Serialized Form

  • Package weka.associations

    • Class weka.associations.AbstractAssociator

      class AbstractAssociator extends Object implements Serializable
      serialVersionUID:
      -3017644543382432070L

      • Serialized Fields

        • m_DoNotCheckCapabilities
          boolean m_DoNotCheckCapabilities
          Whether capabilities should not be checked

    • Class weka.associations.Apriori

      class Apriori extends AbstractAssociator implements Serializable
      serialVersionUID:
      3277498842319212687L

      • Serialized Fields

        • m_allTheRules
          ArrayList<Object>[] 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
          ArrayList<Hashtable<ItemSet,Integer>> 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
          ArrayList<ArrayList<Object>> 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_toStringDelimiters
          String m_toStringDelimiters
          ToString delimiters, if any
        • m_treatZeroAsMissing
          boolean m_treatZeroAsMissing
          Treat zeros as missing (rather than a value in their own right)
        • 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.AssociationRules

      class AssociationRules extends Object implements Serializable
      serialVersionUID:
      8889198755948056749L

      • Serialized Fields

    • Class weka.associations.BinaryItem

      class BinaryItem extends NominalItem implements Serializable
      serialVersionUID:
      -3372941834914147669L

    • Class weka.associations.DefaultAssociationRule

      class DefaultAssociationRule extends AssociationRule implements Serializable
      serialVersionUID:
      -661269018702294489L

      • Serialized Fields

        • m_consequence
          Collection<Item> m_consequence
          The consequence of the rule
        • m_consequenceSupport
          int m_consequenceSupport
          The support for the consequence
        • m_metricType
          DefaultAssociationRule.METRIC_TYPE m_metricType
          The metric type for this rule
        • m_premise
          Collection<Item> 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.FilteredAssociationRules

      class FilteredAssociationRules extends AssociationRules implements Serializable
      serialVersionUID:
      -4223408305476916955L

    • 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
          DefaultAssociationRule.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_numInstances
          int m_numInstances
          The number of instances in the data
        • m_numRulesToFind
          int m_numRulesToFind
          The number of rules to find
        • m_offDiskReportingFrequency
          int m_offDiskReportingFrequency
          When processing data off of disk report progress this frequently (number of instances).
        • m_positiveIndex
          int m_positiveIndex
          The index (1 based) of binary attributes to treat as the positive value
        • m_rules
          List<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.FPTreeNode

      class FPTreeNode extends Object implements Serializable
      serialVersionUID:
      4396315323673737660L

      • Serialized Fields

        • m_children
          Map<BinaryItem,weka.associations.FPGrowth.FPTreeNode> m_children
          the children of this node
        • m_ID
          int m_ID
          ID (for graphing the tree)
        • m_item
          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

        • m_items
          ArrayList<BinaryItem> m_items
          The list of items in the item set
        • m_support
          int m_support
          the support of this item set

    • Class weka.associations.FPGrowth.FrequentItemSets

      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.Item

      class Item extends Object implements Serializable
      serialVersionUID:
      -430198211081183575L

      • Serialized Fields

        • m_attribute
          Attribute m_attribute
          The attribute that backs this item
        • m_frequency
          int m_frequency
          The frequency of this item

    • 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_secondaryCounter
          int m_secondaryCounter
          Holds support of consequence only in the case where this ItemSet is a consequence of a rule (as m_counter in this case actually holds the support of the rule as a whole, i.e. premise and consequence)
        • 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.NominalItem

      class NominalItem extends Item implements Serializable
      serialVersionUID:
      2182122099990462066L

      • Serialized Fields

        • m_valueIndex
          int m_valueIndex
          The index of the value considered to be positive

    • Class weka.associations.NumericItem

      class NumericItem extends Item implements Serializable
      serialVersionUID:
      -7869433770765864800L

      • Serialized Fields

        • m_comparison
          NumericItem.Comparison m_comparison
          The comparison operator
        • m_splitPoint
          double m_splitPoint
          The numeric test

    • Class weka.associations.SingleAssociatorEnhancer

      class SingleAssociatorEnhancer extends AbstractAssociator implements Serializable
      serialVersionUID:
      -3665885256363525164L

      • Serialized Fields

        • m_Associator
          Associator m_Associator
          The base associator to use

  • Package weka.attributeSelection

    • Class weka.attributeSelection.ASEvaluation

      class ASEvaluation extends Object implements Serializable
      serialVersionUID:
      2091705669885950849L

      • Serialized Fields

        • m_DoNotCheckCapabilities
          boolean m_DoNotCheckCapabilities
          Whether capabilities should not be checked

    • 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

    • 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 ArrayList<BestFirst.Link2> 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_debug
          boolean m_debug
          Output debugging info
        • 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 predictive 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_numEntries
          int m_numEntries
          Number of entries in the correlation matrix
        • m_numFilled
          AtomicInteger m_numFilled
          Number of correlations actually computed
        • m_numInstances
          int m_numInstances
          Number of instances in the training data
        • m_numThreads
          int m_numThreads
          The number of threads used to compute the correlation matrix. Used when correlation matrix is precomputed
        • m_poolSize
          int m_poolSize
          The size of the thread pool. Usually set equal to the number of CPUs or CPU cores available
        • m_preComputeCorrelationMatrix
          boolean m_preComputeCorrelationMatrix
        • 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.ClassifierAttributeEval

      class ClassifierAttributeEval extends ASEvaluation implements Serializable
      serialVersionUID:
      2442390690522602284L

      • Serialized Fields

        • m_executionSlots
          int m_executionSlots
          The number of attributes to evaluate in parallel
        • m_leaveOneOut
          boolean m_leaveOneOut
          Whether to leave each attribute out in turn and evaluate rather than just evaluate on each attribute
        • m_merit
          double[] m_merit
          Holds the merit scores for each attribute
        • m_trainInstances
          Instances m_trainInstances
          The training instances.
        • m_wrapperSetup
          String m_wrapperSetup
          Holds toString() info for the wrapper
        • m_wrapperTemplate
          WrapperSubsetEval m_wrapperTemplate
          The configured underlying Wrapper instance to use for evaluation

    • Class weka.attributeSelection.ClassifierSubsetEval

      class ClassifierSubsetEval extends HoldOutSubsetEvaluator implements Serializable
      serialVersionUID:
      7532217899385278710L

      • Serialized Fields

        • m_Classifier
          Classifier m_Classifier
          Holds the classifier used when evaluating single hold-out instances - this is used by RaceSearch and the trained classifier may need to persist between calls to that particular method.
        • m_ClassifierTemplate
          Classifier m_ClassifierTemplate
          holds the template classifier to use for error estimates
        • m_classIndex
          int m_classIndex
          class index
        • m_evaluationMeasure
          Tag m_evaluationMeasure
          The evaluation measure to use
        • m_holdOutFile
          File m_holdOutFile
          the file that contains hold out/test instances
        • m_holdOutInstances
          Instances m_holdOutInstances
          the instances to test on
        • m_IRClassVal
          int m_IRClassVal
          If >= 0, and an IR metric is being used, then evaluate with respect to this class value (0-based index)
        • m_IRClassValS
          String m_IRClassValS
          User supplied option for IR class value (either name or 1-based index)
        • m_numAttribs
          int m_numAttribs
          number of attributes in the training data
        • m_seed
          int m_seed
          Seed for randomizing prior to splitting training data
        • m_splitPercent
          String m_splitPercent
          The split to use if doing a percentage split
        • m_trainingInstances
          Instances m_trainingInstances
          training instances
        • m_usePercentageSplit
          boolean m_usePercentageSplit
          Whether to hold out a percentage of the training data
        • m_useTraining
          boolean m_useTraining
          evaluate on training data rather than separate hold out/test set

    • Class weka.attributeSelection.CorrelationAttributeEval

      class CorrelationAttributeEval extends ASEvaluation implements Serializable
      serialVersionUID:
      -4931946995055872438L

      • Serialized Fields

        • m_correlations
          double[] m_correlations
          The correlation for each attribute
        • m_detailedOutput
          boolean m_detailedOutput
          Whether to output detailed (per value) correlation for nominal attributes
        • m_detailedOutputBuff
          StringBuffer m_detailedOutputBuff
          Holds the detailed output info

    • 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_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.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_debug
          boolean m_debug
          Print debugging output
        • 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_poolSize
          int m_poolSize
        • 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 separate value

    • Class weka.attributeSelection.OneRAttributeEval

      class OneRAttributeEval extends ASEvaluation implements Serializable
      serialVersionUID:
      4386514823886856980L

      • Serialized Fields

        • 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_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
          no.uib.cipr.matrix.UpperSymmDenseMatrix 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.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.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.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 separate value
        • 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_evaluationMeasure
          Tag m_evaluationMeasure
          The evaluation measure to use
        • m_folds
          int m_folds
          number of folds to use for cross validation
        • m_IRClassVal
          int m_IRClassVal
          If >= 0, and an IR metric is being used, then evaluate with respect to this class value (0-based index)
        • m_IRClassValS
          String m_IRClassValS
          User supplied option for IR class value (either name or 1-based index)
        • m_numAttribs
          int m_numAttribs
          number of attributes 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

    • Class weka.attributeSelection.WrapperSubsetEval.PluginTag

      class PluginTag extends Tag implements Serializable
      serialVersionUID:
      -6978438858413428382L

      • Serialized Fields

        • m_metric
          AbstractEvaluationMetric m_metric
          The metric object itself
        • m_statisticName
          String m_statisticName
          The particular statistic from the metric that this tag pertains to

  • Package weka.classifiers

  • Package weka.classifiers.bayes

    • Class weka.classifiers.bayes.BayesNet

      class BayesNet extends AbstractClassifier 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_NumInstances
          int m_NumInstances
          The number of instances the model was built from
        • 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.NaiveBayes

      class NaiveBayes extends AbstractClassifier 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 AbstractClassifier implements Serializable
      serialVersionUID:
      5932177440181257085L

      • Serialized Fields

        • m_headerInfo
          Instances m_headerInfo
          copy of header information for use in toString method
        • 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.NaiveBayesMultinomialText

      class NaiveBayesMultinomialText extends AbstractClassifier implements Serializable
      serialVersionUID:
      2139025532014821394L

      • Serialized Fields

        • m_data
          Instances m_data
          The header of the training data
        • m_leplace
          double m_leplace
          Leplace-like correction factor for zero frequency
        • m_lnorm
          double m_lnorm
          The L-norm to use
        • m_lowercaseTokens
          boolean m_lowercaseTokens
          Whether or not to convert all tokens to lowercase
        • m_minWordP
          double m_minWordP
          Only consider dictionary words (features) that occur at least this many times
        • m_norm
          double m_norm
          The length that each document vector should have in the end
        • m_normalize
          boolean m_normalize
          normailize document length ?
        • m_numModels
          int m_numModels
        • m_periodicP
          int m_periodicP
          The number of training instances at which to periodically prune the dictionary of min frequency words. Empty or null string indicates don't prune
        • m_probOfClass
          double[] m_probOfClass
        • m_probOfWordGivenClass
          Map<Integer,LinkedHashMap<String,weka.classifiers.bayes.NaiveBayesMultinomialText.Count>> m_probOfWordGivenClass
        • m_stemmer
          Stemmer m_stemmer
          The stemming algorithm.
        • m_StopwordsHandler
          StopwordsHandler m_StopwordsHandler
          Stopword handler to use.
        • m_t
          double m_t
          Holds the current instance number
        • m_tokenizer
          Tokenizer m_tokenizer
          The tokenizer to use
        • m_wordFrequencies
          boolean m_wordFrequencies
          Use word frequencies rather than bag-of-words if true
        • m_wordsPerClass
          double[] m_wordsPerClass

    • Class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable

      class NaiveBayesMultinomialUpdateable extends NaiveBayesMultinomial implements Serializable
      serialVersionUID:
      -7204398796974263186L

      • Serialized Fields

        • m_wordsPerClass
          double[] m_wordsPerClass
          the number of words per class.

    • Class weka.classifiers.bayes.NaiveBayesUpdateable

      class NaiveBayesUpdateable extends NaiveBayes implements Serializable
      serialVersionUID:
      -5354015843807192221L

  • 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
          ArrayList<double[]> m_fMarginP
          marginal distributions *
        • m_nCurrentEditAction
          int m_nCurrentEditAction
          current action in undo stack
        • m_nEvidence
          ArrayList<Integer> m_nEvidence
          evidence values, used for evidence propagation *
        • m_nPositionX
          ArrayList<Integer> m_nPositionX
          location of nodes, used for graph drawing *
        • m_nPositionY
          ArrayList<Integer> m_nPositionY
        • m_nSavedPointer
          int m_nSavedPointer
          action that the network is saved
        • m_undoStack
          ArrayList<weka.classifiers.bayes.net.EditableBayesNet.UndoAction> 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<MarginCalculator.JunctionTreeNode> 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
        • m_sInitalBIFFile
          String m_sInitalBIFFile
          File name containing initial network structure. This can be used as starting point for structure search It will be ignored if not speficied. When specified, it overrides the InitAsNaivBayes flag.

  • 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.AbstractEvaluationMetric

      class AbstractEvaluationMetric extends Object implements Serializable
      serialVersionUID:
      -924507718482386887L

      • Serialized Fields

        • m_baseEvaluation
          Evaluation m_baseEvaluation
          Base evaluation object for subclasses to access for statistics. IMPORTANT: subclasses should treat this object as read-only

    • Exception weka.classifiers.evaluation.AbstractEvaluationMetric.UnknownStatisticException

      class UnknownStatisticException extends IllegalArgumentException implements Serializable
      serialVersionUID:
      -8787045492227999839L

    • Class weka.classifiers.evaluation.AggregateableEvaluation

      class AggregateableEvaluation extends Evaluation implements Serializable
      serialVersionUID:
      8734675926526110924L

    • 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.Evaluation

      class Evaluation extends Object implements Serializable
      serialVersionUID:
      -7010314486866816271L

      • Serialized Fields

        • m_ClassIsNominal
          boolean m_ClassIsNominal
          Is the class nominal or numeric?
        • m_ClassNames
          String[] m_ClassNames
          The names of the classes.
        • m_ClassPriors
          double[] m_ClassPriors
          The prior probabilities of the classes.
        • m_ClassPriorsSum
          double m_ClassPriorsSum
          The sum of counts for priors.
        • m_ComplexityStatisticsAvailable
          boolean m_ComplexityStatisticsAvailable
          Whether complexity statistics are available.
        • m_ConfLevel
          double m_ConfLevel
          The confidence level used for coverage statistics.
        • m_ConfusionMatrix
          double[][] m_ConfusionMatrix
          Array for storing the confusion matrix.
        • m_Correct
          double m_Correct
          The weight of all correctly classified instances.
        • m_CostMatrix
          CostMatrix m_CostMatrix
          The cost matrix (if given).
        • m_CoverageStatisticsAvailable
          boolean m_CoverageStatisticsAvailable
          Whether coverage statistics are available.
        • m_DiscardPredictions
          boolean m_DiscardPredictions
          whether to discard predictions (and save memory).
        • m_Header
          Instances m_Header
          The header of the training set.
        • m_Incorrect
          double m_Incorrect
          The weight of all incorrectly classified instances.
        • m_MarginCounts
          double[] m_MarginCounts
          Cumulative margin distribution.
        • m_MaxTarget
          double m_MaxTarget
          Maximum target value.
        • m_metricsToDisplay
          List<String> m_metricsToDisplay
          The list of metrics to display in the output
        • m_MinTarget
          double m_MinTarget
          Minimum target value.
        • m_MissingClass
          double m_MissingClass
          The weight of all instances that had no class assigned to them.
        • m_NoPriors
          boolean m_NoPriors
          enables/disables the use of priors, e.g., if no training set is present in case of de-serialized schemes.
        • m_NumClasses
          int m_NumClasses
          The number of classes.
        • m_NumFolds
          int m_NumFolds
          The number of folds for a cross-validation.
        • m_NumTrainClassVals
          int m_NumTrainClassVals
          Number of non-missing class training instances seen.
        • m_pluginMetrics
          List<AbstractEvaluationMetric> m_pluginMetrics
          Holds plugin evaluation metrics
        • m_Predictions
          ArrayList<Prediction> m_Predictions
          The list of predictions that have been generated (for computing AUC).
        • m_PriorEstimator
          UnivariateKernelEstimator m_PriorEstimator
          Numeric class estimator for prior.
        • m_SumAbsErr
          double m_SumAbsErr
          Sum of absolute errors.
        • m_SumClass
          double m_SumClass
          Sum of class values.
        • m_SumClassPredicted
          double m_SumClassPredicted
          Sum of predicted * class values.
        • m_SumErr
          double m_SumErr
          Sum of errors.
        • m_SumKBInfo
          double m_SumKBInfo
          Total Kononenko & Bratko Information.
        • m_SumPredicted
          double m_SumPredicted
          Sum of predicted values.
        • m_SumPriorAbsErr
          double m_SumPriorAbsErr
          Sum of absolute errors of the prior.
        • m_SumPriorEntropy
          double m_SumPriorEntropy
          Total entropy of prior predictions.
        • m_SumPriorSqrErr
          double m_SumPriorSqrErr
          Sum of absolute errors of the prior.
        • m_SumSchemeEntropy
          double m_SumSchemeEntropy
          Total entropy of scheme predictions.
        • m_SumSqrClass
          double m_SumSqrClass
          Sum of squared class values.
        • m_SumSqrErr
          double m_SumSqrErr
          Sum of squared errors.
        • m_SumSqrPredicted
          double m_SumSqrPredicted
          Sum of squared predicted values.
        • m_TotalCost
          double m_TotalCost
          The total cost of predictions (includes instance weights).
        • m_TotalCoverage
          double m_TotalCoverage
          Total coverage of test cases at the given confidence level.
        • m_TotalSizeOfRegions
          double m_TotalSizeOfRegions
          Total size of predicted regions at the given confidence level.
        • m_TrainClassVals
          double[] m_TrainClassVals
          Array containing all numeric training class values seen.
        • m_TrainClassWeights
          double[] m_TrainClassWeights
          Array containing all numeric training class weights.
        • m_Unclassified
          double m_Unclassified
          The weight of all unclassified instances.
        • m_WithClass
          double m_WithClass
          The weight of all instances that had a class assigned to them.

    • 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_PredictionIntervals
          double[][] m_PredictionIntervals
          the prediction intervals.
        • m_Weight
          double m_Weight
          The weight assigned to this prediction.

  • Package weka.classifiers.evaluation.output.prediction

  • Package weka.classifiers.functions

    • Class weka.classifiers.functions.GaussianProcesses

      class GaussianProcesses extends RandomizableClassifier implements Serializable
      serialVersionUID:
      -8620066949967678545L

      • Serialized Fields

        • m_actualKernel
          Kernel m_actualKernel
          Actual kernel object to use
        • m_Alin
          double m_Alin
          The parameters of the linear transformation realized by the filter on the class attribute
        • m_avg_target
          double m_avg_target
          The training data.
        • m_Blin
          double m_Blin
        • m_checksTurnedOff
          boolean m_checksTurnedOff
          Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class.
        • m_delta
          double m_delta
          Gaussian Noise Value.
        • m_deltaSquared
          double m_deltaSquared
          The squared 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
          Template of kernel to use
        • m_L
          no.uib.cipr.matrix.Matrix m_L
          (negative) covariance matrix in symmetric matrix representation
        • 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
          no.uib.cipr.matrix.Vector m_t
          The vector of target values.
        • m_weights
          double[] m_weights
          The weight of the training instances.

    • Class weka.classifiers.functions.LinearRegression

      class LinearRegression extends AbstractClassifier implements Serializable
      serialVersionUID:
      -3364580862046573747L

      • Serialized Fields

        • 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_df
          int m_df
          The degrees of freedom of the regression model
        • m_EliminateColinearAttributes
          boolean m_EliminateColinearAttributes
          Try to eliminate correlated attributes?
        • m_FStat
          double m_FStat
          The F-statistic of the regression model
        • m_isZeroR
          boolean m_isZeroR
          True if the model is a zero R one
        • m_Means
          double[] m_Means
          The attributes means
        • m_Minimal
          boolean m_Minimal
          Conserve memory?
        • m_MissingFilter
          ReplaceMissingValues m_MissingFilter
          The filter for removing missing values.
        • m_ModelBuilt
          boolean m_ModelBuilt
          Model already built?
        • m_outputAdditionalStats
          boolean m_outputAdditionalStats
          Whether to output additional statistics such as std. dev. of coefficients and t-stats
        • m_Ridge
          double m_Ridge
          The ridge parameter
        • m_RSquared
          double m_RSquared
          The R-squared value of the regression model
        • m_RSquaredAdj
          double m_RSquaredAdj
          The adjusted R-squared value of the regression model
        • m_SelectedAttributes
          boolean[] m_SelectedAttributes
          Which attributes are relevant?
        • m_StdDevs
          double[] m_StdDevs
          The attribute standard deviations
        • m_StdErrorOfCoef
          double[] m_StdErrorOfCoef
          Array for storing the standard error of each coefficient
        • 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.
        • m_TStats
          double[] m_TStats
          Array for storing the t-statistic of each coefficient
        • m_useQRDecomposition
          boolean m_useQRDecomposition
          Use QR decomposition

    • Class weka.classifiers.functions.Logistic

      class Logistic extends AbstractClassifier 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_doNotStandardizeAttributes
          boolean m_doNotStandardizeAttributes
          Whether to turn of standardization of attributes.
        • 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_numModels
          int m_numModels
        • 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
          The header information in the training data.
        • m_useConjugateGradientDescent
          boolean m_useConjugateGradientDescent
          Whether to use conjugate gradient descent rather than BFGS updates.

    • Class weka.classifiers.functions.MultilayerPerceptron

      class MultilayerPerceptron extends AbstractClassifier implements Serializable
      serialVersionUID:
      -5990607817048210779L

      • Serialized Fields

        • bestError
          double bestError
        • driftOff
          double driftOff
          Drift off counter
        • lastRight
          double lastRight
          To keep track of error
        • 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_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_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_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_numItsPerformed
          int m_numItsPerformed
          Number of iterations (epochs) performed in this session of iterating
        • 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_resume
          boolean m_resume
          Whether to allow training to continue at a later point after the initial model is built.
        • m_selected
          ArrayList<NeuralConnection> 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_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
        • numInVal
          int numInVal
          The number of instances in the validation set (if any)
        • originalFormatData
          Instances originalFormatData
          Data in original format (in case learning rate gets reset
        • totalValWeight
          double totalValWeight
          Total weight of the instances in the validation set (if any)
        • totalWeight
          double totalWeight
          Total weight of the instances in the training set
        • valSet
          Instances valSet
          The instances in the validation set (if any)

    • 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.SGD

      class SGD extends RandomizableClassifier 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_epsilon
          double m_epsilon
          The epsilon parameter for epsilon insensitive and Huber loss
        • m_lambda
          double m_lambda
          The regularization parameter
        • m_learningRate
          double m_learningRate
          The learning rate
        • m_loss
          int m_loss
          The current loss function to minimize
        • m_nominalToBinary
          Filter m_nominalToBinary
          Convert nominal attributes to numerically coded binary ones. Uses supervised NominalToBinary in the batch learning case
        • m_normalize
          Normalize m_normalize
          Normalize the training data
        • m_numInstances
          double m_numInstances
          The number of training instances
        • m_numModels
          int m_numModels
        • 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.SGDText

      class SGDText extends RandomizableClassifier implements Serializable
      serialVersionUID:
      7200171484002029584L

      • Serialized Fields

        • m_bias
          double m_bias
          Holds the bias term
        • m_data
          Instances m_data
          The header of the training data
        • m_dictionary
          LinkedHashMap<String,SGDText.Count> m_dictionary
          The dictionary (and term weights)
        • m_epochs
          int m_epochs
          The number of epochs to perform (batch learning). Total iterations is m_epochs * num instances
        • m_fitLogistic
          boolean m_fitLogistic
          True if a logistic regression is to be fit to the output of the SVM for producing probability estimates
        • m_fitLogisticStructure
          Instances m_fitLogisticStructure
        • m_lambda
          double m_lambda
          The regularization parameter
        • m_learningRate
          double m_learningRate
          The learning rate
        • m_lnorm
          double m_lnorm
          The L-norm to use
        • m_loss
          int m_loss
          The current loss function to minimize
        • m_lowercaseTokens
          boolean m_lowercaseTokens
          Whether or not to convert all tokens to lowercase
        • m_minAbsCoefficient
          double m_minAbsCoefficient
          Prune terms from the model that have a coefficient smaller than this.
        • m_minWordP
          double m_minWordP
          Only consider dictionary words (features) that occur at least this many times.
        • m_norm
          double m_norm
          The length that each document vector should have in the end
        • m_normalize
          boolean m_normalize
          Whether to normalized document length or not
        • m_numInstances
          double m_numInstances
          The number of training instances
        • m_numModels
          int m_numModels
        • m_periodicP
          int m_periodicP
          The number of training instances at which to periodically prune the dictionary of min frequency words. Empty or null string indicates don't prune
        • m_stemmer
          Stemmer m_stemmer
          The stemming algorithm.
        • m_StopwordsHandler
          StopwordsHandler m_StopwordsHandler
          Stopword handler to use.
        • m_svmProbs
          SGD m_svmProbs
          Used for producing probabilities for SVM via SGD logistic regression
        • m_t
          double m_t
          Holds the current iteration number
        • m_tokenizer
          Tokenizer m_tokenizer
          The tokenizer to use
        • m_wordFrequencies
          boolean m_wordFrequencies
          Use word frequencies rather than bag-of-words if true

    • Class weka.classifiers.functions.SGDText.Count

      class Count extends Object implements Serializable
      serialVersionUID:
      2104201532017340967L

      • Serialized Fields

        • m_count
          double m_count
        • m_weight
          double m_weight

    • Class weka.classifiers.functions.SimpleLinearRegression

      class SimpleLinearRegression extends AbstractClassifier 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_classMeanForMissing
          double m_classMeanForMissing
          The class mean for missing values
        • m_df
          int m_df
          Degrees of freedom, used in statistical calculations
        • m_fstat
          double m_fstat
          F-statistic for the regression
        • m_intercept
          double m_intercept
          The intercept
        • m_outputAdditionalStats
          boolean m_outputAdditionalStats
          Whether to output additional statistics such as std. dev. of coefficients and t-stats
        • m_rsquared
          double m_rsquared
          R^2 value for the regression
        • m_rsquaredAdj
          double m_rsquaredAdj
          Adjusted R^2 value for the regression
        • m_seIntercept
          double m_seIntercept
          standard error of the intercept
        • m_seSlope
          double m_seSlope
          standard error of the slope
        • m_slope
          double m_slope
          The slope
        • m_suppressErrorMessage
          boolean m_suppressErrorMessage
          If true, suppress error message if no useful attribute was found
        • m_tstatIntercept
          double m_tstatIntercept
          t-statistic of the intercept
        • m_tstatSlope
          double m_tstatSlope
          t-statistic of the slope

    • Class weka.classifiers.functions.SimpleLogistic

      class SimpleLogistic extends AbstractClassifier 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 AbstractClassifier implements Serializable
      serialVersionUID:
      -6585883636378691736L

      • Serialized Fields

        • m_C
          double m_C
          The complexity parameter.
        • m_calibrator
          Classifier m_calibrator
          Determines the calibrator model to use for probability estimate
        • 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_fitCalibratorModels
          boolean m_fitCalibratorModels
          Whether calibrator 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_calibrationDataHeader
          Instances m_calibrationDataHeader
          Reference to the header information for the calibration data
        • m_calibrator
          Classifier m_calibrator
          Stores calibrator model for probability estimate
        • 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 < m_alpha[i] < C}
        • m_I1
          SMOset m_I1
          {i: m_class[i] = 1, m_alpha[i] = 0}
        • m_I2
          SMOset m_I2
          {i: m_class[i] = -1, m_alpha[i] =C}
        • m_I3
          SMOset m_I3
          {i: m_class[i] = 1, m_alpha[i] = C}
        • m_I4
          SMOset m_I4
          {i: m_class[i] = -1, m_alpha[i] = 0}
        • m_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_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_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 AbstractClassifier 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.VotedPerceptron

      class VotedPerceptron extends AbstractClassifier 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

  • 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.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
          This value is now ignored. Checks are always turned off as they are the responsibility of the class using the kernel. We are keeping this to allow deserialization.
        • m_data
          Instances m_data
          The dataset
        • m_Debug
          boolean m_Debug
          enables debugging output
        • m_DoNotCheckCapabilities
          boolean m_DoNotCheckCapabilities
          This value is now ignored. Checks are always turned off as they are the responsibility of the class using the kernel. We are keeping this to allow deserialization.

    • Class weka.classifiers.functions.supportVector.NormalizedPolyKernel

      class NormalizedPolyKernel extends PolyKernel implements Serializable
      serialVersionUID:
      1248574185532130851L

      • Serialized Fields

        • m_diagDotproducts
          double[] m_diagDotproducts
          A cache for the diagonal of the dot product kernel

    • 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
          The gamma parameter for the RBF kernel.
        • m_kernelPrecalc
          double[] m_kernelPrecalc
          The diagonal values of the dot product matrix (name needs to be consistent with J. Lindgren's implementation).

    • 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 < 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 < m_alpha[i] < C || 0 < m_alphaStar[i] < 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.IBk

      class IBk extends AbstractClassifier 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 AbstractClassifier 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.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

  • Package weka.classifiers.meta

    • Class weka.classifiers.meta.AdaBoostM1

      class AdaBoostM1 extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -1178107808933117974L

      • 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_NumItsThisSession
          int m_NumItsThisSession
          Number of iterations performed in this session of iterating
        • m_RandomInstance
          Random m_RandomInstance
          Random number generator to be used for resampling
        • m_resume
          boolean m_resume
          Whether to allow training to continue at a later point after the initial model is built.
        • m_TrainingData
          Instances m_TrainingData
          The (weighted) training data
        • 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_Classifiers
          ArrayList<Classifier> m_Classifiers
          ArrayList for storing the generated base classifiers. Note: we are hiding the variable from IteratedSingleClassifierEnhancer
        • m_Data
          Instances m_Data
          The working data
        • m_Diff
          double m_Diff
          The improvement in the sum of (absolute or squared) residuals.
        • m_Error
          double m_Error
          The sum of (absolute or squared) residuals.
        • m_InitialPrediction
          double m_InitialPrediction
          The mean or median
        • m_MinimizeAbsoluteError
          boolean m_MinimizeAbsoluteError
          Whether to minimise absolute error instead of squared error.
        • m_numItsPerformed
          int m_numItsPerformed
          Number of iterations performed in this session of iterating
        • m_resume
          boolean m_resume
          Whether to allow training to continue at a later point after the initial model is built.
        • m_shrinkage
          double m_shrinkage
          Shrinkage (Learning rate). Default = no shrinkage.
        • m_SuitableData
          boolean m_SuitableData
          whether we have suitable data or nor (if only mean/mode is used)

    • Class weka.classifiers.meta.AttributeSelectedClassifier

      class AttributeSelectedClassifier extends SingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -1151805453487947577L

      • 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

      class Bagging extends RandomizableParallelIteratedSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -115879962237199703L

      • 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_classifiersCache
          List<Classifier> m_classifiersCache
        • m_data
          Instances m_data
          Reference to the training data
        • m_inBag
          boolean[][] m_inBag
          Used to indicate whether an instance is in a bag or not
        • m_Numeric
          boolean m_Numeric
          Whether class is numeric.
        • m_OutOfBagEvaluationObject
          Evaluation m_OutOfBagEvaluationObject
          The evaluation object holding the out of bag error, etc.
        • m_OutputOutOfBagComplexityStatistics
          boolean m_OutputOutOfBagComplexityStatistics
          Whether to output complexity-based statistics when OOB-evaluation is performed.
        • m_printClassifiers
          boolean m_printClassifiers
          Whether to print individual ensemble members in output.
        • m_random
          Random m_random
          Random number generator
        • m_RepresentUsingWeights
          boolean m_RepresentUsingWeights
          Whether to represent copies of instances using weights rather than explicitly
        • m_StoreOutOfBagPredictions
          boolean m_StoreOutOfBagPredictions
          Whether to store the out of bag predictions in the evaluation object.

    • 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:
      -110658209263002404L

      • 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
          Vector<weka.classifiers.meta.CVParameterSelection.CVParameter> 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.FilteredClassifier

      class FilteredClassifier extends RandomizableSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -4523450618538717400L

      • Serialized Fields

        • m_DoNotCheckForModifiedClassAttribute
          boolean m_DoNotCheckForModifiedClassAttribute
          Flag that can be set to true if class attribute is not to be checked for modifications by the filer.
        • m_Filter
          Filter m_Filter
          The filter
        • m_FilteredInstances
          Instances m_FilteredInstances
          The instance structure of the filtered instances
        • m_ReorderFiltered
          Reorder m_ReorderFiltered
        • m_ReorderOriginal
          Reorder m_ReorderOriginal
          If the attributes are resampled, we store the filter for this

    • Class weka.classifiers.meta.IterativeClassifierOptimizer

      class IterativeClassifierOptimizer extends RandomizableClassifier implements Serializable
      serialVersionUID:
      -3665485256313525864L

      • Serialized Fields

        • m_bestNumIts
          int m_bestNumIts
          The best number of iterations identified.
        • m_bestResult
          double m_bestResult
          The best value found for the criterion to be optimized.
        • m_classValueIndex
          int m_classValueIndex
          The class value index to use with information retrieval type metrics. < 0 indicates to use the class weighted average version of the metric".
        • m_evalMetric
          String m_evalMetric
          The evaluation metric to use
        • m_IterativeClassifier
          IterativeClassifier m_IterativeClassifier
          The base classifier to use
        • m_lookAheadIterations
          int m_lookAheadIterations
          The number of iterations to look ahead for to find a better optimum.
        • m_NumFolds
          int m_NumFolds
          The number of folds for the cross-validation.
        • m_NumRuns
          int m_NumRuns
          The number of runs for the cross-validation.
        • m_numThreads
          int m_numThreads
          The number of threads to use for parallel building of classifiers.
        • m_poolSize
          int m_poolSize
          The size of the thread pool.
        • m_preserveOrderInPercentageSplitEvaluation
          boolean m_preserveOrderInPercentageSplitEvaluation
          Whether to preserve order when a percentage split evaluation is performed.
        • m_splitPercentage
          double m_splitPercentage
          The percentage of data to be used for training (if 0, k-fold cross-validation is used).
        • m_StepSize
          int m_StepSize
          The steps size determining when evaluations happen.
        • m_thresholds
          double[] m_thresholds
          The thresholds to be used for classification, if the metric implements ThresholdProducingMetric.
        • m_UseAverage
          boolean m_UseAverage
          Whether to use average.

    • Class weka.classifiers.meta.LogitBoost

      class LogitBoost extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -1105660358715833753L

      • Serialized Fields

        • m_ClassAttribute
          Attribute m_ClassAttribute
          The actual class attribute (for getting class names)
        • m_Classifiers
          ArrayList<Classifier[]> m_Classifiers
          ArrayList for storing the generated base classifiers. Note: we are hiding the variable from IteratedSingleClassifierEnhancer
        • m_data
          Instances m_data
          The training data.
        • m_InitialFs
          double[] m_InitialFs
          The initial F scores (0 by default)
        • m_logLikelihood
          double m_logLikelihood
          The current loglikelihood.
        • m_NumClasses
          int m_NumClasses
          The number of classes
        • m_NumericClassData
          Instances m_NumericClassData
          Dummy dataset with a numeric class
        • m_NumGenerated
          int m_NumGenerated
          The number of successfully generated base classifiers.
        • m_NumItsPerformed
          int m_NumItsPerformed
          Number of iterations performed in this session of iterating
        • m_numThreads
          int m_numThreads
          The number of threads to use at prediction time in batch prediction.
        • m_Offset
          double m_Offset
          The value by which the actual target value for the true class is offset.
        • m_poolSize
          int m_poolSize
          The size of the thread pool.
        • m_Precision
          double m_Precision
          The threshold on the improvement of the likelihood
        • m_probs
          double[][] m_probs
          The probabilities used during the training process.
        • m_RandomInstance
          Random m_RandomInstance
          The random number generator used
        • m_resume
          boolean m_resume
          Whether to allow training to continue at a later point after the initial model is built.
        • m_Shrinkage
          double m_Shrinkage
          The value of the shrinkage parameter
        • m_sumOfWeights
          double m_sumOfWeights
          The total weight of the data.
        • m_trainFs
          double[][] m_trainFs
          The F scores used during the training process.
        • m_trainYs
          double[][] m_trainYs
          The y values used during the training process.
        • m_UseEstimatedPriors
          boolean m_UseEstimatedPriors
          Whether to start with class priors estimated from the training data
        • 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
        • m_zMax
          double m_zMax
          The Z max value to use

    • 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_logLossDecoding
          boolean m_logLossDecoding
          True if log loss decoding is to be used for random and exhaustive codes.
        • 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.MultiClassClassifierUpdateable

      class MultiClassClassifierUpdateable extends MultiClassClassifier implements Serializable
      serialVersionUID:
      -1619685269774366430L

    • 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.RandomCommittee

      class RandomCommittee extends RandomizableParallelIteratedSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      -9204394360557300093L

      • Serialized Fields

    • Class weka.classifiers.meta.RandomizableFilteredClassifier

      class RandomizableFilteredClassifier extends FilteredClassifier implements Serializable
      serialVersionUID:
      -4523466618555717333L

    • Class weka.classifiers.meta.RandomSubSpace

      class RandomSubSpace extends RandomizableParallelIteratedSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      1278172513912424947L

      • Serialized Fields

        • m_data
          Instances m_data
          Training data
        • 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_ClassCounts
          int[] m_ClassCounts
          The class counts for each Discretized class interval.
        • 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_Estimator
          UnivariateDensityEstimator m_Estimator
          Which estimator to use (default: histogram)
        • m_MinimizeAbsoluteError
          boolean m_MinimizeAbsoluteError
          Whether to minimize absolute error, rather than squared error.
        • m_NewTargetValues
          int[] m_NewTargetValues
          The converted target values in the training data
        • m_NumBins
          int m_NumBins
          The number of discretization intervals.
        • m_OldIndexToNewIndex
          int[] m_OldIndexToNewIndex
          Mapping to convert indices in case empty bins are deleted.
        • m_OriginalTargetValues
          double[] m_OriginalTargetValues
          The original target values in the training data
        • m_UseEqualFrequency
          boolean m_UseEqualFrequency
          Use equal-frequency binning

    • Class weka.classifiers.meta.Stacking

      class Stacking extends RandomizableParallelMultipleClassifiersCombiner 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.Vote

      class Vote extends RandomizableMultipleClassifiersCombiner implements Serializable
      serialVersionUID:
      -637891196294399624L

      • Serialized Fields

        • m_classifiersToLoad
          List<String> m_classifiersToLoad
          List of file paths to serialized models to load
        • m_CombinationRule
          int m_CombinationRule
          Combination Rule variable
        • m_dontPrintModels
          boolean m_dontPrintModels
          Print the individual models in the output
        • m_preBuiltClassifiers
          List<Classifier> m_preBuiltClassifiers
          List of de-serialized pre-built classifiers to include in the ensemble
        • m_structure
          Instances m_structure
          Structure of the training data

    • Class weka.classifiers.meta.WeightedInstancesHandlerWrapper

      class WeightedInstancesHandlerWrapper extends RandomizableSingleClassifierEnhancer implements Serializable
      serialVersionUID:
      2980789213434466135L

      • Serialized Fields

        • m_ForceResampleWithWeights
          boolean m_ForceResampleWithWeights
          whether to force resampling with weights.

  • Package weka.classifiers.misc

    • Class weka.classifiers.misc.InputMappedClassifier

      class InputMappedClassifier extends SingleClassifierEnhancer implements Serializable
      serialVersionUID:
      4901630631723287761L

      • Serialized Fields

        • m_ignoreCase
          boolean m_ignoreCase
          Ignore case when matching attribute names and nominal values?
        • m_initialTestStructureKnown
          boolean m_initialTestStructureKnown
          If true, then a call to buildClassifier() will not overwrite any test structure that has been recorded with the current training structure. This is useful for getting a correct mapping report output in toString() after buildClassifier has been called and before any test instance has been seen. Test structure and mapping will get reset if a test instance is received whose structure does not match the recorded test structure.
        • m_modelHeader
          Instances m_modelHeader
          The instances structure used to train the classifier with
        • m_modelPath
          String m_modelPath
          The path to the serialized model to use (if any)
        • m_suppressMappingReport
          boolean m_suppressMappingReport
          Dont output mapping report if set to true
        • m_trim
          boolean m_trim
          Trim white space from both ends of attribute names and nominal values?
        • m_vals
          double[] m_vals
          Holds values for instances constructed for prediction

    • Class weka.classifiers.misc.SerializedClassifier

      class SerializedClassifier extends AbstractClassifier implements Serializable
      serialVersionUID:
      4599593909947628642L

      • Serialized Fields

        • m_ModelFile
          File m_ModelFile
          the file where the serialized model is stored

  • 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 AbstractClassifier 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

    • Class weka.classifiers.pmml.consumer.RuleSetModel

      class RuleSetModel extends PMMLClassifier implements Serializable
      serialVersionUID:
      1993161168811020547L

      • Serialized Fields

        • m_algorithmName
          String m_algorithmName
          The algorithm name (if defined)
        • m_functionType
          weka.classifiers.pmml.consumer.TreeModel.MiningFunction m_functionType
          The mining function
        • m_modelName
          String m_modelName
          The model name (if defined)
        • m_ruleSet
          weka.classifiers.pmml.consumer.RuleSetModel.RuleSet m_ruleSet
          The set of rules

    • Class weka.classifiers.pmml.consumer.SupportVectorMachineModel

      class SupportVectorMachineModel extends PMMLClassifier implements Serializable
      serialVersionUID:
      6225095165118374296L

      • Serialized Fields

        • m_algorithmName
          String m_algorithmName
          The algorithm name (if defined)
        • m_alternateBinaryTargetCategory
          int m_alternateBinaryTargetCategory
          The other class index (in the case of a single binary SVM - PMML 3.2).
        • m_classificationMethod
          weka.classifiers.pmml.consumer.SupportVectorMachineModel.classificationMethod m_classificationMethod
          The classification method (PMML 4.0)
        • m_functionType
          weka.classifiers.pmml.consumer.NeuralNetwork.MiningFunction m_functionType
          The mining function
        • m_kernel
          weka.classifiers.pmml.consumer.SupportVectorMachineModel.Kernel m_kernel
          The kernel function to use
        • m_machines
          List<weka.classifiers.pmml.consumer.SupportVectorMachineModel.SupportVectorMachine> m_machines
          The individual binary SVMs
        • m_modelName
          String m_modelName
          The model name (if defined)
        • m_svmRepresentation
          weka.classifiers.pmml.consumer.SupportVectorMachineModel.SVM_representation m_svmRepresentation
          Do we have support vectors, or just attribute coefficients for a linear machine?
        • m_threshold
          double m_threshold
          PMML 4.0 threshold value
        • m_vectorDictionary
          VectorDictionary m_vectorDictionary
          The dictionary of support vectors

    • Class weka.classifiers.pmml.consumer.TreeModel

      class TreeModel extends PMMLClassifier implements Serializable
      serialVersionUID:
      -2065158088298753129L

      • Serialized Fields

        • m_functionType
          weka.classifiers.pmml.consumer.TreeModel.MiningFunction m_functionType
          The mining function
        • m_missingValuePenalty
          double m_missingValuePenalty
          The missing value penalty (if defined). We don't actually make use of this since we always return full probability distributions.
        • m_missingValueStrategy
          weka.classifiers.pmml.consumer.TreeModel.MissingValueStrategy m_missingValueStrategy
          The missing value strategy
        • m_noTrueChildStrategy
          weka.classifiers.pmml.consumer.TreeModel.NoTrueChildStrategy m_noTrueChildStrategy
          The no true child strategy to use
        • m_root
          weka.classifiers.pmml.consumer.TreeModel.TreeNode m_root
          The root of the tree
        • m_splitCharacteristic
          weka.classifiers.pmml.consumer.TreeModel.SplitCharacteristic m_splitCharacteristic
          The splitting type

  • Package weka.classifiers.rules

    • Class weka.classifiers.rules.DecisionTable

      class DecisionTable extends AbstractClassifier 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<DecisionTableHashKey,double[]> 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.JRip

      class JRip extends AbstractClassifier 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
          ArrayList<double[]> 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
          ArrayList<Rule> m_Ruleset
          The ruleset
        • m_RulesetStats
          ArrayList<RuleStats> 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
          ArrayList<JRip.Antd> 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.OneR

      class OneR extends AbstractClassifier 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 AbstractClassifier implements Serializable
      serialVersionUID:
      8121455039782598361L

      • Serialized Fields

        • m_binarySplits
          boolean m_binarySplits
          Binary splits on nominal attributes?
        • m_CF
          float m_CF
          Confidence level
        • m_doNotMakeSplitPointActualValue
          boolean m_doNotMakeSplitPointActualValue
          Do not relocate split point to actual data value
        • 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?
        • m_useMDLcorrection
          boolean m_useMDLcorrection
          Use MDL correction?

    • 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
          ArrayList<double[]> m_Distributions
          The class distributions predicted by each rule
        • m_Filtered
          ArrayList<Instances[]> m_Filtered
          The set of instances filtered by the ruleset
        • m_Ruleset
          ArrayList<Rule> m_Ruleset
          The specific ruleset in question
        • m_SimpleStats
          ArrayList<double[]> 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 AbstractClassifier 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.DecisionStump

      class DecisionStump extends AbstractClassifier 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.HoeffdingTree

      class HoeffdingTree extends AbstractClassifier implements Serializable
      serialVersionUID:
      7117521775722396251L

      • Serialized Fields

        • m_activeLeafCount
          int m_activeLeafCount
        • m_decisionNodeCount
          int m_decisionNodeCount
        • m_gracePeriod
          double m_gracePeriod
          The number of instances a leaf should observe between split attempts
        • m_header
          Instances m_header
        • m_hoeffdingTieThreshold
          double m_hoeffdingTieThreshold
          Threshold below which a split will be forced to break ties
        • m_inactiveLeafCount
          int m_inactiveLeafCount
        • m_leafStrategy
          int m_leafStrategy
          The leaf prediction strategy to use
        • m_minFracWeightForTwoBranchesGain
          double m_minFracWeightForTwoBranchesGain
          The minimum fraction of weight required down at least two branches for info gain splitting
        • m_nbThreshold
          double m_nbThreshold
          The number of instances (total weight) a leaf should observe before allowing naive Bayes to make predictions
        • m_printLeafModels
          boolean m_printLeafModels
          Print out leaf models in the case of naive Bayes or naive Bayes adaptive leaves
        • m_root
          HNode m_root
        • m_selectedSplitMetric
          int m_selectedSplitMetric
          The splitting metric to use
        • m_splitConfidence
          double m_splitConfidence
          The allowable error in a split decision. Values closer to zero will take longer to decide
        • m_splitMetric
          SplitMetric m_splitMetric

    • Class weka.classifiers.trees.J48

      class J48 extends AbstractClassifier implements Serializable
      serialVersionUID:
      -217733168393644444L

      • Serialized Fields

        • m_binarySplits
          boolean m_binarySplits
          Binary splits on nominal attributes?
        • m_CF
          float m_CF
          Confidence level
        • m_collapseTree
          boolean m_collapseTree
          Collapse tree?
        • m_doNotMakeSplitPointActualValue
          boolean m_doNotMakeSplitPointActualValue
          Do not relocate split point to actual data value
        • 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
        • m_useMDLcorrection
          boolean m_useMDLcorrection
          Use MDL correction?

    • Class weka.classifiers.trees.LMT

      class LMT extends AbstractClassifier implements Serializable
      serialVersionUID:
      -1113212459618104943L

      • Serialized Fields

        • m_convertNominal
          boolean m_convertNominal
          convert nominal attributes to binary ?
        • m_doNotMakeSplitPointActualValue
          boolean m_doNotMakeSplitPointActualValue
          Do not relocate split point to actual data value
        • 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.RandomForest

      class RandomForest extends Bagging implements Serializable
      serialVersionUID:
      1116839470751428698L

      • Serialized Fields

        • m_computeAttributeImportance
          boolean m_computeAttributeImportance
          True to compute attribute importance

    • Class weka.classifiers.trees.RandomTree

      class RandomTree extends AbstractClassifier implements Serializable
      serialVersionUID:
      -9051119597407396024L

      • Serialized Fields

        • m_AllowUnclassifiedInstances
          boolean m_AllowUnclassifiedInstances
          Whether unclassified instances are allowed
        • m_BreakTiesRandomly
          boolean m_BreakTiesRandomly
          Whether to break ties randomly.
        • m_computeImpurityDecreases
          boolean m_computeImpurityDecreases
          Whether to store the impurity decrease/gain sum
        • m_impurityDecreasees
          double[][] m_impurityDecreasees
          Indexed by attribute, each two element array contains impurity decrease/gain sum in first element and count in the second
        • 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_MinVarianceProp
          double m_MinVarianceProp
          The minimum proportion of the total variance (over all the data) required for split.
        • 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 in the nominal case. Holds the mean in the numeric case.
        • m_Distribution
          double[] m_Distribution
          Holds the sum of squared errors and the weight in the numeric case.
        • 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 AbstractClassifier implements Serializable
      serialVersionUID:
      -9216785998198681299L

      • Serialized Fields

        • m_InitialCount
          double m_InitialCount
          The initial class count
        • 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_SpreadInitialCount
          boolean m_SpreadInitialCount
          Whether to spread initial count across all values
        • 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.

  • Package weka.classifiers.trees.ht

  • 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_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_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 AbstractClassifier 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)

    • Class weka.classifiers.trees.lmt.SimpleLinearRegression

      class SimpleLinearRegression extends Object implements Serializable
      serialVersionUID:
      1779336022895414137L

      • Serialized Fields

        • 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

  • Package weka.classifiers.trees.m5

    • Class weka.classifiers.trees.m5.CorrelationSplitInfo

      class CorrelationSplitInfo extends Object implements Serializable
      serialVersionUID:
      4212734895125452770L

      • Serialized Fields

        • 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 AbstractClassifier 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
          ArrayList<Rule> 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 AbstractClassifier 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_numCovered
          int m_numCovered
          the number of instances covered by this rule
        • m_numDecimalPlaces
          int m_numDecimalPlaces
          The number of decimal places used for printing 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 = "<=", 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 AbstractClassifier 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

      • Serialized Fields

        • m_Debug
          boolean m_Debug
          Whether the clusterer is run in debug mode.
        • m_DoNotCheckCapabilities
          boolean m_DoNotCheckCapabilities
          Whether capabilities should not be checked before clusterer is built.

    • Class weka.clusterers.AbstractDensityBasedClusterer

      class AbstractDensityBasedClusterer extends AbstractClusterer implements Serializable
      serialVersionUID:
      -5950728041704213845L

    • Class weka.clusterers.Canopy

      class Canopy extends RandomizableClusterer implements Serializable
      serialVersionUID:
      2067574593448223334L

      • Serialized Fields

        • m_canopies
          Instances m_canopies
          The canopy centers
        • m_canopyCenters
          List<double[][]> m_canopyCenters
        • m_canopyNumMissingForNumerics
          List<double[]> m_canopyNumMissingForNumerics
        • m_canopyT2Density
          List<double[]> m_canopyT2Density
          The T2 density of each canopy
        • m_clusterCanopies
          List<long[]> m_clusterCanopies
          The list of canopies that each canopy is a member of (according to the T1 radius, which can overlap). Each bit position in the long values corresponds to one canopy. Outer list order corresponds to the order of the instances that store the actual canopy centers
        • m_didPruneLastTime
          boolean m_didPruneLastTime
          True if the pruning operation did remove at least one low density canopy the last time it was invoked
        • m_distanceFunction
          NormalizableDistance m_distanceFunction
          The distance function to use
        • m_dontReplaceMissing
          boolean m_dontReplaceMissing
          Replace missing values globally when running in batch mode?
        • m_instanceCount
          int m_instanceCount
          Number of training instances seen so far
        • m_maxCanopyCandidates
          int m_maxCanopyCandidates
          The maximum number of candidate canopies to hold in memory at any one time
        • m_minClusterDensity
          double m_minClusterDensity
          The minimum cluster density (according to T2 distance) allowed. Used when periodically pruning candidate canopies
        • m_missingValuesReplacer
          Filter m_missingValuesReplacer
          If not null, then this is expected to be a filter that can replace missing values immediately (at training and testing time)
        • m_numClustersRequested
          int m_numClustersRequested
          Default is to let the t2 radius determine how many canopies/clusters are formed
        • m_periodicPruningRate
          int m_periodicPruningRate
          Prune low-density candidate canopies after every x instances have been seen
        • m_t1
          double m_t1
          Outer radius
        • m_t2
          double m_t2
          Inner radius
        • m_trainingData
          Instances m_trainingData
          Used to pad out number of cluster centers if fewer canopies are generated than the number of requested clusters and we are running in batch mode.
        • m_userT1
          double m_userT1
          < 0 indicates the multiplier to use for T2 when setting T1, otherwise the value is take as is
        • m_userT2
          double m_userT2
          < 0 means use the heuristic based on std. dev. to set the t2 radius

    • 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
          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.Cobweb.CNode

      class CNode extends Object implements Serializable
      serialVersionUID:
      3452097436933325631L

      • Serialized Fields

        • m_attStats
          AttributeStats[] m_attStats
          Within cluster attribute statistics
        • m_children
          ArrayList<Cobweb.CNode> m_children
          Children of this node
        • m_clusterInstances
          Instances m_clusterInstances
          Instances at this node
        • m_clusterNum
          int m_clusterNum
          Cluster number of this node
        • m_numAttributes
          int m_numAttributes
          Number of attributes
        • m_totalInstances
          double m_totalInstances
          Total instances at this node

    • Class weka.clusterers.EM

      class EM extends RandomizableDensityBasedClusterer implements Serializable
      serialVersionUID:
      8348181483812829475L

      • Serialized Fields

        • m_cvFolds
          int m_cvFolds
          The number of folds to use for cross-validation
        • m_displayModelInOldFormat
          boolean m_displayModelInOldFormat
          display model output in old-style format
        • m_executionSlots
          int m_executionSlots
          Number of threads to use for E and M steps
        • 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_iterationsPerformed
          int m_iterationsPerformed
          The actual number of iterations performed
        • m_max_iterations
          int m_max_iterations
          maximum iterations to perform
        • m_maxValues
          double[] m_maxValues
          attribute max values
        • m_minLogLikelihoodImprovementCV
          double m_minLogLikelihoodImprovementCV
          Minimum improvement to increase number of clusters when cross-validating
        • m_minLogLikelihoodImprovementIterating
          double m_minLogLikelihoodImprovementIterating
          Minimum improvement in log likelihood when iterating
        • 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_NumKMeansRuns
          int m_NumKMeansRuns
          The number of runs of k-means to perform
        • 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
          full training instances
        • m_training
          boolean m_training
          False once training has completed
        • m_upperBoundNumClustersCV
          int m_upperBoundNumClustersCV
          Don't consider more clusters than this under CV (-1 means no upper bound)
        • 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_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.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.SimpleKMeans

      class SimpleKMeans extends RandomizableClusterer implements Serializable
      serialVersionUID:
      -3235809600124455376L

      • Serialized Fields

        • m_Assignments
          int[] m_Assignments
          Assignments obtained.
        • m_canopyClusters
          Canopy m_canopyClusters
          The canopy clusterer (if being used)
        • m_centroidCanopyAssignments
          List<long[]> m_centroidCanopyAssignments
          Canopies that each centroid falls into (determined by T1 radius)
        • m_ClusterCentroids
          Instances m_ClusterCentroids
          holds the cluster centroids.
        • m_ClusterMissingCounts
          double[][] m_ClusterMissingCounts
        • m_ClusterNominalCounts
          double[][][] m_ClusterNominalCounts
          For each cluster, holds the frequency counts for the values of each nominal attribute.
        • m_ClusterSizes
          double[] 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_completed
          int m_completed
        • m_dataPointCanopyAssignments
          List<long[]> m_dataPointCanopyAssignments
          Canopies that each training instance falls into (determined by T1 radius)
        • 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_executionSlots
          int m_executionSlots
          Number of threads to run
        • m_failed
          int m_failed
        • m_FastDistanceCalc
          boolean m_FastDistanceCalc
          whether to use fast calculation of distances (using a cut-off).
        • 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
          double[] m_FullMissingCounts
        • m_FullNominalCounts
          double[][] m_FullNominalCounts
        • m_FullStdDevs
          double[] m_FullStdDevs
        • m_initializationMethod
          int m_initializationMethod
          The initialization method to use
        • m_initialStartPoints
          Instances m_initialStartPoints
          Holds the initial start points, as supplied by the initialization method used
        • m_Iterations
          int m_Iterations
          Keep track of the number of iterations completed before convergence.
        • m_maxCanopyCandidates
          int m_maxCanopyCandidates
          The maximum number of candidate canopies to hold in memory at any one time (if using canopy clustering)
        • m_MaxIterations
          int m_MaxIterations
          Maximum number of iterations to be executed.
        • m_minClusterDensity
          double m_minClusterDensity
          The minimum cluster density (according to T2 distance) allowed. Used when periodically pruning candidate canopies (if using canopy clustering)
        • m_NumClusters
          int m_NumClusters
          number of clusters to generate.
        • m_periodicPruningRate
          int m_periodicPruningRate
          Prune low-density candidate canopies after every x instances have been seen (if using canopy clustering)
        • m_PreserveOrder
          boolean m_PreserveOrder
          Preserve order of instances.
        • m_ReplaceMissingFilter
          ReplaceMissingValues m_ReplaceMissingFilter
          replace missing values in training instances.
        • m_speedUpDistanceCompWithCanopies
          boolean m_speedUpDistanceCompWithCanopies
          Whether to reducet the number of distance calcs done by k-means with canopies
        • m_squaredErrors
          double[] m_squaredErrors
          Holds the squared errors for all clusters.
        • m_t1
          double m_t1
          The t1 radius to pass through to Canopy
        • m_t2
          double m_t2
          The t2 radius to pass through to Canopy

    • Class weka.clusterers.SingleClustererEnhancer

      class SingleClustererEnhancer extends AbstractClusterer implements Serializable
      serialVersionUID:
      4893928362926428671L

      • Serialized Fields

        • m_Clusterer
          Clusterer m_Clusterer
          the clusterer

  • Package weka.core

    • Class weka.core.AbstractInstance

      class AbstractInstance extends Object implements Serializable
      serialVersionUID:
      1482635194499365155L

      • 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.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_AttributeInfo
          weka.core.AttributeInfo m_AttributeInfo
          The attribute info (null for numeric attributes)
        • m_AttributeMetaInfo
          AttributeMetaInfo m_AttributeMetaInfo
          The meta data for the attribute.
        • m_Index
          int m_Index
          The attribute's index.
        • m_Name
          String m_Name
          The attribute's name.
        • m_Type
          int m_Type
          The attribute's type.
        • m_Weight
          double m_Weight
          The attribute's weight.

    • 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
          BitSet m_Attributes
          contains the attribute locations, either true or false Boolean objects
        • 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
          ArrayList<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.AttributeMetaInfo

      class AttributeMetaInfo extends Object implements Serializable

      • Serialized Fields

        • m_HasZeropoint
          boolean m_HasZeropoint
          Whether the attribute has a zeropoint.
        • 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_Ordering
          int m_Ordering
          The attribute's ordering.
        • m_UpperBound
          double m_UpperBound
          The attribute's upper numeric bound.
        • m_UpperBoundIsOpen
          boolean m_UpperBoundIsOpen
          Whether the upper bound is open

    • Class weka.core.AttributeStats

      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
        • nominalWeights
          double[] nominalWeights
          Weight mass for 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<Capabilities.Capability> m_Capabilities
          the hashset for storing the active capabilities
        • m_Dependencies
          HashSet<Capabilities.Capability> 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_InterfaceDefinedCapabilities
          HashSet<Class> m_InterfaceDefinedCapabilities
          the interface-defined capabilities
        • 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.DateAttributeInfo

      class DateAttributeInfo extends Object implements Serializable

      • Serialized Fields

        • m_DateFormat
          SimpleDateFormat m_DateFormat
          Date format specification for date attributes

    • 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.Defaults

      class Defaults extends Object implements Serializable
      serialVersionUID:
      1061521489520308096L

      • Serialized Fields

    • Class weka.core.DenseInstance

      class DenseInstance extends AbstractInstance implements Serializable
      serialVersionUID:
      1482635194499365122L

    • Class weka.core.DictionaryBuilder

      class DictionaryBuilder extends Object implements Serializable
      serialVersionUID:
      5579506627960356012L

      • Serialized Fields

        • m_avgDocLength
          double m_avgDocLength
          The average document length
        • m_classIndex
          int m_classIndex
          Holds the class index
        • m_consolidatedDict
          Map<String,int[]> m_consolidatedDict
          Holds the final dictionary that is consolidated across classes and pruned according to m_wordsToKeep. First element of array contains the index of the word. The second (optional) element contains the document count for the word (i.e. number of training docs the word occurs in).
        • m_count
          int m_count
          Count of input vectors seen
        • m_dictsPerClass
          Map<String,int[]>[] m_dictsPerClass
          Holds the dictionaries (one per class) that are compiled while processing
        • m_docLengthSum
          double m_docLengthSum
          The sum of document lengths
        • m_doNotOperateOnPerClassBasis
          boolean m_doNotOperateOnPerClassBasis
          True if the final number of words to keep should not be applied on a per class basis
        • m_IDFTransform
          boolean m_IDFTransform
          True if the IDF transform is to be applied
        • m_inputContainsStringAttributes
          boolean m_inputContainsStringAttributes
          True if the input data contains string attributes to convert
        • m_inputFormat
          Instances m_inputFormat
          Input structure
        • m_lowerCaseTokens
          boolean m_lowerCaseTokens
          True if all tokens should be downcased.
        • m_minFrequency
          int m_minFrequency
          Minimum frequency to retain dictionary entries
        • m_normalize
          boolean m_normalize
          Whether to normalize to average length of training docs
        • m_numClasses
          int m_numClasses
          Number of classes
        • m_outputCounts
          boolean m_outputCounts
          Whether to output frequency counts instead of presence indicators
        • m_outputFormat
          Instances m_outputFormat
          Output structure
        • m_periodicPruneRate
          long m_periodicPruneRate
          Prune dictionary (per class) of low freq terms after every x documents. 0 = no periodic pruning
        • 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_sortDictionary
          boolean m_sortDictionary
          Whether to keep the dictionary(s) sorted alphabetically
        • m_stemmer
          Stemmer m_stemmer
          the stemming algorithm.
        • m_stopwordsHandler
          StopwordsHandler m_stopwordsHandler
          Stopword handler to use.
        • m_TFTransform
          boolean m_TFTransform
          True if the TF transform is to be applied
        • m_tokenizer
          Tokenizer m_tokenizer
          the tokenizer algorithm to use.
        • 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.core.EnvironmentProperties

      class EnvironmentProperties extends Properties implements Serializable

    • Class weka.core.EuclideanDistance

      class EuclideanDistance extends NormalizableDistance implements Serializable
      serialVersionUID:
      1068606253458807903L

    • Class weka.core.FastVector

      class FastVector extends ArrayList<E> implements Serializable
      serialVersionUID:
      -2173635135622930169L

    • Class weka.core.FilteredDistance

      class FilteredDistance extends Object implements Serializable

      • Serialized Fields

        • m_Distance
          DistanceFunction m_Distance
          The distance function to use.
        • m_Filter
          Filter m_Filter
          The filter to use.
        • m_Remove
          Remove m_Remove
          Remove filter to remove attributes if required.

    • 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
        • m_Range
          Range m_Range
          the range of attributes to use for comparison.

    • Class weka.core.Instances

      class Instances extends AbstractList<Instance> implements Serializable
      serialVersionUID:
      -19412345060742748L

      • Serialized Fields

        • m_Attributes
          ArrayList<Attribute> m_Attributes
          The attribute information.
        • m_ClassIndex
          int m_ClassIndex
          The class attribute's index
        • m_Instances
          ArrayList<Instance> 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_NamesToAttributeIndices
          HashMap<String,Integer> m_NamesToAttributeIndices
          A map to quickly find attribute indices based on their names.
        • m_RelationName
          String m_RelationName
          The dataset's name.

    • 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.MinkowskiDistance

      class MinkowskiDistance extends NormalizableDistance implements Serializable
      serialVersionUID:
      -7446019339455453893L

      • Serialized Fields

        • m_Order
          double m_Order
          the order of the minkowski distance.

    • Class weka.core.NominalAttributeInfo

      class NominalAttributeInfo extends Object implements Serializable

    • Class weka.core.NormalizableDistance

      class NormalizableDistance extends Object implements Serializable
      serialVersionUID:
      -2806520224161351708L

      • 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 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
          ArrayList<String> 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.RelationalAttributeInfo

      class RelationalAttributeInfo extends NominalAttributeInfo implements Serializable

      • Serialized Fields

        • m_Header
          Instances m_Header
          The header information for a relation-valued attribute.

    • Class weka.core.RelationalLocator

      class RelationalLocator extends AttributeLocator implements Serializable
      serialVersionUID:
      4646872277151854732L

    • Class weka.core.SelectedTag

      class SelectedTag extends Object implements Serializable
      serialVersionUID:
      6947341624626504975L

      • Serialized Fields

        • m_Selected
          int m_Selected
          The index of the selected tag
        • m_Tags
          Tag[] m_Tags
          The set of tags to choose from

    • 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.Settings

      class Settings extends Object implements Serializable
      serialVersionUID:
      -4005372566372478008L

      • Serialized Fields

        • m_ID
          String m_ID
          The name of the entry within the store to save to
        • m_settings
          Map<String,Map<Settings.SettingKey,Object>> m_settings
          outer map keyed by perspective ID. Inner map - settings for a perspective.
        • m_storeName
          String m_storeName
          The name of the store that these settings should be saved/loaded to/from

    • Class weka.core.Settings.SettingKey

      class SettingKey extends Object implements Serializable

      • Serialized Fields

        • m_description
          String m_description
          Description/display name of the seting
        • m_key
          String m_key
          Key for this setting
        • m_meta
          Map<String,String> m_meta
          Metadata for this setting - e.g. file property could specify whether it is files only, directories only or both
        • m_pickList
          List<String> m_pickList
          Pick list (can be null if not applicable) for a string-based setting
        • m_toolTip
          String m_toolTip
          Tool tip for the setting

    • 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 AbstractInstance 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 weka.core.UnassignedClassException

      class UnassignedClassException extends RuntimeException implements Serializable
      serialVersionUID:
      6268278694768818235L

    • Exception weka.core.UnassignedDatasetException

      class UnassignedDatasetException extends RuntimeException implements Serializable
      serialVersionUID:
      -9000116786626328854L

    • Exception weka.core.UnsupportedAttributeTypeException

      class UnsupportedAttributeTypeException extends WekaException implements Serializable
      serialVersionUID:
      2658987325328414838L

    • Exception weka.core.UnsupportedClassTypeException

      class UnsupportedClassTypeException extends WekaException implements Serializable
      serialVersionUID:
      5175741076972192151L

    • Exception 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
      serialVersionUID:
      5535537461920594758L

      • 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
      serialVersionUID:
      2399441762235754491L

      • 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
      serialVersionUID:
      2425432084900694551L

      • Serialized Fields

        • m_retrieval
          int m_retrieval
          The current retrieval mode

    • Class weka.core.converters.AbstractSaver

      class AbstractSaver extends Object implements Serializable
      serialVersionUID:
      -27467499727819258L

      • Serialized Fields

        • m_DoNotCheckCapabilities
          boolean m_DoNotCheckCapabilities
          Whether capabilities should not be checked
        • 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

        • m_retainStringVals
          boolean m_retainStringVals
          Whether the values of string attributes should be retained in memory when reading incrementally
        • m_URL
          String m_URL
          the url

    • 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
        • m_MaxDecimalPlaces
          int m_MaxDecimalPlaces
          Max number of decimal places for numeric values

    • 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:
      -1300595850715808438L

      • Serialized Fields

        • m_bufferSize
          int m_bufferSize
          The maximum number of rows to hold in memory at any one time
        • m_current
          ArrayList<Object> m_current
        • 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_FieldSeparator
          String m_FieldSeparator
          the field separator.
        • m_fieldSeparatorAndEnclosures
          String[] m_fieldSeparatorAndEnclosures
          Array holding field separator and enclosures to pass through to the underlying ArffReader
        • m_formatter
          SimpleDateFormat m_formatter
          The formatter to use on dates
        • m_incrementalReader
          ArffLoader.ArffReader m_incrementalReader
          Reader used to process and output data incrementally
        • m_MissingValue
          String m_MissingValue
          The placeholder for missing values.
        • m_noHeaderRow
          boolean m_noHeaderRow
          whether the csv file contains a header row with att names
        • m_NominalAttributes
          Range m_NominalAttributes
          The range of attributes to force to type nominal.
        • m_nominalLabelSpecs
          List<String> m_nominalLabelSpecs
          The user-supplied legal nominal values - each entry in the list is a spec
        • m_nominalVals
          Map<Integer,LinkedHashSet<String>> m_nominalVals
          Lookup for nominal values
        • m_numBufferedRows
          int m_numBufferedRows
        • m_numericAttributes
          Range m_numericAttributes
          The range of attributes to force to type numeric
        • m_rowBuffer
          List<String> m_rowBuffer
          The in memory row buffer
        • m_StringAttributes
          Range m_StringAttributes
          The range of attributes to force to type string.
        • m_types
          weka.core.converters.CSVLoader.TYPE[] m_types

    • Class weka.core.converters.CSVSaver

      class CSVSaver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      476636654410701807L

      • Serialized Fields

        • m_FieldSeparator
          String m_FieldSeparator
          the field separator.
        • m_MaxDecimalPlaces
          int m_MaxDecimalPlaces
          Max number of decimal places for numeric values
        • m_MissingValue
          String m_MissingValue
          The placeholder for missing values.
        • m_noHeaderRow
          boolean m_noHeaderRow
          Set to true to not write the header row

    • 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_CreateSparseData
          boolean m_CreateSparseData
          Determines whether sparse data is created
        • m_CustomPropsFile
          File m_CustomPropsFile
          the custom props file to use instead of default one.
        • 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<String,Double>[] m_nominalIndexes
          Stores the index of a nominal value
        • m_nominalStrings
          ArrayList<String>[] 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
          ArrayList<String> 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 *|&ltcolumn-list> FROM &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_CustomPropsFile
          File m_CustomPropsFile
          the custom props file to use instead of default one.
        • 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_resolvedTableName
          String m_resolvedTableName
          Table name with any environment variables resolved
        • 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_truncate
          boolean m_truncate
          Whether to truncate (i.e. drop and then recreate) the table if it already exists
        • m_URL
          String m_URL
          the database URL.
        • m_Username
          String m_Username
          the user name for the database.

    • Class weka.core.converters.DictionarySaver

      class DictionarySaver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      -19891905988830722L

      • Serialized Fields

        • m_dictionaryBuilder
          DictionaryBuilder m_dictionaryBuilder
          The dictionary builder to use
        • m_dictionaryIsBinary
          boolean m_dictionaryIsBinary
          Whether the dictionary file contains a binary serialized dictionary, rather than plain text
        • m_periodicPruningRate
          long m_periodicPruningRate
          Prune the dictionary every x instances. <=0 means no periodic pruning

    • Class weka.core.converters.JSONLoader

      class JSONLoader extends AbstractFileLoader implements Serializable
      serialVersionUID:
      3764533621135196582L

      • Serialized Fields

        • m_JSON
          JSONNode m_JSON
          the loaded JSON object.
        • m_URL
          String m_URL
          the url.

    • Class weka.core.converters.JSONSaver

      class JSONSaver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      -1047134047244534557L

      • Serialized Fields

        • m_ClassIndex
          SingleIndex m_ClassIndex
          the class index.
        • m_CompressOutput
          boolean m_CompressOutput
          whether to compress the output.

    • Class weka.core.converters.LibSVMLoader

      class LibSVMLoader extends AbstractFileLoader implements Serializable
      serialVersionUID:
      4988360125354664417L

      • Serialized Fields

        • m_Buffer
          Vector<double[]> 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

    • Exception weka.core.converters.Loader.StructureNotReadyException

      class StructureNotReadyException extends IOException implements Serializable
      serialVersionUID:
      1938493033987645828L

    • Class weka.core.converters.MatlabLoader

      class MatlabLoader extends AbstractFileLoader implements Serializable
      serialVersionUID:
      -8861142318612875251L

      • Serialized Fields

    • Class weka.core.converters.MatlabSaver

      class MatlabSaver extends AbstractFileSaver implements Serializable
      serialVersionUID:
      4118356803697172614L

      • Serialized Fields

        • m_Format
          DecimalFormat m_Format
          for formatting the numbers.
        • m_HeaderWritten
          boolean m_HeaderWritten
          whether the header was written already.
        • m_UseDouble
          boolean m_UseDouble
          whether to save in double instead of single precision format.
        • m_UseTabs
          boolean m_UseTabs
          whether to use tabs instead of blanks.

    • 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.StreamTokenizerUtils

      class StreamTokenizerUtils extends Object implements Serializable
      serialVersionUID:
      -5786996944597404253L

    • Class weka.core.converters.SVMLightLoader

      class SVMLightLoader extends AbstractFileLoader implements Serializable
      serialVersionUID:
      4988360125354664417L

      • Serialized Fields

        • m_Buffer
          Vector<double[]> 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_filesByClass
          List<LinkedList<String>> m_filesByClass
        • m_lastClassDir
          int m_lastClassDir
        • 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.expressionlanguage.common

  • Package weka.core.expressionlanguage.core

  • Package weka.core.expressionlanguage.weka

  • Package weka.core.json

  • 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.
        • 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.FilteredNeighbourSearch

      class FilteredNeighbourSearch extends NearestNeighbourSearch implements Serializable
      serialVersionUID:
      1369174644087067375L

      • Serialized Fields

        • m_AddID
          AddID m_AddID
          Need to use ID filter to add ID so that we can identify instances
        • m_Filter
          Filter m_Filter
          The filter object to use.
        • m_IndexOfID
          int m_IndexOfID
          The index of the ID attribute
        • m_ModifiedSearchMethod
          NearestNeighbourSearch m_ModifiedSearchMethod
          The modified search method, where ID is skipped
        • m_SearchMethod
          NearestNeighbourSearch m_SearchMethod
          The neighborhood search method to use.

    • 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
      serialVersionUID:
      7516898393890379876L

      • 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
      serialVersionUID:
      -2233739562654159948L

      • 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
      serialVersionUID:
      982315539809240771L

      • 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.ListNode

      class ListNode extends Object implements Serializable

      • Serialized Fields

        • distance
          double distance
          The distance of the point to the anchor.
        • idx
          int idx
          The index of the point.

    • Class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList

      class MyIdxList extends Object implements Serializable
      serialVersionUID:
      -2283869109722934927L

      • Serialized Fields

        • m_List
          ArrayList<weka.core.neighboursearch.balltrees.MiddleOutConstructor.ListNode> m_List
          The array list backing this list

    • 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.packageManagement

  • Package weka.core.pmml

    • Class weka.core.pmml.Array

      class Array extends Object implements Serializable
      serialVersionUID:
      4286234448957826177L

    • 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
      serialVersionUID:
      875736989396755241L

      • 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
      serialVersionUID:
      -5809107997906180082L

      • 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

      • Serialized Fields

        • m_binValue
          String m_binValue
          The bin value for this DiscretizeBin
        • m_intervals
          ArrayList<FieldMetaInfo.Interval> m_intervals
          The intervals for this DiscretizeBin
        • m_numericBinValue
          double m_numericBinValue
          If the optype is continuous or ordinal, we will attempt to parse the bin value as a number and store it here.

    • Class weka.core.pmml.Expression

      class Expression extends Object implements Serializable
      serialVersionUID:
      4448840549804800321L

    • Class weka.core.pmml.FieldMetaInfo

      class FieldMetaInfo extends Object implements Serializable
      serialVersionUID:
      -6116715567129830143L

      • 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
      serialVersionUID:
      -475467721189397466L

      • 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.SparseArray

      class SparseArray extends Array implements Serializable
      serialVersionUID:
      8129550573612673674L

      • Serialized Fields

        • m_indices
          List<Integer> m_indices
          The indices of the sparse array
        • m_numNonZero
          int m_numNonZero
          The number of non-zero elements
        • m_numValues
          int m_numValues
          The size of the array if known

    • 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

    • Class weka.core.pmml.VectorDictionary

      class VectorDictionary extends Object implements Serializable
      serialVersionUID:
      -5538024467333813123L

      • Serialized Fields

        • m_numberOfVectors
          int m_numberOfVectors
          The number of support vectors in the dictionary
        • m_vectorFields
          List<FieldRef> m_vectorFields
          The fields accessed by the support vectors
        • m_vectorInstances
          Map<String,VectorInstance> m_vectorInstances
          The vectors in the dictionary

    • Class weka.core.pmml.VectorInstance

      class VectorInstance extends Object implements Serializable
      serialVersionUID:
      -7543200367512646290L

      • Serialized Fields

        • m_ID
          String m_ID
          The ID of this instance
        • m_values
          Array m_values
          The usually sparse elements of this vector
        • m_vectorFields
          List<FieldRef> m_vectorFields
          The fields indexed by this VectorInstance

  • Package weka.core.scripting

  • Package weka.core.stemmers

  • Package weka.core.stopwords

  • 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_ClassFlag
          boolean m_ClassFlag
          class flag
        • 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
          This flag is no longer used (left here to maintain compatibility for serialization)
        • 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 specified by the user (relation name will be auto-generated if empty)
        • 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
          ArrayList<weka.datagenerators.classifiers.classification.RDG1.RuleList> 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
          ArrayList<weka.datagenerators.clusterers.BIRCHCluster.Cluster> 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_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_booleanCols
          Range m_booleanCols
          Stores which columns are boolean (default numeric)
        • m_Clusters
          ClusterDefinition[] m_Clusters
          cluster list
        • m_nominalCols
          Range m_nominalCols
          Stores which columns are nominal (default numeric)
        • 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_MinInstNum
          int m_MinInstNum
          minimal number of instances for this cluster
        • 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_valueA
          double[] m_valueA
          min or mean
        • m_valueB
          double[] m_valueB
          max or stddev
        • m_valuesList
          String m_valuesList
          the specification of the list of values as a string

  • 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_FPrior
          double m_FPrior
          Initialization for 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_DoNotCheckCapabilities
          boolean m_DoNotCheckCapabilities
          Whether capabilities should not be checked
        • 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.MultivariateGaussianEstimator

      class MultivariateGaussianEstimator extends Object implements Serializable

      • Serialized Fields

        • covarianceInverse
          no.uib.cipr.matrix.UpperSPDDenseMatrix covarianceInverse
          Inverse of covariance matrix
        • lnconstant
          double lnconstant
          Factor to make density integrate to one (log of this factor)
        • m_Ridge
          double m_Ridge
          Ridge parameter to add to diagonal of covariance matrix
        • mean
          no.uib.cipr.matrix.DenseVector mean
          Mean vector

    • 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

    • Class weka.estimators.UnivariateEqualFrequencyHistogramEstimator

      class UnivariateEqualFrequencyHistogramEstimator extends Object implements Serializable
      serialVersionUID:
      -3180287591539683137L

      • Serialized Fields

        • m_Boundaries
          double[] m_Boundaries
          The interval boundaries.
        • m_Exponent
          double m_Exponent
          The exponent to use in computation of bandwidth (default: -0.25)
        • m_MinWidth
          double m_MinWidth
          The minimum allowed value of the kernel width (default: 1.0E-6)
        • m_NumBins
          int m_NumBins
          The number of bins to use.
        • m_NumIntervals
          int m_NumIntervals
          The number of intervals used to approximate prediction interval.
        • m_SumOfWeights
          double m_SumOfWeights
          The total sum of weights.
        • m_TM
          TreeMap<Double,Double> m_TM
          The collection used to store the weighted values.
        • m_UpdateWeightsOnly
          boolean m_UpdateWeightsOnly
          Whether boundaries are updated or only weights.
        • m_WeightedSum
          double m_WeightedSum
          The weighted sum of values
        • m_WeightedSumSquared
          double m_WeightedSumSquared
          The weighted sum of squared values
        • m_Weights
          double[] m_Weights
          The weight of each interval.
        • m_Width
          double m_Width
          The current bandwidth (only computed when needed)

    • Class weka.estimators.UnivariateKernelEstimator

      class UnivariateKernelEstimator extends Object implements Serializable
      serialVersionUID:
      -1163983347810498880L

      • Serialized Fields

        • m_Exponent
          double m_Exponent
          The exponent to use in computation of bandwidth (default: -0.25)
        • m_MinWidth
          double m_MinWidth
          The minimum allowed value of the kernel width (default: 1.0E-6)
        • m_NumIntervals
          int m_NumIntervals
          The number of intervals used to approximate prediction interval.
        • m_SumOfWeights
          double m_SumOfWeights
          The weight of the values collected so far
        • m_Threshold
          double m_Threshold
          Threshold at which further kernels are no longer added to sum.
        • m_TM
          TreeMap<Double,Double> m_TM
          The collection used to store the weighted values.
        • m_WeightedSum
          double m_WeightedSum
          The weighted sum of values
        • m_WeightedSumSquared
          double m_WeightedSumSquared
          The weighted sum of squared values
        • m_Width
          double m_Width
          The current bandwidth (only computed when needed)

    • Class weka.estimators.UnivariateMixtureEstimator

      class UnivariateMixtureEstimator extends Object implements Serializable
      serialVersionUID:
      -2035274930137353656L

      • Serialized Fields

        • m_Debug
          boolean m_Debug
          Whether to output debug info.
        • m_MaxNumComponents
          int m_MaxNumComponents
          The maximum number of components to use (default is 5)
        • m_MixtureModel
          UnivariateMixtureEstimator.MM m_MixtureModel
          The current mixture model
        • m_NumBootstrapRuns
          int m_NumBootstrapRuns
          The number of Bootstrap runs to use to select the number of components (default is 10)
        • m_NumComponents
          int m_NumComponents
          The number of components to use (default is -1)
        • m_NumIntervals
          int m_NumIntervals
          The number of intervals used to approximate prediction interval.
        • m_NumValues
          int m_NumValues
          The number of values that have been seen
        • m_Random
          Random m_Random
          The random number generator.
        • m_Seed
          int m_Seed
          The random number seed to use (default is 1
        • m_UseNormalizedEntropy
          boolean m_UseNormalizedEntropy
          Whether to use normalized entropy instance of bootstrap.
        • m_Values
          double[] m_Values
          The values used for this estimator
        • m_Weights
          double[] m_Weights
          The weights used for this estimator

    • Class weka.estimators.UnivariateNormalEstimator

      class UnivariateNormalEstimator extends Object implements Serializable
      serialVersionUID:
      -1669009817825826548L

      • Serialized Fields

        • m_Mean
          double m_Mean
          The mean value (only updated when needed)
        • m_MinVar
          double m_MinVar
          The minimum allowed value of the variance (default: 1.0E-6 * 1.0E-6)
        • m_SumOfWeights
          double m_SumOfWeights
          The weight of the values collected so far
        • m_Variance
          double m_Variance
          The variance (only updated when needed)
        • m_WeightedSum
          double m_WeightedSum
          The weighted sum of values
        • m_WeightedSumSquared
          double m_WeightedSumSquared
          The weighted sum of squared values

  • 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
          ArrayList<Object[]> 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
          ArrayList<Object[]> 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_Evaluation
          Evaluation m_Evaluation
          Holds the most recently used Evaluation object
        • m_IRclass
          int m_IRclass
          Class index for information retrieval statistics (default 0)
        • m_NoSizeDetermination
          boolean m_NoSizeDetermination
          whether to skip determination of sizes (train/test/classifier).
        • 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_numPluginStatistics
          int m_numPluginStatistics
        • m_pluginMetrics
          List<AbstractEvaluationMetric> m_pluginMetrics
        • 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.CrossValidationSplitResultProducer

      class CrossValidationSplitResultProducer extends CrossValidationResultProducer implements Serializable
      serialVersionUID:
      1403798164046795073L

    • 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
          ArrayList<String> 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_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

    • Class weka.experiment.DatabaseUtils

      class DatabaseUtils extends Object implements Serializable
      serialVersionUID:
      -8252351994547116729L

      • Serialized Fields

        • DRIVERS
          Vector<String> 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
      serialVersionUID:
      5124501059135692160L

      • 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_Evaluation
          ClusterEvaluation m_Evaluation
          Holds the most recently used ClusterEvaluation object
        • m_NoSizeDetermination
          boolean m_NoSizeDetermination
          whether to skip determination of sizes (train/test/classifier).
        • 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.ExplicitTestsetResultProducer

      class ExplicitTestsetResultProducer extends Object implements Serializable
      serialVersionUID:
      2613585409333652530L

      • 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_RelationFind
          String m_RelationFind
          The regular expression to search for in the relation name.
        • m_RelationReplace
          String m_RelationReplace
          The string to use to replace the matches of the regular expression.
        • m_ResultListener
          ResultListener m_ResultListener
          The ResultListener to send results to.
        • m_SplitEvaluator
          SplitEvaluator m_SplitEvaluator
          The SplitEvaluator used to generate results.
        • m_TestsetDir
          File m_TestsetDir
          The directory containing all the test sets.
        • m_TestsetPrefix
          String m_TestsetPrefix
          The prefix for all the test sets.
        • m_TestsetSuffix
          String m_TestsetSuffix
          The suffix for all the test sets.
        • m_ZipDest
          OutputZipper m_ZipDest
          The output zipper to use for saving raw splitEvaluator output.

    • 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_CustomPropsFile
          File m_CustomPropsFile
          the custom props file to use instead of default one.
        • 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
          ArrayList<weka.experiment.PairedTTester.Resultset> 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.PairedTTester.Dataset

      class Dataset extends Object implements Serializable
      serialVersionUID:
      -2801397601839433282L

    • Class weka.experiment.PairedTTester.DatasetSpecifiers

      class DatasetSpecifiers extends Object implements Serializable
      serialVersionUID:
      -9020938059902723401L

      • Serialized Fields

        • m_Specifiers
          ArrayList<Instance> m_Specifiers
          the specifiers that have been observed

    • Class weka.experiment.PairedTTester.Resultset

      class Resultset extends Object implements Serializable
      serialVersionUID:
      1543786683821339978L

      • Serialized Fields

        • m_Datasets
          ArrayList<weka.experiment.PairedTTester.Dataset> m_Datasets
          the dataset
        • m_Template
          Instance m_Template
          the template

    • 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_Evaluation
          Evaluation m_Evaluation
          Holds the most recently used Evaluation object
        • m_NoSizeDetermination
          boolean m_NoSizeDetermination
          whether to skip determination of sizes (train/test/classifier).
        • m_numPluginStatistics
          int m_numPluginStatistics
        • m_pluginMetrics
          List<AbstractEvaluationMetric> m_pluginMetrics
        • 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<String,TaskStatusInfo> 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
          ArrayList<RemoteExperimentListener> 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.
        • m_splitByProperty
          boolean m_splitByProperty
          If true, then sub experiments are created on the basis of properties
        • 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
      serialVersionUID:
      -1674092706571603720L

    • 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<String> m_HeaderKeys
          contains the keys for the header.
        • m_HeaderValues
          Vector<String> 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, or Double.NaN if no values seen
        • 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
        • stdDevFactor
          double stdDevFactor
          an important factor to calculate the standard deviation incrementally
        • 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_Debug
          boolean m_Debug
          Whether the classifier is run in debug mode.
        • m_DoNotCheckCapabilities
          boolean m_DoNotCheckCapabilities
          Whether capabilities should not be checked before classifier is built.
        • 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_Seed
          int m_Seed
          The random number seed that will be passed through to all filters that are randomizable.
        • m_Streamable
          boolean m_Streamable
          caches the streamable state
        • m_StreamableChecked
          boolean m_StreamableChecked
          whether we already checked the streamable state

    • Class weka.filters.RenameRelation

      class RenameRelation extends Filter implements Serializable
      serialVersionUID:
      8082179220141937043L

      • Serialized Fields

        • m_modType
          weka.filters.RenameRelation.ModType m_modType
          The type of modification to make
        • m_regexMatch
          String m_regexMatch
          Regex string to match
        • m_regexPattern
          Pattern m_regexPattern
          Pattern for regex replacement
        • m_relationNameModText
          String m_relationNameModText
          Text to modify the relation name with
        • m_replaceAll
          boolean m_replaceAll
          Whether to replace all rexex matches, or just the first

    • Class weka.filters.SimpleBatchFilter

      class SimpleBatchFilter extends SimpleFilter implements Serializable
      serialVersionUID:
      8102908673378055114L

    • Class weka.filters.SimpleFilter

      class SimpleFilter extends Filter implements Serializable
      serialVersionUID:
      5702974949137433141L

    • 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_hasClass
          boolean m_hasClass
          True if a class attribute is set in the data
        • m_SelectedAttributes
          int[] m_SelectedAttributes
          holds the selected attributes
        • m_trainSelector
          AttributeSelection m_trainSelector
          the attribute selection evaluation object

    • Class weka.filters.supervised.attribute.ClassConditionalProbabilities

      class ClassConditionalProbabilities extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      1684310720200284263L

      • Serialized Fields

        • m_estimator
          NaiveBayes m_estimator
          The Naive Bayes classifier to use for class conditional estimation
        • m_estimatorLookup
          Map<String,Estimator[]> m_estimatorLookup
          A lookup of estimators from Naive Bayes
        • m_excludeNominalAttributes
          boolean m_excludeNominalAttributes
          True if nominal attributes are to be excluded from the transformation
        • m_excludeNumericAttributes
          boolean m_excludeNumericAttributes
          True if numeric attributes are to be excluded from the transformation
        • m_nominalConversionThreshold
          int m_nominalConversionThreshold
          Don't convert nominal attributes with fewer than this number of values. -1 means always convert
        • m_remove
          Remove m_remove
          Remove filter to use for creating a set of untouched attributes
        • m_SpreadAttributeWeight
          boolean m_SpreadAttributeWeight
          Whether to spread attribute weight when creating binary attributes
        • m_unchanged
          Instances m_unchanged
          The attributes from the original data that are untouched by this transformation

    • 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_BinRangePrecision
          int m_BinRangePrecision
          Precision for bin range labels
        • 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_SpreadAttributeWeight
          boolean m_SpreadAttributeWeight
          Whether to spread attribute weight when creating binary attributes
        • m_UseBetterEncoding
          boolean m_UseBetterEncoding
          Use better encoding of split point for MDL.
        • m_UseBinNumbers
          boolean m_UseBinNumbers
          Use bin numbers rather than ranges for discretized attributes.
        • m_UseKononenko
          boolean m_UseKononenko
          Use Kononenko's MDL criterion instead of Fayyad et al.'s

    • Class weka.filters.supervised.attribute.MergeNominalValues

      class MergeNominalValues extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      7447337831221353842L

      • Serialized Fields

        • m_AttToBeModified
          boolean[] m_AttToBeModified
          Indicators for which attributes need to be changed.
        • m_Indicators
          int[][] m_Indicators
          The indicators used to map the old values.
        • m_SelectCols
          Range m_SelectCols
          Stores which atributes to operate on (or nto)
        • m_SelectedAttributes
          int[] m_SelectedAttributes
          Stores the indexes of the selected attributes in order.
        • m_SigLevel
          double m_SigLevel
          Set the significance level
        • m_UseShortIdentifiers
          boolean m_UseShortIdentifiers
          Use short values

    • 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_SpreadAttributeWeight
          boolean m_SpreadAttributeWeight
          Whether to spread attribute weight when creating binary attributes
        • m_TransformAll
          boolean m_TransformAll
          Are all values transformed into new attributes?

    • Class weka.filters.supervised.attribute.PartitionMembership

      class PartitionMembership extends Filter implements Serializable
      serialVersionUID:
      333532554667754026L

      • Serialized Fields

  • Package weka.filters.supervised.instance

    • Class weka.filters.supervised.instance.ClassBalancer

      class ClassBalancer extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      6237337831221353842L

      • Serialized Fields

        • m_NumIntervals
          int m_NumIntervals
          number of discretization intervals to use if the class is numeric

    • 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.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
          ArrayList<String> m_Labels
          The list of labels for nominal attribute.
        • m_Name
          String m_Name
          The name for the new attribute.
        • m_Weight
          double m_Weight
          The weight for the new attribute.

    • Class weka.filters.unsupervised.attribute.AddCluster

      class AddCluster extends Filter implements Serializable
      serialVersionUID:
      7414280611943807337L

      • Serialized Fields

        • m_ActualClusterer
          Clusterer m_ActualClusterer
          The actual clusterer used to do the clustering.
        • 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.
        • m_SerializedClustererFile
          File m_SerializedClustererFile
          The file from which to load a serialized clusterer.

    • Class weka.filters.unsupervised.attribute.AddExpression

      class AddExpression extends Filter implements Serializable
      serialVersionUID:
      402130384261736245L

      • Serialized Fields

        • 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_Expression
          Primitives.DoubleExpression m_Expression
        • m_infixExpression
          String m_infixExpression
          The infix expression
        • m_InstancesHelper
          InstancesHelper m_InstancesHelper

    • 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.AddUserFields

      class AddUserFields extends Filter implements Serializable
      serialVersionUID:
      -2761427344847891585L

    • Class weka.filters.unsupervised.attribute.AddUserFields.AttributeSpec

      class AttributeSpec extends Object implements Serializable
      serialVersionUID:
      -617328946241474608L

      • Serialized Fields

        • m_dateFormat
          SimpleDateFormat m_dateFormat
          The date format to use (if the new attribute is a date)
        • m_name
          String m_name
          The name of the new attribute
        • m_nameS
          String m_nameS
          The name after resolving any environment variables
        • m_parsedDate
          Date m_parsedDate
          Holds the parsed date value
        • m_type
          String m_type
          The type of the new attribute
        • m_typeS
          String m_typeS
          The type after resolving any environment variables
        • m_value
          String m_value
          The constant value it should assume
        • m_valueS
          String m_valueS
          The value after resolving any environment variables

    • 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
          ArrayList<String> 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.CartesianProduct

      class CartesianProduct extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -227979753639722020L

      • Serialized Fields

        • m_Attributes
          Range m_Attributes
          the attribute range to work on

    • 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.DateToNumeric

      class DateToNumeric extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -6614650822291796239L

      • Serialized Fields

        • m_Cols
          Range m_Cols
          Stores which columns to turn into numeric attributes
        • m_DefaultCols
          String m_DefaultCols
          The default columns to turn into numeric attributes

    • Class weka.filters.unsupervised.attribute.Discretize

      class Discretize extends PotentialClassIgnorer implements Serializable
      serialVersionUID:
      -1358531742174527279L

      • Serialized Fields

        • m_BinRangePrecision
          int m_BinRangePrecision
          Precision for bin range labels
        • 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_SpreadAttributeWeight
          boolean m_SpreadAttributeWeight
          Whether to spread attribute weight when creating binary attributes
        • m_UseBinNumbers
          boolean m_UseBinNumbers
          Use bin numbers rather than ranges for discretized attributes.
        • 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.FixedDictionaryStringToWordVector

      class FixedDictionaryStringToWordVector extends SimpleStreamFilter implements Serializable
      serialVersionUID:
      7990892846966916757L

      • Serialized Fields

        • m_dictionaryFile
          File m_dictionaryFile
        • m_dictionaryIsBinary
          boolean m_dictionaryIsBinary
          Whether the dictionary file contains a binary serialized dictionary, rather than plain text - used when loading from a file in order to differentiate
        • m_vectorizer
          DictionaryBuilder m_vectorizer

    • 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
          Stats[] m_attStats
          Attributes statistics
        • m_CompiledExpression
          Primitives.DoubleExpression m_CompiledExpression
          The compiled modification expression
        • m_CurrentValue
          SimpleVariableDeclarations.VariableInitializer m_CurrentValue
          VariableInitializer for the current value 'A' in an expression
        • m_expression
          String m_expression
          The modification expression
        • m_InstancesHelper
          InstancesHelper m_InstancesHelper
          InstancesHelpers for different indices
        • m_SelectCols
          Range m_SelectCols
          Stores which columns to select as a funky range
        • m_StatsHelper
          StatsHelper m_StatsHelper
          StatsHelpers for different indices

    • Class weka.filters.unsupervised.attribute.MergeInfrequentNominalValues

      class MergeInfrequentNominalValues extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      4444337331921333847L

      • Serialized Fields

        • m_AttToBeModified
          boolean[] m_AttToBeModified
          Indicators for which attributes need to be changed.
        • m_MinimumFrequency
          int m_MinimumFrequency
          Set the minimum frequency for a value not to be merged.
        • m_NewValues
          int[][] m_NewValues
          The new values.
        • m_SelectCols
          Range m_SelectCols
          Stores which atributes to operate on (or nto)
        • m_SelectedAttributes
          int[] m_SelectedAttributes
          Stores the indexes of the selected attributes in order.
        • m_UseShortIDs
          boolean m_UseShortIDs
          Whether to use short identifiers for merge values.

    • Class weka.filters.unsupervised.attribute.MergeManyValues

      class MergeManyValues extends PotentialClassIgnorer implements Serializable
      serialVersionUID:
      4649332102154713625L

      • Serialized Fields

        • m_AttIndex
          SingleIndex m_AttIndex
          The attribute's index setting.
        • m_Label
          String m_Label
          The first value's index setting.
        • m_MergeRange
          Range m_MergeRange
          The merge value's index setting.

    • 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.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_SpreadAttributeWeight
          boolean m_SpreadAttributeWeight
          Whether to spread attribute weight when creating binary attributes
        • 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

      • Serialized Fields

        • m_Cols
          Range m_Cols
          Stores which columns to turn into binary
        • m_DefaultCols
          String m_DefaultCols
          The default columns to turn into binary

    • Class weka.filters.unsupervised.attribute.NumericToDate

      class NumericToDate extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -6514657821295776239L

      • Serialized Fields

        • m_Cols
          Range m_Cols
          Stores which columns to turn into date attributes
        • m_DateFormat
          SimpleDateFormat m_DateFormat
          The output date format.
        • m_DefaultCols
          String m_DefaultCols
          The default columns to turn into date attributes

    • 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.OrdinalToNumeric

      class OrdinalToNumeric extends PotentialClassIgnorer implements Serializable
      serialVersionUID:
      -5199516576940135696L

      • Serialized Fields

        • m_range
          String m_range
          Textual range string
        • m_resolvedRange
          String m_resolvedRange
          Holds the resolved range
        • m_selectedRange
          Range m_selectedRange
          Range of columns to consider

    • 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
          no.uib.cipr.matrix.UpperSymmDenseMatrix 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.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
          int m_rndmSeed
          Stores the random seed used to generate the random matrix
        • m_useReplaceMissing
          boolean m_useReplaceMissing
          Should the missing values be replaced using unsupervised.ReplaceMissingValues filter

    • Class weka.filters.unsupervised.attribute.RandomSubset

      class RandomSubset extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      2911221724251628050L

      • Serialized Fields

        • m_Indices
          int[] m_Indices
          The indices of the attributes that got selected.
        • m_invertSelection
          boolean m_invertSelection
          Whether to randomly remove rather than select
        • 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.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.RemoveByName

      class RemoveByName extends SimpleStreamFilter implements Serializable
      serialVersionUID:
      -3335106965521265631L

      • Serialized Fields

        • m_Expression
          String m_Expression
          the regular expression for selecting the attributes by name.
        • m_InvertSelection
          boolean m_InvertSelection
          whether to invert the matching sense.
        • m_Remove
          Remove m_Remove
          the Remove filter used internally for removing the attributes.

    • 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.RenameAttribute

      class RenameAttribute extends SimpleStreamFilter implements Serializable
      serialVersionUID:
      4216491776378279596L

      • Serialized Fields

        • m_AttributeIndices
          Range m_AttributeIndices
          the attribute range to work on.
        • m_Find
          String m_Find
          the regular expression that the attribute names have to match.
        • m_Replace
          String m_Replace
          the regular expression to replace the attribute name with.
        • m_ReplaceAll
          boolean m_ReplaceAll
          whether to replace all occurrences or just the first.

    • Class weka.filters.unsupervised.attribute.RenameNominalValues

      class RenameNominalValues extends Filter implements Serializable
      serialVersionUID:
      -2121767582746512209L

      • Serialized Fields

        • m_ignoreCase
          boolean m_ignoreCase
          True if case is to be ignored when matching nominal values
        • m_invert
          boolean m_invert
          True if the matching sense (for attributes) is to be inverted
        • m_renameMap
          Map<String,String> m_renameMap
          The map of nominal values and their replacements
        • m_renameVals
          String m_renameVals
          The comma-separated list of nominal values and their replacements
        • m_selectedAttributes
          int[] m_selectedAttributes
          Stores the indexes of the selected attributes in order, once the dataset is seen
        • m_selectedCols
          Range m_selectedCols
          The range object to use if a range has been supplied
        • m_selectedColsString
          String m_selectedColsString
          Range specification or comma-separated list of attribute names

    • 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
        • m_setAllAttributeWeightsToOne
          boolean m_setAllAttributeWeightsToOne
          Whether to set all attribute weights to 1.0 in the reordered data.

    • 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.ReplaceMissingWithUserConstant

      class ReplaceMissingWithUserConstant extends PotentialClassIgnorer implements Serializable
      serialVersionUID:
      -7334039452189350356L

      • Serialized Fields

        • m_dateConstant
          String m_dateConstant
          Constant for replacing missing values in date attributes with
        • m_dateConstVal
          double m_dateConstVal
          Parsed date value as a double
        • m_defaultDateFormat
          String m_defaultDateFormat
          Formatting string to use for parsing the date constant
        • m_nominalStringConstant
          String m_nominalStringConstant
          Constant for replacing missing values in nominal/string atts with
        • m_numericConstant
          String m_numericConstant
          Constant for replacing missing values in numeric attributes with
        • m_numericConstVal
          double m_numericConstVal
          Parsed numeric constant value
        • m_range
          String m_range
        • m_resolvedDateConstant
          String m_resolvedDateConstant
          Replacement value for date atts after resolving environment vars
        • m_resolvedDateFormat
          String m_resolvedDateFormat
          Formatting string after resolving environment vars
        • m_resolvedNominalStringConstant
          String m_resolvedNominalStringConstant
          Replacement value for nominal/string atts after resolving environment vars
        • m_resolvedNumericConstant
          String m_resolvedNumericConstant
          Replacement value for numeric atts after resolving environment vars
        • m_resolvedRange
          String m_resolvedRange
        • m_selectedRange
          Range m_selectedRange
          Range of columns to consider

    • Class weka.filters.unsupervised.attribute.ReplaceWithMissingValue

      class ReplaceWithMissingValue extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      -2356630932899796239L

      • 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
        • m_IgnoreClass
          boolean m_IgnoreClass
          True if the class is to be unset
        • m_Probability
          double m_Probability
          The probability
        • m_Seed
          int m_Seed
          The seed for the random number generator

    • Class weka.filters.unsupervised.attribute.SortLabels

      class SortLabels extends SimpleStreamFilter implements Serializable
      serialVersionUID:
      7815204879694105691L

      • Serialized Fields

        • m_AttributeIndices
          Range m_AttributeIndices
          the range of attributes to process (only relational ones will be processed).
        • m_Comparator
          Comparator<String> m_Comparator
          the comparator to use for sorting.
        • m_NewOrder
          int[][] m_NewOrder
          the new order for the labels.
        • m_SortType
          int m_SortType
          the sort type.

    • Class weka.filters.unsupervised.attribute.SortLabels.CaseInsensitiveComparator

      class CaseInsensitiveComparator extends Object implements Serializable
      serialVersionUID:
      -4515292733342486066L

    • Class weka.filters.unsupervised.attribute.SortLabels.CaseSensitiveComparator

      class CaseSensitiveComparator extends Object implements Serializable
      serialVersionUID:
      7071450356783873277L

    • 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_dictionaryBuilder
          DictionaryBuilder m_dictionaryBuilder
          Used to build and manage the dictionary + vectorization
        • m_dictionaryFile
          File m_dictionaryFile
          File to save the dictionary to
        • m_dictionaryIsBinary
          boolean m_dictionaryIsBinary
          Whether to save the dictionary in serialized form rather than as plain text
        • m_filterType
          int m_filterType
          The normalization to apply.
        • m_PeriodicPruningRate
          double m_PeriodicPruningRate
          The percentage at which to periodically prune the dictionary.

    • 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.Transpose

      class Transpose extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      213999899640387499L

  • Package weka.filters.unsupervised.instance

    • Class weka.filters.unsupervised.instance.NonSparseToSparse

      class NonSparseToSparse extends Filter implements Serializable
      serialVersionUID:
      4694489111366063852L

      • Serialized Fields

        • m_encodeMissingAsZero
          boolean m_encodeMissingAsZero

    • 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.RemoveDuplicates

      class RemoveDuplicates extends SimpleBatchFilter implements Serializable
      serialVersionUID:
      4518686110979589602L

    • 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<String> 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_containsStringAtts
          boolean m_containsStringAtts
          True if the incoming data contains string attributes
        • 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
          Object[] 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.AbstractGUIApplication

      class AbstractGUIApplication extends JPanel implements Serializable
      serialVersionUID:
      -2116770422043462730L

      • Serialized Fields

        • m_applicationSettings
          Settings m_applicationSettings
          The settings for the application
        • m_perspectiveManager
          PerspectiveManager m_perspectiveManager
          Manages perspectives and provides the perspectives toolbar

    • Class weka.gui.AbstractPerspective

      class AbstractPerspective extends JPanel implements Serializable
      serialVersionUID:
      1919714661641262879L

      • Serialized Fields

        • m_isActive
          boolean m_isActive
          True if this perspective is currently the active/visible one
        • m_isLoaded
          boolean m_isLoaded
          True if this perspective has been loaded
        • m_log
          Logger m_log
          Logger for this perspective
        • m_mainApplication
          GUIApplication m_mainApplication
          The main application that is displaying this perspective
        • m_perspectiveIcon
          Icon m_perspectiveIcon
          Icon for this perspective
        • m_perspectiveID
          String m_perspectiveID
          The ID of the perspective
        • m_perspectiveTipText
          String m_perspectiveTipText
          Tip text for this perspective
        • m_perspectiveTitle
          String m_perspectiveTitle
          The title of the perspective

    • 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_allEqualWeights
          boolean m_allEqualWeights
          Do all instances have the same weight
        • 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_AttributeWeightLab
          JLabel m_AttributeWeightLab
          Displays the weight 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
        • m_WeightLab
          JLabel m_WeightLab
          Displays the string "Weight:"

    • 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
          ArrayList<Color> 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
          double[] 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.nominalWeights[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
          double 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.nominalWeights[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.CloseableTabTitle

      class CloseableTabTitle extends JPanel implements Serializable
      serialVersionUID:
      9178081197757118130L

      • Serialized Fields

        • m_callback
          CloseableTabTitle.ClosingCallback m_callback
          Owner to callback on closing widget click
        • m_closeAccelleratorText
          String m_closeAccelleratorText
          Description of the keyboard accellerator used for closing the active tab (if any)
        • m_enclosingPane
          JTabbedPane m_enclosingPane
          The enclosing JTabbedPane
        • m_tabButton
          weka.gui.CloseableTabTitle.TabButton m_tabButton
          The close widget
        • m_tabLabel
          JLabel m_tabLabel
          The label for the tab

    • Class weka.gui.ConverterFileChooser

      class ConverterFileChooser extends WekaFileChooser implements Serializable
      serialVersionUID:
      -5373058011025481738L

      • Serialized Fields

        • m_CapabilitiesFilter
          Capabilities m_CapabilitiesFilter
          the Capabilities filter for the savers.
        • m_CheckBoxOptions
          JCheckBox m_CheckBoxOptions
          the checkbox for bringing up the GenericObjectEditor.
        • 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_Editor
          GenericObjectEditor m_Editor
          the GOE for displaying the options of a loader/saver.
        • m_EditorResult
          int m_EditorResult
          whether the GOE was OKed or Canceled.
        • 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).

    • Class weka.gui.CostBenefitAnalysisPanel

      class CostBenefitAnalysisPanel 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.CostBenefitAnalysisPanel.ConfusionCell m_conf_aa
        • m_conf_ab
          weka.gui.CostBenefitAnalysisPanel.ConfusionCell m_conf_ab
        • m_conf_actualA
          JLabel m_conf_actualA
        • m_conf_actualB
          JLabel m_conf_actualB
        • m_conf_ba
          weka.gui.CostBenefitAnalysisPanel.ConfusionCell m_conf_ba
        • m_conf_bb
          weka.gui.CostBenefitAnalysisPanel.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.CostBenefitAnalysisPanel.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.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.EnvironmentField

      class EnvironmentField extends JPanel implements Serializable
      serialVersionUID:
      -3125404573324734121L

      • Serialized Fields

        • m_combo
          JComboBox m_combo
          The combo box
        • m_currentCaretPos
          int m_currentCaretPos
        • m_currentContents
          String m_currentContents
        • m_env
          Environment m_env
          The current environment variables
        • m_firstCaretPos
          int m_firstCaretPos
        • m_label
          JLabel m_label
          The label for the widget
        • m_previousCaretPos
          int m_previousCaretPos
        • m_support
          PropertyChangeSupport m_support

    • Class weka.gui.EnvironmentField.WideComboBox

      class WideComboBox extends JComboBox implements Serializable
      serialVersionUID:
      -6512065375459733517L

      • Serialized Fields

        • m_layingOut
          boolean m_layingOut

    • Class weka.gui.ETable

      class ETable extends JTable implements Serializable
      serialVersionUID:
      -3028630226368293049L

      • Serialized Fields

        • MAC_FOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR
          Color MAC_FOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR
        • MAC_FOCUSED_SELECTED_VERTICAL_LINE_COLOR
          Color MAC_FOCUSED_SELECTED_VERTICAL_LINE_COLOR
        • MAC_FOCUSED_UNSELECTED_VERTICAL_LINE_COLOR
          Color MAC_FOCUSED_UNSELECTED_VERTICAL_LINE_COLOR
        • MAC_OS_ALTERNATE_ROW_COLOR
          Color MAC_OS_ALTERNATE_ROW_COLOR
        • MAC_UNFOCUSED_SELECTED_CELL_BACKGROUND_COLOR
          Color MAC_UNFOCUSED_SELECTED_CELL_BACKGROUND_COLOR
        • MAC_UNFOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR
          Color MAC_UNFOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR
        • MAC_UNFOCUSED_SELECTED_VERTICAL_LINE_COLOR
          Color MAC_UNFOCUSED_SELECTED_VERTICAL_LINE_COLOR
        • MAC_UNFOCUSED_UNSELECTED_VERTICAL_LINE_COLOR
          Color MAC_UNFOCUSED_UNSELECTED_VERTICAL_LINE_COLOR

    • Class weka.gui.EvaluationMetricSelectionDialog

      class EvaluationMetricSelectionDialog extends JDialog implements Serializable
      serialVersionUID:
      4451184027143094270L

      • Serialized Fields

        • m_selectedEvalMetrics
          List<String> m_selectedEvalMetrics
          The list of selected evaluation metrics

    • Class weka.gui.ExtensionFileFilter

      class ExtensionFileFilter extends FileFilter implements Serializable
      serialVersionUID:
      -5960622841390665131L

      • 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.ExtensionFileFilterWithClass

      class ExtensionFileFilterWithClass extends ExtensionFileFilter implements Serializable

      • Serialized Fields

        • m_FilterClass
          Class m_FilterClass
          the underlying class.

    • Class weka.gui.FileEnvironmentField

      class FileEnvironmentField extends EnvironmentField implements Serializable
      serialVersionUID:
      -233731548086207652L

      • Serialized Fields

        • m_browseBut
          JButton m_browseBut
          The button to pop up the file dialog
        • m_fileEditor
          FileEditor m_fileEditor
          File editor component
        • m_fileEditorDialog
          PropertyDialog m_fileEditorDialog
          Dialog to hold the file editor

    • 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
          WekaFileChooser 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.GenericObjectEditorHistory

      class GenericObjectEditorHistory extends Object implements Serializable
      serialVersionUID:
      -1255734638729633595L

      • Serialized Fields

        • m_History
          Vector<Object> m_History
          the history of objects.

    • Class weka.gui.GenericObjectEditorHistory.HistorySelectionEvent

      class HistorySelectionEvent extends EventObject implements Serializable
      serialVersionUID:
      45824542929908105L

      • Serialized Fields

        • m_HistoryItem
          Object m_HistoryItem
          the selected favorite.

    • Class weka.gui.GUIChooserApp

      class GUIChooserApp extends JFrame implements Serializable
      serialVersionUID:
      9001529425230247914L

      • Serialized Fields

        • m_ChildFrames
          HashSet<Container> m_ChildFrames
          contains the child frames (title <-> object).
        • m_ExperimenterBut
          JButton m_ExperimenterBut
          Click to open the Explorer
        • m_ExplorerBut
          JButton m_ExplorerBut
          Click to open the Explorer
        • m_FileChooserGraphVisualizer
          WekaFileChooser m_FileChooserGraphVisualizer
          filechooser for the GraphVisualizer
        • m_FileChooserPlot
          WekaFileChooser m_FileChooserPlot
          filechooser for Plots
        • m_FileChooserROC
          WekaFileChooser m_FileChooserROC
          filechooser for ROC curves
        • m_FileChooserTreeVisualizer
          WekaFileChooser m_FileChooserTreeVisualizer
          filechooser for the TreeVisualizer
        • m_Frames
          Vector<JFrame> m_Frames
          The frames for the various applications that are open
        • 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_PanelApplications
          JPanel m_PanelApplications
          the panel for the application buttons
        • m_Self
          GUIChooserApp m_Self
          the GUIChooser itself
        • m_settings
          Settings m_settings
          GUIChooser settings
        • m_SimpleBut
          JButton m_SimpleBut
          Click to open the simplecli
        • m_weka
          Image m_weka
          The weka image
        • m_WorkbenchBut
          JButton m_WorkbenchBut
          Click to open the Workbench

    • Class weka.gui.GUIChooserApp.ChildFrameSDI

      class ChildFrameSDI extends JFrame implements Serializable
      serialVersionUID:
      8588293938686425618L

      • Serialized Fields

    • Class weka.gui.GUIChooserApp.GUIChooserDefaults

      class GUIChooserDefaults extends Defaults implements Serializable
      serialVersionUID:
      -8524894440289936685L

    • 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).
        • m_sumOfWeightsLab
          JLabel m_sumOfWeightsLab
          Displays the sum of instance weights

    • Class weka.gui.InteractiveTableModel

      class InteractiveTableModel extends AbstractTableModel implements Serializable
      serialVersionUID:
      -5113873323690309667L

      • Serialized Fields

        • m_columnNames
          String[] m_columnNames
          The names of the columns
        • m_dataVector
          List<List<String>> m_dataVector
          Holds the data
        • m_hidden_index
          int m_hidden_index
          Index of the hidden column

    • Class weka.gui.InteractiveTablePanel

      class InteractiveTablePanel extends JPanel implements Serializable
      serialVersionUID:
      4495705463732140410L

      • Serialized Fields

        • m_columnNames
          String[] m_columnNames
          Holds column names
        • m_scroller
          JScrollPane m_scroller
          Scroll panel for the table
        • m_table
          JTable m_table
          The table itself
        • m_tableModel
          InteractiveTableModel m_tableModel
          Model for the table

    • 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_Frame
          JFrame m_Frame
          The JFrame to use
        • 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
        • jMenuItemToolsGroovyConsole
          JMenuItem jMenuItemToolsGroovyConsole
        • jMenuItemToolsJythonConsole
          JMenuItem jMenuItemToolsJythonConsole
        • 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
          WekaFileChooser m_FileChooserGraphVisualizer
          filechooser for the GraphVisualizer.
        • m_FileChooserPlot
          WekaFileChooser m_FileChooserPlot
          filechooser for Plots.
        • m_FileChooserROC
          WekaFileChooser m_FileChooserROC
          filechooser for ROC curves.
        • m_FileChooserTreeVisualizer
          WekaFileChooser 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.PackageManager

      class PackageManager extends JPanel implements Serializable
      serialVersionUID:
      -7463821313750352385L

      • Serialized Fields

        • m_allBut
          JRadioButton m_allBut
          All radio button
        • m_allPackages
          List<Package> m_allPackages
        • m_availableBut
          JRadioButton m_availableBut
          Available radio button
        • m_availablePackages
          List<Package> m_availablePackages
        • m_backB
          JButton m_backB
        • m_browserHistory
          LinkedList<URL> m_browserHistory
        • m_browserTools
          JToolBar m_browserTools
        • m_cacheEstablished
          boolean m_cacheEstablished
        • m_cacheRefreshInProgress
          boolean m_cacheRefreshInProgress
        • m_detailLabel
          JLabel m_detailLabel
        • m_forceBut
          JCheckBox m_forceBut
        • m_homeB
          JButton m_homeB
        • m_infoPane
          JEditorPane m_infoPane
          An editor pane to display package information
        • m_installBut
          JButton m_installBut
          Button for installing the selected package
        • m_installedBut
          JRadioButton m_installedBut
          Installed radio button
        • m_installedPackages
          List<Package> m_installedPackages
        • m_installing
          boolean m_installing
        • m_model
          DefaultTableModel m_model
        • m_newPackagesAvailableL
          JLabel m_newPackagesAvailableL
        • m_packageComparator
          Comparator<Package> m_packageComparator
        • m_packageDescriptions
          Map<String,String> m_packageDescriptions
        • m_packageLookupInfo
          Map<String,List<Object>> m_packageLookupInfo
        • m_progress
          JProgressBar m_progress
        • m_refreshCacheBut
          JButton m_refreshCacheBut
          Button for refreshing the package meta data cache
        • m_reverseSort
          boolean m_reverseSort
          Reverse the sort order if the user clicks the same column header twice
        • m_searchField
          JTextField m_searchField
        • m_searchHitsLab
          JLabel m_searchHitsLab
        • m_searchResults
          List<Package> m_searchResults
        • m_sortColumn
          int m_sortColumn
          The column in the table to sort the entries by
        • m_splitP
          JSplitPane m_splitP
        • m_table
          JTable m_table
          The JTable for displaying the package names and version numbers
        • m_toggleLoad
          JButton m_toggleLoad
          Button for toggling the load status of an installed package
        • m_uninstallBut
          JButton m_uninstallBut
          Button for uninstalling the selected package
        • m_unofficialBut
          JButton m_unofficialBut
          Button to pop up the file environment field widget
        • m_unofficialChooser
          FileEnvironmentField m_unofficialChooser
          Widget for specifying a URL or path to an unofficial package to install
        • m_unofficialFrame
          JFrame m_unofficialFrame

    • Class weka.gui.PackageManager.ComboBoxEditor

      class ComboBoxEditor extends DefaultCellEditor implements Serializable
      serialVersionUID:
      5240331667759901966L

    • Class weka.gui.PasswordField

      class PasswordField extends JPanel implements Serializable
      serialVersionUID:
      8180782063577036194L

    • Class weka.gui.PerspectiveManager

      class PerspectiveManager extends JPanel implements Serializable
      serialVersionUID:
      -6099806469970666208L

      • Serialized Fields

        • m_allowedPerspectiveClassPrefixes
          List<String> m_allowedPerspectiveClassPrefixes
          Allow these perspectives in the toolbar (empty list means allow all)
        • m_appMenuBar
          JMenuBar m_appMenuBar
          Main application menu bar
        • m_configAndPerspectivesToolBar
          JPanel m_configAndPerspectivesToolBar
          Holds the config/settings button and toolbar
        • m_configAndPerspectivesVisible
          boolean m_configAndPerspectivesVisible
          Whether the toolbar is visible or hidden
        • m_disallowedPerspectiveClassPrefixes
          List<String> m_disallowedPerspectiveClassPrefixes
          Disallow these perspectives (non-empty list overrides meaning of empty allowed list)
        • m_LogPanel
          LogPanel m_LogPanel
          The panel for log and status messages
        • m_logVisible
          boolean m_logVisible
          Whether the log is visible in the current perspective
        • m_mainApp
          GUIApplication m_mainApp
          The main application that owns this perspective manager
        • m_mainPerspective
          Perspective m_mainPerspective
          The main perspective for the application owning this perspective manager
        • m_perspectiveCache
          Map<String,Perspective> m_perspectiveCache
          Cache of perspectives
        • m_perspectiveGroup
          ButtonGroup m_perspectiveGroup
          For grouping the perspectives buttons in the toolbar
        • m_perspectiveNameLookup
          Map<String,String> m_perspectiveNameLookup
          Name lookup for perspectives
        • m_perspectives
          List<Perspective> m_perspectives
          The perspectives that have a button in the toolbar
        • m_perspectiveToolBar
          JToolBar m_perspectiveToolBar
          The actual perspectives toolbar
        • m_programMenu
          JMenu m_programMenu
          Program menu
        • m_togglePerspectivesToolBar
          JMenuItem m_togglePerspectivesToolBar
          Menu item for toggling whether the toolbar is visible or not
        • m_visiblePerspectives
          LinkedHashSet<String> m_visiblePerspectives
          Names of visible perspectives (i.e. those that the user has opted to be available via a button in the toolbar

    • Class weka.gui.PerspectiveManager.SelectedPerspectivePreferences

      class SelectedPerspectivePreferences extends Object implements Serializable
      serialVersionUID:
      -2665480123235382483L

      • Serialized Fields

        • m_perspectivesToolbarAlwaysHidden
          boolean m_perspectivesToolbarAlwaysHidden
          Whether the toolbar should always be hidden (and not able to be toggled from the menu or widget.
        • m_perspectivesToolbarVisibleOnStartup
          boolean m_perspectivesToolbarVisibleOnStartup
          Whether the toolbar should be visible (or hidden) at startup
        • m_userVisiblePerspectives
          LinkedList<String> m_userVisiblePerspectives
          List of user selected perspectives to show

    • 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_Customizer
          GOECustomizer m_Customizer
          Holds the customizer (if one exists) for the object being edited
        • 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_propertyGroupingCategory
          String m_propertyGroupingCategory
          Non-empty if this panel is being used to group/display properties for a particular category
        • m_showAboutPanel
          boolean m_showAboutPanel
          Whether to show the about panel
        • m_Target
          Object m_Target
          The target object being edited.
        • m_TipTexts
          String[] m_TipTexts
          The tool tip text for each property.
        • m_useEnvironmentPropertyEditors
          boolean m_useEnvironmentPropertyEditors
          Whether to use EnvironmentField and FileEnvironmentField for text and file properties respectively
        • 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<String,JTextArea> 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<String,Object> m_Objs
          A hashtable mapping names to arbitrary objects
        • m_Printer
          PrintableComponent m_Printer
          for printing the output to files
        • m_Results
          Hashtable<String,StringBuffer> 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_ClassComboBox
          JComboBox m_ClassComboBox
          the class combobox.
        • m_ClassLabel
          JLabel m_ClassLabel
          the label for the class combobox.
        • 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_showClassComboBox
          boolean m_showClassComboBox
          whether to display a combobox that allows the user to choose the class attribute.
        • 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.SettingsEditor

      class SettingsEditor extends JPanel implements Serializable
      serialVersionUID:
      1453121012707399758L

    • Class weka.gui.SettingsEditor.PerspectiveSelector

      class PerspectiveSelector extends JPanel implements Serializable
      serialVersionUID:
      -4765015948030757897L

      • Serialized Fields

        • m_perspectiveChecks
          List<JCheckBox> m_perspectiveChecks
        • m_toolBarVisibleOnStartup
          JCheckBox m_toolBarVisibleOnStartup

    • Class weka.gui.SettingsEditor.PickList

      class PickList extends JPanel implements Serializable
      serialVersionUID:
      3505647427533464230L

    • Class weka.gui.SettingsEditor.SingleSettingsEditor

      class SingleSettingsEditor extends JPanel implements Serializable
      serialVersionUID:
      8896265984902770239L

    • Class weka.gui.SimpleCLI

      class SimpleCLI extends JFrame implements Serializable
      serialVersionUID:
      -50661410800566036L

    • Class weka.gui.SimpleCLIPanel

      class SimpleCLIPanel extends ScriptingPanel implements Serializable
      serialVersionUID:
      1089039734615114942L

      • Serialized Fields

        • m_CommandHistory
          Vector<String> m_CommandHistory
          The history of commands entered interactively.
        • m_Completion
          SimpleCLIPanel.CommandlineCompletion m_Completion
          The commandline completion.
        • m_HistoryPos
          int m_HistoryPos
          The current position in the command history.
        • m_Input
          JTextField m_Input
          The command input area.
        • m_mainApp
          GUIApplication m_mainApp
          Main application (if any) owning this perspective
        • m_OutputArea
          JTextPane m_OutputArea
          The output area canvas added to the frame.
        • m_perspectiveIcon
          Icon m_perspectiveIcon
          The Icon for this perspective
        • m_RunThread
          Thread m_RunThread
          The thread currently running a class main method.
        • m_Variables
          Map<String,Object> m_Variables
          for storing variables.

    • 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.
        • message
          List<String> message
          Any message to overlay on the image
        • 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_addInstanceButton
          JButton m_addInstanceButton
          Click to add a new instance to the end of the dataset
        • 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.WekaFileChooser

      class WekaFileChooser extends JFileChooser implements Serializable

    • Class weka.gui.WekaFileChooser.Factory

      class Factory extends com.googlecode.jfilechooserbookmarks.DefaultFactory implements Serializable

    • Class weka.gui.WekaFileChooser.FileChooserBookmarksPanel

      class FileChooserBookmarksPanel extends com.googlecode.jfilechooserbookmarks.AbstractBookmarksPanel implements Serializable

    • Class weka.gui.WekaFileChooser.PropertiesHandler

      class PropertiesHandler extends com.googlecode.jfilechooserbookmarks.AbstractPropertiesHandler implements Serializable

    • 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

    • Class weka.gui.WorkbenchApp

      class WorkbenchApp extends AbstractGUIApplication implements Serializable
      serialVersionUID:
      -2357486011273897728L

      • Serialized Fields

        • m_mainPerspective
          PreprocessPanel m_mainPerspective
          The main perspective for this application
        • m_workbenchSettings
          Settings m_workbenchSettings
          Settings for the Workbench

    • Class weka.gui.WorkbenchDefaults

      class WorkbenchDefaults extends Defaults implements Serializable
      serialVersionUID:
      7881327795923189743L

    • Class weka.gui.WrapLayout

      class WrapLayout extends FlowLayout implements Serializable
      serialVersionUID:
      7055023419450383821L

      • Serialized Fields

        • preferredLayoutSize
          Dimension preferredLayoutSize

  • 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<ChangeListener> 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_ShowAttributeIndex
          boolean m_ShowAttributeIndex
          whether to display the attribute index in the table header.
        • 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
        • menuItemInsertInstance
          JMenuItem menuItemInsertInstance
        • menuItemMean
          JMenuItem menuItemMean
        • menuItemOptimalColWidth
          JMenuItem menuItemOptimalColWidth
        • menuItemOptimalColWidths
          JMenuItem menuItemOptimalColWidths
        • menuItemRenameAttribute
          JMenuItem menuItemRenameAttribute
        • menuItemReplaceValues
          JMenuItem menuItemReplaceValues
        • menuItemSearch
          JMenuItem menuItemSearch
        • menuItemSetAllValues
          JMenuItem menuItemSetAllValues
        • menuItemSetAttributeWeight
          JMenuItem menuItemSetAttributeWeight
        • menuItemSetInstanceWeight
          JMenuItem menuItemSetInstanceWeight
        • 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<ChangeListener> 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.ArffTableModel

      class ArffTableModel extends DefaultTableModel implements Serializable
      serialVersionUID:
      3411795562305994946L

      • Serialized Fields

        • m_Cache
          Hashtable<String,String> m_Cache
          for caching long relational and string values that get processed for display.
        • m_Data
          Instances m_Data
          the data
        • m_IgnoreChanges
          boolean m_IgnoreChanges
          whether to ignore changes, i.e. not adding to undo history
        • m_Listeners
          HashSet<TableModelListener> m_Listeners
          the listeners
        • m_NotificationEnabled
          boolean m_NotificationEnabled
          whether notfication is enabled
        • m_ReadOnly
          boolean m_ReadOnly
          whether the table is read-only
        • m_ShowAttributeIndex
          boolean m_ShowAttributeIndex
          whether to display the attribute index in the table header.
        • m_ShowAttributeWeights
          boolean m_ShowAttributeWeights
          whether to show attribute weights.
        • m_ShowInstanceWeights
          boolean m_ShowInstanceWeights
          whether to show instance weights.
        • m_UndoEnabled
          boolean m_UndoEnabled
          whether undo is active
        • m_UndoList
          Vector<File> m_UndoList
          the undo list (contains temp. filenames)

    • 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<EventListener> 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<EventListener> 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<EventListener> m_testListeners
          Objects listening for test set events
        • m_trainingListeners
          Vector<EventListener> 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<EventListener> m_listeners
          Objects listening for training set events
        • m_visual
          BeanVisual m_visual

    • Class weka.gui.beans.Appender

      class Appender extends JPanel implements Serializable
      serialVersionUID:
      9177433051794199463L

      • Serialized Fields

        • m_busy
          boolean m_busy
          True if we are busy
        • m_dataListeners
          ArrayList<DataSourceListener> m_dataListeners
          Downstream steps listening to batch data events
        • m_finishedCount
          int m_finishedCount
          Keeps track of how many incoming instance streams have finished
        • m_ie
          InstanceEvent m_ie
          Instance event to use for incremental mode
        • m_instanceListeners
          ArrayList<InstanceListener> m_instanceListeners
          Downstream steps listening to instance events
        • m_listenees
          Map<Object,Object> m_listenees
        • m_listeneeTypes
          Set<String> m_listeneeTypes
          Upstream components sending us data
        • m_visual
          BeanVisual m_visual
          Default visual for data sources

    • Class weka.gui.beans.Associator

      class Associator extends JPanel implements Serializable
      serialVersionUID:
      -7843500322130210057L

    • 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_activePerspective
          boolean m_activePerspective
        • 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
        • m_runningAsPerspective
          boolean m_runningAsPerspective
        • m_showClassCombo
          boolean m_showClassCombo

    • Class weka.gui.beans.AttributeSummarizerCustomizer

      class AttributeSummarizerCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -6973666187676272788L

    • Class weka.gui.beans.BatchAssociationRulesEvent

      class BatchAssociationRulesEvent extends EventObject implements Serializable
      serialVersionUID:
      6332614648885439492L

    • Class weka.gui.beans.BatchClassifierEvent

      class BatchClassifierEvent extends EventObject implements Serializable
      serialVersionUID:
      878097199815991084L

      • Serialized Fields

        • m_classifier
          Classifier m_classifier
          The classifier
        • m_eventLabel
          String m_eventLabel
          Label for this event
        • 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.BeansProperties

      class BeansProperties extends Object implements Serializable
      serialVersionUID:
      7183413615090695785L

    • 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
          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<String> 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

    • Class weka.gui.beans.ClassAssignerCustomizer

      class ClassAssignerCustomizer extends JPanel implements Serializable
      serialVersionUID:
      476539385765301907L

      • Serialized Fields

        • m_displayColNames
          boolean m_displayColNames

    • Class weka.gui.beans.Classifier

      class Classifier extends JPanel implements Serializable
      serialVersionUID:
      659603893917736008L

      • Serialized Fields

        • m_batchClassifierListeners
          Vector<BatchClassifierListener> m_batchClassifierListeners
          Objects listening for batch classifier events
        • m_binaryFilter
          FileFilter m_binaryFilter
        • m_block
          boolean m_block
          True if we should block rather reject until all processing has been completed.
        • 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<GraphListener> m_graphListeners
          Objects listening for graph events
        • m_ie
          IncrementalClassifierEvent m_ie
        • m_incrementalClassifierListeners
          Vector<IncrementalClassifierListener> 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
          HashMap<String,List<Object>> m_listenees
          Objects talking to us. String connection event key, 2 element list containing source and count
        • m_loadModelFileName
          String m_loadModelFileName
          Optional file to load a pre-trained model to score with (batch, or to score and update (incremental) in the case of testSet only (batch) or instance (incremental) connections
        • m_oldText
          String m_oldText
          Holds original icon label text
        • m_reject
          boolean m_reject
          true if we should reject any further training data sets, until all processing has been finished, once we've received the last fold of the last run.
        • m_resetIncrementalClassifier
          boolean m_resetIncrementalClassifier
          If the classifier is an incremental classifier, should we reset it (i.e. call buildClassifier()) and discard any previously learned model before processing the first instance in the stream. Note that this happens automatically if the incoming instance structure does not match that (if any) that the classifier was trained with previously.
        • m_state
          int m_state
        • m_textListeners
          Vector<TextListener> 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
      serialVersionUID:
      -7918128680624169641L

      • 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_blockOnLastFold
          JCheckBox m_blockOnLastFold
        • m_ClassifierEditor
          PropertySheetPanel m_ClassifierEditor
        • m_dsClassifier
          Classifier m_dsClassifier
        • m_env
          Environment m_env
        • m_executionSlotsLabel
          JLabel m_executionSlotsLabel
        • m_executionSlotsPanel
          JPanel m_executionSlotsPanel
        • m_executionSlotsText
          JTextField m_executionSlotsText
        • m_holderPanel
          JPanel m_holderPanel
        • m_incrementalPanel
          JPanel m_incrementalPanel
        • m_loadModelField
          FileEnvironmentField m_loadModelField
        • m_modifyListener
          BeanCustomizer.ModifyListener m_modifyListener
          Listener that wants to know the the modified status of the object that we're customizing
        • m_panelVisible
          boolean m_panelVisible
        • m_parentWindow
          Window m_parentWindow
        • m_pcSupport
          PropertyChangeSupport m_pcSupport
        • m_resetIncrementalClassifier
          JCheckBox m_resetIncrementalClassifier
        • m_updateIncrementalClassifier
          JCheckBox m_updateIncrementalClassifier

    • Class weka.gui.beans.ClassifierPerformanceEvaluator

      class ClassifierPerformanceEvaluator extends AbstractEvaluator implements Serializable
      serialVersionUID:
      -3511801418192148690L

      • Serialized Fields

        • m_errorPlotPointSizeProportionalToMargin
          boolean m_errorPlotPointSizeProportionalToMargin
        • m_executionSlots
          int m_executionSlots
          Number of threads to use to train models with
        • m_metricsList
          List<String> m_metricsList
        • m_selectedEvalMetrics
          String m_selectedEvalMetrics
          Evaluation metrics to output
        • m_textListeners
          Vector<TextListener> m_textListeners
        • m_thresholdListeners
          Vector<ThresholdDataListener> m_thresholdListeners
        • m_visualizableErrorListeners
          Vector<VisualizableErrorListener> m_visualizableErrorListeners

    • Class weka.gui.beans.ClassifierPerformanceEvaluator.AggregateableClassifierErrorsPlotInstances

      class AggregateableClassifierErrorsPlotInstances extends ClassifierErrorsPlotInstances implements Serializable
      serialVersionUID:
      2012744784036684168L

    • Class weka.gui.beans.ClassifierPerformanceEvaluator.EvaluationTask

      class EvaluationTask extends Object implements Serializable
      serialVersionUID:
      -8939077467030259059L

      • Serialized Fields

        • m_classifier
          Classifier m_classifier
        • m_evalLabel
          String m_evalLabel
        • m_maxSetNum
          int m_maxSetNum
        • m_setNum
          int m_setNum
        • m_stopped
          boolean m_stopped
        • m_testData
          Instances m_testData
        • m_trainData
          Instances m_trainData

    • Class weka.gui.beans.ClassifierPerformanceEvaluatorCustomizer

      class ClassifierPerformanceEvaluatorCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -1055460295546483853L

    • Class weka.gui.beans.ClassValuePicker

      class ClassValuePicker extends JPanel implements Serializable
      serialVersionUID:
      -1196143276710882989L

      • Serialized Fields

    • Class weka.gui.beans.ClassValuePickerCustomizer

      class ClassValuePickerCustomizer extends JPanel implements Serializable
      serialVersionUID:
      8213423053861600469L

    • Class weka.gui.beans.Clusterer

      class Clusterer extends JPanel implements Serializable
      serialVersionUID:
      7729795159836843810L

      • Serialized Fields

        • m_batchClustererListeners
          Vector<BatchClustererListener> 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<GraphListener> m_graphListeners
          Objects listening for graph events
        • m_listenees
          Hashtable<String,Object> m_listenees
          Objects talking to us
        • m_state
          int m_state
        • m_textListeners
          Vector<TextListener> 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

    • Class weka.gui.beans.ConfigurationEvent

      class ConfigurationEvent extends EventObject implements Serializable
      serialVersionUID:
      5433562112093780868L

    • 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_headlessEvents
          List<EventObject> m_headlessEvents
        • 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_dataProvider
          boolean m_dataProvider
        • m_numFolds
          int m_numFolds
        • m_preserveOrder
          boolean m_preserveOrder
        • m_randomSeed
          int m_randomSeed
        • m_testProvider
          boolean m_testProvider
        • m_trainingProvider
          boolean m_trainingProvider

    • 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_additionalOptions
          String m_additionalOptions
          Additional options for the offscreen renderer
        • m_bcSupport
          BeanContextChildSupport m_bcSupport
          BeanContextChild support
        • m_dataSetListeners
          Vector<DataSourceListener> 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_headlessEvents
          List<EventObject> m_headlessEvents
          Events received and stored during headless execution
        • m_height
          String m_height
          Height of offscreen plots
        • m_imageListeners
          ArrayList<ImageListener> m_imageListeners
        • m_listenees
          List<Object> m_listenees
        • m_offscreenRendererName
          String m_offscreenRendererName
          Name of the renderer to use for offscreen chart rendering
        • m_visPanel
          VisualizePanel m_visPanel
        • m_visual
          BeanVisual m_visual
        • m_width
          String m_width
          Width of offscreen plots
        • m_xAxis
          String m_xAxis
          The name of the attribute to use for the x-axis of offscreen plots. If left empty, False Positive Rate is used for threshold curves
        • m_yAxis
          String m_yAxis
          The name of the attribute to use for the y-axis of offscreen plots. If left empty, True Positive Rate is used for threshold curves

    • Class weka.gui.beans.DataVisualizerCustomizer

      class DataVisualizerCustomizer extends JPanel implements Serializable
      serialVersionUID:
      27802741348090392L

    • Class weka.gui.beans.EnvironmentField

      class EnvironmentField extends JPanel implements Serializable
      serialVersionUID:
      -3125404573324734121L

      • Serialized Fields

        • m_combo
          JComboBox m_combo
          Deprecated.
          The combo box
        • m_currentCaretPos
          int m_currentCaretPos
          Deprecated.
        • m_currentContents
          String m_currentContents
          Deprecated.
        • m_env
          Environment m_env
          Deprecated.
          The current environment variables
        • m_firstCaretPos
          int m_firstCaretPos
          Deprecated.
        • m_label
          JLabel m_label
          Deprecated.
          The label for the widget
        • m_previousCaretPos
          int m_previousCaretPos
          Deprecated.
        • m_support
          PropertyChangeSupport m_support
          Deprecated.

    • Class weka.gui.beans.EnvironmentField.WideComboBox

      class WideComboBox extends JComboBox implements Serializable
      serialVersionUID:
      -6512065375459733517L

      • Serialized Fields

        • m_layingOut
          boolean m_layingOut

    • Class weka.gui.beans.FileEnvironmentField

      class FileEnvironmentField extends EnvironmentField implements Serializable
      serialVersionUID:
      -233731548086207652L

      • Serialized Fields

        • m_browseBut
          JButton m_browseBut
          Deprecated.
          The button to pop up the file dialog
        • m_fileEditor
          FileEditor m_fileEditor
          Deprecated.
          File editor component
        • m_fileEditorDialog
          PropertyDialog m_fileEditorDialog
          Deprecated.
          Dialog to hold the file editor

    • Class weka.gui.beans.Filter

      class Filter extends JPanel implements Serializable
      serialVersionUID:
      8249759470189439321L

      • Serialized Fields

        • m_dataListeners
          Vector<DataSourceListener> 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<InstanceListener> m_instanceListeners
          Objects listening for instance events
        • m_listenees
          Hashtable<String,Object> m_listenees
          Objects talking to us
        • m_state
          int m_state
        • m_structurePassedOn
          boolean m_structurePassedOn
        • m_testListeners
          Vector<TestSetListener> m_testListeners
          Objects listening for test set events
        • m_trainingListeners
          Vector<TrainingSetListener> 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.FlowByExpression

      class FlowByExpression extends JPanel implements Serializable
      serialVersionUID:
      2492050246494259885L

      • Serialized Fields

        • m_connectedFormat
          Instances m_connectedFormat
          format of instances for current incoming connection (if any)
        • m_connectionType
          String m_connectionType
          The type of the incoming connection
        • m_customNameOfFalseStep
          String m_customNameOfFalseStep
          Name of the step to receive instances that evaluate to false via the expression
        • m_customNameOfTrueStep
          String m_customNameOfTrueStep
          Name of the step to receive instances that evaluate to true via the expression
        • m_downstream
          Object[] m_downstream
          The one or two downstream steps - one for instances that match the expression and the other for instances that don't
        • m_expressionString
          String m_expressionString
          The expression tree to use in internal textual format
        • m_ie
          InstanceEvent m_ie
          Instance event to use
        • m_indexOfFalseStep
          int m_indexOfFalseStep
        • m_indexOfTrueStep
          int m_indexOfTrueStep
        • m_listenee
          Object m_listenee
          Component talking to us
        • m_root
          weka.gui.beans.FlowByExpression.ExpressionNode m_root
          The root of the expression tree
        • m_visual
          BeanVisual m_visual
          Default visual filters

    • Class weka.gui.beans.FlowByExpression.BracketNode

      class BracketNode extends weka.gui.beans.FlowByExpression.ExpressionNode implements Serializable
      serialVersionUID:
      8732159083173001115L

      • Serialized Fields

        • m_children
          List<weka.gui.beans.FlowByExpression.ExpressionNode> m_children

    • Class weka.gui.beans.FlowByExpression.ExpressionClause

      class ExpressionClause extends weka.gui.beans.FlowByExpression.ExpressionNode implements Serializable
      serialVersionUID:
      2754006654981248325L

      • Serialized Fields

        • m_lhsAttIndex
          int m_lhsAttIndex
          The index of the lhs attribute
        • m_lhsAttributeName
          String m_lhsAttributeName
          The name of the lhs attribute
        • m_numericOperand
          double m_numericOperand
          The rhs operand (if constant and is a number )
        • m_operator
          FlowByExpression.ExpressionClause.ExpressionType m_operator
          The operator for this expression
        • m_regexPattern
          Pattern m_regexPattern
          the compiled regex pattern (if the operator is REGEX)
        • m_resolvedLhsName
          String m_resolvedLhsName
          The name of the lhs attribute after resolving variables
        • m_resolvedRhsOperand
          String m_resolvedRhsOperand
          The rhs operand after resolving variables
        • m_rhsAttIndex
          int m_rhsAttIndex
          index of the rhs if it is an attribute
        • m_rhsIsAttribute
          boolean m_rhsIsAttribute
          True if the rhs operand is an attribute
        • m_rhsOperand
          String m_rhsOperand
          The rhs operand (constant value or attribute name)

    • Class weka.gui.beans.FlowByExpression.ExpressionNode

      class ExpressionNode extends Object implements Serializable
      serialVersionUID:
      -8427857202322768762L

      • Serialized Fields

        • m_isAnOr
          boolean m_isAnOr
          boolean operator for combining with result so far
        • m_isNegated
          boolean m_isNegated
          is this node negated?
        • m_showAndOr
          boolean m_showAndOr
          Whether to show the combination operator in the textual representation

    • Class weka.gui.beans.FlowByExpressionCustomizer

      class FlowByExpressionCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -3574741335754017794L

    • 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.ImageEvent

      class ImageEvent extends EventObject implements Serializable
      serialVersionUID:
      -8126533743311557969L

      • Serialized Fields

    • Class weka.gui.beans.ImageSaver

      class ImageSaver extends JPanel implements Serializable
      serialVersionUID:
      -641438159956934314L

      • Serialized Fields

        • m_fileName
          String m_fileName
          The file to save to
        • 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_visual
          BeanVisual m_visual
          Default visual for data sources

    • Class weka.gui.beans.ImageSaverCustomizer

      class ImageSaverCustomizer extends JPanel implements Serializable
      serialVersionUID:
      5215477777077643368L

    • Class weka.gui.beans.ImageViewer

      class ImageViewer extends JPanel implements Serializable
      serialVersionUID:
      7976930810628389750L

      • Serialized Fields

        • m_history
          ResultHistoryPanel m_history
          Keeps a history of the images received
        • m_plotter
          weka.gui.beans.ImageViewer.ImageDisplayer m_plotter
          Panel for displaying the image
        • m_visual
          BeanVisual m_visual
          Default visual for data sources

    • Class weka.gui.beans.IncrementalClassifierEvaluator

      class IncrementalClassifierEvaluator extends AbstractEvaluator implements Serializable
      serialVersionUID:
      -3105419818939541291L

      • Serialized Fields

        • m_ce
          ChartEvent m_ce
        • m_dataLegend
          Vector<String> m_dataLegend
        • m_dataPoint
          double[] m_dataPoint
        • m_instanceCount
          int m_instanceCount
        • m_listeners
          Vector<ChartListener> 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<TextListener> m_textListeners
        • m_window
          LinkedList<Instance> m_window
        • m_windowedPreds
          LinkedList<double[]> m_windowedPreds
        • m_windowEval
          Evaluation m_windowEval
        • m_windowSize
          int m_windowSize

    • Class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer

      class IncrementalClassifierEvaluatorCustomizer extends JPanel implements Serializable
      serialVersionUID:
      443506897387629418L

    • 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_formatNotificationOnly
          boolean m_formatNotificationOnly
          for FORMAT_AVAILABLE, if this is true then it indicates that this is not the actual start of stream processing, but rather that a file/source has changed via the user from the UI
        • 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.InteractiveTableModel

      class InteractiveTableModel extends InteractiveTableModel implements Serializable
      serialVersionUID:
      7628124449228704885L

    • Class weka.gui.beans.InteractiveTablePanel

      class InteractiveTablePanel extends InteractiveTablePanel implements Serializable
      serialVersionUID:
      -5331129312037269302L

    • Class weka.gui.beans.Join

      class Join extends JPanel implements Serializable
      serialVersionUID:
      398021880509558185L

      • Serialized Fields

        • m_busy
          boolean m_busy
          True if we are busy
        • m_dataListeners
          ArrayList<DataSourceListener> m_dataListeners
          Downstream steps listening to batch data events
        • m_firstInput
          Object m_firstInput
          The first source of data
        • m_firstInputConnectionType
          String m_firstInputConnectionType
          Connection type of the first input
        • m_firstIsWaiting
          boolean m_firstIsWaiting
          True if the first input stream is waiting due to a full buffer (incremental mode only)
        • m_ie
          InstanceEvent m_ie
          Instance event to use for incremental mode
        • m_incomingBatchConnections
          boolean m_incomingBatchConnections
          Upstream components sending us data
        • m_instanceListeners
          ArrayList<InstanceListener> m_instanceListeners
          Downstream steps listening to instance events
        • m_keyIndexesOne
          int[] m_keyIndexesOne
          Indexes of the key fields for the first input
        • m_keyIndexesTwo
          int[] m_keyIndexesTwo
          Indexes of the key fields for the second input
        • m_keySpec
          String m_keySpec
          Holds the internal representation of the key specification
        • m_runningIncrementally
          boolean m_runningIncrementally
          True if the step is running incrementally
        • m_secondInput
          Object m_secondInput
          The second source of data
        • m_secondInputConnectionType
          String m_secondInputConnectionType
          Connection type of the second input
        • m_secondIsWaiting
          boolean m_secondIsWaiting
          True if the second input stream is waiting due to a full buffer (incremental mode only)
        • m_stopRequested
          AtomicBoolean m_stopRequested
          True if the step has been told to stop processing
        • m_stringAttIndexesOne
          Map<String,Integer> m_stringAttIndexesOne
          Holds indexes of string attributes, keyed by attribute name
        • m_stringAttIndexesTwo
          Map<String,Integer> m_stringAttIndexesTwo
          Holds indexes of string attributes, keyed by attribute name
        • m_stringAttsPresent
          boolean m_stringAttsPresent
          True if string attributes are present in the incoming data
        • m_visual
          BeanVisual m_visual
          Default visual for data sources

    • Class weka.gui.beans.Join.InstanceHolder

      class InstanceHolder extends Object implements Serializable
      serialVersionUID:
      -2554438923824758088L

      • Serialized Fields

        • m_instance
          Instance m_instance
          The instance
        • m_stringVals
          Map<String,String> m_stringVals
          for incremental operation, if string attributes are present then we need to store them with each instance - since incremental streaming in the knowledge flow only maintains one string value in memory (and hence in the header) at any one time

    • Class weka.gui.beans.JoinCustomizer

      class JoinCustomizer extends JPanel implements Serializable
      serialVersionUID:
      5797383368777382010L

    • Class weka.gui.beans.KnowledgeFlowApp

      class KnowledgeFlowApp extends JPanel implements Serializable
      serialVersionUID:
      -7064906770289728431L

      • Serialized Fields

        • m_allowMultipleTabs
          boolean m_allowMultipleTabs
          Whether to allow more than one tab or not
        • m_bcSupport
          BeanContextSupport m_bcSupport
        • m_beanLayout
          weka.gui.beans.KnowledgeFlowApp.BeanLayout m_beanLayout
          The layout area
        • m_componentTree
          JTree m_componentTree
          Palette of components stored in a JTree
        • m_configAndPerspectives
          JPanel m_configAndPerspectives
          Panel to hold the perspectives toolbar and config button
        • m_configAndPerspectivesVisible
          boolean m_configAndPerspectivesVisible
          Whether the perspectives toolbar is visible at present (will never be visible if there are no plugin perspectives installed)
        • m_copyB
          JButton m_copyB
        • m_cutB
          JButton m_cutB
        • m_deleteB
          JButton m_deleteB
        • m_editElement
          BeanInstance m_editElement
          Reference to bean being manipulated
        • m_FileChooser
          WekaFileChooser 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_gridSpacing
          int m_gridSpacing
          Snap-to-grid spacing
        • m_groupB
          JButton m_groupB
        • 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_layoutZoom
          int m_layoutZoom
        • m_loadB
          JButton m_loadB
        • m_logPanel
          weka.gui.beans.KnowledgeFlowApp.KFLogPanel m_logPanel
        • m_mainKFPerspective
          KnowledgeFlowApp.MainKFPerspective m_mainKFPerspective
          Shortcut to the main perspective
        • m_mode
          int m_mode
        • m_newB
          JButton m_newB
        • m_noteB
          JButton m_noteB
        • 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_pasteB
          JButton m_pasteB
        • m_pasteBuffer
          StringBuffer m_pasteBuffer
          The clip board
        • m_perspectiveCache
          Map<String,KnowledgeFlowApp.KFPerspective> m_perspectiveCache
          Those perspectives that have been instantiated
        • m_perspectiveConfigurer
          AttributeSelectionPanel m_perspectiveConfigurer
        • m_perspectiveDataLoadThread
          Thread m_perspectiveDataLoadThread
          Thread for loading data for perspectives
        • m_perspectiveGroup
          ButtonGroup m_perspectiveGroup
          Button group to manage all perspective toggle buttons
        • m_perspectiveHolder
          JPanel m_perspectiveHolder
          Component that holds the currently visible perspective
        • m_perspectives
          List<KnowledgeFlowApp.KFPerspective> m_perspectives
          Holds the list of currently loaded perspectives. Element at 0 is always the main KF data mining flow perspective
        • m_perspectiveToolBar
          JToolBar m_perspectiveToolBar
          Toolbar to hold the perspective buttons
        • m_playB
          JButton m_playB
        • m_playBB
          JButton m_playBB
        • m_pluginPerspectiveLookup
          Map<String,String> m_pluginPerspectiveLookup
          Map of all plugin perspectives
        • m_pointerB
          JButton m_pointerB
        • m_PreferredExtension
          String m_PreferredExtension
          the preferred file extension
        • m_saveB
          JButton m_saveB
        • m_saveBB
          JButton m_saveBB
        • 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_selectAllB
          JButton m_selectAllB
        • m_showFileMenu
          boolean m_showFileMenu
        • m_snapToGridB
          JToggleButton m_snapToGridB
        • 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_templatesB
          JButton m_templatesB
        • m_togglePerspectivesB
          JButton m_togglePerspectivesB
        • m_toolBarBean
          Object m_toolBarBean
          Holds the selected toolbar bean
        • m_undoB
          JButton m_undoB
        • m_untitledCount
          int m_untitledCount
          Number of untitled tabs so far
        • m_userCompNode
          DefaultMutableTreeNode m_userCompNode
          The node of the JTree that holds "user" (metabean) components
        • m_userComponents
          Vector<Object> m_userComponents
        • m_UserComponentsInXML
          boolean m_UserComponentsInXML
          whether to store the user components in XML or in binary format
        • 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
        • m_zoomInB
          JButton m_zoomInB
        • m_zoomOutB
          JButton m_zoomOutB

    • Class weka.gui.beans.KnowledgeFlowApp.BeanIconRenderer

      class BeanIconRenderer extends DefaultTreeCellRenderer implements Serializable
      serialVersionUID:
      -4488876734500244945L

    • Class weka.gui.beans.KnowledgeFlowApp.BeanLayout

      class BeanLayout extends PrintablePanel implements Serializable
      serialVersionUID:
      -146377012429662757L

    • Class weka.gui.beans.KnowledgeFlowApp.InvisibleNode

      class InvisibleNode extends DefaultMutableTreeNode implements Serializable
      serialVersionUID:
      -9064396835384819887L

      • Serialized Fields

        • m_isVisible
          boolean m_isVisible

    • Class weka.gui.beans.KnowledgeFlowApp.InvisibleTreeModel

      class InvisibleTreeModel extends DefaultTreeModel implements Serializable
      serialVersionUID:
      6940101211275068260L

      • Serialized Fields

        • m_filterIsActive
          boolean m_filterIsActive

    • Class weka.gui.beans.KnowledgeFlowApp.JTreeLeafDetails

      class JTreeLeafDetails extends Object implements Serializable
      serialVersionUID:
      6197221540272931626L

      • Serialized Fields

        • m_fullyQualifiedCompName
          String m_fullyQualifiedCompName
          fully qualified bean name
        • m_isMeta
          boolean m_isMeta
          true if this is a MetaBean (user component)
        • m_leafLabel
          String m_leafLabel
          the label (usually derived from the qualified name or wrapped algorithm) for the leaf
        • m_metaBean
          Vector<Object> m_metaBean
          XML serialized MetaBean (if this is a user component)
        • m_toolTipText
          String m_toolTipText
          tool tip text to display
        • m_wekaAlgoName
          String m_wekaAlgoName
          the fully qualified wrapped weka algorithm name

    • Class weka.gui.beans.KnowledgeFlowApp.KFLogPanel

      class KFLogPanel extends LogPanel implements Serializable
      serialVersionUID:
      -2224509243343105276L

    • Class weka.gui.beans.KnowledgeFlowApp.MainKFPerspective

      class MainKFPerspective extends JPanel implements Serializable
      serialVersionUID:
      7666381888012259527L

      • Serialized Fields

        • m_beanLayouts
          List<weka.gui.beans.KnowledgeFlowApp.BeanLayout> m_beanLayouts
          List of layouts - one for each tab
        • m_editedList
          List<Boolean> m_editedList
          Keeps track of which tabs have been edited but not saved
        • m_environmentSettings
          List<Environment> m_environmentSettings
          List of environment variable settings - one for each tab
        • m_executingList
          List<Boolean> m_executingList
          Keeps track of which tabs have flows that are executing
        • m_executionThreads
          List<weka.gui.beans.KnowledgeFlowApp.RunThread> m_executionThreads
          Keeps track of the threads used for execution
        • m_filePaths
          List<File> m_filePaths
          List of flow file paths - one for each tab
        • m_flowTabs
          JTabbedPane m_flowTabs
          Holds the tabs of the perspective
        • m_logPanels
          List<weka.gui.beans.KnowledgeFlowApp.KFLogPanel> m_logPanels
          List of log panels - one for each tab
        • m_nodeTextIndex
          Map<String,DefaultMutableTreeNode> m_nodeTextIndex
        • m_selectedBeans
          List<Vector<Object>> m_selectedBeans
          Keeps track of any highlighted beans on the canvas for a tab
        • m_undoBufferList
          List<Stack<File>> m_undoBufferList
          Keeps track of the undo buffers for each tab
        • m_zoomSettings
          List<Integer> m_zoomSettings
          List of zoom settings - one for each tab

    • 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
      serialVersionUID:
      6583097154513435548L

      • 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

    • Class weka.gui.beans.ModelPerformanceChart

      class ModelPerformanceChart extends JPanel implements Serializable
      serialVersionUID:
      -4602034200071195924L

      • Serialized Fields

        • m_additionalOptions
          String m_additionalOptions
          Additional options for the offscreen renderer
        • 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_headlessEvents
          List<EventObject> m_headlessEvents
          Events received and stored during headless execution
        • m_height
          String m_height
          Height of offscreen plots
        • m_imageListeners
          ArrayList<ImageListener> m_imageListeners
        • m_listenees
          List<Object> m_listenees
        • m_offscreenRendererName
          String m_offscreenRendererName
          Name of the renderer to use for offscreen chart rendering
        • m_visual
          BeanVisual m_visual
        • m_width
          String m_width
          Width of offscreen plots
        • m_xAxis
          String m_xAxis
          The name of the attribute to use for the x-axis of offscreen plots. If left empty, False Positive Rate is used for threshold curves
        • m_yAxis
          String m_yAxis
          The name of the attribute to use for the y-axis of offscreen plots. If left empty, True Positive Rate is used for threshold curves

    • Class weka.gui.beans.ModelPerformanceChartCustomizer

      class ModelPerformanceChartCustomizer extends JPanel implements Serializable
      serialVersionUID:
      27802741348090392L

    • Class weka.gui.beans.Note

      class Note extends JPanel implements Serializable
      serialVersionUID:
      -7272355421198069040L

      • Serialized Fields

        • m_fontSizeAdjust
          int m_fontSizeAdjust
          Adjustment for the font size
        • m_label
          JLabel m_label
          The label that displays the note text
        • m_noteText
          String m_noteText
          The note text

    • Class weka.gui.beans.NoteCustomizer

      class NoteCustomizer extends JPanel implements Serializable
      serialVersionUID:
      995648616684953391L

      • Serialized Fields

        • m_modifyListener
          BeanCustomizer.ModifyListener m_modifyListener
          Listener that wants to know the the modified status of the object that we're customizing
        • m_note
          Note m_note
          the note to be edited
        • m_parentWindow
          Window m_parentWindow
          the parent window
        • m_textArea
          JTextArea m_textArea
          text area for displaying the note's text

    • 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<DataSourceListener> 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<InstanceListener> 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<TestSetListener> m_testSetListeners
          Objects listening for test set events
        • m_trainingSetListeners
          Vector<TrainingSetListener> 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_includeRelationName
          boolean m_includeRelationName
          include relation name in filename
        • m_incrementalSaveSchedule
          int m_incrementalSaveSchedule
          How often to save an incremental classifier (<= 0 means only at the end of the stream)
        • 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.ShadowBorder

      class ShadowBorder extends AbstractBorder implements Serializable
      serialVersionUID:
      -2117842133475125463L

      • Serialized Fields

        • m_color
          Color m_color
          the color of the drop shadow
        • m_width
          int m_width
          The width in pixels of the drop shadow

    • Class weka.gui.beans.Sorter

      class Sorter extends JPanel implements Serializable
      serialVersionUID:
      4978227384322482115L

      • Serialized Fields

        • m_bufferSize
          String m_bufferSize
          Size of the in-memory buffer
        • m_bufferSizeI
          int m_bufferSizeI
          Size of the in-memory buffer after resolving any environment vars
        • m_busy
          boolean m_busy
          True if we are busy
        • m_connectedFormat
          Instances m_connectedFormat
          format of instances for current incoming connection (if any)
        • m_connectionType
          String m_connectionType
          The type of the incoming connection
        • m_dataListeners
          ArrayList<DataSourceListener> m_dataListeners
          Downstream steps listening to batch data events
        • m_ie
          InstanceEvent m_ie
          For printing status updates in incremental mode
        • m_instanceListeners
          ArrayList<InstanceListener> m_instanceListeners
          Downstream steps listening to instance events
        • m_listenee
          Object m_listenee
          Step talking to us
        • m_sortDetails
          String m_sortDetails
          Holds the internal textual description of the sort definitions
        • m_stopRequested
          AtomicBoolean m_stopRequested
          True if a stop has been requested
        • m_stringAttIndexes
          Map<String,Integer> m_stringAttIndexes
          Holds indexes of string attributes, keyed by attribute name
        • m_tempDirectory
          String m_tempDirectory
          The directory to hold the temp files - if not set the system tmp directory is used
        • m_visual
          BeanVisual m_visual
          Default visual for data sources

    • Class weka.gui.beans.Sorter.InstanceHolder

      class InstanceHolder extends Object implements Serializable
      serialVersionUID:
      -3985730394250172995L

      • Serialized Fields

        • m_fileNumber
          int m_fileNumber
          index into the list of files on disk
        • m_instance
          Instance m_instance
          The instance
        • m_stringVals
          Map<String,String> m_stringVals
          for incremental operation, if string attributes are present then we need to store them with each instance - since incremental streaming in the knowledge flow only maintains one string value in memory (and hence in the header) at any one time

    • Class weka.gui.beans.SorterCustomizer

      class SorterCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -4860246697276275408L

    • Class weka.gui.beans.SQLViewerPerspective

      class SQLViewerPerspective extends JPanel implements Serializable
      serialVersionUID:
      3684166225482042972L

    • Class weka.gui.beans.StreamThroughput

      class StreamThroughput extends Object implements Serializable
      serialVersionUID:
      2820675210555581676L

    • 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<double[]> 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<String> 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_userRefreshWidth
          int m_userRefreshWidth
        • 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.SubstringLabeler

      class SubstringLabeler extends JPanel implements Serializable
      serialVersionUID:
      6297059699297260134L

      • Serialized Fields

        • m_addFilter
          Add m_addFilter
          Add filter for adding the new attribute
        • m_attName
          String m_attName
          Name of the new attribute
        • m_consumeNonMatchingInstances
          boolean m_consumeNonMatchingInstances
          For multi-valued labeled rules, whether or not to consume non-matching instances or output them with missing value for the match attribute.
        • m_dataListeners
          ArrayList<DataSourceListener> m_dataListeners
          Downstream steps listening to data set events
        • m_ie
          InstanceEvent m_ie
          Instance event to use
        • m_instanceListeners
          ArrayList<InstanceListener> m_instanceListeners
          Downstream steps listening to instance events
        • m_listenee
          Object m_listenee
          Component talking to us
        • m_matchDetails
          String m_matchDetails
          Internally encoded list of match rules
        • m_nominalBinary
          boolean m_nominalBinary
          Whether to make the binary match/non-match attribute a nominal (rather than numeric) binary attribute.
        • m_visual
          BeanVisual m_visual
          Default visual filters

    • Class weka.gui.beans.SubstringLabelerCustomizer

      class SubstringLabelerCustomizer extends JPanel implements Serializable
      serialVersionUID:
      7636584212353183751L

    • Class weka.gui.beans.SubstringLabelerRules

      class SubstringLabelerRules extends Object implements Serializable
      serialVersionUID:
      1392983905562573599L

      • Serialized Fields

        • m_attName
          String m_attName
          The name of the new "label" attribute
        • m_consumeNonMatching
          boolean m_consumeNonMatching
          True if non matching instances should be "consumed" - i.e no output instance created
        • m_hasLabels
          boolean m_hasLabels
          True if the rules have user supplied labels
        • m_inputStructure
          Instances m_inputStructure
          The input structure
        • m_matchRules
          List<SubstringLabelerRules.SubstringLabelerMatchRule> m_matchRules
          The list of rules
        • m_nominalBinary
          boolean m_nominalBinary
          If not multiple labels then should new att be a nominal rather than numeric binary one?
        • m_outputStructure
          Instances m_outputStructure
          The output structure
        • m_statusMessagePrefix
          String m_statusMessagePrefix
          An optional prefix for status log messages
        • m_voteLabels
          boolean m_voteLabels

    • Class weka.gui.beans.SubstringLabelerRules.SubstringLabelerMatchRule

      class SubstringLabelerMatchRule extends Object implements Serializable
      serialVersionUID:
      6518104085439241523L

      • Serialized Fields

        • m_attsToApplyTo
          String m_attsToApplyTo
          The attributes to apply the match-replace rule to
        • m_ignoreCase
          boolean m_ignoreCase
          True if case should be ignored when matching
        • m_label
          String m_label
          The label (if any) for this rule
        • m_labelS
          String m_labelS
          Resolved label string
        • m_logger
          Logger m_logger
          Logger to use
        • m_match
          String m_match
          The substring literal/regex to use for matching
        • m_matchS
          String m_matchS
          Resolved match string
        • m_regex
          boolean m_regex
          True if a regular expression match is to be used
        • m_regexPattern
          Pattern m_regexPattern
          Precompiled regex pattern
        • m_selectedAtts
          int[] m_selectedAtts
          Attributes to apply to
        • m_statusMessagePrefix
          String m_statusMessagePrefix
          Status message prefix

    • Class weka.gui.beans.SubstringReplacer

      class SubstringReplacer extends JPanel implements Serializable
      serialVersionUID:
      5636877747903965818L

      • Serialized Fields

        • m_ie
          InstanceEvent m_ie
          Instance event to use
        • m_instanceListeners
          ArrayList<InstanceListener> m_instanceListeners
          Downstream steps listening to instance events
        • m_listenee
          Object m_listenee
          Component sending us instances
        • m_matchReplaceDetails
          String m_matchReplaceDetails
          Internally encoded list of match-replace rules
        • m_visual
          BeanVisual m_visual
          Default visual filters

    • Class weka.gui.beans.SubstringReplacerCustomizer

      class SubstringReplacerCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -1245155414691393809L

    • Class weka.gui.beans.SubstringReplacerRules

      class SubstringReplacerRules extends Object implements Serializable
      serialVersionUID:
      -7151320452496749698L

    • Class weka.gui.beans.SubstringReplacerRules.SubstringReplacerMatchRule

      class SubstringReplacerMatchRule extends Object implements Serializable
      serialVersionUID:
      5792838913737819728L

      • Serialized Fields

        • m_attsToApplyTo
          String m_attsToApplyTo
          The attributes to apply the match-replace rule to
        • m_ignoreCase
          boolean m_ignoreCase
          True if case should be ignored when matching
        • m_logger
          Logger m_logger
        • m_match
          String m_match
          The substring literal/regex to use for matching
        • m_matchS
          String m_matchS
        • m_regex
          boolean m_regex
          True if a regular expression match is to be used
        • m_regexPattern
          Pattern m_regexPattern
          Precompiled regex
        • m_replace
          String m_replace
          The string to replace with
        • m_replaceS
          String m_replaceS
        • m_selectedAtts
          int[] m_selectedAtts
        • m_statusMessagePrefix
          String m_statusMessagePrefix

    • 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.TextSaver

      class TextSaver extends JPanel implements Serializable
      serialVersionUID:
      6363577506969809332L

      • Serialized Fields

        • m_append
          boolean m_append
          Whether to append to the file or not
        • m_fileName
          String m_fileName
          The file to save to
        • m_visual
          BeanVisual m_visual
          Default visual for data sources

    • Class weka.gui.beans.TextSaverCustomizer

      class TextSaverCustomizer extends JPanel implements Serializable
      serialVersionUID:
      -1012433373647714743L

    • Class weka.gui.beans.TextViewer

      class TextViewer extends JPanel implements Serializable
      serialVersionUID:
      104838186352536832L

      • Serialized Fields

    • 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_dataProvider
          boolean m_dataProvider
        • m_randomSeed
          int m_randomSeed
        • m_splitThread
          Thread m_splitThread
        • m_testProvider
          boolean m_testProvider
        • m_trainingProvider
          boolean m_trainingProvider
        • 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
          ArrayList<Color> 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<ActionListener> 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_hostPollingTime
          int[] m_hostPollingTime
        • m_listeners
          Vector<RemoteExperimentListener> 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<String> 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
          WekaFileChooser 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_random
          Random m_random
          random number generator
        • m_seed
          int m_seed
          random number seed
        • 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
      serialVersionUID:
      -5275252329449241592L

      • Serialized Fields

        • m_attsToWeightOn
          boolean[] m_attsToWeightOn
        • m_classifier
          Classifier m_classifier
        • m_dataGenerator
          DataGenerator m_dataGenerator
        • m_dist
          double[] m_dist
        • 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

  • Package weka.gui.experiment

    • Class weka.gui.experiment.AbstractSetupPanel

      class AbstractSetupPanel extends JPanel implements Serializable

    • Class weka.gui.experiment.AlgorithmListPanel

      class AlgorithmListPanel extends JPanel implements Serializable
      serialVersionUID:
      -7204528834764898671L

      • Serialized Fields

        • m_ActionListener
          ActionListener m_ActionListener
          An action listener that needs to be available globally to permit garbage collection.
        • 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
          WekaFileChooser m_FileChooser
          The file chooser for selecting experiments
        • m_List
          JList<Classifier> 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_PropertyChangeListener
          PropertyChangeListener m_PropertyChangeListener
          An property change listener that needs to be available globally to permit garbage collection.
        • 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_splitByProperty
          JRadioButton m_splitByProperty
          Split experiment up by algorithm.
        • m_splitByRun
          JRadioButton m_splitByRun
          Split experiment up by run number.

    • Class weka.gui.experiment.Experimenter

      class Experimenter extends AbstractPerspective 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
          ArrayList<ActionListener> 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_IgnoreChanges
          boolean m_IgnoreChanges
          whether to ignore updates in the GUI momentarily.
        • m_MeanPrecLabel
          JLabel m_MeanPrecLabel
          the label for the mean precision.
        • 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_OutputFormatClasses
          Vector<Class<?>> m_OutputFormatClasses
          the different classes for outputting the comparison tables.
        • m_OutputFormatComboBox
          JComboBox m_OutputFormatComboBox
          lets the user choose the format for the output.
        • m_OutputFormatLabel
          JLabel m_OutputFormatLabel
          the label for the format.
        • m_OutputFormatNames
          Vector<String> m_OutputFormatNames
          the different names of matrices for outputting the comparison tables.
        • m_RemoveFilterNameCheckBox
          JCheckBox m_RemoveFilterNameCheckBox
          the checkbox for the removing of filter classnames.
        • m_RemoveFilterNameLabel
          JLabel m_RemoveFilterNameLabel
          the label for the removing the filter classnames.
        • m_Result
          int m_Result
          the result of the user's action, either OK or CANCEL.
        • m_ResultMatrix
          ResultMatrix m_ResultMatrix
          the current result matrix.
        • m_ResultMatrixEditor
          GenericObjectEditor m_ResultMatrixEditor
          Lets the user configure the result matrix.
        • m_ResultMatrixLabel
          JLabel m_ResultMatrixLabel
          the label for the GOE.
        • m_ResultMatrixPanel
          PropertyPanel m_ResultMatrixPanel
          the panel for the GOE.
        • m_ShowAverageCheckBox
          JCheckBox m_ShowAverageCheckBox
          the checkbox for outputting the average.
        • m_ShowAverageLabel
          JLabel m_ShowAverageLabel
          the label for showing the average.
        • m_StdDevPrecLabel
          JLabel m_StdDevPrecLabel
          the label for the std dev precision.
        • 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_DatasetAndResultKeysLabel
          JLabel m_DatasetAndResultKeysLabel
          Label for the dataset and result key buttons.
        • m_DatasetKeyBut
          JButton m_DatasetKeyBut
          Click to edit the columns used to determine the scheme.
        • 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_Explorer
          JButton m_Explorer
          Click to load the results instances into the Explorer
        • m_FileChooser
          WekaFileChooser 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_mainPerspective
          Perspective m_mainPerspective
          If running in the Workbench (or other GUIApplication) this will hold a reference to the main perspective. Otherwise it will be null
        • 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_PanelDatasetResultKeys
          JPanel m_PanelDatasetResultKeys
          the panel encapsulating the Rows/Columns/Swap buttons.
        • 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_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_SwapDatasetKeyAndResultKeyBut
          JButton m_SwapDatasetKeyAndResultKeyBut
          For swapping rows and columns.
        • 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_TesterClassesModel
          DefaultComboBoxModel m_TesterClassesModel
          Contains all the available classes implementing the Tester-Interface (the display names).
          See Also:
        • 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

    • Class weka.gui.experiment.SetupPanel

      class SetupPanel extends AbstractSetupPanel 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
          WekaFileChooser 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_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_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 AbstractSetupPanel 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
          WekaFileChooser 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
          WekaFileChooser 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.AbstractPlotInstances

      class AbstractPlotInstances extends Object implements Serializable
      serialVersionUID:
      2375155184845160908L

      • Serialized Fields

        • m_FinishUpCalled
          boolean m_FinishUpCalled
          whether processing has been finished up already.
        • m_Instances
          Instances m_Instances
          the full dataset.
        • m_PlotInstances
          Instances m_PlotInstances
          the plotable instances.

    • Class weka.gui.explorer.AssociationsPanel

      class AssociationsPanel extends AbstractPerspective 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_initialSettingsSet
          boolean m_initialSettingsSet
          Whether start-up settings have been applied (i.e. initial default associator to use
        • 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_storeOutput
          JCheckBox m_storeOutput
          Whether to store any graph or xml rules output in the history list
        • m_TestInstances
          Instances m_TestInstances
          The user-supplied test set (if any)

    • Class weka.gui.explorer.AssociationsPanel.AssociationsPanelDefaults

      class AssociationsPanelDefaults extends Defaults implements Serializable
      serialVersionUID:
      1108450683775771792L

    • Class weka.gui.explorer.AttributeSelectionPanel

      class AttributeSelectionPanel extends AbstractPerspective 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_initialSettingsSet
          boolean m_initialSettingsSet
          True if startup settings have been applied
        • 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.AttributeSelectionPanel.AttributeSelectionPanelDefaults

      class AttributeSelectionPanelDefaults extends Defaults implements Serializable
      serialVersionUID:
      -5413933415469545770L

    • Class weka.gui.explorer.ClassifierErrorsPlotInstances

      class ClassifierErrorsPlotInstances extends AbstractPlotInstances implements Serializable
      serialVersionUID:
      -3941976365792013279L

      • Serialized Fields

        • m_Classifier
          Classifier m_Classifier
          the classifier being used.
        • m_ClassIndex
          int m_ClassIndex
          the class index.
        • m_Evaluation
          Evaluation m_Evaluation
          the Evaluation object to use.
        • m_MaximumPlotSizeNumeric
          int m_MaximumPlotSizeNumeric
          the maximum plot size for numeric errors.
        • m_MinimumPlotSizeNumeric
          int m_MinimumPlotSizeNumeric
          the minimum plot size for numeric errors.
        • m_PlotShapes
          ArrayList<Integer> m_PlotShapes
          for storing the plot shapes.
        • m_PlotSizes
          ArrayList<Object> m_PlotSizes
          for storing the plot sizes.
        • m_pointSizeProportionalToMargin
          boolean m_pointSizeProportionalToMargin
        • m_SaveForVisualization
          boolean m_SaveForVisualization
          whether to save the instances for visualization or just evaluate the instance.

    • Class weka.gui.explorer.ClassifierPanel

      class ClassifierPanel extends AbstractPerspective 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_ClassificationOutputEditor
          GenericObjectEditor m_ClassificationOutputEditor
          Lets the user configure the ClassificationOutput.
        • m_ClassificationOutputPanel
          PropertyPanel m_ClassificationOutputPanel
          ClassificationOutput configuration.
        • m_ClassifierEditor
          GenericObjectEditor m_ClassifierEditor
          Lets the user configure the classifier.
        • m_CollectPredictionsForEvaluationBut
          JCheckBox m_CollectPredictionsForEvaluationBut
          Check to collect the predictions for computing statistics such as AUROC.
        • m_CostMatrixEditor
          CostMatrixEditor m_CostMatrixEditor
          The cost matrix editor for evaluation costs.
        • 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_errorPlotPointSizeProportionalToMargin
          JCheckBox m_errorPlotPointSizeProportionalToMargin
          Check to have the point size in error plots proportional to the prediction margin (classification only)
        • m_EvalWRTCostsBut
          JCheckBox m_EvalWRTCostsBut
          Check to evaluate w.r.t a cost matrix.
        • m_Explorer
          Explorer m_Explorer
          the parent frame.
        • m_FileChooser
          WekaFileChooser m_FileChooser
          The file chooser for selecting model files.
        • m_History
          ResultHistoryPanel m_History
          A panel controlling results viewing.
        • m_initialSettingsSet
          boolean m_initialSettingsSet
          Whether start-up settings have been applied (i.e. initial classifier to use)
        • 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_OutputAdditionalAttributesRange
          Range m_OutputAdditionalAttributesRange
          the range of 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_OutputModelsForTrainingSplitsBut
          JCheckBox m_OutputModelsForTrainingSplitsBut
          Check to output the models built from the training splits.
        • m_OutputPerClassBut
          JCheckBox m_OutputPerClassBut
          Check to output true/false positives, precision/recall for each class.
        • 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_selectedEvalMetrics
          List<String> m_selectedEvalMetrics
          The user's list of selected evaluation metrics
        • 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_StoreTestDataAndPredictionsBut
          JCheckBox m_StoreTestDataAndPredictionsBut
          Check to save the test data and the predictions in the results list for visualizing later on.
        • m_TestClassIndex
          int m_TestClassIndex
          the class index for the supplied test set.
        • 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.ClassifierPanel.ClassifierPanelDefaults

      class ClassifierPanelDefaults extends Defaults implements Serializable
      serialVersionUID:
      7109938811150596359L

    • Class weka.gui.explorer.ClustererAssignmentsPlotInstances

      class ClustererAssignmentsPlotInstances extends AbstractPlotInstances implements Serializable
      serialVersionUID:
      -4748134272046520423L

      • Serialized Fields

        • m_Clusterer
          Clusterer m_Clusterer
          the clusterer being used.
        • m_Evaluation
          ClusterEvaluation m_Evaluation
          the cluster evaluation to use.
        • m_PlotShapes
          int[] m_PlotShapes
          for storing the plot shapes.

    • Class weka.gui.explorer.ClustererPanel

      class ClustererPanel extends AbstractPerspective 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
          WekaFileChooser 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_initialSettingsSet
          boolean m_initialSettingsSet
          Whether startup settings have been applied yet or not
        • 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.ClustererPanel.ClustererPanelDefaults

      class ClustererPanelDefaults extends Defaults implements Serializable
      serialVersionUID:
      2708388782229179493L

    • 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 AbstractPerspective 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_EditM
          JMenuItem m_EditM
        • 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_initialSettingsSet
          boolean m_initialSettingsSet
          True after settings have been applied the first time
        • 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_menus
          List<JMenu> m_menus
          Menus provided by this perspective
        • 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_sendToPerspective
          JMenu m_sendToPerspective
          For sending instances to various perspectives/tabs
        • m_SQLQ
          String m_SQLQ
          Stores the last sql query executed
        • m_StopBut
          JButton m_StopBut
          Click to stop a running filter
        • 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.PreprocessPanel.PreprocessDefaults

      class PreprocessDefaults extends Defaults implements Serializable

    • Class weka.gui.explorer.VisualizePanel

      class VisualizePanel extends AbstractPerspective implements Serializable
      serialVersionUID:
      6084015036853918846L

      • Serialized Fields

        • m_Explorer
          Explorer m_Explorer
          the parent frame
        • m_hasInstancesSet
          boolean m_hasInstancesSet
          True if a set of instances has been set on the panel
        • m_matrixPanel
          MatrixPanel m_matrixPanel

    • Class weka.gui.explorer.VisualizePanel.ScatterDefaults

      class ScatterDefaults extends Defaults implements Serializable
      serialVersionUID:
      -6890761195767034507L

  • Package weka.gui.filters

  • Package weka.gui.graphvisualizer

    • Exception 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
          ArrayList<GraphEdge> 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
          ArrayList<GraphNode> 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.knowledgeflow

    • Class weka.gui.knowledgeflow.AttributeSummaryPerspective

      class AttributeSummaryPerspective extends AbstractPerspective implements Serializable
      serialVersionUID:
      6697308901346612850L

      • Serialized Fields

        • m_coloringIndex
          int m_coloringIndex
          Index on which to color the plots
        • m_visualizeDataSet
          Instances m_visualizeDataSet
          The dataset being visualized

    • Class weka.gui.knowledgeflow.AttributeSummaryPerspective.AttDefaults

      class AttDefaults extends Defaults implements Serializable
      serialVersionUID:
      -32801466385262321L

    • Class weka.gui.knowledgeflow.BaseInteractiveViewer

      class BaseInteractiveViewer extends JPanel implements Serializable
      serialVersionUID:
      -1191494001428785466L

      • Serialized Fields

        • m_buttonHolder
          JPanel m_buttonHolder
          Holds buttons displayed at the bottom of the window
        • m_closeBut
          JButton m_closeBut
          The close button, for closing the viewer
        • m_mainPerspective
          MainKFPerspective m_mainPerspective
          The main Knowledge Flow perspective
        • m_parent
          Window m_parent
          The parent window
        • m_step
          Step m_step
          Holds the step that this interactive viewer relates to

    • Class weka.gui.knowledgeflow.DesignPanel

      class DesignPanel extends JPanel implements Serializable
      serialVersionUID:
      3324733191950871564L

    • Class weka.gui.knowledgeflow.GOEStepEditorDialog

      class GOEStepEditorDialog extends StepEditorDialog implements Serializable
      serialVersionUID:
      -2500973437145276268L

      • Serialized Fields

        • m_editor
          PropertySheetPanel m_editor
          Main editor for the step - used for editing properties of the step or properties of a wrapped algorithm (if the step is a subclass of WekaAlgorithmWrapper.
        • m_editorHolder
          JPanel m_editorHolder
          The main holder panel
        • m_manager
          StepManagerImpl m_manager
          The StepManager for the step being edited
        • m_primaryEditorHolder
          JPanel m_primaryEditorHolder
          The panel that contains the main editor
        • m_secondaryEditor
          PropertySheetPanel m_secondaryEditor
          Secondary editor. Used for additional properties that belong to a Step that extends WekaAlgorithmWrapper
        • m_stepOriginal
          Step m_stepOriginal
          Holds a copy of the step (for restoring after cancel)

    • Class weka.gui.knowledgeflow.InvisibleNode

      class InvisibleNode extends DefaultMutableTreeNode implements Serializable
      serialVersionUID:
      -9064396835384819887L

      • Serialized Fields

        • m_isVisible
          boolean m_isVisible
          True if the node is visible

    • Class weka.gui.knowledgeflow.InvisibleTreeModel

      class InvisibleTreeModel extends DefaultTreeModel implements Serializable
      serialVersionUID:
      6940101211275068260L

      • Serialized Fields

        • m_filterIsActive
          boolean m_filterIsActive
          True if the visibility filter is active

    • Class weka.gui.knowledgeflow.KnowledgeFlowApp

      class KnowledgeFlowApp extends AbstractGUIApplication implements Serializable
      serialVersionUID:
      -1460599392623083983L

      • Serialized Fields

        • m_kfProperties
          Settings m_kfProperties
          Settings for the Knowledge Flow
        • m_mainPerspective
          MainKFPerspective m_mainPerspective
          Main perspective of the Knowledge Flow

    • Class weka.gui.knowledgeflow.KnowledgeFlowApp.KnowledgeFlowGeneralDefaults

      class KnowledgeFlowGeneralDefaults extends Defaults implements Serializable
      serialVersionUID:
      6957165806947500265L

    • Class weka.gui.knowledgeflow.LayoutPanel

      class LayoutPanel extends PrintablePanel implements Serializable
      serialVersionUID:
      4988098224376217099L

      • Serialized Fields

        • m_currentX
          int m_currentX
        • m_currentY
          int m_currentY
        • m_gridSpacing
          int m_gridSpacing
          Grid spacing
        • m_oldX
          int m_oldX
        • m_oldY
          int m_oldY
        • m_perspectiveDataLoadThread
          Thread m_perspectiveDataLoadThread
          Thread for loading data for perspectives
        • m_visLayout
          VisibleLayout m_visLayout
          The flow contained in this LayoutPanel as a visible (graphical) flow

    • Class weka.gui.knowledgeflow.MainKFPerspective

      class MainKFPerspective extends AbstractPerspective implements Serializable
      serialVersionUID:
      3986047323839299447L

      • Serialized Fields

        • m_allowMultipleTabs
          boolean m_allowMultipleTabs
          Whether to allow multiple tabs
        • m_FileChooser
          WekaFileChooser m_FileChooser
          The file chooser for loading layout files
        • m_flowGraphs
          List<VisibleLayout> m_flowGraphs
          List of layouts - one for each tab
        • m_flowTabs
          JTabbedPane m_flowTabs
          Holds the tabs of the perspective
        • m_mainToolBar
          MainKFPerspectiveToolBar m_mainToolBar
          Main toolbar
        • m_palleteSelectedStep
          StepManagerImpl m_palleteSelectedStep
          The current step selected from the design pallete
        • m_pasteBuffer
          String m_pasteBuffer
          The paste buffer
        • m_saveFileChooser
          WekaFileChooser m_saveFileChooser
          The file chooser for saving layout files (only supports saving as json .kf files
        • m_stepTree
          StepTree m_stepTree
          The jtree holding steps
        • m_templateManager
          TemplateManager m_templateManager
          Manages template flows
        • m_untitledCount
          int m_untitledCount
          Count for new "untitled" tabs

    • Class weka.gui.knowledgeflow.MainKFPerspectiveToolBar

      class MainKFPerspectiveToolBar extends JPanel implements Serializable
      serialVersionUID:
      -157986423490835642L

      • Serialized Fields

        • m_mainPerspective
          MainKFPerspective m_mainPerspective
          Reference to the main knowledge flow perspective
        • m_menuItemMap
          Map<String,JMenuItem> m_menuItemMap
          holds a map of menu items (for widgets that have corresponding menu items)
        • m_menuMap
          Map<String,JMenu> m_menuMap
          Holds a map of top level menus
        • m_showMenus
          boolean m_showMenus
          True if menu items corresponding to the toolbar widgets should be shown
        • m_widgetMap
          Map<String,JComponent> m_widgetMap
          holds a map of widgets, keyed by widget name

    • Class weka.gui.knowledgeflow.NoteVisual

      class NoteVisual extends StepVisual implements Serializable
      serialVersionUID:
      -3291021235652124916L

      • Serialized Fields

        • m_fontSizeAdjust
          int m_fontSizeAdjust
          Adjustment for the font size
        • m_label
          JLabel m_label
          The label that displays the note text

    • Class weka.gui.knowledgeflow.ScatterPlotMatrixPerspective

      class ScatterPlotMatrixPerspective extends AbstractPerspective implements Serializable
      serialVersionUID:
      5661598509822826837L

      • Serialized Fields

        • m_matrixPanel
          MatrixPanel m_matrixPanel
          The actual matrix panel
        • m_visualizeDataSet
          Instances m_visualizeDataSet
          The dataset being visualized

    • Class weka.gui.knowledgeflow.ShadowBorder

      class ShadowBorder extends AbstractBorder implements Serializable
      serialVersionUID:
      -2117842133475125463L

      • Serialized Fields

        • m_color
          Color m_color
          the color of the drop shadow
        • m_width
          int m_width
          The width in pixels of the drop shadow

    • Class weka.gui.knowledgeflow.SQLViewerPerspective

      class SQLViewerPerspective extends AbstractPerspective implements Serializable
      serialVersionUID:
      -4771310190331379801L

      • Serialized Fields

        • m_buttonHolder
          JPanel m_buttonHolder
          Panel for holding buttons
        • m_mainKFPerspective
          MainKFPerspective m_mainKFPerspective
          Reference to tne main knowledge flow perspective
        • m_newFlowBut
          JButton m_newFlowBut
          Button for creating a new flow layout with a configured DBLoader step
        • m_viewer
          SqlViewer m_viewer
          The wrapped SQLViewer

    • Class weka.gui.knowledgeflow.SQLViewerPerspective.SQLDefaults

      class SQLDefaults extends Defaults implements Serializable
      serialVersionUID:
      5907476861935295960L

    • Class weka.gui.knowledgeflow.StepEditorDialog

      class StepEditorDialog extends JPanel implements Serializable
      serialVersionUID:
      -4860182109190301676L

      • Serialized Fields

        • m_buttonHolder
          JPanel m_buttonHolder
          Holder for buttons
        • m_cancelBut
          JButton m_cancelBut
          Cancel button
        • m_closingListener
          StepEditorDialog.ClosingListener m_closingListener
          Listener to be informed when the window closes
        • m_commandHandler
          KFGraphicalEnvironmentCommandHandler m_commandHandler
          The handler for graphical commands
        • m_env
          Environment m_env
          Environment variables
        • m_helpBut
          JButton m_helpBut
          About button
        • m_helpText
          StringBuilder m_helpText
          Buffer to hold the help text
        • m_isEdited
          boolean m_isEdited
          True if the step's properties have been altered
        • m_mainPerspective
          MainKFPerspective m_mainPerspective
          Reference to the main perspective
        • m_okBut
          JButton m_okBut
          OK button
        • m_parent
          Window m_parent
          Parent window
        • m_settingsBut
          JButton m_settingsBut
          Settings button
        • m_stepToEdit
          Step m_stepToEdit
          The step to edit

    • Class weka.gui.knowledgeflow.StepTree

      class StepTree extends JTree implements Serializable
      serialVersionUID:
      3646119269455293741L

      • Serialized Fields

    • Class weka.gui.knowledgeflow.StepTree.StepIconRenderer

      class StepIconRenderer extends DefaultTreeCellRenderer implements Serializable
      serialVersionUID:
      -4488876734500244945L

    • Class weka.gui.knowledgeflow.StepTreeLeafDetails

      class StepTreeLeafDetails extends Object implements Serializable
      serialVersionUID:
      6347861816716877761L

      • Serialized Fields

        • m_leafLabel
          String m_leafLabel
          the label (usually derived from the qualified name or wrapped algorithm) for the leaf
        • m_showTipText
          boolean m_showTipText
          If a tool tip text is set, whether to show it or not
        • m_stepClazz
          Class m_stepClazz
          Class of the step stored at this leaf
        • m_toolTipText
          String m_toolTipText
          tool tip text to display
        • m_wrappedWekaAlgoName
          String m_wrappedWekaAlgoName
          The name of the algorithm wrapped by a WekaAlgorithmWrapper step

    • Class weka.gui.knowledgeflow.StepVisual

      class StepVisual extends JPanel implements Serializable
      serialVersionUID:
      4156046438296843760L

      • Serialized Fields

        • m_connectorColor
          Color m_connectorColor
          Current connector dot colour
        • m_displayConnectors
          boolean m_displayConnectors
          Whether to display connector dots
        • m_icon
          ImageIcon m_icon
          The icon for the step this visual represents
        • m_stepManager
          StepManagerImpl m_stepManager
          The step manager for the step this visual represents
        • m_x
          int m_x
          The x coordinate of the step on the graphical layout
        • m_y
          int m_y
          The y coordinate of the step on the graphical layout

    • Class weka.gui.knowledgeflow.VisibleLayout

      class VisibleLayout extends JPanel implements Serializable
      serialVersionUID:
      -3644458365810712479L

      • Serialized Fields

        • m_editConnection
          String m_editConnection
          The name of the user-selected connection if the user has initiated a connection from a source step (stored in m_editStep)
        • m_editStep
          StepVisual m_editStep
          Reference to the step being edited (if any)
        • m_env
          Environment m_env
          Environment variables to use
        • m_filePath
          File m_filePath
          Current path on disk for this flow
        • m_flow
          Flow m_flow
        • m_flowExecutor
          FlowExecutor m_flowExecutor
          The flow executor used to execute the flow
        • m_hasBeenEdited
          boolean m_hasBeenEdited
          True if the flow has been edited, but not yet saved
        • m_isExecuting
          boolean m_isExecuting
          True if this flow is executing
        • m_layout
          LayoutPanel m_layout
          The panel used to render the flow
        • m_logPanel
          weka.gui.knowledgeflow.VisibleLayout.KFLogPanel m_logPanel
          The log panel to use for this layout
        • m_mainPerspective
          MainKFPerspective m_mainPerspective
          A reference to the main perspective
        • m_renderGraph
          List<StepVisual> m_renderGraph
          The steps to be rendered on this layout
        • m_selectedSteps
          List<StepVisual> m_selectedSteps
          Keeps track of any highlighted steps on the canvas
        • m_undoBuffer
          Stack<File> m_undoBuffer
          Keeps track of the undo buffer for this flow
        • m_userOpp
          weka.gui.knowledgeflow.VisibleLayout.LayoutOperation m_userOpp
          The current layout operation
        • m_zoomSetting
          int m_zoomSetting
          Current zoom setting for this layout

    • Class weka.gui.knowledgeflow.VisibleLayout.KFLogPanel

      class KFLogPanel extends LogPanel implements Serializable
      serialVersionUID:
      -2224509243343105276L

  • Package weka.gui.knowledgeflow.steps

  • Package weka.gui.scripting

    • Class weka.gui.scripting.FileScriptingPanel

      class FileScriptingPanel extends ScriptingPanel implements Serializable
      serialVersionUID:
      1583670545010241816L

      • Serialized Fields

        • m_AboutAction
          weka.gui.scripting.FileScriptingPanel.AboutAction m_AboutAction
          the about action.
        • m_Actions
          HashMap<Object,Action> m_Actions
          for storing the actions under their name.
        • m_Args
          String[] m_Args
          the commandline arguments to use.
        • m_ArgsAction
          weka.gui.scripting.FileScriptingPanel.CommandlineArgsAction m_ArgsAction
          the arguments action.
        • m_ClearOutputAction
          weka.gui.scripting.FileScriptingPanel.ClearOutputAction m_ClearOutputAction
          the clear output action.
        • m_CopyAction
          Action m_CopyAction
          the copy action.
        • m_CutAction
          Action m_CutAction
          the cut action.
        • m_ExitAction
          weka.gui.scripting.FileScriptingPanel.ExitAction m_ExitAction
          the exit action.
        • m_FileChooser
          WekaFileChooser m_FileChooser
          for loading/saving file.
        • m_LabelInfo
          JLabel m_LabelInfo
          for informing the user.
        • m_NewAction
          weka.gui.scripting.FileScriptingPanel.NewAction m_NewAction
          the new action.
        • m_OpenAction
          weka.gui.scripting.FileScriptingPanel.OpenAction m_OpenAction
          the open action.
        • m_OutputArea
          JTextArea m_OutputArea
          the output area.
        • m_PasteAction
          Action m_PasteAction
          the paste action.
        • m_PrintAction
          weka.gui.scripting.FileScriptingPanel.PrintAction m_PrintAction
          the Print action.
        • m_RedoAction
          weka.gui.scripting.FileScriptingPanel.RedoAction m_RedoAction
          the redo action.
        • m_SaveAction
          weka.gui.scripting.FileScriptingPanel.SaveAction m_SaveAction
          the Save action.
        • m_SaveAsAction
          weka.gui.scripting.FileScriptingPanel.SaveAction m_SaveAsAction
          the Save as action.
        • m_Script
          Script m_Script
          the script.
        • m_ScriptArea
          JTextArea m_ScriptArea
          the script area.
        • m_StartAction
          weka.gui.scripting.FileScriptingPanel.StartAction m_StartAction
          the start action.
        • m_StopAction
          weka.gui.scripting.FileScriptingPanel.StopAction m_StopAction
          the stop action.
        • m_TextCode
          JTextPane m_TextCode
          the text pane with the code.
        • m_TextOutput
          JTextPane m_TextOutput
          the text pane for the output.
        • m_Undo
          UndoManager m_Undo
          the undo manager.
        • m_UndoAction
          weka.gui.scripting.FileScriptingPanel.UndoAction m_UndoAction
          the undo action.

    • Class weka.gui.scripting.FileScriptingPanel.AboutAction

      class AboutAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      -6420463480569171227L

    • Class weka.gui.scripting.FileScriptingPanel.BasicAction

      class BasicAction extends AbstractAction implements Serializable
      serialVersionUID:
      2821117985661550385L

    • Class weka.gui.scripting.FileScriptingPanel.ClearOutputAction

      class ClearOutputAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      47986890456997211L

    • Class weka.gui.scripting.FileScriptingPanel.CommandlineArgsAction

      class CommandlineArgsAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      -3183470039010826204L

    • Class weka.gui.scripting.FileScriptingPanel.ExitAction

      class ExitAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      -5884709836238884180L

    • Class weka.gui.scripting.FileScriptingPanel.NewAction

      class NewAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      -8665722554539726090L

    • Class weka.gui.scripting.FileScriptingPanel.OpenAction

      class OpenAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      -4496148485267789162L

    • Class weka.gui.scripting.FileScriptingPanel.PrintAction

      class PrintAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      -6246539539545724632L

    • Class weka.gui.scripting.FileScriptingPanel.RedoAction

      class RedoAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      4533966901523279350L

    • Class weka.gui.scripting.FileScriptingPanel.SaveAction

      class SaveAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      -74651145892063975L

      • Serialized Fields

        • m_ShowDialog
          boolean m_ShowDialog
          whether to bring up the save dialog all the time.

    • Class weka.gui.scripting.FileScriptingPanel.StartAction

      class StartAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      -7936456072955996220L

    • Class weka.gui.scripting.FileScriptingPanel.StopAction

      class StopAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      8764023289575718872L

    • Class weka.gui.scripting.FileScriptingPanel.UndoAction

      class UndoAction extends FileScriptingPanel.BasicAction implements Serializable
      serialVersionUID:
      4298096648424808522L

    • Class weka.gui.scripting.GroovyPanel

      class GroovyPanel extends FileScriptingPanel implements Serializable
      serialVersionUID:
      -3475707604414854111L

    • Class weka.gui.scripting.GroovyScript

      class GroovyScript extends Script implements Serializable
      serialVersionUID:
      -3708517162415549420L

    • Class weka.gui.scripting.JythonPanel

      class JythonPanel extends FileScriptingPanel implements Serializable
      serialVersionUID:
      -827358576217085413L

    • Class weka.gui.scripting.JythonScript

      class JythonScript extends Script implements Serializable
      serialVersionUID:
      3469648507172973169L

    • Class weka.gui.scripting.Script

      class Script extends Object implements Serializable
      serialVersionUID:
      5053328052680586401L

      • Serialized Fields

        • m_Document
          Document m_Document
          the document this script is a wrapper around.
        • m_Filename
          File m_Filename
          the filename of the script.
        • m_FinishedListeners
          HashSet<ScriptExecutionListener> m_FinishedListeners
          optional listeners when the script finishes.
        • m_Modified
          boolean m_Modified
          whether the script is modified.
        • m_NewLine
          String m_NewLine
          the newline used on this platform.

    • Class weka.gui.scripting.ScriptingPanel

      class ScriptingPanel extends JPanel implements Serializable
      serialVersionUID:
      7593091442691911406L

      • Serialized Fields

        • m_Debug
          boolean m_Debug
          whether debug mode is on.
        • m_ErrPrintStream
          PrintStream m_ErrPrintStream
          The print stream that is wrapped around the piped error output stream. We need a a reference to it at the class level so that we can remove it from the tee when the panel is terminated.
        • m_ErrRedirector
          ReaderToTextPane m_ErrRedirector
          The thread that sends output from m_POE to the output box.
        • m_ErrTee
          Tee m_ErrTee
          The Tee that the err stream will be part of. We need a reference to it at the class level so that we can remove the stream from it when the panel is terminated.
        • m_OutRedirector
          ReaderToTextPane 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_PrintStream
          PrintStream m_PrintStream
          The print stream that is wrapped around the piped output stream. We need a a reference to it at the class level so that we can remove it from the tee when the panel is terminated.
        • m_Tee
          Tee m_Tee
          The Tee that the output stream will be part of. We need a reference to it at the class level so that we can remove the stream from it when the panel is terminated.
        • m_TitleUpdatedListeners
          HashSet<TitleUpdatedListener> m_TitleUpdatedListeners
          the listeners for the changes in the title.

    • Class weka.gui.scripting.SyntaxDocument

      class SyntaxDocument extends DefaultStyledDocument implements Serializable
      serialVersionUID:
      -3642426465631271381L

      • Serialized Fields

        • m_AddMatchingEndBlocks
          boolean m_AddMatchingEndBlocks
          whether to add matching brackets.
        • m_BackgroundColor
          Color m_BackgroundColor
          the background color.
        • m_BlockEnd
          String m_BlockEnd
          the end of a block.
        • m_BlockStart
          String m_BlockStart
          the start of a block.
        • m_CaseSensitive
          boolean m_CaseSensitive
          whether keywords are case-sensitive.
        • m_Delimiters
          String m_Delimiters
          the delimiters.
        • m_FontName
          String m_FontName
          the font name.
        • m_FontSize
          int m_FontSize
          the font size.
        • m_Indentation
          String m_Indentation
          the number of spaces used for indentation.
        • m_InsideMultiLineComment
          boolean m_InsideMultiLineComment
          whether we're currently in a multi-line comment.
        • m_Keywords
          HashMap<String,MutableAttributeSet> m_Keywords
          the keywords.
        • m_MultiLineComment
          boolean m_MultiLineComment
          whether multi-line comments are enabled.
        • m_MultiLineCommentEnd
          String m_MultiLineCommentEnd
          the multi-line comment end.
        • m_MultiLineCommentStart
          String m_MultiLineCommentStart
          the multi-line comment start.
        • m_QuoteDelimiters
          String m_QuoteDelimiters
          the quote delimiter.
        • m_QuoteEscape
          String m_QuoteEscape
          the quote escape.
        • m_RootElement
          Element m_RootElement
          the root element.
        • m_Self
          DefaultStyledDocument m_Self
          the document.
        • m_SingleLineCommentStart
          String m_SingleLineCommentStart
          the single-line comment start.
        • m_UseBlanks
          boolean m_UseBlanks
          whether to use blanks instead of tabs.

  • Package weka.gui.scripting.event

  • Package weka.gui.simplecli

  • 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_ButtonSetup
          JButton m_ButtonSetup
          the button for the setup.
        • m_ConnectionListeners
          HashSet<ConnectionListener> m_ConnectionListeners
          the connection listeners.
        • m_DbDialog
          DatabaseConnectionDialog m_DbDialog
          the database connection dialog.
        • m_DbUtils
          DbUtils m_DbUtils
          for connecting to the database.
        • m_History
          DefaultListModel m_History
          the history of connections.
        • m_HistoryChangedListeners
          HashSet<HistoryChangedListener> 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_SetupFileChooser
          WekaFileChooser m_SetupFileChooser
          the file chooser for the setup files.
        • 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<HistoryChangedListener> m_HistoryChangedListeners
          the history listeners.
        • m_Parent
          JFrame m_Parent
          the parent of this panel.
        • m_QueryExecuteListeners
          HashSet<QueryExecuteListener> 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<ResultChangedListener> 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_CheckBoxSparseData
          JCheckBox m_CheckBoxSparseData
          whether to return sparse instances or not.
        • 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_autoScale
          JMenuItem m_autoScale
          An option on the win_menu
        • m_BackgroundColor
          Color m_BackgroundColor
          the background color.
        • 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_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
          ArrayList<Color> 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
          ArrayList<AttributePanelListener> 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
          ArrayList<Color> m_colorList
          the list of colours to use for colouring nominal attribute labels
        • m_ColourChangeListeners
          ArrayList<ActionListener> 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_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
          ArrayList<Component> 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.InstanceInfoFrame

      class InstanceInfoFrame extends JFrame implements Serializable
      serialVersionUID:
      4684184733677263009L

      • Serialized Fields

        • m_Data
          Vector<Instances> m_Data
          the underlying data.
        • m_TextInfo
          JTextArea m_TextInfo
          the text area for displaying the info.

    • Class weka.gui.visualize.LegendPanel

      class LegendPanel extends JScrollPane implements Serializable
      serialVersionUID:
      -1262384440543001505L

      • Serialized Fields

        • m_plots
          ArrayList<PlotData2D> m_plots
          the list of plot elements
        • m_Repainters
          ArrayList<Component> 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_backgroundColor
          Color m_backgroundColor
          For the background of the little plots
        • 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_clearOSIPlottedCells
          boolean m_clearOSIPlottedCells
        • m_colorList
          ArrayList<Color> 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_fastScroll
          JCheckBox m_fastScroll
        • 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_plottedCells
          boolean[][] m_plottedCells
        • 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_previousPercent
          double m_previousPercent
        • m_regenerateOSI
          boolean m_regenerateOSI
        • 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_settings
          Settings m_settings
          Settings (if available) to pass through to the VisualizePanels
        • m_settingsOwnerID
          String m_settingsOwnerID
          ID of the owner (perspective, panel etc.) under which to lookup our settings
        • 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
          ArrayList<Color> 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_InstanceInfoFrameClass
          Class<?> m_InstanceInfoFrameClass
          the class for displaying instance info.
        • 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
          ArrayList<PlotData2D> m_plots
          The plots to display
        • 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.PlotData2D

      class PlotData2D extends Object implements Serializable
      serialVersionUID:
      -3979972167982697979L

      • Serialized Fields

        • m_alwaysDisplayPointsOfThisSize
          int m_alwaysDisplayPointsOfThisSize
          If the shape size of a point equals this size then always plot it (i.e. even if it is obscured by other points)
        • m_cIndex
          int m_cIndex
          The colouring index
        • m_connectPoints
          boolean[] m_connectPoints
          Additional optional information to control the drawing of lines between consecutive points. Setting an entry in the array to true indicates that the associated point should have a line connecting it to the previous point.
        • m_customColour
          Color m_customColour
        • m_displayAllPoints
          boolean m_displayAllPoints
          Display all points (ie. those that map to the same display coords)
        • m_maxC
          double m_maxC
        • m_maxX
          double m_maxX
          Holds the min and max values of the x, y and colouring attributes for this plot
        • m_maxY
          double m_maxY
        • m_minC
          double m_minC
        • m_minX
          double m_minX
        • m_minY
          double m_minY
        • m_plotInstances
          Instances m_plotInstances
          The instances
        • m_plotName
          String m_plotName
          The name of this plot
        • m_plotNameHTML
          String m_plotNameHTML
          The name of this plot (possibly in html) suitable for using in a tool tip text.
        • m_pointLookup
          double[][] m_pointLookup
          Panel coordinate cache for data points
        • m_shapeSize
          int[] m_shapeSize
          Additional optional information to control the size of points. The default is shape size 2
        • m_shapeType
          int[] m_shapeType
          Additional optional information to control the point shape for this data. Default is to allow automatic assigning of point shape on the basis of plot number
        • m_useCustomColour
          boolean m_useCustomColour
          Custom colour for this plot
        • m_xIndex
          int m_xIndex
          The x index
        • m_yIndex
          int m_yIndex
          The y index

    • Class weka.gui.visualize.PrintableComponent.JComponentWriterFileFilter

      class JComponentWriterFileFilter extends ExtensionFileFilter implements Serializable
      serialVersionUID:
      8540426888094207515L

    • 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
          ArrayList<Color> 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
          WekaFileChooser 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
          ArrayList<Double> m_shapePoints
          contains the points of the shape currently being drawn.
        • m_shapes
          ArrayList<ArrayList<Double>> m_shapes
          contains all the shapes that have been drawn for these attribs
        • m_sIndex
          int m_sIndex
        • m_xIndex
          int m_xIndex
          Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing
        • m_yIndex
          int m_yIndex

    • Class weka.gui.visualize.VisualizeUtils.VisualizeDefaults

      class VisualizeDefaults extends Defaults implements Serializable
      serialVersionUID:
      4227480313375404688L

  • Package weka.knowledgeflow

  • Package weka.knowledgeflow.steps

    • Class weka.knowledgeflow.steps.AlterRelationName

      class AlterRelationName extends BaseStep implements Serializable
      serialVersionUID:
      5894383194664583303L

      • Serialized Fields

        • m_hasAltered
          Set<String> m_hasAltered
          The set of source step identifiers that have had their data modified so far
        • m_modType
          weka.knowledgeflow.steps.AlterRelationName.ModType m_modType
          The type of modification to make
        • m_regexMatch
          String m_regexMatch
          Regex string to match
        • m_regexPattern
          Pattern m_regexPattern
          For regex replacement
        • m_relationNameModText
          String m_relationNameModText
          Text to modify the relation name with
        • m_replaceAll
          boolean m_replaceAll
          Whether to replace all rexex matches, or just the first

    • Class weka.knowledgeflow.steps.Appender

      class Appender extends BaseStep implements Serializable
      serialVersionUID:
      -3003135257112845998L

      • Serialized Fields

        • m_completed
          Map<Step,Instances> m_completed
          Used to keep track of how many upstream steps have sent us complete data sets (batch) or headers (incremental) so far.
        • m_completeHeader
          Instances m_completeHeader
          Used to hold the final header in the case of incremental operation
        • m_isReset
          boolean m_isReset
          True if this step has been reset
        • m_streamingCountDown
          AtomicInteger m_streamingCountDown
          Gets decremented for each incoming instance stream that has finished
        • m_streamingData
          Data m_streamingData
          Re-usable data object for streaming mode
        • m_tempBatchFiles
          Map<Step,File> m_tempBatchFiles
          Handles on temp files used to store batches of instances in batch mode

    • Class weka.knowledgeflow.steps.ASEvaluator

      class ASEvaluator extends WekaAlgorithmWrapper implements Serializable
      serialVersionUID:
      -1280208826860871742L

      • Serialized Fields

        • m_eval
          AttributeSelection m_eval
          Eval to use when performing a cross-validation and not outputting separate results for each fold
        • m_evaluatorTemplate
          ASEvaluation m_evaluatorTemplate
          The evaluator (attribute or subset) being used
        • m_isDoingXVal
          boolean m_isDoingXVal
          True if we are processing cross-validation folds to produce a summary over the folds (as opposed to producing separate results per fold).
        • m_isRanking
          boolean m_isRanking
          Whether a ranking is being produced by the attribute selection
        • m_isReset
          boolean m_isReset
          True if we've been reset
        • m_numToSelectStore
          Map<Integer,Integer> m_numToSelectStore
          Holds the calculated number of attributes to select (may depend on thresholds) for each training fold
        • m_searchTemplate
          ASSearch m_searchTemplate
          The search strategy being used (retrieved via an incoming "info" connection
        • m_selectedAttsStore
          Map<Integer,int[]> m_selectedAttsStore
          Holds selected attribute indices corresponding to training folds
        • m_setCount
          AtomicInteger m_setCount
          Keeps count of the folds processed
        • m_transformerStore
          Map<Integer,AttributeTransformer> m_transformerStore
          Holds the evaluator trained per fold in the case when it is a transformer (such as PCA)
        • m_treatXValFoldsSeparately
          boolean m_treatXValFoldsSeparately
          Whether to output separate evaluation results for each fold of a xval or report the cross-validation summary
        • m_waitingTestData
          Map<Integer,Instances> m_waitingTestData
          Any test folds waiting to be processed (i.e. have their dimensionality reduced

    • Class weka.knowledgeflow.steps.ASSearchStrategy

      class ASSearchStrategy extends WekaAlgorithmWrapper implements Serializable
      serialVersionUID:
      5038697382280884975L

    • Class weka.knowledgeflow.steps.Associator

      class Associator extends WekaAlgorithmWrapper implements Serializable
      serialVersionUID:
      -589410455393151511L

      • Serialized Fields

        • m_associatorTemplate
          Associator m_associatorTemplate
          Template for the associator in use

    • Class weka.knowledgeflow.steps.AttributeSummarizer

      class AttributeSummarizer extends BaseSimpleDataVisualizer implements Serializable
      serialVersionUID:
      2313372820072708102L

      • Serialized Fields

        • m_additionalOptions
          String m_additionalOptions
          Additional options for the offscreen renderer
        • m_height
          String m_height
          Height of offscreen plots
        • m_offscreenRendererName
          String m_offscreenRendererName
          Name of the renderer to use for offscreen chart rendering
        • m_width
          String m_width
          Width of offscreen plots
        • m_xAxis
          String m_xAxis
          The x-axis attribute name

    • Class weka.knowledgeflow.steps.BaseSimpleDataVisualizer

      class BaseSimpleDataVisualizer extends BaseStep implements Serializable
      serialVersionUID:
      4955068920302509451L

      • Serialized Fields

        • m_data
          List<Data> m_data
          The datasets seen so far

    • Class weka.knowledgeflow.steps.BaseStep

      class BaseStep extends Object implements Serializable
      serialVersionUID:
      -1595753549991953141L

      • Serialized Fields

        • m_stepIsResourceIntensive
          boolean m_stepIsResourceIntensive
          True if the step is resource (cpu/memory) intensive
        • m_stepName
          String m_stepName
          The name of this step component

    • Class weka.knowledgeflow.steps.Block

      class Block extends BaseStep implements Serializable
      serialVersionUID:
      3204082191908877620L

      • Serialized Fields

        • m_stepToWaitFor
          String m_stepToWaitFor
          The name of the step to wait for

    • Class weka.knowledgeflow.steps.BoundaryPlotter

      class BoundaryPlotter extends BaseStep implements Serializable
      serialVersionUID:
      7864251468395026619L

      • Serialized Fields

        • m_classifierTemplates
          List<Classifier> m_classifierTemplates
          Classifiers to use
        • m_clustererTemplates
          List<DensityBasedClusterer> m_clustererTemplates
          Clusterers to use
        • m_Colors
          List<Color> m_Colors
          Holds colors to use
        • m_currentDescription
          String m_currentDescription
          The spec of the scheme being used to render the current image
        • m_dataGenerator
          KDDataGenerator m_dataGenerator
          The data generator to use
        • m_imageHeight
          int m_imageHeight
          Height of images to generate
        • m_imageWidth
          int m_imageWidth
          Width of images to generate
        • m_isReset
          boolean m_isReset
          True if we've been reset
        • m_kBand
          String m_kBand
          User-specified bandwidth
        • m_kernelBandwidth
          int m_kernelBandwidth
          Parsed bandwidth
        • m_maxRowsInParallel
          int m_maxRowsInParallel
          Number of rows of the visualization to compute in parallel. We don't want to dominate the thread pool that is used for executing all steps and step sub-tasks in the KF (this is currently fixed at 50 threads by FlowRunner).
        • m_maxX
          double m_maxX
        • m_maxY
          double m_maxY
        • m_minX
          double m_minX
        • m_minY
          double m_minY
        • m_nSamples
          String m_nSamples
          User-specified num samples
        • m_numSamplesPerRegion
          int m_numSamplesPerRegion
          Parsed samples
        • m_pixHeight
          double m_pixHeight
        • m_pixWidth
          double m_pixWidth
        • m_plotTrainingData
          boolean m_plotTrainingData
          Superimpose the training data on the plot?
        • m_rangeX
          double m_rangeX
        • m_rangeY
          double m_rangeY
        • m_samplesBase
          int m_samplesBase
          Parsed base
        • m_sBase
          String m_sBase
          User-specified base for sampling
        • m_threadClassifiers
          Classifier[] m_threadClassifiers
          Copies of trained classifier to use in parallel for prediction
        • m_threadClusterers
          Clusterer[] m_threadClusterers
          Copies of trained clusterer to use in parallel for prediction
        • m_threadGenerators
          DataGenerator[] m_threadGenerators
          Data generator copies to use in parallel
        • m_xAttName
          String m_xAttName
          X axis attribute name/index
        • m_xAttribute
          int m_xAttribute
        • m_yAttName
          String m_yAttName
          Y axis attribute name/index
        • m_yAttribute
          int m_yAttribute

    • Class weka.knowledgeflow.steps.BoundaryPlotter.SchemeRowTask

      class SchemeRowTask extends StepTask<weka.knowledgeflow.steps.BoundaryPlotter.RowResult> implements Serializable
      serialVersionUID:
      -4144732293602550066L

      • Serialized Fields

        • m_attsToWeightOn
          boolean[] m_attsToWeightOn
        • m_classifier
          Classifier m_classifier
        • m_clusterer
          DensityBasedClusterer m_clusterer
        • m_dataGenerator
          DataGenerator m_dataGenerator
        • m_dist
          double[] m_dist
        • m_imageHeight
          int m_imageHeight
        • m_imageWidth
          int m_imageWidth
        • 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_pixHeight
          double m_pixHeight
        • m_pixWidth
          double m_pixWidth
        • m_predInst
          Instance m_predInst
        • m_random
          Random m_random
        • m_rowNum
          int m_rowNum
        • m_samplesBase
          double m_samplesBase
        • m_trainingData
          Instances m_trainingData
        • m_vals
          double[] m_vals
        • m_weightingAttsValues
          double[] m_weightingAttsValues
        • m_xAtt
          int m_xAtt
        • m_yAtt
          int m_yAtt

    • Class weka.knowledgeflow.steps.ClassAssigner

      class ClassAssigner extends BaseStep implements Serializable
      serialVersionUID:
      -4269063233834866140L

      • Serialized Fields

        • m_classAssigned
          boolean m_classAssigned
          True if the class has already been assigned
        • m_classCol
          String m_classCol
          Holds user-specified class column/index
        • m_classColumnS
          String m_classColumnS
          Holds resoved class column/index
        • m_isInstanceStream
          boolean m_isInstanceStream
          True if processing an instance stream
        • m_streamCount
          int m_streamCount
          Counter used for streams

    • Class weka.knowledgeflow.steps.Classifier

      class Classifier extends WekaAlgorithmWrapper implements Serializable
      serialVersionUID:
      8326706942962123155L

      • Serialized Fields

        • m_classifierIsIncremental
          boolean m_classifierIsIncremental
          True if the classifier in use is Updateable
        • m_classifierTemplate
          Classifier m_classifierTemplate
          The template for constructing concrete instances of the classifier to train
        • m_incrementalData
          Data m_incrementalData
          Reusable data for incremental streaming classifiers
        • m_isReset
          boolean m_isReset
          True if we've been reset
        • m_loadModelFileName
          File m_loadModelFileName
          Optional file to load a pre-trained model to score with (batch, or to score and update (incremental) in the case of testSet only (batch) or instance (incremental) connections
        • m_resetIncrementalClassifier
          boolean m_resetIncrementalClassifier
          True if we should resent an Updateable classifier at the start of processing for an incoming "instance" stream
        • m_streaming
          boolean m_streaming
          True if we are processing streaming data
        • m_trainedClassifier
          Classifier m_trainedClassifier
          Holds the trained classifier in the case of single train/test pairs or instance stream connections
        • m_trainedClassifierHeader
          Instances m_trainedClassifierHeader
        • m_updateIncrementalClassifier
          boolean m_updateIncrementalClassifier
          True if we should update an incremental classifier when there is a incoming "instance" stream

    • Class weka.knowledgeflow.steps.ClassifierPerformanceEvaluator

      class ClassifierPerformanceEvaluator extends BaseStep implements Serializable
      serialVersionUID:
      -2679292079974676672L

      • Serialized Fields

        • m_collectDataForVisAndAUC
          boolean m_collectDataForVisAndAUC
          Whether to store test data and predictions for visualization
        • m_costSensitiveEval
          boolean m_costSensitiveEval
          True to perform cost sensitive evaluation
        • m_costString
          String m_costString
          The cost matrix (string form)
        • m_errorPlotPointSizeProportionalToMargin
          boolean m_errorPlotPointSizeProportionalToMargin
          True if plot point sizes are to be rendered proportional to the size of the prediction margin
        • m_isReset
          boolean m_isReset
          True if the step has been reset
        • m_matrix
          CostMatrix m_matrix
          The cost matrix
        • m_maxSetNum
          int m_maxSetNum
          The maximum set number in the batch of sets being processed
        • m_metricsList
          List<String> m_metricsList
          Holds a list of metric names
        • m_outputConfusionMatrix
          boolean m_outputConfusionMatrix
          True to output confusion matrix
        • m_outputEntropyMetrics
          boolean m_outputEntropyMetrics
          True to output entropy-based metrics
        • m_outputPerClassStats
          boolean m_outputPerClassStats
          True to output of per class stats
        • m_selectedEvalMetrics
          String m_selectedEvalMetrics
          Evaluation metrics to output
        • m_setsToGo
          AtomicInteger m_setsToGo
          For counting down the sets left to process
        • m_taskCount
          AtomicInteger m_taskCount

    • Class weka.knowledgeflow.steps.ClassifierPerformanceEvaluator.AggregateableClassifierErrorsPlotInstances

      class AggregateableClassifierErrorsPlotInstances extends ClassifierErrorsPlotInstances implements Serializable
      serialVersionUID:
      2012744784036684168L

    • Class weka.knowledgeflow.steps.ClassifierPerformanceEvaluator.EvaluationTask

      class EvaluationTask extends StepTask<Object[]> implements Serializable
      serialVersionUID:
      -686972773536075889L

      • Serialized Fields

        • m_classifier
          Classifier m_classifier
        • m_classifierDesc
          String m_classifierDesc
        • m_cMatrix
          CostMatrix m_cMatrix
        • m_collectPreds
          boolean m_collectPreds
        • m_errPlotPtSizePropToMarg
          boolean m_errPlotPtSizePropToMarg
        • m_evalLabel
          String m_evalLabel
        • m_metricsList
          List<String> m_metricsList
        • m_setNum
          int m_setNum
        • m_testData
          Instances m_testData
        • m_trainData
          Instances m_trainData

    • Class weka.knowledgeflow.steps.ClassValuePicker

      class ClassValuePicker extends BaseStep implements Serializable
      serialVersionUID:
      8558445535347028472L

      • Serialized Fields

        • m_classIsNominal
          boolean m_classIsNominal
          True if the class is set and is nominal
        • m_classIsSet
          boolean m_classIsSet
          True if the class is set in the incoming data
        • m_classValue
          String m_classValue
          Class label after environment variables have been resolved
        • m_classValueS
          String m_classValueS
          User specified class label, label index or special identifier (e.g "first"/"last")

    • Class weka.knowledgeflow.steps.Clusterer

      class Clusterer extends WekaAlgorithmWrapper implements Serializable
      serialVersionUID:
      3275754421525338036L

      • Serialized Fields

        • m_clustererTemplate
          Clusterer m_clustererTemplate
          The template for constructing concrete instances of the clusterer to train
        • m_incrementalData
          Data m_incrementalData
          Re-usable Data object for incrementalClusterer output
        • m_isReset
          boolean m_isReset
          True if the step has just been reset
        • m_loadModelFileName
          File m_loadModelFileName
          Optional file to load a pre-trained model to score with (batch, or to score and update (incremental) in the case of testSet only (batch) or instance (incremental) connections
        • m_streaming
          boolean m_streaming
          True if there is a single incoming "instance" connection
        • m_trainedClusterer
          Clusterer m_trainedClusterer
          Holds a trained/loaded clusterer
        • m_trainedClustererHeader
          Instances m_trainedClustererHeader
          Header used to train the clusterer

    • Class weka.knowledgeflow.steps.ClustererPerformanceEvaluator

      class ClustererPerformanceEvaluator extends BaseStep implements Serializable
      serialVersionUID:
      -6337375482954345717L

    • Class weka.knowledgeflow.steps.CostBenefitAnalysis

      class CostBenefitAnalysis extends BaseSimpleDataVisualizer implements Serializable
      serialVersionUID:
      7756281775575854085L

    • Class weka.knowledgeflow.steps.CrossValidationFoldMaker

      class CrossValidationFoldMaker extends BaseStep implements Serializable
      serialVersionUID:
      6090713408437825355L

      • Serialized Fields

        • m_numFolds
          int m_numFolds
          Resolved number of folds
        • m_numFoldsS
          String m_numFoldsS
          User-specified number of folds
        • m_preserveOrder
          boolean m_preserveOrder
          True to preserve order of the instances rather than randomly shuffling
        • m_seed
          long m_seed
          Resolved random seed
        • m_seedS
          String m_seedS
          User-specified random seed

    • Class weka.knowledgeflow.steps.DataGenerator

      class DataGenerator extends WekaAlgorithmWrapper implements Serializable
      serialVersionUID:
      -7716707145987484527L

      • Serialized Fields

        • m_flowThroughput
          StreamThroughput m_flowThroughput
          overall flow throughput when streaming
        • m_incrementalData
          Data m_incrementalData
          reusable data object for streaming

    • Class weka.knowledgeflow.steps.DataGrid

      class DataGrid extends BaseStep implements Serializable
      serialVersionUID:
      1318159328875458847L

      • Serialized Fields

        • m_data
          String m_data
          The instances to output (as a string)
        • m_flowThroughput
          StreamThroughput m_flowThroughput
          For overall stream throughput measuring
        • m_incrementalData
          Data m_incrementalData
          Reusable data object for streaming output

    • Class weka.knowledgeflow.steps.DataVisualizer

      class DataVisualizer extends BaseStep implements Serializable
      serialVersionUID:
      -8013077913672918384L

      • Serialized Fields

        • m_additionalOptions
          String m_additionalOptions
          Additional options for the offscreen renderer
        • m_height
          String m_height
          Height of offscreen plots
        • m_offscreenRendererName
          String m_offscreenRendererName
          Name of the renderer to use for offscreen chart rendering
        • m_plots
          List<PlotData2D> m_plots
          Current set of plots. First element is the master plot
        • m_width
          String m_width
          Width of offscreen plots
        • m_xAxis
          String m_xAxis
          The name of the attribute to use for the x-axis of offscreen plots. If left empty, False Positive Rate is used for threshold curves
        • m_yAxis
          String m_yAxis
          The name of the attribute to use for the y-axis of offscreen plots. If left empty, True Positive Rate is used for threshold curves

    • Class weka.knowledgeflow.steps.Dummy

      class Dummy extends BaseStep implements Serializable
      serialVersionUID:
      -5822675617001689385L

    • Class weka.knowledgeflow.steps.ExecuteProcess

      class ExecuteProcess extends BaseStep implements Serializable
      serialVersionUID:
      -9153714279540182885L

      • Serialized Fields

        • m_argsFieldIndex
          int m_argsFieldIndex
          Resolved attribute index of dynamic arguments
        • m_cmdFieldIndex
          int m_cmdFieldIndex
          Resolved attribute index of dynamic command
        • m_fieldArgs
          String m_fieldArgs
          Name of attribute that will hold optional arguments for dynamic command
        • m_fieldCmd
          String m_fieldCmd
          Name of attribute that will hold dynamic command to be executed
        • m_fieldWorkingDir
          String m_fieldWorkingDir
          Name of attribute that will hold optional working directory for dynamic command
        • m_instanceOutHeader
          Instances m_instanceOutHeader
          Structure of output for outgoing instance connections
        • m_raiseAnExceptionOnCommandFailure
          boolean m_raiseAnExceptionOnCommandFailure
          Whether to raise an exception when a command fails completely (i.e. doesn't exist or something) vs the case of a non-zero exit status. If not raising an exception, then output indicating failure (with exist status = 1 in the case of instance connections) will be generated
        • m_runningProcess
          Process m_runningProcess
          The process that is used to execute the user's command(s)
        • m_staticArgs
          String m_staticArgs
          Arguments (if necessary) for single static command
        • m_staticExecCmd
          String m_staticExecCmd
          Single static command (may use env vars)
        • m_staticWorkingDir
          String m_staticWorkingDir
          Optional working directory for single static command
        • m_stdErrBuffer
          StringBuffer m_stdErrBuffer
          Std err from process
        • m_stdOutbuffer
          StringBuffer m_stdOutbuffer
          Std out from process
        • m_structureCheckComplete
          boolean m_structureCheckComplete
          True if the structure has been checked
        • m_useDynamic
          boolean m_useDynamic
          True to execute commands specified in incoming instance fields
        • m_workingDirFieldIndex
          int m_workingDirFieldIndex
          Resolved attribute index of dynamic working directory

    • Class weka.knowledgeflow.steps.Filter

      class Filter extends WekaAlgorithmWrapper implements Serializable
      serialVersionUID:
      6857031910153224479L

      • Serialized Fields

        • m_filterMap
          Map<Integer,Filter> m_filterMap
          Map of filters that have processed the first batch
        • m_filterTemplate
          Filter m_filterTemplate
          Template filter
        • m_incrementalData
          Data m_incrementalData
          Data object to reuse when processing incrementally
        • m_isReset
          boolean m_isReset
          True if we've been reset
        • m_setCount
          AtomicInteger m_setCount
          Keeps track of the number of train/test batches processed
        • m_streaming
          boolean m_streaming
          True if we're streaming
        • m_streamingFilter
          Filter m_streamingFilter
          Used when processing streaming data
        • m_stringAttsPresent
          boolean m_stringAttsPresent
          True if string attributes are present in streaming case
        • m_waitingTestData
          Map<Integer,Instances> m_waitingTestData
          Map of waiting test sets when batch filtering

    • Class weka.knowledgeflow.steps.FlowByExpression

      class FlowByExpression extends BaseStep implements Serializable
      serialVersionUID:
      7006511778677802572L

      • Serialized Fields

        • m_batchCount
          AtomicInteger m_batchCount
          Keep track of how many incoming batches we've seen
        • m_customNameOfFalseStep
          String m_customNameOfFalseStep
          Name of the step to receive instances that evaluate to false via the expression
        • m_customNameOfTrueStep
          String m_customNameOfTrueStep
          Name of the step to receive instances that evaluate to true via the expression
        • m_expressionString
          String m_expressionString
          The expression tree to use in internal textual format
        • m_incomingStructure
          Instances m_incomingStructure
          Incoming structure
        • m_isReset
          boolean m_isReset
        • m_root
          FlowByExpression.ExpressionNode m_root
          The root of the expression tree
        • m_streamingData
          Data m_streamingData
          Re-usable data object for streaming
        • m_validFalseStep
          boolean m_validFalseStep
          True if the "false" step is valid (i.e. exists in the flow)
        • m_validTrueStep
          boolean m_validTrueStep
          True if the "true" step is valid (i.e. exists in the flow)

    • Class weka.knowledgeflow.steps.FlowByExpression.BracketNode

      class BracketNode extends FlowByExpression.ExpressionNode implements Serializable
      serialVersionUID:
      8732159083173001115L

    • Class weka.knowledgeflow.steps.FlowByExpression.ExpressionClause

      class ExpressionClause extends FlowByExpression.ExpressionNode implements Serializable
      serialVersionUID:
      2754006654981248325L

      • Serialized Fields

        • m_lhsAttIndex
          int m_lhsAttIndex
          The index of the lhs attribute
        • m_lhsAttributeName
          String m_lhsAttributeName
          The name of the lhs attribute
        • m_numericOperand
          double m_numericOperand
          The rhs operand (if constant and is a number )
        • m_operator
          FlowByExpression.ExpressionClause.ExpressionType m_operator
          The operator for this expression
        • m_regexPattern
          Pattern m_regexPattern
          the compiled regex pattern (if the operator is REGEX)
        • m_resolvedLhsName
          String m_resolvedLhsName
          The name of the lhs attribute after resolving variables
        • m_resolvedRhsOperand
          String m_resolvedRhsOperand
          The rhs operand after resolving variables
        • m_rhsAttIndex
          int m_rhsAttIndex
          index of the rhs if it is an attribute
        • m_rhsIsAttribute
          boolean m_rhsIsAttribute
          True if the rhs operand is an attribute
        • m_rhsOperand
          String m_rhsOperand
          The rhs operand (constant value or attribute name)

    • Class weka.knowledgeflow.steps.FlowByExpression.ExpressionNode

      class ExpressionNode extends Object implements Serializable
      serialVersionUID:
      -8427857202322768762L

      • Serialized Fields

        • m_isAnOr
          boolean m_isAnOr
          boolean operator for combining with result so far
        • m_isNegated
          boolean m_isNegated
          is this node negated?
        • m_showAndOr
          boolean m_showAndOr
          Whether to show the combination operator in the textual representation

    • Class weka.knowledgeflow.steps.GetDataFromResult

      class GetDataFromResult extends BaseStep implements Serializable
      serialVersionUID:
      7447845310997458636L

    • Class weka.knowledgeflow.steps.GraphViewer

      class GraphViewer extends BaseSimpleDataVisualizer implements Serializable
      serialVersionUID:
      -3256888744740965144L

    • Class weka.knowledgeflow.steps.ImageSaver

      class ImageSaver extends BaseStep implements Serializable
      serialVersionUID:
      -8766164679635957891L

      • Serialized Fields

        • m_defaultFile
          String m_defaultFile
          Default location to write to, in case a file has not been explicitly set
        • m_defaultFormat
          weka.knowledgeflow.steps.ImageSaver.ImageFormat m_defaultFormat
          Default format to use - read from the settings for this step, and used in the case when the user has selected/left DEFAULT as the format type in the step's options. Must not be set to the type DEFAULT of course :-)
        • m_file
          File m_file
          The file to save to
        • m_format
          weka.knowledgeflow.steps.ImageSaver.ImageFormat m_format
          Format to save to. If set to DEFAULT, then the default format the user has set in the settings for this step is used.
        • m_imageCounter
          int m_imageCounter
          Gets incremented by 1 for each image received during execution. Can be used (via the image_count variable) to ensure that each image gets saved to a different file when there are multiple images expected during execution.

    • Class weka.knowledgeflow.steps.ImageSaver.ImageSaverDefaults

      class ImageSaverDefaults extends Defaults implements Serializable
      serialVersionUID:
      -2739579935119189195L

    • Class weka.knowledgeflow.steps.ImageViewer

      class ImageViewer extends BaseStep implements Serializable
      serialVersionUID:
      -4055716444227948343L

    • Class weka.knowledgeflow.steps.IncrementalClassifierEvaluator

      class IncrementalClassifierEvaluator extends BaseStep implements Serializable
      serialVersionUID:
      -5951569492213633100L

      • Serialized Fields

        • m_chartData
          Data m_chartData
          Re-usable chart data
        • m_classifierName
          String m_classifierName
          Holds the name of the classifier being used
        • m_dataLegend
          List<String> m_dataLegend
          Legend information
        • m_dataPoint
          double[] m_dataPoint
          Actual data point values
        • m_eval
          Evaluation m_eval
          Main eval object
        • m_instanceCount
          int m_instanceCount
          Count of instances seen
        • m_max
          double m_max
        • m_min
          double m_min
        • m_outputInfoRetrievalStats
          boolean m_outputInfoRetrievalStats
        • m_reset
          boolean m_reset
          True if rest
        • m_statusFrequency
          int m_statusFrequency
          how often (in milliseconds) to report throughput to the log
        • m_window
          LinkedList<Instance> m_window
          Window instances
        • m_windowedPreds
          LinkedList<double[]> m_windowedPreds
          Window predictions
        • m_windowEval
          Evaluation m_windowEval
          Evaluation object for window
        • m_windowSize
          int m_windowSize
          window size for computing performance metrics - 0 means no window, i.e don't "forget" performance on any instances

    • Class weka.knowledgeflow.steps.InstanceStreamToBatchMaker

      class InstanceStreamToBatchMaker extends BaseStep implements Serializable
      serialVersionUID:
      5461324282251111320L

      • Serialized Fields

        • m_hasStringAtts
          boolean m_hasStringAtts
          True if the incoming data contains string attributes
        • m_isReset
          boolean m_isReset
          True if we've been reset
        • m_structure
          Instances m_structure
          The structure of the incoming instances

    • Class weka.knowledgeflow.steps.Job

      class Job extends BaseStep implements Serializable
      serialVersionUID:
      -8684065684979500325L

      • Serialized Fields

    • Class weka.knowledgeflow.steps.Join

      class Join extends BaseStep implements Serializable
      serialVersionUID:
      -8248954818247532014L

      • Serialized Fields

        • m_firstInput
          StepManager m_firstInput
          First source of data
        • m_firstInputConnectionType
          String m_firstInputConnectionType
          Connection type of the first input
        • m_firstIsWaiting
          boolean m_firstIsWaiting
          True if the first input stream is waiting due to a full buffer (incremental mode only)
        • m_keyIndexesOne
          int[] m_keyIndexesOne
          Indexes of the key fields for the first input
        • m_keyIndexesTwo
          int[] m_keyIndexesTwo
          Indexes of the key fields for the second input
        • m_keySpec
          String m_keySpec
          Holds the internal representation of the key specification
        • m_runningIncrementally
          boolean m_runningIncrementally
          True if the step is running incrementally
        • m_secondInput
          StepManager m_secondInput
          Second source of data
        • m_secondInputConnectionType
          String m_secondInputConnectionType
          Connection type of the second input
        • m_secondIsWaiting
          boolean m_secondIsWaiting
          True if the second input stream is waiting due to a full buffer (incremental mode only)
        • m_streamingData
          Data m_streamingData
          Reusable data object for streaming output
        • m_stringAttIndexesOne
          Map<String,Integer> m_stringAttIndexesOne
          Holds indexes of string attributes, keyed by attribute name
        • m_stringAttIndexesTwo
          Map<String,Integer> m_stringAttIndexesTwo
          Holds indexes of string attributes, keyed by attribute name
        • m_stringAttsPresent
          boolean m_stringAttsPresent
          True if string attributes are present in the incoming data

    • Class weka.knowledgeflow.steps.Loader

      class Loader extends WekaAlgorithmWrapper implements Serializable
      serialVersionUID:
      -788869066035779154L

      • Serialized Fields

        • m_flowThroughput
          StreamThroughput m_flowThroughput
          For measuring the overall flow throughput
        • m_globalInfo
          String m_globalInfo
          Global info for the wrapped loader (if it exists).
        • m_instanceData
          Data m_instanceData
          Reusable data container
        • m_instanceGeneration
          boolean m_instanceGeneration
          True if we're going to be streaming instance objects
        • m_noOutputs
          boolean m_noOutputs
          True if there are no outgoing connections

    • Class weka.knowledgeflow.steps.MakeResourceIntensive

      class MakeResourceIntensive extends BaseStep implements Serializable
      serialVersionUID:
      -5670771681991035130L

      • Serialized Fields

        • m_setAsResourceIntensive
          boolean m_setAsResourceIntensive
          True if downstream steps are to be made resource intensive

    • Class weka.knowledgeflow.steps.MemoryBasedDataSource

      class MemoryBasedDataSource extends BaseStep implements Serializable
      serialVersionUID:
      -1901014330145130275L

      • Serialized Fields

        • m_instances
          Instances m_instances
          The data that will be output from this step

    • Class weka.knowledgeflow.steps.ModelPerformanceChart

      class ModelPerformanceChart extends BaseStep implements Serializable
      serialVersionUID:
      6166590810777938147L

      • Serialized Fields

        • m_additionalOptions
          String m_additionalOptions
          Additional options for the offscreen renderer
        • m_dataIsThresholdData
          boolean m_dataIsThresholdData
          True if the collected plots contain threshold data
        • m_height
          String m_height
          Height of offscreen plots
        • m_offscreenRendererName
          String m_offscreenRendererName
          Name of the renderer to use for offscreen chart rendering
        • m_plots
          List<PlotData2D> m_plots
          Current set of plots. First element is the master plot
        • m_width
          String m_width
          Width of offscreen plots
        • m_xAxis
          String m_xAxis
          The name of the attribute to use for the x-axis of offscreen plots. If left empty, False Positive Rate is used for threshold curves
        • m_yAxis
          String m_yAxis
          The name of the attribute to use for the y-axis of offscreen plots. If left empty, True Positive Rate is used for threshold curves

    • Class weka.knowledgeflow.steps.Note

      class Note extends BaseStep implements Serializable

      • Serialized Fields

        • m_noteText
          String m_noteText
          The text of the note

    • Class weka.knowledgeflow.steps.PairedDataHelper

      class PairedDataHelper extends Object implements Serializable
      serialVersionUID:
      -7813465607881227514L

      • Serialized Fields

        • m_namedIndexedStore
          Map<String,Map<Integer,Object>> m_namedIndexedStore
          Storage of arbitrary indexed results computed during execution of PairedProcessor.processPrimary()
        • m_primaryConType
          String m_primaryConType
          The type of connection to route to PairedProcessor.processPrimary()
        • m_primaryResultMap
          Map<Integer,P> m_primaryResultMap
          Storage of the indexed primary result
        • m_secondaryConType
          String m_secondaryConType
          The type of connection to route to PairedProcessor.processSecondary()
        • m_secondaryDataMap
          Map<Integer,Data> m_secondaryDataMap
          Holds the secondary data objects, if they arrive before the corresponding primary has been computed

    • Class weka.knowledgeflow.steps.PredictionAppender

      class PredictionAppender extends BaseStep implements Serializable
      serialVersionUID:
      3558618759400903936L

      • Serialized Fields

        • m_appendProbabilities
          boolean m_appendProbabilities
          True if probabilities are to be appended
        • m_instanceData
          Data m_instanceData
          Re-usable Data object for streaming output
        • m_streamingOutputStructure
          Instances m_streamingOutputStructure
          Holds structure of streaming output
        • m_stringAttIndexes
          List<Integer> m_stringAttIndexes
          Keep track of indexes of string attributes in the streaming case

    • Class weka.knowledgeflow.steps.Saver

      class Saver extends WekaAlgorithmWrapper implements Serializable
      serialVersionUID:
      6831606284211403465L

      • Serialized Fields

        • m_isDBSaver
          boolean m_isDBSaver
          True if the saver is a DatabaseSaver
        • 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
          The actual saver instance to use
        • m_structure
          Instances m_structure
          Holds the structure

    • Class weka.knowledgeflow.steps.ScatterPlotMatrix

      class ScatterPlotMatrix extends BaseSimpleDataVisualizer implements Serializable
      serialVersionUID:
      -2033576643553187310L

    • Class weka.knowledgeflow.steps.SendToPerspective

      class SendToPerspective extends BaseStep implements Serializable
      serialVersionUID:
      7322550048407408819L

      • Serialized Fields

        • m_perspectiveName
          String m_perspectiveName

    • Class weka.knowledgeflow.steps.SerializedModelSaver

      class SerializedModelSaver extends BaseStep implements Serializable
      serialVersionUID:
      -8343162241983197708L

      • Serialized Fields

        • m_counter
          int m_counter
          Counter for use when processing incremental classifier connections
        • m_directory
          File m_directory
          The directory to hold the saved model(s)
        • m_filenamePrefix
          String m_filenamePrefix
          The prefix for the file name (model + training set info will be appended)
        • m_includeRelationName
          boolean m_includeRelationName
          Whether to include the relation name of the data in the file name for the model
        • m_incrementalHeader
          Instances m_incrementalHeader
          Stores the header of data used to build an incremental model
        • m_incrementalSaveSchedule
          int m_incrementalSaveSchedule
          How often to save an incremental classifier (<= 0 means only at the end of the stream)

    • Class weka.knowledgeflow.steps.SetPropertiesFromEnvironment

      class SetPropertiesFromEnvironment extends BaseStep implements Serializable
      serialVersionUID:
      -8316084792512232973L

    • Class weka.knowledgeflow.steps.SetVariables

      class SetVariables extends BaseStep implements Serializable
      serialVersionUID:
      8042350408846800738L

      • Serialized Fields

        • m_dynamicInternalRep
          String m_dynamicInternalRep
          Holds dynamic variables in internal representation
        • m_internalRep
          String m_internalRep
          Holds static variables in internal representation
        • m_raiseErrorWhenValueMissing
          boolean m_raiseErrorWhenValueMissing
          True if an exception should be raised when an attribute value being used to set a variable is missing, instead of using a default value
        • m_structureCheckComplete
          boolean m_structureCheckComplete
          True if the structure has been checked
        • m_structureOK
          boolean m_structureOK
          OK if there is at least one specified attribute in the incoming instance structure
        • m_varsToSet
          Map<String,String> m_varsToSet
          Map of variables to set with fixed values
        • m_varsToSetFromIncomingInstances
          Map<String,List<String>> m_varsToSetFromIncomingInstances
          Map of variables to set based on the values of attributes in incoming instances

    • Class weka.knowledgeflow.steps.Sorter

      class Sorter extends BaseStep implements Serializable
      serialVersionUID:
      3373283983192467264L

      • Serialized Fields

        • m_bufferSize
          String m_bufferSize
          Size of the in-memory buffer
        • m_bufferSizeI
          int m_bufferSizeI
          Size of the in-memory buffer after resolving any environment vars
        • m_connectedFormat
          Instances m_connectedFormat
          format of instances for current incoming connection (if any)
        • m_isReset
          boolean m_isReset
          True if we've been reset
        • m_sortDetails
          String m_sortDetails
          Holds the internal textual description of the sort definitions
        • m_streaming
          boolean m_streaming
          True if processing streaming data
        • m_streamingData
          Data m_streamingData
          To (re)use when streaming
        • m_stringAttIndexes
          Map<String,Integer> m_stringAttIndexes
          Holds indexes of string attributes, keyed by attribute name
        • m_tempDirectory
          File m_tempDirectory
          The directory to hold the temp files - if not set the system tmp directory is used

    • Class weka.knowledgeflow.steps.Sorter.InstanceHolder

      class InstanceHolder extends Object implements Serializable
      serialVersionUID:
      -3985730394250172995L

      • Serialized Fields

        • m_fileNumber
          int m_fileNumber
          index into the list of files on disk
        • m_instance
          Instance m_instance
          The instance
        • m_stringVals
          Map<String,String> m_stringVals
          for incremental operation, if string attributes are present then we need to store them with each instance - since incremental streaming in the knowledge flow only maintains one string value in memory (and hence in the header) at any one time

    • Class weka.knowledgeflow.steps.StorePropertiesInEnvironment

      class StorePropertiesInEnvironment extends BaseStep implements Serializable
      serialVersionUID:
      -1526289154505863542L

      • Serialized Fields

        • m_internalRep
          String m_internalRep
          Internal string-based representation of property configs
        • m_propsToSetFromIncomingInstances
          Map<String,List<String>> m_propsToSetFromIncomingInstances
          Map of properties to set based on the values of attributes in incoming instances. Keyed by attribute name/index. List contains target step name, property path (can be empty string to indicate a command line spec for a complete base-scheme config), default property value. If an incoming attribute value is missing, and no default property value is available, an exception will be generated.
        • m_raiseErrorWhenValueMissing
          boolean m_raiseErrorWhenValueMissing
        • m_structureCheckComplete
          boolean m_structureCheckComplete
          True if the structure has been checked
        • m_structureOK
          boolean m_structureOK
          OK if there is at least one specified attribute in the incoming instance structure

    • Class weka.knowledgeflow.steps.StripChart

      class StripChart extends BaseStep implements Serializable
      serialVersionUID:
      -2569383350174947630L

      • Serialized Fields

        • m_instanceWidth
          int m_instanceWidth
          Holds the number of attribute values (10 max) to plot if processing an incoming instance stream
        • m_plotListeners
          List<StripChart.PlotNotificationListener> m_plotListeners
          Parties interested in knowing about updates
        • m_refreshFrequency
          int m_refreshFrequency
          Plot every m_refreshFrequency'th point
        • m_reset
          boolean m_reset
          True if we've been reset
        • m_userRefreshWidth
          int m_userRefreshWidth
        • m_xValFreq
          int m_xValFreq
          Frequency for plotting x values

    • Class weka.knowledgeflow.steps.SubstringLabeler

      class SubstringLabeler extends BaseStep implements Serializable
      serialVersionUID:
      1409175779108600014L

      • Serialized Fields

        • m_addFilter
          Add m_addFilter
          Add filter for adding the new attribute
        • m_attName
          String m_attName
          Name of the new attribute
        • m_consumeNonMatchingInstances
          boolean m_consumeNonMatchingInstances
          For multi-valued labeled rules, whether or not to consume non-matching instances or output them with missing value for the match attribute.
        • m_isReset
          boolean m_isReset
          Step has been reset - i.e. start of processing?
        • m_matchDetails
          String m_matchDetails
          Internally encoded list of match rules
        • m_nominalBinary
          boolean m_nominalBinary
          Whether to make the binary match/non-match attribute a nominal (rather than numeric) binary attribute.
        • m_streaming
          boolean m_streaming
          Streaming instances?
        • m_streamingData
          Data m_streamingData
          Reusable data object for output

    • Class weka.knowledgeflow.steps.SubstringReplacer

      class SubstringReplacer extends BaseStep implements Serializable
      serialVersionUID:
      -8786642000811852824L

      • Serialized Fields

        • m_isReset
          boolean m_isReset
          Step has been reset - i.e. start of processing
        • m_matchReplaceDetails
          String m_matchReplaceDetails
          Internally encoded list of match-replace rules
        • m_streamingData
          Data m_streamingData
          Reusable data object for output

    • Class weka.knowledgeflow.steps.TestSetMaker

      class TestSetMaker extends BaseStep implements Serializable
      serialVersionUID:
      6384920860783839811L

    • Class weka.knowledgeflow.steps.TextSaver

      class TextSaver extends BaseStep implements Serializable
      serialVersionUID:
      -1434752243260858338L

      • Serialized Fields

        • m_append
          boolean m_append
          Whether to append to the file or not
        • m_defaultFile
          String m_defaultFile
          Default location to write to, in case a file has not been explicitly set
        • m_file
          File m_file
          The file to save to
        • m_writeTitleString
          boolean m_writeTitleString
          Whether to write the title string for each textual result too

    • Class weka.knowledgeflow.steps.TextSaver.TextSaverDefaults

      class TextSaverDefaults extends Defaults implements Serializable
      serialVersionUID:
      -2739579935119189195L

    • Class weka.knowledgeflow.steps.TextViewer

      class TextViewer extends BaseStep implements Serializable
      serialVersionUID:
      8602416209256135064L

      • Serialized Fields

    • Class weka.knowledgeflow.steps.TrainingSetMaker

      class TrainingSetMaker extends BaseStep implements Serializable
      serialVersionUID:
      1082946912813721183L

    • Class weka.knowledgeflow.steps.TrainTestSplitMaker

      class TrainTestSplitMaker extends BaseStep implements Serializable
      serialVersionUID:
      7685026723199727685L

      • Serialized Fields

        • m_preserveOrder
          boolean m_preserveOrder
          Whether to preserve the order of the data before making the split, rather than randomly shuffling
        • m_seed
          long m_seed
          Resolved seed
        • m_seedS
          String m_seedS
          Default seed for the random number generator
        • m_trainPercentage
          double m_trainPercentage
          Resolved percentage
        • m_trainPercentageS
          String m_trainPercentageS
          Default split percentage

    • Class weka.knowledgeflow.steps.WekaAlgorithmWrapper

      class WekaAlgorithmWrapper extends BaseStep implements Serializable
      serialVersionUID:
      -1013404060247467085L

      • Serialized Fields

        • m_defaultIconPath
          String m_defaultIconPath
          Icon path to the default icon for the type of wrapped algorithm - e.g. Classifier, Loader etc.
        • m_defaultPackageIconPath
          String m_defaultPackageIconPath
          Icon path to the default icon at the package level - e.g. weka.classifiers.rules
        • m_iconPath
          String m_iconPath
          Icon path to the specific icon for the wrapped algorithim
        • m_wrappedAlgorithm
          Object m_wrappedAlgorithm
          The wrapped algorithm

    • Class weka.knowledgeflow.steps.WriteDataToResult

      class WriteDataToResult extends BaseStep implements Serializable
      serialVersionUID:
      -1932252461151862615L

    • Class weka.knowledgeflow.steps.WriteWekaLog

      class WriteWekaLog extends BaseStep implements Serializable
      serialVersionUID:
      -2306717547200779711L

      • Serialized Fields

        • m_incrCount
          int m_incrCount
          Count of how many incremental data points have been seen so far
        • m_incrementalWriteFrequency
          String m_incrementalWriteFrequency
          How often to write incremental data to the log
        • m_incrFreq
          int m_incrFreq
          Resolved frequency
        • m_inputIsIncremental
          boolean m_inputIsIncremental
          True if the input is incremental
        • m_isReset
          boolean m_isReset
          True if the step has been reset
        • m_logLevel
          LoggingLevel m_logLevel
          Level to log at