Serialized Form

  • Package weka.associations

    • Class weka.associations.AbstractAssociator

      class AbstractAssociator extends Object implements Serializable
      serialVersionUID:
      -3017644543382432070L
    • Class weka.associations.Apriori

      class Apriori extends AbstractAssociator implements Serializable
      serialVersionUID:
      3277498842319212687L
      • Serialized Fields

        • m_allTheRules
          FastVector[] m_allTheRules
          The list of all generated rules.
        • m_car
          boolean m_car
          Flag indicating whether class association rules are mined.
        • m_classIndex
          int m_classIndex
          The class index.
        • m_cycles
          int m_cycles
          Number of cycles used before required number of rules was one.
        • m_delta
          double m_delta
          Delta by which m_minSupport is decreased in each iteration.
        • m_hashtables
          FastVector m_hashtables
          The same information stored in hash tables.
        • m_instances
          Instances m_instances
          The instances (transactions) to be used for generating the association rules.
        • m_lowerBoundMinSupport
          double m_lowerBoundMinSupport
          The lower bound for the minimum support.
        • m_Ls
          FastVector m_Ls
          The set of all sets of itemsets L.
        • m_metricType
          int m_metricType
          The selected metric type.
        • m_minMetric
          double m_minMetric
          The minimum metric score.
        • m_minSupport
          double m_minSupport
          The minimum support.
        • m_numRules
          int m_numRules
          The maximum number of rules that are output.
        • m_onlyClass
          Instances m_onlyClass
          Only the class attribute of all Instances.
        • m_outputItemSets
          boolean m_outputItemSets
          Output itemsets found?
        • m_removeMissingCols
          boolean m_removeMissingCols
          Remove columns with all missing values
        • m_significanceLevel
          double m_significanceLevel
          Significance level for optional significance test.
        • m_upperBoundMinSupport
          double m_upperBoundMinSupport
          The upper bound on the support
        • m_verbose
          boolean m_verbose
          Report progress iteratively
    • Class weka.associations.AprioriItemSet

      class AprioriItemSet extends ItemSet implements Serializable
      serialVersionUID:
      7684467755712672058L
    • Class weka.associations.CaRuleGeneration

      class CaRuleGeneration extends RuleGeneration implements Serializable
      serialVersionUID:
      3065752149646517703L
    • Class weka.associations.FilteredAssociator

      class FilteredAssociator extends SingleAssociatorEnhancer implements Serializable
      serialVersionUID:
      -4523450618538717400L
      • Serialized Fields

        • m_ClassIndex
          int m_ClassIndex
          The class index.
        • m_Filter
          Filter m_Filter
          The filter
        • m_FilteredInstances
          Instances m_FilteredInstances
          The instance structure of the filtered instances
    • Class weka.associations.FPGrowth

      class FPGrowth extends AbstractAssociator implements Serializable
      serialVersionUID:
      3620717108603442911L
      • Serialized Fields

        • 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_largeItemSets
          weka.associations.FPGrowth.FrequentItemSets m_largeItemSets
          Holds the large item sets found
        • m_lowerBoundMinSupport
          double m_lowerBoundMinSupport
          The lower bound on minimum support
        • m_maxItems
          int m_maxItems
        • m_metric
          FPGrowth.AssociationRule.METRIC_TYPE m_metric
        • m_metricThreshold
          double m_metricThreshold
        • m_mustContainOR
          boolean m_mustContainOR
          Use OR rather than AND when considering must contain lists
        • m_numRulesToFind
          int m_numRulesToFind
          The number of rules to find
        • m_positiveIndex
          int m_positiveIndex
          The index (1 based) of binary attributes to treat as the positive value
        • m_rules
          List<FPGrowth.AssociationRule> m_rules
          Holds the rules
        • m_rulesMustContain
          String m_rulesMustContain
          If set, then only output rules containing these itmes
        • m_transactionsMustContain
          String m_transactionsMustContain
          If set, limit the transactions (instances) input to the algorithm to those that contain these items
        • m_upperBoundMinSupport
          double m_upperBoundMinSupport
          The upper bound on the minimum support
    • Class weka.associations.FPGrowth.AssociationRule

      class AssociationRule extends Object implements Serializable
      serialVersionUID:
      -661269018702294489L
      • Serialized Fields

        • m_consequence
          Collection<FPGrowth.BinaryItem> m_consequence
          The consequence of the rule
        • m_consequenceSupport
          int m_consequenceSupport
          The support for the consequence
        • m_metricType
          FPGrowth.AssociationRule.METRIC_TYPE m_metricType
          The metric type for this rule
        • m_premise
          Collection<FPGrowth.BinaryItem> m_premise
          The premise of the rule
        • m_premiseSupport
          int m_premiseSupport
          The support for the premise
        • 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

      class BinaryItem extends Object implements Serializable
      serialVersionUID:
      -3372941834914147669L
      • Serialized Fields

        • m_attribute
          Attribute m_attribute
          The attribute that the item corresponds to
        • m_frequency
          int m_frequency
          The frequency of the item
        • m_valueIndex
          int m_valueIndex
          The index of the value considered to be positive
    • Class weka.associations.FPGrowth.FPTreeNode

      class FPTreeNode extends Object implements Serializable
      serialVersionUID:
      4396315323673737660L
      • Serialized Fields

        • m_children
          Map<FPGrowth.BinaryItem,weka.associations.FPGrowth.FPTreeNode> m_children
          the children of this node
        • m_ID
          int m_ID
          ID (for graphing the tree)
        • m_item
          FPGrowth.BinaryItem m_item
          item at this node
        • 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_projectedCounts
          weka.associations.FPGrowth.ShadowCounts m_projectedCounts
          counts associated with projected versions of this node
    • Class weka.associations.FPGrowth.FPTreeRoot.Header

      class Header extends Object implements Serializable
      serialVersionUID:
      -6583156284891368909L
      • Serialized Fields

        • m_headerList
          List<weka.associations.FPGrowth.FPTreeNode> 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

      class FrequentBinaryItemSet extends Object implements Serializable
      serialVersionUID:
      -6543815873565829448L
      • Serialized Fields

    • Class weka.associations.FPGrowth.FrequentItemSets

      class FrequentItemSets extends Object implements Serializable
      serialVersionUID:
      4173606872363973588L
      • Serialized Fields

        • m_numberOfTransactions
          int m_numberOfTransactions
          The total number of transactions in the data
        • m_sets
          ArrayList<weka.associations.FPGrowth.FrequentBinaryItemSet> m_sets
          The list of frequent item sets
    • Class weka.associations.FPGrowth.ShadowCounts

      class ShadowCounts extends Object implements Serializable
      serialVersionUID:
      4435433714185969155L
      • Serialized Fields

        • m_counts
          ArrayList<Integer> m_counts
          Holds the counts at different recursion levels
    • Class weka.associations.GeneralizedSequentialPatterns

      class GeneralizedSequentialPatterns extends AbstractAssociator implements Serializable
      serialVersionUID:
      -4119691320812254676L
      • Serialized Fields

        • m_AlgorithmStart
          String m_AlgorithmStart
          String indicating the starting time of the algorithm.
        • m_AllSequentialPatterns
          FastVector m_AllSequentialPatterns
          all generated frequent sequences, i.e. sequential patterns
        • m_CycleEnd
          String m_CycleEnd
          String indicating the ending time of an cycle.
        • m_Cycles
          int m_Cycles
          number of cycles performed until termination
        • m_CycleStart
          String m_CycleStart
          String indicating the starting time of an cycle.
        • m_DataSeqID
          int m_DataSeqID
          number indicating the attribute holding the data sequence ID
        • m_Debug
          boolean m_Debug
          Whether the classifier is run in debug mode.
        • m_FilterAttributes
          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_MinSupport
          double m_MinSupport
          the minimum support threshold
        • m_OriginalDataSet
          Instances m_OriginalDataSet
          original sequential data set to be used for sequential patterns extraction
    • Class weka.associations.ItemSet

      class ItemSet extends Object implements Serializable
      serialVersionUID:
      2724000045282835791L
      • Serialized Fields

        • m_counter
          int m_counter
          Counter for how many transactions contain this item set.
        • m_items
          int[] m_items
          The items stored as an array of of ints.
        • m_totalTransactions
          int m_totalTransactions
          The total number of transactions
    • Class weka.associations.LabeledItemSet

      class 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

      class PredictiveApriori extends AbstractAssociator implements Serializable
      serialVersionUID:
      8109088846865075341L
      • Serialized Fields

        • m_allTheRules
          FastVector[] m_allTheRules
          The list of all generated rules.
        • m_best
          TreeSet 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_car
          boolean m_car
          Flag indicating whether class association rules are mined.
        • m_classIndex
          int m_classIndex
          The class index.
        • m_count
          int m_count
          Counter for the time of generation for an association rule.
        • m_expectation
          double m_expectation
          The expected predictive accuracy a rule needs to be a candidate for the output.
        • m_hashtables
          FastVector m_hashtables
          The same information stored in hash tables.
        • m_instances
          Instances m_instances
          The instances (transactions) to be used for generating the association rules.
        • m_Ls
          FastVector m_Ls
          The set of all sets of itemsets.
        • m_midPoints
          double[] m_midPoints
          The mid points of the intervals used for the prior estimation.
        • m_numRules
          int m_numRules
          The maximum number of rules that are output.
        • m_premiseCount
          int m_premiseCount
          The minimum support.
        • m_priorEstimator
          PriorEstimation m_priorEstimator
          The prior estimator.
        • m_priors
          Hashtable m_priors
          The hashtable containing the prior probabilities.
    • Class weka.associations.PriorEstimation

      class PriorEstimation extends Object implements Serializable
      serialVersionUID:
      5570863216522496271L
      • Serialized Fields

        • m_CARs
          boolean m_CARs
          Flag indicating whether standard association rules or class association rules are mined.
        • m_distribution
          Hashtable m_distribution
          Hashtable to store the confidence values of randomly generated rules.
        • m_instances
          Instances m_instances
          The instances for which association rules are mined.
        • m_midPoints
          double[] m_midPoints
          The mid points of the discrete intervals in which the interval [0,1] is divided.
        • m_numIntervals
          int m_numIntervals
          The number of intervals.
        • m_numRandRules
          int m_numRandRules
          The number of rnadom rules.
        • m_priors
          Hashtable m_priors
          Hashtable containing the estimated prior probabilities.
        • m_randNum
          Random m_randNum
          The random number generator.
        • m_sum
          double m_sum
          Sums up the confidences of all rules with a certain length.
    • Class weka.associations.RuleGeneration

      class RuleGeneration extends Object implements Serializable
      serialVersionUID:
      -8927041669872491432L
      • Serialized Fields

        • m_best
          TreeSet m_best
          The list of the actual n best rules.
        • m_change
          boolean m_change
          Flag indicating whether the list fo the best rules has changed.
        • m_count
          int m_count
          Integer indicating the generation time of a rule.
        • m_counter
          int m_counter
          Counter for how many transactions contain this item set.
        • 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_instances
          Instances m_instances
          The instances.
        • m_items
          int[] m_items
          The items stored as an array of of integer.
        • m_midPoints
          double[] m_midPoints
          Sorted array of the mied points of the intervals used for prior estimation.
        • m_minRuleCount
          int m_minRuleCount
          The minimum support a rule needs to be a candidate for the list of the best rules.
        • m_priors
          Hashtable m_priors
          Hashtable conatining the estimated prior probabilities.
        • m_totalTransactions
          int m_totalTransactions
          The total number of transactions
    • Class weka.associations.RuleItem

      class RuleItem extends Object implements Serializable
      serialVersionUID:
      -3761299128347476534L
      • Serialized Fields

        • m_accuracy
          double m_accuracy
          The expected predictive accuracy of a rule.
        • m_consequence
          ItemSet m_consequence
          The consequence of a rule.
        • m_genTime
          int m_genTime
          The generation time of a rule.
        • m_premise
          ItemSet m_premise
          The premise of a rule.
    • Class weka.associations.SingleAssociatorEnhancer

      class SingleAssociatorEnhancer extends AbstractAssociator implements Serializable
      serialVersionUID:
      -3665885256363525164L
      • Serialized Fields

        • m_Associator
          Associator m_Associator
          The base associator to use
    • Class weka.associations.Tertius

      class Tertius extends AbstractAssociator implements Serializable
      serialVersionUID:
      5556726848380738179L
      • Serialized Fields

        • m_best
          int m_best
          Number of best confirmation values to search.
        • m_classification
          boolean m_classification
          Classification bias.
        • m_classIndex
          int m_classIndex
          Index of class attribute.
        • m_confirmationThreshold
          double m_confirmationThreshold
          Confirmation threshold for the rules.
        • m_equivalent
          boolean m_equivalent
          Perform test on equivalent rules ?
        • m_explored
          int m_explored
          Number of hypotheses explored.
        • m_frequencyThreshold
          double m_frequencyThreshold
          Frequency threshold for the body and the negation of the head.
        • m_horn
          boolean m_horn
          Horn clauses bias.
        • m_hypotheses
          int m_hypotheses
          Number of hypotheses considered.
        • m_instances
          Instances m_instances
          Instances used for the search.
        • m_missing
          int m_missing
          Way of handling missing values in the search.
        • m_negation
          int m_negation
          Type of negation used in the rules.
        • m_noiseThreshold
          double m_noiseThreshold
          Maximal number of counter-instances.
        • m_numLiterals
          int m_numLiterals
          Number of literals in a rule.
        • m_parts
          Instances m_parts
          Part instances for individual-based learning.
        • m_partsString
          String m_partsString
          Name of the file containing the parts for individual-based learning.
        • m_predicates
          ArrayList m_predicates
          Predicates used in the rules.
        • m_printValues
          int m_printValues
          Type of values output.
        • m_repeat
          boolean m_repeat
          Repeat attributes ?
        • m_results
          SimpleLinkedList m_results
          The results.
        • m_roc
          boolean m_roc
          Perform ROC analysis ?
        • m_sameClause
          boolean m_sameClause
          Perform test on same clauses ?
        • m_status
          int m_status
          Status of the search.
        • m_subsumption
          boolean m_subsumption
          Perform subsumption test ?
        • m_time
          Date m_time
          Time needed for the search.
        • m_valuesText
          TextField m_valuesText
          Field to output the current values.
  • Package weka.associations.gsp

  • Package weka.associations.tertius

  • Package weka.attributeSelection

    • Class weka.attributeSelection.ASEvaluation

      class ASEvaluation extends Object implements Serializable
      serialVersionUID:
      2091705669885950849L
    • Class weka.attributeSelection.ASSearch

      class ASSearch extends Object implements Serializable
      serialVersionUID:
      7591673350342236548L
    • Class weka.attributeSelection.AttributeSelection

      class AttributeSelection extends Object implements Serializable
      serialVersionUID:
      4170171824147584330L
      • Serialized Fields

        • m_ASEvaluator
          ASEvaluation m_ASEvaluator
          the attribute/subset evaluator
        • m_attributeFilter
          Remove m_attributeFilter
          the attribute filter for processing instances with respect to the most recent feature selection run
        • m_attributeRanking
          double[][] m_attributeRanking
          the attribute indexes and associated merits if a ranking is produced
        • m_doRank
          boolean m_doRank
          rank features (if allowed by the search method)
        • m_doXval
          boolean m_doXval
          do cross validation
        • m_numFolds
          int m_numFolds
          the number of folds to use for cross validation
        • m_numToSelect
          int m_numToSelect
          number of attributes requested from ranked results
        • m_rankResults
          double[][] m_rankResults
          hold statistics for repeated feature selection, such as under cross validation
        • m_searchMethod
          ASSearch m_searchMethod
          the search method
        • m_seed
          int m_seed
          seed used to randomly shuffle instances for cross validation
        • m_selectedAttributeSet
          int[] m_selectedAttributeSet
          the selected attributes
        • m_selectionResults
          StringBuffer m_selectionResults
          holds a string describing the results of the attribute selection
        • m_subsetResults
          double[] m_subsetResults
        • m_trainInstances
          Instances m_trainInstances
          the instances to select attributes from
        • m_transformer
          AttributeTransformer m_transformer
          if a feature selection run involves an attribute transformer
        • m_trials
          int m_trials
    • Class weka.attributeSelection.AttributeSetEvaluator

      class AttributeSetEvaluator extends ASEvaluation implements Serializable
      serialVersionUID:
      -5744881009422257389L
    • Class weka.attributeSelection.BestFirst

      class BestFirst extends ASSearch implements Serializable
      serialVersionUID:
      7841338689536821867L
      • Serialized Fields

        • 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
        • m_classIndex
          int m_classIndex
          holds the class index
        • m_debug
          boolean m_debug
          for debugging
        • m_hasClass
          boolean m_hasClass
          does the data have a class
        • m_maxStale
          int m_maxStale
          maximum number of stale nodes before terminating search
        • m_numAttribs
          int m_numAttribs
          number of attributes in the data
        • 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_totalEvals
          int m_totalEvals
          total number of subsets evaluated during a search
    • Class weka.attributeSelection.BestFirst.Link2

      class Link2 extends Object implements Serializable
      serialVersionUID:
      -8236598311516351420L
      • Serialized Fields

        • m_data
          Object[] m_data
        • m_merit
          double m_merit
    • Class weka.attributeSelection.BestFirst.LinkedList2

      class 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

      class CfsSubsetEval extends ASEvaluation implements Serializable
      serialVersionUID:
      747878400813276317L
      • Serialized Fields

        • m_c_Threshold
          double m_c_Threshold
          Threshold for admitting locally predictive features
        • m_classIndex
          int m_classIndex
          The class index
        • m_corr_matrix
          float[][] m_corr_matrix
          Holds the matrix of attribute correlations
        • m_disTransform
          Discretize m_disTransform
          Discretise attributes when class in nominal
        • m_isNumeric
          boolean m_isNumeric
          Is the class numeric
        • m_locallyPredictive
          boolean m_locallyPredictive
          Include locally predicitive attributes
        • m_missingSeparate
          boolean m_missingSeparate
          Treat missing values as separate values
        • 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_std_devs
          double[] m_std_devs
          Standard deviations of attributes (when using pearsons correlation)
        • m_trainInstances
          Instances m_trainInstances
          The training instances
    • Class weka.attributeSelection.ChiSquaredAttributeEval

      class ChiSquaredAttributeEval extends ASEvaluation implements Serializable
      serialVersionUID:
      -8316857822521717692L
      • Serialized Fields

        • m_Binarize
          boolean m_Binarize
          Just binarize numeric attributes
        • m_ChiSquareds
          double[] m_ChiSquareds
          The chi-squared value for each attribute
        • m_missing_merge
          boolean m_missing_merge
          Treat missing values as a seperate value
    • Class weka.attributeSelection.ClassifierSubsetEval

      class ClassifierSubsetEval extends HoldOutSubsetEvaluator implements Serializable
      serialVersionUID:
      7532217899385278710L
      • Serialized Fields

        • m_Classifier
          Classifier m_Classifier
          holds the classifier to use for error estimates
        • m_classIndex
          int m_classIndex
          class index
        • m_Evaluation
          Evaluation m_Evaluation
          holds the evaluation object to use for evaluating the classifier
        • m_holdOutFile
          File m_holdOutFile
          the file that containts hold out/test instances
        • m_holdOutInstances
          Instances m_holdOutInstances
          the instances to test on
        • m_numAttribs
          int m_numAttribs
          number of attributes in the training data
        • m_numInstances
          int m_numInstances
          number of training instances
        • m_trainingInstances
          Instances m_trainingInstances
          training instances
        • m_useTraining
          boolean m_useTraining
          evaluate on training data rather than seperate hold out/test set
    • Class weka.attributeSelection.ConsistencySubsetEval

      class ConsistencySubsetEval extends ASEvaluation implements Serializable
      serialVersionUID:
      -2880323763295270402L
      • Serialized Fields

        • m_classIndex
          int m_classIndex
          class index
        • m_disTransform
          Discretize m_disTransform
          Discretise numeric attributes
        • 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_table
          Hashtable m_table
          Hash table for evaluating feature subsets
        • m_trainInstances
          Instances m_trainInstances
          training instances
    • Class weka.attributeSelection.ConsistencySubsetEval.hashKey

      class hashKey extends Object implements Serializable
      serialVersionUID:
      6144138512017017408L
      • Serialized Fields

        • attributes
          double[] attributes
          Array of attribute values for an instance
        • key
          int key
          The key
        • missing
          boolean[] missing
          True for an index if the corresponding attribute value is missing.
    • Class weka.attributeSelection.CostSensitiveASEvaluation

      class CostSensitiveASEvaluation extends ASEvaluation implements Serializable
      serialVersionUID:
      -7045833833363396977L
      • Serialized Fields

        • m_CostFile
          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_MatrixSource
          int m_MatrixSource
          Indicates the current cost matrix source
        • m_OnDemandDirectory
          File m_OnDemandDirectory
          The directory used when loading cost files on demand, null indicates current directory
        • m_seed
          int m_seed
          random number seed
    • Class weka.attributeSelection.CostSensitiveAttributeEval

      class CostSensitiveAttributeEval extends CostSensitiveASEvaluation implements Serializable
      serialVersionUID:
      4484876541145458447L
    • Class weka.attributeSelection.CostSensitiveSubsetEval

      class CostSensitiveSubsetEval extends CostSensitiveASEvaluation implements Serializable
      serialVersionUID:
      2924546096103426700L
    • Class weka.attributeSelection.ExhaustiveSearch

      class ExhaustiveSearch extends ASSearch implements Serializable
      serialVersionUID:
      5741842861142379712L
      • Serialized Fields

        • m_bestGroup
          BitSet m_bestGroup
          the best feature set found during the search
        • m_bestMerit
          double m_bestMerit
          the merit of the best subset found
        • m_classIndex
          int m_classIndex
          holds the class index
        • m_evaluations
          int m_evaluations
          the number of subsets evaluated during the search
        • m_hasClass
          boolean m_hasClass
          does the data have a class
        • 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
    • Class weka.attributeSelection.FilteredAttributeEval

      class 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

      class 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

      class GainRatioAttributeEval extends ASEvaluation implements Serializable
      serialVersionUID:
      -8504656625598579926L
      • Serialized Fields

        • m_classIndex
          int m_classIndex
          The class index
        • m_missing_merge
          boolean m_missing_merge
          Merge missing values
        • m_numAttribs
          int m_numAttribs
          The number of attributes
        • m_numClasses
          int m_numClasses
          The number of classes
        • m_numInstances
          int m_numInstances
          The number of instances
        • m_trainInstances
          Instances m_trainInstances
          The training instances
    • Class weka.attributeSelection.GeneticSearch

      class GeneticSearch extends ASSearch implements Serializable
      serialVersionUID:
      -1618264232838472679L
      • Serialized Fields

        • m_avgFitness
          double m_avgFitness
        • 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_classIndex
          int m_classIndex
          holds the class index
        • m_generationReports
          StringBuffer m_generationReports
          holds the generation reports
        • m_hasClass
          boolean m_hasClass
          does the data have a class
        • m_lookupTable
          Hashtable m_lookupTable
          the lookup table
        • m_lookupTableSize
          int m_lookupTableSize
          the number of entries to cache for lookup
        • m_maxFitness
          double m_maxFitness
        • m_maxGenerations
          int m_maxGenerations
          the maximum number of generations to evaluate
        • m_minFitness
          double m_minFitness
        • m_numAttribs
          int m_numAttribs
          number of attributes in the data
        • m_pCrossover
          double m_pCrossover
          the probability of crossover occuring
        • m_pMutation
          double m_pMutation
          the probability of mutation occuring
        • m_popSize
          int m_popSize
          the number of individual solutions
        • m_population
          weka.attributeSelection.GeneticSearch.GABitSet[] m_population
          the current population
        • m_random
          Random m_random
          random number generation
        • m_reportFrequency
          int m_reportFrequency
          how often reports are generated
        • m_seed
          int m_seed
          seed for random number generation
        • 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_sumFitness
          double m_sumFitness
          sum of the current population fitness
    • Class weka.attributeSelection.GeneticSearch.GABitSet

      class GABitSet extends Object implements Serializable
      serialVersionUID:
      -2930607837482622224L
      • Serialized Fields

        • m_chromosome
          BitSet m_chromosome
          the bitset
        • m_fitness
          double m_fitness
          the fitness
        • m_objective
          double m_objective
          holds raw merit
    • Class weka.attributeSelection.GreedyStepwise

      class GreedyStepwise extends ASSearch implements Serializable
      serialVersionUID:
      -6312951970168325471L
      • Serialized Fields

        • m_ASEval
          ASEvaluation m_ASEval
        • m_backward
          boolean m_backward
          Use a backwards search instead of a forwards one
        • m_best_group
          BitSet m_best_group
          the best subset found
        • m_bestMerit
          double m_bestMerit
          the merit of the best subset found
        • m_calculatedNumToSelect
          int m_calculatedNumToSelect
        • m_classIndex
          int m_classIndex
          holds the class index
        • 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
        • m_doneRanking
          boolean m_doneRanking
          used to indicate whether or not ranking has been performed
        • m_doRank
          boolean m_doRank
          go from one side of the search space to the other in order to generate a ranking
        • m_hasClass
          boolean m_hasClass
          does the data have a class
        • m_Instances
          Instances m_Instances
        • m_numAttribs
          int m_numAttribs
          number of attributes in the data
        • 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_rankedAtts
          double[][] m_rankedAtts
          a ranked list of attribute indexes
        • m_rankedSoFar
          int m_rankedSoFar
        • m_rankingRequested
          boolean m_rankingRequested
          true if the user has requested a ranked list of attributes
        • 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_threshold
          double m_threshold
          A threshold by which to discard attributes---used by the AttributeSelection module
    • Class weka.attributeSelection.HoldOutSubsetEvaluator

      class HoldOutSubsetEvaluator extends ASEvaluation implements Serializable
      serialVersionUID:
      8280529785412054174L
    • Class weka.attributeSelection.InfoGainAttributeEval

      class InfoGainAttributeEval extends ASEvaluation implements Serializable
      serialVersionUID:
      -1949849512589218930L
      • Serialized Fields

        • m_Binarize
          boolean m_Binarize
          Just binarize numeric attributes
        • m_InfoGains
          double[] m_InfoGains
          The info gain for each attribute
        • m_missing_merge
          boolean m_missing_merge
          Treat missing values as a seperate value
    • Class weka.attributeSelection.LatentSemanticAnalysis

      class LatentSemanticAnalysis extends UnsupervisedAttributeEvaluator implements Serializable
      serialVersionUID:
      -8712112988018106198L
      • Serialized Fields

        • m_actualRank
          int m_actualRank
          The actual rank number to use for computation
        • m_attributeFilter
          Remove m_attributeFilter
        • m_classIndex
          int m_classIndex
          Class index
        • m_hasClass
          boolean m_hasClass
          Data has a class set
        • m_maxAttributesInName
          int m_maxAttributesInName
          Maximum number of attributes in the transformed attribute name
        • m_nominalToBinaryFilter
          NominalToBinary m_nominalToBinaryFilter
        • m_normalize
          boolean m_normalize
          Normalize the input data?
        • m_normalizeFilter
          Normalize m_normalizeFilter
        • m_numAttributes
          int m_numAttributes
          Number of attributes
        • m_numInstances
          int m_numInstances
          Number of instances
        • m_outputNumAttributes
          int m_outputNumAttributes
          The number of attributes in the LSA transformed data
        • m_rank
          double m_rank
          The approximation rank to use (between 0 and 1 means coverage proportion)
        • m_replaceMissingFilter
          ReplaceMissingValues m_replaceMissingFilter
          Filters for original data
        • m_s
          Matrix m_s
          Will hold the singular values
        • m_sumSquaredSingularValues
          double m_sumSquaredSingularValues
          The sum of the squares of the singular values
        • m_trainHeader
          Instances m_trainHeader
          Keep a copy for the class attribute (if set) and for checking for header compatibility
        • m_trainInstances
          Instances m_trainInstances
          The data to transform analyse/transform
        • m_transformationMatrix
          Matrix m_transformationMatrix
          Will hold the matrix used to transform instances to the new feature space
        • m_transformedFormat
          Instances m_transformedFormat
          The header for the transformed data format
        • m_transpose
          boolean m_transpose
          Is transpose necessary because numAttributes invalid input: '<' numInstances?
        • m_u
          Matrix m_u
          Will hold the left singular vectors
        • m_v
          Matrix m_v
          Will hold the right singular values
    • Class weka.attributeSelection.LFSMethods.Link2

      class Link2 extends Object implements Serializable
      serialVersionUID:
      -7422719407475185086L
      • Serialized Fields

        • m_data
          Object[] m_data
        • m_merit
          double m_merit
    • Class weka.attributeSelection.LFSMethods.LinkedList2

      class LinkedList2 extends FastVector implements Serializable
      serialVersionUID:
      -7776010892419656105L
      • Serialized Fields

        • m_MaxSize
          int m_MaxSize
    • Class weka.attributeSelection.LinearForwardSelection

      class LinearForwardSelection extends ASSearch implements Serializable
      • Serialized Fields

        • 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
        • m_classIndex
          int m_classIndex
          holds the class index
        • m_forwardSearchMethod
          int m_forwardSearchMethod
          0 == forward selection, 1 == floating forward search
        • m_hasClass
          boolean m_hasClass
          does the data have a class
        • m_linearSelectionType
          int m_linearSelectionType
          0 == fixed-set, 1 == fixed-width
        • m_maxStale
          int m_maxStale
          maximum number of stale nodes before terminating search
        • m_numAttribs
          int m_numAttribs
          number of attributes in the data
        • m_numUsedAttributes
          int m_numUsedAttributes
          number of top-ranked attributes that are taken into account for the search
        • m_performRanking
          boolean m_performRanking
          perform initial ranking to select top-ranked attributes
        • 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_totalEvals
          int m_totalEvals
          total number of subsets evaluated during a search
        • m_verbose
          boolean m_verbose
          for debugging
    • Class weka.attributeSelection.OneRAttributeEval

      class OneRAttributeEval extends ASEvaluation implements Serializable
      serialVersionUID:
      4386514823886856980L
      • Serialized Fields

        • m_classIndex
          int m_classIndex
          The class index
        • m_evalUsingTrainingData
          boolean m_evalUsingTrainingData
          Use training data to evaluate merit rather than x-val
        • m_folds
          int m_folds
          Number of folds for cross validation
        • m_minBucketSize
          int m_minBucketSize
          Passed on to OneR
        • 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_trainInstances
          Instances m_trainInstances
          The training instances
    • Class weka.attributeSelection.PrincipalComponents

      class PrincipalComponents extends UnsupervisedAttributeEvaluator implements Serializable
      serialVersionUID:
      -3675307197777734007L
      • Serialized Fields

        • m_attributeFilter
          Remove m_attributeFilter
        • m_center
          boolean m_center
          If true, center (rather than standardize) the data and compute PCA from covariance (rather than correlation) matrix.
        • m_centerFilter
          Center m_centerFilter
        • m_classIndex
          int m_classIndex
          Class index
        • m_correlation
          double[][] m_correlation
          Correlation/covariance matrix for the original data
        • m_coverVariance
          double m_coverVariance
          the amount of variance to cover in the original data when retaining the best n PC's
        • m_eigenvalues
          double[] m_eigenvalues
          Eigenvalues for the corresponding eigenvectors
        • m_eigenvectors
          double[][] m_eigenvectors
          Will hold the unordered linear transformations of the (normalized) original data
        • m_eTranspose
          double[][] m_eTranspose
          holds the transposed eigenvectors for converting back to the original space
        • m_hasClass
          boolean m_hasClass
          Data has a class set
        • m_maxAttrsInName
          int m_maxAttrsInName
          maximum number of attributes in the transformed attribute name
        • m_means
          double[] m_means
        • m_nominalToBinFilter
          NominalToBinary m_nominalToBinFilter
        • m_numAttribs
          int m_numAttribs
          Number of attributes
        • m_numInstances
          int m_numInstances
          Number of instances
        • m_originalSpaceFormat
          Instances m_originalSpaceFormat
          The header for data transformed back to the original space
        • m_outputNumAtts
          int m_outputNumAtts
          The number of attributes in the pc transformed data
        • m_replaceMissingFilter
          ReplaceMissingValues m_replaceMissingFilter
          Filters for original data
        • m_sortedEigens
          int[] m_sortedEigens
          Sorted eigenvalues
        • m_standardizeFilter
          Standardize m_standardizeFilter
        • m_stdDevs
          double[] m_stdDevs
        • m_sumOfEigenValues
          double m_sumOfEigenValues
          sum of the eigenvalues
        • m_trainHeader
          Instances m_trainHeader
          Keep a copy for the class attribute (if set)
        • m_trainInstances
          Instances m_trainInstances
          The data to transform analyse/transform
        • m_transBackToOriginal
          boolean m_transBackToOriginal
          transform the data through the pc space and back to the original space ?
        • m_transformedFormat
          Instances m_transformedFormat
          The header for the transformed data format
    • Class weka.attributeSelection.RaceSearch

      class RaceSearch extends ASSearch implements Serializable
      serialVersionUID:
      4015453851212985720L
      • Serialized Fields

        • m_ASEval
          ASEvaluation m_ASEval
          the attribute evaluator to generate the initial ranking when doing a rank race
        • m_bestMerit
          double m_bestMerit
          holds the merit of the best subset found
        • m_calculatedNumToSelect
          int m_calculatedNumToSelect
        • m_classIndex
          int m_classIndex
          the class index
        • m_debug
          boolean m_debug
          verbose output for monitoring the search and debugging
        • m_delta
          double m_delta
          threshold for comparisons
        • m_Instances
          Instances m_Instances
          the training instances
        • m_numAttribs
          int m_numAttribs
          the number of attributes in the data
        • m_numFolds
          int m_numFolds
          number of cross validation folds---equal to the number of instances for leave-one-out cv
        • 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_raceType
          int m_raceType
          the selected search type
        • 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_Ranking
          int[] m_Ranking
          will hold the attribute ranking produced by the above attribute evaluator if doing a rank search
        • m_rankingRequested
          boolean m_rankingRequested
          If true then produce a ranked list of attributes by fully traversing a forward hillclimb race
        • m_samples
          int m_samples
          the number of samples above which to begin testing for similarity between competing subsets
        • m_sigLevel
          double m_sigLevel
          the significance level for comparisons
        • m_theEvaluator
          HoldOutSubsetEvaluator m_theEvaluator
          the subset evaluator to use
        • m_threshold
          double m_threshold
          the threshold for removing attributes if ranking is requested
        • m_totalEvals
          int m_totalEvals
          the total number of partially/fully evaluated subsets
        • m_xvalType
          int m_xvalType
          the selected xval type
    • Class weka.attributeSelection.RandomSearch

      class RandomSearch extends ASSearch implements Serializable
      serialVersionUID:
      7479392617377425484L
      • Serialized Fields

        • m_bestGroup
          BitSet m_bestGroup
          the best feature set found during the search
        • m_bestMerit
          double m_bestMerit
          the merit of the best subset found
        • m_classIndex
          int m_classIndex
          holds the class index
        • m_hasClass
          boolean m_hasClass
          does the data have a class
        • m_iterations
          int m_iterations
          the number of iterations performed
        • m_numAttribs
          int m_numAttribs
          number of attributes in the data
        • 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_random
          Random m_random
          random number object
        • m_searchSize
          double m_searchSize
          percentage of the search space to consider
        • m_seed
          int m_seed
          seed for random number generation
        • 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_verbose
          boolean m_verbose
          output new best subsets as the search progresses
    • Class weka.attributeSelection.Ranker

      class Ranker extends ASSearch implements Serializable
      serialVersionUID:
      -9086714848510751934L
      • Serialized Fields

        • m_attributeList
          int[] m_attributeList
          Holds the ordered list of attributes
        • m_attributeMerit
          double[] m_attributeMerit
          Holds the list of attribute merit scores
        • m_calculatedNumToSelect
          int m_calculatedNumToSelect
          Used to compute the number to select
        • m_classIndex
          int m_classIndex
          Class index of the data if supervised evaluator
        • m_hasClass
          boolean m_hasClass
          Data has class attribute---if unsupervised evaluator then no class
        • m_numAttribs
          int m_numAttribs
          The number of attribtes
        • 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_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_threshold
          double m_threshold
          A threshold by which to discard attributes---used by the AttributeSelection module
    • Class weka.attributeSelection.RankSearch

      class RankSearch extends ASSearch implements Serializable
      serialVersionUID:
      -7992268736874353755L
      • Serialized Fields

        • m_add
          int m_add
          add this many attributes in each iteration from the ranking
        • m_ASEval
          ASEvaluation m_ASEval
          the attribute evaluator to use for generating the ranking
        • m_best_group
          BitSet m_best_group
          the best subset found
        • m_bestMerit
          double m_bestMerit
          the merit of the best subset found
        • m_classIndex
          int m_classIndex
          holds the class index
        • m_hasClass
          boolean m_hasClass
          does the data have a class
        • m_Instances
          Instances m_Instances
          the training instances
        • m_numAttribs
          int m_numAttribs
          number of attributes in the data
        • m_Ranking
          int[] m_Ranking
          will hold the attribute ranking
        • m_startPoint
          int m_startPoint
          start from this point in the ranking
        • m_SubsetEval
          ASEvaluation m_SubsetEval
          the subset evaluator with which to evaluate the ranking
    • Class weka.attributeSelection.ReliefFAttributeEval

      class ReliefFAttributeEval extends ASEvaluation implements Serializable
      serialVersionUID:
      -8422186665795839379L
      • Serialized Fields

        • m_classIndex
          int m_classIndex
          The class index
        • m_classProbs
          double[] m_classProbs
          Prior class probabilities (discrete class case)
        • m_index
          int[] m_index
          Index in the m_karray of the farthest instance for each class
        • m_karray
          double[][][] m_karray
          k nearest scores + instance indexes for n classes
        • m_Knn
          int m_Knn
          The number of nearest hits/misses
        • m_maxArray
          double[] m_maxArray
          Upper bound for numeric attributes
        • m_minArray
          double[] m_minArray
          Lower bound for numeric attributes
        • m_nda
          double[] m_nda
          Used to hold the prob of different value of an attribute given nearest instances (numeric class case)
        • m_ndc
          double m_ndc
          Used to hold the probability of a different class val given nearest instances (numeric class)
        • 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_numAttribs
          int m_numAttribs
          The number of attributes
        • m_numClasses
          int m_numClasses
          The number of classes if class is nominal
        • m_numericClass
          boolean m_numericClass
          Numeric class
        • m_numInstances
          int m_numInstances
          The number of instances
        • m_sampleM
          int m_sampleM
          The number of instances to sample when estimating attributes default == -1, use all instances
        • m_seed
          int m_seed
          Random number seed used for sampling instances
        • m_sigma
          int m_sigma
        • m_stored
          int[] m_stored
          Number of nearest neighbours stored of each class
        • m_trainInstances
          Instances m_trainInstances
          The training instances
        • m_weightByDistance
          boolean m_weightByDistance
          Weight by distance rather than equal weights
        • m_weights
          double[] m_weights
          Holds the weights that relief assigns to attributes
        • 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_worst
          double[] m_worst
          Keep track of the farthest instance for each class
    • Class weka.attributeSelection.ScatterSearchV1

      class ScatterSearchV1 extends ASSearch implements Serializable
      serialVersionUID:
      -8512041420388121326L
      • Serialized Fields

        • ASEvaluator
          SubsetEvaluator ASEvaluator
          Evaluator used to know the significance of a subset (for guiding the search)
        • m_attributeRanking
          List<ScatterSearchV1.Subset> m_attributeRanking
          holds the attributes ranked
        • m_bestMerit
          double m_bestMerit
          holds the merit of the best subset found
        • 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_classIndex
          int m_classIndex
          holds the class index
        • m_debug
          boolean m_debug
          verbose output for monitoring the search and debugging
        • m_InformationReports
          StringBuffer m_InformationReports
          holds a report of the search
        • m_initialPopSize
          int m_initialPopSize
          holds the user selected initial population size
        • m_initialThreshold
          double m_initialThreshold
          the initial threshold
        • m_numAttribs
          int m_numAttribs
          number of attributes in the data
        • m_popSize
          int m_popSize
          holds the population size
        • m_population
          List<ScatterSearchV1.Subset> m_population
          holds the Initial Population of Subsets
        • m_processinTime
          long m_processinTime
          time for procesing the search method
        • m_random
          Random m_random
          random number generation
        • m_seed
          int m_seed
          seed for random number generation
        • m_totalEvals
          int m_totalEvals
          total number of subsets evaluated during a search
        • m_treshold
          double m_treshold
          holds the treshhold that delimits the good attributes
        • m_typeOfCombination
          int m_typeOfCombination
          the kind of comination betwen parents ((0)greedy combination/(1)reduced greedy combination)
    • Class weka.attributeSelection.ScatterSearchV1.Subset

      class Subset extends Object implements Serializable
      • Serialized Fields

        • merit
          double merit
        • subset
          BitSet subset
    • Class weka.attributeSelection.SubsetSizeForwardSelection

      class SubsetSizeForwardSelection extends ASSearch implements Serializable
      • Serialized Fields

        • 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
        • m_linearSelectionType
          int m_linearSelectionType
          0 == fixed-set, 1 == fixed-width
        • m_numAttribs
          int m_numAttribs
          number of attributes in the data
        • m_numFolds
          int m_numFolds
          Number of cross validation folds for subset size determination (default = 5).
        • m_numUsedAttributes
          int m_numUsedAttributes
          number of top-ranked attributes that are taken into account for the search
        • m_performRanking
          boolean m_performRanking
          perform initial ranking to select top-ranked attributes
        • m_seed
          int m_seed
          Seed for cross validation subset size determination. (default = 1)
        • m_setSizeEval
          ASEvaluation m_setSizeEval
          the subset evaluator to use for subset size determination
        • m_totalEvals
          int m_totalEvals
          total number of subsets evaluated during a search
        • m_verbose
          boolean m_verbose
          for debugging
    • Class weka.attributeSelection.SVMAttributeEval

      class 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_percentThreshold
          int m_percentThreshold
          Threshold below which percent elimination switches to constant elimination
        • m_percentToEliminate
          int m_percentToEliminate
          Percentage rate of attribute elimination, trumps constant rate (above threshold), ignored if = 0
        • m_smoCParameter
          double m_smoCParameter
          Complexity parameter to pass on to SMO
        • m_smoFilterType
          int m_smoFilterType
          Filter parameter to pass on to SMO
        • m_smoPParameter
          double m_smoPParameter
          Epsilon parameter to pass on to SMO
        • m_smoTParameter
          double m_smoTParameter
          Tolerance parameter to pass on to SMO
    • Class weka.attributeSelection.SymmetricalUncertAttributeEval

      class SymmetricalUncertAttributeEval extends ASEvaluation implements Serializable
      serialVersionUID:
      -8096505776132296416L
      • Serialized Fields

        • m_classIndex
          int m_classIndex
          The class index
        • m_missing_merge
          boolean m_missing_merge
          Treat missing values as a seperate value
        • m_numAttribs
          int m_numAttribs
          The number of attributes
        • m_numClasses
          int m_numClasses
          The number of classes
        • m_numInstances
          int m_numInstances
          The number of instances
        • m_trainInstances
          Instances m_trainInstances
          The training instances
    • Class weka.attributeSelection.UnsupervisedAttributeEvaluator

      class UnsupervisedAttributeEvaluator extends ASEvaluation implements Serializable
      serialVersionUID:
      -4100897318675336178L
    • Class weka.attributeSelection.UnsupervisedSubsetEvaluator

      class UnsupervisedSubsetEvaluator extends ASEvaluation implements Serializable
      serialVersionUID:
      627934376267488763L
    • Class weka.attributeSelection.WrapperSubsetEval

      class WrapperSubsetEval extends ASEvaluation implements Serializable
      serialVersionUID:
      -4573057658746728675L
      • Serialized Fields

        • m_BaseClassifier
          Classifier m_BaseClassifier
          holds the base classifier object
        • m_classIndex
          int m_classIndex
          class index
        • m_Evaluation
          Evaluation m_Evaluation
          holds an evaluation object
        • m_folds
          int m_folds
          number of folds to use for cross validation
        • 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_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
        • m_trainInstances
          Instances m_trainInstances
          training instances
  • Package weka.classifiers

  • Package weka.classifiers.bayes

    • Class weka.classifiers.bayes.AODE

      class AODE extends Classifier implements Serializable
      serialVersionUID:
      9197439980415113523L
      • Serialized Fields

        • m_ClassCounts
          double[] m_ClassCounts
          The number of times each class value occurs in the dataset
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • 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_Debug
          boolean m_Debug
          If true, outputs debugging info
        • m_Frequencies
          double[] m_Frequencies
          The frequency of each attribute value for the dataset
        • m_Instances
          Instances m_Instances
          The dataset
        • m_Limit
          int m_Limit
          An att's frequency must be this value or more to be a superParent
        • m_MEstimates
          boolean m_MEstimates
          flag for using m-estimates
        • m_NumAttributes
          int m_NumAttributes
          The number of attributes in dataset, including class
        • m_NumAttValues
          int[] m_NumAttValues
          The number of values for each attribute
        • m_NumClasses
          int m_NumClasses
          The number of classes
        • m_NumInstances
          int m_NumInstances
          The number of instances in the dataset
        • m_StartAttIndex
          int[] m_StartAttIndex
          The starting index (in the m_CondiCounts matrix) of the values for each attribute
        • 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_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_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_Weight
          int m_Weight
          value for m in m-estimate
    • Class weka.classifiers.bayes.AODEsr

      class AODEsr extends Classifier implements Serializable
      serialVersionUID:
      5602143019183068848L
      • Serialized Fields

        • m_ClassCounts
          double[] m_ClassCounts
          The number of times each class value occurs in the dataset
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • 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_Critical
          int m_Critical
          the critical value for the specialization-generalization
        • m_Debug
          boolean m_Debug
          If true, outputs debugging info
        • m_Frequencies
          double[] m_Frequencies
          The frequency of each attribute value for the dataset
        • m_Instances
          Instances m_Instances
          The dataset
        • m_Laplace
          boolean m_Laplace
          Using LapLace estimation or not
        • m_Limit
          int m_Limit
          An att's frequency must be this value or more to be a superParent
        • m_MWeight
          double m_MWeight
          m value for m-estimation
        • m_NumAttributes
          int m_NumAttributes
          The number of attributes in dataset, including class
        • m_NumAttValues
          int[] m_NumAttValues
          The number of values for each attribute
        • m_NumClasses
          int m_NumClasses
          The number of classes
        • m_NumInstances
          int m_NumInstances
          The number of instances in the dataset
        • m_StartAttIndex
          int[] m_StartAttIndex
          The starting index (in the m_CondiCounts matrix) of the values for each attribute
        • 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_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_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.
    • Class weka.classifiers.bayes.BayesianLogisticRegression

      class BayesianLogisticRegression extends Classifier implements Serializable
      serialVersionUID:
      -8013478897911757631L
      • Serialized Fields

        • BetaVector
          double[] BetaVector
          Array for storing coefficients of Bayesian regression model.
        • Change
          double Change
          This variable is used to keep track of change in the value of delta summation of r(i).
        • ClassIndex
          int ClassIndex
          The class index from the training data
        • debug
          boolean debug
          DEBUG Mode
        • Delta
          double[] Delta
          Trust Region Radius
        • DeltaBeta
          double[] DeltaBeta
          Array to store Regression Coefficient updates.
        • DeltaR
          double[] DeltaR
          This vector is used to store the increments on the R(i). It is also used to determining the stopping criterion.
        • DeltaUpdate
          double[] DeltaUpdate
          Trust Region Radius Update
        • HyperparameterRange
          String HyperparameterRange
          CV Hyperparameter Range
        • Hyperparameters
          double[] Hyperparameters
          Array to store Hyperparameter values for each feature.
        • HyperparameterSelection
          int HyperparameterSelection
          Hyperparameter selection method
        • HyperparameterValue
          double HyperparameterValue
          Best hyperparameter for test phase
        • iterationCounter
          int iterationCounter
          Iteration counter
        • 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
        • m_seed
          int m_seed
          seed for randomizing the instances before CV
        • maxIterations
          int maxIterations
          Maximum number of iterations
        • NormalizeData
          boolean NormalizeData
          Choose whether to normalize data or not
        • NumFolds
          int NumFolds
          NumFolds for CV based Hyperparameters selection
        • PriorClass
          int PriorClass
          Distribution Prior class
        • 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
        • Threshold
          double Threshold
          Threshold for binary classification of probabilisitic estimate
        • Tolerance
          double Tolerance
          Tolerance criteria for the stopping criterion.
    • Class weka.classifiers.bayes.BayesNet

      class BayesNet extends Classifier implements Serializable
      serialVersionUID:
      746037443258775954L
      • Serialized Fields

        • m_ADTree
          ADNode m_ADTree
          Datastructure containing ADTree representation of the database. This may result in more efficient access to the data.
        • m_BayesNetEstimator
          BayesNetEstimator m_BayesNetEstimator
          Search algorithm used for learning the structure of a network.
        • m_bUseADTree
          boolean m_bUseADTree
          Use the experimental ADTree datastructure for calculating contingency tables
        • m_DiscretizeFilter
          Discretize m_DiscretizeFilter
          filter used to quantize continuous variables, if any
        • m_Distributions
          Estimator[][] m_Distributions
          The attribute estimators containing CPTs.
        • m_Instances
          Instances m_Instances
          The dataset header for the purposes of printing out a semi-intelligible model
        • m_MissingValuesFilter
          ReplaceMissingValues m_MissingValuesFilter
          filter used to fill in missing values, if any
        • m_nNonDiscreteAttribute
          int m_nNonDiscreteAttribute
          attribute index of a non-nominal attribute
        • m_NumClasses
          int m_NumClasses
          The number of classes
        • m_otherBayesNet
          BIFReader m_otherBayesNet
          Bayes network to compare the structure with.
        • m_ParentSets
          ParentSet[] m_ParentSets
          The parent sets.
        • m_SearchAlgorithm
          SearchAlgorithm m_SearchAlgorithm
          Search algorithm used for learning the structure of a network.
    • Class weka.classifiers.bayes.ComplementNaiveBayes

      class ComplementNaiveBayes extends Classifier implements Serializable
      serialVersionUID:
      7246302925903086397L
      • Serialized Fields

        • header
          Instances header
          The instances header that'll be used in toString
        • 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
        • 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
        • 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]
    • Class weka.classifiers.bayes.DMNBtext

      class DMNBtext extends Classifier implements Serializable
      serialVersionUID:
      5932177450183457085L
      • Serialized Fields

        • m_binaryClassifiers
          DMNBtext.DNBBinary[] m_binaryClassifiers
        • m_headerInfo
          Instances m_headerInfo
        • m_MultinomialWord
          boolean m_MultinomialWord
        • m_numClasses
          int m_numClasses
        • m_NumIterations
          int m_NumIterations
          The number of iterations.
    • Class weka.classifiers.bayes.DMNBtext.DNBBinary

      class DNBBinary extends Object implements Serializable
      • Serialized Fields

        • m_classDistribution
          double[] m_classDistribution
        • m_classIndex
          int m_classIndex
        • m_classRatio
          double m_classRatio
        • m_coefficient
          double[] m_coefficient
        • m_numAttributes
          int m_numAttributes
          number of unique words
        • m_perWordPerClass
          double[][] m_perWordPerClass
          The number of iterations.
        • m_targetClass
          int m_targetClass
        • m_WordLaplace
          double m_WordLaplace
        • m_wordRatio
          double m_wordRatio
        • m_wordsPerClass
          double[] m_wordsPerClass
    • Class weka.classifiers.bayes.HNB

      class HNB extends Classifier implements Serializable
      serialVersionUID:
      -4503874444306113214L
      • Serialized Fields

        • m_ClassAttAttCounts
          double[][][] m_ClassAttAttCounts
          The number of class and two attributes values occurs in the dataset
        • m_ClassCounts
          double[] m_ClassCounts
          The number of each class value occurs in the dataset
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute in the dataset
        • m_condiMutualInfo
          double[][] m_condiMutualInfo
          The 2D array of conditional mutual information of each pair attributes
        • m_NumAttributes
          int m_NumAttributes
          The number of attributes including class in the dataset
        • m_NumAttValues
          int[] m_NumAttValues
          The number of values for each attribute in the dataset
        • m_NumClasses
          int m_NumClasses
          The number of classes in the dataset
        • m_NumInstances
          int m_NumInstances
          The number of instances in the dataset
        • m_StartAttIndex
          int[] m_StartAttIndex
          The starting index of each attribute in the dataset
        • m_TotalAttValues
          int m_TotalAttValues
          The number of values for all attributes in the dataset
    • Class weka.classifiers.bayes.NaiveBayes

      class NaiveBayes extends Classifier implements Serializable
      serialVersionUID:
      5995231201785697655L
      • Serialized Fields

        • m_ClassDistribution
          Estimator m_ClassDistribution
          The class estimator.
        • m_Disc
          Discretize m_Disc
          The discretization filter.
        • m_displayModelInOldFormat
          boolean m_displayModelInOldFormat
        • m_Distributions
          Estimator[][] m_Distributions
          The attribute estimators.
        • m_Instances
          Instances m_Instances
          The dataset header for the purposes of printing out a semi-intelligible model
        • m_NumClasses
          int m_NumClasses
          The number of classes (or 1 for numeric class)
        • m_UseDiscretization
          boolean m_UseDiscretization
          Whether to use discretization than normal distribution for numeric attributes
        • m_UseKernelEstimator
          boolean m_UseKernelEstimator
          Whether to use kernel density estimator rather than normal distribution for numeric attributes
    • Class weka.classifiers.bayes.NaiveBayesMultinomial

      class NaiveBayesMultinomial extends Classifier implements Serializable
      serialVersionUID:
      5932177440181257085L
      • Serialized Fields

        • m_headerInfo
          Instances m_headerInfo
          copy of header information for use in toString method
        • m_lnFactorialCache
          double[] m_lnFactorialCache
          cache lnFactorial computations
        • m_numAttributes
          int m_numAttributes
          number of unique words
        • m_numClasses
          int m_numClasses
          number of class values
        • m_probOfClass
          double[] m_probOfClass
          the probability of a class (i.e. Pr[H])
        • 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]
    • Class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable

      class NaiveBayesMultinomialUpdateable extends NaiveBayesMultinomial implements Serializable
      serialVersionUID:
      -7204398796974263186L
      • Serialized Fields

        • m_wordsPerClass
          double[] m_wordsPerClass
          the word count per class
    • Class weka.classifiers.bayes.NaiveBayesSimple

      class NaiveBayesSimple extends Classifier implements Serializable
      serialVersionUID:
      -1478242251770381214L
      • Serialized Fields

        • m_Counts
          double[][][] m_Counts
          All the counts for nominal attributes.
        • m_Devs
          double[][] m_Devs
          The standard deviations for numeric attributes.
        • m_Instances
          Instances m_Instances
          The instances used for training.
        • m_Means
          double[][] m_Means
          The means for numeric attributes.
        • m_Priors
          double[] m_Priors
          The prior probabilities of the classes.
    • Class weka.classifiers.bayes.NaiveBayesUpdateable

      class NaiveBayesUpdateable extends NaiveBayes implements Serializable
      serialVersionUID:
      -5354015843807192221L
    • Class weka.classifiers.bayes.WAODE

      class WAODE extends Classifier implements Serializable
      serialVersionUID:
      2170978824284697882L
      • Serialized Fields

        • m_AttAttCounts
          double[][] m_AttAttCounts
          The number of two attributes values occurs in the dataset
        • m_AttCounts
          double[] m_AttCounts
          The number of each attribute value occurs in the dataset
        • m_ClassAttAttCounts
          double[][][] m_ClassAttAttCounts
          The number of class and two attributes values occurs in the dataset
        • m_ClassCounts
          double[] m_ClassCounts
          The number of each class value occurs in the dataset
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute in the dataset
        • 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:
        • m_mutualInformation
          double[] m_mutualInformation
          The array of mutual information between each attribute and class
        • m_NumAttributes
          int m_NumAttributes
          The number of attributes including class in the dataset
        • m_NumAttValues
          int[] m_NumAttValues
          The number of values for each attribute in the dataset
        • m_NumClasses
          int m_NumClasses
          The number of classes in the dataset
        • m_NumInstances
          int m_NumInstances
          The number of instances in the dataset
        • m_StartAttIndex
          int[] m_StartAttIndex
          The starting index of each attribute in the dataset
        • m_TotalAttValues
          int m_TotalAttValues
          The number of values for all attributes in the dataset
        • m_ZeroR
          Classifier m_ZeroR
          a ZeroR model in case no model can be built from the data
  • Package weka.classifiers.bayes.blr

  • Package weka.classifiers.bayes.net

    • Class weka.classifiers.bayes.net.ADNode

      class ADNode extends Object implements Serializable
      serialVersionUID:
      397409728366910204L
      • Serialized Fields

        • 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
        • m_VaryNodes
          VaryNode[] m_VaryNodes
          list of VaryNode children
    • Class weka.classifiers.bayes.net.BayesNetGenerator

      class BayesNetGenerator extends EditableBayesNet implements Serializable
      serialVersionUID:
      -7462571170596157720L
      • Serialized Fields

        • m_bGenerateNet
          boolean m_bGenerateNet
        • m_nCardinality
          int m_nCardinality
        • m_nNrOfArcs
          int m_nNrOfArcs
        • m_nNrOfInstances
          int m_nNrOfInstances
        • m_nNrOfNodes
          int m_nNrOfNodes
        • m_nSeed
          int m_nSeed
          the seed value
        • m_sBIFFile
          String m_sBIFFile
        • random
          Random random
          the random number generator
    • Class weka.classifiers.bayes.net.BIFReader

      class 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
          String m_sFile
          the current filename
    • Class weka.classifiers.bayes.net.EditableBayesNet

      class EditableBayesNet extends BayesNet implements Serializable
      serialVersionUID:
      746037443258735954L
      • Serialized Fields

        • 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.
        • m_fMarginP
          FastVector m_fMarginP
          marginal distributions *
        • m_nCurrentEditAction
          int m_nCurrentEditAction
          current action in undo stack
        • m_nEvidence
          FastVector m_nEvidence
          evidence values, used for evidence propagation *
        • m_nPositionX
          FastVector m_nPositionX
          location of nodes, used for graph drawing *
        • m_nPositionY
          FastVector m_nPositionY
        • m_nSavedPointer
          int m_nSavedPointer
          action that the network is saved
        • m_undoStack
          FastVector m_undoStack
          undo stack for undoin edit actions, or redo edit actions
    • Class weka.classifiers.bayes.net.GUI

      class GUI extends JPanel implements Serializable
      serialVersionUID:
      -2038911085935515624L
      • Serialized Fields

        • a_about
          Action a_about
        • a_addarc
          Action a_addarc
        • a_addnode
          weka.classifiers.bayes.net.GUI.ActionAddNode a_addnode
        • a_alignbottom
          Action a_alignbottom
        • a_alignleft
          Action a_alignleft
        • a_alignright
          Action a_alignright
        • a_aligntop
          Action a_aligntop
        • a_centerhorizontal
          Action a_centerhorizontal
        • a_centervertical
          Action a_centervertical
        • a_copynode
          Action a_copynode
        • a_cutnode
          Action a_cutnode
        • a_datagenerator
          Action a_datagenerator
        • a_datasetter
          Action a_datasetter
        • a_delarc
          Action a_delarc
        • a_delnode
          Action a_delnode
        • a_export
          weka.classifiers.bayes.net.GUI.ActionExport a_export
        • a_help
          Action a_help
        • a_layout
          Action a_layout
        • a_learn
          Action a_learn
        • a_learnCPT
          Action a_learnCPT
        • a_load
          Action a_load
        • a_networkgenerator
          Action a_networkgenerator
        • a_new
          Action a_new
          actions triggered by GUI events
        • a_pastenode
          Action a_pastenode
        • a_print
          weka.classifiers.bayes.net.GUI.ActionPrint a_print
        • a_quit
          Action a_quit
        • a_redo
          Action a_redo
        • a_save
          Action a_save
        • a_saveas
          Action a_saveas
        • a_selectall
          Action a_selectall
        • a_spacehorizontal
          Action a_spacehorizontal
        • a_spacevertical
          Action a_spacevertical
        • a_undo
          Action a_undo
        • a_viewstatusbar
          Action a_viewstatusbar
        • a_viewtoolbar
          Action a_viewtoolbar
        • a_zoomin
          Action a_zoomin
        • a_zoomout
          Action a_zoomout
        • ICONPATH
          String ICONPATH
          path for icons
        • m_BayesNet
          EditableBayesNet m_BayesNet
          Container of Bayesian network
        • m_bViewCliques
          boolean m_bViewCliques
        • m_bViewMargins
          boolean m_bViewMargins
          flag indicating whether marginal distributions of each of the nodes should be shown in display.
        • m_clipboard
          weka.classifiers.bayes.net.GUI.ClipBoard m_clipboard
        • m_fScale
          double m_fScale
          current zoom value
        • m_GraphPanel
          weka.classifiers.bayes.net.GUI.GraphPanel m_GraphPanel
          Panel actually displaying the graph
        • m_Instances
          Instances m_Instances
          data selected from file. Used to train a Bayesian network on
        • m_jScrollPane
          JScrollPane m_jScrollPane
          this contains the m_GraphPanel GraphPanel
        • m_jStatusBar
          JLabel m_jStatusBar
          status bar at bottom of window
        • m_jTbTools
          JToolBar m_jTbTools
          toolbar containing buttons at top of window
        • m_jTfNodeHeight
          JTextField m_jTfNodeHeight
          TextField for nodes height
        • m_jTfNodeWidth
          JTextField m_jTfNodeWidth
          TextField for node's width
        • m_jTfZoom
          JTextField m_jTfZoom
          Text field for specifying zoom
        • m_layoutEngine
          LayoutEngine m_layoutEngine
          The current LayoutEngine
        • 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_menuBar
          JMenuBar m_menuBar
          The menu bar
        • m_nCurrentNode
          int m_nCurrentNode
          node currently selected through right clicking
        • m_nNodeHeight
          int m_nNodeHeight
          standard width of node
        • m_nNodeWidth
          int m_nNodeWidth
        • m_nPaddedNodeWidth
          int m_nPaddedNodeWidth
        • m_nSelectedRect
          Rectangle m_nSelectedRect
          selection rectangle drawn through dragging with left mouse button
        • m_nZoomPercents
          int[] m_nZoomPercents
          used when using zoomIn and zoomOut buttons
        • m_Selection
          weka.classifiers.bayes.net.GUI.Selection m_Selection
          selection of nodes
        • m_sFileName
          String m_sFileName
          String containing file name storing current network
    • Class weka.classifiers.bayes.net.MarginCalculator

      class MarginCalculator extends Object implements Serializable
      serialVersionUID:
      650278019241175534L
    • Class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode

      class JunctionTreeNode extends 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_children
          Vector m_children
        • m_fi
          double[] m_fi
          potentials for first network
        • m_MarginalP
          double[][] m_MarginalP
        • m_nCardinality
          int m_nCardinality
          cardinality of the instances of variables in this junction node
        • m_nNodes
          int[] m_nNodes
          nodes of the Bayes net in this junction node
        • m_P
          double[] m_P
          distribution over this junction node according to first Bayes network
        • m_parentSeparator
          MarginCalculator.JunctionTreeSeparator m_parentSeparator
    • Class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator

      class JunctionTreeSeparator extends Object implements Serializable
      serialVersionUID:
      6502780192411755343L
    • Class weka.classifiers.bayes.net.ParentSet

      class ParentSet extends Object implements Serializable
      serialVersionUID:
      4155021284407181838L
      • Serialized Fields

        • m_nCardinalityOfParents
          int m_nCardinalityOfParents
          Holds cardinality of parents (= number of instantiations the parents can take)
        • m_nNrOfParents
          int m_nNrOfParents
          Holds number of parents
        • m_nParents
          int[] m_nParents
          Holds indexes of parents
    • Class weka.classifiers.bayes.net.VaryNode

      class VaryNode extends Object implements Serializable
      serialVersionUID:
      -6196294370675872424L
      • Serialized Fields

        • m_ADNodes
          ADNode[] m_ADNodes
          list of ADNode children
        • m_iNode
          int m_iNode
          index of the node varied
        • m_nMCV
          int m_nMCV
          most common value
  • Package weka.classifiers.bayes.net.estimate

  • Package weka.classifiers.bayes.net.search

    • Class weka.classifiers.bayes.net.search.SearchAlgorithm

      class SearchAlgorithm extends Object implements Serializable
      serialVersionUID:
      6164792240778525312L
      • Serialized Fields

        • 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.
        • m_nMaxNrOfParents
          int m_nMaxNrOfParents
          Holds upper bound on number of parents
  • Package weka.classifiers.bayes.net.search.ci

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

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

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

  • Package weka.classifiers.evaluation

    • Class weka.classifiers.evaluation.ConfusionMatrix

      class ConfusionMatrix extends Matrix implements Serializable
      serialVersionUID:
      -181789981401504090L
      • Serialized Fields

        • m_ClassNames
          String[] m_ClassNames
          Stores the names of the classes
    • Class weka.classifiers.evaluation.NominalPrediction

      class NominalPrediction extends Object implements Serializable
      serialVersionUID:
      -8871333992740492788L
      • Serialized Fields

        • m_Actual
          double m_Actual
          The actual class value
        • m_Distribution
          double[] m_Distribution
          The predicted probabilities
        • 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

      class NumericPrediction extends 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

      class GaussianProcesses extends Classifier implements Serializable
      serialVersionUID:
      -8620066949967678545L
      • Serialized Fields

        • m_Alin
          double m_Alin
          The parameters of the linear transforamtion realized by the filter on the class attribute
        • m_avg_target
          double m_avg_target
          The training data.
        • m_Blin
          double m_Blin
        • m_C
          Matrix m_C
          The covariance matrix.
        • 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_classIndex
          int m_classIndex
          The class index from the training data
        • m_delta
          double m_delta
          Gaussian Noise Value.
        • m_Filter
          Filter m_Filter
          The filter used to standardize/normalize all values.
        • m_filterType
          int m_filterType
          Whether to normalize/standardize/neither
        • m_kernel
          Kernel m_kernel
          Kernel to use
        • m_KernelIsLinear
          boolean m_KernelIsLinear
          whether the kernel is a linear one
        • m_Missing
          ReplaceMissingValues m_Missing
          The filter used to get rid of missing values.
        • m_NominalToBinary
          NominalToBinary m_NominalToBinary
          The filter used to make attributes numeric.
        • m_NumTrain
          int m_NumTrain
          The number of training instances
        • m_t
          Matrix m_t
          The vector of target values.
    • Class weka.classifiers.functions.IsotonicRegression

      class 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_minMsq
          double m_minMsq
          The minimum mean squared error that has been achieved.
        • m_values
          double[] m_values
          The predicted value in each interval.
        • m_ZeroR
          Classifier m_ZeroR
          a ZeroR model in case no model can be built from the data
    • Class weka.classifiers.functions.LeastMedSq

      class LeastMedSq extends Classifier implements Serializable
      serialVersionUID:
      4288954049987652970L
      • Serialized Fields

        • m_bestMedian
          double m_bestMedian
        • m_bestRegression
          LinearRegression m_bestRegression
        • m_currentRegression
          LinearRegression m_currentRegression
        • m_Data
          Instances m_Data
        • m_debug
          boolean m_debug
        • m_israndom
          boolean m_israndom
        • m_ls
          LinearRegression m_ls
        • m_MissingFilter
          ReplaceMissingValues m_MissingFilter
        • m_random
          Random m_random
        • m_randomseed
          long m_randomseed
        • m_Residuals
          double[] m_Residuals
        • m_RLSData
          Instances m_RLSData
        • m_samples
          int m_samples
        • m_samplesize
          int m_samplesize
        • m_scalefactor
          double m_scalefactor
        • m_SplitFilter
          RemoveRange m_SplitFilter
        • m_SSR
          double m_SSR
        • m_SubSample
          Instances m_SubSample
        • m_TransformFilter
          NominalToBinary m_TransformFilter
        • m_weight
          double[] m_weight
    • Class weka.classifiers.functions.LibLINEAR

      class LibLINEAR extends Classifier implements Serializable
      serialVersionUID:
      230504711L
      • Serialized Fields

        • m_Bias
          double m_Bias
          bias term value
        • m_Cost
          double m_Cost
          cost Parameter C
        • m_eps
          double m_eps
          stopping criteria
        • m_Filter
          Filter m_Filter
          for normalizing the data
        • m_Model
          Object m_Model
          LibLINEAR Model
        • m_nominalToBinary
          boolean m_nominalToBinary
          If true, the nominal to binary filter is applied
        • m_NominalToBinary
          NominalToBinary m_NominalToBinary
          The filter used to make attributes numeric.
        • m_noReplaceMissingValues
          boolean m_noReplaceMissingValues
          If true, the replace missing values filter is not applied
        • m_Normalize
          boolean m_Normalize
          normalize input data
        • 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_SVMType
          int m_SVMType
          the SVM solver type
        • m_Weight
          double[] m_Weight
        • m_WeightLabel
          int[] m_WeightLabel
    • Class weka.classifiers.functions.LibSVM

      class LibSVM extends RandomizableClassifier implements Serializable
      serialVersionUID:
      14172L
      • Serialized Fields

        • m_CacheSize
          double m_CacheSize
          in MB
        • m_Coef0
          double m_Coef0
          for poly/sigmoid
        • m_Cost
          double m_Cost
          cost, for C_SVC, EPSILON_SVR and NU_SVR
        • 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_eps
          double m_eps
          stopping criteria
        • m_Filter
          Filter m_Filter
          for normalizing the data
        • m_Gamma
          double m_Gamma
          for poly/rbf/sigmoid
        • m_GammaActual
          double m_GammaActual
          for poly/rbf/sigmoid (the actual gamma)
        • m_KernelType
          int m_KernelType
          the kernel type
        • m_Loss
          double m_Loss
          loss, for EPSILON_SVR
        • m_Model
          Object m_Model
          LibSVM Model
        • m_NominalToBinary
          Filter m_NominalToBinary
          for converting mult-valued nominal attributes to binary
        • m_noReplaceMissingValues
          boolean m_noReplaceMissingValues
          If true, the replace missing values filter is not applied
        • m_Normalize
          boolean m_Normalize
          normalize input data
        • m_nu
          double m_nu
          for NU_SVC, ONE_CLASS, and NU_SVR
        • 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_Shrinking
          boolean m_Shrinking
          use the shrinking heuristics
        • m_SVMType
          int m_SVMType
          the SVM type
        • m_Weight
          double[] m_Weight
          for C_SVC
        • m_WeightLabel
          int[] m_WeightLabel
          for C_SVC
    • Class weka.classifiers.functions.LinearRegression

      class LinearRegression extends Classifier implements Serializable
      serialVersionUID:
      -3364580862046573747L
      • Serialized Fields

        • b_Debug
          boolean b_Debug
          True if debug output will be printed
        • m_AttributeSelection
          int m_AttributeSelection
          The current attribute selection method
        • m_checksTurnedOff
          boolean m_checksTurnedOff
          Turn off all checks and conversions?
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • m_ClassMean
          double m_ClassMean
          The mean of the class attribute
        • m_ClassStdDev
          double m_ClassStdDev
          The standard deviations of the class attribute
        • m_Coefficients
          double[] m_Coefficients
          Array for storing coefficients of linear regression.
        • m_EliminateColinearAttributes
          boolean m_EliminateColinearAttributes
          Try to eliminate correlated attributes?
        • m_Means
          double[] m_Means
          The attributes means
        • m_MissingFilter
          ReplaceMissingValues m_MissingFilter
          The filter for removing missing values.
        • m_Ridge
          double m_Ridge
          The ridge parameter
        • m_SelectedAttributes
          boolean[] m_SelectedAttributes
          Which attributes are relevant?
        • m_StdDevs
          double[] m_StdDevs
          The attribute standard deviations
        • m_TransformedData
          Instances m_TransformedData
          Variable for storing transformed training data.
        • m_TransformFilter
          NominalToBinary m_TransformFilter
          The filter storing the transformation from nominal to binary attributes.
    • Class weka.classifiers.functions.Logistic

      class Logistic extends Classifier implements Serializable
      serialVersionUID:
      3932117032546553727L
      • Serialized Fields

        • m_AttFilter
          RemoveUseless m_AttFilter
          An attribute filter
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • m_Data
          double[][] m_Data
          The data saved as a matrix
        • 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_NominalToBinary
          NominalToBinary m_NominalToBinary
          The filter used to make attributes numeric.
        • m_NumClasses
          int m_NumClasses
          The number of the class labels
        • m_NumPredictors
          int m_NumPredictors
          The number of attributes in the model
        • m_Par
          double[][] m_Par
          The coefficients (optimized parameters) of the model
        • m_ReplaceMissingValues
          ReplaceMissingValues m_ReplaceMissingValues
          The filter used to get rid of missing values.
        • m_Ridge
          double m_Ridge
          The ridge parameter.
        • m_structure
          Instances m_structure
    • Class weka.classifiers.functions.MultilayerPerceptron

      class MultilayerPerceptron extends Classifier implements Serializable
      serialVersionUID:
      -5990607817048210779L
      • Serialized Fields

        • m_accepted
          boolean m_accepted
          a flag to state that the network should be accepted the way it is.
        • m_attributeBases
          double[] m_attributeBases
          The base values for all the attributes.
        • m_attributeRanges
          double[] m_attributeRanges
          The ranges for all the attributes.
        • m_autoBuild
          boolean m_autoBuild
          A flag to tell the build classifier to automatically build a neural net.
        • m_controlPanel
          weka.classifiers.functions.MultilayerPerceptron.ControlPanel m_controlPanel
          The control panel.
        • m_currentInstance
          Instance m_currentInstance
          The current instance running through the network.
        • m_decay
          boolean m_decay
          This flag states that the user wants the learning rate to decay.
        • m_driftThreshold
          int m_driftThreshold
          The number to to use to quit on validation testing.
        • 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_graphers
          FastVector m_graphers
          A Vector list of the graphers.
        • 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_hiddenLayers
          String m_hiddenLayers
          The string that defines the hidden layers
        • m_inputs
          weka.classifiers.functions.MultilayerPerceptron.NeuralEnd[] m_inputs
          The input units.(only feeds the inputs does no calcs)
        • m_instances
          Instances m_instances
          The training instances.
        • m_learningRate
          double m_learningRate
          This is the learning rate for the network.
        • m_linearUnit
          LinearUnit m_linearUnit
          This is a linear unit.
        • m_momentum
          double m_momentum
          This is the momentum for the network.
        • m_neuralNodes
          NeuralConnection[] m_neuralNodes
          All the nodes that actually comprise the logical neural net.
        • m_nextId
          int m_nextId
          The next id number available for default naming.
        • m_nodePanel
          weka.classifiers.functions.MultilayerPerceptron.NodePanel m_nodePanel
          The panel the nodes are displayed on.
        • m_nominalToBinaryFilter
          NominalToBinary m_nominalToBinaryFilter
          The actual filter.
        • m_normalizeAttributes
          boolean m_normalizeAttributes
          This flag states that the user wants the input values normalized.
        • 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_numAttributes
          int m_numAttributes
          The number of attributes.
        • m_numClasses
          int m_numClasses
          The number of classes.
        • m_numEpochs
          int m_numEpochs
          The number of epochs to train through.
        • m_numeric
          boolean m_numeric
          A flag to say that it's a numeric class.
        • m_outputs
          weka.classifiers.functions.MultilayerPerceptron.NeuralEnd[] m_outputs
          The output units.(only feeds the errors, does no calcs)
        • m_random
          Random m_random
          The actual random number generator.
        • m_randomSeed
          int m_randomSeed
          The number used to seed the random number generator.
        • 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_selected
          FastVector m_selected
          A Vector list of the units currently selected.
        • m_sigmoidUnit
          SigmoidUnit m_sigmoidUnit
          this is a sigmoid unit.
        • 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_useDefaultModel
          boolean m_useDefaultModel
          Whether to use the default ZeroR model
        • m_useNomToBin
          boolean m_useNomToBin
          A flag to state that a nominal to binary filter should be used.
        • m_valSize
          int m_valSize
          An int to say how big the validation set should be.
        • m_win
          JFrame m_win
          The window for the network.
        • 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
    • Class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd

      class NeuralEnd extends NeuralConnection implements Serializable
      serialVersionUID:
      7305185603191183338L
      • Serialized Fields

        • m_input
          boolean m_input
          True if node is an input, False if it's an output.
        • 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.
    • Class weka.classifiers.functions.PaceRegression

      class PaceRegression extends Classifier implements Serializable
      serialVersionUID:
      7230266976059115435L
      • Serialized Fields

        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • m_Coefficients
          double[] m_Coefficients
          Array for storing coefficients of linear regression.
        • m_Debug
          boolean m_Debug
          True if debug output will be printed
        • m_Model
          Instances m_Model
          The model used
        • olscThreshold
          double olscThreshold
        • paceEstimator
          int paceEstimator
          the estimator
    • Class weka.classifiers.functions.PLSClassifier

      class PLSClassifier extends Classifier implements Serializable
      serialVersionUID:
      4819775160590973256L
      • Serialized Fields

        • m_ActualFilter
          PLSFilter m_ActualFilter
          the actual filter to use
        • m_Filter
          PLSFilter m_Filter
          the PLS filter
    • Class weka.classifiers.functions.RBFNetwork

      class RBFNetwork extends Classifier implements Serializable
      serialVersionUID:
      -3669814959712675720L
      • Serialized Fields

        • m_basisFilter
          ClusterMembership m_basisFilter
          The filter for producing the meta data
        • m_clusteringSeed
          int m_clusteringSeed
          The seed to pass on to K-means
        • m_linear
          LinearRegression m_linear
          The linear regression for numeric problems
        • m_logistic
          Logistic m_logistic
          The logistic regression for classification problems
        • m_maxIts
          int m_maxIts
          The maximum number of iterations for logistic regression.
        • m_minStdDev
          double m_minStdDev
          The minimum standard deviation
        • 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_standardize
          Standardize m_standardize
          Filter used for normalizing the data
        • m_ZeroR
          Classifier m_ZeroR
          a ZeroR model in case no model can be built from the data
    • Class weka.classifiers.functions.SimpleLinearRegression

      class 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_intercept
          double m_intercept
          The intercept
        • m_slope
          double m_slope
          The slope
        • m_suppressErrorMessage
          boolean m_suppressErrorMessage
          If true, suppress error message if no useful attribute was found
    • Class weka.classifiers.functions.SimpleLogistic

      class SimpleLogistic extends Classifier implements Serializable
      serialVersionUID:
      7397710626304705059L
      • Serialized Fields

        • m_boostedModel
          LogisticBase m_boostedModel
          The actual logistic regression model
        • m_errorOnProbabilities
          boolean m_errorOnProbabilities
          If true, use minimize error on probabilities instead of misclassification error
        • m_heuristicStop
          int m_heuristicStop
          Parameter for the heuristic for early stopping of LogitBoost
        • m_maxBoostingIterations
          int m_maxBoostingIterations
          Maximum number of iterations for LogitBoost
        • m_NominalToBinary
          NominalToBinary m_NominalToBinary
          Filter for converting nominal attributes to binary ones
        • m_numBoostingIterations
          int m_numBoostingIterations
          If non-negative, use this as fixed number of LogitBoost iterations
        • m_ReplaceMissingValues
          ReplaceMissingValues m_ReplaceMissingValues
          Filter for replacing missing values
        • m_useAIC
          boolean m_useAIC
          If true, the AIC is used to choose the best iteration
        • m_useCrossValidation
          boolean m_useCrossValidation
          If true, cross-validate number of LogitBoost iterations
        • 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.functions.SMO

      class SMO extends Classifier implements Serializable
      serialVersionUID:
      -6585883636378691736L
      • Serialized Fields

        • m_C
          double m_C
          The complexity parameter.
        • 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_classAttribute
          Attribute m_classAttribute
          The class attribute
        • m_classifiers
          SMO.BinarySMO[][] m_classifiers
          The binary classifier(s)
        • m_classIndex
          int m_classIndex
          The class index from the training data
        • m_eps
          double m_eps
          Epsilon for rounding.
        • m_Filter
          Filter m_Filter
          The filter used to standardize/normalize all values.
        • m_filterType
          int m_filterType
          Whether to normalize/standardize/neither
        • m_fitLogisticModels
          boolean m_fitLogisticModels
          Whether logistic models are to be fit
        • m_kernel
          Kernel m_kernel
          the kernel to use
        • m_KernelIsLinear
          boolean m_KernelIsLinear
          whether the kernel is a linear one
        • m_Missing
          ReplaceMissingValues m_Missing
          The filter used to get rid of missing values.
        • m_NominalToBinary
          NominalToBinary m_NominalToBinary
          The filter used to make attributes numeric.
        • m_numFolds
          int m_numFolds
          The number of folds for the internal cross-validation
        • m_randomSeed
          int m_randomSeed
          The random number seed
        • m_tol
          double m_tol
          Tolerance for accuracy of result.
    • Class weka.classifiers.functions.SMO.BinarySMO

      class BinarySMO extends 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_class
          double[] m_class
          The transformed class values.
        • m_data
          Instances m_data
          The training data.
        • m_errors
          double[] m_errors
          The current set of errors for all non-bound examples.
        • m_I0
          SMOset m_I0
          {i: 0 invalid input: '<' m_alpha[i] invalid input: '<' 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_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_kernel
          Kernel m_kernel
          Kernel to use
        • m_logistic
          Logistic m_logistic
          Stores logistic regression model for probability estimate
        • m_sparseIndices
          int[] m_sparseIndices
        • m_sparseWeights
          double[] m_sparseWeights
          Variables to hold weight vector in sparse form. (To reduce storage requirements.)
        • m_sumOfWeights
          double m_sumOfWeights
          Stores the weight of the training instances
        • m_supportVectors
          SMOset m_supportVectors
          The set of support vectors
        • m_weights
          double[] m_weights
          Weight vector for linear machine.
    • Class weka.classifiers.functions.SMOreg

      class SMOreg extends Classifier implements Serializable
      serialVersionUID:
      -7149606251113102827L
      • Serialized Fields

        • m_C
          double m_C
          capacity 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_kernel
          Kernel m_kernel
          the configured kernel
        • m_Missing
          ReplaceMissingValues m_Missing
          The filter used to get rid of missing values.
        • m_NominalToBinary
          NominalToBinary m_NominalToBinary
          The filter used to make attributes numeric.
        • m_onlyNumeric
          boolean m_onlyNumeric
          Only numeric attributes in the dataset? If so, less need to filter
        • m_optimizer
          RegOptimizer m_optimizer
          contains the algorithm used for learning
        • m_x0
          double m_x0
        • m_x1
          double m_x1
          coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0
    • Class weka.classifiers.functions.SPegasos

      class SPegasos extends Classifier implements Serializable
      serialVersionUID:
      -3732968666673530290L
      • Serialized Fields

        • m_data
          Instances m_data
          Holds the header of the training data
        • 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_epochs
          int m_epochs
          The number of epochs to perform (batch learning). Total iterations is m_epochs * num instances
        • m_lambda
          double m_lambda
          The regularization parameter
        • m_loss
          int m_loss
          The current loss function to minimize
        • m_nominalToBinary
          NominalToBinary m_nominalToBinary
          Convert nominal attributes to numerically coded binary ones
        • m_normalize
          Normalize m_normalize
          Normalize the training data
        • m_replaceMissing
          ReplaceMissingValues m_replaceMissing
          Replace missing values
        • m_t
          double m_t
          Holds the current iteration number
        • m_weights
          double[] m_weights
          Stores the weights (+ bias in the last element)
    • Class weka.classifiers.functions.VotedPerceptron

      class VotedPerceptron extends Classifier implements Serializable
      serialVersionUID:
      -1072429260104568698L
      • Serialized Fields

        • m_Additions
          int[] m_Additions
          The training instances added to the perceptron
        • m_Exponent
          double m_Exponent
          The exponent
        • m_IsAddition
          boolean[] m_IsAddition
          Addition or subtraction?
        • m_K
          int m_K
          The actual number of alterations
        • m_MaxK
          int m_MaxK
          The maximum number of alterations to the perceptron
        • m_NominalToBinary
          NominalToBinary m_NominalToBinary
          The filter used to make attributes numeric.
        • m_NumIterations
          int m_NumIterations
          The number of iterations
        • m_ReplaceMissingValues
          ReplaceMissingValues m_ReplaceMissingValues
          The filter used to get rid of missing values.
        • m_Seed
          int m_Seed
          Seed used for shuffling the dataset
        • m_Train
          Instances m_Train
          The training instances
        • m_Weights
          int[] m_Weights
          The weights for each perceptron
    • Class weka.classifiers.functions.Winnow

      class Winnow extends Classifier implements Serializable
      serialVersionUID:
      3543770107994321324L
      • Serialized Fields

        • m_actualThreshold
          double m_actualThreshold
          The true threshold used for prediction
        • m_Alpha
          double m_Alpha
          The promotion coefficient
        • m_Balanced
          boolean m_Balanced
          Use the balanced variant?
        • m_Beta
          double m_Beta
          The demotion coefficient
        • m_defaultWeight
          double m_defaultWeight
          Starting weights for the prediction vector(s)
        • m_Mistakes
          int m_Mistakes
          Accumulated mistake count (for statistics)
        • m_NominalToBinary
          NominalToBinary m_NominalToBinary
          The filter used to make attributes numeric.
        • m_numIterations
          int m_numIterations
          The number of iterations
        • m_predNegVector
          double[] m_predNegVector
          The weight vector for prediction (neg)
        • m_predPosVector
          double[] m_predPosVector
          The weight vector for prediction (pos)
        • m_ReplaceMissingValues
          ReplaceMissingValues m_ReplaceMissingValues
          The filter used to get rid of missing values.
        • m_Seed
          int m_Seed
          Random seed used for shuffling the dataset, -1 == disable
        • m_Threshold
          double m_Threshold
          Prediction threshold, invalid input: '<'0 == numAttributes
        • m_Train
          Instances m_Train
          The training instances
  • Package weka.classifiers.functions.neural

    • Class weka.classifiers.functions.neural.LinearUnit

      class LinearUnit extends Object implements Serializable
      serialVersionUID:
      8572152807755673630L
    • Class weka.classifiers.functions.neural.NeuralConnection

      class NeuralConnection extends Object implements Serializable
      serialVersionUID:
      -286208828571059163L
      • Serialized Fields

        • m_id
          String m_id
          The string that uniquely (provided naming is done properly) identifies this unit.
        • m_inputList
          NeuralConnection[] m_inputList
          The list of inputs to this unit.
        • m_inputNums
          int[] m_inputNums
          The numbering for the connections at the other end of the input lines.
        • m_numInputs
          int m_numInputs
          The number of inputs.
        • m_numOutputs
          int m_numOutputs
          The number of outputs.
        • m_outputList
          NeuralConnection[] m_outputList
          The list of outputs from this unit.
        • m_outputNums
          int[] m_outputNums
          The numbering for the connections at the other end of the out lines.
        • m_type
          int m_type
          The type of unit this is.
        • m_unitError
          double m_unitError
          The error value for this unit, NaN if not calculated.
        • m_unitValue
          double m_unitValue
          The output value for this unit, NaN if not calculated.
        • m_weightsUpdated
          boolean m_weightsUpdated
          True if the weights have already been updated.
        • 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

      class NeuralNode extends NeuralConnection implements Serializable
      serialVersionUID:
      -1085750607680839163L
      • Serialized Fields

        • 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_methods
          NeuralMethod m_methods
          Performs the operations for this node. Currently this defines that the node is either a sigmoid or a linear unit.
        • m_random
          Random m_random
        • m_weights
          double[] m_weights
          The weights for each of the input connections, and the threshold.
    • Class weka.classifiers.functions.neural.SigmoidUnit

      class SigmoidUnit extends Object implements Serializable
      serialVersionUID:
      -5162958458177475652L
  • Package weka.classifiers.functions.pace

  • Package weka.classifiers.functions.supportVector

    • Class weka.classifiers.functions.supportVector.CachedKernel

      class CachedKernel extends Kernel implements Serializable
      serialVersionUID:
      702810182699015136L
      • Serialized Fields

        • 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_cacheSlots
          int m_cacheSlots
          number of cache slots in an entry
        • m_kernelEvals
          int m_kernelEvals
          Counts the number of kernel evaluations.
        • m_kernelMatrix
          double[][] m_kernelMatrix
          The kernel matrix if full cache is used (i.e. size is set to 0)
        • m_keys
          long[] m_keys
        • m_numInsts
          int m_numInsts
          The number of instance in the dataset
        • m_storage
          double[] m_storage
          Kernel cache
    • Class weka.classifiers.functions.supportVector.Kernel

      class Kernel extends Object implements Serializable
      serialVersionUID:
      -6102771099905817064L
      • Serialized Fields

        • m_ChecksTurnedOff
          boolean m_ChecksTurnedOff
          Turns off all checks
        • m_data
          Instances m_data
          The dataset
        • m_Debug
          boolean m_Debug
          enables debugging output
    • Class weka.classifiers.functions.supportVector.NormalizedPolyKernel

      class NormalizedPolyKernel extends PolyKernel implements Serializable
      serialVersionUID:
      1248574185532130851L
    • Class weka.classifiers.functions.supportVector.PolyKernel

      class PolyKernel extends CachedKernel implements Serializable
      serialVersionUID:
      -321831645846363201L
      • Serialized Fields

        • m_exponent
          double m_exponent
          The exponent for the polynomial kernel.
        • m_lowerOrder
          boolean m_lowerOrder
          Use lower-order terms?
    • Class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel

      class PrecomputedKernelMatrixKernel extends Kernel implements Serializable
      serialVersionUID:
      -321831645846363333L
      • Serialized Fields

        • m_Counter
          int m_Counter
          A classifier counter.
        • m_KernelMatrix
          Matrix m_KernelMatrix
          The kernel matrix.
        • m_KernelMatrixFile
          File m_KernelMatrixFile
          The file holding the kernel matrix.
    • Class weka.classifiers.functions.supportVector.Puk

      class Puk extends CachedKernel implements Serializable
      serialVersionUID:
      1682161522559978851L
      • Serialized Fields

        • m_factor
          double m_factor
          Cached factor for the Puk kernel.
        • 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.
    • Class weka.classifiers.functions.supportVector.RBFKernel

      class RBFKernel extends CachedKernel implements Serializable
      serialVersionUID:
      5247117544316387852L
      • Serialized Fields

        • m_gamma
          double m_gamma
          Gamma for the RBF kernel.
        • m_kernelPrecalc
          double[] m_kernelPrecalc
          The precalculated dotproducts of <inst_i,inst_i>
    • Class weka.classifiers.functions.supportVector.RegOptimizer

      class RegOptimizer extends 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_bModelBuilt
          boolean m_bModelBuilt
          flag to indicate whether the model is built yet
        • m_C
          double m_C
          capacity parameter, copied from SMOreg
        • m_classIndex
          int m_classIndex
          index of class variable in data set
        • m_data
          Instances m_data
          points to data set
        • m_epsilon
          double m_epsilon
          epsilon of epsilon-insensitive cost function
        • m_kernel
          Kernel m_kernel
          the kernel
        • m_nCacheHits
          int m_nCacheHits
          number of kernel cache hits, used for printing statistics only
        • m_nEvals
          long m_nEvals
          number of kernel evaluations, used for printing statistics only
        • m_nInstances
          int m_nInstances
          number of instances in data set
        • m_nSeed
          int m_nSeed
          seed for initializing random number generator
        • m_random
          Random m_random
          random number generator
        • m_sparseIndices
          int[] m_sparseIndices
        • m_sparseWeights
          double[] m_sparseWeights
          Variables to hold weight vector in sparse form. (To reduce storage requirements.)
        • m_supportVectors
          SMOset m_supportVectors
          set of support vectors, that is, vectors with alpha(*)!=0
        • m_SVM
          SMOreg m_SVM
          parent SMOreg class
        • m_target
          double[] m_target
          class values/desired output vector
        • m_weights
          double[] m_weights
          weights for linear kernel
    • Class weka.classifiers.functions.supportVector.RegSMO

      class RegSMO extends RegOptimizer implements Serializable
      serialVersionUID:
      -7504070793279598638L
      • Serialized Fields

        • 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
        • 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
    • Class weka.classifiers.functions.supportVector.RegSMOImproved

      class RegSMOImproved extends RegSMO implements Serializable
      serialVersionUID:
      471692841446029784L
      • Serialized Fields

        • m_bLow
          double m_bLow
          b.up and b.low boundaries used to determine stopping criterion
        • m_bUp
          double m_bUp
          b.up and b.low boundaries used to determine stopping criterion
        • m_bUseVariant1
          boolean m_bUseVariant1
          set true to use variant 1 of the paper, otherwise use variant 2
        • m_fTolerance
          double m_fTolerance
          tolerance parameter used for checking stopping criterion b.up invalid input: '<' b.low + 2 tol
        • m_I0
          SMOset m_I0
          The different sets used by the algorithm.
        • m_iLow
          int m_iLow
          index of the instance that gave us b.up and b.low
        • m_iSet
          int[] m_iSet
          Index set {i: 0 invalid input: '<' m_alpha[i] invalid input: '<' C || 0 invalid input: '<' m_alphaStar[i] invalid input: '<' C}}
        • m_iUp
          int m_iUp
          index of the instance that gave us b.up and b.low
    • Class weka.classifiers.functions.supportVector.SMOset

      class SMOset extends Object implements Serializable
      serialVersionUID:
      -8364829283188675777L
      • Serialized Fields

        • 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_number
          int m_number
          The current number of elements in the set
        • m_previous
          int[] m_previous
          The previous element for each element
    • Class weka.classifiers.functions.supportVector.StringKernel

      class StringKernel extends Kernel implements Serializable
      serialVersionUID:
      -4902954211202690123L
      • Serialized Fields

        • cachekh
          double[] cachekh
        • cachekh2
          double[] cachekh2
        • cachekh2K
          int[] cachekh2K
        • cachekhK
          int[] cachekhK
        • 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_kernelEvals
          int m_kernelEvals
          Counts the number of kernel evaluations.
        • m_keys
          long[] m_keys
        • m_lambda
          double m_lambda
          the decay factor that penalizes non-continuous substring matches. See [1] for details.
        • m_maxSubsequenceLength
          int m_maxSubsequenceLength
          The maximum substring length for lambda pruning
        • 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_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
        • m_numInsts
          int m_numInsts
          The number of instance in the dataset
        • m_powersOflambda
          double[] m_powersOflambda
          the precalculated powers of lambda
        • m_PruningMethod
          int m_PruningMethod
          the pruning method
        • m_storage
          double[] m_storage
          Kernel cache (i.e., cache for kernel evaluations)
        • m_strAttr
          int m_strAttr
          The attribute number of the string attribute
        • m_subsequenceLength
          int m_subsequenceLength
          The substring length
        • m_useRecursionCache
          boolean m_useRecursionCache
        • maxCache
          int maxCache
          private cache for intermediate results
  • Package weka.classifiers.lazy

    • Class weka.classifiers.lazy.IB1

      class IB1 extends Classifier implements Serializable
      serialVersionUID:
      -6152184127304895851L
      • Serialized Fields

        • m_MaxArray
          double[] m_MaxArray
          The maximum values for numeric attributes.
        • m_MinArray
          double[] m_MinArray
          The minimum values for numeric attributes.
        • m_Train
          Instances m_Train
          The training instances used for classification.
    • Class weka.classifiers.lazy.IBk

      class IBk extends Classifier implements Serializable
      serialVersionUID:
      -3080186098777067172L
      • Serialized Fields

        • m_ClassType
          int m_ClassType
          The class attribute type.
        • m_CrossValidate
          boolean m_CrossValidate
          Whether to select k by cross validation.
        • m_defaultModel
          ZeroR m_defaultModel
          Default ZeroR model to use when there are no training instances
        • m_DistanceWeighting
          int m_DistanceWeighting
          Whether the neighbours should be distance-weighted.
        • 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_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_NumClasses
          int m_NumClasses
          The number of class values (or 1 if predicting numeric).
        • m_Train
          Instances m_Train
          The training instances used for classification.
        • 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.
    • Class weka.classifiers.lazy.KStar

      class KStar extends Classifier implements Serializable
      serialVersionUID:
      332458330800479083L
      • Serialized Fields

        • m_BlendMethod
          int m_BlendMethod
          0 = use specified blend, 1 = entropic blend setting
        • m_Cache
          KStarCache[] m_Cache
          A custom data structure for caching distinct attribute values and their scale factor or stop parameter.
        • m_ClassType
          int m_ClassType
          The class attribute type
        • m_ComputeRandomCols
          int m_ComputeRandomCols
          Flag turning on and off the computation of random class colomns
        • m_GlobalBlend
          int m_GlobalBlend
          default sphere of influence blend setting
        • m_InitFlag
          int m_InitFlag
          Flag turning on and off the initialisation of config variables
        • m_MissingMode
          int m_MissingMode
          missing value treatment
        • m_NumAttributes
          int m_NumAttributes
          The number of attributes
        • m_NumClasses
          int m_NumClasses
          The number of class values
        • m_NumInstances
          int m_NumInstances
          The number of instances in the dataset
        • m_RandClassCols
          int[][] m_RandClassCols
          Table of random class value colomns
        • m_Train
          Instances m_Train
          The training instances used for classification.
    • Class weka.classifiers.lazy.LBR

      class LBR extends Classifier implements Serializable
      serialVersionUID:
      5648559277738985156L
      • Serialized Fields

        • bestCnt
          int bestCnt
        • forCnt
          int forCnt
        • leftHand
          ArrayList leftHand
          best attribute's index list. maybe as output result
        • m_Counts
          int[][][] m_Counts
          All the counts for nominal attributes.
        • m_ErrorFlags
          boolean[] m_ErrorFlags
          leave-one-out error flags on the training dataaet.
        • m_Errors
          int m_Errors
          leave-one-out errors on the training dataset.
        • m_Instances
          Instances m_Instances
          The set of instances used for current training.
        • m_NCV
          boolean m_NCV
          for printing in n-fold cross validation
        • m_numAtts
          int m_numAtts
          number of attributes for the dataset
        • 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_numClasses
          int m_numClasses
          number of classes for dataset
        • m_numInsts
          int m_numInsts
          number of instances in dataset
        • m_Priors
          int[] m_Priors
          The prior probabilities of the classes.
        • m_RemainderErrors
          int m_RemainderErrors
          the number of instances to be classified incorrectly besides the subset.
        • m_subInstances
          LBR.Indexes m_subInstances
          index of instances and attributes for the given dataset
        • m_subOldErrorFlags
          boolean[] m_subOldErrorFlags
          following is defined by wangzh, the number of instances to be classified incorrectly on the subset.
        • m_tCounts
          int[][][] m_tCounts
          All the counts for nominal attributes.
        • m_tPriors
          int[] m_tPriors
          The prior probabilities of the classes.
        • posteriorsArray
          double[] posteriorsArray
          probability values array
        • tempCnt
          int tempCnt
        • tempSubInstances
          LBR.Indexes tempSubInstances
          index of instances and attributes for the given dataset
        • whileCnt
          int whileCnt
    • Class weka.classifiers.lazy.LBR.Indexes

      class Indexes extends Object implements Serializable
      serialVersionUID:
      -2771490019751421307L
      • Serialized Fields

        • m_AttIndexes
          boolean[] m_AttIndexes
          the array attribute indexes
        • m_ClassIndex
          int m_ClassIndex
          the Class Index for the data set
        • m_InstIndexes
          boolean[] m_InstIndexes
          the array instance indexes
        • m_NumAtts
          int m_NumAtts
          the number of attributes indexed
        • m_NumAttsSet
          int m_NumAttsSet
          the number of attributes "in use" or set to a the original value (true or false)
        • m_NumInstances
          int m_NumInstances
          the number of instances indexed
        • m_NumInstsSet
          int m_NumInstsSet
          the number of 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_NumSeqInstsSet
          int m_NumSeqInstsSet
          the number of sequential instances "in use" or set to a the original value (true or false)
        • m_SequentialAttIndex_valid
          boolean m_SequentialAttIndex_valid
          flag to check if sequential array must be rebuilt due to changes to the attribute index
        • 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_SequentialInstIndexes
          int[] m_SequentialInstIndexes
          the array of instance indexes that are set to a either true or false
    • Class weka.classifiers.lazy.LWL

      class LWL extends SingleClassifierEnhancer implements Serializable
      serialVersionUID:
      1979797405383665815L
      • Serialized Fields

        • m_kNN
          int m_kNN
          The number of neighbours used to select the kernel bandwidth.
        • m_NNSearch
          NearestNeighbourSearch m_NNSearch
          The nearest neighbour search algorithm to use. (Default: weka.core.neighboursearch.LinearNNSearch)
        • m_Train
          Instances m_Train
          The training instances used for classification.
        • m_UseAllK
          boolean m_UseAllK
          True if m_kNN should be set to all instances.
        • m_WeightKernel
          int m_WeightKernel
          The weighting kernel method currently selected.
        • 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

      class KStarCache extends Object implements Serializable
      serialVersionUID:
      -7693632394267140678L
    • Class weka.classifiers.lazy.kstar.KStarCache.CacheTable

      class CacheTable extends Object implements Serializable
      serialVersionUID:
      -8086106452588253423L
      • Serialized Fields

        • DEFAULT_LOAD_FACTOR
          float DEFAULT_LOAD_FACTOR
          The default load factor for the hashtable
        • DEFAULT_TABLE_SIZE
          int DEFAULT_TABLE_SIZE
          The default size of the hashtable
        • EPSILON
          double EPSILON
          Accuracy value for equality
        • m_Count
          int m_Count
          The total number of entries in the hash table.
        • m_LoadFactor
          float m_LoadFactor
          The load factor for the hashtable.
        • m_Table
          KStarCache.TableEntry[] m_Table
          The hash table data.
        • m_Threshold
          int m_Threshold
          Rehashes the table when count exceeds this threshold.
    • Class weka.classifiers.lazy.kstar.KStarCache.TableEntry

      class TableEntry extends Object implements Serializable
      serialVersionUID:
      4057602386766259138L
      • Serialized Fields

        • hash
          int hash
          attribute value hash code
        • key
          double key
          attribute value
        • next
          KStarCache.TableEntry next
          next table entry (separate chaining)
        • pmiss
          double pmiss
          transformation probability to missing value
        • value
          double value
          scale factor or stop parameter
  • Package weka.classifiers.meta

    • Class weka.classifiers.meta.AdaBoostM1

      serialVersionUID:
      -7378107808933117974L
      • Serialized Fields

        • m_Betas
          double[] m_Betas
          Array for storing the weights for the votes.
        • m_NumClasses
          int m_NumClasses
          The number of classes
        • m_NumIterationsPerformed
          int m_NumIterationsPerformed
          The number of successfully generated base classifiers.
        • m_UseResampling
          boolean m_UseResampling
          Use boosting with reweighting?
        • m_WeightThreshold
          int m_WeightThreshold
          Weight Threshold. The percentage of weight mass used in training
        • m_ZeroR
          Classifier m_ZeroR
          a ZeroR model in case no model can be built from the data
    • Class weka.classifiers.meta.AdditiveRegression

      class AdditiveRegression extends IteratedSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -2368937577670527151L
      • Serialized Fields

        • m_NumIterationsPerformed
          int m_NumIterationsPerformed
          The number of successfully generated base classifiers.
        • m_shrinkage
          double m_shrinkage
          Shrinkage (Learning rate). Default = no shrinkage.
        • m_SuitableData
          boolean m_SuitableData
          whether we have suitable data or nor (if not, ZeroR model is used)
        • m_zeroR
          ZeroR m_zeroR
          The model for the mean
    • Class weka.classifiers.meta.AttributeSelectedClassifier

      class 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_numAttributesSelected
          double m_numAttributesSelected
          The number of attributes selected by the attribute selection phase
        • m_numClasses
          int m_numClasses
          The number of class vals in the training data (1 if class is numeric)
        • m_ReducedHeader
          Instances m_ReducedHeader
          The header of the dimensionally reduced data
        • m_Search
          ASSearch m_Search
          The search method to use
        • 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

      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

      class ClassificationViaClustering extends Classifier implements Serializable
      serialVersionUID:
      -5687069451420259135L
      • Serialized Fields

        • m_ActualClusterer
          Clusterer m_ActualClusterer
          the actual cluster algorithm being used
        • m_Clusterer
          Clusterer m_Clusterer
          the cluster algorithm used (template)
        • m_ClusteringHeader
          Instances m_ClusteringHeader
          the modified training data header
        • m_ClustersToClasses
          double[] m_ClustersToClasses
          the mapping between clusters and classes
        • m_OriginalHeader
          Instances m_OriginalHeader
          the original training data header
        • m_ZeroR
          Classifier m_ZeroR
          the default model
    • Class weka.classifiers.meta.ClassificationViaRegression

      class ClassificationViaRegression extends SingleClassifierEnhancer implements Serializable
      serialVersionUID:
      4500023123618669859L
      • Serialized Fields

        • m_ClassFilters
          MakeIndicator[] m_ClassFilters
          The filters used to transform the class.
        • m_Classifiers
          Classifier[] m_Classifiers
          The classifiers. (One for each class.)
    • Class weka.classifiers.meta.CostSensitiveClassifier

      class CostSensitiveClassifier extends RandomizableSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -720658209263002404L
      • Serialized Fields

        • m_CostFile
          String m_CostFile
          The name of the cost file, for command line options
        • m_CostMatrix
          CostMatrix m_CostMatrix
          The cost matrix
        • m_MatrixSource
          int m_MatrixSource
          Indicates the current cost matrix source
        • 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)
        • m_OnDemandDirectory
          File m_OnDemandDirectory
          The directory used when loading cost files on demand, null indicates current directory
    • Class weka.classifiers.meta.CVParameterSelection

      class CVParameterSelection extends RandomizableSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -6529603380876641265L
      • Serialized Fields

        • m_BestClassifierOptions
          String[] m_BestClassifierOptions
          The set of all classifier options as determined by cross-validation
        • m_BestPerformance
          double m_BestPerformance
          The cross-validated performance of the best options
        • m_ClassifierOptions
          String[] m_ClassifierOptions
          The base classifier options (not including those being set by cross-validation)
        • m_CVParams
          FastVector m_CVParams
          The set of parameters to cross-validate over
        • m_InitOptions
          String[] m_InitOptions
          The set of all options at initialization time. So that getOptions can return this.
        • m_NumAttributes
          int m_NumAttributes
          The number of attributes in the data
        • m_NumFolds
          int m_NumFolds
          The number of folds used in cross-validation
        • m_TrainFoldSize
          int m_TrainFoldSize
          The number of instances in a training fold
    • Class weka.classifiers.meta.CVParameterSelection.CVParameter

      class CVParameter extends Object implements Serializable
      serialVersionUID:
      -4668812017709421953L
      • Serialized Fields

        • m_AddAtEnd
          boolean m_AddAtEnd
          True if the parameter should be added at the end of the argument list
        • m_Lower
          double m_Lower
          Lower bound for the CV search
        • m_ParamChar
          String m_ParamChar
          Char used to identify the option of interest
        • m_ParamValue
          double m_ParamValue
          The parameter value with the best performance
        • m_RoundParam
          boolean m_RoundParam
          True if the parameter should be rounded to an integer
        • m_Steps
          double m_Steps
          Number of steps during the search
        • m_Upper
          double m_Upper
          Upper bound for the CV search
    • Class weka.classifiers.meta.Dagging

      class 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_Verbose
          boolean m_Verbose
          whether to output some progress information during building
        • m_Vote
          Vote m_Vote
          the classifier used for voting
    • Class weka.classifiers.meta.Decorate

      serialVersionUID:
      -6020193348750269931L
      • Serialized Fields

        • m_ArtSize
          double m_ArtSize
          Amount of artificial/random instances to use - specified as a fraction of the training data size.
        • m_AttributeStats
          Vector m_AttributeStats
          Attribute statistics - used for generating artificial examples.
        • m_Committee
          Vector m_Committee
          Vector of classifiers that make up the committee/ensemble.
        • m_DesiredSize
          int m_DesiredSize
          The desired ensemble size.
        • m_Random
          Random m_Random
          The random number generator.
    • Class weka.classifiers.meta.END

      serialVersionUID:
      -4143242362912214956L
      • Serialized Fields

        • m_hashtable
          Hashtable m_hashtable
          The hashtable containing the classifiers for the END.
    • Class weka.classifiers.meta.FilteredClassifier

      class 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

      class Grading extends Stacking implements Serializable
      serialVersionUID:
      5207837947890081170L
      • Serialized Fields

        • m_InstPerClass
          double[] m_InstPerClass
          InstPerClass
        • m_MetaClassifiers
          Classifier[] m_MetaClassifiers
          The meta classifiers, one for each base classifier.
    • Class weka.classifiers.meta.GridSearch

      class GridSearch extends RandomizableSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -3034773968581595348L
      • Serialized Fields

        • m_BestClassifier
          Classifier m_BestClassifier
          the Classifier with the best setup
        • m_BestFilter
          Filter m_BestFilter
          the Filter with the best setup
        • m_Cache
          weka.classifiers.meta.GridSearch.PerformanceCache m_Cache
          the cache for points in the grid that got calculated
        • m_Data
          Instances m_Data
          the training data
        • m_Evaluation
          int m_Evaluation
          the type of evaluation
        • m_Filter
          Filter m_Filter
          the Filter
        • m_Grid
          weka.classifiers.meta.GridSearch.Grid m_Grid
          the value-pairs grid
        • m_GridExtensionsPerformed
          int m_GridExtensionsPerformed
          the number of extensions performed
        • m_GridIsExtendable
          boolean m_GridIsExtendable
          whether the grid can be extended
        • m_LogFile
          File m_LogFile
          the log file to use
        • m_MaxGridExtensions
          int m_MaxGridExtensions
          maximum number of grid extensions (-1 means unlimited)
        • m_SampleSize
          double m_SampleSize
          the sample size to search the initial grid with
        • m_Traversal
          int m_Traversal
          the traversal
        • m_UniformPerformance
          boolean m_UniformPerformance
          whether all performances in the grid are the same
        • m_Values
          weka.classifiers.meta.GridSearch.PointDouble m_Values
          the best values
        • m_X_Base
          double m_X_Base
          the base for
        • m_X_Expression
          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:
        • m_X_Max
          double m_X_Max
          the maximum of X
        • m_X_Min
          double m_X_Min
          the minimum of X
        • m_X_Property
          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_Step
          double m_X_Step
          the step size of
        • m_Y_Base
          double m_Y_Base
          the base for Y
        • m_Y_Expression
          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:
        • m_Y_Max
          double m_Y_Max
          the maximum of Y
        • m_Y_Min
          double m_Y_Min
          the minimum of Y
        • m_Y_Property
          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_Step
          double m_Y_Step
          the step size of Y
    • Class weka.classifiers.meta.GridSearch.Grid

      class Grid extends Object implements Serializable
      serialVersionUID:
      7290732613611243139L
      • Serialized Fields

        • m_Height
          int m_Height
          the number of points on the Y axis
        • m_LabelX
          String m_LabelX
          the label for the X axis
        • m_LabelY
          String m_LabelY
          the label for the Y axis
        • m_MaxX
          double m_MaxX
          the maximum on the X axis
        • m_MaxY
          double m_MaxY
          the maximum on the Y axis
        • m_MinX
          double m_MinX
          the minimum on the X axis
        • m_MinY
          double m_MinY
          the minimum on the Y axis
        • m_StepX
          double m_StepX
          the step size for the X axis
        • m_StepY
          double m_StepY
          the step size for the Y axis
        • m_Width
          int m_Width
          the number of points on the X axis
    • Class weka.classifiers.meta.GridSearch.Performance

      class Performance extends Object implements Serializable
      serialVersionUID:
      -4374706475277588755L
      • Serialized Fields

        • m_ACC
          double m_ACC
          the Accuracy
        • m_CC
          double m_CC
          the Correlation coefficient
        • m_Kappa
          double m_Kappa
          the kappa value
        • m_MAE
          double m_MAE
          the Mean absolute error
        • m_RAE
          double m_RAE
          the Relative absolute error
        • m_RMSE
          double m_RMSE
          the Root mean squared error
        • m_RRSE
          double m_RRSE
          the Root relative squared error
        • m_Values
          weka.classifiers.meta.GridSearch.PointDouble m_Values
          the value pair the classifier was built with
    • Class weka.classifiers.meta.GridSearch.PerformanceCache

      class PerformanceCache extends Object implements Serializable
      serialVersionUID:
      5838863230451530252L
      • Serialized Fields

        • m_Cache
          Hashtable m_Cache
          the cache for points in the grid that got calculated
    • Class weka.classifiers.meta.GridSearch.PerformanceComparator

      class PerformanceComparator extends Object implements Serializable
      serialVersionUID:
      6507592831825393847L
      • Serialized Fields

    • Class weka.classifiers.meta.GridSearch.PerformanceTable

      class PerformanceTable extends Object implements Serializable
      serialVersionUID:
      5486491313460338379L
      • Serialized Fields

        • m_Grid
          weka.classifiers.meta.GridSearch.Grid m_Grid
          the corresponding grid
        • m_Max
          double m_Max
          the maximum performance
        • m_Min
          double m_Min
          the minimum performance
        • m_Performances
          Vector<weka.classifiers.meta.GridSearch.Performance> m_Performances
          the performances
        • m_Table
          double[][] m_Table
          the table with the values
        • m_Type
          int m_Type
          the type of performance the table was generated for
    • Class weka.classifiers.meta.GridSearch.PointDouble

      class PointDouble extends Point2D.Double implements Serializable
      serialVersionUID:
      7151661776161898119L
    • Class weka.classifiers.meta.GridSearch.PointInt

      class PointInt extends Point implements Serializable
      serialVersionUID:
      -5900415163698021618L
    • Class weka.classifiers.meta.LogitBoost

      serialVersionUID:
      8627452775249625582L
      • Serialized Fields

        • m_ClassAttribute
          Attribute m_ClassAttribute
          The actual class attribute (for getting class names)
        • 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_NumericClassData
          Instances m_NumericClassData
          Dummy dataset with a numeric class
        • m_NumFolds
          int m_NumFolds
          The number of folds for the internal cross-validation.
        • m_NumGenerated
          int m_NumGenerated
          The number of successfully generated base classifiers.
        • m_NumRuns
          int m_NumRuns
          The number of runs for the internal cross-validation.
        • m_Offset
          double m_Offset
          The value by which the actual target value for the true class is offset.
        • m_Precision
          double m_Precision
          The threshold on the improvement of the likelihood
        • m_RandomInstance
          Random m_RandomInstance
          The random number generator used
        • m_Shrinkage
          double m_Shrinkage
          The value of the shrinkage parameter
        • m_UseResampling
          boolean m_UseResampling
          Use boosting with reweighting?
        • m_WeightThreshold
          int m_WeightThreshold
          Weight thresholding. The percentage of weight mass used in training
        • m_ZeroR
          Classifier m_ZeroR
          a ZeroR model in case no model can be built from the data
    • Class weka.classifiers.meta.MetaCost

      class MetaCost extends RandomizableSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      1205317833344726855L
      • Serialized Fields

        • m_BagSizePercent
          int m_BagSizePercent
          The size of each bag sample, as a percentage of the training size
        • m_CostFile
          String m_CostFile
          The name of the cost file, for command line options
        • m_CostMatrix
          CostMatrix m_CostMatrix
          The cost matrix
        • m_MatrixSource
          int m_MatrixSource
          Indicates the current cost matrix source
        • m_NumIterations
          int m_NumIterations
          The number of iterations.
        • m_OnDemandDirectory
          File m_OnDemandDirectory
          The directory used when loading cost files on demand, null indicates current directory
    • Class weka.classifiers.meta.MultiBoostAB

      class MultiBoostAB extends AdaBoostM1 implements Serializable
      serialVersionUID:
      -6681619178187935148L
      • Serialized Fields

        • m_NumSubCmtys
          int m_NumSubCmtys
          The number of sub-committees to use
        • m_Random
          Random m_Random
          Random number generator
    • Class weka.classifiers.meta.MultiClassClassifier

      class MultiClassClassifier extends RandomizableSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -3879602011542849141L
      • Serialized Fields

        • m_ClassAttribute
          Attribute m_ClassAttribute
          Internal copy of the class attribute for output purposes
        • m_ClassFilters
          Filter[] m_ClassFilters
          The filters used to transform the class.
        • m_Classifiers
          Classifier[] m_Classifiers
          The classifiers.
        • m_Method
          int m_Method
          The multiclass method to use
        • m_pairwiseCoupling
          boolean m_pairwiseCoupling
          Use pairwise coupling with 1-vs-1
        • m_RandomWidthFactor
          double m_RandomWidthFactor
          The multiplier when generating random codes. Will generate numClasses * m_RandomWidthFactor codes
        • m_SumOfWeights
          double[] m_SumOfWeights
          Needed for pairwise coupling
        • m_TwoClassDataset
          Instances m_TwoClassDataset
          A transformed dataset header used by the 1-against-1 method
        • m_ZeroR
          ZeroR m_ZeroR
          ZeroR classifier for when all base classifier return zero probability.
    • Class weka.classifiers.meta.MultiScheme

      class 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

      class OrdinalClassClassifier extends SingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -3461971774059603636L
      • Serialized Fields

        • m_ClassFilters
          MakeIndicator[] m_ClassFilters
          The filters used to transform the class.
        • m_Classifiers
          Classifier[] m_Classifiers
          The classifiers. (One for each class.)
        • m_ZeroR
          ZeroR m_ZeroR
          ZeroR classifier for when all base classifier return zero probability.
    • Class weka.classifiers.meta.RacedIncrementalLogitBoost

      class RacedIncrementalLogitBoost extends RandomizableSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      908598343772170052L
      • Serialized Fields

        • m_bestCommittee
          weka.classifiers.meta.RacedIncrementalLogitBoost.Committee m_bestCommittee
          The current best committee
        • m_ClassAttribute
          Attribute m_ClassAttribute
          The actual class attribute (for getting class names)
        • m_committees
          FastVector m_committees
          The committees
        • m_currentSet
          Instances m_currentSet
          The instances currently in memory for training
        • m_maxBatchSizeRequired
          int m_maxBatchSizeRequired
          The maximum number of instances required for processing
        • m_maxChunkSize
          int m_maxChunkSize
          The maimum chunk size used for training
        • m_minChunkSize
          int m_minChunkSize
          The minimum chunk size used for training
        • m_NumClasses
          int m_NumClasses
          The number of classes
        • m_NumericClassData
          Instances m_NumericClassData
          Dummy dataset with a numeric class
        • m_numInstancesConsumed
          int m_numInstancesConsumed
          The number of instances consumed
        • m_PruningType
          int m_PruningType
          The pruning type used
        • m_RandomInstance
          Random m_RandomInstance
          The random number generator used
        • m_UseResampling
          boolean m_UseResampling
          Whether to use resampling
        • m_validationChunkSize
          int m_validationChunkSize
          The size of the validation set
        • m_validationSet
          Instances m_validationSet
          The instances used for validation
        • m_validationSetChanged
          boolean m_validationSetChanged
          Whether the validation set has recently been changed
        • m_zeroR
          ZeroR m_zeroR
          The default scheme used when committees aren't ready
    • Class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee

      class Committee extends Object implements Serializable
      serialVersionUID:
      5559880306684082199L
      • Serialized Fields

        • m_chunkSize
          int m_chunkSize
        • m_instancesConsumed
          int m_instancesConsumed
          number eaten from m_currentSet
        • m_lastLogLikelihood
          double m_lastLogLikelihood
        • m_lastValidationError
          double m_lastValidationError
        • m_modelHasChanged
          boolean m_modelHasChanged
        • m_modelHasChangedLL
          boolean m_modelHasChangedLL
        • m_models
          FastVector m_models
        • m_newValidationFs
          double[][] m_newValidationFs
        • m_validationFs
          double[][] m_validationFs
    • Class weka.classifiers.meta.RandomCommittee

      class RandomCommittee extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -9204394360557300092L
    • Class weka.classifiers.meta.RandomSubSpace

      class 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

      class RegressionByDiscretization extends SingleClassifierEnhancer implements Serializable
      serialVersionUID:
      5066426153134050378L
      • Serialized Fields

        • 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_Discretizer
          Discretize m_Discretizer
          The discretization filter.
        • m_NumBins
          int m_NumBins
          The number of discretization intervals.
        • m_UseEqualFrequency
          boolean m_UseEqualFrequency
          Use equal-frequency binning
    • Class weka.classifiers.meta.RotationForest

      class RotationForest extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -3255631880798499936L
      • Serialized Fields

        • m_Groups
          int[][][] m_Groups
          The attributes of each group
        • m_Headers
          Instances[] m_Headers
          Headers of the transformed dataset
        • m_MaxGroup
          int m_MaxGroup
          The maximum size of a group
        • m_MinGroup
          int m_MinGroup
          The minimum size of a group
        • m_Normalize
          Normalize m_Normalize
          Filter that normalized the attributes
        • m_NumberOfGroups
          boolean m_NumberOfGroups
          Whether minGroup and maxGroup refer to the number of groups or their size
        • m_ProjectionFilter
          Filter m_ProjectionFilter
          The type of projection filter
        • m_ProjectionFilters
          Filter[][] m_ProjectionFilters
          The projection filters
        • m_ReducedHeaders
          Instances[][] m_ReducedHeaders
          Headers of the reduced datasets
        • m_RemovedPercentage
          int m_RemovedPercentage
          The percentage of instances to be removed
        • m_RemoveUseless
          RemoveUseless m_RemoveUseless
          Filter that remove useless attributes
    • Class weka.classifiers.meta.Stacking

      class Stacking extends RandomizableMultipleClassifiersCombiner implements Serializable
      serialVersionUID:
      5134738557155845452L
      • Serialized Fields

        • m_BaseFormat
          Instances m_BaseFormat
          Format for base data
        • m_MetaClassifier
          Classifier m_MetaClassifier
          The meta classifier
        • m_MetaFormat
          Instances m_MetaFormat
          Format for meta data
        • m_NumFolds
          int m_NumFolds
          Set the number of folds for the cross-validation
    • Class weka.classifiers.meta.StackingC

      class StackingC extends Stacking implements Serializable
      serialVersionUID:
      -6717545616603725198L
      • Serialized Fields

        • m_attrFilter
          Remove m_attrFilter
          Filter to transform metaData - Remove
        • m_makeIndicatorFilter
          MakeIndicator m_makeIndicatorFilter
          Filter to transform metaData - MakeIndicator
        • m_MetaClassifiers
          Classifier[] m_MetaClassifiers
          The meta classifiers (one for each class, like in ClassificationViaRegression)
    • Class weka.classifiers.meta.ThresholdSelector

      class ThresholdSelector extends RandomizableSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -1795038053239867444L
      • Serialized Fields

        • 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_ClassMode
          int m_ClassMode
          Method to determine which class to optimize for
        • m_DesignatedClass
          int m_DesignatedClass
          Designated class value, determined during building
        • m_EvalMode
          int m_EvalMode
          The evaluation mode
        • 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_manualThreshold
          boolean m_manualThreshold
          True if a manually set threshold is being used
        • m_manualThresholdValue
          double m_manualThresholdValue
          -1 = not used by default
        • m_nMeasure
          int m_nMeasure
          evaluation measure used for determining threshold
        • m_NumXValFolds
          int m_NumXValFolds
          The number of folds used in cross-validation
        • m_RangeMode
          int m_RangeMode
          The range correction mode
    • Class weka.classifiers.meta.Vote

      serialVersionUID:
      -637891196294399624L
      • Serialized Fields

        • m_CombinationRule
          int m_CombinationRule
          Combination Rule variable
        • m_Random
          Random m_Random
          the random number generator used for breaking ties in majority voting
          See Also:
          • Vote.distributionForInstanceMajorityVoting(Instance)
  • Package weka.classifiers.meta.nestedDichotomies

  • Package weka.classifiers.mi

    • Class weka.classifiers.mi.CitationKNN

      class CitationKNN extends Classifier implements Serializable
      serialVersionUID:
      -8435377743874094852L
      • Serialized Fields

        • m_Attributes
          Instances m_Attributes
          attribute name structure of the relational attribute
        • m_Citers
          int[] m_Citers
          C nearest citers
        • m_CitersDebug
          boolean m_CitersDebug
        • m_Classes
          int[] m_Classes
          Class labels for each bag
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • m_CNN
          weka.classifiers.mi.CitationKNN.NeighborList[] m_CNN
          C nearest neighbors considering all the bags
        • m_CNNDebug
          boolean m_CNNDebug
          Different debugging output
        • m_Debug
          boolean m_Debug
          Debugging output
        • m_Diffs
          double[] m_Diffs
          Normalization of the euclidean distance
        • m_HDistanceDebug
          boolean m_HDistanceDebug
        • m_HDRank
          int m_HDRank
          Rank associated to the Hausdorff distance
        • m_IdIndex
          int m_IdIndex
        • m_Max
          double[] m_Max
        • m_MaxNorm
          double m_MaxNorm
        • m_Min
          double[] m_Min
        • m_MinNorm
          double m_MinNorm
        • m_NeighborListDebug
          boolean m_NeighborListDebug
        • m_NumCiters
          int m_NumCiters
          Number of citers
        • m_NumClasses
          int m_NumClasses
          The number of the class labels
        • m_NumReferences
          int m_NumReferences
          Number of references
        • m_References
          int[] m_References
          R nearest references
        • m_ReferencesDebug
          boolean m_ReferencesDebug
        • m_TrainBags
          Instances m_TrainBags
          Training bags
    • Class weka.classifiers.mi.MDD

      class MDD extends Classifier implements Serializable
      serialVersionUID:
      -7273119490545290581L
      • Serialized Fields

        • m_Attributes
          Instances m_Attributes
          All attribute names
        • m_Classes
          int[] m_Classes
          Class labels for each bag
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • m_Data
          double[][][] m_Data
          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.
        • m_NumClasses
          int m_NumClasses
          The number of the class labels
        • m_Par
          double[] m_Par
    • Class weka.classifiers.mi.MIBoost

      class MIBoost extends SingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -3808427225599279539L
      • Serialized Fields

        • m_Attributes
          Instances m_Attributes
          attributes name for the new dataset used to build the model
        • m_Beta
          double[] m_Beta
          Voting weights of models
        • m_Classes
          int[] m_Classes
          Class labels for each bag
        • m_ConvertToSI
          MultiInstanceToPropositional m_ConvertToSI
          filter used to convert the MI dataset into single-instance dataset
        • m_DiscretizeBin
          int m_DiscretizeBin
          the number of discretization bins
        • m_Filter
          Discretize m_Filter
          filter used for discretization
        • m_MaxIterations
          int m_MaxIterations
          the maximum number of boost iterations
        • m_Models
          Classifier[] m_Models
          the models for the iterations
        • m_NumClasses
          int m_NumClasses
          The number of the class labels
        • m_NumIterations
          int m_NumIterations
          Number of iterations
    • Class weka.classifiers.mi.MIDD

      class MIDD extends Classifier implements Serializable
      serialVersionUID:
      4263507733600536168L
      • Serialized Fields

        • m_Attributes
          Instances m_Attributes
          All attribute names
        • m_Classes
          int[] m_Classes
          Class labels for each bag
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • m_Data
          double[][][] m_Data
          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.
        • m_NumClasses
          int m_NumClasses
          The number of the class labels
        • m_Par
          double[] m_Par
    • Class weka.classifiers.mi.MIEMDD

      class MIEMDD extends RandomizableClassifier implements Serializable
      serialVersionUID:
      3899547154866223734L
      • Serialized Fields

        • m_Attributes
          Instances m_Attributes
          All attribute names
        • m_Classes
          int[] m_Classes
          Class labels for each bag
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • m_Data
          double[][][] m_Data
          MI data
        • 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.
        • m_NumClasses
          int m_NumClasses
          The number of the class labels
        • m_Par
          double[] m_Par
    • Class weka.classifiers.mi.MILR

      class MILR extends Classifier implements Serializable
      serialVersionUID:
      1996101190172373826L
      • Serialized Fields

        • m_AlgorithmType
          int m_AlgorithmType
          the type of processing
        • m_Attributes
          Instances m_Attributes
          All attribute names
        • m_Classes
          int[] m_Classes
          Class labels for each bag
        • m_Data
          double[][][] m_Data
          MI data
        • m_NumClasses
          int m_NumClasses
          The number of the class labels
        • m_Par
          double[] m_Par
        • m_Ridge
          double m_Ridge
          The ridge parameter.
        • xMean
          double[] xMean
        • xSD
          double[] xSD
    • Class weka.classifiers.mi.MINND

      class MINND extends Classifier implements Serializable
      serialVersionUID:
      -4512599203273864994L
      • Serialized Fields

        • m_Attributes
          Instances m_Attributes
          header info of the data
        • m_Change
          double[][] m_Change
          The weights that alter the dimnesion of each exemplar
        • m_Choose
          int m_Choose
          The number of nearest neighbour exemplars in the selection of noises in the test data
        • m_Class
          double[] m_Class
          The class label of each exemplar
        • m_Decay
          double m_Decay
          The decay rate of learning rate
        • m_Dimension
          int m_Dimension
          The dimension of each exemplar, i.e. (numAttributes-2)
        • m_MaxArray
          double[] m_MaxArray
          The maximum values for numeric attributes.
        • m_Mean
          double[][] m_Mean
          The mean for each attribute of each exemplar
        • m_MinArray
          double[] m_MinArray
          The minimum values for numeric attributes.
        • m_Neighbour
          int m_Neighbour
          The number of nearest neighbour for prediction
        • m_NoiseM
          double[][] m_NoiseM
          The noise data of each exemplar
        • m_NoiseV
          double[][] m_NoiseV
          The noise data of each exemplar
        • m_NumClasses
          int m_NumClasses
          The number of class labels in the data
        • m_Rate
          double m_Rate
          The learning rate in the gradient descent
        • m_Select
          int m_Select
          The number of nearest neighbour instances in the selection of noises in the training data
        • m_STOP
          double m_STOP
          The stopping criteria of gradient descent
        • m_ValidM
          double[][] m_ValidM
          The noise data of each exemplar
        • m_ValidV
          double[][] m_ValidV
          The noise data of each exemplar
        • m_Variance
          double[][] m_Variance
          The variance for each attribute of each exemplar
        • m_Weights
          double[] m_Weights
          The weight of each exemplar
    • Class weka.classifiers.mi.MIOptimalBall

      class MIOptimalBall extends Classifier implements Serializable
      serialVersionUID:
      -6465750129576777254L
      • Serialized Fields

        • m_Center
          double[] m_Center
          center of the optimal ball
        • m_ConvertToMI
          PropositionalToMultiInstance m_ConvertToMI
          filter used to convert the single-instance dataset into MI dataset
        • m_ConvertToSI
          MultiInstanceToPropositional m_ConvertToSI
          filter used to convert the MI dataset into single-instance dataset
        • 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_Radius
          double m_Radius
          radius of the optimal ball
    • Class weka.classifiers.mi.MISMO

      class MISMO extends Classifier implements Serializable
      serialVersionUID:
      -5834036950143719712L
      • Serialized Fields

        • m_C
          double m_C
          The complexity parameter.
        • 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_classAttribute
          Attribute m_classAttribute
          The class attribute
        • m_classifiers
          weka.classifiers.mi.MISMO.BinaryMISMO[][] m_classifiers
          The binary classifier(s)
        • m_classIndex
          int m_classIndex
          The class index from the training data
        • m_eps
          double m_eps
          Epsilon for rounding.
        • m_Filter
          Filter m_Filter
          The filter used to standardize/normalize all values.
        • m_filterType
          int m_filterType
          Whether to normalize/standardize/neither
        • m_fitLogisticModels
          boolean m_fitLogisticModels
          Whether logistic models are to be fit
        • m_kernel
          Kernel m_kernel
          Kernel to use
        • m_minimax
          boolean m_minimax
          Use MIMinimax feature space?
        • m_Missing
          ReplaceMissingValues m_Missing
          The filter used to get rid of missing values.
        • m_NominalToBinary
          NominalToBinary m_NominalToBinary
          The filter used to make attributes numeric.
        • m_numFolds
          int m_numFolds
          The number of folds for the internal cross-validation
        • m_randomSeed
          int m_randomSeed
          The random number seed
        • m_tol
          double m_tol
          Tolerance for accuracy of result.
    • Class weka.classifiers.mi.MISMO.BinaryMISMO

      class BinaryMISMO extends 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_class
          double[] m_class
          The transformed class values.
        • m_data
          Instances m_data
          The training data.
        • m_errors
          double[] m_errors
          The current set of errors for all non-bound examples.
        • m_I0
          SMOset m_I0
          {i: 0 invalid input: '<' m_alpha[i] invalid input: '<' 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_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_kernel
          Kernel m_kernel
          Kernel to use
        • m_logistic
          Logistic m_logistic
          Stores logistic regression model for probability estimate
        • m_sparseIndices
          int[] m_sparseIndices
        • m_sparseWeights
          double[] m_sparseWeights
          Variables to hold weight vector in sparse form. (To reduce storage requirements.)
        • m_sumOfWeights
          double m_sumOfWeights
          Stores the weight of the training instances
        • m_supportVectors
          SMOset m_supportVectors
          The set of support vectors {i: 0 invalid input: '<' m_alpha[i]}
        • m_weights
          double[] m_weights
          Weight vector for linear machine.
    • Class weka.classifiers.mi.MISVM

      class MISVM extends Classifier implements Serializable
      serialVersionUID:
      7622231064035278145L
      • Serialized Fields

        • m_C
          double m_C
          The complexity parameter.
        • m_ConvertToProp
          MultiInstanceToPropositional m_ConvertToProp
          filter used to convert the MI dataset into single-instance dataset
        • m_Filter
          Filter m_Filter
          The filter used to standardize/normalize all values.
        • m_filterType
          int m_filterType
          Whether to normalize/standardize/neither
        • m_kernel
          Kernel m_kernel
          the kernel to use
        • m_MaxIterations
          int m_MaxIterations
          the maximum number of iterations to perform
        • 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
    • Class weka.classifiers.mi.MIWrapper

      class MIWrapper extends SingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -7707766152904315910L
      • Serialized Fields

        • m_ConvertToProp
          MultiInstanceToPropositional m_ConvertToProp
          Filter used to convert MI dataset into single-instance dataset
        • m_Method
          int m_Method
          the test method
        • m_NumClasses
          int m_NumClasses
          The number of the class labels
        • m_WeightMethod
          int m_WeightMethod
          the single-instance weight setting method
    • Class weka.classifiers.mi.SimpleMI

      class SimpleMI extends SingleClassifierEnhancer implements Serializable
      serialVersionUID:
      9137795893666592662L
      • Serialized Fields

        • m_TransformMethod
          int m_TransformMethod
          the method used in transformation
  • Package weka.classifiers.mi.supportVector

  • Package weka.classifiers.misc

    • Class weka.classifiers.misc.HyperPipes

      class HyperPipes extends Classifier implements Serializable
      serialVersionUID:
      -7527596632268975274L
      • Serialized Fields

        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • m_HyperPipes
          weka.classifiers.misc.HyperPipes.HyperPipe[] m_HyperPipes
          Stores the HyperPipe for each class
        • m_Instances
          Instances m_Instances
          The structure of the training data
        • m_ZeroR
          Classifier m_ZeroR
          a ZeroR model in case no model can be built from the data
    • Class weka.classifiers.misc.SerializedClassifier

      class SerializedClassifier extends Classifier implements Serializable
      serialVersionUID:
      4599593909947628642L
      • Serialized Fields

        • m_ModelFile
          File m_ModelFile
          the file where the serialized model is stored
    • Class weka.classifiers.misc.VFI

      class VFI extends Classifier implements Serializable
      serialVersionUID:
      8081692166331321866L
      • Serialized Fields

        • m_bias
          double m_bias
          Bias towards more confident intervals
        • m_ClassIndex
          int m_ClassIndex
          The index of the class attribute
        • m_counts
          double[][][] m_counts
          The class counts for each interval of each attribute
        • m_globalCounts
          double[] m_globalCounts
          The global class counts
        • m_Instances
          Instances m_Instances
          The training data
        • m_intervalBounds
          double[][] m_intervalBounds
          The lower bounds for each attribute
        • m_maxEntrop
          double m_maxEntrop
          The maximum entropy for the class
        • m_NumClasses
          int m_NumClasses
          The number of classes
        • m_weightByConfidence
          boolean m_weightByConfidence
          Exponentially bias more confident intervals
        • TINY
          double TINY
  • Package weka.classifiers.pmml.consumer

    • Class weka.classifiers.pmml.consumer.GeneralRegression

      class GeneralRegression extends PMMLClassifier implements Serializable
      serialVersionUID:
      2583880411828388959L
      • Serialized Fields

        • m_algorithmName
          String m_algorithmName
        • m_covariateList
          ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Predictor> m_covariateList
        • m_cumulativeLinkFunction
          weka.classifiers.pmml.consumer.GeneralRegression.CumulativeLinkFunction m_cumulativeLinkFunction
        • m_distParameter
          double m_distParameter
        • m_distribution
          weka.classifiers.pmml.consumer.GeneralRegression.Distribution m_distribution
        • m_factorList
          ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Predictor> m_factorList
        • m_functionType
          int m_functionType
        • m_linkFunction
          weka.classifiers.pmml.consumer.GeneralRegression.LinkFunction m_linkFunction
        • m_linkParameter
          double m_linkParameter
        • m_modelName
          String m_modelName
        • m_modelType
          weka.classifiers.pmml.consumer.GeneralRegression.ModelType m_modelType
        • m_offsetValue
          double m_offsetValue
        • m_offsetVariable
          String m_offsetVariable
        • m_parameterList
          ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Parameter> m_parameterList
        • m_paramMatrix
          weka.classifiers.pmml.consumer.GeneralRegression.PCell[][] m_paramMatrix
        • m_ppMatrix
          weka.classifiers.pmml.consumer.GeneralRegression.PPCell[][] m_ppMatrix
        • m_trialsValue
          double m_trialsValue
        • m_trialsVariable
          String m_trialsVariable
    • Class weka.classifiers.pmml.consumer.NeuralNetwork

      class NeuralNetwork extends PMMLClassifier implements Serializable
      serialVersionUID:
      -4545904813133921249L
      • Serialized Fields

        • m_activationFunction
          weka.classifiers.pmml.consumer.NeuralNetwork.ActivationFunction m_activationFunction
          The activation function to use
        • m_altitude
          double m_altitude
          Altitude for radial basis
        • m_functionType
          weka.classifiers.pmml.consumer.NeuralNetwork.MiningFunction m_functionType
          The mining function
        • m_inputMap
          HashMap<String,Double> m_inputMap
          A map for storing network input values (computed from an incoming instance)
        • m_inputs
          weka.classifiers.pmml.consumer.NeuralNetwork.NeuralInput[] m_inputs
          The inputs to the network
        • m_layers
          weka.classifiers.pmml.consumer.NeuralNetwork.NeuralLayer[] m_layers
          The hidden layers in the network
        • m_normalizationMethod
          weka.classifiers.pmml.consumer.NeuralNetwork.Normalization m_normalizationMethod
          The normalization method
        • 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_outputs
          weka.classifiers.pmml.consumer.NeuralNetwork.NeuralOutputs m_outputs
          The outputs of the network
        • m_threshold
          double m_threshold
          Threshold activation
        • m_width
          double m_width
          Width for radial basis
    • Class weka.classifiers.pmml.consumer.PMMLClassifier

      class PMMLClassifier extends Classifier implements Serializable
      serialVersionUID:
      -5371600590320702971L
      • Serialized Fields

        • m_creatorApplication
          String m_creatorApplication
          Creator application
        • m_dataDictionary
          Instances m_dataDictionary
          The data dictionary
        • m_log
          Logger m_log
          Logger
        • m_miningSchema
          MiningSchema m_miningSchema
          The fields and meta data used by the model
        • m_pmmlVersion
          String m_pmmlVersion
          PMML version
    • Class weka.classifiers.pmml.consumer.Regression

      class Regression extends PMMLClassifier implements Serializable
      serialVersionUID:
      -5551125528409488634L
      • Serialized Fields

        • m_algorithmName
          String m_algorithmName
          Description of the algorithm
        • m_normalizationMethod
          weka.classifiers.pmml.consumer.Regression.Normalization m_normalizationMethod
          The normalization to use
        • m_regressionTables
          weka.classifiers.pmml.consumer.Regression.RegressionTable[] m_regressionTables
          The regression tables for this regression
    • Class weka.classifiers.pmml.consumer.Regression.RegressionTable.CategoricalPredictor

      class CategoricalPredictor extends weka.classifiers.pmml.consumer.Regression.RegressionTable.Predictor implements Serializable
      serialVersionUID:
      3077920125549906819L
      • Serialized Fields

        • m_valueIndex
          int m_valueIndex
          The index of the attribute value for this predictor
        • m_valueName
          String m_valueName
          The attribute value for this predictor
    • Class weka.classifiers.pmml.consumer.Regression.RegressionTable.NumericPredictor

      class 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

      class PredictorTerm extends Object implements Serializable
      serialVersionUID:
      5493100145890252757L
      • Serialized Fields

        • m_coefficient
          double m_coefficient
          The coefficient for this predictor term
        • m_fieldNames
          String[] m_fieldNames
          The names of the terms (attributes) to be multiplied
        • m_indexes
          int[] m_indexes
          the indexes of the terms to be multiplied
  • Package weka.classifiers.rules

    • Class weka.classifiers.rules.ConjunctiveRule

      class ConjunctiveRule extends Classifier implements Serializable
      serialVersionUID:
      -5938309903225087198L
      • Serialized Fields

        • m_Antds
          FastVector m_Antds
          The vector of antecedents of this rule
        • m_ClassAttribute
          Attribute m_ClassAttribute
          The class attribute of the data
        • m_Cnsqt
          double[] m_Cnsqt
          The consequent of this rule
        • m_DefDstr
          double[] m_DefDstr
          The default rule distribution of the data not covered
        • m_Folds
          int m_Folds
          The number of folds to split data into Grow and Prune for REP
        • 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
        • m_NumClasses
          int m_NumClasses
          Number of classes in the training data
        • m_Random
          Random m_Random
          The Random object used for randomization
        • m_Seed
          long m_Seed
          The seed to perform randomization
        • m_Targets
          FastVector m_Targets
          The predicted classes recorded for each antecedent in the growing data
    • Class weka.classifiers.rules.DecisionTable

      class DecisionTable extends Classifier implements Serializable
      serialVersionUID:
      2888557078165701326L
      • Serialized Fields

        • m_classIsNominal
          boolean m_classIsNominal
          Class is nominal
        • m_classPriorCounts
          double[] m_classPriorCounts
          The class priors to use when there is no match in the table
        • m_classPriors
          double[] m_classPriors
        • m_CVFolds
          int m_CVFolds
          Number of folds for cross validating feature sets
        • m_decisionFeatures
          int[] m_decisionFeatures
          Holds the final feature set
        • m_delTransform
          Remove m_delTransform
          Filter used to remove columns discarded by feature selection
        • m_displayRules
          boolean m_displayRules
          Display Rules
        • m_disTransform
          Filter m_disTransform
          Discretization filter
        • m_dtInstances
          Instances m_dtInstances
          Holds the final feature selected set of instances
        • m_entries
          Hashtable m_entries
          The hashtable used to hold training instances
        • m_evaluation
          Evaluation m_evaluation
          The evaluation object used to evaluate subsets
        • m_evaluationMeasure
          int m_evaluationMeasure
        • m_evaluator
          ASEvaluation m_evaluator
          Our own internal evaluator
        • m_ibk
          IBk m_ibk
          IB1 used to classify non matching instances rather than majority class
        • m_majority
          double m_majority
          Holds the majority class
        • 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_rr
          Random m_rr
          Random numbers for use in cross validation
        • m_saveMemory
          boolean m_saveMemory
        • m_search
          ASSearch m_search
          The search method to use
        • m_theInstances
          Instances m_theInstances
          Holds the original training instances
        • m_useIBk
          boolean m_useIBk
          Use the IBk classifier rather than majority class
    • Class weka.classifiers.rules.DecisionTableHashKey

      class DecisionTableHashKey extends Object implements Serializable
      serialVersionUID:
      5674163500154964602L
      • Serialized Fields

        • attributes
          double[] attributes
          Array of attribute values for an instance
        • key
          int key
          The key
        • missing
          boolean[] missing
          True for an index if the corresponding attribute value is missing.
    • Class weka.classifiers.rules.DTNB

      class DTNB extends DecisionTable implements Serializable
      serialVersionUID:
      2999557077765701326L
      • Serialized Fields

        • m_backwardWithDelete
          ASSearch m_backwardWithDelete
        • m_NB
          NaiveBayes m_NB
          The naive Bayes half of the hybrid
        • m_nbFeatures
          int[] m_nbFeatures
          The features used by naive Bayes
        • m_percentDeleted
          double m_percentDeleted
          Percentage of the features features that were dropped entirely
        • m_percentUsedByDT
          double m_percentUsedByDT
          Percentage of the total number of features used by the decision table
    • Class weka.classifiers.rules.DTNB.BackwardsWithDelete

      class BackwardsWithDelete extends ASSearch implements Serializable
    • Class weka.classifiers.rules.DTNB.EvalWithDelete

      class EvalWithDelete extends ASEvaluation implements Serializable
      • Serialized Fields

        • m_deletedFromDTNB
          BitSet m_deletedFromDTNB
    • Class weka.classifiers.rules.JRip

      class JRip extends Classifier implements Serializable
      serialVersionUID:
      -6589312996832147161L
      • Serialized Fields

        • m_CheckErr
          boolean m_CheckErr
          Whether check the error rate >= 0.5 in stopping criteria
        • m_Class
          Attribute m_Class
          The class attribute of the data
        • m_Debug
          boolean m_Debug
          Whether in a debug mode
        • m_Distributions
          FastVector m_Distributions
          The predicted class distribution
        • m_Filter
          Filter m_Filter
          The filter used to randomize the class order
        • 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_Optimizations
          int m_Optimizations
          Runs of optimizations
        • m_Random
          Random m_Random
          Random object used in this class
        • m_Ruleset
          FastVector m_Ruleset
          The ruleset
        • m_RulesetStats
          FastVector m_RulesetStats
          The RuleStats for the ruleset of each class value
        • m_Seed
          long m_Seed
          The seed to perform randomization
        • m_Total
          double m_Total
          # of all the possible conditions in a rule
        • m_UsePruning
          boolean m_UsePruning
          Whether use pruning, i.e. the data is clean or not
    • Class weka.classifiers.rules.JRip.Antd

      class Antd extends Object implements Serializable
      serialVersionUID:
      -8929754772994154334L
      • Serialized Fields

        • accu
          double accu
          The accurate data for this antecedent in the growing data
        • accuRate
          double accuRate
          The accurate rate of this antecedent test on the growing data
        • att
          Attribute att
          The attribute of the antecedent
        • cover
          double cover
          The coverage of this antecedent in the growing data
        • maxInfoGain
          double maxInfoGain
          The maximum infoGain achieved by this antecedent test in the growing data
        • value
          double value
          The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)
    • Class weka.classifiers.rules.JRip.NominalAntd

      class NominalAntd extends JRip.Antd implements Serializable
      serialVersionUID:
      -9102297038837585135L
      • Serialized Fields

        • accurate
          double[] accurate
        • coverage
          double[] coverage
    • Class weka.classifiers.rules.JRip.NumericAntd

      class 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

      class RipperRule extends Rule implements Serializable
      serialVersionUID:
      -2410020717305262952L
      • Serialized Fields

        • m_Antds
          FastVector m_Antds
          The vector of antecedents of this rule
        • m_Consequent
          double m_Consequent
          The internal representation of the class label to be predicted
    • Class weka.classifiers.rules.M5Rules

      class M5Rules extends M5Base implements Serializable
      serialVersionUID:
      -1746114858746563180L
    • Class weka.classifiers.rules.NNge

      class NNge extends Classifier implements Serializable
      serialVersionUID:
      4084742275553788972L
      • Serialized Fields

        • 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_MaxArray
          double[] m_MaxArray
          The maximum values for numeric attributes.
        • m_MI
          double[] m_MI
        • m_MI_MaxArray
          double[] m_MI_MaxArray
        • m_MI_MinArray
          double[] m_MI_MinArray
        • m_MI_NumAttrClassInter
          int[][][] m_MI_NumAttrClassInter
          MUTUAL INFORMATION'S DATAS
        • m_MI_NumAttrClassValue
          int[][][] m_MI_NumAttrClassValue
        • m_MI_NumAttrInter
          int[][] m_MI_NumAttrInter
        • m_MI_NumAttrValue
          int[][] m_MI_NumAttrValue
        • m_MI_NumClass
          int[] m_MI_NumClass
        • m_MI_NumInst
          int m_MI_NumInst
        • m_MinArray
          double[] m_MinArray
          The minimum values for numeric attributes.
        • m_MissingVector
          double[] m_MissingVector
          Values to use for missing value
        • 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_Train
          Instances m_Train
          An empty instances to keep the headers, the classIndex, etc...
    • Class weka.classifiers.rules.OneR

      class OneR extends Classifier implements Serializable
      serialVersionUID:
      -3459427003147861443L
      • Serialized Fields

        • m_minBucketSize
          int m_minBucketSize
          The minimum bucket size
        • m_rule
          weka.classifiers.rules.OneR.OneRRule m_rule
          A 1-R rule
        • m_ZeroR
          Classifier m_ZeroR
          a ZeroR model in case no model can be built from the data
    • Class weka.classifiers.rules.PART

      class PART extends Classifier implements Serializable
      serialVersionUID:
      8121455039782598361L
      • Serialized Fields

        • m_binarySplits
          boolean m_binarySplits
          Binary splits on nominal attributes?
        • m_CF
          float m_CF
          Confidence level
        • m_minNumObj
          int m_minNumObj
          Minimum number of objects
        • m_numFolds
          int m_numFolds
          Number of folds for reduced error pruning.
        • m_reducedErrorPruning
          boolean m_reducedErrorPruning
          Use reduced error pruning?
        • m_root
          MakeDecList m_root
          The decision list
        • m_Seed
          int m_Seed
          The seed for random number generation.
        • m_unpruned
          boolean m_unpruned
          Generate unpruned list?
    • Class weka.classifiers.rules.Prism

      class 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

      class Ridor extends Classifier implements Serializable
      serialVersionUID:
      -7261533075088314436L
      • Serialized Fields

        • 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_Folds
          int m_Folds
          The number of folds to split data into Grow and Prune for IREP
        • 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_MinNo
          double m_MinNo
          The minimal number of instance weights within a split
        • m_Random
          Random m_Random
          Random object for randomization
        • m_Root
          weka.classifiers.rules.Ridor.Ridor_node m_Root
          The root of Ridor
        • m_Seed
          int m_Seed
          The seed to perform randomization
        • m_Shuffle
          int m_Shuffle
          The number of shuffles performed on the data for randomization
    • Class weka.classifiers.rules.Rule

      class Rule extends Object implements Serializable
      serialVersionUID:
      8815687740470471229L
    • Class weka.classifiers.rules.RuleStats

      class RuleStats extends Object implements Serializable
      serialVersionUID:
      -5708153367675298624L
      • Serialized Fields

        • m_Data
          Instances m_Data
          The data on which the stats calculation is based
        • m_Distributions
          FastVector m_Distributions
          The class distributions predicted by each rule
        • m_Filtered
          FastVector m_Filtered
          The set of instances filtered by the ruleset
        • m_Ruleset
          FastVector m_Ruleset
          The specific ruleset in question
        • m_SimpleStats
          FastVector m_SimpleStats
          The simple stats of each rule
        • 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
    • Class weka.classifiers.rules.ZeroR

      class ZeroR extends Classifier implements Serializable
      serialVersionUID:
      48055541465867954L
      • Serialized Fields

        • m_Class
          Attribute m_Class
          The class attribute.
        • 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).
  • Package weka.classifiers.rules.part

  • Package weka.classifiers.trees

    • Class weka.classifiers.trees.ADTree

      class ADTree extends Classifier implements Serializable
      serialVersionUID:
      -1532264837167690683L
      • Serialized Fields

        • m_boostingIterations
          int m_boostingIterations
          Option - the number of boosting iterations o perform
        • m_examplesCounted
          int m_examplesCounted
          Statistics - the number of instances processed during search
        • m_lastAddedSplitNum
          int m_lastAddedSplitNum
          The number of the last splitter added to the tree
        • m_negTrainInstances
          ReferenceInstances m_negTrainInstances
          The training instances with negative class - referencing the training dataset
        • m_nodesExpanded
          int m_nodesExpanded
          Statistics - the number of prediction nodes investigated during search
        • m_nominalAttIndices
          int[] m_nominalAttIndices
          An array containing the inidices to the nominal attributes in the data
        • m_numericAttIndices
          int[] m_numericAttIndices
          An array containing the inidices to the numeric attributes in the data
        • m_posTrainInstances
          ReferenceInstances m_posTrainInstances
          The training instances with positive class - referencing the training dataset
        • m_random
          Random m_random
          The random number generator - used for the random search heuristic
        • m_randomSeed
          int m_randomSeed
          Option - the seed to use for a random search
        • m_root
          PredictionNode m_root
          The root of the tree
        • m_saveInstanceData
          boolean m_saveInstanceData
          Option - whether the tree should remember the instance data
        • m_search_bestInsertionNode
          PredictionNode m_search_bestInsertionNode
          The best node to insert under, as found so far by the latest search
        • m_search_bestPathNegInstances
          Instances m_search_bestPathNegInstances
          The negative instances that apply to the best path found so far
        • m_search_bestPathPosInstances
          Instances m_search_bestPathPosInstances
          The positive instances that apply to the best path found so far
        • 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_searchPath
          int m_searchPath
          Option - the search mode
        • m_trainInstances
          Instances m_trainInstances
          The instances used to train the tree
        • m_trainTotalWeight
          double m_trainTotalWeight
          The total weight of the instances - used to speed Z calculations
    • Class weka.classifiers.trees.BFTree

      class BFTree extends RandomizableClassifier implements Serializable
      serialVersionUID:
      -7035607375962528217L
      • Serialized Fields

        • m_Attribute
          Attribute m_Attribute
          Attribute used for splitting.
        • m_ClassAttribute
          Attribute m_ClassAttribute
          Class attribute of a dataset.
        • m_ClassProbs
          double[] m_ClassProbs
          Class probabilities.
        • m_ClassValue
          double m_ClassValue
          Class value for a node.
        • m_Distribution
          double[] m_Distribution
          Class distributions.
        • m_Dists
          double[][][] m_Dists
          Distributions of each attribute for two successor nodes.
        • 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_isLeaf
          boolean m_isLeaf
          If the ndoe is leaf node.
        • m_minNumObj
          int m_minNumObj
          Minimum number of instances at leaf nodes.
        • m_numFoldsPruning
          int m_numFoldsPruning
          Number of folds for the pruning.
        • m_Props
          double[] m_Props
          Branch proportions.
        • m_PruningStrategy
          int m_PruningStrategy
          the pruning strategy
        • m_SizePer
          double m_SizePer
          The training data size (0-1). Default 1.
        • m_SortedIndices
          int[][] m_SortedIndices
          Sorted indices.
        • m_SplitString
          String m_SplitString
          Split subset (for nominal attributes).
        • m_SplitValue
          double m_SplitValue
          Split point (for numeric attributes).
        • m_Successors
          BFTree[] m_Successors
          Successor nodes.
        • m_TotalWeight
          double m_TotalWeight
          Total weights.
        • 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_UseGini
          boolean m_UseGini
          If use Gini index as the splitting criterion - default (if not, information is used).
        • m_UseOneSE
          boolean m_UseOneSE
          If use the 1SE rule to make the decision.
        • m_Weights
          double[][] m_Weights
          Sorted weights.
    • Class weka.classifiers.trees.DecisionStump

      class DecisionStump extends Classifier implements Serializable
      serialVersionUID:
      1618384535950391L
      • Serialized Fields

        • m_AttIndex
          int m_AttIndex
          The attribute used for classification.
        • 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_SplitPoint
          double m_SplitPoint
          The split point (index respectively).
        • m_ZeroR
          Classifier m_ZeroR
          a ZeroR model in case no model can be built from the data
    • Class weka.classifiers.trees.FT

      class FT extends Classifier implements Serializable
      serialVersionUID:
      -1113212459618105000L
      • Serialized Fields

        • 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_modelType
          int m_modelType
          Model Type, value: 0 is FT, 1 is FTLeaves, 2 is FTInner
        • m_nominalToBinary
          NominalToBinary m_nominalToBinary
          Filter to replace nominal attributes
        • m_numBoostingIterations
          int m_numBoostingIterations
          if non-zero, use fixed number of iterations for LogitBoost
        • m_replaceMissing
          ReplaceMissingValues m_replaceMissing
          Filter to replace missing values
        • m_tree
          FTtree m_tree
          root of the logistic model tree
        • m_useAIC
          boolean m_useAIC
          If true, the AIC is used to choose the best LogitBoost iteration
        • 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.Id3

      class Id3 extends Classifier implements Serializable
      serialVersionUID:
      -2693678647096322561L
      • Serialized Fields

        • m_Attribute
          Attribute m_Attribute
          Attribute used for splitting.
        • m_ClassAttribute
          Attribute m_ClassAttribute
          Class attribute of dataset.
        • m_ClassValue
          double m_ClassValue
          Class value if node is leaf.
        • m_Distribution
          double[] m_Distribution
          Class distribution if node is leaf.
        • m_Successors
          Id3[] m_Successors
          The node's successors.
    • Class weka.classifiers.trees.J48

      class J48 extends Classifier implements Serializable
      serialVersionUID:
      -217733168393644444L
      • Serialized Fields

        • m_binarySplits
          boolean m_binarySplits
          Binary splits on nominal attributes?
        • m_CF
          float m_CF
          Confidence level
        • m_minNumObj
          int m_minNumObj
          Minimum number of instances
        • m_noCleanup
          boolean m_noCleanup
          Cleanup after the tree has been built.
        • m_numFolds
          int m_numFolds
          Number of folds for reduced error pruning.
        • m_reducedErrorPruning
          boolean m_reducedErrorPruning
          Use reduced error pruning?
        • m_root
          ClassifierTree m_root
          The decision tree
        • m_Seed
          int m_Seed
          Random number seed for reduced-error pruning.
        • m_subtreeRaising
          boolean m_subtreeRaising
          Subtree raising to be performed?
        • m_unpruned
          boolean m_unpruned
          Unpruned tree?
        • m_useLaplace
          boolean m_useLaplace
          Determines whether probabilities are smoothed using Laplace correction when predictions are generated
    • Class weka.classifiers.trees.J48graft

      class J48graft extends Classifier implements Serializable
      serialVersionUID:
      8823716098042427799L
      • Serialized Fields

        • m_binarySplits
          boolean m_binarySplits
          Binary splits on nominal attributes?
        • m_CF
          float m_CF
          Confidence level
        • m_minNumObj
          int m_minNumObj
          Minimum number of instances
        • m_noCleanup
          boolean m_noCleanup
          Cleanup after the tree has been built.
        • m_numFolds
          int m_numFolds
          Number of folds for reduced error pruning.
        • m_relabel
          boolean m_relabel
          relabel instances when grafting
        • m_root
          ClassifierTree m_root
          The decision tree
        • m_subtreeRaising
          boolean m_subtreeRaising
          Subtree raising to be performed?
        • m_unpruned
          boolean m_unpruned
          Unpruned tree?
        • m_useLaplace
          boolean m_useLaplace
          Determines whether probabilities are smoothed using Laplace correction when predictions are generated
    • Class weka.classifiers.trees.LADTree

      class LADTree extends Classifier implements Serializable
      serialVersionUID:
      -4940716114518300302L
      • Serialized Fields

        • m_boostingIterations
          int m_boostingIterations
        • m_examplesCounted
          int m_examplesCounted
        • m_lastAddedSplitNum
          int m_lastAddedSplitNum
        • m_nodesExpanded
          int m_nodesExpanded
        • m_numericAttIndices
          int[] m_numericAttIndices
        • m_numOfClasses
          int m_numOfClasses
        • m_root
          weka.classifiers.trees.LADTree.PredictionNode m_root
        • m_search_bestInsertionNode
          weka.classifiers.trees.LADTree.PredictionNode m_search_bestInsertionNode
        • m_search_bestPathInstances
          Instances m_search_bestPathInstances
        • m_search_bestSplitter
          weka.classifiers.trees.LADTree.Splitter m_search_bestSplitter
        • m_search_smallestLeastSquares
          double m_search_smallestLeastSquares
        • m_staticPotentialSplitters2way
          FastVector m_staticPotentialSplitters2way
        • m_trainInstances
          ReferenceInstances m_trainInstances
        • Z_MAX
          double Z_MAX
    • Class weka.classifiers.trees.LADTree.LADInstance

      class LADInstance extends Instance implements Serializable
      • Serialized Fields

        • fVector
          double[] fVector
        • pVector
          double[] pVector
        • wVector
          double[] wVector
        • zVector
          double[] zVector
    • Class weka.classifiers.trees.LADTree.PredictionNode

      class PredictionNode extends Object implements Serializable
      • Serialized Fields

        • children
          FastVector children
        • values
          double[] values
    • Class weka.classifiers.trees.LADTree.Splitter

      class Splitter extends Object implements Serializable
      • Serialized Fields

        • attIndex
          int attIndex
        • orderAdded
          int orderAdded
    • Class weka.classifiers.trees.LADTree.TwoWayNominalSplit

      class TwoWayNominalSplit extends weka.classifiers.trees.LADTree.Splitter implements Serializable
      • Serialized Fields

        • children
          weka.classifiers.trees.LADTree.PredictionNode[] children
        • trueSplitValue
          int trueSplitValue
    • Class weka.classifiers.trees.LADTree.TwoWayNumericSplit

      class TwoWayNumericSplit extends weka.classifiers.trees.LADTree.Splitter implements Serializable
      • Serialized Fields

        • children
          weka.classifiers.trees.LADTree.PredictionNode[] children
        • splitPoint
          double splitPoint
    • Class weka.classifiers.trees.LMT

      class LMT extends Classifier implements Serializable
      serialVersionUID:
      -1113212459618104943L
      • Serialized Fields

        • 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_fastRegression
          boolean m_fastRegression
          use heuristic that determines the number of LogitBoost iterations only once in the beginning?
        • m_minNumInstances
          int m_minNumInstances
          minimum number of instances at which a node is considered for splitting
        • m_nominalToBinary
          NominalToBinary m_nominalToBinary
          Filter to replace nominal attributes
        • m_numBoostingIterations
          int m_numBoostingIterations
          if non-zero, use fixed number of iterations for LogitBoost
        • m_replaceMissing
          ReplaceMissingValues m_replaceMissing
          Filter to replace missing values
        • m_splitOnResiduals
          boolean m_splitOnResiduals
          split on residuals?
        • m_tree
          LMTNode m_tree
          root of the logistic model tree
        • m_useAIC
          boolean m_useAIC
          If true, the AIC is used to choose the best LogitBoost iteration
        • 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.M5P

      class M5P extends M5Base implements Serializable
      serialVersionUID:
      -6118439039768244417L
    • Class weka.classifiers.trees.NBTree

      class 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

      class RandomForest extends Classifier implements Serializable
      serialVersionUID:
      -2260823972777004705L
      • Serialized Fields

        • m_bagger
          Bagging m_bagger
          The bagger.
        • m_KValue
          int m_KValue
          Final number of features that were considered in last build.
        • m_MaxDepth
          int m_MaxDepth
          The maximum depth of the trees (0 = unlimited)
        • m_numFeatures
          int m_numFeatures
          Number of features to consider in random feature selection. If less than 1 will use int(logM+1) )
        • m_numTrees
          int m_numTrees
          Number of trees in forest.
        • m_randomSeed
          int m_randomSeed
          The random seed.
    • Class weka.classifiers.trees.RandomTree

      class RandomTree extends Classifier implements Serializable
      serialVersionUID:
      8934314652175299374L
      • Serialized Fields

        • m_AllowUnclassifiedInstances
          boolean m_AllowUnclassifiedInstances
          Whether unclassified instances are allowed
        • m_Info
          Instances m_Info
          The header information.
        • m_KValue
          int m_KValue
          The number of attributes considered for a split.
        • m_MaxDepth
          int m_MaxDepth
          The maximum depth of the tree (0 = unlimited)
        • m_MinNum
          double m_MinNum
          Minimum number of instances for leaf.
        • m_NumFolds
          int m_NumFolds
          Determines how much data is used for backfitting
        • m_randomSeed
          int m_randomSeed
          The random seed to use.
        • m_Tree
          weka.classifiers.trees.RandomTree.Tree m_Tree
          The Tree object
        • m_zeroR
          Classifier m_zeroR
          a ZeroR model in case no model can be built from the data
    • Class weka.classifiers.trees.RandomTree.Tree

      class Tree extends Object implements Serializable
      serialVersionUID:
      3549573538656522569L
      • Serialized Fields

        • m_Attribute
          int m_Attribute
          The attribute to split on.
        • m_ClassDistribution
          double[] m_ClassDistribution
          Class probabilities from the training data.
        • m_Prop
          double[] m_Prop
          The proportions of training instances going down each branch.
        • m_SplitPoint
          double m_SplitPoint
          The split point.
        • m_Successors
          weka.classifiers.trees.RandomTree.Tree[] m_Successors
          The subtrees appended to this tree.
    • Class weka.classifiers.trees.REPTree

      class REPTree extends Classifier implements Serializable
      serialVersionUID:
      -9216785998198681299L
      • Serialized Fields

        • m_MaxDepth
          int m_MaxDepth
          Upper bound on the tree depth
        • 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_NoPruning
          boolean m_NoPruning
          Don't prune
        • m_NumFolds
          int m_NumFolds
          Number of folds for reduced error pruning.
        • m_Seed
          int m_Seed
          Seed for random data shuffling.
        • m_Tree
          weka.classifiers.trees.REPTree.Tree m_Tree
          The Tree object
        • m_zeroR
          ZeroR m_zeroR
          ZeroR model that is used if no attributes are present.
    • Class weka.classifiers.trees.REPTree.Tree

      class Tree extends Object implements Serializable
      serialVersionUID:
      -1635481717888437935L
      • Serialized Fields

        • m_Attribute
          int m_Attribute
          The attribute to split on.
        • 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.
        • m_Info
          Instances m_Info
          The header information (for printing the tree).
        • m_Prop
          double[] m_Prop
          The proportions of training instances going down each branch.
        • m_SplitPoint
          double m_SplitPoint
          The split point.
        • m_Successors
          weka.classifiers.trees.REPTree.Tree[] m_Successors
          The subtrees of this tree.
    • Class weka.classifiers.trees.SimpleCart

      class SimpleCart extends RandomizableClassifier implements Serializable
      serialVersionUID:
      4154189200352566053L
      • Serialized Fields

        • m_Alpha
          double m_Alpha
          Alpha-value (for pruning) at the node.
        • m_Attribute
          Attribute m_Attribute
          Attribute used to split data.
        • m_ClassAttribute
          Attribute m_ClassAttribute
          Class attriubte of data.
        • m_ClassProbs
          double[] m_ClassProbs
          Class probabilities.
        • m_ClassValue
          double m_ClassValue
          Class value if the node is leaf.
        • 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_isLeaf
          boolean m_isLeaf
          Indicate if the node is a leaf node.
        • 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_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_Props
          double[] m_Props
          Proportion for each branch.
        • m_Prune
          boolean m_Prune
          If use minimal cost-compexity pruning.
        • m_SizePer
          double m_SizePer
          Training data size.
        • m_SplitString
          String m_SplitString
          Split subset used to split data for nominal attributes.
        • m_SplitValue
          double m_SplitValue
          Split point for a numeric attribute.
        • m_Successors
          SimpleCart[] m_Successors
          Successor nodes.
        • m_totalTrainInstances
          int m_totalTrainInstances
          Total number of instances used to build the classifier.
        • m_train
          Instances m_train
          Training data.
        • m_UseOneSE
          boolean m_UseOneSE
          If use the 1SE rule to make final decision tree.
    • Class weka.classifiers.trees.UserClassifier

      class UserClassifier extends Classifier implements Serializable
      serialVersionUID:
      6483901103562809843L
      • Serialized Fields

        • 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_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_propertyDialog
          PropertyDialog m_propertyDialog
          A window for selecting other classifiers.
        • m_top
          weka.classifiers.trees.UserClassifier.TreeClass m_top
          Two references to the structure of the decision tree.
  • Package weka.classifiers.trees.adtree

  • Package weka.classifiers.trees.ft

    • Class weka.classifiers.trees.ft.FTInnerNode

      class FTInnerNode extends FTtree implements Serializable
      serialVersionUID:
      -1125334488640233181L
    • Class weka.classifiers.trees.ft.FTLeavesNode

      class FTLeavesNode extends FTtree implements Serializable
      serialVersionUID:
      950601378326259315L
    • Class weka.classifiers.trees.ft.FTNode

      class FTNode extends FTtree implements Serializable
      serialVersionUID:
      2317688685139295063L
    • Class weka.classifiers.trees.ft.FTtree

      class FTtree extends LogisticBase implements Serializable
      serialVersionUID:
      1862737145870398755L
      • Serialized Fields

        • m_auxLocalModel
          ClassifierSplitModel m_auxLocalModel
          Auxiliary copy ClassifierSplitModel (for splitting)
        • m_CF
          float m_CF
          Confidence level
        • m_constError
          double m_constError
          Constructor error
        • m_hasConstr
          boolean m_hasConstr
          True if node has or splits on constructor
        • m_higherRegressions
          SimpleLinearRegression[][] m_higherRegressions
          Simple regression functions fit by LogitBoost at higher levels in the tree
        • m_id
          int m_id
          Node id
        • m_isLeaf
          boolean m_isLeaf
          True if node is leaf
        • m_leafclass
          int m_leafclass
          Stores leaf class value
        • m_leafModelNum
          int m_leafModelNum
          ID of logistic model at leaf
        • m_localModel
          ClassifierSplitModel m_localModel
          The ClassifierSplitModel (for splitting)
        • 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_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_sons
          FTtree[] m_sons
          Array of children of the node
        • m_totalInstanceWeight
          double m_totalInstanceWeight
          Total number of training instances.
  • Package weka.classifiers.trees.j48

  • Package weka.classifiers.trees.lmt

    • Class weka.classifiers.trees.lmt.LMTNode

      class LMTNode extends LogisticBase implements Serializable
      serialVersionUID:
      1862737145870398755L
      • Serialized Fields

        • m_alpha
          double m_alpha
          Alpha-value (for pruning) at the node
        • m_fastRegression
          boolean m_fastRegression
          Use heuristic that determines the number of LogitBoost iterations only once in the beginning?
        • m_higherRegressions
          SimpleLinearRegression[][] m_higherRegressions
          Simple regression functions fit by LogitBoost at higher levels in the tree
        • m_id
          int m_id
          Node id
        • m_isLeaf
          boolean m_isLeaf
          True if node is leaf
        • m_leafModelNum
          int m_leafModelNum
          ID of logistic model at leaf
        • m_localModel
          ClassifierSplitModel m_localModel
          The ClassifierSplitModel (for splitting)
        • 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_numHigherRegressions
          int m_numHigherRegressions
          Number of simple regression functions fit by LogitBoost at higher levels in the tree
        • 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_numInstances
          int m_numInstances
          Number of instances at the node
        • m_sons
          LMTNode[] m_sons
          Array of children of the node
        • m_totalInstanceWeight
          double m_totalInstanceWeight
          Total number of training instances.
    • Class weka.classifiers.trees.lmt.LogisticBase

      class LogisticBase extends Classifier implements Serializable
      serialVersionUID:
      168765678097825064L
      • Serialized Fields

        • 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_maxIterations
          int m_maxIterations
          The maximum number of LogitBoost iterations
        • m_numClasses
          int m_numClasses
          The number of different classes
        • m_numericData
          Instances m_numericData
          Numeric version of the training data. Original class is replaced by a numeric pseudo-class.
        • m_numericDataHeader
          Instances m_numericDataHeader
          Header-only version of the numeric version of the training data
        • m_numParameters
          double m_numParameters
          Effective number of parameters used for AIC / BIC automatic stopping
        • m_numRegressions
          int m_numRegressions
          The number of LogitBoost iterations performed.
        • m_regressions
          SimpleLinearRegression[][] m_regressions
          Array holding the simple regression functions fit by LogitBoost
        • m_train
          Instances m_train
          Training data
        • m_useAIC
          boolean m_useAIC
          If true, the AIC is used to choose the best iteration
        • m_useCrossValidation
          boolean m_useCrossValidation
          Use cross-validation to determine best number of LogitBoost iterations ?
        • 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

      class ResidualModelSelection extends ModelSelection implements Serializable
      serialVersionUID:
      -293098783159385148L
      • Serialized Fields

        • m_minInfoGain
          double m_minInfoGain
          Minimum information gain for split
        • m_minNumInstances
          int m_minNumInstances
          Minimum number of instances for leaves
    • Class weka.classifiers.trees.lmt.ResidualSplit

      class ResidualSplit extends ClassifierSplitModel implements Serializable
      serialVersionUID:
      -5055883734183713525L
      • Serialized Fields

        • m_attIndex
          int m_attIndex
          The index of the attribute selected for the split
        • m_attribute
          Attribute m_attribute
          The attribute selected for the split
        • m_data
          Instances m_data
          The set of instances
        • m_dataWs
          double[][] m_dataWs
          The LogitBoost-weights for the set of instances
        • m_dataZs
          double[][] m_dataZs
          The Z-values (LogitBoost response) for the set of instances
        • m_numClasses
          int m_numClasses
          Number of classed
        • m_numInstances
          int m_numInstances
          Number of instances in the set
        • m_splitPoint
          double m_splitPoint
          The split point (for numeric attributes)
  • Package weka.classifiers.trees.m5

    • Class weka.classifiers.trees.m5.CorrelationSplitInfo

      class CorrelationSplitInfo extends Object implements Serializable
      serialVersionUID:
      4212734895125452770L
      • Serialized Fields

        • m_first
          int m_first
          the first instance
        • m_last
          int m_last
          the last instance
        • m_maxImpurity
          double m_maxImpurity
          the maximum impurity reduction
        • m_number
          int m_number
          the number of instances
        • m_position
          int m_position
        • m_splitAttr
          int m_splitAttr
          the attribute being tested
        • m_splitValue
          double m_splitValue
          the best value on which to split
    • Class weka.classifiers.trees.m5.M5Base

      class M5Base extends Classifier implements Serializable
      serialVersionUID:
      -4022221950191647679L
      • Serialized Fields

        • m_generateRules
          boolean m_generateRules
          generate a decision list instead of a single tree.
        • m_instances
          Instances m_instances
          the instances covered by the tree/rules
        • m_minNumInstances
          double m_minNumInstances
          The minimum number of instances to allow at a leaf node
        • m_nominalToBinary
          NominalToBinary m_nominalToBinary
          filter to convert nominal attributes to binary
        • m_regressionTree
          boolean m_regressionTree
          Make a regression tree/rule instead of a model tree/rule
        • m_removeUseless
          RemoveUseless m_removeUseless
          for removing useless attributes
        • m_replaceMissing
          ReplaceMissingValues m_replaceMissing
          filter to fill in missing values
        • m_ruleSet
          FastVector m_ruleSet
          the rule set
        • m_saveInstances
          boolean m_saveInstances
          Save instances at each node in an M5 tree for visualization purposes.
        • m_unsmoothedPredictions
          boolean m_unsmoothedPredictions
          use unsmoothed predictions
        • m_useUnpruned
          boolean m_useUnpruned
          Do not prune tree/rules
    • Class weka.classifiers.trees.m5.PreConstructedLinearModel

      class PreConstructedLinearModel extends Classifier implements Serializable
      serialVersionUID:
      2030974097051713247L
      • Serialized Fields

        • m_coefficients
          double[] m_coefficients
          The coefficients
        • m_instancesHeader
          Instances m_instancesHeader
          Holds the instances header for printing the model
        • m_intercept
          double m_intercept
          The intercept
        • m_numParameters
          int m_numParameters
          number of coefficients in the model
    • Class weka.classifiers.trees.m5.Rule

      class Rule extends Object implements Serializable
      serialVersionUID:
      -4458627451682483204L
      • Serialized Fields

        • m_classIndex
          int m_classIndex
          the class index
        • m_covered
          Instances m_covered
          the instances covered by this rule
        • m_globalAbsDev
          double m_globalAbsDev
          the absolute deviation of the class for all the instances
        • m_globalStdDev
          double m_globalStdDev
          the standard deviation of the class for all the instances
        • m_instances
          Instances m_instances
          the instances covered by this rule
        • m_internalNodes
          RuleNode[] m_internalNodes
          the corresponding internal nodes. Used for smoothing rules.
        • m_minNumInstances
          double m_minNumInstances
          The minimum number of instances to allow at a leaf node
        • m_notCovered
          Instances m_notCovered
          the instances not covered by this rule
        • m_numAttributes
          int m_numAttributes
          the number of attributes
        • m_numCovered
          int m_numCovered
          the number of instances covered by this rule
        • m_numInstances
          int m_numInstances
          the number of instances in the dataset
        • m_regressionTree
          boolean m_regressionTree
          Make a regression tree instead of a model tree
        • m_relOps
          int[] m_relOps
          the corresponding relational operators (0 = "invalid input: '<'=", 1 = ">")
        • m_ruleModel
          RuleNode m_ruleModel
          the leaf encapsulating the linear model for this rule
        • m_saveInstances
          boolean m_saveInstances
          Save instances at each node in an M5 tree for visualization purposes.
        • m_smoothPredictions
          boolean m_smoothPredictions
          use the original m5 smoothing procedure
        • 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_topOfTree
          RuleNode m_topOfTree
          the top of the m5 tree for this rule
        • m_useTree
          boolean m_useTree
          use a pruned m5 tree rather than make a rule
        • m_useUnpruned
          boolean m_useUnpruned
          Build unpruned tree/rule
    • Class weka.classifiers.trees.m5.RuleNode

      class RuleNode extends Classifier implements Serializable
      serialVersionUID:
      1979807611124337144L
      • Serialized Fields

        • m_classIndex
          int m_classIndex
          the class index
        • 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_globalAbsDeviation
          double m_globalAbsDeviation
          the absolute deviation of the global class
        • 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_id
          int m_id
          Node id.
        • m_indices
          int[] m_indices
          Indices of the attributes to be used in generating a linear model at this node
        • m_instances
          Instances m_instances
          instances reaching this node
        • m_isLeaf
          boolean m_isLeaf
          Node is a leaf
        • 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_left
          RuleNode m_left
          left child node
        • m_nodeModel
          PreConstructedLinearModel m_nodeModel
          the linear model at this node
        • m_numAttributes
          int m_numAttributes
          the number of attributes
        • m_numInstances
          int m_numInstances
          the number of instances reaching 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_parent
          RuleNode m_parent
          the parent of this node
        • m_pruningMultiplier
          double m_pruningMultiplier
        • m_regressionTree
          boolean m_regressionTree
          Make a regression tree instead of a model tree
        • m_right
          RuleNode m_right
          right child node
        • m_rootMeanSquaredError
          double m_rootMeanSquaredError
          the mean squared error of the model at this node (either linear or subtree)
        • m_saveInstances
          boolean m_saveInstances
          Save the instances at each node (for visualizing in the Explorer's treevisualizer.
        • m_splitAtt
          int m_splitAtt
          attribute this node splits on
        • m_splitNum
          double m_splitNum
          a node will not be split if it contains less then m_splitNum instances
        • m_splitValue
          double m_splitValue
          the value of the split attribute
    • Class weka.classifiers.trees.m5.YongSplitInfo

      class YongSplitInfo extends Object implements Serializable
      serialVersionUID:
      1864267581079767881L
      • Serialized Fields

        • first
          int first
        • last
          int last
        • leftAve
          double leftAve
        • maxImpurity
          double maxImpurity
        • number
          int number
        • position
          int position
        • rightAve
          double rightAve
        • splitAttr
          int splitAttr
        • splitValue
          double splitValue
  • Package weka.clusterers

    • Class weka.clusterers.AbstractClusterer

      class AbstractClusterer extends Object implements Serializable
      serialVersionUID:
      -6099962589663877632L
    • Class weka.clusterers.AbstractDensityBasedClusterer

      class AbstractDensityBasedClusterer extends AbstractClusterer implements Serializable
      serialVersionUID:
      -5950728041704213845L
    • Class weka.clusterers.CLOPE

      class CLOPE extends AbstractClusterer implements Serializable
      serialVersionUID:
      -567567567567588L
      • Serialized Fields

        • clusters
          ArrayList<weka.clusterers.CLOPE.CLOPECluster> clusters
          Array of clusters
        • m_clusterAssignments
          ArrayList<Integer> m_clusterAssignments
        • m_numberOfClusters
          int m_numberOfClusters
          Number of clusters
        • m_numberOfClustersDetermined
          boolean m_numberOfClustersDetermined
          whether the number of clusters was already determined
        • m_numberOfInstances
          int m_numberOfInstances
          Number of instances
        • m_processed_InstanceID
          int m_processed_InstanceID
          Counter for the processed instances
        • m_Repulsion
          double m_Repulsion
          Specifies the repulsion
        • m_RepulsionDefault
          double m_RepulsionDefault
          Specifies the repulsion default
    • Class weka.clusterers.ClusterEvaluation

      class ClusterEvaluation extends Object implements Serializable
      serialVersionUID:
      -830188327319128005L
      • Serialized Fields

        • m_classToCluster
          int[] m_classToCluster
          will hold the mapping of classes to clusters (for class based evaluation)
        • m_clusterAssignments
          double[] m_clusterAssignments
          holds the assigments of instances to clusters for a particular testing dataset
        • m_Clusterer
          Clusterer m_Clusterer
          the clusterer
        • m_clusteringResults
          StringBuffer m_clusteringResults
          holds a string describing the results of clustering the training data
        • m_logL
          double m_logL
          holds the average log likelihood for a particular testing dataset if the clusterer is a DensityBasedClusterer
        • m_numClusters
          int m_numClusters
          holds the number of clusters found by the clusterer
    • Class weka.clusterers.Cobweb

      class Cobweb extends RandomizableClusterer implements Serializable
      serialVersionUID:
      928406656495092318L
      • Serialized Fields

        • m_acuity
          double m_acuity
          Acuity (minimum standard deviation).
        • m_cobwebTree
          weka.clusterers.Cobweb.CNode m_cobwebTree
          Holds the root of the Cobweb tree.
        • m_cutoff
          double m_cutoff
          Cutoff (minimum category utility).
        • m_numberMerges
          int m_numberMerges
          the number of merges that happened
        • 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:
        • 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_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

      class DBSCAN extends AbstractClusterer implements Serializable
      serialVersionUID:
      -1666498248451219728L
      • Serialized Fields

        • clusterID
          int clusterID
          Holds the current clusterID
        • database
          Database database
          The database that is used for DBSCAN
        • database_distanceType
          String database_distanceType
          Holds the distance-type that is used (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject)
        • database_Type
          String database_Type
          Holds the type of the used database (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase)
        • elapsedTime
          double elapsedTime
          Holds the time-value (seconds) for the duration of the clustering-process
        • 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)
        • numberOfGeneratedClusters
          int numberOfGeneratedClusters
          Holds the number of clusters generated
        • processed_InstanceID
          int processed_InstanceID
          Counter for the processed instances
        • replaceMissingValues_Filter
          ReplaceMissingValues replaceMissingValues_Filter
          Replace missing values in training instances
    • Class weka.clusterers.EM

      serialVersionUID:
      8348181483812829475L
      • Serialized Fields

        • m_displayModelInOldFormat
          boolean m_displayModelInOldFormat
          display model output in old-style format
        • 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_loglikely
          double m_loglikely
          the loglikelihood of the data
        • m_max_iterations
          int m_max_iterations
          maximum iterations to perform
        • m_maxValues
          double[] m_maxValues
          attribute max values
        • m_minStdDev
          double m_minStdDev
          default minimum standard deviation
        • m_minStdDevPerAtt
          double[] m_minStdDevPerAtt
        • m_minValues
          double[] m_minValues
          attribute min values
        • 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_modelNormalPrev
          double[][][] m_modelNormalPrev
        • m_modelPrev
          Estimator[][] m_modelPrev
        • m_num_attribs
          int m_num_attribs
          number of attributes
        • m_num_clusters
          int m_num_clusters
          number of clusters selected by the user or cross validation
        • m_num_instances
          int m_num_instances
          number of training instances
        • m_priors
          double[] m_priors
          the prior probabilities for clusters
        • m_priorsPrev
          double[] m_priorsPrev
        • m_replaceMissing
          ReplaceMissingValues m_replaceMissing
          globally replace missing values
        • m_rr
          Random m_rr
          random number generator
        • m_theInstances
          Instances m_theInstances
          training instances
        • m_verbose
          boolean m_verbose
          Verbose?
        • m_weights
          double[][] m_weights
          hold the weights of each instance for each cluster
    • Class weka.clusterers.FarthestFirst

      class FarthestFirst extends RandomizableClusterer implements Serializable
      serialVersionUID:
      7499838100631329509L
      • Serialized Fields

        • m_ClusterCentroids
          Instances m_ClusterCentroids
          holds the cluster centroids
        • m_instances
          Instances m_instances
          training instances, not necessary to keep, could be replaced by m_ClusterCentroids where needed for header info
        • m_Max
          double[] m_Max
          attribute max values
        • m_Min
          double[] m_Min
          attribute min values
        • m_NumClusters
          int m_NumClusters
          number of clusters to generate
        • m_ReplaceMissingFilter
          ReplaceMissingValues m_ReplaceMissingFilter
          replace missing values in training instances
    • Class weka.clusterers.FilteredClusterer

      class 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

      class 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_bPrintNewick
          boolean m_bPrintNewick
        • m_clusters
          weka.clusterers.HierarchicalClusterer.Node[] m_clusters
        • m_DistanceFunction
          DistanceFunction m_DistanceFunction
          distance function used for comparing members of a cluster
        • m_instances
          Instances m_instances
          training data
        • m_nClusterNr
          int[] m_nClusterNr
        • m_nLinkType
          int m_nLinkType
          Holds the Link type used calculate distance between clusters
        • m_nNumClusters
          int m_nNumClusters
          number of clusters desired in clustering
    • Class weka.clusterers.MakeDensityBasedClusterer

      class MakeDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable
      serialVersionUID:
      -5643302427972186631L
      • Serialized Fields

        • m_minStdDev
          double m_minStdDev
          default minimum standard deviation
        • m_model
          DiscreteEstimator[][] m_model
          discrete distributions fitted to each discrete attribute in each cluster
        • m_modelNormal
          double[][][] m_modelNormal
          normal distributions fitted to each numeric attribute in each cluster
        • m_priors
          double[] m_priors
          prior probabilities for the fitted clusters
        • m_replaceMissing
          ReplaceMissingValues m_replaceMissing
          globally replace missing values
        • m_theInstances
          Instances m_theInstances
          holds training instances header information
        • m_wrappedClusterer
          Clusterer m_wrappedClusterer
          The clusterer being wrapped
    • Class weka.clusterers.OPTICS

      class OPTICS extends AbstractClusterer implements Serializable
      serialVersionUID:
      274552680222105221L
      • Serialized Fields

        • database
          Database database
          The database that is used for OPTICS
        • database_distanceType
          String database_distanceType
          Holds the distance-type that is used (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject)
        • database_Type
          String database_Type
          Holds the type of the used database (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase)
        • databaseOutput
          File databaseOutput
          the file to save the generated database object to.
        • elapsedTime
          double elapsedTime
          Holds the time-value (seconds) for the duration of the clustering-process
        • 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)
        • numberOfGeneratedClusters
          int numberOfGeneratedClusters
          Holds the number of clusters generated
        • replaceMissingValues_Filter
          ReplaceMissingValues replaceMissingValues_Filter
          Replace missing values in training instances
        • 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.
        • writeOPTICSresults
          boolean writeOPTICSresults
          Flag that indicates if the results are written to a file or not
    • Class weka.clusterers.RandomizableClusterer

      class RandomizableClusterer extends AbstractClusterer implements Serializable
      serialVersionUID:
      -4819590778152242745L
      • Serialized Fields

        • m_Seed
          int m_Seed
          The random number seed.
        • m_SeedDefault
          int m_SeedDefault
          the default seed value
    • Class weka.clusterers.RandomizableDensityBasedClusterer

      class RandomizableDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable
      serialVersionUID:
      -5325270357918932849L
      • Serialized Fields

        • m_Seed
          int m_Seed
          The random number seed.
        • m_SeedDefault
          int m_SeedDefault
          the default seed value
    • Class weka.clusterers.RandomizableSingleClustererEnhancer

      class RandomizableSingleClustererEnhancer extends AbstractClusterer implements Serializable
      serialVersionUID:
      -644847037106316249L
      • Serialized Fields

        • m_Seed
          int m_Seed
          The random number seed.
        • m_SeedDefault
          int m_SeedDefault
          the default seed value
    • Class weka.clusterers.sIB

      class sIB extends RandomizableClusterer implements Serializable
      serialVersionUID:
      -8652125897352654213L
      • Serialized Fields

        • bestT
          weka.clusterers.sIB.Partition bestT
          Holds the best partition built
        • input
          weka.clusterers.sIB.Input input
          Holds the statistics about the input dataset
        • m_data
          Instances m_data
          Training data
        • m_maxLoop
          int m_maxLoop
          Max number of iterations during each restart
        • m_minChange
          int m_minChange
          Minimum number of changes
        • m_numAttributes
          int m_numAttributes
          Number of attributes
        • m_numCluster
          int m_numCluster
          Number of clusters
        • m_numInstances
          int m_numInstances
          Number of instances
        • m_numRestarts
          int m_numRestarts
          Number of restarts
        • m_replaceMissing
          ReplaceMissingValues m_replaceMissing
          Globally replace missing values
        • m_uniformPrior
          boolean m_uniformPrior
          Uniform prior probability of the documents
        • m_verbose
          boolean m_verbose
          Verbose?
        • random
          Random random
          Randomly generate initial partition
    • Class weka.clusterers.SimpleKMeans

      class SimpleKMeans extends RandomizableClusterer implements Serializable
      serialVersionUID:
      -3235809600124455376L
      • Serialized Fields

        • m_Assignments
          int[] m_Assignments
          Assignments obtained
        • m_ClusterCentroids
          Instances m_ClusterCentroids
          holds the cluster centroids
        • m_ClusterMissingCounts
          int[][] m_ClusterMissingCounts
        • m_ClusterNominalCounts
          int[][][] m_ClusterNominalCounts
          For each cluster, holds the frequency counts for the values of each nominal attribute
        • m_ClusterSizes
          int[] m_ClusterSizes
          The number of instances in each cluster
        • m_ClusterStdDevs
          Instances m_ClusterStdDevs
          Holds the standard deviations of the numeric attributes in each cluster
        • m_displayStdDevs
          boolean m_displayStdDevs
          Display standard deviations for numeric atts
        • m_DistanceFunction
          DistanceFunction m_DistanceFunction
          the distance function used.
        • m_dontReplaceMissing
          boolean m_dontReplaceMissing
          Replace missing values globally?
        • 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_FullMissingCounts
          int[] m_FullMissingCounts
        • m_FullNominalCounts
          int[][] m_FullNominalCounts
        • m_FullStdDevs
          double[] m_FullStdDevs
        • m_Iterations
          int m_Iterations
          Keep track of the number of iterations completed before convergence
        • m_MaxIterations
          int m_MaxIterations
          Maximum number of iterations to be executed
        • m_NumClusters
          int m_NumClusters
          number of clusters to generate
        • m_PreserveOrder
          boolean m_PreserveOrder
          Preserve order of instances
        • m_ReplaceMissingFilter
          ReplaceMissingValues m_ReplaceMissingFilter
          replace missing values in training instances
        • m_squaredErrors
          double[] m_squaredErrors
          Holds the squared errors for all clusters
    • Class weka.clusterers.SingleClustererEnhancer

      class SingleClustererEnhancer extends AbstractClusterer implements Serializable
      serialVersionUID:
      4893928362926428671L
      • Serialized Fields

        • m_Clusterer
          Clusterer m_Clusterer
          the clusterer
    • Class weka.clusterers.XMeans

      class XMeans extends RandomizableClusterer implements Serializable
      serialVersionUID:
      -7941793078404132616L
      • Serialized Fields

        • m_Bic
          double m_Bic
          BIC-Score of the current model.
        • 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_ClusterAssignments
          int[] m_ClusterAssignments
          temporary variable holding cluster assignments while iterating.
        • m_ClusterCenters
          Instances m_ClusterCenters
          cluster centers.
        • m_CurrDebugFlag
          boolean m_CurrDebugFlag
          Flag: I'm debugging.
        • m_CutOffFactor
          double m_CutOffFactor
          cutoff factor - percentage of splits done in Improve-Structure part only relevant, if all children lost.
        • m_DebugLevel
          int m_DebugLevel
          level of debug output, 0 is no output.
        • m_DebugVectors
          Instances m_DebugVectors
          all the debug vectors.
        • m_DebugVectorsFile
          File m_DebugVectorsFile
          file name of the input file for the random vectors.
        • m_DebugVectorsIndex
          int m_DebugVectorsIndex
          the index for the current debug vector.
        • m_DebugVectorsInput
          Reader m_DebugVectorsInput
          input file for the random vectors --> USED FOR DEBUGGING.
        • m_DistanceF
          DistanceFunction m_DistanceF
          the distance function used.
        • m_InputCenterFile
          File m_InputCenterFile
          file name of the output file for the cluster centers.
        • m_Instances
          Instances m_Instances
          training instances.
        • m_IterationCount
          int m_IterationCount
          counts iterations done in main loop.
        • m_KDTree
          KDTree m_KDTree
          KDTrees class if KDTrees are used.
        • m_KMeansStopped
          int m_KMeansStopped
          counter to say how often kMeans was stopped by loop counter.
        • 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_MaxNumClusters
          int m_MaxNumClusters
          max number of clusters to generate.
        • m_MinNumClusters
          int m_MinNumClusters
          min number of clusters to generate.
        • m_Mle
          double[] m_Mle
          Distortion.
        • m_Model
          Instances m_Model
          model information, should increase readability.
        • m_NumClusters
          int m_NumClusters
          The actual number of clusters.
        • 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_OutputCenterFile
          File m_OutputCenterFile
          file name of the output file for the cluster centers.
        • m_ReplaceMissingFilter
          ReplaceMissingValues m_ReplaceMissingFilter
          replace missing values in training instances.
        • m_UseKDTree
          boolean m_UseKDTree
          whether to use the KDTree (the KDTree is only initialized to be configurable from the GUI).
  • Package weka.clusterers.forOPTICSAndDBScan.Databases

    • Class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase

      class SequentialDatabase extends Object implements Serializable
      serialVersionUID:
      787245523118665778L
      • Serialized Fields

        • attributeMaxValues
          double[] attributeMaxValues
          Holds the maximum value for each attribute
        • attributeMinValues
          double[] attributeMinValues
          Holds the minimum value for each attribute
        • instances
          Instances instances
          Holds the original instances delivered from WEKA
        • treeMap
          TreeMap treeMap
          Internal, sorted Treemap for storing all the DataObjects
  • Package weka.clusterers.forOPTICSAndDBScan.DataObjects

    • Class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject

      class EuclideanDataObject extends Object implements Serializable
      serialVersionUID:
      -4408119914898291075L
      • Serialized Fields

        • c_dist
          double c_dist
          Holds the coreDistance for this DataObject
        • clusterID
          int clusterID
          Holds the ID of the cluster, to which this DataObject is assigned
        • database
          Database database
          Holds the database, that is the keeper of this DataObject
        • instance
          Instance instance
          Holds the original instance
        • key
          String key
          Holds the (unique) key that is associated with this DataObject
        • processed
          boolean processed
          Holds the status for this DataObject (true, if it has been processed, else false)
        • r_dist
          double r_dist
          Holds the reachabilityDistance for this DataObject
    • Class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject

      class ManhattanDataObject extends Object implements Serializable
      serialVersionUID:
      -3417720553766544582L
      • Serialized Fields

        • c_dist
          double c_dist
          Holds the coreDistance for this DataObject
        • clusterID
          int clusterID
          Holds the ID of the cluster, to which this DataObject is assigned
        • database
          Database database
          Holds the database, that is the keeper of this DataObject
        • instance
          Instance instance
          Holds the original instance
        • key
          String key
          Holds the (unique) key that is associated with this DataObject
        • processed
          boolean processed
          Holds the status for this DataObject (true, if it has been processed, else false)
        • r_dist
          double r_dist
          Holds the reachabilityDistance for this DataObject
  • Package weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI

    • Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel

      class GraphPanel extends JComponent implements Serializable
      serialVersionUID:
      7917937528738361470L
      • Serialized Fields

        • coreDistanceColor
          Color coreDistanceColor
          Specifies the color for displaying core-distances
        • reachabilityDistanceColor
          Color reachabilityDistanceColor
          Specifies the color for displaying reachability-distances
        • recentIndex
          int recentIndex
          Holds the index of the last toolTip
        • resultVector
          FastVector resultVector
          Holds the clustering results
        • showCoreDistances
          boolean showCoreDistances
          Holds the flag for showCoreDistances
        • showReachabilityDistances
          boolean showReachabilityDistances
          Holds the flag for showrRechabilityDistances
        • 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
        • widthSlider
          int widthSlider
          Specifies the width for displaying the distances
    • Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel

      class ResultVectorTableModel extends 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

      class SERObject extends Object implements Serializable
      serialVersionUID:
      -6022057864970639151L
      • Serialized Fields

        • database_distanceType
          String database_distanceType
        • database_Type
          String database_Type
        • databaseSize
          int databaseSize
        • elapsedTime
          String elapsedTime
        • epsilon
          double epsilon
        • minPoints
          int minPoints
        • numberOfAttributes
          int numberOfAttributes
        • numberOfGeneratedClusters
          int numberOfGeneratedClusters
        • opticsOutputs
          boolean opticsOutputs
        • resultVector
          FastVector resultVector
  • Package weka.core

    • Class weka.core.AbstractStringDistanceFunction

      class AbstractStringDistanceFunction extends NormalizableDistance implements Serializable
    • Class weka.core.AlgVector

      class AlgVector extends Object implements Serializable
      serialVersionUID:
      -4023736016850256591L
      • Serialized Fields

        • m_Elements
          double[] m_Elements
          The values of the matrix
    • Class weka.core.Attribute

      class Attribute extends Object implements Serializable
      serialVersionUID:
      -742180568732916383L
      • Serialized Fields

        • m_DateFormat
          SimpleDateFormat m_DateFormat
          Date format specification for date attributes
        • m_Hashtable
          Hashtable m_Hashtable
          Mapping of values to indices (if nominal or string).
        • m_HasZeropoint
          boolean m_HasZeropoint
          Whether the attribute has a zeropoint.
        • m_Header
          Instances m_Header
          The header information for a relation-valued attribute.
        • m_Index
          int m_Index
          The attribute's index.
        • m_IsAveragable
          boolean m_IsAveragable
          Whether the attribute is averagable.
        • m_IsRegular
          boolean m_IsRegular
          Whether the attribute is regular.
        • m_LowerBound
          double m_LowerBound
          The attribute's lower numeric bound.
        • m_LowerBoundIsOpen
          boolean m_LowerBoundIsOpen
          Whether the lower bound is open.
        • m_Metadata
          ProtectedProperties m_Metadata
          The attribute's metadata.
        • m_Name
          String m_Name
          The attribute's name.
        • m_Ordering
          int m_Ordering
          The attribute's ordering.
        • m_Type
          int m_Type
          The attribute's type.
        • m_UpperBound
          double m_UpperBound
          The attribute's upper numeric bound.
        • m_UpperBoundIsOpen
          boolean m_UpperBoundIsOpen
          Whether the upper bound is open
        • m_Values
          FastVector m_Values
          The attribute's values (if nominal or string).
        • m_Weight
          double m_Weight
          The attribute's weight.
    • Class weka.core.AttributeExpression

      class AttributeExpression extends Object implements Serializable
      serialVersionUID:
      402130123261736245L
      • Serialized Fields

        • m_operatorStack
          Stack m_operatorStack
          Operator stack
        • m_originalInfix
          String m_originalInfix
          Holds the original infix expression
        • m_postFixExpVector
          Vector m_postFixExpVector
          Holds the expression in postfix form
        • m_previousTok
          String m_previousTok
          Holds the previous token
        • m_signMod
          boolean m_signMod
          True if the next numeric constant or attribute index is negative
    • Class weka.core.AttributeLocator

      class AttributeLocator extends Object implements Serializable
      serialVersionUID:
      -2932848827681070345L
      • Serialized Fields

        • m_AllowedIndices
          int[] m_AllowedIndices
          the attribute indices that may be inspected
        • m_Attributes
          Vector<Boolean> m_Attributes
          contains the attribute locations, either true or false Boolean objects
        • m_AttributesEfficient
          BitSet m_AttributesEfficient
          contains the attribute locations, either true or false (efficient replacement)
        • 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
        • m_Locators
          Vector<AttributeLocator> m_Locators
          contains the locator locations, either null or a AttributeLocator reference
        • m_Type
          int m_Type
          the type of the attribute
    • Class weka.core.AttributeStats

      class AttributeStats extends Object implements Serializable
      serialVersionUID:
      4434688832743939380L
      • Serialized Fields

        • distinctCount
          int distinctCount
          The number of distinct values
        • intCount
          int intCount
          The number of int-like values
        • missingCount
          int missingCount
          The number of missing values
        • nominalCounts
          int[] nominalCounts
          Counts of each nominal value
        • numericStats
          Stats numericStats
          Stats on numeric value distributions
        • realCount
          int realCount
          The number of real-like values (i.e. have a fractional part)
        • totalCount
          int totalCount
          The total number of values (i.e. number of instances)
        • uniqueCount
          int uniqueCount
          The number of values that only appear once
    • Class weka.core.BinarySparseInstance

      class BinarySparseInstance extends SparseInstance implements Serializable
      serialVersionUID:
      -5297388762342528737L
    • Class weka.core.Capabilities

      class Capabilities extends Object implements Serializable
      serialVersionUID:
      -5478590032325567849L
      • Serialized Fields

        • m_AttributeTest
          boolean m_AttributeTest
          whether to perform attribute based tests
        • m_Capabilities
          HashSet m_Capabilities
          the hashset for storing the active capabilities
        • m_Dependencies
          HashSet m_Dependencies
          the hashset for storing dependent capabilities, eg for meta-classifiers
        • m_FailReason
          Exception m_FailReason
          the reason why the test failed, used to throw an exception
        • m_InstancesTest
          boolean m_InstancesTest
          whether to perform data based tests
        • m_MinimumNumberInstances
          int m_MinimumNumberInstances
          the minimum number of instances in a dataset
        • m_MinimumNumberInstancesTest
          boolean m_MinimumNumberInstancesTest
          whether to test for minimum number of instances
        • m_MissingClassValuesTest
          boolean m_MissingClassValuesTest
          whether to test for missing class values
        • m_MissingValuesTest
          boolean m_MissingValuesTest
          whether to test for missing values
        • m_Owner
          CapabilitiesHandler m_Owner
          the object that owns this capabilities instance
        • m_Test
          boolean m_Test
          whether to perform any tests at all
    • Class weka.core.ChebyshevDistance

      class ChebyshevDistance extends NormalizableDistance implements Serializable
      serialVersionUID:
      -7739904999895461429L
    • Class weka.core.Debug

      class Debug extends Object implements Serializable
      serialVersionUID:
      66171861743328020L
      • Serialized Fields

        • m_Clock
          Debug.Clock m_Clock
          for clocking
        • m_Enabled
          boolean m_Enabled
          whether logging is enabled
        • m_Log
          Debug.Log m_Log
          for logging
    • Class weka.core.Debug.Clock

      class Clock extends Object implements Serializable
      serialVersionUID:
      4622161807307942201L
      • Serialized Fields

        • m_CanMeasureCpuTime
          boolean m_CanMeasureCpuTime
          whether the system can measure the CPU time
        • m_OutputFormat
          int m_OutputFormat
          the format of the output
        • m_Running
          boolean m_Running
          whether the time is still clocked
        • m_Start
          long m_Start
          the start time
        • m_Stop
          long m_Stop
          the end time
        • m_ThreadID
          long m_ThreadID
          the thread ID
        • m_UseCpuTime
          boolean m_UseCpuTime
          whether to use the CPU time (by default TRUE)
    • Class weka.core.Debug.DBO

      class DBO extends Object implements Serializable
      serialVersionUID:
      -5245628124742606784L
      • Serialized Fields

        • m_outputTypes
          Range m_outputTypes
          range of outputtyp
        • m_verboseOn
          boolean m_verboseOn
          enables/disables output of debug information
    • Class weka.core.Debug.Log

      class Log extends Object implements Serializable
      serialVersionUID:
      1458435732111675823L
      • Serialized Fields

        • m_Filename
          String m_Filename
          the filename, if any
        • m_LoggerInitFailed
          boolean m_LoggerInitFailed
          whether the initialization of the logger failed
        • m_NumFiles
          int m_NumFiles
          the number of files for rotating the logs
        • m_Size
          int m_Size
          the size of the file (in bytes)
    • Class weka.core.Debug.Random

      class Random extends 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

      class SimpleLog extends Object implements Serializable
      serialVersionUID:
      -2671928223819510830L
      • Serialized Fields

        • m_Filename
          String m_Filename
          the file to write to (if null then only stdout is used)
    • Class weka.core.Debug.Timestamp

      class Timestamp extends Object implements Serializable
      serialVersionUID:
      -6099868388466922753L
      • Serialized Fields

        • m_Format
          String m_Format
          the format of the timestamp
        • m_Formatter
          SimpleDateFormat m_Formatter
          handles the format of the output
        • m_Stamp
          Date m_Stamp
          the actual date
    • Class weka.core.EditDistance

      class EditDistance extends AbstractStringDistanceFunction implements Serializable
    • Class weka.core.EuclideanDistance

      class EuclideanDistance extends NormalizableDistance implements Serializable
      serialVersionUID:
      1068606253458807903L
    • Class weka.core.FastVector

      class FastVector extends Object implements Serializable
      serialVersionUID:
      -2173635135622930169L
      • Serialized Fields

        • m_CapacityIncrement
          int m_CapacityIncrement
          The capacity increment
        • m_CapacityMultiplier
          int m_CapacityMultiplier
          The capacity multiplier.
        • m_Objects
          Object[] m_Objects
          The array of objects.
        • m_Size
          int m_Size
          The current size;
    • Class weka.core.Instance

      class Instance extends Object implements Serializable
      serialVersionUID:
      1482635194499365122L
      • Serialized Fields

        • m_AttValues
          double[] m_AttValues
          The instance's attribute values.
        • 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_Weight
          double m_Weight
          The instance's weight.
    • Class weka.core.InstanceComparator

      class InstanceComparator extends Object implements Serializable
      serialVersionUID:
      -6589278678230949683L
      • Serialized Fields

        • m_IncludeClass
          boolean m_IncludeClass
          whether to include the class in the comparison
    • Class weka.core.Instances

      class Instances extends Object implements Serializable
      serialVersionUID:
      -19412345060742748L
      • Serialized Fields

        • m_Attributes
          FastVector m_Attributes
          The attribute information.
        • m_ClassIndex
          int m_ClassIndex
          The class attribute's index
        • m_Instances
          FastVector m_Instances
          The instances.
        • 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:
        • m_RelationName
          String m_RelationName
          The dataset's name.
    • Class weka.core.Jython

      class Jython extends Object implements Serializable
      serialVersionUID:
      -6972298704460209252L
      • Serialized Fields

        • m_Interpreter
          Object m_Interpreter
          the interpreter
    • Class weka.core.ManhattanDistance

      class ManhattanDistance extends NormalizableDistance implements Serializable
      serialVersionUID:
      6783782554224000243L
    • Class weka.core.Matrix

      class Matrix extends Object implements Serializable
      serialVersionUID:
      -3604757095849145838L
      • Serialized Fields

        • m_Matrix
          Matrix m_Matrix
          Deprecated.
          The actual matrix
    • Class weka.core.NormalizableDistance

      class NormalizableDistance extends Object implements Serializable
      • Serialized Fields

        • m_ActiveIndices
          boolean[] m_ActiveIndices
          The boolean flags, whether an attribute will be used or not.
        • m_AttributeIndices
          Range m_AttributeIndices
          The range of attributes to use for calculating the distance.
        • 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_Validated
          boolean m_Validated
          Whether all the necessary preparations have been done.
    • Exception Class weka.core.NoSupportForMissingValuesException

      class NoSupportForMissingValuesException extends WekaException implements Serializable
      serialVersionUID:
      5161175307725893973L
    • Class weka.core.ProtectedProperties

      class ProtectedProperties extends 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

      class Queue extends 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_Size
          int m_Size
          Store the c m_Tail.m_Nexturrent number of elements in the queue
        • m_Tail
          weka.core.Queue.QueueNode m_Tail
          Store a reference to the tail of the queue
    • Class weka.core.Queue.QueueNode

      class QueueNode extends Object implements Serializable
      serialVersionUID:
      -5119358279412097455L
      • Serialized Fields

        • m_Contents
          Object m_Contents
          The nodes contents
        • m_Next
          weka.core.Queue.QueueNode m_Next
          The next node in the queue
    • Class weka.core.RandomVariates

      class RandomVariates extends Random implements Serializable
      serialVersionUID:
      -4763742718209460354L
    • Class weka.core.Range

      class Range extends Object implements Serializable
      serialVersionUID:
      3667337062176835900L
      • Serialized Fields

        • m_Invert
          boolean m_Invert
          Whether matching should be inverted
        • m_RangeStrings
          Vector m_RangeStrings
          Record the string representations of the columns to delete
        • 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

      class RelationalLocator extends AttributeLocator implements Serializable
      serialVersionUID:
      4646872277151854732L
    • Class weka.core.SerializedObject

      class SerializedObject extends Object implements Serializable
      serialVersionUID:
      6635502953928860434L
      • Serialized Fields

        • 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
        • m_storedObjectArray
          byte[] m_storedObjectArray
          The array storing the object.
    • Class weka.core.SingleIndex

      class SingleIndex extends Object implements Serializable
      serialVersionUID:
      5285169134430839303L
      • Serialized Fields

        • m_IndexString
          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

      class 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

      class StringLocator extends AttributeLocator implements Serializable
      serialVersionUID:
      7805522230268783972L
    • Class weka.core.Tag

      class Tag extends Object implements Serializable
      serialVersionUID:
      3326379903447135320L
      • Serialized Fields

        • m_ID
          int m_ID
          The ID
        • m_IDStr
          String m_IDStr
          The unique string for this tag, doesn't have to be numeric
        • m_Readable
          String m_Readable
          The descriptive text
    • Class weka.core.TestInstances

      class TestInstances extends Object implements Serializable
      serialVersionUID:
      -6263968936330390469L
      • Serialized Fields

        • m_ClassIndex
          int m_ClassIndex
          the class index (-1 is LAST, -2 means no class)
          See Also:
        • m_ClassType
          int m_ClassType
          the class type
        • m_Data
          Instances m_Data
          the generated data
        • m_Handler
          CapabilitiesHandler m_Handler
          the CapabilitiesHandler to get the Capabilities from
        • m_MultiInstance
          boolean m_MultiInstance
          whether to generate Multi-Instance data or not
        • m_NumClasses
          int m_NumClasses
          the number of classes (in case of NOMINAL class)
        • m_NumDate
          int m_NumDate
          the number of date attributes
        • m_NumInstances
          int m_NumInstances
          the number of instances
        • m_NumInstancesRelational
          int m_NumInstancesRelational
          the number of instances in relational attributes (applies also for bags in multi-instance)
        • 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_NumRelational
          int m_NumRelational
          the number of relational attributes
        • m_NumRelationalDate
          int m_NumRelationalDate
          the number of date attributes in a relational attribute
        • 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_NumString
          int m_NumString
          the number of string attributes
        • m_Random
          Random m_Random
          the random number generator
        • m_Relation
          String m_Relation
          the name of the relation
        • m_RelationalClassFormat
          Instances m_RelationalClassFormat
          the format of the multi-instance data of the class
        • m_RelationalFormat
          Instances[] m_RelationalFormat
          the format of the multi-instance data
        • m_Seed
          int m_Seed
          the seed value
        • m_Words
          String[] m_Words
          for generating String attributes/classes
        • m_WordSeparators
          String m_WordSeparators
          for generating String attributes/classes
    • Class weka.core.Trie

      class Trie extends Object implements Serializable
      serialVersionUID:
      -5897980928817779048L
      • Serialized Fields

        • 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
        • m_Root
          Trie.TrieNode m_Root
          the root node
    • Class weka.core.Trie.TrieNode

      class TrieNode extends DefaultMutableTreeNode implements Serializable
      serialVersionUID:
      -2252907099391881148L
    • Exception Class weka.core.UnassignedClassException

      class UnassignedClassException extends RuntimeException implements Serializable
      serialVersionUID:
      6268278694768818235L
    • Exception Class weka.core.UnassignedDatasetException

      class UnassignedDatasetException extends RuntimeException implements Serializable
      serialVersionUID:
      -9000116786626328854L
    • Exception Class weka.core.UnsupportedAttributeTypeException

      class UnsupportedAttributeTypeException extends WekaException implements Serializable
      serialVersionUID:
      2658987325328414838L
    • Exception Class weka.core.UnsupportedClassTypeException

      class UnsupportedClassTypeException extends WekaException implements Serializable
      serialVersionUID:
      5175741076972192151L
    • Exception Class weka.core.WekaException

      class WekaException extends Exception implements Serializable
      serialVersionUID:
      6428269989006208585L
  • Package weka.core.converters

    • Class weka.core.converters.AbstractFileLoader

      class AbstractFileLoader extends AbstractLoader implements Serializable
      • Serialized Fields

        • m_File
          String m_File
          the file
        • m_sourceFile
          File m_sourceFile
          Holds the source of the data set.
        • m_useRelativePath
          boolean m_useRelativePath
          use relative file paths
    • Class weka.core.converters.AbstractFileSaver

      class AbstractFileSaver extends AbstractSaver implements Serializable
      • Serialized Fields

        • FILE_EXTENSION
          String FILE_EXTENSION
          The file extension of the destination file.
        • FILE_EXTENSION_COMPRESSED
          String FILE_EXTENSION_COMPRESSED
          the extension for compressed files
        • m_dir
          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_outputFile
          File m_outputFile
          The destination file.
        • m_prefix
          String m_prefix
          The prefix for the filename (chosen in the GUI).
        • m_useRelativePath
          boolean m_useRelativePath
          use relative file paths
    • Class weka.core.converters.AbstractLoader

      class AbstractLoader extends Object implements Serializable
      • Serialized Fields

        • m_retrieval
          int m_retrieval
          The current retrieval mode
    • Class weka.core.converters.AbstractSaver

      class AbstractSaver extends 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

      class ArffLoader extends AbstractFileLoader implements Serializable
      serialVersionUID:
      2726929550544048587L
      • Serialized Fields

    • Class weka.core.converters.ArffSaver

      class ArffSaver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      2223634248900042228L
      • Serialized Fields

        • m_CompressOutput
          boolean m_CompressOutput
          whether to compress the output
    • Class weka.core.converters.C45Loader

      class C45Loader extends AbstractFileLoader implements Serializable
      serialVersionUID:
      5454329403218219L
      • Serialized Fields

        • m_fileStem
          String m_fileStem
          Holds the filestem.
        • m_ignore
          boolean[] m_ignore
          Which attributes are ignore or label. These are *not* included in the arff representation.
        • m_numAttribs
          int m_numAttribs
          Number of attributes in the data (including ignore and label attributes).
        • m_sourceFileData
          File m_sourceFileData
          Describe variable m_sourceFileData here.
    • Class weka.core.converters.C45Saver

      class C45Saver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      -821428878384253377L
    • Class weka.core.converters.ConverterUtils

      class ConverterUtils extends Object implements Serializable
      serialVersionUID:
      -2460855349276148760L
    • Class weka.core.converters.ConverterUtils.DataSink

      class DataSink extends Object implements Serializable
      serialVersionUID:
      -1504966891136411204L
      • Serialized Fields

        • m_Saver
          Saver m_Saver
          the saver to use for storing the data.
        • m_Stream
          OutputStream m_Stream
          the stream to store the data in (always in ARFF format).
    • Class weka.core.converters.ConverterUtils.DataSource

      class DataSource extends Object implements Serializable
      serialVersionUID:
      -613122395928757332L
      • Serialized Fields

        • m_BatchBuffer
          Instances m_BatchBuffer
          the batch buffer.
        • m_BatchCounter
          int m_BatchCounter
          the instance counter for the batch case.
        • m_File
          File m_File
          the file to load.
        • m_Incremental
          boolean m_Incremental
          whether the loader is incremental.
        • m_IncrementalBuffer
          Instance m_IncrementalBuffer
          the last internally read instance.
        • m_Loader
          Loader m_Loader
          the loader.
        • m_URL
          URL m_URL
          the URL to load.
    • Class weka.core.converters.CSVLoader

      class CSVLoader extends AbstractFileLoader implements Serializable
      serialVersionUID:
      5607529739745491340L
      • Serialized Fields

        • m_cumulativeInstances
          FastVector m_cumulativeInstances
          Holds instances accumulated so far.
        • m_cumulativeStructure
          FastVector m_cumulativeStructure
          A list of hash tables for accumulating nominal values during parsing.
        • m_dateAttributes
          Range m_dateAttributes
          The range of attributes to force to type date
        • m_dateFormat
          String m_dateFormat
          The formatting string to use to parse dates
        • m_Enclosures
          String m_Enclosures
          enclosure character(s) to use for strings
        • m_FirstCheck
          boolean m_FirstCheck
          whether the first row has been read.
        • m_formatter
          SimpleDateFormat m_formatter
          The formatter to use on dates
        • m_MissingValue
          String m_MissingValue
          The placeholder for missing values.
        • 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.
    • Class weka.core.converters.CSVSaver

      class CSVSaver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      476636654410701807L
    • Class weka.core.converters.DatabaseConnection

      class DatabaseConnection extends DatabaseUtils implements Serializable
      serialVersionUID:
      1673169848863178695L
    • Class weka.core.converters.DatabaseLoader

      class DatabaseLoader extends AbstractLoader implements Serializable
      serialVersionUID:
      -7936159015338318659L
      • Serialized Fields

        • 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_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_counter
          int m_counter
          Indicates how many rows has already been loaded incrementally
        • m_DataBaseConnection
          DatabaseConnection m_DataBaseConnection
          The database connection
        • m_datasetPseudoInc
          Instances m_datasetPseudoInc
          Used in pseudoincremental mode. The whole dataset from which instances will be read incrementally.
        • m_firstTime
          boolean m_firstTime
          Flag indicating that incremental process wants to read first instance
        • m_idColumn
          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_inc
          boolean m_inc
          Flag indicating that incremental mode is chosen (for command line use only)
        • m_Keys
          String m_Keys
          the keys for unique ordering
        • m_nominalIndexes
          Hashtable[] m_nominalIndexes
          Stores the index of a nominal value
        • m_nominalStrings
          FastVector[] m_nominalStrings
          Stores the nominal value
        • 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_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_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_Password
          String m_Password
          the database password to use
        • 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_query
          String m_query
          The user defined query to load instances. (form: SELECT *|invalid input: '&ltcolumn'-list> FROM invalid input: '&lttable'> [WHERE <condition>])
        • m_rowCount
          int m_rowCount
          The number of rows obtained by m_query, eg the size of the ResultSet to load
        • m_structure
          Instances m_structure
          The header information that is retrieved in the beginning of incremental loading
        • m_URL
          String m_URL
          the JDBC URL to use
        • m_User
          String m_User
          the database user to use
    • Class weka.core.converters.DatabaseSaver

      class DatabaseSaver extends AbstractSaver implements Serializable
      serialVersionUID:
      863971733782624956L
      • Serialized Fields

        • m_count
          int m_count
          counts the rows and used as a primary key value.
        • m_createDate
          String m_createDate
          The database specific type for a date (read in from the properties file).
        • m_createDouble
          String m_createDouble
          The database specific type for a double (read in from the properties file).
        • m_createInt
          String m_createInt
          The database specific type for an int (read in from the properties file).
        • m_createText
          String m_createText
          The database specific type for a string (read in from the properties file).
        • m_DataBaseConnection
          DatabaseConnection m_DataBaseConnection
          The database connection.
        • m_DateFormat
          SimpleDateFormat m_DateFormat
          For converting the date value into a database string.
        • m_id
          boolean m_id
          Flag indicating if a primary key column should be added.
        • m_idColumn
          String m_idColumn
          The name of the primary key column that will be automatically generated (if enabled). The name is read from DatabaseUtils.
        • m_inputFile
          String m_inputFile
          An input arff file (for command line use).
        • m_Password
          String m_Password
          the password for the database.
        • m_tableName
          String m_tableName
          The name of the table in which the instances should be stored.
        • m_tabName
          boolean m_tabName
          Flag indicating whether the default name of the table is the relaion name or not.
        • m_Username
          String m_Username
          the user name for the database.
    • Class weka.core.converters.LibSVMLoader

      class LibSVMLoader extends AbstractFileLoader implements Serializable
      serialVersionUID:
      4988360125354664417L
      • Serialized Fields

        • m_Buffer
          Vector m_Buffer
          the buffer of the rows read so far.
        • m_URL
          String m_URL
          the url.
    • Class weka.core.converters.LibSVMSaver

      class LibSVMSaver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      2792295817125694786L
      • Serialized Fields

        • m_ClassIndex
          SingleIndex m_ClassIndex
          the class index
    • Class weka.core.converters.SerializedInstancesLoader

      class 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

      class SerializedInstancesSaver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      -7717010648500658872L
    • Class weka.core.converters.SVMLightLoader

      class SVMLightLoader extends AbstractFileLoader implements Serializable
      serialVersionUID:
      4988360125354664417L
      • Serialized Fields

        • m_Buffer
          Vector m_Buffer
          the buffer of the rows read so far.
        • m_URL
          String m_URL
          the url.
    • Class weka.core.converters.SVMLightSaver

      class SVMLightSaver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      2605714599263995835L
      • Serialized Fields

        • m_ClassIndex
          SingleIndex m_ClassIndex
          the class index.
    • Class weka.core.converters.TextDirectoryLoader

      class TextDirectoryLoader extends AbstractLoader implements Serializable
      serialVersionUID:
      2592118773712247647L
      • Serialized Fields

        • m_charSet
          String m_charSet
          The charset to use when loading text files (default is to just use the default charset).
        • 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_sourceFile
          File m_sourceFile
          Holds the source of the data set.
        • m_structure
          Instances m_structure
          Holds the determined structure (header) of the data set.
    • Class weka.core.converters.XRFFLoader

      class XRFFLoader extends AbstractFileLoader implements Serializable
      serialVersionUID:
      3764533621135196582L
      • Serialized Fields

        • m_URL
          String m_URL
          the url
        • m_XMLInstances
          XMLInstances m_XMLInstances
          the loaded XML document
    • Class weka.core.converters.XRFFSaver

      class XRFFSaver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      -7226404765213522043L
      • Serialized Fields

        • m_ClassIndex
          SingleIndex m_ClassIndex
          the class index
        • m_CompressOutput
          boolean m_CompressOutput
          whether to compress the output
        • m_XMLInstances
          XMLInstances m_XMLInstances
          the generated XML document
  • Package weka.core.matrix

    • Class weka.core.matrix.CholeskyDecomposition

      class CholeskyDecomposition extends Object implements Serializable
      serialVersionUID:
      -8739775942782694701L
      • Serialized Fields

        • isspd
          boolean isspd
          Symmetric and positive definite flag.
          is symmetric and positive definite flag.
        • L
          double[][] L
          Array for internal storage of decomposition.
          internal array storage.
        • n
          int n
          Row and column dimension (square matrix).
          matrix dimension.
    • Class weka.core.matrix.EigenvalueDecomposition

      class EigenvalueDecomposition extends Object implements Serializable
      serialVersionUID:
      4011654467211422319L
      • Serialized Fields

        • 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.
        • H
          double[][] H
          Array for internal storage of nonsymmetric Hessenberg form.
          internal storage of nonsymmetric Hessenberg form.
        • issymmetric
          boolean issymmetric
          Symmetry flag.
          internal symmetry flag.
        • n
          int n
          Row and column dimension (square matrix).
          matrix dimension.
        • ort
          double[] ort
          Working storage for nonsymmetric algorithm.
          working storage for nonsymmetric algorithm.
        • V
          double[][] V
          Array for internal storage of eigenvectors.
          internal storage of eigenvectors.
    • Class weka.core.matrix.ExponentialFormat

      class ExponentialFormat extends DecimalFormat implements Serializable
      serialVersionUID:
      -5298981701073897741L
      • Serialized Fields

        • digits
          int digits
        • exp
          int exp
        • nf
          DecimalFormat nf
        • sign
          boolean sign
        • trailing
          boolean trailing
    • Class weka.core.matrix.FlexibleDecimalFormat

      class FlexibleDecimalFormat extends DecimalFormat implements Serializable
      serialVersionUID:
      110912192794064140L
      • Serialized Fields

        • decimalDigits
          int decimalDigits
        • digits
          int digits
        • exp
          boolean exp
        • expDecimalDigits
          int expDecimalDigits
        • grouping
          boolean grouping
        • intDigits
          int intDigits
        • nf
          DecimalFormat nf
        • power
          int power
        • sign
          boolean sign
        • trailing
          boolean trailing
    • Class weka.core.matrix.FloatingPointFormat

      class FloatingPointFormat extends DecimalFormat implements Serializable
      serialVersionUID:
      4500373755333429499L
      • Serialized Fields

        • decimal
          int decimal
        • nf
          DecimalFormat nf
        • trailing
          boolean trailing
        • width
          int width
    • Class weka.core.matrix.LUDecomposition

      class LUDecomposition extends 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.
        • piv
          int[] piv
          Internal storage of pivot vector.
          pivot vector.
        • pivsign
          int pivsign
          Row and column dimensions, and pivot sign.
          column dimension.
    • Class weka.core.matrix.Matrix

      class Matrix extends 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

      class QRDecomposition extends Object implements Serializable
      serialVersionUID:
      -5013090736132211418L
      • Serialized Fields

        • m
          int m
          Row and column dimensions.
          column dimension.
        • n
          int n
          Row and column dimensions.
          column dimension.
        • QR
          double[][] QR
          Array for internal storage of decomposition.
          internal array storage.
        • Rdiag
          double[] Rdiag
          Array for internal storage of diagonal of R.
          diagonal of R.
    • Class weka.core.matrix.SingularValueDecomposition

      class SingularValueDecomposition extends Object implements Serializable
      serialVersionUID:
      -8738089610999867951L
      • Serialized Fields

        • m
          int m
          Row and column dimensions.
          row dimension.
        • n
          int n
          Row and column dimensions.
          row dimension.
        • s
          double[] s
          Array for internal storage of singular values.
          internal storage of singular values.
        • 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.
  • Package weka.core.neighboursearch

    • Class weka.core.neighboursearch.BallTree

      class BallTree extends NearestNeighbourSearch implements Serializable
      serialVersionUID:
      728763855952698328L
      • 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_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_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_TreeStats
          TreePerformanceStats m_TreeStats
          Tree Stats variables.
    • Class weka.core.neighboursearch.CoverTree

      class CoverTree extends NearestNeighbourSearch implements Serializable
      serialVersionUID:
      7617412821497807586L
      • Serialized Fields

        • 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.
        • 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.
        • m_DistanceList
          double[] m_DistanceList
          Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours().
        • m_EuclideanDistance
          EuclideanDistance m_EuclideanDistance
          The euclidean distance function to use.
        • m_MaxDepth
          int m_MaxDepth
          Number of nodes in the tree.
        • m_NumLeaves
          int m_NumLeaves
          Number of nodes in the tree.
        • m_NumNodes
          int m_NumNodes
          Number of nodes in the tree.
        • m_Root
          CoverTree.CoverTreeNode m_Root
          The root node.
        • m_TreeStats
          TreePerformanceStats m_TreeStats
          Tree Stats variables.
    • Class weka.core.neighboursearch.CoverTree.CoverTreeNode

      class CoverTreeNode extends Object implements Serializable
      serialVersionUID:
      1808760031169036512L
      • Serialized Fields

        • children
          Stack<CoverTree.CoverTreeNode> children
          The children of the node.
        • idx
          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.
        • nodeid
          int nodeid
          ID for the node.
        • num_children
          int num_children
          The number of children node has.
        • parent_dist
          double parent_dist
          The distance to the nodes parent.
        • scale
          int scale
          The min i that makes base^i <= max_dist.
    • Class weka.core.neighboursearch.KDTree

      class 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_EuclideanDistance
          EuclideanDistance m_EuclideanDistance
          The euclidean distance function to use.
        • 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_MaxDepth
          int m_MaxDepth
          Tree stats.
        • m_MaxInstInLeaf
          int m_MaxInstInLeaf
          maximal number of instances in a leaf.
        • m_MinBoxRelWidth
          double m_MinBoxRelWidth
          minimal relative width of a KDTree rectangle.
        • m_NormalizeNodeWidth
          boolean m_NormalizeNodeWidth
          flag for normalizing.
        • m_NumLeaves
          int m_NumLeaves
          Tree stats.
        • m_NumNodes
          int m_NumNodes
          Tree stats.
        • m_Root
          KDTreeNode m_Root
          The root node of the tree.
        • m_Splitter
          KDTreeNodeSplitter m_Splitter
          The node splitter.
        • m_TreeStats
          TreePerformanceStats m_TreeStats
          Tree Stats variables.
    • Class weka.core.neighboursearch.LinearNNSearch

      class 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

      class NearestNeighbourSearch extends Object implements Serializable
      • Serialized Fields

        • m_DistanceFunction
          DistanceFunction m_DistanceFunction
          the distance function used.
        • 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_MeasurePerformance
          boolean m_MeasurePerformance
          Should we measure Performance.
        • m_Stats
          PerformanceStats m_Stats
          Performance statistics.
    • Class weka.core.neighboursearch.PerformanceStats

      class PerformanceStats extends Object implements Serializable
      serialVersionUID:
      -7215110351388368092L
      • Serialized Fields

        • m_CoordCount
          double m_CoordCount
          The number of coordinates looked at for the current/last query.
        • m_MaxC
          double m_MaxC
          The min and max coordinates(attributes) looked at per query.
        • m_MaxP
          double m_MaxP
          The min and max data points looked for a query by the NNS algorithm.
        • m_MinC
          double m_MinC
          The min and max coordinates(attributes) looked at per query.
        • m_MinP
          double m_MinP
          The min and max data points looked for a query by the NNS algorithm.
        • m_NumQueries
          int m_NumQueries
          The total number of queries looked at.
        • m_PointCount
          double m_PointCount
          The number of data points looked at for the current/last query.
        • m_SumC
          double m_SumC
          The sum of coordinates/attributes looked at for all the queries.
        • m_SumP
          double m_SumP
          The sum of data points looked at for all the queries.
        • m_SumSqC
          double m_SumSqC
          The squared sum of coordinates/attributes looked at for all the queries.
        • m_SumSqP
          double m_SumSqP
          The squared sum of data points looked at for all the queries.
    • Class weka.core.neighboursearch.TreePerformanceStats

      class TreePerformanceStats extends PerformanceStats implements Serializable
      serialVersionUID:
      -6637636693340810373L
      • Serialized Fields

        • m_IntNodeCount
          int m_IntNodeCount
          The number of internal nodes looked at for the current/last query.
        • m_LeafCount
          int m_LeafCount
          The number of leaf nodes looked at for the current/last query.
        • m_MaxIntNodes
          int m_MaxIntNodes
          The min and max number internal 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_MinIntNodes
          int m_MinIntNodes
          The min and max number internal nodes looked for a query by the tree based NNS algorithm.
        • m_MinLeaves
          int m_MinLeaves
          The min and max number leaf 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_SumLeaves
          int m_SumLeaves
          The sum of leaf 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_SumSqLeaves
          int m_SumSqLeaves
          The squared sum of leaf nodes looked at for all the queries.
  • Package weka.core.neighboursearch.balltrees

    • Class weka.core.neighboursearch.balltrees.BallNode

      class BallNode extends Object implements Serializable
      serialVersionUID:
      -8289151861759883510L
      • Serialized Fields

        • 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_Left
          BallNode m_Left
          The left child of the node.
        • m_NodeNumber
          int m_NodeNumber
          The node number/id.
        • m_NumInstances
          int m_NumInstances
          The number of instances/points in 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).
        • m_Right
          BallNode m_Right
          The right child of the node.
        • 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_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.
    • Class weka.core.neighboursearch.balltrees.BallSplitter

      class BallSplitter extends Object implements Serializable
      • Serialized Fields

        • m_DistanceFunction
          EuclideanDistance m_DistanceFunction
          The distance function (metric) from which the tree is (OR is to be) built.
        • m_Instances
          Instances m_Instances
          The instance on which the tree is 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

      class BallTreeConstructor extends Object implements Serializable
      • Serialized Fields

        • m_DistanceFunction
          DistanceFunction m_DistanceFunction
          The distance function to use to build the tree.
        • 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_InstList
          int[] m_InstList
          The master index array.
        • m_MaxDepth
          int m_MaxDepth
          The depth of the built tree.
        • 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_NumLeaves
          int m_NumLeaves
          The number of leaf nodes in the built tree.
        • m_NumNodes
          int m_NumNodes
          The number of internal and leaf nodes in the built tree.
    • Class weka.core.neighboursearch.balltrees.BottomUpConstructor

      class BottomUpConstructor extends BallTreeConstructor implements Serializable
      serialVersionUID:
      5864250777657707687L
    • Class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint

      class MedianDistanceFromArbitraryPoint extends BallSplitter implements Serializable
      serialVersionUID:
      5617378551363700558L
      • Serialized Fields

        • m_Rand
          Random m_Rand
          Random number generator for selecting an abitrary (random) point.
        • m_RandSeed
          int m_RandSeed
          Seed for random number generator.
    • Class weka.core.neighboursearch.balltrees.MedianOfWidestDimension

      class 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

      class MiddleOutConstructor extends BallTreeConstructor implements Serializable
      serialVersionUID:
      -8523314263062524462L
      • Serialized Fields

        • m_RandomInitialAnchor
          boolean m_RandomInitialAnchor
          True if the initial anchor is chosen randomly. False if it is the furthest point from the mean/centroid.
        • m_RSeed
          int m_RSeed
          Seed form random number generator.
        • rand
          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.
    • Class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList

      class MyIdxList extends FastVector implements Serializable
      serialVersionUID:
      -2283869109722934927L
    • Class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren

      class PointsClosestToFurthestChildren extends BallSplitter implements Serializable
      serialVersionUID:
      -2947177543565818260L
    • Class weka.core.neighboursearch.balltrees.TopDownConstructor

      class 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

  • Package weka.core.neighboursearch.kdtrees

  • Package weka.core.pmml

    • Class weka.core.pmml.BuiltInArithmetic

      class 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

      class 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

      class 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

      class Constant extends Expression implements Serializable
      serialVersionUID:
      -304829687822452424L
      • Serialized Fields

        • m_categoricalConst
          String m_categoricalConst
        • m_continuousConst
          double m_continuousConst
    • Class weka.core.pmml.DefineFunction

      class DefineFunction extends Function implements Serializable
      serialVersionUID:
      -1976646917527243888L
      • Serialized Fields

        • m_expression
          Expression m_expression
          The Expression for this function to use
        • m_optype
          FieldMetaInfo.Optype m_optype
          The optype for this function
        • m_parameters
          ArrayList<weka.core.pmml.DefineFunction.ParameterField> 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
    • Class weka.core.pmml.DefineFunction.ParameterField

      class ParameterField extends FieldMetaInfo implements Serializable
      serialVersionUID:
      3918895902507585558L
    • Class weka.core.pmml.DerivedFieldMetaInfo

      class DerivedFieldMetaInfo extends FieldMetaInfo implements Serializable
      • Serialized Fields

        • m_displayName
          String m_displayName
          display name
        • m_expression
          Expression m_expression
          the single expression that defines the value of this field
        • m_values
          ArrayList<FieldMetaInfo.Value> 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
    • Class weka.core.pmml.Discretize

      class Discretize extends Expression implements Serializable
      • Serialized Fields

        • m_bins
          ArrayList<weka.core.pmml.Discretize.DiscretizeBin> m_bins
          The bins for this discretization
        • m_defaultValue
          String m_defaultValue
          The default value (if defined)
        • m_defaultValueDefined
          boolean m_defaultValueDefined
          True if a default value has been specified
        • m_fieldIndex
          int m_fieldIndex
          The index of the field
        • m_fieldName
          String m_fieldName
          The name of the field to be discretized
        • m_mapMissingDefined
          boolean m_mapMissingDefined
          True if a replacement for missing values has been specified
        • m_mapMissingTo
          String m_mapMissingTo
          The value of the missing value replacement (if defined)
        • m_outputDef
          Attribute m_outputDef
          The output structure of this discretization
    • Class weka.core.pmml.Discretize.DiscretizeBin

      class DiscretizeBin extends Object implements Serializable
      serialVersionUID:
      5810063243316808400L
    • Class weka.core.pmml.Expression

      class Expression extends Object implements Serializable
      serialVersionUID:
      4448840549804800321L
    • Class weka.core.pmml.FieldMetaInfo

      class FieldMetaInfo extends Object implements Serializable
      • Serialized Fields

    • Class weka.core.pmml.FieldMetaInfo.Interval

      class Interval extends Object implements Serializable
      serialVersionUID:
      -7339790632684638012L
      • Serialized Fields

        • m_closure
          FieldMetaInfo.Interval.Closure m_closure
        • m_leftMargin
          double m_leftMargin
          The left boundary value
        • m_rightMargin
          double m_rightMargin
          The right boundary value
    • Class weka.core.pmml.FieldMetaInfo.Value

      class Value extends Object implements Serializable
      serialVersionUID:
      -3981030320273649739L
      • Serialized Fields

        • m_displayValue
          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
        • m_value
          String m_value
          The value
    • Class weka.core.pmml.FieldRef

      class FieldRef extends Expression implements Serializable
      serialVersionUID:
      -8009605897876168409L
      • Serialized Fields

        • m_fieldName
          String m_fieldName
          The name of the field to reference
    • Class weka.core.pmml.Function

      class Function extends Object implements Serializable
      serialVersionUID:
      -6997738288201933171L
      • Serialized Fields

        • m_functionName
          String m_functionName
          The name of this function
        • m_parameterDefs
          ArrayList<Attribute> m_parameterDefs
          The structure of the parameters to this function
    • Class weka.core.pmml.MappingInfo

      class MappingInfo extends 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_fieldsMappingText
          String m_fieldsMappingText
          Holds a textual description of the fields mapping
        • m_log
          Logger m_log
          For logging
        • 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.
    • Class weka.core.pmml.MiningFieldMetaInfo

      class MiningFieldMetaInfo extends FieldMetaInfo implements Serializable
      serialVersionUID:
      -1256774332779563185L
      • Serialized Fields

        • m_highValue
          double m_highValue
          outlier high value
        • m_importance
          double m_importance
          importance (if defined)
        • m_index
          int m_index
          the index of the field in the mining schema Instances
        • m_lowValue
          double m_lowValue
          outlier low value
        • m_miningSchemaI
          Instances m_miningSchemaI
          mining schema (needed for toString method)
        • m_missingValueReplacementNominal
          String m_missingValueReplacementNominal
          actual missing value replacements (if specified)
        • m_missingValueReplacementNumeric
          double m_missingValueReplacementNumeric
        • m_missingValueTreatmentMethod
          weka.core.pmml.MiningFieldMetaInfo.Missing m_missingValueTreatmentMethod
          missing values treatment method
        • m_optypeOverride
          FieldMetaInfo.Optype m_optypeOverride
          optype overrides (override data dictionary type - NOT SUPPORTED AT PRESENT)
        • m_outlierTreatmentMethod
          weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod
          outlier treatmemnt method
        • m_usageType
          weka.core.pmml.MiningFieldMetaInfo.Usage m_usageType
          usage type
    • Class weka.core.pmml.MiningSchema

      class MiningSchema extends Object implements Serializable
      serialVersionUID:
      7144380586726330455L
      • Serialized Fields

        • m_derivedMeta
          ArrayList<DerivedFieldMetaInfo> m_derivedMeta
          Meta information about derived fields (those defined in the TransformationDictionary followed by those defined in LocalTransformations)
        • m_fieldInstancesStructure
          Instances m_fieldInstancesStructure
          The structure of all the fields (both mining schema and derived) as Instances
        • m_miningMeta
          ArrayList<MiningFieldMetaInfo> m_miningMeta
          Meta information about the mining schema fields
        • m_miningSchemaInstancesStructure
          Instances m_miningSchemaInstancesStructure
          Just the mining schema fields as Instances
        • m_targetMetaInfo
          TargetMetaInfo m_targetMetaInfo
          target meta info (may be null if not defined)
        • m_transformationDictionary
          weka.core.pmml.TransformationDictionary m_transformationDictionary
          The transformation dictionary (if defined)
    • Class weka.core.pmml.NormContinuous

      class NormContinuous extends Expression implements Serializable
      serialVersionUID:
      4714332374909851542L
      • Serialized Fields

        • m_fieldIndex
          int m_fieldIndex
          The index of the field
        • m_fieldName
          String m_fieldName
          The name of the field to use
        • m_linearNormNorm
          double[] m_linearNormNorm
          norm values for the LinearNorm entries
        • m_linearNormOrig
          double[] m_linearNormOrig
          original values for the LinearNorm entries
        • 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)
    • Class weka.core.pmml.NormDiscrete

      class NormDiscrete extends Expression implements Serializable
      serialVersionUID:
      -8854409417983908220L
      • Serialized Fields

        • m_field
          Attribute m_field
          The actual attribute itself
        • m_fieldIndex
          int m_fieldIndex
          The index of the attribute
        • m_fieldName
          String m_fieldName
          The name of the field to lookup our value in
        • m_fieldValue
          String m_fieldValue
          The actual value (as a String) that will correspond to an output of 1
        • 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.
        • 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)
    • Class weka.core.pmml.TargetMetaInfo

      class TargetMetaInfo extends FieldMetaInfo implements Serializable
      serialVersionUID:
      863500462237904927L
      • Serialized Fields

        • m_castInteger
          String m_castInteger
          cast integers (default no casting)
        • m_defaultValueOrPriorProbs
          double[] m_defaultValueOrPriorProbs
          default value (numeric) or prior distribution (categorical)
        • m_displayValues
          ArrayList<String> m_displayValues
          corresponding display values
        • m_max
          double m_max
        • m_min
          double m_min
          min and max
        • m_rescaleConstant
          double m_rescaleConstant
          re-scaling of target value (if defined)
        • m_rescaleFactor
          double m_rescaleFactor
        • m_values
          ArrayList<String> m_values
          for categorical values. Actual values
  • Package weka.core.stemmers

  • Package weka.core.tokenizers

  • Package weka.core.xml

  • Package weka.datagenerators

    • Class weka.datagenerators.ClassificationGenerator

      class ClassificationGenerator extends DataGenerator implements Serializable
      serialVersionUID:
      -5261662546673517844L
      • Serialized Fields

        • m_NumExamples
          int m_NumExamples
          Number of instances
    • Class weka.datagenerators.ClusterDefinition

      class ClusterDefinition extends Object implements Serializable
      serialVersionUID:
      -5950001207047429961L
      • Serialized Fields

    • Class weka.datagenerators.ClusterGenerator

      class ClusterGenerator extends DataGenerator implements Serializable
      serialVersionUID:
      6131722618472046365L
      • Serialized Fields

        • m_booleanCols
          Range m_booleanCols
          Stores which columns are boolean (default numeric)
        • m_ClassFlag
          boolean m_ClassFlag
          class flag
        • m_nominalCols
          Range m_nominalCols
          Stores which columns are nominal (default numeric)
        • m_NumAttributes
          int m_NumAttributes
          Number of attribute the dataset should have
    • Class weka.datagenerators.DataGenerator

      class DataGenerator extends Object implements Serializable
      serialVersionUID:
      -3698585946221802578L
      • Serialized Fields

        • m_CreatingRelationName
          boolean m_CreatingRelationName
          flag, that indicates whether the relationname is currently assembled
        • m_DatasetFormat
          Instances m_DatasetFormat
          The format for the generated dataset
        • m_Debug
          boolean m_Debug
          Debugging mode
        • 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_Random
          Random m_Random
          random number generator
        • m_RelationName
          String m_RelationName
          Relation name the dataset should have
        • m_Seed
          int m_Seed
          random number generator seed
    • Class weka.datagenerators.RegressionGenerator

      class RegressionGenerator extends DataGenerator implements Serializable
      serialVersionUID:
      3073254041275658221L
      • Serialized Fields

        • m_NumExamples
          int m_NumExamples
          Number of instances
    • Class weka.datagenerators.Test

      class Test extends Object implements Serializable
      serialVersionUID:
      -8890645875887157782L
      • Serialized Fields

        • m_AttIndex
          int m_AttIndex
          the attribute index
        • m_Dataset
          Instances m_Dataset
          the dataset
        • m_Not
          boolean m_Not
          whether to negate the test
        • m_Split
          double m_Split
          the split
  • Package weka.datagenerators.classifiers.classification

    • Class weka.datagenerators.classifiers.classification.Agrawal

      class Agrawal extends ClassificationGenerator implements Serializable
      serialVersionUID:
      2254651939636143025L
      • Serialized Fields

        • m_BalanceClass
          boolean m_BalanceClass
          whether to balance the class
        • m_Function
          int m_Function
          the function to use for generating the data
        • m_lastLabel
          double m_lastLabel
          the last class label that was generated
        • m_nextClassShouldBeZero
          boolean m_nextClassShouldBeZero
          used for balancing the class
        • m_PerturbationFraction
          double m_PerturbationFraction
          the perturabation fraction
    • Class weka.datagenerators.classifiers.classification.BayesNet

      class 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

      class 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

      class RandomRBF extends ClassificationGenerator implements Serializable
      serialVersionUID:
      6069033710635728720L
      • Serialized Fields

        • m_centroidClasses
          int[] m_centroidClasses
          the classes of the centroids
        • m_centroids
          double[][] m_centroids
          the centroids
        • m_centroidStdDevs
          double[] m_centroidStdDevs
          the stddevs of the centroids
        • m_centroidWeights
          double[] m_centroidWeights
          the weights of the centroids
        • m_NumAttributes
          int m_NumAttributes
          Number of attribute the dataset should have
        • m_NumCentroids
          int m_NumCentroids
          the number of centroids to use for generation
        • m_NumClasses
          int m_NumClasses
          Number of Classes the dataset should have
    • Class weka.datagenerators.classifiers.classification.RDG1

      class RDG1 extends ClassificationGenerator implements Serializable
      serialVersionUID:
      7751005204635320414L
      • Serialized Fields

        • m_AttList_Irr
          boolean[] m_AttList_Irr
          array defines which attributes are irrelevant, with: true = attribute is irrelevant; false = attribute is not irrelevant
        • m_DecisionList
          FastVector m_DecisionList
          decision list
        • m_MaxRuleSize
          int m_MaxRuleSize
          maximum rule size
        • m_MinRuleSize
          int m_MinRuleSize
          minimum rule size
        • 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_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
  • Package weka.datagenerators.classifiers.regression

    • Class weka.datagenerators.classifiers.regression.Expression

      class Expression extends MexicanHat implements Serializable
      serialVersionUID:
      -4237047357682277211L
      • Serialized Fields

        • m_Expression
          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

      class MexicanHat extends RegressionGenerator implements Serializable
      serialVersionUID:
      4577016375261512975L
      • Serialized Fields

        • m_Amplitude
          double m_Amplitude
          the amplitude of y
        • m_MaxRange
          double m_MaxRange
          the upper boundary of the range, x is drawn from
        • m_MinRange
          double m_MinRange
          the lower boundary of the range, x is drawn from
        • m_NoiseRandom
          Random m_NoiseRandom
          the random number generator for the noise
        • m_NoiseRate
          double m_NoiseRate
          the rate of the gaussian noise
        • m_NoiseVariance
          double m_NoiseVariance
          the variance of the gaussian noise
  • Package weka.datagenerators.clusterers

    • Class weka.datagenerators.clusterers.BIRCHCluster

      class BIRCHCluster extends ClusterGenerator implements Serializable
      serialVersionUID:
      -334820527230755027L
      • Serialized Fields

        • m_ClusterList
          FastVector m_ClusterList
          cluster list
        • m_DistMult
          double m_DistMult
          distance multiplier (option M)
        • m_GridSize
          int m_GridSize
          grid size
        • m_GridWidth
          double m_GridWidth
          grid width
        • m_InputOrder
          int m_InputOrder
          input order (changed with option O)
        • m_MaxInstNum
          int m_MaxInstNum
          maximal number of instances per cluster (option N)
        • m_MaxRadius
          double m_MaxRadius
          maximum radius (option R)
        • m_MinInstNum
          int m_MinInstNum
          minimal number of instances per cluster (option N)
        • m_MinRadius
          double m_MinRadius
          minimum radius (option R)
        • m_NoiseRate
          double m_NoiseRate
          noise rate in percent (option P, between 0 and 30)
        • m_NumClusters
          int m_NumClusters
          Number of Clusters the dataset should have
        • m_NumCycles
          int m_NumCycles
          number of cycles (option C)
        • m_Pattern
          int m_Pattern
          pattern (changed with options G or S)
    • Class weka.datagenerators.clusterers.SubspaceCluster

      class SubspaceCluster extends ClusterGenerator implements Serializable
      serialVersionUID:
      -3454999858505621128L
      • Serialized Fields

        • m_Clusters
          ClusterDefinition[] m_Clusters
          cluster list
        • m_globalMaxValue
          double[] m_globalMaxValue
          store global max values
        • m_globalMinValue
          double[] m_globalMinValue
          store global min values
        • m_NoiseRate
          double m_NoiseRate
          noise rate in percent (option P, between 0 and 30)
        • m_numValues
          int[] m_numValues
          if nominal, store number of values
    • Class weka.datagenerators.clusterers.SubspaceClusterDefinition

      class SubspaceClusterDefinition extends ClusterDefinition implements Serializable
      serialVersionUID:
      3135678125044007231L
      • Serialized Fields

        • m_attributes
          boolean[] m_attributes
          attributes of this cluster
        • m_AttrIndexRange
          Range m_AttrIndexRange
          range of atttributes
        • m_attrIndices
          int[] m_attrIndices
          global indices of the attributes of the cluster
        • m_clustersubtype
          int m_clustersubtype
          cluster subtypes
        • m_clustertype
          int m_clustertype
          cluster type
        • m_MaxInstNum
          int m_MaxInstNum
          maximal number of instances for this cluster
        • 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_MinInstNum
          int m_MinInstNum
          minimal number of instances for this cluster
        • m_minValue
          double[] m_minValue
          ranges of each attribute (min); not used if gaussian
        • 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_stddevValue
          double[] m_stddevValue
          standarddev; only used if gaussian
  • Package weka.estimators

    • Class weka.estimators.DiscreteEstimator

      class 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

      class Estimator extends Object implements Serializable
      serialVersionUID:
      -5902411487362274342L
      • Serialized Fields

        • m_classValueIndex
          double m_classValueIndex
          The class value index is > -1 if subset is taken with specific class value only
        • m_Debug
          boolean m_Debug
          Debugging mode
        • m_noClass
          boolean m_noClass
          set if class is not important
    • Class weka.estimators.KernelEstimator

      class KernelEstimator extends Estimator implements Serializable
      serialVersionUID:
      3646923563367683925L
      • Serialized Fields

        • m_AllWeightsOne
          boolean m_AllWeightsOne
          Whether we can optimise the kernel summation
        • m_NumValues
          int m_NumValues
          Number of values stored in m_Weights and m_Values so far
        • m_Precision
          double m_Precision
          The precision of data values
        • m_StandardDev
          double m_StandardDev
          The standard deviation
        • m_SumOfWeights
          double m_SumOfWeights
          The sum of the weights so far
        • m_Values
          double[] m_Values
          Vector containing all of the values seen
        • m_Weights
          double[] m_Weights
          Vector containing the associated weights
    • Class weka.estimators.MahalanobisEstimator

      class MahalanobisEstimator extends Estimator implements Serializable
      serialVersionUID:
      8950225468990043868L
      • Serialized Fields

        • m_ConstDelta
          double m_ConstDelta
          The difference between the conditioning value and the conditioning mean
        • m_CovarianceInverse
          Matrix m_CovarianceInverse
          The inverse of the covariance matrix
        • m_Determinant
          double m_Determinant
          The determinant of the covariance matrix
        • m_ValueMean
          double m_ValueMean
          The mean of the values
    • Class weka.estimators.NormalEstimator

      class NormalEstimator extends Estimator implements Serializable
      serialVersionUID:
      93584379632315841L
      • Serialized Fields

        • m_Mean
          double m_Mean
          The current mean
        • m_Precision
          double m_Precision
          The precision of numeric values ( = minimum std dev permitted)
        • m_StandardDev
          double m_StandardDev
          The current standard deviation
        • m_SumOfValues
          double m_SumOfValues
          The sum of the values seen
        • m_SumOfValuesSq
          double m_SumOfValuesSq
          The sum of the values squared
        • m_SumOfWeights
          double m_SumOfWeights
          The sum of the weights
    • Class weka.estimators.PoissonEstimator

      class PoissonEstimator extends Estimator implements Serializable
      serialVersionUID:
      7669362595289236662L
      • Serialized Fields

        • m_Lambda
          double m_Lambda
          The average number of times an event occurs in an interval.
        • m_NumValues
          double m_NumValues
          The number of values seen
        • m_SumOfValues
          double m_SumOfValues
          The sum of the values seen
  • Package weka.experiment

    • Class weka.experiment.AveragingResultProducer

      class AveragingResultProducer extends Object implements Serializable
      serialVersionUID:
      2551284958501991352L
      • Serialized Fields

        • m_AdditionalMeasures
          String[] m_AdditionalMeasures
          The names of any additional measures to look for in SplitEvaluators
        • m_CalculateStdDevs
          boolean m_CalculateStdDevs
          True if standard deviation fields should be produced
        • m_CountFieldName
          String m_CountFieldName
          The name of the field that will contain the number of results averaged over.
        • m_ExpectedResultsPerAverage
          int m_ExpectedResultsPerAverage
          The number of results expected to average over for each run
        • m_Instances
          Instances m_Instances
          The dataset of interest
        • m_KeyFieldName
          String m_KeyFieldName
          The name of the key field to average over
        • m_KeyIndex
          int m_KeyIndex
          The index of the field to average over in the resultproducers key
        • m_Keys
          FastVector m_Keys
          Collects the keys from a single run
        • m_ResultListener
          ResultListener m_ResultListener
          The ResultListener to send results to
        • m_ResultProducer
          ResultProducer m_ResultProducer
          The ResultProducer used to generate results
        • m_Results
          FastVector m_Results
          Collects the results from a single run
    • Class weka.experiment.ClassifierSplitEvaluator

      class ClassifierSplitEvaluator extends Object implements Serializable
      serialVersionUID:
      -8511241602760467265L
      • Serialized Fields

        • m_AdditionalMeasures
          String[] m_AdditionalMeasures
          The names of any additional measures to look for in SplitEvaluators
        • m_attID
          int m_attID
          Attribute index of instance identifier (default -1)
        • m_Classifier
          Classifier m_Classifier
          The classifier used for evaluation
        • m_ClassifierOptions
          String m_ClassifierOptions
          The classifier options (if any)
        • m_ClassifierVersion
          String m_ClassifierVersion
          The classifier version
        • 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_IRclass
          int m_IRclass
          Class index for information retrieval statistics (default 0)
        • 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_predTargetColumn
          boolean m_predTargetColumn
          Flag for prediction and target columns output.
        • m_result
          String m_result
          Holds the statistics for the most recent application of the classifier
        • m_Template
          Classifier m_Template
          The template classifier
    • Class weka.experiment.CostSensitiveClassifierSplitEvaluator

      class CostSensitiveClassifierSplitEvaluator extends ClassifierSplitEvaluator implements Serializable
      serialVersionUID:
      -8069566663019501276L
      • Serialized Fields

        • m_OnDemandDirectory
          File m_OnDemandDirectory
          The directory used when loading cost files on demand, null indicates current directory
    • Class weka.experiment.CrossValidationResultProducer

      class CrossValidationResultProducer extends Object implements Serializable
      serialVersionUID:
      -1580053925080091917L
      • Serialized Fields

        • m_AdditionalMeasures
          String[] m_AdditionalMeasures
          The names of any additional measures to look for in SplitEvaluators
        • m_debugOutput
          boolean m_debugOutput
          Save raw output of split evaluators --- for debugging purposes
        • m_Instances
          Instances m_Instances
          The dataset of interest
        • m_NumFolds
          int m_NumFolds
          The number of folds in the cross-validation
        • m_OutputFile
          File m_OutputFile
          The destination output file/directory for raw output
        • m_ResultListener
          ResultListener m_ResultListener
          The ResultListener to send results to
        • m_SplitEvaluator
          SplitEvaluator m_SplitEvaluator
          The SplitEvaluator used to generate results
        • m_ZipDest
          OutputZipper m_ZipDest
          The output zipper to use for saving raw splitEvaluator output
    • Class weka.experiment.CSVResultListener

      class CSVResultListener extends Object implements Serializable
      serialVersionUID:
      -623185072785174658L
      • Serialized Fields

        • m_OutputFile
          File m_OutputFile
          The destination output file, null sends to System.out
        • m_OutputFileName
          String m_OutputFileName
          The name of the output file. Empty for temporary file.
        • m_RP
          ResultProducer m_RP
          The ResultProducer sending us results
    • Class weka.experiment.DatabaseResultListener

      class DatabaseResultListener extends DatabaseUtils implements Serializable
      serialVersionUID:
      7388014746954652818L
      • Serialized Fields

        • m_Cache
          FastVector m_Cache
          Stores the cached values
        • m_CacheKey
          Object[] m_CacheKey
          Stores the key for which the cache is valid
        • m_CacheKeyIndex
          int m_CacheKeyIndex
          Stores the index of the key column holding the cache key data
        • m_CacheKeyName
          String m_CacheKeyName
          Holds the name of the key field to cache upon, or null if no caching
        • m_Debug
          boolean m_Debug
          True if debugging output should be printed
        • m_ResultProducer
          ResultProducer m_ResultProducer
          The ResultProducer to listen to
        • m_ResultsTableName
          String m_ResultsTableName
          The name of the current results table
    • Class weka.experiment.DatabaseResultProducer

      class DatabaseResultProducer extends DatabaseResultListener implements Serializable
      serialVersionUID:
      -5620660780203158666L
      • Serialized Fields

        • m_AdditionalMeasures
          String[] m_AdditionalMeasures
          The names of any additional measures to look for in SplitEvaluators
        • 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
    • Class weka.experiment.DatabaseUtils

      class DatabaseUtils extends Object implements Serializable
      serialVersionUID:
      -8252351994547116729L
      • Serialized Fields

        • DRIVERS
          Vector DRIVERS
          Holds the jdbc drivers to be used (only to stop them being gc'ed).
        • m_checkForLowerCaseNames
          boolean m_checkForLowerCaseNames
          For databases where Tables and Columns are created in lower case.
        • m_checkForUpperCaseNames
          boolean m_checkForUpperCaseNames
          For databases where Tables and Columns are created in upper case.
        • m_createIndex
          boolean m_createIndex
          create index on the database?
        • m_DatabaseURL
          String m_DatabaseURL
          Database URL.
        • m_Debug
          boolean m_Debug
          True if debugging output should be printed.
        • m_doubleType
          String m_doubleType
          double type for the create table statement.
        • m_intType
          String m_intType
          integer type for the create table statement.
        • m_Keywords
          HashSet<String> m_Keywords
          the keywords for the current database type.
        • m_KeywordsMaskChar
          String m_KeywordsMaskChar
          the character to mask SQL keywords (by appending this character).
        • m_password
          String m_password
          Database Password.
        • m_setAutoCommit
          boolean m_setAutoCommit
          setAutoCommit on the database?
        • m_stringType
          String m_stringType
          string type for the create table statement.
        • m_userName
          String m_userName
          Database username.
        • PROPERTIES
          Properties PROPERTIES
          Properties associated with the database connection.
    • Class weka.experiment.DensityBasedClustererSplitEvaluator

      class DensityBasedClustererSplitEvaluator extends Object implements Serializable
      • Serialized Fields

        • m_additionalMeasures
          String[] m_additionalMeasures
          The names of any additional measures to look for in SplitEvaluators
        • m_clusterer
          DensityBasedClusterer m_clusterer
          The clusterer used for evaluation
        • m_clustererOptions
          String m_clustererOptions
          The clusterer options (if any)
        • m_clustererVersion
          String m_clustererVersion
          The clusterer version
        • 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_removeClassColumn
          boolean m_removeClassColumn
          Remove the class column (if set) from the data
        • m_result
          String m_result
          Holds the statistics for the most recent application of the clusterer
    • Class weka.experiment.Experiment

      class Experiment extends Object implements Serializable
      serialVersionUID:
      44945596742646663L
      • Serialized Fields

        • m_AdditionalMeasures
          String[] m_AdditionalMeasures
          Method names of additional measures of objects contained in the custom property iterator. Only methods names beginning with "measure" and returning doubles are recognised
        • m_AdvanceDataSetFirst
          boolean m_AdvanceDataSetFirst
          If true an experiment will advance the current data set befor any custom itererator
        • m_ClassFirst
          boolean m_ClassFirst
          True if the class attribute is the first attribute for all datasets involved in this experiment.
        • m_Datasets
          DefaultListModel m_Datasets
          An array of dataset files
        • m_Notes
          String m_Notes
          User notes about the experiment
        • m_PropertyArray
          Object m_PropertyArray
          The array of values to set the property to
        • m_PropertyPath
          PropertyNode[] m_PropertyPath
          The path to the iterator property
        • 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_UsePropertyIterator
          boolean m_UsePropertyIterator
          True if the exp should also iterate over a property of the RP
    • Class weka.experiment.InstanceQuery

      class InstanceQuery extends DatabaseUtils implements Serializable
      serialVersionUID:
      718158370917782584L
      • Serialized Fields

        • m_CreateSparseData
          boolean m_CreateSparseData
          Determines whether sparse data is created
        • m_Query
          String m_Query
          Query to execute
    • Class weka.experiment.InstancesResultListener

      class InstancesResultListener extends CSVResultListener implements Serializable
      serialVersionUID:
      -2203808461809311178L
    • Class weka.experiment.LearningRateResultProducer

      class LearningRateResultProducer extends Object implements Serializable
      serialVersionUID:
      -3841159673490861331L
      • Serialized Fields

        • m_AdditionalMeasures
          String[] m_AdditionalMeasures
          The names of any additional measures to look for in SplitEvaluators
        • m_CurrentSize
          int m_CurrentSize
          The current dataset size during stepping
        • m_Instances
          Instances m_Instances
          The dataset of interest
        • 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_ResultListener
          ResultListener m_ResultListener
          The ResultListener to send results to
        • m_ResultProducer
          ResultProducer m_ResultProducer
          The ResultProducer used to generate results
        • m_StepSize
          int m_StepSize
          The number of instances to add at each step
        • m_UpperSize
          int m_UpperSize
          The maximum number of instances to use. -1 indicates no maximum (other than the total number of instances)
    • Class weka.experiment.PairedCorrectedTTester

      class PairedCorrectedTTester extends PairedTTester implements Serializable
      serialVersionUID:
      -3105268939845653323L
    • Class weka.experiment.PairedTTester

      class PairedTTester extends Object implements Serializable
      serialVersionUID:
      8370014624008728610L
      • Serialized Fields

        • m_ColOrder
          int[] m_ColOrder
          The sorting of the columns (test base is always first)
        • m_DatasetKeyColumns
          int[] m_DatasetKeyColumns
          An array containing the indexes of just the selected columns
        • m_DatasetKeyColumnsRange
          Range m_DatasetKeyColumnsRange
          The range of columns that specify a unique "dataset" (eg: scheme plus configuration)
        • m_DatasetSpecifiers
          weka.experiment.PairedTTester.DatasetSpecifiers m_DatasetSpecifiers
          The list of dataset specifiers
        • m_DisplayedResultsets
          int[] m_DisplayedResultsets
          An array containing the indexes of the datasets to display
        • m_FoldColumn
          int m_FoldColumn
          The option setting for the fold number column (-1 means none)
        • m_Instances
          Instances m_Instances
          The set of instances we will analyse
        • m_ResultMatrix
          ResultMatrix m_ResultMatrix
          the instance of the class to produce the output.
        • m_ResultsetKeyColumns
          int[] m_ResultsetKeyColumns
          An array containing the indexes of just the selected columns
        • m_ResultsetKeyColumnsRange
          Range m_ResultsetKeyColumnsRange
          The range of columns that specify a unique result set (eg: scheme plus configuration)
        • 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_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_ShowStdDevs
          boolean m_ShowStdDevs
          Indicates whether standard deviations should be displayed
        • m_SignificanceLevel
          double m_SignificanceLevel
          The significance level for comparisons
        • 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)
    • Class weka.experiment.PropertyNode

      class PropertyNode extends Object implements Serializable
      serialVersionUID:
      -8718165742572631384L
    • Class weka.experiment.RandomSplitResultProducer

      class RandomSplitResultProducer extends Object implements Serializable
      serialVersionUID:
      1403798165056795073L
      • Serialized Fields

        • m_AdditionalMeasures
          String[] m_AdditionalMeasures
          The names of any additional measures to look for in SplitEvaluators
        • m_debugOutput
          boolean m_debugOutput
          Save raw output of split evaluators --- for debugging purposes
        • m_Instances
          Instances m_Instances
          The dataset of interest
        • m_OutputFile
          File m_OutputFile
          The destination output file/directory for raw output
        • m_randomize
          boolean m_randomize
          Whether dataset is to be randomized
        • m_ResultListener
          ResultListener m_ResultListener
          The ResultListener to send results to
        • m_SplitEvaluator
          SplitEvaluator m_SplitEvaluator
          The SplitEvaluator used to generate results
        • m_TrainPercent
          double m_TrainPercent
          The percentage of instances to use for training
        • m_ZipDest
          OutputZipper m_ZipDest
          The output zipper to use for saving raw splitEvaluator output
    • Class weka.experiment.RegressionSplitEvaluator

      class RegressionSplitEvaluator extends Object implements Serializable
      serialVersionUID:
      -328181640503349202L
      • Serialized Fields

        • m_AdditionalMeasures
          String[] m_AdditionalMeasures
          The names of any additional measures to look for in SplitEvaluators
        • m_Classifier
          Classifier m_Classifier
          The classifier used for evaluation
        • m_ClassifierOptions
          String m_ClassifierOptions
          The classifier options (if any)
        • m_ClassifierVersion
          String m_ClassifierVersion
          The classifier version
        • m_doesProduce
          boolean[] m_doesProduce
          Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce
        • m_result
          String m_result
          Holds the statistics for the most recent application of the classifier
        • m_Template
          Classifier m_Template
          The template classifier
    • Class weka.experiment.RemoteEngine

      class RemoteEngine extends UnicastRemoteObject implements Serializable
      serialVersionUID:
      -1021538162895448259L
      • Serialized Fields

        • m_HostName
          String m_HostName
          The name of the host that this engine is started on
        • m_TaskIdQueue
          Queue m_TaskIdQueue
          A queue of corresponding ID's for tasks
        • m_TaskQueue
          Queue m_TaskQueue
          A queue of waiting tasks
        • m_TaskRunning
          boolean m_TaskRunning
          Is there a task running
        • m_TaskStatus
          Hashtable m_TaskStatus
          A hashtable of experiment status
    • Class weka.experiment.RemoteExperiment

      class RemoteExperiment extends Experiment implements Serializable
      serialVersionUID:
      -7357668825635314937L
      • Serialized Fields

        • m_baseExperiment
          Experiment m_baseExperiment
          The base experiment to split up into sub experiments for remote execution
        • 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_failedCount
          int m_failedCount
          The count of failed sub-experiments
        • m_finishedCount
          int m_finishedCount
          The count of successfully completed sub-experiments
        • m_listeners
          FastVector m_listeners
          The list of objects listening for remote experiment events
        • m_remoteHostFailureCounts
          int[] m_remoteHostFailureCounts
          The number of times tasks have failed on each remote host
        • m_remoteHosts
          DefaultListModel m_remoteHosts
          Holds the names of machines with remoteEngine servers running
        • m_remoteHostsQueue
          Queue m_remoteHostsQueue
          The queue of available hosts
        • m_remoteHostsStatus
          int[] m_remoteHostsStatus
          The status of each of the remote hosts
        • m_removedHosts
          int m_removedHosts
          The number of hosts removed due to exceeding max failures
        • m_splitByDataSet
          boolean m_splitByDataSet
          If true, then sub experiments are created on the basis of data sets rather than run number.
        • m_subExpComplete
          int[] m_subExpComplete
          The status of each of the sub-experiments
        • m_subExperiments
          Experiment[] m_subExperiments
          The sub experiments
        • m_subExpQueue
          Queue m_subExpQueue
          The queue of sub experiments waiting to be processed
    • Class weka.experiment.RemoteExperimentEvent

      class RemoteExperimentEvent extends Object implements Serializable
      serialVersionUID:
      7000867987391866451L
      • Serialized Fields

        • m_experimentFinished
          boolean m_experimentFinished
          True if a remote experiment has finished
        • m_logMessage
          boolean m_logMessage
          A log type message
        • m_messageString
          String m_messageString
          The message
        • m_statusMessage
          boolean m_statusMessage
          A status type message
    • Class weka.experiment.RemoteExperimentSubTask

      class RemoteExperimentSubTask extends Object implements Serializable
    • Class weka.experiment.ResultMatrix

      class ResultMatrix extends Object implements Serializable
      serialVersionUID:
      4487179306428209739L
      • Serialized Fields

        • LEFT_PARENTHESES
          String LEFT_PARENTHESES
          the left parentheses for enumerating cols/rows
        • LOSS_STRING
          String LOSS_STRING
          loss string
        • m_ColHidden
          boolean[] m_ColHidden
          whether a column is hidden
        • m_ColNames
          String[] m_ColNames
          the column names
        • m_ColNameWidth
          int m_ColNameWidth
          the size of the names of the columns
        • m_ColOrder
          int[] m_ColOrder
          the ordering of the columns
        • m_Counts
          double[] m_Counts
          the counts for the different datasets
        • m_CountWidth
          int m_CountWidth
          the size of the counts
        • 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_HeaderKeys
          Vector m_HeaderKeys
          contains the keys for the header
        • m_HeaderValues
          Vector m_HeaderValues
          contains the values for the header
        • m_Mean
          double[][] m_Mean
          the values
        • m_MeanPrec
          int m_MeanPrec
          the standard mean precision
        • m_MeanWidth
          int m_MeanWidth
          the size of the mean columns
        • m_NonSigWins
          int[][] m_NonSigWins
          the non-significant wins
        • 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_RankingDiff
          int[] m_RankingDiff
          the difference between wins and losses
        • m_RankingLosses
          int[] m_RankingLosses
          the losses in ranking
        • m_RankingWins
          int[] m_RankingWins
          the wins in ranking
        • m_RemoveFilterName
          boolean m_RemoveFilterName
          whether to remove the filter name from the dataaset name
        • m_RowHidden
          boolean[] m_RowHidden
          whether a row is hidden
        • m_RowNames
          String[] m_RowNames
          the row names
        • m_RowNameWidth
          int m_RowNameWidth
          the size of the names of the rows
        • m_RowOrder
          int[] m_RowOrder
          the ordering of the rows
        • m_ShowAverage
          boolean m_ShowAverage
          whether the average for each column should be printed
        • m_ShowStdDev
          boolean m_ShowStdDev
          whether std. deviations are printed as well
        • m_Significance
          int[][] m_Significance
          the significance
        • m_SignificanceWidth
          int m_SignificanceWidth
          the size of the significance columns
        • m_StdDev
          double[][] m_StdDev
          the standard deviation
        • m_StdDevPrec
          int m_StdDevPrec
          the standard std. deviation preicision
        • m_StdDevWidth
          int m_StdDevWidth
          the size of the std dev columns
        • m_Wins
          int[][] m_Wins
          the significant wins
        • RIGHT_PARENTHESES
          String RIGHT_PARENTHESES
          the right parentheses for enumerating cols/rows
        • TIE_STRING
          String TIE_STRING
          tie string
        • WIN_STRING
          String WIN_STRING
          win string
    • Class weka.experiment.ResultMatrixCSV

      class ResultMatrixCSV extends ResultMatrix implements Serializable
      serialVersionUID:
      -171838863135042743L
    • Class weka.experiment.ResultMatrixGnuPlot

      class ResultMatrixGnuPlot extends ResultMatrix implements Serializable
      serialVersionUID:
      -234648254944790097L
    • Class weka.experiment.ResultMatrixHTML

      class ResultMatrixHTML extends ResultMatrix implements Serializable
      serialVersionUID:
      6672380422544799990L
    • Class weka.experiment.ResultMatrixLatex

      class ResultMatrixLatex extends ResultMatrix implements Serializable
      serialVersionUID:
      777690788447600978L
    • Class weka.experiment.ResultMatrixPlainText

      class ResultMatrixPlainText extends ResultMatrix implements Serializable
      serialVersionUID:
      1502934525382357937L
    • Class weka.experiment.ResultMatrixSignificance

      class ResultMatrixSignificance extends ResultMatrix implements Serializable
      serialVersionUID:
      -1280545644109764206L
    • Class weka.experiment.Stats

      class Stats extends Object implements Serializable
      serialVersionUID:
      -8610544539090024102L
      • Serialized Fields

        • count
          double count
          The number of values seen
        • max
          double max
          The maximum value seen, or Double.NaN if no values seen
        • 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
        • stdDev
          double stdDev
          The std deviation of values at the last calculateDerived() call
        • sum
          double sum
          The sum of values seen
        • sumSq
          double sumSq
          The sum of values squared seen
    • Class weka.experiment.TaskStatusInfo

      class TaskStatusInfo extends Object implements Serializable
      serialVersionUID:
      -6129343303703560015L
      • Serialized Fields

        • m_ExecutionStatus
          int m_ExecutionStatus
          Holds current execution status.
        • m_StatusMessage
          String m_StatusMessage
          Holds current status message.
        • m_TaskResult
          Object m_TaskResult
          Holds task result. Set to null for no returnable result.
  • Package weka.filters

    • Class weka.filters.AllFilter

      class AllFilter extends Filter implements Serializable
      serialVersionUID:
      5022109283147503266L
    • Class weka.filters.Filter

      class Filter extends Object implements Serializable
      serialVersionUID:
      -8835063755891851218L
      • Serialized Fields

        • m_FirstBatchDone
          boolean m_FirstBatchDone
          True if the first batch has been done
        • m_InputFormat
          Instances m_InputFormat
          The input format for instances
        • m_InputRelAtts
          RelationalLocator m_InputRelAtts
          Indices of relational attributes in the input format
        • m_InputStringAtts
          StringLocator m_InputStringAtts
          Indices of string attributes in the input format
        • m_NewBatch
          boolean m_NewBatch
          Record whether the filter is at the start of a batch
        • m_OutputFormat
          Instances m_OutputFormat
          The output format for instances
        • m_OutputQueue
          Queue m_OutputQueue
          The output instance queue
        • m_OutputRelAtts
          RelationalLocator m_OutputRelAtts
          Indices of relational attributes in the output format
        • m_OutputStringAtts
          StringLocator m_OutputStringAtts
          Indices of string attributes in the output format
    • Class weka.filters.MultiFilter

      class 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

      class SimpleBatchFilter extends SimpleFilter implements Serializable
      serialVersionUID:
      8102908673378055114L
    • Class weka.filters.SimpleFilter

      class SimpleFilter extends Filter implements Serializable
      serialVersionUID:
      5702974949137433141L
      • Serialized Fields

        • m_Debug
          boolean m_Debug
          Whether debugging is on
    • Class weka.filters.SimpleStreamFilter

      class SimpleStreamFilter extends SimpleFilter implements Serializable
      serialVersionUID:
      2754882676192747091L
  • Package weka.filters.supervised.attribute

    • Class weka.filters.supervised.attribute.AddClassification

      class AddClassification extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -1931467132568441909L
      • Serialized Fields

        • m_ActualClassifier
          Classifier m_ActualClassifier
          The actual classifier used to do the classification.
        • m_Classifier
          Classifier m_Classifier
          The classifier template used to do the classification.
        • m_OutputClassification
          boolean m_OutputClassification
          whether to output the classification.
        • m_OutputDistribution
          boolean m_OutputDistribution
          whether to output the class distribution.
        • m_OutputErrorFlag
          boolean m_OutputErrorFlag
          whether to output the error flag.
        • m_RemoveOldClass
          boolean m_RemoveOldClass
          whether to remove the old class attribute.
        • m_SerializedClassifierFile
          File m_SerializedClassifierFile
          The file from which to load a serialized classifier.
        • m_SerializedHeader
          Instances m_SerializedHeader
          the header of the file the serialized classifier was trained with.
    • Class weka.filters.supervised.attribute.AttributeSelection

      class AttributeSelection extends Filter implements Serializable
      serialVersionUID:
      -296211247688169716L
      • Serialized Fields

        • m_ASEvaluator
          ASEvaluation m_ASEvaluator
          the attribute evaluator to use
        • m_ASSearch
          ASSearch m_ASSearch
          the search method if any
        • m_FilterOptions
          String[] m_FilterOptions
          holds a copy of the full set of valid options passed to the filter
        • m_hasClass
          boolean m_hasClass
          True if the input format has a class attribute set
        • m_SelectedAttributes
          int[] m_SelectedAttributes
          holds the selected attributes
        • m_trainSelector
          AttributeSelection m_trainSelector
          the attribute selection evaluation object
    • Class weka.filters.supervised.attribute.ClassOrder

      class ClassOrder extends Filter implements Serializable
      serialVersionUID:
      -2116226838887628411L
      • Serialized Fields

        • m_ClassAttribute
          Attribute m_ClassAttribute
          Class attribute of the data
        • m_ClassCounts
          double[] m_ClassCounts
          This class can provide the class distribution in the sorted order as side effect
        • m_ClassOrder
          int m_ClassOrder
          The class order to be sorted
        • m_Converter
          int[] m_Converter
          The 1-1 converting table from the original class values to the new values
        • m_Random
          Random m_Random
          The random object
        • m_Seed
          long m_Seed
          The seed of randomization
    • Class weka.filters.supervised.attribute.Discretize

      class Discretize extends Filter implements Serializable
      serialVersionUID:
      -3141006402280129097L
      • Serialized Fields

        • m_CutPoints
          double[][] m_CutPoints
          Store the current cutpoints
        • m_DiscretizeCols
          Range m_DiscretizeCols
          Stores which columns to Discretize
        • 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

      class NominalToBinary extends Filter implements Serializable
      serialVersionUID:
      -5004607029857673950L
      • Serialized Fields

        • m_Indices
          int[][] m_Indices
          The sorted indices of the attribute values.
        • m_needToTransform
          boolean m_needToTransform
          Whether we need to transform at all
        • 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?
    • Class weka.filters.supervised.attribute.PLSFilter

      class PLSFilter extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -3335106965521265631L
      • Serialized Fields

        • m_Algorithm
          int m_Algorithm
          the type of algorithm
        • m_ClassMean
          double m_ClassMean
          the mean of the class
        • m_ClassStdDev
          double m_ClassStdDev
          the standard deviation of the class
        • m_Filter
          Filter m_Filter
          for centering the data
        • m_Missing
          Filter m_Missing
          for replacing missing values
        • m_NumComponents
          int m_NumComponents
          the maximum number of components to generate
        • m_PerformPrediction
          boolean m_PerformPrediction
          whether to include the prediction, i.e., modifying the class attribute
        • m_PLS1_b_hat
          Matrix m_PLS1_b_hat
          the b-hat vector for PLS1
        • m_PLS1_P
          Matrix m_PLS1_P
          the P matrix for PLS1
        • m_PLS1_RegVector
          Matrix m_PLS1_RegVector
          the regression vector "r-hat" for PLS1
        • m_PLS1_W
          Matrix m_PLS1_W
          the W matrix for PLS1
        • m_Preprocessing
          int m_Preprocessing
          the type of preprocessing
        • m_ReplaceMissing
          boolean m_ReplaceMissing
          whether to replace missing values
        • m_SIMPLS_B
          Matrix m_SIMPLS_B
          the B matrix for SIMPLS (used for prediction)
        • m_SIMPLS_W
          Matrix m_SIMPLS_W
          the W matrix for SIMPLS
  • Package weka.filters.supervised.instance

    • Class weka.filters.supervised.instance.Resample

      class Resample extends Filter implements Serializable
      serialVersionUID:
      7079064953548300681L
      • Serialized Fields

        • m_BiasToUniformClass
          double m_BiasToUniformClass
          The degree of bias towards uniform (nominal) class distribution.
        • m_InvertSelection
          boolean m_InvertSelection
          Whether to invert the selection (only if instances are drawn WITHOUT replacement).
          See Also:
          • Resample.m_NoReplacement
        • m_NoReplacement
          boolean m_NoReplacement
          Whether to perform sampling with replacement or without.
        • m_RandomSeed
          int m_RandomSeed
          The random number generator seed.
        • m_SampleSizePercent
          double m_SampleSizePercent
          The subsample size, percent of original set, default 100%.
    • Class weka.filters.supervised.instance.SMOTE

      class SMOTE extends Filter implements Serializable
      serialVersionUID:
      -1653880819059250364L
      • Serialized Fields

        • m_ClassValueIndex
          String m_ClassValueIndex
          the index of the class value.
        • m_DetectMinorityClass
          boolean m_DetectMinorityClass
          whether to detect the minority class automatically.
        • m_NearestNeighbors
          int m_NearestNeighbors
          the number of neighbors to use.
        • m_Percentage
          double m_Percentage
          the percentage of SMOTE instances to create.
        • m_RandomSeed
          int m_RandomSeed
          the random seed to use.
    • Class weka.filters.supervised.instance.SpreadSubsample

      class SpreadSubsample extends Filter implements Serializable
      serialVersionUID:
      -3947033795243930016L
      • Serialized Fields

        • m_AdjustWeights
          boolean m_AdjustWeights
          True if instance weights will be adjusted to maintain total weight per class.
        • m_DistributionSpread
          double m_DistributionSpread
          True if the first batch has been done
        • m_MaxCount
          int m_MaxCount
          The maximum count of any class
        • m_RandomSeed
          int m_RandomSeed
          The random number generator seed
    • Class weka.filters.supervised.instance.StratifiedRemoveFolds

      class StratifiedRemoveFolds extends Filter implements Serializable
      serialVersionUID:
      -7069148179905814324L
      • Serialized Fields

        • m_Fold
          int m_Fold
          Fold to output
        • 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_Seed
          long m_Seed
          Random number seed.
  • Package weka.filters.unsupervised.attribute

    • Class weka.filters.unsupervised.attribute.AbstractTimeSeries

      class AbstractTimeSeries extends Filter implements Serializable
      serialVersionUID:
      -3795656792078022357L
      • Serialized Fields

        • 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_History
          Queue m_History
          Stores the historical instances to copy values between
        • 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_SelectedCols
          Range m_SelectedCols
          Stores which columns to copy
    • Class weka.filters.unsupervised.attribute.Add

      class Add extends Filter implements Serializable
      serialVersionUID:
      761386447332932389L
      • Serialized Fields

        • m_AttributeType
          int m_AttributeType
          Record the type of attribute to insert.
        • m_DateFormat
          String m_DateFormat
          The date format.
        • 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_Name
          String m_Name
          The name for the new attribute.
    • Class weka.filters.unsupervised.attribute.AddCluster

      class 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

      class AddExpression extends Filter implements Serializable
      serialVersionUID:
      402130384261736245L
      • Serialized Fields

        • m_attributeExpression
          AttributeExpression m_attributeExpression
        • m_attributeName
          String m_attributeName
          Name of the new attribute. "expression" length string will use the provided expression as the new attribute name
        • m_Debug
          boolean m_Debug
          If true, makes the attribute name equal to the postfix parse of the expression
        • m_infixExpression
          String m_infixExpression
          The infix expression
    • Class weka.filters.unsupervised.attribute.AddID

      class AddID extends Filter implements Serializable
      serialVersionUID:
      4734383199819293390L
      • Serialized Fields

        • m_Counter
          int m_Counter
          the counter for the ID
        • m_Index
          SingleIndex m_Index
          the index of the attribute
        • m_Name
          String m_Name
          the name of the attribute
    • Class weka.filters.unsupervised.attribute.AddNoise

      class AddNoise extends Filter implements Serializable
      serialVersionUID:
      -8499673222857299082L
      • Serialized Fields

        • m_AttIndex
          SingleIndex m_AttIndex
          The attribute's index setting.
        • m_Percent
          int m_Percent
          The subsample size, percent of original set, default 10%
        • m_RandomSeed
          int m_RandomSeed
          The random number generator seed
        • m_UseMissing
          boolean m_UseMissing
          Flag if missing values are taken as value.
    • Class weka.filters.unsupervised.attribute.AddValues

      class AddValues extends Filter implements Serializable
      serialVersionUID:
      -8100622241742393656L
      • Serialized Fields

        • m_AttIndex
          SingleIndex m_AttIndex
          The attribute's index setting.
        • m_Labels
          Vector 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

      class Center extends PotentialClassIgnorer implements Serializable
      serialVersionUID:
      -9101338448900581023L
      • Serialized Fields

        • m_Means
          double[] m_Means
          The means
    • Class weka.filters.unsupervised.attribute.ChangeDateFormat

      class ChangeDateFormat extends Filter implements Serializable
      serialVersionUID:
      -1609344074013448737L
      • Serialized Fields

        • m_AttIndex
          SingleIndex m_AttIndex
          The attribute's index setting.
        • m_DateFormat
          SimpleDateFormat m_DateFormat
          The output date format.
        • m_OutputAttribute
          Attribute m_OutputAttribute
          The output attribute.
    • Class weka.filters.unsupervised.attribute.ClassAssigner

      class ClassAssigner extends SimpleStreamFilter implements Serializable
      serialVersionUID:
      1775780193887394115L
      • Serialized Fields

        • m_ClassIndex
          int m_ClassIndex
          the class index.
    • Class weka.filters.unsupervised.attribute.ClusterMembership

      class 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_priors
          double[] m_priors
          The prior probability for each class
        • m_removeAttributes
          Filter m_removeAttributes
          Filter for removing attributes
    • Class weka.filters.unsupervised.attribute.Copy

      class 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

      class Discretize extends PotentialClassIgnorer implements Serializable
      serialVersionUID:
      -1358531742174527279L
      • Serialized Fields

        • m_CutPoints
          double[][] m_CutPoints
          Store the current cutpoints
        • m_DefaultCols
          String m_DefaultCols
          The default columns to discretize
        • m_DesiredWeightOfInstancesPerInterval
          double m_DesiredWeightOfInstancesPerInterval
          The desired weight of instances per bin
        • m_DiscretizeCols
          Range m_DiscretizeCols
          Stores which columns to Discretize
        • m_FindNumBins
          boolean m_FindNumBins
          Find the number of bins using cross-validated entropy.
        • m_MakeBinary
          boolean m_MakeBinary
          Output binary attributes for discretized attributes.
        • m_NumBins
          int m_NumBins
          The number of bins to divide the attribute into
        • m_UseEqualFrequency
          boolean m_UseEqualFrequency
          Use equal-frequency binning if unsupervised discretization turned on
    • Class weka.filters.unsupervised.attribute.FirstOrder

      class 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

      class InterquartileRange extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -227879653639723030L
      • Serialized Fields

        • m_AttributeIndices
          int[] m_AttributeIndices
          the generated indices (only for performance reasons)
        • m_Attributes
          Range m_Attributes
          the attribute range to work on
        • m_DetectionPerAttribute
          boolean m_DetectionPerAttribute
          whether to generate Outlier/ExtremeValue attributes for each attribute instead of a general one
        • m_ExtremeValuesAsOutliers
          boolean m_ExtremeValuesAsOutliers
          whether extreme values are also tagged as outliers
        • m_ExtremeValuesFactor
          double m_ExtremeValuesFactor
          the factor for detecting extreme values, by default 2*m_OutlierFactor
        • m_IQR
          double[] m_IQR
          the interquartile range
        • m_LowerExtremeValue
          double[] m_LowerExtremeValue
          the lower extreme value threshold (= Q1 - EVF*IQR)
        • m_LowerOutlier
          double[] m_LowerOutlier
          the lower outlier threshold (= Q1 - OF*IQR)
        • m_Median
          double[] m_Median
          the median
        • m_OutlierAttributePosition
          int[] m_OutlierAttributePosition
          the position of the outlier attribute
        • m_OutlierFactor
          double m_OutlierFactor
          the factor for detecting outliers
        • 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!
        • 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)
    • Class weka.filters.unsupervised.attribute.KernelFilter

      class KernelFilter extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      213800899640387499L
      • Serialized Fields

        • m_ActualFilter
          Filter m_ActualFilter
          for centering/standardizing the data (the actual filter 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_Filter
          Filter m_Filter
          for centering/standardizing the data
        • m_InitFile
          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_Kernel
          Kernel m_Kernel
          Kernel to use
        • m_KernelFactor
          double m_KernelFactor
          the calculated kernel factor
          See Also:
          • KernelFilter.m_KernelFactorExpression
        • m_KernelFactorExpression
          String m_KernelFactorExpression
          optimizes the kernel with this formula (A = # of attributes, N = # of instances)
        • m_Missing
          ReplaceMissingValues m_Missing
          The filter used to get rid of missing values.
        • m_NominalToBinary
          NominalToBinary m_NominalToBinary
          The filter used to make attributes numeric.
        • m_NumTrainInstances
          int m_NumTrainInstances
          The number of instances in the training data.
    • Class weka.filters.unsupervised.attribute.MakeIndicator

      class MakeIndicator extends Filter implements Serializable
      serialVersionUID:
      766001176862773163L
      • Serialized Fields

        • m_AttIndex
          SingleIndex m_AttIndex
          The attribute's index setting.
        • m_Numeric
          boolean m_Numeric
          Make boolean attribute numeric.
        • m_ValIndex
          Range m_ValIndex
          The value's index
    • Class weka.filters.unsupervised.attribute.MathExpression

      class MathExpression extends PotentialClassIgnorer implements Serializable
      serialVersionUID:
      -3713222714671997901L
      • Serialized Fields

        • m_attStats
          AttributeStats[] m_attStats
          Attributes statistics
        • m_expression
          String m_expression
          The modification expression
        • m_SelectCols
          Range m_SelectCols
          Stores which columns to select as a funky range
    • Class weka.filters.unsupervised.attribute.MergeTwoValues

      class 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

      class MultiInstanceToPropositional extends Filter implements Serializable
      serialVersionUID:
      -4102847628883002530L
      • Serialized Fields

        • m_BagRelAtts
          RelationalLocator m_BagRelAtts
          Indices of relational attributes in the bag
        • m_BagStringAtts
          StringLocator m_BagStringAtts
          Indices of string attributes in the bag
        • m_NumBags
          int m_NumBags
          the total number of bags
        • 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

      class NominalToBinary extends Filter implements Serializable
      serialVersionUID:
      -1130642825710549138L
      • Serialized Fields

        • m_Columns
          Range m_Columns
          Stores which columns to act on
        • m_needToTransform
          boolean m_needToTransform
          Whether we need to transform at all
        • 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?
    • Class weka.filters.unsupervised.attribute.NominalToString

      class 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

      class Normalize extends PotentialClassIgnorer implements Serializable
      serialVersionUID:
      -8158531150984362898L
      • Serialized Fields

        • m_MaxArray
          double[] m_MaxArray
          The maximum values for numeric attributes.
        • m_MinArray
          double[] m_MinArray
          The minimum values for numeric attributes.
        • m_Scale
          double m_Scale
          The scaling factor of the output range.
        • m_Translation
          double m_Translation
          The translation of the output range.
    • Class weka.filters.unsupervised.attribute.NumericCleaner

      class NumericCleaner extends SimpleStreamFilter implements Serializable
      serialVersionUID:
      -352890679895066592L
      • Serialized Fields

        • 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_Decimals
          int m_Decimals
          the number of decimals to round to (-1 means no rounding)
        • m_IncludeClass
          boolean m_IncludeClass
          whether to include the class attribute
        • m_MaxDefault
          double m_MaxDefault
          the maximum default replacement value
        • m_MaxThreshold
          double m_MaxThreshold
          the maximum threshold
        • m_MinDefault
          double m_MinDefault
          the minimum default replacement value
        • m_MinThreshold
          double m_MinThreshold
          the minimum threshold
    • Class weka.filters.unsupervised.attribute.NumericToBinary

      class NumericToBinary extends PotentialClassIgnorer implements Serializable
      serialVersionUID:
      2616879323359470802L
    • Class weka.filters.unsupervised.attribute.NumericToNominal

      class NumericToNominal extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -6614630932899796239L
      • Serialized Fields

        • m_Cols
          Range m_Cols
          Stores which columns to turn into nominals
        • m_DefaultCols
          String m_DefaultCols
          The default columns to turn into nominals
    • Class weka.filters.unsupervised.attribute.NumericTransform

      class NumericTransform extends Filter implements Serializable
      serialVersionUID:
      -8561413333351366934L
      • Serialized Fields

        • m_Class
          String m_Class
          Class containing transformation method.
        • m_Cols
          Range m_Cols
          Stores which columns to transform.
        • m_Method
          String m_Method
          Transformation method.
    • Class weka.filters.unsupervised.attribute.Obfuscate

      class Obfuscate extends Filter implements Serializable
      serialVersionUID:
      -343922772462971561L
    • Class weka.filters.unsupervised.attribute.PartitionedMultiFilter

      class PartitionedMultiFilter extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -6293720886005713120L
      • Serialized Fields

        • m_Filters
          Filter[] m_Filters
          The filters.
        • m_IndicesUnused
          int[] m_IndicesUnused
          the indices of the unused attributes.
        • m_Ranges
          Range[] m_Ranges
          The attribute ranges.
        • m_RemoveUnused
          boolean m_RemoveUnused
          Whether unused attributes are left out of the output.
    • Class weka.filters.unsupervised.attribute.PKIDiscretize

      class PKIDiscretize extends Discretize implements Serializable
      serialVersionUID:
      6153101248977702675L
    • Class weka.filters.unsupervised.attribute.PotentialClassIgnorer

      class PotentialClassIgnorer extends Filter implements Serializable
      serialVersionUID:
      8625371119276845454L
      • Serialized Fields

        • m_ClassIndex
          int m_ClassIndex
          Storing the class index
        • m_IgnoreClass
          boolean m_IgnoreClass
          True if the class is to be unset
    • Class weka.filters.unsupervised.attribute.PrincipalComponents

      class PrincipalComponents extends Filter implements Serializable
      serialVersionUID:
      -5649876869480249303L
      • Serialized Fields

        • m_AttributeFilter
          Remove m_AttributeFilter
          Filter for removing class attribute, nominal attributes with 0 or 1 value.
        • m_center
          boolean m_center
          If true, center (rather than standardize) the data and compute PCA from covariance (rather than correlation) matrix.
        • m_centerFilter
          Center m_centerFilter
          Filter for centering the data
        • m_ClassIndex
          int m_ClassIndex
          Class index.
        • m_Correlation
          double[][] m_Correlation
          Correlation matrix for the original data.
        • m_CoverVariance
          double m_CoverVariance
          the amount of varaince to cover in the original data when retaining the best n PC's.
        • m_Eigenvalues
          double[] m_Eigenvalues
          Eigenvalues for the corresponding eigenvectors.
        • m_Eigenvectors
          double[][] m_Eigenvectors
          Will hold the unordered linear transformations of the (normalized) original data.
        • m_HasClass
          boolean m_HasClass
          Data has a class set.
        • m_MaxAttributes
          int m_MaxAttributes
          maximum number of attributes in the transformed data (-1 for all).
        • m_MaxAttrsInName
          int m_MaxAttrsInName
          maximum number of attributes in the transformed attribute name.
        • m_NominalToBinaryFilter
          NominalToBinary m_NominalToBinaryFilter
          Filter for turning nominal values into numeric ones.
        • m_NumAttribs
          int m_NumAttribs
          Number of attributes.
        • m_NumInstances
          int m_NumInstances
          Number of instances.
        • m_OutputNumAtts
          int m_OutputNumAtts
          The number of attributes in the pc transformed data.
        • m_ReplaceMissingFilter
          ReplaceMissingValues m_ReplaceMissingFilter
          Filters for replacing missing values.
        • m_SortedEigens
          int[] m_SortedEigens
          Sorted eigenvalues.
        • m_standardizeFilter
          Standardize m_standardizeFilter
          Filter for standardizing the data
        • m_SumOfEigenValues
          double m_SumOfEigenValues
          sum of the eigenvalues.
        • m_TrainCopy
          Instances m_TrainCopy
          Keep a copy for the class attribute (if set).
        • m_TrainInstances
          Instances m_TrainInstances
          The data to transform analyse/transform.
        • m_TransformedFormat
          Instances m_TransformedFormat
          The header for the transformed data format.
    • Class weka.filters.unsupervised.attribute.PropositionalToMultiInstance

      class PropositionalToMultiInstance extends Filter implements Serializable
      serialVersionUID:
      5825873573912102482L
      • Serialized Fields

        • m_BagRelAtts
          RelationalLocator m_BagRelAtts
          Indices of relational attributes in the bag
        • m_BagStringAtts
          StringLocator m_BagStringAtts
          Indices of string attributes in the bag
        • m_DoNotWeightBags
          boolean m_DoNotWeightBags
          do not weight bags by number of instances they contain
        • m_Randomize
          boolean m_Randomize
          whether to randomize the output data
        • m_Seed
          int m_Seed
          the seed for randomizing, default is 1
    • Class weka.filters.unsupervised.attribute.RandomProjection

      class RandomProjection extends Filter implements Serializable
      serialVersionUID:
      4428905532728645880L
      • Serialized Fields

        • m_distribution
          int m_distribution
          Stores the distribution to use for calculating the random matrix
        • m_k
          int m_k
          Stores the number of dimensions to reduce the data to
        • m_ntob
          Filter m_ntob
          The NominalToBinary filter applied to the data before this filter
        • m_OutputFormatDefined
          boolean m_OutputFormatDefined
          Keeps track of output format if it is defined or not
        • m_percent
          double m_percent
          Stores the dimensionality the data should be reduced to as percentage of the original dimension
        • m_random
          Random m_random
          The random number generator used for generating the random matrix
        • m_replaceMissing
          Filter m_replaceMissing
          The ReplaceMissingValues filter
        • m_rmatrix
          double[][] m_rmatrix
          The random matrix
        • m_rndmSeed
          long m_rndmSeed
          Stores the random seed used to generate the random matrix
        • m_useGaussian
          boolean m_useGaussian
          Is the random matrix will be computed using Gaussian distribution or not
        • m_useReplaceMissing
          boolean m_useReplaceMissing
          Should the missing values be replaced using unsupervised.ReplaceMissingValues filter
    • Class weka.filters.unsupervised.attribute.RandomSubset

      class RandomSubset extends SimpleStreamFilter implements Serializable
      serialVersionUID:
      2911221724251628050L
      • Serialized Fields

        • m_Indices
          int[] m_Indices
          The indices of the attributes that got selected.
        • 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.
    • Class weka.filters.unsupervised.attribute.RELAGGS

      class RELAGGS extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -3333791375278589231L
      • Serialized Fields

        • m_AttStats
          Hashtable<String,AttributeStats> m_AttStats
          stores the attribute statistics att_index-att_index_in_rel_att <-> AttributeStats
        • 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)
    • Class weka.filters.unsupervised.attribute.Remove

      class 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

      class 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

      class RemoveUseless extends Filter implements Serializable
      serialVersionUID:
      -8659417851407640038L
      • Serialized Fields

        • m_maxVariancePercentage
          double m_maxVariancePercentage
          The type of attribute to delete
        • m_removeFilter
          Remove m_removeFilter
          The filter used to remove attributes
    • Class weka.filters.unsupervised.attribute.Reorder

      class Reorder extends Filter implements Serializable
      serialVersionUID:
      -1135571321097202292L
      • Serialized Fields

        • 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
        • m_NewOrderCols
          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
    • Class weka.filters.unsupervised.attribute.ReplaceMissingValues

      class ReplaceMissingValues extends PotentialClassIgnorer implements Serializable
      serialVersionUID:
      8349568310991609867L
      • Serialized Fields

        • m_ModesAndMeans
          double[] m_ModesAndMeans
          The modes and means
    • Class weka.filters.unsupervised.attribute.Standardize

      class 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

      class 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

      class StringToWordVector extends Filter implements Serializable
      serialVersionUID:
      8249106275278565424L
      • Serialized Fields

        • 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_Dictionary
          TreeMap m_Dictionary
          Contains a mapping of valid words to attribute indexes.
        • 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_doNotOperateOnPerClassBasis
          boolean m_doNotOperateOnPerClassBasis
          whether to operate on a per-class basis.
        • 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_minTermFreq
          int m_minTermFreq
          the minimum (per-class) word frequency.
        • 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_OutputCounts
          boolean m_OutputCounts
          True if output instances should contain word frequency rather than boolean 0 or 1.
        • m_PeriodicPruningRate
          double m_PeriodicPruningRate
          The percentage at which to periodically prune the dictionary.
        • m_Prefix
          String m_Prefix
          A String prefix for the attribute names.
        • m_SelectedRange
          Range m_SelectedRange
          Range of columns to convert to word vectors.
        • m_Stemmer
          Stemmer m_Stemmer
          the stemming algorithm.
        • m_Stopwords
          File m_Stopwords
          a file containing stopwords for using others than the default Rainbow ones.
        • 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_Tokenizer
          Tokenizer m_Tokenizer
          the tokenizer algorithm to use.
        • m_useStoplist
          boolean m_useStoplist
          True if tokens that are on a stoplist are to be ignored.
        • m_WordsToKeep
          int m_WordsToKeep
          The default number of words (per class if there is a class attribute assigned) to attempt to keep.
    • Class weka.filters.unsupervised.attribute.SwapValues

      class 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

      class TimeSeriesDelta extends TimeSeriesTranslate implements Serializable
      serialVersionUID:
      3101490081896634942L
    • Class weka.filters.unsupervised.attribute.TimeSeriesTranslate

      class TimeSeriesTranslate extends AbstractTimeSeries implements Serializable
      serialVersionUID:
      -8901621509691785705L
    • Class weka.filters.unsupervised.attribute.Wavelet

      class Wavelet extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -3335106965521265631L
      • Serialized Fields

        • m_Algorithm
          int m_Algorithm
          the type of algorithm
        • m_Filter
          Filter m_Filter
          an optional filter for preprocessing of the data
        • m_Padding
          int m_Padding
          the type of padding
  • Package weka.filters.unsupervised.instance

    • Class weka.filters.unsupervised.instance.NonSparseToSparse

      class NonSparseToSparse extends Filter implements Serializable
      serialVersionUID:
      4694489111366063852L
    • Class weka.filters.unsupervised.instance.Normalize

      class Normalize extends Filter implements Serializable
      serialVersionUID:
      -7947971807522917395L
      • Serialized Fields

        • m_LNorm
          double m_LNorm
          The L-norm to use
        • m_Norm
          double m_Norm
          The norm that each instance must have at the end
    • Class weka.filters.unsupervised.instance.Randomize

      class Randomize extends Filter implements Serializable
      serialVersionUID:
      8854479785121877582L
      • Serialized Fields

        • m_Random
          Random m_Random
          The current random number generator
        • m_Seed
          int m_Seed
          The random number seed
    • Class weka.filters.unsupervised.instance.RemoveFolds

      class RemoveFolds extends Filter implements Serializable
      serialVersionUID:
      8220373305559055700L
      • Serialized Fields

        • m_Fold
          int m_Fold
          Fold to output
        • 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_Seed
          long m_Seed
          Random number seed.
    • Class weka.filters.unsupervised.instance.RemoveFrequentValues

      class RemoveFrequentValues extends Filter implements Serializable
      serialVersionUID:
      -2447432930070059511L
      • Serialized Fields

        • m_AttIndex
          SingleIndex m_AttIndex
          The attribute's index setting.
        • m_Invert
          boolean m_Invert
          whether to invert the matching sense.
        • m_LeastValues
          boolean m_LeastValues
          whether to retain values with least instances instead of most.
        • 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_NumValues
          int m_NumValues
          the number of values to retain.
        • m_Values
          HashSet m_Values
          contains the values to retain
    • Class weka.filters.unsupervised.instance.RemoveMisclassified

      class RemoveMisclassified extends Filter implements Serializable
      serialVersionUID:
      5469157004717663171L
      • Serialized Fields

        • m_classIndex
          int m_classIndex
          The attribute to treat as the class for purposes of cleansing.
        • m_cleansingClassifier
          Classifier m_cleansingClassifier
          The classifier used to do the cleansing
        • m_firstBatchFinished
          boolean m_firstBatchFinished
          Have we processed the first batch (i.e. training data)?
        • m_invertMatching
          boolean m_invertMatching
          Whether to invert the match so the correctly classified instances are discarded
        • m_numericClassifyThreshold
          double m_numericClassifyThreshold
          The threshold for deciding when a numeric value is correctly classified
        • m_numOfCleansingIterations
          int m_numOfCleansingIterations
          The maximum number of cleansing iterations to perform (<1 = until fully cleansed)
        • m_numOfCrossValidationFolds
          int m_numOfCrossValidationFolds
          The number of cross validation folds to perform (<2 = no cross validation)
    • Class weka.filters.unsupervised.instance.RemovePercentage

      class RemovePercentage extends Filter implements Serializable
      serialVersionUID:
      2150341191158533133L
      • Serialized Fields

        • m_Inverse
          boolean m_Inverse
          Indicates if inverse of selection is to be output.
        • m_Percentage
          double m_Percentage
          Percentage of instances to select.
    • Class weka.filters.unsupervised.instance.RemoveRange

      class 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

      class RemoveWithValues extends Filter implements Serializable
      serialVersionUID:
      4752870193679263361L
      • Serialized Fields

        • m_AttIndex
          SingleIndex m_AttIndex
          The attribute's index setting.
        • m_dontFilterAfterFirstBatch
          boolean m_dontFilterAfterFirstBatch
          Whether to filter instances after the first batch has been processed
        • 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_Value
          double m_Value
          Stores which value of a numeric attribute is to be used for filtering.
        • m_Values
          Range m_Values
          Stores which values of nominal attribute are to be used for filtering.
    • Class weka.filters.unsupervised.instance.Resample

      class Resample extends Filter implements Serializable
      serialVersionUID:
      3119607037607101160L
      • Serialized Fields

        • m_InvertSelection
          boolean m_InvertSelection
          Whether to invert the selection (only if instances are drawn WITHOUT replacement)
          See Also:
          • Resample.m_NoReplacement
        • m_NoReplacement
          boolean m_NoReplacement
          Whether to perform sampling with replacement or without
        • m_RandomSeed
          int m_RandomSeed
          The random number generator seed
        • m_SampleSizePercent
          double m_SampleSizePercent
          The subsample size, percent of original set, default 100%
    • Class weka.filters.unsupervised.instance.ReservoirSample

      class ReservoirSample extends Filter implements Serializable
      serialVersionUID:
      3119607037607101160L
      • Serialized Fields

        • m_currentInst
          int m_currentInst
          The current instance being processed
        • m_random
          Random m_random
          The random number generator
        • m_RandomSeed
          int m_RandomSeed
          The random number generator seed
        • m_SampleSize
          int m_SampleSize
          The subsample size, number of instances%
        • m_subSample
          Instance[] m_subSample
          Holds the sub-sample (reservoir)
    • Class weka.filters.unsupervised.instance.SparseToNonSparse

      class SparseToNonSparse extends Filter implements Serializable
      serialVersionUID:
      2481634184210236074L
    • Class weka.filters.unsupervised.instance.SubsetByExpression

      class SubsetByExpression extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      5628686110979589602L
      • Serialized Fields

        • m_Expression
          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

      class AttributeListPanel extends JPanel implements Serializable
      serialVersionUID:
      -2030706987910400362L
      • Serialized Fields

        • m_Model
          weka.gui.AttributeListPanel.AttributeTableModel m_Model
          The table model containing attribute names
        • m_Table
          JTable m_Table
          The table displaying attribute names
    • Class weka.gui.AttributeSelectionPanel

      class AttributeSelectionPanel extends JPanel implements Serializable
      serialVersionUID:
      627131485290359194L
      • Serialized Fields

        • m_IncludeAll
          JButton m_IncludeAll
          Press to select all attributes
        • m_Invert
          JButton m_Invert
          Press to invert the current selection
        • m_Model
          weka.gui.AttributeSelectionPanel.AttributeTableModel m_Model
          The table model containing attribute names and selection status
        • m_Pattern
          JButton m_Pattern
          Press to enter a perl regular expression for selection
        • m_PatternRegEx
          String m_PatternRegEx
          The current regular expression.
        • m_RemoveAll
          JButton m_RemoveAll
          Press to deselect all attributes
        • m_Table
          JTable m_Table
          The table displaying attribute names and selection status
    • Class weka.gui.AttributeSummaryPanel

      class AttributeSummaryPanel extends JPanel implements Serializable
      serialVersionUID:
      -5434987925737735880L
      • Serialized Fields

        • m_AttributeNameLab
          JLabel m_AttributeNameLab
          Displays the name of the relation
        • m_AttributeStats
          AttributeStats[] m_AttributeStats
          Cached stats on the attributes we've summarized so far
        • m_AttributeTypeLab
          JLabel m_AttributeTypeLab
          Displays the type of attribute
        • m_DistinctLab
          JLabel m_DistinctLab
          Displays the number of distinct values
        • m_Instances
          Instances m_Instances
          The instances we're playing with
        • m_MissingLab
          JLabel m_MissingLab
          Displays the number of missing values
        • m_StatsTable
          JTable m_StatsTable
          Displays other stats in a table
        • m_UniqueLab
          JLabel m_UniqueLab
          Displays the number of unique values
    • Class weka.gui.AttributeVisualizationPanel

      class AttributeVisualizationPanel extends PrintablePanel implements Serializable
      serialVersionUID:
      -8650490488825371193L
      • Serialized Fields

        • 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_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_colorAttrib
          JComboBox m_colorAttrib
          This stores and lets the user select a class attribute. It also has an entry "No Class" if the user does not want to set a class attribute for colouring.
        • m_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
        • m_data
          Instances m_data
          This holds the current set of instances
        • m_displayCurrentAttribute
          boolean m_displayCurrentAttribute
        • m_doneCurrentAttribute
          boolean m_doneCurrentAttribute
        • m_fm
          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_hc
          Thread m_hc
          This stores the BarCalc or HistCalc thread while a new barplot or histogram is being calculated.
        • 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_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_locker
          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_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_threadRun
          boolean m_threadRun
          True if the thread m_hc above is running.
    • Class weka.gui.CheckBoxList

      class CheckBoxList extends JList implements Serializable
      serialVersionUID:
      -4359573373359270258L
    • Class weka.gui.CheckBoxList.CheckBoxListModel

      class CheckBoxListModel extends DefaultListModel implements Serializable
      serialVersionUID:
      7772455499540273507L
    • Class weka.gui.CheckBoxList.CheckBoxListRenderer

      class CheckBoxListRenderer extends JCheckBox implements Serializable
      serialVersionUID:
      1059591605858524586L
    • Class weka.gui.ConverterFileChooser

      class ConverterFileChooser extends JFileChooser implements Serializable
      serialVersionUID:
      -5373058011025481738L
      • Serialized Fields

        • m_CapabilitiesFilter
          Capabilities m_CapabilitiesFilter
          the Capabilities filter for the savers
        • m_ConfigureButton
          JButton m_ConfigureButton
          the configure button
        • m_CoreConvertersOnly
          boolean m_CoreConvertersOnly
          whether to display only core converters (hardcoded in ConverterUtils). Necessary for RMI/Remote Experiments for instance.
          See Also:
        • m_CurrentConverter
          Object m_CurrentConverter
          the converter that was chosen by the user
        • m_DialogType
          int m_DialogType
          the type of dialog to display
        • m_FileMustExist
          boolean m_FileMustExist
          whether the file to be opened must exist (only open dialog)
        • m_LastFilter
          FileFilter m_LastFilter
          the last filter that was used for opening/saving
        • m_Listener
          PropertyChangeListener m_Listener
          the propertychangelistener
        • m_OverwriteWarning
          boolean m_OverwriteWarning
          whether to popup a dialog in case the file already exists (only save dialog)
        • m_Self
          ConverterFileChooser m_Self
          the file chooser itself
    • Class weka.gui.DatabaseConnectionDialog

      class DatabaseConnectionDialog extends JDialog implements Serializable
      serialVersionUID:
      -1081946748666245054L
      • Serialized Fields

        • m_DbaseURLLab
          JLabel m_DbaseURLLab
        • m_DbaseURLText
          JTextField m_DbaseURLText
        • m_DebugCheckBox
          JCheckBox m_DebugCheckBox
        • m_DebugLab
          JLabel m_DebugLab
        • m_PasswordLab
          JLabel m_PasswordLab
        • m_PasswordText
          JPasswordField m_PasswordText
        • m_returnValue
          int m_returnValue
        • m_UserNameLab
          JLabel m_UserNameLab
        • m_UserNameText
          JTextField m_UserNameText
    • Class weka.gui.ExtensionFileFilter

      class ExtensionFileFilter extends FileFilter implements Serializable
      • Serialized Fields

        • m_Description
          String m_Description
          The text description of the types of files accepted
        • m_Extension
          String[] m_Extension
          The filename extensions of accepted files
    • Class weka.gui.GenericArrayEditor

      class GenericArrayEditor extends JPanel implements Serializable
      serialVersionUID:
      3914616975334750480L
      • Serialized Fields

        • m_AddBut
          JButton m_AddBut
          Click to add the current object configuration to the array.
        • m_DeleteBut
          JButton m_DeleteBut
          Click this to delete the selected array values.
        • m_DownBut
          JButton m_DownBut
          Click this to move the selected array value(s) one down.
        • m_EditBut
          JButton m_EditBut
          Click this to edit the selected array value.
        • m_Editor
          PropertyEditor m_Editor
          The property editor for editing existing elements.
        • m_ElementClass
          Class m_ElementClass
          The class of objects allowed in the array.
        • m_ElementEditor
          PropertyEditor m_ElementEditor
          The property editor for the class we are editing.
        • m_ElementList
          JList m_ElementList
          The list component displaying current values.
        • m_InnerActionListener
          ActionListener m_InnerActionListener
          Listens to buttons being pressed and taking the appropriate action.
        • m_InnerMouseListener
          MouseListener m_InnerMouseListener
          Listens to mouse events and takes appropriate action.
        • m_InnerSelectionListener
          ListSelectionListener m_InnerSelectionListener
          Listens to list items being selected and takes appropriate action.
        • m_Label
          JLabel m_Label
          The label for when we can't edit that type.
        • m_ListModel
          DefaultListModel m_ListModel
          The defaultlistmodel holding our data.
        • m_PD
          PropertyDialog m_PD
          The currently displayed property dialog, if any.
        • m_Support
          PropertyChangeSupport m_Support
          Handles property change notification.
        • m_UpBut
          JButton m_UpBut
          Click this to move the selected array value(s) one up.
    • Class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog

      class CapabilitiesFilterDialog extends JDialog implements Serializable
      serialVersionUID:
      -7845503345689646266L
      • Serialized Fields

        • m_CancelButton
          JButton m_CancelButton
          the Cancel button.
        • m_Capabilities
          Capabilities m_Capabilities
          the capabilities used for initializing the dialog.
        • m_InfoLabel
          JLabel m_InfoLabel
          the label, listing the name of the superclass.
        • m_List
          CheckBoxList m_List
          the list with all the capabilities.
        • m_OkButton
          JButton m_OkButton
          the OK button.
        • m_Popup
          JPopupMenu m_Popup
          the popup to display again.
        • m_Self
          JDialog m_Self
          the dialog itself.
    • Class weka.gui.GenericObjectEditor.GOEPanel

      class GOEPanel extends JPanel implements Serializable
      serialVersionUID:
      3656028520876011335L
      • Serialized Fields

        • m_cancelBut
          JButton m_cancelBut
          cancel button.
        • m_ChildPropertySheet
          PropertySheetPanel m_ChildPropertySheet
          The component that performs classifier customization.
        • m_ClassNameLabel
          JLabel m_ClassNameLabel
          The name of the current class.
        • m_FileChooser
          JFileChooser m_FileChooser
          The filechooser for opening and saving object files.
        • m_okBut
          JButton m_okBut
          ok button.
        • m_OpenBut
          JButton m_OpenBut
          Open object from disk.
        • m_SaveBut
          JButton m_SaveBut
          Save object to disk.
    • Class weka.gui.GenericObjectEditor.GOETreeNode

      class GOETreeNode extends DefaultMutableTreeNode implements Serializable
      serialVersionUID:
      -1707872446682150133L
      • Serialized Fields

        • m_Capabilities
          Capabilities m_Capabilities
          the Capabilities object to use for filtering.
        • m_toolTipText
          String m_toolTipText
          tool tip
    • Class weka.gui.GenericObjectEditor.JTreePopupMenu

      class JTreePopupMenu extends JPopupMenu implements Serializable
      serialVersionUID:
      -3404546329655057387L
      • Serialized Fields

        • m_CloseButton
          JButton m_CloseButton
          The button for closing the popup again.
        • m_FilterButton
          JButton m_FilterButton
          The filter button in case of CapabilitiesHandlers.
        • m_RemoveFilterButton
          JButton m_RemoveFilterButton
          The remove filter button in case of CapabilitiesHandlers.
        • m_scroller
          JScrollPane m_scroller
          The scroller.
        • m_Self
          JPopupMenu m_Self
          the popup itself.
        • m_tree
          JTree m_tree
          The tree.
    • Class weka.gui.GUIChooser

      class GUIChooser extends JFrame implements Serializable
      serialVersionUID:
      9001529425230247914L
      • Serialized Fields

        • m_ArffViewers
          Vector m_ArffViewers
          keeps track of the opened ArffViewer instancs
        • m_BayesNetGUIFrame
          JFrame m_BayesNetGUIFrame
          The frame containing the Bayes net GUI
        • m_BoundaryVisualizerFrame
          JFrame m_BoundaryVisualizerFrame
          The frame containing the boundary visualizer
        • m_ChildFrames
          HashSet<Container> m_ChildFrames
          contains the child frames (title <-> object).
        • m_EnsembleLibraryFrame
          JFrame m_EnsembleLibraryFrame
          The frame containing the ensemble library interface
        • m_ExperimenterBut
          JButton m_ExperimenterBut
          Click to open the Explorer
        • m_ExperimenterFrame
          JFrame m_ExperimenterFrame
          The frame containing the experiment interface
        • m_ExplorerBut
          JButton m_ExplorerBut
          Click to open the Explorer
        • m_ExplorerFrame
          JFrame m_ExplorerFrame
          The frame containing the explorer interface
        • m_FileChooserGraphVisualizer
          JFileChooser m_FileChooserGraphVisualizer
          filechooser for the GraphVisualizer
        • m_FileChooserPlot
          JFileChooser m_FileChooserPlot
          filechooser for Plots
        • m_FileChooserROC
          JFileChooser m_FileChooserROC
          filechooser for ROC curves
        • m_FileChooserTreeVisualizer
          JFileChooser m_FileChooserTreeVisualizer
          filechooser for the TreeVisualizer
        • m_GraphVisualizers
          Vector m_GraphVisualizers
          keeps track of the opened graph visualizer instancs
        • m_Icon
          Image m_Icon
          the icon for the frames
        • m_jMenuBar
          JMenuBar m_jMenuBar
        • m_jMenuHelp
          JMenu m_jMenuHelp
        • m_jMenuProgram
          JMenu m_jMenuProgram
        • m_jMenuTools
          JMenu m_jMenuTools
        • m_jMenuVisualization
          JMenu m_jMenuVisualization
        • m_KnowledgeFlowBut
          JButton m_KnowledgeFlowBut
          Click to open the KnowledgeFlow
        • m_KnowledgeFlowFrame
          JFrame m_KnowledgeFlowFrame
          The frame containing the knowledge flow interface
        • m_MemoryUsageFrame
          JFrame m_MemoryUsageFrame
          The frame containing the memory usage
        • m_PanelApplications
          JPanel m_PanelApplications
          the panel for the application buttons
        • m_pendingKnowledgeFlowLoad
          String m_pendingKnowledgeFlowLoad
          Pending file to load on startup of the KnowledgeFlow
        • m_Plots
          Vector m_Plots
          keeps track of the opened plots
        • m_ROCs
          Vector m_ROCs
          keeps track of the opened ROCs
        • m_Self
          GUIChooser m_Self
          the GUIChooser itself
        • m_SimpleBut
          JButton m_SimpleBut
          Click to open the simplecli
        • m_SimpleCLI
          SimpleCLI m_SimpleCLI
          The SimpleCLI
        • m_SqlViewerFrame
          JFrame m_SqlViewerFrame
          The frame containing the SqlViewer
        • m_SystemInfoFrame
          JFrame m_SystemInfoFrame
          The frame containing the system info
        • m_TreeVisualizers
          Vector m_TreeVisualizers
          keeps track of the opened tree visualizer instancs
        • m_weka
          Image m_weka
          The weka image
    • Class weka.gui.GUIChooser.ChildFrameSDI

      class ChildFrameSDI extends JFrame implements Serializable
      serialVersionUID:
      8588293938686425618L
      • Serialized Fields

    • Class weka.gui.HierarchyPropertyParser

      class HierarchyPropertyParser extends Object implements Serializable
      serialVersionUID:
      -4151103338506077544L
      • Serialized Fields

        • m_Current
          weka.gui.HierarchyPropertyParser.TreeNode m_Current
          Keep track of the current node when traversing the tree
        • m_Depth
          int m_Depth
          The depth of the tree
        • m_Root
          weka.gui.HierarchyPropertyParser.TreeNode m_Root
          Keep track of the root of the tree
        • m_Seperator
          String m_Seperator
          The level separate in the path
    • Class weka.gui.InstancesSummaryPanel

      class InstancesSummaryPanel extends JPanel implements Serializable
      serialVersionUID:
      -5243579535296681063L
      • Serialized Fields

        • m_Instances
          Instances m_Instances
          The instances we're playing with
        • m_NumAttributesLab
          JLabel m_NumAttributesLab
          Displays the number of attributes
        • m_NumInstancesLab
          JLabel m_NumInstancesLab
          Displays the number of instances
        • m_RelationNameLab
          JLabel m_RelationNameLab
          Displays the name of the relation
        • 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

      class ListSelectorDialog extends JDialog implements Serializable
      serialVersionUID:
      906147926840288895L
      • Serialized Fields

        • m_CancelBut
          JButton m_CancelBut
          Click to cancel the property selection
        • m_List
          JList m_List
          The list component
        • m_PatternBut
          JButton m_PatternBut
          Click to enter a regex pattern for selection
        • m_PatternRegEx
          String m_PatternRegEx
          The current regular expression.
        • m_Result
          int m_Result
          Whether the selection was made or cancelled
        • m_SelectBut
          JButton m_SelectBut
          Click to choose the currently selected property
    • Class weka.gui.LogPanel

      class LogPanel extends JPanel implements Serializable
      serialVersionUID:
      -4072464549112439484L
      • Serialized Fields

        • m_First
          boolean m_First
          An indicator for whether text has been output yet
        • m_logButton
          JButton m_logButton
          The button for viewing the log
        • m_LogText
          JTextArea m_LogText
          Displays the log messages
        • m_StatusLab
          JLabel m_StatusLab
          Displays the current status
        • m_TaskMonitor
          WekaTaskMonitor m_TaskMonitor
          The panel for monitoring the number of running tasks (if supplied)
    • Class weka.gui.LogWindow

      class LogWindow extends JFrame implements Serializable
      serialVersionUID:
      5650947361381061112L
      • Serialized Fields

        • m_ButtonClear
          JButton m_ButtonClear
          the clear button
        • m_ButtonClose
          JButton m_ButtonClose
          the close button
        • m_CheckBoxWordwrap
          JCheckBox m_CheckBoxWordwrap
          whether to allow wordwrap or not
        • m_LabelCurrentSize
          JLabel m_LabelCurrentSize
          the current size
        • m_Output
          JTextPane m_Output
          the output
        • m_SpinnerMaxSize
          JSpinner m_SpinnerMaxSize
          the spinner for the max number of chars
        • m_UseWordwrap
          boolean m_UseWordwrap
          whether the JTextPane has wordwrap or not
    • Class weka.gui.Main

      class Main extends JFrame implements Serializable
      serialVersionUID:
      1453813254824253849L
      • Serialized Fields

        • jDesktopPane
          JDesktopPane jDesktopPane
        • jMenuApplications
          JMenu jMenuApplications
        • jMenuBar
          JMenuBar jMenuBar
        • jMenuExtensions
          JMenu jMenuExtensions
        • jMenuHelp
          JMenu jMenuHelp
        • jMenuItemApplicationsExperimenter
          JMenuItem jMenuItemApplicationsExperimenter
        • jMenuItemApplicationsExplorer
          JMenuItem jMenuItemApplicationsExplorer
        • jMenuItemApplicationsKnowledgeFlow
          JMenuItem jMenuItemApplicationsKnowledgeFlow
        • jMenuItemApplicationsSimpleCLI
          JMenuItem jMenuItemApplicationsSimpleCLI
        • jMenuItemHelpAbout
          JMenuItem jMenuItemHelpAbout
        • jMenuItemHelpHomepage
          JMenuItem jMenuItemHelpHomepage
        • jMenuItemHelpSourceforge
          JMenuItem jMenuItemHelpSourceforge
        • jMenuItemHelpSystemInfo
          JMenuItem jMenuItemHelpSystemInfo
        • jMenuItemHelpWekaWiki
          JMenuItem jMenuItemHelpWekaWiki
        • jMenuItemProgramExit
          JMenuItem jMenuItemProgramExit
        • jMenuItemProgramLogWindow
          JMenuItem jMenuItemProgramLogWindow
        • jMenuItemProgramMemoryUsage
          JMenuItem jMenuItemProgramMemoryUsage
        • jMenuItemToolsArffViewer
          JMenuItem jMenuItemToolsArffViewer
        • jMenuItemToolsSqlViewer
          JMenuItem jMenuItemToolsSqlViewer
        • jMenuItemVisualizationBoundaryVisualizer
          JMenuItem jMenuItemVisualizationBoundaryVisualizer
        • jMenuItemVisualizationGraphVisualizer
          JMenuItem jMenuItemVisualizationGraphVisualizer
        • jMenuItemVisualizationPlot
          JMenuItem jMenuItemVisualizationPlot
        • jMenuItemVisualizationROC
          JMenuItem jMenuItemVisualizationROC
        • jMenuItemVisualizationTreeVisualizer
          JMenuItem jMenuItemVisualizationTreeVisualizer
        • jMenuProgram
          JMenu jMenuProgram
        • jMenuTools
          JMenu jMenuTools
        • jMenuVisualization
          JMenu jMenuVisualization
        • jMenuWindows
          JMenu jMenuWindows
        • m_ChildFrames
          HashSet<Container> m_ChildFrames
          contains the child frames (title <-> object).
        • m_FileChooserGraphVisualizer
          JFileChooser m_FileChooserGraphVisualizer
          filechooser for the GraphVisualizer.
        • m_FileChooserPlot
          JFileChooser m_FileChooserPlot
          filechooser for Plots.
        • m_FileChooserROC
          JFileChooser m_FileChooserROC
          filechooser for ROC curves.
        • m_FileChooserTreeVisualizer
          JFileChooser m_FileChooserTreeVisualizer
          filechooser for the TreeVisualizer.
        • m_GUIType
          int m_GUIType
          the type of GUI to display.
        • m_Self
          Main m_Self
          the frame itself.
    • Class weka.gui.Main.BackgroundDesktopPane

      class BackgroundDesktopPane extends JDesktopPane implements Serializable
      serialVersionUID:
      2046713123452402745L
      • Serialized Fields

        • m_Background
          Image m_Background
          the actual background image.
    • Class weka.gui.Main.ChildFrameMDI

      class ChildFrameMDI extends JInternalFrame implements Serializable
      serialVersionUID:
      3772573515346899959L
      • Serialized Fields

        • m_Parent
          Main m_Parent
          the parent frame.
    • Class weka.gui.Main.ChildFrameSDI

      class ChildFrameSDI extends JFrame implements Serializable
      serialVersionUID:
      8588293938686425618L
      • Serialized Fields

        • m_Parent
          Main m_Parent
          the parent frame.
    • Class weka.gui.MemoryUsagePanel

      class MemoryUsagePanel extends JPanel implements Serializable
      serialVersionUID:
      -4812319791687471721L
      • Serialized Fields

        • m_BackgroundColor
          Color m_BackgroundColor
          the background color.
        • m_ButtonGC
          JButton m_ButtonGC
          the button for running the garbage collector.
        • m_Colors
          Hashtable<Double,Color> m_Colors
          the corresponding colors for the thresholds.
        • m_DefaultColor
          Color m_DefaultColor
          the default color.
        • m_FrameLocation
          Point m_FrameLocation
          the position for the dialog.
        • m_History
          Vector<Double> 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_Percentages
          Vector<Double> m_Percentages
          the threshold percentages to change color.
    • Class weka.gui.PropertyDialog

      class PropertyDialog extends JDialog implements Serializable
      serialVersionUID:
      -2314850859392433539L
      • Serialized Fields

        • m_Editor
          PropertyEditor m_Editor
          The property editor.
        • m_EditorComponent
          Component m_EditorComponent
          The custom editor component.
    • Class weka.gui.PropertyPanel

      class PropertyPanel extends JPanel implements Serializable
      serialVersionUID:
      5370025273466728904L
      • Serialized Fields

        • m_CustomPanel
          JPanel m_CustomPanel
          The custom panel (if any)
        • m_Editor
          PropertyEditor m_Editor
          The property editor
        • m_HasCustomPanel
          boolean m_HasCustomPanel
          Whether the editor has provided its own panel
        • m_PD
          PropertyDialog m_PD
          The currently displayed property dialog, if any
    • Class weka.gui.PropertySelectorDialog

      class PropertySelectorDialog extends JDialog implements Serializable
      serialVersionUID:
      -3155058124137930518L
      • Serialized Fields

        • m_CancelBut
          JButton m_CancelBut
          Click to cancel the property selection
        • m_Result
          int m_Result
          Whether the selection was made or cancelled
        • m_ResultPath
          Object[] m_ResultPath
          Stores the path to the selected property
        • m_Root
          DefaultMutableTreeNode m_Root
          The root of the property tree
        • m_RootObject
          Object m_RootObject
          The object at the root of the tree
        • m_SelectBut
          JButton m_SelectBut
          Click to choose the currently selected property
        • m_Tree
          JTree m_Tree
          The component displaying the property tree
    • Class weka.gui.PropertySheetPanel

      class PropertySheetPanel extends JPanel implements Serializable
      serialVersionUID:
      -8939835593429918345L
      • Serialized Fields

        • m_aboutPanel
          JPanel m_aboutPanel
          The panel holding global info and help, if provided by the object being editied.
        • m_CapabilitiesBut
          JButton m_CapabilitiesBut
          Button to pop up the capabilities in a separate dialog.
        • m_CapabilitiesDialog
          weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_CapabilitiesDialog
          Capabilities Help dialog.
        • m_CapabilitiesText
          JTextArea m_CapabilitiesText
          the TextArea of the Capabilities help dialog.
        • m_Editors
          PropertyEditor[] m_Editors
          Holds property editors of the object.
        • m_HelpBut
          JButton m_HelpBut
          Button to pop up the full help text in a separate dialog.
        • m_HelpDialog
          JDialog m_HelpDialog
          Help dialog.
        • m_HelpText
          StringBuffer m_HelpText
          StringBuffer containing help text for the object being edited.
        • m_Labels
          JLabel[] m_Labels
          The labels for each property.
        • m_Methods
          MethodDescriptor[] m_Methods
          Holds the methods of the target.
        • m_NumEditable
          int m_NumEditable
          A count of the number of properties we have an editor for.
        • m_Properties
          PropertyDescriptor[] m_Properties
          Holds properties of the target.
        • m_Target
          Object m_Target
          The target object being edited.
        • m_TipTexts
          String[] m_TipTexts
          The tool tip text for each property.
        • m_Values
          Object[] m_Values
          Holds current object values for each property.
        • m_Views
          JComponent[] m_Views
          Stores GUI components containing each editing component.
        • support
          PropertyChangeSupport support
          A support object for handling property change listeners.
    • Class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog

      class CapabilitiesHelpDialog extends JDialog implements Serializable
      serialVersionUID:
      -1404770987103289858L
      • Serialized Fields

        • m_Self
          weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_Self
          the dialog itself.
    • Class weka.gui.ResultHistoryPanel

      class ResultHistoryPanel extends JPanel implements Serializable
      serialVersionUID:
      4297069440135326829L
      • Serialized Fields

        • m_FramedOutput
          Hashtable m_FramedOutput
          A Hashtable mapping names to output text components
        • 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_List
          JList m_List
          The list component
        • m_Model
          DefaultListModel m_Model
          The list model
        • m_Objs
          Hashtable m_Objs
          A hashtable mapping names to arbitrary objects
        • m_Printer
          PrintableComponent m_Printer
          for printing the output to files
        • m_Results
          Hashtable m_Results
          A Hashtable mapping names to result buffers
        • m_SingleName
          String m_SingleName
          The named result being viewed in the single-click display
        • m_SingleText
          JTextComponent m_SingleText
          An optional component for single-click display
    • Class weka.gui.ResultHistoryPanel.RKeyAdapter

      class RKeyAdapter extends KeyAdapter implements Serializable
      serialVersionUID:
      -8675332541861828079L
    • Class weka.gui.ResultHistoryPanel.RMouseAdapter

      class RMouseAdapter extends MouseAdapter implements Serializable
      serialVersionUID:
      -8991922650552358669L
    • Class weka.gui.SetInstancesPanel

      class SetInstancesPanel extends JPanel implements Serializable
      serialVersionUID:
      -384804041420453735L
      • Serialized Fields

        • m_CloseBut
          JButton m_CloseBut
          Click to close the dialog
        • m_CloseButPanel
          JPanel m_CloseButPanel
          the panel the Close-Button is located in
        • m_FileChooser
          ConverterFileChooser m_FileChooser
          The file chooser for selecting arff files
        • m_Instances
          Instances m_Instances
          The current set of instances loaded
        • m_IOThread
          Thread m_IOThread
          The thread we do loading in
        • m_LastURL
          String m_LastURL
          Stores the last URL that instances were loaded from
        • m_Loader
          Loader m_Loader
          The current loader used to obtain the current instances
        • m_OpenFileBut
          JButton m_OpenFileBut
          Click to open instances from a file
        • m_OpenURLBut
          JButton m_OpenURLBut
          Click to open instances from a URL
        • m_ParentFrame
          JFrame m_ParentFrame
          the parent frame. if one is provided, the close-button is displayed
        • 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 ("?").
        • m_Summary
          InstancesSummaryPanel m_Summary
          The instance summary component
        • m_Support
          PropertyChangeSupport m_Support
          Manages sending notifications to people when we change the set of working instances.
    • Class weka.gui.SimpleCLI

      class SimpleCLI extends Frame implements Serializable
      serialVersionUID:
      -50661410800566036L
    • Class weka.gui.SimpleCLIPanel

      class SimpleCLIPanel extends JPanel implements Serializable
      serialVersionUID:
      -7377739469759943231L
      • Serialized Fields

        • m_CommandHistory
          Vector m_CommandHistory
          The history of commands entered interactively.
        • m_Completion
          SimpleCLIPanel.CommandlineCompletion m_Completion
          The commandline completion.
        • m_ErrRedirector
          Thread m_ErrRedirector
          The thread that sends output from m_POE to the output box.
        • m_HistoryPos
          int m_HistoryPos
          The current position in the command history.
        • m_Input
          JTextField m_Input
          The command input area.
        • m_OutputArea
          JTextArea m_OutputArea
          The output area canvas added to the frame.
        • m_OutRedirector
          Thread m_OutRedirector
          The thread that sends output from m_POO to the output box.
        • m_POE
          PipedOutputStream m_POE
          The new output stream for System.err.
        • m_POO
          PipedOutputStream m_POO
          The new output stream for System.out.
        • m_RunThread
          Thread m_RunThread
          The thread currently running a class main method.
    • Class weka.gui.SortedTableModel

      class SortedTableModel extends AbstractTableModel implements Serializable
      serialVersionUID:
      4030907921461127548L
      • Serialized Fields

        • mAscending
          boolean mAscending
          whether sorting is ascending or descending
        • mIndices
          int[] mIndices
          the mapping between displayed and actual index
        • mModel
          TableModel mModel
          the actual table model
        • mSortColumn
          int mSortColumn
          the sort column
    • Class weka.gui.SplashWindow

      class SplashWindow extends Window implements Serializable
      serialVersionUID:
      -2685134277041307795L
      • Serialized Fields

        • image
          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:
    • Class weka.gui.ViewerDialog

      class ViewerDialog extends JDialog implements Serializable
      serialVersionUID:
      6747718484736047752L
      • Serialized Fields

        • m_ArffPanel
          ArffPanel m_ArffPanel
          the panel to display the Instances-object
        • m_CancelButton
          JButton m_CancelButton
          Click to cancel the dialog
        • m_OkButton
          JButton m_OkButton
          Click to activate the current set parameters
        • m_Result
          int m_Result
          the result of the user's action, either OK or CANCEL
        • m_UndoButton
          JButton m_UndoButton
          Click to undo the last action
    • Class weka.gui.WekaTaskMonitor

      class WekaTaskMonitor extends JPanel implements Serializable
      serialVersionUID:
      508309816292197578L
      • Serialized Fields

        • m_ActiveTasks
          int m_ActiveTasks
          The number of running weka threads
        • m_animating
          boolean m_animating
          True if their are active tasks
        • m_iconAnimated
          ImageIcon m_iconAnimated
          The icon for the animated bird
        • m_iconStationary
          ImageIcon m_iconStationary
          The icon for the stationary bird
        • m_MonitorLabel
          JLabel m_MonitorLabel
          The label for displaying info
  • Package weka.gui.arffviewer

    • Class weka.gui.arffviewer.ArffPanel

      class ArffPanel extends JPanel implements Serializable
      serialVersionUID:
      -4697041150989513939L
      • Serialized Fields

        • m_Changed
          boolean m_Changed
          flag for whether data got changed
        • m_ChangeListeners
          HashSet m_ChangeListeners
          the listeners that listen for modifications
        • m_CurrentCol
          int m_CurrentCol
          the currently selected column
        • m_Filename
          String m_Filename
          the filename used in the title
        • m_LabelName
          JLabel m_LabelName
          displays the relation name
        • m_LastReplace
          String m_LastReplace
          the string used in the last replace
        • m_LastSearch
          String m_LastSearch
          the string used in the last search
        • m_PopupHeader
          JPopupMenu m_PopupHeader
          the popup menu for the header row
        • m_PopupRows
          JPopupMenu m_PopupRows
          the popup menu for the data rows
        • m_TableArff
          ArffTable m_TableArff
          the underlying table
        • m_Title
          String m_Title
          the title prefix
        • menuItemAttributeAsClass
          JMenuItem menuItemAttributeAsClass
        • menuItemClearSearch
          JMenuItem menuItemClearSearch
        • menuItemCopy
          JMenuItem menuItemCopy
        • menuItemDeleteAllSelectedInstances
          JMenuItem menuItemDeleteAllSelectedInstances
        • menuItemDeleteAttribute
          JMenuItem menuItemDeleteAttribute
        • menuItemDeleteAttributes
          JMenuItem menuItemDeleteAttributes
        • menuItemDeleteSelectedInstance
          JMenuItem menuItemDeleteSelectedInstance
        • menuItemMean
          JMenuItem menuItemMean
        • menuItemOptimalColWidth
          JMenuItem menuItemOptimalColWidth
        • menuItemOptimalColWidths
          JMenuItem menuItemOptimalColWidths
        • menuItemRenameAttribute
          JMenuItem menuItemRenameAttribute
        • menuItemReplaceValues
          JMenuItem menuItemReplaceValues
        • menuItemSearch
          JMenuItem menuItemSearch
        • menuItemSetAllValues
          JMenuItem menuItemSetAllValues
        • menuItemSetMissingValues
          JMenuItem menuItemSetMissingValues
        • menuItemSortInstances
          JMenuItem menuItemSortInstances
        • menuItemUndo
          JMenuItem menuItemUndo
    • Class weka.gui.arffviewer.ArffSortedTableModel

      class ArffSortedTableModel extends SortedTableModel implements Serializable
      serialVersionUID:
      -5733148376354254030L
    • Class weka.gui.arffviewer.ArffTable

      class ArffTable extends JTable implements Serializable
      serialVersionUID:
      -2016200506908637967L
      • Serialized Fields

        • m_ChangeListeners
          HashSet m_ChangeListeners
          the listeners for changes
        • m_SearchString
          String m_SearchString
          the search string
    • Class weka.gui.arffviewer.ArffTable.RelationalCellEditor

      class RelationalCellEditor extends AbstractCellEditor implements Serializable
      serialVersionUID:
      657969163293205963L
      • Serialized Fields

        • m_Button
          JButton m_Button
          the button for opening the dialog
        • m_ColumnIndex
          int m_ColumnIndex
          the column index this editor is for
        • m_CurrentInst
          Instances m_CurrentInst
          the current instances
        • m_RowIndex
          int m_RowIndex
          the row index this editor is for
    • Class weka.gui.arffviewer.ArffTableCellRenderer

      class ArffTableCellRenderer extends DefaultTableCellRenderer implements Serializable
      serialVersionUID:
      9195794493301191171L
      • Serialized Fields

        • highlightColor
          Color highlightColor
          the color for highlighted values
        • highlightColorSelected
          Color highlightColorSelected
          the color for selected highlighted values
        • missingColor
          Color missingColor
          the color for missing values
        • missingColorSelected
          Color missingColorSelected
          the color for selected missing values
    • Class weka.gui.arffviewer.ArffViewer

      class ArffViewer extends JFrame implements Serializable
      serialVersionUID:
      -7455845566922685175L
    • Class weka.gui.arffviewer.ArffViewerMainPanel

      class ArffViewerMainPanel extends JPanel implements Serializable
      serialVersionUID:
      -8763161167586738753L
      • Serialized Fields

        • confirmExit
          boolean confirmExit
        • exitOnClose
          boolean exitOnClose
        • fileChooser
          ConverterFileChooser fileChooser
        • frameTitle
          String frameTitle
        • height
          int height
        • left
          int left
        • menuBar
          JMenuBar menuBar
        • menuEdit
          JMenu menuEdit
        • menuEditAttributeAsClass
          JMenuItem menuEditAttributeAsClass
        • menuEditClearSearch
          JMenuItem menuEditClearSearch
        • menuEditCopy
          JMenuItem menuEditCopy
        • menuEditDeleteAttribute
          JMenuItem menuEditDeleteAttribute
        • menuEditDeleteAttributes
          JMenuItem menuEditDeleteAttributes
        • menuEditDeleteInstance
          JMenuItem menuEditDeleteInstance
        • menuEditDeleteInstances
          JMenuItem menuEditDeleteInstances
        • menuEditRenameAttribute
          JMenuItem menuEditRenameAttribute
        • menuEditSearch
          JMenuItem menuEditSearch
        • menuEditSortInstances
          JMenuItem menuEditSortInstances
        • menuEditUndo
          JMenuItem menuEditUndo
        • menuFile
          JMenu menuFile
        • menuFileClose
          JMenuItem menuFileClose
        • menuFileCloseAll
          JMenuItem menuFileCloseAll
        • menuFileExit
          JMenuItem menuFileExit
        • menuFileOpen
          JMenuItem menuFileOpen
        • menuFileProperties
          JMenuItem menuFileProperties
        • menuFileSave
          JMenuItem menuFileSave
        • menuFileSaveAs
          JMenuItem menuFileSaveAs
        • menuView
          JMenu menuView
        • menuViewAttributes
          JMenuItem menuViewAttributes
        • menuViewOptimalColWidths
          JMenuItem menuViewOptimalColWidths
        • menuViewValues
          JMenuItem menuViewValues
        • parent
          Container parent
        • tabbedPane
          JTabbedPane tabbedPane
        • top
          int top
        • width
          int width
  • Package weka.gui.beans

    • Class weka.gui.beans.AbstractDataSink

      class AbstractDataSink extends JPanel implements Serializable
      serialVersionUID:
      3956528599473814287L
      • Serialized Fields

        • m_listenee
          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
        • m_visual
          BeanVisual m_visual
          Default visual for data sources
    • Class weka.gui.beans.AbstractDataSource

      class AbstractDataSource extends JPanel implements Serializable
      serialVersionUID:
      -4127257701890044793L
      • Serialized Fields

        • m_bcSupport
          BeanContextChildSupport m_bcSupport
          BeanContextChild support
        • m_design
          boolean m_design
          True if this bean's appearance is the design mode appearance
        • m_listeners
          Vector m_listeners
          Objects listening for events from data sources
        • m_visual
          BeanVisual m_visual
          Default visual for data sources
    • Class weka.gui.beans.AbstractEvaluator

      class AbstractEvaluator extends JPanel implements Serializable
      serialVersionUID:
      3983303541814121632L
      • Serialized Fields

        • m_listenee
          Object m_listenee
        • m_visual
          BeanVisual m_visual
          Default visual for evaluators
    • Class weka.gui.beans.AbstractTestSetProducer

      class AbstractTestSetProducer extends JPanel implements Serializable
      serialVersionUID:
      -7905764845789349839L
      • Serialized Fields

        • m_listenee
          Object m_listenee
          non null if this object is a target for any events.
        • m_listeners
          Vector m_listeners
          Objects listening to us
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.AbstractTrainAndTestSetProducer

      class AbstractTrainAndTestSetProducer extends JPanel implements Serializable
      serialVersionUID:
      -1809339823613492037L
      • Serialized Fields

        • m_listenee
          Object m_listenee
          non null if this object is a target for any events.
        • m_testListeners
          Vector m_testListeners
          Objects listening for test set events
        • m_trainingListeners
          Vector m_trainingListeners
          Objects listening for trainin set events
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.AbstractTrainingSetProducer

      class AbstractTrainingSetProducer extends JPanel implements Serializable
      serialVersionUID:
      -7842746199524591125L
      • Serialized Fields

        • m_listenee
          Object m_listenee
          non null if this object is a target for any events.
        • m_listeners
          Vector m_listeners
          Objects listening for training set events
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.Associator

      class Associator extends JPanel implements Serializable
      serialVersionUID:
      -7843500322130210057L
      • Serialized Fields

        • m_Associator
          Associator m_Associator
        • m_buildThread
          Thread m_buildThread
        • m_globalInfo
          String m_globalInfo
          Global info for the wrapped associator (if it exists).
        • m_graphListeners
          Vector m_graphListeners
          Objects listening for graph events
        • m_listenees
          Hashtable m_listenees
          Objects talking to us
        • m_state
          int m_state
        • m_textListeners
          Vector m_textListeners
          Objects listening for text events
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.AssociatorCustomizer

      class AssociatorCustomizer extends JPanel implements Serializable
      serialVersionUID:
      5767664969353495974L
    • Class weka.gui.beans.AttributeSummarizer

      class AttributeSummarizer extends DataVisualizer implements Serializable
      serialVersionUID:
      -294354961169372758L
      • Serialized Fields

        • m_coloringIndex
          int m_coloringIndex
          Index on which to color the plots.
        • 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
    • Class weka.gui.beans.BatchClassifierEvent

      class BatchClassifierEvent extends EventObject implements Serializable
      serialVersionUID:
      878097199815991084L
      • Serialized Fields

        • m_classifier
          Classifier m_classifier
          The classifier
        • m_groupIdentifier
          long m_groupIdentifier
          An identifier that can be used to group all related runs/sets together.
        • m_maxRunNumber
          int m_maxRunNumber
          The maximum number of runs
        • m_maxSetNumber
          int m_maxSetNumber
          The last set number for this series
        • m_runNumber
          int m_runNumber
          The run number that this classifier was generated for
        • m_setNumber
          int m_setNumber
          The set number for the test set
        • 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)
    • Class weka.gui.beans.BatchClustererEvent

      class BatchClustererEvent extends EventObject implements Serializable
      serialVersionUID:
      7268777944939129714L
      • Serialized Fields

        • m_clusterer
          Clusterer m_clusterer
          The clusterer
        • m_maxSetNumber
          int m_maxSetNumber
          The last set number for this series
        • 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_testSet
          DataSetEvent m_testSet
          Training or Test Instances
    • Class weka.gui.beans.BeanConnection

      class BeanConnection extends Object implements Serializable
      serialVersionUID:
      8804264241791332064L
      • Serialized Fields

        • m_eventName
          String m_eventName
          The name of the event for this connection
        • m_hidden
          boolean m_hidden
        • m_source
          BeanInstance m_source
        • m_target
          BeanInstance m_target
    • Class weka.gui.beans.BeanInstance

      class BeanInstance extends Object implements Serializable
      serialVersionUID:
      -7575653109025406342L
      • Serialized Fields

        • m_bean
          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

      class BeanVisual extends JPanel implements Serializable
      serialVersionUID:
      -6677473561687129614L
      • Serialization Methods

      • Serialized Fields

        • m_animatedIconPath
          String m_animatedIconPath
          Holds name (including path) of the animated icon
        • m_connectorColor
          Color m_connectorColor
        • m_displayConnectors
          boolean m_displayConnectors
        • m_iconPath
          String m_iconPath
          Holds name (including path) of the static icon
        • m_pcs
          PropertyChangeSupport m_pcs
        • m_stationary
          boolean m_stationary
          Container for the icon
        • m_visualLabel
          JLabel m_visualLabel
        • m_visualName
          String m_visualName
          Name for the bean
    • Class weka.gui.beans.ChartEvent

      class ChartEvent extends EventObject implements Serializable
      serialVersionUID:
      7812460715499569390L
      • Serialized Fields

        • m_dataPoint
          double[] m_dataPoint
          Y values of the data points
        • m_legendText
          Vector m_legendText
        • m_max
          double m_max
        • m_min
          double m_min
        • m_reset
          boolean m_reset
    • Class weka.gui.beans.ClassAssigner

      class ClassAssigner extends JPanel implements Serializable
      serialVersionUID:
      4011131665025817924L
      • Serialized Fields

        • m_classColumn
          String m_classColumn
        • m_connectedFormat
          Instances m_connectedFormat
          format of instances for current incoming connection (if any)
        • m_dataFormatListeners
          Vector m_dataFormatListeners
        • m_dataListeners
          Vector m_dataListeners
        • m_dataProvider
          Object m_dataProvider
        • m_instanceListeners
          Vector m_instanceListeners
        • m_instanceProvider
          Object m_instanceProvider
        • m_testListeners
          Vector m_testListeners
        • m_testProvider
          Object m_testProvider
        • m_trainingListeners
          Vector m_trainingListeners
        • m_trainingProvider
          Object m_trainingProvider
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.ClassAssignerCustomizer

      class ClassAssignerCustomizer extends JPanel implements Serializable
      serialVersionUID:
      476539385765301907L
    • Class weka.gui.beans.Classifier

      class Classifier extends JPanel implements Serializable
      serialVersionUID:
      659603893917736008L
      • Serialized Fields

        • m_batchClassifierListeners
          Vector m_batchClassifierListeners
          Objects listening for batch classifier events
        • m_binaryFilter
          FileFilter m_binaryFilter
        • m_block
          boolean m_block
          true if we should block any further training data sets.
        • m_Classifier
          Classifier m_Classifier
        • m_ClassifierTemplate
          Classifier m_ClassifierTemplate
          Template used for creating copies when building in parallel
        • m_executionSlots
          int m_executionSlots
          Number of threads to use to train models with
        • m_globalInfo
          String m_globalInfo
          Global info for the wrapped classifier (if it exists).
        • m_graphListeners
          Vector m_graphListeners
          Objects listening for graph events
        • m_ie
          IncrementalClassifierEvent m_ie
        • m_incrementalClassifierListeners
          Vector m_incrementalClassifierListeners
          Objects listening for incremental classifier events
        • m_incrementalEvent
          InstanceEvent m_incrementalEvent
          Event to handle when processing incremental updates
        • m_KOMLFilter
          FileFilter m_KOMLFilter
        • m_listenees
          Hashtable m_listenees
          Objects talking to us
        • m_oldText
          String m_oldText
          Holds original icon label text
        • m_state
          int m_state
        • m_textListeners
          Vector 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_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_visual
          BeanVisual m_visual
        • m_XStreamFilter
          FileFilter m_XStreamFilter
    • Class weka.gui.beans.Classifier.TrainingTask

      class TrainingTask extends Object implements Serializable
      • Serialized Fields

        • m_maxRunNum
          int m_maxRunNum
        • m_maxSetNum
          int m_maxSetNum
        • m_runNum
          int m_runNum
        • m_setNum
          int m_setNum
        • m_taskInfo
          TaskStatusInfo m_taskInfo
        • m_train
          Instances m_train
    • Class weka.gui.beans.ClassifierCustomizer

      class ClassifierCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -6688000820160821429L
      • Serialized Fields

        • m_backup
          Classifier m_backup
          Copy of the current classifier in case cancel is selected
        • m_ClassifierEditor
          PropertySheetPanel m_ClassifierEditor
        • m_dsClassifier
          Classifier m_dsClassifier
        • m_executionSlotsText
          JTextField m_executionSlotsText
        • m_holderPanel
          JPanel m_holderPanel
        • m_incrementalPanel
          JPanel m_incrementalPanel
        • m_panelVisible
          boolean m_panelVisible
        • m_parentFrame
          JFrame m_parentFrame
        • m_pcSupport
          PropertyChangeSupport m_pcSupport
        • m_updateIncrementalClassifier
          JCheckBox m_updateIncrementalClassifier
    • Class weka.gui.beans.ClassifierPerformanceEvaluator

      class ClassifierPerformanceEvaluator extends AbstractEvaluator implements Serializable
      serialVersionUID:
      -3511801418192148690L
      • Serialized Fields

        • m_rocListenersConnected
          boolean m_rocListenersConnected
        • m_textListeners
          Vector m_textListeners
        • m_thresholdListeners
          Vector m_thresholdListeners
        • m_visualizableErrorListeners
          Vector m_visualizableErrorListeners
    • Class weka.gui.beans.ClassValuePicker

      class ClassValuePicker extends JPanel implements Serializable
      serialVersionUID:
      -1196143276710882989L
      • Serialized Fields

        • m_classValue
          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_dataFormatListeners
          Vector m_dataFormatListeners
        • m_dataListeners
          Vector m_dataListeners
        • m_dataProvider
          Object m_dataProvider
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.ClassValuePickerCustomizer

      class ClassValuePickerCustomizer extends JPanel implements Serializable
      serialVersionUID:
      8213423053861600469L
      • Serialized Fields

    • Class weka.gui.beans.Clusterer

      class Clusterer extends JPanel implements Serializable
      serialVersionUID:
      7729795159836843810L
      • Serialized Fields

        • m_batchClustererListeners
          Vector m_batchClustererListeners
          Objects listening for batch clusterer events
        • m_buildThread
          Thread m_buildThread
        • m_Clusterer
          Clusterer m_Clusterer
        • m_dummy
          Double m_dummy
        • m_globalInfo
          String m_globalInfo
          Global info for the wrapped classifier (if it exists).
        • m_graphListeners
          Vector m_graphListeners
          Objects listening for graph events
        • m_listenees
          Hashtable m_listenees
          Objects talking to us
        • m_state
          int m_state
        • m_textListeners
          Vector m_textListeners
          Objects listening for text events
        • m_trainingSet
          Instances m_trainingSet
          Holds training instances for batch training.
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.ClustererCustomizer

      class ClustererCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -2035688458149534161L
    • Class weka.gui.beans.ClustererPerformanceEvaluator

      class ClustererPerformanceEvaluator extends AbstractEvaluator implements Serializable
      serialVersionUID:
      8041163601333978584L
      • Serialized Fields

        • m_textListeners
          Vector m_textListeners
    • Class weka.gui.beans.CostBenefitAnalysis

      class CostBenefitAnalysis extends JPanel implements Serializable
      serialVersionUID:
      8647471654613320469L
      • Serialized Fields

        • m_bcSupport
          BeanContextChildSupport m_bcSupport
          BeanContextChild support
        • m_design
          boolean m_design
          True if this bean's appearance is the design mode appearance
        • m_framePoppedUp
          boolean m_framePoppedUp
        • m_listenee
          Object m_listenee
          The object sending us data (we allow only one connection at any one time)
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel

      class AnalysisPanel extends JPanel implements Serializable
      serialVersionUID:
      5364871945448769003L
      • Serialized Fields

        • m_benefitR
          JRadioButton m_benefitR
        • m_classAttribute
          Attribute m_classAttribute
          The class attribute from the data that was used to generate the threshold curve
        • m_classificationAccV
          JLabel m_classificationAccV
          Classification accuracy
        • 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_actualA
          JLabel m_conf_actualA
        • m_conf_actualB
          JLabel m_conf_actualB
        • 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_conf_predictedA
          JLabel m_conf_predictedA
        • m_conf_predictedB
          JLabel m_conf_predictedB
        • m_cost_aa
          JTextField m_cost_aa
        • m_cost_ab
          JTextField m_cost_ab
        • m_cost_actualA
          JLabel m_cost_actualA
        • m_cost_actualB
          JLabel m_cost_actualB
        • m_cost_ba
          JTextField m_cost_ba
        • m_cost_bb
          JTextField m_cost_bb
        • m_cost_predictedA
          JLabel m_cost_predictedA
        • m_cost_predictedB
          JLabel m_cost_predictedB
        • m_costBenefit
          PlotData2D m_costBenefit
          Data for the cost/benefit curve
        • m_costBenefitL
          JLabel m_costBenefitL
        • m_costBenefitPanel
          VisualizePanel m_costBenefitPanel
          Displays the cost/benefit (profit/loss) graph
        • m_costBenefitV
          JLabel m_costBenefitV
        • m_costR
          JRadioButton m_costR
        • m_fnPrevious
          double m_fnPrevious
        • m_fpPrevious
          double m_fpPrevious
        • m_gainV
          JLabel m_gainV
        • m_masterPlot
          PlotData2D m_masterPlot
          Data for the threshold curve
        • m_maximizeCB
          JButton m_maximizeCB
        • m_minimizeCB
          JButton m_minimizeCB
        • m_originalPopSize
          int m_originalPopSize
        • m_percOfTarget
          JRadioButton m_percOfTarget
        • m_percOfTargetLab
          JLabel m_percOfTargetLab
        • m_percPop
          JRadioButton m_percPop
        • m_percPopLab
          JLabel m_percPopLab
        • m_performancePanel
          VisualizePanel m_performancePanel
          Displays the performance graphs(s)
        • m_previousShapeIndex
          int m_previousShapeIndex
          The index of the previous plotted point that was highlighted
        • m_randomV
          JLabel m_randomV
        • m_shapeSizes
          int[] m_shapeSizes
          The size of the points being plotted
        • m_threshold
          JRadioButton m_threshold
        • m_thresholdLab
          JLabel m_thresholdLab
        • m_thresholdSlider
          JSlider m_thresholdSlider
          The slider for adjusting the threshold
        • m_tnPrevious
          double m_tnPrevious
        • m_totalPopField
          JTextField m_totalPopField
          Population text field
        • m_totalPopPrevious
          int m_totalPopPrevious
        • m_tpPrevious
          double m_tpPrevious
    • Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell

      class ConfusionCell extends JPanel implements Serializable
      serialVersionUID:
      6148640235434494767L
      • Serialized Fields

        • m_conf_cell
          JLabel m_conf_cell
        • m_conf_perc
          JLabel m_conf_perc
        • m_percentage
          double m_percentage
        • m_percentageP
          JPanel m_percentageP
    • Class weka.gui.beans.CrossValidationFoldMaker

      class CrossValidationFoldMaker extends AbstractTrainAndTestSetProducer implements Serializable
      serialVersionUID:
      -6350179298851891512L
      • Serialized Fields

        • m_numFolds
          int m_numFolds
        • m_randomSeed
          int m_randomSeed
    • Class weka.gui.beans.CrossValidationFoldMakerCustomizer

      class CrossValidationFoldMakerCustomizer extends JPanel implements Serializable
      serialVersionUID:
      1229878140258668581L
    • Class weka.gui.beans.DataSetEvent

      class DataSetEvent extends EventObject implements Serializable
      serialVersionUID:
      -5111218447577318057L
      • Serialized Fields

        • m_dataSet
          Instances m_dataSet
        • m_structureOnly
          boolean m_structureOnly
    • Class weka.gui.beans.DataVisualizer

      class DataVisualizer extends JPanel implements Serializable
      serialVersionUID:
      1949062132560159028L
      • Serialized Fields

        • m_bcSupport
          BeanContextChildSupport m_bcSupport
          BeanContextChild support
        • m_dataSetListeners
          Vector m_dataSetListeners
          Objects listening for data set events
        • m_design
          boolean m_design
          True if this bean's appearance is the design mode appearance
        • m_framePoppedUp
          boolean m_framePoppedUp
        • m_visPanel
          VisualizePanel m_visPanel
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.Filter

      class Filter extends JPanel implements Serializable
      serialVersionUID:
      8249759470189439321L
      • Serialized Fields

        • m_dataListeners
          Vector m_dataListeners
          Objects listening for data set events
        • m_Filter
          Filter m_Filter
          The filter to use.
        • m_globalInfo
          String m_globalInfo
          Global info for the wrapped filter (if it exists).
        • m_ie
          InstanceEvent m_ie
          Instance event object for passing on filtered instance streams
        • m_instanceListeners
          Vector m_instanceListeners
          Objects listening for instance events
        • m_listenees
          Hashtable m_listenees
          Objects talking to us
        • m_state
          int m_state
        • m_structurePassedOn
          boolean m_structurePassedOn
        • m_testListeners
          Vector m_testListeners
          Objects listening for test set events
        • m_trainingListeners
          Vector m_trainingListeners
          Objects listening for training set events
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.FilterCustomizer

      class FilterCustomizer extends JPanel implements Serializable
      serialVersionUID:
      2049895469240109738L
    • Class weka.gui.beans.GraphEvent

      class GraphEvent extends EventObject implements Serializable
      serialVersionUID:
      2099494034652519986L
      • Serialized Fields

        • m_graphString
          String m_graphString
        • m_graphTitle
          String m_graphTitle
        • m_graphType
          int m_graphType
    • Class weka.gui.beans.GraphViewer

      class GraphViewer extends JPanel implements Serializable
      serialVersionUID:
      -5183121972114900617L
      • Serialized Fields

        • m_bcSupport
          BeanContextChildSupport m_bcSupport
          BeanContextChild support
        • m_design
          boolean m_design
          True if this bean's appearance is the design mode appearance
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.IncrementalClassifierEvaluator

      class IncrementalClassifierEvaluator extends AbstractEvaluator implements Serializable
      serialVersionUID:
      -3105419818939541291L
      • Serialized Fields

        • m_ce
          ChartEvent m_ce
        • m_dataLegend
          Vector m_dataLegend
        • m_dataPoint
          double[] m_dataPoint
        • m_instanceCount
          int m_instanceCount
        • m_listeners
          Vector m_listeners
        • m_max
          double m_max
        • m_min
          double m_min
        • m_outputInfoRetrievalStats
          boolean m_outputInfoRetrievalStats
        • m_reset
          boolean m_reset
        • m_statusFrequency
          int m_statusFrequency
        • m_textListeners
          Vector m_textListeners
    • Class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer

      class IncrementalClassifierEvaluatorCustomizer extends JPanel implements Serializable
    • Class weka.gui.beans.IncrementalClassifierEvent

      class IncrementalClassifierEvent extends EventObject implements Serializable
      serialVersionUID:
      28979464317643232L
      • Serialized Fields

        • m_classifier
          Classifier m_classifier
        • m_currentInstance
          Instance m_currentInstance
        • m_status
          int m_status
        • m_structure
          Instances m_structure
    • Class weka.gui.beans.InstanceEvent

      class InstanceEvent extends EventObject implements Serializable
      serialVersionUID:
      6104920894559423946L
      • Serialized Fields

        • m_instance
          Instance m_instance
        • m_status
          int m_status
        • m_structure
          Instances m_structure
    • Class weka.gui.beans.InstanceStreamToBatchMaker

      class InstanceStreamToBatchMaker extends JPanel implements Serializable
      serialVersionUID:
      -7037141087208627799L
    • Class weka.gui.beans.KnowledgeFlowApp

      class KnowledgeFlowApp extends JPanel implements Serializable
      serialVersionUID:
      -7064906770289728431L
      • Serialized Fields

        • m_bcSupport
          BeanContextSupport m_bcSupport
        • m_beanLayout
          weka.gui.beans.KnowledgeFlowApp.BeanLayout m_beanLayout
          The layout area
        • m_editElement
          BeanInstance m_editElement
          Reference to bean being manipulated
        • m_FileChooser
          JFileChooser m_FileChooser
          The file chooser for selecting layout files
        • m_firstUserComponentOpp
          boolean m_firstUserComponentOpp
        • m_flowEnvironment
          Environment m_flowEnvironment
          Environment variables for the current flow
        • m_FlowHeight
          int m_FlowHeight
          the flow layout height
        • m_FlowWidth
          int m_FlowWidth
          the flow layout width
        • m_fontM
          FontMetrics m_fontM
        • m_helpB
          JButton m_helpB
        • m_KfFilter
          FileFilter m_KfFilter
          A filter to ensure only KnowledgeFlow files in binary format get shown in the chooser
        • m_KOMLFilter
          FileFilter m_KOMLFilter
          A filter to ensure only KnowledgeFlow files in KOML format get shown in the chooser
        • m_loadB
          JButton m_loadB
        • m_logPanel
          LogPanel m_logPanel
        • m_mode
          int m_mode
        • m_newB
          JButton m_newB
        • 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_pluginsBoxPanel
          Box m_pluginsBoxPanel
        • m_pluginsToolBar
          JToolBar m_pluginsToolBar
          Stuff relating to plugin beans
        • m_pointerB
          JToggleButton m_pointerB
        • m_PreferredExtension
          String m_PreferredExtension
          the preferred file extension
        • m_saveB
          JButton m_saveB
        • m_ScrollBarIncrementComponents
          int m_ScrollBarIncrementComponents
          the scrollbar increment of the components scrollpane
        • m_ScrollBarIncrementLayout
          int m_ScrollBarIncrementLayout
          the scrollbar increment of the layout scrollpane
        • m_showFileMenu
          boolean m_showFileMenu
        • m_sourceEventSetDescriptor
          EventSetDescriptor m_sourceEventSetDescriptor
          Event set descriptor for the bean being manipulated
        • m_startX
          int m_startX
        • m_startY
          int m_startY
        • m_stopB
          JButton m_stopB
        • m_toolBarBean
          Object m_toolBarBean
          Holds the selected toolbar bean
        • m_toolBarGroup
          ButtonGroup m_toolBarGroup
          Button group to manage all toolbar buttons
        • m_toolBars
          JTabbedPane m_toolBars
          Tabbed pane to hold tool bars
        • m_userBoxPanel
          Box m_userBoxPanel
        • m_userComponents
          Vector m_userComponents
        • m_UserComponentsInXML
          boolean m_UserComponentsInXML
          whether to store the user components in XML or in binary format
        • m_userToolBar
          JToolBar m_userToolBar
          Stuff relating to user created meta beans
        • m_XMLFilter
          FileFilter m_XMLFilter
          A filter to ensure only KnowledgeFlow layout files in XML format get shown in the chooser
        • m_XStreamFilter
          FileFilter m_XStreamFilter
          A filter to ensure only KnowledgeFlow files in XStream format get shown in the chooser
    • Class weka.gui.beans.KnowledgeFlowApp.BeanLayout

      class BeanLayout extends PrintablePanel implements Serializable
      serialVersionUID:
      -146377012429662757L
    • Class weka.gui.beans.Loader

      class Loader extends AbstractDataSource implements Serializable
      serialVersionUID:
      1993738191961163027L
      • Serialization Methods

      • Serialized Fields

        • m_dataSetEventTargets
          int m_dataSetEventTargets
        • m_dbSet
          boolean m_dbSet
          Flag indicating that a database has already been configured
        • m_globalInfo
          String m_globalInfo
          Global info for the wrapped loader (if it exists).
        • m_ie
          InstanceEvent m_ie
        • m_instanceEventTargets
          int m_instanceEventTargets
          Keep track of how many listeners for different types of events there are.
        • m_ioThread
          weka.gui.beans.Loader.LoadThread m_ioThread
          Thread for doing IO in
        • m_Loader
          Loader m_Loader
          Loader
        • m_state
          int m_state
        • m_stopped
          boolean m_stopped
          Asked to stop?
    • Class weka.gui.beans.LoaderCustomizer

      class LoaderCustomizer extends JPanel implements Serializable
      serialVersionUID:
      6990446313118930298L
    • Class weka.gui.beans.LogPanel

      class LogPanel extends JPanel implements Serializable
      • Serialized Fields

        • m_formatter
          DecimalFormat m_formatter
          For formatting timer digits
        • m_logPanel
          LogPanel m_logPanel
          The log panel to delegate log messages to.
        • m_table
          JTable m_table
          The table for the status area
        • m_tableIndexes
          HashMap<String,Integer> m_tableIndexes
          Holds the index (line number) in the JTable of each component being tracked.
        • m_tableModel
          DefaultTableModel m_tableModel
          The table model for the JTable used in the status area
        • m_tabs
          JTabbedPane m_tabs
          Tabbed pane to hold both the status and the log
        • m_timers
          HashMap<String,Timer> m_timers
          Holds the timers associated with each component being tracked.
    • Class weka.gui.beans.MetaBean

      class MetaBean extends JPanel implements Serializable
      serialVersionUID:
      -6582768902038027077L
      • Serialized Fields

        • m_associatedConnections
          Vector m_associatedConnections
        • m_inputs
          Vector m_inputs
        • m_originalCoords
          Vector m_originalCoords
        • m_outputs
          Vector m_outputs
        • m_subFlow
          Vector m_subFlow
        • m_subFlowPreview
          ImageIcon m_subFlowPreview
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.ModelPerformanceChart

      class ModelPerformanceChart extends JPanel implements Serializable
      serialVersionUID:
      -4602034200071195924L
      • Serialized Fields

        • m_bcSupport
          BeanContextChildSupport m_bcSupport
          BeanContextChild support
        • m_design
          boolean m_design
          True if this bean's appearance is the design mode appearance
        • m_framePoppedUp
          boolean m_framePoppedUp
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.PredictionAppender

      class PredictionAppender extends JPanel implements Serializable
      serialVersionUID:
      -2987740065058976673L
      • Serialized Fields

        • m_appendProbabilities
          boolean m_appendProbabilities
          Append classifier's predicted probabilities (if the class is discrete and the classifier is a distribution classifier)
        • m_dataSourceListeners
          Vector m_dataSourceListeners
          Objects listenening for dataset events
        • m_format
          Instances m_format
          Format of instances to be produced.
        • m_instanceEvent
          InstanceEvent m_instanceEvent
        • m_instanceListeners
          Vector m_instanceListeners
          Objects listening for instances events
        • m_listenee
          Object m_listenee
          Non null if this object is a target for any events.
        • m_testSetListeners
          Vector m_testSetListeners
          Objects listening for test set events
        • m_trainingSetListeners
          Vector m_trainingSetListeners
          Objects listening for training set events
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.PredictionAppenderCustomizer

      class PredictionAppenderCustomizer extends JPanel implements Serializable
      serialVersionUID:
      6884933202506331888L
    • Class weka.gui.beans.Saver

      class Saver extends AbstractDataSink implements Serializable
      serialVersionUID:
      5371716690308950755L
      • Serialization Methods

      • Serialized Fields

        • m_count
          int m_count
          Count for structure available messages
        • m_dataSet
          Instances m_dataSet
          Holds the instances to be saved
        • m_fileName
          String m_fileName
          The relation name that becomes part of the file name
        • m_globalInfo
          String m_globalInfo
          Global info for the wrapped loader (if it exists).
        • 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_Saver
          Saver m_Saver
          Saver
        • m_SaverTemplate
          Saver m_SaverTemplate
        • m_structure
          Instances m_structure
          Holds the structure
    • Class weka.gui.beans.SaverCustomizer

      class SaverCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -4874208115942078471L
    • Class weka.gui.beans.ScatterPlotMatrix

      class ScatterPlotMatrix extends DataVisualizer implements Serializable
      serialVersionUID:
      -657856527563507491L
      • Serialized Fields

    • Class weka.gui.beans.SerializedModelSaver

      class SerializedModelSaver extends JPanel implements Serializable
      serialVersionUID:
      3956528599473814287L
      • Serialization Methods

      • Serialized Fields

        • m_directory
          File m_directory
          The directory to hold the saved model(s)
        • m_fileFormat
          Tag m_fileFormat
          File format stuff
        • m_filenamePrefix
          String m_filenamePrefix
          The prefix for the file name (model + training set info will be appended)
        • m_listenee
          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_useRelativePath
          boolean m_useRelativePath
          relative path for the directory (relative to the user.dir (startup directory))?
        • m_visual
          BeanVisual m_visual
          Default visual for data sources
    • Class weka.gui.beans.SerializedModelSaverCustomizer

      class SerializedModelSaverCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -4874208115942078471L
    • Class weka.gui.beans.StripChart

      class StripChart extends JPanel implements Serializable
      serialVersionUID:
      1483649041577695019L
      • Serialization Methods

      • Serialized Fields

        • m_BackgroundColor
          Color m_BackgroundColor
          the background color.
        • m_ce
          ChartEvent m_ce
        • m_colorList
          Color[] m_colorList
          default colours for colouring lines
        • m_dataList
          LinkedList m_dataList
          Holds the data to be plotted. Entries in the list are arrays of y points.
        • m_dataPoint
          double[] m_dataPoint
        • m_iheight
          int m_iheight
          Width and height of the off screen image.
        • m_iwidth
          int m_iwidth
        • m_labelFont
          Font m_labelFont
        • m_labelMetrics
          FontMetrics m_labelMetrics
        • m_legendPanel
          weka.gui.beans.StripChart.LegendPanel m_legendPanel
          the legend.
        • m_LegendPanelBorderColor
          Color m_LegendPanelBorderColor
          the color of the legend panel's border.
        • m_legendText
          Vector m_legendText
        • m_listenee
          Object m_listenee
        • m_max
          double m_max
          Max value for the y axis.
        • m_min
          double m_min
          Min value for the y axis.
        • m_oldMax
          double m_oldMax
        • m_oldMin
          double m_oldMin
        • m_previousY
          double[] m_previousY
        • m_Printer
          PrintableComponent m_Printer
          the class responsible for printing
        • m_refreshFrequency
          int m_refreshFrequency
          Plot every m_refreshFrequency'th point
        • m_refreshWidth
          int m_refreshWidth
          Shift the plot by this many pixels every time a point is plotted
        • m_scalePanel
          weka.gui.beans.StripChart.ScalePanel m_scalePanel
          the scale.
        • m_visual
          BeanVisual m_visual
        • m_xCount
          int m_xCount
        • m_xValFreq
          int m_xValFreq
          Print x axis labels every m_xValFreq points
        • m_yScaleUpdate
          boolean m_yScaleUpdate
          Scale update requested.
    • Class weka.gui.beans.StripChartCustomizer

      class StripChartCustomizer extends JPanel implements Serializable
      serialVersionUID:
      7441741530975984608L
    • Class weka.gui.beans.TestSetEvent

      class TestSetEvent extends EventObject implements Serializable
      serialVersionUID:
      8780718708498854231L
      • Serialized Fields

        • m_maxRunNumber
          int m_maxRunNumber
          Maximum number of runs.
        • m_maxSetNumber
          int m_maxSetNumber
          Maximum number of sets (ie 10 in a 10 fold)
        • m_runNumber
          int m_runNumber
          What run number is this training set from.
        • m_setNumber
          int m_setNumber
          what number is this test set (ie fold 2 of 10 folds)
        • m_structureOnly
          boolean m_structureOnly
        • m_testSet
          Instances m_testSet
          The test set instances
    • Class weka.gui.beans.TestSetMaker

      class TestSetMaker extends AbstractTestSetProducer implements Serializable
      serialVersionUID:
      -8473882857628061841L
      • Serialized Fields

        • m_receivedStopNotification
          boolean m_receivedStopNotification
    • Class weka.gui.beans.TextEvent

      class TextEvent extends EventObject implements Serializable
      serialVersionUID:
      4196810607402973744L
      • Serialized Fields

        • m_text
          String m_text
          The text
        • m_textTitle
          String m_textTitle
          The title for the text. Could be used in a list component
    • Class weka.gui.beans.TextViewer

      class TextViewer extends JPanel implements Serializable
      serialVersionUID:
      104838186352536832L
      • Serialized Fields

        • m_bcSupport
          BeanContextChildSupport m_bcSupport
          BeanContextChild support
        • m_design
          boolean m_design
          True if this bean's appearance is the design mode appearance
        • m_textListeners
          Vector m_textListeners
          Objects listening for text events
        • m_visual
          BeanVisual m_visual
    • Class weka.gui.beans.ThresholdDataEvent

      class ThresholdDataEvent extends EventObject implements Serializable
      serialVersionUID:
      -8309334224492439644L
    • Class weka.gui.beans.TrainingSetEvent

      class TrainingSetEvent extends EventObject implements Serializable
      serialVersionUID:
      5872343811810872662L
      • Serialized Fields

        • m_maxRunNumber
          int m_maxRunNumber
          Maximum number of runs.
        • m_maxSetNumber
          int m_maxSetNumber
          Maximum number of sets (ie 10 in a 10 fold)
        • m_runNumber
          int m_runNumber
          What run number is this training set from.
        • m_setNumber
          int m_setNumber
          what number is this training set (ie fold 2 of 10 folds)
        • m_structureOnly
          boolean m_structureOnly
        • m_trainingSet
          Instances m_trainingSet
          The training instances
    • Class weka.gui.beans.TrainingSetMaker

      class TrainingSetMaker extends AbstractTrainingSetProducer implements Serializable
      serialVersionUID:
      -6152577265471535786L
      • Serialized Fields

        • m_receivedStopNotification
          boolean m_receivedStopNotification
    • Class weka.gui.beans.TrainTestSplitMaker

      class TrainTestSplitMaker extends AbstractTrainAndTestSetProducer implements Serializable
      serialVersionUID:
      7390064039444605943L
      • Serialized Fields

        • m_randomSeed
          int m_randomSeed
        • m_splitThread
          Thread m_splitThread
        • m_trainPercentage
          double m_trainPercentage
    • Class weka.gui.beans.TrainTestSplitMakerCustomizer

      class TrainTestSplitMakerCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -1684662340241807260L
    • Class weka.gui.beans.VisualizableErrorEvent

      class VisualizableErrorEvent extends EventObject implements Serializable
      serialVersionUID:
      -5811819270887223400L
      • Serialized Fields

  • Package weka.gui.boundaryvisualizer

    • Class weka.gui.boundaryvisualizer.BoundaryPanel

      class BoundaryPanel extends JPanel implements Serializable
      serialVersionUID:
      -8499445518744770458L
      • Serialized Fields

        • m_classifier
          Classifier m_classifier
          distribution classifier to use
        • m_classIndex
          int m_classIndex
          index of the class attribute
        • m_Colors
          FastVector m_Colors
        • m_dataGenerator
          DataGenerator m_dataGenerator
          data generator to use
        • m_dummy
          Double m_dummy
        • m_initialTiling
          boolean m_initialTiling
          is the main plot thread performing the initial coarse tiling
        • m_listeners
          Vector m_listeners
          listeners to be notified when plot is complete
        • m_maxX
          double m_maxX
        • m_maxY
          double m_maxY
        • m_minX
          double m_minX
        • m_minY
          double m_minY
        • m_numOfSamplesPerGenerator
          int m_numOfSamplesPerGenerator
        • m_numOfSamplesPerRegion
          int m_numOfSamplesPerRegion
        • m_osi
          Image m_osi
          used for offscreen drawing
        • m_panelHeight
          int m_panelHeight
        • m_panelWidth
          int m_panelWidth
        • m_pausePlotting
          boolean m_pausePlotting
        • m_pixHeight
          double m_pixHeight
        • m_pixWidth
          double m_pixWidth
        • m_plotPanel
          weka.gui.boundaryvisualizer.BoundaryPanel.PlotPanel m_plotPanel
          the actual plotting area
        • m_plotThread
          Thread m_plotThread
          thread for running the plotting operation in
        • m_plotTrainingData
          boolean m_plotTrainingData
          plot the training data
        • m_probabilityCache
          double[][][] m_probabilityCache
          cache of probabilities for fast replotting
        • m_random
          Random m_random
          A random number generator
        • m_rangeX
          double m_rangeX
        • m_rangeY
          double m_rangeY
        • m_samplesBase
          double m_samplesBase
        • m_size
          int m_size
          what size of tile is currently being plotted
        • m_stopPlotting
          boolean m_stopPlotting
          Stop the plotting thread
        • m_stopReplotting
          boolean m_stopReplotting
          Stop any replotting threads
        • m_trainingData
          Instances m_trainingData
          training data
        • m_xAttribute
          int m_xAttribute
        • m_yAttribute
          int m_yAttribute
    • Class weka.gui.boundaryvisualizer.BoundaryPanelDistributed

      class BoundaryPanelDistributed extends BoundaryPanel implements Serializable
      serialVersionUID:
      -1743284397893937776L
      • Serialized Fields

        • m_failedCount
          int m_failedCount
          The count of failed sub-tasks
        • m_finishedCount
          int m_finishedCount
          The count of successfully completed sub-tasks
        • m_hostPollingTime
          int[] m_hostPollingTime
        • m_listeners
          Vector m_listeners
          a list of RemoteExperimentListeners
        • m_minTaskPollTime
          int m_minTaskPollTime
          number of seconds between polling server
        • 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_remoteHostFailureCounts
          int[] m_remoteHostFailureCounts
          The number of times tasks have failed on each remote host
        • m_remoteHosts
          Vector 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_removedHosts
          int m_removedHosts
          The number of hosts removed due to exceeding max failures
        • m_subExpQueue
          Queue m_subExpQueue
          The queue of sub-tasks waiting to be processed
    • Class weka.gui.boundaryvisualizer.BoundaryVisualizer

      class BoundaryVisualizer extends JPanel implements Serializable
      serialVersionUID:
      3933877580074013208L
      • Serialized Fields

        • chooseButton
          JButton chooseButton
        • COMBO_SIZE
          Dimension COMBO_SIZE
        • dataFileLabel
          JLabel dataFileLabel
        • m_addPointsButton
          JRadioButton m_addPointsButton
        • m_addRemovePointsButtonGroup
          ButtonGroup m_addRemovePointsButtonGroup
        • m_addRemovePointsPanel
          JPanel m_addRemovePointsPanel
        • m_arffFileFilter
          ExtensionFileFilter m_arffFileFilter
        • m_boundaryPanel
          BoundaryPanel m_boundaryPanel
          the plotting panel
        • m_classAttBox
          JComboBox m_classAttBox
        • m_classifier
          Classifier m_classifier
          the classifier to use
        • m_classifierEditor
          GenericObjectEditor m_classifierEditor
        • m_ClassifierPanel
          PropertyPanel m_ClassifierPanel
        • m_classPanel
          ClassPanel m_classPanel
        • m_classValueSelector
          JComboBox m_classValueSelector
        • m_controlPanel
          JPanel m_controlPanel
        • m_dataGenerator
          KDDataGenerator m_dataGenerator
        • m_FileChooser
          JFileChooser m_FileChooser
          The file chooser for selecting arff files
        • m_generatorSamplesBase
          int m_generatorSamplesBase
          base for sampling in the non-fixed dimensions
        • m_generatorSamplesText
          JTextField m_generatorSamplesText
        • m_kernelBandwidth
          int m_kernelBandwidth
          Set the kernel bandwidth to cover this many nearest neighbours
        • m_kernelBandwidthText
          JTextField m_kernelBandwidthText
        • m_maxX
          double m_maxX
        • m_maxY
          double m_maxY
        • m_minX
          double m_minX
        • m_minY
          double m_minY
        • m_numberOfSamplesFromEachRegion
          int m_numberOfSamplesFromEachRegion
        • m_plotAreaHeight
          int m_plotAreaHeight
        • m_plotAreaWidth
          int m_plotAreaWidth
        • m_plotTrainingData
          JCheckBox m_plotTrainingData
        • m_regionSamplesText
          JTextField m_regionSamplesText
        • m_removePointsButton
          JRadioButton m_removePointsButton
        • m_startBut
          JButton m_startBut
        • m_trainingInstances
          Instances m_trainingInstances
          the training instances
        • m_xAttBox
          JComboBox m_xAttBox
        • m_xAxisPanel
          weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_xAxisPanel
        • m_xIndex
          int m_xIndex
        • m_yAttBox
          JComboBox m_yAttBox
        • m_yAxisPanel
          weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_yAxisPanel
        • m_yIndex
          int m_yIndex
        • removeAllButton
          JButton removeAllButton
    • Class weka.gui.boundaryvisualizer.KDDataGenerator

      class KDDataGenerator extends Object implements Serializable
      serialVersionUID:
      -958573275606402792L
      • Serialized Fields

        • m_globalMeansOrModes
          double[] m_globalMeansOrModes
          global means or modes to use for missing values
        • m_instances
          Instances m_instances
          the instances to use
        • 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_laplaceConst
          double m_laplaceConst
          Laplace correction for discrete distributions
        • m_Max
          double[] m_Max
          The maximum values for numeric attributes.
        • m_Min
          double[] m_Min
          The minimum values for numeric attributes.
        • m_minStdDev
          double m_minStdDev
          minimum standard deviation for numeric attributes
        • m_random
          Random m_random
          random number generator
        • m_seed
          int m_seed
          random number seed
        • m_standardDeviations
          double[] m_standardDeviations
          standard deviations of the normal distributions for numeric attributes in each KD estimator
        • 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
    • Class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

      class RemoteBoundaryVisualizerSubTask extends Object implements Serializable
      • Serialized Fields

        • m_attsToWeightOn
          boolean[] m_attsToWeightOn
        • m_classifier
          Classifier m_classifier
        • m_dataGenerator
          DataGenerator m_dataGenerator
        • m_dist
          double[] m_dist
        • m_maxX
          double m_maxX
        • m_maxY
          double m_maxY
        • m_minX
          double m_minX
        • m_minY
          double m_minY
        • m_numOfSamplesPerGenerator
          int m_numOfSamplesPerGenerator
        • m_numOfSamplesPerRegion
          int m_numOfSamplesPerRegion
        • m_panelHeight
          int m_panelHeight
        • m_panelWidth
          int m_panelWidth
        • m_pixHeight
          double m_pixHeight
        • m_pixWidth
          double m_pixWidth
        • m_predInst
          Instance m_predInst
        • m_random
          Random m_random
        • m_result
          RemoteResult m_result
        • m_rowNumber
          int m_rowNumber
        • m_samplesBase
          double m_samplesBase
        • m_status
          TaskStatusInfo m_status
        • m_trainingData
          Instances m_trainingData
        • m_vals
          double[] m_vals
        • m_weightingAttsValues
          double[] m_weightingAttsValues
        • m_xAttribute
          int m_xAttribute
        • m_yAttribute
          int m_yAttribute
    • Class weka.gui.boundaryvisualizer.RemoteResult

      class RemoteResult extends Object implements Serializable
      serialVersionUID:
      1873271280044633808L
      • Serialized Fields

        • m_percentCompleted
          int m_percentCompleted
          progress on computing this row
        • m_probabilities
          double[][] m_probabilities
          the result - ie. the probability distributions produced by the classifier for this row in the visualization
        • m_rowLength
          int m_rowLength
          how many pixels in a row
        • m_rowNumber
          int m_rowNumber
          the row number that this result corresponds to
  • Package weka.gui.experiment

    • Class weka.gui.experiment.AlgorithmListPanel

      class AlgorithmListPanel extends JPanel implements Serializable
      serialVersionUID:
      -7204528834764898671L
      • Serialized Fields

        • m_AddBut
          JButton m_AddBut
          Click to add an algorithm
        • m_AlgorithmListModel
          DefaultListModel m_AlgorithmListModel
          The list model used
        • m_ClassifierEditor
          GenericObjectEditor m_ClassifierEditor
          Lets the user configure the classifier
        • m_DeleteBut
          JButton m_DeleteBut
          Click to remove the selected dataset from the list
        • m_DownBut
          JButton m_DownBut
          Click to move the selected algorithm(s) one down
        • m_EditBut
          JButton m_EditBut
          Click to edit the selected algorithm
        • m_Editing
          boolean m_Editing
          Whether an algorithm is added or only edited
        • m_Exp
          Experiment m_Exp
          The experiment to set the algorithm list of
        • m_FileChooser
          JFileChooser m_FileChooser
          The file chooser for selecting experiments
        • m_List
          JList m_List
          The component displaying the algorithm list
        • m_LoadOptionsBut
          JButton m_LoadOptionsBut
          Click to edit the load the options for athe selected algorithm
        • m_PD
          PropertyDialog m_PD
          The currently displayed property dialog, if any
        • m_SaveOptionsBut
          JButton m_SaveOptionsBut
          Click to edit the save the options from selected algorithm
        • m_UpBut
          JButton m_UpBut
          Click to move the selected algorithm(s) one up
        • m_XMLFilter
          FileFilter m_XMLFilter
          A filter to ensure only experiment (in XML format) files get shown in the chooser
    • Class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer

      class ObjectCellRenderer extends DefaultListCellRenderer implements Serializable
      serialVersionUID:
      -5067138526587433808L
    • Class weka.gui.experiment.DatasetListPanel

      class DatasetListPanel extends JPanel implements Serializable
      serialVersionUID:
      7068857852794405769L
      • Serialized Fields

        • m_AddBut
          JButton m_AddBut
          Click to add a dataset.
        • m_DeleteBut
          JButton m_DeleteBut
          Click to remove the selected dataset from the list.
        • m_DownBut
          JButton m_DownBut
          Click to move the selected dataset(s) one down.
        • m_EditBut
          JButton m_EditBut
          Click to edit the selected algorithm.
        • m_Exp
          Experiment m_Exp
          The experiment to set the dataset list of.
        • m_FileChooser
          ConverterFileChooser m_FileChooser
          The file chooser component.
        • m_List
          JList m_List
          The component displaying the dataset list.
        • m_relativeCheck
          JCheckBox m_relativeCheck
          Make file paths relative to the user (start) directory.
        • m_UpBut
          JButton m_UpBut
          Click to move the selected dataset(s) one up.
    • Class weka.gui.experiment.DistributeExperimentPanel

      class DistributeExperimentPanel extends JPanel implements Serializable
      serialVersionUID:
      5206721431754800278L
      • Serialized Fields

        • m_configureHostNames
          JButton m_configureHostNames
          Popup the HostListPanel
        • m_enableDistributedExperiment
          JCheckBox m_enableDistributedExperiment
          Distribute the current experiment to remote hosts
        • m_Exp
          RemoteExperiment m_Exp
          The experiment to configure.
        • m_hostList
          HostListPanel m_hostList
          The host list panel
        • m_radioListener
          ActionListener m_radioListener
          Handle radio buttons
        • m_splitByDataSet
          JRadioButton m_splitByDataSet
          Split experiment up by data set.
        • m_splitByRun
          JRadioButton m_splitByRun
          Split experiment up by run number.
    • Class weka.gui.experiment.Experimenter

      class Experimenter extends JPanel implements Serializable
      serialVersionUID:
      -5751617505738193788L
      • Serialized Fields

        • m_ClassFirst
          boolean m_ClassFirst
          True if the class attribute is the first attribute for all datasets involved in this experiment.
        • m_ResultsPanel
          ResultsPanel m_ResultsPanel
          The panel for analysing experimental results
        • m_RunPanel
          RunPanel m_RunPanel
          The panel for running the experiment
        • m_SetupPanel
          SetupModePanel m_SetupPanel
          The panel for configuring the experiment
        • m_TabbedPane
          JTabbedPane m_TabbedPane
          The tabbed pane that controls which sub-pane we are working with
    • Class weka.gui.experiment.ExperimenterDefaults

      class ExperimenterDefaults extends Object implements Serializable
      serialVersionUID:
      -2835933184632147981L
    • Class weka.gui.experiment.GeneratorPropertyIteratorPanel

      class GeneratorPropertyIteratorPanel extends JPanel implements Serializable
      serialVersionUID:
      -6026938995241632139L
      • Serialized Fields

        • m_ArrayEditor
          GenericArrayEditor m_ArrayEditor
          Allows editing of the custom property values
        • m_ConfigureBut
          JButton m_ConfigureBut
          Click to select the property to iterate over
        • 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
        • m_StatusBox
          JComboBox m_StatusBox
          Controls whether the custom iterator is used or not
    • Class weka.gui.experiment.HostListPanel

      class HostListPanel extends JPanel implements Serializable
      serialVersionUID:
      7182791134585882197L
      • Serialized Fields

        • m_DeleteBut
          JButton m_DeleteBut
          Click to remove the selected host from the list
        • m_Exp
          RemoteExperiment m_Exp
          The remote experiment to set the host list of
        • m_HostField
          JTextField m_HostField
          The field with which to enter host names
        • m_List
          JList m_List
          The component displaying the host list
    • Class weka.gui.experiment.OutputFormatDialog

      class OutputFormatDialog extends JDialog implements Serializable
      serialVersionUID:
      2169792738187807378L
      • Serialized Fields

        • m_CancelButton
          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_MeanPrecSpinner
          JSpinner m_MeanPrecSpinner
          the spinner to choose the precision for the mean from.
        • m_OkButton
          JButton m_OkButton
          Click to activate the current set parameters.
        • m_OutputFormatComboBox
          JComboBox m_OutputFormatComboBox
          lets the user choose the format for the output.
        • m_RemoveFilterName
          boolean m_RemoveFilterName
          whether to remove the filter names from the names.
        • m_RemoveFilterNameCheckBox
          JCheckBox m_RemoveFilterNameCheckBox
          the checkbox for the removing of filter classnames.
        • m_Result
          int m_Result
          the result of the user's action, either OK or CANCEL.
        • m_ResultMatrix
          Class m_ResultMatrix
          the output format specific matrix.
        • m_ShowAverage
          boolean m_ShowAverage
          whether to show the average too.
        • m_ShowAverageCheckBox
          JCheckBox m_ShowAverageCheckBox
          the checkbox for outputting the average.
        • m_StdDevPrec
          int m_StdDevPrec
          the number of digits after the period (= precision) for printing the std. deviation
        • m_StdDevPrecSpinner
          JSpinner m_StdDevPrecSpinner
          the spinner to choose the precision for the std. deviation from
    • Class weka.gui.experiment.ResultsPanel

      class ResultsPanel extends JPanel implements Serializable
      serialVersionUID:
      -4913007978534178569L
      • Serialized Fields

        • COMBO_SIZE
          Dimension COMBO_SIZE
          the size for a combobox.
        • m_arffFileFilter
          ExtensionFileFilter m_arffFileFilter
          ARFF file filter.
        • m_CompareCombo
          JComboBox m_CompareCombo
          Lets the user select which performance measure to analyze.
        • m_CompareModel
          DefaultComboBoxModel m_CompareModel
          The model embedded in m_CompareCombo.
        • m_csvFileFilter
          ExtensionFileFilter m_csvFileFilter
          CSV file filter.
        • m_DatasetKeyBut
          JButton m_DatasetKeyBut
          Click to edit the columns used to determine the scheme.
        • m_DatasetKeyLabel
          JLabel m_DatasetKeyLabel
          Displays the currently selected column names for the scheme invalid input: '&' options.
        • m_DatasetKeyList
          JList m_DatasetKeyList
          Displays the list of selected columns determining the scheme.
        • m_DatasetKeyModel
          DefaultListModel m_DatasetKeyModel
          Stores the list of attributes for selecting the scheme columns.
        • m_DatasetModel
          DefaultComboBoxModel m_DatasetModel
          The model embedded in m_DatasetCombo.
        • m_DisplayedButton
          JButton m_DisplayedButton
          Lets the user select which schemes are compared to base.
        • m_DisplayedList
          JList m_DisplayedList
          Holds the list of schemes to display.
        • m_DisplayedModel
          DefaultListModel m_DisplayedModel
          The model embedded in m_DisplayedList.
        • m_Exp
          Experiment m_Exp
          An experiment (used for identifying a result source) -- optional.
        • m_FileChooser
          JFileChooser m_FileChooser
          The file chooser for selecting result files.
        • m_FromDBaseBut
          JButton m_FromDBaseBut
          Click to load results from a database.
        • m_FromExpBut
          JButton m_FromExpBut
          Click to get results from the destination given in the experiment.
        • m_FromFileBut
          JButton m_FromFileBut
          Click to load results from a file.
        • m_FromLab
          JLabel m_FromLab
          Displays a message about the current result set.
        • m_History
          ResultHistoryPanel m_History
          A panel controlling results viewing.
        • m_InstanceQuery
          InstanceQuery m_InstanceQuery
          Does any database querying for us.
        • m_Instances
          Instances m_Instances
          The instances we're extracting results from.
        • m_LoadThread
          Thread m_LoadThread
          A thread to load results instances from a file or database.
        • m_OutputFormatButton
          JButton m_OutputFormatButton
          lets the user choose the format for the output.
        • m_OutText
          JTextArea m_OutText
          Displays the output of tests.
        • m_PerformBut
          JButton m_PerformBut
          Click to start the test.
        • m_ResultKeyBut
          JButton m_ResultKeyBut
          Click to edit the columns used to determine the scheme.
        • m_ResultKeyLabel
          JLabel m_ResultKeyLabel
          Displays the currently selected column names for the scheme invalid input: '&' options.
        • m_ResultKeyList
          JList m_ResultKeyList
          Displays the list of selected columns determining the scheme.
        • m_ResultKeyModel
          DefaultListModel m_ResultKeyModel
          Stores the list of attributes for selecting the scheme columns.
        • m_ResultMatrix
          ResultMatrix m_ResultMatrix
          the initial result matrix.
        • m_SaveOut
          SaveBuffer m_SaveOut
          The buffer saving object for saving output.
        • m_SaveOutBut
          JButton m_SaveOutBut
          Click to save test output to a file.
        • m_ShowStdDevs
          JCheckBox m_ShowStdDevs
          Lets the user select whether standard deviations are to be output or not.
        • m_SigTex
          JTextField m_SigTex
          Lets the user edit the test significance.
        • m_SortCombo
          JComboBox m_SortCombo
          Lets the user select which column to use for sorting.
        • m_SortModel
          DefaultComboBoxModel m_SortModel
          The model embedded in m_SortCombo.
        • m_TesterClasses
          JComboBox m_TesterClasses
          Lists all the available classes implementing the Tester-Interface.
          See Also:
        • m_TesterClassesLabel
          JLabel m_TesterClassesLabel
          Displays the currently selected Tester-Class.
        • m_TestsButton
          JButton m_TestsButton
          Lets the user select which scheme to base comparisons against.
        • m_TestsList
          JList m_TestsList
          Holds the list of schemes to base the test against.
        • m_TestsModel
          DefaultListModel m_TestsModel
          The model embedded in m_TestsList.
        • m_TTester
          Tester m_TTester
          The PairedTTester object.
    • Class weka.gui.experiment.RunNumberPanel

      class RunNumberPanel extends JPanel implements Serializable
      serialVersionUID:
      -1644336658426067852L
      • Serialized Fields

        • m_Exp
          Experiment m_Exp
          The experiment being configured
        • m_LowerText
          JTextField m_LowerText
          Configures the lower run number
        • m_UpperText
          JTextField m_UpperText
          Configures the upper run number
    • Class weka.gui.experiment.RunPanel

      class RunPanel extends JPanel implements Serializable
      serialVersionUID:
      1691868018596872051L
      • Serialized Fields

        • m_Exp
          Experiment m_Exp
          The experiment to run
        • m_Log
          LogPanel m_Log
        • m_ResultsPanel
          ResultsPanel m_ResultsPanel
          A pointer to the results panel
        • m_RunThread
          Thread m_RunThread
          The thread running the experiment
        • m_StartBut
          JButton m_StartBut
          Click to start running the experiment
        • m_StopBut
          JButton m_StopBut
          Click to signal the running experiment to halt
    • Class weka.gui.experiment.SetupModePanel

      class SetupModePanel extends JPanel implements Serializable
      serialVersionUID:
      -3758035565520727822L
      • Serialized Fields

        • m_advancedPanel
          SetupPanel m_advancedPanel
          The advanced setup panel
        • m_AdvancedSetupRBut
          JRadioButton m_AdvancedSetupRBut
          The button for choosing advanced setup mode
        • m_simplePanel
          SimpleSetupPanel m_simplePanel
          The simple setup panel
        • m_SimpleSetupRBut
          JRadioButton m_SimpleSetupRBut
          The button for choosing simple setup mode
    • Class weka.gui.experiment.SetupPanel

      class SetupPanel extends JPanel implements Serializable
      serialVersionUID:
      6552671886903170033L
      • Serialized Fields

        • m_advanceDataSetFirst
          JRadioButton m_advanceDataSetFirst
          Click to advacne data set before custom generator
        • m_advanceIteratorFirst
          JRadioButton m_advanceIteratorFirst
          Click to advance custom generator before data set
        • m_DatasetListPanel
          DatasetListPanel m_DatasetListPanel
          The panel for configuring selected datasets
        • m_DistributeExperimentPanel
          DistributeExperimentPanel m_DistributeExperimentPanel
          The panel for enabling a distributed experiment
        • m_Exp
          Experiment m_Exp
          The experiment being configured
        • m_ExpFilter
          FileFilter m_ExpFilter
          A filter to ensure only experiment files get shown in the chooser
        • m_FileChooser
          JFileChooser m_FileChooser
          The file chooser for selecting experiments
        • m_GeneratorPropertyPanel
          GeneratorPropertyIteratorPanel m_GeneratorPropertyPanel
          The panel that configures iteration on custom resultproducer property
        • m_KOMLFilter
          FileFilter m_KOMLFilter
          A filter to ensure only experiment (in KOML format) files get shown in the chooser
        • m_NewBut
          JButton m_NewBut
          Click to create a new experiment with default settings
        • m_NotesButton
          JButton m_NotesButton
          A button for bringing up the notes
        • m_NotesFrame
          JFrame m_NotesFrame
          Frame for the notes
        • m_NotesText
          JTextArea m_NotesText
          Area for user notes Default of 10 rows
        • m_OpenBut
          JButton m_OpenBut
          Click to load an experiment
        • m_RadioListener
          ActionListener m_RadioListener
          Handle radio buttons
        • m_RLEditor
          GenericObjectEditor m_RLEditor
          The ResultListener editor
        • m_RLEditorPanel
          PropertyPanel m_RLEditorPanel
          The panel to contain the ResultListener editor
        • m_RPEditor
          GenericObjectEditor m_RPEditor
          The ResultProducer editor
        • m_RPEditorPanel
          PropertyPanel m_RPEditorPanel
          The panel to contain the ResultProducer editor
        • m_RunNumberPanel
          RunNumberPanel m_RunNumberPanel
          The panel for configuring run numbers
        • m_SaveBut
          JButton m_SaveBut
          Click to save an experiment
        • m_Support
          PropertyChangeSupport m_Support
          Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.
        • m_XMLFilter
          FileFilter m_XMLFilter
          A filter to ensure only experiment (in XML format) files get shown in the chooser
    • Class weka.gui.experiment.SimpleSetupPanel

      class SimpleSetupPanel extends JPanel implements Serializable
      serialVersionUID:
      5257424515609176509L
      • Serialized Fields

        • m_AlgorithmListPanel
          AlgorithmListPanel m_AlgorithmListPanel
          The panel for configuring selected algorithms
        • m_arffFileFilter
          ExtensionFileFilter m_arffFileFilter
          FIlter for choosing ARFF files
        • m_BrowseDestinationButton
          JButton m_BrowseDestinationButton
          Button for browsing destination files
        • m_csvFileFilter
          ExtensionFileFilter m_csvFileFilter
          Filter for choosing CSV files
        • m_DatasetListPanel
          DatasetListPanel m_DatasetListPanel
          The panel for configuring selected datasets
        • m_DestFileChooser
          JFileChooser m_DestFileChooser
          The file chooser for selecting result destinations
        • m_destinationDatabaseURL
          String m_destinationDatabaseURL
          The database destination URL to store results into
        • m_destinationFilename
          String m_destinationFilename
          The filename to store results into
        • m_Exp
          Experiment m_Exp
          The experiment being configured
        • m_ExpClassificationRBut
          JRadioButton m_ExpClassificationRBut
          Radio button for choosing classification experiment
        • m_ExperimentParameterLabel
          JLabel m_ExperimentParameterLabel
          Label for parameter field
        • m_ExperimentParameterTField
          JTextField m_ExperimentParameterTField
          Input field for experiment parameter
        • m_ExperimentTypeCBox
          JComboBox m_ExperimentTypeCBox
          Combo box for choosing experiment type
        • m_ExpFilter
          FileFilter m_ExpFilter
          A filter to ensure only experiment files get shown in the chooser
        • m_ExpRegressionRBut
          JRadioButton m_ExpRegressionRBut
          Radio button for choosing regression experiment
        • m_FileChooser
          JFileChooser m_FileChooser
          The file chooser for selecting experiments
        • m_KOMLFilter
          FileFilter m_KOMLFilter
          A filter to ensure only experiment (in KOML format) files get shown in the chooser
        • m_modePanel
          SetupModePanel m_modePanel
          The panel which switched between simple and advanced setup modes
        • m_NewBut
          JButton m_NewBut
          Click to create a new experiment with default settings
        • m_NotesButton
          JButton m_NotesButton
          A button for bringing up the notes
        • m_NotesFrame
          JFrame m_NotesFrame
          Frame for the notes
        • m_NotesText
          JTextArea m_NotesText
          Area for user notes Default of 10 rows
        • m_NumberOfRepetitionsTField
          JTextField m_NumberOfRepetitionsTField
          Input field for number of repetitions
        • m_numFolds
          int m_numFolds
          The number of folds for a cross-validation experiment
        • m_numRepetitions
          int m_numRepetitions
          The number of times to repeat the sub-experiment
        • m_OpenBut
          JButton m_OpenBut
          Click to load an experiment
        • m_OrderAlgorithmsFirstRBut
          JRadioButton m_OrderAlgorithmsFirstRBut
          Radio button for choosing algorithms first in order of execution
        • m_OrderDatasetsFirstRBut
          JRadioButton m_OrderDatasetsFirstRBut
          Radio button for choosing datasets first in order of execution
        • m_ResultsDestinationCBox
          JComboBox m_ResultsDestinationCBox
          Combo box for choosing experiment destination type
        • m_ResultsDestinationPathLabel
          JLabel m_ResultsDestinationPathLabel
          Label for destination field
        • m_ResultsDestinationPathTField
          JTextField m_ResultsDestinationPathTField
          Input field for result destination path
        • m_SaveBut
          JButton m_SaveBut
          Click to save an experiment
        • m_Support
          PropertyChangeSupport m_Support
          Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.
        • m_trainPercent
          double m_trainPercent
          The training percentage for a train/test split experiment
        • m_userHasBeenAskedAboutConversion
          boolean m_userHasBeenAskedAboutConversion
          Whether or not the user has consented for the experiment to be simplified
        • m_XMLFilter
          FileFilter m_XMLFilter
          A filter to ensure only experiment (in XML format) files get shown in the chooser
  • Package weka.gui.explorer

    • Class weka.gui.explorer.AssociationsPanel

      class AssociationsPanel extends JPanel implements Serializable
      serialVersionUID:
      -6867871711865476971L
      • Serialized Fields

        • m_AssociatorEditor
          GenericObjectEditor m_AssociatorEditor
          Lets the user configure the associator
        • m_CEPanel
          PropertyPanel m_CEPanel
          The panel showing the current associator selection
        • m_Explorer
          Explorer m_Explorer
          the parent frame
        • m_History
          ResultHistoryPanel m_History
          A panel controlling results viewing
        • m_Instances
          Instances m_Instances
          The main set of instances we're playing with
        • m_Log
          Logger m_Log
          The destination for log/status messages
        • m_OutText
          JTextArea m_OutText
          The output area for associations
        • m_RunThread
          Thread m_RunThread
          A thread that associator runs in
        • m_SaveOut
          SaveBuffer m_SaveOut
          The buffer saving object for saving output
        • m_StartBut
          JButton m_StartBut
          Click to start running the associator
        • m_StopBut
          JButton m_StopBut
          Click to stop a running associator
        • m_TestInstances
          Instances m_TestInstances
          The user-supplied test set (if any)
    • Class weka.gui.explorer.AttributeSelectionPanel

      class AttributeSelectionPanel extends JPanel implements Serializable
      serialVersionUID:
      5627185966993476142L
      • Serialized Fields

        • COMBO_SIZE
          Dimension COMBO_SIZE
          Stop the class combo from taking up to much space
        • 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_AttributeEvaluatorEditor
          GenericObjectEditor m_AttributeEvaluatorEditor
          Lets the user configure the attribute evaluator
        • m_AttributeSearchEditor
          GenericObjectEditor m_AttributeSearchEditor
          Lets the user configure the search method
        • m_ClassCombo
          JComboBox m_ClassCombo
          Lets the user select the class column
        • m_CVBut
          JRadioButton m_CVBut
          Click to set evaluation mode to cross-validation
        • m_CVLab
          JLabel m_CVLab
          Label by where the cv folds are entered
        • m_CVText
          JTextField m_CVText
          The field where the cv folds are entered
        • m_Explorer
          Explorer m_Explorer
          the parent frame
        • m_History
          ResultHistoryPanel m_History
          A panel controlling results viewing
        • m_Instances
          Instances m_Instances
          The main set of instances we're playing with
        • m_Log
          Logger m_Log
          The destination for log/status messages
        • m_OutText
          JTextArea m_OutText
          The output area for attribute selection results
        • m_RadioListener
          ActionListener m_RadioListener
          Alters the enabled/disabled status of elements associated with each radio button
        • m_RunThread
          Thread m_RunThread
          A thread that attribute selection runs in
        • m_SaveOut
          SaveBuffer m_SaveOut
          The buffer saving object for saving output
        • m_SeedLab
          JLabel m_SeedLab
          Label by where cv random seed is entered
        • m_SeedText
          JTextField m_SeedText
          The field where the seed value is entered
        • m_StartBut
          JButton m_StartBut
          Click to start running the attribute selector
        • m_StopBut
          JButton m_StopBut
          Click to stop a running classifier
        • m_TrainBut
          JRadioButton m_TrainBut
          Click to set test mode to test on training data
    • Class weka.gui.explorer.ClassifierPanel

      class ClassifierPanel extends JPanel implements Serializable
      serialVersionUID:
      6959973704963624003L
      • Serialized Fields

        • COMBO_SIZE
          Dimension COMBO_SIZE
          Stop the class combo from taking up to much space
        • m_CEPanel
          PropertyPanel m_CEPanel
          The panel showing the current classifier selection
        • m_ClassCombo
          JComboBox m_ClassCombo
          Lets the user select the class column
        • m_ClassifierEditor
          GenericObjectEditor m_ClassifierEditor
          Lets the user configure the classifier
        • m_CostMatrixEditor
          CostMatrixEditor m_CostMatrixEditor
          The cost matrix editor for evaluation costs
        • m_CurrentVis
          VisualizePanel m_CurrentVis
          The current visualization object
        • m_CVBut
          JRadioButton m_CVBut
          Click to set test mode to cross-validation
        • m_CVLab
          JLabel m_CVLab
          Label by where the cv folds are entered
        • m_CVText
          JTextField m_CVText
          The field where the cv folds are entered
        • m_EvalWRTCostsBut
          JCheckBox m_EvalWRTCostsBut
          Check to evaluate w.r.t a cost matrix
        • m_Explorer
          Explorer m_Explorer
          the parent frame
        • m_FileChooser
          JFileChooser m_FileChooser
          The file chooser for selecting model files
        • m_History
          ResultHistoryPanel m_History
          A panel controlling results viewing
        • m_Instances
          Instances m_Instances
          The main set of instances we're playing with
        • m_Log
          Logger m_Log
          The destination for log/status messages
        • m_ModelFilter
          FileFilter m_ModelFilter
          Filter to ensure only model files are selected
        • m_MoreOptions
          JButton m_MoreOptions
          Button for further output/visualize options
        • m_OutputAdditionalAttributesLab
          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_OutputAdditionalAttributesText
          JTextField m_OutputAdditionalAttributesText
          Lists indices for additional attributes to output
        • m_OutputConfusionBut
          JCheckBox m_OutputConfusionBut
          Check to output a confusion matrix
        • m_OutputEntropyBut
          JCheckBox m_OutputEntropyBut
          Check to output entropy statistics
        • m_OutputModelBut
          JCheckBox m_OutputModelBut
          Check to output the model built from the training data
        • m_OutputPerClassBut
          JCheckBox m_OutputPerClassBut
          Check to output true/false positives, precision/recall for each class
        • m_OutputPredictionsTextBut
          JCheckBox m_OutputPredictionsTextBut
          Check to output text predictions
        • m_OutputSourceCode
          JCheckBox m_OutputSourceCode
          Whether to output the source code (only for classifiers importing Sourcable)
        • m_OutText
          JTextArea m_OutText
          The output area for classification results
        • m_PercentBut
          JRadioButton m_PercentBut
          Click to set test mode to generate a % split
        • m_PercentLab
          JLabel m_PercentLab
          Label by where the % split is entered
        • m_PercentText
          JTextField m_PercentText
          The field where the % split is entered
        • m_PMMLModelFilter
          FileFilter m_PMMLModelFilter
        • m_PreserveOrderBut
          JCheckBox m_PreserveOrderBut
          Whether randomization is turned off to preserve order
        • m_RadioListener
          ActionListener m_RadioListener
          Alters the enabled/disabled status of elements associated with each radio button
        • m_RandomLab
          JLabel m_RandomLab
          the label for the random seed textfield
        • m_RandomSeedText
          JTextField m_RandomSeedText
          User specified random seed for cross validation or % split
        • m_RunThread
          Thread m_RunThread
          A thread that classification runs in
        • m_SaveOut
          SaveBuffer m_SaveOut
          The buffer saving object for saving output
        • m_SetCostsBut
          JButton m_SetCostsBut
          for the cost matrix
        • m_SetCostsFrame
          PropertyDialog m_SetCostsFrame
          The frame used to show the cost matrix editing panel
        • m_SetTestBut
          JButton m_SetTestBut
          The button used to open a separate test dataset
        • m_SetTestFrame
          JFrame m_SetTestFrame
          The frame used to show the test set selection panel
        • m_SourceCodeClass
          JTextField m_SourceCodeClass
          The name of the generated class (only applicable to Sourcable schemes)
        • m_StartBut
          JButton m_StartBut
          Click to start running the classifier
        • m_StopBut
          JButton m_StopBut
          Click to stop a running classifier
        • m_StorePredictionsBut
          JCheckBox m_StorePredictionsBut
          Check to save the predictions in the results list for visualizing later on
        • m_TestLoader
          Loader m_TestLoader
          The loader used to load the user-supplied test set (if any)
        • m_TestSplitBut
          JRadioButton m_TestSplitBut
          Click to set test mode to a user-specified test set
        • m_TrainBut
          JRadioButton m_TrainBut
          Click to set test mode to test on training data
    • Class weka.gui.explorer.ClustererPanel

      class ClustererPanel extends JPanel implements Serializable
      serialVersionUID:
      -2474932792950820990L
      • Serialized Fields

        • COMBO_SIZE
          Dimension COMBO_SIZE
          Stop the class combo from taking up to much space
        • m_ClassCombo
          JComboBox m_ClassCombo
          Lets the user select the class column for classes to clusters based evaluation
        • m_ClassesToClustersBut
          JRadioButton m_ClassesToClustersBut
          Click to set test mode to classes to clusters based evaluation
        • m_CLPanel
          PropertyPanel m_CLPanel
          The panel showing the current clusterer selection
        • m_ClustererEditor
          GenericObjectEditor m_ClustererEditor
          Lets the user configure the clusterer
        • m_CurrentVis
          VisualizePanel m_CurrentVis
          The current visualization object
        • m_Explorer
          Explorer m_Explorer
          the parent frame
        • m_FileChooser
          JFileChooser m_FileChooser
          The file chooser for selecting model files
        • m_History
          ResultHistoryPanel m_History
          A panel controlling results viewing
        • m_ignoreBut
          JButton m_ignoreBut
          The button used to popup a list for choosing attributes to ignore while clustering
        • m_ignoreKeyList
          JList m_ignoreKeyList
        • m_ignoreKeyModel
          DefaultListModel m_ignoreKeyModel
        • m_Instances
          Instances m_Instances
          The main set of instances we're playing with
        • m_Log
          Logger m_Log
          The destination for log/status messages
        • m_ModelFilter
          FileFilter m_ModelFilter
          Filter to ensure only model files are selected
        • m_OutText
          JTextArea m_OutText
          The output area for classification results
        • m_PercentBut
          JRadioButton m_PercentBut
          Click to set test mode to generate a % split
        • m_PercentLab
          JLabel m_PercentLab
          Label by where the % split is entered
        • m_PercentText
          JTextField m_PercentText
          The field where the % split is entered
        • m_RadioListener
          ActionListener m_RadioListener
          Alters the enabled/disabled status of elements associated with each radio button
        • m_RunThread
          Thread m_RunThread
          A thread that clustering runs in
        • m_SaveOut
          SaveBuffer m_SaveOut
          The buffer saving object for saving output
        • m_SetTestBut
          JButton m_SetTestBut
          The button used to open a separate test dataset
        • m_SetTestFrame
          JFrame m_SetTestFrame
          The frame used to show the test set selection panel
        • m_StartBut
          JButton m_StartBut
          Click to start running the clusterer
        • m_StopBut
          JButton m_StopBut
          Click to stop a running clusterer
        • m_StorePredictionsBut
          JCheckBox m_StorePredictionsBut
          Check to save the predictions in the results list for visualizing later on
        • m_Summary
          InstancesSummaryPanel m_Summary
          The instances summary panel displayed by m_SetTestFrame
        • m_TestInstances
          Instances m_TestInstances
          The user-supplied test set (if any)
        • m_TestSplitBut
          JRadioButton m_TestSplitBut
          Click to set test mode to a user-specified test set
        • m_TrainBut
          JRadioButton m_TrainBut
          Click to set test mode to test on training data
    • Class weka.gui.explorer.DataGeneratorPanel

      class DataGeneratorPanel extends 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_Log
          Logger m_Log
          The destination for log/status messages
        • m_Output
          StringWriter m_Output
          the generated output (as text)
    • Class weka.gui.explorer.Explorer

      class Explorer extends JPanel implements Serializable
      serialVersionUID:
      -7674003708867909578L
      • Serialized Fields

        • m_CapabilitiesFilterChangeListeners
          HashSet<Explorer.CapabilitiesFilterChangeListener> m_CapabilitiesFilterChangeListeners
          the listeners that listen to filter changes
        • m_LogPanel
          LogPanel m_LogPanel
          The panel for log and status messages
        • m_Panels
          Vector<Explorer.ExplorerPanel> m_Panels
          Contains all the additional panels apart from the pre-processing panel
        • m_PreprocessPanel
          PreprocessPanel m_PreprocessPanel
          The panel for preprocessing instances
        • m_TabbedPane
          JTabbedPane m_TabbedPane
          The tabbed pane that controls which sub-pane we are working with
    • Class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent

      class CapabilitiesFilterChangeEvent extends ChangeEvent implements Serializable
      serialVersionUID:
      1194260517270385559L
      • Serialized Fields

    • Class weka.gui.explorer.ExplorerDefaults

      class ExplorerDefaults extends Object implements Serializable
      serialVersionUID:
      4954795757927524225L
    • Class weka.gui.explorer.PreprocessPanel

      class PreprocessPanel extends JPanel implements Serializable
      serialVersionUID:
      6764850273874813049L
      • Serialized Fields

        • m_ApplyFilterBut
          JButton m_ApplyFilterBut
          Click to apply filters and save the results
        • m_AttPanel
          AttributeSelectionPanel m_AttPanel
          Panel to let the user toggle attributes
        • m_AttSummaryPanel
          AttributeSummaryPanel m_AttSummaryPanel
          Displays summary stats on the selected attribute
        • m_AttVisualizePanel
          AttributeVisualizationPanel m_AttVisualizePanel
          The visualization of the attribute values
        • m_DataGenerator
          DataGenerator m_DataGenerator
          The last generator that was selected
        • m_EditBut
          JButton m_EditBut
          Click to open the current instances in a viewer
        • m_Explorer
          Explorer m_Explorer
          the parent frame
        • m_FileChooser
          ConverterFileChooser m_FileChooser
          The file chooser for selecting data files
        • m_FilterEditor
          GenericObjectEditor m_FilterEditor
          Lets the user configure the filter
        • m_FilterPanel
          PropertyPanel m_FilterPanel
          Filter configuration
        • m_GenerateBut
          JButton m_GenerateBut
          Click to generate artificial data
        • m_Instances
          Instances m_Instances
          The working instances
        • m_InstSummaryPanel
          InstancesSummaryPanel m_InstSummaryPanel
          Displays simple stats on the working instances
        • m_IOThread
          Thread m_IOThread
          A thread for loading/saving instances from a file or URL
        • m_LastURL
          String m_LastURL
          Stores the last URL that instances were loaded from
        • m_Log
          Logger m_Log
          The message logger
        • m_OpenDBBut
          JButton m_OpenDBBut
          Click to load base instances from a Database
        • m_OpenFileBut
          JButton m_OpenFileBut
          Click to load base instances from a file
        • m_OpenURLBut
          JButton m_OpenURLBut
          Click to load base instances from a URL
        • m_RemoveButton
          JButton m_RemoveButton
          Button for removing attributes
        • m_SaveBut
          JButton m_SaveBut
          Click to apply filters and save the results
        • m_SQLQ
          String m_SQLQ
          Stores the last sql query executed
        • m_Support
          PropertyChangeSupport m_Support
          Manages sending notifications to people when we change the set of working instances.
        • m_tempUndoFiles
          File[] m_tempUndoFiles
          Keeps track of undo points
        • m_tempUndoIndex
          int m_tempUndoIndex
          The next available slot for an undo point
        • m_UndoBut
          JButton m_UndoBut
          Click to revert back to the last saved point
    • Class weka.gui.explorer.VisualizePanel

      class VisualizePanel extends MatrixPanel implements Serializable
      serialVersionUID:
      6084015036853918846L
      • Serialized Fields

        • m_Explorer
          Explorer m_Explorer
          the parent frame
  • Package weka.gui.graphvisualizer

    • Exception Class weka.gui.graphvisualizer.BIFFormatException

      class BIFFormatException extends Exception implements Serializable
      serialVersionUID:
      -4102518086411708140L
    • Class weka.gui.graphvisualizer.GraphVisualizer

      class GraphVisualizer extends JPanel implements Serializable
      serialVersionUID:
      -2038911085935515624L
      • Serialized Fields

        • fm
          FontMetrics fm
        • graphID
          String graphID
          String containing graph's name
        • ICONPATH
          String ICONPATH
          path for icons
        • jBtLayout
          JButton jBtLayout
          Button for laying out the graph again, necessary after changing node's size or some other property of the layout engine
        • jTfNodeHeight
          JTextField jTfNodeHeight
          TextField for nodes height
        • jTfNodeWidth
          JTextField jTfNodeWidth
          TextField for node's width
        • m_edges
          FastVector m_edges
          Vector containing edges
        • m_gp
          weka.gui.graphvisualizer.GraphVisualizer.GraphPanel m_gp
          Panel actually displaying the graph
        • m_jBtSave
          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.
        • m_js
          JScrollPane m_js
          this contains the m_gp GraphPanel
        • m_le
          LayoutEngine m_le
          The current LayoutEngine
        • m_nodes
          FastVector m_nodes
          Vector containing nodes
        • maxStringWidth
          int maxStringWidth
          used for setting appropriate node size
        • nodeHeight
          int nodeHeight
        • nodeWidth
          int nodeWidth
        • paddedNodeWidth
          int paddedNodeWidth
        • scale
          double scale
        • zoomPercents
          int[] zoomPercents
          used when using zoomIn and zoomOut buttons
    • Class weka.gui.graphvisualizer.LayoutCompleteEvent

      class LayoutCompleteEvent extends EventObject implements Serializable
      serialVersionUID:
      6172467234026258427L
  • Package weka.gui.hierarchyvisualizer

    • Class weka.gui.hierarchyvisualizer.HierarchyVisualizer

      class HierarchyVisualizer extends PrintablePanel implements Serializable
      serialVersionUID:
      1L
      • Serialized Fields

        • m_fHeight
          double m_fHeight
        • m_fScaleX
          double m_fScaleX
        • m_fScaleY
          double m_fScaleY
        • m_fTmpLength
          double m_fTmpLength
        • m_nLeafs
          int m_nLeafs
        • m_sNewick
          String m_sNewick
        • m_tree
          weka.gui.hierarchyvisualizer.HierarchyVisualizer.Node m_tree
  • Package weka.gui.sql

    • Class weka.gui.sql.ConnectionPanel

      class ConnectionPanel extends JPanel implements Serializable
      serialVersionUID:
      3499317023969723490L
      • Serialized Fields

        • m_ButtonConnect
          JButton m_ButtonConnect
          the button for connecting to the database.
        • m_ButtonDatabase
          JButton m_ButtonDatabase
          the button for the DB-Dialog.
        • m_ButtonHistory
          JButton m_ButtonHistory
          the button for the history.
        • m_ConnectionListeners
          HashSet m_ConnectionListeners
          the connection listeners.
        • m_DbDialog
          DatabaseConnectionDialog m_DbDialog
          the databae connection dialog.
        • m_DbUtils
          DbUtils m_DbUtils
          for connecting to the database.
        • m_History
          DefaultListModel m_History
          the history of connections.
        • m_HistoryChangedListeners
          HashSet m_HistoryChangedListeners
          the history listeners.
        • m_LabelURL
          JLabel m_LabelURL
          the label for the URL.
        • m_Parent
          JFrame m_Parent
          the parent frame.
        • m_Password
          String m_Password
          the password to use for connecting to the DB.
        • m_TextURL
          JTextField m_TextURL
          the textfield for the URL.
        • m_URL
          String m_URL
          the URL to use.
        • m_User
          String m_User
          the user to use for connecting to the DB.
    • Class weka.gui.sql.DbUtils

      class DbUtils extends DatabaseUtils implements Serializable
      serialVersionUID:
      103748569037426479L
    • Class weka.gui.sql.InfoPanel

      class InfoPanel extends JPanel implements Serializable
      serialVersionUID:
      -7701133696481997973L
      • Serialized Fields

        • m_ButtonClear
          JButton m_ButtonClear
          the button to clear the area
        • m_ButtonCopy
          JButton m_ButtonCopy
          the button to copy the selected message
        • m_Info
          JList m_Info
          the list the contains the messages
        • m_Model
          DefaultListModel m_Model
          the model for the list
        • m_Parent
          JFrame m_Parent
          the parent of this panel
    • Class weka.gui.sql.InfoPanelCellRenderer

      class InfoPanelCellRenderer extends JLabel implements Serializable
      serialVersionUID:
      -533380118807178531L
    • Class weka.gui.sql.QueryPanel

      class QueryPanel extends JPanel implements Serializable
      serialVersionUID:
      4348967824619706636L
      • Serialized Fields

        • m_ButtonClear
          JButton m_ButtonClear
          the clear button.
        • m_ButtonExecute
          JButton m_ButtonExecute
          the execute button.
        • m_ButtonHistory
          JButton m_ButtonHistory
          the history button.
        • m_Connected
          boolean m_Connected
          whether we have a connection to a database or not.
        • m_DbUtils
          DbUtils m_DbUtils
          for working on the database.
        • m_History
          DefaultListModel m_History
          the query history.
        • m_HistoryChangedListeners
          HashSet m_HistoryChangedListeners
          the history listeners.
        • m_Parent
          JFrame m_Parent
          the parent of this panel.
        • m_QueryExecuteListeners
          HashSet m_QueryExecuteListeners
          the connection listeners.
        • m_SpinnerMaxRows
          JSpinner m_SpinnerMaxRows
          the spinner for the maximum number of rows.
        • m_TextQuery
          JTextArea m_TextQuery
          the textarea for the query.
    • Class weka.gui.sql.ResultPanel

      class ResultPanel extends JPanel implements Serializable
      serialVersionUID:
      278654800344034571L
      • Serialized Fields

        • m_ButtonClose
          JButton m_ButtonClose
          the close button
        • m_ButtonCloseAll
          JButton m_ButtonCloseAll
          the close all button
        • m_ButtonCopyQuery
          JButton m_ButtonCopyQuery
          the button that copies the query into the QueryPanel
        • m_ButtonOptWidth
          JButton m_ButtonOptWidth
          the button for the optimal column width of the current table
        • m_Listeners
          HashSet m_Listeners
          the result change listeners
        • m_NameCounter
          int m_NameCounter
          the counter for the tab names
        • m_Parent
          JFrame m_Parent
          the parent of this panel
        • m_QueryPanel
          QueryPanel m_QueryPanel
          the panel where to output the queries
        • m_TabbedPane
          JTabbedPane m_TabbedPane
          the tabbed pane for the results
    • Class weka.gui.sql.ResultSetTable

      class ResultSetTable extends JTable implements Serializable
      serialVersionUID:
      -3391076671854464137L
      • Serialized Fields

        • m_Password
          String m_Password
          the password that was used to connect to the DB
        • m_Query
          String m_Query
          the query the table model is based on
        • m_URL
          String m_URL
          the connect string with which the query was run
        • m_User
          String m_User
          the user that was used to connect to the DB
    • Class weka.gui.sql.ResultSetTableCellRenderer

      class ResultSetTableCellRenderer extends DefaultTableCellRenderer implements Serializable
      serialVersionUID:
      -8106963669703497351L
      • Serialized Fields

        • missingColor
          Color missingColor
        • missingColorSelected
          Color missingColorSelected
    • Class weka.gui.sql.SqlViewer

      class SqlViewer extends JPanel implements Serializable
      serialVersionUID:
      -4395028775566514329L
      • Serialized Fields

        • m_ConnectionPanel
          ConnectionPanel m_ConnectionPanel
          the connection panel.
        • m_History
          Properties m_History
          stores the history.
        • m_InfoPanel
          InfoPanel m_InfoPanel
          the info panel.
        • m_Parent
          JFrame m_Parent
          the parent of this panel.
        • m_Password
          String m_Password
          the password that was used to connect to the DB.
        • m_Query
          String m_Query
          the currently selected query.
        • m_QueryPanel
          QueryPanel m_QueryPanel
          the query panel.
        • m_ResultPanel
          ResultPanel m_ResultPanel
          the result panel.
        • m_URL
          String m_URL
          the connect string with which the query was run.
        • m_User
          String m_User
          the user that was used to connect to the DB.
    • Class weka.gui.sql.SqlViewerDialog

      class SqlViewerDialog extends JDialog implements Serializable
      serialVersionUID:
      -31619864037233099L
      • Serialized Fields

        • m_ButtonCancel
          JButton m_ButtonCancel
          the Cancel button
        • m_ButtonOK
          JButton m_ButtonOK
          the OK button
        • m_LabelQuery
          JLabel m_LabelQuery
          displays the current query
        • m_PanelButtons
          JPanel m_PanelButtons
          the panel for the buttons
        • m_Parent
          JFrame m_Parent
          the parent frame
        • m_Password
          String m_Password
          the password that was used to connect to the DB
        • m_Query
          String m_Query
          the currently selected query
        • m_ReturnValue
          int m_ReturnValue
          the return value
        • m_URL
          String m_URL
          the connect string with which the query was run
        • m_User
          String m_User
          the user that was used to connect to the DB
        • m_Viewer
          SqlViewer m_Viewer
          the SQL panel
  • Package weka.gui.sql.event

  • Package weka.gui.streams

  • Package weka.gui.treevisualizer

    • Class weka.gui.treevisualizer.TreeVisualizer

      class TreeVisualizer extends PrintablePanel implements Serializable
      serialVersionUID:
      -8668637962504080749L
      • Serialized Fields

        • m_accept
          JMenuItem m_accept
          An option on the win menu.
        • m_addChildren
          JMenuItem m_addChildren
          An add children to Node choice, This is only available if the tree display has a treedisplay listerner added to it.
        • m_autoScale
          JMenuItem m_autoScale
          An option on the win_menu
        • m_BackgroundColor
          Color m_BackgroundColor
          the background color.
        • m_caseSen
          JRadioButton m_caseSen
        • m_classifyChild
          JMenuItem m_classifyChild
          Use this to have J48 classify this node.
        • m_clickAvailable
          boolean m_clickAvailable
          A variable used to determine for the clicked method if any other mouse state has already taken place.
        • m_currentFont
          Font m_currentFont
          The font used to display the tree.
        • m_edges
          weka.gui.treevisualizer.TreeVisualizer.EdgeInfo[] m_edges
          An array with the Edges sorted into it and display information about the Edges.
        • m_fitToScreen
          JMenuItem m_fitToScreen
          An option on the win_menu
        • 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_FontColor
          Color m_FontColor
          the font color.
        • m_fontSize
          FontMetrics m_fontSize
          The size information for the current font.
        • m_frameLimiter
          Timer m_frameLimiter
          A timer to keep the frame rate constant.
        • 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_LineColor
          Color m_LineColor
          the line color.
        • m_listener
          TreeDisplayListener m_listener
        • m_mouseState
          int m_mouseState
          Describes the action the user is performing.
        • m_newMousePos
          Dimension m_newMousePos
          A variable used to tag the most current point of a user action.
        • m_NodeColor
          Color m_NodeColor
          the node color.
        • m_nodeMenu
          JPopupMenu m_nodeMenu
          A right or middle click popup menu for nodes.
        • m_nodes
          weka.gui.treevisualizer.TreeVisualizer.NodeInfo[] m_nodes
          An array with the Nodes sorted into it and display information about the Nodes.
        • m_numLevels
          int m_numLevels
          The number of levels in the tree.
        • m_numNodes
          int m_numNodes
          The number of Nodes in the tree.
        • m_nViewPos
          Dimension m_nViewPos
          A variable used to remember the desired view pos.
        • m_nViewSize
          Dimension m_nViewSize
          A variable used to remember the desired tree size.
        • m_oldMousePos
          Dimension m_oldMousePos
          A variable used to tag the start pos of a user action.
        • m_placer
          NodePlace m_placer
          The placement algorithm for the Node structure.
        • m_remChildren
          JMenuItem m_remChildren
          Similar to add children but now it removes children.
        • m_scaling
          int m_scaling
          The number of frames left to calculate.
        • m_searchString
          JTextField m_searchString
        • m_searchWin
          JDialog m_searchWin
        • m_selectFont
          JMenu m_selectFont
          A sub group on the win_menu
        • m_selectFontGroup
          ButtonGroup m_selectFontGroup
          A grouping for the font choices
        • m_sendInstances
          JMenuItem m_sendInstances
          Use this to dump the instances from this node to the vis panel.
        • m_ShowBorder
          boolean m_ShowBorder
          whether to show the border or not.
        • m_size1
          JRadioButtonMenuItem m_size1
          A font choice.
        • m_size10
          JRadioButtonMenuItem m_size10
          A font choice.
        • m_size12
          JRadioButtonMenuItem m_size12
          A font choice.
        • m_size14
          JRadioButtonMenuItem m_size14
          A font choice.
        • m_size16
          JRadioButtonMenuItem m_size16
          A font choice.
        • m_size18
          JRadioButtonMenuItem m_size18
          A font choice.
        • m_size2
          JRadioButtonMenuItem m_size2
          A font choice.
        • m_size20
          JRadioButtonMenuItem m_size20
          A font choice.
        • m_size22
          JRadioButtonMenuItem m_size22
          A font choice.
        • m_size24
          JRadioButtonMenuItem m_size24
          A font choice.
        • m_size4
          JRadioButtonMenuItem m_size4
          A font choice.
        • m_size6
          JRadioButtonMenuItem m_size6
          A font choice.
        • m_size8
          JRadioButtonMenuItem m_size8
          A font choice.
        • m_topN
          JMenuItem m_topN
          An option on the win_menu
        • m_topNode
          Node m_topNode
          The top Node.
        • m_viewPos
          Dimension m_viewPos
          The postion of the view relative to the tree.
        • m_viewSize
          Dimension m_viewSize
          The size of the tree in pixels.
        • m_visualise
          JMenuItem m_visualise
          A visualize choice for the node, may not be available.
        • m_winMenu
          JPopupMenu m_winMenu
          A right (or middle) click popup menu.
        • m_ZoomBoxColor
          Color m_ZoomBoxColor
          the color of the zoombox.
        • m_ZoomBoxXORColor
          Color m_ZoomBoxXORColor
          the XOR color of the zoombox.
  • Package weka.gui.visualize

    • Class weka.gui.visualize.AttributePanel

      class AttributePanel extends JScrollPane implements Serializable
      serialVersionUID:
      3533330317806757814L
      • Serialized Fields

        • m_backgroundColor
          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_barColour
          Color m_barColour
          The default colour to use for the background of the bars if a colour is not defined in Visualize.props
        • m_cIndex
          int m_cIndex
        • m_colorList
          FastVector m_colorList
          The colour map to use for colouring points
        • m_DefaultColors
          Color[] m_DefaultColors
          default colours for colouring discrete class
        • m_heights
          int[] m_heights
          Holds the random height for each instance.
        • m_Listeners
          FastVector m_Listeners
          The list of things listening to this panel
        • m_maxC
          double m_maxC
          Holds the min and max values of the colouring attributes
        • m_minC
          double m_minC
        • m_plotInstances
          Instances m_plotInstances
          The instances to be plotted
        • m_span
          JPanel m_span
          The container window for the attribute bars, and also where the X,Y or B get printed.
        • m_xIndex
          int m_xIndex
        • m_yIndex
          int m_yIndex
    • Class weka.gui.visualize.AttributePanel.AttributeSpacing

      class AttributeSpacing extends JPanel implements Serializable
      serialVersionUID:
      7220615894321679898L
      • Serialized Fields

        • 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_maxVal
          double m_maxVal
          The min and max values for this attribute.
        • m_minVal
          double m_minVal
        • m_oldWidth
          int m_oldWidth
          Used to determine if the positions need to be recalculated.
        • m_pointDrawn
          boolean[][] m_pointDrawn
          A temporary array used to strike any instances that would be drawn redundantly.
    • Class weka.gui.visualize.ClassPanel

      class ClassPanel extends JPanel implements Serializable
      serialVersionUID:
      -7969401840501661430L
      • Serialized Fields

        • m_backgroundColor
          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
        • 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_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
          Color[] m_DefaultColors
          default colours for colouring discrete class
        • m_fieldWidthC
          int m_fieldWidthC
          Field width for numeric values
        • m_HorizontalPad
          int m_HorizontalPad
          The amount of space to leave either side of the legend
        • m_Instances
          Instances m_Instances
          Instances being plotted
        • 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_labelFont
          Font m_labelFont
          The font used in labeling
        • m_labelMetrics
          FontMetrics m_labelMetrics
          Font metrics
        • 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_oldWidth
          int m_oldWidth
          The old width.
        • m_precisionC
          int m_precisionC
          The precision with which to display real values
        • 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_spectrumHeight
          int m_spectrumHeight
          The height of the spectrum for numeric class
        • m_tickSize
          int m_tickSize
          The size of the ticks
    • Class weka.gui.visualize.LegendPanel

      class LegendPanel extends JScrollPane implements Serializable
      serialVersionUID:
      -1262384440543001505L
      • Serialized Fields

        • m_plots
          FastVector m_plots
          the list of plot elements
        • m_Repainters
          FastVector m_Repainters
          a list of components that need to be repainted when a colour is changed
        • m_span
          JPanel m_span
          the panel that contains the legend entries
    • Class weka.gui.visualize.LegendPanel.LegendEntry

      class LegendEntry extends JPanel implements Serializable
      serialVersionUID:
      3879990289042935670L
      • Serialized Fields

        • m_dataIndex
          int m_dataIndex
          the index (in the list of plots) of the data for this legend--- used to draw the correct shape for this data
        • m_legendText
          JLabel m_legendText
          the text part of this legend
        • m_plotData
          PlotData2D m_plotData
          the data for this legend entry
        • m_pointShape
          JPanel m_pointShape
          displays the point shape associated with this legend entry
    • Class weka.gui.visualize.MatrixPanel

      class MatrixPanel extends JPanel implements Serializable
      serialVersionUID:
      -1232642719869188740L
      • Serialized Fields

        • datapointSize
          int datapointSize
          This stores the size of the datapoint
        • f
          Font f
          font used in column and row names
        • fontColor
          Color fontColor
          color for the font used in column and row names
        • jitterVals
          int[][] jitterVals
          Array containing precalculated jitter values
        • jp
          JSplitPane jp
          Split pane for splitting the matrix and the buttons and bars
        • m_attribList
          JList m_attribList
          The list for selecting the attributes to display the plot matrix
        • m_classAttrib
          JComboBox m_classAttrib
          The combo box to allow user to select the colouring attribute
        • m_classIndex
          int m_classIndex
          This contains the index of the currently selected colouring attribute
        • m_colorList
          FastVector m_colorList
          Contains discrete colours for colouring for nominal attributes
        • m_cp
          ClassPanel m_cp
          The panel that displays the legend of the colouring attribute
        • m_data
          Instances m_data
          The dataset for which this panel will display the plot matrix for
        • m_jitter
          JSlider m_jitter
          The slider to add jitter to the plots
        • m_js
          JScrollPane m_js
          The scroll pane to scrolling the matrix
        • 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_plotLBSizeD
          Dimension m_plotLBSizeD
          Stores the maximum size for PlotSize label to keep it's size constant
        • m_plotSize
          JSlider m_plotSize
          The slider to adjust the size of the cells in the matrix
        • m_plotSizeLb
          JLabel m_plotSizeLb
          Displays the current size beside the slider bar for cell size
        • m_plotsPanel
          weka.gui.visualize.MatrixPanel.Plot m_plotsPanel
          The that panel contains the actual matrix
        • 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_pointLBSizeD
          Dimension m_pointLBSizeD
          Stores the maximum size for PointSize label to keep it's size constant
        • m_points
          int[][] m_points
          This is a local array cache for all the instance values for faster rendering
        • m_pointSize
          JSlider m_pointSize
          The slider to adjust the size of the datapoints
        • m_pointSizeLb
          JLabel m_pointSizeLb
          Displays the current size beside the slider bar for point size
        • m_resampleBt
          JButton m_resampleBt
          The label for resample percentage
        • m_resamplePercent
          JTextField m_resamplePercent
          The text area for percentage to resample data
        • m_rseed
          JTextField m_rseed
          Random seed for random subsample
        • m_selAttrib
          JButton m_selAttrib
          The button to display a window to select attributes
        • m_selectedAttribs
          int[] m_selectedAttribs
          This array contains the indices of the attributes currently selected
        • 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_updateBt
          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
        • optionsPanel
          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
        • rnd
          Random rnd
          For adding random jitter
    • Class weka.gui.visualize.Plot2D

      class Plot2D extends JPanel implements Serializable
      serialVersionUID:
      -1673162410856660442L
      • Serialized Fields

        • m_axisChanged
          boolean m_axisChanged
          if the user changes attribute assigned to an axis
        • m_axisColour
          Color m_axisColour
          Default colour for the axis
        • m_axisPad
          int m_axisPad
          Axis padding
        • m_backgroundColour
          Color m_backgroundColour
          Default colour for the plot background
        • m_cIndex
          int m_cIndex
        • m_colorList
          FastVector m_colorList
          The list of the colors used
        • m_DefaultColors
          Color[] m_DefaultColors
          default colours for colouring discrete class
        • 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_InstanceInfo
          JFrame m_InstanceInfo
          For popping up text info on data points
        • m_InstanceInfoText
          JTextArea m_InstanceInfoText
        • m_JitterVal
          int m_JitterVal
          the level of jitter
        • m_JRand
          Random m_JRand
          random values for perterbing the data points
        • m_labelFont
          Font m_labelFont
          Font for labels
        • m_labelMetrics
          FontMetrics m_labelMetrics
        • m_masterName
          String m_masterName
          The name of the master plot
        • m_masterPlot
          PlotData2D m_masterPlot
          The master plot
        • m_maxC
          double m_maxC
        • m_maxX
          double m_maxX
          Holds the min and max values of the x, y and colouring attributes over all plots
        • m_maxY
          double m_maxY
        • m_minC
          double m_minC
        • m_minX
          double m_minX
        • m_minY
          double m_minY
        • 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_plotInstances
          Instances m_plotInstances
          The instances to be plotted
        • 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_plots
          FastVector m_plots
          The plots to display
        • m_pointLookup
          double[][] m_pointLookup
          lookup table for plotted points
        • m_sIndex
          int m_sIndex
        • m_tickSize
          int m_tickSize
          Tick size
        • m_XaxisEnd
          int m_XaxisEnd
        • m_XaxisStart
          int m_XaxisStart
          the offsets of the axes once label metrics are calculated
        • 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_YaxisEnd
          int m_YaxisEnd
        • m_YaxisStart
          int m_YaxisStart
        • m_yIndex
          int m_yIndex
    • Class weka.gui.visualize.PrintableComponent.JComponentWriterFileFilter

      class JComponentWriterFileFilter extends ExtensionFileFilter implements Serializable
    • Class weka.gui.visualize.PrintablePanel

      class PrintablePanel extends JPanel implements Serializable
      serialVersionUID:
      6281532227633417538L
      • Serialized Fields

    • Class weka.gui.visualize.ThresholdVisualizePanel

      class ThresholdVisualizePanel extends VisualizePanel implements Serializable
      serialVersionUID:
      3070002211779443890L
      • Serialized Fields

        • m_ROCString
          String m_ROCString
          The string to add to the Plot Border.
        • m_savePanelBorderText
          String m_savePanelBorderText
          Original border text
    • Class weka.gui.visualize.VisualizePanel

      class VisualizePanel extends PrintablePanel implements Serializable
      serialVersionUID:
      240108358588153943L
      • Serialized Fields

        • COMBO_SIZE
          Dimension COMBO_SIZE
          Stop the combos from growing out of control
        • listener
          ActionListener listener
          An optional listener that we will inform when ComboBox selections change
        • m_ArffFilter
          FileFilter m_ArffFilter
          Filter to ensure only arff files are selected
        • m_attrib
          AttributePanel m_attrib
          The panel that displays the attributes , using color to represent another attribute.
        • m_cancel
          JButton m_cancel
          Button for the user to remove all splits.
        • m_classPanel
          ClassPanel m_classPanel
          The panel that displays the legend for the colouring attribute
        • m_classSurround
          JPanel m_classSurround
          Panel that surrounds the class panel with a titled border
        • m_colorList
          FastVector m_colorList
          The list of the colors used
        • m_ColourCombo
          JComboBox m_ColourCombo
          Lets the user select the attribute to use for colouring
        • m_DefaultColors
          Color[] m_DefaultColors
          default colours for colouring discrete class
        • m_FileChooser
          JFileChooser m_FileChooser
          file chooser for saving instances
        • m_Jitter
          JSlider m_Jitter
          The jitter slider
        • m_JitterLab
          JLabel m_JitterLab
          Label for the jitter slider
        • m_legendPanel
          LegendPanel m_legendPanel
          The panel that displays legend info if there is more than one plot
        • m_Log
          Logger m_Log
          the logger
        • m_openBut
          JButton m_openBut
          Button for the user to open the visualized set of instances
        • m_plot
          weka.gui.visualize.VisualizePanel.PlotPanel m_plot
          The panel that displays the plot
        • m_plotName
          String m_plotName
          The name of the plot (not currently displayed, but can be used in the containing Frame or Panel)
        • m_plotSurround
          JPanel m_plotSurround
          Panel that surrounds the plot panel with a titled border
        • m_preferredColourDimension
          String m_preferredColourDimension
        • m_preferredXDimension
          String m_preferredXDimension
          These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file
        • m_preferredYDimension
          String m_preferredYDimension
        • m_saveBut
          JButton m_saveBut
          Button for the user to save the visualized set of instances
        • m_ShapeCombo
          JComboBox m_ShapeCombo
          Lets the user select the shape they want to create for instance selection.
        • m_showAttBars
          boolean m_showAttBars
          Show the attribute bar panel
        • m_showClassPanel
          boolean m_showClassPanel
          Show the class panel
        • m_splitListener
          VisualizePanelListener m_splitListener
          An optional listener that we will inform when the user creates a split to seperate instances.
        • m_submit
          JButton m_submit
          Button for the user to enter the splits.
        • m_XCombo
          JComboBox m_XCombo
          Lets the user select the attribute for the x axis
        • m_YCombo
          JComboBox m_YCombo
          Lets the user select the attribute for the y axis
    • Class weka.gui.visualize.VisualizePanel.PlotPanel

      class PlotPanel extends PrintablePanel implements Serializable
      serialVersionUID:
      -4823674171136494204L
      • Serialized Fields

        • m_cIndex
          int m_cIndex
        • m_createShape
          boolean m_createShape
          True if the user is currently dragging a box.
        • m_newMousePos
          Dimension m_newMousePos
          contains the position of the mouse (used for rubberbanding).
        • m_originalPlot
          PlotData2D m_originalPlot
          The master plot
        • m_plot2D
          Plot2D m_plot2D
          The actual generic plotting panel
        • m_plotInstances
          Instances m_plotInstances
          The instances from the master plot
        • m_shapePoints
          FastVector m_shapePoints
          contains the points of the shape currently being drawn.
        • m_shapes
          FastVector m_shapes
          contains all the shapes that have been drawn for these attribs
        • m_sIndex
          int m_sIndex
        • m_XaxisEnd
          int m_XaxisEnd
        • m_XaxisStart
          int m_XaxisStart
          the offsets of the axes once label metrics are calculated
        • 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_YaxisEnd
          int m_YaxisEnd
        • m_YaxisStart
          int m_YaxisStart
        • m_yIndex
          int m_yIndex