# Serialized Form

## Package weka.associations

### Class weka.associations.AbstractAssociator

class AbstractAssociator extends Object implements Serializable

serialVersionUID:
  -3017644543382432070L

### Class weka.associations.Apriori

class Apriori extends AbstractAssociator implements Serializable

serialVersionUID:
  3277498842319212687L

#### Serialized Fields

##### m_allTheRules

    FastVector[] m_allTheRules

The list of all generated rules.

##### m_car

    boolean m_car

Flag indicating whether class association rules are mined.

##### m_classIndex

    int m_classIndex

The class index.

##### m_cycles

    int m_cycles

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

##### m_delta

    double m_delta

Delta by which m_minSupport is decreased in each iteration.

##### m_hashtables

    FastVector m_hashtables

The same information stored in hash tables.

##### m_instances

    Instances m_instances

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

##### m_lowerBoundMinSupport

    double m_lowerBoundMinSupport

The lower bound for the minimum support.

##### m_Ls

    FastVector m_Ls

The set of all sets of itemsets L.

##### m_metricType

    int m_metricType

The selected metric type.

##### m_minMetric

    double m_minMetric

The minimum metric score.

##### m_minSupport

    double m_minSupport

The minimum support.

##### m_numRules

    int m_numRules

The maximum number of rules that are output.

##### m_onlyClass

    Instances m_onlyClass

Only the class attribute of all Instances.

##### m_outputItemSets

    boolean m_outputItemSets

Output itemsets found?

##### m_removeMissingCols

    boolean m_removeMissingCols

Remove columns with all missing values

##### m_significanceLevel

    double m_significanceLevel

Significance level for optional significance test.

##### m_upperBoundMinSupport

    double m_upperBoundMinSupport

The upper bound on the support

##### m_verbose

    boolean m_verbose

Report progress iteratively

### Class weka.associations.AprioriItemSet

class AprioriItemSet extends ItemSet implements Serializable

serialVersionUID:
  7684467755712672058L

### Class weka.associations.CaRuleGeneration

class CaRuleGeneration extends RuleGeneration implements Serializable

serialVersionUID:
  3065752149646517703L

### Class weka.associations.FilteredAssociator

class FilteredAssociator extends SingleAssociatorEnhancer implements Serializable

serialVersionUID:
  -4523450618538717400L

#### Serialized Fields

##### m_ClassIndex

    int m_ClassIndex

The class index.

##### m_Filter

    Filter m_Filter

The filter

##### m_FilteredInstances

    Instances m_FilteredInstances

The instance structure of the filtered instances

### Class weka.associations.FPGrowth

class FPGrowth extends AbstractAssociator implements Serializable

serialVersionUID:
  3620717108603442911L

#### Serialized Fields

##### m_delta

    double m_delta

The amount by which to decrease the support in each iteration

##### m_findAllRulesForSupportLevel

    boolean m_findAllRulesForSupportLevel

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

##### m_largeItemSets

    weka.associations.FPGrowth.FrequentItemSets m_largeItemSets

Holds the large item sets found

##### m_lowerBoundMinSupport

    double m_lowerBoundMinSupport

The lower bound on minimum support

##### m_maxItems

    int m_maxItems

##### m_metric

    FPGrowth.AssociationRule.METRIC_TYPE m_metric

##### m_metricThreshold

    double m_metricThreshold

##### m_mustContainOR

    boolean m_mustContainOR

Use OR rather than AND when considering must contain lists

##### m_numRulesToFind

    int m_numRulesToFind

The number of rules to find

##### m_positiveIndex

    int m_positiveIndex

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

##### m_rules

    List<FPGrowth.AssociationRule> m_rules

Holds the rules

##### m_rulesMustContain

    String m_rulesMustContain

If set, then only output rules containing these itmes

##### m_transactionsMustContain

    String m_transactionsMustContain

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

##### m_upperBoundMinSupport

    double m_upperBoundMinSupport

The upper bound on the minimum support

### Class weka.associations.FPGrowth.AssociationRule

class AssociationRule extends Object implements Serializable

serialVersionUID:
  -661269018702294489L

#### Serialized Fields

##### m_consequence

    Collection<FPGrowth.BinaryItem> m_consequence

The consequence of the rule

##### m_consequenceSupport

    int m_consequenceSupport

The support for the consequence

##### m_metricType

    FPGrowth.AssociationRule.METRIC_TYPE m_metricType

The metric type for this rule

##### m_premise

    Collection<FPGrowth.BinaryItem> m_premise

The premise of the rule

##### m_premiseSupport

    int m_premiseSupport

The support for the premise

##### m_totalSupport

    int m_totalSupport

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

##### m_totalTransactions

    int m_totalTransactions

The total number of transactions in the data

### Class weka.associations.FPGrowth.BinaryItem

class BinaryItem extends Object implements Serializable

serialVersionUID:
  -3372941834914147669L

#### Serialized Fields

##### m_attribute

    Attribute m_attribute

The attribute that the item corresponds to

##### m_frequency

    int m_frequency

The frequency of the item

##### m_valueIndex

    int m_valueIndex

The index of the value considered to be positive

### Class weka.associations.FPGrowth.FPTreeNode

class FPTreeNode extends Object implements Serializable

serialVersionUID:
  4396315323673737660L

#### Serialized Fields

##### m_children

    Map<FPGrowth.BinaryItem,weka.associations.FPGrowth.FPTreeNode> m_children

the children of this node

##### m_ID

    int m_ID

ID (for graphing the tree)

##### m_item

    FPGrowth.BinaryItem m_item

item at this node

##### m_levelSibling

    weka.associations.FPGrowth.FPTreeNode m_levelSibling

link to another sibling at this level in the tree

##### m_parent

    weka.associations.FPGrowth.FPTreeNode m_parent

link to the parent node

##### m_projectedCounts

    weka.associations.FPGrowth.ShadowCounts m_projectedCounts

counts associated with projected versions of this node

### Class weka.associations.FPGrowth.FPTreeRoot.Header

class Header extends Object implements Serializable

serialVersionUID:
  -6583156284891368909L

#### Serialized Fields

##### m_headerList

    List<weka.associations.FPGrowth.FPTreeNode> m_headerList

The list of pointers into the tree structure

##### m_projectedHeaderCounts

    weka.associations.FPGrowth.ShadowCounts m_projectedHeaderCounts

Projected header counts for this entry

### Class weka.associations.FPGrowth.FrequentBinaryItemSet

class FrequentBinaryItemSet extends Object implements Serializable

serialVersionUID:
  -6543815873565829448L

#### Serialized Fields

##### m_items

    ArrayList<FPGrowth.BinaryItem> m_items

The list of items in the item set

##### m_support

    int m_support

the support of this item set

### Class weka.associations.FPGrowth.FrequentItemSets

class FrequentItemSets extends Object implements Serializable

serialVersionUID:
  4173606872363973588L

#### Serialized Fields

##### m_numberOfTransactions

    int m_numberOfTransactions

The total number of transactions in the data

##### m_sets

    ArrayList<weka.associations.FPGrowth.FrequentBinaryItemSet> m_sets

The list of frequent item sets

### Class weka.associations.FPGrowth.ShadowCounts

class ShadowCounts extends Object implements Serializable

serialVersionUID:
  4435433714185969155L

#### Serialized Fields

##### m_counts

    ArrayList<Integer> m_counts

Holds the counts at different recursion levels

### Class weka.associations.GeneralizedSequentialPatterns

class GeneralizedSequentialPatterns extends AbstractAssociator implements Serializable

serialVersionUID:
  -4119691320812254676L

#### Serialized Fields

##### m_AlgorithmStart

    String m_AlgorithmStart

String indicating the starting time of the algorithm.

##### m_AllSequentialPatterns

    FastVector m_AllSequentialPatterns

all generated frequent sequences, i.e. sequential patterns

##### m_CycleEnd

    String m_CycleEnd

String indicating the ending time of an cycle.

##### m_Cycles

    int m_Cycles

number of cycles performed until termination

##### m_CycleStart

    String m_CycleStart

String indicating the starting time of an cycle.

##### m_DataSeqID

    int m_DataSeqID

number indicating the attribute holding the data sequence ID

##### m_Debug

    boolean m_Debug

Whether the classifier is run in debug mode.

##### m_FilterAttributes

    String m_FilterAttributes

String containing the attribute numbers that are used for result filtering; -1 means no filtering

##### m_FilterAttrVector

    FastVector m_FilterAttrVector

Vector containing the attribute numbers that are used for result filtering; -1 means no filtering

##### m_MinSupport

    double m_MinSupport

the minimum support threshold

##### m_OriginalDataSet

    Instances m_OriginalDataSet

original sequential data set to be used for sequential patterns extraction

### Class weka.associations.ItemSet

class ItemSet extends Object implements Serializable

serialVersionUID:
  2724000045282835791L

#### Serialized Fields

##### m_counter

    int m_counter

Counter for how many transactions contain this item set.

##### m_items

    int[] m_items

The items stored as an array of of ints.

##### m_totalTransactions

    int m_totalTransactions

The total number of transactions

### Class weka.associations.LabeledItemSet

class LabeledItemSet extends ItemSet implements Serializable

serialVersionUID:
  4158771925518299903L

#### Serialized Fields

##### m_classLabel

    int m_classLabel

The class label.

##### m_ruleSupCounter

    int m_ruleSupCounter

The support of the rule.

### Class weka.associations.PredictiveApriori

class PredictiveApriori extends AbstractAssociator implements Serializable

serialVersionUID:
  8109088846865075341L

#### Serialized Fields

##### m_allTheRules

    FastVector[] m_allTheRules

The list of all generated rules.

##### m_best

    TreeSet m_best

The n best rules.

##### m_bestChanged

    boolean m_bestChanged

Flag keeping track if the list of the n best rules has changed.

##### m_car

    boolean m_car

Flag indicating whether class association rules are mined.

##### m_classIndex

    int m_classIndex

The class index.

##### m_count

    int m_count

Counter for the time of generation for an association rule.

##### m_expectation

    double m_expectation

The expected predictive accuracy a rule needs to be a candidate for the output.

##### m_hashtables

    FastVector m_hashtables

The same information stored in hash tables.

##### m_instances

    Instances m_instances

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

##### m_Ls

    FastVector m_Ls

The set of all sets of itemsets.

##### m_midPoints

    double[] m_midPoints

The mid points of the intervals used for the prior estimation.

##### m_numRules

    int m_numRules

The maximum number of rules that are output.

##### m_premiseCount

    int m_premiseCount

The minimum support.

##### m_priorEstimator

    PriorEstimation m_priorEstimator

The prior estimator.

##### m_priors

    Hashtable m_priors

The hashtable containing the prior probabilities.

### Class weka.associations.PriorEstimation

class PriorEstimation extends Object implements Serializable

serialVersionUID:
  5570863216522496271L

#### Serialized Fields

##### m_CARs

    boolean m_CARs

Flag indicating whether standard association rules or class association rules are mined.

##### m_distribution

    Hashtable m_distribution

Hashtable to store the confidence values of randomly generated rules.

##### m_instances

    Instances m_instances

The instances for which association rules are mined.

##### m_midPoints

    double[] m_midPoints

The mid points of the discrete intervals in which the interval [0,1] is divided.

##### m_numIntervals

    int m_numIntervals

The number of intervals.

##### m_numRandRules

    int m_numRandRules

The number of rnadom rules.

##### m_priors

    Hashtable m_priors

Hashtable containing the estimated prior probabilities.

##### m_randNum

    Random m_randNum

The random number generator.

##### m_sum

    double m_sum

Sums up the confidences of all rules with a certain length.

### Class weka.associations.RuleGeneration

class RuleGeneration extends Object implements Serializable

serialVersionUID:
  -8927041669872491432L

#### Serialized Fields

##### m_best

    TreeSet m_best

The list of the actual n best rules.

##### m_change

    boolean m_change

Flag indicating whether the list fo the best rules has changed.

##### m_count

    int m_count

Integer indicating the generation time of a rule.

##### m_counter

    int m_counter

Counter for how many transactions contain this item set.

##### m_expectation

    double m_expectation

The minimum expected predictive accuracy that is needed to be a candidate for the list of the best rules.

##### m_instances

    Instances m_instances

The instances.

##### m_items

    int[] m_items

The items stored as an array of of integer.

##### m_midPoints

    double[] m_midPoints

Sorted array of the mied points of the intervals used for prior estimation.

##### m_minRuleCount

    int m_minRuleCount

The minimum support a rule needs to be a candidate for the list of the best rules.

##### m_priors

    Hashtable m_priors

Hashtable conatining the estimated prior probabilities.

##### m_totalTransactions

    int m_totalTransactions

The total number of transactions

### Class weka.associations.RuleItem

class RuleItem extends Object implements Serializable

serialVersionUID:
  -3761299128347476534L

#### Serialized Fields

##### m_accuracy

    double m_accuracy

The expected predictive accuracy of a rule.

##### m_consequence

    ItemSet m_consequence

The consequence of a rule.

##### m_genTime

    int m_genTime

The generation time of a rule.

##### m_premise

    ItemSet m_premise

The premise of a rule.

### Class weka.associations.SingleAssociatorEnhancer

class SingleAssociatorEnhancer extends AbstractAssociator implements Serializable

serialVersionUID:
  -3665885256363525164L

#### Serialized Fields

##### m_Associator

    Associator m_Associator

The base associator to use

### Class weka.associations.Tertius

class Tertius extends AbstractAssociator implements Serializable

serialVersionUID:
  5556726848380738179L

#### Serialized Fields

##### m_best

    int m_best

Number of best confirmation values to search.

##### m_classification

    boolean m_classification

Classification bias.

##### m_classIndex

    int m_classIndex

Index of class attribute.

##### m_confirmationThreshold

    double m_confirmationThreshold

Confirmation threshold for the rules.

##### m_equivalent

    boolean m_equivalent

Perform test on equivalent rules ?

##### m_explored

    int m_explored

Number of hypotheses explored.

##### m_frequencyThreshold

    double m_frequencyThreshold

Frequency threshold for the body and the negation of the head.

##### m_horn

    boolean m_horn

Horn clauses bias.

##### m_hypotheses

    int m_hypotheses

Number of hypotheses considered.

##### m_instances

    Instances m_instances

Instances used for the search.

##### m_missing

    int m_missing

Way of handling missing values in the search.

##### m_negation

    int m_negation

Type of negation used in the rules.

##### m_noiseThreshold

    double m_noiseThreshold

Maximal number of counter-instances.

##### m_numLiterals

    int m_numLiterals

Number of literals in a rule.

##### m_parts

    Instances m_parts

Part instances for individual-based learning.

##### m_partsString

    String m_partsString

Name of the file containing the parts for individual-based learning.

##### m_predicates

    ArrayList m_predicates

Predicates used in the rules.

##### m_printValues

    int m_printValues

Type of values output.

##### m_repeat

    boolean m_repeat

Repeat attributes ?

##### m_results

    SimpleLinkedList m_results

The results.

##### m_roc

    boolean m_roc

Perform ROC analysis ?

##### m_sameClause

    boolean m_sameClause

Perform test on same clauses ?

##### m_status

    int m_status

Status of the search.

##### m_subsumption

    boolean m_subsumption

Perform subsumption test ?

##### m_time

    Date m_time

Time needed for the search.

##### m_valuesText

    TextField m_valuesText

Field to output the current values.

## Package weka.associations.gsp

### Class weka.associations.gsp.Element

class Element extends Object implements Serializable

serialVersionUID:
  -7900701276019516371L

#### Serialized Fields

##### m_Events

    int[] m_Events

events/items stored as an array of ints

### Class weka.associations.gsp.Sequence

class Sequence extends Object implements Serializable

serialVersionUID:
  -5001018056339156390L

#### Serialized Fields

##### m_Elements

    FastVector m_Elements

ordered list of the comprised elements/itemsets

##### m_SupportCount

    int m_SupportCount

the support count of the Sequence

## Package weka.associations.tertius

### Class weka.associations.tertius.AttributeValueLiteral

class AttributeValueLiteral extends Literal implements Serializable

serialVersionUID:
  4077436297281456239L

#### Serialized Fields

##### m_index

    int m_index

##### m_value

    String m_value

### Class weka.associations.tertius.Body

class Body extends LiteralSet implements Serializable

serialVersionUID:
  4870689270432218016L

### Class weka.associations.tertius.Head

class Head extends LiteralSet implements Serializable

serialVersionUID:
  5068076274253706199L

### Class weka.associations.tertius.IndividualInstance

class IndividualInstance extends Instance implements Serializable

serialVersionUID:
  -7903938733476585114L

#### Serialized Fields

##### m_parts

    Instances m_parts

### Class weka.associations.tertius.IndividualInstances

class IndividualInstances extends Instances implements Serializable

serialVersionUID:
  -7355054814895636733L

### Class weka.associations.tertius.IndividualLiteral

class IndividualLiteral extends AttributeValueLiteral implements Serializable

serialVersionUID:
  4712404824517887435L

#### Serialized Fields

##### m_type

    int m_type

### Class weka.associations.tertius.Literal

class Literal extends Object implements Serializable

serialVersionUID:
  2675363669503575771L

#### Serialized Fields

##### m_missing

    int m_missing

##### m_negation

    Literal m_negation

##### m_predicate

    Predicate m_predicate

##### m_sign

    int m_sign

### Class weka.associations.tertius.LiteralSet

class LiteralSet extends Object implements Serializable

serialVersionUID:
  6094536488654503152L

#### Serialized Fields

##### m_counter

    int m_counter

Counter for the number of counter-instances.

##### m_counterInstances

    ArrayList m_counterInstances

Set of counter-instances of this part of the rule.

##### m_lastLiteral

    Literal m_lastLiteral

Last literal added to this set.

##### m_literals

    ArrayList m_literals

Literals contained in this set.

##### m_numInstances

    int m_numInstances

Number of instances in the data the set deals with.

##### m_type

    int m_type

Type of properties expressed in this set (individual or parts properties).

### Class weka.associations.tertius.Predicate

class Predicate extends Object implements Serializable

serialVersionUID:
  -8374702481965026640L

#### Serialized Fields

##### m_index

    int m_index

##### m_isClass

    boolean m_isClass

##### m_literals

    ArrayList m_literals

##### m_name

    String m_name

### Class weka.associations.tertius.Rule

class Rule extends Object implements Serializable

serialVersionUID:
  -7763378359090435505L

#### Serialized Fields

##### m_body

    Body m_body

The body of the rule.

##### m_classRule

    boolean m_classRule

Is this rule a classification rule ?

##### m_confirmation

    double m_confirmation

Confirmation of this rule.

##### m_counter

    int m_counter

Counter for the counter-instances of this rule.

##### m_counterInstances

    ArrayList m_counterInstances

Set of counter-instances of this rule.

##### m_head

    Head m_head

The head of the rule.

##### m_maxLiterals

    int m_maxLiterals

Maximal number of literals in the rule.

##### m_negBody

    boolean m_negBody

Can there be negations in the body ?

##### m_negHead

    boolean m_negHead

Can there be negations in the head ?

##### m_numInstances

    int m_numInstances

Number of instances in the data this rule deals with.

##### m_optimistic

    double m_optimistic

Optimistic estimate of this rule.

##### m_repeatPredicate

    boolean m_repeatPredicate

Can repeat predicates in the rule ?

##### m_singleHead

    boolean m_singleHead

Can there be only one literal in the head ?

### Class weka.associations.tertius.SimpleLinkedList

class SimpleLinkedList extends Object implements Serializable

serialVersionUID:
  -1491148276509976299L

#### Serialization Methods

##### readObject

private void readObject(ObjectInputStream s) throws IOException, ClassNotFoundException
Reconstitute this `LinkedList` instance from a stream (that is deserialize it).

Throws:
  `IOException`
  `ClassNotFoundException`

##### writeObject

private void writeObject(ObjectOutputStream s) throws IOException
Save the state of this `LinkedList` instance to a stream (that is, serialize it).

Serial Data:
  The size of the list (the number of elements it contains) is emitted (int), followed by all of its elements (each an Object) in the proper order.
Throws:
  `IOException`

#### Serialized Fields

##### first

    weka.associations.tertius.SimpleLinkedList.Entry first

##### last

    weka.associations.tertius.SimpleLinkedList.Entry last

### Class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator

class LinkedListInverseIterator extends Object implements Serializable

serialVersionUID:
  6290379064027832108L

#### Serialized Fields

##### current

    weka.associations.tertius.SimpleLinkedList.Entry current

##### lastReturned

    weka.associations.tertius.SimpleLinkedList.Entry lastReturned

### Class weka.associations.tertius.SimpleLinkedList.LinkedListIterator

class LinkedListIterator extends Object implements Serializable

serialVersionUID:
  -2448555236100426759L

#### Serialized Fields

##### current

    weka.associations.tertius.SimpleLinkedList.Entry current

##### lastReturned

    weka.associations.tertius.SimpleLinkedList.Entry lastReturned

## Package weka.attributeSelection

### Class weka.attributeSelection.ASEvaluation

class ASEvaluation extends Object implements Serializable

serialVersionUID:
  2091705669885950849L

### Class weka.attributeSelection.ASSearch

class ASSearch extends Object implements Serializable

serialVersionUID:
  7591673350342236548L

### Class weka.attributeSelection.AttributeSelection

class AttributeSelection extends Object implements Serializable

serialVersionUID:
  4170171824147584330L

#### Serialized Fields

##### m_ASEvaluator

    ASEvaluation m_ASEvaluator

the attribute/subset evaluator

##### m_attributeFilter

    Remove m_attributeFilter

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

##### m_attributeRanking

    double[][] m_attributeRanking

the attribute indexes and associated merits if a ranking is produced

##### m_doRank

    boolean m_doRank

rank features (if allowed by the search method)

##### m_doXval

    boolean m_doXval

do cross validation

##### m_numFolds

    int m_numFolds

the number of folds to use for cross validation

##### m_numToSelect

    int m_numToSelect

number of attributes requested from ranked results

##### m_rankResults

    double[][] m_rankResults

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

##### m_searchMethod

    ASSearch m_searchMethod

the search method

##### m_seed

    int m_seed

seed used to randomly shuffle instances for cross validation

##### m_selectedAttributeSet

    int[] m_selectedAttributeSet

the selected attributes

##### m_selectionResults

    StringBuffer m_selectionResults

holds a string describing the results of the attribute selection

##### m_subsetResults

    double[] m_subsetResults

##### m_trainInstances

    Instances m_trainInstances

the instances to select attributes from

##### m_transformer

    AttributeTransformer m_transformer

if a feature selection run involves an attribute transformer

##### m_trials

    int m_trials

### Class weka.attributeSelection.AttributeSetEvaluator

class AttributeSetEvaluator extends ASEvaluation implements Serializable

serialVersionUID:
  -5744881009422257389L

### Class weka.attributeSelection.BestFirst

class BestFirst extends ASSearch implements Serializable

serialVersionUID:
  7841338689536821867L

#### Serialized Fields

##### m_bestMerit

    double m_bestMerit

holds the merit of the best subset found

##### m_cacheSize

    int m_cacheSize

holds the maximum size of the lookup cache for evaluated subsets

##### m_classIndex

    int m_classIndex

holds the class index

##### m_debug

    boolean m_debug

for debugging

##### m_hasClass

    boolean m_hasClass

does the data have a class

##### m_maxStale

    int m_maxStale

maximum number of stale nodes before terminating search

##### m_numAttribs

    int m_numAttribs

number of attributes in the data

##### m_searchDirection

    int m_searchDirection

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

##### m_starting

    int[] m_starting

holds an array of starting attributes

##### m_startRange

    Range m_startRange

holds the start set for the search as a Range

##### m_totalEvals

    int m_totalEvals

total number of subsets evaluated during a search

### Class weka.attributeSelection.BestFirst.Link2

class Link2 extends Object implements Serializable

serialVersionUID:
  -8236598311516351420L

#### Serialized Fields

##### m_data

    Object[] m_data

##### m_merit

    double m_merit

### Class weka.attributeSelection.BestFirst.LinkedList2

class LinkedList2 extends FastVector implements Serializable

serialVersionUID:
  3250538292330398929L

#### Serialized Fields

##### m_MaxSize

    int m_MaxSize

Max number of elements in the list

### Class weka.attributeSelection.CfsSubsetEval

class CfsSubsetEval extends ASEvaluation implements Serializable

serialVersionUID:
  747878400813276317L

#### Serialized Fields

##### m_c_Threshold

    double m_c_Threshold

Threshold for admitting locally predictive features

##### m_classIndex

    int m_classIndex

The class index

##### m_corr_matrix

    float[][] m_corr_matrix

Holds the matrix of attribute correlations

##### m_disTransform

    Discretize m_disTransform

Discretise attributes when class in nominal

##### m_isNumeric

    boolean m_isNumeric

Is the class numeric

##### m_locallyPredictive

    boolean m_locallyPredictive

Include locally predicitive attributes

##### m_missingSeparate

    boolean m_missingSeparate

Treat missing values as separate values

##### m_numAttribs

    int m_numAttribs

Number of attributes in the training data

##### m_numInstances

    int m_numInstances

Number of instances in the training data

##### m_std_devs

    double[] m_std_devs

Standard deviations of attributes (when using pearsons correlation)

##### m_trainInstances

    Instances m_trainInstances

The training instances

### Class weka.attributeSelection.ChiSquaredAttributeEval

class ChiSquaredAttributeEval extends ASEvaluation implements Serializable

serialVersionUID:
  -8316857822521717692L

#### Serialized Fields

##### m_Binarize

    boolean m_Binarize

Just binarize numeric attributes

##### m_ChiSquareds

    double[] m_ChiSquareds

The chi-squared value for each attribute

##### m_missing_merge

    boolean m_missing_merge

Treat missing values as a seperate value

### Class weka.attributeSelection.ClassifierSubsetEval

class ClassifierSubsetEval extends HoldOutSubsetEvaluator implements Serializable

serialVersionUID:
  7532217899385278710L

#### Serialized Fields

##### m_Classifier

    Classifier m_Classifier

holds the classifier to use for error estimates

##### m_classIndex

    int m_classIndex

class index

##### m_Evaluation

    Evaluation m_Evaluation

holds the evaluation object to use for evaluating the classifier

##### m_holdOutFile

    File m_holdOutFile

the file that containts hold out/test instances

##### m_holdOutInstances

    Instances m_holdOutInstances

the instances to test on

##### m_numAttribs

    int m_numAttribs

number of attributes in the training data

##### m_numInstances

    int m_numInstances

number of training instances

##### m_trainingInstances

    Instances m_trainingInstances

training instances

##### m_useTraining

    boolean m_useTraining

evaluate on training data rather than seperate hold out/test set

### Class weka.attributeSelection.ConsistencySubsetEval

class ConsistencySubsetEval extends ASEvaluation implements Serializable

serialVersionUID:
  -2880323763295270402L

#### Serialized Fields

##### m_classIndex

    int m_classIndex

class index

##### m_disTransform

    Discretize m_disTransform

Discretise numeric attributes

##### m_numAttribs

    int m_numAttribs

number of attributes in the training data

##### m_numInstances

    int m_numInstances

number of instances in the training data

##### m_table

    Hashtable m_table

Hash table for evaluating feature subsets

##### m_trainInstances

    Instances m_trainInstances

training instances

### Class weka.attributeSelection.ConsistencySubsetEval.hashKey

class hashKey extends Object implements Serializable

serialVersionUID:
  6144138512017017408L

#### Serialized Fields

##### attributes

    double[] attributes

Array of attribute values for an instance

##### key

    int key

The key

##### missing

    boolean[] missing

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

### Class weka.attributeSelection.CostSensitiveASEvaluation

class CostSensitiveASEvaluation extends ASEvaluation implements Serializable

serialVersionUID:
  -7045833833363396977L

#### Serialized Fields

##### m_CostFile

    String m_CostFile

The name of the cost file, for command line options

##### m_CostMatrix

    CostMatrix m_CostMatrix

The cost matrix

##### m_evaluator

    ASEvaluation m_evaluator

The base evaluator to use

##### m_MatrixSource

    int m_MatrixSource

Indicates the current cost matrix source

##### m_OnDemandDirectory

    File m_OnDemandDirectory

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

##### m_seed

    int m_seed

random number seed

### Class weka.attributeSelection.CostSensitiveAttributeEval

class CostSensitiveAttributeEval extends CostSensitiveASEvaluation implements Serializable

serialVersionUID:
  4484876541145458447L

### Class weka.attributeSelection.CostSensitiveSubsetEval

class CostSensitiveSubsetEval extends CostSensitiveASEvaluation implements Serializable

serialVersionUID:
  2924546096103426700L

### Class weka.attributeSelection.ExhaustiveSearch

class ExhaustiveSearch extends ASSearch implements Serializable

serialVersionUID:
  5741842861142379712L

#### Serialized Fields

##### m_bestGroup

    BitSet m_bestGroup

the best feature set found during the search

##### m_bestMerit

    double m_bestMerit

the merit of the best subset found

##### m_classIndex

    int m_classIndex

holds the class index

##### m_evaluations

    int m_evaluations

the number of subsets evaluated during the search

##### m_hasClass

    boolean m_hasClass

does the data have a class

##### m_numAttribs

    int m_numAttribs

number of attributes in the data

##### m_verbose

    boolean m_verbose

if true, then ouput new best subsets as the search progresses

### Class weka.attributeSelection.FilteredAttributeEval

class FilteredAttributeEval extends ASEvaluation implements Serializable

serialVersionUID:
  2111121880778327334L

#### Serialized Fields

##### m_evaluator

    AttributeEvaluator m_evaluator

Base evaluator

##### m_filter

    Filter m_filter

Filter

##### m_filteredInstances

    Instances m_filteredInstances

Filtered instances structure

### Class weka.attributeSelection.FilteredSubsetEval

class FilteredSubsetEval extends ASEvaluation implements Serializable

serialVersionUID:
  2111121880778327334L

#### Serialized Fields

##### m_evaluator

    SubsetEvaluator m_evaluator

Base evaluator

##### m_filter

    Filter m_filter

Filter

##### m_filteredInstances

    Instances m_filteredInstances

Filtered instances structure

### Class weka.attributeSelection.GainRatioAttributeEval

class GainRatioAttributeEval extends ASEvaluation implements Serializable

serialVersionUID:
  -8504656625598579926L

#### Serialized Fields

##### m_classIndex

    int m_classIndex

The class index

##### m_missing_merge

    boolean m_missing_merge

Merge missing values

##### m_numAttribs

    int m_numAttribs

The number of attributes

##### m_numClasses

    int m_numClasses

The number of classes

##### m_numInstances

    int m_numInstances

The number of instances

##### m_trainInstances

    Instances m_trainInstances

The training instances

### Class weka.attributeSelection.GeneticSearch

class GeneticSearch extends ASSearch implements Serializable

serialVersionUID:
  -1618264232838472679L

#### Serialized Fields

##### m_avgFitness

    double m_avgFitness

##### m_best

    weka.attributeSelection.GeneticSearch.GABitSet m_best

the best population member found during the search

##### m_bestFeatureCount

    int m_bestFeatureCount

the number of features in the best population member

##### m_classIndex

    int m_classIndex

holds the class index

##### m_generationReports

    StringBuffer m_generationReports

holds the generation reports

##### m_hasClass

    boolean m_hasClass

does the data have a class

##### m_lookupTable

    Hashtable m_lookupTable

the lookup table

##### m_lookupTableSize

    int m_lookupTableSize

the number of entries to cache for lookup

##### m_maxFitness

    double m_maxFitness

##### m_maxGenerations

    int m_maxGenerations

the maximum number of generations to evaluate

##### m_minFitness

    double m_minFitness

##### m_numAttribs

    int m_numAttribs

number of attributes in the data

##### m_pCrossover

    double m_pCrossover

the probability of crossover occuring

##### m_pMutation

    double m_pMutation

the probability of mutation occuring

##### m_popSize

    int m_popSize

the number of individual solutions

##### m_population

    weka.attributeSelection.GeneticSearch.GABitSet[] m_population

the current population

##### m_random

    Random m_random

random number generation

##### m_reportFrequency

    int m_reportFrequency

how often reports are generated

##### m_seed

    int m_seed

seed for random number generation

##### m_starting

    int[] m_starting

holds a starting set as an array of attributes. Becomes one member of the initial random population

##### m_startRange

    Range m_startRange

holds the start set for the search as a Range

##### m_sumFitness

    double m_sumFitness

sum of the current population fitness

### Class weka.attributeSelection.GeneticSearch.GABitSet

class GABitSet extends Object implements Serializable

serialVersionUID:
  -2930607837482622224L

#### Serialized Fields

##### m_chromosome

    BitSet m_chromosome

the bitset

##### m_fitness

    double m_fitness

the fitness

##### m_objective

    double m_objective

holds raw merit

### Class weka.attributeSelection.GreedyStepwise

class GreedyStepwise extends ASSearch implements Serializable

serialVersionUID:
  -6312951970168325471L

#### Serialized Fields

##### m_ASEval

    ASEvaluation m_ASEval

##### m_backward

    boolean m_backward

Use a backwards search instead of a forwards one

##### m_best_group

    BitSet m_best_group

the best subset found

##### m_bestMerit

    double m_bestMerit

the merit of the best subset found

##### m_calculatedNumToSelect

    int m_calculatedNumToSelect

##### m_classIndex

    int m_classIndex

holds the class index

##### m_conservativeSelection

    boolean m_conservativeSelection

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

##### m_doneRanking

    boolean m_doneRanking

used to indicate whether or not ranking has been performed

##### m_doRank

    boolean m_doRank

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

##### m_hasClass

    boolean m_hasClass

does the data have a class

##### m_Instances

    Instances m_Instances

##### m_numAttribs

    int m_numAttribs

number of attributes in the data

##### m_numToSelect

    int m_numToSelect

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

##### m_rankedAtts

    double[][] m_rankedAtts

a ranked list of attribute indexes

##### m_rankedSoFar

    int m_rankedSoFar

##### m_rankingRequested

    boolean m_rankingRequested

true if the user has requested a ranked list of attributes

##### m_starting

    int[] m_starting

holds an array of starting attributes

##### m_startRange

    Range m_startRange

holds the start set for the search as a Range

##### m_threshold

    double m_threshold

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

### Class weka.attributeSelection.HoldOutSubsetEvaluator

class HoldOutSubsetEvaluator extends ASEvaluation implements Serializable

serialVersionUID:
  8280529785412054174L

### Class weka.attributeSelection.InfoGainAttributeEval

class InfoGainAttributeEval extends ASEvaluation implements Serializable

serialVersionUID:
  -1949849512589218930L

#### Serialized Fields

##### m_Binarize

    boolean m_Binarize

Just binarize numeric attributes

##### m_InfoGains

    double[] m_InfoGains

The info gain for each attribute

##### m_missing_merge

    boolean m_missing_merge

Treat missing values as a seperate value

### Class weka.attributeSelection.LatentSemanticAnalysis

class LatentSemanticAnalysis extends UnsupervisedAttributeEvaluator implements Serializable

serialVersionUID:
  -8712112988018106198L

#### Serialized Fields

##### m_actualRank

    int m_actualRank

The actual rank number to use for computation

##### m_attributeFilter

    Remove m_attributeFilter

##### m_classIndex

    int m_classIndex

Class index

##### m_hasClass

    boolean m_hasClass

Data has a class set

##### m_maxAttributesInName

    int m_maxAttributesInName

Maximum number of attributes in the transformed attribute name

##### m_nominalToBinaryFilter

    NominalToBinary m_nominalToBinaryFilter

##### m_normalize

    boolean m_normalize

Normalize the input data?

##### m_normalizeFilter

    Normalize m_normalizeFilter

##### m_numAttributes

    int m_numAttributes

Number of attributes

##### m_numInstances

    int m_numInstances

Number of instances

##### m_outputNumAttributes

    int m_outputNumAttributes

The number of attributes in the LSA transformed data

##### m_rank

    double m_rank

The approximation rank to use (between 0 and 1 means coverage proportion)

##### m_replaceMissingFilter

    ReplaceMissingValues m_replaceMissingFilter

Filters for original data

##### m_s

    Matrix m_s

Will hold the singular values

##### m_sumSquaredSingularValues

    double m_sumSquaredSingularValues

The sum of the squares of the singular values

##### m_trainHeader

    Instances m_trainHeader

Keep a copy for the class attribute (if set) and for checking for header compatibility

##### m_trainInstances

    Instances m_trainInstances

The data to transform analyse/transform

##### m_transformationMatrix

    Matrix m_transformationMatrix

Will hold the matrix used to transform instances to the new feature space

##### m_transformedFormat

    Instances m_transformedFormat

The header for the transformed data format

##### m_transpose

    boolean m_transpose

Is transpose necessary because numAttributes invalid input: '<' numInstances?

##### m_u

    Matrix m_u

Will hold the left singular vectors

##### m_v

    Matrix m_v

Will hold the right singular values

### Class weka.attributeSelection.LFSMethods.Link2

class Link2 extends Object implements Serializable

serialVersionUID:
  -7422719407475185086L

#### Serialized Fields

##### m_data

    Object[] m_data

##### m_merit

    double m_merit

### Class weka.attributeSelection.LFSMethods.LinkedList2

class LinkedList2 extends FastVector implements Serializable

serialVersionUID:
  -7776010892419656105L

#### Serialized Fields

##### m_MaxSize

    int m_MaxSize

### Class weka.attributeSelection.LinearForwardSelection

class LinearForwardSelection extends ASSearch implements Serializable

#### Serialized Fields

##### m_bestMerit

    double m_bestMerit

holds the merit of the best subset found

##### m_cacheSize

    int m_cacheSize

holds the maximum size of the lookup cache for evaluated subsets

##### m_classIndex

    int m_classIndex

holds the class index

##### m_forwardSearchMethod

    int m_forwardSearchMethod

0 == forward selection, 1 == floating forward search

##### m_hasClass

    boolean m_hasClass

does the data have a class

##### m_linearSelectionType

    int m_linearSelectionType

0 == fixed-set, 1 == fixed-width

##### m_maxStale

    int m_maxStale

maximum number of stale nodes before terminating search

##### m_numAttribs

    int m_numAttribs

number of attributes in the data

##### m_numUsedAttributes

    int m_numUsedAttributes

number of top-ranked attributes that are taken into account for the search

##### m_performRanking

    boolean m_performRanking

perform initial ranking to select top-ranked attributes

##### m_starting

    int[] m_starting

holds an array of starting attributes

##### m_startRange

    Range m_startRange

holds the start set for the search as a Range

##### m_totalEvals

    int m_totalEvals

total number of subsets evaluated during a search

##### m_verbose

    boolean m_verbose

for debugging

### Class weka.attributeSelection.OneRAttributeEval

class OneRAttributeEval extends ASEvaluation implements Serializable

serialVersionUID:
  4386514823886856980L

#### Serialized Fields

##### m_classIndex

    int m_classIndex

The class index

##### m_evalUsingTrainingData

    boolean m_evalUsingTrainingData

Use training data to evaluate merit rather than x-val

##### m_folds

    int m_folds

Number of folds for cross validation

##### m_minBucketSize

    int m_minBucketSize

Passed on to OneR

##### m_numAttribs

    int m_numAttribs

The number of attributes

##### m_numInstances

    int m_numInstances

The number of instances

##### m_randomSeed

    int m_randomSeed

Random number seed

##### m_trainInstances

    Instances m_trainInstances

The training instances

### Class weka.attributeSelection.PrincipalComponents

class PrincipalComponents extends UnsupervisedAttributeEvaluator implements Serializable

serialVersionUID:
  -3675307197777734007L

#### Serialized Fields

##### m_attributeFilter

    Remove m_attributeFilter

##### m_center

    boolean m_center

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

##### m_centerFilter

    Center m_centerFilter

##### m_classIndex

    int m_classIndex

Class index

##### m_correlation

    double[][] m_correlation

Correlation/covariance matrix for the original data

##### m_coverVariance

    double m_coverVariance

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

##### m_eigenvalues

    double[] m_eigenvalues

Eigenvalues for the corresponding eigenvectors

##### m_eigenvectors

    double[][] m_eigenvectors

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

##### m_eTranspose

    double[][] m_eTranspose

holds the transposed eigenvectors for converting back to the original space

##### m_hasClass

    boolean m_hasClass

Data has a class set

##### m_maxAttrsInName

    int m_maxAttrsInName

maximum number of attributes in the transformed attribute name

##### m_means

    double[] m_means

##### m_nominalToBinFilter

    NominalToBinary m_nominalToBinFilter

##### m_numAttribs

    int m_numAttribs

Number of attributes

##### m_numInstances

    int m_numInstances

Number of instances

##### m_originalSpaceFormat

    Instances m_originalSpaceFormat

The header for data transformed back to the original space

##### m_outputNumAtts

    int m_outputNumAtts

The number of attributes in the pc transformed data

##### m_replaceMissingFilter

    ReplaceMissingValues m_replaceMissingFilter

Filters for original data

##### m_sortedEigens

    int[] m_sortedEigens

Sorted eigenvalues

##### m_standardizeFilter

    Standardize m_standardizeFilter

##### m_stdDevs

    double[] m_stdDevs

##### m_sumOfEigenValues

    double m_sumOfEigenValues

sum of the eigenvalues

##### m_trainHeader

    Instances m_trainHeader

Keep a copy for the class attribute (if set)

##### m_trainInstances

    Instances m_trainInstances

The data to transform analyse/transform

##### m_transBackToOriginal

    boolean m_transBackToOriginal

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

##### m_transformedFormat

    Instances m_transformedFormat

The header for the transformed data format

### Class weka.attributeSelection.RaceSearch

class RaceSearch extends ASSearch implements Serializable

serialVersionUID:
  4015453851212985720L

#### Serialized Fields

##### m_ASEval

    ASEvaluation m_ASEval

the attribute evaluator to generate the initial ranking when doing a rank race

##### m_bestMerit

    double m_bestMerit

holds the merit of the best subset found

##### m_calculatedNumToSelect

    int m_calculatedNumToSelect

##### m_classIndex

    int m_classIndex

the class index

##### m_debug

    boolean m_debug

verbose output for monitoring the search and debugging

##### m_delta

    double m_delta

threshold for comparisons

##### m_Instances

    Instances m_Instances

the training instances

##### m_numAttribs

    int m_numAttribs

the number of attributes in the data

##### m_numFolds

    int m_numFolds

number of cross validation folds---equal to the number of instances for leave-one-out cv

##### m_numToSelect

    int m_numToSelect

The number of attributes to retain if a ranking is requested. -1 indicates that all attributes are to be retained. Has precedence over m_threshold

##### m_raceType

    int m_raceType

the selected search type

##### m_rankedAtts

    double[][] m_rankedAtts

The ranked list of attributes produced if m_rankingRequested is true

##### m_rankedSoFar

    int m_rankedSoFar

The number of attributes ranked so far (if ranking is requested)

##### m_Ranking

    int[] m_Ranking

will hold the attribute ranking produced by the above attribute evaluator if doing a rank search

##### m_rankingRequested

    boolean m_rankingRequested

If true then produce a ranked list of attributes by fully traversing a forward hillclimb race

##### m_samples

    int m_samples

the number of samples above which to begin testing for similarity between competing subsets

##### m_sigLevel

    double m_sigLevel

the significance level for comparisons

##### m_theEvaluator

    HoldOutSubsetEvaluator m_theEvaluator

the subset evaluator to use

##### m_threshold

    double m_threshold

the threshold for removing attributes if ranking is requested

##### m_totalEvals

    int m_totalEvals

the total number of partially/fully evaluated subsets

##### m_xvalType

    int m_xvalType

the selected xval type

### Class weka.attributeSelection.RandomSearch

class RandomSearch extends ASSearch implements Serializable

serialVersionUID:
  7479392617377425484L

#### Serialized Fields

##### m_bestGroup

    BitSet m_bestGroup

the best feature set found during the search

##### m_bestMerit

    double m_bestMerit

the merit of the best subset found

##### m_classIndex

    int m_classIndex

holds the class index

##### m_hasClass

    boolean m_hasClass

does the data have a class

##### m_iterations

    int m_iterations

the number of iterations performed

##### m_numAttribs

    int m_numAttribs

number of attributes in the data

##### m_onlyConsiderBetterAndSmaller

    boolean m_onlyConsiderBetterAndSmaller

only accept a feature set as being "better" than the best if its merit is better or equal to the best, and it contains as many or fewer features than the best (this allows LVF to be implemented).

##### m_random

    Random m_random

random number object

##### m_searchSize

    double m_searchSize

percentage of the search space to consider

##### m_seed

    int m_seed

seed for random number generation

##### m_starting

    int[] m_starting

holds a starting set as an array of attributes.

##### m_startRange

    Range m_startRange

holds the start set as a range

##### m_verbose

    boolean m_verbose

output new best subsets as the search progresses

### Class weka.attributeSelection.Ranker

class Ranker extends ASSearch implements Serializable

serialVersionUID:
  -9086714848510751934L

#### Serialized Fields

##### m_attributeList

    int[] m_attributeList

Holds the ordered list of attributes

##### m_attributeMerit

    double[] m_attributeMerit

Holds the list of attribute merit scores

##### m_calculatedNumToSelect

    int m_calculatedNumToSelect

Used to compute the number to select

##### m_classIndex

    int m_classIndex

Class index of the data if supervised evaluator

##### m_hasClass

    boolean m_hasClass

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

##### m_numAttribs

    int m_numAttribs

The number of attribtes

##### m_numToSelect

    int m_numToSelect

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

##### m_starting

    int[] m_starting

Holds the starting set as an array of attributes

##### m_startRange

    Range m_startRange

Holds the start set for the search as a range

##### m_threshold

    double m_threshold

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

### Class weka.attributeSelection.RankSearch

class RankSearch extends ASSearch implements Serializable

serialVersionUID:
  -7992268736874353755L

#### Serialized Fields

##### m_add

    int m_add

add this many attributes in each iteration from the ranking

##### m_ASEval

    ASEvaluation m_ASEval

the attribute evaluator to use for generating the ranking

##### m_best_group

    BitSet m_best_group

the best subset found

##### m_bestMerit

    double m_bestMerit

the merit of the best subset found

##### m_classIndex

    int m_classIndex

holds the class index

##### m_hasClass

    boolean m_hasClass

does the data have a class

##### m_Instances

    Instances m_Instances

the training instances

##### m_numAttribs

    int m_numAttribs

number of attributes in the data

##### m_Ranking

    int[] m_Ranking

will hold the attribute ranking

##### m_startPoint

    int m_startPoint

start from this point in the ranking

##### m_SubsetEval

    ASEvaluation m_SubsetEval

the subset evaluator with which to evaluate the ranking

### Class weka.attributeSelection.ReliefFAttributeEval

class ReliefFAttributeEval extends ASEvaluation implements Serializable

serialVersionUID:
  -8422186665795839379L

#### Serialized Fields

##### m_classIndex

    int m_classIndex

The class index

##### m_classProbs

    double[] m_classProbs

Prior class probabilities (discrete class case)

##### m_index

    int[] m_index

Index in the m_karray of the farthest instance for each class

##### m_karray

    double[][][] m_karray

k nearest scores + instance indexes for n classes

##### m_Knn

    int m_Knn

The number of nearest hits/misses

##### m_maxArray

    double[] m_maxArray

Upper bound for numeric attributes

##### m_minArray

    double[] m_minArray

Lower bound for numeric attributes

##### m_nda

    double[] m_nda

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

##### m_ndc

    double m_ndc

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

##### m_ndcda

    double[] m_ndcda

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

##### m_numAttribs

    int m_numAttribs

The number of attributes

##### m_numClasses

    int m_numClasses

The number of classes if class is nominal

##### m_numericClass

    boolean m_numericClass

Numeric class

##### m_numInstances

    int m_numInstances

The number of instances

##### m_sampleM

    int m_sampleM

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

##### m_seed

    int m_seed

Random number seed used for sampling instances

##### m_sigma

    int m_sigma

##### m_stored

    int[] m_stored

Number of nearest neighbours stored of each class

##### m_trainInstances

    Instances m_trainInstances

The training instances

##### m_weightByDistance

    boolean m_weightByDistance

Weight by distance rather than equal weights

##### m_weights

    double[] m_weights

Holds the weights that relief assigns to attributes

##### m_weightsByRank

    double[] m_weightsByRank

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

##### m_worst

    double[] m_worst

Keep track of the farthest instance for each class

### Class weka.attributeSelection.ScatterSearchV1

class ScatterSearchV1 extends ASSearch implements Serializable

serialVersionUID:
  -8512041420388121326L

#### Serialized Fields

##### ASEvaluator

    SubsetEvaluator ASEvaluator

Evaluator used to know the significance of a subset (for guiding the search)

##### m_attributeRanking

    List<ScatterSearchV1.Subset> m_attributeRanking

holds the attributes ranked

##### m_bestMerit

    double m_bestMerit

holds the merit of the best subset found

##### m_calculatedInitialPopSize

    int m_calculatedInitialPopSize

if no initial user pop size, then this holds the initial pop size calculated from the number of attributes in the data (for use in the toString() method)

##### m_classIndex

    int m_classIndex

holds the class index

##### m_debug

    boolean m_debug

verbose output for monitoring the search and debugging

##### m_InformationReports

    StringBuffer m_InformationReports

holds a report of the search

##### m_initialPopSize

    int m_initialPopSize

holds the user selected initial population size

##### m_initialThreshold

    double m_initialThreshold

the initial threshold

##### m_numAttribs

    int m_numAttribs

number of attributes in the data

##### m_popSize

    int m_popSize

holds the population size

##### m_population

    List<ScatterSearchV1.Subset> m_population

holds the Initial Population of Subsets

##### m_processinTime

    long m_processinTime

time for procesing the search method

##### m_random

    Random m_random

random number generation

##### m_seed

    int m_seed

seed for random number generation

##### m_totalEvals

    int m_totalEvals

total number of subsets evaluated during a search

##### m_treshold

    double m_treshold

holds the treshhold that delimits the good attributes

##### m_typeOfCombination

    int m_typeOfCombination

the kind of comination betwen parents ((0)greedy combination/(1)reduced greedy combination)

### Class weka.attributeSelection.ScatterSearchV1.Subset

class Subset extends Object implements Serializable

#### Serialized Fields

##### merit

    double merit

##### subset

    BitSet subset

### Class weka.attributeSelection.SubsetSizeForwardSelection

class SubsetSizeForwardSelection extends ASSearch implements Serializable

#### Serialized Fields

##### m_bestMerit

    double m_bestMerit

holds the merit of the best subset found

##### m_cacheSize

    int m_cacheSize

holds the maximum size of the lookup cache for evaluated subsets

##### m_linearSelectionType

    int m_linearSelectionType

0 == fixed-set, 1 == fixed-width

##### m_numAttribs

    int m_numAttribs

number of attributes in the data

##### m_numFolds

    int m_numFolds

Number of cross validation folds for subset size determination (default = 5).

##### m_numUsedAttributes

    int m_numUsedAttributes

number of top-ranked attributes that are taken into account for the search

##### m_performRanking

    boolean m_performRanking

perform initial ranking to select top-ranked attributes

##### m_seed

    int m_seed

Seed for cross validation subset size determination. (default = 1)

##### m_setSizeEval

    ASEvaluation m_setSizeEval

the subset evaluator to use for subset size determination

##### m_totalEvals

    int m_totalEvals

total number of subsets evaluated during a search

##### m_verbose

    boolean m_verbose

for debugging

### Class weka.attributeSelection.SVMAttributeEval

class SVMAttributeEval extends ASEvaluation implements Serializable

serialVersionUID:
  -6489975709033967447L

#### Serialized Fields

##### m_attScores

    double[] m_attScores

The attribute scores

##### m_numToEliminate

    int m_numToEliminate

Constant rate of attribute elimination per iteration

##### m_percentThreshold

    int m_percentThreshold

Threshold below which percent elimination switches to constant elimination

##### m_percentToEliminate

    int m_percentToEliminate

Percentage rate of attribute elimination, trumps constant rate (above threshold), ignored if = 0

##### m_smoCParameter

    double m_smoCParameter

Complexity parameter to pass on to SMO

##### m_smoFilterType

    int m_smoFilterType

Filter parameter to pass on to SMO

##### m_smoPParameter

    double m_smoPParameter

Epsilon parameter to pass on to SMO

##### m_smoTParameter

    double m_smoTParameter

Tolerance parameter to pass on to SMO

### Class weka.attributeSelection.SymmetricalUncertAttributeEval

class SymmetricalUncertAttributeEval extends ASEvaluation implements Serializable

serialVersionUID:
  -8096505776132296416L

#### Serialized Fields

##### m_classIndex

    int m_classIndex

The class index

##### m_missing_merge

    boolean m_missing_merge

Treat missing values as a seperate value

##### m_numAttribs

    int m_numAttribs

The number of attributes

##### m_numClasses

    int m_numClasses

The number of classes

##### m_numInstances

    int m_numInstances

The number of instances

##### m_trainInstances

    Instances m_trainInstances

The training instances

### Class weka.attributeSelection.UnsupervisedAttributeEvaluator

class UnsupervisedAttributeEvaluator extends ASEvaluation implements Serializable

serialVersionUID:
  -4100897318675336178L

### Class weka.attributeSelection.UnsupervisedSubsetEvaluator

class UnsupervisedSubsetEvaluator extends ASEvaluation implements Serializable

serialVersionUID:
  627934376267488763L

### Class weka.attributeSelection.WrapperSubsetEval

class WrapperSubsetEval extends ASEvaluation implements Serializable

serialVersionUID:
  -4573057658746728675L

#### Serialized Fields

##### m_BaseClassifier

    Classifier m_BaseClassifier

holds the base classifier object

##### m_classIndex

    int m_classIndex

class index

##### m_Evaluation

    Evaluation m_Evaluation

holds an evaluation object

##### m_folds

    int m_folds

number of folds to use for cross validation

##### m_numAttribs

    int m_numAttribs

number of attributes in the training data

##### m_numInstances

    int m_numInstances

number of instances in the training data

##### m_seed

    int m_seed

random number seed

##### m_threshold

    double m_threshold

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

##### m_trainInstances

    Instances m_trainInstances

training instances

## Package weka.classifiers

### Class weka.classifiers.Classifier

class Classifier extends Object implements Serializable

serialVersionUID:
  6502780192411755341L

#### Serialized Fields

##### m_Debug

    boolean m_Debug

Whether the classifier is run in debug mode.

### Class weka.classifiers.CostMatrix

class CostMatrix extends Object implements Serializable

serialVersionUID:
  -1973792250544554965L

#### Serialized Fields

##### m_matrix

    Object[][] m_matrix

[rows][columns]

##### m_size

    int m_size

### Class weka.classifiers.IteratedSingleClassifierEnhancer

class 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

class MultipleClassifiersCombiner extends Classifier implements Serializable

serialVersionUID:
  2776436621129422119L

#### Serialized Fields

##### m_Classifiers

    Classifier[] m_Classifiers

Array for storing the generated base classifiers.

### Class weka.classifiers.RandomizableClassifier

class RandomizableClassifier extends Classifier implements Serializable

serialVersionUID:
  -8816375798262351903L

#### Serialized Fields

##### m_Seed

    int m_Seed

The random number seed.

### Class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

class RandomizableIteratedSingleClassifierEnhancer extends IteratedSingleClassifierEnhancer implements Serializable

serialVersionUID:
  5063351391524938557L

#### Serialized Fields

##### m_Seed

    int m_Seed

The random number seed.

### Class weka.classifiers.RandomizableMultipleClassifiersCombiner

class RandomizableMultipleClassifiersCombiner extends MultipleClassifiersCombiner implements Serializable

serialVersionUID:
  5057936555724785679L

#### Serialized Fields

##### m_Seed

    int m_Seed

The random number seed.

### Class weka.classifiers.RandomizableSingleClassifierEnhancer

class RandomizableSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

serialVersionUID:
  558286687096157160L

#### Serialized Fields

##### m_Seed

    int m_Seed

The random number seed.

### Class weka.classifiers.SingleClassifierEnhancer

class SingleClassifierEnhancer extends Classifier implements Serializable

serialVersionUID:
  -3665885256363525164L

#### Serialized Fields

##### m_Classifier

    Classifier m_Classifier

The base classifier to use

## Package weka.classifiers.bayes

### Class weka.classifiers.bayes.AODE

class AODE extends Classifier implements Serializable

serialVersionUID:
  9197439980415113523L

#### Serialized Fields

##### m_ClassCounts

    double[] m_ClassCounts

The number of times each class value occurs in the dataset

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_CondiCounts

    double[][][] m_CondiCounts

3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts, i.e., the number of times an attribute value occurs in conjunction with another attribute value and a class value.

##### m_Debug

    boolean m_Debug

If true, outputs debugging info

##### m_Frequencies

    double[] m_Frequencies

The frequency of each attribute value for the dataset

##### m_Instances

    Instances m_Instances

The dataset

##### m_Limit

    int m_Limit

An att's frequency must be this value or more to be a superParent

##### m_MEstimates

    boolean m_MEstimates

flag for using m-estimates

##### m_NumAttributes

    int m_NumAttributes

The number of attributes in dataset, including class

##### m_NumAttValues

    int[] m_NumAttValues

The number of values for each attribute

##### m_NumClasses

    int m_NumClasses

The number of classes

##### m_NumInstances

    int m_NumInstances

The number of instances in the dataset

##### m_StartAttIndex

    int[] m_StartAttIndex

The starting index (in the m_CondiCounts matrix) of the values for each attribute

##### m_SumForCounts

    double[][] m_SumForCounts

The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal]

##### m_SumInstances

    double m_SumInstances

The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances.

##### m_TotalAttValues

    int m_TotalAttValues

The total number of values (including an extra for each attribute's missing value, which are included in m_CondiCounts) for all attributes (not including class). E.g., for three atts each with two possible values, m_TotalAttValues would be 9 (6 values + 3 missing). This variable is used when allocating space for m_CondiCounts matrix.

##### m_Weight

    int m_Weight

value for m in m-estimate

### Class weka.classifiers.bayes.AODEsr

class AODEsr extends Classifier implements Serializable

serialVersionUID:
  5602143019183068848L

#### Serialized Fields

##### m_ClassCounts

    double[] m_ClassCounts

The number of times each class value occurs in the dataset

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_CondiCounts

    double[][][] m_CondiCounts

3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts, i.e. the number of times an attribute value occurs in conjunction with another attribute value and a class value.

##### m_CondiCountsNoClass

    double[][] m_CondiCountsNoClass

2D array (m_TotalAttValues * m_TotalAttValues) of attributes counts. similar to m_CondiCounts, but ignoring class value.

##### m_Critical

    int m_Critical

the critical value for the specialization-generalization

##### m_Debug

    boolean m_Debug

If true, outputs debugging info

##### m_Frequencies

    double[] m_Frequencies

The frequency of each attribute value for the dataset

##### m_Instances

    Instances m_Instances

The dataset

##### m_Laplace

    boolean m_Laplace

Using LapLace estimation or not

##### m_Limit

    int m_Limit

An att's frequency must be this value or more to be a superParent

##### m_MWeight

    double m_MWeight

m value for m-estimation

##### m_NumAttributes

    int m_NumAttributes

The number of attributes in dataset, including class

##### m_NumAttValues

    int[] m_NumAttValues

The number of values for each attribute

##### m_NumClasses

    int m_NumClasses

The number of classes

##### m_NumInstances

    int m_NumInstances

The number of instances in the dataset

##### m_StartAttIndex

    int[] m_StartAttIndex

The starting index (in the m_CondiCounts matrix) of the values for each attribute

##### m_SumForCounts

    double[][] m_SumForCounts

The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal]

##### m_SumInstances

    double m_SumInstances

The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances.

##### m_TotalAttValues

    int m_TotalAttValues

The total number of values (including an extra for each attribute's missing value, which are included in m_CondiCounts) for all attributes (not including class). Eg. for three atts each with two possible values, m_TotalAttValues would be 9 (6 values + 3 missing). This variable is used when allocating space for m_CondiCounts matrix.

### Class weka.classifiers.bayes.BayesianLogisticRegression

class BayesianLogisticRegression extends Classifier implements Serializable

serialVersionUID:
  -8013478897911757631L

#### Serialized Fields

##### BetaVector

    double[] BetaVector

Array for storing coefficients of Bayesian regression model.

##### Change

    double Change

This variable is used to keep track of change in the value of delta summation of r(i).

##### ClassIndex

    int ClassIndex

The class index from the training data

##### debug

    boolean debug

DEBUG Mode

##### Delta

    double[] Delta

Trust Region Radius

##### DeltaBeta

    double[] DeltaBeta

Array to store Regression Coefficient updates.

##### DeltaR

    double[] DeltaR

This vector is used to store the increments on the R(i). It is also used to determining the stopping criterion.

##### DeltaUpdate

    double[] DeltaUpdate

Trust Region Radius Update

##### HyperparameterRange

    String HyperparameterRange

CV Hyperparameter Range

##### Hyperparameters

    double[] Hyperparameters

Array to store Hyperparameter values for each feature.

##### HyperparameterSelection

    int HyperparameterSelection

Hyperparameter selection method

##### HyperparameterValue

    double HyperparameterValue

Best hyperparameter for test phase

##### iterationCounter

    int iterationCounter

Iteration counter

##### m_Filter

    Filter m_Filter

Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object

##### m_Instances

    Instances m_Instances

Dataset provided to do Training/Test set.

##### m_PriorUpdate

    Prior m_PriorUpdate

Prior class object interface

##### m_seed

    int m_seed

seed for randomizing the instances before CV

##### maxIterations

    int maxIterations

Maximum number of iterations

##### NormalizeData

    boolean NormalizeData

Choose whether to normalize data or not

##### NumFolds

    int NumFolds

NumFolds for CV based Hyperparameters selection

##### PriorClass

    int PriorClass

Distribution Prior class

##### R

    double[] R

R(i)= BetaVector X x(i) X y(i). This an intermediate value with respect to vector BETA, input values and corresponding class labels

##### Threshold

    double Threshold

Threshold for binary classification of probabilisitic estimate

##### Tolerance

    double Tolerance

Tolerance criteria for the stopping criterion.

### Class weka.classifiers.bayes.BayesNet

class BayesNet extends Classifier implements Serializable

serialVersionUID:
  746037443258775954L

#### Serialized Fields

##### m_ADTree

    ADNode m_ADTree

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

##### m_BayesNetEstimator

    BayesNetEstimator m_BayesNetEstimator

Search algorithm used for learning the structure of a network.

##### m_bUseADTree

    boolean m_bUseADTree

Use the experimental ADTree datastructure for calculating contingency tables

##### m_DiscretizeFilter

    Discretize m_DiscretizeFilter

filter used to quantize continuous variables, if any

##### m_Distributions

    Estimator[][] m_Distributions

The attribute estimators containing CPTs.

##### m_Instances

    Instances m_Instances

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

##### m_MissingValuesFilter

    ReplaceMissingValues m_MissingValuesFilter

filter used to fill in missing values, if any

##### m_nNonDiscreteAttribute

    int m_nNonDiscreteAttribute

attribute index of a non-nominal attribute

##### m_NumClasses

    int m_NumClasses

The number of classes

##### m_otherBayesNet

    BIFReader m_otherBayesNet

Bayes network to compare the structure with.

##### m_ParentSets

    ParentSet[] m_ParentSets

The parent sets.

##### m_SearchAlgorithm

    SearchAlgorithm m_SearchAlgorithm

Search algorithm used for learning the structure of a network.

### Class weka.classifiers.bayes.ComplementNaiveBayes

class ComplementNaiveBayes extends Classifier implements Serializable

serialVersionUID:
  7246302925903086397L

#### Serialized Fields

##### header

    Instances header

The instances header that'll be used in toString

##### m_normalizeWordWeights

    boolean m_normalizeWordWeights

True if the words weights are to be normalized

##### numClasses

    int numClasses

Holds the number of Class values present in the set of specified instances

##### smoothingParameter

    double smoothingParameter

Holds the smoothing value to avoid word probabilities of zero.
P.S.: According to the paper this is the Alpha i parameter

##### wordWeights

    double[][] wordWeights

Weight of words for each class. The weight is actually the log of the probability of a word (w) given a class (c) (i.e. log(Pr[w|c])). The format of the matrix is: wordWeights[class][wordAttribute]

### Class weka.classifiers.bayes.DMNBtext

class DMNBtext extends Classifier implements Serializable

serialVersionUID:
  5932177450183457085L

#### Serialized Fields

##### m_binaryClassifiers

    DMNBtext.DNBBinary[] m_binaryClassifiers

##### m_headerInfo

    Instances m_headerInfo

##### m_MultinomialWord

    boolean m_MultinomialWord

##### m_numClasses

    int m_numClasses

##### m_NumIterations

    int m_NumIterations

The number of iterations.

### Class weka.classifiers.bayes.DMNBtext.DNBBinary

class DNBBinary extends Object implements Serializable

#### Serialized Fields

##### m_classDistribution

    double[] m_classDistribution

##### m_classIndex

    int m_classIndex

##### m_classRatio

    double m_classRatio

##### m_coefficient

    double[] m_coefficient

##### m_numAttributes

    int m_numAttributes

number of unique words

##### m_perWordPerClass

    double[][] m_perWordPerClass

The number of iterations.

##### m_targetClass

    int m_targetClass

##### m_WordLaplace

    double m_WordLaplace

##### m_wordRatio

    double m_wordRatio

##### m_wordsPerClass

    double[] m_wordsPerClass

### Class weka.classifiers.bayes.HNB

class HNB extends Classifier implements Serializable

serialVersionUID:
  -4503874444306113214L

#### Serialized Fields

##### m_ClassAttAttCounts

    double[][][] m_ClassAttAttCounts

The number of class and two attributes values occurs in the dataset

##### m_ClassCounts

    double[] m_ClassCounts

The number of each class value occurs in the dataset

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute in the dataset

##### m_condiMutualInfo

    double[][] m_condiMutualInfo

The 2D array of conditional mutual information of each pair attributes

##### m_NumAttributes

    int m_NumAttributes

The number of attributes including class in the dataset

##### m_NumAttValues

    int[] m_NumAttValues

The number of values for each attribute in the dataset

##### m_NumClasses

    int m_NumClasses

The number of classes in the dataset

##### m_NumInstances

    int m_NumInstances

The number of instances in the dataset

##### m_StartAttIndex

    int[] m_StartAttIndex

The starting index of each attribute in the dataset

##### m_TotalAttValues

    int m_TotalAttValues

The number of values for all attributes in the dataset

### Class weka.classifiers.bayes.NaiveBayes

class NaiveBayes extends Classifier implements Serializable

serialVersionUID:
  5995231201785697655L

#### Serialized Fields

##### m_ClassDistribution

    Estimator m_ClassDistribution

The class estimator.

##### m_Disc

    Discretize m_Disc

The discretization filter.

##### m_displayModelInOldFormat

    boolean m_displayModelInOldFormat

##### m_Distributions

    Estimator[][] m_Distributions

The attribute estimators.

##### m_Instances

    Instances m_Instances

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

##### m_NumClasses

    int m_NumClasses

The number of classes (or 1 for numeric class)

##### m_UseDiscretization

    boolean m_UseDiscretization

Whether to use discretization than normal distribution for numeric attributes

##### m_UseKernelEstimator

    boolean m_UseKernelEstimator

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

### Class weka.classifiers.bayes.NaiveBayesMultinomial

class NaiveBayesMultinomial extends Classifier implements Serializable

serialVersionUID:
  5932177440181257085L

#### Serialized Fields

##### m_headerInfo

    Instances m_headerInfo

copy of header information for use in toString method

##### m_lnFactorialCache

    double[] m_lnFactorialCache

cache lnFactorial computations

##### m_numAttributes

    int m_numAttributes

number of unique words

##### m_numClasses

    int m_numClasses

number of class values

##### m_probOfClass

    double[] m_probOfClass

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

##### m_probOfWordGivenClass

    double[][] m_probOfWordGivenClass

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

### Class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable

class NaiveBayesMultinomialUpdateable extends NaiveBayesMultinomial implements Serializable

serialVersionUID:
  -7204398796974263186L

#### Serialized Fields

##### m_wordsPerClass

    double[] m_wordsPerClass

the word count per class

### Class weka.classifiers.bayes.NaiveBayesSimple

class NaiveBayesSimple extends Classifier implements Serializable

serialVersionUID:
  -1478242251770381214L

#### Serialized Fields

##### m_Counts

    double[][][] m_Counts

All the counts for nominal attributes.

##### m_Devs

    double[][] m_Devs

The standard deviations for numeric attributes.

##### m_Instances

    Instances m_Instances

The instances used for training.

##### m_Means

    double[][] m_Means

The means for numeric attributes.

##### m_Priors

    double[] m_Priors

The prior probabilities of the classes.

### Class weka.classifiers.bayes.NaiveBayesUpdateable

class NaiveBayesUpdateable extends NaiveBayes implements Serializable

serialVersionUID:
  -5354015843807192221L

### Class weka.classifiers.bayes.WAODE

class WAODE extends Classifier implements Serializable

serialVersionUID:
  2170978824284697882L

#### Serialized Fields

##### m_AttAttCounts

    double[][] m_AttAttCounts

The number of two attributes values occurs in the dataset

##### m_AttCounts

    double[] m_AttCounts

The number of each attribute value occurs in the dataset

##### m_ClassAttAttCounts

    double[][][] m_ClassAttAttCounts

The number of class and two attributes values occurs in the dataset

##### m_ClassCounts

    double[] m_ClassCounts

The number of each class value occurs in the dataset

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute in the dataset

##### m_Header

    Instances m_Header

the header information of the training data

##### m_Internals

    boolean m_Internals

whether to print more internals in the toString method

See Also:
  - `WAODE.toString()`

##### m_mutualInformation

    double[] m_mutualInformation

The array of mutual information between each attribute and class

##### m_NumAttributes

    int m_NumAttributes

The number of attributes including class in the dataset

##### m_NumAttValues

    int[] m_NumAttValues

The number of values for each attribute in the dataset

##### m_NumClasses

    int m_NumClasses

The number of classes in the dataset

##### m_NumInstances

    int m_NumInstances

The number of instances in the dataset

##### m_StartAttIndex

    int[] m_StartAttIndex

The starting index of each attribute in the dataset

##### m_TotalAttValues

    int m_TotalAttValues

The number of values for all attributes in the dataset

##### m_ZeroR

    Classifier m_ZeroR

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

## Package weka.classifiers.bayes.blr

### Class weka.classifiers.bayes.blr.GaussianPriorImpl

class GaussianPriorImpl extends Prior implements Serializable

serialVersionUID:
  -2995684220141159223L

### Class weka.classifiers.bayes.blr.LaplacePriorImpl

class LaplacePriorImpl extends Prior implements Serializable

serialVersionUID:
  2353576123257012607L

#### Serialized Fields

##### Beta

    double Beta

##### Delta

    double Delta

##### DeltaUpdate

    double DeltaUpdate

##### Hyperparameter

    double Hyperparameter

##### m_Instances

    Instances m_Instances

##### R

    double[] R

### Class weka.classifiers.bayes.blr.Prior

class Prior extends Object implements Serializable

#### Serialized Fields

##### Beta

    double Beta

##### Delta

    double Delta

##### DeltaUpdate

    double DeltaUpdate

##### Hyperparameter

    double Hyperparameter

##### log_likelihood

    double log_likelihood

##### log_posterior

    double log_posterior

##### m_Instances

    Instances m_Instances

##### penalty

    double penalty

##### R

    double[] R

## Package weka.classifiers.bayes.net

### Class weka.classifiers.bayes.net.ADNode

class ADNode extends Object implements Serializable

serialVersionUID:
  397409728366910204L

#### Serialized Fields

##### m_Instances

    Instance[] m_Instances

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

##### m_nCount

    int m_nCount

count

##### m_nStartNode

    int m_nStartNode

first node in VaryNode array

##### m_VaryNodes

    VaryNode[] m_VaryNodes

list of VaryNode children

### Class weka.classifiers.bayes.net.BayesNetGenerator

class BayesNetGenerator extends EditableBayesNet implements Serializable

serialVersionUID:
  -7462571170596157720L

#### Serialized Fields

##### m_bGenerateNet

    boolean m_bGenerateNet

##### m_nCardinality

    int m_nCardinality

##### m_nNrOfArcs

    int m_nNrOfArcs

##### m_nNrOfInstances

    int m_nNrOfInstances

##### m_nNrOfNodes

    int m_nNrOfNodes

##### m_nSeed

    int m_nSeed

the seed value

##### m_sBIFFile

    String m_sBIFFile

##### random

    Random random

the random number generator

### Class weka.classifiers.bayes.net.BIFReader

class BIFReader extends BayesNet implements Serializable

serialVersionUID:
  -8358864680379881429L

#### Serialized Fields

##### m_nPositionX

    int[] m_nPositionX

##### m_nPositionY

    int[] m_nPositionY

##### m_order

    int[] m_order

##### m_sFile

    String m_sFile

the current filename

### Class weka.classifiers.bayes.net.EditableBayesNet

class EditableBayesNet extends BayesNet implements Serializable

serialVersionUID:
  746037443258735954L

#### Serialized Fields

##### m_bNeedsUndoAction

    boolean m_bNeedsUndoAction

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

##### m_fMarginP

    FastVector m_fMarginP

marginal distributions *

##### m_nCurrentEditAction

    int m_nCurrentEditAction

current action in undo stack

##### m_nEvidence

    FastVector m_nEvidence

evidence values, used for evidence propagation *

##### m_nPositionX

    FastVector m_nPositionX

location of nodes, used for graph drawing *

##### m_nPositionY

    FastVector m_nPositionY

##### m_nSavedPointer

    int m_nSavedPointer

action that the network is saved

##### m_undoStack

    FastVector m_undoStack

undo stack for undoin edit actions, or redo edit actions

### Class weka.classifiers.bayes.net.GUI

class GUI extends JPanel implements Serializable

serialVersionUID:
  -2038911085935515624L

#### Serialized Fields

##### a_about

    Action a_about

##### a_addarc

    Action a_addarc

##### a_addnode

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

##### a_alignbottom

    Action a_alignbottom

##### a_alignleft

    Action a_alignleft

##### a_alignright

    Action a_alignright

##### a_aligntop

    Action a_aligntop

##### a_centerhorizontal

    Action a_centerhorizontal

##### a_centervertical

    Action a_centervertical

##### a_copynode

    Action a_copynode

##### a_cutnode

    Action a_cutnode

##### a_datagenerator

    Action a_datagenerator

##### a_datasetter

    Action a_datasetter

##### a_delarc

    Action a_delarc

##### a_delnode

    Action a_delnode

##### a_export

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

##### a_help

    Action a_help

##### a_layout

    Action a_layout

##### a_learn

    Action a_learn

##### a_learnCPT

    Action a_learnCPT

##### a_load

    Action a_load

##### a_networkgenerator

    Action a_networkgenerator

##### a_new

    Action a_new

actions triggered by GUI events

##### a_pastenode

    Action a_pastenode

##### a_print

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

##### a_quit

    Action a_quit

##### a_redo

    Action a_redo

##### a_save

    Action a_save

##### a_saveas

    Action a_saveas

##### a_selectall

    Action a_selectall

##### a_spacehorizontal

    Action a_spacehorizontal

##### a_spacevertical

    Action a_spacevertical

##### a_undo

    Action a_undo

##### a_viewstatusbar

    Action a_viewstatusbar

##### a_viewtoolbar

    Action a_viewtoolbar

##### a_zoomin

    Action a_zoomin

##### a_zoomout

    Action a_zoomout

##### ICONPATH

    String ICONPATH

path for icons

##### m_BayesNet

    EditableBayesNet m_BayesNet

Container of Bayesian network

##### m_bViewCliques

    boolean m_bViewCliques

##### m_bViewMargins

    boolean m_bViewMargins

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

##### m_clipboard

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

##### m_fScale

    double m_fScale

current zoom value

##### m_GraphPanel

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

Panel actually displaying the graph

##### m_Instances

    Instances m_Instances

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

##### m_jScrollPane

    JScrollPane m_jScrollPane

this contains the m_GraphPanel GraphPanel

##### m_jStatusBar

    JLabel m_jStatusBar

status bar at bottom of window

##### m_jTbTools

    JToolBar m_jTbTools

toolbar containing buttons at top of window

##### m_jTfNodeHeight

    JTextField m_jTfNodeHeight

TextField for nodes height

##### m_jTfNodeWidth

    JTextField m_jTfNodeWidth

TextField for node's width

##### m_jTfZoom

    JTextField m_jTfZoom

Text field for specifying zoom

##### m_layoutEngine

    LayoutEngine m_layoutEngine

The current LayoutEngine

##### m_marginCalculator

    MarginCalculator m_marginCalculator

used for calculating marginals in Bayesian netwowrks

##### m_marginCalculatorWithEvidence

    MarginCalculator m_marginCalculatorWithEvidence

used for calculating marginals in Bayesian netwowrks when evidence is present

##### m_menuBar

    JMenuBar m_menuBar

The menu bar

##### m_nCurrentNode

    int m_nCurrentNode

node currently selected through right clicking

##### m_nNodeHeight

    int m_nNodeHeight

standard width of node

##### m_nNodeWidth

    int m_nNodeWidth

##### m_nPaddedNodeWidth

    int m_nPaddedNodeWidth

##### m_nSelectedRect

    Rectangle m_nSelectedRect

selection rectangle drawn through dragging with left mouse button

##### m_nZoomPercents

    int[] m_nZoomPercents

used when using zoomIn and zoomOut buttons

##### m_Selection

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

selection of nodes

##### m_sFileName

    String m_sFileName

String containing file name storing current network

### Class weka.classifiers.bayes.net.MarginCalculator

class MarginCalculator extends Object implements Serializable

serialVersionUID:
  650278019241175534L

#### Serialized Fields

##### jtNodes

    MarginCalculator.JunctionTreeNode[] jtNodes

##### m_debug

    boolean m_debug

##### m_Margins

    double[][] m_Margins

##### m_root

    MarginCalculator.JunctionTreeNode m_root

### Class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode

class JunctionTreeNode extends Object implements Serializable

serialVersionUID:
  650278019241175536L

#### Serialized Fields

##### m_bayesNet

    BayesNet m_bayesNet

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

##### m_children

    Vector m_children

##### m_fi

    double[] m_fi

potentials for first network

##### m_MarginalP

    double[][] m_MarginalP

##### m_nCardinality

    int m_nCardinality

cardinality of the instances of variables in this junction node

##### m_nNodes

    int[] m_nNodes

nodes of the Bayes net in this junction node

##### m_P

    double[] m_P

distribution over this junction node according to first Bayes network

##### m_parentSeparator

    MarginCalculator.JunctionTreeSeparator m_parentSeparator

### Class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator

class JunctionTreeSeparator extends Object implements Serializable

serialVersionUID:
  6502780192411755343L

#### Serialized Fields

##### m_bayesNet

    BayesNet m_bayesNet

##### m_childNode

    MarginCalculator.JunctionTreeNode m_childNode

##### m_fiChild

    double[] m_fiChild

##### m_fiParent

    double[] m_fiParent

##### m_nCardinality

    int m_nCardinality

##### m_nNodes

    int[] m_nNodes

##### m_parentNode

    MarginCalculator.JunctionTreeNode m_parentNode

### Class weka.classifiers.bayes.net.ParentSet

class ParentSet extends Object implements Serializable

serialVersionUID:
  4155021284407181838L

#### Serialized Fields

##### m_nCardinalityOfParents

    int m_nCardinalityOfParents

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

##### m_nNrOfParents

    int m_nNrOfParents

Holds number of parents

##### m_nParents

    int[] m_nParents

Holds indexes of parents

### Class weka.classifiers.bayes.net.VaryNode

class VaryNode extends Object implements Serializable

serialVersionUID:
  -6196294370675872424L

#### Serialized Fields

##### m_ADNodes

    ADNode[] m_ADNodes

list of ADNode children

##### m_iNode

    int m_iNode

index of the node varied

##### m_nMCV

    int m_nMCV

most common value

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

### Class weka.classifiers.bayes.net.estimate.BayesNetEstimator

class BayesNetEstimator extends Object implements Serializable

serialVersionUID:
  2184330197666253884L

#### Serialized Fields

##### m_fAlpha

    double m_fAlpha

Holds prior on count

### Class weka.classifiers.bayes.net.estimate.BMAEstimator

class 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

class DiscreteEstimatorBayes extends Estimator implements Serializable

serialVersionUID:
  4215400230843212684L

#### Serialized Fields

##### m_Counts

    double[] m_Counts

Hold the counts

##### m_fPrior

    double m_fPrior

Holds the prior probability

##### m_nSymbols

    int m_nSymbols

Holds number of symbols in distribution

##### m_SumOfCounts

    double m_SumOfCounts

Hold the sum of counts

### Class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes

class DiscreteEstimatorFullBayes extends DiscreteEstimatorBayes implements Serializable

serialVersionUID:
  6774941981423312133L

### Class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator

class 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

class SimpleEstimator extends BayesNetEstimator implements Serializable

serialVersionUID:
  5874941612331806172L

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

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

class SearchAlgorithm extends Object implements Serializable

serialVersionUID:
  6164792240778525312L

#### Serialized Fields

##### m_bInitAsNaiveBayes

    boolean m_bInitAsNaiveBayes

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

##### m_bMarkovBlanketClassifier

    boolean m_bMarkovBlanketClassifier

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

##### m_nMaxNrOfParents

    int m_nMaxNrOfParents

Holds upper bound on number of parents

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

### Class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm

class 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

class 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

class FromFile extends SearchAlgorithm implements Serializable

serialVersionUID:
  7334358169507619525L

#### Serialized Fields

##### m_sBIFFile

    String m_sBIFFile

name of file to read structure from

### Class weka.classifiers.bayes.net.search.fixed.NaiveBayes

class NaiveBayes extends SearchAlgorithm implements Serializable

serialVersionUID:
  -4808572519709755811L

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

### Class weka.classifiers.bayes.net.search.global.GeneticSearch

class GeneticSearch extends GlobalScoreSearchAlgorithm implements Serializable

serialVersionUID:
  4236165533882462203L

#### Serialized Fields

##### 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_nDescendantPopulationSize

    int m_nDescendantPopulationSize

size of descendant population

##### m_nPopulationSize

    int m_nPopulationSize

size of population

##### m_nRuns

    int m_nRuns

number of runs

##### m_nSeed

    int m_nSeed

random number seed

##### m_random

    Random m_random

random number generator

### Class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm

class 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_nCVType

    int m_nCVType

Holds the cross validation strategy used to measure quality of network

##### m_nNrOfFolds

    int m_nNrOfFolds

number of folds for k-fold cross validation

### Class weka.classifiers.bayes.net.search.global.HillClimber

class 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

class 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

class 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

    Random m_random

random number generator

### Class weka.classifiers.bayes.net.search.global.SimulatedAnnealing

class SimulatedAnnealing extends GlobalScoreSearchAlgorithm implements Serializable

serialVersionUID:
  -5482721887881010916L

#### Serialized Fields

##### m_bUseArcReversal

    boolean m_bUseArcReversal

use the arc reversal operator

##### m_fDelta

    double m_fDelta

change in temperature at every run

##### m_fTStart

    double m_fTStart

start temperature

##### m_nRuns

    int m_nRuns

number of runs

##### m_nSeed

    int m_nSeed

random number seed

##### m_random

    Random m_random

random number generator

### Class weka.classifiers.bayes.net.search.global.TabuSearch

class 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

class TAN extends GlobalScoreSearchAlgorithm implements Serializable

serialVersionUID:
  1715277053980895298L

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

### Class weka.classifiers.bayes.net.search.local.GeneticSearch

class GeneticSearch extends LocalScoreSearchAlgorithm implements Serializable

serialVersionUID:
  -7037070678911459757L

#### Serialized Fields

##### 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_nDescendantPopulationSize

    int m_nDescendantPopulationSize

size of descendant population

##### m_nPopulationSize

    int m_nPopulationSize

size of population

##### m_nRuns

    int m_nRuns

number of runs

##### m_nSeed

    int m_nSeed

random number seed

##### m_random

    Random m_random

random number generator

### Class weka.classifiers.bayes.net.search.local.HillClimber

class HillClimber extends LocalScoreSearchAlgorithm implements Serializable

serialVersionUID:
  4322783593818122403L

#### Serialized Fields

##### m_bUseArcReversal

    boolean m_bUseArcReversal

use the arc reversal operator

##### m_Cache

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

cache for storing score differences

### Class weka.classifiers.bayes.net.search.local.K2

class 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

class LAGDHillClimber extends HillClimber implements Serializable

serialVersionUID:
  7217437499439184344L

#### Serialized Fields

##### m_nNrOfGoodOperations

    int m_nNrOfGoodOperations

Number of Good Operations per Step

##### m_nNrOfLookAheadSteps

    int m_nNrOfLookAheadSteps

Number of Look Ahead Steps

### Class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm

class 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

class 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

    Random m_random

random number generator

### Class weka.classifiers.bayes.net.search.local.SimulatedAnnealing

class SimulatedAnnealing extends LocalScoreSearchAlgorithm implements Serializable

serialVersionUID:
  6951955606060513191L

#### Serialized Fields

##### m_bUseArcReversal

    boolean m_bUseArcReversal

use the arc reversal operator

##### m_fDelta

    double m_fDelta

change in temperature at every run

##### m_fTStart

    double m_fTStart

start temperature

##### m_nRuns

    int m_nRuns

number of runs

##### m_nSeed

    int m_nSeed

random number seed

##### m_random

    Random m_random

random number generator

### Class weka.classifiers.bayes.net.search.local.TabuSearch

class 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

class TAN extends LocalScoreSearchAlgorithm implements Serializable

serialVersionUID:
  965182127977228690L

## Package weka.classifiers.evaluation

### Class weka.classifiers.evaluation.ConfusionMatrix

class ConfusionMatrix extends Matrix implements Serializable

serialVersionUID:
  -181789981401504090L

#### Serialized Fields

##### m_ClassNames

    String[] m_ClassNames

Stores the names of the classes

### Class weka.classifiers.evaluation.NominalPrediction

class NominalPrediction extends Object implements Serializable

serialVersionUID:
  -8871333992740492788L

#### Serialized Fields

##### m_Actual

    double m_Actual

The actual class value

##### m_Distribution

    double[] m_Distribution

The predicted probabilities

##### m_Predicted

    double m_Predicted

The predicted class value

##### m_Weight

    double m_Weight

The weight assigned to this prediction

### Class weka.classifiers.evaluation.NumericPrediction

class NumericPrediction extends Object implements Serializable

serialVersionUID:
  -4880216423674233887L

#### Serialized Fields

##### m_Actual

    double m_Actual

The actual class value

##### m_Predicted

    double m_Predicted

The predicted class value

##### m_Weight

    double m_Weight

The weight assigned to this prediction

## Package weka.classifiers.functions

### Class weka.classifiers.functions.GaussianProcesses

class GaussianProcesses extends Classifier implements Serializable

serialVersionUID:
  -8620066949967678545L

#### Serialized Fields

##### m_Alin

    double m_Alin

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

##### m_avg_target

    double m_avg_target

The training data.

##### m_Blin

    double m_Blin

##### m_C

    Matrix m_C

The covariance matrix.

##### m_checksTurnedOff

    boolean m_checksTurnedOff

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

##### m_classIndex

    int m_classIndex

The class index from the training data

##### m_delta

    double m_delta

Gaussian Noise Value.

##### m_Filter

    Filter m_Filter

The filter used to standardize/normalize all values.

##### m_filterType

    int m_filterType

Whether to normalize/standardize/neither

##### m_kernel

    Kernel m_kernel

Kernel to use

##### m_KernelIsLinear

    boolean m_KernelIsLinear

whether the kernel is a linear one

##### m_Missing

    ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

##### m_NominalToBinary

    NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

##### m_NumTrain

    int m_NumTrain

The number of training instances

##### m_t

    Matrix m_t

The vector of target values.

### Class weka.classifiers.functions.IsotonicRegression

class IsotonicRegression extends Classifier implements Serializable

serialVersionUID:
  1679336022835454137L

#### Serialized Fields

##### m_attribute

    Attribute m_attribute

The chosen attribute

##### m_cuts

    double[] m_cuts

The array of cut points

##### m_minMsq

    double m_minMsq

The minimum mean squared error that has been achieved.

##### m_values

    double[] m_values

The predicted value in each interval.

##### m_ZeroR

    Classifier m_ZeroR

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

### Class weka.classifiers.functions.LeastMedSq

class LeastMedSq extends Classifier implements Serializable

serialVersionUID:
  4288954049987652970L

#### Serialized Fields

##### m_bestMedian

    double m_bestMedian

##### m_bestRegression

    LinearRegression m_bestRegression

##### m_currentRegression

    LinearRegression m_currentRegression

##### m_Data

    Instances m_Data

##### m_debug

    boolean m_debug

##### m_israndom

    boolean m_israndom

##### m_ls

    LinearRegression m_ls

##### m_MissingFilter

    ReplaceMissingValues m_MissingFilter

##### m_random

    Random m_random

##### m_randomseed

    long m_randomseed

##### m_Residuals

    double[] m_Residuals

##### m_RLSData

    Instances m_RLSData

##### m_samples

    int m_samples

##### m_samplesize

    int m_samplesize

##### m_scalefactor

    double m_scalefactor

##### m_SplitFilter

    RemoveRange m_SplitFilter

##### m_SSR

    double m_SSR

##### m_SubSample

    Instances m_SubSample

##### m_TransformFilter

    NominalToBinary m_TransformFilter

##### m_weight

    double[] m_weight

### Class weka.classifiers.functions.LibLINEAR

class LibLINEAR extends Classifier implements Serializable

serialVersionUID:
  230504711L

#### Serialized Fields

##### m_Bias

    double m_Bias

bias term value

##### m_Cost

    double m_Cost

cost Parameter C

##### m_eps

    double m_eps

stopping criteria

##### m_Filter

    Filter m_Filter

for normalizing the data

##### m_Model

    Object m_Model

LibLINEAR Model

##### m_nominalToBinary

    boolean m_nominalToBinary

If true, the nominal to binary filter is applied

##### m_NominalToBinary

    NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

##### m_noReplaceMissingValues

    boolean m_noReplaceMissingValues

If true, the replace missing values filter is not applied

##### m_Normalize

    boolean m_Normalize

normalize input data

##### m_ProbabilityEstimates

    boolean m_ProbabilityEstimates

whether to generate probability estimates instead of +1/-1 in case of classification problems

##### m_ReplaceMissingValues

    ReplaceMissingValues m_ReplaceMissingValues

The filter used to get rid of missing values.

##### m_SVMType

    int m_SVMType

the SVM solver type

##### m_Weight

    double[] m_Weight

##### m_WeightLabel

    int[] m_WeightLabel

### Class weka.classifiers.functions.LibSVM

class LibSVM extends RandomizableClassifier implements Serializable

serialVersionUID:
  14172L

#### Serialized Fields

##### m_CacheSize

    double m_CacheSize

in MB

##### m_Coef0

    double m_Coef0

for poly/sigmoid

##### m_Cost

    double m_Cost

cost, for C_SVC, EPSILON_SVR and NU_SVR

##### m_Degree

    int m_Degree

for poly - in older versions of libsvm declared as a double. At least since 2.82 it is an int.

##### m_eps

    double m_eps

stopping criteria

##### m_Filter

    Filter m_Filter

for normalizing the data

##### m_Gamma

    double m_Gamma

for poly/rbf/sigmoid

##### m_GammaActual

    double m_GammaActual

for poly/rbf/sigmoid (the actual gamma)

##### m_KernelType

    int m_KernelType

the kernel type

##### m_Loss

    double m_Loss

loss, for EPSILON_SVR

##### m_Model

    Object m_Model

LibSVM Model

##### m_NominalToBinary

    Filter m_NominalToBinary

for converting mult-valued nominal attributes to binary

##### m_noReplaceMissingValues

    boolean m_noReplaceMissingValues

If true, the replace missing values filter is not applied

##### m_Normalize

    boolean m_Normalize

normalize input data

##### m_nu

    double m_nu

for NU_SVC, ONE_CLASS, and NU_SVR

##### m_ProbabilityEstimates

    boolean m_ProbabilityEstimates

whether to generate probability estimates instead of +1/-1 in case of classification problems

##### m_ReplaceMissingValues

    ReplaceMissingValues m_ReplaceMissingValues

The filter used to get rid of missing values.

##### m_Shrinking

    boolean m_Shrinking

use the shrinking heuristics

##### m_SVMType

    int m_SVMType

the SVM type

##### m_Weight

    double[] m_Weight

for C_SVC

##### m_WeightLabel

    int[] m_WeightLabel

for C_SVC

### Class weka.classifiers.functions.LinearRegression

class LinearRegression extends Classifier implements Serializable

serialVersionUID:
  -3364580862046573747L

#### Serialized Fields

##### b_Debug

    boolean b_Debug

True if debug output will be printed

##### m_AttributeSelection

    int m_AttributeSelection

The current attribute selection method

##### m_checksTurnedOff

    boolean m_checksTurnedOff

Turn off all checks and conversions?

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_ClassMean

    double m_ClassMean

The mean of the class attribute

##### m_ClassStdDev

    double m_ClassStdDev

The standard deviations of the class attribute

##### m_Coefficients

    double[] m_Coefficients

Array for storing coefficients of linear regression.

##### m_EliminateColinearAttributes

    boolean m_EliminateColinearAttributes

Try to eliminate correlated attributes?

##### m_Means

    double[] m_Means

The attributes means

##### m_MissingFilter

    ReplaceMissingValues m_MissingFilter

The filter for removing missing values.

##### m_Ridge

    double m_Ridge

The ridge parameter

##### m_SelectedAttributes

    boolean[] m_SelectedAttributes

Which attributes are relevant?

##### m_StdDevs

    double[] m_StdDevs

The attribute standard deviations

##### m_TransformedData

    Instances m_TransformedData

Variable for storing transformed training data.

##### m_TransformFilter

    NominalToBinary m_TransformFilter

The filter storing the transformation from nominal to binary attributes.

### Class weka.classifiers.functions.Logistic

class Logistic extends Classifier implements Serializable

serialVersionUID:
  3932117032546553727L

#### Serialized Fields

##### m_AttFilter

    RemoveUseless m_AttFilter

An attribute filter

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_Data

    double[][] m_Data

The data saved as a matrix

##### m_Debug

    boolean m_Debug

Debugging output

##### m_LL

    double m_LL

Log-likelihood of the searched model

##### m_MaxIts

    int m_MaxIts

The maximum number of iterations.

##### m_NominalToBinary

    NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

##### m_NumClasses

    int m_NumClasses

The number of the class labels

##### m_NumPredictors

    int m_NumPredictors

The number of attributes in the model

##### m_Par

    double[][] m_Par

The coefficients (optimized parameters) of the model

##### m_ReplaceMissingValues

    ReplaceMissingValues m_ReplaceMissingValues

The filter used to get rid of missing values.

##### m_Ridge

    double m_Ridge

The ridge parameter.

##### m_structure

    Instances m_structure

### Class weka.classifiers.functions.MultilayerPerceptron

class MultilayerPerceptron extends Classifier implements Serializable

serialVersionUID:
  -5990607817048210779L

#### Serialized Fields

##### m_accepted

    boolean m_accepted

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

##### m_attributeBases

    double[] m_attributeBases

The base values for all the attributes.

##### m_attributeRanges

    double[] m_attributeRanges

The ranges for all the attributes.

##### m_autoBuild

    boolean m_autoBuild

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

##### m_controlPanel

    weka.classifiers.functions.MultilayerPerceptron.ControlPanel m_controlPanel

The control panel.

##### m_currentInstance

    Instance m_currentInstance

The current instance running through the network.

##### m_decay

    boolean m_decay

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

##### m_driftThreshold

    int m_driftThreshold

The number to to use to quit on validation testing.

##### m_epoch

    int m_epoch

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

##### m_error

    double m_error

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

##### m_graphers

    FastVector m_graphers

A Vector list of the graphers.

##### m_gui

    boolean m_gui

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

##### m_hiddenLayers

    String m_hiddenLayers

The string that defines the hidden layers

##### m_inputs

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

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

##### m_instances

    Instances m_instances

The training instances.

##### m_learningRate

    double m_learningRate

This is the learning rate for the network.

##### m_linearUnit

    LinearUnit m_linearUnit

This is a linear unit.

##### m_momentum

    double m_momentum

This is the momentum for the network.

##### m_neuralNodes

    NeuralConnection[] m_neuralNodes

All the nodes that actually comprise the logical neural net.

##### m_nextId

    int m_nextId

The next id number available for default naming.

##### m_nodePanel

    weka.classifiers.functions.MultilayerPerceptron.NodePanel m_nodePanel

The panel the nodes are displayed on.

##### m_nominalToBinaryFilter

    NominalToBinary m_nominalToBinaryFilter

The actual filter.

##### m_normalizeAttributes

    boolean m_normalizeAttributes

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

##### m_normalizeClass

    boolean m_normalizeClass

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

##### m_numAttributes

    int m_numAttributes

The number of attributes.

##### m_numClasses

    int m_numClasses

The number of classes.

##### m_numEpochs

    int m_numEpochs

The number of epochs to train through.

##### m_numeric

    boolean m_numeric

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

##### m_outputs

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

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

##### m_random

    Random m_random

The actual random number generator.

##### m_randomSeed

    int m_randomSeed

The number used to seed the random number generator.

##### m_reset

    boolean m_reset

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

##### m_selected

    FastVector m_selected

A Vector list of the units currently selected.

##### m_sigmoidUnit

    SigmoidUnit m_sigmoidUnit

this is a sigmoid unit.

##### m_stopIt

    boolean m_stopIt

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

##### m_stopped

    boolean m_stopped

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

##### m_useDefaultModel

    boolean m_useDefaultModel

Whether to use the default ZeroR model

##### m_useNomToBin

    boolean m_useNomToBin

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

##### m_valSize

    int m_valSize

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

##### m_win

    JFrame m_win

The window for the network.

##### m_ZeroR

    Classifier m_ZeroR

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

### Class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd

class NeuralEnd extends NeuralConnection implements Serializable

serialVersionUID:
  7305185603191183338L

#### Serialized Fields

##### m_input

    boolean m_input

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

##### m_link

    int m_link

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

### Class weka.classifiers.functions.PaceRegression

class PaceRegression extends Classifier implements Serializable

serialVersionUID:
  7230266976059115435L

#### Serialized Fields

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_Coefficients

    double[] m_Coefficients

Array for storing coefficients of linear regression.

##### m_Debug

    boolean m_Debug

True if debug output will be printed

##### m_Model

    Instances m_Model

The model used

##### olscThreshold

    double olscThreshold

##### paceEstimator

    int paceEstimator

the estimator

### Class weka.classifiers.functions.PLSClassifier

class PLSClassifier extends Classifier implements Serializable

serialVersionUID:
  4819775160590973256L

#### Serialized Fields

##### m_ActualFilter

    PLSFilter m_ActualFilter

the actual filter to use

##### m_Filter

    PLSFilter m_Filter

the PLS filter

### Class weka.classifiers.functions.RBFNetwork

class RBFNetwork extends Classifier implements Serializable

serialVersionUID:
  -3669814959712675720L

#### Serialized Fields

##### m_basisFilter

    ClusterMembership m_basisFilter

The filter for producing the meta data

##### m_clusteringSeed

    int m_clusteringSeed

The seed to pass on to K-means

##### m_linear

    LinearRegression m_linear

The linear regression for numeric problems

##### m_logistic

    Logistic m_logistic

The logistic regression for classification problems

##### m_maxIts

    int m_maxIts

The maximum number of iterations for logistic regression.

##### m_minStdDev

    double m_minStdDev

The minimum standard deviation

##### m_numClusters

    int m_numClusters

The number of clusters (basis functions to generate)

##### m_ridge

    double m_ridge

The ridge parameter for the logistic regression.

##### m_standardize

    Standardize m_standardize

Filter used for normalizing the data

##### m_ZeroR

    Classifier m_ZeroR

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

### Class weka.classifiers.functions.SimpleLinearRegression

class SimpleLinearRegression extends Classifier implements Serializable

serialVersionUID:
  1679336022895414137L

#### Serialized Fields

##### m_attribute

    Attribute m_attribute

The chosen attribute

##### m_attributeIndex

    int m_attributeIndex

The index of the chosen attribute

##### m_intercept

    double m_intercept

The intercept

##### m_slope

    double m_slope

The slope

##### m_suppressErrorMessage

    boolean m_suppressErrorMessage

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

### Class weka.classifiers.functions.SimpleLogistic

class SimpleLogistic extends Classifier implements Serializable

serialVersionUID:
  7397710626304705059L

#### Serialized Fields

##### m_boostedModel

    LogisticBase m_boostedModel

The actual logistic regression model

##### m_errorOnProbabilities

    boolean m_errorOnProbabilities

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

##### m_heuristicStop

    int m_heuristicStop

Parameter for the heuristic for early stopping of LogitBoost

##### m_maxBoostingIterations

    int m_maxBoostingIterations

Maximum number of iterations for LogitBoost

##### m_NominalToBinary

    NominalToBinary m_NominalToBinary

Filter for converting nominal attributes to binary ones

##### m_numBoostingIterations

    int m_numBoostingIterations

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

##### m_ReplaceMissingValues

    ReplaceMissingValues m_ReplaceMissingValues

Filter for replacing missing values

##### m_useAIC

    boolean m_useAIC

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

##### m_useCrossValidation

    boolean m_useCrossValidation

If true, cross-validate number of LogitBoost iterations

##### m_weightTrimBeta

    double m_weightTrimBeta

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

### Class weka.classifiers.functions.SMO

class SMO extends Classifier implements Serializable

serialVersionUID:
  -6585883636378691736L

#### Serialized Fields

##### m_C

    double m_C

The complexity parameter.

##### m_checksTurnedOff

    boolean m_checksTurnedOff

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

##### m_classAttribute

    Attribute m_classAttribute

The class attribute

##### m_classifiers

    SMO.BinarySMO[][] m_classifiers

The binary classifier(s)

##### m_classIndex

    int m_classIndex

The class index from the training data

##### m_eps

    double m_eps

Epsilon for rounding.

##### m_Filter

    Filter m_Filter

The filter used to standardize/normalize all values.

##### m_filterType

    int m_filterType

Whether to normalize/standardize/neither

##### m_fitLogisticModels

    boolean m_fitLogisticModels

Whether logistic models are to be fit

##### m_kernel

    Kernel m_kernel

the kernel to use

##### m_KernelIsLinear

    boolean m_KernelIsLinear

whether the kernel is a linear one

##### m_Missing

    ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

##### m_NominalToBinary

    NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

##### m_numFolds

    int m_numFolds

The number of folds for the internal cross-validation

##### m_randomSeed

    int m_randomSeed

The random number seed

##### m_tol

    double m_tol

Tolerance for accuracy of result.

### Class weka.classifiers.functions.SMO.BinarySMO

class BinarySMO extends Object implements Serializable

serialVersionUID:
  -8246163625699362456L

#### Serialized Fields

##### m_alpha

    double[] m_alpha

The Lagrange multipliers.

##### m_b

    double m_b

The thresholds.

##### m_bLow

    double m_bLow

The thresholds.

##### m_bUp

    double m_bUp

The thresholds.

##### m_class

    double[] m_class

The transformed class values.

##### m_data

    Instances m_data

The training data.

##### m_errors

    double[] m_errors

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

##### m_I0

    SMOset m_I0

{i: 0 invalid input: '<' m_alpha[i] invalid input: '<' C}

##### m_I1

    SMOset m_I1

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

##### m_I2

    SMOset m_I2

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

##### m_I3

    SMOset m_I3

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

##### m_I4

    SMOset m_I4

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

##### m_iLow

    int m_iLow

The indices for m_bLow and m_bUp

##### m_iUp

    int m_iUp

The indices for m_bLow and m_bUp

##### m_kernel

    Kernel m_kernel

Kernel to use

##### m_logistic

    Logistic m_logistic

Stores logistic regression model for probability estimate

##### m_sparseIndices

    int[] m_sparseIndices

##### m_sparseWeights

    double[] m_sparseWeights

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

##### m_sumOfWeights

    double m_sumOfWeights

Stores the weight of the training instances

##### m_supportVectors

    SMOset m_supportVectors

The set of support vectors

##### m_weights

    double[] m_weights

Weight vector for linear machine.

### Class weka.classifiers.functions.SMOreg

class SMOreg extends Classifier implements Serializable

serialVersionUID:
  -7149606251113102827L

#### Serialized Fields

##### m_C

    double m_C

capacity parameter

##### m_Filter

    Filter m_Filter

The filter used to standardize/normalize all values.

##### m_filterType

    int m_filterType

Whether to normalize/standardize/neither

##### m_kernel

    Kernel m_kernel

the configured kernel

##### m_Missing

    ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

##### m_NominalToBinary

    NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

##### m_onlyNumeric

    boolean m_onlyNumeric

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

##### m_optimizer

    RegOptimizer m_optimizer

contains the algorithm used for learning

##### m_x0

    double m_x0

##### m_x1

    double m_x1

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

### Class weka.classifiers.functions.SPegasos

class SPegasos extends Classifier implements Serializable

serialVersionUID:
  -3732968666673530290L

#### Serialized Fields

##### m_data

    Instances m_data

Holds the header of the training data

##### m_dontNormalize

    boolean m_dontNormalize

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

##### m_dontReplaceMissing

    boolean m_dontReplaceMissing

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

##### m_epochs

    int m_epochs

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

##### m_lambda

    double m_lambda

The regularization parameter

##### m_loss

    int m_loss

The current loss function to minimize

##### m_nominalToBinary

    NominalToBinary m_nominalToBinary

Convert nominal attributes to numerically coded binary ones

##### m_normalize

    Normalize m_normalize

Normalize the training data

##### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

Replace missing values

##### m_t

    double m_t

Holds the current iteration number

##### m_weights

    double[] m_weights

Stores the weights (+ bias in the last element)

### Class weka.classifiers.functions.VotedPerceptron

class VotedPerceptron extends Classifier implements Serializable

serialVersionUID:
  -1072429260104568698L

#### Serialized Fields

##### m_Additions

    int[] m_Additions

The training instances added to the perceptron

##### m_Exponent

    double m_Exponent

The exponent

##### m_IsAddition

    boolean[] m_IsAddition

Addition or subtraction?

##### m_K

    int m_K

The actual number of alterations

##### m_MaxK

    int m_MaxK

The maximum number of alterations to the perceptron

##### m_NominalToBinary

    NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

##### m_NumIterations

    int m_NumIterations

The number of iterations

##### m_ReplaceMissingValues

    ReplaceMissingValues m_ReplaceMissingValues

The filter used to get rid of missing values.

##### m_Seed

    int m_Seed

Seed used for shuffling the dataset

##### m_Train

    Instances m_Train

The training instances

##### m_Weights

    int[] m_Weights

The weights for each perceptron

### Class weka.classifiers.functions.Winnow

class Winnow extends Classifier implements Serializable

serialVersionUID:
  3543770107994321324L

#### Serialized Fields

##### m_actualThreshold

    double m_actualThreshold

The true threshold used for prediction

##### m_Alpha

    double m_Alpha

The promotion coefficient

##### m_Balanced

    boolean m_Balanced

Use the balanced variant?

##### m_Beta

    double m_Beta

The demotion coefficient

##### m_defaultWeight

    double m_defaultWeight

Starting weights for the prediction vector(s)

##### m_Mistakes

    int m_Mistakes

Accumulated mistake count (for statistics)

##### m_NominalToBinary

    NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

##### m_numIterations

    int m_numIterations

The number of iterations

##### m_predNegVector

    double[] m_predNegVector

The weight vector for prediction (neg)

##### m_predPosVector

    double[] m_predPosVector

The weight vector for prediction (pos)

##### m_ReplaceMissingValues

    ReplaceMissingValues m_ReplaceMissingValues

The filter used to get rid of missing values.

##### m_Seed

    int m_Seed

Random seed used for shuffling the dataset, -1 == disable

##### m_Threshold

    double m_Threshold

Prediction threshold, invalid input: '<'0 == numAttributes

##### m_Train

    Instances m_Train

The training instances

## Package weka.classifiers.functions.neural

### Class weka.classifiers.functions.neural.LinearUnit

class LinearUnit extends Object implements Serializable

serialVersionUID:
  8572152807755673630L

### Class weka.classifiers.functions.neural.NeuralConnection

class NeuralConnection extends Object implements Serializable

serialVersionUID:
  -286208828571059163L

#### Serialized Fields

##### m_id

    String m_id

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

##### m_inputList

    NeuralConnection[] m_inputList

The list of inputs to this unit.

##### m_inputNums

    int[] m_inputNums

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

##### m_numInputs

    int m_numInputs

The number of inputs.

##### m_numOutputs

    int m_numOutputs

The number of outputs.

##### m_outputList

    NeuralConnection[] m_outputList

The list of outputs from this unit.

##### m_outputNums

    int[] m_outputNums

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

##### m_type

    int m_type

The type of unit this is.

##### m_unitError

    double m_unitError

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

##### m_unitValue

    double m_unitValue

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

##### m_weightsUpdated

    boolean m_weightsUpdated

True if the weights have already been updated.

##### m_x

    double m_x

The x coord of this unit purely for displaying purposes.

##### m_y

    double m_y

The y coord of this unit purely for displaying purposes.

### Class weka.classifiers.functions.neural.NeuralNode

class NeuralNode extends NeuralConnection implements Serializable

serialVersionUID:
  -1085750607680839163L

#### Serialized Fields

##### m_bestWeights

    double[] m_bestWeights

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

##### m_changeInWeights

    double[] m_changeInWeights

The change in the weights.

##### m_methods

    NeuralMethod m_methods

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

##### m_random

    Random m_random

##### m_weights

    double[] m_weights

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

### Class weka.classifiers.functions.neural.SigmoidUnit

class SigmoidUnit extends Object implements Serializable

serialVersionUID:
  -5162958458177475652L

## Package weka.classifiers.functions.pace

### Class weka.classifiers.functions.pace.PaceMatrix

class PaceMatrix extends Matrix implements Serializable

serialVersionUID:
  2699925616857843973L

## Package weka.classifiers.functions.supportVector

### Class weka.classifiers.functions.supportVector.CachedKernel

class CachedKernel extends Kernel implements Serializable

serialVersionUID:
  702810182699015136L

#### Serialized Fields

##### m_cacheHits

    int m_cacheHits

Counts the number of kernel cache hits.

##### m_cacheSize

    int m_cacheSize

The size of the cache (a prime number)

##### m_cacheSlots

    int m_cacheSlots

number of cache slots in an entry

##### m_kernelEvals

    int m_kernelEvals

Counts the number of kernel evaluations.

##### m_kernelMatrix

    double[][] m_kernelMatrix

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

##### m_keys

    long[] m_keys

##### m_numInsts

    int m_numInsts

The number of instance in the dataset

##### m_storage

    double[] m_storage

Kernel cache

### Class weka.classifiers.functions.supportVector.Kernel

class Kernel extends Object implements Serializable

serialVersionUID:
  -6102771099905817064L

#### Serialized Fields

##### m_ChecksTurnedOff

    boolean m_ChecksTurnedOff

Turns off all checks

##### m_data

    Instances m_data

The dataset

##### m_Debug

    boolean m_Debug

enables debugging output

### Class weka.classifiers.functions.supportVector.NormalizedPolyKernel

class NormalizedPolyKernel extends PolyKernel implements Serializable

serialVersionUID:
  1248574185532130851L

### Class weka.classifiers.functions.supportVector.PolyKernel

class PolyKernel extends CachedKernel implements Serializable

serialVersionUID:
  -321831645846363201L

#### Serialized Fields

##### m_exponent

    double m_exponent

The exponent for the polynomial kernel.

##### m_lowerOrder

    boolean m_lowerOrder

Use lower-order terms?

### Class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel

class PrecomputedKernelMatrixKernel extends Kernel implements Serializable

serialVersionUID:
  -321831645846363333L

#### Serialized Fields

##### m_Counter

    int m_Counter

A classifier counter.

##### m_KernelMatrix

    Matrix m_KernelMatrix

The kernel matrix.

##### m_KernelMatrixFile

    File m_KernelMatrixFile

The file holding the kernel matrix.

### Class weka.classifiers.functions.supportVector.Puk

class Puk extends CachedKernel implements Serializable

serialVersionUID:
  1682161522559978851L

#### Serialized Fields

##### m_factor

    double m_factor

Cached factor for the Puk kernel.

##### m_kernelPrecalc

    double[] m_kernelPrecalc

The precalculated dotproducts of <inst_i,inst_i>

##### m_omega

    double m_omega

Omega for the Puk kernel.

##### m_sigma

    double m_sigma

Sigma for the Puk kernel.

### Class weka.classifiers.functions.supportVector.RBFKernel

class RBFKernel extends CachedKernel implements Serializable

serialVersionUID:
  5247117544316387852L

#### Serialized Fields

##### m_gamma

    double m_gamma

Gamma for the RBF kernel.

##### m_kernelPrecalc

    double[] m_kernelPrecalc

The precalculated dotproducts of <inst_i,inst_i>

### Class weka.classifiers.functions.supportVector.RegOptimizer

class RegOptimizer extends Object implements Serializable

serialVersionUID:
  -2198266997254461814L

#### Serialized Fields

##### m_alpha

    double[] m_alpha

alpha and alpha* arrays containing weights for solving dual problem

##### m_alphaStar

    double[] m_alphaStar

##### m_b

    double m_b

offset

##### m_bModelBuilt

    boolean m_bModelBuilt

flag to indicate whether the model is built yet

##### m_C

    double m_C

capacity parameter, copied from SMOreg

##### m_classIndex

    int m_classIndex

index of class variable in data set

##### m_data

    Instances m_data

points to data set

##### m_epsilon

    double m_epsilon

epsilon of epsilon-insensitive cost function

##### m_kernel

    Kernel m_kernel

the kernel

##### m_nCacheHits

    int m_nCacheHits

number of kernel cache hits, used for printing statistics only

##### m_nEvals

    long m_nEvals

number of kernel evaluations, used for printing statistics only

##### m_nInstances

    int m_nInstances

number of instances in data set

##### m_nSeed

    int m_nSeed

seed for initializing random number generator

##### m_random

    Random m_random

random number generator

##### m_sparseIndices

    int[] m_sparseIndices

##### m_sparseWeights

    double[] m_sparseWeights

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

##### m_supportVectors

    SMOset m_supportVectors

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

##### m_SVM

    SMOreg m_SVM

parent SMOreg class

##### m_target

    double[] m_target

class values/desired output vector

##### m_weights

    double[] m_weights

weights for linear kernel

### Class weka.classifiers.functions.supportVector.RegSMO

class RegSMO extends RegOptimizer implements Serializable

serialVersionUID:
  -7504070793279598638L

#### Serialized Fields

##### m_alpha1

    double m_alpha1

alpha value for first candidate

##### m_alpha1Star

    double m_alpha1Star

alpha* value for first candidate

##### m_alpha2

    double m_alpha2

alpha value for second candidate

##### m_alpha2Star

    double m_alpha2Star

alpha* value for second candidate

##### m_eps

    double m_eps

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

##### m_error

    double[] m_error

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

### Class weka.classifiers.functions.supportVector.RegSMOImproved

class RegSMOImproved extends RegSMO implements Serializable

serialVersionUID:
  471692841446029784L

#### Serialized Fields

##### m_bLow

    double m_bLow

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

##### m_bUp

    double m_bUp

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

##### m_bUseVariant1

    boolean m_bUseVariant1

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

##### m_fTolerance

    double m_fTolerance

tolerance parameter used for checking stopping criterion b.up invalid input: '<' b.low + 2 tol

##### m_I0

    SMOset m_I0

The different sets used by the algorithm.

##### m_iLow

    int m_iLow

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

##### m_iSet

    int[] m_iSet

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

##### m_iUp

    int m_iUp

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

### Class weka.classifiers.functions.supportVector.SMOset

class SMOset extends Object implements Serializable

serialVersionUID:
  -8364829283188675777L

#### Serialized Fields

##### m_first

    int m_first

The first element in the set

##### m_indicators

    boolean[] m_indicators

Indicators

##### m_next

    int[] m_next

The next element for each element

##### m_number

    int m_number

The current number of elements in the set

##### m_previous

    int[] m_previous

The previous element for each element

### Class weka.classifiers.functions.supportVector.StringKernel

class StringKernel extends Kernel implements Serializable

serialVersionUID:
  -4902954211202690123L

#### Serialized Fields

##### cachekh

    double[] cachekh

##### cachekh2

    double[] cachekh2

##### cachekh2K

    int[] cachekh2K

##### cachekhK

    int[] cachekhK

##### m_cacheSize

    int m_cacheSize

The size of the cache (a prime number)

##### m_internalCacheSize

    int m_internalCacheSize

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

##### m_kernelEvals

    int m_kernelEvals

Counts the number of kernel evaluations.

##### m_keys

    long[] m_keys

##### m_lambda

    double m_lambda

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

##### m_maxSubsequenceLength

    int m_maxSubsequenceLength

The maximum substring length for lambda pruning

##### m_multX

    int m_multX

cached indexes for private cache

##### m_multY

    int m_multY

##### m_multZ

    int m_multZ

##### m_multZZ

    int m_multZZ

##### m_normalize

    boolean m_normalize

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

##### m_numInsts

    int m_numInsts

The number of instance in the dataset

##### m_powersOflambda

    double[] m_powersOflambda

the precalculated powers of lambda

##### m_PruningMethod

    int m_PruningMethod

the pruning method

##### m_storage

    double[] m_storage

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

##### m_strAttr

    int m_strAttr

The attribute number of the string attribute

##### m_subsequenceLength

    int m_subsequenceLength

The substring length

##### m_useRecursionCache

    boolean m_useRecursionCache

##### maxCache

    int maxCache

private cache for intermediate results

## Package weka.classifiers.lazy

### Class weka.classifiers.lazy.IB1

class IB1 extends Classifier implements Serializable

serialVersionUID:
  -6152184127304895851L

#### Serialized Fields

##### m_MaxArray

    double[] m_MaxArray

The maximum values for numeric attributes.

##### m_MinArray

    double[] m_MinArray

The minimum values for numeric attributes.

##### m_Train

    Instances m_Train

The training instances used for classification.

### Class weka.classifiers.lazy.IBk

class IBk extends Classifier implements Serializable

serialVersionUID:
  -3080186098777067172L

#### Serialized Fields

##### m_ClassType

    int m_ClassType

The class attribute type.

##### m_CrossValidate

    boolean m_CrossValidate

Whether to select k by cross validation.

##### m_defaultModel

    ZeroR m_defaultModel

Default ZeroR model to use when there are no training instances

##### m_DistanceWeighting

    int m_DistanceWeighting

Whether the neighbours should be distance-weighted.

##### m_kNN

    int m_kNN

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

##### m_kNNUpper

    int m_kNNUpper

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

##### m_kNNValid

    boolean m_kNNValid

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

##### m_MeanSquared

    boolean m_MeanSquared

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

##### m_NNSearch

    NearestNeighbourSearch m_NNSearch

for nearest-neighbor search.

##### m_NumAttributesUsed

    double m_NumAttributesUsed

The number of attributes the contribute to a prediction.

##### m_NumClasses

    int m_NumClasses

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

##### m_Train

    Instances m_Train

The training instances used for classification.

##### m_WindowSize

    int m_WindowSize

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

### Class weka.classifiers.lazy.KStar

class KStar extends Classifier implements Serializable

serialVersionUID:
  332458330800479083L

#### Serialized Fields

##### m_BlendMethod

    int m_BlendMethod

0 = use specified blend, 1 = entropic blend setting

##### m_Cache

    KStarCache[] m_Cache

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

##### m_ClassType

    int m_ClassType

The class attribute type

##### m_ComputeRandomCols

    int m_ComputeRandomCols

Flag turning on and off the computation of random class colomns

##### m_GlobalBlend

    int m_GlobalBlend

default sphere of influence blend setting

##### m_InitFlag

    int m_InitFlag

Flag turning on and off the initialisation of config variables

##### m_MissingMode

    int m_MissingMode

missing value treatment

##### m_NumAttributes

    int m_NumAttributes

The number of attributes

##### m_NumClasses

    int m_NumClasses

The number of class values

##### m_NumInstances

    int m_NumInstances

The number of instances in the dataset

##### m_RandClassCols

    int[][] m_RandClassCols

Table of random class value colomns

##### m_Train

    Instances m_Train

The training instances used for classification.

### Class weka.classifiers.lazy.LBR

class LBR extends Classifier implements Serializable

serialVersionUID:
  5648559277738985156L

#### Serialized Fields

##### bestCnt

    int bestCnt

##### forCnt

    int forCnt

##### leftHand

    ArrayList leftHand

best attribute's index list. maybe as output result

##### m_Counts

    int[][][] m_Counts

All the counts for nominal attributes.

##### m_ErrorFlags

    boolean[] m_ErrorFlags

leave-one-out error flags on the training dataaet.

##### m_Errors

    int m_Errors

leave-one-out errors on the training dataset.

##### m_Instances

    Instances m_Instances

The set of instances used for current training.

##### m_NCV

    boolean m_NCV

for printing in n-fold cross validation

##### m_numAtts

    int m_numAtts

number of attributes for the dataset

##### m_Number

    int m_Number

the number of instance to be processed

##### m_NumberOfInstances

    int m_NumberOfInstances

the Number of Instances to be used in building a classifiers

##### m_numClasses

    int m_numClasses

number of classes for dataset

##### m_numInsts

    int m_numInsts

number of instances in dataset

##### m_Priors

    int[] m_Priors

The prior probabilities of the classes.

##### m_RemainderErrors

    int m_RemainderErrors

the number of instances to be classified incorrectly besides the subset.

##### m_subInstances

    LBR.Indexes m_subInstances

index of instances and attributes for the given dataset

##### m_subOldErrorFlags

    boolean[] m_subOldErrorFlags

following is defined by wangzh, the number of instances to be classified incorrectly on the subset.

##### m_tCounts

    int[][][] m_tCounts

All the counts for nominal attributes.

##### m_tPriors

    int[] m_tPriors

The prior probabilities of the classes.

##### posteriorsArray

    double[] posteriorsArray

probability values array

##### tempCnt

    int tempCnt

##### tempSubInstances

    LBR.Indexes tempSubInstances

index of instances and attributes for the given dataset

##### whileCnt

    int whileCnt

### Class weka.classifiers.lazy.LBR.Indexes

class Indexes extends Object implements Serializable

serialVersionUID:
  -2771490019751421307L

#### Serialized Fields

##### m_AttIndexes

    boolean[] m_AttIndexes

the array attribute indexes

##### m_ClassIndex

    int m_ClassIndex

the Class Index for the data set

##### m_InstIndexes

    boolean[] m_InstIndexes

the array instance indexes

##### m_NumAtts

    int m_NumAtts

the number of attributes indexed

##### m_NumAttsSet

    int m_NumAttsSet

the number of attributes "in use" or set to a the original value (true or false)

##### m_NumInstances

    int m_NumInstances

the number of instances indexed

##### m_NumInstsSet

    int m_NumInstsSet

the number of instances "in use" or set to a the original value (true or false)

##### m_NumSeqAttsSet

    int m_NumSeqAttsSet

the number of sequential attributes "in use" or set to a the original value (true or false)

##### m_NumSeqInstsSet

    int m_NumSeqInstsSet

the number of sequential instances "in use" or set to a the original value (true or false)

##### m_SequentialAttIndex_valid

    boolean m_SequentialAttIndex_valid

flag to check if sequential array must be rebuilt due to changes to the attribute index

##### m_SequentialAttIndexes

    int[] m_SequentialAttIndexes

an array of attribute indexes that are set to either true or false

##### m_SequentialInstanceIndex_valid

    boolean m_SequentialInstanceIndex_valid

flag to check if sequential array must be rebuilt due to changes to the instance index

##### m_SequentialInstIndexes

    int[] m_SequentialInstIndexes

the array of instance indexes that are set to a either true or false

### Class weka.classifiers.lazy.LWL

class LWL extends SingleClassifierEnhancer implements Serializable

serialVersionUID:
  1979797405383665815L

#### Serialized Fields

##### m_kNN

    int m_kNN

The number of neighbours used to select the kernel bandwidth.

##### m_NNSearch

    NearestNeighbourSearch m_NNSearch

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

##### m_Train

    Instances m_Train

The training instances used for classification.

##### m_UseAllK

    boolean m_UseAllK

True if m_kNN should be set to all instances.

##### m_WeightKernel

    int m_WeightKernel

The weighting kernel method currently selected.

##### m_ZeroR

    Classifier m_ZeroR

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

## Package weka.classifiers.lazy.kstar

### Class weka.classifiers.lazy.kstar.KStarCache

class KStarCache extends Object implements Serializable

serialVersionUID:
  -7693632394267140678L

#### Serialized Fields

##### m_Cache

    KStarCache.CacheTable m_Cache

cache table

### Class weka.classifiers.lazy.kstar.KStarCache.CacheTable

class CacheTable extends Object implements Serializable

serialVersionUID:
  -8086106452588253423L

#### Serialized Fields

##### DEFAULT_LOAD_FACTOR

    float DEFAULT_LOAD_FACTOR

The default load factor for the hashtable

##### DEFAULT_TABLE_SIZE

    int DEFAULT_TABLE_SIZE

The default size of the hashtable

##### EPSILON

    double EPSILON

Accuracy value for equality

##### m_Count

    int m_Count

The total number of entries in the hash table.

##### m_LoadFactor

    float m_LoadFactor

The load factor for the hashtable.

##### m_Table

    KStarCache.TableEntry[] m_Table

The hash table data.

##### m_Threshold

    int m_Threshold

Rehashes the table when count exceeds this threshold.

### Class weka.classifiers.lazy.kstar.KStarCache.TableEntry

class TableEntry extends Object implements Serializable

serialVersionUID:
  4057602386766259138L

#### Serialized Fields

##### hash

    int hash

attribute value hash code

##### key

    double key

attribute value

##### next

    KStarCache.TableEntry next

next table entry (separate chaining)

##### pmiss

    double pmiss

transformation probability to missing value

##### value

    double value

scale factor or stop parameter

## Package weka.classifiers.meta

### Class weka.classifiers.meta.AdaBoostM1

class AdaBoostM1 extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -7378107808933117974L

#### Serialized Fields

##### m_Betas

    double[] m_Betas

Array for storing the weights for the votes.

##### m_NumClasses

    int m_NumClasses

The number of classes

##### m_NumIterationsPerformed

    int m_NumIterationsPerformed

The number of successfully generated base classifiers.

##### m_UseResampling

    boolean m_UseResampling

Use boosting with reweighting?

##### m_WeightThreshold

    int m_WeightThreshold

Weight Threshold. The percentage of weight mass used in training

##### m_ZeroR

    Classifier m_ZeroR

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

### Class weka.classifiers.meta.AdditiveRegression

class AdditiveRegression extends IteratedSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -2368937577670527151L

#### Serialized Fields

##### m_NumIterationsPerformed

    int m_NumIterationsPerformed

The number of successfully generated base classifiers.

##### m_shrinkage

    double m_shrinkage

Shrinkage (Learning rate). Default = no shrinkage.

##### m_SuitableData

    boolean m_SuitableData

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

##### m_zeroR

    ZeroR m_zeroR

The model for the mean

### Class weka.classifiers.meta.AttributeSelectedClassifier

class AttributeSelectedClassifier extends SingleClassifierEnhancer implements Serializable

serialVersionUID:
  -5951805453487947577L

#### Serialized Fields

##### m_AttributeSelection

    AttributeSelection m_AttributeSelection

The attribute selection object

##### m_Evaluator

    ASEvaluation m_Evaluator

The attribute evaluator to use

##### m_numAttributesSelected

    double m_numAttributesSelected

The number of attributes selected by the attribute selection phase

##### m_numClasses

    int m_numClasses

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

##### m_ReducedHeader

    Instances m_ReducedHeader

The header of the dimensionally reduced data

##### m_Search

    ASSearch m_Search

The search method to use

##### m_selectionTime

    double m_selectionTime

The time taken to select attributes in milliseconds

##### m_totalTime

    double m_totalTime

The time taken to select attributes AND build the classifier

### Class weka.classifiers.meta.Bagging

class Bagging extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -5178288489778728847L

#### Serialized Fields

##### m_BagSizePercent

    int m_BagSizePercent

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

##### m_CalcOutOfBag

    boolean m_CalcOutOfBag

Whether to calculate the out of bag error

##### m_OutOfBagError

    double m_OutOfBagError

The out of bag error that has been calculated

### Class weka.classifiers.meta.ClassificationViaClustering

class ClassificationViaClustering extends Classifier implements Serializable

serialVersionUID:
  -5687069451420259135L

#### Serialized Fields

##### m_ActualClusterer

    Clusterer m_ActualClusterer

the actual cluster algorithm being used

##### m_Clusterer

    Clusterer m_Clusterer

the cluster algorithm used (template)

##### m_ClusteringHeader

    Instances m_ClusteringHeader

the modified training data header

##### m_ClustersToClasses

    double[] m_ClustersToClasses

the mapping between clusters and classes

##### m_OriginalHeader

    Instances m_OriginalHeader

the original training data header

##### m_ZeroR

    Classifier m_ZeroR

the default model

### Class weka.classifiers.meta.ClassificationViaRegression

class ClassificationViaRegression extends SingleClassifierEnhancer implements Serializable

serialVersionUID:
  4500023123618669859L

#### Serialized Fields

##### m_ClassFilters

    MakeIndicator[] m_ClassFilters

The filters used to transform the class.

##### m_Classifiers

    Classifier[] m_Classifiers

The classifiers. (One for each class.)

### Class weka.classifiers.meta.CostSensitiveClassifier

class CostSensitiveClassifier extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -720658209263002404L

#### Serialized Fields

##### m_CostFile

    String m_CostFile

The name of the cost file, for command line options

##### m_CostMatrix

    CostMatrix m_CostMatrix

The cost matrix

##### m_MatrixSource

    int m_MatrixSource

Indicates the current cost matrix source

##### m_MinimizeExpectedCost

    boolean m_MinimizeExpectedCost

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

##### m_OnDemandDirectory

    File m_OnDemandDirectory

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

### Class weka.classifiers.meta.CVParameterSelection

class CVParameterSelection extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -6529603380876641265L

#### Serialized Fields

##### m_BestClassifierOptions

    String[] m_BestClassifierOptions

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

##### m_BestPerformance

    double m_BestPerformance

The cross-validated performance of the best options

##### m_ClassifierOptions

    String[] m_ClassifierOptions

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

##### m_CVParams

    FastVector m_CVParams

The set of parameters to cross-validate over

##### m_InitOptions

    String[] m_InitOptions

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

##### m_NumAttributes

    int m_NumAttributes

The number of attributes in the data

##### m_NumFolds

    int m_NumFolds

The number of folds used in cross-validation

##### m_TrainFoldSize

    int m_TrainFoldSize

The number of instances in a training fold

### Class weka.classifiers.meta.CVParameterSelection.CVParameter

class CVParameter extends Object implements Serializable

serialVersionUID:
  -4668812017709421953L

#### Serialized Fields

##### m_AddAtEnd

    boolean m_AddAtEnd

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

##### m_Lower

    double m_Lower

Lower bound for the CV search

##### m_ParamChar

    String m_ParamChar

Char used to identify the option of interest

##### m_ParamValue

    double m_ParamValue

The parameter value with the best performance

##### m_RoundParam

    boolean m_RoundParam

True if the parameter should be rounded to an integer

##### m_Steps

    double m_Steps

Number of steps during the search

##### m_Upper

    double m_Upper

Upper bound for the CV search

### Class weka.classifiers.meta.Dagging

class Dagging extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  4560165876570074309L

#### Serialized Fields

##### m_NumFolds

    int m_NumFolds

the number of folds to use to split the training data

##### m_Verbose

    boolean m_Verbose

whether to output some progress information during building

##### m_Vote

    Vote m_Vote

the classifier used for voting

### Class weka.classifiers.meta.Decorate

class Decorate extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -6020193348750269931L

#### Serialized Fields

##### m_ArtSize

    double m_ArtSize

Amount of artificial/random instances to use - specified as a fraction of the training data size.

##### m_AttributeStats

    Vector m_AttributeStats

Attribute statistics - used for generating artificial examples.

##### m_Committee

    Vector m_Committee

Vector of classifiers that make up the committee/ensemble.

##### m_DesiredSize

    int m_DesiredSize

The desired ensemble size.

##### m_Random

    Random m_Random

The random number generator.

### Class weka.classifiers.meta.END

class END extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -4143242362912214956L

#### Serialized Fields

##### m_hashtable

    Hashtable m_hashtable

The hashtable containing the classifiers for the END.

### Class weka.classifiers.meta.FilteredClassifier

class FilteredClassifier extends SingleClassifierEnhancer implements Serializable

serialVersionUID:
  -4523450618538717400L

#### Serialized Fields

##### m_Filter

    Filter m_Filter

The filter

##### m_FilteredInstances

    Instances m_FilteredInstances

The instance structure of the filtered instances

### Class weka.classifiers.meta.Grading

class Grading extends Stacking implements Serializable

serialVersionUID:
  5207837947890081170L

#### Serialized Fields

##### m_InstPerClass

    double[] m_InstPerClass

InstPerClass

##### m_MetaClassifiers

    Classifier[] m_MetaClassifiers

The meta classifiers, one for each base classifier.

### Class weka.classifiers.meta.GridSearch

class GridSearch extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -3034773968581595348L

#### Serialized Fields

##### m_BestClassifier

    Classifier m_BestClassifier

the Classifier with the best setup

##### m_BestFilter

    Filter m_BestFilter

the Filter with the best setup

##### m_Cache

    weka.classifiers.meta.GridSearch.PerformanceCache m_Cache

the cache for points in the grid that got calculated

##### m_Data

    Instances m_Data

the training data

##### m_Evaluation

    int m_Evaluation

the type of evaluation

##### m_Filter

    Filter m_Filter

the Filter

##### m_Grid

    weka.classifiers.meta.GridSearch.Grid m_Grid

the value-pairs grid

##### m_GridExtensionsPerformed

    int m_GridExtensionsPerformed

the number of extensions performed

##### m_GridIsExtendable

    boolean m_GridIsExtendable

whether the grid can be extended

##### m_LogFile

    File m_LogFile

the log file to use

##### m_MaxGridExtensions

    int m_MaxGridExtensions

maximum number of grid extensions (-1 means unlimited)

##### m_SampleSize

    double m_SampleSize

the sample size to search the initial grid with

##### m_Traversal

    int m_Traversal

the traversal

##### m_UniformPerformance

    boolean m_UniformPerformance

whether all performances in the grid are the same

##### m_Values

    weka.classifiers.meta.GridSearch.PointDouble m_Values

the best values

##### m_X_Base

    double m_X_Base

the base for

##### m_X_Expression

    String m_X_Expression

The expression for the X property. Available parameters for the expression:

- BASE
- FROM (= min)
- TO (= max)
- STEP
- I - the current value (from 'from' to 'to' with stepsize 'step')

See Also:
  - `MathematicalExpression`
  - `MathExpression`

##### m_X_Max

    double m_X_Max

the maximum of X

##### m_X_Min

    double m_X_Min

the minimum of X

##### m_X_Property

    String m_X_Property

the X option to work on (without leading dash, preceding 'classifier.' means to set the option for the classifier 'filter.' for the filter)

##### m_X_Step

    double m_X_Step

the step size of

##### m_Y_Base

    double m_Y_Base

the base for Y

##### m_Y_Expression

    String m_Y_Expression

The expression for the Y property. Available parameters for the expression:

- BASE
- FROM (= min)
- TO (= max)
- STEP
- I - the current value (from 'from' to 'to' with stepsize 'step')

See Also:
  - `MathematicalExpression`
  - `MathExpression`

##### m_Y_Max

    double m_Y_Max

the maximum of Y

##### m_Y_Min

    double m_Y_Min

the minimum of Y

##### m_Y_Property

    String m_Y_Property

the Y option to work on (without leading dash, preceding 'classifier.' means to set the option for the classifier 'filter.' for the filter)

##### m_Y_Step

    double m_Y_Step

the step size of Y

### Class weka.classifiers.meta.GridSearch.Grid

class Grid extends Object implements Serializable

serialVersionUID:
  7290732613611243139L

#### Serialized Fields

##### m_Height

    int m_Height

the number of points on the Y axis

##### m_LabelX

    String m_LabelX

the label for the X axis

##### m_LabelY

    String m_LabelY

the label for the Y axis

##### m_MaxX

    double m_MaxX

the maximum on the X axis

##### m_MaxY

    double m_MaxY

the maximum on the Y axis

##### m_MinX

    double m_MinX

the minimum on the X axis

##### m_MinY

    double m_MinY

the minimum on the Y axis

##### m_StepX

    double m_StepX

the step size for the X axis

##### m_StepY

    double m_StepY

the step size for the Y axis

##### m_Width

    int m_Width

the number of points on the X axis

### Class weka.classifiers.meta.GridSearch.Performance

class Performance extends Object implements Serializable

serialVersionUID:
  -4374706475277588755L

#### Serialized Fields

##### m_ACC

    double m_ACC

the Accuracy

##### m_CC

    double m_CC

the Correlation coefficient

##### m_Kappa

    double m_Kappa

the kappa value

##### m_MAE

    double m_MAE

the Mean absolute error

##### m_RAE

    double m_RAE

the Relative absolute error

##### m_RMSE

    double m_RMSE

the Root mean squared error

##### m_RRSE

    double m_RRSE

the Root relative squared error

##### m_Values

    weka.classifiers.meta.GridSearch.PointDouble m_Values

the value pair the classifier was built with

### Class weka.classifiers.meta.GridSearch.PerformanceCache

class PerformanceCache extends Object implements Serializable

serialVersionUID:
  5838863230451530252L

#### Serialized Fields

##### m_Cache

    Hashtable m_Cache

the cache for points in the grid that got calculated

### Class weka.classifiers.meta.GridSearch.PerformanceComparator

class PerformanceComparator extends Object implements Serializable

serialVersionUID:
  6507592831825393847L

#### Serialized Fields

##### m_Evaluation

    int m_Evaluation

the performance measure to use for comparison

See Also:
  - `GridSearch.TAGS_EVALUATION`

### Class weka.classifiers.meta.GridSearch.PerformanceTable

class PerformanceTable extends Object implements Serializable

serialVersionUID:
  5486491313460338379L

#### Serialized Fields

##### m_Grid

    weka.classifiers.meta.GridSearch.Grid m_Grid

the corresponding grid

##### m_Max

    double m_Max

the maximum performance

##### m_Min

    double m_Min

the minimum performance

##### m_Performances

    Vector<weka.classifiers.meta.GridSearch.Performance> m_Performances

the performances

##### m_Table

    double[][] m_Table

the table with the values

##### m_Type

    int m_Type

the type of performance the table was generated for

### Class weka.classifiers.meta.GridSearch.PointDouble

class PointDouble extends Point2D.Double implements Serializable

serialVersionUID:
  7151661776161898119L

### Class weka.classifiers.meta.GridSearch.PointInt

class PointInt extends Point implements Serializable

serialVersionUID:
  -5900415163698021618L

### Class weka.classifiers.meta.LogitBoost

class LogitBoost extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID:
  8627452775249625582L

#### Serialized Fields

##### m_ClassAttribute

    Attribute m_ClassAttribute

The actual class attribute (for getting class names)

##### m_Classifiers

    Classifier[][] m_Classifiers

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

##### m_NumClasses

    int m_NumClasses

The number of classes

##### m_NumericClassData

    Instances m_NumericClassData

Dummy dataset with a numeric class

##### m_NumFolds

    int m_NumFolds

The number of folds for the internal cross-validation.

##### m_NumGenerated

    int m_NumGenerated

The number of successfully generated base classifiers.

##### m_NumRuns

    int m_NumRuns

The number of runs for the internal cross-validation.

##### m_Offset

    double m_Offset

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

##### m_Precision

    double m_Precision

The threshold on the improvement of the likelihood

##### m_RandomInstance

    Random m_RandomInstance

The random number generator used

##### m_Shrinkage

    double m_Shrinkage

The value of the shrinkage parameter

##### m_UseResampling

    boolean m_UseResampling

Use boosting with reweighting?

##### m_WeightThreshold

    int m_WeightThreshold

Weight thresholding. The percentage of weight mass used in training

##### m_ZeroR

    Classifier m_ZeroR

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

### Class weka.classifiers.meta.MetaCost

class MetaCost extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  1205317833344726855L

#### Serialized Fields

##### m_BagSizePercent

    int m_BagSizePercent

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

##### m_CostFile

    String m_CostFile

The name of the cost file, for command line options

##### m_CostMatrix

    CostMatrix m_CostMatrix

The cost matrix

##### m_MatrixSource

    int m_MatrixSource

Indicates the current cost matrix source

##### m_NumIterations

    int m_NumIterations

The number of iterations.

##### m_OnDemandDirectory

    File m_OnDemandDirectory

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

### Class weka.classifiers.meta.MultiBoostAB

class MultiBoostAB extends AdaBoostM1 implements Serializable

serialVersionUID:
  -6681619178187935148L

#### Serialized Fields

##### m_NumSubCmtys

    int m_NumSubCmtys

The number of sub-committees to use

##### m_Random

    Random m_Random

Random number generator

### Class weka.classifiers.meta.MultiClassClassifier

class MultiClassClassifier extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -3879602011542849141L

#### Serialized Fields

##### m_ClassAttribute

    Attribute m_ClassAttribute

Internal copy of the class attribute for output purposes

##### m_ClassFilters

    Filter[] m_ClassFilters

The filters used to transform the class.

##### m_Classifiers

    Classifier[] m_Classifiers

The classifiers.

##### m_Method

    int m_Method

The multiclass method to use

##### m_pairwiseCoupling

    boolean m_pairwiseCoupling

Use pairwise coupling with 1-vs-1

##### m_RandomWidthFactor

    double m_RandomWidthFactor

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

##### m_SumOfWeights

    double[] m_SumOfWeights

Needed for pairwise coupling

##### m_TwoClassDataset

    Instances m_TwoClassDataset

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

##### m_ZeroR

    ZeroR m_ZeroR

ZeroR classifier for when all base classifier return zero probability.

### Class weka.classifiers.meta.MultiScheme

class MultiScheme extends RandomizableMultipleClassifiersCombiner implements Serializable

serialVersionUID:
  5710744346128957520L

#### Serialized Fields

##### m_Classifier

    Classifier m_Classifier

The classifier that had the best performance on training data.

##### m_ClassifierIndex

    int m_ClassifierIndex

The index into the vector for the selected scheme

##### m_NumXValFolds

    int m_NumXValFolds

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

### Class weka.classifiers.meta.OrdinalClassClassifier

class OrdinalClassClassifier extends SingleClassifierEnhancer implements Serializable

serialVersionUID:
  -3461971774059603636L

#### Serialized Fields

##### m_ClassFilters

    MakeIndicator[] m_ClassFilters

The filters used to transform the class.

##### m_Classifiers

    Classifier[] m_Classifiers

The classifiers. (One for each class.)

##### m_ZeroR

    ZeroR m_ZeroR

ZeroR classifier for when all base classifier return zero probability.

### Class weka.classifiers.meta.RacedIncrementalLogitBoost

class RacedIncrementalLogitBoost extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  908598343772170052L

#### Serialized Fields

##### m_bestCommittee

    weka.classifiers.meta.RacedIncrementalLogitBoost.Committee m_bestCommittee

The current best committee

##### m_ClassAttribute

    Attribute m_ClassAttribute

The actual class attribute (for getting class names)

##### m_committees

    FastVector m_committees

The committees

##### m_currentSet

    Instances m_currentSet

The instances currently in memory for training

##### m_maxBatchSizeRequired

    int m_maxBatchSizeRequired

The maximum number of instances required for processing

##### m_maxChunkSize

    int m_maxChunkSize

The maimum chunk size used for training

##### m_minChunkSize

    int m_minChunkSize

The minimum chunk size used for training

##### m_NumClasses

    int m_NumClasses

The number of classes

##### m_NumericClassData

    Instances m_NumericClassData

Dummy dataset with a numeric class

##### m_numInstancesConsumed

    int m_numInstancesConsumed

The number of instances consumed

##### m_PruningType

    int m_PruningType

The pruning type used

##### m_RandomInstance

    Random m_RandomInstance

The random number generator used

##### m_UseResampling

    boolean m_UseResampling

Whether to use resampling

##### m_validationChunkSize

    int m_validationChunkSize

The size of the validation set

##### m_validationSet

    Instances m_validationSet

The instances used for validation

##### m_validationSetChanged

    boolean m_validationSetChanged

Whether the validation set has recently been changed

##### m_zeroR

    ZeroR m_zeroR

The default scheme used when committees aren't ready

### Class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee

class Committee extends Object implements Serializable

serialVersionUID:
  5559880306684082199L

#### Serialized Fields

##### m_chunkSize

    int m_chunkSize

##### m_instancesConsumed

    int m_instancesConsumed

number eaten from m_currentSet

##### m_lastLogLikelihood

    double m_lastLogLikelihood

##### m_lastValidationError

    double m_lastValidationError

##### m_modelHasChanged

    boolean m_modelHasChanged

##### m_modelHasChangedLL

    boolean m_modelHasChangedLL

##### m_models

    FastVector m_models

##### m_newValidationFs

    double[][] m_newValidationFs

##### m_validationFs

    double[][] m_validationFs

### Class weka.classifiers.meta.RandomCommittee

class RandomCommittee extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -9204394360557300092L

### Class weka.classifiers.meta.RandomSubSpace

class RandomSubSpace extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID:
  1278172513912424947L

#### Serialized Fields

##### m_SubSpaceSize

    double m_SubSpaceSize

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

##### m_ZeroR

    Classifier m_ZeroR

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

### Class weka.classifiers.meta.RegressionByDiscretization

class RegressionByDiscretization extends SingleClassifierEnhancer implements Serializable

serialVersionUID:
  5066426153134050378L

#### Serialized Fields

##### m_ClassMeans

    double[] m_ClassMeans

The mean values for each Discretized class interval.

##### m_DeleteEmptyBins

    boolean m_DeleteEmptyBins

Whether to delete empty intervals.

##### m_DiscretizedHeader

    Instances m_DiscretizedHeader

Header of discretized data.

##### m_Discretizer

    Discretize m_Discretizer

The discretization filter.

##### m_NumBins

    int m_NumBins

The number of discretization intervals.

##### m_UseEqualFrequency

    boolean m_UseEqualFrequency

Use equal-frequency binning

### Class weka.classifiers.meta.RotationForest

class RotationForest extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -3255631880798499936L

#### Serialized Fields

##### m_Groups

    int[][][] m_Groups

The attributes of each group

##### m_Headers

    Instances[] m_Headers

Headers of the transformed dataset

##### m_MaxGroup

    int m_MaxGroup

The maximum size of a group

##### m_MinGroup

    int m_MinGroup

The minimum size of a group

##### m_Normalize

    Normalize m_Normalize

Filter that normalized the attributes

##### m_NumberOfGroups

    boolean m_NumberOfGroups

Whether minGroup and maxGroup refer to the number of groups or their size

##### m_ProjectionFilter

    Filter m_ProjectionFilter

The type of projection filter

##### m_ProjectionFilters

    Filter[][] m_ProjectionFilters

The projection filters

##### m_ReducedHeaders

    Instances[][] m_ReducedHeaders

Headers of the reduced datasets

##### m_RemovedPercentage

    int m_RemovedPercentage

The percentage of instances to be removed

##### m_RemoveUseless

    RemoveUseless m_RemoveUseless

Filter that remove useless attributes

### Class weka.classifiers.meta.Stacking

class Stacking extends RandomizableMultipleClassifiersCombiner implements Serializable

serialVersionUID:
  5134738557155845452L

#### Serialized Fields

##### m_BaseFormat

    Instances m_BaseFormat

Format for base data

##### m_MetaClassifier

    Classifier m_MetaClassifier

The meta classifier

##### m_MetaFormat

    Instances m_MetaFormat

Format for meta data

##### m_NumFolds

    int m_NumFolds

Set the number of folds for the cross-validation

### Class weka.classifiers.meta.StackingC

class StackingC extends Stacking implements Serializable

serialVersionUID:
  -6717545616603725198L

#### Serialized Fields

##### m_attrFilter

    Remove m_attrFilter

Filter to transform metaData - Remove

##### m_makeIndicatorFilter

    MakeIndicator m_makeIndicatorFilter

Filter to transform metaData - MakeIndicator

##### m_MetaClassifiers

    Classifier[] m_MetaClassifiers

The meta classifiers (one for each class, like in ClassificationViaRegression)

### Class weka.classifiers.meta.ThresholdSelector

class ThresholdSelector extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -1795038053239867444L

#### Serialized Fields

##### m_BestThreshold

    double m_BestThreshold

The threshold that lead to the best performance

##### m_BestValue

    double m_BestValue

The best value that has been observed

##### m_ClassMode

    int m_ClassMode

Method to determine which class to optimize for

##### m_DesignatedClass

    int m_DesignatedClass

Designated class value, determined during building

##### m_EvalMode

    int m_EvalMode

The evaluation mode

##### m_HighThreshold

    double m_HighThreshold

The upper threshold used as the basis of correction

##### m_LowThreshold

    double m_LowThreshold

The lower threshold used as the basis of correction

##### m_manualThreshold

    boolean m_manualThreshold

True if a manually set threshold is being used

##### m_manualThresholdValue

    double m_manualThresholdValue

-1 = not used by default

##### m_nMeasure

    int m_nMeasure

evaluation measure used for determining threshold

##### m_NumXValFolds

    int m_NumXValFolds

The number of folds used in cross-validation

##### m_RangeMode

    int m_RangeMode

The range correction mode

### Class weka.classifiers.meta.Vote

class Vote extends RandomizableMultipleClassifiersCombiner implements Serializable

serialVersionUID:
  -637891196294399624L

#### Serialized Fields

##### m_CombinationRule

    int m_CombinationRule

Combination Rule variable

##### m_Random

    Random m_Random

the random number generator used for breaking ties in majority voting

See Also:
  - `Vote.distributionForInstanceMajorityVoting(Instance)`

## Package weka.classifiers.meta.nestedDichotomies

### Class weka.classifiers.meta.nestedDichotomies.ClassBalancedND

class ClassBalancedND extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  5944063630650811903L

#### Serialized Fields

##### m_classifiers

    Hashtable m_classifiers

The hashtable for this node.

##### m_FilteredClassifier

    FilteredClassifier m_FilteredClassifier

The filtered classifier in which the base classifier is wrapped.

##### m_FirstSuccessor

    ClassBalancedND m_FirstSuccessor

The first successor

##### m_hashtablegiven

    boolean m_hashtablegiven

Is Hashtable given from END?

##### m_Range

    Range m_Range

The classes that are grouped together at the current node

##### m_SecondSuccessor

    ClassBalancedND m_SecondSuccessor

The second successor

### Class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND

class DataNearBalancedND extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  5117477294209496368L

#### Serialized Fields

##### m_classifiers

    Hashtable m_classifiers

The hashtable for this node.

##### m_FilteredClassifier

    FilteredClassifier m_FilteredClassifier

The filtered classifier in which the base classifier is wrapped.

##### m_FirstSuccessor

    DataNearBalancedND m_FirstSuccessor

The first successor

##### m_hashtablegiven

    boolean m_hashtablegiven

Is Hashtable given from END?

##### m_Range

    Range m_Range

The classes that are grouped together at the current node

##### m_SecondSuccessor

    DataNearBalancedND m_SecondSuccessor

The second successor

### Class weka.classifiers.meta.nestedDichotomies.ND

class ND extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID:
  -6355893369855683820L

#### Serialized Fields

##### m_classifiers

    Hashtable m_classifiers

The hashtable containing all the classifiers

##### m_hashtablegiven

    boolean m_hashtablegiven

Is Hashtable given from END?

##### m_ndtree

    weka.classifiers.meta.nestedDichotomies.ND.NDTree m_ndtree

The tree of classes

### Class weka.classifiers.meta.nestedDichotomies.ND.NDTree

class NDTree extends Object implements Serializable

serialVersionUID:
  4284655952754474880L

#### Serialized Fields

##### m_indices

    FastVector m_indices

The indices associated with this node

##### m_left

    weka.classifiers.meta.nestedDichotomies.ND.NDTree m_left

The left successor

##### m_parent

    weka.classifiers.meta.nestedDichotomies.ND.NDTree m_parent

The parent

##### m_right

    weka.classifiers.meta.nestedDichotomies.ND.NDTree m_right

The right successor

## Package weka.classifiers.mi

### Class weka.classifiers.mi.CitationKNN

class CitationKNN extends Classifier implements Serializable

serialVersionUID:
  -8435377743874094852L

#### Serialized Fields

##### m_Attributes

    Instances m_Attributes

attribute name structure of the relational attribute

##### m_Citers

    int[] m_Citers

C nearest citers

##### m_CitersDebug

    boolean m_CitersDebug

##### m_Classes

    int[] m_Classes

Class labels for each bag

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_CNN

    weka.classifiers.mi.CitationKNN.NeighborList[] m_CNN

C nearest neighbors considering all the bags

##### m_CNNDebug

    boolean m_CNNDebug

Different debugging output

##### m_Debug

    boolean m_Debug

Debugging output

##### m_Diffs

    double[] m_Diffs

Normalization of the euclidean distance

##### m_HDistanceDebug

    boolean m_HDistanceDebug

##### m_HDRank

    int m_HDRank

Rank associated to the Hausdorff distance

##### m_IdIndex

    int m_IdIndex

##### m_Max

    double[] m_Max

##### m_MaxNorm

    double m_MaxNorm

##### m_Min

    double[] m_Min

##### m_MinNorm

    double m_MinNorm

##### m_NeighborListDebug

    boolean m_NeighborListDebug

##### m_NumCiters

    int m_NumCiters

Number of citers

##### m_NumClasses

    int m_NumClasses

The number of the class labels

##### m_NumReferences

    int m_NumReferences

Number of references

##### m_References

    int[] m_References

R nearest references

##### m_ReferencesDebug

    boolean m_ReferencesDebug

##### m_TrainBags

    Instances m_TrainBags

Training bags

### Class weka.classifiers.mi.MDD

class MDD extends Classifier implements Serializable

serialVersionUID:
  -7273119490545290581L

#### Serialized Fields

##### m_Attributes

    Instances m_Attributes

All attribute names

##### m_Classes

    int[] m_Classes

Class labels for each bag

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_Data

    double[][][] m_Data

MI data

##### m_Filter

    Filter m_Filter

The filter used to standardize/normalize all values.

##### m_filterType

    int m_filterType

Whether to normalize/standardize/neither, default:standardize

##### m_Missing

    ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

##### m_NumClasses

    int m_NumClasses

The number of the class labels

##### m_Par

    double[] m_Par

### Class weka.classifiers.mi.MIBoost

class MIBoost extends SingleClassifierEnhancer implements Serializable

serialVersionUID:
  -3808427225599279539L

#### Serialized Fields

##### m_Attributes

    Instances m_Attributes

attributes name for the new dataset used to build the model

##### m_Beta

    double[] m_Beta

Voting weights of models

##### m_Classes

    int[] m_Classes

Class labels for each bag

##### m_ConvertToSI

    MultiInstanceToPropositional m_ConvertToSI

filter used to convert the MI dataset into single-instance dataset

##### m_DiscretizeBin

    int m_DiscretizeBin

the number of discretization bins

##### m_Filter

    Discretize m_Filter

filter used for discretization

##### m_MaxIterations

    int m_MaxIterations

the maximum number of boost iterations

##### m_Models

    Classifier[] m_Models

the models for the iterations

##### m_NumClasses

    int m_NumClasses

The number of the class labels

##### m_NumIterations

    int m_NumIterations

Number of iterations

### Class weka.classifiers.mi.MIDD

class MIDD extends Classifier implements Serializable

serialVersionUID:
  4263507733600536168L

#### Serialized Fields

##### m_Attributes

    Instances m_Attributes

All attribute names

##### m_Classes

    int[] m_Classes

Class labels for each bag

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_Data

    double[][][] m_Data

MI data

##### m_Filter

    Filter m_Filter

The filter used to standardize/normalize all values.

##### m_filterType

    int m_filterType

Whether to normalize/standardize/neither, default:standardize

##### m_Missing

    ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

##### m_NumClasses

    int m_NumClasses

The number of the class labels

##### m_Par

    double[] m_Par

### Class weka.classifiers.mi.MIEMDD

class MIEMDD extends RandomizableClassifier implements Serializable

serialVersionUID:
  3899547154866223734L

#### Serialized Fields

##### m_Attributes

    Instances m_Attributes

All attribute names

##### m_Classes

    int[] m_Classes

Class labels for each bag

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_Data

    double[][][] m_Data

MI data

##### m_emData

    double[][] m_emData

MI data

##### m_Filter

    Filter m_Filter

The filter used to standardize/normalize all values.

##### m_filterType

    int m_filterType

Whether to normalize/standardize/neither, default:standardize

##### m_Missing

    ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

##### m_NumClasses

    int m_NumClasses

The number of the class labels

##### m_Par

    double[] m_Par

### Class weka.classifiers.mi.MILR

class MILR extends Classifier implements Serializable

serialVersionUID:
  1996101190172373826L

#### Serialized Fields

##### m_AlgorithmType

    int m_AlgorithmType

the type of processing

##### m_Attributes

    Instances m_Attributes

All attribute names

##### m_Classes

    int[] m_Classes

Class labels for each bag

##### m_Data

    double[][][] m_Data

MI data

##### m_NumClasses

    int m_NumClasses

The number of the class labels

##### m_Par

    double[] m_Par

##### m_Ridge

    double m_Ridge

The ridge parameter.

##### xMean

    double[] xMean

##### xSD

    double[] xSD

### Class weka.classifiers.mi.MINND

class MINND extends Classifier implements Serializable

serialVersionUID:
  -4512599203273864994L

#### Serialized Fields

##### m_Attributes

    Instances m_Attributes

header info of the data

##### m_Change

    double[][] m_Change

The weights that alter the dimnesion of each exemplar

##### m_Choose

    int m_Choose

The number of nearest neighbour exemplars in the selection of noises in the test data

##### m_Class

    double[] m_Class

The class label of each exemplar

##### m_Decay

    double m_Decay

The decay rate of learning rate

##### m_Dimension

    int m_Dimension

The dimension of each exemplar, i.e. (numAttributes-2)

##### m_MaxArray

    double[] m_MaxArray

The maximum values for numeric attributes.

##### m_Mean

    double[][] m_Mean

The mean for each attribute of each exemplar

##### m_MinArray

    double[] m_MinArray

The minimum values for numeric attributes.

##### m_Neighbour

    int m_Neighbour

The number of nearest neighbour for prediction

##### m_NoiseM

    double[][] m_NoiseM

The noise data of each exemplar

##### m_NoiseV

    double[][] m_NoiseV

The noise data of each exemplar

##### m_NumClasses

    int m_NumClasses

The number of class labels in the data

##### m_Rate

    double m_Rate

The learning rate in the gradient descent

##### m_Select

    int m_Select

The number of nearest neighbour instances in the selection of noises in the training data

##### m_STOP

    double m_STOP

The stopping criteria of gradient descent

##### m_ValidM

    double[][] m_ValidM

The noise data of each exemplar

##### m_ValidV

    double[][] m_ValidV

The noise data of each exemplar

##### m_Variance

    double[][] m_Variance

The variance for each attribute of each exemplar

##### m_Weights

    double[] m_Weights

The weight of each exemplar

### Class weka.classifiers.mi.MIOptimalBall

class MIOptimalBall extends Classifier implements Serializable

serialVersionUID:
  -6465750129576777254L

#### Serialized Fields

##### m_Center

    double[] m_Center

center of the optimal ball

##### m_ConvertToMI

    PropositionalToMultiInstance m_ConvertToMI

filter used to convert the single-instance dataset into MI dataset

##### m_ConvertToSI

    MultiInstanceToPropositional m_ConvertToSI

filter used to convert the MI dataset into single-instance dataset

##### m_Distance

    double[][][] m_Distance

the distances from each instance in a positive bag to each bag

##### m_Filter

    Filter m_Filter

The filter used to standardize/normalize all values.

##### m_filterType

    int m_filterType

Whether to normalize/standardize/neither

##### m_Radius

    double m_Radius

radius of the optimal ball

### Class weka.classifiers.mi.MISMO

class MISMO extends Classifier implements Serializable

serialVersionUID:
  -5834036950143719712L

#### Serialized Fields

##### m_C

    double m_C

The complexity parameter.

##### m_checksTurnedOff

    boolean m_checksTurnedOff

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

##### m_classAttribute

    Attribute m_classAttribute

The class attribute

##### m_classifiers

    weka.classifiers.mi.MISMO.BinaryMISMO[][] m_classifiers

The binary classifier(s)

##### m_classIndex

    int m_classIndex

The class index from the training data

##### m_eps

    double m_eps

Epsilon for rounding.

##### m_Filter

    Filter m_Filter

The filter used to standardize/normalize all values.

##### m_filterType

    int m_filterType

Whether to normalize/standardize/neither

##### m_fitLogisticModels

    boolean m_fitLogisticModels

Whether logistic models are to be fit

##### m_kernel

    Kernel m_kernel

Kernel to use

##### m_minimax

    boolean m_minimax

Use MIMinimax feature space?

##### m_Missing

    ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

##### m_NominalToBinary

    NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

##### m_numFolds

    int m_numFolds

The number of folds for the internal cross-validation

##### m_randomSeed

    int m_randomSeed

The random number seed

##### m_tol

    double m_tol

Tolerance for accuracy of result.

### Class weka.classifiers.mi.MISMO.BinaryMISMO

class BinaryMISMO extends Object implements Serializable

serialVersionUID:
  -7107082483475433531L

#### Serialized Fields

##### m_alpha

    double[] m_alpha

The Lagrange multipliers.

##### m_b

    double m_b

The thresholds.

##### m_bLow

    double m_bLow

The thresholds.

##### m_bUp

    double m_bUp

The thresholds.

##### m_class

    double[] m_class

The transformed class values.

##### m_data

    Instances m_data

The training data.

##### m_errors

    double[] m_errors

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

##### m_I0

    SMOset m_I0

{i: 0 invalid input: '<' m_alpha[i] invalid input: '<' C}

##### m_I1

    SMOset m_I1

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

##### m_I2

    SMOset m_I2

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

##### m_I3

    SMOset m_I3

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

##### m_I4

    SMOset m_I4

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

##### m_iLow

    int m_iLow

The indices for m_bLow and m_bUp

##### m_iUp

    int m_iUp

The indices for m_bLow and m_bUp

##### m_kernel

    Kernel m_kernel

Kernel to use

##### m_logistic

    Logistic m_logistic

Stores logistic regression model for probability estimate

##### m_sparseIndices

    int[] m_sparseIndices

##### m_sparseWeights

    double[] m_sparseWeights

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

##### m_sumOfWeights

    double m_sumOfWeights

Stores the weight of the training instances

##### m_supportVectors

    SMOset m_supportVectors

The set of support vectors {i: 0 invalid input: '<' m_alpha[i]}

##### m_weights

    double[] m_weights

Weight vector for linear machine.

### Class weka.classifiers.mi.MISVM

class MISVM extends Classifier implements Serializable

serialVersionUID:
  7622231064035278145L

#### Serialized Fields

##### m_C

    double m_C

The complexity parameter.

##### m_ConvertToProp

    MultiInstanceToPropositional m_ConvertToProp

filter used to convert the MI dataset into single-instance dataset

##### m_Filter

    Filter m_Filter

The filter used to standardize/normalize all values.

##### m_filterType

    int m_filterType

Whether to normalize/standardize/neither

##### m_kernel

    Kernel m_kernel

the kernel to use

##### m_MaxIterations

    int m_MaxIterations

the maximum number of iterations to perform

##### m_SparseFilter

    Filter m_SparseFilter

The filter used to transform the sparse datasets to nonsparse

##### m_SVM

    weka.classifiers.mi.MISVM.SVM m_SVM

The SMO classifier used to compute SVM soluton w,b for the dataset

### Class weka.classifiers.mi.MIWrapper

class MIWrapper extends SingleClassifierEnhancer implements Serializable

serialVersionUID:
  -7707766152904315910L

#### Serialized Fields

##### m_ConvertToProp

    MultiInstanceToPropositional m_ConvertToProp

Filter used to convert MI dataset into single-instance dataset

##### m_Method

    int m_Method

the test method

##### m_NumClasses

    int m_NumClasses

The number of the class labels

##### m_WeightMethod

    int m_WeightMethod

the single-instance weight setting method

### Class weka.classifiers.mi.SimpleMI

class SimpleMI extends SingleClassifierEnhancer implements Serializable

serialVersionUID:
  9137795893666592662L

#### Serialized Fields

##### m_TransformMethod

    int m_TransformMethod

the method used in transformation

## Package weka.classifiers.mi.supportVector

### Class weka.classifiers.mi.supportVector.MIPolyKernel

class MIPolyKernel extends PolyKernel implements Serializable

serialVersionUID:
  7926421479341051777L

### Class weka.classifiers.mi.supportVector.MIRBFKernel

class MIRBFKernel extends RBFKernel implements Serializable

serialVersionUID:
  -8711882393708956962L

#### Serialized Fields

##### m_kernelPrecalc

    double[][] m_kernelPrecalc

The precalculated dotproducts of <inst_i,inst_i>

## Package weka.classifiers.misc

### Class weka.classifiers.misc.HyperPipes

class HyperPipes extends Classifier implements Serializable

serialVersionUID:
  -7527596632268975274L

#### Serialized Fields

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_HyperPipes

    weka.classifiers.misc.HyperPipes.HyperPipe[] m_HyperPipes

Stores the HyperPipe for each class

##### m_Instances

    Instances m_Instances

The structure of the training data

##### m_ZeroR

    Classifier m_ZeroR

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

### Class weka.classifiers.misc.SerializedClassifier

class SerializedClassifier extends Classifier implements Serializable

serialVersionUID:
  4599593909947628642L

#### Serialized Fields

##### m_ModelFile

    File m_ModelFile

the file where the serialized model is stored

### Class weka.classifiers.misc.VFI

class VFI extends Classifier implements Serializable

serialVersionUID:
  8081692166331321866L

#### Serialized Fields

##### m_bias

    double m_bias

Bias towards more confident intervals

##### m_ClassIndex

    int m_ClassIndex

The index of the class attribute

##### m_counts

    double[][][] m_counts

The class counts for each interval of each attribute

##### m_globalCounts

    double[] m_globalCounts

The global class counts

##### m_Instances

    Instances m_Instances

The training data

##### m_intervalBounds

    double[][] m_intervalBounds

The lower bounds for each attribute

##### m_maxEntrop

    double m_maxEntrop

The maximum entropy for the class

##### m_NumClasses

    int m_NumClasses

The number of classes

##### m_weightByConfidence

    boolean m_weightByConfidence

Exponentially bias more confident intervals

##### TINY

    double TINY

## Package weka.classifiers.pmml.consumer

### Class weka.classifiers.pmml.consumer.GeneralRegression

class GeneralRegression extends PMMLClassifier implements Serializable

serialVersionUID:
  2583880411828388959L

#### Serialized Fields

##### m_algorithmName

    String m_algorithmName

##### m_covariateList

    ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Predictor> m_covariateList

##### m_cumulativeLinkFunction

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

##### m_distParameter

    double m_distParameter

##### m_distribution

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

##### m_factorList

    ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Predictor> m_factorList

##### m_functionType

    int m_functionType

##### m_linkFunction

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

##### m_linkParameter

    double m_linkParameter

##### m_modelName

    String m_modelName

##### m_modelType

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

##### m_offsetValue

    double m_offsetValue

##### m_offsetVariable

    String m_offsetVariable

##### m_parameterList

    ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Parameter> m_parameterList

##### m_paramMatrix

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

##### m_ppMatrix

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

##### m_trialsValue

    double m_trialsValue

##### m_trialsVariable

    String m_trialsVariable

### Class weka.classifiers.pmml.consumer.NeuralNetwork

class NeuralNetwork extends PMMLClassifier implements Serializable

serialVersionUID:
  -4545904813133921249L

#### Serialized Fields

##### m_activationFunction

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

The activation function to use

##### m_altitude

    double m_altitude

Altitude for radial basis

##### m_functionType

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

The mining function

##### m_inputMap

    HashMap<String,Double> m_inputMap

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

##### m_inputs

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

The inputs to the network

##### m_layers

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

The hidden layers in the network

##### m_normalizationMethod

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

The normalization method

##### m_numberOfInputs

    int m_numberOfInputs

The number of inputs to the network

##### m_numberOfLayers

    int m_numberOfLayers

Number of hidden layers in the network

##### m_outputs

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

The outputs of the network

##### m_threshold

    double m_threshold

Threshold activation

##### m_width

    double m_width

Width for radial basis

### Class weka.classifiers.pmml.consumer.PMMLClassifier

class PMMLClassifier extends Classifier implements Serializable

serialVersionUID:
  -5371600590320702971L

#### Serialized Fields

##### m_creatorApplication

    String m_creatorApplication

Creator application

##### m_dataDictionary

    Instances m_dataDictionary

The data dictionary

##### m_log

    Logger m_log

Logger

##### m_miningSchema

    MiningSchema m_miningSchema

The fields and meta data used by the model

##### m_pmmlVersion

    String m_pmmlVersion

PMML version

### Class weka.classifiers.pmml.consumer.Regression

class Regression extends PMMLClassifier implements Serializable

serialVersionUID:
  -5551125528409488634L

#### Serialized Fields

##### m_algorithmName

    String m_algorithmName

Description of the algorithm

##### m_normalizationMethod

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

The normalization to use

##### m_regressionTables

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

The regression tables for this regression

### Class weka.classifiers.pmml.consumer.Regression.RegressionTable.CategoricalPredictor

class CategoricalPredictor extends weka.classifiers.pmml.consumer.Regression.RegressionTable.Predictor implements Serializable

serialVersionUID:
  3077920125549906819L

#### Serialized Fields

##### m_valueIndex

    int m_valueIndex

The index of the attribute value for this predictor

##### m_valueName

    String m_valueName

The attribute value for this predictor

### Class weka.classifiers.pmml.consumer.Regression.RegressionTable.NumericPredictor

class NumericPredictor extends weka.classifiers.pmml.consumer.Regression.RegressionTable.Predictor implements Serializable

serialVersionUID:
  -4335075205696648273L

#### Serialized Fields

##### m_exponent

    double m_exponent

The exponent

### Class weka.classifiers.pmml.consumer.Regression.RegressionTable.PredictorTerm

class PredictorTerm extends Object implements Serializable

serialVersionUID:
  5493100145890252757L

#### Serialized Fields

##### m_coefficient

    double m_coefficient

The coefficient for this predictor term

##### m_fieldNames

    String[] m_fieldNames

The names of the terms (attributes) to be multiplied

##### m_indexes

    int[] m_indexes

the indexes of the terms to be multiplied

## Package weka.classifiers.rules

### Class weka.classifiers.rules.ConjunctiveRule

class ConjunctiveRule extends Classifier implements Serializable

serialVersionUID:
  -5938309903225087198L

#### Serialized Fields

##### m_Antds

    FastVector m_Antds

The vector of antecedents of this rule

##### m_ClassAttribute

    Attribute m_ClassAttribute

The class attribute of the data

##### m_Cnsqt

    double[] m_Cnsqt

The consequent of this rule

##### m_DefDstr

    double[] m_DefDstr

The default rule distribution of the data not covered

##### m_Folds

    int m_Folds

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

##### m_IsExclude

    boolean m_IsExclude

Whether to use exlusive expressions for nominal attributes

##### m_MinNo

    double m_MinNo

The minimal number of instance weights within a split

##### m_NumAntds

    int m_NumAntds

The number of antecedents in pre-pruning

##### m_NumClasses

    int m_NumClasses

Number of classes in the training data

##### m_Random

    Random m_Random

The Random object used for randomization

##### m_Seed

    long m_Seed

The seed to perform randomization

##### m_Targets

    FastVector m_Targets

The predicted classes recorded for each antecedent in the growing data

### Class weka.classifiers.rules.DecisionTable

class DecisionTable extends Classifier implements Serializable

serialVersionUID:
  2888557078165701326L

#### Serialized Fields

##### m_classIsNominal

    boolean m_classIsNominal

Class is nominal

##### m_classPriorCounts

    double[] m_classPriorCounts

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

##### m_classPriors

    double[] m_classPriors

##### m_CVFolds

    int m_CVFolds

Number of folds for cross validating feature sets

##### m_decisionFeatures

    int[] m_decisionFeatures

Holds the final feature set

##### m_delTransform

    Remove m_delTransform

Filter used to remove columns discarded by feature selection

##### m_displayRules

    boolean m_displayRules

Display Rules

##### m_disTransform

    Filter m_disTransform

Discretization filter

##### m_dtInstances

    Instances m_dtInstances

Holds the final feature selected set of instances

##### m_entries

    Hashtable m_entries

The hashtable used to hold training instances

##### m_evaluation

    Evaluation m_evaluation

The evaluation object used to evaluate subsets

##### m_evaluationMeasure

    int m_evaluationMeasure

##### m_evaluator

    ASEvaluation m_evaluator

Our own internal evaluator

##### m_ibk

    IBk m_ibk

IB1 used to classify non matching instances rather than majority class

##### m_majority

    double m_majority

Holds the majority class

##### m_numAttributes

    int m_numAttributes

The number of attributes in the dataset

##### m_numInstances

    int m_numInstances

The number of instances in the dataset

##### m_rr

    Random m_rr

Random numbers for use in cross validation

##### m_saveMemory

    boolean m_saveMemory

##### m_search

    ASSearch m_search

The search method to use

##### m_theInstances

    Instances m_theInstances

Holds the original training instances

##### m_useIBk

    boolean m_useIBk

Use the IBk classifier rather than majority class

### Class weka.classifiers.rules.DecisionTableHashKey

class DecisionTableHashKey extends Object implements Serializable

serialVersionUID:
  5674163500154964602L

#### Serialized Fields

##### attributes

    double[] attributes

Array of attribute values for an instance

##### key

    int key

The key

##### missing

    boolean[] missing

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

### Class weka.classifiers.rules.DTNB

class DTNB extends DecisionTable implements Serializable

serialVersionUID:
  2999557077765701326L

#### Serialized Fields

##### m_backwardWithDelete

    ASSearch m_backwardWithDelete

##### m_NB

    NaiveBayes m_NB

The naive Bayes half of the hybrid

##### m_nbFeatures

    int[] m_nbFeatures

The features used by naive Bayes

##### m_percentDeleted

    double m_percentDeleted

Percentage of the features features that were dropped entirely

##### m_percentUsedByDT

    double m_percentUsedByDT

Percentage of the total number of features used by the decision table

### Class weka.classifiers.rules.DTNB.BackwardsWithDelete

class BackwardsWithDelete extends ASSearch implements Serializable

### Class weka.classifiers.rules.DTNB.EvalWithDelete

class EvalWithDelete extends ASEvaluation implements Serializable

#### Serialized Fields

##### m_deletedFromDTNB

    BitSet m_deletedFromDTNB

### Class weka.classifiers.rules.JRip

class JRip extends Classifier implements Serializable

serialVersionUID:
  -6589312996832147161L

#### Serialized Fields

##### m_CheckErr

    boolean m_CheckErr

Whether check the error rate >= 0.5 in stopping criteria

##### m_Class

    Attribute m_Class

The class attribute of the data

##### m_Debug

    boolean m_Debug

Whether in a debug mode

##### m_Distributions

    FastVector m_Distributions

The predicted class distribution

##### m_Filter

    Filter m_Filter

The filter used to randomize the class order

##### m_Folds

    int m_Folds

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

##### m_MinNo

    double m_MinNo

The minimal number of instance weights within a split

##### m_Optimizations

    int m_Optimizations

Runs of optimizations

##### m_Random

    Random m_Random

Random object used in this class

##### m_Ruleset

    FastVector m_Ruleset

The ruleset

##### m_RulesetStats

    FastVector m_RulesetStats

The RuleStats for the ruleset of each class value

##### m_Seed

    long m_Seed

The seed to perform randomization

##### m_Total

    double m_Total

# of all the possible conditions in a rule

##### m_UsePruning

    boolean m_UsePruning

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

### Class weka.classifiers.rules.JRip.Antd

class Antd extends Object implements Serializable

serialVersionUID:
  -8929754772994154334L

#### Serialized Fields

##### accu

    double accu

The accurate data for this antecedent in the growing data

##### accuRate

    double accuRate

The accurate rate of this antecedent test on the growing data

##### att

    Attribute att

The attribute of the antecedent

##### cover

    double cover

The coverage of this antecedent in the growing data

##### maxInfoGain

    double maxInfoGain

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

##### value

    double value

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

### Class weka.classifiers.rules.JRip.NominalAntd

class NominalAntd extends JRip.Antd implements Serializable

serialVersionUID:
  -9102297038837585135L

#### Serialized Fields

##### accurate

    double[] accurate

##### coverage

    double[] coverage

### Class weka.classifiers.rules.JRip.NumericAntd

class NumericAntd extends JRip.Antd implements Serializable

serialVersionUID:
  5699457269983735442L

#### Serialized Fields

##### splitPoint

    double splitPoint

The split point for this numeric antecedent

### Class weka.classifiers.rules.JRip.RipperRule

class RipperRule extends Rule implements Serializable

serialVersionUID:
  -2410020717305262952L

#### Serialized Fields

##### m_Antds

    FastVector m_Antds

The vector of antecedents of this rule

##### m_Consequent

    double m_Consequent

The internal representation of the class label to be predicted

### Class weka.classifiers.rules.M5Rules

class M5Rules extends M5Base implements Serializable

serialVersionUID:
  -1746114858746563180L

### Class weka.classifiers.rules.NNge

class NNge extends Classifier implements Serializable

serialVersionUID:
  4084742275553788972L

#### Serialized Fields

##### m_Exemplars

    weka.classifiers.rules.NNge.Exemplar m_Exemplars

The list of Exemplars

##### m_ExemplarsByClass

    weka.classifiers.rules.NNge.Exemplar[] m_ExemplarsByClass

The lists of Exemplars by class

##### m_MaxArray

    double[] m_MaxArray

The maximum values for numeric attributes.

##### m_MI

    double[] m_MI

##### m_MI_MaxArray

    double[] m_MI_MaxArray

##### m_MI_MinArray

    double[] m_MI_MinArray

##### m_MI_NumAttrClassInter

    int[][][] m_MI_NumAttrClassInter

MUTUAL INFORMATION'S DATAS

##### m_MI_NumAttrClassValue

    int[][][] m_MI_NumAttrClassValue

##### m_MI_NumAttrInter

    int[][] m_MI_NumAttrInter

##### m_MI_NumAttrValue

    int[][] m_MI_NumAttrValue

##### m_MI_NumClass

    int[] m_MI_NumClass

##### m_MI_NumInst

    int m_MI_NumInst

##### m_MinArray

    double[] m_MinArray

The minimum values for numeric attributes.

##### m_MissingVector

    double[] m_MissingVector

Values to use for missing value

##### m_NumAttemptsOfGene

    int m_NumAttemptsOfGene

The number of try for generalisation

##### m_NumFoldersMI

    int m_NumFoldersMI

The number of folder for the Mutual Information

##### m_Train

    Instances m_Train

An empty instances to keep the headers, the classIndex, etc...

### Class weka.classifiers.rules.OneR

class OneR extends Classifier implements Serializable

serialVersionUID:
  -3459427003147861443L

#### Serialized Fields

##### m_minBucketSize

    int m_minBucketSize

The minimum bucket size

##### m_rule

    weka.classifiers.rules.OneR.OneRRule m_rule

A 1-R rule

##### m_ZeroR

    Classifier m_ZeroR

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

### Class weka.classifiers.rules.PART

class PART extends Classifier implements Serializable

serialVersionUID:
  8121455039782598361L

#### Serialized Fields

##### m_binarySplits

    boolean m_binarySplits

Binary splits on nominal attributes?

##### m_CF

    float m_CF

Confidence level

##### m_minNumObj

    int m_minNumObj

Minimum number of objects

##### m_numFolds

    int m_numFolds

Number of folds for reduced error pruning.

##### m_reducedErrorPruning

    boolean m_reducedErrorPruning

Use reduced error pruning?

##### m_root

    MakeDecList m_root

The decision list

##### m_Seed

    int m_Seed

The seed for random number generation.

##### m_unpruned

    boolean m_unpruned

Generate unpruned list?

### Class weka.classifiers.rules.Prism

class Prism extends Classifier implements Serializable

serialVersionUID:
  1310258880025902106L

#### Serialized Fields

##### m_rules

    weka.classifiers.rules.Prism.PrismRule m_rules

The first rule in the list of rules

### Class weka.classifiers.rules.Ridor

class Ridor extends Classifier implements Serializable

serialVersionUID:
  -7261533075088314436L

#### Serialized Fields

##### m_Class

    Attribute m_Class

The class attribute of the data

##### m_Cover

    double m_Cover

Statistics of the data

##### m_Err

    double m_Err

Statistics of the data

##### m_Folds

    int m_Folds

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

##### m_IsAllErr

    boolean m_IsAllErr

Whether use error rate on all the data

##### m_IsMajority

    boolean m_IsMajority

Whether use majority class as default class

##### m_MinNo

    double m_MinNo

The minimal number of instance weights within a split

##### m_Random

    Random m_Random

Random object for randomization

##### m_Root

    weka.classifiers.rules.Ridor.Ridor_node m_Root

The root of Ridor

##### m_Seed

    int m_Seed

The seed to perform randomization

##### m_Shuffle

    int m_Shuffle

The number of shuffles performed on the data for randomization

### Class weka.classifiers.rules.Rule

class Rule extends Object implements Serializable

serialVersionUID:
  8815687740470471229L

### Class weka.classifiers.rules.RuleStats

class RuleStats extends Object implements Serializable

serialVersionUID:
  -5708153367675298624L

#### Serialized Fields

##### m_Data

    Instances m_Data

The data on which the stats calculation is based

##### m_Distributions

    FastVector m_Distributions

The class distributions predicted by each rule

##### m_Filtered

    FastVector m_Filtered

The set of instances filtered by the ruleset

##### m_Ruleset

    FastVector m_Ruleset

The specific ruleset in question

##### m_SimpleStats

    FastVector m_SimpleStats

The simple stats of each rule

##### m_Total

    double m_Total

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

##### MDL_THEORY_WEIGHT

    double MDL_THEORY_WEIGHT

The theory weight in the MDL calculation

### Class weka.classifiers.rules.ZeroR

class ZeroR extends Classifier implements Serializable

serialVersionUID:
  48055541465867954L

#### Serialized Fields

##### m_Class

    Attribute m_Class

The class attribute.

##### m_ClassValue

    double m_ClassValue

The class value 0R predicts.

##### m_Counts

    double[] m_Counts

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

## Package weka.classifiers.rules.part

### Class weka.classifiers.rules.part.C45PruneableDecList

class C45PruneableDecList extends ClassifierDecList implements Serializable

serialVersionUID:
  -2757684345218324559L

#### Serialized Fields

##### CF

    double CF

CF

### Class weka.classifiers.rules.part.ClassifierDecList

class ClassifierDecList extends Object implements Serializable

serialVersionUID:
  7284358349711992497L

#### Serialized Fields

##### indeX

    int indeX

Which son to expand?

##### m_isEmpty

    boolean m_isEmpty

True if node is empty.

##### m_isLeaf

    boolean m_isLeaf

True if node is leaf.

##### m_localModel

    ClassifierSplitModel m_localModel

Local model at node.

##### m_minNumObj

    int m_minNumObj

Minimum number of objects

##### m_sons

    ClassifierDecList[] m_sons

References to sons.

##### m_test

    Distribution m_test

The pruning instances.

##### m_toSelectModel

    ModelSelection m_toSelectModel

The model selection method.

##### m_train

    Instances m_train

The training instances.

### Class weka.classifiers.rules.part.MakeDecList

class MakeDecList extends Object implements Serializable

serialVersionUID:
  -1427481323245079123L

#### Serialized Fields

##### CF

    double CF

The confidence for C45-type pruning.

##### m_seed

    int m_seed

The seed for random number generation.

##### minNumObj

    int minNumObj

Minimum number of objects

##### numSetS

    int numSetS

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

##### reducedErrorPruning

    boolean reducedErrorPruning

Use reduced error pruning?

##### theRules

    Vector theRules

Vector storing the rules.

##### toSelectModeL

    ModelSelection toSelectModeL

The model selection method.

##### unpruned

    boolean unpruned

Generated unpruned list?

### Class weka.classifiers.rules.part.PruneableDecList

class PruneableDecList extends ClassifierDecList implements Serializable

serialVersionUID:
  -7228103346297172921L

## Package weka.classifiers.trees

### Class weka.classifiers.trees.ADTree

class ADTree extends Classifier implements Serializable

serialVersionUID:
  -1532264837167690683L

#### Serialized Fields

##### m_boostingIterations

    int m_boostingIterations

Option - the number of boosting iterations o perform

##### m_examplesCounted

    int m_examplesCounted

Statistics - the number of instances processed during search

##### m_lastAddedSplitNum

    int m_lastAddedSplitNum

The number of the last splitter added to the tree

##### m_negTrainInstances

    ReferenceInstances m_negTrainInstances

The training instances with negative class - referencing the training dataset

##### m_nodesExpanded

    int m_nodesExpanded

Statistics - the number of prediction nodes investigated during search

##### m_nominalAttIndices

    int[] m_nominalAttIndices

An array containing the inidices to the nominal attributes in the data

##### m_numericAttIndices

    int[] m_numericAttIndices

An array containing the inidices to the numeric attributes in the data

##### m_posTrainInstances

    ReferenceInstances m_posTrainInstances

The training instances with positive class - referencing the training dataset

##### m_random

    Random m_random

The random number generator - used for the random search heuristic

##### m_randomSeed

    int m_randomSeed

Option - the seed to use for a random search

##### m_root

    PredictionNode m_root

The root of the tree

##### m_saveInstanceData

    boolean m_saveInstanceData

Option - whether the tree should remember the instance data

##### m_search_bestInsertionNode

    PredictionNode m_search_bestInsertionNode

The best node to insert under, as found so far by the latest search

##### m_search_bestPathNegInstances

    Instances m_search_bestPathNegInstances

The negative instances that apply to the best path found so far

##### m_search_bestPathPosInstances

    Instances m_search_bestPathPosInstances

The positive instances that apply to the best path found so far

##### m_search_bestSplitter

    Splitter m_search_bestSplitter

The best splitter to insert, as found so far by the latest search

##### m_search_smallestZ

    double m_search_smallestZ

The smallest Z value found so far by the latest search

##### m_searchPath

    int m_searchPath

Option - the search mode

##### m_trainInstances

    Instances m_trainInstances

The instances used to train the tree

##### m_trainTotalWeight

    double m_trainTotalWeight

The total weight of the instances - used to speed Z calculations

### Class weka.classifiers.trees.BFTree

class BFTree extends RandomizableClassifier implements Serializable

serialVersionUID:
  -7035607375962528217L

#### Serialized Fields

##### m_Attribute

    Attribute m_Attribute

Attribute used for splitting.

##### m_ClassAttribute

    Attribute m_ClassAttribute

Class attribute of a dataset.

##### m_ClassProbs

    double[] m_ClassProbs

Class probabilities.

##### m_ClassValue

    double m_ClassValue

Class value for a node.

##### m_Distribution

    double[] m_Distribution

Class distributions.

##### m_Dists

    double[][][] m_Dists

Distributions of each attribute for two successor nodes.

##### m_FixedExpansion

    int m_FixedExpansion

Fixed number of expansions (if no pruning method is used, its value is -1. Otherwise, its value is gotten from internal cross-validation).

##### m_Heuristic

    boolean m_Heuristic

If use huristic search for binary split (default true). Note even if its value is true, it is only used when the number of values of a nominal attribute is larger than 4.

##### m_isLeaf

    boolean m_isLeaf

If the ndoe is leaf node.

##### m_minNumObj

    int m_minNumObj

Minimum number of instances at leaf nodes.

##### m_numFoldsPruning

    int m_numFoldsPruning

Number of folds for the pruning.

##### m_Props

    double[] m_Props

Branch proportions.

##### m_PruningStrategy

    int m_PruningStrategy

the pruning strategy

##### m_SizePer

    double m_SizePer

The training data size (0-1). Default 1.

##### m_SortedIndices

    int[][] m_SortedIndices

Sorted indices.

##### m_SplitString

    String m_SplitString

Split subset (for nominal attributes).

##### m_SplitValue

    double m_SplitValue

Split point (for numeric attributes).

##### m_Successors

    BFTree[] m_Successors

Successor nodes.

##### m_TotalWeight

    double m_TotalWeight

Total weights.

##### m_UseErrorRate

    boolean m_UseErrorRate

If use error rate in internal cross-validation to fix the number of expansions - default (if not, root mean squared error is used).

##### m_UseGini

    boolean m_UseGini

If use Gini index as the splitting criterion - default (if not, information is used).

##### m_UseOneSE

    boolean m_UseOneSE

If use the 1SE rule to make the decision.

##### m_Weights

    double[][] m_Weights

Sorted weights.

### Class weka.classifiers.trees.DecisionStump

class DecisionStump extends Classifier implements Serializable

serialVersionUID:
  1618384535950391L

#### Serialized Fields

##### m_AttIndex

    int m_AttIndex

The attribute used for classification.

##### m_Distribution

    double[][] m_Distribution

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

##### m_Instances

    Instances m_Instances

The instances used for training.

##### m_SplitPoint

    double m_SplitPoint

The split point (index respectively).

##### m_ZeroR

    Classifier m_ZeroR

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

### Class weka.classifiers.trees.FT

class FT extends Classifier implements Serializable

serialVersionUID:
  -1113212459618105000L

#### Serialized Fields

##### m_convertNominal

    boolean m_convertNominal

convert nominal attributes to binary ?

##### m_errorOnProbabilities

    boolean m_errorOnProbabilities

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

##### m_minNumInstances

    int m_minNumInstances

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

##### m_modelType

    int m_modelType

Model Type, value: 0 is FT, 1 is FTLeaves, 2 is FTInner

##### m_nominalToBinary

    NominalToBinary m_nominalToBinary

Filter to replace nominal attributes

##### m_numBoostingIterations

    int m_numBoostingIterations

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

##### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

Filter to replace missing values

##### m_tree

    FTtree m_tree

root of the logistic model tree

##### m_useAIC

    boolean m_useAIC

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

##### m_weightTrimBeta

    double m_weightTrimBeta

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

### Class weka.classifiers.trees.Id3

class Id3 extends Classifier implements Serializable

serialVersionUID:
  -2693678647096322561L

#### Serialized Fields

##### m_Attribute

    Attribute m_Attribute

Attribute used for splitting.

##### m_ClassAttribute

    Attribute m_ClassAttribute

Class attribute of dataset.

##### m_ClassValue

    double m_ClassValue

Class value if node is leaf.

##### m_Distribution

    double[] m_Distribution

Class distribution if node is leaf.

##### m_Successors

    Id3[] m_Successors

The node's successors.

### Class weka.classifiers.trees.J48

class J48 extends Classifier implements Serializable

serialVersionUID:
  -217733168393644444L

#### Serialized Fields

##### m_binarySplits

    boolean m_binarySplits

Binary splits on nominal attributes?

##### m_CF

    float m_CF

Confidence level

##### m_minNumObj

    int m_minNumObj

Minimum number of instances

##### m_noCleanup

    boolean m_noCleanup

Cleanup after the tree has been built.

##### m_numFolds

    int m_numFolds

Number of folds for reduced error pruning.

##### m_reducedErrorPruning

    boolean m_reducedErrorPruning

Use reduced error pruning?

##### m_root

    ClassifierTree m_root

The decision tree

##### m_Seed

    int m_Seed

Random number seed for reduced-error pruning.

##### m_subtreeRaising

    boolean m_subtreeRaising

Subtree raising to be performed?

##### m_unpruned

    boolean m_unpruned

Unpruned tree?

##### m_useLaplace

    boolean m_useLaplace

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

### Class weka.classifiers.trees.J48graft

class J48graft extends Classifier implements Serializable

serialVersionUID:
  8823716098042427799L

#### Serialized Fields

##### m_binarySplits

    boolean m_binarySplits

Binary splits on nominal attributes?

##### m_CF

    float m_CF

Confidence level

##### m_minNumObj

    int m_minNumObj

Minimum number of instances

##### m_noCleanup

    boolean m_noCleanup

Cleanup after the tree has been built.

##### m_numFolds

    int m_numFolds

Number of folds for reduced error pruning.

##### m_relabel

    boolean m_relabel

relabel instances when grafting

##### m_root

    ClassifierTree m_root

The decision tree

##### m_subtreeRaising

    boolean m_subtreeRaising

Subtree raising to be performed?

##### m_unpruned

    boolean m_unpruned

Unpruned tree?

##### m_useLaplace

    boolean m_useLaplace

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

### Class weka.classifiers.trees.LADTree

class LADTree extends Classifier implements Serializable

serialVersionUID:
  -4940716114518300302L

#### Serialized Fields

##### m_boostingIterations

    int m_boostingIterations

##### m_examplesCounted

    int m_examplesCounted

##### m_lastAddedSplitNum

    int m_lastAddedSplitNum

##### m_nodesExpanded

    int m_nodesExpanded

##### m_numericAttIndices

    int[] m_numericAttIndices

##### m_numOfClasses

    int m_numOfClasses

##### m_root

    weka.classifiers.trees.LADTree.PredictionNode m_root

##### m_search_bestInsertionNode

    weka.classifiers.trees.LADTree.PredictionNode m_search_bestInsertionNode

##### m_search_bestPathInstances

    Instances m_search_bestPathInstances

##### m_search_bestSplitter

    weka.classifiers.trees.LADTree.Splitter m_search_bestSplitter

##### m_search_smallestLeastSquares

    double m_search_smallestLeastSquares

##### m_staticPotentialSplitters2way

    FastVector m_staticPotentialSplitters2way

##### m_trainInstances

    ReferenceInstances m_trainInstances

##### Z_MAX

    double Z_MAX

### Class weka.classifiers.trees.LADTree.LADInstance

class LADInstance extends Instance implements Serializable

#### Serialized Fields

##### fVector

    double[] fVector

##### pVector

    double[] pVector

##### wVector

    double[] wVector

##### zVector

    double[] zVector

### Class weka.classifiers.trees.LADTree.PredictionNode

class PredictionNode extends Object implements Serializable

#### Serialized Fields

##### children

    FastVector children

##### values

    double[] values

### Class weka.classifiers.trees.LADTree.Splitter

class Splitter extends Object implements Serializable

#### Serialized Fields

##### attIndex

    int attIndex

##### orderAdded

    int orderAdded

### Class weka.classifiers.trees.LADTree.TwoWayNominalSplit

class TwoWayNominalSplit extends weka.classifiers.trees.LADTree.Splitter implements Serializable

#### Serialized Fields

##### children

    weka.classifiers.trees.LADTree.PredictionNode[] children

##### trueSplitValue

    int trueSplitValue

### Class weka.classifiers.trees.LADTree.TwoWayNumericSplit

class TwoWayNumericSplit extends weka.classifiers.trees.LADTree.Splitter implements Serializable

#### Serialized Fields

##### children

    weka.classifiers.trees.LADTree.PredictionNode[] children

##### splitPoint

    double splitPoint

### Class weka.classifiers.trees.LMT

class LMT extends Classifier implements Serializable

serialVersionUID:
  -1113212459618104943L

#### Serialized Fields

##### m_convertNominal

    boolean m_convertNominal

convert nominal attributes to binary ?

##### m_errorOnProbabilities

    boolean m_errorOnProbabilities

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

##### m_fastRegression

    boolean m_fastRegression

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

##### m_minNumInstances

    int m_minNumInstances

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

##### m_nominalToBinary

    NominalToBinary m_nominalToBinary

Filter to replace nominal attributes

##### m_numBoostingIterations

    int m_numBoostingIterations

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

##### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

Filter to replace missing values

##### m_splitOnResiduals

    boolean m_splitOnResiduals

split on residuals?

##### m_tree

    LMTNode m_tree

root of the logistic model tree

##### m_useAIC

    boolean m_useAIC

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

##### m_weightTrimBeta

    double m_weightTrimBeta

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

### Class weka.classifiers.trees.M5P

class M5P extends M5Base implements Serializable

serialVersionUID:
  -6118439039768244417L

### Class weka.classifiers.trees.NBTree

class NBTree extends Classifier implements Serializable

serialVersionUID:
  -4716005707058256086L

#### Serialized Fields

##### m_minNumObj

    int m_minNumObj

Minimum number of instances

##### m_root

    NBTreeClassifierTree m_root

The root of the tree

### Class weka.classifiers.trees.RandomForest

class RandomForest extends Classifier implements Serializable

serialVersionUID:
  -2260823972777004705L

#### Serialized Fields

##### m_bagger

    Bagging m_bagger

The bagger.

##### m_KValue

    int m_KValue

Final number of features that were considered in last build.

##### m_MaxDepth

    int m_MaxDepth

The maximum depth of the trees (0 = unlimited)

##### m_numFeatures

    int m_numFeatures

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

##### m_numTrees

    int m_numTrees

Number of trees in forest.

##### m_randomSeed

    int m_randomSeed

The random seed.

### Class weka.classifiers.trees.RandomTree

class RandomTree extends Classifier implements Serializable

serialVersionUID:
  8934314652175299374L

#### Serialized Fields

##### m_AllowUnclassifiedInstances

    boolean m_AllowUnclassifiedInstances

Whether unclassified instances are allowed

##### m_Info

    Instances m_Info

The header information.

##### m_KValue

    int m_KValue

The number of attributes considered for a split.

##### m_MaxDepth

    int m_MaxDepth

The maximum depth of the tree (0 = unlimited)

##### m_MinNum

    double m_MinNum

Minimum number of instances for leaf.

##### m_NumFolds

    int m_NumFolds

Determines how much data is used for backfitting

##### m_randomSeed

    int m_randomSeed

The random seed to use.

##### m_Tree

    weka.classifiers.trees.RandomTree.Tree m_Tree

The Tree object

##### m_zeroR

    Classifier m_zeroR

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

### Class weka.classifiers.trees.RandomTree.Tree

class Tree extends Object implements Serializable

serialVersionUID:
  3549573538656522569L

#### Serialized Fields

##### m_Attribute

    int m_Attribute

The attribute to split on.

##### m_ClassDistribution

    double[] m_ClassDistribution

Class probabilities from the training data.

##### m_Prop

    double[] m_Prop

The proportions of training instances going down each branch.

##### m_SplitPoint

    double m_SplitPoint

The split point.

##### m_Successors

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

The subtrees appended to this tree.

### Class weka.classifiers.trees.REPTree

class REPTree extends Classifier implements Serializable

serialVersionUID:
  -9216785998198681299L

#### Serialized Fields

##### m_MaxDepth

    int m_MaxDepth

Upper bound on the tree depth

##### m_MinNum

    double m_MinNum

The minimum number of instances per leaf.

##### m_MinVarianceProp

    double m_MinVarianceProp

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

##### m_NoPruning

    boolean m_NoPruning

Don't prune

##### m_NumFolds

    int m_NumFolds

Number of folds for reduced error pruning.

##### m_Seed

    int m_Seed

Seed for random data shuffling.

##### m_Tree

    weka.classifiers.trees.REPTree.Tree m_Tree

The Tree object

##### m_zeroR

    ZeroR m_zeroR

ZeroR model that is used if no attributes are present.

### Class weka.classifiers.trees.REPTree.Tree

class Tree extends Object implements Serializable

serialVersionUID:
  -1635481717888437935L

#### Serialized Fields

##### m_Attribute

    int m_Attribute

The attribute to split on.

##### m_ClassProbs

    double[] m_ClassProbs

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

##### m_Distribution

    double[] m_Distribution

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

##### m_HoldOutDist

    double[] m_HoldOutDist

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

##### m_HoldOutError

    double m_HoldOutError

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

##### m_Info

    Instances m_Info

The header information (for printing the tree).

##### m_Prop

    double[] m_Prop

The proportions of training instances going down each branch.

##### m_SplitPoint

    double m_SplitPoint

The split point.

##### m_Successors

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

The subtrees of this tree.

### Class weka.classifiers.trees.SimpleCart

class SimpleCart extends RandomizableClassifier implements Serializable

serialVersionUID:
  4154189200352566053L

#### Serialized Fields

##### m_Alpha

    double m_Alpha

Alpha-value (for pruning) at the node.

##### m_Attribute

    Attribute m_Attribute

Attribute used to split data.

##### m_ClassAttribute

    Attribute m_ClassAttribute

Class attriubte of data.

##### m_ClassProbs

    double[] m_ClassProbs

Class probabilities.

##### m_ClassValue

    double m_ClassValue

Class value if the node is leaf.

##### m_Distribution

    double[] m_Distribution

Distributions of leaf node (or temporary leaf node in minimal cost-complexity pruning)

##### m_Heuristic

    boolean m_Heuristic

If use huristic search for nominal attributes in multi-class problems (default true).

##### m_isLeaf

    boolean m_isLeaf

Indicate if the node is a leaf node.

##### m_minNumObj

    double m_minNumObj

Minimum number of instances in at the terminal nodes.

##### m_numFoldsPruning

    int m_numFoldsPruning

Number of folds for minimal cost-complexity pruning.

##### m_numIncorrectModel

    double m_numIncorrectModel

Number of training examples misclassified by the model (subtree rooted).

##### m_numIncorrectTree

    double m_numIncorrectTree

Number of training examples misclassified by the model (subtree not rooted).

##### m_Props

    double[] m_Props

Proportion for each branch.

##### m_Prune

    boolean m_Prune

If use minimal cost-compexity pruning.

##### m_SizePer

    double m_SizePer

Training data size.

##### m_SplitString

    String m_SplitString

Split subset used to split data for nominal attributes.

##### m_SplitValue

    double m_SplitValue

Split point for a numeric attribute.

##### m_Successors

    SimpleCart[] m_Successors

Successor nodes.

##### m_totalTrainInstances

    int m_totalTrainInstances

Total number of instances used to build the classifier.

##### m_train

    Instances m_train

Training data.

##### m_UseOneSE

    boolean m_UseOneSE

If use the 1SE rule to make final decision tree.

### Class weka.classifiers.trees.UserClassifier

class UserClassifier extends Classifier implements Serializable

serialVersionUID:
  6483901103562809843L

#### Serialized Fields

##### m_built

    boolean m_built

The status of whether there is a decision tree ready or not.

##### m_classifiers

    GenericObjectEditor m_classifiers

A list of other m_classifiers.

##### m_focus

    weka.classifiers.trees.UserClassifier.TreeClass m_focus

Two references to the structure of the decision tree.

##### m_nextId

    int m_nextId

The next number that can be used as a unique id for a node.

##### m_propertyDialog

    PropertyDialog m_propertyDialog

A window for selecting other classifiers.

##### m_top

    weka.classifiers.trees.UserClassifier.TreeClass m_top

Two references to the structure of the decision tree.

## Package weka.classifiers.trees.adtree

### Class weka.classifiers.trees.adtree.PredictionNode

class PredictionNode extends Object implements Serializable

serialVersionUID:
  6018958856358698814L

#### Serialized Fields

##### children

    FastVector children

The children of this node - any number of splitter nodes

##### value

    double value

The prediction value stored in this node

### Class weka.classifiers.trees.adtree.ReferenceInstances

class ReferenceInstances extends Instances implements Serializable

serialVersionUID:
  -8022666381920252997L

### Class weka.classifiers.trees.adtree.Splitter

class Splitter extends Object implements Serializable

serialVersionUID:
  8190449848490055L

#### Serialized Fields

##### orderAdded

    int orderAdded

The number this node was in the order of nodes added to the tree

### Class weka.classifiers.trees.adtree.TwoWayNominalSplit

class TwoWayNominalSplit extends Splitter implements Serializable

serialVersionUID:
  -4598366190152721355L

#### Serialized Fields

##### attIndex

    int attIndex

The index of the attribute the split depends on

##### children

    PredictionNode[] children

The children of this split

##### trueSplitValue

    int trueSplitValue

The attribute value that is compared against

### Class weka.classifiers.trees.adtree.TwoWayNumericSplit

class TwoWayNumericSplit extends Splitter implements Serializable

serialVersionUID:
  449769177903158283L

#### Serialized Fields

##### attIndex

    int attIndex

The index of the attribute the split depends on

##### children

    PredictionNode[] children

The children of this split

##### splitPoint

    double splitPoint

The attribute value that is compared against

## Package weka.classifiers.trees.ft

### Class weka.classifiers.trees.ft.FTInnerNode

class FTInnerNode extends FTtree implements Serializable

serialVersionUID:
  -1125334488640233181L

### Class weka.classifiers.trees.ft.FTLeavesNode

class FTLeavesNode extends FTtree implements Serializable

serialVersionUID:
  950601378326259315L

### Class weka.classifiers.trees.ft.FTNode

class FTNode extends FTtree implements Serializable

serialVersionUID:
  2317688685139295063L

### Class weka.classifiers.trees.ft.FTtree

class FTtree extends LogisticBase implements Serializable

serialVersionUID:
  1862737145870398755L

#### Serialized Fields

##### m_auxLocalModel

    ClassifierSplitModel m_auxLocalModel

Auxiliary copy ClassifierSplitModel (for splitting)

##### m_CF

    float m_CF

Confidence level

##### m_constError

    double m_constError

Constructor error

##### m_hasConstr

    boolean m_hasConstr

True if node has or splits on constructor

##### m_higherRegressions

    SimpleLinearRegression[][] m_higherRegressions

Simple regression functions fit by LogitBoost at higher levels in the tree

##### m_id

    int m_id

Node id

##### m_isLeaf

    boolean m_isLeaf

True if node is leaf

##### m_leafclass

    int m_leafclass

Stores leaf class value

##### m_leafModelNum

    int m_leafModelNum

ID of logistic model at leaf

##### m_localModel

    ClassifierSplitModel m_localModel

The ClassifierSplitModel (for splitting)

##### m_minNumInstances

    int m_minNumInstances

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

##### m_modelSelection

    ModelSelection m_modelSelection

ModelSelection object (for splitting)

##### m_nominalToBinary

    NominalToBinary m_nominalToBinary

Filter to convert nominal attributes to binary

##### m_numHigherRegressions

    int m_numHigherRegressions

Number of simple regression functions fit by LogitBoost at higher levels in the tree

##### m_numInstances

    int m_numInstances

Number of instances at the node

##### m_sons

    FTtree[] m_sons

Array of children of the node

##### m_totalInstanceWeight

    double m_totalInstanceWeight

Total number of training instances.

## Package weka.classifiers.trees.j48

### Class weka.classifiers.trees.j48.BinC45ModelSelection

class BinC45ModelSelection extends ModelSelection implements Serializable

serialVersionUID:
  179170923545122001L

#### Serialized Fields

##### m_allData

    Instances m_allData

The FULL training dataset.

##### m_minNoObj

    int m_minNoObj

Minimum number of instances in interval.

### Class weka.classifiers.trees.j48.BinC45Split

class BinC45Split extends ClassifierSplitModel implements Serializable

serialVersionUID:
  -1278776919563022474L

#### Serialized Fields

##### m_attIndex

    int m_attIndex

Attribute to split on.

##### m_gainRatio

    double m_gainRatio

GainRatio of split.

##### m_infoGain

    double m_infoGain

InfoGain of split.

##### m_minNoObj

    int m_minNoObj

Minimum number of objects in a split.

##### m_splitPoint

    double m_splitPoint

Value of split point.

##### m_sumOfWeights

    double m_sumOfWeights

The sum of the weights of the instances.

### Class weka.classifiers.trees.j48.C45ModelSelection

class C45ModelSelection extends ModelSelection implements Serializable

serialVersionUID:
  3372204862440821989L

#### Serialized Fields

##### m_allData

    Instances m_allData

All the training data

##### m_minNoObj

    int m_minNoObj

Minimum number of objects in interval.

### Class weka.classifiers.trees.j48.C45PruneableClassifierTree

class C45PruneableClassifierTree extends ClassifierTree implements Serializable

serialVersionUID:
  -4813820170260388194L

#### Serialized Fields

##### m_CF

    float m_CF

The confidence factor for pruning.

##### m_cleanup

    boolean m_cleanup

Cleanup after the tree has been built.

##### m_pruneTheTree

    boolean m_pruneTheTree

True if the tree is to be pruned.

##### m_subtreeRaising

    boolean m_subtreeRaising

Is subtree raising to be performed?

### Class weka.classifiers.trees.j48.C45PruneableClassifierTreeG

class C45PruneableClassifierTreeG extends ClassifierTree implements Serializable

serialVersionUID:
  66981207374331964L

#### Serialized Fields

##### m_BiProbCrit

    double m_BiProbCrit

binomial probability critical value

##### m_CF

    float m_CF

The confidence factor for pruning.

##### m_cleanup

    boolean m_cleanup

Cleanup after the tree has been built.

##### m_Debug

    boolean m_Debug

##### m_pruneTheTree

    boolean m_pruneTheTree

True if the tree is to be pruned.

##### m_relabel

    boolean m_relabel

flag for using relabelling when grafting

##### m_subtreeRaising

    boolean m_subtreeRaising

Is subtree raising to be performed?

### Class weka.classifiers.trees.j48.C45Split

class C45Split extends ClassifierSplitModel implements Serializable

serialVersionUID:
  3064079330067903161L

#### Serialized Fields

##### m_attIndex

    int m_attIndex

Attribute to split on.

##### m_complexityIndex

    int m_complexityIndex

Desired number of branches.

##### m_gainRatio

    double m_gainRatio

GainRatio of split.

##### m_index

    int m_index

Number of split points.

##### m_infoGain

    double m_infoGain

InfoGain of split.

##### m_minNoObj

    int m_minNoObj

Minimum number of objects in a split.

##### m_splitPoint

    double m_splitPoint

Value of split point.

##### m_sumOfWeights

    double m_sumOfWeights

The sum of the weights of the instances.

### Class weka.classifiers.trees.j48.ClassifierSplitModel

class ClassifierSplitModel extends 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

class ClassifierTree extends Object implements Serializable

serialVersionUID:
  -8722249377542734193L

#### Serialized Fields

##### m_id

    int m_id

The id for the node.

##### m_isEmpty

    boolean m_isEmpty

True if node is empty.

##### m_isLeaf

    boolean m_isLeaf

True if node is leaf.

##### m_localModel

    ClassifierSplitModel m_localModel

Local model at node.

##### m_sons

    ClassifierTree[] m_sons

References to sons.

##### m_test

    Distribution m_test

The pruning instances.

##### m_toSelectModel

    ModelSelection m_toSelectModel

The model selection method.

##### m_train

    Instances m_train

The training instances.

### Class weka.classifiers.trees.j48.Distribution

class Distribution extends Object implements Serializable

serialVersionUID:
  8526859638230806576L

#### Serialized Fields

##### m_perBag

    double[] m_perBag

Weight of instances per bag.

##### m_perClass

    double[] m_perClass

Weight of instances per class.

##### m_perClassPerBag

    double[][] m_perClassPerBag

Weight of instances per class per bag.

##### totaL

    double totaL

Total weight of instances.

### Class weka.classifiers.trees.j48.EntropyBasedSplitCrit

class EntropyBasedSplitCrit extends SplitCriterion implements Serializable

serialVersionUID:
  -2618691439791653056L

### Class weka.classifiers.trees.j48.EntropySplitCrit

class EntropySplitCrit extends EntropyBasedSplitCrit implements Serializable

serialVersionUID:
  5986252682266803935L

### Class weka.classifiers.trees.j48.GainRatioSplitCrit

class GainRatioSplitCrit extends EntropyBasedSplitCrit implements Serializable

serialVersionUID:
  -433336694718670930L

### Class weka.classifiers.trees.j48.GraftSplit

class GraftSplit extends ClassifierSplitModel implements Serializable

serialVersionUID:
  722773260393182051L

#### Serialized Fields

##### m_attIndex

    int m_attIndex

the attribute we are splitting on

##### m_graftdistro

    Distribution m_graftdistro

the distribution for graft values, from cases in atbop

##### m_laplace

    double m_laplace

laplace value of the subset specified by m_testType for m_maxClass

##### m_leafdistro

    Distribution m_leafdistro

leaf for the subset specified by m_testType

##### m_maxClass

    int m_maxClass

dominant class of the subset specified by m_testType

##### m_otherLeafMaxClass

    int m_otherLeafMaxClass

dominant class of the subset not specified by m_testType

##### m_splitPoint

    double m_splitPoint

value of split point (if numeric attribute)

##### m_testType

    int m_testType

type of test: 0: invalid input: '<'= test 1: > test 2: = test 3: != test

### Class weka.classifiers.trees.j48.InfoGainSplitCrit

class InfoGainSplitCrit extends EntropyBasedSplitCrit implements Serializable

serialVersionUID:
  4892105020180728499L

### Class weka.classifiers.trees.j48.ModelSelection

class ModelSelection extends Object implements Serializable

serialVersionUID:
  -4850147125096133642L

### Class weka.classifiers.trees.j48.NBTreeClassifierTree

class NBTreeClassifierTree extends ClassifierTree implements Serializable

serialVersionUID:
  -4472639447877404786L

### Class weka.classifiers.trees.j48.NBTreeModelSelection

class NBTreeModelSelection extends ModelSelection implements Serializable

serialVersionUID:
  990097748931976704L

#### Serialized Fields

##### m_allData

    Instances m_allData

All the training data

##### m_minNoObj

    int m_minNoObj

Minimum number of objects in interval.

### Class weka.classifiers.trees.j48.NBTreeNoSplit

class NBTreeNoSplit extends ClassifierSplitModel implements Serializable

serialVersionUID:
  7824804381545259618L

#### Serialized Fields

##### m_disc

    Discretize m_disc

the discretizer used

##### m_errors

    double m_errors

errors on the training data at this node

##### m_nb

    NaiveBayesUpdateable m_nb

the naive bayes classifier

### Class weka.classifiers.trees.j48.NBTreeSplit

class NBTreeSplit extends ClassifierSplitModel implements Serializable

serialVersionUID:
  8922627123884975070L

#### Serialized Fields

##### m_attIndex

    int m_attIndex

Attribute to split on.

##### m_c45S

    C45Split m_c45S

##### m_complexityIndex

    int m_complexityIndex

Desired number of branches.

##### m_errors

    double m_errors

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

##### m_globalNB

    NBTreeNoSplit m_globalNB

The global naive bayes model for this node

##### m_minNoObj

    int m_minNoObj

Minimum number of objects in a split.

##### m_splitPoint

    double m_splitPoint

Value of split point.

##### m_sumOfWeights

    double m_sumOfWeights

The sum of the weights of the instances.

### Class weka.classifiers.trees.j48.NoSplit

class NoSplit extends ClassifierSplitModel implements Serializable

serialVersionUID:
  -1292620749331337546L

### Class weka.classifiers.trees.j48.PruneableClassifierTree

class PruneableClassifierTree extends ClassifierTree implements Serializable

serialVersionUID:
  -555775736857600201L

#### Serialized Fields

##### m_cleanup

    boolean m_cleanup

Cleanup after the tree has been built.

##### m_seed

    int m_seed

The random number seed.

##### numSets

    int numSets

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

##### pruneTheTree

    boolean pruneTheTree

True if the tree is to be pruned.

### Class weka.classifiers.trees.j48.SplitCriterion

class SplitCriterion extends Object implements Serializable

serialVersionUID:
  5490996638027101259L

## Package weka.classifiers.trees.lmt

### Class weka.classifiers.trees.lmt.LMTNode

class LMTNode extends LogisticBase implements Serializable

serialVersionUID:
  1862737145870398755L

#### Serialized Fields

##### m_alpha

    double m_alpha

Alpha-value (for pruning) at the node

##### m_fastRegression

    boolean m_fastRegression

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

##### m_higherRegressions

    SimpleLinearRegression[][] m_higherRegressions

Simple regression functions fit by LogitBoost at higher levels in the tree

##### m_id

    int m_id

Node id

##### m_isLeaf

    boolean m_isLeaf

True if node is leaf

##### m_leafModelNum

    int m_leafModelNum

ID of logistic model at leaf

##### m_localModel

    ClassifierSplitModel m_localModel

The ClassifierSplitModel (for splitting)

##### m_minNumInstances

    int m_minNumInstances

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

##### m_modelSelection

    ModelSelection m_modelSelection

ModelSelection object (for splitting)

##### m_nominalToBinary

    NominalToBinary m_nominalToBinary

Filter to convert nominal attributes to binary

##### m_numHigherRegressions

    int m_numHigherRegressions

Number of simple regression functions fit by LogitBoost at higher levels in the tree

##### m_numIncorrectModel

    double m_numIncorrectModel

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

##### m_numIncorrectTree

    double m_numIncorrectTree

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

##### m_numInstances

    int m_numInstances

Number of instances at the node

##### m_sons

    LMTNode[] m_sons

Array of children of the node

##### m_totalInstanceWeight

    double m_totalInstanceWeight

Total number of training instances.

### Class weka.classifiers.trees.lmt.LogisticBase

class LogisticBase extends Classifier implements Serializable

serialVersionUID:
  168765678097825064L

#### Serialized Fields

##### m_errorOnProbabilities

    boolean m_errorOnProbabilities

Use error on probabilities for stopping criterion of LogitBoost?

##### m_fixedNumIterations

    int m_fixedNumIterations

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

##### m_heuristicStop

    int m_heuristicStop

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

##### m_maxIterations

    int m_maxIterations

The maximum number of LogitBoost iterations

##### m_numClasses

    int m_numClasses

The number of different classes

##### m_numericData

    Instances m_numericData

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

##### m_numericDataHeader

    Instances m_numericDataHeader

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

##### m_numParameters

    double m_numParameters

Effective number of parameters used for AIC / BIC automatic stopping

##### m_numRegressions

    int m_numRegressions

The number of LogitBoost iterations performed.

##### m_regressions

    SimpleLinearRegression[][] m_regressions

Array holding the simple regression functions fit by LogitBoost

##### m_train

    Instances m_train

Training data

##### m_useAIC

    boolean m_useAIC

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

##### m_useCrossValidation

    boolean m_useCrossValidation

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

##### m_weightTrimBeta

    double m_weightTrimBeta

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

### Class weka.classifiers.trees.lmt.ResidualModelSelection

class ResidualModelSelection extends ModelSelection implements Serializable

serialVersionUID:
  -293098783159385148L

#### Serialized Fields

##### m_minInfoGain

    double m_minInfoGain

Minimum information gain for split

##### m_minNumInstances

    int m_minNumInstances

Minimum number of instances for leaves

### Class weka.classifiers.trees.lmt.ResidualSplit

class ResidualSplit extends ClassifierSplitModel implements Serializable

serialVersionUID:
  -5055883734183713525L

#### Serialized Fields

##### m_attIndex

    int m_attIndex

The index of the attribute selected for the split

##### m_attribute

    Attribute m_attribute

The attribute selected for the split

##### m_data

    Instances m_data

The set of instances

##### m_dataWs

    double[][] m_dataWs

The LogitBoost-weights for the set of instances

##### m_dataZs

    double[][] m_dataZs

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

##### m_numClasses

    int m_numClasses

Number of classed

##### m_numInstances

    int m_numInstances

Number of instances in the set

##### m_splitPoint

    double m_splitPoint

The split point (for numeric attributes)

## Package weka.classifiers.trees.m5

### Class weka.classifiers.trees.m5.CorrelationSplitInfo

class CorrelationSplitInfo extends Object implements Serializable

serialVersionUID:
  4212734895125452770L

#### Serialized Fields

##### m_first

    int m_first

the first instance

##### m_last

    int m_last

the last instance

##### m_maxImpurity

    double m_maxImpurity

the maximum impurity reduction

##### m_number

    int m_number

the number of instances

##### m_position

    int m_position

##### m_splitAttr

    int m_splitAttr

the attribute being tested

##### m_splitValue

    double m_splitValue

the best value on which to split

### Class weka.classifiers.trees.m5.M5Base

class M5Base extends Classifier implements Serializable

serialVersionUID:
  -4022221950191647679L

#### Serialized Fields

##### m_generateRules

    boolean m_generateRules

generate a decision list instead of a single tree.

##### m_instances

    Instances m_instances

the instances covered by the tree/rules

##### m_minNumInstances

    double m_minNumInstances

The minimum number of instances to allow at a leaf node

##### m_nominalToBinary

    NominalToBinary m_nominalToBinary

filter to convert nominal attributes to binary

##### m_regressionTree

    boolean m_regressionTree

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

##### m_removeUseless

    RemoveUseless m_removeUseless

for removing useless attributes

##### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

filter to fill in missing values

##### m_ruleSet

    FastVector m_ruleSet

the rule set

##### m_saveInstances

    boolean m_saveInstances

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

##### m_unsmoothedPredictions

    boolean m_unsmoothedPredictions

use unsmoothed predictions

##### m_useUnpruned

    boolean m_useUnpruned

Do not prune tree/rules

### Class weka.classifiers.trees.m5.PreConstructedLinearModel

class PreConstructedLinearModel extends Classifier implements Serializable

serialVersionUID:
  2030974097051713247L

#### Serialized Fields

##### m_coefficients

    double[] m_coefficients

The coefficients

##### m_instancesHeader

    Instances m_instancesHeader

Holds the instances header for printing the model

##### m_intercept

    double m_intercept

The intercept

##### m_numParameters

    int m_numParameters

number of coefficients in the model

### Class weka.classifiers.trees.m5.Rule

class Rule extends Object implements Serializable

serialVersionUID:
  -4458627451682483204L

#### Serialized Fields

##### m_classIndex

    int m_classIndex

the class index

##### m_covered

    Instances m_covered

the instances covered by this rule

##### m_globalAbsDev

    double m_globalAbsDev

the absolute deviation of the class for all the instances

##### m_globalStdDev

    double m_globalStdDev

the standard deviation of the class for all the instances

##### m_instances

    Instances m_instances

the instances covered by this rule

##### m_internalNodes

    RuleNode[] m_internalNodes

the corresponding internal nodes. Used for smoothing rules.

##### m_minNumInstances

    double m_minNumInstances

The minimum number of instances to allow at a leaf node

##### m_notCovered

    Instances m_notCovered

the instances not covered by this rule

##### m_numAttributes

    int m_numAttributes

the number of attributes

##### m_numCovered

    int m_numCovered

the number of instances covered by this rule

##### m_numInstances

    int m_numInstances

the number of instances in the dataset

##### m_regressionTree

    boolean m_regressionTree

Make a regression tree instead of a model tree

##### m_relOps

    int[] m_relOps

the corresponding relational operators (0 = "invalid input: '<'=", 1 = ">")

##### m_ruleModel

    RuleNode m_ruleModel

the leaf encapsulating the linear model for this rule

##### m_saveInstances

    boolean m_saveInstances

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

##### m_smoothPredictions

    boolean m_smoothPredictions

use the original m5 smoothing procedure

##### m_splitAtts

    int[] m_splitAtts

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

##### m_splitVals

    double[] m_splitVals

the corresponding values of the split points

##### m_topOfTree

    RuleNode m_topOfTree

the top of the m5 tree for this rule

##### m_useTree

    boolean m_useTree

use a pruned m5 tree rather than make a rule

##### m_useUnpruned

    boolean m_useUnpruned

Build unpruned tree/rule

### Class weka.classifiers.trees.m5.RuleNode

class RuleNode extends Classifier implements Serializable

serialVersionUID:
  1979807611124337144L

#### Serialized Fields

##### m_classIndex

    int m_classIndex

the class index

##### m_devFraction

    double m_devFraction

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

##### m_globalAbsDeviation

    double m_globalAbsDeviation

the absolute deviation of the global class

##### m_globalDeviation

    double m_globalDeviation

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

##### m_id

    int m_id

Node id.

##### m_indices

    int[] m_indices

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

##### m_instances

    Instances m_instances

instances reaching this node

##### m_isLeaf

    boolean m_isLeaf

Node is a leaf

##### m_leafModelNum

    int m_leafModelNum

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

##### m_left

    RuleNode m_left

left child node

##### m_nodeModel

    PreConstructedLinearModel m_nodeModel

the linear model at this node

##### m_numAttributes

    int m_numAttributes

the number of attributes

##### m_numInstances

    int m_numInstances

the number of instances reaching this node

##### m_numParameters

    int m_numParameters

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

##### m_parent

    RuleNode m_parent

the parent of this node

##### m_pruningMultiplier

    double m_pruningMultiplier

##### m_regressionTree

    boolean m_regressionTree

Make a regression tree instead of a model tree

##### m_right

    RuleNode m_right

right child node

##### m_rootMeanSquaredError

    double m_rootMeanSquaredError

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

##### m_saveInstances

    boolean m_saveInstances

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

##### m_splitAtt

    int m_splitAtt

attribute this node splits on

##### m_splitNum

    double m_splitNum

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

##### m_splitValue

    double m_splitValue

the value of the split attribute

### Class weka.classifiers.trees.m5.YongSplitInfo

class YongSplitInfo extends Object implements Serializable

serialVersionUID:
  1864267581079767881L

#### Serialized Fields

##### first

    int first

##### last

    int last

##### leftAve

    double leftAve

##### maxImpurity

    double maxImpurity

##### number

    int number

##### position

    int position

##### rightAve

    double rightAve

##### splitAttr

    int splitAttr

##### splitValue

    double splitValue

## Package weka.clusterers

### Class weka.clusterers.AbstractClusterer

class AbstractClusterer extends Object implements Serializable

serialVersionUID:
  -6099962589663877632L

### Class weka.clusterers.AbstractDensityBasedClusterer

class AbstractDensityBasedClusterer extends AbstractClusterer implements Serializable

serialVersionUID:
  -5950728041704213845L

### Class weka.clusterers.CLOPE

class CLOPE extends AbstractClusterer implements Serializable

serialVersionUID:
  -567567567567588L

#### Serialized Fields

##### clusters

    ArrayList<weka.clusterers.CLOPE.CLOPECluster> clusters

Array of clusters

##### m_clusterAssignments

    ArrayList<Integer> m_clusterAssignments

##### m_numberOfClusters

    int m_numberOfClusters

Number of clusters

##### m_numberOfClustersDetermined

    boolean m_numberOfClustersDetermined

whether the number of clusters was already determined

##### m_numberOfInstances

    int m_numberOfInstances

Number of instances

##### m_processed_InstanceID

    int m_processed_InstanceID

Counter for the processed instances

##### m_Repulsion

    double m_Repulsion

Specifies the repulsion

##### m_RepulsionDefault

    double m_RepulsionDefault

Specifies the repulsion default

### Class weka.clusterers.ClusterEvaluation

class ClusterEvaluation extends Object implements Serializable

serialVersionUID:
  -830188327319128005L

#### Serialized Fields

##### m_classToCluster

    int[] m_classToCluster

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

##### m_clusterAssignments

    double[] m_clusterAssignments

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

##### m_Clusterer

    Clusterer m_Clusterer

the clusterer

##### m_clusteringResults

    StringBuffer m_clusteringResults

holds a string describing the results of clustering the training data

##### m_logL

    double m_logL

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

##### m_numClusters

    int m_numClusters

holds the number of clusters found by the clusterer

### Class weka.clusterers.Cobweb

class Cobweb extends RandomizableClusterer implements Serializable

serialVersionUID:
  928406656495092318L

#### Serialized Fields

##### m_acuity

    double m_acuity

Acuity (minimum standard deviation).

##### m_cobwebTree

    weka.clusterers.Cobweb.CNode m_cobwebTree

Holds the root of the Cobweb tree.

##### m_cutoff

    double m_cutoff

Cutoff (minimum category utility).

##### m_numberMerges

    int m_numberMerges

the number of merges that happened

##### m_numberOfClusters

    int m_numberOfClusters

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

See Also:
  - `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_saveInstances

    boolean m_saveInstances

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

### Class weka.clusterers.DBSCAN

class DBSCAN extends AbstractClusterer implements Serializable

serialVersionUID:
  -1666498248451219728L

#### Serialized Fields

##### clusterID

    int clusterID

Holds the current clusterID

##### database

    Database database

The database that is used for DBSCAN

##### database_distanceType

    String database_distanceType

Holds the distance-type that is used (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject)

##### database_Type

    String database_Type

Holds the type of the used database (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase)

##### elapsedTime

    double elapsedTime

Holds the time-value (seconds) for the duration of the clustering-process

##### epsilon

    double epsilon

Specifies the radius for a range-query

##### minPoints

    int minPoints

Specifies the density (the range-query must contain at least minPoints DataObjects)

##### numberOfGeneratedClusters

    int numberOfGeneratedClusters

Holds the number of clusters generated

##### processed_InstanceID

    int processed_InstanceID

Counter for the processed instances

##### replaceMissingValues_Filter

    ReplaceMissingValues replaceMissingValues_Filter

Replace missing values in training instances

### Class weka.clusterers.EM

class EM extends RandomizableDensityBasedClusterer implements Serializable

serialVersionUID:
  8348181483812829475L

#### Serialized Fields

##### m_displayModelInOldFormat

    boolean m_displayModelInOldFormat

display model output in old-style format

##### m_initialNumClusters

    int m_initialNumClusters

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

##### m_loglikely

    double m_loglikely

the loglikelihood of the data

##### m_max_iterations

    int m_max_iterations

maximum iterations to perform

##### m_maxValues

    double[] m_maxValues

attribute max values

##### m_minStdDev

    double m_minStdDev

default minimum standard deviation

##### m_minStdDevPerAtt

    double[] m_minStdDevPerAtt

##### m_minValues

    double[] m_minValues

attribute min values

##### m_model

    Estimator[][] m_model

hold the discrete estimators for each cluster

##### m_modelNormal

    double[][][] m_modelNormal

hold the normal estimators for each cluster

##### m_modelNormalPrev

    double[][][] m_modelNormalPrev

##### m_modelPrev

    Estimator[][] m_modelPrev

##### m_num_attribs

    int m_num_attribs

number of attributes

##### m_num_clusters

    int m_num_clusters

number of clusters selected by the user or cross validation

##### m_num_instances

    int m_num_instances

number of training instances

##### m_priors

    double[] m_priors

the prior probabilities for clusters

##### m_priorsPrev

    double[] m_priorsPrev

##### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

globally replace missing values

##### m_rr

    Random m_rr

random number generator

##### m_theInstances

    Instances m_theInstances

training instances

##### m_verbose

    boolean m_verbose

Verbose?

##### m_weights

    double[][] m_weights

hold the weights of each instance for each cluster

### Class weka.clusterers.FarthestFirst

class FarthestFirst extends RandomizableClusterer implements Serializable

serialVersionUID:
  7499838100631329509L

#### Serialized Fields

##### m_ClusterCentroids

    Instances m_ClusterCentroids

holds the cluster centroids

##### m_instances

    Instances m_instances

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

##### m_Max

    double[] m_Max

attribute max values

##### m_Min

    double[] m_Min

attribute min values

##### m_NumClusters

    int m_NumClusters

number of clusters to generate

##### m_ReplaceMissingFilter

    ReplaceMissingValues m_ReplaceMissingFilter

replace missing values in training instances

### Class weka.clusterers.FilteredClusterer

class FilteredClusterer extends SingleClustererEnhancer implements Serializable

serialVersionUID:
  1420005943163412943L

#### Serialized Fields

##### m_Filter

    Filter m_Filter

The filter.

##### m_FilteredInstances

    Instances m_FilteredInstances

The instance structure of the filtered instances.

### Class weka.clusterers.HierarchicalClusterer

class HierarchicalClusterer extends AbstractClusterer implements Serializable

serialVersionUID:
  1L

#### Serialized Fields

##### m_bDebug

    boolean m_bDebug

Whether the classifier is run in debug mode.

##### m_bDistanceIsBranchLength

    boolean m_bDistanceIsBranchLength

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

##### m_bPrintNewick

    boolean m_bPrintNewick

##### m_clusters

    weka.clusterers.HierarchicalClusterer.Node[] m_clusters

##### m_DistanceFunction

    DistanceFunction m_DistanceFunction

distance function used for comparing members of a cluster

##### m_instances

    Instances m_instances

training data

##### m_nClusterNr

    int[] m_nClusterNr

##### m_nLinkType

    int m_nLinkType

Holds the Link type used calculate distance between clusters

##### m_nNumClusters

    int m_nNumClusters

number of clusters desired in clustering

### Class weka.clusterers.MakeDensityBasedClusterer

class MakeDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable

serialVersionUID:
  -5643302427972186631L

#### Serialized Fields

##### m_minStdDev

    double m_minStdDev

default minimum standard deviation

##### m_model

    DiscreteEstimator[][] m_model

discrete distributions fitted to each discrete attribute in each cluster

##### m_modelNormal

    double[][][] m_modelNormal

normal distributions fitted to each numeric attribute in each cluster

##### m_priors

    double[] m_priors

prior probabilities for the fitted clusters

##### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

globally replace missing values

##### m_theInstances

    Instances m_theInstances

holds training instances header information

##### m_wrappedClusterer

    Clusterer m_wrappedClusterer

The clusterer being wrapped

### Class weka.clusterers.OPTICS

class OPTICS extends AbstractClusterer implements Serializable

serialVersionUID:
  274552680222105221L

#### Serialized Fields

##### database

    Database database

The database that is used for OPTICS

##### database_distanceType

    String database_distanceType

Holds the distance-type that is used (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject)

##### database_Type

    String database_Type

Holds the type of the used database (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase)

##### databaseOutput

    File databaseOutput

the file to save the generated database object to.

##### elapsedTime

    double elapsedTime

Holds the time-value (seconds) for the duration of the clustering-process

##### epsilon

    double epsilon

Specifies the radius for a range-query

##### minPoints

    int minPoints

Specifies the density (the range-query must contain at least minPoints DataObjects)

##### numberOfGeneratedClusters

    int numberOfGeneratedClusters

Holds the number of clusters generated

##### replaceMissingValues_Filter

    ReplaceMissingValues replaceMissingValues_Filter

Replace missing values in training instances

##### resultVector

    FastVector resultVector

Holds the ClusterOrder (dataObjects with their r_dist and c_dist) for the GUI

##### showGUI

    boolean showGUI

whether to display the GUI after building the clusterer or not.

##### writeOPTICSresults

    boolean writeOPTICSresults

Flag that indicates if the results are written to a file or not

### Class weka.clusterers.RandomizableClusterer

class RandomizableClusterer extends AbstractClusterer implements Serializable

serialVersionUID:
  -4819590778152242745L

#### Serialized Fields

##### m_Seed

    int m_Seed

The random number seed.

##### m_SeedDefault

    int m_SeedDefault

the default seed value

### Class weka.clusterers.RandomizableDensityBasedClusterer

class RandomizableDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable

serialVersionUID:
  -5325270357918932849L

#### Serialized Fields

##### m_Seed

    int m_Seed

The random number seed.

##### m_SeedDefault

    int m_SeedDefault

the default seed value

### Class weka.clusterers.RandomizableSingleClustererEnhancer

class RandomizableSingleClustererEnhancer extends AbstractClusterer implements Serializable

serialVersionUID:
  -644847037106316249L

#### Serialized Fields

##### m_Seed

    int m_Seed

The random number seed.

##### m_SeedDefault

    int m_SeedDefault

the default seed value

### Class weka.clusterers.sIB

class sIB extends RandomizableClusterer implements Serializable

serialVersionUID:
  -8652125897352654213L

#### Serialized Fields

##### bestT

    weka.clusterers.sIB.Partition bestT

Holds the best partition built

##### input

    weka.clusterers.sIB.Input input

Holds the statistics about the input dataset

##### m_data

    Instances m_data

Training data

##### m_maxLoop

    int m_maxLoop

Max number of iterations during each restart

##### m_minChange

    int m_minChange

Minimum number of changes

##### m_numAttributes

    int m_numAttributes

Number of attributes

##### m_numCluster

    int m_numCluster

Number of clusters

##### m_numInstances

    int m_numInstances

Number of instances

##### m_numRestarts

    int m_numRestarts

Number of restarts

##### m_replaceMissing

    ReplaceMissingValues m_replaceMissing

Globally replace missing values

##### m_uniformPrior

    boolean m_uniformPrior

Uniform prior probability of the documents

##### m_verbose

    boolean m_verbose

Verbose?

##### random

    Random random

Randomly generate initial partition

### Class weka.clusterers.SimpleKMeans

class SimpleKMeans extends RandomizableClusterer implements Serializable

serialVersionUID:
  -3235809600124455376L

#### Serialized Fields

##### m_Assignments

    int[] m_Assignments

Assignments obtained

##### m_ClusterCentroids

    Instances m_ClusterCentroids

holds the cluster centroids

##### m_ClusterMissingCounts

    int[][] m_ClusterMissingCounts

##### m_ClusterNominalCounts

    int[][][] m_ClusterNominalCounts

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

##### m_ClusterSizes

    int[] m_ClusterSizes

The number of instances in each cluster

##### m_ClusterStdDevs

    Instances m_ClusterStdDevs

Holds the standard deviations of the numeric attributes in each cluster

##### m_displayStdDevs

    boolean m_displayStdDevs

Display standard deviations for numeric atts

##### m_DistanceFunction

    DistanceFunction m_DistanceFunction

the distance function used.

##### m_dontReplaceMissing

    boolean m_dontReplaceMissing

Replace missing values globally?

##### m_FullMeansOrMediansOrModes

    double[] m_FullMeansOrMediansOrModes

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

##### m_FullMissingCounts

    int[] m_FullMissingCounts

##### m_FullNominalCounts

    int[][] m_FullNominalCounts

##### m_FullStdDevs

    double[] m_FullStdDevs

##### m_Iterations

    int m_Iterations

Keep track of the number of iterations completed before convergence

##### m_MaxIterations

    int m_MaxIterations

Maximum number of iterations to be executed

##### m_NumClusters

    int m_NumClusters

number of clusters to generate

##### m_PreserveOrder

    boolean m_PreserveOrder

Preserve order of instances

##### m_ReplaceMissingFilter

    ReplaceMissingValues m_ReplaceMissingFilter

replace missing values in training instances

##### m_squaredErrors

    double[] m_squaredErrors

Holds the squared errors for all clusters

### Class weka.clusterers.SingleClustererEnhancer

class SingleClustererEnhancer extends AbstractClusterer implements Serializable

serialVersionUID:
  4893928362926428671L

#### Serialized Fields

##### m_Clusterer

    Clusterer m_Clusterer

the clusterer

### Class weka.clusterers.XMeans

class XMeans extends RandomizableClusterer implements Serializable

serialVersionUID:
  -7941793078404132616L

#### Serialized Fields

##### m_Bic

    double m_Bic

BIC-Score of the current model.

##### m_BinValue

    double m_BinValue

Distance value between true and false of binary attributes and "same" and "different" of nominal attributes (default = 1.0).

##### m_ClusterAssignments

    int[] m_ClusterAssignments

temporary variable holding cluster assignments while iterating.

##### m_ClusterCenters

    Instances m_ClusterCenters

cluster centers.

##### m_CurrDebugFlag

    boolean m_CurrDebugFlag

Flag: I'm debugging.

##### m_CutOffFactor

    double m_CutOffFactor

cutoff factor - percentage of splits done in Improve-Structure part only relevant, if all children lost.

##### m_DebugLevel

    int m_DebugLevel

level of debug output, 0 is no output.

##### m_DebugVectors

    Instances m_DebugVectors

all the debug vectors.

##### m_DebugVectorsFile

    File m_DebugVectorsFile

file name of the input file for the random vectors.

##### m_DebugVectorsIndex

    int m_DebugVectorsIndex

the index for the current debug vector.

##### m_DebugVectorsInput

    Reader m_DebugVectorsInput

input file for the random vectors --> USED FOR DEBUGGING.

##### m_DistanceF

    DistanceFunction m_DistanceF

the distance function used.

##### m_InputCenterFile

    File m_InputCenterFile

file name of the output file for the cluster centers.

##### m_Instances

    Instances m_Instances

training instances.

##### m_IterationCount

    int m_IterationCount

counts iterations done in main loop.

##### m_KDTree

    KDTree m_KDTree

KDTrees class if KDTrees are used.

##### m_KMeansStopped

    int m_KMeansStopped

counter to say how often kMeans was stopped by loop counter.

##### m_MaxIterations

    int m_MaxIterations

maximum overall iterations.

##### m_MaxKMeans

    int m_MaxKMeans

maximum iterations to perform Kmeans part if negative, iterations are not checked.

##### m_MaxKMeansForChildren

    int m_MaxKMeansForChildren

see above, but for kMeans of splitted clusters.

##### m_MaxNumClusters

    int m_MaxNumClusters

max number of clusters to generate.

##### m_MinNumClusters

    int m_MinNumClusters

min number of clusters to generate.

##### m_Mle

    double[] m_Mle

Distortion.

##### m_Model

    Instances m_Model

model information, should increase readability.

##### m_NumClusters

    int m_NumClusters

The actual number of clusters.

##### m_NumSplits

    int m_NumSplits

Number of splits prepared.

##### m_NumSplitsDone

    int m_NumSplitsDone

Number of splits accepted (including cutoff factor decisions).

##### m_NumSplitsStillDone

    int m_NumSplitsStillDone

Number of splits accepted just because of cutoff factor.

##### m_OutputCenterFile

    File m_OutputCenterFile

file name of the output file for the cluster centers.

##### m_ReplaceMissingFilter

    ReplaceMissingValues m_ReplaceMissingFilter

replace missing values in training instances.

##### m_UseKDTree

    boolean m_UseKDTree

whether to use the KDTree (the KDTree is only initialized to be configurable from the GUI).

## Package weka.clusterers.forOPTICSAndDBScan.Databases

### Class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase

class SequentialDatabase extends Object implements Serializable

serialVersionUID:
  787245523118665778L

#### Serialized Fields

##### attributeMaxValues

    double[] attributeMaxValues

Holds the maximum value for each attribute

##### attributeMinValues

    double[] attributeMinValues

Holds the minimum value for each attribute

##### instances

    Instances instances

Holds the original instances delivered from WEKA

##### treeMap

    TreeMap treeMap

Internal, sorted Treemap for storing all the DataObjects

## Package weka.clusterers.forOPTICSAndDBScan.DataObjects

### Class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject

class EuclideanDataObject extends Object implements Serializable

serialVersionUID:
  -4408119914898291075L

#### Serialized Fields

##### c_dist

    double c_dist

Holds the coreDistance for this DataObject

##### clusterID

    int clusterID

Holds the ID of the cluster, to which this DataObject is assigned

##### database

    Database database

Holds the database, that is the keeper of this DataObject

##### instance

    Instance instance

Holds the original instance

##### key

    String key

Holds the (unique) key that is associated with this DataObject

##### processed

    boolean processed

Holds the status for this DataObject (true, if it has been processed, else false)

##### r_dist

    double r_dist

Holds the reachabilityDistance for this DataObject

### Class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject

class ManhattanDataObject extends Object implements Serializable

serialVersionUID:
  -3417720553766544582L

#### Serialized Fields

##### c_dist

    double c_dist

Holds the coreDistance for this DataObject

##### clusterID

    int clusterID

Holds the ID of the cluster, to which this DataObject is assigned

##### database

    Database database

Holds the database, that is the keeper of this DataObject

##### instance

    Instance instance

Holds the original instance

##### key

    String key

Holds the (unique) key that is associated with this DataObject

##### processed

    boolean processed

Holds the status for this DataObject (true, if it has been processed, else false)

##### r_dist

    double r_dist

Holds the reachabilityDistance for this DataObject

## Package weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI

### Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel

class GraphPanel extends JComponent implements Serializable

serialVersionUID:
  7917937528738361470L

#### Serialized Fields

##### coreDistanceColor

    Color coreDistanceColor

Specifies the color for displaying core-distances

##### reachabilityDistanceColor

    Color reachabilityDistanceColor

Specifies the color for displaying reachability-distances

##### recentIndex

    int recentIndex

Holds the index of the last toolTip

##### resultVector

    FastVector resultVector

Holds the clustering results

##### showCoreDistances

    boolean showCoreDistances

Holds the flag for showCoreDistances

##### showReachabilityDistances

    boolean showReachabilityDistances

Holds the flag for showrRechabilityDistances

##### verticalAdjustment

    int verticalAdjustment

Holds the value that is multiplied with the original values of core- and reachability distances in order to get better graphical views

##### widthSlider

    int widthSlider

Specifies the width for displaying the distances

### Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel

class ResultVectorTableModel extends AbstractTableModel implements Serializable

serialVersionUID:
  -7732711470435549210L

#### Serialized Fields

##### resultVector

    FastVector resultVector

Holds the ClusterOrder (dataObjects with their r_dist and c_dist) for the GUI

### Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject

class SERObject extends Object implements Serializable

serialVersionUID:
  -6022057864970639151L

#### Serialized Fields

##### database_distanceType

    String database_distanceType

##### database_Type

    String database_Type

##### databaseSize

    int databaseSize

##### elapsedTime

    String elapsedTime

##### epsilon

    double epsilon

##### minPoints

    int minPoints

##### numberOfAttributes

    int numberOfAttributes

##### numberOfGeneratedClusters

    int numberOfGeneratedClusters

##### opticsOutputs

    boolean opticsOutputs

##### resultVector

    FastVector resultVector

## Package weka.core

### Class weka.core.AbstractStringDistanceFunction

class AbstractStringDistanceFunction extends NormalizableDistance implements Serializable

### Class weka.core.AlgVector

class AlgVector extends Object implements Serializable

serialVersionUID:
  -4023736016850256591L

#### Serialized Fields

##### m_Elements

    double[] m_Elements

The values of the matrix

### Class weka.core.Attribute

class Attribute extends Object implements Serializable

serialVersionUID:
  -742180568732916383L

#### Serialized Fields

##### m_DateFormat

    SimpleDateFormat m_DateFormat

Date format specification for date attributes

##### m_Hashtable

    Hashtable m_Hashtable

Mapping of values to indices (if nominal or string).

##### m_HasZeropoint

    boolean m_HasZeropoint

Whether the attribute has a zeropoint.

##### m_Header

    Instances m_Header

The header information for a relation-valued attribute.

##### m_Index

    int m_Index

The attribute's index.

##### m_IsAveragable

    boolean m_IsAveragable

Whether the attribute is averagable.

##### m_IsRegular

    boolean m_IsRegular

Whether the attribute is regular.

##### m_LowerBound

    double m_LowerBound

The attribute's lower numeric bound.

##### m_LowerBoundIsOpen

    boolean m_LowerBoundIsOpen

Whether the lower bound is open.

##### m_Metadata

    ProtectedProperties m_Metadata

The attribute's metadata.

##### m_Name

    String m_Name

The attribute's name.

##### m_Ordering

    int m_Ordering

The attribute's ordering.

##### m_Type

    int m_Type

The attribute's type.

##### m_UpperBound

    double m_UpperBound

The attribute's upper numeric bound.

##### m_UpperBoundIsOpen

    boolean m_UpperBoundIsOpen

Whether the upper bound is open

##### m_Values

    FastVector m_Values

The attribute's values (if nominal or string).

##### m_Weight

    double m_Weight

The attribute's weight.

### Class weka.core.AttributeExpression

class AttributeExpression extends Object implements Serializable

serialVersionUID:
  402130123261736245L

#### Serialized Fields

##### m_operatorStack

    Stack m_operatorStack

Operator stack

##### m_originalInfix

    String m_originalInfix

Holds the original infix expression

##### m_postFixExpVector

    Vector m_postFixExpVector

Holds the expression in postfix form

##### m_previousTok

    String m_previousTok

Holds the previous token

##### m_signMod

    boolean m_signMod

True if the next numeric constant or attribute index is negative

### Class weka.core.AttributeLocator

class AttributeLocator extends Object implements Serializable

serialVersionUID:
  -2932848827681070345L

#### Serialized Fields

##### m_AllowedIndices

    int[] m_AllowedIndices

the attribute indices that may be inspected

##### m_Attributes

    Vector<Boolean> m_Attributes

contains the attribute locations, either true or false Boolean objects

##### m_AttributesEfficient

    BitSet m_AttributesEfficient

contains the attribute locations, either true or false (efficient replacement)

##### m_Data

    Instances m_Data

the referenced data

##### m_Indices

    int[] m_Indices

the indices

##### m_LocatorIndices

    int[] m_LocatorIndices

the indices of locator objects

##### m_Locators

    Vector<AttributeLocator> m_Locators

contains the locator locations, either null or a AttributeLocator reference

##### m_Type

    int m_Type

the type of the attribute

### Class weka.core.AttributeStats

class AttributeStats extends Object implements Serializable

serialVersionUID:
  4434688832743939380L

#### Serialized Fields

##### distinctCount

    int distinctCount

The number of distinct values

##### intCount

    int intCount

The number of int-like values

##### missingCount

    int missingCount

The number of missing values

##### nominalCounts

    int[] nominalCounts

Counts of each nominal value

##### numericStats

    Stats numericStats

Stats on numeric value distributions

##### realCount

    int realCount

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

##### totalCount

    int totalCount

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

##### uniqueCount

    int uniqueCount

The number of values that only appear once

### Class weka.core.BinarySparseInstance

class BinarySparseInstance extends SparseInstance implements Serializable

serialVersionUID:
  -5297388762342528737L

### Class weka.core.Capabilities

class Capabilities extends Object implements Serializable

serialVersionUID:
  -5478590032325567849L

#### Serialized Fields

##### m_AttributeTest

    boolean m_AttributeTest

whether to perform attribute based tests

##### m_Capabilities

    HashSet m_Capabilities

the hashset for storing the active capabilities

##### m_Dependencies

    HashSet m_Dependencies

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

##### m_FailReason

    Exception m_FailReason

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

##### m_InstancesTest

    boolean m_InstancesTest

whether to perform data based tests

##### m_MinimumNumberInstances

    int m_MinimumNumberInstances

the minimum number of instances in a dataset

##### m_MinimumNumberInstancesTest

    boolean m_MinimumNumberInstancesTest

whether to test for minimum number of instances

##### m_MissingClassValuesTest

    boolean m_MissingClassValuesTest

whether to test for missing class values

##### m_MissingValuesTest

    boolean m_MissingValuesTest

whether to test for missing values

##### m_Owner

    CapabilitiesHandler m_Owner

the object that owns this capabilities instance

##### m_Test

    boolean m_Test

whether to perform any tests at all

### Class weka.core.ChebyshevDistance

class ChebyshevDistance extends NormalizableDistance implements Serializable

serialVersionUID:
  -7739904999895461429L

### Class weka.core.Debug

class Debug extends Object implements Serializable

serialVersionUID:
  66171861743328020L

#### Serialized Fields

##### m_Clock

    Debug.Clock m_Clock

for clocking

##### m_Enabled

    boolean m_Enabled

whether logging is enabled

##### m_Log

    Debug.Log m_Log

for logging

### Class weka.core.Debug.Clock

class Clock extends Object implements Serializable

serialVersionUID:
  4622161807307942201L

#### Serialized Fields

##### m_CanMeasureCpuTime

    boolean m_CanMeasureCpuTime

whether the system can measure the CPU time

##### m_OutputFormat

    int m_OutputFormat

the format of the output

##### m_Running

    boolean m_Running

whether the time is still clocked

##### m_Start

    long m_Start

the start time

##### m_Stop

    long m_Stop

the end time

##### m_ThreadID

    long m_ThreadID

the thread ID

##### m_UseCpuTime

    boolean m_UseCpuTime

whether to use the CPU time (by default TRUE)

### Class weka.core.Debug.DBO

class DBO extends Object implements Serializable

serialVersionUID:
  -5245628124742606784L

#### Serialized Fields

##### m_outputTypes

    Range m_outputTypes

range of outputtyp

##### m_verboseOn

    boolean m_verboseOn

enables/disables output of debug information

### Class weka.core.Debug.Log

class Log extends Object implements Serializable

serialVersionUID:
  1458435732111675823L

#### Serialized Fields

##### m_Filename

    String m_Filename

the filename, if any

##### m_LoggerInitFailed

    boolean m_LoggerInitFailed

whether the initialization of the logger failed

##### m_NumFiles

    int m_NumFiles

the number of files for rotating the logs

##### m_Size

    int m_Size

the size of the file (in bytes)

### Class weka.core.Debug.Random

class Random extends Random implements Serializable

serialVersionUID:
  1256846887618333956L

#### Serialized Fields

##### m_Debug

    boolean m_Debug

whether to output debug information

##### m_ID

    long m_ID

the unique ID for this number generator

##### m_Log

    Debug.Log m_Log

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

### Class weka.core.Debug.SimpleLog

class SimpleLog extends Object implements Serializable

serialVersionUID:
  -2671928223819510830L

#### Serialized Fields

##### m_Filename

    String m_Filename

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

### Class weka.core.Debug.Timestamp

class Timestamp extends Object implements Serializable

serialVersionUID:
  -6099868388466922753L

#### Serialized Fields

##### m_Format

    String m_Format

the format of the timestamp

##### m_Formatter

    SimpleDateFormat m_Formatter

handles the format of the output

##### m_Stamp

    Date m_Stamp

the actual date

### Class weka.core.EditDistance

class EditDistance extends AbstractStringDistanceFunction implements Serializable

### Class weka.core.EuclideanDistance

class EuclideanDistance extends NormalizableDistance implements Serializable

serialVersionUID:
  1068606253458807903L

### Class weka.core.FastVector

class FastVector extends Object implements Serializable

serialVersionUID:
  -2173635135622930169L

#### Serialized Fields

##### m_CapacityIncrement

    int m_CapacityIncrement

The capacity increment

##### m_CapacityMultiplier

    int m_CapacityMultiplier

The capacity multiplier.

##### m_Objects

    Object[] m_Objects

The array of objects.

##### m_Size

    int m_Size

The current size;

### Class weka.core.Instance

class Instance extends Object implements Serializable

serialVersionUID:
  1482635194499365122L

#### Serialized Fields

##### m_AttValues

    double[] m_AttValues

The instance's attribute values.

##### m_Dataset

    Instances m_Dataset

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

##### m_Weight

    double m_Weight

The instance's weight.

### Class weka.core.InstanceComparator

class InstanceComparator extends Object implements Serializable

serialVersionUID:
  -6589278678230949683L

#### Serialized Fields

##### m_IncludeClass

    boolean m_IncludeClass

whether to include the class in the comparison

### Class weka.core.Instances

class Instances extends Object implements Serializable

serialVersionUID:
  -19412345060742748L

#### Serialized Fields

##### m_Attributes

    FastVector m_Attributes

The attribute information.

##### m_ClassIndex

    int m_ClassIndex

The class attribute's index

##### m_Instances

    FastVector m_Instances

The instances.

##### m_Lines

    int m_Lines

The lines read so far in case of incremental loading. Since the StreamTokenizer will be re-initialized with every instance that is read, we have to keep track of the number of lines read so far.

See Also:
  - `Instances.readInstance(Reader)`

##### m_RelationName

    String m_RelationName

The dataset's name.

### Class weka.core.Jython

class Jython extends Object implements Serializable

serialVersionUID:
  -6972298704460209252L

#### Serialized Fields

##### m_Interpreter

    Object m_Interpreter

the interpreter

### Class weka.core.ManhattanDistance

class ManhattanDistance extends NormalizableDistance implements Serializable

serialVersionUID:
  6783782554224000243L

### Class weka.core.Matrix

class Matrix extends Object implements Serializable

serialVersionUID:
  -3604757095849145838L

#### Serialized Fields

##### m_Matrix

    Matrix m_Matrix

Deprecated.
The actual matrix

### Class weka.core.NormalizableDistance

class NormalizableDistance extends Object implements Serializable

#### Serialized Fields

##### m_ActiveIndices

    boolean[] m_ActiveIndices

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

##### m_AttributeIndices

    Range m_AttributeIndices

The range of attributes to use for calculating the distance.

##### m_Data

    Instances m_Data

the instances used internally.

##### m_DontNormalize

    boolean m_DontNormalize

True if normalization is turned off (default false).

##### m_Ranges

    double[][] m_Ranges

The range of the attributes.

##### m_Validated

    boolean m_Validated

Whether all the necessary preparations have been done.

### Exception Class weka.core.NoSupportForMissingValuesException

class NoSupportForMissingValuesException extends WekaException implements Serializable

serialVersionUID:
  5161175307725893973L

### Class weka.core.ProtectedProperties

class ProtectedProperties extends Properties implements Serializable

serialVersionUID:
  3876658672657323985L

#### Serialized Fields

##### closed

    boolean closed

the properties need to be open during construction of the object

### Class weka.core.Queue

class Queue extends Object implements Serializable

serialVersionUID:
  -1141282001146389780L

#### Serialized Fields

##### m_Head

    weka.core.Queue.QueueNode m_Head

Store a reference to the head of the queue

##### m_Size

    int m_Size

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

##### m_Tail

    weka.core.Queue.QueueNode m_Tail

Store a reference to the tail of the queue

### Class weka.core.Queue.QueueNode

class QueueNode extends Object implements Serializable

serialVersionUID:
  -5119358279412097455L

#### Serialized Fields

##### m_Contents

    Object m_Contents

The nodes contents

##### m_Next

    weka.core.Queue.QueueNode m_Next

The next node in the queue

### Class weka.core.RandomVariates

class RandomVariates extends Random implements Serializable

serialVersionUID:
  -4763742718209460354L

### Class weka.core.Range

class Range extends Object implements Serializable

serialVersionUID:
  3667337062176835900L

#### Serialized Fields

##### m_Invert

    boolean m_Invert

Whether matching should be inverted

##### m_RangeStrings

    Vector m_RangeStrings

Record the string representations of the columns to delete

##### m_SelectFlags

    boolean[] m_SelectFlags

The array of flags for whether an column is selected

##### m_Upper

    int m_Upper

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

### Class weka.core.RelationalLocator

class RelationalLocator extends AttributeLocator implements Serializable

serialVersionUID:
  4646872277151854732L

### Class weka.core.SerializedObject

class SerializedObject extends Object implements Serializable

serialVersionUID:
  6635502953928860434L

#### Serialized Fields

##### m_isCompressed

    boolean m_isCompressed

Whether or not the object is compressed.

##### m_isJython

    boolean m_isJython

Whether it is a Jython object or not

##### m_storedObjectArray

    byte[] m_storedObjectArray

The array storing the object.

### Class weka.core.SingleIndex

class SingleIndex extends Object implements Serializable

serialVersionUID:
  5285169134430839303L

#### Serialized Fields

##### m_IndexString

    String m_IndexString

Record the string representation of the number

##### m_SelectedIndex

    int m_SelectedIndex

The selected index

##### m_Upper

    int m_Upper

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

### Class weka.core.SparseInstance

class SparseInstance extends Instance implements Serializable

serialVersionUID:
  -3579051291332630149L

#### Serialized Fields

##### m_Indices

    int[] m_Indices

The index of the attribute associated with each stored value.

##### m_NumAttributes

    int m_NumAttributes

The maximum number of values that can be stored.

### Class weka.core.StringLocator

class StringLocator extends AttributeLocator implements Serializable

serialVersionUID:
  7805522230268783972L

### Class weka.core.Tag

class Tag extends Object implements Serializable

serialVersionUID:
  3326379903447135320L

#### Serialized Fields

##### m_ID

    int m_ID

The ID

##### m_IDStr

    String m_IDStr

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

##### m_Readable

    String m_Readable

The descriptive text

### Class weka.core.TestInstances

class TestInstances extends Object implements Serializable

serialVersionUID:
  -6263968936330390469L

#### Serialized Fields

##### m_ClassIndex

    int m_ClassIndex

the class index (-1 is LAST, -2 means no class)

See Also:
  - `TestInstances.CLASS_IS_LAST`
  - `TestInstances.NO_CLASS`

##### m_ClassType

    int m_ClassType

the class type

##### m_Data

    Instances m_Data

the generated data

##### m_Handler

    CapabilitiesHandler m_Handler

the CapabilitiesHandler to get the Capabilities from

##### m_MultiInstance

    boolean m_MultiInstance

whether to generate Multi-Instance data or not

##### m_NumClasses

    int m_NumClasses

the number of classes (in case of NOMINAL class)

##### m_NumDate

    int m_NumDate

the number of date attributes

##### m_NumInstances

    int m_NumInstances

the number of instances

##### m_NumInstancesRelational

    int m_NumInstancesRelational

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

##### m_NumNominal

    int m_NumNominal

the number of nominal attributes

##### m_NumNominalValues

    int m_NumNominalValues

the number of values for nominal attributes

##### m_NumNumeric

    int m_NumNumeric

the number of numeric attributes

##### m_NumRelational

    int m_NumRelational

the number of relational attributes

##### m_NumRelationalDate

    int m_NumRelationalDate

the number of date attributes in a relational attribute

##### m_NumRelationalNominal

    int m_NumRelationalNominal

the number of nominal attributes in a relational attribute

##### m_NumRelationalNominalValues

    int m_NumRelationalNominalValues

the number of values for nominal attributes in relational attributes

##### m_NumRelationalNumeric

    int m_NumRelationalNumeric

the number of numeric attributes in a relational attribute

##### m_NumRelationalString

    int m_NumRelationalString

the number of string attributes in a relational attribute

##### m_NumString

    int m_NumString

the number of string attributes

##### m_Random

    Random m_Random

the random number generator

##### m_Relation

    String m_Relation

the name of the relation

##### m_RelationalClassFormat

    Instances m_RelationalClassFormat

the format of the multi-instance data of the class

##### m_RelationalFormat

    Instances[] m_RelationalFormat

the format of the multi-instance data

##### m_Seed

    int m_Seed

the seed value

##### m_Words

    String[] m_Words

for generating String attributes/classes

##### m_WordSeparators

    String m_WordSeparators

for generating String attributes/classes

### Class weka.core.Trie

class Trie extends Object implements Serializable

serialVersionUID:
  -5897980928817779048L

#### Serialized Fields

##### m_HashCode

    int m_HashCode

the hash code

##### m_RecalcHashCode

    boolean m_RecalcHashCode

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

##### m_Root

    Trie.TrieNode m_Root

the root node

### Class weka.core.Trie.TrieNode

class TrieNode extends DefaultMutableTreeNode implements Serializable

serialVersionUID:
  -2252907099391881148L

#### Serialized Fields

##### m_Children

    Hashtable<Character,Trie.TrieNode> m_Children

for fast access to the children

### Exception Class weka.core.UnassignedClassException

class UnassignedClassException extends RuntimeException implements Serializable

serialVersionUID:
  6268278694768818235L

### Exception Class weka.core.UnassignedDatasetException

class UnassignedDatasetException extends RuntimeException implements Serializable

serialVersionUID:
  -9000116786626328854L

### Exception Class weka.core.UnsupportedAttributeTypeException

class UnsupportedAttributeTypeException extends WekaException implements Serializable

serialVersionUID:
  2658987325328414838L

### Exception Class weka.core.UnsupportedClassTypeException

class UnsupportedClassTypeException extends WekaException implements Serializable

serialVersionUID:
  5175741076972192151L

### Exception Class weka.core.WekaException

class WekaException extends Exception implements Serializable

serialVersionUID:
  6428269989006208585L

## Package weka.core.converters

### Class weka.core.converters.AbstractFileLoader

class AbstractFileLoader extends AbstractLoader implements Serializable

#### Serialized Fields

##### m_File

    String m_File

the file

##### m_sourceFile

    File m_sourceFile

Holds the source of the data set.

##### m_useRelativePath

    boolean m_useRelativePath

use relative file paths

### Class weka.core.converters.AbstractFileSaver

class AbstractFileSaver extends AbstractSaver implements Serializable

#### Serialized Fields

##### FILE_EXTENSION

    String FILE_EXTENSION

The file extension of the destination file.

##### FILE_EXTENSION_COMPRESSED

    String FILE_EXTENSION_COMPRESSED

the extension for compressed files

##### m_dir

    String m_dir

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

##### m_incrementalCounter

    int m_incrementalCounter

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

##### m_outputFile

    File m_outputFile

The destination file.

##### m_prefix

    String m_prefix

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

##### m_useRelativePath

    boolean m_useRelativePath

use relative file paths

### Class weka.core.converters.AbstractLoader

class AbstractLoader extends Object implements Serializable

#### Serialized Fields

##### m_retrieval

    int m_retrieval

The current retrieval mode

### Class weka.core.converters.AbstractSaver

class AbstractSaver extends Object implements Serializable

#### Serialized Fields

##### m_instances

    Instances m_instances

The instances that should be stored

##### m_retrieval

    int m_retrieval

The current retrieval mode

##### m_writeMode

    int m_writeMode

The current write mode

### Class weka.core.converters.ArffLoader

class ArffLoader extends AbstractFileLoader implements Serializable

serialVersionUID:
  2726929550544048587L

#### Serialized Fields

##### m_URL

    String m_URL

the url

### Class weka.core.converters.ArffSaver

class ArffSaver extends AbstractFileSaver implements Serializable

serialVersionUID:
  2223634248900042228L

#### Serialized Fields

##### m_CompressOutput

    boolean m_CompressOutput

whether to compress the output

### Class weka.core.converters.C45Loader

class C45Loader extends AbstractFileLoader implements Serializable

serialVersionUID:
  5454329403218219L

#### Serialized Fields

##### m_fileStem

    String m_fileStem

Holds the filestem.

##### m_ignore

    boolean[] m_ignore

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

##### m_numAttribs

    int m_numAttribs

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

##### m_sourceFileData

    File m_sourceFileData

Describe variable `m_sourceFileData` here.

### Class weka.core.converters.C45Saver

class C45Saver extends AbstractFileSaver implements Serializable

serialVersionUID:
  -821428878384253377L

### Class weka.core.converters.ConverterUtils

class ConverterUtils extends Object implements Serializable

serialVersionUID:
  -2460855349276148760L

### Class weka.core.converters.ConverterUtils.DataSink

class DataSink extends Object implements Serializable

serialVersionUID:
  -1504966891136411204L

#### Serialized Fields

##### m_Saver

    Saver m_Saver

the saver to use for storing the data.

##### m_Stream

    OutputStream m_Stream

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

### Class weka.core.converters.ConverterUtils.DataSource

class DataSource extends Object implements Serializable

serialVersionUID:
  -613122395928757332L

#### Serialized Fields

##### m_BatchBuffer

    Instances m_BatchBuffer

the batch buffer.

##### m_BatchCounter

    int m_BatchCounter

the instance counter for the batch case.

##### m_File

    File m_File

the file to load.

##### m_Incremental

    boolean m_Incremental

whether the loader is incremental.

##### m_IncrementalBuffer

    Instance m_IncrementalBuffer

the last internally read instance.

##### m_Loader

    Loader m_Loader

the loader.

##### m_URL

    URL m_URL

the URL to load.

### Class weka.core.converters.CSVLoader

class CSVLoader extends AbstractFileLoader implements Serializable

serialVersionUID:
  5607529739745491340L

#### Serialized Fields

##### m_cumulativeInstances

    FastVector m_cumulativeInstances

Holds instances accumulated so far.

##### m_cumulativeStructure

    FastVector m_cumulativeStructure

A list of hash tables for accumulating nominal values during parsing.

##### m_dateAttributes

    Range m_dateAttributes

The range of attributes to force to type date

##### m_dateFormat

    String m_dateFormat

The formatting string to use to parse dates

##### m_Enclosures

    String m_Enclosures

enclosure character(s) to use for strings

##### m_FirstCheck

    boolean m_FirstCheck

whether the first row has been read.

##### m_formatter

    SimpleDateFormat m_formatter

The formatter to use on dates

##### m_MissingValue

    String m_MissingValue

The placeholder for missing values.

##### m_NominalAttributes

    Range m_NominalAttributes

The range of attributes to force to type nominal.

##### m_StringAttributes

    Range m_StringAttributes

The range of attributes to force to type string.

### Class weka.core.converters.CSVSaver

class CSVSaver extends AbstractFileSaver implements Serializable

serialVersionUID:
  476636654410701807L

### Class weka.core.converters.DatabaseConnection

class DatabaseConnection extends DatabaseUtils implements Serializable

serialVersionUID:
  1673169848863178695L

### Class weka.core.converters.DatabaseLoader

class DatabaseLoader extends AbstractLoader implements Serializable

serialVersionUID:
  -7936159015338318659L

#### Serialized Fields

##### m_checkForTable

    boolean m_checkForTable

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

##### m_choice

    int m_choice

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

##### m_counter

    int m_counter

Indicates how many rows has already been loaded incrementally

##### m_DataBaseConnection

    DatabaseConnection m_DataBaseConnection

The database connection

##### m_datasetPseudoInc

    Instances m_datasetPseudoInc

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

##### m_firstTime

    boolean m_firstTime

Flag indicating that incremental process wants to read first instance

##### m_idColumn

    String m_idColumn

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

##### m_inc

    boolean m_inc

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

##### m_Keys

    String m_Keys

the keys for unique ordering

##### m_nominalIndexes

    Hashtable[] m_nominalIndexes

Stores the index of a nominal value

##### m_nominalStrings

    FastVector[] m_nominalStrings

Stores the nominal value

##### m_nominalToStringLimit

    int m_nominalToStringLimit

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

##### m_oldStructure

    Instances m_oldStructure

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

##### m_orderBy

    FastVector m_orderBy

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

##### m_Password

    String m_Password

the database password to use

##### m_pseudoIncremental

    boolean m_pseudoIncremental

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

##### m_query

    String m_query

The user defined query to load instances. (form: SELECT *|invalid input: '&ltcolumn'-list> FROM invalid input: '&lttable'> [WHERE <condition>])

##### m_rowCount

    int m_rowCount

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

##### m_structure

    Instances m_structure

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

##### m_URL

    String m_URL

the JDBC URL to use

##### m_User

    String m_User

the database user to use

### Class weka.core.converters.DatabaseSaver

class DatabaseSaver extends AbstractSaver implements Serializable

serialVersionUID:
  863971733782624956L

#### Serialized Fields

##### m_count

    int m_count

counts the rows and used as a primary key value.

##### m_createDate

    String m_createDate

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

##### m_createDouble

    String m_createDouble

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

##### m_createInt

    String m_createInt

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

##### m_createText

    String m_createText

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

##### m_DataBaseConnection

    DatabaseConnection m_DataBaseConnection

The database connection.

##### m_DateFormat

    SimpleDateFormat m_DateFormat

For converting the date value into a database string.

##### m_id

    boolean m_id

Flag indicating if a primary key column should be added.

##### m_idColumn

    String m_idColumn

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

##### m_inputFile

    String m_inputFile

An input arff file (for command line use).

##### m_Password

    String m_Password

the password for the database.

##### m_tableName

    String m_tableName

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

##### m_tabName

    boolean m_tabName

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

##### m_Username

    String m_Username

the user name for the database.

### Class weka.core.converters.LibSVMLoader

class LibSVMLoader extends AbstractFileLoader implements Serializable

serialVersionUID:
  4988360125354664417L

#### Serialized Fields

##### m_Buffer

    Vector m_Buffer

the buffer of the rows read so far.

##### m_URL

    String m_URL

the url.

### Class weka.core.converters.LibSVMSaver

class LibSVMSaver extends AbstractFileSaver implements Serializable

serialVersionUID:
  2792295817125694786L

#### Serialized Fields

##### m_ClassIndex

    SingleIndex m_ClassIndex

the class index

### Class weka.core.converters.SerializedInstancesLoader

class SerializedInstancesLoader extends AbstractFileLoader implements Serializable

serialVersionUID:
  2391085836269030715L

#### Serialized Fields

##### m_Dataset

    Instances m_Dataset

Holds the structure (header) of the data set.

##### m_IncrementalIndex

    int m_IncrementalIndex

The current index position for incremental reading

### Class weka.core.converters.SerializedInstancesSaver

class SerializedInstancesSaver extends AbstractFileSaver implements Serializable

serialVersionUID:
  -7717010648500658872L

#### Serialized Fields

##### m_objectstream

    ObjectOutputStream m_objectstream

the output stream.

### Class weka.core.converters.SVMLightLoader

class SVMLightLoader extends AbstractFileLoader implements Serializable

serialVersionUID:
  4988360125354664417L

#### Serialized Fields

##### m_Buffer

    Vector m_Buffer

the buffer of the rows read so far.

##### m_URL

    String m_URL

the url.

### Class weka.core.converters.SVMLightSaver

class SVMLightSaver extends AbstractFileSaver implements Serializable

serialVersionUID:
  2605714599263995835L

#### Serialized Fields

##### m_ClassIndex

    SingleIndex m_ClassIndex

the class index.

### Class weka.core.converters.TextDirectoryLoader

class TextDirectoryLoader extends AbstractLoader implements Serializable

serialVersionUID:
  2592118773712247647L

#### Serialized Fields

##### m_charSet

    String m_charSet

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

##### m_Debug

    boolean m_Debug

whether to print some debug information

##### m_OutputFilename

    boolean m_OutputFilename

whether to include the filename as an extra attribute

##### m_sourceFile

    File m_sourceFile

Holds the source of the data set.

##### m_structure

    Instances m_structure

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

### Class weka.core.converters.XRFFLoader

class XRFFLoader extends AbstractFileLoader implements Serializable

serialVersionUID:
  3764533621135196582L

#### Serialized Fields

##### m_URL

    String m_URL

the url

##### m_XMLInstances

    XMLInstances m_XMLInstances

the loaded XML document

### Class weka.core.converters.XRFFSaver

class XRFFSaver extends AbstractFileSaver implements Serializable

serialVersionUID:
  -7226404765213522043L

#### Serialized Fields

##### m_ClassIndex

    SingleIndex m_ClassIndex

the class index

##### m_CompressOutput

    boolean m_CompressOutput

whether to compress the output

##### m_XMLInstances

    XMLInstances m_XMLInstances

the generated XML document

## Package weka.core.matrix

### Class weka.core.matrix.CholeskyDecomposition

class CholeskyDecomposition extends Object implements Serializable

serialVersionUID:
  -8739775942782694701L

#### Serialized Fields

##### isspd

    boolean isspd

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

##### L

    double[][] L

Array for internal storage of decomposition.
internal array storage.

##### n

    int n

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

### Class weka.core.matrix.EigenvalueDecomposition

class EigenvalueDecomposition extends Object implements Serializable

serialVersionUID:
  4011654467211422319L

#### Serialized Fields

##### d

    double[] d

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

##### e

    double[] e

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

##### H

    double[][] H

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

##### issymmetric

    boolean issymmetric

Symmetry flag.
internal symmetry flag.

##### n

    int n

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

##### ort

    double[] ort

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

##### V

    double[][] V

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

### Class weka.core.matrix.ExponentialFormat

class ExponentialFormat extends DecimalFormat implements Serializable

serialVersionUID:
  -5298981701073897741L

#### Serialized Fields

##### digits

    int digits

##### exp

    int exp

##### nf

    DecimalFormat nf

##### sign

    boolean sign

##### trailing

    boolean trailing

### Class weka.core.matrix.FlexibleDecimalFormat

class FlexibleDecimalFormat extends DecimalFormat implements Serializable

serialVersionUID:
  110912192794064140L

#### Serialized Fields

##### decimalDigits

    int decimalDigits

##### digits

    int digits

##### exp

    boolean exp

##### expDecimalDigits

    int expDecimalDigits

##### grouping

    boolean grouping

##### intDigits

    int intDigits

##### nf

    DecimalFormat nf

##### power

    int power

##### sign

    boolean sign

##### trailing

    boolean trailing

### Class weka.core.matrix.FloatingPointFormat

class FloatingPointFormat extends DecimalFormat implements Serializable

serialVersionUID:
  4500373755333429499L

#### Serialized Fields

##### decimal

    int decimal

##### nf

    DecimalFormat nf

##### trailing

    boolean trailing

##### width

    int width

### Class weka.core.matrix.LUDecomposition

class LUDecomposition extends Object implements Serializable

serialVersionUID:
  -2731022568037808629L

#### Serialized Fields

##### LU

    double[][] LU

Array for internal storage of decomposition.
internal array storage.

##### m

    int m

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

##### n

    int n

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

##### piv

    int[] piv

Internal storage of pivot vector.
pivot vector.

##### pivsign

    int pivsign

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

### Class weka.core.matrix.Matrix

class Matrix extends Object implements Serializable

serialVersionUID:
  7856794138418366180L

#### Serialized Fields

##### A

    double[][] A

Array for internal storage of elements.
internal array storage.

##### m

    int m

Row and column dimensions.
row dimension.

##### n

    int n

Row and column dimensions.
row dimension.

### Class weka.core.matrix.QRDecomposition

class QRDecomposition extends Object implements Serializable

serialVersionUID:
  -5013090736132211418L

#### Serialized Fields

##### m

    int m

Row and column dimensions.
column dimension.

##### n

    int n

Row and column dimensions.
column dimension.

##### QR

    double[][] QR

Array for internal storage of decomposition.
internal array storage.

##### Rdiag

    double[] Rdiag

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

### Class weka.core.matrix.SingularValueDecomposition

class SingularValueDecomposition extends Object implements Serializable

serialVersionUID:
  -8738089610999867951L

#### Serialized Fields

##### m

    int m

Row and column dimensions.
row dimension.

##### n

    int n

Row and column dimensions.
row dimension.

##### s

    double[] s

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

##### U

    double[][] U

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

##### V

    double[][] V

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

## Package weka.core.neighboursearch

### Class weka.core.neighboursearch.BallTree

class BallTree extends NearestNeighbourSearch implements Serializable

serialVersionUID:
  728763855952698328L

#### Serialized Fields

##### m_Distances

    double[] m_Distances

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

##### m_InstList

    int[] m_InstList

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

##### m_MaxInstancesInLeaf

    int m_MaxInstancesInLeaf

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

##### m_Root

    BallNode m_Root

The root node of the BallTree.

##### m_TreeConstructor

    BallTreeConstructor m_TreeConstructor

The constructor method to use to build the tree.

##### m_TreeStats

    TreePerformanceStats m_TreeStats

Tree Stats variables.

### Class weka.core.neighboursearch.CoverTree

class CoverTree extends NearestNeighbourSearch implements Serializable

serialVersionUID:
  7617412821497807586L

#### Serialized Fields

##### il2

    double il2

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

##### m_Base

    double m_Base

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

##### m_DistanceList

    double[] m_DistanceList

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

##### m_EuclideanDistance

    EuclideanDistance m_EuclideanDistance

The euclidean distance function to use.

##### m_MaxDepth

    int m_MaxDepth

Number of nodes in the tree.

##### m_NumLeaves

    int m_NumLeaves

Number of nodes in the tree.

##### m_NumNodes

    int m_NumNodes

Number of nodes in the tree.

##### m_Root

    CoverTree.CoverTreeNode m_Root

The root node.

##### m_TreeStats

    TreePerformanceStats m_TreeStats

Tree Stats variables.

### Class weka.core.neighboursearch.CoverTree.CoverTreeNode

class CoverTreeNode extends Object implements Serializable

serialVersionUID:
  1808760031169036512L

#### Serialized Fields

##### children

    Stack<CoverTree.CoverTreeNode> children

The children of the node.

##### idx

    Integer idx

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

##### max_dist

    double max_dist

The distance of the furthest descendant of the node.

##### nodeid

    int nodeid

ID for the node.

##### num_children

    int num_children

The number of children node has.

##### parent_dist

    double parent_dist

The distance to the nodes parent.

##### scale

    int scale

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

### Class weka.core.neighboursearch.KDTree

class KDTree extends NearestNeighbourSearch implements Serializable

serialVersionUID:
  1505717283763272533L

#### Serialized Fields

##### m_DistanceList

    double[] m_DistanceList

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

##### m_EuclideanDistance

    EuclideanDistance m_EuclideanDistance

The euclidean distance function to use.

##### m_InstList

    int[] m_InstList

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

##### m_MaxDepth

    int m_MaxDepth

Tree stats.

##### m_MaxInstInLeaf

    int m_MaxInstInLeaf

maximal number of instances in a leaf.

##### m_MinBoxRelWidth

    double m_MinBoxRelWidth

minimal relative width of a KDTree rectangle.

##### m_NormalizeNodeWidth

    boolean m_NormalizeNodeWidth

flag for normalizing.

##### m_NumLeaves

    int m_NumLeaves

Tree stats.

##### m_NumNodes

    int m_NumNodes

Tree stats.

##### m_Root

    KDTreeNode m_Root

The root node of the tree.

##### m_Splitter

    KDTreeNodeSplitter m_Splitter

The node splitter.

##### m_TreeStats

    TreePerformanceStats m_TreeStats

Tree Stats variables.

### Class weka.core.neighboursearch.LinearNNSearch

class LinearNNSearch extends NearestNeighbourSearch implements Serializable

serialVersionUID:
  1915484723703917241L

#### Serialized Fields

##### m_Distances

    double[] m_Distances

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

##### m_SkipIdentical

    boolean m_SkipIdentical

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

### Class weka.core.neighboursearch.NearestNeighbourSearch

class NearestNeighbourSearch extends Object implements Serializable

#### Serialized Fields

##### m_DistanceFunction

    DistanceFunction m_DistanceFunction

the distance function used.

##### m_Instances

    Instances m_Instances

The neighbourhood of instances to find neighbours in.

##### m_kNN

    int m_kNN

The number of neighbours to find.

##### m_MeasurePerformance

    boolean m_MeasurePerformance

Should we measure Performance.

##### m_Stats

    PerformanceStats m_Stats

Performance statistics.

### Class weka.core.neighboursearch.PerformanceStats

class PerformanceStats extends Object implements Serializable

serialVersionUID:
  -7215110351388368092L

#### Serialized Fields

##### m_CoordCount

    double m_CoordCount

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

##### m_MaxC

    double m_MaxC

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

##### m_MaxP

    double m_MaxP

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

##### m_MinC

    double m_MinC

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

##### m_MinP

    double m_MinP

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

##### m_NumQueries

    int m_NumQueries

The total number of queries looked at.

##### m_PointCount

    double m_PointCount

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

##### m_SumC

    double m_SumC

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

##### m_SumP

    double m_SumP

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

##### m_SumSqC

    double m_SumSqC

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

##### m_SumSqP

    double m_SumSqP

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

### Class weka.core.neighboursearch.TreePerformanceStats

class TreePerformanceStats extends PerformanceStats implements Serializable

serialVersionUID:
  -6637636693340810373L

#### Serialized Fields

##### m_IntNodeCount

    int m_IntNodeCount

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

##### m_LeafCount

    int m_LeafCount

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

##### m_MaxIntNodes

    int m_MaxIntNodes

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

##### m_MaxLeaves

    int m_MaxLeaves

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

##### m_MinIntNodes

    int m_MinIntNodes

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

##### m_MinLeaves

    int m_MinLeaves

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

##### m_SumIntNodes

    int m_SumIntNodes

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

##### m_SumLeaves

    int m_SumLeaves

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

##### m_SumSqIntNodes

    int m_SumSqIntNodes

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

##### m_SumSqLeaves

    int m_SumSqLeaves

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

## Package weka.core.neighboursearch.balltrees

### Class weka.core.neighboursearch.balltrees.BallNode

class BallNode extends Object implements Serializable

serialVersionUID:
  -8289151861759883510L

#### Serialized Fields

##### m_End

    int m_End

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

##### m_Left

    BallNode m_Left

The left child of the node.

##### m_NodeNumber

    int m_NodeNumber

The node number/id.

##### m_NumInstances

    int m_NumInstances

The number of instances/points in the node.

##### m_Pivot

    Instance m_Pivot

The pivot/centre of the ball.

##### m_Radius

    double m_Radius

The radius of this ball (hyper sphere).

##### m_Right

    BallNode m_Right

The right child of the node.

##### m_SplitAttrib

    int m_SplitAttrib

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

##### m_SplitVal

    double m_SplitVal

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

##### m_Start

    int m_Start

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

### Class weka.core.neighboursearch.balltrees.BallSplitter

class BallSplitter extends Object implements Serializable

#### Serialized Fields

##### m_DistanceFunction

    EuclideanDistance m_DistanceFunction

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

##### m_Instances

    Instances m_Instances

The instance on which the tree is built.

##### m_Instlist

    int[] m_Instlist

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

### Class weka.core.neighboursearch.balltrees.BallTreeConstructor

class BallTreeConstructor extends Object implements Serializable

#### Serialized Fields

##### m_DistanceFunction

    DistanceFunction m_DistanceFunction

The distance function to use to build the tree.

##### m_FullyContainChildBalls

    boolean m_FullyContainChildBalls

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

##### m_Instances

    Instances m_Instances

The instances on which to build the tree.

##### m_InstList

    int[] m_InstList

The master index array.

##### m_MaxDepth

    int m_MaxDepth

The depth of the built tree.

##### m_MaxInstancesInLeaf

    int m_MaxInstancesInLeaf

The maximum number of instances allowed in a leaf.

##### m_MaxRelLeafRadius

    double m_MaxRelLeafRadius

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

##### m_NumLeaves

    int m_NumLeaves

The number of leaf nodes in the built tree.

##### m_NumNodes

    int m_NumNodes

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

### Class weka.core.neighboursearch.balltrees.BottomUpConstructor

class BottomUpConstructor extends BallTreeConstructor implements Serializable

serialVersionUID:
  5864250777657707687L

### Class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint

class MedianDistanceFromArbitraryPoint extends BallSplitter implements Serializable

serialVersionUID:
  5617378551363700558L

#### Serialized Fields

##### m_Rand

    Random m_Rand

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

##### m_RandSeed

    int m_RandSeed

Seed for random number generator.

### Class weka.core.neighboursearch.balltrees.MedianOfWidestDimension

class MedianOfWidestDimension extends BallSplitter implements Serializable

serialVersionUID:
  3054842574468790421L

#### Serialized Fields

##### m_NormalizeDimWidths

    boolean m_NormalizeDimWidths

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

### Class weka.core.neighboursearch.balltrees.MiddleOutConstructor

class MiddleOutConstructor extends BallTreeConstructor implements Serializable

serialVersionUID:
  -8523314263062524462L

#### Serialized Fields

##### m_RandomInitialAnchor

    boolean m_RandomInitialAnchor

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

##### m_RSeed

    int m_RSeed

Seed form random number generator.

##### rand

    Random rand

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

##### rootRadius

    double rootRadius

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

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

class MyIdxList extends FastVector implements Serializable

serialVersionUID:
  -2283869109722934927L

### Class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren

class PointsClosestToFurthestChildren extends BallSplitter implements Serializable

serialVersionUID:
  -2947177543565818260L

### Class weka.core.neighboursearch.balltrees.TopDownConstructor

class TopDownConstructor extends BallTreeConstructor implements Serializable

serialVersionUID:
  -5150140645091889979L

#### Serialized Fields

##### m_Splitter

    BallSplitter m_Splitter

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

## Package weka.core.neighboursearch.covertrees

### Class weka.core.neighboursearch.covertrees.Stack

class Stack extends Object implements Serializable

serialVersionUID:
  5604056321825539264L

#### Serialized Fields

##### elements

    ArrayList<T> elements

The elements inside the stack.

##### length

    int length

The number of elements in the stack.

## Package weka.core.neighboursearch.kdtrees

### Class weka.core.neighboursearch.kdtrees.KDTreeNode

class KDTreeNode extends Object implements Serializable

serialVersionUID:
  -3660396067582792648L

#### Serialized Fields

##### m_End

    int m_End

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

##### m_Left

    KDTreeNode m_Left

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

##### m_NodeNumber

    int m_NodeNumber

node number (only for debug).

##### 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_Right

    KDTreeNode m_Right

right subtree; contains instances with larger than split value.

##### m_SplitDim

    int m_SplitDim

attribute to split on.

##### m_SplitValue

    double m_SplitValue

value to split on.

##### m_Start

    int m_Start

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

### Class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter

class KDTreeNodeSplitter extends Object implements Serializable

#### Serialized Fields

##### m_EuclideanDistance

    EuclideanDistance m_EuclideanDistance

The distance function used for building the tree.

##### m_Instances

    Instances m_Instances

The instances that'll be used for tree construction.

##### 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

class KMeansInpiredMethod extends KDTreeNodeSplitter implements Serializable

serialVersionUID:
  -866783749124714304L

### Class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension

class MedianOfWidestDimension extends KDTreeNodeSplitter implements Serializable

serialVersionUID:
  1383443320160540663L

### Class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension

class MidPointOfWidestDimension extends KDTreeNodeSplitter implements Serializable

serialVersionUID:
  -7617277960046591906L

### Class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide

class SlidingMidPointOfWidestSide extends KDTreeNodeSplitter implements Serializable

serialVersionUID:
  852857628205680562L

## Package weka.core.pmml

### Class weka.core.pmml.BuiltInArithmetic

class BuiltInArithmetic extends Function implements Serializable

serialVersionUID:
  2275009453597279459L

#### Serialized Fields

##### m_operator

    weka.core.pmml.BuiltInArithmetic.Operator m_operator

The operator for this function

### Class weka.core.pmml.BuiltInMath

class BuiltInMath extends Function implements Serializable

serialVersionUID:
  -8092338695602573652L

#### Serialized Fields

##### m_func

    weka.core.pmml.BuiltInMath.MathFunc m_func

The function to apply

### Class weka.core.pmml.BuiltInString

class BuiltInString extends Function implements Serializable

serialVersionUID:
  -7391516909331728653L

#### Serialized Fields

##### m_func

    weka.core.pmml.BuiltInString.StringFunc m_func

The function to apply

##### m_outputDef

    Attribute m_outputDef

The output structure produced by this function

### Class weka.core.pmml.Constant

class Constant extends Expression implements Serializable

serialVersionUID:
  -304829687822452424L

#### Serialized Fields

##### m_categoricalConst

    String m_categoricalConst

##### m_continuousConst

    double m_continuousConst

### Class weka.core.pmml.DefineFunction

class DefineFunction extends Function implements Serializable

serialVersionUID:
  -1976646917527243888L

#### Serialized Fields

##### m_expression

    Expression m_expression

The Expression for this function to use

##### m_optype

    FieldMetaInfo.Optype m_optype

The optype for this function

##### m_parameters

    ArrayList<weka.core.pmml.DefineFunction.ParameterField> m_parameters

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

### Class weka.core.pmml.DefineFunction.ParameterField

class ParameterField extends FieldMetaInfo implements Serializable

serialVersionUID:
  3918895902507585558L

### Class weka.core.pmml.DerivedFieldMetaInfo

class DerivedFieldMetaInfo extends FieldMetaInfo implements Serializable

#### Serialized Fields

##### m_displayName

    String m_displayName

display name

##### m_expression

    Expression m_expression

the single expression that defines the value of this field

##### m_values

    ArrayList<FieldMetaInfo.Value> m_values

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

### Class weka.core.pmml.Discretize

class Discretize extends Expression implements Serializable

#### Serialized Fields

##### m_bins

    ArrayList<weka.core.pmml.Discretize.DiscretizeBin> m_bins

The bins for this discretization

##### m_defaultValue

    String m_defaultValue

The default value (if defined)

##### m_defaultValueDefined

    boolean m_defaultValueDefined

True if a default value has been specified

##### m_fieldIndex

    int m_fieldIndex

The index of the field

##### m_fieldName

    String m_fieldName

The name of the field to be discretized

##### m_mapMissingDefined

    boolean m_mapMissingDefined

True if a replacement for missing values has been specified

##### m_mapMissingTo

    String m_mapMissingTo

The value of the missing value replacement (if defined)

##### m_outputDef

    Attribute m_outputDef

The output structure of this discretization

### Class weka.core.pmml.Discretize.DiscretizeBin

class DiscretizeBin extends Object implements Serializable

serialVersionUID:
  5810063243316808400L

#### Serialized Fields

##### m_binValue

    String m_binValue

The bin value for this DiscretizeBin

##### m_intervals

    ArrayList<FieldMetaInfo.Interval> m_intervals

The intervals for this DiscretizeBin

### Class weka.core.pmml.Expression

class Expression extends Object implements Serializable

serialVersionUID:
  4448840549804800321L

#### Serialized Fields

##### m_fieldDefs

    ArrayList<Attribute> m_fieldDefs

The field defs

##### m_opType

    FieldMetaInfo.Optype m_opType

The optype of this Expression

### Class weka.core.pmml.FieldMetaInfo

class FieldMetaInfo extends Object implements Serializable

#### Serialized Fields

##### m_fieldName

    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

class Interval extends Object implements Serializable

serialVersionUID:
  -7339790632684638012L

#### Serialized Fields

##### m_closure

    FieldMetaInfo.Interval.Closure m_closure

##### m_leftMargin

    double m_leftMargin

The left boundary value

##### m_rightMargin

    double m_rightMargin

The right boundary value

### Class weka.core.pmml.FieldMetaInfo.Value

class Value extends Object implements Serializable

serialVersionUID:
  -3981030320273649739L

#### Serialized Fields

##### m_displayValue

    String m_displayValue

The display value (might hold a human readable value - e.g. product name instead of cryptic code).

##### m_property

    FieldMetaInfo.Value.Property m_property

##### m_value

    String m_value

The value

### Class weka.core.pmml.FieldRef

class FieldRef extends Expression implements Serializable

serialVersionUID:
  -8009605897876168409L

#### Serialized Fields

##### m_fieldName

    String m_fieldName

The name of the field to reference

### Class weka.core.pmml.Function

class Function extends Object implements Serializable

serialVersionUID:
  -6997738288201933171L

#### Serialized Fields

##### m_functionName

    String m_functionName

The name of this function

##### m_parameterDefs

    ArrayList<Attribute> m_parameterDefs

The structure of the parameters to this function

### Class weka.core.pmml.MappingInfo

class MappingInfo extends Object implements Serializable

#### Serialized Fields

##### m_fieldsMap

    int[] m_fieldsMap

Map the incoming attributes to the mining schema attributes. Each entry holds the index of the incoming attribute that corresponds to this mining schema attribute.

##### m_fieldsMappingText

    String m_fieldsMappingText

Holds a textual description of the fields mapping

##### m_log

    Logger m_log

For logging

##### m_nominalValueMaps

    int[][] m_nominalValueMaps

Map indexes for nominal values in incoming structure to those in the mining schema. There will be as many entries as there are attributes in this array. Non-nominal attributes will have null entries. Each non-null entry is an array of integer indexes. Each entry in a given array (for a given attribute) holds the index of the mining schema value that corresponds to this incoming value. UNKNOWN_NOMINAL_VALUE is used as the index for those incoming values that are not defined in the mining schema.

### Class weka.core.pmml.MiningFieldMetaInfo

class MiningFieldMetaInfo extends FieldMetaInfo implements Serializable

serialVersionUID:
  -1256774332779563185L

#### Serialized Fields

##### m_highValue

    double m_highValue

outlier high value

##### m_importance

    double m_importance

importance (if defined)

##### m_index

    int m_index

the index of the field in the mining schema Instances

##### m_lowValue

    double m_lowValue

outlier low value

##### m_miningSchemaI

    Instances m_miningSchemaI

mining schema (needed for toString method)

##### m_missingValueReplacementNominal

    String m_missingValueReplacementNominal

actual missing value replacements (if specified)

##### m_missingValueReplacementNumeric

    double m_missingValueReplacementNumeric

##### m_missingValueTreatmentMethod

    weka.core.pmml.MiningFieldMetaInfo.Missing m_missingValueTreatmentMethod

missing values treatment method

##### m_optypeOverride

    FieldMetaInfo.Optype m_optypeOverride

optype overrides (override data dictionary type - NOT SUPPORTED AT PRESENT)

##### m_outlierTreatmentMethod

    weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod

outlier treatmemnt method

##### m_usageType

    weka.core.pmml.MiningFieldMetaInfo.Usage m_usageType

usage type

### Class weka.core.pmml.MiningSchema

class MiningSchema extends Object implements Serializable

serialVersionUID:
  7144380586726330455L

#### Serialized Fields

##### m_derivedMeta

    ArrayList<DerivedFieldMetaInfo> m_derivedMeta

Meta information about derived fields (those defined in the TransformationDictionary followed by those defined in LocalTransformations)

##### m_fieldInstancesStructure

    Instances m_fieldInstancesStructure

The structure of all the fields (both mining schema and derived) as Instances

##### m_miningMeta

    ArrayList<MiningFieldMetaInfo> m_miningMeta

Meta information about the mining schema fields

##### m_miningSchemaInstancesStructure

    Instances m_miningSchemaInstancesStructure

Just the mining schema fields as Instances

##### m_targetMetaInfo

    TargetMetaInfo m_targetMetaInfo

target meta info (may be null if not defined)

##### m_transformationDictionary

    weka.core.pmml.TransformationDictionary m_transformationDictionary

The transformation dictionary (if defined)

### Class weka.core.pmml.NormContinuous

class NormContinuous extends Expression implements Serializable

serialVersionUID:
  4714332374909851542L

#### Serialized Fields

##### m_fieldIndex

    int m_fieldIndex

The index of the field

##### m_fieldName

    String m_fieldName

The name of the field to use

##### m_linearNormNorm

    double[] m_linearNormNorm

norm values for the LinearNorm entries

##### m_linearNormOrig

    double[] m_linearNormOrig

original values for the LinearNorm entries

##### m_mapMissingDefined

    boolean m_mapMissingDefined

True if a replacement for missing values has been specified

##### m_mapMissingTo

    double m_mapMissingTo

The value of the missing value replacement (if defined)

##### m_outlierTreatmentMethod

    weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod

Outlier treatment method (default = asIs)

### Class weka.core.pmml.NormDiscrete

class NormDiscrete extends Expression implements Serializable

serialVersionUID:
  -8854409417983908220L

#### Serialized Fields

##### m_field

    Attribute m_field

The actual attribute itself

##### m_fieldIndex

    int m_fieldIndex

The index of the attribute

##### m_fieldName

    String m_fieldName

The name of the field to lookup our value in

##### m_fieldValue

    String m_fieldValue

The actual value (as a String) that will correspond to an output of 1

##### m_fieldValueIndex

    int m_fieldValueIndex

If we are referring to a nominal (rather than String) attribute then this holds the index of the value in question. Will be faster than searching for the value each time.

##### m_mapMissingDefined

    boolean m_mapMissingDefined

True if a replacement for missing values has been specified

##### m_mapMissingTo

    double m_mapMissingTo

The value of the missing value replacement (if defined)

### Class weka.core.pmml.TargetMetaInfo

class TargetMetaInfo extends FieldMetaInfo implements Serializable

serialVersionUID:
  863500462237904927L

#### Serialized Fields

##### m_castInteger

    String m_castInteger

cast integers (default no casting)

##### m_defaultValueOrPriorProbs

    double[] m_defaultValueOrPriorProbs

default value (numeric) or prior distribution (categorical)

##### m_displayValues

    ArrayList<String> m_displayValues

corresponding display values

##### m_max

    double m_max

##### m_min

    double m_min

min and max

##### m_rescaleConstant

    double m_rescaleConstant

re-scaling of target value (if defined)

##### m_rescaleFactor

    double m_rescaleFactor

##### m_values

    ArrayList<String> m_values

for categorical values. Actual values

## Package weka.core.stemmers

### Class weka.core.stemmers.IteratedLovinsStemmer

class IteratedLovinsStemmer extends LovinsStemmer implements Serializable

serialVersionUID:
  960689687163788264L

### Class weka.core.stemmers.LovinsStemmer

class LovinsStemmer extends Object implements Serializable

serialVersionUID:
  -6113024782588197L

### Class weka.core.stemmers.NullStemmer

class NullStemmer extends Object implements Serializable

serialVersionUID:
  -3671261636532625496L

### Class weka.core.stemmers.SnowballStemmer

class SnowballStemmer extends Object implements Serializable

serialVersionUID:
  -6111170431963015178L

#### Serialized Fields

##### m_Stemmer

    Object m_Stemmer

the current stemmer.

## Package weka.core.tokenizers

### Class weka.core.tokenizers.AlphabeticTokenizer

class AlphabeticTokenizer extends Tokenizer implements Serializable

serialVersionUID:
  6705199562609861697L

#### Serialized Fields

##### m_CurrentPos

    int m_CurrentPos

the current position

##### m_Str

    char[] m_Str

the characters of the string

### Class weka.core.tokenizers.CharacterDelimitedTokenizer

class CharacterDelimitedTokenizer extends Tokenizer implements Serializable

#### Serialized Fields

##### m_Delimiters

    String m_Delimiters

Delimiters used in tokenization

### Class weka.core.tokenizers.NGramTokenizer

class NGramTokenizer extends CharacterDelimitedTokenizer implements Serializable

serialVersionUID:
  -2181896254171647219L

#### Serialized Fields

##### m_CurrentPosition

    int m_CurrentPosition

the current position for returning elements

##### m_MaxPosition

    int m_MaxPosition

the number of strings available

##### m_N

    int m_N

the current length of the N-grams

##### m_NMax

    int m_NMax

the maximum number of N

##### m_NMin

    int m_NMin

the minimum number of N

##### m_SplitString

    String[] m_SplitString

all the available grams

### Class weka.core.tokenizers.Tokenizer

class Tokenizer extends Object implements Serializable

### Class weka.core.tokenizers.WordTokenizer

class WordTokenizer extends CharacterDelimitedTokenizer implements Serializable

serialVersionUID:
  -930893034037880773L

## Package weka.core.xml

### Class weka.core.xml.XMLInstances

class XMLInstances extends XMLDocument implements Serializable

serialVersionUID:
  3626821327547416099L

#### Serialized Fields

##### m_Instances

    Instances m_Instances

the underlying Instances

##### m_Precision

    int m_Precision

the precision for numbers

## Package weka.datagenerators

### Class weka.datagenerators.ClassificationGenerator

class ClassificationGenerator extends DataGenerator implements Serializable

serialVersionUID:
  -5261662546673517844L

#### Serialized Fields

##### m_NumExamples

    int m_NumExamples

Number of instances

### Class weka.datagenerators.ClusterDefinition

class ClusterDefinition extends Object implements Serializable

serialVersionUID:
  -5950001207047429961L

#### Serialized Fields

##### m_Parent

    ClusterGenerator m_Parent

the parent of the cluster

### Class weka.datagenerators.ClusterGenerator

class ClusterGenerator extends DataGenerator implements Serializable

serialVersionUID:
  6131722618472046365L

#### Serialized Fields

##### m_booleanCols

    Range m_booleanCols

Stores which columns are boolean (default numeric)

##### m_ClassFlag

    boolean m_ClassFlag

class flag

##### m_nominalCols

    Range m_nominalCols

Stores which columns are nominal (default numeric)

##### m_NumAttributes

    int m_NumAttributes

Number of attribute the dataset should have

### Class weka.datagenerators.DataGenerator

class DataGenerator extends Object implements Serializable

serialVersionUID:
  -3698585946221802578L

#### Serialized Fields

##### m_CreatingRelationName

    boolean m_CreatingRelationName

flag, that indicates whether the relationname is currently assembled

##### m_DatasetFormat

    Instances m_DatasetFormat

The format for the generated dataset

##### m_Debug

    boolean m_Debug

Debugging mode

##### m_NumExamplesAct

    int m_NumExamplesAct

Number of instances that should be produced into the dataset this number is by default m_NumExamples, but can be reset by the generator

##### m_Random

    Random m_Random

random number generator

##### m_RelationName

    String m_RelationName

Relation name the dataset should have

##### m_Seed

    int m_Seed

random number generator seed

### Class weka.datagenerators.RegressionGenerator

class RegressionGenerator extends DataGenerator implements Serializable

serialVersionUID:
  3073254041275658221L

#### Serialized Fields

##### m_NumExamples

    int m_NumExamples

Number of instances

### Class weka.datagenerators.Test

class Test extends Object implements Serializable

serialVersionUID:
  -8890645875887157782L

#### Serialized Fields

##### m_AttIndex

    int m_AttIndex

the attribute index

##### m_Dataset

    Instances m_Dataset

the dataset

##### m_Not

    boolean m_Not

whether to negate the test

##### m_Split

    double m_Split

the split

## Package weka.datagenerators.classifiers.classification

### Class weka.datagenerators.classifiers.classification.Agrawal

class Agrawal extends ClassificationGenerator implements Serializable

serialVersionUID:
  2254651939636143025L

#### Serialized Fields

##### m_BalanceClass

    boolean m_BalanceClass

whether to balance the class

##### m_Function

    int m_Function

the function to use for generating the data

##### m_lastLabel

    double m_lastLabel

the last class label that was generated

##### m_nextClassShouldBeZero

    boolean m_nextClassShouldBeZero

used for balancing the class

##### m_PerturbationFraction

    double m_PerturbationFraction

the perturabation fraction

### Class weka.datagenerators.classifiers.classification.BayesNet

class BayesNet extends ClassificationGenerator implements Serializable

serialVersionUID:
  -796118162379901512L

#### Serialized Fields

##### m_Generator

    BayesNetGenerator m_Generator

the bayesian net generator, that produces the actual data

### Class weka.datagenerators.classifiers.classification.LED24

class LED24 extends ClassificationGenerator implements Serializable

serialVersionUID:
  -7880209100415868737L

#### Serialized Fields

##### m_NoisePercent

    double m_NoisePercent

the noise rate

##### m_numIrrelevantAttributes

    int m_numIrrelevantAttributes

used for generating the output, i.e., the additional noise attributes

### Class weka.datagenerators.classifiers.classification.RandomRBF

class RandomRBF extends ClassificationGenerator implements Serializable

serialVersionUID:
  6069033710635728720L

#### Serialized Fields

##### m_centroidClasses

    int[] m_centroidClasses

the classes of the centroids

##### m_centroids

    double[][] m_centroids

the centroids

##### m_centroidStdDevs

    double[] m_centroidStdDevs

the stddevs of the centroids

##### m_centroidWeights

    double[] m_centroidWeights

the weights of the centroids

##### m_NumAttributes

    int m_NumAttributes

Number of attribute the dataset should have

##### m_NumCentroids

    int m_NumCentroids

the number of centroids to use for generation

##### m_NumClasses

    int m_NumClasses

Number of Classes the dataset should have

### Class weka.datagenerators.classifiers.classification.RDG1

class RDG1 extends ClassificationGenerator implements Serializable

serialVersionUID:
  7751005204635320414L

#### Serialized Fields

##### m_AttList_Irr

    boolean[] m_AttList_Irr

array defines which attributes are irrelevant, with: true = attribute is irrelevant; false = attribute is not irrelevant

##### m_DecisionList

    FastVector m_DecisionList

decision list

##### m_MaxRuleSize

    int m_MaxRuleSize

maximum rule size

##### m_MinRuleSize

    int m_MinRuleSize

minimum rule size

##### m_NumAttributes

    int m_NumAttributes

Number of attribute the dataset should have

##### m_NumClasses

    int m_NumClasses

Number of Classes the dataset should have

##### m_NumIrrelevant

    int m_NumIrrelevant

number of irrelevant attributes.

##### m_NumNumeric

    int m_NumNumeric

number of numeric attribute

##### m_VoteFlag

    boolean m_VoteFlag

flag that stores if voting is wished

## Package weka.datagenerators.classifiers.regression

### Class weka.datagenerators.classifiers.regression.Expression

class Expression extends MexicanHat implements Serializable

serialVersionUID:
  -4237047357682277211L

#### Serialized Fields

##### m_Expression

    String m_Expression

the expression for computing y

##### m_Filter

    AddExpression m_Filter

the filter for generating y out of x

##### m_RawData

    Instances m_RawData

the input data structure for the filter

### Class weka.datagenerators.classifiers.regression.MexicanHat

class MexicanHat extends RegressionGenerator implements Serializable

serialVersionUID:
  4577016375261512975L

#### Serialized Fields

##### m_Amplitude

    double m_Amplitude

the amplitude of y

##### m_MaxRange

    double m_MaxRange

the upper boundary of the range, x is drawn from

##### m_MinRange

    double m_MinRange

the lower boundary of the range, x is drawn from

##### m_NoiseRandom

    Random m_NoiseRandom

the random number generator for the noise

##### m_NoiseRate

    double m_NoiseRate

the rate of the gaussian noise

##### m_NoiseVariance

    double m_NoiseVariance

the variance of the gaussian noise

## Package weka.datagenerators.clusterers

### Class weka.datagenerators.clusterers.BIRCHCluster

class BIRCHCluster extends ClusterGenerator implements Serializable

serialVersionUID:
  -334820527230755027L

#### Serialized Fields

##### m_ClusterList

    FastVector m_ClusterList

cluster list

##### m_DistMult

    double m_DistMult

distance multiplier (option M)

##### m_GridSize

    int m_GridSize

grid size

##### m_GridWidth

    double m_GridWidth

grid width

##### m_InputOrder

    int m_InputOrder

input order (changed with option O)

##### m_MaxInstNum

    int m_MaxInstNum

maximal number of instances per cluster (option N)

##### m_MaxRadius

    double m_MaxRadius

maximum radius (option R)

##### m_MinInstNum

    int m_MinInstNum

minimal number of instances per cluster (option N)

##### m_MinRadius

    double m_MinRadius

minimum radius (option R)

##### m_NoiseRate

    double m_NoiseRate

noise rate in percent (option P, between 0 and 30)

##### m_NumClusters

    int m_NumClusters

Number of Clusters the dataset should have

##### m_NumCycles

    int m_NumCycles

number of cycles (option C)

##### m_Pattern

    int m_Pattern

pattern (changed with options G or S)

### Class weka.datagenerators.clusterers.SubspaceCluster

class SubspaceCluster extends ClusterGenerator implements Serializable

serialVersionUID:
  -3454999858505621128L

#### Serialized Fields

##### m_Clusters

    ClusterDefinition[] m_Clusters

cluster list

##### m_globalMaxValue

    double[] m_globalMaxValue

store global max values

##### m_globalMinValue

    double[] m_globalMinValue

store global min values

##### m_NoiseRate

    double m_NoiseRate

noise rate in percent (option P, between 0 and 30)

##### m_numValues

    int[] m_numValues

if nominal, store number of values

### Class weka.datagenerators.clusterers.SubspaceClusterDefinition

class SubspaceClusterDefinition extends ClusterDefinition implements Serializable

serialVersionUID:
  3135678125044007231L

#### Serialized Fields

##### m_attributes

    boolean[] m_attributes

attributes of this cluster

##### m_AttrIndexRange

    Range m_AttrIndexRange

range of atttributes

##### m_attrIndices

    int[] m_attrIndices

global indices of the attributes of the cluster

##### m_clustersubtype

    int m_clustersubtype

cluster subtypes

##### m_clustertype

    int m_clustertype

cluster type

##### m_MaxInstNum

    int m_MaxInstNum

maximal number of instances for this cluster

##### m_maxValue

    double[] m_maxValue

ranges of each attribute (max); not used if gaussian

##### m_meanValue

    double[] m_meanValue

mean ; only used if gaussian

##### m_MinInstNum

    int m_MinInstNum

minimal number of instances for this cluster

##### m_minValue

    double[] m_minValue

ranges of each attribute (min); not used if gaussian

##### m_numClusterAttributes

    int m_numClusterAttributes

number of attributes the cluster is defined for

##### m_numInstances

    int m_numInstances

number of instances for this cluster

##### m_stddevValue

    double[] m_stddevValue

standarddev; only used if gaussian

## Package weka.estimators

### Class weka.estimators.DiscreteEstimator

class DiscreteEstimator extends Estimator implements Serializable

serialVersionUID:
  -5526486742612434779L

#### Serialized Fields

##### m_Counts

    double[] m_Counts

Hold the counts

##### m_SumOfCounts

    double m_SumOfCounts

Hold the sum of counts

### Class weka.estimators.Estimator

class Estimator extends Object implements Serializable

serialVersionUID:
  -5902411487362274342L

#### Serialized Fields

##### m_classValueIndex

    double m_classValueIndex

The class value index is > -1 if subset is taken with specific class value only

##### m_Debug

    boolean m_Debug

Debugging mode

##### m_noClass

    boolean m_noClass

set if class is not important

### Class weka.estimators.KernelEstimator

class KernelEstimator extends Estimator implements Serializable

serialVersionUID:
  3646923563367683925L

#### Serialized Fields

##### m_AllWeightsOne

    boolean m_AllWeightsOne

Whether we can optimise the kernel summation

##### m_NumValues

    int m_NumValues

Number of values stored in m_Weights and m_Values so far

##### m_Precision

    double m_Precision

The precision of data values

##### m_StandardDev

    double m_StandardDev

The standard deviation

##### m_SumOfWeights

    double m_SumOfWeights

The sum of the weights so far

##### m_Values

    double[] m_Values

Vector containing all of the values seen

##### m_Weights

    double[] m_Weights

Vector containing the associated weights

### Class weka.estimators.MahalanobisEstimator

class MahalanobisEstimator extends Estimator implements Serializable

serialVersionUID:
  8950225468990043868L

#### Serialized Fields

##### m_ConstDelta

    double m_ConstDelta

The difference between the conditioning value and the conditioning mean

##### m_CovarianceInverse

    Matrix m_CovarianceInverse

The inverse of the covariance matrix

##### m_Determinant

    double m_Determinant

The determinant of the covariance matrix

##### m_ValueMean

    double m_ValueMean

The mean of the values

### Class weka.estimators.NormalEstimator

class NormalEstimator extends Estimator implements Serializable

serialVersionUID:
  93584379632315841L

#### Serialized Fields

##### m_Mean

    double m_Mean

The current mean

##### m_Precision

    double m_Precision

The precision of numeric values ( = minimum std dev permitted)

##### m_StandardDev

    double m_StandardDev

The current standard deviation

##### m_SumOfValues

    double m_SumOfValues

The sum of the values seen

##### m_SumOfValuesSq

    double m_SumOfValuesSq

The sum of the values squared

##### m_SumOfWeights

    double m_SumOfWeights

The sum of the weights

### Class weka.estimators.PoissonEstimator

class PoissonEstimator extends Estimator implements Serializable

serialVersionUID:
  7669362595289236662L

#### Serialized Fields

##### m_Lambda

    double m_Lambda

The average number of times an event occurs in an interval.

##### m_NumValues

    double m_NumValues

The number of values seen

##### m_SumOfValues

    double m_SumOfValues

The sum of the values seen

## Package weka.experiment

### Class weka.experiment.AveragingResultProducer

class AveragingResultProducer extends Object implements Serializable

serialVersionUID:
  2551284958501991352L

#### Serialized Fields

##### m_AdditionalMeasures

    String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

##### m_CalculateStdDevs

    boolean m_CalculateStdDevs

True if standard deviation fields should be produced

##### m_CountFieldName

    String m_CountFieldName

The name of the field that will contain the number of results averaged over.

##### m_ExpectedResultsPerAverage

    int m_ExpectedResultsPerAverage

The number of results expected to average over for each run

##### m_Instances

    Instances m_Instances

The dataset of interest

##### m_KeyFieldName

    String m_KeyFieldName

The name of the key field to average over

##### m_KeyIndex

    int m_KeyIndex

The index of the field to average over in the resultproducers key

##### m_Keys

    FastVector m_Keys

Collects the keys from a single run

##### m_ResultListener

    ResultListener m_ResultListener

The ResultListener to send results to

##### m_ResultProducer

    ResultProducer m_ResultProducer

The ResultProducer used to generate results

##### m_Results

    FastVector m_Results

Collects the results from a single run

### Class weka.experiment.ClassifierSplitEvaluator

class ClassifierSplitEvaluator extends Object implements Serializable

serialVersionUID:
  -8511241602760467265L

#### Serialized Fields

##### m_AdditionalMeasures

    String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

##### m_attID

    int m_attID

Attribute index of instance identifier (default -1)

##### m_Classifier

    Classifier m_Classifier

The classifier used for evaluation

##### m_ClassifierOptions

    String m_ClassifierOptions

The classifier options (if any)

##### m_ClassifierVersion

    String m_ClassifierVersion

The classifier version

##### m_doesProduce

    boolean[] m_doesProduce

Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce

##### m_IRclass

    int m_IRclass

Class index for information retrieval statistics (default 0)

##### m_numberAdditionalMeasures

    int m_numberAdditionalMeasures

The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results

##### m_predTargetColumn

    boolean m_predTargetColumn

Flag for prediction and target columns output.

##### m_result

    String m_result

Holds the statistics for the most recent application of the classifier

##### m_Template

    Classifier m_Template

The template classifier

### Class weka.experiment.CostSensitiveClassifierSplitEvaluator

class CostSensitiveClassifierSplitEvaluator extends ClassifierSplitEvaluator implements Serializable

serialVersionUID:
  -8069566663019501276L

#### Serialized Fields

##### m_OnDemandDirectory

    File m_OnDemandDirectory

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

### Class weka.experiment.CrossValidationResultProducer

class CrossValidationResultProducer extends Object implements Serializable

serialVersionUID:
  -1580053925080091917L

#### Serialized Fields

##### m_AdditionalMeasures

    String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

##### m_debugOutput

    boolean m_debugOutput

Save raw output of split evaluators --- for debugging purposes

##### m_Instances

    Instances m_Instances

The dataset of interest

##### m_NumFolds

    int m_NumFolds

The number of folds in the cross-validation

##### m_OutputFile

    File m_OutputFile

The destination output file/directory for raw output

##### m_ResultListener

    ResultListener m_ResultListener

The ResultListener to send results to

##### m_SplitEvaluator

    SplitEvaluator m_SplitEvaluator

The SplitEvaluator used to generate results

##### m_ZipDest

    OutputZipper m_ZipDest

The output zipper to use for saving raw splitEvaluator output

### Class weka.experiment.CSVResultListener

class CSVResultListener extends Object implements Serializable

serialVersionUID:
  -623185072785174658L

#### Serialized Fields

##### m_OutputFile

    File m_OutputFile

The destination output file, null sends to System.out

##### m_OutputFileName

    String m_OutputFileName

The name of the output file. Empty for temporary file.

##### m_RP

    ResultProducer m_RP

The ResultProducer sending us results

### Class weka.experiment.DatabaseResultListener

class DatabaseResultListener extends DatabaseUtils implements Serializable

serialVersionUID:
  7388014746954652818L

#### Serialized Fields

##### m_Cache

    FastVector m_Cache

Stores the cached values

##### m_CacheKey

    Object[] m_CacheKey

Stores the key for which the cache is valid

##### m_CacheKeyIndex

    int m_CacheKeyIndex

Stores the index of the key column holding the cache key data

##### m_CacheKeyName

    String m_CacheKeyName

Holds the name of the key field to cache upon, or null if no caching

##### m_Debug

    boolean m_Debug

True if debugging output should be printed

##### m_ResultProducer

    ResultProducer m_ResultProducer

The ResultProducer to listen to

##### m_ResultsTableName

    String m_ResultsTableName

The name of the current results table

### Class weka.experiment.DatabaseResultProducer

class DatabaseResultProducer extends DatabaseResultListener implements Serializable

serialVersionUID:
  -5620660780203158666L

#### Serialized Fields

##### m_AdditionalMeasures

    String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

##### m_Instances

    Instances m_Instances

The dataset of interest

##### m_ResultListener

    ResultListener m_ResultListener

The ResultListener to send results to

##### m_ResultProducer

    ResultProducer m_ResultProducer

The ResultProducer used to generate results

### Class weka.experiment.DatabaseUtils

class DatabaseUtils extends Object implements Serializable

serialVersionUID:
  -8252351994547116729L

#### Serialized Fields

##### DRIVERS

    Vector DRIVERS

Holds the jdbc drivers to be used (only to stop them being gc'ed).

##### m_checkForLowerCaseNames

    boolean m_checkForLowerCaseNames

For databases where Tables and Columns are created in lower case.

##### m_checkForUpperCaseNames

    boolean m_checkForUpperCaseNames

For databases where Tables and Columns are created in upper case.

##### m_createIndex

    boolean m_createIndex

create index on the database?

##### m_DatabaseURL

    String m_DatabaseURL

Database URL.

##### m_Debug

    boolean m_Debug

True if debugging output should be printed.

##### m_doubleType

    String m_doubleType

double type for the create table statement.

##### m_intType

    String m_intType

integer type for the create table statement.

##### m_Keywords

    HashSet<String> m_Keywords

the keywords for the current database type.

##### m_KeywordsMaskChar

    String m_KeywordsMaskChar

the character to mask SQL keywords (by appending this character).

##### m_password

    String m_password

Database Password.

##### m_setAutoCommit

    boolean m_setAutoCommit

setAutoCommit on the database?

##### m_stringType

    String m_stringType

string type for the create table statement.

##### m_userName

    String m_userName

Database username.

##### PROPERTIES

    Properties PROPERTIES

Properties associated with the database connection.

### Class weka.experiment.DensityBasedClustererSplitEvaluator

class DensityBasedClustererSplitEvaluator extends Object implements Serializable

#### Serialized Fields

##### m_additionalMeasures

    String[] m_additionalMeasures

The names of any additional measures to look for in SplitEvaluators

##### m_clusterer

    DensityBasedClusterer m_clusterer

The clusterer used for evaluation

##### m_clustererOptions

    String m_clustererOptions

The clusterer options (if any)

##### m_clustererVersion

    String m_clustererVersion

The clusterer version

##### m_doesProduce

    boolean[] m_doesProduce

Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current clusterer can produce

##### m_numberAdditionalMeasures

    int m_numberAdditionalMeasures

The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results

##### m_removeClassColumn

    boolean m_removeClassColumn

Remove the class column (if set) from the data

##### m_result

    String m_result

Holds the statistics for the most recent application of the clusterer

### Class weka.experiment.Experiment

class Experiment extends Object implements Serializable

serialVersionUID:
  44945596742646663L

#### Serialized Fields

##### m_AdditionalMeasures

    String[] m_AdditionalMeasures

Method names of additional measures of objects contained in the custom property iterator. Only methods names beginning with "measure" and returning doubles are recognised

##### m_AdvanceDataSetFirst

    boolean m_AdvanceDataSetFirst

If true an experiment will advance the current data set befor any custom itererator

##### m_ClassFirst

    boolean m_ClassFirst

True if the class attribute is the first attribute for all datasets involved in this experiment.

##### m_Datasets

    DefaultListModel m_Datasets

An array of dataset files

##### m_Notes

    String m_Notes

User notes about the experiment

##### m_PropertyArray

    Object m_PropertyArray

The array of values to set the property to

##### m_PropertyPath

    PropertyNode[] m_PropertyPath

The path to the iterator property

##### m_ResultListener

    ResultListener m_ResultListener

Where results will be sent

##### m_ResultProducer

    ResultProducer m_ResultProducer

The result producer

##### m_RunLower

    int m_RunLower

Lower run number

##### m_RunUpper

    int m_RunUpper

Upper run number

##### m_UsePropertyIterator

    boolean m_UsePropertyIterator

True if the exp should also iterate over a property of the RP

### Class weka.experiment.InstanceQuery

class InstanceQuery extends DatabaseUtils implements Serializable

serialVersionUID:
  718158370917782584L

#### Serialized Fields

##### m_CreateSparseData

    boolean m_CreateSparseData

Determines whether sparse data is created

##### m_Query

    String m_Query

Query to execute

### Class weka.experiment.InstancesResultListener

class InstancesResultListener extends CSVResultListener implements Serializable

serialVersionUID:
  -2203808461809311178L

### Class weka.experiment.LearningRateResultProducer

class LearningRateResultProducer extends Object implements Serializable

serialVersionUID:
  -3841159673490861331L

#### Serialized Fields

##### m_AdditionalMeasures

    String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

##### m_CurrentSize

    int m_CurrentSize

The current dataset size during stepping

##### m_Instances

    Instances m_Instances

The dataset of interest

##### m_LowerSize

    int m_LowerSize

The minimum number of instances to use. If this is zero, the first step will contain m_StepSize instances

##### m_ResultListener

    ResultListener m_ResultListener

The ResultListener to send results to

##### m_ResultProducer

    ResultProducer m_ResultProducer

The ResultProducer used to generate results

##### m_StepSize

    int m_StepSize

The number of instances to add at each step

##### m_UpperSize

    int m_UpperSize

The maximum number of instances to use. -1 indicates no maximum (other than the total number of instances)

### Class weka.experiment.PairedCorrectedTTester

class PairedCorrectedTTester extends PairedTTester implements Serializable

serialVersionUID:
  -3105268939845653323L

### Class weka.experiment.PairedTTester

class PairedTTester extends Object implements Serializable

serialVersionUID:
  8370014624008728610L

#### Serialized Fields

##### m_ColOrder

    int[] m_ColOrder

The sorting of the columns (test base is always first)

##### m_DatasetKeyColumns

    int[] m_DatasetKeyColumns

An array containing the indexes of just the selected columns

##### m_DatasetKeyColumnsRange

    Range m_DatasetKeyColumnsRange

The range of columns that specify a unique "dataset" (eg: scheme plus configuration)

##### m_DatasetSpecifiers

    weka.experiment.PairedTTester.DatasetSpecifiers m_DatasetSpecifiers

The list of dataset specifiers

##### m_DisplayedResultsets

    int[] m_DisplayedResultsets

An array containing the indexes of the datasets to display

##### m_FoldColumn

    int m_FoldColumn

The option setting for the fold number column (-1 means none)

##### m_Instances

    Instances m_Instances

The set of instances we will analyse

##### m_ResultMatrix

    ResultMatrix m_ResultMatrix

the instance of the class to produce the output.

##### m_ResultsetKeyColumns

    int[] m_ResultsetKeyColumns

An array containing the indexes of just the selected columns

##### m_ResultsetKeyColumnsRange

    Range m_ResultsetKeyColumnsRange

The range of columns that specify a unique result set (eg: scheme plus configuration)

##### m_Resultsets

    FastVector m_Resultsets

Stores a vector for each resultset holding all instances in each set

##### m_ResultsetsValid

    boolean m_ResultsetsValid

Indicates whether the instances have been partitioned

##### m_RunColumn

    int m_RunColumn

The index of the column containing the run number

##### m_RunColumnSet

    int m_RunColumnSet

The option setting for the run number column (-1 means last)

##### m_ShowStdDevs

    boolean m_ShowStdDevs

Indicates whether standard deviations should be displayed

##### m_SignificanceLevel

    double m_SignificanceLevel

The significance level for comparisons

##### m_SortColumn

    int m_SortColumn

The column to sort on (-1 means default sorting)

##### m_SortOrder

    int[] m_SortOrder

The sorting of the datasets (according to the sort column)

### Class weka.experiment.PropertyNode

class PropertyNode extends Object implements Serializable

serialVersionUID:
  -8718165742572631384L

#### Serialization Methods

##### readObject

private void readObject(ObjectInputStream in) throws IOException, ClassNotFoundException

Throws:
  `IOException`
  `ClassNotFoundException`

##### writeObject

private void writeObject(ObjectOutputStream out) throws IOException

Throws:
  `IOException`

#### Serialized Fields

##### parentClass

    Class parentClass

The class of the object with this property

##### property

    PropertyDescriptor property

Other info about the property

##### value

    Object value

The current property value

### Class weka.experiment.RandomSplitResultProducer

class RandomSplitResultProducer extends Object implements Serializable

serialVersionUID:
  1403798165056795073L

#### Serialized Fields

##### m_AdditionalMeasures

    String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

##### m_debugOutput

    boolean m_debugOutput

Save raw output of split evaluators --- for debugging purposes

##### m_Instances

    Instances m_Instances

The dataset of interest

##### m_OutputFile

    File m_OutputFile

The destination output file/directory for raw output

##### m_randomize

    boolean m_randomize

Whether dataset is to be randomized

##### m_ResultListener

    ResultListener m_ResultListener

The ResultListener to send results to

##### m_SplitEvaluator

    SplitEvaluator m_SplitEvaluator

The SplitEvaluator used to generate results

##### m_TrainPercent

    double m_TrainPercent

The percentage of instances to use for training

##### m_ZipDest

    OutputZipper m_ZipDest

The output zipper to use for saving raw splitEvaluator output

### Class weka.experiment.RegressionSplitEvaluator

class RegressionSplitEvaluator extends Object implements Serializable

serialVersionUID:
  -328181640503349202L

#### Serialized Fields

##### m_AdditionalMeasures

    String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

##### m_Classifier

    Classifier m_Classifier

The classifier used for evaluation

##### m_ClassifierOptions

    String m_ClassifierOptions

The classifier options (if any)

##### m_ClassifierVersion

    String m_ClassifierVersion

The classifier version

##### m_doesProduce

    boolean[] m_doesProduce

Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce

##### m_result

    String m_result

Holds the statistics for the most recent application of the classifier

##### m_Template

    Classifier m_Template

The template classifier

### Class weka.experiment.RemoteEngine

class RemoteEngine extends UnicastRemoteObject implements Serializable

serialVersionUID:
  -1021538162895448259L

#### Serialized Fields

##### m_HostName

    String m_HostName

The name of the host that this engine is started on

##### m_TaskIdQueue

    Queue m_TaskIdQueue

A queue of corresponding ID's for tasks

##### m_TaskQueue

    Queue m_TaskQueue

A queue of waiting tasks

##### m_TaskRunning

    boolean m_TaskRunning

Is there a task running

##### m_TaskStatus

    Hashtable m_TaskStatus

A hashtable of experiment status

### Class weka.experiment.RemoteExperiment

class RemoteExperiment extends Experiment implements Serializable

serialVersionUID:
  -7357668825635314937L

#### Serialized Fields

##### m_baseExperiment

    Experiment m_baseExperiment

The base experiment to split up into sub experiments for remote execution

##### m_experimentAborted

    boolean m_experimentAborted

Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts experiment (via gui)

##### m_failedCount

    int m_failedCount

The count of failed sub-experiments

##### m_finishedCount

    int m_finishedCount

The count of successfully completed sub-experiments

##### m_listeners

    FastVector m_listeners

The list of objects listening for remote experiment events

##### m_remoteHostFailureCounts

    int[] m_remoteHostFailureCounts

The number of times tasks have failed on each remote host

##### m_remoteHosts

    DefaultListModel m_remoteHosts

Holds the names of machines with remoteEngine servers running

##### m_remoteHostsQueue

    Queue m_remoteHostsQueue

The queue of available hosts

##### m_remoteHostsStatus

    int[] m_remoteHostsStatus

The status of each of the remote hosts

##### m_removedHosts

    int m_removedHosts

The number of hosts removed due to exceeding max failures

##### m_splitByDataSet

    boolean m_splitByDataSet

If true, then sub experiments are created on the basis of data sets rather than run number.

##### m_subExpComplete

    int[] m_subExpComplete

The status of each of the sub-experiments

##### m_subExperiments

    Experiment[] m_subExperiments

The sub experiments

##### m_subExpQueue

    Queue m_subExpQueue

The queue of sub experiments waiting to be processed

### Class weka.experiment.RemoteExperimentEvent

class RemoteExperimentEvent extends Object implements Serializable

serialVersionUID:
  7000867987391866451L

#### Serialized Fields

##### m_experimentFinished

    boolean m_experimentFinished

True if a remote experiment has finished

##### m_logMessage

    boolean m_logMessage

A log type message

##### m_messageString

    String m_messageString

The message

##### m_statusMessage

    boolean m_statusMessage

A status type message

### Class weka.experiment.RemoteExperimentSubTask

class RemoteExperimentSubTask extends Object implements Serializable

#### Serialized Fields

##### m_experiment

    Experiment m_experiment

##### m_result

    TaskStatusInfo m_result

### Class weka.experiment.ResultMatrix

class ResultMatrix extends Object implements Serializable

serialVersionUID:
  4487179306428209739L

#### Serialized Fields

##### LEFT_PARENTHESES

    String LEFT_PARENTHESES

the left parentheses for enumerating cols/rows

##### LOSS_STRING

    String LOSS_STRING

loss string

##### m_ColHidden

    boolean[] m_ColHidden

whether a column is hidden

##### m_ColNames

    String[] m_ColNames

the column names

##### m_ColNameWidth

    int m_ColNameWidth

the size of the names of the columns

##### m_ColOrder

    int[] m_ColOrder

the ordering of the columns

##### m_Counts

    double[] m_Counts

the counts for the different datasets

##### m_CountWidth

    int m_CountWidth

the size of the counts

##### m_EnumerateColNames

    boolean m_EnumerateColNames

whether a "(x)" is printed before each column name with "x" as the index

##### m_EnumerateRowNames

    boolean m_EnumerateRowNames

whether a "(x)" is printed before each row name with "x" as the index

##### m_HeaderKeys

    Vector m_HeaderKeys

contains the keys for the header

##### m_HeaderValues

    Vector m_HeaderValues

contains the values for the header

##### m_Mean

    double[][] m_Mean

the values

##### m_MeanPrec

    int m_MeanPrec

the standard mean precision

##### m_MeanWidth

    int m_MeanWidth

the size of the mean columns

##### m_NonSigWins

    int[][] m_NonSigWins

the non-significant wins

##### m_PrintColNames

    boolean m_PrintColNames

whether the names or numbers are output as column declarations

##### m_PrintRowNames

    boolean m_PrintRowNames

whether the names or numbers are output as row declarations

##### m_RankingDiff

    int[] m_RankingDiff

the difference between wins and losses

##### m_RankingLosses

    int[] m_RankingLosses

the losses in ranking

##### m_RankingWins

    int[] m_RankingWins

the wins in ranking

##### m_RemoveFilterName

    boolean m_RemoveFilterName

whether to remove the filter name from the dataaset name

##### m_RowHidden

    boolean[] m_RowHidden

whether a row is hidden

##### m_RowNames

    String[] m_RowNames

the row names

##### m_RowNameWidth

    int m_RowNameWidth

the size of the names of the rows

##### m_RowOrder

    int[] m_RowOrder

the ordering of the rows

##### m_ShowAverage

    boolean m_ShowAverage

whether the average for each column should be printed

##### m_ShowStdDev

    boolean m_ShowStdDev

whether std. deviations are printed as well

##### m_Significance

    int[][] m_Significance

the significance

##### m_SignificanceWidth

    int m_SignificanceWidth

the size of the significance columns

##### m_StdDev

    double[][] m_StdDev

the standard deviation

##### m_StdDevPrec

    int m_StdDevPrec

the standard std. deviation preicision

##### m_StdDevWidth

    int m_StdDevWidth

the size of the std dev columns

##### m_Wins

    int[][] m_Wins

the significant wins

##### RIGHT_PARENTHESES

    String RIGHT_PARENTHESES

the right parentheses for enumerating cols/rows

##### TIE_STRING

    String TIE_STRING

tie string

##### WIN_STRING

    String WIN_STRING

win string

### Class weka.experiment.ResultMatrixCSV

class ResultMatrixCSV extends ResultMatrix implements Serializable

serialVersionUID:
  -171838863135042743L

### Class weka.experiment.ResultMatrixGnuPlot

class ResultMatrixGnuPlot extends ResultMatrix implements Serializable

serialVersionUID:
  -234648254944790097L

### Class weka.experiment.ResultMatrixHTML

class ResultMatrixHTML extends ResultMatrix implements Serializable

serialVersionUID:
  6672380422544799990L

### Class weka.experiment.ResultMatrixLatex

class ResultMatrixLatex extends ResultMatrix implements Serializable

serialVersionUID:
  777690788447600978L

### Class weka.experiment.ResultMatrixPlainText

class ResultMatrixPlainText extends ResultMatrix implements Serializable

serialVersionUID:
  1502934525382357937L

### Class weka.experiment.ResultMatrixSignificance

class ResultMatrixSignificance extends ResultMatrix implements Serializable

serialVersionUID:
  -1280545644109764206L

### Class weka.experiment.Stats

class Stats extends Object implements Serializable

serialVersionUID:
  -8610544539090024102L

#### Serialized Fields

##### count

    double count

The number of values seen

##### max

    double max

The maximum value seen, or Double.NaN if no values seen

##### mean

    double mean

The mean of values at the last calculateDerived() call

##### min

    double min

The minimum value seen, or Double.NaN if no values seen

##### stdDev

    double stdDev

The std deviation of values at the last calculateDerived() call

##### sum

    double sum

The sum of values seen

##### sumSq

    double sumSq

The sum of values squared seen

### Class weka.experiment.TaskStatusInfo

class TaskStatusInfo extends Object implements Serializable

serialVersionUID:
  -6129343303703560015L

#### Serialized Fields

##### m_ExecutionStatus

    int m_ExecutionStatus

Holds current execution status.

##### m_StatusMessage

    String m_StatusMessage

Holds current status message.

##### m_TaskResult

    Object m_TaskResult

Holds task result. Set to null for no returnable result.

## Package weka.filters

### Class weka.filters.AllFilter

class AllFilter extends Filter implements Serializable

serialVersionUID:
  5022109283147503266L

### Class weka.filters.Filter

class Filter extends Object implements Serializable

serialVersionUID:
  -8835063755891851218L

#### Serialized Fields

##### m_FirstBatchDone

    boolean m_FirstBatchDone

True if the first batch has been done

##### m_InputFormat

    Instances m_InputFormat

The input format for instances

##### m_InputRelAtts

    RelationalLocator m_InputRelAtts

Indices of relational attributes in the input format

##### m_InputStringAtts

    StringLocator m_InputStringAtts

Indices of string attributes in the input format

##### m_NewBatch

    boolean m_NewBatch

Record whether the filter is at the start of a batch

##### m_OutputFormat

    Instances m_OutputFormat

The output format for instances

##### m_OutputQueue

    Queue m_OutputQueue

The output instance queue

##### m_OutputRelAtts

    RelationalLocator m_OutputRelAtts

Indices of relational attributes in the output format

##### m_OutputStringAtts

    StringLocator m_OutputStringAtts

Indices of string attributes in the output format

### Class weka.filters.MultiFilter

class MultiFilter extends SimpleStreamFilter implements Serializable

serialVersionUID:
  -6293720886005713120L

#### Serialized Fields

##### m_Filters

    Filter[] m_Filters

The filters

##### m_Streamable

    boolean m_Streamable

caches the streamable state

##### m_StreamableChecked

    boolean m_StreamableChecked

whether we already checked the streamable state

### Class weka.filters.SimpleBatchFilter

class SimpleBatchFilter extends SimpleFilter implements Serializable

serialVersionUID:
  8102908673378055114L

### Class weka.filters.SimpleFilter

class SimpleFilter extends Filter implements Serializable

serialVersionUID:
  5702974949137433141L

#### Serialized Fields

##### m_Debug

    boolean m_Debug

Whether debugging is on

### Class weka.filters.SimpleStreamFilter

class SimpleStreamFilter extends SimpleFilter implements Serializable

serialVersionUID:
  2754882676192747091L

## Package weka.filters.supervised.attribute

### Class weka.filters.supervised.attribute.AddClassification

class AddClassification extends SimpleBatchFilter implements Serializable

serialVersionUID:
  -1931467132568441909L

#### Serialized Fields

##### m_ActualClassifier

    Classifier m_ActualClassifier

The actual classifier used to do the classification.

##### m_Classifier

    Classifier m_Classifier

The classifier template used to do the classification.

##### m_OutputClassification

    boolean m_OutputClassification

whether to output the classification.

##### m_OutputDistribution

    boolean m_OutputDistribution

whether to output the class distribution.

##### m_OutputErrorFlag

    boolean m_OutputErrorFlag

whether to output the error flag.

##### m_RemoveOldClass

    boolean m_RemoveOldClass

whether to remove the old class attribute.

##### m_SerializedClassifierFile

    File m_SerializedClassifierFile

The file from which to load a serialized classifier.

##### m_SerializedHeader

    Instances m_SerializedHeader

the header of the file the serialized classifier was trained with.

### Class weka.filters.supervised.attribute.AttributeSelection

class AttributeSelection extends Filter implements Serializable

serialVersionUID:
  -296211247688169716L

#### Serialized Fields

##### m_ASEvaluator

    ASEvaluation m_ASEvaluator

the attribute evaluator to use

##### m_ASSearch

    ASSearch m_ASSearch

the search method if any

##### m_FilterOptions

    String[] m_FilterOptions

holds a copy of the full set of valid options passed to the filter

##### m_hasClass

    boolean m_hasClass

True if the input format has a class attribute set

##### m_SelectedAttributes

    int[] m_SelectedAttributes

holds the selected attributes

##### m_trainSelector

    AttributeSelection m_trainSelector

the attribute selection evaluation object

### Class weka.filters.supervised.attribute.ClassOrder

class ClassOrder extends Filter implements Serializable

serialVersionUID:
  -2116226838887628411L

#### Serialized Fields

##### m_ClassAttribute

    Attribute m_ClassAttribute

Class attribute of the data

##### m_ClassCounts

    double[] m_ClassCounts

This class can provide the class distribution in the sorted order as side effect

##### m_ClassOrder

    int m_ClassOrder

The class order to be sorted

##### m_Converter

    int[] m_Converter

The 1-1 converting table from the original class values to the new values

##### m_Random

    Random m_Random

The random object

##### m_Seed

    long m_Seed

The seed of randomization

### Class weka.filters.supervised.attribute.Discretize

class Discretize extends Filter implements Serializable

serialVersionUID:
  -3141006402280129097L

#### Serialized Fields

##### m_CutPoints

    double[][] m_CutPoints

Store the current cutpoints

##### m_DiscretizeCols

    Range m_DiscretizeCols

Stores which columns to Discretize

##### m_MakeBinary

    boolean m_MakeBinary

Output binary attributes for discretized attributes.

##### m_UseBetterEncoding

    boolean m_UseBetterEncoding

Use better encoding of split point for MDL.

##### m_UseKononenko

    boolean m_UseKononenko

Use Kononenko's MDL criterion instead of Fayyad et al.'s

### Class weka.filters.supervised.attribute.NominalToBinary

class NominalToBinary extends Filter implements Serializable

serialVersionUID:
  -5004607029857673950L

#### Serialized Fields

##### m_Indices

    int[][] m_Indices

The sorted indices of the attribute values.

##### m_needToTransform

    boolean m_needToTransform

Whether we need to transform at all

##### m_Numeric

    boolean m_Numeric

Are the new attributes going to be nominal or numeric ones?

##### m_TransformAll

    boolean m_TransformAll

Are all values transformed into new attributes?

### Class weka.filters.supervised.attribute.PLSFilter

class PLSFilter extends SimpleBatchFilter implements Serializable

serialVersionUID:
  -3335106965521265631L

#### Serialized Fields

##### m_Algorithm

    int m_Algorithm

the type of algorithm

##### m_ClassMean

    double m_ClassMean

the mean of the class

##### m_ClassStdDev

    double m_ClassStdDev

the standard deviation of the class

##### m_Filter

    Filter m_Filter

for centering the data

##### m_Missing

    Filter m_Missing

for replacing missing values

##### m_NumComponents

    int m_NumComponents

the maximum number of components to generate

##### m_PerformPrediction

    boolean m_PerformPrediction

whether to include the prediction, i.e., modifying the class attribute

##### m_PLS1_b_hat

    Matrix m_PLS1_b_hat

the b-hat vector for PLS1

##### m_PLS1_P

    Matrix m_PLS1_P

the P matrix for PLS1

##### m_PLS1_RegVector

    Matrix m_PLS1_RegVector

the regression vector "r-hat" for PLS1

##### m_PLS1_W

    Matrix m_PLS1_W

the W matrix for PLS1

##### m_Preprocessing

    int m_Preprocessing

the type of preprocessing

##### m_ReplaceMissing

    boolean m_ReplaceMissing

whether to replace missing values

##### m_SIMPLS_B

    Matrix m_SIMPLS_B

the B matrix for SIMPLS (used for prediction)

##### m_SIMPLS_W

    Matrix m_SIMPLS_W

the W matrix for SIMPLS

## Package weka.filters.supervised.instance

### Class weka.filters.supervised.instance.Resample

class Resample extends Filter implements Serializable

serialVersionUID:
  7079064953548300681L

#### Serialized Fields

##### m_BiasToUniformClass

    double m_BiasToUniformClass

The degree of bias towards uniform (nominal) class distribution.

##### m_InvertSelection

    boolean m_InvertSelection

Whether to invert the selection (only if instances are drawn WITHOUT replacement).

See Also:
  - `Resample.m_NoReplacement`

##### m_NoReplacement

    boolean m_NoReplacement

Whether to perform sampling with replacement or without.

##### m_RandomSeed

    int m_RandomSeed

The random number generator seed.

##### m_SampleSizePercent

    double m_SampleSizePercent

The subsample size, percent of original set, default 100%.

### Class weka.filters.supervised.instance.SMOTE

class SMOTE extends Filter implements Serializable

serialVersionUID:
  -1653880819059250364L

#### Serialized Fields

##### m_ClassValueIndex

    String m_ClassValueIndex

the index of the class value.

##### m_DetectMinorityClass

    boolean m_DetectMinorityClass

whether to detect the minority class automatically.

##### m_NearestNeighbors

    int m_NearestNeighbors

the number of neighbors to use.

##### m_Percentage

    double m_Percentage

the percentage of SMOTE instances to create.

##### m_RandomSeed

    int m_RandomSeed

the random seed to use.

### Class weka.filters.supervised.instance.SpreadSubsample

class SpreadSubsample extends Filter implements Serializable

serialVersionUID:
  -3947033795243930016L

#### Serialized Fields

##### m_AdjustWeights

    boolean m_AdjustWeights

True if instance weights will be adjusted to maintain total weight per class.

##### m_DistributionSpread

    double m_DistributionSpread

True if the first batch has been done

##### m_MaxCount

    int m_MaxCount

The maximum count of any class

##### m_RandomSeed

    int m_RandomSeed

The random number generator seed

### Class weka.filters.supervised.instance.StratifiedRemoveFolds

class StratifiedRemoveFolds extends Filter implements Serializable

serialVersionUID:
  -7069148179905814324L

#### Serialized Fields

##### m_Fold

    int m_Fold

Fold to output

##### m_Inverse

    boolean m_Inverse

Indicates if inverse of selection is to be output.

##### m_NumFolds

    int m_NumFolds

Number of folds to split dataset into

##### m_Seed

    long m_Seed

Random number seed.

## Package weka.filters.unsupervised.attribute

### Class weka.filters.unsupervised.attribute.AbstractTimeSeries

class AbstractTimeSeries extends Filter implements Serializable

serialVersionUID:
  -3795656792078022357L

#### Serialized Fields

##### m_FillWithMissing

    boolean m_FillWithMissing

True if missing values should be used rather than removing instances where the translated value is not known (due to border effects).

##### m_History

    Queue m_History

Stores the historical instances to copy values between

##### m_InstanceRange

    int m_InstanceRange

The number of instances forward to translate values between. A negative number indicates taking values from a past instance.

##### m_SelectedCols

    Range m_SelectedCols

Stores which columns to copy

### Class weka.filters.unsupervised.attribute.Add

class Add extends Filter implements Serializable

serialVersionUID:
  761386447332932389L

#### Serialized Fields

##### m_AttributeType

    int m_AttributeType

Record the type of attribute to insert.

##### m_DateFormat

    String m_DateFormat

The date format.

##### m_Insert

    SingleIndex m_Insert

The location to insert the new attribute.

##### m_Labels

    FastVector m_Labels

The list of labels for nominal attribute.

##### m_Name

    String m_Name

The name for the new attribute.

### Class weka.filters.unsupervised.attribute.AddCluster

class AddCluster extends Filter implements Serializable

serialVersionUID:
  7414280611943807337L

#### Serialized Fields

##### m_Clusterer

    Clusterer m_Clusterer

The clusterer used to do the cleansing

##### m_IgnoreAttributesRange

    Range m_IgnoreAttributesRange

Range of attributes to ignore

##### m_removeAttributes

    Filter m_removeAttributes

Filter for removing attributes

### Class weka.filters.unsupervised.attribute.AddExpression

class AddExpression extends Filter implements Serializable

serialVersionUID:
  402130384261736245L

#### Serialized Fields

##### m_attributeExpression

    AttributeExpression m_attributeExpression

##### m_attributeName

    String m_attributeName

Name of the new attribute. "expression" length string will use the provided expression as the new attribute name

##### m_Debug

    boolean m_Debug

If true, makes the attribute name equal to the postfix parse of the expression

##### m_infixExpression

    String m_infixExpression

The infix expression

### Class weka.filters.unsupervised.attribute.AddID

class AddID extends Filter implements Serializable

serialVersionUID:
  4734383199819293390L

#### Serialized Fields

##### m_Counter

    int m_Counter

the counter for the ID

##### m_Index

    SingleIndex m_Index

the index of the attribute

##### m_Name

    String m_Name

the name of the attribute

### Class weka.filters.unsupervised.attribute.AddNoise

class AddNoise extends Filter implements Serializable

serialVersionUID:
  -8499673222857299082L

#### Serialized Fields

##### m_AttIndex

    SingleIndex m_AttIndex

The attribute's index setting.

##### m_Percent

    int m_Percent

The subsample size, percent of original set, default 10%

##### m_RandomSeed

    int m_RandomSeed

The random number generator seed

##### m_UseMissing

    boolean m_UseMissing

Flag if missing values are taken as value.

### Class weka.filters.unsupervised.attribute.AddValues

class AddValues extends Filter implements Serializable

serialVersionUID:
  -8100622241742393656L

#### Serialized Fields

##### m_AttIndex

    SingleIndex m_AttIndex

The attribute's index setting.

##### m_Labels

    Vector m_Labels

The values to add.

##### m_Sort

    boolean m_Sort

Whether to sort the values.

##### m_SortedIndices

    int[] m_SortedIndices

the array with the sorted label indices

### Class weka.filters.unsupervised.attribute.Center

class Center extends PotentialClassIgnorer implements Serializable

serialVersionUID:
  -9101338448900581023L

#### Serialized Fields

##### m_Means

    double[] m_Means

The means

### Class weka.filters.unsupervised.attribute.ChangeDateFormat

class ChangeDateFormat extends Filter implements Serializable

serialVersionUID:
  -1609344074013448737L

#### Serialized Fields

##### m_AttIndex

    SingleIndex m_AttIndex

The attribute's index setting.

##### m_DateFormat

    SimpleDateFormat m_DateFormat

The output date format.

##### m_OutputAttribute

    Attribute m_OutputAttribute

The output attribute.

### Class weka.filters.unsupervised.attribute.ClassAssigner

class ClassAssigner extends SimpleStreamFilter implements Serializable

serialVersionUID:
  1775780193887394115L

#### Serialized Fields

##### m_ClassIndex

    int m_ClassIndex

the class index.

### Class weka.filters.unsupervised.attribute.ClusterMembership

class ClusterMembership extends Filter implements Serializable

serialVersionUID:
  6675702504667714026L

#### Serialized Fields

##### m_clusterer

    DensityBasedClusterer m_clusterer

The clusterer

##### m_clusterers

    DensityBasedClusterer[] m_clusterers

Array for storing the clusterers

##### m_ignoreAttributesRange

    Range m_ignoreAttributesRange

Range of attributes to ignore

##### m_priors

    double[] m_priors

The prior probability for each class

##### m_removeAttributes

    Filter m_removeAttributes

Filter for removing attributes

### Class weka.filters.unsupervised.attribute.Copy

class Copy extends Filter implements Serializable

serialVersionUID:
  -8543707493627441566L

#### Serialized Fields

##### m_CopyCols

    Range m_CopyCols

Stores which columns to copy

##### m_SelectedAttributes

    int[] m_SelectedAttributes

Stores the indexes of the selected attributes in order, once the dataset is seen

### Class weka.filters.unsupervised.attribute.Discretize

class Discretize extends PotentialClassIgnorer implements Serializable

serialVersionUID:
  -1358531742174527279L

#### Serialized Fields

##### m_CutPoints

    double[][] m_CutPoints

Store the current cutpoints

##### m_DefaultCols

    String m_DefaultCols

The default columns to discretize

##### m_DesiredWeightOfInstancesPerInterval

    double m_DesiredWeightOfInstancesPerInterval

The desired weight of instances per bin

##### m_DiscretizeCols

    Range m_DiscretizeCols

Stores which columns to Discretize

##### m_FindNumBins

    boolean m_FindNumBins

Find the number of bins using cross-validated entropy.

##### m_MakeBinary

    boolean m_MakeBinary

Output binary attributes for discretized attributes.

##### m_NumBins

    int m_NumBins

The number of bins to divide the attribute into

##### m_UseEqualFrequency

    boolean m_UseEqualFrequency

Use equal-frequency binning if unsupervised discretization turned on

### Class weka.filters.unsupervised.attribute.FirstOrder

class FirstOrder extends Filter implements Serializable

serialVersionUID:
  -7500464545400454179L

#### Serialized Fields

##### m_DeltaCols

    Range m_DeltaCols

Stores which columns to take differences between

### Class weka.filters.unsupervised.attribute.InterquartileRange

class InterquartileRange extends SimpleBatchFilter implements Serializable

serialVersionUID:
  -227879653639723030L

#### Serialized Fields

##### m_AttributeIndices

    int[] m_AttributeIndices

the generated indices (only for performance reasons)

##### m_Attributes

    Range m_Attributes

the attribute range to work on

##### m_DetectionPerAttribute

    boolean m_DetectionPerAttribute

whether to generate Outlier/ExtremeValue attributes for each attribute instead of a general one

##### m_ExtremeValuesAsOutliers

    boolean m_ExtremeValuesAsOutliers

whether extreme values are also tagged as outliers

##### m_ExtremeValuesFactor

    double m_ExtremeValuesFactor

the factor for detecting extreme values, by default 2*m_OutlierFactor

##### m_IQR

    double[] m_IQR

the interquartile range

##### m_LowerExtremeValue

    double[] m_LowerExtremeValue

the lower extreme value threshold (= Q1 - EVF*IQR)

##### m_LowerOutlier

    double[] m_LowerOutlier

the lower outlier threshold (= Q1 - OF*IQR)

##### m_Median

    double[] m_Median

the median

##### m_OutlierAttributePosition

    int[] m_OutlierAttributePosition

the position of the outlier attribute

##### m_OutlierFactor

    double m_OutlierFactor

the factor for detecting outliers

##### m_OutputOffsetMultiplier

    boolean m_OutputOffsetMultiplier

whether to add another attribute called "Offset", that lists the 'multiplier' by which the outlier/extreme value is away from the median, i.e., value = median + 'multiplier' * IQR
automatically enables m_DetectionPerAttribute!

##### m_UpperExtremeValue

    double[] m_UpperExtremeValue

the upper extreme value threshold (= Q3 + EVF*IQR)

##### m_UpperOutlier

    double[] m_UpperOutlier

the upper outlier threshold (= Q3 + OF*IQR)

### Class weka.filters.unsupervised.attribute.KernelFilter

class KernelFilter extends SimpleBatchFilter implements Serializable

serialVersionUID:
  213800899640387499L

#### Serialized Fields

##### m_ActualFilter

    Filter m_ActualFilter

for centering/standardizing the data (the actual filter to use)

##### m_ActualKernel

    Kernel m_ActualKernel

the Kernel which is actually used for computation

##### m_checksTurnedOff

    boolean m_checksTurnedOff

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

##### m_Filter

    Filter m_Filter

for centering/standardizing the data

##### m_InitFile

    File m_InitFile

The dataset to initialize the filter with

##### m_InitFileClassIndex

    SingleIndex m_InitFileClassIndex

the class index for the file to initialized with

See Also:
  - `KernelFilter.m_InitFile`

##### m_Initialized

    boolean m_Initialized

whether the filter was initialized

##### m_Kernel

    Kernel m_Kernel

Kernel to use

##### m_KernelFactor

    double m_KernelFactor

the calculated kernel factor

See Also:
  - `KernelFilter.m_KernelFactorExpression`

##### m_KernelFactorExpression

    String m_KernelFactorExpression

optimizes the kernel with this formula (A = # of attributes, N = # of instances)

##### m_Missing

    ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

##### m_NominalToBinary

    NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

##### m_NumTrainInstances

    int m_NumTrainInstances

The number of instances in the training data.

### Class weka.filters.unsupervised.attribute.MakeIndicator

class MakeIndicator extends Filter implements Serializable

serialVersionUID:
  766001176862773163L

#### Serialized Fields

##### m_AttIndex

    SingleIndex m_AttIndex

The attribute's index setting.

##### m_Numeric

    boolean m_Numeric

Make boolean attribute numeric.

##### m_ValIndex

    Range m_ValIndex

The value's index

### Class weka.filters.unsupervised.attribute.MathExpression

class MathExpression extends PotentialClassIgnorer implements Serializable

serialVersionUID:
  -3713222714671997901L

#### Serialized Fields

##### m_attStats

    AttributeStats[] m_attStats

Attributes statistics

##### m_expression

    String m_expression

The modification expression

##### m_SelectCols

    Range m_SelectCols

Stores which columns to select as a funky range

### Class weka.filters.unsupervised.attribute.MergeTwoValues

class MergeTwoValues extends Filter implements Serializable

serialVersionUID:
  2925048980504034018L

#### Serialized Fields

##### m_AttIndex

    SingleIndex m_AttIndex

The attribute's index setting.

##### m_FirstIndex

    SingleIndex m_FirstIndex

The first value's index setting.

##### m_SecondIndex

    SingleIndex m_SecondIndex

The second value's index setting.

### Class weka.filters.unsupervised.attribute.MultiInstanceToPropositional

class MultiInstanceToPropositional extends Filter implements Serializable

serialVersionUID:
  -4102847628883002530L

#### Serialized Fields

##### m_BagRelAtts

    RelationalLocator m_BagRelAtts

Indices of relational attributes in the bag

##### m_BagStringAtts

    StringLocator m_BagStringAtts

Indices of string attributes in the bag

##### m_NumBags

    int m_NumBags

the total number of bags

##### m_NumInstances

    int m_NumInstances

the total number of the propositional instance in the dataset

##### m_WeightMethod

    int m_WeightMethod

the propositional instance weight setting method

### Class weka.filters.unsupervised.attribute.NominalToBinary

class NominalToBinary extends Filter implements Serializable

serialVersionUID:
  -1130642825710549138L

#### Serialized Fields

##### m_Columns

    Range m_Columns

Stores which columns to act on

##### m_needToTransform

    boolean m_needToTransform

Whether we need to transform at all

##### m_Numeric

    boolean m_Numeric

Are the new attributes going to be nominal or numeric ones?

##### m_TransformAll

    boolean m_TransformAll

Are all values transformed into new attributes?

### Class weka.filters.unsupervised.attribute.NominalToString

class NominalToString extends Filter implements Serializable

serialVersionUID:
  8655492378380068939L

#### Serialized Fields

##### m_AttIndex

    Range m_AttIndex

The attribute's index setting.

### Class weka.filters.unsupervised.attribute.Normalize

class Normalize extends PotentialClassIgnorer implements Serializable

serialVersionUID:
  -8158531150984362898L

#### Serialized Fields

##### m_MaxArray

    double[] m_MaxArray

The maximum values for numeric attributes.

##### m_MinArray

    double[] m_MinArray

The minimum values for numeric attributes.

##### m_Scale

    double m_Scale

The scaling factor of the output range.

##### m_Translation

    double m_Translation

The translation of the output range.

### Class weka.filters.unsupervised.attribute.NumericCleaner

class NumericCleaner extends SimpleStreamFilter implements Serializable

serialVersionUID:
  -352890679895066592L

#### Serialized Fields

##### m_CloseTo

    double m_CloseTo

the number the values are checked for closeness to

##### m_CloseToDefault

    double m_CloseToDefault

the default replacement value for numbers "close-to"

##### m_CloseToTolerance

    double m_CloseToTolerance

the tolerance distance, below which numbers are considered being "close-to"

##### m_Cols

    Range m_Cols

Stores which columns to cleanse

##### m_Decimals

    int m_Decimals

the number of decimals to round to (-1 means no rounding)

##### m_IncludeClass

    boolean m_IncludeClass

whether to include the class attribute

##### m_MaxDefault

    double m_MaxDefault

the maximum default replacement value

##### m_MaxThreshold

    double m_MaxThreshold

the maximum threshold

##### m_MinDefault

    double m_MinDefault

the minimum default replacement value

##### m_MinThreshold

    double m_MinThreshold

the minimum threshold

### Class weka.filters.unsupervised.attribute.NumericToBinary

class NumericToBinary extends PotentialClassIgnorer implements Serializable

serialVersionUID:
  2616879323359470802L

### Class weka.filters.unsupervised.attribute.NumericToNominal

class NumericToNominal extends SimpleBatchFilter implements Serializable

serialVersionUID:
  -6614630932899796239L

#### Serialized Fields

##### m_Cols

    Range m_Cols

Stores which columns to turn into nominals

##### m_DefaultCols

    String m_DefaultCols

The default columns to turn into nominals

### Class weka.filters.unsupervised.attribute.NumericTransform

class NumericTransform extends Filter implements Serializable

serialVersionUID:
  -8561413333351366934L

#### Serialized Fields

##### m_Class

    String m_Class

Class containing transformation method.

##### m_Cols

    Range m_Cols

Stores which columns to transform.

##### m_Method

    String m_Method

Transformation method.

### Class weka.filters.unsupervised.attribute.Obfuscate

class Obfuscate extends Filter implements Serializable

serialVersionUID:
  -343922772462971561L

### Class weka.filters.unsupervised.attribute.PartitionedMultiFilter

class PartitionedMultiFilter extends SimpleBatchFilter implements Serializable

serialVersionUID:
  -6293720886005713120L

#### Serialized Fields

##### m_Filters

    Filter[] m_Filters

The filters.

##### m_IndicesUnused

    int[] m_IndicesUnused

the indices of the unused attributes.

##### m_Ranges

    Range[] m_Ranges

The attribute ranges.

##### m_RemoveUnused

    boolean m_RemoveUnused

Whether unused attributes are left out of the output.

### Class weka.filters.unsupervised.attribute.PKIDiscretize

class PKIDiscretize extends Discretize implements Serializable

serialVersionUID:
  6153101248977702675L

### Class weka.filters.unsupervised.attribute.PotentialClassIgnorer

class PotentialClassIgnorer extends Filter implements Serializable

serialVersionUID:
  8625371119276845454L

#### Serialized Fields

##### m_ClassIndex

    int m_ClassIndex

Storing the class index

##### m_IgnoreClass

    boolean m_IgnoreClass

True if the class is to be unset

### Class weka.filters.unsupervised.attribute.PrincipalComponents

class PrincipalComponents extends Filter implements Serializable

serialVersionUID:
  -5649876869480249303L

#### Serialized Fields

##### m_AttributeFilter

    Remove m_AttributeFilter

Filter for removing class attribute, nominal attributes with 0 or 1 value.

##### m_center

    boolean m_center

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

##### m_centerFilter

    Center m_centerFilter

Filter for centering the data

##### m_ClassIndex

    int m_ClassIndex

Class index.

##### m_Correlation

    double[][] m_Correlation

Correlation matrix for the original data.

##### m_CoverVariance

    double m_CoverVariance

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

##### m_Eigenvalues

    double[] m_Eigenvalues

Eigenvalues for the corresponding eigenvectors.

##### m_Eigenvectors

    double[][] m_Eigenvectors

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

##### m_HasClass

    boolean m_HasClass

Data has a class set.

##### m_MaxAttributes

    int m_MaxAttributes

maximum number of attributes in the transformed data (-1 for all).

##### m_MaxAttrsInName

    int m_MaxAttrsInName

maximum number of attributes in the transformed attribute name.

##### m_NominalToBinaryFilter

    NominalToBinary m_NominalToBinaryFilter

Filter for turning nominal values into numeric ones.

##### m_NumAttribs

    int m_NumAttribs

Number of attributes.

##### m_NumInstances

    int m_NumInstances

Number of instances.

##### m_OutputNumAtts

    int m_OutputNumAtts

The number of attributes in the pc transformed data.

##### m_ReplaceMissingFilter

    ReplaceMissingValues m_ReplaceMissingFilter

Filters for replacing missing values.

##### m_SortedEigens

    int[] m_SortedEigens

Sorted eigenvalues.

##### m_standardizeFilter

    Standardize m_standardizeFilter

Filter for standardizing the data

##### m_SumOfEigenValues

    double m_SumOfEigenValues

sum of the eigenvalues.

##### m_TrainCopy

    Instances m_TrainCopy

Keep a copy for the class attribute (if set).

##### m_TrainInstances

    Instances m_TrainInstances

The data to transform analyse/transform.

##### m_TransformedFormat

    Instances m_TransformedFormat

The header for the transformed data format.

### Class weka.filters.unsupervised.attribute.PropositionalToMultiInstance

class PropositionalToMultiInstance extends Filter implements Serializable

serialVersionUID:
  5825873573912102482L

#### Serialized Fields

##### m_BagRelAtts

    RelationalLocator m_BagRelAtts

Indices of relational attributes in the bag

##### m_BagStringAtts

    StringLocator m_BagStringAtts

Indices of string attributes in the bag

##### m_DoNotWeightBags

    boolean m_DoNotWeightBags

do not weight bags by number of instances they contain

##### m_Randomize

    boolean m_Randomize

whether to randomize the output data

##### m_Seed

    int m_Seed

the seed for randomizing, default is 1

### Class weka.filters.unsupervised.attribute.RandomProjection

class RandomProjection extends Filter implements Serializable

serialVersionUID:
  4428905532728645880L

#### Serialized Fields

##### m_distribution

    int m_distribution

Stores the distribution to use for calculating the random matrix

##### m_k

    int m_k

Stores the number of dimensions to reduce the data to

##### m_ntob

    Filter m_ntob

The NominalToBinary filter applied to the data before this filter

##### m_OutputFormatDefined

    boolean m_OutputFormatDefined

Keeps track of output format if it is defined or not

##### m_percent

    double m_percent

Stores the dimensionality the data should be reduced to as percentage of the original dimension

##### m_random

    Random m_random

The random number generator used for generating the random matrix

##### m_replaceMissing

    Filter m_replaceMissing

The ReplaceMissingValues filter

##### m_rmatrix

    double[][] m_rmatrix

The random matrix

##### m_rndmSeed

    long m_rndmSeed

Stores the random seed used to generate the random matrix

##### m_useGaussian

    boolean m_useGaussian

Is the random matrix will be computed using Gaussian distribution or not

##### m_useReplaceMissing

    boolean m_useReplaceMissing

Should the missing values be replaced using unsupervised.ReplaceMissingValues filter

### Class weka.filters.unsupervised.attribute.RandomSubset

class RandomSubset extends SimpleStreamFilter implements Serializable

serialVersionUID:
  2911221724251628050L

#### Serialized Fields

##### m_Indices

    int[] m_Indices

The indices of the attributes that got selected.

##### m_NumAttributes

    double m_NumAttributes

The number of attributes to randomly choose (>= 1 absolute number of attributes, < 1 percentage).

##### m_Seed

    int m_Seed

The seed value.

### Class weka.filters.unsupervised.attribute.RELAGGS

class RELAGGS extends SimpleBatchFilter implements Serializable

serialVersionUID:
  -3333791375278589231L

#### Serialized Fields

##### m_AttStats

    Hashtable<String,AttributeStats> m_AttStats

stores the attribute statistics `att_index-att_index_in_rel_att <-> AttributeStats`

##### m_MaxCardinality

    int m_MaxCardinality

the max. cardinality for nominal attributes

##### m_SelectedRange

    Range m_SelectedRange

the range of attributes to process (only relational ones will be processed)

### Class weka.filters.unsupervised.attribute.Remove

class Remove extends Filter implements Serializable

serialVersionUID:
  5011337331921522847L

#### Serialized Fields

##### m_SelectCols

    Range m_SelectCols

Stores which columns to select as a funky range

##### m_SelectedAttributes

    int[] m_SelectedAttributes

Stores the indexes of the selected attributes in order, once the dataset is seen

### Class weka.filters.unsupervised.attribute.RemoveType

class RemoveType extends Filter implements Serializable

serialVersionUID:
  -3563999462782486279L

#### Serialized Fields

##### m_attributeFilter

    Remove m_attributeFilter

The attribute filter used to do the filtering

##### m_attTypeToDelete

    int m_attTypeToDelete

The type of attribute to delete

##### m_invert

    boolean m_invert

Whether to invert selection

### Class weka.filters.unsupervised.attribute.RemoveUseless

class RemoveUseless extends Filter implements Serializable

serialVersionUID:
  -8659417851407640038L

#### Serialized Fields

##### m_maxVariancePercentage

    double m_maxVariancePercentage

The type of attribute to delete

##### m_removeFilter

    Remove m_removeFilter

The filter used to remove attributes

### Class weka.filters.unsupervised.attribute.Reorder

class Reorder extends Filter implements Serializable

serialVersionUID:
  -1135571321097202292L

#### Serialized Fields

##### m_InputStringIndex

    int[] m_InputStringIndex

Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated

##### m_NewOrderCols

    String m_NewOrderCols

Stores which columns to reorder

##### m_SelectedAttributes

    int[] m_SelectedAttributes

Stores the indexes of the selected attributes in order, once the dataset is seen

### Class weka.filters.unsupervised.attribute.ReplaceMissingValues

class ReplaceMissingValues extends PotentialClassIgnorer implements Serializable

serialVersionUID:
  8349568310991609867L

#### Serialized Fields

##### m_ModesAndMeans

    double[] m_ModesAndMeans

The modes and means

### Class weka.filters.unsupervised.attribute.Standardize

class Standardize extends PotentialClassIgnorer implements Serializable

serialVersionUID:
  -6830769026855053281L

#### Serialized Fields

##### m_Means

    double[] m_Means

The means

##### m_StdDevs

    double[] m_StdDevs

The variances

### Class weka.filters.unsupervised.attribute.StringToNominal

class StringToNominal extends Filter implements Serializable

serialVersionUID:
  4864084427902797605L

#### Serialized Fields

##### m_AttIndices

    Range m_AttIndices

The attribute's range indices setting.

### Class weka.filters.unsupervised.attribute.StringToWordVector

class StringToWordVector extends Filter implements Serializable

serialVersionUID:
  8249106275278565424L

#### Serialized Fields

##### m_AvgDocLength

    double m_AvgDocLength

Contains the average length of documents (among the first batch of instances aka training data). This is used in length normalization of documents which will be normalized to average document length.

##### m_Dictionary

    TreeMap m_Dictionary

Contains a mapping of valid words to attribute indexes.

##### m_DocsCounts

    int[] m_DocsCounts

Contains the number of documents (instances) a particular word appears in. The counts are stored with the same indexing as given by m_Dictionary.

##### m_doNotOperateOnPerClassBasis

    boolean m_doNotOperateOnPerClassBasis

whether to operate on a per-class basis.

##### m_filterType

    int m_filterType

The normalization to apply.

##### m_IDFTransform

    boolean m_IDFTransform

True if word frequencies should be transformed into fij*log(numOfDocs/numOfDocsWithWordi).

##### m_lowerCaseTokens

    boolean m_lowerCaseTokens

True if all tokens should be downcased.

##### m_minTermFreq

    int m_minTermFreq

the minimum (per-class) word frequency.

##### m_NumInstances

    int m_NumInstances

Contains the number of documents (instances) in the input format from which the dictionary is created. It is used in IDF transform.

##### m_OutputCounts

    boolean m_OutputCounts

True if output instances should contain word frequency rather than boolean 0 or 1.

##### m_PeriodicPruningRate

    double m_PeriodicPruningRate

The percentage at which to periodically prune the dictionary.

##### m_Prefix

    String m_Prefix

A String prefix for the attribute names.

##### m_SelectedRange

    Range m_SelectedRange

Range of columns to convert to word vectors.

##### m_Stemmer

    Stemmer m_Stemmer

the stemming algorithm.

##### m_Stopwords

    File m_Stopwords

a file containing stopwords for using others than the default Rainbow ones.

##### m_TFTransform

    boolean m_TFTransform

True if word frequencies should be transformed into log(1+fi) where fi is the frequency of word i.

##### m_Tokenizer

    Tokenizer m_Tokenizer

the tokenizer algorithm to use.

##### m_useStoplist

    boolean m_useStoplist

True if tokens that are on a stoplist are to be ignored.

##### m_WordsToKeep

    int m_WordsToKeep

The default number of words (per class if there is a class attribute assigned) to attempt to keep.

### Class weka.filters.unsupervised.attribute.SwapValues

class SwapValues extends Filter implements Serializable

serialVersionUID:
  6155834679414275855L

#### Serialized Fields

##### m_AttIndex

    SingleIndex m_AttIndex

The attribute's index setting.

##### m_FirstIndex

    SingleIndex m_FirstIndex

The first value's index setting.

##### m_SecondIndex

    SingleIndex m_SecondIndex

The second value's index setting.

### Class weka.filters.unsupervised.attribute.TimeSeriesDelta

class TimeSeriesDelta extends TimeSeriesTranslate implements Serializable

serialVersionUID:
  3101490081896634942L

### Class weka.filters.unsupervised.attribute.TimeSeriesTranslate

class TimeSeriesTranslate extends AbstractTimeSeries implements Serializable

serialVersionUID:
  -8901621509691785705L

### Class weka.filters.unsupervised.attribute.Wavelet

class Wavelet extends SimpleBatchFilter implements Serializable

serialVersionUID:
  -3335106965521265631L

#### Serialized Fields

##### m_Algorithm

    int m_Algorithm

the type of algorithm

##### m_Filter

    Filter m_Filter

an optional filter for preprocessing of the data

##### m_Padding

    int m_Padding

the type of padding

## Package weka.filters.unsupervised.instance

### Class weka.filters.unsupervised.instance.NonSparseToSparse

class NonSparseToSparse extends Filter implements Serializable

serialVersionUID:
  4694489111366063852L

### Class weka.filters.unsupervised.instance.Normalize

class Normalize extends Filter implements Serializable

serialVersionUID:
  -7947971807522917395L

#### Serialized Fields

##### m_LNorm

    double m_LNorm

The L-norm to use

##### m_Norm

    double m_Norm

The norm that each instance must have at the end

### Class weka.filters.unsupervised.instance.Randomize

class Randomize extends Filter implements Serializable

serialVersionUID:
  8854479785121877582L

#### Serialized Fields

##### m_Random

    Random m_Random

The current random number generator

##### m_Seed

    int m_Seed

The random number seed

### Class weka.filters.unsupervised.instance.RemoveFolds

class RemoveFolds extends Filter implements Serializable

serialVersionUID:
  8220373305559055700L

#### Serialized Fields

##### m_Fold

    int m_Fold

Fold to output

##### m_Inverse

    boolean m_Inverse

Indicates if inverse of selection is to be output.

##### m_NumFolds

    int m_NumFolds

Number of folds to split dataset into

##### m_Seed

    long m_Seed

Random number seed.

### Class weka.filters.unsupervised.instance.RemoveFrequentValues

class RemoveFrequentValues extends Filter implements Serializable

serialVersionUID:
  -2447432930070059511L

#### Serialized Fields

##### m_AttIndex

    SingleIndex m_AttIndex

The attribute's index setting.

##### m_Invert

    boolean m_Invert

whether to invert the matching sense.

##### m_LeastValues

    boolean m_LeastValues

whether to retain values with least instances instead of most.

##### m_ModifyHeader

    boolean m_ModifyHeader

Modify header for nominal attributes?

##### m_NominalMapping

    int[] m_NominalMapping

If m_ModifyHeader, stores a mapping from old to new indexes

##### m_NumValues

    int m_NumValues

the number of values to retain.

##### m_Values

    HashSet m_Values

contains the values to retain

### Class weka.filters.unsupervised.instance.RemoveMisclassified

class RemoveMisclassified extends Filter implements Serializable

serialVersionUID:
  5469157004717663171L

#### Serialized Fields

##### m_classIndex

    int m_classIndex

The attribute to treat as the class for purposes of cleansing.

##### m_cleansingClassifier

    Classifier m_cleansingClassifier

The classifier used to do the cleansing

##### m_firstBatchFinished

    boolean m_firstBatchFinished

Have we processed the first batch (i.e. training data)?

##### m_invertMatching

    boolean m_invertMatching

Whether to invert the match so the correctly classified instances are discarded

##### m_numericClassifyThreshold

    double m_numericClassifyThreshold

The threshold for deciding when a numeric value is correctly classified

##### m_numOfCleansingIterations

    int m_numOfCleansingIterations

The maximum number of cleansing iterations to perform (<1 = until fully cleansed)

##### m_numOfCrossValidationFolds

    int m_numOfCrossValidationFolds

The number of cross validation folds to perform (<2 = no cross validation)

### Class weka.filters.unsupervised.instance.RemovePercentage

class RemovePercentage extends Filter implements Serializable

serialVersionUID:
  2150341191158533133L

#### Serialized Fields

##### m_Inverse

    boolean m_Inverse

Indicates if inverse of selection is to be output.

##### m_Percentage

    double m_Percentage

Percentage of instances to select.

### Class weka.filters.unsupervised.instance.RemoveRange

class RemoveRange extends Filter implements Serializable

serialVersionUID:
  -3064641215340828695L

#### Serialized Fields

##### m_Range

    Range m_Range

Range of instances requested by the user.

### Class weka.filters.unsupervised.instance.RemoveWithValues

class RemoveWithValues extends Filter implements Serializable

serialVersionUID:
  4752870193679263361L

#### Serialized Fields

##### m_AttIndex

    SingleIndex m_AttIndex

The attribute's index setting.

##### m_dontFilterAfterFirstBatch

    boolean m_dontFilterAfterFirstBatch

Whether to filter instances after the first batch has been processed

##### m_MatchMissingValues

    boolean m_MatchMissingValues

True if missing values should count as a match

##### m_ModifyHeader

    boolean m_ModifyHeader

Modify header for nominal attributes?

##### m_NominalMapping

    int[] m_NominalMapping

If m_ModifyHeader, stores a mapping from old to new indexes

##### m_Value

    double m_Value

Stores which value of a numeric attribute is to be used for filtering.

##### m_Values

    Range m_Values

Stores which values of nominal attribute are to be used for filtering.

### Class weka.filters.unsupervised.instance.Resample

class Resample extends Filter implements Serializable

serialVersionUID:
  3119607037607101160L

#### Serialized Fields

##### m_InvertSelection

    boolean m_InvertSelection

Whether to invert the selection (only if instances are drawn WITHOUT replacement)

See Also:
  - `Resample.m_NoReplacement`

##### m_NoReplacement

    boolean m_NoReplacement

Whether to perform sampling with replacement or without

##### m_RandomSeed

    int m_RandomSeed

The random number generator seed

##### m_SampleSizePercent

    double m_SampleSizePercent

The subsample size, percent of original set, default 100%

### Class weka.filters.unsupervised.instance.ReservoirSample

class ReservoirSample extends Filter implements Serializable

serialVersionUID:
  3119607037607101160L

#### Serialized Fields

##### m_currentInst

    int m_currentInst

The current instance being processed

##### m_random

    Random m_random

The random number generator

##### m_RandomSeed

    int m_RandomSeed

The random number generator seed

##### m_SampleSize

    int m_SampleSize

The subsample size, number of instances%

##### m_subSample

    Instance[] m_subSample

Holds the sub-sample (reservoir)

### Class weka.filters.unsupervised.instance.SparseToNonSparse

class SparseToNonSparse extends Filter implements Serializable

serialVersionUID:
  2481634184210236074L

### Class weka.filters.unsupervised.instance.SubsetByExpression

class SubsetByExpression extends SimpleBatchFilter implements Serializable

serialVersionUID:
  5628686110979589602L

#### Serialized Fields

##### m_Expression

    String m_Expression

the expresion to use for filtering.

##### m_filterAfterFirstBatch

    boolean m_filterAfterFirstBatch

Whether to filter instances after the first batch has been processed

## Package weka.gui

### Class weka.gui.AttributeListPanel

class AttributeListPanel extends JPanel implements Serializable

serialVersionUID:
  -2030706987910400362L

#### Serialized Fields

##### m_Model

    weka.gui.AttributeListPanel.AttributeTableModel m_Model

The table model containing attribute names

##### m_Table

    JTable m_Table

The table displaying attribute names

### Class weka.gui.AttributeSelectionPanel

class AttributeSelectionPanel extends JPanel implements Serializable

serialVersionUID:
  627131485290359194L

#### Serialized Fields

##### m_IncludeAll

    JButton m_IncludeAll

Press to select all attributes

##### m_Invert

    JButton m_Invert

Press to invert the current selection

##### m_Model

    weka.gui.AttributeSelectionPanel.AttributeTableModel m_Model

The table model containing attribute names and selection status

##### m_Pattern

    JButton m_Pattern

Press to enter a perl regular expression for selection

##### m_PatternRegEx

    String m_PatternRegEx

The current regular expression.

##### m_RemoveAll

    JButton m_RemoveAll

Press to deselect all attributes

##### m_Table

    JTable m_Table

The table displaying attribute names and selection status

### Class weka.gui.AttributeSummaryPanel

class AttributeSummaryPanel extends JPanel implements Serializable

serialVersionUID:
  -5434987925737735880L

#### Serialized Fields

##### m_AttributeNameLab

    JLabel m_AttributeNameLab

Displays the name of the relation

##### m_AttributeStats

    AttributeStats[] m_AttributeStats

Cached stats on the attributes we've summarized so far

##### m_AttributeTypeLab

    JLabel m_AttributeTypeLab

Displays the type of attribute

##### m_DistinctLab

    JLabel m_DistinctLab

Displays the number of distinct values

##### m_Instances

    Instances m_Instances

The instances we're playing with

##### m_MissingLab

    JLabel m_MissingLab

Displays the number of missing values

##### m_StatsTable

    JTable m_StatsTable

Displays other stats in a table

##### m_UniqueLab

    JLabel m_UniqueLab

Displays the number of unique values

### Class weka.gui.AttributeVisualizationPanel

class AttributeVisualizationPanel extends PrintablePanel implements Serializable

serialVersionUID:
  -8650490488825371193L

#### Serialized Fields

##### m_as

    AttributeStats m_as

This holds the attribute stats of the current attribute on display. It is calculated in setAttribute(int idx) when it is called to set a new attribute index.

##### m_asCache

    AttributeStats[] m_asCache

Cache of attribute stats info for the current data set

##### m_attribIndex

    int m_attribIndex

This holds the index of the current attribute on display and should be set through setAttribute(int idx).

##### m_barRange

    double m_barRange

Contains the range of each bar in a histogram. It is used to work out the range of bar the mouse pointer is on in getToolTipText().

##### m_classIndex

    int m_classIndex

Contains the current class index.

##### m_colorAttrib

    JComboBox m_colorAttrib

This stores and lets the user select a class attribute. It also has an entry "No Class" if the user does not want to set a class attribute for colouring.

##### m_colorList

    FastVector m_colorList

Contains discrete colours for colouring of subbars of histograms and bar plots when the class attribute is set and is nominal

##### m_data

    Instances m_data

This holds the current set of instances

##### m_displayCurrentAttribute

    boolean m_displayCurrentAttribute

##### m_doneCurrentAttribute

    boolean m_doneCurrentAttribute

##### m_fm

    FontMetrics m_fm

Fontmetrics used to get the font size which is required for calculating displayable area size, bar height ratio and width of strings that are displayed on top of bars indicating their count.

##### m_hc

    Thread m_hc

This stores the BarCalc or HistCalc thread while a new barplot or histogram is being calculated.

##### m_histBarClassCounts

    SparseInstance[] m_histBarClassCounts

This array holds the per class count (or per class height) of the each of the bars in a barplot or a histogram. For nominal attributes the format is:
m_histBarClassCounts[nominalValue][classValue+1]. For numeric attributes the format is:
m_histBarClassCounts[interval][classValues+1],
where the number of intervals is calculated by the Scott's method as mentioned above. The array is initialized to have 1+numClasses to accomodate for instances with missing class value. The ones with missing class value are displayed as a black sub par in a histogram or a barplot. NOTE: The values of this array are only calculated if the class attribute is set and it is nominal.

##### m_histBarCounts

    int[] m_histBarCounts

This array holds the count (or height) for the each of the bars in a barplot or a histogram. In case of barplots (and current attribute being nominal) its length (and the number of bars) is equal to the number of nominal values in the current attribute, with each field of the array being equal to the count of each nominal that it represents (the count of ith nominal value of an attribute is given by m_as.nominalCounts[i]). Whereas, in case of histograms (and current attribute being numeric) the width of its intervals is calculated by Scott's(1979) method:
intervalWidth = Max(1, 3.49*Std.Dev*numInstances^(1/3)) And the number of intervals by:
intervals = max(1, Math.round(Range/intervalWidth); Then each field of this array contains the number of values of the current attribute that fall in the histogram interval that it represents.
NOTE: The values of this array are only calculated if the class attribute is not set or if it is numeric.

##### m_locker

    Integer m_locker

Lock variable to synchronize the different threads running currently in this class. There are two to three threads in this class, AWT paint thread which is handled differently in paintComponent() which checks on m_threadRun to determine if it can perform full paint or not, the second thread is the main execution thread and the third is the one represented by m_hc which we start when we want to calculate the internal fields for a bar plot or a histogram.

##### m_maxValue

    int m_maxValue

This holds the max value of the current attribute. In case of nominal attribute it is the highest count that a nominal value has in the attribute (given by m_as.nominalCounts[i]), otherwise in case of numeric attribute it is simply the maximum value present in the attribute (given by m_as.numericStats.max). It is used to calculate the ratio of the height of the bars with respect to the height of the display area.

##### m_threadRun

    boolean m_threadRun

True if the thread m_hc above is running.

### Class weka.gui.CheckBoxList

class CheckBoxList extends JList implements Serializable

serialVersionUID:
  -4359573373359270258L

### Class weka.gui.CheckBoxList.CheckBoxListModel

class CheckBoxListModel extends DefaultListModel implements Serializable

serialVersionUID:
  7772455499540273507L

### Class weka.gui.CheckBoxList.CheckBoxListRenderer

class CheckBoxListRenderer extends JCheckBox implements Serializable

serialVersionUID:
  1059591605858524586L

### Class weka.gui.ConverterFileChooser

class ConverterFileChooser extends JFileChooser implements Serializable

serialVersionUID:
  -5373058011025481738L

#### Serialized Fields

##### m_CapabilitiesFilter

    Capabilities m_CapabilitiesFilter

the Capabilities filter for the savers

##### m_ConfigureButton

    JButton m_ConfigureButton

the configure button

##### m_CoreConvertersOnly

    boolean m_CoreConvertersOnly

whether to display only core converters (hardcoded in ConverterUtils). Necessary for RMI/Remote Experiments for instance.

See Also:
  - `ConverterUtils.CORE_FILE_LOADERS`
  - `ConverterUtils.CORE_FILE_SAVERS`

##### m_CurrentConverter

    Object m_CurrentConverter

the converter that was chosen by the user

##### m_DialogType

    int m_DialogType

the type of dialog to display

##### m_FileMustExist

    boolean m_FileMustExist

whether the file to be opened must exist (only open dialog)

##### m_LastFilter

    FileFilter m_LastFilter

the last filter that was used for opening/saving

##### m_Listener

    PropertyChangeListener m_Listener

the propertychangelistener

##### m_OverwriteWarning

    boolean m_OverwriteWarning

whether to popup a dialog in case the file already exists (only save dialog)

##### m_Self

    ConverterFileChooser m_Self

the file chooser itself

### Class weka.gui.DatabaseConnectionDialog

class DatabaseConnectionDialog extends JDialog implements Serializable

serialVersionUID:
  -1081946748666245054L

#### Serialized Fields

##### m_DbaseURLLab

    JLabel m_DbaseURLLab

##### m_DbaseURLText

    JTextField m_DbaseURLText

##### m_DebugCheckBox

    JCheckBox m_DebugCheckBox

##### m_DebugLab

    JLabel m_DebugLab

##### m_PasswordLab

    JLabel m_PasswordLab

##### m_PasswordText

    JPasswordField m_PasswordText

##### m_returnValue

    int m_returnValue

##### m_UserNameLab

    JLabel m_UserNameLab

##### m_UserNameText

    JTextField m_UserNameText

### Class weka.gui.ExtensionFileFilter

class ExtensionFileFilter extends FileFilter implements Serializable

#### Serialized Fields

##### m_Description

    String m_Description

The text description of the types of files accepted

##### m_Extension

    String[] m_Extension

The filename extensions of accepted files

### Class weka.gui.GenericArrayEditor

class GenericArrayEditor extends JPanel implements Serializable

serialVersionUID:
  3914616975334750480L

#### Serialized Fields

##### m_AddBut

    JButton m_AddBut

Click to add the current object configuration to the array.

##### m_DeleteBut

    JButton m_DeleteBut

Click this to delete the selected array values.

##### m_DownBut

    JButton m_DownBut

Click this to move the selected array value(s) one down.

##### m_EditBut

    JButton m_EditBut

Click this to edit the selected array value.

##### m_Editor

    PropertyEditor m_Editor

The property editor for editing existing elements.

##### m_ElementClass

    Class m_ElementClass

The class of objects allowed in the array.

##### m_ElementEditor

    PropertyEditor m_ElementEditor

The property editor for the class we are editing.

##### m_ElementList

    JList m_ElementList

The list component displaying current values.

##### m_InnerActionListener

    ActionListener m_InnerActionListener

Listens to buttons being pressed and taking the appropriate action.

##### m_InnerMouseListener

    MouseListener m_InnerMouseListener

Listens to mouse events and takes appropriate action.

##### m_InnerSelectionListener

    ListSelectionListener m_InnerSelectionListener

Listens to list items being selected and takes appropriate action.

##### m_Label

    JLabel m_Label

The label for when we can't edit that type.

##### m_ListModel

    DefaultListModel m_ListModel

The defaultlistmodel holding our data.

##### m_PD

    PropertyDialog m_PD

The currently displayed property dialog, if any.

##### m_Support

    PropertyChangeSupport m_Support

Handles property change notification.

##### m_UpBut

    JButton m_UpBut

Click this to move the selected array value(s) one up.

### Class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog

class CapabilitiesFilterDialog extends JDialog implements Serializable

serialVersionUID:
  -7845503345689646266L

#### Serialized Fields

##### m_CancelButton

    JButton m_CancelButton

the Cancel button.

##### m_Capabilities

    Capabilities m_Capabilities

the capabilities used for initializing the dialog.

##### m_InfoLabel

    JLabel m_InfoLabel

the label, listing the name of the superclass.

##### m_List

    CheckBoxList m_List

the list with all the capabilities.

##### m_OkButton

    JButton m_OkButton

the OK button.

##### m_Popup

    JPopupMenu m_Popup

the popup to display again.

##### m_Self

    JDialog m_Self

the dialog itself.

### Class weka.gui.GenericObjectEditor.GOEPanel

class GOEPanel extends JPanel implements Serializable

serialVersionUID:
  3656028520876011335L

#### Serialized Fields

##### m_cancelBut

    JButton m_cancelBut

cancel button.

##### m_ChildPropertySheet

    PropertySheetPanel m_ChildPropertySheet

The component that performs classifier customization.

##### m_ClassNameLabel

    JLabel m_ClassNameLabel

The name of the current class.

##### m_FileChooser

    JFileChooser m_FileChooser

The filechooser for opening and saving object files.

##### m_okBut

    JButton m_okBut

ok button.

##### m_OpenBut

    JButton m_OpenBut

Open object from disk.

##### m_SaveBut

    JButton m_SaveBut

Save object to disk.

### Class weka.gui.GenericObjectEditor.GOETreeNode

class GOETreeNode extends DefaultMutableTreeNode implements Serializable

serialVersionUID:
  -1707872446682150133L

#### Serialized Fields

##### m_Capabilities

    Capabilities m_Capabilities

the Capabilities object to use for filtering.

##### m_toolTipText

    String m_toolTipText

tool tip

### Class weka.gui.GenericObjectEditor.JTreePopupMenu

class JTreePopupMenu extends JPopupMenu implements Serializable

serialVersionUID:
  -3404546329655057387L

#### Serialized Fields

##### m_CloseButton

    JButton m_CloseButton

The button for closing the popup again.

##### m_FilterButton

    JButton m_FilterButton

The filter button in case of CapabilitiesHandlers.

##### m_RemoveFilterButton

    JButton m_RemoveFilterButton

The remove filter button in case of CapabilitiesHandlers.

##### m_scroller

    JScrollPane m_scroller

The scroller.

##### m_Self

    JPopupMenu m_Self

the popup itself.

##### m_tree

    JTree m_tree

The tree.

### Class weka.gui.GUIChooser

class GUIChooser extends JFrame implements Serializable

serialVersionUID:
  9001529425230247914L

#### Serialized Fields

##### m_ArffViewers

    Vector m_ArffViewers

keeps track of the opened ArffViewer instancs

##### m_BayesNetGUIFrame

    JFrame m_BayesNetGUIFrame

The frame containing the Bayes net GUI

##### m_BoundaryVisualizerFrame

    JFrame m_BoundaryVisualizerFrame

The frame containing the boundary visualizer

##### m_ChildFrames

    HashSet<Container> m_ChildFrames

contains the child frames (title <-> object).

##### m_EnsembleLibraryFrame

    JFrame m_EnsembleLibraryFrame

The frame containing the ensemble library interface

##### m_ExperimenterBut

    JButton m_ExperimenterBut

Click to open the Explorer

##### m_ExperimenterFrame

    JFrame m_ExperimenterFrame

The frame containing the experiment interface

##### m_ExplorerBut

    JButton m_ExplorerBut

Click to open the Explorer

##### m_ExplorerFrame

    JFrame m_ExplorerFrame

The frame containing the explorer interface

##### m_FileChooserGraphVisualizer

    JFileChooser m_FileChooserGraphVisualizer

filechooser for the GraphVisualizer

##### m_FileChooserPlot

    JFileChooser m_FileChooserPlot

filechooser for Plots

##### m_FileChooserROC

    JFileChooser m_FileChooserROC

filechooser for ROC curves

##### m_FileChooserTreeVisualizer

    JFileChooser m_FileChooserTreeVisualizer

filechooser for the TreeVisualizer

##### m_GraphVisualizers

    Vector m_GraphVisualizers

keeps track of the opened graph visualizer instancs

##### m_Icon

    Image m_Icon

the icon for the frames

##### m_jMenuBar

    JMenuBar m_jMenuBar

##### m_jMenuHelp

    JMenu m_jMenuHelp

##### m_jMenuProgram

    JMenu m_jMenuProgram

##### m_jMenuTools

    JMenu m_jMenuTools

##### m_jMenuVisualization

    JMenu m_jMenuVisualization

##### m_KnowledgeFlowBut

    JButton m_KnowledgeFlowBut

Click to open the KnowledgeFlow

##### m_KnowledgeFlowFrame

    JFrame m_KnowledgeFlowFrame

The frame containing the knowledge flow interface

##### m_MemoryUsageFrame

    JFrame m_MemoryUsageFrame

The frame containing the memory usage

##### m_PanelApplications

    JPanel m_PanelApplications

the panel for the application buttons

##### m_pendingKnowledgeFlowLoad

    String m_pendingKnowledgeFlowLoad

Pending file to load on startup of the KnowledgeFlow

##### m_Plots

    Vector m_Plots

keeps track of the opened plots

##### m_ROCs

    Vector m_ROCs

keeps track of the opened ROCs

##### m_Self

    GUIChooser m_Self

the GUIChooser itself

##### m_SimpleBut

    JButton m_SimpleBut

Click to open the simplecli

##### m_SimpleCLI

    SimpleCLI m_SimpleCLI

The SimpleCLI

##### m_SqlViewerFrame

    JFrame m_SqlViewerFrame

The frame containing the SqlViewer

##### m_SystemInfoFrame

    JFrame m_SystemInfoFrame

The frame containing the system info

##### m_TreeVisualizers

    Vector m_TreeVisualizers

keeps track of the opened tree visualizer instancs

##### m_weka

    Image m_weka

The weka image

### Class weka.gui.GUIChooser.ChildFrameSDI

class ChildFrameSDI extends JFrame implements Serializable

serialVersionUID:
  8588293938686425618L

#### Serialized Fields

##### m_Parent

    GUIChooser m_Parent

the parent frame.

### Class weka.gui.HierarchyPropertyParser

class HierarchyPropertyParser extends Object implements Serializable

serialVersionUID:
  -4151103338506077544L

#### Serialized Fields

##### m_Current

    weka.gui.HierarchyPropertyParser.TreeNode m_Current

Keep track of the current node when traversing the tree

##### m_Depth

    int m_Depth

The depth of the tree

##### m_Root

    weka.gui.HierarchyPropertyParser.TreeNode m_Root

Keep track of the root of the tree

##### m_Seperator

    String m_Seperator

The level separate in the path

### Class weka.gui.InstancesSummaryPanel

class InstancesSummaryPanel extends JPanel implements Serializable

serialVersionUID:
  -5243579535296681063L

#### Serialized Fields

##### m_Instances

    Instances m_Instances

The instances we're playing with

##### m_NumAttributesLab

    JLabel m_NumAttributesLab

Displays the number of attributes

##### m_NumInstancesLab

    JLabel m_NumInstancesLab

Displays the number of instances

##### m_RelationNameLab

    JLabel m_RelationNameLab

Displays the name of the relation

##### m_showZeroInstancesAsUnknown

    boolean m_showZeroInstancesAsUnknown

Whether to display 0 or ? for the number of instances in cases where a dataset has only structure. Depending on where this panel is used from, the user may have loaded a dataset with no instances or a Loader that can read incrementally may be being used (in which case we don't know how many instances are in the dataset... yet).

### Class weka.gui.ListSelectorDialog

class ListSelectorDialog extends JDialog implements Serializable

serialVersionUID:
  906147926840288895L

#### Serialized Fields

##### m_CancelBut

    JButton m_CancelBut

Click to cancel the property selection

##### m_List

    JList m_List

The list component

##### m_PatternBut

    JButton m_PatternBut

Click to enter a regex pattern for selection

##### m_PatternRegEx

    String m_PatternRegEx

The current regular expression.

##### m_Result

    int m_Result

Whether the selection was made or cancelled

##### m_SelectBut

    JButton m_SelectBut

Click to choose the currently selected property

### Class weka.gui.LogPanel

class LogPanel extends JPanel implements Serializable

serialVersionUID:
  -4072464549112439484L

#### Serialized Fields

##### m_First

    boolean m_First

An indicator for whether text has been output yet

##### m_logButton

    JButton m_logButton

The button for viewing the log

##### m_LogText

    JTextArea m_LogText

Displays the log messages

##### m_StatusLab

    JLabel m_StatusLab

Displays the current status

##### m_TaskMonitor

    WekaTaskMonitor m_TaskMonitor

The panel for monitoring the number of running tasks (if supplied)

### Class weka.gui.LogWindow

class LogWindow extends JFrame implements Serializable

serialVersionUID:
  5650947361381061112L

#### Serialized Fields

##### m_ButtonClear

    JButton m_ButtonClear

the clear button

##### m_ButtonClose

    JButton m_ButtonClose

the close button

##### m_CheckBoxWordwrap

    JCheckBox m_CheckBoxWordwrap

whether to allow wordwrap or not

##### m_LabelCurrentSize

    JLabel m_LabelCurrentSize

the current size

##### m_Output

    JTextPane m_Output

the output

##### m_SpinnerMaxSize

    JSpinner m_SpinnerMaxSize

the spinner for the max number of chars

##### m_UseWordwrap

    boolean m_UseWordwrap

whether the JTextPane has wordwrap or not

### Class weka.gui.Main

class Main extends JFrame implements Serializable

serialVersionUID:
  1453813254824253849L

#### Serialized Fields

##### jDesktopPane

    JDesktopPane jDesktopPane

##### jMenuApplications

    JMenu jMenuApplications

##### jMenuBar

    JMenuBar jMenuBar

##### jMenuExtensions

    JMenu jMenuExtensions

##### jMenuHelp

    JMenu jMenuHelp

##### jMenuItemApplicationsExperimenter

    JMenuItem jMenuItemApplicationsExperimenter

##### jMenuItemApplicationsExplorer

    JMenuItem jMenuItemApplicationsExplorer

##### jMenuItemApplicationsKnowledgeFlow

    JMenuItem jMenuItemApplicationsKnowledgeFlow

##### jMenuItemApplicationsSimpleCLI

    JMenuItem jMenuItemApplicationsSimpleCLI

##### jMenuItemHelpAbout

    JMenuItem jMenuItemHelpAbout

##### jMenuItemHelpHomepage

    JMenuItem jMenuItemHelpHomepage

##### jMenuItemHelpSourceforge

    JMenuItem jMenuItemHelpSourceforge

##### jMenuItemHelpSystemInfo

    JMenuItem jMenuItemHelpSystemInfo

##### jMenuItemHelpWekaWiki

    JMenuItem jMenuItemHelpWekaWiki

##### jMenuItemProgramExit

    JMenuItem jMenuItemProgramExit

##### jMenuItemProgramLogWindow

    JMenuItem jMenuItemProgramLogWindow

##### jMenuItemProgramMemoryUsage

    JMenuItem jMenuItemProgramMemoryUsage

##### jMenuItemToolsArffViewer

    JMenuItem jMenuItemToolsArffViewer

##### jMenuItemToolsSqlViewer

    JMenuItem jMenuItemToolsSqlViewer

##### jMenuItemVisualizationBoundaryVisualizer

    JMenuItem jMenuItemVisualizationBoundaryVisualizer

##### jMenuItemVisualizationGraphVisualizer

    JMenuItem jMenuItemVisualizationGraphVisualizer

##### jMenuItemVisualizationPlot

    JMenuItem jMenuItemVisualizationPlot

##### jMenuItemVisualizationROC

    JMenuItem jMenuItemVisualizationROC

##### jMenuItemVisualizationTreeVisualizer

    JMenuItem jMenuItemVisualizationTreeVisualizer

##### jMenuProgram

    JMenu jMenuProgram

##### jMenuTools

    JMenu jMenuTools

##### jMenuVisualization

    JMenu jMenuVisualization

##### jMenuWindows

    JMenu jMenuWindows

##### m_ChildFrames

    HashSet<Container> m_ChildFrames

contains the child frames (title <-> object).

##### m_FileChooserGraphVisualizer

    JFileChooser m_FileChooserGraphVisualizer

filechooser for the GraphVisualizer.

##### m_FileChooserPlot

    JFileChooser m_FileChooserPlot

filechooser for Plots.

##### m_FileChooserROC

    JFileChooser m_FileChooserROC

filechooser for ROC curves.

##### m_FileChooserTreeVisualizer

    JFileChooser m_FileChooserTreeVisualizer

filechooser for the TreeVisualizer.

##### m_GUIType

    int m_GUIType

the type of GUI to display.

##### m_Self

    Main m_Self

the frame itself.

### Class weka.gui.Main.BackgroundDesktopPane

class BackgroundDesktopPane extends JDesktopPane implements Serializable

serialVersionUID:
  2046713123452402745L

#### Serialized Fields

##### m_Background

    Image m_Background

the actual background image.

### Class weka.gui.Main.ChildFrameMDI

class ChildFrameMDI extends JInternalFrame implements Serializable

serialVersionUID:
  3772573515346899959L

#### Serialized Fields

##### m_Parent

    Main m_Parent

the parent frame.

### Class weka.gui.Main.ChildFrameSDI

class ChildFrameSDI extends JFrame implements Serializable

serialVersionUID:
  8588293938686425618L

#### Serialized Fields

##### m_Parent

    Main m_Parent

the parent frame.

### Class weka.gui.MemoryUsagePanel

class MemoryUsagePanel extends JPanel implements Serializable

serialVersionUID:
  -4812319791687471721L

#### Serialized Fields

##### m_BackgroundColor

    Color m_BackgroundColor

the background color.

##### m_ButtonGC

    JButton m_ButtonGC

the button for running the garbage collector.

##### m_Colors

    Hashtable<Double,Color> m_Colors

the corresponding colors for the thresholds.

##### m_DefaultColor

    Color m_DefaultColor

the default color.

##### m_FrameLocation

    Point m_FrameLocation

the position for the dialog.

##### m_History

    Vector<Double> m_History

the memory usage over time.

##### m_Memory

    Memory m_Memory

for monitoring the memory usage.

##### m_Monitor

    weka.gui.MemoryUsagePanel.MemoryMonitor m_Monitor

the thread for monitoring the memory usage.

##### m_Percentages

    Vector<Double> m_Percentages

the threshold percentages to change color.

### Class weka.gui.PropertyDialog

class PropertyDialog extends JDialog implements Serializable

serialVersionUID:
  -2314850859392433539L

#### Serialized Fields

##### m_Editor

    PropertyEditor m_Editor

The property editor.

##### m_EditorComponent

    Component m_EditorComponent

The custom editor component.

### Class weka.gui.PropertyPanel

class PropertyPanel extends JPanel implements Serializable

serialVersionUID:
  5370025273466728904L

#### Serialized Fields

##### m_CustomPanel

    JPanel m_CustomPanel

The custom panel (if any)

##### m_Editor

    PropertyEditor m_Editor

The property editor

##### m_HasCustomPanel

    boolean m_HasCustomPanel

Whether the editor has provided its own panel

##### m_PD

    PropertyDialog m_PD

The currently displayed property dialog, if any

### Class weka.gui.PropertySelectorDialog

class PropertySelectorDialog extends JDialog implements Serializable

serialVersionUID:
  -3155058124137930518L

#### Serialized Fields

##### m_CancelBut

    JButton m_CancelBut

Click to cancel the property selection

##### m_Result

    int m_Result

Whether the selection was made or cancelled

##### m_ResultPath

    Object[] m_ResultPath

Stores the path to the selected property

##### m_Root

    DefaultMutableTreeNode m_Root

The root of the property tree

##### m_RootObject

    Object m_RootObject

The object at the root of the tree

##### m_SelectBut

    JButton m_SelectBut

Click to choose the currently selected property

##### m_Tree

    JTree m_Tree

The component displaying the property tree

### Class weka.gui.PropertySheetPanel

class PropertySheetPanel extends JPanel implements Serializable

serialVersionUID:
  -8939835593429918345L

#### Serialized Fields

##### m_aboutPanel

    JPanel m_aboutPanel

The panel holding global info and help, if provided by the object being editied.

##### m_CapabilitiesBut

    JButton m_CapabilitiesBut

Button to pop up the capabilities in a separate dialog.

##### m_CapabilitiesDialog

    weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_CapabilitiesDialog

Capabilities Help dialog.

##### m_CapabilitiesText

    JTextArea m_CapabilitiesText

the TextArea of the Capabilities help dialog.

##### m_Editors

    PropertyEditor[] m_Editors

Holds property editors of the object.

##### m_HelpBut

    JButton m_HelpBut

Button to pop up the full help text in a separate dialog.

##### m_HelpDialog

    JDialog m_HelpDialog

Help dialog.

##### m_HelpText

    StringBuffer m_HelpText

StringBuffer containing help text for the object being edited.

##### m_Labels

    JLabel[] m_Labels

The labels for each property.

##### m_Methods

    MethodDescriptor[] m_Methods

Holds the methods of the target.

##### m_NumEditable

    int m_NumEditable

A count of the number of properties we have an editor for.

##### m_Properties

    PropertyDescriptor[] m_Properties

Holds properties of the target.

##### m_Target

    Object m_Target

The target object being edited.

##### m_TipTexts

    String[] m_TipTexts

The tool tip text for each property.

##### m_Values

    Object[] m_Values

Holds current object values for each property.

##### m_Views

    JComponent[] m_Views

Stores GUI components containing each editing component.

##### support

    PropertyChangeSupport support

A support object for handling property change listeners.

### Class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog

class CapabilitiesHelpDialog extends JDialog implements Serializable

serialVersionUID:
  -1404770987103289858L

#### Serialized Fields

##### m_Self

    weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_Self

the dialog itself.

### Class weka.gui.ResultHistoryPanel

class ResultHistoryPanel extends JPanel implements Serializable

serialVersionUID:
  4297069440135326829L

#### Serialized Fields

##### m_FramedOutput

    Hashtable m_FramedOutput

A Hashtable mapping names to output text components

##### m_HandleRightClicks

    boolean m_HandleRightClicks

Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer

##### m_List

    JList m_List

The list component

##### m_Model

    DefaultListModel m_Model

The list model

##### m_Objs

    Hashtable m_Objs

A hashtable mapping names to arbitrary objects

##### m_Printer

    PrintableComponent m_Printer

for printing the output to files

##### m_Results

    Hashtable m_Results

A Hashtable mapping names to result buffers

##### m_SingleName

    String m_SingleName

The named result being viewed in the single-click display

##### m_SingleText

    JTextComponent m_SingleText

An optional component for single-click display

### Class weka.gui.ResultHistoryPanel.RKeyAdapter

class RKeyAdapter extends KeyAdapter implements Serializable

serialVersionUID:
  -8675332541861828079L

### Class weka.gui.ResultHistoryPanel.RMouseAdapter

class RMouseAdapter extends MouseAdapter implements Serializable

serialVersionUID:
  -8991922650552358669L

### Class weka.gui.SetInstancesPanel

class SetInstancesPanel extends JPanel implements Serializable

serialVersionUID:
  -384804041420453735L

#### Serialized Fields

##### m_CloseBut

    JButton m_CloseBut

Click to close the dialog

##### m_CloseButPanel

    JPanel m_CloseButPanel

the panel the Close-Button is located in

##### m_FileChooser

    ConverterFileChooser m_FileChooser

The file chooser for selecting arff files

##### m_Instances

    Instances m_Instances

The current set of instances loaded

##### m_IOThread

    Thread m_IOThread

The thread we do loading in

##### m_LastURL

    String m_LastURL

Stores the last URL that instances were loaded from

##### m_Loader

    Loader m_Loader

The current loader used to obtain the current instances

##### m_OpenFileBut

    JButton m_OpenFileBut

Click to open instances from a file

##### m_OpenURLBut

    JButton m_OpenURLBut

Click to open instances from a URL

##### m_ParentFrame

    JFrame m_ParentFrame

the parent frame. if one is provided, the close-button is displayed

##### m_readIncrementally

    boolean m_readIncrementally

whether to read the instances incrementally, if possible.

##### m_showZeroInstancesAsUnknown

    boolean m_showZeroInstancesAsUnknown

whether to display zero instances as unknown ("?").

##### m_Summary

    InstancesSummaryPanel m_Summary

The instance summary component

##### m_Support

    PropertyChangeSupport m_Support

Manages sending notifications to people when we change the set of working instances.

### Class weka.gui.SimpleCLI

class SimpleCLI extends Frame implements Serializable

serialVersionUID:
  -50661410800566036L

### Class weka.gui.SimpleCLIPanel

class SimpleCLIPanel extends JPanel implements Serializable

serialVersionUID:
  -7377739469759943231L

#### Serialized Fields

##### m_CommandHistory

    Vector m_CommandHistory

The history of commands entered interactively.

##### m_Completion

    SimpleCLIPanel.CommandlineCompletion m_Completion

The commandline completion.

##### m_ErrRedirector

    Thread m_ErrRedirector

The thread that sends output from m_POE to the output box.

##### m_HistoryPos

    int m_HistoryPos

The current position in the command history.

##### m_Input

    JTextField m_Input

The command input area.

##### m_OutputArea

    JTextArea m_OutputArea

The output area canvas added to the frame.

##### m_OutRedirector

    Thread m_OutRedirector

The thread that sends output from m_POO to the output box.

##### m_POE

    PipedOutputStream m_POE

The new output stream for System.err.

##### m_POO

    PipedOutputStream m_POO

The new output stream for System.out.

##### m_RunThread

    Thread m_RunThread

The thread currently running a class main method.

### Class weka.gui.SortedTableModel

class SortedTableModel extends AbstractTableModel implements Serializable

serialVersionUID:
  4030907921461127548L

#### Serialized Fields

##### mAscending

    boolean mAscending

whether sorting is ascending or descending

##### mIndices

    int[] mIndices

the mapping between displayed and actual index

##### mModel

    TableModel mModel

the actual table model

##### mSortColumn

    int mSortColumn

the sort column

### Class weka.gui.SplashWindow

class SplashWindow extends Window implements Serializable

serialVersionUID:
  -2685134277041307795L

#### Serialized Fields

##### image

    Image image

The splash image which is displayed on the splash window.

##### paintCalled

    boolean paintCalled

This attribute indicates whether the method paint(Graphics) has been called at least once since the construction of this window.
This attribute is used to notify method splash(Image) that the window has been drawn at least once by the AWT event dispatcher thread.
This attribute acts like a latch. Once set to true, it will never be changed back to false again.

See Also:
  - `SplashWindow.paint(java.awt.Graphics)`
  - `SplashWindow.splash(java.awt.Image)`

### Class weka.gui.ViewerDialog

class ViewerDialog extends JDialog implements Serializable

serialVersionUID:
  6747718484736047752L

#### Serialized Fields

##### m_ArffPanel

    ArffPanel m_ArffPanel

the panel to display the Instances-object

##### m_CancelButton

    JButton m_CancelButton

Click to cancel the dialog

##### m_OkButton

    JButton m_OkButton

Click to activate the current set parameters

##### m_Result

    int m_Result

the result of the user's action, either OK or CANCEL

##### m_UndoButton

    JButton m_UndoButton

Click to undo the last action

### Class weka.gui.WekaTaskMonitor

class WekaTaskMonitor extends JPanel implements Serializable

serialVersionUID:
  508309816292197578L

#### Serialized Fields

##### m_ActiveTasks

    int m_ActiveTasks

The number of running weka threads

##### m_animating

    boolean m_animating

True if their are active tasks

##### m_iconAnimated

    ImageIcon m_iconAnimated

The icon for the animated bird

##### m_iconStationary

    ImageIcon m_iconStationary

The icon for the stationary bird

##### m_MonitorLabel

    JLabel m_MonitorLabel

The label for displaying info

## Package weka.gui.arffviewer

### Class weka.gui.arffviewer.ArffPanel

class ArffPanel extends JPanel implements Serializable

serialVersionUID:
  -4697041150989513939L

#### Serialized Fields

##### m_Changed

    boolean m_Changed

flag for whether data got changed

##### m_ChangeListeners

    HashSet m_ChangeListeners

the listeners that listen for modifications

##### m_CurrentCol

    int m_CurrentCol

the currently selected column

##### m_Filename

    String m_Filename

the filename used in the title

##### m_LabelName

    JLabel m_LabelName

displays the relation name

##### m_LastReplace

    String m_LastReplace

the string used in the last replace

##### m_LastSearch

    String m_LastSearch

the string used in the last search

##### m_PopupHeader

    JPopupMenu m_PopupHeader

the popup menu for the header row

##### m_PopupRows

    JPopupMenu m_PopupRows

the popup menu for the data rows

##### m_TableArff

    ArffTable m_TableArff

the underlying table

##### m_Title

    String m_Title

the title prefix

##### menuItemAttributeAsClass

    JMenuItem menuItemAttributeAsClass

##### menuItemClearSearch

    JMenuItem menuItemClearSearch

##### menuItemCopy

    JMenuItem menuItemCopy

##### menuItemDeleteAllSelectedInstances

    JMenuItem menuItemDeleteAllSelectedInstances

##### menuItemDeleteAttribute

    JMenuItem menuItemDeleteAttribute

##### menuItemDeleteAttributes

    JMenuItem menuItemDeleteAttributes

##### menuItemDeleteSelectedInstance

    JMenuItem menuItemDeleteSelectedInstance

##### menuItemMean

    JMenuItem menuItemMean

##### menuItemOptimalColWidth

    JMenuItem menuItemOptimalColWidth

##### menuItemOptimalColWidths

    JMenuItem menuItemOptimalColWidths

##### menuItemRenameAttribute

    JMenuItem menuItemRenameAttribute

##### menuItemReplaceValues

    JMenuItem menuItemReplaceValues

##### menuItemSearch

    JMenuItem menuItemSearch

##### menuItemSetAllValues

    JMenuItem menuItemSetAllValues

##### menuItemSetMissingValues

    JMenuItem menuItemSetMissingValues

##### menuItemSortInstances

    JMenuItem menuItemSortInstances

##### menuItemUndo

    JMenuItem menuItemUndo

### Class weka.gui.arffviewer.ArffSortedTableModel

class ArffSortedTableModel extends SortedTableModel implements Serializable

serialVersionUID:
  -5733148376354254030L

### Class weka.gui.arffviewer.ArffTable

class ArffTable extends JTable implements Serializable

serialVersionUID:
  -2016200506908637967L

#### Serialized Fields

##### m_ChangeListeners

    HashSet m_ChangeListeners

the listeners for changes

##### m_SearchString

    String m_SearchString

the search string

### Class weka.gui.arffviewer.ArffTable.RelationalCellEditor

class RelationalCellEditor extends AbstractCellEditor implements Serializable

serialVersionUID:
  657969163293205963L

#### Serialized Fields

##### m_Button

    JButton m_Button

the button for opening the dialog

##### m_ColumnIndex

    int m_ColumnIndex

the column index this editor is for

##### m_CurrentInst

    Instances m_CurrentInst

the current instances

##### m_RowIndex

    int m_RowIndex

the row index this editor is for

### Class weka.gui.arffviewer.ArffTableCellRenderer

class ArffTableCellRenderer extends DefaultTableCellRenderer implements Serializable

serialVersionUID:
  9195794493301191171L

#### Serialized Fields

##### highlightColor

    Color highlightColor

the color for highlighted values

##### highlightColorSelected

    Color highlightColorSelected

the color for selected highlighted values

##### missingColor

    Color missingColor

the color for missing values

##### missingColorSelected

    Color missingColorSelected

the color for selected missing values

### Class weka.gui.arffviewer.ArffViewer

class ArffViewer extends JFrame implements Serializable

serialVersionUID:
  -7455845566922685175L

#### Serialized Fields

##### m_MainPanel

    ArffViewerMainPanel m_MainPanel

the main panel

### Class weka.gui.arffviewer.ArffViewerMainPanel

class ArffViewerMainPanel extends JPanel implements Serializable

serialVersionUID:
  -8763161167586738753L

#### Serialized Fields

##### confirmExit

    boolean confirmExit

##### exitOnClose

    boolean exitOnClose

##### fileChooser

    ConverterFileChooser fileChooser

##### frameTitle

    String frameTitle

##### height

    int height

##### left

    int left

##### menuBar

    JMenuBar menuBar

##### menuEdit

    JMenu menuEdit

##### menuEditAttributeAsClass

    JMenuItem menuEditAttributeAsClass

##### menuEditClearSearch

    JMenuItem menuEditClearSearch

##### menuEditCopy

    JMenuItem menuEditCopy

##### menuEditDeleteAttribute

    JMenuItem menuEditDeleteAttribute

##### menuEditDeleteAttributes

    JMenuItem menuEditDeleteAttributes

##### menuEditDeleteInstance

    JMenuItem menuEditDeleteInstance

##### menuEditDeleteInstances

    JMenuItem menuEditDeleteInstances

##### menuEditRenameAttribute

    JMenuItem menuEditRenameAttribute

##### menuEditSearch

    JMenuItem menuEditSearch

##### menuEditSortInstances

    JMenuItem menuEditSortInstances

##### menuEditUndo

    JMenuItem menuEditUndo

##### menuFile

    JMenu menuFile

##### menuFileClose

    JMenuItem menuFileClose

##### menuFileCloseAll

    JMenuItem menuFileCloseAll

##### menuFileExit

    JMenuItem menuFileExit

##### menuFileOpen

    JMenuItem menuFileOpen

##### menuFileProperties

    JMenuItem menuFileProperties

##### menuFileSave

    JMenuItem menuFileSave

##### menuFileSaveAs

    JMenuItem menuFileSaveAs

##### menuView

    JMenu menuView

##### menuViewAttributes

    JMenuItem menuViewAttributes

##### menuViewOptimalColWidths

    JMenuItem menuViewOptimalColWidths

##### menuViewValues

    JMenuItem menuViewValues

##### parent

    Container parent

##### tabbedPane

    JTabbedPane tabbedPane

##### top

    int top

##### width

    int width

## Package weka.gui.beans

### Class weka.gui.beans.AbstractDataSink

class AbstractDataSink extends JPanel implements Serializable

serialVersionUID:
  3956528599473814287L

#### Serialized Fields

##### m_listenee

    Object m_listenee

Non null if this object is a target for any events. Provides for the simplest case when only one incomming connection is allowed. Subclasses can overide the appropriate BeanCommon methods to change this behaviour and allow multiple connections if desired

##### m_visual

    BeanVisual m_visual

Default visual for data sources

### Class weka.gui.beans.AbstractDataSource

class AbstractDataSource extends JPanel implements Serializable

serialVersionUID:
  -4127257701890044793L

#### Serialized Fields

##### m_bcSupport

    BeanContextChildSupport m_bcSupport

BeanContextChild support

##### m_design

    boolean m_design

True if this bean's appearance is the design mode appearance

##### m_listeners

    Vector m_listeners

Objects listening for events from data sources

##### m_visual

    BeanVisual m_visual

Default visual for data sources

### Class weka.gui.beans.AbstractEvaluator

class AbstractEvaluator extends JPanel implements Serializable

serialVersionUID:
  3983303541814121632L

#### Serialized Fields

##### m_listenee

    Object m_listenee

##### m_visual

    BeanVisual m_visual

Default visual for evaluators

### Class weka.gui.beans.AbstractTestSetProducer

class AbstractTestSetProducer extends JPanel implements Serializable

serialVersionUID:
  -7905764845789349839L

#### Serialized Fields

##### m_listenee

    Object m_listenee

non null if this object is a target for any events.

##### m_listeners

    Vector m_listeners

Objects listening to us

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.AbstractTrainAndTestSetProducer

class AbstractTrainAndTestSetProducer extends JPanel implements Serializable

serialVersionUID:
  -1809339823613492037L

#### Serialized Fields

##### m_listenee

    Object m_listenee

non null if this object is a target for any events.

##### m_testListeners

    Vector m_testListeners

Objects listening for test set events

##### m_trainingListeners

    Vector m_trainingListeners

Objects listening for trainin set events

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.AbstractTrainingSetProducer

class AbstractTrainingSetProducer extends JPanel implements Serializable

serialVersionUID:
  -7842746199524591125L

#### Serialized Fields

##### m_listenee

    Object m_listenee

non null if this object is a target for any events.

##### m_listeners

    Vector m_listeners

Objects listening for training set events

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.Associator

class Associator extends JPanel implements Serializable

serialVersionUID:
  -7843500322130210057L

#### Serialized Fields

##### m_Associator

    Associator m_Associator

##### m_buildThread

    Thread m_buildThread

##### m_globalInfo

    String m_globalInfo

Global info for the wrapped associator (if it exists).

##### m_graphListeners

    Vector m_graphListeners

Objects listening for graph events

##### m_listenees

    Hashtable m_listenees

Objects talking to us

##### m_state

    int m_state

##### m_textListeners

    Vector m_textListeners

Objects listening for text events

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.AssociatorCustomizer

class AssociatorCustomizer extends JPanel implements Serializable

serialVersionUID:
  5767664969353495974L

#### Serialized Fields

##### m_AssociatorEditor

    PropertySheetPanel m_AssociatorEditor

##### m_backup

    Associator m_backup

Backup is user presses cancel

##### m_dsAssociator

    Associator m_dsAssociator

##### m_parentFrame

    JFrame m_parentFrame

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

### Class weka.gui.beans.AttributeSummarizer

class AttributeSummarizer extends DataVisualizer implements Serializable

serialVersionUID:
  -294354961169372758L

#### Serialized Fields

##### m_coloringIndex

    int m_coloringIndex

Index on which to color the plots.

##### m_gridWidth

    int m_gridWidth

The number of plots horizontally in the display

##### m_maxPlots

    int m_maxPlots

The maximum number of plots to show

### Class weka.gui.beans.BatchClassifierEvent

class BatchClassifierEvent extends EventObject implements Serializable

serialVersionUID:
  878097199815991084L

#### Serialized Fields

##### m_classifier

    Classifier m_classifier

The classifier

##### m_groupIdentifier

    long m_groupIdentifier

An identifier that can be used to group all related runs/sets together.

##### m_maxRunNumber

    int m_maxRunNumber

The maximum number of runs

##### m_maxSetNumber

    int m_maxSetNumber

The last set number for this series

##### m_runNumber

    int m_runNumber

The run number that this classifier was generated for

##### m_setNumber

    int m_setNumber

The set number for the test set

##### m_testSet

    DataSetEvent m_testSet

Instances that can be used for testing the classifier

##### m_trainSet

    DataSetEvent m_trainSet

Instances that were used to train the classifier (may be null if not available)

### Class weka.gui.beans.BatchClustererEvent

class BatchClustererEvent extends EventObject implements Serializable

serialVersionUID:
  7268777944939129714L

#### Serialized Fields

##### m_clusterer

    Clusterer m_clusterer

The clusterer

##### m_maxSetNumber

    int m_maxSetNumber

The last set number for this series

##### m_setNumber

    int m_setNumber

The set number for the test set

##### m_testOrTrain

    int m_testOrTrain

Indicates if m_testSet is a training or a test set. 0 for test, >0 for training

##### m_testSet

    DataSetEvent m_testSet

Training or Test Instances

### Class weka.gui.beans.BeanConnection

class BeanConnection extends Object implements Serializable

serialVersionUID:
  8804264241791332064L

#### Serialized Fields

##### m_eventName

    String m_eventName

The name of the event for this connection

##### m_hidden

    boolean m_hidden

##### m_source

    BeanInstance m_source

##### m_target

    BeanInstance m_target

### Class weka.gui.beans.BeanInstance

class BeanInstance extends Object implements Serializable

serialVersionUID:
  -7575653109025406342L

#### Serialized Fields

##### m_bean

    Object m_bean

Holds the bean encapsulated in this instance

##### m_x

    int m_x

##### m_y

    int m_y

### Class weka.gui.beans.BeanVisual

class BeanVisual extends JPanel implements Serializable

serialVersionUID:
  -6677473561687129614L

#### Serialization Methods

##### readObject

private void readObject(ObjectInputStream ois) throws IOException, 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.

Parameters:
  `ois` - an `ObjectInputStream` value
Throws:
  `IOException` - if an error occurs
  `ClassNotFoundException` - if an error occurs

#### Serialized Fields

##### m_animatedIconPath

    String m_animatedIconPath

Holds name (including path) of the animated icon

##### m_connectorColor

    Color m_connectorColor

##### m_displayConnectors

    boolean m_displayConnectors

##### m_iconPath

    String m_iconPath

Holds name (including path) of the static icon

##### m_pcs

    PropertyChangeSupport m_pcs

##### m_stationary

    boolean m_stationary

Container for the icon

##### m_visualLabel

    JLabel m_visualLabel

##### m_visualName

    String m_visualName

Name for the bean

### Class weka.gui.beans.ChartEvent

class ChartEvent extends EventObject implements Serializable

serialVersionUID:
  7812460715499569390L

#### Serialized Fields

##### m_dataPoint

    double[] m_dataPoint

Y values of the data points

##### m_legendText

    Vector m_legendText

##### m_max

    double m_max

##### m_min

    double m_min

##### m_reset

    boolean m_reset

### Class weka.gui.beans.ClassAssigner

class ClassAssigner extends JPanel implements Serializable

serialVersionUID:
  4011131665025817924L

#### Serialized Fields

##### m_classColumn

    String m_classColumn

##### m_connectedFormat

    Instances m_connectedFormat

format of instances for current incoming connection (if any)

##### m_dataFormatListeners

    Vector m_dataFormatListeners

##### m_dataListeners

    Vector m_dataListeners

##### m_dataProvider

    Object m_dataProvider

##### m_instanceListeners

    Vector m_instanceListeners

##### m_instanceProvider

    Object m_instanceProvider

##### m_testListeners

    Vector m_testListeners

##### m_testProvider

    Object m_testProvider

##### m_trainingListeners

    Vector m_trainingListeners

##### m_trainingProvider

    Object m_trainingProvider

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.ClassAssignerCustomizer

class ClassAssignerCustomizer extends JPanel implements Serializable

serialVersionUID:
  476539385765301907L

#### Serialized Fields

##### m_caEditor

    PropertySheetPanel m_caEditor

##### m_classAssigner

    ClassAssigner m_classAssigner

##### m_ClassCombo

    JComboBox m_ClassCombo

##### m_displayColNames

    boolean m_displayColNames

##### m_holderP

    JPanel m_holderP

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

### Class weka.gui.beans.Classifier

class Classifier extends JPanel implements Serializable

serialVersionUID:
  659603893917736008L

#### Serialized Fields

##### m_batchClassifierListeners

    Vector m_batchClassifierListeners

Objects listening for batch classifier events

##### m_binaryFilter

    FileFilter m_binaryFilter

##### m_block

    boolean m_block

true if we should block any further training data sets.

##### m_Classifier

    Classifier m_Classifier

##### m_ClassifierTemplate

    Classifier m_ClassifierTemplate

Template used for creating copies when building in parallel

##### m_executionSlots

    int m_executionSlots

Number of threads to use to train models with

##### m_globalInfo

    String m_globalInfo

Global info for the wrapped classifier (if it exists).

##### m_graphListeners

    Vector m_graphListeners

Objects listening for graph events

##### m_ie

    IncrementalClassifierEvent m_ie

##### m_incrementalClassifierListeners

    Vector m_incrementalClassifierListeners

Objects listening for incremental classifier events

##### m_incrementalEvent

    InstanceEvent m_incrementalEvent

Event to handle when processing incremental updates

##### m_KOMLFilter

    FileFilter m_KOMLFilter

##### m_listenees

    Hashtable m_listenees

Objects talking to us

##### m_oldText

    String m_oldText

Holds original icon label text

##### m_state

    int m_state

##### m_textListeners

    Vector m_textListeners

Objects listening for text events

##### m_trainingSet

    Instances m_trainingSet

Holds training instances for batch training. Not transient because header is retained for validating any instance events that this classifier might be asked to predict in the future.

##### m_updateIncrementalClassifier

    boolean m_updateIncrementalClassifier

If the classifier is an incremental classifier, should we update it (ie train it on incoming instances). This makes it possible incrementally test on a separate stream of instances without updating the classifier, or mix batch training/testing with incremental training/testing

##### m_visual

    BeanVisual m_visual

##### m_XStreamFilter

    FileFilter m_XStreamFilter

### Class weka.gui.beans.Classifier.TrainingTask

class TrainingTask extends Object implements Serializable

#### Serialized Fields

##### m_maxRunNum

    int m_maxRunNum

##### m_maxSetNum

    int m_maxSetNum

##### m_runNum

    int m_runNum

##### m_setNum

    int m_setNum

##### m_taskInfo

    TaskStatusInfo m_taskInfo

##### m_train

    Instances m_train

### Class weka.gui.beans.ClassifierCustomizer

class ClassifierCustomizer extends JPanel implements Serializable

serialVersionUID:
  -6688000820160821429L

#### Serialized Fields

##### m_backup

    Classifier m_backup

Copy of the current classifier in case cancel is selected

##### m_ClassifierEditor

    PropertySheetPanel m_ClassifierEditor

##### m_dsClassifier

    Classifier m_dsClassifier

##### m_executionSlotsText

    JTextField m_executionSlotsText

##### m_holderPanel

    JPanel m_holderPanel

##### m_incrementalPanel

    JPanel m_incrementalPanel

##### m_panelVisible

    boolean m_panelVisible

##### m_parentFrame

    JFrame m_parentFrame

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

##### m_updateIncrementalClassifier

    JCheckBox m_updateIncrementalClassifier

### Class weka.gui.beans.ClassifierPerformanceEvaluator

class ClassifierPerformanceEvaluator extends AbstractEvaluator implements Serializable

serialVersionUID:
  -3511801418192148690L

#### Serialized Fields

##### m_rocListenersConnected

    boolean m_rocListenersConnected

##### m_textListeners

    Vector m_textListeners

##### m_thresholdListeners

    Vector m_thresholdListeners

##### m_visualizableErrorListeners

    Vector m_visualizableErrorListeners

### Class weka.gui.beans.ClassValuePicker

class ClassValuePicker extends JPanel implements Serializable

serialVersionUID:
  -1196143276710882989L

#### Serialized Fields

##### m_classValue

    String m_classValue

the class value considered to be the positive class

##### m_connectedFormat

    Instances m_connectedFormat

format of instances for the current incoming connection (if any)

##### m_dataFormatListeners

    Vector m_dataFormatListeners

##### m_dataListeners

    Vector m_dataListeners

##### m_dataProvider

    Object m_dataProvider

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.ClassValuePickerCustomizer

class ClassValuePickerCustomizer extends JPanel implements Serializable

serialVersionUID:
  8213423053861600469L

#### Serialized Fields

##### m_backup

    String m_backup

##### m_ClassValueCombo

    JComboBox m_ClassValueCombo

##### m_classValuePicker

    ClassValuePicker m_classValuePicker

##### m_displayValNames

    boolean m_displayValNames

##### m_holderP

    JPanel m_holderP

##### m_messageLabel

    JLabel m_messageLabel

##### m_parent

    JFrame m_parent

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

##### m_textBoxEntryMode

    boolean m_textBoxEntryMode

##### m_valueTextBox

    JTextField m_valueTextBox

### Class weka.gui.beans.Clusterer

class Clusterer extends JPanel implements Serializable

serialVersionUID:
  7729795159836843810L

#### Serialized Fields

##### m_batchClustererListeners

    Vector m_batchClustererListeners

Objects listening for batch clusterer events

##### m_buildThread

    Thread m_buildThread

##### m_Clusterer

    Clusterer m_Clusterer

##### m_dummy

    Double m_dummy

##### m_globalInfo

    String m_globalInfo

Global info for the wrapped classifier (if it exists).

##### m_graphListeners

    Vector m_graphListeners

Objects listening for graph events

##### m_listenees

    Hashtable m_listenees

Objects talking to us

##### m_state

    int m_state

##### m_textListeners

    Vector m_textListeners

Objects listening for text events

##### m_trainingSet

    Instances m_trainingSet

Holds training instances for batch training.

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.ClustererCustomizer

class ClustererCustomizer extends JPanel implements Serializable

serialVersionUID:
  -2035688458149534161L

#### Serialized Fields

##### m_backup

    Clusterer m_backup

Backup if the user presses cancel

##### m_ClustererEditor

    PropertySheetPanel m_ClustererEditor

##### m_dsClusterer

    Clusterer m_dsClusterer

##### m_parentFrame

    JFrame m_parentFrame

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

### Class weka.gui.beans.ClustererPerformanceEvaluator

class ClustererPerformanceEvaluator extends AbstractEvaluator implements Serializable

serialVersionUID:
  8041163601333978584L

#### Serialized Fields

##### m_textListeners

    Vector m_textListeners

### Class weka.gui.beans.CostBenefitAnalysis

class CostBenefitAnalysis extends JPanel implements Serializable

serialVersionUID:
  8647471654613320469L

#### Serialized Fields

##### m_bcSupport

    BeanContextChildSupport m_bcSupport

BeanContextChild support

##### m_design

    boolean m_design

True if this bean's appearance is the design mode appearance

##### m_framePoppedUp

    boolean m_framePoppedUp

##### m_listenee

    Object m_listenee

The object sending us data (we allow only one connection at any one time)

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel

class AnalysisPanel extends JPanel implements Serializable

serialVersionUID:
  5364871945448769003L

#### Serialized Fields

##### m_benefitR

    JRadioButton m_benefitR

##### m_classAttribute

    Attribute m_classAttribute

The class attribute from the data that was used to generate the threshold curve

##### m_classificationAccV

    JLabel m_classificationAccV

Classification accuracy

##### m_conf_aa

    weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_aa

##### m_conf_ab

    weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_ab

##### m_conf_actualA

    JLabel m_conf_actualA

##### m_conf_actualB

    JLabel m_conf_actualB

##### m_conf_ba

    weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_ba

##### m_conf_bb

    weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_bb

##### m_conf_predictedA

    JLabel m_conf_predictedA

##### m_conf_predictedB

    JLabel m_conf_predictedB

##### m_cost_aa

    JTextField m_cost_aa

##### m_cost_ab

    JTextField m_cost_ab

##### m_cost_actualA

    JLabel m_cost_actualA

##### m_cost_actualB

    JLabel m_cost_actualB

##### m_cost_ba

    JTextField m_cost_ba

##### m_cost_bb

    JTextField m_cost_bb

##### m_cost_predictedA

    JLabel m_cost_predictedA

##### m_cost_predictedB

    JLabel m_cost_predictedB

##### m_costBenefit

    PlotData2D m_costBenefit

Data for the cost/benefit curve

##### m_costBenefitL

    JLabel m_costBenefitL

##### m_costBenefitPanel

    VisualizePanel m_costBenefitPanel

Displays the cost/benefit (profit/loss) graph

##### m_costBenefitV

    JLabel m_costBenefitV

##### m_costR

    JRadioButton m_costR

##### m_fnPrevious

    double m_fnPrevious

##### m_fpPrevious

    double m_fpPrevious

##### m_gainV

    JLabel m_gainV

##### m_masterPlot

    PlotData2D m_masterPlot

Data for the threshold curve

##### m_maximizeCB

    JButton m_maximizeCB

##### m_minimizeCB

    JButton m_minimizeCB

##### m_originalPopSize

    int m_originalPopSize

##### m_percOfTarget

    JRadioButton m_percOfTarget

##### m_percOfTargetLab

    JLabel m_percOfTargetLab

##### m_percPop

    JRadioButton m_percPop

##### m_percPopLab

    JLabel m_percPopLab

##### m_performancePanel

    VisualizePanel m_performancePanel

Displays the performance graphs(s)

##### m_previousShapeIndex

    int m_previousShapeIndex

The index of the previous plotted point that was highlighted

##### m_randomV

    JLabel m_randomV

##### m_shapeSizes

    int[] m_shapeSizes

The size of the points being plotted

##### m_threshold

    JRadioButton m_threshold

##### m_thresholdLab

    JLabel m_thresholdLab

##### m_thresholdSlider

    JSlider m_thresholdSlider

The slider for adjusting the threshold

##### m_tnPrevious

    double m_tnPrevious

##### m_totalPopField

    JTextField m_totalPopField

Population text field

##### m_totalPopPrevious

    int m_totalPopPrevious

##### m_tpPrevious

    double m_tpPrevious

### Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell

class ConfusionCell extends JPanel implements Serializable

serialVersionUID:
  6148640235434494767L

#### Serialized Fields

##### m_conf_cell

    JLabel m_conf_cell

##### m_conf_perc

    JLabel m_conf_perc

##### m_percentage

    double m_percentage

##### m_percentageP

    JPanel m_percentageP

### Class weka.gui.beans.CrossValidationFoldMaker

class CrossValidationFoldMaker extends AbstractTrainAndTestSetProducer implements Serializable

serialVersionUID:
  -6350179298851891512L

#### Serialized Fields

##### m_numFolds

    int m_numFolds

##### m_randomSeed

    int m_randomSeed

### Class weka.gui.beans.CrossValidationFoldMakerCustomizer

class CrossValidationFoldMakerCustomizer extends JPanel implements Serializable

serialVersionUID:
  1229878140258668581L

#### Serialized Fields

##### m_cvEditor

    PropertySheetPanel m_cvEditor

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

### Class weka.gui.beans.DataSetEvent

class DataSetEvent extends EventObject implements Serializable

serialVersionUID:
  -5111218447577318057L

#### Serialized Fields

##### m_dataSet

    Instances m_dataSet

##### m_structureOnly

    boolean m_structureOnly

### Class weka.gui.beans.DataVisualizer

class DataVisualizer extends JPanel implements Serializable

serialVersionUID:
  1949062132560159028L

#### Serialized Fields

##### m_bcSupport

    BeanContextChildSupport m_bcSupport

BeanContextChild support

##### m_dataSetListeners

    Vector m_dataSetListeners

Objects listening for data set events

##### m_design

    boolean m_design

True if this bean's appearance is the design mode appearance

##### m_framePoppedUp

    boolean m_framePoppedUp

##### m_visPanel

    VisualizePanel m_visPanel

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.Filter

class Filter extends JPanel implements Serializable

serialVersionUID:
  8249759470189439321L

#### Serialized Fields

##### m_dataListeners

    Vector m_dataListeners

Objects listening for data set events

##### m_Filter

    Filter m_Filter

The filter to use.

##### m_globalInfo

    String m_globalInfo

Global info for the wrapped filter (if it exists).

##### m_ie

    InstanceEvent m_ie

Instance event object for passing on filtered instance streams

##### m_instanceListeners

    Vector m_instanceListeners

Objects listening for instance events

##### m_listenees

    Hashtable m_listenees

Objects talking to us

##### m_state

    int m_state

##### m_structurePassedOn

    boolean m_structurePassedOn

##### m_testListeners

    Vector m_testListeners

Objects listening for test set events

##### m_trainingListeners

    Vector m_trainingListeners

Objects listening for training set events

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.FilterCustomizer

class FilterCustomizer extends JPanel implements Serializable

serialVersionUID:
  2049895469240109738L

#### Serialized Fields

##### m_backup

    Filter m_backup

Backup if user presses cancel

##### m_filter

    Filter m_filter

##### m_filterEditor

    PropertySheetPanel m_filterEditor

##### m_parentFrame

    JFrame m_parentFrame

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

### Class weka.gui.beans.GraphEvent

class GraphEvent extends EventObject implements Serializable

serialVersionUID:
  2099494034652519986L

#### Serialized Fields

##### m_graphString

    String m_graphString

##### m_graphTitle

    String m_graphTitle

##### m_graphType

    int m_graphType

### Class weka.gui.beans.GraphViewer

class GraphViewer extends JPanel implements Serializable

serialVersionUID:
  -5183121972114900617L

#### Serialized Fields

##### m_bcSupport

    BeanContextChildSupport m_bcSupport

BeanContextChild support

##### m_design

    boolean m_design

True if this bean's appearance is the design mode appearance

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.IncrementalClassifierEvaluator

class IncrementalClassifierEvaluator extends AbstractEvaluator implements Serializable

serialVersionUID:
  -3105419818939541291L

#### Serialized Fields

##### m_ce

    ChartEvent m_ce

##### m_dataLegend

    Vector m_dataLegend

##### m_dataPoint

    double[] m_dataPoint

##### m_instanceCount

    int m_instanceCount

##### m_listeners

    Vector m_listeners

##### m_max

    double m_max

##### m_min

    double m_min

##### m_outputInfoRetrievalStats

    boolean m_outputInfoRetrievalStats

##### m_reset

    boolean m_reset

##### m_statusFrequency

    int m_statusFrequency

##### m_textListeners

    Vector m_textListeners

### Class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer

class IncrementalClassifierEvaluatorCustomizer extends JPanel implements Serializable

#### Serialized Fields

##### m_ieEditor

    PropertySheetPanel m_ieEditor

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

for serialization

### Class weka.gui.beans.IncrementalClassifierEvent

class IncrementalClassifierEvent extends EventObject implements Serializable

serialVersionUID:
  28979464317643232L

#### Serialized Fields

##### m_classifier

    Classifier m_classifier

##### m_currentInstance

    Instance m_currentInstance

##### m_status

    int m_status

##### m_structure

    Instances m_structure

### Class weka.gui.beans.InstanceEvent

class InstanceEvent extends EventObject implements Serializable

serialVersionUID:
  6104920894559423946L

#### Serialized Fields

##### m_instance

    Instance m_instance

##### m_status

    int m_status

##### m_structure

    Instances m_structure

### Class weka.gui.beans.InstanceStreamToBatchMaker

class InstanceStreamToBatchMaker extends JPanel implements Serializable

serialVersionUID:
  -7037141087208627799L

#### Serialized Fields

##### m_batch

    ArrayList<Instance> m_batch

Collects up the instances.

##### m_dataListeners

    ArrayList<DataSourceListener> m_dataListeners

##### m_listenee

    Object m_listenee

Component connected to us.

##### m_structure

    Instances m_structure

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.KnowledgeFlowApp

class KnowledgeFlowApp extends JPanel implements Serializable

serialVersionUID:
  -7064906770289728431L

#### Serialized Fields

##### m_bcSupport

    BeanContextSupport m_bcSupport

##### m_beanLayout

    weka.gui.beans.KnowledgeFlowApp.BeanLayout m_beanLayout

The layout area

##### m_editElement

    BeanInstance m_editElement

Reference to bean being manipulated

##### m_FileChooser

    JFileChooser m_FileChooser

The file chooser for selecting layout files

##### m_firstUserComponentOpp

    boolean m_firstUserComponentOpp

##### m_flowEnvironment

    Environment m_flowEnvironment

Environment variables for the current flow

##### m_FlowHeight

    int m_FlowHeight

the flow layout height

##### m_FlowWidth

    int m_FlowWidth

the flow layout width

##### m_fontM

    FontMetrics m_fontM

##### m_helpB

    JButton m_helpB

##### m_KfFilter

    FileFilter m_KfFilter

A filter to ensure only KnowledgeFlow files in binary format get shown in the chooser

##### m_KOMLFilter

    FileFilter m_KOMLFilter

A filter to ensure only KnowledgeFlow files in KOML format get shown in the chooser

##### m_loadB

    JButton m_loadB

##### m_logPanel

    LogPanel m_logPanel

##### m_mode

    int m_mode

##### m_newB

    JButton m_newB

##### m_oldX

    int m_oldX

Used to record screen coordinates during move, select and connect operations

##### m_oldY

    int m_oldY

Used to record screen coordinates during move, select and connect operations

##### m_pluginsBoxPanel

    Box m_pluginsBoxPanel

##### m_pluginsToolBar

    JToolBar m_pluginsToolBar

Stuff relating to plugin beans

##### m_pointerB

    JToggleButton m_pointerB

##### m_PreferredExtension

    String m_PreferredExtension

the preferred file extension

##### m_saveB

    JButton m_saveB

##### m_ScrollBarIncrementComponents

    int m_ScrollBarIncrementComponents

the scrollbar increment of the components scrollpane

##### m_ScrollBarIncrementLayout

    int m_ScrollBarIncrementLayout

the scrollbar increment of the layout scrollpane

##### m_showFileMenu

    boolean m_showFileMenu

##### m_sourceEventSetDescriptor

    EventSetDescriptor m_sourceEventSetDescriptor

Event set descriptor for the bean being manipulated

##### m_startX

    int m_startX

##### m_startY

    int m_startY

##### m_stopB

    JButton m_stopB

##### m_toolBarBean

    Object m_toolBarBean

Holds the selected toolbar bean

##### m_toolBarGroup

    ButtonGroup m_toolBarGroup

Button group to manage all toolbar buttons

##### m_toolBars

    JTabbedPane m_toolBars

Tabbed pane to hold tool bars

##### m_userBoxPanel

    Box m_userBoxPanel

##### m_userComponents

    Vector m_userComponents

##### m_UserComponentsInXML

    boolean m_UserComponentsInXML

whether to store the user components in XML or in binary format

##### m_userToolBar

    JToolBar m_userToolBar

Stuff relating to user created meta beans

##### m_XMLFilter

    FileFilter m_XMLFilter

A filter to ensure only KnowledgeFlow layout files in XML format get shown in the chooser

##### m_XStreamFilter

    FileFilter m_XStreamFilter

A filter to ensure only KnowledgeFlow files in XStream format get shown in the chooser

### Class weka.gui.beans.KnowledgeFlowApp.BeanLayout

class BeanLayout extends PrintablePanel implements Serializable

serialVersionUID:
  -146377012429662757L

### Class weka.gui.beans.Loader

class Loader extends AbstractDataSource implements Serializable

serialVersionUID:
  1993738191961163027L

#### Serialization Methods

##### readObject

private void readObject(ObjectInputStream aStream) throws IOException, ClassNotFoundException

Throws:
  `IOException`
  `ClassNotFoundException`

##### readResolve

private Object readResolve() throws ObjectStreamException

Throws:
  `ObjectStreamException`

#### Serialized Fields

##### m_dataSetEventTargets

    int m_dataSetEventTargets

##### m_dbSet

    boolean m_dbSet

Flag indicating that a database has already been configured

##### m_globalInfo

    String m_globalInfo

Global info for the wrapped loader (if it exists).

##### m_ie

    InstanceEvent m_ie

##### m_instanceEventTargets

    int m_instanceEventTargets

Keep track of how many listeners for different types of events there are.

##### m_ioThread

    weka.gui.beans.Loader.LoadThread m_ioThread

Thread for doing IO in

##### m_Loader

    Loader m_Loader

Loader

##### m_state

    int m_state

##### m_stopped

    boolean m_stopped

Asked to stop?

### Class weka.gui.beans.LoaderCustomizer

class LoaderCustomizer extends JPanel implements Serializable

serialVersionUID:
  6990446313118930298L

#### Serialized Fields

##### m_dbaseURLText

    JTextField m_dbaseURLText

##### m_dsLoader

    Loader m_dsLoader

##### m_fileChooser

    JFileChooser m_fileChooser

##### m_keyText

    JTextField m_keyText

##### m_LoaderEditor

    PropertySheetPanel m_LoaderEditor

##### m_parentFrame

    JFrame m_parentFrame

##### m_passwordText

    JPasswordField m_passwordText

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

##### m_queryText

    JTextField m_queryText

##### m_relativeFilePath

    JCheckBox m_relativeFilePath

##### m_userNameText

    JTextField m_userNameText

### Class weka.gui.beans.LogPanel

class LogPanel extends JPanel implements Serializable

#### Serialized Fields

##### m_formatter

    DecimalFormat m_formatter

For formatting timer digits

##### m_logPanel

    LogPanel m_logPanel

The log panel to delegate log messages to.

##### m_table

    JTable m_table

The table for the status area

##### m_tableIndexes

    HashMap<String,Integer> m_tableIndexes

Holds the index (line number) in the JTable of each component being tracked.

##### m_tableModel

    DefaultTableModel m_tableModel

The table model for the JTable used in the status area

##### m_tabs

    JTabbedPane m_tabs

Tabbed pane to hold both the status and the log

##### m_timers

    HashMap<String,Timer> m_timers

Holds the timers associated with each component being tracked.

### Class weka.gui.beans.MetaBean

class MetaBean extends JPanel implements Serializable

serialVersionUID:
  -6582768902038027077L

#### Serialized Fields

##### m_associatedConnections

    Vector m_associatedConnections

##### m_inputs

    Vector m_inputs

##### m_originalCoords

    Vector m_originalCoords

##### m_outputs

    Vector m_outputs

##### m_subFlow

    Vector m_subFlow

##### m_subFlowPreview

    ImageIcon m_subFlowPreview

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.ModelPerformanceChart

class ModelPerformanceChart extends JPanel implements Serializable

serialVersionUID:
  -4602034200071195924L

#### Serialized Fields

##### m_bcSupport

    BeanContextChildSupport m_bcSupport

BeanContextChild support

##### m_design

    boolean m_design

True if this bean's appearance is the design mode appearance

##### m_framePoppedUp

    boolean m_framePoppedUp

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.PredictionAppender

class PredictionAppender extends JPanel implements Serializable

serialVersionUID:
  -2987740065058976673L

#### Serialized Fields

##### m_appendProbabilities

    boolean m_appendProbabilities

Append classifier's predicted probabilities (if the class is discrete and the classifier is a distribution classifier)

##### m_dataSourceListeners

    Vector m_dataSourceListeners

Objects listenening for dataset events

##### m_format

    Instances m_format

Format of instances to be produced.

##### m_instanceEvent

    InstanceEvent m_instanceEvent

##### m_instanceListeners

    Vector m_instanceListeners

Objects listening for instances events

##### m_listenee

    Object m_listenee

Non null if this object is a target for any events.

##### m_testSetListeners

    Vector m_testSetListeners

Objects listening for test set events

##### m_trainingSetListeners

    Vector m_trainingSetListeners

Objects listening for training set events

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.PredictionAppenderCustomizer

class PredictionAppenderCustomizer extends JPanel implements Serializable

serialVersionUID:
  6884933202506331888L

#### Serialized Fields

##### m_paEditor

    PropertySheetPanel m_paEditor

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

### Class weka.gui.beans.Saver

class Saver extends AbstractDataSink implements Serializable

serialVersionUID:
  5371716690308950755L

#### Serialization Methods

##### readObject

private void readObject(ObjectInputStream aStream) throws IOException, ClassNotFoundException

Throws:
  `IOException`
  `ClassNotFoundException`

#### Serialized Fields

##### m_count

    int m_count

Count for structure available messages

##### m_dataSet

    Instances m_dataSet

Holds the instances to be saved

##### m_fileName

    String m_fileName

The relation name that becomes part of the file name

##### m_globalInfo

    String m_globalInfo

Global info for the wrapped loader (if it exists).

##### m_isDBSaver

    boolean m_isDBSaver

Flag indicating that instances will be saved to database. Used because structure information can only be sent after a database has been configured.

##### m_relationNameForFilename

    boolean m_relationNameForFilename

For file-based savers - if true (default), relation name is used as the primary part of the filename. If false, then the prefix is used as the filename. Useful for preventing filenames from getting too long when there are many filters in a flow.

##### m_Saver

    Saver m_Saver

Saver

##### m_SaverTemplate

    Saver m_SaverTemplate

##### m_structure

    Instances m_structure

Holds the structure

### Class weka.gui.beans.SaverCustomizer

class SaverCustomizer extends JPanel implements Serializable

serialVersionUID:
  -4874208115942078471L

#### Serialized Fields

##### m_dbaseURLText

    JTextField m_dbaseURLText

##### m_dsSaver

    Saver m_dsSaver

##### m_fileChooser

    JFileChooser m_fileChooser

##### m_idBox

    JComboBox m_idBox

##### m_parentFrame

    JFrame m_parentFrame

##### m_passwordText

    JPasswordField m_passwordText

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

##### m_prefixText

    JTextField m_prefixText

##### m_relationNameForFilename

    JCheckBox m_relationNameForFilename

##### m_relativeFilePath

    JCheckBox m_relativeFilePath

##### m_SaverEditor

    PropertySheetPanel m_SaverEditor

##### m_tabBox

    JComboBox m_tabBox

##### m_tableText

    JTextField m_tableText

##### m_userNameText

    JTextField m_userNameText

### Class weka.gui.beans.ScatterPlotMatrix

class ScatterPlotMatrix extends DataVisualizer implements Serializable

serialVersionUID:
  -657856527563507491L

#### Serialized Fields

##### m_matrixPanel

    MatrixPanel m_matrixPanel

### Class weka.gui.beans.SerializedModelSaver

class SerializedModelSaver extends JPanel implements Serializable

serialVersionUID:
  3956528599473814287L

#### Serialization Methods

##### readObject

private void readObject(ObjectInputStream aStream) throws IOException, ClassNotFoundException

Throws:
  `IOException`
  `ClassNotFoundException`

#### Serialized Fields

##### m_directory

    File m_directory

The directory to hold the saved model(s)

##### m_fileFormat

    Tag m_fileFormat

File format stuff

##### m_filenamePrefix

    String m_filenamePrefix

The prefix for the file name (model + training set info will be appended)

##### m_listenee

    Object m_listenee

Non null if this object is a target for any events. Provides for the simplest case when only one incomming connection is allowed.

##### m_useRelativePath

    boolean m_useRelativePath

relative path for the directory (relative to the user.dir (startup directory))?

##### m_visual

    BeanVisual m_visual

Default visual for data sources

### Class weka.gui.beans.SerializedModelSaverCustomizer

class SerializedModelSaverCustomizer extends JPanel implements Serializable

serialVersionUID:
  -4874208115942078471L

#### Serialized Fields

##### m_fileChooser

    JFileChooser m_fileChooser

##### m_fileFormatBox

    JComboBox m_fileFormatBox

##### m_parentFrame

    JFrame m_parentFrame

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

##### m_prefixText

    JTextField m_prefixText

##### m_relativeFilePath

    JCheckBox m_relativeFilePath

##### m_SaverEditor

    PropertySheetPanel m_SaverEditor

##### m_smSaver

    SerializedModelSaver m_smSaver

### Class weka.gui.beans.StripChart

class StripChart extends JPanel implements Serializable

serialVersionUID:
  1483649041577695019L

#### Serialization Methods

##### readObject

private void readObject(ObjectInputStream ois) throws IOException, ClassNotFoundException
Provide some necessary initialization after object has been deserialized.

Parameters:
  `ois` - an `ObjectInputStream` value
Throws:
  `IOException` - if an error occurs
  `ClassNotFoundException` - if an error occurs

#### Serialized Fields

##### m_BackgroundColor

    Color m_BackgroundColor

the background color.

##### m_ce

    ChartEvent m_ce

##### m_colorList

    Color[] m_colorList

default colours for colouring lines

##### m_dataList

    LinkedList m_dataList

Holds the data to be plotted. Entries in the list are arrays of y points.

##### m_dataPoint

    double[] m_dataPoint

##### m_iheight

    int m_iheight

Width and height of the off screen image.

##### m_iwidth

    int m_iwidth

##### m_labelFont

    Font m_labelFont

##### m_labelMetrics

    FontMetrics m_labelMetrics

##### m_legendPanel

    weka.gui.beans.StripChart.LegendPanel m_legendPanel

the legend.

##### m_LegendPanelBorderColor

    Color m_LegendPanelBorderColor

the color of the legend panel's border.

##### m_legendText

    Vector m_legendText

##### m_listenee

    Object m_listenee

##### m_max

    double m_max

Max value for the y axis.

##### m_min

    double m_min

Min value for the y axis.

##### m_oldMax

    double m_oldMax

##### m_oldMin

    double m_oldMin

##### m_previousY

    double[] m_previousY

##### m_Printer

    PrintableComponent m_Printer

the class responsible for printing

##### m_refreshFrequency

    int m_refreshFrequency

Plot every m_refreshFrequency'th point

##### m_refreshWidth

    int m_refreshWidth

Shift the plot by this many pixels every time a point is plotted

##### m_scalePanel

    weka.gui.beans.StripChart.ScalePanel m_scalePanel

the scale.

##### m_visual

    BeanVisual m_visual

##### m_xCount

    int m_xCount

##### m_xValFreq

    int m_xValFreq

Print x axis labels every m_xValFreq points

##### m_yScaleUpdate

    boolean m_yScaleUpdate

Scale update requested.

### Class weka.gui.beans.StripChartCustomizer

class StripChartCustomizer extends JPanel implements Serializable

serialVersionUID:
  7441741530975984608L

#### Serialized Fields

##### m_cvEditor

    PropertySheetPanel m_cvEditor

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

### Class weka.gui.beans.TestSetEvent

class TestSetEvent extends EventObject implements Serializable

serialVersionUID:
  8780718708498854231L

#### Serialized Fields

##### m_maxRunNumber

    int m_maxRunNumber

Maximum number of runs.

##### m_maxSetNumber

    int m_maxSetNumber

Maximum number of sets (ie 10 in a 10 fold)

##### m_runNumber

    int m_runNumber

What run number is this training set from.

##### m_setNumber

    int m_setNumber

what number is this test set (ie fold 2 of 10 folds)

##### m_structureOnly

    boolean m_structureOnly

##### m_testSet

    Instances m_testSet

The test set instances

### Class weka.gui.beans.TestSetMaker

class TestSetMaker extends AbstractTestSetProducer implements Serializable

serialVersionUID:
  -8473882857628061841L

#### Serialized Fields

##### m_receivedStopNotification

    boolean m_receivedStopNotification

### Class weka.gui.beans.TextEvent

class TextEvent extends EventObject implements Serializable

serialVersionUID:
  4196810607402973744L

#### Serialized Fields

##### m_text

    String m_text

The text

##### m_textTitle

    String m_textTitle

The title for the text. Could be used in a list component

### Class weka.gui.beans.TextViewer

class TextViewer extends JPanel implements Serializable

serialVersionUID:
  104838186352536832L

#### Serialized Fields

##### m_bcSupport

    BeanContextChildSupport m_bcSupport

BeanContextChild support

##### m_design

    boolean m_design

True if this bean's appearance is the design mode appearance

##### m_textListeners

    Vector m_textListeners

Objects listening for text events

##### m_visual

    BeanVisual m_visual

### Class weka.gui.beans.ThresholdDataEvent

class ThresholdDataEvent extends EventObject implements Serializable

serialVersionUID:
  -8309334224492439644L

#### Serialized Fields

##### m_classAttribute

    Attribute m_classAttribute

##### m_dataSet

    PlotData2D m_dataSet

### Class weka.gui.beans.TrainingSetEvent

class TrainingSetEvent extends EventObject implements Serializable

serialVersionUID:
  5872343811810872662L

#### Serialized Fields

##### m_maxRunNumber

    int m_maxRunNumber

Maximum number of runs.

##### m_maxSetNumber

    int m_maxSetNumber

Maximum number of sets (ie 10 in a 10 fold)

##### m_runNumber

    int m_runNumber

What run number is this training set from.

##### m_setNumber

    int m_setNumber

what number is this training set (ie fold 2 of 10 folds)

##### m_structureOnly

    boolean m_structureOnly

##### m_trainingSet

    Instances m_trainingSet

The training instances

### Class weka.gui.beans.TrainingSetMaker

class TrainingSetMaker extends AbstractTrainingSetProducer implements Serializable

serialVersionUID:
  -6152577265471535786L

#### Serialized Fields

##### m_receivedStopNotification

    boolean m_receivedStopNotification

### Class weka.gui.beans.TrainTestSplitMaker

class TrainTestSplitMaker extends AbstractTrainAndTestSetProducer implements Serializable

serialVersionUID:
  7390064039444605943L

#### Serialized Fields

##### m_randomSeed

    int m_randomSeed

##### m_splitThread

    Thread m_splitThread

##### m_trainPercentage

    double m_trainPercentage

### Class weka.gui.beans.TrainTestSplitMakerCustomizer

class TrainTestSplitMakerCustomizer extends JPanel implements Serializable

serialVersionUID:
  -1684662340241807260L

#### Serialized Fields

##### m_pcSupport

    PropertyChangeSupport m_pcSupport

##### m_splitEditor

    PropertySheetPanel m_splitEditor

### Class weka.gui.beans.VisualizableErrorEvent

class VisualizableErrorEvent extends EventObject implements Serializable

serialVersionUID:
  -5811819270887223400L

#### Serialized Fields

##### m_dataSet

    PlotData2D m_dataSet

## Package weka.gui.boundaryvisualizer

### Class weka.gui.boundaryvisualizer.BoundaryPanel

class BoundaryPanel extends JPanel implements Serializable

serialVersionUID:
  -8499445518744770458L

#### Serialized Fields

##### m_classifier

    Classifier m_classifier

distribution classifier to use

##### m_classIndex

    int m_classIndex

index of the class attribute

##### m_Colors

    FastVector m_Colors

##### m_dataGenerator

    DataGenerator m_dataGenerator

data generator to use

##### m_dummy

    Double m_dummy

##### m_initialTiling

    boolean m_initialTiling

is the main plot thread performing the initial coarse tiling

##### m_listeners

    Vector m_listeners

listeners to be notified when plot is complete

##### m_maxX

    double m_maxX

##### m_maxY

    double m_maxY

##### m_minX

    double m_minX

##### m_minY

    double m_minY

##### m_numOfSamplesPerGenerator

    int m_numOfSamplesPerGenerator

##### m_numOfSamplesPerRegion

    int m_numOfSamplesPerRegion

##### m_osi

    Image m_osi

used for offscreen drawing

##### m_panelHeight

    int m_panelHeight

##### m_panelWidth

    int m_panelWidth

##### m_pausePlotting

    boolean m_pausePlotting

##### m_pixHeight

    double m_pixHeight

##### m_pixWidth

    double m_pixWidth

##### m_plotPanel

    weka.gui.boundaryvisualizer.BoundaryPanel.PlotPanel m_plotPanel

the actual plotting area

##### m_plotThread

    Thread m_plotThread

thread for running the plotting operation in

##### m_plotTrainingData

    boolean m_plotTrainingData

plot the training data

##### m_probabilityCache

    double[][][] m_probabilityCache

cache of probabilities for fast replotting

##### m_random

    Random m_random

A random number generator

##### m_rangeX

    double m_rangeX

##### m_rangeY

    double m_rangeY

##### m_samplesBase

    double m_samplesBase

##### m_size

    int m_size

what size of tile is currently being plotted

##### m_stopPlotting

    boolean m_stopPlotting

Stop the plotting thread

##### m_stopReplotting

    boolean m_stopReplotting

Stop any replotting threads

##### m_trainingData

    Instances m_trainingData

training data

##### m_xAttribute

    int m_xAttribute

##### m_yAttribute

    int m_yAttribute

### Class weka.gui.boundaryvisualizer.BoundaryPanelDistributed

class BoundaryPanelDistributed extends BoundaryPanel implements Serializable

serialVersionUID:
  -1743284397893937776L

#### Serialized Fields

##### m_failedCount

    int m_failedCount

The count of failed sub-tasks

##### m_finishedCount

    int m_finishedCount

The count of successfully completed sub-tasks

##### m_hostPollingTime

    int[] m_hostPollingTime

##### m_listeners

    Vector m_listeners

a list of RemoteExperimentListeners

##### m_minTaskPollTime

    int m_minTaskPollTime

number of seconds between polling server

##### m_plottingAborted

    boolean m_plottingAborted

Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts plotting

##### m_remoteHostFailureCounts

    int[] m_remoteHostFailureCounts

The number of times tasks have failed on each remote host

##### m_remoteHosts

    Vector m_remoteHosts

Holds the names of machines with remoteEngine servers running

##### m_remoteHostsQueue

    Queue m_remoteHostsQueue

The queue of available hosts

##### m_remoteHostsStatus

    int[] m_remoteHostsStatus

The status of each of the remote hosts

##### m_removedHosts

    int m_removedHosts

The number of hosts removed due to exceeding max failures

##### m_subExpQueue

    Queue m_subExpQueue

The queue of sub-tasks waiting to be processed

### Class weka.gui.boundaryvisualizer.BoundaryVisualizer

class BoundaryVisualizer extends JPanel implements Serializable

serialVersionUID:
  3933877580074013208L

#### Serialized Fields

##### chooseButton

    JButton chooseButton

##### COMBO_SIZE

    Dimension COMBO_SIZE

##### dataFileLabel

    JLabel dataFileLabel

##### m_addPointsButton

    JRadioButton m_addPointsButton

##### m_addRemovePointsButtonGroup

    ButtonGroup m_addRemovePointsButtonGroup

##### m_addRemovePointsPanel

    JPanel m_addRemovePointsPanel

##### m_arffFileFilter

    ExtensionFileFilter m_arffFileFilter

##### m_boundaryPanel

    BoundaryPanel m_boundaryPanel

the plotting panel

##### m_classAttBox

    JComboBox m_classAttBox

##### m_classifier

    Classifier m_classifier

the classifier to use

##### m_classifierEditor

    GenericObjectEditor m_classifierEditor

##### m_ClassifierPanel

    PropertyPanel m_ClassifierPanel

##### m_classPanel

    ClassPanel m_classPanel

##### m_classValueSelector

    JComboBox m_classValueSelector

##### m_controlPanel

    JPanel m_controlPanel

##### m_dataGenerator

    KDDataGenerator m_dataGenerator

##### m_FileChooser

    JFileChooser m_FileChooser

The file chooser for selecting arff files

##### m_generatorSamplesBase

    int m_generatorSamplesBase

base for sampling in the non-fixed dimensions

##### m_generatorSamplesText

    JTextField m_generatorSamplesText

##### m_kernelBandwidth

    int m_kernelBandwidth

Set the kernel bandwidth to cover this many nearest neighbours

##### m_kernelBandwidthText

    JTextField m_kernelBandwidthText

##### m_maxX

    double m_maxX

##### m_maxY

    double m_maxY

##### m_minX

    double m_minX

##### m_minY

    double m_minY

##### m_numberOfSamplesFromEachRegion

    int m_numberOfSamplesFromEachRegion

##### m_plotAreaHeight

    int m_plotAreaHeight

##### m_plotAreaWidth

    int m_plotAreaWidth

##### m_plotTrainingData

    JCheckBox m_plotTrainingData

##### m_regionSamplesText

    JTextField m_regionSamplesText

##### m_removePointsButton

    JRadioButton m_removePointsButton

##### m_startBut

    JButton m_startBut

##### m_trainingInstances

    Instances m_trainingInstances

the training instances

##### m_xAttBox

    JComboBox m_xAttBox

##### m_xAxisPanel

    weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_xAxisPanel

##### m_xIndex

    int m_xIndex

##### m_yAttBox

    JComboBox m_yAttBox

##### m_yAxisPanel

    weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_yAxisPanel

##### m_yIndex

    int m_yIndex

##### removeAllButton

    JButton removeAllButton

### Class weka.gui.boundaryvisualizer.KDDataGenerator

class KDDataGenerator extends Object implements Serializable

serialVersionUID:
  -958573275606402792L

#### Serialized Fields

##### m_globalMeansOrModes

    double[] m_globalMeansOrModes

global means or modes to use for missing values

##### m_instances

    Instances m_instances

the instances to use

##### m_kernelBandwidth

    int m_kernelBandwidth

Number of neighbours to use for kernel bandwidth

##### m_kernelParams

    double[][] m_kernelParams

standard deviations for numeric attributes computed from the m_kernelBandwidth nearest neighbours for each kernel.

##### m_laplaceConst

    double m_laplaceConst

Laplace correction for discrete distributions

##### m_Max

    double[] m_Max

The maximum values for numeric attributes.

##### m_Min

    double[] m_Min

The minimum values for numeric attributes.

##### m_minStdDev

    double m_minStdDev

minimum standard deviation for numeric attributes

##### m_random

    Random m_random

random number generator

##### m_seed

    int m_seed

random number seed

##### m_standardDeviations

    double[] m_standardDeviations

standard deviations of the normal distributions for numeric attributes in each KD estimator

##### m_weightingDimensions

    boolean[] m_weightingDimensions

which dimensions to use for computing a weight for each generated instance

##### m_weightingValues

    double[] m_weightingValues

the values for the weighting dimensions to use for computing the weight for the next instance to be generated

### Class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

class RemoteBoundaryVisualizerSubTask extends Object implements Serializable

#### Serialized Fields

##### m_attsToWeightOn

    boolean[] m_attsToWeightOn

##### m_classifier

    Classifier m_classifier

##### m_dataGenerator

    DataGenerator m_dataGenerator

##### m_dist

    double[] m_dist

##### m_maxX

    double m_maxX

##### m_maxY

    double m_maxY

##### m_minX

    double m_minX

##### m_minY

    double m_minY

##### m_numOfSamplesPerGenerator

    int m_numOfSamplesPerGenerator

##### m_numOfSamplesPerRegion

    int m_numOfSamplesPerRegion

##### m_panelHeight

    int m_panelHeight

##### m_panelWidth

    int m_panelWidth

##### m_pixHeight

    double m_pixHeight

##### m_pixWidth

    double m_pixWidth

##### m_predInst

    Instance m_predInst

##### m_random

    Random m_random

##### m_result

    RemoteResult m_result

##### m_rowNumber

    int m_rowNumber

##### m_samplesBase

    double m_samplesBase

##### m_status

    TaskStatusInfo m_status

##### m_trainingData

    Instances m_trainingData

##### m_vals

    double[] m_vals

##### m_weightingAttsValues

    double[] m_weightingAttsValues

##### m_xAttribute

    int m_xAttribute

##### m_yAttribute

    int m_yAttribute

### Class weka.gui.boundaryvisualizer.RemoteResult

class RemoteResult extends Object implements Serializable

serialVersionUID:
  1873271280044633808L

#### Serialized Fields

##### m_percentCompleted

    int m_percentCompleted

progress on computing this row

##### m_probabilities

    double[][] m_probabilities

the result - ie. the probability distributions produced by the classifier for this row in the visualization

##### m_rowLength

    int m_rowLength

how many pixels in a row

##### m_rowNumber

    int m_rowNumber

the row number that this result corresponds to

## Package weka.gui.experiment

### Class weka.gui.experiment.AlgorithmListPanel

class AlgorithmListPanel extends JPanel implements Serializable

serialVersionUID:
  -7204528834764898671L

#### Serialized Fields

##### m_AddBut

    JButton m_AddBut

Click to add an algorithm

##### m_AlgorithmListModel

    DefaultListModel m_AlgorithmListModel

The list model used

##### m_ClassifierEditor

    GenericObjectEditor m_ClassifierEditor

Lets the user configure the classifier

##### m_DeleteBut

    JButton m_DeleteBut

Click to remove the selected dataset from the list

##### m_DownBut

    JButton m_DownBut

Click to move the selected algorithm(s) one down

##### m_EditBut

    JButton m_EditBut

Click to edit the selected algorithm

##### m_Editing

    boolean m_Editing

Whether an algorithm is added or only edited

##### m_Exp

    Experiment m_Exp

The experiment to set the algorithm list of

##### m_FileChooser

    JFileChooser m_FileChooser

The file chooser for selecting experiments

##### m_List

    JList m_List

The component displaying the algorithm list

##### m_LoadOptionsBut

    JButton m_LoadOptionsBut

Click to edit the load the options for athe selected algorithm

##### m_PD

    PropertyDialog m_PD

The currently displayed property dialog, if any

##### m_SaveOptionsBut

    JButton m_SaveOptionsBut

Click to edit the save the options from selected algorithm

##### m_UpBut

    JButton m_UpBut

Click to move the selected algorithm(s) one up

##### m_XMLFilter

    FileFilter m_XMLFilter

A filter to ensure only experiment (in XML format) files get shown in the chooser

### Class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer

class ObjectCellRenderer extends DefaultListCellRenderer implements Serializable

serialVersionUID:
  -5067138526587433808L

### Class weka.gui.experiment.DatasetListPanel

class DatasetListPanel extends JPanel implements Serializable

serialVersionUID:
  7068857852794405769L

#### Serialized Fields

##### m_AddBut

    JButton m_AddBut

Click to add a dataset.

##### m_DeleteBut

    JButton m_DeleteBut

Click to remove the selected dataset from the list.

##### m_DownBut

    JButton m_DownBut

Click to move the selected dataset(s) one down.

##### m_EditBut

    JButton m_EditBut

Click to edit the selected algorithm.

##### m_Exp

    Experiment m_Exp

The experiment to set the dataset list of.

##### m_FileChooser

    ConverterFileChooser m_FileChooser

The file chooser component.

##### m_List

    JList m_List

The component displaying the dataset list.

##### m_relativeCheck

    JCheckBox m_relativeCheck

Make file paths relative to the user (start) directory.

##### m_UpBut

    JButton m_UpBut

Click to move the selected dataset(s) one up.

### Class weka.gui.experiment.DistributeExperimentPanel

class DistributeExperimentPanel extends JPanel implements Serializable

serialVersionUID:
  5206721431754800278L

#### Serialized Fields

##### m_configureHostNames

    JButton m_configureHostNames

Popup the HostListPanel

##### m_enableDistributedExperiment

    JCheckBox m_enableDistributedExperiment

Distribute the current experiment to remote hosts

##### m_Exp

    RemoteExperiment m_Exp

The experiment to configure.

##### m_hostList

    HostListPanel m_hostList

The host list panel

##### m_radioListener

    ActionListener m_radioListener

Handle radio buttons

##### m_splitByDataSet

    JRadioButton m_splitByDataSet

Split experiment up by data set.

##### m_splitByRun

    JRadioButton m_splitByRun

Split experiment up by run number.

### Class weka.gui.experiment.Experimenter

class Experimenter extends JPanel implements Serializable

serialVersionUID:
  -5751617505738193788L

#### Serialized Fields

##### m_ClassFirst

    boolean m_ClassFirst

True if the class attribute is the first attribute for all datasets involved in this experiment.

##### m_ResultsPanel

    ResultsPanel m_ResultsPanel

The panel for analysing experimental results

##### m_RunPanel

    RunPanel m_RunPanel

The panel for running the experiment

##### m_SetupPanel

    SetupModePanel m_SetupPanel

The panel for configuring the experiment

##### m_TabbedPane

    JTabbedPane m_TabbedPane

The tabbed pane that controls which sub-pane we are working with

### Class weka.gui.experiment.ExperimenterDefaults

class ExperimenterDefaults extends Object implements Serializable

serialVersionUID:
  -2835933184632147981L

### Class weka.gui.experiment.GeneratorPropertyIteratorPanel

class GeneratorPropertyIteratorPanel extends JPanel implements Serializable

serialVersionUID:
  -6026938995241632139L

#### Serialized Fields

##### m_ArrayEditor

    GenericArrayEditor m_ArrayEditor

Allows editing of the custom property values

##### m_ConfigureBut

    JButton m_ConfigureBut

Click to select the property to iterate over

##### m_Exp

    Experiment m_Exp

The experiment this all applies to

##### m_Listeners

    FastVector m_Listeners

Listeners who want to be notified about editing status of this panel

##### m_StatusBox

    JComboBox m_StatusBox

Controls whether the custom iterator is used or not

### Class weka.gui.experiment.HostListPanel

class HostListPanel extends JPanel implements Serializable

serialVersionUID:
  7182791134585882197L

#### Serialized Fields

##### m_DeleteBut

    JButton m_DeleteBut

Click to remove the selected host from the list

##### m_Exp

    RemoteExperiment m_Exp

The remote experiment to set the host list of

##### m_HostField

    JTextField m_HostField

The field with which to enter host names

##### m_List

    JList m_List

The component displaying the host list

### Class weka.gui.experiment.OutputFormatDialog

class OutputFormatDialog extends JDialog implements Serializable

serialVersionUID:
  2169792738187807378L

#### Serialized Fields

##### m_CancelButton

    JButton m_CancelButton

Click to cancel the dialog.

##### m_MeanPrec

    int m_MeanPrec

the number of digits after the period (= precision) for printing the mean.

##### m_MeanPrecSpinner

    JSpinner m_MeanPrecSpinner

the spinner to choose the precision for the mean from.

##### m_OkButton

    JButton m_OkButton

Click to activate the current set parameters.

##### m_OutputFormatComboBox

    JComboBox m_OutputFormatComboBox

lets the user choose the format for the output.

##### m_RemoveFilterName

    boolean m_RemoveFilterName

whether to remove the filter names from the names.

##### m_RemoveFilterNameCheckBox

    JCheckBox m_RemoveFilterNameCheckBox

the checkbox for the removing of filter classnames.

##### m_Result

    int m_Result

the result of the user's action, either OK or CANCEL.

##### m_ResultMatrix

    Class m_ResultMatrix

the output format specific matrix.

##### m_ShowAverage

    boolean m_ShowAverage

whether to show the average too.

##### m_ShowAverageCheckBox

    JCheckBox m_ShowAverageCheckBox

the checkbox for outputting the average.

##### m_StdDevPrec

    int m_StdDevPrec

the number of digits after the period (= precision) for printing the std. deviation

##### m_StdDevPrecSpinner

    JSpinner m_StdDevPrecSpinner

the spinner to choose the precision for the std. deviation from

### Class weka.gui.experiment.ResultsPanel

class ResultsPanel extends JPanel implements Serializable

serialVersionUID:
  -4913007978534178569L

#### Serialized Fields

##### COMBO_SIZE

    Dimension COMBO_SIZE

the size for a combobox.

##### m_arffFileFilter

    ExtensionFileFilter m_arffFileFilter

ARFF file filter.

##### m_CompareCombo

    JComboBox m_CompareCombo

Lets the user select which performance measure to analyze.

##### m_CompareModel

    DefaultComboBoxModel m_CompareModel

The model embedded in m_CompareCombo.

##### m_csvFileFilter

    ExtensionFileFilter m_csvFileFilter

CSV file filter.

##### m_DatasetKeyBut

    JButton m_DatasetKeyBut

Click to edit the columns used to determine the scheme.

##### m_DatasetKeyLabel

    JLabel m_DatasetKeyLabel

Displays the currently selected column names for the scheme invalid input: '&' options.

##### m_DatasetKeyList

    JList m_DatasetKeyList

Displays the list of selected columns determining the scheme.

##### m_DatasetKeyModel

    DefaultListModel m_DatasetKeyModel

Stores the list of attributes for selecting the scheme columns.

##### m_DatasetModel

    DefaultComboBoxModel m_DatasetModel

The model embedded in m_DatasetCombo.

##### m_DisplayedButton

    JButton m_DisplayedButton

Lets the user select which schemes are compared to base.

##### m_DisplayedList

    JList m_DisplayedList

Holds the list of schemes to display.

##### m_DisplayedModel

    DefaultListModel m_DisplayedModel

The model embedded in m_DisplayedList.

##### m_Exp

    Experiment m_Exp

An experiment (used for identifying a result source) -- optional.

##### m_FileChooser

    JFileChooser m_FileChooser

The file chooser for selecting result files.

##### m_FromDBaseBut

    JButton m_FromDBaseBut

Click to load results from a database.

##### m_FromExpBut

    JButton m_FromExpBut

Click to get results from the destination given in the experiment.

##### m_FromFileBut

    JButton m_FromFileBut

Click to load results from a file.

##### m_FromLab

    JLabel m_FromLab

Displays a message about the current result set.

##### m_History

    ResultHistoryPanel m_History

A panel controlling results viewing.

##### m_InstanceQuery

    InstanceQuery m_InstanceQuery

Does any database querying for us.

##### m_Instances

    Instances m_Instances

The instances we're extracting results from.

##### m_LoadThread

    Thread m_LoadThread

A thread to load results instances from a file or database.

##### m_OutputFormatButton

    JButton m_OutputFormatButton

lets the user choose the format for the output.

##### m_OutText

    JTextArea m_OutText

Displays the output of tests.

##### m_PerformBut

    JButton m_PerformBut

Click to start the test.

##### m_ResultKeyBut

    JButton m_ResultKeyBut

Click to edit the columns used to determine the scheme.

##### m_ResultKeyLabel

    JLabel m_ResultKeyLabel

Displays the currently selected column names for the scheme invalid input: '&' options.

##### m_ResultKeyList

    JList m_ResultKeyList

Displays the list of selected columns determining the scheme.

##### m_ResultKeyModel

    DefaultListModel m_ResultKeyModel

Stores the list of attributes for selecting the scheme columns.

##### m_ResultMatrix

    ResultMatrix m_ResultMatrix

the initial result matrix.

##### m_SaveOut

    SaveBuffer m_SaveOut

The buffer saving object for saving output.

##### m_SaveOutBut

    JButton m_SaveOutBut

Click to save test output to a file.

##### m_ShowStdDevs

    JCheckBox m_ShowStdDevs

Lets the user select whether standard deviations are to be output or not.

##### m_SigTex

    JTextField m_SigTex

Lets the user edit the test significance.

##### m_SortCombo

    JComboBox m_SortCombo

Lets the user select which column to use for sorting.

##### m_SortModel

    DefaultComboBoxModel m_SortModel

The model embedded in m_SortCombo.

##### m_TesterClasses

    JComboBox m_TesterClasses

Lists all the available classes implementing the Tester-Interface.

See Also:
  - `Tester`

##### m_TesterClassesLabel

    JLabel m_TesterClassesLabel

Displays the currently selected Tester-Class.

##### m_TestsButton

    JButton m_TestsButton

Lets the user select which scheme to base comparisons against.

##### m_TestsList

    JList m_TestsList

Holds the list of schemes to base the test against.

##### m_TestsModel

    DefaultListModel m_TestsModel

The model embedded in m_TestsList.

##### m_TTester

    Tester m_TTester

The PairedTTester object.

### Class weka.gui.experiment.RunNumberPanel

class RunNumberPanel extends JPanel implements Serializable

serialVersionUID:
  -1644336658426067852L

#### Serialized Fields

##### m_Exp

    Experiment m_Exp

The experiment being configured

##### m_LowerText

    JTextField m_LowerText

Configures the lower run number

##### m_UpperText

    JTextField m_UpperText

Configures the upper run number

### Class weka.gui.experiment.RunPanel

class RunPanel extends JPanel implements Serializable

serialVersionUID:
  1691868018596872051L

#### Serialized Fields

##### m_Exp

    Experiment m_Exp

The experiment to run

##### m_Log

    LogPanel m_Log

##### m_ResultsPanel

    ResultsPanel m_ResultsPanel

A pointer to the results panel

##### m_RunThread

    Thread m_RunThread

The thread running the experiment

##### m_StartBut

    JButton m_StartBut

Click to start running the experiment

##### m_StopBut

    JButton m_StopBut

Click to signal the running experiment to halt

### Class weka.gui.experiment.SetupModePanel

class SetupModePanel extends JPanel implements Serializable

serialVersionUID:
  -3758035565520727822L

#### Serialized Fields

##### m_advancedPanel

    SetupPanel m_advancedPanel

The advanced setup panel

##### m_AdvancedSetupRBut

    JRadioButton m_AdvancedSetupRBut

The button for choosing advanced setup mode

##### m_simplePanel

    SimpleSetupPanel m_simplePanel

The simple setup panel

##### m_SimpleSetupRBut

    JRadioButton m_SimpleSetupRBut

The button for choosing simple setup mode

### Class weka.gui.experiment.SetupPanel

class SetupPanel extends JPanel implements Serializable

serialVersionUID:
  6552671886903170033L

#### Serialized Fields

##### m_advanceDataSetFirst

    JRadioButton m_advanceDataSetFirst

Click to advacne data set before custom generator

##### m_advanceIteratorFirst

    JRadioButton m_advanceIteratorFirst

Click to advance custom generator before data set

##### m_DatasetListPanel

    DatasetListPanel m_DatasetListPanel

The panel for configuring selected datasets

##### m_DistributeExperimentPanel

    DistributeExperimentPanel m_DistributeExperimentPanel

The panel for enabling a distributed experiment

##### m_Exp

    Experiment m_Exp

The experiment being configured

##### m_ExpFilter

    FileFilter m_ExpFilter

A filter to ensure only experiment files get shown in the chooser

##### m_FileChooser

    JFileChooser m_FileChooser

The file chooser for selecting experiments

##### m_GeneratorPropertyPanel

    GeneratorPropertyIteratorPanel m_GeneratorPropertyPanel

The panel that configures iteration on custom resultproducer property

##### m_KOMLFilter

    FileFilter m_KOMLFilter

A filter to ensure only experiment (in KOML format) files get shown in the chooser

##### m_NewBut

    JButton m_NewBut

Click to create a new experiment with default settings

##### m_NotesButton

    JButton m_NotesButton

A button for bringing up the notes

##### m_NotesFrame

    JFrame m_NotesFrame

Frame for the notes

##### m_NotesText

    JTextArea m_NotesText

Area for user notes Default of 10 rows

##### m_OpenBut

    JButton m_OpenBut

Click to load an experiment

##### m_RadioListener

    ActionListener m_RadioListener

Handle radio buttons

##### m_RLEditor

    GenericObjectEditor m_RLEditor

The ResultListener editor

##### m_RLEditorPanel

    PropertyPanel m_RLEditorPanel

The panel to contain the ResultListener editor

##### m_RPEditor

    GenericObjectEditor m_RPEditor

The ResultProducer editor

##### m_RPEditorPanel

    PropertyPanel m_RPEditorPanel

The panel to contain the ResultProducer editor

##### m_RunNumberPanel

    RunNumberPanel m_RunNumberPanel

The panel for configuring run numbers

##### m_SaveBut

    JButton m_SaveBut

Click to save an experiment

##### m_Support

    PropertyChangeSupport m_Support

Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.

##### m_XMLFilter

    FileFilter m_XMLFilter

A filter to ensure only experiment (in XML format) files get shown in the chooser

### Class weka.gui.experiment.SimpleSetupPanel

class SimpleSetupPanel extends JPanel implements Serializable

serialVersionUID:
  5257424515609176509L

#### Serialized Fields

##### m_AlgorithmListPanel

    AlgorithmListPanel m_AlgorithmListPanel

The panel for configuring selected algorithms

##### m_arffFileFilter

    ExtensionFileFilter m_arffFileFilter

FIlter for choosing ARFF files

##### m_BrowseDestinationButton

    JButton m_BrowseDestinationButton

Button for browsing destination files

##### m_csvFileFilter

    ExtensionFileFilter m_csvFileFilter

Filter for choosing CSV files

##### m_DatasetListPanel

    DatasetListPanel m_DatasetListPanel

The panel for configuring selected datasets

##### m_DestFileChooser

    JFileChooser m_DestFileChooser

The file chooser for selecting result destinations

##### m_destinationDatabaseURL

    String m_destinationDatabaseURL

The database destination URL to store results into

##### m_destinationFilename

    String m_destinationFilename

The filename to store results into

##### m_Exp

    Experiment m_Exp

The experiment being configured

##### m_ExpClassificationRBut

    JRadioButton m_ExpClassificationRBut

Radio button for choosing classification experiment

##### m_ExperimentParameterLabel

    JLabel m_ExperimentParameterLabel

Label for parameter field

##### m_ExperimentParameterTField

    JTextField m_ExperimentParameterTField

Input field for experiment parameter

##### m_ExperimentTypeCBox

    JComboBox m_ExperimentTypeCBox

Combo box for choosing experiment type

##### m_ExpFilter

    FileFilter m_ExpFilter

A filter to ensure only experiment files get shown in the chooser

##### m_ExpRegressionRBut

    JRadioButton m_ExpRegressionRBut

Radio button for choosing regression experiment

##### m_FileChooser

    JFileChooser m_FileChooser

The file chooser for selecting experiments

##### m_KOMLFilter

    FileFilter m_KOMLFilter

A filter to ensure only experiment (in KOML format) files get shown in the chooser

##### m_modePanel

    SetupModePanel m_modePanel

The panel which switched between simple and advanced setup modes

##### m_NewBut

    JButton m_NewBut

Click to create a new experiment with default settings

##### m_NotesButton

    JButton m_NotesButton

A button for bringing up the notes

##### m_NotesFrame

    JFrame m_NotesFrame

Frame for the notes

##### m_NotesText

    JTextArea m_NotesText

Area for user notes Default of 10 rows

##### m_NumberOfRepetitionsTField

    JTextField m_NumberOfRepetitionsTField

Input field for number of repetitions

##### m_numFolds

    int m_numFolds

The number of folds for a cross-validation experiment

##### m_numRepetitions

    int m_numRepetitions

The number of times to repeat the sub-experiment

##### m_OpenBut

    JButton m_OpenBut

Click to load an experiment

##### m_OrderAlgorithmsFirstRBut

    JRadioButton m_OrderAlgorithmsFirstRBut

Radio button for choosing algorithms first in order of execution

##### m_OrderDatasetsFirstRBut

    JRadioButton m_OrderDatasetsFirstRBut

Radio button for choosing datasets first in order of execution

##### m_ResultsDestinationCBox

    JComboBox m_ResultsDestinationCBox

Combo box for choosing experiment destination type

##### m_ResultsDestinationPathLabel

    JLabel m_ResultsDestinationPathLabel

Label for destination field

##### m_ResultsDestinationPathTField

    JTextField m_ResultsDestinationPathTField

Input field for result destination path

##### m_SaveBut

    JButton m_SaveBut

Click to save an experiment

##### m_Support

    PropertyChangeSupport m_Support

Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.

##### m_trainPercent

    double m_trainPercent

The training percentage for a train/test split experiment

##### m_userHasBeenAskedAboutConversion

    boolean m_userHasBeenAskedAboutConversion

Whether or not the user has consented for the experiment to be simplified

##### m_XMLFilter

    FileFilter m_XMLFilter

A filter to ensure only experiment (in XML format) files get shown in the chooser

## Package weka.gui.explorer

### Class weka.gui.explorer.AssociationsPanel

class AssociationsPanel extends JPanel implements Serializable

serialVersionUID:
  -6867871711865476971L

#### Serialized Fields

##### m_AssociatorEditor

    GenericObjectEditor m_AssociatorEditor

Lets the user configure the associator

##### m_CEPanel

    PropertyPanel m_CEPanel

The panel showing the current associator selection

##### m_Explorer

    Explorer m_Explorer

the parent frame

##### m_History

    ResultHistoryPanel m_History

A panel controlling results viewing

##### m_Instances

    Instances m_Instances

The main set of instances we're playing with

##### m_Log

    Logger m_Log

The destination for log/status messages

##### m_OutText

    JTextArea m_OutText

The output area for associations

##### m_RunThread

    Thread m_RunThread

A thread that associator runs in

##### m_SaveOut

    SaveBuffer m_SaveOut

The buffer saving object for saving output

##### m_StartBut

    JButton m_StartBut

Click to start running the associator

##### m_StopBut

    JButton m_StopBut

Click to stop a running associator

##### m_TestInstances

    Instances m_TestInstances

The user-supplied test set (if any)

### Class weka.gui.explorer.AttributeSelectionPanel

class AttributeSelectionPanel extends JPanel implements Serializable

serialVersionUID:
  5627185966993476142L

#### Serialized Fields

##### COMBO_SIZE

    Dimension COMBO_SIZE

Stop the class combo from taking up to much space

##### m_AEEPanel

    PropertyPanel m_AEEPanel

The panel showing the current attribute evaluation method

##### m_ASEPanel

    PropertyPanel m_ASEPanel

The panel showing the current search method

##### m_AttributeEvaluatorEditor

    GenericObjectEditor m_AttributeEvaluatorEditor

Lets the user configure the attribute evaluator

##### m_AttributeSearchEditor

    GenericObjectEditor m_AttributeSearchEditor

Lets the user configure the search method

##### m_ClassCombo

    JComboBox m_ClassCombo

Lets the user select the class column

##### m_CVBut

    JRadioButton m_CVBut

Click to set evaluation mode to cross-validation

##### m_CVLab

    JLabel m_CVLab

Label by where the cv folds are entered

##### m_CVText

    JTextField m_CVText

The field where the cv folds are entered

##### m_Explorer

    Explorer m_Explorer

the parent frame

##### m_History

    ResultHistoryPanel m_History

A panel controlling results viewing

##### m_Instances

    Instances m_Instances

The main set of instances we're playing with

##### m_Log

    Logger m_Log

The destination for log/status messages

##### m_OutText

    JTextArea m_OutText

The output area for attribute selection results

##### m_RadioListener

    ActionListener m_RadioListener

Alters the enabled/disabled status of elements associated with each radio button

##### m_RunThread

    Thread m_RunThread

A thread that attribute selection runs in

##### m_SaveOut

    SaveBuffer m_SaveOut

The buffer saving object for saving output

##### m_SeedLab

    JLabel m_SeedLab

Label by where cv random seed is entered

##### m_SeedText

    JTextField m_SeedText

The field where the seed value is entered

##### m_StartBut

    JButton m_StartBut

Click to start running the attribute selector

##### m_StopBut

    JButton m_StopBut

Click to stop a running classifier

##### m_TrainBut

    JRadioButton m_TrainBut

Click to set test mode to test on training data

### Class weka.gui.explorer.ClassifierPanel

class ClassifierPanel extends JPanel implements Serializable

serialVersionUID:
  6959973704963624003L

#### Serialized Fields

##### COMBO_SIZE

    Dimension COMBO_SIZE

Stop the class combo from taking up to much space

##### m_CEPanel

    PropertyPanel m_CEPanel

The panel showing the current classifier selection

##### m_ClassCombo

    JComboBox m_ClassCombo

Lets the user select the class column

##### m_ClassifierEditor

    GenericObjectEditor m_ClassifierEditor

Lets the user configure the classifier

##### m_CostMatrixEditor

    CostMatrixEditor m_CostMatrixEditor

The cost matrix editor for evaluation costs

##### m_CurrentVis

    VisualizePanel m_CurrentVis

The current visualization object

##### m_CVBut

    JRadioButton m_CVBut

Click to set test mode to cross-validation

##### m_CVLab

    JLabel m_CVLab

Label by where the cv folds are entered

##### m_CVText

    JTextField m_CVText

The field where the cv folds are entered

##### m_EvalWRTCostsBut

    JCheckBox m_EvalWRTCostsBut

Check to evaluate w.r.t a cost matrix

##### m_Explorer

    Explorer m_Explorer

the parent frame

##### m_FileChooser

    JFileChooser m_FileChooser

The file chooser for selecting model files

##### m_History

    ResultHistoryPanel m_History

A panel controlling results viewing

##### m_Instances

    Instances m_Instances

The main set of instances we're playing with

##### m_Log

    Logger m_Log

The destination for log/status messages

##### m_ModelFilter

    FileFilter m_ModelFilter

Filter to ensure only model files are selected

##### m_MoreOptions

    JButton m_MoreOptions

Button for further output/visualize options

##### m_OutputAdditionalAttributesLab

    JLabel m_OutputAdditionalAttributesLab

Label for the text field with additional attributes in the output

##### m_OutputAdditionalAttributesRange

    Range m_OutputAdditionalAttributesRange

the range of attributes to output

##### m_OutputAdditionalAttributesText

    JTextField m_OutputAdditionalAttributesText

Lists indices for additional attributes to output

##### m_OutputConfusionBut

    JCheckBox m_OutputConfusionBut

Check to output a confusion matrix

##### m_OutputEntropyBut

    JCheckBox m_OutputEntropyBut

Check to output entropy statistics

##### m_OutputModelBut

    JCheckBox m_OutputModelBut

Check to output the model built from the training data

##### m_OutputPerClassBut

    JCheckBox m_OutputPerClassBut

Check to output true/false positives, precision/recall for each class

##### m_OutputPredictionsTextBut

    JCheckBox m_OutputPredictionsTextBut

Check to output text predictions

##### m_OutputSourceCode

    JCheckBox m_OutputSourceCode

Whether to output the source code (only for classifiers importing Sourcable)

##### m_OutText

    JTextArea m_OutText

The output area for classification results

##### m_PercentBut

    JRadioButton m_PercentBut

Click to set test mode to generate a % split

##### m_PercentLab

    JLabel m_PercentLab

Label by where the % split is entered

##### m_PercentText

    JTextField m_PercentText

The field where the % split is entered

##### m_PMMLModelFilter

    FileFilter m_PMMLModelFilter

##### m_PreserveOrderBut

    JCheckBox m_PreserveOrderBut

Whether randomization is turned off to preserve order

##### m_RadioListener

    ActionListener m_RadioListener

Alters the enabled/disabled status of elements associated with each radio button

##### m_RandomLab

    JLabel m_RandomLab

the label for the random seed textfield

##### m_RandomSeedText

    JTextField m_RandomSeedText

User specified random seed for cross validation or % split

##### m_RunThread

    Thread m_RunThread

A thread that classification runs in

##### m_SaveOut

    SaveBuffer m_SaveOut

The buffer saving object for saving output

##### m_SetCostsBut

    JButton m_SetCostsBut

for the cost matrix

##### m_SetCostsFrame

    PropertyDialog m_SetCostsFrame

The frame used to show the cost matrix editing panel

##### m_SetTestBut

    JButton m_SetTestBut

The button used to open a separate test dataset

##### m_SetTestFrame

    JFrame m_SetTestFrame

The frame used to show the test set selection panel

##### m_SourceCodeClass

    JTextField m_SourceCodeClass

The name of the generated class (only applicable to Sourcable schemes)

##### m_StartBut

    JButton m_StartBut

Click to start running the classifier

##### m_StopBut

    JButton m_StopBut

Click to stop a running classifier

##### m_StorePredictionsBut

    JCheckBox m_StorePredictionsBut

Check to save the predictions in the results list for visualizing later on

##### m_TestLoader

    Loader m_TestLoader

The loader used to load the user-supplied test set (if any)

##### m_TestSplitBut

    JRadioButton m_TestSplitBut

Click to set test mode to a user-specified test set

##### m_TrainBut

    JRadioButton m_TrainBut

Click to set test mode to test on training data

### Class weka.gui.explorer.ClustererPanel

class ClustererPanel extends JPanel implements Serializable

serialVersionUID:
  -2474932792950820990L

#### Serialized Fields

##### COMBO_SIZE

    Dimension COMBO_SIZE

Stop the class combo from taking up to much space

##### m_ClassCombo

    JComboBox m_ClassCombo

Lets the user select the class column for classes to clusters based evaluation

##### m_ClassesToClustersBut

    JRadioButton m_ClassesToClustersBut

Click to set test mode to classes to clusters based evaluation

##### m_CLPanel

    PropertyPanel m_CLPanel

The panel showing the current clusterer selection

##### m_ClustererEditor

    GenericObjectEditor m_ClustererEditor

Lets the user configure the clusterer

##### m_CurrentVis

    VisualizePanel m_CurrentVis

The current visualization object

##### m_Explorer

    Explorer m_Explorer

the parent frame

##### m_FileChooser

    JFileChooser m_FileChooser

The file chooser for selecting model files

##### m_History

    ResultHistoryPanel m_History

A panel controlling results viewing

##### m_ignoreBut

    JButton m_ignoreBut

The button used to popup a list for choosing attributes to ignore while clustering

##### m_ignoreKeyList

    JList m_ignoreKeyList

##### m_ignoreKeyModel

    DefaultListModel m_ignoreKeyModel

##### m_Instances

    Instances m_Instances

The main set of instances we're playing with

##### m_Log

    Logger m_Log

The destination for log/status messages

##### m_ModelFilter

    FileFilter m_ModelFilter

Filter to ensure only model files are selected

##### m_OutText

    JTextArea m_OutText

The output area for classification results

##### m_PercentBut

    JRadioButton m_PercentBut

Click to set test mode to generate a % split

##### m_PercentLab

    JLabel m_PercentLab

Label by where the % split is entered

##### m_PercentText

    JTextField m_PercentText

The field where the % split is entered

##### m_RadioListener

    ActionListener m_RadioListener

Alters the enabled/disabled status of elements associated with each radio button

##### m_RunThread

    Thread m_RunThread

A thread that clustering runs in

##### m_SaveOut

    SaveBuffer m_SaveOut

The buffer saving object for saving output

##### m_SetTestBut

    JButton m_SetTestBut

The button used to open a separate test dataset

##### m_SetTestFrame

    JFrame m_SetTestFrame

The frame used to show the test set selection panel

##### m_StartBut

    JButton m_StartBut

Click to start running the clusterer

##### m_StopBut

    JButton m_StopBut

Click to stop a running clusterer

##### m_StorePredictionsBut

    JCheckBox m_StorePredictionsBut

Check to save the predictions in the results list for visualizing later on

##### m_Summary

    InstancesSummaryPanel m_Summary

The instances summary panel displayed by m_SetTestFrame

##### m_TestInstances

    Instances m_TestInstances

The user-supplied test set (if any)

##### m_TestSplitBut

    JRadioButton m_TestSplitBut

Click to set test mode to a user-specified test set

##### m_TrainBut

    JRadioButton m_TrainBut

Click to set test mode to test on training data

### Class weka.gui.explorer.DataGeneratorPanel

class DataGeneratorPanel extends JPanel implements Serializable

serialVersionUID:
  -2520408165350629380L

#### Serialized Fields

##### m_GeneratorEditor

    GenericObjectEditor m_GeneratorEditor

the GOE for the generators

##### m_Instances

    Instances m_Instances

the generated Instances

##### m_Log

    Logger m_Log

The destination for log/status messages

##### m_Output

    StringWriter m_Output

the generated output (as text)

### Class weka.gui.explorer.Explorer

class Explorer extends JPanel implements Serializable

serialVersionUID:
  -7674003708867909578L

#### Serialized Fields

##### m_CapabilitiesFilterChangeListeners

    HashSet<Explorer.CapabilitiesFilterChangeListener> m_CapabilitiesFilterChangeListeners

the listeners that listen to filter changes

##### m_LogPanel

    LogPanel m_LogPanel

The panel for log and status messages

##### m_Panels

    Vector<Explorer.ExplorerPanel> m_Panels

Contains all the additional panels apart from the pre-processing panel

##### m_PreprocessPanel

    PreprocessPanel m_PreprocessPanel

The panel for preprocessing instances

##### m_TabbedPane

    JTabbedPane m_TabbedPane

The tabbed pane that controls which sub-pane we are working with

### Class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent

class CapabilitiesFilterChangeEvent extends ChangeEvent implements Serializable

serialVersionUID:
  1194260517270385559L

#### Serialized Fields

##### m_Filter

    Capabilities m_Filter

the capabilities filter

### Class weka.gui.explorer.ExplorerDefaults

class ExplorerDefaults extends Object implements Serializable

serialVersionUID:
  4954795757927524225L

### Class weka.gui.explorer.PreprocessPanel

class PreprocessPanel extends JPanel implements Serializable

serialVersionUID:
  6764850273874813049L

#### Serialized Fields

##### m_ApplyFilterBut

    JButton m_ApplyFilterBut

Click to apply filters and save the results

##### m_AttPanel

    AttributeSelectionPanel m_AttPanel

Panel to let the user toggle attributes

##### m_AttSummaryPanel

    AttributeSummaryPanel m_AttSummaryPanel

Displays summary stats on the selected attribute

##### m_AttVisualizePanel

    AttributeVisualizationPanel m_AttVisualizePanel

The visualization of the attribute values

##### m_DataGenerator

    DataGenerator m_DataGenerator

The last generator that was selected

##### m_EditBut

    JButton m_EditBut

Click to open the current instances in a viewer

##### m_Explorer

    Explorer m_Explorer

the parent frame

##### m_FileChooser

    ConverterFileChooser m_FileChooser

The file chooser for selecting data files

##### m_FilterEditor

    GenericObjectEditor m_FilterEditor

Lets the user configure the filter

##### m_FilterPanel

    PropertyPanel m_FilterPanel

Filter configuration

##### m_GenerateBut

    JButton m_GenerateBut

Click to generate artificial data

##### m_Instances

    Instances m_Instances

The working instances

##### m_InstSummaryPanel

    InstancesSummaryPanel m_InstSummaryPanel

Displays simple stats on the working instances

##### m_IOThread

    Thread m_IOThread

A thread for loading/saving instances from a file or URL

##### m_LastURL

    String m_LastURL

Stores the last URL that instances were loaded from

##### m_Log

    Logger m_Log

The message logger

##### m_OpenDBBut

    JButton m_OpenDBBut

Click to load base instances from a Database

##### m_OpenFileBut

    JButton m_OpenFileBut

Click to load base instances from a file

##### m_OpenURLBut

    JButton m_OpenURLBut

Click to load base instances from a URL

##### m_RemoveButton

    JButton m_RemoveButton

Button for removing attributes

##### m_SaveBut

    JButton m_SaveBut

Click to apply filters and save the results

##### m_SQLQ

    String m_SQLQ

Stores the last sql query executed

##### m_Support

    PropertyChangeSupport m_Support

Manages sending notifications to people when we change the set of working instances.

##### m_tempUndoFiles

    File[] m_tempUndoFiles

Keeps track of undo points

##### m_tempUndoIndex

    int m_tempUndoIndex

The next available slot for an undo point

##### m_UndoBut

    JButton m_UndoBut

Click to revert back to the last saved point

### Class weka.gui.explorer.VisualizePanel

class VisualizePanel extends MatrixPanel implements Serializable

serialVersionUID:
  6084015036853918846L

#### Serialized Fields

##### m_Explorer

    Explorer m_Explorer

the parent frame

## Package weka.gui.graphvisualizer

### Exception Class weka.gui.graphvisualizer.BIFFormatException

class BIFFormatException extends Exception implements Serializable

serialVersionUID:
  -4102518086411708140L

### Class weka.gui.graphvisualizer.GraphVisualizer

class GraphVisualizer extends JPanel implements Serializable

serialVersionUID:
  -2038911085935515624L

#### Serialized Fields

##### fm

    FontMetrics fm

##### graphID

    String graphID

String containing graph's name

##### ICONPATH

    String ICONPATH

path for icons

##### jBtLayout

    JButton jBtLayout

Button for laying out the graph again, necessary after changing node's size or some other property of the layout engine

##### jTfNodeHeight

    JTextField jTfNodeHeight

TextField for nodes height

##### jTfNodeWidth

    JTextField jTfNodeWidth

TextField for node's width

##### m_edges

    FastVector m_edges

Vector containing edges

##### m_gp

    weka.gui.graphvisualizer.GraphVisualizer.GraphPanel m_gp

Panel actually displaying the graph

##### m_jBtSave

    JButton m_jBtSave

Save button to save the current graph in DOT or XMLBIF format. The graph should be layed out again to get the original form if reloaded from command line, as the formats do not allow saving specific information for a properly layed out graph.

##### m_js

    JScrollPane m_js

this contains the m_gp GraphPanel

##### m_le

    LayoutEngine m_le

The current LayoutEngine

##### m_nodes

    FastVector m_nodes

Vector containing nodes

##### maxStringWidth

    int maxStringWidth

used for setting appropriate node size

##### nodeHeight

    int nodeHeight

##### nodeWidth

    int nodeWidth

##### paddedNodeWidth

    int paddedNodeWidth

##### scale

    double scale

##### zoomPercents

    int[] zoomPercents

used when using zoomIn and zoomOut buttons

### Class weka.gui.graphvisualizer.LayoutCompleteEvent

class LayoutCompleteEvent extends EventObject implements Serializable

serialVersionUID:
  6172467234026258427L

## Package weka.gui.hierarchyvisualizer

### Class weka.gui.hierarchyvisualizer.HierarchyVisualizer

class HierarchyVisualizer extends PrintablePanel implements Serializable

serialVersionUID:
  1L

#### Serialized Fields

##### m_fHeight

    double m_fHeight

##### m_fScaleX

    double m_fScaleX

##### m_fScaleY

    double m_fScaleY

##### m_fTmpLength

    double m_fTmpLength

##### m_nLeafs

    int m_nLeafs

##### m_sNewick

    String m_sNewick

##### m_tree

    weka.gui.hierarchyvisualizer.HierarchyVisualizer.Node m_tree

## Package weka.gui.sql

### Class weka.gui.sql.ConnectionPanel

class ConnectionPanel extends JPanel implements Serializable

serialVersionUID:
  3499317023969723490L

#### Serialized Fields

##### m_ButtonConnect

    JButton m_ButtonConnect

the button for connecting to the database.

##### m_ButtonDatabase

    JButton m_ButtonDatabase

the button for the DB-Dialog.

##### m_ButtonHistory

    JButton m_ButtonHistory

the button for the history.

##### m_ConnectionListeners

    HashSet m_ConnectionListeners

the connection listeners.

##### m_DbDialog

    DatabaseConnectionDialog m_DbDialog

the databae connection dialog.

##### m_DbUtils

    DbUtils m_DbUtils

for connecting to the database.

##### m_History

    DefaultListModel m_History

the history of connections.

##### m_HistoryChangedListeners

    HashSet m_HistoryChangedListeners

the history listeners.

##### m_LabelURL

    JLabel m_LabelURL

the label for the URL.

##### m_Parent

    JFrame m_Parent

the parent frame.

##### m_Password

    String m_Password

the password to use for connecting to the DB.

##### m_TextURL

    JTextField m_TextURL

the textfield for the URL.

##### m_URL

    String m_URL

the URL to use.

##### m_User

    String m_User

the user to use for connecting to the DB.

### Class weka.gui.sql.DbUtils

class DbUtils extends DatabaseUtils implements Serializable

serialVersionUID:
  103748569037426479L

### Class weka.gui.sql.InfoPanel

class InfoPanel extends JPanel implements Serializable

serialVersionUID:
  -7701133696481997973L

#### Serialized Fields

##### m_ButtonClear

    JButton m_ButtonClear

the button to clear the area

##### m_ButtonCopy

    JButton m_ButtonCopy

the button to copy the selected message

##### m_Info

    JList m_Info

the list the contains the messages

##### m_Model

    DefaultListModel m_Model

the model for the list

##### m_Parent

    JFrame m_Parent

the parent of this panel

### Class weka.gui.sql.InfoPanelCellRenderer

class InfoPanelCellRenderer extends JLabel implements Serializable

serialVersionUID:
  -533380118807178531L

### Class weka.gui.sql.QueryPanel

class QueryPanel extends JPanel implements Serializable

serialVersionUID:
  4348967824619706636L

#### Serialized Fields

##### m_ButtonClear

    JButton m_ButtonClear

the clear button.

##### m_ButtonExecute

    JButton m_ButtonExecute

the execute button.

##### m_ButtonHistory

    JButton m_ButtonHistory

the history button.

##### m_Connected

    boolean m_Connected

whether we have a connection to a database or not.

##### m_DbUtils

    DbUtils m_DbUtils

for working on the database.

##### m_History

    DefaultListModel m_History

the query history.

##### m_HistoryChangedListeners

    HashSet m_HistoryChangedListeners

the history listeners.

##### m_Parent

    JFrame m_Parent

the parent of this panel.

##### m_QueryExecuteListeners

    HashSet m_QueryExecuteListeners

the connection listeners.

##### m_SpinnerMaxRows

    JSpinner m_SpinnerMaxRows

the spinner for the maximum number of rows.

##### m_TextQuery

    JTextArea m_TextQuery

the textarea for the query.

### Class weka.gui.sql.ResultPanel

class ResultPanel extends JPanel implements Serializable

serialVersionUID:
  278654800344034571L

#### Serialized Fields

##### m_ButtonClose

    JButton m_ButtonClose

the close button

##### m_ButtonCloseAll

    JButton m_ButtonCloseAll

the close all button

##### m_ButtonCopyQuery

    JButton m_ButtonCopyQuery

the button that copies the query into the QueryPanel

##### m_ButtonOptWidth

    JButton m_ButtonOptWidth

the button for the optimal column width of the current table

##### m_Listeners

    HashSet m_Listeners

the result change listeners

##### m_NameCounter

    int m_NameCounter

the counter for the tab names

##### m_Parent

    JFrame m_Parent

the parent of this panel

##### m_QueryPanel

    QueryPanel m_QueryPanel

the panel where to output the queries

##### m_TabbedPane

    JTabbedPane m_TabbedPane

the tabbed pane for the results

### Class weka.gui.sql.ResultSetTable

class ResultSetTable extends JTable implements Serializable

serialVersionUID:
  -3391076671854464137L

#### Serialized Fields

##### m_Password

    String m_Password

the password that was used to connect to the DB

##### m_Query

    String m_Query

the query the table model is based on

##### m_URL

    String m_URL

the connect string with which the query was run

##### m_User

    String m_User

the user that was used to connect to the DB

### Class weka.gui.sql.ResultSetTableCellRenderer

class ResultSetTableCellRenderer extends DefaultTableCellRenderer implements Serializable

serialVersionUID:
  -8106963669703497351L

#### Serialized Fields

##### missingColor

    Color missingColor

##### missingColorSelected

    Color missingColorSelected

### Class weka.gui.sql.SqlViewer

class SqlViewer extends JPanel implements Serializable

serialVersionUID:
  -4395028775566514329L

#### Serialized Fields

##### m_ConnectionPanel

    ConnectionPanel m_ConnectionPanel

the connection panel.

##### m_History

    Properties m_History

stores the history.

##### m_InfoPanel

    InfoPanel m_InfoPanel

the info panel.

##### m_Parent

    JFrame m_Parent

the parent of this panel.

##### m_Password

    String m_Password

the password that was used to connect to the DB.

##### m_Query

    String m_Query

the currently selected query.

##### m_QueryPanel

    QueryPanel m_QueryPanel

the query panel.

##### m_ResultPanel

    ResultPanel m_ResultPanel

the result panel.

##### m_URL

    String m_URL

the connect string with which the query was run.

##### m_User

    String m_User

the user that was used to connect to the DB.

### Class weka.gui.sql.SqlViewerDialog

class SqlViewerDialog extends JDialog implements Serializable

serialVersionUID:
  -31619864037233099L

#### Serialized Fields

##### m_ButtonCancel

    JButton m_ButtonCancel

the Cancel button

##### m_ButtonOK

    JButton m_ButtonOK

the OK button

##### m_LabelQuery

    JLabel m_LabelQuery

displays the current query

##### m_PanelButtons

    JPanel m_PanelButtons

the panel for the buttons

##### m_Parent

    JFrame m_Parent

the parent frame

##### m_Password

    String m_Password

the password that was used to connect to the DB

##### m_Query

    String m_Query

the currently selected query

##### m_ReturnValue

    int m_ReturnValue

the return value

##### m_URL

    String m_URL

the connect string with which the query was run

##### m_User

    String m_User

the user that was used to connect to the DB

##### m_Viewer

    SqlViewer m_Viewer

the SQL panel

## Package weka.gui.sql.event

### Class weka.gui.sql.event.ConnectionEvent

class ConnectionEvent extends EventObject implements Serializable

serialVersionUID:
  5420308930427835037L

#### Serialized Fields

##### m_DbUtils

    DbUtils m_DbUtils

the databaseutils instance reponsible for the connection

##### m_Exception

    Exception m_Exception

a possible exception that occurred if not successful

##### m_Type

    int m_Type

the type of event, CONNECT or DISCONNECT

### Class weka.gui.sql.event.HistoryChangedEvent

class HistoryChangedEvent extends EventObject implements Serializable

serialVersionUID:
  7476087315774869973L

#### Serialized Fields

##### m_History

    DefaultListModel m_History

the history model

##### m_HistoryName

    String m_HistoryName

the name of the history

### Class weka.gui.sql.event.QueryExecuteEvent

class QueryExecuteEvent extends EventObject implements Serializable

serialVersionUID:
  -5556385019954730740L

#### Serialized Fields

##### m_DbUtils

    DbUtils m_DbUtils

the Db utils instance for the current DB connection

##### m_Exception

    Exception m_Exception

a possible exception, if the query failed

##### m_MaxRows

    int m_MaxRows

the maximum number of rows to retrieve

##### m_Query

    String m_Query

the query that was executed

##### m_ResultSet

    ResultSet m_ResultSet

the produced ResultSet, if any

### Class weka.gui.sql.event.ResultChangedEvent

class ResultChangedEvent extends EventObject implements Serializable

serialVersionUID:
  36042516077236111L

#### Serialized Fields

##### m_Password

    String m_Password

the password that was used to connect to the DB

##### m_Query

    String m_Query

the query that is associated with the active result table

##### m_URL

    String m_URL

the connect string with which the query was run

##### m_User

    String m_User

the user that was used to connect to the DB

## Package weka.gui.streams

### Class weka.gui.streams.InstanceCounter

class InstanceCounter extends JPanel implements Serializable

serialVersionUID:
  -6084967152645935934L

#### Serialized Fields

##### m_Count

    int m_Count

##### m_Count_Lab

    JLabel m_Count_Lab

##### m_Debug

    boolean m_Debug

### Class weka.gui.streams.InstanceEvent

class InstanceEvent extends EventObject implements Serializable

serialVersionUID:
  3207259868110667379L

#### Serialized Fields

##### m_ID

    int m_ID

### Class weka.gui.streams.InstanceJoiner

class InstanceJoiner extends Object implements Serializable

serialVersionUID:
  -6529972700291329656L

#### Serialized Fields

##### b_Debug

    boolean b_Debug

Debugging mode

##### b_FirstInputFinished

    boolean b_FirstInputFinished

Whether the first input batch has finished

##### b_SecondInputFinished

    boolean b_SecondInputFinished

##### listeners

    Vector listeners

The listeners

##### m_InputFormat

    Instances m_InputFormat

The input format for instances

##### m_OutputInstance

    Instance m_OutputInstance

The current output instance

### Class weka.gui.streams.InstanceLoader

class InstanceLoader extends JPanel implements Serializable

serialVersionUID:
  -8725567310271862492L

#### Serialized Fields

##### m_Debug

    boolean m_Debug

##### m_FileNameTex

    JTextField m_FileNameTex

##### m_Listeners

    Vector m_Listeners

##### m_LoaderThread

    Thread m_LoaderThread

##### m_OutputInstance

    Instance m_OutputInstance

##### m_OutputInstances

    Instances m_OutputInstances

##### m_StartBut

    JButton m_StartBut

### Class weka.gui.streams.InstanceSavePanel

class InstanceSavePanel extends Panel implements Serializable

serialVersionUID:
  -6061005366989295026L

#### Serialized Fields

##### arffFile_Tex

    TextField arffFile_Tex

##### b_Debug

    boolean b_Debug

##### count_Lab

    Label count_Lab

##### m_Count

    int m_Count

##### outputWriter

    PrintWriter outputWriter

### Class weka.gui.streams.InstanceTable

class InstanceTable extends JPanel implements Serializable

serialVersionUID:
  -2462533698100834803L

#### Serialized Fields

##### m_Clear

    boolean m_Clear

##### m_Debug

    boolean m_Debug

##### m_Instances

    Instances m_Instances

##### m_InstanceTable

    JTable m_InstanceTable

##### m_UpdateString

    String m_UpdateString

### Class weka.gui.streams.InstanceViewer

class InstanceViewer extends JPanel implements Serializable

serialVersionUID:
  -4925729441294121772L

#### Serialized Fields

##### m_Clear

    boolean m_Clear

##### m_Debug

    boolean m_Debug

##### m_OutputTex

    JTextArea m_OutputTex

##### m_UpdateString

    String m_UpdateString

## Package weka.gui.treevisualizer

### Class weka.gui.treevisualizer.TreeVisualizer

class TreeVisualizer extends PrintablePanel implements Serializable

serialVersionUID:
  -8668637962504080749L

#### Serialized Fields

##### m_accept

    JMenuItem m_accept

An option on the win menu.

##### m_addChildren

    JMenuItem m_addChildren

An add children to Node choice, This is only available if the tree display has a treedisplay listerner added to it.

##### m_autoScale

    JMenuItem m_autoScale

An option on the win_menu

##### m_BackgroundColor

    Color m_BackgroundColor

the background color.

##### m_caseSen

    JRadioButton m_caseSen

##### m_classifyChild

    JMenuItem m_classifyChild

Use this to have J48 classify this node.

##### m_clickAvailable

    boolean m_clickAvailable

A variable used to determine for the clicked method if any other mouse state has already taken place.

##### m_currentFont

    Font m_currentFont

The font used to display the tree.

##### m_edges

    weka.gui.treevisualizer.TreeVisualizer.EdgeInfo[] m_edges

An array with the Edges sorted into it and display information about the Edges.

##### m_fitToScreen

    JMenuItem m_fitToScreen

An option on the win_menu

##### m_focusNode

    int m_focusNode

The subscript for the currently selected node (this is an internal thing, so the user is unaware of this).

##### m_FontColor

    Color m_FontColor

the font color.

##### m_fontSize

    FontMetrics m_fontSize

The size information for the current font.

##### m_frameLimiter

    Timer m_frameLimiter

A timer to keep the frame rate constant.

##### m_highlightNode

    int m_highlightNode

The Node the user is currently focused on , this is similar to focus node except that it is used by other classes rather than this one.

##### m_LineColor

    Color m_LineColor

the line color.

##### m_listener

    TreeDisplayListener m_listener

##### m_mouseState

    int m_mouseState

Describes the action the user is performing.

##### m_newMousePos

    Dimension m_newMousePos

A variable used to tag the most current point of a user action.

##### m_NodeColor

    Color m_NodeColor

the node color.

##### m_nodeMenu

    JPopupMenu m_nodeMenu

A right or middle click popup menu for nodes.

##### m_nodes

    weka.gui.treevisualizer.TreeVisualizer.NodeInfo[] m_nodes

An array with the Nodes sorted into it and display information about the Nodes.

##### m_numLevels

    int m_numLevels

The number of levels in the tree.

##### m_numNodes

    int m_numNodes

The number of Nodes in the tree.

##### m_nViewPos

    Dimension m_nViewPos

A variable used to remember the desired view pos.

##### m_nViewSize

    Dimension m_nViewSize

A variable used to remember the desired tree size.

##### m_oldMousePos

    Dimension m_oldMousePos

A variable used to tag the start pos of a user action.

##### m_placer

    NodePlace m_placer

The placement algorithm for the Node structure.

##### m_remChildren

    JMenuItem m_remChildren

Similar to add children but now it removes children.

##### m_scaling

    int m_scaling

The number of frames left to calculate.

##### m_searchString

    JTextField m_searchString

##### m_searchWin

    JDialog m_searchWin

##### m_selectFont

    JMenu m_selectFont

A sub group on the win_menu

##### m_selectFontGroup

    ButtonGroup m_selectFontGroup

A grouping for the font choices

##### m_sendInstances

    JMenuItem m_sendInstances

Use this to dump the instances from this node to the vis panel.

##### m_ShowBorder

    boolean m_ShowBorder

whether to show the border or not.

##### m_size1

    JRadioButtonMenuItem m_size1

A font choice.

##### m_size10

    JRadioButtonMenuItem m_size10

A font choice.

##### m_size12

    JRadioButtonMenuItem m_size12

A font choice.

##### m_size14

    JRadioButtonMenuItem m_size14

A font choice.

##### m_size16

    JRadioButtonMenuItem m_size16

A font choice.

##### m_size18

    JRadioButtonMenuItem m_size18

A font choice.

##### m_size2

    JRadioButtonMenuItem m_size2

A font choice.

##### m_size20

    JRadioButtonMenuItem m_size20

A font choice.

##### m_size22

    JRadioButtonMenuItem m_size22

A font choice.

##### m_size24

    JRadioButtonMenuItem m_size24

A font choice.

##### m_size4

    JRadioButtonMenuItem m_size4

A font choice.

##### m_size6

    JRadioButtonMenuItem m_size6

A font choice.

##### m_size8

    JRadioButtonMenuItem m_size8

A font choice.

##### m_topN

    JMenuItem m_topN

An option on the win_menu

##### m_topNode

    Node m_topNode

The top Node.

##### m_viewPos

    Dimension m_viewPos

The postion of the view relative to the tree.

##### m_viewSize

    Dimension m_viewSize

The size of the tree in pixels.

##### m_visualise

    JMenuItem m_visualise

A visualize choice for the node, may not be available.

##### m_winMenu

    JPopupMenu m_winMenu

A right (or middle) click popup menu.

##### m_ZoomBoxColor

    Color m_ZoomBoxColor

the color of the zoombox.

##### m_ZoomBoxXORColor

    Color m_ZoomBoxXORColor

the XOR color of the zoombox.

## Package weka.gui.visualize

### Class weka.gui.visualize.AttributePanel

class AttributePanel extends JScrollPane implements Serializable

serialVersionUID:
  3533330317806757814L

#### Serialized Fields

##### m_backgroundColor

    Color m_backgroundColor

If set, it allows this panel to avoid setting a color in the color list that is equal to the background color

##### m_barColour

    Color m_barColour

The default colour to use for the background of the bars if a colour is not defined in Visualize.props

##### m_cIndex

    int m_cIndex

##### m_colorList

    FastVector m_colorList

The colour map to use for colouring points

##### m_DefaultColors

    Color[] m_DefaultColors

default colours for colouring discrete class

##### m_heights

    int[] m_heights

Holds the random height for each instance.

##### m_Listeners

    FastVector m_Listeners

The list of things listening to this panel

##### m_maxC

    double m_maxC

Holds the min and max values of the colouring attributes

##### m_minC

    double m_minC

##### m_plotInstances

    Instances m_plotInstances

The instances to be plotted

##### m_span

    JPanel m_span

The container window for the attribute bars, and also where the X,Y or B get printed.

##### m_xIndex

    int m_xIndex

##### m_yIndex

    int m_yIndex

### Class weka.gui.visualize.AttributePanel.AttributeSpacing

class AttributeSpacing extends JPanel implements Serializable

serialVersionUID:
  7220615894321679898L

#### Serialized Fields

##### m_attrib

    Attribute m_attrib

The attribute itself.

##### m_attribIndex

    int m_attribIndex

The index for this attribute.

##### m_cached

    int[] m_cached

The x position of each point.

##### m_maxVal

    double m_maxVal

The min and max values for this attribute.

##### m_minVal

    double m_minVal

##### m_oldWidth

    int m_oldWidth

Used to determine if the positions need to be recalculated.

##### m_pointDrawn

    boolean[][] m_pointDrawn

A temporary array used to strike any instances that would be drawn redundantly.

### Class weka.gui.visualize.ClassPanel

class ClassPanel extends JPanel implements Serializable

serialVersionUID:
  -7969401840501661430L

#### Serialized Fields

##### m_backgroundColor

    Color m_backgroundColor

if set, it allows this panel to steer away from setting up a color in the color list that is equal to the background color

##### m_cIndex

    int m_cIndex

Index of the colouring attribute

##### m_colorList

    FastVector m_colorList

the list of colours to use for colouring nominal attribute labels

##### m_ColourChangeListeners

    FastVector m_ColourChangeListeners

An optional list of listeners who want to know when a colour changes. Listeners are notified via an ActionEvent

##### m_DefaultColors

    Color[] m_DefaultColors

default colours for colouring discrete class

##### m_fieldWidthC

    int m_fieldWidthC

Field width for numeric values

##### m_HorizontalPad

    int m_HorizontalPad

The amount of space to leave either side of the legend

##### m_Instances

    Instances m_Instances

Instances being plotted

##### m_isEnabled

    boolean m_isEnabled

True when the panel has been enabled (ie after setNumeric or setNominal has been called

##### m_isNumeric

    boolean m_isNumeric

True if the colouring attribute is numeric

##### m_labelFont

    Font m_labelFont

The font used in labeling

##### m_labelMetrics

    FontMetrics m_labelMetrics

Font metrics

##### m_maxC

    double m_maxC

The maximum value for the colouring attribute

##### m_minC

    double m_minC

The minimum value for the colouring attribute

##### m_oldWidth

    int m_oldWidth

The old width.

##### m_precisionC

    int m_precisionC

The precision with which to display real values

##### m_Repainters

    FastVector m_Repainters

An optional list of Components that use the colour list maintained by this class. If the user changes a colour using the colour chooser, then these components need to be repainted in order to display the change

##### m_spectrumHeight

    int m_spectrumHeight

The height of the spectrum for numeric class

##### m_tickSize

    int m_tickSize

The size of the ticks

### Class weka.gui.visualize.LegendPanel

class LegendPanel extends JScrollPane implements Serializable

serialVersionUID:
  -1262384440543001505L

#### Serialized Fields

##### m_plots

    FastVector m_plots

the list of plot elements

##### m_Repainters

    FastVector m_Repainters

a list of components that need to be repainted when a colour is changed

##### m_span

    JPanel m_span

the panel that contains the legend entries

### Class weka.gui.visualize.LegendPanel.LegendEntry

class LegendEntry extends JPanel implements Serializable

serialVersionUID:
  3879990289042935670L

#### Serialized Fields

##### m_dataIndex

    int m_dataIndex

the index (in the list of plots) of the data for this legend--- used to draw the correct shape for this data

##### m_legendText

    JLabel m_legendText

the text part of this legend

##### m_plotData

    PlotData2D m_plotData

the data for this legend entry

##### m_pointShape

    JPanel m_pointShape

displays the point shape associated with this legend entry

### Class weka.gui.visualize.MatrixPanel

class MatrixPanel extends JPanel implements Serializable

serialVersionUID:
  -1232642719869188740L

#### Serialized Fields

##### datapointSize

    int datapointSize

This stores the size of the datapoint

##### f

    Font f

font used in column and row names

##### fontColor

    Color fontColor

color for the font used in column and row names

##### jitterVals

    int[][] jitterVals

Array containing precalculated jitter values

##### jp

    JSplitPane jp

Split pane for splitting the matrix and the buttons and bars

##### m_attribList

    JList m_attribList

The list for selecting the attributes to display the plot matrix

##### m_classAttrib

    JComboBox m_classAttrib

The combo box to allow user to select the colouring attribute

##### m_classIndex

    int m_classIndex

This contains the index of the currently selected colouring attribute

##### m_colorList

    FastVector m_colorList

Contains discrete colours for colouring for nominal attributes

##### m_cp

    ClassPanel m_cp

The panel that displays the legend of the colouring attribute

##### m_data

    Instances m_data

The dataset for which this panel will display the plot matrix for

##### m_jitter

    JSlider m_jitter

The slider to add jitter to the plots

##### m_js

    JScrollPane m_js

The scroll pane to scrolling the matrix

##### m_missing

    boolean[][] m_missing

Contains true for each attribute value (only the selected attributes+class attribute) that is missing, for each instance. m_missing[i][j] == true if m_selectedAttribs[j] is missing in instance i. m_missing[i][m_missing[].length-1] == true if class value is missing in instance i.

##### m_plotLBSizeD

    Dimension m_plotLBSizeD

Stores the maximum size for PlotSize label to keep it's size constant

##### m_plotSize

    JSlider m_plotSize

The slider to adjust the size of the cells in the matrix

##### m_plotSizeLb

    JLabel m_plotSizeLb

Displays the current size beside the slider bar for cell size

##### m_plotsPanel

    weka.gui.visualize.MatrixPanel.Plot m_plotsPanel

The that panel contains the actual matrix

##### m_pointColors

    int[] m_pointColors

This is an array cache for the colour of each of the instances depending on the colouring attribute. If the colouring attribute is nominal then it contains the index of the colour in our colour list. Otherwise, for numeric colouring attribute, it contains the precalculated red component for each instance's colour

##### m_pointLBSizeD

    Dimension m_pointLBSizeD

Stores the maximum size for PointSize label to keep it's size constant

##### m_points

    int[][] m_points

This is a local array cache for all the instance values for faster rendering

##### m_pointSize

    JSlider m_pointSize

The slider to adjust the size of the datapoints

##### m_pointSizeLb

    JLabel m_pointSizeLb

Displays the current size beside the slider bar for point size

##### m_resampleBt

    JButton m_resampleBt

The label for resample percentage

##### m_resamplePercent

    JTextField m_resamplePercent

The text area for percentage to resample data

##### m_rseed

    JTextField m_rseed

Random seed for random subsample

##### m_selAttrib

    JButton m_selAttrib

The button to display a window to select attributes

##### m_selectedAttribs

    int[] m_selectedAttribs

This array contains the indices of the attributes currently selected

##### m_type

    int[] m_type

This array contains for the classAttribute:
m_type[0] = [type of attribute, nominal, string or numeric]
m_type[1] = [number of discrete values of nominal or string attribute
or same as m_type[0] for numeric attribute]

##### m_updateBt

    JButton m_updateBt

The button that updates the display to reflect the changes made by the user. E.g. changed attribute set for the matrix

##### optionsPanel

    JPanel optionsPanel

The panel that contains all the buttons and tools, i.e. resize, jitter bars and sub-sampling buttons etc on the bottom of the panel

##### rnd

    Random rnd

For adding random jitter

### Class weka.gui.visualize.Plot2D

class Plot2D extends JPanel implements Serializable

serialVersionUID:
  -1673162410856660442L

#### Serialized Fields

##### m_axisChanged

    boolean m_axisChanged

if the user changes attribute assigned to an axis

##### m_axisColour

    Color m_axisColour

Default colour for the axis

##### m_axisPad

    int m_axisPad

Axis padding

##### m_backgroundColour

    Color m_backgroundColour

Default colour for the plot background

##### m_cIndex

    int m_cIndex

##### m_colorList

    FastVector m_colorList

The list of the colors used

##### m_DefaultColors

    Color[] m_DefaultColors

default colours for colouring discrete class

##### m_drawnPoints

    int[][] m_drawnPoints

An array used to show if a point is hidden or not. This is used for speeding up the drawing of the plot panel although I am not sure how much performance this grants over not having it.

##### m_InstanceInfo

    JFrame m_InstanceInfo

For popping up text info on data points

##### m_InstanceInfoText

    JTextArea m_InstanceInfoText

##### m_JitterVal

    int m_JitterVal

the level of jitter

##### m_JRand

    Random m_JRand

random values for perterbing the data points

##### m_labelFont

    Font m_labelFont

Font for labels

##### m_labelMetrics

    FontMetrics m_labelMetrics

##### m_masterName

    String m_masterName

The name of the master plot

##### m_masterPlot

    PlotData2D m_masterPlot

The master plot

##### m_maxC

    double m_maxC

##### m_maxX

    double m_maxX

Holds the min and max values of the x, y and colouring attributes over all plots

##### m_maxY

    double m_maxY

##### m_minC

    double m_minC

##### m_minX

    double m_minX

##### m_minY

    double m_minY

##### m_plotCompanion

    Plot2DCompanion m_plotCompanion

An optional "compainion" of the panel. If specified, this class will get to do its thing with our graphics context before we do any drawing. Eg. the visualize panel may need to draw polygons etc. before we draw plot axis and data points

##### m_plotInstances

    Instances m_plotInstances

The instances to be plotted

##### m_plotResize

    boolean m_plotResize

if the user resizes the window, or the attributes selected for the attributes change, then the lookup table for points needs to be recalculated

##### m_plots

    FastVector m_plots

The plots to display

##### m_pointLookup

    double[][] m_pointLookup

lookup table for plotted points

##### m_sIndex

    int m_sIndex

##### m_tickSize

    int m_tickSize

Tick size

##### m_XaxisEnd

    int m_XaxisEnd

##### m_XaxisStart

    int m_XaxisStart

the offsets of the axes once label metrics are calculated

##### m_xIndex

    int m_xIndex

Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing

##### m_YaxisEnd

    int m_YaxisEnd

##### m_YaxisStart

    int m_YaxisStart

##### m_yIndex

    int m_yIndex

### Class weka.gui.visualize.PrintableComponent.JComponentWriterFileFilter

class JComponentWriterFileFilter extends ExtensionFileFilter implements Serializable

#### Serialized Fields

##### m_Writer

    JComponentWriter m_Writer

the associated writer.

### Class weka.gui.visualize.PrintablePanel

class PrintablePanel extends JPanel implements Serializable

serialVersionUID:
  6281532227633417538L

#### Serialized Fields

##### m_Printer

    PrintableComponent m_Printer

the class responsible for printing

### Class weka.gui.visualize.ThresholdVisualizePanel

class ThresholdVisualizePanel extends VisualizePanel implements Serializable

serialVersionUID:
  3070002211779443890L

#### Serialized Fields

##### m_ROCString

    String m_ROCString

The string to add to the Plot Border.

##### m_savePanelBorderText

    String m_savePanelBorderText

Original border text

### Class weka.gui.visualize.VisualizePanel

class VisualizePanel extends PrintablePanel implements Serializable

serialVersionUID:
  240108358588153943L

#### Serialized Fields

##### COMBO_SIZE

    Dimension COMBO_SIZE

Stop the combos from growing out of control

##### listener

    ActionListener listener

An optional listener that we will inform when ComboBox selections change

##### m_ArffFilter

    FileFilter m_ArffFilter

Filter to ensure only arff files are selected

##### m_attrib

    AttributePanel m_attrib

The panel that displays the attributes , using color to represent another attribute.

##### m_cancel

    JButton m_cancel

Button for the user to remove all splits.

##### m_classPanel

    ClassPanel m_classPanel

The panel that displays the legend for the colouring attribute

##### m_classSurround

    JPanel m_classSurround

Panel that surrounds the class panel with a titled border

##### m_colorList

    FastVector m_colorList

The list of the colors used

##### m_ColourCombo

    JComboBox m_ColourCombo

Lets the user select the attribute to use for colouring

##### m_DefaultColors

    Color[] m_DefaultColors

default colours for colouring discrete class

##### m_FileChooser

    JFileChooser m_FileChooser

file chooser for saving instances

##### m_Jitter

    JSlider m_Jitter

The jitter slider

##### m_JitterLab

    JLabel m_JitterLab

Label for the jitter slider

##### m_legendPanel

    LegendPanel m_legendPanel

The panel that displays legend info if there is more than one plot

##### m_Log

    Logger m_Log

the logger

##### m_openBut

    JButton m_openBut

Button for the user to open the visualized set of instances

##### m_plot

    weka.gui.visualize.VisualizePanel.PlotPanel m_plot

The panel that displays the plot

##### m_plotName

    String m_plotName

The name of the plot (not currently displayed, but can be used in the containing Frame or Panel)

##### m_plotSurround

    JPanel m_plotSurround

Panel that surrounds the plot panel with a titled border

##### m_preferredColourDimension

    String m_preferredColourDimension

##### m_preferredXDimension

    String m_preferredXDimension

These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file

##### m_preferredYDimension

    String m_preferredYDimension

##### m_saveBut

    JButton m_saveBut

Button for the user to save the visualized set of instances

##### m_ShapeCombo

    JComboBox m_ShapeCombo

Lets the user select the shape they want to create for instance selection.

##### m_showAttBars

    boolean m_showAttBars

Show the attribute bar panel

##### m_showClassPanel

    boolean m_showClassPanel

Show the class panel

##### m_splitListener

    VisualizePanelListener m_splitListener

An optional listener that we will inform when the user creates a split to seperate instances.

##### m_submit

    JButton m_submit

Button for the user to enter the splits.

##### m_XCombo

    JComboBox m_XCombo

Lets the user select the attribute for the x axis

##### m_YCombo

    JComboBox m_YCombo

Lets the user select the attribute for the y axis

### Class weka.gui.visualize.VisualizePanel.PlotPanel

class PlotPanel extends PrintablePanel implements Serializable

serialVersionUID:
  -4823674171136494204L

#### Serialized Fields

##### m_cIndex

    int m_cIndex

##### m_createShape

    boolean m_createShape

True if the user is currently dragging a box.

##### m_newMousePos

    Dimension m_newMousePos

contains the position of the mouse (used for rubberbanding).

##### m_originalPlot

    PlotData2D m_originalPlot

The master plot

##### m_plot2D

    Plot2D m_plot2D

The actual generic plotting panel

##### m_plotInstances

    Instances m_plotInstances

The instances from the master plot

##### m_shapePoints

    FastVector m_shapePoints

contains the points of the shape currently being drawn.

##### m_shapes

    FastVector m_shapes

contains all the shapes that have been drawn for these attribs

##### m_sIndex

    int m_sIndex

##### m_XaxisEnd

    int m_XaxisEnd

##### m_XaxisStart

    int m_XaxisStart

the offsets of the axes once label metrics are calculated

##### m_xIndex

    int m_xIndex

Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing

##### m_YaxisEnd

    int m_YaxisEnd

##### m_YaxisStart

    int m_YaxisStart

##### m_yIndex

    int m_yIndex