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<E> m_Ls

The set of all sets of itemsets L.

m_hashtables

FastVector<E> m_hashtables

The same information stored in hash tables.

m_allTheRules

FastVector<E>[] m_allTheRules

The list of all generated rules.

m_instances

Instances m_instances

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

m_outputItemSets

boolean m_outputItemSets

Output itemsets found?

m_removeMissingCols

boolean m_removeMissingCols

Remove columns with all missing values

m_verbose

boolean m_verbose

Report progress iteratively

m_onlyClass

Instances m_onlyClass

Only the class attribute of all Instances.

m_classIndex

int m_classIndex

The class index.

m_car

boolean m_car

Flag indicating whether class association rules are mined.

m_treatZeroAsMissing

boolean m_treatZeroAsMissing

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

m_toStringDelimiters

java.lang.String m_toStringDelimiters

ToString delimiters, if any

Class weka.associations.AprioriItemSet extends ItemSet implements Serializable

serialVersionUID: 7684467755712672058L

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

serialVersionUID: 8889198755948056749L

Serialized Fields

m_producer

java.lang.String m_producer

The scheme that produced these rules

m_rules

java.util.List<E> m_rules

The list of rules

Class weka.associations.BinaryItem extends NominalItem implements Serializable

serialVersionUID: -3372941834914147669L

Class weka.associations.DefaultAssociationRule extends AssociationRule implements Serializable

serialVersionUID: -661269018702294489L

Serialized Fields

m_metricType

DefaultAssociationRule.METRIC_TYPE m_metricType

The metric type for this rule

m_premise

java.util.Collection<E> m_premise

The premise of the rule

m_consequence

java.util.Collection<E> m_consequence

The consequence of the rule

m_premiseSupport

int m_premiseSupport

The support for the premise

m_consequenceSupport

int m_consequenceSupport

The support for the consequence

m_totalSupport

int m_totalSupport

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

m_totalTransactions

int m_totalTransactions

The total number of transactions in the data

Class weka.associations.FilteredAssociationRules extends AssociationRules implements Serializable

serialVersionUID: -4223408305476916955L

Serialized Fields

m_filter

Filter m_filter

m_wrappedRules

AssociationRules m_wrappedRules

Class weka.associations.FilteredAssociator extends SingleAssociatorEnhancer implements Serializable

serialVersionUID: -4523450618538717400L

Serialized Fields

m_Filter

Filter m_Filter

The filter

m_FilteredInstances

Instances m_FilteredInstances

The instance structure of the filtered instances

m_ClassIndex

int m_ClassIndex

The class index.

Class weka.associations.FPGrowth extends AbstractAssociator implements Serializable

serialVersionUID: 3620717108603442911L

Serialized Fields

m_numRulesToFind

int m_numRulesToFind

The number of rules to find

m_upperBoundMinSupport

double m_upperBoundMinSupport

The upper bound on the minimum support

m_lowerBoundMinSupport

double m_lowerBoundMinSupport

The lower bound on minimum support

m_delta

double m_delta

The amount by which to decrease the support in each iteration

m_numInstances

int m_numInstances

The number of instances in the data

m_offDiskReportingFrequency

int m_offDiskReportingFrequency

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

m_findAllRulesForSupportLevel

boolean m_findAllRulesForSupportLevel

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

m_positiveIndex

int m_positiveIndex

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

m_metric

DefaultAssociationRule.METRIC_TYPE m_metric

m_metricThreshold

double m_metricThreshold

m_largeItemSets

weka.associations.FPGrowth.FrequentItemSets m_largeItemSets

Holds the large item sets found

m_rules

java.util.List<E> m_rules

Holds the rules

m_maxItems

int m_maxItems

m_transactionsMustContain

java.lang.String m_transactionsMustContain

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

m_mustContainOR

boolean m_mustContainOR

Use OR rather than AND when considering must contain lists

m_rulesMustContain

java.lang.String m_rulesMustContain

If set, then only output rules containing these itmes

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

serialVersionUID: 4396315323673737660L

Serialized Fields

m_levelSibling

weka.associations.FPGrowth.FPTreeNode m_levelSibling

link to another sibling at this level in the tree

m_parent

weka.associations.FPGrowth.FPTreeNode m_parent

link to the parent node

m_item

BinaryItem m_item

item at this node

m_ID

int m_ID

ID (for graphing the tree)

m_children

java.util.Map<K,V> m_children

the children of this node

m_projectedCounts

weka.associations.FPGrowth.ShadowCounts m_projectedCounts

counts associated with projected versions of this node

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

serialVersionUID: -6583156284891368909L

Serialized Fields

m_headerList

java.util.List<E> m_headerList

The list of pointers into the tree structure

m_projectedHeaderCounts

weka.associations.FPGrowth.ShadowCounts m_projectedHeaderCounts

Projected header counts for this entry

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

serialVersionUID: -6543815873565829448L

Serialized Fields

m_items

java.util.ArrayList<E> m_items

The list of items in the item set

m_support

int m_support

the support of this item set

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

serialVersionUID: 4173606872363973588L

Serialized Fields

m_sets

java.util.ArrayList<E> m_sets

The list of frequent item sets

m_numberOfTransactions

int m_numberOfTransactions

The total number of transactions in the data

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

serialVersionUID: 4435433714185969155L

Serialized Fields

m_counts

java.util.ArrayList<E> m_counts

Holds the counts at different recursion levels

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

serialVersionUID: -430198211081183575L

Serialized Fields

m_frequency

int m_frequency

The frequency of this item

m_attribute

Attribute m_attribute

The attribute that backs this item

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

serialVersionUID: 2724000045282835791L

Serialized Fields

m_items

int[] m_items

The items stored as an array of of ints.

m_counter

int m_counter

Counter for how many transactions contain this item set.

m_totalTransactions

int m_totalTransactions

The total number of transactions

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.NominalItem extends Item implements Serializable

serialVersionUID: 2182122099990462066L

Serialized Fields

m_valueIndex

int m_valueIndex

The index of the value considered to be positive

Class weka.associations.NumericItem extends Item implements Serializable

serialVersionUID: -7869433770765864800L

Serialized Fields

m_splitPoint

double m_splitPoint

The numeric test

m_comparison

NumericItem.Comparison m_comparison

The comparison operator

Class weka.associations.SingleAssociatorEnhancer extends AbstractAssociator implements Serializable

serialVersionUID: -3665885256363525164L

Serialized Fields

m_Associator

Associator m_Associator

The base associator to use

Package weka.attributeSelection

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

serialVersionUID: 2091705669885950849L

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

serialVersionUID: 7591673350342236548L

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

serialVersionUID: 4170171824147584330L

Serialized Fields

m_trainInstances

Instances m_trainInstances

the instances to select attributes from

m_ASEvaluator

ASEvaluation m_ASEvaluator

the attribute/subset evaluator

m_searchMethod

ASSearch m_searchMethod

the search method

m_numFolds

int m_numFolds

the number of folds to use for cross validation

m_selectionResults

java.lang.StringBuffer m_selectionResults

holds a string describing the results of the attribute selection

m_doRank

boolean m_doRank

rank features (if allowed by the search method)

m_doXval

boolean m_doXval

do cross validation

m_seed

int m_seed

seed used to randomly shuffle instances for cross validation

m_numToSelect

int m_numToSelect

number of attributes requested from ranked results

m_selectedAttributeSet

int[] m_selectedAttributeSet

the selected attributes

m_attributeRanking

double[][] m_attributeRanking

the attribute indexes and associated merits if a ranking is produced

m_transformer

AttributeTransformer m_transformer

if a feature selection run involves an attribute transformer

m_attributeFilter

Remove m_attributeFilter

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

m_rankResults

double[][] m_rankResults

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

m_subsetResults

double[] m_subsetResults

m_trials

int m_trials

Class weka.attributeSelection.AttributeSetEvaluator extends ASEvaluation implements Serializable

serialVersionUID: -5744881009422257389L

Class weka.attributeSelection.BestFirst extends ASSearch implements Serializable

serialVersionUID: 7841338689536821867L

Serialized Fields

m_maxStale

int m_maxStale

maximum number of stale nodes before terminating search

m_searchDirection

int m_searchDirection

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

m_starting

int[] m_starting

holds an array of starting attributes

m_startRange

Range m_startRange

holds the start set for the search as a Range

m_hasClass

boolean m_hasClass

does the data have a class

m_classIndex

int m_classIndex

holds the class index

m_numAttribs

int m_numAttribs

number of attributes in the data

m_totalEvals

int m_totalEvals

total number of subsets evaluated during a search

m_debug

boolean m_debug

for debugging

m_bestMerit

double m_bestMerit

holds the merit of the best subset found

m_cacheSize

int m_cacheSize

holds the maximum size of the lookup cache for evaluated subsets

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

serialVersionUID: -8236598311516351420L

Serialized Fields

m_data

java.lang.Object[] m_data

m_merit

double m_merit

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

serialVersionUID: 3250538292330398929L

Serialized Fields

m_MaxSize

int m_MaxSize

Max number of elements in the list

Class weka.attributeSelection.CfsSubsetEval extends ASEvaluation implements Serializable

serialVersionUID: 747878400813276317L

Serialized Fields

m_trainInstances

Instances m_trainInstances

The training instances

m_disTransform

Discretize m_disTransform

Discretise attributes when class in nominal

m_classIndex

int m_classIndex

The class index

m_isNumeric

boolean m_isNumeric

Is the class numeric

m_numAttribs

int m_numAttribs

Number of attributes in the training data

m_numInstances

int m_numInstances

Number of instances in the training data

m_missingSeparate

boolean m_missingSeparate

Treat missing values as separate values

m_locallyPredictive

boolean m_locallyPredictive

Include locally predictive attributes

m_corr_matrix

float[][] m_corr_matrix

Holds the matrix of attribute correlations

m_std_devs

double[] m_std_devs

Standard deviations of attributes (when using pearsons correlation)

m_c_Threshold

double m_c_Threshold

Threshold for admitting locally predictive features

Class weka.attributeSelection.CorrelationAttributeEval extends ASEvaluation implements Serializable

serialVersionUID: -4931946995055872438L

Serialized Fields

m_correlations

double[] m_correlations

The correlation for each attribute

m_detailedOutput

boolean m_detailedOutput

Whether to output detailed (per value) correlation for nominal attributes

m_detailedOutputBuff

java.lang.StringBuffer m_detailedOutputBuff

Holds the detailed output info

Class weka.attributeSelection.GainRatioAttributeEval extends ASEvaluation implements Serializable

serialVersionUID: -8504656625598579926L

Serialized Fields

m_trainInstances

Instances m_trainInstances

The training instances

m_classIndex

int m_classIndex

The class index

m_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.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.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: -3675307197777734007L

Serialized Fields

m_trainInstances

Instances m_trainInstances

The data to transform analyse/transform

m_trainHeader

Instances m_trainHeader

Keep a copy for the class attribute (if set)

m_transformedFormat

Instances m_transformedFormat

The header for the transformed data format

m_originalSpaceFormat

Instances m_originalSpaceFormat

The header for data transformed back to the original space

m_hasClass

boolean m_hasClass

Data has a class set

m_classIndex

int m_classIndex

Class index

m_numAttribs

int m_numAttribs

Number of attributes

m_numInstances

int m_numInstances

Number of instances

m_correlation

double[][] m_correlation

Correlation/covariance matrix for the original data

m_means

double[] m_means

m_stdDevs

double[] m_stdDevs

m_center

boolean m_center

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

m_eigenvectors

double[][] m_eigenvectors

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

m_eigenvalues

double[] m_eigenvalues

Eigenvalues for the corresponding eigenvectors

m_sortedEigens

int[] m_sortedEigens

Sorted eigenvalues

m_sumOfEigenValues

double m_sumOfEigenValues

sum of the eigenvalues

m_replaceMissingFilter

ReplaceMissingValues m_replaceMissingFilter

Filters for original data

m_nominalToBinFilter

NominalToBinary m_nominalToBinFilter

m_attributeFilter

Remove m_attributeFilter

m_centerFilter

Center m_centerFilter

m_standardizeFilter

Standardize m_standardizeFilter

m_outputNumAtts

int m_outputNumAtts

The number of attributes in the pc transformed data

m_coverVariance

double m_coverVariance

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

m_transBackToOriginal

boolean m_transBackToOriginal

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

m_maxAttrsInName

int m_maxAttrsInName

maximum number of attributes in the transformed attribute name

m_eTranspose

double[][] m_eTranspose

holds the transposed eigenvectors for converting back to the original space

Class weka.attributeSelection.Ranker extends ASSearch implements Serializable

serialVersionUID: -9086714848510751934L

Serialized Fields

m_starting

int[] m_starting

Holds the starting set as an array of attributes

m_startRange

Range m_startRange

Holds the start set for the search as a range

m_attributeList

int[] m_attributeList

Holds the ordered list of attributes

m_attributeMerit

double[] m_attributeMerit

Holds the list of attribute merit scores

m_hasClass

boolean m_hasClass

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

m_classIndex

int m_classIndex

Class index of the data if supervised evaluator

m_numAttribs

int m_numAttribs

The number of attribtes

m_threshold

double m_threshold

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

m_numToSelect

int m_numToSelect

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

m_calculatedNumToSelect

int m_calculatedNumToSelect

Used to compute the number to select

Class weka.attributeSelection.ReliefFAttributeEval extends ASEvaluation implements Serializable

serialVersionUID: -8422186665795839379L

Serialized Fields

m_trainInstances

Instances m_trainInstances

The training instances

m_classIndex

int m_classIndex

The class index

m_numAttribs

int m_numAttribs

The number of attributes

m_numInstances

int m_numInstances

The number of instances

m_numericClass

boolean m_numericClass

Numeric class

m_numClasses

int m_numClasses

The number of classes if class is nominal

m_ndc

double m_ndc

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

m_nda

double[] m_nda

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

m_ndcda

double[] m_ndcda

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

m_weights

double[] m_weights

Holds the weights that relief assigns to attributes

m_classProbs

double[] m_classProbs

Prior class probabilities (discrete class case)

m_sampleM

int m_sampleM

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

m_Knn

int m_Knn

The number of nearest hits/misses

m_karray

double[][][] m_karray

k nearest scores + instance indexes for n classes

m_maxArray

double[] m_maxArray

Upper bound for numeric attributes

m_minArray

double[] m_minArray

Lower bound for numeric attributes

m_worst

double[] m_worst

Keep track of the farthest instance for each class

m_index

int[] m_index

Index in the m_karray of the farthest instance for each class

m_stored

int[] m_stored

Number of nearest neighbours stored of each class

m_seed

int m_seed

Random number seed used for sampling instances

m_weightsByRank

double[] m_weightsByRank

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

m_sigma

int m_sigma

m_weightByDistance

boolean m_weightByDistance

Weight by distance rather than equal weights

Class weka.attributeSelection.SymmetricalUncertAttributeEval extends ASEvaluation implements Serializable

serialVersionUID: -8096505776132296416L

Serialized Fields

m_trainInstances

Instances m_trainInstances

The training instances

m_classIndex

int m_classIndex

The class index

m_numAttribs

int m_numAttribs

The number of attributes

m_numInstances

int m_numInstances

The number of instances

m_numClasses

int m_numClasses

The number of classes

m_missing_merge

boolean m_missing_merge

Treat missing values as a seperate value

Class weka.attributeSelection.UnsupervisedAttributeEvaluator extends ASEvaluation implements Serializable

serialVersionUID: -4100897318675336178L

Class weka.attributeSelection.UnsupervisedSubsetEvaluator extends ASEvaluation implements Serializable

serialVersionUID: 627934376267488763L

Class weka.attributeSelection.WrapperSubsetEval extends ASEvaluation implements Serializable

serialVersionUID: -4573057658746728675L

Serialized Fields

m_trainInstances

Instances m_trainInstances

training instances

m_classIndex

int m_classIndex

class index

m_numAttribs

int m_numAttribs

number of attributes in the training data

m_numInstances

int m_numInstances

number of instances in the training data

m_Evaluation

Evaluation m_Evaluation

holds an evaluation object

m_BaseClassifier

Classifier m_BaseClassifier

holds the base classifier object

m_folds

int m_folds

number of folds to use for cross validation

m_seed

int m_seed

random number seed

m_threshold

double m_threshold

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

m_evaluationMeasure

int m_evaluationMeasure

The evaluation measure to use

Package weka.classifiers

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

serialVersionUID: 6502780192411755341L

Serialized Fields

m_Debug

boolean m_Debug

Whether the classifier is run in debug mode.

Class weka.classifiers.AggregateableEvaluation extends Evaluation implements Serializable

serialVersionUID: 6850546230173753210L

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

serialVersionUID: -1973792250544554965L

Serialized Fields

m_size

int m_size

m_matrix

java.lang.Object[][] m_matrix

[rows][columns]

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

serialVersionUID: -170766452472965668L

Serialized Fields

m_delegate

Evaluation m_delegate

The actual evaluation object that we delegate to

Class weka.classifiers.IteratedSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

serialVersionUID: -6217979135443319724L

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers

Array for storing the generated base classifiers.

m_NumIterations

int m_NumIterations

The number of iterations.

Class weka.classifiers.MultipleClassifiersCombiner extends AbstractClassifier implements Serializable

serialVersionUID: 2776436621129422119L

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers

Array for storing the generated base classifiers.

Class weka.classifiers.ParallelIteratedSingleClassifierEnhancer extends IteratedSingleClassifierEnhancer implements Serializable

serialVersionUID: -5026378741833046436L

Serialized Fields

m_numExecutionSlots

int m_numExecutionSlots

The number of threads to have executing at any one time

m_completed

int m_completed

The number of classifiers completed so far

m_failed

int m_failed

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

Class weka.classifiers.ParallelMultipleClassifiersCombiner extends MultipleClassifiersCombiner implements Serializable

serialVersionUID: 728109028953726626L

Serialized Fields

m_numExecutionSlots

int m_numExecutionSlots

The number of threads to have executing at any one time

m_completed

int m_completed

The number of classifiers completed so far

m_failed

int m_failed

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

Class weka.classifiers.RandomizableClassifier extends AbstractClassifier implements Serializable

serialVersionUID: -8816375798262351903L

Serialized Fields

m_Seed

int m_Seed

The random number seed.

Class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer extends IteratedSingleClassifierEnhancer implements Serializable

serialVersionUID: 5063351391524938557L

Serialized Fields

m_Seed

int m_Seed

The random number seed.

Class weka.classifiers.RandomizableMultipleClassifiersCombiner extends MultipleClassifiersCombiner implements Serializable

serialVersionUID: 5057936555724785679L

Serialized Fields

m_Seed

int m_Seed

The random number seed.

Class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer extends ParallelIteratedSingleClassifierEnhancer implements Serializable

serialVersionUID: 1298141000373615374L

Serialized Fields

m_Seed

int m_Seed

The random number seed.

Class weka.classifiers.RandomizableParallelMultipleClassifiersCombiner extends ParallelMultipleClassifiersCombiner implements Serializable

serialVersionUID: 8274061943448676943L

Serialized Fields

m_Seed

int m_Seed

The random number seed.

Class weka.classifiers.RandomizableSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable

serialVersionUID: 558286687096157160L

Serialized Fields

m_Seed

int m_Seed

The random number seed.

Class weka.classifiers.SingleClassifierEnhancer extends AbstractClassifier implements Serializable

serialVersionUID: -3665885256363525164L

Serialized Fields

m_Classifier

Classifier m_Classifier

The base classifier to use

Package weka.classifiers.bayes

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

serialVersionUID: 746037443258775954L

Serialized Fields

m_ParentSets

ParentSet[] m_ParentSets

The parent sets.

m_Distributions

Estimator[][] m_Distributions

The attribute estimators containing CPTs.

m_DiscretizeFilter

Discretize m_DiscretizeFilter

filter used to quantize continuous variables, if any

m_nNonDiscreteAttribute

int m_nNonDiscreteAttribute

attribute index of a non-nominal attribute

m_MissingValuesFilter

ReplaceMissingValues m_MissingValuesFilter

filter used to fill in missing values, if any

m_NumClasses

int m_NumClasses

The number of classes

m_Instances

Instances m_Instances

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

m_ADTree

ADNode m_ADTree

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

m_otherBayesNet

BIFReader m_otherBayesNet

Bayes network to compare the structure with.

m_bUseADTree

boolean m_bUseADTree

Use the experimental ADTree datastructure for calculating contingency tables

m_SearchAlgorithm

SearchAlgorithm m_SearchAlgorithm

Search algorithm used for learning the structure of a network.

m_BayesNetEstimator

BayesNetEstimator m_BayesNetEstimator

Search algorithm used for learning the structure of a network.

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

serialVersionUID: 5995231201785697655L

Serialized Fields

m_Distributions

Estimator[][] m_Distributions

The attribute estimators.

m_ClassDistribution

Estimator m_ClassDistribution

The class estimator.

m_UseKernelEstimator

boolean m_UseKernelEstimator

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

m_UseDiscretization

boolean m_UseDiscretization

Whether to use discretization than normal distribution for numeric attributes

m_NumClasses

int m_NumClasses

The number of classes (or 1 for numeric class)

m_Instances

Instances m_Instances

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

m_Disc

Discretize m_Disc

The discretization filter.

m_displayModelInOldFormat

boolean m_displayModelInOldFormat

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

serialVersionUID: 5932177440181257085L

Serialized Fields

m_probOfWordGivenClass

double[][] m_probOfWordGivenClass

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

m_probOfClass

double[] m_probOfClass

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

m_numAttributes

int m_numAttributes

number of unique words

m_numClasses

int m_numClasses

number of class values

m_lnFactorialCache

double[] m_lnFactorialCache

cache lnFactorial computations

m_headerInfo

Instances m_headerInfo

copy of header information for use in toString method

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

serialVersionUID: 2139025532014821394L

Serialized Fields

m_data

Instances m_data

The header of the training data

m_probOfClass

double[] m_probOfClass

m_wordsPerClass

double[] m_wordsPerClass

m_probOfWordGivenClass

java.util.Map<K,V> m_probOfWordGivenClass

m_stopwordsFile

java.io.File m_stopwordsFile

a file containing stopwords for using others than the default Rainbow ones.

m_tokenizer

Tokenizer m_tokenizer

The tokenizer to use

m_lowercaseTokens

boolean m_lowercaseTokens

Whether or not to convert all tokens to lowercase

m_stemmer

Stemmer m_stemmer

The stemming algorithm.

m_useStopList

boolean m_useStopList

Whether or not to use a stop list

m_periodicP

int m_periodicP

The number of training instances at which to periodically prune the dictionary of min frequency words. Empty or null string indicates don't prune

m_minWordP

double m_minWordP

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

m_wordFrequencies

boolean m_wordFrequencies

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

m_normalize

boolean m_normalize

normailize document length ?

m_norm

double m_norm

The length that each document vector should have in the end

m_lnorm

double m_lnorm

The L-norm to use

m_leplace

double m_leplace

Leplace-like correction factor for zero frequency

m_t

double m_t

Holds the current instance number

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

serialVersionUID: -7204398796974263186L

Serialized Fields

m_wordsPerClass

double[] m_wordsPerClass

the word count per class

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

serialVersionUID: -5354015843807192221L

Package weka.classifiers.bayes.net

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

serialVersionUID: 397409728366910204L

Serialized Fields

m_VaryNodes

VaryNode[] m_VaryNodes

list of VaryNode children

m_Instances

Instance[] m_Instances

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

m_nCount

int m_nCount

count

m_nStartNode

int m_nStartNode

first node in VaryNode array

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

serialVersionUID: -7462571170596157720L

Serialized Fields

m_nSeed

int m_nSeed

the seed value

random

java.util.Random random

the random number generator

m_bGenerateNet

boolean m_bGenerateNet

m_nNrOfNodes

int m_nNrOfNodes

m_nNrOfArcs

int m_nNrOfArcs

m_nNrOfInstances

int m_nNrOfInstances

m_nCardinality

int m_nCardinality

m_sBIFFile

java.lang.String m_sBIFFile

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

serialVersionUID: -8358864680379881429L

Serialized Fields

m_nPositionX

int[] m_nPositionX

m_nPositionY

int[] m_nPositionY

m_order

int[] m_order

m_sFile

java.lang.String m_sFile

the current filename

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

serialVersionUID: 746037443258735954L

Serialized Fields

m_nPositionX

FastVector<E> m_nPositionX

location of nodes, used for graph drawing *

m_nPositionY

FastVector<E> m_nPositionY

m_fMarginP

FastVector<E> m_fMarginP

marginal distributions *

m_nEvidence

FastVector<E> m_nEvidence

evidence values, used for evidence propagation *

m_undoStack

FastVector<E> m_undoStack

undo stack for undoin edit actions, or redo edit actions

m_nCurrentEditAction

int m_nCurrentEditAction

current action in undo stack

m_nSavedPointer

int m_nSavedPointer

action that the network is saved

m_bNeedsUndoAction

boolean m_bNeedsUndoAction

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

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

serialVersionUID: -2038911085935515624L

Serialized Fields

m_layoutEngine

LayoutEngine m_layoutEngine

The current LayoutEngine

m_GraphPanel

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

Panel actually displaying the graph

m_BayesNet

EditableBayesNet m_BayesNet

Container of Bayesian network

m_sFileName

java.lang.String m_sFileName

String containing file name storing current network

m_marginCalculator

MarginCalculator m_marginCalculator

used for calculating marginals in Bayesian netwowrks

m_marginCalculatorWithEvidence

MarginCalculator m_marginCalculatorWithEvidence

used for calculating marginals in Bayesian netwowrks when evidence is present

m_bViewMargins

boolean m_bViewMargins

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

m_bViewCliques

boolean m_bViewCliques

m_menuBar

javax.swing.JMenuBar m_menuBar

The menu bar

m_Instances

Instances m_Instances

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

m_jTfZoom

javax.swing.JTextField m_jTfZoom

Text field for specifying zoom

m_jTbTools

javax.swing.JToolBar m_jTbTools

toolbar containing buttons at top of window

m_jStatusBar

javax.swing.JLabel m_jStatusBar

status bar at bottom of window

m_jTfNodeWidth

javax.swing.JTextField m_jTfNodeWidth

TextField for node's width

m_jTfNodeHeight

javax.swing.JTextField m_jTfNodeHeight

TextField for nodes height

m_jScrollPane

javax.swing.JScrollPane m_jScrollPane

this contains the m_GraphPanel GraphPanel

ICONPATH

java.lang.String ICONPATH

path for icons

m_fScale

double m_fScale

current zoom value

m_nNodeHeight

int m_nNodeHeight

standard width of node

m_nNodeWidth

int m_nNodeWidth

m_nPaddedNodeWidth

int m_nPaddedNodeWidth

m_nZoomPercents

int[] m_nZoomPercents

used when using zoomIn and zoomOut buttons

a_new

javax.swing.Action a_new

actions triggered by GUI events

a_quit

javax.swing.Action a_quit

a_save

javax.swing.Action a_save

a_export

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

a_print

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

a_load

javax.swing.Action a_load

a_zoomin

javax.swing.Action a_zoomin

a_zoomout

javax.swing.Action a_zoomout

a_layout

javax.swing.Action a_layout

a_saveas

javax.swing.Action a_saveas

a_viewtoolbar

javax.swing.Action a_viewtoolbar

a_viewstatusbar

javax.swing.Action a_viewstatusbar

a_networkgenerator

javax.swing.Action a_networkgenerator

a_datagenerator

javax.swing.Action a_datagenerator

a_datasetter

javax.swing.Action a_datasetter

a_learn

javax.swing.Action a_learn

a_learnCPT

javax.swing.Action a_learnCPT

a_help

javax.swing.Action a_help

a_about

javax.swing.Action a_about

a_addnode

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

a_delnode

javax.swing.Action a_delnode

a_cutnode

javax.swing.Action a_cutnode

a_copynode

javax.swing.Action a_copynode

a_pastenode

javax.swing.Action a_pastenode

a_selectall

javax.swing.Action a_selectall

a_addarc

javax.swing.Action a_addarc

a_delarc

javax.swing.Action a_delarc

a_undo

javax.swing.Action a_undo

a_redo

javax.swing.Action a_redo

a_alignleft

javax.swing.Action a_alignleft

a_alignright

javax.swing.Action a_alignright

a_aligntop

javax.swing.Action a_aligntop

a_alignbottom

javax.swing.Action a_alignbottom

a_centerhorizontal

javax.swing.Action a_centerhorizontal

a_centervertical

javax.swing.Action a_centervertical

a_spacehorizontal

javax.swing.Action a_spacehorizontal

a_spacevertical

javax.swing.Action a_spacevertical

m_nCurrentNode

int m_nCurrentNode

node currently selected through right clicking

m_Selection

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

selection of nodes

m_nSelectedRect

java.awt.Rectangle m_nSelectedRect

selection rectangle drawn through dragging with left mouse button

m_clipboard

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

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

serialVersionUID: 650278019241175534L

Serialized Fields

m_debug

boolean m_debug

m_root

MarginCalculator.JunctionTreeNode m_root

jtNodes

MarginCalculator.JunctionTreeNode[] jtNodes

m_Margins

double[][] m_Margins

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

serialVersionUID: 650278019241175536L

Serialized Fields

m_bayesNet

BayesNet m_bayesNet

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

m_nNodes

int[] m_nNodes

nodes of the Bayes net in this junction node

m_nCardinality

int m_nCardinality

cardinality of the instances of variables in this junction node

m_fi

double[] m_fi

potentials for first network

m_P

double[] m_P

distribution over this junction node according to first Bayes network

m_MarginalP

double[][] m_MarginalP

m_parentSeparator

MarginCalculator.JunctionTreeSeparator m_parentSeparator

m_children

java.util.Vector<E> m_children

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

serialVersionUID: 6502780192411755343L

Serialized Fields

m_nNodes

int[] m_nNodes

m_nCardinality

int m_nCardinality

m_fiParent

double[] m_fiParent

m_fiChild

double[] m_fiChild

m_parentNode

MarginCalculator.JunctionTreeNode m_parentNode

m_childNode

MarginCalculator.JunctionTreeNode m_childNode

m_bayesNet

BayesNet m_bayesNet

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

serialVersionUID: 4155021284407181838L

Serialized Fields

m_nParents

int[] m_nParents

Holds indexes of parents

m_nNrOfParents

int m_nNrOfParents

Holds number of parents

m_nCardinalityOfParents

int m_nCardinalityOfParents

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

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

serialVersionUID: -6196294370675872424L

Serialized Fields

m_iNode

int m_iNode

index of the node varied

m_nMCV

int m_nMCV

most common value

m_ADNodes

ADNode[] m_ADNodes

list of ADNode children

Package weka.classifiers.bayes.net.estimate

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

serialVersionUID: 2184330197666253884L

Serialized Fields

m_fAlpha

double m_fAlpha

Holds prior on count

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

serialVersionUID: -1846028304233257309L

Serialized Fields

m_bUseK2Prior

boolean m_bUseK2Prior

whether to use K2 prior

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

serialVersionUID: 4215400230843212684L

Serialized Fields

m_Counts

double[] m_Counts

Hold the counts

m_SumOfCounts

double m_SumOfCounts

Hold the sum of counts

m_nSymbols

int m_nSymbols

Holds number of symbols in distribution

m_fPrior

double m_fPrior

Holds the prior probability

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

serialVersionUID: 6774941981423312133L

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

serialVersionUID: 8330705772601586313L

Serialized Fields

m_bUseK2Prior

boolean m_bUseK2Prior

whether to use K2 prior

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

serialVersionUID: 5874941612331806172L

Package weka.classifiers.bayes.net.search

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

serialVersionUID: 6164792240778525312L

Serialized Fields

m_nMaxNrOfParents

int m_nMaxNrOfParents

Holds upper bound on number of parents

m_bInitAsNaiveBayes

boolean m_bInitAsNaiveBayes

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

m_bMarkovBlanketClassifier

boolean m_bMarkovBlanketClassifier

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

m_sInitalBIFFile

java.lang.String m_sInitalBIFFile

File name containing initial network structure. This can be used as starting point for structure search It will be ignored if not speficied. When specified, it overrides the InitAsNaivBayes flag.

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

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

serialVersionUID: 3165802334119704560L

Serialized Fields

m_BayesNet

BayesNet m_BayesNet

m_instances

Instances m_instances

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

serialVersionUID: -2510985917284798576L

Serialized Fields

m_nMaxCardinality

int m_nMaxCardinality

maximum size of separating set

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

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

serialVersionUID: 7334358169507619525L

Serialized Fields

m_sBIFFile

java.lang.String m_sBIFFile

name of file to read structure from

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

serialVersionUID: -4808572519709755811L

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

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

serialVersionUID: 4236165533882462203L

Serialized Fields

m_nRuns

int m_nRuns

number of runs

m_nPopulationSize

int m_nPopulationSize

size of population

m_nDescendantPopulationSize

int m_nDescendantPopulationSize

size of descendant population

m_bUseCrossOver

boolean m_bUseCrossOver

use cross-over?

m_bUseMutation

boolean m_bUseMutation

use mutation?

m_bUseTournamentSelection

boolean m_bUseTournamentSelection

use tournament selection or take best sub-population

m_nSeed

int m_nSeed

random number seed

m_random

java.util.Random m_random

random number generator

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.AbstractEvaluationMetric extends java.lang.Object implements Serializable

serialVersionUID: -924507718482386887L

Serialized Fields

m_baseEvaluation

Evaluation m_baseEvaluation

Base evaluation object for subclasses to access for statistics. IMPORTANT: subclasses should treat this object as read-only

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

serialVersionUID: -8787045492227999839L

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

serialVersionUID: 8734675926526110924L

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

serialVersionUID: -181789981401504090L

Serialized Fields

m_ClassNames

java.lang.String[] m_ClassNames

Stores the names of the classes

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

serialVersionUID: -7010314486866816271L

Serialized Fields

m_NumClasses

int m_NumClasses

The number of classes.

m_NumFolds

int m_NumFolds

The number of folds for a cross-validation.

m_Incorrect

double m_Incorrect

The weight of all incorrectly classified instances.

m_Correct

double m_Correct

The weight of all correctly classified instances.

m_Unclassified

double m_Unclassified

The weight of all unclassified instances.

m_MissingClass

double m_MissingClass

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

m_WithClass

double m_WithClass

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

m_ConfusionMatrix

double[][] m_ConfusionMatrix

Array for storing the confusion matrix.

m_ClassNames

java.lang.String[] m_ClassNames

The names of the classes.

m_ClassIsNominal

boolean m_ClassIsNominal

Is the class nominal or numeric?

m_ClassPriors

double[] m_ClassPriors

The prior probabilities of the classes.

m_ClassPriorsSum

double m_ClassPriorsSum

The sum of counts for priors.

m_CostMatrix

CostMatrix m_CostMatrix

The cost matrix (if given).

m_TotalCost

double m_TotalCost

The total cost of predictions (includes instance weights).

m_SumErr

double m_SumErr

Sum of errors.

m_SumAbsErr

double m_SumAbsErr

Sum of absolute errors.

m_SumSqrErr

double m_SumSqrErr

Sum of squared errors.

m_SumClass

double m_SumClass

Sum of class values.

m_SumSqrClass

double m_SumSqrClass

Sum of squared class values.

m_SumPredicted

double m_SumPredicted

Sum of predicted values.

m_SumSqrPredicted

double m_SumSqrPredicted

Sum of squared predicted values.

m_SumClassPredicted

double m_SumClassPredicted

Sum of predicted * class values.

m_SumPriorAbsErr

double m_SumPriorAbsErr

Sum of absolute errors of the prior.

m_SumPriorSqrErr

double m_SumPriorSqrErr

Sum of absolute errors of the prior.

m_SumKBInfo

double m_SumKBInfo

Total Kononenko & Bratko Information.

m_MarginCounts

double[] m_MarginCounts

Cumulative margin distribution.

m_NumTrainClassVals

int m_NumTrainClassVals

Number of non-missing class training instances seen.

m_TrainClassVals

double[] m_TrainClassVals

Array containing all numeric training class values seen.

m_TrainClassWeights

double[] m_TrainClassWeights

Array containing all numeric training class weights.

m_PriorEstimator

UnivariateKernelEstimator m_PriorEstimator

Numeric class estimator for prior.

m_ComplexityStatisticsAvailable

boolean m_ComplexityStatisticsAvailable

Whether complexity statistics are available.

m_SumPriorEntropy

double m_SumPriorEntropy

Total entropy of prior predictions.

m_SumSchemeEntropy

double m_SumSchemeEntropy

Total entropy of scheme predictions.

m_CoverageStatisticsAvailable

boolean m_CoverageStatisticsAvailable

Whether coverage statistics are available.

m_ConfLevel

double m_ConfLevel

The confidence level used for coverage statistics.

m_TotalSizeOfRegions

double m_TotalSizeOfRegions

Total size of predicted regions at the given confidence level.

m_TotalCoverage

double m_TotalCoverage

Total coverage of test cases at the given confidence level.

m_MinTarget

double m_MinTarget

Minimum target value.

m_MaxTarget

double m_MaxTarget

Maximum target value.

m_Predictions

FastVector<E> m_Predictions

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

m_NoPriors

boolean m_NoPriors

enables/disables the use of priors, e.g., if no training set is present in case of de-serialized schemes.

m_Header

Instances m_Header

The header of the training set.

m_DiscardPredictions

boolean m_DiscardPredictions

whether to discard predictions (and save memory).

m_pluginMetrics

java.util.List<E> m_pluginMetrics

Holds plugin evaluation metrics

m_metricsToDisplay

java.util.List<E> m_metricsToDisplay

The list of metrics to display in the output

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

serialVersionUID: -8871333992740492788L

Serialized Fields

m_Distribution

double[] m_Distribution

The predicted probabilities

m_Actual

double m_Actual

The actual class value

m_Predicted

double m_Predicted

The predicted class value

m_Weight

double m_Weight

The weight assigned to this prediction

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

serialVersionUID: -4880216423674233887L

Serialized Fields

m_Actual

double m_Actual

The actual class value.

m_Predicted

double m_Predicted

The predicted class value.

m_Weight

double m_Weight

The weight assigned to this prediction.

m_PredictionIntervals

double[][] m_PredictionIntervals

the prediction intervals.

Package weka.classifiers.evaluation.output.prediction

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

serialVersionUID: 752696986017306241L

Serialized Fields

m_Header

Instances m_Header

the header of the dataset.

m_Buffer

java.lang.StringBuffer m_Buffer

the buffer to write to.

m_FileBuffer

java.lang.StringBuffer m_FileBuffer

the file buffer to write to.

m_OutputDistribution

boolean m_OutputDistribution

whether to output the class distribution.

m_Attributes

Range m_Attributes

the range of attributes to output.

m_NumDecimals

int m_NumDecimals

the number of decimals after the decimal point.

m_OutputFile

java.io.File m_OutputFile

the file to store the output in.

m_SuppressOutput

boolean m_SuppressOutput

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

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

serialVersionUID: 3401604538169573720L

Serialized Fields

m_Delimiter

java.lang.String m_Delimiter

the delimiter.

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

serialVersionUID: 7241252244954353300L

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

serialVersionUID: 4988413155999044966L

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

serialVersionUID: 2033389864898242735L

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

serialVersionUID: -3165514277316824801L

Package weka.classifiers.functions

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

serialVersionUID: -8620066949967678545L

Serialized Fields

m_NominalToBinary

NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

m_Filter

Filter m_Filter

The filter used to standardize/normalize all values.

m_filterType

int m_filterType

Whether to normalize/standardize/neither

m_Missing

ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

m_checksTurnedOff

boolean m_checksTurnedOff

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

m_delta

double m_delta

Gaussian Noise Value.

m_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_L

double[][] m_L

(negative) covariance matrix in symmetric matrix representation

m_t

Matrix m_t

The vector of target values.

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

serialVersionUID: -3364580862046573747L

Serialized Fields

m_Coefficients

double[] m_Coefficients

Array for storing coefficients of linear regression.

m_SelectedAttributes

boolean[] m_SelectedAttributes

Which attributes are relevant?

m_TransformedData

Instances m_TransformedData

Variable for storing transformed training data.

m_MissingFilter

ReplaceMissingValues m_MissingFilter

The filter for removing missing values.

m_TransformFilter

NominalToBinary m_TransformFilter

The filter storing the transformation from nominal to binary attributes.

m_ClassStdDev

double m_ClassStdDev

The standard deviations of the class attribute

m_ClassMean

double m_ClassMean

The mean of the class attribute

m_ClassIndex

int m_ClassIndex

The index of the class attribute

m_Means

double[] m_Means

The attributes means

m_StdDevs

double[] m_StdDevs

The attribute standard deviations

m_AttributeSelection

int m_AttributeSelection

The current attribute selection method

m_EliminateColinearAttributes

boolean m_EliminateColinearAttributes

Try to eliminate correlated attributes?

m_checksTurnedOff

boolean m_checksTurnedOff

Turn off all checks and conversions?

m_Ridge

double m_Ridge

The ridge parameter

m_Minimal

boolean m_Minimal

Conserve memory?

m_ModelBuilt

boolean m_ModelBuilt

Model already built?

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

serialVersionUID: 3932117032546553727L

Serialized Fields

m_Par

double[][] m_Par

The coefficients (optimized parameters) of the model

m_Data

double[][] m_Data

The data saved as a matrix

m_NumPredictors

int m_NumPredictors

The number of attributes in the model

m_ClassIndex

int m_ClassIndex

The index of the class attribute

m_NumClasses

int m_NumClasses

The number of the class labels

m_Ridge

double m_Ridge

The ridge parameter.

m_AttFilter

RemoveUseless m_AttFilter

An attribute filter

m_NominalToBinary

NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues

The filter used to get rid of missing values.

m_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_useConjugateGradientDescent

boolean m_useConjugateGradientDescent

Wether to use conjugate gradient descent rather than BFGS updates.

m_structure

Instances m_structure

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

serialVersionUID: -5990607817048210779L

Serialized Fields

m_ZeroR

Classifier m_ZeroR

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

m_useDefaultModel

boolean m_useDefaultModel

Whether to use the default ZeroR model

m_instances

Instances m_instances

The training instances.

m_currentInstance

Instance m_currentInstance

The current instance running through the network.

m_numeric

boolean m_numeric

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

m_attributeRanges

double[] m_attributeRanges

The ranges for all the attributes.

m_attributeBases

double[] m_attributeBases

The base values for all the attributes.

m_outputs

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

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

m_inputs

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

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

m_neuralNodes

NeuralConnection[] m_neuralNodes

All the nodes that actually comprise the logical neural net.

m_numClasses

int m_numClasses

The number of classes.

m_numAttributes

int m_numAttributes

The number of attributes.

m_nodePanel

weka.classifiers.functions.MultilayerPerceptron.NodePanel m_nodePanel

The panel the nodes are displayed on.

m_controlPanel

weka.classifiers.functions.MultilayerPerceptron.ControlPanel m_controlPanel

The control panel.

m_nextId

int m_nextId

The next id number available for default naming.

m_selected

FastVector<E> m_selected

A Vector list of the units currently selected.

m_graphers

FastVector<E> 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.SGD extends RandomizableClassifier implements Serializable

serialVersionUID: -3732968666673530290L

Serialized Fields

m_replaceMissing

ReplaceMissingValues m_replaceMissing

Replace missing values

m_nominalToBinary

Filter m_nominalToBinary

Convert nominal attributes to numerically coded binary ones. Uses supervised NominalToBinary in the batch learning case

m_normalize

Normalize m_normalize

Normalize the training data

m_lambda

double m_lambda

The regularization parameter

m_learningRate

double m_learningRate

The learning rate

m_weights

double[] m_weights

Stores the weights (+ bias in the last element)

m_t

double m_t

Holds the current iteration number

m_numInstances

double m_numInstances

The number of training instances

m_epochs

int m_epochs

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

m_dontNormalize

boolean m_dontNormalize

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

m_dontReplaceMissing

boolean m_dontReplaceMissing

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

m_data

Instances m_data

Holds the header of the training data

m_loss

int m_loss

The current loss function to minimize

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

serialVersionUID: 7200171484002029584L

Serialized Fields

m_periodicP

int m_periodicP

The number of training instances at which to periodically prune the dictionary of min frequency words. Empty or null string indicates don't prune

m_minWordP

double m_minWordP

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

m_wordFrequencies

boolean m_wordFrequencies

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

m_normalize

boolean m_normalize

Whether to normalized document length or not

m_norm

double m_norm

The length that each document vector should have in the end

m_lnorm

double m_lnorm

The L-norm to use

m_dictionary

java.util.LinkedHashMap<K,V> m_dictionary

The dictionary (and term weights)

m_stopwordsFile

java.io.File m_stopwordsFile

a file containing stopwords for using others than the default Rainbow ones.

m_tokenizer

Tokenizer m_tokenizer

The tokenizer to use

m_lowercaseTokens

boolean m_lowercaseTokens

Whether or not to convert all tokens to lowercase

m_stemmer

Stemmer m_stemmer

The stemming algorithm.

m_useStopList

boolean m_useStopList

Whether or not to use a stop list

m_lambda

double m_lambda

The regularization parameter

m_learningRate

double m_learningRate

The learning rate

m_t

double m_t

Holds the current iteration number

m_bias

double m_bias

Holds the bias term

m_numInstances

double m_numInstances

The number of training instances

m_data

Instances m_data

The header of the training data

m_epochs

int m_epochs

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

m_loss

int m_loss

The current loss function to minimize

m_svmProbs

SGD m_svmProbs

Used for producing probabilities for SVM via SGD logistic regression

m_fitLogistic

boolean m_fitLogistic

True if a logistic regression is to be fit to the output of the SVM for producing probability estimates

m_fitLogisticStructure

Instances m_fitLogisticStructure

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

serialVersionUID: 2104201532017340967L

Serialized Fields

m_count

double m_count

m_weight

double m_weight

Class weka.classifiers.functions.SimpleLinearRegression extends AbstractClassifier implements Serializable

serialVersionUID: 1679336022895414137L

Serialized Fields

m_attribute

Attribute m_attribute

The chosen attribute

m_attributeIndex

int m_attributeIndex

The index of the chosen attribute

m_slope

double m_slope

The slope

m_intercept

double m_intercept

The intercept

m_suppressErrorMessage

boolean m_suppressErrorMessage

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

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

serialVersionUID: 7397710626304705059L

Serialized Fields

m_boostedModel

LogisticBase m_boostedModel

The actual logistic regression model

m_NominalToBinary

NominalToBinary m_NominalToBinary

Filter for converting nominal attributes to binary ones

m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues

Filter for replacing missing values

m_numBoostingIterations

int m_numBoostingIterations

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

m_maxBoostingIterations

int m_maxBoostingIterations

Maximum number of iterations for LogitBoost

m_heuristicStop

int m_heuristicStop

Parameter for the heuristic for early stopping of LogitBoost

m_useCrossValidation

boolean m_useCrossValidation

If true, cross-validate number of LogitBoost iterations

m_errorOnProbabilities

boolean m_errorOnProbabilities

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

m_weightTrimBeta

double m_weightTrimBeta

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

m_useAIC

boolean m_useAIC

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

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

serialVersionUID: -6585883636378691736L

Serialized Fields

m_classifiers

SMO.BinarySMO[][] m_classifiers

The binary classifier(s)

m_C

double m_C

The complexity parameter.

m_eps

double m_eps

Epsilon for rounding.

m_tol

double m_tol

Tolerance for accuracy of result.

m_filterType

int m_filterType

Whether to normalize/standardize/neither

m_NominalToBinary

NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

m_Filter

Filter m_Filter

The filter used to standardize/normalize all values.

m_Missing

ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

m_classIndex

int m_classIndex

The class index from the training data

m_classAttribute

Attribute m_classAttribute

The class attribute

m_KernelIsLinear

boolean m_KernelIsLinear

whether the kernel is a linear one

m_checksTurnedOff

boolean m_checksTurnedOff

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

m_fitLogisticModels

boolean m_fitLogisticModels

Whether logistic models are to be fit

m_numFolds

int m_numFolds

The number of folds for the internal cross-validation

m_randomSeed

int m_randomSeed

The random number seed

m_kernel

Kernel m_kernel

the kernel to use

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

serialVersionUID: -8246163625699362456L

Serialized Fields

m_alpha

double[] m_alpha

The Lagrange multipliers.

m_b

double m_b

The thresholds.

m_bLow

double m_bLow

The thresholds.

m_bUp

double m_bUp

The thresholds.

m_iLow

int m_iLow

The indices for m_bLow and m_bUp

m_iUp

int m_iUp

The indices for m_bLow and m_bUp

m_data

Instances m_data

The training data.

m_weights

double[] m_weights

Weight vector for linear machine.

m_sparseWeights

double[] m_sparseWeights

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

m_sparseIndices

int[] m_sparseIndices

m_kernel

Kernel m_kernel

Kernel to use

m_class

double[] m_class

The transformed class values.

m_errors

double[] m_errors

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

m_I0

SMOset m_I0

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

m_I1

SMOset m_I1

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

m_I2

SMOset m_I2

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

m_I3

SMOset m_I3

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

m_I4

SMOset m_I4

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

m_supportVectors

SMOset m_supportVectors

The set of support vectors

m_logistic

Logistic m_logistic

Stores logistic regression model for probability estimate

m_sumOfWeights

double m_sumOfWeights

Stores the weight of the training instances

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

serialVersionUID: -7149606251113102827L

Serialized Fields

m_filterType

int m_filterType

Whether to normalize/standardize/neither

m_NominalToBinary

NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

m_Filter

Filter m_Filter

The filter used to standardize/normalize all values.

m_Missing

ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

m_onlyNumeric

boolean m_onlyNumeric

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

m_C

double m_C

capacity parameter

m_x1

double m_x1

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

m_x0

double m_x0

m_optimizer

RegOptimizer m_optimizer

contains the algorithm used for learning

m_kernel

Kernel m_kernel

the configured kernel

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

serialVersionUID: -1072429260104568698L

Serialized Fields

m_MaxK

int m_MaxK

The maximum number of alterations to the perceptron

m_NumIterations

int m_NumIterations

The number of iterations

m_Exponent

double m_Exponent

The exponent

m_K

int m_K

The actual number of alterations

m_Additions

int[] m_Additions

The training instances added to the perceptron

m_IsAddition

boolean[] m_IsAddition

Addition or subtraction?

m_Weights

int[] m_Weights

The weights for each perceptron

m_Train

Instances m_Train

The training instances

m_Seed

int m_Seed

Seed used for shuffling the dataset

m_NominalToBinary

NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues

The filter used to get rid of missing values.

Package weka.classifiers.functions.neural

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

serialVersionUID: 8572152807755673630L

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

serialVersionUID: -286208828571059163L

Serialized Fields

m_inputList

NeuralConnection[] m_inputList

The list of inputs to this unit.

m_outputList

NeuralConnection[] m_outputList

The list of outputs from this unit.

m_inputNums

int[] m_inputNums

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

m_outputNums

int[] m_outputNums

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

m_numInputs

int m_numInputs

The number of inputs.

m_numOutputs

int m_numOutputs

The number of outputs.

m_unitValue

double m_unitValue

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

m_unitError

double m_unitError

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

m_weightsUpdated

boolean m_weightsUpdated

True if the weights have already been updated.

m_id

java.lang.String m_id

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

m_type

int m_type

The type of unit this is.

m_x

double m_x

The x coord of this unit purely for displaying purposes.

m_y

double m_y

The y coord of this unit purely for displaying purposes.

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

serialVersionUID: -1085750607680839163L

Serialized Fields

m_weights

double[] m_weights

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

m_bestWeights

double[] m_bestWeights

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

m_changeInWeights

double[] m_changeInWeights

The change in the weights.

m_random

java.util.Random m_random

m_methods

NeuralMethod m_methods

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

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

serialVersionUID: -5162958458177475652L

Package weka.classifiers.functions.supportVector

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

serialVersionUID: 702810182699015136L

Serialized Fields

m_kernelEvals

int m_kernelEvals

Counts the number of kernel evaluations.

m_cacheHits

int m_cacheHits

Counts the number of kernel cache hits.

m_cacheSize

int m_cacheSize

The size of the cache (a prime number)

m_storage

double[] m_storage

Kernel cache

m_keys

long[] m_keys

m_kernelMatrix

double[][] m_kernelMatrix

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

m_numInsts

int m_numInsts

The number of instance in the dataset

m_cacheSlots

int m_cacheSlots

number of cache slots in an entry

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

serialVersionUID: -6102771099905817064L

Serialized Fields

m_data

Instances m_data

The dataset

m_Debug

boolean m_Debug

enables debugging output

m_ChecksTurnedOff

boolean m_ChecksTurnedOff

Turns off all checks

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

serialVersionUID: 1248574185532130851L

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

serialVersionUID: -321831645846363201L

Serialized Fields

m_lowerOrder

boolean m_lowerOrder

Use lower-order terms?

m_exponent

double m_exponent

The exponent for the polynomial kernel.

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

serialVersionUID: -321831645846363333L

Serialized Fields

m_KernelMatrixFile

java.io.File m_KernelMatrixFile

The file holding the kernel matrix.

m_KernelMatrix

Matrix m_KernelMatrix

The kernel matrix.

m_Counter

int m_Counter

A classifier counter.

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

serialVersionUID: 1682161522559978851L

Serialized Fields

m_kernelPrecalc

double[] m_kernelPrecalc

The precalculated dotproducts of <inst_i,inst_i>

m_omega

double m_omega

Omega for the Puk kernel.

m_sigma

double m_sigma

Sigma for the Puk kernel.

m_factor

double m_factor

Cached factor for the Puk kernel.

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

serialVersionUID: 5247117544316387852L

Serialized Fields

m_kernelPrecalc

double[] m_kernelPrecalc

The precalculated dotproducts of <inst_i,inst_i>

m_gamma

double m_gamma

Gamma for the RBF kernel.

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

serialVersionUID: -2198266997254461814L

Serialized Fields

m_alpha

double[] m_alpha

alpha and alpha* arrays containing weights for solving dual problem

m_alphaStar

double[] m_alphaStar

m_b

double m_b

offset

m_epsilon

double m_epsilon

epsilon of epsilon-insensitive cost function

m_C

double m_C

capacity parameter, copied from SMOreg

m_target

double[] m_target

class values/desired output vector

m_data

Instances m_data

points to data set

m_kernel

Kernel m_kernel

the kernel

m_classIndex

int m_classIndex

index of class variable in data set

m_nInstances

int m_nInstances

number of instances in data set

m_random

java.util.Random m_random

random number generator

m_nSeed

int m_nSeed

seed for initializing random number generator

m_supportVectors

SMOset m_supportVectors

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

m_nEvals

int m_nEvals

number of kernel evaluations, used for printing statistics only

m_nCacheHits

int m_nCacheHits

number of kernel cache hits, used for printing statistics only

m_weights

double[] m_weights

weights for linear kernel

m_sparseWeights

double[] m_sparseWeights

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

m_sparseIndices

int[] m_sparseIndices

m_bModelBuilt

boolean m_bModelBuilt

flag to indicate whether the model is built yet

m_SVM

SMOreg m_SVM

parent SMOreg class

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

serialVersionUID: -7504070793279598638L

Serialized Fields

m_eps

double m_eps

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

m_error

double[] m_error

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

m_alpha1

double m_alpha1

alpha value for first candidate

m_alpha1Star

double m_alpha1Star

alpha* value for first candidate

m_alpha2

double m_alpha2

alpha value for second candidate

m_alpha2Star

double m_alpha2Star

alpha* value for second candidate

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

serialVersionUID: 471692841446029784L

Serialized Fields

m_I0

SMOset m_I0

The different sets used by the algorithm.

m_iSet

int[] m_iSet

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

m_bUp

double m_bUp

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

m_bLow

double m_bLow

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

m_iUp

int m_iUp

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

m_iLow

int m_iLow

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

m_fTolerance

double m_fTolerance

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

m_bUseVariant1

boolean m_bUseVariant1

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

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

serialVersionUID: -8364829283188675777L

Serialized Fields

m_number

int m_number

The current number of elements in the set

m_first

int m_first

The first element in the set

m_indicators

boolean[] m_indicators

Indicators

m_next

int[] m_next

The next element for each element

m_previous

int[] m_previous

The previous element for each element

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

serialVersionUID: -4902954211202690123L

Serialized Fields

m_cacheSize

int m_cacheSize

The size of the cache (a prime number)

m_internalCacheSize

int m_internalCacheSize

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

m_strAttr

int m_strAttr

The attribute number of the string attribute

m_storage

double[] m_storage

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

m_keys

long[] m_keys

m_kernelEvals

int m_kernelEvals

Counts the number of kernel evaluations.

m_numInsts

int m_numInsts

The number of instance in the dataset

m_PruningMethod

int m_PruningMethod

the pruning method

m_lambda

double m_lambda

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

m_subsequenceLength

int m_subsequenceLength

The substring length

m_maxSubsequenceLength

int m_maxSubsequenceLength

The maximum substring length for lambda pruning

m_powersOflambda

double[] m_powersOflambda

the precalculated powers of lambda

m_normalize

boolean m_normalize

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

maxCache

int maxCache

private cache for intermediate results

cachekh

double[] cachekh

cachekhK

int[] cachekhK

cachekh2

double[] cachekh2

cachekh2K

int[] cachekh2K

m_multX

int m_multX

cached indexes for private cache

m_multY

int m_multY

m_multZ

int m_multZ

m_multZZ

int m_multZZ

m_useRecursionCache

boolean m_useRecursionCache

Package weka.classifiers.lazy

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

serialVersionUID: -3080186098777067172L

Serialized Fields

m_Train

Instances m_Train

The training instances used for classification.

m_NumClasses

int m_NumClasses

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

m_ClassType

int m_ClassType

The class attribute type.

m_kNN

int m_kNN

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

m_kNNUpper

int m_kNNUpper

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

m_kNNValid

boolean m_kNNValid

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

m_WindowSize

int m_WindowSize

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

m_DistanceWeighting

int m_DistanceWeighting

Whether the neighbours should be distance-weighted.

m_CrossValidate

boolean m_CrossValidate

Whether to select k by cross validation.

m_MeanSquared

boolean m_MeanSquared

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

m_defaultModel

ZeroR m_defaultModel

Default ZeroR model to use when there are no training instances

m_NNSearch

NearestNeighbourSearch m_NNSearch

for nearest-neighbor search.

m_NumAttributesUsed

double m_NumAttributesUsed

The number of attributes the contribute to a prediction.

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

serialVersionUID: 332458330800479083L

Serialized Fields

m_Train

Instances m_Train

The training instances used for classification.

m_NumInstances

int m_NumInstances

The number of instances in the dataset

m_NumClasses

int m_NumClasses

The number of class values

m_NumAttributes

int m_NumAttributes

The number of attributes

m_ClassType

int m_ClassType

The class attribute type

m_RandClassCols

int[][] m_RandClassCols

Table of random class value colomns

m_ComputeRandomCols

int m_ComputeRandomCols

Flag turning on and off the computation of random class colomns

m_InitFlag

int m_InitFlag

Flag turning on and off the initialisation of config variables

m_Cache

KStarCache[] m_Cache

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

m_MissingMode

int m_MissingMode

missing value treatment

m_BlendMethod

int m_BlendMethod

0 = use specified blend, 1 = entropic blend setting

m_GlobalBlend

int m_GlobalBlend

default sphere of influence blend setting

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

serialVersionUID: 1979797405383665815L

Serialized Fields

m_Train

Instances m_Train

The training instances used for classification.

m_kNN

int m_kNN

The number of neighbours used to select the kernel bandwidth.

m_WeightKernel

int m_WeightKernel

The weighting kernel method currently selected.

m_UseAllK

boolean m_UseAllK

True if m_kNN should be set to all instances.

m_NNSearch

NearestNeighbourSearch m_NNSearch

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

m_ZeroR

Classifier m_ZeroR

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

Package weka.classifiers.lazy.kstar

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

serialVersionUID: -7693632394267140678L

Serialized Fields

m_Cache

KStarCache.CacheTable m_Cache

cache table

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

serialVersionUID: -8086106452588253423L

Serialized Fields

m_Table

KStarCache.TableEntry[] m_Table

The hash table data.

m_Count

int m_Count

The total number of entries in the hash table.

m_Threshold

int m_Threshold

Rehashes the table when count exceeds this threshold.

m_LoadFactor

float m_LoadFactor

The load factor for the hashtable.

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: -1178107808933117974L

Serialized Fields

m_Betas

double[] m_Betas

Array for storing the weights for the votes.

m_NumIterationsPerformed

int m_NumIterationsPerformed

The number of successfully generated base classifiers.

m_WeightThreshold

int m_WeightThreshold

Weight Threshold. The percentage of weight mass used in training

m_UseResampling

boolean m_UseResampling

Use boosting with reweighting?

m_NumClasses

int m_NumClasses

The number of classes

m_ZeroR

Classifier m_ZeroR

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

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: -1151805453487947577L

Serialized Fields

m_AttributeSelection

AttributeSelection m_AttributeSelection

The attribute selection object

m_Evaluator

ASEvaluation m_Evaluator

The attribute evaluator to use

m_Search

ASSearch m_Search

The search method to use

m_ReducedHeader

Instances m_ReducedHeader

The header of the dimensionally reduced data

m_numClasses

int m_numClasses

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

m_numAttributesSelected

double m_numAttributesSelected

The number of attributes selected by the attribute selection phase

m_selectionTime

double m_selectionTime

The time taken to select attributes in milliseconds

m_totalTime

double m_totalTime

The time taken to select attributes AND build the classifier

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

serialVersionUID: -115879962237199703L

Serialized Fields

m_BagSizePercent

int m_BagSizePercent

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

m_CalcOutOfBag

boolean m_CalcOutOfBag

Whether to calculate the out of bag error

m_OutOfBagError

double m_OutOfBagError

The out of bag error that has been calculated

m_random

java.util.Random m_random

m_inBag

boolean[][] m_inBag

m_data

Instances m_data

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

serialVersionUID: 4500023123618669859L

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers

The classifiers. (One for each class.)

m_ClassFilters

MakeIndicator[] m_ClassFilters

The filters used to transform the class.

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

serialVersionUID: -110658209263002404L

Serialized Fields

m_MatrixSource

int m_MatrixSource

Indicates the current cost matrix source

m_OnDemandDirectory

java.io.File m_OnDemandDirectory

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

m_CostFile

java.lang.String m_CostFile

The name of the cost file, for command line options

m_CostMatrix

CostMatrix m_CostMatrix

The cost matrix

m_MinimizeExpectedCost

boolean m_MinimizeExpectedCost

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

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

serialVersionUID: -6529603380876641265L

Serialized Fields

m_ClassifierOptions

java.lang.String[] m_ClassifierOptions

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

m_BestClassifierOptions

java.lang.String[] m_BestClassifierOptions

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

m_InitOptions

java.lang.String[] m_InitOptions

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

m_BestPerformance

double m_BestPerformance

The cross-validated performance of the best options

m_CVParams

FastVector<E> m_CVParams

The set of parameters to cross-validate over

m_NumAttributes

int m_NumAttributes

The number of attributes in the data

m_TrainFoldSize

int m_TrainFoldSize

The number of instances in a training fold

m_NumFolds

int m_NumFolds

The number of folds used in cross-validation

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

serialVersionUID: -4668812017709421953L

Serialized Fields

m_ParamChar

java.lang.String m_ParamChar

Char used to identify the option of interest

m_Lower

double m_Lower

Lower bound for the CV search

m_Upper

double m_Upper

Upper bound for the CV search

m_Steps

double m_Steps

Number of steps during the search

m_ParamValue

double m_ParamValue

The parameter value with the best performance

m_AddAtEnd

boolean m_AddAtEnd

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

m_RoundParam

boolean m_RoundParam

True if the parameter should be rounded to an integer

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

serialVersionUID: -4523450618538717400L

Serialized Fields

m_Filter

Filter m_Filter

The filter

m_FilteredInstances

Instances m_FilteredInstances

The instance structure of the filtered instances

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

serialVersionUID: -1105660358715833753L

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.MultiClassClassifier extends RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: -3879602011542849141L

Serialized Fields

m_Classifiers

Classifier[] m_Classifiers

The classifiers.

m_pairwiseCoupling

boolean m_pairwiseCoupling

Use pairwise coupling with 1-vs-1

m_SumOfWeights

double[] m_SumOfWeights

Needed for pairwise coupling

m_ClassFilters

Filter[] m_ClassFilters

The filters used to transform the class.

m_ZeroR

ZeroR m_ZeroR

ZeroR classifier for when all base classifier return zero probability.

m_ClassAttribute

Attribute m_ClassAttribute

Internal copy of the class attribute for output purposes

m_TwoClassDataset

Instances m_TwoClassDataset

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

m_RandomWidthFactor

double m_RandomWidthFactor

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

m_Method

int m_Method

The multiclass method to use

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

serialVersionUID: -1619685269774366430L

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

serialVersionUID: 5710744346128957520L

Serialized Fields

m_Classifier

Classifier m_Classifier

The classifier that had the best performance on training data.

m_ClassifierIndex

int m_ClassifierIndex

The index into the vector for the selected scheme

m_NumXValFolds

int m_NumXValFolds

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

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

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

serialVersionUID: -9204394360557300093L

Serialized Fields

m_data

Instances m_data

training data

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

serialVersionUID: 1278172513912424947L

Serialized Fields

m_SubSpaceSize

double m_SubSpaceSize

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

m_ZeroR

Classifier m_ZeroR

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

m_data

Instances m_data

Training data

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

serialVersionUID: 5066426153134050378L

Serialized Fields

m_Discretizer

Discretize m_Discretizer

The discretization filter.

m_NumBins

int m_NumBins

The number of discretization intervals.

m_ClassMeans

double[] m_ClassMeans

The mean values for each Discretized class interval.

m_ClassCounts

int[] m_ClassCounts

The class counts for each Discretized class interval.

m_DeleteEmptyBins

boolean m_DeleteEmptyBins

Whether to delete empty intervals.

m_OldIndexToNewIndex

int[] m_OldIndexToNewIndex

Mapping to convert indices in case empty bins are deleted.

m_DiscretizedHeader

Instances m_DiscretizedHeader

Header of discretized data.

m_UseEqualFrequency

boolean m_UseEqualFrequency

Use equal-frequency binning

m_MinimizeAbsoluteError

boolean m_MinimizeAbsoluteError

Whether to minimize absolute error, rather than squared error.

m_estimatorType

int m_estimatorType

Which estimator to use (default: histogram)

m_OriginalTargetValues

double[] m_OriginalTargetValues

The original target values in the training data

m_NewTargetValues

int[] m_NewTargetValues

The converted target values in the training data

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

serialVersionUID: 5134738557155845452L

Serialized Fields

m_MetaClassifier

Classifier m_MetaClassifier

The meta classifier

m_MetaFormat

Instances m_MetaFormat

Format for meta data

m_BaseFormat

Instances m_BaseFormat

Format for base data

m_NumFolds

int m_NumFolds

Set the number of folds for the cross-validation

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

serialVersionUID: -637891196294399624L

Serialized Fields

m_CombinationRule

int m_CombinationRule

Combination Rule variable

m_Random

java.util.Random m_Random

the random number generator used for breaking ties in majority voting

See Also:

Vote.distributionForInstanceMajorityVoting(Instance)

m_classifiersToLoad

java.util.List<E> m_classifiersToLoad

List of file paths to serialized models to load

m_preBuiltClassifiers

java.util.List<E> m_preBuiltClassifiers

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

Package weka.classifiers.misc

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

serialVersionUID: 4901630631723287761L

Serialized Fields

m_modelPath

java.lang.String m_modelPath

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

m_modelHeader

Instances m_modelHeader

The instances structure used to train the classifier with

m_trim

boolean m_trim

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

m_ignoreCase

boolean m_ignoreCase

Ignore case when matching attribute names and nominal values?

m_suppressMappingReport

boolean m_suppressMappingReport

Dont output mapping report if set to true

m_initialTestStructureKnown

boolean m_initialTestStructureKnown

If true, then a call to buildClassifier() will not overwrite any test structure that has been recorded with the current training structure. This is useful for getting a correct mapping report output in toString() after buildClassifier has been called and before any test instance has been seen. Test structure and mapping will get reset if a test instance is received whose structure does not match the recorded test structure.

m_vals

double[] m_vals

Holds values for instances constructed for prediction

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

serialVersionUID: 4599593909947628642L

Serialized Fields

m_ModelFile

java.io.File m_ModelFile

the file where the serialized model is stored

Package weka.classifiers.pmml.consumer

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

serialVersionUID: 2583880411828388959L

Serialized Fields

m_modelType

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

m_modelName

java.lang.String m_modelName

m_algorithmName

java.lang.String m_algorithmName

m_functionType

int m_functionType

m_cumulativeLinkFunction

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

m_linkFunction

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

m_linkParameter

double m_linkParameter

m_trialsVariable

java.lang.String m_trialsVariable

m_trialsValue

double m_trialsValue

m_distribution

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

m_distParameter

double m_distParameter

m_offsetVariable

java.lang.String m_offsetVariable

m_offsetValue

double m_offsetValue

m_parameterList

java.util.ArrayList<E> m_parameterList

m_factorList

java.util.ArrayList<E> m_factorList

m_covariateList

java.util.ArrayList<E> m_covariateList

m_ppMatrix

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

m_paramMatrix

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

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

serialVersionUID: -4545904813133921249L

Serialized Fields

m_functionType

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

The mining function

m_activationFunction

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

The activation function to use

m_normalizationMethod

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

The normalization method

m_threshold

double m_threshold

Threshold activation

m_width

double m_width

Width for radial basis

m_altitude

double m_altitude

Altitude for radial basis

m_numberOfInputs

int m_numberOfInputs

The number of inputs to the network

m_numberOfLayers

int m_numberOfLayers

Number of hidden layers in the network

m_inputs

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

The inputs to the network

m_inputMap

java.util.HashMap<K,V> m_inputMap

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

m_layers

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

The hidden layers in the network

m_outputs

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

The outputs of the network

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

serialVersionUID: -5371600590320702971L

Serialized Fields

m_pmmlVersion

java.lang.String m_pmmlVersion

PMML version

m_creatorApplication

java.lang.String m_creatorApplication

Creator application

m_log

Logger m_log

Logger

m_dataDictionary

Instances m_dataDictionary

The data dictionary

m_miningSchema

MiningSchema m_miningSchema

The fields and meta data used by the model

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

serialVersionUID: -5551125528409488634L

Serialized Fields

m_algorithmName

java.lang.String m_algorithmName

Description of the algorithm

m_regressionTables

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

The regression tables for this regression

m_normalizationMethod

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

The normalization to use

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

serialVersionUID: 3077920125549906819L

Serialized Fields

m_valueName

java.lang.String m_valueName

The attribute value for this predictor

m_valueIndex

int m_valueIndex

The index of the attribute value for this predictor

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

serialVersionUID: -4335075205696648273L

Serialized Fields

m_exponent

double m_exponent

The exponent

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

serialVersionUID: 5493100145890252757L

Serialized Fields

m_coefficient

double m_coefficient

The coefficient for this predictor term

m_indexes

int[] m_indexes

the indexes of the terms to be multiplied

m_fieldNames

java.lang.String[] m_fieldNames

The names of the terms (attributes) to be multiplied

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

serialVersionUID: 1993161168811020547L

Serialized Fields

m_functionType

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

The mining function

m_modelName

java.lang.String m_modelName

The model name (if defined)

m_algorithmName

java.lang.String m_algorithmName

The algorithm name (if defined)

m_ruleSet

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

The set of rules

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

serialVersionUID: 6225095165118374296L

Serialized Fields

m_functionType

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

The mining function

m_classificationMethod

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

The classification method (PMML 4.0)

m_modelName

java.lang.String m_modelName

The model name (if defined)

m_algorithmName

java.lang.String m_algorithmName

The algorithm name (if defined)

m_vectorDictionary

VectorDictionary m_vectorDictionary

The dictionary of support vectors

m_kernel

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

The kernel function to use

m_machines

java.util.List<E> m_machines

The individual binary SVMs

m_alternateBinaryTargetCategory

int m_alternateBinaryTargetCategory

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

m_svmRepresentation

weka.classifiers.pmml.consumer.SupportVectorMachineModel.SVM_representation m_svmRepresentation

Do we have support vectors, or just attribute coefficients for a linear machine?

m_threshold

double m_threshold

PMML 4.0 threshold value

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

serialVersionUID: -2065158088298753129L

Serialized Fields

m_functionType

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

The mining function

m_missingValueStrategy

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

The missing value strategy

m_missingValuePenalty

double m_missingValuePenalty

The missing value penalty (if defined). We don't actually make use of this since we always return full probability distributions.

m_noTrueChildStrategy

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

The no true child strategy to use

m_splitCharacteristic

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

The splitting type

m_root

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

The root of the tree

Package weka.classifiers.rules

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

serialVersionUID: 2888557078165701326L

Serialized Fields

m_entries

java.util.Hashtable<K,V> m_entries

The hashtable used to hold training instances

m_classPriorCounts

double[] m_classPriorCounts

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

m_classPriors

double[] m_classPriors

m_decisionFeatures

int[] m_decisionFeatures

Holds the final feature set

m_disTransform

Filter m_disTransform

Discretization filter

m_delTransform

Remove m_delTransform

Filter used to remove columns discarded by feature selection

m_ibk

IBk m_ibk

IB1 used to classify non matching instances rather than majority class

m_theInstances

Instances m_theInstances

Holds the original training instances

m_dtInstances

Instances m_dtInstances

Holds the final feature selected set of instances

m_numAttributes

int m_numAttributes

The number of attributes in the dataset

m_numInstances

int m_numInstances

The number of instances in the dataset

m_classIsNominal

boolean m_classIsNominal

Class is nominal

m_useIBk

boolean m_useIBk

Use the IBk classifier rather than majority class

m_displayRules

boolean m_displayRules

Display Rules

m_CVFolds

int m_CVFolds

Number of folds for cross validating feature sets

m_rr

java.util.Random m_rr

Random numbers for use in cross validation

m_majority

double m_majority

Holds the majority class

m_search

ASSearch m_search

The search method to use

m_evaluator

ASEvaluation m_evaluator

Our own internal evaluator

m_evaluation

Evaluation m_evaluation

The evaluation object used to evaluate subsets

m_evaluationMeasure

int m_evaluationMeasure

m_saveMemory

boolean m_saveMemory

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

serialVersionUID: 5674163500154964602L

Serialized Fields

attributes

double[] attributes

Array of attribute values for an instance

missing

boolean[] missing

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

key

int key

The key

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

serialVersionUID: -6589312996832147161L

Serialized Fields

m_Class

Attribute m_Class

The class attribute of the data

m_Ruleset

FastVector<E> m_Ruleset

The ruleset

m_Distributions

FastVector<E> m_Distributions

The predicted class distribution

m_Optimizations

int m_Optimizations

Runs of optimizations

m_Random

java.util.Random m_Random

Random object used in this class

m_Total

double m_Total

# of all the possible conditions in a rule

m_Seed

long m_Seed

The seed to perform randomization

m_Folds

int m_Folds

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

m_MinNo

double m_MinNo

The minimal number of instance weights within a split

m_Debug

boolean m_Debug

Whether in a debug mode

m_CheckErr

boolean m_CheckErr

Whether check the error rate >= 0.5 in stopping criteria

m_UsePruning

boolean m_UsePruning

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

m_Filter

Filter m_Filter

The filter used to randomize the class order

m_RulesetStats

FastVector<E> m_RulesetStats

The RuleStats for the ruleset of each class value

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

serialVersionUID: -8929754772994154334L

Serialized Fields

att

Attribute att

The attribute of the antecedent

value

double value

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

maxInfoGain

double maxInfoGain

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

accuRate

double accuRate

The accurate rate of this antecedent test on the growing data

cover

double cover

The coverage of this antecedent in the growing data

accu

double accu

The accurate data for this antecedent in the growing data

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

serialVersionUID: -9102297038837585135L

Serialized Fields

accurate

double[] accurate

coverage

double[] coverage

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

serialVersionUID: 5699457269983735442L

Serialized Fields

splitPoint

double splitPoint

The split point for this numeric antecedent

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

serialVersionUID: -2410020717305262952L

Serialized Fields

m_Consequent

double m_Consequent

The internal representation of the class label to be predicted

m_Antds

FastVector<E> m_Antds

The vector of antecedents of this rule

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

serialVersionUID: -1746114858746563180L

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

serialVersionUID: -2459427002147861445L

Serialized Fields

m_rule

weka.classifiers.rules.OneR.OneRRule m_rule

A 1-R rule

m_minBucketSize

int m_minBucketSize

The minimum bucket size

m_ZeroR

Classifier m_ZeroR

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

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

serialVersionUID: 8121455039782598361L

Serialized Fields

m_root

MakeDecList m_root

The decision list

m_CF

float m_CF

Confidence level

m_minNumObj

int m_minNumObj

Minimum number of objects

m_useMDLcorrection

boolean m_useMDLcorrection

Use MDL correction?

m_reducedErrorPruning

boolean m_reducedErrorPruning

Use reduced error pruning?

m_numFolds

int m_numFolds

Number of folds for reduced error pruning.

m_binarySplits

boolean m_binarySplits

Binary splits on nominal attributes?

m_unpruned

boolean m_unpruned

Generate unpruned list?

m_Seed

int m_Seed

The seed for random number generation.

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

serialVersionUID: 8815687740470471229L

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

serialVersionUID: -5708153367675298624L

Serialized Fields

m_Data

Instances m_Data

The data on which the stats calculation is based

m_Ruleset

FastVector<E> m_Ruleset

The specific ruleset in question

m_SimpleStats

FastVector<E> m_SimpleStats

The simple stats of each rule

m_Filtered

FastVector<E> m_Filtered

The set of instances filtered by the ruleset

m_Total

double m_Total

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

MDL_THEORY_WEIGHT

double MDL_THEORY_WEIGHT

The theory weight in the MDL calculation

m_Distributions

FastVector<E> m_Distributions

The class distributions predicted by each rule

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

serialVersionUID: 48055541465867954L

Serialized Fields

m_ClassValue

double m_ClassValue

The class value 0R predicts.

m_Counts

double[] m_Counts

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

m_Class

Attribute m_Class

The class attribute.

Package weka.classifiers.rules.part

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

serialVersionUID: -2757684345218324559L

Serialized Fields

CF

double CF

CF

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

serialVersionUID: 7284358349711992497L

Serialized Fields

m_minNumObj

int m_minNumObj

Minimum number of objects

m_toSelectModel

ModelSelection m_toSelectModel

The model selection method.

m_localModel

ClassifierSplitModel m_localModel

Local model at node.

m_sons

ClassifierDecList[] m_sons

References to sons.

m_isLeaf

boolean m_isLeaf

True if node is leaf.

m_isEmpty

boolean m_isEmpty

True if node is empty.

m_train

Instances m_train

The training instances.

m_test

Distribution m_test

The pruning instances.

indeX

int indeX

Which son to expand?

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

serialVersionUID: -1427481323245079123L

Serialized Fields

theRules

java.util.Vector<E> theRules

Vector storing the rules.

CF

double CF

The confidence for C45-type pruning.

minNumObj

int minNumObj

Minimum number of objects

toSelectModeL

ModelSelection toSelectModeL

The model selection method.

numSetS

int numSetS

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

reducedErrorPruning

boolean reducedErrorPruning

Use reduced error pruning?

unpruned

boolean unpruned

Generated unpruned list?

m_seed

int m_seed

The seed for random number generation.

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

serialVersionUID: -7228103346297172921L

Package weka.classifiers.trees

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

serialVersionUID: 1618384535950391L

Serialized Fields

m_AttIndex

int m_AttIndex

The attribute used for classification.

m_SplitPoint

double m_SplitPoint

The split point (index respectively).

m_Distribution

double[][] m_Distribution

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

m_Instances

Instances m_Instances

The instances used for training.

m_ZeroR

Classifier m_ZeroR

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

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

Serialized Fields

m_header

Instances m_header

m_root

weka.classifiers.trees.HoeffdingTree.HNode m_root

m_growthAllowed

boolean m_growthAllowed

m_gracePeriod

double m_gracePeriod

m_splitMetric

weka.classifiers.trees.HoeffdingTree.SplitMetric m_splitMetric

m_splitConfidence

double m_splitConfidence

m_hoeffdingTieThreshold

double m_hoeffdingTieThreshold

m_activeLeafCount

int m_activeLeafCount

m_inactiveLeafCount

int m_inactiveLeafCount

m_decisionNodeCount

int m_decisionNodeCount

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

serialVersionUID: -217733168393644444L

Serialized Fields

m_root

ClassifierTree m_root

The decision tree

m_unpruned

boolean m_unpruned

Unpruned tree?

m_collapseTree

boolean m_collapseTree

Collapse tree?

m_CF

float m_CF

Confidence level

m_minNumObj

int m_minNumObj

Minimum number of instances

m_useMDLcorrection

boolean m_useMDLcorrection

Use MDL correction?

m_useLaplace

boolean m_useLaplace

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

m_reducedErrorPruning

boolean m_reducedErrorPruning

Use reduced error pruning?

m_numFolds

int m_numFolds

Number of folds for reduced error pruning.

m_binarySplits

boolean m_binarySplits

Binary splits on nominal attributes?

m_subtreeRaising

boolean m_subtreeRaising

Subtree raising to be performed?

m_noCleanup

boolean m_noCleanup

Cleanup after the tree has been built.

m_Seed

int m_Seed

Random number seed for reduced-error pruning.

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

serialVersionUID: -1113212459618104943L

Serialized Fields

m_replaceMissing

ReplaceMissingValues m_replaceMissing

Filter to replace missing values

m_nominalToBinary

NominalToBinary m_nominalToBinary

Filter to replace nominal attributes

m_tree

LMTNode m_tree

root of the logistic model tree

m_fastRegression

boolean m_fastRegression

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

m_convertNominal

boolean m_convertNominal

convert nominal attributes to binary ?

m_splitOnResiduals

boolean m_splitOnResiduals

split on residuals?

m_errorOnProbabilities

boolean m_errorOnProbabilities

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

m_minNumInstances

int m_minNumInstances

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

m_numBoostingIterations

int m_numBoostingIterations

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

m_weightTrimBeta

double m_weightTrimBeta

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

m_useAIC

boolean m_useAIC

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

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

serialVersionUID: -6118439039768244417L

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

serialVersionUID: 1116839470751428698L

Serialized Fields

m_numTrees

int m_numTrees

Number of trees in forest.

m_numFeatures

int m_numFeatures

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

m_randomSeed

int m_randomSeed

The random seed.

m_KValue

int m_KValue

Final number of features that were considered in last build.

m_bagger

Bagging m_bagger

The bagger.

m_MaxDepth

int m_MaxDepth

The maximum depth of the trees (0 = unlimited)

m_numExecutionSlots

int m_numExecutionSlots

The number of threads to have executing at any one time

m_printTrees

boolean m_printTrees

Print the individual trees in the output

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

serialVersionUID: 8934314652175299374L

Serialized Fields

m_Tree

weka.classifiers.trees.RandomTree.Tree m_Tree

The Tree object

m_Info

Instances m_Info

The header information.

m_MinNum

double m_MinNum

Minimum number of instances for leaf.

m_KValue

int m_KValue

The number of attributes considered for a split.

m_randomSeed

int m_randomSeed

The random seed to use.

m_MaxDepth

int m_MaxDepth

The maximum depth of the tree (0 = unlimited)

m_NumFolds

int m_NumFolds

Determines how much data is used for backfitting

m_AllowUnclassifiedInstances

boolean m_AllowUnclassifiedInstances

Whether unclassified instances are allowed

m_zeroR

Classifier m_zeroR

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

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

serialVersionUID: 3549573538656522569L

Serialized Fields

m_Successors

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

The subtrees appended to this tree.

m_Attribute

int m_Attribute

The attribute to split on.

m_SplitPoint

double m_SplitPoint

The split point.

m_Prop

double[] m_Prop

The proportions of training instances going down each branch.

m_ClassDistribution

double[] m_ClassDistribution

Class probabilities from the training data.

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

serialVersionUID: -9216785998198681299L

Serialized Fields

m_zeroR

ZeroR m_zeroR

ZeroR model that is used if no attributes are present.

m_Tree

weka.classifiers.trees.REPTree.Tree m_Tree

The Tree object

m_NumFolds

int m_NumFolds

Number of folds for reduced error pruning.

m_Seed

int m_Seed

Seed for random data shuffling.

m_NoPruning

boolean m_NoPruning

Don't prune

m_MinNum

double m_MinNum

The minimum number of instances per leaf.

m_MinVarianceProp

double m_MinVarianceProp

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

m_MaxDepth

int m_MaxDepth

Upper bound on the tree depth

m_InitialCount

double m_InitialCount

The initial class count

m_SpreadInitialCount

boolean m_SpreadInitialCount

Whether to spread initial count across all values

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

serialVersionUID: -1635481717888437935L

Serialized Fields

m_Info

Instances m_Info

The header information (for printing the tree).

m_Successors

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

The subtrees of this tree.

m_Attribute

int m_Attribute

The attribute to split on.

m_SplitPoint

double m_SplitPoint

The split point.

m_Prop

double[] m_Prop

The proportions of training instances going down each branch.

m_ClassProbs

double[] m_ClassProbs

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

m_Distribution

double[] m_Distribution

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

m_HoldOutDist

double[] m_HoldOutDist

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

m_HoldOutError

double m_HoldOutError

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

Package weka.classifiers.trees.j48

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

serialVersionUID: 179170923545122001L

Serialized Fields

m_minNoObj

int m_minNoObj

Minimum number of instances in interval.

m_useMDLcorrection

boolean m_useMDLcorrection

Use MDL correction?

m_allData

Instances m_allData

The FULL training dataset.

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

serialVersionUID: -1278776919563022474L

Serialized Fields

m_attIndex

int m_attIndex

Attribute to split on.

m_minNoObj

int m_minNoObj

Minimum number of objects in a split.

m_useMDLcorrection

boolean m_useMDLcorrection

Use MDL correction?

m_splitPoint

double m_splitPoint

Value of split point.

m_infoGain

double m_infoGain

InfoGain of split.

m_gainRatio

double m_gainRatio

GainRatio of split.

m_sumOfWeights

double m_sumOfWeights

The sum of the weights of the instances.

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

serialVersionUID: 3372204862440821989L

Serialized Fields

m_minNoObj

int m_minNoObj

Minimum number of objects in interval.

m_useMDLcorrection

boolean m_useMDLcorrection

Use MDL correction?

m_allData

Instances m_allData

All the training data

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

serialVersionUID: -4813820170260388194L

Serialized Fields

m_pruneTheTree

boolean m_pruneTheTree

True if the tree is to be pruned.

m_collapseTheTree

boolean m_collapseTheTree

True if the tree is to be collapsed.

m_CF

float m_CF

The confidence factor for pruning.

m_subtreeRaising

boolean m_subtreeRaising

Is subtree raising to be performed?

m_cleanup

boolean m_cleanup

Cleanup after the tree has been built.

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

serialVersionUID: 3064079330067903161L

Serialized Fields

m_complexityIndex

int m_complexityIndex

Desired number of branches.

m_attIndex

int m_attIndex

Attribute to split on.

m_minNoObj

int m_minNoObj

Minimum number of objects in a split.

m_useMDLcorrection

boolean m_useMDLcorrection

Use MDL correction?

m_splitPoint

double m_splitPoint

Value of split point.

m_infoGain

double m_infoGain

InfoGain of split.

m_gainRatio

double m_gainRatio

GainRatio of split.

m_sumOfWeights

double m_sumOfWeights

The sum of the weights of the instances.

m_index

int m_index

Number of split points.

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

serialVersionUID: 4280730118393457457L

Serialized Fields

m_distribution

Distribution m_distribution

Distribution of class values.

m_numSubsets

int m_numSubsets

Number of created subsets.

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

serialVersionUID: -8722249377542734193L

Serialized Fields

m_toSelectModel

ModelSelection m_toSelectModel

The model selection method.

m_localModel

ClassifierSplitModel m_localModel

Local model at node.

m_sons

ClassifierTree[] m_sons

References to sons.

m_isLeaf

boolean m_isLeaf

True if node is leaf.

m_isEmpty

boolean m_isEmpty

True if node is empty.

m_train

Instances m_train

The training instances.

m_test

Distribution m_test

The pruning instances.

m_id

int m_id

The id for the node.

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

serialVersionUID: 8526859638230806576L

Serialized Fields

m_perClassPerBag

double[][] m_perClassPerBag

Weight of instances per class per bag.

m_perBag

double[] m_perBag

Weight of instances per bag.

m_perClass

double[] m_perClass

Weight of instances per class.

totaL

double totaL

Total weight of instances.

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

serialVersionUID: -2618691439791653056L

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

serialVersionUID: 5986252682266803935L

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

serialVersionUID: -433336694718670930L

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

serialVersionUID: 4892105020180728499L

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

serialVersionUID: -4850147125096133642L

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

serialVersionUID: -4472639447877404786L

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

serialVersionUID: 990097748931976704L

Serialized Fields

m_minNoObj

int m_minNoObj

Minimum number of objects in interval.

m_allData

Instances m_allData

All the training data

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

serialVersionUID: 7824804381545259618L

Serialized Fields

m_nb

NaiveBayesUpdateable m_nb

the naive bayes classifier

m_disc

Discretize m_disc

the discretizer used

m_errors

double m_errors

errors on the training data at this node

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

serialVersionUID: 8922627123884975070L

Serialized Fields

m_complexityIndex

int m_complexityIndex

Desired number of branches.

m_attIndex

int m_attIndex

Attribute to split on.

m_minNoObj

int m_minNoObj

Minimum number of objects in a split.

m_splitPoint

double m_splitPoint

Value of split point.

m_sumOfWeights

double m_sumOfWeights

The sum of the weights of the instances.

m_errors

double m_errors

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

m_c45S

C45Split m_c45S

m_globalNB

NBTreeNoSplit m_globalNB

The global naive bayes model for this node

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

serialVersionUID: -1292620749331337546L

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

serialVersionUID: -555775736857600201L

Serialized Fields

pruneTheTree

boolean pruneTheTree

True if the tree is to be pruned.

numSets

int numSets

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

m_cleanup

boolean m_cleanup

Cleanup after the tree has been built.

m_seed

int m_seed

The random number seed.

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

serialVersionUID: 5490996638027101259L

Package weka.classifiers.trees.lmt

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

serialVersionUID: 1862737145870398755L

Serialized Fields

m_totalInstanceWeight

double m_totalInstanceWeight

Total number of training instances.

m_id

int m_id

Node id

m_leafModelNum

int m_leafModelNum

ID of logistic model at leaf

m_alpha

double m_alpha

Alpha-value (for pruning) at the node

m_numIncorrectModel

double m_numIncorrectModel

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

m_numIncorrectTree

double m_numIncorrectTree

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

m_minNumInstances

int m_minNumInstances

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

m_modelSelection

ModelSelection m_modelSelection

ModelSelection object (for splitting)

m_nominalToBinary

NominalToBinary m_nominalToBinary

Filter to convert nominal attributes to binary

m_higherRegressions

SimpleLinearRegression[][] m_higherRegressions

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

m_numHigherRegressions

int m_numHigherRegressions

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

m_fastRegression

boolean m_fastRegression

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

m_numInstances

int m_numInstances

Number of instances at the node

m_localModel

ClassifierSplitModel m_localModel

The ClassifierSplitModel (for splitting)

m_sons

LMTNode[] m_sons

Array of children of the node

m_isLeaf

boolean m_isLeaf

True if node is leaf

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

serialVersionUID: 168765678097825064L

Serialized Fields

m_numericDataHeader

Instances m_numericDataHeader

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

m_numericData

Instances m_numericData

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

m_train

Instances m_train

Training data

m_useCrossValidation

boolean m_useCrossValidation

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

m_errorOnProbabilities

boolean m_errorOnProbabilities

Use error on probabilities for stopping criterion of LogitBoost?

m_fixedNumIterations

int m_fixedNumIterations

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

m_heuristicStop

int m_heuristicStop

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

m_numRegressions

int m_numRegressions

The number of LogitBoost iterations performed.

m_maxIterations

int m_maxIterations

The maximum number of LogitBoost iterations

m_numClasses

int m_numClasses

The number of different classes

m_regressions

SimpleLinearRegression[][] m_regressions

Array holding the simple regression functions fit by LogitBoost

m_useAIC

boolean m_useAIC

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

m_numParameters

double m_numParameters

Effective number of parameters used for AIC / BIC automatic stopping

m_weightTrimBeta

double m_weightTrimBeta

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

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

serialVersionUID: -293098783159385148L

Serialized Fields

m_minNumInstances

int m_minNumInstances

Minimum number of instances for leaves

m_minInfoGain

double m_minInfoGain

Minimum information gain for split

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

serialVersionUID: -5055883734183713525L

Serialized Fields

m_attribute

Attribute m_attribute

The attribute selected for the split

m_attIndex

int m_attIndex

The index of the attribute selected for the split

m_numInstances

int m_numInstances

Number of instances in the set

m_numClasses

int m_numClasses

Number of classed

m_data

Instances m_data

The set of instances

m_dataZs

double[][] m_dataZs

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

m_dataWs

double[][] m_dataWs

The LogitBoost-weights for the set of instances

m_splitPoint

double m_splitPoint

The split point (for numeric attributes)

Package weka.classifiers.trees.m5

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

serialVersionUID: 4212734895125452770L

Serialized Fields

m_first

int m_first

the first instance

m_last

int m_last

the last instance

m_position

int m_position

m_maxImpurity

double m_maxImpurity

the maximum impurity reduction

m_splitAttr

int m_splitAttr

the attribute being tested

m_splitValue

double m_splitValue

the best value on which to split

m_number

int m_number

the number of instances

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

serialVersionUID: -4022221950191647679L

Serialized Fields

m_instances

Instances m_instances

the instances covered by the tree/rules

m_ruleSet

FastVector<E> m_ruleSet

the rule set

m_generateRules

boolean m_generateRules

generate a decision list instead of a single tree.

m_unsmoothedPredictions

boolean m_unsmoothedPredictions

use unsmoothed predictions

m_replaceMissing

ReplaceMissingValues m_replaceMissing

filter to fill in missing values

m_nominalToBinary

NominalToBinary m_nominalToBinary

filter to convert nominal attributes to binary

m_removeUseless

RemoveUseless m_removeUseless

for removing useless attributes

m_saveInstances

boolean m_saveInstances

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

m_regressionTree

boolean m_regressionTree

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

m_useUnpruned

boolean m_useUnpruned

Do not prune tree/rules

m_minNumInstances

double m_minNumInstances

The minimum number of instances to allow at a leaf node

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

serialVersionUID: 2030974097051713247L

Serialized Fields

m_coefficients

double[] m_coefficients

The coefficients

m_intercept

double m_intercept

The intercept

m_instancesHeader

Instances m_instancesHeader

Holds the instances header for printing the model

m_numParameters

int m_numParameters

number of coefficients in the model

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

serialVersionUID: -4458627451682483204L

Serialized Fields

m_instances

Instances m_instances

the instances covered by this rule

m_classIndex

int m_classIndex

the class index

m_numAttributes

int m_numAttributes

the number of attributes

m_numInstances

int m_numInstances

the number of instances in the dataset

m_splitAtts

int[] m_splitAtts

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

m_splitVals

double[] m_splitVals

the corresponding values of the split points

m_internalNodes

RuleNode[] m_internalNodes

the corresponding internal nodes. Used for smoothing rules.

m_relOps

int[] m_relOps

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

m_ruleModel

RuleNode m_ruleModel

the leaf encapsulating the linear model for this rule

m_topOfTree

RuleNode m_topOfTree

the top of the m5 tree for this rule

m_globalStdDev

double m_globalStdDev

the standard deviation of the class for all the instances

m_globalAbsDev

double m_globalAbsDev

the absolute deviation of the class for all the instances

m_covered

Instances m_covered

the instances covered by this rule

m_numCovered

int m_numCovered

the number of instances covered by this rule

m_notCovered

Instances m_notCovered

the instances not covered by this rule

m_useTree

boolean m_useTree

use a pruned m5 tree rather than make a rule

m_smoothPredictions

boolean m_smoothPredictions

use the original m5 smoothing procedure

m_saveInstances

boolean m_saveInstances

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

m_regressionTree

boolean m_regressionTree

Make a regression tree instead of a model tree

m_useUnpruned

boolean m_useUnpruned

Build unpruned tree/rule

m_minNumInstances

double m_minNumInstances

The minimum number of instances to allow at a leaf node

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

serialVersionUID: 1979807611124337144L

Serialized Fields

m_instances

Instances m_instances

instances reaching this node

m_classIndex

int m_classIndex

the class index

m_numInstances

int m_numInstances

the number of instances reaching this node

m_numAttributes

int m_numAttributes

the number of attributes

m_isLeaf

boolean m_isLeaf

Node is a leaf

m_splitAtt

int m_splitAtt

attribute this node splits on

m_splitValue

double m_splitValue

the value of the split attribute

m_nodeModel

PreConstructedLinearModel m_nodeModel

the linear model at this node

m_numParameters

int m_numParameters

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

m_rootMeanSquaredError

double m_rootMeanSquaredError

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

m_left

RuleNode m_left

left child node

m_right

RuleNode m_right

right child node

m_parent

RuleNode m_parent

the parent of this node

m_splitNum

double m_splitNum

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

m_devFraction

double m_devFraction

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

m_pruningMultiplier

double m_pruningMultiplier

m_leafModelNum

int m_leafModelNum

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

m_globalDeviation

double m_globalDeviation

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

m_globalAbsDeviation

double m_globalAbsDeviation

the absolute deviation of the global class

m_indices

int[] m_indices

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

m_id

int m_id

Node id.

m_saveInstances

boolean m_saveInstances

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

m_regressionTree

boolean m_regressionTree

Make a regression tree instead of a model tree

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

serialVersionUID: 1864267581079767881L

Serialized Fields

number

int number

first

int first

last

int last

position

int position

maxImpurity

double maxImpurity

leftAve

double leftAve

rightAve

double rightAve

splitAttr

int splitAttr

splitValue

double splitValue

Package weka.clusterers

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

serialVersionUID: -6099962589663877632L

Class weka.clusterers.AbstractDensityBasedClusterer extends AbstractClusterer implements Serializable

serialVersionUID: -5950728041704213845L

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

serialVersionUID: -830188327319128005L

Serialized Fields

m_Clusterer

Clusterer m_Clusterer

the clusterer

m_clusteringResults

java.lang.StringBuffer m_clusteringResults

holds a string describing the results of clustering the training data

m_numClusters

int m_numClusters

holds the number of clusters found by the clusterer

m_clusterAssignments

double[] m_clusterAssignments

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

m_logL

double m_logL

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

m_classToCluster

int[] m_classToCluster

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

Class weka.clusterers.Cobweb extends RandomizableClusterer implements Serializable

serialVersionUID: 928406656495092318L

Serialized Fields

m_acuity

double m_acuity

Acuity (minimum standard deviation).

m_cutoff

double m_cutoff

Cutoff (minimum category utility).

m_cobwebTree

Cobweb.CNode m_cobwebTree

Holds the root of the Cobweb tree.

m_numberOfClusters

int m_numberOfClusters

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

See Also:

Cobweb.numberOfClusters(), Cobweb.m_numberOfClustersDetermined

m_numberOfClustersDetermined

boolean m_numberOfClustersDetermined

whether the number of clusters was already determined

m_numberSplits

int m_numberSplits

the number of splits that happened

m_numberMerges

int m_numberMerges

the number of merges that happened

m_saveInstances

boolean m_saveInstances

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

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

serialVersionUID: 3452097436933325631L

Serialized Fields

m_attStats

AttributeStats[] m_attStats

Within cluster attribute statistics

m_numAttributes

int m_numAttributes

Number of attributes

m_clusterInstances

Instances m_clusterInstances

Instances at this node

m_children

FastVector<E> m_children

Children of this node

m_totalInstances

double m_totalInstances

Total instances at this node

m_clusterNum

int m_clusterNum

Cluster number of this node

Class weka.clusterers.EM extends RandomizableDensityBasedClusterer implements Serializable

serialVersionUID: 8348181483812829475L

Serialized Fields

m_model

Estimator[][] m_model

hold the discrete estimators for each cluster

m_modelNormal

double[][][] m_modelNormal

hold the normal estimators for each cluster

m_minStdDev

double m_minStdDev

default minimum standard deviation

m_minStdDevPerAtt

double[] m_minStdDevPerAtt

m_weights

double[][] m_weights

hold the weights of each instance for each cluster

m_priors

double[] m_priors

the prior probabilities for clusters

m_loglikely

double m_loglikely

the loglikelihood of the data

m_theInstances

Instances m_theInstances

full training instances

m_num_clusters

int m_num_clusters

number of clusters selected by the user or cross validation

m_initialNumClusters

int m_initialNumClusters

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

m_upperBoundNumClustersCV

int m_upperBoundNumClustersCV

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

m_num_attribs

int m_num_attribs

number of attributes

m_num_instances

int m_num_instances

number of training instances

m_max_iterations

int m_max_iterations

maximum iterations to perform

m_minValues

double[] m_minValues

attribute min values

m_maxValues

double[] m_maxValues

attribute max values

m_rr

java.util.Random m_rr

random number generator

m_verbose

boolean m_verbose

Verbose?

m_replaceMissing

ReplaceMissingValues m_replaceMissing

globally replace missing values

m_displayModelInOldFormat

boolean m_displayModelInOldFormat

display model output in old-style format

m_executionSlots

int m_executionSlots

Number of threads to use for E and M steps

m_training

boolean m_training

False once training has completed

m_iterationsPerformed

int m_iterationsPerformed

The actual number of iterations performed

m_minLogLikelihoodImprovementIterating

double m_minLogLikelihoodImprovementIterating

Minimum improvement in log likelihood when iterating

m_minLogLikelihoodImprovementCV

double m_minLogLikelihoodImprovementCV

Minimum improvement to increase number of clusters when cross-validating

m_cvFolds

int m_cvFolds

The number of folds to use for cross-validation

Class weka.clusterers.FarthestFirst extends RandomizableClusterer implements Serializable

serialVersionUID: 7499838100631329509L

Serialized Fields

m_instances

Instances m_instances

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

m_ReplaceMissingFilter

ReplaceMissingValues m_ReplaceMissingFilter

replace missing values in training instances

m_NumClusters

int m_NumClusters

number of clusters to generate

m_ClusterCentroids

Instances m_ClusterCentroids

holds the cluster centroids

m_Min

double[] m_Min

attribute min values

m_Max

double[] m_Max

attribute max values

Class weka.clusterers.FilteredClusterer extends SingleClustererEnhancer implements Serializable

serialVersionUID: 1420005943163412943L

Serialized Fields

m_Filter

Filter m_Filter

The filter.

m_FilteredInstances

Instances m_FilteredInstances

The instance structure of the filtered instances.

Class weka.clusterers.HierarchicalClusterer extends AbstractClusterer implements Serializable

serialVersionUID: 1L

Serialized Fields

m_bDebug

boolean m_bDebug

Whether the classifier is run in debug mode.

m_bDistanceIsBranchLength

boolean m_bDistanceIsBranchLength

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

m_instances

Instances m_instances

training data

m_nNumClusters

int m_nNumClusters

number of clusters desired in clustering

m_DistanceFunction

DistanceFunction m_DistanceFunction

distance function used for comparing members of a cluster

m_nLinkType

int m_nLinkType

Holds the Link type used calculate distance between clusters

m_bPrintNewick

boolean m_bPrintNewick

m_clusters

weka.clusterers.HierarchicalClusterer.Node[] m_clusters

m_nClusterNr

int[] m_nClusterNr

Class weka.clusterers.MakeDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable

serialVersionUID: -5643302427972186631L

Serialized Fields

m_theInstances

Instances m_theInstances

holds training instances header information

m_priors

double[] m_priors

prior probabilities for the fitted clusters

m_modelNormal

double[][][] m_modelNormal

normal distributions fitted to each numeric attribute in each cluster

m_model

DiscreteEstimator[][] m_model

discrete distributions fitted to each discrete attribute in each cluster

m_minStdDev

double m_minStdDev

default minimum standard deviation

m_wrappedClusterer

Clusterer m_wrappedClusterer

The clusterer being wrapped

m_replaceMissing

ReplaceMissingValues m_replaceMissing

globally replace missing values

Class weka.clusterers.RandomizableClusterer extends AbstractClusterer implements Serializable

serialVersionUID: -4819590778152242745L

Serialized Fields

m_SeedDefault

int m_SeedDefault

the default seed value

m_Seed

int m_Seed

The random number seed.

Class weka.clusterers.RandomizableDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable

serialVersionUID: -5325270357918932849L

Serialized Fields

m_SeedDefault

int m_SeedDefault

the default seed value

m_Seed

int m_Seed

The random number seed.

Class weka.clusterers.RandomizableSingleClustererEnhancer extends AbstractClusterer implements Serializable

serialVersionUID: -644847037106316249L

Serialized Fields

m_SeedDefault

int m_SeedDefault

the default seed value

m_Seed

int m_Seed

The random number seed.

Class weka.clusterers.SimpleKMeans extends RandomizableClusterer implements Serializable

serialVersionUID: -3235809600124455376L

Serialized Fields

m_ReplaceMissingFilter

ReplaceMissingValues m_ReplaceMissingFilter

replace missing values in training instances.

m_NumClusters

int m_NumClusters

number of clusters to generate.

m_ClusterCentroids

Instances m_ClusterCentroids

holds the cluster centroids.

m_ClusterStdDevs

Instances m_ClusterStdDevs

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

m_ClusterNominalCounts

int[][][] m_ClusterNominalCounts

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

m_ClusterMissingCounts

int[][] m_ClusterMissingCounts

m_FullMeansOrMediansOrModes

double[] m_FullMeansOrMediansOrModes

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

m_FullStdDevs

double[] m_FullStdDevs

m_FullNominalCounts

int[][] m_FullNominalCounts

m_FullMissingCounts

int[] m_FullMissingCounts

m_displayStdDevs

boolean m_displayStdDevs

Display standard deviations for numeric atts.

m_dontReplaceMissing

boolean m_dontReplaceMissing

Replace missing values globally?

m_ClusterSizes

int[] m_ClusterSizes

The number of instances in each cluster.

m_MaxIterations

int m_MaxIterations

Maximum number of iterations to be executed.

m_Iterations

int m_Iterations

Keep track of the number of iterations completed before convergence.

m_squaredErrors

double[] m_squaredErrors

Holds the squared errors for all clusters.

m_DistanceFunction

DistanceFunction m_DistanceFunction

the distance function used.

m_PreserveOrder

boolean m_PreserveOrder

Preserve order of instances.

m_Assignments

int[] m_Assignments

Assignments obtained.

m_FastDistanceCalc

boolean m_FastDistanceCalc

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

m_initializeWithKMeansPlusPlus

boolean m_initializeWithKMeansPlusPlus

Whether to initialize cluster centers using the k-means++ method

m_executionSlots

int m_executionSlots

m_completed

int m_completed

m_failed

int m_failed

Class weka.clusterers.SingleClustererEnhancer extends AbstractClusterer implements Serializable

serialVersionUID: 4893928362926428671L

Serialized Fields

m_Clusterer

Clusterer m_Clusterer

the clusterer

Package weka.core

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

serialVersionUID: 1482635194499365155L

Serialized Fields

m_Dataset

Instances m_Dataset

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

m_AttValues

double[] m_AttValues

The instance's attribute values.

m_Weight

double m_Weight

The instance's weight.

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

serialVersionUID: -4023736016850256591L

Serialized Fields

m_Elements

double[] m_Elements

The values of the matrix

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

serialVersionUID: -742180568732916383L

Serialized Fields

m_Name

java.lang.String m_Name

The attribute's name.

m_Type

int m_Type

The attribute's type.

m_Values

java.util.ArrayList<E> m_Values

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

m_Hashtable

java.util.Hashtable<K,V> m_Hashtable

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

m_Header

Instances m_Header

The header information for a relation-valued attribute.

m_DateFormat

java.text.SimpleDateFormat m_DateFormat

Date format specification for date attributes

m_Index

int m_Index

The attribute's index.

m_Metadata

ProtectedProperties m_Metadata

The attribute's metadata.

m_Ordering

int m_Ordering

The attribute's ordering.

m_IsRegular

boolean m_IsRegular

Whether the attribute is regular.

m_IsAveragable

boolean m_IsAveragable

Whether the attribute is averagable.

m_HasZeropoint

boolean m_HasZeropoint

Whether the attribute has a zeropoint.

m_Weight

double m_Weight

The attribute's weight.

m_LowerBound

double m_LowerBound

The attribute's lower numeric bound.

m_LowerBoundIsOpen

boolean m_LowerBoundIsOpen

Whether the lower bound is open.

m_UpperBound

double m_UpperBound

The attribute's upper numeric bound.

m_UpperBoundIsOpen

boolean m_UpperBoundIsOpen

Whether the upper bound is open

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

serialVersionUID: 402130123261736245L

Serialized Fields

m_operatorStack

java.util.Stack<E> m_operatorStack

Operator stack

m_originalInfix

java.lang.String m_originalInfix

Holds the original infix expression

m_postFixExpVector

java.util.Vector<E> m_postFixExpVector

Holds the expression in postfix form

m_signMod

boolean m_signMod

True if the next numeric constant or attribute index is negative

m_previousTok

java.lang.String m_previousTok

Holds the previous token

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

serialVersionUID: -2932848827681070345L

Serialized Fields

m_AllowedIndices

int[] m_AllowedIndices

the attribute indices that may be inspected

m_Attributes

java.util.Vector<E> m_Attributes

contains the attribute locations, either true or false Boolean objects

m_Locators

java.util.Vector<E> m_Locators

contains the locator locations, either null or a AttributeLocator reference

m_Type

int m_Type

the type of the attribute

m_Data

Instances m_Data

the referenced data

m_Indices

int[] m_Indices

the indices

m_LocatorIndices

int[] m_LocatorIndices

the indices of locator objects

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

serialVersionUID: 4434688832743939380L

Serialized Fields

intCount

int intCount

The number of int-like values

realCount

int realCount

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

missingCount

int missingCount

The number of missing values

distinctCount

int distinctCount

The number of distinct values

uniqueCount

int uniqueCount

The number of values that only appear once

totalCount

int totalCount

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

numericStats

Stats numericStats

Stats on numeric value distributions

nominalCounts

int[] nominalCounts

Counts of each nominal value

nominalWeights

double[] nominalWeights

Weight mass for each nominal value

Class weka.core.BinarySparseInstance extends SparseInstance implements Serializable

serialVersionUID: -5297388762342528737L

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

serialVersionUID: -5478590032325567849L

Serialized Fields

m_Owner

CapabilitiesHandler m_Owner

the object that owns this capabilities instance

m_Capabilities

java.util.HashSet<E> m_Capabilities

the hashset for storing the active capabilities

m_Dependencies

java.util.HashSet<E> m_Dependencies

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

m_FailReason

java.lang.Exception m_FailReason

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

m_MinimumNumberInstances

int m_MinimumNumberInstances

the minimum number of instances in a dataset

m_Test

boolean m_Test

whether to perform any tests at all

m_InstancesTest

boolean m_InstancesTest

whether to perform data based tests

m_AttributeTest

boolean m_AttributeTest

whether to perform attribute based tests

m_MissingValuesTest

boolean m_MissingValuesTest

whether to test for missing values

m_MissingClassValuesTest

boolean m_MissingClassValuesTest

whether to test for missing class values

m_MinimumNumberInstancesTest

boolean m_MinimumNumberInstancesTest

whether to test for minimum number of instances

Class weka.core.ChebyshevDistance extends NormalizableDistance implements Serializable

serialVersionUID: -7739904999895461429L

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

serialVersionUID: 66171861743328020L

Serialized Fields

m_Enabled

boolean m_Enabled

whether logging is enabled

m_Log

Debug.Log m_Log

for logging

m_Clock

Debug.Clock m_Clock

for clocking

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

serialVersionUID: 4622161807307942201L

Serialized Fields

m_OutputFormat

int m_OutputFormat

the format of the output

m_Start

long m_Start

the start time

m_Stop

long m_Stop

the end time

m_Running

boolean m_Running

whether the time is still clocked

m_ThreadID

long m_ThreadID

the thread ID

m_CanMeasureCpuTime

boolean m_CanMeasureCpuTime

whether the system can measure the CPU time

m_UseCpuTime

boolean m_UseCpuTime

whether to use the CPU time (by default TRUE)

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

serialVersionUID: -5245628124742606784L

Serialized Fields

m_verboseOn

boolean m_verboseOn

enables/disables output of debug information

m_outputTypes

Range m_outputTypes

range of outputtyp

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

serialVersionUID: 1458435732111675823L

Serialized Fields

m_Filename

java.lang.String m_Filename

the filename, if any

m_Size

int m_Size

the size of the file (in bytes)

m_NumFiles

int m_NumFiles

the number of files for rotating the logs

m_LoggerInitFailed

boolean m_LoggerInitFailed

whether the initialization of the logger failed

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

serialVersionUID: 1256846887618333956L

Serialized Fields

m_Debug

boolean m_Debug

whether to output debug information

m_ID

long m_ID

the unique ID for this number generator

m_Log

Debug.Log m_Log

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

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

serialVersionUID: -2671928223819510830L

Serialized Fields

m_Filename

java.lang.String m_Filename

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

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

serialVersionUID: -6099868388466922753L

Serialized Fields

m_Stamp

java.util.Date m_Stamp

the actual date

m_Format

java.lang.String m_Format

the format of the timestamp

m_Formatter

java.text.SimpleDateFormat m_Formatter

handles the format of the output

Class weka.core.DenseInstance extends AbstractInstance implements Serializable

serialVersionUID: 1482635194499365122L

Class weka.core.EuclideanDistance extends NormalizableDistance implements Serializable

serialVersionUID: 1068606253458807903L

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

serialVersionUID: -2173635135622930169L

Class weka.core.InstanceComparator extends java.lang.Object implements Serializable

serialVersionUID: -6589278678230949683L

Serialized Fields

m_IncludeClass

boolean m_IncludeClass

whether to include the class in the comparison

m_Range

Range m_Range

the range of attributes to use for comparison.

Class weka.core.Instances extends java.util.AbstractList<Instance> implements Serializable

serialVersionUID: -19412345060742748L

Serialized Fields

m_RelationName

java.lang.String m_RelationName

The dataset's name.

m_Attributes

java.util.ArrayList<E> m_Attributes

The attribute information.

m_Instances

java.util.ArrayList<E> m_Instances

The instances.

m_ClassIndex

int m_ClassIndex

The class attribute's index

m_Lines

int m_Lines

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

See Also:

Instances.readInstance(Reader)

Class weka.core.ManhattanDistance extends NormalizableDistance implements Serializable

serialVersionUID: 6783782554224000243L

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

serialVersionUID: -3604757095849145838L

Serialized Fields

m_Matrix

Matrix m_Matrix

Deprecated.

The actual matrix

Class weka.core.MinkowskiDistance extends NormalizableDistance implements Serializable

serialVersionUID: -7446019339455453893L

Serialized Fields

m_Order

double m_Order

the order of the minkowski distance.

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

Serialized Fields

m_Data

Instances m_Data

the instances used internally.

m_DontNormalize

boolean m_DontNormalize

True if normalization is turned off (default false).

m_Ranges

double[][] m_Ranges

The range of the attributes.

m_AttributeIndices

Range m_AttributeIndices

The range of attributes to use for calculating the distance.

m_ActiveIndices

boolean[] m_ActiveIndices

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

m_Validated

boolean m_Validated

Whether all the necessary preparations have been done.

Class weka.core.NoSupportForMissingValuesException extends WekaException implements Serializable

serialVersionUID: 5161175307725893973L

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

serialVersionUID: 3876658672657323985L

Serialized Fields

closed

boolean closed

the properties need to be open during construction of the object

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

serialVersionUID: -1141282001146389780L

Serialized Fields

m_Head

weka.core.Queue.QueueNode m_Head

Store a reference to the head of the queue

m_Tail

weka.core.Queue.QueueNode m_Tail

Store a reference to the tail of the queue

m_Size

int m_Size

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

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

serialVersionUID: -5119358279412097455L

Serialized Fields

m_Next

weka.core.Queue.QueueNode m_Next

The next node in the queue

m_Contents

java.lang.Object m_Contents

The nodes contents

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

serialVersionUID: -4763742718209460354L

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

serialVersionUID: 3667337062176835900L

Serialized Fields

m_RangeStrings

java.util.Vector<E> m_RangeStrings

Record the string representations of the columns to delete.

m_Invert

boolean m_Invert

Whether matching should be inverted.

m_SelectFlags

boolean[] m_SelectFlags

The array of flags for whether an column is selected.

m_Upper

int m_Upper

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

Class weka.core.RelationalLocator extends AttributeLocator implements Serializable

serialVersionUID: 4646872277151854732L

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

serialVersionUID: 6635502953928860434L

Serialized Fields

m_storedObjectArray

byte[] m_storedObjectArray

The array storing the object.

m_isCompressed

boolean m_isCompressed

Whether or not the object is compressed.

m_isJython

boolean m_isJython

Whether it is a Jython object or not

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

serialVersionUID: 5285169134430839303L

Serialized Fields

m_IndexString

java.lang.String m_IndexString

Record the string representation of the number.

m_SelectedIndex

int m_SelectedIndex

The selected index.

m_Upper

int m_Upper

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

Class weka.core.SparseInstance extends AbstractInstance implements Serializable

serialVersionUID: -3579051291332630149L

Serialized Fields

m_Indices

int[] m_Indices

The index of the attribute associated with each stored value.

m_NumAttributes

int m_NumAttributes

The maximum number of values that can be stored.

Class weka.core.StringLocator extends AttributeLocator implements Serializable

serialVersionUID: 7805522230268783972L

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

serialVersionUID: 3326379903447135320L

Serialized Fields

m_ID

int m_ID

The ID

m_IDStr

java.lang.String m_IDStr

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

m_Readable

java.lang.String m_Readable

The descriptive text

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

serialVersionUID: -6263968936330390469L

Serialized Fields

m_Words

java.lang.String[] m_Words

for generating String attributes/classes

m_WordSeparators

java.lang.String m_WordSeparators

for generating String attributes/classes

m_Relation

java.lang.String m_Relation

the name of the relation

m_Seed

int m_Seed

the seed value

m_Random

java.util.Random m_Random

the random number generator

m_NumInstances

int m_NumInstances

the number of instances

m_ClassType

int m_ClassType

the class type

m_NumClasses

int m_NumClasses

the number of classes (in case of NOMINAL class)

m_ClassIndex

int m_ClassIndex

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

See Also:

TestInstances.CLASS_IS_LAST, TestInstances.NO_CLASS

m_NumNominal

int m_NumNominal

the number of nominal attributes

m_NumNominalValues

int m_NumNominalValues

the number of values for nominal attributes

m_NumNumeric

int m_NumNumeric

the number of numeric attributes

m_NumString

int m_NumString

the number of string attributes

m_NumDate

int m_NumDate

the number of date attributes

m_NumRelational

int m_NumRelational

the number of relational attributes

m_NumRelationalNominal

int m_NumRelationalNominal

the number of nominal attributes in a relational attribute

m_NumRelationalNominalValues

int m_NumRelationalNominalValues

the number of values for nominal attributes in relational attributes

m_NumRelationalNumeric

int m_NumRelationalNumeric

the number of numeric attributes in a relational attribute

m_NumRelationalString

int m_NumRelationalString

the number of string attributes in a relational attribute

m_NumRelationalDate

int m_NumRelationalDate

the number of date attributes in a relational attribute

m_MultiInstance

boolean m_MultiInstance

whether to generate Multi-Instance data or not

m_NumInstancesRelational

int m_NumInstancesRelational

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

m_RelationalFormat

Instances[] m_RelationalFormat

the format of the multi-instance data

m_RelationalClassFormat

Instances m_RelationalClassFormat

the format of the multi-instance data of the class

m_Data

Instances m_Data

the generated data

m_Handler

CapabilitiesHandler m_Handler

the CapabilitiesHandler to get the Capabilities from

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

serialVersionUID: -5897980928817779048L

Serialized Fields

m_Root

Trie.TrieNode m_Root

the root node

m_HashCode

int m_HashCode

the hash code

m_RecalcHashCode

boolean m_RecalcHashCode

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

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

serialVersionUID: -2252907099391881148L

Serialized Fields

m_Children

java.util.Hashtable<K,V> m_Children

for fast access to the children

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

serialVersionUID: 6268278694768818235L

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

serialVersionUID: -9000116786626328854L

Class weka.core.UnsupportedAttributeTypeException extends WekaException implements Serializable

serialVersionUID: 2658987325328414838L

Class weka.core.UnsupportedClassTypeException extends WekaException implements Serializable

serialVersionUID: 5175741076972192151L

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

serialVersionUID: 6428269989006208585L

Package weka.core.converters

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

Serialized Fields

m_File

java.lang.String m_File

the file

m_sourceFile

java.io.File m_sourceFile

Holds the source of the data set.

m_useRelativePath

boolean m_useRelativePath

use relative file paths

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

Serialized Fields

m_outputFile

java.io.File m_outputFile

The destination file.

FILE_EXTENSION

java.lang.String FILE_EXTENSION

The file extension of the destination file.

FILE_EXTENSION_COMPRESSED

java.lang.String FILE_EXTENSION_COMPRESSED

the extension for compressed files

m_prefix

java.lang.String m_prefix

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

m_dir

java.lang.String m_dir

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

m_incrementalCounter

int m_incrementalCounter

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

m_useRelativePath

boolean m_useRelativePath

use relative file paths

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

Serialized Fields

m_retrieval

int m_retrieval

The current retrieval mode

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

Serialized Fields

m_instances

Instances m_instances

The instances that should be stored

m_retrieval

int m_retrieval

The current retrieval mode

m_writeMode

int m_writeMode

The current write mode

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

serialVersionUID: 2726929550544048587L

Serialized Fields

m_URL

java.lang.String m_URL

the url

Class weka.core.converters.ArffSaver extends AbstractFileSaver implements Serializable

serialVersionUID: 2223634248900042228L

Serialized Fields

m_CompressOutput

boolean m_CompressOutput

whether to compress the output

m_MaxDecimalPlaces

int m_MaxDecimalPlaces

Max number of decimal places for numeric values

Class weka.core.converters.C45Loader extends AbstractFileLoader implements Serializable

serialVersionUID: 5454329403218219L

Serialized Fields

m_sourceFileData

java.io.File m_sourceFileData

Describe variable m_sourceFileData here.

m_fileStem

java.lang.String m_fileStem

Holds the filestem.

m_numAttribs

int m_numAttribs

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

m_ignore

boolean[] m_ignore

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

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

serialVersionUID: -821428878384253377L

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

serialVersionUID: -2460855349276148760L

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

serialVersionUID: -1504966891136411204L

Serialized Fields

m_Saver

Saver m_Saver

the saver to use for storing the data.

m_Stream

java.io.OutputStream m_Stream

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

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

serialVersionUID: -613122395928757332L

Serialized Fields

m_File

java.io.File m_File

the file to load.

m_URL

java.net.URL m_URL

the URL to load.

m_Loader

Loader m_Loader

the loader.

m_Incremental

boolean m_Incremental

whether the loader is incremental.

m_BatchCounter

int m_BatchCounter

the instance counter for the batch case.

m_IncrementalBuffer

Instance m_IncrementalBuffer

the last internally read instance.

m_BatchBuffer

Instances m_BatchBuffer

the batch buffer.

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

serialVersionUID: -1300595850715808438L

Serialized Fields

m_FieldSeparator

java.lang.String m_FieldSeparator

the field separator.

m_MissingValue

java.lang.String m_MissingValue

The placeholder for missing values.

m_NominalAttributes

Range m_NominalAttributes

The range of attributes to force to type nominal.

m_nominalLabelSpecs

java.util.List<E> m_nominalLabelSpecs

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

m_StringAttributes

Range m_StringAttributes

The range of attributes to force to type string.

m_dateAttributes

Range m_dateAttributes

The range of attributes to force to type date

m_dateFormat

java.lang.String m_dateFormat

The formatting string to use to parse dates

m_formatter

java.text.SimpleDateFormat m_formatter

The formatter to use on dates

m_noHeaderRow

boolean m_noHeaderRow

whether the csv file contains a header row with att names

m_Enclosures

java.lang.String m_Enclosures

enclosure character(s) to use for strings

m_rowBuffer

java.util.List<E> m_rowBuffer

The in memory row buffer

m_bufferSize

int m_bufferSize

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

m_nominalVals

java.util.Map<K,V> m_nominalVals

Lookup for nominal values

m_incrementalReader

ArffLoader.ArffReader m_incrementalReader

Reader used to process and output data incrementally

m_numBufferedRows

int m_numBufferedRows

m_current

java.util.ArrayList<E> m_current

m_types

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

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

serialVersionUID: 476636654410701807L

Serialized Fields

m_FieldSeparator

java.lang.String m_FieldSeparator

the field separator.

m_MissingValue

java.lang.String m_MissingValue

The placeholder for missing values.

m_MaxDecimalPlaces

int m_MaxDecimalPlaces

Max number of decimal places for numeric values

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

serialVersionUID: 1673169848863178695L

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

serialVersionUID: -7936159015338318659L

Serialized Fields

m_structure

Instances m_structure

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

m_datasetPseudoInc

Instances m_datasetPseudoInc

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

m_oldStructure

Instances m_oldStructure

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

m_DataBaseConnection

DatabaseConnection m_DataBaseConnection

The database connection

m_query

java.lang.String m_query

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

m_pseudoIncremental

boolean m_pseudoIncremental

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

m_checkForTable

boolean m_checkForTable

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

m_nominalToStringLimit

int m_nominalToStringLimit

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

m_rowCount

int m_rowCount

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

m_counter

int m_counter

Indicates how many rows has already been loaded incrementally

m_choice

int m_choice

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

m_firstTime

boolean m_firstTime

Flag indicating that incremental process wants to read first instance

m_inc

boolean m_inc

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

m_orderBy

java.util.ArrayList<E> m_orderBy

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

m_nominalIndexes

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

Stores the index of a nominal value

m_nominalStrings

java.util.ArrayList<E>[] m_nominalStrings

Stores the nominal value

m_idColumn

java.lang.String m_idColumn

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

m_URL

java.lang.String m_URL

the JDBC URL to use

m_User

java.lang.String m_User

the database user to use

m_Password

java.lang.String m_Password

the database password to use

m_Keys

java.lang.String m_Keys

the keys for unique ordering

m_CustomPropsFile

java.io.File m_CustomPropsFile

the custom props file to use instead of default one.

m_CreateSparseData

boolean m_CreateSparseData

Determines whether sparse data is created

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

serialVersionUID: 863971733782624956L

Serialized Fields

m_DataBaseConnection

DatabaseConnection m_DataBaseConnection

The database connection.

m_tableName

java.lang.String m_tableName

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

m_resolvedTableName

java.lang.String m_resolvedTableName

Table name with any environment variables resolved

m_inputFile

java.lang.String m_inputFile

An input arff file (for command line use).

m_createText

java.lang.String m_createText

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

m_createDouble

java.lang.String m_createDouble

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

m_createInt

java.lang.String m_createInt

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

m_createDate

java.lang.String m_createDate

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

m_DateFormat

java.text.SimpleDateFormat m_DateFormat

For converting the date value into a database string.

m_idColumn

java.lang.String m_idColumn

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

m_count

int m_count

counts the rows and used as a primary key value.

m_id

boolean m_id

Flag indicating if a primary key column should be added.

m_tabName

boolean m_tabName

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

m_URL

java.lang.String m_URL

the database URL.

m_Username

java.lang.String m_Username

the user name for the database.

m_Password

java.lang.String m_Password

the password for the database.

m_CustomPropsFile

java.io.File m_CustomPropsFile

the custom props file to use instead of default one.

m_truncate

boolean m_truncate

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

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

serialVersionUID: 3764533621135196582L

Serialized Fields

m_URL

java.lang.String m_URL

the url.

m_JSON

JSONNode m_JSON

the loaded JSON object.

Class weka.core.converters.JSONSaver extends AbstractFileSaver implements Serializable

serialVersionUID: -1047134047244534557L

Serialized Fields

m_ClassIndex

SingleIndex m_ClassIndex

the class index.

m_CompressOutput

boolean m_CompressOutput

whether to compress the output.

Class weka.core.converters.LibSVMLoader extends AbstractFileLoader implements Serializable

serialVersionUID: 4988360125354664417L

Serialized Fields

m_URL

java.lang.String m_URL

the url.

m_Buffer

java.util.Vector<E> m_Buffer

the buffer of the rows read so far.

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

serialVersionUID: 2792295817125694786L

Serialized Fields

m_ClassIndex

SingleIndex m_ClassIndex

the class index

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

serialVersionUID: -8861142318612875251L

Serialized Fields

m_URL

java.lang.String m_URL

the url.

m_Buffer

java.util.Vector<E> m_Buffer

the buffer of the rows read so far.

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

serialVersionUID: 4118356803697172614L

Serialized Fields

m_UseDouble

boolean m_UseDouble

whether to save in double instead of single precision format.

m_UseTabs

boolean m_UseTabs

whether to use tabs instead of blanks.

m_HeaderWritten

boolean m_HeaderWritten

whether the header was written already.

m_Format

java.text.DecimalFormat m_Format

for formatting the numbers.

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

serialVersionUID: 2391085836269030715L

Serialized Fields

m_Dataset

Instances m_Dataset

Holds the structure (header) of the data set.

m_IncrementalIndex

int m_IncrementalIndex

The current index position for incremental reading

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

serialVersionUID: -7717010648500658872L

Serialized Fields

m_objectstream

java.io.ObjectOutputStream m_objectstream

the output stream.

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

serialVersionUID: -5786996944597404253L

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

serialVersionUID: 4988360125354664417L

Serialized Fields

m_URL

java.lang.String m_URL

the url.

m_Buffer

java.util.Vector<E> m_Buffer

the buffer of the rows read so far.

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

serialVersionUID: 2605714599263995835L

Serialized Fields

m_ClassIndex

SingleIndex m_ClassIndex

the class index.

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

serialVersionUID: 2592118773712247647L

Serialized Fields

m_structure

Instances m_structure

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

m_sourceFile

java.io.File m_sourceFile

Holds the source of the data set.

m_Debug

boolean m_Debug

whether to print some debug information

m_OutputFilename

boolean m_OutputFilename

whether to include the filename as an extra attribute

m_charSet

java.lang.String m_charSet

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

m_filesByClass

java.util.List<E> m_filesByClass

m_lastClassDir

int m_lastClassDir

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

serialVersionUID: 3764533621135196582L

Serialized Fields

m_URL

java.lang.String m_URL

the url

m_XMLInstances

XMLInstances m_XMLInstances

the loaded XML document

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

serialVersionUID: -7226404765213522043L

Serialized Fields

m_ClassIndex

SingleIndex m_ClassIndex

the class index

m_XMLInstances

XMLInstances m_XMLInstances

the generated XML document

m_CompressOutput

boolean m_CompressOutput

whether to compress the output

Package weka.core.json

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

serialVersionUID: -3047440914507883491L

Serialized Fields

m_Name

java.lang.String m_Name

the name of the node.

m_Value

java.lang.Object m_Value

the value of the node.

m_NodeType

JSONNode.NodeType m_NodeType

the type of the node.

Package weka.core.matrix

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

serialVersionUID: -8739775942782694701L

Serialized Fields

L

double[][] L

Array for internal storage of decomposition.

internal array storage.

n

int n

Row and column dimension (square matrix).

matrix dimension.

isspd

boolean isspd

Symmetric and positive definite flag.

is symmetric and positive definite flag.

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

serialVersionUID: 4011654467211422319L

Serialized Fields

n

int n

Row and column dimension (square matrix).

matrix dimension.

issymmetric

boolean issymmetric

Symmetry flag.

internal symmetry flag.

d

double[] d

Arrays for internal storage of eigenvalues.

internal storage of eigenvalues.

e

double[] e

Arrays for internal storage of eigenvalues.

internal storage of eigenvalues.

V

double[][] V

Array for internal storage of eigenvectors.

internal storage of eigenvectors.

H

double[][] H

Array for internal storage of nonsymmetric Hessenberg form.

internal storage of nonsymmetric Hessenberg form.

ort

double[] ort

Working storage for nonsymmetric algorithm.

working storage for nonsymmetric algorithm.

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

serialVersionUID: -5298981701073897741L

Serialized Fields

nf

java.text.DecimalFormat nf

sign

boolean sign

digits

int digits

exp

int exp

trailing

boolean trailing

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

serialVersionUID: 110912192794064140L

Serialized Fields

nf

java.text.DecimalFormat nf

digits

int digits

exp

boolean exp

intDigits

int intDigits

decimalDigits

int decimalDigits

expIntDigits

int expIntDigits

expDecimalDigits

int expDecimalDigits

power

int power

trailing

boolean trailing

grouping

boolean grouping

sign

boolean sign

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

serialVersionUID: 4500373755333429499L

Serialized Fields

nf

java.text.DecimalFormat nf

width

int width

decimal

int decimal

trailing

boolean trailing

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

serialVersionUID: -2731022568037808629L

Serialized Fields

LU

double[][] LU

Array for internal storage of decomposition.

internal array storage.

m

int m

Row and column dimensions, and pivot sign.

column dimension.

n

int n

Row and column dimensions, and pivot sign.

column dimension.

pivsign

int pivsign

Row and column dimensions, and pivot sign.

column dimension.

piv

int[] piv

Internal storage of pivot vector.

pivot vector.

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

serialVersionUID: 7856794138418366180L

Serialized Fields

A

double[][] A

Array for internal storage of elements.

internal array storage.

m

int m

Row and column dimensions.

row dimension.

n

int n

Row and column dimensions.

row dimension.

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

serialVersionUID: -5013090736132211418L

Serialized Fields

QR

double[][] QR

Array for internal storage of decomposition.

internal array storage.

m

int m

Row and column dimensions.

column dimension.

n

int n

Row and column dimensions.

column dimension.

Rdiag

double[] Rdiag

Array for internal storage of diagonal of R.

diagonal of R.

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

serialVersionUID: -8738089610999867951L

Serialized Fields

U

double[][] U

Arrays for internal storage of U and V.

internal storage of U.

V

double[][] V

Arrays for internal storage of U and V.

internal storage of U.

s

double[] s

Array for internal storage of singular values.

internal storage of singular values.

m

int m

Row and column dimensions.

row dimension.

n

int n

Row and column dimensions.

row dimension.

Package weka.core.neighboursearch

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

serialVersionUID: 728763855952698328L

Serialized Fields

m_InstList

int[] m_InstList

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

m_MaxInstancesInLeaf

int m_MaxInstancesInLeaf

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

m_TreeStats

TreePerformanceStats m_TreeStats

Tree Stats variables.

m_Root

BallNode m_Root

The root node of the BallTree.

m_TreeConstructor

BallTreeConstructor m_TreeConstructor

The constructor method to use to build the tree.

m_Distances

double[] m_Distances

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

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

serialVersionUID: 7617412821497807586L

Serialized Fields

m_EuclideanDistance

EuclideanDistance m_EuclideanDistance

The euclidean distance function to use.

m_Root

CoverTree.CoverTreeNode m_Root

The root node.

m_DistanceList

double[] m_DistanceList

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

m_NumNodes

int m_NumNodes

Number of nodes in the tree.

m_NumLeaves

int m_NumLeaves

Number of nodes in the tree.

m_MaxDepth

int m_MaxDepth

Number of nodes in the tree.

m_TreeStats

TreePerformanceStats m_TreeStats

Tree Stats variables.

m_Base

double m_Base

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

il2

double il2

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

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

serialVersionUID: 1808760031169036512L

Serialized Fields

nodeid

int nodeid

ID for the node.

idx

java.lang.Integer idx

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

max_dist

double max_dist

The distance of the furthest descendant of the node.

parent_dist

double parent_dist

The distance to the nodes parent.

children

Stack<T> children

The children of the node.

num_children

int num_children

The number of children node has.

scale

int scale

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

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

serialVersionUID: 1505717283763272533L

Serialized Fields

m_DistanceList

double[] m_DistanceList

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

m_InstList

int[] m_InstList

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

m_Root

KDTreeNode m_Root

The root node of the tree.

m_Splitter

KDTreeNodeSplitter m_Splitter

The node splitter.

m_NumNodes

int m_NumNodes

Tree stats.

m_NumLeaves

int m_NumLeaves

Tree stats.

m_MaxDepth

int m_MaxDepth

Tree stats.

m_TreeStats

TreePerformanceStats m_TreeStats

Tree Stats variables.

m_NormalizeNodeWidth

boolean m_NormalizeNodeWidth

flag for normalizing.

m_EuclideanDistance

EuclideanDistance m_EuclideanDistance

The euclidean distance function to use.

m_MinBoxRelWidth

double m_MinBoxRelWidth

minimal relative width of a KDTree rectangle.

m_MaxInstInLeaf

int m_MaxInstInLeaf

maximal number of instances in a leaf.

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

serialVersionUID: 1915484723703917241L

Serialized Fields

m_Distances

double[] m_Distances

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

m_SkipIdentical

boolean m_SkipIdentical

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

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

Serialized Fields

m_Instances

Instances m_Instances

The neighbourhood of instances to find neighbours in.

m_kNN

int m_kNN

The number of neighbours to find.

m_DistanceFunction

DistanceFunction m_DistanceFunction

the distance function used.

m_Stats

PerformanceStats m_Stats

Performance statistics.

m_MeasurePerformance

boolean m_MeasurePerformance

Should we measure Performance.

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

serialVersionUID: -7215110351388368092L

Serialized Fields

m_NumQueries

int m_NumQueries

The total number of queries looked at.

m_MinP

double m_MinP

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

m_MaxP

double m_MaxP

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

m_SumP

double m_SumP

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

m_SumSqP

double m_SumSqP

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

m_PointCount

double m_PointCount

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

m_MinC

double m_MinC

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

m_MaxC

double m_MaxC

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

m_SumC

double m_SumC

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

m_SumSqC

double m_SumSqC

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

m_CoordCount

double m_CoordCount

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

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

serialVersionUID: -6637636693340810373L

Serialized Fields

m_MinLeaves

int m_MinLeaves

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

m_MaxLeaves

int m_MaxLeaves

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

m_SumLeaves

int m_SumLeaves

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

m_SumSqLeaves

int m_SumSqLeaves

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

m_LeafCount

int m_LeafCount

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

m_MinIntNodes

int m_MinIntNodes

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

m_MaxIntNodes

int m_MaxIntNodes

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

m_SumIntNodes

int m_SumIntNodes

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

m_SumSqIntNodes

int m_SumSqIntNodes

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

m_IntNodeCount

int m_IntNodeCount

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

Package weka.core.neighboursearch.balltrees

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

serialVersionUID: -8289151861759883510L

Serialized Fields

m_Start

int m_Start

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

m_End

int m_End

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

m_NumInstances

int m_NumInstances

The number of instances/points in the node.

m_NodeNumber

int m_NodeNumber

The node number/id.

m_SplitAttrib

int m_SplitAttrib

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

m_SplitVal

double m_SplitVal

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

m_Left

BallNode m_Left

The left child of the node.

m_Right

BallNode m_Right

The right child of the node.

m_Pivot

Instance m_Pivot

The pivot/centre of the ball.

m_Radius

double m_Radius

The radius of this ball (hyper sphere).

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

Serialized Fields

m_Instances

Instances m_Instances

The instance on which the tree is built.

m_DistanceFunction

EuclideanDistance m_DistanceFunction

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

m_Instlist

int[] m_Instlist

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

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

Serialized Fields

m_MaxInstancesInLeaf

int m_MaxInstancesInLeaf

The maximum number of instances allowed in a leaf.

m_MaxRelLeafRadius

double m_MaxRelLeafRadius

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

m_FullyContainChildBalls

boolean m_FullyContainChildBalls

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

m_Instances

Instances m_Instances

The instances on which to build the tree.

m_DistanceFunction

DistanceFunction m_DistanceFunction

The distance function to use to build the tree.

m_NumNodes

int m_NumNodes

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

m_NumLeaves

int m_NumLeaves

The number of leaf nodes in the built tree.

m_MaxDepth

int m_MaxDepth

The depth of the built tree.

m_InstList

int[] m_InstList

The master index array.

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

serialVersionUID: 5864250777657707687L

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

serialVersionUID: 5617378551363700558L

Serialized Fields

m_RandSeed

int m_RandSeed

Seed for random number generator.

m_Rand

java.util.Random m_Rand

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

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

serialVersionUID: 3054842574468790421L

Serialized Fields

m_NormalizeDimWidths

boolean m_NormalizeDimWidths

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

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

serialVersionUID: -8523314263062524462L

Serialized Fields

m_RSeed

int m_RSeed

Seed form random number generator.

rand

java.util.Random rand

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

rootRadius

double rootRadius

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

m_RandomInitialAnchor

boolean m_RandomInitialAnchor

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

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

serialVersionUID: -2283869109722934927L

Serialized Fields

m_List

java.util.ArrayList<E> m_List

The array list backing this list

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

serialVersionUID: -2947177543565818260L

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

serialVersionUID: -5150140645091889979L

Serialized Fields

m_Splitter

BallSplitter m_Splitter

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

Package weka.core.neighboursearch.covertrees

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

serialVersionUID: 5604056321825539264L

Serialized Fields

length

int length

The number of elements in the stack.

elements

java.util.ArrayList<E> elements

The elements inside the stack.

Package weka.core.neighboursearch.kdtrees

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

serialVersionUID: -3660396067582792648L

Serialized Fields

m_NodeNumber

int m_NodeNumber

node number (only for debug).

m_Left

KDTreeNode m_Left

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

m_Right

KDTreeNode m_Right

right subtree; contains instances with larger than split value.

m_SplitValue

double m_SplitValue

value to split on.

m_SplitDim

int m_SplitDim

attribute to split on.

m_NodeRanges

double[][] m_NodeRanges

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

m_NodesRectBounds

double[][] m_NodesRectBounds

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

m_Start

int m_Start

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

m_End

int m_End

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

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

Serialized Fields

m_Instances

Instances m_Instances

The instances that'll be used for tree construction.

m_EuclideanDistance

EuclideanDistance m_EuclideanDistance

The distance function used for building the tree.

m_InstList

int[] m_InstList

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

m_NormalizeNodeWidth

boolean m_NormalizeNodeWidth

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

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

serialVersionUID: -866783749124714304L

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

serialVersionUID: 1383443320160540663L

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

serialVersionUID: -7617277960046591906L

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

serialVersionUID: 852857628205680562L

Package weka.core.pmml

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

Serialized Fields

m_values

java.util.ArrayList<E> m_values

The values of the array

m_type

Array.ArrayType m_type

The type of the array

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

serialVersionUID: 2275009453597279459L

Serialized Fields

m_operator

weka.core.pmml.BuiltInArithmetic.Operator m_operator

The operator for this function

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

serialVersionUID: -8092338695602573652L

Serialized Fields

m_func

weka.core.pmml.BuiltInMath.MathFunc m_func

The function to apply

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

serialVersionUID: -7391516909331728653L

Serialized Fields

m_func

weka.core.pmml.BuiltInString.StringFunc m_func

The function to apply

m_outputDef

Attribute m_outputDef

The output structure produced by this function

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

serialVersionUID: -304829687822452424L

Serialized Fields

m_categoricalConst

java.lang.String m_categoricalConst

m_continuousConst

double m_continuousConst

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

serialVersionUID: -1976646917527243888L

Serialized Fields

m_parameters

java.util.ArrayList<E> m_parameters

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

m_optype

FieldMetaInfo.Optype m_optype

The optype for this function

m_expression

Expression m_expression

The Expression for this function to use

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

serialVersionUID: 3918895902507585558L

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

Serialized Fields

m_displayName

java.lang.String m_displayName

display name

m_values

java.util.ArrayList<E> m_values

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

m_expression

Expression m_expression

the single expression that defines the value of this field

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

Serialized Fields

m_fieldName

java.lang.String m_fieldName

The name of the field to be discretized

m_fieldIndex

int m_fieldIndex

The index of the field

m_mapMissingDefined

boolean m_mapMissingDefined

True if a replacement for missing values has been specified

m_mapMissingTo

java.lang.String m_mapMissingTo

The value of the missing value replacement (if defined)

m_defaultValueDefined

boolean m_defaultValueDefined

True if a default value has been specified

m_defaultValue

java.lang.String m_defaultValue

The default value (if defined)

m_bins

java.util.ArrayList<E> m_bins

The bins for this discretization

m_outputDef

Attribute m_outputDef

The output structure of this discretization

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

serialVersionUID: 5810063243316808400L

Serialized Fields

m_intervals

java.util.ArrayList<E> m_intervals

The intervals for this DiscretizeBin

m_binValue

java.lang.String m_binValue

The bin value for this DiscretizeBin

m_numericBinValue

double m_numericBinValue

If the optype is continuous or ordinal, we will attempt to parse the bin value as a number and store it here.

Class weka.core.pmml.Expression extends java.lang.Object implements Serializable

serialVersionUID: 4448840549804800321L

Serialized Fields

m_opType

FieldMetaInfo.Optype m_opType

The optype of this Expression

m_fieldDefs

java.util.ArrayList<E> m_fieldDefs

The field defs

Class weka.core.pmml.FieldMetaInfo extends java.lang.Object implements Serializable

Serialized Fields

m_fieldName

java.lang.String m_fieldName

the name of the field

m_optype

FieldMetaInfo.Optype m_optype

The optype for the target

Class weka.core.pmml.FieldMetaInfo.Interval extends java.lang.Object implements Serializable

serialVersionUID: -7339790632684638012L

Serialized Fields

m_leftMargin

double m_leftMargin

The left boundary value

m_rightMargin

double m_rightMargin

The right boundary value

m_closure

FieldMetaInfo.Interval.Closure m_closure

Class weka.core.pmml.FieldMetaInfo.Value extends java.lang.Object implements Serializable

serialVersionUID: -3981030320273649739L

Serialized Fields

m_value

java.lang.String m_value

The value

m_displayValue

java.lang.String m_displayValue

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

m_property

FieldMetaInfo.Value.Property m_property

Class weka.core.pmml.FieldRef extends Expression implements Serializable

serialVersionUID: -8009605897876168409L

Serialized Fields

m_fieldName

java.lang.String m_fieldName

The name of the field to reference

Class weka.core.pmml.Function extends java.lang.Object implements Serializable

serialVersionUID: -6997738288201933171L

Serialized Fields

m_functionName

java.lang.String m_functionName

The name of this function

m_parameterDefs

java.util.ArrayList<E> m_parameterDefs

The structure of the parameters to this function

Class weka.core.pmml.MappingInfo extends java.lang.Object implements Serializable

Serialized Fields

m_fieldsMap

int[] m_fieldsMap

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

m_nominalValueMaps

int[][] m_nominalValueMaps

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

m_fieldsMappingText

java.lang.String m_fieldsMappingText

Holds a textual description of the fields mapping

m_log

Logger m_log

For logging

Class weka.core.pmml.MiningFieldMetaInfo extends FieldMetaInfo implements Serializable

serialVersionUID: -1256774332779563185L

Serialized Fields

m_usageType

weka.core.pmml.MiningFieldMetaInfo.Usage m_usageType

usage type

m_outlierTreatmentMethod

weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod

outlier treatmemnt method

m_lowValue

double m_lowValue

outlier low value

m_highValue

double m_highValue

outlier high value

m_missingValueTreatmentMethod

weka.core.pmml.MiningFieldMetaInfo.Missing m_missingValueTreatmentMethod

missing values treatment method

m_missingValueReplacementNominal

java.lang.String m_missingValueReplacementNominal

actual missing value replacements (if specified)

m_missingValueReplacementNumeric

double m_missingValueReplacementNumeric

m_optypeOverride

FieldMetaInfo.Optype m_optypeOverride

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

m_index

int m_index

the index of the field in the mining schema Instances

m_importance

double m_importance

importance (if defined)

m_miningSchemaI

Instances m_miningSchemaI

mining schema (needed for toString method)

Class weka.core.pmml.MiningSchema extends java.lang.Object implements Serializable

serialVersionUID: 7144380586726330455L

Serialized Fields

m_fieldInstancesStructure

Instances m_fieldInstancesStructure

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

m_miningSchemaInstancesStructure

Instances m_miningSchemaInstancesStructure

Just the mining schema fields as Instances

m_miningMeta

java.util.ArrayList<E> m_miningMeta

Meta information about the mining schema fields

m_derivedMeta

java.util.ArrayList<E> m_derivedMeta

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

m_transformationDictionary

weka.core.pmml.TransformationDictionary m_transformationDictionary

The transformation dictionary (if defined)

m_targetMetaInfo

TargetMetaInfo m_targetMetaInfo

target meta info (may be null if not defined)

Class weka.core.pmml.NormContinuous extends Expression implements Serializable

serialVersionUID: 4714332374909851542L

Serialized Fields

m_fieldName

java.lang.String m_fieldName

The name of the field to use

m_fieldIndex

int m_fieldIndex

The index of the field

m_mapMissingDefined

boolean m_mapMissingDefined

True if a replacement for missing values has been specified

m_mapMissingTo

double m_mapMissingTo

The value of the missing value replacement (if defined)

m_outlierTreatmentMethod

weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod

Outlier treatment method (default = asIs)

m_linearNormOrig

double[] m_linearNormOrig

original values for the LinearNorm entries

m_linearNormNorm

double[] m_linearNormNorm

norm values for the LinearNorm entries

Class weka.core.pmml.NormDiscrete extends Expression implements Serializable

serialVersionUID: -8854409417983908220L

Serialized Fields

m_fieldName

java.lang.String m_fieldName

The name of the field to lookup our value in

m_field

Attribute m_field

The actual attribute itself

m_fieldIndex

int m_fieldIndex

The index of the attribute

m_fieldValue

java.lang.String m_fieldValue

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

m_mapMissingDefined

boolean m_mapMissingDefined

True if a replacement for missing values has been specified

m_mapMissingTo

double m_mapMissingTo

The value of the missing value replacement (if defined)

m_fieldValueIndex

int m_fieldValueIndex

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

Class weka.core.pmml.SparseArray extends Array implements Serializable

serialVersionUID: 8129550573612673674L

Serialized Fields

m_numValues

int m_numValues

The size of the array if known

m_numNonZero

int m_numNonZero

The number of non-zero elements

m_indices

java.util.List<E> m_indices

The indices of the sparse array

Class weka.core.pmml.TargetMetaInfo extends FieldMetaInfo implements Serializable

serialVersionUID: 863500462237904927L

Serialized Fields

m_min

double m_min

min and max

m_max

double m_max

m_rescaleConstant

double m_rescaleConstant

re-scaling of target value (if defined)

m_rescaleFactor

double m_rescaleFactor

m_castInteger

java.lang.String m_castInteger

cast integers (default no casting)

m_defaultValueOrPriorProbs

double[] m_defaultValueOrPriorProbs

default value (numeric) or prior distribution (categorical)

m_values

java.util.ArrayList<E> m_values

for categorical values. Actual values

m_displayValues

java.util.ArrayList<E> m_displayValues

corresponding display values

Class weka.core.pmml.VectorDictionary extends java.lang.Object implements Serializable

serialVersionUID: -5538024467333813123L

Serialized Fields

m_numberOfVectors

int m_numberOfVectors

The number of support vectors in the dictionary

m_vectorFields

java.util.List<E> m_vectorFields

The fields accessed by the support vectors

m_vectorInstances

java.util.Map<K,V> m_vectorInstances

The vectors in the dictionary

Class weka.core.pmml.VectorInstance extends java.lang.Object implements Serializable

serialVersionUID: -7543200367512646290L

Serialized Fields

m_ID

java.lang.String m_ID

The ID of this instance

m_values

Array m_values

The usually sparse elements of this vector

m_vectorFields

java.util.List<E> m_vectorFields

The fields indexed by this VectorInstance

Package weka.core.scripting

Class weka.core.scripting.Groovy extends java.lang.Object implements Serializable

serialVersionUID: -2628766602043134673L

Serialized Fields

m_ClassLoader

java.lang.Object m_ClassLoader

the classloader.

Class weka.core.scripting.Jython extends java.lang.Object implements Serializable

serialVersionUID: -6972298704460209252L

Serialized Fields

m_Interpreter

java.lang.Object m_Interpreter

the interpreter

Package weka.core.stemmers

Class weka.core.stemmers.IteratedLovinsStemmer extends LovinsStemmer implements Serializable

serialVersionUID: 960689687163788264L

Class weka.core.stemmers.LovinsStemmer extends java.lang.Object implements Serializable

serialVersionUID: -6113024782588197L

Class weka.core.stemmers.NullStemmer extends java.lang.Object implements Serializable

serialVersionUID: -3671261636532625496L

Class weka.core.stemmers.SnowballStemmer extends java.lang.Object implements Serializable

serialVersionUID: -6111170431963015178L

Serialized Fields

m_Stemmer

java.lang.Object m_Stemmer

the current stemmer.

Package weka.core.tokenizers

Class weka.core.tokenizers.AlphabeticTokenizer extends Tokenizer implements Serializable

serialVersionUID: 6705199562609861697L

Serialized Fields

m_Str

char[] m_Str

the characters of the string

m_CurrentPos

int m_CurrentPos

the current position

Class weka.core.tokenizers.CharacterDelimitedTokenizer extends Tokenizer implements Serializable

Serialized Fields

m_Delimiters

java.lang.String m_Delimiters

Delimiters used in tokenization

Class weka.core.tokenizers.NGramTokenizer extends CharacterDelimitedTokenizer implements Serializable

serialVersionUID: -2181896254171647219L

Serialized Fields

m_NMax

int m_NMax

the maximum number of N

m_NMin

int m_NMin

the minimum number of N

m_N

int m_N

the current length of the N-grams

m_MaxPosition

int m_MaxPosition

the number of strings available

m_CurrentPosition

int m_CurrentPosition

the current position for returning elements

m_SplitString

java.lang.String[] m_SplitString

all the available grams

Class weka.core.tokenizers.Tokenizer extends java.lang.Object implements Serializable

Class weka.core.tokenizers.WordTokenizer extends CharacterDelimitedTokenizer implements Serializable

serialVersionUID: -930893034037880773L

Package weka.core.xml

Class weka.core.xml.XMLInstances extends XMLDocument implements Serializable

serialVersionUID: 3626821327547416099L

Serialized Fields

m_Precision

int m_Precision

the precision for numbers

m_Instances

Instances m_Instances

the underlying Instances

Package weka.datagenerators

Class weka.datagenerators.ClassificationGenerator extends DataGenerator implements Serializable

serialVersionUID: -5261662546673517844L

Serialized Fields

m_NumExamples

int m_NumExamples

Number of instances

Class weka.datagenerators.ClusterDefinition extends java.lang.Object implements Serializable

serialVersionUID: -5950001207047429961L

Serialized Fields

m_Parent

ClusterGenerator m_Parent

the parent of the cluster

Class weka.datagenerators.ClusterGenerator extends DataGenerator implements Serializable

serialVersionUID: 6131722618472046365L

Serialized Fields

m_NumAttributes

int m_NumAttributes

Number of attribute the dataset should have

m_ClassFlag

boolean m_ClassFlag

class flag

m_booleanCols

Range m_booleanCols

Stores which columns are boolean (default numeric)

m_nominalCols

Range m_nominalCols

Stores which columns are nominal (default numeric)

Class weka.datagenerators.DataGenerator extends java.lang.Object implements Serializable

serialVersionUID: -3698585946221802578L

Serialized Fields

m_Debug

boolean m_Debug

Debugging mode

m_DatasetFormat

Instances m_DatasetFormat

The format for the generated dataset

m_RelationName

java.lang.String m_RelationName

Relation name the dataset should have

m_NumExamplesAct

int m_NumExamplesAct

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

m_Seed

int m_Seed

random number generator seed

m_Random

java.util.Random m_Random

random number generator

m_CreatingRelationName

boolean m_CreatingRelationName

flag, that indicates whether the relationname is currently assembled

Class weka.datagenerators.RegressionGenerator extends DataGenerator implements Serializable

serialVersionUID: 3073254041275658221L

Serialized Fields

m_NumExamples

int m_NumExamples

Number of instances

Class weka.datagenerators.Test extends java.lang.Object implements Serializable

serialVersionUID: -8890645875887157782L

Serialized Fields

m_AttIndex

int m_AttIndex

the attribute index

m_Split

double m_Split

the split

m_Not

boolean m_Not

whether to negate the test

m_Dataset

Instances m_Dataset

the dataset

Package weka.datagenerators.classifiers.classification

Class weka.datagenerators.classifiers.classification.Agrawal extends ClassificationGenerator implements Serializable

serialVersionUID: 2254651939636143025L

Serialized Fields

m_Function

int m_Function

the function to use for generating the data

m_BalanceClass

boolean m_BalanceClass

whether to balance the class

m_PerturbationFraction

double m_PerturbationFraction

the perturabation fraction

m_nextClassShouldBeZero

boolean m_nextClassShouldBeZero

used for balancing the class

m_lastLabel

double m_lastLabel

the last class label that was generated

Class weka.datagenerators.classifiers.classification.BayesNet extends ClassificationGenerator implements Serializable

serialVersionUID: -796118162379901512L

Serialized Fields

m_Generator

BayesNetGenerator m_Generator

the bayesian net generator, that produces the actual data

Class weka.datagenerators.classifiers.classification.LED24 extends ClassificationGenerator implements Serializable

serialVersionUID: -7880209100415868737L

Serialized Fields

m_NoisePercent

double m_NoisePercent

the noise rate

m_numIrrelevantAttributes

int m_numIrrelevantAttributes

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

Class weka.datagenerators.classifiers.classification.RandomRBF extends ClassificationGenerator implements Serializable

serialVersionUID: 6069033710635728720L

Serialized Fields

m_NumAttributes

int m_NumAttributes

Number of attribute the dataset should have

m_NumClasses

int m_NumClasses

Number of Classes the dataset should have

m_NumCentroids

int m_NumCentroids

the number of centroids to use for generation

m_centroids

double[][] m_centroids

the centroids

m_centroidClasses

int[] m_centroidClasses

the classes of the centroids

m_centroidWeights

double[] m_centroidWeights

the weights of the centroids

m_centroidStdDevs

double[] m_centroidStdDevs

the stddevs of the centroids

Class weka.datagenerators.classifiers.classification.RDG1 extends ClassificationGenerator implements Serializable

serialVersionUID: 7751005204635320414L

Serialized Fields

m_NumAttributes

int m_NumAttributes

Number of attribute the dataset should have

m_NumClasses

int m_NumClasses

Number of Classes the dataset should have

m_MaxRuleSize

int m_MaxRuleSize

maximum rule size

m_MinRuleSize

int m_MinRuleSize

minimum rule size

m_NumIrrelevant

int m_NumIrrelevant

number of irrelevant attributes.

m_NumNumeric

int m_NumNumeric

number of numeric attribute

m_VoteFlag

boolean m_VoteFlag

flag that stores if voting is wished

m_DecisionList

FastVector<E> m_DecisionList

decision list

m_AttList_Irr

boolean[] m_AttList_Irr

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

Package weka.datagenerators.classifiers.regression

Class weka.datagenerators.classifiers.regression.Expression extends MexicanHat implements Serializable

serialVersionUID: -4237047357682277211L

Serialized Fields

m_Expression

java.lang.String m_Expression

the expression for computing y

m_Filter

AddExpression m_Filter

the filter for generating y out of x

m_RawData

Instances m_RawData

the input data structure for the filter

Class weka.datagenerators.classifiers.regression.MexicanHat extends RegressionGenerator implements Serializable

serialVersionUID: 4577016375261512975L

Serialized Fields

m_Amplitude

double m_Amplitude

the amplitude of y

m_MinRange

double m_MinRange

the lower boundary of the range, x is drawn from

m_MaxRange

double m_MaxRange

the upper boundary of the range, x is drawn from

m_NoiseRate

double m_NoiseRate

the rate of the gaussian noise

m_NoiseVariance

double m_NoiseVariance

the variance of the gaussian noise

m_NoiseRandom

java.util.Random m_NoiseRandom

the random number generator for the noise

Package weka.datagenerators.clusterers

Class weka.datagenerators.clusterers.BIRCHCluster extends ClusterGenerator implements Serializable

serialVersionUID: -334820527230755027L

Serialized Fields

m_NumClusters

int m_NumClusters

Number of Clusters the dataset should have

m_MinInstNum

int m_MinInstNum

minimal number of instances per cluster (option N)

m_MaxInstNum

int m_MaxInstNum

maximal number of instances per cluster (option N)

m_MinRadius

double m_MinRadius

minimum radius (option R)

m_MaxRadius

double m_MaxRadius

maximum radius (option R)

m_Pattern

int m_Pattern

pattern (changed with options G or S)

m_DistMult

double m_DistMult

distance multiplier (option M)

m_NumCycles

int m_NumCycles

number of cycles (option C)

m_InputOrder

int m_InputOrder

input order (changed with option O)

m_NoiseRate

double m_NoiseRate

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

m_ClusterList

FastVector<E> m_ClusterList

cluster list

m_GridSize

int m_GridSize

grid size

m_GridWidth

double m_GridWidth

grid width

Class weka.datagenerators.clusterers.SubspaceCluster extends ClusterGenerator implements Serializable

serialVersionUID: -3454999858505621128L

Serialized Fields

m_NoiseRate

double m_NoiseRate

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

m_Clusters

ClusterDefinition[] m_Clusters

cluster list

m_numValues

int[] m_numValues

if nominal, store number of values

m_globalMinValue

double[] m_globalMinValue

store global min values

m_globalMaxValue

double[] m_globalMaxValue

store global max values

Class weka.datagenerators.clusterers.SubspaceClusterDefinition extends ClusterDefinition implements Serializable

serialVersionUID: 3135678125044007231L

Serialized Fields

m_clustertype

int m_clustertype

cluster type

m_clustersubtype

int m_clustersubtype

cluster subtypes

m_numClusterAttributes

int m_numClusterAttributes

number of attributes the cluster is defined for

m_numInstances

int m_numInstances

number of instances for this cluster

m_MinInstNum

int m_MinInstNum

minimal number of instances for this cluster

m_MaxInstNum

int m_MaxInstNum

maximal number of instances for this cluster

m_AttrIndexRange

Range m_AttrIndexRange

range of atttributes

m_attributes

boolean[] m_attributes

attributes of this cluster

m_attrIndices

int[] m_attrIndices

global indices of the attributes of the cluster

m_minValue

double[] m_minValue

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

m_maxValue

double[] m_maxValue

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

m_meanValue

double[] m_meanValue

mean ; only used if gaussian

m_stddevValue

double[] m_stddevValue

standarddev; only used if gaussian

Package weka.estimators

Class weka.estimators.DiscreteEstimator extends Estimator implements Serializable

serialVersionUID: -5526486742612434779L

Serialized Fields

m_Counts

double[] m_Counts

Hold the counts

m_SumOfCounts

double m_SumOfCounts

Hold the sum of counts

Class weka.estimators.Estimator extends java.lang.Object implements Serializable

serialVersionUID: -5902411487362274342L

Serialized Fields

m_Debug

boolean m_Debug

Debugging mode

m_classValueIndex

double m_classValueIndex

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

m_noClass

boolean m_noClass

set if class is not important

Class weka.estimators.KernelEstimator extends Estimator implements Serializable

serialVersionUID: 3646923563367683925L

Serialized Fields

m_Values

double[] m_Values

Vector containing all of the values seen

m_Weights

double[] m_Weights

Vector containing the associated weights

m_NumValues

int m_NumValues

Number of values stored in m_Weights and m_Values so far

m_SumOfWeights

double m_SumOfWeights

The sum of the weights so far

m_StandardDev

double m_StandardDev

The standard deviation

m_Precision

double m_Precision

The precision of data values

m_AllWeightsOne

boolean m_AllWeightsOne

Whether we can optimise the kernel summation

Class weka.estimators.MahalanobisEstimator extends Estimator implements Serializable

serialVersionUID: 8950225468990043868L

Serialized Fields

m_CovarianceInverse

Matrix m_CovarianceInverse

The inverse of the covariance matrix

m_Determinant

double m_Determinant

The determinant of the covariance matrix

m_ConstDelta

double m_ConstDelta

The difference between the conditioning value and the conditioning mean

m_ValueMean

double m_ValueMean

The mean of the values

Class weka.estimators.NormalEstimator extends Estimator implements Serializable

serialVersionUID: 93584379632315841L

Serialized Fields

m_SumOfWeights

double m_SumOfWeights

The sum of the weights

m_SumOfValues

double m_SumOfValues

The sum of the values seen

m_SumOfValuesSq

double m_SumOfValuesSq

The sum of the values squared

m_Mean

double m_Mean

The current mean

m_StandardDev

double m_StandardDev

The current standard deviation

m_Precision

double m_Precision

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

Class weka.estimators.PoissonEstimator extends Estimator implements Serializable

serialVersionUID: 7669362595289236662L

Serialized Fields

m_NumValues

double m_NumValues

The number of values seen

m_SumOfValues

double m_SumOfValues

The sum of the values seen

m_Lambda

double m_Lambda

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

Package weka.experiment

Class weka.experiment.AveragingResultProducer extends java.lang.Object implements Serializable

serialVersionUID: 2551284958501991352L

Serialized Fields

m_Instances

Instances m_Instances

The dataset of interest

m_ResultListener

ResultListener m_ResultListener

The ResultListener to send results to

m_ResultProducer

ResultProducer m_ResultProducer

The ResultProducer used to generate results

m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

m_ExpectedResultsPerAverage

int m_ExpectedResultsPerAverage

The number of results expected to average over for each run

m_CalculateStdDevs

boolean m_CalculateStdDevs

True if standard deviation fields should be produced

m_CountFieldName

java.lang.String m_CountFieldName

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

m_KeyFieldName

java.lang.String m_KeyFieldName

The name of the key field to average over

m_KeyIndex

int m_KeyIndex

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

m_Keys

FastVector<E> m_Keys

Collects the keys from a single run

m_Results

FastVector<E> m_Results

Collects the results from a single run

Class weka.experiment.ClassifierSplitEvaluator extends java.lang.Object implements Serializable

serialVersionUID: -8511241602760467265L

Serialized Fields

m_Template

Classifier m_Template

The template classifier

m_Classifier

Classifier m_Classifier

The classifier used for evaluation

m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

m_doesProduce

boolean[] m_doesProduce

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

m_numberAdditionalMeasures

int m_numberAdditionalMeasures

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

m_result

java.lang.String m_result

Holds the statistics for the most recent application of the classifier

m_ClassifierOptions

java.lang.String m_ClassifierOptions

The classifier options (if any)

m_ClassifierVersion

java.lang.String m_ClassifierVersion

The classifier version

m_IRclass

int m_IRclass

Class index for information retrieval statistics (default 0)

m_predTargetColumn

boolean m_predTargetColumn

Flag for prediction and target columns output.

m_attID

int m_attID

Attribute index of instance identifier (default -1)

m_NoSizeDetermination

boolean m_NoSizeDetermination

whether to skip determination of sizes (train/test/classifier).

m_pluginMetrics

java.util.List<E> m_pluginMetrics

m_numPluginStatistics

int m_numPluginStatistics

Class weka.experiment.CostSensitiveClassifierSplitEvaluator extends ClassifierSplitEvaluator implements Serializable

serialVersionUID: -8069566663019501276L

Serialized Fields

m_OnDemandDirectory

java.io.File m_OnDemandDirectory

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

Class weka.experiment.CrossValidationResultProducer extends java.lang.Object implements Serializable

serialVersionUID: -1580053925080091917L

Serialized Fields

m_Instances

Instances m_Instances

The dataset of interest

m_ResultListener

ResultListener m_ResultListener

The ResultListener to send results to

m_NumFolds

int m_NumFolds

The number of folds in the cross-validation

m_debugOutput

boolean m_debugOutput

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

m_ZipDest

OutputZipper m_ZipDest

The output zipper to use for saving raw splitEvaluator output

m_OutputFile

java.io.File m_OutputFile

The destination output file/directory for raw output

m_SplitEvaluator

SplitEvaluator m_SplitEvaluator

The SplitEvaluator used to generate results

m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

Class weka.experiment.CrossValidationSplitResultProducer extends CrossValidationResultProducer implements Serializable

serialVersionUID: 1403798164046795073L

Class weka.experiment.CSVResultListener extends java.lang.Object implements Serializable

serialVersionUID: -623185072785174658L

Serialized Fields

m_RP

ResultProducer m_RP

The ResultProducer sending us results

m_OutputFile

java.io.File m_OutputFile

The destination output file, null sends to System.out

m_OutputFileName

java.lang.String m_OutputFileName

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

Class weka.experiment.DatabaseResultListener extends DatabaseUtils implements Serializable

serialVersionUID: 7388014746954652818L

Serialized Fields

m_ResultProducer

ResultProducer m_ResultProducer

The ResultProducer to listen to

m_ResultsTableName

java.lang.String m_ResultsTableName

The name of the current results table

m_Debug

boolean m_Debug

True if debugging output should be printed

m_CacheKeyName

java.lang.String m_CacheKeyName

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

m_CacheKeyIndex

int m_CacheKeyIndex

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

m_CacheKey

java.lang.Object[] m_CacheKey

Stores the key for which the cache is valid

m_Cache

FastVector<E> m_Cache

Stores the cached values

Class weka.experiment.DatabaseResultProducer extends DatabaseResultListener implements Serializable

serialVersionUID: -5620660780203158666L

Serialized Fields

m_Instances

Instances m_Instances

The dataset of interest

m_ResultListener

ResultListener m_ResultListener

The ResultListener to send results to

m_ResultProducer

ResultProducer m_ResultProducer

The ResultProducer used to generate results

m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

Class weka.experiment.DatabaseUtils extends java.lang.Object implements Serializable

serialVersionUID: -8252351994547116729L

Serialized Fields

DRIVERS

java.util.Vector<E> DRIVERS

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

PROPERTIES

java.util.Properties PROPERTIES

Properties associated with the database connection.

m_DatabaseURL

java.lang.String m_DatabaseURL

Database URL.

m_Debug

boolean m_Debug

True if debugging output should be printed.

m_userName

java.lang.String m_userName

Database username.

m_password

java.lang.String m_password

Database Password.

m_stringType

java.lang.String m_stringType

string type for the create table statement.

m_intType

java.lang.String m_intType

integer type for the create table statement.

m_doubleType

java.lang.String m_doubleType

double type for the create table statement.

m_checkForUpperCaseNames

boolean m_checkForUpperCaseNames

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

m_checkForLowerCaseNames

boolean m_checkForLowerCaseNames

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

m_setAutoCommit

boolean m_setAutoCommit

setAutoCommit on the database?

m_createIndex

boolean m_createIndex

create index on the database?

m_Keywords

java.util.HashSet<E> m_Keywords

the keywords for the current database type.

m_KeywordsMaskChar

java.lang.String m_KeywordsMaskChar

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

Class weka.experiment.DensityBasedClustererSplitEvaluator extends java.lang.Object implements Serializable

serialVersionUID: 5124501059135692160L

Serialized Fields

m_removeClassColumn

boolean m_removeClassColumn

Remove the class column (if set) from the data

m_clusterer

DensityBasedClusterer m_clusterer

The clusterer used for evaluation

m_additionalMeasures

java.lang.String[] m_additionalMeasures

The names of any additional measures to look for in SplitEvaluators

m_doesProduce

boolean[] m_doesProduce

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

m_numberAdditionalMeasures

int m_numberAdditionalMeasures

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

m_result

java.lang.String m_result

Holds the statistics for the most recent application of the clusterer

m_clustererOptions

java.lang.String m_clustererOptions

The clusterer options (if any)

m_clustererVersion

java.lang.String m_clustererVersion

The clusterer version

m_NoSizeDetermination

boolean m_NoSizeDetermination

whether to skip determination of sizes (train/test/classifier).

Class weka.experiment.Experiment extends java.lang.Object implements Serializable

serialVersionUID: 44945596742646663L

Serialized Fields

m_ResultListener

ResultListener m_ResultListener

Where results will be sent

m_ResultProducer

ResultProducer m_ResultProducer

The result producer

m_RunLower

int m_RunLower

Lower run number

m_RunUpper

int m_RunUpper

Upper run number

m_Datasets

javax.swing.DefaultListModel m_Datasets

An array of dataset files

m_UsePropertyIterator

boolean m_UsePropertyIterator

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

m_PropertyPath

PropertyNode[] m_PropertyPath

The path to the iterator property

m_PropertyArray

java.lang.Object m_PropertyArray

The array of values to set the property to

m_Notes

java.lang.String m_Notes

User notes about the experiment

m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures

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

m_ClassFirst

boolean m_ClassFirst

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

m_AdvanceDataSetFirst

boolean m_AdvanceDataSetFirst

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

Class weka.experiment.ExplicitTestsetResultProducer extends java.lang.Object implements Serializable

serialVersionUID: 2613585409333652530L

Serialized Fields

m_Instances

Instances m_Instances

The dataset of interest.

m_ResultListener

ResultListener m_ResultListener

The ResultListener to send results to.

m_TestsetDir

java.io.File m_TestsetDir

The directory containing all the test sets.

m_TestsetPrefix

java.lang.String m_TestsetPrefix

The prefix for all the test sets.

m_TestsetSuffix

java.lang.String m_TestsetSuffix

The suffix for all the test sets.

m_RelationFind

java.lang.String m_RelationFind

The regular expression to search for in the relation name.

m_RelationReplace

java.lang.String m_RelationReplace

The string to use to replace the matches of the regular expression.

m_randomize

boolean m_randomize

Whether dataset is to be randomized.

m_SplitEvaluator

SplitEvaluator m_SplitEvaluator

The SplitEvaluator used to generate results.

m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators.

m_debugOutput

boolean m_debugOutput

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

m_ZipDest

OutputZipper m_ZipDest

The output zipper to use for saving raw splitEvaluator output.

m_OutputFile

java.io.File m_OutputFile

The destination output file/directory for raw output.

Class weka.experiment.InstanceQuery extends DatabaseUtils implements Serializable

serialVersionUID: 718158370917782584L

Serialized Fields

m_CreateSparseData

boolean m_CreateSparseData

Determines whether sparse data is created

m_Query

java.lang.String m_Query

Query to execute

m_CustomPropsFile

java.io.File m_CustomPropsFile

the custom props file to use instead of default one.

Class weka.experiment.InstancesResultListener extends CSVResultListener implements Serializable

serialVersionUID: -2203808461809311178L

Class weka.experiment.LearningRateResultProducer extends java.lang.Object implements Serializable

serialVersionUID: -3841159673490861331L

Serialized Fields

m_Instances

Instances m_Instances

The dataset of interest

m_ResultListener

ResultListener m_ResultListener

The ResultListener to send results to

m_ResultProducer

ResultProducer m_ResultProducer

The ResultProducer used to generate results

m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

m_LowerSize

int m_LowerSize

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

m_UpperSize

int m_UpperSize

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

m_StepSize

int m_StepSize

The number of instances to add at each step

m_CurrentSize

int m_CurrentSize

The current dataset size during stepping

Class weka.experiment.PairedCorrectedTTester extends PairedTTester implements Serializable

serialVersionUID: -3105268939845653323L

Class weka.experiment.PairedTTester extends java.lang.Object implements Serializable

serialVersionUID: 8370014624008728610L

Serialized Fields

m_Instances

Instances m_Instances

The set of instances we will analyse

m_RunColumn

int m_RunColumn

The index of the column containing the run number

m_RunColumnSet

int m_RunColumnSet

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

m_FoldColumn

int m_FoldColumn

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

m_SortColumn

int m_SortColumn

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

m_SortOrder

int[] m_SortOrder

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

m_ColOrder

int[] m_ColOrder

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

m_SignificanceLevel

double m_SignificanceLevel

The significance level for comparisons

m_DatasetKeyColumnsRange

Range m_DatasetKeyColumnsRange

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

m_DatasetKeyColumns

int[] m_DatasetKeyColumns

An array containing the indexes of just the selected columns

m_DatasetSpecifiers

weka.experiment.PairedTTester.DatasetSpecifiers m_DatasetSpecifiers

The list of dataset specifiers

m_ResultsetKeyColumnsRange

Range m_ResultsetKeyColumnsRange

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

m_ResultsetKeyColumns

int[] m_ResultsetKeyColumns

An array containing the indexes of just the selected columns

m_DisplayedResultsets

int[] m_DisplayedResultsets

An array containing the indexes of the datasets to display

m_Resultsets

FastVector<E> m_Resultsets

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

m_ResultsetsValid

boolean m_ResultsetsValid

Indicates whether the instances have been partitioned

m_ShowStdDevs

boolean m_ShowStdDevs

Indicates whether standard deviations should be displayed

m_ResultMatrix

ResultMatrix m_ResultMatrix

the instance of the class to produce the output.

Class weka.experiment.PairedTTester.Dataset extends java.lang.Object implements Serializable

serialVersionUID: -2801397601839433282L

Serialized Fields

m_Template

Instance m_Template

the template

m_Dataset

FastVector<E> m_Dataset

the dataset

Class weka.experiment.PairedTTester.DatasetSpecifiers extends java.lang.Object implements Serializable

serialVersionUID: -9020938059902723401L

Serialized Fields

m_Specifiers

FastVector<E> m_Specifiers

the specifiers that have been observed

Class weka.experiment.PairedTTester.Resultset extends java.lang.Object implements Serializable

serialVersionUID: 1543786683821339978L

Serialized Fields

m_Template

Instance m_Template

the template

m_Datasets

FastVector<E> m_Datasets

the dataset

Class weka.experiment.PropertyNode extends java.lang.Object implements Serializable

serialVersionUID: -8718165742572631384L

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream in)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException

Throws:

java.io.IOException

java.lang.ClassNotFoundException

writeObject

private void writeObject(java.io.ObjectOutputStream out)
                  throws java.io.IOException

Throws:

java.io.IOException

Serialized Fields

value

java.lang.Object value

The current property value

parentClass

java.lang.Class<T> parentClass

The class of the object with this property

property

java.beans.PropertyDescriptor property

Other info about the property

Class weka.experiment.RandomSplitResultProducer extends java.lang.Object implements Serializable

serialVersionUID: 1403798165056795073L

Serialized Fields

m_Instances

Instances m_Instances

The dataset of interest

m_ResultListener

ResultListener m_ResultListener

The ResultListener to send results to

m_TrainPercent

double m_TrainPercent

The percentage of instances to use for training

m_randomize

boolean m_randomize

Whether dataset is to be randomized

m_SplitEvaluator

SplitEvaluator m_SplitEvaluator

The SplitEvaluator used to generate results

m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

m_debugOutput

boolean m_debugOutput

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

m_ZipDest

OutputZipper m_ZipDest

The output zipper to use for saving raw splitEvaluator output

m_OutputFile

java.io.File m_OutputFile

The destination output file/directory for raw output

Class weka.experiment.RegressionSplitEvaluator extends java.lang.Object implements Serializable

serialVersionUID: -328181640503349202L

Serialized Fields

m_Template

Classifier m_Template

The template classifier

m_Classifier

Classifier m_Classifier

The classifier used for evaluation

m_AdditionalMeasures

java.lang.String[] m_AdditionalMeasures

The names of any additional measures to look for in SplitEvaluators

m_doesProduce

boolean[] m_doesProduce

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

m_result

java.lang.String m_result

Holds the statistics for the most recent application of the classifier

m_ClassifierOptions

java.lang.String m_ClassifierOptions

The classifier options (if any)

m_ClassifierVersion

java.lang.String m_ClassifierVersion

The classifier version

m_NoSizeDetermination

boolean m_NoSizeDetermination

whether to skip determination of sizes (train/test/classifier).

m_pluginMetrics

java.util.List<E> m_pluginMetrics

m_numPluginStatistics

int m_numPluginStatistics

Class weka.experiment.RemoteEngine extends java.rmi.server.UnicastRemoteObject implements Serializable

serialVersionUID: -1021538162895448259L

Serialized Fields

m_HostName

java.lang.String m_HostName

The name of the host that this engine is started on

m_TaskQueue

Queue m_TaskQueue

A queue of waiting tasks

m_TaskIdQueue

Queue m_TaskIdQueue

A queue of corresponding ID's for tasks

m_TaskStatus

java.util.Hashtable<K,V> m_TaskStatus

A hashtable of experiment status

m_TaskRunning

boolean m_TaskRunning

Is there a task running

Class weka.experiment.RemoteEngine_Stub extends java.rmi.server.RemoteStub implements Serializable

serialVersionUID: 2L

Class weka.experiment.RemoteExperiment extends Experiment implements Serializable

serialVersionUID: -7357668825635314937L

Serialized Fields

m_listeners

FastVector<E> m_listeners

The list of objects listening for remote experiment events

m_remoteHosts

javax.swing.DefaultListModel m_remoteHosts

Holds the names of machines with remoteEngine servers running

m_remoteHostsQueue

Queue m_remoteHostsQueue

The queue of available hosts

m_remoteHostsStatus

int[] m_remoteHostsStatus

The status of each of the remote hosts

m_remoteHostFailureCounts

int[] m_remoteHostFailureCounts

The number of times tasks have failed on each remote host

m_experimentAborted

boolean m_experimentAborted

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

m_removedHosts

int m_removedHosts

The number of hosts removed due to exceeding max failures

m_failedCount

int m_failedCount

The count of failed sub-experiments

m_finishedCount

int m_finishedCount

The count of successfully completed sub-experiments

m_baseExperiment

Experiment m_baseExperiment

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

m_subExperiments

Experiment[] m_subExperiments

The sub experiments

m_subExpQueue

Queue m_subExpQueue

The queue of sub experiments waiting to be processed

m_subExpComplete

int[] m_subExpComplete

The status of each of the sub-experiments

m_splitByDataSet

boolean m_splitByDataSet

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

Class weka.experiment.RemoteExperimentEvent extends java.lang.Object implements Serializable

serialVersionUID: 7000867987391866451L

Serialized Fields

m_statusMessage

boolean m_statusMessage

A status type message

m_logMessage

boolean m_logMessage

A log type message

m_messageString

java.lang.String m_messageString

The message

m_experimentFinished

boolean m_experimentFinished

True if a remote experiment has finished

Class weka.experiment.RemoteExperimentSubTask extends java.lang.Object implements Serializable

Serialized Fields

m_result

TaskStatusInfo m_result

m_experiment

Experiment m_experiment

Class weka.experiment.ResultMatrix extends java.lang.Object implements Serializable

serialVersionUID: 4487179306428209739L

Serialized Fields

TIE_STRING

java.lang.String TIE_STRING

tie string.

WIN_STRING

java.lang.String WIN_STRING

win string.

LOSS_STRING

java.lang.String LOSS_STRING

loss string.

LEFT_PARENTHESES

java.lang.String LEFT_PARENTHESES

the left parentheses for enumerating cols/rows.

RIGHT_PARENTHESES

java.lang.String RIGHT_PARENTHESES

the right parentheses for enumerating cols/rows.

m_ColNames

java.lang.String[] m_ColNames

the column names.

m_RowNames

java.lang.String[] m_RowNames

the row names.

m_ColHidden

boolean[] m_ColHidden

whether a column is hidden.

m_RowHidden

boolean[] m_RowHidden

whether a row is hidden.

m_Significance

int[][] m_Significance

the significance.

m_Mean

double[][] m_Mean

the values.

m_StdDev

double[][] m_StdDev

the standard deviation.

m_Counts

double[] m_Counts

the counts for the different datasets.

m_MeanPrec

int m_MeanPrec

the standard mean precision.

m_StdDevPrec

int m_StdDevPrec

the standard std. deviation preicision.

m_ShowStdDev

boolean m_ShowStdDev

whether std. deviations are printed as well.

m_ShowAverage

boolean m_ShowAverage

whether the average for each column should be printed.

m_PrintColNames

boolean m_PrintColNames

whether the names or numbers are output as column declarations.

m_PrintRowNames

boolean m_PrintRowNames

whether the names or numbers are output as row declarations.

m_EnumerateColNames

boolean m_EnumerateColNames

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

m_EnumerateRowNames

boolean m_EnumerateRowNames

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

m_ColNameWidth

int m_ColNameWidth

the size of the names of the columns.

m_RowNameWidth

int m_RowNameWidth

the size of the names of the rows.

m_MeanWidth

int m_MeanWidth

the size of the mean columns.

m_StdDevWidth

int m_StdDevWidth

the size of the std dev columns.

m_SignificanceWidth

int m_SignificanceWidth

the size of the significance columns.

m_CountWidth

int m_CountWidth

the size of the counts.

m_HeaderKeys

java.util.Vector<E> m_HeaderKeys

contains the keys for the header.

m_HeaderValues

java.util.Vector<E> m_HeaderValues

contains the values for the header.

m_NonSigWins

int[][] m_NonSigWins

the non-significant wins.

m_Wins

int[][] m_Wins

the significant wins.

m_RankingWins

int[] m_RankingWins

the wins in ranking.

m_RankingLosses

int[] m_RankingLosses

the losses in ranking.

m_RankingDiff

int[] m_RankingDiff

the difference between wins and losses.

m_RowOrder

int[] m_RowOrder

the ordering of the rows.

m_ColOrder

int[] m_ColOrder

the ordering of the columns.

m_RemoveFilterName

boolean m_RemoveFilterName

whether to remove the filter name from the dataaset name.

Class weka.experiment.ResultMatrixCSV extends ResultMatrix implements Serializable

serialVersionUID: -171838863135042743L

Class weka.experiment.ResultMatrixGnuPlot extends ResultMatrix implements Serializable

serialVersionUID: -234648254944790097L

Class weka.experiment.ResultMatrixHTML extends ResultMatrix implements Serializable

serialVersionUID: 6672380422544799990L

Class weka.experiment.ResultMatrixLatex extends ResultMatrix implements Serializable

serialVersionUID: 777690788447600978L

Class weka.experiment.ResultMatrixPlainText extends ResultMatrix implements Serializable

serialVersionUID: 1502934525382357937L

Class weka.experiment.ResultMatrixSignificance extends ResultMatrix implements Serializable

serialVersionUID: -1280545644109764206L

Class weka.experiment.Stats extends java.lang.Object implements Serializable

serialVersionUID: -8610544539090024102L

Serialized Fields

count

double count

The number of values seen

sum

double sum

The sum of values seen

sumSq

double sumSq

The sum of values squared seen

stdDev

double stdDev

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

mean

double mean

The mean of values at the last calculateDerived() call

min

double min

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

max

double max

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

Class weka.experiment.TaskStatusInfo extends java.lang.Object implements Serializable

serialVersionUID: -6129343303703560015L

Serialized Fields

m_ExecutionStatus

int m_ExecutionStatus

Holds current execution status.

m_StatusMessage

java.lang.String m_StatusMessage

Holds current status message.

m_TaskResult

java.lang.Object m_TaskResult

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

Package weka.filters

Class weka.filters.AllFilter extends Filter implements Serializable

serialVersionUID: 5022109283147503266L

Class weka.filters.Filter extends java.lang.Object implements Serializable

serialVersionUID: -8835063755891851218L

Serialized Fields

m_OutputFormat

Instances m_OutputFormat

The output format for instances

m_OutputQueue

Queue m_OutputQueue

The output instance queue

m_OutputStringAtts

StringLocator m_OutputStringAtts

Indices of string attributes in the output format

m_InputStringAtts

StringLocator m_InputStringAtts

Indices of string attributes in the input format

m_OutputRelAtts

RelationalLocator m_OutputRelAtts

Indices of relational attributes in the output format

m_InputRelAtts

RelationalLocator m_InputRelAtts

Indices of relational attributes in the input format

m_InputFormat

Instances m_InputFormat

The input format for instances

m_NewBatch

boolean m_NewBatch

Record whether the filter is at the start of a batch

m_FirstBatchDone

boolean m_FirstBatchDone

True if the first batch has been done

Class weka.filters.MultiFilter extends SimpleStreamFilter implements Serializable

serialVersionUID: -6293720886005713120L

Serialized Fields

m_Filters

Filter[] m_Filters

The filters

m_Streamable

boolean m_Streamable

caches the streamable state

m_StreamableChecked

boolean m_StreamableChecked

whether we already checked the streamable state

Class weka.filters.SimpleBatchFilter extends SimpleFilter implements Serializable

serialVersionUID: 8102908673378055114L

Class weka.filters.SimpleFilter extends Filter implements Serializable

serialVersionUID: 5702974949137433141L

Serialized Fields

m_Debug

boolean m_Debug

Whether debugging is on

Class weka.filters.SimpleStreamFilter extends SimpleFilter implements Serializable

serialVersionUID: 2754882676192747091L

Package weka.filters.supervised.attribute

Class weka.filters.supervised.attribute.AddClassification extends SimpleBatchFilter implements Serializable

serialVersionUID: -1931467132568441909L

Serialized Fields

m_Classifier

Classifier m_Classifier

The classifier template used to do the classification.

m_SerializedClassifierFile

java.io.File m_SerializedClassifierFile

The file from which to load a serialized classifier.

m_ActualClassifier

Classifier m_ActualClassifier

The actual classifier used to do the classification.

m_SerializedHeader

Instances m_SerializedHeader

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

m_OutputClassification

boolean m_OutputClassification

whether to output the classification.

m_RemoveOldClass

boolean m_RemoveOldClass

whether to remove the old class attribute.

m_OutputDistribution

boolean m_OutputDistribution

whether to output the class distribution.

m_OutputErrorFlag

boolean m_OutputErrorFlag

whether to output the error flag.

Class weka.filters.supervised.attribute.AttributeSelection extends Filter implements Serializable

serialVersionUID: -296211247688169716L

Serialized Fields

m_trainSelector

AttributeSelection m_trainSelector

the attribute selection evaluation object

m_ASEvaluator

ASEvaluation m_ASEvaluator

the attribute evaluator to use

m_ASSearch

ASSearch m_ASSearch

the search method if any

m_FilterOptions

java.lang.String[] m_FilterOptions

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

m_SelectedAttributes

int[] m_SelectedAttributes

holds the selected attributes

Class weka.filters.supervised.attribute.ClassOrder extends Filter implements Serializable

serialVersionUID: -2116226838887628411L

Serialized Fields

m_Seed

long m_Seed

The seed of randomization

m_Random

java.util.Random m_Random

The random object

m_Converter

int[] m_Converter

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

m_ClassAttribute

Attribute m_ClassAttribute

Class attribute of the data

m_ClassOrder

int m_ClassOrder

The class order to be sorted

m_ClassCounts

double[] m_ClassCounts

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

Class weka.filters.supervised.attribute.Discretize extends Filter implements Serializable

serialVersionUID: -3141006402280129097L

Serialized Fields

m_DiscretizeCols

Range m_DiscretizeCols

Stores which columns to Discretize

m_CutPoints

double[][] m_CutPoints

Store the current cutpoints

m_MakeBinary

boolean m_MakeBinary

Output binary attributes for discretized attributes.

m_UseBinNumbers

boolean m_UseBinNumbers

Use bin numbers rather than ranges for discretized attributes.

m_UseBetterEncoding

boolean m_UseBetterEncoding

Use better encoding of split point for MDL.

m_UseKononenko

boolean m_UseKononenko

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

Class weka.filters.supervised.attribute.NominalToBinary extends Filter implements Serializable

serialVersionUID: -5004607029857673950L

Serialized Fields

m_Indices

int[][] m_Indices

The sorted indices of the attribute values.

m_Numeric

boolean m_Numeric

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

m_TransformAll

boolean m_TransformAll

Are all values transformed into new attributes?

m_needToTransform

boolean m_needToTransform

Whether we need to transform at all

Package weka.filters.supervised.instance

Class weka.filters.supervised.instance.Resample extends Filter implements Serializable

serialVersionUID: 7079064953548300681L

Serialized Fields

m_SampleSizePercent

double m_SampleSizePercent

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

m_RandomSeed

int m_RandomSeed

The random number generator seed.

m_BiasToUniformClass

double m_BiasToUniformClass

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

m_NoReplacement

boolean m_NoReplacement

Whether to perform sampling with replacement or without.

m_InvertSelection

boolean m_InvertSelection

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

See Also:

Resample.m_NoReplacement

Class weka.filters.supervised.instance.SpreadSubsample extends Filter implements Serializable

serialVersionUID: -3947033795243930016L

Serialized Fields

m_RandomSeed

int m_RandomSeed

The random number generator seed

m_MaxCount

int m_MaxCount

The maximum count of any class

m_DistributionSpread

double m_DistributionSpread

True if the first batch has been done

m_AdjustWeights

boolean m_AdjustWeights

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

Class weka.filters.supervised.instance.StratifiedRemoveFolds extends Filter implements Serializable

serialVersionUID: -7069148179905814324L

Serialized Fields

m_Inverse

boolean m_Inverse

Indicates if inverse of selection is to be output.

m_NumFolds

int m_NumFolds

Number of folds to split dataset into

m_Fold

int m_Fold

Fold to output

m_Seed

long m_Seed

Random number seed.

Package weka.filters.unsupervised.attribute

Class weka.filters.unsupervised.attribute.AbstractTimeSeries extends Filter implements Serializable

serialVersionUID: -3795656792078022357L

Serialized Fields

m_SelectedCols

Range m_SelectedCols

Stores which columns to copy

m_FillWithMissing

boolean m_FillWithMissing

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

m_InstanceRange

int m_InstanceRange

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

m_History

Queue m_History

Stores the historical instances to copy values between

Class weka.filters.unsupervised.attribute.Add extends Filter implements Serializable

serialVersionUID: 761386447332932389L

Serialized Fields

m_AttributeType

int m_AttributeType

Record the type of attribute to insert.

m_Name

java.lang.String m_Name

The name for the new attribute.

m_Insert

SingleIndex m_Insert

The location to insert the new attribute.

m_Labels

FastVector<E> m_Labels

The list of labels for nominal attribute.

m_DateFormat

java.lang.String m_DateFormat

The date format.

Class weka.filters.unsupervised.attribute.AddCluster extends Filter implements Serializable

serialVersionUID: 7414280611943807337L

Serialized Fields

m_Clusterer

Clusterer m_Clusterer

The clusterer used to do the cleansing.

m_SerializedClustererFile

java.io.File m_SerializedClustererFile

The file from which to load a serialized clusterer.

m_ActualClusterer

Clusterer m_ActualClusterer

The actual clusterer used to do the clustering.

m_IgnoreAttributesRange

Range m_IgnoreAttributesRange

Range of attributes to ignore.

m_removeAttributes

Filter m_removeAttributes

Filter for removing attributes.

Class weka.filters.unsupervised.attribute.AddExpression extends Filter implements Serializable

serialVersionUID: 402130384261736245L

Serialized Fields

m_infixExpression

java.lang.String m_infixExpression

The infix expression

m_attributeName

java.lang.String m_attributeName

Name of the new attribute. "expression" length string will use the provided expression as the new attribute name

m_Debug

boolean m_Debug

If true, makes the attribute name equal to the postfix parse of the expression

m_attributeExpression

AttributeExpression m_attributeExpression

Class weka.filters.unsupervised.attribute.AddID extends Filter implements Serializable

serialVersionUID: 4734383199819293390L

Serialized Fields

m_Index

SingleIndex m_Index

the index of the attribute

m_Name

java.lang.String m_Name

the name of the attribute

m_Counter

int m_Counter

the counter for the ID

Class weka.filters.unsupervised.attribute.AddNoise extends Filter implements Serializable

serialVersionUID: -8499673222857299082L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex

The attribute's index setting.

m_UseMissing

boolean m_UseMissing

Flag if missing values are taken as value.

m_Percent

int m_Percent

The subsample size, percent of original set, default 10%

m_RandomSeed

int m_RandomSeed

The random number generator seed

Class weka.filters.unsupervised.attribute.AddUserFields extends Filter implements Serializable

serialVersionUID: -2761427344847891585L

Serialized Fields

m_attributeSpecs

java.util.List<E> m_attributeSpecs

The new attributes to create

Class weka.filters.unsupervised.attribute.AddUserFields.AttributeSpec extends java.lang.Object implements Serializable

serialVersionUID: -617328946241474608L

Serialized Fields

m_name

java.lang.String m_name

The name of the new attribute

m_value

java.lang.String m_value

The constant value it should assume

m_type

java.lang.String m_type

The type of the new attribute

m_nameS

java.lang.String m_nameS

The name after resolving any environment variables

m_valueS

java.lang.String m_valueS

The value after resolving any environment variables

m_typeS

java.lang.String m_typeS

The type after resolving any environment variables

m_dateFormat

java.text.SimpleDateFormat m_dateFormat

The date format to use (if the new attribute is a date)

m_parsedDate

java.util.Date m_parsedDate

Holds the parsed date value

Class weka.filters.unsupervised.attribute.AddValues extends Filter implements Serializable

serialVersionUID: -8100622241742393656L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex

The attribute's index setting.

m_Labels

java.util.Vector<E> m_Labels

The values to add.

m_Sort

boolean m_Sort

Whether to sort the values.

m_SortedIndices

int[] m_SortedIndices

the array with the sorted label indices

Class weka.filters.unsupervised.attribute.Center extends PotentialClassIgnorer implements Serializable

serialVersionUID: -9101338448900581023L

Serialized Fields

m_Means

double[] m_Means

The means

Class weka.filters.unsupervised.attribute.ChangeDateFormat extends Filter implements Serializable

serialVersionUID: -1609344074013448737L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex

The attribute's index setting.

m_DateFormat

java.text.SimpleDateFormat m_DateFormat

The output date format.

m_OutputAttribute

Attribute m_OutputAttribute

The output attribute.

Class weka.filters.unsupervised.attribute.ClassAssigner extends SimpleStreamFilter implements Serializable

serialVersionUID: 1775780193887394115L

Serialized Fields

m_ClassIndex

int m_ClassIndex

the class index.

Class weka.filters.unsupervised.attribute.ClusterMembership extends Filter implements Serializable

serialVersionUID: 6675702504667714026L

Serialized Fields

m_clusterer

DensityBasedClusterer m_clusterer

The clusterer

m_clusterers

DensityBasedClusterer[] m_clusterers

Array for storing the clusterers

m_ignoreAttributesRange

Range m_ignoreAttributesRange

Range of attributes to ignore

m_removeAttributes

Filter m_removeAttributes

Filter for removing attributes

m_priors

double[] m_priors

The prior probability for each class

Class weka.filters.unsupervised.attribute.Copy extends Filter implements Serializable

serialVersionUID: -8543707493627441566L

Serialized Fields

m_CopyCols

Range m_CopyCols

Stores which columns to copy

m_SelectedAttributes

int[] m_SelectedAttributes

Stores the indexes of the selected attributes in order, once the dataset is seen

Class weka.filters.unsupervised.attribute.Discretize extends PotentialClassIgnorer implements Serializable

serialVersionUID: -1358531742174527279L

Serialized Fields

m_DiscretizeCols

Range m_DiscretizeCols

Stores which columns to Discretize

m_NumBins

int m_NumBins

The number of bins to divide the attribute into

m_DesiredWeightOfInstancesPerInterval

double m_DesiredWeightOfInstancesPerInterval

The desired weight of instances per bin

m_CutPoints

double[][] m_CutPoints

Store the current cutpoints

m_MakeBinary

boolean m_MakeBinary

Output binary attributes for discretized attributes.

m_UseBinNumbers

boolean m_UseBinNumbers

Use bin numbers rather than ranges for discretized attributes.

m_FindNumBins

boolean m_FindNumBins

Find the number of bins using cross-validated entropy.

m_UseEqualFrequency

boolean m_UseEqualFrequency

Use equal-frequency binning if unsupervised discretization turned on

m_DefaultCols

java.lang.String m_DefaultCols

The default columns to discretize

Class weka.filters.unsupervised.attribute.FirstOrder extends Filter implements Serializable

serialVersionUID: -7500464545400454179L

Serialized Fields

m_DeltaCols

Range m_DeltaCols

Stores which columns to take differences between

Class weka.filters.unsupervised.attribute.InterquartileRange extends SimpleBatchFilter implements Serializable

serialVersionUID: -227879653639723030L

Serialized Fields

m_Attributes

Range m_Attributes

the attribute range to work on

m_AttributeIndices

int[] m_AttributeIndices

the generated indices (only for performance reasons)

m_OutlierFactor

double m_OutlierFactor

the factor for detecting outliers

m_ExtremeValuesFactor

double m_ExtremeValuesFactor

the factor for detecting extreme values, by default 2*m_OutlierFactor

m_ExtremeValuesAsOutliers

boolean m_ExtremeValuesAsOutliers

whether extreme values are also tagged as outliers

m_UpperExtremeValue

double[] m_UpperExtremeValue

the upper extreme value threshold (= Q3 + EVF*IQR)

m_UpperOutlier

double[] m_UpperOutlier

the upper outlier threshold (= Q3 + OF*IQR)

m_LowerOutlier

double[] m_LowerOutlier

the lower outlier threshold (= Q1 - OF*IQR)

m_IQR

double[] m_IQR

the interquartile range

m_Median

double[] m_Median

the median

m_LowerExtremeValue

double[] m_LowerExtremeValue

the lower extreme value threshold (= Q1 - EVF*IQR)

m_DetectionPerAttribute

boolean m_DetectionPerAttribute

whether to generate Outlier/ExtremeValue attributes for each attribute instead of a general one

m_OutlierAttributePosition

int[] m_OutlierAttributePosition

the position of the outlier attribute

m_OutputOffsetMultiplier

boolean m_OutputOffsetMultiplier

whether to add another attribute called "Offset", that lists the 'multiplier' by which the outlier/extreme value is away from the median, i.e., value = median + 'multiplier' * IQR
automatically enables m_DetectionPerAttribute!

Class weka.filters.unsupervised.attribute.KernelFilter extends SimpleBatchFilter implements Serializable

serialVersionUID: 213800899640387499L

Serialized Fields

m_NumTrainInstances

int m_NumTrainInstances

The number of instances in the training data.

m_Kernel

Kernel m_Kernel

Kernel to use

m_ActualKernel

Kernel m_ActualKernel

the Kernel which is actually used for computation

m_checksTurnedOff

boolean m_checksTurnedOff

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

m_NominalToBinary

NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

m_Missing

ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

m_InitFile

java.io.File m_InitFile

The dataset to initialize the filter with

m_InitFileClassIndex

SingleIndex m_InitFileClassIndex

the class index for the file to initialized with

See Also:

KernelFilter.m_InitFile

m_Initialized

boolean m_Initialized

whether the filter was initialized

m_KernelFactorExpression

java.lang.String m_KernelFactorExpression

optimizes the kernel with this formula (A = # of attributes, N = # of instances)

m_KernelFactor

double m_KernelFactor

the calculated kernel factor

See Also:

KernelFilter.m_KernelFactorExpression

m_Filter

Filter m_Filter

for centering/standardizing the data

m_ActualFilter

Filter m_ActualFilter

for centering/standardizing the data (the actual filter to use)

Class weka.filters.unsupervised.attribute.MakeIndicator extends Filter implements Serializable

serialVersionUID: 766001176862773163L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex

The attribute's index setting.

m_ValIndex

Range m_ValIndex

The value's index

m_Numeric

boolean m_Numeric

Make boolean attribute numeric.

Class weka.filters.unsupervised.attribute.MathExpression extends PotentialClassIgnorer implements Serializable

serialVersionUID: -3713222714671997901L

Serialized Fields

m_SelectCols

Range m_SelectCols

Stores which columns to select as a funky range

m_expression

java.lang.String m_expression

The modification expression

m_attStats

AttributeStats[] m_attStats

Attributes statistics

Class weka.filters.unsupervised.attribute.MergeManyValues extends Filter implements Serializable

serialVersionUID: 4649332102154713625L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex

The attribute's index setting.

m_Label

java.lang.String m_Label

The first value's index setting.

m_MergeRange

Range m_MergeRange

The merge value's index setting.

Class weka.filters.unsupervised.attribute.MergeTwoValues extends Filter implements Serializable

serialVersionUID: 2925048980504034018L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex

The attribute's index setting.

m_FirstIndex

SingleIndex m_FirstIndex

The first value's index setting.

m_SecondIndex

SingleIndex m_SecondIndex

The second value's index setting.

Class weka.filters.unsupervised.attribute.NominalToBinary extends Filter implements Serializable

serialVersionUID: -1130642825710549138L

Serialized Fields

m_Columns

Range m_Columns

Stores which columns to act on

m_Numeric

boolean m_Numeric

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

m_TransformAll

boolean m_TransformAll

Are all values transformed into new attributes?

m_needToTransform

boolean m_needToTransform

Whether we need to transform at all

Class weka.filters.unsupervised.attribute.NominalToString extends Filter implements Serializable

serialVersionUID: 8655492378380068939L

Serialized Fields

m_AttIndex

Range m_AttIndex

The attribute's index setting.

Class weka.filters.unsupervised.attribute.Normalize extends PotentialClassIgnorer implements Serializable

serialVersionUID: -8158531150984362898L

Serialized Fields

m_MinArray

double[] m_MinArray

The minimum values for numeric attributes.

m_MaxArray

double[] m_MaxArray

The maximum values for numeric attributes.

m_Translation

double m_Translation

The translation of the output range.

m_Scale

double m_Scale

The scaling factor of the output range.

Class weka.filters.unsupervised.attribute.NumericCleaner extends SimpleStreamFilter implements Serializable

serialVersionUID: -352890679895066592L

Serialized Fields

m_MinThreshold

double m_MinThreshold

the minimum threshold

m_MinDefault

double m_MinDefault

the minimum default replacement value

m_MaxThreshold

double m_MaxThreshold

the maximum threshold

m_MaxDefault

double m_MaxDefault

the maximum default replacement value

m_CloseTo

double m_CloseTo

the number the values are checked for closeness to

m_CloseToDefault

double m_CloseToDefault

the default replacement value for numbers "close-to"

m_CloseToTolerance

double m_CloseToTolerance

the tolerance distance, below which numbers are considered being "close-to"

m_Cols

Range m_Cols

Stores which columns to cleanse

m_IncludeClass

boolean m_IncludeClass

whether to include the class attribute

m_Decimals

int m_Decimals

the number of decimals to round to (-1 means no rounding)

Class weka.filters.unsupervised.attribute.NumericToBinary extends PotentialClassIgnorer implements Serializable

serialVersionUID: 2616879323359470802L

Class weka.filters.unsupervised.attribute.NumericToNominal extends SimpleBatchFilter implements Serializable

serialVersionUID: -6614630932899796239L

Serialized Fields

m_Cols

Range m_Cols

Stores which columns to turn into nominals

m_DefaultCols

java.lang.String m_DefaultCols

The default columns to turn into nominals

Class weka.filters.unsupervised.attribute.NumericTransform extends Filter implements Serializable

serialVersionUID: -8561413333351366934L

Serialized Fields

m_Cols

Range m_Cols

Stores which columns to transform.

m_Class

java.lang.String m_Class

Class containing transformation method.

m_Method

java.lang.String m_Method

Transformation method.

Class weka.filters.unsupervised.attribute.Obfuscate extends Filter implements Serializable

serialVersionUID: -343922772462971561L

Class weka.filters.unsupervised.attribute.PartitionedMultiFilter extends SimpleBatchFilter implements Serializable

serialVersionUID: -6293720886005713120L

Serialized Fields

m_Filters

Filter[] m_Filters

The filters.

m_Ranges

Range[] m_Ranges

The attribute ranges.

m_RemoveUnused

boolean m_RemoveUnused

Whether unused attributes are left out of the output.

m_IndicesUnused

int[] m_IndicesUnused

the indices of the unused attributes.

Class weka.filters.unsupervised.attribute.PartitionMembership extends Filter implements Serializable

serialVersionUID: 333532554667754026L

Serialized Fields

m_partitionGenerator

PartitionGenerator m_partitionGenerator

The partition generator

Class weka.filters.unsupervised.attribute.PKIDiscretize extends Discretize implements Serializable

serialVersionUID: 6153101248977702675L

Class weka.filters.unsupervised.attribute.PotentialClassIgnorer extends Filter implements Serializable

serialVersionUID: 8625371119276845454L

Serialized Fields

m_IgnoreClass

boolean m_IgnoreClass

True if the class is to be unset

m_ClassIndex

int m_ClassIndex

Storing the class index

Class weka.filters.unsupervised.attribute.PrincipalComponents extends Filter implements Serializable

serialVersionUID: -5649876869480249303L

Serialized Fields

m_TrainInstances

Instances m_TrainInstances

The data to transform analyse/transform.

m_TrainCopy

Instances m_TrainCopy

Keep a copy for the class attribute (if set).

m_TransformedFormat

Instances m_TransformedFormat

The header for the transformed data format.

m_HasClass

boolean m_HasClass

Data has a class set.

m_ClassIndex

int m_ClassIndex

Class index.

m_NumAttribs

int m_NumAttribs

Number of attributes.

m_NumInstances

int m_NumInstances

Number of instances.

m_Correlation

double[][] m_Correlation

Correlation matrix for the original data.

m_center

boolean m_center

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

m_Eigenvectors

double[][] m_Eigenvectors

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

m_Eigenvalues

double[] m_Eigenvalues

Eigenvalues for the corresponding eigenvectors.

m_SortedEigens

int[] m_SortedEigens

Sorted eigenvalues.

m_SumOfEigenValues

double m_SumOfEigenValues

sum of the eigenvalues.

m_ReplaceMissingFilter

ReplaceMissingValues m_ReplaceMissingFilter

Filters for replacing missing values.

m_NominalToBinaryFilter

NominalToBinary m_NominalToBinaryFilter

Filter for turning nominal values into numeric ones.

m_AttributeFilter

Remove m_AttributeFilter

Filter for removing class attribute, nominal attributes with 0 or 1 value.

m_standardizeFilter

Standardize m_standardizeFilter

Filter for standardizing the data

m_centerFilter

Center m_centerFilter

Filter for centering the data

m_OutputNumAtts

int m_OutputNumAtts

The number of attributes in the pc transformed data.

m_CoverVariance

double m_CoverVariance

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

m_MaxAttrsInName

int m_MaxAttrsInName

maximum number of attributes in the transformed attribute name.

m_MaxAttributes

int m_MaxAttributes

maximum number of attributes in the transformed data (-1 for all).

Class weka.filters.unsupervised.attribute.RandomProjection extends Filter implements Serializable

serialVersionUID: 4428905532728645880L

Serialized Fields

m_k

int m_k

Stores the number of dimensions to reduce the data to

m_percent

double m_percent

Stores the dimensionality the data should be reduced to as percentage of the original dimension

m_useGaussian

boolean m_useGaussian

Is the random matrix will be computed using Gaussian distribution or not

m_distribution

int m_distribution

Stores the distribution to use for calculating the random matrix

m_useReplaceMissing

boolean m_useReplaceMissing

Should the missing values be replaced using unsupervised.ReplaceMissingValues filter

m_OutputFormatDefined

boolean m_OutputFormatDefined

Keeps track of output format if it is defined or not

m_ntob

Filter m_ntob

The NominalToBinary filter applied to the data before this filter

m_replaceMissing

Filter m_replaceMissing

The ReplaceMissingValues filter

m_rndmSeed

long m_rndmSeed

Stores the random seed used to generate the random matrix

m_rmatrix

double[][] m_rmatrix

The random matrix

m_random

java.util.Random m_random

The random number generator used for generating the random matrix

Class weka.filters.unsupervised.attribute.RandomSubset extends SimpleStreamFilter implements Serializable

serialVersionUID: 2911221724251628050L

Serialized Fields

m_NumAttributes

double m_NumAttributes

The number of attributes to randomly choose (>= 1 absolute number of attributes, < 1 percentage).

m_Seed

int m_Seed

The seed value.

m_Indices

int[] m_Indices

The indices of the attributes that got selected.

Class weka.filters.unsupervised.attribute.Remove extends Filter implements Serializable

serialVersionUID: 5011337331921522847L

Serialized Fields

m_SelectCols

Range m_SelectCols

Stores which columns to select as a funky range

m_SelectedAttributes

int[] m_SelectedAttributes

Stores the indexes of the selected attributes in order, once the dataset is seen

Class weka.filters.unsupervised.attribute.RemoveByName extends SimpleStreamFilter implements Serializable

serialVersionUID: -3335106965521265631L

Serialized Fields

m_Expression

java.lang.String m_Expression

the regular expression for selecting the attributes by name.

m_InvertSelection

boolean m_InvertSelection

whether to invert the matching sense.

m_Remove

Remove m_Remove

the Remove filter used internally for removing the attributes.

Class weka.filters.unsupervised.attribute.RemoveType extends Filter implements Serializable

serialVersionUID: -3563999462782486279L

Serialized Fields

m_attributeFilter

Remove m_attributeFilter

The attribute filter used to do the filtering

m_attTypeToDelete

int m_attTypeToDelete

The type of attribute to delete

m_invert

boolean m_invert

Whether to invert selection

Class weka.filters.unsupervised.attribute.RemoveUseless extends Filter implements Serializable

serialVersionUID: -8659417851407640038L

Serialized Fields

m_removeFilter

Remove m_removeFilter

The filter used to remove attributes

m_maxVariancePercentage

double m_maxVariancePercentage

The type of attribute to delete

Class weka.filters.unsupervised.attribute.RenameAttribute extends SimpleStreamFilter implements Serializable

serialVersionUID: 4216491776378279596L

Serialized Fields

m_Find

java.lang.String m_Find

the regular expression that the attribute names have to match.

m_Replace

java.lang.String m_Replace

the regular expression to replace the attribute name with.

m_AttributeIndices

Range m_AttributeIndices

the attribute range to work on.

m_ReplaceAll

boolean m_ReplaceAll

whether to replace all occurrences or just the first.

Class weka.filters.unsupervised.attribute.Reorder extends Filter implements Serializable

serialVersionUID: -1135571321097202292L

Serialized Fields

m_NewOrderCols

java.lang.String m_NewOrderCols

Stores which columns to reorder

m_SelectedAttributes

int[] m_SelectedAttributes

Stores the indexes of the selected attributes in order, once the dataset is seen

m_InputStringIndex

int[] m_InputStringIndex

Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated

Class weka.filters.unsupervised.attribute.ReplaceMissingValues extends PotentialClassIgnorer implements Serializable

serialVersionUID: 8349568310991609867L

Serialized Fields

m_ModesAndMeans

double[] m_ModesAndMeans

The modes and means

Class weka.filters.unsupervised.attribute.ReplaceMissingWithUserConstant extends PotentialClassIgnorer implements Serializable

serialVersionUID: -7334039452189350356L

Serialized Fields

m_selectedRange

Range m_selectedRange

Range of columns to consider

m_range

java.lang.String m_range

m_resolvedRange

java.lang.String m_resolvedRange

m_nominalStringConstant

java.lang.String m_nominalStringConstant

Constant for replacing missing values in nominal/string atts with

m_resolvedNominalStringConstant

java.lang.String m_resolvedNominalStringConstant

Replacement value for nominal/string atts after resolving environment vars

m_numericConstant

java.lang.String m_numericConstant

Constant for replacing missing values in numeric attributes with

m_resolvedNumericConstant

java.lang.String m_resolvedNumericConstant

Replacement value for numeric atts after resolving environment vars

m_numericConstVal

double m_numericConstVal

Parsed numeric constant value

m_dateConstant

java.lang.String m_dateConstant

Constant for replacing missing values in date attributes with

m_resolvedDateConstant

java.lang.String m_resolvedDateConstant

Replacement value for date atts after resolving environment vars

m_dateConstVal

double m_dateConstVal

Parsed date value as a double

m_defaultDateFormat

java.lang.String m_defaultDateFormat

Formatting string to use for parsing the date constant

m_resolvedDateFormat

java.lang.String m_resolvedDateFormat

Formatting string after resolving environment vars

Class weka.filters.unsupervised.attribute.SortLabels extends SimpleStreamFilter implements Serializable

serialVersionUID: 7815204879694105691L

Serialized Fields

m_AttributeIndices

Range m_AttributeIndices

the range of attributes to process (only relational ones will be processed).

m_NewOrder

int[][] m_NewOrder

the new order for the labels.

m_SortType

int m_SortType

the sort type.

m_Comparator

java.util.Comparator<T> m_Comparator

the comparator to use for sorting.

Class weka.filters.unsupervised.attribute.SortLabels.CaseInsensitiveComparator extends java.lang.Object implements Serializable

serialVersionUID: -4515292733342486066L

Class weka.filters.unsupervised.attribute.SortLabels.CaseSensitiveComparator extends java.lang.Object implements Serializable

serialVersionUID: 7071450356783873277L

Class weka.filters.unsupervised.attribute.Standardize extends PotentialClassIgnorer implements Serializable

serialVersionUID: -6830769026855053281L

Serialized Fields

m_Means

double[] m_Means

The means

m_StdDevs

double[] m_StdDevs

The variances

Class weka.filters.unsupervised.attribute.StringToNominal extends Filter implements Serializable

serialVersionUID: 4864084427902797605L

Serialized Fields

m_AttIndices

Range m_AttIndices

The attribute's range indices setting.

Class weka.filters.unsupervised.attribute.StringToWordVector extends Filter implements Serializable

serialVersionUID: 8249106275278565424L

Serialized Fields

m_SelectedRange

Range m_SelectedRange

Range of columns to convert to word vectors.

m_Dictionary

java.util.TreeMap<K,V> m_Dictionary

Contains a mapping of valid words to attribute indexes.

m_OutputCounts

boolean m_OutputCounts

True if output instances should contain word frequency rather than boolean 0 or 1.

m_Prefix

java.lang.String m_Prefix

A String prefix for the attribute names.

m_DocsCounts

int[] m_DocsCounts

Contains the number of documents (instances) a particular word appears in. The counts are stored with the same indexing as given by m_Dictionary.

m_NumInstances

int m_NumInstances

Contains the number of documents (instances) in the input format from which the dictionary is created. It is used in IDF transform.

m_AvgDocLength

double m_AvgDocLength

Contains the average length of documents (among the first batch of instances aka training data). This is used in length normalization of documents which will be normalized to average document length.

m_WordsToKeep

int m_WordsToKeep

The default number of words (per class if there is a class attribute assigned) to attempt to keep.

m_PeriodicPruningRate

double m_PeriodicPruningRate

The percentage at which to periodically prune the dictionary.

m_TFTransform

boolean m_TFTransform

True if word frequencies should be transformed into log(1+fi) where fi is the frequency of word i.

m_filterType

int m_filterType

The normalization to apply.

m_IDFTransform

boolean m_IDFTransform

True if word frequencies should be transformed into fij*log(numOfDocs/numOfDocsWithWordi).

m_lowerCaseTokens

boolean m_lowerCaseTokens

True if all tokens should be downcased.

m_useStoplist

boolean m_useStoplist

True if tokens that are on a stoplist are to be ignored.

m_Stemmer

Stemmer m_Stemmer

the stemming algorithm.

m_minTermFreq

int m_minTermFreq

the minimum (per-class) word frequency.

m_doNotOperateOnPerClassBasis

boolean m_doNotOperateOnPerClassBasis

whether to operate on a per-class basis.

m_Stopwords

java.io.File m_Stopwords

a file containing stopwords for using others than the default Rainbow ones.

m_Tokenizer

Tokenizer m_Tokenizer

the tokenizer algorithm to use.

Class weka.filters.unsupervised.attribute.SwapValues extends Filter implements Serializable

serialVersionUID: 6155834679414275855L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex

The attribute's index setting.

m_FirstIndex

SingleIndex m_FirstIndex

The first value's index setting.

m_SecondIndex

SingleIndex m_SecondIndex

The second value's index setting.

Class weka.filters.unsupervised.attribute.TimeSeriesDelta extends TimeSeriesTranslate implements Serializable

serialVersionUID: 3101490081896634942L

Class weka.filters.unsupervised.attribute.TimeSeriesTranslate extends AbstractTimeSeries implements Serializable

serialVersionUID: -8901621509691785705L

Package weka.filters.unsupervised.instance

Class weka.filters.unsupervised.instance.NonSparseToSparse extends Filter implements Serializable

serialVersionUID: 4694489111366063852L

Serialized Fields

m_encodeMissingAsZero

boolean m_encodeMissingAsZero

m_insertDummyNominalFirstValue

boolean m_insertDummyNominalFirstValue

Class weka.filters.unsupervised.instance.Randomize extends Filter implements Serializable

serialVersionUID: 8854479785121877582L

Serialized Fields

m_Seed

int m_Seed

The random number seed

m_Random

java.util.Random m_Random

The current random number generator

Class weka.filters.unsupervised.instance.RemoveFolds extends Filter implements Serializable

serialVersionUID: 8220373305559055700L

Serialized Fields

m_Inverse

boolean m_Inverse

Indicates if inverse of selection is to be output.

m_NumFolds

int m_NumFolds

Number of folds to split dataset into

m_Fold

int m_Fold

Fold to output

m_Seed

long m_Seed

Random number seed.

Class weka.filters.unsupervised.instance.RemoveFrequentValues extends Filter implements Serializable

serialVersionUID: -2447432930070059511L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex

The attribute's index setting.

m_NumValues

int m_NumValues

the number of values to retain.

m_LeastValues

boolean m_LeastValues

whether to retain values with least instances instead of most.

m_Invert

boolean m_Invert

whether to invert the matching sense.

m_ModifyHeader

boolean m_ModifyHeader

Modify header for nominal attributes?

m_NominalMapping

int[] m_NominalMapping

If m_ModifyHeader, stores a mapping from old to new indexes

m_Values

java.util.HashSet<E> m_Values

contains the values to retain

Class weka.filters.unsupervised.instance.RemoveMisclassified extends Filter implements Serializable

serialVersionUID: 5469157004717663171L

Serialized Fields

m_cleansingClassifier

Classifier m_cleansingClassifier

The classifier used to do the cleansing

m_classIndex

int m_classIndex

The attribute to treat as the class for purposes of cleansing.

m_numOfCrossValidationFolds

int m_numOfCrossValidationFolds

The number of cross validation folds to perform (<2 = no cross validation)

m_numOfCleansingIterations

int m_numOfCleansingIterations

The maximum number of cleansing iterations to perform (<1 = until fully cleansed)

m_numericClassifyThreshold

double m_numericClassifyThreshold

The threshold for deciding when a numeric value is correctly classified

m_invertMatching

boolean m_invertMatching

Whether to invert the match so the correctly classified instances are discarded

m_firstBatchFinished

boolean m_firstBatchFinished

Have we processed the first batch (i.e. training data)?

Class weka.filters.unsupervised.instance.RemovePercentage extends Filter implements Serializable

serialVersionUID: 2150341191158533133L

Serialized Fields

m_Percentage

double m_Percentage

Percentage of instances to select.

m_Inverse

boolean m_Inverse

Indicates if inverse of selection is to be output.

Class weka.filters.unsupervised.instance.RemoveRange extends Filter implements Serializable

serialVersionUID: -3064641215340828695L

Serialized Fields

m_Range

Range m_Range

Range of instances requested by the user.

Class weka.filters.unsupervised.instance.RemoveWithValues extends Filter implements Serializable

serialVersionUID: 4752870193679263361L

Serialized Fields

m_AttIndex

SingleIndex m_AttIndex

The attribute's index setting.

m_Values

Range m_Values

Stores which values of nominal attribute are to be used for filtering.

m_Value

double m_Value

Stores which value of a numeric attribute is to be used for filtering.

m_MatchMissingValues

boolean m_MatchMissingValues

True if missing values should count as a match

m_ModifyHeader

boolean m_ModifyHeader

Modify header for nominal attributes?

m_NominalMapping

int[] m_NominalMapping

If m_ModifyHeader, stores a mapping from old to new indexes

m_dontFilterAfterFirstBatch

boolean m_dontFilterAfterFirstBatch

Whether to filter instances after the first batch has been processed

Class weka.filters.unsupervised.instance.Resample extends Filter implements Serializable

serialVersionUID: 3119607037607101160L

Serialized Fields

m_SampleSizePercent

double m_SampleSizePercent

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

m_RandomSeed

int m_RandomSeed

The random number generator seed

m_NoReplacement

boolean m_NoReplacement

Whether to perform sampling with replacement or without

m_InvertSelection

boolean m_InvertSelection

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

See Also:

Resample.m_NoReplacement

Class weka.filters.unsupervised.instance.ReservoirSample extends Filter implements Serializable

serialVersionUID: 3119607037607101160L

Serialized Fields

m_SampleSize

int m_SampleSize

The subsample size, number of instances%

m_subSample

Instance[] m_subSample

Holds the sub-sample (reservoir)

m_currentInst

int m_currentInst

The current instance being processed

m_RandomSeed

int m_RandomSeed

The random number generator seed

m_random

java.util.Random m_random

The random number generator

Class weka.filters.unsupervised.instance.SparseToNonSparse extends Filter implements Serializable

serialVersionUID: 2481634184210236074L

Class weka.filters.unsupervised.instance.SubsetByExpression extends SimpleBatchFilter implements Serializable

serialVersionUID: 5628686110979589602L

Serialized Fields

m_Expression

java.lang.String m_Expression

the expresion to use for filtering.

m_filterAfterFirstBatch

boolean m_filterAfterFirstBatch

Whether to filter instances after the first batch has been processed

Package weka.gui

Class weka.gui.AttributeListPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -2030706987910400362L

Serialized Fields

m_Table

javax.swing.JTable m_Table

The table displaying attribute names

m_Model

weka.gui.AttributeListPanel.AttributeTableModel m_Model

The table model containing attribute names

Class weka.gui.AttributeSelectionPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 627131485290359194L

Serialized Fields

m_IncludeAll

javax.swing.JButton m_IncludeAll

Press to select all attributes

m_RemoveAll

javax.swing.JButton m_RemoveAll

Press to deselect all attributes

m_Invert

javax.swing.JButton m_Invert

Press to invert the current selection

m_Pattern

javax.swing.JButton m_Pattern

Press to enter a perl regular expression for selection

m_Table

javax.swing.JTable m_Table

The table displaying attribute names and selection status

m_Model

weka.gui.AttributeSelectionPanel.AttributeTableModel m_Model

The table model containing attribute names and selection status

m_PatternRegEx

java.lang.String m_PatternRegEx

The current regular expression.

Class weka.gui.AttributeSummaryPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -5434987925737735880L

Serialized Fields

m_AttributeNameLab

javax.swing.JLabel m_AttributeNameLab

Displays the name of the relation

m_AttributeTypeLab

javax.swing.JLabel m_AttributeTypeLab

Displays the type of attribute

m_MissingLab

javax.swing.JLabel m_MissingLab

Displays the number of missing values

m_UniqueLab

javax.swing.JLabel m_UniqueLab

Displays the number of unique values

m_DistinctLab

javax.swing.JLabel m_DistinctLab

Displays the number of distinct values

m_StatsTable

javax.swing.JTable m_StatsTable

Displays other stats in a table

m_Instances

Instances m_Instances

The instances we're playing with

m_AttributeStats

AttributeStats[] m_AttributeStats

Cached stats on the attributes we've summarized so far

m_allEqualWeights

boolean m_allEqualWeights

Do all instances have the same weight

Class weka.gui.AttributeVisualizationPanel extends PrintablePanel implements Serializable

serialVersionUID: -8650490488825371193L

Serialized Fields

m_data

Instances m_data

This holds the current set of instances

m_as

AttributeStats m_as

This holds the attribute stats of the current attribute on display. It is calculated in setAttribute(int idx) when it is called to set a new attribute index.

m_asCache

AttributeStats[] m_asCache

Cache of attribute stats info for the current data set

m_attribIndex

int m_attribIndex

This holds the index of the current attribute on display and should be set through setAttribute(int idx).

m_maxValue

double m_maxValue

This holds the max value of the current attribute. In case of nominal attribute it is the highest count that a nominal value has in the attribute (given by m_as.nominalWeights[i]), otherwise in case of numeric attribute it is simply the maximum value present in the attribute (given by m_as.numericStats.max). It is used to calculate the ratio of the height of the bars with respect to the height of the display area.

m_histBarCounts

double[] m_histBarCounts

This array holds the count (or height) for the each of the bars in a barplot or a histogram. In case of barplots (and current attribute being nominal) its length (and the number of bars) is equal to the number of nominal values in the current attribute, with each field of the array being equal to the count of each nominal that it represents (the count of ith nominal value of an attribute is given by m_as.nominalWeights[i]). Whereas, in case of histograms (and current attribute being numeric) the width of its intervals is calculated by Scott's(1979) method:
intervalWidth = Max(1, 3.49*Std.Dev*numInstances^(1/3)) And the number of intervals by:
intervals = max(1, Math.round(Range/intervalWidth); Then each field of this array contains the number of values of the current attribute that fall in the histogram interval that it represents.
NOTE: The values of this array are only calculated if the class attribute is not set or if it is numeric.

m_histBarClassCounts

SparseInstance[] m_histBarClassCounts

This array holds the per class count (or per class height) of the each of the bars in a barplot or a histogram. For nominal attributes the format is:
m_histBarClassCounts[nominalValue][classValue+1]. For numeric attributes the format is:
m_histBarClassCounts[interval][classValues+1],
where the number of intervals is calculated by the Scott's method as mentioned above. The array is initialized to have 1+numClasses to accomodate for instances with missing class value. The ones with missing class value are displayed as a black sub par in a histogram or a barplot. NOTE: The values of this array are only calculated if the class attribute is set and it is nominal.

m_barRange

double m_barRange

Contains the range of each bar in a histogram. It is used to work out the range of bar the mouse pointer is on in getToolTipText().

m_classIndex

int m_classIndex

Contains the current class index.

m_hc

java.lang.Thread m_hc

This stores the BarCalc or HistCalc thread while a new barplot or histogram is being calculated.

m_threadRun

boolean m_threadRun

True if the thread m_hc above is running.

m_doneCurrentAttribute

boolean m_doneCurrentAttribute

m_displayCurrentAttribute

boolean m_displayCurrentAttribute

m_colorAttrib

javax.swing.JComboBox m_colorAttrib

This stores and lets the user select a class attribute. It also has an entry "No Class" if the user does not want to set a class attribute for colouring.

m_fm

java.awt.FontMetrics m_fm

Fontmetrics used to get the font size which is required for calculating displayable area size, bar height ratio and width of strings that are displayed on top of bars indicating their count.

m_locker

java.lang.Integer m_locker

Lock variable to synchronize the different threads running currently in this class. There are two to three threads in this class, AWT paint thread which is handled differently in paintComponent() which checks on m_threadRun to determine if it can perform full paint or not, the second thread is the main execution thread and the third is the one represented by m_hc which we start when we want to calculate the internal fields for a bar plot or a histogram.

m_colorList

FastVector<E> m_colorList

Contains discrete colours for colouring of subbars of histograms and bar plots when the class attribute is set and is nominal

Class weka.gui.CheckBoxList extends javax.swing.JList implements Serializable

serialVersionUID: -4359573373359270258L

Class weka.gui.CheckBoxList.CheckBoxListModel extends javax.swing.DefaultListModel implements Serializable

serialVersionUID: 7772455499540273507L

Class weka.gui.CheckBoxList.CheckBoxListRenderer extends javax.swing.JCheckBox implements Serializable

serialVersionUID: 1059591605858524586L

Class weka.gui.ConverterFileChooser extends javax.swing.JFileChooser implements Serializable

serialVersionUID: -5373058011025481738L

Serialized Fields

m_Self

ConverterFileChooser m_Self

the file chooser itself.

m_DialogType

int m_DialogType

the type of dialog to display.

m_CurrentConverter

java.lang.Object m_CurrentConverter

the converter that was chosen by the user.

m_ConfigureButton

javax.swing.JButton m_ConfigureButton

the configure button.

m_Listener

java.beans.PropertyChangeListener m_Listener

the propertychangelistener.

m_LastFilter

javax.swing.filechooser.FileFilter m_LastFilter

the last filter that was used for opening/saving.

m_CapabilitiesFilter

Capabilities m_CapabilitiesFilter

the Capabilities filter for the savers.

m_OverwriteWarning

boolean m_OverwriteWarning

whether to popup a dialog in case the file already exists (only save dialog).

m_FileMustExist

boolean m_FileMustExist

whether the file to be opened must exist (only open dialog).

m_CheckBoxOptions

javax.swing.JCheckBox m_CheckBoxOptions

the checkbox for bringing up the GenericObjectEditor.

m_LabelOptions

javax.swing.JLabel m_LabelOptions

the note about the options dialog.

m_Editor

GenericObjectEditor m_Editor

the GOE for displaying the options of a loader/saver.

m_EditorResult

int m_EditorResult

whether the GOE was OKed or Canceled.

m_CoreConvertersOnly

boolean m_CoreConvertersOnly

whether to display only core converters (hardcoded in ConverterUtils). Necessary for RMI/Remote Experiments for instance.

See Also:

ConverterUtils.CORE_FILE_LOADERS, ConverterUtils.CORE_FILE_SAVERS

Class weka.gui.DatabaseConnectionDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -1081946748666245054L

Serialized Fields

m_DbaseURLText

javax.swing.JTextField m_DbaseURLText

m_DbaseURLLab

javax.swing.JLabel m_DbaseURLLab

m_UserNameText

javax.swing.JTextField m_UserNameText

m_UserNameLab

javax.swing.JLabel m_UserNameLab

m_PasswordText

javax.swing.JPasswordField m_PasswordText

m_PasswordLab

javax.swing.JLabel m_PasswordLab

m_DebugCheckBox

javax.swing.JCheckBox m_DebugCheckBox

m_DebugLab

javax.swing.JLabel m_DebugLab

m_returnValue

int m_returnValue

Class weka.gui.ETable extends javax.swing.JTable implements Serializable

serialVersionUID: -3028630226368293049L

Serialized Fields

MAC_FOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR

java.awt.Color MAC_FOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR

MAC_UNFOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR

java.awt.Color MAC_UNFOCUSED_SELECTED_CELL_HORIZONTAL_LINE_COLOR

MAC_UNFOCUSED_SELECTED_CELL_BACKGROUND_COLOR

java.awt.Color MAC_UNFOCUSED_SELECTED_CELL_BACKGROUND_COLOR

MAC_FOCUSED_UNSELECTED_VERTICAL_LINE_COLOR

java.awt.Color MAC_FOCUSED_UNSELECTED_VERTICAL_LINE_COLOR

MAC_FOCUSED_SELECTED_VERTICAL_LINE_COLOR

java.awt.Color MAC_FOCUSED_SELECTED_VERTICAL_LINE_COLOR

MAC_UNFOCUSED_UNSELECTED_VERTICAL_LINE_COLOR

java.awt.Color MAC_UNFOCUSED_UNSELECTED_VERTICAL_LINE_COLOR

MAC_UNFOCUSED_SELECTED_VERTICAL_LINE_COLOR

java.awt.Color MAC_UNFOCUSED_SELECTED_VERTICAL_LINE_COLOR

MAC_OS_ALTERNATE_ROW_COLOR

java.awt.Color MAC_OS_ALTERNATE_ROW_COLOR

Class weka.gui.EvaluationMetricSelectionDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: 4451184027143094270L

Serialized Fields

m_selectedEvalMetrics

java.util.List<E> m_selectedEvalMetrics

The list of selected evaluation metrics

Class weka.gui.ExtensionFileFilter extends javax.swing.filechooser.FileFilter implements Serializable

Serialized Fields

m_Description

java.lang.String m_Description

The text description of the types of files accepted

m_Extension

java.lang.String[] m_Extension

The filename extensions of accepted files

Class weka.gui.GenericArrayEditor extends javax.swing.JPanel implements Serializable

serialVersionUID: 3914616975334750480L

Serialized Fields

m_Support

java.beans.PropertyChangeSupport m_Support

Handles property change notification.

m_Label

javax.swing.JLabel m_Label

The label for when we can't edit that type.

m_ElementList

javax.swing.JList m_ElementList

The list component displaying current values.

m_ElementClass

java.lang.Class<T> m_ElementClass

The class of objects allowed in the array.

m_ListModel

javax.swing.DefaultListModel m_ListModel

The defaultlistmodel holding our data.

m_ElementEditor

java.beans.PropertyEditor m_ElementEditor

The property editor for the class we are editing.

m_DeleteBut

javax.swing.JButton m_DeleteBut

Click this to delete the selected array values.

m_EditBut

javax.swing.JButton m_EditBut

Click this to edit the selected array value.

m_UpBut

javax.swing.JButton m_UpBut

Click this to move the selected array value(s) one up.

m_DownBut

javax.swing.JButton m_DownBut

Click this to move the selected array value(s) one down.

m_AddBut

javax.swing.JButton m_AddBut

Click to add the current object configuration to the array.

m_Editor

java.beans.PropertyEditor m_Editor

The property editor for editing existing elements.

m_PD

PropertyDialog m_PD

The currently displayed property dialog, if any.

m_InnerActionListener

java.awt.event.ActionListener m_InnerActionListener

Listens to buttons being pressed and taking the appropriate action.

m_InnerSelectionListener

javax.swing.event.ListSelectionListener m_InnerSelectionListener

Listens to list items being selected and takes appropriate action.

m_InnerMouseListener

java.awt.event.MouseListener m_InnerMouseListener

Listens to mouse events and takes appropriate action.

Class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -7845503345689646266L

Serialized Fields

m_Self

javax.swing.JDialog m_Self

the dialog itself.

m_Popup

javax.swing.JPopupMenu m_Popup

the popup to display again.

m_Capabilities

Capabilities m_Capabilities

the capabilities used for initializing the dialog.

m_InfoLabel

javax.swing.JLabel m_InfoLabel

the label, listing the name of the superclass.

m_List

CheckBoxList m_List

the list with all the capabilities.

m_OkButton

javax.swing.JButton m_OkButton

the OK button.

m_CancelButton

javax.swing.JButton m_CancelButton

the Cancel button.

Class weka.gui.GenericObjectEditor.GOEPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 3656028520876011335L

Serialized Fields

m_ChildPropertySheet

PropertySheetPanel m_ChildPropertySheet

The component that performs classifier customization.

m_ClassNameLabel

javax.swing.JLabel m_ClassNameLabel

The name of the current class.

m_OpenBut

javax.swing.JButton m_OpenBut

Open object from disk.

m_SaveBut

javax.swing.JButton m_SaveBut

Save object to disk.

m_okBut

javax.swing.JButton m_okBut

ok button.

m_cancelBut

javax.swing.JButton m_cancelBut

cancel button.

m_FileChooser

javax.swing.JFileChooser m_FileChooser

The filechooser for opening and saving object files.

Class weka.gui.GenericObjectEditor.GOETreeNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable

serialVersionUID: -1707872446682150133L

Serialized Fields

m_Capabilities

Capabilities m_Capabilities

the Capabilities object to use for filtering.

Class weka.gui.GenericObjectEditor.JTreePopupMenu extends javax.swing.JPopupMenu implements Serializable

serialVersionUID: -3404546329655057387L

Serialized Fields

m_Self

javax.swing.JPopupMenu m_Self

the popup itself.

m_tree

javax.swing.JTree m_tree

The tree.

m_scroller

javax.swing.JScrollPane m_scroller

The scroller.

m_FilterButton

javax.swing.JButton m_FilterButton

The filter button in case of CapabilitiesHandlers.

m_RemoveFilterButton

javax.swing.JButton m_RemoveFilterButton

The remove filter button in case of CapabilitiesHandlers.

m_CloseButton

javax.swing.JButton m_CloseButton

The button for closing the popup again.

Class weka.gui.GenericObjectEditorHistory extends java.lang.Object implements Serializable

serialVersionUID: -1255734638729633595L

Serialized Fields

m_History

java.util.Vector<E> m_History

the history of objects.

Class weka.gui.GenericObjectEditorHistory.HistorySelectionEvent extends java.util.EventObject implements Serializable

serialVersionUID: 45824542929908105L

Serialized Fields

m_HistoryItem

java.lang.Object m_HistoryItem

the selected favorite.

Class weka.gui.GUIChooser extends javax.swing.JFrame implements Serializable

serialVersionUID: 9001529425230247914L

Serialized Fields

m_Self

GUIChooser m_Self

the GUIChooser itself

m_jMenuBar

javax.swing.JMenuBar m_jMenuBar

m_jMenuProgram

javax.swing.JMenu m_jMenuProgram

m_jMenuVisualization

javax.swing.JMenu m_jMenuVisualization

m_jMenuTools

javax.swing.JMenu m_jMenuTools

m_jMenuHelp

javax.swing.JMenu m_jMenuHelp

m_PanelApplications

javax.swing.JPanel m_PanelApplications

the panel for the application buttons

m_ExplorerBut

javax.swing.JButton m_ExplorerBut

Click to open the Explorer

m_ExplorerFrame

javax.swing.JFrame m_ExplorerFrame

The frame containing the explorer interface

m_ExperimenterBut

javax.swing.JButton m_ExperimenterBut

Click to open the Explorer

m_ExperimenterFrame

javax.swing.JFrame m_ExperimenterFrame

The frame containing the experiment interface

m_KnowledgeFlowBut

javax.swing.JButton m_KnowledgeFlowBut

Click to open the KnowledgeFlow

m_pendingKnowledgeFlowLoad

java.lang.String m_pendingKnowledgeFlowLoad

Pending file to load on startup of the KnowledgeFlow

m_KnowledgeFlowFrame

javax.swing.JFrame m_KnowledgeFlowFrame

The frame containing the knowledge flow interface

m_SimpleBut

javax.swing.JButton m_SimpleBut

Click to open the simplecli

m_SimpleCLI

SimpleCLI m_SimpleCLI

The SimpleCLI

m_GroovyConsoleFrame

javax.swing.JFrame m_GroovyConsoleFrame

The frame containing the Groovy console.

m_JythonConsoleFrame

javax.swing.JFrame m_JythonConsoleFrame

The frame containing the Jython console.

m_ArffViewers

java.util.Vector<E> m_ArffViewers

keeps track of the opened ArffViewer instancs

m_SqlViewerFrame

javax.swing.JFrame m_SqlViewerFrame

The frame containing the SqlViewer

m_BayesNetGUIFrame

javax.swing.JFrame m_BayesNetGUIFrame

The frame containing the Bayes net GUI

m_EnsembleLibraryFrame

javax.swing.JFrame m_EnsembleLibraryFrame

The frame containing the ensemble library interface

m_PackageManagerFrame

javax.swing.JFrame m_PackageManagerFrame

The frame containing the package manager

m_Plots

java.util.Vector<E> m_Plots

keeps track of the opened plots

m_ROCs

java.util.Vector<E> m_ROCs

keeps track of the opened ROCs

m_TreeVisualizers

java.util.Vector<E> m_TreeVisualizers

keeps track of the opened tree visualizer instancs

m_GraphVisualizers

java.util.Vector<E> m_GraphVisualizers

keeps track of the opened graph visualizer instancs

m_BoundaryVisualizerFrame

javax.swing.JFrame m_BoundaryVisualizerFrame

The frame containing the boundary visualizer

m_SystemInfoFrame

javax.swing.JFrame m_SystemInfoFrame

The frame containing the system info

m_MemoryUsageFrame

javax.swing.JFrame m_MemoryUsageFrame

The frame containing the memory usage

m_weka

java.awt.Image m_weka

The weka image

m_FileChooserTreeVisualizer

javax.swing.JFileChooser m_FileChooserTreeVisualizer

filechooser for the TreeVisualizer

m_FileChooserGraphVisualizer

javax.swing.JFileChooser m_FileChooserGraphVisualizer

filechooser for the GraphVisualizer

m_FileChooserPlot

javax.swing.JFileChooser m_FileChooserPlot

filechooser for Plots

m_FileChooserROC

javax.swing.JFileChooser m_FileChooserROC

filechooser for ROC curves

m_Icon

java.awt.Image m_Icon

the icon for the frames

m_ChildFrames

java.util.HashSet<E> m_ChildFrames

contains the child frames (title <-> object).

Class weka.gui.GUIChooser.ChildFrameSDI extends javax.swing.JFrame implements Serializable

serialVersionUID: 8588293938686425618L

Serialized Fields

m_Parent

GUIChooser m_Parent

the parent frame.

Class weka.gui.HierarchyPropertyParser extends java.lang.Object implements Serializable

serialVersionUID: -4151103338506077544L

Serialized Fields

m_Root

weka.gui.HierarchyPropertyParser.TreeNode m_Root

Keep track of the root of the tree

m_Current

weka.gui.HierarchyPropertyParser.TreeNode m_Current

Keep track of the current node when traversing the tree

m_Seperator

java.lang.String m_Seperator

The level separate in the path

m_Depth

int m_Depth

The depth of the tree

Class weka.gui.InstancesSummaryPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -5243579535296681063L

Serialized Fields

m_RelationNameLab

javax.swing.JLabel m_RelationNameLab

Displays the name of the relation

m_NumInstancesLab

javax.swing.JLabel m_NumInstancesLab

Displays the number of instances

m_NumAttributesLab

javax.swing.JLabel m_NumAttributesLab

Displays the number of attributes

m_sumOfWeightsLab

javax.swing.JLabel m_sumOfWeightsLab

Displays the sum of instance weights

m_Instances

Instances m_Instances

The instances we're playing with

m_showZeroInstancesAsUnknown

boolean m_showZeroInstancesAsUnknown

Whether to display 0 or ? for the number of instances in cases where a dataset has only structure. Depending on where this panel is used from, the user may have loaded a dataset with no instances or a Loader that can read incrementally may be being used (in which case we don't know how many instances are in the dataset... yet).

Class weka.gui.ListSelectorDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: 906147926840288895L

Serialized Fields

m_SelectBut

javax.swing.JButton m_SelectBut

Click to choose the currently selected property

m_CancelBut

javax.swing.JButton m_CancelBut

Click to cancel the property selection

m_PatternBut

javax.swing.JButton m_PatternBut

Click to enter a regex pattern for selection

m_List

javax.swing.JList m_List

The list component

m_Result

int m_Result

Whether the selection was made or cancelled

m_PatternRegEx

java.lang.String m_PatternRegEx

The current regular expression.

Class weka.gui.LogPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -4072464549112439484L

Serialized Fields

m_StatusLab

javax.swing.JLabel m_StatusLab

Displays the current status

m_LogText

javax.swing.JTextArea m_LogText

Displays the log messages

m_logButton

javax.swing.JButton m_logButton

The button for viewing the log

m_First

boolean m_First

An indicator for whether text has been output yet

m_TaskMonitor

WekaTaskMonitor m_TaskMonitor

The panel for monitoring the number of running tasks (if supplied)

Class weka.gui.LogWindow extends javax.swing.JFrame implements Serializable

serialVersionUID: 5650947361381061112L

Serialized Fields

m_UseWordwrap

boolean m_UseWordwrap

whether the JTextPane has wordwrap or not

m_Output

javax.swing.JTextPane m_Output

the output

m_ButtonClear

javax.swing.JButton m_ButtonClear

the clear button

m_ButtonClose

javax.swing.JButton m_ButtonClose

the close button

m_LabelCurrentSize

javax.swing.JLabel m_LabelCurrentSize

the current size

m_SpinnerMaxSize

javax.swing.JSpinner m_SpinnerMaxSize

the spinner for the max number of chars

m_CheckBoxWordwrap

javax.swing.JCheckBox m_CheckBoxWordwrap

whether to allow wordwrap or not

Class weka.gui.Main extends javax.swing.JFrame implements Serializable

serialVersionUID: 1453813254824253849L

Serialized Fields

m_Self

Main m_Self

the frame itself.

m_GUIType

int m_GUIType

the type of GUI to display.

m_ChildFrames

java.util.HashSet<E> m_ChildFrames

contains the child frames (title <-> object).

m_FileChooserTreeVisualizer

javax.swing.JFileChooser m_FileChooserTreeVisualizer

filechooser for the TreeVisualizer.

m_FileChooserGraphVisualizer

javax.swing.JFileChooser m_FileChooserGraphVisualizer

filechooser for the GraphVisualizer.

m_FileChooserPlot

javax.swing.JFileChooser m_FileChooserPlot

filechooser for Plots.

m_FileChooserROC

javax.swing.JFileChooser m_FileChooserROC

filechooser for ROC curves.

jMenuHelp

javax.swing.JMenu jMenuHelp

jMenuVisualization

javax.swing.JMenu jMenuVisualization

jMenuTools

javax.swing.JMenu jMenuTools

jDesktopPane

javax.swing.JDesktopPane jDesktopPane

jMenuApplications

javax.swing.JMenu jMenuApplications

jMenuItemHelpSystemInfo

javax.swing.JMenuItem jMenuItemHelpSystemInfo

jMenuItemHelpAbout

javax.swing.JMenuItem jMenuItemHelpAbout

jMenuItemHelpHomepage

javax.swing.JMenuItem jMenuItemHelpHomepage

jMenuItemHelpWekaWiki

javax.swing.JMenuItem jMenuItemHelpWekaWiki

jMenuItemHelpWekaDoc

javax.swing.JMenuItem jMenuItemHelpWekaDoc

jMenuItemHelpSourceforge

javax.swing.JMenuItem jMenuItemHelpSourceforge

jMenuItemVisualizationBoundaryVisualizer

javax.swing.JMenuItem jMenuItemVisualizationBoundaryVisualizer

jMenuItemVisualizationGraphVisualizer

javax.swing.JMenuItem jMenuItemVisualizationGraphVisualizer

jMenuItemVisualizationTreeVisualizer

javax.swing.JMenuItem jMenuItemVisualizationTreeVisualizer

jMenuItemVisualizationROC

javax.swing.JMenuItem jMenuItemVisualizationROC

jMenuItemVisualizationPlot

javax.swing.JMenuItem jMenuItemVisualizationPlot

jMenuItemToolsEnsembleLibrary

javax.swing.JMenuItem jMenuItemToolsEnsembleLibrary

jMenuItemToolsSqlViewer

javax.swing.JMenuItem jMenuItemToolsSqlViewer

jMenuItemToolsGroovyConsole

javax.swing.JMenuItem jMenuItemToolsGroovyConsole

jMenuItemToolsJythonConsole

javax.swing.JMenuItem jMenuItemToolsJythonConsole

jMenuItemToolsArffViewer

javax.swing.JMenuItem jMenuItemToolsArffViewer

jMenuItemApplicationsSimpleCLI

javax.swing.JMenuItem jMenuItemApplicationsSimpleCLI

jMenuItemApplicationsKnowledgeFlow

javax.swing.JMenuItem jMenuItemApplicationsKnowledgeFlow

jMenuItemApplicationsExperimenter

javax.swing.JMenuItem jMenuItemApplicationsExperimenter

jMenuItemApplicationsExplorer

javax.swing.JMenuItem jMenuItemApplicationsExplorer

jMenuItemProgramExit

javax.swing.JMenuItem jMenuItemProgramExit

jMenuItemProgramLogWindow

javax.swing.JMenuItem jMenuItemProgramLogWindow

jMenuItemProgramMemoryUsage

javax.swing.JMenuItem jMenuItemProgramMemoryUsage

jMenuItemProgramPreferences

javax.swing.JMenuItem jMenuItemProgramPreferences

jMenuProgram

javax.swing.JMenu jMenuProgram

jMenuExtensions

javax.swing.JMenu jMenuExtensions

jMenuWindows

javax.swing.JMenu jMenuWindows

jMenuBar

javax.swing.JMenuBar jMenuBar

Class weka.gui.Main.BackgroundDesktopPane extends javax.swing.JDesktopPane implements Serializable

serialVersionUID: 2046713123452402745L

Serialized Fields

m_Background

java.awt.Image m_Background

the actual background image.

Class weka.gui.Main.ChildFrameMDI extends javax.swing.JInternalFrame implements Serializable

serialVersionUID: 3772573515346899959L

Serialized Fields

m_Parent

Main m_Parent

the parent frame.

Class weka.gui.Main.ChildFrameSDI extends javax.swing.JFrame implements Serializable

serialVersionUID: 8588293938686425618L

Serialized Fields

m_Parent

Main m_Parent

the parent frame.

Class weka.gui.MemoryUsagePanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -4812319791687471721L

Serialized Fields

m_History

java.util.Vector<E> m_History

the memory usage over time.

m_Memory

Memory m_Memory

for monitoring the memory usage.

m_Monitor

weka.gui.MemoryUsagePanel.MemoryMonitor m_Monitor

the thread for monitoring the memory usage.

m_ButtonGC

javax.swing.JButton m_ButtonGC

the button for running the garbage collector.

m_Percentages

java.util.Vector<E> m_Percentages

the threshold percentages to change color.

m_Colors

java.util.Hashtable<K,V> m_Colors

the corresponding colors for the thresholds.

m_DefaultColor

java.awt.Color m_DefaultColor

the default color.

m_BackgroundColor

java.awt.Color m_BackgroundColor

the background color.

m_FrameLocation

java.awt.Point m_FrameLocation

the position for the dialog.

Class weka.gui.PackageManager extends javax.swing.JPanel implements Serializable

serialVersionUID: -7463821313750352385L

Serialized Fields

m_table

javax.swing.JTable m_table

The JTable for displaying the package names and version numbers

m_splitP

javax.swing.JSplitPane m_splitP

m_infoPane

javax.swing.JEditorPane m_infoPane

An editor pane to display package information

m_installedBut

javax.swing.JRadioButton m_installedBut

Installed radio button

m_availableBut

javax.swing.JRadioButton m_availableBut

Available radio button

m_allBut

javax.swing.JRadioButton m_allBut

All radio button

m_installBut

javax.swing.JButton m_installBut

Button for installing the selected package

m_forceBut

javax.swing.JCheckBox m_forceBut

m_uninstallBut

javax.swing.JButton m_uninstallBut

Button for uninstalling the selected package

m_refreshCacheBut

javax.swing.JButton m_refreshCacheBut

m_progress

javax.swing.JProgressBar m_progress

m_detailLabel

javax.swing.JLabel m_detailLabel

m_backB

javax.swing.JButton m_backB

m_browserHistory

java.util.LinkedList<E> m_browserHistory

m_homeB

javax.swing.JButton m_homeB

m_browserTools

javax.swing.JToolBar m_browserTools

m_newPackagesAvailableL

javax.swing.JLabel m_newPackagesAvailableL

m_model

javax.swing.table.DefaultTableModel m_model

m_packageLookupInfo

java.util.Map<K,V> m_packageLookupInfo

m_allPackages

java.util.List<E> m_allPackages

m_installedPackages

java.util.List<E> m_installedPackages

m_availablePackages

java.util.List<E> m_availablePackages

m_sortColumn

int m_sortColumn

The column in the table to sort the entries by

m_reverseSort

boolean m_reverseSort

Reverse the sort order if the user clicks the same column header twice

m_unofficialBut

javax.swing.JButton m_unofficialBut

Button to pop up the file environment field widget

m_unofficialChooser

FileEnvironmentField m_unofficialChooser

Widget for specifying a URL or path to an unofficial package to install

m_unofficialFrame

javax.swing.JFrame m_unofficialFrame

m_packageComparator

java.util.Comparator<T> m_packageComparator

m_installing

boolean m_installing

m_cacheEstablished

boolean m_cacheEstablished

m_cacheRefreshInProgress

boolean m_cacheRefreshInProgress

Class weka.gui.PackageManager.ComboBoxEditor extends javax.swing.DefaultCellEditor implements Serializable

Class weka.gui.PropertyDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -2314850859392433539L

Serialized Fields

m_Editor

java.beans.PropertyEditor m_Editor

The property editor.

m_EditorComponent

java.awt.Component m_EditorComponent

The custom editor component.

Class weka.gui.PropertyPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 5370025273466728904L

Serialized Fields

m_Editor

java.beans.PropertyEditor m_Editor

The property editor

m_PD

PropertyDialog m_PD

The currently displayed property dialog, if any

m_HasCustomPanel

boolean m_HasCustomPanel

Whether the editor has provided its own panel

m_CustomPanel

javax.swing.JPanel m_CustomPanel

The custom panel (if any)

Class weka.gui.PropertySelectorDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -3155058124137930518L

Serialized Fields

m_SelectBut

javax.swing.JButton m_SelectBut

Click to choose the currently selected property

m_CancelBut

javax.swing.JButton m_CancelBut

Click to cancel the property selection

m_Root

javax.swing.tree.DefaultMutableTreeNode m_Root

The root of the property tree

m_RootObject

java.lang.Object m_RootObject

The object at the root of the tree

m_Result

int m_Result

Whether the selection was made or cancelled

m_ResultPath

java.lang.Object[] m_ResultPath

Stores the path to the selected property

m_Tree

javax.swing.JTree m_Tree

The component displaying the property tree

Class weka.gui.PropertySheetPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -8939835593429918345L

Serialized Fields

m_Target

java.lang.Object m_Target

The target object being edited.

m_Customizer

GOECustomizer m_Customizer

Holds the customizer (if one exists) for the object being edited

m_Properties

java.beans.PropertyDescriptor[] m_Properties

Holds properties of the target.

m_Methods

java.beans.MethodDescriptor[] m_Methods

Holds the methods of the target.

m_Editors

java.beans.PropertyEditor[] m_Editors

Holds property editors of the object.

m_Values

java.lang.Object[] m_Values

Holds current object values for each property.

m_Views

javax.swing.JComponent[] m_Views

Stores GUI components containing each editing component.

m_Labels

javax.swing.JLabel[] m_Labels

The labels for each property.

m_TipTexts

java.lang.String[] m_TipTexts

The tool tip text for each property.

m_HelpText

java.lang.StringBuffer m_HelpText

StringBuffer containing help text for the object being edited.

m_HelpDialog

javax.swing.JDialog m_HelpDialog

Help dialog.

m_CapabilitiesDialog

weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_CapabilitiesDialog

Capabilities Help dialog.

m_HelpBut

javax.swing.JButton m_HelpBut

Button to pop up the full help text in a separate dialog.

m_CapabilitiesBut

javax.swing.JButton m_CapabilitiesBut

Button to pop up the capabilities in a separate dialog.

m_CapabilitiesText

javax.swing.JTextArea m_CapabilitiesText

the TextArea of the Capabilities help dialog.

m_NumEditable

int m_NumEditable

A count of the number of properties we have an editor for.

m_aboutPanel

javax.swing.JPanel m_aboutPanel

The panel holding global info and help, if provided by the object being editied.

support

java.beans.PropertyChangeSupport support

A support object for handling property change listeners.

Class weka.gui.PropertySheetPanel.CapabilitiesHelpDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: -1404770987103289858L

Serialized Fields

m_Self

weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_Self

the dialog itself.

Class weka.gui.ResultHistoryPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: 4297069440135326829L

Serialized Fields

m_SingleText

javax.swing.text.JTextComponent m_SingleText

An optional component for single-click display

m_SingleName

java.lang.String m_SingleName

The named result being viewed in the single-click display

m_Model

javax.swing.DefaultListModel m_Model

The list model

m_List

javax.swing.JList m_List

The list component

m_Results

java.util.Hashtable<K,V> m_Results

A Hashtable mapping names to result buffers

m_FramedOutput

java.util.Hashtable<K,V> m_FramedOutput

A Hashtable mapping names to output text components

m_Objs

java.util.Hashtable<K,V> m_Objs

A hashtable mapping names to arbitrary objects

m_HandleRightClicks

boolean m_HandleRightClicks

Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer

m_Printer

PrintableComponent m_Printer

for printing the output to files

Class weka.gui.ResultHistoryPanel.RKeyAdapter extends java.awt.event.KeyAdapter implements Serializable

serialVersionUID: -8675332541861828079L

Class weka.gui.ResultHistoryPanel.RMouseAdapter extends java.awt.event.MouseAdapter implements Serializable

serialVersionUID: -8991922650552358669L

Class weka.gui.SetInstancesPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -384804041420453735L

Serialized Fields

m_OpenFileBut

javax.swing.JButton m_OpenFileBut

Click to open instances from a file.

m_OpenURLBut

javax.swing.JButton m_OpenURLBut

Click to open instances from a URL.

m_CloseBut

javax.swing.JButton m_CloseBut

Click to close the dialog.

m_Summary

InstancesSummaryPanel m_Summary

The instance summary component.

m_ClassLabel

javax.swing.JLabel m_ClassLabel

the label for the class combobox.

m_ClassComboBox

javax.swing.JComboBox m_ClassComboBox

the class combobox.

m_FileChooser

ConverterFileChooser m_FileChooser

The file chooser for selecting arff files.

m_LastURL

java.lang.String m_LastURL

Stores the last URL that instances were loaded from.

m_IOThread

java.lang.Thread m_IOThread

The thread we do loading in.

m_Support

java.beans.PropertyChangeSupport m_Support

Manages sending notifications to people when we change the set of working instances.

m_Instances

Instances m_Instances

The current set of instances loaded.

m_Loader

Loader m_Loader

The current loader used to obtain the current instances.

m_ParentFrame

javax.swing.JFrame m_ParentFrame

the parent frame. if one is provided, the close-button is displayed

m_CloseButPanel

javax.swing.JPanel m_CloseButPanel

the panel the Close-Button is located in.

m_readIncrementally

boolean m_readIncrementally

whether to read the instances incrementally, if possible.

m_showZeroInstancesAsUnknown

boolean m_showZeroInstancesAsUnknown

whether to display zero instances as unknown ("?").

m_showClassComboBox

boolean m_showClassComboBox

whether to display a combobox that allows the user to choose the class attribute.

Class weka.gui.SimpleCLI extends javax.swing.JFrame implements Serializable

serialVersionUID: -50661410800566036L

Class weka.gui.SimpleCLIPanel extends ScriptingPanel implements Serializable

serialVersionUID: 1089039734615114942L

Serialized Fields

m_OutputArea

javax.swing.JTextPane m_OutputArea

The output area canvas added to the frame.

m_Input

javax.swing.JTextField m_Input

The command input area.

m_CommandHistory

java.util.Vector<E> m_CommandHistory

The history of commands entered interactively.

m_HistoryPos

int m_HistoryPos

The current position in the command history.

m_RunThread

java.lang.Thread m_RunThread

The thread currently running a class main method.

m_Completion

SimpleCLIPanel.CommandlineCompletion m_Completion

The commandline completion.

Class weka.gui.SortedTableModel extends javax.swing.table.AbstractTableModel implements Serializable

serialVersionUID: 4030907921461127548L

Serialized Fields

mModel

javax.swing.table.TableModel mModel

the actual table model

mIndices

int[] mIndices

the mapping between displayed and actual index

mSortColumn

int mSortColumn

the sort column

mAscending

boolean mAscending

whether sorting is ascending or descending

Class weka.gui.SplashWindow extends java.awt.Window implements Serializable

serialVersionUID: -2685134277041307795L

Serialized Fields

image

java.awt.Image image

The splash image which is displayed on the splash window.

paintCalled

boolean paintCalled

This attribute indicates whether the method paint(Graphics) has been called at least once since the construction of this window.
This attribute is used to notify method splash(Image) that the window has been drawn at least once by the AWT event dispatcher thread.
This attribute acts like a latch. Once set to true, it will never be changed back to false again.

See Also:

SplashWindow.paint(java.awt.Graphics), SplashWindow.splash(java.awt.Image)

Class weka.gui.ViewerDialog extends javax.swing.JDialog implements Serializable

serialVersionUID: 6747718484736047752L

Serialized Fields

m_Result

int m_Result

the result of the user's action, either OK or CANCEL

m_OkButton

javax.swing.JButton m_OkButton

Click to activate the current set parameters

m_CancelButton

javax.swing.JButton m_CancelButton

Click to cancel the dialog

m_UndoButton

javax.swing.JButton m_UndoButton

Click to undo the last action

m_ArffPanel

ArffPanel m_ArffPanel

the panel to display the Instances-object

Class weka.gui.WekaTaskMonitor extends javax.swing.JPanel implements Serializable

serialVersionUID: 508309816292197578L

Serialized Fields

m_ActiveTasks

int m_ActiveTasks

The number of running weka threads

m_MonitorLabel

javax.swing.JLabel m_MonitorLabel

The label for displaying info

m_iconStationary

javax.swing.ImageIcon m_iconStationary

The icon for the stationary bird

m_iconAnimated

javax.swing.ImageIcon m_iconAnimated

The icon for the animated bird

m_animating

boolean m_animating

True if their are active tasks

Package weka.gui.arffviewer

Class weka.gui.arffviewer.ArffPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -4697041150989513939L

Serialized Fields

m_TableArff

ArffTable m_TableArff

the underlying table

m_PopupHeader

javax.swing.JPopupMenu m_PopupHeader

the popup menu for the header row

m_PopupRows

javax.swing.JPopupMenu m_PopupRows

the popup menu for the data rows

m_LabelName

javax.swing.JLabel m_LabelName

displays the relation name

m_ShowAttributeIndex

boolean m_ShowAttributeIndex

whether to display the attribute index in the table header.

menuItemMean

javax.swing.JMenuItem menuItemMean

menuItemSetAllValues

javax.swing.JMenuItem menuItemSetAllValues

menuItemSetMissingValues

javax.swing.JMenuItem menuItemSetMissingValues

menuItemReplaceValues

javax.swing.JMenuItem menuItemReplaceValues

menuItemRenameAttribute

javax.swing.JMenuItem menuItemRenameAttribute

menuItemAttributeAsClass

javax.swing.JMenuItem menuItemAttributeAsClass

menuItemDeleteAttribute

javax.swing.JMenuItem menuItemDeleteAttribute

menuItemDeleteAttributes

javax.swing.JMenuItem menuItemDeleteAttributes

menuItemSortInstances

javax.swing.JMenuItem menuItemSortInstances

menuItemDeleteSelectedInstance

javax.swing.JMenuItem menuItemDeleteSelectedInstance

menuItemDeleteAllSelectedInstances

javax.swing.JMenuItem menuItemDeleteAllSelectedInstances

menuItemSearch

javax.swing.JMenuItem menuItemSearch

menuItemClearSearch

javax.swing.JMenuItem menuItemClearSearch

menuItemUndo

javax.swing.JMenuItem menuItemUndo

menuItemCopy

javax.swing.JMenuItem menuItemCopy

menuItemOptimalColWidth

javax.swing.JMenuItem menuItemOptimalColWidth

menuItemOptimalColWidths

javax.swing.JMenuItem menuItemOptimalColWidths

m_Filename

java.lang.String m_Filename

the filename used in the title

m_Title

java.lang.String m_Title

the title prefix

m_CurrentCol

int m_CurrentCol

the currently selected column

m_Changed

boolean m_Changed

flag for whether data got changed

m_ChangeListeners

java.util.HashSet<E> m_ChangeListeners

the listeners that listen for modifications

m_LastSearch

java.lang.String m_LastSearch

the string used in the last search

m_LastReplace

java.lang.String m_LastReplace

the string used in the last replace

Class weka.gui.arffviewer.ArffSortedTableModel extends SortedTableModel implements Serializable

serialVersionUID: -5733148376354254030L

Class weka.gui.arffviewer.ArffTable extends javax.swing.JTable implements Serializable

serialVersionUID: -2016200506908637967L

Serialized Fields

m_SearchString

java.lang.String m_SearchString

the search string

m_ChangeListeners

java.util.HashSet<E> m_ChangeListeners

the listeners for changes

Class weka.gui.arffviewer.ArffTable.RelationalCellEditor extends javax.swing.AbstractCellEditor implements Serializable

serialVersionUID: 657969163293205963L

Serialized Fields

m_Button

javax.swing.JButton m_Button

the button for opening the dialog

m_CurrentInst

Instances m_CurrentInst

the current instances

m_RowIndex

int m_RowIndex

the row index this editor is for

m_ColumnIndex

int m_ColumnIndex

the column index this editor is for

Class weka.gui.arffviewer.ArffTableCellRenderer extends javax.swing.table.DefaultTableCellRenderer implements Serializable

serialVersionUID: 9195794493301191171L

Serialized Fields

missingColor

java.awt.Color missingColor

the color for missing values

missingColorSelected

java.awt.Color missingColorSelected

the color for selected missing values

highlightColor

java.awt.Color highlightColor

the color for highlighted values

highlightColorSelected

java.awt.Color highlightColorSelected

the color for selected highlighted values

Class weka.gui.arffviewer.ArffTableModel extends javax.swing.table.DefaultTableModel implements Serializable

serialVersionUID: 3411795562305994946L

Serialized Fields

m_Listeners

java.util.HashSet<E> m_Listeners

the listeners

m_Data

Instances m_Data

the data

m_NotificationEnabled

boolean m_NotificationEnabled

whether notfication is enabled

m_UndoEnabled

boolean m_UndoEnabled

whether undo is active

m_IgnoreChanges

boolean m_IgnoreChanges

whether to ignore changes, i.e. not adding to undo history

m_UndoList

java.util.Vector<E> m_UndoList

the undo list (contains temp. filenames)

m_ReadOnly

boolean m_ReadOnly

whether the table is read-only

m_ShowAttributeIndex

boolean m_ShowAttributeIndex

whether to display the attribute index in the table header.

m_Cache

java.util.Hashtable<K,V> m_Cache

for caching long relational and string values that get processed for display.

Class weka.gui.arffviewer.ArffViewer extends javax.swing.JFrame implements Serializable

serialVersionUID: -7455845566922685175L

Serialized Fields

m_MainPanel

ArffViewerMainPanel m_MainPanel

the main panel

Class weka.gui.arffviewer.ArffViewerMainPanel extends javax.swing.JPanel implements Serializable

serialVersionUID: -8763161167586738753L

Serialized Fields

parent

java.awt.Container parent

tabbedPane

javax.swing.JTabbedPane tabbedPane

menuBar

javax.swing.JMenuBar menuBar

menuFile

javax.swing.JMenu menuFile

menuFileOpen

javax.swing.JMenuItem menuFileOpen

menuFileSave

javax.swing.JMenuItem menuFileSave

menuFileSaveAs

javax.swing.JMenuItem menuFileSaveAs

menuFileClose

javax.swing.JMenuItem menuFileClose

menuFileCloseAll

javax.swing.JMenuItem menuFileCloseAll

menuFileProperties

javax.swing.JMenuItem menuFileProperties

menuFileExit

javax.swing.JMenuItem menuFileExit

menuEdit

javax.swing.JMenu menuEdit

menuEditUndo

javax.swing.JMenuItem menuEditUndo

menuEditCopy

javax.swing.JMenuItem menuEditCopy

menuEditSearch

javax.swing.JMenuItem menuEditSearch

menuEditClearSearch

javax.swing.JMenuItem menuEditClearSearch

menuEditDeleteAttribute

javax.swing.JMenuItem menuEditDeleteAttribute

menuEditDeleteAttributes

javax.swing.JMenuItem menuEditDeleteAttributes

menuEditRenameAttribute

javax.swing.JMenuItem menuEditRenameAttribute

menuEditAttributeAsClass

javax.swing.JMenuItem menuEditAttributeAsClass

menuEditDeleteInstance

javax.swing.JMenuItem menuEditDeleteInstance

menuEditDeleteInstances

javax.swing.JMenuItem menuEditDeleteInstances

menuEditSortInstances

javax.swing.JMenuItem menuEditSortInstances

menuView

javax.swing.JMenu menuView

menuViewAttributes

javax.swing.JMenuItem menuViewAttributes

menuViewValues

javax.swing.JMenuItem menuViewValues

menuViewOptimalColWidths

javax.swing.JMenuItem menuViewOptimalColWidths

fileChooser

ConverterFileChooser fileChooser

frameTitle

java.lang.String frameTitle

confirmExit

boolean confirmExit

width

int width

height

int height

top

int top

left

int left

exitOnClose

boolean exitOnClose

Package weka.gui.beans

Class weka.gui.beans.AbstractDataSink extends javax.swing.JPanel implements Serializable

serialVersionUID: 3956528599473814287L

Serialized Fields

m_visual

BeanVisual m_visual

Default visual for data sources

m_listenee

java.lang.Object m_listenee

Non null if this object is a target for any events. Provides for the simplest case when only one incomming connection is allowed. Subclasses can overide the appropriate BeanCommon methods to change this behaviour and allow multiple connections if desired

Class weka.gui.beans.AbstractDataSource extends javax.swing.JPanel implements Serializable

serialVersionUID: -4127257701890044793L

Serialized Fields

m_design

boolean m_design

True if this bean's appearance is the design mode appearance

m_bcSupport

java.beans.beancontext.BeanContextChildSupport m_bcSupport

BeanContextChild support

m_visual

BeanVisual m_visual

Default visual for data sources

m_listeners

java.util.Vector<E> m_listeners

Objects listening for events from data sources

Class weka.gui.beans.AbstractEvaluator extends javax.swing.JPanel implements Serializable

serialVersionUID: 3983303541814121632L

Serialized Fields

m_visual

BeanVisual m_visual

Default visual for evaluators

m_listenee

java.lang.Object m_listenee

Class weka.gui.beans.AbstractTestSetProducer extends javax.swing.JPanel implements Serializable

serialVersionUID: -7905764845789349839L

Serialized Fields

m_listeners

java.util.Vector<E> m_listeners

Objects listening to us

m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee

non null if this object is a target for any events.

Class weka.gui.beans.AbstractTrainAndTestSetProducer extends javax.swing.JPanel implements Serializable

serialVersionUID: -1809339823613492037L

Serialized Fields

m_trainingListeners

java.util.Vector<E> m_trainingListeners

Objects listening for trainin set events

m_testListeners

java.util.Vector<E> m_testListeners

Objects listening for test set events

m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee

non null if this object is a target for any events.

Class weka.gui.beans.AbstractTrainingSetProducer extends javax.swing.JPanel implements Serializable

serialVersionUID: -7842746199524591125L

Serialized Fields

m_listeners

java.util.Vector<E> m_listeners

Objects listening for training set events

m_visual

BeanVisual m_visual

m_listenee

java.lang.Object m_listenee

non null if this object is a target for any events.

Class weka.gui.beans.Appender extends javax.swing.JPanel implements Serializable

serialVersionUID: 9177433051794199463L

Serialized Fields

m_listeneeTypes

java.util.Set<E> m_listeneeTypes

Upstream components sending us data

m_listenees

java.util.Map<K,V> m_listenees

m_ie

InstanceEvent m_ie

Instance event to use for incremental mode

m_finishedCount

int m_finishedCount

Keeps track of how many incoming instance streams have finished

m_busy

boolean m_busy

True if we are busy

m_visual

BeanVisual m_visual

Default visual for data sources

m_dataListeners

java.util.ArrayList<E> m_dataListeners

Downstream steps listening to batch data events

m_instanceListeners

java.util.ArrayList<E> m_instanceListeners

Downstream steps listening to instance events

Class weka.gui.beans.Associator extends javax.swing.JPanel implements Serializable

serialVersionUID: -7843500322130210057L

Serialized Fields

m_visual

BeanVisual m_visual

m_state

int m_state

m_buildThread

java.lang.Thread m_buildThread

m_globalInfo

java.lang.String m_globalInfo

Global info for the wrapped associator (if it exists).

m_listenees

java.util.Hashtable<K,V> m_listenees

Objects talking to us

m_textListeners

java.util.Vector<E> m_textListeners

Objects listening for text events

m_graphListeners

java.util.Vector<E> m_graphListeners

Objects listening for graph events

m_rulesListeners

java.util.Vector<E> m_rulesListeners

The objects listening for batchAssociationRules events

m_Associator

Associator m_Associator

Class weka.gui.beans.AssociatorCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 5767664969353495974L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsAssociator

Associator m_dsAssociator

m_AssociatorEditor

PropertySheetPanel m_AssociatorEditor

m_parentWindow

java.awt.Window m_parentWindow

m_backup

Associator m_backup

Backup is user presses cancel

m_modifyListener

BeanCustomizer.ModifyListener m_modifyListener

Class weka.gui.beans.AttributeSummarizer extends DataVisualizer implements Serializable

serialVersionUID: -294354961169372758L

Serialized Fields

m_gridWidth

int m_gridWidth

The number of plots horizontally in the display

m_maxPlots

int m_maxPlots

The maximum number of plots to show

m_coloringIndex

int m_coloringIndex

Index on which to color the plots.

m_showClassCombo

boolean m_showClassCombo

m_runningAsPerspective

boolean m_runningAsPerspective

m_activePerspective

boolean m_activePerspective

Class weka.gui.beans.AttributeSummarizerCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: -6973666187676272788L

Serialized Fields

m_dataVis

DataVisualizer m_dataVis

m_env

Environment m_env

m_modifyListener

BeanCustomizer.ModifyListener m_modifyListener

m_parent

java.awt.Window m_parent

m_rendererNameBack

java.lang.String m_rendererNameBack

m_xAxisBack

java.lang.String m_xAxisBack

m_widthBack

java.lang.String m_widthBack

m_heightBack

java.lang.String m_heightBack

m_optsBack

java.lang.String m_optsBack

m_rendererCombo

javax.swing.JComboBox m_rendererCombo

m_xAxis

EnvironmentField m_xAxis

m_width

EnvironmentField m_width

m_height

EnvironmentField m_height

m_opts

EnvironmentField m_opts

Class weka.gui.beans.BatchAssociationRulesEvent extends java.util.EventObject implements Serializable

serialVersionUID: 6332614648885439492L

Serialized Fields

m_rules

AssociationRules m_rules

The encapsulated rules

Class weka.gui.beans.BatchClassifierEvent extends java.util.EventObject implements Serializable

serialVersionUID: 878097199815991084L

Serialized Fields

m_classifier

Classifier m_classifier

The classifier

m_testSet

DataSetEvent m_testSet

Instances that can be used for testing the classifier

m_trainSet

DataSetEvent m_trainSet

Instances that were used to train the classifier (may be null if not available)

m_runNumber

int m_runNumber

The run number that this classifier was generated for

m_maxRunNumber

int m_maxRunNumber

The maximum number of runs

m_setNumber

int m_setNumber

The set number for the test set

m_maxSetNumber

int m_maxSetNumber

The last set number for this series

m_groupIdentifier

long m_groupIdentifier

An identifier that can be used to group all related runs/sets together.

m_eventLabel

java.lang.String m_eventLabel

Label for this event

Class weka.gui.beans.BatchClustererEvent extends java.util.EventObject implements Serializable

serialVersionUID: 7268777944939129714L

Serialized Fields

m_clusterer

Clusterer m_clusterer

The clusterer

m_testSet

DataSetEvent m_testSet

Training or Test Instances

m_setNumber

int m_setNumber

The set number for the test set

m_testOrTrain

int m_testOrTrain

Indicates if m_testSet is a training or a test set. 0 for test, >0 for training

m_maxSetNumber

int m_maxSetNumber

The last set number for this series

Class weka.gui.beans.BeanConnection extends java.lang.Object implements Serializable

serialVersionUID: 8804264241791332064L

Serialized Fields

m_source

BeanInstance m_source

m_target

BeanInstance m_target

m_eventName

java.lang.String m_eventName

The name of the event for this connection

m_hidden

boolean m_hidden

Class weka.gui.beans.BeanInstance extends java.lang.Object implements Serializable

serialVersionUID: -7575653109025406342L

Serialized Fields

m_bean

java.lang.Object m_bean

Holds the bean encapsulated in this instance

m_x

int m_x

m_y

int m_y

Class weka.gui.beans.BeanVisual extends javax.swing.JPanel implements Serializable

serialVersionUID: -6677473561687129614L

Serialization Methods

readObject

private void readObject(java.io.ObjectInputStream ois)
                 throws java.io.IOException,
                        java.lang.ClassNotFoundException

Overides default read object in order to reload icons. This is necessary because for some strange reason animated gifs stop being animated after being serialized/deserialized.

Throws:

java.io.IOException - if an error occurs

java.lang.ClassNotFoundException - if an error occurs

Serialized Fields

m_iconPath

java.lang.String m_iconPath

Holds name (including path) of the static icon

m_animatedIconPath

java.lang.String m_animatedIconPath

Holds name (including path) of the animated icon

m_visualName

java.lang.String m_visualName

Name for the bean

m_visualLabel

javax.swing.JLabel m_visualLabel

m_stationary

boolean m_stationary

Container for the icon

m_pcs

java.beans.PropertyChangeSupport m_pcs

m_displayConnectors

boolean m_displayConnectors

m_connectorColor

java.awt.Color m_connectorColor

Class weka.gui.beans.ChartEvent extends java.util.EventObject implements Serializable

serialVersionUID: 7812460715499569390L

Serialized Fields

m_legendText

java.util.Vector<E> m_legendText

m_max

double m_max

m_min

double m_min

m_reset

boolean m_reset

m_dataPoint

double[] m_dataPoint

Y values of the data points

Class weka.gui.beans.ClassAssigner extends javax.swing.JPanel implements Serializable

serialVersionUID: 4011131665025817924L

Serialized Fields

m_classColumn

java.lang.String m_classColumn

m_connectedFormat

Instances m_connectedFormat

format of instances for current incoming connection (if any)

m_trainingProvider

java.lang.Object m_trainingProvider

m_testProvider

java.lang.Object m_testProvider

m_dataProvider

java.lang.Object m_dataProvider

m_instanceProvider

java.lang.Object m_instanceProvider

m_trainingListeners

java.util.Vector<E> m_trainingListeners

m_testListeners

java.util.Vector<E> m_testListeners

m_dataListeners

java.util.Vector<E> m_dataListeners

m_instanceListeners

java.util.Vector<E> m_instanceListeners

m_dataFormatListeners

java.util.Vector<E> m_dataFormatListeners

m_visual

BeanVisual m_visual

Class weka.gui.beans.ClassAssignerCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 476539385765301907L

Serialized Fields

m_displayColNames

boolean m_displayColNames

Class weka.gui.beans.Classifier extends javax.swing.JPanel implements Serializable

serialVersionUID: 659603893917736008L

Serialized Fields

m_visual

BeanVisual m_visual

m_state

int m_state

m_globalInfo

java.lang.String m_globalInfo

Global info for the wrapped classifier (if it exists).

m_listenees

java.util.Hashtable<K,V> m_listenees

Objects talking to us

m_batchClassifierListeners

java.util.Vector<E> m_batchClassifierListeners

Objects listening for batch classifier events

m_incrementalClassifierListeners

java.util.Vector<E> m_incrementalClassifierListeners

Objects listening for incremental classifier events

m_graphListeners

java.util.Vector<E> m_graphListeners

Objects listening for graph events

m_textListeners

java.util.Vector<E> m_textListeners

Objects listening for text events

m_trainingSet

Instances m_trainingSet

Holds training instances for batch training. Not transient because header is retained for validating any instance events that this classifier might be asked to predict in the future.

m_Classifier

Classifier m_Classifier

m_ClassifierTemplate

Classifier m_ClassifierTemplate

Template used for creating copies when building in parallel

m_ie

IncrementalClassifierEvent m_ie

m_binaryFilter

javax.swing.filechooser.FileFilter m_binaryFilter

m_KOMLFilter

javax.swing.filechooser.FileFilter m_KOMLFilter

m_XStreamFilter

javax.swing.filechooser.FileFilter m_XStreamFilter

m_resetIncrementalClassifier

boolean m_resetIncrementalClassifier

If the classifier is an incremental classifier, should we reset it (i.e. call buildClassifier()) and discard any previously learned model before processing the first instance in the stream. Note that this happens automatically if the incoming instance structure does not match that (if any) that the classifier was trained with previously.

m_updateIncrementalClassifier

boolean m_updateIncrementalClassifier

If the classifier is an incremental classifier, should we update it (ie train it on incoming instances). This makes it possible incrementally test on a separate stream of instances without updating the classifier, or mix batch training/testing with incremental training/testing

m_incrementalEvent

InstanceEvent m_incrementalEvent

Event to handle when processing incremental updates

m_executionSlots

int m_executionSlots

Number of threads to use to train models with

m_oldText

java.lang.String m_oldText

Holds original icon label text

m_reject

boolean m_reject

true if we should reject any further training data sets, until all processing has been finished, once we've received the last fold of the last run.

m_block

boolean m_block

True if we should block rather reject until all processing has been completed.

m_loadModelFileName

java.lang.String m_loadModelFileName

Optional file to load a pre-trained model to score with (batch, or to score and update (incremental) in the case of testSet only (batch) or instance (incremental) connections

Class weka.gui.beans.Classifier.TrainingTask extends java.lang.Object implements Serializable

Serialized Fields

m_runNum

int m_runNum

m_maxRunNum

int m_maxRunNum

m_setNum

int m_setNum

m_maxSetNum

int m_maxSetNum

m_train

Instances m_train

m_taskInfo

TaskStatusInfo m_taskInfo

Class weka.gui.beans.ClassifierCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: -6688000820160821429L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_dsClassifier

Classifier m_dsClassifier

m_ClassifierEditor

PropertySheetPanel m_ClassifierEditor

m_incrementalPanel

javax.swing.JPanel m_incrementalPanel

m_resetIncrementalClassifier

javax.swing.JCheckBox m_resetIncrementalClassifier

m_updateIncrementalClassifier

javax.swing.JCheckBox m_updateIncrementalClassifier

m_panelVisible

boolean m_panelVisible

m_holderPanel

javax.swing.JPanel m_holderPanel

m_executionSlotsText

javax.swing.JTextField m_executionSlotsText

m_executionSlotsLabel

javax.swing.JLabel m_executionSlotsLabel

m_executionSlotsPanel

javax.swing.JPanel m_executionSlotsPanel

m_blockOnLastFold

javax.swing.JCheckBox m_blockOnLastFold

m_loadModelField

FileEnvironmentField m_loadModelField

m_parentWindow

java.awt.Window m_parentWindow

m_backup

Classifier m_backup

Copy of the current classifier in case cancel is selected

m_env

Environment m_env

m_modifyListener

BeanCustomizer.ModifyListener m_modifyListener

Listener that wants to know the the modified status of the object that we're customizing

Class weka.gui.beans.ClassifierPerformanceEvaluator extends AbstractEvaluator implements Serializable

serialVersionUID: -3511801418192148690L

Serialized Fields

m_textListeners

java.util.Vector<E> m_textListeners

m_thresholdListeners

java.util.Vector<E> m_thresholdListeners

m_visualizableErrorListeners

java.util.Vector<E> m_visualizableErrorListeners

m_errorPlotPointSizeProportionalToMargin

boolean m_errorPlotPointSizeProportionalToMargin

m_executionSlots

int m_executionSlots

Number of threads to use to train models with

m_selectedEvalMetrics

java.lang.String m_selectedEvalMetrics

Evaluation metrics to output

m_metricsList

java.util.List<E> m_metricsList

Class weka.gui.beans.ClassifierPerformanceEvaluator.AggregateableClassifierErrorsPlotInstances extends ClassifierErrorsPlotInstances implements Serializable

serialVersionUID: 2012744784036684168L

Class weka.gui.beans.ClassifierPerformanceEvaluator.EvaluationTask extends java.lang.Object implements Serializable

serialVersionUID: -8939077467030259059L

Serialized Fields

m_testData

Instances m_testData

m_trainData

Instances m_trainData

m_setNum

int m_setNum

m_maxSetNum

int m_maxSetNum

m_classifier

Classifier m_classifier

m_stopped

boolean m_stopped

m_evalLabel

java.lang.String m_evalLabel

Class weka.gui.beans.ClassifierPerformanceEvaluatorCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: -1055460295546483853L

Serialized Fields

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_splitEditor

PropertySheetPanel m_splitEditor

m_cpe

ClassifierPerformanceEvaluator m_cpe

m_modifyListener

BeanCustomizer.ModifyListener m_modifyListener

m_executionSlotsBackup

int m_executionSlotsBackup

m_parent

java.awt.Window m_parent

m_evalMetricsBut

javax.swing.JButton m_evalMetricsBut

m_evaluationMetrics

java.util.List<E> m_evaluationMetrics

Class weka.gui.beans.ClassValuePicker extends javax.swing.JPanel implements Serializable

serialVersionUID: -1196143276710882989L

Serialized Fields

m_classValue

java.lang.String m_classValue

the class value considered to be the positive class

m_connectedFormat

Instances m_connectedFormat

format of instances for the current incoming connection (if any)

m_dataProvider

java.lang.Object m_dataProvider

m_dataListeners

java.util.Vector<E> m_dataListeners

m_dataFormatListeners

java.util.Vector<E> m_dataFormatListeners

m_visual

BeanVisual m_visual

Class weka.gui.beans.ClassValuePickerCustomizer extends javax.swing.JPanel implements Serializable

serialVersionUID: 8213423053861600469L

Serialized Fields

m_displayValNames

boolean m_displayValNames

m_classValuePicker

ClassValuePicker m_classValuePicker

m_pcSupport

java.beans.PropertyChangeSupport m_pcSupport

m_ClassValueCombo

javax.swing.JComboBox m_ClassValueCombo

m_holderP

javax.swing.JPanel m_holderP

m_messageLabel

javax.swing.JLabel m_messageLabel

m_modifyListener

BeanCustomizer.ModifyListener m_modifyListener

m_modified

boolean m_modified

m_parent

java.awt.Window m_parent

m_backup

java.lang.String m_backup

m_textBoxEntryMode

boolean m_textBoxEntryMode

m_valueTextBox

javax.swing.JTextField m_valueTextBox

Class weka.gui.beans.Clusterer extends javax.swing.JPanel implements Serializable

serialVersionUID: 7729795159836843810L

Serialized Fields

m_visual

BeanVisual m_visual

m_state

int m_state

m_buildThread

java.lang.Thread m_buildThread

m_globalInfo

java.lang.String m_globalInfo

Global info for the wrapped classifier (if it exists).

m_listenees

java.util.Hashtable<K,V> m_listenees

Objects talking to us

m_batchClustererListeners

java.util.Vector<E> m_batchClustererListeners

Objects listening for batch clusterer events