# Serialized Form

- ## Package weka.associations

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

    serialVersionUID:
      -3017644543382432070L

    - ### Serialized Fields

      - #### m_DoNotCheckCapabilities

            boolean m_DoNotCheckCapabilities

        Whether capabilities should not be checked

  - ### Class weka.associations.Apriori extends AbstractAssociator implements Serializable

    serialVersionUID:
      3277498842319212687L

    - ### Serialized Fields

      - #### m_minSupport

            double m_minSupport

        The minimum support.

      - #### m_upperBoundMinSupport

            double m_upperBoundMinSupport

        The upper bound on the support

      - #### m_lowerBoundMinSupport

            double m_lowerBoundMinSupport

        The lower bound for the minimum support.

      - #### m_metricType

            int m_metricType

        The selected metric type.

      - #### m_minMetric

            double m_minMetric

        The minimum metric score.

      - #### m_numRules

            int m_numRules

        The maximum number of rules that are output.

      - #### m_delta

            double m_delta

        Delta by which m_minSupport is decreased in each iteration.

      - #### m_significanceLevel

            double m_significanceLevel

        Significance level for optional significance test.

      - #### m_cycles

            int m_cycles

        Number of cycles used before required number of rules was one.

      - #### m_Ls

            java.util.ArrayList<E> m_Ls

        The set of all sets of itemsets L.

      - #### m_hashtables

            java.util.ArrayList<E> m_hashtables

        The same information stored in hash tables.

      - #### m_allTheRules

            java.util.ArrayList<E>[] m_allTheRules

        The list of all generated rules.

      - #### m_instances

            Instances m_instances

        The instances (transactions) to be used for generating the association rules.

      - #### m_outputItemSets

            boolean m_outputItemSets

        Output itemsets found?

      - #### m_removeMissingCols

            boolean m_removeMissingCols

        Remove columns with all missing values

      - #### m_verbose

            boolean m_verbose

        Report progress iteratively

      - #### m_onlyClass

            Instances m_onlyClass

        Only the class attribute of all Instances.

      - #### m_classIndex

            int m_classIndex

        The class index.

      - #### m_car

            boolean m_car

        Flag indicating whether class association rules are mined.

      - #### m_treatZeroAsMissing

            boolean m_treatZeroAsMissing

        Treat zeros as missing (rather than a value in their own right)

      - #### m_toStringDelimiters

            java.lang.String m_toStringDelimiters

        ToString delimiters, if any

  - ### Class weka.associations.AprioriItemSet extends ItemSet implements Serializable

    serialVersionUID:
      7684467755712672058L

  - ### Class weka.associations.AssociationRules extends java.lang.Object implements Serializable

    serialVersionUID:
      8889198755948056749L

    - ### Serialized Fields

      - #### m_producer

            java.lang.String m_producer

        The scheme that produced these rules

      - #### m_rules

            java.util.List<E> m_rules

        The list of rules

  - ### Class weka.associations.BinaryItem extends NominalItem implements Serializable

    serialVersionUID:
      -3372941834914147669L

  - ### Class weka.associations.DefaultAssociationRule extends AssociationRule implements Serializable

    serialVersionUID:
      -661269018702294489L

    - ### Serialized Fields

      - #### m_metricType

            DefaultAssociationRule.METRIC_TYPE m_metricType

        The metric type for this rule

      - #### m_premise

            java.util.Collection<E> m_premise

        The premise of the rule

      - #### m_consequence

            java.util.Collection<E> m_consequence

        The consequence of the rule

      - #### m_premiseSupport

            int m_premiseSupport

        The support for the premise

      - #### m_consequenceSupport

            int m_consequenceSupport

        The support for the consequence

      - #### m_totalSupport

            int m_totalSupport

        The total support for the item set (premise + consequence)

      - #### m_totalTransactions

            int m_totalTransactions

        The total number of transactions in the data

  - ### Class weka.associations.FilteredAssociationRules extends AssociationRules implements Serializable

    serialVersionUID:
      -4223408305476916955L

    - ### Serialized Fields

      - #### m_filter

            Filter m_filter

      - #### m_wrappedRules

            AssociationRules m_wrappedRules

  - ### Class weka.associations.FilteredAssociator extends SingleAssociatorEnhancer implements Serializable

    serialVersionUID:
      -4523450618538717400L

    - ### Serialized Fields

      - #### m_Filter

            Filter m_Filter

        The filter

      - #### m_FilteredInstances

            Instances m_FilteredInstances

        The instance structure of the filtered instances

      - #### m_ClassIndex

            int m_ClassIndex

        The class index.

  - ### Class weka.associations.FPGrowth extends AbstractAssociator implements Serializable

    serialVersionUID:
      3620717108603442911L

    - ### Serialized Fields

      - #### m_numRulesToFind

            int m_numRulesToFind

        The number of rules to find

      - #### m_upperBoundMinSupport

            double m_upperBoundMinSupport

        The upper bound on the minimum support

      - #### m_lowerBoundMinSupport

            double m_lowerBoundMinSupport

        The lower bound on minimum support

      - #### m_delta

            double m_delta

        The amount by which to decrease the support in each iteration

      - #### m_numInstances

            int m_numInstances

        The number of instances in the data

      - #### m_offDiskReportingFrequency

            int m_offDiskReportingFrequency

        When processing data off of disk report progress this frequently (number of instances).

      - #### m_findAllRulesForSupportLevel

            boolean m_findAllRulesForSupportLevel

        If true, just all rules meeting the lower bound on the minimum support will be found. The number of rules to find will be ignored and the iterative reduction of support will not be done.

      - #### m_positiveIndex

            int m_positiveIndex

        The index (1 based) of binary attributes to treat as the positive value

      - #### m_metric

            DefaultAssociationRule.METRIC_TYPE m_metric

      - #### m_metricThreshold

            double m_metricThreshold

      - #### m_largeItemSets

            weka.associations.FPGrowth.FrequentItemSets m_largeItemSets

        Holds the large item sets found

      - #### m_rules

            java.util.List<E> m_rules

        Holds the rules

      - #### m_maxItems

            int m_maxItems

      - #### m_transactionsMustContain

            java.lang.String m_transactionsMustContain

        If set, limit the transactions (instances) input to the algorithm to those that contain these items

      - #### m_mustContainOR

            boolean m_mustContainOR

        Use OR rather than AND when considering must contain lists

      - #### m_rulesMustContain

            java.lang.String m_rulesMustContain

        If set, then only output rules containing these itmes

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

    serialVersionUID:
      4396315323673737660L

    - ### Serialized Fields

      - #### m_levelSibling

            weka.associations.FPGrowth.FPTreeNode m_levelSibling

        link to another sibling at this level in the tree

      - #### m_parent

            weka.associations.FPGrowth.FPTreeNode m_parent

        link to the parent node

      - #### m_item

            BinaryItem m_item

        item at this node

      - #### m_ID

            int m_ID

        ID (for graphing the tree)

      - #### m_children

            java.util.Map<K,V> m_children

        the children of this node

      - #### m_projectedCounts

            weka.associations.FPGrowth.ShadowCounts m_projectedCounts

        counts associated with projected versions of this node

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

    serialVersionUID:
      -6583156284891368909L

    - ### Serialized Fields

      - #### m_headerList

            java.util.List<E> m_headerList

        The list of pointers into the tree structure

      - #### m_projectedHeaderCounts

            weka.associations.FPGrowth.ShadowCounts m_projectedHeaderCounts

        Projected header counts for this entry

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

    serialVersionUID:
      -6543815873565829448L

    - ### Serialized Fields

      - #### m_items

            java.util.ArrayList<E> m_items

        The list of items in the item set

      - #### m_support

            int m_support

        the support of this item set

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

    serialVersionUID:
      4173606872363973588L

    - ### Serialized Fields

      - #### m_sets

            java.util.ArrayList<E> m_sets

        The list of frequent item sets

      - #### m_numberOfTransactions

            int m_numberOfTransactions

        The total number of transactions in the data

  - ### Class weka.associations.FPGrowth.ShadowCounts extends java.lang.Object implements Serializable

    serialVersionUID:
      4435433714185969155L

    - ### Serialized Fields

      - #### m_counts

            java.util.ArrayList<E> m_counts

        Holds the counts at different recursion levels

  - ### Class weka.associations.Item extends java.lang.Object implements Serializable

    serialVersionUID:
      -430198211081183575L

    - ### Serialized Fields

      - #### m_frequency

            int m_frequency

        The frequency of this item

      - #### m_attribute

            Attribute m_attribute

        The attribute that backs this item

  - ### Class weka.associations.ItemSet extends java.lang.Object implements Serializable

    serialVersionUID:
      2724000045282835791L

    - ### Serialized Fields

      - #### m_items

            int[] m_items

        The items stored as an array of of ints.

      - #### m_counter

            int m_counter

        Counter for how many transactions contain this item set.

      - #### m_totalTransactions

            int m_totalTransactions

        The total number of transactions

  - ### Class weka.associations.LabeledItemSet extends ItemSet implements Serializable

    serialVersionUID:
      4158771925518299903L

    - ### Serialized Fields

      - #### m_classLabel

            int m_classLabel

        The class label.

      - #### m_ruleSupCounter

            int m_ruleSupCounter

        The support of the rule.

  - ### Class weka.associations.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 extends Item implements Serializable

    serialVersionUID:
      -7869433770765864800L

    - ### Serialized Fields

      - #### m_splitPoint

            double m_splitPoint

        The numeric test

      - #### m_comparison

            NumericItem.Comparison m_comparison

        The comparison operator

  - ### Class weka.associations.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 extends java.lang.Object implements Serializable

    serialVersionUID:
      2091705669885950849L

    - ### Serialized Fields

      - #### m_DoNotCheckCapabilities

            boolean m_DoNotCheckCapabilities

        Whether capabilities should not be checked

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

    serialVersionUID:
      7591673350342236548L

  - ### Class weka.attributeSelection.AttributeSelection extends java.lang.Object implements Serializable

    serialVersionUID:
      4170171824147584330L

    - ### Serialized Fields

      - #### m_trainInstances

            Instances m_trainInstances

        the instances to select attributes from

      - #### m_ASEvaluator

            ASEvaluation m_ASEvaluator

        the attribute/subset evaluator

      - #### m_searchMethod

            ASSearch m_searchMethod

        the search method

      - #### m_numFolds

            int m_numFolds

        the number of folds to use for cross validation

      - #### m_selectionResults

            java.lang.StringBuffer m_selectionResults

        holds a string describing the results of the attribute selection

      - #### m_doRank

            boolean m_doRank

        rank features (if allowed by the search method)

      - #### m_doXval

            boolean m_doXval

        do cross validation

      - #### m_seed

            int m_seed

        seed used to randomly shuffle instances for cross validation

      - #### m_numToSelect

            int m_numToSelect

        number of attributes requested from ranked results

      - #### m_selectedAttributeSet

            int[] m_selectedAttributeSet

        the selected attributes

      - #### m_attributeRanking

            double[][] m_attributeRanking

        the attribute indexes and associated merits if a ranking is produced

      - #### m_transformer

            AttributeTransformer m_transformer

        if a feature selection run involves an attribute transformer

      - #### m_attributeFilter

            Remove m_attributeFilter

        the attribute filter for processing instances with respect to the most recent feature selection run

      - #### m_rankResults

            double[][] m_rankResults

        hold statistics for repeated feature selection, such as under cross validation

      - #### m_subsetResults

            double[] m_subsetResults

  - ### Class weka.attributeSelection.AttributeSetEvaluator extends ASEvaluation implements Serializable

    serialVersionUID:
      -5744881009422257389L

  - ### Class weka.attributeSelection.BestFirst extends ASSearch implements Serializable

    serialVersionUID:
      7841338689536821867L

    - ### Serialized Fields

      - #### m_maxStale

            int m_maxStale

        maximum number of stale nodes before terminating search

      - #### m_searchDirection

            int m_searchDirection

        0 == backward search, 1 == forward search, 2 == bidirectional

      - #### m_starting

            int[] m_starting

        holds an array of starting attributes

      - #### m_startRange

            Range m_startRange

        holds the start set for the search as a Range

      - #### m_hasClass

            boolean m_hasClass

        does the data have a class

      - #### m_classIndex

            int m_classIndex

        holds the class index

      - #### m_numAttribs

            int m_numAttribs

        number of attributes in the data

      - #### m_totalEvals

            int m_totalEvals

        total number of subsets evaluated during a search

      - #### m_debug

            boolean m_debug

        for debugging

      - #### m_bestMerit

            double m_bestMerit

        holds the merit of the best subset found

      - #### m_cacheSize

            int m_cacheSize

        holds the maximum size of the lookup cache for evaluated subsets

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

    serialVersionUID:
      -8236598311516351420L

    - ### Serialized Fields

      - #### m_data

            java.lang.Object[] m_data

      - #### m_merit

            double m_merit

  - ### Class weka.attributeSelection.BestFirst.LinkedList2 extends java.util.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 extends ASEvaluation implements Serializable

    serialVersionUID:
      747878400813276317L

    - ### Serialized Fields

      - #### m_trainInstances

            Instances m_trainInstances

        The training instances

      - #### m_disTransform

            Discretize m_disTransform

        Discretise attributes when class in nominal

      - #### m_classIndex

            int m_classIndex

        The class index

      - #### m_isNumeric

            boolean m_isNumeric

        Is the class numeric

      - #### m_numAttribs

            int m_numAttribs

        Number of attributes in the training data

      - #### m_numInstances

            int m_numInstances

        Number of instances in the training data

      - #### m_missingSeparate

            boolean m_missingSeparate

        Treat missing values as separate values

      - #### m_locallyPredictive

            boolean m_locallyPredictive

        Include locally predictive attributes

      - #### m_corr_matrix

            float[][] m_corr_matrix

        Holds the matrix of attribute correlations

      - #### m_std_devs

            double[] m_std_devs

        Standard deviations of attributes (when using pearsons correlation)

      - #### m_c_Threshold

            double m_c_Threshold

        Threshold for admitting locally predictive features

      - #### m_debug

            boolean m_debug

        Output debugging info

      - #### m_numEntries

            int m_numEntries

        Number of entries in the correlation matrix

      - #### m_numFilled

            java.util.concurrent.atomic.AtomicInteger m_numFilled

        Number of correlations actually computed

      - #### m_preComputeCorrelationMatrix

            boolean m_preComputeCorrelationMatrix

      - #### 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

  - ### Class weka.attributeSelection.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

            java.lang.StringBuffer m_detailedOutputBuff

        Holds the detailed output info

  - ### Class weka.attributeSelection.GainRatioAttributeEval extends ASEvaluation implements Serializable

    serialVersionUID:
      -8504656625598579926L

    - ### Serialized Fields

      - #### m_trainInstances

            Instances m_trainInstances

        The training instances

      - #### m_classIndex

            int m_classIndex

        The class index

      - #### m_numInstances

            int m_numInstances

        The number of instances

      - #### m_numClasses

            int m_numClasses

        The number of classes

      - #### m_missing_merge

            boolean m_missing_merge

        Merge missing values

  - ### Class weka.attributeSelection.GreedyStepwise extends ASSearch implements Serializable

    serialVersionUID:
      -6312951970168325471L

    - ### Serialized Fields

      - #### m_hasClass

            boolean m_hasClass

        does the data have a class

      - #### m_classIndex

            int m_classIndex

        holds the class index

      - #### m_numAttribs

            int m_numAttribs

        number of attributes in the data

      - #### m_rankingRequested

            boolean m_rankingRequested

        true if the user has requested a ranked list of attributes

      - #### m_doRank

            boolean m_doRank

        go from one side of the search space to the other in order to generate a ranking

      - #### m_doneRanking

            boolean m_doneRanking

        used to indicate whether or not ranking has been performed

      - #### m_threshold

            double m_threshold

        A threshold by which to discard attributes---used by the AttributeSelection module

      - #### m_numToSelect

            int m_numToSelect

        The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold

      - #### m_calculatedNumToSelect

            int m_calculatedNumToSelect

      - #### m_bestMerit

            double m_bestMerit

        the merit of the best subset found

      - #### m_rankedAtts

            double[][] m_rankedAtts

        a ranked list of attribute indexes

      - #### m_rankedSoFar

            int m_rankedSoFar

      - #### m_best_group

            java.util.BitSet m_best_group

        the best subset found

      - #### m_ASEval

            ASEvaluation m_ASEval

      - #### m_Instances

            Instances m_Instances

      - #### m_startRange

            Range m_startRange

        holds the start set for the search as a Range

      - #### m_starting

            int[] m_starting

        holds an array of starting attributes

      - #### m_backward

            boolean m_backward

        Use a backwards search instead of a forwards one

      - #### m_conservativeSelection

            boolean m_conservativeSelection

        If set then attributes will continue to be added during a forward search as long as the merit does not degrade

      - #### m_debug

            boolean m_debug

        Print debugging output

      - #### m_poolSize

            int m_poolSize

  - ### Class weka.attributeSelection.HoldOutSubsetEvaluator extends ASEvaluation implements Serializable

    serialVersionUID:
      8280529785412054174L

  - ### Class weka.attributeSelection.InfoGainAttributeEval extends ASEvaluation implements Serializable

    serialVersionUID:
      -1949849512589218930L

    - ### Serialized Fields

      - #### m_missing_merge

            boolean m_missing_merge

        Treat missing values as a seperate value

      - #### m_Binarize

            boolean m_Binarize

        Just binarize numeric attributes

      - #### m_InfoGains

            double[] m_InfoGains

        The info gain for each attribute

  - ### Class weka.attributeSelection.OneRAttributeEval extends ASEvaluation implements Serializable

    serialVersionUID:
      4386514823886856980L

    - ### Serialized Fields

      - #### m_trainInstances

            Instances m_trainInstances

        The training instances

      - #### m_randomSeed

            int m_randomSeed

        Random number seed

      - #### m_folds

            int m_folds

        Number of folds for cross validation

      - #### m_evalUsingTrainingData

            boolean m_evalUsingTrainingData

        Use training data to evaluate merit rather than x-val

      - #### m_minBucketSize

            int m_minBucketSize

        Passed on to OneR

  - ### Class weka.attributeSelection.PrincipalComponents extends UnsupervisedAttributeEvaluator implements Serializable

    serialVersionUID:
      -3675307197777734007L

    - ### Serialized Fields

      - #### m_trainInstances

            Instances m_trainInstances

        The data to transform analyse/transform

      - #### m_trainHeader

            Instances m_trainHeader

        Keep a copy for the class attribute (if set)

      - #### m_transformedFormat

            Instances m_transformedFormat

        The header for the transformed data format

      - #### m_originalSpaceFormat

            Instances m_originalSpaceFormat

        The header for data transformed back to the original space

      - #### m_hasClass

            boolean m_hasClass

        Data has a class set

      - #### m_classIndex

            int m_classIndex

        Class index

      - #### m_numAttribs

            int m_numAttribs

        Number of attributes

      - #### m_numInstances

            int m_numInstances

        Number of instances

      - #### m_correlation

            double[][] m_correlation

        Correlation/covariance matrix for the original data

      - #### m_means

            double[] m_means

      - #### m_stdDevs

            double[] m_stdDevs

      - #### m_center

            boolean m_center

        If true, center (rather than standardize) the data and compute PCA from covariance (rather than correlation) matrix.

      - #### m_eigenvectors

            double[][] m_eigenvectors

        Will hold the unordered linear transformations of the (normalized) original data

      - #### m_eigenvalues

            double[] m_eigenvalues

        Eigenvalues for the corresponding eigenvectors

      - #### m_sortedEigens

            int[] m_sortedEigens

        Sorted eigenvalues

      - #### m_sumOfEigenValues

            double m_sumOfEigenValues

        sum of the eigenvalues

      - #### m_replaceMissingFilter

            ReplaceMissingValues m_replaceMissingFilter

        Filters for original data

      - #### m_nominalToBinFilter

            NominalToBinary m_nominalToBinFilter

      - #### m_attributeFilter

            Remove m_attributeFilter

      - #### m_centerFilter

            Center m_centerFilter

      - #### m_standardizeFilter

            Standardize m_standardizeFilter

      - #### m_outputNumAtts

            int m_outputNumAtts

        The number of attributes in the pc transformed data

      - #### m_coverVariance

            double m_coverVariance

        the amount of variance to cover in the original data when retaining the best n PC's

      - #### m_transBackToOriginal

            boolean m_transBackToOriginal

        transform the data through the pc space and back to the original space ?

      - #### m_maxAttrsInName

            int m_maxAttrsInName

        maximum number of attributes in the transformed attribute name

      - #### m_eTranspose

            double[][] m_eTranspose

        holds the transposed eigenvectors for converting back to the original space

  - ### Class weka.attributeSelection.Ranker extends ASSearch implements Serializable

    serialVersionUID:
      -9086714848510751934L

    - ### Serialized Fields

      - #### m_starting

            int[] m_starting

        Holds the starting set as an array of attributes

      - #### m_startRange

            Range m_startRange

        Holds the start set for the search as a range

      - #### m_attributeList

            int[] m_attributeList

        Holds the ordered list of attributes

      - #### m_attributeMerit

            double[] m_attributeMerit

        Holds the list of attribute merit scores

      - #### m_hasClass

            boolean m_hasClass

        Data has class attribute---if unsupervised evaluator then no class

      - #### m_classIndex

            int m_classIndex

        Class index of the data if supervised evaluator

      - #### m_numAttribs

            int m_numAttribs

        The number of attribtes

      - #### m_threshold

            double m_threshold

        A threshold by which to discard attributes---used by the AttributeSelection module

      - #### m_numToSelect

            int m_numToSelect

        The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold

      - #### m_calculatedNumToSelect

            int m_calculatedNumToSelect

        Used to compute the number to select

  - ### Class weka.attributeSelection.ReliefFAttributeEval extends ASEvaluation implements Serializable

    serialVersionUID:
      -8422186665795839379L

    - ### Serialized Fields

      - #### m_trainInstances

            Instances m_trainInstances

        The training instances

      - #### m_classIndex

            int m_classIndex

        The class index

      - #### m_numAttribs

            int m_numAttribs

        The number of attributes

      - #### m_numInstances

            int m_numInstances

        The number of instances

      - #### m_numericClass

            boolean m_numericClass

        Numeric class

      - #### m_numClasses

            int m_numClasses

        The number of classes if class is nominal

      - #### m_ndc

            double m_ndc

        Used to hold the probability of a different class val given nearest instances (numeric class)

      - #### m_nda

            double[] m_nda

        Used to hold the prob of different value of an attribute given nearest instances (numeric class case)

      - #### m_ndcda

            double[] m_ndcda

        Used to hold the prob of a different class val and different att val given nearest instances (numeric class case)

      - #### m_weights

            double[] m_weights

        Holds the weights that relief assigns to attributes

      - #### m_classProbs

            double[] m_classProbs

        Prior class probabilities (discrete class case)

      - #### m_sampleM

            int m_sampleM

        The number of instances to sample when estimating attributes default == -1, use all instances

      - #### m_Knn

            int m_Knn

        The number of nearest hits/misses

      - #### m_karray

            double[][][] m_karray

        k nearest scores + instance indexes for n classes

      - #### m_maxArray

            double[] m_maxArray

        Upper bound for numeric attributes

      - #### m_minArray

            double[] m_minArray

        Lower bound for numeric attributes

      - #### m_worst

            double[] m_worst

        Keep track of the farthest instance for each class

      - #### m_index

            int[] m_index

        Index in the m_karray of the farthest instance for each class

      - #### m_stored

            int[] m_stored

        Number of nearest neighbours stored of each class

      - #### m_seed

            int m_seed

        Random number seed used for sampling instances

      - #### m_weightsByRank

            double[] m_weightsByRank

        used to (optionally) weight nearest neighbours by their distance from the instance in question. Each entry holds exp(-((rank(r_i, i_j)/sigma)^2)) where rank(r_i,i_j) is the rank of instance i_j in a sequence of instances ordered by the distance from r_i. sigma is a user defined parameter, default=20

      - #### m_sigma

            int m_sigma

      - #### m_weightByDistance

            boolean m_weightByDistance

        Weight by distance rather than equal weights

  - ### Class weka.attributeSelection.SymmetricalUncertAttributeEval extends ASEvaluation implements Serializable

    serialVersionUID:
      -8096505776132296416L

    - ### Serialized Fields

      - #### m_trainInstances

            Instances m_trainInstances

        The training instances

      - #### m_classIndex

            int m_classIndex

        The class index

      - #### m_numInstances

            int m_numInstances

        The number of instances

      - #### m_numClasses

            int m_numClasses

        The number of classes

      - #### m_missing_merge

            boolean m_missing_merge

        Treat missing values as a seperate value

  - ### Class weka.attributeSelection.UnsupervisedAttributeEvaluator extends ASEvaluation implements Serializable

    serialVersionUID:
      -4100897318675336178L

  - ### Class weka.attributeSelection.UnsupervisedSubsetEvaluator extends ASEvaluation implements Serializable

    serialVersionUID:
      627934376267488763L

  - ### Class weka.attributeSelection.WrapperSubsetEval extends ASEvaluation implements Serializable

    serialVersionUID:
      -4573057658746728675L

    - ### Serialized Fields

      - #### m_trainInstances

            Instances m_trainInstances

        training instances

      - #### m_classIndex

            int m_classIndex

        class index

      - #### m_numAttribs

            int m_numAttribs

        number of attributes in the training data

      - #### m_Evaluation

            Evaluation m_Evaluation

        holds an evaluation object

      - #### m_BaseClassifier

            Classifier m_BaseClassifier

        holds the base classifier object

      - #### m_folds

            int m_folds

        number of folds to use for cross validation

      - #### m_seed

            int m_seed

        random number seed

      - #### m_threshold

            double m_threshold

        the threshold by which to do further cross validations when estimating the accuracy of a subset

      - #### m_evaluationMeasure

            int m_evaluationMeasure

        The evaluation measure to use

      - #### 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

            java.lang.String m_IRClassValS

        User supplied option for IR class value (either name or 1-based index)

- ## Package weka.classifiers

  - ### Class weka.classifiers.AbstractClassifier extends java.lang.Object implements Serializable

    serialVersionUID:
      6502780192411755341L

    - ### 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.

  - ### Class weka.classifiers.AggregateableEvaluation extends Evaluation implements Serializable

    serialVersionUID:
      6850546230173753210L

  - ### Class weka.classifiers.CostMatrix extends java.lang.Object implements Serializable

    serialVersionUID:
      -1973792250544554965L

    - ### Serialized Fields

      - #### m_size

            int m_size

      - #### m_matrix

            java.lang.Object[][] m_matrix

        [rows][columns]

  - ### Class weka.classifiers.Evaluation extends java.lang.Object implements Serializable

    serialVersionUID:
      -170766452472965668L

    - ### Serialized Fields

      - #### m_delegate

            Evaluation m_delegate

        The actual evaluation object that we delegate to

  - ### Class weka.classifiers.IteratedSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

    serialVersionUID:
      -6217979135443319724L

    - ### Serialized Fields

      - #### m_Classifiers

            Classifier[] m_Classifiers

        Array for storing the generated base classifiers.

      - #### m_NumIterations

            int m_NumIterations

        The number of iterations.

  - ### Class weka.classifiers.MultipleClassifiersCombiner extends AbstractClassifier implements Serializable

    serialVersionUID:
      2776436621129422119L

    - ### Serialized Fields

      - #### m_Classifiers

            Classifier[] m_Classifiers

        Array for storing the generated base classifiers.

  - ### Class weka.classifiers.ParallelIteratedSingleClassifierEnhancer extends IteratedSingleClassifierEnhancer implements Serializable

    serialVersionUID:
      -5026378741833046436L

    - ### Serialized Fields

      - #### m_numExecutionSlots

            int m_numExecutionSlots

        The number of threads to have executing at any one time

  - ### Class weka.classifiers.ParallelMultipleClassifiersCombiner extends MultipleClassifiersCombiner implements Serializable

    serialVersionUID:
      728109028953726626L

    - ### Serialized Fields

      - #### m_numExecutionSlots

            int m_numExecutionSlots

        The number of threads to have executing at any one time

      - #### m_completed

            int m_completed

        The number of classifiers completed so far

      - #### m_failed

            int m_failed

        The number of classifiers that experienced a failure of some sort during construction

  - ### Class weka.classifiers.RandomizableClassifier extends AbstractClassifier implements Serializable

    serialVersionUID:
      -8816375798262351903L

    - ### Serialized Fields

      - #### m_Seed

            int m_Seed

        The random number seed.

  - ### Class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer extends IteratedSingleClassifierEnhancer implements Serializable

    serialVersionUID:
      5063351391524938557L

    - ### Serialized Fields

      - #### m_Seed

            int m_Seed

        The random number seed.

  - ### Class weka.classifiers.RandomizableMultipleClassifiersCombiner extends MultipleClassifiersCombiner implements Serializable

    serialVersionUID:
      5057936555724785679L

    - ### Serialized Fields

      - #### m_Seed

            int m_Seed

        The random number seed.

  - ### Class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer extends ParallelIteratedSingleClassifierEnhancer implements Serializable

    serialVersionUID:
      1298141000373615374L

    - ### Serialized Fields

      - #### m_Seed

            int m_Seed

        The random number seed.

  - ### Class weka.classifiers.RandomizableParallelMultipleClassifiersCombiner extends ParallelMultipleClassifiersCombiner implements Serializable

    serialVersionUID:
      8274061943448676943L

    - ### Serialized Fields

      - #### m_Seed

            int m_Seed

        The random number seed.

  - ### Class weka.classifiers.RandomizableSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

    serialVersionUID:
      558286687096157160L

    - ### Serialized Fields

      - #### m_Seed

            int m_Seed

        The random number seed.

  - ### Class weka.classifiers.SingleClassifierEnhancer extends AbstractClassifier implements Serializable

    serialVersionUID:
      -3665885256363525164L

    - ### Serialized Fields

      - #### m_Classifier

            Classifier m_Classifier

        The base classifier to use

- ## Package weka.classifiers.bayes

  - ### Class weka.classifiers.bayes.BayesNet extends AbstractClassifier implements Serializable

    serialVersionUID:
      746037443258775954L

    - ### Serialized Fields

      - #### m_ParentSets

            ParentSet[] m_ParentSets

        The parent sets.

      - #### m_Distributions

            Estimator[][] m_Distributions

        The attribute estimators containing CPTs.

      - #### m_DiscretizeFilter

            Discretize m_DiscretizeFilter

        filter used to quantize continuous variables, if any

      - #### m_nNonDiscreteAttribute

            int m_nNonDiscreteAttribute

        attribute index of a non-nominal attribute

      - #### m_MissingValuesFilter

            ReplaceMissingValues m_MissingValuesFilter

        filter used to fill in missing values, if any

      - #### m_NumClasses

            int m_NumClasses

        The number of classes

      - #### m_Instances

            Instances m_Instances

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

      - #### m_NumInstances

            int m_NumInstances

        The number of instances the model was built from

      - #### m_ADTree

            ADNode m_ADTree

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

      - #### m_otherBayesNet

            BIFReader m_otherBayesNet

        Bayes network to compare the structure with.

      - #### m_bUseADTree

            boolean m_bUseADTree

        Use the experimental ADTree datastructure for calculating contingency tables

      - #### m_SearchAlgorithm

            SearchAlgorithm m_SearchAlgorithm

        Search algorithm used for learning the structure of a network.

      - #### m_BayesNetEstimator

            BayesNetEstimator m_BayesNetEstimator

        Search algorithm used for learning the structure of a network.

  - ### Class weka.classifiers.bayes.NaiveBayes extends AbstractClassifier implements Serializable

    serialVersionUID:
      5995231201785697655L

    - ### Serialized Fields

      - #### m_Distributions

            Estimator[][] m_Distributions

        The attribute estimators.

      - #### m_ClassDistribution

            Estimator m_ClassDistribution

        The class estimator.

      - #### m_UseKernelEstimator

            boolean m_UseKernelEstimator

        Whether to use kernel density estimator rather than normal distribution for numeric attributes

      - #### m_UseDiscretization

            boolean m_UseDiscretization

        Whether to use discretization than normal distribution for numeric attributes

      - #### m_NumClasses

            int m_NumClasses

        The number of classes (or 1 for numeric class)

      - #### m_Instances

            Instances m_Instances

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

      - #### m_Disc

            Discretize m_Disc

        The discretization filter.

      - #### m_displayModelInOldFormat

            boolean m_displayModelInOldFormat

  - ### Class weka.classifiers.bayes.NaiveBayesMultinomial extends AbstractClassifier implements Serializable

    serialVersionUID:
      5932177440181257085L

    - ### Serialized Fields

      - #### m_probOfWordGivenClass

            double[][] m_probOfWordGivenClass

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

      - #### m_probOfClass

            double[] m_probOfClass

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

      - #### m_numAttributes

            int m_numAttributes

        number of unique words

      - #### m_numClasses

            int m_numClasses

        number of class values

      - #### m_lnFactorialCache

            double[] m_lnFactorialCache

        cache lnFactorial computations

      - #### m_headerInfo

            Instances m_headerInfo

        copy of header information for use in toString method

  - ### Class weka.classifiers.bayes.NaiveBayesMultinomialText extends AbstractClassifier implements Serializable

    serialVersionUID:
      2139025532014821394L

    - ### Serialized Fields

      - #### m_data

            Instances m_data

        The header of the training data

      - #### m_probOfClass

            double[] m_probOfClass

      - #### m_wordsPerClass

            double[] m_wordsPerClass

      - #### m_probOfWordGivenClass

            java.util.Map<K,V> m_probOfWordGivenClass

      - #### m_StopwordsHandler

            StopwordsHandler m_StopwordsHandler

        Stopword handler to use.

      - #### m_tokenizer

            Tokenizer m_tokenizer

        The tokenizer to use

      - #### m_lowercaseTokens

            boolean m_lowercaseTokens

        Whether or not to convert all tokens to lowercase

      - #### m_stemmer

            Stemmer m_stemmer

        The stemming algorithm.

      - #### 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_minWordP

            double m_minWordP

        Only consider dictionary words (features) that occur at least this many times

      - #### m_wordFrequencies

            boolean m_wordFrequencies

        Use word frequencies rather than bag-of-words if true

      - #### m_normalize

            boolean m_normalize

        normailize document length ?

      - #### m_norm

            double m_norm

        The length that each document vector should have in the end

      - #### m_lnorm

            double m_lnorm

        The L-norm to use

      - #### m_leplace

            double m_leplace

        Leplace-like correction factor for zero frequency

      - #### m_t

            double m_t

        Holds the current instance number

      - #### m_numModels

            int m_numModels

  - ### Class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable extends NaiveBayesMultinomial implements Serializable

    serialVersionUID:
      -7204398796974263186L

    - ### Serialized Fields

      - #### m_wordsPerClass

            double[] m_wordsPerClass

        the word count per class

  - ### Class weka.classifiers.bayes.NaiveBayesUpdateable extends NaiveBayes implements Serializable

    serialVersionUID:
      -5354015843807192221L

- ## Package weka.classifiers.bayes.net

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

    serialVersionUID:
      397409728366910204L

    - ### Serialized Fields

      - #### m_VaryNodes

            VaryNode[] m_VaryNodes

        list of VaryNode children

      - #### m_Instances

            Instance[] m_Instances

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

      - #### m_nCount

            int m_nCount

        count

      - #### m_nStartNode

            int m_nStartNode

        first node in VaryNode array

  - ### Class weka.classifiers.bayes.net.BayesNetGenerator extends EditableBayesNet implements Serializable

    serialVersionUID:
      -7462571170596157720L

    - ### Serialized Fields

      - #### m_nSeed

            int m_nSeed

        the seed value

      - #### random

            java.util.Random random

        the random number generator

      - #### m_bGenerateNet

            boolean m_bGenerateNet

      - #### m_nNrOfNodes

            int m_nNrOfNodes

      - #### m_nNrOfArcs

            int m_nNrOfArcs

      - #### m_nNrOfInstances

            int m_nNrOfInstances

      - #### m_nCardinality

            int m_nCardinality

      - #### m_sBIFFile

            java.lang.String m_sBIFFile

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

    serialVersionUID:
      -8358864680379881429L

    - ### Serialized Fields

      - #### m_nPositionX

            int[] m_nPositionX

      - #### m_nPositionY

            int[] m_nPositionY

      - #### m_order

            int[] m_order

      - #### m_sFile

            java.lang.String m_sFile

        the current filename

  - ### Class weka.classifiers.bayes.net.EditableBayesNet extends BayesNet implements Serializable

    serialVersionUID:
      746037443258735954L

    - ### Serialized Fields

      - #### m_nPositionX

            java.util.ArrayList<E> m_nPositionX

        location of nodes, used for graph drawing *

      - #### m_nPositionY

            java.util.ArrayList<E> m_nPositionY

      - #### m_fMarginP

            java.util.ArrayList<E> m_fMarginP

        marginal distributions *

      - #### m_nEvidence

            java.util.ArrayList<E> m_nEvidence

        evidence values, used for evidence propagation *

      - #### m_undoStack

            java.util.ArrayList<E> m_undoStack

        undo stack for undoin edit actions, or redo edit actions

      - #### m_nCurrentEditAction

            int m_nCurrentEditAction

        current action in undo stack

      - #### m_nSavedPointer

            int m_nSavedPointer

        action that the network is saved

      - #### m_bNeedsUndoAction

            boolean m_bNeedsUndoAction

        flag to indicate whether an edit action needs to introduce an undo action. This is only false when an undo or redo action is performed.

  - ### Class weka.classifiers.bayes.net.GUI extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -2038911085935515624L

    - ### Serialized Fields

      - #### m_layoutEngine

            LayoutEngine m_layoutEngine

        The current LayoutEngine

      - #### m_GraphPanel

            weka.classifiers.bayes.net.GUI.GraphPanel m_GraphPanel

        Panel actually displaying the graph

      - #### m_BayesNet

            EditableBayesNet m_BayesNet

        Container of Bayesian network

      - #### m_sFileName

            java.lang.String m_sFileName

        String containing file name storing current network

      - #### m_marginCalculator

            MarginCalculator m_marginCalculator

        used for calculating marginals in Bayesian netwowrks

      - #### m_marginCalculatorWithEvidence

            MarginCalculator m_marginCalculatorWithEvidence

        used for calculating marginals in Bayesian netwowrks when evidence is present

      - #### m_bViewMargins

            boolean m_bViewMargins

        flag indicating whether marginal distributions of each of the nodes should be shown in display.

      - #### m_bViewCliques

            boolean m_bViewCliques

      - #### m_menuBar

            javax.swing.JMenuBar m_menuBar

        The menu bar

      - #### m_Instances

            Instances m_Instances

        data selected from file. Used to train a Bayesian network on

      - #### m_jTfZoom

            javax.swing.JTextField m_jTfZoom

        Text field for specifying zoom

      - #### m_jTbTools

            javax.swing.JToolBar m_jTbTools

        toolbar containing buttons at top of window

      - #### m_jStatusBar

            javax.swing.JLabel m_jStatusBar

        status bar at bottom of window

      - #### m_jTfNodeWidth

            javax.swing.JTextField m_jTfNodeWidth

        TextField for node's width

      - #### m_jTfNodeHeight

            javax.swing.JTextField m_jTfNodeHeight

        TextField for nodes height

      - #### m_jScrollPane

            javax.swing.JScrollPane m_jScrollPane

        this contains the m_GraphPanel GraphPanel

      - #### ICONPATH

            java.lang.String ICONPATH

        path for icons

      - #### m_fScale

            double m_fScale

        current zoom value

      - #### m_nNodeHeight

            int m_nNodeHeight

        standard width of node

      - #### m_nNodeWidth

            int m_nNodeWidth

      - #### m_nPaddedNodeWidth

            int m_nPaddedNodeWidth

      - #### m_nZoomPercents

            int[] m_nZoomPercents

        used when using zoomIn and zoomOut buttons

      - #### a_new

            javax.swing.Action a_new

        actions triggered by GUI events

      - #### a_quit

            javax.swing.Action a_quit

      - #### a_save

            javax.swing.Action a_save

      - #### a_export

            weka.classifiers.bayes.net.GUI.ActionExport a_export

      - #### a_print

            weka.classifiers.bayes.net.GUI.ActionPrint a_print

      - #### a_load

            javax.swing.Action a_load

      - #### a_zoomin

            javax.swing.Action a_zoomin

      - #### a_zoomout

            javax.swing.Action a_zoomout

      - #### a_layout

            javax.swing.Action a_layout

      - #### a_saveas

            javax.swing.Action a_saveas

      - #### a_viewtoolbar

            javax.swing.Action a_viewtoolbar

      - #### a_viewstatusbar

            javax.swing.Action a_viewstatusbar

      - #### a_networkgenerator

            javax.swing.Action a_networkgenerator

      - #### a_datagenerator

            javax.swing.Action a_datagenerator

      - #### a_datasetter

            javax.swing.Action a_datasetter

      - #### a_learn

            javax.swing.Action a_learn

      - #### a_learnCPT

            javax.swing.Action a_learnCPT

      - #### a_help

            javax.swing.Action a_help

      - #### a_about

            javax.swing.Action a_about

      - #### a_addnode

            weka.classifiers.bayes.net.GUI.ActionAddNode a_addnode

      - #### a_delnode

            javax.swing.Action a_delnode

      - #### a_cutnode

            javax.swing.Action a_cutnode

      - #### a_copynode

            javax.swing.Action a_copynode

      - #### a_pastenode

            javax.swing.Action a_pastenode

      - #### a_selectall

            javax.swing.Action a_selectall

      - #### a_addarc

            javax.swing.Action a_addarc

      - #### a_delarc

            javax.swing.Action a_delarc

      - #### a_undo

            javax.swing.Action a_undo

      - #### a_redo

            javax.swing.Action a_redo

      - #### a_alignleft

            javax.swing.Action a_alignleft

      - #### a_alignright

            javax.swing.Action a_alignright

      - #### a_aligntop

            javax.swing.Action a_aligntop

      - #### a_alignbottom

            javax.swing.Action a_alignbottom

      - #### a_centerhorizontal

            javax.swing.Action a_centerhorizontal

      - #### a_centervertical

            javax.swing.Action a_centervertical

      - #### a_spacehorizontal

            javax.swing.Action a_spacehorizontal

      - #### a_spacevertical

            javax.swing.Action a_spacevertical

      - #### m_nCurrentNode

            int m_nCurrentNode

        node currently selected through right clicking

      - #### m_Selection

            weka.classifiers.bayes.net.GUI.Selection m_Selection

        selection of nodes

      - #### m_nSelectedRect

            java.awt.Rectangle m_nSelectedRect

        selection rectangle drawn through dragging with left mouse button

      - #### m_clipboard

            weka.classifiers.bayes.net.GUI.ClipBoard m_clipboard

  - ### Class weka.classifiers.bayes.net.MarginCalculator extends java.lang.Object implements Serializable

    serialVersionUID:
      650278019241175534L

    - ### Serialized Fields

      - #### m_debug

            boolean m_debug

      - #### m_root

            MarginCalculator.JunctionTreeNode m_root

      - #### jtNodes

            MarginCalculator.JunctionTreeNode[] jtNodes

      - #### m_Margins

            double[][] m_Margins

  - ### Class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode extends java.lang.Object implements Serializable

    serialVersionUID:
      650278019241175536L

    - ### Serialized Fields

      - #### m_bayesNet

            BayesNet m_bayesNet

        reference Bayes net for information about variables like name, cardinality, etc. but not for relations between nodes

      - #### m_nNodes

            int[] m_nNodes

        nodes of the Bayes net in this junction node

      - #### m_nCardinality

            int m_nCardinality

        cardinality of the instances of variables in this junction node

      - #### m_fi

            double[] m_fi

        potentials for first network

      - #### m_P

            double[] m_P

        distribution over this junction node according to first Bayes network

      - #### m_MarginalP

            double[][] m_MarginalP

      - #### m_parentSeparator

            MarginCalculator.JunctionTreeSeparator m_parentSeparator

      - #### m_children

            java.util.Vector<E> m_children

  - ### Class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator extends java.lang.Object implements Serializable

    serialVersionUID:
      6502780192411755343L

    - ### Serialized Fields

      - #### m_nNodes

            int[] m_nNodes

      - #### m_nCardinality

            int m_nCardinality

      - #### m_fiParent

            double[] m_fiParent

      - #### m_fiChild

            double[] m_fiChild

      - #### m_parentNode

            MarginCalculator.JunctionTreeNode m_parentNode

      - #### m_childNode

            MarginCalculator.JunctionTreeNode m_childNode

      - #### m_bayesNet

            BayesNet m_bayesNet

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

    serialVersionUID:
      4155021284407181838L

    - ### Serialized Fields

      - #### m_nParents

            int[] m_nParents

        Holds indexes of parents

      - #### m_nNrOfParents

            int m_nNrOfParents

        Holds number of parents

      - #### m_nCardinalityOfParents

            int m_nCardinalityOfParents

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

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

    serialVersionUID:
      -6196294370675872424L

    - ### Serialized Fields

      - #### m_iNode

            int m_iNode

        index of the node varied

      - #### m_nMCV

            int m_nMCV

        most common value

      - #### m_ADNodes

            ADNode[] m_ADNodes

        list of ADNode children

- ## Package weka.classifiers.bayes.net.estimate

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

    serialVersionUID:
      2184330197666253884L

    - ### Serialized Fields

      - #### m_fAlpha

            double m_fAlpha

        Holds prior on count

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

    serialVersionUID:
      -1846028304233257309L

    - ### Serialized Fields

      - #### m_bUseK2Prior

            boolean m_bUseK2Prior

        whether to use K2 prior

  - ### Class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes extends Estimator implements Serializable

    serialVersionUID:
      4215400230843212684L

    - ### Serialized Fields

      - #### m_Counts

            double[] m_Counts

        Hold the counts

      - #### m_SumOfCounts

            double m_SumOfCounts

        Hold the sum of counts

      - #### m_nSymbols

            int m_nSymbols

        Holds number of symbols in distribution

      - #### m_fPrior

            double m_fPrior

        Holds the prior probability

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

    serialVersionUID:
      6774941981423312133L

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

    serialVersionUID:
      8330705772601586313L

    - ### Serialized Fields

      - #### m_bUseK2Prior

            boolean m_bUseK2Prior

        whether to use K2 prior

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

    serialVersionUID:
      5874941612331806172L

- ## Package weka.classifiers.bayes.net.search

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

    serialVersionUID:
      6164792240778525312L

    - ### Serialized Fields

      - #### m_nMaxNrOfParents

            int m_nMaxNrOfParents

        Holds upper bound on number of parents

      - #### m_bInitAsNaiveBayes

            boolean m_bInitAsNaiveBayes

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

      - #### m_bMarkovBlanketClassifier

            boolean m_bMarkovBlanketClassifier

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

      - #### m_sInitalBIFFile

            java.lang.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

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

    serialVersionUID:
      3165802334119704560L

    - ### Serialized Fields

      - #### m_BayesNet

            BayesNet m_BayesNet

      - #### m_instances

            Instances m_instances

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

    serialVersionUID:
      -2510985917284798576L

    - ### Serialized Fields

      - #### m_nMaxCardinality

            int m_nMaxCardinality

        maximum size of separating set

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

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

    serialVersionUID:
      7334358169507619525L

    - ### Serialized Fields

      - #### m_sBIFFile

            java.lang.String m_sBIFFile

        name of file to read structure from

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

    serialVersionUID:
      -4808572519709755811L

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

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

    serialVersionUID:
      4236165533882462203L

    - ### Serialized Fields

      - #### m_nRuns

            int m_nRuns

        number of runs

      - #### m_nPopulationSize

            int m_nPopulationSize

        size of population

      - #### m_nDescendantPopulationSize

            int m_nDescendantPopulationSize

        size of descendant population

      - #### m_bUseCrossOver

            boolean m_bUseCrossOver

        use cross-over?

      - #### m_bUseMutation

            boolean m_bUseMutation

        use mutation?

      - #### m_bUseTournamentSelection

            boolean m_bUseTournamentSelection

        use tournament selection or take best sub-population

      - #### m_nSeed

            int m_nSeed

        random number seed

      - #### m_random

            java.util.Random m_random

        random number generator

      - #### g_bIsSquare

            boolean[] g_bIsSquare

        used in BayesNetRepresentation for efficiently determining whether a number is square

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

    serialVersionUID:
      7341389867906199781L

    - ### Serialized Fields

      - #### m_BayesNet

            BayesNet m_BayesNet

        points to Bayes network for which a structure is searched for

      - #### m_bUseProb

            boolean m_bUseProb

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

      - #### m_nNrOfFolds

            int m_nNrOfFolds

        number of folds for k-fold cross validation

      - #### m_nCVType

            int m_nCVType

        Holds the cross validation strategy used to measure quality of network

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

    serialVersionUID:
      -3885042888195820149L

    - ### Serialized Fields

      - #### m_bUseArcReversal

            boolean m_bUseArcReversal

        use the arc reversal operator

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

    serialVersionUID:
      -6626871067466338256L

    - ### Serialized Fields

      - #### m_bRandomOrder

            boolean m_bRandomOrder

        Holds flag to indicate ordering should be random

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

    serialVersionUID:
      -7359197180460703069L

    - ### Serialized Fields

      - #### m_nRuns

            int m_nRuns

        number of runs

      - #### m_nSeed

            int m_nSeed

        random number seed

      - #### m_random

            java.util.Random m_random

        random number generator

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

    serialVersionUID:
      -5482721887881010916L

    - ### Serialized Fields

      - #### m_fTStart

            double m_fTStart

        start temperature

      - #### m_fDelta

            double m_fDelta

        change in temperature at every run

      - #### m_nRuns

            int m_nRuns

        number of runs

      - #### m_bUseArcReversal

            boolean m_bUseArcReversal

        use the arc reversal operator

      - #### m_nSeed

            int m_nSeed

        random number seed

      - #### m_random

            java.util.Random m_random

        random number generator

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

    serialVersionUID:
      1176705618756672292L

    - ### Serialized Fields

      - #### m_nRuns

            int m_nRuns

        number of runs

      - #### m_nTabuList

            int m_nTabuList

        size of tabu list

      - #### m_oTabuList

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

        the actual tabu list

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

    serialVersionUID:
      1715277053980895298L

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

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

    serialVersionUID:
      -7037070678911459757L

    - ### Serialized Fields

      - #### m_nRuns

            int m_nRuns

        number of runs

      - #### m_nPopulationSize

            int m_nPopulationSize

        size of population

      - #### m_nDescendantPopulationSize

            int m_nDescendantPopulationSize

        size of descendant population

      - #### m_bUseCrossOver

            boolean m_bUseCrossOver

        use cross-over?

      - #### m_bUseMutation

            boolean m_bUseMutation

        use mutation?

      - #### m_bUseTournamentSelection

            boolean m_bUseTournamentSelection

        use tournament selection or take best sub-population

      - #### m_nSeed

            int m_nSeed

        random number seed

      - #### m_random

            java.util.Random m_random

        random number generator

      - #### g_bIsSquare

            boolean[] g_bIsSquare

        used in BayesNetRepresentation for efficiently determining whether a number is square

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

    serialVersionUID:
      4322783593818122403L

    - ### Serialized Fields

      - #### m_Cache

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

        cache for storing score differences

      - #### m_bUseArcReversal

            boolean m_bUseArcReversal

        use the arc reversal operator

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

    serialVersionUID:
      6176545934752116631L

    - ### Serialized Fields

      - #### m_bRandomOrder

            boolean m_bRandomOrder

        Holds flag to indicate ordering should be random

  - ### Class weka.classifiers.bayes.net.search.local.LAGDHillClimber extends HillClimber implements Serializable

    serialVersionUID:
      7217437499439184344L

    - ### Serialized Fields

      - #### m_nNrOfLookAheadSteps

            int m_nNrOfLookAheadSteps

        Number of Look Ahead Steps

      - #### m_nNrOfGoodOperations

            int m_nNrOfGoodOperations

        Number of Good Operations per Step

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

    serialVersionUID:
      3325995552474190374L

    - ### Serialized Fields

      - #### m_BayesNet

            BayesNet m_BayesNet

        points to Bayes network for which a structure is searched for

      - #### m_fAlpha

            double m_fAlpha

        Holds prior on count

      - #### m_nScoreType

            int m_nScoreType

        Holds the score type used to measure quality of network

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

    serialVersionUID:
      -6574084564213041174L

    - ### Serialized Fields

      - #### m_nRuns

            int m_nRuns

        number of runs

      - #### m_nSeed

            int m_nSeed

        random number seed

      - #### m_random

            java.util.Random m_random

        random number generator

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

    serialVersionUID:
      6951955606060513191L

    - ### Serialized Fields

      - #### m_fTStart

            double m_fTStart

        start temperature

      - #### m_fDelta

            double m_fDelta

        change in temperature at every run

      - #### m_nRuns

            int m_nRuns

        number of runs

      - #### m_bUseArcReversal

            boolean m_bUseArcReversal

        use the arc reversal operator

      - #### m_nSeed

            int m_nSeed

        random number seed

      - #### m_random

            java.util.Random m_random

        random number generator

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

    serialVersionUID:
      1457344073228786447L

    - ### Serialized Fields

      - #### m_nRuns

            int m_nRuns

        number of runs

      - #### m_nTabuList

            int m_nTabuList

        size of tabu list

      - #### m_oTabuList

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

        the actual tabu list

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

    serialVersionUID:
      965182127977228690L

- ## Package weka.classifiers.evaluation

  - ### Class weka.classifiers.evaluation.AbstractEvaluationMetric extends java.lang.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

  - ### Class weka.classifiers.evaluation.AbstractEvaluationMetric.UnknownStatisticException extends java.lang.IllegalArgumentException implements Serializable

    serialVersionUID:
      -8787045492227999839L

  - ### Class weka.classifiers.evaluation.AggregateableEvaluation extends Evaluation implements Serializable

    serialVersionUID:
      8734675926526110924L

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

    serialVersionUID:
      -181789981401504090L

    - ### Serialized Fields

      - #### m_ClassNames

            java.lang.String[] m_ClassNames

        Stores the names of the classes

  - ### Class weka.classifiers.evaluation.Evaluation extends java.lang.Object implements Serializable

    serialVersionUID:
      -7010314486866816271L

    - ### Serialized Fields

      - #### m_NumClasses

            int m_NumClasses

        The number of classes.

      - #### m_NumFolds

            int m_NumFolds

        The number of folds for a cross-validation.

      - #### m_Incorrect

            double m_Incorrect

        The weight of all incorrectly classified instances.

      - #### m_Correct

            double m_Correct

        The weight of all correctly classified instances.

      - #### m_Unclassified

            double m_Unclassified

        The weight of all unclassified instances.

      - #### m_MissingClass

            double m_MissingClass

        The weight of all instances that had no class assigned to them.

      - #### m_WithClass

            double m_WithClass

        The weight of all instances that had a class assigned to them.

      - #### m_ConfusionMatrix

            double[][] m_ConfusionMatrix

        Array for storing the confusion matrix.

      - #### m_ClassNames

            java.lang.String[] m_ClassNames

        The names of the classes.

      - #### m_ClassIsNominal

            boolean m_ClassIsNominal

        Is the class nominal or numeric?

      - #### m_ClassPriors

            double[] m_ClassPriors

        The prior probabilities of the classes.

      - #### m_ClassPriorsSum

            double m_ClassPriorsSum

        The sum of counts for priors.

      - #### m_CostMatrix

            CostMatrix m_CostMatrix

        The cost matrix (if given).

      - #### m_TotalCost

            double m_TotalCost

        The total cost of predictions (includes instance weights).

      - #### m_SumErr

            double m_SumErr

        Sum of errors.

      - #### m_SumAbsErr

            double m_SumAbsErr

        Sum of absolute errors.

      - #### m_SumSqrErr

            double m_SumSqrErr

        Sum of squared errors.

      - #### m_SumClass

            double m_SumClass

        Sum of class values.

      - #### m_SumSqrClass

            double m_SumSqrClass

        Sum of squared class values.

      - #### m_SumPredicted

            double m_SumPredicted

        Sum of predicted values.

      - #### m_SumSqrPredicted

            double m_SumSqrPredicted

        Sum of squared predicted values.

      - #### m_SumClassPredicted

            double m_SumClassPredicted

        Sum of predicted * class values.

      - #### m_SumPriorAbsErr

            double m_SumPriorAbsErr

        Sum of absolute errors of the prior.

      - #### m_SumPriorSqrErr

            double m_SumPriorSqrErr

        Sum of absolute errors of the prior.

      - #### m_SumKBInfo

            double m_SumKBInfo

        Total Kononenko & Bratko Information.

      - #### m_MarginCounts

            double[] m_MarginCounts

        Cumulative margin distribution.

      - #### m_NumTrainClassVals

            int m_NumTrainClassVals

        Number of non-missing class training instances seen.

      - #### 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_PriorEstimator

            UnivariateKernelEstimator m_PriorEstimator

        Numeric class estimator for prior.

      - #### m_ComplexityStatisticsAvailable

            boolean m_ComplexityStatisticsAvailable

        Whether complexity statistics are available.

      - #### m_SumPriorEntropy

            double m_SumPriorEntropy

        Total entropy of prior predictions.

      - #### m_SumSchemeEntropy

            double m_SumSchemeEntropy

        Total entropy of scheme predictions.

      - #### m_CoverageStatisticsAvailable

            boolean m_CoverageStatisticsAvailable

        Whether coverage statistics are available.

      - #### m_ConfLevel

            double m_ConfLevel

        The confidence level used for coverage statistics.

      - #### m_TotalSizeOfRegions

            double m_TotalSizeOfRegions

        Total size of predicted regions at the given confidence level.

      - #### m_TotalCoverage

            double m_TotalCoverage

        Total coverage of test cases at the given confidence level.

      - #### m_MinTarget

            double m_MinTarget

        Minimum target value.

      - #### m_MaxTarget

            double m_MaxTarget

        Maximum target value.

      - #### m_Predictions

            java.util.ArrayList<E> m_Predictions

        The list of predictions that have been generated (for computing AUC).

      - #### 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_Header

            Instances m_Header

        The header of the training set.

      - #### m_DiscardPredictions

            boolean m_DiscardPredictions

        whether to discard predictions (and save memory).

      - #### m_pluginMetrics

            java.util.List<E> m_pluginMetrics

        Holds plugin evaluation metrics

      - #### m_metricsToDisplay

            java.util.List<E> m_metricsToDisplay

        The list of metrics to display in the output

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

    serialVersionUID:
      -8871333992740492788L

    - ### Serialized Fields

      - #### m_Distribution

            double[] m_Distribution

        The predicted probabilities

      - #### m_Actual

            double m_Actual

        The actual class value

      - #### m_Predicted

            double m_Predicted

        The predicted class value

      - #### m_Weight

            double m_Weight

        The weight assigned to this prediction

  - ### Class weka.classifiers.evaluation.NumericPrediction extends java.lang.Object implements Serializable

    serialVersionUID:
      -4880216423674233887L

    - ### Serialized Fields

      - #### m_Actual

            double m_Actual

        The actual class value.

      - #### m_Predicted

            double m_Predicted

        The predicted class value.

      - #### m_Weight

            double m_Weight

        The weight assigned to this prediction.

      - #### m_PredictionIntervals

            double[][] m_PredictionIntervals

        the prediction intervals.

- ## Package weka.classifiers.evaluation.output.prediction

  - ### Class weka.classifiers.evaluation.output.prediction.AbstractOutput extends java.lang.Object implements Serializable

    serialVersionUID:
      752696986017306241L

    - ### Serialized Fields

      - #### m_Header

            Instances m_Header

        the header of the dataset.

      - #### m_Buffer

            java.lang.StringBuffer m_Buffer

        the buffer to write to.

      - #### m_FileBuffer

            java.lang.StringBuffer m_FileBuffer

        the file buffer to write to.

      - #### m_OutputDistribution

            boolean m_OutputDistribution

        whether to output the class distribution.

      - #### m_Attributes

            Range m_Attributes

        the range of attributes to output.

      - #### m_NumDecimals

            int m_NumDecimals

        the number of decimals after the decimal point.

      - #### m_OutputFile

            java.io.File m_OutputFile

        the file to store the output in.

      - #### m_SuppressOutput

            boolean m_SuppressOutput

        whether to suppress the regular output and only store in file.

  - ### Class weka.classifiers.evaluation.output.prediction.CSV extends AbstractOutput implements Serializable

    serialVersionUID:
      3401604538169573720L

    - ### Serialized Fields

      - #### m_Delimiter

            java.lang.String m_Delimiter

        the delimiter.

  - ### Class weka.classifiers.evaluation.output.prediction.HTML extends AbstractOutput implements Serializable

    serialVersionUID:
      7241252244954353300L

  - ### Class weka.classifiers.evaluation.output.prediction.Null extends AbstractOutput implements Serializable

    serialVersionUID:
      4988413155999044966L

  - ### Class weka.classifiers.evaluation.output.prediction.PlainText extends AbstractOutput implements Serializable

    serialVersionUID:
      2033389864898242735L

  - ### Class weka.classifiers.evaluation.output.prediction.XML extends AbstractOutput implements Serializable

    serialVersionUID:
      -3165514277316824801L

- ## Package weka.classifiers.functions

  - ### Class weka.classifiers.functions.GaussianProcesses extends AbstractClassifier implements Serializable

    serialVersionUID:
      -8620066949967678545L

    - ### Serialized Fields

      - #### m_NominalToBinary

            NominalToBinary m_NominalToBinary

        The filter used to make attributes numeric.

      - #### m_Filter

            Filter m_Filter

        The filter used to standardize/normalize all values.

      - #### m_filterType

            int m_filterType

        Whether to normalize/standardize/neither

      - #### m_Missing

            ReplaceMissingValues m_Missing

        The filter used to get rid of missing values.

      - #### m_checksTurnedOff

            boolean m_checksTurnedOff

        Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class.

      - #### m_delta

            double m_delta

        Gaussian Noise Value.

      - #### m_deltaSquared

            double m_deltaSquared

        The squared noise value.

      - #### m_Alin

            double m_Alin

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

      - #### m_Blin

            double m_Blin

      - #### m_kernel

            Kernel m_kernel

        Kernel to use *

      - #### m_NumTrain

            int m_NumTrain

        The number of training instances

      - #### m_avg_target

            double m_avg_target

        The training data.

      - #### m_L

            double[][] m_L

        (negative) covariance matrix in symmetric matrix representation

      - #### m_t

            Matrix m_t

        The vector of target values.

  - ### Class weka.classifiers.functions.LinearRegression extends AbstractClassifier implements Serializable

    serialVersionUID:
      -3364580862046573747L

    - ### Serialized Fields

      - #### m_Coefficients

            double[] m_Coefficients

        Array for storing coefficients of linear regression.

      - #### m_SelectedAttributes

            boolean[] m_SelectedAttributes

        Which attributes are relevant?

      - #### m_TransformedData

            Instances m_TransformedData

        Variable for storing transformed training data.

      - #### m_MissingFilter

            ReplaceMissingValues m_MissingFilter

        The filter for removing missing values.

      - #### m_TransformFilter

            NominalToBinary m_TransformFilter

        The filter storing the transformation from nominal to binary attributes.

      - #### m_ClassStdDev

            double m_ClassStdDev

        The standard deviations of the class attribute

      - #### m_ClassMean

            double m_ClassMean

        The mean of the class attribute

      - #### m_ClassIndex

            int m_ClassIndex

        The index of the class attribute

      - #### m_Means

            double[] m_Means

        The attributes means

      - #### m_StdDevs

            double[] m_StdDevs

        The attribute standard deviations

      - #### m_outputAdditionalStats

            boolean m_outputAdditionalStats

        Whether to output additional statistics such as std. dev. of coefficients and t-stats

      - #### m_df

            int m_df

        The degrees of freedom of the regression model

      - #### 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_FStat

            double m_FStat

        The F-statistic of the regression model

      - #### m_StdErrorOfCoef

            double[] m_StdErrorOfCoef

        Array for storing the standard error of each coefficient

      - #### m_TStats

            double[] m_TStats

        Array for storing the t-statistic of each coefficient

      - #### m_AttributeSelection

            int m_AttributeSelection

        The current attribute selection method

      - #### m_EliminateColinearAttributes

            boolean m_EliminateColinearAttributes

        Try to eliminate correlated attributes?

      - #### m_checksTurnedOff

            boolean m_checksTurnedOff

        Turn off all checks and conversions?

      - #### m_Ridge

            double m_Ridge

        The ridge parameter

      - #### m_Minimal

            boolean m_Minimal

        Conserve memory?

      - #### m_ModelBuilt

            boolean m_ModelBuilt

        Model already built?

  - ### Class weka.classifiers.functions.Logistic extends AbstractClassifier implements Serializable

    serialVersionUID:
      3932117032546553727L

    - ### Serialized Fields

      - #### m_Par

            double[][] m_Par

        The coefficients (optimized parameters) of the model

      - #### m_Data

            double[][] m_Data

        The data saved as a matrix

      - #### m_NumPredictors

            int m_NumPredictors

        The number of attributes in the model

      - #### m_ClassIndex

            int m_ClassIndex

        The index of the class attribute

      - #### m_NumClasses

            int m_NumClasses

        The number of the class labels

      - #### m_Ridge

            double m_Ridge

        The ridge parameter.

      - #### m_AttFilter

            RemoveUseless m_AttFilter

        An attribute filter

      - #### m_NominalToBinary

            NominalToBinary m_NominalToBinary

        The filter used to make attributes numeric.

      - #### m_ReplaceMissingValues

            ReplaceMissingValues m_ReplaceMissingValues

        The filter used to get rid of missing values.

      - #### m_LL

            double m_LL

        Log-likelihood of the searched model

      - #### m_MaxIts

            int m_MaxIts

        The maximum number of iterations.

      - #### m_useConjugateGradientDescent

            boolean m_useConjugateGradientDescent

        Wether to use conjugate gradient descent rather than BFGS updates.

      - #### m_structure

            Instances m_structure

      - #### m_numModels

            int m_numModels

  - ### Class weka.classifiers.functions.MultilayerPerceptron extends AbstractClassifier implements Serializable

    serialVersionUID:
      -5990607817048210779L

    - ### Serialized Fields

      - #### m_ZeroR

            Classifier m_ZeroR

        a ZeroR model in case no model can be built from the data or the network predicts all zeros for the classes

      - #### m_useDefaultModel

            boolean m_useDefaultModel

        Whether to use the default ZeroR model

      - #### m_instances

            Instances m_instances

        The training instances.

      - #### m_currentInstance

            Instance m_currentInstance

        The current instance running through the network.

      - #### m_numeric

            boolean m_numeric

        A flag to say that it's a numeric class.

      - #### m_attributeRanges

            double[] m_attributeRanges

        The ranges for all the attributes.

      - #### m_attributeBases

            double[] m_attributeBases

        The base values for all the attributes.

      - #### m_outputs

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

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

      - #### m_inputs

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

        The input units.(only feeds the inputs does no calcs)

      - #### m_neuralNodes

            NeuralConnection[] m_neuralNodes

        All the nodes that actually comprise the logical neural net.

      - #### m_numClasses

            int m_numClasses

        The number of classes.

      - #### m_numAttributes

            int m_numAttributes

        The number of attributes.

      - #### m_nodePanel

            weka.classifiers.functions.MultilayerPerceptron.NodePanel m_nodePanel

        The panel the nodes are displayed on.

      - #### m_controlPanel

            weka.classifiers.functions.MultilayerPerceptron.ControlPanel m_controlPanel

        The control panel.

      - #### m_nextId

            int m_nextId

        The next id number available for default naming.

      - #### m_selected

            java.util.ArrayList<E> m_selected

        A Vector list of the units currently selected.

      - #### m_numEpochs

            int m_numEpochs

        The number of epochs to train through.

      - #### m_stopIt

            boolean m_stopIt

        a flag to state if the network should be running, or stopped.

      - #### m_stopped

            boolean m_stopped

        a flag to state that the network has in fact stopped.

      - #### m_accepted

            boolean m_accepted

        a flag to state that the network should be accepted the way it is.

      - #### m_win

            javax.swing.JFrame m_win

        The window for the network.

      - #### m_autoBuild

            boolean m_autoBuild

        A flag to tell the build classifier to automatically build a neural net.

      - #### m_gui

            boolean m_gui

        A flag to state that the gui for the network should be brought up. To allow interaction while training.

      - #### m_valSize

            int m_valSize

        An int to say how big the validation set should be.

      - #### m_driftThreshold

            int m_driftThreshold

        The number to to use to quit on validation testing.

      - #### m_randomSeed

            int m_randomSeed

        The number used to seed the random number generator.

      - #### m_random

            java.util.Random m_random

        The actual random number generator.

      - #### m_useNomToBin

            boolean m_useNomToBin

        A flag to state that a nominal to binary filter should be used.

      - #### m_nominalToBinaryFilter

            NominalToBinary m_nominalToBinaryFilter

        The actual filter.

      - #### m_hiddenLayers

            java.lang.String m_hiddenLayers

        The string that defines the hidden layers

      - #### m_normalizeAttributes

            boolean m_normalizeAttributes

        This flag states that the user wants the input values normalized.

      - #### m_decay

            boolean m_decay

        This flag states that the user wants the learning rate to decay.

      - #### m_learningRate

            double m_learningRate

        This is the learning rate for the network.

      - #### m_momentum

            double m_momentum

        This is the momentum for the network.

      - #### m_epoch

            int m_epoch

        Shows the number of the epoch that the network just finished.

      - #### m_error

            double m_error

        Shows the error of the epoch that the network just finished.

      - #### m_reset

            boolean m_reset

        This flag states that the user wants the network to restart if it is found to be generating infinity or NaN for the error value. This would restart the network with the current options except that the learning rate would be smaller than before, (perhaps half of its current value). This option will not be available if the gui is chosen (if the gui is open the user can fix the network themselves, it is an architectural minefield for the network to be reset with the gui open).

      - #### m_normalizeClass

            boolean m_normalizeClass

        This flag states that the user wants the class to be normalized while processing in the network is done. (the final answer will be in the original range regardless). This option will only be used when the class is numeric.

      - #### m_sigmoidUnit

            SigmoidUnit m_sigmoidUnit

        this is a sigmoid unit.

      - #### m_linearUnit

            LinearUnit m_linearUnit

        This is a linear unit.

  - ### Class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd extends NeuralConnection implements Serializable

    serialVersionUID:
      7305185603191183338L

    - ### Serialized Fields

      - #### m_link

            int m_link

        the value that represents the instance value this node represents. For an input it is the attribute number, for an output, if nominal it is the class value.

      - #### m_input

            boolean m_input

        True if node is an input, False if it's an output.

  - ### Class weka.classifiers.functions.SGD extends RandomizableClassifier implements Serializable

    serialVersionUID:
      -3732968666673530290L

    - ### Serialized Fields

      - #### m_replaceMissing

            ReplaceMissingValues m_replaceMissing

        Replace missing values

      - #### 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_lambda

            double m_lambda

        The regularization parameter

      - #### m_learningRate

            double m_learningRate

        The learning rate

      - #### m_weights

            double[] m_weights

        Stores the weights (+ bias in the last element)

      - #### m_epsilon

            double m_epsilon

        The epsilon parameter for epsilon insensitive and Huber loss

      - #### m_t

            double m_t

        Holds the current iteration number

      - #### m_numInstances

            double m_numInstances

        The number of training instances

      - #### m_epochs

            int m_epochs

        The number of epochs to perform (batch learning). Total iterations is m_epochs * num instances

      - #### m_dontNormalize

            boolean m_dontNormalize

        Turn off normalization of the input data. This option gets forced for incremental training.

      - #### m_dontReplaceMissing

            boolean m_dontReplaceMissing

        Turn off global replacement of missing values. Missing values will be ignored instead. This option gets forced for incremental training.

      - #### m_data

            Instances m_data

        Holds the header of the training data

      - #### m_loss

            int m_loss

        The current loss function to minimize

      - #### m_numModels

            int m_numModels

  - ### Class weka.classifiers.functions.SGDText extends RandomizableClassifier implements Serializable

    serialVersionUID:
      7200171484002029584L

    - ### Serialized Fields

      - #### 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_minWordP

            double m_minWordP

        Only consider dictionary words (features) that occur at least this many times.

      - #### m_minAbsCoefficient

            double m_minAbsCoefficient

        Prune terms from the model that have a coefficient smaller than this.

      - #### m_wordFrequencies

            boolean m_wordFrequencies

        Use word frequencies rather than bag-of-words if true

      - #### m_normalize

            boolean m_normalize

        Whether to normalized document length or not

      - #### m_norm

            double m_norm

        The length that each document vector should have in the end

      - #### m_lnorm

            double m_lnorm

        The L-norm to use

      - #### m_dictionary

            java.util.LinkedHashMap<K,V> m_dictionary

        The dictionary (and term weights)

      - #### m_StopwordsHandler

            StopwordsHandler m_StopwordsHandler

        Stopword handler to use.

      - #### m_tokenizer

            Tokenizer m_tokenizer

        The tokenizer to use

      - #### m_lowercaseTokens

            boolean m_lowercaseTokens

        Whether or not to convert all tokens to lowercase

      - #### m_stemmer

            Stemmer m_stemmer

        The stemming algorithm.

      - #### m_lambda

            double m_lambda

        The regularization parameter

      - #### m_learningRate

            double m_learningRate

        The learning rate

      - #### m_t

            double m_t

        Holds the current iteration number

      - #### m_bias

            double m_bias

        Holds the bias term

      - #### m_numInstances

            double m_numInstances

        The number of training instances

      - #### m_data

            Instances m_data

        The header of the training data

      - #### m_epochs

            int m_epochs

        The number of epochs to perform (batch learning). Total iterations is m_epochs * num instances

      - #### m_loss

            int m_loss

        The current loss function to minimize

      - #### m_svmProbs

            SGD m_svmProbs

        Used for producing probabilities for SVM via SGD logistic regression

      - #### 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_numModels

            int m_numModels

  - ### Class weka.classifiers.functions.SGDText.Count extends java.lang.Object implements Serializable

    serialVersionUID:
      2104201532017340967L

    - ### Serialized Fields

      - #### m_count

            double m_count

      - #### m_weight

            double m_weight

  - ### Class weka.classifiers.functions.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_slope

            double m_slope

        The slope

      - #### m_intercept

            double m_intercept

        The intercept

      - #### m_classMeanForMissing

            double m_classMeanForMissing

        The class mean for missing values

      - #### m_outputAdditionalStats

            boolean m_outputAdditionalStats

        Whether to output additional statistics such as std. dev. of coefficients and t-stats

      - #### m_df

            int m_df

        Degrees of freedom, used in statistical calculations

      - #### m_seSlope

            double m_seSlope

        standard error of the slope

      - #### m_seIntercept

            double m_seIntercept

        standard error of the intercept

      - #### m_tstatSlope

            double m_tstatSlope

        t-statistic of the slope

      - #### m_tstatIntercept

            double m_tstatIntercept

        t-statistic of the intercept

      - #### m_rsquared

            double m_rsquared

        R^2 value for the regression

      - #### m_rsquaredAdj

            double m_rsquaredAdj

        Adjusted R^2 value for the regression

      - #### m_fstat

            double m_fstat

        F-statistic for the regression

      - #### m_suppressErrorMessage

            boolean m_suppressErrorMessage

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

  - ### Class weka.classifiers.functions.SimpleLogistic extends AbstractClassifier implements Serializable

    serialVersionUID:
      7397710626304705059L

    - ### Serialized Fields

      - #### m_boostedModel

            LogisticBase m_boostedModel

        The actual logistic regression model

      - #### m_NominalToBinary

            NominalToBinary m_NominalToBinary

        Filter for converting nominal attributes to binary ones

      - #### m_ReplaceMissingValues

            ReplaceMissingValues m_ReplaceMissingValues

        Filter for replacing missing values

      - #### m_numBoostingIterations

            int m_numBoostingIterations

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

      - #### m_maxBoostingIterations

            int m_maxBoostingIterations

        Maximum number of iterations for LogitBoost

      - #### m_heuristicStop

            int m_heuristicStop

        Parameter for the heuristic for early stopping of LogitBoost

      - #### m_useCrossValidation

            boolean m_useCrossValidation

        If true, cross-validate number of LogitBoost iterations

      - #### m_errorOnProbabilities

            boolean m_errorOnProbabilities

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

      - #### m_weightTrimBeta

            double m_weightTrimBeta

        Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.

      - #### m_useAIC

            boolean m_useAIC

        If true, the AIC is used to choose the best iteration

  - ### Class weka.classifiers.functions.SMO extends AbstractClassifier implements Serializable

    serialVersionUID:
      -6585883636378691736L

    - ### Serialized Fields

      - #### m_classifiers

            SMO.BinarySMO[][] m_classifiers

        The binary classifier(s)

      - #### m_C

            double m_C

        The complexity parameter.

      - #### m_eps

            double m_eps

        Epsilon for rounding.

      - #### m_tol

            double m_tol

        Tolerance for accuracy of result.

      - #### m_filterType

            int m_filterType

        Whether to normalize/standardize/neither

      - #### m_NominalToBinary

            NominalToBinary m_NominalToBinary

        The filter used to make attributes numeric.

      - #### m_Filter

            Filter m_Filter

        The filter used to standardize/normalize all values.

      - #### m_Missing

            ReplaceMissingValues m_Missing

        The filter used to get rid of missing values.

      - #### m_classIndex

            int m_classIndex

        The class index from the training data

      - #### m_classAttribute

            Attribute m_classAttribute

        The class attribute

      - #### m_KernelIsLinear

            boolean m_KernelIsLinear

        whether the kernel is a linear one

      - #### m_checksTurnedOff

            boolean m_checksTurnedOff

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

      - #### m_fitLogisticModels

            boolean m_fitLogisticModels

        Whether logistic models are to be fit

      - #### m_numFolds

            int m_numFolds

        The number of folds for the internal cross-validation

      - #### m_randomSeed

            int m_randomSeed

        The random number seed

      - #### m_kernel

            Kernel m_kernel

        the kernel to use

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

    serialVersionUID:
      -8246163625699362456L

    - ### Serialized Fields

      - #### m_alpha

            double[] m_alpha

        The Lagrange multipliers.

      - #### m_b

            double m_b

        The thresholds.

      - #### m_bLow

            double m_bLow

        The thresholds.

      - #### m_bUp

            double m_bUp

        The thresholds.

      - #### m_iLow

            int m_iLow

        The indices for m_bLow and m_bUp

      - #### m_iUp

            int m_iUp

        The indices for m_bLow and m_bUp

      - #### m_data

            Instances m_data

        The training data.

      - #### m_weights

            double[] m_weights

        Weight vector for linear machine.

      - #### m_sparseWeights

            double[] m_sparseWeights

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

      - #### m_sparseIndices

            int[] m_sparseIndices

      - #### m_kernel

            Kernel m_kernel

        Kernel to use

      - #### m_class

            double[] m_class

        The transformed class values.

      - #### m_errors

            double[] m_errors

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

      - #### m_I0

            SMOset m_I0

        {i: 0 < m_alpha[i] < C}

      - #### m_I1

            SMOset m_I1

        {i: m_class[i] = 1, m_alpha[i] = 0}

      - #### m_I2

            SMOset m_I2

        {i: m_class[i] = -1, m_alpha[i] =C}

      - #### m_I3

            SMOset m_I3

        {i: m_class[i] = 1, m_alpha[i] = C}

      - #### m_I4

            SMOset m_I4

        {i: m_class[i] = -1, m_alpha[i] = 0}

      - #### m_supportVectors

            SMOset m_supportVectors

        The set of support vectors

      - #### m_logistic

            Logistic m_logistic

        Stores logistic regression model for probability estimate

      - #### m_sumOfWeights

            double m_sumOfWeights

        Stores the weight of the training instances

  - ### Class weka.classifiers.functions.SMOreg extends AbstractClassifier implements Serializable

    serialVersionUID:
      -7149606251113102827L

    - ### Serialized Fields

      - #### m_filterType

            int m_filterType

        Whether to normalize/standardize/neither

      - #### m_NominalToBinary

            NominalToBinary m_NominalToBinary

        The filter used to make attributes numeric.

      - #### m_Filter

            Filter m_Filter

        The filter used to standardize/normalize all values.

      - #### m_Missing

            ReplaceMissingValues m_Missing

        The filter used to get rid of missing values.

      - #### m_onlyNumeric

            boolean m_onlyNumeric

        Only numeric attributes in the dataset? If so, less need to filter

      - #### m_C

            double m_C

        capacity parameter

      - #### m_x1

            double m_x1

        coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0

      - #### m_x0

            double m_x0

      - #### m_optimizer

            RegOptimizer m_optimizer

        contains the algorithm used for learning

      - #### m_kernel

            Kernel m_kernel

        the configured kernel

  - ### Class weka.classifiers.functions.VotedPerceptron extends AbstractClassifier implements Serializable

    serialVersionUID:
      -1072429260104568698L

    - ### Serialized Fields

      - #### m_MaxK

            int m_MaxK

        The maximum number of alterations to the perceptron

      - #### m_NumIterations

            int m_NumIterations

        The number of iterations

      - #### m_Exponent

            double m_Exponent

        The exponent

      - #### m_K

            int m_K

        The actual number of alterations

      - #### m_Additions

            int[] m_Additions

        The training instances added to the perceptron

      - #### m_IsAddition

            boolean[] m_IsAddition

        Addition or subtraction?

      - #### m_Weights

            int[] m_Weights

        The weights for each perceptron

      - #### m_Train

            Instances m_Train

        The training instances

      - #### m_Seed

            int m_Seed

        Seed used for shuffling the dataset

      - #### m_NominalToBinary

            NominalToBinary m_NominalToBinary

        The filter used to make attributes numeric.

      - #### m_ReplaceMissingValues

            ReplaceMissingValues m_ReplaceMissingValues

        The filter used to get rid of missing values.

- ## Package weka.classifiers.functions.neural

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

    serialVersionUID:
      8572152807755673630L

  - ### Class weka.classifiers.functions.neural.NeuralConnection extends java.lang.Object implements Serializable

    serialVersionUID:
      -286208828571059163L

    - ### Serialized Fields

      - #### m_inputList

            NeuralConnection[] m_inputList

        The list of inputs to this unit.

      - #### m_outputList

            NeuralConnection[] m_outputList

        The list of outputs from this unit.

      - #### m_inputNums

            int[] m_inputNums

        The numbering for the connections at the other end of the input lines.

      - #### m_outputNums

            int[] m_outputNums

        The numbering for the connections at the other end of the out lines.

      - #### m_numInputs

            int m_numInputs

        The number of inputs.

      - #### m_numOutputs

            int m_numOutputs

        The number of outputs.

      - #### m_unitValue

            double m_unitValue

        The output value for this unit, NaN if not calculated.

      - #### m_unitError

            double m_unitError

        The error value for this unit, NaN if not calculated.

      - #### m_weightsUpdated

            boolean m_weightsUpdated

        True if the weights have already been updated.

      - #### m_id

            java.lang.String m_id

        The string that uniquely (provided naming is done properly) identifies this unit.

      - #### m_type

            int m_type

        The type of unit this is.

      - #### m_x

            double m_x

        The x coord of this unit purely for displaying purposes.

      - #### m_y

            double m_y

        The y coord of this unit purely for displaying purposes.

  - ### Class weka.classifiers.functions.neural.NeuralNode extends NeuralConnection implements Serializable

    serialVersionUID:
      -1085750607680839163L

    - ### Serialized Fields

      - #### m_weights

            double[] m_weights

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

      - #### m_bestWeights

            double[] m_bestWeights

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

      - #### m_changeInWeights

            double[] m_changeInWeights

        The change in the weights.

      - #### m_random

            java.util.Random m_random

      - #### m_methods

            NeuralMethod m_methods

        Performs the operations for this node. Currently this defines that the node is either a sigmoid or a linear unit.

  - ### Class weka.classifiers.functions.neural.SigmoidUnit extends java.lang.Object implements Serializable

    serialVersionUID:
      -5162958458177475652L

- ## Package weka.classifiers.functions.supportVector

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

    serialVersionUID:
      702810182699015136L

    - ### Serialized Fields

      - #### m_kernelEvals

            int m_kernelEvals

        Counts the number of kernel evaluations.

      - #### m_cacheHits

            int m_cacheHits

        Counts the number of kernel cache hits.

      - #### m_cacheSize

            int m_cacheSize

        The size of the cache (a prime number)

      - #### m_storage

            double[] m_storage

        Kernel cache

      - #### m_keys

            long[] m_keys

      - #### m_kernelMatrix

            double[][] m_kernelMatrix

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

      - #### m_numInsts

            int m_numInsts

        The number of instance in the dataset

      - #### m_cacheSlots

            int m_cacheSlots

        number of cache slots in an entry

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

    serialVersionUID:
      -6102771099905817064L

    - ### Serialized Fields

      - #### m_data

            Instances m_data

        The dataset

      - #### m_Debug

            boolean m_Debug

        enables debugging output

      - #### m_ChecksTurnedOff

            boolean m_ChecksTurnedOff

        Turns off all checks

      - #### m_DoNotCheckCapabilities

            boolean m_DoNotCheckCapabilities

        Whether capabilities should not be checked

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

    serialVersionUID:
      1248574185532130851L

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

    serialVersionUID:
      -321831645846363201L

    - ### Serialized Fields

      - #### m_lowerOrder

            boolean m_lowerOrder

        Use lower-order terms?

      - #### m_exponent

            double m_exponent

        The exponent for the polynomial kernel.

  - ### Class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel extends Kernel implements Serializable

    serialVersionUID:
      -321831645846363333L

    - ### Serialized Fields

      - #### m_KernelMatrixFile

            java.io.File m_KernelMatrixFile

        The file holding the kernel matrix.

      - #### m_KernelMatrix

            Matrix m_KernelMatrix

        The kernel matrix.

      - #### m_Counter

            int m_Counter

        A classifier counter.

  - ### Class weka.classifiers.functions.supportVector.Puk extends CachedKernel implements Serializable

    serialVersionUID:
      1682161522559978851L

    - ### Serialized Fields

      - #### m_kernelPrecalc

            double[] m_kernelPrecalc

        The precalculated dotproducts of <inst_i,inst_i>

      - #### m_omega

            double m_omega

        Omega for the Puk kernel.

      - #### m_sigma

            double m_sigma

        Sigma for the Puk kernel.

      - #### m_factor

            double m_factor

        Cached factor for the Puk kernel.

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

    serialVersionUID:
      5247117544316387852L

    - ### Serialized Fields

      - #### m_kernelPrecalc

            double[] m_kernelPrecalc

        The precalculated dotproducts of <inst_i,inst_i>

      - #### m_gamma

            double m_gamma

        Gamma for the RBF kernel.

  - ### Class weka.classifiers.functions.supportVector.RegOptimizer extends java.lang.Object implements Serializable

    serialVersionUID:
      -2198266997254461814L

    - ### Serialized Fields

      - #### m_alpha

            double[] m_alpha

        alpha and alpha* arrays containing weights for solving dual problem

      - #### m_alphaStar

            double[] m_alphaStar

      - #### m_b

            double m_b

        offset

      - #### m_epsilon

            double m_epsilon

        epsilon of epsilon-insensitive cost function

      - #### m_C

            double m_C

        capacity parameter, copied from SMOreg

      - #### m_target

            double[] m_target

        class values/desired output vector

      - #### m_data

            Instances m_data

        points to data set

      - #### m_kernel

            Kernel m_kernel

        the kernel

      - #### m_classIndex

            int m_classIndex

        index of class variable in data set

      - #### m_nInstances

            int m_nInstances

        number of instances in data set

      - #### m_random

            java.util.Random m_random

        random number generator

      - #### m_nSeed

            int m_nSeed

        seed for initializing random number generator

      - #### m_supportVectors

            SMOset m_supportVectors

        set of support vectors, that is, vectors with alpha(*)!=0

      - #### m_nEvals

            int m_nEvals

        number of kernel evaluations, used for printing statistics only

      - #### m_nCacheHits

            int m_nCacheHits

        number of kernel cache hits, used for printing statistics only

      - #### m_weights

            double[] m_weights

        weights for linear kernel

      - #### m_sparseWeights

            double[] m_sparseWeights

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

      - #### m_sparseIndices

            int[] m_sparseIndices

      - #### m_bModelBuilt

            boolean m_bModelBuilt

        flag to indicate whether the model is built yet

      - #### m_SVM

            SMOreg m_SVM

        parent SMOreg class

  - ### Class weka.classifiers.functions.supportVector.RegSMO extends RegOptimizer implements Serializable

    serialVersionUID:
      -7504070793279598638L

    - ### Serialized Fields

      - #### m_eps

            double m_eps

        tolerance parameter, smaller changes on alpha in inner loop will be ignored

      - #### m_error

            double[] m_error

        error cache containing m_error[i] = SVMOutput(i) - m_target[i] - m_b
        note, we don't need m_b in the cache, since if we do, we need to maintain it when m_b is updated

      - #### m_alpha1

            double m_alpha1

        alpha value for first candidate

      - #### m_alpha1Star

            double m_alpha1Star

        alpha* value for first candidate

      - #### m_alpha2

            double m_alpha2

        alpha value for second candidate

      - #### m_alpha2Star

            double m_alpha2Star

        alpha* value for second candidate

  - ### Class weka.classifiers.functions.supportVector.RegSMOImproved extends RegSMO implements Serializable

    serialVersionUID:
      471692841446029784L

    - ### Serialized Fields

      - #### m_I0

            SMOset m_I0

        The different sets used by the algorithm.

      - #### m_iSet

            int[] m_iSet

        Index set {i: 0 < m_alpha[i] < C || 0 < m_alphaStar[i] < C}}

      - #### m_bUp

            double m_bUp

        b.up and b.low boundaries used to determine stopping criterion

      - #### m_bLow

            double m_bLow

        b.up and b.low boundaries used to determine stopping criterion

      - #### m_iUp

            int m_iUp

        index of the instance that gave us b.up and b.low

      - #### m_iLow

            int m_iLow

        index of the instance that gave us b.up and b.low

      - #### m_fTolerance

            double m_fTolerance

        tolerance parameter used for checking stopping criterion b.up < b.low + 2 tol

      - #### m_bUseVariant1

            boolean m_bUseVariant1

        set true to use variant 1 of the paper, otherwise use variant 2

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

    serialVersionUID:
      -8364829283188675777L

    - ### Serialized Fields

      - #### m_number

            int m_number

        The current number of elements in the set

      - #### m_first

            int m_first

        The first element in the set

      - #### m_indicators

            boolean[] m_indicators

        Indicators

      - #### m_next

            int[] m_next

        The next element for each element

      - #### m_previous

            int[] m_previous

        The previous element for each element

  - ### Class weka.classifiers.functions.supportVector.StringKernel extends Kernel implements Serializable

    serialVersionUID:
      -4902954211202690123L

    - ### Serialized Fields

      - #### m_cacheSize

            int m_cacheSize

        The size of the cache (a prime number)

      - #### m_internalCacheSize

            int m_internalCacheSize

        The size of the internal cache for intermediate results (a prime number)

      - #### m_strAttr

            int m_strAttr

        The attribute number of the string attribute

      - #### m_storage

            double[] m_storage

        Kernel cache (i.e., cache for kernel evaluations)

      - #### m_keys

            long[] m_keys

      - #### m_kernelEvals

            int m_kernelEvals

        Counts the number of kernel evaluations.

      - #### m_numInsts

            int m_numInsts

        The number of instance in the dataset

      - #### m_PruningMethod

            int m_PruningMethod

        the pruning method

      - #### m_lambda

            double m_lambda

        the decay factor that penalizes non-continuous substring matches. See [1] for details.

      - #### m_subsequenceLength

            int m_subsequenceLength

        The substring length

      - #### m_maxSubsequenceLength

            int m_maxSubsequenceLength

        The maximum substring length for lambda pruning

      - #### m_powersOflambda

            double[] m_powersOflambda

        the precalculated powers of lambda

      - #### m_normalize

            boolean m_normalize

        flag for switching normalization on or off. This defaults to false and can be turned on by the switch for feature space normalization in SMO

      - #### maxCache

            int maxCache

        private cache for intermediate results

      - #### cachekh

            double[] cachekh

      - #### cachekhK

            int[] cachekhK

      - #### cachekh2

            double[] cachekh2

      - #### cachekh2K

            int[] cachekh2K

      - #### m_multX

            int m_multX

        cached indexes for private cache

      - #### m_multY

            int m_multY

      - #### m_multZ

            int m_multZ

      - #### m_multZZ

            int m_multZZ

      - #### m_useRecursionCache

            boolean m_useRecursionCache

- ## Package weka.classifiers.lazy

  - ### Class weka.classifiers.lazy.IBk extends AbstractClassifier implements Serializable

    serialVersionUID:
      -3080186098777067172L

    - ### Serialized Fields

      - #### m_Train

            Instances m_Train

        The training instances used for classification.

      - #### m_NumClasses

            int m_NumClasses

        The number of class values (or 1 if predicting numeric).

      - #### m_ClassType

            int m_ClassType

        The class attribute type.

      - #### m_kNN

            int m_kNN

        The number of neighbours to use for classification (currently).

      - #### m_kNNUpper

            int m_kNNUpper

        The value of kNN provided by the user. This may differ from m_kNN if cross-validation is being used.

      - #### m_kNNValid

            boolean m_kNNValid

        Whether the value of k selected by cross validation has been invalidated by a change in the training instances.

      - #### m_WindowSize

            int m_WindowSize

        The maximum number of training instances allowed. When this limit is reached, old training instances are removed, so the training data is "windowed". Set to 0 for unlimited numbers of instances.

      - #### m_DistanceWeighting

            int m_DistanceWeighting

        Whether the neighbours should be distance-weighted.

      - #### m_CrossValidate

            boolean m_CrossValidate

        Whether to select k by cross validation.

      - #### m_MeanSquared

            boolean m_MeanSquared

        Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks.

      - #### m_defaultModel

            ZeroR m_defaultModel

        Default ZeroR model to use when there are no training instances

      - #### m_NNSearch

            NearestNeighbourSearch m_NNSearch

        for nearest-neighbor search.

      - #### m_NumAttributesUsed

            double m_NumAttributesUsed

        The number of attributes the contribute to a prediction.

  - ### Class weka.classifiers.lazy.KStar extends AbstractClassifier implements Serializable

    serialVersionUID:
      332458330800479083L

    - ### Serialized Fields

      - #### m_Train

            Instances m_Train

        The training instances used for classification.

      - #### m_NumInstances

            int m_NumInstances

        The number of instances in the dataset

      - #### m_NumClasses

            int m_NumClasses

        The number of class values

      - #### m_NumAttributes

            int m_NumAttributes

        The number of attributes

      - #### m_ClassType

            int m_ClassType

        The class attribute type

      - #### m_RandClassCols

            int[][] m_RandClassCols

        Table of random class value colomns

      - #### m_ComputeRandomCols

            int m_ComputeRandomCols

        Flag turning on and off the computation of random class colomns

      - #### m_InitFlag

            int m_InitFlag

        Flag turning on and off the initialisation of config variables

      - #### m_Cache

            KStarCache[] m_Cache

        A custom data structure for caching distinct attribute values and their scale factor or stop parameter.

      - #### m_MissingMode

            int m_MissingMode

        missing value treatment

      - #### m_BlendMethod

            int m_BlendMethod

        0 = use specified blend, 1 = entropic blend setting

      - #### m_GlobalBlend

            int m_GlobalBlend

        default sphere of influence blend setting

  - ### Class weka.classifiers.lazy.LWL extends SingleClassifierEnhancer implements Serializable

    serialVersionUID:
      1979797405383665815L

    - ### Serialized Fields

      - #### m_Train

            Instances m_Train

        The training instances used for classification.

      - #### m_kNN

            int m_kNN

        The number of neighbours used to select the kernel bandwidth.

      - #### m_WeightKernel

            int m_WeightKernel

        The weighting kernel method currently selected.

      - #### m_UseAllK

            boolean m_UseAllK

        True if m_kNN should be set to all instances.

      - #### m_NNSearch

            NearestNeighbourSearch m_NNSearch

        The nearest neighbour search algorithm to use. (Default: weka.core.neighboursearch.LinearNNSearch)

      - #### m_ZeroR

            Classifier m_ZeroR

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

- ## Package weka.classifiers.lazy.kstar

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

    serialVersionUID:
      -7693632394267140678L

    - ### Serialized Fields

      - #### m_Cache

            KStarCache.CacheTable m_Cache

        cache table

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

    serialVersionUID:
      -8086106452588253423L

    - ### Serialized Fields

      - #### m_Table

            KStarCache.TableEntry[] m_Table

        The hash table data.

      - #### m_Count

            int m_Count

        The total number of entries in the hash table.

      - #### m_Threshold

            int m_Threshold

        Rehashes the table when count exceeds this threshold.

      - #### m_LoadFactor

            float m_LoadFactor

        The load factor for the hashtable.

      - #### EPSILON

            double EPSILON

        Accuracy value for equality

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

    serialVersionUID:
      4057602386766259138L

    - ### Serialized Fields

      - #### hash

            int hash

        attribute value hash code

      - #### key

            double key

        attribute value

      - #### value

            double value

        scale factor or stop parameter

      - #### pmiss

            double pmiss

        transformation probability to missing value

      - #### next

            KStarCache.TableEntry next

        next table entry (separate chaining)

- ## Package weka.classifiers.meta

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

    serialVersionUID:
      -1178107808933117974L

    - ### Serialized Fields

      - #### m_Betas

            double[] m_Betas

        Array for storing the weights for the votes.

      - #### m_NumIterationsPerformed

            int m_NumIterationsPerformed

        The number of successfully generated base classifiers.

      - #### m_WeightThreshold

            int m_WeightThreshold

        Weight Threshold. The percentage of weight mass used in training

      - #### m_UseResampling

            boolean m_UseResampling

        Use boosting with reweighting?

      - #### m_NumClasses

            int m_NumClasses

        The number of classes

      - #### m_ZeroR

            Classifier m_ZeroR

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

      - #### m_TrainingData

            Instances m_TrainingData

        The (weighted) training data

      - #### m_RandomInstance

            java.util.Random m_RandomInstance

        Random number generator to be used for resampling

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

    serialVersionUID:
      -2368937577670527151L

    - ### Serialized Fields

      - #### m_Classifiers

            java.util.ArrayList<E> m_Classifiers

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

      - #### m_shrinkage

            double m_shrinkage

        Shrinkage (Learning rate). Default = no shrinkage.

      - #### m_zeroR

            ZeroR m_zeroR

        The model for the mean

      - #### m_SuitableData

            boolean m_SuitableData

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

      - #### m_Data

            Instances m_Data

        The working data

      - #### m_SSE

            double m_SSE

        The sum of squared errors

      - #### m_Diff

            double m_Diff

        The improvement in squared error

  - ### Class weka.classifiers.meta.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_Search

            ASSearch m_Search

        The search method to use

      - #### m_ReducedHeader

            Instances m_ReducedHeader

        The header of the dimensionally reduced data

      - #### m_numClasses

            int m_numClasses

        The number of class vals in the training data (1 if class is numeric)

      - #### m_numAttributesSelected

            double m_numAttributesSelected

        The number of attributes selected by the attribute selection phase

      - #### m_selectionTime

            double m_selectionTime

        The time taken to select attributes in milliseconds

      - #### m_totalTime

            double m_totalTime

        The time taken to select attributes AND build the classifier

  - ### Class weka.classifiers.meta.Bagging extends RandomizableParallelIteratedSingleClassifierEnhancer implements Serializable

    serialVersionUID:
      -115879962237199703L

    - ### Serialized Fields

      - #### m_BagSizePercent

            int m_BagSizePercent

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

      - #### m_CalcOutOfBag

            boolean m_CalcOutOfBag

        Whether to calculate the out of bag error

      - #### m_RepresentUsingWeights

            boolean m_RepresentUsingWeights

        Whether to represent copies of instances using weights rather than explicitly

      - #### m_OutOfBagError

            double m_OutOfBagError

        The out of bag error that has been calculated

      - #### m_random

            java.util.Random m_random

      - #### m_inBag

            boolean[][] m_inBag

      - #### m_data

            Instances m_data

      - #### m_classifiersCache

            java.util.List<E> m_classifiersCache

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

    serialVersionUID:
      4500023123618669859L

    - ### Serialized Fields

      - #### m_Classifiers

            Classifier[] m_Classifiers

        The classifiers. (One for each class.)

      - #### m_ClassFilters

            MakeIndicator[] m_ClassFilters

        The filters used to transform the class.

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

    serialVersionUID:
      -110658209263002404L

    - ### Serialized Fields

      - #### m_MatrixSource

            int m_MatrixSource

        Indicates the current cost matrix source

      - #### m_OnDemandDirectory

            java.io.File m_OnDemandDirectory

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

      - #### m_CostFile

            java.lang.String m_CostFile

        The name of the cost file, for command line options

      - #### m_CostMatrix

            CostMatrix m_CostMatrix

        The cost matrix

      - #### m_MinimizeExpectedCost

            boolean m_MinimizeExpectedCost

        True if the costs should be used by selecting the minimum expected cost (false means weight training data by the costs)

  - ### Class weka.classifiers.meta.CVParameterSelection extends RandomizableSingleClassifierEnhancer implements Serializable

    serialVersionUID:
      -6529603380876641265L

    - ### Serialized Fields

      - #### m_ClassifierOptions

            java.lang.String[] m_ClassifierOptions

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

      - #### m_BestClassifierOptions

            java.lang.String[] m_BestClassifierOptions

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

      - #### m_InitOptions

            java.lang.String[] m_InitOptions

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

      - #### m_BestPerformance

            double m_BestPerformance

        The cross-validated performance of the best options

      - #### m_CVParams

            java.util.Vector<E> m_CVParams

        The set of parameters to cross-validate over

      - #### m_NumAttributes

            int m_NumAttributes

        The number of attributes in the data

      - #### m_TrainFoldSize

            int m_TrainFoldSize

        The number of instances in a training fold

      - #### m_NumFolds

            int m_NumFolds

        The number of folds used in cross-validation

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

    serialVersionUID:
      -4668812017709421953L

    - ### Serialized Fields

      - #### m_ParamChar

            java.lang.String m_ParamChar

        Char used to identify the option of interest

      - #### m_Lower

            double m_Lower

        Lower bound for the CV search

      - #### m_Upper

            double m_Upper

        Upper bound for the CV search

      - #### m_Steps

            double m_Steps

        Number of steps during the search

      - #### m_ParamValue

            double m_ParamValue

        The parameter value with the best performance

      - #### m_AddAtEnd

            boolean m_AddAtEnd

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

      - #### m_RoundParam

            boolean m_RoundParam

        True if the parameter should be rounded to an integer

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

    serialVersionUID:
      -4523450618538717400L

    - ### Serialized Fields

      - #### m_Filter

            Filter m_Filter

        The filter

      - #### m_FilteredInstances

            Instances m_FilteredInstances

        The instance structure of the filtered instances

  - ### Class weka.classifiers.meta.IterativeClassifierOptimizer extends RandomizableClassifier implements Serializable

    serialVersionUID:
      -3665485256313525864L

    - ### Serialized Fields

      - #### m_IterativeClassifier

            IterativeClassifier m_IterativeClassifier

        The base classifier to use

      - #### 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_StepSize

            int m_StepSize

        The steps size determining when evaluations happen.

      - #### m_UseAverage

            boolean m_UseAverage

        Whether to use average.

      - #### m_lookAheadIterations

            int m_lookAheadIterations

        The number of iterations to look ahead for to find a better optimum.

      - #### m_evalMetric

            java.lang.String m_evalMetric

        The evaluation metric to use

      - #### 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_thresholds

            double[] m_thresholds

        The thresholds to be used for classification, if the metric implements ThresholdProducingMetric.

      - #### m_bestResult

            double m_bestResult

        The best value found for the criterion to be optimized.

      - #### m_bestNumIts

            int m_bestNumIts

        The best number of iterations identified.

      - #### 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.

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

    serialVersionUID:
      -1105660358715833753L

    - ### Serialized Fields

      - #### m_Classifiers

            java.util.ArrayList<E> m_Classifiers

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

      - #### m_NumClasses

            int m_NumClasses

        The number of classes

      - #### m_NumGenerated

            int m_NumGenerated

        The number of successfully generated base classifiers.

      - #### m_WeightThreshold

            int m_WeightThreshold

        Weight thresholding. The percentage of weight mass used in training

      - #### m_NumericClassData

            Instances m_NumericClassData

        Dummy dataset with a numeric class

      - #### m_ClassAttribute

            Attribute m_ClassAttribute

        The actual class attribute (for getting class names)

      - #### m_UseResampling

            boolean m_UseResampling

        Use boosting with reweighting?

      - #### m_Precision

            double m_Precision

        The threshold on the improvement of the likelihood

      - #### m_Shrinkage

            double m_Shrinkage

        The value of the shrinkage parameter

      - #### m_RandomInstance

            java.util.Random m_RandomInstance

        The random number generator used

      - #### m_Offset

            double m_Offset

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

      - #### m_ZeroR

            Classifier m_ZeroR

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

      - #### m_zMax

            double m_zMax

        The Z max value to use

      - #### m_trainYs

            double[][] m_trainYs

        The y values used during the training process.

      - #### m_trainFs

            double[][] m_trainFs

        The F scores used during the training process.

      - #### m_probs

            double[][] m_probs

        The probabilities used during the training process.

      - #### m_logLikelihood

            double m_logLikelihood

        The current loglikelihood.

      - #### m_sumOfWeights

            double m_sumOfWeights

        The total weight of the data.

      - #### m_data

            Instances m_data

        The training data.

      - #### m_numThreads

            int m_numThreads

        The number of threads to use at prediction time in batch prediction.

      - #### m_poolSize

            int m_poolSize

        The size of the thread pool.

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

    serialVersionUID:
      -3879602011542849141L

    - ### Serialized Fields

      - #### m_Classifiers

            Classifier[] m_Classifiers

        The classifiers.

      - #### m_pairwiseCoupling

            boolean m_pairwiseCoupling

        Use pairwise coupling with 1-vs-1

      - #### m_SumOfWeights

            double[] m_SumOfWeights

        Needed for pairwise coupling

      - #### m_ClassFilters

            Filter[] m_ClassFilters

        The filters used to transform the class.

      - #### m_ZeroR

            ZeroR m_ZeroR

        ZeroR classifier for when all base classifier return zero probability.

      - #### m_ClassAttribute

            Attribute m_ClassAttribute

        Internal copy of the class attribute for output purposes

      - #### m_TwoClassDataset

            Instances m_TwoClassDataset

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

      - #### m_RandomWidthFactor

            double m_RandomWidthFactor

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

      - #### m_Method

            int m_Method

        The multiclass method to use

  - ### Class weka.classifiers.meta.MultiClassClassifierUpdateable extends MultiClassClassifier implements Serializable

    serialVersionUID:
      -1619685269774366430L

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

    serialVersionUID:
      5710744346128957520L

    - ### Serialized Fields

      - #### m_Classifier

            Classifier m_Classifier

        The classifier that had the best performance on training data.

      - #### m_ClassifierIndex

            int m_ClassifierIndex

        The index into the vector for the selected scheme

      - #### m_NumXValFolds

            int m_NumXValFolds

        Number of folds to use for cross validation (0 means use training error for selection)

  - ### Class weka.classifiers.meta.RandomCommittee extends RandomizableParallelIteratedSingleClassifierEnhancer implements Serializable

    serialVersionUID:
      -9204394360557300093L

    - ### Serialized Fields

      - #### m_data

            Instances m_data

        training data

  - ### Class weka.classifiers.meta.RandomizableFilteredClassifier extends FilteredClassifier implements Serializable

    serialVersionUID:
      -4523466618555717333L

    - ### Serialized Fields

      - #### m_Seed

            int m_Seed

        The random number seed.

  - ### Class weka.classifiers.meta.RandomSubSpace extends RandomizableParallelIteratedSingleClassifierEnhancer implements Serializable

    serialVersionUID:
      1278172513912424947L

    - ### Serialized Fields

      - #### m_SubSpaceSize

            double m_SubSpaceSize

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

      - #### m_ZeroR

            Classifier m_ZeroR

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

      - #### m_data

            Instances m_data

        Training data

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

    serialVersionUID:
      5066426153134050378L

    - ### Serialized Fields

      - #### m_Discretizer

            Discretize m_Discretizer

        The discretization filter.

      - #### m_NumBins

            int m_NumBins

        The number of discretization intervals.

      - #### m_ClassMeans

            double[] m_ClassMeans

        The mean values for each Discretized class interval.

      - #### m_ClassCounts

            int[] m_ClassCounts

        The class counts for each Discretized class interval.

      - #### m_DeleteEmptyBins

            boolean m_DeleteEmptyBins

        Whether to delete empty intervals.

      - #### m_OldIndexToNewIndex

            int[] m_OldIndexToNewIndex

        Mapping to convert indices in case empty bins are deleted.

      - #### m_DiscretizedHeader

            Instances m_DiscretizedHeader

        Header of discretized data.

      - #### m_UseEqualFrequency

            boolean m_UseEqualFrequency

        Use equal-frequency binning

      - #### m_MinimizeAbsoluteError

            boolean m_MinimizeAbsoluteError

        Whether to minimize absolute error, rather than squared error.

      - #### m_Estimator

            UnivariateDensityEstimator m_Estimator

        Which estimator to use (default: histogram)

      - #### m_OriginalTargetValues

            double[] m_OriginalTargetValues

        The original target values in the training data

      - #### m_NewTargetValues

            int[] m_NewTargetValues

        The converted target values in the training data

  - ### Class weka.classifiers.meta.Stacking extends RandomizableParallelMultipleClassifiersCombiner implements Serializable

    serialVersionUID:
      5134738557155845452L

    - ### Serialized Fields

      - #### m_MetaClassifier

            Classifier m_MetaClassifier

        The meta classifier

      - #### m_MetaFormat

            Instances m_MetaFormat

        Format for meta data

      - #### m_BaseFormat

            Instances m_BaseFormat

        Format for base data

      - #### m_NumFolds

            int m_NumFolds

        Set the number of folds for the cross-validation

  - ### Class weka.classifiers.meta.Vote extends RandomizableMultipleClassifiersCombiner implements Serializable

    serialVersionUID:
      -637891196294399624L

    - ### Serialized Fields

      - #### m_CombinationRule

            int m_CombinationRule

        Combination Rule variable

      - #### m_Random

            java.util.Random m_Random

        the random number generator used for breaking ties in majority voting
        See Also:
          `Vote.distributionForInstanceMajorityVoting(Instance)`

      - #### m_classifiersToLoad

            java.util.List<E> m_classifiersToLoad

        List of file paths to serialized models to load

      - #### m_preBuiltClassifiers

            java.util.List<E> m_preBuiltClassifiers

        List of de-serialized pre-built classifiers to include in the ensemble

      - #### m_structure

            Instances m_structure

        Structure of the training data

      - #### m_dontPrintModels

            boolean m_dontPrintModels

        Print the individual models in the output

- ## Package weka.classifiers.misc

  - ### Class weka.classifiers.misc.InputMappedClassifier extends SingleClassifierEnhancer implements Serializable

    serialVersionUID:
      4901630631723287761L

    - ### Serialized Fields

      - #### m_modelPath

            java.lang.String m_modelPath

        The path to the serialized model to use (if any)

      - #### m_modelHeader

            Instances m_modelHeader

        The instances structure used to train the classifier with

      - #### m_trim

            boolean m_trim

        Trim white space from both ends of attribute names and nominal values?

      - #### m_ignoreCase

            boolean m_ignoreCase

        Ignore case when matching attribute names and nominal values?

      - #### m_suppressMappingReport

            boolean m_suppressMappingReport

        Dont output mapping report if set to true

      - #### 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_vals

            double[] m_vals

        Holds values for instances constructed for prediction

  - ### Class weka.classifiers.misc.SerializedClassifier extends AbstractClassifier implements Serializable

    serialVersionUID:
      4599593909947628642L

    - ### Serialized Fields

      - #### m_ModelFile

            java.io.File m_ModelFile

        the file where the serialized model is stored

- ## Package weka.classifiers.pmml.consumer

  - ### Class weka.classifiers.pmml.consumer.GeneralRegression extends PMMLClassifier implements Serializable

    serialVersionUID:
      2583880411828388959L

    - ### Serialized Fields

      - #### m_modelType

            weka.classifiers.pmml.consumer.GeneralRegression.ModelType m_modelType

      - #### m_modelName

            java.lang.String m_modelName

      - #### m_algorithmName

            java.lang.String m_algorithmName

      - #### m_functionType

            int m_functionType

      - #### m_cumulativeLinkFunction

            weka.classifiers.pmml.consumer.GeneralRegression.CumulativeLinkFunction m_cumulativeLinkFunction

      - #### m_linkFunction

            weka.classifiers.pmml.consumer.GeneralRegression.LinkFunction m_linkFunction

      - #### m_linkParameter

            double m_linkParameter

      - #### m_trialsVariable

            java.lang.String m_trialsVariable

      - #### m_trialsValue

            double m_trialsValue

      - #### m_distribution

            weka.classifiers.pmml.consumer.GeneralRegression.Distribution m_distribution

      - #### m_distParameter

            double m_distParameter

      - #### m_offsetVariable

            java.lang.String m_offsetVariable

      - #### m_offsetValue

            double m_offsetValue

      - #### m_parameterList

            java.util.ArrayList<E> m_parameterList

      - #### m_factorList

            java.util.ArrayList<E> m_factorList

      - #### m_covariateList

            java.util.ArrayList<E> m_covariateList

      - #### m_ppMatrix

            weka.classifiers.pmml.consumer.GeneralRegression.PPCell[][] m_ppMatrix

      - #### m_paramMatrix

            weka.classifiers.pmml.consumer.GeneralRegression.PCell[][] m_paramMatrix

  - ### Class weka.classifiers.pmml.consumer.NeuralNetwork extends PMMLClassifier implements Serializable

    serialVersionUID:
      -4545904813133921249L

    - ### Serialized Fields

      - #### m_functionType

            weka.classifiers.pmml.consumer.NeuralNetwork.MiningFunction m_functionType

        The mining function

      - #### m_activationFunction

            weka.classifiers.pmml.consumer.NeuralNetwork.ActivationFunction m_activationFunction

        The activation function to use

      - #### m_normalizationMethod

            weka.classifiers.pmml.consumer.NeuralNetwork.Normalization m_normalizationMethod

        The normalization method

      - #### m_threshold

            double m_threshold

        Threshold activation

      - #### m_width

            double m_width

        Width for radial basis

      - #### m_altitude

            double m_altitude

        Altitude for radial basis

      - #### m_numberOfInputs

            int m_numberOfInputs

        The number of inputs to the network

      - #### m_numberOfLayers

            int m_numberOfLayers

        Number of hidden layers in the network

      - #### m_inputs

            weka.classifiers.pmml.consumer.NeuralNetwork.NeuralInput[] m_inputs

        The inputs to the network

      - #### m_inputMap

            java.util.HashMap<K,V> m_inputMap

        A map for storing network input values (computed from an incoming instance)

      - #### m_layers

            weka.classifiers.pmml.consumer.NeuralNetwork.NeuralLayer[] m_layers

        The hidden layers in the network

      - #### m_outputs

            weka.classifiers.pmml.consumer.NeuralNetwork.NeuralOutputs m_outputs

        The outputs of the network

  - ### Class weka.classifiers.pmml.consumer.PMMLClassifier extends AbstractClassifier implements Serializable

    serialVersionUID:
      -5371600590320702971L

    - ### Serialized Fields

      - #### m_pmmlVersion

            java.lang.String m_pmmlVersion

        PMML version

      - #### m_creatorApplication

            java.lang.String m_creatorApplication

        Creator application

      - #### m_log

            Logger m_log

        Logger

      - #### m_dataDictionary

            Instances m_dataDictionary

        The data dictionary

      - #### m_miningSchema

            MiningSchema m_miningSchema

        The fields and meta data used by the model

  - ### Class weka.classifiers.pmml.consumer.Regression extends PMMLClassifier implements Serializable

    serialVersionUID:
      -5551125528409488634L

    - ### Serialized Fields

      - #### m_algorithmName

            java.lang.String m_algorithmName

        Description of the algorithm

      - #### m_regressionTables

            weka.classifiers.pmml.consumer.Regression.RegressionTable[] m_regressionTables

        The regression tables for this regression

      - #### m_normalizationMethod

            weka.classifiers.pmml.consumer.Regression.Normalization m_normalizationMethod

        The normalization to use

  - ### Class weka.classifiers.pmml.consumer.Regression.RegressionTable.CategoricalPredictor extends weka.classifiers.pmml.consumer.Regression.RegressionTable.Predictor implements Serializable

    serialVersionUID:
      3077920125549906819L

    - ### Serialized Fields

      - #### m_valueName

            java.lang.String m_valueName

        The attribute value for this predictor

      - #### m_valueIndex

            int m_valueIndex

        The index of the attribute value for this predictor

  - ### Class weka.classifiers.pmml.consumer.Regression.RegressionTable.NumericPredictor extends weka.classifiers.pmml.consumer.Regression.RegressionTable.Predictor implements Serializable

    serialVersionUID:
      -4335075205696648273L

    - ### Serialized Fields

      - #### m_exponent

            double m_exponent

        The exponent

  - ### Class weka.classifiers.pmml.consumer.Regression.RegressionTable.PredictorTerm extends java.lang.Object implements Serializable

    serialVersionUID:
      5493100145890252757L

    - ### Serialized Fields

      - #### m_coefficient

            double m_coefficient

        The coefficient for this predictor term

      - #### m_indexes

            int[] m_indexes

        the indexes of the terms to be multiplied

      - #### m_fieldNames

            java.lang.String[] m_fieldNames

        The names of the terms (attributes) to be multiplied

  - ### Class weka.classifiers.pmml.consumer.RuleSetModel extends PMMLClassifier implements Serializable

    serialVersionUID:
      1993161168811020547L

    - ### Serialized Fields

      - #### m_functionType

            weka.classifiers.pmml.consumer.TreeModel.MiningFunction m_functionType

        The mining function

      - #### m_modelName

            java.lang.String m_modelName

        The model name (if defined)

      - #### m_algorithmName

            java.lang.String m_algorithmName

        The algorithm name (if defined)

      - #### m_ruleSet

            weka.classifiers.pmml.consumer.RuleSetModel.RuleSet m_ruleSet

        The set of rules

  - ### Class weka.classifiers.pmml.consumer.SupportVectorMachineModel extends PMMLClassifier implements Serializable

    serialVersionUID:
      6225095165118374296L

    - ### Serialized Fields

      - #### m_functionType

            weka.classifiers.pmml.consumer.NeuralNetwork.MiningFunction m_functionType

        The mining function

      - #### m_classificationMethod

            weka.classifiers.pmml.consumer.SupportVectorMachineModel.classificationMethod m_classificationMethod

        The classification method (PMML 4.0)

      - #### m_modelName

            java.lang.String m_modelName

        The model name (if defined)

      - #### m_algorithmName

            java.lang.String m_algorithmName

        The algorithm name (if defined)

      - #### m_vectorDictionary

            VectorDictionary m_vectorDictionary

        The dictionary of support vectors

      - #### m_kernel

            weka.classifiers.pmml.consumer.SupportVectorMachineModel.Kernel m_kernel

        The kernel function to use

      - #### m_machines

            java.util.List<E> m_machines

        The individual binary SVMs

      - #### m_alternateBinaryTargetCategory

            int m_alternateBinaryTargetCategory

        The other class index (in the case of a single binary SVM - PMML 3.2).

      - #### 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

  - ### Class weka.classifiers.pmml.consumer.TreeModel extends PMMLClassifier implements Serializable

    serialVersionUID:
      -2065158088298753129L

    - ### Serialized Fields

      - #### m_functionType

            weka.classifiers.pmml.consumer.TreeModel.MiningFunction m_functionType

        The mining function

      - #### m_missingValueStrategy

            weka.classifiers.pmml.consumer.TreeModel.MissingValueStrategy m_missingValueStrategy

        The missing value strategy

      - #### 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_noTrueChildStrategy

            weka.classifiers.pmml.consumer.TreeModel.NoTrueChildStrategy m_noTrueChildStrategy

        The no true child strategy to use

      - #### m_splitCharacteristic

            weka.classifiers.pmml.consumer.TreeModel.SplitCharacteristic m_splitCharacteristic

        The splitting type

      - #### m_root

            weka.classifiers.pmml.consumer.TreeModel.TreeNode m_root

        The root of the tree

- ## Package weka.classifiers.rules

  - ### Class weka.classifiers.rules.DecisionTable extends AbstractClassifier implements Serializable

    serialVersionUID:
      2888557078165701326L

    - ### Serialized Fields

      - #### m_entries

            java.util.Hashtable<K,V> m_entries

        The hashtable used to hold training instances

      - #### m_classPriorCounts

            double[] m_classPriorCounts

        The class priors to use when there is no match in the table

      - #### m_classPriors

            double[] m_classPriors

      - #### m_decisionFeatures

            int[] m_decisionFeatures

        Holds the final feature set

      - #### m_disTransform

            Filter m_disTransform

        Discretization filter

      - #### m_delTransform

            Remove m_delTransform

        Filter used to remove columns discarded by feature selection

      - #### m_ibk

            IBk m_ibk

        IB1 used to classify non matching instances rather than majority class

      - #### m_theInstances

            Instances m_theInstances

        Holds the original training instances

      - #### m_dtInstances

            Instances m_dtInstances

        Holds the final feature selected set of instances

      - #### m_numAttributes

            int m_numAttributes

        The number of attributes in the dataset

      - #### m_numInstances

            int m_numInstances

        The number of instances in the dataset

      - #### m_classIsNominal

            boolean m_classIsNominal

        Class is nominal

      - #### m_useIBk

            boolean m_useIBk

        Use the IBk classifier rather than majority class

      - #### m_displayRules

            boolean m_displayRules

        Display Rules

      - #### m_CVFolds

            int m_CVFolds

        Number of folds for cross validating feature sets

      - #### m_rr

            java.util.Random m_rr

        Random numbers for use in cross validation

      - #### m_majority

            double m_majority

        Holds the majority class

      - #### m_search

            ASSearch m_search

        The search method to use

      - #### m_evaluator

            ASEvaluation m_evaluator

        Our own internal evaluator

      - #### m_evaluation

            Evaluation m_evaluation

        The evaluation object used to evaluate subsets

      - #### m_evaluationMeasure

            int m_evaluationMeasure

      - #### m_saveMemory

            boolean m_saveMemory

  - ### Class weka.classifiers.rules.DecisionTableHashKey extends java.lang.Object implements Serializable

    serialVersionUID:
      5674163500154964602L

    - ### Serialized Fields

      - #### attributes

            double[] attributes

        Array of attribute values for an instance

      - #### missing

            boolean[] missing

        True for an index if the corresponding attribute value is missing.

      - #### key

            int key

        The key

  - ### Class weka.classifiers.rules.JRip extends AbstractClassifier implements Serializable

    serialVersionUID:
      -6589312996832147161L

    - ### Serialized Fields

      - #### m_Class

            Attribute m_Class

        The class attribute of the data

      - #### m_Ruleset

            java.util.ArrayList<E> m_Ruleset

        The ruleset

      - #### m_Distributions

            java.util.ArrayList<E> m_Distributions

        The predicted class distribution

      - #### m_Optimizations

            int m_Optimizations

        Runs of optimizations

      - #### m_Random

            java.util.Random m_Random

        Random object used in this class

      - #### m_Total

            double m_Total

        # of all the possible conditions in a rule

      - #### m_Seed

            long m_Seed

        The seed to perform randomization

      - #### m_Folds

            int m_Folds

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

      - #### m_MinNo

            double m_MinNo

        The minimal number of instance weights within a split

      - #### m_Debug

            boolean m_Debug

        Whether in a debug mode

      - #### m_CheckErr

            boolean m_CheckErr

        Whether check the error rate >= 0.5 in stopping criteria

      - #### m_UsePruning

            boolean m_UsePruning

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

      - #### m_Filter

            Filter m_Filter

        The filter used to randomize the class order

      - #### m_RulesetStats

            java.util.ArrayList<E> m_RulesetStats

        The RuleStats for the ruleset of each class value

  - ### Class weka.classifiers.rules.JRip.Antd extends java.lang.Object implements Serializable

    serialVersionUID:
      -8929754772994154334L

    - ### Serialized Fields

      - #### att

            Attribute att

        The attribute of the antecedent

      - #### value

            double value

        The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)

      - #### maxInfoGain

            double maxInfoGain

        The maximum infoGain achieved by this antecedent test in the growing data

      - #### accuRate

            double accuRate

        The accurate rate of this antecedent test on the growing data

      - #### cover

            double cover

        The coverage of this antecedent in the growing data

      - #### accu

            double accu

        The accurate data for this antecedent in the growing data

  - ### Class weka.classifiers.rules.JRip.NominalAntd extends JRip.Antd implements Serializable

    serialVersionUID:
      -9102297038837585135L

    - ### Serialized Fields

      - #### accurate

            double[] accurate

      - #### coverage

            double[] coverage

  - ### Class weka.classifiers.rules.JRip.NumericAntd extends JRip.Antd implements Serializable

    serialVersionUID:
      5699457269983735442L

    - ### Serialized Fields

      - #### splitPoint

            double splitPoint

        The split point for this numeric antecedent

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

    serialVersionUID:
      -2410020717305262952L

    - ### Serialized Fields

      - #### m_Consequent

            double m_Consequent

        The internal representation of the class label to be predicted

      - #### m_Antds

            java.util.ArrayList<E> m_Antds

        The vector of antecedents of this rule

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

    serialVersionUID:
      -1746114858746563180L

  - ### Class weka.classifiers.rules.OneR extends AbstractClassifier implements Serializable

    serialVersionUID:
      -3459427003147861443L

    - ### Serialized Fields

      - #### m_rule

            weka.classifiers.rules.OneR.OneRRule m_rule

        A 1-R rule

      - #### m_minBucketSize

            int m_minBucketSize

        The minimum bucket size

      - #### m_ZeroR

            Classifier m_ZeroR

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

  - ### Class weka.classifiers.rules.PART extends AbstractClassifier implements Serializable

    serialVersionUID:
      8121455039782598361L

    - ### Serialized Fields

      - #### m_root

            MakeDecList m_root

        The decision list

      - #### m_CF

            float m_CF

        Confidence level

      - #### m_minNumObj

            int m_minNumObj

        Minimum number of objects

      - #### m_useMDLcorrection

            boolean m_useMDLcorrection

        Use MDL correction?

      - #### m_reducedErrorPruning

            boolean m_reducedErrorPruning

        Use reduced error pruning?

      - #### m_numFolds

            int m_numFolds

        Number of folds for reduced error pruning.

      - #### m_binarySplits

            boolean m_binarySplits

        Binary splits on nominal attributes?

      - #### m_unpruned

            boolean m_unpruned

        Generate unpruned list?

      - #### m_Seed

            int m_Seed

        The seed for random number generation.

      - #### m_doNotMakeSplitPointActualValue

            boolean m_doNotMakeSplitPointActualValue

        Do not relocate split point to actual data value

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

    serialVersionUID:
      8815687740470471229L

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

    serialVersionUID:
      -5708153367675298624L

    - ### Serialized Fields

      - #### m_Data

            Instances m_Data

        The data on which the stats calculation is based

      - #### m_Ruleset

            java.util.ArrayList<E> m_Ruleset

        The specific ruleset in question

      - #### m_SimpleStats

            java.util.ArrayList<E> m_SimpleStats

        The simple stats of each rule

      - #### m_Filtered

            java.util.ArrayList<E> m_Filtered

        The set of instances filtered by the ruleset

      - #### m_Total

            double m_Total

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

      - #### MDL_THEORY_WEIGHT

            double MDL_THEORY_WEIGHT

        The theory weight in the MDL calculation

      - #### m_Distributions

            java.util.ArrayList<E> m_Distributions

        The class distributions predicted by each rule

  - ### Class weka.classifiers.rules.ZeroR extends AbstractClassifier implements Serializable

    serialVersionUID:
      48055541465867954L

    - ### Serialized Fields

      - #### m_ClassValue

            double m_ClassValue

        The class value 0R predicts.

      - #### m_Counts

            double[] m_Counts

        The number of instances in each class (null if class numeric).

      - #### m_Class

            Attribute m_Class

        The class attribute.

- ## Package weka.classifiers.rules.part

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

    serialVersionUID:
      -2757684345218324559L

    - ### Serialized Fields

      - #### CF

            double CF

        CF

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

    serialVersionUID:
      7284358349711992497L

    - ### Serialized Fields

      - #### m_minNumObj

            int m_minNumObj

        Minimum number of objects

      - #### m_toSelectModel

            ModelSelection m_toSelectModel

        The model selection method.

      - #### m_localModel

            ClassifierSplitModel m_localModel

        Local model at node.

      - #### m_sons

            ClassifierDecList[] m_sons

        References to sons.

      - #### m_isLeaf

            boolean m_isLeaf

        True if node is leaf.

      - #### m_isEmpty

            boolean m_isEmpty

        True if node is empty.

      - #### m_train

            Instances m_train

        The training instances.

      - #### m_test

            Distribution m_test

        The pruning instances.

      - #### indeX

            int indeX

        Which son to expand?

  - ### Class weka.classifiers.rules.part.MakeDecList extends java.lang.Object implements Serializable

    serialVersionUID:
      -1427481323245079123L

    - ### Serialized Fields

      - #### theRules

            java.util.Vector<E> theRules

        Vector storing the rules.

      - #### CF

            double CF

        The confidence for C45-type pruning.

      - #### minNumObj

            int minNumObj

        Minimum number of objects

      - #### toSelectModeL

            ModelSelection toSelectModeL

        The model selection method.

      - #### numSetS

            int numSetS

        How many subsets of equal size? One used for pruning, the rest for training.

      - #### reducedErrorPruning

            boolean reducedErrorPruning

        Use reduced error pruning?

      - #### unpruned

            boolean unpruned

        Generated unpruned list?

      - #### m_seed

            int m_seed

        The seed for random number generation.

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

    serialVersionUID:
      -7228103346297172921L

- ## Package weka.classifiers.trees

  - ### Class weka.classifiers.trees.DecisionStump extends AbstractClassifier implements Serializable

    serialVersionUID:
      1618384535950391L

    - ### Serialized Fields

      - #### m_AttIndex

            int m_AttIndex

        The attribute used for classification.

      - #### m_SplitPoint

            double m_SplitPoint

        The split point (index respectively).

      - #### m_Distribution

            double[][] m_Distribution

        The distribution of class values or the means in each subset.

      - #### m_Instances

            Instances m_Instances

        The instances used for training.

      - #### m_ZeroR

            Classifier m_ZeroR

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

  - ### Class weka.classifiers.trees.HoeffdingTree extends AbstractClassifier implements Serializable

    serialVersionUID:
      7117521775722396251L

    - ### Serialized Fields

      - #### m_header

            Instances m_header

      - #### m_root

            HNode m_root

      - #### m_gracePeriod

            double m_gracePeriod

        The number of instances a leaf should observe between split attempts

      - #### m_splitConfidence

            double m_splitConfidence

        The allowable error in a split decision. Values closer to zero will take longer to decide

      - #### m_hoeffdingTieThreshold

            double m_hoeffdingTieThreshold

        Threshold below which a split will be forced to break ties

      - #### m_minFracWeightForTwoBranchesGain

            double m_minFracWeightForTwoBranchesGain

        The minimum fraction of weight required down at least two branches for info gain splitting

      - #### m_selectedSplitMetric

            int m_selectedSplitMetric

        The splitting metric to use

      - #### m_splitMetric

            SplitMetric m_splitMetric

      - #### m_leafStrategy

            int m_leafStrategy

        The leaf prediction strategy to use

      - #### m_nbThreshold

            double m_nbThreshold

        The number of instances (total weight) a leaf should observe before allowing naive Bayes to make predictions

      - #### m_activeLeafCount

            int m_activeLeafCount

      - #### m_inactiveLeafCount

            int m_inactiveLeafCount

      - #### m_decisionNodeCount

            int m_decisionNodeCount

      - #### m_printLeafModels

            boolean m_printLeafModels

        Print out leaf models in the case of naive Bayes or naive Bayes adaptive leaves

  - ### Class weka.classifiers.trees.J48 extends AbstractClassifier implements Serializable

    serialVersionUID:
      -217733168393644444L

    - ### Serialized Fields

      - #### m_root

            ClassifierTree m_root

        The decision tree

      - #### m_unpruned

            boolean m_unpruned

        Unpruned tree?

      - #### m_collapseTree

            boolean m_collapseTree

        Collapse tree?

      - #### m_CF

            float m_CF

        Confidence level

      - #### m_minNumObj

            int m_minNumObj

        Minimum number of instances

      - #### m_useMDLcorrection

            boolean m_useMDLcorrection

        Use MDL correction?

      - #### m_useLaplace

            boolean m_useLaplace

        Determines whether probabilities are smoothed using Laplace correction when predictions are generated

      - #### m_reducedErrorPruning

            boolean m_reducedErrorPruning

        Use reduced error pruning?

      - #### m_numFolds

            int m_numFolds

        Number of folds for reduced error pruning.

      - #### m_binarySplits

            boolean m_binarySplits

        Binary splits on nominal attributes?

      - #### m_subtreeRaising

            boolean m_subtreeRaising

        Subtree raising to be performed?

      - #### m_noCleanup

            boolean m_noCleanup

        Cleanup after the tree has been built.

      - #### m_Seed

            int m_Seed

        Random number seed for reduced-error pruning.

      - #### m_doNotMakeSplitPointActualValue

            boolean m_doNotMakeSplitPointActualValue

        Do not relocate split point to actual data value

  - ### Class weka.classifiers.trees.LMT extends AbstractClassifier implements Serializable

    serialVersionUID:
      -1113212459618104943L

    - ### Serialized Fields

      - #### m_replaceMissing

            ReplaceMissingValues m_replaceMissing

        Filter to replace missing values

      - #### m_nominalToBinary

            NominalToBinary m_nominalToBinary

        Filter to replace nominal attributes

      - #### m_tree

            LMTNode m_tree

        root of the logistic model tree

      - #### m_fastRegression

            boolean m_fastRegression

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

      - #### m_convertNominal

            boolean m_convertNominal

        convert nominal attributes to binary ?

      - #### m_splitOnResiduals

            boolean m_splitOnResiduals

        split on residuals?

      - #### m_errorOnProbabilities

            boolean m_errorOnProbabilities

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

      - #### m_minNumInstances

            int m_minNumInstances

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

      - #### m_numBoostingIterations

            int m_numBoostingIterations

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

      - #### m_weightTrimBeta

            double m_weightTrimBeta

        Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.

      - #### m_useAIC

            boolean m_useAIC

        If true, the AIC is used to choose the best LogitBoost iteration

      - #### m_doNotMakeSplitPointActualValue

            boolean m_doNotMakeSplitPointActualValue

        Do not relocate split point to actual data value

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

    serialVersionUID:
      -6118439039768244417L

  - ### Class weka.classifiers.trees.RandomForest extends AbstractClassifier implements Serializable

    serialVersionUID:
      1116839470751428698L

    - ### Serialized Fields

      - #### m_numTrees

            int m_numTrees

        Number of trees in forest.

      - #### m_numFeatures

            int m_numFeatures

        Number of features to consider in random feature selection. If less than 1 will use int(log_2(M)+1) )

      - #### m_randomSeed

            int m_randomSeed

        The random seed.

      - #### m_KValue

            int m_KValue

        Final number of features that were considered in last build.

      - #### m_bagger

            Bagging m_bagger

        The bagger.

      - #### m_MaxDepth

            int m_MaxDepth

        The maximum depth of the trees (0 = unlimited)

      - #### m_numExecutionSlots

            int m_numExecutionSlots

        The number of threads to have executing at any one time

      - #### m_printTrees

            boolean m_printTrees

        Print the individual trees in the output

      - #### m_dontCalculateOutOfBagError

            boolean m_dontCalculateOutOfBagError

        Don't calculate the out of bag error

  - ### Class weka.classifiers.trees.RandomTree extends AbstractClassifier implements Serializable

    serialVersionUID:
      -9051119597407396024L

    - ### Serialized Fields

      - #### m_Tree

            weka.classifiers.trees.RandomTree.Tree m_Tree

        The Tree object

      - #### m_Info

            Instances m_Info

        The header information.

      - #### m_MinNum

            double m_MinNum

        Minimum number of instances for leaf.

      - #### m_KValue

            int m_KValue

        The number of attributes considered for a split.

      - #### m_randomSeed

            int m_randomSeed

        The random seed to use.

      - #### m_MaxDepth

            int m_MaxDepth

        The maximum depth of the tree (0 = unlimited)

      - #### m_NumFolds

            int m_NumFolds

        Determines how much data is used for backfitting

      - #### m_AllowUnclassifiedInstances

            boolean m_AllowUnclassifiedInstances

        Whether unclassified instances are allowed

      - #### m_zeroR

            Classifier m_zeroR

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

      - #### m_MinVarianceProp

            double m_MinVarianceProp

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

  - ### Class weka.classifiers.trees.RandomTree.Tree extends java.lang.Object implements Serializable

    serialVersionUID:
      3549573538656522569L

    - ### Serialized Fields

      - #### m_Successors

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

        The subtrees appended to this tree.

      - #### m_Attribute

            int m_Attribute

        The attribute to split on.

      - #### m_SplitPoint

            double m_SplitPoint

        The split point.

      - #### m_Prop

            double[] m_Prop

        The proportions of training instances going down each branch.

      - #### m_ClassDistribution

            double[] m_ClassDistribution

        Class probabilities from the training data 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.

  - ### Class weka.classifiers.trees.REPTree extends AbstractClassifier implements Serializable

    serialVersionUID:
      -9216785998198681299L

    - ### Serialized Fields

      - #### m_zeroR

            ZeroR m_zeroR

        ZeroR model that is used if no attributes are present.

      - #### m_Tree

            weka.classifiers.trees.REPTree.Tree m_Tree

        The Tree object

      - #### m_NumFolds

            int m_NumFolds

        Number of folds for reduced error pruning.

      - #### m_Seed

            int m_Seed

        Seed for random data shuffling.

      - #### m_NoPruning

            boolean m_NoPruning

        Don't prune

      - #### m_MinNum

            double m_MinNum

        The minimum number of instances per leaf.

      - #### m_MinVarianceProp

            double m_MinVarianceProp

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

      - #### m_MaxDepth

            int m_MaxDepth

        Upper bound on the tree depth

      - #### m_InitialCount

            double m_InitialCount

        The initial class count

      - #### m_SpreadInitialCount

            boolean m_SpreadInitialCount

        Whether to spread initial count across all values

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

    serialVersionUID:
      -1635481717888437935L

    - ### Serialized Fields

      - #### m_Info

            Instances m_Info

        The header information (for printing the tree).

      - #### m_Successors

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

        The subtrees of this tree.

      - #### m_Attribute

            int m_Attribute

        The attribute to split on.

      - #### m_SplitPoint

            double m_SplitPoint

        The split point.

      - #### m_Prop

            double[] m_Prop

        The proportions of training instances going down each branch.

      - #### m_ClassProbs

            double[] m_ClassProbs

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

      - #### m_Distribution

            double[] m_Distribution

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

      - #### m_HoldOutDist

            double[] m_HoldOutDist

        Class distribution of hold-out set at node in the nominal case. Straight sum of weights plus sum of weighted targets in the numeric case (i.e. array has only two elements).

      - #### m_HoldOutError

            double m_HoldOutError

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

- ## Package weka.classifiers.trees.ht

  - ### Class weka.classifiers.trees.ht.ActiveHNode extends LeafNode implements Serializable

    serialVersionUID:
      3284585939739561683L

    - ### Serialized Fields

      - #### m_weightSeenAtLastSplitEval

            double m_weightSeenAtLastSplitEval

        The weight of instances seen at the last split evaluation

      - #### m_nodeStats

            java.util.Map<K,V> m_nodeStats

        Statistics for nominal or numeric attributes conditioned on the class

  - ### Class weka.classifiers.trees.ht.ConditionalSufficientStats extends java.lang.Object implements Serializable

    serialVersionUID:
      8724787722646808376L

    - ### Serialized Fields

      - #### m_classLookup

            java.util.Map<K,V> m_classLookup

        Lookup by class value

  - ### Class weka.classifiers.trees.ht.GaussianConditionalSufficientStats extends ConditionalSufficientStats implements Serializable

    serialVersionUID:
      -1527915607201784762L

    - ### Serialized Fields

      - #### m_minValObservedPerClass

            java.util.Map<K,V> m_minValObservedPerClass

      - #### m_maxValObservedPerClass

            java.util.Map<K,V> m_maxValObservedPerClass

      - #### m_numBins

            int m_numBins

  - ### Class weka.classifiers.trees.ht.GaussianConditionalSufficientStats.GaussianEstimator extends UnivariateNormalEstimator implements Serializable

    serialVersionUID:
      4756032800685001315L

  - ### Class weka.classifiers.trees.ht.GiniSplitMetric extends SplitMetric implements Serializable

    serialVersionUID:
      -2037586582742660298L

  - ### Class weka.classifiers.trees.ht.HNode extends java.lang.Object implements Serializable

    serialVersionUID:
      197233928177240264L

    - ### Serialized Fields

      - #### m_classDistribution

            java.util.Map<K,V> m_classDistribution

        Class distribution at this node

      - #### m_leafNum

            int m_leafNum

        Holds the leaf number (if this is a leaf)

      - #### m_nodeNum

            int m_nodeNum

        Holds the node number (for graphing purposes)

  - ### Class weka.classifiers.trees.ht.InactiveHNode extends LeafNode implements Serializable

    serialVersionUID:
      -8747567733141700911L

  - ### Class weka.classifiers.trees.ht.InfoGainSplitMetric extends SplitMetric implements Serializable

    serialVersionUID:
      2173840581308675428L

    - ### Serialized Fields

      - #### m_minFracWeightForTwoBranches

            double m_minFracWeightForTwoBranches

  - ### Class weka.classifiers.trees.ht.LeafNode extends HNode implements Serializable

    serialVersionUID:
      -3359429731894384404L

    - ### Serialized Fields

      - #### m_theNode

            HNode m_theNode

        The actual node for this leaf

      - #### m_parentNode

            SplitNode m_parentNode

        Parent split node

      - #### m_parentBranch

            java.lang.String m_parentBranch

        Parent branch leading to this node

  - ### Class weka.classifiers.trees.ht.NBNode extends ActiveHNode implements Serializable

    serialVersionUID:
      -1872415764817690961L

    - ### Serialized Fields

      - #### m_bayes

            NaiveBayesUpdateable m_bayes

        The naive Bayes model at the node

      - #### m_nbWeightThreshold

            double m_nbWeightThreshold

        The weight of instances that need to be seen by this node before allowing naive Bayes to make predictions

  - ### Class weka.classifiers.trees.ht.NBNodeAdaptive extends NBNode implements Serializable

    serialVersionUID:
      -4509802312019989686L

    - ### Serialized Fields

      - #### m_majClassCorrectWeight

            double m_majClassCorrectWeight

        The number of correct predictions made by the majority class

      - #### m_nbCorrectWeight

            double m_nbCorrectWeight

        The number of correct predictions made by naive Bayes

  - ### Class weka.classifiers.trees.ht.NominalConditionalSufficientStats extends ConditionalSufficientStats implements Serializable

    serialVersionUID:
      -669902060601313488L

    - ### Serialized Fields

      - #### m_totalWeight

            double m_totalWeight

      - #### m_missingWeight

            double m_missingWeight

  - ### Class weka.classifiers.trees.ht.NominalConditionalSufficientStats.ValueDistribution extends java.lang.Object implements Serializable

    serialVersionUID:
      -61711544350888154L

    - ### Serialized Fields

      - #### m_dist

            java.util.Map<K,V> m_dist

      - #### m_sum

            double m_sum

  - ### Class weka.classifiers.trees.ht.Split extends java.lang.Object implements Serializable

    serialVersionUID:
      5390368487675958092L

    - ### Serialized Fields

      - #### m_splitAttNames

            java.util.List<E> m_splitAttNames

        name(s) of attribute(s) involved in the split

  - ### Class weka.classifiers.trees.ht.SplitMetric extends java.lang.Object implements Serializable

    serialVersionUID:
      2891555018707080818L

  - ### Class weka.classifiers.trees.ht.SplitNode extends HNode implements Serializable

    serialVersionUID:
      1558033628618451073L

    - ### Serialized Fields

      - #### m_split

            Split m_split

        The split itself

      - #### m_children

            java.util.Map<K,V> m_children

        Child nodes

  - ### Class weka.classifiers.trees.ht.UnivariateNominalMultiwaySplit extends Split implements Serializable

    serialVersionUID:
      -9094590488097956665L

  - ### Class weka.classifiers.trees.ht.UnivariateNumericBinarySplit extends Split implements Serializable

    serialVersionUID:
      -7392204582942741097L

    - ### Serialized Fields

      - #### m_splitPoint

            double m_splitPoint

        The split point

  - ### Class weka.classifiers.trees.ht.WeightMass extends java.lang.Object implements Serializable

    serialVersionUID:
      6794839107050779425L

    - ### Serialized Fields

      - #### m_weight

            double m_weight

- ## Package weka.classifiers.trees.j48

  - ### Class weka.classifiers.trees.j48.BinC45ModelSelection extends ModelSelection implements Serializable

    serialVersionUID:
      179170923545122001L

    - ### Serialized Fields

      - #### m_minNoObj

            int m_minNoObj

        Minimum number of instances in interval.

      - #### m_useMDLcorrection

            boolean m_useMDLcorrection

        Use MDL correction?

      - #### m_allData

            Instances m_allData

        The FULL training dataset.

      - #### m_doNotMakeSplitPointActualValue

            boolean m_doNotMakeSplitPointActualValue

        Do not relocate split point to actual data value

  - ### Class weka.classifiers.trees.j48.BinC45Split extends ClassifierSplitModel implements Serializable

    serialVersionUID:
      -1278776919563022474L

    - ### Serialized Fields

      - #### m_attIndex

            int m_attIndex

        Attribute to split on.

      - #### m_minNoObj

            int m_minNoObj

        Minimum number of objects in a split.

      - #### m_useMDLcorrection

            boolean m_useMDLcorrection

        Use MDL correction?

      - #### m_splitPoint

            double m_splitPoint

        Value of split point.

      - #### m_infoGain

            double m_infoGain

        InfoGain of split.

      - #### m_gainRatio

            double m_gainRatio

        GainRatio of split.

      - #### m_sumOfWeights

            double m_sumOfWeights

        The sum of the weights of the instances.

  - ### Class weka.classifiers.trees.j48.C45ModelSelection extends ModelSelection implements Serializable

    serialVersionUID:
      3372204862440821989L

    - ### Serialized Fields

      - #### m_minNoObj

            int m_minNoObj

        Minimum number of objects in interval.

      - #### m_useMDLcorrection

            boolean m_useMDLcorrection

        Use MDL correction?

      - #### m_allData

            Instances m_allData

        All the training data

      - #### m_doNotMakeSplitPointActualValue

            boolean m_doNotMakeSplitPointActualValue

        Do not relocate split point to actual data value

  - ### Class weka.classifiers.trees.j48.C45PruneableClassifierTree extends ClassifierTree implements Serializable

    serialVersionUID:
      -4813820170260388194L

    - ### Serialized Fields

      - #### m_pruneTheTree

            boolean m_pruneTheTree

        True if the tree is to be pruned.

      - #### m_collapseTheTree

            boolean m_collapseTheTree

        True if the tree is to be collapsed.

      - #### m_CF

            float m_CF

        The confidence factor for pruning.

      - #### m_subtreeRaising

            boolean m_subtreeRaising

        Is subtree raising to be performed?

      - #### m_cleanup

            boolean m_cleanup

        Cleanup after the tree has been built.

  - ### Class weka.classifiers.trees.j48.C45Split extends ClassifierSplitModel implements Serializable

    serialVersionUID:
      3064079330067903161L

    - ### Serialized Fields

      - #### m_complexityIndex

            int m_complexityIndex

        Desired number of branches.

      - #### m_attIndex

            int m_attIndex

        Attribute to split on.

      - #### m_minNoObj

            int m_minNoObj

        Minimum number of objects in a split.

      - #### m_useMDLcorrection

            boolean m_useMDLcorrection

        Use MDL correction?

      - #### m_splitPoint

            double m_splitPoint

        Value of split point.

      - #### m_infoGain

            double m_infoGain

        InfoGain of split.

      - #### m_gainRatio

            double m_gainRatio

        GainRatio of split.

      - #### m_sumOfWeights

            double m_sumOfWeights

        The sum of the weights of the instances.

      - #### m_index

            int m_index

        Number of split points.

  - ### Class weka.classifiers.trees.j48.ClassifierSplitModel extends java.lang.Object implements Serializable

    serialVersionUID:
      4280730118393457457L

    - ### Serialized Fields

      - #### m_distribution

            Distribution m_distribution

        Distribution of class values.

      - #### m_numSubsets

            int m_numSubsets

        Number of created subsets.

  - ### Class weka.classifiers.trees.j48.ClassifierTree extends java.lang.Object implements Serializable

    serialVersionUID:
      -8722249377542734193L

    - ### Serialized Fields

      - #### m_toSelectModel

            ModelSelection m_toSelectModel

        The model selection method.

      - #### m_localModel

            ClassifierSplitModel m_localModel

        Local model at node.

      - #### m_sons

            ClassifierTree[] m_sons

        References to sons.

      - #### m_isLeaf

            boolean m_isLeaf

        True if node is leaf.

      - #### m_isEmpty

            boolean m_isEmpty

        True if node is empty.

      - #### m_train

            Instances m_train

        The training instances.

      - #### m_test

            Distribution m_test

        The pruning instances.

      - #### m_id

            int m_id

        The id for the node.

  - ### Class weka.classifiers.trees.j48.Distribution extends java.lang.Object implements Serializable

    serialVersionUID:
      8526859638230806576L

    - ### Serialized Fields

      - #### m_perClassPerBag

            double[][] m_perClassPerBag

        Weight of instances per class per bag.

      - #### m_perBag

            double[] m_perBag

        Weight of instances per bag.

      - #### m_perClass

            double[] m_perClass

        Weight of instances per class.

      - #### totaL

            double totaL

        Total weight of instances.

  - ### Class weka.classifiers.trees.j48.EntropyBasedSplitCrit extends SplitCriterion implements Serializable

    serialVersionUID:
      -2618691439791653056L

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

    serialVersionUID:
      5986252682266803935L

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

    serialVersionUID:
      -433336694718670930L

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

    serialVersionUID:
      4892105020180728499L

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

    serialVersionUID:
      -4850147125096133642L

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

    serialVersionUID:
      -4472639447877404786L

  - ### Class weka.classifiers.trees.j48.NBTreeModelSelection extends ModelSelection implements Serializable

    serialVersionUID:
      990097748931976704L

    - ### Serialized Fields

      - #### m_minNoObj

            int m_minNoObj

        Minimum number of objects in interval.

      - #### m_allData

            Instances m_allData

        All the training data

  - ### Class weka.classifiers.trees.j48.NBTreeNoSplit extends ClassifierSplitModel implements Serializable

    serialVersionUID:
      7824804381545259618L

    - ### Serialized Fields

      - #### m_nb

            NaiveBayesUpdateable m_nb

        the naive bayes classifier

      - #### m_disc

            Discretize m_disc

        the discretizer used

      - #### m_errors

            double m_errors

        errors on the training data at this node

  - ### Class weka.classifiers.trees.j48.NBTreeSplit extends ClassifierSplitModel implements Serializable

    serialVersionUID:
      8922627123884975070L

    - ### Serialized Fields

      - #### m_complexityIndex

            int m_complexityIndex

        Desired number of branches.

      - #### m_attIndex

            int m_attIndex

        Attribute to split on.

      - #### m_sumOfWeights

            double m_sumOfWeights

        The sum of the weights of the instances.

      - #### m_errors

            double m_errors

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

      - #### m_c45S

            C45Split m_c45S

      - #### m_globalNB

            NBTreeNoSplit m_globalNB

        The global naive bayes model for this node

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

    serialVersionUID:
      -1292620749331337546L

  - ### Class weka.classifiers.trees.j48.PruneableClassifierTree extends ClassifierTree implements Serializable

    serialVersionUID:
      -555775736857600201L

    - ### Serialized Fields

      - #### pruneTheTree

            boolean pruneTheTree

        True if the tree is to be pruned.

      - #### numSets

            int numSets

        How many subsets of equal size? One used for pruning, the rest for training.

      - #### m_cleanup

            boolean m_cleanup

        Cleanup after the tree has been built.

      - #### m_seed

            int m_seed

        The random number seed.

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

    serialVersionUID:
      5490996638027101259L

- ## Package weka.classifiers.trees.lmt

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

    serialVersionUID:
      1862737145870398755L

    - ### Serialized Fields

      - #### m_totalInstanceWeight

            double m_totalInstanceWeight

        Total number of training instances.

      - #### m_id

            int m_id

        Node id

      - #### m_leafModelNum

            int m_leafModelNum

        ID of logistic model at leaf

      - #### m_alpha

            double m_alpha

        Alpha-value (for pruning) at the node

      - #### m_numIncorrectModel

            double m_numIncorrectModel

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

      - #### m_numIncorrectTree

            double m_numIncorrectTree

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

      - #### m_minNumInstances

            int m_minNumInstances

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

      - #### m_modelSelection

            ModelSelection m_modelSelection

        ModelSelection object (for splitting)

      - #### m_nominalToBinary

            NominalToBinary m_nominalToBinary

        Filter to convert nominal attributes to binary

      - #### m_fastRegression

            boolean m_fastRegression

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

      - #### m_numInstances

            int m_numInstances

        Number of instances at the node

      - #### m_localModel

            ClassifierSplitModel m_localModel

        The ClassifierSplitModel (for splitting)

      - #### m_sons

            LMTNode[] m_sons

        Array of children of the node

      - #### m_isLeaf

            boolean m_isLeaf

        True if node is leaf

  - ### Class weka.classifiers.trees.lmt.LogisticBase extends AbstractClassifier implements Serializable

    serialVersionUID:
      168765678097825064L

    - ### Serialized Fields

      - #### m_numericDataHeader

            Instances m_numericDataHeader

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

      - #### m_numericData

            Instances m_numericData

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

      - #### m_train

            Instances m_train

        Training data

      - #### m_useCrossValidation

            boolean m_useCrossValidation

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

      - #### m_errorOnProbabilities

            boolean m_errorOnProbabilities

        Use error on probabilities for stopping criterion of LogitBoost?

      - #### m_fixedNumIterations

            int m_fixedNumIterations

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

      - #### m_heuristicStop

            int m_heuristicStop

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

      - #### m_numRegressions

            int m_numRegressions

        The number of LogitBoost iterations performed.

      - #### m_maxIterations

            int m_maxIterations

        The maximum number of LogitBoost iterations

      - #### m_numClasses

            int m_numClasses

        The number of different classes

      - #### m_regressions

            SimpleLinearRegression[][] m_regressions

        Array holding the simple regression functions fit by LogitBoost

      - #### m_useAIC

            boolean m_useAIC

        If true, the AIC is used to choose the best iteration

      - #### m_numParameters

            double m_numParameters

        Effective number of parameters used for AIC / BIC automatic stopping

      - #### m_weightTrimBeta

            double m_weightTrimBeta

        Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.

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

    serialVersionUID:
      -293098783159385148L

    - ### Serialized Fields

      - #### m_minNumInstances

            int m_minNumInstances

        Minimum number of instances for leaves

      - #### m_minInfoGain

            double m_minInfoGain

        Minimum information gain for split

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

    serialVersionUID:
      -5055883734183713525L

    - ### Serialized Fields

      - #### m_attribute

            Attribute m_attribute

        The attribute selected for the split

      - #### m_attIndex

            int m_attIndex

        The index of the attribute selected for the split

      - #### m_numInstances

            int m_numInstances

        Number of instances in the set

      - #### m_numClasses

            int m_numClasses

        Number of classed

      - #### m_data

            Instances m_data

        The set of instances

      - #### m_dataZs

            double[][] m_dataZs

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

      - #### m_dataWs

            double[][] m_dataWs

        The LogitBoost-weights for the set of instances

      - #### m_splitPoint

            double m_splitPoint

        The split point (for numeric attributes)

  - ### Class weka.classifiers.trees.lmt.SimpleLinearRegression extends java.lang.Object implements Serializable

    serialVersionUID:
      1779336022895414137L

    - ### Serialized Fields

      - #### m_attributeIndex

            int m_attributeIndex

        The index of the chosen attribute

      - #### m_slope

            double m_slope

        The slope

      - #### m_intercept

            double m_intercept

        The intercept

- ## Package weka.classifiers.trees.m5

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

    serialVersionUID:
      4212734895125452770L

    - ### Serialized Fields

      - #### m_position

            int m_position

      - #### m_maxImpurity

            double m_maxImpurity

        the maximum impurity reduction

      - #### m_splitAttr

            int m_splitAttr

        the attribute being tested

      - #### m_splitValue

            double m_splitValue

        the best value on which to split

      - #### m_number

            int m_number

        the number of instances

  - ### Class weka.classifiers.trees.m5.M5Base extends AbstractClassifier implements Serializable

    serialVersionUID:
      -4022221950191647679L

    - ### Serialized Fields

      - #### m_instances

            Instances m_instances

        the instances covered by the tree/rules

      - #### m_ruleSet

            java.util.ArrayList<E> m_ruleSet

        the rule set

      - #### m_generateRules

            boolean m_generateRules

        generate a decision list instead of a single tree.

      - #### m_unsmoothedPredictions

            boolean m_unsmoothedPredictions

        use unsmoothed predictions

      - #### m_replaceMissing

            ReplaceMissingValues m_replaceMissing

        filter to fill in missing values

      - #### m_nominalToBinary

            NominalToBinary m_nominalToBinary

        filter to convert nominal attributes to binary

      - #### m_removeUseless

            RemoveUseless m_removeUseless

        for removing useless attributes

      - #### m_saveInstances

            boolean m_saveInstances

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

      - #### m_regressionTree

            boolean m_regressionTree

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

      - #### m_useUnpruned

            boolean m_useUnpruned

        Do not prune tree/rules

      - #### m_minNumInstances

            double m_minNumInstances

        The minimum number of instances to allow at a leaf node

  - ### Class weka.classifiers.trees.m5.PreConstructedLinearModel extends AbstractClassifier implements Serializable

    serialVersionUID:
      2030974097051713247L

    - ### Serialized Fields

      - #### m_coefficients

            double[] m_coefficients

        The coefficients

      - #### m_intercept

            double m_intercept

        The intercept

      - #### m_instancesHeader

            Instances m_instancesHeader

        Holds the instances header for printing the model

      - #### m_numParameters

            int m_numParameters

        number of coefficients in the model

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

    serialVersionUID:
      -4458627451682483204L

    - ### Serialized Fields

      - #### m_instances

            Instances m_instances

        the instances covered by this rule

      - #### m_classIndex

            int m_classIndex

        the class index

      - #### m_numInstances

            int m_numInstances

        the number of instances in the dataset

      - #### m_splitAtts

            int[] m_splitAtts

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

      - #### m_splitVals

            double[] m_splitVals

        the corresponding values of the split points

      - #### m_internalNodes

            RuleNode[] m_internalNodes

        the corresponding internal nodes. Used for smoothing rules.

      - #### m_relOps

            int[] m_relOps

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

      - #### m_ruleModel

            RuleNode m_ruleModel

        the leaf encapsulating the linear model for this rule

      - #### m_topOfTree

            RuleNode m_topOfTree

        the top of the m5 tree for this rule

      - #### m_globalStdDev

            double m_globalStdDev

        the standard deviation of the class for all the instances

      - #### m_globalAbsDev

            double m_globalAbsDev

        the absolute deviation of the class for all the instances

      - #### m_covered

            Instances m_covered

        the instances covered by this rule

      - #### m_numCovered

            int m_numCovered

        the number of instances covered by this rule

      - #### m_notCovered

            Instances m_notCovered

        the instances not covered by this rule

      - #### m_useTree

            boolean m_useTree

        use a pruned m5 tree rather than make a rule

      - #### m_smoothPredictions

            boolean m_smoothPredictions

        use the original m5 smoothing procedure

      - #### m_saveInstances

            boolean m_saveInstances

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

      - #### m_regressionTree

            boolean m_regressionTree

        Make a regression tree instead of a model tree

      - #### m_useUnpruned

            boolean m_useUnpruned

        Build unpruned tree/rule

      - #### m_minNumInstances

            double m_minNumInstances

        The minimum number of instances to allow at a leaf node

  - ### Class weka.classifiers.trees.m5.RuleNode extends AbstractClassifier implements Serializable

    serialVersionUID:
      1979807611124337144L

    - ### Serialized Fields

      - #### m_instances

            Instances m_instances

        instances reaching this node

      - #### m_classIndex

            int m_classIndex

        the class index

      - #### m_numInstances

            int m_numInstances

        the number of instances reaching this node

      - #### m_numAttributes

            int m_numAttributes

        the number of attributes

      - #### m_isLeaf

            boolean m_isLeaf

        Node is a leaf

      - #### m_splitAtt

            int m_splitAtt

        attribute this node splits on

      - #### m_splitValue

            double m_splitValue

        the value of the split attribute

      - #### m_nodeModel

            PreConstructedLinearModel m_nodeModel

        the linear model at this node

      - #### m_numParameters

            int m_numParameters

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

      - #### m_rootMeanSquaredError

            double m_rootMeanSquaredError

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

      - #### m_left

            RuleNode m_left

        left child node

      - #### m_right

            RuleNode m_right

        right child node

      - #### m_parent

            RuleNode m_parent

        the parent of this node

      - #### m_splitNum

            double m_splitNum

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

      - #### m_devFraction

            double m_devFraction

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

      - #### m_pruningMultiplier

            double m_pruningMultiplier

      - #### m_leafModelNum

            int m_leafModelNum

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

      - #### m_globalDeviation

            double m_globalDeviation

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

      - #### m_globalAbsDeviation

            double m_globalAbsDeviation

        the absolute deviation of the global class

      - #### m_indices

            int[] m_indices

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

      - #### m_id

            int m_id

        Node id.

      - #### m_saveInstances

            boolean m_saveInstances

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

      - #### m_regressionTree

            boolean m_regressionTree

        Make a regression tree instead of a model tree

  - ### Class weka.classifiers.trees.m5.YongSplitInfo extends java.lang.Object implements Serializable

    serialVersionUID:
      1864267581079767881L

    - ### Serialized Fields

      - #### number

            int number

      - #### first

            int first

      - #### last

            int last

      - #### position

            int position

      - #### maxImpurity

            double maxImpurity

      - #### leftAve

            double leftAve

      - #### rightAve

            double rightAve

      - #### splitAttr

            int splitAttr

      - #### splitValue

            double splitValue

- ## Package weka.clusterers

  - ### Class weka.clusterers.AbstractClusterer extends java.lang.Object implements Serializable

    serialVersionUID:
      -6099962589663877632L

    - ### 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 extends AbstractClusterer implements Serializable

    serialVersionUID:
      -5950728041704213845L

  - ### Class weka.clusterers.Canopy extends RandomizableClusterer implements Serializable

    serialVersionUID:
      2067574593448223334L

    - ### Serialized Fields

      - #### m_canopies

            Instances m_canopies

        The canopy centers

      - #### m_canopyT2Density

            java.util.List<E> m_canopyT2Density

        The T2 density of each canopy

      - #### m_canopyCenters

            java.util.List<E> m_canopyCenters

      - #### m_canopyNumMissingForNumerics

            java.util.List<E> m_canopyNumMissingForNumerics

      - #### m_clusterCanopies

            java.util.List<E> 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_userT2

            double m_userT2

        < 0 means use the heuristic based on std. dev. to set the t2 radius

      - #### m_userT1

            double m_userT1

        < 0 indicates the multiplier to use for T2 when setting T1, otherwise the value is take as is

      - #### m_t1

            double m_t1

        Outer radius

      - #### m_t2

            double m_t2

        Inner radius

      - #### m_periodicPruningRate

            int m_periodicPruningRate

        Prune low-density candidate canopies after every x instances have been seen

      - #### m_minClusterDensity

            double m_minClusterDensity

        The minimum cluster density (according to T2 distance) allowed. Used when periodically pruning candidate canopies

      - #### m_maxCanopyCandidates

            int m_maxCanopyCandidates

        The maximum number of candidate canopies to hold in memory at any one time

      - #### 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_instanceCount

            int m_instanceCount

        Number of training instances seen so far

      - #### m_numClustersRequested

            int m_numClustersRequested

        Default is to let the t2 radius determine how many canopies/clusters are formed

      - #### 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_dontReplaceMissing

            boolean m_dontReplaceMissing

        Replace missing values globally when running in batch mode?

      - #### m_distanceFunction

            NormalizableDistance m_distanceFunction

        The distance function to use

      - #### 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.

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

    serialVersionUID:
      -830188327319128005L

    - ### Serialized Fields

      - #### m_Clusterer

            Clusterer m_Clusterer

        the clusterer

      - #### m_clusteringResults

            java.lang.StringBuffer m_clusteringResults

        holds a string describing the results of clustering the training data

      - #### m_numClusters

            int m_numClusters

        holds the number of clusters found by the clusterer

      - #### m_clusterAssignments

            double[] m_clusterAssignments

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

      - #### m_logL

            double m_logL

        holds the average log likelihood for a particular testing dataset if the clusterer is a DensityBasedClusterer

      - #### m_classToCluster

            int[] m_classToCluster

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

  - ### Class weka.clusterers.Cobweb extends RandomizableClusterer implements Serializable

    serialVersionUID:
      928406656495092318L

    - ### Serialized Fields

      - #### m_acuity

            double m_acuity

        Acuity (minimum standard deviation).

      - #### m_cutoff

            double m_cutoff

        Cutoff (minimum category utility).

      - #### m_cobwebTree

            Cobweb.CNode m_cobwebTree

        Holds the root of the Cobweb tree.

      - #### m_numberOfClusters

            int m_numberOfClusters

        Number of clusters (nodes in the tree). Must never be queried directly, only via the method numberOfClusters(). Otherwise it's not guaranteed that it contains the correct value.
        See Also:
          `Cobweb.numberOfClusters()`, `Cobweb.m_numberOfClustersDetermined`

      - #### m_numberOfClustersDetermined

            boolean m_numberOfClustersDetermined

        whether the number of clusters was already determined

      - #### m_numberSplits

            int m_numberSplits

        the number of splits that happened

      - #### m_numberMerges

            int m_numberMerges

        the number of merges that happened

      - #### m_saveInstances

            boolean m_saveInstances

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

  - ### Class weka.clusterers.Cobweb.CNode extends java.lang.Object implements Serializable

    serialVersionUID:
      3452097436933325631L

    - ### Serialized Fields

      - #### m_attStats

            AttributeStats[] m_attStats

        Within cluster attribute statistics

      - #### m_numAttributes

            int m_numAttributes

        Number of attributes

      - #### m_clusterInstances

            Instances m_clusterInstances

        Instances at this node

      - #### m_children

            java.util.ArrayList<E> m_children

        Children of this node

      - #### m_totalInstances

            double m_totalInstances

        Total instances at this node

      - #### m_clusterNum

            int m_clusterNum

        Cluster number of this node

  - ### Class weka.clusterers.EM extends RandomizableDensityBasedClusterer implements Serializable

    serialVersionUID:
      8348181483812829475L

    - ### Serialized Fields

      - #### m_modelPrev

            Estimator[][] m_modelPrev

      - #### m_modelNormalPrev

            double[][][] m_modelNormalPrev

      - #### m_priorsPrev

            double[] m_priorsPrev

      - #### m_model

            Estimator[][] m_model

        hold the discrete estimators for each cluster

      - #### m_modelNormal

            double[][][] m_modelNormal

        hold the normal estimators for each cluster

      - #### m_minStdDev

            double m_minStdDev

        default minimum standard deviation

      - #### m_minStdDevPerAtt

            double[] m_minStdDevPerAtt

      - #### m_weights

            double[][] m_weights

        hold the weights of each instance for each cluster

      - #### m_priors

            double[] m_priors

        the prior probabilities for clusters

      - #### m_theInstances

            Instances m_theInstances

        full training instances

      - #### m_num_clusters

            int m_num_clusters

        number of clusters selected by the user or cross validation

      - #### m_initialNumClusters

            int m_initialNumClusters

        the initial number of clusters requested by the user--- -1 if xval is to be used to find the number of clusters

      - #### m_upperBoundNumClustersCV

            int m_upperBoundNumClustersCV

        Don't consider more clusters than this under CV (-1 means no upper bound)

      - #### m_num_attribs

            int m_num_attribs

        number of attributes

      - #### m_num_instances

            int m_num_instances

        number of training instances

      - #### m_max_iterations

            int m_max_iterations

        maximum iterations to perform

      - #### m_minValues

            double[] m_minValues

        attribute min values

      - #### m_maxValues

            double[] m_maxValues

        attribute max values

      - #### m_rr

            java.util.Random m_rr

        random number generator

      - #### m_verbose

            boolean m_verbose

        Verbose?

      - #### m_replaceMissing

            ReplaceMissingValues m_replaceMissing

        globally replace missing values

      - #### m_displayModelInOldFormat

            boolean m_displayModelInOldFormat

        display model output in old-style format

      - #### m_executionSlots

            int m_executionSlots

        Number of threads to use for E and M steps

      - #### m_training

            boolean m_training

        False once training has completed

      - #### m_iterationsPerformed

            int m_iterationsPerformed

        The actual number of iterations performed

      - #### m_minLogLikelihoodImprovementIterating

            double m_minLogLikelihoodImprovementIterating

        Minimum improvement in log likelihood when iterating

      - #### m_minLogLikelihoodImprovementCV

            double m_minLogLikelihoodImprovementCV

        Minimum improvement to increase number of clusters when cross-validating

      - #### m_cvFolds

            int m_cvFolds

        The number of folds to use for cross-validation

      - #### m_NumKMeansRuns

            int m_NumKMeansRuns

        The number of runs of k-means to perform

  - ### Class weka.clusterers.FarthestFirst extends RandomizableClusterer implements Serializable

    serialVersionUID:
      7499838100631329509L

    - ### Serialized Fields

      - #### m_instances

            Instances m_instances

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

      - #### m_ReplaceMissingFilter

            ReplaceMissingValues m_ReplaceMissingFilter

        replace missing values in training instances

      - #### m_NumClusters

            int m_NumClusters

        number of clusters to generate

      - #### m_ClusterCentroids

            Instances m_ClusterCentroids

        holds the cluster centroids

      - #### m_Min

            double[] m_Min

        attribute min values

      - #### m_Max

            double[] m_Max

        attribute max values

  - ### Class weka.clusterers.FilteredClusterer extends SingleClustererEnhancer implements Serializable

    serialVersionUID:
      1420005943163412943L

    - ### Serialized Fields

      - #### m_Filter

            Filter m_Filter

        The filter.

      - #### m_FilteredInstances

            Instances m_FilteredInstances

        The instance structure of the filtered instances.

  - ### Class weka.clusterers.HierarchicalClusterer extends AbstractClusterer implements Serializable

    serialVersionUID:
      1L

    - ### Serialized Fields

      - #### m_bDistanceIsBranchLength

            boolean m_bDistanceIsBranchLength

        Whether the distance represent node height (if false) or branch length (if true).

      - #### m_instances

            Instances m_instances

        training data

      - #### m_nNumClusters

            int m_nNumClusters

        number of clusters desired in clustering

      - #### m_DistanceFunction

            DistanceFunction m_DistanceFunction

        distance function used for comparing members of a cluster

      - #### m_nLinkType

            int m_nLinkType

        Holds the Link type used calculate distance between clusters

      - #### m_bPrintNewick

            boolean m_bPrintNewick

      - #### m_clusters

            weka.clusterers.HierarchicalClusterer.Node[] m_clusters

      - #### m_nClusterNr

            int[] m_nClusterNr

  - ### Class weka.clusterers.MakeDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable

    serialVersionUID:
      -5643302427972186631L

    - ### Serialized Fields

      - #### m_theInstances

            Instances m_theInstances

        holds training instances header information

      - #### m_priors

            double[] m_priors

        prior probabilities for the fitted clusters

      - #### m_modelNormal

            double[][][] m_modelNormal

        normal distributions fitted to each numeric attribute in each cluster

      - #### m_model

            DiscreteEstimator[][] m_model

        discrete distributions fitted to each discrete attribute in each cluster

      - #### m_minStdDev

            double m_minStdDev

        default minimum standard deviation

      - #### m_wrappedClusterer

            Clusterer m_wrappedClusterer

        The clusterer being wrapped

      - #### m_replaceMissing

            ReplaceMissingValues m_replaceMissing

        globally replace missing values

  - ### Class weka.clusterers.RandomizableClusterer extends AbstractClusterer implements Serializable

    serialVersionUID:
      -4819590778152242745L

    - ### Serialized Fields

      - #### m_SeedDefault

            int m_SeedDefault

        the default seed value

      - #### m_Seed

            int m_Seed

        The random number seed.

  - ### Class weka.clusterers.RandomizableDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable

    serialVersionUID:
      -5325270357918932849L

    - ### Serialized Fields

      - #### m_SeedDefault

            int m_SeedDefault

        the default seed value

      - #### m_Seed

            int m_Seed

        The random number seed.

  - ### Class weka.clusterers.RandomizableSingleClustererEnhancer extends AbstractClusterer implements Serializable

    serialVersionUID:
      -644847037106316249L

    - ### Serialized Fields

      - #### m_SeedDefault

            int m_SeedDefault

        the default seed value

      - #### m_Seed

            int m_Seed

        The random number seed.

  - ### Class weka.clusterers.SimpleKMeans extends RandomizableClusterer implements Serializable

    serialVersionUID:
      -3235809600124455376L

    - ### Serialized Fields

      - #### m_ReplaceMissingFilter

            ReplaceMissingValues m_ReplaceMissingFilter

        replace missing values in training instances.

      - #### m_NumClusters

            int m_NumClusters

        number of clusters to generate.

      - #### m_initialStartPoints

            Instances m_initialStartPoints

        Holds the initial start points, as supplied by the initialization method used

      - #### m_ClusterCentroids

            Instances m_ClusterCentroids

        holds the cluster centroids.

      - #### m_ClusterStdDevs

            Instances m_ClusterStdDevs

        Holds the standard deviations of the numeric attributes in each cluster.

      - #### m_ClusterNominalCounts

            double[][][] m_ClusterNominalCounts

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

      - #### m_ClusterMissingCounts

            double[][] m_ClusterMissingCounts

      - #### m_FullMeansOrMediansOrModes

            double[] m_FullMeansOrMediansOrModes

        Stats on the full data set for comparison purposes. In case the attribute is numeric the value is the mean if is being used the Euclidian distance or the median if Manhattan distance and if the attribute is nominal then it's mode is saved.

      - #### m_FullStdDevs

            double[] m_FullStdDevs

      - #### m_FullNominalCounts

            double[][] m_FullNominalCounts

      - #### m_FullMissingCounts

            double[] m_FullMissingCounts

      - #### m_displayStdDevs

            boolean m_displayStdDevs

        Display standard deviations for numeric atts.

      - #### m_dontReplaceMissing

            boolean m_dontReplaceMissing

        Replace missing values globally?

      - #### m_ClusterSizes

            double[] m_ClusterSizes

        The number of instances in each cluster.

      - #### m_MaxIterations

            int m_MaxIterations

        Maximum number of iterations to be executed.

      - #### m_Iterations

            int m_Iterations

        Keep track of the number of iterations completed before convergence.

      - #### m_squaredErrors

            double[] m_squaredErrors

        Holds the squared errors for all clusters.

      - #### m_DistanceFunction

            DistanceFunction m_DistanceFunction

        the distance function used.

      - #### m_PreserveOrder

            boolean m_PreserveOrder

        Preserve order of instances.

      - #### m_Assignments

            int[] m_Assignments

        Assignments obtained.

      - #### m_FastDistanceCalc

            boolean m_FastDistanceCalc

        whether to use fast calculation of distances (using a cut-off).

      - #### m_initializationMethod

            int m_initializationMethod

        The initialization method to use

      - #### m_speedUpDistanceCompWithCanopies

            boolean m_speedUpDistanceCompWithCanopies

        Whether to reducet the number of distance calcs done by k-means with canopies

      - #### m_centroidCanopyAssignments

            java.util.List<E> m_centroidCanopyAssignments

        Canopies that each centroid falls into (determined by T1 radius)

      - #### m_dataPointCanopyAssignments

            java.util.List<E> m_dataPointCanopyAssignments

        Canopies that each training instance falls into (determined by T1 radius)

      - #### m_canopyClusters

            Canopy m_canopyClusters

        The canopy clusterer (if being used)

      - #### m_maxCanopyCandidates

            int m_maxCanopyCandidates

        The maximum number of candidate canopies to hold in memory at any one time (if using canopy clustering)

      - #### m_periodicPruningRate

            int m_periodicPruningRate

        Prune low-density candidate canopies after every x instances have been seen (if using canopy clustering)

      - #### 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_t2

            double m_t2

        The t2 radius to pass through to Canopy

      - #### m_t1

            double m_t1

        The t1 radius to pass through to Canopy

      - #### m_executionSlots

            int m_executionSlots

        Number of threads to run

      - #### m_completed

            int m_completed

      - #### m_failed

            int m_failed

  - ### Class weka.clusterers.SingleClustererEnhancer extends AbstractClusterer implements Serializable

    serialVersionUID:
      4893928362926428671L

    - ### Serialized Fields

      - #### m_Clusterer

            Clusterer m_Clusterer

        the clusterer

- ## Package weka.core

  - ### Class weka.core.AbstractInstance extends java.lang.Object implements Serializable

    serialVersionUID:
      1482635194499365155L

    - ### Serialized Fields

      - #### m_Dataset

            Instances m_Dataset

        The dataset the instance has access to. Null if the instance doesn't have access to any dataset. Only if an instance has access to a dataset, it knows about the actual attribute types.

      - #### m_AttValues

            double[] m_AttValues

        The instance's attribute values.

      - #### m_Weight

            double m_Weight

        The instance's weight.

  - ### Class weka.core.AlgVector extends java.lang.Object implements Serializable

    serialVersionUID:
      -4023736016850256591L

    - ### Serialized Fields

      - #### m_Elements

            double[] m_Elements

        The values of the matrix

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

    serialVersionUID:
      -742180568732916383L

    - ### Serialized Fields

      - #### m_Name

            java.lang.String m_Name

        The attribute's name.

      - #### m_Type

            int m_Type

        The attribute's type.

      - #### m_AttributeInfo

            weka.core.AttributeInfo m_AttributeInfo

        The attribute info (null for numeric attributes)

      - #### m_Index

            int m_Index

        The attribute's index.

      - #### m_Weight

            double m_Weight

        The attribute's weight.

      - #### m_AttributeMetaInfo

            AttributeMetaInfo m_AttributeMetaInfo

        The meta data for the attribute.

  - ### Class weka.core.AttributeExpression extends java.lang.Object implements Serializable

    serialVersionUID:
      402130123261736245L

    - ### Serialized Fields

      - #### m_operatorStack

            java.util.Stack<E> m_operatorStack

        Operator stack

      - #### m_originalInfix

            java.lang.String m_originalInfix

        Holds the original infix expression

      - #### m_postFixExpVector

            java.util.Vector<E> m_postFixExpVector

        Holds the expression in postfix form

      - #### m_signMod

            boolean m_signMod

        True if the next numeric constant or attribute index is negative

      - #### m_previousTok

            java.lang.String m_previousTok

        Holds the previous token

  - ### Class weka.core.AttributeLocator extends java.lang.Object implements Serializable

    serialVersionUID:
      -2932848827681070345L

    - ### Serialized Fields

      - #### m_AllowedIndices

            int[] m_AllowedIndices

        the attribute indices that may be inspected

      - #### m_Attributes

            java.util.BitSet m_Attributes

        contains the attribute locations, either true or false Boolean objects

      - #### m_Locators

            java.util.ArrayList<E> m_Locators

        contains the locator locations, either null or a AttributeLocator reference

      - #### m_Type

            int m_Type

        the type of the attribute

      - #### m_Data

            Instances m_Data

        the referenced data

      - #### m_Indices

            int[] m_Indices

        the indices

      - #### m_LocatorIndices

            int[] m_LocatorIndices

        the indices of locator objects

  - ### Class weka.core.AttributeMetaInfo extends java.lang.Object implements Serializable

    - ### Serialized Fields

      - #### m_Metadata

            ProtectedProperties m_Metadata

        The attribute's metadata.

      - #### m_Ordering

            int m_Ordering

        The attribute's ordering.

      - #### m_IsRegular

            boolean m_IsRegular

        Whether the attribute is regular.

      - #### m_IsAveragable

            boolean m_IsAveragable

        Whether the attribute is averagable.

      - #### m_HasZeropoint

            boolean m_HasZeropoint

        Whether the attribute has a zeropoint.

      - #### m_LowerBound

            double m_LowerBound

        The attribute's lower numeric bound.

      - #### m_LowerBoundIsOpen

            boolean m_LowerBoundIsOpen

        Whether the lower bound is open.

      - #### m_UpperBound

            double m_UpperBound

        The attribute's upper numeric bound.

      - #### m_UpperBoundIsOpen

            boolean m_UpperBoundIsOpen

        Whether the upper bound is open

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

    serialVersionUID:
      4434688832743939380L

    - ### Serialized Fields

      - #### intCount

            int intCount

        The number of int-like values

      - #### realCount

            int realCount

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

      - #### missingCount

            int missingCount

        The number of missing values

      - #### distinctCount

            int distinctCount

        The number of distinct values

      - #### uniqueCount

            int uniqueCount

        The number of values that only appear once

      - #### totalCount

            int totalCount

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

      - #### numericStats

            Stats numericStats

        Stats on numeric value distributions

      - #### nominalCounts

            int[] nominalCounts

        Counts of each nominal value

      - #### nominalWeights

            double[] nominalWeights

        Weight mass for each nominal value

  - ### Class weka.core.BinarySparseInstance extends SparseInstance implements Serializable

    serialVersionUID:
      -5297388762342528737L

  - ### Class weka.core.Capabilities extends java.lang.Object implements Serializable

    serialVersionUID:
      -5478590032325567849L

    - ### Serialized Fields

      - #### m_Owner

            CapabilitiesHandler m_Owner

        the object that owns this capabilities instance

      - #### m_Capabilities

            java.util.HashSet<E> m_Capabilities

        the hashset for storing the active capabilities

      - #### m_Dependencies

            java.util.HashSet<E> m_Dependencies

        the hashset for storing dependent capabilities, eg for meta-classifiers

      - #### m_FailReason

            java.lang.Exception m_FailReason

        the reason why the test failed, used to throw an exception

      - #### m_MinimumNumberInstances

            int m_MinimumNumberInstances

        the minimum number of instances in a dataset

      - #### m_Test

            boolean m_Test

        whether to perform any tests at all

      - #### m_InstancesTest

            boolean m_InstancesTest

        whether to perform data based tests

      - #### m_AttributeTest

            boolean m_AttributeTest

        whether to perform attribute based tests

      - #### m_MissingValuesTest

            boolean m_MissingValuesTest

        whether to test for missing values

      - #### m_MissingClassValuesTest

            boolean m_MissingClassValuesTest

        whether to test for missing class values

      - #### m_MinimumNumberInstancesTest

            boolean m_MinimumNumberInstancesTest

        whether to test for minimum number of instances

  - ### Class weka.core.ChebyshevDistance extends NormalizableDistance implements Serializable

    serialVersionUID:
      -7739904999895461429L

  - ### Class weka.core.DateAttributeInfo extends java.lang.Object implements Serializable

    - ### Serialized Fields

      - #### m_DateFormat

            java.text.SimpleDateFormat m_DateFormat

        Date format specification for date attributes

  - ### Class weka.core.Debug extends java.lang.Object implements Serializable

    serialVersionUID:
      66171861743328020L

    - ### Serialized Fields

      - #### m_Enabled

            boolean m_Enabled

        whether logging is enabled

      - #### m_Log

            Debug.Log m_Log

        for logging

      - #### m_Clock

            Debug.Clock m_Clock

        for clocking

  - ### Class weka.core.Debug.Clock extends java.lang.Object implements Serializable

    serialVersionUID:
      4622161807307942201L

    - ### Serialized Fields

      - #### m_OutputFormat

            int m_OutputFormat

        the format of the output

      - #### m_Start

            long m_Start

        the start time

      - #### m_Stop

            long m_Stop

        the end time

      - #### m_Running

            boolean m_Running

        whether the time is still clocked

      - #### m_ThreadID

            long m_ThreadID

        the thread ID

      - #### m_CanMeasureCpuTime

            boolean m_CanMeasureCpuTime

        whether the system can measure the CPU time

      - #### m_UseCpuTime

            boolean m_UseCpuTime

        whether to use the CPU time (by default TRUE)

  - ### Class weka.core.Debug.DBO extends java.lang.Object implements Serializable

    serialVersionUID:
      -5245628124742606784L

    - ### Serialized Fields

      - #### m_verboseOn

            boolean m_verboseOn

        enables/disables output of debug information

      - #### m_outputTypes

            Range m_outputTypes

        range of outputtyp

  - ### Class weka.core.Debug.Log extends java.lang.Object implements Serializable

    serialVersionUID:
      1458435732111675823L

    - ### Serialized Fields

      - #### m_Filename

            java.lang.String m_Filename

        the filename, if any

      - #### m_Size

            int m_Size

        the size of the file (in bytes)

      - #### m_NumFiles

            int m_NumFiles

        the number of files for rotating the logs

      - #### m_LoggerInitFailed

            boolean m_LoggerInitFailed

        whether the initialization of the logger failed

  - ### Class weka.core.Debug.Random extends java.util.Random implements Serializable

    serialVersionUID:
      1256846887618333956L

    - ### Serialized Fields

      - #### m_Debug

            boolean m_Debug

        whether to output debug information

      - #### m_ID

            long m_ID

        the unique ID for this number generator

      - #### m_Log

            Debug.Log m_Log

        the log to use for outputting the data, otherwise just stdout

  - ### Class weka.core.Debug.SimpleLog extends java.lang.Object implements Serializable

    serialVersionUID:
      -2671928223819510830L

    - ### Serialized Fields

      - #### m_Filename

            java.lang.String m_Filename

        the file to write to (if null then only stdout is used)

  - ### Class weka.core.Debug.Timestamp extends java.lang.Object implements Serializable

    serialVersionUID:
      -6099868388466922753L

    - ### Serialized Fields

      - #### m_Stamp

            java.util.Date m_Stamp

        the actual date

      - #### m_Format

            java.lang.String m_Format

        the format of the timestamp

      - #### m_Formatter

            java.text.SimpleDateFormat m_Formatter

        handles the format of the output

  - ### Class weka.core.DenseInstance extends AbstractInstance implements Serializable

    serialVersionUID:
      1482635194499365122L

  - ### Class weka.core.EuclideanDistance extends NormalizableDistance implements Serializable

    serialVersionUID:
      1068606253458807903L

  - ### Class weka.core.FastVector extends java.util.ArrayList<E> implements Serializable

    serialVersionUID:
      -2173635135622930169L

  - ### Class weka.core.FilteredDistance extends java.lang.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 extends java.lang.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 extends java.util.AbstractList<Instance> implements Serializable

    serialVersionUID:
      -19412345060742748L

    - ### Serialized Fields

      - #### m_RelationName

            java.lang.String m_RelationName

        The dataset's name.

      - #### m_Attributes

            java.util.ArrayList<E> m_Attributes

        The attribute information.

      - #### m_NamesToAttributeIndices

            java.util.HashMap<K,V> m_NamesToAttributeIndices

        A map to quickly find attribute indices based on their names.

      - #### m_Instances

            java.util.ArrayList<E> m_Instances

        The instances.

      - #### m_ClassIndex

            int m_ClassIndex

        The class attribute's index

      - #### m_Lines

            int m_Lines

        The lines read so far in case of incremental loading. Since the StreamTokenizer will be re-initialized with every instance that is read, we have to keep track of the number of lines read so far.
        See Also:
          `Instances.readInstance(Reader)`

  - ### Class weka.core.ManhattanDistance extends NormalizableDistance implements Serializable

    serialVersionUID:
      6783782554224000243L

  - ### Class weka.core.Matrix extends java.lang.Object implements Serializable

    serialVersionUID:
      -3604757095849145838L

    - ### Serialized Fields

      - #### m_Matrix

            Matrix m_Matrix

        Deprecated.
        The actual matrix

  - ### Class weka.core.MinkowskiDistance extends NormalizableDistance implements Serializable

    serialVersionUID:
      -7446019339455453893L

    - ### Serialized Fields

      - #### m_Order

            double m_Order

        the order of the minkowski distance.

  - ### Class weka.core.NominalAttributeInfo extends java.lang.Object implements Serializable

    - ### Serialized Fields

      - #### m_Values

            java.util.ArrayList<E> m_Values

        The attribute's values.

      - #### m_Hashtable

            java.util.Hashtable<K,V> m_Hashtable

        Mapping of values to indices.

  - ### Class weka.core.NormalizableDistance extends java.lang.Object implements Serializable

    serialVersionUID:
      -2806520224161351708L

    - ### Serialized Fields

      - #### m_Data

            Instances m_Data

        the instances used internally.

      - #### m_DontNormalize

            boolean m_DontNormalize

        True if normalization is turned off (default false).

      - #### m_Ranges

            double[][] m_Ranges

        The range of the attributes.

      - #### m_AttributeIndices

            Range m_AttributeIndices

        The range of attributes to use for calculating the distance.

      - #### m_ActiveIndices

            boolean[] m_ActiveIndices

        The boolean flags, whether an attribute will be used or not.

      - #### m_Validated

            boolean m_Validated

        Whether all the necessary preparations have been done.

  - ### Class weka.core.NoSupportForMissingValuesException extends WekaException implements Serializable

    serialVersionUID:
      5161175307725893973L

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

    serialVersionUID:
      3876658672657323985L

    - ### Serialized Fields

      - #### closed

            boolean closed

        the properties need to be open during construction of the object

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

    serialVersionUID:
      -1141282001146389780L

    - ### Serialized Fields

      - #### m_Head

            weka.core.Queue.QueueNode m_Head

        Store a reference to the head of the queue

      - #### m_Tail

            weka.core.Queue.QueueNode m_Tail

        Store a reference to the tail of the queue

      - #### m_Size

            int m_Size

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

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

    serialVersionUID:
      -5119358279412097455L

    - ### Serialized Fields

      - #### m_Next

            weka.core.Queue.QueueNode m_Next

        The next node in the queue

      - #### m_Contents

            java.lang.Object m_Contents

        The nodes contents

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

    serialVersionUID:
      -4763742718209460354L

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

    serialVersionUID:
      3667337062176835900L

    - ### Serialized Fields

      - #### m_RangeStrings

            java.util.ArrayList<E> m_RangeStrings

        Record the string representations of the columns to delete.

      - #### m_Invert

            boolean m_Invert

        Whether matching should be inverted.

      - #### m_SelectFlags

            boolean[] m_SelectFlags

        The array of flags for whether an column is selected.

      - #### m_Upper

            int m_Upper

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

  - ### Class weka.core.RelationalAttributeInfo extends NominalAttributeInfo implements Serializable

    - ### Serialized Fields

      - #### m_Header

            Instances m_Header

        The header information for a relation-valued attribute.

  - ### Class weka.core.RelationalLocator extends AttributeLocator implements Serializable

    serialVersionUID:
      4646872277151854732L

  - ### Class weka.core.SerializedObject extends java.lang.Object implements Serializable

    serialVersionUID:
      6635502953928860434L

    - ### Serialized Fields

      - #### m_storedObjectArray

            byte[] m_storedObjectArray

        The array storing the object.

      - #### m_isCompressed

            boolean m_isCompressed

        Whether or not the object is compressed.

      - #### m_isJython

            boolean m_isJython

        Whether it is a Jython object or not

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

    serialVersionUID:
      5285169134430839303L

    - ### Serialized Fields

      - #### m_IndexString

            java.lang.String m_IndexString

        Record the string representation of the number.

      - #### m_SelectedIndex

            int m_SelectedIndex

        The selected index.

      - #### m_Upper

            int m_Upper

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

  - ### Class weka.core.SparseInstance extends 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 extends AttributeLocator implements Serializable

    serialVersionUID:
      7805522230268783972L

  - ### Class weka.core.Tag extends java.lang.Object implements Serializable

    serialVersionUID:
      3326379903447135320L

    - ### Serialized Fields

      - #### m_ID

            int m_ID

        The ID

      - #### m_IDStr

            java.lang.String m_IDStr

        The unique string for this tag, doesn't have to be numeric

      - #### m_Readable

            java.lang.String m_Readable

        The descriptive text

  - ### Class weka.core.TestInstances extends java.lang.Object implements Serializable

    serialVersionUID:
      -6263968936330390469L

    - ### Serialized Fields

      - #### m_Words

            java.lang.String[] m_Words

        for generating String attributes/classes

      - #### m_WordSeparators

            java.lang.String m_WordSeparators

        for generating String attributes/classes

      - #### m_Relation

            java.lang.String m_Relation

        the name of the relation

      - #### m_Seed

            int m_Seed

        the seed value

      - #### m_Random

            java.util.Random m_Random

        the random number generator

      - #### m_NumInstances

            int m_NumInstances

        the number of instances

      - #### m_ClassType

            int m_ClassType

        the class type

      - #### m_NumClasses

            int m_NumClasses

        the number of classes (in case of NOMINAL class)

      - #### m_ClassIndex

            int m_ClassIndex

        the class index (-1 is LAST, -2 means no class)
        See Also:
          `TestInstances.CLASS_IS_LAST`, `TestInstances.NO_CLASS`

      - #### m_NumNominal

            int m_NumNominal

        the number of nominal attributes

      - #### m_NumNominalValues

            int m_NumNominalValues

        the number of values for nominal attributes

      - #### m_NumNumeric

            int m_NumNumeric

        the number of numeric attributes

      - #### m_NumString

            int m_NumString

        the number of string attributes

      - #### m_NumDate

            int m_NumDate

        the number of date attributes

      - #### m_NumRelational

            int m_NumRelational

        the number of relational attributes

      - #### m_NumRelationalNominal

            int m_NumRelationalNominal

        the number of nominal attributes in a relational attribute

      - #### m_NumRelationalNominalValues

            int m_NumRelationalNominalValues

        the number of values for nominal attributes in relational attributes

      - #### m_NumRelationalNumeric

            int m_NumRelationalNumeric

        the number of numeric attributes in a relational attribute

      - #### m_NumRelationalString

            int m_NumRelationalString

        the number of string attributes in a relational attribute

      - #### m_NumRelationalDate

            int m_NumRelationalDate

        the number of date attributes in a relational attribute

      - #### m_MultiInstance

            boolean m_MultiInstance

        whether to generate Multi-Instance data or not

      - #### m_NumInstancesRelational

            int m_NumInstancesRelational

        the number of instances in relational attributes (applies also for bags in multi-instance)

      - #### m_RelationalFormat

            Instances[] m_RelationalFormat

        the format of the multi-instance data

      - #### m_RelationalClassFormat

            Instances m_RelationalClassFormat

        the format of the multi-instance data of the class

      - #### m_Data

            Instances m_Data

        the generated data

      - #### m_Handler

            CapabilitiesHandler m_Handler

        the CapabilitiesHandler to get the Capabilities from

  - ### Class weka.core.Trie extends java.lang.Object implements Serializable

    serialVersionUID:
      -5897980928817779048L

    - ### Serialized Fields

      - #### m_Root

            Trie.TrieNode m_Root

        the root node

      - #### m_HashCode

            int m_HashCode

        the hash code

      - #### m_RecalcHashCode

            boolean m_RecalcHashCode

        whether the structure got modified and the hash code needs to be re-calculated

  - ### Class weka.core.Trie.TrieNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

    serialVersionUID:
      -2252907099391881148L

    - ### Serialized Fields

      - #### m_Children

            java.util.Hashtable<K,V> m_Children

        for fast access to the children

  - ### Class weka.core.UnassignedClassException extends java.lang.RuntimeException implements Serializable

    serialVersionUID:
      6268278694768818235L

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

    serialVersionUID:
      -9000116786626328854L

  - ### Class weka.core.UnsupportedAttributeTypeException extends WekaException implements Serializable

    serialVersionUID:
      2658987325328414838L

  - ### Class weka.core.UnsupportedClassTypeException extends WekaException implements Serializable

    serialVersionUID:
      5175741076972192151L

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

    serialVersionUID:
      6428269989006208585L

- ## Package weka.core.converters

  - ### Class weka.core.converters.AbstractFileLoader extends AbstractLoader implements Serializable

    serialVersionUID:
      5535537461920594758L

    - ### Serialized Fields

      - #### m_File

            java.lang.String m_File

        the file

      - #### m_sourceFile

            java.io.File m_sourceFile

        Holds the source of the data set.

      - #### m_useRelativePath

            boolean m_useRelativePath

        use relative file paths

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

    serialVersionUID:
      2399441762235754491L

    - ### Serialized Fields

      - #### m_outputFile

            java.io.File m_outputFile

        The destination file.

      - #### FILE_EXTENSION

            java.lang.String FILE_EXTENSION

        The file extension of the destination file.

      - #### FILE_EXTENSION_COMPRESSED

            java.lang.String FILE_EXTENSION_COMPRESSED

        the extension for compressed files

      - #### m_prefix

            java.lang.String m_prefix

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

      - #### m_dir

            java.lang.String m_dir

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

      - #### m_incrementalCounter

            int m_incrementalCounter

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

      - #### m_useRelativePath

            boolean m_useRelativePath

        use relative file paths

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

    serialVersionUID:
      2425432084900694551L

    - ### Serialized Fields

      - #### m_retrieval

            int m_retrieval

        The current retrieval mode

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

    serialVersionUID:
      -27467499727819258L

    - ### Serialized Fields

      - #### m_instances

            Instances m_instances

        The instances that should be stored

      - #### m_retrieval

            int m_retrieval

        The current retrieval mode

      - #### m_writeMode

            int m_writeMode

        The current write mode

      - #### m_DoNotCheckCapabilities

            boolean m_DoNotCheckCapabilities

        Whether capabilities should not be checked

  - ### Class weka.core.converters.ArffLoader extends AbstractFileLoader implements Serializable

    serialVersionUID:
      2726929550544048587L

    - ### Serialized Fields

      - #### m_URL

            java.lang.String m_URL

        the url

      - #### m_retainStringVals

            boolean m_retainStringVals

        Whether the values of string attributes should be retained in memory when reading incrementally

  - ### Class weka.core.converters.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 extends AbstractFileLoader implements Serializable

    serialVersionUID:
      5454329403218219L

    - ### Serialized Fields

      - #### m_sourceFileData

            java.io.File m_sourceFileData

        Describe variable `m_sourceFileData` here.

      - #### m_fileStem

            java.lang.String m_fileStem

        Holds the filestem.

      - #### m_numAttribs

            int m_numAttribs

        Number of attributes in the data (including ignore and label attributes).

      - #### m_ignore

            boolean[] m_ignore

        Which attributes are ignore or label. These are *not* included in the arff representation.

  - ### Class weka.core.converters.C45Saver extends AbstractFileSaver implements Serializable

    serialVersionUID:
      -821428878384253377L

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

    serialVersionUID:
      -2460855349276148760L

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

    serialVersionUID:
      -1504966891136411204L

    - ### Serialized Fields

      - #### m_Saver

            Saver m_Saver

        the saver to use for storing the data.

      - #### m_Stream

            java.io.OutputStream m_Stream

        the stream to store the data in (always in ARFF format).

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

    serialVersionUID:
      -613122395928757332L

    - ### Serialized Fields

      - #### m_File

            java.io.File m_File

        the file to load.

      - #### m_URL

            java.net.URL m_URL

        the URL to load.

      - #### m_Loader

            Loader m_Loader

        the loader.

      - #### m_Incremental

            boolean m_Incremental

        whether the loader is incremental.

      - #### m_BatchCounter

            int m_BatchCounter

        the instance counter for the batch case.

      - #### m_IncrementalBuffer

            Instance m_IncrementalBuffer

        the last internally read instance.

      - #### m_BatchBuffer

            Instances m_BatchBuffer

        the batch buffer.

  - ### Class weka.core.converters.CSVLoader extends AbstractFileLoader implements Serializable

    serialVersionUID:
      -1300595850715808438L

    - ### Serialized Fields

      - #### m_FieldSeparator

            java.lang.String m_FieldSeparator

        the field separator.

      - #### m_MissingValue

            java.lang.String m_MissingValue

        The placeholder for missing values.

      - #### m_NominalAttributes

            Range m_NominalAttributes

        The range of attributes to force to type nominal.

      - #### m_nominalLabelSpecs

            java.util.List<E> m_nominalLabelSpecs

        The user-supplied legal nominal values - each entry in the list is a spec

      - #### m_StringAttributes

            Range m_StringAttributes

        The range of attributes to force to type string.

      - #### m_dateAttributes

            Range m_dateAttributes

        The range of attributes to force to type date

      - #### m_dateFormat

            java.lang.String m_dateFormat

        The formatting string to use to parse dates

      - #### m_formatter

            java.text.SimpleDateFormat m_formatter

        The formatter to use on dates

      - #### m_noHeaderRow

            boolean m_noHeaderRow

        whether the csv file contains a header row with att names

      - #### m_Enclosures

            java.lang.String m_Enclosures

        enclosure character(s) to use for strings

      - #### m_rowBuffer

            java.util.List<E> m_rowBuffer

        The in memory row buffer

      - #### m_bufferSize

            int m_bufferSize

        The maximum number of rows to hold in memory at any one time

      - #### m_nominalVals

            java.util.Map<K,V> m_nominalVals

        Lookup for nominal values

      - #### m_incrementalReader

            ArffLoader.ArffReader m_incrementalReader

        Reader used to process and output data incrementally

      - #### m_fieldSeparatorAndEnclosures

            java.lang.String[] m_fieldSeparatorAndEnclosures

        Array holding field separator and enclosures to pass through to the underlying ArffReader

      - #### m_numBufferedRows

            int m_numBufferedRows

      - #### m_current

            java.util.ArrayList<E> m_current

      - #### m_types

            weka.core.converters.CSVLoader.TYPE[] m_types

  - ### Class weka.core.converters.CSVSaver extends AbstractFileSaver implements Serializable

    serialVersionUID:
      476636654410701807L

    - ### Serialized Fields

      - #### m_FieldSeparator

            java.lang.String m_FieldSeparator

        the field separator.

      - #### m_MissingValue

            java.lang.String m_MissingValue

        The placeholder for missing values.

      - #### m_MaxDecimalPlaces

            int m_MaxDecimalPlaces

        Max number of decimal places for numeric values

      - #### m_noHeaderRow

            boolean m_noHeaderRow

        Set to true to not write the header row

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

    serialVersionUID:
      1673169848863178695L

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

    serialVersionUID:
      -7936159015338318659L

    - ### Serialized Fields

      - #### m_structure

            Instances m_structure

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

      - #### m_datasetPseudoInc

            Instances m_datasetPseudoInc

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

      - #### m_oldStructure

            Instances m_oldStructure

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

      - #### m_DataBaseConnection

            DatabaseConnection m_DataBaseConnection

        The database connection

      - #### m_query

            java.lang.String m_query

        The user defined query to load instances. (form: SELECT *|<column-list> FROM <table> [WHERE <condition>])

      - #### m_pseudoIncremental

            boolean m_pseudoIncremental

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

      - #### m_checkForTable

            boolean m_checkForTable

        If true it checks whether or not the table exists in the database before loading depending on jdbc metadata information. Set flag to false if no check is required or if jdbc metadata is not complete.

      - #### m_nominalToStringLimit

            int m_nominalToStringLimit

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

      - #### m_rowCount

            int m_rowCount

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

      - #### m_counter

            int m_counter

        Indicates how many rows has already been loaded incrementally

      - #### m_choice

            int m_choice

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

      - #### m_firstTime

            boolean m_firstTime

        Flag indicating that incremental process wants to read first instance

      - #### m_inc

            boolean m_inc

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

      - #### m_orderBy

            java.util.ArrayList<E> m_orderBy

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

      - #### m_nominalIndexes

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

        Stores the index of a nominal value

      - #### m_nominalStrings

            java.util.ArrayList<E>[] m_nominalStrings

        Stores the nominal value

      - #### m_idColumn

            java.lang.String m_idColumn

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

      - #### m_URL

            java.lang.String m_URL

        the JDBC URL to use

      - #### m_User

            java.lang.String m_User

        the database user to use

      - #### m_Password

            java.lang.String m_Password

        the database password to use

      - #### m_Keys

            java.lang.String m_Keys

        the keys for unique ordering

      - #### m_CustomPropsFile

            java.io.File m_CustomPropsFile

        the custom props file to use instead of default one.

      - #### m_CreateSparseData

            boolean m_CreateSparseData

        Determines whether sparse data is created

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

    serialVersionUID:
      863971733782624956L

    - ### Serialized Fields

      - #### m_DataBaseConnection

            DatabaseConnection m_DataBaseConnection

        The database connection.

      - #### m_tableName

            java.lang.String m_tableName

        The name of the table in which the instances should be stored.

      - #### m_resolvedTableName

            java.lang.String m_resolvedTableName

        Table name with any environment variables resolved

      - #### m_inputFile

            java.lang.String m_inputFile

        An input arff file (for command line use).

      - #### m_createText

            java.lang.String m_createText

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

      - #### m_createDouble

            java.lang.String m_createDouble

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

      - #### m_createInt

            java.lang.String m_createInt

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

      - #### m_createDate

            java.lang.String m_createDate

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

      - #### m_DateFormat

            java.text.SimpleDateFormat m_DateFormat

        For converting the date value into a database string.

      - #### m_idColumn

            java.lang.String m_idColumn

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

      - #### m_count

            int m_count

        counts the rows and used as a primary key value.

      - #### m_id

            boolean m_id

        Flag indicating if a primary key column should be added.

      - #### m_tabName

            boolean m_tabName

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

      - #### m_URL

            java.lang.String m_URL

        the database URL.

      - #### m_Username

            java.lang.String m_Username

        the user name for the database.

      - #### m_Password

            java.lang.String m_Password

        the password for the database.

      - #### m_CustomPropsFile

            java.io.File m_CustomPropsFile

        the custom props file to use instead of default one.

      - #### m_truncate

            boolean m_truncate

        Whether to truncate (i.e. drop and then recreate) the table if it already exists

  - ### Class weka.core.converters.JSONLoader extends AbstractFileLoader implements Serializable

    serialVersionUID:
      3764533621135196582L

    - ### Serialized Fields

      - #### m_URL

            java.lang.String m_URL

        the url.

      - #### m_JSON

            JSONNode m_JSON

        the loaded JSON object.

  - ### Class weka.core.converters.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 extends AbstractFileLoader implements Serializable

    serialVersionUID:
      4988360125354664417L

    - ### Serialized Fields

      - #### m_URL

            java.lang.String m_URL

        the url.

      - #### m_Buffer

            java.util.Vector<E> m_Buffer

        the buffer of the rows read so far.

  - ### Class weka.core.converters.LibSVMSaver extends AbstractFileSaver implements Serializable

    serialVersionUID:
      2792295817125694786L

    - ### Serialized Fields

      - #### m_ClassIndex

            SingleIndex m_ClassIndex

        the class index

  - ### Class weka.core.converters.Loader.StructureNotReadyException extends java.io.IOException implements Serializable

    serialVersionUID:
      1938493033987645828L

  - ### Class weka.core.converters.MatlabLoader extends AbstractFileLoader implements Serializable

    serialVersionUID:
      -8861142318612875251L

    - ### Serialized Fields

      - #### m_URL

            java.lang.String m_URL

        the url.

      - #### m_Buffer

            java.util.Vector<E> m_Buffer

        the buffer of the rows read so far.

  - ### Class weka.core.converters.MatlabSaver extends AbstractFileSaver implements Serializable

    serialVersionUID:
      4118356803697172614L

    - ### Serialized Fields

      - #### 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.

      - #### m_HeaderWritten

            boolean m_HeaderWritten

        whether the header was written already.

      - #### m_Format

            java.text.DecimalFormat m_Format

        for formatting the numbers.

  - ### Class weka.core.converters.SerializedInstancesLoader extends AbstractFileLoader implements Serializable

    serialVersionUID:
      2391085836269030715L

    - ### Serialized Fields

      - #### m_Dataset

            Instances m_Dataset

        Holds the structure (header) of the data set.

      - #### m_IncrementalIndex

            int m_IncrementalIndex

        The current index position for incremental reading

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

    serialVersionUID:
      -7717010648500658872L

    - ### Serialized Fields

      - #### m_objectstream

            java.io.ObjectOutputStream m_objectstream

        the output stream.

  - ### Class weka.core.converters.StreamTokenizerUtils extends java.lang.Object implements Serializable

    serialVersionUID:
      -5786996944597404253L

  - ### Class weka.core.converters.SVMLightLoader extends AbstractFileLoader implements Serializable

    serialVersionUID:
      4988360125354664417L

    - ### Serialized Fields

      - #### m_URL

            java.lang.String m_URL

        the url.

      - #### m_Buffer

            java.util.Vector<E> m_Buffer

        the buffer of the rows read so far.

  - ### Class weka.core.converters.SVMLightSaver extends AbstractFileSaver implements Serializable

    serialVersionUID:
      2605714599263995835L

    - ### Serialized Fields

      - #### m_ClassIndex

            SingleIndex m_ClassIndex

        the class index.

  - ### Class weka.core.converters.TextDirectoryLoader extends AbstractLoader implements Serializable

    serialVersionUID:
      2592118773712247647L

    - ### Serialized Fields

      - #### m_structure

            Instances m_structure

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

      - #### m_sourceFile

            java.io.File m_sourceFile

        Holds the source of the data set.

      - #### m_Debug

            boolean m_Debug

        whether to print some debug information

      - #### m_OutputFilename

            boolean m_OutputFilename

        whether to include the filename as an extra attribute

      - #### m_charSet

            java.lang.String m_charSet

        The charset to use when loading text files (default is to just use the default charset).

      - #### m_filesByClass

            java.util.List<E> m_filesByClass

      - #### m_lastClassDir

            int m_lastClassDir

  - ### Class weka.core.converters.XRFFLoader extends AbstractFileLoader implements Serializable

    serialVersionUID:
      3764533621135196582L

    - ### Serialized Fields

      - #### m_URL

            java.lang.String m_URL

        the url

      - #### m_XMLInstances

            XMLInstances m_XMLInstances

        the loaded XML document

  - ### Class weka.core.converters.XRFFSaver extends AbstractFileSaver implements Serializable

    serialVersionUID:
      -7226404765213522043L

    - ### Serialized Fields

      - #### m_ClassIndex

            SingleIndex m_ClassIndex

        the class index

      - #### m_XMLInstances

            XMLInstances m_XMLInstances

        the generated XML document

      - #### m_CompressOutput

            boolean m_CompressOutput

        whether to compress the output

- ## Package weka.core.json

  - ### Class weka.core.json.JSONNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

    serialVersionUID:
      -3047440914507883491L

    - ### Serialized Fields

      - #### m_Name

            java.lang.String m_Name

        the name of the node.

      - #### m_Value

            java.lang.Object m_Value

        the value of the node.

      - #### m_NodeType

            JSONNode.NodeType m_NodeType

        the type of the node.

- ## Package weka.core.matrix

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

    serialVersionUID:
      -8739775942782694701L

    - ### Serialized Fields

      - #### L

            double[][] L

        Array for internal storage of decomposition.
        internal array storage.

      - #### n

            int n

        Row and column dimension (square matrix).
        matrix dimension.

      - #### isspd

            boolean isspd

        Symmetric and positive definite flag.
        is symmetric and positive definite flag.

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

    serialVersionUID:
      4011654467211422319L

    - ### Serialized Fields

      - #### n

            int n

        Row and column dimension (square matrix).
        matrix dimension.

      - #### issymmetric

            boolean issymmetric

        Symmetry flag.
        internal symmetry flag.

      - #### d

            double[] d

        Arrays for internal storage of eigenvalues.
        internal storage of eigenvalues.

      - #### e

            double[] e

        Arrays for internal storage of eigenvalues.
        internal storage of eigenvalues.

      - #### V

            double[][] V

        Array for internal storage of eigenvectors.
        internal storage of eigenvectors.

      - #### H

            double[][] H

        Array for internal storage of nonsymmetric Hessenberg form.
        internal storage of nonsymmetric Hessenberg form.

      - #### ort

            double[] ort

        Working storage for nonsymmetric algorithm.
        working storage for nonsymmetric algorithm.

  - ### Class weka.core.matrix.ExponentialFormat extends java.text.DecimalFormat implements Serializable

    serialVersionUID:
      -5298981701073897741L

    - ### Serialized Fields

      - #### nf

            java.text.DecimalFormat nf

      - #### sign

            boolean sign

      - #### digits

            int digits

      - #### exp

            int exp

      - #### trailing

            boolean trailing

  - ### Class weka.core.matrix.FlexibleDecimalFormat extends java.text.DecimalFormat implements Serializable

    serialVersionUID:
      110912192794064140L

    - ### Serialized Fields

      - #### nf

            java.text.DecimalFormat nf

      - #### digits

            int digits

      - #### exp

            boolean exp

      - #### intDigits

            int intDigits

      - #### decimalDigits

            int decimalDigits

      - #### expDecimalDigits

            int expDecimalDigits

      - #### power

            int power

      - #### trailing

            boolean trailing

      - #### grouping

            boolean grouping

      - #### sign

            boolean sign

  - ### Class weka.core.matrix.FloatingPointFormat extends java.text.DecimalFormat implements Serializable

    serialVersionUID:
      4500373755333429499L

    - ### Serialized Fields

      - #### nf

            java.text.DecimalFormat nf

      - #### width

            int width

      - #### decimal

            int decimal

      - #### trailing

            boolean trailing

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

    serialVersionUID:
      -2731022568037808629L

    - ### Serialized Fields

      - #### LU

            double[][] LU

        Array for internal storage of decomposition.
        internal array storage.

      - #### m

            int m

        Row and column dimensions, and pivot sign.
        column dimension.

      - #### n

            int n

        Row and column dimensions, and pivot sign.
        column dimension.

      - #### pivsign

            int pivsign

        Row and column dimensions, and pivot sign.
        column dimension.

      - #### piv

            int[] piv

        Internal storage of pivot vector.
        pivot vector.

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

    serialVersionUID:
      7856794138418366180L

    - ### Serialized Fields

      - #### A

            double[][] A

        Array for internal storage of elements.
        internal array storage.

      - #### m

            int m

        Row and column dimensions.
        row dimension.

      - #### n

            int n

        Row and column dimensions.
        row dimension.

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

    serialVersionUID:
      -5013090736132211418L

    - ### Serialized Fields

      - #### QR

            double[][] QR

        Array for internal storage of decomposition.
        internal array storage.

      - #### m

            int m

        Row and column dimensions.
        column dimension.

      - #### n

            int n

        Row and column dimensions.
        column dimension.

      - #### Rdiag

            double[] Rdiag

        Array for internal storage of diagonal of R.
        diagonal of R.

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

    serialVersionUID:
      -8738089610999867951L

    - ### Serialized Fields

      - #### U

            double[][] U

        Arrays for internal storage of U and V.
        internal storage of U.

      - #### V

            double[][] V

        Arrays for internal storage of U and V.
        internal storage of U.

      - #### s

            double[] s

        Array for internal storage of singular values.
        internal storage of singular values.

      - #### m

            int m

        Row and column dimensions.
        row dimension.

      - #### n

            int n

        Row and column dimensions.
        row dimension.

- ## Package weka.core.neighboursearch

  - ### Class weka.core.neighboursearch.BallTree extends NearestNeighbourSearch implements Serializable

    serialVersionUID:
      728763855952698328L

    - ### Serialized Fields

      - #### m_InstList

            int[] m_InstList

        The instances indices sorted inorder of appearence in the tree from left most leaf node to the right most leaf node.

      - #### m_MaxInstancesInLeaf

            int m_MaxInstancesInLeaf

        The maximum number of instances in a leaf. A node is made into a leaf if the number of instances in it become less than or equal to this value.

      - #### m_TreeStats

            TreePerformanceStats m_TreeStats

        Tree Stats variables.

      - #### m_Root

            BallNode m_Root

        The root node of the BallTree.

      - #### m_TreeConstructor

            BallTreeConstructor m_TreeConstructor

        The constructor method to use to build the tree.

      - #### m_Distances

            double[] m_Distances

        Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours().

  - ### Class weka.core.neighboursearch.CoverTree extends NearestNeighbourSearch implements Serializable

    serialVersionUID:
      7617412821497807586L

    - ### Serialized Fields

      - #### m_EuclideanDistance

            EuclideanDistance m_EuclideanDistance

        The euclidean distance function to use.

      - #### m_Root

            CoverTree.CoverTreeNode m_Root

        The root node.

      - #### m_DistanceList

            double[] m_DistanceList

        Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours().

      - #### m_NumNodes

            int m_NumNodes

        Number of nodes in the tree.

      - #### m_NumLeaves

            int m_NumLeaves

        Number of nodes in the tree.

      - #### m_MaxDepth

            int m_MaxDepth

        Number of nodes in the tree.

      - #### m_TreeStats

            TreePerformanceStats m_TreeStats

        Tree Stats variables.

      - #### m_Base

            double m_Base

        The base of our expansion constant. In other words the 2 in 2^i used in covering tree and separation invariants of a cover tree. P.S.: In paper it's suggested the separation invariant is relaxed in batch construction.

      - #### il2

            double il2

        if we have base 2 then this can be viewed as 1/ln(2), which can be used later on to do il2*ln(d) instead of ln(d)/ln(2), to get log2(d), in get_scale method.

  - ### Class weka.core.neighboursearch.CoverTree.CoverTreeNode extends java.lang.Object implements Serializable

    serialVersionUID:
      1808760031169036512L

    - ### Serialized Fields

      - #### idx

            java.lang.Integer idx

        Index of the instance represented by this node in the index array.

      - #### max_dist

            double max_dist

        The distance of the furthest descendant of the node.

      - #### parent_dist

            double parent_dist

        The distance to the nodes parent.

      - #### children

            Stack<T> children

        The children of the node.

      - #### num_children

            int num_children

        The number of children node has.

      - #### scale

            int scale

        The min i that makes base^i <= max_dist.

  - ### Class weka.core.neighboursearch.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_IndexOfID

            int m_IndexOfID

        The index of the ID attribute

      - #### m_Filter

            Filter m_Filter

        The filter object to use.

      - #### m_SearchMethod

            NearestNeighbourSearch m_SearchMethod

        The neighborhood search method to use.

      - #### m_ModifiedSearchMethod

            NearestNeighbourSearch m_ModifiedSearchMethod

        The modified search method, where ID is skipped

  - ### Class weka.core.neighboursearch.KDTree extends NearestNeighbourSearch implements Serializable

    serialVersionUID:
      1505717283763272533L

    - ### Serialized Fields

      - #### m_DistanceList

            double[] m_DistanceList

        Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours().

      - #### m_InstList

            int[] m_InstList

        Indexlist of the instances of this kdtree. Instances get sorted according to the splits. the nodes of the KDTree just hold their start and end indices

      - #### m_Root

            KDTreeNode m_Root

        The root node of the tree.

      - #### m_Splitter

            KDTreeNodeSplitter m_Splitter

        The node splitter.

      - #### m_NumNodes

            int m_NumNodes

        Tree stats.

      - #### m_NumLeaves

            int m_NumLeaves

        Tree stats.

      - #### m_MaxDepth

            int m_MaxDepth

        Tree stats.

      - #### m_TreeStats

            TreePerformanceStats m_TreeStats

        Tree Stats variables.

      - #### m_NormalizeNodeWidth

            boolean m_NormalizeNodeWidth

        flag for normalizing.

      - #### m_EuclideanDistance

            EuclideanDistance m_EuclideanDistance

        The euclidean distance function to use.

      - #### m_MinBoxRelWidth

            double m_MinBoxRelWidth

        minimal relative width of a KDTree rectangle.

      - #### m_MaxInstInLeaf

            int m_MaxInstInLeaf

        maximal number of instances in a leaf.

  - ### Class weka.core.neighboursearch.LinearNNSearch extends NearestNeighbourSearch implements Serializable

    serialVersionUID:
      1915484723703917241L

    - ### Serialized Fields

      - #### m_Distances

            double[] m_Distances

        Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours().

      - #### m_SkipIdentical

            boolean m_SkipIdentical

        Whether to skip instances from the neighbours that are identical to the query instance.

  - ### Class weka.core.neighboursearch.NearestNeighbourSearch extends java.lang.Object implements Serializable

    serialVersionUID:
      7516898393890379876L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        The neighbourhood of instances to find neighbours in.

      - #### m_kNN

            int m_kNN

        The number of neighbours to find.

      - #### m_DistanceFunction

            DistanceFunction m_DistanceFunction

        the distance function used.

      - #### m_Stats

            PerformanceStats m_Stats

        Performance statistics.

      - #### m_MeasurePerformance

            boolean m_MeasurePerformance

        Should we measure Performance.

  - ### Class weka.core.neighboursearch.PerformanceStats extends java.lang.Object implements Serializable

    serialVersionUID:
      -7215110351388368092L

    - ### Serialized Fields

      - #### m_NumQueries

            int m_NumQueries

        The total number of queries looked at.

      - #### m_MinP

            double m_MinP

        The min and max data points looked for a query by the NNS algorithm.

      - #### m_MaxP

            double m_MaxP

        The min and max data points looked for a query by the NNS algorithm.

      - #### m_SumP

            double m_SumP

        The sum of data points looked at for all the queries.

      - #### m_SumSqP

            double m_SumSqP

        The squared sum of data points looked at for all the queries.

      - #### m_PointCount

            double m_PointCount

        The number of data points looked at for the current/last query.

      - #### m_MinC

            double m_MinC

        The min and max coordinates(attributes) looked at per query.

      - #### m_MaxC

            double m_MaxC

        The min and max coordinates(attributes) looked at per query.

      - #### m_SumC

            double m_SumC

        The sum of coordinates/attributes looked at for all the queries.

      - #### m_SumSqC

            double m_SumSqC

        The squared sum of coordinates/attributes looked at for all the queries.

      - #### m_CoordCount

            double m_CoordCount

        The number of coordinates looked at for the current/last query.

  - ### Class weka.core.neighboursearch.TreePerformanceStats extends PerformanceStats implements Serializable

    serialVersionUID:
      -6637636693340810373L

    - ### Serialized Fields

      - #### m_MinLeaves

            int m_MinLeaves

        The min and max number leaf nodes looked for a query by the tree based NNS algorithm.

      - #### m_MaxLeaves

            int m_MaxLeaves

        The min and max number leaf nodes looked for a query by the tree based NNS algorithm.

      - #### m_SumLeaves

            int m_SumLeaves

        The sum of leaf nodes looked at for all the queries.

      - #### m_SumSqLeaves

            int m_SumSqLeaves

        The squared sum of leaf nodes looked at for all the queries.

      - #### m_LeafCount

            int m_LeafCount

        The number of leaf nodes looked at for the current/last query.

      - #### m_MinIntNodes

            int m_MinIntNodes

        The min and max number internal nodes looked for a query by the tree based NNS algorithm.

      - #### m_MaxIntNodes

            int m_MaxIntNodes

        The min and max number internal nodes looked for a query by the tree based NNS algorithm.

      - #### m_SumIntNodes

            int m_SumIntNodes

        The sum of internal nodes looked at for all the queries.

      - #### m_SumSqIntNodes

            int m_SumSqIntNodes

        The squared sum of internal nodes looked at for all the queries.

      - #### m_IntNodeCount

            int m_IntNodeCount

        The number of internal nodes looked at for the current/last query.

- ## Package weka.core.neighboursearch.balltrees

  - ### Class weka.core.neighboursearch.balltrees.BallNode extends java.lang.Object implements Serializable

    serialVersionUID:
      -8289151861759883510L

    - ### Serialized Fields

      - #### m_Start

            int m_Start

        The start index of the portion of the master index array, which stores the indices of the instances/points the node contains.

      - #### m_End

            int m_End

        The end index of the portion of the master index array, which stores indices of the instances/points the node contains.

      - #### m_NumInstances

            int m_NumInstances

        The number of instances/points in the node.

      - #### m_NodeNumber

            int m_NodeNumber

        The node number/id.

      - #### m_SplitAttrib

            int m_SplitAttrib

        The attribute that splits this node (not always used).

      - #### m_SplitVal

            double m_SplitVal

        The value of m_SpiltAttrib that splits this node (not always used).

      - #### m_Left

            BallNode m_Left

        The left child of the node.

      - #### m_Right

            BallNode m_Right

        The right child of the node.

      - #### m_Pivot

            Instance m_Pivot

        The pivot/centre of the ball.

      - #### m_Radius

            double m_Radius

        The radius of this ball (hyper sphere).

  - ### Class weka.core.neighboursearch.balltrees.BallSplitter extends java.lang.Object implements Serializable

    serialVersionUID:
      -2233739562654159948L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        The instance on which the tree is built.

      - #### m_DistanceFunction

            EuclideanDistance m_DistanceFunction

        The distance function (metric) from which the tree is (OR is to be) built.

      - #### m_Instlist

            int[] m_Instlist

        The master index array that'll be reshuffled as nodes are split (and the tree is constructed).

  - ### Class weka.core.neighboursearch.balltrees.BallTreeConstructor extends java.lang.Object implements Serializable

    serialVersionUID:
      982315539809240771L

    - ### Serialized Fields

      - #### m_MaxInstancesInLeaf

            int m_MaxInstancesInLeaf

        The maximum number of instances allowed in a leaf.

      - #### m_MaxRelLeafRadius

            double m_MaxRelLeafRadius

        The maximum relative radius of a leaf node (relative to the smallest ball enclosing all the data (training) points).

      - #### m_FullyContainChildBalls

            boolean m_FullyContainChildBalls

        Should a parent ball completely enclose the balls of its two children, or only the points inside its children.

      - #### m_Instances

            Instances m_Instances

        The instances on which to build the tree.

      - #### m_DistanceFunction

            DistanceFunction m_DistanceFunction

        The distance function to use to build the tree.

      - #### m_NumNodes

            int m_NumNodes

        The number of internal and leaf nodes in the built tree.

      - #### m_NumLeaves

            int m_NumLeaves

        The number of leaf nodes in the built tree.

      - #### m_MaxDepth

            int m_MaxDepth

        The depth of the built tree.

      - #### m_InstList

            int[] m_InstList

        The master index array.

  - ### Class weka.core.neighboursearch.balltrees.BottomUpConstructor extends BallTreeConstructor implements Serializable

    serialVersionUID:
      5864250777657707687L

  - ### Class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint extends BallSplitter implements Serializable

    serialVersionUID:
      5617378551363700558L

    - ### Serialized Fields

      - #### m_RandSeed

            int m_RandSeed

        Seed for random number generator.

      - #### m_Rand

            java.util.Random m_Rand

        Random number generator for selecting an abitrary (random) point.

  - ### Class weka.core.neighboursearch.balltrees.MedianOfWidestDimension extends BallSplitter implements Serializable

    serialVersionUID:
      3054842574468790421L

    - ### Serialized Fields

      - #### m_NormalizeDimWidths

            boolean m_NormalizeDimWidths

        Should we normalize the widths(ranges) of the dimensions (attributes) before selecting the widest one.

  - ### Class weka.core.neighboursearch.balltrees.MiddleOutConstructor extends BallTreeConstructor implements Serializable

    serialVersionUID:
      -8523314263062524462L

    - ### Serialized Fields

      - #### m_RSeed

            int m_RSeed

        Seed form random number generator.

      - #### rand

            java.util.Random rand

        The random number generator for selecting the first anchor point randomly (if selecting randomly).

      - #### rootRadius

            double rootRadius

        The radius of the smallest ball enclosing all the data points.

      - #### m_RandomInitialAnchor

            boolean m_RandomInitialAnchor

        True if the initial anchor is chosen randomly. False if it is the furthest point from the mean/centroid.

  - ### Class weka.core.neighboursearch.balltrees.MiddleOutConstructor.ListNode extends java.lang.Object implements Serializable

    - ### Serialized Fields

      - #### idx

            int idx

        The index of the point.

      - #### distance

            double distance

        The distance of the point to the anchor.

  - ### Class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList extends java.lang.Object implements Serializable

    serialVersionUID:
      -2283869109722934927L

    - ### Serialized Fields

      - #### m_List

            java.util.ArrayList<E> m_List

        The array list backing this list

  - ### Class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren extends BallSplitter implements Serializable

    serialVersionUID:
      -2947177543565818260L

  - ### Class weka.core.neighboursearch.balltrees.TopDownConstructor extends BallTreeConstructor implements Serializable

    serialVersionUID:
      -5150140645091889979L

    - ### Serialized Fields

      - #### m_Splitter

            BallSplitter m_Splitter

        The BallSplitter algorithm used by the TopDown BallTree constructor, if it is selected.

- ## Package weka.core.neighboursearch.covertrees

  - ### Class weka.core.neighboursearch.covertrees.Stack extends java.lang.Object implements Serializable

    serialVersionUID:
      5604056321825539264L

    - ### Serialized Fields

      - #### length

            int length

        The number of elements in the stack.

      - #### elements

            java.util.ArrayList<E> elements

        The elements inside the stack.

- ## Package weka.core.neighboursearch.kdtrees

  - ### Class weka.core.neighboursearch.kdtrees.KDTreeNode extends java.lang.Object implements Serializable

    serialVersionUID:
      -3660396067582792648L

    - ### Serialized Fields

      - #### m_NodeNumber

            int m_NodeNumber

        node number (only for debug).

      - #### m_Left

            KDTreeNode m_Left

        left subtree; contains instances with smaller or equal to split value.

      - #### m_Right

            KDTreeNode m_Right

        right subtree; contains instances with larger than split value.

      - #### m_SplitValue

            double m_SplitValue

        value to split on.

      - #### m_SplitDim

            int m_SplitDim

        attribute to split on.

      - #### m_NodeRanges

            double[][] m_NodeRanges

        lowest and highest value and width (= high - low) for each dimension.

      - #### m_NodesRectBounds

            double[][] m_NodesRectBounds

        The lo and high bounds of the hyper rectangle described by the node.

      - #### m_Start

            int m_Start

        The start index of the portion of the master index array, which stores the indices of the instances/points the node contains.

      - #### m_End

            int m_End

        The end index of the portion of the master index array, which stores indices of the instances/points the node contains.

  - ### Class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter extends java.lang.Object implements Serializable

    serialVersionUID:
      7222420817095067166L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        The instances that'll be used for tree construction.

      - #### m_EuclideanDistance

            EuclideanDistance m_EuclideanDistance

        The distance function used for building the tree.

      - #### m_InstList

            int[] m_InstList

        The master index array that'll be reshuffled as nodes are split and the tree is constructed.

      - #### m_NormalizeNodeWidth

            boolean m_NormalizeNodeWidth

        Stores whether if the width of a KDTree node is normalized or not.

  - ### Class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod extends KDTreeNodeSplitter implements Serializable

    serialVersionUID:
      -866783749124714304L

  - ### Class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension extends KDTreeNodeSplitter implements Serializable

    serialVersionUID:
      1383443320160540663L

  - ### Class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension extends KDTreeNodeSplitter implements Serializable

    serialVersionUID:
      -7617277960046591906L

  - ### Class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide extends KDTreeNodeSplitter implements Serializable

    serialVersionUID:
      852857628205680562L

- ## Package weka.core.pmml

  - ### Class weka.core.pmml.Array extends java.lang.Object implements Serializable

    serialVersionUID:
      4286234448957826177L

    - ### Serialized Fields

      - #### m_values

            java.util.ArrayList<E> m_values

        The values of the array

      - #### m_type

            Array.ArrayType m_type

        The type of the array

  - ### Class weka.core.pmml.BuiltInArithmetic extends Function implements Serializable

    serialVersionUID:
      2275009453597279459L

    - ### Serialized Fields

      - #### m_operator

            weka.core.pmml.BuiltInArithmetic.Operator m_operator

        The operator for this function

  - ### Class weka.core.pmml.BuiltInMath extends Function implements Serializable

    serialVersionUID:
      -8092338695602573652L

    - ### Serialized Fields

      - #### m_func

            weka.core.pmml.BuiltInMath.MathFunc m_func

        The function to apply

  - ### Class weka.core.pmml.BuiltInString extends Function implements Serializable

    serialVersionUID:
      -7391516909331728653L

    - ### Serialized Fields

      - #### m_func

            weka.core.pmml.BuiltInString.StringFunc m_func

        The function to apply

      - #### m_outputDef

            Attribute m_outputDef

        The output structure produced by this function

  - ### Class weka.core.pmml.Constant extends Expression implements Serializable

    serialVersionUID:
      -304829687822452424L

    - ### Serialized Fields

      - #### m_categoricalConst

            java.lang.String m_categoricalConst

      - #### m_continuousConst

            double m_continuousConst

  - ### Class weka.core.pmml.DefineFunction extends Function implements Serializable

    serialVersionUID:
      -1976646917527243888L

    - ### Serialized Fields

      - #### m_parameters

            java.util.ArrayList<E> m_parameters

        The list of parameters expected by this function. We can use this to do some error/type checking when users call setParameterDefs() on us

      - #### m_optype

            FieldMetaInfo.Optype m_optype

        The optype for this function

      - #### m_expression

            Expression m_expression

        The Expression for this function to use

  - ### Class weka.core.pmml.DefineFunction.ParameterField extends FieldMetaInfo implements Serializable

    serialVersionUID:
      3918895902507585558L

  - ### Class weka.core.pmml.DerivedFieldMetaInfo extends FieldMetaInfo implements Serializable

    serialVersionUID:
      875736989396755241L

    - ### Serialized Fields

      - #### m_displayName

            java.lang.String m_displayName

        display name

      - #### m_values

            java.util.ArrayList<E> m_values

        the list of values (if the field is ordinal) - may be of size zero if none are specified. If none are specified, we may be able to construct this by querying the Expression in this derived field

      - #### m_expression

            Expression m_expression

        the single expression that defines the value of this field

  - ### Class weka.core.pmml.Discretize extends Expression implements Serializable

    serialVersionUID:
      -5809107997906180082L

    - ### Serialized Fields

      - #### m_fieldName

            java.lang.String m_fieldName

        The name of the field to be discretized

      - #### m_fieldIndex

            int m_fieldIndex

        The index of the field

      - #### m_mapMissingDefined

            boolean m_mapMissingDefined

        True if a replacement for missing values has been specified

      - #### m_mapMissingTo

            java.lang.String m_mapMissingTo

        The value of the missing value replacement (if defined)

      - #### m_defaultValueDefined

            boolean m_defaultValueDefined

        True if a default value has been specified

      - #### m_defaultValue

            java.lang.String m_defaultValue

        The default value (if defined)

      - #### m_bins

            java.util.ArrayList<E> m_bins

        The bins for this discretization

      - #### m_outputDef

            Attribute m_outputDef

        The output structure of this discretization

  - ### Class weka.core.pmml.Discretize.DiscretizeBin extends java.lang.Object implements Serializable

    serialVersionUID:
      5810063243316808400L

    - ### Serialized Fields

      - #### m_intervals

            java.util.ArrayList<E> m_intervals

        The intervals for this DiscretizeBin

      - #### m_binValue

            java.lang.String m_binValue

        The bin value for this DiscretizeBin

      - #### 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 extends java.lang.Object implements Serializable

    serialVersionUID:
      4448840549804800321L

    - ### Serialized Fields

      - #### m_opType

            FieldMetaInfo.Optype m_opType

        The optype of this Expression

      - #### m_fieldDefs

            java.util.ArrayList<E> m_fieldDefs

        The field defs

  - ### Class weka.core.pmml.FieldMetaInfo extends java.lang.Object implements Serializable

    serialVersionUID:
      -6116715567129830143L

    - ### Serialized Fields

      - #### m_fieldName

            java.lang.String m_fieldName

        the name of the field

      - #### m_optype

            FieldMetaInfo.Optype m_optype

        The optype for the target

  - ### Class weka.core.pmml.FieldMetaInfo.Interval extends java.lang.Object implements Serializable

    serialVersionUID:
      -7339790632684638012L

    - ### Serialized Fields

      - #### m_leftMargin

            double m_leftMargin

        The left boundary value

      - #### m_rightMargin

            double m_rightMargin

        The right boundary value

      - #### m_closure

            FieldMetaInfo.Interval.Closure m_closure

  - ### Class weka.core.pmml.FieldMetaInfo.Value extends java.lang.Object implements Serializable

    serialVersionUID:
      -3981030320273649739L

    - ### Serialized Fields

      - #### m_value

            java.lang.String m_value

        The value

      - #### m_displayValue

            java.lang.String m_displayValue

        The display value (might hold a human readable value - e.g. product name instead of cryptic code).

      - #### m_property

            FieldMetaInfo.Value.Property m_property

  - ### Class weka.core.pmml.FieldRef extends Expression implements Serializable

    serialVersionUID:
      -8009605897876168409L

    - ### Serialized Fields

      - #### m_fieldName

            java.lang.String m_fieldName

        The name of the field to reference

  - ### Class weka.core.pmml.Function extends java.lang.Object implements Serializable

    serialVersionUID:
      -6997738288201933171L

    - ### Serialized Fields

      - #### m_functionName

            java.lang.String m_functionName

        The name of this function

      - #### m_parameterDefs

            java.util.ArrayList<E> m_parameterDefs

        The structure of the parameters to this function

  - ### Class weka.core.pmml.MappingInfo extends java.lang.Object implements Serializable

    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_nominalValueMaps

            int[][] m_nominalValueMaps

        Map indexes for nominal values in incoming structure to those in the mining schema. There will be as many entries as there are attributes in this array. Non-nominal attributes will have null entries. Each non-null entry is an array of integer indexes. Each entry in a given array (for a given attribute) holds the index of the mining schema value that corresponds to this incoming value. UNKNOWN_NOMINAL_VALUE is used as the index for those incoming values that are not defined in the mining schema.

      - #### m_fieldsMappingText

            java.lang.String m_fieldsMappingText

        Holds a textual description of the fields mapping

      - #### m_log

            Logger m_log

        For logging

  - ### Class weka.core.pmml.MiningFieldMetaInfo extends FieldMetaInfo implements Serializable

    serialVersionUID:
      -1256774332779563185L

    - ### Serialized Fields

      - #### m_usageType

            weka.core.pmml.MiningFieldMetaInfo.Usage m_usageType

        usage type

      - #### m_outlierTreatmentMethod

            weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod

        outlier treatmemnt method

      - #### m_lowValue

            double m_lowValue

        outlier low value

      - #### m_highValue

            double m_highValue

        outlier high value

      - #### m_missingValueTreatmentMethod

            weka.core.pmml.MiningFieldMetaInfo.Missing m_missingValueTreatmentMethod

        missing values treatment method

      - #### m_missingValueReplacementNominal

            java.lang.String m_missingValueReplacementNominal

        actual missing value replacements (if specified)

      - #### m_missingValueReplacementNumeric

            double m_missingValueReplacementNumeric

      - #### m_optypeOverride

            FieldMetaInfo.Optype m_optypeOverride

        optype overrides (override data dictionary type - NOT SUPPORTED AT PRESENT)

      - #### m_index

            int m_index

        the index of the field in the mining schema Instances

      - #### m_importance

            double m_importance

        importance (if defined)

      - #### m_miningSchemaI

            Instances m_miningSchemaI

        mining schema (needed for toString method)

  - ### Class weka.core.pmml.MiningSchema extends java.lang.Object implements Serializable

    serialVersionUID:
      7144380586726330455L

    - ### Serialized Fields

      - #### m_fieldInstancesStructure

            Instances m_fieldInstancesStructure

        The structure of all the fields (both mining schema and derived) as Instances

      - #### m_miningSchemaInstancesStructure

            Instances m_miningSchemaInstancesStructure

        Just the mining schema fields as Instances

      - #### m_miningMeta

            java.util.ArrayList<E> m_miningMeta

        Meta information about the mining schema fields

      - #### m_derivedMeta

            java.util.ArrayList<E> m_derivedMeta

        Meta information about derived fields (those defined in the TransformationDictionary followed by those defined in LocalTransformations)

      - #### m_transformationDictionary

            weka.core.pmml.TransformationDictionary m_transformationDictionary

        The transformation dictionary (if defined)

      - #### m_targetMetaInfo

            TargetMetaInfo m_targetMetaInfo

        target meta info (may be null if not defined)

  - ### Class weka.core.pmml.NormContinuous extends Expression implements Serializable

    serialVersionUID:
      4714332374909851542L

    - ### Serialized Fields

      - #### m_fieldName

            java.lang.String m_fieldName

        The name of the field to use

      - #### m_fieldIndex

            int m_fieldIndex

        The index of the field

      - #### m_mapMissingDefined

            boolean m_mapMissingDefined

        True if a replacement for missing values has been specified

      - #### m_mapMissingTo

            double m_mapMissingTo

        The value of the missing value replacement (if defined)

      - #### m_outlierTreatmentMethod

            weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod

        Outlier treatment method (default = asIs)

      - #### m_linearNormOrig

            double[] m_linearNormOrig

        original values for the LinearNorm entries

      - #### m_linearNormNorm

            double[] m_linearNormNorm

        norm values for the LinearNorm entries

  - ### Class weka.core.pmml.NormDiscrete extends Expression implements Serializable

    serialVersionUID:
      -8854409417983908220L

    - ### Serialized Fields

      - #### m_fieldName

            java.lang.String m_fieldName

        The name of the field to lookup our value in

      - #### m_field

            Attribute m_field

        The actual attribute itself

      - #### m_fieldIndex

            int m_fieldIndex

        The index of the attribute

      - #### m_fieldValue

            java.lang.String m_fieldValue

        The actual value (as a String) that will correspond to an output of 1

      - #### m_mapMissingDefined

            boolean m_mapMissingDefined

        True if a replacement for missing values has been specified

      - #### m_mapMissingTo

            double m_mapMissingTo

        The value of the missing value replacement (if defined)

      - #### m_fieldValueIndex

            int m_fieldValueIndex

        If we are referring to a nominal (rather than String) attribute then this holds the index of the value in question. Will be faster than searching for the value each time.

  - ### Class weka.core.pmml.SparseArray extends Array implements Serializable

    serialVersionUID:
      8129550573612673674L

    - ### Serialized Fields

      - #### m_numValues

            int m_numValues

        The size of the array if known

      - #### m_numNonZero

            int m_numNonZero

        The number of non-zero elements

      - #### m_indices

            java.util.List<E> m_indices

        The indices of the sparse array

  - ### Class weka.core.pmml.TargetMetaInfo extends FieldMetaInfo implements Serializable

    serialVersionUID:
      863500462237904927L

    - ### Serialized Fields

      - #### m_min

            double m_min

        min and max

      - #### m_max

            double m_max

      - #### m_rescaleConstant

            double m_rescaleConstant

        re-scaling of target value (if defined)

      - #### m_rescaleFactor

            double m_rescaleFactor

      - #### m_castInteger

            java.lang.String m_castInteger

        cast integers (default no casting)

      - #### m_defaultValueOrPriorProbs

            double[] m_defaultValueOrPriorProbs

        default value (numeric) or prior distribution (categorical)

      - #### m_values

            java.util.ArrayList<E> m_values

        for categorical values. Actual values

      - #### m_displayValues

            java.util.ArrayList<E> m_displayValues

        corresponding display values

  - ### Class weka.core.pmml.VectorDictionary extends java.lang.Object implements Serializable

    serialVersionUID:
      -5538024467333813123L

    - ### Serialized Fields

      - #### m_numberOfVectors

            int m_numberOfVectors

        The number of support vectors in the dictionary

      - #### m_vectorFields

            java.util.List<E> m_vectorFields

        The fields accessed by the support vectors

      - #### m_vectorInstances

            java.util.Map<K,V> m_vectorInstances

        The vectors in the dictionary

  - ### Class weka.core.pmml.VectorInstance extends java.lang.Object implements Serializable

    serialVersionUID:
      -7543200367512646290L

    - ### Serialized Fields

      - #### m_ID

            java.lang.String m_ID

        The ID of this instance

      - #### m_values

            Array m_values

        The usually sparse elements of this vector

      - #### m_vectorFields

            java.util.List<E> m_vectorFields

        The fields indexed by this VectorInstance

- ## Package weka.core.scripting

  - ### Class weka.core.scripting.Groovy extends java.lang.Object implements Serializable

    serialVersionUID:
      -2628766602043134673L

    - ### Serialized Fields

      - #### m_ClassLoader

            java.lang.Object m_ClassLoader

        the classloader.

  - ### Class weka.core.scripting.Jython extends java.lang.Object implements Serializable

    serialVersionUID:
      -6972298704460209252L

    - ### Serialized Fields

      - #### m_Interpreter

            java.lang.Object m_Interpreter

        the interpreter

- ## Package weka.core.stemmers

  - ### Class weka.core.stemmers.IteratedLovinsStemmer extends LovinsStemmer implements Serializable

    serialVersionUID:
      960689687163788264L

  - ### Class weka.core.stemmers.LovinsStemmer extends java.lang.Object implements Serializable

    serialVersionUID:
      -6113024782588197L

  - ### Class weka.core.stemmers.NullStemmer extends java.lang.Object implements Serializable

    serialVersionUID:
      -3671261636532625496L

  - ### Class weka.core.stemmers.SnowballStemmer extends java.lang.Object implements Serializable

    serialVersionUID:
      -6111170431963015178L

    - ### Serialized Fields

      - #### m_Stemmer

            java.lang.Object m_Stemmer

        the current stemmer.

- ## Package weka.core.stopwords

  - ### Class weka.core.stopwords.AbstractFileBasedStopwords extends AbstractStopwords implements Serializable

    serialVersionUID:
      -8568762652879773063L

    - ### Serialized Fields

      - #### m_Stopwords

            java.io.File m_Stopwords

        a file containing stopwords.

  - ### Class weka.core.stopwords.AbstractStopwords extends java.lang.Object implements Serializable

    serialVersionUID:
      -1975256329586388142L

    - ### Serialized Fields

      - #### m_Initialized

            boolean m_Initialized

        whether the scheme has been initialized.

      - #### m_Debug

            boolean m_Debug

        debugging flag.

  - ### Class weka.core.stopwords.MultiStopwords extends AbstractStopwords implements Serializable

    serialVersionUID:
      -8568762652879773063L

    - ### Serialized Fields

      - #### m_Stopwords

            StopwordsHandler[] m_Stopwords

        the stopwords algorithms to use.

  - ### Class weka.core.stopwords.Null extends AbstractStopwords implements Serializable

    serialVersionUID:
      -3319681866579617385L

  - ### Class weka.core.stopwords.Rainbow extends AbstractStopwords implements Serializable

    serialVersionUID:
      -722795295494945193L

    - ### Serialized Fields

      - #### m_Words

            java.util.HashSet<E> m_Words

        The hash set containing the list of stopwords.

  - ### Class weka.core.stopwords.RegExpFromFile extends AbstractFileBasedStopwords implements Serializable

    serialVersionUID:
      -722795295494945193L

    - ### Serialized Fields

      - #### m_Patterns

            java.util.List<E> m_Patterns

        The list of regular expressions.

  - ### Class weka.core.stopwords.WordsFromFile extends AbstractFileBasedStopwords implements Serializable

    serialVersionUID:
      -722795295494945193L

    - ### Serialized Fields

      - #### m_Words

            java.util.HashSet<E> m_Words

        The hash set containing the list of stopwords.

- ## Package weka.core.tokenizers

  - ### Class weka.core.tokenizers.AlphabeticTokenizer extends Tokenizer implements Serializable

    serialVersionUID:
      6705199562609861697L

    - ### Serialized Fields

      - #### m_Str

            char[] m_Str

        the characters of the string

      - #### m_CurrentPos

            int m_CurrentPos

        the current position

  - ### Class weka.core.tokenizers.CharacterDelimitedTokenizer extends Tokenizer implements Serializable

    serialVersionUID:
      -3091468793633408477L

    - ### Serialized Fields

      - #### m_Delimiters

            java.lang.String m_Delimiters

        Delimiters used in tokenization

  - ### Class weka.core.tokenizers.CharacterNGramTokenizer extends Tokenizer implements Serializable

    serialVersionUID:
      -1181896253171647218L

    - ### Serialized Fields

      - #### m_NMax

            int m_NMax

        the maximum number of N

      - #### m_NMin

            int m_NMin

        the minimum number of N

      - #### m_N

            int m_N

        the current length of the N-grams

      - #### m_CurrentPosition

            int m_CurrentPosition

        the current position for returning elements

      - #### m_String

            java.lang.String m_String

        the string to tokenize

  - ### Class weka.core.tokenizers.NGramTokenizer extends CharacterDelimitedTokenizer implements Serializable

    serialVersionUID:
      -2181896254171647219L

    - ### Serialized Fields

      - #### m_NMax

            int m_NMax

        the maximum number of N

      - #### m_NMin

            int m_NMin

        the minimum number of N

      - #### m_N

            int m_N

        the current length of the N-grams

      - #### m_MaxPosition

            int m_MaxPosition

        the number of strings available

      - #### m_CurrentPosition

            int m_CurrentPosition

        the current position for returning elements

      - #### m_SplitString

            java.lang.String[] m_SplitString

        all the available grams

  - ### Class weka.core.tokenizers.Tokenizer extends java.lang.Object implements Serializable

    serialVersionUID:
      7781271062738973996L

  - ### Class weka.core.tokenizers.WordTokenizer extends CharacterDelimitedTokenizer implements Serializable

    serialVersionUID:
      -930893034037880773L

- ## Package weka.core.xml

  - ### Class weka.core.xml.XMLInstances extends XMLDocument implements Serializable

    serialVersionUID:
      3626821327547416099L

    - ### Serialized Fields

      - #### m_Precision

            int m_Precision

        the precision for numbers

      - #### m_Instances

            Instances m_Instances

        the underlying Instances

- ## Package weka.datagenerators

  - ### Class weka.datagenerators.ClassificationGenerator extends DataGenerator implements Serializable

    serialVersionUID:
      -5261662546673517844L

    - ### Serialized Fields

      - #### m_NumExamples

            int m_NumExamples

        Number of instances

  - ### Class weka.datagenerators.ClusterDefinition extends java.lang.Object implements Serializable

    serialVersionUID:
      -5950001207047429961L

    - ### Serialized Fields

      - #### m_Parent

            ClusterGenerator m_Parent

        the parent of the cluster

  - ### Class weka.datagenerators.ClusterGenerator extends DataGenerator implements Serializable

    serialVersionUID:
      6131722618472046365L

    - ### Serialized Fields

      - #### m_NumAttributes

            int m_NumAttributes

        Number of attribute the dataset should have

      - #### m_ClassFlag

            boolean m_ClassFlag

        class flag

      - #### m_booleanCols

            Range m_booleanCols

        Stores which columns are boolean (default numeric)

      - #### m_nominalCols

            Range m_nominalCols

        Stores which columns are nominal (default numeric)

  - ### Class weka.datagenerators.DataGenerator extends java.lang.Object implements Serializable

    serialVersionUID:
      -3698585946221802578L

    - ### Serialized Fields

      - #### m_Debug

            boolean m_Debug

        Debugging mode

      - #### m_DatasetFormat

            Instances m_DatasetFormat

        The format for the generated dataset

      - #### m_RelationName

            java.lang.String m_RelationName

        Relation name the dataset should have

      - #### m_NumExamplesAct

            int m_NumExamplesAct

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

      - #### m_Seed

            int m_Seed

        random number generator seed

      - #### m_Random

            java.util.Random m_Random

        random number generator

      - #### m_CreatingRelationName

            boolean m_CreatingRelationName

        flag, that indicates whether the relationname is currently assembled

  - ### Class weka.datagenerators.RegressionGenerator extends DataGenerator implements Serializable

    serialVersionUID:
      3073254041275658221L

    - ### Serialized Fields

      - #### m_NumExamples

            int m_NumExamples

        Number of instances

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

    serialVersionUID:
      -8890645875887157782L

    - ### Serialized Fields

      - #### m_AttIndex

            int m_AttIndex

        the attribute index

      - #### m_Split

            double m_Split

        the split

      - #### m_Not

            boolean m_Not

        whether to negate the test

      - #### m_Dataset

            Instances m_Dataset

        the dataset

- ## Package weka.datagenerators.classifiers.classification

  - ### Class weka.datagenerators.classifiers.classification.Agrawal extends ClassificationGenerator implements Serializable

    serialVersionUID:
      2254651939636143025L

    - ### Serialized Fields

      - #### m_Function

            int m_Function

        the function to use for generating the data

      - #### m_BalanceClass

            boolean m_BalanceClass

        whether to balance the class

      - #### m_PerturbationFraction

            double m_PerturbationFraction

        the perturabation fraction

      - #### m_nextClassShouldBeZero

            boolean m_nextClassShouldBeZero

        used for balancing the class

      - #### m_lastLabel

            double m_lastLabel

        the last class label that was generated

  - ### Class weka.datagenerators.classifiers.classification.BayesNet extends ClassificationGenerator implements Serializable

    serialVersionUID:
      -796118162379901512L

    - ### Serialized Fields

      - #### m_Generator

            BayesNetGenerator m_Generator

        the bayesian net generator, that produces the actual data

  - ### Class weka.datagenerators.classifiers.classification.LED24 extends ClassificationGenerator implements Serializable

    serialVersionUID:
      -7880209100415868737L

    - ### Serialized Fields

      - #### m_NoisePercent

            double m_NoisePercent

        the noise rate

      - #### m_numIrrelevantAttributes

            int m_numIrrelevantAttributes

        used for generating the output, i.e., the additional noise attributes

  - ### Class weka.datagenerators.classifiers.classification.RandomRBF extends ClassificationGenerator implements Serializable

    serialVersionUID:
      6069033710635728720L

    - ### Serialized Fields

      - #### m_NumAttributes

            int m_NumAttributes

        Number of attribute the dataset should have

      - #### m_NumClasses

            int m_NumClasses

        Number of Classes the dataset should have

      - #### m_NumCentroids

            int m_NumCentroids

        the number of centroids to use for generation

      - #### m_centroids

            double[][] m_centroids

        the centroids

      - #### m_centroidClasses

            int[] m_centroidClasses

        the classes of the centroids

      - #### m_centroidWeights

            double[] m_centroidWeights

        the weights of the centroids

      - #### m_centroidStdDevs

            double[] m_centroidStdDevs

        the stddevs of the centroids

  - ### Class weka.datagenerators.classifiers.classification.RDG1 extends ClassificationGenerator implements Serializable

    serialVersionUID:
      7751005204635320414L

    - ### Serialized Fields

      - #### m_NumAttributes

            int m_NumAttributes

        Number of attribute the dataset should have

      - #### m_NumClasses

            int m_NumClasses

        Number of Classes the dataset should have

      - #### m_MaxRuleSize

            int m_MaxRuleSize

        maximum rule size

      - #### m_MinRuleSize

            int m_MinRuleSize

        minimum rule size

      - #### m_NumIrrelevant

            int m_NumIrrelevant

        number of irrelevant attributes.

      - #### m_NumNumeric

            int m_NumNumeric

        number of numeric attribute

      - #### m_VoteFlag

            boolean m_VoteFlag

        flag that stores if voting is wished

      - #### m_DecisionList

            java.util.ArrayList<E> m_DecisionList

        decision list

      - #### m_AttList_Irr

            boolean[] m_AttList_Irr

        array defines which attributes are irrelevant, with: true = attribute is irrelevant; false = attribute is not irrelevant

- ## Package weka.datagenerators.classifiers.regression

  - ### Class weka.datagenerators.classifiers.regression.Expression extends MexicanHat implements Serializable

    serialVersionUID:
      -4237047357682277211L

    - ### Serialized Fields

      - #### m_Expression

            java.lang.String m_Expression

        the expression for computing y

      - #### m_Filter

            AddExpression m_Filter

        the filter for generating y out of x

      - #### m_RawData

            Instances m_RawData

        the input data structure for the filter

  - ### Class weka.datagenerators.classifiers.regression.MexicanHat extends RegressionGenerator implements Serializable

    serialVersionUID:
      4577016375261512975L

    - ### Serialized Fields

      - #### m_Amplitude

            double m_Amplitude

        the amplitude of y

      - #### m_MinRange

            double m_MinRange

        the lower boundary of the range, x is drawn from

      - #### m_MaxRange

            double m_MaxRange

        the upper boundary of the range, x is drawn from

      - #### m_NoiseRate

            double m_NoiseRate

        the rate of the gaussian noise

      - #### m_NoiseVariance

            double m_NoiseVariance

        the variance of the gaussian noise

      - #### m_NoiseRandom

            java.util.Random m_NoiseRandom

        the random number generator for the noise

- ## Package weka.datagenerators.clusterers

  - ### Class weka.datagenerators.clusterers.BIRCHCluster extends ClusterGenerator implements Serializable

    serialVersionUID:
      -334820527230755027L

    - ### Serialized Fields

      - #### m_NumClusters

            int m_NumClusters

        Number of Clusters the dataset should have

      - #### m_MinInstNum

            int m_MinInstNum

        minimal number of instances per cluster (option N)

      - #### m_MaxInstNum

            int m_MaxInstNum

        maximal number of instances per cluster (option N)

      - #### m_MinRadius

            double m_MinRadius

        minimum radius (option R)

      - #### m_MaxRadius

            double m_MaxRadius

        maximum radius (option R)

      - #### m_Pattern

            int m_Pattern

        pattern (changed with options G or S)

      - #### m_DistMult

            double m_DistMult

        distance multiplier (option M)

      - #### m_NumCycles

            int m_NumCycles

        number of cycles (option C)

      - #### m_InputOrder

            int m_InputOrder

        input order (changed with option O)

      - #### m_NoiseRate

            double m_NoiseRate

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

      - #### m_ClusterList

            java.util.ArrayList<E> m_ClusterList

        cluster list

      - #### m_GridSize

            int m_GridSize

        grid size

      - #### m_GridWidth

            double m_GridWidth

        grid width

  - ### Class weka.datagenerators.clusterers.SubspaceCluster extends ClusterGenerator implements Serializable

    serialVersionUID:
      -3454999858505621128L

    - ### Serialized Fields

      - #### m_NoiseRate

            double m_NoiseRate

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

      - #### m_Clusters

            ClusterDefinition[] m_Clusters

        cluster list

      - #### m_numValues

            int[] m_numValues

        if nominal, store number of values

      - #### m_globalMinValue

            double[] m_globalMinValue

        store global min values

      - #### m_globalMaxValue

            double[] m_globalMaxValue

        store global max values

  - ### Class weka.datagenerators.clusterers.SubspaceClusterDefinition extends ClusterDefinition implements Serializable

    serialVersionUID:
      3135678125044007231L

    - ### Serialized Fields

      - #### m_clustertype

            int m_clustertype

        cluster type

      - #### m_clustersubtype

            int m_clustersubtype

        cluster subtypes

      - #### m_numClusterAttributes

            int m_numClusterAttributes

        number of attributes the cluster is defined for

      - #### m_numInstances

            int m_numInstances

        number of instances for this cluster

      - #### m_MinInstNum

            int m_MinInstNum

        minimal number of instances for this cluster

      - #### m_MaxInstNum

            int m_MaxInstNum

        maximal number of instances for this cluster

      - #### m_AttrIndexRange

            Range m_AttrIndexRange

        range of atttributes

      - #### m_attributes

            boolean[] m_attributes

        attributes of this cluster

      - #### m_attrIndices

            int[] m_attrIndices

        global indices of the attributes of the cluster

      - #### m_minValue

            double[] m_minValue

        ranges of each attribute (min); not used if gaussian

      - #### m_maxValue

            double[] m_maxValue

        ranges of each attribute (max); not used if gaussian

      - #### m_meanValue

            double[] m_meanValue

        mean ; only used if gaussian

      - #### m_stddevValue

            double[] m_stddevValue

        standarddev; only used if gaussian

- ## Package weka.estimators

  - ### Class weka.estimators.DiscreteEstimator extends Estimator implements Serializable

    serialVersionUID:
      -5526486742612434779L

    - ### Serialized Fields

      - #### m_Counts

            double[] m_Counts

        Hold the counts

      - #### m_SumOfCounts

            double m_SumOfCounts

        Hold the sum of counts

      - #### m_FPrior

            double m_FPrior

        Initialization for counts

  - ### Class weka.estimators.Estimator extends java.lang.Object implements Serializable

    serialVersionUID:
      -5902411487362274342L

    - ### Serialized Fields

      - #### m_Debug

            boolean m_Debug

        Debugging mode

      - #### m_classValueIndex

            double m_classValueIndex

        The class value index is > -1 if subset is taken with specific class value only

      - #### m_noClass

            boolean m_noClass

        set if class is not important

      - #### m_DoNotCheckCapabilities

            boolean m_DoNotCheckCapabilities

        Whether capabilities should not be checked

  - ### Class weka.estimators.KernelEstimator extends Estimator implements Serializable

    serialVersionUID:
      3646923563367683925L

    - ### Serialized Fields

      - #### m_Values

            double[] m_Values

        Vector containing all of the values seen

      - #### m_Weights

            double[] m_Weights

        Vector containing the associated weights

      - #### m_NumValues

            int m_NumValues

        Number of values stored in m_Weights and m_Values so far

      - #### m_SumOfWeights

            double m_SumOfWeights

        The sum of the weights so far

      - #### m_StandardDev

            double m_StandardDev

        The standard deviation

      - #### m_Precision

            double m_Precision

        The precision of data values

      - #### m_AllWeightsOne

            boolean m_AllWeightsOne

        Whether we can optimise the kernel summation

  - ### Class weka.estimators.MahalanobisEstimator extends Estimator implements Serializable

    serialVersionUID:
      8950225468990043868L

    - ### Serialized Fields

      - #### m_CovarianceInverse

            Matrix m_CovarianceInverse

        The inverse of the covariance matrix

      - #### m_Determinant

            double m_Determinant

        The determinant of the covariance matrix

      - #### m_ConstDelta

            double m_ConstDelta

        The difference between the conditioning value and the conditioning mean

      - #### m_ValueMean

            double m_ValueMean

        The mean of the values

  - ### Class weka.estimators.NormalEstimator extends Estimator implements Serializable

    serialVersionUID:
      93584379632315841L

    - ### Serialized Fields

      - #### m_SumOfWeights

            double m_SumOfWeights

        The sum of the weights

      - #### m_SumOfValues

            double m_SumOfValues

        The sum of the values seen

      - #### m_SumOfValuesSq

            double m_SumOfValuesSq

        The sum of the values squared

      - #### m_Mean

            double m_Mean

        The current mean

      - #### m_StandardDev

            double m_StandardDev

        The current standard deviation

      - #### m_Precision

            double m_Precision

        The precision of numeric values ( = minimum std dev permitted)

  - ### Class weka.estimators.PoissonEstimator extends Estimator implements Serializable

    serialVersionUID:
      7669362595289236662L

    - ### Serialized Fields

      - #### m_NumValues

            double m_NumValues

        The number of values seen

      - #### m_SumOfValues

            double m_SumOfValues

        The sum of the values seen

      - #### m_Lambda

            double m_Lambda

        The average number of times an event occurs in an interval.

  - ### Class weka.estimators.UnivariateEqualFrequencyHistogramEstimator extends java.lang.Object implements Serializable

    serialVersionUID:
      -3180287591539683137L

    - ### Serialized Fields

      - #### m_TM

            java.util.TreeMap<K,V> m_TM

        The collection used to store the weighted values.

      - #### m_Boundaries

            double[] m_Boundaries

        The interval boundaries.

      - #### m_Weights

            double[] m_Weights

        The weight of each interval.

      - #### m_WeightedSum

            double m_WeightedSum

        The weighted sum of values

      - #### m_WeightedSumSquared

            double m_WeightedSumSquared

        The weighted sum of squared values

      - #### m_SumOfWeights

            double m_SumOfWeights

        The total sum of weights.

      - #### m_NumBins

            int m_NumBins

        The number of bins to use.

      - #### m_Width

            double m_Width

        The current bandwidth (only computed when needed)

      - #### 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_UpdateWeightsOnly

            boolean m_UpdateWeightsOnly

        Whether boundaries are updated or only weights.

  - ### Class weka.estimators.UnivariateKernelEstimator extends java.lang.Object implements Serializable

    serialVersionUID:
      -1163983347810498880L

    - ### Serialized Fields

      - #### m_TM

            java.util.TreeMap<K,V> 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_SumOfWeights

            double m_SumOfWeights

        The weight of the values collected so far

      - #### m_Width

            double m_Width

        The current bandwidth (only computed when needed)

      - #### 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_Threshold

            double m_Threshold

        Threshold at which further kernels are no longer added to sum.

      - #### m_NumIntervals

            int m_NumIntervals

        The number of intervals used to approximate prediction interval.

  - ### Class weka.estimators.UnivariateMixtureEstimator extends java.lang.Object implements Serializable

    serialVersionUID:
      -2035274930137353656L

    - ### Serialized Fields

      - #### m_Values

            double[] m_Values

        The values used for this estimator

      - #### m_Weights

            double[] m_Weights

        The weights used for this estimator

      - #### m_NumValues

            int m_NumValues

        The number of values that have been seen

      - #### m_MixtureModel

            UnivariateMixtureEstimator.MM m_MixtureModel

        The current mixture model

      - #### m_NumComponents

            int m_NumComponents

        The number of components to use (default is -1)

      - #### m_MaxNumComponents

            int m_MaxNumComponents

        The maximum number of components to use (default is 5)

      - #### m_Seed

            int m_Seed

        The random number seed to use (default is 1

      - #### m_NumBootstrapRuns

            int m_NumBootstrapRuns

        The number of Bootstrap runs to use to select the number of components (default is 10)

      - #### m_NumIntervals

            int m_NumIntervals

        The number of intervals used to approximate prediction interval.

      - #### m_UseNormalizedEntropy

            boolean m_UseNormalizedEntropy

        Whether to use normalized entropy instance of bootstrap.

      - #### m_Debug

            boolean m_Debug

        Whether to output debug info.

      - #### m_Random

            java.util.Random m_Random

        The random number generator.

  - ### Class weka.estimators.UnivariateNormalEstimator extends java.lang.Object implements Serializable

    serialVersionUID:
      -1669009817825826548L

    - ### Serialized Fields

      - #### m_WeightedSum

            double m_WeightedSum

        The weighted sum of values

      - #### m_WeightedSumSquared

            double m_WeightedSumSquared

        The weighted sum of squared values

      - #### m_SumOfWeights

            double m_SumOfWeights

        The weight of the values collected so far

      - #### m_Mean

            double m_Mean

        The mean value (only updated when needed)

      - #### m_Variance

            double m_Variance

        The variance (only updated when needed)

      - #### m_MinVar

            double m_MinVar

        The minimum allowed value of the variance (default: 1.0E-6 * 1.0E-6)

- ## Package weka.experiment

  - ### Class weka.experiment.AveragingResultProducer extends java.lang.Object implements Serializable

    serialVersionUID:
      2551284958501991352L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        The dataset of interest

      - #### m_ResultListener

            ResultListener m_ResultListener

        The ResultListener to send results to

      - #### m_ResultProducer

            ResultProducer m_ResultProducer

        The ResultProducer used to generate results

      - #### m_AdditionalMeasures

            java.lang.String[] m_AdditionalMeasures

        The names of any additional measures to look for in SplitEvaluators

      - #### m_ExpectedResultsPerAverage

            int m_ExpectedResultsPerAverage

        The number of results expected to average over for each run

      - #### m_CalculateStdDevs

            boolean m_CalculateStdDevs

        True if standard deviation fields should be produced

      - #### m_CountFieldName

            java.lang.String m_CountFieldName

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

      - #### m_KeyFieldName

            java.lang.String m_KeyFieldName

        The name of the key field to average over

      - #### m_KeyIndex

            int m_KeyIndex

        The index of the field to average over in the resultproducers key

      - #### m_Keys

            java.util.ArrayList<E> m_Keys

        Collects the keys from a single run

      - #### m_Results

            java.util.ArrayList<E> m_Results

        Collects the results from a single run

  - ### Class weka.experiment.ClassifierSplitEvaluator extends java.lang.Object implements Serializable

    serialVersionUID:
      -8511241602760467265L

    - ### Serialized Fields

      - #### m_Template

            Classifier m_Template

        The template classifier

      - #### m_Classifier

            Classifier m_Classifier

        The classifier used for evaluation

      - #### m_Evaluation

            Evaluation m_Evaluation

        Holds the most recently used Evaluation object

      - #### m_AdditionalMeasures

            java.lang.String[] m_AdditionalMeasures

        The names of any additional measures to look for in SplitEvaluators

      - #### m_doesProduce

            boolean[] m_doesProduce

        Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce

      - #### m_numberAdditionalMeasures

            int m_numberAdditionalMeasures

        The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results

      - #### m_result

            java.lang.String m_result

        Holds the statistics for the most recent application of the classifier

      - #### m_ClassifierOptions

            java.lang.String m_ClassifierOptions

        The classifier options (if any)

      - #### m_ClassifierVersion

            java.lang.String m_ClassifierVersion

        The classifier version

      - #### m_IRclass

            int m_IRclass

        Class index for information retrieval statistics (default 0)

      - #### m_predTargetColumn

            boolean m_predTargetColumn

        Flag for prediction and target columns output.

      - #### m_attID

            int m_attID

        Attribute index of instance identifier (default -1)

      - #### m_NoSizeDetermination

            boolean m_NoSizeDetermination

        whether to skip determination of sizes (train/test/classifier).

      - #### m_pluginMetrics

            java.util.List<E> m_pluginMetrics

      - #### m_numPluginStatistics

            int m_numPluginStatistics

  - ### Class weka.experiment.CostSensitiveClassifierSplitEvaluator extends ClassifierSplitEvaluator implements Serializable

    serialVersionUID:
      -8069566663019501276L

    - ### Serialized Fields

      - #### m_OnDemandDirectory

            java.io.File m_OnDemandDirectory

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

  - ### Class weka.experiment.CrossValidationResultProducer extends java.lang.Object implements Serializable

    serialVersionUID:
      -1580053925080091917L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        The dataset of interest

      - #### m_ResultListener

            ResultListener m_ResultListener

        The ResultListener to send results to

      - #### m_NumFolds

            int m_NumFolds

        The number of folds in the cross-validation

      - #### m_debugOutput

            boolean m_debugOutput

        Save raw output of split evaluators --- for debugging purposes

      - #### m_ZipDest

            OutputZipper m_ZipDest

        The output zipper to use for saving raw splitEvaluator output

      - #### m_OutputFile

            java.io.File m_OutputFile

        The destination output file/directory for raw output

      - #### m_SplitEvaluator

            SplitEvaluator m_SplitEvaluator

        The SplitEvaluator used to generate results

      - #### m_AdditionalMeasures

            java.lang.String[] m_AdditionalMeasures

        The names of any additional measures to look for in SplitEvaluators

  - ### Class weka.experiment.CrossValidationSplitResultProducer extends CrossValidationResultProducer implements Serializable

    serialVersionUID:
      1403798164046795073L

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

    serialVersionUID:
      -623185072785174658L

    - ### Serialized Fields

      - #### m_RP

            ResultProducer m_RP

        The ResultProducer sending us results

      - #### m_OutputFile

            java.io.File m_OutputFile

        The destination output file, null sends to System.out

      - #### m_OutputFileName

            java.lang.String m_OutputFileName

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

  - ### Class weka.experiment.DatabaseResultListener extends DatabaseUtils implements Serializable

    serialVersionUID:
      7388014746954652818L

    - ### Serialized Fields

      - #### m_ResultProducer

            ResultProducer m_ResultProducer

        The ResultProducer to listen to

      - #### m_ResultsTableName

            java.lang.String m_ResultsTableName

        The name of the current results table

      - #### m_CacheKeyName

            java.lang.String m_CacheKeyName

        Holds the name of the key field to cache upon, or null if no caching

      - #### m_CacheKeyIndex

            int m_CacheKeyIndex

        Stores the index of the key column holding the cache key data

      - #### m_CacheKey

            java.lang.Object[] m_CacheKey

        Stores the key for which the cache is valid

      - #### m_Cache

            java.util.ArrayList<E> m_Cache

        Stores the cached values

  - ### Class weka.experiment.DatabaseResultProducer extends DatabaseResultListener implements Serializable

    serialVersionUID:
      -5620660780203158666L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        The dataset of interest

      - #### m_ResultListener

            ResultListener m_ResultListener

        The ResultListener to send results to

      - #### m_AdditionalMeasures

            java.lang.String[] m_AdditionalMeasures

        The names of any additional measures to look for in SplitEvaluators

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

    serialVersionUID:
      -8252351994547116729L

    - ### Serialized Fields

      - #### DRIVERS

            java.util.Vector<E> DRIVERS

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

      - #### PROPERTIES

            java.util.Properties PROPERTIES

        Properties associated with the database connection.

      - #### m_DatabaseURL

            java.lang.String m_DatabaseURL

        Database URL.

      - #### m_Debug

            boolean m_Debug

        True if debugging output should be printed.

      - #### m_userName

            java.lang.String m_userName

        Database username.

      - #### m_password

            java.lang.String m_password

        Database Password.

      - #### m_stringType

            java.lang.String m_stringType

        string type for the create table statement.

      - #### m_intType

            java.lang.String m_intType

        integer type for the create table statement.

      - #### m_doubleType

            java.lang.String m_doubleType

        double type for the create table statement.

      - #### m_checkForUpperCaseNames

            boolean m_checkForUpperCaseNames

        For databases where Tables and Columns are created in upper case.

      - #### m_checkForLowerCaseNames

            boolean m_checkForLowerCaseNames

        For databases where Tables and Columns are created in lower case.

      - #### m_setAutoCommit

            boolean m_setAutoCommit

        setAutoCommit on the database?

      - #### m_createIndex

            boolean m_createIndex

        create index on the database?

      - #### m_Keywords

            java.util.HashSet<E> m_Keywords

        the keywords for the current database type.

      - #### m_KeywordsMaskChar

            java.lang.String m_KeywordsMaskChar

        the character to mask SQL keywords (by appending this character).

  - ### Class weka.experiment.DensityBasedClustererSplitEvaluator extends java.lang.Object implements Serializable

    serialVersionUID:
      5124501059135692160L

    - ### Serialized Fields

      - #### m_removeClassColumn

            boolean m_removeClassColumn

        Remove the class column (if set) from the data

      - #### m_clusterer

            DensityBasedClusterer m_clusterer

        The clusterer used for evaluation

      - #### m_Evaluation

            ClusterEvaluation m_Evaluation

        Holds the most recently used ClusterEvaluation object

      - #### m_additionalMeasures

            java.lang.String[] m_additionalMeasures

        The names of any additional measures to look for in SplitEvaluators

      - #### m_doesProduce

            boolean[] m_doesProduce

        Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current clusterer can produce

      - #### m_numberAdditionalMeasures

            int m_numberAdditionalMeasures

        The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results

      - #### m_result

            java.lang.String m_result

        Holds the statistics for the most recent application of the clusterer

      - #### m_clustererOptions

            java.lang.String m_clustererOptions

        The clusterer options (if any)

      - #### m_clustererVersion

            java.lang.String m_clustererVersion

        The clusterer version

      - #### m_NoSizeDetermination

            boolean m_NoSizeDetermination

        whether to skip determination of sizes (train/test/classifier).

  - ### Class weka.experiment.Experiment extends java.lang.Object implements Serializable

    serialVersionUID:
      44945596742646663L

    - ### Serialized Fields

      - #### m_ResultListener

            ResultListener m_ResultListener

        Where results will be sent

      - #### m_ResultProducer

            ResultProducer m_ResultProducer

        The result producer

      - #### m_RunLower

            int m_RunLower

        Lower run number

      - #### m_RunUpper

            int m_RunUpper

        Upper run number

      - #### m_Datasets

            javax.swing.DefaultListModel<E> m_Datasets

        An array of dataset files

      - #### m_UsePropertyIterator

            boolean m_UsePropertyIterator

        True if the exp should also iterate over a property of the RP

      - #### m_PropertyPath

            PropertyNode[] m_PropertyPath

        The path to the iterator property

      - #### m_PropertyArray

            java.lang.Object m_PropertyArray

        The array of values to set the property to

      - #### m_Notes

            java.lang.String m_Notes

        User notes about the experiment

      - #### m_AdditionalMeasures

            java.lang.String[] m_AdditionalMeasures

        Method names of additional measures of objects contained in the custom property iterator. Only methods names beginning with "measure" and returning doubles are recognised

      - #### m_ClassFirst

            boolean m_ClassFirst

        True if the class attribute is the first attribute for all datasets involved in this experiment.

      - #### m_AdvanceDataSetFirst

            boolean m_AdvanceDataSetFirst

        If true an experiment will advance the current data set befor any custom itererator

  - ### Class weka.experiment.ExplicitTestsetResultProducer extends java.lang.Object implements Serializable

    serialVersionUID:
      2613585409333652530L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        The dataset of interest.

      - #### m_ResultListener

            ResultListener m_ResultListener

        The ResultListener to send results to.

      - #### m_TestsetDir

            java.io.File m_TestsetDir

        The directory containing all the test sets.

      - #### m_TestsetPrefix

            java.lang.String m_TestsetPrefix

        The prefix for all the test sets.

      - #### m_TestsetSuffix

            java.lang.String m_TestsetSuffix

        The suffix for all the test sets.

      - #### m_RelationFind

            java.lang.String m_RelationFind

        The regular expression to search for in the relation name.

      - #### m_RelationReplace

            java.lang.String m_RelationReplace

        The string to use to replace the matches of the regular expression.

      - #### m_randomize

            boolean m_randomize

        Whether dataset is to be randomized.

      - #### m_SplitEvaluator

            SplitEvaluator m_SplitEvaluator

        The SplitEvaluator used to generate results.

      - #### m_AdditionalMeasures

            java.lang.String[] m_AdditionalMeasures

        The names of any additional measures to look for in SplitEvaluators.

      - #### m_debugOutput

            boolean m_debugOutput

        Save raw output of split evaluators --- for debugging purposes.

      - #### m_ZipDest

            OutputZipper m_ZipDest

        The output zipper to use for saving raw splitEvaluator output.

      - #### m_OutputFile

            java.io.File m_OutputFile

        The destination output file/directory for raw output.

  - ### Class weka.experiment.InstanceQuery extends DatabaseUtils implements Serializable

    serialVersionUID:
      718158370917782584L

    - ### Serialized Fields

      - #### m_CreateSparseData

            boolean m_CreateSparseData

        Determines whether sparse data is created

      - #### m_Query

            java.lang.String m_Query

        Query to execute

      - #### m_CustomPropsFile

            java.io.File m_CustomPropsFile

        the custom props file to use instead of default one.

  - ### Class weka.experiment.InstancesResultListener extends CSVResultListener implements Serializable

    serialVersionUID:
      -2203808461809311178L

  - ### Class weka.experiment.LearningRateResultProducer extends java.lang.Object implements Serializable

    serialVersionUID:
      -3841159673490861331L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        The dataset of interest

      - #### m_ResultListener

            ResultListener m_ResultListener

        The ResultListener to send results to

      - #### m_ResultProducer

            ResultProducer m_ResultProducer

        The ResultProducer used to generate results

      - #### m_AdditionalMeasures

            java.lang.String[] m_AdditionalMeasures

        The names of any additional measures to look for in SplitEvaluators

      - #### m_LowerSize

            int m_LowerSize

        The minimum number of instances to use. If this is zero, the first step will contain m_StepSize instances

      - #### m_UpperSize

            int m_UpperSize

        The maximum number of instances to use. -1 indicates no maximum (other than the total number of instances)

      - #### m_StepSize

            int m_StepSize

        The number of instances to add at each step

      - #### m_CurrentSize

            int m_CurrentSize

        The current dataset size during stepping

  - ### Class weka.experiment.PairedCorrectedTTester extends PairedTTester implements Serializable

    serialVersionUID:
      -3105268939845653323L

  - ### Class weka.experiment.PairedTTester extends java.lang.Object implements Serializable

    serialVersionUID:
      8370014624008728610L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        The set of instances we will analyse

      - #### m_RunColumn

            int m_RunColumn

        The index of the column containing the run number

      - #### m_RunColumnSet

            int m_RunColumnSet

        The option setting for the run number column (-1 means last)

      - #### m_FoldColumn

            int m_FoldColumn

        The option setting for the fold number column (-1 means none)

      - #### m_SortColumn

            int m_SortColumn

        The column to sort on (-1 means default sorting)

      - #### m_SortOrder

            int[] m_SortOrder

        The sorting of the datasets (according to the sort column)

      - #### m_ColOrder

            int[] m_ColOrder

        The sorting of the columns (test base is always first)

      - #### m_SignificanceLevel

            double m_SignificanceLevel

        The significance level for comparisons

      - #### m_DatasetKeyColumnsRange

            Range m_DatasetKeyColumnsRange

        The range of columns that specify a unique "dataset" (eg: scheme plus configuration)

      - #### m_DatasetKeyColumns

            int[] m_DatasetKeyColumns

        An array containing the indexes of just the selected columns

      - #### m_DatasetSpecifiers

            weka.experiment.PairedTTester.DatasetSpecifiers m_DatasetSpecifiers

        The list of dataset specifiers

      - #### m_ResultsetKeyColumnsRange

            Range m_ResultsetKeyColumnsRange

        The range of columns that specify a unique result set (eg: scheme plus configuration)

      - #### m_ResultsetKeyColumns

            int[] m_ResultsetKeyColumns

        An array containing the indexes of just the selected columns

      - #### m_DisplayedResultsets

            int[] m_DisplayedResultsets

        An array containing the indexes of the datasets to display

      - #### m_Resultsets

            java.util.ArrayList<E> m_Resultsets

        Stores a vector for each resultset holding all instances in each set

      - #### m_ResultsetsValid

            boolean m_ResultsetsValid

        Indicates whether the instances have been partitioned

      - #### m_ShowStdDevs

            boolean m_ShowStdDevs

        Indicates whether standard deviations should be displayed

      - #### m_ResultMatrix

            ResultMatrix m_ResultMatrix

        the instance of the class to produce the output.

  - ### Class weka.experiment.PairedTTester.Dataset extends java.lang.Object implements Serializable

    serialVersionUID:
      -2801397601839433282L

    - ### Serialized Fields

      - #### m_Template

            Instance m_Template

        the template

      - #### m_Dataset

            java.util.ArrayList<E> m_Dataset

        the dataset

  - ### Class weka.experiment.PairedTTester.DatasetSpecifiers extends java.lang.Object implements Serializable

    serialVersionUID:
      -9020938059902723401L

    - ### Serialized Fields

      - #### m_Specifiers

            java.util.ArrayList<E> m_Specifiers

        the specifiers that have been observed

  - ### Class weka.experiment.PairedTTester.Resultset extends java.lang.Object implements Serializable

    serialVersionUID:
      1543786683821339978L

    - ### Serialized Fields

      - #### m_Template

            Instance m_Template

        the template

      - #### m_Datasets

            java.util.ArrayList<E> m_Datasets

        the dataset

  - ### Class weka.experiment.PropertyNode extends java.lang.Object implements Serializable

    serialVersionUID:
      -8718165742572631384L

    - ### Serialization Methods

      - #### readObject

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

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

      - #### writeObject

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

        Throws:
          `java.io.IOException`

    - ### Serialized Fields

      - #### value

            java.lang.Object value

        The current property value

      - #### parentClass

            java.lang.Class<T> parentClass

        The class of the object with this property

      - #### property

            java.beans.PropertyDescriptor property

        Other info about the property

  - ### Class weka.experiment.RandomSplitResultProducer extends java.lang.Object implements Serializable

    serialVersionUID:
      1403798165056795073L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        The dataset of interest

      - #### m_ResultListener

            ResultListener m_ResultListener

        The ResultListener to send results to

      - #### m_TrainPercent

            double m_TrainPercent

        The percentage of instances to use for training

      - #### m_randomize

            boolean m_randomize

        Whether dataset is to be randomized

      - #### m_SplitEvaluator

            SplitEvaluator m_SplitEvaluator

        The SplitEvaluator used to generate results

      - #### m_AdditionalMeasures

            java.lang.String[] m_AdditionalMeasures

        The names of any additional measures to look for in SplitEvaluators

      - #### m_debugOutput

            boolean m_debugOutput

        Save raw output of split evaluators --- for debugging purposes

      - #### m_ZipDest

            OutputZipper m_ZipDest

        The output zipper to use for saving raw splitEvaluator output

      - #### m_OutputFile

            java.io.File m_OutputFile

        The destination output file/directory for raw output

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

    serialVersionUID:
      -328181640503349202L

    - ### Serialized Fields

      - #### m_Template

            Classifier m_Template

        The template classifier

      - #### m_Classifier

            Classifier m_Classifier

        The classifier used for evaluation

      - #### m_Evaluation

            Evaluation m_Evaluation

        Holds the most recently used Evaluation object

      - #### m_AdditionalMeasures

            java.lang.String[] m_AdditionalMeasures

        The names of any additional measures to look for in SplitEvaluators

      - #### m_doesProduce

            boolean[] m_doesProduce

        Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce

      - #### m_result

            java.lang.String m_result

        Holds the statistics for the most recent application of the classifier

      - #### m_ClassifierOptions

            java.lang.String m_ClassifierOptions

        The classifier options (if any)

      - #### m_ClassifierVersion

            java.lang.String m_ClassifierVersion

        The classifier version

      - #### m_NoSizeDetermination

            boolean m_NoSizeDetermination

        whether to skip determination of sizes (train/test/classifier).

      - #### m_pluginMetrics

            java.util.List<E> m_pluginMetrics

      - #### m_numPluginStatistics

            int m_numPluginStatistics

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

    serialVersionUID:
      -1021538162895448259L

    - ### Serialized Fields

      - #### m_HostName

            java.lang.String m_HostName

        The name of the host that this engine is started on

      - #### m_TaskQueue

            Queue m_TaskQueue

        A queue of waiting tasks

      - #### m_TaskIdQueue

            Queue m_TaskIdQueue

        A queue of corresponding ID's for tasks

      - #### m_TaskStatus

            java.util.Hashtable<K,V> m_TaskStatus

        A hashtable of experiment status

      - #### m_TaskRunning

            boolean m_TaskRunning

        Is there a task running

  - ### Class weka.experiment.RemoteEngine_Stub extends java.rmi.server.RemoteStub implements Serializable

    serialVersionUID:
      2L

  - ### Class weka.experiment.RemoteExperiment extends Experiment implements Serializable

    serialVersionUID:
      -7357668825635314937L

    - ### Serialized Fields

      - #### m_listeners

            java.util.ArrayList<E> m_listeners

        The list of objects listening for remote experiment events

      - #### m_remoteHosts

            javax.swing.DefaultListModel<E> m_remoteHosts

        Holds the names of machines with remoteEngine servers running

      - #### m_remoteHostsQueue

            Queue m_remoteHostsQueue

        The queue of available hosts

      - #### m_remoteHostsStatus

            int[] m_remoteHostsStatus

        The status of each of the remote hosts

      - #### m_remoteHostFailureCounts

            int[] m_remoteHostFailureCounts

        The number of times tasks have failed on each remote host

      - #### m_experimentAborted

            boolean m_experimentAborted

        Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts experiment (via gui)

      - #### m_removedHosts

            int m_removedHosts

        The number of hosts removed due to exceeding max failures

      - #### m_failedCount

            int m_failedCount

        The count of failed sub-experiments

      - #### m_finishedCount

            int m_finishedCount

        The count of successfully completed sub-experiments

      - #### m_baseExperiment

            Experiment m_baseExperiment

        The base experiment to split up into sub experiments for remote execution

      - #### m_subExperiments

            Experiment[] m_subExperiments

        The sub experiments

      - #### m_subExpQueue

            Queue m_subExpQueue

        The queue of sub experiments waiting to be processed

      - #### m_subExpComplete

            int[] m_subExpComplete

        The status of each of the sub-experiments

      - #### m_splitByDataSet

            boolean m_splitByDataSet

        If true, then sub experiments are created on the basis of data sets rather than run number.

  - ### Class weka.experiment.RemoteExperimentEvent extends java.lang.Object implements Serializable

    serialVersionUID:
      7000867987391866451L

    - ### Serialized Fields

      - #### m_statusMessage

            boolean m_statusMessage

        A status type message

      - #### m_logMessage

            boolean m_logMessage

        A log type message

      - #### m_messageString

            java.lang.String m_messageString

        The message

      - #### m_experimentFinished

            boolean m_experimentFinished

        True if a remote experiment has finished

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

    serialVersionUID:
      -1674092706571603720L

    - ### Serialized Fields

      - #### m_result

            TaskStatusInfo m_result

      - #### m_experiment

            Experiment m_experiment

  - ### Class weka.experiment.ResultMatrix extends java.lang.Object implements Serializable

    serialVersionUID:
      4487179306428209739L

    - ### Serialized Fields

      - #### TIE_STRING

            java.lang.String TIE_STRING

        tie string.

      - #### WIN_STRING

            java.lang.String WIN_STRING

        win string.

      - #### LOSS_STRING

            java.lang.String LOSS_STRING

        loss string.

      - #### LEFT_PARENTHESES

            java.lang.String LEFT_PARENTHESES

        the left parentheses for enumerating cols/rows.

      - #### RIGHT_PARENTHESES

            java.lang.String RIGHT_PARENTHESES

        the right parentheses for enumerating cols/rows.

      - #### m_ColNames

            java.lang.String[] m_ColNames

        the column names.

      - #### m_RowNames

            java.lang.String[] m_RowNames

        the row names.

      - #### m_ColHidden

            boolean[] m_ColHidden

        whether a column is hidden.

      - #### m_RowHidden

            boolean[] m_RowHidden

        whether a row is hidden.

      - #### m_Significance

            int[][] m_Significance

        the significance.

      - #### m_Mean

            double[][] m_Mean

        the values.

      - #### m_StdDev

            double[][] m_StdDev

        the standard deviation.

      - #### m_Counts

            double[] m_Counts

        the counts for the different datasets.

      - #### m_MeanPrec

            int m_MeanPrec

        the standard mean precision.

      - #### m_StdDevPrec

            int m_StdDevPrec

        the standard std. deviation preicision.

      - #### m_ShowStdDev

            boolean m_ShowStdDev

        whether std. deviations are printed as well.

      - #### m_ShowAverage

            boolean m_ShowAverage

        whether the average for each column should be printed.

      - #### m_PrintColNames

            boolean m_PrintColNames

        whether the names or numbers are output as column declarations.

      - #### m_PrintRowNames

            boolean m_PrintRowNames

        whether the names or numbers are output as row declarations.

      - #### m_EnumerateColNames

            boolean m_EnumerateColNames

        whether a "(x)" is printed before each column name with "x" as the index.

      - #### m_EnumerateRowNames

            boolean m_EnumerateRowNames

        whether a "(x)" is printed before each row name with "x" as the index.

      - #### m_ColNameWidth

            int m_ColNameWidth

        the size of the names of the columns.

      - #### m_RowNameWidth

            int m_RowNameWidth

        the size of the names of the rows.

      - #### m_MeanWidth

            int m_MeanWidth

        the size of the mean columns.

      - #### m_StdDevWidth

            int m_StdDevWidth

        the size of the std dev columns.

      - #### m_SignificanceWidth

            int m_SignificanceWidth

        the size of the significance columns.

      - #### m_CountWidth

            int m_CountWidth

        the size of the counts.

      - #### m_HeaderKeys

            java.util.Vector<E> m_HeaderKeys

        contains the keys for the header.

      - #### m_HeaderValues

            java.util.Vector<E> m_HeaderValues

        contains the values for the header.

      - #### m_NonSigWins

            int[][] m_NonSigWins

        the non-significant wins.

      - #### m_Wins

            int[][] m_Wins

        the significant wins.

      - #### m_RankingWins

            int[] m_RankingWins

        the wins in ranking.

      - #### m_RankingLosses

            int[] m_RankingLosses

        the losses in ranking.

      - #### m_RankingDiff

            int[] m_RankingDiff

        the difference between wins and losses.

      - #### m_RowOrder

            int[] m_RowOrder

        the ordering of the rows.

      - #### m_ColOrder

            int[] m_ColOrder

        the ordering of the columns.

      - #### m_RemoveFilterName

            boolean m_RemoveFilterName

        whether to remove the filter name from the dataaset name.

  - ### Class weka.experiment.ResultMatrixCSV extends ResultMatrix implements Serializable

    serialVersionUID:
      -171838863135042743L

  - ### Class weka.experiment.ResultMatrixGnuPlot extends ResultMatrix implements Serializable

    serialVersionUID:
      -234648254944790097L

  - ### Class weka.experiment.ResultMatrixHTML extends ResultMatrix implements Serializable

    serialVersionUID:
      6672380422544799990L

  - ### Class weka.experiment.ResultMatrixLatex extends ResultMatrix implements Serializable

    serialVersionUID:
      777690788447600978L

  - ### Class weka.experiment.ResultMatrixPlainText extends ResultMatrix implements Serializable

    serialVersionUID:
      1502934525382357937L

  - ### Class weka.experiment.ResultMatrixSignificance extends ResultMatrix implements Serializable

    serialVersionUID:
      -1280545644109764206L

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

    serialVersionUID:
      -8610544539090024102L

    - ### Serialized Fields

      - #### count

            double count

        The number of values seen

      - #### sum

            double sum

        The sum of values seen

      - #### sumSq

            double sumSq

        The sum of values squared seen

      - #### stdDev

            double stdDev

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

      - #### mean

            double mean

        The mean of values at the last calculateDerived() call

      - #### min

            double min

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

      - #### max

            double max

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

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

    serialVersionUID:
      -6129343303703560015L

    - ### Serialized Fields

      - #### m_ExecutionStatus

            int m_ExecutionStatus

        Holds current execution status.

      - #### m_StatusMessage

            java.lang.String m_StatusMessage

        Holds current status message.

      - #### m_TaskResult

            java.lang.Object m_TaskResult

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

- ## Package weka.filters

  - ### Class weka.filters.AllFilter extends Filter implements Serializable

    serialVersionUID:
      5022109283147503266L

  - ### Class weka.filters.Filter extends java.lang.Object implements Serializable

    serialVersionUID:
      -8835063755891851218L

    - ### Serialized Fields

      - #### m_OutputFormat

            Instances m_OutputFormat

        The output format for instances

      - #### m_OutputQueue

            Queue m_OutputQueue

        The output instance queue

      - #### m_OutputStringAtts

            StringLocator m_OutputStringAtts

        Indices of string attributes in the output format

      - #### m_InputStringAtts

            StringLocator m_InputStringAtts

        Indices of string attributes in the input format

      - #### m_OutputRelAtts

            RelationalLocator m_OutputRelAtts

        Indices of relational attributes in the output format

      - #### m_InputRelAtts

            RelationalLocator m_InputRelAtts

        Indices of relational attributes in the input format

      - #### m_InputFormat

            Instances m_InputFormat

        The input format for instances

      - #### m_NewBatch

            boolean m_NewBatch

        Record whether the filter is at the start of a batch

      - #### m_FirstBatchDone

            boolean m_FirstBatchDone

        True if the first batch has been done

      - #### 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.

  - ### Class weka.filters.MultiFilter extends SimpleStreamFilter implements Serializable

    serialVersionUID:
      -6293720886005713120L

    - ### Serialized Fields

      - #### m_Filters

            Filter[] m_Filters

        The filters

      - #### m_Streamable

            boolean m_Streamable

        caches the streamable state

      - #### m_StreamableChecked

            boolean m_StreamableChecked

        whether we already checked the streamable state

  - ### Class weka.filters.SimpleBatchFilter extends SimpleFilter implements Serializable

    serialVersionUID:
      8102908673378055114L

  - ### Class weka.filters.SimpleFilter extends Filter implements Serializable

    serialVersionUID:
      5702974949137433141L

  - ### Class weka.filters.SimpleStreamFilter extends SimpleFilter implements Serializable

    serialVersionUID:
      2754882676192747091L

- ## Package weka.filters.supervised.attribute

  - ### Class weka.filters.supervised.attribute.AddClassification extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      -1931467132568441909L

    - ### Serialized Fields

      - #### m_Classifier

            Classifier m_Classifier

        The classifier template used to do the classification.

      - #### m_SerializedClassifierFile

            java.io.File m_SerializedClassifierFile

        The file from which to load a serialized classifier.

      - #### m_ActualClassifier

            Classifier m_ActualClassifier

        The actual classifier used to do the classification.

      - #### m_SerializedHeader

            Instances m_SerializedHeader

        the header of the file the serialized classifier was trained with.

      - #### m_OutputClassification

            boolean m_OutputClassification

        whether to output the classification.

      - #### m_RemoveOldClass

            boolean m_RemoveOldClass

        whether to remove the old class attribute.

      - #### m_OutputDistribution

            boolean m_OutputDistribution

        whether to output the class distribution.

      - #### m_OutputErrorFlag

            boolean m_OutputErrorFlag

        whether to output the error flag.

  - ### Class weka.filters.supervised.attribute.AttributeSelection extends Filter implements Serializable

    serialVersionUID:
      -296211247688169716L

    - ### Serialized Fields

      - #### m_trainSelector

            AttributeSelection m_trainSelector

        the attribute selection evaluation object

      - #### m_ASEvaluator

            ASEvaluation m_ASEvaluator

        the attribute evaluator to use

      - #### m_ASSearch

            ASSearch m_ASSearch

        the search method if any

      - #### m_SelectedAttributes

            int[] m_SelectedAttributes

        holds the selected attributes

      - #### m_hasClass

            boolean m_hasClass

        True if a class attribute is set in the data

  - ### Class weka.filters.supervised.attribute.ClassOrder extends Filter implements Serializable

    serialVersionUID:
      -2116226838887628411L

    - ### Serialized Fields

      - #### m_Seed

            long m_Seed

        The seed of randomization

      - #### m_Random

            java.util.Random m_Random

        The random object

      - #### m_Converter

            int[] m_Converter

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

      - #### m_ClassAttribute

            Attribute m_ClassAttribute

        Class attribute of the data

      - #### m_ClassOrder

            int m_ClassOrder

        The class order to be sorted

      - #### m_ClassCounts

            double[] m_ClassCounts

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

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

    serialVersionUID:
      -3141006402280129097L

    - ### Serialized Fields

      - #### m_DiscretizeCols

            Range m_DiscretizeCols

        Stores which columns to Discretize

      - #### m_CutPoints

            double[][] m_CutPoints

        Store the current cutpoints

      - #### m_MakeBinary

            boolean m_MakeBinary

        Output binary attributes for discretized attributes.

      - #### m_UseBinNumbers

            boolean m_UseBinNumbers

        Use bin numbers rather than ranges for discretized attributes.

      - #### m_UseBetterEncoding

            boolean m_UseBetterEncoding

        Use better encoding of split point for MDL.

      - #### m_UseKononenko

            boolean m_UseKononenko

        Use Kononenko's MDL criterion instead of Fayyad et al.'s

      - #### m_BinRangePrecision

            int m_BinRangePrecision

        Precision for bin range labels

  - ### Class weka.filters.supervised.attribute.MergeNominalValues extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      7447337831221353842L

    - ### Serialized Fields

      - #### m_SigLevel

            double m_SigLevel

        Set the significance level

      - #### 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_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_UseShortIdentifiers

            boolean m_UseShortIdentifiers

        Use short values

  - ### Class weka.filters.supervised.attribute.NominalToBinary extends Filter implements Serializable

    serialVersionUID:
      -5004607029857673950L

    - ### Serialized Fields

      - #### m_Indices

            int[][] m_Indices

        The sorted indices of the attribute values.

      - #### m_Numeric

            boolean m_Numeric

        Are the new attributes going to be nominal or numeric ones?

      - #### m_TransformAll

            boolean m_TransformAll

        Are all values transformed into new attributes?

      - #### m_needToTransform

            boolean m_needToTransform

        Whether we need to transform at all

  - ### Class weka.filters.supervised.attribute.PartitionMembership extends Filter implements Serializable

    serialVersionUID:
      333532554667754026L

    - ### Serialized Fields

      - #### m_partitionGenerator

            PartitionGenerator m_partitionGenerator

        The partition generator

- ## Package weka.filters.supervised.instance

  - ### Class weka.filters.supervised.instance.ClassBalancer extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      6237337831221353842L

  - ### Class weka.filters.supervised.instance.Resample extends Filter implements Serializable

    serialVersionUID:
      7079064953548300681L

    - ### Serialized Fields

      - #### m_SampleSizePercent

            double m_SampleSizePercent

        The subsample size, percent of original set, default 100%.

      - #### m_RandomSeed

            int m_RandomSeed

        The random number generator seed.

      - #### m_BiasToUniformClass

            double m_BiasToUniformClass

        The degree of bias towards uniform (nominal) class distribution.

      - #### m_NoReplacement

            boolean m_NoReplacement

        Whether to perform sampling with replacement or without.

      - #### m_InvertSelection

            boolean m_InvertSelection

        Whether to invert the selection (only if instances are drawn WITHOUT replacement).
        See Also:
          `Resample.m_NoReplacement`

  - ### Class weka.filters.supervised.instance.SpreadSubsample extends Filter implements Serializable

    serialVersionUID:
      -3947033795243930016L

    - ### Serialized Fields

      - #### m_RandomSeed

            int m_RandomSeed

        The random number generator seed

      - #### m_MaxCount

            int m_MaxCount

        The maximum count of any class

      - #### m_DistributionSpread

            double m_DistributionSpread

        True if the first batch has been done

      - #### m_AdjustWeights

            boolean m_AdjustWeights

        True if instance weights will be adjusted to maintain total weight per class.

  - ### Class weka.filters.supervised.instance.StratifiedRemoveFolds extends Filter implements Serializable

    serialVersionUID:
      -7069148179905814324L

    - ### Serialized Fields

      - #### m_Inverse

            boolean m_Inverse

        Indicates if inverse of selection is to be output.

      - #### m_NumFolds

            int m_NumFolds

        Number of folds to split dataset into

      - #### m_Fold

            int m_Fold

        Fold to output

      - #### m_Seed

            long m_Seed

        Random number seed.

- ## Package weka.filters.unsupervised.attribute

  - ### Class weka.filters.unsupervised.attribute.AbstractTimeSeries extends Filter implements Serializable

    serialVersionUID:
      -3795656792078022357L

    - ### Serialized Fields

      - #### m_SelectedCols

            Range m_SelectedCols

        Stores which columns to copy

      - #### m_FillWithMissing

            boolean m_FillWithMissing

        True if missing values should be used rather than removing instances where the translated value is not known (due to border effects).

      - #### m_InstanceRange

            int m_InstanceRange

        The number of instances forward to translate values between. A negative number indicates taking values from a past instance.

      - #### m_History

            Queue m_History

        Stores the historical instances to copy values between

  - ### Class weka.filters.unsupervised.attribute.Add extends Filter implements Serializable

    serialVersionUID:
      761386447332932389L

    - ### Serialized Fields

      - #### m_AttributeType

            int m_AttributeType

        Record the type of attribute to insert.

      - #### m_Name

            java.lang.String m_Name

        The name for the new attribute.

      - #### m_Insert

            SingleIndex m_Insert

        The location to insert the new attribute.

      - #### m_Labels

            java.util.ArrayList<E> m_Labels

        The list of labels for nominal attribute.

      - #### m_DateFormat

            java.lang.String m_DateFormat

        The date format.

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

    serialVersionUID:
      7414280611943807337L

    - ### Serialized Fields

      - #### m_Clusterer

            Clusterer m_Clusterer

        The clusterer used to do the cleansing.

      - #### m_SerializedClustererFile

            java.io.File m_SerializedClustererFile

        The file from which to load a serialized clusterer.

      - #### m_ActualClusterer

            Clusterer m_ActualClusterer

        The actual clusterer used to do the clustering.

      - #### m_IgnoreAttributesRange

            Range m_IgnoreAttributesRange

        Range of attributes to ignore.

      - #### m_removeAttributes

            Filter m_removeAttributes

        Filter for removing attributes.

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

    serialVersionUID:
      402130384261736245L

    - ### Serialized Fields

      - #### m_infixExpression

            java.lang.String m_infixExpression

        The infix expression

      - #### m_attributeName

            java.lang.String m_attributeName

        Name of the new attribute. "expression" length string will use the provided expression as the new attribute name

      - #### m_Debug

            boolean m_Debug

        If true, makes the attribute name equal to the postfix parse of the expression

      - #### m_attributeExpression

            AttributeExpression m_attributeExpression

  - ### Class weka.filters.unsupervised.attribute.AddID extends Filter implements Serializable

    serialVersionUID:
      4734383199819293390L

    - ### Serialized Fields

      - #### m_Index

            SingleIndex m_Index

        the index of the attribute

      - #### m_Name

            java.lang.String m_Name

        the name of the attribute

      - #### m_Counter

            int m_Counter

        the counter for the ID

  - ### Class weka.filters.unsupervised.attribute.AddNoise extends Filter implements Serializable

    serialVersionUID:
      -8499673222857299082L

    - ### Serialized Fields

      - #### m_AttIndex

            SingleIndex m_AttIndex

        The attribute's index setting.

      - #### m_UseMissing

            boolean m_UseMissing

        Flag if missing values are taken as value.

      - #### m_Percent

            int m_Percent

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

      - #### m_RandomSeed

            int m_RandomSeed

        The random number generator seed

  - ### Class weka.filters.unsupervised.attribute.AddUserFields extends Filter implements Serializable

    serialVersionUID:
      -2761427344847891585L

    - ### Serialized Fields

      - #### m_attributeSpecs

            java.util.List<E> m_attributeSpecs

        The new attributes to create

  - ### Class weka.filters.unsupervised.attribute.AddUserFields.AttributeSpec extends java.lang.Object implements Serializable

    serialVersionUID:
      -617328946241474608L

    - ### Serialized Fields

      - #### m_name

            java.lang.String m_name

        The name of the new attribute

      - #### m_value

            java.lang.String m_value

        The constant value it should assume

      - #### m_type

            java.lang.String m_type

        The type of the new attribute

      - #### m_nameS

            java.lang.String m_nameS

        The name after resolving any environment variables

      - #### m_valueS

            java.lang.String m_valueS

        The value after resolving any environment variables

      - #### m_typeS

            java.lang.String m_typeS

        The type after resolving any environment variables

      - #### m_dateFormat

            java.text.SimpleDateFormat m_dateFormat

        The date format to use (if the new attribute is a date)

      - #### m_parsedDate

            java.util.Date m_parsedDate

        Holds the parsed date value

  - ### Class weka.filters.unsupervised.attribute.AddValues extends Filter implements Serializable

    serialVersionUID:
      -8100622241742393656L

    - ### Serialized Fields

      - #### m_AttIndex

            SingleIndex m_AttIndex

        The attribute's index setting.

      - #### m_Labels

            java.util.ArrayList<E> m_Labels

        The values to add.

      - #### m_Sort

            boolean m_Sort

        Whether to sort the values.

      - #### m_SortedIndices

            int[] m_SortedIndices

        the array with the sorted label indices

  - ### Class weka.filters.unsupervised.attribute.Center extends PotentialClassIgnorer implements Serializable

    serialVersionUID:
      -9101338448900581023L

    - ### Serialized Fields

      - #### m_Means

            double[] m_Means

        The means

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

    serialVersionUID:
      -1609344074013448737L

    - ### Serialized Fields

      - #### m_AttIndex

            SingleIndex m_AttIndex

        The attribute's index setting.

      - #### m_DateFormat

            java.text.SimpleDateFormat m_DateFormat

        The output date format.

      - #### m_OutputAttribute

            Attribute m_OutputAttribute

        The output attribute.

  - ### Class weka.filters.unsupervised.attribute.ClassAssigner extends SimpleStreamFilter implements Serializable

    serialVersionUID:
      1775780193887394115L

    - ### Serialized Fields

      - #### m_ClassIndex

            int m_ClassIndex

        the class index.

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

    serialVersionUID:
      6675702504667714026L

    - ### Serialized Fields

      - #### m_clusterer

            DensityBasedClusterer m_clusterer

        The clusterer

      - #### m_clusterers

            DensityBasedClusterer[] m_clusterers

        Array for storing the clusterers

      - #### m_ignoreAttributesRange

            Range m_ignoreAttributesRange

        Range of attributes to ignore

      - #### m_removeAttributes

            Filter m_removeAttributes

        Filter for removing attributes

      - #### m_priors

            double[] m_priors

        The prior probability for each class

  - ### Class weka.filters.unsupervised.attribute.Copy extends Filter implements Serializable

    serialVersionUID:
      -8543707493627441566L

    - ### Serialized Fields

      - #### m_CopyCols

            Range m_CopyCols

        Stores which columns to copy

      - #### m_SelectedAttributes

            int[] m_SelectedAttributes

        Stores the indexes of the selected attributes in order, once the dataset is seen

  - ### Class weka.filters.unsupervised.attribute.Discretize extends PotentialClassIgnorer implements Serializable

    serialVersionUID:
      -1358531742174527279L

    - ### Serialized Fields

      - #### m_DiscretizeCols

            Range m_DiscretizeCols

        Stores which columns to Discretize

      - #### m_NumBins

            int m_NumBins

        The number of bins to divide the attribute into

      - #### m_DesiredWeightOfInstancesPerInterval

            double m_DesiredWeightOfInstancesPerInterval

        The desired weight of instances per bin

      - #### m_CutPoints

            double[][] m_CutPoints

        Store the current cutpoints

      - #### m_MakeBinary

            boolean m_MakeBinary

        Output binary attributes for discretized attributes.

      - #### m_UseBinNumbers

            boolean m_UseBinNumbers

        Use bin numbers rather than ranges for discretized attributes.

      - #### m_FindNumBins

            boolean m_FindNumBins

        Find the number of bins using cross-validated entropy.

      - #### m_UseEqualFrequency

            boolean m_UseEqualFrequency

        Use equal-frequency binning if unsupervised discretization turned on

      - #### m_DefaultCols

            java.lang.String m_DefaultCols

        The default columns to discretize

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

    serialVersionUID:
      -7500464545400454179L

    - ### Serialized Fields

      - #### m_DeltaCols

            Range m_DeltaCols

        Stores which columns to take differences between

  - ### Class weka.filters.unsupervised.attribute.InterquartileRange extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      -227879653639723030L

    - ### Serialized Fields

      - #### m_Attributes

            Range m_Attributes

        the attribute range to work on

      - #### m_AttributeIndices

            int[] m_AttributeIndices

        the generated indices (only for performance reasons)

      - #### m_OutlierFactor

            double m_OutlierFactor

        the factor for detecting outliers

      - #### m_ExtremeValuesFactor

            double m_ExtremeValuesFactor

        the factor for detecting extreme values, by default 2*m_OutlierFactor

      - #### m_ExtremeValuesAsOutliers

            boolean m_ExtremeValuesAsOutliers

        whether extreme values are also tagged as outliers

      - #### m_UpperExtremeValue

            double[] m_UpperExtremeValue

        the upper extreme value threshold (= Q3 + EVF*IQR)

      - #### m_UpperOutlier

            double[] m_UpperOutlier

        the upper outlier threshold (= Q3 + OF*IQR)

      - #### m_LowerOutlier

            double[] m_LowerOutlier

        the lower outlier threshold (= Q1 - OF*IQR)

      - #### m_IQR

            double[] m_IQR

        the interquartile range

      - #### m_Median

            double[] m_Median

        the median

      - #### m_LowerExtremeValue

            double[] m_LowerExtremeValue

        the lower extreme value threshold (= Q1 - EVF*IQR)

      - #### m_DetectionPerAttribute

            boolean m_DetectionPerAttribute

        whether to generate Outlier/ExtremeValue attributes for each attribute instead of a general one

      - #### m_OutlierAttributePosition

            int[] m_OutlierAttributePosition

        the position of the outlier attribute

      - #### m_OutputOffsetMultiplier

            boolean m_OutputOffsetMultiplier

        whether to add another attribute called "Offset", that lists the 'multiplier' by which the outlier/extreme value is away from the median, i.e., value = median + 'multiplier' * IQR
        automatically enables m_DetectionPerAttribute!

  - ### Class weka.filters.unsupervised.attribute.KernelFilter extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      213800899640387499L

    - ### Serialized Fields

      - #### m_NumTrainInstances

            int m_NumTrainInstances

        The number of instances in the training data.

      - #### m_Kernel

            Kernel m_Kernel

        Kernel to use

      - #### m_ActualKernel

            Kernel m_ActualKernel

        the Kernel which is actually used for computation

      - #### m_checksTurnedOff

            boolean m_checksTurnedOff

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

      - #### m_NominalToBinary

            NominalToBinary m_NominalToBinary

        The filter used to make attributes numeric.

      - #### m_Missing

            ReplaceMissingValues m_Missing

        The filter used to get rid of missing values.

      - #### m_InitFile

            java.io.File m_InitFile

        The dataset to initialize the filter with

      - #### m_InitFileClassIndex

            SingleIndex m_InitFileClassIndex

        the class index for the file to initialized with
        See Also:
          `KernelFilter.m_InitFile`

      - #### m_Initialized

            boolean m_Initialized

        whether the filter was initialized

      - #### m_KernelFactorExpression

            java.lang.String m_KernelFactorExpression

        optimizes the kernel with this formula (A = # of attributes, N = # of instances)

      - #### m_KernelFactor

            double m_KernelFactor

        the calculated kernel factor
        See Also:
          `KernelFilter.m_KernelFactorExpression`

      - #### m_Filter

            Filter m_Filter

        for centering/standardizing the data

      - #### m_ActualFilter

            Filter m_ActualFilter

        for centering/standardizing the data (the actual filter to use)

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

    serialVersionUID:
      766001176862773163L

    - ### Serialized Fields

      - #### m_AttIndex

            SingleIndex m_AttIndex

        The attribute's index setting.

      - #### m_ValIndex

            Range m_ValIndex

        The value's index

      - #### m_Numeric

            boolean m_Numeric

        Make boolean attribute numeric.

  - ### Class weka.filters.unsupervised.attribute.MathExpression extends PotentialClassIgnorer implements Serializable

    serialVersionUID:
      -3713222714671997901L

    - ### Serialized Fields

      - #### m_SelectCols

            Range m_SelectCols

        Stores which columns to select as a funky range

      - #### m_expression

            java.lang.String m_expression

        The modification expression

      - #### m_attStats

            AttributeStats[] m_attStats

        Attributes statistics

  - ### Class weka.filters.unsupervised.attribute.MergeInfrequentNominalValues extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      4444337331921333847L

    - ### Serialized Fields

      - #### m_MinimumFrequency

            int m_MinimumFrequency

        Set the minimum frequency for a value not to be merged.

      - #### 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_AttToBeModified

            boolean[] m_AttToBeModified

        Indicators for which attributes need to be changed.

      - #### m_NewValues

            int[][] m_NewValues

        The new values.

  - ### Class weka.filters.unsupervised.attribute.MergeManyValues extends Filter implements Serializable

    serialVersionUID:
      4649332102154713625L

    - ### Serialized Fields

      - #### m_AttIndex

            SingleIndex m_AttIndex

        The attribute's index setting.

      - #### m_Label

            java.lang.String m_Label

        The first value's index setting.

      - #### m_MergeRange

            Range m_MergeRange

        The merge value's index setting.

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

    serialVersionUID:
      2925048980504034018L

    - ### Serialized Fields

      - #### m_AttIndex

            SingleIndex m_AttIndex

        The attribute's index setting.

      - #### m_FirstIndex

            SingleIndex m_FirstIndex

        The first value's index setting.

      - #### m_SecondIndex

            SingleIndex m_SecondIndex

        The second value's index setting.

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

    serialVersionUID:
      -1130642825710549138L

    - ### Serialized Fields

      - #### m_Columns

            Range m_Columns

        Stores which columns to act on

      - #### m_Numeric

            boolean m_Numeric

        Are the new attributes going to be nominal or numeric ones?

      - #### m_TransformAll

            boolean m_TransformAll

        Are all values transformed into new attributes?

      - #### m_needToTransform

            boolean m_needToTransform

        Whether we need to transform at all

  - ### Class weka.filters.unsupervised.attribute.NominalToString extends Filter implements Serializable

    serialVersionUID:
      8655492378380068939L

    - ### Serialized Fields

      - #### m_AttIndex

            Range m_AttIndex

        The attribute's index setting.

  - ### Class weka.filters.unsupervised.attribute.Normalize extends PotentialClassIgnorer implements Serializable

    serialVersionUID:
      -8158531150984362898L

    - ### Serialized Fields

      - #### m_MinArray

            double[] m_MinArray

        The minimum values for numeric attributes.

      - #### m_MaxArray

            double[] m_MaxArray

        The maximum values for numeric attributes.

      - #### m_Translation

            double m_Translation

        The translation of the output range.

      - #### m_Scale

            double m_Scale

        The scaling factor of the output range.

  - ### Class weka.filters.unsupervised.attribute.NumericCleaner extends SimpleStreamFilter implements Serializable

    serialVersionUID:
      -352890679895066592L

    - ### Serialized Fields

      - #### m_MinThreshold

            double m_MinThreshold

        the minimum threshold

      - #### m_MinDefault

            double m_MinDefault

        the minimum default replacement value

      - #### m_MaxThreshold

            double m_MaxThreshold

        the maximum threshold

      - #### m_MaxDefault

            double m_MaxDefault

        the maximum default replacement value

      - #### m_CloseTo

            double m_CloseTo

        the number the values are checked for closeness to

      - #### m_CloseToDefault

            double m_CloseToDefault

        the default replacement value for numbers "close-to"

      - #### m_CloseToTolerance

            double m_CloseToTolerance

        the tolerance distance, below which numbers are considered being "close-to"

      - #### m_Cols

            Range m_Cols

        Stores which columns to cleanse

      - #### m_IncludeClass

            boolean m_IncludeClass

        whether to include the class attribute

      - #### m_Decimals

            int m_Decimals

        the number of decimals to round to (-1 means no rounding)

  - ### Class weka.filters.unsupervised.attribute.NumericToBinary extends PotentialClassIgnorer implements Serializable

    serialVersionUID:
      2616879323359470802L

  - ### Class weka.filters.unsupervised.attribute.NumericToNominal extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      -6614630932899796239L

    - ### Serialized Fields

      - #### m_Cols

            Range m_Cols

        Stores which columns to turn into nominals

      - #### m_DefaultCols

            java.lang.String m_DefaultCols

        The default columns to turn into nominals

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

    serialVersionUID:
      -8561413333351366934L

    - ### Serialized Fields

      - #### m_Cols

            Range m_Cols

        Stores which columns to transform.

      - #### m_Class

            java.lang.String m_Class

        Class containing transformation method.

      - #### m_Method

            java.lang.String m_Method

        Transformation method.

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

    serialVersionUID:
      -343922772462971561L

  - ### Class weka.filters.unsupervised.attribute.PartitionedMultiFilter extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      -6293720886005713120L

    - ### Serialized Fields

      - #### m_Filters

            Filter[] m_Filters

        The filters.

      - #### m_Ranges

            Range[] m_Ranges

        The attribute ranges.

      - #### m_RemoveUnused

            boolean m_RemoveUnused

        Whether unused attributes are left out of the output.

      - #### m_IndicesUnused

            int[] m_IndicesUnused

        the indices of the unused attributes.

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

    serialVersionUID:
      6153101248977702675L

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

    serialVersionUID:
      8625371119276845454L

    - ### Serialized Fields

      - #### m_IgnoreClass

            boolean m_IgnoreClass

        True if the class is to be unset

      - #### m_ClassIndex

            int m_ClassIndex

        Storing the class index

  - ### Class weka.filters.unsupervised.attribute.PrincipalComponents extends Filter implements Serializable

    serialVersionUID:
      -5649876869480249303L

    - ### Serialized Fields

      - #### m_TrainInstances

            Instances m_TrainInstances

        The data to transform analyse/transform.

      - #### m_TrainCopy

            Instances m_TrainCopy

        Keep a copy for the class attribute (if set).

      - #### m_TransformedFormat

            Instances m_TransformedFormat

        The header for the transformed data format.

      - #### m_HasClass

            boolean m_HasClass

        Data has a class set.

      - #### m_ClassIndex

            int m_ClassIndex

        Class index.

      - #### m_NumAttribs

            int m_NumAttribs

        Number of attributes.

      - #### m_NumInstances

            int m_NumInstances

        Number of instances.

      - #### m_Correlation

            double[][] m_Correlation

        Correlation matrix for the original data.

      - #### m_center

            boolean m_center

        If true, center (rather than standardize) the data and compute PCA from covariance (rather than correlation) matrix.

      - #### m_Eigenvectors

            double[][] m_Eigenvectors

        Will hold the unordered linear transformations of the (normalized) original data.

      - #### m_Eigenvalues

            double[] m_Eigenvalues

        Eigenvalues for the corresponding eigenvectors.

      - #### m_SortedEigens

            int[] m_SortedEigens

        Sorted eigenvalues.

      - #### m_SumOfEigenValues

            double m_SumOfEigenValues

        sum of the eigenvalues.

      - #### m_ReplaceMissingFilter

            ReplaceMissingValues m_ReplaceMissingFilter

        Filters for replacing missing values.

      - #### m_NominalToBinaryFilter

            NominalToBinary m_NominalToBinaryFilter

        Filter for turning nominal values into numeric ones.

      - #### m_AttributeFilter

            Remove m_AttributeFilter

        Filter for removing class attribute, nominal attributes with 0 or 1 value.

      - #### m_standardizeFilter

            Standardize m_standardizeFilter

        Filter for standardizing the data

      - #### m_centerFilter

            Center m_centerFilter

        Filter for centering the data

      - #### m_OutputNumAtts

            int m_OutputNumAtts

        The number of attributes in the pc transformed data.

      - #### m_CoverVariance

            double m_CoverVariance

        the amount of varaince to cover in the original data when retaining the best n PC's.

      - #### m_MaxAttrsInName

            int m_MaxAttrsInName

        maximum number of attributes in the transformed attribute name.

      - #### m_MaxAttributes

            int m_MaxAttributes

        maximum number of attributes in the transformed data (-1 for all).

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

    serialVersionUID:
      4428905532728645880L

    - ### Serialized Fields

      - #### m_k

            int m_k

        Stores the number of dimensions to reduce the data to

      - #### m_percent

            double m_percent

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

      - #### m_distribution

            int m_distribution

        Stores the distribution to use for calculating the random matrix

      - #### m_useReplaceMissing

            boolean m_useReplaceMissing

        Should the missing values be replaced using unsupervised.ReplaceMissingValues filter

      - #### m_OutputFormatDefined

            boolean m_OutputFormatDefined

        Keeps track of output format if it is defined or not

      - #### m_ntob

            Filter m_ntob

        The NominalToBinary filter applied to the data before this filter

      - #### m_replaceMissing

            Filter m_replaceMissing

        The ReplaceMissingValues filter

      - #### m_rndmSeed

            int m_rndmSeed

        Stores the random seed used to generate the random matrix

      - #### m_rmatrix

            double[][] m_rmatrix

        The random matrix

      - #### m_random

            java.util.Random m_random

        The random number generator used for generating the random matrix

  - ### Class weka.filters.unsupervised.attribute.RandomSubset extends SimpleStreamFilter implements Serializable

    serialVersionUID:
      2911221724251628050L

    - ### Serialized Fields

      - #### m_NumAttributes

            double m_NumAttributes

        The number of attributes to randomly choose (>= 1 absolute number of attributes, < 1 percentage).

      - #### m_Seed

            int m_Seed

        The seed value.

      - #### m_Indices

            int[] m_Indices

        The indices of the attributes that got selected.

      - #### m_invertSelection

            boolean m_invertSelection

        Whether to randomly remove rather than select

  - ### Class weka.filters.unsupervised.attribute.Remove extends Filter implements Serializable

    serialVersionUID:
      5011337331921522847L

    - ### Serialized Fields

      - #### m_SelectCols

            Range m_SelectCols

        Stores which columns to select as a funky range

      - #### m_SelectedAttributes

            int[] m_SelectedAttributes

        Stores the indexes of the selected attributes in order, once the dataset is seen

  - ### Class weka.filters.unsupervised.attribute.RemoveByName extends SimpleStreamFilter implements Serializable

    serialVersionUID:
      -3335106965521265631L

    - ### Serialized Fields

      - #### m_Expression

            java.lang.String m_Expression

        the regular expression for selecting the attributes by name.

      - #### m_InvertSelection

            boolean m_InvertSelection

        whether to invert the matching sense.

      - #### m_Remove

            Remove m_Remove

        the Remove filter used internally for removing the attributes.

  - ### Class weka.filters.unsupervised.attribute.RemoveType extends Filter implements Serializable

    serialVersionUID:
      -3563999462782486279L

    - ### Serialized Fields

      - #### m_attributeFilter

            Remove m_attributeFilter

        The attribute filter used to do the filtering

      - #### m_attTypeToDelete

            int m_attTypeToDelete

        The type of attribute to delete

      - #### m_invert

            boolean m_invert

        Whether to invert selection

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

    serialVersionUID:
      -8659417851407640038L

    - ### Serialized Fields

      - #### m_removeFilter

            Remove m_removeFilter

        The filter used to remove attributes

      - #### m_maxVariancePercentage

            double m_maxVariancePercentage

        The type of attribute to delete

  - ### Class weka.filters.unsupervised.attribute.RenameAttribute extends SimpleStreamFilter implements Serializable

    serialVersionUID:
      4216491776378279596L

    - ### Serialized Fields

      - #### m_Find

            java.lang.String m_Find

        the regular expression that the attribute names have to match.

      - #### m_Replace

            java.lang.String m_Replace

        the regular expression to replace the attribute name with.

      - #### m_AttributeIndices

            Range m_AttributeIndices

        the attribute range to work on.

      - #### m_ReplaceAll

            boolean m_ReplaceAll

        whether to replace all occurrences or just the first.

  - ### Class weka.filters.unsupervised.attribute.RenameNominalValues extends Filter implements Serializable

    serialVersionUID:
      -2121767582746512209L

    - ### Serialized Fields

      - #### m_selectedColsString

            java.lang.String m_selectedColsString

        Range specification or comma-separated list of attribute names

      - #### m_selectedCols

            Range m_selectedCols

        The range object to use if a range has been supplied

      - #### m_renameVals

            java.lang.String m_renameVals

        The comma-separated list of nominal values and their replacements

      - #### m_ignoreCase

            boolean m_ignoreCase

        True if case is to be ignored when matching nominal values

      - #### m_invert

            boolean m_invert

        True if the matching sense (for attributes) is to be inverted

      - #### m_selectedAttributes

            int[] m_selectedAttributes

        Stores the indexes of the selected attributes in order, once the dataset is seen

      - #### m_renameMap

            java.util.Map<K,V> m_renameMap

        The map of nominal values and their replacements

  - ### Class weka.filters.unsupervised.attribute.Reorder extends Filter implements Serializable

    serialVersionUID:
      -1135571321097202292L

    - ### Serialized Fields

      - #### m_NewOrderCols

            java.lang.String m_NewOrderCols

        Stores which columns to reorder

      - #### m_SelectedAttributes

            int[] m_SelectedAttributes

        Stores the indexes of the selected attributes in order, once the dataset is seen

      - #### m_InputStringIndex

            int[] m_InputStringIndex

        Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated

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

    serialVersionUID:
      8349568310991609867L

    - ### Serialized Fields

      - #### m_ModesAndMeans

            double[] m_ModesAndMeans

        The modes and means

  - ### Class weka.filters.unsupervised.attribute.ReplaceMissingWithUserConstant extends PotentialClassIgnorer implements Serializable

    serialVersionUID:
      -7334039452189350356L

    - ### Serialized Fields

      - #### m_selectedRange

            Range m_selectedRange

        Range of columns to consider

      - #### m_range

            java.lang.String m_range

      - #### m_resolvedRange

            java.lang.String m_resolvedRange

      - #### m_nominalStringConstant

            java.lang.String m_nominalStringConstant

        Constant for replacing missing values in nominal/string atts with

      - #### m_resolvedNominalStringConstant

            java.lang.String m_resolvedNominalStringConstant

        Replacement value for nominal/string atts after resolving environment vars

      - #### m_numericConstant

            java.lang.String m_numericConstant

        Constant for replacing missing values in numeric attributes with

      - #### m_resolvedNumericConstant

            java.lang.String m_resolvedNumericConstant

        Replacement value for numeric atts after resolving environment vars

      - #### m_numericConstVal

            double m_numericConstVal

        Parsed numeric constant value

      - #### m_dateConstant

            java.lang.String m_dateConstant

        Constant for replacing missing values in date attributes with

      - #### m_resolvedDateConstant

            java.lang.String m_resolvedDateConstant

        Replacement value for date atts after resolving environment vars

      - #### m_dateConstVal

            double m_dateConstVal

        Parsed date value as a double

      - #### m_defaultDateFormat

            java.lang.String m_defaultDateFormat

        Formatting string to use for parsing the date constant

      - #### m_resolvedDateFormat

            java.lang.String m_resolvedDateFormat

        Formatting string after resolving environment vars

  - ### Class weka.filters.unsupervised.attribute.SortLabels extends SimpleStreamFilter implements Serializable

    serialVersionUID:
      7815204879694105691L

    - ### Serialized Fields

      - #### m_AttributeIndices

            Range m_AttributeIndices

        the range of attributes to process (only relational ones will be processed).

      - #### m_NewOrder

            int[][] m_NewOrder

        the new order for the labels.

      - #### m_SortType

            int m_SortType

        the sort type.

      - #### m_Comparator

            java.util.Comparator<T> m_Comparator

        the comparator to use for sorting.

  - ### Class weka.filters.unsupervised.attribute.SortLabels.CaseInsensitiveComparator extends java.lang.Object implements Serializable

    serialVersionUID:
      -4515292733342486066L

  - ### Class weka.filters.unsupervised.attribute.SortLabels.CaseSensitiveComparator extends java.lang.Object implements Serializable

    serialVersionUID:
      7071450356783873277L

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

    serialVersionUID:
      -6830769026855053281L

    - ### Serialized Fields

      - #### m_Means

            double[] m_Means

        The means

      - #### m_StdDevs

            double[] m_StdDevs

        The variances

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

    serialVersionUID:
      4864084427902797605L

    - ### Serialized Fields

      - #### m_AttIndices

            Range m_AttIndices

        The attribute's range indices setting.

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

    serialVersionUID:
      8249106275278565424L

    - ### Serialized Fields

      - #### m_SelectedRange

            Range m_SelectedRange

        Range of columns to convert to word vectors.

      - #### m_Dictionary

            java.util.TreeMap<K,V> m_Dictionary

        Contains a mapping of valid words to attribute indexes.

      - #### m_OutputCounts

            boolean m_OutputCounts

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

      - #### m_Prefix

            java.lang.String m_Prefix

        A String prefix for the attribute names.

      - #### m_DocsCounts

            int[] m_DocsCounts

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

      - #### m_NumInstances

            int m_NumInstances

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

      - #### m_AvgDocLength

            double m_AvgDocLength

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

      - #### m_WordsToKeep

            int m_WordsToKeep

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

      - #### m_PeriodicPruningRate

            double m_PeriodicPruningRate

        The percentage at which to periodically prune the dictionary.

      - #### m_TFTransform

            boolean m_TFTransform

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

      - #### m_filterType

            int m_filterType

        The normalization to apply.

      - #### m_IDFTransform

            boolean m_IDFTransform

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

      - #### m_lowerCaseTokens

            boolean m_lowerCaseTokens

        True if all tokens should be downcased.

      - #### m_Stemmer

            Stemmer m_Stemmer

        the stemming algorithm.

      - #### m_minTermFreq

            int m_minTermFreq

        the minimum (per-class) word frequency.

      - #### m_doNotOperateOnPerClassBasis

            boolean m_doNotOperateOnPerClassBasis

        whether to operate on a per-class basis.

      - #### m_StopwordsHandler

            StopwordsHandler m_StopwordsHandler

        Stopword handler to use.

      - #### m_Tokenizer

            Tokenizer m_Tokenizer

        the tokenizer algorithm to use.

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

    serialVersionUID:
      6155834679414275855L

    - ### Serialized Fields

      - #### m_AttIndex

            SingleIndex m_AttIndex

        The attribute's index setting.

      - #### m_FirstIndex

            SingleIndex m_FirstIndex

        The first value's index setting.

      - #### m_SecondIndex

            SingleIndex m_SecondIndex

        The second value's index setting.

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

    serialVersionUID:
      3101490081896634942L

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

    serialVersionUID:
      -8901621509691785705L

  - ### Class weka.filters.unsupervised.attribute.Transpose extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      213999899640387499L

- ## Package weka.filters.unsupervised.instance

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

    serialVersionUID:
      4694489111366063852L

    - ### Serialized Fields

      - #### m_encodeMissingAsZero

            boolean m_encodeMissingAsZero

      - #### m_insertDummyNominalFirstValue

            boolean m_insertDummyNominalFirstValue

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

    serialVersionUID:
      8854479785121877582L

    - ### Serialized Fields

      - #### m_Seed

            int m_Seed

        The random number seed

      - #### m_Random

            java.util.Random m_Random

        The current random number generator

  - ### Class weka.filters.unsupervised.instance.RemoveDuplicates extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      4518686110979589602L

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

    serialVersionUID:
      8220373305559055700L

    - ### Serialized Fields

      - #### m_Inverse

            boolean m_Inverse

        Indicates if inverse of selection is to be output.

      - #### m_NumFolds

            int m_NumFolds

        Number of folds to split dataset into

      - #### m_Fold

            int m_Fold

        Fold to output

      - #### m_Seed

            long m_Seed

        Random number seed.

  - ### Class weka.filters.unsupervised.instance.RemoveFrequentValues extends Filter implements Serializable

    serialVersionUID:
      -2447432930070059511L

    - ### Serialized Fields

      - #### m_AttIndex

            SingleIndex m_AttIndex

        The attribute's index setting.

      - #### m_NumValues

            int m_NumValues

        the number of values to retain.

      - #### m_LeastValues

            boolean m_LeastValues

        whether to retain values with least instances instead of most.

      - #### m_Invert

            boolean m_Invert

        whether to invert the matching sense.

      - #### m_ModifyHeader

            boolean m_ModifyHeader

        Modify header for nominal attributes?

      - #### m_NominalMapping

            int[] m_NominalMapping

        If m_ModifyHeader, stores a mapping from old to new indexes

      - #### m_Values

            java.util.HashSet<E> m_Values

        contains the values to retain

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

    serialVersionUID:
      5469157004717663171L

    - ### Serialized Fields

      - #### m_cleansingClassifier

            Classifier m_cleansingClassifier

        The classifier used to do the cleansing

      - #### m_classIndex

            int m_classIndex

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

      - #### m_numOfCrossValidationFolds

            int m_numOfCrossValidationFolds

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

      - #### m_numOfCleansingIterations

            int m_numOfCleansingIterations

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

      - #### m_numericClassifyThreshold

            double m_numericClassifyThreshold

        The threshold for deciding when a numeric value is correctly classified

      - #### m_invertMatching

            boolean m_invertMatching

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

      - #### m_firstBatchFinished

            boolean m_firstBatchFinished

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

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

    serialVersionUID:
      2150341191158533133L

    - ### Serialized Fields

      - #### m_Percentage

            double m_Percentage

        Percentage of instances to select.

      - #### m_Inverse

            boolean m_Inverse

        Indicates if inverse of selection is to be output.

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

    serialVersionUID:
      -3064641215340828695L

    - ### Serialized Fields

      - #### m_Range

            Range m_Range

        Range of instances requested by the user.

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

    serialVersionUID:
      4752870193679263361L

    - ### Serialized Fields

      - #### m_AttIndex

            SingleIndex m_AttIndex

        The attribute's index setting.

      - #### m_Values

            Range m_Values

        Stores which values of nominal attribute are to be used for filtering.

      - #### m_Value

            double m_Value

        Stores which value of a numeric attribute is to be used for filtering.

      - #### m_MatchMissingValues

            boolean m_MatchMissingValues

        True if missing values should count as a match

      - #### m_ModifyHeader

            boolean m_ModifyHeader

        Modify header for nominal attributes?

      - #### m_NominalMapping

            int[] m_NominalMapping

        If m_ModifyHeader, stores a mapping from old to new indexes

      - #### m_dontFilterAfterFirstBatch

            boolean m_dontFilterAfterFirstBatch

        Whether to filter instances after the first batch has been processed

  - ### Class weka.filters.unsupervised.instance.Resample extends Filter implements Serializable

    serialVersionUID:
      3119607037607101160L

    - ### Serialized Fields

      - #### m_SampleSizePercent

            double m_SampleSizePercent

        The subsample size, percent of original set, default 100%

      - #### m_RandomSeed

            int m_RandomSeed

        The random number generator seed

      - #### m_NoReplacement

            boolean m_NoReplacement

        Whether to perform sampling with replacement or without

      - #### m_InvertSelection

            boolean m_InvertSelection

        Whether to invert the selection (only if instances are drawn WITHOUT replacement)
        See Also:
          `Resample.m_NoReplacement`

  - ### Class weka.filters.unsupervised.instance.ReservoirSample extends Filter implements Serializable

    serialVersionUID:
      3119607037607101160L

    - ### Serialized Fields

      - #### m_SampleSize

            int m_SampleSize

        The subsample size, number of instances%

      - #### m_subSample

            Instance[] m_subSample

        Holds the sub-sample (reservoir)

      - #### m_currentInst

            int m_currentInst

        The current instance being processed

      - #### m_RandomSeed

            int m_RandomSeed

        The random number generator seed

      - #### m_random

            java.util.Random m_random

        The random number generator

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

    serialVersionUID:
      2481634184210236074L

  - ### Class weka.filters.unsupervised.instance.SubsetByExpression extends SimpleBatchFilter implements Serializable

    serialVersionUID:
      5628686110979589602L

    - ### Serialized Fields

      - #### m_Expression

            java.lang.String m_Expression

        the expresion to use for filtering.

      - #### m_filterAfterFirstBatch

            boolean m_filterAfterFirstBatch

        Whether to filter instances after the first batch has been processed

- ## Package weka.gui

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

    serialVersionUID:
      -2030706987910400362L

    - ### Serialized Fields

      - #### m_Table

            javax.swing.JTable m_Table

        The table displaying attribute names

      - #### m_Model

            weka.gui.AttributeListPanel.AttributeTableModel m_Model

        The table model containing attribute names

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

    serialVersionUID:
      627131485290359194L

    - ### Serialized Fields

      - #### m_IncludeAll

            javax.swing.JButton m_IncludeAll

        Press to select all attributes

      - #### m_RemoveAll

            javax.swing.JButton m_RemoveAll

        Press to deselect all attributes

      - #### m_Invert

            javax.swing.JButton m_Invert

        Press to invert the current selection

      - #### m_Pattern

            javax.swing.JButton m_Pattern

        Press to enter a perl regular expression for selection

      - #### m_Table

            javax.swing.JTable m_Table

        The table displaying attribute names and selection status

      - #### m_Model

            weka.gui.AttributeSelectionPanel.AttributeTableModel m_Model

        The table model containing attribute names and selection status

      - #### m_PatternRegEx

            java.lang.String m_PatternRegEx

        The current regular expression.

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

    serialVersionUID:
      -5434987925737735880L

    - ### Serialized Fields

      - #### m_AttributeNameLab

            javax.swing.JLabel m_AttributeNameLab

        Displays the name of the relation

      - #### m_AttributeTypeLab

            javax.swing.JLabel m_AttributeTypeLab

        Displays the type of attribute

      - #### m_MissingLab

            javax.swing.JLabel m_MissingLab

        Displays the number of missing values

      - #### m_UniqueLab

            javax.swing.JLabel m_UniqueLab

        Displays the number of unique values

      - #### m_DistinctLab

            javax.swing.JLabel m_DistinctLab

        Displays the number of distinct values

      - #### m_StatsTable

            javax.swing.JTable m_StatsTable

        Displays other stats in a table

      - #### m_Instances

            Instances m_Instances

        The instances we're playing with

      - #### m_AttributeStats

            AttributeStats[] m_AttributeStats

        Cached stats on the attributes we've summarized so far

      - #### m_allEqualWeights

            boolean m_allEqualWeights

        Do all instances have the same weight

  - ### Class weka.gui.AttributeVisualizationPanel extends PrintablePanel implements Serializable

    serialVersionUID:
      -8650490488825371193L

    - ### Serialized Fields

      - #### m_data

            Instances m_data

        This holds the current set of instances

      - #### m_as

            AttributeStats m_as

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

      - #### m_asCache

            AttributeStats[] m_asCache

        Cache of attribute stats info for the current data set

      - #### m_attribIndex

            int m_attribIndex

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

      - #### m_maxValue

            double m_maxValue

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

      - #### m_histBarCounts

            double[] m_histBarCounts

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

      - #### m_histBarClassCounts

            SparseInstance[] m_histBarClassCounts

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

      - #### m_barRange

            double m_barRange

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

      - #### m_classIndex

            int m_classIndex

        Contains the current class index.

      - #### m_hc

            java.lang.Thread m_hc

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

      - #### m_threadRun

            boolean m_threadRun

        True if the thread m_hc above is running.

      - #### m_doneCurrentAttribute

            boolean m_doneCurrentAttribute

      - #### m_displayCurrentAttribute

            boolean m_displayCurrentAttribute

      - #### m_colorAttrib

            javax.swing.JComboBox<E> m_colorAttrib

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

      - #### m_fm

            java.awt.FontMetrics m_fm

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

      - #### m_locker

            java.lang.Integer m_locker

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

      - #### m_colorList

            java.util.ArrayList<E> m_colorList

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

  - ### Class weka.gui.CheckBoxList extends javax.swing.JList implements Serializable

    serialVersionUID:
      -4359573373359270258L

  - ### Class weka.gui.CheckBoxList.CheckBoxListModel extends javax.swing.DefaultListModel implements Serializable

    serialVersionUID:
      7772455499540273507L

  - ### Class weka.gui.CheckBoxList.CheckBoxListRenderer extends javax.swing.JCheckBox implements Serializable

    serialVersionUID:
      1059591605858524586L

  - ### Class weka.gui.ConverterFileChooser extends javax.swing.JFileChooser implements Serializable

    serialVersionUID:
      -5373058011025481738L

    - ### Serialized Fields

      - #### m_Self

            ConverterFileChooser m_Self

        the file chooser itself.

      - #### m_DialogType

            int m_DialogType

        the type of dialog to display.

      - #### m_CurrentConverter

            java.lang.Object m_CurrentConverter

        the converter that was chosen by the user.

      - #### m_ConfigureButton

            javax.swing.JButton m_ConfigureButton

        the configure button.

      - #### m_Listener

            java.beans.PropertyChangeListener m_Listener

        the propertychangelistener.

      - #### m_LastFilter

            javax.swing.filechooser.FileFilter m_LastFilter

        the last filter that was used for opening/saving.

      - #### m_CapabilitiesFilter

            Capabilities m_CapabilitiesFilter

        the Capabilities filter for the savers.

      - #### m_OverwriteWarning

            boolean m_OverwriteWarning

        whether to popup a dialog in case the file already exists (only save dialog).

      - #### m_FileMustExist

            boolean m_FileMustExist

        whether the file to be opened must exist (only open dialog).

      - #### m_CheckBoxOptions

            javax.swing.JCheckBox m_CheckBoxOptions

        the checkbox for bringing up the GenericObjectEditor.

      - #### m_LabelOptions

            javax.swing.JLabel m_LabelOptions

        the note about the options dialog.

      - #### m_Editor

            GenericObjectEditor m_Editor

        the GOE for displaying the options of a loader/saver.

      - #### m_EditorResult

            int m_EditorResult

        whether the GOE was OKed or Canceled.

      - #### m_CoreConvertersOnly

            boolean m_CoreConvertersOnly

        whether to display only core converters (hardcoded in ConverterUtils). Necessary for RMI/Remote Experiments for instance.
        See Also:
          `ConverterUtils.CORE_FILE_LOADERS`, `ConverterUtils.CORE_FILE_SAVERS`

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

    serialVersionUID:
      -1081946748666245054L

    - ### Serialized Fields

      - #### m_DbaseURLText

            javax.swing.JTextField m_DbaseURLText

      - #### m_DbaseURLLab

            javax.swing.JLabel m_DbaseURLLab

      - #### m_UserNameText

            javax.swing.JTextField m_UserNameText

      - #### m_UserNameLab

            javax.swing.JLabel m_UserNameLab

      - #### m_PasswordText

            javax.swing.JPasswordField m_PasswordText

      - #### m_PasswordLab

            javax.swing.JLabel m_PasswordLab

      - #### m_DebugCheckBox

            javax.swing.JCheckBox m_DebugCheckBox

      - #### m_DebugLab

            javax.swing.JLabel m_DebugLab

      - #### m_returnValue

            int m_returnValue

  - ### Class weka.gui.ETable extends javax.swing.JTable implements Serializable

    serialVersionUID:
      -3028630226368293049L

    - ### Serialized Fields

      - #### MAC_FOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR

            java.awt.Color MAC_FOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR

      - #### MAC_UNFOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR

            java.awt.Color MAC_UNFOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR

      - #### MAC_UNFOCUSED_SELECTED_CELL_BACKGROUND_COLOR

            java.awt.Color MAC_UNFOCUSED_SELECTED_CELL_BACKGROUND_COLOR

      - #### MAC_FOCUSED_UNSELECTED_VERTICAL_LINE_COLOR

            java.awt.Color MAC_FOCUSED_UNSELECTED_VERTICAL_LINE_COLOR

      - #### MAC_FOCUSED_SELECTED_VERTICAL_LINE_COLOR

            java.awt.Color MAC_FOCUSED_SELECTED_VERTICAL_LINE_COLOR

      - #### MAC_UNFOCUSED_UNSELECTED_VERTICAL_LINE_COLOR

            java.awt.Color MAC_UNFOCUSED_UNSELECTED_VERTICAL_LINE_COLOR

      - #### MAC_UNFOCUSED_SELECTED_VERTICAL_LINE_COLOR

            java.awt.Color MAC_UNFOCUSED_SELECTED_VERTICAL_LINE_COLOR

      - #### MAC_OS_ALTERNATE_ROW_COLOR

            java.awt.Color MAC_OS_ALTERNATE_ROW_COLOR

  - ### Class weka.gui.EvaluationMetricSelectionDialog extends javax.swing.JDialog implements Serializable

    serialVersionUID:
      4451184027143094270L

    - ### Serialized Fields

      - #### m_selectedEvalMetrics

            java.util.List<E> m_selectedEvalMetrics

        The list of selected evaluation metrics

  - ### Class weka.gui.ExtensionFileFilter extends javax.swing.filechooser.FileFilter implements Serializable

    serialVersionUID:
      -5960622841390665131L

    - ### Serialized Fields

      - #### m_Description

            java.lang.String m_Description

        The text description of the types of files accepted

      - #### m_Extension

            java.lang.String[] m_Extension

        The filename extensions of accepted files

  - ### Class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog extends javax.swing.JDialog implements Serializable

    serialVersionUID:
      -7845503345689646266L

    - ### Serialized Fields

      - #### m_Self

            javax.swing.JDialog m_Self

        the dialog itself.

      - #### m_Popup

            javax.swing.JPopupMenu m_Popup

        the popup to display again.

      - #### m_Capabilities

            Capabilities m_Capabilities

        the capabilities used for initializing the dialog.

      - #### m_InfoLabel

            javax.swing.JLabel m_InfoLabel

        the label, listing the name of the superclass.

      - #### m_List

            CheckBoxList m_List

        the list with all the capabilities.

      - #### m_OkButton

            javax.swing.JButton m_OkButton

        the OK button.

      - #### m_CancelButton

            javax.swing.JButton m_CancelButton

        the Cancel button.

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

    serialVersionUID:
      3656028520876011335L

    - ### Serialized Fields

      - #### m_ChildPropertySheet

            PropertySheetPanel m_ChildPropertySheet

        The component that performs classifier customization.

      - #### m_ClassNameLabel

            javax.swing.JLabel m_ClassNameLabel

        The name of the current class.

      - #### m_OpenBut

            javax.swing.JButton m_OpenBut

        Open object from disk.

      - #### m_SaveBut

            javax.swing.JButton m_SaveBut

        Save object to disk.

      - #### m_okBut

            javax.swing.JButton m_okBut

        ok button.

      - #### m_cancelBut

            javax.swing.JButton m_cancelBut

        cancel button.

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        The filechooser for opening and saving object files.

  - ### Class weka.gui.GenericObjectEditor.GOETreeNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

    serialVersionUID:
      -1707872446682150133L

    - ### Serialized Fields

      - #### m_Capabilities

            Capabilities m_Capabilities

        the Capabilities object to use for filtering.

      - #### m_toolTipText

            java.lang.String m_toolTipText

        tool tip

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

    serialVersionUID:
      -3404546329655057387L

    - ### Serialized Fields

      - #### m_Self

            javax.swing.JPopupMenu m_Self

        the popup itself.

      - #### m_tree

            javax.swing.JTree m_tree

        The tree.

      - #### m_scroller

            javax.swing.JScrollPane m_scroller

        The scroller.

      - #### m_FilterButton

            javax.swing.JButton m_FilterButton

        The filter button in case of CapabilitiesHandlers.

      - #### m_RemoveFilterButton

            javax.swing.JButton m_RemoveFilterButton

        The remove filter button in case of CapabilitiesHandlers.

      - #### m_CloseButton

            javax.swing.JButton m_CloseButton

        The button for closing the popup again.

  - ### Class weka.gui.GenericObjectEditorHistory extends java.lang.Object implements Serializable

    serialVersionUID:
      -1255734638729633595L

    - ### Serialized Fields

      - #### m_History

            java.util.Vector<E> m_History

        the history of objects.

  - ### Class weka.gui.GenericObjectEditorHistory.HistorySelectionEvent extends java.util.EventObject implements Serializable

    serialVersionUID:
      45824542929908105L

    - ### Serialized Fields

      - #### m_HistoryItem

            java.lang.Object m_HistoryItem

        the selected favorite.

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

    serialVersionUID:
      9001529425230247914L

    - ### Serialized Fields

      - #### m_Self

            GUIChooser m_Self

        the GUIChooser itself

      - #### m_jMenuBar

            javax.swing.JMenuBar m_jMenuBar

      - #### m_jMenuProgram

            javax.swing.JMenu m_jMenuProgram

      - #### m_jMenuVisualization

            javax.swing.JMenu m_jMenuVisualization

      - #### m_jMenuTools

            javax.swing.JMenu m_jMenuTools

      - #### m_jMenuHelp

            javax.swing.JMenu m_jMenuHelp

      - #### m_PanelApplications

            javax.swing.JPanel m_PanelApplications

        the panel for the application buttons

      - #### m_ExplorerBut

            javax.swing.JButton m_ExplorerBut

        Click to open the Explorer

      - #### m_ExplorerFrame

            javax.swing.JFrame m_ExplorerFrame

        The frame containing the explorer interface

      - #### m_ExperimenterBut

            javax.swing.JButton m_ExperimenterBut

        Click to open the Explorer

      - #### m_ExperimenterFrame

            javax.swing.JFrame m_ExperimenterFrame

        The frame containing the experiment interface

      - #### m_KnowledgeFlowBut

            javax.swing.JButton m_KnowledgeFlowBut

        Click to open the KnowledgeFlow

      - #### m_pendingKnowledgeFlowLoad

            java.lang.String m_pendingKnowledgeFlowLoad

        Pending file to load on startup of the KnowledgeFlow

      - #### m_KnowledgeFlowFrame

            javax.swing.JFrame m_KnowledgeFlowFrame

        The frame containing the knowledge flow interface

      - #### m_SimpleBut

            javax.swing.JButton m_SimpleBut

        Click to open the simplecli

      - #### m_SimpleCLI

            SimpleCLI m_SimpleCLI

        The SimpleCLI

      - #### m_GroovyConsoleFrame

            javax.swing.JFrame m_GroovyConsoleFrame

        The frame containing the Groovy console.

      - #### m_JythonConsoleFrame

            javax.swing.JFrame m_JythonConsoleFrame

        The frame containing the Jython console.

      - #### m_ArffViewers

            java.util.Vector<E> m_ArffViewers

        keeps track of the opened ArffViewer instances

      - #### m_menuPlugins

            java.util.List<E> m_menuPlugins

        keeps track of the opened GUIChooserMenuPlugins (if any)

      - #### m_SqlViewerFrame

            javax.swing.JFrame m_SqlViewerFrame

        The frame containing the SqlViewer

      - #### m_BayesNetGUIFrame

            javax.swing.JFrame m_BayesNetGUIFrame

        The frame containing the Bayes net GUI

      - #### m_EnsembleLibraryFrame

            javax.swing.JFrame m_EnsembleLibraryFrame

        The frame containing the ensemble library interface

      - #### m_PackageManagerFrame

            javax.swing.JFrame m_PackageManagerFrame

        The frame containing the package manager

      - #### m_Plots

            java.util.Vector<E> m_Plots

        keeps track of the opened plots

      - #### m_ROCs

            java.util.Vector<E> m_ROCs

        keeps track of the opened ROCs

      - #### m_TreeVisualizers

            java.util.Vector<E> m_TreeVisualizers

        keeps track of the opened tree visualizer instancs

      - #### m_GraphVisualizers

            java.util.Vector<E> m_GraphVisualizers

        keeps track of the opened graph visualizer instancs

      - #### m_BoundaryVisualizerFrame

            javax.swing.JFrame m_BoundaryVisualizerFrame

        The frame containing the boundary visualizer

      - #### m_SystemInfoFrame

            javax.swing.JFrame m_SystemInfoFrame

        The frame containing the system info

      - #### m_MemoryUsageFrame

            javax.swing.JFrame m_MemoryUsageFrame

        The frame containing the memory usage

      - #### m_weka

            java.awt.Image m_weka

        The weka image

      - #### m_FileChooserTreeVisualizer

            javax.swing.JFileChooser m_FileChooserTreeVisualizer

        filechooser for the TreeVisualizer

      - #### m_FileChooserGraphVisualizer

            javax.swing.JFileChooser m_FileChooserGraphVisualizer

        filechooser for the GraphVisualizer

      - #### m_FileChooserPlot

            javax.swing.JFileChooser m_FileChooserPlot

        filechooser for Plots

      - #### m_FileChooserROC

            javax.swing.JFileChooser m_FileChooserROC

        filechooser for ROC curves

      - #### m_Icon

            java.awt.Image m_Icon

        the icon for the frames

      - #### m_ChildFrames

            java.util.HashSet<E> m_ChildFrames

        contains the child frames (title <-> object).

  - ### Class weka.gui.GUIChooser.ChildFrameSDI extends javax.swing.JFrame implements Serializable

    serialVersionUID:
      8588293938686425618L

    - ### Serialized Fields

      - #### m_Parent

            GUIChooser m_Parent

        the parent frame.

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

    serialVersionUID:
      -4151103338506077544L

    - ### Serialized Fields

      - #### m_Root

            weka.gui.HierarchyPropertyParser.TreeNode m_Root

        Keep track of the root of the tree

      - #### m_Current

            weka.gui.HierarchyPropertyParser.TreeNode m_Current

        Keep track of the current node when traversing the tree

      - #### m_Seperator

            java.lang.String m_Seperator

        The level separate in the path

      - #### m_Depth

            int m_Depth

        The depth of the tree

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

    serialVersionUID:
      -5243579535296681063L

    - ### Serialized Fields

      - #### m_RelationNameLab

            javax.swing.JLabel m_RelationNameLab

        Displays the name of the relation

      - #### m_NumInstancesLab

            javax.swing.JLabel m_NumInstancesLab

        Displays the number of instances

      - #### m_NumAttributesLab

            javax.swing.JLabel m_NumAttributesLab

        Displays the number of attributes

      - #### m_sumOfWeightsLab

            javax.swing.JLabel m_sumOfWeightsLab

        Displays the sum of instance weights

      - #### m_Instances

            Instances m_Instances

        The instances we're playing with

      - #### m_showZeroInstancesAsUnknown

            boolean m_showZeroInstancesAsUnknown

        Whether to display 0 or ? for the number of instances in cases where a dataset has only structure. Depending on where this panel is used from, the user may have loaded a dataset with no instances or a Loader that can read incrementally may be being used (in which case we don't know how many instances are in the dataset... yet).

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

    serialVersionUID:
      906147926840288895L

    - ### Serialized Fields

      - #### m_SelectBut

            javax.swing.JButton m_SelectBut

        Click to choose the currently selected property

      - #### m_CancelBut

            javax.swing.JButton m_CancelBut

        Click to cancel the property selection

      - #### m_PatternBut

            javax.swing.JButton m_PatternBut

        Click to enter a regex pattern for selection

      - #### m_List

            javax.swing.JList<E> m_List

        The list component

      - #### m_Result

            int m_Result

        Whether the selection was made or cancelled

      - #### m_PatternRegEx

            java.lang.String m_PatternRegEx

        The current regular expression.

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

    serialVersionUID:
      -4072464549112439484L

    - ### Serialized Fields

      - #### m_StatusLab

            javax.swing.JLabel m_StatusLab

        Displays the current status

      - #### m_LogText

            javax.swing.JTextArea m_LogText

        Displays the log messages

      - #### m_logButton

            javax.swing.JButton m_logButton

        The button for viewing the log

      - #### m_First

            boolean m_First

        An indicator for whether text has been output yet

      - #### m_TaskMonitor

            WekaTaskMonitor m_TaskMonitor

        The panel for monitoring the number of running tasks (if supplied)

  - ### Class weka.gui.LogWindow extends javax.swing.JFrame implements Serializable

    serialVersionUID:
      5650947361381061112L

    - ### Serialized Fields

      - #### m_UseWordwrap

            boolean m_UseWordwrap

        whether the JTextPane has wordwrap or not

      - #### m_Output

            javax.swing.JTextPane m_Output

        the output

      - #### m_ButtonClear

            javax.swing.JButton m_ButtonClear

        the clear button

      - #### m_ButtonClose

            javax.swing.JButton m_ButtonClose

        the close button

      - #### m_LabelCurrentSize

            javax.swing.JLabel m_LabelCurrentSize

        the current size

      - #### m_SpinnerMaxSize

            javax.swing.JSpinner m_SpinnerMaxSize

        the spinner for the max number of chars

      - #### m_CheckBoxWordwrap

            javax.swing.JCheckBox m_CheckBoxWordwrap

        whether to allow wordwrap or not

  - ### Class weka.gui.Main extends javax.swing.JFrame implements Serializable

    serialVersionUID:
      1453813254824253849L

    - ### Serialized Fields

      - #### m_Self

            Main m_Self

        the frame itself.

      - #### m_GUIType

            int m_GUIType

        the type of GUI to display.

      - #### m_ChildFrames

            java.util.HashSet<E> m_ChildFrames

        contains the child frames (title <-> object).

      - #### m_FileChooserTreeVisualizer

            javax.swing.JFileChooser m_FileChooserTreeVisualizer

        filechooser for the TreeVisualizer.

      - #### m_FileChooserGraphVisualizer

            javax.swing.JFileChooser m_FileChooserGraphVisualizer

        filechooser for the GraphVisualizer.

      - #### m_FileChooserPlot

            javax.swing.JFileChooser m_FileChooserPlot

        filechooser for Plots.

      - #### m_FileChooserROC

            javax.swing.JFileChooser m_FileChooserROC

        filechooser for ROC curves.

      - #### jMenuHelp

            javax.swing.JMenu jMenuHelp

      - #### jMenuVisualization

            javax.swing.JMenu jMenuVisualization

      - #### jMenuTools

            javax.swing.JMenu jMenuTools

      - #### jDesktopPane

            javax.swing.JDesktopPane jDesktopPane

      - #### jMenuApplications

            javax.swing.JMenu jMenuApplications

      - #### jMenuItemHelpSystemInfo

            javax.swing.JMenuItem jMenuItemHelpSystemInfo

      - #### jMenuItemHelpAbout

            javax.swing.JMenuItem jMenuItemHelpAbout

      - #### jMenuItemHelpHomepage

            javax.swing.JMenuItem jMenuItemHelpHomepage

      - #### jMenuItemHelpWekaWiki

            javax.swing.JMenuItem jMenuItemHelpWekaWiki

      - #### jMenuItemHelpSourceforge

            javax.swing.JMenuItem jMenuItemHelpSourceforge

      - #### jMenuItemVisualizationBoundaryVisualizer

            javax.swing.JMenuItem jMenuItemVisualizationBoundaryVisualizer

      - #### jMenuItemVisualizationGraphVisualizer

            javax.swing.JMenuItem jMenuItemVisualizationGraphVisualizer

      - #### jMenuItemVisualizationTreeVisualizer

            javax.swing.JMenuItem jMenuItemVisualizationTreeVisualizer

      - #### jMenuItemVisualizationROC

            javax.swing.JMenuItem jMenuItemVisualizationROC

      - #### jMenuItemVisualizationPlot

            javax.swing.JMenuItem jMenuItemVisualizationPlot

      - #### jMenuItemToolsSqlViewer

            javax.swing.JMenuItem jMenuItemToolsSqlViewer

      - #### jMenuItemToolsGroovyConsole

            javax.swing.JMenuItem jMenuItemToolsGroovyConsole

      - #### jMenuItemToolsJythonConsole

            javax.swing.JMenuItem jMenuItemToolsJythonConsole

      - #### jMenuItemToolsArffViewer

            javax.swing.JMenuItem jMenuItemToolsArffViewer

      - #### jMenuItemApplicationsSimpleCLI

            javax.swing.JMenuItem jMenuItemApplicationsSimpleCLI

      - #### jMenuItemApplicationsKnowledgeFlow

            javax.swing.JMenuItem jMenuItemApplicationsKnowledgeFlow

      - #### jMenuItemApplicationsExperimenter

            javax.swing.JMenuItem jMenuItemApplicationsExperimenter

      - #### jMenuItemApplicationsExplorer

            javax.swing.JMenuItem jMenuItemApplicationsExplorer

      - #### jMenuItemProgramExit

            javax.swing.JMenuItem jMenuItemProgramExit

      - #### jMenuItemProgramLogWindow

            javax.swing.JMenuItem jMenuItemProgramLogWindow

      - #### jMenuItemProgramMemoryUsage

            javax.swing.JMenuItem jMenuItemProgramMemoryUsage

      - #### jMenuProgram

            javax.swing.JMenu jMenuProgram

      - #### jMenuExtensions

            javax.swing.JMenu jMenuExtensions

      - #### jMenuWindows

            javax.swing.JMenu jMenuWindows

      - #### jMenuBar

            javax.swing.JMenuBar jMenuBar

  - ### Class weka.gui.Main.BackgroundDesktopPane extends javax.swing.JDesktopPane implements Serializable

    serialVersionUID:
      2046713123452402745L

    - ### Serialized Fields

      - #### m_Background

            java.awt.Image m_Background

        the actual background image.

  - ### Class weka.gui.Main.ChildFrameMDI extends javax.swing.JInternalFrame implements Serializable

    serialVersionUID:
      3772573515346899959L

    - ### Serialized Fields

      - #### m_Parent

            Main m_Parent

        the parent frame.

  - ### Class weka.gui.Main.ChildFrameSDI extends javax.swing.JFrame implements Serializable

    serialVersionUID:
      8588293938686425618L

    - ### Serialized Fields

      - #### m_Parent

            Main m_Parent

        the parent frame.

  - ### Class weka.gui.MemoryUsagePanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -4812319791687471721L

    - ### Serialized Fields

      - #### m_History

            java.util.Vector<E> m_History

        the memory usage over time.

      - #### m_Memory

            Memory m_Memory

        for monitoring the memory usage.

      - #### m_Monitor

            weka.gui.MemoryUsagePanel.MemoryMonitor m_Monitor

        the thread for monitoring the memory usage.

      - #### m_ButtonGC

            javax.swing.JButton m_ButtonGC

        the button for running the garbage collector.

      - #### m_Percentages

            java.util.Vector<E> m_Percentages

        the threshold percentages to change color.

      - #### m_Colors

            java.util.Hashtable<K,V> m_Colors

        the corresponding colors for the thresholds.

      - #### m_DefaultColor

            java.awt.Color m_DefaultColor

        the default color.

      - #### m_BackgroundColor

            java.awt.Color m_BackgroundColor

        the background color.

      - #### m_FrameLocation

            java.awt.Point m_FrameLocation

        the position for the dialog.

  - ### Class weka.gui.PackageManager extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -7463821313750352385L

    - ### Serialized Fields

      - #### m_table

            javax.swing.JTable m_table

        The JTable for displaying the package names and version numbers

      - #### m_splitP

            javax.swing.JSplitPane m_splitP

      - #### m_infoPane

            javax.swing.JEditorPane m_infoPane

        An editor pane to display package information

      - #### m_installedBut

            javax.swing.JRadioButton m_installedBut

        Installed radio button

      - #### m_availableBut

            javax.swing.JRadioButton m_availableBut

        Available radio button

      - #### m_allBut

            javax.swing.JRadioButton m_allBut

        All radio button

      - #### m_installBut

            javax.swing.JButton m_installBut

        Button for installing the selected package

      - #### m_forceBut

            javax.swing.JCheckBox m_forceBut

      - #### m_uninstallBut

            javax.swing.JButton m_uninstallBut

        Button for uninstalling the selected package

      - #### m_refreshCacheBut

            javax.swing.JButton m_refreshCacheBut

        Button for refreshing the package meta data cache

      - #### m_toggleLoad

            javax.swing.JButton m_toggleLoad

        Button for toggling the load status of an installed package

      - #### m_progress

            javax.swing.JProgressBar m_progress

      - #### m_detailLabel

            javax.swing.JLabel m_detailLabel

      - #### m_backB

            javax.swing.JButton m_backB

      - #### m_browserHistory

            java.util.LinkedList<E> m_browserHistory

      - #### m_homeB

            javax.swing.JButton m_homeB

      - #### m_browserTools

            javax.swing.JToolBar m_browserTools

      - #### m_newPackagesAvailableL

            javax.swing.JLabel m_newPackagesAvailableL

      - #### m_model

            javax.swing.table.DefaultTableModel m_model

      - #### m_packageLookupInfo

            java.util.Map<K,V> m_packageLookupInfo

      - #### m_allPackages

            java.util.List<E> m_allPackages

      - #### m_installedPackages

            java.util.List<E> m_installedPackages

      - #### m_availablePackages

            java.util.List<E> m_availablePackages

      - #### m_sortColumn

            int m_sortColumn

        The column in the table to sort the entries by

      - #### m_reverseSort

            boolean m_reverseSort

        Reverse the sort order if the user clicks the same column header twice

      - #### m_unofficialBut

            javax.swing.JButton m_unofficialBut

        Button to pop up the file environment field widget

      - #### m_unofficialChooser

            FileEnvironmentField m_unofficialChooser

        Widget for specifying a URL or path to an unofficial package to install

      - #### m_unofficialFrame

            javax.swing.JFrame m_unofficialFrame

      - #### m_packageComparator

            java.util.Comparator<T> m_packageComparator

      - #### m_installing

            boolean m_installing

      - #### m_cacheEstablished

            boolean m_cacheEstablished

      - #### m_cacheRefreshInProgress

            boolean m_cacheRefreshInProgress

  - ### Class weka.gui.PackageManager.ComboBoxEditor extends javax.swing.DefaultCellEditor implements Serializable

    serialVersionUID:
      5240331667759901966L

  - ### Class weka.gui.PropertyDialog extends javax.swing.JDialog implements Serializable

    serialVersionUID:
      -2314850859392433539L

    - ### Serialized Fields

      - #### m_Editor

            java.beans.PropertyEditor m_Editor

        The property editor.

      - #### m_EditorComponent

            java.awt.Component m_EditorComponent

        The custom editor component.

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

    serialVersionUID:
      5370025273466728904L

    - ### Serialized Fields

      - #### m_Editor

            java.beans.PropertyEditor m_Editor

        The property editor

      - #### m_PD

            PropertyDialog m_PD

        The currently displayed property dialog, if any

      - #### m_HasCustomPanel

            boolean m_HasCustomPanel

        Whether the editor has provided its own panel

      - #### m_CustomPanel

            javax.swing.JPanel m_CustomPanel

        The custom panel (if any)

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

    serialVersionUID:
      -3155058124137930518L

    - ### Serialized Fields

      - #### m_SelectBut

            javax.swing.JButton m_SelectBut

        Click to choose the currently selected property

      - #### m_CancelBut

            javax.swing.JButton m_CancelBut

        Click to cancel the property selection

      - #### m_Root

            javax.swing.tree.DefaultMutableTreeNode m_Root

        The root of the property tree

      - #### m_RootObject

            java.lang.Object m_RootObject

        The object at the root of the tree

      - #### m_Result

            int m_Result

        Whether the selection was made or cancelled

      - #### m_ResultPath

            java.lang.Object[] m_ResultPath

        Stores the path to the selected property

      - #### m_Tree

            javax.swing.JTree m_Tree

        The component displaying the property tree

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

    serialVersionUID:
      -8939835593429918345L

    - ### Serialized Fields

      - #### m_Target

            java.lang.Object m_Target

        The target object being edited.

      - #### m_Customizer

            GOECustomizer m_Customizer

        Holds the customizer (if one exists) for the object being edited

      - #### m_Properties

            java.beans.PropertyDescriptor[] m_Properties

        Holds properties of the target.

      - #### m_Methods

            java.beans.MethodDescriptor[] m_Methods

        Holds the methods of the target.

      - #### m_Editors

            java.beans.PropertyEditor[] m_Editors

        Holds property editors of the object.

      - #### m_Values

            java.lang.Object[] m_Values

        Holds current object values for each property.

      - #### m_Views

            javax.swing.JComponent[] m_Views

        Stores GUI components containing each editing component.

      - #### m_Labels

            javax.swing.JLabel[] m_Labels

        The labels for each property.

      - #### m_TipTexts

            java.lang.String[] m_TipTexts

        The tool tip text for each property.

      - #### m_HelpText

            java.lang.StringBuffer m_HelpText

        StringBuffer containing help text for the object being edited.

      - #### m_HelpDialog

            javax.swing.JDialog m_HelpDialog

        Help dialog.

      - #### m_CapabilitiesDialog

            weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_CapabilitiesDialog

        Capabilities Help dialog.

      - #### m_HelpBut

            javax.swing.JButton m_HelpBut

        Button to pop up the full help text in a separate dialog.

      - #### m_CapabilitiesBut

            javax.swing.JButton m_CapabilitiesBut

        Button to pop up the capabilities in a separate dialog.

      - #### m_CapabilitiesText

            javax.swing.JTextArea m_CapabilitiesText

        the TextArea of the Capabilities help dialog.

      - #### m_NumEditable

            int m_NumEditable

        A count of the number of properties we have an editor for.

      - #### m_aboutPanel

            javax.swing.JPanel m_aboutPanel

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

      - #### support

            java.beans.PropertyChangeSupport support

        A support object for handling property change listeners.

  - ### Class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog extends javax.swing.JDialog implements Serializable

    serialVersionUID:
      -1404770987103289858L

    - ### Serialized Fields

      - #### m_Self

            weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_Self

        the dialog itself.

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

    serialVersionUID:
      4297069440135326829L

    - ### Serialized Fields

      - #### m_SingleText

            javax.swing.text.JTextComponent m_SingleText

        An optional component for single-click display

      - #### m_SingleName

            java.lang.String m_SingleName

        The named result being viewed in the single-click display

      - #### m_Model

            javax.swing.DefaultListModel<E> m_Model

        The list model

      - #### m_List

            javax.swing.JList<E> m_List

        The list component

      - #### m_Results

            java.util.Hashtable<K,V> m_Results

        A Hashtable mapping names to result buffers

      - #### m_FramedOutput

            java.util.Hashtable<K,V> m_FramedOutput

        A Hashtable mapping names to output text components

      - #### m_Objs

            java.util.Hashtable<K,V> m_Objs

        A hashtable mapping names to arbitrary objects

      - #### m_HandleRightClicks

            boolean m_HandleRightClicks

        Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer

      - #### m_Printer

            PrintableComponent m_Printer

        for printing the output to files

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

    serialVersionUID:
      -8675332541861828079L

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

    serialVersionUID:
      -8991922650552358669L

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

    serialVersionUID:
      -384804041420453735L

    - ### Serialized Fields

      - #### m_OpenFileBut

            javax.swing.JButton m_OpenFileBut

        Click to open instances from a file.

      - #### m_OpenURLBut

            javax.swing.JButton m_OpenURLBut

        Click to open instances from a URL.

      - #### m_CloseBut

            javax.swing.JButton m_CloseBut

        Click to close the dialog.

      - #### m_Summary

            InstancesSummaryPanel m_Summary

        The instance summary component.

      - #### m_ClassLabel

            javax.swing.JLabel m_ClassLabel

        the label for the class combobox.

      - #### m_ClassComboBox

            javax.swing.JComboBox<E> m_ClassComboBox

        the class combobox.

      - #### m_FileChooser

            ConverterFileChooser m_FileChooser

        The file chooser for selecting arff files.

      - #### m_LastURL

            java.lang.String m_LastURL

        Stores the last URL that instances were loaded from.

      - #### m_IOThread

            java.lang.Thread m_IOThread

        The thread we do loading in.

      - #### m_Support

            java.beans.PropertyChangeSupport m_Support

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

      - #### m_Instances

            Instances m_Instances

        The current set of instances loaded.

      - #### m_Loader

            Loader m_Loader

        The current loader used to obtain the current instances.

      - #### m_ParentFrame

            javax.swing.JFrame m_ParentFrame

        the parent frame. if one is provided, the close-button is displayed

      - #### m_CloseButPanel

            javax.swing.JPanel m_CloseButPanel

        the panel the Close-Button is located in.

      - #### m_readIncrementally

            boolean m_readIncrementally

        whether to read the instances incrementally, if possible.

      - #### m_showZeroInstancesAsUnknown

            boolean m_showZeroInstancesAsUnknown

        whether to display zero instances as unknown ("?").

      - #### m_showClassComboBox

            boolean m_showClassComboBox

        whether to display a combobox that allows the user to choose the class attribute.

  - ### Class weka.gui.SimpleCLI extends javax.swing.JFrame implements Serializable

    serialVersionUID:
      -50661410800566036L

  - ### Class weka.gui.SimpleCLIPanel extends ScriptingPanel implements Serializable

    serialVersionUID:
      1089039734615114942L

    - ### Serialized Fields

      - #### m_OutputArea

            javax.swing.JTextPane m_OutputArea

        The output area canvas added to the frame.

      - #### m_Input

            javax.swing.JTextField m_Input

        The command input area.

      - #### m_CommandHistory

            java.util.Vector<E> m_CommandHistory

        The history of commands entered interactively.

      - #### m_HistoryPos

            int m_HistoryPos

        The current position in the command history.

      - #### m_RunThread

            java.lang.Thread m_RunThread

        The thread currently running a class main method.

      - #### m_Completion

            SimpleCLIPanel.CommandlineCompletion m_Completion

        The commandline completion.

  - ### Class weka.gui.SortedTableModel extends javax.swing.table.AbstractTableModel implements Serializable

    serialVersionUID:
      4030907921461127548L

    - ### Serialized Fields

      - #### mModel

            javax.swing.table.TableModel mModel

        the actual table model

      - #### mIndices

            int[] mIndices

        the mapping between displayed and actual index

      - #### mSortColumn

            int mSortColumn

        the sort column

      - #### mAscending

            boolean mAscending

        whether sorting is ascending or descending

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

    serialVersionUID:
      -2685134277041307795L

    - ### Serialized Fields

      - #### image

            java.awt.Image image

        The splash image which is displayed on the splash window.

      - #### paintCalled

            boolean paintCalled

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

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

    serialVersionUID:
      6747718484736047752L

    - ### Serialized Fields

      - #### m_Result

            int m_Result

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

      - #### m_OkButton

            javax.swing.JButton m_OkButton

        Click to activate the current set parameters

      - #### m_CancelButton

            javax.swing.JButton m_CancelButton

        Click to cancel the dialog

      - #### m_UndoButton

            javax.swing.JButton m_UndoButton

        Click to undo the last action

      - #### m_ArffPanel

            ArffPanel m_ArffPanel

        the panel to display the Instances-object

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

    serialVersionUID:
      508309816292197578L

    - ### Serialized Fields

      - #### m_ActiveTasks

            int m_ActiveTasks

        The number of running weka threads

      - #### m_MonitorLabel

            javax.swing.JLabel m_MonitorLabel

        The label for displaying info

      - #### m_iconStationary

            javax.swing.ImageIcon m_iconStationary

        The icon for the stationary bird

      - #### m_iconAnimated

            javax.swing.ImageIcon m_iconAnimated

        The icon for the animated bird

      - #### m_animating

            boolean m_animating

        True if their are active tasks

- ## Package weka.gui.arffviewer

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

    serialVersionUID:
      -4697041150989513939L

    - ### Serialized Fields

      - #### m_TableArff

            ArffTable m_TableArff

        the underlying table

      - #### m_PopupHeader

            javax.swing.JPopupMenu m_PopupHeader

        the popup menu for the header row

      - #### m_PopupRows

            javax.swing.JPopupMenu m_PopupRows

        the popup menu for the data rows

      - #### m_LabelName

            javax.swing.JLabel m_LabelName

        displays the relation name

      - #### m_ShowAttributeIndex

            boolean m_ShowAttributeIndex

        whether to display the attribute index in the table header.

      - #### menuItemMean

            javax.swing.JMenuItem menuItemMean

      - #### menuItemSetAllValues

            javax.swing.JMenuItem menuItemSetAllValues

      - #### menuItemSetMissingValues

            javax.swing.JMenuItem menuItemSetMissingValues

      - #### menuItemReplaceValues

            javax.swing.JMenuItem menuItemReplaceValues

      - #### menuItemRenameAttribute

            javax.swing.JMenuItem menuItemRenameAttribute

      - #### menuItemAttributeAsClass

            javax.swing.JMenuItem menuItemAttributeAsClass

      - #### menuItemDeleteAttribute

            javax.swing.JMenuItem menuItemDeleteAttribute

      - #### menuItemDeleteAttributes

            javax.swing.JMenuItem menuItemDeleteAttributes

      - #### menuItemSortInstances

            javax.swing.JMenuItem menuItemSortInstances

      - #### menuItemDeleteSelectedInstance

            javax.swing.JMenuItem menuItemDeleteSelectedInstance

      - #### menuItemDeleteAllSelectedInstances

            javax.swing.JMenuItem menuItemDeleteAllSelectedInstances

      - #### menuItemSearch

            javax.swing.JMenuItem menuItemSearch

      - #### menuItemClearSearch

            javax.swing.JMenuItem menuItemClearSearch

      - #### menuItemUndo

            javax.swing.JMenuItem menuItemUndo

      - #### menuItemCopy

            javax.swing.JMenuItem menuItemCopy

      - #### menuItemOptimalColWidth

            javax.swing.JMenuItem menuItemOptimalColWidth

      - #### menuItemOptimalColWidths

            javax.swing.JMenuItem menuItemOptimalColWidths

      - #### m_Filename

            java.lang.String m_Filename

        the filename used in the title

      - #### m_Title

            java.lang.String m_Title

        the title prefix

      - #### m_CurrentCol

            int m_CurrentCol

        the currently selected column

      - #### m_Changed

            boolean m_Changed

        flag for whether data got changed

      - #### m_ChangeListeners

            java.util.HashSet<E> m_ChangeListeners

        the listeners that listen for modifications

      - #### m_LastSearch

            java.lang.String m_LastSearch

        the string used in the last search

      - #### m_LastReplace

            java.lang.String m_LastReplace

        the string used in the last replace

  - ### Class weka.gui.arffviewer.ArffSortedTableModel extends SortedTableModel implements Serializable

    serialVersionUID:
      -5733148376354254030L

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

    serialVersionUID:
      -2016200506908637967L

    - ### Serialized Fields

      - #### m_SearchString

            java.lang.String m_SearchString

        the search string

      - #### m_ChangeListeners

            java.util.HashSet<E> m_ChangeListeners

        the listeners for changes

  - ### Class weka.gui.arffviewer.ArffTable.RelationalCellEditor extends javax.swing.AbstractCellEditor implements Serializable

    serialVersionUID:
      657969163293205963L

    - ### Serialized Fields

      - #### m_Button

            javax.swing.JButton m_Button

        the button for opening the dialog

      - #### m_CurrentInst

            Instances m_CurrentInst

        the current instances

      - #### m_RowIndex

            int m_RowIndex

        the row index this editor is for

      - #### m_ColumnIndex

            int m_ColumnIndex

        the column index this editor is for

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

    serialVersionUID:
      9195794493301191171L

    - ### Serialized Fields

      - #### missingColor

            java.awt.Color missingColor

        the color for missing values

      - #### missingColorSelected

            java.awt.Color missingColorSelected

        the color for selected missing values

      - #### highlightColor

            java.awt.Color highlightColor

        the color for highlighted values

      - #### highlightColorSelected

            java.awt.Color highlightColorSelected

        the color for selected highlighted values

  - ### Class weka.gui.arffviewer.ArffTableModel extends javax.swing.table.DefaultTableModel implements Serializable

    serialVersionUID:
      3411795562305994946L

    - ### Serialized Fields

      - #### m_Listeners

            java.util.HashSet<E> m_Listeners

        the listeners

      - #### m_Data

            Instances m_Data

        the data

      - #### m_NotificationEnabled

            boolean m_NotificationEnabled

        whether notfication is enabled

      - #### m_UndoEnabled

            boolean m_UndoEnabled

        whether undo is active

      - #### m_IgnoreChanges

            boolean m_IgnoreChanges

        whether to ignore changes, i.e. not adding to undo history

      - #### m_UndoList

            java.util.Vector<E> m_UndoList

        the undo list (contains temp. filenames)

      - #### m_ReadOnly

            boolean m_ReadOnly

        whether the table is read-only

      - #### m_ShowAttributeIndex

            boolean m_ShowAttributeIndex

        whether to display the attribute index in the table header.

      - #### m_Cache

            java.util.Hashtable<K,V> m_Cache

        for caching long relational and string values that get processed for display.

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

    serialVersionUID:
      -7455845566922685175L

    - ### Serialized Fields

      - #### m_MainPanel

            ArffViewerMainPanel m_MainPanel

        the main panel

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

    serialVersionUID:
      -8763161167586738753L

    - ### Serialized Fields

      - #### parent

            java.awt.Container parent

      - #### tabbedPane

            javax.swing.JTabbedPane tabbedPane

      - #### menuBar

            javax.swing.JMenuBar menuBar

      - #### menuFile

            javax.swing.JMenu menuFile

      - #### menuFileOpen

            javax.swing.JMenuItem menuFileOpen

      - #### menuFileSave

            javax.swing.JMenuItem menuFileSave

      - #### menuFileSaveAs

            javax.swing.JMenuItem menuFileSaveAs

      - #### menuFileClose

            javax.swing.JMenuItem menuFileClose

      - #### menuFileCloseAll

            javax.swing.JMenuItem menuFileCloseAll

      - #### menuFileProperties

            javax.swing.JMenuItem menuFileProperties

      - #### menuFileExit

            javax.swing.JMenuItem menuFileExit

      - #### menuEdit

            javax.swing.JMenu menuEdit

      - #### menuEditUndo

            javax.swing.JMenuItem menuEditUndo

      - #### menuEditCopy

            javax.swing.JMenuItem menuEditCopy

      - #### menuEditSearch

            javax.swing.JMenuItem menuEditSearch

      - #### menuEditClearSearch

            javax.swing.JMenuItem menuEditClearSearch

      - #### menuEditDeleteAttribute

            javax.swing.JMenuItem menuEditDeleteAttribute

      - #### menuEditDeleteAttributes

            javax.swing.JMenuItem menuEditDeleteAttributes

      - #### menuEditRenameAttribute

            javax.swing.JMenuItem menuEditRenameAttribute

      - #### menuEditAttributeAsClass

            javax.swing.JMenuItem menuEditAttributeAsClass

      - #### menuEditDeleteInstance

            javax.swing.JMenuItem menuEditDeleteInstance

      - #### menuEditDeleteInstances

            javax.swing.JMenuItem menuEditDeleteInstances

      - #### menuEditSortInstances

            javax.swing.JMenuItem menuEditSortInstances

      - #### menuView

            javax.swing.JMenu menuView

      - #### menuViewAttributes

            javax.swing.JMenuItem menuViewAttributes

      - #### menuViewValues

            javax.swing.JMenuItem menuViewValues

      - #### menuViewOptimalColWidths

            javax.swing.JMenuItem menuViewOptimalColWidths

      - #### fileChooser

            ConverterFileChooser fileChooser

      - #### frameTitle

            java.lang.String frameTitle

      - #### confirmExit

            boolean confirmExit

      - #### width

            int width

      - #### height

            int height

      - #### top

            int top

      - #### left

            int left

      - #### exitOnClose

            boolean exitOnClose

- ## Package weka.gui.beans

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

    serialVersionUID:
      3956528599473814287L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

        Default visual for data sources

      - #### m_listenee

            java.lang.Object m_listenee

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

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

    serialVersionUID:
      -4127257701890044793L

    - ### Serialized Fields

      - #### m_design

            boolean m_design

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

      - #### m_bcSupport

            java.beans.beancontext.BeanContextChildSupport m_bcSupport

        BeanContextChild support

      - #### m_visual

            BeanVisual m_visual

        Default visual for data sources

      - #### m_listeners

            java.util.Vector<E> m_listeners

        Objects listening for events from data sources

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

    serialVersionUID:
      3983303541814121632L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

        Default visual for evaluators

      - #### m_listenee

            java.lang.Object m_listenee

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

    serialVersionUID:
      -7905764845789349839L

    - ### Serialized Fields

      - #### m_listeners

            java.util.Vector<E> m_listeners

        Objects listening to us

      - #### m_visual

            BeanVisual m_visual

      - #### m_listenee

            java.lang.Object m_listenee

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

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

    serialVersionUID:
      -1809339823613492037L

    - ### Serialized Fields

      - #### m_trainingListeners

            java.util.Vector<E> m_trainingListeners

        Objects listening for trainin set events

      - #### m_testListeners

            java.util.Vector<E> m_testListeners

        Objects listening for test set events

      - #### m_visual

            BeanVisual m_visual

      - #### m_listenee

            java.lang.Object m_listenee

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

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

    serialVersionUID:
      -7842746199524591125L

    - ### Serialized Fields

      - #### m_listeners

            java.util.Vector<E> m_listeners

        Objects listening for training set events

      - #### m_visual

            BeanVisual m_visual

      - #### m_listenee

            java.lang.Object m_listenee

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

  - ### Class weka.gui.beans.Appender extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      9177433051794199463L

    - ### Serialized Fields

      - #### m_listeneeTypes

            java.util.Set<E> m_listeneeTypes

        Upstream components sending us data

      - #### m_listenees

            java.util.Map<K,V> m_listenees

      - #### m_ie

            InstanceEvent m_ie

        Instance event to use for incremental mode

      - #### m_finishedCount

            int m_finishedCount

        Keeps track of how many incoming instance streams have finished

      - #### m_busy

            boolean m_busy

        True if we are busy

      - #### m_visual

            BeanVisual m_visual

        Default visual for data sources

      - #### m_dataListeners

            java.util.ArrayList<E> m_dataListeners

        Downstream steps listening to batch data events

      - #### m_instanceListeners

            java.util.ArrayList<E> m_instanceListeners

        Downstream steps listening to instance events

  - ### Class weka.gui.beans.Associator extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -7843500322130210057L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_state

            int m_state

      - #### m_buildThread

            java.lang.Thread m_buildThread

      - #### m_globalInfo

            java.lang.String m_globalInfo

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

      - #### m_listenees

            java.util.Hashtable<K,V> m_listenees

        Objects talking to us

      - #### m_textListeners

            java.util.Vector<E> m_textListeners

        Objects listening for text events

      - #### m_graphListeners

            java.util.Vector<E> m_graphListeners

        Objects listening for graph events

      - #### m_rulesListeners

            java.util.Vector<E> m_rulesListeners

        The objects listening for batchAssociationRules events

      - #### m_Associator

            Associator m_Associator

  - ### Class weka.gui.beans.AssociatorCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      5767664969353495974L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_dsAssociator

            Associator m_dsAssociator

      - #### m_AssociatorEditor

            PropertySheetPanel m_AssociatorEditor

      - #### m_parentWindow

            java.awt.Window m_parentWindow

      - #### m_backup

            Associator m_backup

        Backup is user presses cancel

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

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

    serialVersionUID:
      -294354961169372758L

    - ### Serialized Fields

      - #### m_gridWidth

            int m_gridWidth

        The number of plots horizontally in the display

      - #### m_maxPlots

            int m_maxPlots

        The maximum number of plots to show

      - #### m_coloringIndex

            int m_coloringIndex

        Index on which to color the plots.

      - #### m_showClassCombo

            boolean m_showClassCombo

      - #### m_runningAsPerspective

            boolean m_runningAsPerspective

      - #### m_activePerspective

            boolean m_activePerspective

  - ### Class weka.gui.beans.AttributeSummarizerCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -6973666187676272788L

    - ### Serialized Fields

      - #### m_dataVis

            DataVisualizer m_dataVis

      - #### m_env

            Environment m_env

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_parent

            java.awt.Window m_parent

      - #### m_rendererNameBack

            java.lang.String m_rendererNameBack

      - #### m_xAxisBack

            java.lang.String m_xAxisBack

      - #### m_widthBack

            java.lang.String m_widthBack

      - #### m_heightBack

            java.lang.String m_heightBack

      - #### m_optsBack

            java.lang.String m_optsBack

      - #### m_rendererCombo

            javax.swing.JComboBox<E> m_rendererCombo

      - #### m_xAxis

            EnvironmentField m_xAxis

      - #### m_width

            EnvironmentField m_width

      - #### m_height

            EnvironmentField m_height

      - #### m_opts

            EnvironmentField m_opts

  - ### Class weka.gui.beans.BatchAssociationRulesEvent extends java.util.EventObject implements Serializable

    serialVersionUID:
      6332614648885439492L

    - ### Serialized Fields

      - #### m_rules

            AssociationRules m_rules

        The encapsulated rules

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

    serialVersionUID:
      878097199815991084L

    - ### Serialized Fields

      - #### m_classifier

            Classifier m_classifier

        The classifier

      - #### m_testSet

            DataSetEvent m_testSet

        Instances that can be used for testing the classifier

      - #### m_trainSet

            DataSetEvent m_trainSet

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

      - #### m_runNumber

            int m_runNumber

        The run number that this classifier was generated for

      - #### m_maxRunNumber

            int m_maxRunNumber

        The maximum number of runs

      - #### m_setNumber

            int m_setNumber

        The set number for the test set

      - #### m_maxSetNumber

            int m_maxSetNumber

        The last set number for this series

      - #### m_groupIdentifier

            long m_groupIdentifier

        An identifier that can be used to group all related runs/sets together.

      - #### m_eventLabel

            java.lang.String m_eventLabel

        Label for this event

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

    serialVersionUID:
      7268777944939129714L

    - ### Serialized Fields

      - #### m_clusterer

            Clusterer m_clusterer

        The clusterer

      - #### m_testSet

            DataSetEvent m_testSet

        Training or Test Instances

      - #### m_setNumber

            int m_setNumber

        The set number for the test set

      - #### m_testOrTrain

            int m_testOrTrain

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

      - #### m_maxSetNumber

            int m_maxSetNumber

        The last set number for this series

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

    serialVersionUID:
      8804264241791332064L

    - ### Serialized Fields

      - #### m_source

            BeanInstance m_source

      - #### m_target

            BeanInstance m_target

      - #### m_eventName

            java.lang.String m_eventName

        The name of the event for this connection

      - #### m_hidden

            boolean m_hidden

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

    serialVersionUID:
      -7575653109025406342L

    - ### Serialized Fields

      - #### m_bean

            java.lang.Object m_bean

        Holds the bean encapsulated in this instance

      - #### m_x

            int m_x

      - #### m_y

            int m_y

  - ### Class weka.gui.beans.BeansProperties extends java.lang.Object implements Serializable

    serialVersionUID:
      7183413615090695785L

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

    serialVersionUID:
      -6677473561687129614L

    - ### Serialization Methods

      - #### readObject

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

        Overides default read object in order to reload icons. This is necessary because for some strange reason animated gifs stop being animated after being serialized/deserialized.
        Throws:
          `java.io.IOException` - if an error occurs
          `java.lang.ClassNotFoundException` - if an error occurs

    - ### Serialized Fields

      - #### m_iconPath

            java.lang.String m_iconPath

        Holds name (including path) of the static icon

      - #### m_animatedIconPath

            java.lang.String m_animatedIconPath

        Holds name (including path) of the animated icon

      - #### m_visualName

            java.lang.String m_visualName

        Name for the bean

      - #### m_visualLabel

            javax.swing.JLabel m_visualLabel

      - #### m_pcs

            java.beans.PropertyChangeSupport m_pcs

        Container for the icon

      - #### m_displayConnectors

            boolean m_displayConnectors

      - #### m_connectorColor

            java.awt.Color m_connectorColor

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

    serialVersionUID:
      7812460715499569390L

    - ### Serialized Fields

      - #### m_legendText

            java.util.Vector<E> m_legendText

      - #### m_max

            double m_max

      - #### m_min

            double m_min

      - #### m_reset

            boolean m_reset

      - #### m_dataPoint

            double[] m_dataPoint

        Y values of the data points

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

    serialVersionUID:
      4011131665025817924L

    - ### Serialized Fields

      - #### m_classColumn

            java.lang.String m_classColumn

      - #### m_connectedFormat

            Instances m_connectedFormat

        format of instances for current incoming connection (if any)

      - #### m_trainingProvider

            java.lang.Object m_trainingProvider

      - #### m_testProvider

            java.lang.Object m_testProvider

      - #### m_dataProvider

            java.lang.Object m_dataProvider

      - #### m_instanceProvider

            java.lang.Object m_instanceProvider

      - #### m_trainingListeners

            java.util.Vector<E> m_trainingListeners

      - #### m_testListeners

            java.util.Vector<E> m_testListeners

      - #### m_dataListeners

            java.util.Vector<E> m_dataListeners

      - #### m_instanceListeners

            java.util.Vector<E> m_instanceListeners

      - #### m_dataFormatListeners

            java.util.Vector<E> m_dataFormatListeners

      - #### m_visual

            BeanVisual m_visual

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

    serialVersionUID:
      476539385765301907L

    - ### Serialized Fields

      - #### m_displayColNames

            boolean m_displayColNames

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

    serialVersionUID:
      659603893917736008L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_state

            int m_state

      - #### m_globalInfo

            java.lang.String m_globalInfo

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

      - #### m_listenees

            java.util.HashMap<K,V> m_listenees

        Objects talking to us. String connection event key, 2 element list containing source and count

      - #### m_batchClassifierListeners

            java.util.Vector<E> m_batchClassifierListeners

        Objects listening for batch classifier events

      - #### m_incrementalClassifierListeners

            java.util.Vector<E> m_incrementalClassifierListeners

        Objects listening for incremental classifier events

      - #### m_graphListeners

            java.util.Vector<E> m_graphListeners

        Objects listening for graph events

      - #### m_textListeners

            java.util.Vector<E> m_textListeners

        Objects listening for text events

      - #### m_trainingSet

            Instances m_trainingSet

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

      - #### m_Classifier

            Classifier m_Classifier

      - #### m_ClassifierTemplate

            Classifier m_ClassifierTemplate

        Template used for creating copies when building in parallel

      - #### m_ie

            IncrementalClassifierEvent m_ie

      - #### m_binaryFilter

            javax.swing.filechooser.FileFilter m_binaryFilter

      - #### m_KOMLFilter

            javax.swing.filechooser.FileFilter m_KOMLFilter

      - #### m_XStreamFilter

            javax.swing.filechooser.FileFilter m_XStreamFilter

      - #### m_resetIncrementalClassifier

            boolean m_resetIncrementalClassifier

        If the classifier is an incremental classifier, should we reset it (i.e. call buildClassifier()) and discard any previously learned model before processing the first instance in the stream. Note that this happens automatically if the incoming instance structure does not match that (if any) that the classifier was trained with previously.

      - #### m_updateIncrementalClassifier

            boolean m_updateIncrementalClassifier

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

      - #### m_incrementalEvent

            InstanceEvent m_incrementalEvent

        Event to handle when processing incremental updates

      - #### m_executionSlots

            int m_executionSlots

        Number of threads to use to train models with

      - #### m_oldText

            java.lang.String m_oldText

        Holds original icon label text

      - #### m_reject

            boolean m_reject

        true if we should reject any further training data sets, until all processing has been finished, once we've received the last fold of the last run.

      - #### m_block

            boolean m_block

        True if we should block rather reject until all processing has been completed.

      - #### m_loadModelFileName

            java.lang.String m_loadModelFileName

        Optional file to load a pre-trained model to score with (batch, or to score and update (incremental) in the case of testSet only (batch) or instance (incremental) connections

  - ### Class weka.gui.beans.Classifier.TrainingTask extends java.lang.Object implements Serializable

    serialVersionUID:
      -7918128680624169641L

    - ### Serialized Fields

      - #### m_runNum

            int m_runNum

      - #### m_maxRunNum

            int m_maxRunNum

      - #### m_setNum

            int m_setNum

      - #### m_maxSetNum

            int m_maxSetNum

      - #### m_train

            Instances m_train

      - #### m_taskInfo

            TaskStatusInfo m_taskInfo

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

    serialVersionUID:
      -6688000820160821429L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_dsClassifier

            Classifier m_dsClassifier

      - #### m_ClassifierEditor

            PropertySheetPanel m_ClassifierEditor

      - #### m_incrementalPanel

            javax.swing.JPanel m_incrementalPanel

      - #### m_resetIncrementalClassifier

            javax.swing.JCheckBox m_resetIncrementalClassifier

      - #### m_updateIncrementalClassifier

            javax.swing.JCheckBox m_updateIncrementalClassifier

      - #### m_panelVisible

            boolean m_panelVisible

      - #### m_holderPanel

            javax.swing.JPanel m_holderPanel

      - #### m_executionSlotsText

            javax.swing.JTextField m_executionSlotsText

      - #### m_executionSlotsLabel

            javax.swing.JLabel m_executionSlotsLabel

      - #### m_executionSlotsPanel

            javax.swing.JPanel m_executionSlotsPanel

      - #### m_blockOnLastFold

            javax.swing.JCheckBox m_blockOnLastFold

      - #### m_loadModelField

            FileEnvironmentField m_loadModelField

      - #### m_parentWindow

            java.awt.Window m_parentWindow

      - #### m_backup

            Classifier m_backup

        Copy of the current classifier in case cancel is selected

      - #### m_env

            Environment m_env

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

        Listener that wants to know the the modified status of the object that we're customizing

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

    serialVersionUID:
      -3511801418192148690L

    - ### Serialized Fields

      - #### m_textListeners

            java.util.Vector<E> m_textListeners

      - #### m_thresholdListeners

            java.util.Vector<E> m_thresholdListeners

      - #### m_visualizableErrorListeners

            java.util.Vector<E> m_visualizableErrorListeners

      - #### m_errorPlotPointSizeProportionalToMargin

            boolean m_errorPlotPointSizeProportionalToMargin

      - #### m_executionSlots

            int m_executionSlots

        Number of threads to use to train models with

      - #### m_selectedEvalMetrics

            java.lang.String m_selectedEvalMetrics

        Evaluation metrics to output

      - #### m_metricsList

            java.util.List<E> m_metricsList

  - ### Class weka.gui.beans.ClassifierPerformanceEvaluator.AggregateableClassifierErrorsPlotInstances extends ClassifierErrorsPlotInstances implements Serializable

    serialVersionUID:
      2012744784036684168L

  - ### Class weka.gui.beans.ClassifierPerformanceEvaluator.EvaluationTask extends java.lang.Object implements Serializable

    serialVersionUID:
      -8939077467030259059L

    - ### Serialized Fields

      - #### m_testData

            Instances m_testData

      - #### m_trainData

            Instances m_trainData

      - #### m_setNum

            int m_setNum

      - #### m_maxSetNum

            int m_maxSetNum

      - #### m_classifier

            Classifier m_classifier

      - #### m_stopped

            boolean m_stopped

      - #### m_evalLabel

            java.lang.String m_evalLabel

  - ### Class weka.gui.beans.ClassifierPerformanceEvaluatorCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -1055460295546483853L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_splitEditor

            PropertySheetPanel m_splitEditor

      - #### m_cpe

            ClassifierPerformanceEvaluator m_cpe

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_executionSlotsBackup

            int m_executionSlotsBackup

      - #### m_parent

            java.awt.Window m_parent

      - #### m_evalMetricsBut

            javax.swing.JButton m_evalMetricsBut

      - #### m_evaluationMetrics

            java.util.List<E> m_evaluationMetrics

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

    serialVersionUID:
      -1196143276710882989L

    - ### Serialized Fields

      - #### m_classValue

            java.lang.String m_classValue

        the class value considered to be the positive class

      - #### m_connectedFormat

            Instances m_connectedFormat

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

      - #### m_dataProvider

            java.lang.Object m_dataProvider

      - #### m_dataListeners

            java.util.Vector<E> m_dataListeners

      - #### m_dataFormatListeners

            java.util.Vector<E> m_dataFormatListeners

      - #### m_visual

            BeanVisual m_visual

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

    serialVersionUID:
      8213423053861600469L

    - ### Serialized Fields

      - #### m_displayValNames

            boolean m_displayValNames

      - #### m_classValuePicker

            ClassValuePicker m_classValuePicker

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_ClassValueCombo

            javax.swing.JComboBox<E> m_ClassValueCombo

      - #### m_holderP

            javax.swing.JPanel m_holderP

      - #### m_messageLabel

            javax.swing.JLabel m_messageLabel

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_modified

            boolean m_modified

      - #### m_parent

            java.awt.Window m_parent

      - #### m_backup

            java.lang.String m_backup

      - #### m_textBoxEntryMode

            boolean m_textBoxEntryMode

      - #### m_valueTextBox

            javax.swing.JTextField m_valueTextBox

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

    serialVersionUID:
      7729795159836843810L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_state

            int m_state

      - #### m_buildThread

            java.lang.Thread m_buildThread

      - #### m_globalInfo

            java.lang.String m_globalInfo

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

      - #### m_listenees

            java.util.Hashtable<K,V> m_listenees

        Objects talking to us

      - #### m_batchClustererListeners

            java.util.Vector<E> m_batchClustererListeners

        Objects listening for batch clusterer events

      - #### m_graphListeners

            java.util.Vector<E> m_graphListeners

        Objects listening for graph events

      - #### m_textListeners

            java.util.Vector<E> m_textListeners

        Objects listening for text events

      - #### m_trainingSet

            Instances m_trainingSet

        Holds training instances for batch training.

      - #### m_Clusterer

            Clusterer m_Clusterer

      - #### m_dummy

            java.lang.Double m_dummy

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

    serialVersionUID:
      -2035688458149534161L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_dsClusterer

            Clusterer m_dsClusterer

      - #### m_ClustererEditor

            PropertySheetPanel m_ClustererEditor

      - #### m_parentWindow

            java.awt.Window m_parentWindow

      - #### m_backup

            Clusterer m_backup

        Backup if the user presses cancel

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

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

    serialVersionUID:
      8041163601333978584L

    - ### Serialized Fields

      - #### m_textListeners

            java.util.Vector<E> m_textListeners

  - ### Class weka.gui.beans.ConfigurationEvent extends java.util.EventObject implements Serializable

    serialVersionUID:
      5433562112093780868L

  - ### Class weka.gui.beans.CostBenefitAnalysis extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      8647471654613320469L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_framePoppedUp

            boolean m_framePoppedUp

      - #### m_design

            boolean m_design

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

      - #### m_bcSupport

            java.beans.beancontext.BeanContextChildSupport m_bcSupport

        BeanContextChild support

      - #### m_listenee

            java.lang.Object m_listenee

        The object sending us data (we allow only one connection at any one time)

      - #### m_headlessEvents

            java.util.List<E> m_headlessEvents

  - ### Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      5364871945448769003L

    - ### Serialized Fields

      - #### m_performancePanel

            VisualizePanel m_performancePanel

        Displays the performance graphs(s)

      - #### m_costBenefitPanel

            VisualizePanel m_costBenefitPanel

        Displays the cost/benefit (profit/loss) graph

      - #### m_classAttribute

            Attribute m_classAttribute

        The class attribute from the data that was used to generate the threshold curve

      - #### m_masterPlot

            PlotData2D m_masterPlot

        Data for the threshold curve

      - #### m_costBenefit

            PlotData2D m_costBenefit

        Data for the cost/benefit curve

      - #### m_shapeSizes

            int[] m_shapeSizes

        The size of the points being plotted

      - #### m_previousShapeIndex

            int m_previousShapeIndex

        The index of the previous plotted point that was highlighted

      - #### m_thresholdSlider

            javax.swing.JSlider m_thresholdSlider

        The slider for adjusting the threshold

      - #### m_percPop

            javax.swing.JRadioButton m_percPop

      - #### m_percOfTarget

            javax.swing.JRadioButton m_percOfTarget

      - #### m_threshold

            javax.swing.JRadioButton m_threshold

      - #### m_percPopLab

            javax.swing.JLabel m_percPopLab

      - #### m_percOfTargetLab

            javax.swing.JLabel m_percOfTargetLab

      - #### m_thresholdLab

            javax.swing.JLabel m_thresholdLab

      - #### m_conf_predictedA

            javax.swing.JLabel m_conf_predictedA

      - #### m_conf_predictedB

            javax.swing.JLabel m_conf_predictedB

      - #### m_conf_actualA

            javax.swing.JLabel m_conf_actualA

      - #### m_conf_actualB

            javax.swing.JLabel m_conf_actualB

      - #### m_conf_aa

            weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_aa

      - #### m_conf_ab

            weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_ab

      - #### m_conf_ba

            weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_ba

      - #### m_conf_bb

            weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_bb

      - #### m_cost_predictedA

            javax.swing.JLabel m_cost_predictedA

      - #### m_cost_predictedB

            javax.swing.JLabel m_cost_predictedB

      - #### m_cost_actualA

            javax.swing.JLabel m_cost_actualA

      - #### m_cost_actualB

            javax.swing.JLabel m_cost_actualB

      - #### m_cost_aa

            javax.swing.JTextField m_cost_aa

      - #### m_cost_ab

            javax.swing.JTextField m_cost_ab

      - #### m_cost_ba

            javax.swing.JTextField m_cost_ba

      - #### m_cost_bb

            javax.swing.JTextField m_cost_bb

      - #### m_maximizeCB

            javax.swing.JButton m_maximizeCB

      - #### m_minimizeCB

            javax.swing.JButton m_minimizeCB

      - #### m_costR

            javax.swing.JRadioButton m_costR

      - #### m_benefitR

            javax.swing.JRadioButton m_benefitR

      - #### m_costBenefitL

            javax.swing.JLabel m_costBenefitL

      - #### m_costBenefitV

            javax.swing.JLabel m_costBenefitV

      - #### m_randomV

            javax.swing.JLabel m_randomV

      - #### m_gainV

            javax.swing.JLabel m_gainV

      - #### m_originalPopSize

            int m_originalPopSize

      - #### m_totalPopField

            javax.swing.JTextField m_totalPopField

        Population text field

      - #### m_totalPopPrevious

            int m_totalPopPrevious

      - #### m_classificationAccV

            javax.swing.JLabel m_classificationAccV

        Classification accuracy

      - #### m_tpPrevious

            double m_tpPrevious

      - #### m_fpPrevious

            double m_fpPrevious

      - #### m_tnPrevious

            double m_tnPrevious

      - #### m_fnPrevious

            double m_fnPrevious

  - ### Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      6148640235434494767L

    - ### Serialized Fields

      - #### m_conf_cell

            javax.swing.JLabel m_conf_cell

      - #### m_conf_perc

            javax.swing.JLabel m_conf_perc

      - #### m_percentageP

            javax.swing.JPanel m_percentageP

      - #### m_percentage

            double m_percentage

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

    serialVersionUID:
      -6350179298851891512L

    - ### Serialized Fields

      - #### m_numFolds

            int m_numFolds

      - #### m_randomSeed

            int m_randomSeed

      - #### m_preserveOrder

            boolean m_preserveOrder

      - #### m_dataProvider

            boolean m_dataProvider

      - #### m_trainingProvider

            boolean m_trainingProvider

      - #### m_testProvider

            boolean m_testProvider

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

    serialVersionUID:
      1229878140258668581L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_cvEditor

            PropertySheetPanel m_cvEditor

      - #### m_cvMaker

            CrossValidationFoldMaker m_cvMaker

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_foldsBackup

            int m_foldsBackup

      - #### m_orderBackup

            boolean m_orderBackup

      - #### m_seedBackup

            int m_seedBackup

      - #### m_parent

            java.awt.Window m_parent

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

    serialVersionUID:
      -5111218447577318057L

    - ### Serialized Fields

      - #### m_dataSet

            Instances m_dataSet

      - #### m_structureOnly

            boolean m_structureOnly

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

    serialVersionUID:
      1949062132560159028L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_framePoppedUp

            boolean m_framePoppedUp

      - #### m_design

            boolean m_design

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

      - #### m_visPanel

            VisualizePanel m_visPanel

      - #### m_headlessEvents

            java.util.List<E> m_headlessEvents

        Events received and stored during headless execution

      - #### m_imageListeners

            java.util.ArrayList<E> m_imageListeners

      - #### m_listenees

            java.util.List<E> m_listenees

      - #### m_dataSetListeners

            java.util.Vector<E> m_dataSetListeners

        Objects listening for data set events

      - #### m_offscreenRendererName

            java.lang.String m_offscreenRendererName

        Name of the renderer to use for offscreen chart rendering

      - #### m_xAxis

            java.lang.String m_xAxis

        The name of the attribute to use for the x-axis of offscreen plots. If left empty, False Positive Rate is used for threshold curves

      - #### m_yAxis

            java.lang.String m_yAxis

        The name of the attribute to use for the y-axis of offscreen plots. If left empty, True Positive Rate is used for threshold curves

      - #### m_additionalOptions

            java.lang.String m_additionalOptions

        Additional options for the offscreen renderer

      - #### m_width

            java.lang.String m_width

        Width of offscreen plots

      - #### m_height

            java.lang.String m_height

        Height of offscreen plots

      - #### m_bcSupport

            java.beans.beancontext.BeanContextChildSupport m_bcSupport

        BeanContextChild support

  - ### Class weka.gui.beans.DataVisualizerCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      27802741348090392L

    - ### Serialized Fields

      - #### m_dataVis

            DataVisualizer m_dataVis

      - #### m_env

            Environment m_env

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_parent

            java.awt.Window m_parent

      - #### m_rendererNameBack

            java.lang.String m_rendererNameBack

      - #### m_xAxisBack

            java.lang.String m_xAxisBack

      - #### m_yAxisBack

            java.lang.String m_yAxisBack

      - #### m_widthBack

            java.lang.String m_widthBack

      - #### m_heightBack

            java.lang.String m_heightBack

      - #### m_optsBack

            java.lang.String m_optsBack

      - #### m_rendererCombo

            javax.swing.JComboBox<E> m_rendererCombo

      - #### m_xAxis

            EnvironmentField m_xAxis

      - #### m_yAxis

            EnvironmentField m_yAxis

      - #### m_width

            EnvironmentField m_width

      - #### m_height

            EnvironmentField m_height

      - #### m_opts

            EnvironmentField m_opts

  - ### Class weka.gui.beans.EnvironmentField extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -3125404573324734121L

    - ### Serialized Fields

      - #### m_label

            javax.swing.JLabel m_label

        The label for the widget

      - #### m_combo

            javax.swing.JComboBox<E> m_combo

        The combo box

      - #### m_env

            Environment m_env

        The current environment variables

      - #### m_currentContents

            java.lang.String m_currentContents

      - #### m_firstCaretPos

            int m_firstCaretPos

      - #### m_previousCaretPos

            int m_previousCaretPos

      - #### m_currentCaretPos

            int m_currentCaretPos

      - #### m_support

            java.beans.PropertyChangeSupport m_support

  - ### Class weka.gui.beans.EnvironmentField.WideComboBox extends javax.swing.JComboBox implements Serializable

    serialVersionUID:
      -6512065375459733517L

    - ### Serialized Fields

      - #### m_layingOut

            boolean m_layingOut

  - ### Class weka.gui.beans.FileEnvironmentField extends EnvironmentField implements Serializable

    serialVersionUID:
      -233731548086207652L

    - ### Serialized Fields

      - #### m_fileEditor

            FileEditor m_fileEditor

        File editor component

      - #### m_fileEditorDialog

            PropertyDialog m_fileEditorDialog

        Dialog to hold the file editor

      - #### m_browseBut

            javax.swing.JButton m_browseBut

        The button to pop up the file dialog

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

    serialVersionUID:
      8249759470189439321L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_state

            int m_state

      - #### m_globalInfo

            java.lang.String m_globalInfo

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

      - #### m_listenees

            java.util.Hashtable<K,V> m_listenees

        Objects talking to us

      - #### m_trainingListeners

            java.util.Vector<E> m_trainingListeners

        Objects listening for training set events

      - #### m_testListeners

            java.util.Vector<E> m_testListeners

        Objects listening for test set events

      - #### m_instanceListeners

            java.util.Vector<E> m_instanceListeners

        Objects listening for instance events

      - #### m_dataListeners

            java.util.Vector<E> m_dataListeners

        Objects listening for data set events

      - #### m_Filter

            Filter m_Filter

        The filter to use.

      - #### m_ie

            InstanceEvent m_ie

        Instance event object for passing on filtered instance streams

      - #### m_structurePassedOn

            boolean m_structurePassedOn

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

    serialVersionUID:
      2049895469240109738L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_filter

            Filter m_filter

      - #### m_backup

            Filter m_backup

        Backup if user presses cancel

      - #### m_filterEditor

            PropertySheetPanel m_filterEditor

      - #### m_parentWindow

            java.awt.Window m_parentWindow

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

  - ### Class weka.gui.beans.FlowByExpression extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      2492050246494259885L

    - ### Serialized Fields

      - #### m_root

            weka.gui.beans.FlowByExpression.ExpressionNode m_root

        The root of the expression tree

      - #### m_expressionString

            java.lang.String m_expressionString

        The expression tree to use in internal textual format

      - #### m_downstream

            java.lang.Object[] m_downstream

        The one or two downstream steps - one for instances that match the expression and the other for instances that don't

      - #### m_customNameOfTrueStep

            java.lang.String m_customNameOfTrueStep

        Name of the step to receive instances that evaluate to true via the expression

      - #### m_customNameOfFalseStep

            java.lang.String m_customNameOfFalseStep

        Name of the step to receive instances that evaluate to false via the expression

      - #### m_indexOfTrueStep

            int m_indexOfTrueStep

      - #### m_indexOfFalseStep

            int m_indexOfFalseStep

      - #### m_listenee

            java.lang.Object m_listenee

        Component talking to us

      - #### m_connectionType

            java.lang.String m_connectionType

        The type of the incoming connection

      - #### m_connectedFormat

            Instances m_connectedFormat

        format of instances for current incoming connection (if any)

      - #### m_ie

            InstanceEvent m_ie

        Instance event to use

      - #### m_visual

            BeanVisual m_visual

        Default visual filters

  - ### Class weka.gui.beans.FlowByExpression.BracketNode extends weka.gui.beans.FlowByExpression.ExpressionNode implements Serializable

    serialVersionUID:
      8732159083173001115L

    - ### Serialized Fields

      - #### m_children

            java.util.List<E> m_children

  - ### Class weka.gui.beans.FlowByExpression.ExpressionClause extends weka.gui.beans.FlowByExpression.ExpressionNode implements Serializable

    serialVersionUID:
      2754006654981248325L

    - ### Serialized Fields

      - #### m_operator

            FlowByExpression.ExpressionClause.ExpressionType m_operator

        The operator for this expression

      - #### m_lhsAttributeName

            java.lang.String m_lhsAttributeName

        The name of the lhs attribute

      - #### m_lhsAttIndex

            int m_lhsAttIndex

        The index of the lhs attribute

      - #### m_rhsOperand

            java.lang.String m_rhsOperand

        The rhs operand (constant value or attribute name)

      - #### m_rhsIsAttribute

            boolean m_rhsIsAttribute

        True if the rhs operand is an attribute

      - #### m_rhsAttIndex

            int m_rhsAttIndex

        index of the rhs if it is an attribute

      - #### m_resolvedLhsName

            java.lang.String m_resolvedLhsName

        The name of the lhs attribute after resolving variables

      - #### m_resolvedRhsOperand

            java.lang.String m_resolvedRhsOperand

        The rhs operand after resolving variables

      - #### m_regexPattern

            java.util.regex.Pattern m_regexPattern

        the compiled regex pattern (if the operator is REGEX)

      - #### m_numericOperand

            double m_numericOperand

        The rhs operand (if constant and is a number )

  - ### Class weka.gui.beans.FlowByExpression.ExpressionNode extends java.lang.Object implements Serializable

    serialVersionUID:
      -8427857202322768762L

    - ### Serialized Fields

      - #### m_isAnOr

            boolean m_isAnOr

        boolean operator for combining with result so far

      - #### m_isNegated

            boolean m_isNegated

        is this node negated?

      - #### m_showAndOr

            boolean m_showAndOr

        Whether to show the combination operator in the textual representation

  - ### Class weka.gui.beans.FlowByExpressionCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -3574741335754017794L

    - ### Serialized Fields

      - #### m_env

            Environment m_env

      - #### m_modifyL

            BeanCustomizer.ModifyListener m_modifyL

      - #### m_expression

            FlowByExpression m_expression

      - #### m_lhsField

            javax.swing.JComboBox<E> m_lhsField

      - #### m_operatorCombo

            javax.swing.JComboBox<E> m_operatorCombo

      - #### m_rhsField

            javax.swing.JComboBox<E> m_rhsField

      - #### m_rhsIsAttribute

            javax.swing.JCheckBox m_rhsIsAttribute

      - #### m_expressionLab

            javax.swing.JLabel m_expressionLab

      - #### m_trueData

            javax.swing.JComboBox<E> m_trueData

      - #### m_falseData

            javax.swing.JComboBox<E> m_falseData

      - #### m_expressionTree

            javax.swing.JTree m_expressionTree

      - #### m_treeRoot

            javax.swing.tree.DefaultMutableTreeNode m_treeRoot

      - #### m_addExpressionNode

            javax.swing.JButton m_addExpressionNode

      - #### m_addBracketNode

            javax.swing.JButton m_addBracketNode

      - #### m_toggleNegation

            javax.swing.JButton m_toggleNegation

      - #### m_andOr

            javax.swing.JButton m_andOr

      - #### m_deleteNode

            javax.swing.JButton m_deleteNode

      - #### m_tempEditor

            PropertySheetPanel m_tempEditor

      - #### m_parent

            java.awt.Window m_parent

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

    serialVersionUID:
      2099494034652519986L

    - ### Serialized Fields

      - #### m_graphString

            java.lang.String m_graphString

      - #### m_graphTitle

            java.lang.String m_graphTitle

      - #### m_graphType

            int m_graphType

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

    serialVersionUID:
      -5183121972114900617L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_bcSupport

            java.beans.beancontext.BeanContextChildSupport m_bcSupport

        BeanContextChild support

      - #### m_design

            boolean m_design

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

  - ### Class weka.gui.beans.ImageEvent extends java.util.EventObject implements Serializable

    serialVersionUID:
      -8126533743311557969L

    - ### Serialized Fields

      - #### m_image

            java.awt.image.BufferedImage m_image

        The image

      - #### m_imageName

            java.lang.String m_imageName

        The name of the image

  - ### Class weka.gui.beans.ImageSaver extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -641438159956934314L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

        Default visual for data sources

      - #### m_listenee

            java.lang.Object m_listenee

        Non null if this object is a target for any events. Provides for the simplest case when only one incomming connection is allowed.

      - #### m_fileName

            java.lang.String m_fileName

        The file to save to

  - ### Class weka.gui.beans.ImageSaverCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      5215477777077643368L

    - ### Serialized Fields

      - #### m_imageSaver

            ImageSaver m_imageSaver

      - #### m_fileEditor

            FileEnvironmentField m_fileEditor

      - #### m_env

            Environment m_env

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_parent

            java.awt.Window m_parent

      - #### m_fileBackup

            java.lang.String m_fileBackup

  - ### Class weka.gui.beans.ImageViewer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      7976930810628389750L

    - ### Serialized Fields

      - #### m_plotter

            weka.gui.beans.ImageViewer.ImageDisplayer m_plotter

        Panel for displaying the image

      - #### m_history

            ResultHistoryPanel m_history

        Keeps a history of the images received

      - #### m_visual

            BeanVisual m_visual

        Default visual for data sources

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

    serialVersionUID:
      -3105419818939541291L

    - ### Serialized Fields

      - #### m_listeners

            java.util.Vector<E> m_listeners

      - #### m_textListeners

            java.util.Vector<E> m_textListeners

      - #### m_dataLegend

            java.util.Vector<E> m_dataLegend

      - #### m_ce

            ChartEvent m_ce

      - #### m_dataPoint

            double[] m_dataPoint

      - #### m_reset

            boolean m_reset

      - #### m_min

            double m_min

      - #### m_max

            double m_max

      - #### m_statusFrequency

            int m_statusFrequency

      - #### m_instanceCount

            int m_instanceCount

      - #### m_outputInfoRetrievalStats

            boolean m_outputInfoRetrievalStats

      - #### m_windowSize

            int m_windowSize

      - #### m_windowEval

            Evaluation m_windowEval

      - #### m_window

            java.util.LinkedList<E> m_window

      - #### m_windowedPreds

            java.util.LinkedList<E> m_windowedPreds

  - ### Class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      443506897387629418L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

        for serialization

      - #### m_ieEditor

            PropertySheetPanel m_ieEditor

      - #### m_evaluator

            IncrementalClassifierEvaluator m_evaluator

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_parent

            java.awt.Window m_parent

      - #### m_freqBackup

            int m_freqBackup

      - #### m_perClassBackup

            boolean m_perClassBackup

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

    serialVersionUID:
      28979464317643232L

    - ### Serialized Fields

      - #### m_structure

            Instances m_structure

      - #### m_status

            int m_status

      - #### m_classifier

            Classifier m_classifier

      - #### m_currentInstance

            Instance m_currentInstance

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

    serialVersionUID:
      6104920894559423946L

    - ### Serialized Fields

      - #### m_structure

            Instances m_structure

      - #### m_instance

            Instance m_instance

      - #### m_status

            int m_status

      - #### m_formatNotificationOnly

            boolean m_formatNotificationOnly

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

  - ### Class weka.gui.beans.InstanceStreamToBatchMaker extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -7037141087208627799L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_listenee

            java.lang.Object m_listenee

        Component connected to us.

      - #### m_dataListeners

            java.util.ArrayList<E> m_dataListeners

      - #### m_batch

            java.util.List<E> m_batch

        Collects up the instances.

      - #### m_structure

            Instances m_structure

  - ### Class weka.gui.beans.InteractiveTableModel extends javax.swing.table.AbstractTableModel implements Serializable

    serialVersionUID:
      -5113873323690309667L

    - ### Serialized Fields

      - #### m_hidden_index

            int m_hidden_index

        Index of the hidden column

      - #### m_columnNames

            java.lang.String[] m_columnNames

        The names of the columns

      - #### m_dataVector

            java.util.List<E> m_dataVector

        Holds the data

  - ### Class weka.gui.beans.InteractiveTablePanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      4495705463732140410L

    - ### Serialized Fields

      - #### m_columnNames

            java.lang.String[] m_columnNames

        Holds column names

      - #### m_table

            javax.swing.JTable m_table

        The table itself

      - #### m_scroller

            javax.swing.JScrollPane m_scroller

        Scroll panel for the table

      - #### m_tableModel

            InteractiveTableModel m_tableModel

        Model for the table

  - ### Class weka.gui.beans.Join extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      398021880509558185L

    - ### Serialized Fields

      - #### m_incomingBatchConnections

            boolean m_incomingBatchConnections

        Upstream components sending us data

      - #### m_firstInput

            java.lang.Object m_firstInput

        The first source of data

      - #### m_secondInput

            java.lang.Object m_secondInput

        The second source of data

      - #### m_firstInputConnectionType

            java.lang.String m_firstInputConnectionType

        Connection type of the first input

      - #### m_secondInputConnectionType

            java.lang.String m_secondInputConnectionType

        Connection type of the second input

      - #### m_ie

            InstanceEvent m_ie

        Instance event to use for incremental mode

      - #### m_stringAttsPresent

            boolean m_stringAttsPresent

        True if string attributes are present in the incoming data

      - #### m_runningIncrementally

            boolean m_runningIncrementally

        True if the step is running incrementally

      - #### m_keyIndexesOne

            int[] m_keyIndexesOne

        Indexes of the key fields for the first input

      - #### m_keyIndexesTwo

            int[] m_keyIndexesTwo

        Indexes of the key fields for the second input

      - #### m_keySpec

            java.lang.String m_keySpec

        Holds the internal representation of the key specification

      - #### m_busy

            boolean m_busy

        True if we are busy

      - #### m_stopRequested

            java.util.concurrent.atomic.AtomicBoolean m_stopRequested

        True if the step has been told to stop processing

      - #### m_stringAttIndexesOne

            java.util.Map<K,V> m_stringAttIndexesOne

        Holds indexes of string attributes, keyed by attribute name

      - #### m_stringAttIndexesTwo

            java.util.Map<K,V> m_stringAttIndexesTwo

        Holds indexes of string attributes, keyed by attribute name

      - #### m_firstIsWaiting

            boolean m_firstIsWaiting

        True if the first input stream is waiting due to a full buffer (incremental mode only)

      - #### m_secondIsWaiting

            boolean m_secondIsWaiting

        True if the second input stream is waiting due to a full buffer (incremental mode only)

      - #### m_visual

            BeanVisual m_visual

        Default visual for data sources

      - #### m_dataListeners

            java.util.ArrayList<E> m_dataListeners

        Downstream steps listening to batch data events

      - #### m_instanceListeners

            java.util.ArrayList<E> m_instanceListeners

        Downstream steps listening to instance events

  - ### Class weka.gui.beans.Join.InstanceHolder extends java.lang.Object implements Serializable

    serialVersionUID:
      -2554438923824758088L

    - ### Serialized Fields

      - #### m_instance

            Instance m_instance

        The instance

      - #### m_stringVals

            java.util.Map<K,V> m_stringVals

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

  - ### Class weka.gui.beans.JoinCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      5797383368777382010L

    - ### Serialized Fields

      - #### m_env

            Environment m_env

      - #### m_modifyL

            BeanCustomizer.ModifyListener m_modifyL

      - #### m_join

            Join m_join

      - #### m_firstKeyFields

            javax.swing.JComboBox<E> m_firstKeyFields

      - #### m_secondKeyFields

            javax.swing.JComboBox<E> m_secondKeyFields

      - #### m_firstList

            javax.swing.JList<E> m_firstList

      - #### m_secondList

            javax.swing.JList<E> m_secondList

      - #### m_firstListModel

            javax.swing.DefaultListModel<E> m_firstListModel

      - #### m_secondListModel

            javax.swing.DefaultListModel<E> m_secondListModel

      - #### m_addOneBut

            javax.swing.JButton m_addOneBut

      - #### m_deleteOneBut

            javax.swing.JButton m_deleteOneBut

      - #### m_upOneBut

            javax.swing.JButton m_upOneBut

      - #### m_downOneBut

            javax.swing.JButton m_downOneBut

      - #### m_addTwoBut

            javax.swing.JButton m_addTwoBut

      - #### m_deleteTwoBut

            javax.swing.JButton m_deleteTwoBut

      - #### m_upTwoBut

            javax.swing.JButton m_upTwoBut

      - #### m_downTwoBut

            javax.swing.JButton m_downTwoBut

      - #### m_parent

            java.awt.Window m_parent

      - #### m_tempEditor

            PropertySheetPanel m_tempEditor

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

    serialVersionUID:
      -7064906770289728431L

    - ### Serialized Fields

      - #### m_pluginPerspectiveLookup

            java.util.Map<K,V> m_pluginPerspectiveLookup

        Map of all plugin perspectives

      - #### m_perspectiveCache

            java.util.Map<K,V> m_perspectiveCache

        Those perspectives that have been instantiated

      - #### m_fontM

            java.awt.FontMetrics m_fontM

      - #### m_mode

            int m_mode

      - #### m_toolBarBean

            java.lang.Object m_toolBarBean

        Holds the selected toolbar bean

      - #### m_gridSpacing

            int m_gridSpacing

        Snap-to-grid spacing

      - #### m_untitledCount

            int m_untitledCount

        Number of untitled tabs so far

      - #### m_beanLayout

            weka.gui.beans.KnowledgeFlowApp.BeanLayout m_beanLayout

        The layout area

      - #### m_layoutZoom

            int m_layoutZoom

      - #### m_allowMultipleTabs

            boolean m_allowMultipleTabs

        Whether to allow more than one tab or not

      - #### m_userComponents

            java.util.Vector<E> m_userComponents

      - #### m_firstUserComponentOpp

            boolean m_firstUserComponentOpp

      - #### m_pointerB

            javax.swing.JButton m_pointerB

      - #### m_saveB

            javax.swing.JButton m_saveB

      - #### m_saveBB

            javax.swing.JButton m_saveBB

      - #### m_loadB

            javax.swing.JButton m_loadB

      - #### m_stopB

            javax.swing.JButton m_stopB

      - #### m_playB

            javax.swing.JButton m_playB

      - #### m_playBB

            javax.swing.JButton m_playBB

      - #### m_helpB

            javax.swing.JButton m_helpB

      - #### m_newB

            javax.swing.JButton m_newB

      - #### m_togglePerspectivesB

            javax.swing.JButton m_togglePerspectivesB

      - #### m_templatesB

            javax.swing.JButton m_templatesB

      - #### m_groupB

            javax.swing.JButton m_groupB

      - #### m_cutB

            javax.swing.JButton m_cutB

      - #### m_copyB

            javax.swing.JButton m_copyB

      - #### m_pasteB

            javax.swing.JButton m_pasteB

      - #### m_deleteB

            javax.swing.JButton m_deleteB

      - #### m_noteB

            javax.swing.JButton m_noteB

      - #### m_selectAllB

            javax.swing.JButton m_selectAllB

      - #### m_undoB

            javax.swing.JButton m_undoB

      - #### m_zoomInB

            javax.swing.JButton m_zoomInB

      - #### m_zoomOutB

            javax.swing.JButton m_zoomOutB

      - #### m_snapToGridB

            javax.swing.JToggleButton m_snapToGridB

      - #### m_editElement

            BeanInstance m_editElement

        Reference to bean being manipulated

      - #### m_sourceEventSetDescriptor

            java.beans.EventSetDescriptor m_sourceEventSetDescriptor

        Event set descriptor for the bean being manipulated

      - #### m_oldX

            int m_oldX

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

      - #### m_oldY

            int m_oldY

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

      - #### m_startX

            int m_startX

      - #### m_startY

            int m_startY

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        The file chooser for selecting layout files

      - #### m_logPanel

            weka.gui.beans.KnowledgeFlowApp.KFLogPanel m_logPanel

      - #### m_perspectiveToolBar

            javax.swing.JToolBar m_perspectiveToolBar

        Toolbar to hold the perspective buttons

      - #### m_configAndPerspectives

            javax.swing.JPanel m_configAndPerspectives

        Panel to hold the perspectives toolbar and config button

      - #### m_configAndPerspectivesVisible

            boolean m_configAndPerspectivesVisible

        Whether the perspectives toolbar is visible at present (will never be visible if there are no plugin perspectives installed)

      - #### m_perspectiveGroup

            javax.swing.ButtonGroup m_perspectiveGroup

        Button group to manage all perspective toggle buttons

      - #### m_perspectiveHolder

            javax.swing.JPanel m_perspectiveHolder

        Component that holds the currently visible perspective

      - #### m_perspectives

            java.util.List<E> m_perspectives

        Holds the list of currently loaded perspectives. Element at 0 is always the main KF data mining flow perspective

      - #### m_perspectiveDataLoadThread

            java.lang.Thread m_perspectiveDataLoadThread

        Thread for loading data for perspectives

      - #### m_perspectiveConfigurer

            AttributeSelectionPanel m_perspectiveConfigurer

      - #### m_mainKFPerspective

            KnowledgeFlowApp.MainKFPerspective m_mainKFPerspective

        Shortcut to the main perspective

      - #### m_bcSupport

            java.beans.beancontext.BeanContextSupport m_bcSupport

      - #### m_KfFilter

            javax.swing.filechooser.FileFilter m_KfFilter

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

      - #### m_KOMLFilter

            javax.swing.filechooser.FileFilter m_KOMLFilter

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

      - #### m_XStreamFilter

            javax.swing.filechooser.FileFilter m_XStreamFilter

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

      - #### m_XMLFilter

            javax.swing.filechooser.FileFilter m_XMLFilter

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

      - #### m_ScrollBarIncrementLayout

            int m_ScrollBarIncrementLayout

        the scrollbar increment of the layout scrollpane

      - #### m_ScrollBarIncrementComponents

            int m_ScrollBarIncrementComponents

        the scrollbar increment of the components scrollpane

      - #### m_FlowWidth

            int m_FlowWidth

        the flow layout width

      - #### m_FlowHeight

            int m_FlowHeight

        the flow layout height

      - #### m_PreferredExtension

            java.lang.String m_PreferredExtension

        the preferred file extension

      - #### m_UserComponentsInXML

            boolean m_UserComponentsInXML

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

      - #### m_flowEnvironment

            Environment m_flowEnvironment

        Environment variables for the current flow

      - #### m_componentTree

            javax.swing.JTree m_componentTree

        Palette of components stored in a JTree

      - #### m_userCompNode

            javax.swing.tree.DefaultMutableTreeNode m_userCompNode

        The node of the JTree that holds "user" (metabean) components

      - #### m_pasteBuffer

            java.lang.StringBuffer m_pasteBuffer

        The clip board

      - #### m_showFileMenu

            boolean m_showFileMenu

  - ### Class weka.gui.beans.KnowledgeFlowApp.BeanIconRenderer extends javax.swing.tree.DefaultTreeCellRenderer implements Serializable

    serialVersionUID:
      -4488876734500244945L

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

    serialVersionUID:
      -146377012429662757L

  - ### Class weka.gui.beans.KnowledgeFlowApp.InvisibleNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

    serialVersionUID:
      -9064396835384819887L

    - ### Serialized Fields

      - #### m_isVisible

            boolean m_isVisible

  - ### Class weka.gui.beans.KnowledgeFlowApp.InvisibleTreeModel extends javax.swing.tree.DefaultTreeModel implements Serializable

    serialVersionUID:
      6940101211275068260L

    - ### Serialized Fields

      - #### m_filterIsActive

            boolean m_filterIsActive

  - ### Class weka.gui.beans.KnowledgeFlowApp.JTreeLeafDetails extends java.lang.Object implements Serializable

    serialVersionUID:
      6197221540272931626L

    - ### Serialized Fields

      - #### m_fullyQualifiedCompName

            java.lang.String m_fullyQualifiedCompName

        fully qualified bean name

      - #### m_leafLabel

            java.lang.String m_leafLabel

        the label (usually derived from the qualified name or wrapped algorithm) for the leaf

      - #### m_wekaAlgoName

            java.lang.String m_wekaAlgoName

        the fully qualified wrapped weka algorithm name

      - #### m_metaBean

            java.util.Vector<E> m_metaBean

        XML serialized MetaBean (if this is a user component)

      - #### m_isMeta

            boolean m_isMeta

        true if this is a MetaBean (user component)

      - #### m_toolTipText

            java.lang.String m_toolTipText

        tool tip text to display

  - ### Class weka.gui.beans.KnowledgeFlowApp.KFLogPanel extends LogPanel implements Serializable

    serialVersionUID:
      -2224509243343105276L

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

    serialVersionUID:
      7666381888012259527L

    - ### Serialized Fields

      - #### m_flowTabs

            javax.swing.JTabbedPane m_flowTabs

        Holds the tabs of the perspective

      - #### m_beanLayouts

            java.util.List<E> m_beanLayouts

        List of layouts - one for each tab

      - #### m_zoomSettings

            java.util.List<E> m_zoomSettings

        List of zoom settings - one for each tab

      - #### m_logPanels

            java.util.List<E> m_logPanels

        List of log panels - one for each tab

      - #### m_environmentSettings

            java.util.List<E> m_environmentSettings

        List of environment variable settings - one for each tab

      - #### m_filePaths

            java.util.List<E> m_filePaths

        List of flow file paths - one for each tab

      - #### m_editedList

            java.util.List<E> m_editedList

        Keeps track of which tabs have been edited but not saved

      - #### m_executingList

            java.util.List<E> m_executingList

        Keeps track of which tabs have flows that are executing

      - #### m_executionThreads

            java.util.List<E> m_executionThreads

        Keeps track of the threads used for execution

      - #### m_selectedBeans

            java.util.List<E> m_selectedBeans

        Keeps track of any highlighted beans on the canvas for a tab

      - #### m_undoBufferList

            java.util.List<E> m_undoBufferList

        Keeps track of the undo buffers for each tab

      - #### m_nodeTextIndex

            java.util.Map<K,V> m_nodeTextIndex

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

    serialVersionUID:
      1993738191961163027L

    - ### Serialization Methods

      - #### readObject

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

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

      - #### readResolve

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

        Throws:
          `java.io.ObjectStreamException`

    - ### Serialized Fields

      - #### m_globalInfo

            java.lang.String m_globalInfo

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

      - #### m_ioThread

            weka.gui.beans.Loader.LoadThread m_ioThread

        Thread for doing IO in

      - #### m_state

            int m_state

      - #### m_Loader

            Loader m_Loader

        Loader

      - #### m_ie

            InstanceEvent m_ie

      - #### m_instanceEventTargets

            int m_instanceEventTargets

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

      - #### m_dataSetEventTargets

            int m_dataSetEventTargets

      - #### m_dbSet

            boolean m_dbSet

        Flag indicating that a database has already been configured

      - #### m_stopped

            boolean m_stopped

        Asked to stop?

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

    serialVersionUID:
      6990446313118930298L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_dsLoader

            Loader m_dsLoader

      - #### m_LoaderEditor

            PropertySheetPanel m_LoaderEditor

      - #### m_fileChooser

            javax.swing.JFileChooser m_fileChooser

      - #### m_parentWindow

            java.awt.Window m_parentWindow

      - #### m_fileChooserFrame

            javax.swing.JDialog m_fileChooserFrame

      - #### m_dbaseURLText

            EnvironmentField m_dbaseURLText

      - #### m_userNameText

            EnvironmentField m_userNameText

      - #### m_queryText

            EnvironmentField m_queryText

      - #### m_keyText

            EnvironmentField m_keyText

      - #### m_passwordText

            javax.swing.JPasswordField m_passwordText

      - #### m_fileText

            EnvironmentField m_fileText

      - #### m_env

            Environment m_env

      - #### m_dbProps

            FileEnvironmentField m_dbProps

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_backup

            Loader m_backup

  - ### Class weka.gui.beans.LogPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      6583097154513435548L

    - ### Serialized Fields

      - #### m_tableIndexes

            java.util.HashMap<K,V> m_tableIndexes

        Holds the index (line number) in the JTable of each component being tracked.

      - #### m_timers

            java.util.HashMap<K,V> m_timers

        Holds the timers associated with each component being tracked.

      - #### m_tableModel

            javax.swing.table.DefaultTableModel m_tableModel

        The table model for the JTable used in the status area

      - #### m_table

            javax.swing.JTable m_table

        The table for the status area

      - #### m_tabs

            javax.swing.JTabbedPane m_tabs

        Tabbed pane to hold both the status and the log

      - #### m_formatter

            java.text.DecimalFormat m_formatter

        For formatting timer digits

      - #### m_logPanel

            LogPanel m_logPanel

        The log panel to delegate log messages to.

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

    serialVersionUID:
      -6582768902038027077L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_subFlow

            java.util.Vector<E> m_subFlow

      - #### m_inputs

            java.util.Vector<E> m_inputs

      - #### m_outputs

            java.util.Vector<E> m_outputs

      - #### m_associatedConnections

            java.util.Vector<E> m_associatedConnections

      - #### m_subFlowPreview

            javax.swing.ImageIcon m_subFlowPreview

      - #### m_xCreate

            int m_xCreate

      - #### m_yCreate

            int m_yCreate

      - #### m_xDrop

            int m_xDrop

      - #### m_yDrop

            int m_yDrop

      - #### m_originalCoords

            java.util.Vector<E> m_originalCoords

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

    serialVersionUID:
      -4602034200071195924L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_offscreenRendererName

            java.lang.String m_offscreenRendererName

        Name of the renderer to use for offscreen chart rendering

      - #### m_xAxis

            java.lang.String m_xAxis

        The name of the attribute to use for the x-axis of offscreen plots. If left empty, False Positive Rate is used for threshold curves

      - #### m_yAxis

            java.lang.String m_yAxis

        The name of the attribute to use for the y-axis of offscreen plots. If left empty, True Positive Rate is used for threshold curves

      - #### m_additionalOptions

            java.lang.String m_additionalOptions

        Additional options for the offscreen renderer

      - #### m_width

            java.lang.String m_width

        Width of offscreen plots

      - #### m_height

            java.lang.String m_height

        Height of offscreen plots

      - #### m_framePoppedUp

            boolean m_framePoppedUp

      - #### m_headlessEvents

            java.util.List<E> m_headlessEvents

        Events received and stored during headless execution

      - #### m_imageListeners

            java.util.ArrayList<E> m_imageListeners

      - #### m_listenees

            java.util.List<E> m_listenees

      - #### m_design

            boolean m_design

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

      - #### m_bcSupport

            java.beans.beancontext.BeanContextChildSupport m_bcSupport

        BeanContextChild support

  - ### Class weka.gui.beans.ModelPerformanceChartCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      27802741348090392L

    - ### Serialized Fields

      - #### m_modelPC

            ModelPerformanceChart m_modelPC

      - #### m_env

            Environment m_env

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_parent

            java.awt.Window m_parent

      - #### m_rendererNameBack

            java.lang.String m_rendererNameBack

      - #### m_xAxisBack

            java.lang.String m_xAxisBack

      - #### m_yAxisBack

            java.lang.String m_yAxisBack

      - #### m_widthBack

            java.lang.String m_widthBack

      - #### m_heightBack

            java.lang.String m_heightBack

      - #### m_optsBack

            java.lang.String m_optsBack

      - #### m_rendererCombo

            javax.swing.JComboBox<E> m_rendererCombo

      - #### m_xAxis

            EnvironmentField m_xAxis

      - #### m_yAxis

            EnvironmentField m_yAxis

      - #### m_width

            EnvironmentField m_width

      - #### m_height

            EnvironmentField m_height

      - #### m_opts

            EnvironmentField m_opts

  - ### Class weka.gui.beans.Note extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -7272355421198069040L

    - ### Serialized Fields

      - #### m_noteText

            java.lang.String m_noteText

        The note text

      - #### m_label

            javax.swing.JLabel m_label

        The label that displays the note text

      - #### m_fontSizeAdjust

            int m_fontSizeAdjust

        Adjustment for the font size

  - ### Class weka.gui.beans.NoteCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      995648616684953391L

    - ### Serialized Fields

      - #### m_parentWindow

            java.awt.Window m_parentWindow

        the parent window

      - #### m_note

            Note m_note

        the note to be edited

      - #### m_textArea

            javax.swing.JTextArea m_textArea

        text area for displaying the note's text

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

        Listener that wants to know the the modified status of the object that we're customizing

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

    serialVersionUID:
      -2987740065058976673L

    - ### Serialized Fields

      - #### m_dataSourceListeners

            java.util.Vector<E> m_dataSourceListeners

        Objects listenening for dataset events

      - #### m_instanceListeners

            java.util.Vector<E> m_instanceListeners

        Objects listening for instances events

      - #### m_trainingSetListeners

            java.util.Vector<E> m_trainingSetListeners

        Objects listening for training set events

      - #### m_testSetListeners

            java.util.Vector<E> m_testSetListeners

        Objects listening for test set events

      - #### m_listenee

            java.lang.Object m_listenee

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

      - #### m_format

            Instances m_format

        Format of instances to be produced.

      - #### m_visual

            BeanVisual m_visual

      - #### m_appendProbabilities

            boolean m_appendProbabilities

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

      - #### m_instanceEvent

            InstanceEvent m_instanceEvent

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

    serialVersionUID:
      6884933202506331888L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_paEditor

            PropertySheetPanel m_paEditor

      - #### m_appender

            PredictionAppender m_appender

      - #### m_appendProbsBackup

            boolean m_appendProbsBackup

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_parent

            java.awt.Window m_parent

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

    serialVersionUID:
      5371716690308950755L

    - ### Serialization Methods

      - #### readObject

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

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

    - ### Serialized Fields

      - #### m_dataSet

            Instances m_dataSet

        Holds the instances to be saved

      - #### m_structure

            Instances m_structure

        Holds the structure

      - #### m_globalInfo

            java.lang.String m_globalInfo

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

      - #### m_Saver

            Saver m_Saver

        Saver

      - #### m_SaverTemplate

            Saver m_SaverTemplate

      - #### m_fileName

            java.lang.String m_fileName

        The relation name that becomes part of the file name

      - #### m_isDBSaver

            boolean m_isDBSaver

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

      - #### m_relationNameForFilename

            boolean m_relationNameForFilename

        For file-based savers - if true (default), relation name is used as the primary part of the filename. If false, then the prefix is used as the filename. Useful for preventing filenames from getting too long when there are many filters in a flow.

      - #### m_count

            int m_count

        Count for structure available messages

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

    serialVersionUID:
      -4874208115942078471L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_dsSaver

            Saver m_dsSaver

      - #### m_SaverEditor

            PropertySheetPanel m_SaverEditor

      - #### m_fileChooser

            javax.swing.JFileChooser m_fileChooser

      - #### m_parentWindow

            java.awt.Window m_parentWindow

      - #### m_fileChooserFrame

            javax.swing.JDialog m_fileChooserFrame

      - #### m_dbaseURLText

            EnvironmentField m_dbaseURLText

      - #### m_userNameText

            EnvironmentField m_userNameText

      - #### m_passwordText

            javax.swing.JPasswordField m_passwordText

      - #### m_tableText

            EnvironmentField m_tableText

      - #### m_truncateBox

            javax.swing.JCheckBox m_truncateBox

      - #### m_idBox

            javax.swing.JCheckBox m_idBox

      - #### m_tabBox

            javax.swing.JCheckBox m_tabBox

      - #### m_prefixText

            EnvironmentField m_prefixText

      - #### m_relativeFilePath

            javax.swing.JCheckBox m_relativeFilePath

      - #### m_relationNameForFilename

            javax.swing.JCheckBox m_relationNameForFilename

      - #### m_env

            Environment m_env

      - #### m_directoryText

            EnvironmentField m_directoryText

      - #### m_dbProps

            FileEnvironmentField m_dbProps

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

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

    serialVersionUID:
      -657856527563507491L

    - ### Serialized Fields

      - #### m_matrixPanel

            MatrixPanel m_matrixPanel

  - ### Class weka.gui.beans.SerializedModelSaver extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      3956528599473814287L

    - ### Serialization Methods

      - #### readObject

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

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

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

        Default visual for data sources

      - #### m_listenee

            java.lang.Object m_listenee

        Non null if this object is a target for any events. Provides for the simplest case when only one incomming connection is allowed.

      - #### m_filenamePrefix

            java.lang.String m_filenamePrefix

        The prefix for the file name (model + training set info will be appended)

      - #### m_incrementalSaveSchedule

            int m_incrementalSaveSchedule

        How often to save an incremental classifier (<= 0 means only at the end of the stream)

      - #### m_directory

            java.io.File m_directory

        The directory to hold the saved model(s)

      - #### m_fileFormat

            Tag m_fileFormat

        File format stuff

      - #### m_useRelativePath

            boolean m_useRelativePath

        relative path for the directory (relative to the user.dir (startup directory))?

      - #### m_includeRelationName

            boolean m_includeRelationName

        include relation name in filename

  - ### Class weka.gui.beans.SerializedModelSaverCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -4874208115942078471L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_smSaver

            SerializedModelSaver m_smSaver

      - #### m_SaverEditor

            PropertySheetPanel m_SaverEditor

      - #### m_fileChooser

            javax.swing.JFileChooser m_fileChooser

      - #### m_parentWindow

            java.awt.Window m_parentWindow

      - #### m_fileChooserFrame

            javax.swing.JDialog m_fileChooserFrame

      - #### m_prefixText

            EnvironmentField m_prefixText

      - #### m_incrementalSaveSchedule

            javax.swing.JTextField m_incrementalSaveSchedule

      - #### m_fileFormatBox

            javax.swing.JComboBox<E> m_fileFormatBox

      - #### m_relativeFilePath

            javax.swing.JCheckBox m_relativeFilePath

      - #### m_includeRelationName

            javax.swing.JCheckBox m_includeRelationName

      - #### m_env

            Environment m_env

      - #### m_directoryText

            EnvironmentField m_directoryText

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_prefixBackup

            java.lang.String m_prefixBackup

      - #### m_directoryBackup

            java.io.File m_directoryBackup

      - #### m_relativeBackup

            boolean m_relativeBackup

      - #### m_relationBackup

            boolean m_relationBackup

      - #### m_formatBackup

            Tag m_formatBackup

  - ### Class weka.gui.beans.ShadowBorder extends javax.swing.border.AbstractBorder implements Serializable

    serialVersionUID:
      -2117842133475125463L

    - ### Serialized Fields

      - #### m_width

            int m_width

        The width in pixels of the drop shadow

      - #### m_color

            java.awt.Color m_color

        the color of the drop shadow

  - ### Class weka.gui.beans.Sorter extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      4978227384322482115L

    - ### Serialized Fields

      - #### m_listenee

            java.lang.Object m_listenee

        Step talking to us

      - #### m_connectionType

            java.lang.String m_connectionType

        The type of the incoming connection

      - #### m_ie

            InstanceEvent m_ie

        For printing status updates in incremental mode

      - #### m_busy

            boolean m_busy

        True if we are busy

      - #### m_stopRequested

            java.util.concurrent.atomic.AtomicBoolean m_stopRequested

        True if a stop has been requested

      - #### m_sortDetails

            java.lang.String m_sortDetails

        Holds the internal textual description of the sort definitions

      - #### m_bufferSize

            java.lang.String m_bufferSize

        Size of the in-memory buffer

      - #### m_bufferSizeI

            int m_bufferSizeI

        Size of the in-memory buffer after resolving any environment vars

      - #### m_stringAttIndexes

            java.util.Map<K,V> m_stringAttIndexes

        Holds indexes of string attributes, keyed by attribute name

      - #### m_tempDirectory

            java.lang.String m_tempDirectory

        The directory to hold the temp files - if not set the system tmp directory is used

      - #### m_connectedFormat

            Instances m_connectedFormat

        format of instances for current incoming connection (if any)

      - #### m_visual

            BeanVisual m_visual

        Default visual for data sources

      - #### m_dataListeners

            java.util.ArrayList<E> m_dataListeners

        Downstream steps listening to batch data events

      - #### m_instanceListeners

            java.util.ArrayList<E> m_instanceListeners

        Downstream steps listening to instance events

  - ### Class weka.gui.beans.Sorter.InstanceHolder extends java.lang.Object implements Serializable

    serialVersionUID:
      -3985730394250172995L

    - ### Serialized Fields

      - #### m_instance

            Instance m_instance

        The instance

      - #### m_fileNumber

            int m_fileNumber

        index into the list of files on disk

      - #### m_stringVals

            java.util.Map<K,V> m_stringVals

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

  - ### Class weka.gui.beans.SorterCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -4860246697276275408L

    - ### Serialized Fields

      - #### m_sorter

            Sorter m_sorter

        The Sorter we are editing

      - #### m_env

            Environment m_env

      - #### m_modifyL

            BeanCustomizer.ModifyListener m_modifyL

      - #### m_attCombo

            javax.swing.JComboBox<E> m_attCombo

      - #### m_descending

            javax.swing.JComboBox<E> m_descending

      - #### m_buffSize

            EnvironmentField m_buffSize

      - #### m_tempDir

            FileEnvironmentField m_tempDir

      - #### m_parent

            java.awt.Window m_parent

      - #### m_list

            javax.swing.JList<E> m_list

      - #### m_listModel

            javax.swing.DefaultListModel<E> m_listModel

      - #### m_newBut

            javax.swing.JButton m_newBut

      - #### m_deleteBut

            javax.swing.JButton m_deleteBut

      - #### m_upBut

            javax.swing.JButton m_upBut

      - #### m_downBut

            javax.swing.JButton m_downBut

      - #### m_tempEditor

            PropertySheetPanel m_tempEditor

  - ### Class weka.gui.beans.SQLViewerPerspective extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      3684166225482042972L

    - ### Serialized Fields

      - #### m_mainPerspective

            KnowledgeFlowApp.MainKFPerspective m_mainPerspective

      - #### m_viewer

            SqlViewer m_viewer

      - #### m_newFlowBut

            javax.swing.JButton m_newFlowBut

  - ### Class weka.gui.beans.StreamThroughput extends java.lang.Object implements Serializable

    serialVersionUID:
      2820675210555581676L

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

    serialVersionUID:
      1483649041577695019L

    - ### Serialization Methods

      - #### readObject

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

        Provide some necessary initialization after object has been deserialized.
        Throws:
          `java.io.IOException` - if an error occurs
          `java.lang.ClassNotFoundException` - if an error occurs

    - ### Serialized Fields

      - #### m_colorList

            java.awt.Color[] m_colorList

        default colours for colouring lines

      - #### m_BackgroundColor

            java.awt.Color m_BackgroundColor

        the background color.

      - #### m_LegendPanelBorderColor

            java.awt.Color m_LegendPanelBorderColor

        the color of the legend panel's border.

      - #### m_iheight

            int m_iheight

        Width and height of the off screen image.

      - #### m_iwidth

            int m_iwidth

      - #### m_max

            double m_max

        Max value for the y axis.

      - #### m_min

            double m_min

        Min value for the y axis.

      - #### m_yScaleUpdate

            boolean m_yScaleUpdate

        Scale update requested.

      - #### m_oldMax

            double m_oldMax

      - #### m_oldMin

            double m_oldMin

      - #### m_labelFont

            java.awt.Font m_labelFont

      - #### m_labelMetrics

            java.awt.FontMetrics m_labelMetrics

      - #### m_legendText

            java.util.Vector<E> m_legendText

      - #### m_scalePanel

            weka.gui.beans.StripChart.ScalePanel m_scalePanel

        the scale.

      - #### m_legendPanel

            weka.gui.beans.StripChart.LegendPanel m_legendPanel

        the legend.

      - #### m_dataList

            java.util.LinkedList<E> m_dataList

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

      - #### m_previousY

            double[] m_previousY

      - #### m_visual

            BeanVisual m_visual

      - #### m_listenee

            java.lang.Object m_listenee

      - #### m_xValFreq

            int m_xValFreq

        Print x axis labels every m_xValFreq points

      - #### m_xCount

            int m_xCount

      - #### m_refreshWidth

            int m_refreshWidth

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

      - #### m_refreshFrequency

            int m_refreshFrequency

        Plot every m_refreshFrequency'th point

      - #### m_Printer

            PrintableComponent m_Printer

        the class responsible for printing

      - #### m_ce

            ChartEvent m_ce

      - #### m_dataPoint

            double[] m_dataPoint

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

    serialVersionUID:
      7441741530975984608L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_cvEditor

            PropertySheetPanel m_cvEditor

  - ### Class weka.gui.beans.SubstringLabeler extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      6297059699297260134L

    - ### Serialized Fields

      - #### m_matchDetails

            java.lang.String m_matchDetails

        Internally encoded list of match rules

      - #### m_listenee

            java.lang.Object m_listenee

        Component talking to us

      - #### m_instanceListeners

            java.util.ArrayList<E> m_instanceListeners

        Downstream steps listening to instance events

      - #### m_dataListeners

            java.util.ArrayList<E> m_dataListeners

        Downstream steps listening to data set events

      - #### m_nominalBinary

            boolean m_nominalBinary

        Whether to make the binary match/non-match attribute a nominal (rather than numeric) binary attribute.

      - #### m_consumeNonMatchingInstances

            boolean m_consumeNonMatchingInstances

        For multi-valued labeled rules, whether or not to consume non-matching instances or output them with missing value for the match attribute.

      - #### m_addFilter

            Add m_addFilter

        Add filter for adding the new attribute

      - #### m_attName

            java.lang.String m_attName

        Name of the new attribute

      - #### m_ie

            InstanceEvent m_ie

        Instance event to use

      - #### m_visual

            BeanVisual m_visual

        Default visual filters

  - ### Class weka.gui.beans.SubstringLabelerCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      7636584212353183751L

    - ### Serialized Fields

      - #### m_env

            Environment m_env

      - #### m_modifyL

            BeanCustomizer.ModifyListener m_modifyL

      - #### m_labeler

            SubstringLabeler m_labeler

      - #### m_matchAttNameField

            EnvironmentField m_matchAttNameField

      - #### m_attListField

            EnvironmentField m_attListField

      - #### m_matchField

            EnvironmentField m_matchField

      - #### m_labelField

            EnvironmentField m_labelField

      - #### m_regexCheck

            javax.swing.JCheckBox m_regexCheck

      - #### m_ignoreCaseCheck

            javax.swing.JCheckBox m_ignoreCaseCheck

      - #### m_nominalBinaryCheck

            javax.swing.JCheckBox m_nominalBinaryCheck

      - #### m_consumeNonMatchingCheck

            javax.swing.JCheckBox m_consumeNonMatchingCheck

      - #### m_list

            javax.swing.JList<E> m_list

      - #### m_listModel

            javax.swing.DefaultListModel<E> m_listModel

      - #### m_newBut

            javax.swing.JButton m_newBut

      - #### m_deleteBut

            javax.swing.JButton m_deleteBut

      - #### m_upBut

            javax.swing.JButton m_upBut

      - #### m_downBut

            javax.swing.JButton m_downBut

      - #### m_parent

            java.awt.Window m_parent

      - #### m_tempEditor

            PropertySheetPanel m_tempEditor

  - ### Class weka.gui.beans.SubstringReplacer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      5636877747903965818L

    - ### Serialized Fields

      - #### m_matchReplaceDetails

            java.lang.String m_matchReplaceDetails

        Internally encoded list of match-replace rules

      - #### m_listenee

            java.lang.Object m_listenee

        Component sending us instances

      - #### m_instanceListeners

            java.util.ArrayList<E> m_instanceListeners

        Downstream steps listening to instance events

      - #### m_ie

            InstanceEvent m_ie

        Instance event to use

      - #### m_visual

            BeanVisual m_visual

        Default visual filters

  - ### Class weka.gui.beans.SubstringReplacerCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -1245155414691393809L

    - ### Serialized Fields

      - #### m_env

            Environment m_env

      - #### m_modifyL

            BeanCustomizer.ModifyListener m_modifyL

      - #### m_replacer

            SubstringReplacer m_replacer

      - #### m_attListField

            EnvironmentField m_attListField

      - #### m_matchField

            EnvironmentField m_matchField

      - #### m_replaceField

            EnvironmentField m_replaceField

      - #### m_regexCheck

            javax.swing.JCheckBox m_regexCheck

      - #### m_ignoreCaseCheck

            javax.swing.JCheckBox m_ignoreCaseCheck

      - #### m_list

            javax.swing.JList<E> m_list

      - #### m_listModel

            javax.swing.DefaultListModel<E> m_listModel

      - #### m_newBut

            javax.swing.JButton m_newBut

      - #### m_deleteBut

            javax.swing.JButton m_deleteBut

      - #### m_upBut

            javax.swing.JButton m_upBut

      - #### m_downBut

            javax.swing.JButton m_downBut

      - #### m_parent

            java.awt.Window m_parent

      - #### m_tempEditor

            PropertySheetPanel m_tempEditor

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

    serialVersionUID:
      8780718708498854231L

    - ### Serialized Fields

      - #### m_testSet

            Instances m_testSet

        The test set instances

      - #### m_structureOnly

            boolean m_structureOnly

      - #### m_runNumber

            int m_runNumber

        What run number is this training set from.

      - #### m_maxRunNumber

            int m_maxRunNumber

        Maximum number of runs.

      - #### m_setNumber

            int m_setNumber

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

      - #### m_maxSetNumber

            int m_maxSetNumber

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

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

    serialVersionUID:
      -8473882857628061841L

    - ### Serialized Fields

      - #### m_receivedStopNotification

            boolean m_receivedStopNotification

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

    serialVersionUID:
      4196810607402973744L

    - ### Serialized Fields

      - #### m_text

            java.lang.String m_text

        The text

      - #### m_textTitle

            java.lang.String m_textTitle

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

  - ### Class weka.gui.beans.TextSaver extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      6363577506969809332L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

        Default visual for data sources

      - #### m_fileName

            java.lang.String m_fileName

        The file to save to

      - #### m_append

            boolean m_append

        Whether to append to the file or not

  - ### Class weka.gui.beans.TextSaverCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -1012433373647714743L

    - ### Serialized Fields

      - #### m_textSaver

            TextSaver m_textSaver

      - #### m_fileEditor

            FileEnvironmentField m_fileEditor

      - #### m_append

            javax.swing.JCheckBox m_append

      - #### m_env

            Environment m_env

      - #### m_modifyListener

            BeanCustomizer.ModifyListener m_modifyListener

      - #### m_parent

            java.awt.Window m_parent

      - #### m_fileBackup

            java.lang.String m_fileBackup

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

    serialVersionUID:
      104838186352536832L

    - ### Serialized Fields

      - #### m_visual

            BeanVisual m_visual

      - #### m_headlessEvents

            java.util.List<E> m_headlessEvents

      - #### m_design

            boolean m_design

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

      - #### m_bcSupport

            java.beans.beancontext.BeanContextChildSupport m_bcSupport

        BeanContextChild support

      - #### m_textListeners

            java.util.Vector<E> m_textListeners

        Objects listening for text events

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

    serialVersionUID:
      -8309334224492439644L

    - ### Serialized Fields

      - #### m_dataSet

            PlotData2D m_dataSet

      - #### m_classAttribute

            Attribute m_classAttribute

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

    serialVersionUID:
      5872343811810872662L

    - ### Serialized Fields

      - #### m_trainingSet

            Instances m_trainingSet

        The training instances

      - #### m_structureOnly

            boolean m_structureOnly

      - #### m_runNumber

            int m_runNumber

        What run number is this training set from.

      - #### m_maxRunNumber

            int m_maxRunNumber

        Maximum number of runs.

      - #### m_setNumber

            int m_setNumber

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

      - #### m_maxSetNumber

            int m_maxSetNumber

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

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

    serialVersionUID:
      -6152577265471535786L

    - ### Serialized Fields

      - #### m_receivedStopNotification

            boolean m_receivedStopNotification

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

    serialVersionUID:
      7390064039444605943L

    - ### Serialized Fields

      - #### m_trainPercentage

            double m_trainPercentage

      - #### m_randomSeed

            int m_randomSeed

      - #### m_splitThread

            java.lang.Thread m_splitThread

      - #### m_dataProvider

            boolean m_dataProvider

      - #### m_trainingProvider

            boolean m_trainingProvider

      - #### m_testProvider

            boolean m_testProvider

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

    serialVersionUID:
      -1684662340241807260L

    - ### Serialized Fields

      - #### m_pcSupport

            java.beans.PropertyChangeSupport m_pcSupport

      - #### m_splitEditor

            PropertySheetPanel m_splitEditor

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

    serialVersionUID:
      -5811819270887223400L

    - ### Serialized Fields

      - #### m_dataSet

            PlotData2D m_dataSet

- ## Package weka.gui.boundaryvisualizer

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

    serialVersionUID:
      -8499445518744770458L

    - ### Serialized Fields

      - #### m_Colors

            java.util.ArrayList<E> m_Colors

      - #### m_trainingData

            Instances m_trainingData

        training data

      - #### m_classifier

            Classifier m_classifier

        distribution classifier to use

      - #### m_dataGenerator

            DataGenerator m_dataGenerator

        data generator to use

      - #### m_classIndex

            int m_classIndex

        index of the class attribute

      - #### m_xAttribute

            int m_xAttribute

      - #### m_yAttribute

            int m_yAttribute

      - #### m_minX

            double m_minX

      - #### m_minY

            double m_minY

      - #### m_maxX

            double m_maxX

      - #### m_maxY

            double m_maxY

      - #### m_rangeX

            double m_rangeX

      - #### m_rangeY

            double m_rangeY

      - #### m_pixHeight

            double m_pixHeight

      - #### m_pixWidth

            double m_pixWidth

      - #### m_osi

            java.awt.Image m_osi

        used for offscreen drawing

      - #### m_panelWidth

            int m_panelWidth

      - #### m_panelHeight

            int m_panelHeight

      - #### m_numOfSamplesPerRegion

            int m_numOfSamplesPerRegion

      - #### m_numOfSamplesPerGenerator

            int m_numOfSamplesPerGenerator

      - #### m_samplesBase

            double m_samplesBase

      - #### m_listeners

            java.util.Vector<E> m_listeners

        listeners to be notified when plot is complete

      - #### m_plotPanel

            weka.gui.boundaryvisualizer.BoundaryPanel.PlotPanel m_plotPanel

        the actual plotting area

      - #### m_plotThread

            java.lang.Thread m_plotThread

        thread for running the plotting operation in

      - #### m_stopPlotting

            boolean m_stopPlotting

        Stop the plotting thread

      - #### m_stopReplotting

            boolean m_stopReplotting

        Stop any replotting threads

      - #### m_dummy

            java.lang.Double m_dummy

      - #### m_pausePlotting

            boolean m_pausePlotting

      - #### m_size

            int m_size

        what size of tile is currently being plotted

      - #### m_initialTiling

            boolean m_initialTiling

        is the main plot thread performing the initial coarse tiling

      - #### m_random

            java.util.Random m_random

        A random number generator

      - #### m_probabilityCache

            double[][][] m_probabilityCache

        cache of probabilities for fast replotting

      - #### m_plotTrainingData

            boolean m_plotTrainingData

        plot the training data

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

    serialVersionUID:
      -1743284397893937776L

    - ### Serialized Fields

      - #### m_listeners

            java.util.Vector<E> m_listeners

        a list of RemoteExperimentListeners

      - #### m_remoteHosts

            java.util.Vector<E> m_remoteHosts

        Holds the names of machines with remoteEngine servers running

      - #### m_remoteHostsQueue

            Queue m_remoteHostsQueue

        The queue of available hosts

      - #### m_remoteHostsStatus

            int[] m_remoteHostsStatus

        The status of each of the remote hosts

      - #### m_remoteHostFailureCounts

            int[] m_remoteHostFailureCounts

        The number of times tasks have failed on each remote host

      - #### m_plottingAborted

            boolean m_plottingAborted

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

      - #### m_removedHosts

            int m_removedHosts

        The number of hosts removed due to exceeding max failures

      - #### m_failedCount

            int m_failedCount

        The count of failed sub-tasks

      - #### m_subExpQueue

            Queue m_subExpQueue

        The queue of sub-tasks waiting to be processed

      - #### m_minTaskPollTime

            int m_minTaskPollTime

        number of seconds between polling server

      - #### m_hostPollingTime

            int[] m_hostPollingTime

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

    serialVersionUID:
      3933877580074013208L

    - ### Serialized Fields

      - #### m_trainingInstances

            Instances m_trainingInstances

        the training instances

      - #### m_classifier

            Classifier m_classifier

        the classifier to use

      - #### m_plotAreaWidth

            int m_plotAreaWidth

      - #### m_plotAreaHeight

            int m_plotAreaHeight

      - #### m_boundaryPanel

            BoundaryPanel m_boundaryPanel

        the plotting panel

      - #### m_classAttBox

            javax.swing.JComboBox<E> m_classAttBox

      - #### m_xAttBox

            javax.swing.JComboBox<E> m_xAttBox

      - #### m_yAttBox

            javax.swing.JComboBox<E> m_yAttBox

      - #### COMBO_SIZE

            java.awt.Dimension COMBO_SIZE

      - #### m_startBut

            javax.swing.JButton m_startBut

      - #### m_plotTrainingData

            javax.swing.JCheckBox m_plotTrainingData

      - #### m_controlPanel

            javax.swing.JPanel m_controlPanel

      - #### m_classPanel

            ClassPanel m_classPanel

      - #### m_xAxisPanel

            weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_xAxisPanel

      - #### m_yAxisPanel

            weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_yAxisPanel

      - #### m_maxX

            double m_maxX

      - #### m_maxY

            double m_maxY

      - #### m_minX

            double m_minX

      - #### m_minY

            double m_minY

      - #### m_xIndex

            int m_xIndex

      - #### m_yIndex

            int m_yIndex

      - #### m_dataGenerator

            KDDataGenerator m_dataGenerator

      - #### m_numberOfSamplesFromEachRegion

            int m_numberOfSamplesFromEachRegion

      - #### m_generatorSamplesBase

            int m_generatorSamplesBase

        base for sampling in the non-fixed dimensions

      - #### m_kernelBandwidth

            int m_kernelBandwidth

        Set the kernel bandwidth to cover this many nearest neighbours

      - #### m_regionSamplesText

            javax.swing.JTextField m_regionSamplesText

      - #### m_generatorSamplesText

            javax.swing.JTextField m_generatorSamplesText

      - #### m_kernelBandwidthText

            javax.swing.JTextField m_kernelBandwidthText

      - #### m_classifierEditor

            GenericObjectEditor m_classifierEditor

      - #### m_ClassifierPanel

            PropertyPanel m_ClassifierPanel

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        The file chooser for selecting arff files

      - #### m_arffFileFilter

            ExtensionFileFilter m_arffFileFilter

      - #### dataFileLabel

            javax.swing.JLabel dataFileLabel

      - #### m_addRemovePointsPanel

            javax.swing.JPanel m_addRemovePointsPanel

      - #### m_classValueSelector

            javax.swing.JComboBox<E> m_classValueSelector

      - #### m_addPointsButton

            javax.swing.JRadioButton m_addPointsButton

      - #### m_removePointsButton

            javax.swing.JRadioButton m_removePointsButton

      - #### m_addRemovePointsButtonGroup

            javax.swing.ButtonGroup m_addRemovePointsButtonGroup

      - #### removeAllButton

            javax.swing.JButton removeAllButton

      - #### chooseButton

            javax.swing.JButton chooseButton

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

    serialVersionUID:
      -958573275606402792L

    - ### Serialized Fields

      - #### m_instances

            Instances m_instances

        the instances to use

      - #### m_globalMeansOrModes

            double[] m_globalMeansOrModes

        global means or modes to use for missing values

      - #### m_laplaceConst

            double m_laplaceConst

        Laplace correction for discrete distributions

      - #### m_seed

            int m_seed

        random number seed

      - #### m_random

            java.util.Random m_random

        random number generator

      - #### m_weightingDimensions

            boolean[] m_weightingDimensions

        which dimensions to use for computing a weight for each generated instance

      - #### m_weightingValues

            double[] m_weightingValues

        the values for the weighting dimensions to use for computing the weight for the next instance to be generated

      - #### m_kernelBandwidth

            int m_kernelBandwidth

        Number of neighbours to use for kernel bandwidth

      - #### m_kernelParams

            double[][] m_kernelParams

        standard deviations for numeric attributes computed from the m_kernelBandwidth nearest neighbours for each kernel.

      - #### m_Min

            double[] m_Min

        The minimum values for numeric attributes.

      - #### m_Max

            double[] m_Max

        The maximum values for numeric attributes.

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

    serialVersionUID:
      -5275252329449241592L

    - ### Serialized Fields

      - #### m_status

            TaskStatusInfo m_status

      - #### m_result

            RemoteResult m_result

      - #### m_rowNumber

            int m_rowNumber

      - #### m_panelHeight

            int m_panelHeight

      - #### m_panelWidth

            int m_panelWidth

      - #### m_classifier

            Classifier m_classifier

      - #### m_dataGenerator

            DataGenerator m_dataGenerator

      - #### m_trainingData

            Instances m_trainingData

      - #### m_xAttribute

            int m_xAttribute

      - #### m_yAttribute

            int m_yAttribute

      - #### m_pixHeight

            double m_pixHeight

      - #### m_pixWidth

            double m_pixWidth

      - #### m_minX

            double m_minX

      - #### m_minY

            double m_minY

      - #### m_numOfSamplesPerRegion

            int m_numOfSamplesPerRegion

      - #### m_numOfSamplesPerGenerator

            int m_numOfSamplesPerGenerator

      - #### m_samplesBase

            double m_samplesBase

      - #### m_random

            java.util.Random m_random

      - #### m_weightingAttsValues

            double[] m_weightingAttsValues

      - #### m_attsToWeightOn

            boolean[] m_attsToWeightOn

      - #### m_vals

            double[] m_vals

      - #### m_dist

            double[] m_dist

      - #### m_predInst

            Instance m_predInst

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

    serialVersionUID:
      1873271280044633808L

    - ### Serialized Fields

      - #### m_probabilities

            double[][] m_probabilities

        the result - ie. the probability distributions produced by the classifier for this row in the visualization

      - #### m_percentCompleted

            int m_percentCompleted

        progress on computing this row

- ## Package weka.gui.experiment

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

    serialVersionUID:
      -7204528834764898671L

    - ### Serialized Fields

      - #### m_Exp

            Experiment m_Exp

        The experiment to set the algorithm list of

      - #### m_List

            javax.swing.JList<E> m_List

        The component displaying the algorithm list

      - #### m_AddBut

            javax.swing.JButton m_AddBut

        Click to add an algorithm

      - #### m_EditBut

            javax.swing.JButton m_EditBut

        Click to edit the selected algorithm

      - #### m_DeleteBut

            javax.swing.JButton m_DeleteBut

        Click to remove the selected dataset from the list

      - #### m_LoadOptionsBut

            javax.swing.JButton m_LoadOptionsBut

        Click to edit the load the options for athe selected algorithm

      - #### m_SaveOptionsBut

            javax.swing.JButton m_SaveOptionsBut

        Click to edit the save the options from selected algorithm

      - #### m_UpBut

            javax.swing.JButton m_UpBut

        Click to move the selected algorithm(s) one up

      - #### m_DownBut

            javax.swing.JButton m_DownBut

        Click to move the selected algorithm(s) one down

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        The file chooser for selecting experiments

      - #### m_XMLFilter

            javax.swing.filechooser.FileFilter m_XMLFilter

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

      - #### m_Editing

            boolean m_Editing

        Whether an algorithm is added or only edited

      - #### m_ClassifierEditor

            GenericObjectEditor m_ClassifierEditor

        Lets the user configure the classifier

      - #### m_PD

            PropertyDialog m_PD

        The currently displayed property dialog, if any

      - #### m_AlgorithmListModel

            javax.swing.DefaultListModel<E> m_AlgorithmListModel

        The list model used

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

    serialVersionUID:
      -5067138526587433808L

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

    serialVersionUID:
      7068857852794405769L

    - ### Serialized Fields

      - #### m_Exp

            Experiment m_Exp

        The experiment to set the dataset list of.

      - #### m_List

            javax.swing.JList<E> m_List

        The component displaying the dataset list.

      - #### m_AddBut

            javax.swing.JButton m_AddBut

        Click to add a dataset.

      - #### m_EditBut

            javax.swing.JButton m_EditBut

        Click to edit the selected algorithm.

      - #### m_DeleteBut

            javax.swing.JButton m_DeleteBut

        Click to remove the selected dataset from the list.

      - #### m_UpBut

            javax.swing.JButton m_UpBut

        Click to move the selected dataset(s) one up.

      - #### m_DownBut

            javax.swing.JButton m_DownBut

        Click to move the selected dataset(s) one down.

      - #### m_relativeCheck

            javax.swing.JCheckBox m_relativeCheck

        Make file paths relative to the user (start) directory.

      - #### m_FileChooser

            ConverterFileChooser m_FileChooser

        The file chooser component.

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

    serialVersionUID:
      5206721431754800278L

    - ### Serialized Fields

      - #### m_Exp

            RemoteExperiment m_Exp

        The experiment to configure.

      - #### m_enableDistributedExperiment

            javax.swing.JCheckBox m_enableDistributedExperiment

        Distribute the current experiment to remote hosts

      - #### m_configureHostNames

            javax.swing.JButton m_configureHostNames

        Popup the HostListPanel

      - #### m_hostList

            HostListPanel m_hostList

        The host list panel

      - #### m_splitByDataSet

            javax.swing.JRadioButton m_splitByDataSet

        Split experiment up by data set.

      - #### m_splitByRun

            javax.swing.JRadioButton m_splitByRun

        Split experiment up by run number.

      - #### m_radioListener

            java.awt.event.ActionListener m_radioListener

        Handle radio buttons

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

    serialVersionUID:
      -5751617505738193788L

    - ### Serialized Fields

      - #### m_SetupPanel

            SetupModePanel m_SetupPanel

        The panel for configuring the experiment

      - #### m_RunPanel

            RunPanel m_RunPanel

        The panel for running the experiment

      - #### m_ResultsPanel

            ResultsPanel m_ResultsPanel

        The panel for analysing experimental results

      - #### m_TabbedPane

            javax.swing.JTabbedPane m_TabbedPane

        The tabbed pane that controls which sub-pane we are working with

      - #### m_ClassFirst

            boolean m_ClassFirst

        True if the class attribute is the first attribute for all datasets involved in this experiment.

  - ### Class weka.gui.experiment.ExperimenterDefaults extends java.lang.Object implements Serializable

    serialVersionUID:
      -2835933184632147981L

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

    serialVersionUID:
      -6026938995241632139L

    - ### Serialized Fields

      - #### m_ConfigureBut

            javax.swing.JButton m_ConfigureBut

        Click to select the property to iterate over

      - #### m_StatusBox

            javax.swing.JComboBox<E> m_StatusBox

        Controls whether the custom iterator is used or not

      - #### m_ArrayEditor

            GenericArrayEditor m_ArrayEditor

        Allows editing of the custom property values

      - #### m_Exp

            Experiment m_Exp

        The experiment this all applies to

      - #### m_Listeners

            java.util.ArrayList<E> m_Listeners

        Listeners who want to be notified about editing status of this panel

  - ### Class weka.gui.experiment.HostListPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      7182791134585882197L

    - ### Serialized Fields

      - #### m_Exp

            RemoteExperiment m_Exp

        The remote experiment to set the host list of

      - #### m_List

            javax.swing.JList<E> m_List

        The component displaying the host list

      - #### m_DeleteBut

            javax.swing.JButton m_DeleteBut

        Click to remove the selected host from the list

      - #### m_HostField

            javax.swing.JTextField m_HostField

        The field with which to enter host names

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

    serialVersionUID:
      2169792738187807378L

    - ### Serialized Fields

      - #### m_Result

            int m_Result

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

      - #### m_OutputFormatClasses

            java.util.Vector<E> m_OutputFormatClasses

        the different classes for outputting the comparison tables.

      - #### m_OutputFormatNames

            java.util.Vector<E> m_OutputFormatNames

        the different names of matrices for outputting the comparison tables.

      - #### m_ResultMatrixEditor

            GenericObjectEditor m_ResultMatrixEditor

        Lets the user configure the result matrix.

      - #### m_ResultMatrixPanel

            PropertyPanel m_ResultMatrixPanel

        the panel for the GOE.

      - #### m_ResultMatrixLabel

            javax.swing.JLabel m_ResultMatrixLabel

        the label for the GOE.

      - #### m_ResultMatrix

            ResultMatrix m_ResultMatrix

        the current result matrix.

      - #### m_OutputFormatComboBox

            javax.swing.JComboBox<E> m_OutputFormatComboBox

        lets the user choose the format for the output.

      - #### m_OutputFormatLabel

            javax.swing.JLabel m_OutputFormatLabel

        the label for the format.

      - #### m_MeanPrecSpinner

            javax.swing.JSpinner m_MeanPrecSpinner

        the spinner to choose the precision for the mean from.

      - #### m_MeanPrecLabel

            javax.swing.JLabel m_MeanPrecLabel

        the label for the mean precision.

      - #### m_StdDevPrecSpinner

            javax.swing.JSpinner m_StdDevPrecSpinner

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

      - #### m_StdDevPrecLabel

            javax.swing.JLabel m_StdDevPrecLabel

        the label for the std dev precision.

      - #### m_ShowAverageCheckBox

            javax.swing.JCheckBox m_ShowAverageCheckBox

        the checkbox for outputting the average.

      - #### m_ShowAverageLabel

            javax.swing.JLabel m_ShowAverageLabel

        the label for showing the average.

      - #### m_RemoveFilterNameCheckBox

            javax.swing.JCheckBox m_RemoveFilterNameCheckBox

        the checkbox for the removing of filter classnames.

      - #### m_RemoveFilterNameLabel

            javax.swing.JLabel m_RemoveFilterNameLabel

        the label for the removing the filter classnames.

      - #### m_OkButton

            javax.swing.JButton m_OkButton

        Click to activate the current set parameters.

      - #### m_CancelButton

            javax.swing.JButton m_CancelButton

        Click to cancel the dialog.

      - #### m_IgnoreChanges

            boolean m_IgnoreChanges

        whether to ignore updates in the GUI momentarily.

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

    serialVersionUID:
      -4913007978534178569L

    - ### Serialized Fields

      - #### m_FromFileBut

            javax.swing.JButton m_FromFileBut

        Click to load results from a file.

      - #### m_FromDBaseBut

            javax.swing.JButton m_FromDBaseBut

        Click to load results from a database.

      - #### m_FromExpBut

            javax.swing.JButton m_FromExpBut

        Click to get results from the destination given in the experiment.

      - #### m_FromLab

            javax.swing.JLabel m_FromLab

        Displays a message about the current result set.

      - #### m_DatasetModel

            javax.swing.DefaultComboBoxModel<E> m_DatasetModel

        The model embedded in m_DatasetCombo.

      - #### m_CompareModel

            javax.swing.DefaultComboBoxModel<E> m_CompareModel

        The model embedded in m_CompareCombo.

      - #### m_SortModel

            javax.swing.DefaultComboBoxModel<E> m_SortModel

        The model embedded in m_SortCombo.

      - #### m_TestsModel

            javax.swing.DefaultListModel<E> m_TestsModel

        The model embedded in m_TestsList.

      - #### m_DisplayedModel

            javax.swing.DefaultListModel<E> m_DisplayedModel

        The model embedded in m_DisplayedList.

      - #### m_TesterClassesLabel

            javax.swing.JLabel m_TesterClassesLabel

        Displays the currently selected Tester-Class.

      - #### m_TesterClasses

            javax.swing.JComboBox<E> m_TesterClasses

        Lists all the available classes implementing the Tester-Interface.
        See Also:
          `Tester`

      - #### m_DatasetAndResultKeysLabel

            javax.swing.JLabel m_DatasetAndResultKeysLabel

        Label for the dataset and result key buttons.

      - #### m_PanelDatasetResultKeys

            javax.swing.JPanel m_PanelDatasetResultKeys

        the panel encapsulating the Rows/Columns/Swap buttons.

      - #### m_DatasetKeyBut

            javax.swing.JButton m_DatasetKeyBut

        Click to edit the columns used to determine the scheme.

      - #### m_DatasetKeyModel

            javax.swing.DefaultListModel<E> m_DatasetKeyModel

        Stores the list of attributes for selecting the scheme columns.

      - #### m_DatasetKeyList

            javax.swing.JList<E> m_DatasetKeyList

        Displays the list of selected columns determining the scheme.

      - #### m_ResultKeyBut

            javax.swing.JButton m_ResultKeyBut

        Click to edit the columns used to determine the scheme.

      - #### m_SwapDatasetKeyAndResultKeyBut

            javax.swing.JButton m_SwapDatasetKeyAndResultKeyBut

        For swapping rows and columns.

      - #### m_ResultKeyModel

            javax.swing.DefaultListModel<E> m_ResultKeyModel

        Stores the list of attributes for selecting the scheme columns.

      - #### m_ResultKeyList

            javax.swing.JList<E> m_ResultKeyList

        Displays the list of selected columns determining the scheme.

      - #### m_TestsButton

            javax.swing.JButton m_TestsButton

        Lets the user select which scheme to base comparisons against.

      - #### m_DisplayedButton

            javax.swing.JButton m_DisplayedButton

        Lets the user select which schemes are compared to base.

      - #### m_TestsList

            javax.swing.JList<E> m_TestsList

        Holds the list of schemes to base the test against.

      - #### m_DisplayedList

            javax.swing.JList<E> m_DisplayedList

        Holds the list of schemes to display.

      - #### m_CompareCombo

            javax.swing.JComboBox<E> m_CompareCombo

        Lets the user select which performance measure to analyze.

      - #### m_SortCombo

            javax.swing.JComboBox<E> m_SortCombo

        Lets the user select which column to use for sorting.

      - #### m_SigTex

            javax.swing.JTextField m_SigTex

        Lets the user edit the test significance.

      - #### m_ShowStdDevs

            javax.swing.JCheckBox m_ShowStdDevs

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

      - #### m_OutputFormatButton

            javax.swing.JButton m_OutputFormatButton

        lets the user choose the format for the output.

      - #### m_PerformBut

            javax.swing.JButton m_PerformBut

        Click to start the test.

      - #### m_SaveOutBut

            javax.swing.JButton m_SaveOutBut

        Click to save test output to a file.

      - #### m_SaveOut

            SaveBuffer m_SaveOut

        The buffer saving object for saving output.

      - #### m_OutText

            javax.swing.JTextArea m_OutText

        Displays the output of tests.

      - #### m_History

            ResultHistoryPanel m_History

        A panel controlling results viewing.

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        The file chooser for selecting result files.

      - #### m_csvFileFilter

            ExtensionFileFilter m_csvFileFilter

        CSV file filter.

      - #### m_arffFileFilter

            ExtensionFileFilter m_arffFileFilter

        ARFF file filter.

      - #### m_TTester

            Tester m_TTester

        The PairedTTester object.

      - #### m_Instances

            Instances m_Instances

        The instances we're extracting results from.

      - #### m_InstanceQuery

            InstanceQuery m_InstanceQuery

        Does any database querying for us.

      - #### m_LoadThread

            java.lang.Thread m_LoadThread

        A thread to load results instances from a file or database.

      - #### m_Exp

            Experiment m_Exp

        An experiment (used for identifying a result source) -- optional.

      - #### COMBO_SIZE

            java.awt.Dimension COMBO_SIZE

        the size for a combobox.

      - #### m_ResultMatrix

            ResultMatrix m_ResultMatrix

        the initial result matrix.

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

    serialVersionUID:
      -1644336658426067852L

    - ### Serialized Fields

      - #### m_LowerText

            javax.swing.JTextField m_LowerText

        Configures the lower run number

      - #### m_UpperText

            javax.swing.JTextField m_UpperText

        Configures the upper run number

      - #### m_Exp

            Experiment m_Exp

        The experiment being configured

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

    serialVersionUID:
      1691868018596872051L

    - ### Serialized Fields

      - #### m_StartBut

            javax.swing.JButton m_StartBut

        Click to start running the experiment

      - #### m_StopBut

            javax.swing.JButton m_StopBut

        Click to signal the running experiment to halt

      - #### m_Log

            LogPanel m_Log

      - #### m_Exp

            Experiment m_Exp

        The experiment to run

      - #### m_RunThread

            java.lang.Thread m_RunThread

        The thread running the experiment

      - #### m_ResultsPanel

            ResultsPanel m_ResultsPanel

        A pointer to the results panel

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

    serialVersionUID:
      -3758035565520727822L

    - ### Serialized Fields

      - #### m_SimpleSetupRBut

            javax.swing.JRadioButton m_SimpleSetupRBut

        The button for choosing simple setup mode

      - #### m_AdvancedSetupRBut

            javax.swing.JRadioButton m_AdvancedSetupRBut

        The button for choosing advanced setup mode

      - #### m_simplePanel

            SimpleSetupPanel m_simplePanel

        The simple setup panel

      - #### m_advancedPanel

            SetupPanel m_advancedPanel

        The advanced setup panel

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

    serialVersionUID:
      6552671886903170033L

    - ### Serialized Fields

      - #### m_Exp

            Experiment m_Exp

        The experiment being configured

      - #### m_OpenBut

            javax.swing.JButton m_OpenBut

        Click to load an experiment

      - #### m_SaveBut

            javax.swing.JButton m_SaveBut

        Click to save an experiment

      - #### m_NewBut

            javax.swing.JButton m_NewBut

        Click to create a new experiment with default settings

      - #### m_ExpFilter

            javax.swing.filechooser.FileFilter m_ExpFilter

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

      - #### m_KOMLFilter

            javax.swing.filechooser.FileFilter m_KOMLFilter

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

      - #### m_XMLFilter

            javax.swing.filechooser.FileFilter m_XMLFilter

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

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        The file chooser for selecting experiments

      - #### m_RPEditor

            GenericObjectEditor m_RPEditor

        The ResultProducer editor

      - #### m_RPEditorPanel

            PropertyPanel m_RPEditorPanel

        The panel to contain the ResultProducer editor

      - #### m_RLEditor

            GenericObjectEditor m_RLEditor

        The ResultListener editor

      - #### m_RLEditorPanel

            PropertyPanel m_RLEditorPanel

        The panel to contain the ResultListener editor

      - #### m_GeneratorPropertyPanel

            GeneratorPropertyIteratorPanel m_GeneratorPropertyPanel

        The panel that configures iteration on custom resultproducer property

      - #### m_RunNumberPanel

            RunNumberPanel m_RunNumberPanel

        The panel for configuring run numbers

      - #### m_DistributeExperimentPanel

            DistributeExperimentPanel m_DistributeExperimentPanel

        The panel for enabling a distributed experiment

      - #### m_DatasetListPanel

            DatasetListPanel m_DatasetListPanel

        The panel for configuring selected datasets

      - #### m_NotesButton

            javax.swing.JButton m_NotesButton

        A button for bringing up the notes

      - #### m_NotesFrame

            javax.swing.JFrame m_NotesFrame

        Frame for the notes

      - #### m_NotesText

            javax.swing.JTextArea m_NotesText

        Area for user notes Default of 10 rows

      - #### m_Support

            java.beans.PropertyChangeSupport m_Support

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

      - #### m_advanceDataSetFirst

            javax.swing.JRadioButton m_advanceDataSetFirst

        Click to advacne data set before custom generator

      - #### m_advanceIteratorFirst

            javax.swing.JRadioButton m_advanceIteratorFirst

        Click to advance custom generator before data set

      - #### m_RadioListener

            java.awt.event.ActionListener m_RadioListener

        Handle radio buttons

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

    serialVersionUID:
      5257424515609176509L

    - ### Serialized Fields

      - #### m_Exp

            Experiment m_Exp

        The experiment being configured

      - #### m_modePanel

            SetupModePanel m_modePanel

        The panel which switched between simple and advanced setup modes

      - #### m_destinationDatabaseURL

            java.lang.String m_destinationDatabaseURL

        The database destination URL to store results into

      - #### m_destinationFilename

            java.lang.String m_destinationFilename

        The filename to store results into

      - #### m_numFolds

            int m_numFolds

        The number of folds for a cross-validation experiment

      - #### m_trainPercent

            double m_trainPercent

        The training percentage for a train/test split experiment

      - #### m_numRepetitions

            int m_numRepetitions

        The number of times to repeat the sub-experiment

      - #### m_userHasBeenAskedAboutConversion

            boolean m_userHasBeenAskedAboutConversion

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

      - #### m_csvFileFilter

            ExtensionFileFilter m_csvFileFilter

        Filter for choosing CSV files

      - #### m_arffFileFilter

            ExtensionFileFilter m_arffFileFilter

        FIlter for choosing ARFF files

      - #### m_OpenBut

            javax.swing.JButton m_OpenBut

        Click to load an experiment

      - #### m_SaveBut

            javax.swing.JButton m_SaveBut

        Click to save an experiment

      - #### m_NewBut

            javax.swing.JButton m_NewBut

        Click to create a new experiment with default settings

      - #### m_ExpFilter

            javax.swing.filechooser.FileFilter m_ExpFilter

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

      - #### m_KOMLFilter

            javax.swing.filechooser.FileFilter m_KOMLFilter

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

      - #### m_XMLFilter

            javax.swing.filechooser.FileFilter m_XMLFilter

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

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        The file chooser for selecting experiments

      - #### m_DestFileChooser

            javax.swing.JFileChooser m_DestFileChooser

        The file chooser for selecting result destinations

      - #### m_ResultsDestinationCBox

            javax.swing.JComboBox<E> m_ResultsDestinationCBox

        Combo box for choosing experiment destination type

      - #### m_ResultsDestinationPathLabel

            javax.swing.JLabel m_ResultsDestinationPathLabel

        Label for destination field

      - #### m_ResultsDestinationPathTField

            javax.swing.JTextField m_ResultsDestinationPathTField

        Input field for result destination path

      - #### m_BrowseDestinationButton

            javax.swing.JButton m_BrowseDestinationButton

        Button for browsing destination files

      - #### m_ExperimentTypeCBox

            javax.swing.JComboBox<E> m_ExperimentTypeCBox

        Combo box for choosing experiment type

      - #### m_ExperimentParameterLabel

            javax.swing.JLabel m_ExperimentParameterLabel

        Label for parameter field

      - #### m_ExperimentParameterTField

            javax.swing.JTextField m_ExperimentParameterTField

        Input field for experiment parameter

      - #### m_ExpClassificationRBut

            javax.swing.JRadioButton m_ExpClassificationRBut

        Radio button for choosing classification experiment

      - #### m_ExpRegressionRBut

            javax.swing.JRadioButton m_ExpRegressionRBut

        Radio button for choosing regression experiment

      - #### m_NumberOfRepetitionsTField

            javax.swing.JTextField m_NumberOfRepetitionsTField

        Input field for number of repetitions

      - #### m_OrderDatasetsFirstRBut

            javax.swing.JRadioButton m_OrderDatasetsFirstRBut

        Radio button for choosing datasets first in order of execution

      - #### m_OrderAlgorithmsFirstRBut

            javax.swing.JRadioButton m_OrderAlgorithmsFirstRBut

        Radio button for choosing algorithms first in order of execution

      - #### m_DatasetListPanel

            DatasetListPanel m_DatasetListPanel

        The panel for configuring selected datasets

      - #### m_AlgorithmListPanel

            AlgorithmListPanel m_AlgorithmListPanel

        The panel for configuring selected algorithms

      - #### m_NotesButton

            javax.swing.JButton m_NotesButton

        A button for bringing up the notes

      - #### m_NotesFrame

            javax.swing.JFrame m_NotesFrame

        Frame for the notes

      - #### m_NotesText

            javax.swing.JTextArea m_NotesText

        Area for user notes Default of 10 rows

      - #### m_Support

            java.beans.PropertyChangeSupport m_Support

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

- ## Package weka.gui.explorer

  - ### Class weka.gui.explorer.AbstractPlotInstances extends java.lang.Object implements Serializable

    serialVersionUID:
      2375155184845160908L

    - ### Serialized Fields

      - #### m_Instances

            Instances m_Instances

        the full dataset.

      - #### m_PlotInstances

            Instances m_PlotInstances

        the plotable instances.

      - #### m_FinishUpCalled

            boolean m_FinishUpCalled

        whether processing has been finished up already.

  - ### Class weka.gui.explorer.AssociationsPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -6867871711865476971L

    - ### Serialized Fields

      - #### m_Explorer

            Explorer m_Explorer

        the parent frame

      - #### m_AssociatorEditor

            GenericObjectEditor m_AssociatorEditor

        Lets the user configure the associator

      - #### m_CEPanel

            PropertyPanel m_CEPanel

        The panel showing the current associator selection

      - #### m_OutText

            javax.swing.JTextArea m_OutText

        The output area for associations

      - #### m_Log

            Logger m_Log

        The destination for log/status messages

      - #### m_SaveOut

            SaveBuffer m_SaveOut

        The buffer saving object for saving output

      - #### m_History

            ResultHistoryPanel m_History

        A panel controlling results viewing

      - #### m_StartBut

            javax.swing.JButton m_StartBut

        Click to start running the associator

      - #### m_StopBut

            javax.swing.JButton m_StopBut

        Click to stop a running associator

      - #### m_storeOutput

            javax.swing.JCheckBox m_storeOutput

        Whether to store any graph or xml rules output in the history list

      - #### m_Instances

            Instances m_Instances

        The main set of instances we're playing with

      - #### m_TestInstances

            Instances m_TestInstances

        The user-supplied test set (if any)

      - #### m_RunThread

            java.lang.Thread m_RunThread

        A thread that associator runs in

  - ### Class weka.gui.explorer.AttributeSelectionPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      5627185966993476142L

    - ### Serialized Fields

      - #### m_Explorer

            Explorer m_Explorer

        the parent frame

      - #### m_AttributeEvaluatorEditor

            GenericObjectEditor m_AttributeEvaluatorEditor

        Lets the user configure the attribute evaluator

      - #### m_AttributeSearchEditor

            GenericObjectEditor m_AttributeSearchEditor

        Lets the user configure the search method

      - #### m_AEEPanel

            PropertyPanel m_AEEPanel

        The panel showing the current attribute evaluation method

      - #### m_ASEPanel

            PropertyPanel m_ASEPanel

        The panel showing the current search method

      - #### m_OutText

            javax.swing.JTextArea m_OutText

        The output area for attribute selection results

      - #### m_Log

            Logger m_Log

        The destination for log/status messages

      - #### m_SaveOut

            SaveBuffer m_SaveOut

        The buffer saving object for saving output

      - #### m_History

            ResultHistoryPanel m_History

        A panel controlling results viewing

      - #### m_ClassCombo

            javax.swing.JComboBox<E> m_ClassCombo

        Lets the user select the class column

      - #### m_CVBut

            javax.swing.JRadioButton m_CVBut

        Click to set evaluation mode to cross-validation

      - #### m_TrainBut

            javax.swing.JRadioButton m_TrainBut

        Click to set test mode to test on training data

      - #### m_CVLab

            javax.swing.JLabel m_CVLab

        Label by where the cv folds are entered

      - #### m_CVText

            javax.swing.JTextField m_CVText

        The field where the cv folds are entered

      - #### m_SeedLab

            javax.swing.JLabel m_SeedLab

        Label by where cv random seed is entered

      - #### m_SeedText

            javax.swing.JTextField m_SeedText

        The field where the seed value is entered

      - #### m_RadioListener

            java.awt.event.ActionListener m_RadioListener

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

      - #### m_StartBut

            javax.swing.JButton m_StartBut

        Click to start running the attribute selector

      - #### m_StopBut

            javax.swing.JButton m_StopBut

        Click to stop a running classifier

      - #### COMBO_SIZE

            java.awt.Dimension COMBO_SIZE

        Stop the class combo from taking up to much space

      - #### m_Instances

            Instances m_Instances

        The main set of instances we're playing with

      - #### m_RunThread

            java.lang.Thread m_RunThread

        A thread that attribute selection runs in

  - ### Class weka.gui.explorer.ClassifierErrorsPlotInstances extends AbstractPlotInstances implements Serializable

    serialVersionUID:
      -3941976365792013279L

    - ### Serialized Fields

      - #### m_MinimumPlotSizeNumeric

            int m_MinimumPlotSizeNumeric

        the minimum plot size for numeric errors.

      - #### m_MaximumPlotSizeNumeric

            int m_MaximumPlotSizeNumeric

        the maximum plot size for numeric errors.

      - #### m_SaveForVisualization

            boolean m_SaveForVisualization

        whether to save the instances for visualization or just evaluate the instance.

      - #### m_pointSizeProportionalToMargin

            boolean m_pointSizeProportionalToMargin

      - #### m_PlotShapes

            java.util.ArrayList<E> m_PlotShapes

        for storing the plot shapes.

      - #### m_PlotSizes

            java.util.ArrayList<E> m_PlotSizes

        for storing the plot sizes.

      - #### m_Classifier

            Classifier m_Classifier

        the classifier being used.

      - #### m_ClassIndex

            int m_ClassIndex

        the class index.

      - #### m_Evaluation

            Evaluation m_Evaluation

        the Evaluation object to use.

  - ### Class weka.gui.explorer.ClassifierPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      6959973704963624003L

    - ### Serialized Fields

      - #### m_Explorer

            Explorer m_Explorer

        the parent frame.

      - #### m_ClassifierEditor

            GenericObjectEditor m_ClassifierEditor

        Lets the user configure the classifier.

      - #### m_CEPanel

            PropertyPanel m_CEPanel

        The panel showing the current classifier selection.

      - #### m_OutText

            javax.swing.JTextArea m_OutText

        The output area for classification results.

      - #### m_Log

            Logger m_Log

        The destination for log/status messages.

      - #### m_SaveOut

            SaveBuffer m_SaveOut

        The buffer saving object for saving output.

      - #### m_History

            ResultHistoryPanel m_History

        A panel controlling results viewing.

      - #### m_ClassCombo

            javax.swing.JComboBox<E> m_ClassCombo

        Lets the user select the class column.

      - #### m_CVBut

            javax.swing.JRadioButton m_CVBut

        Click to set test mode to cross-validation.

      - #### m_PercentBut

            javax.swing.JRadioButton m_PercentBut

        Click to set test mode to generate a % split.

      - #### m_TrainBut

            javax.swing.JRadioButton m_TrainBut

        Click to set test mode to test on training data.

      - #### m_TestSplitBut

            javax.swing.JRadioButton m_TestSplitBut

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

      - #### m_StorePredictionsBut

            javax.swing.JCheckBox m_StorePredictionsBut

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

      - #### m_errorPlotPointSizeProportionalToMargin

            javax.swing.JCheckBox m_errorPlotPointSizeProportionalToMargin

        Check to have the point size in error plots proportional to the prediction margin (classification only)

      - #### m_OutputModelBut

            javax.swing.JCheckBox m_OutputModelBut

        Check to output the model built from the training data.

      - #### m_OutputPerClassBut

            javax.swing.JCheckBox m_OutputPerClassBut

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

      - #### m_OutputConfusionBut

            javax.swing.JCheckBox m_OutputConfusionBut

        Check to output a confusion matrix.

      - #### m_OutputEntropyBut

            javax.swing.JCheckBox m_OutputEntropyBut

        Check to output entropy statistics.

      - #### m_ClassificationOutputEditor

            GenericObjectEditor m_ClassificationOutputEditor

        Lets the user configure the ClassificationOutput.

      - #### m_ClassificationOutputPanel

            PropertyPanel m_ClassificationOutputPanel

        ClassificationOutput configuration.

      - #### m_OutputAdditionalAttributesRange

            Range m_OutputAdditionalAttributesRange

        the range of attributes to output.

      - #### m_EvalWRTCostsBut

            javax.swing.JCheckBox m_EvalWRTCostsBut

        Check to evaluate w.r.t a cost matrix.

      - #### m_SetCostsBut

            javax.swing.JButton m_SetCostsBut

        for the cost matrix.

      - #### m_CVLab

            javax.swing.JLabel m_CVLab

        Label by where the cv folds are entered.

      - #### m_CVText

            javax.swing.JTextField m_CVText

        The field where the cv folds are entered.

      - #### m_PercentLab

            javax.swing.JLabel m_PercentLab

        Label by where the % split is entered.

      - #### m_PercentText

            javax.swing.JTextField m_PercentText

        The field where the % split is entered.

      - #### m_SetTestBut

            javax.swing.JButton m_SetTestBut

        The button used to open a separate test dataset.

      - #### m_SetTestFrame

            javax.swing.JFrame m_SetTestFrame

        The frame used to show the test set selection panel.

      - #### m_SetCostsFrame

            PropertyDialog m_SetCostsFrame

        The frame used to show the cost matrix editing panel.

      - #### m_RadioListener

            java.awt.event.ActionListener m_RadioListener

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

      - #### m_MoreOptions

            javax.swing.JButton m_MoreOptions

        Button for further output/visualize options.

      - #### m_RandomSeedText

            javax.swing.JTextField m_RandomSeedText

        User specified random seed for cross validation or % split.

      - #### m_RandomLab

            javax.swing.JLabel m_RandomLab

        the label for the random seed textfield.

      - #### m_PreserveOrderBut

            javax.swing.JCheckBox m_PreserveOrderBut

        Whether randomization is turned off to preserve order.

      - #### m_OutputSourceCode

            javax.swing.JCheckBox m_OutputSourceCode

        Whether to output the source code (only for classifiers importing Sourcable).

      - #### m_SourceCodeClass

            javax.swing.JTextField m_SourceCodeClass

        The name of the generated class (only applicable to Sourcable schemes).

      - #### m_StartBut

            javax.swing.JButton m_StartBut

        Click to start running the classifier.

      - #### m_StopBut

            javax.swing.JButton m_StopBut

        Click to stop a running classifier.

      - #### COMBO_SIZE

            java.awt.Dimension COMBO_SIZE

        Stop the class combo from taking up to much space.

      - #### m_CostMatrixEditor

            CostMatrixEditor m_CostMatrixEditor

        The cost matrix editor for evaluation costs.

      - #### m_Instances

            Instances m_Instances

        The main set of instances we're playing with.

      - #### m_TestLoader

            Loader m_TestLoader

        The loader used to load the user-supplied test set (if any).

      - #### m_TestClassIndex

            int m_TestClassIndex

        the class index for the supplied test set.

      - #### m_RunThread

            java.lang.Thread m_RunThread

        A thread that classification runs in.

      - #### m_CurrentVis

            VisualizePanel m_CurrentVis

        The current visualization object.

      - #### m_ModelFilter

            javax.swing.filechooser.FileFilter m_ModelFilter

        Filter to ensure only model files are selected.

      - #### m_PMMLModelFilter

            javax.swing.filechooser.FileFilter m_PMMLModelFilter

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        The file chooser for selecting model files.

      - #### m_selectedEvalMetrics

            java.util.List<E> m_selectedEvalMetrics

        The user's list of selected evaluation metrics

  - ### Class weka.gui.explorer.ClustererAssignmentsPlotInstances extends AbstractPlotInstances implements Serializable

    serialVersionUID:
      -4748134272046520423L

    - ### Serialized Fields

      - #### m_PlotShapes

            int[] m_PlotShapes

        for storing the plot shapes.

      - #### m_Clusterer

            Clusterer m_Clusterer

        the clusterer being used.

      - #### m_Evaluation

            ClusterEvaluation m_Evaluation

        the cluster evaluation to use.

  - ### Class weka.gui.explorer.ClustererPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -2474932792950820990L

    - ### Serialized Fields

      - #### m_Explorer

            Explorer m_Explorer

        the parent frame

      - #### m_ClustererEditor

            GenericObjectEditor m_ClustererEditor

        Lets the user configure the clusterer

      - #### m_CLPanel

            PropertyPanel m_CLPanel

        The panel showing the current clusterer selection

      - #### m_OutText

            javax.swing.JTextArea m_OutText

        The output area for classification results

      - #### m_Log

            Logger m_Log

        The destination for log/status messages

      - #### m_SaveOut

            SaveBuffer m_SaveOut

        The buffer saving object for saving output

      - #### m_History

            ResultHistoryPanel m_History

        A panel controlling results viewing

      - #### m_PercentBut

            javax.swing.JRadioButton m_PercentBut

        Click to set test mode to generate a % split

      - #### m_TrainBut

            javax.swing.JRadioButton m_TrainBut

        Click to set test mode to test on training data

      - #### m_TestSplitBut

            javax.swing.JRadioButton m_TestSplitBut

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

      - #### m_ClassesToClustersBut

            javax.swing.JRadioButton m_ClassesToClustersBut

        Click to set test mode to classes to clusters based evaluation

      - #### m_ClassCombo

            javax.swing.JComboBox<E> m_ClassCombo

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

      - #### m_PercentLab

            javax.swing.JLabel m_PercentLab

        Label by where the % split is entered

      - #### m_PercentText

            javax.swing.JTextField m_PercentText

        The field where the % split is entered

      - #### m_SetTestBut

            javax.swing.JButton m_SetTestBut

        The button used to open a separate test dataset

      - #### m_SetTestFrame

            javax.swing.JFrame m_SetTestFrame

        The frame used to show the test set selection panel

      - #### m_ignoreBut

            javax.swing.JButton m_ignoreBut

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

      - #### m_ignoreKeyModel

            javax.swing.DefaultListModel<E> m_ignoreKeyModel

      - #### m_ignoreKeyList

            javax.swing.JList<E> m_ignoreKeyList

      - #### m_RadioListener

            java.awt.event.ActionListener m_RadioListener

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

      - #### m_StartBut

            javax.swing.JButton m_StartBut

        Click to start running the clusterer

      - #### COMBO_SIZE

            java.awt.Dimension COMBO_SIZE

        Stop the class combo from taking up to much space

      - #### m_StopBut

            javax.swing.JButton m_StopBut

        Click to stop a running clusterer

      - #### m_Instances

            Instances m_Instances

        The main set of instances we're playing with

      - #### m_TestInstances

            Instances m_TestInstances

        The user-supplied test set (if any)

      - #### m_CurrentVis

            VisualizePanel m_CurrentVis

        The current visualization object

      - #### m_StorePredictionsBut

            javax.swing.JCheckBox m_StorePredictionsBut

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

      - #### m_RunThread

            java.lang.Thread m_RunThread

        A thread that clustering runs in

      - #### m_Summary

            InstancesSummaryPanel m_Summary

        The instances summary panel displayed by m_SetTestFrame

      - #### m_ModelFilter

            javax.swing.filechooser.FileFilter m_ModelFilter

        Filter to ensure only model files are selected

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        The file chooser for selecting model files

  - ### Class weka.gui.explorer.DataGeneratorPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -2520408165350629380L

    - ### Serialized Fields

      - #### m_GeneratorEditor

            GenericObjectEditor m_GeneratorEditor

        the GOE for the generators

      - #### m_Instances

            Instances m_Instances

        the generated Instances

      - #### m_Output

            java.io.StringWriter m_Output

        the generated output (as text)

      - #### m_Log

            Logger m_Log

        The destination for log/status messages

  - ### Class weka.gui.explorer.Explorer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -7674003708867909578L

    - ### Serialized Fields

      - #### m_PreprocessPanel

            PreprocessPanel m_PreprocessPanel

        The panel for preprocessing instances

      - #### m_Panels

            java.util.Vector<E> m_Panels

        Contains all the additional panels apart from the pre-processing panel

      - #### m_TabbedPane

            javax.swing.JTabbedPane m_TabbedPane

        The tabbed pane that controls which sub-pane we are working with

      - #### m_LogPanel

            LogPanel m_LogPanel

        The panel for log and status messages

      - #### m_CapabilitiesFilterChangeListeners

            java.util.HashSet<E> m_CapabilitiesFilterChangeListeners

        the listeners that listen to filter changes

  - ### Class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent extends javax.swing.event.ChangeEvent implements Serializable

    serialVersionUID:
      1194260517270385559L

    - ### Serialized Fields

      - #### m_Filter

            Capabilities m_Filter

        the capabilities filter

  - ### Class weka.gui.explorer.ExplorerDefaults extends java.lang.Object implements Serializable

    serialVersionUID:
      4954795757927524225L

  - ### Class weka.gui.explorer.PreprocessPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      6764850273874813049L

    - ### Serialized Fields

      - #### m_InstSummaryPanel

            InstancesSummaryPanel m_InstSummaryPanel

        Displays simple stats on the working instances

      - #### m_OpenFileBut

            javax.swing.JButton m_OpenFileBut

        Click to load base instances from a file

      - #### m_OpenURLBut

            javax.swing.JButton m_OpenURLBut

        Click to load base instances from a URL

      - #### m_OpenDBBut

            javax.swing.JButton m_OpenDBBut

        Click to load base instances from a Database

      - #### m_GenerateBut

            javax.swing.JButton m_GenerateBut

        Click to generate artificial data

      - #### m_UndoBut

            javax.swing.JButton m_UndoBut

        Click to revert back to the last saved point

      - #### m_EditBut

            javax.swing.JButton m_EditBut

        Click to open the current instances in a viewer

      - #### m_SaveBut

            javax.swing.JButton m_SaveBut

        Click to apply filters and save the results

      - #### m_AttPanel

            AttributeSelectionPanel m_AttPanel

        Panel to let the user toggle attributes

      - #### m_RemoveButton

            javax.swing.JButton m_RemoveButton

        Button for removing attributes

      - #### m_AttSummaryPanel

            AttributeSummaryPanel m_AttSummaryPanel

        Displays summary stats on the selected attribute

      - #### m_FilterEditor

            GenericObjectEditor m_FilterEditor

        Lets the user configure the filter

      - #### m_FilterPanel

            PropertyPanel m_FilterPanel

        Filter configuration

      - #### m_ApplyFilterBut

            javax.swing.JButton m_ApplyFilterBut

        Click to apply filters and save the results

      - #### m_FileChooser

            ConverterFileChooser m_FileChooser

        The file chooser for selecting data files

      - #### m_LastURL

            java.lang.String m_LastURL

        Stores the last URL that instances were loaded from

      - #### m_SQLQ

            java.lang.String m_SQLQ

        Stores the last sql query executed

      - #### m_Instances

            Instances m_Instances

        The working instances

      - #### m_DataGenerator

            DataGenerator m_DataGenerator

        The last generator that was selected

      - #### m_AttVisualizePanel

            AttributeVisualizationPanel m_AttVisualizePanel

        The visualization of the attribute values

      - #### m_tempUndoFiles

            java.io.File[] m_tempUndoFiles

        Keeps track of undo points

      - #### m_tempUndoIndex

            int m_tempUndoIndex

        The next available slot for an undo point

      - #### m_Support

            java.beans.PropertyChangeSupport m_Support

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

      - #### m_IOThread

            java.lang.Thread m_IOThread

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

      - #### m_Log

            Logger m_Log

        The message logger

      - #### m_Explorer

            Explorer m_Explorer

        the parent frame

  - ### Class weka.gui.explorer.VisualizePanel extends MatrixPanel implements Serializable

    serialVersionUID:
      6084015036853918846L

    - ### Serialized Fields

      - #### m_Explorer

            Explorer m_Explorer

        the parent frame

- ## Package weka.gui.filters

  - ### Class weka.gui.filters.AddUserFieldsCustomizer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -3823417827142174931L

    - ### Serialized Fields

      - #### m_modifyL

            BeanCustomizer.ModifyListener m_modifyL

      - #### m_env

            Environment m_env

      - #### m_filter

            AddUserFields m_filter

        THe filter being customized

      - #### m_nameField

            EnvironmentField m_nameField

      - #### m_typeField

            javax.swing.JComboBox<E> m_typeField

      - #### m_dateFormatField

            EnvironmentField m_dateFormatField

      - #### m_valueField

            EnvironmentField m_valueField

      - #### m_list

            javax.swing.JList<E> m_list

      - #### m_listModel

            javax.swing.DefaultListModel<E> m_listModel

      - #### m_newBut

            javax.swing.JButton m_newBut

      - #### m_deleteBut

            javax.swing.JButton m_deleteBut

      - #### m_upBut

            javax.swing.JButton m_upBut

      - #### m_downBut

            javax.swing.JButton m_downBut

      - #### m_dontShowButs

            boolean m_dontShowButs

- ## Package weka.gui.graphvisualizer

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

    serialVersionUID:
      -4102518086411708140L

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

    serialVersionUID:
      -2038911085935515624L

    - ### Serialized Fields

      - #### m_nodes

            java.util.ArrayList<E> m_nodes

        Vector containing nodes

      - #### m_edges

            java.util.ArrayList<E> m_edges

        Vector containing edges

      - #### m_le

            LayoutEngine m_le

        The current LayoutEngine

      - #### m_gp

            weka.gui.graphvisualizer.GraphVisualizer.GraphPanel m_gp

        Panel actually displaying the graph

      - #### graphID

            java.lang.String graphID

        String containing graph's name

      - #### m_jBtSave

            javax.swing.JButton m_jBtSave

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

      - #### ICONPATH

            java.lang.String ICONPATH

        path for icons

      - #### fm

            java.awt.FontMetrics fm

      - #### scale

            double scale

      - #### nodeHeight

            int nodeHeight

      - #### nodeWidth

            int nodeWidth

      - #### paddedNodeWidth

            int paddedNodeWidth

      - #### jTfNodeWidth

            javax.swing.JTextField jTfNodeWidth

        TextField for node's width

      - #### jTfNodeHeight

            javax.swing.JTextField jTfNodeHeight

        TextField for nodes height

      - #### jBtLayout

            javax.swing.JButton jBtLayout

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

      - #### maxStringWidth

            int maxStringWidth

        used for setting appropriate node size

      - #### zoomPercents

            int[] zoomPercents

        used when using zoomIn and zoomOut buttons

      - #### m_js

            javax.swing.JScrollPane m_js

        this contains the m_gp GraphPanel

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

    serialVersionUID:
      6172467234026258427L

- ## Package weka.gui.hierarchyvisualizer

  - ### Class weka.gui.hierarchyvisualizer.HierarchyVisualizer extends PrintablePanel implements Serializable

    serialVersionUID:
      1L

    - ### Serialized Fields

      - #### m_sNewick

            java.lang.String m_sNewick

      - #### m_tree

            weka.gui.hierarchyvisualizer.HierarchyVisualizer.Node m_tree

      - #### m_nLeafs

            int m_nLeafs

      - #### m_fHeight

            double m_fHeight

      - #### m_fScaleX

            double m_fScaleX

      - #### m_fScaleY

            double m_fScaleY

      - #### m_fTmpLength

            double m_fTmpLength

- ## Package weka.gui.scripting

  - ### Class weka.gui.scripting.FileScriptingPanel extends ScriptingPanel implements Serializable

    serialVersionUID:
      1583670545010241816L

    - ### Serialized Fields

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        for loading/saving file.

      - #### m_Script

            Script m_Script

        the script.

      - #### m_ScriptArea

            javax.swing.JTextArea m_ScriptArea

        the script area.

      - #### m_OutputArea

            javax.swing.JTextArea m_OutputArea

        the output area.

      - #### m_LabelInfo

            javax.swing.JLabel m_LabelInfo

        for informing the user.

      - #### m_Actions

            java.util.HashMap<K,V> m_Actions

        for storing the actions under their name.

      - #### m_NewAction

            weka.gui.scripting.FileScriptingPanel.NewAction m_NewAction

        the new action.

      - #### m_OpenAction

            weka.gui.scripting.FileScriptingPanel.OpenAction m_OpenAction

        the open action.

      - #### m_SaveAction

            weka.gui.scripting.FileScriptingPanel.SaveAction m_SaveAction

        the Save action.

      - #### m_SaveAsAction

            weka.gui.scripting.FileScriptingPanel.SaveAction m_SaveAsAction

        the Save as action.

      - #### m_PrintAction

            weka.gui.scripting.FileScriptingPanel.PrintAction m_PrintAction

        the Print action.

      - #### m_ClearOutputAction

            weka.gui.scripting.FileScriptingPanel.ClearOutputAction m_ClearOutputAction

        the clear output action.

      - #### m_ExitAction

            weka.gui.scripting.FileScriptingPanel.ExitAction m_ExitAction

        the exit action.

      - #### m_UndoAction

            weka.gui.scripting.FileScriptingPanel.UndoAction m_UndoAction

        the undo action.

      - #### m_RedoAction

            weka.gui.scripting.FileScriptingPanel.RedoAction m_RedoAction

        the redo action.

      - #### m_CutAction

            javax.swing.Action m_CutAction

        the cut action.

      - #### m_CopyAction

            javax.swing.Action m_CopyAction

        the copy action.

      - #### m_PasteAction

            javax.swing.Action m_PasteAction

        the paste action.

      - #### m_StartAction

            weka.gui.scripting.FileScriptingPanel.StartAction m_StartAction

        the start action.

      - #### m_StopAction

            weka.gui.scripting.FileScriptingPanel.StopAction m_StopAction

        the stop action.

      - #### m_ArgsAction

            weka.gui.scripting.FileScriptingPanel.CommandlineArgsAction m_ArgsAction

        the arguments action.

      - #### m_AboutAction

            weka.gui.scripting.FileScriptingPanel.AboutAction m_AboutAction

        the about action.

      - #### m_Undo

            javax.swing.undo.UndoManager m_Undo

        the undo manager.

      - #### m_TextCode

            javax.swing.JTextPane m_TextCode

        the text pane with the code.

      - #### m_TextOutput

            javax.swing.JTextPane m_TextOutput

        the text pane for the output.

      - #### m_Args

            java.lang.String[] m_Args

        the commandline arguments to use.

  - ### Class weka.gui.scripting.FileScriptingPanel.AboutAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      -6420463480569171227L

  - ### Class weka.gui.scripting.FileScriptingPanel.BasicAction extends javax.swing.AbstractAction implements Serializable

    serialVersionUID:
      2821117985661550385L

  - ### Class weka.gui.scripting.FileScriptingPanel.ClearOutputAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      47986890456997211L

  - ### Class weka.gui.scripting.FileScriptingPanel.CommandlineArgsAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      -3183470039010826204L

  - ### Class weka.gui.scripting.FileScriptingPanel.ExitAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      -5884709836238884180L

  - ### Class weka.gui.scripting.FileScriptingPanel.NewAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      -8665722554539726090L

  - ### Class weka.gui.scripting.FileScriptingPanel.OpenAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      -4496148485267789162L

  - ### Class weka.gui.scripting.FileScriptingPanel.PrintAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      -6246539539545724632L

  - ### Class weka.gui.scripting.FileScriptingPanel.RedoAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      4533966901523279350L

  - ### Class weka.gui.scripting.FileScriptingPanel.SaveAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      -74651145892063975L

    - ### Serialized Fields

      - #### m_ShowDialog

            boolean m_ShowDialog

        whether to bring up the save dialog all the time.

  - ### Class weka.gui.scripting.FileScriptingPanel.StartAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      -7936456072955996220L

  - ### Class weka.gui.scripting.FileScriptingPanel.StopAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      8764023289575718872L

  - ### Class weka.gui.scripting.FileScriptingPanel.UndoAction extends FileScriptingPanel.BasicAction implements Serializable

    serialVersionUID:
      4298096648424808522L

  - ### Class weka.gui.scripting.GroovyPanel extends FileScriptingPanel implements Serializable

    serialVersionUID:
      -3475707604414854111L

  - ### Class weka.gui.scripting.GroovyScript extends Script implements Serializable

    serialVersionUID:
      -3708517162415549420L

  - ### Class weka.gui.scripting.JythonPanel extends FileScriptingPanel implements Serializable

    serialVersionUID:
      -827358576217085413L

  - ### Class weka.gui.scripting.JythonScript extends Script implements Serializable

    serialVersionUID:
      3469648507172973169L

  - ### Class weka.gui.scripting.Script extends java.lang.Object implements Serializable

    serialVersionUID:
      5053328052680586401L

    - ### Serialized Fields

      - #### m_Document

            javax.swing.text.Document m_Document

        the document this script is a wrapper around.

      - #### m_Filename

            java.io.File m_Filename

        the filename of the script.

      - #### m_NewLine

            java.lang.String m_NewLine

        the newline used on this platform.

      - #### m_Modified

            boolean m_Modified

        whether the script is modified.

      - #### m_FinishedListeners

            java.util.HashSet<E> m_FinishedListeners

        optional listeners when the script finishes.

  - ### Class weka.gui.scripting.ScriptingPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      7593091442691911406L

    - ### Serialized Fields

      - #### m_POO

            java.io.PipedOutputStream m_POO

        The new output stream for System.out.

      - #### m_POE

            java.io.PipedOutputStream m_POE

        The new output stream for System.err.

      - #### m_OutRedirector

            ReaderToTextPane m_OutRedirector

        The thread that sends output from m_POO to the output box.

      - #### m_ErrRedirector

            ReaderToTextPane m_ErrRedirector

        The thread that sends output from m_POE to the output box.

      - #### m_Debug

            boolean m_Debug

        whether debug mode is on.

      - #### m_TitleUpdatedListeners

            java.util.HashSet<E> m_TitleUpdatedListeners

        the listeners for the changes in the title.

  - ### Class weka.gui.scripting.SyntaxDocument extends javax.swing.text.DefaultStyledDocument implements Serializable

    serialVersionUID:
      -3642426465631271381L

    - ### Serialized Fields

      - #### m_Self

            javax.swing.text.DefaultStyledDocument m_Self

        the document.

      - #### m_RootElement

            javax.swing.text.Element m_RootElement

        the root element.

      - #### m_InsideMultiLineComment

            boolean m_InsideMultiLineComment

        whether we're currently in a multi-line comment.

      - #### m_Keywords

            java.util.HashMap<K,V> m_Keywords

        the keywords.

      - #### m_Delimiters

            java.lang.String m_Delimiters

        the delimiters.

      - #### m_QuoteDelimiters

            java.lang.String m_QuoteDelimiters

        the quote delimiter.

      - #### m_QuoteEscape

            java.lang.String m_QuoteEscape

        the quote escape.

      - #### m_MultiLineCommentStart

            java.lang.String m_MultiLineCommentStart

        the multi-line comment start.

      - #### m_MultiLineCommentEnd

            java.lang.String m_MultiLineCommentEnd

        the multi-line comment end.

      - #### m_SingleLineCommentStart

            java.lang.String m_SingleLineCommentStart

        the single-line comment start.

      - #### m_BlockStart

            java.lang.String m_BlockStart

        the start of a block.

      - #### m_BlockEnd

            java.lang.String m_BlockEnd

        the end of a block.

      - #### m_FontSize

            int m_FontSize

        the font size.

      - #### m_FontName

            java.lang.String m_FontName

        the font name.

      - #### m_BackgroundColor

            java.awt.Color m_BackgroundColor

        the background color.

      - #### m_Indentation

            java.lang.String m_Indentation

        the number of spaces used for indentation.

      - #### m_AddMatchingEndBlocks

            boolean m_AddMatchingEndBlocks

        whether to add matching brackets.

      - #### m_UseBlanks

            boolean m_UseBlanks

        whether to use blanks instead of tabs.

      - #### m_MultiLineComment

            boolean m_MultiLineComment

        whether multi-line comments are enabled.

      - #### m_CaseSensitive

            boolean m_CaseSensitive

        whether keywords are case-sensitive.

- ## Package weka.gui.scripting.event

  - ### Class weka.gui.scripting.event.ScriptExecutionEvent extends java.util.EventObject implements Serializable

    serialVersionUID:
      -8357216611114356632L

    - ### Serialized Fields

      - #### m_Type

            ScriptExecutionEvent.Type m_Type

        the type of event.

      - #### m_Additional

            java.lang.Object m_Additional

        optional additional information.

  - ### Class weka.gui.scripting.event.TitleUpdatedEvent extends java.util.EventObject implements Serializable

    serialVersionUID:
      4971569742479666535L

- ## Package weka.gui.sql

  - ### Class weka.gui.sql.ConnectionPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      3499317023969723490L

    - ### Serialized Fields

      - #### m_Parent

            javax.swing.JFrame m_Parent

        the parent frame.

      - #### m_DbDialog

            DatabaseConnectionDialog m_DbDialog

        the databae connection dialog.

      - #### m_URL

            java.lang.String m_URL

        the URL to use.

      - #### m_User

            java.lang.String m_User

        the user to use for connecting to the DB.

      - #### m_Password

            java.lang.String m_Password

        the password to use for connecting to the DB.

      - #### m_LabelURL

            javax.swing.JLabel m_LabelURL

        the label for the URL.

      - #### m_TextURL

            javax.swing.JTextField m_TextURL

        the textfield for the URL.

      - #### m_ButtonDatabase

            javax.swing.JButton m_ButtonDatabase

        the button for the DB-Dialog.

      - #### m_ButtonConnect

            javax.swing.JButton m_ButtonConnect

        the button for connecting to the database.

      - #### m_ButtonHistory

            javax.swing.JButton m_ButtonHistory

        the button for the history.

      - #### m_ButtonSetup

            javax.swing.JButton m_ButtonSetup

        the button for the setup.

      - #### m_ConnectionListeners

            java.util.HashSet<E> m_ConnectionListeners

        the connection listeners.

      - #### m_HistoryChangedListeners

            java.util.HashSet<E> m_HistoryChangedListeners

        the history listeners.

      - #### m_DbUtils

            DbUtils m_DbUtils

        for connecting to the database.

      - #### m_History

            javax.swing.DefaultListModel<E> m_History

        the history of connections.

      - #### m_SetupFileChooser

            javax.swing.JFileChooser m_SetupFileChooser

        the file chooser for the setup files.

  - ### Class weka.gui.sql.DbUtils extends DatabaseUtils implements Serializable

    serialVersionUID:
      103748569037426479L

  - ### Class weka.gui.sql.InfoPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -7701133696481997973L

    - ### Serialized Fields

      - #### m_Parent

            javax.swing.JFrame m_Parent

        the parent of this panel

      - #### m_Info

            javax.swing.JList<E> m_Info

        the list the contains the messages

      - #### m_Model

            javax.swing.DefaultListModel<E> m_Model

        the model for the list

      - #### m_ButtonClear

            javax.swing.JButton m_ButtonClear

        the button to clear the area

      - #### m_ButtonCopy

            javax.swing.JButton m_ButtonCopy

        the button to copy the selected message

  - ### Class weka.gui.sql.InfoPanelCellRenderer extends javax.swing.JLabel implements Serializable

    serialVersionUID:
      -533380118807178531L

  - ### Class weka.gui.sql.QueryPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      4348967824619706636L

    - ### Serialized Fields

      - #### m_Parent

            javax.swing.JFrame m_Parent

        the parent of this panel.

      - #### m_TextQuery

            javax.swing.JTextArea m_TextQuery

        the textarea for the query.

      - #### m_ButtonExecute

            javax.swing.JButton m_ButtonExecute

        the execute button.

      - #### m_ButtonClear

            javax.swing.JButton m_ButtonClear

        the clear button.

      - #### m_ButtonHistory

            javax.swing.JButton m_ButtonHistory

        the history button.

      - #### m_SpinnerMaxRows

            javax.swing.JSpinner m_SpinnerMaxRows

        the spinner for the maximum number of rows.

      - #### m_QueryExecuteListeners

            java.util.HashSet<E> m_QueryExecuteListeners

        the connection listeners.

      - #### m_HistoryChangedListeners

            java.util.HashSet<E> m_HistoryChangedListeners

        the history listeners.

      - #### m_DbUtils

            DbUtils m_DbUtils

        for working on the database.

      - #### m_Connected

            boolean m_Connected

        whether we have a connection to a database or not.

      - #### m_History

            javax.swing.DefaultListModel<E> m_History

        the query history.

  - ### Class weka.gui.sql.ResultPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      278654800344034571L

    - ### Serialized Fields

      - #### m_Parent

            javax.swing.JFrame m_Parent

        the parent of this panel

      - #### m_Listeners

            java.util.HashSet<E> m_Listeners

        the result change listeners

      - #### m_QueryPanel

            QueryPanel m_QueryPanel

        the panel where to output the queries

      - #### m_TabbedPane

            javax.swing.JTabbedPane m_TabbedPane

        the tabbed pane for the results

      - #### m_ButtonClose

            javax.swing.JButton m_ButtonClose

        the close button

      - #### m_ButtonCloseAll

            javax.swing.JButton m_ButtonCloseAll

        the close all button

      - #### m_ButtonCopyQuery

            javax.swing.JButton m_ButtonCopyQuery

        the button that copies the query into the QueryPanel

      - #### m_ButtonOptWidth

            javax.swing.JButton m_ButtonOptWidth

        the button for the optimal column width of the current table

      - #### m_NameCounter

            int m_NameCounter

        the counter for the tab names

  - ### Class weka.gui.sql.ResultSetTable extends javax.swing.JTable implements Serializable

    serialVersionUID:
      -3391076671854464137L

    - ### Serialized Fields

      - #### m_Query

            java.lang.String m_Query

        the query the table model is based on

      - #### m_URL

            java.lang.String m_URL

        the connect string with which the query was run

      - #### m_User

            java.lang.String m_User

        the user that was used to connect to the DB

      - #### m_Password

            java.lang.String m_Password

        the password that was used to connect to the DB

  - ### Class weka.gui.sql.ResultSetTableCellRenderer extends javax.swing.table.DefaultTableCellRenderer implements Serializable

    serialVersionUID:
      -8106963669703497351L

    - ### Serialized Fields

      - #### missingColor

            java.awt.Color missingColor

      - #### missingColorSelected

            java.awt.Color missingColorSelected

  - ### Class weka.gui.sql.SqlViewer extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -4395028775566514329L

    - ### Serialized Fields

      - #### m_Parent

            javax.swing.JFrame m_Parent

        the parent of this panel.

      - #### m_ConnectionPanel

            ConnectionPanel m_ConnectionPanel

        the connection panel.

      - #### m_QueryPanel

            QueryPanel m_QueryPanel

        the query panel.

      - #### m_ResultPanel

            ResultPanel m_ResultPanel

        the result panel.

      - #### m_InfoPanel

            InfoPanel m_InfoPanel

        the info panel.

      - #### m_URL

            java.lang.String m_URL

        the connect string with which the query was run.

      - #### m_User

            java.lang.String m_User

        the user that was used to connect to the DB.

      - #### m_Password

            java.lang.String m_Password

        the password that was used to connect to the DB.

      - #### m_Query

            java.lang.String m_Query

        the currently selected query.

      - #### m_History

            java.util.Properties m_History

        stores the history.

  - ### Class weka.gui.sql.SqlViewerDialog extends javax.swing.JDialog implements Serializable

    serialVersionUID:
      -31619864037233099L

    - ### Serialized Fields

      - #### m_Parent

            javax.swing.JFrame m_Parent

        the parent frame.

      - #### m_Viewer

            SqlViewer m_Viewer

        the SQL panel.

      - #### m_PanelButtons

            javax.swing.JPanel m_PanelButtons

        the panel for the buttons.

      - #### m_ButtonOK

            javax.swing.JButton m_ButtonOK

        the OK button.

      - #### m_ButtonCancel

            javax.swing.JButton m_ButtonCancel

        the Cancel button.

      - #### m_LabelQuery

            javax.swing.JLabel m_LabelQuery

        displays the current query.

      - #### m_CheckBoxSparseData

            javax.swing.JCheckBox m_CheckBoxSparseData

        whether to return sparse instances or not.

      - #### m_ReturnValue

            int m_ReturnValue

        the return value.

      - #### m_URL

            java.lang.String m_URL

        the connect string with which the query was run.

      - #### m_User

            java.lang.String m_User

        the user that was used to connect to the DB.

      - #### m_Password

            java.lang.String m_Password

        the password that was used to connect to the DB.

      - #### m_Query

            java.lang.String m_Query

        the currently selected query.

- ## Package weka.gui.sql.event

  - ### Class weka.gui.sql.event.ConnectionEvent extends java.util.EventObject implements Serializable

    serialVersionUID:
      5420308930427835037L

    - ### Serialized Fields

      - #### m_Type

            int m_Type

        the type of event, CONNECT or DISCONNECT

      - #### m_DbUtils

            DbUtils m_DbUtils

        the databaseutils instance reponsible for the connection

      - #### m_Exception

            java.lang.Exception m_Exception

        a possible exception that occurred if not successful

  - ### Class weka.gui.sql.event.HistoryChangedEvent extends java.util.EventObject implements Serializable

    serialVersionUID:
      7476087315774869973L

    - ### Serialized Fields

      - #### m_HistoryName

            java.lang.String m_HistoryName

        the name of the history

      - #### m_History

            javax.swing.DefaultListModel<E> m_History

        the history model

  - ### Class weka.gui.sql.event.QueryExecuteEvent extends java.util.EventObject implements Serializable

    serialVersionUID:
      -5556385019954730740L

    - ### Serialized Fields

      - #### m_DbUtils

            DbUtils m_DbUtils

        the Db utils instance for the current DB connection

      - #### m_Query

            java.lang.String m_Query

        the query that was executed

      - #### m_ResultSet

            java.sql.ResultSet m_ResultSet

        the produced ResultSet, if any

      - #### m_Exception

            java.lang.Exception m_Exception

        a possible exception, if the query failed

      - #### m_MaxRows

            int m_MaxRows

        the maximum number of rows to retrieve

  - ### Class weka.gui.sql.event.ResultChangedEvent extends java.util.EventObject implements Serializable

    serialVersionUID:
      36042516077236111L

    - ### Serialized Fields

      - #### m_Query

            java.lang.String m_Query

        the query that is associated with the active result table

      - #### m_URL

            java.lang.String m_URL

        the connect string with which the query was run

      - #### m_User

            java.lang.String m_User

        the user that was used to connect to the DB

      - #### m_Password

            java.lang.String m_Password

        the password that was used to connect to the DB

- ## Package weka.gui.streams

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

    serialVersionUID:
      -6084967152645935934L

    - ### Serialized Fields

      - #### m_Count_Lab

            javax.swing.JLabel m_Count_Lab

      - #### m_Count

            int m_Count

      - #### m_Debug

            boolean m_Debug

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

    serialVersionUID:
      3207259868110667379L

    - ### Serialized Fields

      - #### m_ID

            int m_ID

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

    serialVersionUID:
      -6529972700291329656L

    - ### Serialized Fields

      - #### listeners

            java.util.Vector<E> listeners

        The listeners

      - #### b_Debug

            boolean b_Debug

        Debugging mode

      - #### m_InputFormat

            Instances m_InputFormat

        The input format for instances

      - #### m_OutputInstance

            Instance m_OutputInstance

        The current output instance

      - #### b_FirstInputFinished

            boolean b_FirstInputFinished

        Whether the first input batch has finished

  - ### Class weka.gui.streams.InstanceLoader extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -8725567310271862492L

    - ### Serialized Fields

      - #### m_Listeners

            java.util.Vector<E> m_Listeners

      - #### m_LoaderThread

            java.lang.Thread m_LoaderThread

      - #### m_OutputInstance

            Instance m_OutputInstance

      - #### m_OutputInstances

            Instances m_OutputInstances

      - #### m_Debug

            boolean m_Debug

      - #### m_StartBut

            javax.swing.JButton m_StartBut

      - #### m_FileNameTex

            javax.swing.JTextField m_FileNameTex

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

    serialVersionUID:
      -6061005366989295026L

    - ### Serialized Fields

      - #### count_Lab

            java.awt.Label count_Lab

      - #### m_Count

            int m_Count

      - #### arffFile_Tex

            java.awt.TextField arffFile_Tex

      - #### b_Debug

            boolean b_Debug

      - #### outputWriter

            java.io.PrintWriter outputWriter

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

    serialVersionUID:
      -2462533698100834803L

    - ### Serialized Fields

      - #### m_InstanceTable

            javax.swing.JTable m_InstanceTable

      - #### m_Debug

            boolean m_Debug

      - #### m_Instances

            Instances m_Instances

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

    serialVersionUID:
      -4925729441294121772L

    - ### Serialized Fields

      - #### m_OutputTex

            javax.swing.JTextArea m_OutputTex

      - #### m_Debug

            boolean m_Debug

      - #### m_Clear

            boolean m_Clear

      - #### m_UpdateString

            java.lang.String m_UpdateString

- ## Package weka.gui.treevisualizer

  - ### Class weka.gui.treevisualizer.TreeVisualizer extends PrintablePanel implements Serializable

    serialVersionUID:
      -8668637962504080749L

    - ### Serialized Fields

      - #### m_placer

            NodePlace m_placer

        The placement algorithm for the Node structure.

      - #### m_topNode

            Node m_topNode

        The top Node.

      - #### m_viewPos

            java.awt.Dimension m_viewPos

        The postion of the view relative to the tree.

      - #### m_viewSize

            java.awt.Dimension m_viewSize

        The size of the tree in pixels.

      - #### m_currentFont

            java.awt.Font m_currentFont

        The font used to display the tree.

      - #### m_fontSize

            java.awt.FontMetrics m_fontSize

        The size information for the current font.

      - #### m_numNodes

            int m_numNodes

        The number of Nodes in the tree.

      - #### m_numLevels

            int m_numLevels

        The number of levels in the tree.

      - #### m_nodes

            weka.gui.treevisualizer.TreeVisualizer.NodeInfo[] m_nodes

        An array with the Nodes sorted into it and display information about the Nodes.

      - #### m_edges

            weka.gui.treevisualizer.TreeVisualizer.EdgeInfo[] m_edges

        An array with the Edges sorted into it and display information about the Edges.

      - #### m_frameLimiter

            javax.swing.Timer m_frameLimiter

        A timer to keep the frame rate constant.

      - #### m_mouseState

            int m_mouseState

        Describes the action the user is performing.

      - #### m_oldMousePos

            java.awt.Dimension m_oldMousePos

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

      - #### m_newMousePos

            java.awt.Dimension m_newMousePos

        A variable used to tag the most current point of a user action.

      - #### m_clickAvailable

            boolean m_clickAvailable

        A variable used to determine for the clicked method if any other mouse state has already taken place.

      - #### m_nViewPos

            java.awt.Dimension m_nViewPos

        A variable used to remember the desired view pos.

      - #### m_nViewSize

            java.awt.Dimension m_nViewSize

        A variable used to remember the desired tree size.

      - #### m_scaling

            int m_scaling

        The number of frames left to calculate.

      - #### m_winMenu

            javax.swing.JPopupMenu m_winMenu

        A right (or middle) click popup menu.

      - #### m_topN

            javax.swing.JMenuItem m_topN

        An option on the win_menu

      - #### m_fitToScreen

            javax.swing.JMenuItem m_fitToScreen

        An option on the win_menu

      - #### m_autoScale

            javax.swing.JMenuItem m_autoScale

        An option on the win_menu

      - #### m_selectFont

            javax.swing.JMenu m_selectFont

        A sub group on the win_menu

      - #### m_selectFontGroup

            javax.swing.ButtonGroup m_selectFontGroup

        A grouping for the font choices

      - #### m_size24

            javax.swing.JRadioButtonMenuItem m_size24

        A font choice.

      - #### m_size22

            javax.swing.JRadioButtonMenuItem m_size22

        A font choice.

      - #### m_size20

            javax.swing.JRadioButtonMenuItem m_size20

        A font choice.

      - #### m_size18

            javax.swing.JRadioButtonMenuItem m_size18

        A font choice.

      - #### m_size16

            javax.swing.JRadioButtonMenuItem m_size16

        A font choice.

      - #### m_size14

            javax.swing.JRadioButtonMenuItem m_size14

        A font choice.

      - #### m_size12

            javax.swing.JRadioButtonMenuItem m_size12

        A font choice.

      - #### m_size10

            javax.swing.JRadioButtonMenuItem m_size10

        A font choice.

      - #### m_size8

            javax.swing.JRadioButtonMenuItem m_size8

        A font choice.

      - #### m_size6

            javax.swing.JRadioButtonMenuItem m_size6

        A font choice.

      - #### m_size4

            javax.swing.JRadioButtonMenuItem m_size4

        A font choice.

      - #### m_size2

            javax.swing.JRadioButtonMenuItem m_size2

        A font choice.

      - #### m_size1

            javax.swing.JRadioButtonMenuItem m_size1

        A font choice.

      - #### m_accept

            javax.swing.JMenuItem m_accept

        An option on the win menu.

      - #### m_nodeMenu

            javax.swing.JPopupMenu m_nodeMenu

        A right or middle click popup menu for nodes.

      - #### m_visualise

            javax.swing.JMenuItem m_visualise

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

      - #### m_remChildren

            javax.swing.JMenuItem m_remChildren

        Similar to add children but now it removes children.

      - #### m_classifyChild

            javax.swing.JMenuItem m_classifyChild

        Use this to have J48 classify this node.

      - #### m_sendInstances

            javax.swing.JMenuItem m_sendInstances

        Use this to dump the instances from this node to the vis panel.

      - #### m_focusNode

            int m_focusNode

        The subscript for the currently selected node (this is an internal thing, so the user is unaware of this).

      - #### m_highlightNode

            int m_highlightNode

        The Node the user is currently focused on , this is similar to focus node except that it is used by other classes rather than this one.

      - #### m_listener

            TreeDisplayListener m_listener

      - #### m_FontColor

            java.awt.Color m_FontColor

        the font color.

      - #### m_BackgroundColor

            java.awt.Color m_BackgroundColor

        the background color.

      - #### m_NodeColor

            java.awt.Color m_NodeColor

        the node color.

      - #### m_LineColor

            java.awt.Color m_LineColor

        the line color.

      - #### m_ZoomBoxColor

            java.awt.Color m_ZoomBoxColor

        the color of the zoombox.

      - #### m_ZoomBoxXORColor

            java.awt.Color m_ZoomBoxXORColor

        the XOR color of the zoombox.

      - #### m_ShowBorder

            boolean m_ShowBorder

        whether to show the border or not.

- ## Package weka.gui.visualize

  - ### Class weka.gui.visualize.AttributePanel extends javax.swing.JScrollPane implements Serializable

    serialVersionUID:
      3533330317806757814L

    - ### Serialized Fields

      - #### m_plotInstances

            Instances m_plotInstances

        The instances to be plotted

      - #### m_maxC

            double m_maxC

        Holds the min and max values of the colouring attributes

      - #### m_minC

            double m_minC

      - #### m_cIndex

            int m_cIndex

      - #### m_xIndex

            int m_xIndex

      - #### m_yIndex

            int m_yIndex

      - #### m_colorList

            java.util.ArrayList<E> m_colorList

        The colour map to use for colouring points

      - #### m_DefaultColors

            java.awt.Color[] m_DefaultColors

        default colours for colouring discrete class

      - #### m_backgroundColor

            java.awt.Color m_backgroundColor

        If set, it allows this panel to avoid setting a color in the color list that is equal to the background color

      - #### m_Listeners

            java.util.ArrayList<E> m_Listeners

        The list of things listening to this panel

      - #### m_heights

            int[] m_heights

        Holds the random height for each instance.

      - #### m_span

            javax.swing.JPanel m_span

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

      - #### m_barColour

            java.awt.Color m_barColour

        The default colour to use for the background of the bars if a colour is not defined in Visualize.props

  - ### Class weka.gui.visualize.AttributePanel.AttributeSpacing extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      7220615894321679898L

    - ### Serialized Fields

      - #### m_maxVal

            double m_maxVal

        The min and max values for this attribute.

      - #### m_minVal

            double m_minVal

      - #### m_attrib

            Attribute m_attrib

        The attribute itself.

      - #### m_attribIndex

            int m_attribIndex

        The index for this attribute.

      - #### m_cached

            int[] m_cached

        The x position of each point.

      - #### m_pointDrawn

            boolean[][] m_pointDrawn

        A temporary array used to strike any instances that would be drawn redundantly.

      - #### m_oldWidth

            int m_oldWidth

        Used to determine if the positions need to be recalculated.

  - ### Class weka.gui.visualize.ClassPanel extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      -7969401840501661430L

    - ### Serialized Fields

      - #### m_isEnabled

            boolean m_isEnabled

        True when the panel has been enabled (ie after setNumeric or setNominal has been called

      - #### m_isNumeric

            boolean m_isNumeric

        True if the colouring attribute is numeric

      - #### m_spectrumHeight

            int m_spectrumHeight

        The height of the spectrum for numeric class

      - #### m_maxC

            double m_maxC

        The maximum value for the colouring attribute

      - #### m_minC

            double m_minC

        The minimum value for the colouring attribute

      - #### m_tickSize

            int m_tickSize

        The size of the ticks

      - #### m_labelMetrics

            java.awt.FontMetrics m_labelMetrics

        Font metrics

      - #### m_labelFont

            java.awt.Font m_labelFont

        The font used in labeling

      - #### m_HorizontalPad

            int m_HorizontalPad

        The amount of space to leave either side of the legend

      - #### m_precisionC

            int m_precisionC

        The precision with which to display real values

      - #### m_oldWidth

            int m_oldWidth

        The old width.

      - #### m_Instances

            Instances m_Instances

        Instances being plotted

      - #### m_cIndex

            int m_cIndex

        Index of the colouring attribute

      - #### m_colorList

            java.util.ArrayList<E> m_colorList

        the list of colours to use for colouring nominal attribute labels

      - #### m_Repainters

            java.util.ArrayList<E> m_Repainters

        An optional list of Components that use the colour list maintained by this class. If the user changes a colour using the colour chooser, then these components need to be repainted in order to display the change

      - #### m_ColourChangeListeners

            java.util.ArrayList<E> m_ColourChangeListeners

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

      - #### m_DefaultColors

            java.awt.Color[] m_DefaultColors

        default colours for colouring discrete class

      - #### m_backgroundColor

            java.awt.Color m_backgroundColor

        if set, it allows this panel to steer away from setting up a color in the color list that is equal to the background color

  - ### Class weka.gui.visualize.InstanceInfoFrame extends javax.swing.JFrame implements Serializable

    serialVersionUID:
      4684184733677263009L

    - ### Serialized Fields

      - #### m_Data

            java.util.Vector<E> m_Data

        the underlying data.

      - #### m_TextInfo

            javax.swing.JTextArea m_TextInfo

        the text area for displaying the info.

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

    serialVersionUID:
      -1262384440543001505L

    - ### Serialized Fields

      - #### m_plots

            java.util.ArrayList<E> m_plots

        the list of plot elements

      - #### m_span

            javax.swing.JPanel m_span

        the panel that contains the legend entries

      - #### m_Repainters

            java.util.ArrayList<E> m_Repainters

        a list of components that need to be repainted when a colour is changed

  - ### Class weka.gui.visualize.LegendPanel.LegendEntry extends javax.swing.JPanel implements Serializable

    serialVersionUID:
      3879990289042935670L

    - ### Serialized Fields

      - #### m_plotData

            PlotData2D m_plotData

        the data for this legend entry

      - #### m_dataIndex

            int m_dataIndex

        the index (in the list of plots) of the data for this legend--- used to draw the correct shape for this data

      - #### m_legendText

            javax.swing.JLabel m_legendText

        the text part of this legend

      - #### m_pointShape

            javax.swing.JPanel m_pointShape

        displays the point shape associated with this legend entry

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

    serialVersionUID:
      -1232642719869188740L

    - ### Serialized Fields

      - #### m_plotsPanel

            weka.gui.visualize.MatrixPanel.Plot m_plotsPanel

        The that panel contains the actual matrix

      - #### m_cp

            ClassPanel m_cp

        The panel that displays the legend of the colouring attribute

      - #### optionsPanel

            javax.swing.JPanel optionsPanel

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

      - #### jp

            javax.swing.JSplitPane jp

        Split pane for splitting the matrix and the buttons and bars

      - #### m_updateBt

            javax.swing.JButton m_updateBt

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

      - #### m_selAttrib

            javax.swing.JButton m_selAttrib

        The button to display a window to select attributes

      - #### m_data

            Instances m_data

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

      - #### m_attribList

            javax.swing.JList<E> m_attribList

        The list for selecting the attributes to display the plot matrix

      - #### m_js

            javax.swing.JScrollPane m_js

        The scroll pane to scrolling the matrix

      - #### m_classAttrib

            javax.swing.JComboBox<E> m_classAttrib

        The combo box to allow user to select the colouring attribute

      - #### m_plotSize

            javax.swing.JSlider m_plotSize

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

      - #### m_pointSize

            javax.swing.JSlider m_pointSize

        The slider to adjust the size of the datapoints

      - #### m_jitter

            javax.swing.JSlider m_jitter

        The slider to add jitter to the plots

      - #### rnd

            java.util.Random rnd

        For adding random jitter

      - #### jitterVals

            int[][] jitterVals

        Array containing precalculated jitter values

      - #### datapointSize

            int datapointSize

        This stores the size of the datapoint

      - #### m_resamplePercent

            javax.swing.JTextField m_resamplePercent

        The text area for percentage to resample data

      - #### m_resampleBt

            javax.swing.JButton m_resampleBt

        The label for resample percentage

      - #### m_rseed

            javax.swing.JTextField m_rseed

        Random seed for random subsample

      - #### m_plotSizeLb

            javax.swing.JLabel m_plotSizeLb

        Displays the current size beside the slider bar for cell size

      - #### m_pointSizeLb

            javax.swing.JLabel m_pointSizeLb

        Displays the current size beside the slider bar for point size

      - #### m_selectedAttribs

            int[] m_selectedAttribs

        This array contains the indices of the attributes currently selected

      - #### m_classIndex

            int m_classIndex

        This contains the index of the currently selected colouring attribute

      - #### m_points

            int[][] m_points

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

      - #### m_pointColors

            int[] m_pointColors

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

      - #### m_missing

            boolean[][] m_missing

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

      - #### m_type

            int[] m_type

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

      - #### m_plotLBSizeD

            java.awt.Dimension m_plotLBSizeD

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

      - #### m_pointLBSizeD

            java.awt.Dimension m_pointLBSizeD

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

      - #### m_colorList

            java.util.ArrayList<E> m_colorList

        Contains discrete colours for colouring for nominal attributes

      - #### fontColor

            java.awt.Color fontColor

        color for the font used in column and row names

      - #### f

            java.awt.Font f

        font used in column and row names

      - #### m_plottedCells

            boolean[][] m_plottedCells

      - #### m_regenerateOSI

            boolean m_regenerateOSI

      - #### m_clearOSIPlottedCells

            boolean m_clearOSIPlottedCells

      - #### m_previousPercent

            double m_previousPercent

      - #### m_fastScroll

            javax.swing.JCheckBox m_fastScroll

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

    serialVersionUID:
      -1673162410856660442L

    - ### Serialized Fields

      - #### m_axisColour

            java.awt.Color m_axisColour

        Default colour for the axis

      - #### m_backgroundColour

            java.awt.Color m_backgroundColour

        Default colour for the plot background

      - #### m_plots

            java.util.ArrayList<E> m_plots

        The plots to display

      - #### m_masterPlot

            PlotData2D m_masterPlot

        The master plot

      - #### m_masterName

            java.lang.String m_masterName

        The name of the master plot

      - #### m_plotInstances

            Instances m_plotInstances

        The instances to be plotted

      - #### m_plotCompanion

            Plot2DCompanion m_plotCompanion

        An optional "compainion" of the panel. If specified, this class will get to do its thing with our graphics context before we do any drawing. Eg. the visualize panel may need to draw polygons etc. before we draw plot axis and data points

      - #### m_InstanceInfoFrameClass

            java.lang.Class<T> m_InstanceInfoFrameClass

        the class for displaying instance info.

      - #### m_InstanceInfo

            javax.swing.JFrame m_InstanceInfo

        For popping up text info on data points

      - #### m_colorList

            java.util.ArrayList<E> m_colorList

        The list of the colors used

      - #### m_DefaultColors

            java.awt.Color[] m_DefaultColors

        default colours for colouring discrete class

      - #### m_xIndex

            int m_xIndex

        Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing

      - #### m_yIndex

            int m_yIndex

      - #### m_cIndex

            int m_cIndex

      - #### m_sIndex

            int m_sIndex

      - #### m_maxX

            double m_maxX

        Holds the min and max values of the x, y and colouring attributes over all plots

      - #### m_minX

            double m_minX

      - #### m_maxY

            double m_maxY

      - #### m_minY

            double m_minY

      - #### m_maxC

            double m_maxC

      - #### m_minC

            double m_minC

      - #### m_axisPad

            int m_axisPad

        Axis padding

      - #### m_tickSize

            int m_tickSize

        Tick size

      - #### m_XaxisStart

            int m_XaxisStart

        the offsets of the axes once label metrics are calculated

      - #### m_YaxisStart

            int m_YaxisStart

      - #### m_XaxisEnd

            int m_XaxisEnd

      - #### m_YaxisEnd

            int m_YaxisEnd

      - #### m_plotResize

            boolean m_plotResize

        if the user resizes the window, or the attributes selected for the attributes change, then the lookup table for points needs to be recalculated

      - #### m_axisChanged

            boolean m_axisChanged

        if the user changes attribute assigned to an axis

      - #### m_drawnPoints

            int[][] m_drawnPoints

        An array used to show if a point is hidden or not. This is used for speeding up the drawing of the plot panel although I am not sure how much performance this grants over not having it.

      - #### m_labelFont

            java.awt.Font m_labelFont

        Font for labels

      - #### m_labelMetrics

            java.awt.FontMetrics m_labelMetrics

      - #### m_JitterVal

            int m_JitterVal

        the level of jitter

      - #### m_JRand

            java.util.Random m_JRand

        random values for perterbing the data points

  - ### Class weka.gui.visualize.PlotData2D extends java.lang.Object implements Serializable

    serialVersionUID:
      -3979972167982697979L

    - ### Serialized Fields

      - #### m_plotInstances

            Instances m_plotInstances

        The instances

      - #### m_plotName

            java.lang.String m_plotName

        The name of this plot

      - #### m_plotNameHTML

            java.lang.String m_plotNameHTML

        The name of this plot (possibly in html) suitable for using in a tool tip text.

      - #### m_useCustomColour

            boolean m_useCustomColour

        Custom colour for this plot

      - #### m_customColour

            java.awt.Color m_customColour

      - #### m_displayAllPoints

            boolean m_displayAllPoints

        Display all points (ie. those that map to the same display coords)

      - #### m_alwaysDisplayPointsOfThisSize

            int m_alwaysDisplayPointsOfThisSize

        If the shape size of a point equals this size then always plot it (i.e. even if it is obscured by other points)

      - #### m_pointLookup

            double[][] m_pointLookup

        Panel coordinate cache for data points

      - #### m_shapeSize

            int[] m_shapeSize

        Additional optional information to control the size of points. The default is shape size 2

      - #### m_shapeType

            int[] m_shapeType

        Additional optional information to control the point shape for this data. Default is to allow automatic assigning of point shape on the basis of plot number

      - #### m_connectPoints

            boolean[] m_connectPoints

        Additional optional information to control the drawing of lines between consecutive points. Setting an entry in the array to true indicates that the associated point should have a line connecting it to the previous point.

      - #### m_xIndex

            int m_xIndex

        The x index

      - #### m_yIndex

            int m_yIndex

        The y index

      - #### m_cIndex

            int m_cIndex

        The colouring index

      - #### m_maxX

            double m_maxX

        Holds the min and max values of the x, y and colouring attributes for this plot

      - #### m_minX

            double m_minX

      - #### m_maxY

            double m_maxY

      - #### m_minY

            double m_minY

      - #### m_maxC

            double m_maxC

      - #### m_minC

            double m_minC

  - ### Class weka.gui.visualize.PrintableComponent.JComponentWriterFileFilter extends ExtensionFileFilter implements Serializable

    serialVersionUID:
      8540426888094207515L

    - ### Serialized Fields

      - #### m_Writer

            JComponentWriter m_Writer

        the associated writer.

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

    serialVersionUID:
      6281532227633417538L

    - ### Serialized Fields

      - #### m_Printer

            PrintableComponent m_Printer

        the class responsible for printing

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

    serialVersionUID:
      3070002211779443890L

    - ### Serialized Fields

      - #### m_ROCString

            java.lang.String m_ROCString

        The string to add to the Plot Border.

      - #### m_savePanelBorderText

            java.lang.String m_savePanelBorderText

        Original border text

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

    serialVersionUID:
      240108358588153943L

    - ### Serialized Fields

      - #### m_DefaultColors

            java.awt.Color[] m_DefaultColors

        default colours for colouring discrete class

      - #### m_XCombo

            javax.swing.JComboBox<E> m_XCombo

        Lets the user select the attribute for the x axis

      - #### m_YCombo

            javax.swing.JComboBox<E> m_YCombo

        Lets the user select the attribute for the y axis

      - #### m_ColourCombo

            javax.swing.JComboBox<E> m_ColourCombo

        Lets the user select the attribute to use for colouring

      - #### m_ShapeCombo

            javax.swing.JComboBox<E> m_ShapeCombo

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

      - #### m_submit

            javax.swing.JButton m_submit

        Button for the user to enter the splits.

      - #### m_cancel

            javax.swing.JButton m_cancel

        Button for the user to remove all splits.

      - #### m_openBut

            javax.swing.JButton m_openBut

        Button for the user to open the visualized set of instances

      - #### m_saveBut

            javax.swing.JButton m_saveBut

        Button for the user to save the visualized set of instances

      - #### COMBO_SIZE

            java.awt.Dimension COMBO_SIZE

        Stop the combos from growing out of control

      - #### m_FileChooser

            javax.swing.JFileChooser m_FileChooser

        file chooser for saving instances

      - #### m_ArffFilter

            javax.swing.filechooser.FileFilter m_ArffFilter

        Filter to ensure only arff files are selected

      - #### m_JitterLab

            javax.swing.JLabel m_JitterLab

        Label for the jitter slider

      - #### m_Jitter

            javax.swing.JSlider m_Jitter

        The jitter slider

      - #### m_plot

            weka.gui.visualize.VisualizePanel.PlotPanel m_plot

        The panel that displays the plot

      - #### m_attrib

            AttributePanel m_attrib

        The panel that displays the attributes , using color to represent another attribute.

      - #### m_legendPanel

            LegendPanel m_legendPanel

        The panel that displays legend info if there is more than one plot

      - #### m_plotSurround

            javax.swing.JPanel m_plotSurround

        Panel that surrounds the plot panel with a titled border

      - #### m_classSurround

            javax.swing.JPanel m_classSurround

        Panel that surrounds the class panel with a titled border

      - #### listener

            java.awt.event.ActionListener listener

        An optional listener that we will inform when ComboBox selections change

      - #### m_splitListener

            VisualizePanelListener m_splitListener

        An optional listener that we will inform when the user creates a split to seperate instances.

      - #### m_plotName

            java.lang.String m_plotName

        The name of the plot (not currently displayed, but can be used in the containing Frame or Panel)

      - #### m_classPanel

            ClassPanel m_classPanel

        The panel that displays the legend for the colouring attribute

      - #### m_colorList

            java.util.ArrayList<E> m_colorList

        The list of the colors used

      - #### m_preferredXDimension

            java.lang.String m_preferredXDimension

        These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file

      - #### m_preferredYDimension

            java.lang.String m_preferredYDimension

      - #### m_preferredColourDimension

            java.lang.String m_preferredColourDimension

      - #### m_showAttBars

            boolean m_showAttBars

        Show the attribute bar panel

      - #### m_showClassPanel

            boolean m_showClassPanel

        Show the class panel

      - #### m_Log

            Logger m_Log

        the logger

  - ### Class weka.gui.visualize.VisualizePanel.PlotPanel extends PrintablePanel implements Serializable

    serialVersionUID:
      -4823674171136494204L

    - ### Serialized Fields

      - #### m_plot2D

            Plot2D m_plot2D

        The actual generic plotting panel

      - #### m_plotInstances

            Instances m_plotInstances

        The instances from the master plot

      - #### m_originalPlot

            PlotData2D m_originalPlot

        The master plot

      - #### m_xIndex

            int m_xIndex

        Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing

      - #### m_yIndex

            int m_yIndex

      - #### m_cIndex

            int m_cIndex

      - #### m_sIndex

            int m_sIndex

      - #### m_createShape

            boolean m_createShape

        True if the user is currently dragging a box.

      - #### m_shapes

            java.util.ArrayList<E> m_shapes

        contains all the shapes that have been drawn for these attribs

      - #### m_shapePoints

            java.util.ArrayList<E> m_shapePoints

        contains the points of the shape currently being drawn.

      - #### m_newMousePos

            java.awt.Dimension m_newMousePos

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