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

      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

      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

      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

      serialVersionUID:
      -9204394360557300093L
      • Serialized Fields

    • Class weka.classifiers.meta.RandomizableFilteredClassifier

      class RandomizableFilteredClassifier extends FilteredClassifier implements Serializable
      serialVersionUID:
      -4523466618555717333L
    • Class weka.classifiers.meta.RandomSubSpace

      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

      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

      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

      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