets - Variable in class weka.classifiers.bayes.BayesNet
The parent sets.
m_PasswordLab - Variable in class weka.gui.DatabaseConnectionDialog
 
m_PasswordText - Variable in class weka.gui.DatabaseConnectionDialog
 
m_Pattern - Variable in class weka.datagenerators.BIRCHCluster
 
m_Percent - Variable in class weka.filters.unsupervised.attribute.AddNoise
The subsample size, percent of original set, default 10%
m_PercentBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to generate a % split
m_PercentBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to generate a % split
m_PercentLab - Variable in class weka.gui.explorer.ClassifierPanel
Label by where the % split is entered
m_PercentLab - Variable in class weka.gui.explorer.ClustererPanel
Label by where the % split is entered
m_PercentText - Variable in class weka.gui.explorer.ClassifierPanel
The field where the % split is entered
m_PercentText - Variable in class weka.gui.explorer.ClustererPanel
The field where the % split is entered
m_Percentage - Variable in class weka.filters.unsupervised.instance.RemovePercentage
Percentage of instances to select.
m_PerformBut - Variable in class weka.gui.experiment.ResultsPanel
Click to start the test
m_PositiveCount - Variable in class weka.classifiers.rules.NNge.Exemplar
Number of correct prediction for this examplar
m_PreInst - Variable in class weka.classifiers.rules.NNge.Exemplar
 
m_PreMaxBorder - Variable in class weka.classifiers.rules.NNge.Exemplar
the arrays used by preGeneralise
m_PreMinBorder - Variable in class weka.classifiers.rules.NNge.Exemplar
 
m_PreRange - Variable in class weka.classifiers.rules.NNge.Exemplar
 
m_Precision - Variable in class weka.classifiers.meta.LogitBoost
The threshold on the improvement of the likelihood
m_Precision - Variable in class weka.estimators.KKConditionalEstimator
The numeric precision
m_Precision - Variable in class weka.estimators.KernelEstimator
The precision of data values
m_Precision - Variable in class weka.estimators.NormalEstimator
The precision of numeric values ( = minimum std dev permitted)
m_Predicted - Variable in class weka.classifiers.evaluation.NominalPrediction
The predicted class value
m_Predicted - Variable in class weka.classifiers.evaluation.NumericPrediction
The predicted class value
m_Prefix - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
A String prefix for the attribute names
m_PreparedStatement - Variable in class weka.experiment.DatabaseUtils
The prepared statement used for database queries.
m_PreprocessPanel - Variable in class weka.gui.explorer.Explorer
The panel for preprocessing instances
m_PriorErrorEstimator - Variable in class weka.classifiers.Evaluation
Numeric class error estimator for prior
m_Priors - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The prior probabilities of the classes.
m_Priors - Variable in class weka.classifiers.lazy.LBR
The prior probabilities of the classes.
m_Prop - Variable in class weka.classifiers.trees.REPTree.Tree
The proportions of training instances going down each branch.
m_Prop - Variable in class weka.classifiers.trees.RandomTree
The proportions of training instances going down each branch.
m_Properties - Variable in class weka.gui.PropertySheetPanel
Holds properties of the target
m_PropertyArray - Variable in class weka.experiment.Experiment
The array of values to set the property to
m_PropertyNumber - Variable in class weka.experiment.Experiment
The current custom property value index when the experiment is running
m_PropertyPath - Variable in class weka.experiment.Experiment
The path to the iterator property
m_PruningType - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The pruning type used
m_Query - Variable in class weka.experiment.InstanceQuery
Query to execute
m_RLEditor - Variable in class weka.gui.experiment.SetupPanel
The ResultListener editor
m_RLEditorPanel - Variable in class weka.gui.experiment.SetupPanel
The panel to contain the ResultListener editor
m_RLSData - Variable in class weka.classifiers.functions.LeastMedSq
 
m_ROCString - Variable in class weka.gui.visualize.ThresholdVisualizePanel
The string to add to the Plot Border.
m_RP - Variable in class weka.experiment.CSVResultListener
The ResultProducer sending us results
m_RPEditor - Variable in class weka.gui.experiment.SetupPanel
The ResultProducer editor
m_RPEditorPanel - Variable in class weka.gui.experiment.SetupPanel
The panel to contain the ResultProducer editor
m_RadioListener - Variable in class weka.gui.experiment.SetupPanel
Handle radio buttons
m_RadioListener - Variable in class weka.gui.explorer.AttributeSelectionPanel
Alters the enabled/disabled status of elements associated with each radio button
m_RadioListener - Variable in class weka.gui.explorer.ClassifierPanel
Alters the enabled/disabled status of elements associated with each radio button
m_RadioListener - Variable in class weka.gui.explorer.ClustererPanel
Alters the enabled/disabled status of elements associated with each radio button
m_Radius - Variable in class weka.datagenerators.BIRCHCluster.Cluster
 
m_RandClassCols - Variable in class weka.classifiers.lazy.KStar
Table of random class value colomns
m_RandClassCols - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Set of colomns: each colomn representing a randomised version of the train dataset class colomn
m_RandClassCols - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Set of colomns: each colomn representing a randomised version of the train dataset class colomn
m_Random - Variable in class weka.classifiers.meta.Decorate
The random number generator.
m_Random - Variable in class weka.classifiers.meta.MultiBoostAB
Random number generator
m_Random - Variable in class weka.classifiers.meta.TreeBasedMultiClassClassifier
The random number generator.
m_Random - Variable in class weka.classifiers.rules.ConjunctiveRule
The Random object used for randomization
m_Random - Variable in class weka.classifiers.rules.JRip
Random object used in this class
m_Random - Variable in class weka.classifiers.rules.Ridor
Random object for randomization
m_Random - Variable in class weka.datagenerators.BIRCHCluster
 
m_Random - Variable in class weka.datagenerators.RDG1
 
m_Random - Variable in class weka.filters.supervised.attribute.ClassOrder
The random object
m_Random - Variable in class weka.filters.unsupervised.instance.Randomize
The current random number generator
m_RandomInstance - Variable in class weka.classifiers.meta.LogitBoost
The random number generator used
m_RandomInstance - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The random number generator used
m_RandomLab - Variable in class weka.gui.explorer.ClassifierPanel
 
m_RandomSeed - Variable in class weka.filters.supervised.instance.Resample
The random number generator seed
m_RandomSeed - Variable in class weka.filters.supervised.instance.SpreadSubsample
The random number generator seed
m_RandomSeed - Variable in class weka.filters.unsupervised.attribute.AddNoise
The random number generator seed
m_RandomSeed - Variable in class weka.filters.unsupervised.instance.Resample
The random number generator seed
m_RandomSeedText - Variable in class weka.gui.explorer.ClassifierPanel
User specified random seed for cross validation or % split
m_RandomWidthFactor - Variable in class weka.classifiers.meta.MultiClassClassifier
The multiplier when generating random codes.
m_Range - Variable in class weka.classifiers.meta.TreeBasedMultiClassClassifier
The classes that are grouped together at the current node
m_Range - Variable in class weka.classifiers.rules.NNge.Exemplar
The ranges of the hyperrectangle for nominal attributes
m_Range - Variable in class weka.filters.unsupervised.instance.RemoveRange
Range of instances provided by user.
m_RangeMode - Variable in class weka.classifiers.meta.ThresholdSelector
The range correction mode
m_RangeStrings - Variable in class weka.core.Range
Record the string representations of the columns to delete
m_Ranking - Variable in class weka.attributeSelection.RaceSearch
will hold the attribute ranking produced by the above attribute evaluator if doing a rank search
m_Ranking - Variable in class weka.attributeSelection.RankSearch
will hold the attribute ranking
m_Readable - Variable in class weka.core.Tag
The descriptive text
m_ReducedHeader - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The header of the dimensionally reduced data
m_RelationName - Variable in class weka.core.Instances
The dataset's name.
m_RelationName - Variable in class weka.datagenerators.ClusterGenerator
 
m_RelationName - Variable in class weka.datagenerators.Generator
 
m_RelationNameLab - Variable in class weka.gui.InstancesSummaryPanel
Displays the name of the relation
m_RemainderErrors - Variable in class weka.classifiers.lazy.LBR
 
m_RemoveAll - Variable in class weka.gui.AttributeSelectionPanel
Press to deselect all attributes
m_Repainters - Variable in class weka.gui.visualize.ClassPanel
An optional list of Components that use the colour list maintained by this class.
m_Repainters - Variable in class weka.gui.visualize.LegendPanel
a list of components that need to be repainted when a colour is changed
m_ReplaceMissingFilter - Variable in class weka.clusterers.FarthestFirst
replace missing values in training instances
m_ReplaceMissingFilter - Variable in class weka.clusterers.SimpleKMeans
replace missing values in training instances
m_ReplaceMissingValues - Variable in class weka.classifiers.functions.Logistic
The filter used to get rid of missing values.
m_ReplaceMissingValues - Variable in class weka.classifiers.functions.SimpleLogistic
Filter for replacing missing values
m_ReplaceMissingValues - Variable in class weka.classifiers.functions.VotedPerceptron
The filter used to get rid of missing values.
m_ReplaceMissingValues - Variable in class weka.classifiers.functions.Winnow
The filter used to get rid of missing values.
m_Residuals - Variable in class weka.classifiers.functions.LeastMedSq
 
m_Result - Variable in class weka.gui.ListSelectorDialog
Whether the selection was made or cancelled
m_Result - Variable in class weka.gui.PropertySelectorDialog
Whether the selection was made or cancelled
m_ResultKeyBut - Variable in class weka.gui.experiment.ResultsPanel
Click to edit the columns used to determine the scheme
m_ResultKeyLabel - Variable in class weka.gui.experiment.ResultsPanel
Displays the currently selected column names for the scheme & options
m_ResultKeyList - Variable in class weka.gui.experiment.ResultsPanel
Displays the list of selected columns determining the scheme
m_ResultKeyModel - Variable in class weka.gui.experiment.ResultsPanel
Stores the list of attributes for selecting the scheme columns
m_ResultListener - Variable in class weka.experiment.AveragingResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.CrossValidationResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.DatabaseResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.Experiment
Where results will be sent
m_ResultListener - Variable in class weka.experiment.LearningRateResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.RandomSplitResultProducer
The ResultListener to send results to
m_ResultPath - Variable in class weka.gui.PropertySelectorDialog
Stores the path to the selected property
m_ResultProducer - Variable in class weka.experiment.AveragingResultProducer
The ResultProducer used to generate results
m_ResultProducer - Variable in class weka.experiment.DatabaseResultListener
The ResultProducer to listen to
m_ResultProducer - Variable in class weka.experiment.DatabaseResultProducer
The ResultProducer used to generate results
m_ResultProducer - Variable in class weka.experiment.Experiment
The result producer
m_ResultProducer - Variable in class weka.experiment.LearningRateResultProducer
The ResultProducer used to generate results
m_Results - Variable in class weka.experiment.AveragingResultProducer
Collects the results from a single run
m_Results - Variable in class weka.gui.ResultHistoryPanel
A Hashtable mapping names to result buffers
m_ResultsDestinationCBox - Variable in class weka.gui.experiment.SimpleSetupPanel
Combo box for choosing experiment destination type
m_ResultsDestinationPathLabel - Variable in class weka.gui.experiment.SimpleSetupPanel
Label for destination field
m_ResultsDestinationPathTField - Variable in class weka.gui.experiment.SimpleSetupPanel
Input field for result destination path
m_ResultsPanel - Variable in class weka.gui.experiment.Experimenter
The panel for analysing experimental results
m_ResultsTableName - Variable in class weka.experiment.DatabaseResultListener
The name of the current results table
m_ResultsetKeyColumns - Variable in class weka.experiment.PairedTTester
An array containing the indexes of just the selected columns
m_ResultsetKeyColumnsRange - Variable in class weka.experiment.PairedTTester
The range of columns that specify a unique result set (eg: scheme plus configuration)
m_Resultsets - Variable in class weka.experiment.PairedTTester
Stores a vector for each resultset holding all instances in each set
m_ResultsetsValid - Variable in class weka.experiment.PairedTTester
Indicates whether the instances have been partitioned
m_Rhoa - Variable in class weka.classifiers.misc.FLR
 
m_Ridge - Variable in class weka.classifiers.functions.LinearRegression
The ridge parameter
m_Ridge - Variable in class weka.classifiers.functions.Logistic
The ridge parameter.
m_Root - Variable in class weka.classifiers.rules.Ridor
The root of Ridor
m_Root - Variable in class weka.gui.HierarchyPropertyParser
Keep track of the root of the tree
m_Root - Variable in class weka.gui.PropertySelectorDialog
The root of the property tree
m_RootObject - Variable in class weka.gui.PropertySelectorDialog
The object at the root of the tree
m_RoundParam - Variable in class weka.classifiers.meta.CVParameterSelection.CVParameter
True if the parameter should be rounded to an integer
m_RuleList - Variable in class weka.datagenerators.RDG1.RuleList
 
m_Ruleset - Variable in class weka.classifiers.rules.JRip
The ruleset
m_Ruleset - Variable in class weka.classifiers.rules.RuleStats
The specific ruleset in question
m_RulesetStats - Variable in class weka.classifiers.rules.JRip
The RuleStats for the ruleset of each class value
m_RunColumn - Variable in class weka.experiment.PairedTTester
The index of the column containing the run number
m_RunColumnSet - Variable in class weka.experiment.PairedTTester
The option setting for the run number column (-1 means last)
m_RunCombo - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select which column contains the run number
m_RunLower - Variable in class weka.experiment.Experiment
Lower run number
m_RunModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_RunCombo
m_RunNumber - Variable in class weka.experiment.Experiment
The current run number when the experiment is running
m_RunNumberPanel - Variable in class weka.gui.experiment.SetupPanel
The panel for configuring run numbers
m_RunPanel - Variable in class weka.gui.experiment.Experimenter
The panel for running the experiment
m_RunThread - Variable in class weka.gui.SimpleCLI
The thread currently running a class main method
m_RunThread - Variable in class weka.gui.experiment.RunPanel
The thread running the experiment
m_RunThread - Variable in class weka.gui.explorer.AssociationsPanel
A thread that associator runs in
m_RunThread - Variable in class weka.gui.explorer.AttributeSelectionPanel
A thread that attribute selection runs in
m_RunThread - Variable in class weka.gui.explorer.ClassifierPanel
A thread that classification runs in
m_RunThread - Variable in class weka.gui.explorer.ClustererPanel
A thread that clustering runs in
m_RunUpper - Variable in class weka.experiment.Experiment
Upper run number
m_SQLQ - Variable in class weka.gui.explorer.PreprocessPanel
Stores the last sql query executed
m_SSR - Variable in class weka.classifiers.functions.LeastMedSq
 
m_STPMX - Variable in class weka.core.Optimization
 
m_SampleSizePercent - Variable in class weka.filters.supervised.instance.Resample
The subsample size, percent of original set, default 100%
m_SampleSizePercent - Variable in class weka.filters.unsupervised.instance.Resample
The subsample size, percent of original set, default 100%
m_SaveBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
Save object to disk
m_SaveBut - Variable in class weka.gui.experiment.SetupPanel
Click to save an experiment
m_SaveBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Click to save an experiment
m_SaveBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to apply filters and save the results
m_SaveOut - Variable in class weka.gui.experiment.ResultsPanel
The buffer saving object for saving output
m_SaveOut - Variable in class weka.gui.explorer.AssociationsPanel
The buffer saving object for saving output
m_SaveOut - Variable in class weka.gui.explorer.AttributeSelectionPanel
The buffer saving object for saving output
m_SaveOut - Variable in class weka.gui.explorer.ClassifierPanel
The buffer saving object for saving output
m_SaveOut - Variable in class weka.gui.explorer.ClustererPanel
The buffer saving object for saving output
m_SaveOutBut - Variable in class weka.gui.experiment.ResultsPanel
Click to save test output to a file
m_Scale - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The scale parameter
m_Search - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The search method to use
m_SecondIndex - Variable in class weka.filters.unsupervised.attribute.MergeTwoValues
The second value's index setting.
m_SecondIndex - Variable in class weka.filters.unsupervised.attribute.SwapValues
The second value's index setting.
m_SecondSuccessor - Variable in class weka.classifiers.meta.TreeBasedMultiClassClassifier
The second successor
m_Seed - Variable in class weka.classifiers.BVDecompose
The random number seed
m_Seed - Variable in class weka.classifiers.BVDecomposeSegCVSub
The random number seed
m_Seed - Variable in class weka.classifiers.RandomizableClassifier
The random number seed.
m_Seed - Variable in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
The random number seed.
m_Seed - Variable in class weka.classifiers.RandomizableMultipleClassifiersCombiner
The random number seed.
m_Seed - Variable in class weka.classifiers.RandomizableSingleClassifierEnhancer
The random number seed.
m_Seed - Variable in class weka.classifiers.evaluation.EvaluationUtils
Seed used to randomize data in cross-validation
m_Seed - Variable in class weka.classifiers.functions.VotedPerceptron
Seed used for shuffling the dataset
m_Seed - Variable in class weka.classifiers.functions.Winnow
Random seed used for shuffling the dataset, -1 == disable
m_Seed - Variable in class weka.classifiers.meta.CostSensitiveClassifier
Seed for reweighting using resampling.
m_Seed - Variable in class weka.classifiers.meta.Decorate
The seed for random number generation.
m_Seed - Variable in class weka.classifiers.meta.MultiClassClassifier
Random number seed
m_Seed - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Seed for boosting with resampling.
m_Seed - Variable in class weka.classifiers.meta.ThresholdSelector
Random number seed
m_Seed - Variable in class weka.classifiers.meta.TreeBasedMultiClassClassifier
Random number seed
m_Seed - Variable in class weka.classifiers.rules.ConjunctiveRule
The seed to perform randomization
m_Seed - Variable in class weka.classifiers.rules.JRip
The seed to perform randomization
m_Seed - Variable in class weka.classifiers.rules.PART
The seed for random number generation.
m_Seed - Variable in class weka.classifiers.rules.Ridor
The seed to perform randomization
m_Seed - Variable in class weka.classifiers.trees.J48
Random number seed for reduced-error pruning.
m_Seed - Variable in class weka.classifiers.trees.REPTree
Seed for random data shuffling.
m_Seed - Variable in class weka.clusterers.FarthestFirst
random seed
m_Seed - Variable in class weka.clusterers.SimpleKMeans
random seed
m_Seed - Variable in class weka.datagenerators.BIRCHCluster
 
m_Seed - Variable in class weka.datagenerators.RDG1
 
m_Seed - Variable in class weka.filters.supervised.attribute.ClassOrder
The seed of randomization
m_Seed - Variable in class weka.filters.supervised.instance.StratifiedRemoveFolds
Random number seed.
m_Seed - Variable in class weka.filters.unsupervised.instance.Randomize
The random number seed
m_Seed - Variable in class weka.filters.unsupervised.instance.RemoveFolds
Random number seed.
m_SeedLab - Variable in class weka.gui.explorer.AttributeSelectionPanel
Label by where cv random seed is entered
m_SeedText - Variable in class weka.gui.explorer.AttributeSelectionPanel
The field where the seed value is entered
m_SelectBut - Variable in class weka.gui.ListSelectorDialog
Click to choose the currently selected property
m_SelectBut - Variable in class weka.gui.PropertySelectorDialog
Click to choose the currently selected property
m_SelectCols - Variable in class weka.filters.unsupervised.attribute.Remove
Stores which columns to select as a funky range
m_SelectFlags - Variable in class weka.core.Range
The array of flags for whether an column is selected
m_Selected - Variable in class weka.core.SelectedTag
The index of the selected tag
m_Selected - Variable in class weka.gui.AttributeSelectionPanel.AttributeTableModel
The flag for whether the instance will be included
m_SelectedAttributes - Variable in class weka.classifiers.functions.LinearRegression
Which attributes are relevant?
m_SelectedAttributes - Variable in class weka.filters.supervised.attribute.AttributeSelection
holds the selected attributes
m_SelectedAttributes - Variable in class weka.filters.unsupervised.attribute.Copy
Stores the indexes of the selected attributes in order, once the dataset is seen
m_SelectedAttributes - Variable in class weka.filters.unsupervised.attribute.Remove
Stores the indexes of the selected attributes in order, once the dataset is seen
m_SelectedCols - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Stores which columns to copy
m_SelectedIndex - Variable in class weka.core.SingleIndex
The selected index
m_SelectedRange - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
Range of columns to convert to word vectors
m_Seperator - Variable in class weka.gui.HierarchyPropertyParser
The level separate in the path
m_SequentialAttIndex_valid - Variable in class weka.classifiers.lazy.LBR.Indexes
flag to check if sequential array must be rebuilt due to changes to the attribute index
m_SequentialAttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
an array of attribute indexes that are set to either true or false
m_SequentialInstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
the array of instance indexes that are set to a either true or false
m_SequentialInstanceIndex_valid - Variable in class weka.classifiers.lazy.LBR.Indexes
flag to check if sequential array must be rebuilt due to changes to the instance index
m_SetCostsBut - Variable in class weka.gui.explorer.ClassifierPanel
 
m_SetCostsFrame - Variable in class weka.gui.explorer.ClassifierPanel
The frame used to show the cost matrix editing panel
m_SetTestBut - Variable in class weka.gui.explorer.ClassifierPanel
The button used to open a separate test dataset
m_SetTestBut - Variable in class weka.gui.explorer.ClustererPanel
The button used to open a separate test dataset
m_SetTestFrame - Variable in class weka.gui.explorer.ClassifierPanel
The frame used to show the test set selection panel
m_SetTestFrame - Variable in class weka.gui.explorer.ClustererPanel
The frame used to show the test set selection panel
m_SetupPanel - Variable in class weka.gui.experiment.Experimenter
The panel for configuring the experiment
m_ShapeCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the shape they want to create for instance selection.
m_ShowStdDevs - Variable in class weka.experiment.PairedTTester
Indicates whether standard deviations should be displayed
m_ShowStdDevs - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select whether standard deviations are to be output or not
m_Shrinkage - Variable in class weka.classifiers.meta.LogitBoost
The value of the shrinkage parameter
m_Shuffle - Variable in class weka.classifiers.rules.Ridor
The number of shuffles performed on the data for randomization
m_SigTex - Variable in class weka.gui.experiment.ResultsPanel
Lets the user edit the test significance
m_Sigma - Variable in class weka.classifiers.BVDecompose
The calculated sigma (squared)
m_SignificanceLevel - Variable in class weka.experiment.PairedTTester
The significance level for comparisons
m_SimpleBut - Variable in class weka.gui.GUIChooser
Click to open the simplecli
m_SimpleCLI - Variable in class weka.gui.GUIChooser
The SimpleCLI
m_SimpleSetupRBut - Variable in class weka.gui.experiment.SetupModePanel
The button for choosing simple setup mode
m_SimpleStats - Variable in class weka.classifiers.rules.RuleStats
The simple stats of each rule
m_SingleName - Variable in class weka.gui.ResultHistoryPanel
The named result being viewed in the single-click display
m_SingleText - Variable in class weka.gui.ResultHistoryPanel
An optional component for single-click display
m_Size - Variable in class weka.core.FastVector
The current size;
m_Size - Variable in class weka.core.Queue
Store the current number of elements in the queue
m_Size - Variable in class weka.datagenerators.BIRCHCluster.GridVector
 
m_Slope - Variable in class weka.core.Optimization
 
m_SmallestProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Smallest probability of test attribute transforming into train attribute
m_SmallestProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Smallest probability of test attribute transforming into train attribute
m_SpecialElement - Variable in class weka.core.FastVector.FastVectorEnumeration
Special element.
m_Specifiers - Variable in class weka.experiment.PairedTTester.DatasetSpecifiers
 
m_Split - Variable in class weka.datagenerators.Test
 
m_SplitEvaluator - Variable in class weka.experiment.CrossValidationResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.RandomSplitResultProducer
The SplitEvaluator used to generate results
m_SplitFilter - Variable in class weka.classifiers.functions.LeastMedSq
 
m_SplitPoint - Variable in class weka.classifiers.trees.DecisionStump
The split point (index respectively).
m_SplitPoint - Variable in class weka.classifiers.trees.REPTree.Tree
The split point.
m_SplitPoint - Variable in class weka.classifiers.trees.RandomTree
The split point.
m_StandardDev - Variable in class weka.estimators.KKConditionalEstimator
Current standard dev
m_StandardDev - Variable in class weka.estimators.KernelEstimator
The standard deviation
m_StandardDev - Variable in class weka.estimators.NormalEstimator
The current standard deviation
m_StartAttIndex - Variable in class weka.classifiers.bayes.AODE
The starting index (in the m_CondiCounts matrix) of each attribute
m_StartBut - Variable in class weka.gui.experiment.RunPanel
Click to start running the experiment
m_StartBut - Variable in class weka.gui.explorer.AssociationsPanel
Click to start running the associator
m_StartBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to start running the attribute selector
m_StartBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to start running the classifier
m_StartBut - Variable in class weka.gui.explorer.ClustererPanel
Click to start running the clusterer
m_StartBut - Variable in class weka.gui.streams.InstanceLoader
 
m_StatsTable - Variable in class weka.gui.AttributeSummaryPanel
Displays other stats in a table
m_StatusBox - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Controls whether the custom iterator is used or not
m_StatusLab - Variable in class weka.gui.LogPanel
Displays the current status
m_StatusMessage - Variable in class weka.experiment.TaskStatusInfo
Holds current status message.
m_StdDevs - Variable in class weka.classifiers.functions.LinearRegression
The attribute standard deviations
m_StdDevs - Variable in class weka.filters.unsupervised.attribute.Standardize
The variances
m_StepSize - Variable in class weka.experiment.LearningRateResultProducer
The number of instances to add at each step
m_Steps - Variable in class weka.classifiers.meta.CVParameterSelection.CVParameter
Increment during the search
m_Stop - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The stop parameter
m_StopBut - Variable in class weka.gui.experiment.RunPanel
Click to signal the running experiment to halt
m_StopBut - Variable in class weka.gui.explorer.AssociationsPanel
Click to stop a running associator
m_StopBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to stop a running classifier
m_StopBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to stop a running classifier
m_StopBut - Variable in class weka.gui.explorer.ClustererPanel
Click to stop a running clusterer
m_Stopwords - Static variable in class weka.core.Stopwords
The hashtable containing the list of stopwords
m_StorePredictionsBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to save the predictions in the results list for visualizing later on
m_StorePredictionsBut - Variable in class weka.gui.explorer.ClustererPanel
Check to save the predictions in the results list for visualizing later on
m_SubSample - Variable in class weka.classifiers.functions.LeastMedSq
 
m_SubsetEval - Variable in class weka.attributeSelection.RankSearch
the subset evaluator with which to evaluate the ranking
m_Successors - Variable in class weka.classifiers.trees.Id3
The node's successors.
m_Successors - Variable in class weka.classifiers.trees.REPTree.Tree
The subtrees of this tree.
m_Successors - Variable in class weka.classifiers.trees.RandomTree
The subtrees appended to this tree.
m_SumAbsErr - Variable in class weka.classifiers.Evaluation
Sum of absolute errors.
m_SumClass - Variable in class weka.classifiers.Evaluation
Sum of class values.
m_SumClassPredicted - Variable in class weka.classifiers.Evaluation
Sum of predicted * class values.
m_SumErr - Variable in class weka.classifiers.Evaluation
Sum of errors.
m_SumForCounts - Variable in class weka.classifiers.bayes.AODE
The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal]
m_SumInstances - Variable in class weka.classifiers.bayes.AODE
The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances.
m_SumKBInfo - Variable in class weka.classifiers.Evaluation
Total Kononenko & Bratko Information
m_SumOfCounts - Variable in class weka.classifiers.bayes.DiscreteEstimatorBayes
Hold the sum of counts
m_SumOfCounts - Variable in class weka.estimators.DiscreteEstimator
Hold the sum of counts
m_SumOfValues - Variable in class weka.estimators.NormalEstimator
The sum of the values seen
m_SumOfValues - Variable in class weka.estimators.PoissonEstimator
The sum of the values seen
m_SumOfValuesSq - Variable in class weka.estimators.NormalEstimator
The sum of the values squared
m_SumOfWeights - Variable in class weka.estimators.KKConditionalEstimator
The sum of the weights so far
m_SumOfWeights - Variable in class weka.estimators.KernelEstimator
The sum of the weights so far
m_SumOfWeights - Variable in class weka.estimators.NNConditionalEstimator
The sum of the weights so far
m_SumOfWeights - Variable in class weka.estimators.NormalEstimator
The sum of the weights
m_SumPredicted - Variable in class weka.classifiers.Evaluation
Sum of predicted values.
m_SumPriorAbsErr - Variable in class weka.classifiers.Evaluation
Sum of absolute errors of the prior
m_SumPriorEntropy - Variable in class weka.classifiers.Evaluation
Total entropy of prior predictions
m_SumPriorSqrErr - Variable in class weka.classifiers.Evaluation
Sum of absolute errors of the prior
m_SumSchemeEntropy - Variable in class weka.classifiers.Evaluation
Total entropy of scheme predictions
m_SumSqrClass - Variable in class weka.classifiers.Evaluation
Sum of squared class values.
m_SumSqrErr - Variable in class weka.classifiers.Evaluation
Sum of squared errors.
m_SumSqrPredicted - Variable in class weka.classifiers.Evaluation
Sum of squared predicted values.
m_Summary - Variable in class weka.gui.SetInstancesPanel
The instance summary component
m_Summary - Variable in class weka.gui.explorer.ClassifierPanel
The instances summary panel displayed by m_SetTestFrame
m_Summary - Variable in class weka.gui.explorer.ClustererPanel
The instances summary panel displayed by m_SetTestFrame
m_Support - Variable in class weka.gui.GenericArrayEditor
Handles property change notification
m_Support - Variable in class weka.gui.GenericObjectEditor
Handles property change notification
m_Support - Variable in class weka.gui.SetInstancesPanel
Manages sending notifications to people when we change the set of working instances.
m_Support - Variable in class weka.gui.experiment.SetupPanel
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.
m_Support - Variable in class weka.gui.experiment.SimpleSetupPanel
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.
m_Support - Variable in class weka.gui.explorer.PreprocessPanel
Manages sending notifications to people when we change the set of working instances.
m_Synopsis - Variable in class weka.core.Option
The synopsis.
m_TFTransform - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
True if word frequencies should be transformed into log(1+fi) where fi is the frequency of word i
m_TOLX - Variable in class weka.core.Optimization
 
m_TTester - Variable in class weka.gui.experiment.ResultsPanel
The PairedTTester object
m_TabbedPane - Variable in class weka.gui.experiment.Experimenter
The tabbed pane that controls which sub-pane we are working with
m_TabbedPane - Variable in class weka.gui.explorer.Explorer
The tabbed pane that controls which sub-pane we are working with
m_Table - Variable in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
The hash table data.
m_Table - Variable in class weka.gui.AttributeListPanel
The table displaying attribute names
m_Table - Variable in class weka.gui.AttributeSelectionPanel
The table displaying attribute names and selection status
m_Tags - Variable in class weka.core.SelectedTag
The set of tags to choose from
m_Tail - Variable in class weka.core.Queue
Store a reference to the tail of the queue
m_Target - Variable in class weka.gui.PropertySheetPanel
The target object being edited
m_Targets - Variable in class weka.classifiers.rules.ConjunctiveRule
The predicted classes recorded for each antecedent in the growing data
m_TaskIdQueue - Variable in class weka.experiment.RemoteEngine
A queue of corresponding ID's for tasks
m_TaskMonitor - Variable in class weka.gui.LogPanel
The panel for monitoring the number of running tasks (if supplied)
m_TaskQueue - Variable in class weka.experiment.RemoteEngine
A queue of waiting tasks
m_TaskResult - Variable in class weka.experiment.TaskStatusInfo
Holds task result.
m_TaskRunning - Variable in class weka.experiment.RemoteEngine
Is there a task running
m_TaskStatus - Variable in class weka.experiment.RemoteEngine
A hashtable of experiment status
m_Template - Variable in class weka.experiment.PairedTTester.Dataset
 
m_Template - Variable in class weka.experiment.PairedTTester.Resultset
 
m_Test - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The test instance
m_Test - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The test instance
m_TestInstances - Variable in class weka.gui.explorer.AssociationsPanel
The user-supplied test set (if any)
m_TestInstances - Variable in class weka.gui.explorer.ClassifierPanel
The user-supplied test set (if any)
m_TestInstances - Variable in class weka.gui.explorer.ClustererPanel
The user-supplied test set (if any)
m_TestInstancesCopy - Variable in class weka.gui.explorer.ClustererPanel
The user supplied test set after preprocess filters have been applied
m_TestSplitBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to a user-specified test set
m_TestSplitBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to a user-specified test set
m_TestsButton - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select which scheme to base comparisons against
m_TestsList - Variable in class weka.gui.experiment.ResultsPanel
Holds the list of schemes to base the test against
m_TestsModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_TestsList
m_Threshold - Variable in class weka.classifiers.functions.Winnow
Prediction threshold, <0 == numAttributes
m_Threshold - Variable in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Rehashes the table when count exceeds this threshold.
m_TipTexts - Variable in class weka.gui.PropertySheetPanel
The tool tip text for each property
m_Total - Variable in class weka.classifiers.rules.JRip
# of all the possible conditions in a rule
m_Total - Variable in class weka.classifiers.rules.RuleStats
The total number of possible conditions that could appear in a rule
m_TotalAttValues - Variable in class weka.classifiers.bayes.AODE
The total number of values for all attributes (not including class).
m_TotalCost - Variable in class weka.classifiers.Evaluation
The total cost of predictions (includes instance weights)
m_TotalCount - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Number of trai instances with no missing attribute values
m_Train - Variable in class weka.classifiers.functions.VotedPerceptron
The training instances
m_Train - Variable in class weka.classifiers.functions.Winnow
The training instances
m_Train - Variable in class weka.classifiers.lazy.IB1
The training instances used for classification.
m_Train - Variable in class weka.classifiers.lazy.IBk
The training instances used for classification.
m_Train - Variable in class weka.classifiers.lazy.KStar
The training instances used for classification.
m_Train - Variable in class weka.classifiers.lazy.LWL
The training instances used for classification.
m_Train - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The train instance
m_Train - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The train instance
m_Train - Variable in class weka.classifiers.rules.NNge
An empty instances to keep the headers, the classIndex, etc...
m_TrainBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to set test mode to test on training data
m_TrainBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to test on training data
m_TrainBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to test on training data
m_TrainClassVals - Variable in class weka.classifiers.Evaluation
Array containing all numeric training class values seen
m_TrainClassWeights - Variable in class weka.classifiers.Evaluation
Array containing all numeric training class weights
m_TrainFoldSize - Variable in class weka.classifiers.meta.CVParameterSelection
The number of instances in a training fold
m_TrainIterations - Variable in class weka.classifiers.BVDecompose
The number of train iterations
m_TrainPercent - Variable in class weka.experiment.RandomSplitResultProducer
The percentage of instances to use for training
m_TrainPoolSize - Variable in class weka.classifiers.BVDecompose
The number of instances used in the training pool
m_TrainSet - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The training instances used for classification.
m_TrainSet - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The training instances used for classification.
m_TrainSize - Variable in class weka.classifiers.BVDecomposeSegCVSub
The training set size
m_TransformFilter - Variable in class weka.classifiers.functions.LeastMedSq
 
m_TransformFilter - Variable in class weka.classifiers.functions.LinearRegression
The filter storing the transformation from nominal to binary attributes.
m_TransformedData - Variable in class weka.classifiers.functions.LinearRegression
Variable for storing transformed training data.
m_Tree - Variable in class weka.classifiers.trees.REPTree
The Tree object
m_Tree - Variable in class weka.gui.PropertySelectorDialog
The component displaying the property tree
m_TrueNeg - Variable in class weka.classifiers.evaluation.TwoClassStats
Neg predicted as neg
m_TruePos - Variable in class weka.classifiers.evaluation.TwoClassStats
Pos predicted as pos
m_TwoClassDataset - Variable in class weka.classifiers.meta.MultiClassClassifier
A transformed dataset header used by the 1-against-1 method
m_Type - Variable in class weka.core.Attribute
The attribute's type.
m_Unclassified - Variable in class weka.classifiers.Evaluation
The weight of all unclassified instances.
m_UndoBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to revert back to the last saved point
m_UniqueLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the number of unique values
m_UpdateString - Variable in class weka.gui.streams.InstanceTable
 
m_UpdateString - Variable in class weka.gui.streams.InstanceViewer
 
m_Upper - Variable in class weka.classifiers.meta.CVParameterSelection.CVParameter
Upper bound for the CV search
m_Upper - Variable in class weka.core.Range
Store the maximum value permitted in the range. -1 indicates that no upper value has been set
m_Upper - Variable in class weka.core.SingleIndex
Store the maximum value permitted. -1 indicates that no upper value has been set
m_UpperBound - Variable in class weka.core.Attribute
The attribute's upper numeric bound.
m_UpperBoundIsOpen - Variable in class weka.core.Attribute
Whether the upper bound is open
m_UpperSize - Variable in class weka.experiment.LearningRateResultProducer
The maximum number of instances to use. -1 indicates no maximum (other than the total number of instances)
m_UpperText - Variable in class weka.gui.experiment.RunNumberPanel
Configures the upper run number
m_UseAllK - Variable in class weka.classifiers.lazy.LWL
True if m_kNN should be set to all instances
m_UseBetterEncoding - Variable in class weka.filters.supervised.attribute.Discretize
Use better encoding of split point for MDL.
m_UseDiscretization - Variable in class weka.classifiers.bayes.NaiveBayes
Whether to use discretization than normal distribution for numeric attributes
m_UseEqualFrequency - Variable in class weka.filters.unsupervised.attribute.Discretize
Use equal-frequency binning if unsupervised discretization turned on
m_UseKernelEstimator - Variable in class weka.classifiers.bayes.NaiveBayes
Whether to use kernel density estimator rather than normal distribution for numeric attributes
m_UseKononenko - Variable in class weka.filters.supervised.attribute.Discretize
Use Kononenko's MDL criterion instead of Fayyad et al.'
m_UseMissing - Variable in class weka.filters.unsupervised.attribute.AddNoise
Flag if missing values are taken as value.
m_UsePropertyIterator - Variable in class weka.experiment.Experiment
True if the exp should also iterate over a property of the RP
m_UsePruning - Variable in class weka.classifiers.rules.JRip
Whether use pruning, i.e. the data is clean or not
m_UseRandomSelection - Variable in class weka.classifiers.meta.TreeBasedMultiClassClassifier
Whether to split classes randomly
m_UseResampling - Variable in class weka.classifiers.meta.AdaBoostM1
Use boosting with reweighting?
m_UseResampling - Variable in class weka.classifiers.meta.LogitBoost
Use boosting with reweighting?
m_UseResampling - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Whether to use resampling
m_UserDir - Variable in class weka.gui.experiment.DatasetListPanel
The user (start) directory
m_UserNameLab - Variable in class weka.gui.DatabaseConnectionDialog
 
m_UserNameText - Variable in class weka.gui.DatabaseConnectionDialog
 
m_ValIndex - Variable in class weka.filters.unsupervised.attribute.MakeIndicator
The value's index
m_Value - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Stores which value of a numeric attribute is to be used for filtering.
m_ValueBuffer - Variable in class weka.core.Instances
Buffer of values for sparse instance
m_ValueClass - Variable in class weka.gui.GenericArrayEditor.EditorListCellRenderer
The class of the array values
m_ValueMean - Variable in class weka.estimators.MahalanobisEstimator
The mean of the values
m_ValueMean - Variable in class weka.estimators.NNConditionalEstimator
Current Values mean
m_Values - Variable in class weka.core.Attribute
The attribute's values (if nominal or string).
m_Values - Variable in class weka.estimators.KKConditionalEstimator
Vector containing all of the values seen
m_Values - Variable in class weka.estimators.KernelEstimator
Vector containing all of the values seen
m_Values - Variable in class weka.estimators.NNConditionalEstimator
Vector containing all of the values seen
m_Values - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Stores which values of nominal attribute are to be used for filtering.
m_Values - Variable in class weka.gui.PropertySheetPanel
Holds current object values for each property
m_Variance - Variable in class weka.classifiers.BVDecompose
The calculated variance
m_VaryNodes - Variable in class weka.classifiers.bayes.ADNode
list of VaryNode children
m_Vector - Variable in class weka.core.FastVector.FastVectorEnumeration
The vector.
m_Views - Variable in class weka.gui.PropertySheetPanel
Stores GUI components containing each editing component
m_VisualizePanel - Variable in class weka.gui.explorer.Explorer
Label for a panel that still need to be implemented
m_VoteFlag - Variable in class weka.datagenerators.RDG1
 
m_WBias - Variable in class weka.classifiers.BVDecomposeSegCVSub
The calculated Webb bias
m_WVariance - Variable in class weka.classifiers.BVDecomposeSegCVSub
The calculated Webb variance
m_Weight - Variable in class weka.classifiers.evaluation.NominalPrediction
The weight assigned to this prediction
m_Weight - Variable in class weka.classifiers.evaluation.NumericPrediction
The weight assigned to this prediction
m_Weight - Variable in class weka.core.Attribute
The attribute's weight.
m_Weight - Variable in class weka.core.Instance
The instance's weight.
m_WeightKernel - Variable in class weka.classifiers.lazy.LWL
The weighting kernel method currently selected
m_WeightThreshold - Variable in class weka.classifiers.meta.AdaBoostM1
Weight Threshold.
m_WeightThreshold - Variable in class weka.classifiers.meta.LogitBoost
Weight thresholding.
m_Weights - Variable in class weka.classifiers.functions.VotedPerceptron
The weights for each perceptron
m_Weights - Variable in class weka.estimators.DKConditionalEstimator
Hold the weights for each of the sub-estimators
m_Weights - Variable in class weka.estimators.DNConditionalEstimator
Hold the weights for each of the sub-estimators
m_Weights - Variable in class weka.estimators.KKConditionalEstimator
Vector containing the associated weights
m_Weights - Variable in class weka.estimators.KernelEstimator
Vector containing the associated weights
m_Weights - Variable in class weka.estimators.NNConditionalEstimator
Vector containing the associated weights
m_WindowSize - Variable in class weka.classifiers.lazy.IBk
The maximum number of training instances allowed.
m_WithClass - Variable in class weka.classifiers.Evaluation
The weight of all instances that had a class assigned to them.
m_WordsToKeep - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
The default number of words (per class if there is a class attribute assigned) to attempt to keep.
m_Worth - Variable in class weka.classifiers.rules.Ridor.RidorRule
The worth value of this rule, in this case, accurate # in pruning data
m_WorthRate - Variable in class weka.classifiers.rules.Ridor.RidorRule
The worth rate of this rule, in this case, accuracy rate in the pruning data
m_X - Variable in class weka.core.Optimization
 
m_XCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the attribute for the x axis
m_XaxisEnd - Variable in class weka.gui.visualize.Plot2D
 
m_XaxisEnd - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_XaxisStart - Variable in class weka.gui.visualize.Plot2D
the offsets of the axes once label metrics are calculated
m_XaxisStart - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
the offsets of the axes once label metrics are calculated
m_YCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the attribute for the y axis
m_YaxisEnd - Variable in class weka.gui.visualize.Plot2D
 
m_YaxisEnd - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_YaxisStart - Variable in class weka.gui.visualize.Plot2D
 
m_YaxisStart - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_Zero - Static variable in class weka.core.Optimization
 
m_ZeroR - Variable in class weka.classifiers.meta.MultiClassClassifier
ZeroR classifier for when all base classifier return zero probability.
m_ZeroR - Variable in class weka.classifiers.meta.OrdinalClassClassifier
ZeroR classifier for when all base classifier return zero probability.
m_ZipDest - Variable in class weka.experiment.CrossValidationResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.RandomSplitResultProducer
The output zipper to use for saving raw splitEvaluator output
m_aEdges - Variable in class weka.gui.treevisualizer.TreeBuild
An array with all the edges initially constructed into it.
m_aNodes - Variable in class weka.gui.treevisualizer.TreeBuild
An array with all the nodes initially constructed into it.
m_accept - Variable in class weka.gui.treevisualizer.TreeVisualizer
An option on the win menu.
m_acceptButton - Variable in class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
The button to accept the network (even if it hasn't done all epochs.
m_accepted - Variable in class weka.classifiers.functions.MultilayerPerceptron
a flag to state that the network should be accepted the way it is.
m_actualThreshold - Variable in class weka.classifiers.functions.Winnow
The true threshold used for prediction
m_acuity - Variable in class weka.clusterers.Cobweb
Acuity (minimum standard deviation).
m_addChildren - Variable in class weka.gui.treevisualizer.TreeVisualizer
An add children to Node choice, This is only available if the tree display has a treedisplay listerner added to it.
m_additiveModels - Variable in class weka.classifiers.meta.AdditiveRegression
The list of iteratively generated models.
m_advanceDataSetFirst - Variable in class weka.gui.experiment.SetupPanel
Click to advacne data set before custom generator
m_advanceIteratorFirst - Variable in class weka.gui.experiment.SetupPanel
Click to advance custom generator before data set
m_advancedPanel - Variable in class weka.gui.experiment.SetupModePanel
The advanced setup panel
m_allData - Variable in class weka.classifiers.trees.j48.BinC45ModelSelection
The FULL training dataset.
m_allData - Variable in class weka.classifiers.trees.j48.C45ModelSelection
All the training data
m_allTheRules - Variable in class weka.associations.Apriori
The list of all generated rules.
m_alpha - Variable in class weka.classifiers.functions.SMO.BinarySMO
The Lagrange multipliers.
m_alpha - Variable in class weka.classifiers.functions.SMOreg
The Lagrange multipliers
m_alpha - Variable in class weka.classifiers.trees.lmt.LMTNode
Alpha-value (for pruning) at the node
m_alpha_ - Variable in class weka.classifiers.functions.SMOreg
 
m_amount - Variable in class weka.gui.treevisualizer.PlaceNode2.Ease
The distance they were tangled.
m_animatedIcon - Variable in class weka.gui.beans.BeanVisual
 
m_animatedIconPath - Variable in class weka.gui.beans.BeanVisual
Holds name (including path) of the animated icon
m_animating - Variable in class weka.gui.WekaTaskMonitor
True if their are active tasks
m_appendProbabilities - Variable in class weka.gui.beans.PredictionAppender
Append classifier's predicted probabilities (if the class is discrete and the classifier is a distribution classifier)
m_arffFileFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
FIlter for choosing ARFF files
m_attIndex - Variable in class weka.classifiers.trees.j48.BinC45Split
Attribute to split on.
m_attIndex - Variable in class weka.classifiers.trees.j48.C45Split
Attribute to split on.
m_attIndex - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The index of the attribute selected for the split
m_attScores - Variable in class weka.attributeSelection.SVMAttributeEval
The attribute scores
m_attStats - Variable in class weka.clusterers.Cobweb.CNode
Within cluster attribute statistics
m_attTypeToDelete - Variable in class weka.filters.unsupervised.attribute.RemoveType
The type of attribute to delete
m_attr - Variable in class weka.classifiers.rules.OneR.OneRRule
Attribute to test
m_attr - Variable in class weka.classifiers.rules.Prism.Test
Attribute to test
m_attrFilter - Variable in class weka.classifiers.meta.StackingC
Filters to transform metaData
m_attrib - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The attribute itself.
m_attrib - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays the attributes , using color to represent another attribute.
m_attrib1 - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
 
m_attrib1 - Variable in class weka.gui.visualize.VisualizePanelEvent
The attribute along the x axis.
m_attrib2 - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
 
m_attrib2 - Variable in class weka.gui.visualize.VisualizePanelEvent
The attribute along the y axis.
m_attribFilter - Variable in class weka.attributeSelection.PrincipalComponents
used to remove the class column if a class column is set
m_attribIndex - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The index for this attribute.
m_attribList - Variable in class weka.gui.visualize.MatrixPanel
The list for selecting the attributes to display the plot matrix
m_attribute - Variable in class weka.classifiers.functions.SimpleLinearRegression
The chosen attribute
m_attribute - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The attribute selected for the split
m_attributeBases - Variable in class weka.classifiers.functions.MultilayerPerceptron
The base values for all the attributes.
m_attributeFilter - Variable in class weka.attributeSelection.AttributeSelection
the attribute filter for processing instances with respect to the most recent feature selection run
m_attributeFilter - Variable in class weka.attributeSelection.PrincipalComponents
 
m_attributeFilter - Variable in class weka.filters.unsupervised.attribute.RemoveType
The attribute filter used to do the filtering
m_attributeIndex - Variable in class weka.classifiers.functions.SimpleLinearRegression
The index of the chosen attribute
m_attributeIndex - Variable in class weka.filters.unsupervised.attribute.AddExpression.AttributeOperand
the index of the attribute
m_attributeList - Variable in class weka.attributeSelection.Ranker
Holds the ordered list of attributes
m_attributeMerit - Variable in class weka.attributeSelection.Ranker
Holds the list of attribute merit scores
m_attributeName - Variable in class weka.filters.unsupervised.attribute.AddExpression
Name of the new attribute.
m_attributeRanges - Variable in class weka.classifiers.functions.MultilayerPerceptron
The ranges for all the attributes.
m_attributeRanking - Variable in class weka.attributeSelection.AttributeSelection
the attribute indexes and associated merits if a ranking is produced
m_attsToWeightOn - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel.PlotThread
 
m_attsToWeightOn - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_autoBuild - Variable in class weka.classifiers.functions.MultilayerPerceptron
A flag to tell the build classifier to automatically build a neural net.
m_autoScale - Variable in class weka.gui.treevisualizer.TreeVisualizer
An option on the win_menu
m_avgFitness - Variable in class weka.attributeSelection.GeneticSearch
 
m_axisChanged - Variable in class weka.gui.visualize.Plot2D
if the user changes attribute assigned to an axis
m_axisColour - Variable in class weka.gui.visualize.Plot2D
Default colour for the axis
m_axisPad - Variable in class weka.gui.visualize.Plot2D
Axis padding
m_axisPad - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
Axis padding
m_b - Variable in class weka.classifiers.functions.SMO.BinarySMO
The thresholds.
m_b - Variable in class weka.classifiers.functions.SMOreg
The thresholds.
m_bInitAsNaiveBayes - Variable in class weka.classifiers.bayes.BayesNet
determines whether initial structure is an empty graph or a Naive Bayes network
m_bLow - Variable in class weka.classifiers.functions.SMO.BinarySMO
The thresholds.
m_bLow - Variable in class weka.classifiers.functions.SMOreg
The thresholds.
m_bRandomOrder - Variable in class weka.classifiers.bayes.BayesNetK2
Holds flag to indicate ordering should be random
m_bUp - Variable in class weka.classifiers.functions.SMO.BinarySMO
The thresholds.
m_bUp - Variable in class weka.classifiers.functions.SMOreg
The thresholds.
m_bUseADTree - Variable in class weka.classifiers.bayes.BayesNet
Use the experimental ADTree datastructure for calculating contingency tables
m_backgroundColour - Variable in class weka.gui.visualize.Plot2D
Default colour for the plot background
m_backstyle - Variable in class weka.gui.treevisualizer.Node
The fill mode for the node (not in use).
m_bagger - Variable in class weka.classifiers.trees.RandomForest
The bagger.
m_barColour - Variable in class weka.gui.visualize.AttributePanel
The default colour to use for the background of the bars if a colour is not defined in Visualize.props
m_barRange - Variable in class weka.gui.AttributeVisualizationPanel
 
m_baseExperiment - Variable in class weka.experiment.RemoteExperiment
The base experiment to split up into sub experiments for remote execution
m_basisFilter - Variable in class weka.classifiers.functions.RBFNetwork
The filter for producing the meta data
m_batchClassifierListeners - Variable in class weka.gui.beans.Classifier
Objects listening for batch classifier events
m_bcSupport - Variable in class weka.gui.beans.AbstractDataSource
BeanContextChild support
m_bcSupport - Variable in class weka.gui.beans.DataVisualizer
BeanContextChild support
m_bcSupport - Variable in class weka.gui.beans.KnowledgeFlow
 
m_bcSupport - Variable in class weka.gui.beans.TextViewer
BeanContextChild support
m_bean - Variable in class weka.gui.beans.BeanInstance
Holds the bean encapsulated in this instance
m_beanContext - Variable in class weka.gui.beans.AbstractDataSource
BeanContex that this bean might be contained within
m_beanContext - Variable in class weka.gui.beans.DataVisualizer
BeanContex that this bean might be contained within
m_beanContext - Variable in class weka.gui.beans.TextViewer
BeanContex that this bean might be contained within
m_beanLayout - Variable in class weka.gui.beans.KnowledgeFlow
The layout area
m_best - Variable in class weka.associations.Tertius
Number of best confirmation values to search.
m_best - Variable in class weka.attributeSelection.GeneticSearch
the best population member found during the search
m_bestCommittee - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The current best committee
m_bestFeatureCount - Variable in class weka.attributeSelection.GeneticSearch
the number of features in the best population member
m_bestGroup - Variable in class weka.attributeSelection.ExhaustiveSearch
the best feature set found during the search
m_bestGroup - Variable in class weka.attributeSelection.RandomSearch
the best feature set found during the search
m_bestMedian - Variable in class weka.classifiers.functions.LeastMedSq
 
m_bestMerit - Variable in class weka.attributeSelection.BestFirst
holds the merit of the best subset found
m_bestMerit - Variable in class weka.attributeSelection.ExhaustiveSearch
the merit of the best subset found
m_bestMerit - Variable in class weka.attributeSelection.ForwardSelection
the merit of the best subset found
m_bestMerit - Variable in class weka.attributeSelection.RaceSearch
holds the merit of the best subset found
m_bestMerit - Variable in class weka.attributeSelection.RandomSearch
the merit of the best subset found
m_bestMerit - Variable in class weka.attributeSelection.RankSearch
the merit of the best subset found
m_bestRegression - Variable in class weka.classifiers.functions.LeastMedSq
 
m_best_group - Variable in class weka.attributeSelection.ForwardSelection
the best subset found
m_best_group - Variable in class weka.attributeSelection.RankSearch
the best subset found
m_bias - Variable in class weka.classifiers.misc.VFI
Bias towards more confident intervals
m_binarySplits - Variable in class weka.classifiers.rules.PART
Binary splits on nominal attributes?
m_binarySplits - Variable in class weka.classifiers.trees.J48
Binary splits on nominal attributes?
m_body - Variable in class weka.associations.tertius.Rule
The body of the rule.
m_boostedModel - Variable in class weka.classifiers.functions.SimpleLogistic
The actual logistic regression model
m_boostingIterations - Variable in class weka.classifiers.trees.ADTree
Option - the number of boosting iterations o perform
m_boundaryPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_breakpoints - Variable in class weka.classifiers.rules.OneR.OneRRule
Breakpoints (numeric attributes only)
m_buildThread - Variable in class weka.gui.beans.Classifier
 
m_built - Variable in class weka.classifiers.trees.UserClassifier
The status of whether there is a decision tree ready or not.
m_cIndex - Variable in class weka.gui.visualize.AttributePanel
 
m_cIndex - Variable in class weka.gui.visualize.ClassPanel
Index of the colouring attribute
m_cIndex - Variable in class weka.gui.visualize.Plot2D
 
m_cIndex - Variable in class weka.gui.visualize.PlotData2D
The colouring index
m_cIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_cVisible - Variable in class weka.gui.treevisualizer.Node
true if this nodes descendants are visible (not in use currently).
m_c_Threshold - Variable in class weka.attributeSelection.CfsSubsetEval
Threshold for admitting locally predictive features
m_caEditor - Variable in class weka.gui.beans.ClassAssignerCustomizer
 
m_cacheSize - Variable in class weka.attributeSelection.BestFirst
holds the maximum size of the lookup cache for evaluated subsets
m_cacheSize - Variable in class weka.classifiers.functions.SMO
The size of the cache (a prime number)
m_cacheSize - Variable in class weka.classifiers.functions.SMOreg
The size of the cache (a prime number)
m_cacheSize - Variable in class weka.classifiers.functions.supportVector.PolyKernel
The size of the cache (a prime number)
m_cacheSize - Variable in class weka.classifiers.functions.supportVector.RBFKernel
The size of the cache (a prime number)
m_cached - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The x position of each point.
m_calculatedNumToSelect - Variable in class weka.attributeSelection.ForwardSelection
 
m_calculatedNumToSelect - Variable in class weka.attributeSelection.RaceSearch
 
m_calculatedNumToSelect - Variable in class weka.attributeSelection.Ranker
Used to compute the number to select
m_canChangeClassInDialog - Variable in class weka.gui.GenericObjectEditor
 
m_cancel - Variable in class weka.gui.visualize.VisualizePanel
Button for the user to remove all splits.
m_cancelBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
cancel button
m_caseSen - Variable in class weka.gui.treevisualizer.TreeVisualizer
 
m_ce - Variable in class weka.gui.beans.IncrementalClassifierEvaluator
 
m_center - Variable in class weka.gui.treevisualizer.Node
The center of the node (between 0 and 1).
m_center - Variable in class weka.gui.treevisualizer.TreeVisualizer.NodeInfo
The x pos of the node on screen.
m_change - Variable in class weka.gui.treevisualizer.TreeVisualizer.NodeInfo
True if the node is at the start (left) of a new level (not sibling group).
m_changeEpochs - Variable in class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
A text field to allow the changing of the total number of epochs.
m_changeInWeights - Variable in class weka.classifiers.functions.neural.NeuralNode
The change in the weights.
m_changeLearning - Variable in class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
A text field to allow the changing of the learning rate.
m_changeMomentum - Variable in class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
A text field to allow the changing of the momentum.
m_checkForUpperCaseNames - Variable in class weka.experiment.DatabaseUtils
 
m_checksTurnedOff - Variable in class weka.classifiers.functions.LinearRegression
Turn off all checks and conversions?
m_checksTurnedOff - Variable in class weka.classifiers.functions.SMO
Turn off all checks and conversions?
m_checksTurnedOff - Variable in class weka.classifiers.functions.SMOreg
Turn off all checks and conversions?
m_child - Variable in class weka.gui.treevisualizer.TreeVisualizer.EdgeInfo
The child subscript (for a Node).
m_children - Variable in class weka.clusterers.Cobweb.CNode
Children of this node
m_children - Variable in class weka.gui.treevisualizer.Node
An array containing references to all the child edges.
m_chromosome - Variable in class weka.attributeSelection.GeneticSearch.GABitSet
 
m_chunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_class - Variable in class weka.classifiers.functions.SMO.BinarySMO
The transformed class values.
m_class - Variable in class weka.classifiers.rules.OneR.OneRRule
The class attribute.
m_classAttBox - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_classAttrib - Variable in class weka.gui.visualize.MatrixPanel
The combo box to allow user to select the colouring attribute
m_classAttribute - Variable in class weka.classifiers.functions.SMO
The class attribute
m_classColumn - Variable in class weka.gui.beans.ClassAssigner
 
m_classIndex - Variable in class weka.associations.Tertius
Index of class attribute.
m_classIndex - Variable in class weka.attributeSelection.BestFirst
holds the class index
m_classIndex - Variable in class weka.attributeSelection.CfsSubsetEval
The class index
m_classIndex - Variable in class weka.attributeSelection.ClassifierSubsetEval
class index
m_classIndex - Variable in class weka.attributeSelection.ConsistencySubsetEval
class index
m_classIndex - Variable in class weka.attributeSelection.ExhaustiveSearch
holds the class index
m_classIndex - Variable in class weka.attributeSelection.ForwardSelection
holds the class index
m_classIndex - Variable in class weka.attributeSelection.GainRatioAttributeEval
The class index
m_classIndex - Variable in class weka.attributeSelection.GeneticSearch
holds the class index
m_classIndex - Variable in class weka.attributeSelection.OneRAttributeEval
The class index
m_classIndex - Variable in class weka.attributeSelection.PrincipalComponents
Class index
m_classIndex - Variable in class weka.attributeSelection.RaceSearch
the class index
m_classIndex - Variable in class weka.attributeSelection.RandomSearch
holds the class index
m_classIndex - Variable in class weka.attributeSelection.RankSearch
holds the class index
m_classIndex - Variable in class weka.attributeSelection.Ranker
Class index of the data if supervised evaluator
m_classIndex - Variable in class weka.attributeSelection.ReliefFAttributeEval
The class index
m_classIndex - Variable in class weka.attributeSelection.SymmetricalUncertAttributeEval
The class index
m_classIndex - Variable in class weka.attributeSelection.WrapperSubsetEval
class index
m_classIndex - Variable in class weka.classifiers.functions.SMO
The class index from the training data
m_classIndex - Variable in class weka.classifiers.functions.SMOreg
The class index from the training data
m_classIndex - Variable in class weka.classifiers.meta.AdditiveRegression
Class index.
m_classIndex - Variable in class weka.classifiers.trees.m5.M5Base
the class index
m_classIndex - Variable in class weka.classifiers.trees.m5.Rule
the class index
m_classIndex - Variable in class weka.classifiers.trees.m5.RuleNode
the class index
m_classIndex - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The attribute to treat as the class for purposes of cleansing.
m_classIndex - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_classIndex - Variable in class weka.gui.visualize.MatrixPanel
This contains the index of the currently selected colouring attribute
m_classIsNominal - Variable in class weka.classifiers.rules.DecisionTable
Class is nominal
m_classObject - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
Used instead of the standard leaf if one exists.
m_classPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_classPanel - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays the legend for the colouring attribute
m_classProbs - Variable in class weka.attributeSelection.ReliefFAttributeEval
Prior class probabilities (discrete class case)
m_classRule - Variable in class weka.associations.tertius.Rule
Is this rule a classification rule ?
m_classSurround - Variable in class weka.gui.visualize.VisualizePanel
Panel that surrounds the class panel with a titled border
m_classToCluster - Variable in class weka.clusterers.ClusterEvaluation
will hold the mapping of classes to clusters (for class based evaluation)
m_classesField - Variable in class weka.gui.CostMatrixEditor.CustomEditor
The field for changing the size of the cost matrix
m_classification - Variable in class weka.associations.Tertius
Classification bias.
m_classification - Variable in class weka.classifiers.rules.Prism.PrismRule
The classification
m_classifications - Variable in class weka.classifiers.rules.OneR.OneRRule
Predicted class for each value of attr
m_classifier - Variable in class weka.classifiers.meta.ND
The base classifier .
m_classifier - Variable in class weka.gui.beans.BatchClassifierEvent
The classifier
m_classifier - Variable in class weka.gui.beans.ClassifierPerformanceEvaluator
Holds the classifier to be evaluated
m_classifier - Variable in class weka.gui.beans.IncrementalClassifierEvaluator
 
m_classifier - Variable in class weka.gui.beans.IncrementalClassifierEvent
 
m_classifier - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_classifier - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_classifier - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_classifiers - Variable in class weka.classifiers.functions.SMO
The binary classifier(s)
m_classifiers - Variable in class weka.classifiers.meta.ND
The hashtable containing all the classifiers
m_classifiers - Variable in class weka.classifiers.trees.UserClassifier
A list of other m_classifiers.
m_classifyChild - Variable in class weka.gui.treevisualizer.TreeVisualizer
Use this to have J48 classify this node.
m_cleansingClassifier - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The classifier used to do the cleansing
m_cleanup - Variable in class weka.classifiers.trees.j48.C45PruneableClassifierTree
Cleanup after the tree has been built.
m_cleanup - Variable in class weka.classifiers.trees.j48.PruneableClassifierTree
Cleanup after the tree has been built.
m_clickAvailable - Variable in class weka.gui.treevisualizer.TreeVisualizer
A variable used to determine for the clicked method if any other mouse state has already taken place.
m_clusterAssignments - Variable in class weka.clusterers.ClusterEvaluation
holds the assigments of instances to clusters for a particular testing dataset
m_clusterInstances - Variable in class weka.clusterers.Cobweb.CNode
Instances at this node
m_clusterNum - Variable in class weka.clusterers.Cobweb.CNode
Cluster number of this node
m_clusterer - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
The clusterer
m_clusteringResults - Variable in class weka.clusterers.ClusterEvaluation
holds a string describing the results of clustering the training data
m_clusteringSeed - Variable in class weka.classifiers.functions.RBFNetwork
The seed to pass on to K-means
m_cobwebTree - Variable in class weka.clusterers.Cobweb
Holds the root of the Cobweb tree.
m_coefficients - Variable in class weka.classifiers.trees.m5.PreConstructedLinearModel
 
m_col - Variable in class weka.gui.treevisualizer.NamedColor
The actual color object
m_color - Variable in class weka.gui.treevisualizer.Node
The color of the node.
m_color - Variable in class weka.gui.treevisualizer.TreeBuild.InfoObject
The color name for the object.
m_colorAttrib - Variable in class weka.gui.AttributeVisualizationPanel
 
m_colorList - Variable in class weka.gui.AttributeVisualizationPanel
Contains discrete colours for colouring for nominal attributes
m_colorList - Variable in class weka.gui.beans.StripChart
default colours for colouring lines
m_colorList - Variable in class weka.gui.visualize.AttributePanel
The colour map to use for colouring points
m_colorList - Variable in class weka.gui.visualize.ClassPanel
the list of colours to use for colouring nominal attribute labels
m_colorList - Variable in class weka.gui.visualize.MatrixPanel
Contains discrete colours for colouring for nominal attributes
m_colorList - Variable in class weka.gui.visualize.Plot2D
The list of the colors used
m_colorList - Variable in class weka.gui.visualize.VisualizePanel
The list of the colors used
m_colorTable - Variable in class weka.gui.treevisualizer.TreeBuild
A table containing all the colors.
m_coloringIndex - Variable in class weka.gui.beans.AttributeSummarizer
Index on which to color the plots. -1 indicates that we let the attribute visualize panels set this on the basis of the class index in the data.
m_cols - Variable in class weka.gui.treevisualizer.Colors
The array with all the colors input
m_command - Variable in class weka.gui.treevisualizer.TreeDisplayEvent
The int representing the action.
m_committees - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The committees
m_completeReLayout - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
This tells the the LayoutGraph method if a completeReLayout should be performed when it is called.
m_complexityIndex - Variable in class weka.classifiers.trees.j48.C45Split
Desired number of branches.
m_configureHostNames - Variable in class weka.gui.experiment.DistributeExperimentPanel
Popup the HostListPanel
m_confirmation - Variable in class weka.associations.tertius.Rule
Confirmation of this rule.
m_confirmationThreshold - Variable in class weka.associations.Tertius
Confirmation threshold for the rules.
m_connectPoints - Variable in class weka.gui.visualize.PlotData2D
Additional optional information to control the drawing of lines between consecutive points.
m_controlPanel - Variable in class weka.classifiers.functions.MultilayerPerceptron
The control panel.
m_controlPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_controlsPanel - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
The panel containing extra options, specific to this LayoutEngine, for greater control over layout of the graph
m_convertNominal - Variable in class weka.classifiers.trees.LMT
convert nominal attributes to binary ?
m_corr_matrix - Variable in class weka.attributeSelection.CfsSubsetEval
Holds the matrix of attribute correlations
m_correct - Variable in class weka.classifiers.rules.OneR.OneRRule
Training set examples this rule gets right
m_correlation - Variable in class weka.attributeSelection.PrincipalComponents
Correlation matrix for the original data
m_counter - Variable in class weka.associations.ItemSet
Counter for how many transactions contain this item set.
m_counter - Variable in class weka.associations.tertius.LiteralSet
Counter for the number of counter-instances.
m_counter - Variable in class weka.associations.tertius.Rule
Counter for the counter-instances of this rule.
m_counterInstances - Variable in class weka.associations.tertius.LiteralSet
Set of counter-instances of this part of the rule.
m_counterInstances - Variable in class weka.associations.tertius.Rule
Set of counter-instances of this rule.
m_counts - Variable in class weka.classifiers.misc.VFI
The class counts for each interval of each attribute
m_coverVariance - Variable in class weka.attributeSelection.PrincipalComponents
the amount of varaince to cover in the original data when retaining the best n PC's
m_covered - Variable in class weka.classifiers.trees.m5.Rule
the instances covered by this rule
m_cp - Variable in class weka.gui.visualize.MatrixPanel
The panel that displays the legend of the colouring attribute
m_createIndex - Variable in class weka.experiment.DatabaseUtils
 
m_createShape - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
True if the user is currently dragging a box.
m_csvFileFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
Filter for choosing CSV files
m_cumulativeInstances - Variable in class weka.core.converters.CSVLoader
Holds instances accumulated so far
m_cumulativeStructure - Variable in class weka.core.converters.CSVLoader
A list of hash tables for accumulating nominal values during parsing.
m_currentFont - Variable in class weka.gui.treevisualizer.TreeVisualizer
The font used to display the tree.
m_currentInstance - Variable in class weka.classifiers.functions.MultilayerPerceptron
The current instance running through the network.
m_currentInstance - Variable in class weka.gui.beans.IncrementalClassifierEvent
 
m_currentRegression - Variable in class weka.classifiers.functions.LeastMedSq
 
m_currentSet - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The instances currently in memory for training
m_customColour - Variable in class weka.gui.visualize.PlotData2D
 
m_customEditor - Variable in class weka.gui.CostMatrixEditor
An instance of the custom editor
m_cutoff - Variable in class weka.clusterers.Cobweb
Cutoff (minimum category utility).
m_cvEditor - Variable in class weka.gui.beans.CrossValidationFoldMakerCustomizer
 
m_cvEditor - Variable in class weka.gui.beans.StripChartCustomizer
 
m_cycles - Variable in class weka.associations.Apriori
Number of cycles used before required number of rules was one.
m_data - Variable in class weka.classifiers.functions.SMO.BinarySMO
The training data.
m_data - Variable in class weka.classifiers.functions.SMOreg
The training data.
m_data - Variable in class weka.classifiers.functions.supportVector.Kernel
The dataset
m_data - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The set of instances
m_data - Variable in class weka.gui.AttributeVisualizationPanel
 
m_data - Variable in class weka.gui.treevisualizer.Node
A String containing extra information about the node.
m_data - Variable in class weka.gui.treevisualizer.TreeBuild.InfoObject
The data for this object.
m_data - Variable in class weka.gui.visualize.MatrixPanel
The dataset for which this panel will display the plot matrix for
m_dataGenerator - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_dataGenerator - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_dataGenerator - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_dataIndex - Variable in class weka.gui.visualize.LegendPanel.LegendEntry
the index (in the list of plots) of the data for this legend--- used to draw the correct shape for this data
m_dataLegend - Variable in class weka.gui.beans.IncrementalClassifierEvaluator
 
m_dataList - Variable in class weka.gui.beans.StripChart
Holds the data to be plotted.
m_dataListeners - Variable in class weka.gui.beans.ClassAssigner
 
m_dataListeners - Variable in class weka.gui.beans.Filter
Objects listening for data set events
m_dataPoint - Variable in class weka.gui.beans.ChartEvent
Y values of the data points
m_dataPoint - Variable in class weka.gui.beans.IncrementalClassifierEvaluator
 
m_dataProvider - Variable in class weka.gui.beans.ClassAssigner
 
m_dataReader - Variable in class weka.core.converters.C45Loader
Reader for data file
m_dataSet - Variable in class weka.gui.beans.DataSetEvent
 
m_dataSet - Variable in class weka.gui.beans.Loader
Holds the instances loaded
m_dataSetEventTargets - Variable in class weka.gui.beans.Loader
 
m_dataSourceListeners - Variable in class weka.gui.beans.PredictionAppender
Objects listenening for dataset events
m_dataWs - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The LogitBoost-weights for the set of instances
m_dataZs - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The Z-values (LogitBoost response) for the set of instances
m_debug - Variable in class weka.attributeSelection.BestFirst
for debugging
m_debug - Variable in class weka.attributeSelection.RaceSearch
verbose output for monitoring the search and debugging
m_debug - Variable in class weka.classifiers.functions.LeastMedSq
 
m_debug - Variable in class weka.classifiers.meta.AdditiveRegression
Produce debugging output.
m_debug - Variable in class weka.classifiers.rules.DecisionTable
Output debug info
m_debugOutput - Variable in class weka.experiment.CrossValidationResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.RandomSplitResultProducer
Save raw output of split evaluators --- for debugging purposes
m_decay - Variable in class weka.classifiers.functions.MultilayerPerceptron
This flag states that the user wants the learning rate to decay.
m_decisionFeatures - Variable in class weka.classifiers.rules.DecisionTable
Holds the final feature set
m_defaultButton - Variable in class weka.gui.CostMatrixEditor.CustomEditor
The button for setting default matrix values
m_defaultColors - Static variable in class weka.gui.AttributeVisualizationPanel
default colour list
m_defaultColors - Static variable in class weka.gui.visualize.MatrixPanel
default colour list
m_defaultWeight - Variable in class weka.classifiers.functions.Winnow
Starting weights for the prediction vector(s)
m_delTransform - Variable in class weka.classifiers.rules.DecisionTable
Filter used to remove columns discarded by feature selection
m_delta - Variable in class weka.associations.Apriori
Delta by which m_minSupport is decreased in each iteration.
m_delta - Variable in class weka.attributeSelection.RaceSearch
threshold for comparisons
m_design - Variable in class weka.gui.beans.AbstractDataSource
True if this bean's appearance is the design mode appearance
m_design - Variable in class weka.gui.beans.DataVisualizer
True if this bean's appearance is the design mode appearance
m_design - Variable in class weka.gui.beans.TextViewer
True if this bean's appearance is the design mode appearance
m_destination - Variable in class weka.experiment.OutputZipper
 
m_destinationDatabaseURL - Variable in class weka.gui.experiment.SimpleSetupPanel
The database destination URL to store results into
m_destinationFilename - Variable in class weka.gui.experiment.SimpleSetupPanel
The filename to store results into
m_devFraction - Variable in class weka.classifiers.trees.m5.RuleNode
a node will not be split if its class standard deviation is less than 5% of the class standard deviation of all the instances
m_digraph - Variable in class weka.gui.treevisualizer.TreeBuild
true if it is a digraph.
m_disTransform - Variable in class weka.attributeSelection.CfsSubsetEval
Discretise attributes when class in nominal
m_disTransform - Variable in class weka.attributeSelection.ConsistencySubsetEval
Discretise numeric attributes
m_disTransform - Variable in class weka.classifiers.rules.DecisionTable
Discretization filter
m_displayAllPoints - Variable in class weka.gui.visualize.PlotData2D
Display all points (ie. those that map to the same display coords)
m_displayConnectors - Variable in class weka.gui.beans.BeanVisual
 
m_displayRules - Variable in class weka.classifiers.rules.DecisionTable
Display Rules
m_dist - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel.PlotThread
 
m_dist - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_distribution - Variable in class weka.classifiers.trees.j48.ClassifierSplitModel
Distribution of class values.
m_distribution - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Stores the distribution to use for calculating the random matrix
m_doRank - Variable in class weka.attributeSelection.AttributeSelection
rank features (if allowed by the search method)
m_doRank - Variable in class weka.attributeSelection.ForwardSelection
go from one side of the search space to the other in order to generate a ranking
m_doXval - Variable in class weka.attributeSelection.AttributeSelection
do cross validation
m_doXval - Variable in class weka.clusterers.ClusterEvaluation
do cross validation (DensityBasedClusterers only)
m_doesProduce - Variable in class weka.experiment.ClassifierSplitEvaluator
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce
m_doesProduce - Variable in class weka.experiment.RegressionSplitEvaluator
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce
m_doneRanking - Variable in class weka.attributeSelection.ForwardSelection
used to indicate whether or not ranking has been performed
m_doubleType - Variable in class weka.experiment.DatabaseUtils
 
m_drawnPoints - Variable in class weka.gui.visualize.Plot2D
An array used to show if a point is hidden or not.
m_driftThreshold - Variable in class weka.classifiers.functions.MultilayerPerceptron
The number to to use to quit on validation testing.
m_dsClassifier - Variable in class weka.gui.beans.ClassifierCustomizer
 
m_dsLoader - Variable in class weka.gui.beans.LoaderCustomizer
 
m_dummy - Variable in class weka.gui.beans.Classifier
 
m_dummy - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_eTranspose - Variable in class weka.attributeSelection.PrincipalComponents
holds the transposed eigenvectors for converting back to the original space
m_edObj - Variable in class weka.gui.treevisualizer.TreeBuild
An object setup to take edge data.
m_edge - Variable in class weka.gui.treevisualizer.TreeVisualizer.EdgeInfo
The Edge itself.
m_edges - Variable in class weka.gui.graphvisualizer.BIFParser
These holds the nodes and edges of the graph
m_edges - Variable in class weka.gui.graphvisualizer.DotParser
These holds the nodes and edges of the graph
m_edges - Variable in class weka.gui.graphvisualizer.GraphVisualizer
Vector containing edges
m_edges - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
FastVector containing nodes and edges
m_edges - Variable in class weka.gui.treevisualizer.TreeBuild
An arry containing a structure that describes the edge without actually creating it.
m_edges - Variable in class weka.gui.treevisualizer.TreeVisualizer
An array with the Edges sorted into it and display information about the Edges.
m_editElement - Variable in class weka.gui.beans.KnowledgeFlow
Reference to bean being manipulated
m_eigenvalues - Variable in class weka.attributeSelection.PrincipalComponents
Eigenvalues for the corresponding eigenvectors
m_eigenvectors - Variable in class weka.attributeSelection.PrincipalComponents
Will hold the unordered linear transformations of the (normalized) original data
m_enableDistributedExperiment - Variable in class weka.gui.experiment.DistributeExperimentPanel
Distribute the current experiment to remote hosts
m_end - Variable in class weka.gui.treevisualizer.PlaceNode2.Group
The end node of this group.
m_end - Variable in class weka.gui.treevisualizer.PlaceNode2.Level
The number for the group on the right of this level.
m_entries - Variable in class weka.classifiers.rules.DecisionTable
The hashtable used to hold training instances
m_epoch - Variable in class weka.classifiers.functions.MultilayerPerceptron
Shows the number of the epoch that the network just finished.
m_epochsLabel - Variable in class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
A label to state the number of epochs processed so far.
m_eps - Variable in class weka.classifiers.functions.SMO
Epsilon for rounding.
m_eps - Variable in class weka.classifiers.functions.SMOreg
The parameter eps
m_epsilon - Variable in class weka.classifiers.functions.SMOreg
The parameter epsilon
m_equivalent - Variable in class weka.associations.Tertius
Perform test on equivalent rules ?
m_error - Variable in class weka.classifiers.functions.MultilayerPerceptron
Shows the error of the epoch that the network just finished.
m_errorLabel - Variable in class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
A label to state roughly the accuracy of the network.
m_errorOnProbabilities - Variable in class weka.classifiers.functions.SimpleLogistic
If true, use minimize error on probabilities instead of misclassification error
m_errorOnProbabilities - Variable in class weka.classifiers.trees.LMT
use error on probabilties instead of misclassification for stopping criterion of LogitBoost?
m_errorOnProbabilities - Variable in class weka.classifiers.trees.lmt.LogisticBase
Use error on probabilities for stopping criterion of LogitBoost?
m_errors - Variable in class weka.classifiers.functions.SMO.BinarySMO
The current set of errors for all non-bound examples.
m_errors - Variable in class weka.classifiers.rules.Prism.PrismRule
Number of errors made by this rule (will end up 0)
m_eval - Variable in class weka.gui.beans.ClassifierPerformanceEvaluator
Evaluation object used for evaluating a classifier
m_eval - Variable in class weka.gui.beans.IncrementalClassifierEvaluator
 
m_evalUsingTrainingData - Variable in class weka.attributeSelection.OneRAttributeEval
Use training data to evaluate merit rather than x-val
m_evaluateThread - Variable in class weka.gui.beans.ClassifierPerformanceEvaluator
 
m_evaluations - Variable in class weka.attributeSelection.ExhaustiveSearch
the number of subsets evaluated during the search
m_eventName - Variable in class weka.gui.beans.BeanConnection
The name of the event for this connection
m_examplesCounted - Variable in class weka.classifiers.trees.ADTree
Statistics - the number of instances processed during search
m_experiment - Variable in class weka.experiment.RemoteExperimentSubTask
 
m_experimentAborted - Variable in class weka.experiment.RemoteExperiment
Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts experiment (via gui)
m_experimentFinished - Variable in class weka.experiment.RemoteExperimentEvent
True if a remote experiment has finished
m_explored - Variable in class weka.associations.Tertius
Number of hypotheses explored.
m_exponent - Variable in class weka.classifiers.functions.SMO
The exponent for the polynomial kernel.
m_exponent - Variable in class weka.classifiers.functions.SMOreg
The exponent for the polynomial kernel.
m_exponent - Variable in class weka.classifiers.functions.supportVector.PolyKernel
The exponent for the polynomial kernel.
m_f - Variable in class weka.core.Optimization
 
m_fAlpha - Variable in class weka.classifiers.bayes.BayesNet
Holds prior on count
m_fPrior - Variable in class weka.classifiers.bayes.DiscreteEstimatorBayes
Holds the prior probability
m_failedCount - Variable in class weka.experiment.RemoteExperiment
The count of failed sub-experiments
m_failedCount - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
The count of failed sub-tasks
m_fastRegression - Variable in class weka.classifiers.trees.LMT
use heuristic that determines the number of LogitBoost iterations only once in the beginning?
m_fastRegression - Variable in class weka.classifiers.trees.lmt.LMTNode
Use heuristic that determines the number of LogitBoost iterations only once in the beginning?
m_fcache - Variable in class weka.classifiers.functions.SMOreg
The current set of errors for all non-bound examples.
m_featureSpaceNormalization - Variable in class weka.classifiers.functions.SMO
Feature-space normalization?
m_featureSpaceNormalization - Variable in class weka.classifiers.functions.SMOreg
Feature-space normalization?
m_fieldWidthC - Variable in class weka.gui.visualize.ClassPanel
Field width for numeric values
m_fileChooser - Variable in class weka.gui.CostMatrixEditor
The file chooser for the user to select cost files to save and load
m_fileEditor - Variable in class weka.gui.beans.LoaderCustomizer
 
m_fileStem - Variable in class weka.core.converters.C45Loader
Holds the filestem.
m_fileStem - Variable in class weka.gui.beans.CSVDataSink
 
m_filter - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
Used on the instances while classifying if one exists.
m_filter - Variable in class weka.gui.beans.FilterCustomizer
 
m_filterEditor - Variable in class weka.gui.beans.FilterCustomizer
 
m_filterThread - Variable in class weka.gui.beans.Filter
 
m_filterType - Variable in class weka.classifiers.functions.SMO
Whether to normalize/standardize/neither
m_filterType - Variable in class weka.classifiers.functions.SMOreg
Whether to normalize/standardize/neither
m_finishedCount - Variable in class weka.experiment.RemoteExperiment
The count of successfully completed sub-experiments
m_finishedCount - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
The count of successfully completed sub-tasks
m_first - Variable in class weka.classifiers.functions.supportVector.SMOset
The first element in the set
m_first - Variable in class weka.classifiers.trees.m5.CorrelationSplitInfo
the first instance
m_firstBatchFinished - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
Have we processed the first batch (i.e. training data)?
m_fitLogisticModels - Variable in class weka.classifiers.functions.SMO
Whether logistic models are to be fit
m_fitToScreen - Variable in class weka.gui.treevisualizer.TreeVisualizer
An option on the win_menu
m_fitness - Variable in class weka.attributeSelection.GeneticSearch.GABitSet
 
m_fixedNumIterations - Variable in class weka.classifiers.trees.lmt.LogisticBase
Use fixed number of iterations for LogitBoost?
m_focus - Variable in class weka.classifiers.trees.UserClassifier
Two references to the structure of the decision tree.
m_focusNode - Variable in class weka.gui.treevisualizer.TreeVisualizer
The subscript for the currently selected node (this is an internal thing, so the user is unaware of this).
m_foldThread - Variable in class weka.gui.beans.CrossValidationFoldMaker
 
m_folds - Variable in class weka.attributeSelection.OneRAttributeEval
Number of folds for cross validation
m_folds - Variable in class weka.attributeSelection.WrapperSubsetEval
number of folds to use for cross validation
m_fontColor - Variable in class weka.gui.treevisualizer.TreeBuild.InfoObject
The font color for the object. not in use.
m_fontHeight - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel
 
m_fontMetrics - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel
 
m_fontSize - Variable in class weka.gui.treevisualizer.TreeBuild.InfoObject
The fontsize for the object. not in use.
m_fontSize - Variable in class weka.gui.treevisualizer.TreeVisualizer
The size information for the current font.
m_frameLimiter - Variable in class weka.gui.treevisualizer.TreeVisualizer
A timer to keep the frame rate constant.
m_framePoppedUp - Variable in class weka.gui.beans.DataVisualizer
 
m_frequencyThreshold - Variable in class weka.associations.Tertius
Frequency threshold for the body and the negation of the head.
m_gainRatio - Variable in class weka.classifiers.trees.j48.BinC45Split
GainRatio of split.
m_gainRatio - Variable in class weka.classifiers.trees.j48.C45Split
GainRatio of split.
m_gainRatioCrit - Static variable in class weka.classifiers.trees.j48.BinC45Split
Static reference to splitting criterion.
m_gamma - Variable in class weka.classifiers.functions.SMO
Gamma for the RBF kernel.
m_gamma - Variable in class weka.classifiers.functions.SMOreg
Gamma for the RBF kernel.
m_gamma - Variable in class weka.classifiers.functions.supportVector.RBFKernel
Gamma for the RBF kernel.
m_gap - Variable in class weka.gui.treevisualizer.PlaceNode2.Group
The gap size for the distance between the nodes in this group.
m_generateRules - Variable in class weka.classifiers.trees.m5.M5Base
generate a decision list instead of a single tree.
m_generationReports - Variable in class weka.attributeSelection.GeneticSearch
holds the generation reports
m_generatorSamplesBase - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_generatorSamplesText - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_globalAbsDev - Variable in class weka.classifiers.trees.m5.Rule
the absolute deviation of the class for all the instances
m_globalAbsDeviation - Variable in class weka.classifiers.trees.m5.RuleNode
the absolute deviation of the global class
m_globalCounts - Variable in class weka.classifiers.misc.VFI
The global class counts
m_globalDeviation - Variable in class weka.classifiers.trees.m5.RuleNode
a node will not be split if the class deviation of its instances is less than m_devFraction of the deviation of the global class
m_globalMeansOrModes - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_globalStdDev - Variable in class weka.classifiers.trees.m5.Rule
the standard deviation of the class for all the instances
m_gp - Variable in class weka.gui.graphvisualizer.GraphVisualizer
Panel actually displaying the graph
m_grObj - Variable in class weka.gui.treevisualizer.TreeBuild
An object setup to take graph data.
m_graphListeners - Variable in class weka.gui.beans.Classifier
Objects listening for graph events
m_graphName - Variable in class weka.gui.graphvisualizer.DotParser
This holds the name of the graph if there is any otherwise it is null
m_graphName - Variable in class weka.gui.treevisualizer.TreeBuild
The name of the tree, Not in use.
m_graphString - Variable in class weka.gui.beans.GraphEvent
 
m_graphTitle - Variable in class weka.gui.beans.GraphEvent
 
m_graphers - Variable in class weka.classifiers.functions.MultilayerPerceptron
A Vector list of the graphers.
m_gridWidth - Variable in class weka.gui.beans.AttributeSummarizer
The number of plots horizontally in the display
m_group - Variable in class weka.classifiers.rules.DecisionTable.Link
The group
m_groupNum - Variable in class weka.gui.treevisualizer.PlaceNode2
The Number of groups the tree has
m_groups - Variable in class weka.gui.treevisualizer.PlaceNode2
An array that lists the groups and information about them.
m_gui - Variable in class weka.classifiers.functions.MultilayerPerceptron
A flag to state that the gui for the network should be brought up.
m_hasClass - Variable in class weka.attributeSelection.BestFirst
does the data have a class
m_hasClass - Variable in class weka.attributeSelection.ExhaustiveSearch
does the data have a class
m_hasClass - Variable in class weka.attributeSelection.ForwardSelection
does the data have a class
m_hasClass - Variable in class weka.attributeSelection.GeneticSearch
does the data have a class
m_hasClass - Variable in class weka.attributeSelection.PrincipalComponents
Data has a class set
m_hasClass - Variable in class weka.attributeSelection.RandomSearch
does the data have a class
m_hasClass - Variable in class weka.attributeSelection.RankSearch
does the data have a class
m_hasClass - Variable in class weka.attributeSelection.Ranker
Data has class attribute---if unsupervised evaluator then no class
m_hashtable - Variable in class weka.classifiers.meta.END
The hashtable containing the classifiers for the END.
m_hashtablegiven - Variable in class weka.classifiers.meta.ND
Is Hashtable given from END?
m_hashtables - Variable in class weka.associations.Apriori
The same information stored in hash tables.
m_head - Variable in class weka.associations.tertius.Rule
The head of the rule.
m_height - Variable in class weka.gui.treevisualizer.TreeVisualizer.EdgeInfo
The height of the text.
m_height - Variable in class weka.gui.treevisualizer.TreeVisualizer.NodeInfo
The height of the node.
m_heights - Variable in class weka.gui.visualize.AttributePanel
Holds the random height for each instance.
m_helpB - Variable in class weka.gui.beans.KnowledgeFlow
 
m_heuristicStop - Variable in class weka.classifiers.functions.SimpleLogistic
Parameter for the heuristic for early stopping of LogitBoost
m_heuristicStop - Variable in class weka.classifiers.trees.lmt.LogisticBase
Use heuristic to stop performing LogitBoost iterations earlier?
m_hiddenLayers - Variable in class weka.classifiers.functions.MultilayerPerceptron
The string that defines the hidden layers
m_higherRegressions - Variable in class weka.classifiers.trees.lmt.LMTNode
Simple regression functions fit by LogitBoost at higher levels in the tree
m_highlightNode - Variable in class weka.gui.treevisualizer.TreeVisualizer
The Node the user is currently focused on , this is similar to focus node except that it is used by other classes rather than this one.
m_history - Variable in class weka.gui.beans.GraphViewer
 
m_history - Variable in class weka.gui.beans.TextViewer
List of text revieved so far
m_holdOutFile - Variable in class weka.attributeSelection.ClassifierSubsetEval
the file that containts hold out/test instances
m_holdOutInstances - Variable in class weka.attributeSelection.ClassifierSubsetEval
the instances to test on
m_horn - Variable in class weka.associations.Tertius
Horn clauses bias.
m_hostList - Variable in class weka.gui.experiment.DistributeExperimentPanel
The host list panel
m_hostPollingTime - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
 
m_hypotheses - Variable in class weka.associations.Tertius
Number of hypotheses considered.
m_iLow - Variable in class weka.classifiers.functions.SMO.BinarySMO
The indices for m_bLow and m_bUp
m_iLow - Variable in class weka.classifiers.functions.SMOreg
The indices for m_bLow and m_bUp
m_iNode - Variable in class weka.classifiers.bayes.VaryNode
index of the node varied
m_iUp - Variable in class weka.classifiers.functions.SMO.BinarySMO
The indices for m_bLow and m_bUp
m_iUp - Variable in class weka.classifiers.functions.SMOreg
The indices for m_bLow and m_bUp
m_iView - Variable in class weka.classifiers.trees.UserClassifier
The instances display.
m_ibk - Variable in class weka.classifiers.rules.DecisionTable
IB1 used to classify non matching instances rather than majority class
m_icon - Variable in class weka.gui.beans.BeanVisual
ImageIcons for the icons.
m_iconAnimated - Variable in class weka.gui.WekaTaskMonitor
The icon for the animated bird
m_iconPath - Variable in class weka.gui.beans.BeanVisual
Holds name (including path) of the static icon
m_iconStationary - Variable in class weka.gui.WekaTaskMonitor
The icon for the stationary bird
m_id - Variable in class weka.classifiers.functions.neural.NeuralConnection
The string that uniquely (provided naming is done properly) identifies this unit.
m_id - Variable in class weka.classifiers.trees.j48.ClassifierTree
The id for the node.
m_id - Variable in class weka.classifiers.trees.lmt.LMTNode
Node id
m_id - Variable in class weka.classifiers.trees.m5.RuleNode
Node id.
m_id - Variable in class weka.gui.treevisualizer.PlaceNode2.Group
The group number for this group.
m_id - Variable in class weka.gui.treevisualizer.TreeBuild.InfoObject
The ID string for th object.
m_identity - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
A string to uniquely identify this node.
m_ie - Variable in class weka.gui.beans.Classifier
 
m_ie - Variable in class weka.gui.beans.Filter
Instance event object for passing on filtered instance streams
m_ie - Variable in class weka.gui.beans.Loader
 
m_ignore - Variable in class weka.core.converters.C45Loader
Which attributes are ignore or label.
m_ignoreAttributesRange - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
Range of attributes to ignore
m_ignoreBut - Variable in class weka.gui.explorer.ClustererPanel
The button used to popup a list for choosing attributes to ignore while clustering
m_ignoreKeyList - Variable in class weka.gui.explorer.ClustererPanel
 
m_ignoreKeyModel - Variable in class weka.gui.explorer.ClustererPanel
 
m_iheight - Variable in class weka.gui.beans.StripChart
Width and height of the off screen image
m_incrementalClassifierListeners - Variable in class weka.gui.beans.Classifier
Objects listening for incremental classifier events
m_incrementalEvent - Variable in class weka.gui.beans.Classifier
Event to handle when processing incremental updates
m_incrementalPanel - Variable in class weka.gui.beans.ClassifierCustomizer
 
m_incrementalStructure - Variable in class weka.gui.beans.PredictionAppender
 
m_index - Variable in class weka.associations.tertius.AttributeValueLiteral
 
m_index - Variable in class weka.associations.tertius.Predicate
 
m_index - Variable in class weka.attributeSelection.ReliefFAttributeEval
Index in the m_karray of the farthest instance for each class
m_index - Variable in class weka.classifiers.trees.j48.C45Split
Number of split points.
m_index - Variable in class weka.gui.visualize.ClassPanel.NomLabel
 
m_indexVal - Variable in class weka.gui.visualize.AttributePanelEvent
The index for the new attribute
m_indicators - Variable in class weka.classifiers.functions.supportVector.SMOset
Indicators
m_indices - Variable in class weka.classifiers.meta.ND.NDTree
The indices associated with this node
m_indices - Variable in class weka.classifiers.trees.m5.RuleNode
Indices of the attributes to be used in generating a linear model at this node
m_infixExpression - Variable in class weka.filters.unsupervised.attribute.AddExpression
The infix expression
m_infoGain - Variable in class weka.classifiers.trees.j48.BinC45Split
InfoGain of split.
m_infoGain - Variable in class weka.classifiers.trees.j48.C45Split
InfoGain of split.
m_infoGainCrit - Static variable in class weka.classifiers.trees.j48.BinC45Split
Static reference to splitting criterion.
m_initialNumClusters - Variable in class weka.clusterers.EM
the initial number of clusters requested by the user--- -1 if xval is to be used to find the number of clusters
m_initialTiling - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_input - Variable in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
True if node is an input, False if it's an output.
m_input - Variable in class weka.gui.graphvisualizer.DotParser
This is the input containing DOT stream to be parsed
m_inputList - Variable in class weka.classifiers.functions.neural.NeuralConnection
The list of inputs to this unit.
m_inputNums - Variable in class weka.classifiers.functions.neural.NeuralConnection
The numbering for the connections at the other end of the input lines.
m_inputs - Variable in class weka.classifiers.functions.MultilayerPerceptron
The input units.
m_inst - Variable in class weka.gui.visualize.VisualizePanelEvent
The instances that fall inside the shapes described in m_values.
m_inst2 - Variable in class weka.gui.visualize.VisualizePanelEvent
The instances that fall outside the shapes described in m_values.
m_instance - Variable in class weka.gui.beans.InstanceEvent
 
m_instanceEvent - Variable in class weka.gui.beans.PredictionAppender
 
m_instanceEventTargets - Variable in class weka.gui.beans.Loader
Keep track of how many listeners for different types of events there are.
m_instanceListeners - Variable in class weka.gui.beans.ClassAssigner
 
m_instanceListeners - Variable in class weka.gui.beans.Filter
Objects listening for instance events
m_instanceListeners - Variable in class weka.gui.beans.PredictionAppender
Objects listening for instances events
m_instanceProvider - Variable in class weka.gui.beans.ClassAssigner
 
m_instanceVals - Variable in class weka.gui.beans.PredictionAppender
 
m_instances - Variable in class weka.associations.Apriori
The instances (transactions) to be used for generating the association rules.
m_instances - Variable in class weka.associations.Tertius
Instances used for the search.
m_instances - Variable in class weka.classifiers.functions.MultilayerPerceptron
The training instances.
m_instances - Variable in class weka.classifiers.rules.Prism.PrismRule
The instance
m_instances - Variable in class weka.classifiers.trees.m5.M5Base
the instances covered by the tree/rules
m_instances - Variable in class weka.classifiers.trees.m5.Rule
the instances covered by this rule
m_instances - Variable in class weka.classifiers.trees.m5.RuleNode
instances reaching this node
m_instances - Variable in class weka.clusterers.FarthestFirst
training instances, not necessary to keep, could be replaced by m_ClusterCentroids where needed for header info
m_instances - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_instancesConsumed - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_instancesHeader - Variable in class weka.classifiers.trees.m5.PreConstructedLinearModel
 
m_intType - Variable in class weka.experiment.DatabaseUtils
 
m_intercept - Variable in class weka.classifiers.functions.SimpleLinearRegression
The intercept
m_intercept - Variable in class weka.classifiers.trees.m5.PreConstructedLinearModel
 
m_internalNodes - Variable in class weka.classifiers.trees.m5.Rule
the corresponding internal nodes.
m_intervalBounds - Variable in class weka.classifiers.misc.VFI
The lower bounds for each attribute
m_invert - Variable in class weka.filters.unsupervised.attribute.RemoveType
Whether to invert selection
m_invertMatching - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
Whether to invert the match so the correctly classified instances are discarded
m_ioThread - Variable in class weka.gui.beans.Loader
Thread for doing IO in
m_isClass - Variable in class weka.associations.tertius.Predicate
 
m_isCompressed - Variable in class weka.core.SerializedObject
Whether or not the object is compressed.
m_isEmpty - Variable in class weka.classifiers.rules.part.ClassifierDecList
True if node is empty.
m_isEmpty - Variable in class weka.classifiers.trees.j48.ClassifierTree
True if node is empty.
m_isEnabled - Variable in class weka.gui.visualize.ClassPanel
True when the panel has been enabled (ie after setNumeric or setNominal has been called
m_isLeaf - Variable in class weka.classifiers.rules.part.ClassifierDecList
True if node is leaf.
m_isLeaf - Variable in class weka.classifiers.trees.j48.ClassifierTree
True if node is leaf.
m_isLeaf - Variable in class weka.classifiers.trees.lmt.LMTNode
True if node is leaf
m_isLeaf - Variable in class weka.classifiers.trees.m5.RuleNode
Node is a leaf
m_isNumeric - Variable in class weka.attributeSelection.CfsSubsetEval
Is the class numeric
m_isNumeric - Variable in class weka.gui.visualize.ClassPanel
True if the colouring attribute is numeric
m_israndom - Variable in class weka.classifiers.functions.LeastMedSq
 
m_items - Variable in class weka.associations.ItemSet
The items stored as an array of of ints.
m_iterations - Variable in class weka.attributeSelection.RandomSearch
the number of iterations performed
m_iwidth - Variable in class weka.gui.beans.StripChart
 
m_jBtSave - Variable in class weka.gui.graphvisualizer.GraphVisualizer
Save button to save the current graph in DOT or XMLBIF format.
m_jCbEdgeConcentration - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
controls edge concentration by concentrating multilple singular dummy child nodes into one plural dummy child node
m_jRbBottomup - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
m_jRbNaiveLayout - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
m_jRbPriorityLayout - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
m_jRbTopdown - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
m_jitter - Variable in class weka.gui.visualize.MatrixPanel
The slider to add jitter to the plots
m_js - Variable in class weka.gui.graphvisualizer.GraphVisualizer
this contains the m_gp GraphPanel
m_js - Variable in class weka.gui.visualize.MatrixPanel
The scroll pane to scrolling the matrix
m_k - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Stores the number of dimensions to reduce the data to
m_kNN - Variable in class weka.classifiers.lazy.IBk
The number of neighbours to use for classification (currently)
m_kNN - Variable in class weka.classifiers.lazy.LWL
The number of neighbours used to select the kernel bandwidth
m_kNNUpper - Variable in class weka.classifiers.lazy.IBk
The value of kNN provided by the user.
m_kNNValid - Variable in class weka.classifiers.lazy.IBk
Whether the value of k selected by cross validation has been invalidated by a change in the training instances
m_karray - Variable in class weka.attributeSelection.ReliefFAttributeEval
k nearest scores + instance indexes for n classes
m_kernel - Variable in class weka.classifiers.functions.SMO.BinarySMO
Kernel to use
m_kernel - Variable in class weka.classifiers.functions.SMOreg
Kernel to use
m_kernelBandwidth - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_kernelBandwidth - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_kernelBandwidthText - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_kernelEvals - Variable in class weka.classifiers.functions.supportVector.PolyKernel
Counts the number of kernel evaluations.
m_kernelEvals - Variable in class weka.classifiers.functions.supportVector.RBFKernel
Counts the number of kernel evaluations.
m_kernelParams - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_kernelPrecalc - Variable in class weka.classifiers.functions.supportVector.RBFKernel
The precalculated dotproducts of
m_keys - Variable in class weka.classifiers.functions.supportVector.PolyKernel
 
m_keys - Variable in class weka.classifiers.functions.supportVector.RBFKernel
 
m_label - Variable in class weka.gui.treevisualizer.Edge
The text caption for the edge.
m_label - Variable in class weka.gui.treevisualizer.Node
the text for the node.
m_label - Variable in class weka.gui.treevisualizer.TreeBuild.InfoObject
The label for the object.
m_labelFont - Variable in class weka.gui.beans.StripChart
 
m_labelFont - Variable in class weka.gui.visualize.ClassPanel
The font used in labeling
m_labelFont - Variable in class weka.gui.visualize.Plot2D
Font for labels
m_labelMetrics - Variable in class weka.gui.beans.StripChart
 
m_labelMetrics - Variable in class weka.gui.visualize.ClassPanel
Font metrics
m_labelMetrics - Variable in class weka.gui.visualize.Plot2D
 
m_laplaceConst - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_last - Variable in class weka.classifiers.trees.m5.CorrelationSplitInfo
the last instance
m_lastAddedSplitNum - Variable in class weka.classifiers.trees.ADTree
The number of the last splitter added to the tree
m_lastLiteral - Variable in class weka.associations.tertius.LiteralSet
Last literal added to this set.
m_lastLogLikelihood - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_lastValidationError - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_lastvisitedDirectory - Variable in class weka.gui.SaveBuffer
Last directory selected from the file chooser
m_latexOutput - Variable in class weka.experiment.PairedTTester
Produce tables in latex format
m_le - Variable in class weka.gui.graphvisualizer.GraphVisualizer
The current LayoutEngine
m_leafModelNum - Variable in class weka.classifiers.trees.lmt.LMTNode
ID of logistic model at leaf
m_leafModelNum - Variable in class weka.classifiers.trees.m5.RuleNode
the number assigned to the linear model if this node is a leaf
m_learningLabel - Variable in class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
A label to state the learning rate.
m_learningRate - Variable in class weka.classifiers.functions.MultilayerPerceptron
This is the learning rate for the network.
m_left - Variable in class weka.classifiers.meta.ND.NDTree
The left successor
m_left - Variable in class weka.classifiers.trees.m5.RuleNode
child nodes
m_left - Variable in class weka.gui.treevisualizer.PlaceNode2.Group
The leftmost position of this group.
m_left - Variable in class weka.gui.treevisualizer.PlaceNode2.Level
These two params would appear to not be used.
m_legendPanel - Variable in class weka.gui.beans.StripChart
Class providing a panel for the legend
m_legendPanel - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays legend info if there is more than one plot
m_legendText - Variable in class weka.gui.beans.ChartEvent
 
m_legendText - Variable in class weka.gui.beans.StripChart
 
m_legendText - Variable in class weka.gui.visualize.LegendPanel.LegendEntry
the text part of this legend
m_lev - Variable in class weka.gui.treevisualizer.PlaceNode2.Ease
The level on which they were tangled.
m_levelNode - Variable in class weka.gui.treevisualizer.PlaceNode1
An array containing the current node place for each level to place each node accordingly.
m_levelNum - Variable in class weka.gui.treevisualizer.PlaceNode2
The number of levels the group tree has
m_levels - Variable in class weka.gui.treevisualizer.PlaceNode1
An array containing the spacing value for each level
m_levels - Variable in class weka.gui.treevisualizer.PlaceNode2
An array that lists the levels and information about them.
m_linear - Variable in class weka.classifiers.functions.RBFNetwork
The linear regression for numeric problems
m_linearUnit - Variable in class weka.classifiers.functions.MultilayerPerceptron
This is a linear unit.
m_lines - Variable in class weka.gui.treevisualizer.Edge
The label broken up into lines.
m_lines - Variable in class weka.gui.treevisualizer.Node
the text broken up into lines
m_link - Variable in class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd
the value that represents the instance value this node represents.
m_listenee - Variable in class weka.gui.beans.AbstractDataSink
Non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.AbstractEvaluator
 
m_listenee - Variable in class weka.gui.beans.AbstractTestSetProducer
non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.AbstractTrainingSetProducer
non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.PredictionAppender
Non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.StripChart
 
m_listenees - Variable in class weka.gui.beans.Classifier
Objects talking to us
m_listenees - Variable in class weka.gui.beans.Filter
Objects talking to us
m_listener - Variable in class weka.gui.treevisualizer.TreeVisualizer
 
m_listeners - Variable in class weka.experiment.RemoteExperiment
The list of objects listening for remote experiment events
m_listeners - Variable in class weka.gui.beans.AbstractDataSource
Objects listening for events from data sources
m_listeners - Variable in class weka.gui.beans.AbstractTestSetProducer
Objects listening to us
m_listeners - Variable in class weka.gui.beans.AbstractTrainingSetProducer
Objects listening for training set events
m_listeners - Variable in class weka.gui.beans.ClassifierPerformanceEvaluator
 
m_listeners - Variable in class weka.gui.beans.IncrementalClassifierEvaluator
 
m_listeners - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_listeners - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
a list of RemoteExperimentListeners
m_literals - Variable in class weka.associations.tertius.LiteralSet
Literals contained in this set.
m_literals - Variable in class weka.associations.tertius.Predicate
 
m_loadB - Variable in class weka.gui.beans.KnowledgeFlow
 
m_localModel - Variable in class weka.classifiers.rules.part.ClassifierDecList
Local model at node.
m_localModel - Variable in class weka.classifiers.trees.j48.ClassifierTree
Local model at node.
m_localModel - Variable in class weka.classifiers.trees.lmt.LMTNode
The ClassifierSplitModel (for splitting)
m_locallyPredictive - Variable in class weka.attributeSelection.CfsSubsetEval
Include locally predicitive attributes
m_locker - Variable in class weka.gui.AttributeVisualizationPanel
 
m_log - Variable in class weka.gui.beans.Classifier
 
m_log - Variable in class weka.gui.beans.Filter
 
m_log - Variable in class weka.gui.beans.StripChart
 
m_logButton - Variable in class weka.gui.LogPanel
The button for viewing the log
m_logMessage - Variable in class weka.experiment.RemoteExperimentEvent
A log type message
m_logPanel - Variable in class weka.gui.beans.KnowledgeFlow
 
m_logger - Variable in class weka.gui.beans.AbstractDataSink
 
m_logger - Variable in class weka.gui.beans.AbstractEvaluator
 
m_logger - Variable in class weka.gui.beans.AbstractTestSetProducer
Logger
m_logger - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
 
m_logger - Variable in class weka.gui.beans.AbstractTrainingSetProducer
 
m_logger - Variable in class weka.gui.beans.ClassAssigner
 
m_logger - Variable in class weka.gui.beans.PredictionAppender
 
m_logistic - Variable in class weka.classifiers.functions.RBFNetwork
The logistic regression for classification problems
m_logistic - Variable in class weka.classifiers.functions.SMO.BinarySMO
Stores logistic regression model for probability estimate
m_loglikely - Variable in class weka.clusterers.EM
the loglikelihood of the data
m_lookupTable - Variable in class weka.attributeSelection.GeneticSearch
the lookup table
m_lookupTableSize - Variable in class weka.attributeSelection.GeneticSearch
the number of entries to cache for lookup
m_lowerBoundMinSupport - Variable in class weka.associations.Apriori
The lower bound for the minimum support.
m_lowerCaseTokens - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
True if all tokens should be downcased
m_lowerOrder - Variable in class weka.classifiers.functions.SMO
Use lower-order terms?
m_lowerOrder - Variable in class weka.classifiers.functions.SMOreg
Use lower-order terms?
m_lowerOrder - Variable in class weka.classifiers.functions.supportVector.PolyKernel
Use lower-order terms?
m_ls - Variable in class weka.classifiers.functions.LeastMedSq
 
m_m_AdvanceRunFirst - Variable in class weka.experiment.Experiment
 
m_mainWin - Variable in class weka.classifiers.trees.UserClassifier
The window.
m_majority - Variable in class weka.classifiers.rules.DecisionTable
Holds the majority class
m_makeIndicatorFilter - Variable in class weka.classifiers.meta.StackingC
 
m_masterName - Variable in class weka.gui.visualize.Plot2D
The name of the master plot
m_masterPlot - Variable in class weka.gui.visualize.Plot2D
The master plot
m_matrix - Variable in class weka.gui.CostMatrixEditor
The cost matrix being edited
m_matrixPanel - Variable in class weka.gui.beans.ScatterPlotMatrix
 
m_max - Variable in class weka.gui.beans.ChartEvent
 
m_max - Variable in class weka.gui.beans.IncrementalClassifierEvaluator
 
m_max - Variable in class weka.gui.beans.StripChart
Max value for the y axis
m_maxArray - Variable in class weka.attributeSelection.ReliefFAttributeEval
Upper bound for numeric attributes
m_maxBatchSizeRequired - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The maximum number of instances required for processing
m_maxBoostingIterations - Variable in class weka.classifiers.functions.SimpleLogistic
Maximum number of iterations for LogitBoost
m_maxC - Variable in class weka.gui.visualize.AttributePanel
Holds the min and max values of the colouring attributes
m_maxC - Variable in class weka.gui.visualize.ClassPanel
The maximum value for the colouring attribute
m_maxC - Variable in class weka.gui.visualize.Plot2D
 
m_maxC - Variable in class weka.gui.visualize.PlotData2D
 
m_maxChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The maimum chunk size used for training
m_maxEntrop - Variable in class weka.classifiers.misc.VFI
The maximum entropy for the class
m_maxFitness - Variable in class weka.attributeSelection.GeneticSearch
 
m_maxGenerations - Variable in class weka.attributeSelection.GeneticSearch
the maximum number of generations to evaluate
m_maxImpurity - Variable in class weka.classifiers.trees.m5.CorrelationSplitInfo
the maximum impurity reduction
m_maxIterations - Variable in class weka.classifiers.trees.lmt.LogisticBase
The maximum number of LogitBoost iterations
m_maxIts - Variable in class weka.classifiers.functions.RBFNetwork
The maximum number of iterations for logistic regression.
m_maxLiterals - Variable in class weka.associations.tertius.Rule
Maximal number of literals in the rule.
m_maxModels - Variable in class weka.classifiers.meta.AdditiveRegression
Maximum number of models to produce. -1 indicates keep going until the error threshold is met.
m_maxPlots - Variable in class weka.gui.beans.AttributeSummarizer
The maximum number of plots to show
m_maxSetNumber - Variable in class weka.gui.beans.BatchClassifierEvent
The last set number for this series
m_maxSetNumber - Variable in class weka.gui.beans.TestSetEvent
Maximum number of sets (ie 10 in a 10 fold)
m_maxSetNumber - Variable in class weka.gui.beans.TrainingSetEvent
Maximum number of sets (ie 10 in a 10 fold)
m_maxStale - Variable in class weka.attributeSelection.BestFirst
maximum number of stale nodes before terminating search
m_maxStale - Variable in class weka.classifiers.rules.DecisionTable
Maximum number of fully expanded non improving subsets for a best first search.
m_maxVal - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The min and max values for this attribute.
m_maxValues - Variable in class weka.clusterers.EM
attribute max values
m_maxVariancePercentage - Variable in class weka.filters.unsupervised.attribute.RemoveUseless
The type of attribute to delete
m_maxX - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_maxX - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_maxX - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_maxX - Variable in class weka.gui.visualize.Plot2D
Holds the min and max values of the x, y and colouring attributes over all plots
m_maxX - Variable in class weka.gui.visualize.PlotData2D
Holds the min and max values of the x, y and colouring attributes for this plot
m_maxY - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_maxY - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_maxY - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_maxY - Variable in class weka.gui.visualize.Plot2D
 
m_maxY - Variable in class weka.gui.visualize.PlotData2D
 
m_max_iterations - Variable in class weka.clusterers.EM
maximum iterations to perform
m_merit - Variable in class weka.classifiers.rules.DecisionTable.Link
The merit
m_messageString - Variable in class weka.experiment.RemoteExperimentEvent
The message
m_methods - Variable in class weka.classifiers.functions.neural.NeuralNode
Performs the operations for this node.
m_metricType - Variable in class weka.associations.Apriori
The selected metric type.
m_min - Variable in class weka.gui.beans.ChartEvent
 
m_min - Variable in class weka.gui.beans.IncrementalClassifierEvaluator
 
m_min - Variable in class weka.gui.beans.StripChart
Min value for the y axis
m_minArray - Variable in class weka.attributeSelection.ReliefFAttributeEval
Lower bound for numeric attributes
m_minBucketSize - Variable in class weka.attributeSelection.OneRAttributeEval
Passed on to OneR
m_minBucketSize - Variable in class weka.classifiers.rules.OneR
The minimum bucket size
m_minC - Variable in class weka.gui.visualize.AttributePanel
 
m_minC - Variable in class weka.gui.visualize.ClassPanel
The minimum value for the colouring attribute
m_minC - Variable in class weka.gui.visualize.Plot2D
 
m_minC - Variable in class weka.gui.visualize.PlotData2D
 
m_minChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The minimum chunk size used for training
m_minFitness - Variable in class weka.attributeSelection.GeneticSearch
 
m_minInfoGain - Variable in class weka.classifiers.trees.lmt.ResidualModelSelection
Minimum information gain for split
m_minMetric - Variable in class weka.associations.Apriori
The minimum metric score.
m_minNoObj - Variable in class weka.classifiers.trees.j48.BinC45ModelSelection
Minimum number of instances in interval.
m_minNoObj - Variable in class weka.classifiers.trees.j48.BinC45Split
Minimum number of objects in a split.
m_minNoObj - Variable in class weka.classifiers.trees.j48.C45ModelSelection
Minimum number of objects in interval.
m_minNoObj - Variable in class weka.classifiers.trees.j48.C45Split
Minimum number of objects in a split.
m_minNumInstances - Variable in class weka.classifiers.trees.LMT
minimum number of instances at which a node is considered for splitting
m_minNumInstances - Variable in class weka.classifiers.trees.lmt.LMTNode
minimum number of instances at which a node is considered for splitting
m_minNumInstances - Variable in class weka.classifiers.trees.lmt.ResidualModelSelection
Minimum number of instances for leaves
m_minNumInstances - Variable in class weka.classifiers.trees.m5.M5Base
The minimum number of instances to allow at a leaf node
m_minNumInstances - Variable in class weka.classifiers.trees.m5.Rule
The minimum number of instances to allow at a leaf node
m_minNumObj - Variable in class weka.classifiers.rules.PART
Minimum number of objects
m_minNumObj - Variable in class weka.classifiers.rules.part.ClassifierDecList
Minimum number of objects
m_minNumObj - Variable in class weka.classifiers.trees.J48
Minimum number of instances
m_minStdDev - Variable in class weka.clusterers.EM
default minimum standard deviation
m_minStdDev - Variable in class weka.clusterers.MakeDensityBasedClusterer
default minimum standard deviation
m_minStdDev - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_minSupport - Variable in class weka.associations.Apriori
The minimum support.
m_minTaskPollTime - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
 
m_minVal - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
 
m_minValues - Variable in class weka.clusterers.EM
attribute min values
m_minX - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_minX - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_minX - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_minX - Variable in class weka.gui.visualize.Plot2D
 
m_minX - Variable in class weka.gui.visualize.PlotData2D
 
m_minY - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_minY - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_minY - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_minY - Variable in class weka.gui.visualize.Plot2D
 
m_minY - Variable in class weka.gui.visualize.PlotData2D
 
m_missing - Variable in class weka.associations.Tertius
Way of handling missing values in the search.
m_missing - Variable in class weka.associations.tertius.Literal
 
m_missing - Variable in class weka.gui.visualize.MatrixPanel
Contains true for each value that is missing, for each instance
m_missingSeperate - Variable in class weka.attributeSelection.CfsSubsetEval
Treat missing values as seperate values
m_missingValueClass - Variable in class weka.classifiers.rules.OneR.OneRRule
Predicted class for missing values
m_missing_merge - Variable in class weka.attributeSelection.ChiSquaredAttributeEval
Treat missing values as a seperate value
m_missing_merge - Variable in class weka.attributeSelection.GainRatioAttributeEval
Merge missing values
m_missing_merge - Variable in class weka.attributeSelection.InfoGainAttributeEval
Treat missing values as a seperate value
m_missing_merge - Variable in class weka.attributeSelection.SymmetricalUncertAttributeEval
Treat missing values as a seperate value
m_mode - Variable in class weka.gui.beans.KnowledgeFlow
 
m_modePanel - Variable in class weka.gui.experiment.SimpleSetupPanel
The panel which switched between simple and advanced setup modes
m_model - Variable in class weka.clusterers.EM
hold the discrete estimators for each cluster
m_model - Variable in class weka.clusterers.MakeDensityBasedClusterer
discrete distributions fitted to each discrete attribute in each cluster
m_modelHasChanged - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_modelHasChangedLL - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_modelNormal - Variable in class weka.clusterers.EM
hold the normal estimators for each cluster
m_modelNormal - Variable in class weka.clusterers.MakeDensityBasedClusterer
normal distributions fitted to each numeric attribute in each cluster
m_modelSelection - Variable in class weka.classifiers.trees.lmt.LMTNode
ModelSelection object (for splitting)
m_models - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_momentum - Variable in class weka.classifiers.functions.MultilayerPerceptron
This is the momentum for the network.
m_momentumLabel - Variable in class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
A label to state the momentum.
m_mouseState - Variable in class weka.gui.treevisualizer.TreeVisualizer
Describes the action the user is performing.
m_nCardinalityOfParents - Variable in class weka.classifiers.bayes.ParentSet
Holds cardinality of parents (= number of instantiations the parents can take)
m_nCount - Variable in class weka.classifiers.bayes.ADNode
count
m_nMCV - Variable in class weka.classifiers.bayes.VaryNode
most common value
m_nMaxNrOfParents - Variable in class weka.classifiers.bayes.BayesNet
Holds upper bound on number of parents
m_nNrOfParents - Variable in class weka.classifiers.bayes.ParentSet
Holds number of parents
m_nOrder - Variable in class weka.classifiers.bayes.BayesNet
topological ordering of the network
m_nParents - Variable in class weka.classifiers.bayes.ParentSet
Holds indexes of parents
m_nScoreType - Variable in class weka.classifiers.bayes.BayesNet
Holds the score type used to measure quality of network
m_nStartNode - Variable in class weka.classifiers.bayes.ADNode
first node in VaryNode array
m_nSymbols - Variable in class weka.classifiers.bayes.DiscreteEstimatorBayes
Holds number of symbols in distribution
m_nViewPos - Variable in class weka.gui.treevisualizer.TreeVisualizer
A variable used to remember the desired view pos.
m_nViewSize - Variable in class weka.gui.treevisualizer.TreeVisualizer
A variable used to remember the desired tree size.
m_name - Variable in class weka.associations.tertius.Predicate
 
m_name - Variable in class weka.gui.treevisualizer.NamedColor
The name of the color
m_namesReader - Variable in class weka.core.converters.C45Loader
Reader for names file
m_nda - Variable in class weka.attributeSelection.ReliefFAttributeEval
Used to hold the prob of different value of an attribute given nearest instances (numeric class case)
m_ndc - Variable in class weka.attributeSelection.ReliefFAttributeEval
Used to hold the probability of a different class val given nearest instances (numeric class)
m_ndcda - Variable in class weka.attributeSelection.ReliefFAttributeEval
Used to hold the prob of a different class val and different att val given nearest instances (numeric class case)
m_ndtree - Variable in class weka.classifiers.meta.ND
The tree of classes
m_negBody - Variable in class weka.associations.tertius.Rule
Can there be negations in the body ?
m_negHead - Variable in class weka.associations.tertius.Rule
Can there be negations in the head ?
m_negTrainInstances - Variable in class weka.classifiers.trees.ADTree
The training instances with negative class - referencing the training dataset
m_negation - Variable in class weka.associations.Tertius
Type of negation used in the rules.
m_negation - Variable in class weka.associations.tertius.Literal
 
m_negative - Variable in class weka.filters.unsupervised.attribute.AddExpression.AttributeOperand
true if the value of the attribute are to be multiplied by -1
m_neuralNodes - Variable in class weka.classifiers.functions.MultilayerPerceptron
All the nodes that actually comprise the logical neural net.
m_newMousePos - Variable in class weka.gui.treevisualizer.TreeVisualizer
A variable used to tag the most current point of a user action.
m_newMousePos - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
contains the position of the mouse (used for rubberbanding).
m_newValidationFs - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_next - Variable in class weka.classifiers.functions.supportVector.SMOset
The next element for each element
m_next - Variable in class weka.classifiers.rules.Prism.PrismRule
The next rule in the list
m_next - Variable in class weka.classifiers.rules.Prism.Test
The next test in the rule
m_nextId - Variable in class weka.classifiers.functions.MultilayerPerceptron
The next id number available for default naming.
m_nextId - Variable in class weka.classifiers.trees.UserClassifier
The next number that can be used as a unique id for a node.
m_noCleanup - Variable in class weka.classifiers.trees.J48
Cleanup after the tree has been built.
m_noLevels - Variable in class weka.gui.treevisualizer.PlaceNode1
The number of levels in the tree
m_noObj - Variable in class weka.gui.treevisualizer.TreeBuild
An object setup to take node data.
m_node - Variable in class weka.gui.treevisualizer.TreeVisualizer.NodeInfo
The Node itself.
m_nodeHeight - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
The nodeWidth and nodeHeight
m_nodeId - Variable in class weka.gui.treevisualizer.TreeDisplayEvent
The id string for the node to alter.
m_nodeMenu - Variable in class weka.gui.treevisualizer.TreeVisualizer
A right or middle click popup menu for nodes.
m_nodeModel - Variable in class weka.classifiers.trees.m5.RuleNode
the linear model at this node
m_nodePanel - Variable in class weka.classifiers.functions.MultilayerPerceptron
The panel the nodes are displayed on.
m_nodeWidth - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
The nodeWidth and nodeHeight
m_nodes - Variable in class weka.gui.graphvisualizer.BIFParser
These holds the nodes and edges of the graph
m_nodes - Variable in class weka.gui.graphvisualizer.DotParser
These holds the nodes and edges of the graph
m_nodes - Variable in class weka.gui.graphvisualizer.GraphVisualizer
Vector containing nodes
m_nodes - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
FastVector containing nodes and edges
m_nodes - Variable in class weka.gui.treevisualizer.TreeBuild
An array containing a structure that describes the node without actually creating it.
m_nodes - Variable in class weka.gui.treevisualizer.TreeVisualizer
An array with the Nodes sorted into it and display information about the Nodes.
m_nodesExpanded - Variable in class weka.classifiers.trees.ADTree
Statistics - the number of prediction nodes investigated during search
m_noiseThreshold - Variable in class weka.associations.Tertius
Maximal number of counter-instances.
m_nominalAttIndices - Variable in class weka.classifiers.trees.ADTree
An array containing the inidices to the nominal attributes in the data
m_nominalToBinFilter - Variable in class weka.attributeSelection.PrincipalComponents
 
m_nominalToBinary - Variable in class weka.classifiers.trees.LMT
Filter to replace nominal attributes
m_nominalToBinary - Variable in class weka.classifiers.trees.lmt.LMTNode
Filter to convert nominal attributes to binary
m_nominalToBinary - Variable in class weka.classifiers.trees.m5.M5Base
filter to convert nominal attributes to binary
m_nominalToBinaryFilter - Variable in class weka.classifiers.functions.MultilayerPerceptron
The actual filter.
m_normConst - Static variable in class weka.clusterers.EM
Constant for normal distribution.
m_normConst - Static variable in class weka.clusterers.MakeDensityBasedClusterer
Constant for normal distribution.
m_normConst - Static variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_normal - Static variable in class weka.clusterers.Cobweb
Normal constant.
m_normalize - Variable in class weka.attributeSelection.PrincipalComponents
normalize the input data?
m_normalizeAttributes - Variable in class weka.classifiers.functions.MultilayerPerceptron
This flag states that the user wants the input values normalized.
m_normalizeClass - Variable in class weka.classifiers.functions.MultilayerPerceptron
This flag states that the user wants the class to be normalized while processing in the network is done.
m_normalizeDocLength - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
True if document's (instance's) word frequencies are to be normalized.
m_normalizeFilter - Variable in class weka.attributeSelection.PrincipalComponents
 
m_normalizeWordWeights - Variable in class weka.classifiers.bayes.ComplementNaiveBayes
True if the words weights are to be normalized
m_notCovered - Variable in class weka.classifiers.trees.m5.Rule
the instances not covered by this rule
m_numAttribs - Variable in class weka.attributeSelection.BestFirst
number of attributes in the data
m_numAttribs - Variable in class weka.attributeSelection.CfsSubsetEval
Number of attributes in the training data
m_numAttribs - Variable in class weka.attributeSelection.ClassifierSubsetEval
number of attributes in the training data
m_numAttribs - Variable in class weka.attributeSelection.ConsistencySubsetEval
number of attributes in the training data
m_numAttribs - Variable in class weka.attributeSelection.ExhaustiveSearch
number of attributes in the data
m_numAttribs - Variable in class weka.attributeSelection.ForwardSelection
number of attributes in the data
m_numAttribs - Variable in class weka.attributeSelection.GainRatioAttributeEval
The number of attributes
m_numAttribs - Variable in class weka.attributeSelection.GeneticSearch
number of attributes in the data
m_numAttribs - Variable in class weka.attributeSelection.OneRAttributeEval
The number of attributes
m_numAttribs - Variable in class weka.attributeSelection.PrincipalComponents
Number of attributes
m_numAttribs - Variable in class weka.attributeSelection.RaceSearch
the number of attributes in the data
m_numAttribs - Variable in class weka.attributeSelection.RandomSearch
number of attributes in the data
m_numAttribs - Variable in class weka.attributeSelection.RankSearch
number of attributes in the data
m_numAttribs - Variable in class weka.attributeSelection.Ranker
The number of attribtes
m_numAttribs - Variable in class weka.attributeSelection.ReliefFAttributeEval
The number of attributes
m_numAttribs - Variable in class weka.attributeSelection.SymmetricalUncertAttributeEval
The number of attributes
m_numAttribs - Variable in class weka.attributeSelection.WrapperSubsetEval
number of attributes in the training data
m_numAttribs - Variable in class weka.core.converters.C45Loader
Number of attributes in the data (including ignore and label attributes).
m_numAttributes - Variable in class weka.classifiers.functions.MultilayerPerceptron
The number of attributes.
m_numAttributes - Variable in class weka.classifiers.rules.DecisionTable
The number of attributes in the dataset
m_numAttributes - Variable in class weka.classifiers.trees.m5.M5Base
the number of attributes
m_numAttributes - Variable in class weka.classifiers.trees.m5.Rule
the number of attributes
m_numAttributes - Variable in class weka.classifiers.trees.m5.RuleNode
the number of attributes
m_numAttributes - Variable in class weka.clusterers.Cobweb.CNode
Number of attributes
m_numAttributesSelected - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The number of attributes selected by the attribute selection phase
m_numAtts - Variable in class weka.classifiers.lazy.LBR
number of attributes for the dataset
m_numBoostingIterations - Variable in class weka.classifiers.functions.SimpleLogistic
If non-negative, use this as fixed number of LogitBoost iterations
m_numBoostingIterations - Variable in class weka.classifiers.trees.LMT
if non-zero, use fixed number of iterations for LogitBoost
m_numClasses - Variable in class weka.attributeSelection.GainRatioAttributeEval
The number of classes
m_numClasses - Variable in class weka.attributeSelection.ReliefFAttributeEval
The number of classes if class is nominal
m_numClasses - Variable in class weka.attributeSelection.SymmetricalUncertAttributeEval
The number of classes
m_numClasses - Variable in class weka.classifiers.functions.MultilayerPerceptron
The number of classes.
m_numClasses - Variable in class weka.classifiers.lazy.LBR
number of classes for dataset
m_numClasses - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The number of class vals in the training data (1 if class is numeric)
m_numClasses - Variable in class weka.classifiers.trees.lmt.LogisticBase
The number of different classes
m_numClasses - Variable in class weka.classifiers.trees.lmt.ResidualSplit
Number of classed
m_numClusters - Variable in class weka.classifiers.functions.RBFNetwork
The number of clusters (basis functions to generate)
m_numClusters - Variable in class weka.clusterers.ClusterEvaluation
holds the number of clusters found by the clusterer
m_numCovered - Variable in class weka.classifiers.trees.m5.Rule
the number of instances covered by this rule
m_numEpochs - Variable in class weka.classifiers.functions.MultilayerPerceptron
The number of epochs to train through.
m_numFeatures - Variable in class weka.classifiers.trees.RandomForest
Number of features to consider in random feature selection.
m_numFolds - Variable in class weka.attributeSelection.AttributeSelection
the number of folds to use for cross validation
m_numFolds - Variable in class weka.attributeSelection.RaceSearch
number of cross validation folds---equal to the number of instances for leave-one-out cv
m_numFolds - Variable in class weka.classifiers.functions.SMO
The number of folds for the internal cross-validation
m_numFolds - Variable in class weka.classifiers.rules.PART
Number of folds for reduced error pruning.
m_numFolds - Variable in class weka.classifiers.trees.J48
Number of folds for reduced error pruning.
m_numFolds - Variable in class weka.clusterers.ClusterEvaluation
the number of folds to use for cross validation
m_numFolds - Variable in class weka.gui.beans.CrossValidationFoldMaker
 
m_numFolds - Variable in class weka.gui.experiment.SimpleSetupPanel
The number of folds for a cross-validation experiment
m_numFoldsBoosting - Static variable in class weka.classifiers.trees.lmt.LogisticBase
Number of folds for cross-validating number of LogitBoost iterations
m_numFoldsPruning - Static variable in class weka.classifiers.trees.lmt.LMTNode
Number of folds for CART pruning
m_numHigherRegressions - Variable in class weka.classifiers.trees.lmt.LMTNode
Number of simple regression functions fit by LogitBoost at higher levels in the tree
m_numIncorrectModel - Variable in class weka.classifiers.trees.lmt.LMTNode
Weighted number of training examples currently misclassified by the logistic model at the node
m_numIncorrectTree - Variable in class weka.classifiers.trees.lmt.LMTNode
Weighted number of training examples currently misclassified by the subtree rooted at the node
m_numInputs - Variable in class weka.classifiers.functions.neural.NeuralConnection
The number of inputs.
m_numInst - Variable in class weka.classifiers.rules.OneR.OneRRule
The number of instances used for building the rule.
m_numInstances - Variable in class weka.associations.tertius.LiteralSet
Number of instances in the data the set deals with.
m_numInstances - Variable in class weka.associations.tertius.Rule
Number of instances in the data this rule deals with.
m_numInstances - Variable in class weka.attributeSelection.CfsSubsetEval
Number of instances in the training data
m_numInstances - Variable in class weka.attributeSelection.ClassifierSubsetEval
number of training instances
m_numInstances - Variable in class weka.attributeSelection.ConsistencySubsetEval
number of instances in the training data
m_numInstances - Variable in class weka.attributeSelection.GainRatioAttributeEval
The number of instances
m_numInstances - Variable in class weka.attributeSelection.OneRAttributeEval
The number of instances
m_numInstances - Variable in class weka.attributeSelection.PrincipalComponents
Number of instances
m_numInstances - Variable in class weka.attributeSelection.ReliefFAttributeEval
The number of instances
m_numInstances - Variable in class weka.attributeSelection.SymmetricalUncertAttributeEval
The number of instances
m_numInstances - Variable in class weka.attributeSelection.WrapperSubsetEval
number of instances in the training data
m_numInstances - Variable in class weka.classifiers.rules.DecisionTable
The number of instances in the dataset
m_numInstances - Variable in class weka.classifiers.trees.lmt.LMTNode
Number of instances at the node
m_numInstances - Variable in class weka.classifiers.trees.lmt.ResidualSplit
Number of instances in the set
m_numInstances - Variable in class weka.classifiers.trees.m5.M5Base
the number of instances in the dataset
m_numInstances - Variable in class weka.classifiers.trees.m5.Rule
the number of instances in the dataset
m_numInstances - Variable in class weka.classifiers.trees.m5.RuleNode
the number of instances reaching this node
m_numInstancesConsumed - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The number of instances consumed
m_numInsts - Variable in class weka.classifiers.functions.supportVector.PolyKernel
The number of instance in the dataset
m_numInsts - Variable in class weka.classifiers.functions.supportVector.RBFKernel
The number of instance in the dataset
m_numInsts - Variable in class weka.classifiers.lazy.LBR
number of instances in dataset
m_numIterations - Variable in class weka.classifiers.functions.Winnow
The number of iterations
m_numLevels - Variable in class weka.gui.treevisualizer.TreeVisualizer
The number of levels in the tree.
m_numLiterals - Variable in class weka.associations.Tertius
Number of literals in a rule.
m_numNodes - Variable in class weka.gui.treevisualizer.TreeVisualizer
The number of Nodes in the tree.
m_numOfCleansingIterations - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The maximum number of cleansing iterations to perform (<1 = until fully cleansed)
m_numOfCrossValidationFolds - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The number of cross validation folds to perform (<2 = no cross validation)
m_numOfSamplesPerGenerator - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_numOfSamplesPerGenerator - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_numOfSamplesPerRegion - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_numOfSamplesPerRegion - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_numOutputs - Variable in class weka.classifiers.functions.neural.NeuralConnection
The number of outputs.
m_numParameters - Variable in class weka.classifiers.trees.m5.PreConstructedLinearModel
 
m_numParameters - Variable in class weka.classifiers.trees.m5.RuleNode
the number of paramters in the chosen model for this node---either the subtree model or the linear model.
m_numRegressions - Variable in class weka.classifiers.trees.lmt.LogisticBase
The number of LogitBoost iterations performed.
m_numRepetitions - Variable in class weka.gui.experiment.SimpleSetupPanel
The number of times to repeat the sub-experiment
m_numRules - Variable in class weka.associations.Apriori
The maximum number of rules that are output.
m_numSubsets - Variable in class weka.classifiers.trees.j48.ClassifierSplitModel
Number of created subsets.
m_numToEliminate - Variable in class weka.attributeSelection.SVMAttributeEval
Constant rate of attribute elimination per iteration
m_numToSelect - Variable in class weka.attributeSelection.AttributeSelection
number of attributes requested from ranked results
m_numToSelect - Variable in class weka.attributeSelection.ForwardSelection
The number of attributes to select. -1 indicates that all attributes are to be retained.
m_numToSelect - Variable in class weka.attributeSelection.RaceSearch
The number of attributes to retain if a ranking is requested. -1 indicates that all attributes are to be retained.
m_numToSelect - Variable in class weka.attributeSelection.Ranker
The number of attributes to select. -1 indicates that all attributes are to be retained.
m_numTrees - Variable in class weka.classifiers.trees.RandomForest
Number of trees in forest.
m_num_attribs - Variable in class weka.clusterers.EM
number of attributes
m_num_clusters - Variable in class weka.clusterers.EM
number of clusters selected by the user or cross validation
m_num_instances - Variable in class weka.clusterers.EM
number of training instances
m_number - Variable in class weka.classifiers.functions.supportVector.SMOset
The current number of elements in the set
m_number - Variable in class weka.classifiers.trees.m5.CorrelationSplitInfo
the number of instances
m_numberAdditionalMeasures - Variable in class weka.experiment.ClassifierSplitEvaluator
The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results
m_numberMerges - Variable in class weka.clusterers.Cobweb
 
m_numberOfClusters - Variable in class weka.clusterers.Cobweb
Number of clusters (nodes in the tree).
m_numberOfSamplesFromEachRegion - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_numberSplits - Variable in class weka.clusterers.Cobweb
 
m_numeric - Variable in class weka.classifiers.functions.MultilayerPerceptron
A flag to say that it's a numeric class.
m_numericAttIndices - Variable in class weka.classifiers.trees.ADTree
An array containing the inidices to the numeric attributes in the data
m_numericClass - Variable in class weka.attributeSelection.ReliefFAttributeEval
Numeric class
m_numericClassifyThreshold - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The threshold for deciding when a numeric value is correctly classified
m_numericConst - Variable in class weka.filters.unsupervised.attribute.AddExpression.NumericOperand
numeric constant
m_numericData - Variable in class weka.classifiers.trees.lmt.LogisticBase
Numeric version of the training data.
m_numericDataHeader - Variable in class weka.classifiers.trees.lmt.LogisticBase
Header-only version of the numeric version of the training data
m_objective - Variable in class weka.attributeSelection.GeneticSearch.GABitSet
holds raw merit
m_okBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
ok button
m_oldMax - Variable in class weka.gui.beans.StripChart
 
m_oldMin - Variable in class weka.gui.beans.StripChart
 
m_oldMousePos - Variable in class weka.gui.treevisualizer.TreeVisualizer
A variable used to tag the start pos of a user action.
m_oldWidth - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
Used to determine if the positions need to be recalculated.
m_oldWidth - Variable in class weka.gui.visualize.ClassPanel
The old width.
m_oldX - Variable in class weka.gui.beans.KnowledgeFlow
Used to record screen coordinates during move and connect operations
m_oldY - Variable in class weka.gui.beans.KnowledgeFlow
Used to record screen coordinates during move and connect operations
m_onlyAlphabeticTokens - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
True if tokens are to be formed only from alphabetic sequences of characters.
m_onlyConsiderBetterAndSmaller - Variable in class weka.attributeSelection.RandomSearch
only accept a feature set as being "better" than the best if its merit is better or equal to the best, and it contains fewer features than the best (this allows LVF to be implimented).
m_onlyNumeric - Variable in class weka.classifiers.functions.SMO
Only numeric attributes in the dataset?
m_onlyNumeric - Variable in class weka.classifiers.functions.SMOreg
Only numeric attributes in the dataset?
m_openButton - Variable in class weka.gui.CostMatrixEditor.CustomEditor
The button for opening a cost matrix from a file
m_operator - Variable in class weka.filters.unsupervised.attribute.AddExpression.Operator
the operator
m_operatorStack - Variable in class weka.filters.unsupervised.attribute.AddExpression
Operator stack
m_optimistic - Variable in class weka.associations.tertius.Rule
Optimistic estimate of this rule.
m_originalPlot - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
The master plot
m_originalSpaceFormat - Variable in class weka.attributeSelection.PrincipalComponents
The header for data transformed back to the original space
m_osi - Variable in class weka.gui.beans.StripChart
The off screen image for rendering to
m_osi - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_outText - Variable in class weka.gui.beans.TextViewer
Output area for a piece of text
m_outputFrame - Variable in class weka.gui.beans.StripChart
 
m_outputItemSets - Variable in class weka.associations.Apriori
Output itemsets found?
m_outputList - Variable in class weka.classifiers.functions.neural.NeuralConnection
The list of outputs from this unit.
m_outputNumAtts - Variable in class weka.attributeSelection.PrincipalComponents
The number of attributes in the pc transformed data
m_outputNums - Variable in class weka.classifiers.functions.neural.NeuralConnection
The numbering for the connections at the other end of the out lines.
m_outputs - Variable in class weka.classifiers.functions.MultilayerPerceptron
The output units.
m_p - Variable in class weka.gui.treevisualizer.PlaceNode2.Group
The parent node of this group.
m_pCrossover - Variable in class weka.attributeSelection.GeneticSearch
the probability of crossover occuring
m_pMutation - Variable in class weka.attributeSelection.GeneticSearch
the probability of mutation occuring
m_paEditor - Variable in class weka.gui.beans.PredictionAppenderCustomizer
 
m_panelHeight - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_panelHeight - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_panelVisible - Variable in class weka.gui.beans.ClassifierCustomizer
 
m_panelWidth - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_panelWidth - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_parent - Variable in class weka.classifiers.meta.ND.NDTree
The parent
m_parent - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
 
m_parent - Variable in class weka.classifiers.trees.m5.RuleNode
the parent of this node
m_parent - Variable in class weka.gui.treevisualizer.Node
An array containing references to all the parent edges (only 1 currently).
m_parent - Variable in class weka.gui.treevisualizer.TreeVisualizer.EdgeInfo
The parent subscript (for a Node).
m_parent - Variable in class weka.gui.treevisualizer.TreeVisualizer.NodeInfo
The subscript number of the Nodes parent.
m_parentComponent - Variable in class weka.gui.SaveBuffer
The parent component requesting the save
m_parts - Variable in class weka.associations.Tertius
Part instances for individual-based learning.
m_parts - Variable in class weka.associations.tertius.IndividualInstance
 
m_partsString - Variable in class weka.associations.Tertius
Name of the file containing the parts for individual-based learning.
m_password - Variable in class weka.experiment.DatabaseUtils
Database Password
m_pausePlotting - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_pcSupport - Variable in class weka.gui.beans.ClassAssignerCustomizer
 
m_pcSupport - Variable in class weka.gui.beans.ClassifierCustomizer
 
m_pcSupport - Variable in class weka.gui.beans.CrossValidationFoldMakerCustomizer
 
m_pcSupport - Variable in class weka.gui.beans.FilterCustomizer
 
m_pcSupport - Variable in class weka.gui.beans.LoaderCustomizer
 
m_pcSupport - Variable in class weka.gui.beans.PredictionAppenderCustomizer
 
m_pcSupport - Variable in class weka.gui.beans.StripChartCustomizer
 
m_pcSupport - Variable in class weka.gui.beans.TrainTestSplitMakerCustomizer
 
m_pcs - Variable in class weka.gui.beans.BeanVisual
 
m_perBag - Variable in class weka.classifiers.trees.j48.Distribution
Weight of instances per bag.
m_perClass - Variable in class weka.classifiers.trees.j48.Distribution
Weight of instances per class.
m_perClassPerBag - Variable in class weka.classifiers.trees.j48.Distribution
Weight of instances per class per bag.
m_percent - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Stores the dimensionality the data should be reduced to as percentage of the original dimension
m_percentCompleted - Variable in class weka.gui.boundaryvisualizer.RemoteResult
 
m_percentThreshold - Variable in class weka.attributeSelection.SVMAttributeEval
Threshold below which percent elimination switches to constant elimination
m_percentToEliminate - Variable in class weka.attributeSelection.SVMAttributeEval
Percentage rate of attribute elimination, trumps constant rate (above threshold), ignored if = 0
m_pg - Variable in class weka.gui.treevisualizer.PlaceNode2.Group
The group number for the parent of this group.
m_pixHeight - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_pixHeight - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_pixWidth - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_pixWidth - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_place - Variable in class weka.gui.treevisualizer.PlaceNode2.Ease
The number of the group on the left of the tangle.
m_placer - Variable in class weka.gui.treevisualizer.TreeVisualizer
The placement algorithm for the Node structure.
m_plot - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays the plot
m_plot2D - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
The actual generic plotting panel
m_plotAreaHeight - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_plotAreaWidth - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_plotCompanion - Variable in class weka.gui.visualize.Plot2D
An optional "compainion" of the panel.
m_plotData - Variable in class weka.gui.visualize.LegendPanel.LegendEntry
the data for this legend entry
m_plotInstances - Variable in class weka.gui.visualize.AttributePanel
The instances to be plotted
m_plotInstances - Variable in class weka.gui.visualize.Plot2D
The instances to be plotted
m_plotInstances - Variable in class weka.gui.visualize.PlotData2D
The instances
m_plotInstances - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
The instances from the master plot
m_plotLBSizeD - Variable in class weka.gui.visualize.MatrixPanel
Stores the maximum size for PlotSize label to keep it's size constant
m_plotName - Variable in class weka.gui.visualize.PlotData2D
The name of this plot
m_plotName - Variable in class weka.gui.visualize.VisualizePanel
The name of the plot (not currently displayed, but can be used in the containing Frame or Panel)
m_plotPanel - Variable in class weka.gui.beans.StripChart
 
m_plotPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_plotResize - Variable in class weka.gui.visualize.Plot2D
if the user resizes the window, or the attributes selected for the attributes change, then the lookup table for points needs to be recalculated
m_plotSize - Variable in class weka.gui.visualize.MatrixPanel
The slider to adjust the size of the cells in the matrix
m_plotSizeLb - Variable in class weka.gui.visualize.MatrixPanel
Displays the current size beside the slider bar for cell size
m_plotSurround - Variable in class weka.gui.visualize.VisualizePanel
Panel that surrounds the plot panel with a titled border
m_plotThread - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_plotTrainingData - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_plotTrainingData - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_plots - Variable in class weka.gui.visualize.LegendPanel
the list of plot elements
m_plots - Variable in class weka.gui.visualize.Plot2D
The plots to display
m_plotsPanel - Variable in class weka.gui.visualize.MatrixPanel
The that panel contains the actual matrix
m_plottingAborted - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts plotting
m_pointColors - Variable in class weka.gui.visualize.MatrixPanel
This is an array cache for the colour of each of the instances depending on the colouring attribute.
m_pointDrawn - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
A temporary array used to strike any instances that would be drawn redundantly.
m_pointLBSizeD - Variable in class weka.gui.visualize.MatrixPanel
Stores the maximum size for PointSize label to keep it's size constant
m_pointLookup - Variable in class weka.gui.visualize.Plot2D
lookup table for plotted points
m_pointLookup - Variable in class weka.gui.visualize.PlotData2D
Panel coordinate cache for data points
m_pointShape - Variable in class weka.gui.visualize.LegendPanel.LegendEntry
displays the point shape associated with this legend entry
m_pointSize - Variable in class weka.gui.visualize.MatrixPanel
The slider to adjust the size of the datapoints
m_pointSizeLb - Variable in class weka.gui.visualize.MatrixPanel
Displays the current size beside the slider bar for point size
m_pointerB - Variable in class weka.gui.beans.KnowledgeFlow
 
m_points - Variable in class weka.gui.visualize.MatrixPanel
This is a local array cache for all the instance values for faster rendering
m_popSize - Variable in class weka.attributeSelection.GeneticSearch
the number of individual solutions
m_population - Variable in class weka.attributeSelection.GeneticSearch
the current population
m_posTrainInstances - Variable in class weka.classifiers.trees.ADTree
The training instances with positive class - referencing the training dataset
m_position - Variable in class weka.classifiers.trees.m5.CorrelationSplitInfo
 
m_postFixExpVector - Variable in class weka.filters.unsupervised.attribute.AddExpression
Holds the expression in postfix form
m_precisionC - Variable in class weka.gui.visualize.ClassPanel
The precision with which to display real values
m_predInst - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel.PlotThread
 
m_predInst - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_predNegVector - Variable in class weka.classifiers.functions.Winnow
 
m_predPosVector - Variable in class weka.classifiers.functions.Winnow
The weight vectors for prediction
m_predicate - Variable in class weka.associations.tertius.Literal
 
m_predicates - Variable in class weka.associations.Tertius
Predicates used in the rules.
m_preferredColourDimension - Variable in class weka.gui.visualize.VisualizePanel
 
m_preferredXDimension - Variable in class weka.gui.visualize.VisualizePanel
These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file
m_preferredYDimension - Variable in class weka.gui.visualize.VisualizePanel
 
m_previous - Variable in class weka.classifiers.functions.supportVector.SMOset
The previous element for each element
m_previousTok - Variable in class weka.filters.unsupervised.attribute.AddExpression
Holds the previous token
m_previousY - Variable in class weka.gui.beans.StripChart
 
m_printValues - Variable in class weka.associations.Tertius
Type of values output.
m_priors - Variable in class weka.clusterers.EM
the prior probabilities for clusters
m_priors - Variable in class weka.clusterers.MakeDensityBasedClusterer
prior probabilities for the fitted clusters
m_probabilities - Variable in class weka.gui.boundaryvisualizer.RemoteResult
 
m_probabilityCache - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_progress - Variable in class weka.gui.graphvisualizer.HierarchicalBCEngine
The progress bar to show the progress of the layout process
m_propSupport - Variable in class weka.gui.CostMatrixEditor
A helper class for notifying listeners
m_propertyDialog - Variable in class weka.classifiers.trees.UserClassifier
A window for selecting other classifiers.
m_pruneTheTree - Variable in class weka.classifiers.trees.j48.C45PruneableClassifierTree
True if the tree is to be pruned.
m_pruningMultiplier - Variable in class weka.classifiers.trees.m5.RuleNode
 
m_quad - Variable in class weka.gui.treevisualizer.TreeVisualizer.NodeInfo
The rough position of the node relative to the screen.
m_raceType - Variable in class weka.attributeSelection.RaceSearch
the selected search type
m_radioListener - Variable in class weka.gui.experiment.DistributeExperimentPanel
Handle radio buttons
m_random - Variable in class weka.attributeSelection.GeneticSearch
random number generation
m_random - Variable in class weka.attributeSelection.RandomSearch
random number object
m_random - Variable in class weka.classifiers.functions.LeastMedSq
 
m_random - Variable in class weka.classifiers.functions.MultilayerPerceptron
The actual random number generator.
m_random - Variable in class weka.classifiers.functions.neural.NeuralNode
 
m_random - Variable in class weka.classifiers.trees.ADTree
The random number generator - used for the random search heuristic
m_random - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_random - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_random - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_randomSeed - Variable in class weka.attributeSelection.OneRAttributeEval
Random number seed
m_randomSeed - Variable in class weka.classifiers.functions.MultilayerPerceptron
The number used to seed the random number generator.
m_randomSeed - Variable in class weka.classifiers.functions.SMO
The random number seed
m_randomSeed - Variable in class weka.classifiers.trees.ADTree
Option - the seed to use for a random search
m_randomSeed - Variable in class weka.classifiers.trees.RandomForest
The random seed.
m_randomSeed - Variable in class weka.classifiers.trees.RandomTree
The random seed to use.
m_randomSeed - Variable in class weka.gui.beans.CrossValidationFoldMaker
 
m_randomSeed - Variable in class weka.gui.beans.TrainTestSplitMaker
 
m_randomize - Variable in class weka.experiment.RandomSplitResultProducer
Whether dataset is to be randomized
m_randomseed - Variable in class weka.classifiers.functions.LeastMedSq
 
m_rangeX - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_rangeY - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_ranges - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
This contains the info for the coords of the shape converted to attrib coords, for polygon the first attrib is the number of points, This is not more object oriented because that would be over kill.
m_rankResults - Variable in class weka.attributeSelection.AttributeSelection
hold statistics for repeated feature selection, such as under cross validation
m_rankedAtts - Variable in class weka.attributeSelection.ForwardSelection
a ranked list of attribute indexes
m_rankedAtts - Variable in class weka.attributeSelection.RaceSearch
The ranked list of attributes produced if m_rankingRequested is true
m_rankedSoFar - Variable in class weka.attributeSelection.ForwardSelection
 
m_rankedSoFar - Variable in class weka.attributeSelection.RaceSearch
The number of attributes ranked so far (if ranking is requested)
m_rankingRequested - Variable in class weka.attributeSelection.ForwardSelection
true if the user has requested a ranked list of attributes
m_rankingRequested - Variable in class weka.attributeSelection.RaceSearch
If true then produce a ranked list of attributes by fully traversing a forward hillclimb race
m_reducedErrorPruning - Variable in class weka.classifiers.rules.PART
Use reduced error pruning?
m_reducedErrorPruning - Variable in class weka.classifiers.trees.J48
Use reduced error pruning?
m_refer - Variable in class weka.gui.treevisualizer.Node
The ID string for this node (used for construction purposes)
m_refreshFrequency - Variable in class weka.gui.beans.StripChart
Plot every m_refreshFrequency'th point
m_refreshWidth - Variable in class weka.gui.beans.StripChart
Shift the plot by this many pixels every time a point is plotted
m_regionSamplesText - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_regressionTree - Variable in class weka.classifiers.trees.m5.M5Base
Make a regression tree/rule instead of a model tree/rule
m_regressionTree - Variable in class weka.classifiers.trees.m5.Rule
Make a regression tree instead of a model tree
m_regressionTree - Variable in class weka.classifiers.trees.m5.RuleNode
Make a regression tree instead of a model tree
m_regressions - Variable in class weka.classifiers.trees.lmt.LogisticBase
Array holding the simple regression functions fit by LogitBoost
m_relOps - Variable in class weka.classifiers.trees.m5.Rule
the corresponding relational operators (0 = "<=", 1 = ">")
m_relativeCheck - Variable in class weka.gui.experiment.DatasetListPanel
Make file paths relative to the user (start) directory
m_remChildren - Variable in class weka.gui.treevisualizer.TreeVisualizer
Similar to add children but now it removes children.
m_remoteHostFailureCounts - Variable in class weka.experiment.RemoteExperiment
The number of times tasks have failed on each remote host
m_remoteHostFailureCounts - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
The number of times tasks have failed on each remote host
m_remoteHosts - Variable in class weka.experiment.RemoteExperiment
Holds the names of machines with remoteEngine servers running
m_remoteHosts - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Holds the names of machines with remoteEngine servers running
m_remoteHostsQueue - Variable in class weka.experiment.RemoteExperiment
The queue of available hosts
m_remoteHostsQueue - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
The queue of available hosts
m_remoteHostsStatus - Variable in class weka.experiment.RemoteExperiment
The status of each of the remote hosts
m_remoteHostsStatus - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
The status of each of the remote hosts
m_removeAttributes - Variable in class weka.filters.unsupervised.attribute.AddCluster
Filter for removing attributes
m_removeAttributes - Variable in class weka.filters.unsupervised.attribute.ClusterMembership
Filter for removing attributes
m_removeFilter - Variable in class weka.filters.unsupervised.attribute.RemoveUseless
The filter used to remove attributes
m_removeMissingCols - Variable in class weka.associations.Apriori
 
m_removedHosts - Variable in class weka.experiment.RemoteExperiment
The number of hosts removed due to exceeding max failures
m_removedHosts - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
The number of hosts removed due to exceeding max failures
m_repeat - Variable in class weka.associations.Tertius
Repeat attributes ?
m_repeatPredicate - Variable in class weka.associations.tertius.Rule
Can repeat predicates in the rule ?
m_replaceMissing - Variable in class weka.classifiers.trees.LMT
Filter to replace missing values
m_replaceMissing - Variable in class weka.classifiers.trees.m5.M5Base
filter to fill in missing values
m_replaceMissing - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Should the missing values be replaced using unsupervised.ReplaceMissingValues filter
m_replaceMissingFilter - Variable in class weka.attributeSelection.PrincipalComponents
Filters for original data
m_reportFrequency - Variable in class weka.attributeSelection.GeneticSearch
how often reports are generated
m_reps - Variable in class weka.classifiers.trees.UserClassifier
The tabbed window for the tree and instances view.
m_resampleBt - Variable in class weka.gui.visualize.MatrixPanel
The label for resample percentage
m_resamplePercent - Variable in class weka.gui.visualize.MatrixPanel
The text area for percentage to resample data
m_reset - Variable in class weka.classifiers.functions.MultilayerPerceptron
This flag states that the user wants the network to restart if it is found to be generating infinity or NaN for the error value.
m_reset - Variable in class weka.gui.beans.ChartEvent
 
m_reset - Variable in class weka.gui.beans.IncrementalClassifierEvaluator
 
m_result - Variable in class weka.experiment.ClassifierSplitEvaluator
Holds the statistics for the most recent application of the classifier
m_result - Variable in class weka.experiment.RegressionSplitEvaluator
Holds the statistics for the most recent application of the classifier
m_result - Variable in class weka.experiment.RemoteExperimentSubTask
 
m_result - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_results - Variable in class weka.associations.Tertius
The results.
m_resultsFrame - Variable in class weka.gui.beans.GraphViewer
 
m_resultsFrame - Variable in class weka.gui.beans.TextViewer
 
m_retrieval - Variable in class weka.core.converters.AbstractLoader
The current retrieval mode
m_returnValue - Variable in class weka.gui.DatabaseConnectionDialog
 
m_ridge - Variable in class weka.classifiers.functions.RBFNetwork
The ridge parameter for the logistic regression.
m_right - Variable in class weka.classifiers.meta.ND.NDTree
The right successor
m_right - Variable in class weka.classifiers.trees.m5.RuleNode
 
m_right - Variable in class weka.gui.treevisualizer.PlaceNode2.Group
The rightmost position of this group.
m_right - Variable in class weka.gui.treevisualizer.PlaceNode2.Level
 
m_rndmSeed - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Stores the random seed used to generate the random matrix
m_roc - Variable in class weka.associations.Tertius
Perform ROC analysis ?
m_root - Variable in class weka.classifiers.rules.PART
The decision list
m_root - Variable in class weka.classifiers.trees.ADTree
The root of the tree
m_root - Variable in class weka.classifiers.trees.J48
The decision tree
m_root - Variable in class weka.gui.treevisualizer.Node
true if this is the top of the tree. ie has no parent
m_rootMeanSquaredError - Variable in class weka.classifiers.trees.m5.RuleNode
the mean squared error of the model at this node (either linear or subtree)
m_rowLength - Variable in class weka.gui.boundaryvisualizer.RemoteResult
 
m_rowNumber - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_rowNumber - Variable in class weka.gui.boundaryvisualizer.RemoteResult
 
m_rr - Variable in class weka.classifiers.rules.DecisionTable
Random numbers for use in cross validation
m_rr - Variable in class weka.clusterers.EM
random numbers and seed
m_rseed - Variable in class weka.clusterers.EM
 
m_rseed - Variable in class weka.gui.visualize.MatrixPanel
Random seed for random subsample
m_rsource - Variable in class weka.gui.treevisualizer.Edge
The ID string of the parent Node of this edge (used for consrtuction purposes).
m_rtarget - Variable in class weka.gui.treevisualizer.Edge
The ID string of the child Node of this edge (used for construction purposes).
m_rule - Variable in class weka.classifiers.rules.OneR
A 1-R rule
m_ruleModel - Variable in class weka.classifiers.trees.m5.Rule
the leaf encapsulating the linear model for this rule
m_ruleSet - Variable in class weka.classifiers.trees.m5.M5Base
the rule set
m_rules - Variable in class weka.classifiers.rules.Prism
The first rule in the list of rules
m_sIndex - Variable in class weka.gui.visualize.Plot2D
 
m_sIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_sameClause - Variable in class weka.associations.Tertius
Perform test on same clauses ?
m_sampleM - Variable in class weka.attributeSelection.ReliefFAttributeEval
The number of instances to sample when estimating attributes default == -1, use all instances
m_samples - Variable in class weka.attributeSelection.RaceSearch
the number of samples above which to begin testing for similarity between competing subsets
m_samples - Variable in class weka.classifiers.functions.LeastMedSq
 
m_samplesBase - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_samplesBase - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_samplesize - Variable in class weka.classifiers.functions.LeastMedSq
 
m_saveB - Variable in class weka.gui.beans.KnowledgeFlow
 
m_saveBut - Variable in class weka.gui.visualize.VisualizePanel
Button for the user to save the visualized set of instances
m_saveButton - Variable in class weka.gui.CostMatrixEditor.CustomEditor
The button for saving a cost matrix to a file
m_saveInstanceData - Variable in class weka.classifiers.trees.ADTree
Option - whether the tree should remember the instance data
m_saveInstances - Variable in class weka.classifiers.trees.m5.M5Base
Save instances at each node in an M5 tree for visualization purposes.
m_saveInstances - Variable in class weka.classifiers.trees.m5.Rule
Save instances at each node in an M5 tree for visualization purposes.
m_saveInstances - Variable in class weka.classifiers.trees.m5.RuleNode
Save the instances at each node (for visualizing in the Explorer's treevisualizer.
m_saveInstances - Variable in class weka.clusterers.Cobweb
Output instances in graph representation of Cobweb tree (Allows instances at nodes in the tree to be visualized in the Explorer).
m_savePanelBorderText - Variable in class weka.gui.visualize.ThresholdVisualizePanel
Original border text
m_scalePanel - Variable in class weka.gui.beans.StripChart
Class providing a panel for displaying the y axis
m_scalefactor - Variable in class weka.classifiers.functions.LeastMedSq
 
m_scaling - Variable in class weka.gui.treevisualizer.TreeVisualizer
The number of frames left to calculate.
m_scroller - Variable in class weka.gui.GenericObjectEditor.JTreePopupMenu
The scroller
m_searchDirection - Variable in class weka.attributeSelection.BestFirst
0 == backward search, 1 == forward search, 2 == bidirectional
m_searchMethod - Variable in class weka.attributeSelection.AttributeSelection
the search method
m_searchPath - Variable in class weka.classifiers.trees.ADTree
Option - the search mode
m_searchSize - Variable in class weka.attributeSelection.RandomSearch
percentage of the search space to consider
m_searchString - Variable in class weka.gui.treevisualizer.TreeVisualizer
 
m_searchWin - Variable in class weka.gui.treevisualizer.TreeVisualizer
 
m_search_bestInsertionNode - Variable in class weka.classifiers.trees.ADTree
The best node to insert under, as found so far by the latest search
m_search_bestPathNegInstances - Variable in class weka.classifiers.trees.ADTree
The negative instances that apply to the best path found so far
m_search_bestPathPosInstances - Variable in class weka.classifiers.trees.ADTree
The positive instances that apply to the best path found so far
m_search_bestSplitter - Variable in class weka.classifiers.trees.ADTree
The best splitter to insert, as found so far by the latest search
m_search_smallestZ - Variable in class weka.classifiers.trees.ADTree
The smallest Z value found so far by the latest search
m_seed - Variable in class weka.attributeSelection.AttributeSelection
seed used to randomly shuffle instances for cross validation
m_seed - Variable in class weka.attributeSelection.GeneticSearch
seed for random number generation
m_seed - Variable in class weka.attributeSelection.RandomSearch
seed for random number generation
m_seed - Variable in class weka.attributeSelection.ReliefFAttributeEval
Random number seed used for sampling instances
m_seed - Variable in class weka.attributeSelection.WrapperSubsetEval
random number seed
m_seed - Variable in class weka.classifiers.meta.ND
The random number seed used
m_seed - Variable in class weka.classifiers.rules.part.MakeDecList
The seed for random number generation.
m_seed - Variable in class weka.classifiers.trees.j48.PruneableClassifierTree
The random number seed.
m_seed - Variable in class weka.clusterers.ClusterEvaluation
seed to use for cross validation
m_seed - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_selAttrib - Variable in class weka.gui.visualize.MatrixPanel
The button to display a window to select attributes
m_selectFont - Variable in class weka.gui.treevisualizer.TreeVisualizer
A ub group on the win_menu
m_selectFontGroup - Variable in class weka.gui.treevisualizer.TreeVisualizer
A grouping for the font choices
m_selected - Variable in class weka.classifiers.functions.MultilayerPerceptron
A Vector list of the units currently selected.
m_selectedAttribs - Variable in class weka.gui.visualize.MatrixPanel
This array contains the indices of the attributes currently selected
m_selectedAttributeSet - Variable in class weka.attributeSelection.AttributeSelection
the selected attributes
m_selectionResults - Variable in class weka.attributeSelection.AttributeSelection
holds a string describing the results of the attribute selection
m_selectionTime - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The time taken to select attributes in milliseconds
m_sendInstances - Variable in class weka.gui.treevisualizer.TreeVisualizer
Use this to dump the instances from this node to the vis panel.
m_set1 - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
 
m_set2 - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
 
m_setAutoCommit - Variable in class weka.experiment.DatabaseUtils
 
m_setNumber - Variable in class weka.gui.beans.BatchClassifierEvent
The set number for the test set
m_setNumber - Variable in class weka.gui.beans.TestSetEvent
what number is this test set (ie fold 2 of 10 folds)
m_setNumber - Variable in class weka.gui.beans.TrainingSetEvent
what number is this training set (ie fold 2 of 10 folds)
m_shape - Variable in class weka.gui.treevisualizer.Node
The shape of the node.
m_shape - Variable in class weka.gui.treevisualizer.TreeBuild.InfoObject
The shape name of for the object.
m_shapePoints - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
contains the points of the shape currently being drawn.
m_shapeSize - Variable in class weka.gui.visualize.PlotData2D
Additional optional information to control the size of points.
m_shapeType - Variable in class weka.gui.visualize.PlotData2D
Additional optional information to control the point shape for this data.
m_shapes - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
contains all the shapes that have been drawn for these attribs
m_showAttBars - Variable in class weka.gui.visualize.VisualizePanel
Show the attribute bar panel
m_showRules - Variable in class weka.classifiers.misc.FLR
 
m_shrinkage - Variable in class weka.classifiers.meta.AdditiveRegression
Shrinkage (Learning rate).
m_side - Variable in class weka.gui.treevisualizer.TreeVisualizer.EdgeInfo
The distance from the center of the text to either side.
m_side - Variable in class weka.gui.treevisualizer.TreeVisualizer.NodeInfo
The offset to get to the left or right of the node.
m_sigLevel - Variable in class weka.attributeSelection.RaceSearch
the significance level for comparisons
m_sigma - Variable in class weka.attributeSelection.ReliefFAttributeEval
 
m_sigmoidUnit - Variable in class weka.classifiers.functions.MultilayerPerceptron
this is a sigmoid unit.
m_sign - Variable in class weka.associations.tertius.Literal
 
m_signMod - Variable in class weka.filters.unsupervised.attribute.AddExpression
True if the next numeric constant or attribute index is negative
m_significanceLevel - Variable in class weka.associations.Apriori
Significance level for optional significance test.
m_simplePanel - Variable in class weka.gui.experiment.SetupModePanel
The simple setup panel
m_singleHead - Variable in class weka.associations.tertius.Rule
Can there be only one literal in the head ?
m_size - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_size - Variable in class weka.gui.treevisualizer.PlaceNode2.Group
The size of this group.
m_size1 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size10 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size12 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size14 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size16 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size18 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size2 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size20 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size22 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size24 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size4 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size6 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_size8 - Variable in class weka.gui.treevisualizer.TreeVisualizer
A font choice.
m_slope - Variable in class weka.classifiers.functions.SimpleLinearRegression
The slope
m_smoCParameter - Variable in class weka.attributeSelection.SVMAttributeEval
Complexity parameter to pass on to SMO
m_smoFilterType - Variable in class weka.attributeSelection.SVMAttributeEval
Filter parameter to pass on to SMO
m_smoPParameter - Variable in class weka.attributeSelection.SVMAttributeEval
Epsilon parameter to pass on to SMO
m_smoTParameter - Variable in class weka.attributeSelection.SVMAttributeEval
Tolerance parameter to pass on to SMO
m_smoothPredictions - Variable in class weka.classifiers.trees.m5.Rule
use the original m5 smoothing procedure
m_sons - Variable in class weka.classifiers.rules.part.ClassifierDecList
References to sons.
m_sons - Variable in class weka.classifiers.trees.j48.ClassifierTree
References to sons.
m_sons - Variable in class weka.classifiers.trees.lmt.LMTNode
Array of children of the node
m_sortedEigens - Variable in class weka.attributeSelection.PrincipalComponents
Sorted eigenvalues
m_source - Variable in class weka.gui.beans.BeanConnection
 
m_source - Variable in class weka.gui.treevisualizer.Edge
The parent Node of this edge.
m_source - Variable in class weka.gui.treevisualizer.TreeBuild.InfoObject
The source ID of the object.
m_sourceEventSetDescriptor - Variable in class weka.gui.beans.KnowledgeFlow
Event set descriptor for the bean being manipulated
m_sourceFile - Variable in class weka.core.converters.C45Loader
Holds the source of the data set.
m_sourceFile - Variable in class weka.core.converters.CSVLoader
Holds the source of the data set.
m_sourceFileData - Variable in class weka.core.converters.C45Loader
Describe variable m_sourceFileData here.
m_sourceReader - Variable in class weka.core.converters.ArffLoader
The reader for the source file.
m_span - Variable in class weka.gui.visualize.AttributePanel
The container window for the attribute bars, and also where the X,Y or B get printed.
m_span - Variable in class weka.gui.visualize.LegendPanel
the panel that contains the legend entries
m_sparseIndices - Variable in class weka.classifiers.functions.SMO.BinarySMO
 
m_sparseIndices - Variable in class weka.classifiers.functions.SMOreg
 
m_sparseWeights - Variable in class weka.classifiers.functions.SMO.BinarySMO
Variables to hold weight vector in sparse form.
m_sparseWeights - Variable in class weka.classifiers.functions.SMOreg
Variables to hold weight vector in sparse form.
m_spectrumHeight - Variable in class weka.gui.visualize.ClassPanel
The height of the spectrum for numeric class
m_splitAtt - Variable in class weka.classifiers.trees.m5.RuleNode
attribute this node splits on
m_splitAttr - Variable in class weka.classifiers.trees.m5.CorrelationSplitInfo
the attribute being tested
m_splitAtts - Variable in class weka.classifiers.trees.m5.Rule
the indexes of the attributes used to split on for this rule
m_splitByDataSet - Variable in class weka.experiment.RemoteExperiment
If true, then sub experiments are created on the basis of data sets rather than run number.
m_splitByDataSet - Variable in class weka.gui.experiment.DistributeExperimentPanel
Split experiment up by data set.
m_splitByRun - Variable in class weka.gui.experiment.DistributeExperimentPanel
Split experiment up by run number.
m_splitCrit - Static variable in class weka.classifiers.rules.part.ClassifierDecList
To compute the entropy.
m_splitEditor - Variable in class weka.gui.beans.TrainTestSplitMakerCustomizer
 
m_splitListener - Variable in class weka.gui.visualize.VisualizePanel
An optional listener that we will inform when the user creates a split to seperate instances.
m_splitNum - Variable in class weka.classifiers.trees.m5.RuleNode
a node will not be split if it contains less then m_splitNum instances
m_splitOnResiduals - Variable in class weka.classifiers.trees.LMT
split on residuals?
m_splitPoint - Variable in class weka.classifiers.trees.j48.BinC45Split
Value of split point.
m_splitPoint - Variable in class weka.classifiers.trees.j48.C45Split
Value of split point.
m_splitPoint - Variable in class weka.classifiers.trees.lmt.ResidualSplit
The split point (for numeric attributes)
m_splitThread - Variable in class weka.gui.beans.TrainTestSplitMaker
 
m_splitVals - Variable in class weka.classifiers.trees.m5.Rule
the corresponding values of the split points
m_splitValue - Variable in class weka.classifiers.trees.m5.CorrelationSplitInfo
the best value on which to split
m_splitValue - Variable in class weka.classifiers.trees.m5.RuleNode
the value of the split attribute
m_st - Variable in class weka.gui.treevisualizer.TreeBuild
The stream to parse.
m_standardDeviations - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_start - Variable in class weka.gui.treevisualizer.PlaceNode2.Group
The start node of this group.
m_start - Variable in class weka.gui.treevisualizer.PlaceNode2.Level
The number for the group on the left of this level.
m_startBut - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_startRange - Variable in class weka.attributeSelection.BestFirst
holds the start set for the search as a Range
m_startRange - Variable in class weka.attributeSelection.ExhaustiveSearch
the start set as a Range
m_startRange - Variable in class weka.attributeSelection.ForwardSelection
holds the start set for the search as a Range
m_startRange - Variable in class weka.attributeSelection.GeneticSearch
holds the start set for the search as a Range
m_startRange - Variable in class weka.attributeSelection.RandomSearch
holds the start set as a range
m_startRange - Variable in class weka.attributeSelection.Ranker
Holds the start set for the search as a range
m_startStop - Variable in class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
The start stop button.
m_startX - Variable in class weka.gui.beans.KnowledgeFlow
 
m_startY - Variable in class weka.gui.beans.KnowledgeFlow
 
m_starting - Variable in class weka.attributeSelection.BestFirst
holds an array of starting attributes
m_starting - Variable in class weka.attributeSelection.ExhaustiveSearch
holds a starting set as an array of attributes.
m_starting - Variable in class weka.attributeSelection.ForwardSelection
holds an array of starting attributes
m_starting - Variable in class weka.attributeSelection.GeneticSearch
holds a starting set as an array of attributes.
m_starting - Variable in class weka.attributeSelection.RandomSearch
holds a starting set as an array of attributes.
m_starting - Variable in class weka.attributeSelection.Ranker
Holds the starting set as an array of attributes
m_state - Variable in class weka.gui.beans.Classifier
 
m_state - Variable in class weka.gui.beans.Filter
 
m_stationary - Variable in class weka.gui.beans.BeanVisual
Container for the icon
m_status - Variable in class weka.associations.Tertius
Status of the search.
m_status - Variable in class weka.gui.beans.IncrementalClassifierEvent
 
m_status - Variable in class weka.gui.beans.InstanceEvent
 
m_status - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_statusMessage - Variable in class weka.experiment.RemoteExperimentEvent
A status type message
m_std_devs - Variable in class weka.attributeSelection.CfsSubsetEval
Standard deviations of attributes (when using pearsons correlation)
m_stopAfterFirst - Variable in class weka.attributeSelection.ExhaustiveSearch
stop after finding the first subset equal to or better than the supplied start set (set to true if start set is supplied).
m_stopB - Variable in class weka.gui.beans.KnowledgeFlow
 
m_stopIt - Variable in class weka.classifiers.functions.MultilayerPerceptron
a flag to state if the network should be running, or stopped.
m_stopPlotting - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_stopReplotting - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_stopped - Variable in class weka.classifiers.functions.MultilayerPerceptron
a flag to state that the network has in fact stopped.
m_storage - Variable in class weka.classifiers.functions.supportVector.PolyKernel
Kernel cache
m_storage - Variable in class weka.classifiers.functions.supportVector.RBFKernel
Kernel cache
m_stored - Variable in class weka.attributeSelection.ReliefFAttributeEval
Number of nearest neighbours stored of each class
m_storedObjectArray - Variable in class weka.core.SerializedObject
The array storing the object.
m_stringType - Variable in class weka.experiment.DatabaseUtils
mappings used for creating Tables.
m_structure - Variable in class weka.core.converters.ArffLoader
Holds the determined structure (header) of the data set.
m_structure - Variable in class weka.core.converters.C45Loader
Holds the determined structure (header) of the data set.
m_structure - Variable in class weka.core.converters.CSVLoader
Holds the determined structure (header) of the data set.
m_style - Variable in class weka.gui.treevisualizer.TreeBuild.InfoObject
The backstyle name for the object.
m_subExpComplete - Variable in class weka.experiment.RemoteExperiment
The status of each of the sub-experiments
m_subExpQueue - Variable in class weka.experiment.RemoteExperiment
The queue of sub experiments waiting to be processed
m_subExpQueue - Variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
The queue of sub-tasks waiting to be processed
m_subExperiments - Variable in class weka.experiment.RemoteExperiment
The sub experiments
m_subInstances - Variable in class weka.classifiers.lazy.LBR
 
m_subOldErrorFlags - Variable in class weka.classifiers.lazy.LBR
 
m_submit - Variable in class weka.gui.visualize.VisualizePanel
Button for the user to enter the splits.
m_subsetResults - Variable in class weka.attributeSelection.AttributeSelection
 
m_subsumption - Variable in class weka.associations.Tertius
Perform subsumption test ?
m_subtreeRaising - Variable in class weka.classifiers.trees.J48
Subtree raising to be performed?
m_subtreeRaising - Variable in class weka.classifiers.trees.j48.C45PruneableClassifierTree
Is subtree raising to be performed?
m_sumFitness - Variable in class weka.attributeSelection.GeneticSearch
sum of the current population fitness
m_sumOfEigenValues - Variable in class weka.attributeSelection.PrincipalComponents
sum of the eigenvalues
m_sumOfWeights - Variable in class weka.classifiers.functions.SMO.BinarySMO
Stores the weight of the training instances
m_sumOfWeights - Variable in class weka.classifiers.trees.j48.BinC45Split
The sum of the weights of the instances.
m_sumOfWeights - Variable in class weka.classifiers.trees.j48.C45Split
The sum of the weights of the instances.
m_supportVectors - Variable in class weka.classifiers.functions.SMO.BinarySMO
The set of support vectors
m_suppressErrorMessage - Variable in class weka.classifiers.functions.SimpleLinearRegression
If true, suppress error message if no useful attribute was found
m_tCounts - Variable in class weka.classifiers.lazy.LBR
All the counts for nominal attributes.
m_tPriors - Variable in class weka.classifiers.lazy.LBR
The prior probabilities of the classes.
m_tView - Variable in class weka.classifiers.trees.UserClassifier
The tree display panel.
m_table - Variable in class weka.attributeSelection.ConsistencySubsetEval
Hash table for evaluating feature subsets
m_tableModel - Variable in class weka.gui.CostMatrixEditor.CustomEditor
The table model of the cost matrix being edited
m_target - Variable in class weka.gui.beans.BeanConnection
 
m_target - Variable in class weka.gui.treevisualizer.Edge
The child Node of this edge.
m_target - Variable in class weka.gui.treevisualizer.TreeBuild.InfoObject
The target ID of the object.
m_tb - Variable in class weka.gui.treevisualizer.TreeVisualizer.EdgeInfo
The distance from the center of the text to top or bottom.
m_tempUndoFiles - Variable in class weka.gui.explorer.PreprocessPanel
Keeps track of undo points
m_tempUndoIndex - Variable in class weka.gui.explorer.PreprocessPanel
The next available slot for an undo point
m_test - Variable in class weka.classifiers.rules.Prism.PrismRule
First test of this rule
m_test - Variable in class weka.classifiers.rules.part.ClassifierDecList
The pruning instances.
m_test - Variable in class weka.classifiers.trees.j48.ClassifierTree
The pruning instances.
m_testListeners - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
Objects listening for test set events
m_testListeners - Variable in class weka.gui.beans.ClassAssigner
 
m_testListeners - Variable in class weka.gui.beans.Filter
Objects listening for test set events
m_testProvider - Variable in class weka.gui.beans.ClassAssigner
 
m_testSet - Variable in class weka.gui.beans.BatchClassifierEvent
Instances that can be used for testing the classifier
m_testSet - Variable in class weka.gui.beans.TestSetEvent
The test set instances
m_testTrainRatio - Variable in class weka.experiment.PairedStatsCorrected
The ratio used to correct the significane test
m_testingSet - Variable in class weka.gui.beans.Classifier
 
m_testingSet - Variable in class weka.gui.beans.Filter
 
m_text - Variable in class weka.gui.beans.TextEvent
The text
m_textListeners - Variable in class weka.gui.beans.Classifier
Objects listening for text events
m_textListeners - Variable in class weka.gui.beans.IncrementalClassifierEvaluator
 
m_textTitle - Variable in class weka.gui.beans.TextEvent
The title for the text.
m_theData - Variable in class weka.gui.treevisualizer.Node
An Instances variable generated from the data.
m_theEvaluator - Variable in class weka.attributeSelection.RaceSearch
the subset evaluator to use
m_theInstances - Variable in class weka.classifiers.rules.DecisionTable
Holds the training instances
m_theInstances - Variable in class weka.clusterers.EM
training instances
m_theInstances - Variable in class weka.clusterers.MakeDensityBasedClusterer
holds training instances header information
m_threshold - Variable in class weka.attributeSelection.AttributeSelection
cutoff value by which to select attributes for ranked results
m_threshold - Variable in class weka.attributeSelection.ForwardSelection
A threshold by which to discard attributes---used by the AttributeSelection module
m_threshold - Variable in class weka.attributeSelection.RaceSearch
the threshold for removing attributes if ranking is requested
m_threshold - Variable in class weka.attributeSelection.Ranker
A threshold by which to discard attributes---used by the AttributeSelection module
m_threshold - Variable in class weka.attributeSelection.WrapperSubsetEval
the threshold by which to do further cross validations when estimating the accuracy of a subset
m_tickSize - Variable in class weka.gui.visualize.ClassPanel
The size of the ticks
m_tickSize - Variable in class weka.gui.visualize.Plot2D
Tick size
m_tickSize - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
Tick size
m_time - Variable in class weka.associations.Tertius
Time needed for the search.
m_toSelectModel - Variable in class weka.classifiers.rules.part.ClassifierDecList
The model selection method.
m_toSelectModel - Variable in class weka.classifiers.trees.j48.ClassifierTree
The model selection method.
m_tol - Variable in class weka.classifiers.functions.SMO
Tolerance for accuracy of result.
m_tol - Variable in class weka.classifiers.functions.SMOreg
The parameter tol
m_toolBarBean - Variable in class weka.gui.beans.KnowledgeFlow
Holds the selected toolbar bean
m_toolBarGroup - Variable in class weka.gui.beans.KnowledgeFlow
Button group to manage all toolbar buttons
m_toolBars - Variable in class weka.gui.beans.KnowledgeFlow
Tabbed pane to hold tool bars
m_top - Variable in class weka.classifiers.trees.UserClassifier
Two references to the structure of the decision tree.
m_top - Variable in class weka.gui.treevisualizer.Node
The top of the node (between 0 and 1).
m_top - Variable in class weka.gui.treevisualizer.TreeVisualizer.NodeInfo
The y pos of the node on screen.
m_topN - Variable in class weka.gui.treevisualizer.TreeVisualizer
An option on the win_menu
m_topNode - Variable in class weka.gui.treevisualizer.TreeVisualizer
The top Node.
m_topOfTree - Variable in class weka.classifiers.trees.m5.Rule
the top of the m5 tree for this rule
m_totalEpochsLabel - Variable in class weka.classifiers.functions.MultilayerPerceptron.ControlPanel
A label to state the total number of epochs to be processed.
m_totalEvals - Variable in class weka.attributeSelection.BestFirst
total number of subsets evaluated during a search
m_totalEvals - Variable in class weka.attributeSelection.RaceSearch
the total number of partially/fully evaluated subsets
m_totalInstanceWeight - Variable in class weka.classifiers.trees.lmt.LMTNode
Total number of training instances.
m_totalInstances - Variable in class weka.clusterers.Cobweb.CNode
Total instances at this node
m_totalTime - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The time taken to select attributes AND build the classifier
m_totalTransactions - Variable in class weka.associations.ItemSet
The total number of transactions
m_train - Variable in class weka.classifiers.rules.part.ClassifierDecList
The training instances.
m_train - Variable in class weka.classifiers.trees.j48.ClassifierTree
The training instances.
m_train - Variable in class weka.classifiers.trees.lmt.LogisticBase
Training data
m_trainCopy - Variable in class weka.attributeSelection.PrincipalComponents
Keep a copy for the class attribute (if set)
m_trainInstances - Variable in class weka.attributeSelection.AttributeSelection
the instances to select attributes from
m_trainInstances - Variable in class weka.attributeSelection.CfsSubsetEval
The training instances
m_trainInstances - Variable in class weka.attributeSelection.ConsistencySubsetEval
training instances
m_trainInstances - Variable in class weka.attributeSelection.GainRatioAttributeEval
The training instances
m_trainInstances - Variable in class weka.attributeSelection.OneRAttributeEval
The training instances
m_trainInstances - Variable in class weka.attributeSelection.PrincipalComponents
The data to transform analyse/transform
m_trainInstances - Variable in class weka.attributeSelection.ReliefFAttributeEval
The training instances
m_trainInstances - Variable in class weka.attributeSelection.SymmetricalUncertAttributeEval
The training instances
m_trainInstances - Variable in class weka.attributeSelection.WrapperSubsetEval
training instances
m_trainInstances - Variable in class weka.classifiers.trees.ADTree
The instances used to train the tree
m_trainInstances - Variable in class weka.clusterers.ClusterEvaluation
the instances to cluster
m_trainPercent - Variable in class weka.gui.experiment.SimpleSetupPanel
The training percentage for a train/test split experiment
m_trainPercentage - Variable in class weka.gui.beans.TrainTestSplitMaker
 
m_trainSelector - Variable in class weka.filters.supervised.attribute.AttributeSelection
the attribute selection evaluation object
m_trainTotalWeight - Variable in class weka.classifiers.trees.ADTree
The total weight of the instances - used to speed Z calculations
m_training - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
 
m_trainingData - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_trainingData - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_trainingInstances - Variable in class weka.attributeSelection.ClassifierSubsetEval
training instances
m_trainingInstances - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_trainingListeners - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
Objects listening for trainin set events
m_trainingListeners - Variable in class weka.gui.beans.ClassAssigner
 
m_trainingListeners - Variable in class weka.gui.beans.Filter
Objects listening for training set events
m_trainingProvider - Variable in class weka.gui.beans.ClassAssigner
 
m_trainingSet - Variable in class weka.gui.beans.Classifier
Holds training instances for batch training.
m_trainingSet - Variable in class weka.gui.beans.Filter
 
m_trainingSet - Variable in class weka.gui.beans.TrainingSetEvent
The training instances
m_transBackToOriginal - Variable in class weka.attributeSelection.PrincipalComponents
transform the data through the pc space and back to the original space ?
m_transformedFormat - Variable in class weka.attributeSelection.PrincipalComponents
The header for the transformed data format
m_transformer - Variable in class weka.attributeSelection.AttributeSelection
if a feature selection run involves an attribute transformer
m_tree - Variable in class weka.classifiers.trees.LMT
root of the logistic model tree
m_tree - Variable in class weka.gui.GenericObjectEditor.JTreePopupMenu
The tree
m_treeFrame - Variable in class weka.classifiers.trees.UserClassifier
These two frames aren't used anymore.
m_treeNodeOfCurrentObject - Variable in class weka.gui.GenericObjectEditor
The tree node of the current object so we can re-select it for the user
m_trials - Variable in class weka.attributeSelection.AttributeSelection
 
m_type - Variable in class weka.associations.tertius.IndividualLiteral
 
m_type - Variable in class weka.associations.tertius.LiteralSet
Type of properties expressed in this set (individual or parts properties).
m_type - Variable in class weka.classifiers.functions.neural.NeuralConnection
The type of unit this is.
m_type - Variable in class weka.gui.visualize.MatrixPanel
This array contains:
m_type[0][i] = [type of attribute, nominal, string or numeric]
m_type[1][i] = [number of discrete values of nominal or string attribute
or same as m_type[0][i] for numeric attribute]
m_unitError - Variable in class weka.classifiers.functions.neural.NeuralConnection
The error value for this unit, NaN if not calculated.
m_unitValue - Variable in class weka.classifiers.functions.neural.NeuralConnection
The output value for this unit, NaN if not calculated.
m_unpruned - Variable in class weka.classifiers.rules.PART
Generate unpruned list?
m_unpruned - Variable in class weka.classifiers.trees.J48
Unpruned tree?
m_unsmoothedPredictions - Variable in class weka.classifiers.trees.m5.M5Base
use unsmoothed predictions
m_updateBt - Variable in class weka.gui.visualize.MatrixPanel
The button that updates the display to reflect the changes made by the user.
m_updateHandler - Variable in class weka.gui.beans.StripChart
 
m_updateIncrementalClassifier - Variable in class weka.gui.beans.Classifier
If the classifier is an incremental classifier, should we update it (ie train it on incoming instances).
m_updateIncrementalClassifier - Variable in class weka.gui.beans.ClassifierCustomizer
 
m_upperBoundMinSupport - Variable in class weka.associations.Apriori
The upper bound on the support
m_useCrossValidation - Variable in class weka.classifiers.functions.SimpleLogistic
If true, cross-validate number of LogitBoost iterations
m_useCrossValidation - Variable in class weka.classifiers.trees.lmt.LogisticBase
Use cross-validation to determine best number of LogitBoost iterations ?
m_useCustomColour - Variable in class weka.gui.visualize.PlotData2D
Custom colour for this plot
m_useGaussian - Variable in class weka.filters.unsupervised.attribute.RandomProjection
Is the random matrix will be computed using Gaussian distribution or not
m_useIBk - Variable in class weka.classifiers.rules.DecisionTable
Use the IBk classifier rather than majority class
m_useLaplace - Variable in class weka.classifiers.trees.J48
Determines whether probabilities are smoothed using Laplace correction when predictions are generated
m_useNomToBin - Variable in class weka.classifiers.functions.MultilayerPerceptron
A flag to state that a nominal to binary filter should be used.
m_useRBF - Variable in class weka.classifiers.functions.SMO
Use RBF kernel?
m_useRBF - Variable in class weka.classifiers.functions.SMOreg
Use RBF kernel?
m_useStoplist - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
True if tokens that are on a stoplist are to be ignored.
m_useTraining - Variable in class weka.attributeSelection.ClassifierSubsetEval
evaluate on training data rather than seperate hold out/test set
m_useTree - Variable in class weka.classifiers.trees.m5.Rule
use a pruned m5 tree rather than make a rule
m_useUnpruned - Variable in class weka.classifiers.trees.m5.M5Base
Do not prune tree/rules
m_useUnpruned - Variable in class weka.classifiers.trees.m5.Rule
Build unpruned tree/rule
m_userHasBeenAskedAboutConversion - Variable in class weka.gui.experiment.SimpleSetupPanel
Whether or not the user has consented for the experiment to be simplified
m_userName - Variable in class weka.experiment.DatabaseUtils
Database username
m_val - Variable in class weka.classifiers.rules.Prism.Test
The attribute's value
m_valSize - Variable in class weka.classifiers.functions.MultilayerPerceptron
An int to say how big the validation set should be.
m_validationChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The size of the validation set
m_validationFs - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_validationSet - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The instances used for validation
m_validationSetChanged - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Whether the validation set has recently been changed
m_vals - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel.PlotThread
 
m_vals - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_value - Variable in class weka.associations.tertius.AttributeValueLiteral
 
m_values - Variable in class weka.gui.visualize.VisualizePanelEvent
Contains FastVectors, each one containing the points for an object.
m_valuesText - Variable in class weka.associations.Tertius
Field to output the current values.
m_verbose - Variable in class weka.associations.Apriori
Report progress iteratively
m_verbose - Variable in class weka.attributeSelection.ExhaustiveSearch
if true, then ouput new best subsets as the search progresses
m_verbose - Variable in class weka.attributeSelection.RandomSearch
output new best subsets as the search progresses
m_verbose - Variable in class weka.clusterers.EM
Verbose?
m_vertical - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel
 
m_viewPos - Variable in class weka.gui.treevisualizer.TreeVisualizer
The postion of the view relative to the tree.
m_viewSize - Variable in class weka.gui.treevisualizer.TreeVisualizer
The size of the tree in pixels.
m_visFrame - Variable in class weka.classifiers.trees.UserClassifier
 
m_visPanel - Variable in class weka.gui.beans.DataVisualizer
 
m_visXIndex - Variable in class weka.gui.explorer.ClassifierPanel
default x index for visualizing
m_visXIndex - Variable in class weka.gui.explorer.ClustererPanel
default x index for visualizing
m_visYIndex - Variable in class weka.gui.explorer.ClassifierPanel
default y index for visualizing
m_visYIndex - Variable in class weka.gui.explorer.ClustererPanel
default y index for visualizing
m_visible - Variable in class weka.gui.treevisualizer.Node
true if this node is visible (not currently in use).
m_visual - Variable in class weka.gui.beans.AbstractDataSink
Default visual for data sources
m_visual - Variable in class weka.gui.beans.AbstractDataSource
Default visual for data sources
m_visual - Variable in class weka.gui.beans.AbstractEvaluator
Default visual for evaluators
m_visual - Variable in class weka.gui.beans.AbstractTestSetProducer
 
m_visual - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
 
m_visual - Variable in class weka.gui.beans.AbstractTrainingSetProducer
 
m_visual - Variable in class weka.gui.beans.ClassAssigner
 
m_visual - Variable in class weka.gui.beans.Classifier
 
m_visual - Variable in class weka.gui.beans.DataVisualizer
 
m_visual - Variable in class weka.gui.beans.Filter
 
m_visual - Variable in class weka.gui.beans.GraphViewer
 
m_visual - Variable in class weka.gui.beans.PredictionAppender
 
m_visual - Variable in class weka.gui.beans.StripChart
 
m_visual - Variable in class weka.gui.beans.TextViewer
 
m_visualLabel - Variable in class weka.gui.beans.BeanVisual
 
m_visualName - Variable in class weka.gui.beans.BeanVisual
Name for the bean
m_visualise - Variable in class weka.gui.treevisualizer.TreeVisualizer
A visualize choice for the node, may not be available.
m_visualizeDataSet - Variable in class weka.gui.beans.DataVisualizer
 
m_weight - Variable in class weka.classifiers.functions.LeastMedSq
 
m_weight - Variable in class weka.classifiers.trees.UserClassifier.TreeClass
 
m_weightByConfidence - Variable in class weka.classifiers.misc.VFI
Exponentially bias more confident intervals
m_weightByDistance - Variable in class weka.attributeSelection.ReliefFAttributeEval
Weight by distance rather than equal weights
m_weightingAttsValues - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel.PlotThread
 
m_weightingAttsValues - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_weightingDimensions - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_weightingValues - Variable in class weka.gui.boundaryvisualizer.KDDataGenerator
 
m_weights - Variable in class weka.attributeSelection.ReliefFAttributeEval
Holds the weights that relief assigns to attributes
m_weights - Variable in class weka.classifiers.functions.SMO.BinarySMO
Weight vector for linear machine.
m_weights - Variable in class weka.classifiers.functions.SMOreg
Weight vector for linear machine.
m_weights - Variable in class weka.classifiers.functions.neural.NeuralNode
The weights for each of the input connections, and the threshold.
m_weights - Variable in class weka.clusterers.EM
hold the weights of each instance for each cluster
m_weightsByRank - Variable in class weka.attributeSelection.ReliefFAttributeEval
used to (optionally) weight nearest neighbours by their distance from the instance in question.
m_weightsUpdated - Variable in class weka.classifiers.functions.neural.NeuralConnection
True if the weights have already been updated.
m_weka - Variable in class weka.gui.GUIChooser
The weka image
m_width - Variable in class weka.gui.treevisualizer.TreeVisualizer.EdgeInfo
The width of the text.
m_width - Variable in class weka.gui.treevisualizer.TreeVisualizer.NodeInfo
The width of the node.
m_win - Variable in class weka.classifiers.functions.MultilayerPerceptron
The window for the network.
m_winMenu - Variable in class weka.gui.treevisualizer.TreeVisualizer
A right (or middle) click popup menu.
m_worst - Variable in class weka.attributeSelection.ReliefFAttributeEval
Keep track of the farthest instance for each class
m_wrappedClusterer - Variable in class weka.clusterers.MakeDensityBasedClusterer
The clusterer being wrapped
m_x - Variable in class weka.classifiers.functions.neural.NeuralConnection
The x coord of this unit purely for displaying purposes.
m_x - Variable in class weka.gui.beans.BeanInstance
 
m_xAttBox - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_xAttribute - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_xAttribute - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_xAxisPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_xChange - Variable in class weka.gui.visualize.AttributePanelEvent
True if the x selection changed
m_xCount - Variable in class weka.gui.beans.StripChart
 
m_xIndex - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_xIndex - Variable in class weka.gui.visualize.AttributePanel
 
m_xIndex - Variable in class weka.gui.visualize.Plot2D
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing
m_xIndex - Variable in class weka.gui.visualize.PlotData2D
The x index
m_xIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing
m_xValFreq - Variable in class weka.gui.beans.StripChart
Print x axis labels every m_xValFreq points
m_xvalType - Variable in class weka.attributeSelection.RaceSearch
the selected xval type
m_y - Variable in class weka.classifiers.functions.neural.NeuralConnection
The y coord of this unit purely for displaying purposes.
m_y - Variable in class weka.gui.beans.BeanInstance
 
m_yAttBox - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_yAttribute - Variable in class weka.gui.boundaryvisualizer.BoundaryPanel
 
m_yAttribute - Variable in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
 
m_yAxisPanel - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_yChange - Variable in class weka.gui.visualize.AttributePanelEvent
True if the y selection changed
m_yIndex - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
 
m_yIndex - Variable in class weka.gui.visualize.AttributePanel
 
m_yIndex - Variable in class weka.gui.visualize.Plot2D
 
m_yIndex - Variable in class weka.gui.visualize.PlotData2D
The y index
m_yIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_yRatio - Variable in class weka.gui.treevisualizer.PlaceNode1
The distance between each level.
m_yRatio - Variable in class weka.gui.treevisualizer.PlaceNode2
The space each row will take up.
m_yScaleUpdate - Variable in class weka.gui.beans.StripChart
Scale update requested
m_zeroR - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The default scheme used when committees aren't ready
m_zipOut - Variable in class weka.experiment.OutputZipper
 
m_zs - Variable in class weka.experiment.OutputZipper
 
main(String[]) - Static method in class weka.associations.Apriori
Main method for testing this class.
main(String[]) - Static method in class weka.associations.Tertius
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.AttributeSelection
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CfsSubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ChiSquaredAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ClassifierSubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ConsistencySubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.GainRatioAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.InfoGainAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.OneRAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.PrincipalComponents
Main method for testing this class
main(String[]) - Static method in class weka.attributeSelection.ReliefFAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.SVMAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.WrapperSubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.BVDecompose
Test method for this class
main(String[]) - Static method in class weka.classifiers.BVDecomposeSegCVSub
Test method for this class
main(String[]) - Static method in class weka.classifiers.CheckClassifier
Test method for this class
main(String[]) - Static method in class weka.classifiers.Evaluation
A test method for this class.
main(String[]) - Static method in class weka.classifiers.bayes.AODE
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesNet
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesNetB
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesNetB2
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesNetK2
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.ComplementNaiveBayes
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.DiscreteEstimatorBayes
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayes
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesMultinomial
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesSimple
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesUpdateable
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.evaluation.CostCurve
Tests the CostCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.evaluation.MarginCurve
Tests the MarginCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.evaluation.ThresholdCurve
Tests the ThresholdCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.functions.LeastMedSq
generate a Linear regression predictor for testing
main(String[]) - Static method in class weka.classifiers.functions.LinearRegression
Generates a linear regression function predictor.
main(String[]) - Static method in class weka.classifiers.functions.Logistic
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.MultilayerPerceptron
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.PaceRegression
Generates a linear regression function predictor.
main(String[]) - Static method in class weka.classifiers.functions.RBFNetwork
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.SMO
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.SMOreg
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.SimpleLinearRegression
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.functions.SimpleLogistic
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.functions.VotedPerceptron
Main method.
main(String[]) - Static method in class weka.classifiers.functions.Winnow
Main method.
main(String[]) - Static method in class weka.classifiers.functions.pace.ChisqMixture
Method to test this class
main(String[]) - Static method in class weka.classifiers.functions.pace.DiscreteFunction
 
main(String[]) - Static method in class weka.classifiers.functions.pace.DoubleVector
 
main(String[]) - Static method in class weka.classifiers.functions.pace.IntVector
Tests the IntVector class
main(String[]) - Static method in class weka.classifiers.functions.pace.NormalMixture
Method to test this class
main(String[]) - Static method in class weka.classifiers.functions.pace.PaceMatrix
 
main(String[]) - Static method in class weka.classifiers.lazy.IB1
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.IBk
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.KStar
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.LBR
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.LWL
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AdaBoostM1
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AdditiveRegression
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AttributeSelectedClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Bagging
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.CVParameterSelection
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.ClassificationViaRegression
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.CostSensitiveClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Decorate
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.END
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.FilteredClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Grading
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.HND
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.LogitBoost
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MetaCost
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiBoostAB
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiClassClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiScheme
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.ND
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.OrdinalClassClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Main method for this class.
main(String[]) - Static method in class weka.classifiers.meta.RandomCommittee
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RegressionByDiscretization
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Stacking
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.StackingC
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.ThresholdSelector
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.TreeBasedMultiClassClassifier
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.meta.Vote
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.FLR
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.HyperPipes
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.VFI
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.ConjunctiveRule
Main method.
main(String[]) - Static method in class weka.classifiers.rules.DecisionTable
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.JRip
Main method.
main(String[]) - Static method in class weka.classifiers.rules.M5Rules
Main method by which this class can be tested
main(String[]) - Static method in class weka.classifiers.rules.NNge
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.OneR
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.rules.PART
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.Prism
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.rules.Ridor
Main method.
main(String[]) - Static method in class weka.classifiers.rules.ZeroR
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.ADTree
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.DecisionStump
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.Id3
Main method.
main(String[]) - Static method in class weka.classifiers.trees.J48
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.LMT
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.M5P
Main method by which this class can be tested
main(String[]) - Static method in class weka.classifiers.trees.REPTree
Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.RandomForest
Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.RandomTree
Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.UserClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.ClusterEvaluation
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.Cobweb
 
main(String[]) - Static method in class weka.clusterers.EM
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.FarthestFirst
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.MakeDensityBasedClusterer
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.SimpleKMeans
Main method for testing this class.
main(String[]) - Static method in class weka.core.Attribute
Simple main method for testing this class.
main(String[]) - Static method in class weka.core.BinarySparseInstance
Main method for testing this class.
main(String[]) - Static method in class weka.core.CheckOptionHandler
Main method for using the CheckOptionHandler.
main(String[]) - Static method in class weka.core.ContingencyTables
Main method for testing this class.
main(String[]) - Static method in class weka.core.Instance
Main method for testing this class.
main(String[]) - Static method in class weka.core.Instances
Main method for this class -- just prints a summary of a set of instances.
main(String[]) - Static method in class weka.core.Matrix
Main method for testing this class.
main(String[]) - Static method in class weka.core.Queue
Main method for testing this class.
main(String[]) - Static method in class weka.core.RandomVariates
Main method for testing this class.
main(String[]) - Static method in class weka.core.Range
Main method for testing this class.
main(String[]) - Static method in class weka.core.SingleIndex
Main method for testing this class.
main(String[]) - Static method in class weka.core.SparseInstance
Main method for testing this class.
main(String[]) - Static method in class weka.core.SpecialFunctions
Main method for testing this class.
main(String[]) - Static method in class weka.core.Statistics
Main method for testing this class.
main(String[]) - Static method in class weka.core.Utils
Main method for testing this class.
main(String[]) - Static method in class weka.core.converters.ArffFileMerger
 
main(String[]) - Static method in class weka.core.converters.ArffLoader
Main method.
main(String[]) - Static method in class weka.core.converters.C45Loader
Main method for testing this class.
main(String[]) - Static method in class weka.core.converters.CSVLoader
Main method.
main(String[]) - Static method in class weka.core.converters.ClassTreeParser
Method to test a hierarchy with respect to given Instances.
main(String[]) - Static method in class weka.core.converters.HierarchicalCostMatrix
Method to use the class as stand-alone tool.
main(String[]) - Static method in class weka.core.converters.SerializedInstancesLoader
Main method.
main(String[]) - Static method in class weka.datagenerators.BIRCHCluster
Main method for testing this class.
main(String[]) - Static method in class weka.datagenerators.RDG1
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DDConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DKConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DNConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DiscreteEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KDConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KKConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KernelEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.MahalanobisEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NDConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NNConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NormalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.PoissonEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.experiment.CrossValidationResultProducer
 
main(String[]) - Static method in class weka.experiment.Experiment
Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.experiment.InstanceQuery
Test the class from the command line.
main(String[]) - Static method in class weka.experiment.OutputZipper
Main method for testing this class
main(String[]) - Static method in class weka.experiment.PairedCorrectedTTester
Test the class from the command line.
main(String[]) - Static method in class weka.experiment.PairedStats
Tests the paired stats object from the command line.
main(String[]) - Static method in class weka.experiment.PairedTTester
Test the class from the command line.
main(String[]) - Static method in class weka.experiment.RemoteEngine
Main method.
main(String[]) - Static method in class weka.experiment.RemoteExperiment
Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.experiment.Stats
Tests the paired stats object from the command line.
main(String[]) - Static method in class weka.filters.AllFilter
Main method for testing this class.
main(String[]) - Static method in class weka.filters.Filter
Main method for testing this class.
main(String[]) - Static method in class weka.filters.NullFilter
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.AttributeSelection
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.ClassOrder
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.Discretize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.NominalToBinary
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.instance.Resample
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.instance.SpreadSubsample
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Add
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddCluster
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddExpression
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddNoise
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ClusterMembership
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Copy
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Discretize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.FirstOrder
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MakeIndicator
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MergeTwoValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NominalToBinary
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Normalize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericToBinary
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericTransform
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Obfuscate
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PKIDiscretize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RandomProjection
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Remove
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveType
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveUseless
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Standardize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToNominal
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToWordVector
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.SwapValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.NonSparseToSparse
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Randomize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveFolds
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveMisclassified
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemovePercentage
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveRange
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveWithValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Resample
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.SparseToNonSparse
Main method for testing this class.
main(String[]) - Static method in class weka.gui.AttributeListPanel
Tests the attribute list panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeSelectionPanel
Tests the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeSummaryPanel
Tests out the attribute summary panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeVisualizationPanel
Main method to test this class from command line
main(String[]) - Static method in class weka.gui.DatabaseConnectionDialog
 
main(String[]) - Static method in class weka.gui.GUIChooser
Tests out the GUIChooser environment.
main(String[]) - Static method in class weka.gui.GenericArrayEditor
Tests out the array editor from the command line.
main(String[]) - Static method in class weka.gui.GenericObjectEditor
Tests out the Object editor from the command line.
main(String[]) - Static method in class weka.gui.HierarchyPropertyParser
Tests out the parser.
main(String[]) - Static method in class weka.gui.InstancesSummaryPanel
Tests out the instance summary panel from the command line.
main(String[]) - Static method in class weka.gui.ListSelectorDialog
Tests out the list selector from the command line.
main(String[]) - Static method in class weka.gui.LogPanel
Tests out the log panel from the command line.
main(String[]) - Static method in class weka.gui.PropertySelectorDialog
Tests out the property selector from the command line.
main(String[]) - Static method in class weka.gui.ResultHistoryPanel
Tests out the result history from the command line.
main(String[]) - Static method in class weka.gui.SaveBuffer
Main method for testing this class
main(String[]) - Static method in class weka.gui.SelectedTagEditor
Tests out the selectedtag editor from the command line.
main(String[]) - Static method in class weka.gui.SimpleCLI
Method to start up the simple cli
main(String[]) - Static method in class weka.gui.WekaTaskMonitor
Main method for testing this class
main(String[]) - Static method in class weka.gui.beans.AttributeSummarizer
 
main(String[]) - Static method in class weka.gui.beans.DataVisualizer
 
main(String[]) - Static method in class weka.gui.beans.KnowledgeFlow
Main method.
main(String[]) - Static method in class weka.gui.beans.Loader
 
main(String[]) - Static method in class weka.gui.beans.ScatterPlotMatrix
 
main(String[]) - Static method in class weka.gui.beans.StripChart
Tests out the StripChart from the command line
main(String[]) - Static method in class weka.gui.beans.TextViewer
 
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryPanel
Main method for testing this class
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
Main method for testing this class
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Main method for testing this class
main(String[]) - Static method in class weka.gui.experiment.AlgorithmListPanel
Tests out the algorithm list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DatasetListPanel
Tests out the dataset list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DistributeExperimentPanel
Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.Experimenter
Tests out the experiment environment.
main(String[]) - Static method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.HostListPanel
Tests out the host list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.ResultsPanel
Tests out the results panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunNumberPanel
Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunPanel
Tests out the run panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.SetupPanel
Tests out the experiment setup from the command line.
main(String[]) - Static method in class weka.gui.experiment.SimpleSetupPanel
Tests out the experiment setup from the command line.
main(String[]) - Static method in class weka.gui.explorer.AssociationsPanel
Tests out the Associator panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.AttributeSelectionPanel
Tests out the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClassifierPanel
Tests out the classifier panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClustererPanel
Tests out the clusterer panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.Explorer
Tests out the explorer environment.
main(String[]) - Static method in class weka.gui.explorer.PreprocessPanel
Tests out the instance-preprocessing panel from the command line.
main(String[]) - Static method in class weka.gui.graphvisualizer.GraphVisualizer
Main method to load a text file with the description of a graph from the command line
main(String[]) - Static method in class weka.gui.treevisualizer.TreeVisualizer
Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.AttributePanel
Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.ClassPanel
Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.LegendPanel
Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.MatrixPanel
Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.Plot2D
Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.VisualizePanel
Main method for testing this class
majorityClassTipText() - Method in class weka.classifiers.rules.Ridor
Returns the tip text for this property
makeBinaryTipText() - Method in class weka.filters.supervised.attribute.Discretize
Returns the tip text for this property
makeBinaryTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
makeCopies(Associator, int) - Static method in class weka.associations.Associator
Creates copies of the current associator.
makeCopies(ASEvaluation, int) - Static method in class weka.attributeSelection.ASEvaluation
Creates copies of the current evaluator.
makeCopies(Classifier, int) - Static method in class weka.classifiers.Classifier
Creates copies of the current classifier, which can then be used for boosting etc.
makeCopies(Clusterer, int) - Static method in class weka.clusterers.Clusterer
Creates copies of the current clusterer.
makeData(ClusterGenerator, String[]) - Static method in class weka.datagenerators.ClusterGenerator
Calls the data generator.
makeData(Generator, String[]) - Static method in class weka.datagenerators.Generator
Calls the data generator.
makeDataSetClass(Instances, Classifier, String) - Method in class weka.gui.beans.PredictionAppender
 
makeDataSetProbabilities(Instances, Classifier, String) - Method in class weka.gui.beans.PredictionAppender
 
makeDistribution(double) - Method in class weka.classifiers.Evaluation
Convert a single prediction into a probability distribution with all zero probabilities except the predicted value which has probability 1.0;
makeDistribution(double, int) - Static method in class weka.classifiers.evaluation.NominalPrediction
Convert a single prediction into a probability distribution with all zero probabilities except the predicted value which has probability 1.0.
makeDistribution(IBk.NeighborList) - Method in class weka.classifiers.lazy.IBk
Turn the list of nearest neighbors into a probability distribution
makeGUIPanel(boolean) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This methods makes the gui extra controls panel "m_controlsPanel"
makeHeader() - Method in class weka.classifiers.evaluation.CostCurve
 
makeHeader() - Method in class weka.classifiers.evaluation.MarginCurve
Creates an Instances object with the attributes we will be calculating.
makeHeader() - Method in class weka.classifiers.evaluation.ThresholdCurve
 
makeInstance(double, int, int) - Method in class weka.classifiers.evaluation.MarginCurve
Creates an Instance object with the attributes calculated.
makeInstance(TwoClassStats, double) - Method in class weka.classifiers.evaluation.ThresholdCurve
 
makeOptionString(ASEvaluation, ASSearch) - Static method in class weka.attributeSelection.AttributeSelection
Make up the help string giving all the command line options
makeOptionString(Classifier) - Static method in class weka.classifiers.Evaluation
Make up the help string giving all the command line options
makeOptionString(Clusterer) - Static method in class weka.clusterers.ClusterEvaluation
Make up the help string giving all the command line options
makePanel() - Method in class weka.gui.beans.AttributeSummarizer
 
makePolygon(FastVector) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
This will convert a polyline to a polygon for drawing purposes So that I can simply use the polygon drawing function.
makePrediction(int, Instance) - Method in class weka.classifiers.functions.VotedPerceptron
Compute a prediction from a perceptron
makePrediction(Instance) - Method in class weka.classifiers.functions.Winnow
Compute the actual prediction for prefiltered instance
makePredictionBalanced(Instance) - Method in class weka.classifiers.functions.Winnow
Compute our prediction (Balanced) for prefiltered instance
makeProperHierarchy() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
 
makeRule() - Method in class weka.classifiers.trees.m5.Rule
Make the single best rule from a pruned m5 model tree
makeTestDataset(int, int, int, int, int, boolean) - Method in class weka.classifiers.CheckClassifier
Make a simple set of instances, which can later be modified for use in specific tests.
makeTree(Instances) - Method in class weka.classifiers.trees.Id3
Method building Id3 tree.
makeUniformDistribution(int) - Static method in class weka.classifiers.evaluation.NominalPrediction
Creates a uniform probability distribution -- where each of the possible classes is assigned equal probability.
makeWeighted(CostMatrix) - Method in class weka.classifiers.evaluation.ConfusionMatrix
Makes a copy of this ConfusionMatrix after applying the supplied CostMatrix to the cells.
map(String, String) - Method in class weka.classifiers.functions.pace.DoubleVector
Applies a method to the vector
mapClasses(int, int[][], int[], double[], double[], int) - Method in class weka.clusterers.ClusterEvaluation
Finds the minimum error mapping of classes to clusters.
margin() - Method in class weka.classifiers.evaluation.NominalPrediction
Calculates the prediction margin.
matchMissingValuesTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the tip text for this property
matchesTemplate(Object[], Object[]) - Method in class weka.experiment.AveragingResultProducer
Compares a key to a template to see whether they match.
matchesTemplate(Instance) - Method in class weka.experiment.PairedTTester.Dataset
Returns true if the two instances match on those attributes that have been designated key columns (eg: scheme name and scheme options)
matchesTemplate(Instance) - Method in class weka.experiment.PairedTTester.Resultset
Returns true if the two instances match on those attributes that have been designated key columns (eg: scheme name and scheme options)
matrix() - Method in class weka.classifiers.trees.j48.Distribution
Returns matrix with distribution of class values.
matrixChanged() - Method in class weka.gui.CostMatrixEditor.CustomEditor
Responds to a change in structure of the matrix being edited.
matrixToString(double[][]) - Method in class weka.attributeSelection.PrincipalComponents
Return a matrix as a String
max() - Method in class weka.classifiers.functions.pace.DoubleVector
Returns the maximum value of all elements
max - Variable in class weka.classifiers.misc.FLR.FuzzyLattice
 
max - Variable in class weka.experiment.Stats
The maximum value seen, or Double.NaN if no values seen
maxAbs() - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the maximum absolute value of all elements
maxAbs(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the maximum absolute value of some elements of a column, that is, the elements of A[i0:i1][j].
maxBag() - Method in class weka.classifiers.trees.j48.Distribution
Returns index of bag containing maximum number of instances.
maxBoostingIterationsTipText() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the tip text for this property
maxChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
maxClass() - Method in class weka.classifiers.trees.j48.Distribution
Returns class with highest frequency over all bags.
maxClass(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns class with highest frequency for given bag.
maxCountTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample
Returns the tip text for this property
maxDepthTipText() - Method in class weka.classifiers.trees.REPTree
Returns the tip text for this property
maxGenerationsTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
maxImpurity() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Returns the impurity of this split
maxImpurity() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
Returns the impurity of this split
maxImpurity - Variable in class weka.classifiers.trees.m5.YongSplitInfo
 
maxImpurity() - Method in class weka.classifiers.trees.m5.YongSplitInfo
Returns the impurity of this split
maxIndex(double[]) - Static method in class weka.core.Utils
Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class weka.core.Utils
Returns index of maximum element in a given array of integers.
maxInfoGain - Variable in class weka.classifiers.rules.ConjunctiveRule.Antd
The maximum infoGain achieved by this antecedent test
maxInfoGain - Variable in class weka.classifiers.rules.JRip.Antd
 
maxInfoGain - Variable in class weka.classifiers.rules.Ridor.Antd
 
maxIterationsTipText() - Method in class weka.clusterers.EM
Returns the tip text for this property
maxIterationsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the tip text for this property
maxItsTipText() - Method in class weka.classifiers.functions.Logistic
Returns the tip text for this property
maxItsTipText() - Method in class weka.classifiers.functions.RBFNetwork
Returns the tip text for this property
maxKTipText() - Method in class weka.classifiers.functions.VotedPerceptron
Returns the tip text for this property
maxModelsTipText() - Method in class weka.classifiers.meta.AdditiveRegression
Returns the tip text for this property
maxNrOfParentsTipText() - Method in class weka.classifiers.bayes.BayesNet
 
maxNumSupportPoints - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
maxStaleTipText() - Method in class weka.classifiers.rules.DecisionTable
Returns the tip text for this property
maxStringWidth - Variable in class weka.gui.graphvisualizer.GraphVisualizer
used for setting appropriate node size
maxValue - Variable in class weka.gui.AttributeVisualizationPanel
 
maximumVariancePercentageAllowedTipText() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Returns the tip text for this property
mean(double[]) - Static method in class weka.core.Utils
Computes the mean for an array of doubles.
mean - Variable in class weka.experiment.Stats
The mean of values at the last calculateDerived() call
meanAbsoluteError() - Method in class weka.classifiers.Evaluation
Returns the mean absolute error.
meanOrMode(int) - Method in class weka.core.Instances
Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(Attribute) - Method in class weka.core.Instances
Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanPriorAbsoluteError() - Method in class weka.classifiers.Evaluation
Returns the mean absolute error of the prior.
meanSquaredError(Instances, double) - Method in class weka.classifiers.rules.ConjunctiveRule
Private function to compute the squared error of the specified data and the specified mean
meanSquaredTipText() - Method in class weka.classifiers.lazy.IBk
Returns the tip text for this property
measureAttributesUsed() - Method in class weka.classifiers.functions.SimpleLogistic
Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
measureExamplesProcessed() - Method in class weka.classifiers.trees.ADTree
Returns the number of examples "counted".
measureNodesExpanded() - Method in class weka.classifiers.trees.ADTree
Returns the number of nodes expanded.
measureNumAttributesSelected() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Additional measure --- number of attributes selected
measureNumIterations() - Method in class weka.classifiers.meta.AdditiveRegression
return the number of iterations (base classifiers) completed
measureNumLeaves() - Method in class weka.classifiers.trees.ADTree
Calls measure function for leaf size - the number of prediction nodes.
measureNumLeaves() - Method in class weka.classifiers.trees.J48
Returns the number of leaves
measureNumLeaves() - Method in class weka.classifiers.trees.LMT
Returns the number of leaves in the tree
measureNumPredictionLeaves() - Method in class weka.classifiers.trees.ADTree
Calls measure function for prediction leaf size - the number of prediction nodes without children.
measureNumRules() - Method in class weka.classifiers.misc.FLR
Additional measure Number of Rules
measureNumRules() - Method in class weka.classifiers.rules.DecisionTable
Returns the number of rules
measureNumRules() - Method in class weka.classifiers.rules.PART
Return the number of rules.
measureNumRules() - Method in class weka.classifiers.trees.J48
Returns the number of rules (same as number of leaves)
measureNumRules() - Method in class weka.classifiers.trees.m5.M5Base
return the number of rules
measureOutOfBagError() - Method in class weka.classifiers.meta.Bagging
Gets the out of bag error that was calculated as the classifier was built.
measureOutOfBagError() - Method in class weka.classifiers.trees.RandomForest
Gets the out of bag error that was calculated as the classifier was built.
measureSelectionTime() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Additional measure --- time taken (milliseconds) to select the attributes
measureTime() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Additional measure --- time taken (milliseconds) to select attributes and build the classifier
measureTreeSize() - Method in class weka.classifiers.trees.ADTree
Calls measure function for tree size - the total number of nodes.
measureTreeSize() - Method in class weka.classifiers.trees.J48
Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.LMT
Returns the size of the tree
merge(SimpleLinkedList, Comparator) - Method in class weka.associations.tertius.SimpleLinkedList
 
merge(ADTree) - Method in class weka.classifiers.trees.ADTree
Merges two trees together.
merge(PredictionNode, ADTree) - Method in class weka.classifiers.trees.adtree.PredictionNode
Merges this node with another.
mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.ItemSet
Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.
mergeArrays(SimpleLinearRegression[][], SimpleLinearRegression[][]) - Method in class weka.classifiers.trees.lmt.LMTNode
Merges two arrays of regression functions into one
mergeClasses(Instance) - Method in interface weka.core.ClassHierarchy
Returns a new Instance with classes selected and merged to superclasses according to the superclasses of this hierarchy.
mergeClasses(Instances) - Method in interface weka.core.ClassHierarchy
Returns a new Instances with classes merged to superclasses according to the superclasses of this hierarchy.
mergeClasses(Instance) - Method in class weka.core.ClassTree
Returns a new Instance with classes selected and merged to superclasses according to the superclasses of this ClassTree.
mergeClasses(Instances) - Method in class weka.core.ClassTree
Returns a new Instances with classes merged to superclasses according to the superclasses of this ClassTree.
mergeInstance(Instance) - Method in class weka.core.BinarySparseInstance
Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class weka.core.Instance
Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class weka.core.SparseInstance
Merges this instance with the given instance and returns the result.
mergeInstances(Instances, Instances) - Static method in class weka.core.Instances
Merges two sets of Instances together.
mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
merit - Variable in class weka.attributeSelection.BestFirst.Link2
 
metaClassifierTipText() - Method in class weka.classifiers.meta.Stacking
Returns the tip text for this property
metaFormat(Instances) - Method in class weka.classifiers.meta.Grading
Makes the format for the level-1 data.
metaFormat(Instances) - Method in class weka.classifiers.meta.Stacking
Makes the format for the level-1 data.
metaInstance(Instance, int) - Method in class weka.classifiers.meta.Grading
Makes a level-1 instance from the given instance.
metaInstance(Instance) - Method in class weka.classifiers.meta.Stacking
Makes a level-1 instance from the given instance.
metaOption() - Method in class weka.classifiers.meta.Stacking
String describing option for setting meta classifier
metaOption() - Method in class weka.classifiers.meta.StackingC
String describing option for setting meta classifier
methodNameTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Returns the tip text for this property
methodTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
 
metricTypeTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
min - Variable in class weka.classifiers.misc.FLR.FuzzyLattice
 
min - Variable in class weka.experiment.Stats
The minimum value seen, or Double.NaN if no values seen
minAbs(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the minimum absolute value of some elements of a column, that is, the elements of A[i0:i1][j].
minBucketSizeTipText() - Method in class weka.classifiers.rules.OneR
Returns the tip text for this property
minChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
minDataDLIfDeleted(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats
Compute the minimal data description length of the ruleset if the rule in the given position is deleted.
minDataDLIfExists(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats
Compute the minimal data description length of the ruleset if the rule in the given position is NOT deleted.
minIndex(int[]) - Static method in class weka.core.Utils
Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class weka.core.Utils
Returns index of minimum element in a given array of doubles.
minMetricTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
minNoTipText() - Method in class weka.classifiers.rules.ConjunctiveRule
Returns the tip text for this property
minNoTipText() - Method in class weka.classifiers.rules.JRip
Returns the tip text for this property
minNoTipText() - Method in class weka.classifiers.rules.Ridor
Returns the tip text for this property
minNumInstancesTipText() - Method in class weka.classifiers.trees.LMT
Returns the tip text for this property
minNumObj - Variable in class weka.classifiers.rules.part.MakeDecList
Minimum number of objects
minNumObjTipText() - Method in class weka.classifiers.rules.PART
Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.trees.J48
Returns the tip text for this property
minNumTipText() - Method in class weka.classifiers.trees.REPTree
Returns the tip text for this property
minNumTipText() - Method in class weka.classifiers.trees.RandomTree
Returns the tip text for this property
minProb - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
used/reused to hold the smallest transformation probability
minStdDevTipText() - Method in class weka.clusterers.EM
Returns the tip text for this property
minStdDevTipText() - Method in class weka.clusterers.MakeDensityBasedClusterer
Returns the tip text for this property
minVariancePropTipText() - Method in class weka.classifiers.trees.REPTree
Returns the tip text for this property
minimizeCrossings(boolean, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine
This method minimizes the number of edge crossings using the BaryCenter heuristics given by Sugiyama et al. 1981 This method processes the graph topdown if reversed is false, otherwise it does bottomup.
minimizeExpectedCostTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
minimumBucketSizeTipText() - Method in class weka.attributeSelection.OneRAttributeEval
Returns a string for this option suitable for display in the gui as a tip text
minsAndMaxs(Instances, double[][], int) - Method in class weka.classifiers.trees.j48.C45Split
Returns the minsAndMaxs of the index.th subset.
minus(double) - Method in class weka.classifiers.functions.pace.DoubleVector
Subtracts a value
minus(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector
Subtracts another DoubleVector element by element
minus(Matrix) - Method in class weka.classifiers.functions.pace.Matrix
C = A - B
minusEquals(double) - Method in class weka.classifiers.functions.pace.DoubleVector
Subtracts a value in place
minusEquals(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector
Subtracts another DoubleVector element by element in place
minusEquals(Matrix) - Method in class weka.classifiers.functions.pace.Matrix
A = A - B
missing - Variable in class weka.attributeSelection.ConsistencySubsetEval.hashKey
True for an index if the corresponding attribute value is missing.
missing - Variable in class weka.classifiers.rules.DecisionTable.hashKey
True for an index if the corresponding attribute value is missing.
missingCount - Variable in class weka.core.AttributeStats
The number of missing values
missingInstances - Variable in class weka.classifiers.trees.m5.Values
 
missingMergeTipText() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.GainRatioAttributeEval
Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns the tip text for this property
missingModeTipText() - Method in class weka.classifiers.lazy.KStar
Returns the tip text for this property
missingSeperateTipText() - Method in class weka.attributeSelection.CfsSubsetEval
Returns the tip text for this property
missingValue() - Static method in class weka.core.Instance
Returns the double that codes "missing".
missingValuesTipText() - Method in class weka.associations.Tertius
Returns the tip text for this property.
mixingDistribution - Variable in class weka.classifiers.functions.pace.MixtureDistribution
 
modelDistributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LMTNode
Returns the class probabilities for an instance according to the logistic model at the node.
modelErrors() - Method in class weka.classifiers.trees.lmt.LMTNode
Updates the numIncorrectModel field for all nodes.
modelsToString() - Method in class weka.classifiers.trees.lmt.LMTNode
Returns a string describing the logistic regression function at the node.
modifyHeaderTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Returns the tip text for this property
momentumTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
 
moreComplexRules(FastVector[], int, int, double, FastVector) - Method in class weka.associations.ItemSet
Generates rules with more than one item in the consequence.
mostExplainingColumn(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns the index of the column that has the largest (squared) response, when each of columns pvt[ks:] is moved to become the ks-th column.
mouseClicked(MouseEvent) - Method in class weka.gui.graphvisualizer.GraphVisualizer.GraphVisualizerMouseListener
If the mouse is clicked on a node then this method displays a dialog box with the probability distribution table for that node IF it exists
mouseClicked(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mouseClicked(MouseEvent) - Method in class weka.gui.visualize.MatrixPanel.Plot
 
mouseDragged(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Performs intermediate updates to what the user wishes to do.
mouseDragged(MouseEvent) - Method in class weka.gui.visualize.MatrixPanel.Plot
 
mouseEntered(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mouseEntered(MouseEvent) - Method in class weka.gui.visualize.MatrixPanel.Plot
 
mouseExited(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mouseExited(MouseEvent) - Method in class weka.gui.visualize.MatrixPanel.Plot
 
mouseInBounds(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Checks to see if the coordinates of the mouse lie on this JPanel.
mouseMoved(MouseEvent) - Method in class weka.gui.graphvisualizer.GraphVisualizer.GraphVisualizerMouseMotionListener
 
mouseMoved(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mouseMoved(MouseEvent) - Method in class weka.gui.visualize.MatrixPanel.Plot
 
mousePressed(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Determines what action the user wants to perform.
mousePressed(MouseEvent) - Method in class weka.gui.visualize.MatrixPanel.Plot
 
mouseReleased(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Performs the final stages of what the user wants to perform.
mouseReleased(MouseEvent) - Method in class weka.gui.visualize.MatrixPanel.Plot
 
moveSubtree(int, double) - Method in class weka.gui.treevisualizer.PlaceNode2
This will recursively shift a sub there to be centered about a particular value.
multiResultsetFull(int, int) - Method in class weka.experiment.PairedTTester
Creates a comparison table where a base resultset is compared to the other resultsets.
multiResultsetFullLatex(int, int, int, int) - Method in class weka.experiment.PairedTTester
Generates a comparison table in latex table format
multiResultsetFullPlainText(int, int, int, int) - Method in class weka.experiment.PairedTTester
Generates a comparison table in latex table format
multiResultsetRanking(int) - Method in class weka.experiment.PairedTTester
 
multiResultsetSummary(int) - Method in class weka.experiment.PairedTTester
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiResultsetWins(int) - Method in class weka.experiment.PairedTTester
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiplier - Variable in class weka.core.converters.HierarchicalCostMatrix
Default multiplier to increase penalty for a wrong class per differing branch in a hierarchy.
multiply(Matrix) - Method in class weka.core.Matrix
Returns the multiplication of two matrices
mutationProbTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property

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