Serialized Form


Package weka.associations

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

serialVersionUID: -3017644543382432070L

Class weka.associations.Apriori extends AbstractAssociator implements Serializable

serialVersionUID: 3277498842319212687L

Serialized Fields

m_minSupport

double m_minSupport
The minimum support.


m_upperBoundMinSupport

double m_upperBoundMinSupport
The upper bound on the support


m_lowerBoundMinSupport

double m_lowerBoundMinSupport
The lower bound for the minimum support.


m_metricType

int m_metricType
The selected metric type.


m_minMetric

double m_minMetric
The minimum metric score.


m_numRules

int m_numRules
The maximum number of rules that are output.


m_delta

double m_delta
Delta by which m_minSupport is decreased in each iteration.


m_significanceLevel

double m_significanceLevel
Significance level for optional significance test.


m_cycles

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


m_Ls

FastVector m_Ls
The set of all sets of itemsets L.


m_hashtables

FastVector m_hashtables
The same information stored in hash tables.


m_allTheRules

FastVector[] m_allTheRules
The list of all generated rules.


m_instances

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


m_outputItemSets

boolean m_outputItemSets
Output itemsets found?


m_removeMissingCols

boolean m_removeMissingCols
Remove columns with all missing values


m_verbose

boolean m_verbose
Report progress iteratively


m_onlyClass

Instances m_onlyClass
Only the class attribute of all Instances.


m_classIndex

int m_classIndex
The class index.


m_car

boolean m_car
Flag indicating whether class association rules are mined.

Class weka.associations.AprioriItemSet extends ItemSet implements Serializable

serialVersionUID: 7684467755712672058L

Class weka.associations.CaRuleGeneration extends RuleGeneration implements Serializable

serialVersionUID: 3065752149646517703L

Class weka.associations.FilteredAssociator extends SingleAssociatorEnhancer implements Serializable

serialVersionUID: -4523450618538717400L

Serialized Fields

m_Filter

Filter m_Filter
The filter


m_FilteredInstances

Instances m_FilteredInstances
The instance structure of the filtered instances


m_ClassIndex

int m_ClassIndex
The class index.

Class weka.associations.GeneralizedSequentialPatterns extends AbstractAssociator implements Serializable

serialVersionUID: -4119691320812254676L

Serialized Fields

m_MinSupport

double m_MinSupport
the minimum support threshold


m_DataSeqID

int m_DataSeqID
number indicating the attribute holding the data sequence ID


m_OriginalDataSet

Instances m_OriginalDataSet
original sequential data set to be used for sequential patterns extraction


m_AllSequentialPatterns

FastVector m_AllSequentialPatterns
all generated frequent sequences, i.e. sequential patterns


m_Cycles

int m_Cycles
number of cycles performed until termination


m_CycleStart

java.lang.String m_CycleStart
String indicating the starting time of an cycle.


m_CycleEnd

java.lang.String m_CycleEnd
String indicating the ending time of an cycle.


m_AlgorithmStart

java.lang.String m_AlgorithmStart
String indicating the starting time of the algorithm.


m_FilterAttributes

java.lang.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_Debug

boolean m_Debug
Whether the classifier is run in debug mode.

Class weka.associations.HotSpot extends java.lang.Object implements Serializable

serialVersionUID: 42972325096347677L

Serialized Fields

m_targetSI

SingleIndex m_targetSI
index of the target attribute


m_target

int m_target

m_support

double m_support
Support as a fraction of the total training set


m_supportCount

int m_supportCount
Support as an instance count


m_globalTarget

double m_globalTarget
The global value of the attribute of interest (mean or probability)


m_minImprovement

double m_minImprovement
The minimum improvement necessary to justify adding a test


m_globalSupport

int m_globalSupport
Actual global support of the target value (discrete target only)


m_targetIndexSI

SingleIndex m_targetIndexSI
For discrete target, the index of the value of interest


m_targetIndex

int m_targetIndex

m_maxBranchingFactor

int m_maxBranchingFactor
At each level of the tree consider at most this number extensions


m_numInstances

int m_numInstances
Number of instances in the full data


m_head

weka.associations.HotSpot.HotNode m_head
The head of the tree


m_header

Instances m_header
Header of the training data


m_lookups

int m_lookups
Debugging stuff


m_insertions

int m_insertions

m_hits

int m_hits

m_debug

boolean m_debug

m_minimize

boolean m_minimize
Minimize, rather than maximize the target


m_errorMessage

java.lang.String m_errorMessage
Error messages relating to too large/small support values


m_ruleLookup

java.util.HashMap<K,V> m_ruleLookup
Rule lookup table

Class weka.associations.HotSpot.HotNode extends java.lang.Object implements Serializable

Serialized Fields

m_insts

Instances m_insts

m_targetValue

double m_targetValue

m_children

weka.associations.HotSpot.HotNode[] m_children

m_testDetails

weka.associations.HotSpot.HotNode.HotTestDetails[] m_testDetails

m_id

int m_id

Class weka.associations.HotSpot.HotNode.HotTestDetails extends java.lang.Object implements Serializable

Serialized Fields

m_merit

double m_merit

m_support

int m_support

m_subsetSize

int m_subsetSize

m_splitAttIndex

int m_splitAttIndex

m_splitValue

double m_splitValue

m_lessThan

boolean m_lessThan

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

serialVersionUID: 2724000045282835791L

Serialized Fields

m_items

int[] m_items
The items stored as an array of of ints.


m_counter

int m_counter
Counter for how many transactions contain this item set.


m_totalTransactions

int m_totalTransactions
The total number of transactions

Class weka.associations.LabeledItemSet extends ItemSet implements Serializable

serialVersionUID: 4158771925518299903L

Serialized Fields

m_classLabel

int m_classLabel
The class label.


m_ruleSupCounter

int m_ruleSupCounter
The support of the rule.

Class weka.associations.PredictiveApriori extends AbstractAssociator implements Serializable

serialVersionUID: 8109088846865075341L

Serialized Fields

m_premiseCount

int m_premiseCount
The minimum support.


m_numRules

int m_numRules
The maximum number of rules that are output.


m_Ls

FastVector m_Ls
The set of all sets of itemsets.


m_hashtables

FastVector m_hashtables
The same information stored in hash tables.


m_allTheRules

FastVector[] m_allTheRules
The list of all generated rules.


m_instances

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


m_priors

java.util.Hashtable<K,V> m_priors
The hashtable containing the prior probabilities.


m_midPoints

double[] m_midPoints
The mid points of the intervals used for the prior estimation.


m_expectation

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


m_best

java.util.TreeSet<E> 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_count

int m_count
Counter for the time of generation for an association rule.


m_priorEstimator

PriorEstimation m_priorEstimator
The prior estimator.


m_classIndex

int m_classIndex
The class index.


m_car

boolean m_car
Flag indicating whether class association rules are mined.

Class weka.associations.PriorEstimation extends java.lang.Object implements Serializable

serialVersionUID: 5570863216522496271L

Serialized Fields

m_numRandRules

int m_numRandRules
The number of rnadom rules.


m_numIntervals

int m_numIntervals
The number of intervals.


m_randNum

java.util.Random m_randNum
The random number generator.


m_instances

Instances m_instances
The instances for which association rules are mined.


m_CARs

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


m_distribution

java.util.Hashtable<K,V> m_distribution
Hashtable to store the confidence values of randomly generated rules.


m_priors

java.util.Hashtable<K,V> m_priors
Hashtable containing the estimated prior probabilities.


m_sum

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


m_midPoints

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

Class weka.associations.RuleGeneration extends java.lang.Object implements Serializable

serialVersionUID: -8927041669872491432L

Serialized Fields

m_items

int[] m_items
The items stored as an array of of integer.


m_counter

int m_counter
Counter for how many transactions contain this item set.


m_totalTransactions

int m_totalTransactions
The total number of transactions


m_change

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


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_minRuleCount

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


m_midPoints

double[] m_midPoints
Sorted array of the mied points of the intervals used for prior estimation.


m_priors

java.util.Hashtable<K,V> m_priors
Hashtable conatining the estimated prior probabilities.


m_best

java.util.TreeSet<E> m_best
The list of the actual n best rules.


m_count

int m_count
Integer indicating the generation time of a rule.


m_instances

Instances m_instances
The instances.

Class weka.associations.RuleItem extends java.lang.Object implements Serializable

serialVersionUID: -3761299128347476534L

Serialized Fields

m_premise

ItemSet m_premise
The premise of a rule.


m_consequence

ItemSet m_consequence
The consequence of a rule.


m_accuracy

double m_accuracy
The expected predictive accuracy of a rule.


m_genTime

int m_genTime
The generation time of a rule.

Class weka.associations.SingleAssociatorEnhancer extends AbstractAssociator implements Serializable

serialVersionUID: -3665885256363525164L

Serialized Fields

m_Associator

Associator m_Associator
The base associator to use

Class weka.associations.Tertius extends AbstractAssociator implements Serializable

serialVersionUID: 5556726848380738179L

Serialized Fields

m_results

SimpleLinkedList m_results
The results.


m_hypotheses

int m_hypotheses
Number of hypotheses considered.


m_explored

int m_explored
Number of hypotheses explored.


m_time

java.util.Date m_time
Time needed for the search.


m_valuesText

java.awt.TextField m_valuesText
Field to output the current values.


m_instances

Instances m_instances
Instances used for the search.


m_predicates

java.util.ArrayList<E> m_predicates
Predicates used in the rules.


m_status

int m_status
Status of the search.


m_best

int m_best
Number of best confirmation values to search.


m_frequencyThreshold

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


m_confirmationThreshold

double m_confirmationThreshold
Confirmation threshold for the rules.


m_noiseThreshold

double m_noiseThreshold
Maximal number of counter-instances.


m_repeat

boolean m_repeat
Repeat attributes ?


m_numLiterals

int m_numLiterals
Number of literals in a rule.


m_negation

int m_negation
Type of negation used in the rules.


m_classification

boolean m_classification
Classification bias.


m_classIndex

int m_classIndex
Index of class attribute.


m_horn

boolean m_horn
Horn clauses bias.


m_equivalent

boolean m_equivalent
Perform test on equivalent rules ?


m_sameClause

boolean m_sameClause
Perform test on same clauses ?


m_subsumption

boolean m_subsumption
Perform subsumption test ?


m_missing

int m_missing
Way of handling missing values in the search.


m_roc

boolean m_roc
Perform ROC analysis ?


m_partsString

java.lang.String m_partsString
Name of the file containing the parts for individual-based learning.


m_parts

Instances m_parts
Part instances for individual-based learning.


m_printValues

int m_printValues
Type of values output.


Package weka.associations.gsp

Class weka.associations.gsp.Element extends java.lang.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 extends java.lang.Object implements Serializable

serialVersionUID: -5001018056339156390L

Serialized Fields

m_SupportCount

int m_SupportCount
the support count of the Sequence


m_Elements

FastVector m_Elements
ordered list of the comprised elements/itemsets


Package weka.associations.tertius

Class weka.associations.tertius.AttributeValueLiteral extends Literal implements Serializable

serialVersionUID: 4077436297281456239L

Serialized Fields

m_value

java.lang.String m_value

m_index

int m_index

Class weka.associations.tertius.Body extends LiteralSet implements Serializable

serialVersionUID: 4870689270432218016L

Class weka.associations.tertius.Head extends LiteralSet implements Serializable

serialVersionUID: 5068076274253706199L

Class weka.associations.tertius.IndividualInstance extends Instance implements Serializable

serialVersionUID: -7903938733476585114L

Serialized Fields

m_parts

Instances m_parts

Class weka.associations.tertius.IndividualInstances extends Instances implements Serializable

serialVersionUID: -7355054814895636733L

Class weka.associations.tertius.IndividualLiteral extends AttributeValueLiteral implements Serializable

serialVersionUID: 4712404824517887435L

Serialized Fields

m_type

int m_type

Class weka.associations.tertius.Literal extends java.lang.Object implements Serializable

serialVersionUID: 2675363669503575771L

Serialized Fields

m_predicate

Predicate m_predicate

m_sign

int m_sign

m_negation

Literal m_negation

m_missing

int m_missing

Class weka.associations.tertius.LiteralSet extends java.lang.Object implements Serializable

serialVersionUID: 6094536488654503152L

Serialized Fields

m_literals

java.util.ArrayList<E> m_literals
Literals contained in this set.


m_lastLiteral

Literal m_lastLiteral
Last literal added to this set.


m_numInstances

int m_numInstances
Number of instances in the data the set deals with.


m_counterInstances

java.util.ArrayList<E> m_counterInstances
Set of counter-instances of this part of the rule.


m_counter

int m_counter
Counter for the number of counter-instances.


m_type

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

Class weka.associations.tertius.Predicate extends java.lang.Object implements Serializable

serialVersionUID: -8374702481965026640L

Serialized Fields

m_literals

java.util.ArrayList<E> m_literals

m_name

java.lang.String m_name

m_index

int m_index

m_isClass

boolean m_isClass

Class weka.associations.tertius.Rule extends java.lang.Object implements Serializable

serialVersionUID: -7763378359090435505L

Serialized Fields

m_body

Body m_body
The body of the rule.


m_head

Head m_head
The head of the rule.


m_repeatPredicate

boolean m_repeatPredicate
Can repeat predicates in 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_classRule

boolean m_classRule
Is this rule a classification rule ?


m_singleHead

boolean m_singleHead
Can there be only one literal in the head ?


m_numInstances

int m_numInstances
Number of instances in the data this rule deals with.


m_counterInstances

java.util.ArrayList<E> m_counterInstances
Set of counter-instances of this rule.


m_counter

int m_counter
Counter for the counter-instances of this rule.


m_confirmation

double m_confirmation
Confirmation of this rule.


m_optimistic

double m_optimistic
Optimistic estimate of this rule.

Class weka.associations.tertius.SimpleLinkedList extends java.lang.Object implements Serializable

serialVersionUID: -1491148276509976299L

Serialization Methods

readObject

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

Throws:
java.io.IOException
java.lang.ClassNotFoundException

writeObject

private void writeObject(java.io.ObjectOutputStream s)
                  throws java.io.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:
java.io.IOException
Serialized Fields

first

weka.associations.tertius.SimpleLinkedList.Entry first

last

weka.associations.tertius.SimpleLinkedList.Entry last

Class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator extends java.lang.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 extends java.lang.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 extends java.lang.Object implements Serializable

serialVersionUID: 2091705669885950849L

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

serialVersionUID: 7591673350342236548L

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

serialVersionUID: 4170171824147584330L

Serialized Fields

m_trainInstances

Instances m_trainInstances
the instances to select attributes from


m_ASEvaluator

ASEvaluation m_ASEvaluator
the attribute/subset evaluator


m_searchMethod

ASSearch m_searchMethod
the search method


m_numFolds

int m_numFolds
the number of folds to use for cross validation


m_selectionResults

java.lang.StringBuffer m_selectionResults
holds a string describing the results of the attribute selection


m_doRank

boolean m_doRank
rank features (if allowed by the search method)


m_doXval

boolean m_doXval
do cross validation


m_seed

int m_seed
seed used to randomly shuffle instances for cross validation


m_numToSelect

int m_numToSelect
number of attributes requested from ranked results


m_selectedAttributeSet

int[] m_selectedAttributeSet
the selected attributes


m_attributeRanking

double[][] m_attributeRanking
the attribute indexes and associated merits if a ranking is produced


m_transformer

AttributeTransformer m_transformer
if a feature selection run involves an attribute transformer


m_attributeFilter

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


m_rankResults

double[][] m_rankResults
hold statistics for repeated feature selection, such as under cross validation


m_subsetResults

double[] m_subsetResults

m_trials

int m_trials

Class weka.attributeSelection.AttributeSetEvaluator extends ASEvaluation implements Serializable

serialVersionUID: -5744881009422257389L

Class weka.attributeSelection.BestFirst extends ASSearch implements Serializable

serialVersionUID: 7841338689536821867L

Serialized Fields

m_maxStale

int m_maxStale
maximum number of stale nodes before terminating search


m_searchDirection

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


m_starting

int[] m_starting
holds an array of starting attributes


m_startRange

Range m_startRange
holds the start set for the search as a Range


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_totalEvals

int m_totalEvals
total number of subsets evaluated during a search


m_debug

boolean m_debug
for debugging


m_bestMerit

double m_bestMerit
holds the merit of the best subset found


m_cacheSize

int m_cacheSize
holds the maximum size of the lookup cache for evaluated subsets

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

serialVersionUID: -8236598311516351420L

Serialized Fields

m_data

java.lang.Object[] m_data

m_merit

double m_merit

Class weka.attributeSelection.BestFirst.LinkedList2 extends FastVector implements Serializable

serialVersionUID: 3250538292330398929L

Serialized Fields

m_MaxSize

int m_MaxSize
Max number of elements in the list

Class weka.attributeSelection.CfsSubsetEval extends ASEvaluation implements Serializable

serialVersionUID: 747878400813276317L

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_disTransform

Discretize m_disTransform
Discretise attributes when class in nominal


m_classIndex

int m_classIndex
The class index


m_isNumeric

boolean m_isNumeric
Is the class numeric


m_numAttribs

int m_numAttribs
Number of attributes in the training data


m_numInstances

int m_numInstances
Number of instances in the training data


m_missingSeparate

boolean m_missingSeparate
Treat missing values as separate values


m_locallyPredictive

boolean m_locallyPredictive
Include locally predicitive attributes


m_corr_matrix

float[][] m_corr_matrix
Holds the matrix of attribute correlations


m_std_devs

double[] m_std_devs
Standard deviations of attributes (when using pearsons correlation)


m_c_Threshold

double m_c_Threshold
Threshold for admitting locally predictive features

Class weka.attributeSelection.ChiSquaredAttributeEval extends ASEvaluation implements Serializable

serialVersionUID: -8316857822521717692L

Serialized Fields

m_missing_merge

boolean m_missing_merge
Treat missing values as a seperate value


m_Binarize

boolean m_Binarize
Just binarize numeric attributes


m_ChiSquareds

double[] m_ChiSquareds
The chi-squared value for each attribute

Class weka.attributeSelection.ClassifierSubsetEval extends HoldOutSubsetEvaluator implements Serializable

serialVersionUID: 7532217899385278710L

Serialized Fields

m_trainingInstances

Instances m_trainingInstances
training instances


m_classIndex

int m_classIndex
class index


m_numAttribs

int m_numAttribs
number of attributes in the training data


m_numInstances

int m_numInstances
number of training instances


m_Classifier

Classifier m_Classifier
holds the classifier to use for error estimates


m_Evaluation

Evaluation m_Evaluation
holds the evaluation object to use for evaluating the classifier


m_holdOutFile

java.io.File m_holdOutFile
the file that containts hold out/test instances


m_holdOutInstances

Instances m_holdOutInstances
the instances to test on


m_useTraining

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

Class weka.attributeSelection.ConsistencySubsetEval extends ASEvaluation implements Serializable

serialVersionUID: -2880323763295270402L

Serialized Fields

m_trainInstances

Instances m_trainInstances
training instances


m_classIndex

int m_classIndex
class index


m_numAttribs

int m_numAttribs
number of attributes in the training data


m_numInstances

int m_numInstances
number of instances in the training data


m_disTransform

Discretize m_disTransform
Discretise numeric attributes


m_table

java.util.Hashtable<K,V> m_table
Hash table for evaluating feature subsets

Class weka.attributeSelection.ConsistencySubsetEval.hashKey extends java.lang.Object implements Serializable

serialVersionUID: 6144138512017017408L

Serialized Fields

attributes

double[] attributes
Array of attribute values for an instance


missing

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


key

int key
The key

Class weka.attributeSelection.CostSensitiveASEvaluation extends ASEvaluation implements Serializable

serialVersionUID: -7045833833363396977L

Serialized Fields

m_MatrixSource

int m_MatrixSource
Indicates the current cost matrix source


m_OnDemandDirectory

java.io.File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory


m_CostFile

java.lang.String m_CostFile
The name of the cost file, for command line options


m_CostMatrix

CostMatrix m_CostMatrix
The cost matrix


m_evaluator

ASEvaluation m_evaluator
The base evaluator to use


m_seed

int m_seed
random number seed

Class weka.attributeSelection.CostSensitiveAttributeEval extends CostSensitiveASEvaluation implements Serializable

serialVersionUID: 4484876541145458447L

Class weka.attributeSelection.CostSensitiveSubsetEval extends CostSensitiveASEvaluation implements Serializable

serialVersionUID: 2924546096103426700L

Class weka.attributeSelection.ExhaustiveSearch extends ASSearch implements Serializable

serialVersionUID: 5741842861142379712L

Serialized Fields

m_bestGroup

java.util.BitSet m_bestGroup
the best feature set found during the search


m_bestMerit

double m_bestMerit
the merit of the best subset found


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_verbose

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


m_evaluations

int m_evaluations
the number of subsets evaluated during the search

Class weka.attributeSelection.FCBFSearch extends ASSearch implements Serializable

serialVersionUID: 8209699587428369942L

Serialized Fields

m_starting

int[] m_starting
Holds the starting set as an array of attributes


m_startRange

Range m_startRange
Holds the start set for the search as a range


m_attributeList

int[] m_attributeList
Holds the ordered list of attributes


m_attributeMerit

double[] m_attributeMerit
Holds the list of attribute merit scores


m_hasClass

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


m_classIndex

int m_classIndex
Class index of the data if supervised evaluator


m_numAttribs

int m_numAttribs
The number of attribtes


m_threshold

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


m_numToSelect

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


m_calculatedNumToSelect

int m_calculatedNumToSelect
Used to compute the number to select


m_generateOutput

boolean m_generateOutput
Used to determine whether we create a new dataset according to the selected features


m_asEval

ASEvaluation m_asEval
Used to store the ref of the Evaluator we use


m_rankedFCBF

double[][] m_rankedFCBF
Holds the list of attribute merit scores generated by FCBF


m_selectedFeatures

double[][] m_selectedFeatures
Hold the list of selected features

Class weka.attributeSelection.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 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 extends ASEvaluation implements Serializable

serialVersionUID: -8504656625598579926L

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_numClasses

int m_numClasses
The number of classes


m_missing_merge

boolean m_missing_merge
Merge missing values

Class weka.attributeSelection.GeneticSearch extends ASSearch implements Serializable

serialVersionUID: -1618264232838472679L

Serialized Fields

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_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_population

weka.attributeSelection.GeneticSearch.GABitSet[] m_population
the current population


m_popSize

int m_popSize
the number of individual solutions


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_lookupTableSize

int m_lookupTableSize
the number of entries to cache for lookup


m_lookupTable

java.util.Hashtable<K,V> m_lookupTable
the lookup table


m_random

java.util.Random m_random
random number generation


m_seed

int m_seed
seed for random number generation


m_pCrossover

double m_pCrossover
the probability of crossover occuring


m_pMutation

double m_pMutation
the probability of mutation occuring


m_sumFitness

double m_sumFitness
sum of the current population fitness


m_maxFitness

double m_maxFitness

m_minFitness

double m_minFitness

m_avgFitness

double m_avgFitness

m_maxGenerations

int m_maxGenerations
the maximum number of generations to evaluate


m_reportFrequency

int m_reportFrequency
how often reports are generated


m_generationReports

java.lang.StringBuffer m_generationReports
holds the generation reports

Class weka.attributeSelection.GeneticSearch.GABitSet extends java.lang.Object implements Serializable

serialVersionUID: -2930607837482622224L

Serialized Fields

m_chromosome

java.util.BitSet m_chromosome
the bitset


m_objective

double m_objective
holds raw merit


m_fitness

double m_fitness
the fitness

Class weka.attributeSelection.GreedyStepwise extends ASSearch implements Serializable

serialVersionUID: -6312951970168325471L

Serialized Fields

m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_rankingRequested

boolean m_rankingRequested
true if the user has requested a ranked list of attributes


m_doRank

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


m_doneRanking

boolean m_doneRanking
used to indicate whether or not ranking has been performed


m_threshold

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


m_numToSelect

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


m_calculatedNumToSelect

int m_calculatedNumToSelect

m_bestMerit

double m_bestMerit
the merit of the best subset found


m_rankedAtts

double[][] m_rankedAtts
a ranked list of attribute indexes


m_rankedSoFar

int m_rankedSoFar

m_best_group

java.util.BitSet m_best_group
the best subset found


m_ASEval

ASEvaluation m_ASEval

m_Instances

Instances m_Instances

m_startRange

Range m_startRange
holds the start set for the search as a Range


m_starting

int[] m_starting
holds an array of starting attributes


m_backward

boolean m_backward
Use a backwards search instead of a forwards one


m_conservativeSelection

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

Class weka.attributeSelection.HoldOutSubsetEvaluator extends ASEvaluation implements Serializable

serialVersionUID: 8280529785412054174L

Class weka.attributeSelection.InfoGainAttributeEval extends ASEvaluation implements Serializable

serialVersionUID: -1949849512589218930L

Serialized Fields

m_missing_merge

boolean m_missing_merge
Treat missing values as a seperate value


m_Binarize

boolean m_Binarize
Just binarize numeric attributes


m_InfoGains

double[] m_InfoGains
The info gain for each attribute

Class weka.attributeSelection.LatentSemanticAnalysis extends UnsupervisedAttributeEvaluator implements Serializable

serialVersionUID: -8712112988018106198L

Serialized Fields

m_trainInstances

Instances m_trainInstances
The data to transform analyse/transform


m_trainHeader

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


m_transformedFormat

Instances m_transformedFormat
The header for the transformed data format


m_hasClass

boolean m_hasClass
Data has a class set


m_classIndex

int m_classIndex
Class index


m_numAttributes

int m_numAttributes
Number of attributes


m_numInstances

int m_numInstances
Number of instances


m_transpose

boolean m_transpose
Is transpose necessary because numAttributes < numInstances?


m_u

Matrix m_u
Will hold the left singular vectors


m_s

Matrix m_s
Will hold the singular values


m_v

Matrix m_v
Will hold the right singular values


m_transformationMatrix

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


m_replaceMissingFilter

ReplaceMissingValues m_replaceMissingFilter
Filters for original data


m_normalizeFilter

Normalize m_normalizeFilter

m_nominalToBinaryFilter

NominalToBinary m_nominalToBinaryFilter

m_attributeFilter

Remove m_attributeFilter

m_outputNumAttributes

int m_outputNumAttributes
The number of attributes in the LSA transformed data


m_normalize

boolean m_normalize
Normalize the input data?


m_rank

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


m_sumSquaredSingularValues

double m_sumSquaredSingularValues
The sum of the squares of the singular values


m_actualRank

int m_actualRank
The actual rank number to use for computation


m_maxAttributesInName

int m_maxAttributesInName
Maximum number of attributes in the transformed attribute name

Class weka.attributeSelection.LFSMethods.Link2 extends java.lang.Object implements Serializable

serialVersionUID: -7422719407475185086L

Serialized Fields

m_data

java.lang.Object[] m_data

m_merit

double m_merit

Class weka.attributeSelection.LFSMethods.LinkedList2 extends FastVector implements Serializable

serialVersionUID: -7776010892419656105L

Serialized Fields

m_MaxSize

int m_MaxSize

Class weka.attributeSelection.LinearForwardSelection extends ASSearch implements Serializable

Serialized Fields

m_maxStale

int m_maxStale
maximum number of stale nodes before terminating search


m_forwardSearchMethod

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


m_performRanking

boolean m_performRanking
perform initial ranking to select top-ranked attributes


m_numUsedAttributes

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


m_linearSelectionType

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


m_starting

int[] m_starting
holds an array of starting attributes


m_startRange

Range m_startRange
holds the start set for the search as a Range


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_totalEvals

int m_totalEvals
total number of subsets evaluated during a search


m_verbose

boolean m_verbose
for debugging


m_bestMerit

double m_bestMerit
holds the merit of the best subset found


m_cacheSize

int m_cacheSize
holds the maximum size of the lookup cache for evaluated subsets

Class weka.attributeSelection.OneRAttributeEval extends ASEvaluation implements Serializable

serialVersionUID: 4386514823886856980L

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_randomSeed

int m_randomSeed
Random number seed


m_folds

int m_folds
Number of folds for cross validation


m_evalUsingTrainingData

boolean m_evalUsingTrainingData
Use training data to evaluate merit rather than x-val


m_minBucketSize

int m_minBucketSize
Passed on to OneR

Class weka.attributeSelection.PrincipalComponents extends UnsupervisedAttributeEvaluator implements Serializable

serialVersionUID: 3310137541055815078L

Serialized Fields

m_trainInstances

Instances m_trainInstances
The data to transform analyse/transform


m_trainHeader

Instances m_trainHeader
Keep a copy for the class attribute (if set)


m_transformedFormat

Instances m_transformedFormat
The header for the transformed data format


m_originalSpaceFormat

Instances m_originalSpaceFormat
The header for data transformed back to the original space


m_hasClass

boolean m_hasClass
Data has a class set


m_classIndex

int m_classIndex
Class index


m_numAttribs

int m_numAttribs
Number of attributes


m_numInstances

int m_numInstances
Number of instances


m_correlation

double[][] m_correlation
Correlation matrix for the original data


m_eigenvectors

double[][] m_eigenvectors
Will hold the unordered linear transformations of the (normalized) original data


m_eigenvalues

double[] m_eigenvalues
Eigenvalues for the corresponding eigenvectors


m_sortedEigens

int[] m_sortedEigens
Sorted eigenvalues


m_sumOfEigenValues

double m_sumOfEigenValues
sum of the eigenvalues


m_replaceMissingFilter

ReplaceMissingValues m_replaceMissingFilter
Filters for original data


m_normalizeFilter

Normalize m_normalizeFilter

m_nominalToBinFilter

NominalToBinary m_nominalToBinFilter

m_attributeFilter

Remove m_attributeFilter

m_attribFilter

Remove m_attribFilter
used to remove the class column if a class column is set


m_outputNumAtts

int m_outputNumAtts
The number of attributes in the pc transformed data


m_normalize

boolean m_normalize
normalize the input data?


m_coverVariance

double m_coverVariance
the amount of varaince to cover in the original data when retaining the best n PC's


m_transBackToOriginal

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


m_maxAttrsInName

int m_maxAttrsInName
maximum number of attributes in the transformed attribute name


m_eTranspose

double[][] m_eTranspose
holds the transposed eigenvectors for converting back to the original space

Class weka.attributeSelection.RaceSearch extends ASSearch implements Serializable

serialVersionUID: 4015453851212985720L

Serialized Fields

m_Instances

Instances m_Instances
the training instances


m_raceType

int m_raceType
the selected search type


m_xvalType

int m_xvalType
the selected xval type


m_classIndex

int m_classIndex
the class index


m_numAttribs

int m_numAttribs
the number of attributes in the data


m_totalEvals

int m_totalEvals
the total number of partially/fully evaluated subsets


m_bestMerit

double m_bestMerit
holds the merit of the best subset found


m_theEvaluator

HoldOutSubsetEvaluator m_theEvaluator
the subset evaluator to use


m_sigLevel

double m_sigLevel
the significance level for comparisons


m_delta

double m_delta
threshold for comparisons


m_samples

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


m_numFolds

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


m_ASEval

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


m_Ranking

int[] m_Ranking
will hold the attribute ranking produced by the above attribute evaluator if doing a rank search


m_debug

boolean m_debug
verbose output for monitoring the search and debugging


m_rankingRequested

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


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_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_calculatedNumToSelect

int m_calculatedNumToSelect

m_threshold

double m_threshold
the threshold for removing attributes if ranking is requested

Class weka.attributeSelection.RandomSearch extends ASSearch implements Serializable

serialVersionUID: 7479392617377425484L

Serialized Fields

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_bestGroup

java.util.BitSet m_bestGroup
the best feature set found during the search


m_bestMerit

double m_bestMerit
the merit of the best subset found


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 fewer features than the best (this allows LVF to be implimented).


m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_seed

int m_seed
seed for random number generation


m_searchSize

double m_searchSize
percentage of the search space to consider


m_iterations

int m_iterations
the number of iterations performed


m_random

java.util.Random m_random
random number object


m_verbose

boolean m_verbose
output new best subsets as the search progresses

Class weka.attributeSelection.Ranker extends ASSearch implements Serializable

serialVersionUID: -9086714848510751934L

Serialized Fields

m_starting

int[] m_starting
Holds the starting set as an array of attributes


m_startRange

Range m_startRange
Holds the start set for the search as a range


m_attributeList

int[] m_attributeList
Holds the ordered list of attributes


m_attributeMerit

double[] m_attributeMerit
Holds the list of attribute merit scores


m_hasClass

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


m_classIndex

int m_classIndex
Class index of the data if supervised evaluator


m_numAttribs

int m_numAttribs
The number of attribtes


m_threshold

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


m_numToSelect

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


m_calculatedNumToSelect

int m_calculatedNumToSelect
Used to compute the number to select

Class weka.attributeSelection.RankSearch extends ASSearch implements Serializable

serialVersionUID: -7992268736874353755L

Serialized Fields

m_hasClass

boolean m_hasClass
does the data have a class


m_classIndex

int m_classIndex
holds the class index


m_numAttribs

int m_numAttribs
number of attributes in the data


m_best_group

java.util.BitSet m_best_group
the best subset found


m_ASEval

ASEvaluation m_ASEval
the attribute evaluator to use for generating the ranking


m_SubsetEval

ASEvaluation m_SubsetEval
the subset evaluator with which to evaluate the ranking


m_Instances

Instances m_Instances
the training instances


m_bestMerit

double m_bestMerit
the merit of the best subset found


m_Ranking

int[] m_Ranking
will hold the attribute ranking


m_add

int m_add
add this many attributes in each iteration from the ranking


m_startPoint

int m_startPoint
start from this point in the ranking

Class weka.attributeSelection.ReliefFAttributeEval extends ASEvaluation implements Serializable

serialVersionUID: -8422186665795839379L

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_numericClass

boolean m_numericClass
Numeric class


m_numClasses

int m_numClasses
The number of classes if class is nominal


m_ndc

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


m_nda

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


m_ndcda

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


m_weights

double[] m_weights
Holds the weights that relief assigns to attributes


m_classProbs

double[] m_classProbs
Prior class probabilities (discrete class case)


m_sampleM

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


m_Knn

int m_Knn
The number of nearest hits/misses


m_karray

double[][][] m_karray
k nearest scores + instance indexes for n classes


m_maxArray

double[] m_maxArray
Upper bound for numeric attributes


m_minArray

double[] m_minArray
Lower bound for numeric attributes


m_worst

double[] m_worst
Keep track of the farthest instance for each class


m_index

int[] m_index
Index in the m_karray of the farthest instance for each class


m_stored

int[] m_stored
Number of nearest neighbours stored of each class


m_seed

int m_seed
Random number seed used for sampling instances


m_weightsByRank

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


m_sigma

int m_sigma

m_weightByDistance

boolean m_weightByDistance
Weight by distance rather than equal weights

Class weka.attributeSelection.ScatterSearchV1 extends ASSearch implements Serializable

serialVersionUID: -8512041420388121326L

Serialized Fields

m_numAttribs

int m_numAttribs
number of attributes in the data


m_classIndex

int m_classIndex
holds the class index


m_treshold

double m_treshold
holds the treshhold that delimits the good attributes


m_initialThreshold

double m_initialThreshold
the initial threshold


m_typeOfCombination

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


m_random

java.util.Random m_random
random number generation


m_seed

int m_seed
seed for random number generation


m_debug

boolean m_debug
verbose output for monitoring the search and debugging


m_InformationReports

java.lang.StringBuffer m_InformationReports
holds a report of the search


m_totalEvals

int m_totalEvals
total number of subsets evaluated during a search


m_bestMerit

double m_bestMerit
holds the merit of the best subset found


m_processinTime

long m_processinTime
time for procesing the search method


m_population

java.util.List<E> m_population
holds the Initial Population of Subsets


m_popSize

int m_popSize
holds the population size


m_initialPopSize

int m_initialPopSize
holds the user selected initial population size


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_attributeRanking

java.util.List<E> m_attributeRanking
holds the attributes ranked


ASEvaluator

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

Class weka.attributeSelection.ScatterSearchV1.Subset extends java.lang.Object implements Serializable

Serialized Fields

merit

double merit

subset

java.util.BitSet subset

Class weka.attributeSelection.SubsetSizeForwardSelection extends ASSearch implements Serializable

Serialized Fields

m_performRanking

boolean m_performRanking
perform initial ranking to select top-ranked attributes


m_numUsedAttributes

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


m_linearSelectionType

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


m_setSizeEval

ASEvaluation m_setSizeEval
the subset evaluator to use for subset size determination


m_numFolds

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


m_seed

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


m_numAttribs

int m_numAttribs
number of attributes in the data


m_totalEvals

int m_totalEvals
total number of subsets evaluated during a search


m_verbose

boolean m_verbose
for debugging


m_bestMerit

double m_bestMerit
holds the merit of the best subset found


m_cacheSize

int m_cacheSize
holds the maximum size of the lookup cache for evaluated subsets

Class weka.attributeSelection.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_percentToEliminate

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


m_percentThreshold

int m_percentThreshold
Threshold below which percent elimination switches to constant elimination


m_smoCParameter

double m_smoCParameter
Complexity parameter to pass on to SMO


m_smoTParameter

double m_smoTParameter
Tolerance parameter to pass on to SMO


m_smoPParameter

double m_smoPParameter
Epsilon parameter to pass on to SMO


m_smoFilterType

int m_smoFilterType
Filter parameter to pass on to SMO

Class weka.attributeSelection.SymmetricalUncertAttributeEval extends ASEvaluation implements Serializable

serialVersionUID: -8096505776132296416L

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_numClasses

int m_numClasses
The number of classes


m_missing_merge

boolean m_missing_merge
Treat missing values as a seperate value

Class weka.attributeSelection.SymmetricalUncertAttributeSetEval extends AttributeSetEvaluator implements Serializable

serialVersionUID: 8351377335495873202L

Serialized Fields

m_trainInstances

Instances m_trainInstances
The training instances


m_classIndex

int m_classIndex
The class index


m_numAttribs

int m_numAttribs
The number of attributes


m_numInstances

int m_numInstances
The number of instances


m_numClasses

int m_numClasses
The number of classes


m_missing_merge

boolean m_missing_merge
Treat missing values as a seperate value

Class weka.attributeSelection.UnsupervisedAttributeEvaluator extends ASEvaluation implements Serializable

serialVersionUID: -4100897318675336178L

Class weka.attributeSelection.UnsupervisedSubsetEvaluator extends ASEvaluation implements Serializable

serialVersionUID: 627934376267488763L

Class weka.attributeSelection.WrapperSubsetEval extends ASEvaluation implements Serializable

serialVersionUID: -4573057658746728675L

Serialized Fields

m_trainInstances

Instances m_trainInstances
training instances


m_classIndex

int m_classIndex
class index


m_numAttribs

int m_numAttribs
number of attributes in the training data


m_numInstances

int m_numInstances
number of instances in the training data


m_Evaluation

Evaluation m_Evaluation
holds an evaluation object


m_BaseClassifier

Classifier m_BaseClassifier
holds the base classifier object


m_folds

int m_folds
number of folds to use for cross validation


m_seed

int m_seed
random number seed


m_threshold

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


Package weka.classifiers

Class weka.classifiers.Classifier extends java.lang.Object implements Serializable

serialVersionUID: 6502780192411755341L

Serialized Fields

m_Debug

boolean m_Debug
Whether the classifier is run in debug mode.

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

serialVersionUID: -1973792250544554965L

Serialized Fields

m_size

int m_size

m_matrix

java.lang.Object[][] m_matrix
[rows][columns]

Class weka.classifiers.EnsembleLibrary extends java.lang.Object implements Serializable

serialVersionUID: -7987178904923706760L

Serialized Fields

m_Models

java.util.TreeSet<E> m_Models
the set of classifiers that constitute the library

Class weka.classifiers.EnsembleLibraryModel extends java.lang.Object implements Serializable

serialVersionUID: 7932816660173443200L

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream stream)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
Custom serialization method

Throws:
java.io.IOException - if something goes wrong IO-wise
java.lang.ClassNotFoundException - if class couldn't be found

writeObject

private void writeObject(java.io.ObjectOutputStream stream)
                  throws java.io.IOException
Custom serialization method

Throws:
java.io.IOException - if something goes wrong IO-wise
Serialized Fields

m_Classifier

Classifier m_Classifier
this is an array of options


m_DescriptionText

java.lang.String m_DescriptionText
the description of this classifier


m_ErrorText

java.lang.String m_ErrorText
this is also saved separately


m_OptionsWereValid

boolean m_OptionsWereValid
a boolean variable tracking whether or not this classifier was able to be built successfully with the given set of options


m_StringRepresentation

java.lang.String m_StringRepresentation
this is stores redundantly to speed up the many operations that frequently need to get the model string representations (like JList renderers)

Class weka.classifiers.EnsembleLibraryModelComparator extends java.lang.Object implements Serializable

serialVersionUID: -6522464740036141188L

Class weka.classifiers.IteratedSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

serialVersionUID: -6217979135443319724L

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
Array for storing the generated base classifiers.


m_NumIterations

int m_NumIterations
The number of iterations.

Class weka.classifiers.JythonClassifier extends Classifier implements Serializable

serialVersionUID: -9078371491735496175L

Serialized Fields

m_JythonModule

java.io.File m_JythonModule
the Jython module


m_JythonOptions

java.lang.String[] m_JythonOptions
the options for the Jython module


m_JythonPaths

java.io.File[] m_JythonPaths
additional paths for the Jython module (will be added to "sys.path")

Class weka.classifiers.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 extends Classifier implements Serializable

serialVersionUID: -8816375798262351903L

Serialized Fields

m_Seed

int m_Seed
The random number seed.

Class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer extends IteratedSingleClassifierEnhancer implements Serializable

serialVersionUID: 5063351391524938557L

Serialized Fields

m_Seed

int m_Seed
The random number seed.

Class weka.classifiers.RandomizableMultipleClassifiersCombiner extends MultipleClassifiersCombiner implements Serializable

serialVersionUID: 5057936555724785679L

Serialized Fields

m_Seed

int m_Seed
The random number seed.

Class weka.classifiers.RandomizableSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

serialVersionUID: 558286687096157160L

Serialized Fields

m_Seed

int m_Seed
The random number seed.

Class weka.classifiers.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 extends Classifier implements Serializable

serialVersionUID: 9197439980415113523L

Serialized Fields

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_ClassCounts

double[] m_ClassCounts
The number of times each class value occurs in the dataset


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_NumClasses

int m_NumClasses
The number of classes


m_NumAttributes

int m_NumAttributes
The number of attributes in dataset, including class


m_NumInstances

int m_NumInstances
The number of instances in the dataset


m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Instances

Instances m_Instances
The dataset


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_StartAttIndex

int[] m_StartAttIndex
The starting index (in the m_CondiCounts matrix) of the values for each attribute


m_NumAttValues

int[] m_NumAttValues
The number of values for each attribute


m_Frequencies

double[] m_Frequencies
The frequency of each attribute value for the dataset


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_Limit

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


m_Debug

boolean m_Debug
If true, outputs debugging info


m_MEstimates

boolean m_MEstimates
flag for using m-estimates


m_Weight

int m_Weight
value for m in m-estimate

Class weka.classifiers.bayes.AODEsr extends Classifier implements Serializable

serialVersionUID: 5602143019183068848L

Serialized Fields

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_ClassCounts

double[] m_ClassCounts
The number of times each class value occurs in the dataset


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_NumClasses

int m_NumClasses
The number of classes


m_NumAttributes

int m_NumAttributes
The number of attributes in dataset, including class


m_NumInstances

int m_NumInstances
The number of instances in the dataset


m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Instances

Instances m_Instances
The dataset


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.


m_StartAttIndex

int[] m_StartAttIndex
The starting index (in the m_CondiCounts matrix) of the values for each attribute


m_NumAttValues

int[] m_NumAttValues
The number of values for each attribute


m_Frequencies

double[] m_Frequencies
The frequency of each attribute value for the dataset


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_Limit

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


m_Debug

boolean m_Debug
If true, outputs debugging info


m_MWeight

double m_MWeight
m value for m-estimation


m_Laplace

boolean m_Laplace
Using LapLace estimation or not


m_Critical

int m_Critical
the critical value for the specialization-generalization

Class weka.classifiers.bayes.BayesianLogisticRegression extends Classifier implements Serializable

serialVersionUID: -8013478897911757631L

Serialized Fields

debug

boolean debug
DEBUG Mode


NormalizeData

boolean NormalizeData
Choose whether to normalize data or not


Tolerance

double Tolerance
Tolerance criteria for the stopping criterion.


Threshold

double Threshold
Threshold for binary classification of probabilisitic estimate


PriorClass

int PriorClass
Distribution Prior class


NumFolds

int NumFolds
NumFolds for CV based Hyperparameters selection


HyperparameterSelection

int HyperparameterSelection
Hyperparameter selection method


ClassIndex

int ClassIndex
The class index from the training data


HyperparameterValue

double HyperparameterValue
Best hyperparameter for test phase


HyperparameterRange

java.lang.String HyperparameterRange
CV Hyperparameter Range


maxIterations

int maxIterations
Maximum number of iterations


iterationCounter

int iterationCounter
Iteration counter


BetaVector

double[] BetaVector
Array for storing coefficients of Bayesian regression model.


DeltaBeta

double[] DeltaBeta
Array to store Regression Coefficient updates.


DeltaUpdate

double[] DeltaUpdate
Trust Region Radius Update


Delta

double[] Delta
Trust Region Radius


Hyperparameters

double[] Hyperparameters
Array to store Hyperparameter values for each feature.


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


DeltaR

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


Change

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


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

Class weka.classifiers.bayes.BayesNet extends Classifier implements Serializable

serialVersionUID: 746037443258775954L

Serialized Fields

m_ParentSets

ParentSet[] m_ParentSets
The parent sets.


m_Distributions

Estimator[][] m_Distributions
The attribute estimators containing CPTs.


m_DiscretizeFilter

Discretize m_DiscretizeFilter
filter used to quantize continuous variables, if any


m_nNonDiscreteAttribute

int m_nNonDiscreteAttribute
attribute index of a non-nominal attribute


m_MissingValuesFilter

ReplaceMissingValues m_MissingValuesFilter
filter used to fill in missing values, if any


m_NumClasses

int m_NumClasses
The number of classes


m_Instances

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


m_ADTree

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


m_otherBayesNet

BIFReader m_otherBayesNet
Bayes network to compare the structure with.


m_bUseADTree

boolean m_bUseADTree
Use the experimental ADTree datastructure for calculating contingency tables


m_SearchAlgorithm

SearchAlgorithm m_SearchAlgorithm
Search algorithm used for learning the structure of a network.


m_BayesNetEstimator

BayesNetEstimator m_BayesNetEstimator
Search algorithm used for learning the structure of a network.

Class weka.classifiers.bayes.ComplementNaiveBayes extends Classifier implements Serializable

serialVersionUID: 7246302925903086397L

Serialized Fields

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]


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


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


header

Instances header
The instances header that'll be used in toString

Class weka.classifiers.bayes.DMNBtext extends Classifier implements Serializable

serialVersionUID: 5932177450183457085L

Serialized Fields

m_NumIterations

int m_NumIterations
The number of iterations.


m_BinaryWord

boolean m_BinaryWord

m_numClasses

int m_numClasses

m_headerInfo

Instances m_headerInfo

m_binaryClassifiers

DMNBtext.DNBBinary[] m_binaryClassifiers

Class weka.classifiers.bayes.DMNBtext.DNBBinary extends java.lang.Object implements Serializable

Serialized Fields

m_perWordPerClass

double[][] m_perWordPerClass
The number of iterations.


m_wordsPerClass

double[] m_wordsPerClass

m_classIndex

int m_classIndex

m_classDistribution

double[] m_classDistribution

m_numAttributes

int m_numAttributes
number of unique words


m_targetClass

int m_targetClass

m_WordLaplace

double m_WordLaplace

m_coefficient

double[] m_coefficient

m_classRatio

double m_classRatio

m_wordRatio

double m_wordRatio

Class weka.classifiers.bayes.HNB extends Classifier implements Serializable

serialVersionUID: -4503874444306113214L

Serialized Fields

m_ClassCounts

double[] m_ClassCounts
The number of each class value occurs in the dataset


m_ClassAttAttCounts

double[][][] m_ClassAttAttCounts
The number of class and two attributes values occurs in the dataset


m_NumAttValues

int[] m_NumAttValues
The number of values for each attribute in the dataset


m_TotalAttValues

int m_TotalAttValues
The number of values for all attributes in the dataset


m_NumClasses

int m_NumClasses
The number of classes in the dataset


m_NumAttributes

int m_NumAttributes
The number of attributes including class in the dataset


m_NumInstances

int m_NumInstances
The number of instances in the dataset


m_ClassIndex

int m_ClassIndex
The index of the class attribute in the dataset


m_StartAttIndex

int[] m_StartAttIndex
The starting index of each attribute in the dataset


m_condiMutualInfo

double[][] m_condiMutualInfo
The 2D array of conditional mutual information of each pair attributes

Class weka.classifiers.bayes.NaiveBayes extends Classifier implements Serializable

serialVersionUID: 5995231201785697655L

Serialized Fields

m_Distributions

Estimator[][] m_Distributions
The attribute estimators.


m_ClassDistribution

Estimator m_ClassDistribution
The class estimator.


m_UseKernelEstimator

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


m_UseDiscretization

boolean m_UseDiscretization
Whether to use discretization than normal distribution for numeric attributes


m_NumClasses

int m_NumClasses
The number of classes (or 1 for numeric class)


m_Instances

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


m_Disc

Discretize m_Disc
The discretization filter.


m_displayModelInOldFormat

boolean m_displayModelInOldFormat

Class weka.classifiers.bayes.NaiveBayesMultinomial extends Classifier implements Serializable

serialVersionUID: 5932177440181257085L

Serialized Fields

m_probOfWordGivenClass

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


m_probOfClass

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


m_numAttributes

int m_numAttributes
number of unique words


m_numClasses

int m_numClasses
number of class values


m_lnFactorialCache

double[] m_lnFactorialCache
cache lnFactorial computations


m_headerInfo

Instances m_headerInfo
copy of header information for use in toString method

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

serialVersionUID: -7204398796974263186L

Serialized Fields

m_wordsPerClass

double[] m_wordsPerClass
the word count per class

Class weka.classifiers.bayes.NaiveBayesSimple extends Classifier implements Serializable

serialVersionUID: -1478242251770381214L

Serialized Fields

m_Counts

double[][][] m_Counts
All the counts for nominal attributes.


m_Means

double[][] m_Means
The means for numeric attributes.


m_Devs

double[][] m_Devs
The standard deviations for numeric attributes.


m_Priors

double[] m_Priors
The prior probabilities of the classes.


m_Instances

Instances m_Instances
The instances used for training.

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

serialVersionUID: -5354015843807192221L

Class weka.classifiers.bayes.WAODE extends Classifier implements Serializable

serialVersionUID: 2170978824284697882L

Serialized Fields

m_ClassCounts

double[] m_ClassCounts
The number of each class value occurs in the dataset


m_AttCounts

double[] m_AttCounts
The number of each attribute value occurs in the dataset


m_AttAttCounts

double[][] m_AttAttCounts
The number of two attributes values occurs in the dataset


m_ClassAttAttCounts

double[][][] m_ClassAttAttCounts
The number of class and two attributes values occurs in the dataset


m_NumAttValues

int[] m_NumAttValues
The number of values for each attribute in the dataset


m_TotalAttValues

int m_TotalAttValues
The number of values for all attributes in the dataset


m_NumClasses

int m_NumClasses
The number of classes in the dataset


m_NumAttributes

int m_NumAttributes
The number of attributes including class in the dataset


m_NumInstances

int m_NumInstances
The number of instances in the dataset


m_ClassIndex

int m_ClassIndex
The index of the class attribute in the dataset


m_StartAttIndex

int[] m_StartAttIndex
The starting index of each attribute in the dataset


m_mutualInformation

double[] m_mutualInformation
The array of mutual information between each attribute and class


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_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 extends Prior implements Serializable

serialVersionUID: -2995684220141159223L

Class weka.classifiers.bayes.blr.LaplacePriorImpl extends Prior implements Serializable

serialVersionUID: 2353576123257012607L

Serialized Fields

m_Instances

Instances m_Instances

Beta

double Beta

Hyperparameter

double Hyperparameter

DeltaUpdate

double DeltaUpdate

R

double[] R

Delta

double Delta

Class weka.classifiers.bayes.blr.Prior extends java.lang.Object implements Serializable

Serialized Fields

m_Instances

Instances m_Instances

Beta

double Beta

Hyperparameter

double Hyperparameter

DeltaUpdate

double DeltaUpdate

R

double[] R

Delta

double Delta

log_posterior

double log_posterior

log_likelihood

double log_likelihood

penalty

double penalty

Package weka.classifiers.bayes.net

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

serialVersionUID: 397409728366910204L

Serialized Fields

m_VaryNodes

VaryNode[] m_VaryNodes
list of VaryNode children


m_Instances

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


m_nCount

int m_nCount
count


m_nStartNode

int m_nStartNode
first node in VaryNode array

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

serialVersionUID: -7462571170596157720L

Serialized Fields

m_nSeed

int m_nSeed
the seed value


random

java.util.Random random
the random number generator


m_bGenerateNet

boolean m_bGenerateNet

m_nNrOfNodes

int m_nNrOfNodes

m_nNrOfArcs

int m_nNrOfArcs

m_nNrOfInstances

int m_nNrOfInstances

m_nCardinality

int m_nCardinality

m_sBIFFile

java.lang.String m_sBIFFile

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

serialVersionUID: -8358864680379881429L

Serialized Fields

m_nPositionX

int[] m_nPositionX

m_nPositionY

int[] m_nPositionY

m_order

int[] m_order

m_sFile

java.lang.String m_sFile
the current filename

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

serialVersionUID: 746037443258735954L

Serialized Fields

m_nPositionX

FastVector m_nPositionX
location of nodes, used for graph drawing *


m_nPositionY

FastVector m_nPositionY

m_fMarginP

FastVector m_fMarginP
marginal distributions *


m_nEvidence

FastVector m_nEvidence
evidence values, used for evidence propagation *


m_undoStack

FastVector m_undoStack
undo stack for undoin edit actions, or redo edit actions


m_nCurrentEditAction

int m_nCurrentEditAction
current action in undo stack


m_nSavedPointer

int m_nSavedPointer
action that the network is saved


m_bNeedsUndoAction

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

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

serialVersionUID: -2038911085935515624L

Serialized Fields

m_layoutEngine

LayoutEngine m_layoutEngine
The current LayoutEngine


m_GraphPanel

weka.classifiers.bayes.net.GUI.GraphPanel m_GraphPanel
Panel actually displaying the graph


m_BayesNet

EditableBayesNet m_BayesNet
Container of Bayesian network


m_sFileName

java.lang.String m_sFileName
String containing file name storing current network


m_marginCalculator

MarginCalculator m_marginCalculator
used for calculating marginals in Bayesian netwowrks


m_marginCalculatorWithEvidence

MarginCalculator m_marginCalculatorWithEvidence
used for calculating marginals in Bayesian netwowrks when evidence is present


m_bViewMargins

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


m_bViewCliques

boolean m_bViewCliques

m_menuBar

javax.swing.JMenuBar m_menuBar
The menu bar


m_Instances

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


m_jTfZoom

javax.swing.JTextField m_jTfZoom
Text field for specifying zoom


m_jTbTools

javax.swing.JToolBar m_jTbTools
toolbar containing buttons at top of window


m_jStatusBar

javax.swing.JLabel m_jStatusBar
status bar at bottom of window


m_jTfNodeWidth

javax.swing.JTextField m_jTfNodeWidth
TextField for node's width


m_jTfNodeHeight

javax.swing.JTextField m_jTfNodeHeight
TextField for nodes height


m_jScrollPane

javax.swing.JScrollPane m_jScrollPane
this contains the m_GraphPanel GraphPanel


ICONPATH

java.lang.String ICONPATH
path for icons


m_fScale

double m_fScale
current zoom value


m_nNodeHeight

int m_nNodeHeight
standard width of node


m_nNodeWidth

int m_nNodeWidth

m_nPaddedNodeWidth

int m_nPaddedNodeWidth

m_nZoomPercents

int[] m_nZoomPercents
used when using zoomIn and zoomOut buttons


a_new

javax.swing.Action a_new
actions triggered by GUI events


a_quit

javax.swing.Action a_quit

a_save

javax.swing.Action a_save

a_export

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

a_print

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

a_load

javax.swing.Action a_load

a_zoomin

javax.swing.Action a_zoomin

a_zoomout

javax.swing.Action a_zoomout

a_layout

javax.swing.Action a_layout

a_saveas

javax.swing.Action a_saveas

a_viewtoolbar

javax.swing.Action a_viewtoolbar

a_viewstatusbar

javax.swing.Action a_viewstatusbar

a_networkgenerator

javax.swing.Action a_networkgenerator

a_datagenerator

javax.swing.Action a_datagenerator

a_datasetter

javax.swing.Action a_datasetter

a_learn

javax.swing.Action a_learn

a_learnCPT

javax.swing.Action a_learnCPT

a_help

javax.swing.Action a_help

a_about

javax.swing.Action a_about

a_addnode

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

a_delnode

javax.swing.Action a_delnode

a_cutnode

javax.swing.Action a_cutnode

a_copynode

javax.swing.Action a_copynode

a_pastenode

javax.swing.Action a_pastenode

a_selectall

javax.swing.Action a_selectall

a_addarc

javax.swing.Action a_addarc

a_delarc

javax.swing.Action a_delarc

a_undo

javax.swing.Action a_undo

a_redo

javax.swing.Action a_redo

a_alignleft

javax.swing.Action a_alignleft

a_alignright

javax.swing.Action a_alignright

a_aligntop

javax.swing.Action a_aligntop

a_alignbottom

javax.swing.Action a_alignbottom

a_centerhorizontal

javax.swing.Action a_centerhorizontal

a_centervertical

javax.swing.Action a_centervertical

a_spacehorizontal

javax.swing.Action a_spacehorizontal

a_spacevertical

javax.swing.Action a_spacevertical

m_nCurrentNode

int m_nCurrentNode
node currently selected through right clicking


m_Selection

weka.classifiers.bayes.net.GUI.Selection m_Selection
selection of nodes


m_nSelectedRect

java.awt.Rectangle m_nSelectedRect
selection rectangle drawn through dragging with left mouse button


m_clipboard

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

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

serialVersionUID: 650278019241175534L

Serialized Fields

m_debug

boolean m_debug

m_root

MarginCalculator.JunctionTreeNode m_root

jtNodes

MarginCalculator.JunctionTreeNode[] jtNodes

m_Margins

double[][] m_Margins

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

serialVersionUID: 650278019241175536L

Serialized Fields

m_bayesNet

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


m_nNodes

int[] m_nNodes
nodes of the Bayes net in this junction node


m_nCardinality

int m_nCardinality
cardinality of the instances of variables in this junction node


m_fi

double[] m_fi
potentials for first network


m_P

double[] m_P
distribution over this junction node according to first Bayes network


m_MarginalP

double[][] m_MarginalP

m_parentSeparator

MarginCalculator.JunctionTreeSeparator m_parentSeparator

m_children

java.util.Vector<E> m_children

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

serialVersionUID: 6502780192411755343L

Serialized Fields

m_nNodes

int[] m_nNodes

m_nCardinality

int m_nCardinality

m_fiParent

double[] m_fiParent

m_fiChild

double[] m_fiChild

m_parentNode

MarginCalculator.JunctionTreeNode m_parentNode

m_childNode

MarginCalculator.JunctionTreeNode m_childNode

m_bayesNet

BayesNet m_bayesNet

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

serialVersionUID: 4155021284407181838L

Serialized Fields

m_nParents

int[] m_nParents
Holds indexes of parents


m_nNrOfParents

int m_nNrOfParents
Holds number of parents


m_nCardinalityOfParents

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

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

serialVersionUID: -6196294370675872424L

Serialized Fields

m_iNode

int m_iNode
index of the node varied


m_nMCV

int m_nMCV
most common value


m_ADNodes

ADNode[] m_ADNodes
list of ADNode children


Package weka.classifiers.bayes.net.estimate

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

serialVersionUID: 2184330197666253884L

Serialized Fields

m_fAlpha

double m_fAlpha
Holds prior on count

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

serialVersionUID: -1846028304233257309L

Serialized Fields

m_bUseK2Prior

boolean m_bUseK2Prior
whether to use K2 prior

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

serialVersionUID: 4215400230843212684L

Serialized Fields

m_Counts

double[] m_Counts
Hold the counts


m_SumOfCounts

double m_SumOfCounts
Hold the sum of counts


m_nSymbols

int m_nSymbols
Holds number of symbols in distribution


m_fPrior

double m_fPrior
Holds the prior probability

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

serialVersionUID: 6774941981423312133L

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

serialVersionUID: 8330705772601586313L

Serialized Fields

m_bUseK2Prior

boolean m_bUseK2Prior
whether to use K2 prior

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

serialVersionUID: 5874941612331806172L


Package weka.classifiers.bayes.net.search

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

serialVersionUID: 6164792240778525312L

Serialized Fields

m_nMaxNrOfParents

int m_nMaxNrOfParents
Holds upper bound on number of parents


m_bInitAsNaiveBayes

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


m_bMarkovBlanketClassifier

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


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

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

serialVersionUID: 3165802334119704560L

Serialized Fields

m_BayesNet

BayesNet m_BayesNet

m_instances

Instances m_instances

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

serialVersionUID: -2510985917284798576L

Serialized Fields

m_nMaxCardinality

int m_nMaxCardinality
maximum size of separating set


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

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

serialVersionUID: 7334358169507619525L

Serialized Fields

m_sBIFFile

java.lang.String m_sBIFFile
name of file to read structure from

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

serialVersionUID: -4808572519709755811L


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

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

serialVersionUID: 4236165533882462203L

Serialized Fields

m_nRuns

int m_nRuns
number of runs


m_nPopulationSize

int m_nPopulationSize
size of population


m_nDescendantPopulationSize

int m_nDescendantPopulationSize
size of descendant population


m_bUseCrossOver

boolean m_bUseCrossOver
use cross-over?


m_bUseMutation

boolean m_bUseMutation
use mutation?


m_bUseTournamentSelection

boolean m_bUseTournamentSelection
use tournament selection or take best sub-population


m_nSeed

int m_nSeed
random number seed


m_random

java.util.Random m_random
random number generator

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

serialVersionUID: 7341389867906199781L

Serialized Fields

m_BayesNet

BayesNet m_BayesNet
points to Bayes network for which a structure is searched for


m_bUseProb

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


m_nNrOfFolds

int m_nNrOfFolds
number of folds for k-fold cross validation


m_nCVType

int m_nCVType
Holds the cross validation strategy used to measure quality of network

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

serialVersionUID: -3885042888195820149L

Serialized Fields

m_bUseArcReversal

boolean m_bUseArcReversal
use the arc reversal operator

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

serialVersionUID: -6626871067466338256L

Serialized Fields

m_bRandomOrder

boolean m_bRandomOrder
Holds flag to indicate ordering should be random

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

serialVersionUID: -7359197180460703069L

Serialized Fields

m_nRuns

int m_nRuns
number of runs


m_nSeed

int m_nSeed
random number seed


m_random

java.util.Random m_random
random number generator

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

serialVersionUID: -5482721887881010916L

Serialized Fields

m_fTStart

double m_fTStart
start temperature


m_fDelta

double m_fDelta
change in temperature at every run


m_nRuns

int m_nRuns
number of runs


m_bUseArcReversal

boolean m_bUseArcReversal
use the arc reversal operator


m_nSeed

int m_nSeed
random number seed


m_random

java.util.Random m_random
random number generator

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

serialVersionUID: 1176705618756672292L

Serialized Fields

m_nRuns

int m_nRuns
number of runs


m_nTabuList

int m_nTabuList
size of tabu list


m_oTabuList

weka.classifiers.bayes.net.search.global.HillClimber.Operation[] m_oTabuList
the actual tabu list

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

serialVersionUID: 1715277053980895298L


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

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

serialVersionUID: -7037070678911459757L

Serialized Fields

m_nRuns

int m_nRuns
number of runs


m_nPopulationSize

int m_nPopulationSize
size of population


m_nDescendantPopulationSize

int m_nDescendantPopulationSize
size of descendant population


m_bUseCrossOver

boolean m_bUseCrossOver
use cross-over?


m_bUseMutation

boolean m_bUseMutation
use mutation?


m_bUseTournamentSelection

boolean m_bUseTournamentSelection
use tournament selection or take best sub-population


m_nSeed

int m_nSeed
random number seed


m_random

java.util.Random m_random
random number generator

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

serialVersionUID: 4322783593818122403L

Serialized Fields

m_Cache

weka.classifiers.bayes.net.search.local.HillClimber.Cache m_Cache
cache for storing score differences


m_bUseArcReversal

boolean m_bUseArcReversal
use the arc reversal operator

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

serialVersionUID: 6176545934752116631L

Serialized Fields

m_bRandomOrder

boolean m_bRandomOrder
Holds flag to indicate ordering should be random

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

serialVersionUID: 7217437499439184344L

Serialized Fields

m_nNrOfLookAheadSteps

int m_nNrOfLookAheadSteps
Number of Look Ahead Steps


m_nNrOfGoodOperations

int m_nNrOfGoodOperations
Number of Good Operations per Step

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

serialVersionUID: 3325995552474190374L

Serialized Fields

m_BayesNet

BayesNet m_BayesNet
points to Bayes network for which a structure is searched for


m_fAlpha

double m_fAlpha
Holds prior on count


m_nScoreType

int m_nScoreType
Holds the score type used to measure quality of network

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

serialVersionUID: -6574084564213041174L

Serialized Fields

m_nRuns

int m_nRuns
number of runs


m_nSeed

int m_nSeed
random number seed


m_random

java.util.Random m_random
random number generator

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

serialVersionUID: 6951955606060513191L

Serialized Fields

m_fTStart

double m_fTStart
start temperature


m_fDelta

double m_fDelta
change in temperature at every run


m_nRuns

int m_nRuns
number of runs


m_bUseArcReversal

boolean m_bUseArcReversal
use the arc reversal operator


m_nSeed

int m_nSeed
random number seed


m_random

java.util.Random m_random
random number generator

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

serialVersionUID: 1457344073228786447L

Serialized Fields

m_nRuns

int m_nRuns
number of runs


m_nTabuList

int m_nTabuList
size of tabu list


m_oTabuList

weka.classifiers.bayes.net.search.local.HillClimber.Operation[] m_oTabuList
the actual tabu list

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

serialVersionUID: 965182127977228690L


Package weka.classifiers.evaluation

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

serialVersionUID: -181789981401504090L

Serialized Fields

m_ClassNames

java.lang.String[] m_ClassNames
Stores the names of the classes

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

serialVersionUID: -8871333992740492788L

Serialized Fields

m_Distribution

double[] m_Distribution
The predicted probabilities


m_Actual

double m_Actual
The actual class value


m_Predicted

double m_Predicted
The predicted class value


m_Weight

double m_Weight
The weight assigned to this prediction

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

serialVersionUID: -4880216423674233887L

Serialized Fields

m_Actual

double m_Actual
The actual class value


m_Predicted

double m_Predicted
The predicted class value


m_Weight

double m_Weight
The weight assigned to this prediction


Package weka.classifiers.functions

Class weka.classifiers.functions.GaussianProcesses extends Classifier implements Serializable

serialVersionUID: -8620066949967678545L

Serialized Fields

m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_Filter

Filter m_Filter
The filter used to standardize/normalize all values.


m_filterType

int m_filterType
Whether to normalize/standardize/neither


m_Missing

ReplaceMissingValues m_Missing
The filter used to get rid of missing values.


m_checksTurnedOff

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


m_delta

double m_delta
Gaussian Noise Value.


m_classIndex

int m_classIndex
The class index from the training data


m_Alin

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


m_Blin

double m_Blin

m_kernel

Kernel m_kernel
Kernel to use


m_NumTrain

int m_NumTrain
The number of training instances


m_avg_target

double m_avg_target
The training data.


m_C

Matrix m_C
The covariance matrix.


m_t

Matrix m_t
The vector of target values.


m_KernelIsLinear

boolean m_KernelIsLinear
whether the kernel is a linear one

Class weka.classifiers.functions.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_values

double[] m_values
The predicted value in each interval.


m_minMsq

double m_minMsq
The minimum mean squared error that has been achieved.


m_ZeroR

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

Class weka.classifiers.functions.LeastMedSq extends Classifier implements Serializable

serialVersionUID: 4288954049987652970L

Serialized Fields

m_Residuals

double[] m_Residuals

m_weight

double[] m_weight

m_SSR

double m_SSR

m_scalefactor

double m_scalefactor

m_bestMedian

double m_bestMedian

m_currentRegression

LinearRegression m_currentRegression

m_bestRegression

LinearRegression m_bestRegression

m_ls

LinearRegression m_ls

m_Data

Instances m_Data

m_RLSData

Instances m_RLSData

m_SubSample

Instances m_SubSample

m_MissingFilter

ReplaceMissingValues m_MissingFilter

m_TransformFilter

NominalToBinary m_TransformFilter

m_SplitFilter

RemoveRange m_SplitFilter

m_samplesize

int m_samplesize

m_samples

int m_samples

m_israndom

boolean m_israndom

m_debug

boolean m_debug

m_random

java.util.Random m_random

m_randomseed

long m_randomseed

Class weka.classifiers.functions.LibLINEAR extends Classifier implements Serializable

serialVersionUID: 230504711L

Serialized Fields

m_Model

java.lang.Object m_Model
LibLINEAR Model


m_Filter

Filter m_Filter
for normalizing the data


m_Normalize

boolean m_Normalize
normalize input data


m_SVMType

int m_SVMType
the SVM solver type


m_eps

double m_eps
stopping criteria


m_Cost

double m_Cost
cost Parameter C


m_Bias

double m_Bias
bias term value


m_WeightLabel

int[] m_WeightLabel

m_Weight

double[] m_Weight

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_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_nominalToBinary

boolean m_nominalToBinary
If true, the nominal to binary filter is applied


m_noReplaceMissingValues

boolean m_noReplaceMissingValues
If true, the replace missing values filter is not applied

Class weka.classifiers.functions.LibSVM extends Classifier implements Serializable

serialVersionUID: 14172L

Serialized Fields

m_Model

java.lang.Object m_Model
LibSVM Model


m_Filter

Filter m_Filter
for normalizing the data


m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values.


m_Normalize

boolean m_Normalize
normalize input data


m_noReplaceMissingValues

boolean m_noReplaceMissingValues
If true, the replace missing values filter is not applied


m_SVMType

int m_SVMType
the SVM type


m_KernelType

int m_KernelType
the kernel type


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_Gamma

double m_Gamma
for poly/rbf/sigmoid


m_GammaActual

double m_GammaActual
for poly/rbf/sigmoid (the actual gamma)


m_Coef0

double m_Coef0
for poly/sigmoid


m_CacheSize

double m_CacheSize
in MB


m_eps

double m_eps
stopping criteria


m_Cost

double m_Cost
cost, for C_SVC, EPSILON_SVR and NU_SVR


m_WeightLabel

int[] m_WeightLabel
for C_SVC


m_Weight

double[] m_Weight
for C_SVC


m_nu

double m_nu
for NU_SVC, ONE_CLASS, and NU_SVR


m_Loss

double m_Loss
loss, for EPSILON_SVR


m_Shrinking

boolean m_Shrinking
use the shrinking heuristics


m_ProbabilityEstimates

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

Class weka.classifiers.functions.LinearRegression extends Classifier implements Serializable

serialVersionUID: -3364580862046573747L

Serialized Fields

m_Coefficients

double[] m_Coefficients
Array for storing coefficients of linear regression.


m_SelectedAttributes

boolean[] m_SelectedAttributes
Which attributes are relevant?


m_TransformedData

Instances m_TransformedData
Variable for storing transformed training data.


m_MissingFilter

ReplaceMissingValues m_MissingFilter
The filter for removing missing values.


m_TransformFilter

NominalToBinary m_TransformFilter
The filter storing the transformation from nominal to binary attributes.


m_ClassStdDev

double m_ClassStdDev
The standard deviations of the class attribute


m_ClassMean

double m_ClassMean
The mean of the class attribute


m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Means

double[] m_Means
The attributes means


m_StdDevs

double[] m_StdDevs
The attribute standard deviations


b_Debug

boolean b_Debug
True if debug output will be printed


m_AttributeSelection

int m_AttributeSelection
The current attribute selection method


m_EliminateColinearAttributes

boolean m_EliminateColinearAttributes
Try to eliminate correlated attributes?


m_checksTurnedOff

boolean m_checksTurnedOff
Turn off all checks and conversions?


m_Ridge

double m_Ridge
The ridge parameter

Class weka.classifiers.functions.Logistic extends Classifier implements Serializable

serialVersionUID: 3932117032546553727L

Serialized Fields

m_Par

double[][] m_Par
The coefficients (optimized parameters) of the model


m_Data

double[][] m_Data
The data saved as a matrix


m_NumPredictors

int m_NumPredictors
The number of attributes in the model


m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_NumClasses

int m_NumClasses
The number of the class labels


m_Ridge

double m_Ridge
The ridge parameter.


m_AttFilter

RemoveUseless m_AttFilter
An attribute filter


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values.


m_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_structure

Instances m_structure

Class weka.classifiers.functions.MultilayerPerceptron extends Classifier implements Serializable

serialVersionUID: 572250905027665169L

Serialized Fields

m_ZeroR

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


m_instances

Instances m_instances
The training instances.


m_currentInstance

Instance m_currentInstance
The current instance running through the network.


m_numeric

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


m_attributeRanges

double[] m_attributeRanges
The ranges for all the attributes.


m_attributeBases

double[] m_attributeBases
The base values for all the attributes.


m_outputs

weka.classifiers.functions.MultilayerPerceptron.NeuralEnd[] m_outputs
The output units.(only feeds the errors, does no calcs)


m_inputs

weka.classifiers.functions.MultilayerPerceptron.NeuralEnd[] m_inputs
The input units.(only feeds the inputs does no calcs)


m_neuralNodes

NeuralConnection[] m_neuralNodes
All the nodes that actually comprise the logical neural net.


m_numClasses

int m_numClasses
The number of classes.


m_numAttributes

int m_numAttributes
The number of attributes.


m_nodePanel

weka.classifiers.functions.MultilayerPerceptron.NodePanel m_nodePanel
The panel the nodes are displayed on.


m_controlPanel

weka.classifiers.functions.MultilayerPerceptron.ControlPanel m_controlPanel
The control panel.


m_nextId

int m_nextId
The next id number available for default naming.


m_selected

FastVector m_selected
A Vector list of the units currently selected.


m_graphers

FastVector m_graphers
A Vector list of the graphers.


m_numEpochs

int m_numEpochs
The number of epochs to train through.


m_stopIt

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


m_stopped

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


m_accepted

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


m_win

javax.swing.JFrame m_win
The window for the network.


m_autoBuild

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


m_gui

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


m_valSize

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


m_driftThreshold

int m_driftThreshold
The number to to use to quit on validation testing.


m_randomSeed

int m_randomSeed
The number used to seed the random number generator.


m_random

java.util.Random m_random
The actual random number generator.


m_useNomToBin

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


m_nominalToBinaryFilter

NominalToBinary m_nominalToBinaryFilter
The actual filter.


m_hiddenLayers

java.lang.String m_hiddenLayers
The string that defines the hidden layers


m_normalizeAttributes

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


m_decay

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


m_learningRate

double m_learningRate
This is the learning rate for the network.


m_momentum

double m_momentum
This is the momentum for the network.


m_epoch

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


m_error

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


m_reset

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


m_normalizeClass

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


m_sigmoidUnit

SigmoidUnit m_sigmoidUnit
this is a sigmoid unit.


m_linearUnit

LinearUnit m_linearUnit
This is a linear unit.

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

serialVersionUID: 7305185603191183338L

Serialized Fields

m_link

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


m_input

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

Class weka.classifiers.functions.PaceRegression extends Classifier implements Serializable

serialVersionUID: 7230266976059115435L

Serialized Fields

m_Model

Instances m_Model
The model used


m_Coefficients

double[] m_Coefficients
Array for storing coefficients of linear regression.


m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Debug

boolean m_Debug
True if debug output will be printed


paceEstimator

int paceEstimator
the estimator


olscThreshold

double olscThreshold

Class weka.classifiers.functions.PLSClassifier extends Classifier implements Serializable

serialVersionUID: 4819775160590973256L

Serialized Fields

m_Filter

PLSFilter m_Filter
the PLS filter


m_ActualFilter

PLSFilter m_ActualFilter
the actual filter to use

Class weka.classifiers.functions.RBFNetwork extends Classifier implements Serializable

serialVersionUID: -3669814959712675720L

Serialized Fields

m_logistic

Logistic m_logistic
The logistic regression for classification problems


m_linear

LinearRegression m_linear
The linear regression for numeric problems


m_basisFilter

ClusterMembership m_basisFilter
The filter for producing the meta data


m_standardize

Standardize m_standardize
Filter used for normalizing the data


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_maxIts

int m_maxIts
The maximum number of iterations for logistic regression.


m_clusteringSeed

int m_clusteringSeed
The seed to pass on to K-means


m_minStdDev

double m_minStdDev
The minimum standard deviation


m_ZeroR

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

Class weka.classifiers.functions.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_slope

double m_slope
The slope


m_intercept

double m_intercept
The intercept


m_suppressErrorMessage

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

Class weka.classifiers.functions.SimpleLogistic extends Classifier implements Serializable

serialVersionUID: 7397710626304705059L

Serialized Fields

m_boostedModel

LogisticBase m_boostedModel
The actual logistic regression model


m_NominalToBinary

NominalToBinary m_NominalToBinary
Filter for converting nominal attributes to binary ones


m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues
Filter for replacing missing values


m_numBoostingIterations

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


m_maxBoostingIterations

int m_maxBoostingIterations
Maximum number of iterations for LogitBoost


m_heuristicStop

int m_heuristicStop
Parameter for the heuristic for early stopping of LogitBoost


m_useCrossValidation

boolean m_useCrossValidation
If true, cross-validate number of LogitBoost iterations


m_errorOnProbabilities

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


m_weightTrimBeta

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


m_useAIC

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

Class weka.classifiers.functions.SMO extends Classifier implements Serializable

serialVersionUID: -6585883636378691736L

Serialized Fields

m_classifiers

SMO.BinarySMO[][] m_classifiers
The binary classifier(s)


m_C

double m_C
The complexity parameter.


m_eps

double m_eps
Epsilon for rounding.


m_tol

double m_tol
Tolerance for accuracy of result.


m_filterType

int m_filterType
Whether to normalize/standardize/neither


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_Filter

Filter m_Filter
The filter used to standardize/normalize all values.


m_Missing

ReplaceMissingValues m_Missing
The filter used to get rid of missing values.


m_classIndex

int m_classIndex
The class index from the training data


m_classAttribute

Attribute m_classAttribute
The class attribute


m_KernelIsLinear

boolean m_KernelIsLinear
whether the kernel is a linear one


m_checksTurnedOff

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


m_fitLogisticModels

boolean m_fitLogisticModels
Whether logistic models are to be fit


m_numFolds

int m_numFolds
The number of folds for the internal cross-validation


m_randomSeed

int m_randomSeed
The random number seed


m_kernel

Kernel m_kernel
the kernel to use

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

serialVersionUID: -8246163625699362456L

Serialized Fields

m_alpha

double[] m_alpha
The Lagrange multipliers.


m_b

double m_b
The thresholds.


m_bLow

double m_bLow
The thresholds.


m_bUp

double m_bUp
The thresholds.


m_iLow

int m_iLow
The indices for m_bLow and m_bUp


m_iUp

int m_iUp
The indices for m_bLow and m_bUp


m_data

Instances m_data
The training data.


m_weights

double[] m_weights
Weight vector for linear machine.


m_sparseWeights

double[] m_sparseWeights
Variables to hold weight vector in sparse form. (To reduce storage requirements.)


m_sparseIndices

int[] m_sparseIndices

m_kernel

Kernel m_kernel
Kernel to use


m_class

double[] m_class
The transformed class values.


m_errors

double[] m_errors
The current set of errors for all non-bound examples.


m_I0

SMOset m_I0
{i: 0 < m_alpha[i] < C}


m_I1

SMOset m_I1
{i: m_class[i] = 1, m_alpha[i] = 0}


m_I2

SMOset m_I2
{i: m_class[i] = -1, m_alpha[i] =C}


m_I3

SMOset m_I3
{i: m_class[i] = 1, m_alpha[i] = C}


m_I4

SMOset m_I4
{i: m_class[i] = -1, m_alpha[i] = 0}


m_supportVectors

SMOset m_supportVectors
The set of support vectors


m_logistic

Logistic m_logistic
Stores logistic regression model for probability estimate


m_sumOfWeights

double m_sumOfWeights
Stores the weight of the training instances

Class weka.classifiers.functions.SMOreg extends Classifier implements Serializable

serialVersionUID: -7149606251113102827L

Serialized Fields

m_filterType

int m_filterType
Whether to normalize/standardize/neither


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_Filter

Filter m_Filter
The filter used to standardize/normalize all values.


m_Missing

ReplaceMissingValues m_Missing
The filter used to get rid of missing values.


m_onlyNumeric

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


m_C

double m_C
capacity parameter


m_x1

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


m_x0

double m_x0

m_optimizer

RegOptimizer m_optimizer
contains the algorithm used for learning


m_kernel

Kernel m_kernel
the configured kernel

Class weka.classifiers.functions.VotedPerceptron extends Classifier implements Serializable

serialVersionUID: -1072429260104568698L

Serialized Fields

m_MaxK

int m_MaxK
The maximum number of alterations to the perceptron


m_NumIterations

int m_NumIterations
The number of iterations


m_Exponent

double m_Exponent
The exponent


m_K

int m_K
The actual number of alterations


m_Additions

int[] m_Additions
The training instances added to the perceptron


m_IsAddition

boolean[] m_IsAddition
Addition or subtraction?


m_Weights

int[] m_Weights
The weights for each perceptron


m_Train

Instances m_Train
The training instances


m_Seed

int m_Seed
Seed used for shuffling the dataset


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values.

Class weka.classifiers.functions.Winnow extends Classifier implements Serializable

serialVersionUID: 3543770107994321324L

Serialized Fields

m_Balanced

boolean m_Balanced
Use the balanced variant?


m_numIterations

int m_numIterations
The number of iterations


m_Alpha

double m_Alpha
The promotion coefficient


m_Beta

double m_Beta
The demotion coefficient


m_Threshold

double m_Threshold
Prediction threshold, <0 == numAttributes


m_Seed

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


m_Mistakes

int m_Mistakes
Accumulated mistake count (for statistics)


m_defaultWeight

double m_defaultWeight
Starting weights for the prediction vector(s)


m_predPosVector

double[] m_predPosVector
The weight vector for prediction (pos)


m_predNegVector

double[] m_predNegVector
The weight vector for prediction (neg)


m_actualThreshold

double m_actualThreshold
The true threshold used for prediction


m_Train

Instances m_Train
The training instances


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values.


Package weka.classifiers.functions.neural

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

serialVersionUID: 8572152807755673630L

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

serialVersionUID: -286208828571059163L

Serialized Fields

m_inputList

NeuralConnection[] m_inputList
The list of inputs to this unit.


m_outputList

NeuralConnection[] m_outputList
The list of outputs from this unit.


m_inputNums

int[] m_inputNums
The numbering for the connections at the other end of the input lines.


m_outputNums

int[] m_outputNums
The numbering for the connections at the other end of the out lines.


m_numInputs

int m_numInputs
The number of inputs.


m_numOutputs

int m_numOutputs
The number of outputs.


m_unitValue

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


m_unitError

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


m_weightsUpdated

boolean m_weightsUpdated
True if the weights have already been updated.


m_id

java.lang.String m_id
The string that uniquely (provided naming is done properly) identifies this unit.


m_type

int m_type
The type of unit this is.


m_x

double m_x
The x coord of this unit purely for displaying purposes.


m_y

double m_y
The y coord of this unit purely for displaying purposes.

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

serialVersionUID: -1085750607680839163L

Serialized Fields

m_weights

double[] m_weights
The weights for each of the input connections, and the threshold.


m_bestWeights

double[] m_bestWeights
The best (lowest error) weights. Only used when validation set is used


m_changeInWeights

double[] m_changeInWeights
The change in the weights.


m_random

java.util.Random m_random

m_methods

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

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

serialVersionUID: -5162958458177475652L


Package weka.classifiers.functions.pace

Class weka.classifiers.functions.pace.PaceMatrix extends Matrix implements Serializable

serialVersionUID: 2699925616857843973L


Package weka.classifiers.functions.supportVector

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

serialVersionUID: 702810182699015136L

Serialized Fields

m_kernelEvals

int m_kernelEvals
Counts the number of kernel evaluations.


m_cacheHits

int m_cacheHits
Counts the number of kernel cache hits.


m_cacheSize

int m_cacheSize
The size of the cache (a prime number)


m_storage

double[] m_storage
Kernel cache


m_keys

long[] m_keys

m_kernelMatrix

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


m_numInsts

int m_numInsts
The number of instance in the dataset


m_cacheSlots

int m_cacheSlots
number of cache slots in an entry

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

serialVersionUID: -6102771099905817064L

Serialized Fields

m_data

Instances m_data
The dataset


m_Debug

boolean m_Debug
enables debugging output


m_ChecksTurnedOff

boolean m_ChecksTurnedOff
Turns off all checks

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

serialVersionUID: 1248574185532130851L

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

serialVersionUID: -321831645846363201L

Serialized Fields

m_lowerOrder

boolean m_lowerOrder
Use lower-order terms?


m_exponent

double m_exponent
The exponent for the polynomial kernel.

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

serialVersionUID: -321831645846363333L

Serialized Fields

m_KernelMatrixFile

java.io.File m_KernelMatrixFile
The file holding the kernel matrix.


m_KernelMatrix

Matrix m_KernelMatrix
The kernel matrix.


m_Counter

int m_Counter
A classifier counter.

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

serialVersionUID: 1682161522559978851L

Serialized Fields

m_kernelPrecalc

double[] m_kernelPrecalc
The precalculated dotproducts of <inst_i,inst_i>


m_omega

double m_omega
Omega for the Puk kernel.


m_sigma

double m_sigma
Sigma for the Puk kernel.


m_factor

double m_factor
Cached factor for the Puk kernel.

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

serialVersionUID: 5247117544316387852L

Serialized Fields

m_kernelPrecalc

double[] m_kernelPrecalc
The precalculated dotproducts of <inst_i,inst_i>


m_gamma

double m_gamma
Gamma for the RBF kernel.

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

serialVersionUID: -2198266997254461814L

Serialized Fields

m_alpha

double[] m_alpha
alpha and alpha* arrays containing weights for solving dual problem


m_alphaStar

double[] m_alphaStar

m_b

double m_b
offset


m_epsilon

double m_epsilon
epsilon of epsilon-insensitive cost function


m_C

double m_C
capacity parameter, copied from SMOreg


m_target

double[] m_target
class values/desired output vector


m_data

Instances m_data
points to data set


m_kernel

Kernel m_kernel
the kernel


m_classIndex

int m_classIndex
index of class variable in data set


m_nInstances

int m_nInstances
number of instances in data set


m_random

java.util.Random m_random
random number generator


m_nSeed

int m_nSeed
seed for initializing random number generator


m_supportVectors

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


m_nEvals

int m_nEvals
number of kernel evaluations, used for printing statistics only


m_nCacheHits

int m_nCacheHits
number of kernel cache hits, used for printing statistics only


m_weights

double[] m_weights
weights for linear kernel


m_sparseWeights

double[] m_sparseWeights
Variables to hold weight vector in sparse form. (To reduce storage requirements.)


m_sparseIndices

int[] m_sparseIndices

m_bModelBuilt

boolean m_bModelBuilt
flag to indicate whether the model is built yet


m_SVM

SMOreg m_SVM
parent SMOreg class

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

serialVersionUID: -7504070793279598638L

Serialized Fields

m_eps

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


m_error

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


m_alpha1

double m_alpha1
alpha value for first candidate


m_alpha1Star

double m_alpha1Star
alpha* value for first candidate


m_alpha2

double m_alpha2
alpha value for second candidate


m_alpha2Star

double m_alpha2Star
alpha* value for second candidate

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

serialVersionUID: 471692841446029784L

Serialized Fields

m_I0

SMOset m_I0
The different sets used by the algorithm.


m_iSet

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


m_bUp

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


m_bLow

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


m_iUp

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


m_iLow

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


m_fTolerance

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


m_bUseVariant1

boolean m_bUseVariant1
set true to use variant 1 of the paper, otherwise use variant 2

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

serialVersionUID: -8364829283188675777L

Serialized Fields

m_number

int m_number
The current number of elements in the set


m_first

int m_first
The first element in the set


m_indicators

boolean[] m_indicators
Indicators


m_next

int[] m_next
The next element for each element


m_previous

int[] m_previous
The previous element for each element

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

serialVersionUID: -4902954211202690123L

Serialized Fields

m_cacheSize

int m_cacheSize
The size of the cache (a prime number)


m_internalCacheSize

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


m_strAttr

int m_strAttr
The attribute number of the string attribute


m_storage

double[] m_storage
Kernel cache (i.e., cache for kernel evaluations)


m_keys

long[] m_keys

m_kernelEvals

int m_kernelEvals
Counts the number of kernel evaluations.


m_numInsts

int m_numInsts
The number of instance in the dataset


m_PruningMethod

int m_PruningMethod
the pruning method


m_lambda

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


m_subsequenceLength

int m_subsequenceLength
The substring length


m_maxSubsequenceLength

int m_maxSubsequenceLength
The maximum substring length for lambda pruning


m_powersOflambda

double[] m_powersOflambda
the precalculated powers of lambda


m_normalize

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


maxCache

int maxCache
private cache for intermediate results


cachekh

double[] cachekh

cachekhK

int[] cachekhK

cachekh2

double[] cachekh2

cachekh2K

int[] cachekh2K

m_multX

int m_multX
cached indexes for private cache


m_multY

int m_multY

m_multZ

int m_multZ

m_multZZ

int m_multZZ

m_useRecursionCache

boolean m_useRecursionCache

Package weka.classifiers.lazy

Class weka.classifiers.lazy.IB1 extends Classifier implements Serializable

serialVersionUID: -6152184127304895851L

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.

Class weka.classifiers.lazy.IBk extends Classifier implements Serializable

serialVersionUID: -3080186098777067172L

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_NumClasses

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


m_ClassType

int m_ClassType
The class attribute type.


m_kNN

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


m_kNNUpper

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


m_kNNValid

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


m_WindowSize

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


m_DistanceWeighting

int m_DistanceWeighting
Whether the neighbours should be distance-weighted.


m_CrossValidate

boolean m_CrossValidate
Whether to select k by cross validation.


m_MeanSquared

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


m_NNSearch

NearestNeighbourSearch m_NNSearch
for nearest-neighbor search.


m_NumAttributesUsed

double m_NumAttributesUsed
The number of attributes the contribute to a prediction.

Class weka.classifiers.lazy.KStar extends Classifier implements Serializable

serialVersionUID: 332458330800479083L

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_NumInstances

int m_NumInstances
The number of instances in the dataset


m_NumClasses

int m_NumClasses
The number of class values


m_NumAttributes

int m_NumAttributes
The number of attributes


m_ClassType

int m_ClassType
The class attribute type


m_RandClassCols

int[][] m_RandClassCols
Table of random class value colomns


m_ComputeRandomCols

int m_ComputeRandomCols
Flag turning on and off the computation of random class colomns


m_InitFlag

int m_InitFlag
Flag turning on and off the initialisation of config variables


m_Cache

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


m_MissingMode

int m_MissingMode
missing value treatment


m_BlendMethod

int m_BlendMethod
0 = use specified blend, 1 = entropic blend setting


m_GlobalBlend

int m_GlobalBlend
default sphere of influence blend setting

Class weka.classifiers.lazy.LBR extends Classifier implements Serializable

serialVersionUID: 5648559277738985156L

Serialized Fields

m_Counts

int[][][] m_Counts
All the counts for nominal attributes.


m_tCounts

int[][][] m_tCounts
All the counts for nominal attributes.


m_Priors

int[] m_Priors
The prior probabilities of the classes.


m_tPriors

int[] m_tPriors
The prior probabilities of the classes.


m_numAtts

int m_numAtts
number of attributes for the dataset


m_numClasses

int m_numClasses
number of classes for dataset


m_numInsts

int m_numInsts
number of instances in dataset


m_Instances

Instances m_Instances
The set of instances used for current training.


m_Errors

int m_Errors
leave-one-out errors on the training dataset.


m_ErrorFlags

boolean[] m_ErrorFlags
leave-one-out error flags on the training dataaet.


leftHand

java.util.ArrayList<E> leftHand
best attribute's index list. maybe as output result


m_subOldErrorFlags

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


m_RemainderErrors

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


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_NCV

boolean m_NCV
for printing in n-fold cross validation


m_subInstances

LBR.Indexes m_subInstances
index of instances and attributes for the given dataset


tempSubInstances

LBR.Indexes tempSubInstances
index of instances and attributes for the given dataset


posteriorsArray

double[] posteriorsArray
probability values array


bestCnt

int bestCnt

tempCnt

int tempCnt

forCnt

int forCnt

whileCnt

int whileCnt

Class weka.classifiers.lazy.LBR.Indexes extends java.lang.Object implements Serializable

serialVersionUID: -2771490019751421307L

Serialized Fields

m_InstIndexes

boolean[] m_InstIndexes
the array instance indexes


m_AttIndexes

boolean[] m_AttIndexes
the array attribute indexes


m_NumInstances

int m_NumInstances
the number of instances indexed


m_NumAtts

int m_NumAtts
the number of attributes indexed


m_SequentialInstIndexes

int[] m_SequentialInstIndexes
the array of instance indexes that are set to a either true or false


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_SequentialAttIndex_valid

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


m_NumInstsSet

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


m_NumAttsSet

int m_NumAttsSet
the number of 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_NumSeqAttsSet

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


m_ClassIndex

int m_ClassIndex
the Class Index for the data set

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

serialVersionUID: 1979797405383665815L

Serialized Fields

m_Train

Instances m_Train
The training instances used for classification.


m_kNN

int m_kNN
The number of neighbours used to select the kernel bandwidth.


m_WeightKernel

int m_WeightKernel
The weighting kernel method currently selected.


m_UseAllK

boolean m_UseAllK
True if m_kNN should be set to all instances.


m_NNSearch

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


m_ZeroR

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


Package weka.classifiers.lazy.kstar

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

serialVersionUID: -7693632394267140678L

Serialized Fields

m_Cache

KStarCache.CacheTable m_Cache
cache table

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

serialVersionUID: -8086106452588253423L

Serialized Fields

m_Table

KStarCache.TableEntry[] m_Table
The hash table data.


m_Count

int m_Count
The total number of entries in the hash table.


m_Threshold

int m_Threshold
Rehashes the table when count exceeds this threshold.


m_LoadFactor

float m_LoadFactor
The load factor for the hashtable.


DEFAULT_TABLE_SIZE

int DEFAULT_TABLE_SIZE
The default size of the hashtable


DEFAULT_LOAD_FACTOR

float DEFAULT_LOAD_FACTOR
The default load factor for the hashtable


EPSILON

double EPSILON
Accuracy value for equality

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

serialVersionUID: 4057602386766259138L

Serialized Fields

hash

int hash
attribute value hash code


key

double key
attribute value


value

double value
scale factor or stop parameter


pmiss

double pmiss
transformation probability to missing value


next

KStarCache.TableEntry next
next table entry (separate chaining)


Package weka.classifiers.meta

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

serialVersionUID: -7378107808933117974L

Serialized Fields

m_Betas

double[] m_Betas
Array for storing the weights for the votes.


m_NumIterationsPerformed

int m_NumIterationsPerformed
The number of successfully generated base classifiers.


m_WeightThreshold

int m_WeightThreshold
Weight Threshold. The percentage of weight mass used in training


m_UseResampling

boolean m_UseResampling
Use boosting with reweighting?


m_NumClasses

int m_NumClasses
The number of classes


m_ZeroR

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

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

serialVersionUID: -2368937577670527151L

Serialized Fields

m_shrinkage

double m_shrinkage
Shrinkage (Learning rate). Default = no shrinkage.


m_NumIterationsPerformed

int m_NumIterationsPerformed
The number of successfully generated base classifiers.


m_zeroR

ZeroR m_zeroR
The model for the mean


m_SuitableData

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

Class weka.classifiers.meta.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_Search

ASSearch m_Search
The search method to use


m_ReducedHeader

Instances m_ReducedHeader
The header of the dimensionally reduced data


m_numClasses

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


m_numAttributesSelected

double m_numAttributesSelected
The number of attributes selected by the attribute selection phase


m_selectionTime

double m_selectionTime
The time taken to select attributes in milliseconds


m_totalTime

double m_totalTime
The time taken to select attributes AND build the classifier

Class weka.classifiers.meta.Bagging extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID: -505879962237199703L

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 extends Classifier implements Serializable

serialVersionUID: -5687069451420259135L

Serialized Fields

m_Clusterer

Clusterer m_Clusterer
the cluster algorithm used (template)


m_ActualClusterer

Clusterer m_ActualClusterer
the actual cluster algorithm being used


m_OriginalHeader

Instances m_OriginalHeader
the original training data header


m_ClusteringHeader

Instances m_ClusteringHeader
the modified training data header


m_ClustersToClasses

double[] m_ClustersToClasses
the mapping between clusters and classes


m_ZeroR

Classifier m_ZeroR
the default model

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

serialVersionUID: 4500023123618669859L

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
The classifiers. (One for each class.)


m_ClassFilters

MakeIndicator[] m_ClassFilters
The filters used to transform the class.

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

serialVersionUID: -720658209263002404L

Serialized Fields

m_MatrixSource

int m_MatrixSource
Indicates the current cost matrix source


m_OnDemandDirectory

java.io.File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory


m_CostFile

java.lang.String m_CostFile
The name of the cost file, for command line options


m_CostMatrix

CostMatrix m_CostMatrix
The cost matrix


m_MinimizeExpectedCost

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

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

serialVersionUID: -6529603380876641265L

Serialized Fields

m_ClassifierOptions

java.lang.String[] m_ClassifierOptions
The base classifier options (not including those being set by cross-validation)


m_BestClassifierOptions

java.lang.String[] m_BestClassifierOptions
The set of all classifier options as determined by cross-validation


m_InitOptions

java.lang.String[] m_InitOptions
The set of all options at initialization time. So that getOptions can return this.


m_BestPerformance

double m_BestPerformance
The cross-validated performance of the best options


m_CVParams

FastVector m_CVParams
The set of parameters to cross-validate over


m_NumAttributes

int m_NumAttributes
The number of attributes in the data


m_TrainFoldSize

int m_TrainFoldSize
The number of instances in a training fold


m_NumFolds

int m_NumFolds
The number of folds used in cross-validation

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

serialVersionUID: -4668812017709421953L

Serialized Fields

m_ParamChar

char m_ParamChar
Char used to identify the option of interest


m_Lower

double m_Lower
Lower bound for the CV search


m_Upper

double m_Upper
Upper bound for the CV search


m_Steps

double m_Steps
Number of steps during the search


m_ParamValue

double m_ParamValue
The parameter value with the best performance


m_AddAtEnd

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


m_RoundParam

boolean m_RoundParam
True if the parameter should be rounded to an integer

Class weka.classifiers.meta.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_Vote

Vote m_Vote
the classifier used for voting


m_Verbose

boolean m_Verbose
whether to output some progress information during building

Class weka.classifiers.meta.Decorate extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID: -6020193348750269931L

Serialized Fields

m_Committee

java.util.Vector<E> m_Committee
Vector of classifiers that make up the committee/ensemble.


m_DesiredSize

int m_DesiredSize
The desired ensemble size.


m_ArtSize

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


m_Random

java.util.Random m_Random
The random number generator.


m_AttributeStats

java.util.Vector<E> m_AttributeStats
Attribute statistics - used for generating artificial examples.

Class weka.classifiers.meta.END extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID: -4143242362912214956L

Serialized Fields

m_hashtable

java.util.Hashtable<K,V> m_hashtable
The hashtable containing the classifiers for the END.

Class weka.classifiers.meta.EnsembleSelection extends RandomizableClassifier implements Serializable

serialVersionUID: -1744155148765058511L

Serialized Fields

m_library

EnsembleSelectionLibrary m_library
The Library of models, from which we can select our ensemble. Usually loaded from a model list file (.mlf or .model.xml) using the -L command-line option.


m_chosen_models

EnsembleSelectionLibraryModel[] m_chosen_models
List of models chosen by EnsembleSelection. Populated by buildClassifier.


m_chosen_model_weights

int[] m_chosen_model_weights
An array of weights for the chosen models. Elements are parallel to those in m_chosen_models. That is, m_chosen_model_weights[i] is the weight associated with the model at m_chosen_models[i].


m_total_weight

int m_total_weight
Total weight of all chosen models.


m_modelRatio

double m_modelRatio
ratio of library models that will be randomly chosen to be used for each model bag


m_validationRatio

double m_validationRatio
Indicates the fraction of the given training set that should be used for hillclimbing/validation. This fraction is set aside and not used for training. It is assumed that any loaded models were also not trained on set-aside data. (If the same percentage and random seed were used previously to train the models in the library, this will work as expected - i.e., those models will be valid)


m_modelLibraryFileName

java.lang.String m_modelLibraryFileName
The name of the Model Library File (if one is specified) which lists models from which ensemble selection will choose. This is only used when run from the command-line, as otherwise m_library is responsible for this.


m_numModelBags

int m_numModelBags
The number of "model bags". Using 1 is equivalent to no bagging at all.


m_hillclimbMetric

int m_hillclimbMetric
The metric for which the ensemble will be optimized.


m_algorithm

int m_algorithm
The algorithm used for ensemble selection.


m_hillclimbIterations

int m_hillclimbIterations
number of hillclimbing iterations for the ensemble selection algorithm


m_sortInitializationRatio

double m_sortInitializationRatio
ratio of library models to be used for sort initialization


m_replacement

boolean m_replacement
specifies whether or not the ensemble algorithm is allowed to include a specific model in the library more than once in each ensemble


m_greedySortInitialization

boolean m_greedySortInitialization
specifies whether we use "greedy" sort initialization. If false, we simply add the best m_sortInitializationRatio models of the bag blindly. If true, we add the best models in order up to m_sortInitializationRatio until adding the next model would not help performance.


m_verboseOutput

boolean m_verboseOutput
Specifies whether or not we will output metrics for all models


m_cachedPredictions

java.util.Map<K,V> m_cachedPredictions
Hash map of cached predictions. The key is a stringified Instance. Each entry is a 2d array, first indexed by classifier index (i.e., the one used in m_chosen_model). The second index is the usual "distribution" index across classes.


m_workingDirectory

java.io.File m_workingDirectory
This string will store the working directory where all models , temporary prediction values, and modellist logs are to be built and stored.


m_NumFolds

int m_NumFolds
Indicates the number of folds for cross-validation. A value of 1 indicates there is no cross-validation. Cross validation is done in the "embedded" fashion described by Caruana, Niculescu, and Munson (unpublished work - tech report forthcoming)

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

serialVersionUID: -4523450618538717400L

Serialized Fields

m_Filter

Filter m_Filter
The filter


m_FilteredInstances

Instances m_FilteredInstances
The instance structure of the filtered instances

Class weka.classifiers.meta.Grading extends Stacking implements Serializable

serialVersionUID: 5207837947890081170L

Serialized Fields

m_MetaClassifiers

Classifier[] m_MetaClassifiers
The meta classifiers, one for each base classifier.


m_InstPerClass

double[] m_InstPerClass
InstPerClass

Class weka.classifiers.meta.GridSearch extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: -3034773968581595348L

Serialized Fields

m_Filter

Filter m_Filter
the Filter


m_BestFilter

Filter m_BestFilter
the Filter with the best setup


m_BestClassifier

Classifier m_BestClassifier
the Classifier with the best setup


m_Values

weka.classifiers.meta.GridSearch.PointDouble m_Values
the best values


m_Evaluation

int m_Evaluation
the type of evaluation


m_Y_Property

java.lang.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_Min

double m_Y_Min
the minimum of Y


m_Y_Max

double m_Y_Max
the maximum of Y


m_Y_Step

double m_Y_Step
the step size of Y


m_Y_Base

double m_Y_Base
the base for Y


m_Y_Expression

java.lang.String m_Y_Expression
The expression for the Y property. Available parameters for the expression:

See Also:
MathematicalExpression, MathExpression

m_X_Property

java.lang.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_Min

double m_X_Min
the minimum of X


m_X_Max

double m_X_Max
the maximum of X


m_X_Step

double m_X_Step
the step size of


m_X_Base

double m_X_Base
the base for


m_X_Expression

java.lang.String m_X_Expression
The expression for the X property. Available parameters for the expression:

See Also:
MathematicalExpression, MathExpression

m_GridIsExtendable

boolean m_GridIsExtendable
whether the grid can be extended


m_MaxGridExtensions

int m_MaxGridExtensions
maximum number of grid extensions (-1 means unlimited)


m_GridExtensionsPerformed

int m_GridExtensionsPerformed
the number of extensions performed


m_SampleSize

double m_SampleSize
the sample size to search the initial grid with


m_Traversal

int m_Traversal
the traversal


m_LogFile

java.io.File m_LogFile
the log file to use


m_Grid

weka.classifiers.meta.GridSearch.Grid m_Grid
the value-pairs grid


m_Data

Instances m_Data
the training data


m_Cache

weka.classifiers.meta.GridSearch.PerformanceCache m_Cache
the cache for points in the grid that got calculated


m_UniformPerformance

boolean m_UniformPerformance
whether all performances in the grid are the same

Class weka.classifiers.meta.GridSearch.Grid extends java.lang.Object implements Serializable

serialVersionUID: 7290732613611243139L

Serialized Fields

m_MinX

double m_MinX
the minimum on the X axis


m_MaxX

double m_MaxX
the maximum on the X axis


m_StepX

double m_StepX
the step size for the X axis


m_LabelX

java.lang.String m_LabelX
the label for the X axis


m_MinY

double m_MinY
the minimum on the Y axis


m_MaxY

double m_MaxY
the maximum on the Y axis


m_StepY

double m_StepY
the step size for the Y axis


m_LabelY

java.lang.String m_LabelY
the label for the Y axis


m_Width

int m_Width
the number of points on the X axis


m_Height

int m_Height
the number of points on the Y axis

Class weka.classifiers.meta.GridSearch.Performance extends java.lang.Object implements Serializable

serialVersionUID: -4374706475277588755L

Serialized Fields

m_Values

weka.classifiers.meta.GridSearch.PointDouble m_Values
the value pair the classifier was built with


m_CC

double m_CC
the Correlation coefficient


m_RMSE

double m_RMSE
the Root mean squared error


m_RRSE

double m_RRSE
the Root relative squared error


m_MAE

double m_MAE
the Mean absolute error


m_RAE

double m_RAE
the Relative absolute error


m_ACC

double m_ACC
the Accuracy


m_Kappa

double m_Kappa
the kappa value

Class weka.classifiers.meta.GridSearch.PerformanceCache extends java.lang.Object implements Serializable

serialVersionUID: 5838863230451530252L

Serialized Fields

m_Cache

java.util.Hashtable<K,V> m_Cache
the cache for points in the grid that got calculated

Class weka.classifiers.meta.GridSearch.PerformanceComparator extends java.lang.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 extends java.lang.Object implements Serializable

serialVersionUID: 5486491313460338379L

Serialized Fields

m_Grid

weka.classifiers.meta.GridSearch.Grid m_Grid
the corresponding grid


m_Performances

java.util.Vector<E> m_Performances
the performances


m_Type

int m_Type
the type of performance the table was generated for


m_Table

double[][] m_Table
the table with the values


m_Min

double m_Min
the minimum performance


m_Max

double m_Max
the maximum performance

Class weka.classifiers.meta.GridSearch.PointDouble extends java.awt.geom.Point2D.Double implements Serializable

serialVersionUID: 7151661776161898119L

Class weka.classifiers.meta.GridSearch.PointInt extends java.awt.Point implements Serializable

serialVersionUID: -5900415163698021618L

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

serialVersionUID: -3905660358715833753L

Serialized Fields

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_NumGenerated

int m_NumGenerated
The number of successfully generated base classifiers.


m_NumFolds

int m_NumFolds
The number of folds for the internal cross-validation.


m_NumRuns

int m_NumRuns
The number of runs for the internal cross-validation.


m_WeightThreshold

int m_WeightThreshold
Weight thresholding. The percentage of weight mass used in training


m_NumericClassData

Instances m_NumericClassData
Dummy dataset with a numeric class


m_ClassAttribute

Attribute m_ClassAttribute
The actual class attribute (for getting class names)


m_UseResampling

boolean m_UseResampling
Use boosting with reweighting?


m_Precision

double m_Precision
The threshold on the improvement of the likelihood


m_Shrinkage

double m_Shrinkage
The value of the shrinkage parameter


m_RandomInstance

java.util.Random m_RandomInstance
The random number generator used


m_Offset

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


m_ZeroR

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

Class weka.classifiers.meta.MetaCost extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: 1205317833344726855L

Serialized Fields

m_MatrixSource

int m_MatrixSource
Indicates the current cost matrix source


m_OnDemandDirectory

java.io.File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory


m_CostFile

java.lang.String m_CostFile
The name of the cost file, for command line options


m_CostMatrix

CostMatrix m_CostMatrix
The cost matrix


m_NumIterations

int m_NumIterations
The number of iterations.


m_BagSizePercent

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

Class weka.classifiers.meta.MultiBoostAB extends AdaBoostM1 implements Serializable

serialVersionUID: -6681619178187935148L

Serialized Fields

m_NumSubCmtys

int m_NumSubCmtys
The number of sub-committees to use


m_Random

java.util.Random m_Random
Random number generator

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

serialVersionUID: -3879602011542849141L

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
The classifiers.


m_pairwiseCoupling

boolean m_pairwiseCoupling
Use pairwise coupling with 1-vs-1


m_SumOfWeights

double[] m_SumOfWeights
Needed for pairwise coupling


m_ClassFilters

Filter[] m_ClassFilters
The filters used to transform the class.


m_ZeroR

ZeroR m_ZeroR
ZeroR classifier for when all base classifier return zero probability.


m_ClassAttribute

Attribute m_ClassAttribute
Internal copy of the class attribute for output purposes


m_TwoClassDataset

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


m_RandomWidthFactor

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


m_Method

int m_Method
The multiclass method to use

Class weka.classifiers.meta.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 extends SingleClassifierEnhancer implements Serializable

serialVersionUID: -3461971774059603636L

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers
The classifiers. (One for each class.)


m_ClassFilters

MakeIndicator[] m_ClassFilters
The filters used to transform the class.


m_ZeroR

ZeroR m_ZeroR
ZeroR classifier for when all base classifier return zero probability.

Class weka.classifiers.meta.RacedIncrementalLogitBoost extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: 908598343772170052L

Serialized Fields

m_committees

FastVector m_committees
The committees


m_PruningType

int m_PruningType
The pruning type used


m_UseResampling

boolean m_UseResampling
Whether to use resampling


m_NumClasses

int m_NumClasses
The number of classes


m_NumericClassData

Instances m_NumericClassData
Dummy dataset with a numeric class


m_ClassAttribute

Attribute m_ClassAttribute
The actual class attribute (for getting class names)


m_minChunkSize

int m_minChunkSize
The minimum chunk size used for training


m_maxChunkSize

int m_maxChunkSize
The maimum chunk size used for training


m_validationChunkSize

int m_validationChunkSize
The size of the validation set


m_numInstancesConsumed

int m_numInstancesConsumed
The number of instances consumed


m_validationSet

Instances m_validationSet
The instances used for validation


m_currentSet

Instances m_currentSet
The instances currently in memory for training


m_bestCommittee

weka.classifiers.meta.RacedIncrementalLogitBoost.Committee m_bestCommittee
The current best committee


m_zeroR

ZeroR m_zeroR
The default scheme used when committees aren't ready


m_validationSetChanged

boolean m_validationSetChanged
Whether the validation set has recently been changed


m_maxBatchSizeRequired

int m_maxBatchSizeRequired
The maximum number of instances required for processing


m_RandomInstance

java.util.Random m_RandomInstance
The random number generator used

Class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee extends java.lang.Object implements Serializable

serialVersionUID: 5559880306684082199L

Serialized Fields

m_chunkSize

int m_chunkSize

m_instancesConsumed

int m_instancesConsumed
number eaten from m_currentSet


m_models

FastVector m_models

m_lastValidationError

double m_lastValidationError

m_lastLogLikelihood

double m_lastLogLikelihood

m_modelHasChanged

boolean m_modelHasChanged

m_modelHasChangedLL

boolean m_modelHasChangedLL

m_validationFs

double[][] m_validationFs

m_newValidationFs

double[][] m_newValidationFs

Class weka.classifiers.meta.RandomCommittee extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID: -9204394360557300092L

Class weka.classifiers.meta.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 extends SingleClassifierEnhancer implements Serializable

serialVersionUID: 5066426153134050378L

Serialized Fields

m_Discretizer

Discretize m_Discretizer
The discretization filter.


m_NumBins

int m_NumBins
The number of discretization intervals.


m_ClassMeans

double[] m_ClassMeans
The mean values for each Discretized class interval.


m_DeleteEmptyBins

boolean m_DeleteEmptyBins
Whether to delete empty intervals.


m_DiscretizedHeader

Instances m_DiscretizedHeader
Header of discretized data.


m_UseEqualFrequency

boolean m_UseEqualFrequency
Use equal-frequency binning

Class weka.classifiers.meta.RotationForest extends RandomizableIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID: -3255631880798499936L

Serialized Fields

m_MinGroup

int m_MinGroup
The minimum size of a group


m_MaxGroup

int m_MaxGroup
The maximum size of a group


m_NumberOfGroups

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


m_RemovedPercentage

int m_RemovedPercentage
The percentage of instances to be removed


m_Groups

int[][][] m_Groups
The attributes of each group


m_ProjectionFilter

Filter m_ProjectionFilter
The type of projection filter


m_ProjectionFilters

Filter[][] m_ProjectionFilters
The projection filters


m_Headers

Instances[] m_Headers
Headers of the transformed dataset


m_ReducedHeaders

Instances[][] m_ReducedHeaders
Headers of the reduced datasets


m_RemoveUseless

RemoveUseless m_RemoveUseless
Filter that remove useless attributes


m_Normalize

Normalize m_Normalize
Filter that normalized the attributes

Class weka.classifiers.meta.Stacking extends RandomizableMultipleClassifiersCombiner implements Serializable

serialVersionUID: 5134738557155845452L

Serialized Fields

m_MetaClassifier

Classifier m_MetaClassifier
The meta classifier


m_MetaFormat

Instances m_MetaFormat
Format for meta data


m_BaseFormat

Instances m_BaseFormat
Format for base data


m_NumFolds

int m_NumFolds
Set the number of folds for the cross-validation

Class weka.classifiers.meta.StackingC extends Stacking implements Serializable

serialVersionUID: -6717545616603725198L

Serialized Fields

m_MetaClassifiers

Classifier[] m_MetaClassifiers
The meta classifiers (one for each class, like in ClassificationViaRegression)


m_attrFilter

Remove m_attrFilter
Filter to transform metaData - Remove


m_makeIndicatorFilter

MakeIndicator m_makeIndicatorFilter
Filter to transform metaData - MakeIndicator

Class weka.classifiers.meta.ThresholdSelector extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: -1795038053239867444L

Serialized Fields

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_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_NumXValFolds

int m_NumXValFolds
The number of folds used in cross-validation


m_DesignatedClass

int m_DesignatedClass
Designated class value, determined during building


m_ClassMode

int m_ClassMode
Method to determine which class to optimize for


m_EvalMode

int m_EvalMode
The evaluation mode


m_RangeMode

int m_RangeMode
The range correction mode


m_nMeasure

int m_nMeasure
evaluation measure used for determining threshold


m_manualThreshold

boolean m_manualThreshold
True if a manually set threshold is being used


m_manualThresholdValue

double m_manualThresholdValue
-1 = not used by default

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

serialVersionUID: -637891196294399624L

Serialized Fields

m_CombinationRule

int m_CombinationRule
Combination Rule variable


m_Random

java.util.Random m_Random
the random number generator used for breaking ties in majority voting

See Also:
Vote.distributionForInstanceMajorityVoting(Instance)

Package weka.classifiers.meta.ensembleSelection

Class weka.classifiers.meta.ensembleSelection.EnsembleModelMismatchException extends java.lang.Exception implements Serializable

serialVersionUID: 4660917211181280739L

Class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibrary extends EnsembleLibrary implements Serializable

serialVersionUID: -6444026512552917835L

Serialized Fields

m_workingDirectory

java.io.File m_workingDirectory
the working ensemble library directory.


m_modelListFile

java.lang.String m_modelListFile
tha name of the model list file storing the list of models currently being used by the model library


m_trainingData

Instances[] m_trainingData
the training data used to build the library. One per fold.


m_hillclimbData

Instances[] m_hillclimbData
the test data used for hillclimbing. One per fold.


m_predictions

double[][][] m_predictions
the predictions of each model. Built by trainAll. First index is for the model. Second is for the instance. third is for the class (we use distributionForInstance).


m_seed

int m_seed
the random seed used to partition the training data into validation and training folds


m_folds

int m_folds
the number of folds


m_validationRatio

double m_validationRatio
the ratio of validation data used to train the model

Class weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel extends EnsembleLibraryModel implements Serializable

serialVersionUID: -6426075459862947640L

Serialized Fields

m_models

Classifier[] m_models
the models


m_seed

int m_seed
The seed that was used to create this model


m_checksum

java.lang.String m_checksum
The checksum of the instances.arff object that was used to create this model


m_validationRatio

double m_validationRatio
The validation ratio that was used to create this model


m_folds

int m_folds
The number of folds, or number of CV models that was used to create this "model"


m_fileName

java.lang.String m_fileName
The .elm file name that this model should be saved/loaded to/from


m_validationPredictions

double[][] m_validationPredictions
the validation predictions of this model. First index for the instance. third is for the class (we use distributionForInstance).


Package weka.classifiers.meta.nestedDichotomies

Class weka.classifiers.meta.nestedDichotomies.ClassBalancedND extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: 5944063630650811903L

Serialized Fields

m_FilteredClassifier

FilteredClassifier m_FilteredClassifier
The filtered classifier in which the base classifier is wrapped.


m_classifiers

java.util.Hashtable<K,V> m_classifiers
The hashtable for this node.


m_FirstSuccessor

ClassBalancedND m_FirstSuccessor
The first successor


m_SecondSuccessor

ClassBalancedND m_SecondSuccessor
The second successor


m_Range

Range m_Range
The classes that are grouped together at the current node


m_hashtablegiven

boolean m_hashtablegiven
Is Hashtable given from END?

Class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: 5117477294209496368L

Serialized Fields

m_FilteredClassifier

FilteredClassifier m_FilteredClassifier
The filtered classifier in which the base classifier is wrapped.


m_classifiers

java.util.Hashtable<K,V> m_classifiers
The hashtable for this node.


m_FirstSuccessor

DataNearBalancedND m_FirstSuccessor
The first successor


m_SecondSuccessor

DataNearBalancedND m_SecondSuccessor
The second successor


m_Range

Range m_Range
The classes that are grouped together at the current node


m_hashtablegiven

boolean m_hashtablegiven
Is Hashtable given from END?

Class weka.classifiers.meta.nestedDichotomies.ND extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: -6355893369855683820L

Serialized Fields

m_ndtree

weka.classifiers.meta.nestedDichotomies.ND.NDTree m_ndtree
The tree of classes


m_classifiers

java.util.Hashtable<K,V> m_classifiers
The hashtable containing all the classifiers


m_hashtablegiven

boolean m_hashtablegiven
Is Hashtable given from END?

Class weka.classifiers.meta.nestedDichotomies.ND.NDTree extends java.lang.Object implements Serializable

serialVersionUID: 4284655952754474880L

Serialized Fields

m_indices

FastVector m_indices
The indices associated with this node


m_parent

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


m_left

weka.classifiers.meta.nestedDichotomies.ND.NDTree m_left
The left successor


m_right

weka.classifiers.meta.nestedDichotomies.ND.NDTree m_right
The right successor


Package weka.classifiers.mi

Class weka.classifiers.mi.CitationKNN extends Classifier implements Serializable

serialVersionUID: -8435377743874094852L

Serialized Fields

m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_NumClasses

int m_NumClasses
The number of the class labels


m_IdIndex

int m_IdIndex

m_Debug

boolean m_Debug
Debugging output


m_Classes

int[] m_Classes
Class labels for each bag


m_Attributes

Instances m_Attributes
attribute name structure of the relational attribute


m_NumReferences

int m_NumReferences
Number of references


m_NumCiters

int m_NumCiters
Number of citers


m_TrainBags

Instances m_TrainBags
Training bags


m_CNNDebug

boolean m_CNNDebug
Different debugging output


m_CitersDebug

boolean m_CitersDebug

m_ReferencesDebug

boolean m_ReferencesDebug

m_HDistanceDebug

boolean m_HDistanceDebug

m_NeighborListDebug

boolean m_NeighborListDebug

m_CNN

weka.classifiers.mi.CitationKNN.NeighborList[] m_CNN
C nearest neighbors considering all the bags


m_Citers

int[] m_Citers
C nearest citers


m_References

int[] m_References
R nearest references


m_HDRank

int m_HDRank
Rank associated to the Hausdorff distance


m_Diffs

double[] m_Diffs
Normalization of the euclidean distance


m_Min

double[] m_Min

m_MinNorm

double m_MinNorm

m_Max

double[] m_Max

m_MaxNorm

double m_MaxNorm

Class weka.classifiers.mi.MDD extends Classifier implements Serializable

serialVersionUID: -7273119490545290581L

Serialized Fields

m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Par

double[] m_Par

m_NumClasses

int m_NumClasses
The number of the class labels


m_Classes

int[] m_Classes
Class labels for each bag


m_Data

double[][][] m_Data
MI data


m_Attributes

Instances m_Attributes
All attribute names


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.

Class weka.classifiers.mi.MIBoost extends SingleClassifierEnhancer implements Serializable

serialVersionUID: -3808427225599279539L

Serialized Fields

m_Models

Classifier[] m_Models
the models for the iterations


m_NumClasses

int m_NumClasses
The number of the class labels


m_Classes

int[] m_Classes
Class labels for each bag


m_Attributes

Instances m_Attributes
attributes name for the new dataset used to build the model


m_NumIterations

int m_NumIterations
Number of iterations


m_Beta

double[] m_Beta
Voting weights of models


m_MaxIterations

int m_MaxIterations
the maximum number of boost iterations


m_DiscretizeBin

int m_DiscretizeBin
the number of discretization bins


m_Filter

Discretize m_Filter
filter used for discretization


m_ConvertToSI

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

Class weka.classifiers.mi.MIDD extends Classifier implements Serializable

serialVersionUID: 4263507733600536168L

Serialized Fields

m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Par

double[] m_Par

m_NumClasses

int m_NumClasses
The number of the class labels


m_Classes

int[] m_Classes
Class labels for each bag


m_Data

double[][][] m_Data
MI data


m_Attributes

Instances m_Attributes
All attribute names


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.

Class weka.classifiers.mi.MIEMDD extends RandomizableClassifier implements Serializable

serialVersionUID: 3899547154866223734L

Serialized Fields

m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Par

double[] m_Par

m_NumClasses

int m_NumClasses
The number of the class labels


m_Classes

int[] m_Classes
Class labels for each bag


m_Data

double[][][] m_Data
MI data


m_Attributes

Instances m_Attributes
All attribute names


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.

Class weka.classifiers.mi.MILR extends Classifier implements Serializable

serialVersionUID: 1996101190172373826L

Serialized Fields

m_Par

double[] m_Par

m_NumClasses

int m_NumClasses
The number of the class labels


m_Ridge

double m_Ridge
The ridge parameter.


m_Classes

int[] m_Classes
Class labels for each bag


m_Data

double[][][] m_Data
MI data


m_Attributes

Instances m_Attributes
All attribute names


xMean

double[] xMean

xSD

double[] xSD

m_AlgorithmType

int m_AlgorithmType
the type of processing

Class weka.classifiers.mi.MINND extends Classifier implements Serializable

serialVersionUID: -4512599203273864994L

Serialized Fields

m_Neighbour

int m_Neighbour
The number of nearest neighbour for prediction


m_Mean

double[][] m_Mean
The mean for each attribute of each exemplar


m_Variance

double[][] m_Variance
The variance for each attribute of each exemplar


m_Dimension

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


m_Attributes

Instances m_Attributes
header info of the data


m_Class

double[] m_Class
The class label of each exemplar


m_NumClasses

int m_NumClasses
The number of class labels in the data


m_Weights

double[] m_Weights
The weight of each exemplar


m_Rate

double m_Rate
The learning rate in the gradient descent


m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.


m_STOP

double m_STOP
The stopping criteria of gradient descent


m_Change

double[][] m_Change
The weights that alter the dimnesion of each exemplar


m_NoiseM

double[][] m_NoiseM
The noise data of each exemplar


m_NoiseV

double[][] m_NoiseV
The noise data of each exemplar


m_ValidM

double[][] m_ValidM
The noise data of each exemplar


m_ValidV

double[][] m_ValidV
The noise data of each exemplar


m_Select

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


m_Choose

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


m_Decay

double m_Decay
The decay rate of learning rate

Class weka.classifiers.mi.MIOptimalBall extends Classifier implements Serializable

serialVersionUID: -6465750129576777254L

Serialized Fields

m_Center

double[] m_Center
center of the optimal ball


m_Radius

double m_Radius
radius of the optimal ball


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_ConvertToSI

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


m_ConvertToMI

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

Class weka.classifiers.mi.MISMO extends Classifier implements Serializable

serialVersionUID: -5834036950143719712L

Serialized Fields

m_classifiers

weka.classifiers.mi.MISMO.BinaryMISMO[][] m_classifiers
The binary classifier(s)


m_C

double m_C
The complexity parameter.


m_eps

double m_eps
Epsilon for rounding.


m_tol

double m_tol
Tolerance for accuracy of result.


m_filterType

int m_filterType
Whether to normalize/standardize/neither


m_minimax

boolean m_minimax
Use MIMinimax feature space?


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_Filter

Filter m_Filter
The filter used to standardize/normalize all values.


m_Missing

ReplaceMissingValues m_Missing
The filter used to get rid of missing values.


m_classIndex

int m_classIndex
The class index from the training data


m_classAttribute

Attribute m_classAttribute
The class attribute


m_kernel

Kernel m_kernel
Kernel to use


m_checksTurnedOff

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


m_fitLogisticModels

boolean m_fitLogisticModels
Whether logistic models are to be fit


m_numFolds

int m_numFolds
The number of folds for the internal cross-validation


m_randomSeed

int m_randomSeed
The random number seed

Class weka.classifiers.mi.MISMO.BinaryMISMO extends java.lang.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_iLow

int m_iLow
The indices for m_bLow and m_bUp


m_iUp

int m_iUp
The indices for m_bLow and m_bUp


m_data

Instances m_data
The training data.


m_weights

double[] m_weights
Weight vector for linear machine.


m_sparseWeights

double[] m_sparseWeights
Variables to hold weight vector in sparse form. (To reduce storage requirements.)


m_sparseIndices

int[] m_sparseIndices

m_kernel

Kernel m_kernel
Kernel to use


m_class

double[] m_class
The transformed class values.


m_errors

double[] m_errors
The current set of errors for all non-bound examples.


m_I0

SMOset m_I0
{i: 0 < m_alpha[i] < C}


m_I1

SMOset m_I1
{i: m_class[i] = 1, m_alpha[i] = 0}


m_I2

SMOset m_I2
{i: m_class[i] = -1, m_alpha[i] = C}


m_I3

SMOset m_I3
{i: m_class[i] = 1, m_alpha[i] = C}


m_I4

SMOset m_I4
{i: m_class[i] = -1, m_alpha[i] = 0}


m_supportVectors

SMOset m_supportVectors
The set of support vectors {i: 0 < m_alpha[i]}


m_logistic

Logistic m_logistic
Stores logistic regression model for probability estimate


m_sumOfWeights

double m_sumOfWeights
Stores the weight of the training instances

Class weka.classifiers.mi.MISVM extends Classifier implements Serializable

serialVersionUID: 7622231064035278145L

Serialized Fields

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


m_kernel

Kernel m_kernel
the kernel to use


m_C

double m_C
The complexity 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_MaxIterations

int m_MaxIterations
the maximum number of iterations to perform


m_ConvertToProp

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

Class weka.classifiers.mi.MIWrapper extends SingleClassifierEnhancer implements Serializable

serialVersionUID: -7707766152904315910L

Serialized Fields

m_NumClasses

int m_NumClasses
The number of the class labels


m_Method

int m_Method
the test method


m_ConvertToProp

MultiInstanceToPropositional m_ConvertToProp
Filter used to convert MI dataset into single-instance dataset


m_WeightMethod

int m_WeightMethod
the single-instance weight setting method

Class weka.classifiers.mi.SimpleMI extends SingleClassifierEnhancer implements Serializable

serialVersionUID: 9137795893666592662L

Serialized Fields

m_TransformMethod

int m_TransformMethod
the method used in transformation

Class weka.classifiers.mi.TLD extends RandomizableClassifier implements Serializable

serialVersionUID: 6657315525171152210L

Serialized Fields

m_MeanP

double[][] m_MeanP
The mean for each attribute of each positive exemplar


m_VarianceP

double[][] m_VarianceP
The variance for each attribute of each positive exemplar


m_MeanN

double[][] m_MeanN
The mean for each attribute of each negative exemplar


m_VarianceN

double[][] m_VarianceN
The variance for each attribute of each negative exemplar


m_SumP

double[][] m_SumP
The effective sum of weights of each positive exemplar in each dimension


m_SumN

double[][] m_SumN
The effective sum of weights of each negative exemplar in each dimension


m_ParamsP

double[] m_ParamsP
The parameters to be estimated for each positive exemplar


m_ParamsN

double[] m_ParamsN
The parameters to be estimated for each negative exemplar


m_Dimension

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


m_Class

double[] m_Class
The class label of each exemplar


m_NumClasses

int m_NumClasses
The number of class labels in the data


m_Run

int m_Run
The number of runs to perform


m_Cutoff

double m_Cutoff

m_UseEmpiricalCutOff

boolean m_UseEmpiricalCutOff

Class weka.classifiers.mi.TLDSimple extends RandomizableClassifier implements Serializable

serialVersionUID: 9040995947243286591L

Serialized Fields

m_MeanP

double[][] m_MeanP
The mean for each attribute of each positive exemplar


m_MeanN

double[][] m_MeanN
The mean for each attribute of each negative exemplar


m_SumP

double[][] m_SumP
The effective sum of weights of each positive exemplar in each dimension


m_SumN

double[][] m_SumN
The effective sum of weights of each negative exemplar in each dimension


m_SgmSqP

double[] m_SgmSqP
Estimated sigma^2 in positive bags


m_SgmSqN

double[] m_SgmSqN
Estimated sigma^2 in negative bags


m_ParamsP

double[] m_ParamsP
The parameters to be estimated for each positive exemplar


m_ParamsN

double[] m_ParamsN
The parameters to be estimated for each negative exemplar


m_Dimension

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


m_Class

double[] m_Class
The class label of each exemplar


m_NumClasses

int m_NumClasses
The number of class labels in the data


m_Run

int m_Run

m_Cutoff

double m_Cutoff

m_UseEmpiricalCutOff

boolean m_UseEmpiricalCutOff

m_LkRatio

double[] m_LkRatio

m_Attribute

Instances m_Attribute

Package weka.classifiers.mi.supportVector

Class weka.classifiers.mi.supportVector.MIPolyKernel extends PolyKernel implements Serializable

serialVersionUID: 7926421479341051777L

Class weka.classifiers.mi.supportVector.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.FLR extends Classifier implements Serializable

serialVersionUID: 3337906540579569626L

Serialized Fields

learnedCode

java.util.Vector<E> learnedCode
the RuleSet: a vector keeping the learned Fuzzy Lattices


m_Rhoa

double m_Rhoa
a double keeping the vignilance parameter rhoa


bounds

weka.classifiers.misc.FLR.FuzzyLattice bounds
a Fuzzy Lattice keeping the metric space


m_BoundsFile

java.io.File m_BoundsFile
a File pointing to the boundaries file (bounds.txt)


m_showRules

boolean m_showRules
a flag indicating whether the RuleSet will be displayed


index

int[] index
an index of the RuleSet (keeps how many rules are needed for each class)


classNames

java.lang.String[] classNames
an array of the names of the classes

Class weka.classifiers.misc.HyperPipes extends Classifier implements Serializable

serialVersionUID: -7527596632268975274L

Serialized Fields

m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_Instances

Instances m_Instances
The structure of the training data


m_HyperPipes

weka.classifiers.misc.HyperPipes.HyperPipe[] m_HyperPipes
Stores the HyperPipe for each class


m_ZeroR

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

Class weka.classifiers.misc.SerializedClassifier extends Classifier implements Serializable

serialVersionUID: 4599593909947628642L

Serialized Fields

m_ModelFile

java.io.File m_ModelFile
the file where the serialized model is stored

Class weka.classifiers.misc.VFI extends Classifier implements Serializable

serialVersionUID: 8081692166331321866L

Serialized Fields

m_ClassIndex

int m_ClassIndex
The index of the class attribute


m_NumClasses

int m_NumClasses
The number of classes


m_Instances

Instances m_Instances
The training data


m_counts

double[][][] m_counts
The class counts for each interval of each attribute


m_globalCounts

double[] m_globalCounts
The global class counts


m_intervalBounds

double[][] m_intervalBounds
The lower bounds for each attribute


m_maxEntrop

double m_maxEntrop
The maximum entropy for the class


m_weightByConfidence

boolean m_weightByConfidence
Exponentially bias more confident intervals


m_bias

double m_bias
Bias towards more confident intervals


TINY

double TINY

Package weka.classifiers.pmml.consumer

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

serialVersionUID: 2583880411828388959L

Serialized Fields

m_modelType

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

m_modelName

java.lang.String m_modelName

m_algorithmName

java.lang.String m_algorithmName

m_functionType

int m_functionType

m_cumulativeLinkFunction

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

m_linkFunction

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

m_linkParameter

double m_linkParameter

m_trialsVariable

java.lang.String m_trialsVariable

m_trialsValue

double m_trialsValue

m_distribution

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

m_distParameter

double m_distParameter

m_offsetVariable

java.lang.String m_offsetVariable

m_offsetValue

double m_offsetValue

m_parameterList

java.util.ArrayList<E> m_parameterList

m_factorList

java.util.ArrayList<E> m_factorList

m_covariateList

java.util.ArrayList<E> m_covariateList

m_ppMatrix

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

m_paramMatrix

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

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

serialVersionUID: -4545904813133921249L

Serialized Fields

m_functionType

weka.classifiers.pmml.consumer.NeuralNetwork.MiningFunction m_functionType
The mining function


m_activationFunction

weka.classifiers.pmml.consumer.NeuralNetwork.ActivationFunction m_activationFunction
The activation function to use


m_normalizationMethod

weka.classifiers.pmml.consumer.NeuralNetwork.Normalization m_normalizationMethod
The normalization method


m_threshold

double m_threshold
Threshold activation


m_width

double m_width
Width for radial basis


m_altitude

double m_altitude
Altitude for radial basis


m_numberOfInputs

int m_numberOfInputs
The number of inputs to the network


m_numberOfLayers

int m_numberOfLayers
Number of hidden layers in the network


m_inputs

weka.classifiers.pmml.consumer.NeuralNetwork.NeuralInput[] m_inputs
The inputs to the network


m_inputMap

java.util.HashMap<K,V> m_inputMap
A map for storing network input values (computed from an incoming instance)


m_layers

weka.classifiers.pmml.consumer.NeuralNetwork.NeuralLayer[] m_layers
The hidden layers in the network


m_outputs

weka.classifiers.pmml.consumer.NeuralNetwork.NeuralOutputs m_outputs
The outputs of the network

Class weka.classifiers.pmml.consumer.PMMLClassifier extends Classifier implements Serializable

serialVersionUID: -5371600590320702971L

Serialized Fields

m_pmmlVersion

java.lang.String m_pmmlVersion
PMML version


m_creatorApplication

java.lang.String m_creatorApplication
Creator application


m_log

Logger m_log
Logger


m_dataDictionary

Instances m_dataDictionary
The data dictionary


m_miningSchema

MiningSchema m_miningSchema
The fields and meta data used by the model

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

serialVersionUID: -5551125528409488634L

Serialized Fields

m_algorithmName

java.lang.String m_algorithmName
Description of the algorithm


m_regressionTables

weka.classifiers.pmml.consumer.Regression.RegressionTable[] m_regressionTables
The regression tables for this regression


m_normalizationMethod

weka.classifiers.pmml.consumer.Regression.Normalization m_normalizationMethod
The normalization to use

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

serialVersionUID: 3077920125549906819L

Serialized Fields

m_valueName

java.lang.String m_valueName
The attribute value for this predictor


m_valueIndex

int m_valueIndex
The index of the attribute value for this predictor

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

serialVersionUID: -4335075205696648273L

Serialized Fields

m_exponent

double m_exponent
The exponent

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

serialVersionUID: 5493100145890252757L

Serialized Fields

m_coefficient

double m_coefficient
The coefficient for this predictor term


m_indexes

int[] m_indexes
the indexes of the terms to be multiplied


m_fieldNames

java.lang.String[] m_fieldNames
The names of the terms (attributes) to be multiplied


Package weka.classifiers.rules

Class weka.classifiers.rules.ConjunctiveRule extends Classifier implements Serializable

serialVersionUID: -5938309903225087198L

Serialized Fields

m_Folds

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


m_ClassAttribute

Attribute m_ClassAttribute
The class attribute of the data


m_Antds

FastVector m_Antds
The vector of antecedents of this rule


m_DefDstr

double[] m_DefDstr
The default rule distribution of the data not covered


m_Cnsqt

double[] m_Cnsqt
The consequent of this rule


m_NumClasses

int m_NumClasses
Number of classes in the training data


m_Seed

long m_Seed
The seed to perform randomization


m_Random

java.util.Random m_Random
The Random object used for randomization


m_Targets

FastVector m_Targets
The predicted classes recorded for each antecedent in the growing data


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

Class weka.classifiers.rules.DecisionTable extends Classifier implements Serializable

serialVersionUID: 2888557078165701326L

Serialized Fields

m_entries

java.util.Hashtable<K,V> m_entries
The hashtable used to hold training instances


m_classPriorCounts

double[] m_classPriorCounts
The class priors to use when there is no match in the table


m_classPriors

double[] m_classPriors

m_decisionFeatures

int[] m_decisionFeatures
Holds the final feature set


m_disTransform

Filter m_disTransform
Discretization filter


m_delTransform

Remove m_delTransform
Filter used to remove columns discarded by feature selection


m_ibk

IBk m_ibk
IB1 used to classify non matching instances rather than majority class


m_theInstances

Instances m_theInstances
Holds the original training instances


m_dtInstances

Instances m_dtInstances
Holds the final feature selected set of instances


m_numAttributes

int m_numAttributes
The number of attributes in the dataset


m_numInstances

int m_numInstances
The number of instances in the dataset


m_classIsNominal

boolean m_classIsNominal
Class is nominal


m_useIBk

boolean m_useIBk
Use the IBk classifier rather than majority class


m_displayRules

boolean m_displayRules
Display Rules


m_CVFolds

int m_CVFolds
Number of folds for cross validating feature sets


m_rr

java.util.Random m_rr
Random numbers for use in cross validation


m_majority

double m_majority
Holds the majority class


m_search

ASSearch m_search
The search method to use


m_evaluator

ASEvaluation m_evaluator
Our own internal evaluator


m_evaluation

Evaluation m_evaluation
The evaluation object used to evaluate subsets


m_evaluationMeasure

int m_evaluationMeasure

m_saveMemory

boolean m_saveMemory

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

serialVersionUID: 5674163500154964602L

Serialized Fields

attributes

double[] attributes
Array of attribute values for an instance


missing

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


key

int key
The key

Class weka.classifiers.rules.DTNB extends DecisionTable implements Serializable

serialVersionUID: 2999557077765701326L

Serialized Fields

m_NB

NaiveBayes m_NB
The naive Bayes half of the hybrid


m_nbFeatures

int[] m_nbFeatures
The features used by naive Bayes


m_percentUsedByDT

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


m_percentDeleted

double m_percentDeleted
Percentage of the features features that were dropped entirely


m_backwardWithDelete

ASSearch m_backwardWithDelete

Class weka.classifiers.rules.DTNB.BackwardsWithDelete extends ASSearch implements Serializable

Class weka.classifiers.rules.DTNB.EvalWithDelete extends ASEvaluation implements Serializable

Serialized Fields

m_deletedFromDTNB

java.util.BitSet m_deletedFromDTNB

Class weka.classifiers.rules.JRip extends Classifier implements Serializable

serialVersionUID: -6589312996832147161L

Serialized Fields

m_Class

Attribute m_Class
The class attribute of the data


m_Ruleset

FastVector m_Ruleset
The ruleset


m_Distributions

FastVector m_Distributions
The predicted class distribution


m_Optimizations

int m_Optimizations
Runs of optimizations


m_Random

java.util.Random m_Random
Random object used in this class


m_Total

double m_Total
# of all the possible conditions in a rule


m_Seed

long m_Seed
The seed to perform randomization


m_Folds

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


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_Debug

boolean m_Debug
Whether in a debug mode


m_CheckErr

boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria


m_UsePruning

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


m_Filter

Filter m_Filter
The filter used to randomize the class order


m_RulesetStats

FastVector m_RulesetStats
The RuleStats for the ruleset of each class value

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

serialVersionUID: -2410020717305262952L

Serialized Fields

m_Consequent

double m_Consequent
The internal representation of the class label to be predicted


m_Antds

FastVector m_Antds
The vector of antecedents of this rule

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

serialVersionUID: -1746114858746563180L

Class weka.classifiers.rules.NNge extends Classifier implements Serializable

serialVersionUID: 4084742275553788972L

Serialized Fields

m_Train

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


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_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.


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_MissingVector

double[] m_MissingVector
Values to use for missing value


m_MI_NumAttrClassInter

int[][][] m_MI_NumAttrClassInter
MUTUAL INFORMATION'S DATAS


m_MI_NumAttrInter

int[][] m_MI_NumAttrInter

m_MI_MaxArray

double[] m_MI_MaxArray

m_MI_MinArray

double[] m_MI_MinArray

m_MI_NumAttrClassValue

int[][][] m_MI_NumAttrClassValue

m_MI_NumAttrValue

int[][] m_MI_NumAttrValue

m_MI_NumClass

int[] m_MI_NumClass

m_MI_NumInst

int m_MI_NumInst

m_MI

double[] m_MI

Class weka.classifiers.rules.OneR extends Classifier implements Serializable

serialVersionUID: -2459427002147861445L

Serialized Fields

m_rule

weka.classifiers.rules.OneR.OneRRule m_rule
A 1-R rule


m_minBucketSize

int m_minBucketSize
The minimum bucket size


m_ZeroR

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

Class weka.classifiers.rules.PART extends Classifier implements Serializable

serialVersionUID: 8121455039782598361L

Serialized Fields

m_root

MakeDecList m_root
The decision list


m_CF

float m_CF
Confidence level


m_minNumObj

int m_minNumObj
Minimum number of objects


m_reducedErrorPruning

boolean m_reducedErrorPruning
Use reduced error pruning?


m_numFolds

int m_numFolds
Number of folds for reduced error pruning.


m_binarySplits

boolean m_binarySplits
Binary splits on nominal attributes?


m_unpruned

boolean m_unpruned
Generate unpruned list?


m_Seed

int m_Seed
The seed for random number generation.

Class weka.classifiers.rules.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 extends Classifier implements Serializable

serialVersionUID: -7261533075088314436L

Serialized Fields

m_Folds

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


m_Shuffle

int m_Shuffle
The number of shuffles performed on the data for randomization


m_Random

java.util.Random m_Random
Random object for randomization


m_Seed

int m_Seed
The seed to perform randomization


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_Root

weka.classifiers.rules.Ridor.Ridor_node m_Root
The root of Ridor


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_MinNo

double m_MinNo
The minimal number of instance weights within a split

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

serialVersionUID: 8815687740470471229L

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

serialVersionUID: -5708153367675298624L

Serialized Fields

m_Data

Instances m_Data
The data on which the stats calculation is based


m_Ruleset

FastVector m_Ruleset
The specific ruleset in question


m_SimpleStats

FastVector m_SimpleStats
The simple stats of each rule


m_Filtered

FastVector m_Filtered
The set of instances filtered by the ruleset


m_Total

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


MDL_THEORY_WEIGHT

double MDL_THEORY_WEIGHT
The theory weight in the MDL calculation


m_Distributions

FastVector m_Distributions
The class distributions predicted by each rule

Class weka.classifiers.rules.ZeroR extends Classifier implements Serializable

serialVersionUID: 48055541465867954L

Serialized Fields

m_ClassValue

double m_ClassValue
The class value 0R predicts.


m_Counts

double[] m_Counts
The number of instances in each class (null if class numeric).


m_Class

Attribute m_Class
The class attribute.


Package weka.classifiers.rules.part

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

serialVersionUID: -2757684345218324559L

Serialized Fields

CF

double CF
CF

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

serialVersionUID: 7284358349711992497L

Serialized Fields

m_minNumObj

int m_minNumObj
Minimum number of objects


m_toSelectModel

ModelSelection m_toSelectModel
The model selection method.


m_localModel

ClassifierSplitModel m_localModel
Local model at node.


m_sons

ClassifierDecList[] m_sons
References to sons.


m_isLeaf

boolean m_isLeaf
True if node is leaf.


m_isEmpty

boolean m_isEmpty
True if node is empty.


m_train

Instances m_train
The training instances.


m_test

Distribution m_test
The pruning instances.


indeX

int indeX
Which son to expand?

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

serialVersionUID: -1427481323245079123L

Serialized Fields

theRules

java.util.Vector<E> theRules
Vector storing the rules.


CF

double CF
The confidence for C45-type pruning.


minNumObj

int minNumObj
Minimum number of objects


toSelectModeL

ModelSelection toSelectModeL
The model selection method.


numSetS

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


reducedErrorPruning

boolean reducedErrorPruning
Use reduced error pruning?


unpruned

boolean unpruned
Generated unpruned list?


m_seed

int m_seed
The seed for random number generation.

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

serialVersionUID: -7228103346297172921L


Package weka.classifiers.trees

Class weka.classifiers.trees.ADTree extends Classifier implements Serializable

serialVersionUID: -1532264837167690683L

Serialized Fields

m_trainInstances

Instances m_trainInstances
The instances used to train the tree


m_root

PredictionNode m_root
The root of the tree


m_random

java.util.Random m_random
The random number generator - used for the random search heuristic


m_lastAddedSplitNum

int m_lastAddedSplitNum
The number of the last splitter added to the tree


m_numericAttIndices

int[] m_numericAttIndices
An array containing the inidices to the numeric attributes in the data


m_nominalAttIndices

int[] m_nominalAttIndices
An array containing the inidices to the nominal attributes in the data


m_trainTotalWeight

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


m_posTrainInstances

ReferenceInstances m_posTrainInstances
The training instances with positive class - referencing the training dataset


m_negTrainInstances

ReferenceInstances m_negTrainInstances
The training instances with negative class - referencing the training dataset


m_search_bestInsertionNode

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


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_search_bestPathPosInstances

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


m_search_bestPathNegInstances

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


m_nodesExpanded

int m_nodesExpanded
Statistics - the number of prediction nodes investigated during search


m_examplesCounted

int m_examplesCounted
Statistics - the number of instances processed during search


m_boostingIterations

int m_boostingIterations
Option - the number of boosting iterations o perform


m_searchPath

int m_searchPath
Option - the search mode


m_randomSeed

int m_randomSeed
Option - the seed to use for a random search


m_saveInstanceData

boolean m_saveInstanceData
Option - whether the tree should remember the instance data

Class weka.classifiers.trees.BFTree extends RandomizableClassifier implements Serializable

serialVersionUID: -7035607375962528217L

Serialized Fields

m_PruningStrategy

int m_PruningStrategy
the pruning strategy


m_Successors

BFTree[] m_Successors
Successor nodes.


m_Attribute

Attribute m_Attribute
Attribute used for splitting.


m_SplitValue

double m_SplitValue
Split point (for numeric attributes).


m_SplitString

java.lang.String m_SplitString
Split subset (for nominal attributes).


m_ClassValue

double m_ClassValue
Class value for a node.


m_ClassAttribute

Attribute m_ClassAttribute
Class attribute of a dataset.


m_minNumObj

int m_minNumObj
Minimum number of instances at leaf nodes.


m_numFoldsPruning

int m_numFoldsPruning
Number of folds for the pruning.


m_isLeaf

boolean m_isLeaf
If the ndoe is leaf node.


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_UseGini

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


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_UseOneSE

boolean m_UseOneSE
If use the 1SE rule to make the decision.


m_Distribution

double[] m_Distribution
Class distributions.


m_Props

double[] m_Props
Branch proportions.


m_SortedIndices

int[][] m_SortedIndices
Sorted indices.


m_Weights

double[][] m_Weights
Sorted weights.


m_Dists

double[][][] m_Dists
Distributions of each attribute for two successor nodes.


m_ClassProbs

double[] m_ClassProbs
Class probabilities.


m_TotalWeight

double m_TotalWeight
Total weights.


m_SizePer

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

Class weka.classifiers.trees.DecisionStump extends Classifier implements Serializable

serialVersionUID: 1618384535950391L

Serialized Fields

m_AttIndex

int m_AttIndex
The attribute used for classification.


m_SplitPoint

double m_SplitPoint
The split point (index respectively).


m_Distribution

double[][] m_Distribution
The distribution of class values or the means in each subset.


m_Instances

Instances m_Instances
The instances used for training.


m_ZeroR

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

Class weka.classifiers.trees.FT extends Classifier implements Serializable

serialVersionUID: -1113212459618105000L

Serialized Fields

m_replaceMissing

ReplaceMissingValues m_replaceMissing
Filter to replace missing values


m_nominalToBinary

NominalToBinary m_nominalToBinary
Filter to replace nominal attributes


m_tree

FTtree m_tree
root of the logistic model tree


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_numBoostingIterations

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


m_modelType

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


m_weightTrimBeta

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


m_useAIC

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

Class weka.classifiers.trees.Id3 extends Classifier implements Serializable

serialVersionUID: -2693678647096322561L

Serialized Fields

m_Successors

Id3[] m_Successors
The node's successors.


m_Attribute

Attribute m_Attribute
Attribute used for splitting.


m_ClassValue

double m_ClassValue
Class value if node is leaf.


m_Distribution

double[] m_Distribution
Class distribution if node is leaf.


m_ClassAttribute

Attribute m_ClassAttribute
Class attribute of dataset.

Class weka.classifiers.trees.J48 extends Classifier implements Serializable

serialVersionUID: -217733168393644444L

Serialized Fields

m_root

ClassifierTree m_root
The decision tree


m_unpruned

boolean m_unpruned
Unpruned tree?


m_CF

float m_CF
Confidence level


m_minNumObj

int m_minNumObj
Minimum number of instances


m_useLaplace

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


m_reducedErrorPruning

boolean m_reducedErrorPruning
Use reduced error pruning?


m_numFolds

int m_numFolds
Number of folds for reduced error pruning.


m_binarySplits

boolean m_binarySplits
Binary splits on nominal attributes?


m_subtreeRaising

boolean m_subtreeRaising
Subtree raising to be performed?


m_noCleanup

boolean m_noCleanup
Cleanup after the tree has been built.


m_Seed

int m_Seed
Random number seed for reduced-error pruning.

Class weka.classifiers.trees.J48graft extends Classifier implements Serializable

serialVersionUID: 8823716098042427799L

Serialized Fields

m_root

ClassifierTree m_root
The decision tree


m_unpruned

boolean m_unpruned
Unpruned tree?


m_CF

float m_CF
Confidence level


m_minNumObj

int m_minNumObj
Minimum number of instances


m_useLaplace

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


m_numFolds

int m_numFolds
Number of folds for reduced error pruning.


m_binarySplits

boolean m_binarySplits
Binary splits on nominal attributes?


m_subtreeRaising

boolean m_subtreeRaising
Subtree raising to be performed?


m_noCleanup

boolean m_noCleanup
Cleanup after the tree has been built.


m_relabel

boolean m_relabel
relabel instances when grafting

Class weka.classifiers.trees.LADTree extends Classifier implements Serializable

serialVersionUID: -4940716114518300302L

Serialized Fields

Z_MAX

double Z_MAX

m_numOfClasses

int m_numOfClasses

m_trainInstances

ReferenceInstances m_trainInstances

m_root

weka.classifiers.trees.LADTree.PredictionNode m_root

m_lastAddedSplitNum

int m_lastAddedSplitNum

m_numericAttIndices

int[] m_numericAttIndices

m_search_smallestLeastSquares

double m_search_smallestLeastSquares

m_search_bestInsertionNode

weka.classifiers.trees.LADTree.PredictionNode m_search_bestInsertionNode

m_search_bestSplitter

weka.classifiers.trees.LADTree.Splitter m_search_bestSplitter

m_search_bestPathInstances

Instances m_search_bestPathInstances

m_staticPotentialSplitters2way

FastVector m_staticPotentialSplitters2way

m_nodesExpanded

int m_nodesExpanded

m_examplesCounted

int m_examplesCounted

m_boostingIterations

int m_boostingIterations

Class weka.classifiers.trees.LADTree.LADInstance extends Instance implements Serializable

Serialized Fields

fVector

double[] fVector

wVector

double[] wVector

pVector

double[] pVector

zVector

double[] zVector

Class weka.classifiers.trees.LADTree.PredictionNode extends java.lang.Object implements Serializable

Serialized Fields

values

double[] values

children

FastVector children

Class weka.classifiers.trees.LADTree.Splitter extends java.lang.Object implements Serializable

Serialized Fields

attIndex

int attIndex

orderAdded

int orderAdded

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

Serialized Fields

trueSplitValue

int trueSplitValue

children

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

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

Serialized Fields

splitPoint

double splitPoint

children

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

Class weka.classifiers.trees.LMT extends Classifier implements Serializable

serialVersionUID: -1113212459618104943L

Serialized Fields

m_replaceMissing

ReplaceMissingValues m_replaceMissing
Filter to replace missing values


m_nominalToBinary

NominalToBinary m_nominalToBinary
Filter to replace nominal attributes


m_tree

LMTNode m_tree
root of the logistic model tree


m_fastRegression

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


m_convertNominal

boolean m_convertNominal
convert nominal attributes to binary ?


m_splitOnResiduals

boolean m_splitOnResiduals
split on residuals?


m_errorOnProbabilities

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


m_minNumInstances

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


m_numBoostingIterations

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


m_weightTrimBeta

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


m_useAIC

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

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

serialVersionUID: -6118439039768244417L

Class weka.classifiers.trees.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 extends Classifier implements Serializable

serialVersionUID: 4216839470751428698L

Serialized Fields

m_numTrees

int m_numTrees
Number of trees in forest.


m_numFeatures

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


m_randomSeed

int m_randomSeed
The random seed.


m_KValue

int m_KValue
Final number of features that were considered in last build.


m_bagger

Bagging m_bagger
The bagger.


m_MaxDepth

int m_MaxDepth
The maximum depth of the trees (0 = unlimited)

Class weka.classifiers.trees.RandomTree extends Classifier implements Serializable

serialVersionUID: 8934314652175299374L

Serialized Fields

m_Successors

RandomTree[] m_Successors
The subtrees appended to this tree.


m_Attribute

int m_Attribute
The attribute to split on.


m_SplitPoint

double m_SplitPoint
The split point.


m_Info

Instances m_Info
The header information.


m_Prop

double[] m_Prop
The proportions of training instances going down each branch.


m_ClassDistribution

double[] m_ClassDistribution
Class probabilities from the training data.


m_MinNum

double m_MinNum
Minimum number of instances for leaf.


m_KValue

int m_KValue
The number of attributes considered for a split.


m_randomSeed

int m_randomSeed
The random seed to use.


m_MaxDepth

int m_MaxDepth
The maximum depth of the tree (0 = unlimited)


m_NumFolds

int m_NumFolds
Determines how much data is used for backfitting


m_AllowUnclassifiedInstances

boolean m_AllowUnclassifiedInstances
Whether unclassified instances are allowed


m_ZeroR

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

Class weka.classifiers.trees.REPTree extends Classifier implements Serializable

serialVersionUID: -8562443428621539458L

Serialized Fields

m_zeroR

ZeroR m_zeroR
ZeroR model that is used if no attributes are present.


m_Tree

weka.classifiers.trees.REPTree.Tree m_Tree
The Tree object


m_NumFolds

int m_NumFolds
Number of folds for reduced error pruning.


m_Seed

int m_Seed
Seed for random data shuffling.


m_NoPruning

boolean m_NoPruning
Don't prune


m_MinNum

double m_MinNum
The minimum number of instances per leaf.


m_MinVarianceProp

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


m_MaxDepth

int m_MaxDepth
Upper bound on the tree depth

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

serialVersionUID: -1635481717888437935L

Serialized Fields

m_Info

Instances m_Info
The header information (for printing the tree).


m_Successors

weka.classifiers.trees.REPTree.Tree[] m_Successors
The subtrees of this tree.


m_Attribute

int m_Attribute
The attribute to split on.


m_SplitPoint

double m_SplitPoint
The split point.


m_Prop

double[] m_Prop
The proportions of training instances going down each branch.


m_ClassProbs

double[] m_ClassProbs
Class probabilities from the training data in the nominal case. Holds the mean in the numeric case.


m_Distribution

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


m_HoldOutDist

double[] m_HoldOutDist
Class distribution of hold-out set at node in the nominal case. Straight sum of weights in the numeric case (i.e. array has only one element.


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.

Class weka.classifiers.trees.SimpleCart extends RandomizableClassifier implements Serializable

serialVersionUID: 4154189200352566053L

Serialized Fields

m_train

Instances m_train
Training data.


m_Successors

SimpleCart[] m_Successors
Successor nodes.


m_Attribute

Attribute m_Attribute
Attribute used to split data.


m_SplitValue

double m_SplitValue
Split point for a numeric attribute.


m_SplitString

java.lang.String m_SplitString
Split subset used to split data for nominal attributes.


m_ClassValue

double m_ClassValue
Class value if the node is leaf.


m_ClassAttribute

Attribute m_ClassAttribute
Class attriubte of data.


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_Alpha

double m_Alpha
Alpha-value (for pruning) at the node.


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_isLeaf

boolean m_isLeaf
Indicate if the node is a leaf node.


m_Prune

boolean m_Prune
If use minimal cost-compexity pruning.


m_totalTrainInstances

int m_totalTrainInstances
Total number of instances used to build the classifier.


m_Props

double[] m_Props
Proportion for each branch.


m_ClassProbs

double[] m_ClassProbs
Class probabilities.


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_UseOneSE

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


m_SizePer

double m_SizePer
Training data size.

Class weka.classifiers.trees.UserClassifier extends Classifier implements Serializable

serialVersionUID: 6483901103562809843L

Serialized Fields

m_top

weka.classifiers.trees.UserClassifier.TreeClass m_top
Two references to the structure of the decision tree.


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_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_propertyDialog

PropertyDialog m_propertyDialog
A window for selecting other classifiers.


Package weka.classifiers.trees.adtree

Class weka.classifiers.trees.adtree.PredictionNode extends java.lang.Object implements Serializable

serialVersionUID: 6018958856358698814L

Serialized Fields

value

double value
The prediction value stored in this node


children

FastVector children
The children of this node - any number of splitter nodes

Class weka.classifiers.trees.adtree.ReferenceInstances extends Instances implements Serializable

serialVersionUID: -8022666381920252997L

Class weka.classifiers.trees.adtree.Splitter extends java.lang.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 extends Splitter implements Serializable

serialVersionUID: -4598366190152721355L

Serialized Fields

attIndex

int attIndex
The index of the attribute the split depends on


trueSplitValue

int trueSplitValue
The attribute value that is compared against


children

PredictionNode[] children
The children of this split

Class weka.classifiers.trees.adtree.TwoWayNumericSplit extends Splitter implements Serializable

serialVersionUID: 449769177903158283L

Serialized Fields

attIndex

int attIndex
The index of the attribute the split depends on


splitPoint

double splitPoint
The attribute value that is compared against


children

PredictionNode[] children
The children of this split


Package weka.classifiers.trees.ft

Class weka.classifiers.trees.ft.FTInnerNode extends FTtree implements Serializable

serialVersionUID: -1125334488640233181L

Class weka.classifiers.trees.ft.FTLeavesNode extends FTtree implements Serializable

serialVersionUID: 950601378326259315L

Class weka.classifiers.trees.ft.FTNode extends FTtree implements Serializable

serialVersionUID: 2317688685139295063L

Class weka.classifiers.trees.ft.FTtree extends LogisticBase implements Serializable

serialVersionUID: 1862737145870398755L

Serialized Fields

m_totalInstanceWeight

double m_totalInstanceWeight
Total number of training instances.


m_id

int m_id
Node id


m_leafModelNum

int m_leafModelNum
ID of logistic model at leaf


m_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_higherRegressions

SimpleLinearRegression[][] m_higherRegressions
Simple regression functions fit by LogitBoost at higher levels in the tree


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_localModel

ClassifierSplitModel m_localModel
The ClassifierSplitModel (for splitting)


m_auxLocalModel

ClassifierSplitModel m_auxLocalModel
Auxiliary copy ClassifierSplitModel (for splitting)


m_sons

FTtree[] m_sons
Array of children of the node


m_leafclass

int m_leafclass
Stores leaf class value


m_isLeaf

boolean m_isLeaf
True if node is leaf


m_hasConstr

boolean m_hasConstr
True if node has or splits on constructor


m_constError

double m_constError
Constructor error


m_CF

float m_CF
Confidence level


Package weka.classifiers.trees.j48

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

serialVersionUID: 179170923545122001L

Serialized Fields

m_minNoObj

int m_minNoObj
Minimum number of instances in interval.


m_allData

Instances m_allData
The FULL training dataset.

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

serialVersionUID: -1278776919563022474L

Serialized Fields

m_attIndex

int m_attIndex
Attribute to split on.


m_minNoObj

int m_minNoObj
Minimum number of objects in a split.


m_splitPoint

double m_splitPoint
Value of split point.


m_infoGain

double m_infoGain
InfoGain of split.


m_gainRatio

double m_gainRatio
GainRatio of split.


m_sumOfWeights

double m_sumOfWeights
The sum of the weights of the instances.

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

serialVersionUID: 3372204862440821989L

Serialized Fields

m_minNoObj

int m_minNoObj
Minimum number of objects in interval.


m_allData

Instances m_allData
All the training data

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

serialVersionUID: -4813820170260388194L

Serialized Fields

m_pruneTheTree

boolean m_pruneTheTree
True if the tree is to be pruned.


m_CF

float m_CF
The confidence factor for pruning.


m_subtreeRaising

boolean m_subtreeRaising
Is subtree raising to be performed?


m_cleanup

boolean m_cleanup
Cleanup after the tree has been built.

Class weka.classifiers.trees.j48.C45PruneableClassifierTreeG extends ClassifierTree implements Serializable

serialVersionUID: 66981207374331964L

Serialized Fields

m_pruneTheTree

boolean m_pruneTheTree
True if the tree is to be pruned.


m_CF

float m_CF
The confidence factor for pruning.


m_subtreeRaising

boolean m_subtreeRaising
Is subtree raising to be performed?


m_cleanup

boolean m_cleanup
Cleanup after the tree has been built.


m_relabel

boolean m_relabel
flag for using relabelling when grafting


m_BiProbCrit

double m_BiProbCrit
binomial probability critical value


m_Debug

boolean m_Debug

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

serialVersionUID: 3064079330067903161L

Serialized Fields

m_complexityIndex

int m_complexityIndex
Desired number of branches.


m_attIndex

int m_attIndex
Attribute to split on.


m_minNoObj

int m_minNoObj
Minimum number of objects in a split.


m_splitPoint

double m_splitPoint
Value of split point.


m_infoGain

double m_infoGain
InfoGain of split.


m_gainRatio

double m_gainRatio
GainRatio of split.


m_sumOfWeights

double m_sumOfWeights
The sum of the weights of the instances.


m_index

int m_index
Number of split points.

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

serialVersionUID: 4280730118393457457L

Serialized Fields

m_distribution

Distribution m_distribution
Distribution of class values.


m_numSubsets

int m_numSubsets
Number of created subsets.

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

serialVersionUID: -8722249377542734193L

Serialized Fields

m_toSelectModel

ModelSelection m_toSelectModel
The model selection method.


m_localModel

ClassifierSplitModel m_localModel
Local model at node.


m_sons

ClassifierTree[] m_sons
References to sons.


m_isLeaf

boolean m_isLeaf
True if node is leaf.


m_isEmpty

boolean m_isEmpty
True if node is empty.


m_train

Instances m_train
The training instances.


m_test

Distribution m_test
The pruning instances.


m_id

int m_id
The id for the node.

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

serialVersionUID: 8526859638230806576L

Serialized Fields

m_perClassPerBag

double[][] m_perClassPerBag
Weight of instances per class per bag.


m_perBag

double[] m_perBag
Weight of instances per bag.


m_perClass

double[] m_perClass
Weight of instances per class.


totaL

double totaL
Total weight of instances.

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

serialVersionUID: -2618691439791653056L

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

serialVersionUID: 5986252682266803935L

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

serialVersionUID: -433336694718670930L

Class weka.classifiers.trees.j48.GraftSplit extends ClassifierSplitModel implements Serializable

serialVersionUID: 722773260393182051L

Serialized Fields

m_graftdistro

Distribution m_graftdistro
the distribution for graft values, from cases in atbop


m_attIndex

int m_attIndex
the attribute we are splitting on


m_splitPoint

double m_splitPoint
value of split point (if numeric attribute)


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_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_testType

int m_testType
type of test: 0: <= test 1: > test 2: = test 3: != test

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

serialVersionUID: 4892105020180728499L

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

serialVersionUID: -4850147125096133642L

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

serialVersionUID: -4472639447877404786L

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

serialVersionUID: 990097748931976704L

Serialized Fields

m_minNoObj

int m_minNoObj
Minimum number of objects in interval.


m_allData

Instances m_allData
All the training data

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

serialVersionUID: 7824804381545259618L

Serialized Fields

m_nb

NaiveBayesUpdateable m_nb
the naive bayes classifier


m_disc

Discretize m_disc
the discretizer used


m_errors

double m_errors
errors on the training data at this node

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

serialVersionUID: 8922627123884975070L

Serialized Fields

m_complexityIndex

int m_complexityIndex
Desired number of branches.


m_attIndex

int m_attIndex
Attribute to split on.


m_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.


m_errors

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


m_c45S

C45Split m_c45S

m_globalNB

NBTreeNoSplit m_globalNB
The global naive bayes model for this node

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

serialVersionUID: -1292620749331337546L

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

serialVersionUID: -555775736857600201L

Serialized Fields

pruneTheTree

boolean pruneTheTree
True if the tree is to be pruned.


numSets

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


m_cleanup

boolean m_cleanup
Cleanup after the tree has been built.


m_seed

int m_seed
The random number seed.

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

serialVersionUID: 5490996638027101259L


Package weka.classifiers.trees.lmt

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

serialVersionUID: 1862737145870398755L

Serialized Fields

m_totalInstanceWeight

double m_totalInstanceWeight
Total number of training instances.


m_id

int m_id
Node id


m_leafModelNum

int m_leafModelNum
ID of logistic model at leaf


m_alpha

double m_alpha
Alpha-value (for pruning) at the node


m_numIncorrectModel

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


m_numIncorrectTree

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


m_minNumInstances

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


m_modelSelection

ModelSelection m_modelSelection
ModelSelection object (for splitting)


m_nominalToBinary

NominalToBinary m_nominalToBinary
Filter to convert nominal attributes to binary


m_higherRegressions

SimpleLinearRegression[][] m_higherRegressions
Simple regression functions fit by LogitBoost at higher levels in the tree


m_numHigherRegressions

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


m_fastRegression

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


m_numInstances

int m_numInstances
Number of instances at the node


m_localModel

ClassifierSplitModel m_localModel
The ClassifierSplitModel (for splitting)


m_sons

LMTNode[] m_sons
Array of children of the node


m_isLeaf

boolean m_isLeaf
True if node is leaf

Class weka.classifiers.trees.lmt.LogisticBase extends Classifier implements Serializable

serialVersionUID: 168765678097825064L

Serialized Fields

m_numericDataHeader

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


m_numericData

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


m_train

Instances m_train
Training data


m_useCrossValidation

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


m_errorOnProbabilities

boolean m_errorOnProbabilities
Use error on probabilities for stopping criterion of LogitBoost?


m_fixedNumIterations

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


m_heuristicStop

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


m_numRegressions

int m_numRegressions
The number of LogitBoost iterations performed.


m_maxIterations

int m_maxIterations
The maximum number of LogitBoost iterations


m_numClasses

int m_numClasses
The number of different classes


m_regressions

SimpleLinearRegression[][] m_regressions
Array holding the simple regression functions fit by LogitBoost


m_useAIC

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


m_numParameters

double m_numParameters
Effective number of parameters used for AIC / BIC automatic stopping


m_weightTrimBeta

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

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

serialVersionUID: -293098783159385148L

Serialized Fields

m_minNumInstances

int m_minNumInstances
Minimum number of instances for leaves


m_minInfoGain

double m_minInfoGain
Minimum information gain for split

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

serialVersionUID: -5055883734183713525L

Serialized Fields

m_attribute

Attribute m_attribute
The attribute selected for the split


m_attIndex

int m_attIndex
The index of the attribute selected for the split


m_numInstances

int m_numInstances
Number of instances in the set


m_numClasses

int m_numClasses
Number of classed


m_data

Instances m_data
The set of instances


m_dataZs

double[][] m_dataZs
The Z-values (LogitBoost response) for the set of instances


m_dataWs

double[][] m_dataWs
The LogitBoost-weights for the set of instances


m_splitPoint

double m_splitPoint
The split point (for numeric attributes)


Package weka.classifiers.trees.m5

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

serialVersionUID: 4212734895125452770L

Serialized Fields

m_first

int m_first
the first instance


m_last

int m_last
the last instance


m_position

int m_position

m_maxImpurity

double m_maxImpurity
the maximum impurity reduction


m_splitAttr

int m_splitAttr
the attribute being tested


m_splitValue

double m_splitValue
the best value on which to split


m_number

int m_number
the number of instances

Class weka.classifiers.trees.m5.M5Base extends Classifier implements Serializable

serialVersionUID: -4022221950191647679L

Serialized Fields

m_instances

Instances m_instances
the instances covered by the tree/rules


m_ruleSet

FastVector m_ruleSet
the rule set


m_generateRules

boolean m_generateRules
generate a decision list instead of a single tree.


m_unsmoothedPredictions

boolean m_unsmoothedPredictions
use unsmoothed predictions


m_replaceMissing

ReplaceMissingValues m_replaceMissing
filter to fill in missing values


m_nominalToBinary

NominalToBinary m_nominalToBinary
filter to convert nominal attributes to binary


m_removeUseless

RemoveUseless m_removeUseless
for removing useless attributes


m_saveInstances

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


m_regressionTree

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


m_useUnpruned

boolean m_useUnpruned
Do not prune tree/rules


m_minNumInstances

double m_minNumInstances
The minimum number of instances to allow at a leaf node

Class weka.classifiers.trees.m5.PreConstructedLinearModel extends Classifier implements Serializable

serialVersionUID: 2030974097051713247L

Serialized Fields

m_coefficients

double[] m_coefficients
The coefficients


m_intercept

double m_intercept
The intercept


m_instancesHeader

Instances m_instancesHeader
Holds the instances header for printing the model


m_numParameters

int m_numParameters
number of coefficients in the model

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

serialVersionUID: -4458627451682483204L

Serialized Fields

m_instances

Instances m_instances
the instances covered by this rule


m_classIndex

int m_classIndex
the class index


m_numAttributes

int m_numAttributes
the number of attributes


m_numInstances

int m_numInstances
the number of instances in the dataset


m_splitAtts

int[] m_splitAtts
the indexes of the attributes used to split on for this rule


m_splitVals

double[] m_splitVals
the corresponding values of the split points


m_internalNodes

RuleNode[] m_internalNodes
the corresponding internal nodes. Used for smoothing rules.


m_relOps

int[] m_relOps
the corresponding relational operators (0 = "<=", 1 = ">")


m_ruleModel

RuleNode m_ruleModel
the leaf encapsulating the linear model for this rule


m_topOfTree

RuleNode m_topOfTree
the top of the m5 tree for this rule


m_globalStdDev

double m_globalStdDev
the standard deviation of the class for all the instances


m_globalAbsDev

double m_globalAbsDev
the absolute deviation of the class for all the instances


m_covered

Instances m_covered
the instances covered by this rule


m_numCovered

int m_numCovered
the number of instances covered by this rule


m_notCovered

Instances m_notCovered
the instances not covered by this rule


m_useTree

boolean m_useTree
use a pruned m5 tree rather than make a rule


m_smoothPredictions

boolean m_smoothPredictions
use the original m5 smoothing procedure


m_saveInstances

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


m_regressionTree

boolean m_regressionTree
Make a regression tree instead of a model tree


m_useUnpruned

boolean m_useUnpruned
Build unpruned tree/rule


m_minNumInstances

double m_minNumInstances
The minimum number of instances to allow at a leaf node

Class weka.classifiers.trees.m5.RuleNode extends Classifier implements Serializable

serialVersionUID: 1979807611124337144L

Serialized Fields

m_instances

Instances m_instances
instances reaching this node


m_classIndex

int m_classIndex
the class index


m_numInstances

int m_numInstances
the number of instances reaching this node


m_numAttributes

int m_numAttributes
the number of attributes


m_isLeaf

boolean m_isLeaf
Node is a leaf


m_splitAtt

int m_splitAtt
attribute this node splits on


m_splitValue

double m_splitValue
the value of the split attribute


m_nodeModel

PreConstructedLinearModel m_nodeModel
the linear model at this node


m_numParameters

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


m_rootMeanSquaredError

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


m_left

RuleNode m_left
left child node


m_right

RuleNode m_right
right child node


m_parent

RuleNode m_parent
the parent of this node


m_splitNum

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


m_devFraction

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


m_pruningMultiplier

double m_pruningMultiplier

m_leafModelNum

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


m_globalDeviation

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


m_globalAbsDeviation

double m_globalAbsDeviation
the absolute deviation of the global class


m_indices

int[] m_indices
Indices of the attributes to be used in generating a linear model at this node


m_id

int m_id
Node id.


m_saveInstances

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


m_regressionTree

boolean m_regressionTree
Make a regression tree instead of a model tree

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

serialVersionUID: 1864267581079767881L

Serialized Fields

number

int number

first

int first

last

int last

position

int position

maxImpurity

double maxImpurity

leftAve

double leftAve

rightAve

double rightAve

splitAttr

int splitAttr

splitValue

double splitValue

Package weka.clusterers

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

serialVersionUID: -6099962589663877632L

Class weka.clusterers.AbstractDensityBasedClusterer extends AbstractClusterer implements Serializable

serialVersionUID: -5950728041704213845L

Class weka.clusterers.CLOPE extends AbstractClusterer implements Serializable

serialVersionUID: -567567567567588L

Serialized Fields

clusters

java.util.ArrayList<E> clusters
Array of clusters


m_RepulsionDefault

double m_RepulsionDefault
Specifies the repulsion default


m_Repulsion

double m_Repulsion
Specifies the repulsion


m_numberOfClusters

int m_numberOfClusters
Number of clusters


m_processed_InstanceID

int m_processed_InstanceID
Counter for the processed instances


m_numberOfInstances

int m_numberOfInstances
Number of instances


m_clusterAssignments

java.util.ArrayList<E> m_clusterAssignments

m_numberOfClustersDetermined

boolean m_numberOfClustersDetermined
whether the number of clusters was already determined

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

serialVersionUID: -830188327319128005L

Serialized Fields

m_Clusterer

Clusterer m_Clusterer
the clusterer


m_clusteringResults

java.lang.StringBuffer m_clusteringResults
holds a string describing the results of clustering the training data


m_numClusters

int m_numClusters
holds the number of clusters found by the clusterer


m_clusterAssignments

double[] m_clusterAssignments
holds the assigments of instances to clusters for a particular testing dataset


m_logL

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


m_classToCluster

int[] m_classToCluster
will hold the mapping of classes to clusters (for class based evaluation)

Class weka.clusterers.Cobweb extends RandomizableClusterer implements Serializable

serialVersionUID: 928406656495092318L

Serialized Fields

m_acuity

double m_acuity
Acuity (minimum standard deviation).


m_cutoff

double m_cutoff
Cutoff (minimum category utility).


m_cobwebTree

weka.clusterers.Cobweb.CNode m_cobwebTree
Holds the root of the Cobweb tree.


m_numberOfClusters

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

See Also:
Cobweb.numberOfClusters(), Cobweb.m_numberOfClustersDetermined

m_numberOfClustersDetermined

boolean m_numberOfClustersDetermined
whether the number of clusters was already determined


m_numberSplits

int m_numberSplits
the number of splits that happened


m_numberMerges

int m_numberMerges
the number of merges that happened


m_saveInstances

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

Class weka.clusterers.DBScan extends AbstractClusterer implements Serializable

serialVersionUID: -1666498248451219728L

Serialized Fields

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)


replaceMissingValues_Filter

ReplaceMissingValues replaceMissingValues_Filter
Replace missing values in training instances


numberOfGeneratedClusters

int numberOfGeneratedClusters
Holds the number of clusters generated


database_distanceType

java.lang.String database_distanceType
Holds the distance-type that is used (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject)


database_Type

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


database

Database database
The database that is used for DBScan


clusterID

int clusterID
Holds the current clusterID


processed_InstanceID

int processed_InstanceID
Counter for the processed instances


elapsedTime

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

Class weka.clusterers.EM extends RandomizableDensityBasedClusterer implements Serializable

serialVersionUID: 8348181483812829475L

Serialized Fields

m_model

Estimator[][] m_model
hold the discrete estimators for each cluster


m_modelNormal

double[][][] m_modelNormal
hold the normal estimators for each cluster


m_minStdDev

double m_minStdDev
default minimum standard deviation


m_minStdDevPerAtt

double[] m_minStdDevPerAtt

m_weights

double[][] m_weights
hold the weights of each instance for each cluster


m_priors

double[] m_priors
the prior probabilities for clusters


m_loglikely

double m_loglikely
the loglikelihood of the data


m_theInstances

Instances m_theInstances
training instances


m_num_clusters

int m_num_clusters
number of clusters selected by the user or cross validation


m_initialNumClusters

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


m_num_attribs

int m_num_attribs
number of attributes


m_num_instances

int m_num_instances
number of training instances


m_max_iterations

int m_max_iterations
maximum iterations to perform


m_minValues

double[] m_minValues
attribute min values


m_maxValues

double[] m_maxValues
attribute max values


m_rr

java.util.Random m_rr
random number generator


m_verbose

boolean m_verbose
Verbose?


m_replaceMissing

ReplaceMissingValues m_replaceMissing
globally replace missing values


m_displayModelInOldFormat

boolean m_displayModelInOldFormat
display model output in old-style format

Class weka.clusterers.FarthestFirst extends RandomizableClusterer implements Serializable

serialVersionUID: 7499838100631329509L

Serialized Fields

m_instances

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


m_ReplaceMissingFilter

ReplaceMissingValues m_ReplaceMissingFilter
replace missing values in training instances


m_NumClusters

int m_NumClusters
number of clusters to generate


m_ClusterCentroids

Instances m_ClusterCentroids
holds the cluster centroids


m_Min

double[] m_Min
attribute min values


m_Max

double[] m_Max
attribute max values

Class weka.clusterers.FilteredClusterer extends SingleClustererEnhancer implements Serializable

serialVersionUID: 1420005943163412943L

Serialized Fields

m_Filter

Filter m_Filter
The filter.


m_FilteredInstances

Instances m_FilteredInstances
The instance structure of the filtered instances.

Class weka.clusterers.MakeDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable

serialVersionUID: -5643302427972186631L

Serialized Fields

m_theInstances

Instances m_theInstances
holds training instances header information


m_priors

double[] m_priors
prior probabilities for the fitted clusters


m_modelNormal

double[][][] m_modelNormal
normal distributions fitted to each numeric attribute in each cluster


m_model

DiscreteEstimator[][] m_model
discrete distributions fitted to each discrete attribute in each cluster


m_minStdDev

double m_minStdDev
default minimum standard deviation


m_wrappedClusterer

Clusterer m_wrappedClusterer
The clusterer being wrapped


m_replaceMissing

ReplaceMissingValues m_replaceMissing
globally replace missing values

Class weka.clusterers.OPTICS extends AbstractClusterer implements Serializable

serialVersionUID: 274552680222105221L

Serialized Fields

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)


replaceMissingValues_Filter

ReplaceMissingValues replaceMissingValues_Filter
Replace missing values in training instances


numberOfGeneratedClusters

int numberOfGeneratedClusters
Holds the number of clusters generated


database_distanceType

java.lang.String database_distanceType
Holds the distance-type that is used (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject)


database_Type

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


database

Database database
The database that is used for OPTICS


elapsedTime

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


writeOPTICSresults

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


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.


databaseOutput

java.io.File databaseOutput
the file to save the generated database object to.

Class weka.clusterers.RandomizableClusterer extends AbstractClusterer implements Serializable

serialVersionUID: -4819590778152242745L

Serialized Fields

m_SeedDefault

int m_SeedDefault
the default seed value


m_Seed

int m_Seed
The random number seed.

Class weka.clusterers.RandomizableDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable

serialVersionUID: -5325270357918932849L

Serialized Fields

m_SeedDefault

int m_SeedDefault
the default seed value


m_Seed

int m_Seed
The random number seed.

Class weka.clusterers.RandomizableSingleClustererEnhancer extends AbstractClusterer implements Serializable

serialVersionUID: -644847037106316249L

Serialized Fields

m_SeedDefault

int m_SeedDefault
the default seed value


m_Seed

int m_Seed
The random number seed.

Class weka.clusterers.sIB extends RandomizableClusterer implements Serializable

serialVersionUID: -8652125897352654213L

Serialized Fields

m_data

Instances m_data
Training data


m_numCluster

int m_numCluster
Number of clusters


m_numRestarts

int m_numRestarts
Number of restarts


m_verbose

boolean m_verbose
Verbose?


m_uniformPrior

boolean m_uniformPrior
Uniform prior probability of the documents


m_maxLoop

int m_maxLoop
Max number of iterations during each restart


m_minChange

int m_minChange
Minimum number of changes


m_replaceMissing

ReplaceMissingValues m_replaceMissing
Globally replace missing values


m_numInstances

int m_numInstances
Number of instances


m_numAttributes

int m_numAttributes
Number of attributes


random

java.util.Random random
Randomly generate initial partition


bestT

weka.clusterers.sIB.Partition bestT
Holds the best partition built


input

weka.clusterers.sIB.Input input
Holds the statistics about the input dataset

Class weka.clusterers.SimpleKMeans extends RandomizableClusterer implements Serializable

serialVersionUID: -3235809600124455376L

Serialized Fields

m_ReplaceMissingFilter

ReplaceMissingValues m_ReplaceMissingFilter
replace missing values in training instances


m_NumClusters

int m_NumClusters
number of clusters to generate


m_ClusterCentroids

Instances m_ClusterCentroids
holds the cluster centroids


m_ClusterStdDevs

Instances m_ClusterStdDevs
Holds the standard deviations of the numeric attributes in each cluster


m_ClusterNominalCounts

int[][][] m_ClusterNominalCounts
For each cluster, holds the frequency counts for the values of each nominal attribute


m_ClusterMissingCounts

int[][] m_ClusterMissingCounts

m_FullMeansOrMediansOrModes

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


m_FullStdDevs

double[] m_FullStdDevs

m_FullNominalCounts

int[][] m_FullNominalCounts

m_FullMissingCounts

int[] m_FullMissingCounts

m_displayStdDevs

boolean m_displayStdDevs
Display standard deviations for numeric atts


m_dontReplaceMissing

boolean m_dontReplaceMissing
Replace missing values globally?


m_ClusterSizes

int[] m_ClusterSizes
The number of instances in each cluster


m_MaxIterations

int m_MaxIterations
Maximum number of iterations to be executed


m_Iterations

int m_Iterations
Keep track of the number of iterations completed before convergence


m_squaredErrors

double[] m_squaredErrors
Holds the squared errors for all clusters


m_DistanceFunction

DistanceFunction m_DistanceFunction
the distance function used.


m_PreserveOrder

boolean m_PreserveOrder
Preserve order of instances


m_Assignments

int[] m_Assignments
Assignments obtained

Class weka.clusterers.SingleClustererEnhancer extends AbstractClusterer implements Serializable

serialVersionUID: 4893928362926428671L

Serialized Fields

m_Clusterer

Clusterer m_Clusterer
the clusterer

Class weka.clusterers.XMeans extends RandomizableClusterer implements Serializable

serialVersionUID: -7941793078404132616L

Serialized Fields

m_Instances

Instances m_Instances
training instances.


m_Model

Instances m_Model
model information, should increase readability.


m_ReplaceMissingFilter

ReplaceMissingValues m_ReplaceMissingFilter
replace missing values in training instances.


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_Bic

double m_Bic
BIC-Score of the current model.


m_Mle

double[] m_Mle
Distortion.


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_NumClusters

int m_NumClusters
The actual number of clusters.


m_MinNumClusters

int m_MinNumClusters
min number of clusters to generate.


m_MaxNumClusters

int m_MaxNumClusters
max number of clusters to generate.


m_DistanceF

DistanceFunction m_DistanceF
the distance function used.


m_ClusterCenters

Instances m_ClusterCenters
cluster centers.


m_InputCenterFile

java.io.File m_InputCenterFile
file name of the output file for the cluster centers.


m_DebugVectorsInput

java.io.Reader m_DebugVectorsInput
input file for the random vectors --> USED FOR DEBUGGING.


m_DebugVectorsIndex

int m_DebugVectorsIndex
the index for the current debug vector.


m_DebugVectors

Instances m_DebugVectors
all the debug vectors.


m_DebugVectorsFile

java.io.File m_DebugVectorsFile
file name of the input file for the random vectors.


m_CenterInput

java.io.Reader m_CenterInput
input file for the cluster centers.


m_OutputCenterFile

java.io.File m_OutputCenterFile
file name of the output file for the cluster centers.


m_CenterOutput

java.io.PrintWriter m_CenterOutput
output file for the cluster centers.


m_ClusterAssignments

int[] m_ClusterAssignments
temporary variable holding cluster assignments while iterating.


m_CutOffFactor

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


m_KDTree

KDTree m_KDTree
KDTrees class if KDTrees are used.


m_UseKDTree

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


m_IterationCount

int m_IterationCount
counts iterations done in main loop.


m_KMeansStopped

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


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_DebugLevel

int m_DebugLevel
level of debug output, 0 is no output.


m_CurrDebugFlag

boolean m_CurrDebugFlag
Flag: I'm debugging.


Package weka.clusterers.forOPTICSAndDBScan.Databases

Class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase extends java.lang.Object implements Serializable

serialVersionUID: 787245523118665778L

Serialized Fields

treeMap

java.util.TreeMap<K,V> treeMap
Internal, sorted Treemap for storing all the DataObjects


instances

Instances instances
Holds the original instances delivered from WEKA


attributeMinValues

double[] attributeMinValues
Holds the minimum value for each attribute


attributeMaxValues

double[] attributeMaxValues
Holds the maximum value for each attribute


Package weka.clusterers.forOPTICSAndDBScan.DataObjects

Class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject extends java.lang.Object implements Serializable

serialVersionUID: -4408119914898291075L

Serialized Fields

instance

Instance instance
Holds the original instance


key

java.lang.String key
Holds the (unique) key that is associated with this DataObject


clusterID

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


processed

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


c_dist

double c_dist
Holds the coreDistance for this DataObject


r_dist

double r_dist
Holds the reachabilityDistance for this DataObject


database

Database database
Holds the database, that is the keeper of this DataObject

Class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject extends java.lang.Object implements Serializable

serialVersionUID: -3417720553766544582L

Serialized Fields

instance

Instance instance
Holds the original instance


key

java.lang.String key
Holds the (unique) key that is associated with this DataObject


clusterID

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


processed

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


c_dist

double c_dist
Holds the coreDistance for this DataObject


r_dist

double r_dist
Holds the reachabilityDistance for this DataObject


database

Database database
Holds the database, that is the keeper of this DataObject


Package weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI

Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel extends javax.swing.JComponent implements Serializable

serialVersionUID: 7917937528738361470L

Serialized Fields

resultVector

FastVector resultVector
Holds the clustering results


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


coreDistanceColor

java.awt.Color coreDistanceColor
Specifies the color for displaying core-distances


reachabilityDistanceColor

java.awt.Color reachabilityDistanceColor
Specifies the color for displaying reachability-distances


widthSlider

int widthSlider
Specifies the width for displaying the distances


showCoreDistances

boolean showCoreDistances
Holds the flag for showCoreDistances


showReachabilityDistances

boolean showReachabilityDistances
Holds the flag for showrRechabilityDistances


recentIndex

int recentIndex
Holds the index of the last toolTip

Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel extends javax.swing.table.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 extends java.lang.Object implements Serializable

serialVersionUID: -6022057864970639151L

Serialized Fields

resultVector

FastVector resultVector

databaseSize

int databaseSize

numberOfAttributes

int numberOfAttributes

epsilon

double epsilon

minPoints

int minPoints

opticsOutputs

boolean opticsOutputs

database_Type

java.lang.String database_Type

database_distanceType

java.lang.String database_distanceType

numberOfGeneratedClusters

int numberOfGeneratedClusters

elapsedTime

java.lang.String elapsedTime

Package weka.core

Class weka.core.AbstractStringDistanceFunction extends NormalizableDistance implements Serializable

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

serialVersionUID: -4023736016850256591L

Serialized Fields

m_Elements

double[] m_Elements
The values of the matrix

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

serialVersionUID: -742180568732916383L

Serialized Fields

m_Name

java.lang.String m_Name
The attribute's name.


m_Type

int m_Type
The attribute's type.


m_Values

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


m_Hashtable

java.util.Hashtable<K,V> m_Hashtable
Mapping of values to indices (if nominal or string).


m_Header

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


m_DateFormat

java.text.SimpleDateFormat m_DateFormat
Date format specification for date attributes


m_Index

int m_Index
The attribute's index.


m_Metadata

ProtectedProperties m_Metadata
The attribute's metadata.


m_Ordering

int m_Ordering
The attribute's ordering.


m_IsRegular

boolean m_IsRegular
Whether the attribute is regular.


m_IsAveragable

boolean m_IsAveragable
Whether the attribute is averagable.


m_HasZeropoint

boolean m_HasZeropoint
Whether the attribute has a zeropoint.


m_Weight

double m_Weight
The attribute's weight.


m_LowerBound

double m_LowerBound
The attribute's lower numeric bound.


m_LowerBoundIsOpen

boolean m_LowerBoundIsOpen
Whether the lower bound is open.


m_UpperBound

double m_UpperBound
The attribute's upper numeric bound.


m_UpperBoundIsOpen

boolean m_UpperBoundIsOpen
Whether the upper bound is open

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

serialVersionUID: 402130123261736245L

Serialized Fields

m_operatorStack

java.util.Stack<E> m_operatorStack
Operator stack


m_originalInfix

java.lang.String m_originalInfix
Holds the original infix expression


m_postFixExpVector

java.util.Vector<E> m_postFixExpVector
Holds the expression in postfix form


m_signMod

boolean m_signMod
True if the next numeric constant or attribute index is negative


m_previousTok

java.lang.String m_previousTok
Holds the previous token

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

serialVersionUID: -2932848827681070345L

Serialized Fields

m_AllowedIndices

int[] m_AllowedIndices
the attribute indices that may be inspected


m_Attributes

java.util.Vector<E> m_Attributes
contains the attribute locations, either true or false Boolean objects


m_Locators

java.util.Vector<E> m_Locators
contains the locator locations, either null or a AttributeLocator reference


m_Type

int m_Type
the type of the attribute


m_Data

Instances m_Data
the referenced data


m_Indices

int[] m_Indices
the indices


m_LocatorIndices

int[] m_LocatorIndices
the indices of locator objects

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

serialVersionUID: 4434688832743939380L

Serialized Fields

intCount

int intCount
The number of int-like values


realCount

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


missingCount

int missingCount
The number of missing values


distinctCount

int distinctCount
The number of distinct values


uniqueCount

int uniqueCount
The number of values that only appear once


totalCount

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


numericStats

Stats numericStats
Stats on numeric value distributions


nominalCounts

int[] nominalCounts
Counts of each nominal value

Class weka.core.BinarySparseInstance extends SparseInstance implements Serializable

serialVersionUID: -5297388762342528737L

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

serialVersionUID: -5478590032325567849L

Serialized Fields

m_Owner

CapabilitiesHandler m_Owner
the object that owns this capabilities instance


m_Capabilities

java.util.HashSet<E> m_Capabilities
the hashset for storing the active capabilities


m_Dependencies

java.util.HashSet<E> m_Dependencies
the hashset for storing dependent capabilities, eg for meta-classifiers


m_FailReason

java.lang.Exception m_FailReason
the reason why the test failed, used to throw an exception


m_MinimumNumberInstances

int m_MinimumNumberInstances
the minimum number of instances in a dataset


m_Test

boolean m_Test
whether to perform any tests at all


m_InstancesTest

boolean m_InstancesTest
whether to perform data based tests


m_AttributeTest

boolean m_AttributeTest
whether to perform attribute based tests


m_MissingValuesTest

boolean m_MissingValuesTest
whether to test for missing values


m_MissingClassValuesTest

boolean m_MissingClassValuesTest
whether to test for missing class values


m_MinimumNumberInstancesTest

boolean m_MinimumNumberInstancesTest
whether to test for minimum number of instances

Class weka.core.ChebyshevDistance extends NormalizableDistance implements Serializable

serialVersionUID: -7739904999895461429L

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

serialVersionUID: 66171861743328020L

Serialized Fields

m_Enabled

boolean m_Enabled
whether logging is enabled


m_Log

Debug.Log m_Log
for logging


m_Clock

Debug.Clock m_Clock
for clocking

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

serialVersionUID: 4622161807307942201L

Serialized Fields

m_OutputFormat

int m_OutputFormat
the format of the output


m_Start

long m_Start
the start time


m_Stop

long m_Stop
the end time


m_Running

boolean m_Running
whether the time is still clocked


m_ThreadID

long m_ThreadID
the thread ID


m_CanMeasureCpuTime

boolean m_CanMeasureCpuTime
whether the system can measure the CPU time


m_UseCpuTime

boolean m_UseCpuTime
whether to use the CPU time (by default TRUE)

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

serialVersionUID: -5245628124742606784L

Serialized Fields

m_verboseOn

boolean m_verboseOn
enables/disables output of debug information


m_outputTypes

Range m_outputTypes
range of outputtyp

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

serialVersionUID: 1458435732111675823L

Serialized Fields

m_Filename

java.lang.String m_Filename
the filename, if any


m_Size

int m_Size
the size of the file (in bytes)


m_NumFiles

int m_NumFiles
the number of files for rotating the logs


m_LoggerInitFailed

boolean m_LoggerInitFailed
whether the initialization of the logger failed

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

serialVersionUID: 1256846887618333956L

Serialized Fields

m_Debug

boolean m_Debug
whether to output debug information


m_ID

long m_ID
the unique ID for this number generator


m_Log

Debug.Log m_Log
the log to use for outputting the data, otherwise just stdout

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

serialVersionUID: -2671928223819510830L

Serialized Fields

m_Filename

java.lang.String m_Filename
the file to write to (if null then only stdout is used)

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

serialVersionUID: -6099868388466922753L

Serialized Fields

m_Stamp

java.util.Date m_Stamp
the actual date


m_Format

java.lang.String m_Format
the format of the timestamp


m_Formatter

java.text.SimpleDateFormat m_Formatter
handles the format of the output

Class weka.core.EditDistance extends AbstractStringDistanceFunction implements Serializable

Class weka.core.EuclideanDistance extends NormalizableDistance implements Serializable

serialVersionUID: 1068606253458807903L

Class weka.core.FastVector extends java.lang.Object implements Serializable

serialVersionUID: -2173635135622930169L

Serialized Fields

m_Objects

java.lang.Object[] m_Objects
The array of objects.


m_Size

int m_Size
The current size;


m_CapacityIncrement

int m_CapacityIncrement
The capacity increment


m_CapacityMultiplier

int m_CapacityMultiplier
The capacity multiplier.

Class weka.core.Instance extends java.lang.Object implements Serializable

serialVersionUID: 1482635194499365122L

Serialized Fields

m_Dataset

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


m_AttValues

double[] m_AttValues
The instance's attribute values.


m_Weight

double m_Weight
The instance's weight.

Class weka.core.InstanceComparator extends java.lang.Object implements Serializable

serialVersionUID: -6589278678230949683L

Serialized Fields

m_IncludeClass

boolean m_IncludeClass
whether to include the class in the comparison

Class weka.core.Instances extends java.lang.Object implements Serializable

serialVersionUID: -19412345060742748L

Serialized Fields

m_RelationName

java.lang.String m_RelationName
The dataset's name.


m_Attributes

FastVector m_Attributes
The attribute information.


m_Instances

FastVector m_Instances
The instances.


m_ClassIndex

int m_ClassIndex
The class attribute's index


m_Lines

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

See Also:
Instances.readInstance(Reader)

attIdx4Randomization

int attIdx4Randomization
used in randomizeAttribute and undoRandomizeAttribute to store/restore the index of attribute that was last shuffled, and it's original values


attIdxOrigValues

double[] attIdxOrigValues

Class weka.core.Jython extends java.lang.Object implements Serializable

serialVersionUID: -6972298704460209252L

Serialized Fields

m_Interpreter

java.lang.Object m_Interpreter
the interpreter

Class weka.core.ManhattanDistance extends NormalizableDistance implements Serializable

serialVersionUID: 6783782554224000243L

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

serialVersionUID: -3604757095849145838L

Serialized Fields

m_Matrix

Matrix m_Matrix
Deprecated. 
The actual matrix

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

Serialized Fields

m_Data

Instances m_Data
the instances used internally.


m_DontNormalize

boolean m_DontNormalize
True if normalization is turned off (default false).


m_Ranges

double[][] m_Ranges
The range of the attributes.


m_AttributeIndices

Range m_AttributeIndices
The range of attributes to use for calculating the distance.


m_ActiveIndices

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


m_Validated

boolean m_Validated
Whether all the necessary preparations have been done.

Class weka.core.NoSupportForMissingValuesException extends WekaException implements Serializable

serialVersionUID: 5161175307725893973L

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

serialVersionUID: 3876658672657323985L

Serialized Fields

closed

boolean closed
the properties need to be open during construction of the object

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

serialVersionUID: -1141282001146389780L

Serialized Fields

m_Head

weka.core.Queue.QueueNode m_Head
Store a reference to the head of the queue


m_Tail

weka.core.Queue.QueueNode m_Tail
Store a reference to the tail of the queue


m_Size

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

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

serialVersionUID: -5119358279412097455L

Serialized Fields

m_Next

weka.core.Queue.QueueNode m_Next
The next node in the queue


m_Contents

java.lang.Object m_Contents
The nodes contents

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

serialVersionUID: -4763742718209460354L

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

serialVersionUID: 3667337062176835900L

Serialized Fields

m_RangeStrings

java.util.Vector<E> m_RangeStrings
Record the string representations of the columns to delete


m_Invert

boolean m_Invert
Whether matching should be inverted


m_SelectFlags

boolean[] m_SelectFlags
The array of flags for whether an column is selected


m_Upper

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

Class weka.core.RelationalLocator extends AttributeLocator implements Serializable

serialVersionUID: 4646872277151854732L

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

serialVersionUID: 6635502953928860434L

Serialized Fields

m_storedObjectArray

byte[] m_storedObjectArray
The array storing the object.


m_isCompressed

boolean m_isCompressed
Whether or not the object is compressed.


m_isJython

boolean m_isJython
Whether it is a Jython object or not

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

serialVersionUID: 5285169134430839303L

Serialized Fields

m_IndexString

java.lang.String m_IndexString
Record the string representation of the number


m_SelectedIndex

int m_SelectedIndex
The selected index


m_Upper

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

Class weka.core.SparseInstance extends 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 extends AttributeLocator implements Serializable

serialVersionUID: 7805522230268783972L

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

serialVersionUID: 3326379903447135320L

Serialized Fields

m_ID

int m_ID
The ID


m_IDStr

java.lang.String m_IDStr
The unique string for this tag, doesn't have to be numeric


m_Readable

java.lang.String m_Readable
The descriptive text

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

serialVersionUID: -6263968936330390469L

Serialized Fields

m_Words

java.lang.String[] m_Words
for generating String attributes/classes


m_WordSeparators

java.lang.String m_WordSeparators
for generating String attributes/classes


m_Relation

java.lang.String m_Relation
the name of the relation


m_Seed

int m_Seed
the seed value


m_Random

java.util.Random m_Random
the random number generator


m_NumInstances

int m_NumInstances
the number of instances


m_ClassType

int m_ClassType
the class type


m_NumClasses

int m_NumClasses
the number of classes (in case of NOMINAL class)


m_ClassIndex

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

See Also:
TestInstances.CLASS_IS_LAST, TestInstances.NO_CLASS

m_NumNominal

int m_NumNominal
the number of nominal attributes


m_NumNominalValues

int m_NumNominalValues
the number of values for nominal attributes


m_NumNumeric

int m_NumNumeric
the number of numeric attributes


m_NumString

int m_NumString
the number of string attributes


m_NumDate

int m_NumDate
the number of date attributes


m_NumRelational

int m_NumRelational
the number of relational attributes


m_NumRelationalNominal

int m_NumRelationalNominal
the number of nominal attributes in a relational attribute


m_NumRelationalNominalValues

int m_NumRelationalNominalValues
the number of values for nominal attributes in relational attributes


m_NumRelationalNumeric

int m_NumRelationalNumeric
the number of numeric attributes in a relational attribute


m_NumRelationalString

int m_NumRelationalString
the number of string attributes in a relational attribute


m_NumRelationalDate

int m_NumRelationalDate
the number of date attributes in a relational attribute


m_MultiInstance

boolean m_MultiInstance
whether to generate Multi-Instance data or not


m_NumInstancesRelational

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


m_RelationalFormat

Instances[] m_RelationalFormat
the format of the multi-instance data


m_RelationalClassFormat

Instances m_RelationalClassFormat
the format of the multi-instance data of the class


m_Data

Instances m_Data
the generated data


m_Handler

CapabilitiesHandler m_Handler
the CapabilitiesHandler to get the Capabilities from

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

serialVersionUID: -5897980928817779048L

Serialized Fields

m_Root

Trie.TrieNode m_Root
the root node


m_HashCode

int m_HashCode
the hash code


m_RecalcHashCode

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

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

serialVersionUID: -2252907099391881148L

Serialized Fields

m_Children

java.util.Hashtable<K,V> m_Children
for fast access to the children

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

serialVersionUID: 6268278694768818235L

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

serialVersionUID: -9000116786626328854L

Class weka.core.UnsupportedAttributeTypeException extends WekaException implements Serializable

serialVersionUID: 2658987325328414838L

Class weka.core.UnsupportedClassTypeException extends WekaException implements Serializable

serialVersionUID: 5175741076972192151L

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

serialVersionUID: 6428269989006208585L


Package weka.core.converters

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

Serialized Fields

m_File

java.lang.String m_File
the file


m_structure

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


m_sourceFile

java.io.File m_sourceFile
Holds the source of the data set.


m_useRelativePath

boolean m_useRelativePath
use relative file paths

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

Serialized Fields

m_outputFile

java.io.File m_outputFile
The destination file.


FILE_EXTENSION

java.lang.String FILE_EXTENSION
The file extension of the destination file.


m_prefix

java.lang.String m_prefix
The prefix for the filename (chosen in the GUI).


m_dir

java.lang.String m_dir
The directory of the file (chosen in the GUI).


m_incrementalCounter

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


m_useRelativePath

boolean m_useRelativePath
use relative file paths

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

Serialized Fields

m_retrieval

int m_retrieval
The current retrieval mode

Class weka.core.converters.AbstractSaver extends java.lang.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 extends AbstractFileLoader implements Serializable

serialVersionUID: 2726929550544048587L

Serialized Fields

m_URL

java.lang.String m_URL
the url

Class weka.core.converters.ArffSaver extends AbstractFileSaver implements Serializable

serialVersionUID: 2223634248900042228L

Class weka.core.converters.C45Loader extends AbstractFileLoader implements Serializable

serialVersionUID: 5454329403218219L

Serialized Fields

m_sourceFileData

java.io.File m_sourceFileData
Describe variable m_sourceFileData here.


m_fileStem

java.lang.String m_fileStem
Holds the filestem.


m_numAttribs

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


m_ignore

boolean[] m_ignore
Which attributes are ignore or label. These are *not* included in the arff representation.

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

serialVersionUID: -821428878384253377L

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

serialVersionUID: -2460855349276148760L

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

serialVersionUID: -1504966891136411204L

Serialized Fields

m_Saver

Saver m_Saver
the saver to use for storing the data.


m_Stream

java.io.OutputStream m_Stream
the stream to store the data in (always in ARFF format).

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

serialVersionUID: -613122395928757332L

Serialized Fields

m_File

java.io.File m_File
the file to load.


m_URL

java.net.URL m_URL
the URL to load.


m_Loader

Loader m_Loader
the loader.


m_Incremental

boolean m_Incremental
whether the loader is incremental.


m_BatchCounter

int m_BatchCounter
the instance counter for the batch case.


m_IncrementalBuffer

Instance m_IncrementalBuffer
the last internally read instance.


m_BatchBuffer

Instances m_BatchBuffer
the batch buffer.

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

serialVersionUID: 5607529739745491340L

Serialized Fields

m_cumulativeStructure

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


m_cumulativeInstances

FastVector m_cumulativeInstances
Holds instances accumulated so far.


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.


m_MissingValue

java.lang.String m_MissingValue
The placeholder for missing values.


m_FirstCheck

boolean m_FirstCheck
whether the first row has been read.

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

serialVersionUID: 476636654410701807L

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

serialVersionUID: 1673169848863178695L

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

serialVersionUID: -7936159015338318659L

Serialized Fields

m_structure

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


m_datasetPseudoInc

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


m_oldStructure

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


m_DataBaseConnection

DatabaseConnection m_DataBaseConnection
The database connection


m_query

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


m_pseudoIncremental

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


m_checkForTable

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


m_nominalToStringLimit

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


m_rowCount

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


m_counter

int m_counter
Indicates how many rows has already been loaded incrementally


m_choice

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


m_firstTime

boolean m_firstTime
Flag indicating that incremental process wants to read first instance


m_inc

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


m_orderBy

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_nominalIndexes

java.util.Hashtable<K,V>[] m_nominalIndexes
Stores the index of a nominal value


m_nominalStrings

FastVector[] m_nominalStrings
Stores the nominal value


m_idColumn

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


m_URL

java.lang.String m_URL
the JDBC URL to use


m_User

java.lang.String m_User
the database user to use


m_Password

java.lang.String m_Password
the database password to use


m_Keys

java.lang.String m_Keys
the keys for unique ordering

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

serialVersionUID: 863971733782624956L

Serialized Fields

m_DataBaseConnection

DatabaseConnection m_DataBaseConnection
The database connection.


m_tableName

java.lang.String m_tableName
The name of the table in which the instances should be stored.


m_inputFile

java.lang.String m_inputFile
An input arff file (for command line use).


m_createText

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


m_createDouble

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


m_createInt

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


m_createDate

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


m_DateFormat

java.text.SimpleDateFormat m_DateFormat
For converting the date value into a database string.


m_idColumn

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


m_count

int m_count
counts the rows and used as a primary key value.


m_id

boolean m_id
Flag indicating if a primary key column should be added.


m_tabName

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


m_Username

java.lang.String m_Username
the user name for the database.


m_Password

java.lang.String m_Password
the password for the database.

Class weka.core.converters.LibSVMLoader extends AbstractFileLoader implements Serializable

serialVersionUID: 4988360125354664417L

Serialized Fields

m_URL

java.lang.String m_URL
the url.


m_Buffer

java.util.Vector<E> m_Buffer
the buffer of the rows read so far.

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

serialVersionUID: 2792295817125694786L

Serialized Fields

m_ClassIndex

SingleIndex m_ClassIndex
the class index

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

serialVersionUID: 2391085836269030715L

Serialized Fields

m_Dataset

Instances m_Dataset
Holds the structure (header) of the data set.


m_IncrementalIndex

int m_IncrementalIndex
The current index position for incremental reading

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

serialVersionUID: -7717010648500658872L

Serialized Fields

m_objectstream

java.io.ObjectOutputStream m_objectstream
the output stream.

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

serialVersionUID: 4988360125354664417L

Serialized Fields

m_URL

java.lang.String m_URL
the url.


m_Buffer

java.util.Vector<E> m_Buffer
the buffer of the rows read so far.

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

serialVersionUID: 2605714599263995835L

Serialized Fields

m_ClassIndex

SingleIndex m_ClassIndex
the class index.

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

serialVersionUID: 2592118773712247647L

Serialized Fields

m_structure

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


m_sourceFile

java.io.File m_sourceFile
Holds the source of the data set.


m_Debug

boolean m_Debug
whether to print some debug information


m_OutputFilename

boolean m_OutputFilename
whether to include the filename as an extra attribute

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

serialVersionUID: 3764533621135196582L

Serialized Fields

m_URL

java.lang.String m_URL
the url


m_XMLInstances

XMLInstances m_XMLInstances
the loaded XML document

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

serialVersionUID: -7226404765213522043L

Serialized Fields

m_ClassIndex

SingleIndex m_ClassIndex
the class index


m_XMLInstances

XMLInstances m_XMLInstances
the generated XML document


m_CompressOutput

boolean m_CompressOutput
whether to compress the output


Package weka.core.matrix

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

serialVersionUID: -8739775942782694701L

Serialized Fields

L

double[][] L
Array for internal storage of decomposition.

internal array storage.

n

int n
Row and column dimension (square matrix).

matrix dimension.

isspd

boolean isspd
Symmetric and positive definite flag.

is symmetric and positive definite flag.

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

serialVersionUID: 4011654467211422319L

Serialized Fields

n

int n
Row and column dimension (square matrix).

matrix dimension.

issymmetric

boolean issymmetric
Symmetry flag.

internal symmetry flag.

d

double[] d
Arrays for internal storage of eigenvalues.

internal storage of eigenvalues.

e

double[] e
Arrays for internal storage of eigenvalues.

internal storage of eigenvalues.

V

double[][] V
Array for internal storage of eigenvectors.

internal storage of eigenvectors.

H

double[][] H
Array for internal storage of nonsymmetric Hessenberg form.

internal storage of nonsymmetric Hessenberg form.

ort

double[] ort
Working storage for nonsymmetric algorithm.

working storage for nonsymmetric algorithm.

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

serialVersionUID: -5298981701073897741L

Serialized Fields

nf

java.text.DecimalFormat nf

sign

boolean sign

digits

int digits

exp

int exp

trailing

boolean trailing

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

serialVersionUID: 110912192794064140L

Serialized Fields

nf

java.text.DecimalFormat nf

digits

int digits

exp

boolean exp

intDigits

int intDigits

decimalDigits

int decimalDigits

expIntDigits

int expIntDigits

expDecimalDigits

int expDecimalDigits

power

int power

trailing

boolean trailing

grouping

boolean grouping

sign

boolean sign

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

serialVersionUID: 4500373755333429499L

Serialized Fields

nf

java.text.DecimalFormat nf

width

int width

decimal

int decimal

trailing

boolean trailing

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

serialVersionUID: -2731022568037808629L

Serialized Fields

LU

double[][] LU
Array for internal storage of decomposition.

internal array storage.

m

int m
Row and column dimensions, and pivot sign.

column dimension.

n

int n
Row and column dimensions, and pivot sign.

column dimension.

pivsign

int pivsign
Row and column dimensions, and pivot sign.

column dimension.

piv

int[] piv
Internal storage of pivot vector.

pivot vector.

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

serialVersionUID: 7856794138418366180L

Serialized Fields

A

double[][] A
Array for internal storage of elements.

internal array storage.

m

int m
Row and column dimensions.

row dimension.

n

int n
Row and column dimensions.

row dimension.

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

serialVersionUID: -5013090736132211418L

Serialized Fields

QR

double[][] QR
Array for internal storage of decomposition.

internal array storage.

m

int m
Row and column dimensions.

column dimension.

n

int n
Row and column dimensions.

column dimension.

Rdiag

double[] Rdiag
Array for internal storage of diagonal of R.

diagonal of R.

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

serialVersionUID: -8738089610999867951L

Serialized Fields

U

double[][] U
Arrays for internal storage of U and V.

internal storage of U.

V

double[][] V
Arrays for internal storage of U and V.

internal storage of U.

s

double[] s
Array for internal storage of singular values.

internal storage of singular values.

m

int m
Row and column dimensions.

row dimension.

n

int n
Row and column dimensions.

row dimension.

Package weka.core.neighboursearch

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

serialVersionUID: 728763855952698328L

Serialized Fields

m_InstList

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


m_MaxInstancesInLeaf

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


m_TreeStats

TreePerformanceStats m_TreeStats
Tree Stats variables.


m_Root

BallNode m_Root
The root node of the BallTree.


m_TreeConstructor

BallTreeConstructor m_TreeConstructor
The constructor method to use to build the tree.


m_Distances

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

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

serialVersionUID: 7617412821497807586L

Serialized Fields

m_EuclideanDistance

EuclideanDistance m_EuclideanDistance
The euclidean distance function to use.


m_Root

CoverTree.CoverTreeNode m_Root
The root node.


m_DistanceList

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


m_NumNodes

int m_NumNodes
Number of nodes in the tree.


m_NumLeaves

int m_NumLeaves
Number of nodes in the tree.


m_MaxDepth

int m_MaxDepth
Number of nodes in the tree.


m_TreeStats

TreePerformanceStats m_TreeStats
Tree Stats variables.


m_Base

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


il2

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

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

serialVersionUID: 1808760031169036512L

Serialized Fields

nodeid

int nodeid
ID for the node.


idx

java.lang.Integer idx
Index of the instance represented by this node in the index array.


max_dist

double max_dist
The distance of the furthest descendant of the node.


parent_dist

double parent_dist
The distance to the nodes parent.


children

Stack<T> children
The children of the node.


num_children

int num_children
The number of children node has.


scale

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

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

serialVersionUID: 1505717283763272533L

Serialized Fields

m_DistanceList

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


m_InstList

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


m_Root

KDTreeNode m_Root
The root node of the tree.


m_Splitter

KDTreeNodeSplitter m_Splitter
The node splitter.


m_NumNodes

int m_NumNodes
Tree stats.


m_NumLeaves

int m_NumLeaves
Tree stats.


m_MaxDepth

int m_MaxDepth
Tree stats.


m_TreeStats

TreePerformanceStats m_TreeStats
Tree Stats variables.


m_NormalizeNodeWidth

boolean m_NormalizeNodeWidth
flag for normalizing.


m_EuclideanDistance

EuclideanDistance m_EuclideanDistance
The euclidean distance function to use.


m_MinBoxRelWidth

double m_MinBoxRelWidth
minimal relative width of a KDTree rectangle.


m_MaxInstInLeaf

int m_MaxInstInLeaf
maximal number of instances in a leaf.

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

serialVersionUID: 1915484723703917241L

Serialized Fields

m_Distances

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


m_SkipIdentical

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

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

Serialized Fields

m_Instances

Instances m_Instances
The neighbourhood of instances to find neighbours in.


m_kNN

int m_kNN
The number of neighbours to find.


m_DistanceFunction

DistanceFunction m_DistanceFunction
the distance function used.


m_Stats

PerformanceStats m_Stats
Performance statistics.


m_MeasurePerformance

boolean m_MeasurePerformance
Should we measure Performance.

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

serialVersionUID: -7215110351388368092L

Serialized Fields

m_NumQueries

int m_NumQueries
The total number of queries looked at.


m_MinP

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


m_MaxP

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


m_SumP

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


m_SumSqP

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


m_PointCount

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


m_MinC

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


m_MaxC

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


m_SumC

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


m_SumSqC

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


m_CoordCount

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

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

serialVersionUID: -6637636693340810373L

Serialized Fields

m_MinLeaves

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


m_MaxLeaves

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


m_SumLeaves

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


m_SumSqLeaves

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


m_LeafCount

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


m_MinIntNodes

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


m_MaxIntNodes

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


m_SumIntNodes

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


m_SumSqIntNodes

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


m_IntNodeCount

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


Package weka.core.neighboursearch.balltrees

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

serialVersionUID: -8289151861759883510L

Serialized Fields

m_Start

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


m_End

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


m_NumInstances

int m_NumInstances
The number of instances/points in the node.


m_NodeNumber

int m_NodeNumber
The node number/id.


m_SplitAttrib

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


m_SplitVal

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


m_Left

BallNode m_Left
The left child of the node.


m_Right

BallNode m_Right
The right child of the node.


m_Pivot

Instance m_Pivot
The pivot/centre of the ball.


m_Radius

double m_Radius
The radius of this ball (hyper sphere).

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

Serialized Fields

m_Instances

Instances m_Instances
The instance on which the tree is built.


m_DistanceFunction

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


m_Instlist

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

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

Serialized Fields

m_MaxInstancesInLeaf

int m_MaxInstancesInLeaf
The maximum number of instances allowed in a leaf.


m_MaxRelLeafRadius

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


m_FullyContainChildBalls

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


m_Instances

Instances m_Instances
The instances on which to build the tree.


m_DistanceFunction

DistanceFunction m_DistanceFunction
The distance function to use to build the tree.


m_NumNodes

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


m_NumLeaves

int m_NumLeaves
The number of leaf nodes in the built tree.


m_MaxDepth

int m_MaxDepth
The depth of the built tree.


m_InstList

int[] m_InstList
The master index array.

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

serialVersionUID: 5864250777657707687L

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

serialVersionUID: 5617378551363700558L

Serialized Fields

m_RandSeed

int m_RandSeed
Seed for random number generator.


m_Rand

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

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

serialVersionUID: 3054842574468790421L

Serialized Fields

m_NormalizeDimWidths

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

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

serialVersionUID: -8523314263062524462L

Serialized Fields

m_RSeed

int m_RSeed
Seed form random number generator.


rand

java.util.Random rand
The random number generator for selecting the first anchor point randomly (if selecting randomly).


rootRadius

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


m_RandomInitialAnchor

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

Class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList extends FastVector implements Serializable

serialVersionUID: -2283869109722934927L

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

serialVersionUID: -2947177543565818260L

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

serialVersionUID: -5150140645091889979L

Serialized Fields

m_Splitter

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


Package weka.core.neighboursearch.covertrees

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

serialVersionUID: 5604056321825539264L

Serialized Fields

length

int length
The number of elements in the stack.


elements

java.util.ArrayList<E> elements
The elements inside the stack.


Package weka.core.neighboursearch.kdtrees

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

serialVersionUID: -3660396067582792648L

Serialized Fields

m_NodeNumber

int m_NodeNumber
node number (only for debug).


m_Left

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


m_Right

KDTreeNode m_Right
right subtree; contains instances with larger than split value.


m_SplitValue

double m_SplitValue
value to split on.


m_SplitDim

int m_SplitDim
attribute to split on.


m_NodeRanges

double[][] m_NodeRanges
lowest and highest value and width (= high - low) for each dimension.


m_NodesRectBounds

double[][] m_NodesRectBounds
The lo and high bounds of the hyper rectangle described by the node.


m_Start

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


m_End

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

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

Serialized Fields

m_Instances

Instances m_Instances
The instances that'll be used for tree construction.


m_EuclideanDistance

EuclideanDistance m_EuclideanDistance
The distance function used for building the tree.


m_InstList

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


m_NormalizeNodeWidth

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

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

serialVersionUID: -866783749124714304L

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

serialVersionUID: 1383443320160540663L

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

serialVersionUID: -7617277960046591906L

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

serialVersionUID: 852857628205680562L


Package weka.core.pmml

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

serialVersionUID: 2275009453597279459L

Serialized Fields

m_operator

weka.core.pmml.BuiltInArithmetic.Operator m_operator
The operator for this function

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

serialVersionUID: -8092338695602573652L

Serialized Fields

m_func

weka.core.pmml.BuiltInMath.MathFunc m_func
The function to apply

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

serialVersionUID: -7391516909331728653L

Serialized Fields

m_func

weka.core.pmml.BuiltInString.StringFunc m_func
The function to apply


m_outputDef

Attribute m_outputDef
The output structure produced by this function

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

serialVersionUID: -304829687822452424L

Serialized Fields

m_categoricalConst

java.lang.String m_categoricalConst

m_continuousConst

double m_continuousConst

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

serialVersionUID: -1976646917527243888L

Serialized Fields

m_parameters

java.util.ArrayList<E> m_parameters
The list of parameters expected by this function. We can use this to do some error/type checking when users call setParameterDefs() on us


m_optype

FieldMetaInfo.Optype m_optype
The optype for this function


m_expression

Expression m_expression
The Expression for this function to use

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

serialVersionUID: 3918895902507585558L

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

Serialized Fields

m_displayName

java.lang.String m_displayName
display name


m_values

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


m_expression

Expression m_expression
the single expression that defines the value of this field

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

Serialized Fields

m_fieldName

java.lang.String m_fieldName
The name of the field to be discretized


m_fieldIndex

int m_fieldIndex
The index of the field


m_mapMissingDefined

boolean m_mapMissingDefined
True if a replacement for missing values has been specified


m_mapMissingTo

java.lang.String m_mapMissingTo
The value of the missing value replacement (if defined)


m_defaultValueDefined

boolean m_defaultValueDefined
True if a default value has been specified


m_defaultValue

java.lang.String m_defaultValue
The default value (if defined)


m_bins

java.util.ArrayList<E> m_bins
The bins for this discretization


m_outputDef

Attribute m_outputDef
The output structure of this discretization

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

serialVersionUID: 5810063243316808400L

Serialized Fields

m_intervals

java.util.ArrayList<E> m_intervals
The intervals for this DiscretizeBin


m_binValue

java.lang.String m_binValue
The bin value for this DiscretizeBin

Class weka.core.pmml.Expression extends java.lang.Object implements Serializable

serialVersionUID: 4448840549804800321L

Serialized Fields

m_opType

FieldMetaInfo.Optype m_opType
The optype of this Expression


m_fieldDefs

java.util.ArrayList<E> m_fieldDefs
The field defs

Class weka.core.pmml.FieldMetaInfo extends java.lang.Object implements Serializable

Serialized Fields

m_fieldName

java.lang.String m_fieldName
the name of the field


m_optype

FieldMetaInfo.Optype m_optype
The optype for the target

Class weka.core.pmml.FieldMetaInfo.Interval extends java.lang.Object implements Serializable

serialVersionUID: -7339790632684638012L

Serialized Fields

m_leftMargin

double m_leftMargin
The left boundary value


m_rightMargin

double m_rightMargin
The right boundary value


m_closure

FieldMetaInfo.Interval.Closure m_closure

Class weka.core.pmml.FieldMetaInfo.Value extends java.lang.Object implements Serializable

serialVersionUID: -3981030320273649739L

Serialized Fields

m_value

java.lang.String m_value
The value


m_displayValue

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


m_property

FieldMetaInfo.Value.Property m_property

Class weka.core.pmml.FieldRef extends Expression implements Serializable

Serialized Fields

m_fieldName

java.lang.String m_fieldName
The name of the field to reference

Class weka.core.pmml.Function extends java.lang.Object implements Serializable

serialVersionUID: -6997738288201933171L

Serialized Fields

m_functionName

java.lang.String m_functionName
The name of this function


m_parameterDefs

java.util.ArrayList<E> m_parameterDefs
The structure of the parameters to this function

Class weka.core.pmml.MappingInfo extends java.lang.Object implements Serializable

Serialized Fields

m_fieldsMap

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


m_nominalValueMaps

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


m_fieldsMappingText

java.lang.String m_fieldsMappingText
Holds a textual description of the fields mapping


m_log

Logger m_log
For logging

Class weka.core.pmml.MiningFieldMetaInfo extends FieldMetaInfo implements Serializable

serialVersionUID: -1256774332779563185L

Serialized Fields

m_usageType

weka.core.pmml.MiningFieldMetaInfo.Usage m_usageType
usage type


m_outlierTreatmentMethod

weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod
outlier treatmemnt method


m_lowValue

double m_lowValue
outlier low value


m_highValue

double m_highValue
outlier high value


m_missingValueTreatmentMethod

weka.core.pmml.MiningFieldMetaInfo.Missing m_missingValueTreatmentMethod
missing values treatment method


m_missingValueReplacementNominal

java.lang.String m_missingValueReplacementNominal
actual missing value replacements (if specified)


m_missingValueReplacementNumeric

double m_missingValueReplacementNumeric

m_optypeOverride

FieldMetaInfo.Optype m_optypeOverride
optype overrides (override data dictionary type - NOT SUPPORTED AT PRESENT)


m_index

int m_index
the index of the field in the mining schema Instances


m_importance

double m_importance
importance (if defined)


m_miningSchemaI

Instances m_miningSchemaI
mining schema (needed for toString method)

Class weka.core.pmml.MiningSchema extends java.lang.Object implements Serializable

serialVersionUID: 7144380586726330455L

Serialized Fields

m_fieldInstancesStructure

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


m_miningSchemaInstancesStructure

Instances m_miningSchemaInstancesStructure
Just the mining schema fields as Instances


m_miningMeta

java.util.ArrayList<E> m_miningMeta
Meta information about the mining schema fields


m_derivedMeta

java.util.ArrayList<E> m_derivedMeta
Meta information about derived fields (those defined in the TransformationDictionary followed by those defined in LocalTransformations)


m_transformationDictionary

weka.core.pmml.TransformationDictionary m_transformationDictionary
The transformation dictionary (if defined)


m_targetMetaInfo

TargetMetaInfo m_targetMetaInfo
target meta info (may be null if not defined)

Class weka.core.pmml.NormContinuous extends Expression implements Serializable

serialVersionUID: 4714332374909851542L

Serialized Fields

m_fieldName

java.lang.String m_fieldName
The name of the field to use


m_fieldIndex

int m_fieldIndex
The index of the field


m_mapMissingDefined

boolean m_mapMissingDefined
True if a replacement for missing values has been specified


m_mapMissingTo

double m_mapMissingTo
The value of the missing value replacement (if defined)


m_outlierTreatmentMethod

weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod
Outlier treatment method (default = asIs)


m_linearNormOrig

double[] m_linearNormOrig
original values for the LinearNorm entries


m_linearNormNorm

double[] m_linearNormNorm
norm values for the LinearNorm entries

Class weka.core.pmml.NormDiscrete extends Expression implements Serializable

serialVersionUID: -8854409417983908220L

Serialized Fields

m_fieldName

java.lang.String m_fieldName
The name of the field to lookup our value in


m_field

Attribute m_field
The actual attribute itself


m_fieldIndex

int m_fieldIndex
The index of the attribute


m_fieldValue

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


m_mapMissingDefined

boolean m_mapMissingDefined
True if a replacement for missing values has been specified


m_mapMissingTo

double m_mapMissingTo
The value of the missing value replacement (if defined)


m_fieldValueIndex

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

Class weka.core.pmml.TargetMetaInfo extends FieldMetaInfo implements Serializable

serialVersionUID: 863500462237904927L

Serialized Fields

m_min

double m_min
min and max


m_max

double m_max

m_rescaleConstant

double m_rescaleConstant
re-scaling of target value (if defined)


m_rescaleFactor

double m_rescaleFactor

m_castInteger

java.lang.String m_castInteger
cast integers (default no casting)


m_defaultValueOrPriorProbs

double[] m_defaultValueOrPriorProbs
default value (numeric) or prior distribution (categorical)


m_values

java.util.ArrayList<E> m_values
for categorical values. Actual values


m_displayValues

java.util.ArrayList<E> m_displayValues
corresponding display values


Package weka.core.stemmers

Class weka.core.stemmers.IteratedLovinsStemmer extends LovinsStemmer implements Serializable

serialVersionUID: 960689687163788264L

Class weka.core.stemmers.LovinsStemmer extends java.lang.Object implements Serializable

serialVersionUID: -6113024782588197L

Class weka.core.stemmers.NullStemmer extends java.lang.Object implements Serializable

serialVersionUID: -3671261636532625496L

Class weka.core.stemmers.SnowballStemmer extends java.lang.Object implements Serializable

serialVersionUID: -6111170431963015178L

Serialized Fields

m_Stemmer

java.lang.Object m_Stemmer
the current stemmer.


Package weka.core.tokenizers

Class weka.core.tokenizers.AlphabeticTokenizer extends Tokenizer implements Serializable

serialVersionUID: 6705199562609861697L

Serialized Fields

m_Str

char[] m_Str
the characters of the string


m_CurrentPos

int m_CurrentPos
the current position

Class weka.core.tokenizers.CharacterDelimitedTokenizer extends Tokenizer implements Serializable

Serialized Fields

m_Delimiters

java.lang.String m_Delimiters
Delimiters used in tokenization

Class weka.core.tokenizers.NGramTokenizer extends CharacterDelimitedTokenizer implements Serializable

serialVersionUID: -2181896254171647219L

Serialized Fields

m_NMax

int m_NMax
the maximum number of N


m_NMin

int m_NMin
the minimum number of N


m_N

int m_N
the current length of the N-grams


m_MaxPosition

int m_MaxPosition
the number of strings available


m_CurrentPosition

int m_CurrentPosition
the current position for returning elements


m_SplitString

java.lang.String[] m_SplitString
all the available grams

Class weka.core.tokenizers.Tokenizer extends java.lang.Object implements Serializable

Class weka.core.tokenizers.WordTokenizer extends CharacterDelimitedTokenizer implements Serializable

serialVersionUID: -930893034037880773L


Package weka.core.xml

Class weka.core.xml.XMLInstances extends XMLDocument implements Serializable

serialVersionUID: 3626821327547416099L

Serialized Fields

m_Precision

int m_Precision
the precision for numbers


m_Instances

Instances m_Instances
the underlying Instances


Package weka.datagenerators

Class weka.datagenerators.ClassificationGenerator extends DataGenerator implements Serializable

serialVersionUID: -5261662546673517844L

Serialized Fields

m_NumExamples

int m_NumExamples
Number of instances

Class weka.datagenerators.ClusterDefinition extends java.lang.Object implements Serializable

serialVersionUID: -5950001207047429961L

Serialized Fields

m_Parent

ClusterGenerator m_Parent
the parent of the cluster

Class weka.datagenerators.ClusterGenerator extends DataGenerator implements Serializable

serialVersionUID: 6131722618472046365L

Serialized Fields

m_NumAttributes

int m_NumAttributes
Number of attribute the dataset should have


m_ClassFlag

boolean m_ClassFlag
class flag


m_booleanCols

Range m_booleanCols
Stores which columns are boolean (default numeric)


m_nominalCols

Range m_nominalCols
Stores which columns are nominal (default numeric)

Class weka.datagenerators.DataGenerator extends java.lang.Object implements Serializable

serialVersionUID: -3698585946221802578L

Serialized Fields

m_Debug

boolean m_Debug
Debugging mode


m_DatasetFormat

Instances m_DatasetFormat
The format for the generated dataset


m_RelationName

java.lang.String m_RelationName
Relation name the dataset should have


m_NumExamplesAct

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


m_Seed

int m_Seed
random number generator seed


m_Random

java.util.Random m_Random
random number generator


m_CreatingRelationName

boolean m_CreatingRelationName
flag, that indicates whether the relationname is currently assembled

Class weka.datagenerators.RegressionGenerator extends DataGenerator implements Serializable

serialVersionUID: 3073254041275658221L

Serialized Fields

m_NumExamples

int m_NumExamples
Number of instances

Class weka.datagenerators.Test extends java.lang.Object implements Serializable

serialVersionUID: -8890645875887157782L

Serialized Fields

m_AttIndex

int m_AttIndex
the attribute index


m_Split

double m_Split
the split


m_Not

boolean m_Not
whether to negate the test


m_Dataset

Instances m_Dataset
the dataset


Package weka.datagenerators.classifiers.classification

Class weka.datagenerators.classifiers.classification.Agrawal extends ClassificationGenerator implements Serializable

serialVersionUID: 2254651939636143025L

Serialized Fields

m_Function

int m_Function
the function to use for generating the data


m_BalanceClass

boolean m_BalanceClass
whether to balance the class


m_PerturbationFraction

double m_PerturbationFraction
the perturabation fraction


m_nextClassShouldBeZero

boolean m_nextClassShouldBeZero
used for balancing the class


m_lastLabel

double m_lastLabel
the last class label that was generated

Class weka.datagenerators.classifiers.classification.BayesNet extends ClassificationGenerator implements Serializable

serialVersionUID: -796118162379901512L

Serialized Fields

m_Generator

BayesNetGenerator m_Generator
the bayesian net generator, that produces the actual data

Class weka.datagenerators.classifiers.classification.LED24 extends ClassificationGenerator implements Serializable

serialVersionUID: -7880209100415868737L

Serialized Fields

m_NoisePercent

double m_NoisePercent
the noise rate


m_numIrrelevantAttributes

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

Class weka.datagenerators.classifiers.classification.RandomRBF extends ClassificationGenerator implements Serializable

serialVersionUID: 6069033710635728720L

Serialized Fields

m_NumAttributes

int m_NumAttributes
Number of attribute the dataset should have


m_NumClasses

int m_NumClasses
Number of Classes the dataset should have


m_NumCentroids

int m_NumCentroids
the number of centroids to use for generation


m_centroids

double[][] m_centroids
the centroids


m_centroidClasses

int[] m_centroidClasses
the classes of the centroids


m_centroidWeights

double[] m_centroidWeights
the weights of the centroids


m_centroidStdDevs

double[] m_centroidStdDevs
the stddevs of the centroids

Class weka.datagenerators.classifiers.classification.RDG1 extends ClassificationGenerator implements Serializable

serialVersionUID: 7751005204635320414L

Serialized Fields

m_NumAttributes

int m_NumAttributes
Number of attribute the dataset should have


m_NumClasses

int m_NumClasses
Number of Classes the dataset should have


m_MaxRuleSize

int m_MaxRuleSize
maximum rule size


m_MinRuleSize

int m_MinRuleSize
minimum rule size


m_NumIrrelevant

int m_NumIrrelevant
number of irrelevant attributes.


m_NumNumeric

int m_NumNumeric
number of numeric attribute


m_VoteFlag

boolean m_VoteFlag
flag that stores if voting is wished


m_DecisionList

FastVector m_DecisionList
decision list


m_AttList_Irr

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


Package weka.datagenerators.classifiers.regression

Class weka.datagenerators.classifiers.regression.Expression extends MexicanHat implements Serializable

serialVersionUID: -4237047357682277211L

Serialized Fields

m_Expression

java.lang.String m_Expression
the expression for computing y


m_Filter

AddExpression m_Filter
the filter for generating y out of x


m_RawData

Instances m_RawData
the input data structure for the filter

Class weka.datagenerators.classifiers.regression.MexicanHat extends RegressionGenerator implements Serializable

serialVersionUID: 4577016375261512975L

Serialized Fields

m_Amplitude

double m_Amplitude
the amplitude of y


m_MinRange

double m_MinRange
the lower boundary of the range, x is drawn from


m_MaxRange

double m_MaxRange
the upper boundary of the range, x is drawn from


m_NoiseRate

double m_NoiseRate
the rate of the gaussian noise


m_NoiseVariance

double m_NoiseVariance
the variance of the gaussian noise


m_NoiseRandom

java.util.Random m_NoiseRandom
the random number generator for the noise


Package weka.datagenerators.clusterers

Class weka.datagenerators.clusterers.BIRCHCluster extends ClusterGenerator implements Serializable

serialVersionUID: -334820527230755027L

Serialized Fields

m_NumClusters

int m_NumClusters
Number of Clusters the dataset should have


m_MinInstNum

int m_MinInstNum
minimal number of instances per cluster (option N)


m_MaxInstNum

int m_MaxInstNum
maximal number of instances per cluster (option N)


m_MinRadius

double m_MinRadius
minimum radius (option R)


m_MaxRadius

double m_MaxRadius
maximum radius (option R)


m_Pattern

int m_Pattern
pattern (changed with options G or S)


m_DistMult

double m_DistMult
distance multiplier (option M)


m_NumCycles

int m_NumCycles
number of cycles (option C)


m_InputOrder

int m_InputOrder
input order (changed with option O)


m_NoiseRate

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


m_ClusterList

FastVector m_ClusterList
cluster list


m_GridSize

int m_GridSize
grid size


m_GridWidth

double m_GridWidth
grid width

Class weka.datagenerators.clusterers.SubspaceCluster extends ClusterGenerator implements Serializable

serialVersionUID: -3454999858505621128L

Serialized Fields

m_NoiseRate

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


m_Clusters

ClusterDefinition[] m_Clusters
cluster list


m_numValues

int[] m_numValues
if nominal, store number of values


m_globalMinValue

double[] m_globalMinValue
store global min values


m_globalMaxValue

double[] m_globalMaxValue
store global max values

Class weka.datagenerators.clusterers.SubspaceClusterDefinition extends ClusterDefinition implements Serializable

serialVersionUID: 3135678125044007231L

Serialized Fields

m_clustertype

int m_clustertype
cluster type


m_clustersubtype

int m_clustersubtype
cluster subtypes


m_numClusterAttributes

int m_numClusterAttributes
number of attributes the cluster is defined for


m_numInstances

int m_numInstances
number of instances for this cluster


m_MinInstNum

int m_MinInstNum
minimal number of instances for this cluster


m_MaxInstNum

int m_MaxInstNum
maximal number of instances for this cluster


m_AttrIndexRange

Range m_AttrIndexRange
range of atttributes


m_attributes

boolean[] m_attributes
attributes of this cluster


m_attrIndices

int[] m_attrIndices
global indices of the attributes of the cluster


m_minValue

double[] m_minValue
ranges of each attribute (min); not used if gaussian


m_maxValue

double[] m_maxValue
ranges of each attribute (max); not used if gaussian


m_meanValue

double[] m_meanValue
mean ; only used if gaussian


m_stddevValue

double[] m_stddevValue
standarddev; only used if gaussian


Package weka.estimators

Class weka.estimators.DiscreteEstimator extends Estimator implements Serializable

serialVersionUID: -5526486742612434779L

Serialized Fields

m_Counts

double[] m_Counts
Hold the counts


m_SumOfCounts

double m_SumOfCounts
Hold the sum of counts

Class weka.estimators.Estimator extends java.lang.Object implements Serializable

serialVersionUID: -5902411487362274342L

Serialized Fields

m_Debug

boolean m_Debug
Debugging mode


m_classValueIndex

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


m_noClass

boolean m_noClass
set if class is not important

Class weka.estimators.KernelEstimator extends Estimator implements Serializable

serialVersionUID: 3646923563367683925L

Serialized Fields

m_Values

double[] m_Values
Vector containing all of the values seen


m_Weights

double[] m_Weights
Vector containing the associated weights


m_NumValues

int m_NumValues
Number of values stored in m_Weights and m_Values so far


m_SumOfWeights

double m_SumOfWeights
The sum of the weights so far


m_StandardDev

double m_StandardDev
The standard deviation


m_Precision

double m_Precision
The precision of data values


m_AllWeightsOne

boolean m_AllWeightsOne
Whether we can optimise the kernel summation

Class weka.estimators.MahalanobisEstimator extends Estimator implements Serializable

serialVersionUID: 8950225468990043868L

Serialized Fields

m_CovarianceInverse

Matrix m_CovarianceInverse
The inverse of the covariance matrix


m_Determinant

double m_Determinant
The determinant of the covariance matrix


m_ConstDelta

double m_ConstDelta
The difference between the conditioning value and the conditioning mean


m_ValueMean

double m_ValueMean
The mean of the values

Class weka.estimators.NormalEstimator extends Estimator implements Serializable

serialVersionUID: 93584379632315841L

Serialized Fields

m_SumOfWeights

double m_SumOfWeights
The sum of the weights


m_SumOfValues

double m_SumOfValues
The sum of the values seen


m_SumOfValuesSq

double m_SumOfValuesSq
The sum of the values squared


m_Mean

double m_Mean
The current mean


m_StandardDev

double m_StandardDev
The current standard deviation


m_Precision

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

Class weka.estimators.PoissonEstimator extends Estimator implements Serializable

serialVersionUID: 7669362595289236662L

Serialized Fields

m_NumValues

double m_NumValues
The number of values seen


m_SumOfValues

double m_SumOfValues
The sum of the values seen


m_Lambda

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


Package weka.experiment

Class weka.experiment.AveragingResultProducer extends java.lang.Object implements Serializable

serialVersionUID: 2551284958501991352L

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer used to generate results


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_ExpectedResultsPerAverage

int m_ExpectedResultsPerAverage
The number of results expected to average over for each run


m_CalculateStdDevs

boolean m_CalculateStdDevs
True if standard deviation fields should be produced


m_CountFieldName

java.lang.String m_CountFieldName
The name of the field that will contain the number of results averaged over.


m_KeyFieldName

java.lang.String m_KeyFieldName
The name of the key field to average over


m_KeyIndex

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


m_Keys

FastVector m_Keys
Collects the keys from a single run


m_Results

FastVector m_Results
Collects the results from a single run

Class weka.experiment.ClassifierSplitEvaluator extends java.lang.Object implements Serializable

serialVersionUID: -8511241602760467265L

Serialized Fields

m_Template

Classifier m_Template
The template classifier


m_Classifier

Classifier m_Classifier
The classifier used for evaluation


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_doesProduce

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


m_numberAdditionalMeasures

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


m_result

java.lang.String m_result
Holds the statistics for the most recent application of the classifier


m_ClassifierOptions

java.lang.String m_ClassifierOptions
The classifier options (if any)


m_ClassifierVersion

java.lang.String m_ClassifierVersion
The classifier version


m_IRclass

int m_IRclass
Class index for information retrieval statistics (default 0)


m_predTargetColumn

boolean m_predTargetColumn
Flag for prediction and target columns output.


m_attID

int m_attID
Attribute index of instance identifier (default -1)

Class weka.experiment.CostSensitiveClassifierSplitEvaluator extends ClassifierSplitEvaluator implements Serializable

serialVersionUID: -8069566663019501276L

Serialized Fields

m_OnDemandDirectory

java.io.File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory

Class weka.experiment.CrossValidationResultProducer extends java.lang.Object implements Serializable

serialVersionUID: -1580053925080091917L

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_NumFolds

int m_NumFolds
The number of folds in the cross-validation


m_debugOutput

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


m_ZipDest

OutputZipper m_ZipDest
The output zipper to use for saving raw splitEvaluator output


m_OutputFile

java.io.File m_OutputFile
The destination output file/directory for raw output


m_SplitEvaluator

SplitEvaluator m_SplitEvaluator
The SplitEvaluator used to generate results


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators

Class weka.experiment.CSVResultListener extends java.lang.Object implements Serializable

serialVersionUID: -623185072785174658L

Serialized Fields

m_RP

ResultProducer m_RP
The ResultProducer sending us results


m_OutputFile

java.io.File m_OutputFile
The destination output file, null sends to System.out


m_OutputFileName

java.lang.String m_OutputFileName
The name of the output file. Empty for temporary file.

Class weka.experiment.DatabaseResultListener extends DatabaseUtils implements Serializable

serialVersionUID: 7388014746954652818L

Serialized Fields

m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer to listen to


m_ResultsTableName

java.lang.String m_ResultsTableName
The name of the current results table


m_Debug

boolean m_Debug
True if debugging output should be printed


m_CacheKeyName

java.lang.String m_CacheKeyName
Holds the name of the key field to cache upon, or null if no caching


m_CacheKeyIndex

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


m_CacheKey

java.lang.Object[] m_CacheKey
Stores the key for which the cache is valid


m_Cache

FastVector m_Cache
Stores the cached values

Class weka.experiment.DatabaseResultProducer extends DatabaseResultListener implements Serializable

serialVersionUID: -5620660780203158666L

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer used to generate results


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators

Class weka.experiment.DatabaseUtils extends java.lang.Object implements Serializable

serialVersionUID: -8252351994547116729L

Serialized Fields

DRIVERS

java.util.Vector<E> DRIVERS
Holds the jdbc drivers to be used (only to stop them being gc'ed).


PROPERTIES

java.util.Properties PROPERTIES
Properties associated with the database connection.


m_DatabaseURL

java.lang.String m_DatabaseURL
Database URL.


m_Debug

boolean m_Debug
True if debugging output should be printed.


m_userName

java.lang.String m_userName
Database username.


m_password

java.lang.String m_password
Database Password.


m_stringType

java.lang.String m_stringType
string type for the create table statement.


m_intType

java.lang.String m_intType
integer type for the create table statement.


m_doubleType

java.lang.String m_doubleType
double type for the create table statement.


m_checkForUpperCaseNames

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


m_checkForLowerCaseNames

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


m_setAutoCommit

boolean m_setAutoCommit
setAutoCommit on the database?


m_createIndex

boolean m_createIndex
create index on the database?


m_Keywords

java.util.HashSet<E> m_Keywords
the keywords for the current database type.


m_KeywordsMaskChar

java.lang.String m_KeywordsMaskChar
the character to mask SQL keywords (by appending this character).

Class weka.experiment.DensityBasedClustererSplitEvaluator extends java.lang.Object implements Serializable

Serialized Fields

m_removeClassColumn

boolean m_removeClassColumn
Remove the class column (if set) from the data


m_clusterer

DensityBasedClusterer m_clusterer
The clusterer used for evaluation


m_additionalMeasures

java.lang.String[] m_additionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_doesProduce

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


m_numberAdditionalMeasures

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


m_result

java.lang.String m_result
Holds the statistics for the most recent application of the clusterer


m_clustererOptions

java.lang.String m_clustererOptions
The clusterer options (if any)


m_clustererVersion

java.lang.String m_clustererVersion
The clusterer version

Class weka.experiment.Experiment extends java.lang.Object implements Serializable

serialVersionUID: 44945596742646663L

Serialized Fields

m_ResultListener

ResultListener m_ResultListener
Where results will be sent


m_ResultProducer

ResultProducer m_ResultProducer
The result producer


m_RunLower

int m_RunLower
Lower run number


m_RunUpper

int m_RunUpper
Upper run number


m_Datasets

javax.swing.DefaultListModel m_Datasets
An array of dataset files


m_UsePropertyIterator

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


m_PropertyPath

PropertyNode[] m_PropertyPath
The path to the iterator property


m_PropertyArray

java.lang.Object m_PropertyArray
The array of values to set the property to


m_Notes

java.lang.String m_Notes
User notes about the experiment


m_AdditionalMeasures

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


m_ClassFirst

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


m_AdvanceDataSetFirst

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

Class weka.experiment.InstanceQuery extends DatabaseUtils implements Serializable

serialVersionUID: 718158370917782584L

Serialized Fields

m_CreateSparseData

boolean m_CreateSparseData
Determines whether sparse data is created


m_Query

java.lang.String m_Query
Query to execute

Class weka.experiment.InstancesResultListener extends CSVResultListener implements Serializable

serialVersionUID: -2203808461809311178L

Class weka.experiment.LearningRateResultProducer extends java.lang.Object implements Serializable

serialVersionUID: -3841159673490861331L

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_ResultProducer

ResultProducer m_ResultProducer
The ResultProducer used to generate results


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_LowerSize

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


m_UpperSize

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


m_StepSize

int m_StepSize
The number of instances to add at each step


m_CurrentSize

int m_CurrentSize
The current dataset size during stepping

Class weka.experiment.PairedCorrectedTTester extends PairedTTester implements Serializable

serialVersionUID: -3105268939845653323L

Class weka.experiment.PairedTTester extends java.lang.Object implements Serializable

serialVersionUID: 8370014624008728610L

Serialized Fields

m_Instances

Instances m_Instances
The set of instances we will analyse


m_RunColumn

int m_RunColumn
The index of the column containing the run number


m_RunColumnSet

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


m_FoldColumn

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


m_SortColumn

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


m_SortOrder

int[] m_SortOrder
The sorting of the datasets (according to the sort column)


m_ColOrder

int[] m_ColOrder
The sorting of the columns (test base is always first)


m_SignificanceLevel

double m_SignificanceLevel
The significance level for comparisons


m_DatasetKeyColumnsRange

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


m_DatasetKeyColumns

int[] m_DatasetKeyColumns
An array containing the indexes of just the selected columns


m_DatasetSpecifiers

weka.experiment.PairedTTester.DatasetSpecifiers m_DatasetSpecifiers
The list of dataset specifiers


m_ResultsetKeyColumnsRange

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


m_ResultsetKeyColumns

int[] m_ResultsetKeyColumns
An array containing the indexes of just the selected columns


m_DisplayedResultsets

int[] m_DisplayedResultsets
An array containing the indexes of the datasets to display


m_Resultsets

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_ShowStdDevs

boolean m_ShowStdDevs
Indicates whether standard deviations should be displayed


m_ResultMatrix

ResultMatrix m_ResultMatrix
the instance of the class to produce the output.

Class weka.experiment.PropertyNode extends java.lang.Object implements Serializable

serialVersionUID: -8718165742572631384L

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream in)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
Throws:
java.io.IOException
java.lang.ClassNotFoundException

writeObject

private void writeObject(java.io.ObjectOutputStream out)
                  throws java.io.IOException
Throws:
java.io.IOException
Serialized Fields

value

java.lang.Object value
The current property value


parentClass

java.lang.Class<T> parentClass
The class of the object with this property


property

java.beans.PropertyDescriptor property
Other info about the property

Class weka.experiment.RandomSplitResultProducer extends java.lang.Object implements Serializable

serialVersionUID: 1403798165056795073L

Serialized Fields

m_Instances

Instances m_Instances
The dataset of interest


m_ResultListener

ResultListener m_ResultListener
The ResultListener to send results to


m_TrainPercent

double m_TrainPercent
The percentage of instances to use for training


m_randomize

boolean m_randomize
Whether dataset is to be randomized


m_SplitEvaluator

SplitEvaluator m_SplitEvaluator
The SplitEvaluator used to generate results


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_debugOutput

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


m_ZipDest

OutputZipper m_ZipDest
The output zipper to use for saving raw splitEvaluator output


m_OutputFile

java.io.File m_OutputFile
The destination output file/directory for raw output

Class weka.experiment.RegressionSplitEvaluator extends java.lang.Object implements Serializable

serialVersionUID: -328181640503349202L

Serialized Fields

m_Template

Classifier m_Template
The template classifier


m_Classifier

Classifier m_Classifier
The classifier used for evaluation


m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators


m_doesProduce

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


m_result

java.lang.String m_result
Holds the statistics for the most recent application of the classifier


m_ClassifierOptions

java.lang.String m_ClassifierOptions
The classifier options (if any)


m_ClassifierVersion

java.lang.String m_ClassifierVersion
The classifier version

Class weka.experiment.RemoteEngine extends java.rmi.server.UnicastRemoteObject implements Serializable

serialVersionUID: -1021538162895448259L

Serialized Fields

m_HostName

java.lang.String m_HostName
The name of the host that this engine is started on


m_TaskQueue

Queue m_TaskQueue
A queue of waiting tasks


m_TaskIdQueue

Queue m_TaskIdQueue
A queue of corresponding ID's for tasks


m_TaskStatus

java.util.Hashtable<K,V> m_TaskStatus
A hashtable of experiment status


m_TaskRunning

boolean m_TaskRunning
Is there a task running

Class weka.experiment.RemoteEngine_Stub extends java.rmi.server.RemoteStub implements Serializable

serialVersionUID: 2L

Class weka.experiment.RemoteExperiment extends Experiment implements Serializable

serialVersionUID: -7357668825635314937L

Serialized Fields

m_listeners

FastVector m_listeners
The list of objects listening for remote experiment events


m_remoteHosts

javax.swing.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_remoteHostFailureCounts

int[] m_remoteHostFailureCounts
The number of times tasks have failed on each remote host


m_experimentAborted

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


m_removedHosts

int m_removedHosts
The number of hosts removed due to exceeding max failures


m_failedCount

int m_failedCount
The count of failed sub-experiments


m_finishedCount

int m_finishedCount
The count of successfully completed sub-experiments


m_baseExperiment

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


m_subExperiments

Experiment[] m_subExperiments
The sub experiments


m_subExpQueue

Queue m_subExpQueue
The queue of sub experiments waiting to be processed


m_subExpComplete

int[] m_subExpComplete
The status of each of the sub-experiments


m_splitByDataSet

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

Class weka.experiment.RemoteExperimentEvent extends java.lang.Object implements Serializable

serialVersionUID: 7000867987391866451L

Serialized Fields

m_statusMessage

boolean m_statusMessage
A status type message


m_logMessage

boolean m_logMessage
A log type message


m_messageString

java.lang.String m_messageString
The message


m_experimentFinished

boolean m_experimentFinished
True if a remote experiment has finished

Class weka.experiment.RemoteExperimentSubTask extends java.lang.Object implements Serializable

Serialized Fields

m_result

TaskStatusInfo m_result

m_experiment

Experiment m_experiment

Class weka.experiment.ResultMatrix extends java.lang.Object implements Serializable

serialVersionUID: 4487179306428209739L

Serialized Fields

TIE_STRING

java.lang.String TIE_STRING
tie string


WIN_STRING

java.lang.String WIN_STRING
win string


LOSS_STRING

java.lang.String LOSS_STRING
loss string


LEFT_PARENTHESES

java.lang.String LEFT_PARENTHESES
the left parentheses for enumerating cols/rows


RIGHT_PARENTHESES

java.lang.String RIGHT_PARENTHESES
the right parentheses for enumerating cols/rows


m_ColNames

java.lang.String[] m_ColNames
the column names


m_RowNames

java.lang.String[] m_RowNames
the row names


m_ColHidden

boolean[] m_ColHidden
whether a column is hidden


m_RowHidden

boolean[] m_RowHidden
whether a row is hidden


m_Significance

int[][] m_Significance
the significance


m_Mean

double[][] m_Mean
the values


m_StdDev

double[][] m_StdDev
the standard deviation


m_Counts

double[] m_Counts
the counts for the different datasets


m_MeanPrec

int m_MeanPrec
the standard mean precision


m_StdDevPrec

int m_StdDevPrec
the standard std. deviation preicision


m_ShowStdDev

boolean m_ShowStdDev
whether std. deviations are printed as well


m_ShowAverage

boolean m_ShowAverage
whether the average for each column should be printed


m_PrintColNames

boolean m_PrintColNames
whether the names or numbers are output as column declarations


m_PrintRowNames

boolean m_PrintRowNames
whether the names or numbers are output as row declarations


m_EnumerateColNames

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


m_EnumerateRowNames

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


m_ColNameWidth

int m_ColNameWidth
the size of the names of the columns


m_RowNameWidth

int m_RowNameWidth
the size of the names of the rows


m_MeanWidth

int m_MeanWidth
the size of the mean columns


m_StdDevWidth

int m_StdDevWidth
the size of the std dev columns


m_SignificanceWidth

int m_SignificanceWidth
the size of the significance columns


m_CountWidth

int m_CountWidth
the size of the counts


m_HeaderKeys

java.util.Vector<E> m_HeaderKeys
contains the keys for the header


m_HeaderValues

java.util.Vector<E> m_HeaderValues
contains the values for the header


m_NonSigWins

int[][] m_NonSigWins
the non-significant wins


m_Wins

int[][] m_Wins
the significant wins


m_RankingWins

int[] m_RankingWins
the wins in ranking


m_RankingLosses

int[] m_RankingLosses
the losses in ranking


m_RankingDiff

int[] m_RankingDiff
the difference between wins and losses


m_RowOrder

int[] m_RowOrder
the ordering of the rows


m_ColOrder

int[] m_ColOrder
the ordering of the columns


m_RemoveFilterName

boolean m_RemoveFilterName
whether to remove the filter name from the dataaset name

Class weka.experiment.ResultMatrixCSV extends ResultMatrix implements Serializable

serialVersionUID: -171838863135042743L

Class weka.experiment.ResultMatrixGnuPlot extends ResultMatrix implements Serializable

serialVersionUID: -234648254944790097L

Class weka.experiment.ResultMatrixHTML extends ResultMatrix implements Serializable

serialVersionUID: 6672380422544799990L

Class weka.experiment.ResultMatrixLatex extends ResultMatrix implements Serializable

serialVersionUID: 777690788447600978L

Class weka.experiment.ResultMatrixPlainText extends ResultMatrix implements Serializable

serialVersionUID: 1502934525382357937L

Class weka.experiment.ResultMatrixSignificance extends ResultMatrix implements Serializable

serialVersionUID: -1280545644109764206L

Class weka.experiment.Stats extends java.lang.Object implements Serializable

serialVersionUID: -8610544539090024102L

Serialized Fields

count

double count
The number of values seen


sum

double sum
The sum of values seen


sumSq

double sumSq
The sum of values squared seen


stdDev

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


mean

double mean
The mean of values at the last calculateDerived() call


min

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


max

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

Class weka.experiment.TaskStatusInfo extends java.lang.Object implements Serializable

serialVersionUID: -6129343303703560015L

Serialized Fields

m_ExecutionStatus

int m_ExecutionStatus
Holds current execution status.


m_StatusMessage

java.lang.String m_StatusMessage
Holds current status message.


m_TaskResult

java.lang.Object m_TaskResult
Holds task result. Set to null for no returnable result.


Package weka.filters

Class weka.filters.AllFilter extends Filter implements Serializable

serialVersionUID: 5022109283147503266L

Class weka.filters.Filter extends java.lang.Object implements Serializable

serialVersionUID: -8835063755891851218L

Serialized Fields

m_OutputFormat

Instances m_OutputFormat
The output format for instances


m_OutputQueue

Queue m_OutputQueue
The output instance queue


m_OutputStringAtts

StringLocator m_OutputStringAtts
Indices of string attributes in the output format


m_InputStringAtts

StringLocator m_InputStringAtts
Indices of string attributes in the input format


m_OutputRelAtts

RelationalLocator m_OutputRelAtts
Indices of relational attributes in the output format


m_InputRelAtts

RelationalLocator m_InputRelAtts
Indices of relational attributes in the input format


m_InputFormat

Instances m_InputFormat
The input format for instances


m_NewBatch

boolean m_NewBatch
Record whether the filter is at the start of a batch


m_FirstBatchDone

boolean m_FirstBatchDone
True if the first batch has been done

Class weka.filters.MultiFilter extends SimpleStreamFilter implements Serializable

serialVersionUID: -6293720886005713120L

Serialized Fields

m_Filters

Filter[] m_Filters
The filters


m_Streamable

boolean m_Streamable
caches the streamable state


m_StreamableChecked

boolean m_StreamableChecked
whether we already checked the streamable state

Class weka.filters.SimpleBatchFilter extends SimpleFilter implements Serializable

serialVersionUID: 8102908673378055114L

Class weka.filters.SimpleFilter extends Filter implements Serializable

serialVersionUID: 5702974949137433141L

Serialized Fields

m_Debug

boolean m_Debug
Whether debugging is on

Class weka.filters.SimpleStreamFilter extends SimpleFilter implements Serializable

serialVersionUID: 2754882676192747091L


Package weka.filters.supervised.attribute

Class weka.filters.supervised.attribute.AddClassification extends SimpleBatchFilter implements Serializable

serialVersionUID: -1931467132568441909L

Serialized Fields

m_Classifier

Classifier m_Classifier
The classifier template used to do the classification


m_SerializedClassifierFile

java.io.File m_SerializedClassifierFile
The file from which to load a serialized classifier


m_ActualClassifier

Classifier m_ActualClassifier
The actual classifier used to do the classification


m_OutputClassification

boolean m_OutputClassification
whether to output the classification


m_RemoveOldClass

boolean m_RemoveOldClass
whether to remove the old class attribute


m_OutputDistribution

boolean m_OutputDistribution
whether to output the class distribution


m_OutputErrorFlag

boolean m_OutputErrorFlag
whether to output the error flag

Class weka.filters.supervised.attribute.AttributeSelection extends Filter implements Serializable

serialVersionUID: -296211247688169716L

Serialized Fields

m_trainSelector

AttributeSelection m_trainSelector
the attribute selection evaluation object


m_ASEvaluator

ASEvaluation m_ASEvaluator
the attribute evaluator to use


m_ASSearch

ASSearch m_ASSearch
the search method if any


m_FilterOptions

java.lang.String[] m_FilterOptions
holds a copy of the full set of valid options passed to the filter


m_SelectedAttributes

int[] m_SelectedAttributes
holds the selected attributes

Class weka.filters.supervised.attribute.ClassOrder extends Filter implements Serializable

serialVersionUID: -2116226838887628411L

Serialized Fields

m_Seed

long m_Seed
The seed of randomization


m_Random

java.util.Random m_Random
The random object


m_Converter

int[] m_Converter
The 1-1 converting table from the original class values to the new values


m_ClassAttribute

Attribute m_ClassAttribute
Class attribute of the data


m_ClassOrder

int m_ClassOrder
The class order to be sorted


m_ClassCounts

double[] m_ClassCounts
This class can provide the class distribution in the sorted order as side effect

Class weka.filters.supervised.attribute.Discretize extends Filter implements Serializable

serialVersionUID: -3141006402280129097L

Serialized Fields

m_DiscretizeCols

Range m_DiscretizeCols
Stores which columns to Discretize


m_CutPoints

double[][] m_CutPoints
Store the current cutpoints


m_MakeBinary

boolean m_MakeBinary
Output binary attributes for discretized attributes.


m_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 extends Filter implements Serializable

serialVersionUID: -5004607029857673950L

Serialized Fields

m_Indices

int[][] m_Indices
The sorted indices of the attribute values.


m_Numeric

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


m_TransformAll

boolean m_TransformAll
Are all values transformed into new attributes?

Class weka.filters.supervised.attribute.PLSFilter extends SimpleBatchFilter implements Serializable

serialVersionUID: -3335106965521265631L

Serialized Fields

m_NumComponents

int m_NumComponents
the maximum number of components to generate


m_Algorithm

int m_Algorithm
the type of algorithm


m_PLS1_RegVector

Matrix m_PLS1_RegVector
the regression vector "r-hat" for PLS1


m_PLS1_P

Matrix m_PLS1_P
the P matrix for PLS1


m_PLS1_W

Matrix m_PLS1_W
the W matrix for PLS1


m_PLS1_b_hat

Matrix m_PLS1_b_hat
the b-hat vector for PLS1


m_SIMPLS_W

Matrix m_SIMPLS_W
the W matrix for SIMPLS


m_SIMPLS_B

Matrix m_SIMPLS_B
the B matrix for SIMPLS (used for prediction)


m_PerformPrediction

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


m_Missing

Filter m_Missing
for replacing missing values


m_ReplaceMissing

boolean m_ReplaceMissing
whether to replace missing values


m_Filter

Filter m_Filter
for centering the data


m_Preprocessing

int m_Preprocessing
the type of preprocessing


m_ClassMean

double m_ClassMean
the mean of the class


m_ClassStdDev

double m_ClassStdDev
the standard deviation of the class


Package weka.filters.supervised.instance

Class weka.filters.supervised.instance.Resample extends Filter implements Serializable

serialVersionUID: 7079064953548300681L

Serialized Fields

m_SampleSizePercent

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


m_RandomSeed

int m_RandomSeed
The random number generator seed.


m_BiasToUniformClass

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


m_NoReplacement

boolean m_NoReplacement
Whether to perform sampling with replacement or without.


m_InvertSelection

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

See Also:
Resample.m_NoReplacement

Class weka.filters.supervised.instance.SMOTE extends Filter implements Serializable

serialVersionUID: -1653880819059250364L

Serialized Fields

m_NearestNeighbors

int m_NearestNeighbors
the number of neighbors to use.


m_RandomSeed

int m_RandomSeed
the random seed to use.


m_Percentage

double m_Percentage
the percentage of SMOTE instances to create.


m_ClassValueIndex

java.lang.String m_ClassValueIndex
the index of the class value.


m_DetectMinorityClass

boolean m_DetectMinorityClass
whether to detect the minority class automatically.

Class weka.filters.supervised.instance.SpreadSubsample extends Filter implements Serializable

serialVersionUID: -3947033795243930016L

Serialized Fields

m_RandomSeed

int m_RandomSeed
The random number generator seed


m_MaxCount

int m_MaxCount
The maximum count of any class


m_DistributionSpread

double m_DistributionSpread
True if the first batch has been done


m_AdjustWeights

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

Class weka.filters.supervised.instance.StratifiedRemoveFolds extends Filter implements Serializable

serialVersionUID: -7069148179905814324L

Serialized Fields

m_Inverse

boolean m_Inverse
Indicates if inverse of selection is to be output.


m_NumFolds

int m_NumFolds
Number of folds to split dataset into


m_Fold

int m_Fold
Fold to output


m_Seed

long m_Seed
Random number seed.


Package weka.filters.unsupervised.attribute

Class weka.filters.unsupervised.attribute.AbstractTimeSeries extends Filter implements Serializable

serialVersionUID: -3795656792078022357L

Serialized Fields

m_SelectedCols

Range m_SelectedCols
Stores which columns to copy


m_FillWithMissing

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


m_InstanceRange

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


m_History

Queue m_History
Stores the historical instances to copy values between

Class weka.filters.unsupervised.attribute.Add extends Filter implements Serializable

serialVersionUID: 761386447332932389L

Serialized Fields

m_AttributeType

int m_AttributeType
Record the type of attribute to insert.


m_Name

java.lang.String m_Name
The name for the new attribute.


m_Insert

SingleIndex m_Insert
The location to insert the new attribute.


m_Labels

FastVector m_Labels
The list of labels for nominal attribute.


m_DateFormat

java.lang.String m_DateFormat
The date format.

Class weka.filters.unsupervised.attribute.AddCluster extends Filter implements Serializable

serialVersionUID: 7414280611943807337L

Serialized Fields

m_Clusterer

Clusterer m_Clusterer
The clusterer used to do the cleansing


m_IgnoreAttributesRange

Range m_IgnoreAttributesRange
Range of attributes to ignore


m_removeAttributes

Filter m_removeAttributes
Filter for removing attributes

Class weka.filters.unsupervised.attribute.AddExpression extends Filter implements Serializable

serialVersionUID: 402130384261736245L

Serialized Fields

m_infixExpression

java.lang.String m_infixExpression
The infix expression


m_attributeName

java.lang.String m_attributeName
Name of the new attribute. "expression" length string will use the provided expression as the new attribute name


m_Debug

boolean m_Debug
If true, makes the attribute name equal to the postfix parse of the expression


m_attributeExpression

AttributeExpression m_attributeExpression

Class weka.filters.unsupervised.attribute.AddID extends Filter implements Serializable

serialVersionUID: 4734383199819293390L

Serialized Fields

m_Index

SingleIndex m_Index
the index of the attribute


m_Name

java.lang.String m_Name
the name of the attribute


m_Counter

int m_Counter
the counter for the ID

Class weka.filters.unsupervised.attribute.AddNoise extends Filter implements Serializable

serialVersionUID: -8499673222857299082L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_UseMissing

boolean m_UseMissing
Flag if missing values are taken as value.


m_Percent

int m_Percent
The subsample size, percent of original set, default 10%


m_RandomSeed

int m_RandomSeed
The random number generator seed

Class weka.filters.unsupervised.attribute.AddValues extends Filter implements Serializable

serialVersionUID: -8100622241742393656L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_Labels

java.util.Vector<E> m_Labels
The values to add.


m_Sort

boolean m_Sort
Whether to sort the values.


m_SortedIndices

int[] m_SortedIndices
the array with the sorted label indices

Class weka.filters.unsupervised.attribute.Center extends PotentialClassIgnorer implements Serializable

serialVersionUID: -9101338448900581023L

Serialized Fields

m_Means

double[] m_Means
The means

Class weka.filters.unsupervised.attribute.ChangeDateFormat extends Filter implements Serializable

serialVersionUID: -1609344074013448737L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_DateFormat

java.text.SimpleDateFormat m_DateFormat
The output date format.


m_OutputAttribute

Attribute m_OutputAttribute
The output attribute.

Class weka.filters.unsupervised.attribute.ClassAssigner extends SimpleStreamFilter implements Serializable

serialVersionUID: 1775780193887394115L

Serialized Fields

m_ClassIndex

int m_ClassIndex
the class index.

Class weka.filters.unsupervised.attribute.ClusterMembership extends Filter implements Serializable

serialVersionUID: 6675702504667714026L

Serialized Fields

m_clusterer

DensityBasedClusterer m_clusterer
The clusterer


m_clusterers

DensityBasedClusterer[] m_clusterers
Array for storing the clusterers


m_ignoreAttributesRange

Range m_ignoreAttributesRange
Range of attributes to ignore


m_removeAttributes

Filter m_removeAttributes
Filter for removing attributes


m_priors

double[] m_priors
The prior probability for each class

Class weka.filters.unsupervised.attribute.Copy extends Filter implements Serializable

serialVersionUID: -8543707493627441566L

Serialized Fields

m_CopyCols

Range m_CopyCols
Stores which columns to copy


m_SelectedAttributes

int[] m_SelectedAttributes
Stores the indexes of the selected attributes in order, once the dataset is seen

Class weka.filters.unsupervised.attribute.Discretize extends PotentialClassIgnorer implements Serializable

serialVersionUID: -1358531742174527279L

Serialized Fields

m_DiscretizeCols

Range m_DiscretizeCols
Stores which columns to Discretize


m_NumBins

int m_NumBins
The number of bins to divide the attribute into


m_DesiredWeightOfInstancesPerInterval

double m_DesiredWeightOfInstancesPerInterval
The desired weight of instances per bin


m_CutPoints

double[][] m_CutPoints
Store the current cutpoints


m_MakeBinary

boolean m_MakeBinary
Output binary attributes for discretized attributes.


m_FindNumBins

boolean m_FindNumBins
Find the number of bins using cross-validated entropy.


m_UseEqualFrequency

boolean m_UseEqualFrequency
Use equal-frequency binning if unsupervised discretization turned on


m_DefaultCols

java.lang.String m_DefaultCols
The default columns to discretize

Class weka.filters.unsupervised.attribute.FirstOrder extends Filter implements Serializable

serialVersionUID: -7500464545400454179L

Serialized Fields

m_DeltaCols

Range m_DeltaCols
Stores which columns to take differences between

Class weka.filters.unsupervised.attribute.InterquartileRange extends SimpleBatchFilter implements Serializable

serialVersionUID: -227879653639723030L

Serialized Fields

m_Attributes

Range m_Attributes
the attribute range to work on


m_AttributeIndices

int[] m_AttributeIndices
the generated indices (only for performance reasons)


m_OutlierFactor

double m_OutlierFactor
the factor for detecting outliers


m_ExtremeValuesFactor

double m_ExtremeValuesFactor
the factor for detecting extreme values, by default 2*m_OutlierFactor


m_ExtremeValuesAsOutliers

boolean m_ExtremeValuesAsOutliers
whether extreme values are also tagged as outliers


m_UpperExtremeValue

double[] m_UpperExtremeValue
the upper extreme value threshold (= Q3 + EVF*IQR)


m_UpperOutlier

double[] m_UpperOutlier
the upper outlier threshold (= Q3 + OF*IQR)


m_LowerOutlier

double[] m_LowerOutlier
the lower outlier threshold (= Q1 - OF*IQR)


m_IQR

double[] m_IQR
the interquartile range


m_Median

double[] m_Median
the median


m_LowerExtremeValue

double[] m_LowerExtremeValue
the lower extreme value threshold (= Q1 - EVF*IQR)


m_DetectionPerAttribute

boolean m_DetectionPerAttribute
whether to generate Outlier/ExtremeValue attributes for each attribute instead of a general one


m_OutlierAttributePosition

int[] m_OutlierAttributePosition
the position of the outlier attribute


m_OutputOffsetMultiplier

boolean m_OutputOffsetMultiplier
whether to add another attribute called "Offset", that lists the 'multiplier' by which the outlier/extreme value is away from the median, i.e., value = median + 'multiplier' * IQR
automatically enables m_DetectionPerAttribute!

Class weka.filters.unsupervised.attribute.KernelFilter extends SimpleBatchFilter implements Serializable

serialVersionUID: 213800899640387499L

Serialized Fields

m_NumTrainInstances

int m_NumTrainInstances
The number of instances in the training data.


m_Kernel

Kernel m_Kernel
Kernel to use


m_ActualKernel

Kernel m_ActualKernel
the Kernel which is actually used for computation


m_checksTurnedOff

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


m_NominalToBinary

NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.


m_Missing

ReplaceMissingValues m_Missing
The filter used to get rid of missing values.


m_InitFile

java.io.File m_InitFile
The dataset to initialize the filter with


m_InitFileClassIndex

SingleIndex m_InitFileClassIndex
the class index for the file to initialized with

See Also:
KernelFilter.m_InitFile

m_Initialized

boolean m_Initialized
whether the filter was initialized


m_KernelFactorExpression

java.lang.String m_KernelFactorExpression
optimizes the kernel with this formula (A = # of attributes, N = # of instances)


m_KernelFactor

double m_KernelFactor
the calculated kernel factor

See Also:
KernelFilter.m_KernelFactorExpression

m_Filter

Filter m_Filter
for centering/standardizing the data


m_ActualFilter

Filter m_ActualFilter
for centering/standardizing the data (the actual filter to use)

Class weka.filters.unsupervised.attribute.MakeIndicator extends Filter implements Serializable

serialVersionUID: 766001176862773163L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_ValIndex

Range m_ValIndex
The value's index


m_Numeric

boolean m_Numeric
Make boolean attribute numeric.

Class weka.filters.unsupervised.attribute.MathExpression extends PotentialClassIgnorer implements Serializable

serialVersionUID: -3713222714671997901L

Serialized Fields

m_SelectCols

Range m_SelectCols
Stores which columns to select as a funky range


m_expression

java.lang.String m_expression
The modification expression


m_attStats

AttributeStats[] m_attStats
Attributes statistics

Class weka.filters.unsupervised.attribute.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 extends Filter implements Serializable

serialVersionUID: -4102847628883002530L

Serialized Fields

m_NumBags

int m_NumBags
the total number of bags


m_BagStringAtts

StringLocator m_BagStringAtts
Indices of string attributes in the bag


m_BagRelAtts

RelationalLocator m_BagRelAtts
Indices of relational attributes in the bag


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 extends Filter implements Serializable

serialVersionUID: -1130642825710549138L

Serialized Fields

m_Columns

Range m_Columns
Stores which columns to act on


m_Numeric

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


m_TransformAll

boolean m_TransformAll
Are all values transformed into new attributes?

Class weka.filters.unsupervised.attribute.NominalToString extends Filter implements Serializable

serialVersionUID: 8655492378380068939L

Serialized Fields

m_AttIndex

Range m_AttIndex
The attribute's index setting.

Class weka.filters.unsupervised.attribute.Normalize extends PotentialClassIgnorer implements Serializable

serialVersionUID: -8158531150984362898L

Serialized Fields

m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.


m_Translation

double m_Translation
The translation of the output range.


m_Scale

double m_Scale
The scaling factor of the output range.

Class weka.filters.unsupervised.attribute.NumericCleaner extends SimpleStreamFilter implements Serializable

serialVersionUID: -352890679895066592L

Serialized Fields

m_MinThreshold

double m_MinThreshold
the minimum threshold


m_MinDefault

double m_MinDefault
the minimum default replacement value


m_MaxThreshold

double m_MaxThreshold
the maximum threshold


m_MaxDefault

double m_MaxDefault
the maximum default replacement value


m_CloseTo

double m_CloseTo
the number the values are checked for closeness to


m_CloseToDefault

double m_CloseToDefault
the default replacement value for numbers "close-to"


m_CloseToTolerance

double m_CloseToTolerance
the tolerance distance, below which numbers are considered being "close-to"


m_Cols

Range m_Cols
Stores which columns to cleanse


m_IncludeClass

boolean m_IncludeClass
whether to include the class attribute


m_Decimals

int m_Decimals
the number of decimals to round to (-1 means no rounding)

Class weka.filters.unsupervised.attribute.NumericToBinary extends PotentialClassIgnorer implements Serializable

serialVersionUID: 2616879323359470802L

Class weka.filters.unsupervised.attribute.NumericToNominal extends SimpleBatchFilter implements Serializable

serialVersionUID: -6614630932899796239L

Serialized Fields

m_Cols

Range m_Cols
Stores which columns to turn into nominals


m_DefaultCols

java.lang.String m_DefaultCols
The default columns to turn into nominals

Class weka.filters.unsupervised.attribute.NumericTransform extends Filter implements Serializable

serialVersionUID: -8561413333351366934L

Serialized Fields

m_Cols

Range m_Cols
Stores which columns to transform.


m_Class

java.lang.String m_Class
Class containing transformation method.


m_Method

java.lang.String m_Method
Transformation method.

Class weka.filters.unsupervised.attribute.Obfuscate extends Filter implements Serializable

serialVersionUID: -343922772462971561L

Class weka.filters.unsupervised.attribute.PartitionedMultiFilter extends SimpleBatchFilter implements Serializable

serialVersionUID: -6293720886005713120L

Serialized Fields

m_Filters

Filter[] m_Filters
The filters


m_Ranges

Range[] m_Ranges
The attribute ranges


m_RemoveUnused

boolean m_RemoveUnused
Whether unused attributes are left out of the output


m_IndicesUnused

int[] m_IndicesUnused
the indices of the unused attributes

Class weka.filters.unsupervised.attribute.PKIDiscretize extends Discretize implements Serializable

serialVersionUID: 6153101248977702675L

Class weka.filters.unsupervised.attribute.PotentialClassIgnorer extends Filter implements Serializable

serialVersionUID: 8625371119276845454L

Serialized Fields

m_IgnoreClass

boolean m_IgnoreClass
True if the class is to be unset


m_ClassIndex

int m_ClassIndex
Storing the class index

Class weka.filters.unsupervised.attribute.PrincipalComponents extends Filter implements Serializable

serialVersionUID: 4626939780964387784L

Serialized Fields

m_TrainInstances

Instances m_TrainInstances
The data to transform analyse/transform.


m_TrainCopy

Instances m_TrainCopy
Keep a copy for the class attribute (if set).


m_TransformedFormat

Instances m_TransformedFormat
The header for the transformed data format.


m_HasClass

boolean m_HasClass
Data has a class set.


m_ClassIndex

int m_ClassIndex
Class index.


m_NumAttribs

int m_NumAttribs
Number of attributes.


m_NumInstances

int m_NumInstances
Number of instances.


m_Correlation

double[][] m_Correlation
Correlation matrix for the original data.


m_Eigenvectors

double[][] m_Eigenvectors
Will hold the unordered linear transformations of the (normalized) original data.


m_Eigenvalues

double[] m_Eigenvalues
Eigenvalues for the corresponding eigenvectors.


m_SortedEigens

int[] m_SortedEigens
Sorted eigenvalues.


m_SumOfEigenValues

double m_SumOfEigenValues
sum of the eigenvalues.


m_ReplaceMissingFilter

ReplaceMissingValues m_ReplaceMissingFilter
Filters for replacing missing values.


m_NormalizeFilter

Normalize m_NormalizeFilter
Filter for normalizing the data.


m_NominalToBinaryFilter

NominalToBinary m_NominalToBinaryFilter
Filter for turning nominal values into numeric ones.


m_AttributeFilter

Remove m_AttributeFilter
Filter for removing class attribute, nominal attributes with 0 or 1 value.


m_OutputNumAtts

int m_OutputNumAtts
The number of attributes in the pc transformed data.


m_Normalize

boolean m_Normalize
normalize the input data?


m_CoverVariance

double m_CoverVariance
the amount of varaince to cover in the original data when retaining the best n PC's.


m_MaxAttrsInName

int m_MaxAttrsInName
maximum number of attributes in the transformed attribute name.


m_MaxAttributes

int m_MaxAttributes
maximum number of attributes in the transformed data (-1 for all).

Class weka.filters.unsupervised.attribute.PropositionalToMultiInstance extends Filter implements Serializable

serialVersionUID: 5825873573912102482L

Serialized Fields

m_Seed

int m_Seed
the seed for randomizing, default is 1


m_Randomize

boolean m_Randomize
whether to randomize the output data


m_BagStringAtts

StringLocator m_BagStringAtts
Indices of string attributes in the bag


m_BagRelAtts

RelationalLocator m_BagRelAtts
Indices of relational attributes in the bag

Class weka.filters.unsupervised.attribute.RandomProjection extends Filter implements Serializable

serialVersionUID: 4428905532728645880L

Serialized Fields

m_k

int m_k
Stores the number of dimensions to reduce the data to


m_percent

double m_percent
Stores the dimensionality the data should be reduced to as percentage of the original dimension


m_useGaussian

boolean m_useGaussian
Is the random matrix will be computed using Gaussian distribution or not


m_distribution

int m_distribution
Stores the distribution to use for calculating the random matrix


m_useReplaceMissing

boolean m_useReplaceMissing
Should the missing values be replaced using unsupervised.ReplaceMissingValues filter


m_OutputFormatDefined

boolean m_OutputFormatDefined
Keeps track of output format if it is defined or not


m_ntob

Filter m_ntob
The NominalToBinary filter applied to the data before this filter


m_replaceMissing

Filter m_replaceMissing
The ReplaceMissingValues filter


m_rndmSeed

long m_rndmSeed
Stores the random seed used to generate the random matrix


m_rmatrix

double[][] m_rmatrix
The random matrix


m_random

java.util.Random m_random
The random number generator used for generating the random matrix

Class weka.filters.unsupervised.attribute.RandomSubset extends SimpleStreamFilter implements Serializable

serialVersionUID: 2911221724251628050L

Serialized Fields

m_NumAttributes

double m_NumAttributes
The number of attributes to randomly choose (>= 1 absolute number of attributes, < 1 percentage).


m_Seed

int m_Seed
The seed value.


m_Indices

int[] m_Indices
The indices of the attributes that got selected.

Class weka.filters.unsupervised.attribute.RELAGGS extends SimpleBatchFilter implements Serializable

serialVersionUID: -3333791375278589231L

Serialized Fields

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)


m_AttStats

java.util.Hashtable<K,V> m_AttStats
stores the attribute statistics att_index-att_index_in_rel_att <-> AttributeStats

Class weka.filters.unsupervised.attribute.Remove extends Filter implements Serializable

serialVersionUID: 5011337331921522847L

Serialized Fields

m_SelectCols

Range m_SelectCols
Stores which columns to select as a funky range


m_SelectedAttributes

int[] m_SelectedAttributes
Stores the indexes of the selected attributes in order, once the dataset is seen

Class weka.filters.unsupervised.attribute.RemoveType extends Filter implements Serializable

serialVersionUID: -3563999462782486279L

Serialized Fields

m_attributeFilter

Remove m_attributeFilter
The attribute filter used to do the filtering


m_attTypeToDelete

int m_attTypeToDelete
The type of attribute to delete


m_invert

boolean m_invert
Whether to invert selection

Class weka.filters.unsupervised.attribute.RemoveUseless extends Filter implements Serializable

serialVersionUID: -8659417851407640038L

Serialized Fields

m_removeFilter

Remove m_removeFilter
The filter used to remove attributes


m_maxVariancePercentage

double m_maxVariancePercentage
The type of attribute to delete

Class weka.filters.unsupervised.attribute.Reorder extends Filter implements Serializable

serialVersionUID: -1135571321097202292L

Serialized Fields

m_NewOrderCols

java.lang.String m_NewOrderCols
Stores which columns to reorder


m_SelectedAttributes

int[] m_SelectedAttributes
Stores the indexes of the selected attributes in order, once the dataset is seen


m_InputStringIndex

int[] m_InputStringIndex
Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated

Class weka.filters.unsupervised.attribute.ReplaceMissingValues extends PotentialClassIgnorer implements Serializable

serialVersionUID: 8349568310991609867L

Serialized Fields

m_ModesAndMeans

double[] m_ModesAndMeans
The modes and means

Class weka.filters.unsupervised.attribute.Standardize extends PotentialClassIgnorer implements Serializable

serialVersionUID: -6830769026855053281L

Serialized Fields

m_Means

double[] m_Means
The means


m_StdDevs

double[] m_StdDevs
The variances

Class weka.filters.unsupervised.attribute.StringToNominal extends Filter implements Serializable

serialVersionUID: 4864084427902797605L

Serialized Fields

m_AttIndices

Range m_AttIndices
The attribute's range indices setting.

Class weka.filters.unsupervised.attribute.StringToWordVector extends Filter implements Serializable

serialVersionUID: 8249106275278565424L

Serialized Fields

m_SelectedRange

Range m_SelectedRange
Range of columns to convert to word vectors.


m_Dictionary

java.util.TreeMap<K,V> m_Dictionary
Contains a mapping of valid words to attribute indexes.


m_OutputCounts

boolean m_OutputCounts
True if output instances should contain word frequency rather than boolean 0 or 1.


m_Prefix

java.lang.String m_Prefix
A String prefix for the attribute names.


m_DocsCounts

int[] m_DocsCounts
Contains the number of documents (instances) a particular word appears in. The counts are stored with the same indexing as given by m_Dictionary.


m_NumInstances

int m_NumInstances
Contains the number of documents (instances) in the input format from which the dictionary is created. It is used in IDF transform.


m_AvgDocLength

double m_AvgDocLength
Contains the average length of documents (among the first batch of instances aka training data). This is used in length normalization of documents which will be normalized to average document length.


m_WordsToKeep

int m_WordsToKeep
The default number of words (per class if there is a class attribute assigned) to attempt to keep.


m_PeriodicPruningRate

double m_PeriodicPruningRate
The percentage at which to periodically prune the dictionary.


m_TFTransform

boolean m_TFTransform
True if word frequencies should be transformed into log(1+fi) where fi is the frequency of word i.


m_filterType

int m_filterType
The normalization to apply.


m_IDFTransform

boolean m_IDFTransform
True if word frequencies should be transformed into fij*log(numOfDocs/numOfDocsWithWordi).


m_lowerCaseTokens

boolean m_lowerCaseTokens
True if all tokens should be downcased.


m_useStoplist

boolean m_useStoplist
True if tokens that are on a stoplist are to be ignored.


m_Stemmer

Stemmer m_Stemmer
the stemming algorithm.


m_minTermFreq

int m_minTermFreq
the minimum (per-class) word frequency.


m_doNotOperateOnPerClassBasis

boolean m_doNotOperateOnPerClassBasis
whether to operate on a per-class basis.


m_Stopwords

java.io.File m_Stopwords
a file containing stopwords for using others than the default Rainbow ones.


m_Tokenizer

Tokenizer m_Tokenizer
the tokenizer algorithm to use.

Class weka.filters.unsupervised.attribute.SwapValues extends Filter implements Serializable

serialVersionUID: 6155834679414275855L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_FirstIndex

SingleIndex m_FirstIndex
The first value's index setting.


m_SecondIndex

SingleIndex m_SecondIndex
The second value's index setting.

Class weka.filters.unsupervised.attribute.TimeSeriesDelta extends TimeSeriesTranslate implements Serializable

serialVersionUID: 3101490081896634942L

Class weka.filters.unsupervised.attribute.TimeSeriesTranslate extends AbstractTimeSeries implements Serializable

serialVersionUID: -8901621509691785705L

Class weka.filters.unsupervised.attribute.Wavelet extends SimpleBatchFilter implements Serializable

serialVersionUID: -3335106965521265631L

Serialized Fields

m_Filter

Filter m_Filter
an optional filter for preprocessing of the data


m_Algorithm

int m_Algorithm
the type of algorithm


m_Padding

int m_Padding
the type of padding


Package weka.filters.unsupervised.instance

Class weka.filters.unsupervised.instance.NonSparseToSparse extends Filter implements Serializable

serialVersionUID: 4694489111366063852L

Class weka.filters.unsupervised.instance.Normalize extends Filter implements Serializable

serialVersionUID: -7947971807522917395L

Serialized Fields

m_Norm

double m_Norm
The norm that each instance must have at the end


m_LNorm

double m_LNorm
The L-norm to use

Class weka.filters.unsupervised.instance.Randomize extends Filter implements Serializable

serialVersionUID: 8854479785121877582L

Serialized Fields

m_Seed

int m_Seed
The random number seed


m_Random

java.util.Random m_Random
The current random number generator

Class weka.filters.unsupervised.instance.RemoveFolds extends Filter implements Serializable

serialVersionUID: 8220373305559055700L

Serialized Fields

m_Inverse

boolean m_Inverse
Indicates if inverse of selection is to be output.


m_NumFolds

int m_NumFolds
Number of folds to split dataset into


m_Fold

int m_Fold
Fold to output


m_Seed

long m_Seed
Random number seed.

Class weka.filters.unsupervised.instance.RemoveFrequentValues extends Filter implements Serializable

serialVersionUID: -2447432930070059511L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_NumValues

int m_NumValues
the number of values to retain.


m_LeastValues

boolean m_LeastValues
whether to retain values with least instances instead of most.


m_Invert

boolean m_Invert
whether to invert the matching sense.


m_ModifyHeader

boolean m_ModifyHeader
Modify header for nominal attributes?


m_NominalMapping

int[] m_NominalMapping
If m_ModifyHeader, stores a mapping from old to new indexes


m_Values

java.util.HashSet<E> m_Values
contains the values to retain

Class weka.filters.unsupervised.instance.RemoveMisclassified extends Filter implements Serializable

serialVersionUID: 5469157004717663171L

Serialized Fields

m_cleansingClassifier

Classifier m_cleansingClassifier
The classifier used to do the cleansing


m_classIndex

int m_classIndex
The attribute to treat as the class for purposes of cleansing.


m_numOfCrossValidationFolds

int m_numOfCrossValidationFolds
The number of cross validation folds to perform (<2 = no cross validation)


m_numOfCleansingIterations

int m_numOfCleansingIterations
The maximum number of cleansing iterations to perform (<1 = until fully cleansed)


m_numericClassifyThreshold

double m_numericClassifyThreshold
The threshold for deciding when a numeric value is correctly classified


m_invertMatching

boolean m_invertMatching
Whether to invert the match so the correctly classified instances are discarded


m_firstBatchFinished

boolean m_firstBatchFinished
Have we processed the first batch (i.e. training data)?

Class weka.filters.unsupervised.instance.RemovePercentage extends Filter implements Serializable

serialVersionUID: 2150341191158533133L

Serialized Fields

m_Percentage

double m_Percentage
Percentage of instances to select.


m_Inverse

boolean m_Inverse
Indicates if inverse of selection is to be output.

Class weka.filters.unsupervised.instance.RemoveRange extends Filter implements Serializable

serialVersionUID: -3064641215340828695L

Serialized Fields

m_Range

Range m_Range
Range of instances requested by the user.

Class weka.filters.unsupervised.instance.RemoveWithValues extends Filter implements Serializable

serialVersionUID: 4752870193679263361L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex
The attribute's index setting.


m_Values

Range m_Values
Stores which values of nominal attribute are to be used for filtering.


m_Value

double m_Value
Stores which value of a numeric attribute is to be used for filtering.


m_MatchMissingValues

boolean m_MatchMissingValues
True if missing values should count as a match


m_ModifyHeader

boolean m_ModifyHeader
Modify header for nominal attributes?


m_NominalMapping

int[] m_NominalMapping
If m_ModifyHeader, stores a mapping from old to new indexes

Class weka.filters.unsupervised.instance.Resample extends Filter implements Serializable

serialVersionUID: 3119607037607101160L

Serialized Fields

m_SampleSizePercent

double m_SampleSizePercent
The subsample size, percent of original set, default 100%


m_RandomSeed

int m_RandomSeed
The random number generator seed


m_NoReplacement

boolean m_NoReplacement
Whether to perform sampling with replacement or without


m_InvertSelection

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

See Also:
Resample.m_NoReplacement

Class weka.filters.unsupervised.instance.ReservoirSample extends Filter implements Serializable

serialVersionUID: 3119607037607101160L

Serialized Fields

m_SampleSize

int m_SampleSize
The subsample size, number of instances%


m_subSample

Instance[] m_subSample
Holds the sub-sample (reservoir)


m_currentInst

int m_currentInst
The current instance being processed


m_RandomSeed

int m_RandomSeed
The random number generator seed


m_random

java.util.Random m_random
The random number generator

Class weka.filters.unsupervised.instance.SparseToNonSparse extends Filter implements Serializable

serialVersionUID: 2481634184210236074L

Class weka.filters.unsupervised.instance.SubsetByExpression extends SimpleBatchFilter implements Serializable

serialVersionUID: 5628686110979589602L

Serialized Fields

m_Expression

java.lang.String m_Expression
the expresion to use for filtering.


Package weka.gui

Class weka.gui.AttributeListPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -2030706987910400362L

Serialized Fields

m_Table

javax.swing.JTable m_Table
The table displaying attribute names


m_Model

weka.gui.AttributeListPanel.AttributeTableModel m_Model
The table model containing attribute names

Class weka.gui.AttributeSelectionPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 627131485290359194L

Serialized Fields

m_IncludeAll

javax.swing.JButton m_IncludeAll
Press to select all attributes


m_RemoveAll

javax.swing.JButton m_RemoveAll
Press to deselect all attributes


m_Invert

javax.swing.JButton m_Invert
Press to invert the current selection


m_Pattern

javax.swing.JButton m_Pattern
Press to enter a perl regular expression for selection


m_Table

javax.swing.JTable m_Table
The table displaying attribute names and selection status


m_Model

weka.gui.AttributeSelectionPanel.AttributeTableModel m_Model
The table model containingn attribute names and selection status


m_PatternRegEx

java.lang.String m_PatternRegEx
The current regular expression.

Class weka.gui.AttributeSummaryPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -5434987925737735880L

Serialized Fields

m_AttributeNameLab

javax.swing.JLabel m_AttributeNameLab
Displays the name of the relation


m_AttributeTypeLab

javax.swing.JLabel m_AttributeTypeLab
Displays the type of attribute


m_MissingLab

javax.swing.JLabel m_MissingLab
Displays the number of missing values


m_UniqueLab

javax.swing.JLabel m_UniqueLab
Displays the number of unique values


m_DistinctLab

javax.swing.JLabel m_DistinctLab
Displays the number of distinct values


m_StatsTable

javax.swing.JTable m_StatsTable
Displays other stats in a table


m_Instances

Instances m_Instances
The instances we're playing with


m_AttributeStats

AttributeStats[] m_AttributeStats
Cached stats on the attributes we've summarized so far

Class weka.gui.AttributeVisualizationPanel extends PrintablePanel implements Serializable

serialVersionUID: -8650490488825371193L

Serialized Fields

m_data

Instances m_data
This holds the current set of instances


m_as

AttributeStats m_as
This holds the attribute stats of the current attribute on display. It is calculated in setAttribute(int idx) when it is called to set a new attribute index.


m_attribIndex

int m_attribIndex
This holds the index of the current attribute on display and should be set through setAttribute(int idx).


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_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_histBarClassCounts

SparseInstance[] m_histBarClassCounts
This array holds the per class count (or per class height) of the each of the bars in a barplot or a histogram. For nominal attributes the format is:
m_histBarClassCounts[nominalValue][classValue+1]. For numeric attributes the format is:
m_histBarClassCounts[interval][classValues+1],
where the number of intervals is calculated by the Scott's method as mentioned above. The array is initialized to have 1+numClasses to accomodate for instances with missing class value. The ones with missing class value are displayed as a black sub par in a histogram or a barplot. NOTE: The values of this array are only calculated if the class attribute is set and it is nominal.


m_barRange

double m_barRange
Contains the range of each bar in a histogram. It is used to work out the range of bar the mouse pointer is on in getToolTipText().


m_classIndex

int m_classIndex
Contains the current class index.


m_hc

java.lang.Thread m_hc
This stores the BarCalc or HistCalc thread while a new barplot or histogram is being calculated.


m_threadRun

boolean m_threadRun
True if the thread m_hc above is running.


m_doneCurrentAttribute

boolean m_doneCurrentAttribute

m_displayCurrentAttribute

boolean m_displayCurrentAttribute

m_colorAttrib

javax.swing.JComboBox m_colorAttrib
This stores and lets the user select a class attribute. It also has an entry "No Class" if the user does not want to set a class attribute for colouring.


m_fm

java.awt.FontMetrics m_fm
Fontmetrics used to get the font size which is required for calculating displayable area size, bar height ratio and width of strings that are displayed on top of bars indicating their count.


m_locker

java.lang.Integer m_locker
Lock variable to synchronize the different threads running currently in this class. There are two to three threads in this class, AWT paint thread which is handled differently in paintComponent() which checks on m_threadRun to determine if it can perform full paint or not, the second thread is the main execution thread and the third is the one represented by m_hc which we start when we want to calculate the internal fields for a bar plot or a histogram.


m_colorList

FastVector m_colorList
Contains discrete colours for colouring of subbars of histograms and bar plots when the class attribute is set and is nominal

Class weka.gui.CheckBoxList extends javax.swing.JList implements Serializable

serialVersionUID: -4359573373359270258L

Class weka.gui.CheckBoxList.CheckBoxListModel extends javax.swing.DefaultListModel implements Serializable

serialVersionUID: 7772455499540273507L

Class weka.gui.CheckBoxList.CheckBoxListRenderer extends javax.swing.JCheckBox implements Serializable

serialVersionUID: 1059591605858524586L

Class weka.gui.ConverterFileChooser extends javax.swing.JFileChooser implements Serializable

serialVersionUID: -5373058011025481738L

Serialized Fields

m_Self

ConverterFileChooser m_Self
the file chooser itself


m_DialogType

int m_DialogType
the type of dialog to display


m_CurrentConverter

java.lang.Object m_CurrentConverter
the converter that was chosen by the user


m_ConfigureButton

javax.swing.JButton m_ConfigureButton
the configure button


m_Listener

java.beans.PropertyChangeListener m_Listener
the propertychangelistener


m_LastFilter

javax.swing.filechooser.FileFilter m_LastFilter
the last filter that was used for opening/saving


m_CapabilitiesFilter

Capabilities m_CapabilitiesFilter
the Capabilities filter for the savers


m_OverwriteWarning

boolean m_OverwriteWarning
whether to popup a dialog in case the file already exists (only save dialog)


m_FileMustExist

boolean m_FileMustExist
whether the file to be opened must exist (only open dialog)


m_CoreConvertersOnly

boolean m_CoreConvertersOnly
whether to display only core converters (hardcoded in ConverterUtils). Necessary for RMI/Remote Experiments for instance.

See Also:
ConverterUtils.CORE_FILE_LOADERS, ConverterUtils.CORE_FILE_SAVERS

Class weka.gui.DatabaseConnectionDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -1081946748666245054L

Serialized Fields

m_DbaseURLText

javax.swing.JTextField m_DbaseURLText

m_DbaseURLLab

javax.swing.JLabel m_DbaseURLLab

m_UserNameText

javax.swing.JTextField m_UserNameText

m_UserNameLab

javax.swing.JLabel m_UserNameLab

m_PasswordText

javax.swing.JPasswordField m_PasswordText

m_PasswordLab

javax.swing.JLabel m_PasswordLab

m_DebugCheckBox

javax.swing.JCheckBox m_DebugCheckBox

m_DebugLab

javax.swing.JLabel m_DebugLab

m_returnValue

int m_returnValue

Class weka.gui.ExtensionFileFilter extends javax.swing.filechooser.FileFilter implements Serializable

Serialized Fields

m_Description

java.lang.String m_Description
The text description of the types of files accepted


m_Extension

java.lang.String[] m_Extension
The filename extensions of accepted files

Class weka.gui.GenericArrayEditor extends javax.swing.JPanel implements Serializable

serialVersionUID: 3914616975334750480L

Serialized Fields

m_Support

java.beans.PropertyChangeSupport m_Support
Handles property change notification.


m_Label

javax.swing.JLabel m_Label
The label for when we can't edit that type.


m_ElementList

javax.swing.JList m_ElementList
The list component displaying current values.


m_ElementClass

java.lang.Class<T> m_ElementClass
The class of objects allowed in the array.


m_ListModel

javax.swing.DefaultListModel m_ListModel
The defaultlistmodel holding our data.


m_ElementEditor

java.beans.PropertyEditor m_ElementEditor
The property editor for the class we are editing.


m_DeleteBut

javax.swing.JButton m_DeleteBut
Click this to delete the selected array values.


m_EditBut

javax.swing.JButton m_EditBut
Click this to edit the selected array value.


m_UpBut

javax.swing.JButton m_UpBut
Click this to move the selected array value(s) one up.


m_DownBut

javax.swing.JButton m_DownBut
Click this to move the selected array value(s) one down.


m_AddBut

javax.swing.JButton m_AddBut
Click to add the current object configuration to the array.


m_Editor

java.beans.PropertyEditor m_Editor
The property editor for editing existing elements.


m_PD

PropertyDialog m_PD
The currently displayed property dialog, if any.


m_InnerActionListener

java.awt.event.ActionListener m_InnerActionListener
Listens to buttons being pressed and taking the appropriate action.


m_InnerSelectionListener

javax.swing.event.ListSelectionListener m_InnerSelectionListener
Listens to list items being selected and takes appropriate action.


m_InnerMouseListener

java.awt.event.MouseListener m_InnerMouseListener
Listens to mouse events and takes appropriate action.

Class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -7845503345689646266L

Serialized Fields

m_Self

javax.swing.JDialog m_Self
the dialog itself.


m_Popup

javax.swing.JPopupMenu m_Popup
the popup to display again.


m_Capabilities

Capabilities m_Capabilities
the capabilities used for initializing the dialog.


m_InfoLabel

javax.swing.JLabel m_InfoLabel
the label, listing the name of the superclass.


m_List

CheckBoxList m_List
the list with all the capabilities.


m_OkButton

javax.swing.JButton m_OkButton
the OK button.


m_CancelButton

javax.swing.JButton m_CancelButton
the Cancel button.

Class weka.gui.GenericObjectEditor.GOEPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 3656028520876011335L

Serialized Fields

m_ChildPropertySheet

PropertySheetPanel m_ChildPropertySheet
The component that performs classifier customization.


m_ClassNameLabel

javax.swing.JLabel m_ClassNameLabel
The name of the current class.


m_OpenBut

javax.swing.JButton m_OpenBut
Open object from disk.


m_SaveBut

javax.swing.JButton m_SaveBut
Save object to disk.


m_okBut

javax.swing.JButton m_okBut
ok button.


m_cancelBut

javax.swing.JButton m_cancelBut
cancel button.


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The filechooser for opening and saving object files.

Class weka.gui.GenericObjectEditor.GOETreeNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

serialVersionUID: -1707872446682150133L

Serialized Fields

m_Capabilities

Capabilities m_Capabilities
the Capabilities object to use for filtering.

Class weka.gui.GenericObjectEditor.JTreePopupMenu extends javax.swing.JPopupMenu implements Serializable

serialVersionUID: -3404546329655057387L

Serialized Fields

m_Self

javax.swing.JPopupMenu m_Self
the popup itself.


m_tree

javax.swing.JTree m_tree
The tree.


m_scroller

javax.swing.JScrollPane m_scroller
The scroller.


m_FilterButton

javax.swing.JButton m_FilterButton
The filter button in case of CapabilitiesHandlers.


m_RemoveFilterButton

javax.swing.JButton m_RemoveFilterButton
The remove filter button in case of CapabilitiesHandlers.


m_CloseButton

javax.swing.JButton m_CloseButton
The button for closing the popup again.

Class weka.gui.GUIChooser extends javax.swing.JFrame implements Serializable

serialVersionUID: 9001529425230247914L

Serialized Fields

m_Self

GUIChooser m_Self
the GUIChooser itself


m_jMenuBar

javax.swing.JMenuBar m_jMenuBar

m_jMenuProgram

javax.swing.JMenu m_jMenuProgram

m_jMenuVisualization

javax.swing.JMenu m_jMenuVisualization

m_jMenuTools

javax.swing.JMenu m_jMenuTools

m_jMenuHelp

javax.swing.JMenu m_jMenuHelp

m_PanelApplications

javax.swing.JPanel m_PanelApplications
the panel for the application buttons


m_ExplorerBut

javax.swing.JButton m_ExplorerBut
Click to open the Explorer


m_ExplorerFrame

javax.swing.JFrame m_ExplorerFrame
The frame containing the explorer interface


m_ExperimenterBut

javax.swing.JButton m_ExperimenterBut
Click to open the Explorer


m_ExperimenterFrame

javax.swing.JFrame m_ExperimenterFrame
The frame containing the experiment interface


m_KnowledgeFlowBut

javax.swing.JButton m_KnowledgeFlowBut
Click to open the KnowledgeFlow


m_KnowledgeFlowFrame

javax.swing.JFrame m_KnowledgeFlowFrame
The frame containing the knowledge flow interface


m_SimpleBut

javax.swing.JButton m_SimpleBut
Click to open the simplecli


m_SimpleCLI

SimpleCLI m_SimpleCLI
The SimpleCLI


m_ArffViewers

java.util.Vector<E> m_ArffViewers
keeps track of the opened ArffViewer instancs


m_SqlViewerFrame

javax.swing.JFrame m_SqlViewerFrame
The frame containing the SqlViewer


m_BayesNetGUIFrame

javax.swing.JFrame m_BayesNetGUIFrame
The frame containing the Bayes net GUI


m_EnsembleLibraryFrame

javax.swing.JFrame m_EnsembleLibraryFrame
The frame containing the ensemble library interface


m_Plots

java.util.Vector<E> m_Plots
keeps track of the opened plots


m_ROCs

java.util.Vector<E> m_ROCs
keeps track of the opened ROCs


m_TreeVisualizers

java.util.Vector<E> m_TreeVisualizers
keeps track of the opened tree visualizer instancs


m_GraphVisualizers

java.util.Vector<E> m_GraphVisualizers
keeps track of the opened graph visualizer instancs


m_BoundaryVisualizerFrame

javax.swing.JFrame m_BoundaryVisualizerFrame
The frame containing the boundary visualizer


m_SystemInfoFrame

javax.swing.JFrame m_SystemInfoFrame
The frame containing the system info


m_MemoryUsageFrame

javax.swing.JFrame m_MemoryUsageFrame
The frame containing the memory usage


m_weka

java.awt.Image m_weka
The weka image


m_FileChooserTreeVisualizer

javax.swing.JFileChooser m_FileChooserTreeVisualizer
filechooser for the TreeVisualizer


m_FileChooserGraphVisualizer

javax.swing.JFileChooser m_FileChooserGraphVisualizer
filechooser for the GraphVisualizer


m_FileChooserPlot

javax.swing.JFileChooser m_FileChooserPlot
filechooser for Plots


m_FileChooserROC

javax.swing.JFileChooser m_FileChooserROC
filechooser for ROC curves


m_Icon

java.awt.Image m_Icon
the icon for the frames


m_ChildFrames

java.util.HashSet<E> m_ChildFrames
contains the child frames (title <-> object).

Class weka.gui.GUIChooser.ChildFrameSDI extends javax.swing.JFrame implements Serializable

serialVersionUID: 8588293938686425618L

Serialized Fields

m_Parent

GUIChooser m_Parent
the parent frame.

Class weka.gui.HierarchyPropertyParser extends java.lang.Object implements Serializable

serialVersionUID: -4151103338506077544L

Serialized Fields

m_Root

weka.gui.HierarchyPropertyParser.TreeNode m_Root
Keep track of the root of the tree


m_Current

weka.gui.HierarchyPropertyParser.TreeNode m_Current
Keep track of the current node when traversing the tree


m_Seperator

java.lang.String m_Seperator
The level separate in the path


m_Depth

int m_Depth
The depth of the tree

Class weka.gui.InstancesSummaryPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -5243579535296681063L

Serialized Fields

m_RelationNameLab

javax.swing.JLabel m_RelationNameLab
Displays the name of the relation


m_NumInstancesLab

javax.swing.JLabel m_NumInstancesLab
Displays the number of instances


m_NumAttributesLab

javax.swing.JLabel m_NumAttributesLab
Displays the number of attributes


m_Instances

Instances m_Instances
The instances we're playing with

Class weka.gui.ListSelectorDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: 906147926840288895L

Serialized Fields

m_SelectBut

javax.swing.JButton m_SelectBut
Click to choose the currently selected property


m_CancelBut

javax.swing.JButton m_CancelBut
Click to cancel the property selection


m_PatternBut

javax.swing.JButton m_PatternBut
Click to enter a regex pattern for selection


m_List

javax.swing.JList m_List
The list component


m_Result

int m_Result
Whether the selection was made or cancelled


m_PatternRegEx

java.lang.String m_PatternRegEx
The current regular expression.

Class weka.gui.LogPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -4072464549112439484L

Serialized Fields

m_StatusLab

javax.swing.JLabel m_StatusLab
Displays the current status


m_LogText

javax.swing.JTextArea m_LogText
Displays the log messages


m_logButton

javax.swing.JButton m_logButton
The button for viewing the log


m_First

boolean m_First
An indicator for whether text has been output yet


m_TaskMonitor

WekaTaskMonitor m_TaskMonitor
The panel for monitoring the number of running tasks (if supplied)

Class weka.gui.LogWindow extends javax.swing.JFrame implements Serializable

serialVersionUID: 5650947361381061112L

Serialized Fields

m_UseWordwrap

boolean m_UseWordwrap
whether the JTextPane has wordwrap or not


m_Output

javax.swing.JTextPane m_Output
the output


m_ButtonClear

javax.swing.JButton m_ButtonClear
the clear button


m_ButtonClose

javax.swing.JButton m_ButtonClose
the close button


m_LabelCurrentSize

javax.swing.JLabel m_LabelCurrentSize
the current size


m_SpinnerMaxSize

javax.swing.JSpinner m_SpinnerMaxSize
the spinner for the max number of chars


m_CheckBoxWordwrap

javax.swing.JCheckBox m_CheckBoxWordwrap
whether to allow wordwrap or not

Class weka.gui.Main extends javax.swing.JFrame implements Serializable

serialVersionUID: 1453813254824253849L

Serialized Fields

m_Self

Main m_Self
the frame itself.


m_GUIType

int m_GUIType
the type of GUI to display.


m_ChildFrames

java.util.HashSet<E> m_ChildFrames
contains the child frames (title <-> object).


m_FileChooserTreeVisualizer

javax.swing.JFileChooser m_FileChooserTreeVisualizer
filechooser for the TreeVisualizer.


m_FileChooserGraphVisualizer

javax.swing.JFileChooser m_FileChooserGraphVisualizer
filechooser for the GraphVisualizer.


m_FileChooserPlot

javax.swing.JFileChooser m_FileChooserPlot
filechooser for Plots.


m_FileChooserROC

javax.swing.JFileChooser m_FileChooserROC
filechooser for ROC curves.


jMenuHelp

javax.swing.JMenu jMenuHelp

jMenuVisualization

javax.swing.JMenu jMenuVisualization

jMenuTools

javax.swing.JMenu jMenuTools

jDesktopPane

javax.swing.JDesktopPane jDesktopPane

jMenuApplications

javax.swing.JMenu jMenuApplications

jMenuItemHelpSystemInfo

javax.swing.JMenuItem jMenuItemHelpSystemInfo

jMenuItemHelpAbout

javax.swing.JMenuItem jMenuItemHelpAbout

jMenuItemHelpHomepage

javax.swing.JMenuItem jMenuItemHelpHomepage

jMenuItemHelpWekaWiki

javax.swing.JMenuItem jMenuItemHelpWekaWiki

jMenuItemHelpWekaDoc

javax.swing.JMenuItem jMenuItemHelpWekaDoc

jMenuItemHelpSourceforge

javax.swing.JMenuItem jMenuItemHelpSourceforge

jMenuItemVisualizationBoundaryVisualizer

javax.swing.JMenuItem jMenuItemVisualizationBoundaryVisualizer

jMenuItemVisualizationGraphVisualizer

javax.swing.JMenuItem jMenuItemVisualizationGraphVisualizer

jMenuItemVisualizationTreeVisualizer

javax.swing.JMenuItem jMenuItemVisualizationTreeVisualizer

jMenuItemVisualizationROC

javax.swing.JMenuItem jMenuItemVisualizationROC

jMenuItemVisualizationPlot

javax.swing.JMenuItem jMenuItemVisualizationPlot

jMenuItemToolsEnsembleLibrary

javax.swing.JMenuItem jMenuItemToolsEnsembleLibrary

jMenuItemToolsSqlViewer

javax.swing.JMenuItem jMenuItemToolsSqlViewer

jMenuItemToolsArffViewer

javax.swing.JMenuItem jMenuItemToolsArffViewer

jMenuItemApplicationsSimpleCLI

javax.swing.JMenuItem jMenuItemApplicationsSimpleCLI

jMenuItemApplicationsKnowledgeFlow

javax.swing.JMenuItem jMenuItemApplicationsKnowledgeFlow

jMenuItemApplicationsExperimenter

javax.swing.JMenuItem jMenuItemApplicationsExperimenter

jMenuItemApplicationsExplorer

javax.swing.JMenuItem jMenuItemApplicationsExplorer

jMenuItemProgramExit

javax.swing.JMenuItem jMenuItemProgramExit

jMenuItemProgramLogWindow

javax.swing.JMenuItem jMenuItemProgramLogWindow

jMenuItemProgramMemoryUsage

javax.swing.JMenuItem jMenuItemProgramMemoryUsage

jMenuItemProgramPreferences

javax.swing.JMenuItem jMenuItemProgramPreferences

jMenuProgram

javax.swing.JMenu jMenuProgram

jMenuExtensions

javax.swing.JMenu jMenuExtensions

jMenuWindows

javax.swing.JMenu jMenuWindows

jMenuBar

javax.swing.JMenuBar jMenuBar

Class weka.gui.Main.BackgroundDesktopPane extends javax.swing.JDesktopPane implements Serializable

serialVersionUID: 2046713123452402745L

Serialized Fields

m_Background

java.awt.Image m_Background
the actual background image.

Class weka.gui.Main.ChildFrameMDI extends javax.swing.JInternalFrame implements Serializable

serialVersionUID: 3772573515346899959L

Serialized Fields

m_Parent

Main m_Parent
the parent frame.

Class weka.gui.Main.ChildFrameSDI extends javax.swing.JFrame implements Serializable

serialVersionUID: 8588293938686425618L

Serialized Fields

m_Parent

Main m_Parent
the parent frame.

Class weka.gui.MemoryUsagePanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -4812319791687471721L

Serialized Fields

m_History

java.util.Vector<E> m_History
the memory usage over time.


m_Memory

Memory m_Memory
for monitoring the memory usage.


m_Monitor

weka.gui.MemoryUsagePanel.MemoryMonitor m_Monitor
the thread for monitoring the memory usage.


m_ButtonGC

javax.swing.JButton m_ButtonGC
the button for running the garbage collector.


m_Percentages

java.util.Vector<E> m_Percentages
the threshold percentages to change color.


m_Colors

java.util.Hashtable<K,V> m_Colors
the corresponding colors for the thresholds.


m_DefaultColor

java.awt.Color m_DefaultColor
the default color.


m_BackgroundColor

java.awt.Color m_BackgroundColor
the background color.


m_FrameLocation

java.awt.Point m_FrameLocation
the position for the dialog.

Class weka.gui.PropertyDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -2314850859392433539L

Serialized Fields

m_Editor

java.beans.PropertyEditor m_Editor
The property editor.


m_EditorComponent

java.awt.Component m_EditorComponent
The custom editor component.

Class weka.gui.PropertyPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 5370025273466728904L

Serialized Fields

m_Editor

java.beans.PropertyEditor m_Editor
The property editor


m_PD

PropertyDialog m_PD
The currently displayed property dialog, if any


m_HasCustomPanel

boolean m_HasCustomPanel
Whether the editor has provided its own panel


m_CustomPanel

javax.swing.JPanel m_CustomPanel
The custom panel (if any)

Class weka.gui.PropertySelectorDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -3155058124137930518L

Serialized Fields

m_SelectBut

javax.swing.JButton m_SelectBut
Click to choose the currently selected property


m_CancelBut

javax.swing.JButton m_CancelBut
Click to cancel the property selection


m_Root

javax.swing.tree.DefaultMutableTreeNode m_Root
The root of the property tree


m_RootObject

java.lang.Object m_RootObject
The object at the root of the tree


m_Result

int m_Result
Whether the selection was made or cancelled


m_ResultPath

java.lang.Object[] m_ResultPath
Stores the path to the selected property


m_Tree

javax.swing.JTree m_Tree
The component displaying the property tree

Class weka.gui.PropertySheetPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -8939835593429918345L

Serialized Fields

m_Target

java.lang.Object m_Target
The target object being edited.


m_Properties

java.beans.PropertyDescriptor[] m_Properties
Holds properties of the target.


m_Methods

java.beans.MethodDescriptor[] m_Methods
Holds the methods of the target.


m_Editors

java.beans.PropertyEditor[] m_Editors
Holds property editors of the object.


m_Values

java.lang.Object[] m_Values
Holds current object values for each property.


m_Views

javax.swing.JComponent[] m_Views
Stores GUI components containing each editing component.


m_Labels

javax.swing.JLabel[] m_Labels
The labels for each property.


m_TipTexts

java.lang.String[] m_TipTexts
The tool tip text for each property.


m_HelpText

java.lang.StringBuffer m_HelpText
StringBuffer containing help text for the object being edited.


m_HelpDialog

javax.swing.JDialog m_HelpDialog
Help dialog.


m_CapabilitiesDialog

weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_CapabilitiesDialog
Capabilities Help dialog.


m_HelpBut

javax.swing.JButton m_HelpBut
Button to pop up the full help text in a separate dialog.


m_CapabilitiesBut

javax.swing.JButton m_CapabilitiesBut
Button to pop up the capabilities in a separate dialog.


m_CapabilitiesText

javax.swing.JTextArea m_CapabilitiesText
the TextArea of the Capabilities help dialog.


m_NumEditable

int m_NumEditable
A count of the number of properties we have an editor for.


m_aboutPanel

javax.swing.JPanel m_aboutPanel
The panel holding global info and help, if provided by the object being editied.


support

java.beans.PropertyChangeSupport support
A support object for handling property change listeners.

Class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -1404770987103289858L

Serialized Fields

m_Self

weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_Self
the dialog itself.

Class weka.gui.ResultHistoryPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 4297069440135326829L

Serialized Fields

m_SingleText

javax.swing.text.JTextComponent m_SingleText
An optional component for single-click display


m_SingleName

java.lang.String m_SingleName
The named result being viewed in the single-click display


m_Model

javax.swing.DefaultListModel m_Model
The list model


m_List

javax.swing.JList m_List
The list component


m_Results

java.util.Hashtable<K,V> m_Results
A Hashtable mapping names to result buffers


m_FramedOutput

java.util.Hashtable<K,V> m_FramedOutput
A Hashtable mapping names to output text components


m_Objs

java.util.Hashtable<K,V> m_Objs
A hashtable mapping names to arbitrary objects


m_HandleRightClicks

boolean m_HandleRightClicks
Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer


m_Printer

PrintableComponent m_Printer
for printing the output to files

Class weka.gui.ResultHistoryPanel.RKeyAdapter extends java.awt.event.KeyAdapter implements Serializable

serialVersionUID: -8675332541861828079L

Class weka.gui.ResultHistoryPanel.RMouseAdapter extends java.awt.event.MouseAdapter implements Serializable

serialVersionUID: -8991922650552358669L

Class weka.gui.SetInstancesPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -384804041420453735L

Serialized Fields

m_OpenFileBut

javax.swing.JButton m_OpenFileBut
Click to open instances from a file


m_OpenURLBut

javax.swing.JButton m_OpenURLBut
Click to open instances from a URL


m_CloseBut

javax.swing.JButton m_CloseBut
Click to close the dialog


m_Summary

InstancesSummaryPanel m_Summary
The instance summary component


m_FileChooser

ConverterFileChooser m_FileChooser
The file chooser for selecting arff files


m_LastURL

java.lang.String m_LastURL
Stores the last URL that instances were loaded from


m_IOThread

java.lang.Thread m_IOThread
The thread we do loading in


m_Support

java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the set of working instances.


m_Instances

Instances m_Instances
The current set of instances loaded


m_Loader

Loader m_Loader
The current loader used to obtain the current instances


m_ParentFrame

javax.swing.JFrame m_ParentFrame
the parent frame. if one is provided, the close-button is displayed


m_CloseButPanel

javax.swing.JPanel m_CloseButPanel
the panel the Close-Button is located in


m_readIncrementally

boolean m_readIncrementally

Class weka.gui.SimpleCLI extends java.awt.Frame implements Serializable

serialVersionUID: -50661410800566036L

Class weka.gui.SimpleCLIPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -7377739469759943231L

Serialized Fields

m_OutputArea

javax.swing.JTextArea m_OutputArea
The output area canvas added to the frame.


m_Input

javax.swing.JTextField m_Input
The command input area.


m_CommandHistory

java.util.Vector<E> m_CommandHistory
The history of commands entered interactively.


m_HistoryPos

int m_HistoryPos
The current position in the command history.


m_POO

java.io.PipedOutputStream m_POO
The new output stream for System.out.


m_POE

java.io.PipedOutputStream m_POE
The new output stream for System.err.


m_OutRedirector

java.lang.Thread m_OutRedirector
The thread that sends output from m_POO to the output box.


m_ErrRedirector

java.lang.Thread m_ErrRedirector
The thread that sends output from m_POE to the output box.


m_RunThread

java.lang.Thread m_RunThread
The thread currently running a class main method.


m_Completion

SimpleCLIPanel.CommandlineCompletion m_Completion
The commandline completion.

Class weka.gui.SortedTableModel extends javax.swing.table.AbstractTableModel implements Serializable

serialVersionUID: 4030907921461127548L

Serialized Fields

mModel

javax.swing.table.TableModel mModel
the actual table model


mIndices

int[] mIndices
the mapping between displayed and actual index


mSortColumn

int mSortColumn
the sort column


mAscending

boolean mAscending
whether sorting is ascending or descending

Class weka.gui.SplashWindow extends java.awt.Window implements Serializable

serialVersionUID: -2685134277041307795L

Serialized Fields

image

java.awt.Image image
The splash image which is displayed on the splash window.


paintCalled

boolean paintCalled
This attribute indicates whether the method paint(Graphics) has been called at least once since the construction of this window.
This attribute is used to notify method splash(Image) that the window has been drawn at least once by the AWT event dispatcher thread.
This attribute acts like a latch. Once set to true, it will never be changed back to false again.

See Also:
SplashWindow.paint(java.awt.Graphics), SplashWindow.splash(java.awt.Image)

Class weka.gui.ViewerDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: 6747718484736047752L

Serialized Fields

m_Result

int m_Result
the result of the user's action, either OK or CANCEL


m_OkButton

javax.swing.JButton m_OkButton
Click to activate the current set parameters


m_CancelButton

javax.swing.JButton m_CancelButton
Click to cancel the dialog


m_UndoButton

javax.swing.JButton m_UndoButton
Click to undo the last action


m_ArffPanel

ArffPanel m_ArffPanel
the panel to display the Instances-object

Class weka.gui.WekaTaskMonitor extends javax.swing.JPanel implements Serializable

serialVersionUID: 508309816292197578L

Serialized Fields

m_ActiveTasks

int m_ActiveTasks
The number of running weka threads


m_MonitorLabel

javax.swing.JLabel m_MonitorLabel
The label for displaying info


m_iconStationary

javax.swing.ImageIcon m_iconStationary
The icon for the stationary bird


m_iconAnimated

javax.swing.ImageIcon m_iconAnimated
The icon for the animated bird


m_animating

boolean m_animating
True if their are active tasks


Package weka.gui.arffviewer

Class weka.gui.arffviewer.ArffPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -4697041150989513939L

Serialized Fields

m_TableArff

ArffTable m_TableArff
the underlying table


m_PopupHeader

javax.swing.JPopupMenu m_PopupHeader
the popup menu for the header row


m_PopupRows

javax.swing.JPopupMenu m_PopupRows
the popup menu for the data rows


m_LabelName

javax.swing.JLabel m_LabelName
displays the relation name


menuItemMean

javax.swing.JMenuItem menuItemMean

menuItemSetAllValues

javax.swing.JMenuItem menuItemSetAllValues

menuItemSetMissingValues

javax.swing.JMenuItem menuItemSetMissingValues

menuItemReplaceValues

javax.swing.JMenuItem menuItemReplaceValues

menuItemRenameAttribute

javax.swing.JMenuItem menuItemRenameAttribute

menuItemAttributeAsClass

javax.swing.JMenuItem menuItemAttributeAsClass

menuItemDeleteAttribute

javax.swing.JMenuItem menuItemDeleteAttribute

menuItemDeleteAttributes

javax.swing.JMenuItem menuItemDeleteAttributes

menuItemSortInstances

javax.swing.JMenuItem menuItemSortInstances

menuItemDeleteSelectedInstance

javax.swing.JMenuItem menuItemDeleteSelectedInstance

menuItemDeleteAllSelectedInstances

javax.swing.JMenuItem menuItemDeleteAllSelectedInstances

menuItemSearch

javax.swing.JMenuItem menuItemSearch

menuItemClearSearch

javax.swing.JMenuItem menuItemClearSearch

menuItemUndo

javax.swing.JMenuItem menuItemUndo

menuItemCopy

javax.swing.JMenuItem menuItemCopy

menuItemOptimalColWidth

javax.swing.JMenuItem menuItemOptimalColWidth

menuItemOptimalColWidths

javax.swing.JMenuItem menuItemOptimalColWidths

m_Filename

java.lang.String m_Filename
the filename used in the title


m_Title

java.lang.String m_Title
the title prefix


m_CurrentCol

int m_CurrentCol
the currently selected column


m_Changed

boolean m_Changed
flag for whether data got changed


m_ChangeListeners

java.util.HashSet<E> m_ChangeListeners
the listeners that listen for modifications


m_LastSearch

java.lang.String m_LastSearch
the string used in the last search


m_LastReplace

java.lang.String m_LastReplace
the string used in the last replace

Class weka.gui.arffviewer.ArffSortedTableModel extends SortedTableModel implements Serializable

serialVersionUID: -5733148376354254030L

Class weka.gui.arffviewer.ArffTable extends javax.swing.JTable implements Serializable

serialVersionUID: -2016200506908637967L

Serialized Fields

m_SearchString

java.lang.String m_SearchString
the search string


m_ChangeListeners

java.util.HashSet<E> m_ChangeListeners
the listeners for changes

Class weka.gui.arffviewer.ArffTable.RelationalCellEditor extends javax.swing.AbstractCellEditor implements Serializable

serialVersionUID: 657969163293205963L

Serialized Fields

m_Button

javax.swing.JButton m_Button
the button for opening the dialog


m_CurrentInst

Instances m_CurrentInst
the current instances


m_RowIndex

int m_RowIndex
the row index this editor is for


m_ColumnIndex

int m_ColumnIndex
the column index this editor is for

Class weka.gui.arffviewer.ArffTableCellRenderer extends javax.swing.table.DefaultTableCellRenderer implements Serializable

serialVersionUID: 9195794493301191171L

Serialized Fields

missingColor

java.awt.Color missingColor
the color for missing values


missingColorSelected

java.awt.Color missingColorSelected
the color for selected missing values


highlightColor

java.awt.Color highlightColor
the color for highlighted values


highlightColorSelected

java.awt.Color highlightColorSelected
the color for selected highlighted values

Class weka.gui.arffviewer.ArffViewer extends javax.swing.JFrame implements Serializable

serialVersionUID: -7455845566922685175L

Serialized Fields

m_MainPanel

ArffViewerMainPanel m_MainPanel
the main panel

Class weka.gui.arffviewer.ArffViewerMainPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -8763161167586738753L

Serialized Fields

parent

java.awt.Container parent

tabbedPane

javax.swing.JTabbedPane tabbedPane

menuBar

javax.swing.JMenuBar menuBar

menuFile

javax.swing.JMenu menuFile

menuFileOpen

javax.swing.JMenuItem menuFileOpen

menuFileSave

javax.swing.JMenuItem menuFileSave

menuFileSaveAs

javax.swing.JMenuItem menuFileSaveAs

menuFileClose

javax.swing.JMenuItem menuFileClose

menuFileCloseAll

javax.swing.JMenuItem menuFileCloseAll

menuFileProperties

javax.swing.JMenuItem menuFileProperties

menuFileExit

javax.swing.JMenuItem menuFileExit

menuEdit

javax.swing.JMenu menuEdit

menuEditUndo

javax.swing.JMenuItem menuEditUndo

menuEditCopy

javax.swing.JMenuItem menuEditCopy

menuEditSearch

javax.swing.JMenuItem menuEditSearch

menuEditClearSearch

javax.swing.JMenuItem menuEditClearSearch

menuEditDeleteAttribute

javax.swing.JMenuItem menuEditDeleteAttribute

menuEditDeleteAttributes

javax.swing.JMenuItem menuEditDeleteAttributes

menuEditRenameAttribute

javax.swing.JMenuItem menuEditRenameAttribute

menuEditAttributeAsClass

javax.swing.JMenuItem menuEditAttributeAsClass

menuEditDeleteInstance

javax.swing.JMenuItem menuEditDeleteInstance

menuEditDeleteInstances

javax.swing.JMenuItem menuEditDeleteInstances

menuEditSortInstances

javax.swing.JMenuItem menuEditSortInstances

menuView

javax.swing.JMenu menuView

menuViewAttributes

javax.swing.JMenuItem menuViewAttributes

menuViewValues

javax.swing.JMenuItem menuViewValues

menuViewOptimalColWidths

javax.swing.JMenuItem menuViewOptimalColWidths

fileChooser

ConverterFileChooser fileChooser

frameTitle

java.lang.String frameTitle

confirmExit

boolean confirmExit

width

int width

height

int height

top

int top

left

int left

exitOnClose

boolean exitOnClose

Package weka.gui.beans

Class weka.gui.beans.AbstractDataSink extends javax.swing.JPanel implements Serializable

serialVersionUID: 3956528599473814287L

Serialized Fields

m_visual

BeanVisual m_visual
Default visual for data sources


m_listenee

java.lang.Object m_listenee
Non null if this object is a target for any events. Provides for the simplest case when only one incomming connection is allowed. Subclasses can overide the appropriate BeanCommon methods to change this behaviour and allow multiple connections if desired

Class weka.gui.beans.AbstractDataSource extends javax.swing.JPanel implements Serializable

serialVersionUID: -4127257701890044793L

Serialized Fields

m_design

boolean m_design
True if this bean's appearance is the design mode appearance


m_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support


m_visual

BeanVisual m_visual
Default visual for data sources


m_listeners

java.util.Vector<E> m_listeners
Objects listening for events from data sources

Class weka.gui.beans.AbstractEvaluator extends javax.swing.JPanel implements Serializable

serialVersionUID: 3983303541814121632L

Serialized Fields

m_visual

BeanVisual m_visual
Default visual for evaluators


m_listenee

java.lang.Object m_listenee

Class weka.gui.beans.AbstractTestSetProducer extends javax.swing.JPanel implements Serializable

serialVersionUID: -7905764845789349839L

Serialized Fields

m_listeners

java.util.Vector<E> m_listeners
Objects listening to us


m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee
non null if this object is a target for any events.

Class weka.gui.beans.AbstractTrainAndTestSetProducer extends javax.swing.JPanel implements Serializable

serialVersionUID: -1809339823613492037L

Serialized Fields

m_trainingListeners

java.util.Vector<E> m_trainingListeners
Objects listening for trainin set events


m_testListeners

java.util.Vector<E> m_testListeners
Objects listening for test set events


m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee
non null if this object is a target for any events.

Class weka.gui.beans.AbstractTrainingSetProducer extends javax.swing.JPanel implements Serializable

serialVersionUID: -7842746199524591125L

Serialized Fields

m_listeners

java.util.Vector<E> m_listeners
Objects listening for training set events


m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee
non null if this object is a target for any events.

Class weka.gui.beans.Associator extends javax.swing.JPanel implements Serializable

serialVersionUID: -7843500322130210057L

Serialized Fields

m_visual

BeanVisual m_visual

m_state

int m_state

m_buildThread

java.lang.Thread m_buildThread

m_globalInfo

java.lang.String m_globalInfo
Global info for the wrapped associator (if it exists).


m_listenees

java.util.Hashtable<K,V> m_listenees
Objects talking to us


m_textListeners

java.util.Vector<E> m_textListeners
Objects listening for text events


m_graphListeners

java.util.Vector<E> m_graphListeners
Objects listening for graph events


m_Associator

Associator m_Associator

Class weka.gui.beans.AssociatorCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 5767664969353495974L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsAssociator

Associator m_dsAssociator

m_AssociatorEditor

PropertySheetPanel m_AssociatorEditor

m_parentFrame

javax.swing.JFrame m_parentFrame

m_backup

Associator m_backup
Backup is user presses cancel

Class weka.gui.beans.AttributeSummarizer extends DataVisualizer implements Serializable

serialVersionUID: -294354961169372758L

Serialized Fields

m_gridWidth

int m_gridWidth
The number of plots horizontally in the display


m_maxPlots

int m_maxPlots
The maximum number of plots to show


m_coloringIndex

int m_coloringIndex
Index on which to color the plots.

Class weka.gui.beans.BatchClassifierEvent extends java.util.EventObject implements Serializable

serialVersionUID: 878097199815991084L

Serialized Fields

m_classifier

Classifier m_classifier
The classifier


m_testSet

DataSetEvent m_testSet
Instances that can be used for testing the classifier


m_trainSet

DataSetEvent m_trainSet
Instances that were used to train the classifier (may be null if not available)


m_runNumber

int m_runNumber
The run number that this classifier was generated for


m_maxRunNumber

int m_maxRunNumber
The maximum number of runs


m_setNumber

int m_setNumber
The set number for the test set


m_maxSetNumber

int m_maxSetNumber
The last set number for this series


m_groupIdentifier

long m_groupIdentifier
An identifier that can be used to group all related runs/sets together.

Class weka.gui.beans.BatchClustererEvent extends java.util.EventObject implements Serializable

serialVersionUID: 7268777944939129714L

Serialized Fields

m_clusterer

Clusterer m_clusterer
The clusterer


m_testSet

DataSetEvent m_testSet
Training or Test Instances


m_setNumber

int m_setNumber
The set number for the test set


m_testOrTrain

int m_testOrTrain
Indicates if m_testSet is a training or a test set. 0 for test, >0 for training


m_maxSetNumber

int m_maxSetNumber
The last set number for this series

Class weka.gui.beans.BeanConnection extends java.lang.Object implements Serializable

serialVersionUID: 8804264241791332064L

Serialized Fields

m_source

BeanInstance m_source

m_target

BeanInstance m_target

m_eventName

java.lang.String m_eventName
The name of the event for this connection


m_hidden

boolean m_hidden

Class weka.gui.beans.BeanInstance extends java.lang.Object implements Serializable

serialVersionUID: -7575653109025406342L

Serialized Fields

m_bean

java.lang.Object m_bean
Holds the bean encapsulated in this instance


m_x

int m_x

m_y

int m_y

Class weka.gui.beans.BeanVisual extends javax.swing.JPanel implements Serializable

serialVersionUID: -6677473561687129614L

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream ois)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
Overides default read object in order to reload icons. This is necessary because for some strange reason animated gifs stop being animated after being serialized/deserialized.

Throws:
java.io.IOException - if an error occurs
java.lang.ClassNotFoundException - if an error occurs
Serialized Fields

m_iconPath

java.lang.String m_iconPath
Holds name (including path) of the static icon


m_animatedIconPath

java.lang.String m_animatedIconPath
Holds name (including path) of the animated icon


m_visualName

java.lang.String m_visualName
Name for the bean


m_visualLabel

javax.swing.JLabel m_visualLabel

m_stationary

boolean m_stationary
Container for the icon


m_pcs

java.beans.PropertyChangeSupport m_pcs

m_displayConnectors

boolean m_displayConnectors

m_connectorColor

java.awt.Color m_connectorColor

Class weka.gui.beans.ChartEvent extends java.util.EventObject implements Serializable

serialVersionUID: 7812460715499569390L

Serialized Fields

m_legendText

java.util.Vector<E> m_legendText

m_max

double m_max

m_min

double m_min

m_reset

boolean m_reset

m_dataPoint

double[] m_dataPoint
Y values of the data points

Class weka.gui.beans.ClassAssigner extends javax.swing.JPanel implements Serializable

serialVersionUID: 4011131665025817924L

Serialized Fields

m_classColumn

java.lang.String m_classColumn

m_connectedFormat

Instances m_connectedFormat
format of instances for current incoming connection (if any)


m_trainingProvider

java.lang.Object m_trainingProvider

m_testProvider

java.lang.Object m_testProvider

m_dataProvider

java.lang.Object m_dataProvider

m_instanceProvider

java.lang.Object m_instanceProvider

m_trainingListeners

java.util.Vector<E> m_trainingListeners

m_testListeners

java.util.Vector<E> m_testListeners

m_dataListeners

java.util.Vector<E> m_dataListeners

m_instanceListeners

java.util.Vector<E> m_instanceListeners

m_dataFormatListeners

java.util.Vector<E> m_dataFormatListeners

m_visual

BeanVisual m_visual

Class weka.gui.beans.ClassAssignerCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 476539385765301907L

Serialized Fields

m_displayColNames

boolean m_displayColNames

m_classAssigner

ClassAssigner m_classAssigner

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_caEditor

PropertySheetPanel m_caEditor

m_ClassCombo

javax.swing.JComboBox m_ClassCombo

m_holderP

javax.swing.JPanel m_holderP

Class weka.gui.beans.Classifier extends javax.swing.JPanel implements Serializable

serialVersionUID: 659603893917736008L

Serialized Fields

m_visual

BeanVisual m_visual

m_state

int m_state

m_globalInfo

java.lang.String m_globalInfo
Global info for the wrapped classifier (if it exists).


m_listenees

java.util.Hashtable<K,V> m_listenees
Objects talking to us


m_batchClassifierListeners

java.util.Vector<E> m_batchClassifierListeners
Objects listening for batch classifier events


m_incrementalClassifierListeners

java.util.Vector<E> m_incrementalClassifierListeners
Objects listening for incremental classifier events


m_graphListeners

java.util.Vector<E> m_graphListeners
Objects listening for graph events


m_textListeners

java.util.Vector<E> m_textListeners
Objects listening for text events


m_trainingSet

Instances m_trainingSet
Holds training instances for batch training. Not transient because header is retained for validating any instance events that this classifier might be asked to predict in the future.


m_Classifier

Classifier m_Classifier

m_ClassifierTemplate

Classifier m_ClassifierTemplate
Template used for creating copies when building in parallel


m_ie

IncrementalClassifierEvent m_ie

m_binaryFilter

javax.swing.filechooser.FileFilter m_binaryFilter

m_KOMLFilter

javax.swing.filechooser.FileFilter m_KOMLFilter

m_XStreamFilter

javax.swing.filechooser.FileFilter m_XStreamFilter

m_updateIncrementalClassifier

boolean m_updateIncrementalClassifier
If the classifier is an incremental classifier, should we update it (ie train it on incoming instances). This makes it possible incrementally test on a separate stream of instances without updating the classifier, or mix batch training/testing with incremental training/testing


m_incrementalEvent

InstanceEvent m_incrementalEvent
Event to handle when processing incremental updates


m_executionSlots

int m_executionSlots
Number of threads to use to train models with


m_oldText

java.lang.String m_oldText
Holds original icon label text


m_block

boolean m_block
true if we should block any further training data sets.

Class weka.gui.beans.Classifier.TrainingTask extends java.lang.Object implements Serializable

Serialized Fields

m_runNum

int m_runNum

m_maxRunNum

int m_maxRunNum

m_setNum

int m_setNum

m_maxSetNum

int m_maxSetNum

m_train

Instances m_train

m_taskInfo

TaskStatusInfo m_taskInfo

Class weka.gui.beans.ClassifierCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: -6688000820160821429L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsClassifier

Classifier m_dsClassifier

m_ClassifierEditor

PropertySheetPanel m_ClassifierEditor

m_incrementalPanel

javax.swing.JPanel m_incrementalPanel

m_updateIncrementalClassifier

javax.swing.JCheckBox m_updateIncrementalClassifier

m_panelVisible

boolean m_panelVisible

m_holderPanel

javax.swing.JPanel m_holderPanel

m_executionSlotsText

javax.swing.JTextField m_executionSlotsText

m_parentFrame

javax.swing.JFrame m_parentFrame

m_backup

Classifier m_backup
Copy of the current classifier in case cancel is selected

Class weka.gui.beans.ClassifierPerformanceEvaluator extends AbstractEvaluator implements Serializable

serialVersionUID: -3511801418192148690L

Serialized Fields

m_textListeners

java.util.Vector<E> m_textListeners

m_thresholdListeners

java.util.Vector<E> m_thresholdListeners

m_visualizableErrorListeners

java.util.Vector<E> m_visualizableErrorListeners

m_rocListenersConnected

boolean m_rocListenersConnected

Class weka.gui.beans.ClassValuePicker extends javax.swing.JPanel implements Serializable

serialVersionUID: -1196143276710882989L

Serialized Fields

m_classValueIndex

int m_classValueIndex
the class value index considered to be the positive class


m_connectedFormat

Instances m_connectedFormat
format of instances for the current incoming connection (if any)


m_dataProvider

java.lang.Object m_dataProvider

m_dataListeners

java.util.Vector<E> m_dataListeners

m_dataFormatListeners

java.util.Vector<E> m_dataFormatListeners

m_visual

BeanVisual m_visual

Class weka.gui.beans.ClassValuePickerCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 8213423053861600469L

Serialized Fields

m_displayValNames

boolean m_displayValNames

m_classValuePicker

ClassValuePicker m_classValuePicker

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_ClassValueCombo

javax.swing.JComboBox m_ClassValueCombo

m_holderP

javax.swing.JPanel m_holderP

m_messageLabel

javax.swing.JLabel m_messageLabel

Class weka.gui.beans.Clusterer extends javax.swing.JPanel implements Serializable

serialVersionUID: 7729795159836843810L

Serialized Fields

m_visual

BeanVisual m_visual

m_state

int m_state

m_buildThread

java.lang.Thread m_buildThread

m_globalInfo

java.lang.String m_globalInfo
Global info for the wrapped classifier (if it exists).


m_listenees

java.util.Hashtable<K,V> m_listenees
Objects talking to us


m_batchClustererListeners

java.util.Vector<E> m_batchClustererListeners
Objects listening for batch clusterer events


m_graphListeners

java.util.Vector<E> m_graphListeners
Objects listening for graph events


m_textListeners

java.util.Vector<E> m_textListeners
Objects listening for text events


m_trainingSet

Instances m_trainingSet
Holds training instances for batch training.


m_Clusterer

Clusterer m_Clusterer

m_dummy

java.lang.Double m_dummy

Class weka.gui.beans.ClustererCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: -2035688458149534161L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsClusterer

Clusterer m_dsClusterer

m_ClustererEditor

PropertySheetPanel m_ClustererEditor

m_parentFrame

javax.swing.JFrame m_parentFrame

m_backup

Clusterer m_backup
Backup if the user presses cancel

Class weka.gui.beans.ClustererPerformanceEvaluator extends AbstractEvaluator implements Serializable

serialVersionUID: 8041163601333978584L

Serialized Fields

m_textListeners

java.util.Vector<E> m_textListeners

Class weka.gui.beans.CostBenefitAnalysis extends javax.swing.JPanel implements Serializable

serialVersionUID: 8647471654613320469L

Serialized Fields

m_visual

BeanVisual m_visual

m_framePoppedUp

boolean m_framePoppedUp

m_design

boolean m_design
True if this bean's appearance is the design mode appearance


m_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support


m_listenee

java.lang.Object m_listenee
The object sending us data (we allow only one connection at any one time)

Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 5364871945448769003L

Serialized Fields

m_performancePanel

VisualizePanel m_performancePanel
Displays the performance graphs(s)


m_costBenefitPanel

VisualizePanel m_costBenefitPanel
Displays the cost/benefit (profit/loss) graph


m_classAttribute

Attribute m_classAttribute
The class attribute from the data that was used to generate the threshold curve


m_masterPlot

PlotData2D m_masterPlot
Data for the threshold curve


m_costBenefit

PlotData2D m_costBenefit
Data for the cost/benefit curve


m_shapeSizes

int[] m_shapeSizes
The size of the points being plotted


m_previousShapeIndex

int m_previousShapeIndex
The index of the previous plotted point that was highlighted


m_thresholdSlider

javax.swing.JSlider m_thresholdSlider
The slider for adjusting the threshold


m_percPop

javax.swing.JRadioButton m_percPop

m_percOfTarget

javax.swing.JRadioButton m_percOfTarget

m_threshold

javax.swing.JRadioButton m_threshold

m_percPopLab

javax.swing.JLabel m_percPopLab

m_percOfTargetLab

javax.swing.JLabel m_percOfTargetLab

m_thresholdLab

javax.swing.JLabel m_thresholdLab

m_conf_predictedA

javax.swing.JLabel m_conf_predictedA

m_conf_predictedB

javax.swing.JLabel m_conf_predictedB

m_conf_actualA

javax.swing.JLabel m_conf_actualA

m_conf_actualB

javax.swing.JLabel m_conf_actualB

m_conf_aa

weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_aa

m_conf_ab

weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_ab

m_conf_ba

weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_ba

m_conf_bb

weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_bb

m_cost_predictedA

javax.swing.JLabel m_cost_predictedA

m_cost_predictedB

javax.swing.JLabel m_cost_predictedB

m_cost_actualA

javax.swing.JLabel m_cost_actualA

m_cost_actualB

javax.swing.JLabel m_cost_actualB

m_cost_aa

javax.swing.JTextField m_cost_aa

m_cost_ab

javax.swing.JTextField m_cost_ab

m_cost_ba

javax.swing.JTextField m_cost_ba

m_cost_bb

javax.swing.JTextField m_cost_bb

m_maximizeCB

javax.swing.JButton m_maximizeCB

m_minimizeCB

javax.swing.JButton m_minimizeCB

m_costR

javax.swing.JRadioButton m_costR

m_benefitR

javax.swing.JRadioButton m_benefitR

m_costBenefitL

javax.swing.JLabel m_costBenefitL

m_costBenefitV

javax.swing.JLabel m_costBenefitV

m_randomV

javax.swing.JLabel m_randomV

m_gainV

javax.swing.JLabel m_gainV

m_originalPopSize

int m_originalPopSize

m_totalPopField

javax.swing.JTextField m_totalPopField
Population text field


m_totalPopPrevious

int m_totalPopPrevious

m_classificationAccV

javax.swing.JLabel m_classificationAccV
Classification accuracy


m_tpPrevious

double m_tpPrevious

m_fpPrevious

double m_fpPrevious

m_tnPrevious

double m_tnPrevious

m_fnPrevious

double m_fnPrevious

Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell extends javax.swing.JPanel implements Serializable

serialVersionUID: 6148640235434494767L

Serialized Fields

m_conf_cell

javax.swing.JLabel m_conf_cell

m_conf_perc

javax.swing.JLabel m_conf_perc

m_percentageP

javax.swing.JPanel m_percentageP

m_percentage

double m_percentage

Class weka.gui.beans.CrossValidationFoldMaker extends AbstractTrainAndTestSetProducer implements Serializable

serialVersionUID: -6350179298851891512L

Serialized Fields

m_numFolds

int m_numFolds

m_randomSeed

int m_randomSeed

Class weka.gui.beans.CrossValidationFoldMakerCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 1229878140258668581L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_cvEditor

PropertySheetPanel m_cvEditor

Class weka.gui.beans.DataSetEvent extends java.util.EventObject implements Serializable

serialVersionUID: -5111218447577318057L

Serialized Fields

m_dataSet

Instances m_dataSet

m_structureOnly

boolean m_structureOnly

Class weka.gui.beans.DataVisualizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 1949062132560159028L

Serialized Fields

m_visual

BeanVisual m_visual

m_framePoppedUp

boolean m_framePoppedUp

m_design

boolean m_design
True if this bean's appearance is the design mode appearance


m_visPanel

VisualizePanel m_visPanel

m_dataSetListeners

java.util.Vector<E> m_dataSetListeners
Objects listening for data set events


m_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support

Class weka.gui.beans.Filter extends javax.swing.JPanel implements Serializable

serialVersionUID: 8249759470189439321L

Serialized Fields

m_visual

BeanVisual m_visual

m_state

int m_state

m_filterThread

java.lang.Thread m_filterThread

m_globalInfo

java.lang.String m_globalInfo
Global info for the wrapped filter (if it exists).


m_listenees

java.util.Hashtable<K,V> m_listenees
Objects talking to us


m_trainingListeners

java.util.Vector<E> m_trainingListeners
Objects listening for training set events


m_testListeners

java.util.Vector<E> m_testListeners
Objects listening for test set events


m_instanceListeners

java.util.Vector<E> m_instanceListeners
Objects listening for instance events


m_dataListeners

java.util.Vector<E> m_dataListeners
Objects listening for data set events


m_Filter

Filter m_Filter
The filter to use.


m_ie

InstanceEvent m_ie
Instance event object for passing on filtered instance streams


m_structurePassedOn

boolean m_structurePassedOn

Class weka.gui.beans.FilterCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 2049895469240109738L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_filter

Filter m_filter

m_backup

Filter m_backup
Backup if user presses cancel


m_filterEditor

PropertySheetPanel m_filterEditor

m_parentFrame

javax.swing.JFrame m_parentFrame

Class weka.gui.beans.GraphEvent extends java.util.EventObject implements Serializable

serialVersionUID: 2099494034652519986L

Serialized Fields

m_graphString

java.lang.String m_graphString

m_graphTitle

java.lang.String m_graphTitle

m_graphType

int m_graphType

Class weka.gui.beans.GraphViewer extends javax.swing.JPanel implements Serializable

serialVersionUID: -5183121972114900617L

Serialized Fields

m_visual

BeanVisual m_visual

m_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support


m_design

boolean m_design
True if this bean's appearance is the design mode appearance

Class weka.gui.beans.IncrementalClassifierEvaluator extends AbstractEvaluator implements Serializable

serialVersionUID: -3105419818939541291L

Serialized Fields

m_listeners

java.util.Vector<E> m_listeners

m_textListeners

java.util.Vector<E> m_textListeners

m_dataLegend

java.util.Vector<E> m_dataLegend

m_ce

ChartEvent m_ce

m_dataPoint

double[] m_dataPoint

m_reset

boolean m_reset

m_min

double m_min

m_max

double m_max

m_statusFrequency

int m_statusFrequency

m_instanceCount

int m_instanceCount

m_outputInfoRetrievalStats

boolean m_outputInfoRetrievalStats

Class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer extends javax.swing.JPanel implements Serializable

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport
for serialization


m_ieEditor

PropertySheetPanel m_ieEditor

Class weka.gui.beans.IncrementalClassifierEvent extends java.util.EventObject implements Serializable

serialVersionUID: 28979464317643232L

Serialized Fields

m_structure

Instances m_structure

m_status

int m_status

m_classifier

Classifier m_classifier

m_currentInstance

Instance m_currentInstance

Class weka.gui.beans.InstanceEvent extends java.util.EventObject implements Serializable

serialVersionUID: 6104920894559423946L

Serialized Fields

m_structure

Instances m_structure

m_instance

Instance m_instance

m_status

int m_status

Class weka.gui.beans.InstanceStreamToBatchMaker extends javax.swing.JPanel implements Serializable

serialVersionUID: -7037141087208627799L

Serialized Fields

m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee
Component connected to us.


m_dataListeners

java.util.ArrayList<E> m_dataListeners

m_batch

java.util.ArrayList<E> m_batch
Collects up the instances.


m_structure

Instances m_structure

Class weka.gui.beans.KnowledgeFlowApp extends javax.swing.JPanel implements Serializable

serialVersionUID: -7064906770289728431L

Serialized Fields

m_fontM

java.awt.FontMetrics m_fontM

m_mode

int m_mode

m_toolBarGroup

javax.swing.ButtonGroup m_toolBarGroup
Button group to manage all toolbar buttons


m_toolBarBean

java.lang.Object m_toolBarBean
Holds the selected toolbar bean


m_beanLayout

weka.gui.beans.KnowledgeFlowApp.BeanLayout m_beanLayout
The layout area


m_toolBars

javax.swing.JTabbedPane m_toolBars
Tabbed pane to hold tool bars


m_pluginsToolBar

javax.swing.JToolBar m_pluginsToolBar
Stuff relating to plugin beans


m_pluginsBoxPanel

javax.swing.Box m_pluginsBoxPanel

m_userToolBar

javax.swing.JToolBar m_userToolBar
Stuff relating to user created meta beans


m_userBoxPanel

javax.swing.Box m_userBoxPanel

m_userComponents

java.util.Vector<E> m_userComponents

m_firstUserComponentOpp

boolean m_firstUserComponentOpp

m_pointerB

javax.swing.JToggleButton m_pointerB

m_saveB

javax.swing.JButton m_saveB

m_loadB

javax.swing.JButton m_loadB

m_stopB

javax.swing.JButton m_stopB

m_helpB

javax.swing.JButton m_helpB

m_newB

javax.swing.JButton m_newB

m_editElement

BeanInstance m_editElement
Reference to bean being manipulated


m_sourceEventSetDescriptor

java.beans.EventSetDescriptor m_sourceEventSetDescriptor
Event set descriptor for the bean being manipulated


m_oldX

int m_oldX
Used to record screen coordinates during move, select and connect operations


m_oldY

int m_oldY
Used to record screen coordinates during move, select and connect operations


m_startX

int m_startX

m_startY

int m_startY

m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting layout files


m_logPanel

LogPanel m_logPanel

m_bcSupport

java.beans.beancontext.BeanContextSupport m_bcSupport

m_KfFilter

javax.swing.filechooser.FileFilter m_KfFilter
A filter to ensure only KnowledgeFlow files in binary format get shown in the chooser


m_KOMLFilter

javax.swing.filechooser.FileFilter m_KOMLFilter
A filter to ensure only KnowledgeFlow files in KOML format get shown in the chooser


m_XStreamFilter

javax.swing.filechooser.FileFilter m_XStreamFilter
A filter to ensure only KnowledgeFlow files in XStream format get shown in the chooser


m_XMLFilter

javax.swing.filechooser.FileFilter m_XMLFilter
A filter to ensure only KnowledgeFlow layout files in XML format get shown in the chooser


m_ScrollBarIncrementLayout

int m_ScrollBarIncrementLayout
the scrollbar increment of the layout scrollpane


m_ScrollBarIncrementComponents

int m_ScrollBarIncrementComponents
the scrollbar increment of the components scrollpane


m_FlowWidth

int m_FlowWidth
the flow layout width


m_FlowHeight

int m_FlowHeight
the flow layout height


m_PreferredExtension

java.lang.String m_PreferredExtension
the preferred file extension


m_UserComponentsInXML

boolean m_UserComponentsInXML
whether to store the user components in XML or in binary format


m_flowEnvironment

Environment m_flowEnvironment
Environment variables for the current flow


m_showFileMenu

boolean m_showFileMenu

Class weka.gui.beans.KnowledgeFlowApp.BeanLayout extends PrintablePanel implements Serializable

serialVersionUID: -146377012429662757L

Class weka.gui.beans.Loader extends AbstractDataSource implements Serializable

serialVersionUID: 1993738191961163027L

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream aStream)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
Throws:
java.io.IOException
java.lang.ClassNotFoundException

readResolve

private java.lang.Object readResolve()
                              throws java.io.ObjectStreamException
Throws:
java.io.ObjectStreamException
Serialized Fields

m_globalInfo

java.lang.String m_globalInfo
Global info for the wrapped loader (if it exists).


m_ioThread

weka.gui.beans.Loader.LoadThread m_ioThread
Thread for doing IO in


m_state

int m_state

m_Loader

Loader m_Loader
Loader


m_ie

InstanceEvent m_ie

m_instanceEventTargets

int m_instanceEventTargets
Keep track of how many listeners for different types of events there are.


m_dataSetEventTargets

int m_dataSetEventTargets

m_dbSet

boolean m_dbSet
Flag indicating that a database has already been configured


m_stopped

boolean m_stopped
Asked to stop?

Class weka.gui.beans.LoaderCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 6990446313118930298L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsLoader

Loader m_dsLoader

m_LoaderEditor

PropertySheetPanel m_LoaderEditor

m_fileChooser

javax.swing.JFileChooser m_fileChooser

m_parentFrame

javax.swing.JFrame m_parentFrame

m_dbaseURLText

javax.swing.JTextField m_dbaseURLText

m_userNameText

javax.swing.JTextField m_userNameText

m_queryText

javax.swing.JTextField m_queryText

m_keyText

javax.swing.JTextField m_keyText

m_passwordText

javax.swing.JPasswordField m_passwordText

m_relativeFilePath

javax.swing.JCheckBox m_relativeFilePath

Class weka.gui.beans.LogPanel extends javax.swing.JPanel implements Serializable

Serialized Fields

m_tableIndexes

java.util.HashMap<K,V> m_tableIndexes
Holds the index (line number) in the JTable of each component being tracked.


m_timers

java.util.HashMap<K,V> m_timers
Holds the timers associated with each component being tracked.


m_tableModel

javax.swing.table.DefaultTableModel m_tableModel
The table model for the JTable used in the status area


m_table

javax.swing.JTable m_table
The table for the status area


m_tabs

javax.swing.JTabbedPane m_tabs
Tabbed pane to hold both the status and the log


m_logPanel

LogPanel m_logPanel
The log panel to delegate log messages to.

Class weka.gui.beans.MetaBean extends javax.swing.JPanel implements Serializable

serialVersionUID: -6582768902038027077L

Serialized Fields

m_visual

BeanVisual m_visual

m_subFlow

java.util.Vector<E> m_subFlow

m_inputs

java.util.Vector<E> m_inputs

m_outputs

java.util.Vector<E> m_outputs

m_associatedConnections

java.util.Vector<E> m_associatedConnections

m_subFlowPreview

javax.swing.ImageIcon m_subFlowPreview

m_originalCoords

java.util.Vector<E> m_originalCoords

Class weka.gui.beans.ModelPerformanceChart extends javax.swing.JPanel implements Serializable

serialVersionUID: -4602034200071195924L

Serialized Fields

m_visual

BeanVisual m_visual

m_framePoppedUp

boolean m_framePoppedUp

m_design

boolean m_design
True if this bean's appearance is the design mode appearance


m_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support

Class weka.gui.beans.PredictionAppender extends javax.swing.JPanel implements Serializable

serialVersionUID: -2987740065058976673L

Serialized Fields

m_dataSourceListeners

java.util.Vector<E> m_dataSourceListeners
Objects listenening for dataset events


m_instanceListeners

java.util.Vector<E> m_instanceListeners
Objects listening for instances events


m_trainingSetListeners

java.util.Vector<E> m_trainingSetListeners
Objects listening for training set events


m_testSetListeners

java.util.Vector<E> m_testSetListeners
Objects listening for test set events


m_listenee

java.lang.Object m_listenee
Non null if this object is a target for any events.


m_format

Instances m_format
Format of instances to be produced.


m_visual

BeanVisual m_visual

m_appendProbabilities

boolean m_appendProbabilities
Append classifier's predicted probabilities (if the class is discrete and the classifier is a distribution classifier)


m_instanceEvent

InstanceEvent m_instanceEvent

Class weka.gui.beans.PredictionAppenderCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 6884933202506331888L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_paEditor

PropertySheetPanel m_paEditor

Class weka.gui.beans.Saver extends AbstractDataSink implements Serializable

serialVersionUID: 5371716690308950755L

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream aStream)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
Throws:
java.io.IOException
java.lang.ClassNotFoundException
Serialized Fields

m_dataSet

Instances m_dataSet
Holds the instances to be saved


m_structure

Instances m_structure
Holds the structure


m_globalInfo

java.lang.String m_globalInfo
Global info for the wrapped loader (if it exists).


m_Saver

Saver m_Saver
Saver


m_fileName

java.lang.String m_fileName
The relation name that becomes part of the file name


m_isDBSaver

boolean m_isDBSaver
Flag indicating that instances will be saved to database. Used because structure information can only be sent after a database has been configured.


m_relationNameForFilename

boolean m_relationNameForFilename
For file-based savers - if true (default), relation name is used as the primary part of the filename. If false, then the prefix is used as the filename. Useful for preventing filenames from getting too long when there are many filters in a flow.


m_count

int m_count
Count for structure available messages

Class weka.gui.beans.SaverCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: -4874208115942078471L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsSaver

Saver m_dsSaver

m_SaverEditor

PropertySheetPanel m_SaverEditor

m_fileChooser

javax.swing.JFileChooser m_fileChooser

m_parentFrame

javax.swing.JFrame m_parentFrame

m_dbaseURLText

javax.swing.JTextField m_dbaseURLText

m_userNameText

javax.swing.JTextField m_userNameText

m_passwordText

javax.swing.JPasswordField m_passwordText

m_tableText

javax.swing.JTextField m_tableText

m_idBox

javax.swing.JComboBox m_idBox

m_tabBox

javax.swing.JComboBox m_tabBox

m_prefixText

javax.swing.JTextField m_prefixText

m_relativeFilePath

javax.swing.JCheckBox m_relativeFilePath

m_relationNameForFilename

javax.swing.JCheckBox m_relationNameForFilename

Class weka.gui.beans.ScatterPlotMatrix extends DataVisualizer implements Serializable

serialVersionUID: -657856527563507491L

Serialized Fields

m_matrixPanel

MatrixPanel m_matrixPanel

Class weka.gui.beans.SerializedModelSaver extends javax.swing.JPanel implements Serializable

serialVersionUID: 3956528599473814287L

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream aStream)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
Throws:
java.io.IOException
java.lang.ClassNotFoundException
Serialized Fields

m_visual

BeanVisual m_visual
Default visual for data sources


m_listenee

java.lang.Object m_listenee
Non null if this object is a target for any events. Provides for the simplest case when only one incomming connection is allowed.


m_filenamePrefix

java.lang.String m_filenamePrefix
The prefix for the file name (model + training set info will be appended)


m_directory

java.io.File m_directory
The directory to hold the saved model(s)


m_fileFormat

Tag m_fileFormat
File format stuff


m_useRelativePath

boolean m_useRelativePath
relative path for the directory (relative to the user.dir (startup directory))?

Class weka.gui.beans.SerializedModelSaverCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: -4874208115942078471L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_smSaver

SerializedModelSaver m_smSaver

m_SaverEditor

PropertySheetPanel m_SaverEditor

m_fileChooser

javax.swing.JFileChooser m_fileChooser

m_parentFrame

javax.swing.JFrame m_parentFrame

m_prefixText

javax.swing.JTextField m_prefixText

m_fileFormatBox

javax.swing.JComboBox m_fileFormatBox

m_relativeFilePath

javax.swing.JCheckBox m_relativeFilePath

Class weka.gui.beans.StripChart extends javax.swing.JPanel implements Serializable

serialVersionUID: 1483649041577695019L

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream ois)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException
Provide some necessary initialization after object has been deserialized.

Throws:
java.io.IOException - if an error occurs
java.lang.ClassNotFoundException - if an error occurs
Serialized Fields

m_colorList

java.awt.Color[] m_colorList
default colours for colouring lines


m_BackgroundColor

java.awt.Color m_BackgroundColor
the background color.


m_LegendPanelBorderColor

java.awt.Color m_LegendPanelBorderColor
the color of the legend panel's border.


m_iheight

int m_iheight
Width and height of the off screen image.


m_iwidth

int m_iwidth

m_max

double m_max
Max value for the y axis.


m_min

double m_min
Min value for the y axis.


m_yScaleUpdate

boolean m_yScaleUpdate
Scale update requested.


m_oldMax

double m_oldMax

m_oldMin

double m_oldMin

m_labelFont

java.awt.Font m_labelFont

m_labelMetrics

java.awt.FontMetrics m_labelMetrics

m_legendText

java.util.Vector<E> m_legendText

m_scalePanel

weka.gui.beans.StripChart.ScalePanel m_scalePanel
the scale.


m_legendPanel

weka.gui.beans.StripChart.LegendPanel m_legendPanel
the legend.


m_dataList

java.util.LinkedList<E> m_dataList
Holds the data to be plotted. Entries in the list are arrays of y points.


m_previousY

double[] m_previousY

m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee

m_xValFreq

int m_xValFreq
Print x axis labels every m_xValFreq points


m_xCount

int m_xCount

m_refreshWidth

int m_refreshWidth
Shift the plot by this many pixels every time a point is plotted


m_refreshFrequency

int m_refreshFrequency
Plot every m_refreshFrequency'th point


m_Printer

PrintableComponent m_Printer
the class responsible for printing


m_ce

ChartEvent m_ce

m_dataPoint

double[] m_dataPoint

Class weka.gui.beans.StripChartCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 7441741530975984608L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_cvEditor

PropertySheetPanel m_cvEditor

Class weka.gui.beans.TestSetEvent extends java.util.EventObject implements Serializable

serialVersionUID: 8780718708498854231L

Serialized Fields

m_testSet

Instances m_testSet
The test set instances


m_structureOnly

boolean m_structureOnly

m_runNumber

int m_runNumber
What run number is this training set from.


m_maxRunNumber

int m_maxRunNumber
Maximum number of runs.


m_setNumber

int m_setNumber
what number is this test set (ie fold 2 of 10 folds)


m_maxSetNumber

int m_maxSetNumber
Maximum number of sets (ie 10 in a 10 fold)

Class weka.gui.beans.TestSetMaker extends AbstractTestSetProducer implements Serializable

serialVersionUID: -8473882857628061841L

Serialized Fields

m_receivedStopNotification

boolean m_receivedStopNotification

Class weka.gui.beans.TextEvent extends java.util.EventObject implements Serializable

serialVersionUID: 4196810607402973744L

Serialized Fields

m_text

java.lang.String m_text
The text


m_textTitle

java.lang.String m_textTitle
The title for the text. Could be used in a list component

Class weka.gui.beans.TextViewer extends javax.swing.JPanel implements Serializable

serialVersionUID: 104838186352536832L

Serialized Fields

m_visual

BeanVisual m_visual

m_design

boolean m_design
True if this bean's appearance is the design mode appearance


m_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support


m_textListeners

java.util.Vector<E> m_textListeners
Objects listening for text events

Class weka.gui.beans.ThresholdDataEvent extends java.util.EventObject implements Serializable

serialVersionUID: -8309334224492439644L

Serialized Fields

m_dataSet

PlotData2D m_dataSet

m_classAttribute

Attribute m_classAttribute

Class weka.gui.beans.TrainingSetEvent extends java.util.EventObject implements Serializable

serialVersionUID: 5872343811810872662L

Serialized Fields

m_trainingSet

Instances m_trainingSet
The training instances


m_structureOnly

boolean m_structureOnly

m_runNumber

int m_runNumber
What run number is this training set from.


m_maxRunNumber

int m_maxRunNumber
Maximum number of runs.


m_setNumber

int m_setNumber
what number is this training set (ie fold 2 of 10 folds)


m_maxSetNumber

int m_maxSetNumber
Maximum number of sets (ie 10 in a 10 fold)

Class weka.gui.beans.TrainingSetMaker extends AbstractTrainingSetProducer implements Serializable

serialVersionUID: -6152577265471535786L

Serialized Fields

m_receivedStopNotification

boolean m_receivedStopNotification

Class weka.gui.beans.TrainTestSplitMaker extends AbstractTrainAndTestSetProducer implements Serializable

serialVersionUID: 7390064039444605943L

Serialized Fields

m_trainPercentage

double m_trainPercentage

m_randomSeed

int m_randomSeed

m_splitThread

java.lang.Thread m_splitThread

Class weka.gui.beans.TrainTestSplitMakerCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: -1684662340241807260L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_splitEditor

PropertySheetPanel m_splitEditor

Class weka.gui.beans.VisualizableErrorEvent extends java.util.EventObject implements Serializable

serialVersionUID: -5811819270887223400L

Serialized Fields

m_dataSet

PlotData2D m_dataSet

Package weka.gui.boundaryvisualizer

Class weka.gui.boundaryvisualizer.BoundaryPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -8499445518744770458L

Serialized Fields

m_Colors

FastVector m_Colors

m_trainingData

Instances m_trainingData
training data


m_classifier

Classifier m_classifier
distribution classifier to use


m_dataGenerator

DataGenerator m_dataGenerator
data generator to use


m_classIndex

int m_classIndex
index of the class attribute


m_xAttribute

int m_xAttribute

m_yAttribute

int m_yAttribute

m_minX

double m_minX

m_minY

double m_minY

m_maxX

double m_maxX

m_maxY

double m_maxY

m_rangeX

double m_rangeX

m_rangeY

double m_rangeY

m_pixHeight

double m_pixHeight

m_pixWidth

double m_pixWidth

m_osi

java.awt.Image m_osi
used for offscreen drawing


m_panelWidth

int m_panelWidth

m_panelHeight

int m_panelHeight

m_numOfSamplesPerRegion

int m_numOfSamplesPerRegion

m_numOfSamplesPerGenerator

int m_numOfSamplesPerGenerator

m_samplesBase

double m_samplesBase

m_listeners

java.util.Vector<E> m_listeners
listeners to be notified when plot is complete


m_plotPanel

weka.gui.boundaryvisualizer.BoundaryPanel.PlotPanel m_plotPanel
the actual plotting area


m_plotThread

java.lang.Thread m_plotThread
thread for running the plotting operation in


m_stopPlotting

boolean m_stopPlotting
Stop the plotting thread


m_stopReplotting

boolean m_stopReplotting
Stop any replotting threads


m_dummy

java.lang.Double m_dummy

m_pausePlotting

boolean m_pausePlotting

m_size

int m_size
what size of tile is currently being plotted


m_initialTiling

boolean m_initialTiling
is the main plot thread performing the initial coarse tiling


m_random

java.util.Random m_random
A random number generator


m_probabilityCache

double[][][] m_probabilityCache
cache of probabilities for fast replotting


m_plotTrainingData

boolean m_plotTrainingData
plot the training data

Class weka.gui.boundaryvisualizer.BoundaryPanelDistributed extends BoundaryPanel implements Serializable

serialVersionUID: -1743284397893937776L

Serialized Fields

m_listeners

java.util.Vector<E> m_listeners
a list of RemoteExperimentListeners


m_remoteHosts

java.util.Vector<E> m_remoteHosts
Holds the names of machines with remoteEngine servers running


m_remoteHostsQueue

Queue m_remoteHostsQueue
The queue of available hosts


m_remoteHostsStatus

int[] m_remoteHostsStatus
The status of each of the remote hosts


m_remoteHostFailureCounts

int[] m_remoteHostFailureCounts
The number of times tasks have failed on each remote host


m_plottingAborted

boolean m_plottingAborted
Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts plotting


m_removedHosts

int m_removedHosts
The number of hosts removed due to exceeding max failures


m_failedCount

int m_failedCount
The count of failed sub-tasks


m_finishedCount

int m_finishedCount
The count of successfully completed sub-tasks


m_subExpQueue

Queue m_subExpQueue
The queue of sub-tasks waiting to be processed


m_minTaskPollTime

int m_minTaskPollTime
number of seconds between polling server


m_hostPollingTime

int[] m_hostPollingTime

Class weka.gui.boundaryvisualizer.BoundaryVisualizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 3933877580074013208L

Serialized Fields

m_trainingInstances

Instances m_trainingInstances
the training instances


m_classifier

Classifier m_classifier
the classifier to use


m_plotAreaWidth

int m_plotAreaWidth

m_plotAreaHeight

int m_plotAreaHeight

m_boundaryPanel

BoundaryPanel m_boundaryPanel
the plotting panel


m_classAttBox

javax.swing.JComboBox m_classAttBox

m_xAttBox

javax.swing.JComboBox m_xAttBox

m_yAttBox

javax.swing.JComboBox m_yAttBox

COMBO_SIZE

java.awt.Dimension COMBO_SIZE

m_startBut

javax.swing.JButton m_startBut

m_plotTrainingData

javax.swing.JCheckBox m_plotTrainingData

m_controlPanel

javax.swing.JPanel m_controlPanel

m_classPanel

ClassPanel m_classPanel

m_xAxisPanel

weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_xAxisPanel

m_yAxisPanel

weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_yAxisPanel

m_maxX

double m_maxX

m_maxY

double m_maxY

m_minX

double m_minX

m_minY

double m_minY

m_xIndex

int m_xIndex

m_yIndex

int m_yIndex

m_dataGenerator

KDDataGenerator m_dataGenerator

m_numberOfSamplesFromEachRegion

int m_numberOfSamplesFromEachRegion

m_generatorSamplesBase

int m_generatorSamplesBase
base for sampling in the non-fixed dimensions


m_kernelBandwidth

int m_kernelBandwidth
Set the kernel bandwidth to cover this many nearest neighbours


m_regionSamplesText

javax.swing.JTextField m_regionSamplesText

m_generatorSamplesText

javax.swing.JTextField m_generatorSamplesText

m_kernelBandwidthText

javax.swing.JTextField m_kernelBandwidthText

m_classifierEditor

GenericObjectEditor m_classifierEditor

m_ClassifierPanel

PropertyPanel m_ClassifierPanel

m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting arff files


m_arffFileFilter

ExtensionFileFilter m_arffFileFilter

dataFileLabel

javax.swing.JLabel dataFileLabel

m_addRemovePointsPanel

javax.swing.JPanel m_addRemovePointsPanel

m_classValueSelector

javax.swing.JComboBox m_classValueSelector

m_addPointsButton

javax.swing.JRadioButton m_addPointsButton

m_removePointsButton

javax.swing.JRadioButton m_removePointsButton

m_addRemovePointsButtonGroup

javax.swing.ButtonGroup m_addRemovePointsButtonGroup

removeAllButton

javax.swing.JButton removeAllButton

chooseButton

javax.swing.JButton chooseButton

Class weka.gui.boundaryvisualizer.KDDataGenerator extends java.lang.Object implements Serializable

serialVersionUID: -958573275606402792L

Serialized Fields

m_instances

Instances m_instances
the instances to use


m_standardDeviations

double[] m_standardDeviations
standard deviations of the normal distributions for numeric attributes in each KD estimator


m_globalMeansOrModes

double[] m_globalMeansOrModes
global means or modes to use for missing values


m_minStdDev

double m_minStdDev
minimum standard deviation for numeric attributes


m_laplaceConst

double m_laplaceConst
Laplace correction for discrete distributions


m_seed

int m_seed
random number seed


m_random

java.util.Random m_random
random number generator


m_weightingDimensions

boolean[] m_weightingDimensions
which dimensions to use for computing a weight for each generated instance


m_weightingValues

double[] m_weightingValues
the values for the weighting dimensions to use for computing the weight for the next instance to be generated


m_kernelBandwidth

int m_kernelBandwidth
Number of neighbours to use for kernel bandwidth


m_kernelParams

double[][] m_kernelParams
standard deviations for numeric attributes computed from the m_kernelBandwidth nearest neighbours for each kernel.


m_Min

double[] m_Min
The minimum values for numeric attributes.


m_Max

double[] m_Max
The maximum values for numeric attributes.

Class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask extends java.lang.Object implements Serializable

Serialized Fields

m_status

TaskStatusInfo m_status

m_result

RemoteResult m_result

m_rowNumber

int m_rowNumber

m_panelHeight

int m_panelHeight

m_panelWidth

int m_panelWidth

m_classifier

Classifier m_classifier

m_dataGenerator

DataGenerator m_dataGenerator

m_trainingData

Instances m_trainingData

m_xAttribute

int m_xAttribute

m_yAttribute

int m_yAttribute

m_pixHeight

double m_pixHeight

m_pixWidth

double m_pixWidth

m_minX

double m_minX

m_minY

double m_minY

m_maxX

double m_maxX

m_maxY

double m_maxY

m_numOfSamplesPerRegion

int m_numOfSamplesPerRegion

m_numOfSamplesPerGenerator

int m_numOfSamplesPerGenerator

m_samplesBase

double m_samplesBase

m_random

java.util.Random m_random

m_weightingAttsValues

double[] m_weightingAttsValues

m_attsToWeightOn

boolean[] m_attsToWeightOn

m_vals

double[] m_vals

m_dist

double[] m_dist

m_predInst

Instance m_predInst

Class weka.gui.boundaryvisualizer.RemoteResult extends java.lang.Object implements Serializable

serialVersionUID: 1873271280044633808L

Serialized Fields

m_rowNumber

int m_rowNumber
the row number that this result corresponds to


m_rowLength

int m_rowLength
how many pixels in a row


m_probabilities

double[][] m_probabilities
the result - ie. the probability distributions produced by the classifier for this row in the visualization


m_percentCompleted

int m_percentCompleted
progress on computing this row


Package weka.gui.ensembleLibraryEditor

Class weka.gui.ensembleLibraryEditor.AddModelsPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 4874639416371962573L

Serialized Fields

m_ListModelsPanel

ListModelsPanel m_ListModelsPanel
This is a reference to the main gui object that is responsible for displaying the model library. This panel will add models to the main panel through methods in this object.


m_Tree

javax.swing.JTree m_Tree
The JTree that will display the classifier options available in the currently select3ed model type


m_TreeModel

javax.swing.tree.DefaultTreeModel m_TreeModel
The tree model that will be used to add and remove nodes from the currently selected model type


m_GenerateButton

javax.swing.JButton m_GenerateButton
This button will allow users to generate a group of models from the currently selected classifer options in the m_Tree object.


m_GenerateLabel

javax.swing.JLabel m_GenerateLabel
This will display messages associated with model generation. Currently the number of models generated and the number of them that had errors.


m_RemoveSelectedButton

javax.swing.JButton m_RemoveSelectedButton
This button will allow users to remove all of the models currently selected in the m_ModeList object


m_RemoveInvalidButton

javax.swing.JButton m_RemoveInvalidButton
This button will remove all of the models that had errors during model generation.


m_AddSelectedButton

javax.swing.JButton m_AddSelectedButton
This button will add all of the models that had are currently selected in the model list.


m_AddAllButton

javax.swing.JButton m_AddAllButton
This button will allow users to add all models currently in the model list to the model library in the ListModelsPanel. Note that this operation will exclude any models that had errors


m_ModelList

ModelList m_ModelList
This object will store all of the model sets generated from the m_Tree. The ModelList class is a custom class in weka.gui that knows how to display library model objects in a JList


m_TreeView

javax.swing.JScrollPane m_TreeView
the scroll pane holding our classifer parameters

Class weka.gui.ensembleLibraryEditor.DefaultModelsPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -6123488873592563339L

Serialized Fields

EXCLUDE_OPTIONS

java.lang.String[] EXCLUDE_OPTIONS
options to exclude


m_DefaultFileNames

java.lang.String[] m_DefaultFileNames
an array of libray files to be used in populating the default list comboBox


m_LargeFileSizeModels

java.lang.String[] m_LargeFileSizeModels
an array of model Strings that should be excluded if the file size option is selected


m_LargeTrainTimeModels

java.lang.String[] m_LargeTrainTimeModels
an array of model Strings that should be excluded if the train time option is selected


m_LargeTestTimeModels

java.lang.String[] m_LargeTestTimeModels
an array of model Strings that should be excluded if the test time option is selected


m_ExcludeModelsComboBox

javax.swing.JComboBox m_ExcludeModelsComboBox
this is a combo box that will allow the user to select which set of models to remove from the list


m_ExcludeModelsButton

javax.swing.JButton m_ExcludeModelsButton
this is a button that will allow the user to select which set of models to remove from the list


m_DefaultFilesComboBox

javax.swing.JComboBox m_DefaultFilesComboBox
this is a combo box that will allow the user to select the default model file


m_RefreshButton

javax.swing.JButton m_RefreshButton
allows the user to reload the default set


m_ModelList

ModelList m_ModelList
This object will store all of the models that can be selected from the default list. The ModelList class is a custom class in weka.gui that knows how to display library model objects in a JList


m_RemoveSelectedButton

javax.swing.JButton m_RemoveSelectedButton
This button allows the user to remove all the models currently selected in the model List from those that will be added


m_AddAllButton

javax.swing.JButton m_AddAllButton
This button allows the user to add all the in the current working default set to the library


m_AddSelectedButton

javax.swing.JButton m_AddSelectedButton
This button allows the user to add all the models currently selected to the current set of models in this library, after this is selected it should also send the user back to the main interface


m_ListModelsPanel

ListModelsPanel m_ListModelsPanel
This is a reference to the main gui object that is responsible for displaying the model library. This panel will add models to the main panel through methods in this object.


m_ListUpdatePending

boolean m_ListUpdatePending
whether an update is pending

Class weka.gui.ensembleLibraryEditor.ListModelsPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -1986253077944432252L

Serialized Fields

m_Library

EnsembleLibrary m_Library
The library being edited


m_RemoveSelectedButton

javax.swing.JButton m_RemoveSelectedButton
The button for removing selected models


m_OpenModelFileButton

javax.swing.JButton m_OpenModelFileButton
The button for opening a model list from a file


m_SaveModelFileButton

javax.swing.JButton m_SaveModelFileButton
The button for saving a model list to a file


m_ModelList

ModelList m_ModelList
The ModelList object that displays all currently selected models


m_modelListChooser

javax.swing.JFileChooser m_modelListChooser
The file chooser for the user to select model list files to save and load

Class weka.gui.ensembleLibraryEditor.LoadModelsPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -6961209378227736515L

Serialized Fields

m_ListModelsPanel

ListModelsPanel m_ListModelsPanel
This is a reference to the main gui object that is responsible for displaying the model library. This panel will add models to the main panel through methods in this object.


m_LoadingLabel

javax.swing.JLabel m_LoadingLabel
This will display messages associated with model loading. Currently the number of models found and the number of data sets.


m_DirectoryLabel

javax.swing.JLabel m_DirectoryLabel
This will display the current working Directory of the Ensemble Library.


m_RefreshListButton

javax.swing.JButton m_RefreshListButton
This button will refresh the model list currently displayed in the case that either the working directory changed or the models stored in it changed.


m_RemoveSelectedButton

javax.swing.JButton m_RemoveSelectedButton
This button will allow users to remove all of the models currently selected in the m_ModeList object


m_AddAllButton

javax.swing.JButton m_AddAllButton
This button will allow users to add all models currently in the model list to the model library in the ListModelsPanel. Note that this operation will exclude any models that had errors


m_AddSelectedButton

javax.swing.JButton m_AddSelectedButton
This button will add all of the models that had are currently selected in the model list.


m_ModelList

ModelList m_ModelList
This object will store all of the models that were found in the library's current working directory. The ModelList class is JList list = new JList(listModel); a custom class in weka.gui that knows how to display library model objects in a JList


m_Library

EnsembleSelectionLibrary m_Library
A reference to the main library object so that we can get the current working Directory.


m_EnsembleLibraryEditor

EnsembleSelectionLibraryEditor m_EnsembleLibraryEditor
A reference to the libary editor that contains this panel so that we can see if we're selected or not.


m_workingDirectoryChanged

boolean m_workingDirectoryChanged

Class weka.gui.ensembleLibraryEditor.ModelList extends javax.swing.JList implements Serializable

serialVersionUID: -421567241792939539L

Class weka.gui.ensembleLibraryEditor.ModelList.ModelListRenderer extends javax.swing.DefaultListCellRenderer implements Serializable

serialVersionUID: -7061163240718897794L

Class weka.gui.ensembleLibraryEditor.ModelList.SortedListModel extends javax.swing.AbstractListModel implements Serializable

serialVersionUID: -8334675481243839371L

Serialized Fields

m_Models

java.util.SortedSet<E> m_Models
Define a SortedSet


Package weka.gui.ensembleLibraryEditor.tree

Class weka.gui.ensembleLibraryEditor.tree.CheckBoxNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

serialVersionUID: 727140674668443817L

Serialized Fields

m_Selected

boolean m_Selected
tracks whether this node is currently selected as a model parameter


m_ToolTipText

java.lang.String m_ToolTipText
the tip text for our node editor to display

Class weka.gui.ensembleLibraryEditor.tree.CheckBoxNodeEditor extends javax.swing.JPanel implements Serializable

serialVersionUID: -1506685976284982111L

Serialized Fields

m_SelectedCheckBox

javax.swing.JCheckBox m_SelectedCheckBox
the checkbox the user will interact with


m_Label

javax.swing.JLabel m_Label
the label that will display the name of this parameter value


textForeground

java.awt.Color textForeground
colors we'll use


textBackground

java.awt.Color textBackground
colors we'll use


m_Node

CheckBoxNode m_Node
a reference to the node this editor represents

Class weka.gui.ensembleLibraryEditor.tree.DefaultNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

serialVersionUID: -2182147677358461880L

Serialized Fields

m_Name

java.lang.String m_Name
the name of this node


m_ToolTipText

java.lang.String m_ToolTipText
the tip text for our node editor to display


m_PropertyEditor

java.beans.PropertyEditor m_PropertyEditor
The default PropertyEditor that was supplied for this node

Class weka.gui.ensembleLibraryEditor.tree.GenericObjectNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

serialVersionUID: 688096727663132485L

Serialized Fields

m_Properties

java.beans.PropertyDescriptor[] m_Properties
Holds properties of the target


m_UsedPropertyIndexes

java.util.Vector<E> m_UsedPropertyIndexes
this tracks which indexes of the m_Properties


m_Methods

java.beans.MethodDescriptor[] m_Methods
Holds the methods of the target


m_Editors

java.beans.PropertyEditor[] m_Editors
Holds property editors of the object


m_Values

java.lang.Object[] m_Values
Holds current object values for each property


m_Names

java.lang.String[] m_Names
The labels for each property


m_TipTexts

java.lang.String[] m_TipTexts
The tool tip text for each property


m_HelpText

java.lang.StringBuffer m_HelpText
StringBuffer containing help text for the object being edited


m_GenericObjectEditor

GenericObjectEditor m_GenericObjectEditor
the GenericObjectEditor that was supplied for this node


m_WorkingSetCombinations

java.util.Vector<E> m_WorkingSetCombinations
this Vector stores all of the possible combinations of parameters that it obtains from its child nodes. These combinations are created by the recursive combineAllValues method


m_ToolTipText

java.lang.String m_ToolTipText
the tip text for our node editor to display


m_TreeModel

javax.swing.tree.DefaultTreeModel m_TreeModel
a reference to the tree model is necessary to be able to add and remove nodes in the tree


m_Tree

javax.swing.JTree m_Tree
this is a reference to the Tree object that this node is contained within. Its required for this node to be able to add/remove nodes from the JTree


m_ParentPanel

AddModelsPanel m_ParentPanel
This is a reference to the parent panel of the JTree so that we can supply it as the required argument when supplying warning JDialog messages

Class weka.gui.ensembleLibraryEditor.tree.GenericObjectNodeEditor extends javax.swing.JPanel implements Serializable

serialVersionUID: -2382339640932830323L

Serialized Fields

m_GenericObjectEditor

GenericObjectEditor m_GenericObjectEditor
a reference to the GenericObjectEditor for this node


m_Label

javax.swing.JLabel m_Label
the label that will display the node object type


m_ChooseClassButton

javax.swing.JButton m_ChooseClassButton
the button that will create the JPopupMenu to choose the object type


m_MoreInfoButton

javax.swing.JButton m_MoreInfoButton
Button to pop up the full help text in a separate frame


m_HelpFrame

javax.swing.JFrame m_HelpFrame
Help frame


m_propertyChangeSupport

java.beans.PropertyChangeSupport m_propertyChangeSupport
a propertyChangeSupportListener to inform the TreeNodeEditor when we stop editing - needs to be done manually for that popup


textForeground

java.awt.Color textForeground
the colors


textBackground

java.awt.Color textBackground
the colors


m_Node

GenericObjectNode m_Node
A reference to the node


m_popup

javax.swing.JPopupMenu m_popup
the popup menu to show the node parameter GUI

Class weka.gui.ensembleLibraryEditor.tree.InvalidInputException extends java.lang.Exception implements Serializable

serialVersionUID: 9192136737177003882L

Class weka.gui.ensembleLibraryEditor.tree.ModelTreeNodeEditor extends javax.swing.AbstractCellEditor implements Serializable

serialVersionUID: 7057924814405386358L

Serialized Fields

m_Tree

javax.swing.JTree m_Tree
This is the underlying tree holding all our parameter nodes in the GUI.

Class weka.gui.ensembleLibraryEditor.tree.NumberClassNotFoundException extends java.lang.Exception implements Serializable

serialVersionUID: -4896867049872120453L

Class weka.gui.ensembleLibraryEditor.tree.NumberNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

serialVersionUID: -2505599954089243851L

Serialized Fields

m_Name

java.lang.String m_Name
the name of the node to be displayed


m_IteratorType

int m_IteratorType
the iterator type, NOT_ITERATOR, TIMES_EQUAL, or PLUS_EQUAL


m_Checkable

boolean m_Checkable
this stores whether or not this node should have a checkbox


m_Selected

boolean m_Selected
this stores the node's selected state


m_ToolTipText

java.lang.String m_ToolTipText
the node's tipText

Class weka.gui.ensembleLibraryEditor.tree.NumberNodeEditor extends javax.swing.JPanel implements Serializable

serialVersionUID: 3486848815982334460L

Serialized Fields

m_IteratorButton

javax.swing.JButton m_IteratorButton
the button that toggles the type of iterator that this node represents


m_NumberField

javax.swing.JFormattedTextField m_NumberField
the textField that allows editing of the number value


m_SelectedCheckBox

javax.swing.JCheckBox m_SelectedCheckBox
the checkBox that allows thi snode to be selected/unselected


m_Label

javax.swing.JLabel m_Label
the label that prints the name of this node


textForeground

java.awt.Color textForeground
the colors to use


textBackground

java.awt.Color textBackground
the colors to use


m_Node

NumberNode m_Node
a reference to the node this editor is rendering

Class weka.gui.ensembleLibraryEditor.tree.PropertyNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

serialVersionUID: 8179038568780212829L

Serialized Fields

m_ParentPanel

AddModelsPanel m_ParentPanel
this is a reference to the parent panel of the JTree which is needed to display correctly anchored JDialogs


m_Name

java.lang.String m_Name
the name of the node to be displayed


m_ToolTipText

java.lang.String m_ToolTipText
the node's tip text


m_PropertyEditor

java.beans.PropertyEditor m_PropertyEditor
The propertyEditor created for the node, this is very useful in figuring out exactly waht kind of child editor nodes to create


m_TreeModel

javax.swing.tree.DefaultTreeModel m_TreeModel
a reference to the tree model is necessary to be able to add and remove nodes in the tree


m_Tree

javax.swing.JTree m_Tree
a reference to the tree


Package weka.gui.experiment

Class weka.gui.experiment.AlgorithmListPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -7204528834764898671L

Serialized Fields

m_Exp

Experiment m_Exp
The experiment to set the algorithm list of


m_List

javax.swing.JList m_List
The component displaying the algorithm list


m_AddBut

javax.swing.JButton m_AddBut
Click to add an algorithm


m_EditBut

javax.swing.JButton m_EditBut
Click to edit the selected algorithm


m_DeleteBut

javax.swing.JButton m_DeleteBut
Click to remove the selected dataset from the list


m_LoadOptionsBut

javax.swing.JButton m_LoadOptionsBut
Click to edit the load the options for athe selected algorithm


m_SaveOptionsBut

javax.swing.JButton m_SaveOptionsBut
Click to edit the save the options from selected algorithm


m_UpBut

javax.swing.JButton m_UpBut
Click to move the selected algorithm(s) one up


m_DownBut

javax.swing.JButton m_DownBut
Click to move the selected algorithm(s) one down


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting experiments


m_XMLFilter

javax.swing.filechooser.FileFilter m_XMLFilter
A filter to ensure only experiment (in XML format) files get shown in the chooser


m_Editing

boolean m_Editing
Whether an algorithm is added or only edited


m_ClassifierEditor

GenericObjectEditor m_ClassifierEditor
Lets the user configure the classifier


m_PD

PropertyDialog m_PD
The currently displayed property dialog, if any


m_AlgorithmListModel

javax.swing.DefaultListModel m_AlgorithmListModel
The list model used

Class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer extends javax.swing.DefaultListCellRenderer implements Serializable

serialVersionUID: -5067138526587433808L

Class weka.gui.experiment.DatasetListPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 7068857852794405769L

Serialized Fields

m_Exp

Experiment m_Exp
The experiment to set the dataset list of.


m_List

javax.swing.JList m_List
The component displaying the dataset list.


m_AddBut

javax.swing.JButton m_AddBut
Click to add a dataset.


m_EditBut

javax.swing.JButton m_EditBut
Click to edit the selected algorithm.


m_DeleteBut

javax.swing.JButton m_DeleteBut
Click to remove the selected dataset from the list.


m_UpBut

javax.swing.JButton m_UpBut
Click to move the selected dataset(s) one up.


m_DownBut

javax.swing.JButton m_DownBut
Click to move the selected dataset(s) one down.


m_relativeCheck

javax.swing.JCheckBox m_relativeCheck
Make file paths relative to the user (start) directory.


m_FileChooser

ConverterFileChooser m_FileChooser
The file chooser component.

Class weka.gui.experiment.DistributeExperimentPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 5206721431754800278L

Serialized Fields

m_Exp

RemoteExperiment m_Exp
The experiment to configure.


m_enableDistributedExperiment

javax.swing.JCheckBox m_enableDistributedExperiment
Distribute the current experiment to remote hosts


m_configureHostNames

javax.swing.JButton m_configureHostNames
Popup the HostListPanel


m_hostList

HostListPanel m_hostList
The host list panel


m_splitByDataSet

javax.swing.JRadioButton m_splitByDataSet
Split experiment up by data set.


m_splitByRun

javax.swing.JRadioButton m_splitByRun
Split experiment up by run number.


m_radioListener

java.awt.event.ActionListener m_radioListener
Handle radio buttons

Class weka.gui.experiment.Experimenter extends javax.swing.JPanel implements Serializable

serialVersionUID: -5751617505738193788L

Serialized Fields

m_SetupPanel

SetupModePanel m_SetupPanel
The panel for configuring the experiment


m_RunPanel

RunPanel m_RunPanel
The panel for running the experiment


m_ResultsPanel

ResultsPanel m_ResultsPanel
The panel for analysing experimental results


m_TabbedPane

javax.swing.JTabbedPane m_TabbedPane
The tabbed pane that controls which sub-pane we are working with


m_ClassFirst

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

Class weka.gui.experiment.ExperimenterDefaults extends java.lang.Object implements Serializable

serialVersionUID: -2835933184632147981L

Class weka.gui.experiment.GeneratorPropertyIteratorPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -6026938995241632139L

Serialized Fields

m_ConfigureBut

javax.swing.JButton m_ConfigureBut
Click to select the property to iterate over


m_StatusBox

javax.swing.JComboBox m_StatusBox
Controls whether the custom iterator is used or not


m_ArrayEditor

GenericArrayEditor m_ArrayEditor
Allows editing of the custom property values


m_Exp

Experiment m_Exp
The experiment this all applies to


m_Listeners

FastVector m_Listeners
Listeners who want to be notified about editing status of this panel

Class weka.gui.experiment.HostListPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 7182791134585882197L

Serialized Fields

m_Exp

RemoteExperiment m_Exp
The remote experiment to set the host list of


m_List

javax.swing.JList m_List
The component displaying the host list


m_DeleteBut

javax.swing.JButton m_DeleteBut
Click to remove the selected host from the list


m_HostField

javax.swing.JTextField m_HostField
The field with which to enter host names

Class weka.gui.experiment.OutputFormatDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: 2169792738187807378L

Serialized Fields

m_Result

int m_Result
the result of the user's action, either OK or CANCEL.


m_ResultMatrix

java.lang.Class<T> m_ResultMatrix
the output format specific matrix.


m_OutputFormatComboBox

javax.swing.JComboBox m_OutputFormatComboBox
lets the user choose the format for the output.


m_MeanPrecSpinner

javax.swing.JSpinner m_MeanPrecSpinner
the spinner to choose the precision for the mean from.


m_StdDevPrecSpinner

javax.swing.JSpinner m_StdDevPrecSpinner
the spinner to choose the precision for the std. deviation from


m_ShowAverageCheckBox

javax.swing.JCheckBox m_ShowAverageCheckBox
the checkbox for outputting the average.


m_RemoveFilterNameCheckBox

javax.swing.JCheckBox m_RemoveFilterNameCheckBox
the checkbox for the removing of filter classnames.


m_OkButton

javax.swing.JButton m_OkButton
Click to activate the current set parameters.


m_CancelButton

javax.swing.JButton m_CancelButton
Click to cancel the dialog.


m_MeanPrec

int m_MeanPrec
the number of digits after the period (= precision) for printing the mean.


m_StdDevPrec

int m_StdDevPrec
the number of digits after the period (= precision) for printing the std. deviation


m_RemoveFilterName

boolean m_RemoveFilterName
whether to remove the filter names from the names.


m_ShowAverage

boolean m_ShowAverage
whether to show the average too.

Class weka.gui.experiment.ResultsPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -4913007978534178569L

Serialized Fields

m_FromFileBut

javax.swing.JButton m_FromFileBut
Click to load results from a file.


m_FromDBaseBut

javax.swing.JButton m_FromDBaseBut
Click to load results from a database.


m_FromExpBut

javax.swing.JButton m_FromExpBut
Click to get results from the destination given in the experiment.


m_FromLab

javax.swing.JLabel m_FromLab
Displays a message about the current result set.


m_DatasetModel

javax.swing.DefaultComboBoxModel m_DatasetModel
The model embedded in m_DatasetCombo.


m_CompareModel

javax.swing.DefaultComboBoxModel m_CompareModel
The model embedded in m_CompareCombo.


m_SortModel

javax.swing.DefaultComboBoxModel m_SortModel
The model embedded in m_SortCombo.


m_TestsModel

javax.swing.DefaultListModel m_TestsModel
The model embedded in m_TestsList.


m_DisplayedModel

javax.swing.DefaultListModel m_DisplayedModel
The model embedded in m_DisplayedList.


m_TesterClassesLabel

javax.swing.JLabel m_TesterClassesLabel
Displays the currently selected Tester-Class.


m_TesterClasses

javax.swing.JComboBox m_TesterClasses
Lists all the available classes implementing the Tester-Interface.

See Also:
Tester

m_DatasetKeyLabel

javax.swing.JLabel m_DatasetKeyLabel
Displays the currently selected column names for the scheme & options.


m_DatasetKeyBut

javax.swing.JButton m_DatasetKeyBut
Click to edit the columns used to determine the scheme.


m_DatasetKeyModel

javax.swing.DefaultListModel m_DatasetKeyModel
Stores the list of attributes for selecting the scheme columns.


m_DatasetKeyList

javax.swing.JList m_DatasetKeyList
Displays the list of selected columns determining the scheme.


m_ResultKeyLabel

javax.swing.JLabel m_ResultKeyLabel
Displays the currently selected column names for the scheme & options.


m_ResultKeyBut

javax.swing.JButton m_ResultKeyBut
Click to edit the columns used to determine the scheme.


m_ResultKeyModel

javax.swing.DefaultListModel m_ResultKeyModel
Stores the list of attributes for selecting the scheme columns.


m_ResultKeyList

javax.swing.JList m_ResultKeyList
Displays the list of selected columns determining the scheme.


m_TestsButton

javax.swing.JButton m_TestsButton
Lets the user select which scheme to base comparisons against.


m_DisplayedButton

javax.swing.JButton m_DisplayedButton
Lets the user select which schemes are compared to base.


m_TestsList

javax.swing.JList m_TestsList
Holds the list of schemes to base the test against.


m_DisplayedList

javax.swing.JList m_DisplayedList
Holds the list of schemes to display.


m_CompareCombo

javax.swing.JComboBox m_CompareCombo
Lets the user select which performance measure to analyze.


m_SortCombo

javax.swing.JComboBox m_SortCombo
Lets the user select which column to use for sorting.


m_SigTex

javax.swing.JTextField m_SigTex
Lets the user edit the test significance.


m_ShowStdDevs

javax.swing.JCheckBox m_ShowStdDevs
Lets the user select whether standard deviations are to be output or not.


m_OutputFormatButton

javax.swing.JButton m_OutputFormatButton
lets the user choose the format for the output.


m_PerformBut

javax.swing.JButton m_PerformBut
Click to start the test.


m_SaveOutBut

javax.swing.JButton m_SaveOutBut
Click to save test output to a file.


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output.


m_OutText

javax.swing.JTextArea m_OutText
Displays the output of tests.


m_History

ResultHistoryPanel m_History
A panel controlling results viewing.


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting result files.


m_csvFileFilter

ExtensionFileFilter m_csvFileFilter
CSV file filter.


m_arffFileFilter

ExtensionFileFilter m_arffFileFilter
ARFF file filter.


m_TTester

Tester m_TTester
The PairedTTester object.


m_Instances

Instances m_Instances
The instances we're extracting results from.


m_InstanceQuery

InstanceQuery m_InstanceQuery
Does any database querying for us.


m_LoadThread

java.lang.Thread m_LoadThread
A thread to load results instances from a file or database.


m_Exp

Experiment m_Exp
An experiment (used for identifying a result source) -- optional.


COMBO_SIZE

java.awt.Dimension COMBO_SIZE
the size for a combobox.


m_ResultMatrix

ResultMatrix m_ResultMatrix
the initial result matrix.

Class weka.gui.experiment.RunNumberPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -1644336658426067852L

Serialized Fields

m_LowerText

javax.swing.JTextField m_LowerText
Configures the lower run number


m_UpperText

javax.swing.JTextField m_UpperText
Configures the upper run number


m_Exp

Experiment m_Exp
The experiment being configured

Class weka.gui.experiment.RunPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 1691868018596872051L

Serialized Fields

m_StartBut

javax.swing.JButton m_StartBut
Click to start running the experiment


m_StopBut

javax.swing.JButton m_StopBut
Click to signal the running experiment to halt


m_Log

LogPanel m_Log

m_Exp

Experiment m_Exp
The experiment to run


m_RunThread

java.lang.Thread m_RunThread
The thread running the experiment


m_ResultsPanel

ResultsPanel m_ResultsPanel
A pointer to the results panel

Class weka.gui.experiment.SetupModePanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -3758035565520727822L

Serialized Fields

m_SimpleSetupRBut

javax.swing.JRadioButton m_SimpleSetupRBut
The button for choosing simple setup mode


m_AdvancedSetupRBut

javax.swing.JRadioButton m_AdvancedSetupRBut
The button for choosing advanced setup mode


m_simplePanel

SimpleSetupPanel m_simplePanel
The simple setup panel


m_advancedPanel

SetupPanel m_advancedPanel
The advanced setup panel

Class weka.gui.experiment.SetupPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 6552671886903170033L

Serialized Fields

m_Exp

Experiment m_Exp
The experiment being configured


m_OpenBut

javax.swing.JButton m_OpenBut
Click to load an experiment


m_SaveBut

javax.swing.JButton m_SaveBut
Click to save an experiment


m_NewBut

javax.swing.JButton m_NewBut
Click to create a new experiment with default settings


m_ExpFilter

javax.swing.filechooser.FileFilter m_ExpFilter
A filter to ensure only experiment files get shown in the chooser


m_KOMLFilter

javax.swing.filechooser.FileFilter m_KOMLFilter
A filter to ensure only experiment (in KOML format) files get shown in the chooser


m_XMLFilter

javax.swing.filechooser.FileFilter m_XMLFilter
A filter to ensure only experiment (in XML format) files get shown in the chooser


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting experiments


m_RPEditor

GenericObjectEditor m_RPEditor
The ResultProducer editor


m_RPEditorPanel

PropertyPanel m_RPEditorPanel
The panel to contain the ResultProducer editor


m_RLEditor

GenericObjectEditor m_RLEditor
The ResultListener editor


m_RLEditorPanel

PropertyPanel m_RLEditorPanel
The panel to contain the ResultListener editor


m_GeneratorPropertyPanel

GeneratorPropertyIteratorPanel m_GeneratorPropertyPanel
The panel that configures iteration on custom resultproducer property


m_RunNumberPanel

RunNumberPanel m_RunNumberPanel
The panel for configuring run numbers


m_DistributeExperimentPanel

DistributeExperimentPanel m_DistributeExperimentPanel
The panel for enabling a distributed experiment


m_DatasetListPanel

DatasetListPanel m_DatasetListPanel
The panel for configuring selected datasets


m_NotesButton

javax.swing.JButton m_NotesButton
A button for bringing up the notes


m_NotesFrame

javax.swing.JFrame m_NotesFrame
Frame for the notes


m_NotesText

javax.swing.JTextArea m_NotesText
Area for user notes Default of 10 rows


m_Support

java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.


m_advanceDataSetFirst

javax.swing.JRadioButton m_advanceDataSetFirst
Click to advacne data set before custom generator


m_advanceIteratorFirst

javax.swing.JRadioButton m_advanceIteratorFirst
Click to advance custom generator before data set


m_RadioListener

java.awt.event.ActionListener m_RadioListener
Handle radio buttons

Class weka.gui.experiment.SimpleSetupPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 5257424515609176509L

Serialized Fields

m_Exp

Experiment m_Exp
The experiment being configured


m_modePanel

SetupModePanel m_modePanel
The panel which switched between simple and advanced setup modes


m_destinationDatabaseURL

java.lang.String m_destinationDatabaseURL
The database destination URL to store results into


m_destinationFilename

java.lang.String m_destinationFilename
The filename to store results into


m_numFolds

int m_numFolds
The number of folds for a cross-validation experiment


m_trainPercent

double m_trainPercent
The training percentage for a train/test split experiment


m_numRepetitions

int m_numRepetitions
The number of times to repeat the sub-experiment


m_userHasBeenAskedAboutConversion

boolean m_userHasBeenAskedAboutConversion
Whether or not the user has consented for the experiment to be simplified


m_csvFileFilter

ExtensionFileFilter m_csvFileFilter
Filter for choosing CSV files


m_arffFileFilter

ExtensionFileFilter m_arffFileFilter
FIlter for choosing ARFF files


m_OpenBut

javax.swing.JButton m_OpenBut
Click to load an experiment


m_SaveBut

javax.swing.JButton m_SaveBut
Click to save an experiment


m_NewBut

javax.swing.JButton m_NewBut
Click to create a new experiment with default settings


m_ExpFilter

javax.swing.filechooser.FileFilter m_ExpFilter
A filter to ensure only experiment files get shown in the chooser


m_KOMLFilter

javax.swing.filechooser.FileFilter m_KOMLFilter
A filter to ensure only experiment (in KOML format) files get shown in the chooser


m_XMLFilter

javax.swing.filechooser.FileFilter m_XMLFilter
A filter to ensure only experiment (in XML format) files get shown in the chooser


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting experiments


m_DestFileChooser

javax.swing.JFileChooser m_DestFileChooser
The file chooser for selecting result destinations


m_ResultsDestinationCBox

javax.swing.JComboBox m_ResultsDestinationCBox
Combo box for choosing experiment destination type


m_ResultsDestinationPathLabel

javax.swing.JLabel m_ResultsDestinationPathLabel
Label for destination field


m_ResultsDestinationPathTField

javax.swing.JTextField m_ResultsDestinationPathTField
Input field for result destination path


m_BrowseDestinationButton

javax.swing.JButton m_BrowseDestinationButton
Button for browsing destination files


m_ExperimentTypeCBox

javax.swing.JComboBox m_ExperimentTypeCBox
Combo box for choosing experiment type


m_ExperimentParameterLabel

javax.swing.JLabel m_ExperimentParameterLabel
Label for parameter field


m_ExperimentParameterTField

javax.swing.JTextField m_ExperimentParameterTField
Input field for experiment parameter


m_ExpClassificationRBut

javax.swing.JRadioButton m_ExpClassificationRBut
Radio button for choosing classification experiment


m_ExpRegressionRBut

javax.swing.JRadioButton m_ExpRegressionRBut
Radio button for choosing regression experiment


m_NumberOfRepetitionsTField

javax.swing.JTextField m_NumberOfRepetitionsTField
Input field for number of repetitions


m_OrderDatasetsFirstRBut

javax.swing.JRadioButton m_OrderDatasetsFirstRBut
Radio button for choosing datasets first in order of execution


m_OrderAlgorithmsFirstRBut

javax.swing.JRadioButton m_OrderAlgorithmsFirstRBut
Radio button for choosing algorithms first in order of execution


m_DatasetListPanel

DatasetListPanel m_DatasetListPanel
The panel for configuring selected datasets


m_AlgorithmListPanel

AlgorithmListPanel m_AlgorithmListPanel
The panel for configuring selected algorithms


m_NotesButton

javax.swing.JButton m_NotesButton
A button for bringing up the notes


m_NotesFrame

javax.swing.JFrame m_NotesFrame
Frame for the notes


m_NotesText

javax.swing.JTextArea m_NotesText
Area for user notes Default of 10 rows


m_Support

java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.


Package weka.gui.explorer

Class weka.gui.explorer.AssociationsPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -6867871711865476971L

Serialized Fields

m_Explorer

Explorer m_Explorer
the parent frame


m_AssociatorEditor

GenericObjectEditor m_AssociatorEditor
Lets the user configure the associator


m_CEPanel

PropertyPanel m_CEPanel
The panel showing the current associator selection


m_OutText

javax.swing.JTextArea m_OutText
The output area for associations


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_StartBut

javax.swing.JButton m_StartBut
Click to start running the associator


m_StopBut

javax.swing.JButton m_StopBut
Click to stop a running associator


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_TestInstances

Instances m_TestInstances
The user-supplied test set (if any)


m_RunThread

java.lang.Thread m_RunThread
A thread that associator runs in

Class weka.gui.explorer.AttributeSelectionPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 5627185966993476142L

Serialized Fields

m_Explorer

Explorer m_Explorer
the parent frame


m_AttributeEvaluatorEditor

GenericObjectEditor m_AttributeEvaluatorEditor
Lets the user configure the attribute evaluator


m_AttributeSearchEditor

GenericObjectEditor m_AttributeSearchEditor
Lets the user configure the search method


m_AEEPanel

PropertyPanel m_AEEPanel
The panel showing the current attribute evaluation method


m_ASEPanel

PropertyPanel m_ASEPanel
The panel showing the current search method


m_OutText

javax.swing.JTextArea m_OutText
The output area for attribute selection results


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_ClassCombo

javax.swing.JComboBox m_ClassCombo
Lets the user select the class column


m_CVBut

javax.swing.JRadioButton m_CVBut
Click to set evaluation mode to cross-validation


m_TrainBut

javax.swing.JRadioButton m_TrainBut
Click to set test mode to test on training data


m_CVLab

javax.swing.JLabel m_CVLab
Label by where the cv folds are entered


m_CVText

javax.swing.JTextField m_CVText
The field where the cv folds are entered


m_SeedLab

javax.swing.JLabel m_SeedLab
Label by where cv random seed is entered


m_SeedText

javax.swing.JTextField m_SeedText
The field where the seed value is entered


m_RadioListener

java.awt.event.ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button


m_StartBut

javax.swing.JButton m_StartBut
Click to start running the attribute selector


m_StopBut

javax.swing.JButton m_StopBut
Click to stop a running classifier


COMBO_SIZE

java.awt.Dimension COMBO_SIZE
Stop the class combo from taking up to much space


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_RunThread

java.lang.Thread m_RunThread
A thread that attribute selection runs in

Class weka.gui.explorer.ClassifierPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 6959973704963624003L

Serialized Fields

m_Explorer

Explorer m_Explorer
the parent frame


m_ClassifierEditor

GenericObjectEditor m_ClassifierEditor
Lets the user configure the classifier


m_CEPanel

PropertyPanel m_CEPanel
The panel showing the current classifier selection


m_OutText

javax.swing.JTextArea m_OutText
The output area for classification results


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_ClassCombo

javax.swing.JComboBox m_ClassCombo
Lets the user select the class column


m_CVBut

javax.swing.JRadioButton m_CVBut
Click to set test mode to cross-validation


m_PercentBut

javax.swing.JRadioButton m_PercentBut
Click to set test mode to generate a % split


m_TrainBut

javax.swing.JRadioButton m_TrainBut
Click to set test mode to test on training data


m_TestSplitBut

javax.swing.JRadioButton m_TestSplitBut
Click to set test mode to a user-specified test set


m_StorePredictionsBut

javax.swing.JCheckBox m_StorePredictionsBut
Check to save the predictions in the results list for visualizing later on


m_OutputModelBut

javax.swing.JCheckBox m_OutputModelBut
Check to output the model built from the training data


m_OutputPerClassBut

javax.swing.JCheckBox m_OutputPerClassBut
Check to output true/false positives, precision/recall for each class


m_OutputConfusionBut

javax.swing.JCheckBox m_OutputConfusionBut
Check to output a confusion matrix


m_OutputEntropyBut

javax.swing.JCheckBox m_OutputEntropyBut
Check to output entropy statistics


m_OutputPredictionsTextBut

javax.swing.JCheckBox m_OutputPredictionsTextBut
Check to output text predictions


m_OutputAdditionalAttributesText

javax.swing.JTextField m_OutputAdditionalAttributesText
Lists indices for additional attributes to output


m_OutputAdditionalAttributesLab

javax.swing.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_EvalWRTCostsBut

javax.swing.JCheckBox m_EvalWRTCostsBut
Check to evaluate w.r.t a cost matrix


m_SetCostsBut

javax.swing.JButton m_SetCostsBut
for the cost matrix


m_CVLab

javax.swing.JLabel m_CVLab
Label by where the cv folds are entered


m_CVText

javax.swing.JTextField m_CVText
The field where the cv folds are entered


m_PercentLab

javax.swing.JLabel m_PercentLab
Label by where the % split is entered


m_PercentText

javax.swing.JTextField m_PercentText
The field where the % split is entered


m_SetTestBut

javax.swing.JButton m_SetTestBut
The button used to open a separate test dataset


m_SetTestFrame

javax.swing.JFrame m_SetTestFrame
The frame used to show the test set selection panel


m_SetCostsFrame

PropertyDialog m_SetCostsFrame
The frame used to show the cost matrix editing panel


m_RadioListener

java.awt.event.ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button


m_MoreOptions

javax.swing.JButton m_MoreOptions
Button for further output/visualize options


m_RandomSeedText

javax.swing.JTextField m_RandomSeedText
User specified random seed for cross validation or % split


m_RandomLab

javax.swing.JLabel m_RandomLab
the label for the random seed textfield


m_PreserveOrderBut

javax.swing.JCheckBox m_PreserveOrderBut
Whether randomization is turned off to preserve order


m_OutputSourceCode

javax.swing.JCheckBox m_OutputSourceCode
Whether to output the source code (only for classifiers importing Sourcable)


m_SourceCodeClass

javax.swing.JTextField m_SourceCodeClass
The name of the generated class (only applicable to Sourcable schemes)


m_StartBut

javax.swing.JButton m_StartBut
Click to start running the classifier


m_StopBut

javax.swing.JButton m_StopBut
Click to stop a running classifier


COMBO_SIZE

java.awt.Dimension COMBO_SIZE
Stop the class combo from taking up to much space


m_CostMatrixEditor

CostMatrixEditor m_CostMatrixEditor
The cost matrix editor for evaluation costs


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_TestLoader

Loader m_TestLoader
The loader used to load the user-supplied test set (if any)


m_RunThread

java.lang.Thread m_RunThread
A thread that classification runs in


m_CurrentVis

VisualizePanel m_CurrentVis
The current visualization object


m_ModelFilter

javax.swing.filechooser.FileFilter m_ModelFilter
Filter to ensure only model files are selected


m_PMMLModelFilter

javax.swing.filechooser.FileFilter m_PMMLModelFilter

m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting model files

Class weka.gui.explorer.ClustererPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -2474932792950820990L

Serialized Fields

m_Explorer

Explorer m_Explorer
the parent frame


m_ClustererEditor

GenericObjectEditor m_ClustererEditor
Lets the user configure the clusterer


m_CLPanel

PropertyPanel m_CLPanel
The panel showing the current clusterer selection


m_OutText

javax.swing.JTextArea m_OutText
The output area for classification results


m_Log

Logger m_Log
The destination for log/status messages


m_SaveOut

SaveBuffer m_SaveOut
The buffer saving object for saving output


m_History

ResultHistoryPanel m_History
A panel controlling results viewing


m_PercentBut

javax.swing.JRadioButton m_PercentBut
Click to set test mode to generate a % split


m_TrainBut

javax.swing.JRadioButton m_TrainBut
Click to set test mode to test on training data


m_TestSplitBut

javax.swing.JRadioButton m_TestSplitBut
Click to set test mode to a user-specified test set


m_ClassesToClustersBut

javax.swing.JRadioButton m_ClassesToClustersBut
Click to set test mode to classes to clusters based evaluation


m_ClassCombo

javax.swing.JComboBox m_ClassCombo
Lets the user select the class column for classes to clusters based evaluation


m_PercentLab

javax.swing.JLabel m_PercentLab
Label by where the % split is entered


m_PercentText

javax.swing.JTextField m_PercentText
The field where the % split is entered


m_SetTestBut

javax.swing.JButton m_SetTestBut
The button used to open a separate test dataset


m_SetTestFrame

javax.swing.JFrame m_SetTestFrame
The frame used to show the test set selection panel


m_ignoreBut

javax.swing.JButton m_ignoreBut
The button used to popup a list for choosing attributes to ignore while clustering


m_ignoreKeyModel

javax.swing.DefaultListModel m_ignoreKeyModel

m_ignoreKeyList

javax.swing.JList m_ignoreKeyList

m_RadioListener

java.awt.event.ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button


m_StartBut

javax.swing.JButton m_StartBut
Click to start running the clusterer


COMBO_SIZE

java.awt.Dimension COMBO_SIZE
Stop the class combo from taking up to much space


m_StopBut

javax.swing.JButton m_StopBut
Click to stop a running clusterer


m_Instances

Instances m_Instances
The main set of instances we're playing with


m_TestInstances

Instances m_TestInstances
The user-supplied test set (if any)


m_CurrentVis

VisualizePanel m_CurrentVis
The current visualization object


m_StorePredictionsBut

javax.swing.JCheckBox m_StorePredictionsBut
Check to save the predictions in the results list for visualizing later on


m_RunThread

java.lang.Thread m_RunThread
A thread that clustering runs in


m_Summary

InstancesSummaryPanel m_Summary
The instances summary panel displayed by m_SetTestFrame


m_ModelFilter

javax.swing.filechooser.FileFilter m_ModelFilter
Filter to ensure only model files are selected


m_FileChooser

javax.swing.JFileChooser m_FileChooser
The file chooser for selecting model files

Class weka.gui.explorer.DataGeneratorPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -2520408165350629380L

Serialized Fields

m_GeneratorEditor

GenericObjectEditor m_GeneratorEditor
the GOE for the generators


m_Instances

Instances m_Instances
the generated Instances


m_Output

java.io.StringWriter m_Output
the generated output (as text)


m_Log

Logger m_Log
The destination for log/status messages

Class weka.gui.explorer.Explorer extends javax.swing.JPanel implements Serializable

serialVersionUID: -7674003708867909578L

Serialized Fields

m_PreprocessPanel

PreprocessPanel m_PreprocessPanel
The panel for preprocessing instances


m_Panels

java.util.Vector<E> m_Panels
Contains all the additional panels apart from the pre-processing panel


m_TabbedPane

javax.swing.JTabbedPane m_TabbedPane
The tabbed pane that controls which sub-pane we are working with


m_LogPanel

LogPanel m_LogPanel
The panel for log and status messages


m_CapabilitiesFilterChangeListeners

java.util.HashSet<E> m_CapabilitiesFilterChangeListeners
the listeners that listen to filter changes

Class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent extends javax.swing.event.ChangeEvent implements Serializable

serialVersionUID: 1194260517270385559L

Serialized Fields

m_Filter

Capabilities m_Filter
the capabilities filter

Class weka.gui.explorer.ExplorerDefaults extends java.lang.Object implements Serializable

serialVersionUID: 4954795757927524225L

Class weka.gui.explorer.PreprocessPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 6764850273874813049L

Serialized Fields

m_InstSummaryPanel

InstancesSummaryPanel m_InstSummaryPanel
Displays simple stats on the working instances


m_OpenFileBut

javax.swing.JButton m_OpenFileBut
Click to load base instances from a file


m_OpenURLBut

javax.swing.JButton m_OpenURLBut
Click to load base instances from a URL


m_OpenDBBut

javax.swing.JButton m_OpenDBBut
Click to load base instances from a Database


m_GenerateBut

javax.swing.JButton m_GenerateBut
Click to generate artificial data


m_UndoBut

javax.swing.JButton m_UndoBut
Click to revert back to the last saved point


m_EditBut

javax.swing.JButton m_EditBut
Click to open the current instances in a viewer


m_SaveBut

javax.swing.JButton m_SaveBut
Click to apply filters and save the results


m_AttPanel

AttributeSelectionPanel m_AttPanel
Panel to let the user toggle attributes


m_RemoveButton

javax.swing.JButton m_RemoveButton
Button for removing attributes


m_AttSummaryPanel

AttributeSummaryPanel m_AttSummaryPanel
Displays summary stats on the selected attribute


m_FilterEditor

GenericObjectEditor m_FilterEditor
Lets the user configure the filter


m_FilterPanel

PropertyPanel m_FilterPanel
Filter configuration


m_ApplyFilterBut

javax.swing.JButton m_ApplyFilterBut
Click to apply filters and save the results


m_FileChooser

ConverterFileChooser m_FileChooser
The file chooser for selecting data files


m_LastURL

java.lang.String m_LastURL
Stores the last URL that instances were loaded from


m_SQLQ

java.lang.String m_SQLQ
Stores the last sql query executed


m_Instances

Instances m_Instances
The working instances


m_DataGenerator

DataGenerator m_DataGenerator
The last generator that was selected


m_AttVisualizePanel

AttributeVisualizationPanel m_AttVisualizePanel
The visualization of the attribute values


m_tempUndoFiles

java.io.File[] m_tempUndoFiles
Keeps track of undo points


m_tempUndoIndex

int m_tempUndoIndex
The next available slot for an undo point


m_Support

java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the set of working instances.


m_IOThread

java.lang.Thread m_IOThread
A thread for loading/saving instances from a file or URL


m_Log

Logger m_Log
The message logger


m_Explorer

Explorer m_Explorer
the parent frame

Class weka.gui.explorer.VisualizePanel extends MatrixPanel implements Serializable

serialVersionUID: 6084015036853918846L

Serialized Fields

m_Explorer

Explorer m_Explorer
the parent frame


Package weka.gui.graphvisualizer

Class weka.gui.graphvisualizer.BIFFormatException extends java.lang.Exception implements Serializable

serialVersionUID: -4102518086411708140L

Class weka.gui.graphvisualizer.GraphVisualizer extends javax.swing.JPanel implements Serializable

serialVersionUID: -2038911085935515624L

Serialized Fields

m_nodes

FastVector m_nodes
Vector containing nodes


m_edges

FastVector m_edges
Vector containing edges


m_le

LayoutEngine m_le
The current LayoutEngine


m_gp

weka.gui.graphvisualizer.GraphVisualizer.GraphPanel m_gp
Panel actually displaying the graph


graphID

java.lang.String graphID
String containing graph's name


m_jBtSave

javax.swing.JButton m_jBtSave
Save button to save the current graph in DOT or XMLBIF format. The graph should be layed out again to get the original form if reloaded from command line, as the formats do not allow saving specific information for a properly layed out graph.


ICONPATH

java.lang.String ICONPATH
path for icons


fm

java.awt.FontMetrics fm

scale

double scale

nodeHeight

int nodeHeight

nodeWidth

int nodeWidth

paddedNodeWidth

int paddedNodeWidth

jTfNodeWidth

javax.swing.JTextField jTfNodeWidth
TextField for node's width


jTfNodeHeight

javax.swing.JTextField jTfNodeHeight
TextField for nodes height


jBtLayout

javax.swing.JButton jBtLayout
Button for laying out the graph again, necessary after changing node's size or some other property of the layout engine


maxStringWidth

int maxStringWidth
used for setting appropriate node size


zoomPercents

int[] zoomPercents
used when using zoomIn and zoomOut buttons


m_js

javax.swing.JScrollPane m_js
this contains the m_gp GraphPanel

Class weka.gui.graphvisualizer.LayoutCompleteEvent extends java.util.EventObject implements Serializable

serialVersionUID: 6172467234026258427L


Package weka.gui.sql

Class weka.gui.sql.ConnectionPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 3499317023969723490L

Serialized Fields

m_Parent

javax.swing.JFrame m_Parent
the parent frame.


m_DbDialog

DatabaseConnectionDialog m_DbDialog
the databae connection dialog.


m_URL

java.lang.String m_URL
the URL to use.


m_User

java.lang.String m_User
the user to use for connecting to the DB.


m_Password

java.lang.String m_Password
the password to use for connecting to the DB.


m_LabelURL

javax.swing.JLabel m_LabelURL
the label for the URL.


m_TextURL

javax.swing.JTextField m_TextURL
the textfield for the URL.


m_ButtonDatabase

javax.swing.JButton m_ButtonDatabase
the button for the DB-Dialog.


m_ButtonConnect

javax.swing.JButton m_ButtonConnect
the button for connecting to the database.


m_ButtonHistory

javax.swing.JButton m_ButtonHistory
the button for the history.


m_ConnectionListeners

java.util.HashSet<E> m_ConnectionListeners
the connection listeners.


m_HistoryChangedListeners

java.util.HashSet<E> m_HistoryChangedListeners
the history listeners.


m_DbUtils

DbUtils m_DbUtils
for connecting to the database.


m_History

javax.swing.DefaultListModel m_History
the history of connections.

Class weka.gui.sql.DbUtils extends DatabaseUtils implements Serializable

serialVersionUID: 103748569037426479L

Class weka.gui.sql.InfoPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -7701133696481997973L

Serialized Fields

m_Parent

javax.swing.JFrame m_Parent
the parent of this panel


m_Info

javax.swing.JList m_Info
the list the contains the messages


m_Model

javax.swing.DefaultListModel m_Model
the model for the list


m_ButtonClear

javax.swing.JButton m_ButtonClear
the button to clear the area


m_ButtonCopy

javax.swing.JButton m_ButtonCopy
the button to copy the selected message

Class weka.gui.sql.InfoPanelCellRenderer extends javax.swing.JLabel implements Serializable

serialVersionUID: -533380118807178531L

Class weka.gui.sql.QueryPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 4348967824619706636L

Serialized Fields

m_Parent

javax.swing.JFrame m_Parent
the parent of this panel.


m_TextQuery

javax.swing.JTextArea m_TextQuery
the textarea for the query.


m_ButtonExecute

javax.swing.JButton m_ButtonExecute
the execute button.


m_ButtonClear

javax.swing.JButton m_ButtonClear
the clear button.


m_ButtonHistory

javax.swing.JButton m_ButtonHistory
the history button.


m_SpinnerMaxRows

javax.swing.JSpinner m_SpinnerMaxRows
the spinner for the maximum number of rows.


m_QueryExecuteListeners

java.util.HashSet<E> m_QueryExecuteListeners
the connection listeners.


m_HistoryChangedListeners

java.util.HashSet<E> m_HistoryChangedListeners
the history listeners.


m_DbUtils

DbUtils m_DbUtils
for working on the database.


m_Connected

boolean m_Connected
whether we have a connection to a database or not.


m_History

javax.swing.DefaultListModel m_History
the query history.

Class weka.gui.sql.ResultPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 278654800344034571L

Serialized Fields

m_Parent

javax.swing.JFrame m_Parent
the parent of this panel


m_Listeners

java.util.HashSet<E> m_Listeners
the result change listeners


m_QueryPanel

QueryPanel m_QueryPanel
the panel where to output the queries


m_TabbedPane

javax.swing.JTabbedPane m_TabbedPane
the tabbed pane for the results


m_ButtonClose

javax.swing.JButton m_ButtonClose
the close button


m_ButtonCloseAll

javax.swing.JButton m_ButtonCloseAll
the close all button


m_ButtonCopyQuery

javax.swing.JButton m_ButtonCopyQuery
the button that copies the query into the QueryPanel


m_ButtonOptWidth

javax.swing.JButton m_ButtonOptWidth
the button for the optimal column width of the current table


m_NameCounter

int m_NameCounter
the counter for the tab names

Class weka.gui.sql.ResultSetTable extends javax.swing.JTable implements Serializable

serialVersionUID: -3391076671854464137L

Serialized Fields

m_Query

java.lang.String m_Query
the query the table model is based on


m_URL

java.lang.String m_URL
the connect string with which the query was run


m_User

java.lang.String m_User
the user that was used to connect to the DB


m_Password

java.lang.String m_Password
the password that was used to connect to the DB

Class weka.gui.sql.ResultSetTableCellRenderer extends javax.swing.table.DefaultTableCellRenderer implements Serializable

serialVersionUID: -8106963669703497351L

Serialized Fields

missingColor

java.awt.Color missingColor

missingColorSelected

java.awt.Color missingColorSelected

Class weka.gui.sql.SqlViewer extends javax.swing.JPanel implements Serializable

serialVersionUID: -4395028775566514329L

Serialized Fields

m_Parent

javax.swing.JFrame m_Parent
the parent of this panel.


m_ConnectionPanel

ConnectionPanel m_ConnectionPanel
the connection panel.


m_QueryPanel

QueryPanel m_QueryPanel
the query panel.


m_ResultPanel

ResultPanel m_ResultPanel
the result panel.


m_InfoPanel

InfoPanel m_InfoPanel
the info panel.


m_URL

java.lang.String m_URL
the connect string with which the query was run.


m_User

java.lang.String m_User
the user that was used to connect to the DB.


m_Password

java.lang.String m_Password
the password that was used to connect to the DB.


m_Query

java.lang.String m_Query
the currently selected query.


m_History

java.util.Properties m_History
stores the history.

Class weka.gui.sql.SqlViewerDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -31619864037233099L

Serialized Fields

m_Parent

javax.swing.JFrame m_Parent
the parent frame


m_Viewer

SqlViewer m_Viewer
the SQL panel


m_PanelButtons

javax.swing.JPanel m_PanelButtons
the panel for the buttons


m_ButtonOK

javax.swing.JButton m_ButtonOK
the OK button


m_ButtonCancel

javax.swing.JButton m_ButtonCancel
the Cancel button


m_LabelQuery

javax.swing.JLabel m_LabelQuery
displays the current query


m_ReturnValue

int m_ReturnValue
the return value


m_URL

java.lang.String m_URL
the connect string with which the query was run


m_User

java.lang.String m_User
the user that was used to connect to the DB


m_Password

java.lang.String m_Password
the password that was used to connect to the DB


m_Query

java.lang.String m_Query
the currently selected query


Package weka.gui.sql.event

Class weka.gui.sql.event.ConnectionEvent extends java.util.EventObject implements Serializable

serialVersionUID: 5420308930427835037L

Serialized Fields

m_Type

int m_Type
the type of event, CONNECT or DISCONNECT


m_DbUtils

DbUtils m_DbUtils
the databaseutils instance reponsible for the connection


m_Exception

java.lang.Exception m_Exception
a possible exception that occurred if not successful

Class weka.gui.sql.event.HistoryChangedEvent extends java.util.EventObject implements Serializable

serialVersionUID: 7476087315774869973L

Serialized Fields

m_HistoryName

java.lang.String m_HistoryName
the name of the history


m_History

javax.swing.DefaultListModel m_History
the history model

Class weka.gui.sql.event.QueryExecuteEvent extends java.util.EventObject implements Serializable

serialVersionUID: -5556385019954730740L

Serialized Fields

m_DbUtils

DbUtils m_DbUtils
the Db utils instance for the current DB connection


m_Query

java.lang.String m_Query
the query that was executed


m_ResultSet

java.sql.ResultSet m_ResultSet
the produced ResultSet, if any


m_Exception

java.lang.Exception m_Exception
a possible exception, if the query failed


m_MaxRows

int m_MaxRows
the maximum number of rows to retrieve

Class weka.gui.sql.event.ResultChangedEvent extends java.util.EventObject implements Serializable

serialVersionUID: 36042516077236111L

Serialized Fields

m_Query

java.lang.String m_Query
the query that is associated with the active result table


m_URL

java.lang.String m_URL
the connect string with which the query was run


m_User

java.lang.String m_User
the user that was used to connect to the DB


m_Password

java.lang.String m_Password
the password that was used to connect to the DB


Package weka.gui.streams

Class weka.gui.streams.InstanceCounter extends javax.swing.JPanel implements Serializable

serialVersionUID: -6084967152645935934L

Serialized Fields

m_Count_Lab

javax.swing.JLabel m_Count_Lab

m_Count

int m_Count

m_Debug

boolean m_Debug

Class weka.gui.streams.InstanceEvent extends java.util.EventObject implements Serializable

serialVersionUID: 3207259868110667379L

Serialized Fields

m_ID

int m_ID

Class weka.gui.streams.InstanceJoiner extends java.lang.Object implements Serializable

serialVersionUID: -6529972700291329656L

Serialized Fields

listeners

java.util.Vector<E> listeners
The listeners


b_Debug

boolean b_Debug
Debugging mode


m_InputFormat

Instances m_InputFormat
The input format for instances


m_OutputInstance

Instance m_OutputInstance
The current output instance


b_FirstInputFinished

boolean b_FirstInputFinished
Whether the first input batch has finished


b_SecondInputFinished

boolean b_SecondInputFinished

Class weka.gui.streams.InstanceLoader extends javax.swing.JPanel implements Serializable

serialVersionUID: -8725567310271862492L

Serialized Fields

m_Listeners

java.util.Vector<E> m_Listeners

m_LoaderThread

java.lang.Thread m_LoaderThread

m_OutputInstance

Instance m_OutputInstance

m_OutputInstances

Instances m_OutputInstances

m_Debug

boolean m_Debug

m_StartBut

javax.swing.JButton m_StartBut

m_FileNameTex

javax.swing.JTextField m_FileNameTex

Class weka.gui.streams.InstanceSavePanel extends java.awt.Panel implements Serializable

serialVersionUID: -6061005366989295026L

Serialized Fields

count_Lab

java.awt.Label count_Lab

m_Count

int m_Count

arffFile_Tex

java.awt.TextField arffFile_Tex

b_Debug

boolean b_Debug

outputWriter

java.io.PrintWriter outputWriter

Class weka.gui.streams.InstanceTable extends javax.swing.JPanel implements Serializable

serialVersionUID: -2462533698100834803L

Serialized Fields

m_InstanceTable

javax.swing.JTable m_InstanceTable

m_Debug

boolean m_Debug

m_Clear

boolean m_Clear

m_UpdateString

java.lang.String m_UpdateString

m_Instances

Instances m_Instances

Class weka.gui.streams.InstanceViewer extends javax.swing.JPanel implements Serializable

serialVersionUID: -4925729441294121772L

Serialized Fields

m_OutputTex

javax.swing.JTextArea m_OutputTex

m_Debug

boolean m_Debug

m_Clear

boolean m_Clear

m_UpdateString

java.lang.String m_UpdateString

Package weka.gui.treevisualizer

Class weka.gui.treevisualizer.TreeVisualizer extends PrintablePanel implements Serializable

serialVersionUID: -8668637962504080749L

Serialized Fields

m_placer

NodePlace m_placer
The placement algorithm for the Node structure.


m_topNode

Node m_topNode
The top Node.


m_viewPos

java.awt.Dimension m_viewPos
The postion of the view relative to the tree.


m_viewSize

java.awt.Dimension m_viewSize
The size of the tree in pixels.


m_currentFont

java.awt.Font m_currentFont
The font used to display the tree.


m_fontSize

java.awt.FontMetrics m_fontSize
The size information for the current font.


m_numNodes

int m_numNodes
The number of Nodes in the tree.


m_numLevels

int m_numLevels
The number of levels in the tree.


m_nodes

weka.gui.treevisualizer.TreeVisualizer.NodeInfo[] m_nodes
An array with the Nodes sorted into it and display information about the Nodes.


m_edges

weka.gui.treevisualizer.TreeVisualizer.EdgeInfo[] m_edges
An array with the Edges sorted into it and display information about the Edges.


m_frameLimiter

javax.swing.Timer m_frameLimiter
A timer to keep the frame rate constant.


m_mouseState

int m_mouseState
Describes the action the user is performing.


m_oldMousePos

java.awt.Dimension m_oldMousePos
A variable used to tag the start pos of a user action.


m_newMousePos

java.awt.Dimension m_newMousePos
A variable used to tag the most current point of a user action.


m_clickAvailable

boolean m_clickAvailable
A variable used to determine for the clicked method if any other mouse state has already taken place.


m_nViewPos

java.awt.Dimension m_nViewPos
A variable used to remember the desired view pos.


m_nViewSize

java.awt.Dimension m_nViewSize
A variable used to remember the desired tree size.


m_scaling

int m_scaling
The number of frames left to calculate.


m_winMenu

javax.swing.JPopupMenu m_winMenu
A right (or middle) click popup menu.


m_topN

javax.swing.JMenuItem m_topN
An option on the win_menu


m_fitToScreen

javax.swing.JMenuItem m_fitToScreen
An option on the win_menu


m_autoScale

javax.swing.JMenuItem m_autoScale
An option on the win_menu


m_selectFont

javax.swing.JMenu m_selectFont
A sub group on the win_menu


m_selectFontGroup

javax.swing.ButtonGroup m_selectFontGroup
A grouping for the font choices


m_size24

javax.swing.JRadioButtonMenuItem m_size24
A font choice.


m_size22

javax.swing.JRadioButtonMenuItem m_size22
A font choice.


m_size20

javax.swing.JRadioButtonMenuItem m_size20
A font choice.


m_size18

javax.swing.JRadioButtonMenuItem m_size18
A font choice.


m_size16

javax.swing.JRadioButtonMenuItem m_size16
A font choice.


m_size14

javax.swing.JRadioButtonMenuItem m_size14
A font choice.


m_size12

javax.swing.JRadioButtonMenuItem m_size12
A font choice.


m_size10

javax.swing.JRadioButtonMenuItem m_size10
A font choice.


m_size8

javax.swing.JRadioButtonMenuItem m_size8
A font choice.


m_size6

javax.swing.JRadioButtonMenuItem m_size6
A font choice.


m_size4

javax.swing.JRadioButtonMenuItem m_size4
A font choice.


m_size2

javax.swing.JRadioButtonMenuItem m_size2
A font choice.


m_size1

javax.swing.JRadioButtonMenuItem m_size1
A font choice.


m_accept

javax.swing.JMenuItem m_accept
An option on the win menu.


m_nodeMenu

javax.swing.JPopupMenu m_nodeMenu
A right or middle click popup menu for nodes.


m_visualise

javax.swing.JMenuItem m_visualise
A visualize choice for the node, may not be available.


m_addChildren

javax.swing.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_remChildren

javax.swing.JMenuItem m_remChildren
Similar to add children but now it removes children.


m_classifyChild

javax.swing.JMenuItem m_classifyChild
Use this to have J48 classify this node.


m_sendInstances

javax.swing.JMenuItem m_sendInstances
Use this to dump the instances from this node to the vis panel.


m_focusNode

int m_focusNode
The subscript for the currently selected node (this is an internal thing, so the user is unaware of this).


m_highlightNode

int m_highlightNode
The Node the user is currently focused on , this is similar to focus node except that it is used by other classes rather than this one.


m_listener

TreeDisplayListener m_listener

m_searchString

javax.swing.JTextField m_searchString

m_searchWin

javax.swing.JDialog m_searchWin

m_caseSen

javax.swing.JRadioButton m_caseSen

m_FontColor

java.awt.Color m_FontColor
the font color.


m_BackgroundColor

java.awt.Color m_BackgroundColor
the background color.


m_NodeColor

java.awt.Color m_NodeColor
the node color.


m_LineColor

java.awt.Color m_LineColor
the line color.


m_ZoomBoxColor

java.awt.Color m_ZoomBoxColor
the color of the zoombox.


m_ZoomBoxXORColor

java.awt.Color m_ZoomBoxXORColor
the XOR color of the zoombox.


m_ShowBorder

boolean m_ShowBorder
whether to show the border or not.


Package weka.gui.visualize

Class weka.gui.visualize.AttributePanel extends javax.swing.JScrollPane implements Serializable

serialVersionUID: 3533330317806757814L

Serialized Fields

m_plotInstances

Instances m_plotInstances
The instances to be plotted


m_maxC

double m_maxC
Holds the min and max values of the colouring attributes


m_minC

double m_minC

m_cIndex

int m_cIndex

m_xIndex

int m_xIndex

m_yIndex

int m_yIndex

m_colorList

FastVector m_colorList
The colour map to use for colouring points


m_DefaultColors

java.awt.Color[] m_DefaultColors
default colours for colouring discrete class


m_backgroundColor

java.awt.Color m_backgroundColor
If set, it allows this panel to avoid setting a color in the color list that is equal to the background color


m_Listeners

FastVector m_Listeners
The list of things listening to this panel


m_heights

int[] m_heights
Holds the random height for each instance.


m_span

javax.swing.JPanel m_span
The container window for the attribute bars, and also where the X,Y or B get printed.


m_barColour

java.awt.Color m_barColour
The default colour to use for the background of the bars if a colour is not defined in Visualize.props

Class weka.gui.visualize.AttributePanel.AttributeSpacing extends javax.swing.JPanel implements Serializable

serialVersionUID: 7220615894321679898L

Serialized Fields

m_maxVal

double m_maxVal
The min and max values for this attribute.


m_minVal

double m_minVal

m_attrib

Attribute m_attrib
The attribute itself.


m_attribIndex

int m_attribIndex
The index for this attribute.


m_cached

int[] m_cached
The x position of each point.


m_pointDrawn

boolean[][] m_pointDrawn
A temporary array used to strike any instances that would be drawn redundantly.


m_oldWidth

int m_oldWidth
Used to determine if the positions need to be recalculated.

Class weka.gui.visualize.ClassPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -7969401840501661430L

Serialized Fields

m_isEnabled

boolean m_isEnabled
True when the panel has been enabled (ie after setNumeric or setNominal has been called


m_isNumeric

boolean m_isNumeric
True if the colouring attribute is numeric


m_spectrumHeight

int m_spectrumHeight
The height of the spectrum for numeric class


m_maxC

double m_maxC
The maximum value for the colouring attribute


m_minC

double m_minC
The minimum value for the colouring attribute


m_tickSize

int m_tickSize
The size of the ticks


m_labelMetrics

java.awt.FontMetrics m_labelMetrics
Font metrics


m_labelFont

java.awt.Font m_labelFont
The font used in labeling


m_HorizontalPad

int m_HorizontalPad
The amount of space to leave either side of the legend


m_precisionC

int m_precisionC
The precision with which to display real values


m_fieldWidthC

int m_fieldWidthC
Field width for numeric values


m_oldWidth

int m_oldWidth
The old width.


m_Instances

Instances m_Instances
Instances being plotted


m_cIndex

int m_cIndex
Index of the colouring attribute


m_colorList

FastVector m_colorList
the list of colours to use for colouring nominal attribute labels


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_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

java.awt.Color[] m_DefaultColors
default colours for colouring discrete class


m_backgroundColor

java.awt.Color m_backgroundColor
if set, it allows this panel to steer away from setting up a color in the color list that is equal to the background color

Class weka.gui.visualize.LegendPanel extends javax.swing.JScrollPane implements Serializable

serialVersionUID: -1262384440543001505L

Serialized Fields

m_plots

FastVector m_plots
the list of plot elements


m_span

javax.swing.JPanel m_span
the panel that contains the legend entries


m_Repainters

FastVector m_Repainters
a list of components that need to be repainted when a colour is changed

Class weka.gui.visualize.LegendPanel.LegendEntry extends javax.swing.JPanel implements Serializable

serialVersionUID: 3879990289042935670L

Serialized Fields

m_plotData

PlotData2D m_plotData
the data for this legend entry


m_dataIndex

int m_dataIndex
the index (in the list of plots) of the data for this legend--- used to draw the correct shape for this data


m_legendText

javax.swing.JLabel m_legendText
the text part of this legend


m_pointShape

javax.swing.JPanel m_pointShape
displays the point shape associated with this legend entry

Class weka.gui.visualize.MatrixPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -1232642719869188740L

Serialized Fields

m_plotsPanel

weka.gui.visualize.MatrixPanel.Plot m_plotsPanel
The that panel contains the actual matrix


m_cp

ClassPanel m_cp
The panel that displays the legend of the colouring attribute


optionsPanel

javax.swing.JPanel optionsPanel
The panel that contains all the buttons and tools, i.e. resize, jitter bars and sub-sampling buttons etc on the bottom of the panel


jp

javax.swing.JSplitPane jp
Split pane for splitting the matrix and the buttons and bars


m_updateBt

javax.swing.JButton m_updateBt
The button that updates the display to reflect the changes made by the user. E.g. changed attribute set for the matrix


m_selAttrib

javax.swing.JButton m_selAttrib
The button to display a window to select attributes


m_data

Instances m_data
The dataset for which this panel will display the plot matrix for


m_attribList

javax.swing.JList m_attribList
The list for selecting the attributes to display the plot matrix


m_js

javax.swing.JScrollPane m_js
The scroll pane to scrolling the matrix


m_classAttrib

javax.swing.JComboBox m_classAttrib
The combo box to allow user to select the colouring attribute


m_plotSize

javax.swing.JSlider m_plotSize
The slider to adjust the size of the cells in the matrix


m_pointSize

javax.swing.JSlider m_pointSize
The slider to adjust the size of the datapoints


m_jitter

javax.swing.JSlider m_jitter
The slider to add jitter to the plots


rnd

java.util.Random rnd
For adding random jitter


jitterVals

int[][] jitterVals
Array containing precalculated jitter values


datapointSize

int datapointSize
This stores the size of the datapoint


m_resamplePercent

javax.swing.JTextField m_resamplePercent
The text area for percentage to resample data


m_resampleBt

javax.swing.JButton m_resampleBt
The label for resample percentage


m_rseed

javax.swing.JTextField m_rseed
Random seed for random subsample


m_plotSizeLb

javax.swing.JLabel m_plotSizeLb
Displays the current size beside the slider bar for cell size


m_pointSizeLb

javax.swing.JLabel m_pointSizeLb
Displays the current size beside the slider bar for point size


m_selectedAttribs

int[] m_selectedAttribs
This array contains the indices of the attributes currently selected


m_classIndex

int m_classIndex
This contains the index of the currently selected colouring attribute


m_points

int[][] m_points
This is a local array cache for all the instance values for faster rendering


m_pointColors

int[] m_pointColors
This is an array cache for the colour of each of the instances depending on the colouring attribute. If the colouring attribute is nominal then it contains the index of the colour in our colour list. Otherwise, for numeric colouring attribute, it contains the precalculated red component for each instance's colour


m_missing

boolean[][] m_missing
Contains true for each attribute value (only the selected attributes+class attribute) that is missing, for each instance. m_missing[i][j] == true if m_selectedAttribs[j] is missing in instance i. m_missing[i][m_missing[].length-1] == true if class value is missing in instance i.


m_type

int[] m_type
This array contains for the classAttribute:
m_type[0] = [type of attribute, nominal, string or numeric]
m_type[1] = [number of discrete values of nominal or string attribute
or same as m_type[0] for numeric attribute]


m_plotLBSizeD

java.awt.Dimension m_plotLBSizeD
Stores the maximum size for PlotSize label to keep it's size constant


m_pointLBSizeD

java.awt.Dimension m_pointLBSizeD
Stores the maximum size for PointSize label to keep it's size constant


m_colorList

FastVector m_colorList
Contains discrete colours for colouring for nominal attributes


fontColor

java.awt.Color fontColor
color for the font used in column and row names


f

java.awt.Font f
font used in column and row names

Class weka.gui.visualize.Plot2D extends javax.swing.JPanel implements Serializable

serialVersionUID: -1673162410856660442L

Serialized Fields

m_axisColour

java.awt.Color m_axisColour
Default colour for the axis


m_backgroundColour

java.awt.Color m_backgroundColour
Default colour for the plot background


m_plots

FastVector m_plots
The plots to display


m_masterPlot

PlotData2D m_masterPlot
The master plot


m_masterName

java.lang.String m_masterName
The name of the master plot


m_plotInstances

Instances m_plotInstances
The instances to be plotted


m_plotCompanion

Plot2DCompanion m_plotCompanion
An optional "compainion" of the panel. If specified, this class will get to do its thing with our graphics context before we do any drawing. Eg. the visualize panel may need to draw polygons etc. before we draw plot axis and data points


m_InstanceInfo

javax.swing.JFrame m_InstanceInfo
For popping up text info on data points


m_InstanceInfoText

javax.swing.JTextArea m_InstanceInfoText

m_colorList

FastVector m_colorList
The list of the colors used


m_DefaultColors

java.awt.Color[] m_DefaultColors
default colours for colouring discrete class


m_xIndex

int m_xIndex
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing


m_yIndex

int m_yIndex

m_cIndex

int m_cIndex

m_sIndex

int m_sIndex

m_maxX

double m_maxX
Holds the min and max values of the x, y and colouring attributes over all plots


m_minX

double m_minX

m_maxY

double m_maxY

m_minY

double m_minY

m_maxC

double m_maxC

m_minC

double m_minC

m_axisPad

int m_axisPad
Axis padding


m_tickSize

int m_tickSize
Tick size


m_XaxisStart

int m_XaxisStart
the offsets of the axes once label metrics are calculated


m_YaxisStart

int m_YaxisStart

m_XaxisEnd

int m_XaxisEnd

m_YaxisEnd

int m_YaxisEnd

m_plotResize

boolean m_plotResize
if the user resizes the window, or the attributes selected for the attributes change, then the lookup table for points needs to be recalculated


m_axisChanged

boolean m_axisChanged
if the user changes attribute assigned to an axis


m_drawnPoints

int[][] m_drawnPoints
An array used to show if a point is hidden or not. This is used for speeding up the drawing of the plot panel although I am not sure how much performance this grants over not having it.


m_labelFont

java.awt.Font m_labelFont
Font for labels


m_labelMetrics

java.awt.FontMetrics m_labelMetrics

m_JitterVal

int m_JitterVal
the level of jitter


m_JRand

java.util.Random m_JRand
random values for perterbing the data points


m_pointLookup

double[][] m_pointLookup
lookup table for plotted points

Class weka.gui.visualize.PrintableComponent.JComponentWriterFileFilter extends ExtensionFileFilter implements Serializable

Serialized Fields

m_Writer

JComponentWriter m_Writer
the associated writer.

Class weka.gui.visualize.PrintablePanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 6281532227633417538L

Serialized Fields

m_Printer

PrintableComponent m_Printer
the class responsible for printing

Class weka.gui.visualize.ThresholdVisualizePanel extends VisualizePanel implements Serializable

serialVersionUID: 3070002211779443890L

Serialized Fields

m_ROCString

java.lang.String m_ROCString
The string to add to the Plot Border.


m_savePanelBorderText

java.lang.String m_savePanelBorderText
Original border text

Class weka.gui.visualize.VisualizePanel extends PrintablePanel implements Serializable

serialVersionUID: 240108358588153943L

Serialized Fields

m_DefaultColors

java.awt.Color[] m_DefaultColors
default colours for colouring discrete class


m_XCombo

javax.swing.JComboBox m_XCombo
Lets the user select the attribute for the x axis


m_YCombo

javax.swing.JComboBox m_YCombo
Lets the user select the attribute for the y axis


m_ColourCombo

javax.swing.JComboBox m_ColourCombo
Lets the user select the attribute to use for colouring


m_ShapeCombo

javax.swing.JComboBox m_ShapeCombo
Lets the user select the shape they want to create for instance selection.


m_submit

javax.swing.JButton m_submit
Button for the user to enter the splits.


m_cancel

javax.swing.JButton m_cancel
Button for the user to remove all splits.


m_openBut

javax.swing.JButton m_openBut
Button for the user to open the visualized set of instances


m_saveBut

javax.swing.JButton m_saveBut
Button for the user to save the visualized set of instances


COMBO_SIZE

java.awt.Dimension COMBO_SIZE
Stop the combos from growing out of control


m_FileChooser

javax.swing.JFileChooser m_FileChooser
file chooser for saving instances


m_ArffFilter

javax.swing.filechooser.FileFilter m_ArffFilter
Filter to ensure only arff files are selected


m_JitterLab

javax.swing.JLabel m_JitterLab
Label for the jitter slider


m_Jitter

javax.swing.JSlider m_Jitter
The jitter slider


m_plot

weka.gui.visualize.VisualizePanel.PlotPanel m_plot
The panel that displays the plot


m_attrib

AttributePanel m_attrib
The panel that displays the attributes , using color to represent another attribute.


m_legendPanel

LegendPanel m_legendPanel
The panel that displays legend info if there is more than one plot


m_plotSurround

javax.swing.JPanel m_plotSurround
Panel that surrounds the plot panel with a titled border


m_classSurround

javax.swing.JPanel m_classSurround
Panel that surrounds the class panel with a titled border


listener

java.awt.event.ActionListener listener
An optional listener that we will inform when ComboBox selections change


m_splitListener

VisualizePanelListener m_splitListener
An optional listener that we will inform when the user creates a split to seperate instances.


m_plotName

java.lang.String m_plotName
The name of the plot (not currently displayed, but can be used in the containing Frame or Panel)


m_classPanel

ClassPanel m_classPanel
The panel that displays the legend for the colouring attribute


m_colorList

FastVector m_colorList
The list of the colors used


m_preferredXDimension

java.lang.String m_preferredXDimension
These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file


m_preferredYDimension

java.lang.String m_preferredYDimension

m_preferredColourDimension

java.lang.String m_preferredColourDimension

m_showAttBars

boolean m_showAttBars
Show the attribute bar panel


m_showClassPanel

boolean m_showClassPanel
Show the class panel


m_Log

Logger m_Log
the logger

Class weka.gui.visualize.VisualizePanel.PlotPanel extends PrintablePanel implements Serializable

serialVersionUID: -4823674171136494204L

Serialized Fields

m_plot2D

Plot2D m_plot2D
The actual generic plotting panel


m_plotInstances

Instances m_plotInstances
The instances from the master plot


m_originalPlot

PlotData2D m_originalPlot
The master plot


m_xIndex

int m_xIndex
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing


m_yIndex

int m_yIndex

m_cIndex

int m_cIndex

m_sIndex

int m_sIndex

m_XaxisStart

int m_XaxisStart
the offsets of the axes once label metrics are calculated


m_YaxisStart

int m_YaxisStart

m_XaxisEnd

int m_XaxisEnd

m_YaxisEnd

int m_YaxisEnd

m_createShape

boolean m_createShape
True if the user is currently dragging a box.


m_shapes

FastVector m_shapes
contains all the shapes that have been drawn for these attribs


m_shapePoints

FastVector m_shapePoints
contains the points of the shape currently being drawn.


m_newMousePos

java.awt.Dimension m_newMousePos
contains the position of the mouse (used for rubberbanding).