Serialized Form

Package weka.associations
- Class weka.associations.AbstractAssociator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -3017644543382432070L
- Class weka.associations.Apriori extends AbstractAssociator implements Serializable
  
  serialVersionUID:
  
  3277498842319212687L
  - 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
```
      Remove columns with all missing values
    - m_verbose
```
boolean m_verbose
```
      Report progress iteratively
    - m_onlyClass
```
Instances m_onlyClass
```
      Only the class attribute of all Instances.
    - m_classIndex
```
int m_classIndex
```
      The class index.
    - m_car
```
boolean m_car
```
      Flag indicating whether class association rules are mined.
- Class weka.associations.AprioriItemSet extends ItemSet implements Serializable
  
  serialVersionUID:
  
  7684467755712672058L
- Class weka.associations.CaRuleGeneration extends RuleGeneration implements Serializable
  
  serialVersionUID:
  
  3065752149646517703L
- Class weka.associations.FilteredAssociator extends SingleAssociatorEnhancer implements Serializable
  
  serialVersionUID:
  
  -4523450618538717400L
  - Serialized Fields
    - m_Filter
```
Filter m_Filter
```
      The filter
    - m_FilteredInstances
```
Instances m_FilteredInstances
```
      The instance structure of the filtered instances
    - m_ClassIndex
```
int m_ClassIndex
```
      The class index.
- Class weka.associations.FPGrowth extends AbstractAssociator implements Serializable
  
  serialVersionUID:
  
  3620717108603442911L
  - Serialized Fields
    - m_numRulesToFind
```
int m_numRulesToFind
```
      The number of rules to find
    - m_upperBoundMinSupport
```
double m_upperBoundMinSupport
```
      The upper bound on the minimum support
    - m_lowerBoundMinSupport
```
double m_lowerBoundMinSupport
```
      The lower bound on minimum support
    - m_delta
```
double m_delta
```
      The amount by which to decrease the support in each iteration
    - m_findAllRulesForSupportLevel
```
boolean m_findAllRulesForSupportLevel
```
      If true, just all rules meeting the lower bound on the minimum support will be found. The number of rules to find will be ignored and the iterative reduction of support will not be done.
    - m_positiveIndex
```
int m_positiveIndex
```
      The index (1 based) of binary attributes to treat as the positive value
    - m_metric
```
FPGrowth.AssociationRule.METRIC_TYPE m_metric
```
    - m_metricThreshold
```
double m_metricThreshold
```
    - m_largeItemSets
```
weka.associations.FPGrowth.FrequentItemSets m_largeItemSets
```
      Holds the large item sets found
    - m_rules
```
java.util.List<E> m_rules
```
      Holds the rules
    - m_maxItems
```
int m_maxItems
```
    - m_transactionsMustContain
```
java.lang.String m_transactionsMustContain
```
      If set, limit the transactions (instances) input to the algorithm to those that contain these items
    - m_mustContainOR
```
boolean m_mustContainOR
```
      Use OR rather than AND when considering must contain lists
    - m_rulesMustContain
```
java.lang.String m_rulesMustContain
```
      If set, then only output rules containing these itmes
- Class weka.associations.FPGrowth.AssociationRule extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -661269018702294489L
  - Serialized Fields
    - m_metricType
```
FPGrowth.AssociationRule.METRIC_TYPE m_metricType
```
      The metric type for this rule
    - m_premise
```
java.util.Collection<E> m_premise
```
      The premise of the rule
    - m_consequence
```
java.util.Collection<E> m_consequence
```
      The consequence of the rule
    - m_premiseSupport
```
int m_premiseSupport
```
      The support for the premise
    - m_consequenceSupport
```
int m_consequenceSupport
```
      The support for the consequence
    - m_totalSupport
```
int m_totalSupport
```
      The total support for the item set (premise + consequence)
    - m_totalTransactions
```
int m_totalTransactions
```
      The total number of transactions in the data
- Class weka.associations.FPGrowth.BinaryItem extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -3372941834914147669L
  - Serialized Fields
    - m_frequency
```
int m_frequency
```
      The frequency of the item
    - m_attribute
```
Attribute m_attribute
```
      The attribute that the item corresponds to
    - m_valueIndex
```
int m_valueIndex
```
      The index of the value considered to be positive
- Class weka.associations.FPGrowth.FPTreeNode extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4396315323673737660L
  - Serialized Fields
    - m_levelSibling
```
weka.associations.FPGrowth.FPTreeNode m_levelSibling
```
      link to another sibling at this level in the tree
    - m_parent
```
weka.associations.FPGrowth.FPTreeNode m_parent
```
      link to the parent node
    - m_item
```
FPGrowth.BinaryItem m_item
```
      item at this node
    - m_ID
```
int m_ID
```
      ID (for graphing the tree)
    - m_children
```
java.util.Map<K,V> m_children
```
      the children of this node
    - m_projectedCounts
```
weka.associations.FPGrowth.ShadowCounts m_projectedCounts
```
      counts associated with projected versions of this node
- Class weka.associations.FPGrowth.FPTreeRoot.Header extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6583156284891368909L
  - Serialized Fields
    - m_headerList
```
java.util.List<E> m_headerList
```
      The list of pointers into the tree structure
    - m_projectedHeaderCounts
```
weka.associations.FPGrowth.ShadowCounts m_projectedHeaderCounts
```
      Projected header counts for this entry
- Class weka.associations.FPGrowth.FrequentBinaryItemSet extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6543815873565829448L
  - Serialized Fields
    - m_items
```
java.util.ArrayList<E> m_items
```
      The list of items in the item set
    - m_support
```
int m_support
```
      the support of this item set
- Class weka.associations.FPGrowth.FrequentItemSets extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4173606872363973588L
  - Serialized Fields
    - m_sets
```
java.util.ArrayList<E> m_sets
```
      The list of frequent item sets
    - m_numberOfTransactions
```
int m_numberOfTransactions
```
      The total number of transactions in the data
- Class weka.associations.FPGrowth.ShadowCounts extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4435433714185969155L
  - Serialized Fields
    - m_counts
```
java.util.ArrayList<E> m_counts
```
      Holds the counts at different recursion levels
- Class weka.associations.GeneralizedSequentialPatterns extends AbstractAssociator implements Serializable
  
  serialVersionUID:
  
  -4119691320812254676L
  - Serialized Fields
    - m_MinSupport
```
double m_MinSupport
```
      the minimum support threshold
    - m_DataSeqID
```
int m_DataSeqID
```
      number indicating the attribute holding the data sequence ID
    - m_OriginalDataSet
```
Instances m_OriginalDataSet
```
      original sequential data set to be used for sequential patterns extraction
    - m_AllSequentialPatterns
```
FastVector m_AllSequentialPatterns
```
      all generated frequent sequences, i.e. sequential patterns
    - m_Cycles
```
int m_Cycles
```
      number of cycles performed until termination
    - m_CycleStart
```
java.lang.String m_CycleStart
```
      String indicating the starting time of an cycle.
    - m_CycleEnd
```
java.lang.String m_CycleEnd
```
      String indicating the ending time of an cycle.
    - m_AlgorithmStart
```
java.lang.String m_AlgorithmStart
```
      String indicating the starting time of the algorithm.
    - m_FilterAttributes
```
java.lang.String m_FilterAttributes
```
      String containing the attribute numbers that are used for result filtering; -1 means no filtering
    - m_FilterAttrVector
```
FastVector m_FilterAttrVector
```
      Vector containing the attribute numbers that are used for result filtering; -1 means no filtering
    - m_Debug
```
boolean m_Debug
```
      Whether the classifier is run in debug mode.
- Class weka.associations.ItemSet extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  2724000045282835791L
  - 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
  
  serialVersionUID:
  
  4158771925518299903L
  - 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 AbstractAssociator implements Serializable
  
  serialVersionUID:
  
  8109088846865075341L
  - 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.
    - m_classIndex
```
int m_classIndex
```
      The class index.
    - m_car
```
boolean m_car
```
      Flag indicating whether class association rules are mined.
- Class weka.associations.PriorEstimation extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  5570863216522496271L
  - 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
  
  serialVersionUID:
  
  -8927041669872491432L
  - 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 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
  
  serialVersionUID:
  
  -3761299128347476534L
  - 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.SingleAssociatorEnhancer extends AbstractAssociator implements Serializable
  
  serialVersionUID:
  
  -3665885256363525164L
  - Serialized Fields
    - m_Associator
```
Associator m_Associator
```
      The base associator to use
- Class weka.associations.Tertius extends AbstractAssociator implements Serializable
  
  serialVersionUID:
  
  5556726848380738179L
  - 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.gsp
- Class weka.associations.gsp.Element extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -7900701276019516371L
  - Serialized Fields
    - m_Events
```
int[] m_Events
```
      events/items stored as an array of ints
- Class weka.associations.gsp.Sequence extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -5001018056339156390L
  - Serialized Fields
    - m_SupportCount
```
int m_SupportCount
```
      the support count of the Sequence
    - m_Elements
```
FastVector m_Elements
```
      ordered list of the comprised elements/itemsets
Package weka.associations.tertius
- Class weka.associations.tertius.AttributeValueLiteral extends Literal implements Serializable
  
  serialVersionUID:
  
  4077436297281456239L
  - Serialized Fields
    - m_value
```
java.lang.String m_value
```
    - m_index
```
int m_index
```
- Class weka.associations.tertius.Body extends LiteralSet implements Serializable
  
  serialVersionUID:
  
  4870689270432218016L
- Class weka.associations.tertius.Head extends LiteralSet implements Serializable
  
  serialVersionUID:
  
  5068076274253706199L
- Class weka.associations.tertius.IndividualInstance extends Instance implements Serializable
  
  serialVersionUID:
  
  -7903938733476585114L
  - Serialized Fields
    - m_parts
```
Instances m_parts
```
- Class weka.associations.tertius.IndividualInstances extends Instances implements Serializable
  
  serialVersionUID:
  
  -7355054814895636733L
- Class weka.associations.tertius.IndividualLiteral extends AttributeValueLiteral implements Serializable
  
  serialVersionUID:
  
  4712404824517887435L
  - Serialized Fields
    - m_type
```
int m_type
```
- Class weka.associations.tertius.Literal extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  2675363669503575771L
  - 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
  
  serialVersionUID:
  
  6094536488654503152L
  - 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
  
  serialVersionUID:
  
  -8374702481965026640L
  - 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
  
  serialVersionUID:
  
  -7763378359090435505L
  - 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
  
  serialVersionUID:
  
  -1491148276509976299L
  - 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
  
  serialVersionUID:
  
  6290379064027832108L
  - 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
  
  serialVersionUID:
  
  -2448555236100426759L
  - 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
  
  serialVersionUID:
  
  2091705669885950849L
- Class weka.attributeSelection.ASSearch extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  7591673350342236548L
- Class weka.attributeSelection.AttributeSelection extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4170171824147584330L
  - 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.AttributeSetEvaluator extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -5744881009422257389L
- Class weka.attributeSelection.BestFirst extends ASSearch implements Serializable
  
  serialVersionUID:
  
  7841338689536821867L
  - 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
  
  serialVersionUID:
  
  -8236598311516351420L
  - Serialized Fields
    - m_data
```
java.lang.Object[] m_data
```
    - m_merit
```
double m_merit
```
- Class weka.attributeSelection.BestFirst.LinkedList2 extends FastVector implements Serializable
  
  serialVersionUID:
  
  3250538292330398929L
  - Serialized Fields
    - m_MaxSize
```
int m_MaxSize
```
      Max number of elements in the list
- Class weka.attributeSelection.CfsSubsetEval extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  747878400813276317L
  - 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_missingSeparate
```
boolean m_missingSeparate
```
      Treat missing values as separate 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 ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -8316857822521717692L
  - 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
  
  serialVersionUID:
  
  7532217899385278710L
  - 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 ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -2880323763295270402L
  - 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
  
  serialVersionUID:
  
  6144138512017017408L
  - 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.
    - key
```
int key
```
      The key
- Class weka.attributeSelection.CostSensitiveASEvaluation extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -7045833833363396977L
  - 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_evaluator
```
ASEvaluation m_evaluator
```
      The base evaluator to use
    - m_seed
```
int m_seed
```
      random number seed
- Class weka.attributeSelection.CostSensitiveAttributeEval extends CostSensitiveASEvaluation implements Serializable
  
  serialVersionUID:
  
  4484876541145458447L
- Class weka.attributeSelection.CostSensitiveSubsetEval extends CostSensitiveASEvaluation implements Serializable
  
  serialVersionUID:
  
  2924546096103426700L
- Class weka.attributeSelection.ExhaustiveSearch extends ASSearch implements Serializable
  
  serialVersionUID:
  
  5741842861142379712L
  - 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.FilteredAttributeEval extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  2111121880778327334L
  - Serialized Fields
    - m_evaluator
```
AttributeEvaluator m_evaluator
```
      Base evaluator
    - m_filter
```
Filter m_filter
```
      Filter
    - m_filteredInstances
```
Instances m_filteredInstances
```
      Filtered instances structure
- Class weka.attributeSelection.FilteredSubsetEval extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  2111121880778327334L
  - Serialized Fields
    - m_evaluator
```
SubsetEvaluator m_evaluator
```
      Base evaluator
    - m_filter
```
Filter m_filter
```
      Filter
    - m_filteredInstances
```
Instances m_filteredInstances
```
      Filtered instances structure
- Class weka.attributeSelection.GainRatioAttributeEval extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -8504656625598579926L
  - 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
  
  serialVersionUID:
  
  -1618264232838472679L
  - 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
  
  serialVersionUID:
  
  -2930607837482622224L
  - Serialized Fields
    - m_chromosome
```
java.util.BitSet m_chromosome
```
      the bitset
    - m_objective
```
double m_objective
```
      holds raw merit
    - m_fitness
```
double m_fitness
```
      the fitness
- Class weka.attributeSelection.GreedyStepwise extends ASSearch implements Serializable
  
  serialVersionUID:
  
  -6312951970168325471L
  - 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
    - m_conservativeSelection
```
boolean m_conservativeSelection
```
      If set then attributes will continue to be added during a forward search as long as the merit does not degrade
- Class weka.attributeSelection.HoldOutSubsetEvaluator extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  8280529785412054174L
- Class weka.attributeSelection.InfoGainAttributeEval extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -1949849512589218930L
  - 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.LatentSemanticAnalysis extends UnsupervisedAttributeEvaluator implements Serializable
  
  serialVersionUID:
  
  -8712112988018106198L
  - Serialized Fields
    - m_trainInstances
```
Instances m_trainInstances
```
      The data to transform analyse/transform
    - m_trainHeader
```
Instances m_trainHeader
```
      Keep a copy for the class attribute (if set) and for checking for header compatibility
    - m_transformedFormat
```
Instances m_transformedFormat
```
      The header for the transformed data format
    - m_hasClass
```
boolean m_hasClass
```
      Data has a class set
    - m_classIndex
```
int m_classIndex
```
      Class index
    - m_numAttributes
```
int m_numAttributes
```
      Number of attributes
    - m_numInstances
```
int m_numInstances
```
      Number of instances
    - m_transpose
```
boolean m_transpose
```
      Is transpose necessary because numAttributes < numInstances?
    - m_u
```
Matrix m_u
```
      Will hold the left singular vectors
    - m_s
```
Matrix m_s
```
      Will hold the singular values
    - m_v
```
Matrix m_v
```
      Will hold the right singular values
    - m_transformationMatrix
```
Matrix m_transformationMatrix
```
      Will hold the matrix used to transform instances to the new feature space
    - m_replaceMissingFilter
```
ReplaceMissingValues m_replaceMissingFilter
```
      Filters for original data
    - m_normalizeFilter
```
Normalize m_normalizeFilter
```
    - m_nominalToBinaryFilter
```
NominalToBinary m_nominalToBinaryFilter
```
    - m_attributeFilter
```
Remove m_attributeFilter
```
    - m_outputNumAttributes
```
int m_outputNumAttributes
```
      The number of attributes in the LSA transformed data
    - m_normalize
```
boolean m_normalize
```
      Normalize the input data?
    - m_rank
```
double m_rank
```
      The approximation rank to use (between 0 and 1 means coverage proportion)
    - m_sumSquaredSingularValues
```
double m_sumSquaredSingularValues
```
      The sum of the squares of the singular values
    - m_actualRank
```
int m_actualRank
```
      The actual rank number to use for computation
    - m_maxAttributesInName
```
int m_maxAttributesInName
```
      Maximum number of attributes in the transformed attribute name
- Class weka.attributeSelection.LFSMethods.Link2 extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -7422719407475185086L
  - Serialized Fields
    - m_data
```
java.lang.Object[] m_data
```
    - m_merit
```
double m_merit
```
- Class weka.attributeSelection.LFSMethods.LinkedList2 extends FastVector implements Serializable
  
  serialVersionUID:
  
  -7776010892419656105L
  - Serialized Fields
    - m_MaxSize
```
int m_MaxSize
```
- Class weka.attributeSelection.LinearForwardSelection extends ASSearch implements Serializable
  - Serialized Fields
    - m_maxStale
```
int m_maxStale
```
      maximum number of stale nodes before terminating search
    - m_forwardSearchMethod
```
int m_forwardSearchMethod
```
      0 == forward selection, 1 == floating forward search
    - m_performRanking
```
boolean m_performRanking
```
      perform initial ranking to select top-ranked attributes
    - m_numUsedAttributes
```
int m_numUsedAttributes
```
      number of top-ranked attributes that are taken into account for the search
    - m_linearSelectionType
```
int m_linearSelectionType
```
      0 == fixed-set, 1 == fixed-width
    - 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_verbose
```
boolean m_verbose
```
      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.OneRAttributeEval extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  4386514823886856980L
  - 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
  
  serialVersionUID:
  
  -3675307197777734007L
  - Serialized Fields
    - m_trainInstances
```
Instances m_trainInstances
```
      The data to transform analyse/transform
    - m_trainHeader
```
Instances m_trainHeader
```
      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/covariance matrix for the original data
    - m_means
```
double[] m_means
```
    - m_stdDevs
```
double[] m_stdDevs
```
    - m_center
```
boolean m_center
```
      If true, center (rather than standardize) the data and compute PCA from covariance (rather than correlation) matrix.
    - 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_nominalToBinFilter
```
NominalToBinary m_nominalToBinFilter
```
    - m_attributeFilter
```
Remove m_attributeFilter
```
    - m_centerFilter
```
Center m_centerFilter
```
    - m_standardizeFilter
```
Standardize m_standardizeFilter
```
    - m_outputNumAtts
```
int m_outputNumAtts
```
      The number of attributes in the pc transformed data
    - m_coverVariance
```
double m_coverVariance
```
      the amount of variance 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
  
  serialVersionUID:
  
  4015453851212985720L
  - Serialized Fields
    - m_Instances
```
Instances m_Instances
```
      the training 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
  
  serialVersionUID:
  
  7479392617377425484L
  - 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 as many or fewer features than the best (this allows LVF to be implemented).
    - 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
  
  serialVersionUID:
  
  -9086714848510751934L
  - 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
  
  serialVersionUID:
  
  -7992268736874353755L
  - 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
    - m_add
```
int m_add
```
      add this many attributes in each iteration from the ranking
    - m_startPoint
```
int m_startPoint
```
      start from this point in the ranking
- Class weka.attributeSelection.ReliefFAttributeEval extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -8422186665795839379L
  - 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.ScatterSearchV1 extends ASSearch implements Serializable
  
  serialVersionUID:
  
  -8512041420388121326L
  - Serialized Fields
    - m_numAttribs
```
int m_numAttribs
```
      number of attributes in the data
    - m_classIndex
```
int m_classIndex
```
      holds the class index
    - m_treshold
```
double m_treshold
```
      holds the treshhold that delimits the good attributes
    - m_initialThreshold
```
double m_initialThreshold
```
      the initial threshold
    - m_typeOfCombination
```
int m_typeOfCombination
```
      the kind of comination betwen parents ((0)greedy combination/(1)reduced greedy combination)
    - m_random
```
java.util.Random m_random
```
      random number generation
    - m_seed
```
int m_seed
```
      seed for random number generation
    - m_debug
```
boolean m_debug
```
      verbose output for monitoring the search and debugging
    - m_InformationReports
```
java.lang.StringBuffer m_InformationReports
```
      holds a report of the search
    - m_totalEvals
```
int m_totalEvals
```
      total number of subsets evaluated during a search
    - m_bestMerit
```
double m_bestMerit
```
      holds the merit of the best subset found
    - m_processinTime
```
long m_processinTime
```
      time for procesing the search method
    - m_population
```
java.util.List<E> m_population
```
      holds the Initial Population of Subsets
    - m_popSize
```
int m_popSize
```
      holds the population size
    - m_initialPopSize
```
int m_initialPopSize
```
      holds the user selected initial population size
    - m_calculatedInitialPopSize
```
int m_calculatedInitialPopSize
```
      if no initial user pop size, then this holds the initial pop size calculated from the number of attributes in the data (for use in the toString() method)
    - m_attributeRanking
```
java.util.List<E> m_attributeRanking
```
      holds the attributes ranked
    - ASEvaluator
```
SubsetEvaluator ASEvaluator
```
      Evaluator used to know the significance of a subset (for guiding the search)
- Class weka.attributeSelection.ScatterSearchV1.Subset extends java.lang.Object implements Serializable
  - Serialized Fields
    - merit
```
double merit
```
    - subset
```
java.util.BitSet subset
```
- Class weka.attributeSelection.SubsetSizeForwardSelection extends ASSearch implements Serializable
  - Serialized Fields
    - m_performRanking
```
boolean m_performRanking
```
      perform initial ranking to select top-ranked attributes
    - m_numUsedAttributes
```
int m_numUsedAttributes
```
      number of top-ranked attributes that are taken into account for the search
    - m_linearSelectionType
```
int m_linearSelectionType
```
      0 == fixed-set, 1 == fixed-width
    - m_setSizeEval
```
ASEvaluation m_setSizeEval
```
      the subset evaluator to use for subset size determination
    - m_numFolds
```
int m_numFolds
```
      Number of cross validation folds for subset size determination (default = 5).
    - m_seed
```
int m_seed
```
      Seed for cross validation subset size determination. (default = 1)
    - 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_verbose
```
boolean m_verbose
```
      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.SVMAttributeEval extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -6489975709033967447L
  - 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 ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -8096505776132296416L
  - 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 ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -4100897318675336178L
- Class weka.attributeSelection.UnsupervisedSubsetEvaluator extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  627934376267488763L
- Class weka.attributeSelection.WrapperSubsetEval extends ASEvaluation implements Serializable
  
  serialVersionUID:
  
  -4573057658746728675L
  - 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
  
  serialVersionUID:
  
  6502780192411755341L
  - Serialized Fields
    - m_Debug
```
boolean m_Debug
```
      Whether the classifier is run in debug mode.
- Class weka.classifiers.CostMatrix extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -1973792250544554965L
  - Serialized Fields
    - m_size
```
int m_size
```
    - m_matrix
```
java.lang.Object[][] m_matrix
```
      [rows][columns]
- Class weka.classifiers.IteratedSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -6217979135443319724L
  - 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
  
  serialVersionUID:
  
  2776436621129422119L
  - Serialized Fields
    - m_Classifiers
```
Classifier[] m_Classifiers
```
      Array for storing the generated base classifiers.
- Class weka.classifiers.RandomizableClassifier extends Classifier implements Serializable
  
  serialVersionUID:
  
  -8816375798262351903L
  - Serialized Fields
    - m_Seed
```
int m_Seed
```
      The random number seed.
- Class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer extends IteratedSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  5063351391524938557L
  - Serialized Fields
    - m_Seed
```
int m_Seed
```
      The random number seed.
- Class weka.classifiers.RandomizableMultipleClassifiersCombiner extends MultipleClassifiersCombiner implements Serializable
  
  serialVersionUID:
  
  5057936555724785679L
  - Serialized Fields
    - m_Seed
```
int m_Seed
```
      The random number seed.
- Class weka.classifiers.RandomizableSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  558286687096157160L
  - Serialized Fields
    - m_Seed
```
int m_Seed
```
      The random number seed.
- Class weka.classifiers.SingleClassifierEnhancer extends Classifier implements Serializable
  
  serialVersionUID:
  
  -3665885256363525164L
  - 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
  
  serialVersionUID:
  
  9197439980415113523L
  - 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). E.g., 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
    - m_MEstimates
```
boolean m_MEstimates
```
      flag for using m-estimates
    - m_Weight
```
int m_Weight
```
      value for m in m-estimate
- Class weka.classifiers.bayes.AODEsr extends Classifier implements Serializable
  
  serialVersionUID:
  
  5602143019183068848L
  - 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_CondiCountsNoClass
```
double[][] m_CondiCountsNoClass
```
      2D array (m_TotalAttValues * m_TotalAttValues) of attributes counts. similar to m_CondiCounts, but ignoring 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
    - m_MWeight
```
double m_MWeight
```
      m value for m-estimation
    - m_Laplace
```
boolean m_Laplace
```
      Using LapLace estimation or not
    - m_Critical
```
int m_Critical
```
      the critical value for the specialization-generalization
- Class weka.classifiers.bayes.BayesianLogisticRegression extends Classifier implements Serializable
  
  serialVersionUID:
  
  -8013478897911757631L
  - Serialized Fields
    - debug
```
boolean debug
```
      DEBUG Mode
    - NormalizeData
```
boolean NormalizeData
```
      Choose whether to normalize data or not
    - Tolerance
```
double Tolerance
```
      Tolerance criteria for the stopping criterion.
    - Threshold
```
double Threshold
```
      Threshold for binary classification of probabilisitic estimate
    - PriorClass
```
int PriorClass
```
      Distribution Prior class
    - NumFolds
```
int NumFolds
```
      NumFolds for CV based Hyperparameters selection
    - m_seed
```
int m_seed
```
      seed for randomizing the instances before CV
    - HyperparameterSelection
```
int HyperparameterSelection
```
      Hyperparameter selection method
    - ClassIndex
```
int ClassIndex
```
      The class index from the training data
    - HyperparameterValue
```
double HyperparameterValue
```
      Best hyperparameter for test phase
    - HyperparameterRange
```
java.lang.String HyperparameterRange
```
      CV Hyperparameter Range
    - maxIterations
```
int maxIterations
```
      Maximum number of iterations
    - iterationCounter
```
int iterationCounter
```
      Iteration counter
    - BetaVector
```
double[] BetaVector
```
      Array for storing coefficients of Bayesian regression model.
    - DeltaBeta
```
double[] DeltaBeta
```
      Array to store Regression Coefficient updates.
    - DeltaUpdate
```
double[] DeltaUpdate
```
      Trust Region Radius Update
    - Delta
```
double[] Delta
```
      Trust Region Radius
    - Hyperparameters
```
double[] Hyperparameters
```
      Array to store Hyperparameter values for each feature.
    - R
```
double[] R
```
      R(i)= BetaVector X x(i) X y(i). This an intermediate value with respect to vector BETA, input values and corresponding class labels
    - DeltaR
```
double[] DeltaR
```
      This vector is used to store the increments on the R(i). It is also used to determining the stopping criterion.
    - Change
```
double Change
```
      This variable is used to keep track of change in the value of delta summation of r(i).
    - m_Filter
```
Filter m_Filter
```
      Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object
    - m_Instances
```
Instances m_Instances
```
      Dataset provided to do Training/Test set.
    - m_PriorUpdate
```
Prior m_PriorUpdate
```
      Prior class object interface
- Class weka.classifiers.bayes.BayesNet extends Classifier implements Serializable
  
  serialVersionUID:
  
  746037443258775954L
  - 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
```
      attribute index of a non-nominal attribute
    - 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
  
  serialVersionUID:
  
  7246302925903086397L
  - 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.DMNBtext extends Classifier implements Serializable
  
  serialVersionUID:
  
  5932177450183457085L
  - Serialized Fields
    - m_NumIterations
```
int m_NumIterations
```
      The number of iterations.
    - m_MultinomialWord
```
boolean m_MultinomialWord
```
    - m_numClasses
```
int m_numClasses
```
    - m_headerInfo
```
Instances m_headerInfo
```
    - m_binaryClassifiers
```
DMNBtext.DNBBinary[] m_binaryClassifiers
```
- Class weka.classifiers.bayes.DMNBtext.DNBBinary extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_perWordPerClass
```
double[][] m_perWordPerClass
```
      The number of iterations.
    - m_wordsPerClass
```
double[] m_wordsPerClass
```
    - m_classIndex
```
int m_classIndex
```
    - m_classDistribution
```
double[] m_classDistribution
```
    - m_numAttributes
```
int m_numAttributes
```
      number of unique words
    - m_targetClass
```
int m_targetClass
```
    - m_WordLaplace
```
double m_WordLaplace
```
    - m_coefficient
```
double[] m_coefficient
```
    - m_classRatio
```
double m_classRatio
```
    - m_wordRatio
```
double m_wordRatio
```
- Class weka.classifiers.bayes.HNB extends Classifier implements Serializable
  
  serialVersionUID:
  
  -4503874444306113214L
  - Serialized Fields
    - m_ClassCounts
```
double[] m_ClassCounts
```
      The number of each class value occurs in the dataset
    - m_ClassAttAttCounts
```
double[][][] m_ClassAttAttCounts
```
      The number of class and two attributes values occurs in the dataset
    - m_NumAttValues
```
int[] m_NumAttValues
```
      The number of values for each attribute in the dataset
    - m_TotalAttValues
```
int m_TotalAttValues
```
      The number of values for all attributes in the dataset
    - m_NumClasses
```
int m_NumClasses
```
      The number of classes in the dataset
    - m_NumAttributes
```
int m_NumAttributes
```
      The number of attributes including class in the dataset
    - m_NumInstances
```
int m_NumInstances
```
      The number of instances in the dataset
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute in the dataset
    - m_StartAttIndex
```
int[] m_StartAttIndex
```
      The starting index of each attribute in the dataset
    - m_condiMutualInfo
```
double[][] m_condiMutualInfo
```
      The 2D array of conditional mutual information of each pair attributes
- Class weka.classifiers.bayes.NaiveBayes extends Classifier implements Serializable
  
  serialVersionUID:
  
  5995231201785697655L
  - 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.
    - m_displayModelInOldFormat
```
boolean m_displayModelInOldFormat
```
- Class weka.classifiers.bayes.NaiveBayesMultinomial extends Classifier implements Serializable
  
  serialVersionUID:
  
  5932177440181257085L
  - Serialized Fields
    - m_probOfWordGivenClass
```
double[][] m_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]
    - m_probOfClass
```
double[] m_probOfClass
```
      the probability of a class (i.e. Pr[H])
    - m_numAttributes
```
int m_numAttributes
```
      number of unique words
    - m_numClasses
```
int m_numClasses
```
      number of class values
    - m_lnFactorialCache
```
double[] m_lnFactorialCache
```
      cache lnFactorial computations
    - m_headerInfo
```
Instances m_headerInfo
```
      copy of header information for use in toString method
- Class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable extends NaiveBayesMultinomial implements Serializable
  
  serialVersionUID:
  
  -7204398796974263186L
  - Serialized Fields
    - m_wordsPerClass
```
double[] m_wordsPerClass
```
      the word count per class
- Class weka.classifiers.bayes.NaiveBayesSimple extends Classifier implements Serializable
  
  serialVersionUID:
  
  -1478242251770381214L
  - 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
  
  serialVersionUID:
  
  -5354015843807192221L
- Class weka.classifiers.bayes.WAODE extends Classifier implements Serializable
  
  serialVersionUID:
  
  2170978824284697882L
  - Serialized Fields
    - m_ClassCounts
```
double[] m_ClassCounts
```
      The number of each class value occurs in the dataset
    - m_AttCounts
```
double[] m_AttCounts
```
      The number of each attribute value occurs in the dataset
    - m_AttAttCounts
```
double[][] m_AttAttCounts
```
      The number of two attributes values occurs in the dataset
    - m_ClassAttAttCounts
```
double[][][] m_ClassAttAttCounts
```
      The number of class and two attributes values occurs in the dataset
    - m_NumAttValues
```
int[] m_NumAttValues
```
      The number of values for each attribute in the dataset
    - m_TotalAttValues
```
int m_TotalAttValues
```
      The number of values for all attributes in the dataset
    - m_NumClasses
```
int m_NumClasses
```
      The number of classes in the dataset
    - m_NumAttributes
```
int m_NumAttributes
```
      The number of attributes including class in the dataset
    - m_NumInstances
```
int m_NumInstances
```
      The number of instances in the dataset
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute in the dataset
    - m_StartAttIndex
```
int[] m_StartAttIndex
```
      The starting index of each attribute in the dataset
    - m_mutualInformation
```
double[] m_mutualInformation
```
      The array of mutual information between each attribute and class
    - m_Header
```
Instances m_Header
```
      the header information of the training data
    - m_Internals
```
boolean m_Internals
```
      whether to print more internals in the toString method
      
      See Also:
      
      WAODE.toString()
    - m_ZeroR
```
Classifier m_ZeroR
```
      a ZeroR model in case no model can be built from the data
Package weka.classifiers.bayes.blr
- Class weka.classifiers.bayes.blr.GaussianPriorImpl extends Prior implements Serializable
  
  serialVersionUID:
  
  -2995684220141159223L
- Class weka.classifiers.bayes.blr.LaplacePriorImpl extends Prior implements Serializable
  
  serialVersionUID:
  
  2353576123257012607L
  - Serialized Fields
    - m_Instances
```
Instances m_Instances
```
    - Beta
```
double Beta
```
    - Hyperparameter
```
double Hyperparameter
```
    - DeltaUpdate
```
double DeltaUpdate
```
    - R
```
double[] R
```
    - Delta
```
double Delta
```
- Class weka.classifiers.bayes.blr.Prior extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_Instances
```
Instances m_Instances
```
    - Beta
```
double Beta
```
    - Hyperparameter
```
double Hyperparameter
```
    - DeltaUpdate
```
double DeltaUpdate
```
    - R
```
double[] R
```
    - Delta
```
double Delta
```
    - log_posterior
```
double log_posterior
```
    - log_likelihood
```
double log_likelihood
```
    - penalty
```
double penalty
```
Package weka.classifiers.bayes.net
- Class weka.classifiers.bayes.net.ADNode extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  397409728366910204L
  - 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 EditableBayesNet implements Serializable
  
  serialVersionUID:
  
  -7462571170596157720L
  - Serialized Fields
    - m_nSeed
```
int m_nSeed
```
      the seed value
    - random
```
java.util.Random random
```
      the random number generator
    - 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
  
  serialVersionUID:
  
  -8358864680379881429L
  - Serialized Fields
    - m_nPositionX
```
int[] m_nPositionX
```
    - m_nPositionY
```
int[] m_nPositionY
```
    - m_order
```
int[] m_order
```
    - m_sFile
```
java.lang.String m_sFile
```
      the current filename
- Class weka.classifiers.bayes.net.EditableBayesNet extends BayesNet implements Serializable
  
  serialVersionUID:
  
  746037443258735954L
  - Serialized Fields
    - m_nPositionX
```
FastVector m_nPositionX
```
      location of nodes, used for graph drawing *
    - m_nPositionY
```
FastVector m_nPositionY
```
    - m_fMarginP
```
FastVector m_fMarginP
```
      marginal distributions *
    - m_nEvidence
```
FastVector m_nEvidence
```
      evidence values, used for evidence propagation *
    - m_undoStack
```
FastVector m_undoStack
```
      undo stack for undoin edit actions, or redo edit actions
    - m_nCurrentEditAction
```
int m_nCurrentEditAction
```
      current action in undo stack
    - m_nSavedPointer
```
int m_nSavedPointer
```
      action that the network is saved
    - m_bNeedsUndoAction
```
boolean m_bNeedsUndoAction
```
      flag to indicate whether an edit action needs to introduce an undo action. This is only false when an undo or redo action is performed.
- Class weka.classifiers.bayes.net.GUI extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -2038911085935515624L
  - Serialized Fields
    - m_layoutEngine
```
LayoutEngine m_layoutEngine
```
      The current LayoutEngine
    - m_GraphPanel
```
weka.classifiers.bayes.net.GUI.GraphPanel m_GraphPanel
```
      Panel actually displaying the graph
    - m_BayesNet
```
EditableBayesNet m_BayesNet
```
      Container of Bayesian network
    - m_sFileName
```
java.lang.String m_sFileName
```
      String containing file name storing current network
    - m_marginCalculator
```
MarginCalculator m_marginCalculator
```
      used for calculating marginals in Bayesian netwowrks
    - m_marginCalculatorWithEvidence
```
MarginCalculator m_marginCalculatorWithEvidence
```
      used for calculating marginals in Bayesian netwowrks when evidence is present
    - m_bViewMargins
```
boolean m_bViewMargins
```
      flag indicating whether marginal distributions of each of the nodes should be shown in display.
    - m_bViewCliques
```
boolean m_bViewCliques
```
    - m_menuBar
```
javax.swing.JMenuBar m_menuBar
```
      The menu bar
    - m_Instances
```
Instances m_Instances
```
      data selected from file. Used to train a Bayesian network on
    - m_jTfZoom
```
javax.swing.JTextField m_jTfZoom
```
      Text field for specifying zoom
    - m_jTbTools
```
javax.swing.JToolBar m_jTbTools
```
      toolbar containing buttons at top of window
    - m_jStatusBar
```
javax.swing.JLabel m_jStatusBar
```
      status bar at bottom of window
    - m_jTfNodeWidth
```
javax.swing.JTextField m_jTfNodeWidth
```
      TextField for node's width
    - m_jTfNodeHeight
```
javax.swing.JTextField m_jTfNodeHeight
```
      TextField for nodes height
    - m_jScrollPane
```
javax.swing.JScrollPane m_jScrollPane
```
      this contains the m_GraphPanel GraphPanel
    - ICONPATH
```
java.lang.String ICONPATH
```
      path for icons
    - m_fScale
```
double m_fScale
```
      current zoom value
    - m_nNodeHeight
```
int m_nNodeHeight
```
      standard width of node
    - m_nNodeWidth
```
int m_nNodeWidth
```
    - m_nPaddedNodeWidth
```
int m_nPaddedNodeWidth
```
    - m_nZoomPercents
```
int[] m_nZoomPercents
```
      used when using zoomIn and zoomOut buttons
    - a_new
```
javax.swing.Action a_new
```
      actions triggered by GUI events
    - a_quit
```
javax.swing.Action a_quit
```
    - a_save
```
javax.swing.Action a_save
```
    - a_export
```
weka.classifiers.bayes.net.GUI.ActionExport a_export
```
    - a_print
```
weka.classifiers.bayes.net.GUI.ActionPrint a_print
```
    - a_load
```
javax.swing.Action a_load
```
    - a_zoomin
```
javax.swing.Action a_zoomin
```
    - a_zoomout
```
javax.swing.Action a_zoomout
```
    - a_layout
```
javax.swing.Action a_layout
```
    - a_saveas
```
javax.swing.Action a_saveas
```
    - a_viewtoolbar
```
javax.swing.Action a_viewtoolbar
```
    - a_viewstatusbar
```
javax.swing.Action a_viewstatusbar
```
    - a_networkgenerator
```
javax.swing.Action a_networkgenerator
```
    - a_datagenerator
```
javax.swing.Action a_datagenerator
```
    - a_datasetter
```
javax.swing.Action a_datasetter
```
    - a_learn
```
javax.swing.Action a_learn
```
    - a_learnCPT
```
javax.swing.Action a_learnCPT
```
    - a_help
```
javax.swing.Action a_help
```
    - a_about
```
javax.swing.Action a_about
```
    - a_addnode
```
weka.classifiers.bayes.net.GUI.ActionAddNode a_addnode
```
    - a_delnode
```
javax.swing.Action a_delnode
```
    - a_cutnode
```
javax.swing.Action a_cutnode
```
    - a_copynode
```
javax.swing.Action a_copynode
```
    - a_pastenode
```
javax.swing.Action a_pastenode
```
    - a_selectall
```
javax.swing.Action a_selectall
```
    - a_addarc
```
javax.swing.Action a_addarc
```
    - a_delarc
```
javax.swing.Action a_delarc
```
    - a_undo
```
javax.swing.Action a_undo
```
    - a_redo
```
javax.swing.Action a_redo
```
    - a_alignleft
```
javax.swing.Action a_alignleft
```
    - a_alignright
```
javax.swing.Action a_alignright
```
    - a_aligntop
```
javax.swing.Action a_aligntop
```
    - a_alignbottom
```
javax.swing.Action a_alignbottom
```
    - a_centerhorizontal
```
javax.swing.Action a_centerhorizontal
```
    - a_centervertical
```
javax.swing.Action a_centervertical
```
    - a_spacehorizontal
```
javax.swing.Action a_spacehorizontal
```
    - a_spacevertical
```
javax.swing.Action a_spacevertical
```
    - m_nCurrentNode
```
int m_nCurrentNode
```
      node currently selected through right clicking
    - m_Selection
```
weka.classifiers.bayes.net.GUI.Selection m_Selection
```
      selection of nodes
    - m_nSelectedRect
```
java.awt.Rectangle m_nSelectedRect
```
      selection rectangle drawn through dragging with left mouse button
    - m_clipboard
```
weka.classifiers.bayes.net.GUI.ClipBoard m_clipboard
```
- Class weka.classifiers.bayes.net.MarginCalculator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  650278019241175534L
  - Serialized Fields
    - m_debug
```
boolean m_debug
```
    - m_root
```
MarginCalculator.JunctionTreeNode m_root
```
    - jtNodes
```
MarginCalculator.JunctionTreeNode[] jtNodes
```
    - m_Margins
```
double[][] m_Margins
```
- Class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  650278019241175536L
  - Serialized Fields
    - m_bayesNet
```
BayesNet m_bayesNet
```
      reference Bayes net for information about variables like name, cardinality, etc. but not for relations between nodes
    - m_nNodes
```
int[] m_nNodes
```
      nodes of the Bayes net in this junction node
    - m_nCardinality
```
int m_nCardinality
```
      cardinality of the instances of variables in this junction node
    - m_fi
```
double[] m_fi
```
      potentials for first network
    - m_P
```
double[] m_P
```
      distribution over this junction node according to first Bayes network
    - m_MarginalP
```
double[][] m_MarginalP
```
    - m_parentSeparator
```
MarginCalculator.JunctionTreeSeparator m_parentSeparator
```
    - m_children
```
java.util.Vector<E> m_children
```
- Class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  6502780192411755343L
  - Serialized Fields
    - m_nNodes
```
int[] m_nNodes
```
    - m_nCardinality
```
int m_nCardinality
```
    - m_fiParent
```
double[] m_fiParent
```
    - m_fiChild
```
double[] m_fiChild
```
    - m_parentNode
```
MarginCalculator.JunctionTreeNode m_parentNode
```
    - m_childNode
```
MarginCalculator.JunctionTreeNode m_childNode
```
    - m_bayesNet
```
BayesNet m_bayesNet
```
- Class weka.classifiers.bayes.net.ParentSet extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4155021284407181838L
  - 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
  
  serialVersionUID:
  
  -6196294370675872424L
  - 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
  
  serialVersionUID:
  
  2184330197666253884L
  - Serialized Fields
    - m_fAlpha
```
double m_fAlpha
```
      Holds prior on count
- Class weka.classifiers.bayes.net.estimate.BMAEstimator extends SimpleEstimator implements Serializable
  
  serialVersionUID:
  
  -1846028304233257309L
  - Serialized Fields
    - m_bUseK2Prior
```
boolean m_bUseK2Prior
```
      whether to use K2 prior
- Class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes extends Estimator implements Serializable
  
  serialVersionUID:
  
  4215400230843212684L
  - 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
  
  serialVersionUID:
  
  6774941981423312133L
- Class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator extends BayesNetEstimator implements Serializable
  
  serialVersionUID:
  
  8330705772601586313L
  - Serialized Fields
    - m_bUseK2Prior
```
boolean m_bUseK2Prior
```
      whether to use K2 prior
- Class weka.classifiers.bayes.net.estimate.SimpleEstimator extends BayesNetEstimator implements Serializable
  
  serialVersionUID:
  
  5874941612331806172L
Package weka.classifiers.bayes.net.search
- Class weka.classifiers.bayes.net.search.SearchAlgorithm extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  6164792240778525312L
  - 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
  
  serialVersionUID:
  
  3165802334119704560L
  - Serialized Fields
    - m_BayesNet
```
BayesNet m_BayesNet
```
    - m_instances
```
Instances m_instances
```
- Class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm extends CISearchAlgorithm implements Serializable
  
  serialVersionUID:
  
  -2510985917284798576L
  - 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
  
  serialVersionUID:
  
  7334358169507619525L
  - 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
  
  serialVersionUID:
  
  -4808572519709755811L
Package weka.classifiers.bayes.net.search.global
- Class weka.classifiers.bayes.net.search.global.GeneticSearch extends GlobalScoreSearchAlgorithm implements Serializable
  
  serialVersionUID:
  
  4236165533882462203L
  - 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
  
  serialVersionUID:
  
  7341389867906199781L
  - 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
  
  serialVersionUID:
  
  -3885042888195820149L
  - Serialized Fields
    - m_bUseArcReversal
```
boolean m_bUseArcReversal
```
      use the arc reversal operator
- Class weka.classifiers.bayes.net.search.global.K2 extends GlobalScoreSearchAlgorithm implements Serializable
  
  serialVersionUID:
  
  -6626871067466338256L
  - 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
  
  serialVersionUID:
  
  -7359197180460703069L
  - 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
  
  serialVersionUID:
  
  -5482721887881010916L
  - 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
  
  serialVersionUID:
  
  1176705618756672292L
  - 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
  
  serialVersionUID:
  
  1715277053980895298L
Package weka.classifiers.bayes.net.search.local
- Class weka.classifiers.bayes.net.search.local.GeneticSearch extends LocalScoreSearchAlgorithm implements Serializable
  
  serialVersionUID:
  
  -7037070678911459757L
  - 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
  
  serialVersionUID:
  
  4322783593818122403L
  - 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
  
  serialVersionUID:
  
  6176545934752116631L
  - Serialized Fields
    - m_bRandomOrder
```
boolean m_bRandomOrder
```
      Holds flag to indicate ordering should be random
- Class weka.classifiers.bayes.net.search.local.LAGDHillClimber extends HillClimber implements Serializable
  
  serialVersionUID:
  
  7217437499439184344L
  - Serialized Fields
    - m_nNrOfLookAheadSteps
```
int m_nNrOfLookAheadSteps
```
      Number of Look Ahead Steps
    - m_nNrOfGoodOperations
```
int m_nNrOfGoodOperations
```
      Number of Good Operations per Step
- Class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm extends SearchAlgorithm implements Serializable
  
  serialVersionUID:
  
  3325995552474190374L
  - 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
  
  serialVersionUID:
  
  -6574084564213041174L
  - 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
  
  serialVersionUID:
  
  6951955606060513191L
  - 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
  
  serialVersionUID:
  
  1457344073228786447L
  - 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
  
  serialVersionUID:
  
  965182127977228690L
Package weka.classifiers.evaluation
- Class weka.classifiers.evaluation.ConfusionMatrix extends Matrix implements Serializable
  
  serialVersionUID:
  
  -181789981401504090L
  - 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
  
  serialVersionUID:
  
  -4880216423674233887L
  - 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.GaussianProcesses extends Classifier implements Serializable
  
  serialVersionUID:
  
  -8620066949967678545L
  - Serialized Fields
    - 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.
    - m_delta
```
double m_delta
```
      Gaussian Noise Value.
    - m_classIndex
```
int m_classIndex
```
      The class index from the training data
    - 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_kernel
```
Kernel m_kernel
```
      Kernel to use
    - m_NumTrain
```
int m_NumTrain
```
      The number of training instances
    - m_avg_target
```
double m_avg_target
```
      The training data.
    - m_C
```
Matrix m_C
```
      The covariance matrix.
    - m_t
```
Matrix m_t
```
      The vector of target values.
    - m_KernelIsLinear
```
boolean m_KernelIsLinear
```
      whether the kernel is a linear one
- Class weka.classifiers.functions.IsotonicRegression extends Classifier implements Serializable
  
  serialVersionUID:
  
  1679336022835454137L
  - Serialized Fields
    - m_attribute
```
Attribute m_attribute
```
      The chosen attribute
    - m_cuts
```
double[] m_cuts
```
      The array of cut points
    - m_values
```
double[] m_values
```
      The predicted value in each interval.
    - m_minMsq
```
double m_minMsq
```
      The minimum mean squared error that has been achieved.
    - m_ZeroR
```
Classifier m_ZeroR
```
      a ZeroR model in case no model can be built from the data
- Class weka.classifiers.functions.LeastMedSq extends Classifier implements Serializable
  
  serialVersionUID:
  
  4288954049987652970L
  - 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.LibLINEAR extends Classifier implements Serializable
  
  serialVersionUID:
  
  230504711L
  - Serialized Fields
    - m_Model
```
java.lang.Object m_Model
```
      LibLINEAR Model
    - m_Filter
```
Filter m_Filter
```
      for normalizing the data
    - m_Normalize
```
boolean m_Normalize
```
      normalize input data
    - m_SVMType
```
int m_SVMType
```
      the SVM solver type
    - m_eps
```
double m_eps
```
      stopping criteria
    - m_Cost
```
double m_Cost
```
      cost Parameter C
    - m_Bias
```
double m_Bias
```
      bias term value
    - m_WeightLabel
```
int[] m_WeightLabel
```
    - m_Weight
```
double[] m_Weight
```
    - m_ProbabilityEstimates
```
boolean m_ProbabilityEstimates
```
      whether to generate probability estimates instead of +1/-1 in case of classification problems
    - m_ReplaceMissingValues
```
ReplaceMissingValues m_ReplaceMissingValues
```
      The filter used to get rid of missing values.
    - m_NominalToBinary
```
NominalToBinary m_NominalToBinary
```
      The filter used to make attributes numeric.
    - m_nominalToBinary
```
boolean m_nominalToBinary
```
      If true, the nominal to binary filter is applied
    - m_noReplaceMissingValues
```
boolean m_noReplaceMissingValues
```
      If true, the replace missing values filter is not applied
- Class weka.classifiers.functions.LibSVM extends RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  14172L
  - Serialized Fields
    - m_Model
```
java.lang.Object m_Model
```
      LibSVM Model
    - m_Filter
```
Filter m_Filter
```
      for normalizing the data
    - m_NominalToBinary
```
Filter m_NominalToBinary
```
      for converting mult-valued nominal attributes to binary
    - m_ReplaceMissingValues
```
ReplaceMissingValues m_ReplaceMissingValues
```
      The filter used to get rid of missing values.
    - m_Normalize
```
boolean m_Normalize
```
      normalize input data
    - m_noReplaceMissingValues
```
boolean m_noReplaceMissingValues
```
      If true, the replace missing values filter is not applied
    - m_SVMType
```
int m_SVMType
```
      the SVM type
    - m_KernelType
```
int m_KernelType
```
      the kernel type
    - m_Degree
```
int m_Degree
```
      for poly - in older versions of libsvm declared as a double. At least since 2.82 it is an int.
    - m_Gamma
```
double m_Gamma
```
      for poly/rbf/sigmoid
    - m_GammaActual
```
double m_GammaActual
```
      for poly/rbf/sigmoid (the actual gamma)
    - m_Coef0
```
double m_Coef0
```
      for poly/sigmoid
    - m_CacheSize
```
double m_CacheSize
```
      in MB
    - m_eps
```
double m_eps
```
      stopping criteria
    - m_Cost
```
double m_Cost
```
      cost, for C_SVC, EPSILON_SVR and NU_SVR
    - m_WeightLabel
```
int[] m_WeightLabel
```
      for C_SVC
    - m_Weight
```
double[] m_Weight
```
      for C_SVC
    - m_nu
```
double m_nu
```
      for NU_SVC, ONE_CLASS, and NU_SVR
    - m_Loss
```
double m_Loss
```
      loss, for EPSILON_SVR
    - m_Shrinking
```
boolean m_Shrinking
```
      use the shrinking heuristics
    - m_ProbabilityEstimates
```
boolean m_ProbabilityEstimates
```
      whether to generate probability estimates instead of +1/-1 in case of classification problems
- Class weka.classifiers.functions.LinearRegression extends Classifier implements Serializable
  
  serialVersionUID:
  
  -3364580862046573747L
  - 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
  
  serialVersionUID:
  
  3932117032546553727L
  - 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
```
      An attribute filter
    - 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.
    - m_structure
```
Instances m_structure
```
- Class weka.classifiers.functions.MultilayerPerceptron extends Classifier implements Serializable
  
  serialVersionUID:
  
  -5990607817048210779L
  - Serialized Fields
    - m_ZeroR
```
Classifier m_ZeroR
```
      a ZeroR model in case no model can be built from the data or the network predicts all zeros for the classes
    - m_useDefaultModel
```
boolean m_useDefaultModel
```
      Whether to use the default ZeroR model
    - 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
  
  serialVersionUID:
  
  7305185603191183338L
  - 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
  
  serialVersionUID:
  
  7230266976059115435L
  - 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
```
      the estimator
    - olscThreshold
```
double olscThreshold
```
- Class weka.classifiers.functions.PLSClassifier extends Classifier implements Serializable
  
  serialVersionUID:
  
  4819775160590973256L
  - Serialized Fields
    - m_Filter
```
PLSFilter m_Filter
```
      the PLS filter
    - m_ActualFilter
```
PLSFilter m_ActualFilter
```
      the actual filter to use
- Class weka.classifiers.functions.RBFNetwork extends Classifier implements Serializable
  
  serialVersionUID:
  
  -3669814959712675720L
  - 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
  
  serialVersionUID:
  
  1679336022895414137L
  - 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
  
  serialVersionUID:
  
  7397710626304705059L
  - 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
    - m_weightTrimBeta
```
double m_weightTrimBeta
```
      Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.
    - m_useAIC
```
boolean m_useAIC
```
      If true, the AIC is used to choose the best iteration
- Class weka.classifiers.functions.SMO extends Classifier implements Serializable
  
  serialVersionUID:
  
  -6585883636378691736L
  - Serialized Fields
    - m_classifiers
```
SMO.BinarySMO[][] m_classifiers
```
      The binary classifier(s)
    - 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_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_classIndex
```
int m_classIndex
```
      The class index from the training data
    - m_classAttribute
```
Attribute m_classAttribute
```
      The class attribute
    - m_KernelIsLinear
```
boolean m_KernelIsLinear
```
      whether the kernel is a linear one
    - 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
    - m_kernel
```
Kernel m_kernel
```
      the kernel to use
- Class weka.classifiers.functions.SMO.BinarySMO extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8246163625699362456L
  - 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
```
      {i: 0 < m_alpha[i] < C}
    - m_I1
```
SMOset m_I1
```
      {i: m_class[i] = 1, m_alpha[i] = 0}
    - m_I2
```
SMOset m_I2
```
      {i: m_class[i] = -1, m_alpha[i] =C}
    - m_I3
```
SMOset m_I3
```
      {i: m_class[i] = 1, m_alpha[i] = C}
    - m_I4
```
SMOset m_I4
```
      {i: m_class[i] = -1, m_alpha[i] = 0}
    - 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
  
  serialVersionUID:
  
  -7149606251113102827L
  - Serialized Fields
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither
    - 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? If so, less need to filter
    - m_C
```
double m_C
```
      capacity parameter
    - m_x1
```
double m_x1
```
      coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0
    - m_x0
```
double m_x0
```
    - m_optimizer
```
RegOptimizer m_optimizer
```
      contains the algorithm used for learning
    - m_kernel
```
Kernel m_kernel
```
      the configured kernel
- Class weka.classifiers.functions.SPegasos extends Classifier implements Serializable
  
  serialVersionUID:
  
  -3732968666673530290L
  - Serialized Fields
    - m_replaceMissing
```
ReplaceMissingValues m_replaceMissing
```
      Replace missing values
    - m_nominalToBinary
```
NominalToBinary m_nominalToBinary
```
      Convert nominal attributes to numerically coded binary ones
    - m_normalize
```
Normalize m_normalize
```
      Normalize the training data
    - m_lambda
```
double m_lambda
```
      The regularization parameter
    - m_weights
```
double[] m_weights
```
      Stores the weights (+ bias in the last element)
    - m_t
```
double m_t
```
      Holds the current iteration number
    - m_epochs
```
int m_epochs
```
      The number of epochs to perform (batch learning). Total iterations is m_epochs * num instances
    - m_dontNormalize
```
boolean m_dontNormalize
```
      Turn off normalization of the input data. This option gets forced for incremental training.
    - m_dontReplaceMissing
```
boolean m_dontReplaceMissing
```
      Turn off global replacement of missing values. Missing values will be ignored instead. This option gets forced for incremental training.
    - m_data
```
Instances m_data
```
      Holds the header of the training data
    - m_loss
```
int m_loss
```
      The current loss function to minimize
- Class weka.classifiers.functions.VotedPerceptron extends Classifier implements Serializable
  
  serialVersionUID:
  
  -1072429260104568698L
  - 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
  
  serialVersionUID:
  
  3543770107994321324L
  - 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 vector for prediction (pos)
    - m_predNegVector
```
double[] m_predNegVector
```
      The weight vector for prediction (neg)
    - 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
  
  serialVersionUID:
  
  8572152807755673630L
- Class weka.classifiers.functions.neural.NeuralConnection extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -286208828571059163L
  - 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
  
  serialVersionUID:
  
  -1085750607680839163L
  - 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
  
  serialVersionUID:
  
  -5162958458177475652L
Package weka.classifiers.functions.pace
- Class weka.classifiers.functions.pace.PaceMatrix extends Matrix implements Serializable
  
  serialVersionUID:
  
  2699925616857843973L
Package weka.classifiers.functions.supportVector
- Class weka.classifiers.functions.supportVector.CachedKernel extends Kernel implements Serializable
  
  serialVersionUID:
  
  702810182699015136L
  - 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
  
  serialVersionUID:
  
  -6102771099905817064L
  - Serialized Fields
    - m_data
```
Instances m_data
```
      The dataset
    - m_Debug
```
boolean m_Debug
```
      enables debugging output
    - m_ChecksTurnedOff
```
boolean m_ChecksTurnedOff
```
      Turns off all checks
- Class weka.classifiers.functions.supportVector.NormalizedPolyKernel extends PolyKernel implements Serializable
  
  serialVersionUID:
  
  1248574185532130851L
- Class weka.classifiers.functions.supportVector.PolyKernel extends CachedKernel implements Serializable
  
  serialVersionUID:
  
  -321831645846363201L
  - 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.PrecomputedKernelMatrixKernel extends Kernel implements Serializable
  
  serialVersionUID:
  
  -321831645846363333L
  - Serialized Fields
    - m_KernelMatrixFile
```
java.io.File m_KernelMatrixFile
```
      The file holding the kernel matrix.
    - m_KernelMatrix
```
Matrix m_KernelMatrix
```
      The kernel matrix.
    - m_Counter
```
int m_Counter
```
      A classifier counter.
- Class weka.classifiers.functions.supportVector.Puk extends CachedKernel implements Serializable
  
  serialVersionUID:
  
  1682161522559978851L
  - Serialized Fields
    - m_kernelPrecalc
```
double[] m_kernelPrecalc
```
      The precalculated dotproducts of <inst_i,inst_i>
    - m_omega
```
double m_omega
```
      Omega for the Puk kernel.
    - m_sigma
```
double m_sigma
```
      Sigma for the Puk kernel.
    - m_factor
```
double m_factor
```
      Cached factor for the Puk kernel.
- Class weka.classifiers.functions.supportVector.RBFKernel extends CachedKernel implements Serializable
  
  serialVersionUID:
  
  5247117544316387852L
  - Serialized Fields
    - m_kernelPrecalc
```
double[] m_kernelPrecalc
```
      The precalculated dotproducts of <inst_i,inst_i>
    - m_gamma
```
double m_gamma
```
      Gamma for the RBF kernel.
- Class weka.classifiers.functions.supportVector.RegOptimizer extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -2198266997254461814L
  - Serialized Fields
    - m_alpha
```
double[] m_alpha
```
      alpha and alpha* arrays containing weights for solving dual problem
    - m_alphaStar
```
double[] m_alphaStar
```
    - m_b
```
double m_b
```
      offset
    - m_epsilon
```
double m_epsilon
```
      epsilon of epsilon-insensitive cost function
    - m_C
```
double m_C
```
      capacity parameter, copied from SMOreg
    - m_target
```
double[] m_target
```
      class values/desired output vector
    - m_data
```
Instances m_data
```
      points to data set
    - m_kernel
```
Kernel m_kernel
```
      the kernel
    - m_classIndex
```
int m_classIndex
```
      index of class variable in data set
    - m_nInstances
```
int m_nInstances
```
      number of instances in data set
    - m_random
```
java.util.Random m_random
```
      random number generator
    - m_nSeed
```
int m_nSeed
```
      seed for initializing random number generator
    - m_supportVectors
```
SMOset m_supportVectors
```
      set of support vectors, that is, vectors with alpha(*)!=0
    - m_nEvals
```
long m_nEvals
```
      number of kernel evaluations, used for printing statistics only
    - m_nCacheHits
```
int m_nCacheHits
```
      number of kernel cache hits, used for printing statistics only
    - m_weights
```
double[] m_weights
```
      weights for linear kernel
    - m_sparseWeights
```
double[] m_sparseWeights
```
      Variables to hold weight vector in sparse form. (To reduce storage requirements.)
    - m_sparseIndices
```
int[] m_sparseIndices
```
    - m_bModelBuilt
```
boolean m_bModelBuilt
```
      flag to indicate whether the model is built yet
    - m_SVM
```
SMOreg m_SVM
```
      parent SMOreg class
- Class weka.classifiers.functions.supportVector.RegSMO extends RegOptimizer implements Serializable
  
  serialVersionUID:
  
  -7504070793279598638L
  - Serialized Fields
    - m_eps
```
double m_eps
```
      tolerance parameter, smaller changes on alpha in inner loop will be ignored
    - m_error
```
double[] m_error
```
      error cache containing m_error[i] = SVMOutput(i) - m_target[i] - m_b
      note, we don't need m_b in the cache, since if we do, we need to maintain it when m_b is updated
    - m_alpha1
```
double m_alpha1
```
      alpha value for first candidate
    - m_alpha1Star
```
double m_alpha1Star
```
      alpha* value for first candidate
    - m_alpha2
```
double m_alpha2
```
      alpha value for second candidate
    - m_alpha2Star
```
double m_alpha2Star
```
      alpha* value for second candidate
- Class weka.classifiers.functions.supportVector.RegSMOImproved extends RegSMO implements Serializable
  
  serialVersionUID:
  
  471692841446029784L
  - Serialized Fields
    - m_I0
```
SMOset m_I0
```
      The different sets used by the algorithm.
    - m_iSet
```
int[] m_iSet
```
      Index set {i: 0 < m_alpha[i] < C || 0 < m_alphaStar[i] < C}}
    - m_bUp
```
double m_bUp
```
      b.up and b.low boundaries used to determine stopping criterion
    - m_bLow
```
double m_bLow
```
      b.up and b.low boundaries used to determine stopping criterion
    - m_iUp
```
int m_iUp
```
      index of the instance that gave us b.up and b.low
    - m_iLow
```
int m_iLow
```
      index of the instance that gave us b.up and b.low
    - m_fTolerance
```
double m_fTolerance
```
      tolerance parameter used for checking stopping criterion b.up < b.low + 2 tol
    - m_bUseVariant1
```
boolean m_bUseVariant1
```
      set true to use variant 1 of the paper, otherwise use variant 2
- Class weka.classifiers.functions.supportVector.SMOset extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8364829283188675777L
  - 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
- Class weka.classifiers.functions.supportVector.StringKernel extends Kernel implements Serializable
  
  serialVersionUID:
  
  -4902954211202690123L
  - Serialized Fields
    - m_cacheSize
```
int m_cacheSize
```
      The size of the cache (a prime number)
    - m_internalCacheSize
```
int m_internalCacheSize
```
      The size of the internal cache for intermediate results (a prime number)
    - m_strAttr
```
int m_strAttr
```
      The attribute number of the string attribute
    - m_storage
```
double[] m_storage
```
      Kernel cache (i.e., cache for kernel evaluations)
    - m_keys
```
long[] m_keys
```
    - m_kernelEvals
```
int m_kernelEvals
```
      Counts the number of kernel evaluations.
    - m_numInsts
```
int m_numInsts
```
      The number of instance in the dataset
    - m_PruningMethod
```
int m_PruningMethod
```
      the pruning method
    - m_lambda
```
double m_lambda
```
      the decay factor that penalizes non-continuous substring matches. See [1] for details.
    - m_subsequenceLength
```
int m_subsequenceLength
```
      The substring length
    - m_maxSubsequenceLength
```
int m_maxSubsequenceLength
```
      The maximum substring length for lambda pruning
    - m_powersOflambda
```
double[] m_powersOflambda
```
      the precalculated powers of lambda
    - m_normalize
```
boolean m_normalize
```
      flag for switching normalization on or off. This defaults to false and can be turned on by the switch for feature space normalization in SMO
    - maxCache
```
int maxCache
```
      private cache for intermediate results
    - cachekh
```
double[] cachekh
```
    - cachekhK
```
int[] cachekhK
```
    - cachekh2
```
double[] cachekh2
```
    - cachekh2K
```
int[] cachekh2K
```
    - m_multX
```
int m_multX
```
      cached indexes for private cache
    - m_multY
```
int m_multY
```
    - m_multZ
```
int m_multZ
```
    - m_multZZ
```
int m_multZZ
```
    - m_useRecursionCache
```
boolean m_useRecursionCache
```
Package weka.classifiers.lazy
- Class weka.classifiers.lazy.IB1 extends Classifier implements Serializable
  
  serialVersionUID:
  
  -6152184127304895851L
  - 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
  
  serialVersionUID:
  
  -3080186098777067172L
  - 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_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_NNSearch
```
NearestNeighbourSearch m_NNSearch
```
      for nearest-neighbor search.
    - m_NumAttributesUsed
```
double m_NumAttributesUsed
```
      The number of attributes the contribute to a prediction.
    - m_defaultModel
```
ZeroR m_defaultModel
```
      Default ZeroR model to use when there are no training instances
- Class weka.classifiers.lazy.KStar extends Classifier implements Serializable
  
  serialVersionUID:
  
  332458330800479083L
  - 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
  
  serialVersionUID:
  
  5648559277738985156L
  - 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
```
      leave-one-out errors on the training dataset.
    - m_ErrorFlags
```
boolean[] m_ErrorFlags
```
      leave-one-out error flags on the training dataaet.
    - leftHand
```
java.util.ArrayList<E> leftHand
```
      best attribute's index list. maybe as output result
    - m_subOldErrorFlags
```
boolean[] m_subOldErrorFlags
```
      following is defined by wangzh, the number of instances to be classified incorrectly on the subset.
    - m_RemainderErrors
```
int m_RemainderErrors
```
      the number of instances to be classified incorrectly besides the subset.
    - m_Number
```
int m_Number
```
      the number of instance to be processed
    - m_NumberOfInstances
```
int m_NumberOfInstances
```
      the Number of Instances to be used in building a classifiers
    - m_NCV
```
boolean m_NCV
```
      for printing in n-fold cross validation
    - m_subInstances
```
LBR.Indexes m_subInstances
```
      index of instances and attributes for the given dataset
    - tempSubInstances
```
LBR.Indexes tempSubInstances
```
      index of instances and attributes for the given dataset
    - posteriorsArray
```
double[] posteriorsArray
```
      probability values array
    - bestCnt
```
int bestCnt
```
    - tempCnt
```
int tempCnt
```
    - forCnt
```
int forCnt
```
    - whileCnt
```
int whileCnt
```
- Class weka.classifiers.lazy.LBR.Indexes extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -2771490019751421307L
  - 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
  
  serialVersionUID:
  
  1979797405383665815L
  - Serialized Fields
    - m_Train
```
Instances m_Train
```
      The training instances used for classification.
    - 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_NNSearch
```
NearestNeighbourSearch m_NNSearch
```
      The nearest neighbour search algorithm to use. (Default: weka.core.neighboursearch.LinearNNSearch)
    - 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
  
  serialVersionUID:
  
  -7693632394267140678L
  - Serialized Fields
    - m_Cache
```
KStarCache.CacheTable m_Cache
```
      cache table
- Class weka.classifiers.lazy.kstar.KStarCache.CacheTable extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8086106452588253423L
  - 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
  
  serialVersionUID:
  
  4057602386766259138L
  - 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
  
  serialVersionUID:
  
  -7378107808933117974L
  - 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
  
  serialVersionUID:
  
  -2368937577670527151L
  - Serialized Fields
    - 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
  
  serialVersionUID:
  
  -5951805453487947577L
  - 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
  
  serialVersionUID:
  
  -5178288489778728847L
  - 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.ClassificationViaClustering extends Classifier implements Serializable
  
  serialVersionUID:
  
  -5687069451420259135L
  - Serialized Fields
    - m_Clusterer
```
Clusterer m_Clusterer
```
      the cluster algorithm used (template)
    - m_ActualClusterer
```
Clusterer m_ActualClusterer
```
      the actual cluster algorithm being used
    - m_OriginalHeader
```
Instances m_OriginalHeader
```
      the original training data header
    - m_ClusteringHeader
```
Instances m_ClusteringHeader
```
      the modified training data header
    - m_ClustersToClasses
```
double[] m_ClustersToClasses
```
      the mapping between clusters and classes
    - m_ZeroR
```
Classifier m_ZeroR
```
      the default model
- Class weka.classifiers.meta.ClassificationViaRegression extends SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  4500023123618669859L
  - 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
  
  serialVersionUID:
  
  -720658209263002404L
  - 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
  
  serialVersionUID:
  
  -6529603380876641265L
  - 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
  
  serialVersionUID:
  
  -4668812017709421953L
  - Serialized Fields
    - m_ParamChar
```
java.lang.String 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.Dagging extends RandomizableSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  4560165876570074309L
  - Serialized Fields
    - m_NumFolds
```
int m_NumFolds
```
      the number of folds to use to split the training data
    - m_Vote
```
Vote m_Vote
```
      the classifier used for voting
    - m_Verbose
```
boolean m_Verbose
```
      whether to output some progress information during building
- Class weka.classifiers.meta.Decorate extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -6020193348750269931L
  - 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.END extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -4143242362912214956L
  - Serialized Fields
    - m_hashtable
```
java.util.Hashtable<K,V> m_hashtable
```
      The hashtable containing the classifiers for the END.
- Class weka.classifiers.meta.FilteredClassifier extends SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -4523450618538717400L
  - 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
  
  serialVersionUID:
  
  5207837947890081170L
  - 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.GridSearch extends RandomizableSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -3034773968581595348L
  - Serialized Fields
    - m_Filter
```
Filter m_Filter
```
      the Filter
    - m_BestFilter
```
Filter m_BestFilter
```
      the Filter with the best setup
    - m_BestClassifier
```
Classifier m_BestClassifier
```
      the Classifier with the best setup
    - m_Values
```
weka.classifiers.meta.GridSearch.PointDouble m_Values
```
      the best values
    - m_Evaluation
```
int m_Evaluation
```
      the type of evaluation
    - m_Y_Property
```
java.lang.String m_Y_Property
```
      the Y option to work on (without leading dash, preceding 'classifier.' means to set the option for the classifier 'filter.' for the filter)
    - m_Y_Min
```
double m_Y_Min
```
      the minimum of Y
    - m_Y_Max
```
double m_Y_Max
```
      the maximum of Y
    - m_Y_Step
```
double m_Y_Step
```
      the step size of Y
    - m_Y_Base
```
double m_Y_Base
```
      the base for Y
    - m_Y_Expression
```
java.lang.String m_Y_Expression
```
      The expression for the Y property. Available parameters for the expression:
      
      BASE
      
      FROM (= min)
      
      TO (= max)
      
      STEP
      
      I - the current value (from 'from' to 'to' with stepsize 'step')
      See Also:
      
      MathematicalExpression, MathExpression
    - m_X_Property
```
java.lang.String m_X_Property
```
      the X option to work on (without leading dash, preceding 'classifier.' means to set the option for the classifier 'filter.' for the filter)
    - m_X_Min
```
double m_X_Min
```
      the minimum of X
    - m_X_Max
```
double m_X_Max
```
      the maximum of X
    - m_X_Step
```
double m_X_Step
```
      the step size of
    - m_X_Base
```
double m_X_Base
```
      the base for
    - m_X_Expression
```
java.lang.String m_X_Expression
```
      The expression for the X property. Available parameters for the expression:
      
      BASE
      
      FROM (= min)
      
      TO (= max)
      
      STEP
      
      I - the current value (from 'from' to 'to' with stepsize 'step')
      See Also:
      
      MathematicalExpression, MathExpression
    - m_GridIsExtendable
```
boolean m_GridIsExtendable
```
      whether the grid can be extended
    - m_MaxGridExtensions
```
int m_MaxGridExtensions
```
      maximum number of grid extensions (-1 means unlimited)
    - m_GridExtensionsPerformed
```
int m_GridExtensionsPerformed
```
      the number of extensions performed
    - m_SampleSize
```
double m_SampleSize
```
      the sample size to search the initial grid with
    - m_Traversal
```
int m_Traversal
```
      the traversal
    - m_LogFile
```
java.io.File m_LogFile
```
      the log file to use
    - m_Grid
```
weka.classifiers.meta.GridSearch.Grid m_Grid
```
      the value-pairs grid
    - m_Data
```
Instances m_Data
```
      the training data
    - m_Cache
```
weka.classifiers.meta.GridSearch.PerformanceCache m_Cache
```
      the cache for points in the grid that got calculated
    - m_UniformPerformance
```
boolean m_UniformPerformance
```
      whether all performances in the grid are the same
- Class weka.classifiers.meta.GridSearch.Grid extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  7290732613611243139L
  - Serialized Fields
    - m_MinX
```
double m_MinX
```
      the minimum on the X axis
    - m_MaxX
```
double m_MaxX
```
      the maximum on the X axis
    - m_StepX
```
double m_StepX
```
      the step size for the X axis
    - m_LabelX
```
java.lang.String m_LabelX
```
      the label for the X axis
    - m_MinY
```
double m_MinY
```
      the minimum on the Y axis
    - m_MaxY
```
double m_MaxY
```
      the maximum on the Y axis
    - m_StepY
```
double m_StepY
```
      the step size for the Y axis
    - m_LabelY
```
java.lang.String m_LabelY
```
      the label for the Y axis
    - m_Width
```
int m_Width
```
      the number of points on the X axis
    - m_Height
```
int m_Height
```
      the number of points on the Y axis
- Class weka.classifiers.meta.GridSearch.Performance extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -4374706475277588755L
  - Serialized Fields
    - m_Values
```
weka.classifiers.meta.GridSearch.PointDouble m_Values
```
      the value pair the classifier was built with
    - m_CC
```
double m_CC
```
      the Correlation coefficient
    - m_RMSE
```
double m_RMSE
```
      the Root mean squared error
    - m_RRSE
```
double m_RRSE
```
      the Root relative squared error
    - m_MAE
```
double m_MAE
```
      the Mean absolute error
    - m_RAE
```
double m_RAE
```
      the Relative absolute error
    - m_ACC
```
double m_ACC
```
      the Accuracy
    - m_Kappa
```
double m_Kappa
```
      the kappa value
- Class weka.classifiers.meta.GridSearch.PerformanceCache extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  5838863230451530252L
  - Serialized Fields
    - m_Cache
```
java.util.Hashtable<K,V> m_Cache
```
      the cache for points in the grid that got calculated
- Class weka.classifiers.meta.GridSearch.PerformanceComparator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  6507592831825393847L
  - Serialized Fields
    - m_Evaluation
```
int m_Evaluation
```
      the performance measure to use for comparison
      
      See Also:
      
      GridSearch.TAGS_EVALUATION
- Class weka.classifiers.meta.GridSearch.PerformanceTable extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  5486491313460338379L
  - Serialized Fields
    - m_Grid
```
weka.classifiers.meta.GridSearch.Grid m_Grid
```
      the corresponding grid
    - m_Performances
```
java.util.Vector<E> m_Performances
```
      the performances
    - m_Type
```
int m_Type
```
      the type of performance the table was generated for
    - m_Table
```
double[][] m_Table
```
      the table with the values
    - m_Min
```
double m_Min
```
      the minimum performance
    - m_Max
```
double m_Max
```
      the maximum performance
- Class weka.classifiers.meta.GridSearch.PointDouble extends java.awt.geom.Point2D.Double implements Serializable
  
  serialVersionUID:
  
  7151661776161898119L
- Class weka.classifiers.meta.GridSearch.PointInt extends java.awt.Point implements Serializable
  
  serialVersionUID:
  
  -5900415163698021618L
- Class weka.classifiers.meta.LogitBoost extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  8627452775249625582L
  - 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
  
  serialVersionUID:
  
  1205317833344726855L
  - 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
  
  serialVersionUID:
  
  -6681619178187935148L
  - 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
  
  serialVersionUID:
  
  -3879602011542849141L
  - Serialized Fields
    - m_Classifiers
```
Classifier[] m_Classifiers
```
      The classifiers.
    - m_pairwiseCoupling
```
boolean m_pairwiseCoupling
```
      Use pairwise coupling with 1-vs-1
    - m_SumOfWeights
```
double[] m_SumOfWeights
```
      Needed for pairwise coupling
    - 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
  
  serialVersionUID:
  
  5710744346128957520L
  - 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
  
  serialVersionUID:
  
  -3461971774059603636L
  - 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_ZeroR
```
ZeroR m_ZeroR
```
      ZeroR classifier for when all base classifier return zero probability.
- Class weka.classifiers.meta.RacedIncrementalLogitBoost extends RandomizableSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  908598343772170052L
  - 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
  
  serialVersionUID:
  
  5559880306684082199L
  - Serialized Fields
    - m_chunkSize
```
int m_chunkSize
```
    - m_instancesConsumed
```
int m_instancesConsumed
```
      number eaten from m_currentSet
    - 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
  
  serialVersionUID:
  
  -9204394360557300092L
- Class weka.classifiers.meta.RandomSubSpace extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  1278172513912424947L
  - Serialized Fields
    - m_SubSpaceSize
```
double m_SubSpaceSize
```
      The size of each bag sample, as a percentage of the training size
    - m_ZeroR
```
Classifier m_ZeroR
```
      a ZeroR model in case no model can be built from the data
- Class weka.classifiers.meta.RegressionByDiscretization extends SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  5066426153134050378L
  - 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.
    - m_DeleteEmptyBins
```
boolean m_DeleteEmptyBins
```
      Whether to delete empty intervals.
    - m_DiscretizedHeader
```
Instances m_DiscretizedHeader
```
      Header of discretized data.
    - m_UseEqualFrequency
```
boolean m_UseEqualFrequency
```
      Use equal-frequency binning
- Class weka.classifiers.meta.RotationForest extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -3255631880798499936L
  - Serialized Fields
    - m_MinGroup
```
int m_MinGroup
```
      The minimum size of a group
    - m_MaxGroup
```
int m_MaxGroup
```
      The maximum size of a group
    - m_NumberOfGroups
```
boolean m_NumberOfGroups
```
      Whether minGroup and maxGroup refer to the number of groups or their size
    - m_RemovedPercentage
```
int m_RemovedPercentage
```
      The percentage of instances to be removed
    - m_Groups
```
int[][][] m_Groups
```
      The attributes of each group
    - m_ProjectionFilter
```
Filter m_ProjectionFilter
```
      The type of projection filter
    - m_ProjectionFilters
```
Filter[][] m_ProjectionFilters
```
      The projection filters
    - m_Headers
```
Instances[] m_Headers
```
      Headers of the transformed dataset
    - m_ReducedHeaders
```
Instances[][] m_ReducedHeaders
```
      Headers of the reduced datasets
    - m_RemoveUseless
```
RemoveUseless m_RemoveUseless
```
      Filter that remove useless attributes
    - m_Normalize
```
Normalize m_Normalize
```
      Filter that normalized the attributes
- Class weka.classifiers.meta.Stacking extends RandomizableMultipleClassifiersCombiner implements Serializable
  
  serialVersionUID:
  
  5134738557155845452L
  - 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
  
  serialVersionUID:
  
  -6717545616603725198L
  - Serialized Fields
    - m_MetaClassifiers
```
Classifier[] m_MetaClassifiers
```
      The meta classifiers (one for each class, like in ClassificationViaRegression)
    - m_attrFilter
```
Remove m_attrFilter
```
      Filter to transform metaData - Remove
    - m_makeIndicatorFilter
```
MakeIndicator m_makeIndicatorFilter
```
      Filter to transform metaData - MakeIndicator
- Class weka.classifiers.meta.ThresholdSelector extends RandomizableSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -1795038053239867444L
  - 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
    - m_nMeasure
```
int m_nMeasure
```
      evaluation measure used for determining threshold
    - m_manualThreshold
```
boolean m_manualThreshold
```
      True if a manually set threshold is being used
    - m_manualThresholdValue
```
double m_manualThresholdValue
```
      -1 = not used by default
- Class weka.classifiers.meta.Vote extends RandomizableMultipleClassifiersCombiner implements Serializable
  
  serialVersionUID:
  
  -637891196294399624L
  - Serialized Fields
    - m_CombinationRule
```
int m_CombinationRule
```
      Combination Rule variable
    - m_Random
```
java.util.Random m_Random
```
      the random number generator used for breaking ties in majority voting
      
      See Also:
      
      Vote.distributionForInstanceMajorityVoting(Instance)
Package weka.classifiers.meta.nestedDichotomies
- Class weka.classifiers.meta.nestedDichotomies.ClassBalancedND extends RandomizableSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  5944063630650811903L
  - Serialized Fields
    - m_FilteredClassifier
```
FilteredClassifier m_FilteredClassifier
```
      The filtered classifier in which the base classifier is wrapped.
    - m_classifiers
```
java.util.Hashtable<K,V> m_classifiers
```
      The hashtable for this node.
    - m_FirstSuccessor
```
ClassBalancedND m_FirstSuccessor
```
      The first successor
    - m_SecondSuccessor
```
ClassBalancedND m_SecondSuccessor
```
      The second successor
    - m_Range
```
Range m_Range
```
      The classes that are grouped together at the current node
    - m_hashtablegiven
```
boolean m_hashtablegiven
```
      Is Hashtable given from END?
- Class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND extends RandomizableSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  5117477294209496368L
  - Serialized Fields
    - m_FilteredClassifier
```
FilteredClassifier m_FilteredClassifier
```
      The filtered classifier in which the base classifier is wrapped.
    - m_classifiers
```
java.util.Hashtable<K,V> m_classifiers
```
      The hashtable for this node.
    - m_FirstSuccessor
```
DataNearBalancedND m_FirstSuccessor
```
      The first successor
    - m_SecondSuccessor
```
DataNearBalancedND m_SecondSuccessor
```
      The second successor
    - m_Range
```
Range m_Range
```
      The classes that are grouped together at the current node
    - m_hashtablegiven
```
boolean m_hashtablegiven
```
      Is Hashtable given from END?
- Class weka.classifiers.meta.nestedDichotomies.ND extends RandomizableSingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -6355893369855683820L
  - Serialized Fields
    - m_ndtree
```
weka.classifiers.meta.nestedDichotomies.ND.NDTree m_ndtree
```
      The tree of classes
    - m_classifiers
```
java.util.Hashtable<K,V> m_classifiers
```
      The hashtable containing all the classifiers
    - m_hashtablegiven
```
boolean m_hashtablegiven
```
      Is Hashtable given from END?
- Class weka.classifiers.meta.nestedDichotomies.ND.NDTree extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4284655952754474880L
  - Serialized Fields
    - m_indices
```
FastVector m_indices
```
      The indices associated with this node
    - m_parent
```
weka.classifiers.meta.nestedDichotomies.ND.NDTree m_parent
```
      The parent
    - m_left
```
weka.classifiers.meta.nestedDichotomies.ND.NDTree m_left
```
      The left successor
    - m_right
```
weka.classifiers.meta.nestedDichotomies.ND.NDTree m_right
```
      The right successor
Package weka.classifiers.mi
- Class weka.classifiers.mi.CitationKNN extends Classifier implements Serializable
  
  serialVersionUID:
  
  -8435377743874094852L
  - Serialized Fields
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_IdIndex
```
int m_IdIndex
```
    - m_Debug
```
boolean m_Debug
```
      Debugging output
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Attributes
```
Instances m_Attributes
```
      attribute name structure of the relational attribute
    - m_NumReferences
```
int m_NumReferences
```
      Number of references
    - m_NumCiters
```
int m_NumCiters
```
      Number of citers
    - m_TrainBags
```
Instances m_TrainBags
```
      Training bags
    - m_CNNDebug
```
boolean m_CNNDebug
```
      Different debugging output
    - m_CitersDebug
```
boolean m_CitersDebug
```
    - m_ReferencesDebug
```
boolean m_ReferencesDebug
```
    - m_HDistanceDebug
```
boolean m_HDistanceDebug
```
    - m_NeighborListDebug
```
boolean m_NeighborListDebug
```
    - m_CNN
```
weka.classifiers.mi.CitationKNN.NeighborList[] m_CNN
```
      C nearest neighbors considering all the bags
    - m_Citers
```
int[] m_Citers
```
      C nearest citers
    - m_References
```
int[] m_References
```
      R nearest references
    - m_HDRank
```
int m_HDRank
```
      Rank associated to the Hausdorff distance
    - m_Diffs
```
double[] m_Diffs
```
      Normalization of the euclidean distance
    - m_Min
```
double[] m_Min
```
    - m_MinNorm
```
double m_MinNorm
```
    - m_Max
```
double[] m_Max
```
    - m_MaxNorm
```
double m_MaxNorm
```
- Class weka.classifiers.mi.MDD extends Classifier implements Serializable
  
  serialVersionUID:
  
  -7273119490545290581L
  - Serialized Fields
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute
    - m_Par
```
double[] m_Par
```
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Data
```
double[][][] m_Data
```
      MI data
    - m_Attributes
```
Instances m_Attributes
```
      All attribute names
    - m_Filter
```
Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither, default:standardize
    - m_Missing
```
ReplaceMissingValues m_Missing
```
      The filter used to get rid of missing values.
- Class weka.classifiers.mi.MIBoost extends SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -3808427225599279539L
  - Serialized Fields
    - m_Models
```
Classifier[] m_Models
```
      the models for the iterations
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Attributes
```
Instances m_Attributes
```
      attributes name for the new dataset used to build the model
    - m_NumIterations
```
int m_NumIterations
```
      Number of iterations
    - m_Beta
```
double[] m_Beta
```
      Voting weights of models
    - m_MaxIterations
```
int m_MaxIterations
```
      the maximum number of boost iterations
    - m_DiscretizeBin
```
int m_DiscretizeBin
```
      the number of discretization bins
    - m_Filter
```
Discretize m_Filter
```
      filter used for discretization
    - m_ConvertToSI
```
MultiInstanceToPropositional m_ConvertToSI
```
      filter used to convert the MI dataset into single-instance dataset
- Class weka.classifiers.mi.MIDD extends Classifier implements Serializable
  
  serialVersionUID:
  
  4263507733600536168L
  - Serialized Fields
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute
    - m_Par
```
double[] m_Par
```
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Data
```
double[][][] m_Data
```
      MI data
    - m_Attributes
```
Instances m_Attributes
```
      All attribute names
    - m_Filter
```
Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither, default:standardize
    - m_Missing
```
ReplaceMissingValues m_Missing
```
      The filter used to get rid of missing values.
- Class weka.classifiers.mi.MIEMDD extends RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  3899547154866223734L
  - Serialized Fields
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute
    - m_Par
```
double[] m_Par
```
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Data
```
double[][][] m_Data
```
      MI data
    - m_Attributes
```
Instances m_Attributes
```
      All attribute names
    - m_emData
```
double[][] m_emData
```
      MI data
    - m_Filter
```
Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither, default:standardize
    - m_Missing
```
ReplaceMissingValues m_Missing
```
      The filter used to get rid of missing values.
- Class weka.classifiers.mi.MILR extends Classifier implements Serializable
  
  serialVersionUID:
  
  1996101190172373826L
  - Serialized Fields
    - m_Par
```
double[] m_Par
```
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Ridge
```
double m_Ridge
```
      The ridge parameter.
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Data
```
double[][][] m_Data
```
      MI data
    - m_Attributes
```
Instances m_Attributes
```
      All attribute names
    - xMean
```
double[] xMean
```
    - xSD
```
double[] xSD
```
    - m_AlgorithmType
```
int m_AlgorithmType
```
      the type of processing
- Class weka.classifiers.mi.MINND extends Classifier implements Serializable
  
  serialVersionUID:
  
  -4512599203273864994L
  - Serialized Fields
    - m_Neighbour
```
int m_Neighbour
```
      The number of nearest neighbour for prediction
    - m_Mean
```
double[][] m_Mean
```
      The mean for each attribute of each exemplar
    - m_Variance
```
double[][] m_Variance
```
      The variance for each attribute of each exemplar
    - m_Dimension
```
int m_Dimension
```
      The dimension of each exemplar, i.e. (numAttributes-2)
    - m_Attributes
```
Instances m_Attributes
```
      header info of the data
    - m_Class
```
double[] m_Class
```
      The class label of each exemplar
    - m_NumClasses
```
int m_NumClasses
```
      The number of class labels in the data
    - m_Weights
```
double[] m_Weights
```
      The weight of each exemplar
    - m_Rate
```
double m_Rate
```
      The learning rate in the gradient descent
    - m_MinArray
```
double[] m_MinArray
```
      The minimum values for numeric attributes.
    - m_MaxArray
```
double[] m_MaxArray
```
      The maximum values for numeric attributes.
    - m_STOP
```
double m_STOP
```
      The stopping criteria of gradient descent
    - m_Change
```
double[][] m_Change
```
      The weights that alter the dimnesion of each exemplar
    - m_NoiseM
```
double[][] m_NoiseM
```
      The noise data of each exemplar
    - m_NoiseV
```
double[][] m_NoiseV
```
      The noise data of each exemplar
    - m_ValidM
```
double[][] m_ValidM
```
      The noise data of each exemplar
    - m_ValidV
```
double[][] m_ValidV
```
      The noise data of each exemplar
    - m_Select
```
int m_Select
```
      The number of nearest neighbour instances in the selection of noises in the training data
    - m_Choose
```
int m_Choose
```
      The number of nearest neighbour exemplars in the selection of noises in the test data
    - m_Decay
```
double m_Decay
```
      The decay rate of learning rate
- Class weka.classifiers.mi.MIOptimalBall extends Classifier implements Serializable
  
  serialVersionUID:
  
  -6465750129576777254L
  - Serialized Fields
    - m_Center
```
double[] m_Center
```
      center of the optimal ball
    - m_Radius
```
double m_Radius
```
      radius of the optimal ball
    - m_Distance
```
double[][][] m_Distance
```
      the distances from each instance in a positive bag to each bag
    - m_Filter
```
Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither
    - m_ConvertToSI
```
MultiInstanceToPropositional m_ConvertToSI
```
      filter used to convert the MI dataset into single-instance dataset
    - m_ConvertToMI
```
PropositionalToMultiInstance m_ConvertToMI
```
      filter used to convert the single-instance dataset into MI dataset
- Class weka.classifiers.mi.MISMO extends Classifier implements Serializable
  
  serialVersionUID:
  
  -5834036950143719712L
  - Serialized Fields
    - m_classifiers
```
weka.classifiers.mi.MISMO.BinaryMISMO[][] m_classifiers
```
      The binary classifier(s)
    - 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_minimax
```
boolean m_minimax
```
      Use MIMinimax feature space?
    - 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_classIndex
```
int m_classIndex
```
      The class index from the training data
    - m_classAttribute
```
Attribute m_classAttribute
```
      The class attribute
    - m_kernel
```
Kernel m_kernel
```
      Kernel to use
    - 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.mi.MISMO.BinaryMISMO extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -7107082483475433531L
  - 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
```
      {i: 0 < m_alpha[i] < C}
    - m_I1
```
SMOset m_I1
```
      {i: m_class[i] = 1, m_alpha[i] = 0}
    - m_I2
```
SMOset m_I2
```
      {i: m_class[i] = -1, m_alpha[i] = C}
    - m_I3
```
SMOset m_I3
```
      {i: m_class[i] = 1, m_alpha[i] = C}
    - m_I4
```
SMOset m_I4
```
      {i: m_class[i] = -1, m_alpha[i] = 0}
    - m_supportVectors
```
SMOset m_supportVectors
```
      The set of support vectors {i: 0 < m_alpha[i]}
    - 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.mi.MISVM extends Classifier implements Serializable
  
  serialVersionUID:
  
  7622231064035278145L
  - Serialized Fields
    - m_SparseFilter
```
Filter m_SparseFilter
```
      The filter used to transform the sparse datasets to nonsparse
    - m_SVM
```
weka.classifiers.mi.MISVM.SVM m_SVM
```
      The SMO classifier used to compute SVM soluton w,b for the dataset
    - m_kernel
```
Kernel m_kernel
```
      the kernel to use
    - m_C
```
double m_C
```
      The complexity parameter.
    - m_Filter
```
Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither
    - m_MaxIterations
```
int m_MaxIterations
```
      the maximum number of iterations to perform
    - m_ConvertToProp
```
MultiInstanceToPropositional m_ConvertToProp
```
      filter used to convert the MI dataset into single-instance dataset
- Class weka.classifiers.mi.MIWrapper extends SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -7707766152904315910L
  - Serialized Fields
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Method
```
int m_Method
```
      the test method
    - m_ConvertToProp
```
MultiInstanceToPropositional m_ConvertToProp
```
      Filter used to convert MI dataset into single-instance dataset
    - m_WeightMethod
```
int m_WeightMethod
```
      the single-instance weight setting method
- Class weka.classifiers.mi.SimpleMI extends SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  9137795893666592662L
  - Serialized Fields
    - m_TransformMethod
```
int m_TransformMethod
```
      the method used in transformation
Package weka.classifiers.mi.supportVector
- Class weka.classifiers.mi.supportVector.MIPolyKernel extends PolyKernel implements Serializable
  
  serialVersionUID:
  
  7926421479341051777L
- Class weka.classifiers.mi.supportVector.MIRBFKernel extends RBFKernel implements Serializable
  
  serialVersionUID:
  
  -8711882393708956962L
  - Serialized Fields
    - m_kernelPrecalc
```
double[][] m_kernelPrecalc
```
      The precalculated dotproducts of <inst_i,inst_i>
Package weka.classifiers.misc
- Class weka.classifiers.misc.HyperPipes extends Classifier implements Serializable
  
  serialVersionUID:
  
  -7527596632268975274L
  - 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.SerializedClassifier extends Classifier implements Serializable
  
  serialVersionUID:
  
  4599593909947628642L
  - Serialized Fields
    - m_ModelFile
```
java.io.File m_ModelFile
```
      the file where the serialized model is stored
- Class weka.classifiers.misc.VFI extends Classifier implements Serializable
  
  serialVersionUID:
  
  8081692166331321866L
  - 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.pmml.consumer
- Class weka.classifiers.pmml.consumer.GeneralRegression extends PMMLClassifier implements Serializable
  
  serialVersionUID:
  
  2583880411828388959L
  - Serialized Fields
    - m_modelType
```
weka.classifiers.pmml.consumer.GeneralRegression.ModelType m_modelType
```
    - m_modelName
```
java.lang.String m_modelName
```
    - m_algorithmName
```
java.lang.String m_algorithmName
```
    - m_functionType
```
int m_functionType
```
    - m_cumulativeLinkFunction
```
weka.classifiers.pmml.consumer.GeneralRegression.CumulativeLinkFunction m_cumulativeLinkFunction
```
    - m_linkFunction
```
weka.classifiers.pmml.consumer.GeneralRegression.LinkFunction m_linkFunction
```
    - m_linkParameter
```
double m_linkParameter
```
    - m_trialsVariable
```
java.lang.String m_trialsVariable
```
    - m_trialsValue
```
double m_trialsValue
```
    - m_distribution
```
weka.classifiers.pmml.consumer.GeneralRegression.Distribution m_distribution
```
    - m_distParameter
```
double m_distParameter
```
    - m_offsetVariable
```
java.lang.String m_offsetVariable
```
    - m_offsetValue
```
double m_offsetValue
```
    - m_parameterList
```
java.util.ArrayList<E> m_parameterList
```
    - m_factorList
```
java.util.ArrayList<E> m_factorList
```
    - m_covariateList
```
java.util.ArrayList<E> m_covariateList
```
    - m_ppMatrix
```
weka.classifiers.pmml.consumer.GeneralRegression.PPCell[][] m_ppMatrix
```
    - m_paramMatrix
```
weka.classifiers.pmml.consumer.GeneralRegression.PCell[][] m_paramMatrix
```
- Class weka.classifiers.pmml.consumer.NeuralNetwork extends PMMLClassifier implements Serializable
  
  serialVersionUID:
  
  -4545904813133921249L
  - Serialized Fields
    - m_functionType
```
weka.classifiers.pmml.consumer.NeuralNetwork.MiningFunction m_functionType
```
      The mining function
    - m_activationFunction
```
weka.classifiers.pmml.consumer.NeuralNetwork.ActivationFunction m_activationFunction
```
      The activation function to use
    - m_normalizationMethod
```
weka.classifiers.pmml.consumer.NeuralNetwork.Normalization m_normalizationMethod
```
      The normalization method
    - m_threshold
```
double m_threshold
```
      Threshold activation
    - m_width
```
double m_width
```
      Width for radial basis
    - m_altitude
```
double m_altitude
```
      Altitude for radial basis
    - m_numberOfInputs
```
int m_numberOfInputs
```
      The number of inputs to the network
    - m_numberOfLayers
```
int m_numberOfLayers
```
      Number of hidden layers in the network
    - m_inputs
```
weka.classifiers.pmml.consumer.NeuralNetwork.NeuralInput[] m_inputs
```
      The inputs to the network
    - m_inputMap
```
java.util.HashMap<K,V> m_inputMap
```
      A map for storing network input values (computed from an incoming instance)
    - m_layers
```
weka.classifiers.pmml.consumer.NeuralNetwork.NeuralLayer[] m_layers
```
      The hidden layers in the network
    - m_outputs
```
weka.classifiers.pmml.consumer.NeuralNetwork.NeuralOutputs m_outputs
```
      The outputs of the network
- Class weka.classifiers.pmml.consumer.PMMLClassifier extends Classifier implements Serializable
  
  serialVersionUID:
  
  -5371600590320702971L
  - Serialized Fields
    - m_pmmlVersion
```
java.lang.String m_pmmlVersion
```
      PMML version
    - m_creatorApplication
```
java.lang.String m_creatorApplication
```
      Creator application
    - m_log
```
Logger m_log
```
      Logger
    - m_dataDictionary
```
Instances m_dataDictionary
```
      The data dictionary
    - m_miningSchema
```
MiningSchema m_miningSchema
```
      The fields and meta data used by the model
- Class weka.classifiers.pmml.consumer.Regression extends PMMLClassifier implements Serializable
  
  serialVersionUID:
  
  -5551125528409488634L
  - Serialized Fields
    - m_algorithmName
```
java.lang.String m_algorithmName
```
      Description of the algorithm
    - m_regressionTables
```
weka.classifiers.pmml.consumer.Regression.RegressionTable[] m_regressionTables
```
      The regression tables for this regression
    - m_normalizationMethod
```
weka.classifiers.pmml.consumer.Regression.Normalization m_normalizationMethod
```
      The normalization to use
- Class weka.classifiers.pmml.consumer.Regression.RegressionTable.CategoricalPredictor extends weka.classifiers.pmml.consumer.Regression.RegressionTable.Predictor implements Serializable
  
  serialVersionUID:
  
  3077920125549906819L
  - Serialized Fields
    - m_valueName
```
java.lang.String m_valueName
```
      The attribute value for this predictor
    - m_valueIndex
```
int m_valueIndex
```
      The index of the attribute value for this predictor
- Class weka.classifiers.pmml.consumer.Regression.RegressionTable.NumericPredictor extends weka.classifiers.pmml.consumer.Regression.RegressionTable.Predictor implements Serializable
  
  serialVersionUID:
  
  -4335075205696648273L
  - Serialized Fields
    - m_exponent
```
double m_exponent
```
      The exponent
- Class weka.classifiers.pmml.consumer.Regression.RegressionTable.PredictorTerm extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  5493100145890252757L
  - Serialized Fields
    - m_coefficient
```
double m_coefficient
```
      The coefficient for this predictor term
    - m_indexes
```
int[] m_indexes
```
      the indexes of the terms to be multiplied
    - m_fieldNames
```
java.lang.String[] m_fieldNames
```
      The names of the terms (attributes) to be multiplied
Package weka.classifiers.rules
- Class weka.classifiers.rules.ConjunctiveRule extends Classifier implements Serializable
  
  serialVersionUID:
  
  -5938309903225087198L
  - 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
  
  serialVersionUID:
  
  2888557078165701326L
  - Serialized Fields
    - m_entries
```
java.util.Hashtable<K,V> m_entries
```
      The hashtable used to hold training instances
    - m_classPriorCounts
```
double[] m_classPriorCounts
```
      The class priors to use when there is no match in the table
    - m_classPriors
```
double[] m_classPriors
```
    - 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 original training instances
    - m_dtInstances
```
Instances m_dtInstances
```
      Holds the final feature selected set of 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_useIBk
```
boolean m_useIBk
```
      Use the IBk classifier rather than majority class
    - m_displayRules
```
boolean m_displayRules
```
      Display Rules
    - 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
    - m_search
```
ASSearch m_search
```
      The search method to use
    - m_evaluator
```
ASEvaluation m_evaluator
```
      Our own internal evaluator
    - m_evaluation
```
Evaluation m_evaluation
```
      The evaluation object used to evaluate subsets
    - m_evaluationMeasure
```
int m_evaluationMeasure
```
    - m_saveMemory
```
boolean m_saveMemory
```
- Class weka.classifiers.rules.DecisionTableHashKey extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  5674163500154964602L
  - 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.
    - key
```
int key
```
      The key
- Class weka.classifiers.rules.DTNB extends DecisionTable implements Serializable
  
  serialVersionUID:
  
  2999557077765701326L
  - Serialized Fields
    - m_NB
```
NaiveBayes m_NB
```
      The naive Bayes half of the hybrid
    - m_nbFeatures
```
int[] m_nbFeatures
```
      The features used by naive Bayes
    - m_percentUsedByDT
```
double m_percentUsedByDT
```
      Percentage of the total number of features used by the decision table
    - m_percentDeleted
```
double m_percentDeleted
```
      Percentage of the features features that were dropped entirely
    - m_backwardWithDelete
```
ASSearch m_backwardWithDelete
```
- Class weka.classifiers.rules.DTNB.BackwardsWithDelete extends ASSearch implements Serializable
- Class weka.classifiers.rules.DTNB.EvalWithDelete extends ASEvaluation implements Serializable
  - Serialized Fields
    - m_deletedFromDTNB
```
java.util.BitSet m_deletedFromDTNB
```
- Class weka.classifiers.rules.JRip extends Classifier implements Serializable
  
  serialVersionUID:
  
  -6589312996832147161L
  - 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.Antd extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8929754772994154334L
  - Serialized Fields
    - att
```
Attribute att
```
      The attribute of the antecedent
    - value
```
double value
```
      The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)
    - maxInfoGain
```
double maxInfoGain
```
      The maximum infoGain achieved by this antecedent test in the growing data
    - accuRate
```
double accuRate
```
      The accurate rate of this antecedent test on the growing data
    - cover
```
double cover
```
      The coverage of this antecedent in the growing data
    - accu
```
double accu
```
      The accurate data for this antecedent in the growing data
- Class weka.classifiers.rules.JRip.NominalAntd extends JRip.Antd implements Serializable
  
  serialVersionUID:
  
  -9102297038837585135L
  - Serialized Fields
    - accurate
```
double[] accurate
```
    - coverage
```
double[] coverage
```
- Class weka.classifiers.rules.JRip.NumericAntd extends JRip.Antd implements Serializable
  
  serialVersionUID:
  
  5699457269983735442L
  - Serialized Fields
    - splitPoint
```
double splitPoint
```
      The split point for this numeric antecedent
- Class weka.classifiers.rules.JRip.RipperRule extends Rule implements Serializable
  
  serialVersionUID:
  
  -2410020717305262952L
  - 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
  
  serialVersionUID:
  
  -1746114858746563180L
- Class weka.classifiers.rules.NNge extends Classifier implements Serializable
  
  serialVersionUID:
  
  4084742275553788972L
  - 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
  
  serialVersionUID:
  
  -3459427003147861443L
  - 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
  
  serialVersionUID:
  
  8121455039782598361L
  - 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
  
  serialVersionUID:
  
  1310258880025902106L
  - 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
  
  serialVersionUID:
  
  -7261533075088314436L
  - 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
  
  serialVersionUID:
  
  8815687740470471229L
- Class weka.classifiers.rules.RuleStats extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -5708153367675298624L
  - 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
  
  serialVersionUID:
  
  48055541465867954L
  - 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
  
  serialVersionUID:
  
  -2757684345218324559L
  - Serialized Fields
    - CF
```
double CF
```
      CF
- Class weka.classifiers.rules.part.ClassifierDecList extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  7284358349711992497L
  - 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
  
  serialVersionUID:
  
  -1427481323245079123L
  - 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
  
  serialVersionUID:
  
  -7228103346297172921L
Package weka.classifiers.trees
- Class weka.classifiers.trees.ADTree extends Classifier implements Serializable
  
  serialVersionUID:
  
  -1532264837167690683L
  - 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.BFTree extends RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  -7035607375962528217L
  - Serialized Fields
    - m_PruningStrategy
```
int m_PruningStrategy
```
      the pruning strategy
    - m_Successors
```
BFTree[] m_Successors
```
      Successor nodes.
    - m_Attribute
```
Attribute m_Attribute
```
      Attribute used for splitting.
    - m_SplitValue
```
double m_SplitValue
```
      Split point (for numeric attributes).
    - m_SplitString
```
java.lang.String m_SplitString
```
      Split subset (for nominal attributes).
    - m_ClassValue
```
double m_ClassValue
```
      Class value for a node.
    - m_ClassAttribute
```
Attribute m_ClassAttribute
```
      Class attribute of a dataset.
    - m_minNumObj
```
int m_minNumObj
```
      Minimum number of instances at leaf nodes.
    - m_numFoldsPruning
```
int m_numFoldsPruning
```
      Number of folds for the pruning.
    - m_isLeaf
```
boolean m_isLeaf
```
      If the ndoe is leaf node.
    - m_FixedExpansion
```
int m_FixedExpansion
```
      Fixed number of expansions (if no pruning method is used, its value is -1. Otherwise, its value is gotten from internal cross-validation).
    - m_Heuristic
```
boolean m_Heuristic
```
      If use huristic search for binary split (default true). Note even if its value is true, it is only used when the number of values of a nominal attribute is larger than 4.
    - m_UseGini
```
boolean m_UseGini
```
      If use Gini index as the splitting criterion - default (if not, information is used).
    - m_UseErrorRate
```
boolean m_UseErrorRate
```
      If use error rate in internal cross-validation to fix the number of expansions - default (if not, root mean squared error is used).
    - m_UseOneSE
```
boolean m_UseOneSE
```
      If use the 1SE rule to make the decision.
    - m_Distribution
```
double[] m_Distribution
```
      Class distributions.
    - m_Props
```
double[] m_Props
```
      Branch proportions.
    - m_SortedIndices
```
int[][] m_SortedIndices
```
      Sorted indices.
    - m_Weights
```
double[][] m_Weights
```
      Sorted weights.
    - m_Dists
```
double[][][] m_Dists
```
      Distributions of each attribute for two successor nodes.
    - m_ClassProbs
```
double[] m_ClassProbs
```
      Class probabilities.
    - m_TotalWeight
```
double m_TotalWeight
```
      Total weights.
    - m_SizePer
```
double m_SizePer
```
      The training data size (0-1). Default 1.
- Class weka.classifiers.trees.DecisionStump extends Classifier implements Serializable
  
  serialVersionUID:
  
  1618384535950391L
  - 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.FT extends Classifier implements Serializable
  
  serialVersionUID:
  
  -1113212459618105000L
  - Serialized Fields
    - m_replaceMissing
```
ReplaceMissingValues m_replaceMissing
```
      Filter to replace missing values
    - m_nominalToBinary
```
NominalToBinary m_nominalToBinary
```
      Filter to replace nominal attributes
    - m_tree
```
FTtree m_tree
```
      root of the logistic model tree
    - m_convertNominal
```
boolean m_convertNominal
```
      convert nominal attributes to binary ?
    - 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
    - m_modelType
```
int m_modelType
```
      Model Type, value: 0 is FT, 1 is FTLeaves, 2 is FTInner
    - m_weightTrimBeta
```
double m_weightTrimBeta
```
      Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.
    - m_useAIC
```
boolean m_useAIC
```
      If true, the AIC is used to choose the best LogitBoost iteration
- Class weka.classifiers.trees.Id3 extends Classifier implements Serializable
  
  serialVersionUID:
  
  -2693678647096322561L
  - 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.J48graft extends Classifier implements Serializable
  
  serialVersionUID:
  
  8823716098042427799L
  - 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_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_relabel
```
boolean m_relabel
```
      relabel instances when grafting
- Class weka.classifiers.trees.LADTree extends Classifier implements Serializable
  
  serialVersionUID:
  
  -4940716114518300302L
  - Serialized Fields
    - Z_MAX
```
double Z_MAX
```
    - m_numOfClasses
```
int m_numOfClasses
```
    - m_trainInstances
```
ReferenceInstances m_trainInstances
```
    - m_root
```
weka.classifiers.trees.LADTree.PredictionNode m_root
```
    - m_lastAddedSplitNum
```
int m_lastAddedSplitNum
```
    - m_numericAttIndices
```
int[] m_numericAttIndices
```
    - m_search_smallestLeastSquares
```
double m_search_smallestLeastSquares
```
    - m_search_bestInsertionNode
```
weka.classifiers.trees.LADTree.PredictionNode m_search_bestInsertionNode
```
    - m_search_bestSplitter
```
weka.classifiers.trees.LADTree.Splitter m_search_bestSplitter
```
    - m_search_bestPathInstances
```
Instances m_search_bestPathInstances
```
    - m_staticPotentialSplitters2way
```
FastVector m_staticPotentialSplitters2way
```
    - m_nodesExpanded
```
int m_nodesExpanded
```
    - m_examplesCounted
```
int m_examplesCounted
```
    - m_boostingIterations
```
int m_boostingIterations
```
- Class weka.classifiers.trees.LADTree.LADInstance extends Instance implements Serializable
  - Serialized Fields
    - fVector
```
double[] fVector
```
    - wVector
```
double[] wVector
```
    - pVector
```
double[] pVector
```
    - zVector
```
double[] zVector
```
- Class weka.classifiers.trees.LADTree.PredictionNode extends java.lang.Object implements Serializable
  - Serialized Fields
    - values
```
double[] values
```
    - children
```
FastVector children
```
- Class weka.classifiers.trees.LADTree.Splitter extends java.lang.Object implements Serializable
  - Serialized Fields
    - attIndex
```
int attIndex
```
    - orderAdded
```
int orderAdded
```
- Class weka.classifiers.trees.LADTree.TwoWayNominalSplit extends weka.classifiers.trees.LADTree.Splitter implements Serializable
  - Serialized Fields
    - trueSplitValue
```
int trueSplitValue
```
    - children
```
weka.classifiers.trees.LADTree.PredictionNode[] children
```
- Class weka.classifiers.trees.LADTree.TwoWayNumericSplit extends weka.classifiers.trees.LADTree.Splitter implements Serializable
  - Serialized Fields
    - splitPoint
```
double splitPoint
```
    - children
```
weka.classifiers.trees.LADTree.PredictionNode[] children
```
- Class weka.classifiers.trees.LMT extends Classifier implements Serializable
  
  serialVersionUID:
  
  -1113212459618104943L
  - 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
    - m_weightTrimBeta
```
double m_weightTrimBeta
```
      Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.
    - m_useAIC
```
boolean m_useAIC
```
      If true, the AIC is used to choose the best LogitBoost iteration
- Class weka.classifiers.trees.M5P extends M5Base implements Serializable
  
  serialVersionUID:
  
  -6118439039768244417L
- Class weka.classifiers.trees.NBTree extends Classifier implements Serializable
  
  serialVersionUID:
  
  -4716005707058256086L
  - 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
  
  serialVersionUID:
  
  -2260823972777004705L
  - 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.
    - m_MaxDepth
```
int m_MaxDepth
```
      The maximum depth of the trees (0 = unlimited)
- Class weka.classifiers.trees.RandomTree extends Classifier implements Serializable
  
  serialVersionUID:
  
  8934314652175299374L
  - Serialized Fields
    - m_Tree
```
weka.classifiers.trees.RandomTree.Tree m_Tree
```
      The Tree object
    - m_Info
```
Instances m_Info
```
      The header information.
    - m_MinNum
```
double m_MinNum
```
      Minimum number of instances for leaf.
    - m_KValue
```
int m_KValue
```
      The number of attributes considered for a split.
    - m_randomSeed
```
int m_randomSeed
```
      The random seed to use.
    - m_MaxDepth
```
int m_MaxDepth
```
      The maximum depth of the tree (0 = unlimited)
    - m_NumFolds
```
int m_NumFolds
```
      Determines how much data is used for backfitting
    - m_AllowUnclassifiedInstances
```
boolean m_AllowUnclassifiedInstances
```
      Whether unclassified instances are allowed
    - m_zeroR
```
Classifier m_zeroR
```
      a ZeroR model in case no model can be built from the data
- Class weka.classifiers.trees.RandomTree.Tree extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  3549573538656522569L
  - Serialized Fields
    - m_Successors
```
weka.classifiers.trees.RandomTree.Tree[] 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_Prop
```
double[] m_Prop
```
      The proportions of training instances going down each branch.
    - m_ClassDistribution
```
double[] m_ClassDistribution
```
      Class probabilities from the training data.
- Class weka.classifiers.trees.REPTree extends Classifier implements Serializable
  
  serialVersionUID:
  
  -9216785998198681299L
  - 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
  
  serialVersionUID:
  
  -1635481717888437935L
  - 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 plus sum of weighted targets in the numeric case (i.e. array has only two elements).
    - 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.SimpleCart extends RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  4154189200352566053L
  - Serialized Fields
    - m_train
```
Instances m_train
```
      Training data.
    - m_Successors
```
SimpleCart[] m_Successors
```
      Successor nodes.
    - m_Attribute
```
Attribute m_Attribute
```
      Attribute used to split data.
    - m_SplitValue
```
double m_SplitValue
```
      Split point for a numeric attribute.
    - m_SplitString
```
java.lang.String m_SplitString
```
      Split subset used to split data for nominal attributes.
    - m_ClassValue
```
double m_ClassValue
```
      Class value if the node is leaf.
    - m_ClassAttribute
```
Attribute m_ClassAttribute
```
      Class attriubte of data.
    - m_minNumObj
```
double m_minNumObj
```
      Minimum number of instances in at the terminal nodes.
    - m_numFoldsPruning
```
int m_numFoldsPruning
```
      Number of folds for minimal cost-complexity pruning.
    - m_Alpha
```
double m_Alpha
```
      Alpha-value (for pruning) at the node.
    - m_numIncorrectModel
```
double m_numIncorrectModel
```
      Number of training examples misclassified by the model (subtree rooted).
    - m_numIncorrectTree
```
double m_numIncorrectTree
```
      Number of training examples misclassified by the model (subtree not rooted).
    - m_isLeaf
```
boolean m_isLeaf
```
      Indicate if the node is a leaf node.
    - m_Prune
```
boolean m_Prune
```
      If use minimal cost-compexity pruning.
    - m_totalTrainInstances
```
int m_totalTrainInstances
```
      Total number of instances used to build the classifier.
    - m_Props
```
double[] m_Props
```
      Proportion for each branch.
    - m_ClassProbs
```
double[] m_ClassProbs
```
      Class probabilities.
    - m_Distribution
```
double[] m_Distribution
```
      Distributions of leaf node (or temporary leaf node in minimal cost-complexity pruning)
    - m_Heuristic
```
boolean m_Heuristic
```
      If use huristic search for nominal attributes in multi-class problems (default true).
    - m_UseOneSE
```
boolean m_UseOneSE
```
      If use the 1SE rule to make final decision tree.
    - m_SizePer
```
double m_SizePer
```
      Training data size.
- Class weka.classifiers.trees.UserClassifier extends Classifier implements Serializable
  
  serialVersionUID:
  
  6483901103562809843L
  - 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
  
  serialVersionUID:
  
  6018958856358698814L
  - 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
  
  serialVersionUID:
  
  -8022666381920252997L
- Class weka.classifiers.trees.adtree.Splitter extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  8190449848490055L
  - 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
  
  serialVersionUID:
  
  -4598366190152721355L
  - 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
  
  serialVersionUID:
  
  449769177903158283L
  - 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.ft
- Class weka.classifiers.trees.ft.FTInnerNode extends FTtree implements Serializable
  
  serialVersionUID:
  
  -1125334488640233181L
- Class weka.classifiers.trees.ft.FTLeavesNode extends FTtree implements Serializable
  
  serialVersionUID:
  
  950601378326259315L
- Class weka.classifiers.trees.ft.FTNode extends FTtree implements Serializable
  
  serialVersionUID:
  
  2317688685139295063L
- Class weka.classifiers.trees.ft.FTtree extends LogisticBase implements Serializable
  
  serialVersionUID:
  
  1862737145870398755L
  - 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_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_numInstances
```
int m_numInstances
```
      Number of instances at the node
    - m_localModel
```
ClassifierSplitModel m_localModel
```
      The ClassifierSplitModel (for splitting)
    - m_auxLocalModel
```
ClassifierSplitModel m_auxLocalModel
```
      Auxiliary copy ClassifierSplitModel (for splitting)
    - m_sons
```
FTtree[] m_sons
```
      Array of children of the node
    - m_leafclass
```
int m_leafclass
```
      Stores leaf class value
    - m_isLeaf
```
boolean m_isLeaf
```
      True if node is leaf
    - m_hasConstr
```
boolean m_hasConstr
```
      True if node has or splits on constructor
    - m_constError
```
double m_constError
```
      Constructor error
    - m_CF
```
float m_CF
```
      Confidence level
Package weka.classifiers.trees.j48
- Class weka.classifiers.trees.j48.BinC45ModelSelection extends ModelSelection implements Serializable
  
  serialVersionUID:
  
  179170923545122001L
  - 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
  
  serialVersionUID:
  
  -1278776919563022474L
  - 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
  
  serialVersionUID:
  
  3372204862440821989L
  - 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
  
  serialVersionUID:
  
  -4813820170260388194L
  - 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.C45PruneableClassifierTreeG extends ClassifierTree implements Serializable
  
  serialVersionUID:
  
  66981207374331964L
  - 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.
    - m_relabel
```
boolean m_relabel
```
      flag for using relabelling when grafting
    - m_BiProbCrit
```
double m_BiProbCrit
```
      binomial probability critical value
    - m_Debug
```
boolean m_Debug
```
- Class weka.classifiers.trees.j48.C45Split extends ClassifierSplitModel implements Serializable
  
  serialVersionUID:
  
  3064079330067903161L
  - 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
  
  serialVersionUID:
  
  4280730118393457457L
  - 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
  
  serialVersionUID:
  
  -8722249377542734193L
  - 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
  
  serialVersionUID:
  
  8526859638230806576L
  - 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
  
  serialVersionUID:
  
  -2618691439791653056L
- Class weka.classifiers.trees.j48.EntropySplitCrit extends EntropyBasedSplitCrit implements Serializable
  
  serialVersionUID:
  
  5986252682266803935L
- Class weka.classifiers.trees.j48.GainRatioSplitCrit extends EntropyBasedSplitCrit implements Serializable
  
  serialVersionUID:
  
  -433336694718670930L
- Class weka.classifiers.trees.j48.GraftSplit extends ClassifierSplitModel implements Serializable
  
  serialVersionUID:
  
  722773260393182051L
  - Serialized Fields
    - m_graftdistro
```
Distribution m_graftdistro
```
      the distribution for graft values, from cases in atbop
    - m_attIndex
```
int m_attIndex
```
      the attribute we are splitting on
    - m_splitPoint
```
double m_splitPoint
```
      value of split point (if numeric attribute)
    - m_maxClass
```
int m_maxClass
```
      dominant class of the subset specified by m_testType
    - m_otherLeafMaxClass
```
int m_otherLeafMaxClass
```
      dominant class of the subset not specified by m_testType
    - m_laplace
```
double m_laplace
```
      laplace value of the subset specified by m_testType for m_maxClass
    - m_leafdistro
```
Distribution m_leafdistro
```
      leaf for the subset specified by m_testType
    - m_testType
```
int m_testType
```
      type of test: 0: <= test 1: > test 2: = test 3: != test
- Class weka.classifiers.trees.j48.InfoGainSplitCrit extends EntropyBasedSplitCrit implements Serializable
  
  serialVersionUID:
  
  4892105020180728499L
- Class weka.classifiers.trees.j48.ModelSelection extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -4850147125096133642L
- Class weka.classifiers.trees.j48.NBTreeClassifierTree extends ClassifierTree implements Serializable
  
  serialVersionUID:
  
  -4472639447877404786L
- Class weka.classifiers.trees.j48.NBTreeModelSelection extends ModelSelection implements Serializable
  
  serialVersionUID:
  
  990097748931976704L
  - 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
  
  serialVersionUID:
  
  7824804381545259618L
  - 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
  
  serialVersionUID:
  
  8922627123884975070L
  - 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
  
  serialVersionUID:
  
  -1292620749331337546L
- Class weka.classifiers.trees.j48.PruneableClassifierTree extends ClassifierTree implements Serializable
  
  serialVersionUID:
  
  -555775736857600201L
  - 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
  
  serialVersionUID:
  
  5490996638027101259L
Package weka.classifiers.trees.lmt
- Class weka.classifiers.trees.lmt.LMTNode extends LogisticBase implements Serializable
  
  serialVersionUID:
  
  1862737145870398755L
  - 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
  
  serialVersionUID:
  
  168765678097825064L
  - 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
    - m_useAIC
```
boolean m_useAIC
```
      If true, the AIC is used to choose the best iteration
    - m_numParameters
```
double m_numParameters
```
      Effective number of parameters used for AIC / BIC automatic stopping
    - m_weightTrimBeta
```
double m_weightTrimBeta
```
      Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.
- Class weka.classifiers.trees.lmt.ResidualModelSelection extends ModelSelection implements Serializable
  
  serialVersionUID:
  
  -293098783159385148L
  - 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
  
  serialVersionUID:
  
  -5055883734183713525L
  - 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
  
  serialVersionUID:
  
  4212734895125452770L
  - 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
  
  serialVersionUID:
  
  -4022221950191647679L
  - Serialized Fields
    - m_instances
```
Instances m_instances
```
      the instances covered by the tree/rules
    - 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_removeUseless
```
RemoveUseless m_removeUseless
```
      for removing useless attributes
    - 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
  
  serialVersionUID:
  
  2030974097051713247L
  - Serialized Fields
    - m_coefficients
```
double[] m_coefficients
```
      The coefficients
    - m_intercept
```
double m_intercept
```
      The intercept
    - m_instancesHeader
```
Instances m_instancesHeader
```
      Holds the instances header for printing the model
    - m_numParameters
```
int m_numParameters
```
      number of coefficients in the model
- Class weka.classifiers.trees.m5.Rule extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -4458627451682483204L
  - 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
  
  serialVersionUID:
  
  1979807611124337144L
  - 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
```
      left child node
    - m_right
```
RuleNode m_right
```
      right child node
    - 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
  
  serialVersionUID:
  
  1864267581079767881L
  - 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.AbstractClusterer extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6099962589663877632L
- Class weka.clusterers.AbstractDensityBasedClusterer extends AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  -5950728041704213845L
- Class weka.clusterers.CLOPE extends AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  -567567567567588L
  - Serialized Fields
    - clusters
```
java.util.ArrayList<E> clusters
```
      Array of clusters
    - m_RepulsionDefault
```
double m_RepulsionDefault
```
      Specifies the repulsion default
    - m_Repulsion
```
double m_Repulsion
```
      Specifies the repulsion
    - m_numberOfClusters
```
int m_numberOfClusters
```
      Number of clusters
    - m_processed_InstanceID
```
int m_processed_InstanceID
```
      Counter for the processed instances
    - m_numberOfInstances
```
int m_numberOfInstances
```
      Number of instances
    - m_clusterAssignments
```
java.util.ArrayList<E> m_clusterAssignments
```
    - m_numberOfClustersDetermined
```
boolean m_numberOfClustersDetermined
```
      whether the number of clusters was already determined
- Class weka.clusterers.ClusterEvaluation extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -830188327319128005L
  - Serialized Fields
    - 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
```
      holds the average log likelihood for a particular testing dataset if the clusterer is a DensityBasedClusterer
    - m_classToCluster
```
int[] m_classToCluster
```
      will hold the mapping of classes to clusters (for class based evaluation)
- Class weka.clusterers.Cobweb extends RandomizableClusterer implements Serializable
  
  serialVersionUID:
  
  928406656495092318L
  - 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). Must never be queried directly, only via the method numberOfClusters(). Otherwise it's not guaranteed that it contains the correct value.
      
      See Also:
      
      Cobweb.numberOfClusters(), Cobweb.m_numberOfClustersDetermined
    - m_numberOfClustersDetermined
```
boolean m_numberOfClustersDetermined
```
      whether the number of clusters was already determined
    - m_numberSplits
```
int m_numberSplits
```
      the number of splits that happened
    - m_numberMerges
```
int m_numberMerges
```
      the number of merges that happened
    - 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.DBSCAN extends AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  -1666498248451219728L
  - Serialized Fields
    - epsilon
```
double epsilon
```
      Specifies the radius for a range-query
    - minPoints
```
int minPoints
```
      Specifies the density (the range-query must contain at least minPoints DataObjects)
    - replaceMissingValues_Filter
```
ReplaceMissingValues replaceMissingValues_Filter
```
      Replace missing values in training instances
    - numberOfGeneratedClusters
```
int numberOfGeneratedClusters
```
      Holds the number of clusters generated
    - database_distanceType
```
java.lang.String database_distanceType
```
      Holds the distance-type that is used (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject)
    - database_Type
```
java.lang.String database_Type
```
      Holds the type of the used database (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase)
    - database
```
Database database
```
      The database that is used for DBSCAN
    - clusterID
```
int clusterID
```
      Holds the current clusterID
    - processed_InstanceID
```
int processed_InstanceID
```
      Counter for the processed instances
    - elapsedTime
```
double elapsedTime
```
      Holds the time-value (seconds) for the duration of the clustering-process
- Class weka.clusterers.EM extends RandomizableDensityBasedClusterer implements Serializable
  
  serialVersionUID:
  
  8348181483812829475L
  - Serialized Fields
    - m_modelPrev
```
Estimator[][] m_modelPrev
```
    - m_modelNormalPrev
```
double[][][] m_modelNormalPrev
```
    - m_priorsPrev
```
double[] m_priorsPrev
```
    - 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 number generator
    - m_verbose
```
boolean m_verbose
```
      Verbose?
    - m_replaceMissing
```
ReplaceMissingValues m_replaceMissing
```
      globally replace missing values
    - m_displayModelInOldFormat
```
boolean m_displayModelInOldFormat
```
      display model output in old-style format
- Class weka.clusterers.FarthestFirst extends RandomizableClusterer implements Serializable
  
  serialVersionUID:
  
  7499838100631329509L
  - 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
- Class weka.clusterers.FilteredClusterer extends SingleClustererEnhancer implements Serializable
  
  serialVersionUID:
  
  1420005943163412943L
  - Serialized Fields
    - m_Filter
```
Filter m_Filter
```
      The filter.
    - m_FilteredInstances
```
Instances m_FilteredInstances
```
      The instance structure of the filtered instances.
- Class weka.clusterers.HierarchicalClusterer extends AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - m_bDebug
```
boolean m_bDebug
```
      Whether the classifier is run in debug mode.
    - m_bDistanceIsBranchLength
```
boolean m_bDistanceIsBranchLength
```
      Whether the distance represent node height (if false) or branch length (if true).
    - m_instances
```
Instances m_instances
```
      training data
    - m_nNumClusters
```
int m_nNumClusters
```
      number of clusters desired in clustering
    - m_DistanceFunction
```
DistanceFunction m_DistanceFunction
```
      distance function used for comparing members of a cluster
    - m_nLinkType
```
int m_nLinkType
```
      Holds the Link type used calculate distance between clusters
    - m_bPrintNewick
```
boolean m_bPrintNewick
```
    - m_clusters
```
weka.clusterers.HierarchicalClusterer.Node[] m_clusters
```
    - m_nClusterNr
```
int[] m_nClusterNr
```
- Class weka.clusterers.MakeDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable
  
  serialVersionUID:
  
  -5643302427972186631L
  - 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.OPTICS extends AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  274552680222105221L
  - Serialized Fields
    - epsilon
```
double epsilon
```
      Specifies the radius for a range-query
    - minPoints
```
int minPoints
```
      Specifies the density (the range-query must contain at least minPoints DataObjects)
    - replaceMissingValues_Filter
```
ReplaceMissingValues replaceMissingValues_Filter
```
      Replace missing values in training instances
    - numberOfGeneratedClusters
```
int numberOfGeneratedClusters
```
      Holds the number of clusters generated
    - database_distanceType
```
java.lang.String database_distanceType
```
      Holds the distance-type that is used (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject)
    - database_Type
```
java.lang.String database_Type
```
      Holds the type of the used database (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase)
    - database
```
Database database
```
      The database that is used for OPTICS
    - elapsedTime
```
double elapsedTime
```
      Holds the time-value (seconds) for the duration of the clustering-process
    - writeOPTICSresults
```
boolean writeOPTICSresults
```
      Flag that indicates if the results are written to a file or not
    - resultVector
```
FastVector resultVector
```
      Holds the ClusterOrder (dataObjects with their r_dist and c_dist) for the GUI
    - showGUI
```
boolean showGUI
```
      whether to display the GUI after building the clusterer or not.
    - databaseOutput
```
java.io.File databaseOutput
```
      the file to save the generated database object to.
- Class weka.clusterers.RandomizableClusterer extends AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  -4819590778152242745L
  - Serialized Fields
    - m_SeedDefault
```
int m_SeedDefault
```
      the default seed value
    - m_Seed
```
int m_Seed
```
      The random number seed.
- Class weka.clusterers.RandomizableDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable
  
  serialVersionUID:
  
  -5325270357918932849L
  - Serialized Fields
    - m_SeedDefault
```
int m_SeedDefault
```
      the default seed value
    - m_Seed
```
int m_Seed
```
      The random number seed.
- Class weka.clusterers.RandomizableSingleClustererEnhancer extends AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  -644847037106316249L
  - Serialized Fields
    - m_SeedDefault
```
int m_SeedDefault
```
      the default seed value
    - m_Seed
```
int m_Seed
```
      The random number seed.
- Class weka.clusterers.sIB extends RandomizableClusterer implements Serializable
  
  serialVersionUID:
  
  -8652125897352654213L
  - Serialized Fields
    - m_data
```
Instances m_data
```
      Training data
    - m_numCluster
```
int m_numCluster
```
      Number of clusters
    - m_numRestarts
```
int m_numRestarts
```
      Number of restarts
    - m_verbose
```
boolean m_verbose
```
      Verbose?
    - m_uniformPrior
```
boolean m_uniformPrior
```
      Uniform prior probability of the documents
    - m_maxLoop
```
int m_maxLoop
```
      Max number of iterations during each restart
    - m_minChange
```
int m_minChange
```
      Minimum number of changes
    - m_replaceMissing
```
ReplaceMissingValues m_replaceMissing
```
      Globally replace missing values
    - m_numInstances
```
int m_numInstances
```
      Number of instances
    - m_numAttributes
```
int m_numAttributes
```
      Number of attributes
    - random
```
java.util.Random random
```
      Randomly generate initial partition
    - bestT
```
weka.clusterers.sIB.Partition bestT
```
      Holds the best partition built
    - input
```
weka.clusterers.sIB.Input input
```
      Holds the statistics about the input dataset
- Class weka.clusterers.SimpleKMeans extends RandomizableClusterer implements Serializable
  
  serialVersionUID:
  
  -3235809600124455376L
  - 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_ClusterMissingCounts
```
int[][] m_ClusterMissingCounts
```
    - m_FullMeansOrMediansOrModes
```
double[] m_FullMeansOrMediansOrModes
```
      Stats on the full data set for comparison purposes In case the attribute is numeric the value is the mean if is being used the Euclidian distance or the median if Manhattan distance and if the attribute is nominal then it's mode is saved
    - m_FullStdDevs
```
double[] m_FullStdDevs
```
    - m_FullNominalCounts
```
int[][] m_FullNominalCounts
```
    - m_FullMissingCounts
```
int[] m_FullMissingCounts
```
    - m_displayStdDevs
```
boolean m_displayStdDevs
```
      Display standard deviations for numeric atts
    - m_dontReplaceMissing
```
boolean m_dontReplaceMissing
```
      Replace missing values globally?
    - m_ClusterSizes
```
int[] m_ClusterSizes
```
      The number of instances in each cluster
    - m_MaxIterations
```
int m_MaxIterations
```
      Maximum number of iterations to be executed
    - m_Iterations
```
int m_Iterations
```
      Keep track of the number of iterations completed before convergence
    - m_squaredErrors
```
double[] m_squaredErrors
```
      Holds the squared errors for all clusters
    - m_DistanceFunction
```
DistanceFunction m_DistanceFunction
```
      the distance function used.
    - m_PreserveOrder
```
boolean m_PreserveOrder
```
      Preserve order of instances
    - m_Assignments
```
int[] m_Assignments
```
      Assignments obtained
- Class weka.clusterers.SingleClustererEnhancer extends AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  4893928362926428671L
  - Serialized Fields
    - m_Clusterer
```
Clusterer m_Clusterer
```
      the clusterer
- Class weka.clusterers.XMeans extends RandomizableClusterer implements Serializable
  
  serialVersionUID:
  
  -7941793078404132616L
  - Serialized Fields
    - m_Instances
```
Instances m_Instances
```
      training instances.
    - m_Model
```
Instances m_Model
```
      model information, should increase readability.
    - m_ReplaceMissingFilter
```
ReplaceMissingValues m_ReplaceMissingFilter
```
      replace missing values in training instances.
    - m_BinValue
```
double m_BinValue
```
      Distance value between true and false of binary attributes and "same" and "different" of nominal attributes (default = 1.0).
    - m_Bic
```
double m_Bic
```
      BIC-Score of the current model.
    - m_Mle
```
double[] m_Mle
```
      Distortion.
    - m_MaxIterations
```
int m_MaxIterations
```
      maximum overall iterations.
    - m_MaxKMeans
```
int m_MaxKMeans
```
      maximum iterations to perform Kmeans part if negative, iterations are not checked.
    - m_MaxKMeansForChildren
```
int m_MaxKMeansForChildren
```
      see above, but for kMeans of splitted clusters.
    - m_NumClusters
```
int m_NumClusters
```
      The actual number of clusters.
    - m_MinNumClusters
```
int m_MinNumClusters
```
      min number of clusters to generate.
    - m_MaxNumClusters
```
int m_MaxNumClusters
```
      max number of clusters to generate.
    - m_DistanceF
```
DistanceFunction m_DistanceF
```
      the distance function used.
    - m_ClusterCenters
```
Instances m_ClusterCenters
```
      cluster centers.
    - m_InputCenterFile
```
java.io.File m_InputCenterFile
```
      file name of the output file for the cluster centers.
    - m_DebugVectorsInput
```
java.io.Reader m_DebugVectorsInput
```
      input file for the random vectors --> USED FOR DEBUGGING.
    - m_DebugVectorsIndex
```
int m_DebugVectorsIndex
```
      the index for the current debug vector.
    - m_DebugVectors
```
Instances m_DebugVectors
```
      all the debug vectors.
    - m_DebugVectorsFile
```
java.io.File m_DebugVectorsFile
```
      file name of the input file for the random vectors.
    - m_OutputCenterFile
```
java.io.File m_OutputCenterFile
```
      file name of the output file for the cluster centers.
    - m_ClusterAssignments
```
int[] m_ClusterAssignments
```
      temporary variable holding cluster assignments while iterating.
    - m_CutOffFactor
```
double m_CutOffFactor
```
      cutoff factor - percentage of splits done in Improve-Structure part only relevant, if all children lost.
    - m_KDTree
```
KDTree m_KDTree
```
      KDTrees class if KDTrees are used.
    - m_UseKDTree
```
boolean m_UseKDTree
```
      whether to use the KDTree (the KDTree is only initialized to be configurable from the GUI).
    - m_IterationCount
```
int m_IterationCount
```
      counts iterations done in main loop.
    - m_KMeansStopped
```
int m_KMeansStopped
```
      counter to say how often kMeans was stopped by loop counter.
    - m_NumSplits
```
int m_NumSplits
```
      Number of splits prepared.
    - m_NumSplitsDone
```
int m_NumSplitsDone
```
      Number of splits accepted (including cutoff factor decisions).
    - m_NumSplitsStillDone
```
int m_NumSplitsStillDone
```
      Number of splits accepted just because of cutoff factor.
    - m_DebugLevel
```
int m_DebugLevel
```
      level of debug output, 0 is no output.
    - m_CurrDebugFlag
```
boolean m_CurrDebugFlag
```
      Flag: I'm debugging.
Package weka.clusterers.forOPTICSAndDBScan.Databases
- Class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  787245523118665778L
  - Serialized Fields
    - treeMap
```
java.util.TreeMap<K,V> treeMap
```
      Internal, sorted Treemap for storing all the DataObjects
    - instances
```
Instances instances
```
      Holds the original instances delivered from WEKA
    - attributeMinValues
```
double[] attributeMinValues
```
      Holds the minimum value for each attribute
    - attributeMaxValues
```
double[] attributeMaxValues
```
      Holds the maximum value for each attribute
Package weka.clusterers.forOPTICSAndDBScan.DataObjects
- Class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -4408119914898291075L
  - Serialized Fields
    - instance
```
Instance instance
```
      Holds the original instance
    - key
```
java.lang.String key
```
      Holds the (unique) key that is associated with this DataObject
    - clusterID
```
int clusterID
```
      Holds the ID of the cluster, to which this DataObject is assigned
    - processed
```
boolean processed
```
      Holds the status for this DataObject (true, if it has been processed, else false)
    - c_dist
```
double c_dist
```
      Holds the coreDistance for this DataObject
    - r_dist
```
double r_dist
```
      Holds the reachabilityDistance for this DataObject
    - database
```
Database database
```
      Holds the database, that is the keeper of this DataObject
- Class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -3417720553766544582L
  - Serialized Fields
    - instance
```
Instance instance
```
      Holds the original instance
    - key
```
java.lang.String key
```
      Holds the (unique) key that is associated with this DataObject
    - clusterID
```
int clusterID
```
      Holds the ID of the cluster, to which this DataObject is assigned
    - processed
```
boolean processed
```
      Holds the status for this DataObject (true, if it has been processed, else false)
    - c_dist
```
double c_dist
```
      Holds the coreDistance for this DataObject
    - r_dist
```
double r_dist
```
      Holds the reachabilityDistance for this DataObject
    - database
```
Database database
```
      Holds the database, that is the keeper of this DataObject
Package weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
- Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel extends javax.swing.JComponent implements Serializable
  
  serialVersionUID:
  
  7917937528738361470L
  - Serialized Fields
    - resultVector
```
FastVector resultVector
```
      Holds the clustering results
    - verticalAdjustment
```
int verticalAdjustment
```
      Holds the value that is multiplied with the original values of core- and reachability distances in order to get better graphical views
    - coreDistanceColor
```
java.awt.Color coreDistanceColor
```
      Specifies the color for displaying core-distances
    - reachabilityDistanceColor
```
java.awt.Color reachabilityDistanceColor
```
      Specifies the color for displaying reachability-distances
    - widthSlider
```
int widthSlider
```
      Specifies the width for displaying the distances
    - showCoreDistances
```
boolean showCoreDistances
```
      Holds the flag for showCoreDistances
    - showReachabilityDistances
```
boolean showReachabilityDistances
```
      Holds the flag for showrRechabilityDistances
    - recentIndex
```
int recentIndex
```
      Holds the index of the last toolTip
- Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel extends javax.swing.table.AbstractTableModel implements Serializable
  
  serialVersionUID:
  
  -7732711470435549210L
  - Serialized Fields
    - resultVector
```
FastVector resultVector
```
      Holds the ClusterOrder (dataObjects with their r_dist and c_dist) for the GUI
- Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6022057864970639151L
  - Serialized Fields
    - resultVector
```
FastVector resultVector
```
    - databaseSize
```
int databaseSize
```
    - numberOfAttributes
```
int numberOfAttributes
```
    - epsilon
```
double epsilon
```
    - minPoints
```
int minPoints
```
    - opticsOutputs
```
boolean opticsOutputs
```
    - database_Type
```
java.lang.String database_Type
```
    - database_distanceType
```
java.lang.String database_distanceType
```
    - numberOfGeneratedClusters
```
int numberOfGeneratedClusters
```
    - elapsedTime
```
java.lang.String elapsedTime
```
Package weka.core
- Class weka.core.AbstractStringDistanceFunction extends NormalizableDistance implements Serializable
- Class weka.core.AlgVector extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -4023736016850256591L
  - Serialized Fields
    - m_Elements
```
double[] m_Elements
```
      The values of the matrix
- Class weka.core.Attribute extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -742180568732916383L
  - 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_Header
```
Instances m_Header
```
      The header information for a relation-valued attribute.
    - 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.AttributeExpression extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  402130123261736245L
  - Serialized Fields
    - m_operatorStack
```
java.util.Stack<E> m_operatorStack
```
      Operator stack
    - m_originalInfix
```
java.lang.String m_originalInfix
```
      Holds the original infix expression
    - 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
- Class weka.core.AttributeLocator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -2932848827681070345L
  - Serialized Fields
    - m_AllowedIndices
```
int[] m_AllowedIndices
```
      the attribute indices that may be inspected
    - m_Attributes
```
java.util.Vector<E> m_Attributes
```
      contains the attribute locations, either true or false Boolean objects
    - m_AttributesEfficient
```
java.util.BitSet m_AttributesEfficient
```
      contains the attribute locations, either true or false (efficient replacement)
    - m_Locators
```
java.util.Vector<E> m_Locators
```
      contains the locator locations, either null or a AttributeLocator reference
    - m_Type
```
int m_Type
```
      the type of the attribute
    - m_Data
```
Instances m_Data
```
      the referenced data
    - m_Indices
```
int[] m_Indices
```
      the indices
    - m_LocatorIndices
```
int[] m_LocatorIndices
```
      the indices of locator objects
- Class weka.core.AttributeStats extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4434688832743939380L
  - 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
  
  serialVersionUID:
  
  -5297388762342528737L
- Class weka.core.Capabilities extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -5478590032325567849L
  - Serialized Fields
    - m_Owner
```
CapabilitiesHandler m_Owner
```
      the object that owns this capabilities instance
    - m_Capabilities
```
java.util.HashSet<E> m_Capabilities
```
      the hashset for storing the active capabilities
    - m_Dependencies
```
java.util.HashSet<E> m_Dependencies
```
      the hashset for storing dependent capabilities, eg for meta-classifiers
    - m_FailReason
```
java.lang.Exception m_FailReason
```
      the reason why the test failed, used to throw an exception
    - m_MinimumNumberInstances
```
int m_MinimumNumberInstances
```
      the minimum number of instances in a dataset
    - m_Test
```
boolean m_Test
```
      whether to perform any tests at all
    - m_InstancesTest
```
boolean m_InstancesTest
```
      whether to perform data based tests
    - m_AttributeTest
```
boolean m_AttributeTest
```
      whether to perform attribute based tests
    - m_MissingValuesTest
```
boolean m_MissingValuesTest
```
      whether to test for missing values
    - m_MissingClassValuesTest
```
boolean m_MissingClassValuesTest
```
      whether to test for missing class values
    - m_MinimumNumberInstancesTest
```
boolean m_MinimumNumberInstancesTest
```
      whether to test for minimum number of instances
- Class weka.core.ChebyshevDistance extends NormalizableDistance implements Serializable
  
  serialVersionUID:
  
  -7739904999895461429L
- Class weka.core.Debug extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  66171861743328020L
  - Serialized Fields
    - m_Enabled
```
boolean m_Enabled
```
      whether logging is enabled
    - m_Log
```
Debug.Log m_Log
```
      for logging
    - m_Clock
```
Debug.Clock m_Clock
```
      for clocking
- Class weka.core.Debug.Clock extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4622161807307942201L
  - Serialized Fields
    - m_OutputFormat
```
int m_OutputFormat
```
      the format of the output
    - m_Start
```
long m_Start
```
      the start time
    - m_Stop
```
long m_Stop
```
      the end time
    - m_Running
```
boolean m_Running
```
      whether the time is still clocked
    - m_ThreadID
```
long m_ThreadID
```
      the thread ID
    - m_CanMeasureCpuTime
```
boolean m_CanMeasureCpuTime
```
      whether the system can measure the CPU time
    - m_UseCpuTime
```
boolean m_UseCpuTime
```
      whether to use the CPU time (by default TRUE)
- Class weka.core.Debug.DBO extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -5245628124742606784L
  - Serialized Fields
    - m_verboseOn
```
boolean m_verboseOn
```
      enables/disables output of debug information
    - m_outputTypes
```
Range m_outputTypes
```
      range of outputtyp
- Class weka.core.Debug.Log extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1458435732111675823L
  - Serialized Fields
    - m_Filename
```
java.lang.String m_Filename
```
      the filename, if any
    - m_Size
```
int m_Size
```
      the size of the file (in bytes)
    - m_NumFiles
```
int m_NumFiles
```
      the number of files for rotating the logs
    - m_LoggerInitFailed
```
boolean m_LoggerInitFailed
```
      whether the initialization of the logger failed
- Class weka.core.Debug.Random extends java.util.Random implements Serializable
  
  serialVersionUID:
  
  1256846887618333956L
  - Serialized Fields
    - m_Debug
```
boolean m_Debug
```
      whether to output debug information
    - m_ID
```
long m_ID
```
      the unique ID for this number generator
    - m_Log
```
Debug.Log m_Log
```
      the log to use for outputting the data, otherwise just stdout
- Class weka.core.Debug.SimpleLog extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -2671928223819510830L
  - Serialized Fields
    - m_Filename
```
java.lang.String m_Filename
```
      the file to write to (if null then only stdout is used)
- Class weka.core.Debug.Timestamp extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6099868388466922753L
  - Serialized Fields
    - m_Stamp
```
java.util.Date m_Stamp
```
      the actual date
    - m_Format
```
java.lang.String m_Format
```
      the format of the timestamp
    - m_Formatter
```
java.text.SimpleDateFormat m_Formatter
```
      handles the format of the output
- Class weka.core.EditDistance extends AbstractStringDistanceFunction implements Serializable
- Class weka.core.EuclideanDistance extends NormalizableDistance implements Serializable
  
  serialVersionUID:
  
  1068606253458807903L
- Class weka.core.FastVector extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -2173635135622930169L
  - 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
  
  serialVersionUID:
  
  1482635194499365122L
  - 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
  
  serialVersionUID:
  
  -6589278678230949683L
  - 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
  
  serialVersionUID:
  
  -19412345060742748L
  - 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_Lines
```
int m_Lines
```
      The lines read so far in case of incremental loading. Since the StreamTokenizer will be re-initialized with every instance that is read, we have to keep track of the number of lines read so far.
      
      See Also:
      
      Instances.readInstance(Reader)
- Class weka.core.Jython extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6972298704460209252L
  - Serialized Fields
    - m_Interpreter
```
java.lang.Object m_Interpreter
```
      the interpreter
- Class weka.core.ManhattanDistance extends NormalizableDistance implements Serializable
  
  serialVersionUID:
  
  6783782554224000243L
- Class weka.core.Matrix extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -3604757095849145838L
  - Serialized Fields
    - m_Matrix
```
Matrix m_Matrix
```
      Deprecated.
      
      The actual matrix
- Class weka.core.NormalizableDistance extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_Data
```
Instances m_Data
```
      the instances used internally.
    - m_DontNormalize
```
boolean m_DontNormalize
```
      True if normalization is turned off (default false).
    - m_Ranges
```
double[][] m_Ranges
```
      The range of the attributes.
    - m_AttributeIndices
```
Range m_AttributeIndices
```
      The range of attributes to use for calculating the distance.
    - m_ActiveIndices
```
boolean[] m_ActiveIndices
```
      The boolean flags, whether an attribute will be used or not.
    - m_Validated
```
boolean m_Validated
```
      Whether all the necessary preparations have been done.
- Class weka.core.NoSupportForMissingValuesException extends WekaException implements Serializable
  
  serialVersionUID:
  
  5161175307725893973L
- Class weka.core.ProtectedProperties extends java.util.Properties implements Serializable
  
  serialVersionUID:
  
  3876658672657323985L
  - Serialized Fields
    - closed
```
boolean closed
```
      the properties need to be open during construction of the object
- Class weka.core.Queue extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -1141282001146389780L
  - 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
  
  serialVersionUID:
  
  -5119358279412097455L
  - 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
  
  serialVersionUID:
  
  -4763742718209460354L
- Class weka.core.Range extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  3667337062176835900L
  - 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.RelationalLocator extends AttributeLocator implements Serializable
  
  serialVersionUID:
  
  4646872277151854732L
- Class weka.core.SerializedObject extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  6635502953928860434L
  - Serialized Fields
    - m_storedObjectArray
```
byte[] m_storedObjectArray
```
      The array storing the object.
    - m_isCompressed
```
boolean m_isCompressed
```
      Whether or not the object is compressed.
    - m_isJython
```
boolean m_isJython
```
      Whether it is a Jython object or not
- Class weka.core.SingleIndex extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  5285169134430839303L
  - 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
  
  serialVersionUID:
  
  -3579051291332630149L
  - 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.StringLocator extends AttributeLocator implements Serializable
  
  serialVersionUID:
  
  7805522230268783972L
- Class weka.core.Tag extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  3326379903447135320L
  - Serialized Fields
    - m_ID
```
int m_ID
```
      The ID
    - m_IDStr
```
java.lang.String m_IDStr
```
      The unique string for this tag, doesn't have to be numeric
    - m_Readable
```
java.lang.String m_Readable
```
      The descriptive text
- Class weka.core.TestInstances extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6263968936330390469L
  - Serialized Fields
    - m_Words
```
java.lang.String[] m_Words
```
      for generating String attributes/classes
    - m_WordSeparators
```
java.lang.String m_WordSeparators
```
      for generating String attributes/classes
    - m_Relation
```
java.lang.String m_Relation
```
      the name of the relation
    - m_Seed
```
int m_Seed
```
      the seed value
    - m_Random
```
java.util.Random m_Random
```
      the random number generator
    - m_NumInstances
```
int m_NumInstances
```
      the number of instances
    - m_ClassType
```
int m_ClassType
```
      the class type
    - m_NumClasses
```
int m_NumClasses
```
      the number of classes (in case of NOMINAL class)
    - m_ClassIndex
```
int m_ClassIndex
```
      the class index (-1 is LAST, -2 means no class)
      
      See Also:
      
      TestInstances.CLASS_IS_LAST, TestInstances.NO_CLASS
    - m_NumNominal
```
int m_NumNominal
```
      the number of nominal attributes
    - m_NumNominalValues
```
int m_NumNominalValues
```
      the number of values for nominal attributes
    - m_NumNumeric
```
int m_NumNumeric
```
      the number of numeric attributes
    - m_NumString
```
int m_NumString
```
      the number of string attributes
    - m_NumDate
```
int m_NumDate
```
      the number of date attributes
    - m_NumRelational
```
int m_NumRelational
```
      the number of relational attributes
    - m_NumRelationalNominal
```
int m_NumRelationalNominal
```
      the number of nominal attributes in a relational attribute
    - m_NumRelationalNominalValues
```
int m_NumRelationalNominalValues
```
      the number of values for nominal attributes in relational attributes
    - m_NumRelationalNumeric
```
int m_NumRelationalNumeric
```
      the number of numeric attributes in a relational attribute
    - m_NumRelationalString
```
int m_NumRelationalString
```
      the number of string attributes in a relational attribute
    - m_NumRelationalDate
```
int m_NumRelationalDate
```
      the number of date attributes in a relational attribute
    - m_MultiInstance
```
boolean m_MultiInstance
```
      whether to generate Multi-Instance data or not
    - m_NumInstancesRelational
```
int m_NumInstancesRelational
```
      the number of instances in relational attributes (applies also for bags in multi-instance)
    - m_RelationalFormat
```
Instances[] m_RelationalFormat
```
      the format of the multi-instance data
    - m_RelationalClassFormat
```
Instances m_RelationalClassFormat
```
      the format of the multi-instance data of the class
    - m_Data
```
Instances m_Data
```
      the generated data
    - m_Handler
```
CapabilitiesHandler m_Handler
```
      the CapabilitiesHandler to get the Capabilities from
- Class weka.core.Trie extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -5897980928817779048L
  - Serialized Fields
    - m_Root
```
Trie.TrieNode m_Root
```
      the root node
    - m_HashCode
```
int m_HashCode
```
      the hash code
    - m_RecalcHashCode
```
boolean m_RecalcHashCode
```
      whether the structure got modified and the hash code needs to be re-calculated
- Class weka.core.Trie.TrieNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable
  
  serialVersionUID:
  
  -2252907099391881148L
  - Serialized Fields
    - m_Children
```
java.util.Hashtable<K,V> m_Children
```
      for fast access to the children
- Class weka.core.UnassignedClassException extends java.lang.RuntimeException implements Serializable
  
  serialVersionUID:
  
  6268278694768818235L
- Class weka.core.UnassignedDatasetException extends java.lang.RuntimeException implements Serializable
  
  serialVersionUID:
  
  -9000116786626328854L
- Class weka.core.UnsupportedAttributeTypeException extends WekaException implements Serializable
  
  serialVersionUID:
  
  2658987325328414838L
- Class weka.core.UnsupportedClassTypeException extends WekaException implements Serializable
  
  serialVersionUID:
  
  5175741076972192151L
- Class weka.core.WekaException extends java.lang.Exception implements Serializable
  
  serialVersionUID:
  
  6428269989006208585L
Package weka.core.converters
- Class weka.core.converters.AbstractFileLoader extends AbstractLoader implements Serializable
  - Serialized Fields
    - m_File
```
java.lang.String m_File
```
      the file
    - m_sourceFile
```
java.io.File m_sourceFile
```
      Holds the source of the data set.
    - m_useRelativePath
```
boolean m_useRelativePath
```
      use relative file paths
- 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.
    - FILE_EXTENSION_COMPRESSED
```
java.lang.String FILE_EXTENSION_COMPRESSED
```
      the extension for compressed files
    - 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.
    - m_useRelativePath
```
boolean m_useRelativePath
```
      use relative file paths
- 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 AbstractFileLoader implements Serializable
  
  serialVersionUID:
  
  2726929550544048587L
  - Serialized Fields
    - m_URL
```
java.lang.String m_URL
```
      the url
- Class weka.core.converters.ArffSaver extends AbstractFileSaver implements Serializable
  
  serialVersionUID:
  
  2223634248900042228L
  - Serialized Fields
    - m_CompressOutput
```
boolean m_CompressOutput
```
      whether to compress the output
- Class weka.core.converters.C45Loader extends AbstractFileLoader implements Serializable
  
  serialVersionUID:
  
  5454329403218219L
  - Serialized Fields
    - 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
  
  serialVersionUID:
  
  -821428878384253377L
- Class weka.core.converters.ConverterUtils extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -2460855349276148760L
- Class weka.core.converters.ConverterUtils.DataSink extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -1504966891136411204L
  - Serialized Fields
    - m_Saver
```
Saver m_Saver
```
      the saver to use for storing the data.
    - m_Stream
```
java.io.OutputStream m_Stream
```
      the stream to store the data in (always in ARFF format).
- Class weka.core.converters.ConverterUtils.DataSource extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -613122395928757332L
  - Serialized Fields
    - m_File
```
java.io.File m_File
```
      the file to load.
    - m_URL
```
java.net.URL m_URL
```
      the URL to load.
    - m_Loader
```
Loader m_Loader
```
      the loader.
    - m_Incremental
```
boolean m_Incremental
```
      whether the loader is incremental.
    - m_BatchCounter
```
int m_BatchCounter
```
      the instance counter for the batch case.
    - m_IncrementalBuffer
```
Instance m_IncrementalBuffer
```
      the last internally read instance.
    - m_BatchBuffer
```
Instances m_BatchBuffer
```
      the batch buffer.
- Class weka.core.converters.CSVLoader extends AbstractFileLoader implements Serializable
  
  serialVersionUID:
  
  5607529739745491340L
  - Serialized Fields
    - 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.
    - m_NominalAttributes
```
Range m_NominalAttributes
```
      The range of attributes to force to type nominal.
    - m_StringAttributes
```
Range m_StringAttributes
```
      The range of attributes to force to type string.
    - m_dateAttributes
```
Range m_dateAttributes
```
      The range of attributes to force to type date
    - m_dateFormat
```
java.lang.String m_dateFormat
```
      The formatting string to use to parse dates
    - m_formatter
```
java.text.SimpleDateFormat m_formatter
```
      The formatter to use on dates
    - m_MissingValue
```
java.lang.String m_MissingValue
```
      The placeholder for missing values.
    - m_FirstCheck
```
boolean m_FirstCheck
```
      whether the first row has been read.
    - m_Enclosures
```
java.lang.String m_Enclosures
```
      enclosure character(s) to use for strings
- Class weka.core.converters.CSVSaver extends AbstractFileSaver implements Serializable
  
  serialVersionUID:
  
  476636654410701807L
- Class weka.core.converters.DatabaseConnection extends DatabaseUtils implements Serializable
  
  serialVersionUID:
  
  1673169848863178695L
- Class weka.core.converters.DatabaseLoader extends AbstractLoader implements Serializable
  
  serialVersionUID:
  
  -7936159015338318659L
  - 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 *|<column-list> FROM <table> [WHERE <condition>])
    - 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_checkForTable
```
boolean m_checkForTable
```
      If true it checks whether or not the table exists in the database before loading depending on jdbc metadata information. Set flag to false if no check is required or if jdbc metadata is not complete.
    - 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
  
  serialVersionUID:
  
  863971733782624956L
  - Serialized Fields
    - m_DataBaseConnection
```
DatabaseConnection m_DataBaseConnection
```
      The database connection.
    - m_tableName
```
java.lang.String m_tableName
```
      The name of the table in 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 rows and 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.
    - m_Username
```
java.lang.String m_Username
```
      the user name for the database.
    - m_Password
```
java.lang.String m_Password
```
      the password for the database.
- Class weka.core.converters.LibSVMLoader extends AbstractFileLoader implements Serializable
  
  serialVersionUID:
  
  4988360125354664417L
  - Serialized Fields
    - m_URL
```
java.lang.String m_URL
```
      the url.
    - m_Buffer
```
java.util.Vector<E> m_Buffer
```
      the buffer of the rows read so far.
- Class weka.core.converters.LibSVMSaver extends AbstractFileSaver implements Serializable
  
  serialVersionUID:
  
  2792295817125694786L
  - Serialized Fields
    - m_ClassIndex
```
SingleIndex m_ClassIndex
```
      the class index
- Class weka.core.converters.SerializedInstancesLoader extends AbstractFileLoader implements Serializable
  
  serialVersionUID:
  
  2391085836269030715L
  - Serialized Fields
    - m_Dataset
```
Instances m_Dataset
```
      Holds the structure (header) of the data set.
    - m_IncrementalIndex
```
int m_IncrementalIndex
```
      The current index position for incremental reading
- Class weka.core.converters.SerializedInstancesSaver extends AbstractFileSaver implements Serializable
  
  serialVersionUID:
  
  -7717010648500658872L
  - Serialized Fields
    - m_objectstream
```
java.io.ObjectOutputStream m_objectstream
```
      the output stream.
- Class weka.core.converters.SVMLightLoader extends AbstractFileLoader implements Serializable
  
  serialVersionUID:
  
  4988360125354664417L
  - Serialized Fields
    - m_URL
```
java.lang.String m_URL
```
      the url.
    - m_Buffer
```
java.util.Vector<E> m_Buffer
```
      the buffer of the rows read so far.
- Class weka.core.converters.SVMLightSaver extends AbstractFileSaver implements Serializable
  
  serialVersionUID:
  
  2605714599263995835L
  - Serialized Fields
    - m_ClassIndex
```
SingleIndex m_ClassIndex
```
      the class index.
- Class weka.core.converters.TextDirectoryLoader extends AbstractLoader implements Serializable
  
  serialVersionUID:
  
  2592118773712247647L
  - Serialized Fields
    - 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_Debug
```
boolean m_Debug
```
      whether to print some debug information
    - m_OutputFilename
```
boolean m_OutputFilename
```
      whether to include the filename as an extra attribute
    - m_charSet
```
java.lang.String m_charSet
```
      The charset to use when loading text files (default is to just use the default charset).
- Class weka.core.converters.XRFFLoader extends AbstractFileLoader implements Serializable
  
  serialVersionUID:
  
  3764533621135196582L
  - Serialized Fields
    - m_URL
```
java.lang.String m_URL
```
      the url
    - m_XMLInstances
```
XMLInstances m_XMLInstances
```
      the loaded XML document
- Class weka.core.converters.XRFFSaver extends AbstractFileSaver implements Serializable
  
  serialVersionUID:
  
  -7226404765213522043L
  - Serialized Fields
    - m_ClassIndex
```
SingleIndex m_ClassIndex
```
      the class index
    - m_XMLInstances
```
XMLInstances m_XMLInstances
```
      the generated XML document
    - m_CompressOutput
```
boolean m_CompressOutput
```
      whether to compress the output
Package weka.core.matrix
- Class weka.core.matrix.CholeskyDecomposition extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8739775942782694701L
  - 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
  
  serialVersionUID:
  
  4011654467211422319L
  - 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.ExponentialFormat extends java.text.DecimalFormat implements Serializable
  
  serialVersionUID:
  
  -5298981701073897741L
  - Serialized Fields
    - nf
```
java.text.DecimalFormat nf
```
    - sign
```
boolean sign
```
    - digits
```
int digits
```
    - exp
```
int exp
```
    - trailing
```
boolean trailing
```
- Class weka.core.matrix.FlexibleDecimalFormat extends java.text.DecimalFormat implements Serializable
  
  serialVersionUID:
  
  110912192794064140L
  - Serialized Fields
    - nf
```
java.text.DecimalFormat nf
```
    - digits
```
int digits
```
    - exp
```
boolean exp
```
    - intDigits
```
int intDigits
```
    - decimalDigits
```
int decimalDigits
```
    - expDecimalDigits
```
int expDecimalDigits
```
    - power
```
int power
```
    - trailing
```
boolean trailing
```
    - grouping
```
boolean grouping
```
    - sign
```
boolean sign
```
- Class weka.core.matrix.FloatingPointFormat extends java.text.DecimalFormat implements Serializable
  
  serialVersionUID:
  
  4500373755333429499L
  - Serialized Fields
    - nf
```
java.text.DecimalFormat nf
```
    - width
```
int width
```
    - decimal
```
int decimal
```
    - trailing
```
boolean trailing
```
- Class weka.core.matrix.LUDecomposition extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -2731022568037808629L
  - 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
  
  serialVersionUID:
  
  7856794138418366180L
  - 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
  
  serialVersionUID:
  
  -5013090736132211418L
  - 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
  
  serialVersionUID:
  
  -8738089610999867951L
  - 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.core.neighboursearch
- Class weka.core.neighboursearch.BallTree extends NearestNeighbourSearch implements Serializable
  
  serialVersionUID:
  
  728763855952698328L
  - Serialized Fields
    - m_InstList
```
int[] m_InstList
```
      The instances indices sorted inorder of appearence in the tree from left most leaf node to the right most leaf node.
    - m_MaxInstancesInLeaf
```
int m_MaxInstancesInLeaf
```
      The maximum number of instances in a leaf. A node is made into a leaf if the number of instances in it become less than or equal to this value.
    - m_TreeStats
```
TreePerformanceStats m_TreeStats
```
      Tree Stats variables.
    - m_Root
```
BallNode m_Root
```
      The root node of the BallTree.
    - m_TreeConstructor
```
BallTreeConstructor m_TreeConstructor
```
      The constructor method to use to build the tree.
    - m_Distances
```
double[] m_Distances
```
      Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours().
- Class weka.core.neighboursearch.CoverTree extends NearestNeighbourSearch implements Serializable
  
  serialVersionUID:
  
  7617412821497807586L
  - Serialized Fields
    - m_EuclideanDistance
```
EuclideanDistance m_EuclideanDistance
```
      The euclidean distance function to use.
    - m_Root
```
CoverTree.CoverTreeNode m_Root
```
      The root node.
    - m_DistanceList
```
double[] m_DistanceList
```
      Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours().
    - m_NumNodes
```
int m_NumNodes
```
      Number of nodes in the tree.
    - m_NumLeaves
```
int m_NumLeaves
```
      Number of nodes in the tree.
    - m_MaxDepth
```
int m_MaxDepth
```
      Number of nodes in the tree.
    - m_TreeStats
```
TreePerformanceStats m_TreeStats
```
      Tree Stats variables.
    - m_Base
```
double m_Base
```
      The base of our expansion constant. In other words the 2 in 2^i used in covering tree and separation invariants of a cover tree. P.S.: In paper it's suggested the separation invariant is relaxed in batch construction.
    - il2
```
double il2
```
      if we have base 2 then this can be viewed as 1/ln(2), which can be used later on to do il2*ln(d) instead of ln(d)/ln(2), to get log2(d), in get_scale method.
- Class weka.core.neighboursearch.CoverTree.CoverTreeNode extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1808760031169036512L
  - Serialized Fields
    - nodeid
```
int nodeid
```
      ID for the node.
    - idx
```
java.lang.Integer idx
```
      Index of the instance represented by this node in the index array.
    - max_dist
```
double max_dist
```
      The distance of the furthest descendant of the node.
    - parent_dist
```
double parent_dist
```
      The distance to the nodes parent.
    - children
```
Stack<T> children
```
      The children of the node.
    - num_children
```
int num_children
```
      The number of children node has.
    - scale
```
int scale
```
      The min i that makes base^i <= max_dist.
- Class weka.core.neighboursearch.KDTree extends NearestNeighbourSearch implements Serializable
  
  serialVersionUID:
  
  1505717283763272533L
  - Serialized Fields
    - m_DistanceList
```
double[] m_DistanceList
```
      Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours().
    - m_InstList
```
int[] m_InstList
```
      Indexlist of the instances of this kdtree. Instances get sorted according to the splits. the nodes of the KDTree just hold their start and end indices
    - m_Root
```
KDTreeNode m_Root
```
      The root node of the tree.
    - m_Splitter
```
KDTreeNodeSplitter m_Splitter
```
      The node splitter.
    - m_NumNodes
```
int m_NumNodes
```
      Tree stats.
    - m_NumLeaves
```
int m_NumLeaves
```
      Tree stats.
    - m_MaxDepth
```
int m_MaxDepth
```
      Tree stats.
    - m_TreeStats
```
TreePerformanceStats m_TreeStats
```
      Tree Stats variables.
    - m_NormalizeNodeWidth
```
boolean m_NormalizeNodeWidth
```
      flag for normalizing.
    - m_EuclideanDistance
```
EuclideanDistance m_EuclideanDistance
```
      The euclidean distance function to use.
    - m_MinBoxRelWidth
```
double m_MinBoxRelWidth
```
      minimal relative width of a KDTree rectangle.
    - m_MaxInstInLeaf
```
int m_MaxInstInLeaf
```
      maximal number of instances in a leaf.
- Class weka.core.neighboursearch.LinearNNSearch extends NearestNeighbourSearch implements Serializable
  
  serialVersionUID:
  
  1915484723703917241L
  - Serialized Fields
    - m_Distances
```
double[] m_Distances
```
      Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours().
    - m_SkipIdentical
```
boolean m_SkipIdentical
```
      Whether to skip instances from the neighbours that are identical to the query instance.
- Class weka.core.neighboursearch.NearestNeighbourSearch extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_Instances
```
Instances m_Instances
```
      The neighbourhood of instances to find neighbours in.
    - m_kNN
```
int m_kNN
```
      The number of neighbours to find.
    - m_DistanceFunction
```
DistanceFunction m_DistanceFunction
```
      the distance function used.
    - m_Stats
```
PerformanceStats m_Stats
```
      Performance statistics.
    - m_MeasurePerformance
```
boolean m_MeasurePerformance
```
      Should we measure Performance.
- Class weka.core.neighboursearch.PerformanceStats extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -7215110351388368092L
  - Serialized Fields
    - m_NumQueries
```
int m_NumQueries
```
      The total number of queries looked at.
    - m_MinP
```
double m_MinP
```
      The min and max data points looked for a query by the NNS algorithm.
    - m_MaxP
```
double m_MaxP
```
      The min and max data points looked for a query by the NNS algorithm.
    - m_SumP
```
double m_SumP
```
      The sum of data points looked at for all the queries.
    - m_SumSqP
```
double m_SumSqP
```
      The squared sum of data points looked at for all the queries.
    - m_PointCount
```
double m_PointCount
```
      The number of data points looked at for the current/last query.
    - m_MinC
```
double m_MinC
```
      The min and max coordinates(attributes) looked at per query.
    - m_MaxC
```
double m_MaxC
```
      The min and max coordinates(attributes) looked at per query.
    - m_SumC
```
double m_SumC
```
      The sum of coordinates/attributes looked at for all the queries.
    - m_SumSqC
```
double m_SumSqC
```
      The squared sum of coordinates/attributes looked at for all the queries.
    - m_CoordCount
```
double m_CoordCount
```
      The number of coordinates looked at for the current/last query.
- Class weka.core.neighboursearch.TreePerformanceStats extends PerformanceStats implements Serializable
  
  serialVersionUID:
  
  -6637636693340810373L
  - Serialized Fields
    - m_MinLeaves
```
int m_MinLeaves
```
      The min and max number leaf nodes looked for a query by the tree based NNS algorithm.
    - m_MaxLeaves
```
int m_MaxLeaves
```
      The min and max number leaf nodes looked for a query by the tree based NNS algorithm.
    - m_SumLeaves
```
int m_SumLeaves
```
      The sum of leaf nodes looked at for all the queries.
    - m_SumSqLeaves
```
int m_SumSqLeaves
```
      The squared sum of leaf nodes looked at for all the queries.
    - m_LeafCount
```
int m_LeafCount
```
      The number of leaf nodes looked at for the current/last query.
    - m_MinIntNodes
```
int m_MinIntNodes
```
      The min and max number internal nodes looked for a query by the tree based NNS algorithm.
    - m_MaxIntNodes
```
int m_MaxIntNodes
```
      The min and max number internal nodes looked for a query by the tree based NNS algorithm.
    - m_SumIntNodes
```
int m_SumIntNodes
```
      The sum of internal nodes looked at for all the queries.
    - m_SumSqIntNodes
```
int m_SumSqIntNodes
```
      The squared sum of internal nodes looked at for all the queries.
    - m_IntNodeCount
```
int m_IntNodeCount
```
      The number of internal nodes looked at for the current/last query.
Package weka.core.neighboursearch.balltrees
- Class weka.core.neighboursearch.balltrees.BallNode extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8289151861759883510L
  - Serialized Fields
    - m_Start
```
int m_Start
```
      The start index of the portion of the master index array, which stores the indices of the instances/points the node contains.
    - m_End
```
int m_End
```
      The end index of the portion of the master index array, which stores indices of the instances/points the node contains.
    - m_NumInstances
```
int m_NumInstances
```
      The number of instances/points in the node.
    - m_NodeNumber
```
int m_NodeNumber
```
      The node number/id.
    - m_SplitAttrib
```
int m_SplitAttrib
```
      The attribute that splits this node (not always used).
    - m_SplitVal
```
double m_SplitVal
```
      The value of m_SpiltAttrib that splits this node (not always used).
    - m_Left
```
BallNode m_Left
```
      The left child of the node.
    - m_Right
```
BallNode m_Right
```
      The right child of the node.
    - m_Pivot
```
Instance m_Pivot
```
      The pivot/centre of the ball.
    - m_Radius
```
double m_Radius
```
      The radius of this ball (hyper sphere).
- Class weka.core.neighboursearch.balltrees.BallSplitter extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_Instances
```
Instances m_Instances
```
      The instance on which the tree is built.
    - m_DistanceFunction
```
EuclideanDistance m_DistanceFunction
```
      The distance function (metric) from which the tree is (OR is to be) built.
    - m_Instlist
```
int[] m_Instlist
```
      The master index array that'll be reshuffled as nodes are split (and the tree is constructed).
- Class weka.core.neighboursearch.balltrees.BallTreeConstructor extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_MaxInstancesInLeaf
```
int m_MaxInstancesInLeaf
```
      The maximum number of instances allowed in a leaf.
    - m_MaxRelLeafRadius
```
double m_MaxRelLeafRadius
```
      The maximum relative radius of a leaf node (relative to the smallest ball enclosing all the data (training) points).
    - m_FullyContainChildBalls
```
boolean m_FullyContainChildBalls
```
      Should a parent ball completely enclose the balls of its two children, or only the points inside its children.
    - m_Instances
```
Instances m_Instances
```
      The instances on which to build the tree.
    - m_DistanceFunction
```
DistanceFunction m_DistanceFunction
```
      The distance function to use to build the tree.
    - m_NumNodes
```
int m_NumNodes
```
      The number of internal and leaf nodes in the built tree.
    - m_NumLeaves
```
int m_NumLeaves
```
      The number of leaf nodes in the built tree.
    - m_MaxDepth
```
int m_MaxDepth
```
      The depth of the built tree.
    - m_InstList
```
int[] m_InstList
```
      The master index array.
- Class weka.core.neighboursearch.balltrees.BottomUpConstructor extends BallTreeConstructor implements Serializable
  
  serialVersionUID:
  
  5864250777657707687L
- Class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint extends BallSplitter implements Serializable
  
  serialVersionUID:
  
  5617378551363700558L
  - Serialized Fields
    - m_RandSeed
```
int m_RandSeed
```
      Seed for random number generator.
    - m_Rand
```
java.util.Random m_Rand
```
      Random number generator for selecting an abitrary (random) point.
- Class weka.core.neighboursearch.balltrees.MedianOfWidestDimension extends BallSplitter implements Serializable
  
  serialVersionUID:
  
  3054842574468790421L
  - Serialized Fields
    - m_NormalizeDimWidths
```
boolean m_NormalizeDimWidths
```
      Should we normalize the widths(ranges) of the dimensions (attributes) before selecting the widest one.
- Class weka.core.neighboursearch.balltrees.MiddleOutConstructor extends BallTreeConstructor implements Serializable
  
  serialVersionUID:
  
  -8523314263062524462L
  - Serialized Fields
    - m_RSeed
```
int m_RSeed
```
      Seed form random number generator.
    - rand
```
java.util.Random rand
```
      The random number generator for selecting the first anchor point randomly (if selecting randomly).
    - rootRadius
```
double rootRadius
```
      The radius of the smallest ball enclosing all the data points.
    - m_RandomInitialAnchor
```
boolean m_RandomInitialAnchor
```
      True if the initial anchor is chosen randomly. False if it is the furthest point from the mean/centroid.
- Class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList extends FastVector implements Serializable
  
  serialVersionUID:
  
  -2283869109722934927L
- Class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren extends BallSplitter implements Serializable
  
  serialVersionUID:
  
  -2947177543565818260L
- Class weka.core.neighboursearch.balltrees.TopDownConstructor extends BallTreeConstructor implements Serializable
  
  serialVersionUID:
  
  -5150140645091889979L
  - Serialized Fields
    - m_Splitter
```
BallSplitter m_Splitter
```
      The BallSplitter algorithm used by the TopDown BallTree constructor, if it is selected.
Package weka.core.neighboursearch.covertrees
- Class weka.core.neighboursearch.covertrees.Stack extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  5604056321825539264L
  - Serialized Fields
    - length
```
int length
```
      The number of elements in the stack.
    - elements
```
java.util.ArrayList<E> elements
```
      The elements inside the stack.
Package weka.core.neighboursearch.kdtrees
- Class weka.core.neighboursearch.kdtrees.KDTreeNode extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -3660396067582792648L
  - Serialized Fields
    - m_NodeNumber
```
int m_NodeNumber
```
      node number (only for debug).
    - m_Left
```
KDTreeNode m_Left
```
      left subtree; contains instances with smaller or equal to split value.
    - m_Right
```
KDTreeNode m_Right
```
      right subtree; contains instances with larger than split value.
    - m_SplitValue
```
double m_SplitValue
```
      value to split on.
    - m_SplitDim
```
int m_SplitDim
```
      attribute to split on.
    - m_NodeRanges
```
double[][] m_NodeRanges
```
      lowest and highest value and width (= high - low) for each dimension.
    - m_NodesRectBounds
```
double[][] m_NodesRectBounds
```
      The lo and high bounds of the hyper rectangle described by the node.
    - m_Start
```
int m_Start
```
      The start index of the portion of the master index array, which stores the indices of the instances/points the node contains.
    - m_End
```
int m_End
```
      The end index of the portion of the master index array, which stores indices of the instances/points the node contains.
- Class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_Instances
```
Instances m_Instances
```
      The instances that'll be used for tree construction.
    - m_EuclideanDistance
```
EuclideanDistance m_EuclideanDistance
```
      The distance function used for building the tree.
    - m_InstList
```
int[] m_InstList
```
      The master index array that'll be reshuffled as nodes are split and the tree is constructed.
    - m_NormalizeNodeWidth
```
boolean m_NormalizeNodeWidth
```
      Stores whether if the width of a KDTree node is normalized or not.
- Class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod extends KDTreeNodeSplitter implements Serializable
  
  serialVersionUID:
  
  -866783749124714304L
- Class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension extends KDTreeNodeSplitter implements Serializable
  
  serialVersionUID:
  
  1383443320160540663L
- Class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension extends KDTreeNodeSplitter implements Serializable
  
  serialVersionUID:
  
  -7617277960046591906L
- Class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide extends KDTreeNodeSplitter implements Serializable
  
  serialVersionUID:
  
  852857628205680562L
Package weka.core.pmml
- Class weka.core.pmml.BuiltInArithmetic extends Function implements Serializable
  
  serialVersionUID:
  
  2275009453597279459L
  - Serialized Fields
    - m_operator
```
weka.core.pmml.BuiltInArithmetic.Operator m_operator
```
      The operator for this function
- Class weka.core.pmml.BuiltInMath extends Function implements Serializable
  
  serialVersionUID:
  
  -8092338695602573652L
  - Serialized Fields
    - m_func
```
weka.core.pmml.BuiltInMath.MathFunc m_func
```
      The function to apply
- Class weka.core.pmml.BuiltInString extends Function implements Serializable
  
  serialVersionUID:
  
  -7391516909331728653L
  - Serialized Fields
    - m_func
```
weka.core.pmml.BuiltInString.StringFunc m_func
```
      The function to apply
    - m_outputDef
```
Attribute m_outputDef
```
      The output structure produced by this function
- Class weka.core.pmml.Constant extends Expression implements Serializable
  
  serialVersionUID:
  
  -304829687822452424L
  - Serialized Fields
    - m_categoricalConst
```
java.lang.String m_categoricalConst
```
    - m_continuousConst
```
double m_continuousConst
```
- Class weka.core.pmml.DefineFunction extends Function implements Serializable
  
  serialVersionUID:
  
  -1976646917527243888L
  - Serialized Fields
    - m_parameters
```
java.util.ArrayList<E> m_parameters
```
      The list of parameters expected by this function. We can use this to do some error/type checking when users call setParameterDefs() on us
    - m_optype
```
FieldMetaInfo.Optype m_optype
```
      The optype for this function
    - m_expression
```
Expression m_expression
```
      The Expression for this function to use
- Class weka.core.pmml.DefineFunction.ParameterField extends FieldMetaInfo implements Serializable
  
  serialVersionUID:
  
  3918895902507585558L
- Class weka.core.pmml.DerivedFieldMetaInfo extends FieldMetaInfo implements Serializable
  - Serialized Fields
    - m_displayName
```
java.lang.String m_displayName
```
      display name
    - m_values
```
java.util.ArrayList<E> m_values
```
      the list of values (if the field is ordinal) - may be of size zero if none are specified. If none are specified, we may be able to construct this by querying the Expression in this derived field
    - m_expression
```
Expression m_expression
```
      the single expression that defines the value of this field
- Class weka.core.pmml.Discretize extends Expression implements Serializable
  - Serialized Fields
    - m_fieldName
```
java.lang.String m_fieldName
```
      The name of the field to be discretized
    - m_fieldIndex
```
int m_fieldIndex
```
      The index of the field
    - m_mapMissingDefined
```
boolean m_mapMissingDefined
```
      True if a replacement for missing values has been specified
    - m_mapMissingTo
```
java.lang.String m_mapMissingTo
```
      The value of the missing value replacement (if defined)
    - m_defaultValueDefined
```
boolean m_defaultValueDefined
```
      True if a default value has been specified
    - m_defaultValue
```
java.lang.String m_defaultValue
```
      The default value (if defined)
    - m_bins
```
java.util.ArrayList<E> m_bins
```
      The bins for this discretization
    - m_outputDef
```
Attribute m_outputDef
```
      The output structure of this discretization
- Class weka.core.pmml.Discretize.DiscretizeBin extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  5810063243316808400L
  - Serialized Fields
    - m_intervals
```
java.util.ArrayList<E> m_intervals
```
      The intervals for this DiscretizeBin
    - m_binValue
```
java.lang.String m_binValue
```
      The bin value for this DiscretizeBin
- Class weka.core.pmml.Expression extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4448840549804800321L
  - Serialized Fields
    - m_opType
```
FieldMetaInfo.Optype m_opType
```
      The optype of this Expression
    - m_fieldDefs
```
java.util.ArrayList<E> m_fieldDefs
```
      The field defs
- Class weka.core.pmml.FieldMetaInfo extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_fieldName
```
java.lang.String m_fieldName
```
      the name of the field
    - m_optype
```
FieldMetaInfo.Optype m_optype
```
      The optype for the target
- Class weka.core.pmml.FieldMetaInfo.Interval extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -7339790632684638012L
  - Serialized Fields
    - m_leftMargin
```
double m_leftMargin
```
      The left boundary value
    - m_rightMargin
```
double m_rightMargin
```
      The right boundary value
    - m_closure
```
FieldMetaInfo.Interval.Closure m_closure
```
- Class weka.core.pmml.FieldMetaInfo.Value extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -3981030320273649739L
  - Serialized Fields
    - m_value
```
java.lang.String m_value
```
      The value
    - m_displayValue
```
java.lang.String m_displayValue
```
      The display value (might hold a human readable value - e.g. product name instead of cryptic code).
    - m_property
```
FieldMetaInfo.Value.Property m_property
```
- Class weka.core.pmml.FieldRef extends Expression implements Serializable
  
  serialVersionUID:
  
  -8009605897876168409L
  - Serialized Fields
    - m_fieldName
```
java.lang.String m_fieldName
```
      The name of the field to reference
- Class weka.core.pmml.Function extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6997738288201933171L
  - Serialized Fields
    - m_functionName
```
java.lang.String m_functionName
```
      The name of this function
    - m_parameterDefs
```
java.util.ArrayList<E> m_parameterDefs
```
      The structure of the parameters to this function
- Class weka.core.pmml.MappingInfo extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_fieldsMap
```
int[] m_fieldsMap
```
      Map the incoming attributes to the mining schema attributes. Each entry holds the index of the incoming attribute that corresponds to this mining schema attribute.
    - m_nominalValueMaps
```
int[][] m_nominalValueMaps
```
      Map indexes for nominal values in incoming structure to those in the mining schema. There will be as many entries as there are attributes in this array. Non-nominal attributes will have null entries. Each non-null entry is an array of integer indexes. Each entry in a given array (for a given attribute) holds the index of the mining schema value that corresponds to this incoming value. UNKNOWN_NOMINAL_VALUE is used as the index for those incoming values that are not defined in the mining schema.
    - m_fieldsMappingText
```
java.lang.String m_fieldsMappingText
```
      Holds a textual description of the fields mapping
    - m_log
```
Logger m_log
```
      For logging
- Class weka.core.pmml.MiningFieldMetaInfo extends FieldMetaInfo implements Serializable
  
  serialVersionUID:
  
  -1256774332779563185L
  - Serialized Fields
    - m_usageType
```
weka.core.pmml.MiningFieldMetaInfo.Usage m_usageType
```
      usage type
    - m_outlierTreatmentMethod
```
weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod
```
      outlier treatmemnt method
    - m_lowValue
```
double m_lowValue
```
      outlier low value
    - m_highValue
```
double m_highValue
```
      outlier high value
    - m_missingValueTreatmentMethod
```
weka.core.pmml.MiningFieldMetaInfo.Missing m_missingValueTreatmentMethod
```
      missing values treatment method
    - m_missingValueReplacementNominal
```
java.lang.String m_missingValueReplacementNominal
```
      actual missing value replacements (if specified)
    - m_missingValueReplacementNumeric
```
double m_missingValueReplacementNumeric
```
    - m_optypeOverride
```
FieldMetaInfo.Optype m_optypeOverride
```
      optype overrides (override data dictionary type - NOT SUPPORTED AT PRESENT)
    - m_index
```
int m_index
```
      the index of the field in the mining schema Instances
    - m_importance
```
double m_importance
```
      importance (if defined)
    - m_miningSchemaI
```
Instances m_miningSchemaI
```
      mining schema (needed for toString method)
- Class weka.core.pmml.MiningSchema extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  7144380586726330455L
  - Serialized Fields
    - m_fieldInstancesStructure
```
Instances m_fieldInstancesStructure
```
      The structure of all the fields (both mining schema and derived) as Instances
    - m_miningSchemaInstancesStructure
```
Instances m_miningSchemaInstancesStructure
```
      Just the mining schema fields as Instances
    - m_miningMeta
```
java.util.ArrayList<E> m_miningMeta
```
      Meta information about the mining schema fields
    - m_derivedMeta
```
java.util.ArrayList<E> m_derivedMeta
```
      Meta information about derived fields (those defined in the TransformationDictionary followed by those defined in LocalTransformations)
    - m_transformationDictionary
```
weka.core.pmml.TransformationDictionary m_transformationDictionary
```
      The transformation dictionary (if defined)
    - m_targetMetaInfo
```
TargetMetaInfo m_targetMetaInfo
```
      target meta info (may be null if not defined)
- Class weka.core.pmml.NormContinuous extends Expression implements Serializable
  
  serialVersionUID:
  
  4714332374909851542L
  - Serialized Fields
    - m_fieldName
```
java.lang.String m_fieldName
```
      The name of the field to use
    - m_fieldIndex
```
int m_fieldIndex
```
      The index of the field
    - m_mapMissingDefined
```
boolean m_mapMissingDefined
```
      True if a replacement for missing values has been specified
    - m_mapMissingTo
```
double m_mapMissingTo
```
      The value of the missing value replacement (if defined)
    - m_outlierTreatmentMethod
```
weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod
```
      Outlier treatment method (default = asIs)
    - m_linearNormOrig
```
double[] m_linearNormOrig
```
      original values for the LinearNorm entries
    - m_linearNormNorm
```
double[] m_linearNormNorm
```
      norm values for the LinearNorm entries
- Class weka.core.pmml.NormDiscrete extends Expression implements Serializable
  
  serialVersionUID:
  
  -8854409417983908220L
  - Serialized Fields
    - m_fieldName
```
java.lang.String m_fieldName
```
      The name of the field to lookup our value in
    - m_field
```
Attribute m_field
```
      The actual attribute itself
    - m_fieldIndex
```
int m_fieldIndex
```
      The index of the attribute
    - m_fieldValue
```
java.lang.String m_fieldValue
```
      The actual value (as a String) that will correspond to an output of 1
    - m_mapMissingDefined
```
boolean m_mapMissingDefined
```
      True if a replacement for missing values has been specified
    - m_mapMissingTo
```
double m_mapMissingTo
```
      The value of the missing value replacement (if defined)
    - m_fieldValueIndex
```
int m_fieldValueIndex
```
      If we are referring to a nominal (rather than String) attribute then this holds the index of the value in question. Will be faster than searching for the value each time.
- Class weka.core.pmml.TargetMetaInfo extends FieldMetaInfo implements Serializable
  
  serialVersionUID:
  
  863500462237904927L
  - Serialized Fields
    - m_min
```
double m_min
```
      min and max
    - m_max
```
double m_max
```
    - m_rescaleConstant
```
double m_rescaleConstant
```
      re-scaling of target value (if defined)
    - m_rescaleFactor
```
double m_rescaleFactor
```
    - m_castInteger
```
java.lang.String m_castInteger
```
      cast integers (default no casting)
    - m_defaultValueOrPriorProbs
```
double[] m_defaultValueOrPriorProbs
```
      default value (numeric) or prior distribution (categorical)
    - m_values
```
java.util.ArrayList<E> m_values
```
      for categorical values. Actual values
    - m_displayValues
```
java.util.ArrayList<E> m_displayValues
```
      corresponding display values
Package weka.core.stemmers
- Class weka.core.stemmers.IteratedLovinsStemmer extends LovinsStemmer implements Serializable
  
  serialVersionUID:
  
  960689687163788264L
- Class weka.core.stemmers.LovinsStemmer extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6113024782588197L
- Class weka.core.stemmers.NullStemmer extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -3671261636532625496L
- Class weka.core.stemmers.SnowballStemmer extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6111170431963015178L
  - Serialized Fields
    - m_Stemmer
```
java.lang.Object m_Stemmer
```
      the current stemmer.
Package weka.core.tokenizers
- Class weka.core.tokenizers.AlphabeticTokenizer extends Tokenizer implements Serializable
  
  serialVersionUID:
  
  6705199562609861697L
  - Serialized Fields
    - m_Str
```
char[] m_Str
```
      the characters of the string
    - m_CurrentPos
```
int m_CurrentPos
```
      the current position
- Class weka.core.tokenizers.CharacterDelimitedTokenizer extends Tokenizer implements Serializable
  - Serialized Fields
    - m_Delimiters
```
java.lang.String m_Delimiters
```
      Delimiters used in tokenization
- Class weka.core.tokenizers.NGramTokenizer extends CharacterDelimitedTokenizer implements Serializable
  
  serialVersionUID:
  
  -2181896254171647219L
  - Serialized Fields
    - m_NMax
```
int m_NMax
```
      the maximum number of N
    - m_NMin
```
int m_NMin
```
      the minimum number of N
    - m_N
```
int m_N
```
      the current length of the N-grams
    - m_MaxPosition
```
int m_MaxPosition
```
      the number of strings available
    - m_CurrentPosition
```
int m_CurrentPosition
```
      the current position for returning elements
    - m_SplitString
```
java.lang.String[] m_SplitString
```
      all the available grams
- Class weka.core.tokenizers.Tokenizer extends java.lang.Object implements Serializable
- Class weka.core.tokenizers.WordTokenizer extends CharacterDelimitedTokenizer implements Serializable
  
  serialVersionUID:
  
  -930893034037880773L
Package weka.core.xml
- Class weka.core.xml.XMLInstances extends XMLDocument implements Serializable
  
  serialVersionUID:
  
  3626821327547416099L
  - Serialized Fields
    - m_Precision
```
int m_Precision
```
      the precision for numbers
    - m_Instances
```
Instances m_Instances
```
      the underlying Instances
Package weka.datagenerators
- Class weka.datagenerators.ClassificationGenerator extends DataGenerator implements Serializable
  
  serialVersionUID:
  
  -5261662546673517844L
  - Serialized Fields
    - m_NumExamples
```
int m_NumExamples
```
      Number of instances
- Class weka.datagenerators.ClusterDefinition extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -5950001207047429961L
  - Serialized Fields
    - m_Parent
```
ClusterGenerator m_Parent
```
      the parent of the cluster
- Class weka.datagenerators.ClusterGenerator extends DataGenerator implements Serializable
  
  serialVersionUID:
  
  6131722618472046365L
  - Serialized Fields
    - m_NumAttributes
```
int m_NumAttributes
```
      Number of attribute the dataset should have
    - m_ClassFlag
```
boolean m_ClassFlag
```
      class flag
    - m_booleanCols
```
Range m_booleanCols
```
      Stores which columns are boolean (default numeric)
    - m_nominalCols
```
Range m_nominalCols
```
      Stores which columns are nominal (default numeric)
- Class weka.datagenerators.DataGenerator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -3698585946221802578L
  - Serialized Fields
    - m_Debug
```
boolean m_Debug
```
      Debugging mode
    - m_DatasetFormat
```
Instances m_DatasetFormat
```
      The format for the generated dataset
    - m_RelationName
```
java.lang.String m_RelationName
```
      Relation name the dataset should have
    - 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_Seed
```
int m_Seed
```
      random number generator seed
    - m_Random
```
java.util.Random m_Random
```
      random number generator
    - m_CreatingRelationName
```
boolean m_CreatingRelationName
```
      flag, that indicates whether the relationname is currently assembled
- Class weka.datagenerators.RegressionGenerator extends DataGenerator implements Serializable
  
  serialVersionUID:
  
  3073254041275658221L
  - Serialized Fields
    - m_NumExamples
```
int m_NumExamples
```
      Number of instances
- Class weka.datagenerators.Test extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8890645875887157782L
  - Serialized Fields
    - m_AttIndex
```
int m_AttIndex
```
      the attribute index
    - m_Split
```
double m_Split
```
      the split
    - m_Not
```
boolean m_Not
```
      whether to negate the test
    - m_Dataset
```
Instances m_Dataset
```
      the dataset
Package weka.datagenerators.classifiers.classification
- Class weka.datagenerators.classifiers.classification.Agrawal extends ClassificationGenerator implements Serializable
  
  serialVersionUID:
  
  2254651939636143025L
  - Serialized Fields
    - m_Function
```
int m_Function
```
      the function to use for generating the data
    - m_BalanceClass
```
boolean m_BalanceClass
```
      whether to balance the class
    - m_PerturbationFraction
```
double m_PerturbationFraction
```
      the perturabation fraction
    - m_nextClassShouldBeZero
```
boolean m_nextClassShouldBeZero
```
      used for balancing the class
    - m_lastLabel
```
double m_lastLabel
```
      the last class label that was generated
- Class weka.datagenerators.classifiers.classification.BayesNet extends ClassificationGenerator implements Serializable
  
  serialVersionUID:
  
  -796118162379901512L
  - Serialized Fields
    - m_Generator
```
BayesNetGenerator m_Generator
```
      the bayesian net generator, that produces the actual data
- Class weka.datagenerators.classifiers.classification.LED24 extends ClassificationGenerator implements Serializable
  
  serialVersionUID:
  
  -7880209100415868737L
  - Serialized Fields
    - m_NoisePercent
```
double m_NoisePercent
```
      the noise rate
    - m_numIrrelevantAttributes
```
int m_numIrrelevantAttributes
```
      used for generating the output, i.e., the additional noise attributes
- Class weka.datagenerators.classifiers.classification.RandomRBF extends ClassificationGenerator implements Serializable
  
  serialVersionUID:
  
  6069033710635728720L
  - Serialized Fields
    - 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_NumCentroids
```
int m_NumCentroids
```
      the number of centroids to use for generation
    - m_centroids
```
double[][] m_centroids
```
      the centroids
    - m_centroidClasses
```
int[] m_centroidClasses
```
      the classes of the centroids
    - m_centroidWeights
```
double[] m_centroidWeights
```
      the weights of the centroids
    - m_centroidStdDevs
```
double[] m_centroidStdDevs
```
      the stddevs of the centroids
- Class weka.datagenerators.classifiers.classification.RDG1 extends ClassificationGenerator implements Serializable
  
  serialVersionUID:
  
  7751005204635320414L
  - Serialized Fields
    - 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_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_VoteFlag
```
boolean m_VoteFlag
```
      flag that stores if voting is wished
    - m_DecisionList
```
FastVector m_DecisionList
```
      decision list
    - m_AttList_Irr
```
boolean[] m_AttList_Irr
```
      array defines which attributes are irrelevant, with: true = attribute is irrelevant; false = attribute is not irrelevant
Package weka.datagenerators.classifiers.regression
- Class weka.datagenerators.classifiers.regression.Expression extends MexicanHat implements Serializable
  
  serialVersionUID:
  
  -4237047357682277211L
  - Serialized Fields
    - m_Expression
```
java.lang.String m_Expression
```
      the expression for computing y
    - m_Filter
```
AddExpression m_Filter
```
      the filter for generating y out of x
    - m_RawData
```
Instances m_RawData
```
      the input data structure for the filter
- Class weka.datagenerators.classifiers.regression.MexicanHat extends RegressionGenerator implements Serializable
  
  serialVersionUID:
  
  4577016375261512975L
  - Serialized Fields
    - m_Amplitude
```
double m_Amplitude
```
      the amplitude of y
    - m_MinRange
```
double m_MinRange
```
      the lower boundary of the range, x is drawn from
    - m_MaxRange
```
double m_MaxRange
```
      the upper boundary of the range, x is drawn from
    - m_NoiseRate
```
double m_NoiseRate
```
      the rate of the gaussian noise
    - m_NoiseVariance
```
double m_NoiseVariance
```
      the variance of the gaussian noise
    - m_NoiseRandom
```
java.util.Random m_NoiseRandom
```
      the random number generator for the noise
Package weka.datagenerators.clusterers
- Class weka.datagenerators.clusterers.BIRCHCluster extends ClusterGenerator implements Serializable
  
  serialVersionUID:
  
  -334820527230755027L
  - Serialized Fields
    - m_NumClusters
```
int m_NumClusters
```
      Number of Clusters the dataset should have
    - 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_ClusterList
```
FastVector m_ClusterList
```
      cluster list
    - m_GridSize
```
int m_GridSize
```
      grid size
    - m_GridWidth
```
double m_GridWidth
```
      grid width
- Class weka.datagenerators.clusterers.SubspaceCluster extends ClusterGenerator implements Serializable
  
  serialVersionUID:
  
  -3454999858505621128L
  - Serialized Fields
    - m_NoiseRate
```
double m_NoiseRate
```
      noise rate in percent (option P, between 0 and 30)
    - m_Clusters
```
ClusterDefinition[] m_Clusters
```
      cluster list
    - m_numValues
```
int[] m_numValues
```
      if nominal, store number of values
    - m_globalMinValue
```
double[] m_globalMinValue
```
      store global min values
    - m_globalMaxValue
```
double[] m_globalMaxValue
```
      store global max values
- Class weka.datagenerators.clusterers.SubspaceClusterDefinition extends ClusterDefinition implements Serializable
  
  serialVersionUID:
  
  3135678125044007231L
  - Serialized Fields
    - m_clustertype
```
int m_clustertype
```
      cluster type
    - m_clustersubtype
```
int m_clustersubtype
```
      cluster subtypes
    - m_numClusterAttributes
```
int m_numClusterAttributes
```
      number of attributes the cluster is defined for
    - m_numInstances
```
int m_numInstances
```
      number of instances for this cluster
    - m_MinInstNum
```
int m_MinInstNum
```
      minimal number of instances for this cluster
    - m_MaxInstNum
```
int m_MaxInstNum
```
      maximal number of instances for this cluster
    - m_AttrIndexRange
```
Range m_AttrIndexRange
```
      range of atttributes
    - m_attributes
```
boolean[] m_attributes
```
      attributes of this cluster
    - m_attrIndices
```
int[] m_attrIndices
```
      global indices of the attributes of the cluster
    - m_minValue
```
double[] m_minValue
```
      ranges of each attribute (min); not used if gaussian
    - m_maxValue
```
double[] m_maxValue
```
      ranges of each attribute (max); not used if gaussian
    - m_meanValue
```
double[] m_meanValue
```
      mean ; only used if gaussian
    - m_stddevValue
```
double[] m_stddevValue
```
      standarddev; only used if gaussian
Package weka.estimators
- Class weka.estimators.DiscreteEstimator extends Estimator implements Serializable
  
  serialVersionUID:
  
  -5526486742612434779L
  - Serialized Fields
    - m_Counts
```
double[] m_Counts
```
      Hold the counts
    - m_SumOfCounts
```
double m_SumOfCounts
```
      Hold the sum of counts
- Class weka.estimators.Estimator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -5902411487362274342L
  - Serialized Fields
    - m_Debug
```
boolean m_Debug
```
      Debugging mode
    - m_classValueIndex
```
double m_classValueIndex
```
      The class value index is > -1 if subset is taken with specific class value only
    - m_noClass
```
boolean m_noClass
```
      set if class is not important
- Class weka.estimators.KernelEstimator extends Estimator implements Serializable
  
  serialVersionUID:
  
  3646923563367683925L
  - 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 Estimator implements Serializable
  
  serialVersionUID:
  
  8950225468990043868L
  - 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 Estimator implements Serializable
  
  serialVersionUID:
  
  93584379632315841L
  - 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 Estimator implements Serializable
  
  serialVersionUID:
  
  7669362595289236662L
  - 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
  
  serialVersionUID:
  
  2551284958501991352L
  - 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
  
  serialVersionUID:
  
  -8511241602760467265L
  - 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
  
  serialVersionUID:
  
  -8069566663019501276L
  - 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
  
  serialVersionUID:
  
  -1580053925080091917L
  - 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
  
  serialVersionUID:
  
  -623185072785174658L
  - 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
  
  serialVersionUID:
  
  7388014746954652818L
  - 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
  
  serialVersionUID:
  
  -5620660780203158666L
  - 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?
    - m_Keywords
```
java.util.HashSet<E> m_Keywords
```
      the keywords for the current database type.
    - m_KeywordsMaskChar
```
java.lang.String m_KeywordsMaskChar
```
      the character to mask SQL keywords (by appending this character).
- Class weka.experiment.DensityBasedClustererSplitEvaluator extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_removeClassColumn
```
boolean m_removeClassColumn
```
      Remove the class column (if set) from the data
    - m_clusterer
```
DensityBasedClusterer m_clusterer
```
      The clusterer 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 clusterer 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 clusterer
    - m_clustererOptions
```
java.lang.String m_clustererOptions
```
      The clusterer options (if any)
    - m_clustererVersion
```
java.lang.String m_clustererVersion
```
      The clusterer version
- Class weka.experiment.Experiment extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  44945596742646663L
  - 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<E> 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
- Class weka.experiment.InstanceQuery extends DatabaseUtils implements Serializable
  
  serialVersionUID:
  
  718158370917782584L
  - 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
  
  serialVersionUID:
  
  -2203808461809311178L
- Class weka.experiment.LearningRateResultProducer extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -3841159673490861331L
  - 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.PairedCorrectedTTester extends PairedTTester implements Serializable
  
  serialVersionUID:
  
  -3105268939845653323L
- Class weka.experiment.PairedTTester extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  8370014624008728610L
  - Serialized Fields
    - m_Instances
```
Instances m_Instances
```
      The set of instances we will analyse
    - m_RunColumn
```
int m_RunColumn
```
      The index of the column containing the run number
    - m_RunColumnSet
```
int m_RunColumnSet
```
      The option setting for the run number column (-1 means last)
    - m_FoldColumn
```
int m_FoldColumn
```
      The option setting for the fold number column (-1 means none)
    - m_SortColumn
```
int m_SortColumn
```
      The column to sort on (-1 means default sorting)
    - m_SortOrder
```
int[] m_SortOrder
```
      The sorting of the datasets (according to the sort column)
    - m_ColOrder
```
int[] m_ColOrder
```
      The sorting of the columns (test base is always first)
    - m_SignificanceLevel
```
double m_SignificanceLevel
```
      The significance level for comparisons
    - m_DatasetKeyColumnsRange
```
Range m_DatasetKeyColumnsRange
```
      The range of columns that specify a unique "dataset" (eg: scheme plus configuration)
    - m_DatasetKeyColumns
```
int[] m_DatasetKeyColumns
```
      An array containing the indexes of just the selected columns
    - m_DatasetSpecifiers
```
weka.experiment.PairedTTester.DatasetSpecifiers m_DatasetSpecifiers
```
      The list of dataset specifiers
    - m_ResultsetKeyColumnsRange
```
Range m_ResultsetKeyColumnsRange
```
      The range of columns that specify a unique result set (eg: scheme plus configuration)
    - m_ResultsetKeyColumns
```
int[] m_ResultsetKeyColumns
```
      An array containing the indexes of just the selected columns
    - m_DisplayedResultsets
```
int[] m_DisplayedResultsets
```
      An array containing the indexes of the datasets to display
    - m_Resultsets
```
FastVector m_Resultsets
```
      Stores a vector for each resultset holding all instances in each set
    - m_ResultsetsValid
```
boolean m_ResultsetsValid
```
      Indicates whether the instances have been partitioned
    - m_ShowStdDevs
```
boolean m_ShowStdDevs
```
      Indicates whether standard deviations should be displayed
    - m_ResultMatrix
```
ResultMatrix m_ResultMatrix
```
      the instance of the class to produce the output.
- Class weka.experiment.PropertyNode extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8718165742572631384L
  - 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
  
  serialVersionUID:
  
  1403798165056795073L
  - 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
  
  serialVersionUID:
  
  -328181640503349202L
  - 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
  
  serialVersionUID:
  
  -1021538162895448259L
  - 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.RemoteEngine_Stub extends java.rmi.server.RemoteStub implements Serializable
  
  serialVersionUID:
  
  2L
- Class weka.experiment.RemoteExperiment extends Experiment implements Serializable
  
  serialVersionUID:
  
  -7357668825635314937L
  - Serialized Fields
    - m_listeners
```
FastVector m_listeners
```
      The list of objects listening for remote experiment events
    - m_remoteHosts
```
javax.swing.DefaultListModel<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_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
  
  serialVersionUID:
  
  7000867987391866451L
  - 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.ResultMatrix extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4487179306428209739L
  - Serialized Fields
    - TIE_STRING
```
java.lang.String TIE_STRING
```
      tie string
    - WIN_STRING
```
java.lang.String WIN_STRING
```
      win string
    - LOSS_STRING
```
java.lang.String LOSS_STRING
```
      loss string
    - LEFT_PARENTHESES
```
java.lang.String LEFT_PARENTHESES
```
      the left parentheses for enumerating cols/rows
    - RIGHT_PARENTHESES
```
java.lang.String RIGHT_PARENTHESES
```
      the right parentheses for enumerating cols/rows
    - m_ColNames
```
java.lang.String[] m_ColNames
```
      the column names
    - m_RowNames
```
java.lang.String[] m_RowNames
```
      the row names
    - m_ColHidden
```
boolean[] m_ColHidden
```
      whether a column is hidden
    - m_RowHidden
```
boolean[] m_RowHidden
```
      whether a row is hidden
    - m_Significance
```
int[][] m_Significance
```
      the significance
    - m_Mean
```
double[][] m_Mean
```
      the values
    - m_StdDev
```
double[][] m_StdDev
```
      the standard deviation
    - m_Counts
```
double[] m_Counts
```
      the counts for the different datasets
    - m_MeanPrec
```
int m_MeanPrec
```
      the standard mean precision
    - m_StdDevPrec
```
int m_StdDevPrec
```
      the standard std. deviation preicision
    - m_ShowStdDev
```
boolean m_ShowStdDev
```
      whether std. deviations are printed as well
    - m_ShowAverage
```
boolean m_ShowAverage
```
      whether the average for each column should be printed
    - m_PrintColNames
```
boolean m_PrintColNames
```
      whether the names or numbers are output as column declarations
    - m_PrintRowNames
```
boolean m_PrintRowNames
```
      whether the names or numbers are output as row declarations
    - m_EnumerateColNames
```
boolean m_EnumerateColNames
```
      whether a "(x)" is printed before each column name with "x" as the index
    - m_EnumerateRowNames
```
boolean m_EnumerateRowNames
```
      whether a "(x)" is printed before each row name with "x" as the index
    - m_ColNameWidth
```
int m_ColNameWidth
```
      the size of the names of the columns
    - m_RowNameWidth
```
int m_RowNameWidth
```
      the size of the names of the rows
    - m_MeanWidth
```
int m_MeanWidth
```
      the size of the mean columns
    - m_StdDevWidth
```
int m_StdDevWidth
```
      the size of the std dev columns
    - m_SignificanceWidth
```
int m_SignificanceWidth
```
      the size of the significance columns
    - m_CountWidth
```
int m_CountWidth
```
      the size of the counts
    - m_HeaderKeys
```
java.util.Vector<E> m_HeaderKeys
```
      contains the keys for the header
    - m_HeaderValues
```
java.util.Vector<E> m_HeaderValues
```
      contains the values for the header
    - m_NonSigWins
```
int[][] m_NonSigWins
```
      the non-significant wins
    - m_Wins
```
int[][] m_Wins
```
      the significant wins
    - m_RankingWins
```
int[] m_RankingWins
```
      the wins in ranking
    - m_RankingLosses
```
int[] m_RankingLosses
```
      the losses in ranking
    - m_RankingDiff
```
int[] m_RankingDiff
```
      the difference between wins and losses
    - m_RowOrder
```
int[] m_RowOrder
```
      the ordering of the rows
    - m_ColOrder
```
int[] m_ColOrder
```
      the ordering of the columns
    - m_RemoveFilterName
```
boolean m_RemoveFilterName
```
      whether to remove the filter name from the dataaset name
- Class weka.experiment.ResultMatrixCSV extends ResultMatrix implements Serializable
  
  serialVersionUID:
  
  -171838863135042743L
- Class weka.experiment.ResultMatrixGnuPlot extends ResultMatrix implements Serializable
  
  serialVersionUID:
  
  -234648254944790097L
- Class weka.experiment.ResultMatrixHTML extends ResultMatrix implements Serializable
  
  serialVersionUID:
  
  6672380422544799990L
- Class weka.experiment.ResultMatrixLatex extends ResultMatrix implements Serializable
  
  serialVersionUID:
  
  777690788447600978L
- Class weka.experiment.ResultMatrixPlainText extends ResultMatrix implements Serializable
  
  serialVersionUID:
  
  1502934525382357937L
- Class weka.experiment.ResultMatrixSignificance extends ResultMatrix implements Serializable
  
  serialVersionUID:
  
  -1280545644109764206L
- Class weka.experiment.Stats extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8610544539090024102L
  - 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
  
  serialVersionUID:
  
  -6129343303703560015L
  - 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
  
  serialVersionUID:
  
  5022109283147503266L
- Class weka.filters.Filter extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8835063755891851218L
  - Serialized Fields
    - m_OutputFormat
```
Instances m_OutputFormat
```
      The output format for instances
    - m_OutputQueue
```
Queue m_OutputQueue
```
      The output instance queue
    - m_OutputStringAtts
```
StringLocator m_OutputStringAtts
```
      Indices of string attributes in the output format
    - m_InputStringAtts
```
StringLocator m_InputStringAtts
```
      Indices of string attributes in the input format
    - m_OutputRelAtts
```
RelationalLocator m_OutputRelAtts
```
      Indices of relational attributes in the output format
    - m_InputRelAtts
```
RelationalLocator m_InputRelAtts
```
      Indices of relational 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.MultiFilter extends SimpleStreamFilter implements Serializable
  
  serialVersionUID:
  
  -6293720886005713120L
  - Serialized Fields
    - m_Filters
```
Filter[] m_Filters
```
      The filters
    - m_Streamable
```
boolean m_Streamable
```
      caches the streamable state
    - m_StreamableChecked
```
boolean m_StreamableChecked
```
      whether we already checked the streamable state
- Class weka.filters.SimpleBatchFilter extends SimpleFilter implements Serializable
  
  serialVersionUID:
  
  8102908673378055114L
- Class weka.filters.SimpleFilter extends Filter implements Serializable
  
  serialVersionUID:
  
  5702974949137433141L
  - Serialized Fields
    - m_Debug
```
boolean m_Debug
```
      Whether debugging is on
- Class weka.filters.SimpleStreamFilter extends SimpleFilter implements Serializable
  
  serialVersionUID:
  
  2754882676192747091L
Package weka.filters.supervised.attribute
- Class weka.filters.supervised.attribute.AddClassification extends SimpleBatchFilter implements Serializable
  
  serialVersionUID:
  
  -1931467132568441909L
  - Serialized Fields
    - m_Classifier
```
Classifier m_Classifier
```
      The classifier template used to do the classification.
    - m_SerializedClassifierFile
```
java.io.File m_SerializedClassifierFile
```
      The file from which to load a serialized classifier.
    - m_ActualClassifier
```
Classifier m_ActualClassifier
```
      The actual classifier used to do the classification.
    - m_SerializedHeader
```
Instances m_SerializedHeader
```
      the header of the file the serialized classifier was trained with.
    - m_OutputClassification
```
boolean m_OutputClassification
```
      whether to output the classification.
    - m_RemoveOldClass
```
boolean m_RemoveOldClass
```
      whether to remove the old class attribute.
    - m_OutputDistribution
```
boolean m_OutputDistribution
```
      whether to output the class distribution.
    - m_OutputErrorFlag
```
boolean m_OutputErrorFlag
```
      whether to output the error flag.
- Class weka.filters.supervised.attribute.AttributeSelection extends Filter implements Serializable
  
  serialVersionUID:
  
  -296211247688169716L
  - 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
    - m_hasClass
```
boolean m_hasClass
```
      True if the input format has a class attribute set
- Class weka.filters.supervised.attribute.ClassOrder extends Filter implements Serializable
  
  serialVersionUID:
  
  -2116226838887628411L
  - 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
  
  serialVersionUID:
  
  -3141006402280129097L
  - 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
  
  serialVersionUID:
  
  -5004607029857673950L
  - 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?
    - m_needToTransform
```
boolean m_needToTransform
```
      Whether we need to transform at all
- Class weka.filters.supervised.attribute.PLSFilter extends SimpleBatchFilter implements Serializable
  
  serialVersionUID:
  
  -3335106965521265631L
  - Serialized Fields
    - m_NumComponents
```
int m_NumComponents
```
      the maximum number of components to generate
    - m_Algorithm
```
int m_Algorithm
```
      the type of algorithm
    - m_PLS1_RegVector
```
Matrix m_PLS1_RegVector
```
      the regression vector "r-hat" for PLS1
    - m_PLS1_P
```
Matrix m_PLS1_P
```
      the P matrix for PLS1
    - m_PLS1_W
```
Matrix m_PLS1_W
```
      the W matrix for PLS1
    - m_PLS1_b_hat
```
Matrix m_PLS1_b_hat
```
      the b-hat vector for PLS1
    - m_SIMPLS_W
```
Matrix m_SIMPLS_W
```
      the W matrix for SIMPLS
    - m_SIMPLS_B
```
Matrix m_SIMPLS_B
```
      the B matrix for SIMPLS (used for prediction)
    - m_PerformPrediction
```
boolean m_PerformPrediction
```
      whether to include the prediction, i.e., modifying the class attribute
    - m_Missing
```
Filter m_Missing
```
      for replacing missing values
    - m_ReplaceMissing
```
boolean m_ReplaceMissing
```
      whether to replace missing values
    - m_Filter
```
Filter m_Filter
```
      for centering the data
    - m_Preprocessing
```
int m_Preprocessing
```
      the type of preprocessing
    - m_ClassMean
```
double m_ClassMean
```
      the mean of the class
    - m_ClassStdDev
```
double m_ClassStdDev
```
      the standard deviation of the class
Package weka.filters.supervised.instance
- Class weka.filters.supervised.instance.Resample extends Filter implements Serializable
  
  serialVersionUID:
  
  7079064953548300681L
  - Serialized Fields
    - m_SampleSizePercent
```
double m_SampleSizePercent
```
      The subsample size, percent of original set, default 100%.
    - m_RandomSeed
```
int m_RandomSeed
```
      The random number generator seed.
    - m_BiasToUniformClass
```
double m_BiasToUniformClass
```
      The degree of bias towards uniform (nominal) class distribution.
    - m_NoReplacement
```
boolean m_NoReplacement
```
      Whether to perform sampling with replacement or without.
    - m_InvertSelection
```
boolean m_InvertSelection
```
      Whether to invert the selection (only if instances are drawn WITHOUT replacement).
      
      See Also:
      
      Resample.m_NoReplacement
- Class weka.filters.supervised.instance.SMOTE extends Filter implements Serializable
  
  serialVersionUID:
  
  -1653880819059250364L
  - Serialized Fields
    - m_NearestNeighbors
```
int m_NearestNeighbors
```
      the number of neighbors to use.
    - m_RandomSeed
```
int m_RandomSeed
```
      the random seed to use.
    - m_Percentage
```
double m_Percentage
```
      the percentage of SMOTE instances to create.
    - m_ClassValueIndex
```
java.lang.String m_ClassValueIndex
```
      the index of the class value.
    - m_DetectMinorityClass
```
boolean m_DetectMinorityClass
```
      whether to detect the minority class automatically.
- Class weka.filters.supervised.instance.SpreadSubsample extends Filter implements Serializable
  
  serialVersionUID:
  
  -3947033795243930016L
  - 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
  
  serialVersionUID:
  
  -7069148179905814324L
  - 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
  
  serialVersionUID:
  
  -3795656792078022357L
  - 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
  
  serialVersionUID:
  
  761386447332932389L
  - 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.
    - m_DateFormat
```
java.lang.String m_DateFormat
```
      The date format.
- Class weka.filters.unsupervised.attribute.AddCluster extends Filter implements Serializable
  
  serialVersionUID:
  
  7414280611943807337L
  - 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
  
  serialVersionUID:
  
  402130384261736245L
  - Serialized Fields
    - m_infixExpression
```
java.lang.String m_infixExpression
```
      The infix expression
    - 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
    - m_attributeExpression
```
AttributeExpression m_attributeExpression
```
- Class weka.filters.unsupervised.attribute.AddID extends Filter implements Serializable
  
  serialVersionUID:
  
  4734383199819293390L
  - Serialized Fields
    - m_Index
```
SingleIndex m_Index
```
      the index of the attribute
    - m_Name
```
java.lang.String m_Name
```
      the name of the attribute
    - m_Counter
```
int m_Counter
```
      the counter for the ID
- Class weka.filters.unsupervised.attribute.AddNoise extends Filter implements Serializable
  
  serialVersionUID:
  
  -8499673222857299082L
  - 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.AddValues extends Filter implements Serializable
  
  serialVersionUID:
  
  -8100622241742393656L
  - Serialized Fields
    - m_AttIndex
```
SingleIndex m_AttIndex
```
      The attribute's index setting.
    - m_Labels
```
java.util.Vector<E> m_Labels
```
      The values to add.
    - m_Sort
```
boolean m_Sort
```
      Whether to sort the values.
    - m_SortedIndices
```
int[] m_SortedIndices
```
      the array with the sorted label indices
- Class weka.filters.unsupervised.attribute.Center extends PotentialClassIgnorer implements Serializable
  
  serialVersionUID:
  
  -9101338448900581023L
  - Serialized Fields
    - m_Means
```
double[] m_Means
```
      The means
- Class weka.filters.unsupervised.attribute.ChangeDateFormat extends Filter implements Serializable
  
  serialVersionUID:
  
  -1609344074013448737L
  - 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.ClassAssigner extends SimpleStreamFilter implements Serializable
  
  serialVersionUID:
  
  1775780193887394115L
  - Serialized Fields
    - m_ClassIndex
```
int m_ClassIndex
```
      the class index.
- Class weka.filters.unsupervised.attribute.ClusterMembership extends Filter implements Serializable
  
  serialVersionUID:
  
  6675702504667714026L
  - 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
  
  serialVersionUID:
  
  -8543707493627441566L
  - 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
- Class weka.filters.unsupervised.attribute.Discretize extends PotentialClassIgnorer implements Serializable
  
  serialVersionUID:
  
  -1358531742174527279L
  - 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
  
  serialVersionUID:
  
  -7500464545400454179L
  - Serialized Fields
    - m_DeltaCols
```
Range m_DeltaCols
```
      Stores which columns to take differences between
- Class weka.filters.unsupervised.attribute.InterquartileRange extends SimpleBatchFilter implements Serializable
  
  serialVersionUID:
  
  -227879653639723030L
  - Serialized Fields
    - m_Attributes
```
Range m_Attributes
```
      the attribute range to work on
    - m_AttributeIndices
```
int[] m_AttributeIndices
```
      the generated indices (only for performance reasons)
    - m_OutlierFactor
```
double m_OutlierFactor
```
      the factor for detecting outliers
    - m_ExtremeValuesFactor
```
double m_ExtremeValuesFactor
```
      the factor for detecting extreme values, by default 2*m_OutlierFactor
    - m_ExtremeValuesAsOutliers
```
boolean m_ExtremeValuesAsOutliers
```
      whether extreme values are also tagged as outliers
    - m_UpperExtremeValue
```
double[] m_UpperExtremeValue
```
      the upper extreme value threshold (= Q3 + EVF*IQR)
    - m_UpperOutlier
```
double[] m_UpperOutlier
```
      the upper outlier threshold (= Q3 + OF*IQR)
    - m_LowerOutlier
```
double[] m_LowerOutlier
```
      the lower outlier threshold (= Q1 - OF*IQR)
    - m_IQR
```
double[] m_IQR
```
      the interquartile range
    - m_Median
```
double[] m_Median
```
      the median
    - m_LowerExtremeValue
```
double[] m_LowerExtremeValue
```
      the lower extreme value threshold (= Q1 - EVF*IQR)
    - m_DetectionPerAttribute
```
boolean m_DetectionPerAttribute
```
      whether to generate Outlier/ExtremeValue attributes for each attribute instead of a general one
    - m_OutlierAttributePosition
```
int[] m_OutlierAttributePosition
```
      the position of the outlier attribute
    - m_OutputOffsetMultiplier
```
boolean m_OutputOffsetMultiplier
```
      whether to add another attribute called "Offset", that lists the 'multiplier' by which the outlier/extreme value is away from the median, i.e., value = median + 'multiplier' * IQR
      automatically enables m_DetectionPerAttribute!
- Class weka.filters.unsupervised.attribute.KernelFilter extends SimpleBatchFilter implements Serializable
  
  serialVersionUID:
  
  213800899640387499L
  - Serialized Fields
    - m_NumTrainInstances
```
int m_NumTrainInstances
```
      The number of instances in the training data.
    - m_Kernel
```
Kernel m_Kernel
```
      Kernel to use
    - m_ActualKernel
```
Kernel m_ActualKernel
```
      the Kernel which is actually used for computation
    - 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_NominalToBinary
```
NominalToBinary m_NominalToBinary
```
      The filter used to make attributes numeric.
    - m_Missing
```
ReplaceMissingValues m_Missing
```
      The filter used to get rid of missing values.
    - m_InitFile
```
java.io.File m_InitFile
```
      The dataset to initialize the filter with
    - m_InitFileClassIndex
```
SingleIndex m_InitFileClassIndex
```
      the class index for the file to initialized with
      
      See Also:
      
      KernelFilter.m_InitFile
    - m_Initialized
```
boolean m_Initialized
```
      whether the filter was initialized
    - m_KernelFactorExpression
```
java.lang.String m_KernelFactorExpression
```
      optimizes the kernel with this formula (A = # of attributes, N = # of instances)
    - m_KernelFactor
```
double m_KernelFactor
```
      the calculated kernel factor
      
      See Also:
      
      KernelFilter.m_KernelFactorExpression
    - m_Filter
```
Filter m_Filter
```
      for centering/standardizing the data
    - m_ActualFilter
```
Filter m_ActualFilter
```
      for centering/standardizing the data (the actual filter to use)
- Class weka.filters.unsupervised.attribute.MakeIndicator extends Filter implements Serializable
  
  serialVersionUID:
  
  766001176862773163L
  - 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.MathExpression extends PotentialClassIgnorer implements Serializable
  
  serialVersionUID:
  
  -3713222714671997901L
  - Serialized Fields
    - m_SelectCols
```
Range m_SelectCols
```
      Stores which columns to select as a funky range
    - m_expression
```
java.lang.String m_expression
```
      The modification expression
    - m_attStats
```
AttributeStats[] m_attStats
```
      Attributes statistics
- Class weka.filters.unsupervised.attribute.MergeTwoValues extends Filter implements Serializable
  
  serialVersionUID:
  
  2925048980504034018L
  - 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.MultiInstanceToPropositional extends Filter implements Serializable
  
  serialVersionUID:
  
  -4102847628883002530L
  - Serialized Fields
    - m_NumBags
```
int m_NumBags
```
      the total number of bags
    - m_BagStringAtts
```
StringLocator m_BagStringAtts
```
      Indices of string attributes in the bag
    - m_BagRelAtts
```
RelationalLocator m_BagRelAtts
```
      Indices of relational attributes in the bag
    - m_NumInstances
```
int m_NumInstances
```
      the total number of the propositional instance in the dataset
    - m_WeightMethod
```
int m_WeightMethod
```
      the propositional instance weight setting method
- Class weka.filters.unsupervised.attribute.NominalToBinary extends Filter implements Serializable
  
  serialVersionUID:
  
  -1130642825710549138L
  - Serialized Fields
    - m_Columns
```
Range m_Columns
```
      Stores which columns to act on
    - 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?
    - m_needToTransform
```
boolean m_needToTransform
```
      Whether we need to transform at all
- Class weka.filters.unsupervised.attribute.NominalToString extends Filter implements Serializable
  
  serialVersionUID:
  
  8655492378380068939L
  - Serialized Fields
    - m_AttIndex
```
Range m_AttIndex
```
      The attribute's index setting.
- Class weka.filters.unsupervised.attribute.Normalize extends PotentialClassIgnorer implements Serializable
  
  serialVersionUID:
  
  -8158531150984362898L
  - Serialized Fields
    - m_MinArray
```
double[] m_MinArray
```
      The minimum values for numeric attributes.
    - m_MaxArray
```
double[] m_MaxArray
```
      The maximum values for numeric attributes.
    - m_Translation
```
double m_Translation
```
      The translation of the output range.
    - m_Scale
```
double m_Scale
```
      The scaling factor of the output range.
- Class weka.filters.unsupervised.attribute.NumericCleaner extends SimpleStreamFilter implements Serializable
  
  serialVersionUID:
  
  -352890679895066592L
  - Serialized Fields
    - m_MinThreshold
```
double m_MinThreshold
```
      the minimum threshold
    - m_MinDefault
```
double m_MinDefault
```
      the minimum default replacement value
    - m_MaxThreshold
```
double m_MaxThreshold
```
      the maximum threshold
    - m_MaxDefault
```
double m_MaxDefault
```
      the maximum default replacement value
    - m_CloseTo
```
double m_CloseTo
```
      the number the values are checked for closeness to
    - m_CloseToDefault
```
double m_CloseToDefault
```
      the default replacement value for numbers "close-to"
    - m_CloseToTolerance
```
double m_CloseToTolerance
```
      the tolerance distance, below which numbers are considered being "close-to"
    - m_Cols
```
Range m_Cols
```
      Stores which columns to cleanse
    - m_IncludeClass
```
boolean m_IncludeClass
```
      whether to include the class attribute
    - m_Decimals
```
int m_Decimals
```
      the number of decimals to round to (-1 means no rounding)
- Class weka.filters.unsupervised.attribute.NumericToBinary extends PotentialClassIgnorer implements Serializable
  
  serialVersionUID:
  
  2616879323359470802L
- Class weka.filters.unsupervised.attribute.NumericToNominal extends SimpleBatchFilter implements Serializable
  
  serialVersionUID:
  
  -6614630932899796239L
  - Serialized Fields
    - m_Cols
```
Range m_Cols
```
      Stores which columns to turn into nominals
    - m_DefaultCols
```
java.lang.String m_DefaultCols
```
      The default columns to turn into nominals
- Class weka.filters.unsupervised.attribute.NumericTransform extends Filter implements Serializable
  
  serialVersionUID:
  
  -8561413333351366934L
  - 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
  
  serialVersionUID:
  
  -343922772462971561L
- Class weka.filters.unsupervised.attribute.PartitionedMultiFilter extends SimpleBatchFilter implements Serializable
  
  serialVersionUID:
  
  -6293720886005713120L
  - Serialized Fields
    - m_Filters
```
Filter[] m_Filters
```
      The filters.
    - m_Ranges
```
Range[] m_Ranges
```
      The attribute ranges.
    - m_RemoveUnused
```
boolean m_RemoveUnused
```
      Whether unused attributes are left out of the output.
    - m_IndicesUnused
```
int[] m_IndicesUnused
```
      the indices of the unused attributes.
- Class weka.filters.unsupervised.attribute.PKIDiscretize extends Discretize implements Serializable
  
  serialVersionUID:
  
  6153101248977702675L
- Class weka.filters.unsupervised.attribute.PotentialClassIgnorer extends Filter implements Serializable
  
  serialVersionUID:
  
  8625371119276845454L
  - 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.PrincipalComponents extends Filter implements Serializable
  
  serialVersionUID:
  
  -5649876869480249303L
  - 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_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_center
```
boolean m_center
```
      If true, center (rather than standardize) the data and compute PCA from covariance (rather than correlation) matrix.
    - 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 replacing missing values.
    - m_NominalToBinaryFilter
```
NominalToBinary m_NominalToBinaryFilter
```
      Filter for turning nominal values into numeric ones.
    - m_AttributeFilter
```
Remove m_AttributeFilter
```
      Filter for removing class attribute, nominal attributes with 0 or 1 value.
    - m_standardizeFilter
```
Standardize m_standardizeFilter
```
      Filter for standardizing the data
    - m_centerFilter
```
Center m_centerFilter
```
      Filter for centering the data
    - m_OutputNumAtts
```
int m_OutputNumAtts
```
      The number of attributes in the pc transformed data.
    - m_CoverVariance
```
double m_CoverVariance
```
      the amount of varaince to cover in the original data when retaining the best n PC's.
    - m_MaxAttrsInName
```
int m_MaxAttrsInName
```
      maximum number of attributes in the transformed attribute name.
    - m_MaxAttributes
```
int m_MaxAttributes
```
      maximum number of attributes in the transformed data (-1 for all).
- Class weka.filters.unsupervised.attribute.PropositionalToMultiInstance extends Filter implements Serializable
  
  serialVersionUID:
  
  5825873573912102482L
  - Serialized Fields
    - m_DoNotWeightBags
```
boolean m_DoNotWeightBags
```
      do not weight bags by number of instances they contain
    - m_Seed
```
int m_Seed
```
      the seed for randomizing, default is 1
    - m_Randomize
```
boolean m_Randomize
```
      whether to randomize the output data
    - m_BagStringAtts
```
StringLocator m_BagStringAtts
```
      Indices of string attributes in the bag
    - m_BagRelAtts
```
RelationalLocator m_BagRelAtts
```
      Indices of relational attributes in the bag
- Class weka.filters.unsupervised.attribute.RandomProjection extends Filter implements Serializable
  
  serialVersionUID:
  
  4428905532728645880L
  - 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_useReplaceMissing
```
boolean m_useReplaceMissing
```
      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
    - m_ntob
```
Filter m_ntob
```
      The NominalToBinary filter applied to the data before this filter
    - m_replaceMissing
```
Filter m_replaceMissing
```
      The ReplaceMissingValues filter
    - m_rndmSeed
```
long m_rndmSeed
```
      Stores the random seed used to generate the random matrix
    - m_rmatrix
```
double[][] m_rmatrix
```
      The random matrix
    - m_random
```
java.util.Random m_random
```
      The random number generator used for generating the random matrix
- Class weka.filters.unsupervised.attribute.RandomSubset extends SimpleStreamFilter implements Serializable
  
  serialVersionUID:
  
  2911221724251628050L
  - Serialized Fields
    - m_NumAttributes
```
double m_NumAttributes
```
      The number of attributes to randomly choose (>= 1 absolute number of attributes, < 1 percentage).
    - m_Seed
```
int m_Seed
```
      The seed value.
    - m_Indices
```
int[] m_Indices
```
      The indices of the attributes that got selected.
- Class weka.filters.unsupervised.attribute.RELAGGS extends SimpleBatchFilter implements Serializable
  
  serialVersionUID:
  
  -3333791375278589231L
  - Serialized Fields
    - m_MaxCardinality
```
int m_MaxCardinality
```
      the max. cardinality for nominal attributes
    - m_SelectedRange
```
Range m_SelectedRange
```
      the range of attributes to process (only relational ones will be processed)
    - m_AttStats
```
java.util.Hashtable<K,V> m_AttStats
```
      stores the attribute statistics att_index-att_index_in_rel_att <-> AttributeStats
- Class weka.filters.unsupervised.attribute.Remove extends Filter implements Serializable
  
  serialVersionUID:
  
  5011337331921522847L
  - 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
- Class weka.filters.unsupervised.attribute.RemoveType extends Filter implements Serializable
  
  serialVersionUID:
  
  -3563999462782486279L
  - 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
  
  serialVersionUID:
  
  -8659417851407640038L
  - 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.Reorder extends Filter implements Serializable
  
  serialVersionUID:
  
  -1135571321097202292L
  - Serialized Fields
    - m_NewOrderCols
```
java.lang.String m_NewOrderCols
```
      Stores which columns to reorder
    - 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.ReplaceMissingValues extends PotentialClassIgnorer implements Serializable
  
  serialVersionUID:
  
  8349568310991609867L
  - Serialized Fields
    - m_ModesAndMeans
```
double[] m_ModesAndMeans
```
      The modes and means
- Class weka.filters.unsupervised.attribute.Standardize extends PotentialClassIgnorer implements Serializable
  
  serialVersionUID:
  
  -6830769026855053281L
  - 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
  
  serialVersionUID:
  
  4864084427902797605L
  - Serialized Fields
    - m_AttIndices
```
Range m_AttIndices
```
      The attribute's range indices setting.
- Class weka.filters.unsupervised.attribute.StringToWordVector extends Filter implements Serializable
  
  serialVersionUID:
  
  8249106275278565424L
  - Serialized Fields
    - 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.
    - m_DocsCounts
```
int[] m_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.
    - m_NumInstances
```
int m_NumInstances
```
      Contains the number of documents (instances) in the input format from which the dictionary is created. It is used in IDF transform.
    - m_AvgDocLength
```
double m_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_PeriodicPruningRate
```
double m_PeriodicPruningRate
```
      The percentage at which to periodically prune the dictionary.
    - 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_filterType
```
int m_filterType
```
      The normalization to apply.
    - m_IDFTransform
```
boolean m_IDFTransform
```
      True if word frequencies should be transformed into fij*log(numOfDocs/numOfDocsWithWordi).
    - 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.
    - m_Stemmer
```
Stemmer m_Stemmer
```
      the stemming algorithm.
    - m_minTermFreq
```
int m_minTermFreq
```
      the minimum (per-class) word frequency.
    - m_doNotOperateOnPerClassBasis
```
boolean m_doNotOperateOnPerClassBasis
```
      whether to operate on a per-class basis.
    - m_Stopwords
```
java.io.File m_Stopwords
```
      a file containing stopwords for using others than the default Rainbow ones.
    - m_Tokenizer
```
Tokenizer m_Tokenizer
```
      the tokenizer algorithm to use.
- Class weka.filters.unsupervised.attribute.SwapValues extends Filter implements Serializable
  
  serialVersionUID:
  
  6155834679414275855L
  - 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
  
  serialVersionUID:
  
  3101490081896634942L
- Class weka.filters.unsupervised.attribute.TimeSeriesTranslate extends AbstractTimeSeries implements Serializable
  
  serialVersionUID:
  
  -8901621509691785705L
- Class weka.filters.unsupervised.attribute.Wavelet extends SimpleBatchFilter implements Serializable
  
  serialVersionUID:
  
  -3335106965521265631L
  - Serialized Fields
    - m_Filter
```
Filter m_Filter
```
      an optional filter for preprocessing of the data
    - m_Algorithm
```
int m_Algorithm
```
      the type of algorithm
    - m_Padding
```
int m_Padding
```
      the type of padding
Package weka.filters.unsupervised.instance
- Class weka.filters.unsupervised.instance.NonSparseToSparse extends Filter implements Serializable
  
  serialVersionUID:
  
  4694489111366063852L
- Class weka.filters.unsupervised.instance.Normalize extends Filter implements Serializable
  
  serialVersionUID:
  
  -7947971807522917395L
  - 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
  
  serialVersionUID:
  
  8854479785121877582L
  - 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
  
  serialVersionUID:
  
  8220373305559055700L
  - 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.RemoveFrequentValues extends Filter implements Serializable
  
  serialVersionUID:
  
  -2447432930070059511L
  - Serialized Fields
    - m_AttIndex
```
SingleIndex m_AttIndex
```
      The attribute's index setting.
    - m_NumValues
```
int m_NumValues
```
      the number of values to retain.
    - m_LeastValues
```
boolean m_LeastValues
```
      whether to retain values with least instances instead of most.
    - m_Invert
```
boolean m_Invert
```
      whether to invert the matching sense.
    - 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
    - m_Values
```
java.util.HashSet<E> m_Values
```
      contains the values to retain
- Class weka.filters.unsupervised.instance.RemoveMisclassified extends Filter implements Serializable
  
  serialVersionUID:
  
  5469157004717663171L
  - 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
  
  serialVersionUID:
  
  2150341191158533133L
  - Serialized Fields
    - m_Percentage
```
double 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
  
  serialVersionUID:
  
  -3064641215340828695L
  - Serialized Fields
    - m_Range
```
Range m_Range
```
      Range of instances requested by the user.
- Class weka.filters.unsupervised.instance.RemoveWithValues extends Filter implements Serializable
  
  serialVersionUID:
  
  4752870193679263361L
  - 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
    - m_dontFilterAfterFirstBatch
```
boolean m_dontFilterAfterFirstBatch
```
      Whether to filter instances after the first batch has been processed
- Class weka.filters.unsupervised.instance.Resample extends Filter implements Serializable
  
  serialVersionUID:
  
  3119607037607101160L
  - 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_NoReplacement
```
boolean m_NoReplacement
```
      Whether to perform sampling with replacement or without
    - m_InvertSelection
```
boolean m_InvertSelection
```
      Whether to invert the selection (only if instances are drawn WITHOUT replacement)
      
      See Also:
      
      Resample.m_NoReplacement
- Class weka.filters.unsupervised.instance.ReservoirSample extends Filter implements Serializable
  
  serialVersionUID:
  
  3119607037607101160L
  - Serialized Fields
    - m_SampleSize
```
int m_SampleSize
```
      The subsample size, number of instances%
    - m_subSample
```
Instance[] m_subSample
```
      Holds the sub-sample (reservoir)
    - m_currentInst
```
int m_currentInst
```
      The current instance being processed
    - m_RandomSeed
```
int m_RandomSeed
```
      The random number generator seed
    - m_random
```
java.util.Random m_random
```
      The random number generator
- Class weka.filters.unsupervised.instance.SparseToNonSparse extends Filter implements Serializable
  
  serialVersionUID:
  
  2481634184210236074L
- Class weka.filters.unsupervised.instance.SubsetByExpression extends SimpleBatchFilter implements Serializable
  
  serialVersionUID:
  
  5628686110979589602L
  - Serialized Fields
    - m_Expression
```
java.lang.String m_Expression
```
      the expresion to use for filtering.
    - m_filterAfterFirstBatch
```
boolean m_filterAfterFirstBatch
```
      Whether to filter instances after the first batch has been processed
Package weka.gui
- Class weka.gui.AttributeListPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -2030706987910400362L
  - 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
  
  serialVersionUID:
  
  627131485290359194L
  - 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_Pattern
```
javax.swing.JButton m_Pattern
```
      Press to enter a perl regular expression for 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 containing attribute names and selection status
    - m_PatternRegEx
```
java.lang.String m_PatternRegEx
```
      The current regular expression.
- Class weka.gui.AttributeSummaryPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -5434987925737735880L
  - 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
  
  serialVersionUID:
  
  -8650490488825371193L
  - 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_asCache
```
AttributeStats[] m_asCache
```
      Cache of attribute stats info for the current data set
    - 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
```
SparseInstance[] 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_doneCurrentAttribute
```
boolean m_doneCurrentAttribute
```
    - m_displayCurrentAttribute
```
boolean m_displayCurrentAttribute
```
    - m_colorAttrib
```
javax.swing.JComboBox<E> 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.CheckBoxList extends javax.swing.JList implements Serializable
  
  serialVersionUID:
  
  -4359573373359270258L
- Class weka.gui.CheckBoxList.CheckBoxListModel extends javax.swing.DefaultListModel implements Serializable
  
  serialVersionUID:
  
  7772455499540273507L
- Class weka.gui.CheckBoxList.CheckBoxListRenderer extends javax.swing.JCheckBox implements Serializable
  
  serialVersionUID:
  
  1059591605858524586L
- Class weka.gui.ConverterFileChooser extends javax.swing.JFileChooser implements Serializable
  
  serialVersionUID:
  
  -5373058011025481738L
  - Serialized Fields
    - m_Self
```
ConverterFileChooser m_Self
```
      the file chooser itself
    - m_DialogType
```
int m_DialogType
```
      the type of dialog to display
    - m_CurrentConverter
```
java.lang.Object m_CurrentConverter
```
      the converter that was chosen by the user
    - m_ConfigureButton
```
javax.swing.JButton m_ConfigureButton
```
      the configure button
    - m_Listener
```
java.beans.PropertyChangeListener m_Listener
```
      the propertychangelistener
    - m_LastFilter
```
javax.swing.filechooser.FileFilter m_LastFilter
```
      the last filter that was used for opening/saving
    - m_CapabilitiesFilter
```
Capabilities m_CapabilitiesFilter
```
      the Capabilities filter for the savers
    - m_OverwriteWarning
```
boolean m_OverwriteWarning
```
      whether to popup a dialog in case the file already exists (only save dialog)
    - m_FileMustExist
```
boolean m_FileMustExist
```
      whether the file to be opened must exist (only open dialog)
    - m_CoreConvertersOnly
```
boolean m_CoreConvertersOnly
```
      whether to display only core converters (hardcoded in ConverterUtils). Necessary for RMI/Remote Experiments for instance.
      
      See Also:
      
      ConverterUtils.CORE_FILE_LOADERS, ConverterUtils.CORE_FILE_SAVERS
- Class weka.gui.DatabaseConnectionDialog extends javax.swing.JDialog implements Serializable
  
  serialVersionUID:
  
  -1081946748666245054L
  - 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_DebugCheckBox
```
javax.swing.JCheckBox m_DebugCheckBox
```
    - m_DebugLab
```
javax.swing.JLabel m_DebugLab
```
    - m_returnValue
```
int m_returnValue
```
- Class weka.gui.ExtensionFileFilter extends javax.swing.filechooser.FileFilter implements Serializable
  - Serialized Fields
    - m_Description
```
java.lang.String m_Description
```
      The text description of the types of files accepted
    - m_Extension
```
java.lang.String[] m_Extension
```
      The filename extensions of accepted files
- Class weka.gui.GenericArrayEditor extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  3914616975334750480L
  - 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<E> 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<E> 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_EditBut
```
javax.swing.JButton m_EditBut
```
      Click this to edit the selected array value.
    - m_UpBut
```
javax.swing.JButton m_UpBut
```
      Click this to move the selected array value(s) one up.
    - m_DownBut
```
javax.swing.JButton m_DownBut
```
      Click this to move the selected array value(s) one down.
    - m_AddBut
```
javax.swing.JButton m_AddBut
```
      Click to add the current object configuration to the array.
    - m_Editor
```
java.beans.PropertyEditor m_Editor
```
      The property editor for editing existing elements.
    - m_PD
```
PropertyDialog m_PD
```
      The currently displayed property dialog, if any.
    - 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.
    - m_InnerMouseListener
```
java.awt.event.MouseListener m_InnerMouseListener
```
      Listens to mouse events and takes appropriate action.
- Class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog extends javax.swing.JDialog implements Serializable
  
  serialVersionUID:
  
  -7845503345689646266L
  - Serialized Fields
    - m_Self
```
javax.swing.JDialog m_Self
```
      the dialog itself.
    - m_Popup
```
javax.swing.JPopupMenu m_Popup
```
      the popup to display again.
    - m_Capabilities
```
Capabilities m_Capabilities
```
      the capabilities used for initializing the dialog.
    - m_InfoLabel
```
javax.swing.JLabel m_InfoLabel
```
      the label, listing the name of the superclass.
    - m_List
```
CheckBoxList m_List
```
      the list with all the capabilities.
    - m_OkButton
```
javax.swing.JButton m_OkButton
```
      the OK button.
    - m_CancelButton
```
javax.swing.JButton m_CancelButton
```
      the Cancel button.
- Class weka.gui.GenericObjectEditor.GOEPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  3656028520876011335L
  - 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.GOETreeNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable
  
  serialVersionUID:
  
  -1707872446682150133L
  - Serialized Fields
    - m_Capabilities
```
Capabilities m_Capabilities
```
      the Capabilities object to use for filtering.
    - m_toolTipText
```
java.lang.String m_toolTipText
```
      tool tip
- Class weka.gui.GenericObjectEditor.JTreePopupMenu extends javax.swing.JPopupMenu implements Serializable
  
  serialVersionUID:
  
  -3404546329655057387L
  - Serialized Fields
    - m_Self
```
javax.swing.JPopupMenu m_Self
```
      the popup itself.
    - m_tree
```
javax.swing.JTree m_tree
```
      The tree.
    - m_scroller
```
javax.swing.JScrollPane m_scroller
```
      The scroller.
    - m_FilterButton
```
javax.swing.JButton m_FilterButton
```
      The filter button in case of CapabilitiesHandlers.
    - m_RemoveFilterButton
```
javax.swing.JButton m_RemoveFilterButton
```
      The remove filter button in case of CapabilitiesHandlers.
    - m_CloseButton
```
javax.swing.JButton m_CloseButton
```
      The button for closing the popup again.
- Class weka.gui.GUIChooser extends javax.swing.JFrame implements Serializable
  
  serialVersionUID:
  
  9001529425230247914L
  - Serialized Fields
    - m_Self
```
GUIChooser m_Self
```
      the GUIChooser itself
    - m_jMenuBar
```
javax.swing.JMenuBar m_jMenuBar
```
    - m_jMenuProgram
```
javax.swing.JMenu m_jMenuProgram
```
    - m_jMenuVisualization
```
javax.swing.JMenu m_jMenuVisualization
```
    - m_jMenuTools
```
javax.swing.JMenu m_jMenuTools
```
    - m_jMenuHelp
```
javax.swing.JMenu m_jMenuHelp
```
    - m_PanelApplications
```
javax.swing.JPanel m_PanelApplications
```
      the panel for the application buttons
    - m_ExplorerBut
```
javax.swing.JButton m_ExplorerBut
```
      Click to open the Explorer
    - m_ExplorerFrame
```
javax.swing.JFrame m_ExplorerFrame
```
      The frame containing the explorer interface
    - m_ExperimenterBut
```
javax.swing.JButton m_ExperimenterBut
```
      Click to open the Explorer
    - m_ExperimenterFrame
```
javax.swing.JFrame m_ExperimenterFrame
```
      The frame containing the experiment interface
    - m_KnowledgeFlowBut
```
javax.swing.JButton m_KnowledgeFlowBut
```
      Click to open the KnowledgeFlow
    - m_pendingKnowledgeFlowLoad
```
java.lang.String m_pendingKnowledgeFlowLoad
```
      Pending file to load on startup of the KnowledgeFlow
    - m_KnowledgeFlowFrame
```
javax.swing.JFrame m_KnowledgeFlowFrame
```
      The frame containing the knowledge flow interface
    - m_SimpleBut
```
javax.swing.JButton m_SimpleBut
```
      Click to open the simplecli
    - m_SimpleCLI
```
SimpleCLI m_SimpleCLI
```
      The SimpleCLI
    - m_ArffViewers
```
java.util.Vector<E> m_ArffViewers
```
      keeps track of the opened ArffViewer instancs
    - m_SqlViewerFrame
```
javax.swing.JFrame m_SqlViewerFrame
```
      The frame containing the SqlViewer
    - m_BayesNetGUIFrame
```
javax.swing.JFrame m_BayesNetGUIFrame
```
      The frame containing the Bayes net GUI
    - m_EnsembleLibraryFrame
```
javax.swing.JFrame m_EnsembleLibraryFrame
```
      The frame containing the ensemble library interface
    - m_Plots
```
java.util.Vector<E> m_Plots
```
      keeps track of the opened plots
    - m_ROCs
```
java.util.Vector<E> m_ROCs
```
      keeps track of the opened ROCs
    - m_TreeVisualizers
```
java.util.Vector<E> m_TreeVisualizers
```
      keeps track of the opened tree visualizer instancs
    - m_GraphVisualizers
```
java.util.Vector<E> m_GraphVisualizers
```
      keeps track of the opened graph visualizer instancs
    - m_BoundaryVisualizerFrame
```
javax.swing.JFrame m_BoundaryVisualizerFrame
```
      The frame containing the boundary visualizer
    - m_SystemInfoFrame
```
javax.swing.JFrame m_SystemInfoFrame
```
      The frame containing the system info
    - m_MemoryUsageFrame
```
javax.swing.JFrame m_MemoryUsageFrame
```
      The frame containing the memory usage
    - m_weka
```
java.awt.Image m_weka
```
      The weka image
    - m_FileChooserTreeVisualizer
```
javax.swing.JFileChooser m_FileChooserTreeVisualizer
```
      filechooser for the TreeVisualizer
    - m_FileChooserGraphVisualizer
```
javax.swing.JFileChooser m_FileChooserGraphVisualizer
```
      filechooser for the GraphVisualizer
    - m_FileChooserPlot
```
javax.swing.JFileChooser m_FileChooserPlot
```
      filechooser for Plots
    - m_FileChooserROC
```
javax.swing.JFileChooser m_FileChooserROC
```
      filechooser for ROC curves
    - m_Icon
```
java.awt.Image m_Icon
```
      the icon for the frames
    - m_ChildFrames
```
java.util.HashSet<E> m_ChildFrames
```
      contains the child frames (title <-> object).
- Class weka.gui.GUIChooser.ChildFrameSDI extends javax.swing.JFrame implements Serializable
  
  serialVersionUID:
  
  8588293938686425618L
  - Serialized Fields
    - m_Parent
```
GUIChooser m_Parent
```
      the parent frame.
- Class weka.gui.HierarchyPropertyParser extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -4151103338506077544L
  - 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
  
  serialVersionUID:
  
  -5243579535296681063L
  - 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
    - m_showZeroInstancesAsUnknown
```
boolean m_showZeroInstancesAsUnknown
```
      Whether to display 0 or ? for the number of instances in cases where a dataset has only structure. Depending on where this panel is used from, the user may have loaded a dataset with no instances or a Loader that can read incrementally may be being used (in which case we don't know how many instances are in the dataset... yet).
- Class weka.gui.ListSelectorDialog extends javax.swing.JDialog implements Serializable
  
  serialVersionUID:
  
  906147926840288895L
  - 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_PatternBut
```
javax.swing.JButton m_PatternBut
```
      Click to enter a regex pattern for selection
    - m_List
```
javax.swing.JList<E> m_List
```
      The list component
    - m_Result
```
int m_Result
```
      Whether the selection was made or cancelled
    - m_PatternRegEx
```
java.lang.String m_PatternRegEx
```
      The current regular expression.
- 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.LogWindow extends javax.swing.JFrame implements Serializable
  
  serialVersionUID:
  
  5650947361381061112L
  - Serialized Fields
    - m_UseWordwrap
```
boolean m_UseWordwrap
```
      whether the JTextPane has wordwrap or not
    - m_Output
```
javax.swing.JTextPane m_Output
```
      the output
    - m_ButtonClear
```
javax.swing.JButton m_ButtonClear
```
      the clear button
    - m_ButtonClose
```
javax.swing.JButton m_ButtonClose
```
      the close button
    - m_LabelCurrentSize
```
javax.swing.JLabel m_LabelCurrentSize
```
      the current size
    - m_SpinnerMaxSize
```
javax.swing.JSpinner m_SpinnerMaxSize
```
      the spinner for the max number of chars
    - m_CheckBoxWordwrap
```
javax.swing.JCheckBox m_CheckBoxWordwrap
```
      whether to allow wordwrap or not
- Class weka.gui.Main extends javax.swing.JFrame implements Serializable
  
  serialVersionUID:
  
  1453813254824253849L
  - Serialized Fields
    - m_Self
```
Main m_Self
```
      the frame itself.
    - m_GUIType
```
int m_GUIType
```
      the type of GUI to display.
    - m_ChildFrames
```
java.util.HashSet<E> m_ChildFrames
```
      contains the child frames (title <-> object).
    - m_FileChooserTreeVisualizer
```
javax.swing.JFileChooser m_FileChooserTreeVisualizer
```
      filechooser for the TreeVisualizer.
    - m_FileChooserGraphVisualizer
```
javax.swing.JFileChooser m_FileChooserGraphVisualizer
```
      filechooser for the GraphVisualizer.
    - m_FileChooserPlot
```
javax.swing.JFileChooser m_FileChooserPlot
```
      filechooser for Plots.
    - m_FileChooserROC
```
javax.swing.JFileChooser m_FileChooserROC
```
      filechooser for ROC curves.
    - jMenuHelp
```
javax.swing.JMenu jMenuHelp
```
    - jMenuVisualization
```
javax.swing.JMenu jMenuVisualization
```
    - jMenuTools
```
javax.swing.JMenu jMenuTools
```
    - jDesktopPane
```
javax.swing.JDesktopPane jDesktopPane
```
    - jMenuApplications
```
javax.swing.JMenu jMenuApplications
```
    - jMenuItemHelpSystemInfo
```
javax.swing.JMenuItem jMenuItemHelpSystemInfo
```
    - jMenuItemHelpAbout
```
javax.swing.JMenuItem jMenuItemHelpAbout
```
    - jMenuItemHelpHomepage
```
javax.swing.JMenuItem jMenuItemHelpHomepage
```
    - jMenuItemHelpWekaWiki
```
javax.swing.JMenuItem jMenuItemHelpWekaWiki
```
    - jMenuItemHelpSourceforge
```
javax.swing.JMenuItem jMenuItemHelpSourceforge
```
    - jMenuItemVisualizationBoundaryVisualizer
```
javax.swing.JMenuItem jMenuItemVisualizationBoundaryVisualizer
```
    - jMenuItemVisualizationGraphVisualizer
```
javax.swing.JMenuItem jMenuItemVisualizationGraphVisualizer
```
    - jMenuItemVisualizationTreeVisualizer
```
javax.swing.JMenuItem jMenuItemVisualizationTreeVisualizer
```
    - jMenuItemVisualizationROC
```
javax.swing.JMenuItem jMenuItemVisualizationROC
```
    - jMenuItemVisualizationPlot
```
javax.swing.JMenuItem jMenuItemVisualizationPlot
```
    - jMenuItemToolsSqlViewer
```
javax.swing.JMenuItem jMenuItemToolsSqlViewer
```
    - jMenuItemToolsArffViewer
```
javax.swing.JMenuItem jMenuItemToolsArffViewer
```
    - jMenuItemApplicationsSimpleCLI
```
javax.swing.JMenuItem jMenuItemApplicationsSimpleCLI
```
    - jMenuItemApplicationsKnowledgeFlow
```
javax.swing.JMenuItem jMenuItemApplicationsKnowledgeFlow
```
    - jMenuItemApplicationsExperimenter
```
javax.swing.JMenuItem jMenuItemApplicationsExperimenter
```
    - jMenuItemApplicationsExplorer
```
javax.swing.JMenuItem jMenuItemApplicationsExplorer
```
    - jMenuItemProgramExit
```
javax.swing.JMenuItem jMenuItemProgramExit
```
    - jMenuItemProgramLogWindow
```
javax.swing.JMenuItem jMenuItemProgramLogWindow
```
    - jMenuItemProgramMemoryUsage
```
javax.swing.JMenuItem jMenuItemProgramMemoryUsage
```
    - jMenuProgram
```
javax.swing.JMenu jMenuProgram
```
    - jMenuExtensions
```
javax.swing.JMenu jMenuExtensions
```
    - jMenuWindows
```
javax.swing.JMenu jMenuWindows
```
    - jMenuBar
```
javax.swing.JMenuBar jMenuBar
```
- Class weka.gui.Main.BackgroundDesktopPane extends javax.swing.JDesktopPane implements Serializable
  
  serialVersionUID:
  
  2046713123452402745L
  - Serialized Fields
    - m_Background
```
java.awt.Image m_Background
```
      the actual background image.
- Class weka.gui.Main.ChildFrameMDI extends javax.swing.JInternalFrame implements Serializable
  
  serialVersionUID:
  
  3772573515346899959L
  - Serialized Fields
    - m_Parent
```
Main m_Parent
```
      the parent frame.
- Class weka.gui.Main.ChildFrameSDI extends javax.swing.JFrame implements Serializable
  
  serialVersionUID:
  
  8588293938686425618L
  - Serialized Fields
    - m_Parent
```
Main m_Parent
```
      the parent frame.
- Class weka.gui.MemoryUsagePanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -4812319791687471721L
  - Serialized Fields
    - m_History
```
java.util.Vector<E> m_History
```
      the memory usage over time.
    - m_Memory
```
Memory m_Memory
```
      for monitoring the memory usage.
    - m_Monitor
```
weka.gui.MemoryUsagePanel.MemoryMonitor m_Monitor
```
      the thread for monitoring the memory usage.
    - m_ButtonGC
```
javax.swing.JButton m_ButtonGC
```
      the button for running the garbage collector.
    - m_Percentages
```
java.util.Vector<E> m_Percentages
```
      the threshold percentages to change color.
    - m_Colors
```
java.util.Hashtable<K,V> m_Colors
```
      the corresponding colors for the thresholds.
    - m_DefaultColor
```
java.awt.Color m_DefaultColor
```
      the default color.
    - m_BackgroundColor
```
java.awt.Color m_BackgroundColor
```
      the background color.
    - m_FrameLocation
```
java.awt.Point m_FrameLocation
```
      the position for the dialog.
- Class weka.gui.PropertyDialog extends javax.swing.JDialog implements Serializable
  
  serialVersionUID:
  
  -2314850859392433539L
  - 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
  
  serialVersionUID:
  
  5370025273466728904L
  - 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
    - m_CustomPanel
```
javax.swing.JPanel m_CustomPanel
```
      The custom panel (if any)
- Class weka.gui.PropertySelectorDialog extends javax.swing.JDialog implements Serializable
  
  serialVersionUID:
  
  -3155058124137930518L
  - 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
  
  serialVersionUID:
  
  -8939835593429918345L
  - 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_HelpDialog
```
javax.swing.JDialog m_HelpDialog
```
      Help dialog.
    - m_CapabilitiesDialog
```
weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_CapabilitiesDialog
```
      Capabilities Help dialog.
    - m_HelpBut
```
javax.swing.JButton m_HelpBut
```
      Button to pop up the full help text in a separate dialog.
    - m_CapabilitiesBut
```
javax.swing.JButton m_CapabilitiesBut
```
      Button to pop up the capabilities in a separate dialog.
    - m_CapabilitiesText
```
javax.swing.JTextArea m_CapabilitiesText
```
      the TextArea of the Capabilities help dialog.
    - 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.PropertySheetPanel.CapabilitiesHelpDialog extends javax.swing.JDialog implements Serializable
  
  serialVersionUID:
  
  -1404770987103289858L
  - Serialized Fields
    - m_Self
```
weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_Self
```
      the dialog itself.
- Class weka.gui.ResultHistoryPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  4297069440135326829L
  - 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<E> m_Model
```
      The list model
    - m_List
```
javax.swing.JList<E> 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
  
  serialVersionUID:
  
  -8675332541861828079L
- Class weka.gui.ResultHistoryPanel.RMouseAdapter extends java.awt.event.MouseAdapter implements Serializable
  
  serialVersionUID:
  
  -8991922650552358669L
- Class weka.gui.SetInstancesPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -384804041420453735L
  - 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_CloseBut
```
javax.swing.JButton m_CloseBut
```
      Click to close the dialog
    - m_Summary
```
InstancesSummaryPanel m_Summary
```
      The instance summary component
    - m_FileChooser
```
ConverterFileChooser 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
    - m_Loader
```
Loader m_Loader
```
      The current loader used to obtain the current instances
    - m_ParentFrame
```
javax.swing.JFrame m_ParentFrame
```
      the parent frame. if one is provided, the close-button is displayed
    - m_CloseButPanel
```
javax.swing.JPanel m_CloseButPanel
```
      the panel the Close-Button is located in
    - m_readIncrementally
```
boolean m_readIncrementally
```
      whether to read the instances incrementally, if possible.
    - m_showZeroInstancesAsUnknown
```
boolean m_showZeroInstancesAsUnknown
```
      whether to display zero instances as unknown ("?").
- Class weka.gui.SimpleCLI extends java.awt.Frame implements Serializable
  
  serialVersionUID:
  
  -50661410800566036L
- Class weka.gui.SimpleCLIPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -7377739469759943231L
  - Serialized Fields
    - m_OutputArea
```
javax.swing.JTextArea m_OutputArea
```
      The output area canvas added to the frame.
    - m_Input
```
javax.swing.JTextField 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.
    - m_Completion
```
SimpleCLIPanel.CommandlineCompletion m_Completion
```
      The commandline completion.
- Class weka.gui.SortedTableModel extends javax.swing.table.AbstractTableModel implements Serializable
  
  serialVersionUID:
  
  4030907921461127548L
  - Serialized Fields
    - mModel
```
javax.swing.table.TableModel mModel
```
      the actual table model
    - mIndices
```
int[] mIndices
```
      the mapping between displayed and actual index
    - mSortColumn
```
int mSortColumn
```
      the sort column
    - mAscending
```
boolean mAscending
```
      whether sorting is ascending or descending
- Class weka.gui.SplashWindow extends java.awt.Window implements Serializable
  
  serialVersionUID:
  
  -2685134277041307795L
  - 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.ViewerDialog extends javax.swing.JDialog implements Serializable
  
  serialVersionUID:
  
  6747718484736047752L
  - 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
  
  serialVersionUID:
  
  -4697041150989513939L
  - Serialized Fields
    - m_TableArff
```
ArffTable m_TableArff
```
      the underlying table
    - m_PopupHeader
```
javax.swing.JPopupMenu m_PopupHeader
```
      the popup menu for the header row
    - m_PopupRows
```
javax.swing.JPopupMenu m_PopupRows
```
      the popup menu for the data rows
    - m_LabelName
```
javax.swing.JLabel m_LabelName
```
      displays the relation name
    - 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
```
    - menuItemAttributeAsClass
```
javax.swing.JMenuItem menuItemAttributeAsClass
```
    - 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
```
    - menuItemOptimalColWidth
```
javax.swing.JMenuItem menuItemOptimalColWidth
```
    - menuItemOptimalColWidths
```
javax.swing.JMenuItem menuItemOptimalColWidths
```
    - m_Filename
```
java.lang.String m_Filename
```
      the filename used in the title
    - m_Title
```
java.lang.String m_Title
```
      the title prefix
    - m_CurrentCol
```
int m_CurrentCol
```
      the currently selected column
    - m_Changed
```
boolean m_Changed
```
      flag for whether data got changed
    - m_ChangeListeners
```
java.util.HashSet<E> m_ChangeListeners
```
      the listeners that listen for modifications
    - m_LastSearch
```
java.lang.String m_LastSearch
```
      the string used in the last search
    - m_LastReplace
```
java.lang.String m_LastReplace
```
      the string used in the last replace
- Class weka.gui.arffviewer.ArffSortedTableModel extends SortedTableModel implements Serializable
  
  serialVersionUID:
  
  -5733148376354254030L
- Class weka.gui.arffviewer.ArffTable extends javax.swing.JTable implements Serializable
  
  serialVersionUID:
  
  -2016200506908637967L
  - Serialized Fields
    - m_SearchString
```
java.lang.String m_SearchString
```
      the search string
    - m_ChangeListeners
```
java.util.HashSet<E> m_ChangeListeners
```
      the listeners for changes
- Class weka.gui.arffviewer.ArffTable.RelationalCellEditor extends javax.swing.AbstractCellEditor implements Serializable
  
  serialVersionUID:
  
  657969163293205963L
  - Serialized Fields
    - m_Button
```
javax.swing.JButton m_Button
```
      the button for opening the dialog
    - m_CurrentInst
```
Instances m_CurrentInst
```
      the current instances
    - m_RowIndex
```
int m_RowIndex
```
      the row index this editor is for
    - m_ColumnIndex
```
int m_ColumnIndex
```
      the column index this editor is for
- Class weka.gui.arffviewer.ArffTableCellRenderer extends javax.swing.table.DefaultTableCellRenderer implements Serializable
  
  serialVersionUID:
  
  9195794493301191171L
  - Serialized Fields
    - missingColor
```
java.awt.Color missingColor
```
      the color for missing values
    - missingColorSelected
```
java.awt.Color missingColorSelected
```
      the color for selected missing values
    - highlightColor
```
java.awt.Color highlightColor
```
      the color for highlighted values
    - highlightColorSelected
```
java.awt.Color highlightColorSelected
```
      the color for selected highlighted values
- Class weka.gui.arffviewer.ArffViewer extends javax.swing.JFrame implements Serializable
  
  serialVersionUID:
  
  -7455845566922685175L
  - Serialized Fields
    - m_MainPanel
```
ArffViewerMainPanel m_MainPanel
```
      the main panel
- Class weka.gui.arffviewer.ArffViewerMainPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -8763161167586738753L
  - Serialized Fields
    - parent
```
java.awt.Container 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
```
    - menuEditAttributeAsClass
```
javax.swing.JMenuItem menuEditAttributeAsClass
```
    - 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
```
    - menuViewOptimalColWidths
```
javax.swing.JMenuItem menuViewOptimalColWidths
```
    - fileChooser
```
ConverterFileChooser fileChooser
```
    - frameTitle
```
java.lang.String frameTitle
```
    - confirmExit
```
boolean confirmExit
```
    - width
```
int width
```
    - height
```
int height
```
    - top
```
int top
```
    - left
```
int left
```
    - exitOnClose
```
boolean exitOnClose
```
Package weka.gui.beans
- Class weka.gui.beans.AbstractDataSink extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  3956528599473814287L
  - 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
  
  serialVersionUID:
  
  -4127257701890044793L
  - 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
  
  serialVersionUID:
  
  3983303541814121632L
  - 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
  
  serialVersionUID:
  
  -7905764845789349839L
  - 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
  
  serialVersionUID:
  
  -1809339823613492037L
  - 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
  
  serialVersionUID:
  
  -7842746199524591125L
  - 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.Associator extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -7843500322130210057L
  - 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 associator (if it exists).
    - m_listenees
```
java.util.Hashtable<K,V> m_listenees
```
      Objects talking to us
    - m_textListeners
```
java.util.Vector<E> m_textListeners
```
      Objects listening for text events
    - m_graphListeners
```
java.util.Vector<E> m_graphListeners
```
      Objects listening for graph events
    - m_Associator
```
Associator m_Associator
```
- Class weka.gui.beans.AssociatorCustomizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  5767664969353495974L
  - Serialized Fields
    - m_pcSupport
```
java.beans.PropertyChangeSupport m_pcSupport
```
    - m_dsAssociator
```
Associator m_dsAssociator
```
    - m_AssociatorEditor
```
PropertySheetPanel m_AssociatorEditor
```
    - m_parentFrame
```
javax.swing.JFrame m_parentFrame
```
    - m_backup
```
Associator m_backup
```
      Backup is user presses cancel
- Class weka.gui.beans.AttributeSummarizer extends DataVisualizer implements Serializable
  
  serialVersionUID:
  
  -294354961169372758L
  - 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_runNumber
```
int m_runNumber
```
      The run number that this classifier was generated for
    - m_maxRunNumber
```
int m_maxRunNumber
```
      The maximum number of runs
    - m_setNumber
```
int m_setNumber
```
      The set number for the test set
    - m_maxSetNumber
```
int m_maxSetNumber
```
      The last set number for this series
    - m_groupIdentifier
```
long m_groupIdentifier
```
      An identifier that can be used to group all related runs/sets together.
- Class weka.gui.beans.BatchClustererEvent extends java.util.EventObject implements Serializable
  
  serialVersionUID:
  
  7268777944939129714L
  - 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
  
  serialVersionUID:
  
  8804264241791332064L
  - 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
  
  serialVersionUID:
  
  -7575653109025406342L
  - 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
  
  serialVersionUID:
  
  -6677473561687129614L
  - 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
  
  serialVersionUID:
  
  7812460715499569390L
  - 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
  
  serialVersionUID:
  
  4011131665025817924L
  - 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
  
  serialVersionUID:
  
  476539385765301907L
  - 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<E> 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_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_ClassifierTemplate
```
Classifier m_ClassifierTemplate
```
      Template used for creating copies when building in parallel
    - m_ie
```
IncrementalClassifierEvent m_ie
```
    - m_binaryFilter
```
javax.swing.filechooser.FileFilter m_binaryFilter
```
    - m_KOMLFilter
```
javax.swing.filechooser.FileFilter m_KOMLFilter
```
    - m_XStreamFilter
```
javax.swing.filechooser.FileFilter m_XStreamFilter
```
    - 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_executionSlots
```
int m_executionSlots
```
      Number of threads to use to train models with
    - m_oldText
```
java.lang.String m_oldText
```
      Holds original icon label text
    - m_block
```
boolean m_block
```
      true if we should block any further training data sets.
- Class weka.gui.beans.Classifier.TrainingTask extends java.lang.Object implements Serializable
  - Serialized Fields
    - m_runNum
```
int m_runNum
```
    - m_maxRunNum
```
int m_maxRunNum
```
    - m_setNum
```
int m_setNum
```
    - m_maxSetNum
```
int m_maxSetNum
```
    - m_train
```
Instances m_train
```
    - m_taskInfo
```
TaskStatusInfo m_taskInfo
```
- Class weka.gui.beans.ClassifierCustomizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -6688000820160821429L
  - 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
```
    - m_holderPanel
```
javax.swing.JPanel m_holderPanel
```
    - m_executionSlotsText
```
javax.swing.JTextField m_executionSlotsText
```
    - m_parentFrame
```
javax.swing.JFrame m_parentFrame
```
    - m_backup
```
Classifier m_backup
```
      Copy of the current classifier in case cancel is selected
- Class weka.gui.beans.ClassifierPerformanceEvaluator extends AbstractEvaluator implements Serializable
  
  serialVersionUID:
  
  -3511801418192148690L
  - 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
```
- Class weka.gui.beans.ClassValuePicker extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -1196143276710882989L
  - Serialized Fields
    - m_classValue
```
java.lang.String m_classValue
```
      the class value 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
  
  serialVersionUID:
  
  8213423053861600469L
  - Serialized Fields
    - m_displayValNames
```
boolean m_displayValNames
```
    - m_classValuePicker
```
ClassValuePicker m_classValuePicker
```
    - m_pcSupport
```
java.beans.PropertyChangeSupport m_pcSupport
```
    - m_ClassValueCombo
```
javax.swing.JComboBox<E> m_ClassValueCombo
```
    - m_holderP
```
javax.swing.JPanel m_holderP
```
    - m_messageLabel
```
javax.swing.JLabel m_messageLabel
```
    - m_parent
```
javax.swing.JFrame m_parent
```
    - m_backup
```
java.lang.String m_backup
```
    - m_textBoxEntryMode
```
boolean m_textBoxEntryMode
```
    - m_valueTextBox
```
javax.swing.JTextField m_valueTextBox
```
- Class weka.gui.beans.Clusterer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  7729795159836843810L
  - 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
  
  serialVersionUID:
  
  -2035688458149534161L
  - Serialized Fields
    - m_pcSupport
```
java.beans.PropertyChangeSupport m_pcSupport
```
    - m_dsClusterer
```
Clusterer m_dsClusterer
```
    - m_ClustererEditor
```
PropertySheetPanel m_ClustererEditor
```
    - m_parentFrame
```
javax.swing.JFrame m_parentFrame
```
    - m_backup
```
Clusterer m_backup
```
      Backup if the user presses cancel
- Class weka.gui.beans.ClustererPerformanceEvaluator extends AbstractEvaluator implements Serializable
  
  serialVersionUID:
  
  8041163601333978584L
  - Serialized Fields
    - m_textListeners
```
java.util.Vector<E> m_textListeners
```
- Class weka.gui.beans.CostBenefitAnalysis extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  8647471654613320469L
  - 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
    - m_listenee
```
java.lang.Object m_listenee
```
      The object sending us data (we allow only one connection at any one time)
- Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  5364871945448769003L
  - Serialized Fields
    - m_performancePanel
```
VisualizePanel m_performancePanel
```
      Displays the performance graphs(s)
    - m_costBenefitPanel
```
VisualizePanel m_costBenefitPanel
```
      Displays the cost/benefit (profit/loss) graph
    - m_classAttribute
```
Attribute m_classAttribute
```
      The class attribute from the data that was used to generate the threshold curve
    - m_masterPlot
```
PlotData2D m_masterPlot
```
      Data for the threshold curve
    - m_costBenefit
```
PlotData2D m_costBenefit
```
      Data for the cost/benefit curve
    - m_shapeSizes
```
int[] m_shapeSizes
```
      The size of the points being plotted
    - m_previousShapeIndex
```
int m_previousShapeIndex
```
      The index of the previous plotted point that was highlighted
    - m_thresholdSlider
```
javax.swing.JSlider m_thresholdSlider
```
      The slider for adjusting the threshold
    - m_percPop
```
javax.swing.JRadioButton m_percPop
```
    - m_percOfTarget
```
javax.swing.JRadioButton m_percOfTarget
```
    - m_threshold
```
javax.swing.JRadioButton m_threshold
```
    - m_percPopLab
```
javax.swing.JLabel m_percPopLab
```
    - m_percOfTargetLab
```
javax.swing.JLabel m_percOfTargetLab
```
    - m_thresholdLab
```
javax.swing.JLabel m_thresholdLab
```
    - m_conf_predictedA
```
javax.swing.JLabel m_conf_predictedA
```
    - m_conf_predictedB
```
javax.swing.JLabel m_conf_predictedB
```
    - m_conf_actualA
```
javax.swing.JLabel m_conf_actualA
```
    - m_conf_actualB
```
javax.swing.JLabel m_conf_actualB
```
    - m_conf_aa
```
weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_aa
```
    - m_conf_ab
```
weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_ab
```
    - m_conf_ba
```
weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_ba
```
    - m_conf_bb
```
weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_bb
```
    - m_cost_predictedA
```
javax.swing.JLabel m_cost_predictedA
```
    - m_cost_predictedB
```
javax.swing.JLabel m_cost_predictedB
```
    - m_cost_actualA
```
javax.swing.JLabel m_cost_actualA
```
    - m_cost_actualB
```
javax.swing.JLabel m_cost_actualB
```
    - m_cost_aa
```
javax.swing.JTextField m_cost_aa
```
    - m_cost_ab
```
javax.swing.JTextField m_cost_ab
```
    - m_cost_ba
```
javax.swing.JTextField m_cost_ba
```
    - m_cost_bb
```
javax.swing.JTextField m_cost_bb
```
    - m_maximizeCB
```
javax.swing.JButton m_maximizeCB
```
    - m_minimizeCB
```
javax.swing.JButton m_minimizeCB
```
    - m_costR
```
javax.swing.JRadioButton m_costR
```
    - m_benefitR
```
javax.swing.JRadioButton m_benefitR
```
    - m_costBenefitL
```
javax.swing.JLabel m_costBenefitL
```
    - m_costBenefitV
```
javax.swing.JLabel m_costBenefitV
```
    - m_randomV
```
javax.swing.JLabel m_randomV
```
    - m_gainV
```
javax.swing.JLabel m_gainV
```
    - m_originalPopSize
```
int m_originalPopSize
```
    - m_totalPopField
```
javax.swing.JTextField m_totalPopField
```
      Population text field
    - m_totalPopPrevious
```
int m_totalPopPrevious
```
    - m_classificationAccV
```
javax.swing.JLabel m_classificationAccV
```
      Classification accuracy
    - m_tpPrevious
```
double m_tpPrevious
```
    - m_fpPrevious
```
double m_fpPrevious
```
    - m_tnPrevious
```
double m_tnPrevious
```
    - m_fnPrevious
```
double m_fnPrevious
```
- Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  6148640235434494767L
  - Serialized Fields
    - m_conf_cell
```
javax.swing.JLabel m_conf_cell
```
    - m_conf_perc
```
javax.swing.JLabel m_conf_perc
```
    - m_percentageP
```
javax.swing.JPanel m_percentageP
```
    - m_percentage
```
double m_percentage
```
- Class weka.gui.beans.CrossValidationFoldMaker extends AbstractTrainAndTestSetProducer implements Serializable
  
  serialVersionUID:
  
  -6350179298851891512L
  - Serialized Fields
    - m_numFolds
```
int m_numFolds
```
    - m_randomSeed
```
int m_randomSeed
```
- Class weka.gui.beans.CrossValidationFoldMakerCustomizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  1229878140258668581L
  - 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
  
  serialVersionUID:
  
  -5111218447577318057L
  - Serialized Fields
    - m_dataSet
```
Instances m_dataSet
```
    - m_structureOnly
```
boolean m_structureOnly
```
- Class weka.gui.beans.DataVisualizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  1949062132560159028L
  - 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_dataSetListeners
```
java.util.Vector<E> m_dataSetListeners
```
      Objects listening for data set events
    - m_bcSupport
```
java.beans.beancontext.BeanContextChildSupport m_bcSupport
```
      BeanContextChild support
- Class weka.gui.beans.Filter extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  8249759470189439321L
  - Serialized Fields
    - m_visual
```
BeanVisual m_visual
```
    - m_state
```
int m_state
```
    - 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
  
  serialVersionUID:
  
  2049895469240109738L
  - Serialized Fields
    - m_pcSupport
```
java.beans.PropertyChangeSupport m_pcSupport
```
    - m_filter
```
Filter m_filter
```
    - m_backup
```
Filter m_backup
```
      Backup if user presses cancel
    - m_filterEditor
```
PropertySheetPanel m_filterEditor
```
    - m_parentFrame
```
javax.swing.JFrame m_parentFrame
```
- Class weka.gui.beans.GraphEvent extends java.util.EventObject implements Serializable
  
  serialVersionUID:
  
  2099494034652519986L
  - 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
  
  serialVersionUID:
  
  -5183121972114900617L
  - Serialized Fields
    - m_visual
```
BeanVisual m_visual
```
    - m_bcSupport
```
java.beans.beancontext.BeanContextChildSupport m_bcSupport
```
      BeanContextChild support
    - m_design
```
boolean m_design
```
      True if this bean's appearance is the design mode appearance
- Class weka.gui.beans.IncrementalClassifierEvaluator extends AbstractEvaluator implements Serializable
  
  serialVersionUID:
  
  -3105419818939541291L
  - 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
```
    - m_statusFrequency
```
int m_statusFrequency
```
    - m_instanceCount
```
int m_instanceCount
```
    - m_outputInfoRetrievalStats
```
boolean m_outputInfoRetrievalStats
```
- Class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer extends javax.swing.JPanel implements Serializable
  - Serialized Fields
    - m_pcSupport
```
java.beans.PropertyChangeSupport m_pcSupport
```
      for serialization
    - m_ieEditor
```
PropertySheetPanel m_ieEditor
```
- Class weka.gui.beans.IncrementalClassifierEvent extends java.util.EventObject implements Serializable
  
  serialVersionUID:
  
  28979464317643232L
  - 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
  
  serialVersionUID:
  
  6104920894559423946L
  - Serialized Fields
    - m_structure
```
Instances m_structure
```
    - m_instance
```
Instance m_instance
```
    - m_status
```
int m_status
```
- Class weka.gui.beans.InstanceStreamToBatchMaker extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -7037141087208627799L
  - Serialized Fields
    - m_visual
```
BeanVisual m_visual
```
    - m_listenee
```
java.lang.Object m_listenee
```
      Component connected to us.
    - m_dataListeners
```
java.util.ArrayList<E> m_dataListeners
```
    - m_batch
```
java.util.ArrayList<E> m_batch
```
      Collects up the instances.
    - m_structure
```
Instances m_structure
```
- Class weka.gui.beans.KnowledgeFlowApp extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -7064906770289728431L
  - 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_pluginsToolBar
```
javax.swing.JToolBar m_pluginsToolBar
```
      Stuff relating to plugin beans
    - m_pluginsBoxPanel
```
javax.swing.Box m_pluginsBoxPanel
```
    - 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_XStreamFilter
```
javax.swing.filechooser.FileFilter m_XStreamFilter
```
      A filter to ensure only KnowledgeFlow files in XStream 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
    - m_flowEnvironment
```
Environment m_flowEnvironment
```
      Environment variables for the current flow
    - m_showFileMenu
```
boolean m_showFileMenu
```
- Class weka.gui.beans.KnowledgeFlowApp.BeanLayout extends PrintablePanel implements Serializable
  
  serialVersionUID:
  
  -146377012429662757L
- Class weka.gui.beans.Loader extends AbstractDataSource implements Serializable
  
  serialVersionUID:
  
  1993738191961163027L
  - Serialization Methods
    - readObject
```
private void readObject(java.io.ObjectInputStream aStream)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
```
      Throws:
      
      java.io.IOException
      
      java.lang.ClassNotFoundException
    - 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_state
```
int m_state
```
    - 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
    - m_stopped
```
boolean m_stopped
```
      Asked to stop?
- Class weka.gui.beans.LoaderCustomizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  6990446313118930298L
  - 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
```
    - m_relativeFilePath
```
javax.swing.JCheckBox m_relativeFilePath
```
- Class weka.gui.beans.LogPanel extends javax.swing.JPanel implements Serializable
  - Serialized Fields
    - m_tableIndexes
```
java.util.HashMap<K,V> m_tableIndexes
```
      Holds the index (line number) in the JTable of each component being tracked.
    - m_timers
```
java.util.HashMap<K,V> m_timers
```
      Holds the timers associated with each component being tracked.
    - m_tableModel
```
javax.swing.table.DefaultTableModel m_tableModel
```
      The table model for the JTable used in the status area
    - m_table
```
javax.swing.JTable m_table
```
      The table for the status area
    - m_tabs
```
javax.swing.JTabbedPane m_tabs
```
      Tabbed pane to hold both the status and the log
    - m_formatter
```
java.text.DecimalFormat m_formatter
```
      For formatting timer digits
    - m_logPanel
```
LogPanel m_logPanel
```
      The log panel to delegate log messages to.
- Class weka.gui.beans.MetaBean extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -6582768902038027077L
  - 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_subFlowPreview
```
javax.swing.ImageIcon m_subFlowPreview
```
    - m_originalCoords
```
java.util.Vector<E> m_originalCoords
```
- Class weka.gui.beans.ModelPerformanceChart extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -4602034200071195924L
  - 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
```
- Class weka.gui.beans.PredictionAppenderCustomizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  6884933202506331888L
  - Serialized Fields
    - m_pcSupport
```
java.beans.PropertyChangeSupport m_pcSupport
```
    - m_paEditor
```
PropertySheetPanel m_paEditor
```
- Class weka.gui.beans.Saver extends AbstractDataSink implements Serializable
  
  serialVersionUID:
  
  5371716690308950755L
  - Serialization Methods
    - readObject
```
private void readObject(java.io.ObjectInputStream aStream)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
```
      Throws:
      
      java.io.IOException
      
      java.lang.ClassNotFoundException
  - 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_SaverTemplate
```
Saver m_SaverTemplate
```
    - 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_relationNameForFilename
```
boolean m_relationNameForFilename
```
      For file-based savers - if true (default), relation name is used as the primary part of the filename. If false, then the prefix is used as the filename. Useful for preventing filenames from getting too long when there are many filters in a flow.
    - m_count
```
int m_count
```
      Count for structure available messages
- Class weka.gui.beans.SaverCustomizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -4874208115942078471L
  - 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<E> m_idBox
```
    - m_tabBox
```
javax.swing.JComboBox<E> m_tabBox
```
    - m_prefixText
```
javax.swing.JTextField m_prefixText
```
    - m_relativeFilePath
```
javax.swing.JCheckBox m_relativeFilePath
```
    - m_relationNameForFilename
```
javax.swing.JCheckBox m_relationNameForFilename
```
- Class weka.gui.beans.ScatterPlotMatrix extends DataVisualizer implements Serializable
  
  serialVersionUID:
  
  -657856527563507491L
  - Serialized Fields
    - m_matrixPanel
```
MatrixPanel m_matrixPanel
```
- Class weka.gui.beans.SerializedModelSaver extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  3956528599473814287L
  - Serialization Methods
    - readObject
```
private void readObject(java.io.ObjectInputStream aStream)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
```
      Throws:
      
      java.io.IOException
      
      java.lang.ClassNotFoundException
  - 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.
    - m_filenamePrefix
```
java.lang.String m_filenamePrefix
```
      The prefix for the file name (model + training set info will be appended)
    - m_directory
```
java.io.File m_directory
```
      The directory to hold the saved model(s)
    - m_fileFormat
```
Tag m_fileFormat
```
      File format stuff
    - m_useRelativePath
```
boolean m_useRelativePath
```
      relative path for the directory (relative to the user.dir (startup directory))?
- Class weka.gui.beans.SerializedModelSaverCustomizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -4874208115942078471L
  - Serialized Fields
    - m_pcSupport
```
java.beans.PropertyChangeSupport m_pcSupport
```
    - m_smSaver
```
SerializedModelSaver m_smSaver
```
    - m_SaverEditor
```
PropertySheetPanel m_SaverEditor
```
    - m_fileChooser
```
javax.swing.JFileChooser m_fileChooser
```
    - m_parentFrame
```
javax.swing.JFrame m_parentFrame
```
    - m_prefixText
```
javax.swing.JTextField m_prefixText
```
    - m_fileFormatBox
```
javax.swing.JComboBox<E> m_fileFormatBox
```
    - m_relativeFilePath
```
javax.swing.JCheckBox m_relativeFilePath
```
- Class weka.gui.beans.StripChart extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  1483649041577695019L
  - 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_BackgroundColor
```
java.awt.Color m_BackgroundColor
```
      the background color.
    - m_LegendPanelBorderColor
```
java.awt.Color m_LegendPanelBorderColor
```
      the color of the legend panel's border.
    - 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
```
weka.gui.beans.StripChart.ScalePanel m_scalePanel
```
      the scale.
    - m_legendPanel
```
weka.gui.beans.StripChart.LegendPanel m_legendPanel
```
      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_Printer
```
PrintableComponent m_Printer
```
      the class responsible for printing
    - m_ce
```
ChartEvent m_ce
```
    - m_dataPoint
```
double[] m_dataPoint
```
- Class weka.gui.beans.StripChartCustomizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  7441741530975984608L
  - 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
  
  serialVersionUID:
  
  8780718708498854231L
  - Serialized Fields
    - m_testSet
```
Instances m_testSet
```
      The test set instances
    - m_structureOnly
```
boolean m_structureOnly
```
    - m_runNumber
```
int m_runNumber
```
      What run number is this training set from.
    - m_maxRunNumber
```
int m_maxRunNumber
```
      Maximum number of runs.
    - 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
  
  serialVersionUID:
  
  -8473882857628061841L
  - Serialized Fields
    - m_receivedStopNotification
```
boolean m_receivedStopNotification
```
- Class weka.gui.beans.TextEvent extends java.util.EventObject implements Serializable
  
  serialVersionUID:
  
  4196810607402973744L
  - 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
  
  serialVersionUID:
  
  104838186352536832L
  - 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
    - m_textListeners
```
java.util.Vector<E> m_textListeners
```
      Objects listening for text events
- Class weka.gui.beans.ThresholdDataEvent extends java.util.EventObject implements Serializable
  
  serialVersionUID:
  
  -8309334224492439644L
  - Serialized Fields
    - m_dataSet
```
PlotData2D m_dataSet
```
    - m_classAttribute
```
Attribute m_classAttribute
```
- Class weka.gui.beans.TrainingSetEvent extends java.util.EventObject implements Serializable
  
  serialVersionUID:
  
  5872343811810872662L
  - Serialized Fields
    - m_trainingSet
```
Instances m_trainingSet
```
      The training instances
    - m_structureOnly
```
boolean m_structureOnly
```
    - m_runNumber
```
int m_runNumber
```
      What run number is this training set from.
    - m_maxRunNumber
```
int m_maxRunNumber
```
      Maximum number of runs.
    - 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
  
  serialVersionUID:
  
  -6152577265471535786L
  - Serialized Fields
    - m_receivedStopNotification
```
boolean m_receivedStopNotification
```
- Class weka.gui.beans.TrainTestSplitMaker extends AbstractTrainAndTestSetProducer implements Serializable
  
  serialVersionUID:
  
  7390064039444605943L
  - Serialized Fields
    - m_trainPercentage
```
double 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
  
  serialVersionUID:
  
  -1684662340241807260L
  - 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
  
  serialVersionUID:
  
  -5811819270887223400L
  - Serialized Fields
    - m_dataSet
```
PlotData2D m_dataSet
```
Package weka.gui.boundaryvisualizer
- Class weka.gui.boundaryvisualizer.BoundaryPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -8499445518744770458L
  - Serialized Fields
    - m_Colors
```
FastVector m_Colors
```
    - m_trainingData
```
Instances m_trainingData
```
      training data
    - m_classifier
```
Classifier m_classifier
```
      distribution classifier to use
    - m_dataGenerator
```
DataGenerator m_dataGenerator
```
      data generator to use
    - m_classIndex
```
int m_classIndex
```
      index of the class attribute
    - 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
```
      used for offscreen drawing
    - 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
```
      listeners to be notified when plot is complete
    - m_plotPanel
```
weka.gui.boundaryvisualizer.BoundaryPanel.PlotPanel m_plotPanel
```
      the actual plotting area
    - m_plotThread
```
java.lang.Thread m_plotThread
```
      thread for running the plotting operation in
    - m_stopPlotting
```
boolean m_stopPlotting
```
      Stop the plotting thread
    - m_stopReplotting
```
boolean m_stopReplotting
```
      Stop any replotting threads
    - m_dummy
```
java.lang.Double m_dummy
```
    - m_pausePlotting
```
boolean m_pausePlotting
```
    - m_size
```
int m_size
```
      what size of tile is currently being plotted
    - m_initialTiling
```
boolean m_initialTiling
```
      is the main plot thread performing the initial coarse tiling
    - m_random
```
java.util.Random m_random
```
      A random number generator
    - m_probabilityCache
```
double[][][] m_probabilityCache
```
      cache of probabilities for fast replotting
    - m_plotTrainingData
```
boolean m_plotTrainingData
```
      plot the training data
- Class weka.gui.boundaryvisualizer.BoundaryPanelDistributed extends BoundaryPanel implements Serializable
  
  serialVersionUID:
  
  -1743284397893937776L
  - 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
```
      number of seconds between polling server
    - m_hostPollingTime
```
int[] m_hostPollingTime
```
- Class weka.gui.boundaryvisualizer.BoundaryVisualizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  3933877580074013208L
  - Serialized Fields
    - m_trainingInstances
```
Instances m_trainingInstances
```
      the training instances
    - m_classifier
```
Classifier m_classifier
```
      the classifier to use
    - m_plotAreaWidth
```
int m_plotAreaWidth
```
    - m_plotAreaHeight
```
int m_plotAreaHeight
```
    - m_boundaryPanel
```
BoundaryPanel m_boundaryPanel
```
      the plotting panel
    - m_classAttBox
```
javax.swing.JComboBox<E> m_classAttBox
```
    - m_xAttBox
```
javax.swing.JComboBox<E> m_xAttBox
```
    - m_yAttBox
```
javax.swing.JComboBox<E> 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
```
      base for sampling in the non-fixed dimensions
    - m_kernelBandwidth
```
int m_kernelBandwidth
```
      Set the kernel bandwidth to cover this many nearest neighbours
    - m_regionSamplesText
```
javax.swing.JTextField m_regionSamplesText
```
    - m_generatorSamplesText
```
javax.swing.JTextField m_generatorSamplesText
```
    - m_kernelBandwidthText
```
javax.swing.JTextField m_kernelBandwidthText
```
    - m_classifierEditor
```
GenericObjectEditor m_classifierEditor
```
    - m_ClassifierPanel
```
PropertyPanel m_ClassifierPanel
```
    - m_FileChooser
```
javax.swing.JFileChooser m_FileChooser
```
      The file chooser for selecting arff files
    - m_arffFileFilter
```
ExtensionFileFilter m_arffFileFilter
```
    - dataFileLabel
```
javax.swing.JLabel dataFileLabel
```
    - m_addRemovePointsPanel
```
javax.swing.JPanel m_addRemovePointsPanel
```
    - m_classValueSelector
```
javax.swing.JComboBox<E> m_classValueSelector
```
    - m_addPointsButton
```
javax.swing.JRadioButton m_addPointsButton
```
    - m_removePointsButton
```
javax.swing.JRadioButton m_removePointsButton
```
    - m_addRemovePointsButtonGroup
```
javax.swing.ButtonGroup m_addRemovePointsButtonGroup
```
    - removeAllButton
```
javax.swing.JButton removeAllButton
```
    - chooseButton
```
javax.swing.JButton chooseButton
```
- Class weka.gui.boundaryvisualizer.KDDataGenerator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -958573275606402792L
  - Serialized Fields
    - m_instances
```
Instances m_instances
```
      the instances to use
    - m_standardDeviations
```
double[] m_standardDeviations
```
      standard deviations of the normal distributions for numeric attributes in each KD estimator
    - m_globalMeansOrModes
```
double[] m_globalMeansOrModes
```
      global means or modes to use for missing values
    - m_minStdDev
```
double m_minStdDev
```
      minimum standard deviation for numeric attributes
    - m_laplaceConst
```
double m_laplaceConst
```
      Laplace correction for discrete distributions
    - m_seed
```
int m_seed
```
      random number seed
    - m_random
```
java.util.Random m_random
```
      random number generator
    - m_weightingDimensions
```
boolean[] m_weightingDimensions
```
      which dimensions to use for computing a weight for each generated instance
    - m_weightingValues
```
double[] m_weightingValues
```
      the values for the weighting dimensions to use for computing the weight for the next instance to be generated
    - m_kernelBandwidth
```
int m_kernelBandwidth
```
      Number of neighbours to use for kernel bandwidth
    - m_kernelParams
```
double[][] m_kernelParams
```
      standard deviations for numeric attributes computed from the m_kernelBandwidth nearest neighbours for each kernel.
    - 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
  
  serialVersionUID:
  
  1873271280044633808L
  - Serialized Fields
    - m_rowNumber
```
int m_rowNumber
```
      the row number that this result corresponds to
    - m_rowLength
```
int m_rowLength
```
      how many pixels in a row
    - m_probabilities
```
double[][] m_probabilities
```
      the result - ie. the probability distributions produced by the classifier for this row in the visualization
    - m_percentCompleted
```
int m_percentCompleted
```
      progress on computing this row
Package weka.gui.experiment
- Class weka.gui.experiment.AlgorithmListPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -7204528834764898671L
  - Serialized Fields
    - m_Exp
```
Experiment m_Exp
```
      The experiment to set the algorithm list of
    - m_List
```
javax.swing.JList<E> 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_UpBut
```
javax.swing.JButton m_UpBut
```
      Click to move the selected algorithm(s) one up
    - m_DownBut
```
javax.swing.JButton m_DownBut
```
      Click to move the selected algorithm(s) one down
    - 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<E> m_AlgorithmListModel
```
      The list model used
- Class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer extends javax.swing.DefaultListCellRenderer implements Serializable
  
  serialVersionUID:
  
  -5067138526587433808L
- Class weka.gui.experiment.DatasetListPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  7068857852794405769L
  - Serialized Fields
    - m_Exp
```
Experiment m_Exp
```
      The experiment to set the dataset list of.
    - m_List
```
javax.swing.JList<E> m_List
```
      The component displaying the dataset list.
    - m_AddBut
```
javax.swing.JButton m_AddBut
```
      Click to add a dataset.
    - 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_UpBut
```
javax.swing.JButton m_UpBut
```
      Click to move the selected dataset(s) one up.
    - m_DownBut
```
javax.swing.JButton m_DownBut
```
      Click to move the selected dataset(s) one down.
    - m_relativeCheck
```
javax.swing.JCheckBox m_relativeCheck
```
      Make file paths relative to the user (start) directory.
    - m_FileChooser
```
ConverterFileChooser m_FileChooser
```
      The file chooser component.
- Class weka.gui.experiment.DistributeExperimentPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  5206721431754800278L
  - 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
  
  serialVersionUID:
  
  -5751617505738193788L
  - 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
  
  serialVersionUID:
  
  -2835933184632147981L
- Class weka.gui.experiment.GeneratorPropertyIteratorPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -6026938995241632139L
  - Serialized Fields
    - m_ConfigureBut
```
javax.swing.JButton m_ConfigureBut
```
      Click to select the property to iterate over
    - m_StatusBox
```
javax.swing.JComboBox<E> 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
  
  serialVersionUID:
  
  7182791134585882197L
  - Serialized Fields
    - m_Exp
```
RemoteExperiment m_Exp
```
      The remote experiment to set the host list of
    - m_List
```
javax.swing.JList<E> 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
  
  serialVersionUID:
  
  2169792738187807378L
  - Serialized Fields
    - m_Result
```
int m_Result
```
      the result of the user's action, either OK or CANCEL.
    - m_ResultMatrix
```
java.lang.Class<T> m_ResultMatrix
```
      the output format specific matrix.
    - m_OutputFormatComboBox
```
javax.swing.JComboBox<E> 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_ShowAverageCheckBox
```
javax.swing.JCheckBox m_ShowAverageCheckBox
```
      the checkbox for outputting the average.
    - m_RemoveFilterNameCheckBox
```
javax.swing.JCheckBox m_RemoveFilterNameCheckBox
```
      the checkbox for the removing of filter classnames.
    - 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_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
    - m_RemoveFilterName
```
boolean m_RemoveFilterName
```
      whether to remove the filter names from the names.
    - m_ShowAverage
```
boolean m_ShowAverage
```
      whether to show the average too.
- Class weka.gui.experiment.ResultsPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -4913007978534178569L
  - 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<E> m_DatasetModel
```
      The model embedded in m_DatasetCombo.
    - m_CompareModel
```
javax.swing.DefaultComboBoxModel<E> m_CompareModel
```
      The model embedded in m_CompareCombo.
    - m_SortModel
```
javax.swing.DefaultComboBoxModel<E> m_SortModel
```
      The model embedded in m_SortCombo.
    - m_TestsModel
```
javax.swing.DefaultListModel<E> m_TestsModel
```
      The model embedded in m_TestsList.
    - m_DisplayedModel
```
javax.swing.DefaultListModel<E> m_DisplayedModel
```
      The model embedded in m_DisplayedList.
    - m_TesterClassesLabel
```
javax.swing.JLabel m_TesterClassesLabel
```
      Displays the currently selected Tester-Class.
    - m_TesterClasses
```
javax.swing.JComboBox<E> m_TesterClasses
```
      Lists all the available classes implementing the Tester-Interface.
      
      See Also:
      
      Tester
    - 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<E> m_DatasetKeyModel
```
      Stores the list of attributes for selecting the scheme columns.
    - m_DatasetKeyList
```
javax.swing.JList<E> 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<E> m_ResultKeyModel
```
      Stores the list of attributes for selecting the scheme columns.
    - m_ResultKeyList
```
javax.swing.JList<E> 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<E> m_TestsList
```
      Holds the list of schemes to base the test against.
    - m_DisplayedList
```
javax.swing.JList<E> m_DisplayedList
```
      Holds the list of schemes to display.
    - m_CompareCombo
```
javax.swing.JComboBox<E> m_CompareCombo
```
      Lets the user select which performance measure to analyze.
    - m_SortCombo
```
javax.swing.JComboBox<E> m_SortCombo
```
      Lets the user select which column to use for sorting.
    - 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
```
      CSV file filter.
    - m_arffFileFilter
```
ExtensionFileFilter m_arffFileFilter
```
      ARFF file filter.
    - m_TTester
```
Tester 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
```
      the size for a combobox.
    - m_ResultMatrix
```
ResultMatrix m_ResultMatrix
```
      the initial result matrix.
- Class weka.gui.experiment.RunNumberPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -1644336658426067852L
  - 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
  
  serialVersionUID:
  
  1691868018596872051L
  - 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
  
  serialVersionUID:
  
  -3758035565520727822L
  - 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
  
  serialVersionUID:
  
  6552671886903170033L
  - 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
  
  serialVersionUID:
  
  5257424515609176509L
  - 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<E> 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<E> 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
  
  serialVersionUID:
  
  -6867871711865476971L
  - Serialized Fields
    - m_Explorer
```
Explorer m_Explorer
```
      the parent frame
    - 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
  
  serialVersionUID:
  
  5627185966993476142L
  - Serialized Fields
    - m_Explorer
```
Explorer m_Explorer
```
      the parent frame
    - 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<E> 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
  
  serialVersionUID:
  
  6959973704963624003L
  - Serialized Fields
    - m_Explorer
```
Explorer m_Explorer
```
      the parent frame
    - 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<E> 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_OutputAdditionalAttributesText
```
javax.swing.JTextField m_OutputAdditionalAttributesText
```
      Lists indices for additional attributes to output
    - m_OutputAdditionalAttributesLab
```
javax.swing.JLabel m_OutputAdditionalAttributesLab
```
      Label for the text field with additional attributes in the output
    - m_OutputAdditionalAttributesRange
```
Range m_OutputAdditionalAttributesRange
```
      the range of attributes to output
    - m_EvalWRTCostsBut
```
javax.swing.JCheckBox m_EvalWRTCostsBut
```
      Check to evaluate w.r.t a cost matrix
    - m_SetCostsBut
```
javax.swing.JButton m_SetCostsBut
```
      for the cost matrix
    - 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
```
      the label for the random seed textfield
    - m_PreserveOrderBut
```
javax.swing.JCheckBox m_PreserveOrderBut
```
      Whether randomization is turned off to preserve order
    - m_OutputSourceCode
```
javax.swing.JCheckBox m_OutputSourceCode
```
      Whether to output the source code (only for classifiers importing Sourcable)
    - m_SourceCodeClass
```
javax.swing.JTextField m_SourceCodeClass
```
      The name of the generated class (only applicable to Sourcable schemes)
    - 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_TestLoader
```
Loader m_TestLoader
```
      The loader used to load 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_ModelFilter
```
javax.swing.filechooser.FileFilter m_ModelFilter
```
      Filter to ensure only model files are selected
    - m_PMMLModelFilter
```
javax.swing.filechooser.FileFilter m_PMMLModelFilter
```
    - 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
  
  serialVersionUID:
  
  -2474932792950820990L
  - Serialized Fields
    - m_Explorer
```
Explorer m_Explorer
```
      the parent frame
    - 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<E> 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<E> m_ignoreKeyModel
```
    - m_ignoreKeyList
```
javax.swing.JList<E> 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.DataGeneratorPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -2520408165350629380L
  - Serialized Fields
    - m_GeneratorEditor
```
GenericObjectEditor m_GeneratorEditor
```
      the GOE for the generators
    - m_Instances
```
Instances m_Instances
```
      the generated Instances
    - m_Output
```
java.io.StringWriter m_Output
```
      the generated output (as text)
    - m_Log
```
Logger m_Log
```
      The destination for log/status messages
- Class weka.gui.explorer.Explorer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -7674003708867909578L
  - Serialized Fields
    - m_PreprocessPanel
```
PreprocessPanel m_PreprocessPanel
```
      The panel for preprocessing instances
    - m_Panels
```
java.util.Vector<E> m_Panels
```
      Contains all the additional panels apart from the pre-processing panel
    - 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
    - m_CapabilitiesFilterChangeListeners
```
java.util.HashSet<E> m_CapabilitiesFilterChangeListeners
```
      the listeners that listen to filter changes
- Class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent extends javax.swing.event.ChangeEvent implements Serializable
  
  serialVersionUID:
  
  1194260517270385559L
  - Serialized Fields
    - m_Filter
```
Capabilities m_Filter
```
      the capabilities filter
- Class weka.gui.explorer.ExplorerDefaults extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4954795757927524225L
- Class weka.gui.explorer.PreprocessPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  6764850273874813049L
  - 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_GenerateBut
```
javax.swing.JButton m_GenerateBut
```
      Click to generate artificial data
    - 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
```
ConverterFileChooser m_FileChooser
```
      The file chooser for selecting data files
    - 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_DataGenerator
```
DataGenerator m_DataGenerator
```
      The last generator that was selected
    - 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
    - m_Explorer
```
Explorer m_Explorer
```
      the parent frame
- Class weka.gui.explorer.VisualizePanel extends MatrixPanel implements Serializable
  
  serialVersionUID:
  
  6084015036853918846L
  - Serialized Fields
    - m_Explorer
```
Explorer m_Explorer
```
      the parent frame
Package weka.gui.graphvisualizer
- Class weka.gui.graphvisualizer.BIFFormatException extends java.lang.Exception implements Serializable
  
  serialVersionUID:
  
  -4102518086411708140L
- Class weka.gui.graphvisualizer.GraphVisualizer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -2038911085935515624L
  - 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
  
  serialVersionUID:
  
  6172467234026258427L
Package weka.gui.hierarchyvisualizer
- Class weka.gui.hierarchyvisualizer.HierarchyVisualizer extends PrintablePanel implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - m_sNewick
```
java.lang.String m_sNewick
```
    - m_tree
```
weka.gui.hierarchyvisualizer.HierarchyVisualizer.Node m_tree
```
    - m_nLeafs
```
int m_nLeafs
```
    - m_fHeight
```
double m_fHeight
```
    - m_fScaleX
```
double m_fScaleX
```
    - m_fScaleY
```
double m_fScaleY
```
    - m_fTmpLength
```
double m_fTmpLength
```
Package weka.gui.sql
- Class weka.gui.sql.ConnectionPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  3499317023969723490L
  - Serialized Fields
    - m_Parent
```
javax.swing.JFrame m_Parent
```
      the parent frame.
    - m_DbDialog
```
DatabaseConnectionDialog m_DbDialog
```
      the databae connection dialog.
    - m_URL
```
java.lang.String m_URL
```
      the URL to use.
    - m_User
```
java.lang.String m_User
```
      the user to use for connecting to the DB.
    - m_Password
```
java.lang.String m_Password
```
      the password to use for connecting to the DB.
    - m_LabelURL
```
javax.swing.JLabel m_LabelURL
```
      the label for the URL.
    - m_TextURL
```
javax.swing.JTextField m_TextURL
```
      the textfield for the URL.
    - m_ButtonDatabase
```
javax.swing.JButton m_ButtonDatabase
```
      the button for the DB-Dialog.
    - m_ButtonConnect
```
javax.swing.JButton m_ButtonConnect
```
      the button for connecting to the database.
    - m_ButtonHistory
```
javax.swing.JButton m_ButtonHistory
```
      the button for the history.
    - m_ConnectionListeners
```
java.util.HashSet<E> m_ConnectionListeners
```
      the connection listeners.
    - m_HistoryChangedListeners
```
java.util.HashSet<E> m_HistoryChangedListeners
```
      the history listeners.
    - m_DbUtils
```
DbUtils m_DbUtils
```
      for connecting to the database.
    - m_History
```
javax.swing.DefaultListModel<E> m_History
```
      the history of connections.
- Class weka.gui.sql.DbUtils extends DatabaseUtils implements Serializable
  
  serialVersionUID:
  
  103748569037426479L
- Class weka.gui.sql.InfoPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -7701133696481997973L
  - Serialized Fields
    - m_Parent
```
javax.swing.JFrame m_Parent
```
      the parent of this panel
    - m_Info
```
javax.swing.JList<E> m_Info
```
      the list the contains the messages
    - m_Model
```
javax.swing.DefaultListModel<E> m_Model
```
      the model for the list
    - m_ButtonClear
```
javax.swing.JButton m_ButtonClear
```
      the button to clear the area
    - m_ButtonCopy
```
javax.swing.JButton m_ButtonCopy
```
      the button to copy the selected message
- Class weka.gui.sql.InfoPanelCellRenderer extends javax.swing.JLabel implements Serializable
  
  serialVersionUID:
  
  -533380118807178531L
- Class weka.gui.sql.QueryPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  4348967824619706636L
  - Serialized Fields
    - m_Parent
```
javax.swing.JFrame m_Parent
```
      the parent of this panel.
    - m_TextQuery
```
javax.swing.JTextArea m_TextQuery
```
      the textarea for the query.
    - m_ButtonExecute
```
javax.swing.JButton m_ButtonExecute
```
      the execute button.
    - m_ButtonClear
```
javax.swing.JButton m_ButtonClear
```
      the clear button.
    - m_ButtonHistory
```
javax.swing.JButton m_ButtonHistory
```
      the history button.
    - m_SpinnerMaxRows
```
javax.swing.JSpinner m_SpinnerMaxRows
```
      the spinner for the maximum number of rows.
    - m_QueryExecuteListeners
```
java.util.HashSet<E> m_QueryExecuteListeners
```
      the connection listeners.
    - m_HistoryChangedListeners
```
java.util.HashSet<E> m_HistoryChangedListeners
```
      the history listeners.
    - m_DbUtils
```
DbUtils m_DbUtils
```
      for working on the database.
    - m_Connected
```
boolean m_Connected
```
      whether we have a connection to a database or not.
    - m_History
```
javax.swing.DefaultListModel<E> m_History
```
      the query history.
- Class weka.gui.sql.ResultPanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  278654800344034571L
  - Serialized Fields
    - m_Parent
```
javax.swing.JFrame m_Parent
```
      the parent of this panel
    - m_Listeners
```
java.util.HashSet<E> m_Listeners
```
      the result change listeners
    - m_QueryPanel
```
QueryPanel m_QueryPanel
```
      the panel where to output the queries
    - m_TabbedPane
```
javax.swing.JTabbedPane m_TabbedPane
```
      the tabbed pane for the results
    - m_ButtonClose
```
javax.swing.JButton m_ButtonClose
```
      the close button
    - m_ButtonCloseAll
```
javax.swing.JButton m_ButtonCloseAll
```
      the close all button
    - m_ButtonCopyQuery
```
javax.swing.JButton m_ButtonCopyQuery
```
      the button that copies the query into the QueryPanel
    - m_ButtonOptWidth
```
javax.swing.JButton m_ButtonOptWidth
```
      the button for the optimal column width of the current table
    - m_NameCounter
```
int m_NameCounter
```
      the counter for the tab names
- Class weka.gui.sql.ResultSetTable extends javax.swing.JTable implements Serializable
  
  serialVersionUID:
  
  -3391076671854464137L
  - Serialized Fields
    - m_Query
```
java.lang.String m_Query
```
      the query the table model is based on
    - m_URL
```
java.lang.String m_URL
```
      the connect string with which the query was run
    - m_User
```
java.lang.String m_User
```
      the user that was used to connect to the DB
    - m_Password
```
java.lang.String m_Password
```
      the password that was used to connect to the DB
- Class weka.gui.sql.ResultSetTableCellRenderer extends javax.swing.table.DefaultTableCellRenderer implements Serializable
  
  serialVersionUID:
  
  -8106963669703497351L
  - Serialized Fields
    - missingColor
```
java.awt.Color missingColor
```
    - missingColorSelected
```
java.awt.Color missingColorSelected
```
- Class weka.gui.sql.SqlViewer extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -4395028775566514329L
  - Serialized Fields
    - m_Parent
```
javax.swing.JFrame m_Parent
```
      the parent of this panel.
    - m_ConnectionPanel
```
ConnectionPanel m_ConnectionPanel
```
      the connection panel.
    - m_QueryPanel
```
QueryPanel m_QueryPanel
```
      the query panel.
    - m_ResultPanel
```
ResultPanel m_ResultPanel
```
      the result panel.
    - m_InfoPanel
```
InfoPanel m_InfoPanel
```
      the info panel.
    - m_URL
```
java.lang.String m_URL
```
      the connect string with which the query was run.
    - m_User
```
java.lang.String m_User
```
      the user that was used to connect to the DB.
    - m_Password
```
java.lang.String m_Password
```
      the password that was used to connect to the DB.
    - m_Query
```
java.lang.String m_Query
```
      the currently selected query.
    - m_History
```
java.util.Properties m_History
```
      stores the history.
- Class weka.gui.sql.SqlViewerDialog extends javax.swing.JDialog implements Serializable
  
  serialVersionUID:
  
  -31619864037233099L
  - Serialized Fields
    - m_Parent
```
javax.swing.JFrame m_Parent
```
      the parent frame
    - m_Viewer
```
SqlViewer m_Viewer
```
      the SQL panel
    - m_PanelButtons
```
javax.swing.JPanel m_PanelButtons
```
      the panel for the buttons
    - m_ButtonOK
```
javax.swing.JButton m_ButtonOK
```
      the OK button
    - m_ButtonCancel
```
javax.swing.JButton m_ButtonCancel
```
      the Cancel button
    - m_LabelQuery
```
javax.swing.JLabel m_LabelQuery
```
      displays the current query
    - m_ReturnValue
```
int m_ReturnValue
```
      the return value
    - m_URL
```
java.lang.String m_URL
```
      the connect string with which the query was run
    - m_User
```
java.lang.String m_User
```
      the user that was used to connect to the DB
    - m_Password
```
java.lang.String m_Password
```
      the password that was used to connect to the DB
    - m_Query
```
java.lang.String m_Query
```
      the currently selected query
Package weka.gui.sql.event
- Class weka.gui.sql.event.ConnectionEvent extends java.util.EventObject implements Serializable
  
  serialVersionUID:
  
  5420308930427835037L
  - Serialized Fields
    - m_Type
```
int m_Type
```
      the type of event, CONNECT or DISCONNECT
    - m_DbUtils
```
DbUtils m_DbUtils
```
      the databaseutils instance reponsible for the connection
    - m_Exception
```
java.lang.Exception m_Exception
```
      a possible exception that occurred if not successful
- Class weka.gui.sql.event.HistoryChangedEvent extends java.util.EventObject implements Serializable
  
  serialVersionUID:
  
  7476087315774869973L
  - Serialized Fields
    - m_HistoryName
```
java.lang.String m_HistoryName
```
      the name of the history
    - m_History
```
javax.swing.DefaultListModel<E> m_History
```
      the history model
- Class weka.gui.sql.event.QueryExecuteEvent extends java.util.EventObject implements Serializable
  
  serialVersionUID:
  
  -5556385019954730740L
  - Serialized Fields
    - m_DbUtils
```
DbUtils m_DbUtils
```
      the Db utils instance for the current DB connection
    - m_Query
```
java.lang.String m_Query
```
      the query that was executed
    - m_ResultSet
```
java.sql.ResultSet m_ResultSet
```
      the produced ResultSet, if any
    - m_Exception
```
java.lang.Exception m_Exception
```
      a possible exception, if the query failed
    - m_MaxRows
```
int m_MaxRows
```
      the maximum number of rows to retrieve
- Class weka.gui.sql.event.ResultChangedEvent extends java.util.EventObject implements Serializable
  
  serialVersionUID:
  
  36042516077236111L
  - Serialized Fields
    - m_Query
```
java.lang.String m_Query
```
      the query that is associated with the active result table
    - m_URL
```
java.lang.String m_URL
```
      the connect string with which the query was run
    - m_User
```
java.lang.String m_User
```
      the user that was used to connect to the DB
    - m_Password
```
java.lang.String m_Password
```
      the password that was used to connect to the DB
Package weka.gui.streams
- Class weka.gui.streams.InstanceCounter extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  -6084967152645935934L
  - 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
  
  serialVersionUID:
  
  3207259868110667379L
  - Serialized Fields
    - m_ID
```
int m_ID
```
- Class weka.gui.streams.InstanceJoiner extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6529972700291329656L
  - 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
  
  serialVersionUID:
  
  -8725567310271862492L
  - 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
  
  serialVersionUID:
  
  -6061005366989295026L
  - 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
  
  serialVersionUID:
  
  -2462533698100834803L
  - 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
  
  serialVersionUID:
  
  -4925729441294121772L
  - 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
  
  serialVersionUID:
  
  -8668637962504080749L
  - 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 sub 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
```
    - m_FontColor
```
java.awt.Color m_FontColor
```
      the font color.
    - m_BackgroundColor
```
java.awt.Color m_BackgroundColor
```
      the background color.
    - m_NodeColor
```
java.awt.Color m_NodeColor
```
      the node color.
    - m_LineColor
```
java.awt.Color m_LineColor
```
      the line color.
    - m_ZoomBoxColor
```
java.awt.Color m_ZoomBoxColor
```
      the color of the zoombox.
    - m_ZoomBoxXORColor
```
java.awt.Color m_ZoomBoxXORColor
```
      the XOR color of the zoombox.
    - m_ShowBorder
```
boolean m_ShowBorder
```
      whether to show the border or not.
Package weka.gui.visualize
- Class weka.gui.visualize.AttributePanel extends javax.swing.JScrollPane implements Serializable
  
  serialVersionUID:
  
  3533330317806757814L
  - 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_backgroundColor
```
java.awt.Color m_backgroundColor
```
      If set, it allows this panel to avoid setting a color in the color list that is equal to the background color
    - 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
  
  serialVersionUID:
  
  7220615894321679898L
  - 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
  
  serialVersionUID:
  
  -7969401840501661430L
  - 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
    - m_backgroundColor
```
java.awt.Color m_backgroundColor
```
      if set, it allows this panel to steer away from setting up a color in the color list that is equal to the background color
- Class weka.gui.visualize.LegendPanel extends javax.swing.JScrollPane implements Serializable
  
  serialVersionUID:
  
  -1262384440543001505L
  - 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
  
  serialVersionUID:
  
  3879990289042935670L
  - 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
  
  serialVersionUID:
  
  -1232642719869188740L
  - 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<E> 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<E> 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
  
  serialVersionUID:
  
  -1673162410856660442L
  - 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.PrintableComponent.JComponentWriterFileFilter extends ExtensionFileFilter implements Serializable
  - Serialized Fields
    - m_Writer
```
JComponentWriter m_Writer
```
      the associated writer.
- Class weka.gui.visualize.PrintablePanel extends javax.swing.JPanel implements Serializable
  
  serialVersionUID:
  
  6281532227633417538L
  - Serialized Fields
    - m_Printer
```
PrintableComponent m_Printer
```
      the class responsible for printing
- Class weka.gui.visualize.ThresholdVisualizePanel extends VisualizePanel implements Serializable
  
  serialVersionUID:
  
  3070002211779443890L
  - 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
  
  serialVersionUID:
  
  240108358588153943L
  - Serialized Fields
    - m_DefaultColors
```
java.awt.Color[] m_DefaultColors
```
      default colours for colouring discrete class
    - m_XCombo
```
javax.swing.JComboBox<E> m_XCombo
```
      Lets the user select the attribute for the x axis
    - m_YCombo
```
javax.swing.JComboBox<E> m_YCombo
```
      Lets the user select the attribute for the y axis
    - m_ColourCombo
```
javax.swing.JComboBox<E> m_ColourCombo
```
      Lets the user select the attribute to use for colouring
    - m_ShapeCombo
```
javax.swing.JComboBox<E> 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_openBut
```
javax.swing.JButton m_openBut
```
      Button for the user to open the visualized set of instances
    - 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_showClassPanel
```
boolean m_showClassPanel
```
      Show the class panel
    - m_Log
```
Logger m_Log
```
      the logger
- Class weka.gui.visualize.VisualizePanel.PlotPanel extends PrintablePanel implements Serializable
  
  serialVersionUID:
  
  -4823674171136494204L
  - 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_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).

Serialized Form

Package weka.associations

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

Class weka.associations.Apriori extends AbstractAssociator implements Serializable

Serialized Fields

m_minSupport

m_upperBoundMinSupport

m_lowerBoundMinSupport

m_metricType

m_minMetric

m_numRules

m_delta

m_significanceLevel

m_cycles

m_Ls

m_hashtables

m_allTheRules

m_instances

m_outputItemSets

m_removeMissingCols

m_verbose

m_onlyClass

m_classIndex

m_car

Class weka.associations.AprioriItemSet extends ItemSet implements Serializable

Class weka.associations.CaRuleGeneration extends RuleGeneration implements Serializable

Class weka.associations.FilteredAssociator extends SingleAssociatorEnhancer implements Serializable

Serialized Fields

m_Filter

m_FilteredInstances

m_ClassIndex

Class weka.associations.FPGrowth extends AbstractAssociator implements Serializable

Serialized Fields

m_numRulesToFind

m_upperBoundMinSupport

m_lowerBoundMinSupport

m_delta

m_findAllRulesForSupportLevel

m_positiveIndex

m_metric

m_metricThreshold

m_largeItemSets

m_rules

m_maxItems

m_transactionsMustContain

m_mustContainOR

m_rulesMustContain

Class weka.associations.FPGrowth.AssociationRule extends java.lang.Object implements Serializable

Serialized Fields

m_metricType

m_premise

m_consequence

m_premiseSupport

m_consequenceSupport

m_totalSupport

m_totalTransactions

Class weka.associations.FPGrowth.BinaryItem extends java.lang.Object implements Serializable

Serialized Fields

m_frequency

m_attribute

m_valueIndex

Class weka.associations.FPGrowth.FPTreeNode extends java.lang.Object implements Serializable

Serialized Fields

m_levelSibling

m_parent

m_item

m_ID

m_children

m_projectedCounts

Class weka.associations.FPGrowth.FPTreeRoot.Header extends java.lang.Object implements Serializable

Serialized Fields

m_headerList

m_projectedHeaderCounts

Class weka.associations.FPGrowth.FrequentBinaryItemSet extends java.lang.Object implements Serializable

Serialized Fields

m_items

m_support

Class weka.associations.FPGrowth.FrequentItemSets extends java.lang.Object implements Serializable

Serialized Fields

m_sets