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.


m_treatZeroAsMissing

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

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


m_treatZeroAsMissing

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

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_missingSeperate

boolean m_missingSeperate
Treat missing values as seperate 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)


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.SignificanceAttributeEval 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.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.TabuSearch extends ASSearch implements Serializable

serialVersionUID: -8812132617585120414L

Serialized Fields

m_numAttribs

int m_numAttribs
number of attributes in the data


m_classIndex

int m_classIndex
holds the class index


m_random

java.util.Random m_random
random number generation


m_seed

int m_seed
seed for random number generation


m_diversificationProb

double m_diversificationProb
probabilities of diversification


m_numIterations

int m_numIterations
number of iterations for getting the best subset


m_totalEvals

int m_totalEvals
total number of subsets evaluated during a search


m_Sbest

TabuSearch.Subset m_Sbest
holds the best Subset found


m_numNeighborhood

int m_numNeighborhood
holds the number of neighborhood to generate from a Subset


m_processinTime

long m_processinTime
time for procesing the search method


m_initialSize

int m_initialSize
holds the solution size


m_initialSolution

TabuSearch.Subset m_initialSolution
Subset holding the initial solution


m_rankedAttribs

java.util.List<E> m_rankedAttribs
attribute ranking


m_vectorTabu

java.util.List<E> m_vectorTabu
tabu List


ASEvaluator

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

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

Serialized Fields

merit

double merit

subset

java.util.BitSet subset

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


m_evaluationMeasure

int m_evaluationMeasure
The evaluation measure to use


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.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.evaluation.output.prediction

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

serialVersionUID: 752696986017306241L

Serialized Fields

m_Header

Instances m_Header
the header of the dataset.


m_Buffer

java.lang.StringBuffer m_Buffer
the buffer to write to.


m_FileBuffer

java.lang.StringBuffer m_FileBuffer
the file buffer to write to.


m_OutputDistribution

boolean m_OutputDistribution
whether to output the class distribution.


m_Attributes

Range m_Attributes
the range of attributes to output.


m_NumDecimals

int m_NumDecimals
the number of decimals after the decimal point.


m_OutputFile

java.io.File m_OutputFile
the file to store the output in.


m_SuppressOutput

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

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

serialVersionUID: 3401604538169573720L

Serialized Fields

m_Delimiter

java.lang.String m_Delimiter
the delimiter.

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

serialVersionUID: 7241252244954353300L

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

serialVersionUID: 4988413155999044966L

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

serialVersionUID: 2033389864898242735L

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

serialVersionUID: -3165514277316824801L


Package weka.classifiers.functions

Class weka.classifiers.functions.GaussianProcesses extends 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.


m_ModelFile

java.io.File m_ModelFile
the file to save the libsvm-internal model to.

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.


m_UseSmoothing

boolean m_UseSmoothing
Whether to use smoothing to prevent negative "probabilities".

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