Serialized Form


Package weka.associations

Class weka.associations.Apriori implements Serializable

Serialized Fields

m_minSupport

double m_minSupport
The minimum support.


m_upperBoundMinSupport

double m_upperBoundMinSupport
The upper bound on the support


m_lowerBoundMinSupport

double m_lowerBoundMinSupport
The lower bound for the minimum support.


m_metricType

int m_metricType
The selected metric type.


m_minMetric

double m_minMetric
The minimum metric score.


m_numRules

int m_numRules
The maximum number of rules that are output.


m_delta

double m_delta
Delta by which m_minSupport is decreased in each iteration.


m_significanceLevel

double m_significanceLevel
Significance level for optional significance test.


m_cycles

int m_cycles
Number of cycles used before required number of rules was one.


m_Ls

FastVector m_Ls
The set of all sets of itemsets L.


m_hashtables

FastVector m_hashtables
The same information stored in hash tables.


m_allTheRules

FastVector[] m_allTheRules
The list of all generated rules.


m_instances

Instances m_instances
The instances (transactions) to be used for generating the association rules.


m_outputItemSets

boolean m_outputItemSets
Output itemsets found?


m_removeMissingCols

boolean m_removeMissingCols

m_verbose

boolean m_verbose
Report progress iteratively

Class weka.associations.Associator implements Serializable

Class weka.associations.ItemSet implements Serializable

Serialized Fields

m_items

int[] m_items
The items stored as an array of of ints.


m_counter

int m_counter
Counter for how many transactions contain this item set.


m_totalTransactions

int m_totalTransactions
The total number of transactions


Package weka.attributeSelection

Class weka.attributeSelection.ASEvaluation implements Serializable

Class weka.attributeSelection.ASSearch implements Serializable

Class weka.attributeSelection.AttributeEvaluator implements Serializable

Class weka.attributeSelection.AttributeSelection implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
the instances to select attributes from


m_ASEvaluator

ASEvaluation m_ASEvaluator
the attribute/subset evaluator


m_searchMethod

ASSearch m_searchMethod
the search method


m_numFolds

int m_numFolds
the number of folds to use for cross validation


m_selectionResults

StringBuffer m_selectionResults
holds a string describing the results of the attribute selection


m_doRank

boolean m_doRank
rank features (if allowed by the search method)


m_doXval

boolean m_doXval
do cross validation


m_seed

int m_seed
seed used to randomly shuffle instances for cross validation


m_threshold

double m_threshold
cutoff value by which to select attributes for ranked results


m_numToSelect

int m_numToSelect
number of attributes requested from ranked results


m_selectedAttributeSet

int[] m_selectedAttributeSet
the selected attributes


m_attributeRanking

double[][] m_attributeRanking
the attribute indexes and associated merits if a ranking is produced


m_transformer

AttributeTransformer m_transformer
if a feature selection run involves an attribute transformer


m_attributeFilter

AttributeFilter m_attributeFilter
the attribute filter for processing instances with respect to the most recent feature selection run


m_rankResults

double[][] m_rankResults
hold statistics for repeated feature selection, such as under cross validation


m_subsetResults

double[] m_subsetResults

m_trials

int m_trials

Class weka.attributeSelection.BestFirst implements Serializable

Serialized Fields

m_maxStale

int m_maxStale
maximum number of stale nodes before terminating search


m_searchDirection

int m_searchDirection
0 == backward search, 1 == forward search, 2 == bidirectional


m_starting

int[] m_starting
holds an array of starting attributes


m_startRange

Range m_startRange
holds the start set for the search as a Range


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_totalEvals

int m_totalEvals
total number of subsets evaluated during a search


m_debug

boolean m_debug
for debugging


m_bestMerit

double m_bestMerit
holds the merit of the best subset found

Class weka.attributeSelection.BestFirst.LinkedList2 implements Serializable

Serialized Fields

m_MaxSize

int m_MaxSize

Class weka.attributeSelection.CfsSubsetEval implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_disTransform

DiscretizeFilter m_disTransform
Discretise attributes when class in nominal


m_classIndex

int m_classIndex
The class index


m_isNumeric

boolean m_isNumeric
Is the class numeric


m_numAttribs

int m_numAttribs
Number of attributes in the training data


m_numInstances

int m_numInstances
Number of instances in the training data


m_missingSeperate

boolean m_missingSeperate
Treat missing values as seperate values


m_locallyPredictive

boolean m_locallyPredictive
Include locally predicitive attributes


m_corr_matrix

Matrix m_corr_matrix
Holds the matrix of attribute correlations


m_std_devs

double[] m_std_devs
Standard deviations of attributes (when using pearsons correlation)


m_c_Threshold

double m_c_Threshold
Threshold for admitting locally predictive features

Class weka.attributeSelection.ChiSquaredAttributeEval implements Serializable

Serialized Fields

m_missing_merge

boolean m_missing_merge
Treat missing values as a seperate value


m_Binarize

boolean m_Binarize
Just binarize numeric attributes


m_ChiSquareds

double[] m_ChiSquareds
The chi-squared value for each attribute

Class weka.attributeSelection.ClassifierSubsetEval implements Serializable

Serialized Fields

m_trainingInstances

Instances m_trainingInstances
training instances


m_classIndex

int m_classIndex
class index


m_numAttribs

int m_numAttribs
number of attributes in the training data


m_numInstances

int m_numInstances
number of training instances


m_Classifier

Classifier m_Classifier
holds the classifier to use for error estimates


m_Evaluation

Evaluation m_Evaluation
holds the evaluation object to use for evaluating the classifier


m_holdOutFile

File m_holdOutFile
the file that containts hold out/test instances


m_holdOutInstances

Instances m_holdOutInstances
the instances to test on


m_useTraining

boolean m_useTraining
evaluate on training data rather than seperate hold out/test set

Class weka.attributeSelection.ConsistencySubsetEval implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
training instances


m_classIndex

int m_classIndex
class index


m_numAttribs

int m_numAttribs
number of attributes in the training data


m_numInstances

int m_numInstances
number of instances in the training data


m_disTransform

DiscretizeFilter m_disTransform
Discretise numeric attributes


m_table

Hashtable m_table
Hash table for evaluating feature subsets

Class weka.attributeSelection.ExhaustiveSearch implements Serializable

Serialized Fields

m_starting

int[] m_starting
holds a starting set as an array of attributes.


m_startRange

Range m_startRange
the start set as a Range


m_bestGroup

BitSet m_bestGroup
the best feature set found during the search


m_bestMerit

double m_bestMerit
the merit of the best subset found


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_verbose

boolean m_verbose
if true, then ouput new best subsets as the search progresses


m_stopAfterFirst

boolean m_stopAfterFirst
stop after finding the first subset equal to or better than the supplied start set (set to true if start set is supplied).


m_evaluations

int m_evaluations
the number of subsets evaluated during the search

Class weka.attributeSelection.ForwardSelection implements Serializable

Serialized Fields

m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_rankingRequested

boolean m_rankingRequested
true if the user has requested a ranked list of attributes


m_doRank

boolean m_doRank
go from one side of the search space to the other in order to generate a ranking


m_doneRanking

boolean m_doneRanking
used to indicate whether or not ranking has been performed


m_threshold

double m_threshold
A threshold by which to discard attributes---used by the AttributeSelection module


m_numToSelect

int m_numToSelect
The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold


m_calculatedNumToSelect

int m_calculatedNumToSelect

m_bestMerit

double m_bestMerit
the merit of the best subset found


m_rankedAtts

double[][] m_rankedAtts
a ranked list of attribute indexes


m_rankedSoFar

int m_rankedSoFar

m_best_group

BitSet m_best_group
the best subset found


m_ASEval

ASEvaluation m_ASEval

m_Instances

Instances m_Instances

m_startRange

Range m_startRange
holds the start set for the search as a Range


m_starting

int[] m_starting
holds an array of starting attributes

Class weka.attributeSelection.GainRatioAttributeEval implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_numClasses

int m_numClasses
The number of classes


m_missing_merge

boolean m_missing_merge
Merge missing values

Class weka.attributeSelection.GeneticSearch implements Serializable

Serialized Fields

m_starting

int[] m_starting
holds a starting set as an array of attributes. Becomes one member of the initial random population


m_startRange

Range m_startRange
holds the start set for the search as a Range


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_population

GeneticSearch.GABitSet[] m_population
the current population


m_popSize

int m_popSize
the number of individual solutions


m_best

GeneticSearch.GABitSet m_best
the best population member found during the search


m_bestFeatureCount

int m_bestFeatureCount
the number of features in the best population member


m_lookupTableSize

int m_lookupTableSize
the number of entries to cache for lookup


m_lookupTable

Hashtable m_lookupTable
the lookup table


m_random

Random m_random
random number generation


m_seed

int m_seed
seed for random number generation


m_pCrossover

double m_pCrossover
the probability of crossover occuring


m_pMutation

double m_pMutation
the probability of mutation occuring


m_sumFitness

double m_sumFitness
sum of the current population fitness


m_maxFitness

double m_maxFitness

m_minFitness

double m_minFitness

m_avgFitness

double m_avgFitness

m_maxGenerations

int m_maxGenerations
the maximum number of generations to evaluate


m_reportFrequency

int m_reportFrequency
how often reports are generated


m_generationReports

StringBuffer m_generationReports
holds the generation reports

Class weka.attributeSelection.HoldOutSubsetEvaluator implements Serializable

Class weka.attributeSelection.InfoGainAttributeEval implements Serializable

Serialized Fields

m_missing_merge

boolean m_missing_merge
Treat missing values as a seperate value


m_Binarize

boolean m_Binarize
Just binarize numeric attributes


m_InfoGains

double[] m_InfoGains
The info gain for each attribute

Class weka.attributeSelection.OneRAttributeEval implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances

Class weka.attributeSelection.PrincipalComponents implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The data to transform analyse/transform


m_trainCopy

Instances m_trainCopy
Keep a copy for the class attribute (if set)


m_transformedFormat

Instances m_transformedFormat
The header for the transformed data format


m_originalSpaceFormat

Instances m_originalSpaceFormat
The header for data transformed back to the original space


m_hasClass

boolean m_hasClass
Data has a class set


m_classIndex

int m_classIndex
Class index


m_numAttribs

int m_numAttribs
Number of attributes


m_numInstances

int m_numInstances
Number of instances


m_correlation

double[][] m_correlation
Correlation matrix for the original data


m_eigenvectors

double[][] m_eigenvectors
Will hold the unordered linear transformations of the (normalized) original data


m_eigenvalues

double[] m_eigenvalues
Eigenvalues for the corresponding eigenvectors


m_sortedEigens

int[] m_sortedEigens
Sorted eigenvalues


m_sumOfEigenValues

double m_sumOfEigenValues
sum of the eigenvalues


m_replaceMissingFilter

ReplaceMissingValuesFilter m_replaceMissingFilter
Filters for original data


m_normalizeFilter

NormalizationFilter m_normalizeFilter

m_nominalToBinFilter

NominalToBinaryFilter m_nominalToBinFilter

m_attributeFilter

AttributeFilter m_attributeFilter

m_attribFilter

AttributeFilter m_attribFilter
used to remove the class column if a class column is set


m_outputNumAtts

int m_outputNumAtts
The number of attributes in the pc transformed data


m_normalize

boolean m_normalize
normalize the input data?


m_coverVariance

double m_coverVariance
the amount of varaince to cover in the original data when retaining the best n PC's


m_transBackToOriginal

boolean m_transBackToOriginal
transform the data through the pc space and back to the original space ?


m_eTranspose

double[][] m_eTranspose
holds the transposed eigenvectors for converting back to the original space

Class weka.attributeSelection.RaceSearch implements Serializable

Serialized Fields

m_Instances

Instances m_Instances

m_raceType

int m_raceType
the selected search type


m_xvalType

int m_xvalType
the selected xval type


m_classIndex

int m_classIndex
the class index


m_numAttribs

int m_numAttribs
the number of attributes in the data


m_totalEvals

int m_totalEvals
the total number of partially/fully evaluated subsets


m_bestMerit

double m_bestMerit
holds the merit of the best subset found


m_theEvaluator

HoldOutSubsetEvaluator m_theEvaluator
the subset evaluator to use


m_sigLevel

double m_sigLevel
the significance level for comparisons


m_delta

double m_delta
threshold for comparisons


m_samples

int m_samples
the number of samples above which to begin testing for similarity between competing subsets


m_numFolds

int m_numFolds
number of cross validation folds---equal to the number of instances for leave-one-out cv


m_ASEval

ASEvaluation m_ASEval
the attribute evaluator to generate the initial ranking when doing a rank race


m_Ranking

int[] m_Ranking
will hold the attribute ranking produced by the above attribute evaluator if doing a rank search


m_debug

boolean m_debug
verbose output for monitoring the search and debugging


m_rankingRequested

boolean m_rankingRequested
If true then produce a ranked list of attributes by fully traversing a forward hillclimb race


m_rankedAtts

double[][] m_rankedAtts
The ranked list of attributes produced if m_rankingRequested is true


m_rankedSoFar

int m_rankedSoFar
The number of attributes ranked so far (if ranking is requested)


m_numToSelect

int m_numToSelect
The number of attributes to retain if a ranking is requested. -1 indicates that all attributes are to be retained. Has precedence over m_threshold


m_calculatedNumToSelect

int m_calculatedNumToSelect

m_threshold

double m_threshold
the threshold for removing attributes if ranking is requested

Class weka.attributeSelection.RandomSearch implements Serializable

Serialized Fields

m_starting

int[] m_starting
holds a starting set as an array of attributes.


m_startRange

Range m_startRange
holds the start set as a range


m_bestGroup

BitSet m_bestGroup
the best feature set found during the search


m_bestMerit

double m_bestMerit
the merit of the best subset found


m_onlyConsiderBetterAndSmaller

boolean m_onlyConsiderBetterAndSmaller
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_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_seed

int m_seed
seed for random number generation


m_searchSize

double m_searchSize
percentage of the search space to consider


m_iterations

int m_iterations
the number of iterations performed


m_random

Random m_random
random number object


m_verbose

boolean m_verbose
output new best subsets as the search progresses

Class weka.attributeSelection.Ranker implements Serializable

Serialized Fields

m_starting

int[] m_starting
Holds the starting set as an array of attributes


m_startRange

Range m_startRange
Holds the start set for the search as a range


m_attributeList

int[] m_attributeList
Holds the ordered list of attributes


m_attributeMerit

double[] m_attributeMerit
Holds the list of attribute merit scores


m_hasClass

boolean m_hasClass
Data has class attribute---if unsupervised evaluator then no class


m_classIndex

int m_classIndex
Class index of the data if supervised evaluator


m_numAttribs

int m_numAttribs
The number of attribtes


m_threshold

double m_threshold
A threshold by which to discard attributes---used by the AttributeSelection module


m_numToSelect

int m_numToSelect
The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold


m_calculatedNumToSelect

int m_calculatedNumToSelect
Used to compute the number to select

Class weka.attributeSelection.RankSearch implements Serializable

Serialized Fields

m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_best_group

BitSet m_best_group
the best subset found


m_ASEval

ASEvaluation m_ASEval
the attribute evaluator to use for generating the ranking


m_SubsetEval

ASEvaluation m_SubsetEval
the subset evaluator with which to evaluate the ranking


m_Instances

Instances m_Instances
the training instances


m_bestMerit

double m_bestMerit
the merit of the best subset found


m_Ranking

int[] m_Ranking
will hold the attribute ranking

Class weka.attributeSelection.ReliefFAttributeEval implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_numericClass

boolean m_numericClass
Numeric class


m_numClasses

int m_numClasses
The number of classes if class is nominal


m_ndc

double m_ndc
Used to hold the probability of a different class val given nearest instances (numeric class)


m_nda

double[] m_nda
Used to hold the prob of different value of an attribute given nearest instances (numeric class case)


m_ndcda

double[] m_ndcda
Used to hold the prob of a different class val and different att val given nearest instances (numeric class case)


m_weights

double[] m_weights
Holds the weights that relief assigns to attributes


m_classProbs

double[] m_classProbs
Prior class probabilities (discrete class case)


m_sampleM

int m_sampleM
The number of instances to sample when estimating attributes default == -1, use all instances


m_Knn

int m_Knn
The number of nearest hits/misses


m_karray

double[][][] m_karray
k nearest scores + instance indexes for n classes


m_maxArray

double[] m_maxArray
Upper bound for numeric attributes


m_minArray

double[] m_minArray
Lower bound for numeric attributes


m_worst

double[] m_worst
Keep track of the farthest instance for each class


m_index

int[] m_index
Index in the m_karray of the farthest instance for each class


m_stored

int[] m_stored
Number of nearest neighbours stored of each class


m_seed

int m_seed
Random number seed used for sampling instances


m_weightsByRank

double[] m_weightsByRank
used to (optionally) weight nearest neighbours by their distance from the instance in question. Each entry holds exp(-((rank(r_i, i_j)/sigma)^2)) where rank(r_i,i_j) is the rank of instance i_j in a sequence of instances ordered by the distance from r_i. sigma is a user defined parameter, default=20


m_sigma

int m_sigma

m_weightByDistance

boolean m_weightByDistance
Weight by distance rather than equal weights

Class weka.attributeSelection.SubsetEvaluator implements Serializable

Class weka.attributeSelection.SymmetricalUncertAttributeEval implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_numClasses

int m_numClasses
The number of classes


m_missing_merge

boolean m_missing_merge
Treat missing values as a seperate value

Class weka.attributeSelection.UnsupervisedAttributeEvaluator implements Serializable

Class weka.attributeSelection.UnsupervisedSubsetEvaluator implements Serializable

Class weka.attributeSelection.WrapperSubsetEval implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
training instances


m_classIndex

int m_classIndex
class index


m_numAttribs

int m_numAttribs
number of attributes in the training data


m_numInstances

int m_numInstances
number of instances in the training data


m_Evaluation

Evaluation m_Evaluation
holds an evaluation object


m_BaseClassifier

Classifier m_BaseClassifier
holds the base classifier object


m_folds

int m_folds
number of folds to use for cross validation


m_seed

int m_seed
random number seed


m_threshold

double m_threshold
the threshold by which to do further cross validations when estimating the accuracy of a subset


Package weka.classifiers

Class weka.classifiers.AdaBoostM1 implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The model base classifier to use


m_Classifiers

Classifier[] m_Classifiers
Array for storing the generated base classifiers.


m_Betas

double[] m_Betas
Array for storing the weights for the votes.


m_MaxIterations

int m_MaxIterations
The maximum number of boost iterations


m_NumIterations

int m_NumIterations
The number of successfully generated base classifiers.


m_WeightThreshold

int m_WeightThreshold
Weight Threshold. The percentage of weight mass used in training


m_Debug

boolean m_Debug
Debugging mode, gives extra output if true


m_UseResampling

boolean m_UseResampling
Use boosting with reweighting?


m_Seed

int m_Seed
Seed for boosting with resampling.


m_NumClasses

int m_NumClasses
The number of classes

Class weka.classifiers.AdditiveRegression implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
Base classifier.


m_classIndex

int m_classIndex
Class index.


m_shrinkage

double m_shrinkage
Shrinkage (Learning rate). Default = no shrinkage.


m_additiveModels

FastVector m_additiveModels
The list of iteratively generated models.


m_debug

boolean m_debug
Produce debugging output.


m_maxModels

int m_maxModels
Maximum number of models to produce. -1 indicates keep going until the error threshold is met.

Class weka.classifiers.AttributeSelectedClassifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier


m_AttributeSelection

AttributeSelection m_AttributeSelection
The attribute selection object


m_Evaluator

ASEvaluation m_Evaluator
The attribute evaluator to use


m_Search

ASSearch m_Search
The search method to use


m_ReducedHeader

Instances m_ReducedHeader
The header of the dimensionally reduced data


m_numClasses

int m_numClasses
The number of class vals in the training data (1 if class is numeric)


m_numAttributesSelected

double m_numAttributesSelected
The number of attributes selected by the attribute selection phase


m_selectionTime

double m_selectionTime
The time taken to select attributes in milliseconds


m_totalTime

double m_totalTime
The time taken to select attributes AND build the classifier

Class weka.classifiers.Bagging implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The model base classifier to use


m_Classifiers

Classifier[] m_Classifiers
Array for storing the generated base classifiers.


m_NumIterations

int m_NumIterations
The number of iterations.


m_Seed

int m_Seed
The seed for random number generation.


m_BagSizePercent

int m_BagSizePercent
The size of each bag sample, as a percentage of the training size

Class weka.classifiers.ClassificationViaRegression implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
The classifiers. (One for each class.)


m_ClassFilters

MakeIndicatorFilter[] m_ClassFilters
The filters used to transform the class.


m_Classifier

Classifier m_Classifier
The class name of the base classifier.

Class weka.classifiers.Classifier implements Serializable

Class weka.classifiers.CostMatrix implements Serializable

Class weka.classifiers.CostSensitiveClassifier implements Serializable

Serialized Fields

m_MatrixSource

int m_MatrixSource
Indicates the current cost matrix source


m_OnDemandDirectory

File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory


m_CostFile

String m_CostFile
The name of the cost file, for command line options


m_CostMatrix

CostMatrix m_CostMatrix
The cost matrix


m_Classifier

Classifier m_Classifier
The classifier


m_Seed

int m_Seed
Seed for reweighting using resampling.


m_MinimizeExpectedCost

boolean m_MinimizeExpectedCost
True if the costs should be used by selecting the minimum expected cost (false means weight training data by the costs)

Class weka.classifiers.CVParameterSelection implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The generated base classifier


m_ClassifierOptions

String[] m_ClassifierOptions
The base classifier options (not including those being set by cross-validation)


m_BestClassifierOptions

String[] m_BestClassifierOptions
The set of all classifier options as determined by cross-validation


m_BestPerformance

double m_BestPerformance
The cross-validated performance of the best options


m_CVParams

FastVector m_CVParams
The set of parameters to cross-validate over


m_NumAttributes

int m_NumAttributes
The number of attributes in the data


m_TrainFoldSize

int m_TrainFoldSize
The number of instances in a training fold


m_NumFolds

int m_NumFolds
The number of folds used in cross-validation


m_Seed

int m_Seed
Random number seed


m_Debug

boolean m_Debug
Debugging mode, gives extra output if true

Class weka.classifiers.DecisionStump implements Serializable

Serialized Fields

m_AttIndex

int m_AttIndex
The attribute used for classification.


m_SplitPoint

double m_SplitPoint
The split point (index respectively).


m_Distribution

double[][] m_Distribution
The distribution of class values or the means in each subset.


m_Instances

Instances m_Instances
The instances used for training.

Class weka.classifiers.DecisionTable implements Serializable

Serialized Fields

m_entries

Hashtable m_entries
The hashtable used to hold training instances


m_decisionFeatures

int[] m_decisionFeatures
Holds the final feature set


m_disTransform

DiscretizeFilter m_disTransform
Discretization filter


m_delTransform

AttributeFilter m_delTransform
Filter used to remove columns discarded by feature selection


m_ibk

IBk m_ibk
IB1 used to classify non matching instances rather than majority class


m_theInstances

Instances m_theInstances
Holds the training instances


m_numAttributes

int m_numAttributes
The number of attributes in the dataset


m_numInstances

int m_numInstances
The number of instances in the dataset


m_classIsNominal

boolean m_classIsNominal
Class is nominal


m_debug

boolean m_debug
Output debug info


m_useIBk

boolean m_useIBk
Use the IBk classifier rather than majority class


m_displayRules

boolean m_displayRules
Display Rules


m_maxStale

int m_maxStale
Maximum number of fully expanded non improving subsets for a best first search.


m_CVFolds

int m_CVFolds
Number of folds for cross validating feature sets


m_rr

Random m_rr
Random numbers for use in cross validation


m_majority

double m_majority
Holds the majority class

Class weka.classifiers.DecisionTable.hashKey implements Serializable

Serialized Fields

attributes

double[] attributes
Array of attribute values for an instance


missing

boolean[] missing
True for an index if the corresponding attribute value is missing.


values

String[] values
The values


key

int key
The key

Class weka.classifiers.DecisionTable.LinkedList implements Serializable

Class weka.classifiers.DistributionClassifier implements Serializable

Class weka.classifiers.DistributionMetaClassifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier.

Class weka.classifiers.FilteredClassifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier


m_Filter

Filter m_Filter
The filter


m_FilteredInstances

Instances m_FilteredInstances
The instance structure of the filtered instances

Class weka.classifiers.HyperPipes implements Serializable

Serialized Fields

m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Instances

Instances m_Instances
The structure of the training data


m_HyperPipes

HyperPipes.HyperPipe[] m_HyperPipes
Stores the HyperPipe for each class

Class weka.classifiers.IB1 implements Serializable

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.

Class weka.classifiers.IBk implements Serializable

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_NumClasses

int m_NumClasses
The number of class values (or 1 if predicting numeric)


m_ClassType

int m_ClassType
The class attribute type


m_Min

double[] m_Min
The minimum values for numeric attributes.


m_Max

double[] m_Max
The maximum values for numeric attributes.


m_kNN

int m_kNN
The number of neighbours to use for classification (currently)


m_kNNUpper

int m_kNNUpper
The value of kNN provided by the user. This may differ from m_kNN if cross-validation is being used


m_kNNValid

boolean m_kNNValid
Whether the value of k selected by cross validation has been invalidated by a change in the training instances


m_WindowSize

int m_WindowSize
The maximum number of training instances allowed. When this limit is reached, old training instances are removed, so the training data is "windowed". Set to 0 for unlimited numbers of instances.


m_DistanceWeighting

int m_DistanceWeighting
Whether the neighbours should be distance-weighted


m_CrossValidate

boolean m_CrossValidate
Whether to select k by cross validation


m_MeanSquared

boolean m_MeanSquared
Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks


m_Debug

boolean m_Debug
True if debugging output should be printed


m_DontNormalize

boolean m_DontNormalize
True if normalization is turned off


m_NumAttributesUsed

double m_NumAttributesUsed
The number of attributes the contribute to a prediction

Class weka.classifiers.Id3 implements Serializable

Serialized Fields

m_Successors

Id3[] m_Successors
The node's successors.


m_Attribute

Attribute m_Attribute
Attribute used for splitting.


m_ClassValue

double m_ClassValue
Class value if node is leaf.


m_Distribution

double[] m_Distribution
Class distribution if node is leaf.


m_ClassAttribute

Attribute m_ClassAttribute
Class attribute of dataset.

Class weka.classifiers.KernelDensity implements Serializable

Serialized Fields

m_Counts

double[] m_Counts
The number of instances in each class (null if class numeric).


m_Instances

Instances m_Instances
The instances used for "training".


m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.

Class weka.classifiers.LEM2 implements Serializable

Serialized Fields

instances

Instances instances
The dataset header for the purposes of printing out a semi-intelligible model


debug

int debug

Class weka.classifiers.LinearRegression implements Serializable

Serialized Fields

m_Coefficients

double[] m_Coefficients
Array for storing coefficients of linear regression.


m_SelectedAttributes

boolean[] m_SelectedAttributes
Which attributes are relevant?


m_TransformedData

Instances m_TransformedData
Variable for storing transformed training data.


m_MissingFilter

ReplaceMissingValuesFilter m_MissingFilter
The filter for removing missing values.


m_TransformFilter

NominalToBinaryFilter m_TransformFilter
The filter storing the transformation from nominal to binary attributes.


m_StdDev

double[] m_StdDev
The standard deviations of the attributes


m_ClassIndex

int m_ClassIndex
The index of the class attribute


b_Debug

boolean b_Debug
True if debug output will be printed


m_AttributeSelection

int m_AttributeSelection
The current attribute selection method

Class weka.classifiers.Logistic implements Serializable

Serialized Fields

m_LL

double m_LL
The log-likelihood of the built model


m_LLn

double m_LLn
The log-likelihood of the null model


m_Par

double[] m_Par
The coefficients of the model


m_NumPredictors

int m_NumPredictors
The number of attributes in the model


m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Ridge

double m_Ridge
The ridge parameter.


m_NominalToBinary

NominalToBinaryFilter m_NominalToBinary
The filter used to make attributes numeric.


m_ReplaceMissingValues

ReplaceMissingValuesFilter m_ReplaceMissingValues
The filter used to get rid of missing values.


m_Debug

boolean m_Debug
Debugging output


m_f

double m_f

fvec

double[] fvec

m_nn

int m_nn

m_check

int m_check

m_ALF

double m_ALF

m_TOLX

double m_TOLX

m_TOLMIN

double m_TOLMIN

m_STPMX

double m_STPMX

m_MAXITS

int m_MAXITS

Class weka.classifiers.LogitBoost implements Serializable

Serialized Fields

m_Classifiers

Classifier[][] m_Classifiers
Array for storing the generated base classifiers.


m_Classifier

Classifier m_Classifier
An instantiated base classifier used for getting and testing options


m_MaxIterations

int m_MaxIterations
The maximum number of boost iterations


m_NumClasses

int m_NumClasses
The number of classes


m_NumIterations

int m_NumIterations
The number of successfully generated base classifiers.


m_WeightThreshold

int m_WeightThreshold
Weight thresholding. The percentage of weight mass used in training


m_Debug

boolean m_Debug
Debugging mode, gives extra output if true


m_NumericClassData

Instances m_NumericClassData
Dummy dataset with a numeric class


m_ClassAttribute

Attribute m_ClassAttribute
The actual class attribute (for getting class names)


m_UseResampling

boolean m_UseResampling
Use boosting with reweighting?


m_Seed

int m_Seed
Seed for boosting with resampling.

Class weka.classifiers.LWR implements Serializable

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_Min

double[] m_Min
The minimum values for numeric attributes.


m_Max

double[] m_Max
The maximum values for numeric attributes.


m_Debug

boolean m_Debug
True if debugging output should be printed


m_kNN

int m_kNN
The number of neighbours used to select the kernel bandwidth


m_WeightKernel

int m_WeightKernel
The weighting kernel method currently selected


m_UseAllK

boolean m_UseAllK
True if m_kNN should be set to all instances

Class weka.classifiers.MetaCost implements Serializable

Serialized Fields

m_MatrixSource

int m_MatrixSource
Indicates the current cost matrix source


m_OnDemandDirectory

File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory


m_CostFile

String m_CostFile
The name of the cost file, for command line options


m_Classifier

Classifier m_Classifier
The classifier


m_CostMatrix

CostMatrix m_CostMatrix
The cost matrix


m_NumIterations

int m_NumIterations
The number of iterations.


m_Seed

int m_Seed
Seed for reweighting using resampling.


m_BagSizePercent

int m_BagSizePercent
The size of each bag sample, as a percentage of the training size

Class weka.classifiers.MultiClassClassifier implements Serializable

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
The classifiers. (One for each class.)


m_ClassFilters

MakeIndicatorFilter[] m_ClassFilters
The filters used to transform the class.


m_Classifier

DistributionClassifier m_Classifier
The class name of the base classifier.


m_ZeroR

ZeroR m_ZeroR
ZeroR classifier for when all base classifier return zero probability.


m_ClassAttribute

Attribute m_ClassAttribute
Internal copy of the class attribute for output purposes


m_RandomWidthFactor

double m_RandomWidthFactor
The multiplier when generating random codes. Will generate numClasses * m_RandomWidthFactor codes


m_ErrorMode

int m_ErrorMode
The error-correcting output code method to use

Class weka.classifiers.MultiScheme implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier that had the best performance on training data.


m_Classifiers

Classifier[] m_Classifiers
The list of classifiers


m_ClassifierIndex

int m_ClassifierIndex
The index into the vector for the selected scheme


m_NumXValFolds

int m_NumXValFolds
Number of folds to use for cross validation (0 means use training error for selection)


m_Debug

boolean m_Debug
Debugging mode, gives extra output if true


m_Seed

int m_Seed
Random number seed

Class weka.classifiers.NaiveBayes implements Serializable

Serialized Fields

m_Distributions

Estimator[][] m_Distributions
The attribute estimators.


m_ClassDistribution

Estimator m_ClassDistribution
The class estimator.


m_UseKernelEstimator

boolean m_UseKernelEstimator
Whether to use kernel density estimator rather than normal distribution for numeric attributes


m_NumClasses

int m_NumClasses
The number of classes (or 1 for numeric class)


m_Instances

Instances m_Instances
The dataset header for the purposes of printing out a semi-intelligible model

Class weka.classifiers.NaiveBayesSimple implements Serializable

Serialized Fields

m_Counts

double[][][] m_Counts
All the counts for nominal attributes.


m_Means

double[][] m_Means
The means for numeric attributes.


m_Devs

double[][] m_Devs
The standard deviations for numeric attributes.


m_Priors

double[] m_Priors
The prior probabilities of the classes.


m_Instances

Instances m_Instances
The instances used for training.

Class weka.classifiers.OneR implements Serializable

Serialized Fields

m_rule

weka.classifiers.OneR.OneRRule m_rule
A 1-R rule


m_minBucketSize

int m_minBucketSize
The minimum bucket size

Class weka.classifiers.Prism implements Serializable

Serialized Fields

m_rules

weka.classifiers.Prism.PrismRule m_rules
The first rule in the list of rules

Class weka.classifiers.RegressionByDiscretization implements Serializable

Serialized Fields

m_Classifier

DistributionClassifier m_Classifier
The subclassifier.


m_Discretizer

DiscretizeFilter m_Discretizer
The discretization filter.


m_NumBins

int m_NumBins
The number of classes in the Discretized training data.


m_ClassMeans

double[] m_ClassMeans
The mean values for each Discretized class interval.


m_Debug

boolean m_Debug
Whether debugging output will be printed


m_OptimizeBins

boolean m_OptimizeBins
Whether the Discretizer will optimise the number of bins

Class weka.classifiers.RuleClassifier implements Serializable

Serialized Fields

rules

Rules rules

ruleStrengthOption

String ruleStrengthOption

accuracy

double accuracy

reducedSetAccuracy

double reducedSetAccuracy

Class weka.classifiers.SMO implements Serializable

Serialized Fields

m_exponent

double m_exponent
The exponent for the polnomial kernel.


m_C

double m_C
The complexity parameter.


m_eps

double m_eps
Epsilon for rounding.


m_tol

double m_tol
Tolerance for accuracy of result.


m_alpha

double[] m_alpha
The Lagrange multipliers.


m_b

double m_b
The thresholds.


m_bLow

double m_bLow
The thresholds.


m_bUp

double m_bUp
The thresholds.


m_iLow

int m_iLow
The indices for m_bLow and m_bUp


m_iUp

int m_iUp
The indices for m_bLow and m_bUp


m_data

Instances m_data
The training data.


m_weights

double[] m_weights
Weight vector for linear machine.


m_storage

double[] m_storage
Kernel function cache


m_keys

int[] m_keys

m_class

double[] m_class
The transformed class values.


m_errors

double[] m_errors
The current set of errors for all non-bound examples.


m_I0

weka.classifiers.SMO.SMOset m_I0
The five different sets used by the algorithm.


m_I1

weka.classifiers.SMO.SMOset m_I1

m_I2

weka.classifiers.SMO.SMOset m_I2

m_I3

weka.classifiers.SMO.SMOset m_I3

m_I4

weka.classifiers.SMO.SMOset m_I4

m_supportVectors

weka.classifiers.SMO.SMOset m_supportVectors
The set of support vectors


m_NominalToBinary

NominalToBinaryFilter m_NominalToBinary
The filter used to make attributes numeric.


m_Normalization

NormalizationFilter m_Normalization
The filter used to normalize all values.


m_Missing

ReplaceMissingValuesFilter m_Missing
The filter used to get rid of missing values.


m_kernelEvals

int m_kernelEvals
Counts the number of kernel evaluations.


m_cacheSize

int m_cacheSize
The size of the cache (a prime number)


m_Normalize

boolean m_Normalize
True if we don't want to normalize


m_rescale

boolean m_rescale
Rescale?


m_lowerOrder

boolean m_lowerOrder
Use lower-order terms?


m_onlyNumeric

boolean m_onlyNumeric
Only numeric attributes in the dataset?

Class weka.classifiers.Stacking implements Serializable

Serialized Fields

m_MetaClassifier

Classifier m_MetaClassifier
The meta classifier.


m_BaseClassifiers

Classifier[] m_BaseClassifiers
The base classifiers.


m_MetaFormat

Instances m_MetaFormat
Format for meta data


m_BaseFormat

Instances m_BaseFormat
Format for base data


m_NumFolds

int m_NumFolds
Set the number of folds for the cross-validation


m_Seed

int m_Seed
Random number seed

Class weka.classifiers.ThresholdSelector implements Serializable

Serialized Fields

m_Classifier

DistributionClassifier m_Classifier
The generated base classifier


m_HighThreshold

double m_HighThreshold
The upper threshold used as the basis of correction


m_LowThreshold

double m_LowThreshold
The lower threshold used as the basis of correction


m_BestThreshold

double m_BestThreshold
The threshold that lead to the best performance


m_BestValue

double m_BestValue
The best value that has been observed


m_NumXValFolds

int m_NumXValFolds
The number of folds used in cross-validation


m_Seed

int m_Seed
Random number seed


m_DesignatedClass

int m_DesignatedClass
Designated class value, determined during building


m_ClassMode

int m_ClassMode
Method to determine which class to optimize for


m_EvalMode

int m_EvalMode
The evaluation mode


m_RangeMode

int m_RangeMode
The range correction mode

Class weka.classifiers.UserClassifier implements Serializable

Serialized Fields

m_tView

TreeVisualizer m_tView
The tree display panel.


m_iView

VisualizePanel m_iView
The instances display.


m_top

weka.classifiers.UserClassifier.TreeClass m_top
Two references to the structure of the decision tree.


m_focus

weka.classifiers.UserClassifier.TreeClass m_focus
Two references to the structure of the decision tree.


m_nextId

int m_nextId
The next number that can be used as a unique id for a node.


m_treeFrame

JFrame m_treeFrame
These two frames aren't used anymore.


m_visFrame

JFrame m_visFrame

m_reps

JTabbedPane m_reps
The tabbed window for the tree and instances view.


m_mainWin

JFrame m_mainWin
The window.


m_built

boolean m_built
The status of whether there is a decision tree ready or not.


m_classifiers

GenericObjectEditor m_classifiers
A list of other m_classifiers.


m_propertyDialog

PropertyDialog m_propertyDialog
A window for selecting other classifiers.

Class weka.classifiers.VFI implements Serializable

Serialized Fields

m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_NumClasses

int m_NumClasses
The number of classes


m_Instances

Instances m_Instances
The training data


m_counts

double[][][] m_counts
The class counts for each interval of each attribute


m_globalCounts

double[] m_globalCounts
The global class counts


m_intervalBounds

double[][] m_intervalBounds
The lower bounds for each attribute


m_maxEntrop

double m_maxEntrop
The maximum entropy for the class


m_weightByConfidence

boolean m_weightByConfidence
Exponentially bias more confident intervals


m_bias

double m_bias
Bias towards more confident intervals


TINY

double TINY

Class weka.classifiers.VotedPerceptron implements Serializable

Serialized Fields

m_MaxK

int m_MaxK
The maximum number of alterations to the perceptron


m_NumIterations

int m_NumIterations
The number of iterations


m_Exponent

double m_Exponent
The exponent


m_K

int m_K
The actual number of alterations


m_Additions

int[] m_Additions
The training instances added to the perceptron


m_IsAddition

boolean[] m_IsAddition
Addition or subtraction?


m_Weights

int[] m_Weights
The weights for each perceptron


m_Train

Instances m_Train
The training instances


m_Seed

int m_Seed
Seed used for shuffling the dataset


m_NominalToBinary

NominalToBinaryFilter m_NominalToBinary
The filter used to make attributes numeric.


m_ReplaceMissingValues

ReplaceMissingValuesFilter m_ReplaceMissingValues
The filter used to get rid of missing values.

Class weka.classifiers.Voter implements Serializable

Serialized Fields

debug

int debug

instances

Instances instances

accuracy

double accuracy

Class weka.classifiers.ZeroR implements Serializable

Serialized Fields

m_ClassValue

double m_ClassValue
The class value 0R predicts.


m_Counts

double[] m_Counts
The number of instances in each class (null if class numeric).


m_Class

Attribute m_Class
The class attribute.


Package weka.classifiers.adtree

Class weka.classifiers.adtree.ADTree implements Serializable

Serialized Fields

m_trainInstances

Instances m_trainInstances
The instances used to train the tree


m_root

PredictionNode m_root
The root of the tree


m_random

Random m_random
The random number generator - used for the random search heuristic


m_lastAddedSplitNum

int m_lastAddedSplitNum
The number of the last splitter added to the tree


m_numericAttIndices

int[] m_numericAttIndices
An array containing the inidices to the numeric attributes in the data


m_nominalAttIndices

int[] m_nominalAttIndices
An array containing the inidices to the nominal attributes in the data


m_trainTotalWeight

double m_trainTotalWeight
The total weight of the instances - used to speed Z calculations


m_posTrainInstances

ReferenceInstances m_posTrainInstances
The training instances with positive class - referencing the training dataset


m_negTrainInstances

ReferenceInstances m_negTrainInstances
The training instances with negative class - referencing the training dataset


m_search_bestInsertionNode

PredictionNode m_search_bestInsertionNode
The best node to insert under, as found so far by the latest search


m_search_bestSplitter

Splitter m_search_bestSplitter
The best splitter to insert, as found so far by the latest search


m_search_smallestZ

double m_search_smallestZ
The smallest Z value found so far by the latest search


m_search_bestPathPosInstances

Instances m_search_bestPathPosInstances
The positive instances that apply to the best path found so far


m_search_bestPathNegInstances

Instances m_search_bestPathNegInstances
The negative instances that apply to the best path found so far


m_nodesExpanded

int m_nodesExpanded
Statistics - the number of prediction nodes investigated during search


m_examplesCounted

int m_examplesCounted
Statistics - the number of instances processed during search


m_boostingIterations

int m_boostingIterations
Option - the number of boosting iterations o perform


m_searchPath

int m_searchPath
Option - the search mode


m_randomSeed

int m_randomSeed
Option - the seed to use for a random search


m_saveInstanceData

boolean m_saveInstanceData
Option - whether the tree should remember the instance data

Class weka.classifiers.adtree.PredictionNode implements Serializable

Serialized Fields

value

double value
The prediction value stored in this node


children

FastVector children
The children of this node - any number of splitter nodes

Class weka.classifiers.adtree.ReferenceInstances implements Serializable

Class weka.classifiers.adtree.Splitter implements Serializable

Serialized Fields

orderAdded

int orderAdded
The number this node was in the order of nodes added to the tree

Class weka.classifiers.adtree.TwoWayNominalSplit implements Serializable

Serialized Fields

attIndex

int attIndex
The index of the attribute the split depends on


trueSplitValue

int trueSplitValue
The attribute value that is compared against


children

PredictionNode[] children
The children of this split

Class weka.classifiers.adtree.TwoWayNumericSplit implements Serializable

Serialized Fields

attIndex

int attIndex
The index of the attribute the split depends on


splitPoint

double splitPoint
The attribute value that is compared against


children

PredictionNode[] children
The children of this split


Package weka.classifiers.evaluation

Class weka.classifiers.evaluation.ConfusionMatrix implements Serializable

Serialized Fields

m_ClassNames

String[] m_ClassNames
Stores the names of the classes

Class weka.classifiers.evaluation.NominalPrediction implements Serializable

Serialized Fields

m_Distribution

double[] m_Distribution
The predicted probabilities


m_Actual

double m_Actual
The actual class value


m_Predicted

double m_Predicted
The predicted class value


m_Weight

double m_Weight
The weight assigned to this prediction

Class weka.classifiers.evaluation.NumericPrediction implements Serializable

Serialized Fields

m_Actual

double m_Actual
The actual class value


m_Predicted

double m_Predicted
The predicted class value


m_Weight

double m_Weight
The weight assigned to this prediction


Package weka.classifiers.j48

Class weka.classifiers.j48.BinC45ModelSelection implements Serializable

Serialized Fields

m_minNoObj

int m_minNoObj
Minimum number of instances in interval.


m_allData

Instances m_allData
The FULL training dataset.

Class weka.classifiers.j48.BinC45Split implements Serializable

Serialized Fields

m_attIndex

int m_attIndex
Attribute to split on.


m_minNoObj

int m_minNoObj
Minimum number of objects in a split.


m_splitPoint

double m_splitPoint
Value of split point.


m_infoGain

double m_infoGain
InfoGain of split.


m_gainRatio

double m_gainRatio
GainRatio of split.


m_sumOfWeights

double m_sumOfWeights
The sum of the weights of the instances.

Class weka.classifiers.j48.C45ModelSelection implements Serializable

Serialized Fields

m_minNoObj

int m_minNoObj
Minimum number of objects in interval.


m_allData

Instances m_allData
All the training data

Class weka.classifiers.j48.C45PruneableClassifierTree implements Serializable

Serialized Fields

m_pruneTheTree

boolean m_pruneTheTree
True if the tree is to be pruned.


m_CF

float m_CF
The confidence factor for pruning.


m_subtreeRaising

boolean m_subtreeRaising
Is subtree raising to be performed?


m_cleanup

boolean m_cleanup
Cleanup after the tree has been built.

Class weka.classifiers.j48.C45PruneableDecList implements Serializable

Serialized Fields

CF

double CF
CF


minNumObj

int minNumObj
Minimum number of objects

Class weka.classifiers.j48.C45Split implements Serializable

Serialized Fields

m_complexityIndex

int m_complexityIndex
Desired number of branches.


m_attIndex

int m_attIndex
Attribute to split on.


m_minNoObj

int m_minNoObj
Minimum number of objects in a split.


m_splitPoint

double m_splitPoint
Value of split point.


m_infoGain

double m_infoGain
InfoGain of split.


m_gainRatio

double m_gainRatio
GainRatio of split.


m_sumOfWeights

double m_sumOfWeights
The sum of the weights of the instances.


m_index

int m_index
Number of split points.

Class weka.classifiers.j48.ClassifierDecList implements Serializable

Serialized Fields

m_toSelectModel

ModelSelection m_toSelectModel
The model selection method.


m_localModel

ClassifierSplitModel m_localModel
Local model at node.


m_sons

ClassifierDecList[] m_sons
References to sons.


m_isLeaf

boolean m_isLeaf
True if node is leaf.


m_isEmpty

boolean m_isEmpty
True if node is empty.


m_train

Instances m_train
The training instances.


m_test

Distribution m_test
The pruning instances.


indeX

int indeX
Which son to expand?

Class weka.classifiers.j48.ClassifierSplitModel implements Serializable

Serialized Fields

m_distribution

Distribution m_distribution
Distribution of class values.


m_numSubsets

int m_numSubsets
Number of created subsets.

Class weka.classifiers.j48.ClassifierTree implements Serializable

Serialized Fields

m_toSelectModel

ModelSelection m_toSelectModel
The model selection method.


m_localModel

ClassifierSplitModel m_localModel
Local model at node.


m_sons

ClassifierTree[] m_sons
References to sons.


m_isLeaf

boolean m_isLeaf
True if node is leaf.


m_isEmpty

boolean m_isEmpty
True if node is empty.


m_train

Instances m_train
The training instances.


m_test

Distribution m_test
The pruning instances.


m_id

int m_id
The id for the node.

Class weka.classifiers.j48.Distribution implements Serializable

Serialized Fields

m_perClassPerBag

double[][] m_perClassPerBag
Weight of instances per class per bag.


m_perBag

double[] m_perBag
Weight of instances per bag.


m_perClass

double[] m_perClass
Weight of instances per class.


totaL

double totaL
Total weight of instances.

Class weka.classifiers.j48.EntropyBasedSplitCrit implements Serializable

Class weka.classifiers.j48.EntropySplitCrit implements Serializable

Class weka.classifiers.j48.GainRatioSplitCrit implements Serializable

Class weka.classifiers.j48.InfoGainSplitCrit implements Serializable

Class weka.classifiers.j48.J48 implements Serializable

Serialized Fields

m_root

ClassifierTree m_root
The decision tree


m_unpruned

boolean m_unpruned
Unpruned tree?


m_CF

float m_CF
Confidence level


m_minNumObj

int m_minNumObj
Minimum number of instances


m_useLaplace

boolean m_useLaplace
Determines whether probabilities are smoothed using Laplace correction when predictions are generated


m_reducedErrorPruning

boolean m_reducedErrorPruning
Use reduced error pruning?


m_numFolds

int m_numFolds
Number of folds for reduced error pruning.


m_binarySplits

boolean m_binarySplits
Binary splits on nominal attributes?


m_subtreeRaising

boolean m_subtreeRaising
Subtree raising to be performed?


m_noCleanup

boolean m_noCleanup
Cleanup after the tree has been built.

Class weka.classifiers.j48.MakeDecList implements Serializable

Serialized Fields

theRules

Vector theRules
Vector storing the rules.


CF

double CF
The confidence for C45-type pruning.


minNumObj

int minNumObj
Minimum number of objects


toSelectModeL

ModelSelection toSelectModeL
The model selection method.


numSetS

int numSetS
How many subsets of equal size? One used for pruning, the rest for training.


reducedErrorPruning

boolean reducedErrorPruning
Use reduced error pruning?

Class weka.classifiers.j48.ModelSelection implements Serializable

Class weka.classifiers.j48.NoSplit implements Serializable

Class weka.classifiers.j48.PART implements Serializable

Serialized Fields

m_root

MakeDecList m_root
The decision list


m_CF

float m_CF
Confidence level


m_minNumObj

int m_minNumObj
Minimum number of objects


m_reducedErrorPruning

boolean m_reducedErrorPruning
Use reduced error pruning?


m_numFolds

int m_numFolds
Number of folds for reduced error pruning.


m_binarySplits

boolean m_binarySplits
Binary splits on nominal attributes?

Class weka.classifiers.j48.PruneableClassifierTree implements Serializable

Serialized Fields

pruneTheTree

boolean pruneTheTree
True if the tree is to be pruned.


numSets

int numSets
How many subsets of equal size? One used for pruning, the rest for training.


m_cleanup

boolean m_cleanup
Cleanup after the tree has been built.

Class weka.classifiers.j48.PruneableDecList implements Serializable

Serialized Fields

m_MinNumObj

int m_MinNumObj
Minimum number of objects

Class weka.classifiers.j48.SplitCriterion implements Serializable


Package weka.classifiers.kstar

Class weka.classifiers.kstar.KStar implements Serializable

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_NumInstances

int m_NumInstances
The number of instances in the dataset


m_NumClasses

int m_NumClasses
The number of class values


m_NumAttributes

int m_NumAttributes
The number of attributes


m_ClassType

int m_ClassType
The class attribute type


m_RandClassCols

int[][] m_RandClassCols
Table of random class value colomns


m_ComputeRandomCols

int m_ComputeRandomCols
Flag turning on and off the computation of random class colomns


m_InitFlag

int m_InitFlag
Flag turning on and off the initialisation of config variables


m_Cache

KStarCache[] m_Cache
A custom data structure for caching distinct attribute values and their scale factor or stop parameter.


m_MissingMode

int m_MissingMode
missing value treatment


m_BlendMethod

int m_BlendMethod
0 = use specified blend, 1 = entropic blend setting


m_GlobalBlend

int m_GlobalBlend
default sphere of influence blend setting


Package weka.classifiers.m5

Class weka.classifiers.m5.Errors implements Serializable

Serialized Fields

numInstances

int numInstances

missingInstances

int missingInstances

sumErr

double sumErr

sumAbsErr

double sumAbsErr

sumSqrErr

double sumSqrErr

meanSqrErr

double meanSqrErr

rootMeanSqrErr

double rootMeanSqrErr

meanAbsErr

double meanAbsErr

Class weka.classifiers.m5.Function implements Serializable

Serialized Fields

terms

int[] terms

coeffs

double[] coeffs

Class weka.classifiers.m5.M5Prime implements Serializable

Serialized Fields

m_root

Node[] m_root
The root node


options

Options options
The options


m_UseUnsmoothed

boolean m_UseUnsmoothed
No smoothing?


m_PruningFactor

double m_PruningFactor
Pruning factor


m_Model

int m_Model
Type of model


m_Verbosity

int m_Verbosity
Verbosity


m_ReplaceMissingValuesFilter

ReplaceMissingValuesFilter m_ReplaceMissingValuesFilter
Filter for replacing missing values.


m_NominalToBinaryFilter

NominalToBinaryFilter m_NominalToBinaryFilter
Filter for replacing nominal attributes with numeric binary ones.

Class weka.classifiers.m5.Node implements Serializable

Serialized Fields

type

boolean type

splitAttr

int splitAttr

splitValue

double splitValue

unsmoothed

Function unsmoothed

smoothed

Function smoothed

valueNode

boolean valueNode

upNode

Node upNode

leftNode

Node leftNode

rightNode

Node rightNode

errors

Errors errors

numParameters

int numParameters

sf

SplitInfo sf

lm

int lm

instances

Instances instances

model

int model

pruningFactor

double pruningFactor

deviation

double deviation

Class weka.classifiers.m5.Options implements Serializable

Serialized Fields

smooth

boolean smooth

randomSeed

int randomSeed

classcol

int classcol

verbosity

int verbosity

model

int model

numFolds

int numFolds

pruningFactor

double pruningFactor

trainFile

String trainFile

testFile

String testFile

lmNo

int lmNo

deviation

double deviation

Class weka.classifiers.m5.SplitInfo implements Serializable

Serialized Fields

number

int number

first

int first

last

int last

position

int position

maxImpurity

double maxImpurity

leftAve

double leftAve

rightAve

double rightAve

splitAttr

int splitAttr

splitValue

double splitValue


Package weka.classifiers.neural

Class weka.classifiers.neural.LinearUnit implements Serializable

Class weka.classifiers.neural.NeuralConnection implements Serializable

Serialized Fields

m_inputList

NeuralConnection[] m_inputList
The list of inputs to this unit.


m_outputList

NeuralConnection[] m_outputList
The list of outputs from this unit.


m_inputNums

int[] m_inputNums
The numbering for the connections at the other end of the input lines.


m_outputNums

int[] m_outputNums
The numbering for the connections at the other end of the out lines.


m_numInputs

int m_numInputs
The number of inputs.


m_numOutputs

int m_numOutputs
The number of outputs.


m_unitValue

double m_unitValue
The output value for this unit, NaN if not calculated.


m_unitError

double m_unitError
The error value for this unit, NaN if not calculated.


m_weightsUpdated

boolean m_weightsUpdated
True if the weights have already been updated.


m_id

String m_id
The string that uniquely (provided naming is done properly) identifies this unit.


m_type

int m_type
The type of unit this is.


m_x

double m_x
The x coord of this unit purely for displaying purposes.


m_y

double m_y
The y coord of this unit purely for displaying purposes.

Class weka.classifiers.neural.NeuralNetwork implements Serializable

Serialized Fields

m_instances

Instances m_instances
The training instances.


m_currentInstance

Instance m_currentInstance
The current instance running through the network.


m_numeric

boolean m_numeric
A flag to say that it's a numeric class.


m_attributeRanges

double[] m_attributeRanges
The ranges for all the attributes.


m_attributeBases

double[] m_attributeBases
The base values for all the attributes.


m_outputs

NeuralNetwork.NeuralEnd[] m_outputs
The output units.(only feeds the errors, does no calcs)


m_inputs

NeuralNetwork.NeuralEnd[] m_inputs
The input units.(only feeds the inputs does no calcs)


m_neuralNodes

NeuralConnection[] m_neuralNodes
All the nodes that actually comprise the logical neural net.


m_numClasses

int m_numClasses
The number of classes.


m_numAttributes

int m_numAttributes
The number of attributes.


m_nodePanel

weka.classifiers.neural.NeuralNetwork.NodePanel m_nodePanel
The panel the nodes are displayed on.


m_controlPanel

NeuralNetwork.ControlPanel m_controlPanel
The control panel.


m_nextId

int m_nextId
The next id number available for default naming.


m_selected

FastVector m_selected
A Vector list of the units currently selected.


m_graphers

FastVector m_graphers
A Vector list of the graphers.


m_numEpochs

int m_numEpochs
The number of epochs to train through.


m_stopIt

boolean m_stopIt
a flag to state if the network should be running, or stopped.


m_stopped

boolean m_stopped
a flag to state that the network has in fact stopped.


m_accepted

boolean m_accepted
a flag to state that the network should be accepted the way it is.


m_win

JFrame m_win
The window for the network.


m_autoBuild

boolean m_autoBuild
A flag to tell the build classifier to automatically build a neural net.


m_gui

boolean m_gui
A flag to state that the gui for the network should be brought up. To allow interaction while training.


m_valSize

int m_valSize
An int to say how big the validation set should be.


m_driftThreshold

int m_driftThreshold
The number to to use to quit on validation testing.


m_randomSeed

long m_randomSeed
The number used to seed the random number generator.


m_random

Random m_random
The actual random number generator.


m_useNomToBin

boolean m_useNomToBin
A flag to state that a nominal to binary filter should be used.


m_nominalToBinaryFilter

NominalToBinaryFilter m_nominalToBinaryFilter
The actual filter.


m_hiddenLayers

String m_hiddenLayers
The string that defines the hidden layers


m_normalizeAttributes

boolean m_normalizeAttributes
This flag states that the user wants the input values normalized.


m_decay

boolean m_decay
This flag states that the user wants the learning rate to decay.


m_learningRate

double m_learningRate
This is the learning rate for the network.


m_momentum

double m_momentum
This is the momentum for the network.


m_epoch

int m_epoch
Shows the number of the epoch that the network just finished.


m_error

double m_error
Shows the error of the epoch that the network just finished.


m_reset

boolean m_reset
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. This would restart the network with the current options except that the learning rate would be smaller than before, (perhaps half of its current value). This option will not be available if the gui is chosen (if the gui is open the user can fix the network themselves, it is an architectural minefield for the network to be reset with the gui open).


m_normalizeClass

boolean m_normalizeClass
This flag states that the user wants the class to be normalized while processing in the network is done. (the final answer will be in the original range regardless). This option will only be used when the class is numeric.


m_sigmoidUnit

SigmoidUnit m_sigmoidUnit
this is a sigmoid unit.


m_linearUnit

LinearUnit m_linearUnit
This is a linear unit.

Class weka.classifiers.neural.NeuralNetwork.NeuralEnd implements Serializable

Serialized Fields

m_link

int m_link
the value that represents the instance value this node represents. For an input it is the attribute number, for an output, if nominal it is the class value.


m_input

boolean m_input
True if node is an input, False if it's an output.

Class weka.classifiers.neural.NeuralNode implements Serializable

Serialized Fields

m_weights

double[] m_weights
The weights for each of the input connections, and the threshold.


m_changeInWeights

double[] m_changeInWeights
The change in the weights.


m_random

Random m_random

m_methods

NeuralMethod m_methods
Performs the operations for this node. Currently this defines that the node is either a sigmoid or a linear unit.

Class weka.classifiers.neural.SigmoidUnit implements Serializable


Package weka.clusterers

Class weka.clusterers.Clusterer implements Serializable

Class weka.clusterers.Cobweb implements Serializable

Serialized Fields

tree

Cobweb.CTree tree
the cobweb tree


numClusters

int numClusters
number of clusters

Class weka.clusterers.DistributionClusterer implements Serializable

Class weka.clusterers.DistributionMetaClusterer implements Serializable

Serialized Fields

m_Clusterer

Clusterer m_Clusterer
The clusterer.

Class weka.clusterers.EM implements Serializable

Serialized Fields

m_model

Estimator[][] m_model
hold the discrete estimators for each cluster


m_modelNormal

double[][][] m_modelNormal
hold the normal estimators for each cluster


m_minStdDev

double m_minStdDev
default minimum standard deviation


m_weights

double[][] m_weights
hold the weights of each instance for each cluster


m_priors

double[] m_priors
the prior probabilities for clusters


m_loglikely

double m_loglikely
the loglikelihood of the data


m_theInstances

Instances m_theInstances
training instances


m_num_clusters

int m_num_clusters
number of clusters selected by the user or cross validation


m_initialNumClusters

int m_initialNumClusters
the initial number of clusters requested by the user--- -1 if xval is to be used to find the number of clusters


m_num_attribs

int m_num_attribs
number of attributes


m_num_instances

int m_num_instances
number of training instances


m_max_iterations

int m_max_iterations
maximum iterations to perform


m_rr

Random m_rr
random numbers and seed


m_rseed

int m_rseed

m_verbose

boolean m_verbose
Verbose?

Class weka.clusterers.SimpleKMeans implements Serializable

Serialized Fields

m_instances

Instances m_instances
training instances


m_ReplaceMissingFilter

ReplaceMissingValuesFilter m_ReplaceMissingFilter
replace missing values in training instances


m_NumClusters

int m_NumClusters
number of clusters to generate


m_ClusterCentroids

Instances m_ClusterCentroids
holds the cluster centroids


m_ClusterAssignments

int[] m_ClusterAssignments
temporary variable holding cluster assignments while iterating


m_Seed

int m_Seed
random seed


m_Min

double[] m_Min
attribute min values


m_Max

double[] m_Max
attribute max values


Package weka.core

Class weka.core.Attribute implements Serializable

Serialized Fields

m_Name

String m_Name
The attribute's name.


m_Type

int m_Type
The attribute's type.


m_Values

FastVector m_Values
The attribute's values (if nominal or string).


m_Hashtable

Hashtable m_Hashtable
Mapping of values to indices (if nominal or string).


m_Index

int m_Index
The attribute's index.

Class weka.core.BinarySparseInstance implements Serializable

Class weka.core.FastVector implements Serializable

Serialized Fields

m_Objects

Object[] m_Objects
The array of objects.


m_Size

int m_Size
The current size;


m_CapacityIncrement

int m_CapacityIncrement
The capacity increment


m_CapacityMultiplier

double m_CapacityMultiplier
The capacity multiplier.

Class weka.core.Instance implements Serializable

Serialized Fields

m_Dataset

Instances m_Dataset
The dataset the instance has access to. Null if the instance doesn't have access to any dataset. Only if an instance has access to a dataset, it knows about the actual attribute types.


m_AttValues

double[] m_AttValues
The instance's attribute values.


m_Weight

double m_Weight
The instance's weight.

Class weka.core.Instances implements Serializable

Serialized Fields

m_RelationName

String m_RelationName
The dataset's name.


m_Attributes

FastVector m_Attributes
The attribute information.


m_Instances

FastVector m_Instances
The instances.


m_ClassIndex

int m_ClassIndex
The class attribute's index


m_ValueBuffer

double[] m_ValueBuffer
Buffer of values for sparse instance


m_IndicesBuffer

int[] m_IndicesBuffer
Buffer of indices for sparse instance

Class weka.core.Matrix implements Serializable

Serialized Fields

m_Elements

double[][] m_Elements
The data in the matrix.

Class weka.core.Queue implements Serializable

Serialized Fields

m_Head

Queue.QueueNode m_Head
Store a reference to the head of the queue


m_Tail

Queue.QueueNode m_Tail
Store a reference to the tail of the queue


m_Size

int m_Size
Store the current number of elements in the queue

Class weka.core.Queue.QueueNode implements Serializable

Serialized Fields

m_Next

Queue.QueueNode m_Next
The next node in the queue


m_Contents

Object m_Contents
The nodes contents

Class weka.core.Range implements Serializable

Serialized Fields

m_RangeStrings

Vector m_RangeStrings
Record the string representations of the columns to delete


m_Invert

boolean m_Invert
Whether matching should be inverted


m_SelectFlags

boolean[] m_SelectFlags
The array of flags for whether an column is selected


m_Upper

int m_Upper
Store the maximum value permitted in the range. -1 indicates that no upper value has been set

Class weka.core.SerializedObject implements Serializable

Serialized Fields

m_Serialized

byte[] m_Serialized
Stores the serialized object


m_Compressed

boolean m_Compressed
True if the object has been compressed during storage

Class weka.core.SparseInstance implements Serializable

Serialized Fields

m_Indices

int[] m_Indices
The index of the attribute associated with each stored value.


m_NumAttributes

int m_NumAttributes
The maximum number of values that can be stored.

Class weka.core.UnassignedClassException implements Serializable

Class weka.core.UnassignedDatasetException implements Serializable

Class weka.core.UnsupportedAttributeTypeException implements Serializable

Class weka.core.UnsupportedClassTypeException implements Serializable

Class weka.core.WekaException implements Serializable


Package weka.core.converters

Class weka.core.converters.AbstractLoader implements Serializable

Serialized Fields

m_Retrieval

int m_Retrieval

Class weka.core.converters.ArffLoader implements Serializable

Serialized Fields

m_structure

Instances m_structure
Holds the determined structure (header) of the data set.

Class weka.core.converters.C45Loader implements Serializable

Serialized Fields

m_structure

Instances m_structure
Holds the determined structure (header) of the data set.


m_sourceFile

File m_sourceFile
Holds the source of the data set. In this case the names file of the data set. m_sourceFileData is the data file.


m_sourceFileData

File m_sourceFileData
Describe variable m_sourceFileData here.


m_fileStem

String m_fileStem
Holds the filestem.


m_numAttribs

int m_numAttribs
Number of attributes in the data (including ignore and label attributes).


m_ignore

boolean[] m_ignore
Which attributes are ignore or label. These are *not* included in the arff representation.

Class weka.core.converters.ConverterUtils implements Serializable

Class weka.core.converters.CSVLoader implements Serializable

Serialized Fields

m_structure

Instances m_structure
Holds the determined structure (header) of the data set.


m_sourceFile

File m_sourceFile
Holds the source of the data set.


m_cumulativeStructure

FastVector m_cumulativeStructure
A list of hash tables for accumulating nominal values during parsing.


m_cumulativeInstances

FastVector m_cumulativeInstances
Holds instances accumulated so far

Class weka.core.converters.SerializedInstancesLoader implements Serializable

Serialized Fields

m_Dataset

Instances m_Dataset
Holds the structure (header) of the data set.


m_IncrementalIndex

int m_IncrementalIndex
The current index position for incremental reading


Package weka.estimators

Class weka.estimators.DiscreteEstimator implements Serializable

Serialized Fields

m_Counts

double[] m_Counts
Hold the counts


m_SumOfCounts

double m_SumOfCounts
Hold the sum of counts

Class weka.estimators.KernelEstimator implements Serializable

Serialized Fields

m_Values

double[] m_Values
Vector containing all of the values seen


m_Weights

double[] m_Weights
Vector containing the associated weights


m_NumValues

int m_NumValues
Number of values stored in m_Weights and m_Values so far


m_SumOfWeights

double m_SumOfWeights
The sum of the weights so far


m_StandardDev

double m_StandardDev
The standard deviation


m_Precision

double m_Precision
The precision of data values


m_AllWeightsOne

boolean m_AllWeightsOne
Whether we can optimise the kernel summation

Class weka.estimators.MahalanobisEstimator implements Serializable

Serialized Fields

m_CovarianceInverse

Matrix m_CovarianceInverse
The inverse of the covariance matrix


m_Determinant

double m_Determinant
The determinant of the covariance matrix


m_ConstDelta

double m_ConstDelta
The difference between the conditioning value and the conditioning mean


m_ValueMean

double m_ValueMean
The mean of the values

Class weka.estimators.NormalEstimator implements Serializable

Serialized Fields

m_SumOfWeights

double m_SumOfWeights
The sum of the weights


m_SumOfValues

double m_SumOfValues
The sum of the values seen


m_SumOfValuesSq

double m_SumOfValuesSq
The sum of the values squared


m_Mean

double m_Mean
The current mean


m_StandardDev

double m_StandardDev
The current standard deviation


m_Precision

double m_Precision
The precision of numeric values ( = minimum std dev permitted)

Class weka.estimators.PoissonEstimator implements Serializable

Serialized Fields

m_NumValues

double m_NumValues
The number of values seen


m_SumOfValues

double m_SumOfValues
The sum of the values seen


m_Lambda

double m_Lambda
The average number of times an event occurs in an interval.


Package weka.experiment

Class weka.experiment.AveragingResultProducer implements Serializable

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer used to generate results


m_AdditionalMeasures

String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_ExpectedResultsPerAverage

int m_ExpectedResultsPerAverage
The number of results expected to average over for each run


m_CalculateStdDevs

boolean m_CalculateStdDevs
True if standard deviation fields should be produced


m_CountFieldName

String m_CountFieldName
The name of the field that will contain the number of results averaged over.


m_KeyFieldName

String m_KeyFieldName
The name of the key field to average over


m_KeyIndex

int m_KeyIndex
The index of the field to average over in the resultproducers key


m_Keys

FastVector m_Keys
Collects the keys from a single run


m_Results

FastVector m_Results
Collects the results from a single run

Class weka.experiment.ClassifierSplitEvaluator implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier used for evaluation


m_AdditionalMeasures

String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_doesProduce

boolean[] m_doesProduce
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce


m_numberAdditionalMeasures

int m_numberAdditionalMeasures
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_result

String m_result
Holds the statistics for the most recent application of the classifier


m_ClassifierOptions

String m_ClassifierOptions
The classifier options (if any)


m_ClassifierVersion

String m_ClassifierVersion
The classifier version


m_IRclass

int m_IRclass
Class index for information retrieval statistics (default 0)

Class weka.experiment.CostSensitiveClassifierSplitEvaluator implements Serializable

Serialized Fields

m_OnDemandDirectory

File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory

Class weka.experiment.CrossValidationResultProducer implements Serializable

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_NumFolds

int m_NumFolds
The number of folds in the cross-validation


m_debugOutput

boolean m_debugOutput
Save raw output of split evaluators --- for debugging purposes


m_ZipDest

OutputZipper m_ZipDest
The output zipper to use for saving raw splitEvaluator output


m_OutputFile

File m_OutputFile
The destination output file/directory for raw output


m_SplitEvaluator

SplitEvaluator m_SplitEvaluator
The SplitEvaluator used to generate results


m_AdditionalMeasures

String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators

Class weka.experiment.CSVResultListener implements Serializable

Serialized Fields

m_RP

ResultProducer m_RP
The ResultProducer sending us results


m_OutputFile

File m_OutputFile
The destination output file, null sends to System.out

Class weka.experiment.DatabaseResultListener implements Serializable

Serialized Fields

m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer to listen to


m_ResultsTableName

String m_ResultsTableName
The name of the current results table


m_Debug

boolean m_Debug
True if debugging output should be printed


m_CacheKeyName

String m_CacheKeyName
Holds the name of the key field to cache upon, or null if no caching


m_CacheKeyIndex

int m_CacheKeyIndex
Stores the index of the key column holding the cache key data


m_CacheKey

Object[] m_CacheKey
Stores the key for which the cache is valid


m_Cache

FastVector m_Cache
Stores the cached values

Class weka.experiment.DatabaseResultProducer implements Serializable

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer used to generate results


m_AdditionalMeasures

String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators

Class weka.experiment.DatabaseUtils implements Serializable

Serialized Fields

m_DatabaseURL

String m_DatabaseURL
Database URL


m_Connection

Connection m_Connection
The database connection


m_Statement

Statement m_Statement
The statement used for database queries


m_Debug

boolean m_Debug
True if debugging output should be printed

Class weka.experiment.Experiment implements Serializable

Serialized Fields

m_ResultListener

ResultListener m_ResultListener
Where results will be sent


m_ResultProducer

ResultProducer m_ResultProducer
The result producer


m_RunLower

int m_RunLower
Lower run number


m_RunUpper

int m_RunUpper
Upper run number


m_Datasets

DefaultListModel m_Datasets
An array of dataset files


m_UsePropertyIterator

boolean m_UsePropertyIterator
True if the exp should also iterate over a property of the RP


m_PropertyPath

PropertyNode[] m_PropertyPath
The path to the iterator property


m_PropertyArray

Object m_PropertyArray
The array of values to set the property to


m_Notes

String m_Notes
User notes about the experiment


m_AdditionalMeasures

String[] m_AdditionalMeasures
Method names of additional measures of objects contained in the custom property iterator. Only methods names beginning with "measure" and returning doubles are recognised


m_ClassFirst

boolean m_ClassFirst
True if the class attribute is the first attribute for all datasets involved in this experiment.


m_AdvanceDataSetFirst

boolean m_AdvanceDataSetFirst
If true an experiment will advance the current data set befor any custom itererator


m_m_AdvanceRunFirst

boolean m_m_AdvanceRunFirst

Class weka.experiment.InstanceQuery implements Serializable

Serialized Fields

m_CreateSparseData

boolean m_CreateSparseData
Determines whether sparse data is created


m_Query

String m_Query
Query to execute

Class weka.experiment.InstancesResultListener implements Serializable

Class weka.experiment.LearningRateResultProducer implements Serializable

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer used to generate results


m_AdditionalMeasures

String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_LowerSize

int m_LowerSize
The minimum number of instances to use. If this is zero, the first step will contain m_StepSize instances


m_UpperSize

int m_UpperSize
The maximum number of instances to use. -1 indicates no maximum (other than the total number of instances)


m_StepSize

int m_StepSize
The number of instances to add at each step


m_CurrentSize

int m_CurrentSize
The current dataset size during stepping

Class weka.experiment.PropertyNode implements Serializable

Serialization Methods

readObject

private void readObject(ObjectInputStream in)
                 throws IOException,
                        ClassNotFoundException

writeObject

private void writeObject(ObjectOutputStream out)
                  throws IOException
Serialized Fields

value

Object value
The current property value


parentClass

Class parentClass
The class of the object with this property


property

PropertyDescriptor property
Other info about the property

Class weka.experiment.RandomSplitResultProducer implements Serializable

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_TrainPercent

double m_TrainPercent
The percentage of instances to use for training


m_randomize

boolean m_randomize
Whether dataset is to be randomized


m_SplitEvaluator

SplitEvaluator m_SplitEvaluator
The SplitEvaluator used to generate results


m_AdditionalMeasures

String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_debugOutput

boolean m_debugOutput
Save raw output of split evaluators --- for debugging purposes


m_ZipDest

OutputZipper m_ZipDest
The output zipper to use for saving raw splitEvaluator output


m_OutputFile

File m_OutputFile
The destination output file/directory for raw output

Class weka.experiment.RegressionSplitEvaluator implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier used for evaluation


m_AdditionalMeasures

String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_doesProduce

boolean[] m_doesProduce
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce


m_result

String m_result
Holds the statistics for the most recent application of the classifier


m_ClassifierOptions

String m_ClassifierOptions
The classifier options (if any)


m_ClassifierVersion

String m_ClassifierVersion
The classifier version

Class weka.experiment.RemoteEngine implements Serializable

Serialized Fields

m_HostName

String m_HostName
The name of the host that this engine is started on


m_TaskQueue

Queue m_TaskQueue
A queue of waiting tasks


m_TaskIdQueue

Queue m_TaskIdQueue
A queue of corresponding ID's for tasks


m_TaskStatus

Hashtable m_TaskStatus
A hashtable of experiment status


m_TaskRunning

boolean m_TaskRunning
Is there a task running

Class weka.experiment.RemoteEngine_Stub implements Serializable

Class weka.experiment.RemoteExperiment implements Serializable

Serialized Fields

m_listeners

FastVector m_listeners
The list of objects listening for remote experiment events


m_remoteHosts

DefaultListModel m_remoteHosts
Holds the names of machines with remoteEngine servers running


m_remoteHostsQueue

Queue m_remoteHostsQueue
The queue of available hosts


m_remoteHostsStatus

int[] m_remoteHostsStatus
The status of each of the remote hosts


m_remoteHostFailureCounts

int[] m_remoteHostFailureCounts
The number of times tasks have failed on each remote host


m_experimentAborted

boolean m_experimentAborted
Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts experiment (via gui)


m_removedHosts

int m_removedHosts
The number of hosts removed due to exceeding max failures


m_failedCount

int m_failedCount
The count of failed sub-experiments


m_finishedCount

int m_finishedCount
The count of successfully completed sub-experiments


m_baseExperiment

Experiment m_baseExperiment
The base experiment to split up into sub experiments for remote execution


m_subExperiments

Experiment[] m_subExperiments
The sub experiments


m_subExpQueue

Queue m_subExpQueue
The queue of sub experiments waiting to be processed


m_subExpComplete

int[] m_subExpComplete
The status of each of the sub-experiments


m_splitByDataSet

boolean m_splitByDataSet
If true, then sub experiments are created on the basis of data sets rather than run number.

Class weka.experiment.RemoteExperimentEvent implements Serializable

Serialized Fields

m_statusMessage

boolean m_statusMessage
A status type message


m_logMessage

boolean m_logMessage
A log type message


m_messageString

String m_messageString
The message


m_experimentFinished

boolean m_experimentFinished
True if a remote experiment has finished

Class weka.experiment.RemoteExperimentSubTask implements Serializable

Serialized Fields

m_experiment

Experiment m_experiment

Class weka.experiment.TaskStatusInfo implements Serializable

Serialized Fields

m_ExecutionStatus

int m_ExecutionStatus
Holds current execution status.


m_StatusMessage

String m_StatusMessage
Holds current status message.


m_TaskResult

Object m_TaskResult
Holds task result. Set to null for no returnable result.


Package weka.filters

Class weka.filters.AbstractTimeSeriesFilter implements Serializable

Serialized Fields

m_SelectedCols

Range m_SelectedCols
Stores which columns to copy


m_FillWithMissing

boolean m_FillWithMissing
True if missing values should be used rather than removing instances where the translated value is not known (due to border effects).


m_InstanceRange

int m_InstanceRange
The number of instances forward to translate values between. A negative number indicates taking values from a past instance.


m_History

Queue m_History
Stores the historical instances to copy values between

Class weka.filters.AddFilter implements Serializable

Serialized Fields

m_AttributeType

int m_AttributeType
Record the type of attribute to insert


m_Name

String m_Name
The name for the new attribute


m_Insert

int m_Insert
The location to insert the new attribute


m_Labels

FastVector m_Labels
The list of labels for nominal attribute

Class weka.filters.AllFilter implements Serializable

Class weka.filters.AttributeExpressionFilter implements Serializable

Serialized Fields

m_infixExpression

String m_infixExpression
The infix expression


m_operatorStack

Stack m_operatorStack
Operator stack


m_postFixExpVector

Vector m_postFixExpVector
Holds the expression in postfix form


m_signMod

boolean m_signMod
True if the next numeric constant or attribute index is negative


m_previousTok

String m_previousTok
Holds the previous token


m_attributeName

String m_attributeName
Name of the new attribute. "expression" length string will use the provided expression as the new attribute name


m_Debug

boolean m_Debug
If true, makes the attribute name equal to the postfix parse of the expression

Class weka.filters.AttributeFilter implements Serializable

Serialized Fields

m_SelectCols

Range m_SelectCols
Stores which columns to select as a funky range


m_SelectedAttributes

int[] m_SelectedAttributes
Stores the indexes of the selected attributes in order, once the dataset is seen


m_InputStringIndex

int[] m_InputStringIndex
Contains an index of string attributes in the input format that will survive the filtering process

Class weka.filters.AttributeSelectionFilter implements Serializable

Serialized Fields

m_trainSelector

AttributeSelection m_trainSelector
the attribute selection evaluation object


m_ASEvaluator

ASEvaluation m_ASEvaluator
the attribute evaluator to use


m_ASSearch

ASSearch m_ASSearch
the search method if any


m_FilterOptions

String[] m_FilterOptions
holds a copy of the full set of valid options passed to the filter


m_SelectedAttributes

int[] m_SelectedAttributes
holds the selected attributes

Class weka.filters.AttributeTypeFilter implements Serializable

Serialized Fields

m_DeleteType

int m_DeleteType
Stores which type of attribute to delete

Class weka.filters.CopyAttributesFilter implements Serializable

Serialized Fields

m_CopyCols

Range m_CopyCols
Stores which columns to copy


m_SelectedAttributes

int[] m_SelectedAttributes
Stores the indexes of the selected attributes in order, once the dataset is seen


m_InputStringIndex

int[] m_InputStringIndex
Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated

Class weka.filters.DiscretizeFilter implements Serializable

Serialized Fields

m_DiscretizeCols

Range m_DiscretizeCols
Stores which columns to Discretize


m_NumBins

int m_NumBins
The number of bins to divide the attribute into


m_CutPoints

double[][] m_CutPoints
Store the current cutpoints


m_UseMDL

boolean m_UseMDL
True if discretisation will be done by MDL rather than binning


m_MakeBinary

boolean m_MakeBinary
Output binary attributes for discretized attributes.


m_UseBetterEncoding

boolean m_UseBetterEncoding
Use better encoding of split point for MDL.


m_UseKononenko

boolean m_UseKononenko
Use Kononenko's MDL criterion instead of Fayyad et al.'s


m_FindNumBins

boolean m_FindNumBins
Find the number of bins using cross-validated entropy.

Class weka.filters.DynamicReductDiscretize implements Serializable

Serialized Fields

debug

int debug

numSubtables

int numSubtables

reductsRatio

double reductsRatio

maxError

double maxError

confidence

double confidence

dynamicStability

double dynamicStability

fast

boolean fast

cutPoints

double[][] cutPoints
Store the current cutpoints

Class weka.filters.EmptyAttributeFilter implements Serializable

Serialized Fields

m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.


m_Keep

boolean[] m_Keep
An array of attribute indices that shall be kept.

Class weka.filters.Filter implements Serializable

Serialized Fields

m_Debug

boolean m_Debug
Debugging mode


m_OutputFormat

Instances m_OutputFormat
The output format for instances


m_OutputQueue

Queue m_OutputQueue
The output instance queue


m_OutputStringAtts

int[] m_OutputStringAtts
Indices of string attributes in the output format


m_InputStringAtts

int[] m_InputStringAtts
Indices of string attributes in the input format


m_InputFormat

Instances m_InputFormat
The input format for instances


m_NewBatch

boolean m_NewBatch
Record whether the filter is at the start of a batch

Class weka.filters.FirstOrderFilter implements Serializable

Serialized Fields

m_DeltaCols

Range m_DeltaCols
Stores which columns to take differences between

Class weka.filters.InstanceFilter implements Serializable

Serialized Fields

m_AttributeSet

int m_AttributeSet
Stores the attribute setting


m_Attribute

int m_Attribute
Stores which attribute to be used for filtering


m_Values

Range m_Values
Stores which values of nominal attribute are to be used for filtering.


m_Value

double m_Value
Stores which value of a numeric attribute is to be used for filtering.


m_Inverse

boolean m_Inverse
Inverse of test to be used?


m_MatchMissingValues

boolean m_MatchMissingValues
True if missing values should count as a match


m_ModifyHeader

boolean m_ModifyHeader
Modify header for nominal attributes?


m_NominalMapping

int[] m_NominalMapping
If m_ModifyHeader, stores a mapping from old to new indexes

Class weka.filters.MakeIndicatorFilter implements Serializable

Serialized Fields

m_AttIndexSet

int m_AttIndexSet
The attribute's index option setting.


m_AttIndex

int m_AttIndex
The attribute's index


m_ValIndex

Range m_ValIndex
The value's index


m_Numeric

boolean m_Numeric
Make boolean attribute numeric.

Class weka.filters.MergeTwoValuesFilter implements Serializable

Serialized Fields

m_AttIndexSet

int m_AttIndexSet
The attribute's index setting.


m_FirstIndexSet

int m_FirstIndexSet
The first value's index setting.


m_SecondIndexSet

int m_SecondIndexSet
The second value's index setting.


m_AttIndex

int m_AttIndex
The attribute's index.


m_FirstIndex

int m_FirstIndex
The first value's index.


m_SecondIndex

int m_SecondIndex
The second value's index.

Class weka.filters.NominalToBinaryFilter implements Serializable

Serialized Fields

m_Indices

int[][] m_Indices
The sorted indices of the attribute values.


m_Numeric

boolean m_Numeric
Are the new attributes going to be nominal or numeric ones?

Class weka.filters.NonSparseToSparseFilter implements Serializable

Class weka.filters.NormalizationFilter implements Serializable

Serialized Fields

m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.

Class weka.filters.NullFilter implements Serializable

Class weka.filters.NumericToBinaryFilter implements Serializable

Class weka.filters.NumericTransformFilter implements Serializable

Serialized Fields

m_Cols

Range m_Cols
Stores which columns to transform.


m_Class

Class m_Class
Class containing transformation method.


m_Method

Method m_Method
Transformation method.

Class weka.filters.ObfuscateFilter implements Serializable

Class weka.filters.RandomizeFilter implements Serializable

Serialized Fields

m_Seed

int m_Seed
The random number seed


m_Random

Random m_Random
The current random number generator

Class weka.filters.ReplaceMissingValuesFilter implements Serializable

Serialized Fields

m_ModesAndMeans

double[] m_ModesAndMeans
The modes and means

Class weka.filters.ResampleFilter implements Serializable

Serialized Fields

m_SampleSizePercent

double m_SampleSizePercent
The subsample size, percent of original set, default 100%


m_RandomSeed

int m_RandomSeed
The random number generator seed


m_BiasToUniformClass

double m_BiasToUniformClass
The degree of bias towards uniform (nominal) class distribution


m_FirstBatchDone

boolean m_FirstBatchDone
True if the first batch has been done

Class weka.filters.RSDiscretize implements Serializable

Serialized Fields

debug

int debug

globalDiscretization

boolean globalDiscretization

keepValues

boolean keepValues

cutPoints

double[][] cutPoints
Store the current cutpoints


bestCuts

MDCutPoints bestCuts

Class weka.filters.RSNumerizeNominal implements Serializable

Serialized Fields

range

Range range

debug

int debug

Class weka.filters.RSReplaceMissingValues implements Serializable

Serialized Fields

conversionVector

double[] conversionVector
The mean values of numeric attributes are collected in a vector that is used for conversion.


counts

Hashtable counts
This hashtable has a number of sub-hashtables for each decision value that keeps track of how many instances have a certain value for a certain attribute, given the decision value.


debug

int debug
Debugging level, used for development purposes alone.

Class weka.filters.SparseToNonSparseFilter implements Serializable

Class weka.filters.SplitDatasetFilter implements Serializable

Serialized Fields

m_Range

Range m_Range
Range of instances provided by user.


m_Inverse

boolean m_Inverse
Indicates if inverse of selection is to be output.


m_NumFolds

int m_NumFolds
Number of folds to split dataset into


m_Fold

int m_Fold
Fold to output


m_Seed

long m_Seed
Random number seed.


m_DontStratifyData

boolean m_DontStratifyData
Don't stratify data if class index is set?

Class weka.filters.SpreadSubsampleFilter implements Serializable

Serialized Fields

m_RandomSeed

int m_RandomSeed
The random number generator seed


m_MaxCount

int m_MaxCount
The maximum count of any class


m_FirstBatchDone

boolean m_FirstBatchDone
True if the first batch has been done


m_DistributionSpread

double m_DistributionSpread
True if the first batch has been done


m_AdjustWeights

boolean m_AdjustWeights
True if instance weights will be adjusted to maintain total weight per class.

Class weka.filters.StringToNominalFilter implements Serializable

Serialized Fields

m_AttIndexSet

int m_AttIndexSet
The attribute index setting (allows -1 = last).


m_AttIndex

int m_AttIndex
The attribute index.

Class weka.filters.SwapAttributeValuesFilter implements Serializable

Serialized Fields

m_AttIndexSet

int m_AttIndexSet
The attribute's index setting.


m_FirstIndexSet

int m_FirstIndexSet
The first value's index setting.


m_SecondIndexSet

int m_SecondIndexSet
The second value's index setting.


m_AttIndex

int m_AttIndex
The attribute's index.


m_FirstIndex

int m_FirstIndex
The first value's index.


m_SecondIndex

int m_SecondIndex
The second value's index.

Class weka.filters.TimeSeriesDeltaFilter implements Serializable

Class weka.filters.TimeSeriesTranslateFilter implements Serializable


Package weka.gui

Class weka.gui.AttributeSelectionPanel implements Serializable

Serialized Fields

m_IncludeAll

JButton m_IncludeAll
Press to select all attributes


m_RemoveAll

JButton m_RemoveAll
Press to deselect all attributes


m_Invert

JButton m_Invert
Press to invert the current selection


m_Table

JTable m_Table
The table displaying attribute names and selection status


m_Model

AttributeSelectionPanel.AttributeTableModel m_Model
The table model containingn attribute names and selection status

Class weka.gui.AttributeSummaryPanel implements Serializable

Serialized Fields

m_AttributeNameLab

JLabel m_AttributeNameLab
Displays the name of the relation


m_AttributeTypeLab

JLabel m_AttributeTypeLab
Displays the type of attribute


m_MissingLab

JLabel m_MissingLab
Displays the number of missing values


m_UniqueLab

JLabel m_UniqueLab
Displays the number of unique values


m_DistinctLab

JLabel m_DistinctLab
Displays the number of distinct values


m_StatsTable

JTable m_StatsTable
Displays other stats in a table


m_Instances

Instances m_Instances
The instances we're playing with


m_AttributeStats

AttributeStats[] m_AttributeStats
Cached stats on the attributes we've summarized so far

Class weka.gui.GenericArrayEditor implements Serializable

Serialized Fields

m_Support

PropertyChangeSupport m_Support
Handles property change notification


m_Label

JLabel m_Label
The label for when we can't edit that type


m_ElementList

JList m_ElementList
The list component displaying current values


m_ElementClass

Class m_ElementClass
The class of objects allowed in the array


m_ListModel

DefaultListModel m_ListModel
The defaultlistmodel holding our data


m_ElementEditor

PropertyEditor m_ElementEditor
The property editor for the class we are editing


m_DeleteBut

JButton m_DeleteBut
Click this to delete the selected array values


m_AddBut

JButton m_AddBut
Click to add the current object configuration to the array


m_InnerActionListener

ActionListener m_InnerActionListener
Listens to buttons being pressed and taking the appropriate action


m_InnerSelectionListener

ListSelectionListener m_InnerSelectionListener
Listens to list items being selected and takes appropriate action

Class weka.gui.GenericObjectEditor.GOEPanel implements Serializable

Serialized Fields

m_ObjectChooser

JComboBox m_ObjectChooser
The chooser component


m_ChildPropertySheet

PropertySheetPanel m_ChildPropertySheet
The component that performs classifier customization


m_ObjectNames

DefaultComboBoxModel m_ObjectNames
The model containing the list of names to select from


m_OpenBut

JButton m_OpenBut
Open object from disk


m_SaveBut

JButton m_SaveBut
Save object to disk


m_okBut

JButton m_okBut
ok button


m_cancelBut

JButton m_cancelBut
cancel button


m_FileChooser

JFileChooser m_FileChooser
The filechooser for opening and saving object files

Class weka.gui.GUIChooser implements Serializable

Serialized Fields

m_SimpleBut

Button m_SimpleBut
Click to open the simplecli


m_ExplorerBut

Button m_ExplorerBut
Click to open the Explorer


m_ExperimenterBut

Button m_ExperimenterBut
Click to open the Explorer


m_SimpleCLI

SimpleCLI m_SimpleCLI
The SimpleCLI


m_ExplorerFrame

JFrame m_ExplorerFrame
The frame containing the explorer interface


m_ExperimenterFrame

JFrame m_ExperimenterFrame
The frame containing the experiment interface


m_weka

Image m_weka
The weka image

Class weka.gui.InstancesSummaryPanel implements Serializable

Serialized Fields

m_RelationNameLab

JLabel m_RelationNameLab
Displays the name of the relation


m_NumInstancesLab

JLabel m_NumInstancesLab
Displays the number of instances


m_NumAttributesLab

JLabel m_NumAttributesLab
Displays the number of attributes


m_Instances

Instances m_Instances
The instances we're playing with

Class weka.gui.ListSelectorDialog implements Serializable

Serialized Fields

m_SelectBut

JButton m_SelectBut
Click to choose the currently selected property


m_CancelBut

JButton m_CancelBut
Click to cancel the property selection


m_List

JList m_List
The list component


m_Result

int m_Result
Whether the selection was made or cancelled

Class weka.gui.LogPanel implements Serializable

Serialized Fields

m_StatusLab

JLabel m_StatusLab
Displays the current status


m_LogText

JTextArea m_LogText
Displays the log messages


m_First

boolean m_First
An indicator for whether text has been output yet


m_TaskMonitor

WekaTaskMonitor m_TaskMonitor
The panel for monitoring the number of running tasks (if supplied)

Class weka.gui.PropertyDialog implements Serializable

Serialized Fields

m_Editor

PropertyEditor m_Editor
The property editor


m_EditorComponent

Component m_EditorComponent
The custom editor component

Class weka.gui.PropertyPanel implements Serializable

Serialized Fields

m_Editor

PropertyEditor m_Editor
The property editor


m_PD

PropertyDialog m_PD
The currently displayed property dialog, if any

Class weka.gui.PropertySelectorDialog implements Serializable

Serialized Fields

m_SelectBut

JButton m_SelectBut
Click to choose the currently selected property


m_CancelBut

JButton m_CancelBut
Click to cancel the property selection


m_Root

DefaultMutableTreeNode m_Root
The root of the property tree


m_RootObject

Object m_RootObject
The object at the root of the tree


m_Result

int m_Result
Whether the selection was made or cancelled


m_ResultPath

Object[] m_ResultPath
Stores the path to the selected property


m_Tree

JTree m_Tree
The component displaying the property tree

Class weka.gui.PropertySheetPanel implements Serializable

Serialized Fields

m_Target

Object m_Target
The target object being edited


m_Properties

PropertyDescriptor[] m_Properties
Holds properties of the target


m_Methods

MethodDescriptor[] m_Methods
Holds the methods of the target


m_Editors

PropertyEditor[] m_Editors
Holds property editors of the object


m_Values

Object[] m_Values
Holds current object values for each property


m_Views

JComponent[] m_Views
Stores GUI components containing each editing component


m_Labels

JLabel[] m_Labels
The labels for each property


m_TipTexts

String[] m_TipTexts
The tool tip text for each property


m_HelpText

StringBuffer m_HelpText
StringBuffer containing help text for the object being edited


m_HelpFrame

JFrame m_HelpFrame
Help frame


m_HelpBut

JButton m_HelpBut
Button to pop up the full help text in a separate frame


m_NumEditable

int m_NumEditable
A count of the number of properties we have an editor for


support

PropertyChangeSupport support
A support object for handling property change listeners

Class weka.gui.ResultHistoryPanel implements Serializable

Serialized Fields

m_SingleText

JTextComponent m_SingleText
An optional component for single-click display


m_SingleName

String m_SingleName
The named result being viewed in the single-click display


m_Model

DefaultListModel m_Model
The list model


m_List

JList m_List
The list component


m_Results

Hashtable m_Results
A Hashtable mapping names to result buffers


m_FramedOutput

Hashtable m_FramedOutput
A Hashtable mapping names to output text components


m_Objs

Hashtable m_Objs
A hashtable mapping names to arbitrary objects


m_HandleRightClicks

boolean m_HandleRightClicks
Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer

Class weka.gui.SetInstancesPanel implements Serializable

Serialized Fields

m_OpenFileBut

JButton m_OpenFileBut
Click to open instances from a file


m_OpenURLBut

JButton m_OpenURLBut
Click to open instances from a URL


m_Summary

InstancesSummaryPanel m_Summary
The instance summary component


m_ArffFilter

FileFilter m_ArffFilter
Filter to ensure only arff files are selected


m_FileChooser

JFileChooser m_FileChooser
The file chooser for selecting arff files


m_LastURL

String m_LastURL
Stores the last URL that instances were loaded from


m_IOThread

Thread m_IOThread
The thread we do loading in


m_Support

PropertyChangeSupport m_Support
Manages sending notifications to people when we change the set of working instances.


m_Instances

Instances m_Instances
The current set of instances loaded

Class weka.gui.SimpleCLI implements Serializable

Serialized Fields

m_OutputArea

TextArea m_OutputArea
The output area canvas added to the frame


m_Input

TextField m_Input
The command input area


m_CommandHistory

Vector m_CommandHistory
The history of commands entered interactively


m_HistoryPos

int m_HistoryPos
The current position in the command history


m_POO

PipedOutputStream m_POO
The new output stream for System.out


m_POE

PipedOutputStream m_POE
The new output stream for System.err


m_OutRedirector

Thread m_OutRedirector
The thread that sends output from m_POO to the output box


m_ErrRedirector

Thread m_ErrRedirector
The thread that sends output from m_POE to the output box


m_RunThread

Thread m_RunThread
The thread currently running a class main method

Class weka.gui.WekaTaskMonitor implements Serializable

Serialized Fields

m_ActiveTasks

int m_ActiveTasks
The number of running weka threads


m_MonitorLabel

JLabel m_MonitorLabel
The label for displaying info


m_iconStationary

ImageIcon m_iconStationary
The icon for the stationary bird


m_iconAnimated

ImageIcon m_iconAnimated
The icon for the animated bird


m_animating

boolean m_animating
True if their are active tasks


Package weka.gui.experiment

Class weka.gui.experiment.DatasetListPanel implements Serializable

Serialized Fields

m_Exp

Experiment m_Exp
The experiment to set the dataset list of


m_List

JList m_List
The component displaying the dataset list


m_AddBut

JButton m_AddBut
Click to add a dataset


m_DeleteBut

JButton m_DeleteBut
Click to remove the selected dataset from the list


m_relativeCheck

JCheckBox m_relativeCheck
Make file paths relative to the user (start) directory


m_ArffFilter

FileFilter m_ArffFilter
A filter to ensure only arff files get selected


m_UserDir

File m_UserDir
The user (start) directory


m_FileChooser

JFileChooser m_FileChooser
The file chooser component

Class weka.gui.experiment.DistributeExperimentPanel implements Serializable

Serialized Fields

m_Exp

RemoteExperiment m_Exp
The experiment to configure.


m_enableDistributedExperiment

JCheckBox m_enableDistributedExperiment
Distribute the current experiment to remote hosts


m_configureHostNames

JButton m_configureHostNames
Popup the HostListPanel


m_hostList

HostListPanel m_hostList
The host list panel


m_splitByDataSet

JRadioButton m_splitByDataSet
Split experiment up by data set.


m_splitByRun

JRadioButton m_splitByRun
Split experiment up by run number.


m_radioListener

ActionListener m_radioListener
Handle radio buttons

Class weka.gui.experiment.Experimenter implements Serializable

Serialized Fields

m_SetupPanel

SetupPanel m_SetupPanel
The panel for configuring the experiment


m_RunPanel

RunPanel m_RunPanel
The panel for running the experiment


m_ResultsPanel

ResultsPanel m_ResultsPanel
The panel for analysing experimental results


m_TabbedPane

JTabbedPane m_TabbedPane
The tabbed pane that controls which sub-pane we are working with


m_ClassFirst

boolean m_ClassFirst
True if the class attribute is the first attribute for all datasets involved in this experiment.

Class weka.gui.experiment.GeneratorPropertyIteratorPanel implements Serializable

Serialized Fields

m_ConfigureBut

JButton m_ConfigureBut
Click to select the property to iterate over


m_StatusBox

JComboBox m_StatusBox
Controls whether the custom iterator is used or not


m_ArrayEditor

GenericArrayEditor m_ArrayEditor
Allows editing of the custom property values


m_Exp

Experiment m_Exp
The experiment this all applies to


m_Listeners

FastVector m_Listeners
Listeners who want to be notified about editing status of this panel

Class weka.gui.experiment.HostListPanel implements Serializable

Serialized Fields

m_Exp

RemoteExperiment m_Exp
The remote experiment to set the host list of


m_List

JList m_List
The component displaying the host list


m_DeleteBut

JButton m_DeleteBut
Click to remove the selected host from the list


m_HostField

JTextField m_HostField
The field with which to enter host names

Class weka.gui.experiment.ResultsPanel implements Serializable

Serialized Fields

m_FromFileBut

JButton m_FromFileBut
Click to load results from a file


m_FromDBaseBut

JButton m_FromDBaseBut
Click to load results from a database


m_FromExpBut

JButton m_FromExpBut
Click to get results from the destination given in the experiment


m_FromLab

JLabel m_FromLab
Displays a message about the current result set


m_DatasetModel

DefaultComboBoxModel m_DatasetModel
The model embedded in m_DatasetCombo


m_RunModel

DefaultComboBoxModel m_RunModel
The model embedded in m_RunCombo


m_CompareModel

DefaultComboBoxModel m_CompareModel
The model embedded in m_CompareCombo


m_TestsModel

DefaultListModel m_TestsModel
The model embedded in m_TestsList


m_DatasetKeyLabel

JLabel m_DatasetKeyLabel
Displays the currently selected column names for the scheme & options


m_DatasetKeyBut

JButton m_DatasetKeyBut
Click to edit the columns used to determine the scheme


m_DatasetKeyModel

DefaultListModel m_DatasetKeyModel
Stores the list of attributes for selecting the scheme columns


m_DatasetKeyList

JList m_DatasetKeyList
Displays the list of selected columns determining the scheme


m_RunCombo

JComboBox m_RunCombo
Lets the user select which column contains the run number


m_ResultKeyLabel

JLabel m_ResultKeyLabel
Displays the currently selected column names for the scheme & options


m_ResultKeyBut

JButton m_ResultKeyBut
Click to edit the columns used to determine the scheme


m_ResultKeyModel

DefaultListModel m_ResultKeyModel
Stores the list of attributes for selecting the scheme columns


m_ResultKeyList

JList m_ResultKeyList
Displays the list of selected columns determining the scheme


m_TestsButton

JButton m_TestsButton
Lets the user select which scheme to base comparisons against


m_TestsList

JList m_TestsList
Holds the list of schemes to base the test against


m_CompareCombo

JComboBox m_CompareCombo
Lets the user select which performance measure to analyze


m_SigTex

JTextField m_SigTex
Lets the user edit the test significance


m_ShowStdDevs

JCheckBox m_ShowStdDevs
Lets the user select whether standard deviations are to be output or not


m_PerformBut

JButton m_PerformBut
Click to start the test


m_SaveOutBut

JButton m_SaveOutBut
Click to save test output to a file


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_OutText

JTextArea m_OutText
Displays the output of tests


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_ArffFilter

FileFilter m_ArffFilter
Filter to ensure only arff files are selected for result files


m_FileChooser

JFileChooser m_FileChooser
The file chooser for selecting result files


m_TTester

PairedTTester m_TTester
The PairedTTester object


m_Instances

Instances m_Instances
The instances we're extracting results from


m_InstanceQuery

InstanceQuery m_InstanceQuery
Does any database querying for us


m_LoadThread

Thread m_LoadThread
A thread to load results instances from a file or database


m_Exp

Experiment m_Exp
An experiment (used for identifying a result source) -- optional


m_ConfigureListener

ActionListener m_ConfigureListener
An actionlisteners that updates ttest settings


COMBO_SIZE

Dimension COMBO_SIZE

Class weka.gui.experiment.RunNumberPanel implements Serializable

Serialized Fields

m_LowerText

JTextField m_LowerText
Configures the lower run number


m_UpperText

JTextField m_UpperText
Configures the upper run number


m_Exp

Experiment m_Exp
The experiment being configured

Class weka.gui.experiment.RunPanel implements Serializable

Serialized Fields

m_StartBut

JButton m_StartBut
Click to start running the experiment


m_StopBut

JButton m_StopBut
Click to signal the running experiment to halt


m_Log

LogPanel m_Log

m_Exp

Experiment m_Exp
The experiment to run


m_RunThread

Thread m_RunThread
The thread running the experiment

Class weka.gui.experiment.SetupPanel implements Serializable

Serialized Fields

m_Exp

Experiment m_Exp
The experiment being configured


m_OpenBut

JButton m_OpenBut
Click to load an experiment


m_SaveBut

JButton m_SaveBut
Click to save an experiment


m_NewBut

JButton m_NewBut
Click to create a new experiment with default settings


m_ExpFilter

FileFilter m_ExpFilter
A filter to ensure only experiment files get shown in the chooser


m_FileChooser

JFileChooser m_FileChooser
The file chooser for selecting experiments


m_RPEditor

GenericObjectEditor m_RPEditor
The ResultProducer editor


m_RPEditorPanel

PropertyPanel m_RPEditorPanel
The panel to contain the ResultProducer editor


m_RLEditor

GenericObjectEditor m_RLEditor
The ResultListener editor


m_RLEditorPanel

PropertyPanel m_RLEditorPanel
The panel to contain the ResultListener editor


m_GeneratorPropertyPanel

GeneratorPropertyIteratorPanel m_GeneratorPropertyPanel
The panel that configures iteration on custom resultproducer property


m_RunNumberPanel

RunNumberPanel m_RunNumberPanel
The panel for configuring run numbers


m_DistributeExperimentPanel

DistributeExperimentPanel m_DistributeExperimentPanel
The panel for enabling a distributed experiment


m_DatasetListPanel

DatasetListPanel m_DatasetListPanel
The panel for configuring selected datasets


m_NotesText

JTextArea m_NotesText
Area for user notes Default of 5 rows


m_Support

PropertyChangeSupport m_Support
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.


m_advanceDataSetFirst

JRadioButton m_advanceDataSetFirst
Click to advacne data set before custom generator


m_advanceIteratorFirst

JRadioButton m_advanceIteratorFirst
Click to advance custom generator before data set


m_RadioListener

ActionListener m_RadioListener
Handle radio buttons


Package weka.gui.explorer

Class weka.gui.explorer.AssociationsPanel implements Serializable

Serialized Fields

m_AssociatorEditor

GenericObjectEditor m_AssociatorEditor
Lets the user configure the associator


m_CEPanel

PropertyPanel m_CEPanel
The panel showing the current associator selection


m_OutText

JTextArea m_OutText
The output area for associations


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_StartBut

JButton m_StartBut
Click to start running the associator


m_StopBut

JButton m_StopBut
Click to stop a running associator


m_SaveOutBut

JButton m_SaveOutBut
Click to save the output associated with the currently selected result


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_TestInstances

Instances m_TestInstances
The user-supplied test set (if any)


m_RunThread

Thread m_RunThread
A thread that associator runs in

Class weka.gui.explorer.AttributeSelectionPanel implements Serializable

Serialized Fields

m_AttributeEvaluatorEditor

GenericObjectEditor m_AttributeEvaluatorEditor
Lets the user configure the attribute evaluator


m_AttributeSearchEditor

GenericObjectEditor m_AttributeSearchEditor
Lets the user configure the search method


m_AEEPanel

PropertyPanel m_AEEPanel
The panel showing the current attribute evaluation method


m_ASEPanel

PropertyPanel m_ASEPanel
The panel showing the current search method


m_OutText

JTextArea m_OutText
The output area for attribute selection results


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_ClassCombo

JComboBox m_ClassCombo
Lets the user select the class column


m_CVBut

JRadioButton m_CVBut
Click to set evaluation mode to cross-validation


m_TrainBut

JRadioButton m_TrainBut
Click to set test mode to test on training data


m_CVLab

JLabel m_CVLab
Label by where the cv folds are entered


m_CVText

JTextField m_CVText
The field where the cv folds are entered


m_SeedLab

JLabel m_SeedLab
Label by where cv random seed is entered


m_SeedText

JTextField m_SeedText
The field where the seed value is entered


m_RadioListener

ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button


m_StartBut

JButton m_StartBut
Click to start running the attribute selector


m_StopBut

JButton m_StopBut
Click to stop a running classifier


COMBO_SIZE

Dimension COMBO_SIZE
Stop the class combo from taking up to much space


m_CurrentVis

VisualizePanel m_CurrentVis
The current visualization object


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_RunThread

Thread m_RunThread
A thread that attribute selection runs in

Class weka.gui.explorer.ClassifierPanel implements Serializable

Serialized Fields

m_ClassifierEditor

GenericObjectEditor m_ClassifierEditor
Lets the user configure the classifier


m_CEPanel

PropertyPanel m_CEPanel
The panel showing the current classifier selection


m_OutText

JTextArea m_OutText
The output area for classification results


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_ClassCombo

JComboBox m_ClassCombo
Lets the user select the class column


m_CVBut

JRadioButton m_CVBut
Click to set test mode to cross-validation


m_PercentBut

JRadioButton m_PercentBut
Click to set test mode to generate a % split


m_TrainBut

JRadioButton m_TrainBut
Click to set test mode to test on training data


m_TestSplitBut

JRadioButton m_TestSplitBut
Click to set test mode to a user-specified test set


m_StorePredictionsBut

JCheckBox m_StorePredictionsBut
Check to save the predictions in the results list for visualizing later on


m_OutputModelBut

JCheckBox m_OutputModelBut
Check to output the model built from the training data


m_OutputPerClassBut

JCheckBox m_OutputPerClassBut
Check to output true/false positives, precision/recall for each class


m_OutputConfusionBut

JCheckBox m_OutputConfusionBut
Check to output a confusion matrix


m_OutputEntropyBut

JCheckBox m_OutputEntropyBut
Check to output entropy statistics


m_EvalWRTCostsBut

JCheckBox m_EvalWRTCostsBut
Check to evaluate w.r.t a cost matrix


m_SetCostsBut

JButton m_SetCostsBut

m_CVLab

JLabel m_CVLab
Label by where the cv folds are entered


m_CVText

JTextField m_CVText
The field where the cv folds are entered


m_PercentLab

JLabel m_PercentLab
Label by where the % split is entered


m_PercentText

JTextField m_PercentText
The field where the % split is entered


m_SetTestBut

JButton m_SetTestBut
The button used to open a separate test dataset


m_SetTestFrame

JFrame m_SetTestFrame
The frame used to show the test set selection panel


m_SetCostsFrame

PropertyDialog m_SetCostsFrame
The frame used to show the cost matrix editing panel


m_RadioListener

ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button


m_MoreOptions

JButton m_MoreOptions
Button for further output/visualize options


m_StartBut

JButton m_StartBut
Click to start running the classifier


m_StopBut

JButton m_StopBut
Click to stop a running classifier


COMBO_SIZE

Dimension COMBO_SIZE
Stop the class combo from taking up to much space


m_CostMatrixEditor

CostMatrixEditor m_CostMatrixEditor
The cost matrix editor for evaluation costs


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_TestInstances

Instances m_TestInstances
The user-supplied test set (if any)


m_TestInstancesCopy

Instances m_TestInstancesCopy
The user supplied test set after preprocess filters have been applied


m_RunThread

Thread m_RunThread
A thread that classification runs in


m_visXIndex

int m_visXIndex
default x index for visualizing


m_visYIndex

int m_visYIndex
default y index for visualizing


m_CurrentVis

VisualizePanel m_CurrentVis
The current visualization object


m_Preprocess

PreprocessPanel m_Preprocess
The preprocess panel through which filters can be applied to user supplied test data sets


m_Summary

InstancesSummaryPanel m_Summary
The instances summary panel displayed by m_SetTestFrame

Class weka.gui.explorer.ClustererPanel implements Serializable

Serialized Fields

m_ClustererEditor

GenericObjectEditor m_ClustererEditor
Lets the user configure the clusterer


m_CLPanel

PropertyPanel m_CLPanel
The panel showing the current clusterer selection


m_OutText

JTextArea m_OutText
The output area for classification results


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_PercentBut

JRadioButton m_PercentBut
Click to set test mode to generate a % split


m_TrainBut

JRadioButton m_TrainBut
Click to set test mode to test on training data


m_TestSplitBut

JRadioButton m_TestSplitBut
Click to set test mode to a user-specified test set


m_ClassesToClustersBut

JRadioButton m_ClassesToClustersBut
Click to set test mode to classes to clusters based evaluation


m_ClassCombo

JComboBox m_ClassCombo
Lets the user select the class column for classes to clusters based evaluation


m_PercentLab

JLabel m_PercentLab
Label by where the % split is entered


m_PercentText

JTextField m_PercentText
The field where the % split is entered


m_SetTestBut

JButton m_SetTestBut
The button used to open a separate test dataset


m_SetTestFrame

JFrame m_SetTestFrame
The frame used to show the test set selection panel


m_ignoreBut

JButton m_ignoreBut
The button used to popup a list for choosing attributes to ignore while clustering


m_ignoreKeyModel

DefaultListModel m_ignoreKeyModel

m_ignoreKeyList

JList m_ignoreKeyList

m_RadioListener

ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button


m_StartBut

JButton m_StartBut
Click to start running the clusterer


COMBO_SIZE

Dimension COMBO_SIZE
Stop the class combo from taking up to much space


m_StopBut

JButton m_StopBut
Click to stop a running clusterer


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_TestInstances

Instances m_TestInstances
The user-supplied test set (if any)


m_TestInstancesCopy

Instances m_TestInstancesCopy
The user supplied test set after preprocess filters have been applied


m_CurrentVis

VisualizePanel m_CurrentVis
The current visualization object


m_visXIndex

int m_visXIndex
default x index for visualizing


m_visYIndex

int m_visYIndex
default y index for visualizing


m_StorePredictionsBut

JCheckBox m_StorePredictionsBut
Check to save the predictions in the results list for visualizing later on


m_RunThread

Thread m_RunThread
A thread that clustering runs in


m_Preprocess

PreprocessPanel m_Preprocess
The pre-process object from which to fetch filters for applying to a user specified test set


m_Summary

InstancesSummaryPanel m_Summary
The instances summary panel displayed by m_SetTestFrame

Class weka.gui.explorer.Explorer implements Serializable

Serialized Fields

m_PreprocessPanel

PreprocessPanel m_PreprocessPanel
The panel for preprocessing instances


m_ClassifierPanel

ClassifierPanel m_ClassifierPanel
The panel for running classifiers


m_ClustererPanel

ClustererPanel m_ClustererPanel
Label for a panel that still need to be implemented


m_AssociationPanel

AssociationsPanel m_AssociationPanel
Label for a panel that still need to be implemented


m_AttributeSelectionPanel

AttributeSelectionPanel m_AttributeSelectionPanel
Label for a panel that still need to be implemented


m_VisualizePanel

VisualizePanel m_VisualizePanel
Label for a panel that still need to be implemented


m_TabbedPane

JTabbedPane m_TabbedPane
The tabbed pane that controls which sub-pane we are working with


m_LogPanel

LogPanel m_LogPanel
The panel for log and status messages

Class weka.gui.explorer.PreprocessPanel implements Serializable

Serialized Fields

m_BaseInstPanel

InstancesSummaryPanel m_BaseInstPanel
Displays simple stats on the base instances


m_WorkingInstPanel

InstancesSummaryPanel m_WorkingInstPanel
Displays simple stats on the working instances


m_OpenFileBut

JButton m_OpenFileBut
Click to load base instances from a file


m_OpenURLBut

JButton m_OpenURLBut
Click to load base instances from a URL


m_OpenDBBut

JButton m_OpenDBBut
Click to load base instances from a Database


m_DatabaseQueryEditor

GenericObjectEditor m_DatabaseQueryEditor

m_ApplyBut

JButton m_ApplyBut
Click to apply filters and replace the working dataset


m_ReplaceBut

JButton m_ReplaceBut
Click to replace the base dataset with the working dataset


m_SaveBut

JButton m_SaveBut
Click to apply filters and save the results


m_AttPanel

AttributeSelectionPanel m_AttPanel
Panel to let the user toggle attributes


m_Filters

GenericArrayEditor m_Filters
Lets the user add a series of filters


m_AttSummaryPanel

AttributeSummaryPanel m_AttSummaryPanel
Displays summary stats on the selected attribute


m_ArffFilter

FileFilter m_ArffFilter
Filter to ensure only arff files are selected


m_FileChooser

JFileChooser m_FileChooser
The file chooser for selecting arff files


m_LastURL

String m_LastURL
Stores the last URL that instances were loaded from


m_SQLQ

String m_SQLQ
Stores the last sql query executed


m_BaseInstances

Instances m_BaseInstances
The unadulterated instances


m_WorkingInstances

Instances m_WorkingInstances
The working (filtered) copy


m_Support

PropertyChangeSupport m_Support
Manages sending notifications to people when we change the set of working instances.


m_IOThread

Thread m_IOThread
A thread to loading/saving instances from a file or URL


m_Log

Logger m_Log

m_FiltersCopy

SerializedObject m_FiltersCopy
A copy of the most recently applied filters


Package weka.gui.streams

Class weka.gui.streams.InstanceCounter implements Serializable

Serialized Fields

m_Count_Lab

JLabel m_Count_Lab

m_Count

int m_Count

m_Debug

boolean m_Debug

Class weka.gui.streams.InstanceEvent implements Serializable

Serialized Fields

m_ID

int m_ID

Class weka.gui.streams.InstanceJoiner implements Serializable

Serialized Fields

listeners

Vector listeners
The listeners


b_Debug

boolean b_Debug
Debugging mode


m_InputFormat

Instances m_InputFormat
The input format for instances


m_OutputInstance

Instance m_OutputInstance
The current output instance


b_FirstInputFinished

boolean b_FirstInputFinished
Whether the first input batch has finished


b_SecondInputFinished

boolean b_SecondInputFinished

Class weka.gui.streams.InstanceLoader implements Serializable

Serialized Fields

m_Listeners

Vector m_Listeners

m_LoaderThread

Thread m_LoaderThread

m_OutputInstance

Instance m_OutputInstance

m_OutputInstances

Instances m_OutputInstances

m_Debug

boolean m_Debug

m_StartBut

JButton m_StartBut

m_FileNameTex

JTextField m_FileNameTex

Class weka.gui.streams.InstanceSavePanel implements Serializable

Serialized Fields

count_Lab

Label count_Lab

m_Count

int m_Count

arffFile_Tex

TextField arffFile_Tex

b_Debug

boolean b_Debug

outputWriter

PrintWriter outputWriter

Class weka.gui.streams.InstanceTable implements Serializable

Serialized Fields

m_InstanceTable

JTable m_InstanceTable

m_Debug

boolean m_Debug

m_Clear

boolean m_Clear

m_UpdateString

String m_UpdateString

m_Instances

Instances m_Instances

Class weka.gui.streams.InstanceViewer implements Serializable

Serialized Fields

m_OutputTex

JTextArea m_OutputTex

m_Debug

boolean m_Debug

m_Clear

boolean m_Clear

m_UpdateString

String m_UpdateString


Package weka.gui.treevisualizer

Class weka.gui.treevisualizer.TreeVisualizer implements Serializable

Serialized Fields

m_placer

NodePlace m_placer
The placement algorithm for the Node structure.


m_topNode

Node m_topNode
The top Node.


m_viewPos

Dimension m_viewPos
The postion of the view relative to the tree.


m_viewSize

Dimension m_viewSize
The size of the tree in pixels.


m_currentFont

Font m_currentFont
The font used to display the tree.


m_fontSize

FontMetrics m_fontSize
The size information for the current font.


m_numNodes

int m_numNodes
The number of Nodes in the tree.


m_numLevels

int m_numLevels
The number of levels in the tree.


m_nodes

weka.gui.treevisualizer.TreeVisualizer.NodeInfo[] m_nodes
An array with the Nodes sorted into it and display information about the Nodes.


m_edges

weka.gui.treevisualizer.TreeVisualizer.EdgeInfo[] m_edges
An array with the Edges sorted into it and display information about the Edges.


m_frameLimiter

Timer m_frameLimiter
A timer to keep the frame rate constant.


m_mouseState

int m_mouseState
Describes the action the user is performing.


m_oldMousePos

Dimension m_oldMousePos
A variable used to tag the start pos of a user action.


m_newMousePos

Dimension m_newMousePos
A variable used to tag the most current point of a user action.


m_clickAvailable

boolean m_clickAvailable
A variable used to determine for the clicked method if any other mouse state has already taken place.


m_nViewPos

Dimension m_nViewPos
A variable used to remember the desired view pos.


m_nViewSize

Dimension m_nViewSize
A variable used to remember the desired tree size.


m_scaling

int m_scaling
The number of frames left to calculate.


m_winMenu

JPopupMenu m_winMenu
A right (or middle) click popup menu.


m_topN

JMenuItem m_topN
An option on the win_menu


m_fitToScreen

JMenuItem m_fitToScreen
An option on the win_menu


m_autoScale

JMenuItem m_autoScale
An option on the win_menu


m_selectFont

JMenu m_selectFont
A ub group on the win_menu


m_selectFontGroup

ButtonGroup m_selectFontGroup
A grouping for the font choices


m_size24

JRadioButtonMenuItem m_size24
A font choice.


m_size22

JRadioButtonMenuItem m_size22
A font choice.


m_size20

JRadioButtonMenuItem m_size20
A font choice.


m_size18

JRadioButtonMenuItem m_size18
A font choice.


m_size16

JRadioButtonMenuItem m_size16
A font choice.


m_size14

JRadioButtonMenuItem m_size14
A font choice.


m_size12

JRadioButtonMenuItem m_size12
A font choice.


m_size10

JRadioButtonMenuItem m_size10
A font choice.


m_size8

JRadioButtonMenuItem m_size8
A font choice.


m_size6

JRadioButtonMenuItem m_size6
A font choice.


m_size4

JRadioButtonMenuItem m_size4
A font choice.


m_size2

JRadioButtonMenuItem m_size2
A font choice.


m_size1

JRadioButtonMenuItem m_size1
A font choice.


m_accept

JMenuItem m_accept
An option on the win menu.


m_nodeMenu

JPopupMenu m_nodeMenu
A right or middle click popup menu for nodes.


m_visualise

JMenuItem m_visualise
A visualize choice for the node, may not be available.


m_addChildren

JMenuItem m_addChildren
An add children to Node choice, This is only available if the tree display has a treedisplay listerner added to it.


m_remChildren

JMenuItem m_remChildren
Similar to add children but now it removes children.


m_classifyChild

JMenuItem m_classifyChild
Use this to have J48 classify this node.


m_sendInstances

JMenuItem m_sendInstances
Use this to dump the instances from this node to the vis panel.


m_focusNode

int m_focusNode
The subscript for the currently selected node (this is an internal thing, so the user is unaware of this).


m_highlightNode

int m_highlightNode
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_listener

TreeDisplayListener m_listener

m_searchString

JTextField m_searchString

m_searchWin

JDialog m_searchWin

m_caseSen

JRadioButton m_caseSen


Package weka.gui.visualize

Class weka.gui.visualize.AttributePanel implements Serializable

Serialized Fields

m_plotInstances

Instances m_plotInstances
The instances to be plotted


m_maxC

double m_maxC
Holds the min and max values of the colouring attributes


m_minC

double m_minC

m_cIndex

int m_cIndex

m_xIndex

int m_xIndex

m_yIndex

int m_yIndex

m_colorList

FastVector m_colorList
The colour map to use for colouring points


m_DefaultColors

Color[] m_DefaultColors
default colours for colouring discrete class


m_Listeners

FastVector m_Listeners
The list of things listening to this panel


m_heights

int[] m_heights
Holds the random height for each instance.


m_span

JPanel m_span
The container window for the attribute bars, and also where the X,Y or B get printed.


m_barColour

Color m_barColour
The default colour to use for the background of the bars if a colour is not defined in Visualize.props

Class weka.gui.visualize.AttributePanel.AttributeSpacing implements Serializable

Serialized Fields

m_maxVal

double m_maxVal
The min and max values for this attribute.


m_minVal

double m_minVal

m_attrib

Attribute m_attrib
The attribute itself.


m_attribIndex

int m_attribIndex
The index for this attribute.


m_cached

int[] m_cached
The x position of each point.


m_pointDrawn

boolean[][] m_pointDrawn
A temporary array used to strike any instances that would be drawn redundantly.


m_oldWidth

int m_oldWidth
Used to determine if the positions need to be recalculated.

Class weka.gui.visualize.ClassPanel implements Serializable

Serialized Fields

m_isEnabled

boolean m_isEnabled
True when the panel has been enabled (ie after setNumeric or setNominal has been called


m_isNumeric

boolean m_isNumeric
True if the colouring attribute is numeric


m_spectrumHeight

int m_spectrumHeight
The height of the spectrum for numeric class

See Also:
Constant Field Values

m_maxC

double m_maxC
The maximum value for the colouring attribute


m_minC

double m_minC
The minimum value for the colouring attribute


m_tickSize

int m_tickSize
The size of the ticks

See Also:
Constant Field Values

m_labelMetrics

FontMetrics m_labelMetrics
Font metrics


m_labelFont

Font m_labelFont
The font used in labeling


m_HorizontalPad

int m_HorizontalPad
The amount of space to leave either side of the legend


m_precisionC

int m_precisionC
The precision with which to display real values


m_fieldWidthC

int m_fieldWidthC
Field width for numeric values


m_oldWidth

int m_oldWidth
The old width.


m_Instances

Instances m_Instances
Instances being plotted


m_cIndex

int m_cIndex
Index of the colouring attribute


m_colorList

FastVector m_colorList
the list of colours to use for colouring nominal attribute labels


m_Repainters

FastVector m_Repainters
An optional list of Components that use the colour list maintained by this class. If the user changes a colour using the colour chooser, then these components need to be repainted in order to display the change


m_DefaultColors

Color[] m_DefaultColors
default colours for colouring discrete class

Class weka.gui.visualize.LegendPanel implements Serializable

Serialized Fields

m_plots

FastVector m_plots
the list of plot elements


m_span

JPanel m_span
the panel that contains the legend entries


m_Repainters

FastVector m_Repainters
a list of components that need to be repainted when a colour is changed

Class weka.gui.visualize.LegendPanel.LegendEntry implements Serializable

Serialized Fields

m_plotData

PlotData2D m_plotData
the data for this legend entry


m_dataIndex

int m_dataIndex
the index (in the list of plots) of the data for this legend--- used to draw the correct shape for this data


m_legendText

JLabel m_legendText
the text part of this legend


m_pointShape

JPanel m_pointShape
displays the point shape associated with this legend entry

Class weka.gui.visualize.Plot2D implements Serializable

Serialized Fields

m_axisColour

Color m_axisColour
Default colour for the axis


m_backgroundColour

Color m_backgroundColour
Default colour for the plot background


m_plots

FastVector m_plots
The plots to display


m_masterPlot

PlotData2D m_masterPlot
The master plot


m_masterName

String m_masterName
The name of the master plot


m_plotInstances

Instances m_plotInstances
The instances to be plotted


m_plotCompanion

Plot2DCompanion m_plotCompanion
An optional "compainion" of the panel. If specified, this class will get to do its thing with our graphics context before we do any drawing. Eg. the visualize panel may need to draw polygons etc. before we draw plot axis and data points


m_InstanceInfo

JFrame m_InstanceInfo
For popping up text info on data points


m_InstanceInfoText

JTextArea m_InstanceInfoText

m_colorList

FastVector m_colorList
The list of the colors used


m_DefaultColors

Color[] m_DefaultColors
default colours for colouring discrete class


m_xIndex

int m_xIndex
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_yIndex

int m_yIndex

m_cIndex

int m_cIndex

m_sIndex

int m_sIndex

m_maxX

double m_maxX
Holds the min and max values of the x, y and colouring attributes over all plots


m_minX

double m_minX

m_maxY

double m_maxY

m_minY

double m_minY

m_maxC

double m_maxC

m_minC

double m_minC

m_axisPad

int m_axisPad
Axis padding

See Also:
Constant Field Values

m_tickSize

int m_tickSize
Tick size

See Also:
Constant Field Values

m_XaxisStart

int m_XaxisStart
the offsets of the axes once label metrics are calculated


m_YaxisStart

int m_YaxisStart

m_XaxisEnd

int m_XaxisEnd

m_YaxisEnd

int m_YaxisEnd

m_plotResize

boolean m_plotResize
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_axisChanged

boolean m_axisChanged
if the user changes attribute assigned to an axis


m_drawnPoints

int[][] m_drawnPoints
An array used to show if a point is hidden or not. This is used for speeding up the drawing of the plot panel although I am not sure how much performance this grants over not having it.


m_labelFont

Font m_labelFont
Font for labels


m_labelMetrics

FontMetrics m_labelMetrics

m_JitterVal

int m_JitterVal
the level of jitter


m_JRand

Random m_JRand
random values for perterbing the data points


m_pointLookup

double[][] m_pointLookup
lookup table for plotted points

Class weka.gui.visualize.VisualizePanel implements Serializable

Serialized Fields

m_DefaultColors

Color[] m_DefaultColors
default colours for colouring discrete class


m_XCombo

JComboBox m_XCombo
Lets the user select the attribute for the x axis


m_YCombo

JComboBox m_YCombo
Lets the user select the attribute for the y axis


m_ColourCombo

JComboBox m_ColourCombo
Lets the user select the attribute to use for colouring


m_ShapeCombo

JComboBox m_ShapeCombo
Lets the user select the shape they want to create for instance selection.


m_submit

JButton m_submit
Button for the user to enter the splits.


m_cancel

JButton m_cancel
Button for the user to remove all splits.


m_saveBut

JButton m_saveBut
Button for the user to save the visualized set of instances


COMBO_SIZE

Dimension COMBO_SIZE
Stop the combos from growing out of control


m_FileChooser

JFileChooser m_FileChooser
file chooser for saving instances


m_ArffFilter

FileFilter m_ArffFilter
Filter to ensure only arff files are selected


m_JitterLab

JLabel m_JitterLab
Label for the jitter slider


m_Jitter

JSlider m_Jitter
The jitter slider


m_plot

VisualizePanel.PlotPanel m_plot
The panel that displays the plot


m_attrib

AttributePanel m_attrib
The panel that displays the attributes , using color to represent another attribute.


m_legendPanel

LegendPanel m_legendPanel
The panel that displays legend info if there is more than one plot


m_plotSurround

JPanel m_plotSurround
Panel that surrounds the plot panel with a titled border


m_classSurround

JPanel m_classSurround
Panel that surrounds the class panel with a titled border


listener

ActionListener listener
An optional listener that we will inform when ComboBox selections change


m_splitListener

VisualizePanelListener m_splitListener
An optional listener that we will inform when the user creates a split to seperate instances.


m_plotName

String m_plotName
The name of the plot (not currently displayed, but can be used in the containing Frame or Panel)


m_classPanel

ClassPanel m_classPanel
The panel that displays the legend for the colouring attribute


m_colorList

FastVector m_colorList
The list of the colors used


m_preferredXDimension

String m_preferredXDimension
These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file


m_preferredYDimension

String m_preferredYDimension

m_preferredColourDimension

String m_preferredColourDimension

m_showAttBars

boolean m_showAttBars
Show the attribute bar panel


m_Log

Logger m_Log
the logger

Class weka.gui.visualize.VisualizePanel.PlotPanel implements Serializable

Serialized Fields

m_plot2D

Plot2D m_plot2D
The actual generic plotting panel


m_plotInstances

Instances m_plotInstances
The instances from the master plot


m_originalPlot

PlotData2D m_originalPlot
The master plot


m_xIndex

int m_xIndex
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_yIndex

int m_yIndex

m_cIndex

int m_cIndex

m_sIndex

int m_sIndex

m_axisPad

int m_axisPad
Axis padding

See Also:
Constant Field Values

m_tickSize

int m_tickSize
Tick size

See Also:
Constant Field Values

m_XaxisStart

int m_XaxisStart
the offsets of the axes once label metrics are calculated


m_YaxisStart

int m_YaxisStart

m_XaxisEnd

int m_XaxisEnd

m_YaxisEnd

int m_YaxisEnd

m_createShape

boolean m_createShape
True if the user is currently dragging a box.


m_shapes

FastVector m_shapes
contains all the shapes that have been drawn for these attribs


m_shapePoints

FastVector m_shapePoints
contains the points of the shape currently being drawn.


m_newMousePos

Dimension m_newMousePos
contains the position of the mouse (used for rubberbanding).


Package weka.roughset

Class weka.roughset.RoughInstance implements Serializable

Serialized Fields

dataSet

RoughInstances dataSet

Class weka.roughset.RoughInstances implements Serializable

Serialized Fields

roughInstances

ArrayList roughInstances

activeAttributes

Attributes activeAttributes

pairs

AttributeValuePairs pairs

compareAll

boolean compareAll

compareDecision

boolean compareDecision

missingIsWildcard

boolean missingIsWildcard