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# Serialized Form

Package weka.associations

Class weka.associations.Apriori extends Associator 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.AprioriItemSet extends ItemSet implements Serializable

Class weka.associations.Associator extends java.lang.Object implements Serializable

Class weka.associations.ItemSet extends java.lang.Object 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

Class weka.associations.LabeledItemSet extends ItemSet implements Serializable

Serialized Fields

### m_classLabel

    int m_classLabel

  The class label.

### m_ruleSupCounter

    int m_ruleSupCounter

  The support of the rule.

Class weka.associations.PredictiveApriori extends Associator implements Serializable

Serialized Fields

### m_premiseCount

    int m_premiseCount

  The minimum support.

### m_numRules

    int m_numRules

  The maximum number of rules that are output.

### m_Ls

    FastVector m_Ls

  The set of all sets of itemsets.

### 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_priors

    java.util.Hashtable<K,V> m_priors

  The hashtable containing the prior probabilities.

### m_midPoints

    double[] m_midPoints

  The mid points of the intervals used for the prior estimation.

### m_expectation

    double m_expectation

  The expected predictive accuracy a rule needs to be a candidate for the output.

### m_best

    java.util.TreeSet<E> m_best

  The n best rules.

### m_bestChanged

    boolean m_bestChanged

  Flag keeping track if the list of the n best rules has changed.

### m_count

    int m_count

  Counter for the time of generation for an association rule.

### m_priorEstimator

    PriorEstimation m_priorEstimator

  The prior estimator.

Class weka.associations.PriorEstimation extends java.lang.Object implements Serializable

Serialized Fields

### m_numRandRules

    int m_numRandRules

  The number of rnadom rules.

### m_numIntervals

    int m_numIntervals

  The number of intervals.

### m_randNum

    java.util.Random m_randNum

  The random number generator.

### m_instances

    Instances m_instances

  The instances for which association rules are mined.

### m_CARs

    boolean m_CARs

  Flag indicating whether standard association rules or class association rules are mined.

### m_distribution

    java.util.Hashtable<K,V> m_distribution

  Hashtable to store the confidence values of randomly generated rules.

### m_priors

    java.util.Hashtable<K,V> m_priors

  Hashtable containing the estimated prior probabilities.

### m_sum

    double m_sum

  Sums up the confidences of all rules with a certain length.

### m_midPoints

    double[] m_midPoints

  The mid points of the discrete intervals in which the interval [0,1] is divided.

Class weka.associations.RuleGeneration extends java.lang.Object implements Serializable

Serialized Fields

### m_items

    int[] m_items

  The items stored as an array of of integer.

### m_counter

    int m_counter

  Counter for how many transactions contain this item set.

### m_totalTransactions

    int m_totalTransactions

  The total number of transactions

### m_change

    boolean m_change

  Flag indicating whether the list fo the best rules has changed.

### m_expectation

    double m_expectation

  The minimum expected predictive accuracy that is needed to be a candidate for the list of the best rules.

### m_minRuleCount

    int m_minRuleCount

  The minimum support a rule needs to be a candidate for the list of the best rules.

### m_midPoints

    double[] m_midPoints

  Sorted array of the mied points of the intervals used for prior estimation.

### m_priors

    java.util.Hashtable<K,V> m_priors

  Hashtable conatining the estimated prior probabilities.

### m_best

    java.util.TreeSet<E> m_best

  The list of the actual n<\i> best rules.

### m_count

    int m_count

  Integer indicating the generation time of a rule.

### m_instances

    Instances m_instances

  The instances.

Class weka.associations.RuleItem extends java.lang.Object implements Serializable

Serialized Fields

### m_premise

    ItemSet m_premise

  The premise of a rule.

### m_consequence

    ItemSet m_consequence

  The consequence of a rule.

### m_accuracy

    double m_accuracy

  The expected predictive accuracy of a rule.

### m_genTime

    int m_genTime

  The generation time of a rule.

Class weka.associations.Tertius extends Associator implements Serializable

Serialized Fields

### m_results

    SimpleLinkedList m_results

  The results.

### m_hypotheses

    int m_hypotheses

  Number of hypotheses considered.

### m_explored

    int m_explored

  Number of hypotheses explored.

### m_time

    java.util.Date m_time

  Time needed for the search.

### m_valuesText

    java.awt.TextField m_valuesText

  Field to output the current values.

### m_instances

    Instances m_instances

  Instances used for the search.

### m_predicates

    java.util.ArrayList<E> m_predicates

  Predicates used in the rules.

### m_status

    int m_status

  Status of the search.

### m_best

    int m_best

  Number of best confirmation values to search.

### m_frequencyThreshold

    double m_frequencyThreshold

  Frequency threshold for the body and the negation of the head.

### m_confirmationThreshold

    double m_confirmationThreshold

  Confirmation threshold for the rules.

### m_noiseThreshold

    double m_noiseThreshold

  Maximal number of counter-instances.

### m_repeat

    boolean m_repeat

  Repeat attributes ?

### m_numLiterals

    int m_numLiterals

  Number of literals in a rule.

### m_negation

    int m_negation

  Type of negation used in the rules.

### m_classification

    boolean m_classification

  Classification bias.

### m_classIndex

    int m_classIndex

  Index of class attribute.

### m_horn

    boolean m_horn

  Horn clauses bias.

### m_equivalent

    boolean m_equivalent

  Perform test on equivalent rules ?

### m_sameClause

    boolean m_sameClause

  Perform test on same clauses ?

### m_subsumption

    boolean m_subsumption

  Perform subsumption test ?

### m_missing

    int m_missing

  Way of handling missing values in the search.

### m_roc

    boolean m_roc

  Perform ROC analysis ?

### m_partsString

    java.lang.String m_partsString

  Name of the file containing the parts for individual-based learning.

### m_parts

    Instances m_parts

  Part instances for individual-based learning.

### m_printValues

    int m_printValues

  Type of values output.

Package weka.associations.tertius

Class weka.associations.tertius.AttributeValueLiteral extends Literal implements Serializable

Serialized Fields

### m_value

    java.lang.String m_value

### m_index

    int m_index

Class weka.associations.tertius.Body extends LiteralSet implements Serializable

Class weka.associations.tertius.Head extends LiteralSet implements Serializable

Class weka.associations.tertius.IndividualInstance extends Instance implements Serializable

Serialized Fields

### m_parts

    Instances m_parts

Class weka.associations.tertius.IndividualInstances extends Instances implements Serializable

Class weka.associations.tertius.IndividualLiteral extends AttributeValueLiteral implements Serializable

Serialized Fields

### m_type

    int m_type

Class weka.associations.tertius.Literal extends java.lang.Object implements Serializable

Serialized Fields

### m_predicate

    Predicate m_predicate

### m_sign

    int m_sign

### m_negation

    Literal m_negation

### m_missing

    int m_missing

Class weka.associations.tertius.LiteralSet extends java.lang.Object implements Serializable

Serialized Fields

### m_literals

    java.util.ArrayList<E> m_literals

  Literals contained in this set.

### m_lastLiteral

    Literal m_lastLiteral

  Last literal added to this set.

### m_numInstances

    int m_numInstances

  Number of instances in the data the set deals with.

### m_counterInstances

    java.util.ArrayList<E> m_counterInstances

  Set of counter-instances of this part of the rule.

### m_counter

    int m_counter

  Counter for the number of counter-instances.

### m_type

    int m_type

  Type of properties expressed in this set (individual or parts properties).

Class weka.associations.tertius.Predicate extends java.lang.Object implements Serializable

Serialized Fields

### m_literals

    java.util.ArrayList<E> m_literals

### m_name

    java.lang.String m_name

### m_index

    int m_index

### m_isClass

    boolean m_isClass

Class weka.associations.tertius.Rule extends java.lang.Object implements Serializable

Serialized Fields

### m_body

    Body m_body

  The body of the rule.

### m_head

    Head m_head

  The head of the rule.

### m_repeatPredicate

    boolean m_repeatPredicate

  Can repeat predicates in the rule ?

### m_maxLiterals

    int m_maxLiterals

  Maximal number of literals in the rule.

### m_negBody

    boolean m_negBody

  Can there be negations in the body ?

### m_negHead

    boolean m_negHead

  Can there be negations in the head ?

### m_classRule

    boolean m_classRule

  Is this rule a classification rule ?

### m_singleHead

    boolean m_singleHead

  Can there be only one literal in the head ?

### m_numInstances

    int m_numInstances

  Number of instances in the data this rule deals with.

### m_counterInstances

    java.util.ArrayList<E> m_counterInstances

  Set of counter-instances of this rule.

### m_counter

    int m_counter

  Counter for the counter-instances of this rule.

### m_confirmation

    double m_confirmation

  Confirmation of this rule.

### m_optimistic

    double m_optimistic

  Optimistic estimate of this rule.

Class weka.associations.tertius.SimpleLinkedList extends java.lang.Object implements Serializable

Serialization Methods

### readObject

    private void readObject(java.io.ObjectInputStream s)
                     throws java.io.IOException,
                            java.lang.ClassNotFoundException

  Reconstitute this `LinkedList` instance from a stream (that is deserialize it).

  Throws:
    `java.io.IOException`
    `java.lang.ClassNotFoundException`

### writeObject

    private void writeObject(java.io.ObjectOutputStream s)
                      throws java.io.IOException

  Save the state of this `LinkedList` instance to a stream (that is, serialize it).

  Serial Data:
    The size of the list (the number of elements it contains) is emitted (int), followed by all of its elements (each an Object) in the proper order.
  Throws:
    `java.io.IOException`

Serialized Fields

### first

    weka.associations.tertius.SimpleLinkedList.Entry first

### last

    weka.associations.tertius.SimpleLinkedList.Entry last

Class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator extends java.lang.Object implements Serializable

Serialized Fields

### current

    weka.associations.tertius.SimpleLinkedList.Entry current

### lastReturned

    weka.associations.tertius.SimpleLinkedList.Entry lastReturned

Class weka.associations.tertius.SimpleLinkedList.LinkedListIterator extends java.lang.Object implements Serializable

Serialized Fields

### current

    weka.associations.tertius.SimpleLinkedList.Entry current

### lastReturned

    weka.associations.tertius.SimpleLinkedList.Entry lastReturned

Package weka.attributeSelection

Class weka.attributeSelection.ASEvaluation extends java.lang.Object implements Serializable

Class weka.attributeSelection.ASSearch extends java.lang.Object implements Serializable

Class weka.attributeSelection.AttributeEvaluator extends ASEvaluation implements Serializable

Class weka.attributeSelection.AttributeSelection extends java.lang.Object 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

    java.lang.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_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

    Remove 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 extends ASSearch 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

### m_cacheSize

    int m_cacheSize

  holds the maximum size of the lookup cache for evaluated subsets

Class weka.attributeSelection.BestFirst.Link2 extends java.lang.Object implements Serializable

Serialized Fields

### m_data

    java.lang.Object[] m_data

### m_merit

    double m_merit

Class weka.attributeSelection.BestFirst.LinkedList2 extends FastVector implements Serializable

Serialized Fields

### m_MaxSize

    int m_MaxSize

Class weka.attributeSelection.CfsSubsetEval extends SubsetEvaluator implements Serializable

Serialized Fields

### m_trainInstances

    Instances m_trainInstances

  The training instances

### m_disTransform

    Discretize 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

    float[][] 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 extends AttributeEvaluator 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 extends HoldOutSubsetEvaluator 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

    java.io.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 extends SubsetEvaluator 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

    Discretize m_disTransform

  Discretise numeric attributes

### m_table

    java.util.Hashtable<K,V> m_table

  Hash table for evaluating feature subsets

Class weka.attributeSelection.ConsistencySubsetEval.hashKey extends java.lang.Object 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

    java.lang.String[] values

  The values

### key

    int key

  The key

Class weka.attributeSelection.ExhaustiveSearch extends ASSearch implements Serializable

Serialized Fields

### m_bestGroup

    java.util.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_evaluations

    int m_evaluations

  the number of subsets evaluated during the search

Class weka.attributeSelection.GainRatioAttributeEval extends AttributeEvaluator 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 extends ASSearch 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

    weka.attributeSelection.GeneticSearch.GABitSet[] m_population

  the current population

### m_popSize

    int m_popSize

  the number of individual solutions

### m_best

    weka.attributeSelection.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

    java.util.Hashtable<K,V> m_lookupTable

  the lookup table

### m_random

    java.util.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

    java.lang.StringBuffer m_generationReports

  holds the generation reports

Class weka.attributeSelection.GeneticSearch.GABitSet extends java.lang.Object implements Serializable

Serialized Fields

### m_chromosome

    java.util.BitSet m_chromosome

### m_objective

    double m_objective

  holds raw merit

### m_fitness

    double m_fitness

Class weka.attributeSelection.GreedyStepwise extends ASSearch 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

    java.util.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

### m_backward

    boolean m_backward

  Use a backwards search instead of a forwards one

Class weka.attributeSelection.HoldOutSubsetEvaluator extends SubsetEvaluator implements Serializable

Class weka.attributeSelection.InfoGainAttributeEval extends AttributeEvaluator 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 extends AttributeEvaluator 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_randomSeed

    int m_randomSeed

  Random number seed

### m_folds

    int m_folds

  Number of folds for cross validation

### m_evalUsingTrainingData

    boolean m_evalUsingTrainingData

  Use training data to evaluate merit rather than x-val

### m_minBucketSize

    int m_minBucketSize

  Passed on to OneR

Class weka.attributeSelection.PrincipalComponents extends UnsupervisedAttributeEvaluator 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

    ReplaceMissingValues m_replaceMissingFilter

  Filters for original data

### m_normalizeFilter

    Normalize m_normalizeFilter

### m_nominalToBinFilter

    NominalToBinary m_nominalToBinFilter

### m_attributeFilter

    Remove m_attributeFilter

### m_attribFilter

    Remove 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_maxAttrsInName

    int m_maxAttrsInName

  maximum number of attributes in the transformed attribute name

### m_eTranspose

    double[][] m_eTranspose

  holds the transposed eigenvectors for converting back to the original space

Class weka.attributeSelection.RaceSearch extends ASSearch 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 extends ASSearch 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

    java.util.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

    java.util.Random m_random

  random number object

### m_verbose

    boolean m_verbose

  output new best subsets as the search progresses

Class weka.attributeSelection.Ranker extends ASSearch 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 extends ASSearch 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

    java.util.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 extends AttributeEvaluator 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 extends ASEvaluation implements Serializable

Class weka.attributeSelection.SVMAttributeEval extends AttributeEvaluator implements Serializable

Serialized Fields

### m_attScores

    double[] m_attScores

  The attribute scores

### m_numToEliminate

    int m_numToEliminate

  Constant rate of attribute elimination per iteration

### m_percentToEliminate

    int m_percentToEliminate

  Percentage rate of attribute elimination, trumps constant rate (above threshold), ignored if = 0

### m_percentThreshold

    int m_percentThreshold

  Threshold below which percent elimination switches to constant elimination

### m_smoCParameter

    double m_smoCParameter

  Complexity parameter to pass on to SMO

### m_smoTParameter

    double m_smoTParameter

  Tolerance parameter to pass on to SMO

### m_smoPParameter

    double m_smoPParameter

  Epsilon parameter to pass on to SMO

### m_smoFilterType

    int m_smoFilterType

  Filter parameter to pass on to SMO

Class weka.attributeSelection.SymmetricalUncertAttributeEval extends AttributeEvaluator 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 extends AttributeEvaluator implements Serializable

Class weka.attributeSelection.UnsupervisedSubsetEvaluator extends SubsetEvaluator implements Serializable

Class weka.attributeSelection.WrapperSubsetEval extends SubsetEvaluator 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.Classifier extends java.lang.Object implements Serializable

Serialized Fields

### m_Debug

    boolean m_Debug

  Whether the classifier is run in debug mode.

Class weka.classifiers.CostMatrix extends Matrix implements Serializable

Class weka.classifiers.IteratedSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Classifiers

    Classifier[] m_Classifiers

  Array for storing the generated base classifiers.

### m_NumIterations

    int m_NumIterations

  The number of iterations.

Class weka.classifiers.MultipleClassifiersCombiner extends Classifier implements Serializable

Serialized Fields

### m_Classifiers

    Classifier[] m_Classifiers

  Array for storing the generated base classifiers.

Class weka.classifiers.RandomizableClassifier extends Classifier implements Serializable

Serialized Fields

### m_Seed

    int m_Seed

  The random number seed.

Class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer extends IteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Seed

    int m_Seed

  The random number seed.

Class weka.classifiers.RandomizableMultipleClassifiersCombiner extends MultipleClassifiersCombiner implements Serializable

Serialized Fields

### m_Seed

    int m_Seed

  The random number seed.

Class weka.classifiers.RandomizableSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Seed

    int m_Seed

  The random number seed.

Class weka.classifiers.SingleClassifierEnhancer extends Classifier implements Serializable

Serialized Fields

### m_Classifier

    Classifier m_Classifier

  The base classifier to use

Package weka.classifiers.bayes

Class weka.classifiers.bayes.AODE extends Classifier implements Serializable

Serialized Fields

### m_CondiCounts

    double[][][] m_CondiCounts

  3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts, i.e. the number of times an attribute value occurs in conjunction with another attribute value and a class value.

### m_ClassCounts

    double[] m_ClassCounts

  The number of times each class value occurs in the dataset

### m_SumForCounts

    double[][] m_SumForCounts

  The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal]

### m_NumClasses

    int m_NumClasses

  The number of classes

### m_NumAttributes

    int m_NumAttributes

  The number of attributes in dataset, including class

### m_NumInstances

    int m_NumInstances

  The number of instances in the dataset

### m_ClassIndex

    int m_ClassIndex

  The index of the class attribute

### m_Instances

    Instances m_Instances

  The dataset

### m_TotalAttValues

    int m_TotalAttValues

  The total number of values (including an extra for each attribute's missing value, which are included in m_CondiCounts) for all attributes (not including class). Eg. for three atts each with two possible values, m_TotalAttValues would be 9 (6 values + 3 missing). This variable is used when allocating space for m_CondiCounts matrix.

### m_StartAttIndex

    int[] m_StartAttIndex

  The starting index (in the m_CondiCounts matrix) of the values for each attribute

### m_NumAttValues

    int[] m_NumAttValues

  The number of values for each attribute

### m_Frequencies

    double[] m_Frequencies

  The frequency of each attribute value for the dataset

### m_SumInstances

    double m_SumInstances

  The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances.

### m_Limit

    int m_Limit

  An att's frequency must be this value or more to be a superParent

### m_Debug

    boolean m_Debug

  If true, outputs debugging info

Class weka.classifiers.bayes.BayesNet extends Classifier implements Serializable

Serialized Fields

### m_ParentSets

    ParentSet[] m_ParentSets

  The parent sets.

### m_Distributions

    Estimator[][] m_Distributions

  The attribute estimators containing CPTs.

### m_DiscretizeFilter

    Discretize m_DiscretizeFilter

  filter used to quantize continuous variables, if any

### m_nNonDiscreteAttribute

    int m_nNonDiscreteAttribute

### m_MissingValuesFilter

    ReplaceMissingValues m_MissingValuesFilter

  filter used to fill in missing values, if any

### m_NumClasses

    int m_NumClasses

  The number of classes

### m_Instances

    Instances m_Instances

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

### m_ADTree

    ADNode m_ADTree

  Datastructure containing ADTree representation of the database. This may result in more efficient access to the data.

### m_otherBayesNet

    BIFReader m_otherBayesNet

  Bayes network to compare the structure with.

### m_bUseADTree

    boolean m_bUseADTree

  Use the experimental ADTree datastructure for calculating contingency tables

### m_SearchAlgorithm

    SearchAlgorithm m_SearchAlgorithm

  Search algorithm used for learning the structure of a network.

### m_BayesNetEstimator

    BayesNetEstimator m_BayesNetEstimator

  Search algorithm used for learning the structure of a network.

Class weka.classifiers.bayes.ComplementNaiveBayes extends Classifier implements Serializable

Serialized Fields

### wordWeights

    double[][] wordWeights

  Weight of words for each class. The weight is actually the log of the probability of a word (w) given a class (c) (i.e. log(Pr[w|c])). The format of the matrix is: wordWeights[class][wordAttribute]

### smoothingParameter

    double smoothingParameter

  Holds the smoothing value to avoid word probabilities of zero.
  P.S.: According to the paper this is the Alpha i parameter

### m_normalizeWordWeights

    boolean m_normalizeWordWeights

  True if the words weights are to be normalized

### numClasses

    int numClasses

  Holds the number of Class values present in the set of specified instances

### header

    Instances header

  The instances header that'll be used in toString

Class weka.classifiers.bayes.NaiveBayes extends Classifier 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_UseDiscretization

    boolean m_UseDiscretization

  Whether to use discretization 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

### m_Disc

    Discretize m_Disc

  The discretization filter.

Class weka.classifiers.bayes.NaiveBayesMultinomial extends Classifier implements Serializable

Serialized Fields

### probOfWordGivenClass

    double[][] probOfWordGivenClass

  probability that a word (w) exists in a class (H) (i.e. Pr[w|H]) The matrix is in the this format: probOfWordGivenClass[class][wordAttribute] NOTE: the values are actually the log of Pr[w|H]

### probOfClass

    double[] probOfClass

  the probability of a class (i.e. Pr[H])

### numAttributes

    int numAttributes

  number of unique words

### numClasses

    int numClasses

  number of class values

### lnFactorialCache

    double[] lnFactorialCache

  cache lnFactorial computations

### headerInfo

    Instances headerInfo

  copy of header information for use in toString method

Class weka.classifiers.bayes.NaiveBayesSimple extends Classifier 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.bayes.NaiveBayesUpdateable extends NaiveBayes implements Serializable

Package weka.classifiers.bayes.net

Class weka.classifiers.bayes.net.ADNode extends java.lang.Object implements Serializable

Serialized Fields

### m_VaryNodes

    VaryNode[] m_VaryNodes

  list of VaryNode children

### m_Instances

    Instance[] m_Instances

  list of Instance children (either m_Instances or m_VaryNodes is instantiated)

### m_nCount

    int m_nCount

  count

### m_nStartNode

    int m_nStartNode

  first node in VaryNode array

Class weka.classifiers.bayes.net.BayesNetGenerator extends BayesNet implements Serializable

Serialized Fields

### m_nSeed

    int m_nSeed

### random

    java.util.Random random

### m_bGenerateNet

    boolean m_bGenerateNet

### m_nNrOfNodes

    int m_nNrOfNodes

### m_nNrOfArcs

    int m_nNrOfArcs

### m_nNrOfInstances

    int m_nNrOfInstances

### m_nCardinality

    int m_nCardinality

### m_sBIFFile

    java.lang.String m_sBIFFile

Class weka.classifiers.bayes.net.BIFReader extends BayesNet implements Serializable

Serialized Fields

### m_nPositionX

    int[] m_nPositionX

### m_nPositionY

    int[] m_nPositionY

### m_order

    int[] m_order

### m_sFile

    java.lang.String m_sFile

Class weka.classifiers.bayes.net.ParentSet extends java.lang.Object implements Serializable

Serialized Fields

### m_nParents

    int[] m_nParents

  Holds indexes of parents

### m_nNrOfParents

    int m_nNrOfParents

  Holds number of parents

### m_nCardinalityOfParents

    int m_nCardinalityOfParents

  Holds cardinality of parents (= number of instantiations the parents can take)

Class weka.classifiers.bayes.net.VaryNode extends java.lang.Object implements Serializable

Serialized Fields

### m_iNode

    int m_iNode

  index of the node varied

### m_nMCV

    int m_nMCV

  most common value

### m_ADNodes

    ADNode[] m_ADNodes

  list of ADNode children

Package weka.classifiers.bayes.net.estimate

Class weka.classifiers.bayes.net.estimate.BayesNetEstimator extends java.lang.Object implements Serializable

Serialized Fields

### m_fAlpha

    double m_fAlpha

  Holds prior on count

Class weka.classifiers.bayes.net.estimate.BMAEstimator extends SimpleEstimator implements Serializable

Serialized Fields

### m_bUseK2Prior

    boolean m_bUseK2Prior

Class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes extends java.lang.Object implements Serializable

Serialized Fields

### m_Counts

    double[] m_Counts

  Hold the counts

### m_SumOfCounts

    double m_SumOfCounts

  Hold the sum of counts

### m_nSymbols

    int m_nSymbols

  Holds number of symbols in distribution

### m_fPrior

    double m_fPrior

  Holds the prior probability

Class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes extends DiscreteEstimatorBayes implements Serializable

Class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator extends BayesNetEstimator implements Serializable

Serialized Fields

### m_bUseK2Prior

    boolean m_bUseK2Prior

Class weka.classifiers.bayes.net.estimate.SimpleEstimator extends BayesNetEstimator implements Serializable

Package weka.classifiers.bayes.net.search

Class weka.classifiers.bayes.net.search.SearchAlgorithm extends java.lang.Object implements Serializable

Serialized Fields

### m_nMaxNrOfParents

    int m_nMaxNrOfParents

  Holds upper bound on number of parents

### m_bInitAsNaiveBayes

    boolean m_bInitAsNaiveBayes

  determines whether initial structure is an empty graph or a Naive Bayes network

### m_bMarkovBlanketClassifier

    boolean m_bMarkovBlanketClassifier

  Determines whether after structure is found a MarkovBlanketClassifier correction should be applied If this is true, m_bInitAsNaiveBayes is overridden and interpreted as false.

Package weka.classifiers.bayes.net.search.ci

Class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm extends LocalScoreSearchAlgorithm implements Serializable

Serialized Fields

### m_BayesNet

    BayesNet m_BayesNet

### m_instances

    Instances m_instances

Class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm extends CISearchAlgorithm implements Serializable

Serialized Fields

### m_nMaxCardinality

    int m_nMaxCardinality

  maximum size of separating set

Package weka.classifiers.bayes.net.search.fixed

Class weka.classifiers.bayes.net.search.fixed.FromFile extends SearchAlgorithm implements Serializable

Serialized Fields

### m_sBIFFile

    java.lang.String m_sBIFFile

  name of file to read structure from

Class weka.classifiers.bayes.net.search.fixed.NaiveBayes extends SearchAlgorithm implements Serializable

Package weka.classifiers.bayes.net.search.global

Class weka.classifiers.bayes.net.search.global.GeneticSearch extends GlobalScoreSearchAlgorithm implements Serializable

Serialized Fields

### m_nRuns

    int m_nRuns

  number of runs

### m_nPopulationSize

    int m_nPopulationSize

  size of population

### m_nDescendantPopulationSize

    int m_nDescendantPopulationSize

  size of descendant population

### m_bUseCrossOver

    boolean m_bUseCrossOver

  use cross-over?

### m_bUseMutation

    boolean m_bUseMutation

  use mutation?

### m_bUseTournamentSelection

    boolean m_bUseTournamentSelection

  use tournament selection or take best sub-population

### m_nSeed

    int m_nSeed

  random number seed

### m_random

    java.util.Random m_random

  random number generator

Class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm extends SearchAlgorithm implements Serializable

Serialized Fields

### m_BayesNet

    BayesNet m_BayesNet

  points to Bayes network for which a structure is searched for

### m_bUseProb

    boolean m_bUseProb

  toggle between scoring using accuracy = 0-1 loss (when false) or class probabilities (when true)

### m_nNrOfFolds

    int m_nNrOfFolds

  number of folds for k-fold cross validation

### m_nCVType

    int m_nCVType

  Holds the cross validation strategy used to measure quality of network

Class weka.classifiers.bayes.net.search.global.HillClimber extends GlobalScoreSearchAlgorithm implements Serializable

Serialized Fields

### m_bUseArcReversal

    boolean m_bUseArcReversal

  use the arc reversal operator

Class weka.classifiers.bayes.net.search.global.K2 extends GlobalScoreSearchAlgorithm implements Serializable

Serialized Fields

### m_bRandomOrder

    boolean m_bRandomOrder

  Holds flag to indicate ordering should be random

Class weka.classifiers.bayes.net.search.global.RepeatedHillClimber extends HillClimber implements Serializable

Serialized Fields

### m_nRuns

    int m_nRuns

  number of runs

### m_nSeed

    int m_nSeed

  random number seed

### m_random

    java.util.Random m_random

  random number generator

Class weka.classifiers.bayes.net.search.global.SimulatedAnnealing extends GlobalScoreSearchAlgorithm implements Serializable

Serialized Fields

### m_fTStart

    double m_fTStart

  start temperature

### m_fDelta

    double m_fDelta

  change in temperature at every run

### m_nRuns

    int m_nRuns

  number of runs

### m_bUseArcReversal

    boolean m_bUseArcReversal

  use the arc reversal operator

### m_nSeed

    int m_nSeed

  random number seed

### m_random

    java.util.Random m_random

  random number generator

Class weka.classifiers.bayes.net.search.global.TabuSearch extends HillClimber implements Serializable

Serialized Fields

### m_nRuns

    int m_nRuns

  number of runs

### m_nTabuList

    int m_nTabuList

  size of tabu list

### m_oTabuList

    weka.classifiers.bayes.net.search.global.HillClimber.Operation[] m_oTabuList

  the actual tabu list

Class weka.classifiers.bayes.net.search.global.TAN extends GlobalScoreSearchAlgorithm implements Serializable

Package weka.classifiers.bayes.net.search.local

Class weka.classifiers.bayes.net.search.local.GeneticSearch extends LocalScoreSearchAlgorithm implements Serializable

Serialized Fields

### m_nRuns

    int m_nRuns

  number of runs

### m_nPopulationSize

    int m_nPopulationSize

  size of population

### m_nDescendantPopulationSize

    int m_nDescendantPopulationSize

  size of descendant population

### m_bUseCrossOver

    boolean m_bUseCrossOver

  use cross-over?

### m_bUseMutation

    boolean m_bUseMutation

  use mutation?

### m_bUseTournamentSelection

    boolean m_bUseTournamentSelection

  use tournament selection or take best sub-population

### m_nSeed

    int m_nSeed

  random number seed

### m_random

    java.util.Random m_random

  random number generator

Class weka.classifiers.bayes.net.search.local.HillClimber extends LocalScoreSearchAlgorithm implements Serializable

Serialized Fields

### m_Cache

    weka.classifiers.bayes.net.search.local.HillClimber.Cache m_Cache

  cache for storing score differences

### m_bUseArcReversal

    boolean m_bUseArcReversal

  use the arc reversal operator

Class weka.classifiers.bayes.net.search.local.K2 extends LocalScoreSearchAlgorithm implements Serializable

Serialized Fields

### m_bRandomOrder

    boolean m_bRandomOrder

  Holds flag to indicate ordering should be random

Class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm extends SearchAlgorithm implements Serializable

Serialized Fields

### m_BayesNet

    BayesNet m_BayesNet

  points to Bayes network for which a structure is searched for

### m_fAlpha

    double m_fAlpha

  Holds prior on count

### m_nScoreType

    int m_nScoreType

  Holds the score type used to measure quality of network

Class weka.classifiers.bayes.net.search.local.RepeatedHillClimber extends HillClimber implements Serializable

Serialized Fields

### m_nRuns

    int m_nRuns

  number of runs

### m_nSeed

    int m_nSeed

  random number seed

### m_random

    java.util.Random m_random

  random number generator

Class weka.classifiers.bayes.net.search.local.SimulatedAnnealing extends LocalScoreSearchAlgorithm implements Serializable

Serialized Fields

### m_fTStart

    double m_fTStart

  start temperature

### m_fDelta

    double m_fDelta

  change in temperature at every run

### m_nRuns

    int m_nRuns

  number of runs

### m_bUseArcReversal

    boolean m_bUseArcReversal

  use the arc reversal operator

### m_nSeed

    int m_nSeed

  random number seed

### m_random

    java.util.Random m_random

  random number generator

Class weka.classifiers.bayes.net.search.local.TabuSearch extends HillClimber implements Serializable

Serialized Fields

### m_nRuns

    int m_nRuns

  number of runs

### m_nTabuList

    int m_nTabuList

  size of tabu list

### m_oTabuList

    weka.classifiers.bayes.net.search.local.HillClimber.Operation[] m_oTabuList

  the actual tabu list

Class weka.classifiers.bayes.net.search.local.TAN extends LocalScoreSearchAlgorithm implements Serializable

Package weka.classifiers.evaluation

Class weka.classifiers.evaluation.ConfusionMatrix extends Matrix implements Serializable

Serialized Fields

### m_ClassNames

    java.lang.String[] m_ClassNames

  Stores the names of the classes

Class weka.classifiers.evaluation.NominalPrediction extends java.lang.Object implements Serializable

serialVersionUID: -8871333992740492788L

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 extends java.lang.Object 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.functions

Class weka.classifiers.functions.LeastMedSq extends Classifier implements Serializable

Serialized Fields

### m_Residuals

    double[] m_Residuals

### m_weight

    double[] m_weight

### m_SSR

    double m_SSR

### m_scalefactor

    double m_scalefactor

### m_bestMedian

    double m_bestMedian

### m_currentRegression

    LinearRegression m_currentRegression

### m_bestRegression

    LinearRegression m_bestRegression

### m_ls

    LinearRegression m_ls

### m_Data

    Instances m_Data

### m_RLSData

    Instances m_RLSData

### m_SubSample

    Instances m_SubSample

### m_MissingFilter

    ReplaceMissingValues m_MissingFilter

### m_TransformFilter

    NominalToBinary m_TransformFilter

### m_SplitFilter

    RemoveRange m_SplitFilter

### m_samplesize

    int m_samplesize

### m_samples

    int m_samples

### m_israndom

    boolean m_israndom

### m_debug

    boolean m_debug

### m_random

    java.util.Random m_random

### m_randomseed

    long m_randomseed

Class weka.classifiers.functions.LinearRegression extends Classifier 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

    ReplaceMissingValues m_MissingFilter

  The filter for removing missing values.

### m_TransformFilter

    NominalToBinary m_TransformFilter

  The filter storing the transformation from nominal to binary attributes.

### m_ClassStdDev

    double m_ClassStdDev

  The standard deviations of the class attribute

### m_ClassMean

    double m_ClassMean

  The mean of the class attribute

### m_ClassIndex

    int m_ClassIndex

  The index of the class attribute

### m_Means

    double[] m_Means

  The attributes means

### m_StdDevs

    double[] m_StdDevs

  The attribute standard deviations

### b_Debug

    boolean b_Debug

  True if debug output will be printed

### m_AttributeSelection

    int m_AttributeSelection

  The current attribute selection method

### m_EliminateColinearAttributes

    boolean m_EliminateColinearAttributes

  Try to eliminate correlated attributes?

### m_checksTurnedOff

    boolean m_checksTurnedOff

  Turn off all checks and conversions?

### m_Ridge

    double m_Ridge

  The ridge parameter

Class weka.classifiers.functions.Logistic extends Classifier implements Serializable

Serialized Fields

### m_Par

    double[][] m_Par

  The coefficients (optimized parameters) of the model

### m_Data

    double[][] m_Data

  The data saved as a matrix

### m_NumPredictors

    int m_NumPredictors

  The number of attributes in the model

### m_ClassIndex

    int m_ClassIndex

  The index of the class attribute

### m_NumClasses

    int m_NumClasses

  The number of the class labels

### m_Ridge

    double m_Ridge

  The ridge parameter.

### m_AttFilter

    RemoveUseless m_AttFilter

### m_NominalToBinary

    NominalToBinary m_NominalToBinary

  The filter used to make attributes numeric.

### m_ReplaceMissingValues

    ReplaceMissingValues m_ReplaceMissingValues

  The filter used to get rid of missing values.

### m_Debug

    boolean m_Debug

  Debugging output

### m_LL

    double m_LL

  Log-likelihood of the searched model

### m_MaxIts

    int m_MaxIts

  The maximum number of iterations.

Class weka.classifiers.functions.MultilayerPerceptron extends Classifier implements Serializable

Serialized Fields

### m_ZeroR

    Classifier m_ZeroR

  a ZeroR model in case no model can be built from the data

### 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

    weka.classifiers.functions.MultilayerPerceptron.NeuralEnd[] m_outputs

  The output units.(only feeds the errors, does no calcs)

### m_inputs

    weka.classifiers.functions.MultilayerPerceptron.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.functions.MultilayerPerceptron.NodePanel m_nodePanel

  The panel the nodes are displayed on.

### m_controlPanel

    weka.classifiers.functions.MultilayerPerceptron.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

    javax.swing.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

    int m_randomSeed

  The number used to seed the random number generator.

### m_random

    java.util.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

    NominalToBinary m_nominalToBinaryFilter

  The actual filter.

### m_hiddenLayers

    java.lang.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.functions.MultilayerPerceptron.NeuralEnd extends NeuralConnection 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.functions.PaceRegression extends Classifier implements Serializable

Serialized Fields

### m_Model

    Instances m_Model

  The model used

### m_Coefficients

    double[] m_Coefficients

  Array for storing coefficients of linear regression.

### m_ClassIndex

    int m_ClassIndex

  The index of the class attribute

### m_Debug

    boolean m_Debug

  True if debug output will be printed

### paceEstimator

    int paceEstimator

### olscThreshold

    double olscThreshold

Class weka.classifiers.functions.RBFNetwork extends Classifier implements Serializable

Serialized Fields

### m_logistic

    Logistic m_logistic

  The logistic regression for classification problems

### m_linear

    LinearRegression m_linear

  The linear regression for numeric problems

### m_basisFilter

    ClusterMembership m_basisFilter

  The filter for producing the meta data

### m_standardize

    Standardize m_standardize

  Filter used for normalizing the data

### m_numClusters

    int m_numClusters

  The number of clusters (basis functions to generate)

### m_ridge

    double m_ridge

  The ridge parameter for the logistic regression.

### m_maxIts

    int m_maxIts

  The maximum number of iterations for logistic regression.

### m_clusteringSeed

    int m_clusteringSeed

  The seed to pass on to K-means

### m_minStdDev

    double m_minStdDev

  The minimum standard deviation

### m_ZeroR

    Classifier m_ZeroR

  a ZeroR model in case no model can be built from the data

Class weka.classifiers.functions.SimpleLinearRegression extends Classifier implements Serializable

Serialized Fields

### m_attribute

    Attribute m_attribute

  The chosen attribute

### m_attributeIndex

    int m_attributeIndex

  The index of the chosen attribute

### m_slope

    double m_slope

  The slope

### m_intercept

    double m_intercept

  The intercept

### m_suppressErrorMessage

    boolean m_suppressErrorMessage

  If true, suppress error message if no useful attribute was found

Class weka.classifiers.functions.SimpleLogistic extends Classifier implements Serializable

Serialized Fields

### m_boostedModel

    LogisticBase m_boostedModel

  The actual logistic regression model

### m_NominalToBinary

    NominalToBinary m_NominalToBinary

  Filter for converting nominal attributes to binary ones

### m_ReplaceMissingValues

    ReplaceMissingValues m_ReplaceMissingValues

  Filter for replacing missing values

### m_numBoostingIterations

    int m_numBoostingIterations

  If non-negative, use this as fixed number of LogitBoost iterations

### m_maxBoostingIterations

    int m_maxBoostingIterations

  Maximum number of iterations for LogitBoost

### m_heuristicStop

    int m_heuristicStop

  Parameter for the heuristic for early stopping of LogitBoost

### m_useCrossValidation

    boolean m_useCrossValidation

  If true, cross-validate number of LogitBoost iterations

### m_errorOnProbabilities

    boolean m_errorOnProbabilities

  If true, use minimize error on probabilities instead of misclassification error

Class weka.classifiers.functions.SMO extends Classifier implements Serializable

Serialized Fields

### m_classifiers

    weka.classifiers.functions.SMO.BinarySMO[][] m_classifiers

  The binary classifier(s)

### m_exponent

    double m_exponent

  The exponent for the polynomial kernel.

### m_lowerOrder

    boolean m_lowerOrder

  Use lower-order terms?

### m_gamma

    double m_gamma

  Gamma for the RBF 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_filterType

    int m_filterType

  Whether to normalize/standardize/neither

### m_featureSpaceNormalization

    boolean m_featureSpaceNormalization

  Feature-space normalization?

### m_useRBF

    boolean m_useRBF

  Use RBF kernel? (default: poly)

### m_cacheSize

    int m_cacheSize

  The size of the cache (a prime number)

### m_NominalToBinary

    NominalToBinary m_NominalToBinary

  The filter used to make attributes numeric.

### m_Filter

    Filter m_Filter

  The filter used to standardize/normalize all values.

### m_Missing

    ReplaceMissingValues m_Missing

  The filter used to get rid of missing values.

### m_onlyNumeric

    boolean m_onlyNumeric

  Only numeric attributes in the dataset?

### m_classIndex

    int m_classIndex

  The class index from the training data

### m_classAttribute

    Attribute m_classAttribute

  The class attribute

### m_checksTurnedOff

    boolean m_checksTurnedOff

  Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0.

### m_fitLogisticModels

    boolean m_fitLogisticModels

  Whether logistic models are to be fit

### m_numFolds

    int m_numFolds

  The number of folds for the internal cross-validation

### m_randomSeed

    int m_randomSeed

  The random number seed

Class weka.classifiers.functions.SMO.BinarySMO extends java.lang.Object implements Serializable

Serialized Fields

### 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_sparseWeights

    double[] m_sparseWeights

  Variables to hold weight vector in sparse form. (To reduce storage requirements.)

### m_sparseIndices

    int[] m_sparseIndices

### m_kernel

    Kernel m_kernel

  Kernel to use

### 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

    SMOset m_I0

  The five different sets used by the algorithm.

### m_I1

    SMOset m_I1

### m_I2

    SMOset m_I2

### m_I3

    SMOset m_I3

### m_I4

    SMOset m_I4

### m_supportVectors

    SMOset m_supportVectors

  The set of support vectors

### m_logistic

    Logistic m_logistic

  Stores logistic regression model for probability estimate

### m_sumOfWeights

    double m_sumOfWeights

  Stores the weight of the training instances

Class weka.classifiers.functions.SMOreg extends Classifier implements Serializable

Serialized Fields

### m_featureSpaceNormalization

    boolean m_featureSpaceNormalization

  Feature-space normalization?

### m_useRBF

    boolean m_useRBF

  Use RBF kernel? (default: poly)

### m_cacheSize

    int m_cacheSize

  The size of the cache (a prime number)

### m_kernel

    Kernel m_kernel

  Kernel to use

### m_lowerOrder

    boolean m_lowerOrder

  Use lower-order terms?

### m_exponent

    double m_exponent

  The exponent for the polynomial kernel.

### m_gamma

    double m_gamma

  Gamma for the RBF kernel.

### m_classIndex

    int m_classIndex

  The class index from the training data

### m_onlyNumeric

    boolean m_onlyNumeric

  Only numeric attributes in the dataset?

### m_NominalToBinary

    NominalToBinary m_NominalToBinary

  The filter used to make attributes numeric.

### m_Filter

    Filter m_Filter

  The filter used to standardize/normalize all values.

### m_filterType

    int m_filterType

  Whether to normalize/standardize/neither

### m_Missing

    ReplaceMissingValues m_Missing

  The filter used to get rid of missing values.

### m_checksTurnedOff

    boolean m_checksTurnedOff

  Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0.

### m_data

    Instances m_data

  The training data.

### m_C

    double m_C

  The complexity parameter

### m_alpha

    double[] m_alpha

  The Lagrange multipliers

### m_alpha_

    double[] m_alpha_

### 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_weights

    double[] m_weights

  Weight vector for linear machine.

### m_fcache

    double[] m_fcache

  The current set of errors for all non-bound examples.

### m_I0

    SMOset m_I0

  The five different sets used by the algorithm.

### m_I1

    SMOset m_I1

### m_I2

    SMOset m_I2

### m_I3

    SMOset m_I3

### m_epsilon

    double m_epsilon

  The parameter epsilon

### m_tol

    double m_tol

  The parameter tol

### m_eps

    double m_eps

  The parameter eps

### m_Alin

    double m_Alin

  The parameters of the linear transforamtion realized by the filter on the class attribute

### m_Blin

    double m_Blin

### m_sparseWeights

    double[] m_sparseWeights

  Variables to hold weight vector in sparse form. (To reduce storage requirements.)

### m_sparseIndices

    int[] m_sparseIndices

Class weka.classifiers.functions.VotedPerceptron extends Classifier 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

    NominalToBinary m_NominalToBinary

  The filter used to make attributes numeric.

### m_ReplaceMissingValues

    ReplaceMissingValues m_ReplaceMissingValues

  The filter used to get rid of missing values.

Class weka.classifiers.functions.Winnow extends Classifier implements Serializable

Serialized Fields

### m_Balanced

    boolean m_Balanced

  Use the balanced variant?

### m_numIterations

    int m_numIterations

  The number of iterations

### m_Alpha

    double m_Alpha

  The promotion coefficient

### m_Beta

    double m_Beta

  The demotion coefficient

### m_Threshold

    double m_Threshold

  Prediction threshold, <0 == numAttributes

### m_Seed

    int m_Seed

  Random seed used for shuffling the dataset, -1 == disable

### m_Mistakes

    int m_Mistakes

  Accumulated mistake count (for statistics)

### m_defaultWeight

    double m_defaultWeight

  Starting weights for the prediction vector(s)

### m_predPosVector

    double[] m_predPosVector

  The weight vectors for prediction

### m_predNegVector

    double[] m_predNegVector

### m_actualThreshold

    double m_actualThreshold

  The true threshold used for prediction

### m_Train

    Instances m_Train

  The training instances

### m_NominalToBinary

    NominalToBinary m_NominalToBinary

  The filter used to make attributes numeric.

### m_ReplaceMissingValues

    ReplaceMissingValues m_ReplaceMissingValues

  The filter used to get rid of missing values.

Package weka.classifiers.functions.neural

Class weka.classifiers.functions.neural.LinearUnit extends java.lang.Object implements Serializable

Class weka.classifiers.functions.neural.NeuralConnection extends java.lang.Object 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

    java.lang.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.functions.neural.NeuralNode extends NeuralConnection implements Serializable

Serialized Fields

### m_weights

    double[] m_weights

  The weights for each of the input connections, and the threshold.

### m_bestWeights

    double[] m_bestWeights

  The best (lowest error) weights. Only used when validation set is used

### m_changeInWeights

    double[] m_changeInWeights

  The change in the weights.

### m_random

    java.util.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.functions.neural.SigmoidUnit extends java.lang.Object implements Serializable

Package weka.classifiers.functions.pace

Class weka.classifiers.functions.pace.ExponentialFormat extends java.text.DecimalFormat implements Serializable

Serialized Fields

### nf

    java.text.DecimalFormat nf

### sign

    boolean sign

### digits

    int digits

### exp

    int exp

### trailing

    boolean trailing

Class weka.classifiers.functions.pace.FlexibleDecimalFormat extends java.text.DecimalFormat implements Serializable

Serialized Fields

### nf

    java.text.DecimalFormat nf

### digits

    int digits

### exp

    boolean exp

### intDigits

    int intDigits

### decimalDigits

    int decimalDigits

### expIntDigits

    int expIntDigits

### expDecimalDigits

    int expDecimalDigits

### power

    int power

### trailing

    boolean trailing

### grouping

    boolean grouping

### sign

    boolean sign

Class weka.classifiers.functions.pace.FloatingPointFormat extends java.text.DecimalFormat implements Serializable

Serialized Fields

### nf

    java.text.DecimalFormat nf

### width

    int width

### decimal

    int decimal

### trailing

    boolean trailing

Class weka.classifiers.functions.pace.Matrix extends java.lang.Object implements Serializable

Serialized Fields

### A

    double[][] A

  Array for internal storage of elements.

  internal array storage.

### m

    int m

  Row and column dimensions.

  row dimension.

### n

    int n

  Row and column dimensions.

  row dimension.

Class weka.classifiers.functions.pace.PaceMatrix extends Matrix implements Serializable

Package weka.classifiers.functions.supportVector

Class weka.classifiers.functions.supportVector.CachedKernel extends Kernel implements Serializable

Serialized Fields

### m_kernelEvals

    int m_kernelEvals

  Counts the number of kernel evaluations.

### m_cacheHits

    int m_cacheHits

  Counts the number of kernel cache hits.

### m_cacheSize

    int m_cacheSize

  The size of the cache (a prime number)

### m_storage

    double[] m_storage

  Kernel cache

### m_keys

    long[] m_keys

### m_kernelMatrix

    double[][] m_kernelMatrix

  The kernel matrix if full cache is used (i.e. size is set to 0)

### m_numInsts

    int m_numInsts

  The number of instance in the dataset

### m_cacheSlots

    int m_cacheSlots

  number of cache slots in an entry

Class weka.classifiers.functions.supportVector.Kernel extends java.lang.Object implements Serializable

Serialized Fields

### m_data

    Instances m_data

  The dataset

Class weka.classifiers.functions.supportVector.NormalizedPolyKernel extends PolyKernel implements Serializable

Class weka.classifiers.functions.supportVector.PolyKernel extends CachedKernel implements Serializable

Serialized Fields

### m_lowerOrder

    boolean m_lowerOrder

  Use lower-order terms?

### m_exponent

    double m_exponent

  The exponent for the polynomial kernel.

Class weka.classifiers.functions.supportVector.RBFKernel extends CachedKernel implements Serializable

Serialized Fields

### m_kernelPrecalc

    double[] m_kernelPrecalc

  The precalculated dotproducts of

### m_gamma

    double m_gamma

  Gamma for the RBF kernel.

Class weka.classifiers.functions.supportVector.SMOset extends java.lang.Object implements Serializable

Serialized Fields

### m_number

    int m_number

  The current number of elements in the set

### m_first

    int m_first

  The first element in the set

### m_indicators

    boolean[] m_indicators

  Indicators

### m_next

    int[] m_next

  The next element for each element

### m_previous

    int[] m_previous

  The previous element for each element

Package weka.classifiers.lazy

Class weka.classifiers.lazy.IB1 extends Classifier 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.lazy.IBk extends Classifier 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_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.lazy.KStar extends Classifier 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

Class weka.classifiers.lazy.LBR extends Classifier implements Serializable

Serialized Fields

### m_Counts

    int[][][] m_Counts

  All the counts for nominal attributes.

### m_tCounts

    int[][][] m_tCounts

  All the counts for nominal attributes.

### m_Priors

    int[] m_Priors

  The prior probabilities of the classes.

### m_tPriors

    int[] m_tPriors

  The prior probabilities of the classes.

### m_numAtts

    int m_numAtts

  number of attributes for the dataset

### m_numClasses

    int m_numClasses

  number of classes for dataset

### m_numInsts

    int m_numInsts

  number of instances in dataset

### m_Instances

    Instances m_Instances

  The set of instances used for current training.

### m_Errors

    int m_Errors

### m_ErrorFlags

    boolean[] m_ErrorFlags

### leftHand

    java.util.ArrayList<E> leftHand

### m_subOldErrorFlags

    boolean[] m_subOldErrorFlags

### m_RemainderErrors

    int m_RemainderErrors

### m_Number

    int m_Number

### m_NumberOfInstances

    int m_NumberOfInstances

### m_NCV

    boolean m_NCV

### m_subInstances

    LBR.Indexes m_subInstances

### tempSubInstances

    LBR.Indexes tempSubInstances

### posteriorsArray

    double[] posteriorsArray

### bestCnt

    int bestCnt

### tempCnt

    int tempCnt

### forCnt

    int forCnt

### whileCnt

    int whileCnt

Class weka.classifiers.lazy.LBR.Indexes extends java.lang.Object implements Serializable

Serialized Fields

### m_InstIndexes

    boolean[] m_InstIndexes

  the array instance indexes

### m_AttIndexes

    boolean[] m_AttIndexes

  the array attribute indexes

### m_NumInstances

    int m_NumInstances

  the number of instances indexed

### m_NumAtts

    int m_NumAtts

  the number of attributes indexed

### m_SequentialInstIndexes

    int[] m_SequentialInstIndexes

  the array of instance indexes that are set to a either true or false

### m_SequentialAttIndexes

    int[] m_SequentialAttIndexes

  an array of attribute indexes that are set to either true or false

### m_SequentialInstanceIndex_valid

    boolean m_SequentialInstanceIndex_valid

  flag to check if sequential array must be rebuilt due to changes to the instance index

### m_SequentialAttIndex_valid

    boolean m_SequentialAttIndex_valid

  flag to check if sequential array must be rebuilt due to changes to the attribute index

### m_NumInstsSet

    int m_NumInstsSet

  the number of instances "in use" or set to a the original value (true or false)

### m_NumAttsSet

    int m_NumAttsSet

  the number of attributes "in use" or set to a the original value (true or false)

### m_NumSeqInstsSet

    int m_NumSeqInstsSet

  the number of sequential instances "in use" or set to a the original value (true or false)

### m_NumSeqAttsSet

    int m_NumSeqAttsSet

  the number of sequential attributes "in use" or set to a the original value (true or false)

### m_ClassIndex

    int m_ClassIndex

  the Class Index for the data set

Class weka.classifiers.lazy.LWL extends SingleClassifierEnhancer 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_NoAttribNorm

    boolean m_NoAttribNorm

  True if numeric attributes' values should not be normalized in distance calculation.

### 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

### m_ZeroR

    Classifier m_ZeroR

  a ZeroR model in case no model can be built from the data

Package weka.classifiers.lazy.kstar

Class weka.classifiers.lazy.kstar.KStarCache extends java.lang.Object implements Serializable

Serialized Fields

### m_Cache

    KStarCache.CacheTable m_Cache

  cache table

Class weka.classifiers.lazy.kstar.KStarCache.CacheTable extends java.lang.Object implements Serializable

Serialized Fields

### m_Table

    KStarCache.TableEntry[] m_Table

  The hash table data.

### m_Count

    int m_Count

  The total number of entries in the hash table.

### m_Threshold

    int m_Threshold

  Rehashes the table when count exceeds this threshold.

### m_LoadFactor

    float m_LoadFactor

  The load factor for the hashtable.

### DEFAULT_TABLE_SIZE

    int DEFAULT_TABLE_SIZE

  The default size of the hashtable

### DEFAULT_LOAD_FACTOR

    float DEFAULT_LOAD_FACTOR

  The default load factor for the hashtable

### EPSILON

    double EPSILON

  Accuracy value for equality

Class weka.classifiers.lazy.kstar.KStarCache.TableEntry extends java.lang.Object implements Serializable

Serialized Fields

### hash

    int hash

  attribute value hash code

### key

    double key

  attribute value

### value

    double value

  scale factor or stop parameter

### pmiss

    double pmiss

  transformation probability to missing value

### next

    KStarCache.TableEntry next

  next table entry (separate chaining)

Package weka.classifiers.meta

Class weka.classifiers.meta.AdaBoostM1 extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Betas

    double[] m_Betas

  Array for storing the weights for the votes.

### m_NumIterationsPerformed

    int m_NumIterationsPerformed

  The number of successfully generated base classifiers.

### m_WeightThreshold

    int m_WeightThreshold

  Weight Threshold. The percentage of weight mass used in training

### m_UseResampling

    boolean m_UseResampling

  Use boosting with reweighting?

### m_NumClasses

    int m_NumClasses

  The number of classes

### m_ZeroR

    Classifier m_ZeroR

  a ZeroR model in case no model can be built from the data

Class weka.classifiers.meta.AdditiveRegression extends IteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_classIndex

    int m_classIndex

  Class index.

### m_shrinkage

    double m_shrinkage

  Shrinkage (Learning rate). Default = no shrinkage.

### m_NumIterationsPerformed

    int m_NumIterationsPerformed

  The number of successfully generated base classifiers.

### m_zeroR

    ZeroR m_zeroR

  The model for the mean

### m_SuitableData

    boolean m_SuitableData

  whether we have suitable data or nor (if not, ZeroR model is used)

Class weka.classifiers.meta.AttributeSelectedClassifier extends SingleClassifierEnhancer implements Serializable

Serialized Fields

### 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.meta.Bagging extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_BagSizePercent

    int m_BagSizePercent

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

### m_CalcOutOfBag

    boolean m_CalcOutOfBag

  Whether to calculate the out of bag error

### m_OutOfBagError

    double m_OutOfBagError

  The out of bag error that has been calculated

Class weka.classifiers.meta.ClassificationViaRegression extends SingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Classifiers

    Classifier[] m_Classifiers

  The classifiers. (One for each class.)

### m_ClassFilters

    MakeIndicator[] m_ClassFilters

  The filters used to transform the class.

Class weka.classifiers.meta.CostSensitiveClassifier extends RandomizableSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_MatrixSource

    int m_MatrixSource

  Indicates the current cost matrix source

### m_OnDemandDirectory

    java.io.File m_OnDemandDirectory

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

### m_CostFile

    java.lang.String m_CostFile

  The name of the cost file, for command line options

### m_CostMatrix

    CostMatrix m_CostMatrix

  The cost matrix

### 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.meta.CVParameterSelection extends RandomizableSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_ClassifierOptions

    java.lang.String[] m_ClassifierOptions

  The base classifier options (not including those being set by cross-validation)

### m_BestClassifierOptions

    java.lang.String[] m_BestClassifierOptions

  The set of all classifier options as determined by cross-validation

### m_InitOptions

    java.lang.String[] m_InitOptions

  The set of all options at initialization time. So that getOptions can return this.

### 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

Class weka.classifiers.meta.CVParameterSelection.CVParameter extends java.lang.Object implements Serializable

Serialized Fields

### m_ParamChar

    char m_ParamChar

  Char used to identify the option of interest

### m_Lower

    double m_Lower

  Lower bound for the CV search

### m_Upper

    double m_Upper

  Upper bound for the CV search

### m_Steps

    double m_Steps

  Number of steps during the search

### m_ParamValue

    double m_ParamValue

  The parameter value with the best performance

### m_AddAtEnd

    boolean m_AddAtEnd

  True if the parameter should be added at the end of the argument list

### m_RoundParam

    boolean m_RoundParam

  True if the parameter should be rounded to an integer

Class weka.classifiers.meta.Decorate extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Committee

    java.util.Vector<E> m_Committee

  Vector of classifiers that make up the committee/ensemble.

### m_DesiredSize

    int m_DesiredSize

  The desired ensemble size.

### m_ArtSize

    double m_ArtSize

  Amount of artificial/random instances to use - specified as a fraction of the training data size.

### m_Random

    java.util.Random m_Random

  The random number generator.

### m_AttributeStats

    java.util.Vector<E> m_AttributeStats

  Attribute statistics - used for generating artificial examples.

Class weka.classifiers.meta.FilteredClassifier extends SingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Filter

    Filter m_Filter

  The filter

### m_FilteredInstances

    Instances m_FilteredInstances

  The instance structure of the filtered instances

Class weka.classifiers.meta.Grading extends Stacking implements Serializable

Serialized Fields

### m_MetaClassifiers

    Classifier[] m_MetaClassifiers

  The meta classifiers, one for each base classifier.

### m_InstPerClass

    double[] m_InstPerClass

  InstPerClass

Class weka.classifiers.meta.LogitBoost extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Classifiers

    Classifier[][] m_Classifiers

  Array for storing the generated base classifiers. Note: we are hiding the variable from IteratedSingleClassifierEnhancer

### m_NumClasses

    int m_NumClasses

  The number of classes

### m_NumGenerated

    int m_NumGenerated

  The number of successfully generated base classifiers.

### m_NumFolds

    int m_NumFolds

  The number of folds for the internal cross-validation.

### m_NumRuns

    int m_NumRuns

  The number of runs for the internal cross-validation.

### m_WeightThreshold

    int m_WeightThreshold

  Weight thresholding. The percentage of weight mass used in training

### 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_Precision

    double m_Precision

  The threshold on the improvement of the likelihood

### m_Shrinkage

    double m_Shrinkage

  The value of the shrinkage parameter

### m_RandomInstance

    java.util.Random m_RandomInstance

  The random number generator used

### m_Offset

    double m_Offset

  The value by which the actual target value for the true class is offset.

### m_ZeroR

    Classifier m_ZeroR

  a ZeroR model in case no model can be built from the data

Class weka.classifiers.meta.MetaCost extends RandomizableSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_MatrixSource

    int m_MatrixSource

  Indicates the current cost matrix source

### m_OnDemandDirectory

    java.io.File m_OnDemandDirectory

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

### m_CostFile

    java.lang.String m_CostFile

  The name of the cost file, for command line options

### m_CostMatrix

    CostMatrix m_CostMatrix

  The cost matrix

### m_NumIterations

    int m_NumIterations

  The number of iterations.

### m_BagSizePercent

    int m_BagSizePercent

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

Class weka.classifiers.meta.MultiBoostAB extends AdaBoostM1 implements Serializable

Serialized Fields

### m_NumSubCmtys

    int m_NumSubCmtys

  The number of sub-committees to use

### m_Random

    java.util.Random m_Random

  Random number generator

Class weka.classifiers.meta.MultiClassClassifier extends RandomizableSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Classifiers

    Classifier[] m_Classifiers

  The classifiers.

### m_ClassFilters

    Filter[] m_ClassFilters

  The filters used to transform the class.

### 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_TwoClassDataset

    Instances m_TwoClassDataset

  A transformed dataset header used by the 1-against-1 method

### m_RandomWidthFactor

    double m_RandomWidthFactor

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

### m_Method

    int m_Method

  The multiclass method to use

Class weka.classifiers.meta.MultiScheme extends RandomizableMultipleClassifiersCombiner implements Serializable

Serialized Fields

### m_Classifier

    Classifier m_Classifier

  The classifier that had the best performance on training data.

### 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)

Class weka.classifiers.meta.OrdinalClassClassifier extends SingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Classifiers

    Classifier[] m_Classifiers

  The classifiers. (One for each class.)

### m_ClassFilters

    MakeIndicator[] m_ClassFilters

  The filters used to transform the class.

### m_ClassAttribute

    Attribute m_ClassAttribute

  Internal copy of the class attribute for output purposes

### m_ZeroR

    ZeroR m_ZeroR

  ZeroR classifier for when all base classifier return zero probability.

Class weka.classifiers.meta.RacedIncrementalLogitBoost extends RandomizableSingleClassifierEnhancer implements Serializable

Serialized Fields

### m_committees

    FastVector m_committees

  The committees

### m_PruningType

    int m_PruningType

  The pruning type used

### m_UseResampling

    boolean m_UseResampling

  Whether to use resampling

### m_NumClasses

    int m_NumClasses

  The number of classes

### 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_minChunkSize

    int m_minChunkSize

  The minimum chunk size used for training

### m_maxChunkSize

    int m_maxChunkSize

  The maimum chunk size used for training

### m_validationChunkSize

    int m_validationChunkSize

  The size of the validation set

### m_numInstancesConsumed

    int m_numInstancesConsumed

  The number of instances consumed

### m_validationSet

    Instances m_validationSet

  The instances used for validation

### m_currentSet

    Instances m_currentSet

  The instances currently in memory for training

### m_bestCommittee

    weka.classifiers.meta.RacedIncrementalLogitBoost.Committee m_bestCommittee

  The current best committee

### m_zeroR

    ZeroR m_zeroR

  The default scheme used when committees aren't ready

### m_validationSetChanged

    boolean m_validationSetChanged

  Whether the validation set has recently been changed

### m_maxBatchSizeRequired

    int m_maxBatchSizeRequired

  The maximum number of instances required for processing

### m_RandomInstance

    java.util.Random m_RandomInstance

  The random number generator used

Class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee extends java.lang.Object implements Serializable

Serialized Fields

### m_chunkSize

    int m_chunkSize

### m_instancesConsumed

    int m_instancesConsumed

### m_models

    FastVector m_models

### m_lastValidationError

    double m_lastValidationError

### m_lastLogLikelihood

    double m_lastLogLikelihood

### m_modelHasChanged

    boolean m_modelHasChanged

### m_modelHasChangedLL

    boolean m_modelHasChangedLL

### m_validationFs

    double[][] m_validationFs

### m_newValidationFs

    double[][] m_newValidationFs

Class weka.classifiers.meta.RandomCommittee extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

Class weka.classifiers.meta.RegressionByDiscretization extends SingleClassifierEnhancer implements Serializable

Serialized Fields

### m_Discretizer

    Discretize m_Discretizer

  The discretization filter.

### m_NumBins

    int m_NumBins

  The number of discretization intervals.

### m_ClassMeans

    double[] m_ClassMeans

  The mean values for each Discretized class interval.

Class weka.classifiers.meta.Stacking extends RandomizableMultipleClassifiersCombiner implements Serializable

Serialized Fields

### m_MetaClassifier

    Classifier m_MetaClassifier

  The meta classifier

### 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

Class weka.classifiers.meta.StackingC extends Stacking implements Serializable

Serialized Fields

### m_MetaClassifiers

    Classifier[] m_MetaClassifiers

  The meta classifiers (one for each class, like in ClassificationViaRegression)

### m_attrFilter

    Remove m_attrFilter

  Filters to transform metaData

### m_makeIndicatorFilter

    MakeIndicator m_makeIndicatorFilter

Class weka.classifiers.meta.ThresholdSelector extends RandomizableSingleClassifierEnhancer implements Serializable

Serialized Fields

### 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_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.meta.Vote extends MultipleClassifiersCombiner implements Serializable

Package weka.classifiers.misc

Class weka.classifiers.misc.HyperPipes extends Classifier 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

    weka.classifiers.misc.HyperPipes.HyperPipe[] m_HyperPipes

  Stores the HyperPipe for each class

### m_ZeroR

    Classifier m_ZeroR

  a ZeroR model in case no model can be built from the data

Class weka.classifiers.misc.VFI extends Classifier 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

Package weka.classifiers.rules

Class weka.classifiers.rules.ConjunctiveRule extends Classifier implements Serializable

Serialized Fields

### m_Folds

    int m_Folds

  The number of folds to split data into Grow and Prune for REP

### m_ClassAttribute

    Attribute m_ClassAttribute

  The class attribute of the data

### m_Antds

    FastVector m_Antds

  The vector of antecedents of this rule

### m_DefDstr

    double[] m_DefDstr

  The default rule distribution of the data not covered

### m_Cnsqt

    double[] m_Cnsqt

  The consequent of this rule

### m_NumClasses

    int m_NumClasses

  Number of classes in the training data

### m_Seed

    long m_Seed

  The seed to perform randomization

### m_Random

    java.util.Random m_Random

  The Random object used for randomization

### m_Targets

    FastVector m_Targets

  The predicted classes recorded for each antecedent in the growing data

### m_IsExclude

    boolean m_IsExclude

  Whether to use exlusive expressions for nominal attributes

### m_MinNo

    double m_MinNo

  The minimal number of instance weights within a split

### m_NumAntds

    int m_NumAntds

  The number of antecedents in pre-pruning

Class weka.classifiers.rules.DecisionTable extends Classifier implements Serializable

Serialized Fields

### m_entries

    java.util.Hashtable<K,V> m_entries

  The hashtable used to hold training instances

### m_decisionFeatures

    int[] m_decisionFeatures

  Holds the final feature set

### m_disTransform

    Filter m_disTransform

  Discretization filter

### m_delTransform

    Remove 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

    java.util.Random m_rr

  Random numbers for use in cross validation

### m_majority

    double m_majority

  Holds the majority class

Class weka.classifiers.rules.DecisionTable.hashKey extends java.lang.Object 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

    java.lang.String[] values

  The values

### key

    int key

  The key

Class weka.classifiers.rules.DecisionTable.LinkedList extends FastVector implements Serializable

Class weka.classifiers.rules.JRip extends Classifier implements Serializable

Serialized Fields

### m_Class

    Attribute m_Class

  The class attribute of the data

### m_Ruleset

    FastVector m_Ruleset

  The ruleset

### m_Distributions

    FastVector m_Distributions

  The predicted class distribution

### m_Optimizations

    int m_Optimizations

  Runs of optimizations

### m_Random

    java.util.Random m_Random

  Random object used in this class

### m_Total

    double m_Total

  # of all the possible conditions in a rule

### m_Seed

    long m_Seed

  The seed to perform randomization

### m_Folds

    int m_Folds

  The number of folds to split data into Grow and Prune for IREP

### m_MinNo

    double m_MinNo

  The minimal number of instance weights within a split

### m_Debug

    boolean m_Debug

  Whether in a debug mode

### m_CheckErr

    boolean m_CheckErr

  Whether check the error rate >= 0.5 in stopping criteria

### m_UsePruning

    boolean m_UsePruning

  Whether use pruning, i.e. the data is clean or not

### m_Filter

    Filter m_Filter

  The filter used to randomize the class order

### m_RulesetStats

    FastVector m_RulesetStats

  The RuleStats for the ruleset of each class value

Class weka.classifiers.rules.JRip.RipperRule extends Rule implements Serializable

Serialized Fields

### m_Consequent

    double m_Consequent

  The internal representation of the class label to be predicted

### m_Antds

    FastVector m_Antds

  The vector of antecedents of this rule

Class weka.classifiers.rules.M5Rules extends M5Base implements Serializable

Class weka.classifiers.rules.NNge extends Classifier implements Serializable

Serialized Fields

### m_Train

    Instances m_Train

  An empty instances to keep the headers, the classIndex, etc...

### m_Exemplars

    weka.classifiers.rules.NNge.Exemplar m_Exemplars

  The list of Exemplars

### m_ExemplarsByClass

    weka.classifiers.rules.NNge.Exemplar[] m_ExemplarsByClass

  The lists of Exemplars by class

### m_MinArray

    double[] m_MinArray

  The minimum values for numeric attributes.

### m_MaxArray

    double[] m_MaxArray

  The maximum values for numeric attributes.

### m_NumAttemptsOfGene

    int m_NumAttemptsOfGene

  The number of try for generalisation

### m_NumFoldersMI

    int m_NumFoldersMI

  The number of folder for the Mutual Information

### m_MissingVector

    double[] m_MissingVector

  Values to use for missing value

### m_MI_NumAttrClassInter

    int[][][] m_MI_NumAttrClassInter

  MUTUAL INFORMATION'S DATAS

### m_MI_NumAttrInter

    int[][] m_MI_NumAttrInter

### m_MI_MaxArray

    double[] m_MI_MaxArray

### m_MI_MinArray

    double[] m_MI_MinArray

### m_MI_NumAttrClassValue

    int[][][] m_MI_NumAttrClassValue

### m_MI_NumAttrValue

    int[][] m_MI_NumAttrValue

### m_MI_NumClass

    int[] m_MI_NumClass

### m_MI_NumInst

    int m_MI_NumInst

### m_MI

    double[] m_MI

Class weka.classifiers.rules.OneR extends Classifier implements Serializable

Serialized Fields

### m_rule

    weka.classifiers.rules.OneR.OneRRule m_rule

  A 1-R rule

### m_minBucketSize

    int m_minBucketSize

  The minimum bucket size

### m_ZeroR

    Classifier m_ZeroR

  a ZeroR model in case no model can be built from the data

Class weka.classifiers.rules.PART extends Classifier 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?

### m_unpruned

    boolean m_unpruned

  Generate unpruned list?

### m_Seed

    int m_Seed

  The seed for random number generation.

Class weka.classifiers.rules.Prism extends Classifier implements Serializable

Serialized Fields

### m_rules

    weka.classifiers.rules.Prism.PrismRule m_rules

  The first rule in the list of rules

Class weka.classifiers.rules.Ridor extends Classifier implements Serializable

Serialized Fields

### m_Folds

    int m_Folds

  The number of folds to split data into Grow and Prune for IREP

### m_Shuffle

    int m_Shuffle

  The number of shuffles performed on the data for randomization

### m_Random

    java.util.Random m_Random

  Random object for randomization

### m_Seed

    int m_Seed

  The seed to perform randomization

### m_IsAllErr

    boolean m_IsAllErr

  Whether use error rate on all the data

### m_IsMajority

    boolean m_IsMajority

  Whether use majority class as default class

### m_Root

    weka.classifiers.rules.Ridor.Ridor_node m_Root

  The root of Ridor

### m_Class

    Attribute m_Class

  The class attribute of the data

### m_Cover

    double m_Cover

  Statistics of the data

### m_Err

    double m_Err

  Statistics of the data

### m_MinNo

    double m_MinNo

  The minimal number of instance weights within a split

Class weka.classifiers.rules.Rule extends java.lang.Object implements Serializable

Class weka.classifiers.rules.RuleStats extends java.lang.Object implements Serializable

Serialized Fields

### m_Data

    Instances m_Data

  The data on which the stats calculation is based

### m_Ruleset

    FastVector m_Ruleset

  The specific ruleset in question

### m_SimpleStats

    FastVector m_SimpleStats

  The simple stats of each rule

### m_Filtered

    FastVector m_Filtered

  The set of instances filtered by the ruleset

### m_Total

    double m_Total

  The total number of possible conditions that could appear in a rule

### MDL_THEORY_WEIGHT

    double MDL_THEORY_WEIGHT

  The theory weight in the MDL calculation

### m_Distributions

    FastVector m_Distributions

  The class distributions predicted by each rule

Class weka.classifiers.rules.ZeroR extends Classifier 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.rules.part

Class weka.classifiers.rules.part.C45PruneableDecList extends ClassifierDecList implements Serializable

Serialized Fields

### CF

    double CF

  CF

Class weka.classifiers.rules.part.ClassifierDecList extends java.lang.Object implements Serializable

Serialized Fields

### m_minNumObj

    int m_minNumObj

  Minimum number of objects

### 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.rules.part.MakeDecList extends java.lang.Object implements Serializable

Serialized Fields

### theRules

    java.util.Vector<E> 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?

### unpruned

    boolean unpruned

  Generated unpruned list?

### m_seed

    int m_seed

  The seed for random number generation.

Class weka.classifiers.rules.part.PruneableDecList extends ClassifierDecList implements Serializable

Package weka.classifiers.trees

Class weka.classifiers.trees.ADTree extends Classifier 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

    java.util.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.trees.DecisionStump extends Classifier 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.

### m_ZeroR

    Classifier m_ZeroR

  a ZeroR model in case no model can be built from the data

Class weka.classifiers.trees.Id3 extends Classifier 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.trees.J48 extends Classifier implements Serializable

serialVersionUID: -217733168393644444L

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.

### m_Seed

    int m_Seed

  Random number seed for reduced-error pruning.

Class weka.classifiers.trees.LMT extends Classifier implements Serializable

Serialized Fields

### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

  Filter to replace missing values

### m_nominalToBinary

    NominalToBinary m_nominalToBinary

  Filter to replace nominal attributes

### m_tree

    LMTNode m_tree

  root of the logistic model tree

### m_fastRegression

    boolean m_fastRegression

  use heuristic that determines the number of LogitBoost iterations only once in the beginning?

### m_convertNominal

    boolean m_convertNominal

  convert nominal attributes to binary ?

### m_splitOnResiduals

    boolean m_splitOnResiduals

  split on residuals?

### m_errorOnProbabilities

    boolean m_errorOnProbabilities

  use error on probabilties instead of misclassification for stopping criterion of LogitBoost?

### m_minNumInstances

    int m_minNumInstances

  minimum number of instances at which a node is considered for splitting

### m_numBoostingIterations

    int m_numBoostingIterations

  if non-zero, use fixed number of iterations for LogitBoost

Class weka.classifiers.trees.M5P extends M5Base implements Serializable

Class weka.classifiers.trees.NBTree extends Classifier implements Serializable

Serialized Fields

### m_minNumObj

    int m_minNumObj

  Minimum number of instances

### m_root

    NBTreeClassifierTree m_root

  The root of the tree

Class weka.classifiers.trees.RandomForest extends Classifier implements Serializable

Serialized Fields

### m_numTrees

    int m_numTrees

  Number of trees in forest.

### m_numFeatures

    int m_numFeatures

  Number of features to consider in random feature selection. If less than 1 will use int(logM+1) )

### m_randomSeed

    int m_randomSeed

  The random seed.

### m_KValue

    int m_KValue

  Final number of features that were considered in last build.

### m_bagger

    Bagging m_bagger

  The bagger.

Class weka.classifiers.trees.RandomTree extends Classifier implements Serializable

Serialized Fields

### m_Successors

    RandomTree[] m_Successors

  The subtrees appended to this tree.

### m_Attribute

    int m_Attribute

  The attribute to split on.

### m_SplitPoint

    double m_SplitPoint

  The split point.

### m_ClassDistribution

    double[] m_ClassDistribution

  The class distribution from the training data.

### m_Info

    Instances m_Info

  The header information.

### m_Prop

    double[] m_Prop

  The proportions of training instances going down each branch.

### m_MinNum

    double m_MinNum

  Minimum number of instances for leaf.

### m_Debug

    boolean m_Debug

  Debug info

### m_KValue

    int m_KValue

  The number of attributes considered for a split.

### m_randomSeed

    int m_randomSeed

  The random seed to use.

Class weka.classifiers.trees.REPTree extends Classifier implements Serializable

Serialized Fields

### m_zeroR

    ZeroR m_zeroR

  ZeroR model that is used if no attributes are present.

### m_Tree

    weka.classifiers.trees.REPTree.Tree m_Tree

  The Tree object

### m_NumFolds

    int m_NumFolds

  Number of folds for reduced error pruning.

### m_Seed

    int m_Seed

  Seed for random data shuffling.

### m_NoPruning

    boolean m_NoPruning

  Don't prune

### m_MinNum

    double m_MinNum

  The minimum number of instances per leaf.

### m_MinVarianceProp

    double m_MinVarianceProp

  The minimum proportion of the total variance (over all the data) required for split.

### m_MaxDepth

    int m_MaxDepth

  Upper bound on the tree depth

Class weka.classifiers.trees.REPTree.Tree extends java.lang.Object implements Serializable

Serialized Fields

### m_Info

    Instances m_Info

  The header information (for printing the tree).

### m_Successors

    weka.classifiers.trees.REPTree.Tree[] m_Successors

  The subtrees of this tree.

### m_Attribute

    int m_Attribute

  The attribute to split on.

### m_SplitPoint

    double m_SplitPoint

  The split point.

### m_Prop

    double[] m_Prop

  The proportions of training instances going down each branch.

### m_ClassProbs

    double[] m_ClassProbs

  Class probabilities from the training data in the nominal case. Holds the mean in the numeric case.

### m_Distribution

    double[] m_Distribution

  The (unnormalized) class distribution in the nominal case. Holds the sum of squared errors and the weight in the numeric case.

### m_HoldOutDist

    double[] m_HoldOutDist

  Class distribution of hold-out set at node in the nominal case. Straight sum of weights in the numeric case (i.e. array has only one element.

### m_HoldOutError

    double m_HoldOutError

  The hold-out error of the node. The number of miss-classified instances in the nominal case, the sum of squared errors in the numeric case.

Class weka.classifiers.trees.UserClassifier extends Classifier implements Serializable

Serialized Fields

### m_top

    weka.classifiers.trees.UserClassifier.TreeClass m_top

  Two references to the structure of the decision tree.

### m_focus

    weka.classifiers.trees.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_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.

Package weka.classifiers.trees.adtree

Class weka.classifiers.trees.adtree.PredictionNode extends java.lang.Object 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.trees.adtree.ReferenceInstances extends Instances implements Serializable

Class weka.classifiers.trees.adtree.Splitter extends java.lang.Object implements Serializable

Serialized Fields

### orderAdded

    int orderAdded

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

Class weka.classifiers.trees.adtree.TwoWayNominalSplit extends Splitter 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.trees.adtree.TwoWayNumericSplit extends Splitter 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.trees.j48

Class weka.classifiers.trees.j48.BinC45ModelSelection extends ModelSelection 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.trees.j48.BinC45Split extends ClassifierSplitModel 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.trees.j48.C45ModelSelection extends ModelSelection 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.trees.j48.C45PruneableClassifierTree extends ClassifierTree 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.trees.j48.C45Split extends ClassifierSplitModel 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.trees.j48.ClassifierSplitModel extends java.lang.Object 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.trees.j48.ClassifierTree extends java.lang.Object 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.trees.j48.Distribution extends java.lang.Object 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.trees.j48.EntropyBasedSplitCrit extends SplitCriterion implements Serializable

Class weka.classifiers.trees.j48.EntropySplitCrit extends EntropyBasedSplitCrit implements Serializable

Class weka.classifiers.trees.j48.GainRatioSplitCrit extends EntropyBasedSplitCrit implements Serializable

Class weka.classifiers.trees.j48.InfoGainSplitCrit extends EntropyBasedSplitCrit implements Serializable

Class weka.classifiers.trees.j48.ModelSelection extends java.lang.Object implements Serializable

Class weka.classifiers.trees.j48.NBTreeClassifierTree extends ClassifierTree implements Serializable

Class weka.classifiers.trees.j48.NBTreeModelSelection extends ModelSelection 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.trees.j48.NBTreeNoSplit extends ClassifierSplitModel implements Serializable

Serialized Fields

### m_nb

    NaiveBayesUpdateable m_nb

  the naive bayes classifier

### m_disc

    Discretize m_disc

  the discretizer used

### m_errors

    double m_errors

  errors on the training data at this node

Class weka.classifiers.trees.j48.NBTreeSplit extends ClassifierSplitModel 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_sumOfWeights

    double m_sumOfWeights

  The sum of the weights of the instances.

### m_errors

    double m_errors

  The weight of the instances incorrectly classified by the naive bayes models arising from this split

### m_c45S

    C45Split m_c45S

### m_globalNB

    NBTreeNoSplit m_globalNB

  The global naive bayes model for this node

Class weka.classifiers.trees.j48.NoSplit extends ClassifierSplitModel implements Serializable

Class weka.classifiers.trees.j48.PruneableClassifierTree extends ClassifierTree 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.

### m_seed

    int m_seed

  The random number seed.

Class weka.classifiers.trees.j48.SplitCriterion extends java.lang.Object implements Serializable

Package weka.classifiers.trees.lmt

Class weka.classifiers.trees.lmt.LMTNode extends LogisticBase implements Serializable

Serialized Fields

### m_totalInstanceWeight

    double m_totalInstanceWeight

  Total number of training instances.

### m_id

    int m_id

  Node id

### m_leafModelNum

    int m_leafModelNum

  ID of logistic model at leaf

### m_alpha

    double m_alpha

  Alpha-value (for pruning) at the node

### m_numIncorrectModel

    double m_numIncorrectModel

  Weighted number of training examples currently misclassified by the logistic model at the node

### m_numIncorrectTree

    double m_numIncorrectTree

  Weighted number of training examples currently misclassified by the subtree rooted at the node

### m_minNumInstances

    int m_minNumInstances

  minimum number of instances at which a node is considered for splitting

### m_modelSelection

    ModelSelection m_modelSelection

  ModelSelection object (for splitting)

### m_nominalToBinary

    NominalToBinary m_nominalToBinary

  Filter to convert nominal attributes to binary

### m_higherRegressions

    SimpleLinearRegression[][] m_higherRegressions

  Simple regression functions fit by LogitBoost at higher levels in the tree

### m_numHigherRegressions

    int m_numHigherRegressions

  Number of simple regression functions fit by LogitBoost at higher levels in the tree

### m_fastRegression

    boolean m_fastRegression

  Use heuristic that determines the number of LogitBoost iterations only once in the beginning?

### m_numInstances

    int m_numInstances

  Number of instances at the node

### m_localModel

    ClassifierSplitModel m_localModel

  The ClassifierSplitModel (for splitting)

### m_sons

    LMTNode[] m_sons

  Array of children of the node

### m_isLeaf

    boolean m_isLeaf

  True if node is leaf

Class weka.classifiers.trees.lmt.LogisticBase extends Classifier implements Serializable

Serialized Fields

### m_numericDataHeader

    Instances m_numericDataHeader

  Header-only version of the numeric version of the training data

### m_numericData

    Instances m_numericData

  Numeric version of the training data. Original class is replaced by a numeric pseudo-class.

### m_train

    Instances m_train

  Training data

### m_useCrossValidation

    boolean m_useCrossValidation

  Use cross-validation to determine best number of LogitBoost iterations ?

### m_errorOnProbabilities

    boolean m_errorOnProbabilities

  Use error on probabilities for stopping criterion of LogitBoost?

### m_fixedNumIterations

    int m_fixedNumIterations

  Use fixed number of iterations for LogitBoost? (if negative, cross-validate number of iterations)

### m_heuristicStop

    int m_heuristicStop

  Use heuristic to stop performing LogitBoost iterations earlier? If enabled, LogitBoost is stopped if the current (local) minimum of the error on a test set as a function of the number of iterations has not changed for m_heuristicStop iterations.

### m_numRegressions

    int m_numRegressions

  The number of LogitBoost iterations performed.

### m_maxIterations

    int m_maxIterations

  The maximum number of LogitBoost iterations

### m_numClasses

    int m_numClasses

  The number of different classes

### m_regressions

    SimpleLinearRegression[][] m_regressions

  Array holding the simple regression functions fit by LogitBoost

Class weka.classifiers.trees.lmt.ResidualModelSelection extends ModelSelection implements Serializable

Serialized Fields

### m_minNumInstances

    int m_minNumInstances

  Minimum number of instances for leaves

### m_minInfoGain

    double m_minInfoGain

  Minimum information gain for split

Class weka.classifiers.trees.lmt.ResidualSplit extends ClassifierSplitModel implements Serializable

Serialized Fields

### m_attribute

    Attribute m_attribute

  The attribute selected for the split

### m_attIndex

    int m_attIndex

  The index of the attribute selected for the split

### m_numInstances

    int m_numInstances

  Number of instances in the set

### m_numClasses

    int m_numClasses

  Number of classed

### m_data

    Instances m_data

  The set of instances

### m_dataZs

    double[][] m_dataZs

  The Z-values (LogitBoost response) for the set of instances

### m_dataWs

    double[][] m_dataWs

  The LogitBoost-weights for the set of instances

### m_splitPoint

    double m_splitPoint

  The split point (for numeric attributes)

Package weka.classifiers.trees.m5

Class weka.classifiers.trees.m5.CorrelationSplitInfo extends java.lang.Object implements Serializable

Serialized Fields

### m_first

    int m_first

  the first instance

### m_last

    int m_last

  the last instance

### m_position

    int m_position

### m_maxImpurity

    double m_maxImpurity

  the maximum impurity reduction

### m_splitAttr

    int m_splitAttr

  the attribute being tested

### m_splitValue

    double m_splitValue

  the best value on which to split

### m_number

    int m_number

  the number of instances

Class weka.classifiers.trees.m5.M5Base extends Classifier implements Serializable

Serialized Fields

### m_instances

    Instances m_instances

  the instances covered by the tree/rules

### m_classIndex

    int m_classIndex

  the class index

### m_numAttributes

    int m_numAttributes

  the number of attributes

### m_numInstances

    int m_numInstances

  the number of instances in the dataset

### m_ruleSet

    FastVector m_ruleSet

  the rule set

### m_generateRules

    boolean m_generateRules

  generate a decision list instead of a single tree.

### m_unsmoothedPredictions

    boolean m_unsmoothedPredictions

  use unsmoothed predictions

### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

  filter to fill in missing values

### m_nominalToBinary

    NominalToBinary m_nominalToBinary

  filter to convert nominal attributes to binary

### m_saveInstances

    boolean m_saveInstances

  Save instances at each node in an M5 tree for visualization purposes.

### m_regressionTree

    boolean m_regressionTree

  Make a regression tree/rule instead of a model tree/rule

### m_useUnpruned

    boolean m_useUnpruned

  Do not prune tree/rules

### m_minNumInstances

    double m_minNumInstances

  The minimum number of instances to allow at a leaf node

Class weka.classifiers.trees.m5.PreConstructedLinearModel extends Classifier implements Serializable

Serialized Fields

### m_coefficients

    double[] m_coefficients

### m_intercept

    double m_intercept

### m_instancesHeader

    Instances m_instancesHeader

### m_numParameters

    int m_numParameters

Class weka.classifiers.trees.m5.Rule extends java.lang.Object implements Serializable

Serialized Fields

### m_instances

    Instances m_instances

  the instances covered by this rule

### m_classIndex

    int m_classIndex

  the class index

### m_numAttributes

    int m_numAttributes

  the number of attributes

### m_numInstances

    int m_numInstances

  the number of instances in the dataset

### m_splitAtts

    int[] m_splitAtts

  the indexes of the attributes used to split on for this rule

### m_splitVals

    double[] m_splitVals

  the corresponding values of the split points

### m_internalNodes

    RuleNode[] m_internalNodes

  the corresponding internal nodes. Used for smoothing rules.

### m_relOps

    int[] m_relOps

  the corresponding relational operators (0 = "<=", 1 = ">")

### m_ruleModel

    RuleNode m_ruleModel

  the leaf encapsulating the linear model for this rule

### m_topOfTree

    RuleNode m_topOfTree

  the top of the m5 tree for this rule

### m_globalStdDev

    double m_globalStdDev

  the standard deviation of the class for all the instances

### m_globalAbsDev

    double m_globalAbsDev

  the absolute deviation of the class for all the instances

### m_covered

    Instances m_covered

  the instances covered by this rule

### m_numCovered

    int m_numCovered

  the number of instances covered by this rule

### m_notCovered

    Instances m_notCovered

  the instances not covered by this rule

### m_useTree

    boolean m_useTree

  use a pruned m5 tree rather than make a rule

### m_smoothPredictions

    boolean m_smoothPredictions

  use the original m5 smoothing procedure

### m_saveInstances

    boolean m_saveInstances

  Save instances at each node in an M5 tree for visualization purposes.

### m_regressionTree

    boolean m_regressionTree

  Make a regression tree instead of a model tree

### m_useUnpruned

    boolean m_useUnpruned

  Build unpruned tree/rule

### m_minNumInstances

    double m_minNumInstances

  The minimum number of instances to allow at a leaf node

Class weka.classifiers.trees.m5.RuleNode extends Classifier implements Serializable

Serialized Fields

### m_instances

    Instances m_instances

  instances reaching this node

### m_classIndex

    int m_classIndex

  the class index

### m_numInstances

    int m_numInstances

  the number of instances reaching this node

### m_numAttributes

    int m_numAttributes

  the number of attributes

### m_isLeaf

    boolean m_isLeaf

  Node is a leaf

### m_splitAtt

    int m_splitAtt

  attribute this node splits on

### m_splitValue

    double m_splitValue

  the value of the split attribute

### m_nodeModel

    PreConstructedLinearModel m_nodeModel

  the linear model at this node

### m_numParameters

    int m_numParameters

  the number of paramters in the chosen model for this node---either the subtree model or the linear model. The constant term is counted as a paramter---this is for pruning purposes

### m_rootMeanSquaredError

    double m_rootMeanSquaredError

  the mean squared error of the model at this node (either linear or subtree)

### m_left

    RuleNode m_left

  child nodes

### m_right

    RuleNode m_right

### m_parent

    RuleNode m_parent

  the parent of this node

### m_splitNum

    double m_splitNum

  a node will not be split if it contains less then m_splitNum instances

### m_devFraction

    double m_devFraction

  a node will not be split if its class standard deviation is less than 5% of the class standard deviation of all the instances

### m_pruningMultiplier

    double m_pruningMultiplier

### m_leafModelNum

    int m_leafModelNum

  the number assigned to the linear model if this node is a leaf. = 0 if this node is not a leaf

### m_globalDeviation

    double m_globalDeviation

  a node will not be split if the class deviation of its instances is less than m_devFraction of the deviation of the global class

### m_globalAbsDeviation

    double m_globalAbsDeviation

  the absolute deviation of the global class

### m_indices

    int[] m_indices

  Indices of the attributes to be used in generating a linear model at this node

### m_id

    int m_id

  Node id.

### m_saveInstances

    boolean m_saveInstances

  Save the instances at each node (for visualizing in the Explorer's treevisualizer.

### m_regressionTree

    boolean m_regressionTree

  Make a regression tree instead of a model tree

Class weka.classifiers.trees.m5.YongSplitInfo extends java.lang.Object 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.clusterers

Class weka.clusterers.Clusterer extends java.lang.Object implements Serializable

Class weka.clusterers.ClusterEvaluation extends java.lang.Object implements Serializable

Serialized Fields

### m_trainInstances

    Instances m_trainInstances

  the instances to cluster

### m_Clusterer

    Clusterer m_Clusterer

  the clusterer

### m_clusteringResults

    java.lang.StringBuffer m_clusteringResults

  holds a string describing the results of clustering the training data

### m_numClusters

    int m_numClusters

  holds the number of clusters found by the clusterer

### m_clusterAssignments

    double[] m_clusterAssignments

  holds the assigments of instances to clusters for a particular testing dataset

### m_logL

    double m_logL

### m_classToCluster

    int[] m_classToCluster

  will hold the mapping of classes to clusters (for class based evaluation)

Class weka.clusterers.Cobweb extends Clusterer implements Serializable

Serialized Fields

### m_acuity

    double m_acuity

  Acuity (minimum standard deviation).

### m_cutoff

    double m_cutoff

  Cutoff (minimum category utility).

### m_cobwebTree

    weka.clusterers.Cobweb.CNode m_cobwebTree

  Holds the root of the Cobweb tree.

### m_numberOfClusters

    int m_numberOfClusters

  Number of clusters (nodes in the tree).

### m_numberSplits

    int m_numberSplits

### m_numberMerges

    int m_numberMerges

### m_saveInstances

    boolean m_saveInstances

  Output instances in graph representation of Cobweb tree (Allows instances at nodes in the tree to be visualized in the Explorer).

Class weka.clusterers.DensityBasedClusterer extends Clusterer implements Serializable

Class weka.clusterers.EM extends DensityBasedClusterer 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_minStdDevPerAtt

    double[] m_minStdDevPerAtt

### 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_minValues

    double[] m_minValues

  attribute min values

### m_maxValues

    double[] m_maxValues

  attribute max values

### m_rr

    java.util.Random m_rr

  random numbers and seed

### m_rseed

    int m_rseed

### m_verbose

    boolean m_verbose

  Verbose?

### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

  globally replace missing values

Class weka.clusterers.FarthestFirst extends Clusterer implements Serializable

Serialized Fields

### m_instances

    Instances m_instances

  training instances, not necessary to keep, could be replaced by m_ClusterCentroids where needed for header info

### m_ReplaceMissingFilter

    ReplaceMissingValues 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_Min

    double[] m_Min

  attribute min values

### m_Max

    double[] m_Max

  attribute max values

### m_Seed

    int m_Seed

  random seed

Class weka.clusterers.MakeDensityBasedClusterer extends DensityBasedClusterer implements Serializable

Serialized Fields

### m_theInstances

    Instances m_theInstances

  holds training instances header information

### m_priors

    double[] m_priors

  prior probabilities for the fitted clusters

### m_modelNormal

    double[][][] m_modelNormal

  normal distributions fitted to each numeric attribute in each cluster

### m_model

    DiscreteEstimator[][] m_model

  discrete distributions fitted to each discrete attribute in each cluster

### m_minStdDev

    double m_minStdDev

  default minimum standard deviation

### m_wrappedClusterer

    Clusterer m_wrappedClusterer

  The clusterer being wrapped

### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

  globally replace missing values

Class weka.clusterers.SimpleKMeans extends Clusterer implements Serializable

Serialized Fields

### m_ReplaceMissingFilter

    ReplaceMissingValues 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_ClusterStdDevs

    Instances m_ClusterStdDevs

  Holds the standard deviations of the numeric attributes in each cluster

### m_ClusterNominalCounts

    int[][][] m_ClusterNominalCounts

  For each cluster, holds the frequency counts for the values of each nominal attribute

### m_ClusterSizes

    int[] m_ClusterSizes

  The number of instances in each cluster

### m_Seed

    int m_Seed

  random seed

### m_Min

    double[] m_Min

  attribute min values

### m_Max

    double[] m_Max

  attribute max values

### m_Iterations

    int m_Iterations

  Keep track of the number of iterations completed before convergence

### m_squaredErrors

    double[] m_squaredErrors

Package weka.core

Class weka.core.Attribute extends java.lang.Object implements Serializable

Serialized Fields

### m_Name

    java.lang.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

    java.util.Hashtable<K,V> m_Hashtable

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

### m_DateFormat

    java.text.SimpleDateFormat m_DateFormat

  Date format specification for date attributes

### m_Index

    int m_Index

  The attribute's index.

### m_Metadata

    ProtectedProperties m_Metadata

  The attribute's metadata.

### m_Ordering

    int m_Ordering

  The attribute's ordering.

### m_IsRegular

    boolean m_IsRegular

  Whether the attribute is regular.

### m_IsAveragable

    boolean m_IsAveragable

  Whether the attribute is averagable.

### m_HasZeropoint

    boolean m_HasZeropoint

  Whether the attribute has a zeropoint.

### m_Weight

    double m_Weight

  The attribute's weight.

### m_LowerBound

    double m_LowerBound

  The attribute's lower numeric bound.

### m_LowerBoundIsOpen

    boolean m_LowerBoundIsOpen

  Whether the lower bound is open.

### m_UpperBound

    double m_UpperBound

  The attribute's upper numeric bound.

### m_UpperBoundIsOpen

    boolean m_UpperBoundIsOpen

  Whether the upper bound is open

Class weka.core.AttributeStats extends java.lang.Object implements Serializable

Serialized Fields

### intCount

    int intCount

  The number of int-like values

### realCount

    int realCount

  The number of real-like values (i.e. have a fractional part)

### missingCount

    int missingCount

  The number of missing values

### distinctCount

    int distinctCount

  The number of distinct values

### uniqueCount

    int uniqueCount

  The number of values that only appear once

### totalCount

    int totalCount

  The total number of values (i.e. number of instances)

### numericStats

    Stats numericStats

  Stats on numeric value distributions

### nominalCounts

    int[] nominalCounts

  Counts of each nominal value

Class weka.core.BinarySparseInstance extends SparseInstance implements Serializable

Class weka.core.FastVector extends java.lang.Object implements Serializable

Serialized Fields

### m_Objects

    java.lang.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

    int m_CapacityMultiplier

  The capacity multiplier.

Class weka.core.Instance extends java.lang.Object 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.InstanceComparator extends java.lang.Object implements Serializable

Serialized Fields

### m_IncludeClass

    boolean m_IncludeClass

  whether to include the class in the comparison

Class weka.core.Instances extends java.lang.Object implements Serializable

Serialized Fields

### m_RelationName

    java.lang.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 extends java.lang.Object implements Serializable

Serialized Fields

### m_Matrix

    Matrix m_Matrix

  Deprecated.
  The actual matrix

Class weka.core.NoSupportForMissingValuesException extends WekaException implements Serializable

Class weka.core.ProtectedProperties extends java.util.Properties implements Serializable

Serialized Fields

### closed

    boolean closed

Class weka.core.Queue extends java.lang.Object implements Serializable

Serialized Fields

### m_Head

    weka.core.Queue.QueueNode m_Head

  Store a reference to the head of the queue

### m_Tail

    weka.core.Queue.QueueNode m_Tail

  Store a reference to the tail of the queue

### m_Size

    int m_Size

  Store the c m_Tail.m_Nexturrent number of elements in the queue

Class weka.core.Queue.QueueNode extends java.lang.Object implements Serializable

Serialized Fields

### m_Next

    weka.core.Queue.QueueNode m_Next

  The next node in the queue

### m_Contents

    java.lang.Object m_Contents

  The nodes contents

Class weka.core.RandomVariates extends java.util.Random implements Serializable

Class weka.core.Range extends java.lang.Object implements Serializable

Serialized Fields

### m_RangeStrings

    java.util.Vector<E> 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 extends java.lang.Object implements Serializable

Serialized Fields

### m_storedObjectArray

    byte[] m_storedObjectArray

  The array storing the object.

### m_isCompressed

    boolean m_isCompressed

  Whether or not the object is compressed.

Class weka.core.SingleIndex extends java.lang.Object implements Serializable

Serialized Fields

### m_IndexString

    java.lang.String m_IndexString

  Record the string representation of the number

### m_SelectedIndex

    int m_SelectedIndex

  The selected index

### m_Upper

    int m_Upper

  Store the maximum value permitted. -1 indicates that no upper value has been set

Class weka.core.SparseInstance extends Instance 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 extends java.lang.RuntimeException implements Serializable

Class weka.core.UnassignedDatasetException extends java.lang.RuntimeException implements Serializable

Class weka.core.UnsupportedAttributeTypeException extends WekaException implements Serializable

Class weka.core.UnsupportedClassTypeException extends WekaException implements Serializable

Class weka.core.WekaException extends java.lang.Exception implements Serializable

Package weka.core.converters

Class weka.core.converters.AbstractFileSaver extends AbstractSaver implements Serializable

Serialized Fields

### m_outputFile

    java.io.File m_outputFile

  The destination file.

### FILE_EXTENSION

    java.lang.String FILE_EXTENSION

  The file extension of the destination file.

### m_prefix

    java.lang.String m_prefix

  The prefix for the filename (chosen in the GUI).

### m_dir

    java.lang.String m_dir

  The directory of the file (chosen in the GUI).

### m_incrementalCounter

    int m_incrementalCounter

  Counter. In incremental mode after reading 100 instances they will be written to a file.

Class weka.core.converters.AbstractLoader extends java.lang.Object implements Serializable

Serialized Fields

### m_retrieval

    int m_retrieval

  The current retrieval mode

Class weka.core.converters.AbstractSaver extends java.lang.Object implements Serializable

Serialized Fields

### m_instances

    Instances m_instances

  The instances that should be stored

### m_retrieval

    int m_retrieval

  The current retrieval mode

### m_writeMode

    int m_writeMode

  The current write mode

Class weka.core.converters.ArffLoader extends AbstractLoader implements Serializable

Serialized Fields

### m_File

    java.lang.String m_File

Class weka.core.converters.ArffSaver extends AbstractFileSaver implements Serializable

Class weka.core.converters.C45Loader extends AbstractLoader implements Serializable

Serialized Fields

### m_File

    java.lang.String m_File

### m_structure

    Instances m_structure

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

### m_sourceFile

    java.io.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

    java.io.File m_sourceFileData

  Describe variable `m_sourceFileData` here.

### m_fileStem

    java.lang.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.C45Saver extends AbstractFileSaver implements Serializable

Class weka.core.converters.ConverterUtils extends java.lang.Object implements Serializable

Class weka.core.converters.CSVLoader extends AbstractLoader implements Serializable

Serialized Fields

### m_File

    java.lang.String m_File

### m_structure

    Instances m_structure

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

### m_sourceFile

    java.io.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.CSVSaver extends AbstractFileSaver implements Serializable

Class weka.core.converters.DatabaseConnection extends DatabaseUtils implements Serializable

serialVersionUID: 1673169848863178695L

Class weka.core.converters.DatabaseLoader extends AbstractLoader implements Serializable

Serialized Fields

### m_structure

    Instances m_structure

  The header information that is retrieved in the beginning of incremental loading

### m_datasetPseudoInc

    Instances m_datasetPseudoInc

  Used in pseudoincremental mode. The whole dataset from which instances will be read incrementally.

### m_oldStructure

    Instances m_oldStructure

  Set of instances that equals m_structure except that the auto_generated_id column is not included as an attribute

### m_DataBaseConnection

    DatabaseConnection m_DataBaseConnection

  The database connection

### m_query

    java.lang.String m_query

  The user defined query to load instances. (form: SELECT *| FROM

  [WHERE ])

### m_pseudoIncremental

    boolean m_pseudoIncremental

  Flag indicating that pseudo incremental mode is used (all instances load at once into main memeory and then incrementally from main memory instead of the database)

### m_nominalToStringLimit

    int m_nominalToStringLimit

  Limit when an attribute is treated as string attribute and not as a nominal one because it has to many values.

### m_rowCount

    int m_rowCount

  The number of rows obtained by m_query, eg the size of the ResultSet to load

### m_counter

    int m_counter

  Indicates how many rows has already been loaded incrementally

### m_choice

    int m_choice

  Decides which SQL statement to limit the number of rows should be used. DBMS dependent. Algorithm just tries several possibilities.

### m_firstTime

    boolean m_firstTime

  Flag indicating that incremental process wants to read first instance

### m_inc

    boolean m_inc

  Flag indicating that incremental mode is chosen (for command line use only)

### m_orderBy

    FastVector m_orderBy

  Contains the name of the columns that uniquely define a row in the ResultSet. Ensures a unique ordering of instances for indremental loading.

### m_nominalIndexes

    java.util.Hashtable<K,V>[] m_nominalIndexes

  Stores the index of a nominal value

### m_nominalStrings

    FastVector[] m_nominalStrings

  Stores the nominal value

### m_idColumn

    java.lang.String m_idColumn

  Name of the primary key column that will allow unique ordering necessary for incremental loading. The name is specified in the DatabaseUtils file.

### m_URL

    java.lang.String m_URL

  the JDBC URL to use

### m_User

    java.lang.String m_User

  the database user to use

### m_Password

    java.lang.String m_Password

  the database password to use

### m_Keys

    java.lang.String m_Keys

  the keys for unique ordering

Class weka.core.converters.DatabaseSaver extends AbstractSaver implements Serializable

Serialized Fields

### m_DataBaseConnection

    DatabaseConnection m_DataBaseConnection

  The database connection

### m_tableName

    java.lang.String m_tableName

  The name of the tablein which the instances should be stored

### m_inputFile

    java.lang.String m_inputFile

  An input arff file (for command line use)

### m_createText

    java.lang.String m_createText

  The database specific type for a string (read in from the properties file)

### m_createDouble

    java.lang.String m_createDouble

  The database specific type for a double (read in from the properties file)

### m_createInt

    java.lang.String m_createInt

  The database specific type for an int (read in from the properties file)

### m_createDate

    java.lang.String m_createDate

  The database specific type for a date (read in from the properties file)

### m_DateFormat

    java.text.SimpleDateFormat m_DateFormat

  For converting the date value into a database string.

### m_idColumn

    java.lang.String m_idColumn

  The name of the primary key column that will be automatically generated (if enabled). The name is read from DatabaseUtils.

### m_count

    int m_count

  counts the rowsand used as a primary key value

### m_id

    boolean m_id

  Flag indicating if a primary key column should be added

### m_tabName

    boolean m_tabName

  Flag indicating whether the default name of the table is the relaion name or not.

Class weka.core.converters.SerializedInstancesLoader extends AbstractLoader implements Serializable

Serialized Fields

### m_File

    java.lang.String m_File

### 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

Class weka.core.converters.SerializedInstancesSaver extends AbstractFileSaver implements Serializable

Package weka.core.matrix

Class weka.core.matrix.CholeskyDecomposition extends java.lang.Object implements Serializable

Serialized Fields

### L

    double[][] L

  Array for internal storage of decomposition.

  internal array storage.

### n

    int n

  Row and column dimension (square matrix).

  matrix dimension.

### isspd

    boolean isspd

  Symmetric and positive definite flag.

  is symmetric and positive definite flag.

Class weka.core.matrix.EigenvalueDecomposition extends java.lang.Object implements Serializable

Serialized Fields

### n

    int n

  Row and column dimension (square matrix).

  matrix dimension.

### issymmetric

    boolean issymmetric

  Symmetry flag.

  internal symmetry flag.

### d

    double[] d

  Arrays for internal storage of eigenvalues.

  internal storage of eigenvalues.

### e

    double[] e

  Arrays for internal storage of eigenvalues.

  internal storage of eigenvalues.

### V

    double[][] V

  Array for internal storage of eigenvectors.

  internal storage of eigenvectors.

### H

    double[][] H

  Array for internal storage of nonsymmetric Hessenberg form.

  internal storage of nonsymmetric Hessenberg form.

### ort

    double[] ort

  Working storage for nonsymmetric algorithm.

  working storage for nonsymmetric algorithm.

Class weka.core.matrix.LUDecomposition extends java.lang.Object implements Serializable

Serialized Fields

### LU

    double[][] LU

  Array for internal storage of decomposition.

  internal array storage.

### m

    int m

  Row and column dimensions, and pivot sign.

  column dimension.

### n

    int n

  Row and column dimensions, and pivot sign.

  column dimension.

### pivsign

    int pivsign

  Row and column dimensions, and pivot sign.

  column dimension.

### piv

    int[] piv

  Internal storage of pivot vector.

  pivot vector.

Class weka.core.matrix.Matrix extends java.lang.Object implements Serializable

Serialized Fields

### A

    double[][] A

  Array for internal storage of elements.

  internal array storage.

### m

    int m

  Row and column dimensions.

  row dimension.

### n

    int n

  Row and column dimensions.

  row dimension.

Class weka.core.matrix.QRDecomposition extends java.lang.Object implements Serializable

Serialized Fields

### QR

    double[][] QR

  Array for internal storage of decomposition.

  internal array storage.

### m

    int m

  Row and column dimensions.

  column dimension.

### n

    int n

  Row and column dimensions.

  column dimension.

### Rdiag

    double[] Rdiag

  Array for internal storage of diagonal of R.

  diagonal of R.

Class weka.core.matrix.SingularValueDecomposition extends java.lang.Object implements Serializable

Serialized Fields

### U

    double[][] U

  Arrays for internal storage of U and V.

  internal storage of U.

### V

    double[][] V

  Arrays for internal storage of U and V.

  internal storage of U.

### s

    double[] s

  Array for internal storage of singular values.

  internal storage of singular values.

### m

    int m

  Row and column dimensions.

  row dimension.

### n

    int n

  Row and column dimensions.

  row dimension.

Package weka.datagenerators

Class weka.datagenerators.BIRCHCluster extends ClusterGenerator implements Serializable

Serialized Fields

### m_MinInstNum

    int m_MinInstNum

  minimal number of instances per cluster (option N)

### m_MaxInstNum

    int m_MaxInstNum

  maximal number of instances per cluster (option N)

### m_MinRadius

    double m_MinRadius

  minimum radius (option R)

### m_MaxRadius

    double m_MaxRadius

  maximum radius (option R)

### m_Pattern

    int m_Pattern

  pattern (changed with options G or S)

### m_DistMult

    double m_DistMult

  distance multiplier (option M)

### m_NumCycles

    int m_NumCycles

  number of cycles (option C)

### m_InputOrder

    int m_InputOrder

  input order (changed with option O)

### m_NoiseRate

    double m_NoiseRate

  noise rate in percent (option P, between 0 and 30)

### m_Seed

    int m_Seed

  random number generator seed (option S)

### m_DatasetFormat

    Instances m_DatasetFormat

  dataset format

### m_Random

    java.util.Random m_Random

  random number generator

### m_Debug

    int m_Debug

  debug flag

### m_ClusterList

    FastVector m_ClusterList

  cluster list

### m_GridSize

    int m_GridSize

  grid size

### m_GridWidth

    double m_GridWidth

  grid width

Class weka.datagenerators.ClusterGenerator extends java.lang.Object implements Serializable

Serialized Fields

### m_Debug

    boolean m_Debug

  Debugging mode

### m_Format

    Instances m_Format

  The format for the generated dataset

### m_RelationName

    java.lang.String m_RelationName

  Relation name the dataset should have

### m_NumAttributes

    int m_NumAttributes

  Number of attribute the dataset should have

### m_NumClusters

    int m_NumClusters

  Number of Clusters the dataset should have

### m_ClassFlag

    boolean m_ClassFlag

  class flag

### m_NumExamplesAct

    int m_NumExamplesAct

  Number of instances that should be produced into the dataset this number is by default m_NumExamples, but can be reset by the generator

### m_Output

    java.io.PrintWriter m_Output

  PrintWriter

Class weka.datagenerators.Generator extends java.lang.Object implements Serializable

Serialized Fields

### m_Debug

    boolean m_Debug

  Debugging mode

### m_Format

    Instances m_Format

  The format for the generated dataset

### m_RelationName

    java.lang.String m_RelationName

  Relation name the dataset should have

### m_NumAttributes

    int m_NumAttributes

  Number of attribute the dataset should have

### m_NumClasses

    int m_NumClasses

  Number of Classes the dataset should have

### m_NumExamples

    int m_NumExamples

  Number of instances

### m_NumExamplesAct

    int m_NumExamplesAct

  Number of instances that should be produced into the dataset this number is by default m_NumExamples, but can be reset by the generator

### m_Output

    java.io.PrintWriter m_Output

  PrintWriter

Class weka.datagenerators.RDG1 extends Generator implements Serializable

Serialized Fields

### m_MaxRuleSize

    int m_MaxRuleSize

  maximum rule size

### m_MinRuleSize

    int m_MinRuleSize

  minimum rule size

### m_NumIrrelevant

    int m_NumIrrelevant

  number of irrelevant attributes.

### m_NumNumeric

    int m_NumNumeric

  number of numeric attribute

### m_Seed

    int m_Seed

  random number generator seed

### m_VoteFlag

    boolean m_VoteFlag

  flag that stores if voting is wished

### m_DatasetFormat

    Instances m_DatasetFormat

  dataset format

### m_Random

    java.util.Random m_Random

  random number generator

### m_DecisionList

    FastVector m_DecisionList

  decision list

### m_AttList_Irr

    boolean[] m_AttList_Irr

  array defines which attributes are irrelevant, with:

### m_Debug

    int m_Debug

  debug flag

Class weka.datagenerators.Test extends java.lang.Object implements Serializable

Serialized Fields

### m_AttIndex

    int m_AttIndex

### m_Split

    double m_Split

### m_Not

    boolean m_Not

### m_Dataset

    Instances m_Dataset

Package weka.estimators

Class weka.estimators.DiscreteEstimator extends java.lang.Object 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 extends java.lang.Object 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 extends java.lang.Object 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 extends java.lang.Object 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 extends java.lang.Object 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 extends java.lang.Object 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

    java.lang.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

    java.lang.String m_CountFieldName

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

### m_KeyFieldName

    java.lang.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 extends java.lang.Object implements Serializable

Serialized Fields

### m_Template

    Classifier m_Template

  The template classifier

### m_Classifier

    Classifier m_Classifier

  The classifier used for evaluation

### m_AdditionalMeasures

    java.lang.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

    java.lang.String m_result

  Holds the statistics for the most recent application of the classifier

### m_ClassifierOptions

    java.lang.String m_ClassifierOptions

  The classifier options (if any)

### m_ClassifierVersion

    java.lang.String m_ClassifierVersion

  The classifier version

### m_IRclass

    int m_IRclass

  Class index for information retrieval statistics (default 0)

### m_predTargetColumn

    boolean m_predTargetColumn

  Flag for prediction and target columns output.

### m_attID

    int m_attID

  Attribute index of instance identifier (default -1)

Class weka.experiment.CostSensitiveClassifierSplitEvaluator extends ClassifierSplitEvaluator implements Serializable

Serialized Fields

### m_OnDemandDirectory

    java.io.File m_OnDemandDirectory

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

Class weka.experiment.CrossValidationResultProducer extends java.lang.Object 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

    java.io.File m_OutputFile

  The destination output file/directory for raw output

### m_SplitEvaluator

    SplitEvaluator m_SplitEvaluator

  The SplitEvaluator used to generate results

### m_AdditionalMeasures

    java.lang.String[] m_AdditionalMeasures

  The names of any additional measures to look for in SplitEvaluators

Class weka.experiment.CSVResultListener extends java.lang.Object implements Serializable

Serialized Fields

### m_RP

    ResultProducer m_RP

  The ResultProducer sending us results

### m_OutputFile

    java.io.File m_OutputFile

  The destination output file, null sends to System.out

### m_OutputFileName

    java.lang.String m_OutputFileName

  The name of the output file. Empty for temporary file.

Class weka.experiment.DatabaseResultListener extends DatabaseUtils implements Serializable

Serialized Fields

### m_ResultProducer

    ResultProducer m_ResultProducer

  The ResultProducer to listen to

### m_ResultsTableName

    java.lang.String m_ResultsTableName

  The name of the current results table

### m_Debug

    boolean m_Debug

  True if debugging output should be printed

### m_CacheKeyName

    java.lang.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

    java.lang.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 extends DatabaseResultListener 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

    java.lang.String[] m_AdditionalMeasures

  The names of any additional measures to look for in SplitEvaluators

Class weka.experiment.DatabaseUtils extends java.lang.Object implements Serializable

serialVersionUID: -8252351994547116729L

Serialized Fields

### DRIVERS

    java.util.Vector<E> DRIVERS

  Holds the jdbc drivers to be used (only to stop them being gc'ed)

### PROPERTIES

    java.util.Properties PROPERTIES

  Properties associated with the database connection

### m_DatabaseURL

    java.lang.String m_DatabaseURL

  Database URL

### m_Debug

    boolean m_Debug

  True if debugging output should be printed

### m_userName

    java.lang.String m_userName

  Database username

### m_password

    java.lang.String m_password

  Database Password

### m_stringType

    java.lang.String m_stringType

  string type for the create table statement

### m_intType

    java.lang.String m_intType

  integer type for the create table statement

### m_doubleType

    java.lang.String m_doubleType

  double type for the create table statement

### m_checkForUpperCaseNames

    boolean m_checkForUpperCaseNames

  For databases where Tables and Columns are created in upper case

### m_checkForLowerCaseNames

    boolean m_checkForLowerCaseNames

  For databases where Tables and Columns are created in lower case

### m_setAutoCommit

    boolean m_setAutoCommit

  setAutoCommit on the database?

### m_createIndex

    boolean m_createIndex

  create index on the database?

Class weka.experiment.Experiment extends java.lang.Object 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

    javax.swing.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

    java.lang.Object m_PropertyArray

  The array of values to set the property to

### m_Notes

    java.lang.String m_Notes

  User notes about the experiment

### m_AdditionalMeasures

    java.lang.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 extends DatabaseUtils implements Serializable

Serialized Fields

### m_CreateSparseData

    boolean m_CreateSparseData

  Determines whether sparse data is created

### m_Query

    java.lang.String m_Query

  Query to execute

Class weka.experiment.InstancesResultListener extends CSVResultListener implements Serializable

Class weka.experiment.LearningRateResultProducer extends java.lang.Object 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

    java.lang.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 extends java.lang.Object implements Serializable

Serialization Methods

### readObject

    private void readObject(java.io.ObjectInputStream in)
                     throws java.io.IOException,
                            java.lang.ClassNotFoundException

  Throws:
    `java.io.IOException`
    `java.lang.ClassNotFoundException`

### writeObject

    private void writeObject(java.io.ObjectOutputStream out)
                      throws java.io.IOException

  Throws:
    `java.io.IOException`

Serialized Fields

### value

    java.lang.Object value

  The current property value

### parentClass

    java.lang.Class<T> parentClass

  The class of the object with this property

### property

    java.beans.PropertyDescriptor property

  Other info about the property

Class weka.experiment.RandomSplitResultProducer extends java.lang.Object 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

    java.lang.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

    java.io.File m_OutputFile

  The destination output file/directory for raw output

Class weka.experiment.RegressionSplitEvaluator extends java.lang.Object implements Serializable

Serialized Fields

### m_Template

    Classifier m_Template

  The template classifier

### m_Classifier

    Classifier m_Classifier

  The classifier used for evaluation

### m_AdditionalMeasures

    java.lang.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

    java.lang.String m_result

  Holds the statistics for the most recent application of the classifier

### m_ClassifierOptions

    java.lang.String m_ClassifierOptions

  The classifier options (if any)

### m_ClassifierVersion

    java.lang.String m_ClassifierVersion

  The classifier version

Class weka.experiment.RemoteEngine extends java.rmi.server.UnicastRemoteObject implements Serializable

Serialized Fields

### m_HostName

    java.lang.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

    java.util.Hashtable<K,V> m_TaskStatus

  A hashtable of experiment status

### m_TaskRunning

    boolean m_TaskRunning

  Is there a task running

Class weka.experiment.RemoteExperiment extends Experiment implements Serializable

Serialized Fields

### m_listeners

    FastVector m_listeners

  The list of objects listening for remote experiment events

### m_remoteHosts

    javax.swing.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 extends java.lang.Object implements Serializable

Serialized Fields

### m_statusMessage

    boolean m_statusMessage

  A status type message

### m_logMessage

    boolean m_logMessage

  A log type message

### m_messageString

    java.lang.String m_messageString

  The message

### m_experimentFinished

    boolean m_experimentFinished

  True if a remote experiment has finished

Class weka.experiment.RemoteExperimentSubTask extends java.lang.Object implements Serializable

Serialized Fields

### m_result

    TaskStatusInfo m_result

### m_experiment

    Experiment m_experiment

Class weka.experiment.Stats extends java.lang.Object implements Serializable

Serialized Fields

### count

    double count

  The number of values seen

### sum

    double sum

  The sum of values seen

### sumSq

    double sumSq

  The sum of values squared seen

### stdDev

    double stdDev

  The std deviation of values at the last calculateDerived() call

### mean

    double mean

  The mean of values at the last calculateDerived() call

### min

    double min

  The minimum value seen, or Double.NaN if no values seen

### max

    double max

  The maximum value seen, or Double.NaN if no values seen

Class weka.experiment.TaskStatusInfo extends java.lang.Object implements Serializable

Serialized Fields

### m_ExecutionStatus

    int m_ExecutionStatus

  Holds current execution status.

### m_StatusMessage

    java.lang.String m_StatusMessage

  Holds current status message.

### m_TaskResult

    java.lang.Object m_TaskResult

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

Package weka.filters

Class weka.filters.AllFilter extends Filter implements Serializable

Class weka.filters.Filter extends java.lang.Object 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

### m_FirstBatchDone

    boolean m_FirstBatchDone

  True if the first batch has been done

Class weka.filters.NullFilter extends Filter implements Serializable

Package weka.filters.supervised.attribute

Class weka.filters.supervised.attribute.AttributeSelection extends Filter 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

    java.lang.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.supervised.attribute.ClassOrder extends Filter implements Serializable

Serialized Fields

### m_Seed

    long m_Seed

  The seed of randomization

### m_Random

    java.util.Random m_Random

  The random object

### m_Converter

    int[] m_Converter

  The 1-1 converting table from the original class values to the new values

### m_ClassAttribute

    Attribute m_ClassAttribute

  Class attribute of the data

### m_ClassOrder

    int m_ClassOrder

  The class order to be sorted

### m_ClassCounts

    double[] m_ClassCounts

  This class can provide the class distribution in the sorted order as side effect

Class weka.filters.supervised.attribute.Discretize extends Filter implements Serializable

Serialized Fields

### m_DiscretizeCols

    Range m_DiscretizeCols

  Stores which columns to Discretize

### m_CutPoints

    double[][] m_CutPoints

  Store the current cutpoints

### 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

Class weka.filters.supervised.attribute.NominalToBinary extends Filter 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?

### m_TransformAll

    boolean m_TransformAll

  Are all values transformed into new attributes?

Package weka.filters.supervised.instance

Class weka.filters.supervised.instance.Resample extends Filter 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

Class weka.filters.supervised.instance.SpreadSubsample extends Filter 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_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.supervised.instance.StratifiedRemoveFolds extends Filter implements Serializable

Serialized Fields

### 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.

Package weka.filters.unsupervised.attribute

Class weka.filters.unsupervised.attribute.AbstractTimeSeries extends Filter 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.unsupervised.attribute.Add extends Filter implements Serializable

Serialized Fields

### m_AttributeType

    int m_AttributeType

  Record the type of attribute to insert

### m_Name

    java.lang.String m_Name

  The name for the new attribute

### m_Insert

    SingleIndex m_Insert

  The location to insert the new attribute

### m_Labels

    FastVector m_Labels

  The list of labels for nominal attribute

Class weka.filters.unsupervised.attribute.AddCluster extends Filter implements Serializable

Serialized Fields

### m_Clusterer

    Clusterer m_Clusterer

  The clusterer used to do the cleansing

### m_IgnoreAttributesRange

    Range m_IgnoreAttributesRange

  Range of attributes to ignore

### m_removeAttributes

    Filter m_removeAttributes

  Filter for removing attributes

Class weka.filters.unsupervised.attribute.AddExpression extends Filter implements Serializable

Serialized Fields

### m_infixExpression

    java.lang.String m_infixExpression

  The infix expression

### m_operatorStack

    java.util.Stack<E> m_operatorStack

  Operator stack

### m_postFixExpVector

    java.util.Vector<E> 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

    java.lang.String m_previousTok

  Holds the previous token

### m_attributeName

    java.lang.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.unsupervised.attribute.AddNoise extends Filter implements Serializable

Serialized Fields

### m_AttIndex

    SingleIndex m_AttIndex

  The attribute's index setting.

### m_UseMissing

    boolean m_UseMissing

  Flag if missing values are taken as value.

### m_Percent

    int m_Percent

  The subsample size, percent of original set, default 10%

### m_RandomSeed

    int m_RandomSeed

  The random number generator seed

Class weka.filters.unsupervised.attribute.ChangeDateFormat extends Filter implements Serializable

Serialized Fields

### m_AttIndex

    SingleIndex m_AttIndex

  The attribute's index setting.

### m_DateFormat

    java.text.SimpleDateFormat m_DateFormat

  The output date format.

### m_OutputAttribute

    Attribute m_OutputAttribute

  The output attribute.

Class weka.filters.unsupervised.attribute.ClusterMembership extends Filter implements Serializable

Serialized Fields

### m_clusterer

    DensityBasedClusterer m_clusterer

  The clusterer

### m_clusterers

    DensityBasedClusterer[] m_clusterers

  Array for storing the clusterers

### m_ignoreAttributesRange

    Range m_ignoreAttributesRange

  Range of attributes to ignore

### m_removeAttributes

    Filter m_removeAttributes

  Filter for removing attributes

### m_priors

    double[] m_priors

  The prior probability for each class

Class weka.filters.unsupervised.attribute.Copy extends Filter 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.unsupervised.attribute.Discretize extends PotentialClassIgnorer 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_DesiredWeightOfInstancesPerInterval

    double m_DesiredWeightOfInstancesPerInterval

  The desired weight of instances per bin

### m_CutPoints

    double[][] m_CutPoints

  Store the current cutpoints

### m_MakeBinary

    boolean m_MakeBinary

  Output binary attributes for discretized attributes.

### m_FindNumBins

    boolean m_FindNumBins

  Find the number of bins using cross-validated entropy.

### m_UseEqualFrequency

    boolean m_UseEqualFrequency

  Use equal-frequency binning if unsupervised discretization turned on

### m_DefaultCols

    java.lang.String m_DefaultCols

  The default columns to discretize

Class weka.filters.unsupervised.attribute.FirstOrder extends Filter implements Serializable

Serialized Fields

### m_DeltaCols

    Range m_DeltaCols

  Stores which columns to take differences between

Class weka.filters.unsupervised.attribute.MakeIndicator extends Filter implements Serializable

Serialized Fields

### m_AttIndex

    SingleIndex m_AttIndex

  The attribute's index setting.

### m_ValIndex

    Range m_ValIndex

  The value's index

### m_Numeric

    boolean m_Numeric

  Make boolean attribute numeric.

Class weka.filters.unsupervised.attribute.MergeTwoValues extends Filter implements Serializable

Serialized Fields

### m_AttIndex

    SingleIndex m_AttIndex

  The attribute's index setting.

### m_FirstIndex

    SingleIndex m_FirstIndex

  The first value's index setting.

### m_SecondIndex

    SingleIndex m_SecondIndex

  The second value's index setting.

Class weka.filters.unsupervised.attribute.NominalToBinary extends Filter implements Serializable

Serialized Fields

### m_Columns

    Range m_Columns

  Stores which columns to act on

### 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.unsupervised.attribute.Normalize extends PotentialClassIgnorer 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.unsupervised.attribute.NumericToBinary extends PotentialClassIgnorer implements Serializable

Class weka.filters.unsupervised.attribute.NumericTransform extends Filter implements Serializable

Serialized Fields

### m_Cols

    Range m_Cols

  Stores which columns to transform.

### m_Class

    java.lang.String m_Class

  Class containing transformation method.

### m_Method

    java.lang.String m_Method

  Transformation method.

Class weka.filters.unsupervised.attribute.Obfuscate extends Filter implements Serializable

Class weka.filters.unsupervised.attribute.PKIDiscretize extends Discretize implements Serializable

Class weka.filters.unsupervised.attribute.PotentialClassIgnorer extends Filter implements Serializable

Serialized Fields

### m_IgnoreClass

    boolean m_IgnoreClass

  True if the class is to be unset

### m_ClassIndex

    int m_ClassIndex

  Storing the class index

Class weka.filters.unsupervised.attribute.RandomProjection extends Filter implements Serializable

Serialized Fields

### m_k

    int m_k

  Stores the number of dimensions to reduce the data to

### m_percent

    double m_percent

  Stores the dimensionality the data should be reduced to as percentage of the original dimension

### m_useGaussian

    boolean m_useGaussian

  Is the random matrix will be computed using Gaussian distribution or not

### m_distribution

    int m_distribution

  Stores the distribution to use for calculating the random matrix

### m_replaceMissing

    boolean m_replaceMissing

  Should the missing values be replaced using unsupervised.ReplaceMissingValues filter

### m_OutputFormatDefined

    boolean m_OutputFormatDefined

  Keeps track of output format if it is defined or not

### ntob

    Filter ntob

  The NominalToBinary filter applied to the data before this filter

### replaceMissing

    Filter replaceMissing

  The ReplaceMissingValues filter

### m_rndmSeed

    long m_rndmSeed

  Stores the random seed used to generate the random matrix

### rmatrix

    double[][] rmatrix

  The random matrix

### r

    java.util.Random r

  The random number generator used for generating the random matrix

Class weka.filters.unsupervised.attribute.Remove extends Filter 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.unsupervised.attribute.RemoveType extends Filter implements Serializable

Serialized Fields

### m_attributeFilter

    Remove m_attributeFilter

  The attribute filter used to do the filtering

### m_attTypeToDelete

    int m_attTypeToDelete

  The type of attribute to delete

### m_invert

    boolean m_invert

  Whether to invert selection

Class weka.filters.unsupervised.attribute.RemoveUseless extends Filter implements Serializable

Serialized Fields

### m_removeFilter

    Remove m_removeFilter

  The filter used to remove attributes

### m_maxVariancePercentage

    double m_maxVariancePercentage

  The type of attribute to delete

Class weka.filters.unsupervised.attribute.ReplaceMissingValues extends PotentialClassIgnorer implements Serializable

Serialized Fields

### m_ModesAndMeans

    double[] m_ModesAndMeans

  The modes and means

Class weka.filters.unsupervised.attribute.Standardize extends PotentialClassIgnorer implements Serializable

Serialized Fields

### m_Means

    double[] m_Means

  The means

### m_StdDevs

    double[] m_StdDevs

  The variances

Class weka.filters.unsupervised.attribute.StringToNominal extends Filter implements Serializable

Serialized Fields

### m_AttIndex

    SingleIndex m_AttIndex

  The attribute's index setting.

Class weka.filters.unsupervised.attribute.StringToWordVector extends Filter implements Serializable

Serialized Fields

### delimiters

    java.lang.String delimiters

  Delimiters used in tokenization

### m_SelectedRange

    Range m_SelectedRange

  Range of columns to convert to word vectors

### m_Dictionary

    java.util.TreeMap<K,V> m_Dictionary

  Contains a mapping of valid words to attribute indexes

### m_OutputCounts

    boolean m_OutputCounts

  True if output instances should contain word frequency rather than boolean 0 or 1.

### m_Prefix

    java.lang.String m_Prefix

  A String prefix for the attribute names

### docsCounts

    int[] docsCounts

  Contains the number of documents (instances) a particular word appears in. The counts are stored with the same indexing as given by m_Dictionary.

### numInstances

    int numInstances

  Contains the number of documents (instances) in the input format from which the dictionary is created. It is used in IDF transform.

### avgDocLength

    double avgDocLength

  Contains the average length of documents (among the first batch of instances aka training data). This is used in length normalization of documents which will be normalized to average document length.

### m_WordsToKeep

    int m_WordsToKeep

  The default number of words (per class if there is a class attribute assigned) to attempt to keep.

### m_TFTransform

    boolean m_TFTransform

  True if word frequencies should be transformed into log(1+fi) where fi is the frequency of word i

### m_normalizeDocLength

    boolean m_normalizeDocLength

  True if document's (instance's) word frequencies are to be normalized. The are normalized to average length of documents specified as input format.

### m_IDFTransform

    boolean m_IDFTransform

  True if word frequencies should be transformed into fij*log(numOfDocs/numOfDocsWithWordi)

### m_onlyAlphabeticTokens

    boolean m_onlyAlphabeticTokens

  True if tokens are to be formed only from alphabetic sequences of characters. (The delimiters string property is ignored if this is true).

### m_lowerCaseTokens

    boolean m_lowerCaseTokens

  True if all tokens should be downcased

### m_useStoplist

    boolean m_useStoplist

  True if tokens that are on a stoplist are to be ignored.

Class weka.filters.unsupervised.attribute.SwapValues extends Filter implements Serializable

Serialized Fields

### m_AttIndex

    SingleIndex m_AttIndex

  The attribute's index setting.

### m_FirstIndex

    SingleIndex m_FirstIndex

  The first value's index setting.

### m_SecondIndex

    SingleIndex m_SecondIndex

  The second value's index setting.

Class weka.filters.unsupervised.attribute.TimeSeriesDelta extends TimeSeriesTranslate implements Serializable

Class weka.filters.unsupervised.attribute.TimeSeriesTranslate extends AbstractTimeSeries implements Serializable

Package weka.filters.unsupervised.instance

Class weka.filters.unsupervised.instance.NonSparseToSparse extends Filter implements Serializable

Class weka.filters.unsupervised.instance.Normalize extends Filter implements Serializable

Serialized Fields

### m_Norm

    double m_Norm

  The norm that each instance must have at the end

### m_LNorm

    double m_LNorm

  The L-norm to use

Class weka.filters.unsupervised.instance.Randomize extends Filter implements Serializable

Serialized Fields

### m_Seed

    int m_Seed

  The random number seed

### m_Random

    java.util.Random m_Random

  The current random number generator

Class weka.filters.unsupervised.instance.RemoveFolds extends Filter implements Serializable

Serialized Fields

### 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.

Class weka.filters.unsupervised.instance.RemoveMisclassified extends Filter implements Serializable

Serialized Fields

### m_cleansingClassifier

    Classifier m_cleansingClassifier

  The classifier used to do the cleansing

### m_classIndex

    int m_classIndex

  The attribute to treat as the class for purposes of cleansing.

### m_numOfCrossValidationFolds

    int m_numOfCrossValidationFolds

  The number of cross validation folds to perform (<2 = no cross validation)

### m_numOfCleansingIterations

    int m_numOfCleansingIterations

  The maximum number of cleansing iterations to perform (<1 = until fully cleansed)

### m_numericClassifyThreshold

    double m_numericClassifyThreshold

  The threshold for deciding when a numeric value is correctly classified

### m_invertMatching

    boolean m_invertMatching

  Whether to invert the match so the correctly classified instances are discarded

### m_firstBatchFinished

    boolean m_firstBatchFinished

  Have we processed the first batch (i.e. training data)?

Class weka.filters.unsupervised.instance.RemovePercentage extends Filter implements Serializable

Serialized Fields

### m_Percentage

    int m_Percentage

  Percentage of instances to select.

### m_Inverse

    boolean m_Inverse

  Indicates if inverse of selection is to be output.

Class weka.filters.unsupervised.instance.RemoveRange extends Filter implements Serializable

Serialized Fields

### m_Range

    Range m_Range

  Range of instances requested by the user.

Class weka.filters.unsupervised.instance.RemoveWithValues extends Filter implements Serializable

Serialized Fields

### m_AttIndex

    SingleIndex m_AttIndex

  The attribute's index setting.

### 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_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.unsupervised.instance.Resample extends Filter 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

Class weka.filters.unsupervised.instance.SparseToNonSparse extends Filter implements Serializable

Package weka.gui

Class weka.gui.AttributeListPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Table

    javax.swing.JTable m_Table

  The table displaying attribute names

### m_Model

    weka.gui.AttributeListPanel.AttributeTableModel m_Model

  The table model containing attribute names

Class weka.gui.AttributeSelectionPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_IncludeAll

    javax.swing.JButton m_IncludeAll

  Press to select all attributes

### m_RemoveAll

    javax.swing.JButton m_RemoveAll

  Press to deselect all attributes

### m_Invert

    javax.swing.JButton m_Invert

  Press to invert the current selection

### m_Table

    javax.swing.JTable m_Table

  The table displaying attribute names and selection status

### m_Model

    weka.gui.AttributeSelectionPanel.AttributeTableModel m_Model

  The table model containingn attribute names and selection status

Class weka.gui.AttributeSummaryPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_AttributeNameLab

    javax.swing.JLabel m_AttributeNameLab

  Displays the name of the relation

### m_AttributeTypeLab

    javax.swing.JLabel m_AttributeTypeLab

  Displays the type of attribute

### m_MissingLab

    javax.swing.JLabel m_MissingLab

  Displays the number of missing values

### m_UniqueLab

    javax.swing.JLabel m_UniqueLab

  Displays the number of unique values

### m_DistinctLab

    javax.swing.JLabel m_DistinctLab

  Displays the number of distinct values

### m_StatsTable

    javax.swing.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.AttributeVisualizationPanel extends PrintablePanel implements Serializable

Serialized Fields

### m_data

    Instances m_data

  This holds the current set of instances

### m_as

    AttributeStats m_as

  This holds the attribute stats of the current attribute on display. It is calculated in setAttribute(int idx) when it is called to set a new attribute index.

### m_attribIndex

    int m_attribIndex

  This holds the index of the current attribute on display and should be set through setAttribute(int idx).

### m_maxValue

    int m_maxValue

  This holds the max value of the current attribute. In case of nominal attribute it is the highest count that a nominal value has in the attribute (given by m_as.nominalCounts[i]), otherwise in case of numeric attribute it is simply the maximum value present in the attribute (given by m_as.numericStats.max). It is used to calculate the ratio of the height of the bars with respect to the height of the display area.

### m_histBarCounts

    int[] m_histBarCounts

  This array holds the count (or height) for the each of the bars in a barplot or a histogram. In case of barplots (and current attribute being nominal) its length (and the number of bars) is equal to the number of nominal values in the current attribute, with each field of the array being equal to the count of each nominal that it represents (the count of ith nominal value of an attribute is given by m_as.nominalCounts[i]). Whereas, in case of histograms (and current attribute being numeric) the width of its intervals is calculated by Scott's(1979) method:
  intervalWidth = Max(1, 3.49*Std.Dev*numInstances^(1/3)) And the number of intervals by:
  intervals = max(1, Math.round(Range/intervalWidth); Then each field of this array contains the number of values of the current attribute that fall in the histogram interval that it represents.
  NOTE: The values of this array are only calculated if the class attribute is not set or if it is numeric.

### m_histBarClassCounts

    int[][] m_histBarClassCounts

  This array holds the per class count (or per class height) of the each of the bars in a barplot or a histogram. For nominal attributes the format is:
  m_histBarClassCounts[nominalValue][classValue+1]. For numeric attributes the format is:
  m_histBarClassCounts[interval][classValues+1],
  where the number of intervals is calculated by the Scott's method as mentioned above. The array is initialized to have 1+numClasses to accomodate for instances with missing class value. The ones with missing class value are displayed as a black sub par in a histogram or a barplot. NOTE: The values of this array are only calculated if the class attribute is set and it is nominal.

### m_barRange

    double m_barRange

  Contains the range of each bar in a histogram. It is used to work out the range of bar the mouse pointer is on in getToolTipText().

### m_classIndex

    int m_classIndex

  Contains the current class index.

### m_hc

    java.lang.Thread m_hc

  This stores the BarCalc or HistCalc thread while a new barplot or histogram is being calculated.

### m_threadRun

    boolean m_threadRun

  True if the thread m_hc above is running.

### m_colorAttrib

    javax.swing.JComboBox m_colorAttrib

  This stores and lets the user select a class attribute. It also has an entry "No Class" if the user does not want to set a class attribute for colouring.

### m_fm

    java.awt.FontMetrics m_fm

  Fontmetrics used to get the font size which is required for calculating displayable area size, bar height ratio and width of strings that are displayed on top of bars indicating their count.

### m_locker

    java.lang.Integer m_locker

  Lock variable to synchronize the different threads running currently in this class. There are two to three threads in this class, AWT paint thread which is handled differently in paintComponent() which checks on m_threadRun to determine if it can perform full paint or not, the second thread is the main execution thread and the third is the one represented by m_hc which we start when we want to calculate the internal fields for a bar plot or a histogram.

### m_colorList

    FastVector m_colorList

  Contains discrete colours for colouring of subbars of histograms and bar plots when the class attribute is set and is nominal

Class weka.gui.DatabaseConnectionDialog extends javax.swing.JDialog implements Serializable

Serialized Fields

### m_DbaseURLText

    javax.swing.JTextField m_DbaseURLText

### m_DbaseURLLab

    javax.swing.JLabel m_DbaseURLLab

### m_UserNameText

    javax.swing.JTextField m_UserNameText

### m_UserNameLab

    javax.swing.JLabel m_UserNameLab

### m_PasswordText

    javax.swing.JPasswordField m_PasswordText

### m_PasswordLab

    javax.swing.JLabel m_PasswordLab

### m_returnValue

    int m_returnValue

Class weka.gui.GenericArrayEditor extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Support

    java.beans.PropertyChangeSupport m_Support

  Handles property change notification

### m_Label

    javax.swing.JLabel m_Label

  The label for when we can't edit that type

### m_ElementList

    javax.swing.JList m_ElementList

  The list component displaying current values

### m_ElementClass

    java.lang.Class<T> m_ElementClass

  The class of objects allowed in the array

### m_ListModel

    javax.swing.DefaultListModel m_ListModel

  The defaultlistmodel holding our data

### m_ElementEditor

    java.beans.PropertyEditor m_ElementEditor

  The property editor for the class we are editing

### m_DeleteBut

    javax.swing.JButton m_DeleteBut

  Click this to delete the selected array values

### m_AddBut

    javax.swing.JButton m_AddBut

  Click to add the current object configuration to the array

### m_InnerActionListener

    java.awt.event.ActionListener m_InnerActionListener

  Listens to buttons being pressed and taking the appropriate action

### m_InnerSelectionListener

    javax.swing.event.ListSelectionListener m_InnerSelectionListener

  Listens to list items being selected and takes appropriate action

Class weka.gui.GenericObjectEditor.GOEPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_ChildPropertySheet

    PropertySheetPanel m_ChildPropertySheet

  The component that performs classifier customization

### m_ClassNameLabel

    javax.swing.JLabel m_ClassNameLabel

  The name of the current class

### m_OpenBut

    javax.swing.JButton m_OpenBut

  Open object from disk

### m_SaveBut

    javax.swing.JButton m_SaveBut

  Save object to disk

### m_okBut

    javax.swing.JButton m_okBut

  ok button

### m_cancelBut

    javax.swing.JButton m_cancelBut

  cancel button

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  The filechooser for opening and saving object files

Class weka.gui.GenericObjectEditor.JTreePopupMenu extends javax.swing.JPopupMenu implements Serializable

Serialized Fields

### m_tree

    javax.swing.JTree m_tree

  The tree

### m_scroller

    javax.swing.JScrollPane m_scroller

  The scroller

Class weka.gui.GUIChooser extends javax.swing.JFrame implements Serializable

Serialized Fields

### m_SimpleBut

    java.awt.Button m_SimpleBut

  Click to open the simplecli

### m_ExplorerBut

    java.awt.Button m_ExplorerBut

  Click to open the Explorer

### m_ExperimenterBut

    java.awt.Button m_ExperimenterBut

  Click to open the Explorer

### m_KnowledgeFlowBut

    java.awt.Button m_KnowledgeFlowBut

  Click to open the KnowledgeFlow

### m_SimpleCLI

    SimpleCLI m_SimpleCLI

  The SimpleCLI

### m_ExplorerFrame

    javax.swing.JFrame m_ExplorerFrame

  The frame containing the explorer interface

### m_ExperimenterFrame

    javax.swing.JFrame m_ExperimenterFrame

  The frame containing the experiment interface

### m_KnowledgeFlowFrame

    javax.swing.JFrame m_KnowledgeFlowFrame

  The frame containing the knowledge flow interface

### m_weka

    java.awt.Image m_weka

  The weka image

Class weka.gui.HierarchyPropertyParser extends java.lang.Object implements Serializable

Serialized Fields

### m_Root

    weka.gui.HierarchyPropertyParser.TreeNode m_Root

  Keep track of the root of the tree

### m_Current

    weka.gui.HierarchyPropertyParser.TreeNode m_Current

  Keep track of the current node when traversing the tree

### m_Seperator

    java.lang.String m_Seperator

  The level separate in the path

### m_Depth

    int m_Depth

  The depth of the tree

Class weka.gui.InstancesSummaryPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_RelationNameLab

    javax.swing.JLabel m_RelationNameLab

  Displays the name of the relation

### m_NumInstancesLab

    javax.swing.JLabel m_NumInstancesLab

  Displays the number of instances

### m_NumAttributesLab

    javax.swing.JLabel m_NumAttributesLab

  Displays the number of attributes

### m_Instances

    Instances m_Instances

  The instances we're playing with

Class weka.gui.ListSelectorDialog extends javax.swing.JDialog implements Serializable

Serialized Fields

### m_SelectBut

    javax.swing.JButton m_SelectBut

  Click to choose the currently selected property

### m_CancelBut

    javax.swing.JButton m_CancelBut

  Click to cancel the property selection

### m_List

    javax.swing.JList m_List

  The list component

### m_Result

    int m_Result

  Whether the selection was made or cancelled

Class weka.gui.LogPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -4072464549112439484L

Serialized Fields

### m_StatusLab

    javax.swing.JLabel m_StatusLab

  Displays the current status

### m_LogText

    javax.swing.JTextArea m_LogText

  Displays the log messages

### m_logButton

    javax.swing.JButton m_logButton

  The button for viewing the log

### 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 extends javax.swing.JFrame implements Serializable

Serialized Fields

### m_Editor

    java.beans.PropertyEditor m_Editor

  The property editor

### m_EditorComponent

    java.awt.Component m_EditorComponent

  The custom editor component

Class weka.gui.PropertyPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Editor

    java.beans.PropertyEditor m_Editor

  The property editor

### m_PD

    PropertyDialog m_PD

  The currently displayed property dialog, if any

### m_HasCustomPanel

    boolean m_HasCustomPanel

  Whether the editor has provided its own panel

Class weka.gui.PropertySelectorDialog extends javax.swing.JDialog implements Serializable

Serialized Fields

### m_SelectBut

    javax.swing.JButton m_SelectBut

  Click to choose the currently selected property

### m_CancelBut

    javax.swing.JButton m_CancelBut

  Click to cancel the property selection

### m_Root

    javax.swing.tree.DefaultMutableTreeNode m_Root

  The root of the property tree

### m_RootObject

    java.lang.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

    java.lang.Object[] m_ResultPath

  Stores the path to the selected property

### m_Tree

    javax.swing.JTree m_Tree

  The component displaying the property tree

Class weka.gui.PropertySheetPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Target

    java.lang.Object m_Target

  The target object being edited

### m_Properties

    java.beans.PropertyDescriptor[] m_Properties

  Holds properties of the target

### m_Methods

    java.beans.MethodDescriptor[] m_Methods

  Holds the methods of the target

### m_Editors

    java.beans.PropertyEditor[] m_Editors

  Holds property editors of the object

### m_Values

    java.lang.Object[] m_Values

  Holds current object values for each property

### m_Views

    javax.swing.JComponent[] m_Views

  Stores GUI components containing each editing component

### m_Labels

    javax.swing.JLabel[] m_Labels

  The labels for each property

### m_TipTexts

    java.lang.String[] m_TipTexts

  The tool tip text for each property

### m_HelpText

    java.lang.StringBuffer m_HelpText

  StringBuffer containing help text for the object being edited

### m_HelpFrame

    javax.swing.JFrame m_HelpFrame

  Help frame

### m_HelpBut

    javax.swing.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

### m_aboutPanel

    javax.swing.JPanel m_aboutPanel

  The panel holding global info and help, if provided by the object being editied

### support

    java.beans.PropertyChangeSupport support

  A support object for handling property change listeners

Class weka.gui.ResultHistoryPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_SingleText

    javax.swing.text.JTextComponent m_SingleText

  An optional component for single-click display

### m_SingleName

    java.lang.String m_SingleName

  The named result being viewed in the single-click display

### m_Model

    javax.swing.DefaultListModel m_Model

  The list model

### m_List

    javax.swing.JList m_List

  The list component

### m_Results

    java.util.Hashtable<K,V> m_Results

  A Hashtable mapping names to result buffers

### m_FramedOutput

    java.util.Hashtable<K,V> m_FramedOutput

  A Hashtable mapping names to output text components

### m_Objs

    java.util.Hashtable<K,V> 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

### m_Printer

    PrintableComponent m_Printer

  for printing the output to files

Class weka.gui.ResultHistoryPanel.RKeyAdapter extends java.awt.event.KeyAdapter implements Serializable

Class weka.gui.ResultHistoryPanel.RMouseAdapter extends java.awt.event.MouseAdapter implements Serializable

Class weka.gui.SetInstancesPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_OpenFileBut

    javax.swing.JButton m_OpenFileBut

  Click to open instances from a file

### m_OpenURLBut

    javax.swing.JButton m_OpenURLBut

  Click to open instances from a URL

### m_Summary

    InstancesSummaryPanel m_Summary

  The instance summary component

### m_ArffFilter

    javax.swing.filechooser.FileFilter m_ArffFilter

  Filter to ensure only arff files are selected

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  The file chooser for selecting arff files

### m_LastURL

    java.lang.String m_LastURL

  Stores the last URL that instances were loaded from

### m_IOThread

    java.lang.Thread m_IOThread

  The thread we do loading in

### m_Support

    java.beans.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 extends java.awt.Frame implements Serializable

Serialized Fields

### m_OutputArea

    java.awt.TextArea m_OutputArea

  The output area canvas added to the frame

### m_Input

    java.awt.TextField m_Input

  The command input area

### m_CommandHistory

    java.util.Vector<E> m_CommandHistory

  The history of commands entered interactively

### m_HistoryPos

    int m_HistoryPos

  The current position in the command history

### m_POO

    java.io.PipedOutputStream m_POO

  The new output stream for System.out

### m_POE

    java.io.PipedOutputStream m_POE

  The new output stream for System.err

### m_OutRedirector

    java.lang.Thread m_OutRedirector

  The thread that sends output from m_POO to the output box

### m_ErrRedirector

    java.lang.Thread m_ErrRedirector

  The thread that sends output from m_POE to the output box

### m_RunThread

    java.lang.Thread m_RunThread

  The thread currently running a class main method

Class weka.gui.SplashWindow extends java.awt.Window implements Serializable

Serialized Fields

### image

    java.awt.Image image

  The splash image which is displayed on the splash window.

### paintCalled

    boolean paintCalled

  This attribute indicates whether the method paint(Graphics) has been called at least once since the construction of this window.
  This attribute is used to notify method splash(Image) that the window has been drawn at least once by the AWT event dispatcher thread.
  This attribute acts like a latch. Once set to true, it will never be changed back to false again.

  See Also:
    `SplashWindow.paint(java.awt.Graphics)`, `SplashWindow.splash(java.awt.Image)`

Class weka.gui.TableMap extends javax.swing.table.AbstractTableModel implements Serializable

Serialized Fields

### model

    javax.swing.table.TableModel model

Class weka.gui.TableSorter extends TableMap implements Serializable

Serialized Fields

### indexes

    int[] indexes

### sortingColumns

    java.util.Vector<E> sortingColumns

### ascending

    boolean ascending

### compares

    int compares

Class weka.gui.ViewerDialog extends javax.swing.JDialog implements Serializable

Serialized Fields

### m_Result

    int m_Result

  the result of the user's action, either OK or CANCEL

### m_OkButton

    javax.swing.JButton m_OkButton

  Click to activate the current set parameters

### m_CancelButton

    javax.swing.JButton m_CancelButton

  Click to cancel the dialog

### m_UndoButton

    javax.swing.JButton m_UndoButton

  Click to undo the last action

### m_ArffPanel

    ArffPanel m_ArffPanel

  the panel to display the Instances-object

Class weka.gui.WekaTaskMonitor extends javax.swing.JPanel implements Serializable

serialVersionUID: 508309816292197578L

Serialized Fields

### m_ActiveTasks

    int m_ActiveTasks

  The number of running weka threads

### m_MonitorLabel

    javax.swing.JLabel m_MonitorLabel

  The label for displaying info

### m_iconStationary

    javax.swing.ImageIcon m_iconStationary

  The icon for the stationary bird

### m_iconAnimated

    javax.swing.ImageIcon m_iconAnimated

  The icon for the animated bird

### m_animating

    boolean m_animating

  True if their are active tasks

Package weka.gui.arffviewer

Class weka.gui.arffviewer.ArffPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### tableArff

    ArffTable tableArff

### popupHeader

    javax.swing.JPopupMenu popupHeader

### popupRows

    javax.swing.JPopupMenu popupRows

### labelName

    javax.swing.JLabel labelName

### menuItemMean

    javax.swing.JMenuItem menuItemMean

### menuItemSetAllValues

    javax.swing.JMenuItem menuItemSetAllValues

### menuItemSetMissingValues

    javax.swing.JMenuItem menuItemSetMissingValues

### menuItemReplaceValues

    javax.swing.JMenuItem menuItemReplaceValues

### menuItemRenameAttribute

    javax.swing.JMenuItem menuItemRenameAttribute

### menuItemDeleteAttribute

    javax.swing.JMenuItem menuItemDeleteAttribute

### menuItemDeleteAttributes

    javax.swing.JMenuItem menuItemDeleteAttributes

### menuItemSortInstances

    javax.swing.JMenuItem menuItemSortInstances

### menuItemDeleteSelectedInstance

    javax.swing.JMenuItem menuItemDeleteSelectedInstance

### menuItemDeleteAllSelectedInstances

    javax.swing.JMenuItem menuItemDeleteAllSelectedInstances

### menuItemSearch

    javax.swing.JMenuItem menuItemSearch

### menuItemClearSearch

    javax.swing.JMenuItem menuItemClearSearch

### menuItemUndo

    javax.swing.JMenuItem menuItemUndo

### menuItemCopy

    javax.swing.JMenuItem menuItemCopy

### filename

    java.lang.String filename

### title

    java.lang.String title

### currentCol

    int currentCol

### changed

    boolean changed

### changeListeners

    java.util.HashSet<E> changeListeners

### lastSearch

    java.lang.String lastSearch

### lastReplace

    java.lang.String lastReplace

Class weka.gui.arffviewer.ArffTable extends javax.swing.JTable implements Serializable

Serialized Fields

### searchString

    java.lang.String searchString

### changeListeners

    java.util.HashSet<E> changeListeners

Class weka.gui.arffviewer.ArffTableCellRenderer extends javax.swing.table.DefaultTableCellRenderer implements Serializable

Serialized Fields

### missingColor

    java.awt.Color missingColor

### missingColorSelected

    java.awt.Color missingColorSelected

### highlightColor

    java.awt.Color highlightColor

### highlightColorSelected

    java.awt.Color highlightColorSelected

Class weka.gui.arffviewer.ArffTableSorter extends TableSorter implements Serializable

Class weka.gui.arffviewer.ArffViewer extends javax.swing.JFrame implements Serializable

Serialized Fields

### m_MainPanel

    ArffViewerMainPanel m_MainPanel

  the main panel

Class weka.gui.arffviewer.ArffViewerMainPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### parent

    javax.swing.JFrame parent

### tabbedPane

    javax.swing.JTabbedPane tabbedPane

### menuBar

    javax.swing.JMenuBar menuBar

### menuFile

    javax.swing.JMenu menuFile

### menuFileOpen

    javax.swing.JMenuItem menuFileOpen

### menuFileSave

    javax.swing.JMenuItem menuFileSave

### menuFileSaveAs

    javax.swing.JMenuItem menuFileSaveAs

### menuFileClose

    javax.swing.JMenuItem menuFileClose

### menuFileCloseAll

    javax.swing.JMenuItem menuFileCloseAll

### menuFileProperties

    javax.swing.JMenuItem menuFileProperties

### menuFileExit

    javax.swing.JMenuItem menuFileExit

### menuEdit

    javax.swing.JMenu menuEdit

### menuEditUndo

    javax.swing.JMenuItem menuEditUndo

### menuEditCopy

    javax.swing.JMenuItem menuEditCopy

### menuEditSearch

    javax.swing.JMenuItem menuEditSearch

### menuEditClearSearch

    javax.swing.JMenuItem menuEditClearSearch

### menuEditDeleteAttribute

    javax.swing.JMenuItem menuEditDeleteAttribute

### menuEditDeleteAttributes

    javax.swing.JMenuItem menuEditDeleteAttributes

### menuEditRenameAttribute

    javax.swing.JMenuItem menuEditRenameAttribute

### menuEditDeleteInstance

    javax.swing.JMenuItem menuEditDeleteInstance

### menuEditDeleteInstances

    javax.swing.JMenuItem menuEditDeleteInstances

### menuEditSortInstances

    javax.swing.JMenuItem menuEditSortInstances

### menuView

    javax.swing.JMenu menuView

### menuViewAttributes

    javax.swing.JMenuItem menuViewAttributes

### menuViewValues

    javax.swing.JMenuItem menuViewValues

### fileChooser

    FileChooser fileChooser

### arffFilter

    ExtensionFileFilter arffFilter

### csvFilter

    ExtensionFileFilter csvFilter

### frameTitle

    java.lang.String frameTitle

### confirmExit

    boolean confirmExit

### width

    int width

### height

    int height

### top

    int top

### left

    int left

### exitOnClose

    boolean exitOnClose

Class weka.gui.arffviewer.FileChooser extends javax.swing.JFileChooser implements Serializable

Package weka.gui.beans

Class weka.gui.beans.AbstractDataSink extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_visual

    BeanVisual m_visual

  Default visual for data sources

### m_listenee

    java.lang.Object m_listenee

  Non null if this object is a target for any events. Provides for the simplest case when only one incomming connection is allowed. Subclasses can overide the appropriate BeanCommon methods to change this behaviour and allow multiple connections if desired

Class weka.gui.beans.AbstractDataSource extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_design

    boolean m_design

  True if this bean's appearance is the design mode appearance

### m_bcSupport

    java.beans.beancontext.BeanContextChildSupport m_bcSupport

  BeanContextChild support

### m_visual

    BeanVisual m_visual

  Default visual for data sources

### m_listeners

    java.util.Vector<E> m_listeners

  Objects listening for events from data sources

Class weka.gui.beans.AbstractEvaluator extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_visual

    BeanVisual m_visual

  Default visual for evaluators

### m_listenee

    java.lang.Object m_listenee

Class weka.gui.beans.AbstractTestSetProducer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_listeners

    java.util.Vector<E> m_listeners

  Objects listening to us

### m_visual

    BeanVisual m_visual

### m_listenee

    java.lang.Object m_listenee

  non null if this object is a target for any events.

Class weka.gui.beans.AbstractTrainAndTestSetProducer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_trainingListeners

    java.util.Vector<E> m_trainingListeners

  Objects listening for trainin set events

### m_testListeners

    java.util.Vector<E> m_testListeners

  Objects listening for test set events

### m_visual

    BeanVisual m_visual

### m_listenee

    java.lang.Object m_listenee

  non null if this object is a target for any events.

Class weka.gui.beans.AbstractTrainingSetProducer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_listeners

    java.util.Vector<E> m_listeners

  Objects listening for training set events

### m_visual

    BeanVisual m_visual

### m_listenee

    java.lang.Object m_listenee

  non null if this object is a target for any events.

Class weka.gui.beans.AttributeSummarizer extends DataVisualizer implements Serializable

Serialized Fields

### m_gridWidth

    int m_gridWidth

  The number of plots horizontally in the display

### m_maxPlots

    int m_maxPlots

  The maximum number of plots to show

### m_coloringIndex

    int m_coloringIndex

  Index on which to color the plots.

Class weka.gui.beans.BatchClassifierEvent extends java.util.EventObject implements Serializable

serialVersionUID: 878097199815991084L

Serialized Fields

### m_classifier

    Classifier m_classifier

  The classifier

### m_testSet

    DataSetEvent m_testSet

  Instances that can be used for testing the classifier

### m_trainSet

    DataSetEvent m_trainSet

  Instances that were used to train the classifier (may be null if not available)

### m_setNumber

    int m_setNumber

  The set number for the test set

### m_maxSetNumber

    int m_maxSetNumber

  The last set number for this series

Class weka.gui.beans.BatchClustererEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_clusterer

    Clusterer m_clusterer

  The clusterer

### m_testSet

    DataSetEvent m_testSet

  Training or Test Instances

### m_setNumber

    int m_setNumber

  The set number for the test set

### m_testOrTrain

    int m_testOrTrain

  Indicates if m_testSet is a training or a test set. 0 for test, >0 for training

### m_maxSetNumber

    int m_maxSetNumber

  The last set number for this series

Class weka.gui.beans.BeanConnection extends java.lang.Object implements Serializable

Serialized Fields

### m_source

    BeanInstance m_source

### m_target

    BeanInstance m_target

### m_eventName

    java.lang.String m_eventName

  The name of the event for this connection

### m_hidden

    boolean m_hidden

Class weka.gui.beans.BeanInstance extends java.lang.Object implements Serializable

Serialized Fields

### m_bean

    java.lang.Object m_bean

  Holds the bean encapsulated in this instance

### m_x

    int m_x

### m_y

    int m_y

Class weka.gui.beans.BeanVisual extends javax.swing.JPanel implements Serializable

Serialization Methods

### readObject

    private void readObject(java.io.ObjectInputStream ois)
                     throws java.io.IOException,
                            java.lang.ClassNotFoundException

  Overides default read object in order to reload icons. This is necessary because for some strange reason animated gifs stop being animated after being serialized/deserialized.

  Throws:
    `java.io.IOException` - if an error occurs
    `java.lang.ClassNotFoundException` - if an error occurs

Serialized Fields

### m_iconPath

    java.lang.String m_iconPath

  Holds name (including path) of the static icon

### m_animatedIconPath

    java.lang.String m_animatedIconPath

  Holds name (including path) of the animated icon

### m_visualName

    java.lang.String m_visualName

  Name for the bean

### m_visualLabel

    javax.swing.JLabel m_visualLabel

### m_stationary

    boolean m_stationary

  Container for the icon

### m_pcs

    java.beans.PropertyChangeSupport m_pcs

### m_displayConnectors

    boolean m_displayConnectors

### m_connectorColor

    java.awt.Color m_connectorColor

Class weka.gui.beans.ChartEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_legendText

    java.util.Vector<E> m_legendText

### m_max

    double m_max

### m_min

    double m_min

### m_reset

    boolean m_reset

### m_dataPoint

    double[] m_dataPoint

  Y values of the data points

Class weka.gui.beans.ClassAssigner extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_classColumn

    java.lang.String m_classColumn

### m_connectedFormat

    Instances m_connectedFormat

  format of instances for current incoming connection (if any)

### m_trainingProvider

    java.lang.Object m_trainingProvider

### m_testProvider

    java.lang.Object m_testProvider

### m_dataProvider

    java.lang.Object m_dataProvider

### m_instanceProvider

    java.lang.Object m_instanceProvider

### m_trainingListeners

    java.util.Vector<E> m_trainingListeners

### m_testListeners

    java.util.Vector<E> m_testListeners

### m_dataListeners

    java.util.Vector<E> m_dataListeners

### m_instanceListeners

    java.util.Vector<E> m_instanceListeners

### m_dataFormatListeners

    java.util.Vector<E> m_dataFormatListeners

### m_visual

    BeanVisual m_visual

Class weka.gui.beans.ClassAssignerCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_displayColNames

    boolean m_displayColNames

### m_classAssigner

    ClassAssigner m_classAssigner

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_caEditor

    PropertySheetPanel m_caEditor

### m_ClassCombo

    javax.swing.JComboBox m_ClassCombo

### m_holderP

    javax.swing.JPanel m_holderP

Class weka.gui.beans.Classifier extends javax.swing.JPanel implements Serializable

serialVersionUID: 659603893917736008L

Serialized Fields

### m_visual

    BeanVisual m_visual

### m_state

    int m_state

### m_buildThread

    java.lang.Thread m_buildThread

### m_globalInfo

    java.lang.String m_globalInfo

  Global info for the wrapped classifier (if it exists).

### m_listenees

    java.util.Hashtable<K,V> m_listenees

  Objects talking to us

### m_batchClassifierListeners

    java.util.Vector<E> m_batchClassifierListeners

  Objects listening for batch classifier events

### m_incrementalClassifierListeners

    java.util.Vector<E> m_incrementalClassifierListeners

  Objects listening for incremental classifier events

### m_graphListeners

    java.util.Vector<E> m_graphListeners

  Objects listening for graph events

### m_textListeners

    java.util.Vector<E> m_textListeners

  Objects listening for text events

### m_trainingSet

    Instances m_trainingSet

  Holds training instances for batch training. Not transient because header is retained for validating any instance events that this classifier might be asked to predict in the future.

### m_Classifier

    Classifier m_Classifier

### m_ie

    IncrementalClassifierEvent m_ie

### m_updateIncrementalClassifier

    boolean m_updateIncrementalClassifier

  If the classifier is an incremental classifier, should we update it (ie train it on incoming instances). This makes it possible incrementally test on a separate stream of instances without updating the classifier, or mix batch training/testing with incremental training/testing

### m_incrementalEvent

    InstanceEvent m_incrementalEvent

  Event to handle when processing incremental updates

### m_dummy

    java.lang.Double m_dummy

Class weka.gui.beans.ClassifierCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_dsClassifier

    Classifier m_dsClassifier

### m_ClassifierEditor

    PropertySheetPanel m_ClassifierEditor

### m_incrementalPanel

    javax.swing.JPanel m_incrementalPanel

### m_updateIncrementalClassifier

    javax.swing.JCheckBox m_updateIncrementalClassifier

### m_panelVisible

    boolean m_panelVisible

Class weka.gui.beans.ClassifierPerformanceEvaluator extends AbstractEvaluator implements Serializable

Serialized Fields

### m_textListeners

    java.util.Vector<E> m_textListeners

### m_thresholdListeners

    java.util.Vector<E> m_thresholdListeners

### m_visualizableErrorListeners

    java.util.Vector<E> m_visualizableErrorListeners

### m_rocListenersConnected

    boolean m_rocListenersConnected

### m_predInstances

    Instances m_predInstances

### m_preds

    FastVector m_preds

### m_plotShape

    FastVector m_plotShape

### m_plotSize

    FastVector m_plotSize

Class weka.gui.beans.ClassValuePicker extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_classValueIndex

    int m_classValueIndex

  the class value index considered to be the positive class

### m_connectedFormat

    Instances m_connectedFormat

  format of instances for the current incoming connection (if any)

### m_dataProvider

    java.lang.Object m_dataProvider

### m_dataListeners

    java.util.Vector<E> m_dataListeners

### m_dataFormatListeners

    java.util.Vector<E> m_dataFormatListeners

### m_visual

    BeanVisual m_visual

Class weka.gui.beans.ClassValuePickerCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_displayValNames

    boolean m_displayValNames

### m_classValuePicker

    ClassValuePicker m_classValuePicker

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_ClassValueCombo

    javax.swing.JComboBox m_ClassValueCombo

### m_holderP

    javax.swing.JPanel m_holderP

### m_messageLabel

    javax.swing.JLabel m_messageLabel

Class weka.gui.beans.Clusterer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_visual

    BeanVisual m_visual

### m_state

    int m_state

### m_buildThread

    java.lang.Thread m_buildThread

### m_globalInfo

    java.lang.String m_globalInfo

  Global info for the wrapped classifier (if it exists).

### m_listenees

    java.util.Hashtable<K,V> m_listenees

  Objects talking to us

### m_batchClustererListeners

    java.util.Vector<E> m_batchClustererListeners

  Objects listening for batch clusterer events

### m_graphListeners

    java.util.Vector<E> m_graphListeners

  Objects listening for graph events

### m_textListeners

    java.util.Vector<E> m_textListeners

  Objects listening for text events

### m_trainingSet

    Instances m_trainingSet

  Holds training instances for batch training.

### m_Clusterer

    Clusterer m_Clusterer

### m_dummy

    java.lang.Double m_dummy

Class weka.gui.beans.ClustererCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_dsClusterer

    Clusterer m_dsClusterer

### m_ClustererEditor

    PropertySheetPanel m_ClustererEditor

Class weka.gui.beans.ClustererPerformanceEvaluator extends AbstractEvaluator implements Serializable

Serialized Fields

### m_textListeners

    java.util.Vector<E> m_textListeners

Class weka.gui.beans.CrossValidationFoldMaker extends AbstractTrainAndTestSetProducer implements Serializable

Serialized Fields

### m_numFolds

    int m_numFolds

### m_randomSeed

    int m_randomSeed

### m_foldThread

    java.lang.Thread m_foldThread

Class weka.gui.beans.CrossValidationFoldMakerCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_cvEditor

    PropertySheetPanel m_cvEditor

Class weka.gui.beans.DataSetEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_dataSet

    Instances m_dataSet

### m_structureOnly

    boolean m_structureOnly

Class weka.gui.beans.DataVisualizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_visual

    BeanVisual m_visual

### m_framePoppedUp

    boolean m_framePoppedUp

### m_design

    boolean m_design

  True if this bean's appearance is the design mode appearance

### m_visPanel

    VisualizePanel m_visPanel

### m_bcSupport

    java.beans.beancontext.BeanContextChildSupport m_bcSupport

  BeanContextChild support

Class weka.gui.beans.Filter extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_visual

    BeanVisual m_visual

### m_state

    int m_state

### m_filterThread

    java.lang.Thread m_filterThread

### m_globalInfo

    java.lang.String m_globalInfo

  Global info for the wrapped filter (if it exists).

### m_listenees

    java.util.Hashtable<K,V> m_listenees

  Objects talking to us

### m_trainingListeners

    java.util.Vector<E> m_trainingListeners

  Objects listening for training set events

### m_testListeners

    java.util.Vector<E> m_testListeners

  Objects listening for test set events

### m_instanceListeners

    java.util.Vector<E> m_instanceListeners

  Objects listening for instance events

### m_dataListeners

    java.util.Vector<E> m_dataListeners

  Objects listening for data set events

### m_Filter

    Filter m_Filter

  The filter to use.

### m_ie

    InstanceEvent m_ie

  Instance event object for passing on filtered instance streams

### m_structurePassedOn

    boolean m_structurePassedOn

Class weka.gui.beans.FilterCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_filter

    Filter m_filter

### m_filterEditor

    GenericObjectEditor m_filterEditor

Class weka.gui.beans.GraphEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_graphString

    java.lang.String m_graphString

### m_graphTitle

    java.lang.String m_graphTitle

### m_graphType

    int m_graphType

Class weka.gui.beans.GraphViewer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_visual

    BeanVisual m_visual

Class weka.gui.beans.IncrementalClassifierEvaluator extends AbstractEvaluator implements Serializable

Serialized Fields

### m_listeners

    java.util.Vector<E> m_listeners

### m_textListeners

    java.util.Vector<E> m_textListeners

### m_dataLegend

    java.util.Vector<E> m_dataLegend

### m_ce

    ChartEvent m_ce

### m_dataPoint

    double[] m_dataPoint

### m_reset

    boolean m_reset

### m_min

    double m_min

### m_max

    double m_max

Class weka.gui.beans.IncrementalClassifierEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_structure

    Instances m_structure

### m_status

    int m_status

### m_classifier

    Classifier m_classifier

### m_currentInstance

    Instance m_currentInstance

Class weka.gui.beans.InstanceEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_structure

    Instances m_structure

### m_instance

    Instance m_instance

### m_status

    int m_status

Class weka.gui.beans.KnowledgeFlowApp extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_fontM

    java.awt.FontMetrics m_fontM

### m_mode

    int m_mode

### m_toolBarGroup

    javax.swing.ButtonGroup m_toolBarGroup

  Button group to manage all toolbar buttons

### m_toolBarBean

    java.lang.Object m_toolBarBean

  Holds the selected toolbar bean

### m_beanLayout

    weka.gui.beans.KnowledgeFlowApp.BeanLayout m_beanLayout

  The layout area

### m_toolBars

    javax.swing.JTabbedPane m_toolBars

  Tabbed pane to hold tool bars

### m_userToolBar

    javax.swing.JToolBar m_userToolBar

  Stuff relating to user created meta beans

### m_userBoxPanel

    javax.swing.Box m_userBoxPanel

### m_userComponents

    java.util.Vector<E> m_userComponents

### m_firstUserComponentOpp

    boolean m_firstUserComponentOpp

### m_pointerB

    javax.swing.JToggleButton m_pointerB

### m_saveB

    javax.swing.JButton m_saveB

### m_loadB

    javax.swing.JButton m_loadB

### m_stopB

    javax.swing.JButton m_stopB

### m_helpB

    javax.swing.JButton m_helpB

### m_newB

    javax.swing.JButton m_newB

### m_editElement

    BeanInstance m_editElement

  Reference to bean being manipulated

### m_sourceEventSetDescriptor

    java.beans.EventSetDescriptor m_sourceEventSetDescriptor

  Event set descriptor for the bean being manipulated

### m_oldX

    int m_oldX

  Used to record screen coordinates during move, select and connect operations

### m_oldY

    int m_oldY

  Used to record screen coordinates during move, select and connect operations

### m_startX

    int m_startX

### m_startY

    int m_startY

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  The file chooser for selecting layout files

### m_logPanel

    LogPanel m_logPanel

### m_bcSupport

    java.beans.beancontext.BeanContextSupport m_bcSupport

### m_KfFilter

    javax.swing.filechooser.FileFilter m_KfFilter

  A filter to ensure only KnowledgeFlow files in binary format get shown in the chooser

### m_KOMLFilter

    javax.swing.filechooser.FileFilter m_KOMLFilter

  A filter to ensure only KnowledgeFlow files in KOML format get shown in the chooser

### m_XMLFilter

    javax.swing.filechooser.FileFilter m_XMLFilter

  A filter to ensure only KnowledgeFlow layout files in XML format get shown in the chooser

### m_ScrollBarIncrementLayout

    int m_ScrollBarIncrementLayout

  the scrollbar increment of the layout scrollpane

### m_ScrollBarIncrementComponents

    int m_ScrollBarIncrementComponents

  the scrollbar increment of the components scrollpane

### m_FlowWidth

    int m_FlowWidth

  the flow layout width

### m_FlowHeight

    int m_FlowHeight

  the flow layout height

### m_PreferredExtension

    java.lang.String m_PreferredExtension

  the preferred file extension

### m_UserComponentsInXML

    boolean m_UserComponentsInXML

  whether to store the user components in XML or in binary format

Class weka.gui.beans.KnowledgeFlowApp.BeanLayout extends PrintablePanel implements Serializable

Class weka.gui.beans.Loader extends AbstractDataSource implements Serializable

Serialization Methods

### readResolve

    private java.lang.Object readResolve()
                                  throws java.io.ObjectStreamException

  Throws:
    `java.io.ObjectStreamException`

Serialized Fields

### m_globalInfo

    java.lang.String m_globalInfo

  Global info for the wrapped loader (if it exists).

### m_ioThread

    weka.gui.beans.Loader.LoadThread m_ioThread

  Thread for doing IO in

### m_Loader

    Loader m_Loader

  Loader

### m_ie

    InstanceEvent m_ie

### m_instanceEventTargets

    int m_instanceEventTargets

  Keep track of how many listeners for different types of events there are.

### m_dataSetEventTargets

    int m_dataSetEventTargets

### m_dbSet

    boolean m_dbSet

  Flag indicating that a database has already been configured

Class weka.gui.beans.LoaderCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_dsLoader

    Loader m_dsLoader

### m_LoaderEditor

    PropertySheetPanel m_LoaderEditor

### m_fileChooser

    javax.swing.JFileChooser m_fileChooser

### m_parentFrame

    javax.swing.JFrame m_parentFrame

### m_dbaseURLText

    javax.swing.JTextField m_dbaseURLText

### m_userNameText

    javax.swing.JTextField m_userNameText

### m_queryText

    javax.swing.JTextField m_queryText

### m_keyText

    javax.swing.JTextField m_keyText

### m_passwordText

    javax.swing.JPasswordField m_passwordText

Class weka.gui.beans.MetaBean extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_visual

    BeanVisual m_visual

### m_subFlow

    java.util.Vector<E> m_subFlow

### m_inputs

    java.util.Vector<E> m_inputs

### m_outputs

    java.util.Vector<E> m_outputs

### m_associatedConnections

    java.util.Vector<E> m_associatedConnections

### m_originalCoords

    java.util.Vector<E> m_originalCoords

Class weka.gui.beans.ModelPerformanceChart extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_visual

    BeanVisual m_visual

### m_framePoppedUp

    boolean m_framePoppedUp

### m_design

    boolean m_design

  True if this bean's appearance is the design mode appearance

### m_bcSupport

    java.beans.beancontext.BeanContextChildSupport m_bcSupport

  BeanContextChild support

Class weka.gui.beans.PredictionAppender extends javax.swing.JPanel implements Serializable

serialVersionUID: -2987740065058976673L

Serialized Fields

### m_dataSourceListeners

    java.util.Vector<E> m_dataSourceListeners

  Objects listenening for dataset events

### m_instanceListeners

    java.util.Vector<E> m_instanceListeners

  Objects listening for instances events

### m_trainingSetListeners

    java.util.Vector<E> m_trainingSetListeners

  Objects listening for training set events

### m_testSetListeners

    java.util.Vector<E> m_testSetListeners

  Objects listening for test set events

### m_listenee

    java.lang.Object m_listenee

  Non null if this object is a target for any events.

### m_format

    Instances m_format

  Format of instances to be produced.

### m_visual

    BeanVisual m_visual

### m_appendProbabilities

    boolean m_appendProbabilities

  Append classifier's predicted probabilities (if the class is discrete and the classifier is a distribution classifier)

### m_instanceEvent

    InstanceEvent m_instanceEvent

### m_instanceVals

    double[] m_instanceVals

Class weka.gui.beans.PredictionAppenderCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_paEditor

    PropertySheetPanel m_paEditor

Class weka.gui.beans.Saver extends AbstractDataSink implements Serializable

Serialized Fields

### m_dataSet

    Instances m_dataSet

  Holds the instances to be saved

### m_structure

    Instances m_structure

  Holds the structure

### m_globalInfo

    java.lang.String m_globalInfo

  Global info for the wrapped loader (if it exists).

### m_Saver

    Saver m_Saver

  Saver

### m_fileName

    java.lang.String m_fileName

  The relation name that becomes part of the file name

### m_isDBSaver

    boolean m_isDBSaver

  Flag indicating that instances will be saved to database. Used because structure information can only be sent after a database has been configured.

### m_count

    int m_count

  Count for structure available messages

Class weka.gui.beans.SaverCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_dsSaver

    Saver m_dsSaver

### m_SaverEditor

    PropertySheetPanel m_SaverEditor

### m_fileChooser

    javax.swing.JFileChooser m_fileChooser

### m_parentFrame

    javax.swing.JFrame m_parentFrame

### m_dbaseURLText

    javax.swing.JTextField m_dbaseURLText

### m_userNameText

    javax.swing.JTextField m_userNameText

### m_passwordText

    javax.swing.JPasswordField m_passwordText

### m_tableText

    javax.swing.JTextField m_tableText

### m_idBox

    javax.swing.JComboBox m_idBox

### m_tabBox

    javax.swing.JComboBox m_tabBox

### m_prefixText

    javax.swing.JTextField m_prefixText

Class weka.gui.beans.ScatterPlotMatrix extends DataVisualizer implements Serializable

Serialized Fields

### m_matrixPanel

    MatrixPanel m_matrixPanel

Class weka.gui.beans.StripChart extends javax.swing.JPanel implements Serializable

Serialization Methods

### readObject

    private void readObject(java.io.ObjectInputStream ois)
                     throws java.io.IOException,
                            java.lang.ClassNotFoundException

  Provide some necessary initialization after object has been deserialized.

  Throws:
    `java.io.IOException` - if an error occurs
    `java.lang.ClassNotFoundException` - if an error occurs

Serialized Fields

### m_colorList

    java.awt.Color[] m_colorList

  default colours for colouring lines

### m_iheight

    int m_iheight

  Width and height of the off screen image

### m_iwidth

    int m_iwidth

### m_max

    double m_max

  Max value for the y axis

### m_min

    double m_min

  Min value for the y axis

### m_yScaleUpdate

    boolean m_yScaleUpdate

  Scale update requested

### m_oldMax

    double m_oldMax

### m_oldMin

    double m_oldMin

### m_labelFont

    java.awt.Font m_labelFont

### m_labelMetrics

    java.awt.FontMetrics m_labelMetrics

### m_legendText

    java.util.Vector<E> m_legendText

### m_scalePanel

    javax.swing.JPanel m_scalePanel

  Class providing a panel for displaying the y axis

### m_legendPanel

    javax.swing.JPanel m_legendPanel

  Class providing a panel for the legend

### m_dataList

    java.util.LinkedList<E> m_dataList

  Holds the data to be plotted. Entries in the list are arrays of y points.

### m_previousY

    double[] m_previousY

### m_visual

    BeanVisual m_visual

### m_listenee

    java.lang.Object m_listenee

### m_xValFreq

    int m_xValFreq

  Print x axis labels every m_xValFreq points

### m_xCount

    int m_xCount

### m_refreshWidth

    int m_refreshWidth

  Shift the plot by this many pixels every time a point is plotted

### m_refreshFrequency

    int m_refreshFrequency

  Plot every m_refreshFrequency'th point

### m_ce

    ChartEvent m_ce

### m_dataPoint

    double[] m_dataPoint

Class weka.gui.beans.StripChartCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_cvEditor

    PropertySheetPanel m_cvEditor

Class weka.gui.beans.TestSetEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_testSet

    Instances m_testSet

  The test set instances

### m_structureOnly

    boolean m_structureOnly

### m_setNumber

    int m_setNumber

  what number is this test set (ie fold 2 of 10 folds)

### m_maxSetNumber

    int m_maxSetNumber

  Maximum number of sets (ie 10 in a 10 fold)

Class weka.gui.beans.TestSetMaker extends AbstractTestSetProducer implements Serializable

Class weka.gui.beans.TextEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_text

    java.lang.String m_text

  The text

### m_textTitle

    java.lang.String m_textTitle

  The title for the text. Could be used in a list component

Class weka.gui.beans.TextViewer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_visual

    BeanVisual m_visual

### m_design

    boolean m_design

  True if this bean's appearance is the design mode appearance

### m_bcSupport

    java.beans.beancontext.BeanContextChildSupport m_bcSupport

  BeanContextChild support

Class weka.gui.beans.ThresholdDataEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_dataSet

    PlotData2D m_dataSet

Class weka.gui.beans.TrainingSetEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_trainingSet

    Instances m_trainingSet

  The training instances

### m_structureOnly

    boolean m_structureOnly

### m_setNumber

    int m_setNumber

  what number is this training set (ie fold 2 of 10 folds)

### m_maxSetNumber

    int m_maxSetNumber

  Maximum number of sets (ie 10 in a 10 fold)

Class weka.gui.beans.TrainingSetMaker extends AbstractTrainingSetProducer implements Serializable

Class weka.gui.beans.TrainTestSplitMaker extends AbstractTrainAndTestSetProducer implements Serializable

Serialized Fields

### m_trainPercentage

    int m_trainPercentage

### m_randomSeed

    int m_randomSeed

### m_splitThread

    java.lang.Thread m_splitThread

Class weka.gui.beans.TrainTestSplitMakerCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_pcSupport

    java.beans.PropertyChangeSupport m_pcSupport

### m_splitEditor

    PropertySheetPanel m_splitEditor

Class weka.gui.beans.VisualizableErrorEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_dataSet

    PlotData2D m_dataSet

Package weka.gui.boundaryvisualizer

Class weka.gui.boundaryvisualizer.BoundaryPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Colors

    FastVector m_Colors

### m_trainingData

    Instances m_trainingData

### m_classifier

    Classifier m_classifier

### m_dataGenerator

    DataGenerator m_dataGenerator

### m_classIndex

    int m_classIndex

### m_xAttribute

    int m_xAttribute

### m_yAttribute

    int m_yAttribute

### m_minX

    double m_minX

### m_minY

    double m_minY

### m_maxX

    double m_maxX

### m_maxY

    double m_maxY

### m_rangeX

    double m_rangeX

### m_rangeY

    double m_rangeY

### m_pixHeight

    double m_pixHeight

### m_pixWidth

    double m_pixWidth

### m_osi

    java.awt.Image m_osi

### m_panelWidth

    int m_panelWidth

### m_panelHeight

    int m_panelHeight

### m_numOfSamplesPerRegion

    int m_numOfSamplesPerRegion

### m_numOfSamplesPerGenerator

    int m_numOfSamplesPerGenerator

### m_samplesBase

    double m_samplesBase

### m_listeners

    java.util.Vector<E> m_listeners

### m_plotPanel

    weka.gui.boundaryvisualizer.BoundaryPanel.PlotPanel m_plotPanel

### m_plotThread

    java.lang.Thread m_plotThread

### m_stopPlotting

    boolean m_stopPlotting

### m_stopReplotting

    boolean m_stopReplotting

### m_dummy

    java.lang.Double m_dummy

### m_pausePlotting

    boolean m_pausePlotting

### m_size

    int m_size

### m_initialTiling

    boolean m_initialTiling

### m_random

    java.util.Random m_random

### m_probabilityCache

    double[][][] m_probabilityCache

### m_plotTrainingData

    boolean m_plotTrainingData

Class weka.gui.boundaryvisualizer.BoundaryPanelDistributed extends BoundaryPanel implements Serializable

Serialized Fields

### m_listeners

    java.util.Vector<E> m_listeners

  a list of RemoteExperimentListeners

### m_remoteHosts

    java.util.Vector<E> 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_plottingAborted

    boolean m_plottingAborted

  Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts plotting

### 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-tasks

### m_finishedCount

    int m_finishedCount

  The count of successfully completed sub-tasks

### m_subExpQueue

    Queue m_subExpQueue

  The queue of sub-tasks waiting to be processed

### m_minTaskPollTime

    int m_minTaskPollTime

### m_hostPollingTime

    int[] m_hostPollingTime

Class weka.gui.boundaryvisualizer.BoundaryVisualizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_trainingInstances

    Instances m_trainingInstances

### m_classifier

    Classifier m_classifier

### m_plotAreaWidth

    int m_plotAreaWidth

### m_plotAreaHeight

    int m_plotAreaHeight

### m_boundaryPanel

    BoundaryPanel m_boundaryPanel

### m_classAttBox

    javax.swing.JComboBox m_classAttBox

### m_xAttBox

    javax.swing.JComboBox m_xAttBox

### m_yAttBox

    javax.swing.JComboBox m_yAttBox

### COMBO_SIZE

    java.awt.Dimension COMBO_SIZE

### m_startBut

    javax.swing.JButton m_startBut

### m_plotTrainingData

    javax.swing.JCheckBox m_plotTrainingData

### m_controlPanel

    javax.swing.JPanel m_controlPanel

### m_classPanel

    ClassPanel m_classPanel

### m_xAxisPanel

    weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_xAxisPanel

### m_yAxisPanel

    weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_yAxisPanel

### m_maxX

    double m_maxX

### m_maxY

    double m_maxY

### m_minX

    double m_minX

### m_minY

    double m_minY

### m_xIndex

    int m_xIndex

### m_yIndex

    int m_yIndex

### m_dataGenerator

    KDDataGenerator m_dataGenerator

### m_numberOfSamplesFromEachRegion

    int m_numberOfSamplesFromEachRegion

### m_generatorSamplesBase

    int m_generatorSamplesBase

### m_kernelBandwidth

    int m_kernelBandwidth

### m_regionSamplesText

    javax.swing.JTextField m_regionSamplesText

### m_generatorSamplesText

    javax.swing.JTextField m_generatorSamplesText

### m_kernelBandwidthText

    javax.swing.JTextField m_kernelBandwidthText

Class weka.gui.boundaryvisualizer.KDDataGenerator extends java.lang.Object implements Serializable

Serialized Fields

### m_instances

    Instances m_instances

### m_standardDeviations

    double[] m_standardDeviations

### m_globalMeansOrModes

    double[] m_globalMeansOrModes

### m_minStdDev

    double m_minStdDev

### m_laplaceConst

    double m_laplaceConst

### m_seed

    int m_seed

### m_random

    java.util.Random m_random

### m_weightingDimensions

    boolean[] m_weightingDimensions

### m_weightingValues

    double[] m_weightingValues

### m_kernelBandwidth

    int m_kernelBandwidth

### m_kernelParams

    double[][] m_kernelParams

### m_Min

    double[] m_Min

  The minimum values for numeric attributes.

### m_Max

    double[] m_Max

  The maximum values for numeric attributes.

Class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask extends java.lang.Object implements Serializable

Serialized Fields

### m_status

    TaskStatusInfo m_status

### m_result

    RemoteResult m_result

### m_rowNumber

    int m_rowNumber

### m_panelHeight

    int m_panelHeight

### m_panelWidth

    int m_panelWidth

### m_classifier

    Classifier m_classifier

### m_dataGenerator

    DataGenerator m_dataGenerator

### m_trainingData

    Instances m_trainingData

### m_xAttribute

    int m_xAttribute

### m_yAttribute

    int m_yAttribute

### m_pixHeight

    double m_pixHeight

### m_pixWidth

    double m_pixWidth

### m_minX

    double m_minX

### m_minY

    double m_minY

### m_maxX

    double m_maxX

### m_maxY

    double m_maxY

### m_numOfSamplesPerRegion

    int m_numOfSamplesPerRegion

### m_numOfSamplesPerGenerator

    int m_numOfSamplesPerGenerator

### m_samplesBase

    double m_samplesBase

### m_random

    java.util.Random m_random

### m_weightingAttsValues

    double[] m_weightingAttsValues

### m_attsToWeightOn

    boolean[] m_attsToWeightOn

### m_vals

    double[] m_vals

### m_dist

    double[] m_dist

### m_predInst

    Instance m_predInst

Class weka.gui.boundaryvisualizer.RemoteResult extends java.lang.Object implements Serializable

Serialized Fields

### m_rowNumber

    int m_rowNumber

### m_rowLength

    int m_rowLength

### m_probabilities

    double[][] m_probabilities

### m_percentCompleted

    int m_percentCompleted

Package weka.gui.experiment

Class weka.gui.experiment.AlgorithmListPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Exp

    Experiment m_Exp

  The experiment to set the algorithm list of

### m_List

    javax.swing.JList m_List

  The component displaying the algorithm list

### m_AddBut

    javax.swing.JButton m_AddBut

  Click to add an algorithm

### m_EditBut

    javax.swing.JButton m_EditBut

  Click to edit the selected algorithm

### m_DeleteBut

    javax.swing.JButton m_DeleteBut

  Click to remove the selected dataset from the list

### m_LoadOptionsBut

    javax.swing.JButton m_LoadOptionsBut

  Click to edit the load the options for athe selected algorithm

### m_SaveOptionsBut

    javax.swing.JButton m_SaveOptionsBut

  Click to edit the save the options from selected algorithm

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  The file chooser for selecting experiments

### m_XMLFilter

    javax.swing.filechooser.FileFilter m_XMLFilter

  A filter to ensure only experiment (in XML format) files get shown in the chooser

### m_Editing

    boolean m_Editing

  Whether an algorithm is added or only edited

### m_ClassifierEditor

    GenericObjectEditor m_ClassifierEditor

  Lets the user configure the classifier

### m_PD

    PropertyDialog m_PD

  The currently displayed property dialog, if any

### m_AlgorithmListModel

    javax.swing.DefaultListModel m_AlgorithmListModel

  The list model used

Class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer extends javax.swing.DefaultListCellRenderer implements Serializable

Class weka.gui.experiment.DatasetListPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Exp

    Experiment m_Exp

  The experiment to set the dataset list of

### m_List

    javax.swing.JList m_List

  The component displaying the dataset list

### m_AddBut

    javax.swing.JButton m_AddBut

  Click to add a dataset

### m_DeleteBut

    javax.swing.JButton m_DeleteBut

  Click to remove the selected dataset from the list

### m_relativeCheck

    javax.swing.JCheckBox m_relativeCheck

  Make file paths relative to the user (start) directory

### m_ArffFilter

    javax.swing.filechooser.FileFilter m_ArffFilter

  A filter to ensure only arff files get selected

### m_UserDir

    java.io.File m_UserDir

  The user (start) directory

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  The file chooser component

Class weka.gui.experiment.DistributeExperimentPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Exp

    RemoteExperiment m_Exp

  The experiment to configure.

### m_enableDistributedExperiment

    javax.swing.JCheckBox m_enableDistributedExperiment

  Distribute the current experiment to remote hosts

### m_configureHostNames

    javax.swing.JButton m_configureHostNames

  Popup the HostListPanel

### m_hostList

    HostListPanel m_hostList

  The host list panel

### m_splitByDataSet

    javax.swing.JRadioButton m_splitByDataSet

  Split experiment up by data set.

### m_splitByRun

    javax.swing.JRadioButton m_splitByRun

  Split experiment up by run number.

### m_radioListener

    java.awt.event.ActionListener m_radioListener

  Handle radio buttons

Class weka.gui.experiment.Experimenter extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_SetupPanel

    SetupModePanel 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

    javax.swing.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.ExperimenterDefaults extends java.lang.Object implements Serializable

Class weka.gui.experiment.GeneratorPropertyIteratorPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_ConfigureBut

    javax.swing.JButton m_ConfigureBut

  Click to select the property to iterate over

### m_StatusBox

    javax.swing.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 extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Exp

    RemoteExperiment m_Exp

  The remote experiment to set the host list of

### m_List

    javax.swing.JList m_List

  The component displaying the host list

### m_DeleteBut

    javax.swing.JButton m_DeleteBut

  Click to remove the selected host from the list

### m_HostField

    javax.swing.JTextField m_HostField

  The field with which to enter host names

Class weka.gui.experiment.OutputFormatDialog extends javax.swing.JDialog implements Serializable

Serialized Fields

### m_Result

    int m_Result

  the result of the user's action, either OK or CANCEL

### m_OutputFormatComboBox

    javax.swing.JComboBox m_OutputFormatComboBox

  lets the user choose the format for the output

### m_MeanPrecSpinner

    javax.swing.JSpinner m_MeanPrecSpinner

  the spinner to choose the precision for the mean from

### m_StdDevPrecSpinner

    javax.swing.JSpinner m_StdDevPrecSpinner

  the spinner to choose the precision for the std. deviation from

### m_OkButton

    javax.swing.JButton m_OkButton

  Click to activate the current set parameters

### m_CancelButton

    javax.swing.JButton m_CancelButton

  Click to cancel the dialog

### m_latexOutput

    boolean m_latexOutput

  Produce tables in latex format

### m_csvOutput

    boolean m_csvOutput

  Produce tables in csv format

### m_MeanPrec

    int m_MeanPrec

  the number of digits after the period (= precision) for printing the mean

### m_StdDevPrec

    int m_StdDevPrec

  the number of digits after the period (= precision) for printing the std. deviation

Class weka.gui.experiment.ResultsPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_FromFileBut

    javax.swing.JButton m_FromFileBut

  Click to load results from a file

### m_FromDBaseBut

    javax.swing.JButton m_FromDBaseBut

  Click to load results from a database

### m_FromExpBut

    javax.swing.JButton m_FromExpBut

  Click to get results from the destination given in the experiment

### m_FromLab

    javax.swing.JLabel m_FromLab

  Displays a message about the current result set

### m_DatasetModel

    javax.swing.DefaultComboBoxModel m_DatasetModel

  The model embedded in m_DatasetCombo

### m_CompareModel

    javax.swing.DefaultComboBoxModel m_CompareModel

  The model embedded in m_CompareCombo

### m_TestsModel

    javax.swing.DefaultListModel m_TestsModel

  The model embedded in m_TestsList

### m_DisplayedModel

    javax.swing.DefaultListModel m_DisplayedModel

  The model embedded in m_DisplayedList

### m_DatasetKeyLabel

    javax.swing.JLabel m_DatasetKeyLabel

  Displays the currently selected column names for the scheme & options

### m_DatasetKeyBut

    javax.swing.JButton m_DatasetKeyBut

  Click to edit the columns used to determine the scheme

### m_DatasetKeyModel

    javax.swing.DefaultListModel m_DatasetKeyModel

  Stores the list of attributes for selecting the scheme columns

### m_DatasetKeyList

    javax.swing.JList m_DatasetKeyList

  Displays the list of selected columns determining the scheme

### m_ResultKeyLabel

    javax.swing.JLabel m_ResultKeyLabel

  Displays the currently selected column names for the scheme & options

### m_ResultKeyBut

    javax.swing.JButton m_ResultKeyBut

  Click to edit the columns used to determine the scheme

### m_ResultKeyModel

    javax.swing.DefaultListModel m_ResultKeyModel

  Stores the list of attributes for selecting the scheme columns

### m_ResultKeyList

    javax.swing.JList m_ResultKeyList

  Displays the list of selected columns determining the scheme

### m_TestsButton

    javax.swing.JButton m_TestsButton

  Lets the user select which scheme to base comparisons against

### m_DisplayedButton

    javax.swing.JButton m_DisplayedButton

  Lets the user select which schemes are compared to base

### m_TestsList

    javax.swing.JList m_TestsList

  Holds the list of schemes to base the test against

### m_DisplayedList

    javax.swing.JList m_DisplayedList

  Holds the list of schemes to display

### m_CompareCombo

    javax.swing.JComboBox m_CompareCombo

  Lets the user select which performance measure to analyze

### m_SigTex

    javax.swing.JTextField m_SigTex

  Lets the user edit the test significance

### m_ShowStdDevs

    javax.swing.JCheckBox m_ShowStdDevs

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

### m_OutputFormatButton

    javax.swing.JButton m_OutputFormatButton

  lets the user choose the format for the output

### m_PerformBut

    javax.swing.JButton m_PerformBut

  Click to start the test

### m_SaveOutBut

    javax.swing.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

    javax.swing.JTextArea m_OutText

  Displays the output of tests

### m_History

    ResultHistoryPanel m_History

  A panel controlling results viewing

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  The file chooser for selecting result files

### m_csvFileFilter

    ExtensionFileFilter m_csvFileFilter

  File filters for various file types

### m_arffFileFilter

    ExtensionFileFilter m_arffFileFilter

### 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

    java.lang.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

### COMBO_SIZE

    java.awt.Dimension COMBO_SIZE

### m_latexOutput

    boolean m_latexOutput

  Produce tables in latex format

### m_csvOutput

    boolean m_csvOutput

  Produce tables in csv format

### m_MeanPrec

    int m_MeanPrec

  the number of digits after the period (= precision) for printing the mean

### m_StdDevPrec

    int m_StdDevPrec

  the number of digits after the period (= precision) for printing the std. deviation

Class weka.gui.experiment.RunNumberPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_LowerText

    javax.swing.JTextField m_LowerText

  Configures the lower run number

### m_UpperText

    javax.swing.JTextField m_UpperText

  Configures the upper run number

### m_Exp

    Experiment m_Exp

  The experiment being configured

Class weka.gui.experiment.RunPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_StartBut

    javax.swing.JButton m_StartBut

  Click to start running the experiment

### m_StopBut

    javax.swing.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

    java.lang.Thread m_RunThread

  The thread running the experiment

### m_ResultsPanel

    ResultsPanel m_ResultsPanel

  A pointer to the results panel

Class weka.gui.experiment.SetupModePanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_SimpleSetupRBut

    javax.swing.JRadioButton m_SimpleSetupRBut

  The button for choosing simple setup mode

### m_AdvancedSetupRBut

    javax.swing.JRadioButton m_AdvancedSetupRBut

  The button for choosing advanced setup mode

### m_simplePanel

    SimpleSetupPanel m_simplePanel

  The simple setup panel

### m_advancedPanel

    SetupPanel m_advancedPanel

  The advanced setup panel

Class weka.gui.experiment.SetupPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Exp

    Experiment m_Exp

  The experiment being configured

### m_OpenBut

    javax.swing.JButton m_OpenBut

  Click to load an experiment

### m_SaveBut

    javax.swing.JButton m_SaveBut

  Click to save an experiment

### m_NewBut

    javax.swing.JButton m_NewBut

  Click to create a new experiment with default settings

### m_ExpFilter

    javax.swing.filechooser.FileFilter m_ExpFilter

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

### m_KOMLFilter

    javax.swing.filechooser.FileFilter m_KOMLFilter

  A filter to ensure only experiment (in KOML format) files get shown in the chooser

### m_XMLFilter

    javax.swing.filechooser.FileFilter m_XMLFilter

  A filter to ensure only experiment (in XML format) files get shown in the chooser

### m_FileChooser

    javax.swing.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_NotesButton

    javax.swing.JButton m_NotesButton

  A button for bringing up the notes

### m_NotesFrame

    javax.swing.JFrame m_NotesFrame

  Frame for the notes

### m_NotesText

    javax.swing.JTextArea m_NotesText

  Area for user notes Default of 10 rows

### m_Support

    java.beans.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

    javax.swing.JRadioButton m_advanceDataSetFirst

  Click to advacne data set before custom generator

### m_advanceIteratorFirst

    javax.swing.JRadioButton m_advanceIteratorFirst

  Click to advance custom generator before data set

### m_RadioListener

    java.awt.event.ActionListener m_RadioListener

  Handle radio buttons

Class weka.gui.experiment.SimpleSetupPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Exp

    Experiment m_Exp

  The experiment being configured

### m_modePanel

    SetupModePanel m_modePanel

  The panel which switched between simple and advanced setup modes

### m_destinationDatabaseURL

    java.lang.String m_destinationDatabaseURL

  The database destination URL to store results into

### m_destinationFilename

    java.lang.String m_destinationFilename

  The filename to store results into

### m_numFolds

    int m_numFolds

  The number of folds for a cross-validation experiment

### m_trainPercent

    double m_trainPercent

  The training percentage for a train/test split experiment

### m_numRepetitions

    int m_numRepetitions

  The number of times to repeat the sub-experiment

### m_userHasBeenAskedAboutConversion

    boolean m_userHasBeenAskedAboutConversion

  Whether or not the user has consented for the experiment to be simplified

### m_csvFileFilter

    ExtensionFileFilter m_csvFileFilter

  Filter for choosing CSV files

### m_arffFileFilter

    ExtensionFileFilter m_arffFileFilter

  FIlter for choosing ARFF files

### m_OpenBut

    javax.swing.JButton m_OpenBut

  Click to load an experiment

### m_SaveBut

    javax.swing.JButton m_SaveBut

  Click to save an experiment

### m_NewBut

    javax.swing.JButton m_NewBut

  Click to create a new experiment with default settings

### m_ExpFilter

    javax.swing.filechooser.FileFilter m_ExpFilter

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

### m_KOMLFilter

    javax.swing.filechooser.FileFilter m_KOMLFilter

  A filter to ensure only experiment (in KOML format) files get shown in the chooser

### m_XMLFilter

    javax.swing.filechooser.FileFilter m_XMLFilter

  A filter to ensure only experiment (in XML format) files get shown in the chooser

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  The file chooser for selecting experiments

### m_DestFileChooser

    javax.swing.JFileChooser m_DestFileChooser

  The file chooser for selecting result destinations

### m_ResultsDestinationCBox

    javax.swing.JComboBox m_ResultsDestinationCBox

  Combo box for choosing experiment destination type

### m_ResultsDestinationPathLabel

    javax.swing.JLabel m_ResultsDestinationPathLabel

  Label for destination field

### m_ResultsDestinationPathTField

    javax.swing.JTextField m_ResultsDestinationPathTField

  Input field for result destination path

### m_BrowseDestinationButton

    javax.swing.JButton m_BrowseDestinationButton

  Button for browsing destination files

### m_ExperimentTypeCBox

    javax.swing.JComboBox m_ExperimentTypeCBox

  Combo box for choosing experiment type

### m_ExperimentParameterLabel

    javax.swing.JLabel m_ExperimentParameterLabel

  Label for parameter field

### m_ExperimentParameterTField

    javax.swing.JTextField m_ExperimentParameterTField

  Input field for experiment parameter

### m_ExpClassificationRBut

    javax.swing.JRadioButton m_ExpClassificationRBut

  Radio button for choosing classification experiment

### m_ExpRegressionRBut

    javax.swing.JRadioButton m_ExpRegressionRBut

  Radio button for choosing regression experiment

### m_NumberOfRepetitionsTField

    javax.swing.JTextField m_NumberOfRepetitionsTField

  Input field for number of repetitions

### m_OrderDatasetsFirstRBut

    javax.swing.JRadioButton m_OrderDatasetsFirstRBut

  Radio button for choosing datasets first in order of execution

### m_OrderAlgorithmsFirstRBut

    javax.swing.JRadioButton m_OrderAlgorithmsFirstRBut

  Radio button for choosing algorithms first in order of execution

### m_DatasetListPanel

    DatasetListPanel m_DatasetListPanel

  The panel for configuring selected datasets

### m_AlgorithmListPanel

    AlgorithmListPanel m_AlgorithmListPanel

  The panel for configuring selected algorithms

### m_NotesButton

    javax.swing.JButton m_NotesButton

  A button for bringing up the notes

### m_NotesFrame

    javax.swing.JFrame m_NotesFrame

  Frame for the notes

### m_NotesText

    javax.swing.JTextArea m_NotesText

  Area for user notes Default of 10 rows

### m_Support

    java.beans.PropertyChangeSupport m_Support

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

Package weka.gui.explorer

Class weka.gui.explorer.AssociationsPanel extends javax.swing.JPanel 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

    javax.swing.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

    javax.swing.JButton m_StartBut

  Click to start running the associator

### m_StopBut

    javax.swing.JButton m_StopBut

  Click to stop a running associator

### 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

    java.lang.Thread m_RunThread

  A thread that associator runs in

Class weka.gui.explorer.AttributeSelectionPanel extends javax.swing.JPanel 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

    javax.swing.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

    javax.swing.JComboBox m_ClassCombo

  Lets the user select the class column

### m_CVBut

    javax.swing.JRadioButton m_CVBut

  Click to set evaluation mode to cross-validation

### m_TrainBut

    javax.swing.JRadioButton m_TrainBut

  Click to set test mode to test on training data

### m_CVLab

    javax.swing.JLabel m_CVLab

  Label by where the cv folds are entered

### m_CVText

    javax.swing.JTextField m_CVText

  The field where the cv folds are entered

### m_SeedLab

    javax.swing.JLabel m_SeedLab

  Label by where cv random seed is entered

### m_SeedText

    javax.swing.JTextField m_SeedText

  The field where the seed value is entered

### m_RadioListener

    java.awt.event.ActionListener m_RadioListener

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

### m_StartBut

    javax.swing.JButton m_StartBut

  Click to start running the attribute selector

### m_StopBut

    javax.swing.JButton m_StopBut

  Click to stop a running classifier

### COMBO_SIZE

    java.awt.Dimension COMBO_SIZE

  Stop the class combo from taking up to much space

### m_Instances

    Instances m_Instances

  The main set of instances we're playing with

### m_RunThread

    java.lang.Thread m_RunThread

  A thread that attribute selection runs in

Class weka.gui.explorer.ClassifierPanel extends javax.swing.JPanel 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

    javax.swing.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

    javax.swing.JComboBox m_ClassCombo

  Lets the user select the class column

### m_CVBut

    javax.swing.JRadioButton m_CVBut

  Click to set test mode to cross-validation

### m_PercentBut

    javax.swing.JRadioButton m_PercentBut

  Click to set test mode to generate a % split

### m_TrainBut

    javax.swing.JRadioButton m_TrainBut

  Click to set test mode to test on training data

### m_TestSplitBut

    javax.swing.JRadioButton m_TestSplitBut

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

### m_StorePredictionsBut

    javax.swing.JCheckBox m_StorePredictionsBut

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

### m_OutputModelBut

    javax.swing.JCheckBox m_OutputModelBut

  Check to output the model built from the training data

### m_OutputPerClassBut

    javax.swing.JCheckBox m_OutputPerClassBut

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

### m_OutputConfusionBut

    javax.swing.JCheckBox m_OutputConfusionBut

  Check to output a confusion matrix

### m_OutputEntropyBut

    javax.swing.JCheckBox m_OutputEntropyBut

  Check to output entropy statistics

### m_OutputPredictionsTextBut

    javax.swing.JCheckBox m_OutputPredictionsTextBut

  Check to output text predictions

### m_EvalWRTCostsBut

    javax.swing.JCheckBox m_EvalWRTCostsBut

  Check to evaluate w.r.t a cost matrix

### m_SetCostsBut

    javax.swing.JButton m_SetCostsBut

### m_CVLab

    javax.swing.JLabel m_CVLab

  Label by where the cv folds are entered

### m_CVText

    javax.swing.JTextField m_CVText

  The field where the cv folds are entered

### m_PercentLab

    javax.swing.JLabel m_PercentLab

  Label by where the % split is entered

### m_PercentText

    javax.swing.JTextField m_PercentText

  The field where the % split is entered

### m_SetTestBut

    javax.swing.JButton m_SetTestBut

  The button used to open a separate test dataset

### m_SetTestFrame

    javax.swing.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

    java.awt.event.ActionListener m_RadioListener

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

### m_MoreOptions

    javax.swing.JButton m_MoreOptions

  Button for further output/visualize options

### m_RandomSeedText

    javax.swing.JTextField m_RandomSeedText

  User specified random seed for cross validation or % split

### m_RandomLab

    javax.swing.JLabel m_RandomLab

### m_StartBut

    javax.swing.JButton m_StartBut

  Click to start running the classifier

### m_StopBut

    javax.swing.JButton m_StopBut

  Click to stop a running classifier

### COMBO_SIZE

    java.awt.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_RunThread

    java.lang.Thread m_RunThread

  A thread that classification runs in

### m_CurrentVis

    VisualizePanel m_CurrentVis

  The current visualization object

### m_Summary

    InstancesSummaryPanel m_Summary

  The instances summary panel displayed by m_SetTestFrame

### m_ModelFilter

    javax.swing.filechooser.FileFilter m_ModelFilter

  Filter to ensure only model files are selected

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  The file chooser for selecting model files

Class weka.gui.explorer.ClustererPanel extends javax.swing.JPanel 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

    javax.swing.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

    javax.swing.JRadioButton m_PercentBut

  Click to set test mode to generate a % split

### m_TrainBut

    javax.swing.JRadioButton m_TrainBut

  Click to set test mode to test on training data

### m_TestSplitBut

    javax.swing.JRadioButton m_TestSplitBut

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

### m_ClassesToClustersBut

    javax.swing.JRadioButton m_ClassesToClustersBut

  Click to set test mode to classes to clusters based evaluation

### m_ClassCombo

    javax.swing.JComboBox m_ClassCombo

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

### m_PercentLab

    javax.swing.JLabel m_PercentLab

  Label by where the % split is entered

### m_PercentText

    javax.swing.JTextField m_PercentText

  The field where the % split is entered

### m_SetTestBut

    javax.swing.JButton m_SetTestBut

  The button used to open a separate test dataset

### m_SetTestFrame

    javax.swing.JFrame m_SetTestFrame

  The frame used to show the test set selection panel

### m_ignoreBut

    javax.swing.JButton m_ignoreBut

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

### m_ignoreKeyModel

    javax.swing.DefaultListModel m_ignoreKeyModel

### m_ignoreKeyList

    javax.swing.JList m_ignoreKeyList

### m_RadioListener

    java.awt.event.ActionListener m_RadioListener

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

### m_StartBut

    javax.swing.JButton m_StartBut

  Click to start running the clusterer

### COMBO_SIZE

    java.awt.Dimension COMBO_SIZE

  Stop the class combo from taking up to much space

### m_StopBut

    javax.swing.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_CurrentVis

    VisualizePanel m_CurrentVis

  The current visualization object

### m_StorePredictionsBut

    javax.swing.JCheckBox m_StorePredictionsBut

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

### m_RunThread

    java.lang.Thread m_RunThread

  A thread that clustering runs in

### m_Summary

    InstancesSummaryPanel m_Summary

  The instances summary panel displayed by m_SetTestFrame

### m_ModelFilter

    javax.swing.filechooser.FileFilter m_ModelFilter

  Filter to ensure only model files are selected

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  The file chooser for selecting model files

Class weka.gui.explorer.Explorer extends javax.swing.JPanel 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

    MatrixPanel m_VisualizePanel

  Label for a panel that still need to be implemented

### m_TabbedPane

    javax.swing.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 extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_InstSummaryPanel

    InstancesSummaryPanel m_InstSummaryPanel

  Displays simple stats on the working instances

### m_OpenFileBut

    javax.swing.JButton m_OpenFileBut

  Click to load base instances from a file

### m_OpenURLBut

    javax.swing.JButton m_OpenURLBut

  Click to load base instances from a URL

### m_OpenDBBut

    javax.swing.JButton m_OpenDBBut

  Click to load base instances from a Database

### m_DatabaseQueryEditor

    GenericObjectEditor m_DatabaseQueryEditor

  Lets the user enter a DB query

### m_UndoBut

    javax.swing.JButton m_UndoBut

  Click to revert back to the last saved point

### m_EditBut

    javax.swing.JButton m_EditBut

  Click to open the current instances in a viewer

### m_SaveBut

    javax.swing.JButton m_SaveBut

  Click to apply filters and save the results

### m_AttPanel

    AttributeSelectionPanel m_AttPanel

  Panel to let the user toggle attributes

### m_RemoveButton

    javax.swing.JButton m_RemoveButton

  Button for removing attributes

### m_AttSummaryPanel

    AttributeSummaryPanel m_AttSummaryPanel

  Displays summary stats on the selected attribute

### m_FilterEditor

    GenericObjectEditor m_FilterEditor

  Lets the user configure the filter

### m_FilterPanel

    PropertyPanel m_FilterPanel

  Filter configuration

### m_ApplyFilterBut

    javax.swing.JButton m_ApplyFilterBut

  Click to apply filters and save the results

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  The file chooser for selecting arff files

### m_bsiFileFilter

    ExtensionFileFilter m_bsiFileFilter

  File filters for various file types

### m_c45FileFilter

    ExtensionFileFilter m_c45FileFilter

### m_csvFileFilter

    ExtensionFileFilter m_csvFileFilter

### m_arffFileFilter

    ExtensionFileFilter m_arffFileFilter

### m_LastURL

    java.lang.String m_LastURL

  Stores the last URL that instances were loaded from

### m_SQLQ

    java.lang.String m_SQLQ

  Stores the last sql query executed

### m_Instances

    Instances m_Instances

  The working instances

### m_AttVisualizePanel

    AttributeVisualizationPanel m_AttVisualizePanel

  The visualization of the attribute values

### m_tempUndoFiles

    java.io.File[] m_tempUndoFiles

  Keeps track of undo points

### m_tempUndoIndex

    int m_tempUndoIndex

  The next available slot for an undo point

### m_Support

    java.beans.PropertyChangeSupport m_Support

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

### m_IOThread

    java.lang.Thread m_IOThread

  A thread for loading/saving instances from a file or URL

### m_Log

    Logger m_Log

  The message logger

Package weka.gui.graphvisualizer

Class weka.gui.graphvisualizer.BIFFormatException extends java.lang.Exception implements Serializable

Class weka.gui.graphvisualizer.GraphVisualizer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_nodes

    FastVector m_nodes

  Vector containing nodes

### m_edges

    FastVector m_edges

  Vector containing edges

### m_le

    LayoutEngine m_le

  The current LayoutEngine

### m_gp

    weka.gui.graphvisualizer.GraphVisualizer.GraphPanel m_gp

  Panel actually displaying the graph

### graphID

    java.lang.String graphID

  String containing graph's name

### m_jBtSave

    javax.swing.JButton m_jBtSave

  Save button to save the current graph in DOT or XMLBIF format. The graph should be layed out again to get the original form if reloaded from command line, as the formats do not allow saving specific information for a properly layed out graph.

### ICONPATH

    java.lang.String ICONPATH

  path for icons

### fm

    java.awt.FontMetrics fm

### scale

    double scale

### nodeHeight

    int nodeHeight

### nodeWidth

    int nodeWidth

### paddedNodeWidth

    int paddedNodeWidth

### jTfNodeWidth

    javax.swing.JTextField jTfNodeWidth

  TextField for node's width

### jTfNodeHeight

    javax.swing.JTextField jTfNodeHeight

  TextField for nodes height

### jBtLayout

    javax.swing.JButton jBtLayout

  Button for laying out the graph again, necessary after changing node's size or some other property of the layout engine

### maxStringWidth

    int maxStringWidth

  used for setting appropriate node size

### zoomPercents

    int[] zoomPercents

  used when using zoomIn and zoomOut buttons

### m_js

    javax.swing.JScrollPane m_js

  this contains the m_gp GraphPanel

Class weka.gui.graphvisualizer.LayoutCompleteEvent extends java.util.EventObject implements Serializable

Package weka.gui.streams

Class weka.gui.streams.InstanceCounter extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Count_Lab

    javax.swing.JLabel m_Count_Lab

### m_Count

    int m_Count

### m_Debug

    boolean m_Debug

Class weka.gui.streams.InstanceEvent extends java.util.EventObject implements Serializable

Serialized Fields

### m_ID

    int m_ID

Class weka.gui.streams.InstanceJoiner extends java.lang.Object implements Serializable

Serialized Fields

### listeners

    java.util.Vector<E> 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 extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Listeners

    java.util.Vector<E> m_Listeners

### m_LoaderThread

    java.lang.Thread m_LoaderThread

### m_OutputInstance

    Instance m_OutputInstance

### m_OutputInstances

    Instances m_OutputInstances

### m_Debug

    boolean m_Debug

### m_StartBut

    javax.swing.JButton m_StartBut

### m_FileNameTex

    javax.swing.JTextField m_FileNameTex

Class weka.gui.streams.InstanceSavePanel extends java.awt.Panel implements Serializable

Serialized Fields

### count_Lab

    java.awt.Label count_Lab

### m_Count

    int m_Count

### arffFile_Tex

    java.awt.TextField arffFile_Tex

### b_Debug

    boolean b_Debug

### outputWriter

    java.io.PrintWriter outputWriter

Class weka.gui.streams.InstanceTable extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_InstanceTable

    javax.swing.JTable m_InstanceTable

### m_Debug

    boolean m_Debug

### m_Clear

    boolean m_Clear

### m_UpdateString

    java.lang.String m_UpdateString

### m_Instances

    Instances m_Instances

Class weka.gui.streams.InstanceViewer extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_OutputTex

    javax.swing.JTextArea m_OutputTex

### m_Debug

    boolean m_Debug

### m_Clear

    boolean m_Clear

### m_UpdateString

    java.lang.String m_UpdateString

Package weka.gui.treevisualizer

Class weka.gui.treevisualizer.TreeVisualizer extends PrintablePanel 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

    java.awt.Dimension m_viewPos

  The postion of the view relative to the tree.

### m_viewSize

    java.awt.Dimension m_viewSize

  The size of the tree in pixels.

### m_currentFont

    java.awt.Font m_currentFont

  The font used to display the tree.

### m_fontSize

    java.awt.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

    javax.swing.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

    java.awt.Dimension m_oldMousePos

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

### m_newMousePos

    java.awt.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

    java.awt.Dimension m_nViewPos

  A variable used to remember the desired view pos.

### m_nViewSize

    java.awt.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

    javax.swing.JPopupMenu m_winMenu

  A right (or middle) click popup menu.

### m_topN

    javax.swing.JMenuItem m_topN

  An option on the win_menu

### m_fitToScreen

    javax.swing.JMenuItem m_fitToScreen

  An option on the win_menu

### m_autoScale

    javax.swing.JMenuItem m_autoScale

  An option on the win_menu

### m_selectFont

    javax.swing.JMenu m_selectFont

  A ub group on the win_menu

### m_selectFontGroup

    javax.swing.ButtonGroup m_selectFontGroup

  A grouping for the font choices

### m_size24

    javax.swing.JRadioButtonMenuItem m_size24

  A font choice.

### m_size22

    javax.swing.JRadioButtonMenuItem m_size22

  A font choice.

### m_size20

    javax.swing.JRadioButtonMenuItem m_size20

  A font choice.

### m_size18

    javax.swing.JRadioButtonMenuItem m_size18

  A font choice.

### m_size16

    javax.swing.JRadioButtonMenuItem m_size16

  A font choice.

### m_size14

    javax.swing.JRadioButtonMenuItem m_size14

  A font choice.

### m_size12

    javax.swing.JRadioButtonMenuItem m_size12

  A font choice.

### m_size10

    javax.swing.JRadioButtonMenuItem m_size10

  A font choice.

### m_size8

    javax.swing.JRadioButtonMenuItem m_size8

  A font choice.

### m_size6

    javax.swing.JRadioButtonMenuItem m_size6

  A font choice.

### m_size4

    javax.swing.JRadioButtonMenuItem m_size4

  A font choice.

### m_size2

    javax.swing.JRadioButtonMenuItem m_size2

  A font choice.

### m_size1

    javax.swing.JRadioButtonMenuItem m_size1

  A font choice.

### m_accept

    javax.swing.JMenuItem m_accept

  An option on the win menu.

### m_nodeMenu

    javax.swing.JPopupMenu m_nodeMenu

  A right or middle click popup menu for nodes.

### m_visualise

    javax.swing.JMenuItem m_visualise

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

### m_addChildren

    javax.swing.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

    javax.swing.JMenuItem m_remChildren

  Similar to add children but now it removes children.

### m_classifyChild

    javax.swing.JMenuItem m_classifyChild

  Use this to have J48 classify this node.

### m_sendInstances

    javax.swing.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

    javax.swing.JTextField m_searchString

### m_searchWin

    javax.swing.JDialog m_searchWin

### m_caseSen

    javax.swing.JRadioButton m_caseSen

Package weka.gui.visualize

Class weka.gui.visualize.AttributePanel extends javax.swing.JScrollPane 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

    java.awt.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

    javax.swing.JPanel m_span

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

### m_barColour

    java.awt.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 extends javax.swing.JPanel 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 extends javax.swing.JPanel 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

### 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

### m_labelMetrics

    java.awt.FontMetrics m_labelMetrics

  Font metrics

### m_labelFont

    java.awt.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_ColourChangeListeners

    FastVector m_ColourChangeListeners

  An optional list of listeners who want to know when a colour changes. Listeners are notified via an ActionEvent

### m_DefaultColors

    java.awt.Color[] m_DefaultColors

  default colours for colouring discrete class

Class weka.gui.visualize.LegendPanel extends javax.swing.JScrollPane implements Serializable

Serialized Fields

### m_plots

    FastVector m_plots

  the list of plot elements

### m_span

    javax.swing.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 extends javax.swing.JPanel 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

    javax.swing.JLabel m_legendText

  the text part of this legend

### m_pointShape

    javax.swing.JPanel m_pointShape

  displays the point shape associated with this legend entry

Class weka.gui.visualize.MatrixPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_plotsPanel

    weka.gui.visualize.MatrixPanel.Plot m_plotsPanel

  The that panel contains the actual matrix

### m_cp

    ClassPanel m_cp

  The panel that displays the legend of the colouring attribute

### optionsPanel

    javax.swing.JPanel optionsPanel

  The panel that contains all the buttons and tools, i.e. resize, jitter bars and sub-sampling buttons etc on the bottom of the panel

### jp

    javax.swing.JSplitPane jp

  Split pane for splitting the matrix and the buttons and bars

### m_updateBt

    javax.swing.JButton m_updateBt

  The button that updates the display to reflect the changes made by the user. E.g. changed attribute set for the matrix

### m_selAttrib

    javax.swing.JButton m_selAttrib

  The button to display a window to select attributes

### m_data

    Instances m_data

  The dataset for which this panel will display the plot matrix for

### m_attribList

    javax.swing.JList m_attribList

  The list for selecting the attributes to display the plot matrix

### m_js

    javax.swing.JScrollPane m_js

  The scroll pane to scrolling the matrix

### m_classAttrib

    javax.swing.JComboBox m_classAttrib

  The combo box to allow user to select the colouring attribute

### m_plotSize

    javax.swing.JSlider m_plotSize

  The slider to adjust the size of the cells in the matrix

### m_pointSize

    javax.swing.JSlider m_pointSize

  The slider to adjust the size of the datapoints

### m_jitter

    javax.swing.JSlider m_jitter

  The slider to add jitter to the plots

### rnd

    java.util.Random rnd

  For adding random jitter

### jitterVals

    int[][] jitterVals

  Array containing precalculated jitter values

### datapointSize

    int datapointSize

  This stores the size of the datapoint

### m_resamplePercent

    javax.swing.JTextField m_resamplePercent

  The text area for percentage to resample data

### m_resampleBt

    javax.swing.JButton m_resampleBt

  The label for resample percentage

### m_rseed

    javax.swing.JTextField m_rseed

  Random seed for random subsample

### m_plotSizeLb

    javax.swing.JLabel m_plotSizeLb

  Displays the current size beside the slider bar for cell size

### m_pointSizeLb

    javax.swing.JLabel m_pointSizeLb

  Displays the current size beside the slider bar for point size

### m_selectedAttribs

    int[] m_selectedAttribs

  This array contains the indices of the attributes currently selected

### m_classIndex

    int m_classIndex

  This contains the index of the currently selected colouring attribute

### m_points

    int[][] m_points

  This is a local array cache for all the instance values for faster rendering

### m_pointColors

    int[] m_pointColors

  This is an array cache for the colour of each of the instances depending on the colouring attribute. If the colouring attribute is nominal then it contains the index of the colour in our colour list. Otherwise, for numeric colouring attribute, it contains the precalculated red component for each instance's colour

### m_missing

    boolean[][] m_missing

  Contains true for each attribute value (only the selected attributes+class attribute) that is missing, for each instance. m_missing[i][j] == true if m_selectedAttribs[j] is missing in instance i. m_missing[i][m_missing[].length-1] == true if class value is missing in instance i.

### m_type

    int[] m_type

  This array contains for the classAttribute:
  m_type[0] = [type of attribute, nominal, string or numeric]
  m_type[1] = [number of discrete values of nominal or string attribute
  or same as m_type[0] for numeric attribute]

### m_plotLBSizeD

    java.awt.Dimension m_plotLBSizeD

  Stores the maximum size for PlotSize label to keep it's size constant

### m_pointLBSizeD

    java.awt.Dimension m_pointLBSizeD

  Stores the maximum size for PointSize label to keep it's size constant

### m_colorList

    FastVector m_colorList

  Contains discrete colours for colouring for nominal attributes

### fontColor

    java.awt.Color fontColor

  color for the font used in column and row names

### f

    java.awt.Font f

  font used in column and row names

Class weka.gui.visualize.Plot2D extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_axisColour

    java.awt.Color m_axisColour

  Default colour for the axis

### m_backgroundColour

    java.awt.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

    java.lang.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

    javax.swing.JFrame m_InstanceInfo

  For popping up text info on data points

### m_InstanceInfoText

    javax.swing.JTextArea m_InstanceInfoText

### m_colorList

    FastVector m_colorList

  The list of the colors used

### m_DefaultColors

    java.awt.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

### m_tickSize

    int m_tickSize

  Tick size

### 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

    java.awt.Font m_labelFont

  Font for labels

### m_labelMetrics

    java.awt.FontMetrics m_labelMetrics

### m_JitterVal

    int m_JitterVal

  the level of jitter

### m_JRand

    java.util.Random m_JRand

  random values for perterbing the data points

### m_pointLookup

    double[][] m_pointLookup

  lookup table for plotted points

Class weka.gui.visualize.PrintablePanel extends javax.swing.JPanel implements Serializable

Serialized Fields

### m_Printer

    PrintableComponent m_Printer

  the class responsible for printing

Class weka.gui.visualize.ThresholdVisualizePanel extends VisualizePanel implements Serializable

Serialized Fields

### m_ROCString

    java.lang.String m_ROCString

  The string to add to the Plot Border.

### m_savePanelBorderText

    java.lang.String m_savePanelBorderText

  Original border text

Class weka.gui.visualize.VisualizePanel extends PrintablePanel implements Serializable

Serialized Fields

### m_DefaultColors

    java.awt.Color[] m_DefaultColors

  default colours for colouring discrete class

### m_XCombo

    javax.swing.JComboBox m_XCombo

  Lets the user select the attribute for the x axis

### m_YCombo

    javax.swing.JComboBox m_YCombo

  Lets the user select the attribute for the y axis

### m_ColourCombo

    javax.swing.JComboBox m_ColourCombo

  Lets the user select the attribute to use for colouring

### m_ShapeCombo

    javax.swing.JComboBox m_ShapeCombo

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

### m_submit

    javax.swing.JButton m_submit

  Button for the user to enter the splits.

### m_cancel

    javax.swing.JButton m_cancel

  Button for the user to remove all splits.

### m_saveBut

    javax.swing.JButton m_saveBut

  Button for the user to save the visualized set of instances

### COMBO_SIZE

    java.awt.Dimension COMBO_SIZE

  Stop the combos from growing out of control

### m_FileChooser

    javax.swing.JFileChooser m_FileChooser

  file chooser for saving instances

### m_ArffFilter

    javax.swing.filechooser.FileFilter m_ArffFilter

  Filter to ensure only arff files are selected

### m_JitterLab

    javax.swing.JLabel m_JitterLab

  Label for the jitter slider

### m_Jitter

    javax.swing.JSlider m_Jitter

  The jitter slider

### m_plot

    weka.gui.visualize.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

    javax.swing.JPanel m_plotSurround

  Panel that surrounds the plot panel with a titled border

### m_classSurround

    javax.swing.JPanel m_classSurround

  Panel that surrounds the class panel with a titled border

### listener

    java.awt.event.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

    java.lang.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

    java.lang.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

    java.lang.String m_preferredYDimension

### m_preferredColourDimension

    java.lang.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 extends PrintablePanel 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

### m_tickSize

    int m_tickSize

  Tick size

### 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

    java.awt.Dimension m_newMousePos

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

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