Serialized Form
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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_allTheRules
FastVector[] m_allTheRules
The list of all generated rules. -
m_car
boolean m_car
Flag indicating whether class association rules are mined. -
m_classIndex
int m_classIndex
The class index. -
m_cycles
int m_cycles
Number of cycles used before required number of rules was one. -
m_delta
double m_delta
Delta by which m_minSupport is decreased in each iteration. -
m_hashtables
FastVector m_hashtables
The same information stored in hash tables. -
m_instances
Instances m_instances
The instances (transactions) to be used for generating the association rules. -
m_lowerBoundMinSupport
double m_lowerBoundMinSupport
The lower bound for the minimum support. -
m_Ls
FastVector m_Ls
The set of all sets of itemsets L. -
m_metricType
int m_metricType
The selected metric type. -
m_minMetric
double m_minMetric
The minimum metric score. -
m_minSupport
double m_minSupport
The minimum support. -
m_numRules
int m_numRules
The maximum number of rules that are output. -
m_onlyClass
Instances m_onlyClass
Only the class attribute of all Instances. -
m_outputItemSets
boolean m_outputItemSets
Output itemsets found? -
m_removeMissingCols
boolean m_removeMissingCols
Remove columns with all missing values -
m_significanceLevel
double m_significanceLevel
Significance level for optional significance test. -
m_upperBoundMinSupport
double m_upperBoundMinSupport
The upper bound on the support -
m_verbose
boolean m_verbose
Report progress iteratively
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-
Class weka.associations.AprioriItemSet extends ItemSet implements Serializable
- serialVersionUID:
- 7684467755712672058L
-
Class weka.associations.CaRuleGeneration extends RuleGeneration implements Serializable
- serialVersionUID:
- 3065752149646517703L
-
Class weka.associations.FilteredAssociator extends SingleAssociatorEnhancer implements Serializable
- serialVersionUID:
- -4523450618538717400L
-
Class weka.associations.FPGrowth extends AbstractAssociator implements Serializable
- serialVersionUID:
- 3620717108603442911L
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Serialized Fields
-
m_delta
double m_delta
The amount by which to decrease the support in each iteration -
m_findAllRulesForSupportLevel
boolean m_findAllRulesForSupportLevel
If true, just all rules meeting the lower bound on the minimum support will be found. The number of rules to find will be ignored and the iterative reduction of support will not be done. -
m_largeItemSets
weka.associations.FPGrowth.FrequentItemSets m_largeItemSets
Holds the large item sets found -
m_lowerBoundMinSupport
double m_lowerBoundMinSupport
The lower bound on minimum support -
m_maxItems
int m_maxItems
-
m_metric
FPGrowth.AssociationRule.METRIC_TYPE m_metric
-
m_metricThreshold
double m_metricThreshold
-
m_mustContainOR
boolean m_mustContainOR
Use OR rather than AND when considering must contain lists -
m_numRulesToFind
int m_numRulesToFind
The number of rules to find -
m_positiveIndex
int m_positiveIndex
The index (1 based) of binary attributes to treat as the positive value -
m_rules
java.util.List<FPGrowth.AssociationRule> m_rules
Holds the rules -
m_rulesMustContain
java.lang.String m_rulesMustContain
If set, then only output rules containing these itmes -
m_transactionsMustContain
java.lang.String m_transactionsMustContain
If set, limit the transactions (instances) input to the algorithm to those that contain these items -
m_upperBoundMinSupport
double m_upperBoundMinSupport
The upper bound on the minimum support
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Class weka.associations.FPGrowth.AssociationRule extends java.lang.Object implements Serializable
- serialVersionUID:
- -661269018702294489L
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Serialized Fields
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m_consequence
java.util.Collection<FPGrowth.BinaryItem> m_consequence
The consequence of the rule -
m_consequenceSupport
int m_consequenceSupport
The support for the consequence -
m_metricType
FPGrowth.AssociationRule.METRIC_TYPE m_metricType
The metric type for this rule -
m_premise
java.util.Collection<FPGrowth.BinaryItem> m_premise
The premise of the rule -
m_premiseSupport
int m_premiseSupport
The support for the premise -
m_totalSupport
int m_totalSupport
The total support for the item set (premise + consequence) -
m_totalTransactions
int m_totalTransactions
The total number of transactions in the data
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Class weka.associations.FPGrowth.BinaryItem extends java.lang.Object implements Serializable
- serialVersionUID:
- -3372941834914147669L
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Serialized Fields
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m_attribute
Attribute m_attribute
The attribute that the item corresponds to -
m_frequency
int m_frequency
The frequency of the item -
m_valueIndex
int m_valueIndex
The index of the value considered to be positive
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Class weka.associations.FPGrowth.FPTreeNode extends java.lang.Object implements Serializable
- serialVersionUID:
- 4396315323673737660L
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Serialized Fields
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m_children
java.util.Map<FPGrowth.BinaryItem,weka.associations.FPGrowth.FPTreeNode> m_children
the children of this node -
m_ID
int m_ID
ID (for graphing the tree) -
m_item
FPGrowth.BinaryItem m_item
item at this node -
m_levelSibling
weka.associations.FPGrowth.FPTreeNode m_levelSibling
link to another sibling at this level in the tree -
m_parent
weka.associations.FPGrowth.FPTreeNode m_parent
link to the parent node -
m_projectedCounts
weka.associations.FPGrowth.ShadowCounts m_projectedCounts
counts associated with projected versions of this node
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Class weka.associations.FPGrowth.FPTreeRoot.Header extends java.lang.Object implements Serializable
- serialVersionUID:
- -6583156284891368909L
-
Serialized Fields
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m_headerList
java.util.List<weka.associations.FPGrowth.FPTreeNode> m_headerList
The list of pointers into the tree structure -
m_projectedHeaderCounts
weka.associations.FPGrowth.ShadowCounts m_projectedHeaderCounts
Projected header counts for this entry
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Class weka.associations.FPGrowth.FrequentBinaryItemSet extends java.lang.Object implements Serializable
- serialVersionUID:
- -6543815873565829448L
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Serialized Fields
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m_items
java.util.ArrayList<FPGrowth.BinaryItem> m_items
The list of items in the item set -
m_support
int m_support
the support of this item set
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-
Class weka.associations.FPGrowth.FrequentItemSets extends java.lang.Object implements Serializable
- serialVersionUID:
- 4173606872363973588L
-
Serialized Fields
-
m_numberOfTransactions
int m_numberOfTransactions
The total number of transactions in the data -
m_sets
java.util.ArrayList<weka.associations.FPGrowth.FrequentBinaryItemSet> m_sets
The list of frequent item sets
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-
Class weka.associations.FPGrowth.ShadowCounts extends java.lang.Object implements Serializable
- serialVersionUID:
- 4435433714185969155L
-
Serialized Fields
-
m_counts
java.util.ArrayList<java.lang.Integer> m_counts
Holds the counts at different recursion levels
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-
Class weka.associations.GeneralizedSequentialPatterns extends AbstractAssociator implements Serializable
- serialVersionUID:
- -4119691320812254676L
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Serialized Fields
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m_AlgorithmStart
java.lang.String m_AlgorithmStart
String indicating the starting time of the algorithm. -
m_AllSequentialPatterns
FastVector m_AllSequentialPatterns
all generated frequent sequences, i.e. sequential patterns -
m_CycleEnd
java.lang.String m_CycleEnd
String indicating the ending time of an cycle. -
m_Cycles
int m_Cycles
number of cycles performed until termination -
m_CycleStart
java.lang.String m_CycleStart
String indicating the starting time of an cycle. -
m_DataSeqID
int m_DataSeqID
number indicating the attribute holding the data sequence ID -
m_Debug
boolean m_Debug
Whether the classifier is run in debug mode. -
m_FilterAttributes
java.lang.String m_FilterAttributes
String containing the attribute numbers that are used for result filtering; -1 means no filtering -
m_FilterAttrVector
FastVector m_FilterAttrVector
Vector containing the attribute numbers that are used for result filtering; -1 means no filtering -
m_MinSupport
double m_MinSupport
the minimum support threshold -
m_OriginalDataSet
Instances m_OriginalDataSet
original sequential data set to be used for sequential patterns extraction
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Class weka.associations.ItemSet extends java.lang.Object implements Serializable
- serialVersionUID:
- 2724000045282835791L
-
Serialized Fields
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m_counter
int m_counter
Counter for how many transactions contain this item set. -
m_items
int[] m_items
The items stored as an array of of ints. -
m_totalTransactions
int m_totalTransactions
The total number of transactions
-
-
Class weka.associations.LabeledItemSet extends ItemSet implements Serializable
- serialVersionUID:
- 4158771925518299903L
-
Serialized Fields
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m_classLabel
int m_classLabel
The class label. -
m_ruleSupCounter
int m_ruleSupCounter
The support of the rule.
-
-
Class weka.associations.PredictiveApriori extends AbstractAssociator implements Serializable
- serialVersionUID:
- 8109088846865075341L
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Serialized Fields
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m_allTheRules
FastVector[] m_allTheRules
The list of all generated rules. -
m_best
java.util.TreeSet m_best
The n best rules. -
m_bestChanged
boolean m_bestChanged
Flag keeping track if the list of the n best rules has changed. -
m_car
boolean m_car
Flag indicating whether class association rules are mined. -
m_classIndex
int m_classIndex
The class index. -
m_count
int m_count
Counter for the time of generation for an association rule. -
m_expectation
double m_expectation
The expected predictive accuracy a rule needs to be a candidate for the output. -
m_hashtables
FastVector m_hashtables
The same information stored in hash tables. -
m_instances
Instances m_instances
The instances (transactions) to be used for generating the association rules. -
m_Ls
FastVector m_Ls
The set of all sets of itemsets. -
m_midPoints
double[] m_midPoints
The mid points of the intervals used for the prior estimation. -
m_numRules
int m_numRules
The maximum number of rules that are output. -
m_premiseCount
int m_premiseCount
The minimum support. -
m_priorEstimator
PriorEstimation m_priorEstimator
The prior estimator. -
m_priors
java.util.Hashtable m_priors
The hashtable containing the prior probabilities.
-
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Class weka.associations.PriorEstimation extends java.lang.Object implements Serializable
- serialVersionUID:
- 5570863216522496271L
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Serialized Fields
-
m_CARs
boolean m_CARs
Flag indicating whether standard association rules or class association rules are mined. -
m_distribution
java.util.Hashtable m_distribution
Hashtable to store the confidence values of randomly generated rules. -
m_instances
Instances m_instances
The instances for which association rules are mined. -
m_midPoints
double[] m_midPoints
The mid points of the discrete intervals in which the interval [0,1] is divided. -
m_numIntervals
int m_numIntervals
The number of intervals. -
m_numRandRules
int m_numRandRules
The number of rnadom rules. -
m_priors
java.util.Hashtable m_priors
Hashtable containing the estimated prior probabilities. -
m_randNum
java.util.Random m_randNum
The random number generator. -
m_sum
double m_sum
Sums up the confidences of all rules with a certain length.
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Class weka.associations.RuleGeneration extends java.lang.Object implements Serializable
- serialVersionUID:
- -8927041669872491432L
-
Serialized Fields
-
m_best
java.util.TreeSet m_best
The list of the actual n best rules. -
m_change
boolean m_change
Flag indicating whether the list fo the best rules has changed. -
m_count
int m_count
Integer indicating the generation time of a rule. -
m_counter
int m_counter
Counter for how many transactions contain this item set. -
m_expectation
double m_expectation
The minimum expected predictive accuracy that is needed to be a candidate for the list of the best rules. -
m_instances
Instances m_instances
The instances. -
m_items
int[] m_items
The items stored as an array of of integer. -
m_midPoints
double[] m_midPoints
Sorted array of the mied points of the intervals used for prior estimation. -
m_minRuleCount
int m_minRuleCount
The minimum support a rule needs to be a candidate for the list of the best rules. -
m_priors
java.util.Hashtable m_priors
Hashtable conatining the estimated prior probabilities. -
m_totalTransactions
int m_totalTransactions
The total number of transactions
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-
Class weka.associations.RuleItem extends java.lang.Object implements Serializable
- serialVersionUID:
- -3761299128347476534L
-
Class weka.associations.SingleAssociatorEnhancer extends AbstractAssociator implements Serializable
- serialVersionUID:
- -3665885256363525164L
-
Serialized Fields
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m_Associator
Associator m_Associator
The base associator to use
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Class weka.associations.Tertius extends AbstractAssociator implements Serializable
- serialVersionUID:
- 5556726848380738179L
-
Serialized Fields
-
m_best
int m_best
Number of best confirmation values to search. -
m_classification
boolean m_classification
Classification bias. -
m_classIndex
int m_classIndex
Index of class attribute. -
m_confirmationThreshold
double m_confirmationThreshold
Confirmation threshold for the rules. -
m_equivalent
boolean m_equivalent
Perform test on equivalent rules ? -
m_explored
int m_explored
Number of hypotheses explored. -
m_frequencyThreshold
double m_frequencyThreshold
Frequency threshold for the body and the negation of the head. -
m_horn
boolean m_horn
Horn clauses bias. -
m_hypotheses
int m_hypotheses
Number of hypotheses considered. -
m_instances
Instances m_instances
Instances used for the search. -
m_missing
int m_missing
Way of handling missing values in the search. -
m_negation
int m_negation
Type of negation used in the rules. -
m_noiseThreshold
double m_noiseThreshold
Maximal number of counter-instances. -
m_numLiterals
int m_numLiterals
Number of literals in a rule. -
m_parts
Instances m_parts
Part instances for individual-based learning. -
m_partsString
java.lang.String m_partsString
Name of the file containing the parts for individual-based learning. -
m_predicates
java.util.ArrayList m_predicates
Predicates used in the rules. -
m_printValues
int m_printValues
Type of values output. -
m_repeat
boolean m_repeat
Repeat attributes ? -
m_results
SimpleLinkedList m_results
The results. -
m_roc
boolean m_roc
Perform ROC analysis ? -
m_sameClause
boolean m_sameClause
Perform test on same clauses ? -
m_status
int m_status
Status of the search. -
m_subsumption
boolean m_subsumption
Perform subsumption test ? -
m_time
java.util.Date m_time
Time needed for the search. -
m_valuesText
java.awt.TextField m_valuesText
Field to output the current values.
-
-
-
Package weka.associations.gsp
-
Class weka.associations.gsp.Element extends java.lang.Object implements Serializable
- serialVersionUID:
- -7900701276019516371L
-
Serialized Fields
-
m_Events
int[] m_Events
events/items stored as an array of ints
-
-
Class weka.associations.gsp.Sequence extends java.lang.Object implements Serializable
- serialVersionUID:
- -5001018056339156390L
-
Serialized Fields
-
m_Elements
FastVector m_Elements
ordered list of the comprised elements/itemsets -
m_SupportCount
int m_SupportCount
the support count of the Sequence
-
-
-
Package weka.associations.tertius
-
Class weka.associations.tertius.AttributeValueLiteral extends Literal implements Serializable
- serialVersionUID:
- 4077436297281456239L
-
Serialized Fields
-
m_index
int m_index
-
m_value
java.lang.String m_value
-
-
Class weka.associations.tertius.Body extends LiteralSet implements Serializable
- serialVersionUID:
- 4870689270432218016L
-
Class weka.associations.tertius.Head extends LiteralSet implements Serializable
- serialVersionUID:
- 5068076274253706199L
-
Class weka.associations.tertius.IndividualInstance extends Instance implements Serializable
- serialVersionUID:
- -7903938733476585114L
-
Serialized Fields
-
m_parts
Instances m_parts
-
-
Class weka.associations.tertius.IndividualInstances extends Instances implements Serializable
- serialVersionUID:
- -7355054814895636733L
-
Class weka.associations.tertius.IndividualLiteral extends AttributeValueLiteral implements Serializable
- serialVersionUID:
- 4712404824517887435L
-
Serialized Fields
-
m_type
int m_type
-
-
Class weka.associations.tertius.Literal extends java.lang.Object implements Serializable
- serialVersionUID:
- 2675363669503575771L
-
Class weka.associations.tertius.LiteralSet extends java.lang.Object implements Serializable
- serialVersionUID:
- 6094536488654503152L
-
Serialized Fields
-
m_counter
int m_counter
Counter for the number of counter-instances. -
m_counterInstances
java.util.ArrayList m_counterInstances
Set of counter-instances of this part of the rule. -
m_lastLiteral
Literal m_lastLiteral
Last literal added to this set. -
m_literals
java.util.ArrayList m_literals
Literals contained in this set. -
m_numInstances
int m_numInstances
Number of instances in the data the set deals with. -
m_type
int m_type
Type of properties expressed in this set (individual or parts properties).
-
-
Class weka.associations.tertius.Predicate extends java.lang.Object implements Serializable
- serialVersionUID:
- -8374702481965026640L
-
Serialized Fields
-
m_index
int m_index
-
m_isClass
boolean m_isClass
-
m_literals
java.util.ArrayList m_literals
-
m_name
java.lang.String m_name
-
-
Class weka.associations.tertius.Rule extends java.lang.Object implements Serializable
- serialVersionUID:
- -7763378359090435505L
-
Serialized Fields
-
m_body
Body m_body
The body of the rule. -
m_classRule
boolean m_classRule
Is this rule a classification rule ? -
m_confirmation
double m_confirmation
Confirmation of this rule. -
m_counter
int m_counter
Counter for the counter-instances of this rule. -
m_counterInstances
java.util.ArrayList m_counterInstances
Set of counter-instances of this rule. -
m_head
Head m_head
The head of the rule. -
m_maxLiterals
int m_maxLiterals
Maximal number of literals in the rule. -
m_negBody
boolean m_negBody
Can there be negations in the body ? -
m_negHead
boolean m_negHead
Can there be negations in the head ? -
m_numInstances
int m_numInstances
Number of instances in the data this rule deals with. -
m_optimistic
double m_optimistic
Optimistic estimate of this rule. -
m_repeatPredicate
boolean m_repeatPredicate
Can repeat predicates in the rule ? -
m_singleHead
boolean m_singleHead
Can there be only one literal in the head ?
-
-
Class weka.associations.tertius.SimpleLinkedList extends java.lang.Object implements Serializable
- serialVersionUID:
- -1491148276509976299L
-
Serialization Methods
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readObject
private void readObject(java.io.ObjectInputStream s) throws java.io.IOException, java.lang.ClassNotFoundException
Reconstitute this LinkedList instance from a stream (that is deserialize it).- Throws:
java.io.IOException
java.lang.ClassNotFoundException
-
writeObject
private void writeObject(java.io.ObjectOutputStream s) throws java.io.IOException
Save the state of this LinkedList instance to a stream (that is, serialize it).- Serial Data:
- The size of the list (the number of elements it contains) is emitted (int), followed by all of its elements (each an Object) in the proper order.
- Throws:
java.io.IOException
-
-
Serialized Fields
-
first
weka.associations.tertius.SimpleLinkedList.Entry first
-
last
weka.associations.tertius.SimpleLinkedList.Entry last
-
-
Class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator extends java.lang.Object implements Serializable
- serialVersionUID:
- 6290379064027832108L
-
Serialized Fields
-
current
weka.associations.tertius.SimpleLinkedList.Entry current
-
lastReturned
weka.associations.tertius.SimpleLinkedList.Entry lastReturned
-
-
Class weka.associations.tertius.SimpleLinkedList.LinkedListIterator extends java.lang.Object implements Serializable
- serialVersionUID:
- -2448555236100426759L
-
Serialized Fields
-
current
weka.associations.tertius.SimpleLinkedList.Entry current
-
lastReturned
weka.associations.tertius.SimpleLinkedList.Entry lastReturned
-
-
-
Package weka.attributeSelection
-
Class weka.attributeSelection.ASEvaluation extends java.lang.Object implements Serializable
- serialVersionUID:
- 2091705669885950849L
-
Class weka.attributeSelection.ASSearch extends java.lang.Object implements Serializable
- serialVersionUID:
- 7591673350342236548L
-
Class weka.attributeSelection.AttributeSelection extends java.lang.Object implements Serializable
- serialVersionUID:
- 4170171824147584330L
-
Serialized Fields
-
m_ASEvaluator
ASEvaluation m_ASEvaluator
the attribute/subset evaluator -
m_attributeFilter
Remove m_attributeFilter
the attribute filter for processing instances with respect to the most recent feature selection run -
m_attributeRanking
double[][] m_attributeRanking
the attribute indexes and associated merits if a ranking is produced -
m_doRank
boolean m_doRank
rank features (if allowed by the search method) -
m_doXval
boolean m_doXval
do cross validation -
m_numFolds
int m_numFolds
the number of folds to use for cross validation -
m_numToSelect
int m_numToSelect
number of attributes requested from ranked results -
m_rankResults
double[][] m_rankResults
hold statistics for repeated feature selection, such as under cross validation -
m_searchMethod
ASSearch m_searchMethod
the search method -
m_seed
int m_seed
seed used to randomly shuffle instances for cross validation -
m_selectedAttributeSet
int[] m_selectedAttributeSet
the selected attributes -
m_selectionResults
java.lang.StringBuffer m_selectionResults
holds a string describing the results of the attribute selection -
m_subsetResults
double[] m_subsetResults
-
m_trainInstances
Instances m_trainInstances
the instances to select attributes from -
m_transformer
AttributeTransformer m_transformer
if a feature selection run involves an attribute transformer -
m_trials
int m_trials
-
-
Class weka.attributeSelection.AttributeSetEvaluator extends ASEvaluation implements Serializable
- serialVersionUID:
- -5744881009422257389L
-
Class weka.attributeSelection.BestFirst extends ASSearch implements Serializable
- serialVersionUID:
- 7841338689536821867L
-
Serialized Fields
-
m_bestMerit
double m_bestMerit
holds the merit of the best subset found -
m_cacheSize
int m_cacheSize
holds the maximum size of the lookup cache for evaluated subsets -
m_classIndex
int m_classIndex
holds the class index -
m_debug
boolean m_debug
for debugging -
m_hasClass
boolean m_hasClass
does the data have a class -
m_maxStale
int m_maxStale
maximum number of stale nodes before terminating search -
m_numAttribs
int m_numAttribs
number of attributes in the data -
m_searchDirection
int m_searchDirection
0 == backward search, 1 == forward search, 2 == bidirectional -
m_starting
int[] m_starting
holds an array of starting attributes -
m_startRange
Range m_startRange
holds the start set for the search as a Range -
m_totalEvals
int m_totalEvals
total number of subsets evaluated during a search
-
-
Class weka.attributeSelection.BestFirst.Link2 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_c_Threshold
double m_c_Threshold
Threshold for admitting locally predictive features -
m_classIndex
int m_classIndex
The class index -
m_corr_matrix
float[][] m_corr_matrix
Holds the matrix of attribute correlations -
m_disTransform
Discretize m_disTransform
Discretise attributes when class in nominal -
m_isNumeric
boolean m_isNumeric
Is the class numeric -
m_locallyPredictive
boolean m_locallyPredictive
Include locally predicitive attributes -
m_missingSeparate
boolean m_missingSeparate
Treat missing values as separate values -
m_numAttribs
int m_numAttribs
Number of attributes in the training data -
m_numInstances
int m_numInstances
Number of instances in the training data -
m_std_devs
double[] m_std_devs
Standard deviations of attributes (when using pearsons correlation) -
m_trainInstances
Instances m_trainInstances
The training instances
-
-
Class weka.attributeSelection.ChiSquaredAttributeEval extends ASEvaluation implements Serializable
- serialVersionUID:
- -8316857822521717692L
-
Serialized Fields
-
m_Binarize
boolean m_Binarize
Just binarize numeric attributes -
m_ChiSquareds
double[] m_ChiSquareds
The chi-squared value for each attribute -
m_missing_merge
boolean m_missing_merge
Treat missing values as a seperate value
-
-
Class weka.attributeSelection.ClassifierSubsetEval extends HoldOutSubsetEvaluator implements Serializable
- serialVersionUID:
- 7532217899385278710L
-
Serialized Fields
-
m_Classifier
Classifier m_Classifier
holds the classifier to use for error estimates -
m_classIndex
int m_classIndex
class index -
m_Evaluation
Evaluation m_Evaluation
holds the evaluation object to use for evaluating the classifier -
m_holdOutFile
java.io.File m_holdOutFile
the file that containts hold out/test instances -
m_holdOutInstances
Instances m_holdOutInstances
the instances to test on -
m_numAttribs
int m_numAttribs
number of attributes in the training data -
m_numInstances
int m_numInstances
number of training instances -
m_trainingInstances
Instances m_trainingInstances
training instances -
m_useTraining
boolean m_useTraining
evaluate on training data rather than seperate hold out/test set
-
-
Class weka.attributeSelection.ConsistencySubsetEval extends ASEvaluation implements Serializable
- serialVersionUID:
- -2880323763295270402L
-
Serialized Fields
-
m_classIndex
int m_classIndex
class index -
m_disTransform
Discretize m_disTransform
Discretise numeric attributes -
m_numAttribs
int m_numAttribs
number of attributes in the training data -
m_numInstances
int m_numInstances
number of instances in the training data -
m_table
java.util.Hashtable m_table
Hash table for evaluating feature subsets -
m_trainInstances
Instances m_trainInstances
training instances
-
-
Class weka.attributeSelection.ConsistencySubsetEval.hashKey extends java.lang.Object implements Serializable
- serialVersionUID:
- 6144138512017017408L
-
Serialized Fields
-
attributes
double[] attributes
Array of attribute values for an instance -
key
int key
The key -
missing
boolean[] missing
True for an index if the corresponding attribute value is missing.
-
-
Class weka.attributeSelection.CostSensitiveASEvaluation extends ASEvaluation implements Serializable
- serialVersionUID:
- -7045833833363396977L
-
Serialized Fields
-
m_CostFile
java.lang.String m_CostFile
The name of the cost file, for command line options -
m_CostMatrix
CostMatrix m_CostMatrix
The cost matrix -
m_evaluator
ASEvaluation m_evaluator
The base evaluator to use -
m_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_seed
int m_seed
random number seed
-
-
Class weka.attributeSelection.CostSensitiveAttributeEval extends CostSensitiveASEvaluation implements Serializable
- serialVersionUID:
- 4484876541145458447L
-
Class weka.attributeSelection.CostSensitiveSubsetEval extends CostSensitiveASEvaluation implements Serializable
- serialVersionUID:
- 2924546096103426700L
-
Class weka.attributeSelection.ExhaustiveSearch extends ASSearch implements Serializable
- serialVersionUID:
- 5741842861142379712L
-
Serialized Fields
-
m_bestGroup
java.util.BitSet m_bestGroup
the best feature set found during the search -
m_bestMerit
double m_bestMerit
the merit of the best subset found -
m_classIndex
int m_classIndex
holds the class index -
m_evaluations
int m_evaluations
the number of subsets evaluated during the search -
m_hasClass
boolean m_hasClass
does the data have a class -
m_numAttribs
int m_numAttribs
number of attributes in the data -
m_verbose
boolean m_verbose
if true, then ouput new best subsets as the search progresses
-
-
Class weka.attributeSelection.FilteredAttributeEval extends ASEvaluation implements Serializable
- serialVersionUID:
- 2111121880778327334L
-
Serialized Fields
-
m_evaluator
AttributeEvaluator m_evaluator
Base evaluator -
m_filter
Filter m_filter
Filter -
m_filteredInstances
Instances m_filteredInstances
Filtered instances structure
-
-
Class weka.attributeSelection.FilteredSubsetEval extends ASEvaluation implements Serializable
- serialVersionUID:
- 2111121880778327334L
-
Serialized Fields
-
m_evaluator
SubsetEvaluator m_evaluator
Base evaluator -
m_filter
Filter m_filter
Filter -
m_filteredInstances
Instances m_filteredInstances
Filtered instances structure
-
-
Class weka.attributeSelection.GainRatioAttributeEval extends ASEvaluation implements Serializable
- serialVersionUID:
- -8504656625598579926L
-
Serialized Fields
-
m_classIndex
int m_classIndex
The class index -
m_missing_merge
boolean m_missing_merge
Merge missing values -
m_numAttribs
int m_numAttribs
The number of attributes -
m_numClasses
int m_numClasses
The number of classes -
m_numInstances
int m_numInstances
The number of instances -
m_trainInstances
Instances m_trainInstances
The training instances
-
-
Class weka.attributeSelection.GeneticSearch extends ASSearch implements Serializable
- serialVersionUID:
- -1618264232838472679L
-
Serialized Fields
-
m_avgFitness
double m_avgFitness
-
m_best
weka.attributeSelection.GeneticSearch.GABitSet m_best
the best population member found during the search -
m_bestFeatureCount
int m_bestFeatureCount
the number of features in the best population member -
m_classIndex
int m_classIndex
holds the class index -
m_generationReports
java.lang.StringBuffer m_generationReports
holds the generation reports -
m_hasClass
boolean m_hasClass
does the data have a class -
m_lookupTable
java.util.Hashtable m_lookupTable
the lookup table -
m_lookupTableSize
int m_lookupTableSize
the number of entries to cache for lookup -
m_maxFitness
double m_maxFitness
-
m_maxGenerations
int m_maxGenerations
the maximum number of generations to evaluate -
m_minFitness
double m_minFitness
-
m_numAttribs
int m_numAttribs
number of attributes in the data -
m_pCrossover
double m_pCrossover
the probability of crossover occuring -
m_pMutation
double m_pMutation
the probability of mutation occuring -
m_popSize
int m_popSize
the number of individual solutions -
m_population
weka.attributeSelection.GeneticSearch.GABitSet[] m_population
the current population -
m_random
java.util.Random m_random
random number generation -
m_reportFrequency
int m_reportFrequency
how often reports are generated -
m_seed
int m_seed
seed for random number generation -
m_starting
int[] m_starting
holds a starting set as an array of attributes. Becomes one member of the initial random population -
m_startRange
Range m_startRange
holds the start set for the search as a Range -
m_sumFitness
double m_sumFitness
sum of the current population fitness
-
-
Class weka.attributeSelection.GeneticSearch.GABitSet extends java.lang.Object implements Serializable
- serialVersionUID:
- -2930607837482622224L
-
Serialized Fields
-
m_chromosome
java.util.BitSet m_chromosome
the bitset -
m_fitness
double m_fitness
the fitness -
m_objective
double m_objective
holds raw merit
-
-
Class weka.attributeSelection.GreedyStepwise extends ASSearch implements Serializable
- serialVersionUID:
- -6312951970168325471L
-
Serialized Fields
-
m_ASEval
ASEvaluation m_ASEval
-
m_backward
boolean m_backward
Use a backwards search instead of a forwards one -
m_best_group
java.util.BitSet m_best_group
the best subset found -
m_bestMerit
double m_bestMerit
the merit of the best subset found -
m_calculatedNumToSelect
int m_calculatedNumToSelect
-
m_classIndex
int m_classIndex
holds the class index -
m_conservativeSelection
boolean m_conservativeSelection
If set then attributes will continue to be added during a forward search as long as the merit does not degrade -
m_doneRanking
boolean m_doneRanking
used to indicate whether or not ranking has been performed -
m_doRank
boolean m_doRank
go from one side of the search space to the other in order to generate a ranking -
m_hasClass
boolean m_hasClass
does the data have a class -
m_Instances
Instances m_Instances
-
m_numAttribs
int m_numAttribs
number of attributes in the data -
m_numToSelect
int m_numToSelect
The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold -
m_rankedAtts
double[][] m_rankedAtts
a ranked list of attribute indexes -
m_rankedSoFar
int m_rankedSoFar
-
m_rankingRequested
boolean m_rankingRequested
true if the user has requested a ranked list of attributes -
m_starting
int[] m_starting
holds an array of starting attributes -
m_startRange
Range m_startRange
holds the start set for the search as a Range -
m_threshold
double m_threshold
A threshold by which to discard attributes---used by the AttributeSelection module
-
-
Class weka.attributeSelection.HoldOutSubsetEvaluator extends ASEvaluation implements Serializable
- serialVersionUID:
- 8280529785412054174L
-
Class weka.attributeSelection.InfoGainAttributeEval extends ASEvaluation implements Serializable
- serialVersionUID:
- -1949849512589218930L
-
Serialized Fields
-
m_Binarize
boolean m_Binarize
Just binarize numeric attributes -
m_InfoGains
double[] m_InfoGains
The info gain for each attribute -
m_missing_merge
boolean m_missing_merge
Treat missing values as a seperate value
-
-
Class weka.attributeSelection.LatentSemanticAnalysis extends UnsupervisedAttributeEvaluator implements Serializable
- serialVersionUID:
- -8712112988018106198L
-
Serialized Fields
-
m_actualRank
int m_actualRank
The actual rank number to use for computation -
m_attributeFilter
Remove m_attributeFilter
-
m_classIndex
int m_classIndex
Class index -
m_hasClass
boolean m_hasClass
Data has a class set -
m_maxAttributesInName
int m_maxAttributesInName
Maximum number of attributes in the transformed attribute name -
m_nominalToBinaryFilter
NominalToBinary m_nominalToBinaryFilter
-
m_normalize
boolean m_normalize
Normalize the input data? -
m_normalizeFilter
Normalize m_normalizeFilter
-
m_numAttributes
int m_numAttributes
Number of attributes -
m_numInstances
int m_numInstances
Number of instances -
m_outputNumAttributes
int m_outputNumAttributes
The number of attributes in the LSA transformed data -
m_rank
double m_rank
The approximation rank to use (between 0 and 1 means coverage proportion) -
m_replaceMissingFilter
ReplaceMissingValues m_replaceMissingFilter
Filters for original data -
m_s
Matrix m_s
Will hold the singular values -
m_sumSquaredSingularValues
double m_sumSquaredSingularValues
The sum of the squares of the singular values -
m_trainHeader
Instances m_trainHeader
Keep a copy for the class attribute (if set) and for checking for header compatibility -
m_trainInstances
Instances m_trainInstances
The data to transform analyse/transform -
m_transformationMatrix
Matrix m_transformationMatrix
Will hold the matrix used to transform instances to the new feature space -
m_transformedFormat
Instances m_transformedFormat
The header for the transformed data format -
m_transpose
boolean m_transpose
Is transpose necessary because numAttributes < numInstances? -
m_u
Matrix m_u
Will hold the left singular vectors -
m_v
Matrix m_v
Will hold the right singular values
-
-
Class weka.attributeSelection.LFSMethods.Link2 extends java.lang.Object implements Serializable
- serialVersionUID:
- -7422719407475185086L
-
Serialized Fields
-
m_data
java.lang.Object[] m_data
-
m_merit
double m_merit
-
-
Class weka.attributeSelection.LFSMethods.LinkedList2 extends FastVector implements Serializable
- serialVersionUID:
- -7776010892419656105L
-
Serialized Fields
-
m_MaxSize
int m_MaxSize
-
-
Class weka.attributeSelection.LinearForwardSelection extends ASSearch implements Serializable
-
Serialized Fields
-
m_bestMerit
double m_bestMerit
holds the merit of the best subset found -
m_cacheSize
int m_cacheSize
holds the maximum size of the lookup cache for evaluated subsets -
m_classIndex
int m_classIndex
holds the class index -
m_forwardSearchMethod
int m_forwardSearchMethod
0 == forward selection, 1 == floating forward search -
m_hasClass
boolean m_hasClass
does the data have a class -
m_linearSelectionType
int m_linearSelectionType
0 == fixed-set, 1 == fixed-width -
m_maxStale
int m_maxStale
maximum number of stale nodes before terminating search -
m_numAttribs
int m_numAttribs
number of attributes in the data -
m_numUsedAttributes
int m_numUsedAttributes
number of top-ranked attributes that are taken into account for the search -
m_performRanking
boolean m_performRanking
perform initial ranking to select top-ranked attributes -
m_starting
int[] m_starting
holds an array of starting attributes -
m_startRange
Range m_startRange
holds the start set for the search as a Range -
m_totalEvals
int m_totalEvals
total number of subsets evaluated during a search -
m_verbose
boolean m_verbose
for debugging
-
-
-
Class weka.attributeSelection.OneRAttributeEval extends ASEvaluation implements Serializable
- serialVersionUID:
- 4386514823886856980L
-
Serialized Fields
-
m_classIndex
int m_classIndex
The class index -
m_evalUsingTrainingData
boolean m_evalUsingTrainingData
Use training data to evaluate merit rather than x-val -
m_folds
int m_folds
Number of folds for cross validation -
m_minBucketSize
int m_minBucketSize
Passed on to OneR -
m_numAttribs
int m_numAttribs
The number of attributes -
m_numInstances
int m_numInstances
The number of instances -
m_randomSeed
int m_randomSeed
Random number seed -
m_trainInstances
Instances m_trainInstances
The training instances
-
-
Class weka.attributeSelection.PrincipalComponents extends UnsupervisedAttributeEvaluator implements Serializable
- serialVersionUID:
- -3675307197777734007L
-
Serialized Fields
-
m_attributeFilter
Remove m_attributeFilter
-
m_center
boolean m_center
If true, center (rather than standardize) the data and compute PCA from covariance (rather than correlation) matrix. -
m_centerFilter
Center m_centerFilter
-
m_classIndex
int m_classIndex
Class index -
m_correlation
double[][] m_correlation
Correlation/covariance matrix for the original data -
m_coverVariance
double m_coverVariance
the amount of variance to cover in the original data when retaining the best n PC's -
m_eigenvalues
double[] m_eigenvalues
Eigenvalues for the corresponding eigenvectors -
m_eigenvectors
double[][] m_eigenvectors
Will hold the unordered linear transformations of the (normalized) original data -
m_eTranspose
double[][] m_eTranspose
holds the transposed eigenvectors for converting back to the original space -
m_hasClass
boolean m_hasClass
Data has a class set -
m_maxAttrsInName
int m_maxAttrsInName
maximum number of attributes in the transformed attribute name -
m_means
double[] m_means
-
m_nominalToBinFilter
NominalToBinary m_nominalToBinFilter
-
m_numAttribs
int m_numAttribs
Number of attributes -
m_numInstances
int m_numInstances
Number of instances -
m_originalSpaceFormat
Instances m_originalSpaceFormat
The header for data transformed back to the original space -
m_outputNumAtts
int m_outputNumAtts
The number of attributes in the pc transformed data -
m_replaceMissingFilter
ReplaceMissingValues m_replaceMissingFilter
Filters for original data -
m_sortedEigens
int[] m_sortedEigens
Sorted eigenvalues -
m_standardizeFilter
Standardize m_standardizeFilter
-
m_stdDevs
double[] m_stdDevs
-
m_sumOfEigenValues
double m_sumOfEigenValues
sum of the eigenvalues -
m_trainHeader
Instances m_trainHeader
Keep a copy for the class attribute (if set) -
m_trainInstances
Instances m_trainInstances
The data to transform analyse/transform -
m_transBackToOriginal
boolean m_transBackToOriginal
transform the data through the pc space and back to the original space ? -
m_transformedFormat
Instances m_transformedFormat
The header for the transformed data format
-
-
Class weka.attributeSelection.RaceSearch extends ASSearch implements Serializable
- serialVersionUID:
- 4015453851212985720L
-
Serialized Fields
-
m_ASEval
ASEvaluation m_ASEval
the attribute evaluator to generate the initial ranking when doing a rank race -
m_bestMerit
double m_bestMerit
holds the merit of the best subset found -
m_calculatedNumToSelect
int m_calculatedNumToSelect
-
m_classIndex
int m_classIndex
the class index -
m_debug
boolean m_debug
verbose output for monitoring the search and debugging -
m_delta
double m_delta
threshold for comparisons -
m_Instances
Instances m_Instances
the training instances -
m_numAttribs
int m_numAttribs
the number of attributes in the data -
m_numFolds
int m_numFolds
number of cross validation folds---equal to the number of instances for leave-one-out cv -
m_numToSelect
int m_numToSelect
The number of attributes to retain if a ranking is requested. -1 indicates that all attributes are to be retained. Has precedence over m_threshold -
m_raceType
int m_raceType
the selected search type -
m_rankedAtts
double[][] m_rankedAtts
The ranked list of attributes produced if m_rankingRequested is true -
m_rankedSoFar
int m_rankedSoFar
The number of attributes ranked so far (if ranking is requested) -
m_Ranking
int[] m_Ranking
will hold the attribute ranking produced by the above attribute evaluator if doing a rank search -
m_rankingRequested
boolean m_rankingRequested
If true then produce a ranked list of attributes by fully traversing a forward hillclimb race -
m_samples
int m_samples
the number of samples above which to begin testing for similarity between competing subsets -
m_sigLevel
double m_sigLevel
the significance level for comparisons -
m_theEvaluator
HoldOutSubsetEvaluator m_theEvaluator
the subset evaluator to use -
m_threshold
double m_threshold
the threshold for removing attributes if ranking is requested -
m_totalEvals
int m_totalEvals
the total number of partially/fully evaluated subsets -
m_xvalType
int m_xvalType
the selected xval type
-
-
Class weka.attributeSelection.RandomSearch extends ASSearch implements Serializable
- serialVersionUID:
- 7479392617377425484L
-
Serialized Fields
-
m_bestGroup
java.util.BitSet m_bestGroup
the best feature set found during the search -
m_bestMerit
double m_bestMerit
the merit of the best subset found -
m_classIndex
int m_classIndex
holds the class index -
m_hasClass
boolean m_hasClass
does the data have a class -
m_iterations
int m_iterations
the number of iterations performed -
m_numAttribs
int m_numAttribs
number of attributes in the data -
m_onlyConsiderBetterAndSmaller
boolean m_onlyConsiderBetterAndSmaller
only accept a feature set as being "better" than the best if its merit is better or equal to the best, and it contains as many or fewer features than the best (this allows LVF to be implemented). -
m_random
java.util.Random m_random
random number object -
m_searchSize
double m_searchSize
percentage of the search space to consider -
m_seed
int m_seed
seed for random number generation -
m_starting
int[] m_starting
holds a starting set as an array of attributes. -
m_startRange
Range m_startRange
holds the start set as a range -
m_verbose
boolean m_verbose
output new best subsets as the search progresses
-
-
Class weka.attributeSelection.Ranker extends ASSearch implements Serializable
- serialVersionUID:
- -9086714848510751934L
-
Serialized Fields
-
m_attributeList
int[] m_attributeList
Holds the ordered list of attributes -
m_attributeMerit
double[] m_attributeMerit
Holds the list of attribute merit scores -
m_calculatedNumToSelect
int m_calculatedNumToSelect
Used to compute the number to select -
m_classIndex
int m_classIndex
Class index of the data if supervised evaluator -
m_hasClass
boolean m_hasClass
Data has class attribute---if unsupervised evaluator then no class -
m_numAttribs
int m_numAttribs
The number of attribtes -
m_numToSelect
int m_numToSelect
The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold -
m_starting
int[] m_starting
Holds the starting set as an array of attributes -
m_startRange
Range m_startRange
Holds the start set for the search as a range -
m_threshold
double m_threshold
A threshold by which to discard attributes---used by the AttributeSelection module
-
-
Class weka.attributeSelection.RankSearch extends ASSearch implements Serializable
- serialVersionUID:
- -7992268736874353755L
-
Serialized Fields
-
m_add
int m_add
add this many attributes in each iteration from the ranking -
m_ASEval
ASEvaluation m_ASEval
the attribute evaluator to use for generating the ranking -
m_best_group
java.util.BitSet m_best_group
the best subset found -
m_bestMerit
double m_bestMerit
the merit of the best subset found -
m_classIndex
int m_classIndex
holds the class index -
m_hasClass
boolean m_hasClass
does the data have a class -
m_Instances
Instances m_Instances
the training instances -
m_numAttribs
int m_numAttribs
number of attributes in the data -
m_Ranking
int[] m_Ranking
will hold the attribute ranking -
m_startPoint
int m_startPoint
start from this point in the ranking -
m_SubsetEval
ASEvaluation m_SubsetEval
the subset evaluator with which to evaluate the ranking
-
-
Class weka.attributeSelection.ReliefFAttributeEval extends ASEvaluation implements Serializable
- serialVersionUID:
- -8422186665795839379L
-
Serialized Fields
-
m_classIndex
int m_classIndex
The class index -
m_classProbs
double[] m_classProbs
Prior class probabilities (discrete class case) -
m_index
int[] m_index
Index in the m_karray of the farthest instance for each class -
m_karray
double[][][] m_karray
k nearest scores + instance indexes for n classes -
m_Knn
int m_Knn
The number of nearest hits/misses -
m_maxArray
double[] m_maxArray
Upper bound for numeric attributes -
m_minArray
double[] m_minArray
Lower bound for numeric attributes -
m_nda
double[] m_nda
Used to hold the prob of different value of an attribute given nearest instances (numeric class case) -
m_ndc
double m_ndc
Used to hold the probability of a different class val given nearest instances (numeric class) -
m_ndcda
double[] m_ndcda
Used to hold the prob of a different class val and different att val given nearest instances (numeric class case) -
m_numAttribs
int m_numAttribs
The number of attributes -
m_numClasses
int m_numClasses
The number of classes if class is nominal -
m_numericClass
boolean m_numericClass
Numeric class -
m_numInstances
int m_numInstances
The number of instances -
m_sampleM
int m_sampleM
The number of instances to sample when estimating attributes default == -1, use all instances -
m_seed
int m_seed
Random number seed used for sampling instances -
m_sigma
int m_sigma
-
m_stored
int[] m_stored
Number of nearest neighbours stored of each class -
m_trainInstances
Instances m_trainInstances
The training instances -
m_weightByDistance
boolean m_weightByDistance
Weight by distance rather than equal weights -
m_weights
double[] m_weights
Holds the weights that relief assigns to attributes -
m_weightsByRank
double[] m_weightsByRank
used to (optionally) weight nearest neighbours by their distance from the instance in question. Each entry holds exp(-((rank(r_i, i_j)/sigma)^2)) where rank(r_i,i_j) is the rank of instance i_j in a sequence of instances ordered by the distance from r_i. sigma is a user defined parameter, default=20 -
m_worst
double[] m_worst
Keep track of the farthest instance for each class
-
-
Class weka.attributeSelection.ScatterSearchV1 extends ASSearch implements Serializable
- serialVersionUID:
- -8512041420388121326L
-
Serialized Fields
-
ASEvaluator
SubsetEvaluator ASEvaluator
Evaluator used to know the significance of a subset (for guiding the search) -
m_attributeRanking
java.util.List<ScatterSearchV1.Subset> m_attributeRanking
holds the attributes ranked -
m_bestMerit
double m_bestMerit
holds the merit of the best subset found -
m_calculatedInitialPopSize
int m_calculatedInitialPopSize
if no initial user pop size, then this holds the initial pop size calculated from the number of attributes in the data (for use in the toString() method) -
m_classIndex
int m_classIndex
holds the class index -
m_debug
boolean m_debug
verbose output for monitoring the search and debugging -
m_InformationReports
java.lang.StringBuffer m_InformationReports
holds a report of the search -
m_initialPopSize
int m_initialPopSize
holds the user selected initial population size -
m_initialThreshold
double m_initialThreshold
the initial threshold -
m_numAttribs
int m_numAttribs
number of attributes in the data -
m_popSize
int m_popSize
holds the population size -
m_population
java.util.List<ScatterSearchV1.Subset> m_population
holds the Initial Population of Subsets -
m_processinTime
long m_processinTime
time for procesing the search method -
m_random
java.util.Random m_random
random number generation -
m_seed
int m_seed
seed for random number generation -
m_totalEvals
int m_totalEvals
total number of subsets evaluated during a search -
m_treshold
double m_treshold
holds the treshhold that delimits the good attributes -
m_typeOfCombination
int m_typeOfCombination
the kind of comination betwen parents ((0)greedy combination/(1)reduced greedy combination)
-
-
Class weka.attributeSelection.ScatterSearchV1.Subset extends java.lang.Object implements Serializable
-
Serialized Fields
-
merit
double merit
-
subset
java.util.BitSet subset
-
-
-
Class weka.attributeSelection.SubsetSizeForwardSelection extends ASSearch implements Serializable
-
Serialized Fields
-
m_bestMerit
double m_bestMerit
holds the merit of the best subset found -
m_cacheSize
int m_cacheSize
holds the maximum size of the lookup cache for evaluated subsets -
m_linearSelectionType
int m_linearSelectionType
0 == fixed-set, 1 == fixed-width -
m_numAttribs
int m_numAttribs
number of attributes in the data -
m_numFolds
int m_numFolds
Number of cross validation folds for subset size determination (default = 5). -
m_numUsedAttributes
int m_numUsedAttributes
number of top-ranked attributes that are taken into account for the search -
m_performRanking
boolean m_performRanking
perform initial ranking to select top-ranked attributes -
m_seed
int m_seed
Seed for cross validation subset size determination. (default = 1) -
m_setSizeEval
ASEvaluation m_setSizeEval
the subset evaluator to use for subset size determination -
m_totalEvals
int m_totalEvals
total number of subsets evaluated during a search -
m_verbose
boolean m_verbose
for debugging
-
-
-
Class weka.attributeSelection.SVMAttributeEval extends ASEvaluation implements Serializable
- serialVersionUID:
- -6489975709033967447L
-
Serialized Fields
-
m_attScores
double[] m_attScores
The attribute scores -
m_numToEliminate
int m_numToEliminate
Constant rate of attribute elimination per iteration -
m_percentThreshold
int m_percentThreshold
Threshold below which percent elimination switches to constant elimination -
m_percentToEliminate
int m_percentToEliminate
Percentage rate of attribute elimination, trumps constant rate (above threshold), ignored if = 0 -
m_smoCParameter
double m_smoCParameter
Complexity parameter to pass on to SMO -
m_smoFilterType
int m_smoFilterType
Filter parameter to pass on to SMO -
m_smoPParameter
double m_smoPParameter
Epsilon parameter to pass on to SMO -
m_smoTParameter
double m_smoTParameter
Tolerance parameter to pass on to SMO
-
-
Class weka.attributeSelection.SymmetricalUncertAttributeEval extends ASEvaluation implements Serializable
- serialVersionUID:
- -8096505776132296416L
-
Serialized Fields
-
m_classIndex
int m_classIndex
The class index -
m_missing_merge
boolean m_missing_merge
Treat missing values as a seperate value -
m_numAttribs
int m_numAttribs
The number of attributes -
m_numClasses
int m_numClasses
The number of classes -
m_numInstances
int m_numInstances
The number of instances -
m_trainInstances
Instances m_trainInstances
The training instances
-
-
Class weka.attributeSelection.UnsupervisedAttributeEvaluator 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_BaseClassifier
Classifier m_BaseClassifier
holds the base classifier object -
m_classIndex
int m_classIndex
class index -
m_Evaluation
Evaluation m_Evaluation
holds an evaluation object -
m_folds
int m_folds
number of folds to use for cross validation -
m_numAttribs
int m_numAttribs
number of attributes in the training data -
m_numInstances
int m_numInstances
number of instances in the training data -
m_seed
int m_seed
random number seed -
m_threshold
double m_threshold
the threshold by which to do further cross validations when estimating the accuracy of a subset -
m_trainInstances
Instances m_trainInstances
training instances
-
-
-
Package weka.classifiers
-
Class weka.classifiers.Classifier extends java.lang.Object implements Serializable
- serialVersionUID:
- 6502780192411755341L
-
Serialized Fields
-
m_Debug
boolean m_Debug
Whether the classifier is run in debug mode.
-
-
Class weka.classifiers.CostMatrix extends java.lang.Object implements Serializable
- serialVersionUID:
- -1973792250544554965L
-
Serialized Fields
-
m_matrix
java.lang.Object[][] m_matrix
[rows][columns] -
m_size
int m_size
-
-
Class weka.classifiers.IteratedSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -6217979135443319724L
-
Serialized Fields
-
m_Classifiers
Classifier[] m_Classifiers
Array for storing the generated base classifiers. -
m_NumIterations
int m_NumIterations
The number of iterations.
-
-
Class weka.classifiers.MultipleClassifiersCombiner extends Classifier implements Serializable
- serialVersionUID:
- 2776436621129422119L
-
Serialized Fields
-
m_Classifiers
Classifier[] m_Classifiers
Array for storing the generated base classifiers.
-
-
Class weka.classifiers.RandomizableClassifier extends Classifier implements Serializable
- serialVersionUID:
- -8816375798262351903L
-
Serialized Fields
-
m_Seed
int m_Seed
The random number seed.
-
-
Class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer extends IteratedSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 5063351391524938557L
-
Serialized Fields
-
m_Seed
int m_Seed
The random number seed.
-
-
Class weka.classifiers.RandomizableMultipleClassifiersCombiner extends MultipleClassifiersCombiner implements Serializable
- serialVersionUID:
- 5057936555724785679L
-
Serialized Fields
-
m_Seed
int m_Seed
The random number seed.
-
-
Class weka.classifiers.RandomizableSingleClassifierEnhancer extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 558286687096157160L
-
Serialized Fields
-
m_Seed
int m_Seed
The random number seed.
-
-
Class weka.classifiers.SingleClassifierEnhancer extends Classifier implements Serializable
- serialVersionUID:
- -3665885256363525164L
-
Serialized Fields
-
m_Classifier
Classifier m_Classifier
The base classifier to use
-
-
-
Package weka.classifiers.bayes
-
Class weka.classifiers.bayes.AODE extends Classifier implements Serializable
- serialVersionUID:
- 9197439980415113523L
-
Serialized Fields
-
m_ClassCounts
double[] m_ClassCounts
The number of times each class value occurs in the dataset -
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_CondiCounts
double[][][] m_CondiCounts
3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts, i.e., the number of times an attribute value occurs in conjunction with another attribute value and a class value. -
m_Debug
boolean m_Debug
If true, outputs debugging info -
m_Frequencies
double[] m_Frequencies
The frequency of each attribute value for the dataset -
m_Instances
Instances m_Instances
The dataset -
m_Limit
int m_Limit
An att's frequency must be this value or more to be a superParent -
m_MEstimates
boolean m_MEstimates
flag for using m-estimates -
m_NumAttributes
int m_NumAttributes
The number of attributes in dataset, including class -
m_NumAttValues
int[] m_NumAttValues
The number of values for each attribute -
m_NumClasses
int m_NumClasses
The number of classes -
m_NumInstances
int m_NumInstances
The number of instances in the dataset -
m_StartAttIndex
int[] m_StartAttIndex
The starting index (in the m_CondiCounts matrix) of the values for each attribute -
m_SumForCounts
double[][] m_SumForCounts
The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal] -
m_SumInstances
double m_SumInstances
The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances. -
m_TotalAttValues
int m_TotalAttValues
The total number of values (including an extra for each attribute's missing value, which are included in m_CondiCounts) for all attributes (not including class). E.g., for three atts each with two possible values, m_TotalAttValues would be 9 (6 values + 3 missing). This variable is used when allocating space for m_CondiCounts matrix. -
m_Weight
int m_Weight
value for m in m-estimate
-
-
Class weka.classifiers.bayes.AODEsr extends Classifier implements Serializable
- serialVersionUID:
- 5602143019183068848L
-
Serialized Fields
-
m_ClassCounts
double[] m_ClassCounts
The number of times each class value occurs in the dataset -
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_CondiCounts
double[][][] m_CondiCounts
3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts, i.e. the number of times an attribute value occurs in conjunction with another attribute value and a class value. -
m_CondiCountsNoClass
double[][] m_CondiCountsNoClass
2D array (m_TotalAttValues * m_TotalAttValues) of attributes counts. similar to m_CondiCounts, but ignoring class value. -
m_Critical
int m_Critical
the critical value for the specialization-generalization -
m_Debug
boolean m_Debug
If true, outputs debugging info -
m_Frequencies
double[] m_Frequencies
The frequency of each attribute value for the dataset -
m_Instances
Instances m_Instances
The dataset -
m_Laplace
boolean m_Laplace
Using LapLace estimation or not -
m_Limit
int m_Limit
An att's frequency must be this value or more to be a superParent -
m_MWeight
double m_MWeight
m value for m-estimation -
m_NumAttributes
int m_NumAttributes
The number of attributes in dataset, including class -
m_NumAttValues
int[] m_NumAttValues
The number of values for each attribute -
m_NumClasses
int m_NumClasses
The number of classes -
m_NumInstances
int m_NumInstances
The number of instances in the dataset -
m_StartAttIndex
int[] m_StartAttIndex
The starting index (in the m_CondiCounts matrix) of the values for each attribute -
m_SumForCounts
double[][] m_SumForCounts
The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal] -
m_SumInstances
double m_SumInstances
The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances. -
m_TotalAttValues
int m_TotalAttValues
The total number of values (including an extra for each attribute's missing value, which are included in m_CondiCounts) for all attributes (not including class). Eg. for three atts each with two possible values, m_TotalAttValues would be 9 (6 values + 3 missing). This variable is used when allocating space for m_CondiCounts matrix.
-
-
Class weka.classifiers.bayes.BayesianLogisticRegression extends Classifier implements Serializable
- serialVersionUID:
- -8013478897911757631L
-
Serialized Fields
-
BetaVector
double[] BetaVector
Array for storing coefficients of Bayesian regression model. -
Change
double Change
This variable is used to keep track of change in the value of delta summation of r(i). -
ClassIndex
int ClassIndex
The class index from the training data -
debug
boolean debug
DEBUG Mode -
Delta
double[] Delta
Trust Region Radius -
DeltaBeta
double[] DeltaBeta
Array to store Regression Coefficient updates. -
DeltaR
double[] DeltaR
This vector is used to store the increments on the R(i). It is also used to determining the stopping criterion. -
DeltaUpdate
double[] DeltaUpdate
Trust Region Radius Update -
HyperparameterRange
java.lang.String HyperparameterRange
CV Hyperparameter Range -
Hyperparameters
double[] Hyperparameters
Array to store Hyperparameter values for each feature. -
HyperparameterSelection
int HyperparameterSelection
Hyperparameter selection method -
HyperparameterValue
double HyperparameterValue
Best hyperparameter for test phase -
iterationCounter
int iterationCounter
Iteration counter -
m_Filter
Filter m_Filter
Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object -
m_Instances
Instances m_Instances
Dataset provided to do Training/Test set. -
m_PriorUpdate
Prior m_PriorUpdate
Prior class object interface -
m_seed
int m_seed
seed for randomizing the instances before CV -
maxIterations
int maxIterations
Maximum number of iterations -
NormalizeData
boolean NormalizeData
Choose whether to normalize data or not -
NumFolds
int NumFolds
NumFolds for CV based Hyperparameters selection -
PriorClass
int PriorClass
Distribution Prior class -
R
double[] R
R(i)= BetaVector X x(i) X y(i). This an intermediate value with respect to vector BETA, input values and corresponding class labels -
Threshold
double Threshold
Threshold for binary classification of probabilisitic estimate -
Tolerance
double Tolerance
Tolerance criteria for the stopping criterion.
-
-
Class weka.classifiers.bayes.BayesNet extends Classifier implements Serializable
- serialVersionUID:
- 746037443258775954L
-
Serialized Fields
-
m_ADTree
ADNode m_ADTree
Datastructure containing ADTree representation of the database. This may result in more efficient access to the data. -
m_BayesNetEstimator
BayesNetEstimator m_BayesNetEstimator
Search algorithm used for learning the structure of a network. -
m_bUseADTree
boolean m_bUseADTree
Use the experimental ADTree datastructure for calculating contingency tables -
m_DiscretizeFilter
Discretize m_DiscretizeFilter
filter used to quantize continuous variables, if any -
m_Distributions
Estimator[][] m_Distributions
The attribute estimators containing CPTs. -
m_Instances
Instances m_Instances
The dataset header for the purposes of printing out a semi-intelligible model -
m_MissingValuesFilter
ReplaceMissingValues m_MissingValuesFilter
filter used to fill in missing values, if any -
m_nNonDiscreteAttribute
int m_nNonDiscreteAttribute
attribute index of a non-nominal attribute -
m_NumClasses
int m_NumClasses
The number of classes -
m_otherBayesNet
BIFReader m_otherBayesNet
Bayes network to compare the structure with. -
m_ParentSets
ParentSet[] m_ParentSets
The parent sets. -
m_SearchAlgorithm
SearchAlgorithm m_SearchAlgorithm
Search algorithm used for learning the structure of a network.
-
-
Class weka.classifiers.bayes.ComplementNaiveBayes extends Classifier implements Serializable
- serialVersionUID:
- 7246302925903086397L
-
Serialized Fields
-
header
Instances header
The instances header that'll be used in toString -
m_normalizeWordWeights
boolean m_normalizeWordWeights
True if the words weights are to be normalized -
numClasses
int numClasses
Holds the number of Class values present in the set of specified instances -
smoothingParameter
double smoothingParameter
Holds the smoothing value to avoid word probabilities of zero.
P.S.: According to the paper this is the Alpha i parameter -
wordWeights
double[][] wordWeights
Weight of words for each class. The weight is actually the log of the probability of a word (w) given a class (c) (i.e. log(Pr[w|c])). The format of the matrix is: wordWeights[class][wordAttribute]
-
-
Class weka.classifiers.bayes.DMNBtext extends Classifier implements Serializable
- serialVersionUID:
- 5932177450183457085L
-
Serialized Fields
-
m_binaryClassifiers
DMNBtext.DNBBinary[] m_binaryClassifiers
-
m_headerInfo
Instances m_headerInfo
-
m_MultinomialWord
boolean m_MultinomialWord
-
m_numClasses
int m_numClasses
-
m_NumIterations
int m_NumIterations
The number of iterations.
-
-
Class weka.classifiers.bayes.DMNBtext.DNBBinary extends java.lang.Object implements Serializable
-
Serialized Fields
-
m_classDistribution
double[] m_classDistribution
-
m_classIndex
int m_classIndex
-
m_classRatio
double m_classRatio
-
m_coefficient
double[] m_coefficient
-
m_numAttributes
int m_numAttributes
number of unique words -
m_perWordPerClass
double[][] m_perWordPerClass
The number of iterations. -
m_targetClass
int m_targetClass
-
m_WordLaplace
double m_WordLaplace
-
m_wordRatio
double m_wordRatio
-
m_wordsPerClass
double[] m_wordsPerClass
-
-
-
Class weka.classifiers.bayes.HNB extends Classifier implements Serializable
- serialVersionUID:
- -4503874444306113214L
-
Serialized Fields
-
m_ClassAttAttCounts
double[][][] m_ClassAttAttCounts
The number of class and two attributes values occurs in the dataset -
m_ClassCounts
double[] m_ClassCounts
The number of each class value occurs in the dataset -
m_ClassIndex
int m_ClassIndex
The index of the class attribute in the dataset -
m_condiMutualInfo
double[][] m_condiMutualInfo
The 2D array of conditional mutual information of each pair attributes -
m_NumAttributes
int m_NumAttributes
The number of attributes including class in the dataset -
m_NumAttValues
int[] m_NumAttValues
The number of values for each attribute in the dataset -
m_NumClasses
int m_NumClasses
The number of classes in the dataset -
m_NumInstances
int m_NumInstances
The number of instances in the dataset -
m_StartAttIndex
int[] m_StartAttIndex
The starting index of each attribute in the dataset -
m_TotalAttValues
int m_TotalAttValues
The number of values for all attributes in the dataset
-
-
Class weka.classifiers.bayes.NaiveBayes extends Classifier implements Serializable
- serialVersionUID:
- 5995231201785697655L
-
Serialized Fields
-
m_ClassDistribution
Estimator m_ClassDistribution
The class estimator. -
m_Disc
Discretize m_Disc
The discretization filter. -
m_displayModelInOldFormat
boolean m_displayModelInOldFormat
-
m_Distributions
Estimator[][] m_Distributions
The attribute estimators. -
m_Instances
Instances m_Instances
The dataset header for the purposes of printing out a semi-intelligible model -
m_NumClasses
int m_NumClasses
The number of classes (or 1 for numeric class) -
m_UseDiscretization
boolean m_UseDiscretization
Whether to use discretization than normal distribution for numeric attributes -
m_UseKernelEstimator
boolean m_UseKernelEstimator
Whether to use kernel density estimator rather than normal distribution for numeric attributes
-
-
Class weka.classifiers.bayes.NaiveBayesMultinomial extends Classifier implements Serializable
- serialVersionUID:
- 5932177440181257085L
-
Serialized Fields
-
m_headerInfo
Instances m_headerInfo
copy of header information for use in toString method -
m_lnFactorialCache
double[] m_lnFactorialCache
cache lnFactorial computations -
m_numAttributes
int m_numAttributes
number of unique words -
m_numClasses
int m_numClasses
number of class values -
m_probOfClass
double[] m_probOfClass
the probability of a class (i.e. Pr[H]) -
m_probOfWordGivenClass
double[][] m_probOfWordGivenClass
probability that a word (w) exists in a class (H) (i.e. Pr[w|H]) The matrix is in the this format: probOfWordGivenClass[class][wordAttribute] NOTE: the values are actually the log of Pr[w|H]
-
-
Class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable extends NaiveBayesMultinomial implements Serializable
- serialVersionUID:
- -7204398796974263186L
-
Serialized Fields
-
m_wordsPerClass
double[] m_wordsPerClass
the word count per class
-
-
Class weka.classifiers.bayes.NaiveBayesSimple extends Classifier implements Serializable
- serialVersionUID:
- -1478242251770381214L
-
Serialized Fields
-
m_Counts
double[][][] m_Counts
All the counts for nominal attributes. -
m_Devs
double[][] m_Devs
The standard deviations for numeric attributes. -
m_Instances
Instances m_Instances
The instances used for training. -
m_Means
double[][] m_Means
The means for numeric attributes. -
m_Priors
double[] m_Priors
The prior probabilities of the classes.
-
-
Class weka.classifiers.bayes.NaiveBayesUpdateable extends NaiveBayes implements Serializable
- serialVersionUID:
- -5354015843807192221L
-
Class weka.classifiers.bayes.WAODE extends Classifier implements Serializable
- serialVersionUID:
- 2170978824284697882L
-
Serialized Fields
-
m_AttAttCounts
double[][] m_AttAttCounts
The number of two attributes values occurs in the dataset -
m_AttCounts
double[] m_AttCounts
The number of each attribute value occurs in the dataset -
m_ClassAttAttCounts
double[][][] m_ClassAttAttCounts
The number of class and two attributes values occurs in the dataset -
m_ClassCounts
double[] m_ClassCounts
The number of each class value occurs in the dataset -
m_ClassIndex
int m_ClassIndex
The index of the class attribute in the dataset -
m_Header
Instances m_Header
the header information of the training data -
m_Internals
boolean m_Internals
whether to print more internals in the toString method- See Also:
WAODE.toString()
-
m_mutualInformation
double[] m_mutualInformation
The array of mutual information between each attribute and class -
m_NumAttributes
int m_NumAttributes
The number of attributes including class in the dataset -
m_NumAttValues
int[] m_NumAttValues
The number of values for each attribute in the dataset -
m_NumClasses
int m_NumClasses
The number of classes in the dataset -
m_NumInstances
int m_NumInstances
The number of instances in the dataset -
m_StartAttIndex
int[] m_StartAttIndex
The starting index of each attribute in the dataset -
m_TotalAttValues
int m_TotalAttValues
The number of values for all attributes in the dataset -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data
-
-
-
Package weka.classifiers.bayes.blr
-
Class weka.classifiers.bayes.blr.GaussianPriorImpl extends Prior implements Serializable
- serialVersionUID:
- -2995684220141159223L
-
Class weka.classifiers.bayes.blr.LaplacePriorImpl extends Prior implements Serializable
- serialVersionUID:
- 2353576123257012607L
-
Serialized Fields
-
Beta
double Beta
-
Delta
double Delta
-
DeltaUpdate
double DeltaUpdate
-
Hyperparameter
double Hyperparameter
-
m_Instances
Instances m_Instances
-
R
double[] R
-
-
Class weka.classifiers.bayes.blr.Prior extends java.lang.Object implements Serializable
-
Serialized Fields
-
Beta
double Beta
-
Delta
double Delta
-
DeltaUpdate
double DeltaUpdate
-
Hyperparameter
double Hyperparameter
-
log_likelihood
double log_likelihood
-
log_posterior
double log_posterior
-
m_Instances
Instances m_Instances
-
penalty
double penalty
-
R
double[] R
-
-
-
-
Package weka.classifiers.bayes.net
-
Class weka.classifiers.bayes.net.ADNode extends java.lang.Object implements Serializable
- serialVersionUID:
- 397409728366910204L
-
Class weka.classifiers.bayes.net.BayesNetGenerator extends EditableBayesNet implements Serializable
- serialVersionUID:
- -7462571170596157720L
-
Serialized Fields
-
m_bGenerateNet
boolean m_bGenerateNet
-
m_nCardinality
int m_nCardinality
-
m_nNrOfArcs
int m_nNrOfArcs
-
m_nNrOfInstances
int m_nNrOfInstances
-
m_nNrOfNodes
int m_nNrOfNodes
-
m_nSeed
int m_nSeed
the seed value -
m_sBIFFile
java.lang.String m_sBIFFile
-
random
java.util.Random random
the random number generator
-
-
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_bNeedsUndoAction
boolean m_bNeedsUndoAction
flag to indicate whether an edit action needs to introduce an undo action. This is only false when an undo or redo action is performed. -
m_fMarginP
FastVector m_fMarginP
marginal distributions * -
m_nCurrentEditAction
int m_nCurrentEditAction
current action in undo stack -
m_nEvidence
FastVector m_nEvidence
evidence values, used for evidence propagation * -
m_nPositionX
FastVector m_nPositionX
location of nodes, used for graph drawing * -
m_nPositionY
FastVector m_nPositionY
-
m_nSavedPointer
int m_nSavedPointer
action that the network is saved -
m_undoStack
FastVector m_undoStack
undo stack for undoin edit actions, or redo edit actions
-
-
Class weka.classifiers.bayes.net.GUI extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -2038911085935515624L
-
Serialized Fields
-
a_about
javax.swing.Action a_about
-
a_addarc
javax.swing.Action a_addarc
-
a_addnode
weka.classifiers.bayes.net.GUI.ActionAddNode a_addnode
-
a_alignbottom
javax.swing.Action a_alignbottom
-
a_alignleft
javax.swing.Action a_alignleft
-
a_alignright
javax.swing.Action a_alignright
-
a_aligntop
javax.swing.Action a_aligntop
-
a_centerhorizontal
javax.swing.Action a_centerhorizontal
-
a_centervertical
javax.swing.Action a_centervertical
-
a_copynode
javax.swing.Action a_copynode
-
a_cutnode
javax.swing.Action a_cutnode
-
a_datagenerator
javax.swing.Action a_datagenerator
-
a_datasetter
javax.swing.Action a_datasetter
-
a_delarc
javax.swing.Action a_delarc
-
a_delnode
javax.swing.Action a_delnode
-
a_export
weka.classifiers.bayes.net.GUI.ActionExport a_export
-
a_help
javax.swing.Action a_help
-
a_layout
javax.swing.Action a_layout
-
a_learn
javax.swing.Action a_learn
-
a_learnCPT
javax.swing.Action a_learnCPT
-
a_load
javax.swing.Action a_load
-
a_networkgenerator
javax.swing.Action a_networkgenerator
-
a_new
javax.swing.Action a_new
actions triggered by GUI events -
a_pastenode
javax.swing.Action a_pastenode
-
a_print
weka.classifiers.bayes.net.GUI.ActionPrint a_print
-
a_quit
javax.swing.Action a_quit
-
a_redo
javax.swing.Action a_redo
-
a_save
javax.swing.Action a_save
-
a_saveas
javax.swing.Action a_saveas
-
a_selectall
javax.swing.Action a_selectall
-
a_spacehorizontal
javax.swing.Action a_spacehorizontal
-
a_spacevertical
javax.swing.Action a_spacevertical
-
a_undo
javax.swing.Action a_undo
-
a_viewstatusbar
javax.swing.Action a_viewstatusbar
-
a_viewtoolbar
javax.swing.Action a_viewtoolbar
-
a_zoomin
javax.swing.Action a_zoomin
-
a_zoomout
javax.swing.Action a_zoomout
-
ICONPATH
java.lang.String ICONPATH
path for icons -
m_BayesNet
EditableBayesNet m_BayesNet
Container of Bayesian network -
m_bViewCliques
boolean m_bViewCliques
-
m_bViewMargins
boolean m_bViewMargins
flag indicating whether marginal distributions of each of the nodes should be shown in display. -
m_clipboard
weka.classifiers.bayes.net.GUI.ClipBoard m_clipboard
-
m_fScale
double m_fScale
current zoom value -
m_GraphPanel
weka.classifiers.bayes.net.GUI.GraphPanel m_GraphPanel
Panel actually displaying the graph -
m_Instances
Instances m_Instances
data selected from file. Used to train a Bayesian network on -
m_jScrollPane
javax.swing.JScrollPane m_jScrollPane
this contains the m_GraphPanel GraphPanel -
m_jStatusBar
javax.swing.JLabel m_jStatusBar
status bar at bottom of window -
m_jTbTools
javax.swing.JToolBar m_jTbTools
toolbar containing buttons at top of window -
m_jTfNodeHeight
javax.swing.JTextField m_jTfNodeHeight
TextField for nodes height -
m_jTfNodeWidth
javax.swing.JTextField m_jTfNodeWidth
TextField for node's width -
m_jTfZoom
javax.swing.JTextField m_jTfZoom
Text field for specifying zoom -
m_layoutEngine
LayoutEngine m_layoutEngine
The current LayoutEngine -
m_marginCalculator
MarginCalculator m_marginCalculator
used for calculating marginals in Bayesian netwowrks -
m_marginCalculatorWithEvidence
MarginCalculator m_marginCalculatorWithEvidence
used for calculating marginals in Bayesian netwowrks when evidence is present -
m_menuBar
javax.swing.JMenuBar m_menuBar
The menu bar -
m_nCurrentNode
int m_nCurrentNode
node currently selected through right clicking -
m_nNodeHeight
int m_nNodeHeight
standard width of node -
m_nNodeWidth
int m_nNodeWidth
-
m_nPaddedNodeWidth
int m_nPaddedNodeWidth
-
m_nSelectedRect
java.awt.Rectangle m_nSelectedRect
selection rectangle drawn through dragging with left mouse button -
m_nZoomPercents
int[] m_nZoomPercents
used when using zoomIn and zoomOut buttons -
m_Selection
weka.classifiers.bayes.net.GUI.Selection m_Selection
selection of nodes -
m_sFileName
java.lang.String m_sFileName
String containing file name storing current network
-
-
Class weka.classifiers.bayes.net.MarginCalculator extends java.lang.Object implements Serializable
- serialVersionUID:
- 650278019241175534L
-
Serialized Fields
-
jtNodes
MarginCalculator.JunctionTreeNode[] jtNodes
-
m_debug
boolean m_debug
-
m_Margins
double[][] m_Margins
-
m_root
MarginCalculator.JunctionTreeNode m_root
-
-
Class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode 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_children
java.util.Vector m_children
-
m_fi
double[] m_fi
potentials for first network -
m_MarginalP
double[][] m_MarginalP
-
m_nCardinality
int m_nCardinality
cardinality of the instances of variables in this junction node -
m_nNodes
int[] m_nNodes
nodes of the Bayes net in this junction node -
m_P
double[] m_P
distribution over this junction node according to first Bayes network -
m_parentSeparator
MarginCalculator.JunctionTreeSeparator m_parentSeparator
-
-
Class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator extends java.lang.Object implements Serializable
- serialVersionUID:
- 6502780192411755343L
-
Serialized Fields
-
m_bayesNet
BayesNet m_bayesNet
-
m_childNode
MarginCalculator.JunctionTreeNode m_childNode
-
m_fiChild
double[] m_fiChild
-
m_fiParent
double[] m_fiParent
-
m_nCardinality
int m_nCardinality
-
m_nNodes
int[] m_nNodes
-
m_parentNode
MarginCalculator.JunctionTreeNode m_parentNode
-
-
Class weka.classifiers.bayes.net.ParentSet extends java.lang.Object implements Serializable
- serialVersionUID:
- 4155021284407181838L
-
Serialized Fields
-
m_nCardinalityOfParents
int m_nCardinalityOfParents
Holds cardinality of parents (= number of instantiations the parents can take) -
m_nNrOfParents
int m_nNrOfParents
Holds number of parents -
m_nParents
int[] m_nParents
Holds indexes of parents
-
-
Class weka.classifiers.bayes.net.VaryNode extends java.lang.Object implements Serializable
- serialVersionUID:
- -6196294370675872424L
-
Serialized Fields
-
m_ADNodes
ADNode[] m_ADNodes
list of ADNode children -
m_iNode
int m_iNode
index of the node varied -
m_nMCV
int m_nMCV
most common value
-
-
-
Package weka.classifiers.bayes.net.estimate
-
Class weka.classifiers.bayes.net.estimate.BayesNetEstimator 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_fPrior
double m_fPrior
Holds the prior probability -
m_nSymbols
int m_nSymbols
Holds number of symbols in distribution -
m_SumOfCounts
double m_SumOfCounts
Hold the sum of counts
-
-
Class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes 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_bInitAsNaiveBayes
boolean m_bInitAsNaiveBayes
determines whether initial structure is an empty graph or a Naive Bayes network -
m_bMarkovBlanketClassifier
boolean m_bMarkovBlanketClassifier
Determines whether after structure is found a MarkovBlanketClassifier correction should be applied If this is true, m_bInitAsNaiveBayes is overridden and interpreted as false. -
m_nMaxNrOfParents
int m_nMaxNrOfParents
Holds upper bound on number of parents
-
-
-
Package weka.classifiers.bayes.net.search.ci
-
Class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm extends LocalScoreSearchAlgorithm implements Serializable
- serialVersionUID:
- 3165802334119704560L
-
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_bUseCrossOver
boolean m_bUseCrossOver
use cross-over? -
m_bUseMutation
boolean m_bUseMutation
use mutation? -
m_bUseTournamentSelection
boolean m_bUseTournamentSelection
use tournament selection or take best sub-population -
m_nDescendantPopulationSize
int m_nDescendantPopulationSize
size of descendant population -
m_nPopulationSize
int m_nPopulationSize
size of population -
m_nRuns
int m_nRuns
number of runs -
m_nSeed
int m_nSeed
random number seed -
m_random
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_nCVType
int m_nCVType
Holds the cross validation strategy used to measure quality of network -
m_nNrOfFolds
int m_nNrOfFolds
number of folds for k-fold cross validation
-
-
Class weka.classifiers.bayes.net.search.global.HillClimber 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_bUseArcReversal
boolean m_bUseArcReversal
use the arc reversal operator -
m_fDelta
double m_fDelta
change in temperature at every run -
m_fTStart
double m_fTStart
start temperature -
m_nRuns
int m_nRuns
number of runs -
m_nSeed
int m_nSeed
random number seed -
m_random
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_bUseCrossOver
boolean m_bUseCrossOver
use cross-over? -
m_bUseMutation
boolean m_bUseMutation
use mutation? -
m_bUseTournamentSelection
boolean m_bUseTournamentSelection
use tournament selection or take best sub-population -
m_nDescendantPopulationSize
int m_nDescendantPopulationSize
size of descendant population -
m_nPopulationSize
int m_nPopulationSize
size of population -
m_nRuns
int m_nRuns
number of runs -
m_nSeed
int m_nSeed
random number seed -
m_random
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_bUseArcReversal
boolean m_bUseArcReversal
use the arc reversal operator -
m_Cache
weka.classifiers.bayes.net.search.local.HillClimber.Cache m_Cache
cache for storing score differences
-
-
Class weka.classifiers.bayes.net.search.local.K2 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_nNrOfGoodOperations
int m_nNrOfGoodOperations
Number of Good Operations per Step -
m_nNrOfLookAheadSteps
int m_nNrOfLookAheadSteps
Number of Look Ahead Steps
-
-
Class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm 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_bUseArcReversal
boolean m_bUseArcReversal
use the arc reversal operator -
m_fDelta
double m_fDelta
change in temperature at every run -
m_fTStart
double m_fTStart
start temperature -
m_nRuns
int m_nRuns
number of runs -
m_nSeed
int m_nSeed
random number seed -
m_random
java.util.Random m_random
random number generator
-
-
Class weka.classifiers.bayes.net.search.local.TabuSearch extends HillClimber implements Serializable
- serialVersionUID:
- 1457344073228786447L
-
Serialized Fields
-
m_nRuns
int m_nRuns
number of runs -
m_nTabuList
int m_nTabuList
size of tabu list -
m_oTabuList
weka.classifiers.bayes.net.search.local.HillClimber.Operation[] m_oTabuList
the actual tabu list
-
-
Class weka.classifiers.bayes.net.search.local.TAN extends LocalScoreSearchAlgorithm implements Serializable
- serialVersionUID:
- 965182127977228690L
-
-
Package weka.classifiers.evaluation
-
Class weka.classifiers.evaluation.ConfusionMatrix extends Matrix implements Serializable
- serialVersionUID:
- -181789981401504090L
-
Serialized Fields
-
m_ClassNames
java.lang.String[] m_ClassNames
Stores the names of the classes
-
-
Class weka.classifiers.evaluation.NominalPrediction extends java.lang.Object implements Serializable
- serialVersionUID:
- -8871333992740492788L
-
Serialized Fields
-
m_Actual
double m_Actual
The actual class value -
m_Distribution
double[] m_Distribution
The predicted probabilities -
m_Predicted
double m_Predicted
The predicted class value -
m_Weight
double m_Weight
The weight assigned to this prediction
-
-
Class weka.classifiers.evaluation.NumericPrediction extends java.lang.Object implements Serializable
- serialVersionUID:
- -4880216423674233887L
-
Serialized Fields
-
m_Actual
double m_Actual
The actual class value -
m_Predicted
double m_Predicted
The predicted class value -
m_Weight
double m_Weight
The weight assigned to this prediction
-
-
-
Package weka.classifiers.functions
-
Class weka.classifiers.functions.GaussianProcesses extends Classifier implements Serializable
- serialVersionUID:
- -8620066949967678545L
-
Serialized Fields
-
m_Alin
double m_Alin
The parameters of the linear transforamtion realized by the filter on the class attribute -
m_avg_target
double m_avg_target
The training data. -
m_Blin
double m_Blin
-
m_C
Matrix m_C
The covariance matrix. -
m_checksTurnedOff
boolean m_checksTurnedOff
Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class. -
m_classIndex
int m_classIndex
The class index from the training data -
m_delta
double m_delta
Gaussian Noise Value. -
m_Filter
Filter m_Filter
The filter used to standardize/normalize all values. -
m_filterType
int m_filterType
Whether to normalize/standardize/neither -
m_kernel
Kernel m_kernel
Kernel to use -
m_KernelIsLinear
boolean m_KernelIsLinear
whether the kernel is a linear one -
m_Missing
ReplaceMissingValues m_Missing
The filter used to get rid of missing values. -
m_NominalToBinary
NominalToBinary m_NominalToBinary
The filter used to make attributes numeric. -
m_NumTrain
int m_NumTrain
The number of training instances -
m_t
Matrix m_t
The vector of target values.
-
-
Class weka.classifiers.functions.IsotonicRegression extends Classifier implements Serializable
- serialVersionUID:
- 1679336022835454137L
-
Serialized Fields
-
m_attribute
Attribute m_attribute
The chosen attribute -
m_cuts
double[] m_cuts
The array of cut points -
m_minMsq
double m_minMsq
The minimum mean squared error that has been achieved. -
m_values
double[] m_values
The predicted value in each interval. -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data
-
-
Class weka.classifiers.functions.LeastMedSq extends Classifier implements Serializable
- serialVersionUID:
- 4288954049987652970L
-
Serialized Fields
-
m_bestMedian
double m_bestMedian
-
m_bestRegression
LinearRegression m_bestRegression
-
m_currentRegression
LinearRegression m_currentRegression
-
m_Data
Instances m_Data
-
m_debug
boolean m_debug
-
m_israndom
boolean m_israndom
-
m_ls
LinearRegression m_ls
-
m_MissingFilter
ReplaceMissingValues m_MissingFilter
-
m_random
java.util.Random m_random
-
m_randomseed
long m_randomseed
-
m_Residuals
double[] m_Residuals
-
m_RLSData
Instances m_RLSData
-
m_samples
int m_samples
-
m_samplesize
int m_samplesize
-
m_scalefactor
double m_scalefactor
-
m_SplitFilter
RemoveRange m_SplitFilter
-
m_SSR
double m_SSR
-
m_SubSample
Instances m_SubSample
-
m_TransformFilter
NominalToBinary m_TransformFilter
-
m_weight
double[] m_weight
-
-
Class weka.classifiers.functions.LibLINEAR extends Classifier implements Serializable
- serialVersionUID:
- 230504711L
-
Serialized Fields
-
m_Bias
double m_Bias
bias term value -
m_Cost
double m_Cost
cost Parameter C -
m_eps
double m_eps
stopping criteria -
m_Filter
Filter m_Filter
for normalizing the data -
m_Model
java.lang.Object m_Model
LibLINEAR Model -
m_nominalToBinary
boolean m_nominalToBinary
If true, the nominal to binary filter is applied -
m_NominalToBinary
NominalToBinary m_NominalToBinary
The filter used to make attributes numeric. -
m_noReplaceMissingValues
boolean m_noReplaceMissingValues
If true, the replace missing values filter is not applied -
m_Normalize
boolean m_Normalize
normalize input data -
m_ProbabilityEstimates
boolean m_ProbabilityEstimates
whether to generate probability estimates instead of +1/-1 in case of classification problems -
m_ReplaceMissingValues
ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values. -
m_SVMType
int m_SVMType
the SVM solver type -
m_Weight
double[] m_Weight
-
m_WeightLabel
int[] m_WeightLabel
-
-
Class weka.classifiers.functions.LibSVM extends RandomizableClassifier implements Serializable
- serialVersionUID:
- 14172L
-
Serialized Fields
-
m_CacheSize
double m_CacheSize
in MB -
m_Coef0
double m_Coef0
for poly/sigmoid -
m_Cost
double m_Cost
cost, for C_SVC, EPSILON_SVR and NU_SVR -
m_Degree
int m_Degree
for poly - in older versions of libsvm declared as a double. At least since 2.82 it is an int. -
m_eps
double m_eps
stopping criteria -
m_Filter
Filter m_Filter
for normalizing the data -
m_Gamma
double m_Gamma
for poly/rbf/sigmoid -
m_GammaActual
double m_GammaActual
for poly/rbf/sigmoid (the actual gamma) -
m_KernelType
int m_KernelType
the kernel type -
m_Loss
double m_Loss
loss, for EPSILON_SVR -
m_Model
java.lang.Object m_Model
LibSVM Model -
m_NominalToBinary
Filter m_NominalToBinary
for converting mult-valued nominal attributes to binary -
m_noReplaceMissingValues
boolean m_noReplaceMissingValues
If true, the replace missing values filter is not applied -
m_Normalize
boolean m_Normalize
normalize input data -
m_nu
double m_nu
for NU_SVC, ONE_CLASS, and NU_SVR -
m_ProbabilityEstimates
boolean m_ProbabilityEstimates
whether to generate probability estimates instead of +1/-1 in case of classification problems -
m_ReplaceMissingValues
ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values. -
m_Shrinking
boolean m_Shrinking
use the shrinking heuristics -
m_SVMType
int m_SVMType
the SVM type -
m_Weight
double[] m_Weight
for C_SVC -
m_WeightLabel
int[] m_WeightLabel
for C_SVC
-
-
Class weka.classifiers.functions.LinearRegression extends Classifier implements Serializable
- serialVersionUID:
- -3364580862046573747L
-
Serialized Fields
-
b_Debug
boolean b_Debug
True if debug output will be printed -
m_AttributeSelection
int m_AttributeSelection
The current attribute selection method -
m_checksTurnedOff
boolean m_checksTurnedOff
Turn off all checks and conversions? -
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_ClassMean
double m_ClassMean
The mean of the class attribute -
m_ClassStdDev
double m_ClassStdDev
The standard deviations of the class attribute -
m_Coefficients
double[] m_Coefficients
Array for storing coefficients of linear regression. -
m_EliminateColinearAttributes
boolean m_EliminateColinearAttributes
Try to eliminate correlated attributes? -
m_Means
double[] m_Means
The attributes means -
m_MissingFilter
ReplaceMissingValues m_MissingFilter
The filter for removing missing values. -
m_Ridge
double m_Ridge
The ridge parameter -
m_SelectedAttributes
boolean[] m_SelectedAttributes
Which attributes are relevant? -
m_StdDevs
double[] m_StdDevs
The attribute standard deviations -
m_TransformedData
Instances m_TransformedData
Variable for storing transformed training data. -
m_TransformFilter
NominalToBinary m_TransformFilter
The filter storing the transformation from nominal to binary attributes.
-
-
Class weka.classifiers.functions.Logistic extends Classifier implements Serializable
- serialVersionUID:
- 3932117032546553727L
-
Serialized Fields
-
m_AttFilter
RemoveUseless m_AttFilter
An attribute filter -
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_Data
double[][] m_Data
The data saved as a matrix -
m_Debug
boolean m_Debug
Debugging output -
m_LL
double m_LL
Log-likelihood of the searched model -
m_MaxIts
int m_MaxIts
The maximum number of iterations. -
m_NominalToBinary
NominalToBinary m_NominalToBinary
The filter used to make attributes numeric. -
m_NumClasses
int m_NumClasses
The number of the class labels -
m_NumPredictors
int m_NumPredictors
The number of attributes in the model -
m_Par
double[][] m_Par
The coefficients (optimized parameters) of the model -
m_ReplaceMissingValues
ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values. -
m_Ridge
double m_Ridge
The ridge parameter. -
m_structure
Instances m_structure
-
-
Class weka.classifiers.functions.MultilayerPerceptron extends Classifier implements Serializable
- serialVersionUID:
- -5990607817048210779L
-
Serialized Fields
-
m_accepted
boolean m_accepted
a flag to state that the network should be accepted the way it is. -
m_attributeBases
double[] m_attributeBases
The base values for all the attributes. -
m_attributeRanges
double[] m_attributeRanges
The ranges for all the attributes. -
m_autoBuild
boolean m_autoBuild
A flag to tell the build classifier to automatically build a neural net. -
m_controlPanel
weka.classifiers.functions.MultilayerPerceptron.ControlPanel m_controlPanel
The control panel. -
m_currentInstance
Instance m_currentInstance
The current instance running through the network. -
m_decay
boolean m_decay
This flag states that the user wants the learning rate to decay. -
m_driftThreshold
int m_driftThreshold
The number to to use to quit on validation testing. -
m_epoch
int m_epoch
Shows the number of the epoch that the network just finished. -
m_error
double m_error
Shows the error of the epoch that the network just finished. -
m_graphers
FastVector m_graphers
A Vector list of the graphers. -
m_gui
boolean m_gui
A flag to state that the gui for the network should be brought up. To allow interaction while training. -
m_hiddenLayers
java.lang.String m_hiddenLayers
The string that defines the hidden layers -
m_inputs
weka.classifiers.functions.MultilayerPerceptron.NeuralEnd[] m_inputs
The input units.(only feeds the inputs does no calcs) -
m_instances
Instances m_instances
The training instances. -
m_learningRate
double m_learningRate
This is the learning rate for the network. -
m_linearUnit
LinearUnit m_linearUnit
This is a linear unit. -
m_momentum
double m_momentum
This is the momentum for the network. -
m_neuralNodes
NeuralConnection[] m_neuralNodes
All the nodes that actually comprise the logical neural net. -
m_nextId
int m_nextId
The next id number available for default naming. -
m_nodePanel
weka.classifiers.functions.MultilayerPerceptron.NodePanel m_nodePanel
The panel the nodes are displayed on. -
m_nominalToBinaryFilter
NominalToBinary m_nominalToBinaryFilter
The actual filter. -
m_normalizeAttributes
boolean m_normalizeAttributes
This flag states that the user wants the input values normalized. -
m_normalizeClass
boolean m_normalizeClass
This flag states that the user wants the class to be normalized while processing in the network is done. (the final answer will be in the original range regardless). This option will only be used when the class is numeric. -
m_numAttributes
int m_numAttributes
The number of attributes. -
m_numClasses
int m_numClasses
The number of classes. -
m_numEpochs
int m_numEpochs
The number of epochs to train through. -
m_numeric
boolean m_numeric
A flag to say that it's a numeric class. -
m_outputs
weka.classifiers.functions.MultilayerPerceptron.NeuralEnd[] m_outputs
The output units.(only feeds the errors, does no calcs) -
m_random
java.util.Random m_random
The actual random number generator. -
m_randomSeed
int m_randomSeed
The number used to seed the random number generator. -
m_reset
boolean m_reset
This flag states that the user wants the network to restart if it is found to be generating infinity or NaN for the error value. This would restart the network with the current options except that the learning rate would be smaller than before, (perhaps half of its current value). This option will not be available if the gui is chosen (if the gui is open the user can fix the network themselves, it is an architectural minefield for the network to be reset with the gui open). -
m_selected
FastVector m_selected
A Vector list of the units currently selected. -
m_sigmoidUnit
SigmoidUnit m_sigmoidUnit
this is a sigmoid unit. -
m_stopIt
boolean m_stopIt
a flag to state if the network should be running, or stopped. -
m_stopped
boolean m_stopped
a flag to state that the network has in fact stopped. -
m_useDefaultModel
boolean m_useDefaultModel
Whether to use the default ZeroR model -
m_useNomToBin
boolean m_useNomToBin
A flag to state that a nominal to binary filter should be used. -
m_valSize
int m_valSize
An int to say how big the validation set should be. -
m_win
javax.swing.JFrame m_win
The window for the network. -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data or the network predicts all zeros for the classes
-
-
Class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd extends NeuralConnection implements Serializable
- serialVersionUID:
- 7305185603191183338L
-
Serialized Fields
-
m_input
boolean m_input
True if node is an input, False if it's an output. -
m_link
int m_link
the value that represents the instance value this node represents. For an input it is the attribute number, for an output, if nominal it is the class value.
-
-
Class weka.classifiers.functions.PaceRegression extends Classifier implements Serializable
- serialVersionUID:
- 7230266976059115435L
-
Serialized Fields
-
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_Coefficients
double[] m_Coefficients
Array for storing coefficients of linear regression. -
m_Debug
boolean m_Debug
True if debug output will be printed -
m_Model
Instances m_Model
The model used -
olscThreshold
double olscThreshold
-
paceEstimator
int paceEstimator
the estimator
-
-
Class weka.classifiers.functions.PLSClassifier extends Classifier implements Serializable
- serialVersionUID:
- 4819775160590973256L
-
Class weka.classifiers.functions.RBFNetwork extends Classifier implements Serializable
- serialVersionUID:
- -3669814959712675720L
-
Serialized Fields
-
m_basisFilter
ClusterMembership m_basisFilter
The filter for producing the meta data -
m_clusteringSeed
int m_clusteringSeed
The seed to pass on to K-means -
m_linear
LinearRegression m_linear
The linear regression for numeric problems -
m_logistic
Logistic m_logistic
The logistic regression for classification problems -
m_maxIts
int m_maxIts
The maximum number of iterations for logistic regression. -
m_minStdDev
double m_minStdDev
The minimum standard deviation -
m_numClusters
int m_numClusters
The number of clusters (basis functions to generate) -
m_ridge
double m_ridge
The ridge parameter for the logistic regression. -
m_standardize
Standardize m_standardize
Filter used for normalizing the data -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data
-
-
Class weka.classifiers.functions.SimpleLinearRegression extends Classifier implements Serializable
- serialVersionUID:
- 1679336022895414137L
-
Serialized Fields
-
m_attribute
Attribute m_attribute
The chosen attribute -
m_attributeIndex
int m_attributeIndex
The index of the chosen attribute -
m_intercept
double m_intercept
The intercept -
m_slope
double m_slope
The slope -
m_suppressErrorMessage
boolean m_suppressErrorMessage
If true, suppress error message if no useful attribute was found
-
-
Class weka.classifiers.functions.SimpleLogistic extends Classifier implements Serializable
- serialVersionUID:
- 7397710626304705059L
-
Serialized Fields
-
m_boostedModel
LogisticBase m_boostedModel
The actual logistic regression model -
m_errorOnProbabilities
boolean m_errorOnProbabilities
If true, use minimize error on probabilities instead of misclassification error -
m_heuristicStop
int m_heuristicStop
Parameter for the heuristic for early stopping of LogitBoost -
m_maxBoostingIterations
int m_maxBoostingIterations
Maximum number of iterations for LogitBoost -
m_NominalToBinary
NominalToBinary m_NominalToBinary
Filter for converting nominal attributes to binary ones -
m_numBoostingIterations
int m_numBoostingIterations
If non-negative, use this as fixed number of LogitBoost iterations -
m_ReplaceMissingValues
ReplaceMissingValues m_ReplaceMissingValues
Filter for replacing missing values -
m_useAIC
boolean m_useAIC
If true, the AIC is used to choose the best iteration -
m_useCrossValidation
boolean m_useCrossValidation
If true, cross-validate number of LogitBoost iterations -
m_weightTrimBeta
double m_weightTrimBeta
Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.
-
-
Class weka.classifiers.functions.SMO extends Classifier implements Serializable
- serialVersionUID:
- -6585883636378691736L
-
Serialized Fields
-
m_C
double m_C
The complexity parameter. -
m_checksTurnedOff
boolean m_checksTurnedOff
Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0. -
m_classAttribute
Attribute m_classAttribute
The class attribute -
m_classifiers
SMO.BinarySMO[][] m_classifiers
The binary classifier(s) -
m_classIndex
int m_classIndex
The class index from the training data -
m_eps
double m_eps
Epsilon for rounding. -
m_Filter
Filter m_Filter
The filter used to standardize/normalize all values. -
m_filterType
int m_filterType
Whether to normalize/standardize/neither -
m_fitLogisticModels
boolean m_fitLogisticModels
Whether logistic models are to be fit -
m_kernel
Kernel m_kernel
the kernel to use -
m_KernelIsLinear
boolean m_KernelIsLinear
whether the kernel is a linear one -
m_Missing
ReplaceMissingValues m_Missing
The filter used to get rid of missing values. -
m_NominalToBinary
NominalToBinary m_NominalToBinary
The filter used to make attributes numeric. -
m_numFolds
int m_numFolds
The number of folds for the internal cross-validation -
m_randomSeed
int m_randomSeed
The random number seed -
m_tol
double m_tol
Tolerance for accuracy of result.
-
-
Class weka.classifiers.functions.SMO.BinarySMO 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_class
double[] m_class
The transformed class values. -
m_data
Instances m_data
The training data. -
m_errors
double[] m_errors
The current set of errors for all non-bound examples. -
m_I0
SMOset m_I0
{i: 0 < 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_iLow
int m_iLow
The indices for m_bLow and m_bUp -
m_iUp
int m_iUp
The indices for m_bLow and m_bUp -
m_kernel
Kernel m_kernel
Kernel to use -
m_logistic
Logistic m_logistic
Stores logistic regression model for probability estimate -
m_sparseIndices
int[] m_sparseIndices
-
m_sparseWeights
double[] m_sparseWeights
Variables to hold weight vector in sparse form. (To reduce storage requirements.) -
m_sumOfWeights
double m_sumOfWeights
Stores the weight of the training instances -
m_supportVectors
SMOset m_supportVectors
The set of support vectors -
m_weights
double[] m_weights
Weight vector for linear machine.
-
-
Class weka.classifiers.functions.SMOreg extends Classifier implements Serializable
- serialVersionUID:
- -7149606251113102827L
-
Serialized Fields
-
m_C
double m_C
capacity parameter -
m_Filter
Filter m_Filter
The filter used to standardize/normalize all values. -
m_filterType
int m_filterType
Whether to normalize/standardize/neither -
m_kernel
Kernel m_kernel
the configured kernel -
m_Missing
ReplaceMissingValues m_Missing
The filter used to get rid of missing values. -
m_NominalToBinary
NominalToBinary m_NominalToBinary
The filter used to make attributes numeric. -
m_onlyNumeric
boolean m_onlyNumeric
Only numeric attributes in the dataset? If so, less need to filter -
m_optimizer
RegOptimizer m_optimizer
contains the algorithm used for learning -
m_x0
double m_x0
-
m_x1
double m_x1
coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0
-
-
Class weka.classifiers.functions.SPegasos extends Classifier implements Serializable
- serialVersionUID:
- -3732968666673530290L
-
Serialized Fields
-
m_data
Instances m_data
Holds the header of the training data -
m_dontNormalize
boolean m_dontNormalize
Turn off normalization of the input data. This option gets forced for incremental training. -
m_dontReplaceMissing
boolean m_dontReplaceMissing
Turn off global replacement of missing values. Missing values will be ignored instead. This option gets forced for incremental training. -
m_epochs
int m_epochs
The number of epochs to perform (batch learning). Total iterations is m_epochs * num instances -
m_lambda
double m_lambda
The regularization parameter -
m_loss
int m_loss
The current loss function to minimize -
m_nominalToBinary
NominalToBinary m_nominalToBinary
Convert nominal attributes to numerically coded binary ones -
m_normalize
Normalize m_normalize
Normalize the training data -
m_replaceMissing
ReplaceMissingValues m_replaceMissing
Replace missing values -
m_t
double m_t
Holds the current iteration number -
m_weights
double[] m_weights
Stores the weights (+ bias in the last element)
-
-
Class weka.classifiers.functions.VotedPerceptron extends Classifier implements Serializable
- serialVersionUID:
- -1072429260104568698L
-
Serialized Fields
-
m_Additions
int[] m_Additions
The training instances added to the perceptron -
m_Exponent
double m_Exponent
The exponent -
m_IsAddition
boolean[] m_IsAddition
Addition or subtraction? -
m_K
int m_K
The actual number of alterations -
m_MaxK
int m_MaxK
The maximum number of alterations to the perceptron -
m_NominalToBinary
NominalToBinary m_NominalToBinary
The filter used to make attributes numeric. -
m_NumIterations
int m_NumIterations
The number of iterations -
m_ReplaceMissingValues
ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values. -
m_Seed
int m_Seed
Seed used for shuffling the dataset -
m_Train
Instances m_Train
The training instances -
m_Weights
int[] m_Weights
The weights for each perceptron
-
-
Class weka.classifiers.functions.Winnow extends Classifier implements Serializable
- serialVersionUID:
- 3543770107994321324L
-
Serialized Fields
-
m_actualThreshold
double m_actualThreshold
The true threshold used for prediction -
m_Alpha
double m_Alpha
The promotion coefficient -
m_Balanced
boolean m_Balanced
Use the balanced variant? -
m_Beta
double m_Beta
The demotion coefficient -
m_defaultWeight
double m_defaultWeight
Starting weights for the prediction vector(s) -
m_Mistakes
int m_Mistakes
Accumulated mistake count (for statistics) -
m_NominalToBinary
NominalToBinary m_NominalToBinary
The filter used to make attributes numeric. -
m_numIterations
int m_numIterations
The number of iterations -
m_predNegVector
double[] m_predNegVector
The weight vector for prediction (neg) -
m_predPosVector
double[] m_predPosVector
The weight vector for prediction (pos) -
m_ReplaceMissingValues
ReplaceMissingValues m_ReplaceMissingValues
The filter used to get rid of missing values. -
m_Seed
int m_Seed
Random seed used for shuffling the dataset, -1 == disable -
m_Threshold
double m_Threshold
Prediction threshold, <0 == numAttributes -
m_Train
Instances m_Train
The training instances
-
-
-
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_id
java.lang.String m_id
The string that uniquely (provided naming is done properly) identifies this unit. -
m_inputList
NeuralConnection[] m_inputList
The list of inputs to this unit. -
m_inputNums
int[] m_inputNums
The numbering for the connections at the other end of the input lines. -
m_numInputs
int m_numInputs
The number of inputs. -
m_numOutputs
int m_numOutputs
The number of outputs. -
m_outputList
NeuralConnection[] m_outputList
The list of outputs from this unit. -
m_outputNums
int[] m_outputNums
The numbering for the connections at the other end of the out lines. -
m_type
int m_type
The type of unit this is. -
m_unitError
double m_unitError
The error value for this unit, NaN if not calculated. -
m_unitValue
double m_unitValue
The output value for this unit, NaN if not calculated. -
m_weightsUpdated
boolean m_weightsUpdated
True if the weights have already been updated. -
m_x
double m_x
The x coord of this unit purely for displaying purposes. -
m_y
double m_y
The y coord of this unit purely for displaying purposes.
-
-
Class weka.classifiers.functions.neural.NeuralNode extends NeuralConnection implements Serializable
- serialVersionUID:
- -1085750607680839163L
-
Serialized Fields
-
m_bestWeights
double[] m_bestWeights
The best (lowest error) weights. Only used when validation set is used -
m_changeInWeights
double[] m_changeInWeights
The change in the weights. -
m_methods
NeuralMethod m_methods
Performs the operations for this node. Currently this defines that the node is either a sigmoid or a linear unit. -
m_random
java.util.Random m_random
-
m_weights
double[] m_weights
The weights for each of the input connections, and the threshold.
-
-
Class weka.classifiers.functions.neural.SigmoidUnit extends java.lang.Object implements Serializable
- serialVersionUID:
- -5162958458177475652L
-
-
Package weka.classifiers.functions.pace
-
Class weka.classifiers.functions.pace.PaceMatrix extends Matrix implements Serializable
- serialVersionUID:
- 2699925616857843973L
-
-
Package weka.classifiers.functions.supportVector
-
Class weka.classifiers.functions.supportVector.CachedKernel extends Kernel implements Serializable
- serialVersionUID:
- 702810182699015136L
-
Serialized Fields
-
m_cacheHits
int m_cacheHits
Counts the number of kernel cache hits. -
m_cacheSize
int m_cacheSize
The size of the cache (a prime number) -
m_cacheSlots
int m_cacheSlots
number of cache slots in an entry -
m_kernelEvals
int m_kernelEvals
Counts the number of kernel evaluations. -
m_kernelMatrix
double[][] m_kernelMatrix
The kernel matrix if full cache is used (i.e. size is set to 0) -
m_keys
long[] m_keys
-
m_numInsts
int m_numInsts
The number of instance in the dataset -
m_storage
double[] m_storage
Kernel cache
-
-
Class weka.classifiers.functions.supportVector.Kernel extends java.lang.Object implements Serializable
- serialVersionUID:
- -6102771099905817064L
-
Serialized Fields
-
m_ChecksTurnedOff
boolean m_ChecksTurnedOff
Turns off all checks -
m_data
Instances m_data
The dataset -
m_Debug
boolean m_Debug
enables debugging output
-
-
Class weka.classifiers.functions.supportVector.NormalizedPolyKernel extends PolyKernel implements Serializable
- serialVersionUID:
- 1248574185532130851L
-
Class weka.classifiers.functions.supportVector.PolyKernel extends CachedKernel implements Serializable
- serialVersionUID:
- -321831645846363201L
-
Serialized Fields
-
m_exponent
double m_exponent
The exponent for the polynomial kernel. -
m_lowerOrder
boolean m_lowerOrder
Use lower-order terms?
-
-
Class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel extends Kernel implements Serializable
- serialVersionUID:
- -321831645846363333L
-
Serialized Fields
-
m_Counter
int m_Counter
A classifier counter. -
m_KernelMatrix
Matrix m_KernelMatrix
The kernel matrix. -
m_KernelMatrixFile
java.io.File m_KernelMatrixFile
The file holding the kernel matrix.
-
-
Class weka.classifiers.functions.supportVector.Puk extends CachedKernel implements Serializable
- serialVersionUID:
- 1682161522559978851L
-
Serialized Fields
-
m_factor
double m_factor
Cached factor for the Puk kernel. -
m_kernelPrecalc
double[] m_kernelPrecalc
The precalculated dotproducts of <inst_i,inst_i> -
m_omega
double m_omega
Omega for the Puk kernel. -
m_sigma
double m_sigma
Sigma for the Puk kernel.
-
-
Class weka.classifiers.functions.supportVector.RBFKernel extends CachedKernel implements Serializable
- serialVersionUID:
- 5247117544316387852L
-
Serialized Fields
-
m_gamma
double m_gamma
Gamma for the RBF kernel. -
m_kernelPrecalc
double[] m_kernelPrecalc
The precalculated dotproducts of <inst_i,inst_i>
-
-
Class weka.classifiers.functions.supportVector.RegOptimizer 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_bModelBuilt
boolean m_bModelBuilt
flag to indicate whether the model is built yet -
m_C
double m_C
capacity parameter, copied from SMOreg -
m_classIndex
int m_classIndex
index of class variable in data set -
m_data
Instances m_data
points to data set -
m_epsilon
double m_epsilon
epsilon of epsilon-insensitive cost function -
m_kernel
Kernel m_kernel
the kernel -
m_nCacheHits
int m_nCacheHits
number of kernel cache hits, used for printing statistics only -
m_nEvals
long m_nEvals
number of kernel evaluations, used for printing statistics only -
m_nInstances
int m_nInstances
number of instances in data set -
m_nSeed
int m_nSeed
seed for initializing random number generator -
m_random
java.util.Random m_random
random number generator -
m_sparseIndices
int[] m_sparseIndices
-
m_sparseWeights
double[] m_sparseWeights
Variables to hold weight vector in sparse form. (To reduce storage requirements.) -
m_supportVectors
SMOset m_supportVectors
set of support vectors, that is, vectors with alpha(*)!=0 -
m_SVM
SMOreg m_SVM
parent SMOreg class -
m_target
double[] m_target
class values/desired output vector -
m_weights
double[] m_weights
weights for linear kernel
-
-
Class weka.classifiers.functions.supportVector.RegSMO extends RegOptimizer implements Serializable
- serialVersionUID:
- -7504070793279598638L
-
Serialized Fields
-
m_alpha1
double m_alpha1
alpha value for first candidate -
m_alpha1Star
double m_alpha1Star
alpha* value for first candidate -
m_alpha2
double m_alpha2
alpha value for second candidate -
m_alpha2Star
double m_alpha2Star
alpha* value for second candidate -
m_eps
double m_eps
tolerance parameter, smaller changes on alpha in inner loop will be ignored -
m_error
double[] m_error
error cache containing m_error[i] = SVMOutput(i) - m_target[i] - m_b
note, we don't need m_b in the cache, since if we do, we need to maintain it when m_b is updated
-
-
Class weka.classifiers.functions.supportVector.RegSMOImproved extends RegSMO implements Serializable
- serialVersionUID:
- 471692841446029784L
-
Serialized Fields
-
m_bLow
double m_bLow
b.up and b.low boundaries used to determine stopping criterion -
m_bUp
double m_bUp
b.up and b.low boundaries used to determine stopping criterion -
m_bUseVariant1
boolean m_bUseVariant1
set true to use variant 1 of the paper, otherwise use variant 2 -
m_fTolerance
double m_fTolerance
tolerance parameter used for checking stopping criterion b.up < b.low + 2 tol -
m_I0
SMOset m_I0
The different sets used by the algorithm. -
m_iLow
int m_iLow
index of the instance that gave us b.up and b.low -
m_iSet
int[] m_iSet
Index set {i: 0 < m_alpha[i] < C || 0 < m_alphaStar[i] < C}} -
m_iUp
int m_iUp
index of the instance that gave us b.up and b.low
-
-
Class weka.classifiers.functions.supportVector.SMOset extends java.lang.Object implements Serializable
- serialVersionUID:
- -8364829283188675777L
-
Serialized Fields
-
m_first
int m_first
The first element in the set -
m_indicators
boolean[] m_indicators
Indicators -
m_next
int[] m_next
The next element for each element -
m_number
int m_number
The current number of elements in the set -
m_previous
int[] m_previous
The previous element for each element
-
-
Class weka.classifiers.functions.supportVector.StringKernel extends Kernel implements Serializable
- serialVersionUID:
- -4902954211202690123L
-
Serialized Fields
-
cachekh
double[] cachekh
-
cachekh2
double[] cachekh2
-
cachekh2K
int[] cachekh2K
-
cachekhK
int[] cachekhK
-
m_cacheSize
int m_cacheSize
The size of the cache (a prime number) -
m_internalCacheSize
int m_internalCacheSize
The size of the internal cache for intermediate results (a prime number) -
m_kernelEvals
int m_kernelEvals
Counts the number of kernel evaluations. -
m_keys
long[] m_keys
-
m_lambda
double m_lambda
the decay factor that penalizes non-continuous substring matches. See [1] for details. -
m_maxSubsequenceLength
int m_maxSubsequenceLength
The maximum substring length for lambda pruning -
m_multX
int m_multX
cached indexes for private cache -
m_multY
int m_multY
-
m_multZ
int m_multZ
-
m_multZZ
int m_multZZ
-
m_normalize
boolean m_normalize
flag for switching normalization on or off. This defaults to false and can be turned on by the switch for feature space normalization in SMO -
m_numInsts
int m_numInsts
The number of instance in the dataset -
m_powersOflambda
double[] m_powersOflambda
the precalculated powers of lambda -
m_PruningMethod
int m_PruningMethod
the pruning method -
m_storage
double[] m_storage
Kernel cache (i.e., cache for kernel evaluations) -
m_strAttr
int m_strAttr
The attribute number of the string attribute -
m_subsequenceLength
int m_subsequenceLength
The substring length -
m_useRecursionCache
boolean m_useRecursionCache
-
maxCache
int maxCache
private cache for intermediate results
-
-
-
Package weka.classifiers.lazy
-
Class weka.classifiers.lazy.IB1 extends Classifier implements Serializable
- serialVersionUID:
- -6152184127304895851L
-
Serialized Fields
-
m_MaxArray
double[] m_MaxArray
The maximum values for numeric attributes. -
m_MinArray
double[] m_MinArray
The minimum values for numeric attributes. -
m_Train
Instances m_Train
The training instances used for classification.
-
-
Class weka.classifiers.lazy.IBk extends Classifier implements Serializable
- serialVersionUID:
- -3080186098777067172L
-
Serialized Fields
-
m_ClassType
int m_ClassType
The class attribute type. -
m_CrossValidate
boolean m_CrossValidate
Whether to select k by cross validation. -
m_defaultModel
ZeroR m_defaultModel
Default ZeroR model to use when there are no training instances -
m_DistanceWeighting
int m_DistanceWeighting
Whether the neighbours should be distance-weighted. -
m_kNN
int m_kNN
The number of neighbours to use for classification (currently). -
m_kNNUpper
int m_kNNUpper
The value of kNN provided by the user. This may differ from m_kNN if cross-validation is being used. -
m_kNNValid
boolean m_kNNValid
Whether the value of k selected by cross validation has been invalidated by a change in the training instances. -
m_MeanSquared
boolean m_MeanSquared
Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks. -
m_NNSearch
NearestNeighbourSearch m_NNSearch
for nearest-neighbor search. -
m_NumAttributesUsed
double m_NumAttributesUsed
The number of attributes the contribute to a prediction. -
m_NumClasses
int m_NumClasses
The number of class values (or 1 if predicting numeric). -
m_Train
Instances m_Train
The training instances used for classification. -
m_WindowSize
int m_WindowSize
The maximum number of training instances allowed. When this limit is reached, old training instances are removed, so the training data is "windowed". Set to 0 for unlimited numbers of instances.
-
-
Class weka.classifiers.lazy.KStar extends Classifier implements Serializable
- serialVersionUID:
- 332458330800479083L
-
Serialized Fields
-
m_BlendMethod
int m_BlendMethod
0 = use specified blend, 1 = entropic blend setting -
m_Cache
KStarCache[] m_Cache
A custom data structure for caching distinct attribute values and their scale factor or stop parameter. -
m_ClassType
int m_ClassType
The class attribute type -
m_ComputeRandomCols
int m_ComputeRandomCols
Flag turning on and off the computation of random class colomns -
m_GlobalBlend
int m_GlobalBlend
default sphere of influence blend setting -
m_InitFlag
int m_InitFlag
Flag turning on and off the initialisation of config variables -
m_MissingMode
int m_MissingMode
missing value treatment -
m_NumAttributes
int m_NumAttributes
The number of attributes -
m_NumClasses
int m_NumClasses
The number of class values -
m_NumInstances
int m_NumInstances
The number of instances in the dataset -
m_RandClassCols
int[][] m_RandClassCols
Table of random class value colomns -
m_Train
Instances m_Train
The training instances used for classification.
-
-
Class weka.classifiers.lazy.LBR extends Classifier implements Serializable
- serialVersionUID:
- 5648559277738985156L
-
Serialized Fields
-
bestCnt
int bestCnt
-
forCnt
int forCnt
-
leftHand
java.util.ArrayList leftHand
best attribute's index list. maybe as output result -
m_Counts
int[][][] m_Counts
All the counts for nominal attributes. -
m_ErrorFlags
boolean[] m_ErrorFlags
leave-one-out error flags on the training dataaet. -
m_Errors
int m_Errors
leave-one-out errors on the training dataset. -
m_Instances
Instances m_Instances
The set of instances used for current training. -
m_NCV
boolean m_NCV
for printing in n-fold cross validation -
m_numAtts
int m_numAtts
number of attributes for the dataset -
m_Number
int m_Number
the number of instance to be processed -
m_NumberOfInstances
int m_NumberOfInstances
the Number of Instances to be used in building a classifiers -
m_numClasses
int m_numClasses
number of classes for dataset -
m_numInsts
int m_numInsts
number of instances in dataset -
m_Priors
int[] m_Priors
The prior probabilities of the classes. -
m_RemainderErrors
int m_RemainderErrors
the number of instances to be classified incorrectly besides the subset. -
m_subInstances
LBR.Indexes m_subInstances
index of instances and attributes for the given dataset -
m_subOldErrorFlags
boolean[] m_subOldErrorFlags
following is defined by wangzh, the number of instances to be classified incorrectly on the subset. -
m_tCounts
int[][][] m_tCounts
All the counts for nominal attributes. -
m_tPriors
int[] m_tPriors
The prior probabilities of the classes. -
posteriorsArray
double[] posteriorsArray
probability values array -
tempCnt
int tempCnt
-
tempSubInstances
LBR.Indexes tempSubInstances
index of instances and attributes for the given dataset -
whileCnt
int whileCnt
-
-
Class weka.classifiers.lazy.LBR.Indexes extends java.lang.Object implements Serializable
- serialVersionUID:
- -2771490019751421307L
-
Serialized Fields
-
m_AttIndexes
boolean[] m_AttIndexes
the array attribute indexes -
m_ClassIndex
int m_ClassIndex
the Class Index for the data set -
m_InstIndexes
boolean[] m_InstIndexes
the array instance indexes -
m_NumAtts
int m_NumAtts
the number of attributes indexed -
m_NumAttsSet
int m_NumAttsSet
the number of attributes "in use" or set to a the original value (true or false) -
m_NumInstances
int m_NumInstances
the number of instances indexed -
m_NumInstsSet
int m_NumInstsSet
the number of instances "in use" or set to a the original value (true or false) -
m_NumSeqAttsSet
int m_NumSeqAttsSet
the number of sequential attributes "in use" or set to a the original value (true or false) -
m_NumSeqInstsSet
int m_NumSeqInstsSet
the number of sequential instances "in use" or set to a the original value (true or false) -
m_SequentialAttIndex_valid
boolean m_SequentialAttIndex_valid
flag to check if sequential array must be rebuilt due to changes to the attribute index -
m_SequentialAttIndexes
int[] m_SequentialAttIndexes
an array of attribute indexes that are set to either true or false -
m_SequentialInstanceIndex_valid
boolean m_SequentialInstanceIndex_valid
flag to check if sequential array must be rebuilt due to changes to the instance index -
m_SequentialInstIndexes
int[] m_SequentialInstIndexes
the array of instance indexes that are set to a either true or false
-
-
Class weka.classifiers.lazy.LWL extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 1979797405383665815L
-
Serialized Fields
-
m_kNN
int m_kNN
The number of neighbours used to select the kernel bandwidth. -
m_NNSearch
NearestNeighbourSearch m_NNSearch
The nearest neighbour search algorithm to use. (Default: weka.core.neighboursearch.LinearNNSearch) -
m_Train
Instances m_Train
The training instances used for classification. -
m_UseAllK
boolean m_UseAllK
True if m_kNN should be set to all instances. -
m_WeightKernel
int m_WeightKernel
The weighting kernel method currently selected. -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data.
-
-
-
Package weka.classifiers.lazy.kstar
-
Class weka.classifiers.lazy.kstar.KStarCache 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
-
DEFAULT_LOAD_FACTOR
float DEFAULT_LOAD_FACTOR
The default load factor for the hashtable -
DEFAULT_TABLE_SIZE
int DEFAULT_TABLE_SIZE
The default size of the hashtable -
EPSILON
double EPSILON
Accuracy value for equality -
m_Count
int m_Count
The total number of entries in the hash table. -
m_LoadFactor
float m_LoadFactor
The load factor for the hashtable. -
m_Table
KStarCache.TableEntry[] m_Table
The hash table data. -
m_Threshold
int m_Threshold
Rehashes the table when count exceeds this threshold.
-
-
Class weka.classifiers.lazy.kstar.KStarCache.TableEntry extends java.lang.Object implements Serializable
- serialVersionUID:
- 4057602386766259138L
-
Serialized Fields
-
hash
int hash
attribute value hash code -
key
double key
attribute value -
next
KStarCache.TableEntry next
next table entry (separate chaining) -
pmiss
double pmiss
transformation probability to missing value -
value
double value
scale factor or stop parameter
-
-
-
Package weka.classifiers.meta
-
Class weka.classifiers.meta.AdaBoostM1 extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -7378107808933117974L
-
Serialized Fields
-
m_Betas
double[] m_Betas
Array for storing the weights for the votes. -
m_NumClasses
int m_NumClasses
The number of classes -
m_NumIterationsPerformed
int m_NumIterationsPerformed
The number of successfully generated base classifiers. -
m_UseResampling
boolean m_UseResampling
Use boosting with reweighting? -
m_WeightThreshold
int m_WeightThreshold
Weight Threshold. The percentage of weight mass used in training -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data
-
-
Class weka.classifiers.meta.AdditiveRegression extends IteratedSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -2368937577670527151L
-
Serialized Fields
-
m_NumIterationsPerformed
int m_NumIterationsPerformed
The number of successfully generated base classifiers. -
m_shrinkage
double m_shrinkage
Shrinkage (Learning rate). Default = no shrinkage. -
m_SuitableData
boolean m_SuitableData
whether we have suitable data or nor (if not, ZeroR model is used) -
m_zeroR
ZeroR m_zeroR
The model for the mean
-
-
Class weka.classifiers.meta.AttributeSelectedClassifier extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -5951805453487947577L
-
Serialized Fields
-
m_AttributeSelection
AttributeSelection m_AttributeSelection
The attribute selection object -
m_Evaluator
ASEvaluation m_Evaluator
The attribute evaluator to use -
m_numAttributesSelected
double m_numAttributesSelected
The number of attributes selected by the attribute selection phase -
m_numClasses
int m_numClasses
The number of class vals in the training data (1 if class is numeric) -
m_ReducedHeader
Instances m_ReducedHeader
The header of the dimensionally reduced data -
m_Search
ASSearch m_Search
The search method to use -
m_selectionTime
double m_selectionTime
The time taken to select attributes in milliseconds -
m_totalTime
double m_totalTime
The time taken to select attributes AND build the classifier
-
-
Class weka.classifiers.meta.Bagging extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -5178288489778728847L
-
Serialized Fields
-
m_BagSizePercent
int m_BagSizePercent
The size of each bag sample, as a percentage of the training size -
m_CalcOutOfBag
boolean m_CalcOutOfBag
Whether to calculate the out of bag error -
m_OutOfBagError
double m_OutOfBagError
The out of bag error that has been calculated
-
-
Class weka.classifiers.meta.ClassificationViaClustering extends Classifier implements Serializable
- serialVersionUID:
- -5687069451420259135L
-
Serialized Fields
-
m_ActualClusterer
Clusterer m_ActualClusterer
the actual cluster algorithm being used -
m_Clusterer
Clusterer m_Clusterer
the cluster algorithm used (template) -
m_ClusteringHeader
Instances m_ClusteringHeader
the modified training data header -
m_ClustersToClasses
double[] m_ClustersToClasses
the mapping between clusters and classes -
m_OriginalHeader
Instances m_OriginalHeader
the original training data header -
m_ZeroR
Classifier m_ZeroR
the default model
-
-
Class weka.classifiers.meta.ClassificationViaRegression extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 4500023123618669859L
-
Serialized Fields
-
m_ClassFilters
MakeIndicator[] m_ClassFilters
The filters used to transform the class. -
m_Classifiers
Classifier[] m_Classifiers
The classifiers. (One for each class.)
-
-
Class weka.classifiers.meta.CostSensitiveClassifier extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -720658209263002404L
-
Serialized Fields
-
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_MatrixSource
int m_MatrixSource
Indicates the current cost matrix source -
m_MinimizeExpectedCost
boolean m_MinimizeExpectedCost
True if the costs should be used by selecting the minimum expected cost (false means weight training data by the costs) -
m_OnDemandDirectory
java.io.File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory
-
-
Class weka.classifiers.meta.CVParameterSelection extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -6529603380876641265L
-
Serialized Fields
-
m_BestClassifierOptions
java.lang.String[] m_BestClassifierOptions
The set of all classifier options as determined by cross-validation -
m_BestPerformance
double m_BestPerformance
The cross-validated performance of the best options -
m_ClassifierOptions
java.lang.String[] m_ClassifierOptions
The base classifier options (not including those being set by cross-validation) -
m_CVParams
FastVector m_CVParams
The set of parameters to cross-validate over -
m_InitOptions
java.lang.String[] m_InitOptions
The set of all options at initialization time. So that getOptions can return this. -
m_NumAttributes
int m_NumAttributes
The number of attributes in the data -
m_NumFolds
int m_NumFolds
The number of folds used in cross-validation -
m_TrainFoldSize
int m_TrainFoldSize
The number of instances in a training fold
-
-
Class weka.classifiers.meta.CVParameterSelection.CVParameter extends java.lang.Object implements Serializable
- serialVersionUID:
- -4668812017709421953L
-
Serialized Fields
-
m_AddAtEnd
boolean m_AddAtEnd
True if the parameter should be added at the end of the argument list -
m_Lower
double m_Lower
Lower bound for the CV search -
m_ParamChar
java.lang.String m_ParamChar
Char used to identify the option of interest -
m_ParamValue
double m_ParamValue
The parameter value with the best performance -
m_RoundParam
boolean m_RoundParam
True if the parameter should be rounded to an integer -
m_Steps
double m_Steps
Number of steps during the search -
m_Upper
double m_Upper
Upper bound for the CV search
-
-
Class weka.classifiers.meta.Dagging extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 4560165876570074309L
-
Serialized Fields
-
m_NumFolds
int m_NumFolds
the number of folds to use to split the training data -
m_Verbose
boolean m_Verbose
whether to output some progress information during building -
m_Vote
Vote m_Vote
the classifier used for voting
-
-
Class weka.classifiers.meta.Decorate extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -6020193348750269931L
-
Serialized Fields
-
m_ArtSize
double m_ArtSize
Amount of artificial/random instances to use - specified as a fraction of the training data size. -
m_AttributeStats
java.util.Vector m_AttributeStats
Attribute statistics - used for generating artificial examples. -
m_Committee
java.util.Vector m_Committee
Vector of classifiers that make up the committee/ensemble. -
m_DesiredSize
int m_DesiredSize
The desired ensemble size. -
m_Random
java.util.Random m_Random
The random number generator.
-
-
Class weka.classifiers.meta.END extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -4143242362912214956L
-
Serialized Fields
-
m_hashtable
java.util.Hashtable m_hashtable
The hashtable containing the classifiers for the END.
-
-
Class weka.classifiers.meta.FilteredClassifier extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -4523450618538717400L
-
Class weka.classifiers.meta.Grading extends Stacking implements Serializable
- serialVersionUID:
- 5207837947890081170L
-
Serialized Fields
-
m_InstPerClass
double[] m_InstPerClass
InstPerClass -
m_MetaClassifiers
Classifier[] m_MetaClassifiers
The meta classifiers, one for each base classifier.
-
-
Class weka.classifiers.meta.GridSearch extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -3034773968581595348L
-
Serialized Fields
-
m_BestClassifier
Classifier m_BestClassifier
the Classifier with the best setup -
m_BestFilter
Filter m_BestFilter
the Filter with the best setup -
m_Cache
weka.classifiers.meta.GridSearch.PerformanceCache m_Cache
the cache for points in the grid that got calculated -
m_Data
Instances m_Data
the training data -
m_Evaluation
int m_Evaluation
the type of evaluation -
m_Filter
Filter m_Filter
the Filter -
m_Grid
weka.classifiers.meta.GridSearch.Grid m_Grid
the value-pairs grid -
m_GridExtensionsPerformed
int m_GridExtensionsPerformed
the number of extensions performed -
m_GridIsExtendable
boolean m_GridIsExtendable
whether the grid can be extended -
m_LogFile
java.io.File m_LogFile
the log file to use -
m_MaxGridExtensions
int m_MaxGridExtensions
maximum number of grid extensions (-1 means unlimited) -
m_SampleSize
double m_SampleSize
the sample size to search the initial grid with -
m_Traversal
int m_Traversal
the traversal -
m_UniformPerformance
boolean m_UniformPerformance
whether all performances in the grid are the same -
m_Values
weka.classifiers.meta.GridSearch.PointDouble m_Values
the best values -
m_X_Base
double m_X_Base
the base for -
m_X_Expression
java.lang.String m_X_Expression
The expression for the X property. Available parameters for the expression:- BASE
- FROM (= min)
- TO (= max)
- STEP
- I - the current value (from 'from' to 'to' with stepsize 'step')
- See Also:
MathematicalExpression
,MathExpression
-
m_X_Max
double m_X_Max
the maximum of X -
m_X_Min
double m_X_Min
the minimum of X -
m_X_Property
java.lang.String m_X_Property
the X option to work on (without leading dash, preceding 'classifier.' means to set the option for the classifier 'filter.' for the filter) -
m_X_Step
double m_X_Step
the step size of -
m_Y_Base
double m_Y_Base
the base for Y -
m_Y_Expression
java.lang.String m_Y_Expression
The expression for the Y property. Available parameters for the expression:- BASE
- FROM (= min)
- TO (= max)
- STEP
- I - the current value (from 'from' to 'to' with stepsize 'step')
- See Also:
MathematicalExpression
,MathExpression
-
m_Y_Max
double m_Y_Max
the maximum of Y -
m_Y_Min
double m_Y_Min
the minimum of Y -
m_Y_Property
java.lang.String m_Y_Property
the Y option to work on (without leading dash, preceding 'classifier.' means to set the option for the classifier 'filter.' for the filter) -
m_Y_Step
double m_Y_Step
the step size of Y
-
-
Class weka.classifiers.meta.GridSearch.Grid extends java.lang.Object implements Serializable
- serialVersionUID:
- 7290732613611243139L
-
Serialized Fields
-
m_Height
int m_Height
the number of points on the Y axis -
m_LabelX
java.lang.String m_LabelX
the label for the X axis -
m_LabelY
java.lang.String m_LabelY
the label for the Y axis -
m_MaxX
double m_MaxX
the maximum on the X axis -
m_MaxY
double m_MaxY
the maximum on the Y axis -
m_MinX
double m_MinX
the minimum on the X axis -
m_MinY
double m_MinY
the minimum on the Y axis -
m_StepX
double m_StepX
the step size for the X axis -
m_StepY
double m_StepY
the step size for the Y axis -
m_Width
int m_Width
the number of points on the X axis
-
-
Class weka.classifiers.meta.GridSearch.Performance extends java.lang.Object implements Serializable
- serialVersionUID:
- -4374706475277588755L
-
Serialized Fields
-
m_ACC
double m_ACC
the Accuracy -
m_CC
double m_CC
the Correlation coefficient -
m_Kappa
double m_Kappa
the kappa value -
m_MAE
double m_MAE
the Mean absolute error -
m_RAE
double m_RAE
the Relative absolute error -
m_RMSE
double m_RMSE
the Root mean squared error -
m_RRSE
double m_RRSE
the Root relative squared error -
m_Values
weka.classifiers.meta.GridSearch.PointDouble m_Values
the value pair the classifier was built with
-
-
Class weka.classifiers.meta.GridSearch.PerformanceCache extends java.lang.Object implements Serializable
- serialVersionUID:
- 5838863230451530252L
-
Serialized Fields
-
m_Cache
java.util.Hashtable m_Cache
the cache for points in the grid that got calculated
-
-
Class weka.classifiers.meta.GridSearch.PerformanceComparator extends java.lang.Object implements Serializable
- serialVersionUID:
- 6507592831825393847L
-
Serialized Fields
-
m_Evaluation
int m_Evaluation
the performance measure to use for comparison- See Also:
GridSearch.TAGS_EVALUATION
-
-
Class weka.classifiers.meta.GridSearch.PerformanceTable extends java.lang.Object implements Serializable
- serialVersionUID:
- 5486491313460338379L
-
Serialized Fields
-
m_Grid
weka.classifiers.meta.GridSearch.Grid m_Grid
the corresponding grid -
m_Max
double m_Max
the maximum performance -
m_Min
double m_Min
the minimum performance -
m_Performances
java.util.Vector<weka.classifiers.meta.GridSearch.Performance> m_Performances
the performances -
m_Table
double[][] m_Table
the table with the values -
m_Type
int m_Type
the type of performance the table was generated for
-
-
Class weka.classifiers.meta.GridSearch.PointDouble extends java.awt.geom.Point2D.Double implements Serializable
- serialVersionUID:
- 7151661776161898119L
-
Class weka.classifiers.meta.GridSearch.PointInt extends java.awt.Point implements Serializable
- serialVersionUID:
- -5900415163698021618L
-
Class weka.classifiers.meta.LogitBoost extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 8627452775249625582L
-
Serialized Fields
-
m_ClassAttribute
Attribute m_ClassAttribute
The actual class attribute (for getting class names) -
m_Classifiers
Classifier[][] m_Classifiers
Array for storing the generated base classifiers. Note: we are hiding the variable from IteratedSingleClassifierEnhancer -
m_NumClasses
int m_NumClasses
The number of classes -
m_NumericClassData
Instances m_NumericClassData
Dummy dataset with a numeric class -
m_NumFolds
int m_NumFolds
The number of folds for the internal cross-validation. -
m_NumGenerated
int m_NumGenerated
The number of successfully generated base classifiers. -
m_NumRuns
int m_NumRuns
The number of runs for the internal cross-validation. -
m_Offset
double m_Offset
The value by which the actual target value for the true class is offset. -
m_Precision
double m_Precision
The threshold on the improvement of the likelihood -
m_RandomInstance
java.util.Random m_RandomInstance
The random number generator used -
m_Shrinkage
double m_Shrinkage
The value of the shrinkage parameter -
m_UseResampling
boolean m_UseResampling
Use boosting with reweighting? -
m_WeightThreshold
int m_WeightThreshold
Weight thresholding. The percentage of weight mass used in training -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data
-
-
Class weka.classifiers.meta.MetaCost extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 1205317833344726855L
-
Serialized Fields
-
m_BagSizePercent
int m_BagSizePercent
The size of each bag sample, as a percentage of the training size -
m_CostFile
java.lang.String m_CostFile
The name of the cost file, for command line options -
m_CostMatrix
CostMatrix m_CostMatrix
The cost matrix -
m_MatrixSource
int m_MatrixSource
Indicates the current cost matrix source -
m_NumIterations
int m_NumIterations
The number of iterations. -
m_OnDemandDirectory
java.io.File m_OnDemandDirectory
The directory used when loading cost files on demand, null indicates current directory
-
-
Class weka.classifiers.meta.MultiBoostAB extends AdaBoostM1 implements Serializable
- serialVersionUID:
- -6681619178187935148L
-
Serialized Fields
-
m_NumSubCmtys
int m_NumSubCmtys
The number of sub-committees to use -
m_Random
java.util.Random m_Random
Random number generator
-
-
Class weka.classifiers.meta.MultiClassClassifier extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -3879602011542849141L
-
Serialized Fields
-
m_ClassAttribute
Attribute m_ClassAttribute
Internal copy of the class attribute for output purposes -
m_ClassFilters
Filter[] m_ClassFilters
The filters used to transform the class. -
m_Classifiers
Classifier[] m_Classifiers
The classifiers. -
m_Method
int m_Method
The multiclass method to use -
m_pairwiseCoupling
boolean m_pairwiseCoupling
Use pairwise coupling with 1-vs-1 -
m_RandomWidthFactor
double m_RandomWidthFactor
The multiplier when generating random codes. Will generate numClasses * m_RandomWidthFactor codes -
m_SumOfWeights
double[] m_SumOfWeights
Needed for pairwise coupling -
m_TwoClassDataset
Instances m_TwoClassDataset
A transformed dataset header used by the 1-against-1 method -
m_ZeroR
ZeroR m_ZeroR
ZeroR classifier for when all base classifier return zero probability.
-
-
Class weka.classifiers.meta.MultiScheme extends RandomizableMultipleClassifiersCombiner implements Serializable
- serialVersionUID:
- 5710744346128957520L
-
Serialized Fields
-
m_Classifier
Classifier m_Classifier
The classifier that had the best performance on training data. -
m_ClassifierIndex
int m_ClassifierIndex
The index into the vector for the selected scheme -
m_NumXValFolds
int m_NumXValFolds
Number of folds to use for cross validation (0 means use training error for selection)
-
-
Class weka.classifiers.meta.OrdinalClassClassifier extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -3461971774059603636L
-
Serialized Fields
-
m_ClassFilters
MakeIndicator[] m_ClassFilters
The filters used to transform the class. -
m_Classifiers
Classifier[] m_Classifiers
The classifiers. (One for each class.) -
m_ZeroR
ZeroR m_ZeroR
ZeroR classifier for when all base classifier return zero probability.
-
-
Class weka.classifiers.meta.RacedIncrementalLogitBoost extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 908598343772170052L
-
Serialized Fields
-
m_bestCommittee
weka.classifiers.meta.RacedIncrementalLogitBoost.Committee m_bestCommittee
The current best committee -
m_ClassAttribute
Attribute m_ClassAttribute
The actual class attribute (for getting class names) -
m_committees
FastVector m_committees
The committees -
m_currentSet
Instances m_currentSet
The instances currently in memory for training -
m_maxBatchSizeRequired
int m_maxBatchSizeRequired
The maximum number of instances required for processing -
m_maxChunkSize
int m_maxChunkSize
The maimum chunk size used for training -
m_minChunkSize
int m_minChunkSize
The minimum chunk size used for training -
m_NumClasses
int m_NumClasses
The number of classes -
m_NumericClassData
Instances m_NumericClassData
Dummy dataset with a numeric class -
m_numInstancesConsumed
int m_numInstancesConsumed
The number of instances consumed -
m_PruningType
int m_PruningType
The pruning type used -
m_RandomInstance
java.util.Random m_RandomInstance
The random number generator used -
m_UseResampling
boolean m_UseResampling
Whether to use resampling -
m_validationChunkSize
int m_validationChunkSize
The size of the validation set -
m_validationSet
Instances m_validationSet
The instances used for validation -
m_validationSetChanged
boolean m_validationSetChanged
Whether the validation set has recently been changed -
m_zeroR
ZeroR m_zeroR
The default scheme used when committees aren't ready
-
-
Class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee extends java.lang.Object implements Serializable
- serialVersionUID:
- 5559880306684082199L
-
Serialized Fields
-
m_chunkSize
int m_chunkSize
-
m_instancesConsumed
int m_instancesConsumed
number eaten from m_currentSet -
m_lastLogLikelihood
double m_lastLogLikelihood
-
m_lastValidationError
double m_lastValidationError
-
m_modelHasChanged
boolean m_modelHasChanged
-
m_modelHasChangedLL
boolean m_modelHasChangedLL
-
m_models
FastVector m_models
-
m_newValidationFs
double[][] m_newValidationFs
-
m_validationFs
double[][] m_validationFs
-
-
Class weka.classifiers.meta.RandomCommittee extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -9204394360557300092L
-
Class weka.classifiers.meta.RandomSubSpace extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 1278172513912424947L
-
Serialized Fields
-
m_SubSpaceSize
double m_SubSpaceSize
The size of each bag sample, as a percentage of the training size -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data
-
-
Class weka.classifiers.meta.RegressionByDiscretization extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 5066426153134050378L
-
Serialized Fields
-
m_ClassMeans
double[] m_ClassMeans
The mean values for each Discretized class interval. -
m_DeleteEmptyBins
boolean m_DeleteEmptyBins
Whether to delete empty intervals. -
m_DiscretizedHeader
Instances m_DiscretizedHeader
Header of discretized data. -
m_Discretizer
Discretize m_Discretizer
The discretization filter. -
m_NumBins
int m_NumBins
The number of discretization intervals. -
m_UseEqualFrequency
boolean m_UseEqualFrequency
Use equal-frequency binning
-
-
Class weka.classifiers.meta.RotationForest extends RandomizableIteratedSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -3255631880798499936L
-
Serialized Fields
-
m_Groups
int[][][] m_Groups
The attributes of each group -
m_Headers
Instances[] m_Headers
Headers of the transformed dataset -
m_MaxGroup
int m_MaxGroup
The maximum size of a group -
m_MinGroup
int m_MinGroup
The minimum size of a group -
m_Normalize
Normalize m_Normalize
Filter that normalized the attributes -
m_NumberOfGroups
boolean m_NumberOfGroups
Whether minGroup and maxGroup refer to the number of groups or their size -
m_ProjectionFilter
Filter m_ProjectionFilter
The type of projection filter -
m_ProjectionFilters
Filter[][] m_ProjectionFilters
The projection filters -
m_ReducedHeaders
Instances[][] m_ReducedHeaders
Headers of the reduced datasets -
m_RemovedPercentage
int m_RemovedPercentage
The percentage of instances to be removed -
m_RemoveUseless
RemoveUseless m_RemoveUseless
Filter that remove useless attributes
-
-
Class weka.classifiers.meta.Stacking extends RandomizableMultipleClassifiersCombiner implements Serializable
- serialVersionUID:
- 5134738557155845452L
-
Serialized Fields
-
m_BaseFormat
Instances m_BaseFormat
Format for base data -
m_MetaClassifier
Classifier m_MetaClassifier
The meta classifier -
m_MetaFormat
Instances m_MetaFormat
Format for meta data -
m_NumFolds
int m_NumFolds
Set the number of folds for the cross-validation
-
-
Class weka.classifiers.meta.StackingC extends Stacking implements Serializable
- serialVersionUID:
- -6717545616603725198L
-
Serialized Fields
-
m_attrFilter
Remove m_attrFilter
Filter to transform metaData - Remove -
m_makeIndicatorFilter
MakeIndicator m_makeIndicatorFilter
Filter to transform metaData - MakeIndicator -
m_MetaClassifiers
Classifier[] m_MetaClassifiers
The meta classifiers (one for each class, like in ClassificationViaRegression)
-
-
Class weka.classifiers.meta.ThresholdSelector extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -1795038053239867444L
-
Serialized Fields
-
m_BestThreshold
double m_BestThreshold
The threshold that lead to the best performance -
m_BestValue
double m_BestValue
The best value that has been observed -
m_ClassMode
int m_ClassMode
Method to determine which class to optimize for -
m_DesignatedClass
int m_DesignatedClass
Designated class value, determined during building -
m_EvalMode
int m_EvalMode
The evaluation mode -
m_HighThreshold
double m_HighThreshold
The upper threshold used as the basis of correction -
m_LowThreshold
double m_LowThreshold
The lower threshold used as the basis of correction -
m_manualThreshold
boolean m_manualThreshold
True if a manually set threshold is being used -
m_manualThresholdValue
double m_manualThresholdValue
-1 = not used by default -
m_nMeasure
int m_nMeasure
evaluation measure used for determining threshold -
m_NumXValFolds
int m_NumXValFolds
The number of folds used in cross-validation -
m_RangeMode
int m_RangeMode
The range correction mode
-
-
Class weka.classifiers.meta.Vote 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
-
-
-
Package weka.classifiers.meta.nestedDichotomies
-
Class weka.classifiers.meta.nestedDichotomies.ClassBalancedND extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 5944063630650811903L
-
Serialized Fields
-
m_classifiers
java.util.Hashtable m_classifiers
The hashtable for this node. -
m_FilteredClassifier
FilteredClassifier m_FilteredClassifier
The filtered classifier in which the base classifier is wrapped. -
m_FirstSuccessor
ClassBalancedND m_FirstSuccessor
The first successor -
m_hashtablegiven
boolean m_hashtablegiven
Is Hashtable given from END? -
m_Range
Range m_Range
The classes that are grouped together at the current node -
m_SecondSuccessor
ClassBalancedND m_SecondSuccessor
The second successor
-
-
Class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 5117477294209496368L
-
Serialized Fields
-
m_classifiers
java.util.Hashtable m_classifiers
The hashtable for this node. -
m_FilteredClassifier
FilteredClassifier m_FilteredClassifier
The filtered classifier in which the base classifier is wrapped. -
m_FirstSuccessor
DataNearBalancedND m_FirstSuccessor
The first successor -
m_hashtablegiven
boolean m_hashtablegiven
Is Hashtable given from END? -
m_Range
Range m_Range
The classes that are grouped together at the current node -
m_SecondSuccessor
DataNearBalancedND m_SecondSuccessor
The second successor
-
-
Class weka.classifiers.meta.nestedDichotomies.ND extends RandomizableSingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -6355893369855683820L
-
Serialized Fields
-
m_classifiers
java.util.Hashtable m_classifiers
The hashtable containing all the classifiers -
m_hashtablegiven
boolean m_hashtablegiven
Is Hashtable given from END? -
m_ndtree
weka.classifiers.meta.nestedDichotomies.ND.NDTree m_ndtree
The tree of classes
-
-
Class weka.classifiers.meta.nestedDichotomies.ND.NDTree extends java.lang.Object implements Serializable
- serialVersionUID:
- 4284655952754474880L
-
Serialized Fields
-
m_indices
FastVector m_indices
The indices associated with this node -
m_left
weka.classifiers.meta.nestedDichotomies.ND.NDTree m_left
The left successor -
m_parent
weka.classifiers.meta.nestedDichotomies.ND.NDTree m_parent
The parent -
m_right
weka.classifiers.meta.nestedDichotomies.ND.NDTree m_right
The right successor
-
-
-
Package weka.classifiers.mi
-
Class weka.classifiers.mi.CitationKNN extends Classifier implements Serializable
- serialVersionUID:
- -8435377743874094852L
-
Serialized Fields
-
m_Attributes
Instances m_Attributes
attribute name structure of the relational attribute -
m_Citers
int[] m_Citers
C nearest citers -
m_CitersDebug
boolean m_CitersDebug
-
m_Classes
int[] m_Classes
Class labels for each bag -
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_CNN
weka.classifiers.mi.CitationKNN.NeighborList[] m_CNN
C nearest neighbors considering all the bags -
m_CNNDebug
boolean m_CNNDebug
Different debugging output -
m_Debug
boolean m_Debug
Debugging output -
m_Diffs
double[] m_Diffs
Normalization of the euclidean distance -
m_HDistanceDebug
boolean m_HDistanceDebug
-
m_HDRank
int m_HDRank
Rank associated to the Hausdorff distance -
m_IdIndex
int m_IdIndex
-
m_Max
double[] m_Max
-
m_MaxNorm
double m_MaxNorm
-
m_Min
double[] m_Min
-
m_MinNorm
double m_MinNorm
-
m_NeighborListDebug
boolean m_NeighborListDebug
-
m_NumCiters
int m_NumCiters
Number of citers -
m_NumClasses
int m_NumClasses
The number of the class labels -
m_NumReferences
int m_NumReferences
Number of references -
m_References
int[] m_References
R nearest references -
m_ReferencesDebug
boolean m_ReferencesDebug
-
m_TrainBags
Instances m_TrainBags
Training bags
-
-
Class weka.classifiers.mi.MDD extends Classifier implements Serializable
- serialVersionUID:
- -7273119490545290581L
-
Serialized Fields
-
m_Attributes
Instances m_Attributes
All attribute names -
m_Classes
int[] m_Classes
Class labels for each bag -
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_Data
double[][][] m_Data
MI data -
m_Filter
Filter m_Filter
The filter used to standardize/normalize all values. -
m_filterType
int m_filterType
Whether to normalize/standardize/neither, default:standardize -
m_Missing
ReplaceMissingValues m_Missing
The filter used to get rid of missing values. -
m_NumClasses
int m_NumClasses
The number of the class labels -
m_Par
double[] m_Par
-
-
Class weka.classifiers.mi.MIBoost extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -3808427225599279539L
-
Serialized Fields
-
m_Attributes
Instances m_Attributes
attributes name for the new dataset used to build the model -
m_Beta
double[] m_Beta
Voting weights of models -
m_Classes
int[] m_Classes
Class labels for each bag -
m_ConvertToSI
MultiInstanceToPropositional m_ConvertToSI
filter used to convert the MI dataset into single-instance dataset -
m_DiscretizeBin
int m_DiscretizeBin
the number of discretization bins -
m_Filter
Discretize m_Filter
filter used for discretization -
m_MaxIterations
int m_MaxIterations
the maximum number of boost iterations -
m_Models
Classifier[] m_Models
the models for the iterations -
m_NumClasses
int m_NumClasses
The number of the class labels -
m_NumIterations
int m_NumIterations
Number of iterations
-
-
Class weka.classifiers.mi.MIDD extends Classifier implements Serializable
- serialVersionUID:
- 4263507733600536168L
-
Serialized Fields
-
m_Attributes
Instances m_Attributes
All attribute names -
m_Classes
int[] m_Classes
Class labels for each bag -
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_Data
double[][][] m_Data
MI data -
m_Filter
Filter m_Filter
The filter used to standardize/normalize all values. -
m_filterType
int m_filterType
Whether to normalize/standardize/neither, default:standardize -
m_Missing
ReplaceMissingValues m_Missing
The filter used to get rid of missing values. -
m_NumClasses
int m_NumClasses
The number of the class labels -
m_Par
double[] m_Par
-
-
Class weka.classifiers.mi.MIEMDD extends RandomizableClassifier implements Serializable
- serialVersionUID:
- 3899547154866223734L
-
Serialized Fields
-
m_Attributes
Instances m_Attributes
All attribute names -
m_Classes
int[] m_Classes
Class labels for each bag -
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_Data
double[][][] m_Data
MI data -
m_emData
double[][] m_emData
MI data -
m_Filter
Filter m_Filter
The filter used to standardize/normalize all values. -
m_filterType
int m_filterType
Whether to normalize/standardize/neither, default:standardize -
m_Missing
ReplaceMissingValues m_Missing
The filter used to get rid of missing values. -
m_NumClasses
int m_NumClasses
The number of the class labels -
m_Par
double[] m_Par
-
-
Class weka.classifiers.mi.MILR extends Classifier implements Serializable
- serialVersionUID:
- 1996101190172373826L
-
Serialized Fields
-
m_AlgorithmType
int m_AlgorithmType
the type of processing -
m_Attributes
Instances m_Attributes
All attribute names -
m_Classes
int[] m_Classes
Class labels for each bag -
m_Data
double[][][] m_Data
MI data -
m_NumClasses
int m_NumClasses
The number of the class labels -
m_Par
double[] m_Par
-
m_Ridge
double m_Ridge
The ridge parameter. -
xMean
double[] xMean
-
xSD
double[] xSD
-
-
Class weka.classifiers.mi.MINND extends Classifier implements Serializable
- serialVersionUID:
- -4512599203273864994L
-
Serialized Fields
-
m_Attributes
Instances m_Attributes
header info of the data -
m_Change
double[][] m_Change
The weights that alter the dimnesion of each exemplar -
m_Choose
int m_Choose
The number of nearest neighbour exemplars in the selection of noises in the test data -
m_Class
double[] m_Class
The class label of each exemplar -
m_Decay
double m_Decay
The decay rate of learning rate -
m_Dimension
int m_Dimension
The dimension of each exemplar, i.e. (numAttributes-2) -
m_MaxArray
double[] m_MaxArray
The maximum values for numeric attributes. -
m_Mean
double[][] m_Mean
The mean for each attribute of each exemplar -
m_MinArray
double[] m_MinArray
The minimum values for numeric attributes. -
m_Neighbour
int m_Neighbour
The number of nearest neighbour for prediction -
m_NoiseM
double[][] m_NoiseM
The noise data of each exemplar -
m_NoiseV
double[][] m_NoiseV
The noise data of each exemplar -
m_NumClasses
int m_NumClasses
The number of class labels in the data -
m_Rate
double m_Rate
The learning rate in the gradient descent -
m_Select
int m_Select
The number of nearest neighbour instances in the selection of noises in the training data -
m_STOP
double m_STOP
The stopping criteria of gradient descent -
m_ValidM
double[][] m_ValidM
The noise data of each exemplar -
m_ValidV
double[][] m_ValidV
The noise data of each exemplar -
m_Variance
double[][] m_Variance
The variance for each attribute of each exemplar -
m_Weights
double[] m_Weights
The weight of each exemplar
-
-
Class weka.classifiers.mi.MIOptimalBall extends Classifier implements Serializable
- serialVersionUID:
- -6465750129576777254L
-
Serialized Fields
-
m_Center
double[] m_Center
center of the optimal ball -
m_ConvertToMI
PropositionalToMultiInstance m_ConvertToMI
filter used to convert the single-instance dataset into MI dataset -
m_ConvertToSI
MultiInstanceToPropositional m_ConvertToSI
filter used to convert the MI dataset into single-instance dataset -
m_Distance
double[][][] m_Distance
the distances from each instance in a positive bag to each bag -
m_Filter
Filter m_Filter
The filter used to standardize/normalize all values. -
m_filterType
int m_filterType
Whether to normalize/standardize/neither -
m_Radius
double m_Radius
radius of the optimal ball
-
-
Class weka.classifiers.mi.MISMO extends Classifier implements Serializable
- serialVersionUID:
- -5834036950143719712L
-
Serialized Fields
-
m_C
double m_C
The complexity parameter. -
m_checksTurnedOff
boolean m_checksTurnedOff
Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0. -
m_classAttribute
Attribute m_classAttribute
The class attribute -
m_classifiers
weka.classifiers.mi.MISMO.BinaryMISMO[][] m_classifiers
The binary classifier(s) -
m_classIndex
int m_classIndex
The class index from the training data -
m_eps
double m_eps
Epsilon for rounding. -
m_Filter
Filter m_Filter
The filter used to standardize/normalize all values. -
m_filterType
int m_filterType
Whether to normalize/standardize/neither -
m_fitLogisticModels
boolean m_fitLogisticModels
Whether logistic models are to be fit -
m_kernel
Kernel m_kernel
Kernel to use -
m_minimax
boolean m_minimax
Use MIMinimax feature space? -
m_Missing
ReplaceMissingValues m_Missing
The filter used to get rid of missing values. -
m_NominalToBinary
NominalToBinary m_NominalToBinary
The filter used to make attributes numeric. -
m_numFolds
int m_numFolds
The number of folds for the internal cross-validation -
m_randomSeed
int m_randomSeed
The random number seed -
m_tol
double m_tol
Tolerance for accuracy of result.
-
-
Class weka.classifiers.mi.MISMO.BinaryMISMO extends java.lang.Object implements Serializable
- serialVersionUID:
- -7107082483475433531L
-
Serialized Fields
-
m_alpha
double[] m_alpha
The Lagrange multipliers. -
m_b
double m_b
The thresholds. -
m_bLow
double m_bLow
The thresholds. -
m_bUp
double m_bUp
The thresholds. -
m_class
double[] m_class
The transformed class values. -
m_data
Instances m_data
The training data. -
m_errors
double[] m_errors
The current set of errors for all non-bound examples. -
m_I0
SMOset m_I0
{i: 0 < 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_iLow
int m_iLow
The indices for m_bLow and m_bUp -
m_iUp
int m_iUp
The indices for m_bLow and m_bUp -
m_kernel
Kernel m_kernel
Kernel to use -
m_logistic
Logistic m_logistic
Stores logistic regression model for probability estimate -
m_sparseIndices
int[] m_sparseIndices
-
m_sparseWeights
double[] m_sparseWeights
Variables to hold weight vector in sparse form. (To reduce storage requirements.) -
m_sumOfWeights
double m_sumOfWeights
Stores the weight of the training instances -
m_supportVectors
SMOset m_supportVectors
The set of support vectors {i: 0 < m_alpha[i]} -
m_weights
double[] m_weights
Weight vector for linear machine.
-
-
Class weka.classifiers.mi.MISVM extends Classifier implements Serializable
- serialVersionUID:
- 7622231064035278145L
-
Serialized Fields
-
m_C
double m_C
The complexity parameter. -
m_ConvertToProp
MultiInstanceToPropositional m_ConvertToProp
filter used to convert the MI dataset into single-instance dataset -
m_Filter
Filter m_Filter
The filter used to standardize/normalize all values. -
m_filterType
int m_filterType
Whether to normalize/standardize/neither -
m_kernel
Kernel m_kernel
the kernel to use -
m_MaxIterations
int m_MaxIterations
the maximum number of iterations to perform -
m_SparseFilter
Filter m_SparseFilter
The filter used to transform the sparse datasets to nonsparse -
m_SVM
weka.classifiers.mi.MISVM.SVM m_SVM
The SMO classifier used to compute SVM soluton w,b for the dataset
-
-
Class weka.classifiers.mi.MIWrapper extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- -7707766152904315910L
-
Serialized Fields
-
m_ConvertToProp
MultiInstanceToPropositional m_ConvertToProp
Filter used to convert MI dataset into single-instance dataset -
m_Method
int m_Method
the test method -
m_NumClasses
int m_NumClasses
The number of the class labels -
m_WeightMethod
int m_WeightMethod
the single-instance weight setting method
-
-
Class weka.classifiers.mi.SimpleMI extends SingleClassifierEnhancer implements Serializable
- serialVersionUID:
- 9137795893666592662L
-
Serialized Fields
-
m_TransformMethod
int m_TransformMethod
the method used in transformation
-
-
-
Package weka.classifiers.mi.supportVector
-
Class weka.classifiers.mi.supportVector.MIPolyKernel extends PolyKernel implements Serializable
- serialVersionUID:
- 7926421479341051777L
-
Class weka.classifiers.mi.supportVector.MIRBFKernel extends RBFKernel implements Serializable
- serialVersionUID:
- -8711882393708956962L
-
Serialized Fields
-
m_kernelPrecalc
double[][] m_kernelPrecalc
The precalculated dotproducts of <inst_i,inst_i>
-
-
-
Package weka.classifiers.misc
-
Class weka.classifiers.misc.HyperPipes extends Classifier implements Serializable
- serialVersionUID:
- -7527596632268975274L
-
Serialized Fields
-
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_HyperPipes
weka.classifiers.misc.HyperPipes.HyperPipe[] m_HyperPipes
Stores the HyperPipe for each class -
m_Instances
Instances m_Instances
The structure of the training data -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data
-
-
Class weka.classifiers.misc.SerializedClassifier extends Classifier implements Serializable
- serialVersionUID:
- 4599593909947628642L
-
Serialized Fields
-
m_ModelFile
java.io.File m_ModelFile
the file where the serialized model is stored
-
-
Class weka.classifiers.misc.VFI extends Classifier implements Serializable
- serialVersionUID:
- 8081692166331321866L
-
Serialized Fields
-
m_bias
double m_bias
Bias towards more confident intervals -
m_ClassIndex
int m_ClassIndex
The index of the class attribute -
m_counts
double[][][] m_counts
The class counts for each interval of each attribute -
m_globalCounts
double[] m_globalCounts
The global class counts -
m_Instances
Instances m_Instances
The training data -
m_intervalBounds
double[][] m_intervalBounds
The lower bounds for each attribute -
m_maxEntrop
double m_maxEntrop
The maximum entropy for the class -
m_NumClasses
int m_NumClasses
The number of classes -
m_weightByConfidence
boolean m_weightByConfidence
Exponentially bias more confident intervals -
TINY
double TINY
-
-
-
Package weka.classifiers.pmml.consumer
-
Class weka.classifiers.pmml.consumer.GeneralRegression extends PMMLClassifier implements Serializable
- serialVersionUID:
- 2583880411828388959L
-
Serialized Fields
-
m_algorithmName
java.lang.String m_algorithmName
-
m_covariateList
java.util.ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Predictor> m_covariateList
-
m_cumulativeLinkFunction
weka.classifiers.pmml.consumer.GeneralRegression.CumulativeLinkFunction m_cumulativeLinkFunction
-
m_distParameter
double m_distParameter
-
m_distribution
weka.classifiers.pmml.consumer.GeneralRegression.Distribution m_distribution
-
m_factorList
java.util.ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Predictor> m_factorList
-
m_functionType
int m_functionType
-
m_linkFunction
weka.classifiers.pmml.consumer.GeneralRegression.LinkFunction m_linkFunction
-
m_linkParameter
double m_linkParameter
-
m_modelName
java.lang.String m_modelName
-
m_modelType
weka.classifiers.pmml.consumer.GeneralRegression.ModelType m_modelType
-
m_offsetValue
double m_offsetValue
-
m_offsetVariable
java.lang.String m_offsetVariable
-
m_parameterList
java.util.ArrayList<weka.classifiers.pmml.consumer.GeneralRegression.Parameter> m_parameterList
-
m_paramMatrix
weka.classifiers.pmml.consumer.GeneralRegression.PCell[][] m_paramMatrix
-
m_ppMatrix
weka.classifiers.pmml.consumer.GeneralRegression.PPCell[][] m_ppMatrix
-
m_trialsValue
double m_trialsValue
-
m_trialsVariable
java.lang.String m_trialsVariable
-
-
Class weka.classifiers.pmml.consumer.NeuralNetwork extends PMMLClassifier implements Serializable
- serialVersionUID:
- -4545904813133921249L
-
Serialized Fields
-
m_activationFunction
weka.classifiers.pmml.consumer.NeuralNetwork.ActivationFunction m_activationFunction
The activation function to use -
m_altitude
double m_altitude
Altitude for radial basis -
m_functionType
weka.classifiers.pmml.consumer.NeuralNetwork.MiningFunction m_functionType
The mining function -
m_inputMap
java.util.HashMap<java.lang.String,java.lang.Double> m_inputMap
A map for storing network input values (computed from an incoming instance) -
m_inputs
weka.classifiers.pmml.consumer.NeuralNetwork.NeuralInput[] m_inputs
The inputs to the network -
m_layers
weka.classifiers.pmml.consumer.NeuralNetwork.NeuralLayer[] m_layers
The hidden layers in the network -
m_normalizationMethod
weka.classifiers.pmml.consumer.NeuralNetwork.Normalization m_normalizationMethod
The normalization method -
m_numberOfInputs
int m_numberOfInputs
The number of inputs to the network -
m_numberOfLayers
int m_numberOfLayers
Number of hidden layers in the network -
m_outputs
weka.classifiers.pmml.consumer.NeuralNetwork.NeuralOutputs m_outputs
The outputs of the network -
m_threshold
double m_threshold
Threshold activation -
m_width
double m_width
Width for radial basis
-
-
Class weka.classifiers.pmml.consumer.PMMLClassifier extends Classifier implements Serializable
- serialVersionUID:
- -5371600590320702971L
-
Serialized Fields
-
m_creatorApplication
java.lang.String m_creatorApplication
Creator application -
m_dataDictionary
Instances m_dataDictionary
The data dictionary -
m_log
Logger m_log
Logger -
m_miningSchema
MiningSchema m_miningSchema
The fields and meta data used by the model -
m_pmmlVersion
java.lang.String m_pmmlVersion
PMML version
-
-
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_normalizationMethod
weka.classifiers.pmml.consumer.Regression.Normalization m_normalizationMethod
The normalization to use -
m_regressionTables
weka.classifiers.pmml.consumer.Regression.RegressionTable[] m_regressionTables
The regression tables for this regression
-
-
Class weka.classifiers.pmml.consumer.Regression.RegressionTable.CategoricalPredictor extends weka.classifiers.pmml.consumer.Regression.RegressionTable.Predictor implements Serializable
- serialVersionUID:
- 3077920125549906819L
-
Serialized Fields
-
m_valueIndex
int m_valueIndex
The index of the attribute value for this predictor -
m_valueName
java.lang.String m_valueName
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_fieldNames
java.lang.String[] m_fieldNames
The names of the terms (attributes) to be multiplied -
m_indexes
int[] m_indexes
the indexes of the terms to be multiplied
-
-
-
Package weka.classifiers.rules
-
Class weka.classifiers.rules.ConjunctiveRule extends Classifier implements Serializable
- serialVersionUID:
- -5938309903225087198L
-
Serialized Fields
-
m_Antds
FastVector m_Antds
The vector of antecedents of this rule -
m_ClassAttribute
Attribute m_ClassAttribute
The class attribute of the data -
m_Cnsqt
double[] m_Cnsqt
The consequent of this rule -
m_DefDstr
double[] m_DefDstr
The default rule distribution of the data not covered -
m_Folds
int m_Folds
The number of folds to split data into Grow and Prune for REP -
m_IsExclude
boolean m_IsExclude
Whether to use exlusive expressions for nominal attributes -
m_MinNo
double m_MinNo
The minimal number of instance weights within a split -
m_NumAntds
int m_NumAntds
The number of antecedents in pre-pruning -
m_NumClasses
int m_NumClasses
Number of classes in the training data -
m_Random
java.util.Random m_Random
The Random object used for randomization -
m_Seed
long m_Seed
The seed to perform randomization -
m_Targets
FastVector m_Targets
The predicted classes recorded for each antecedent in the growing data
-
-
Class weka.classifiers.rules.DecisionTable extends Classifier implements Serializable
- serialVersionUID:
- 2888557078165701326L
-
Serialized Fields
-
m_classIsNominal
boolean m_classIsNominal
Class is nominal -
m_classPriorCounts
double[] m_classPriorCounts
The class priors to use when there is no match in the table -
m_classPriors
double[] m_classPriors
-
m_CVFolds
int m_CVFolds
Number of folds for cross validating feature sets -
m_decisionFeatures
int[] m_decisionFeatures
Holds the final feature set -
m_delTransform
Remove m_delTransform
Filter used to remove columns discarded by feature selection -
m_displayRules
boolean m_displayRules
Display Rules -
m_disTransform
Filter m_disTransform
Discretization filter -
m_dtInstances
Instances m_dtInstances
Holds the final feature selected set of instances -
m_entries
java.util.Hashtable m_entries
The hashtable used to hold training instances -
m_evaluation
Evaluation m_evaluation
The evaluation object used to evaluate subsets -
m_evaluationMeasure
int m_evaluationMeasure
-
m_evaluator
ASEvaluation m_evaluator
Our own internal evaluator -
m_ibk
IBk m_ibk
IB1 used to classify non matching instances rather than majority class -
m_majority
double m_majority
Holds the majority class -
m_numAttributes
int m_numAttributes
The number of attributes in the dataset -
m_numInstances
int m_numInstances
The number of instances in the dataset -
m_rr
java.util.Random m_rr
Random numbers for use in cross validation -
m_saveMemory
boolean m_saveMemory
-
m_search
ASSearch m_search
The search method to use -
m_theInstances
Instances m_theInstances
Holds the original training instances -
m_useIBk
boolean m_useIBk
Use the IBk classifier rather than majority class
-
-
Class weka.classifiers.rules.DecisionTableHashKey extends java.lang.Object implements Serializable
- serialVersionUID:
- 5674163500154964602L
-
Serialized Fields
-
attributes
double[] attributes
Array of attribute values for an instance -
key
int key
The key -
missing
boolean[] missing
True for an index if the corresponding attribute value is missing.
-
-
Class weka.classifiers.rules.DTNB extends DecisionTable implements Serializable
- serialVersionUID:
- 2999557077765701326L
-
Serialized Fields
-
m_backwardWithDelete
ASSearch m_backwardWithDelete
-
m_NB
NaiveBayes m_NB
The naive Bayes half of the hybrid -
m_nbFeatures
int[] m_nbFeatures
The features used by naive Bayes -
m_percentDeleted
double m_percentDeleted
Percentage of the features features that were dropped entirely -
m_percentUsedByDT
double m_percentUsedByDT
Percentage of the total number of features used by the decision table
-
-
Class weka.classifiers.rules.DTNB.BackwardsWithDelete extends ASSearch implements Serializable
-
Class weka.classifiers.rules.DTNB.EvalWithDelete extends ASEvaluation implements Serializable
-
Serialized Fields
-
m_deletedFromDTNB
java.util.BitSet m_deletedFromDTNB
-
-
-
Class weka.classifiers.rules.JRip extends Classifier implements Serializable
- serialVersionUID:
- -6589312996832147161L
-
Serialized Fields
-
m_CheckErr
boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria -
m_Class
Attribute m_Class
The class attribute of the data -
m_Debug
boolean m_Debug
Whether in a debug mode -
m_Distributions
FastVector m_Distributions
The predicted class distribution -
m_Filter
Filter m_Filter
The filter used to randomize the class order -
m_Folds
int m_Folds
The number of folds to split data into Grow and Prune for IREP -
m_MinNo
double m_MinNo
The minimal number of instance weights within a split -
m_Optimizations
int m_Optimizations
Runs of optimizations -
m_Random
java.util.Random m_Random
Random object used in this class -
m_Ruleset
FastVector m_Ruleset
The ruleset -
m_RulesetStats
FastVector m_RulesetStats
The RuleStats for the ruleset of each class value -
m_Seed
long m_Seed
The seed to perform randomization -
m_Total
double m_Total
# of all the possible conditions in a rule -
m_UsePruning
boolean m_UsePruning
Whether use pruning, i.e. the data is clean or not
-
-
Class weka.classifiers.rules.JRip.Antd extends java.lang.Object implements Serializable
- serialVersionUID:
- -8929754772994154334L
-
Serialized Fields
-
accu
double accu
The accurate data for this antecedent in the growing data -
accuRate
double accuRate
The accurate rate of this antecedent test on the growing data -
att
Attribute att
The attribute of the antecedent -
cover
double cover
The coverage of this antecedent in the growing data -
maxInfoGain
double maxInfoGain
The maximum infoGain achieved by this antecedent test in the growing data -
value
double value
The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)
-
-
Class weka.classifiers.rules.JRip.NominalAntd 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_Antds
FastVector m_Antds
The vector of antecedents of this rule -
m_Consequent
double m_Consequent
The internal representation of the class label to be predicted
-
-
Class weka.classifiers.rules.M5Rules extends M5Base implements Serializable
- serialVersionUID:
- -1746114858746563180L
-
Class weka.classifiers.rules.NNge extends Classifier implements Serializable
- serialVersionUID:
- 4084742275553788972L
-
Serialized Fields
-
m_Exemplars
weka.classifiers.rules.NNge.Exemplar m_Exemplars
The list of Exemplars -
m_ExemplarsByClass
weka.classifiers.rules.NNge.Exemplar[] m_ExemplarsByClass
The lists of Exemplars by class -
m_MaxArray
double[] m_MaxArray
The maximum values for numeric attributes. -
m_MI
double[] m_MI
-
m_MI_MaxArray
double[] m_MI_MaxArray
-
m_MI_MinArray
double[] m_MI_MinArray
-
m_MI_NumAttrClassInter
int[][][] m_MI_NumAttrClassInter
MUTUAL INFORMATION'S DATAS -
m_MI_NumAttrClassValue
int[][][] m_MI_NumAttrClassValue
-
m_MI_NumAttrInter
int[][] m_MI_NumAttrInter
-
m_MI_NumAttrValue
int[][] m_MI_NumAttrValue
-
m_MI_NumClass
int[] m_MI_NumClass
-
m_MI_NumInst
int m_MI_NumInst
-
m_MinArray
double[] m_MinArray
The minimum values for numeric attributes. -
m_MissingVector
double[] m_MissingVector
Values to use for missing value -
m_NumAttemptsOfGene
int m_NumAttemptsOfGene
The number of try for generalisation -
m_NumFoldersMI
int m_NumFoldersMI
The number of folder for the Mutual Information -
m_Train
Instances m_Train
An empty instances to keep the headers, the classIndex, etc...
-
-
Class weka.classifiers.rules.OneR extends Classifier implements Serializable
- serialVersionUID:
- -3459427003147861443L
-
Serialized Fields
-
m_minBucketSize
int m_minBucketSize
The minimum bucket size -
m_rule
weka.classifiers.rules.OneR.OneRRule m_rule
A 1-R rule -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data
-
-
Class weka.classifiers.rules.PART extends Classifier implements Serializable
- serialVersionUID:
- 8121455039782598361L
-
Serialized Fields
-
m_binarySplits
boolean m_binarySplits
Binary splits on nominal attributes? -
m_CF
float m_CF
Confidence level -
m_minNumObj
int m_minNumObj
Minimum number of objects -
m_numFolds
int m_numFolds
Number of folds for reduced error pruning. -
m_reducedErrorPruning
boolean m_reducedErrorPruning
Use reduced error pruning? -
m_root
MakeDecList m_root
The decision list -
m_Seed
int m_Seed
The seed for random number generation. -
m_unpruned
boolean m_unpruned
Generate unpruned list?
-
-
Class weka.classifiers.rules.Prism extends Classifier implements Serializable
- serialVersionUID:
- 1310258880025902106L
-
Serialized Fields
-
m_rules
weka.classifiers.rules.Prism.PrismRule m_rules
The first rule in the list of rules
-
-
Class weka.classifiers.rules.Ridor extends Classifier implements Serializable
- serialVersionUID:
- -7261533075088314436L
-
Serialized Fields
-
m_Class
Attribute m_Class
The class attribute of the data -
m_Cover
double m_Cover
Statistics of the data -
m_Err
double m_Err
Statistics of the data -
m_Folds
int m_Folds
The number of folds to split data into Grow and Prune for IREP -
m_IsAllErr
boolean m_IsAllErr
Whether use error rate on all the data -
m_IsMajority
boolean m_IsMajority
Whether use majority class as default class -
m_MinNo
double m_MinNo
The minimal number of instance weights within a split -
m_Random
java.util.Random m_Random
Random object for randomization -
m_Root
weka.classifiers.rules.Ridor.Ridor_node m_Root
The root of Ridor -
m_Seed
int m_Seed
The seed to perform randomization -
m_Shuffle
int m_Shuffle
The number of shuffles performed on the data for randomization
-
-
Class weka.classifiers.rules.Rule 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_Distributions
FastVector m_Distributions
The class distributions predicted by each rule -
m_Filtered
FastVector m_Filtered
The set of instances filtered by the ruleset -
m_Ruleset
FastVector m_Ruleset
The specific ruleset in question -
m_SimpleStats
FastVector m_SimpleStats
The simple stats of each rule -
m_Total
double m_Total
The total number of possible conditions that could appear in a rule -
MDL_THEORY_WEIGHT
double MDL_THEORY_WEIGHT
The theory weight in the MDL calculation
-
-
Class weka.classifiers.rules.ZeroR extends Classifier implements Serializable
- serialVersionUID:
- 48055541465867954L
-
Serialized Fields
-
m_Class
Attribute m_Class
The class attribute. -
m_ClassValue
double m_ClassValue
The class value 0R predicts. -
m_Counts
double[] m_Counts
The number of instances in each class (null if class numeric).
-
-
-
Package weka.classifiers.rules.part
-
Class weka.classifiers.rules.part.C45PruneableDecList 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
-
indeX
int indeX
Which son to expand? -
m_isEmpty
boolean m_isEmpty
True if node is empty. -
m_isLeaf
boolean m_isLeaf
True if node is leaf. -
m_localModel
ClassifierSplitModel m_localModel
Local model at node. -
m_minNumObj
int m_minNumObj
Minimum number of objects -
m_sons
ClassifierDecList[] m_sons
References to sons. -
m_test
Distribution m_test
The pruning instances. -
m_toSelectModel
ModelSelection m_toSelectModel
The model selection method. -
m_train
Instances m_train
The training instances.
-
-
Class weka.classifiers.rules.part.MakeDecList extends java.lang.Object implements Serializable
- serialVersionUID:
- -1427481323245079123L
-
Serialized Fields
-
CF
double CF
The confidence for C45-type pruning. -
m_seed
int m_seed
The seed for random number generation. -
minNumObj
int minNumObj
Minimum number of objects -
numSetS
int numSetS
How many subsets of equal size? One used for pruning, the rest for training. -
reducedErrorPruning
boolean reducedErrorPruning
Use reduced error pruning? -
theRules
java.util.Vector theRules
Vector storing the rules. -
toSelectModeL
ModelSelection toSelectModeL
The model selection method. -
unpruned
boolean unpruned
Generated unpruned list?
-
-
Class weka.classifiers.rules.part.PruneableDecList extends ClassifierDecList implements Serializable
- serialVersionUID:
- -7228103346297172921L
-
-
Package weka.classifiers.trees
-
Class weka.classifiers.trees.ADTree extends Classifier implements Serializable
- serialVersionUID:
- -1532264837167690683L
-
Serialized Fields
-
m_boostingIterations
int m_boostingIterations
Option - the number of boosting iterations o perform -
m_examplesCounted
int m_examplesCounted
Statistics - the number of instances processed during search -
m_lastAddedSplitNum
int m_lastAddedSplitNum
The number of the last splitter added to the tree -
m_negTrainInstances
ReferenceInstances m_negTrainInstances
The training instances with negative class - referencing the training dataset -
m_nodesExpanded
int m_nodesExpanded
Statistics - the number of prediction nodes investigated during search -
m_nominalAttIndices
int[] m_nominalAttIndices
An array containing the inidices to the nominal attributes in the data -
m_numericAttIndices
int[] m_numericAttIndices
An array containing the inidices to the numeric attributes in the data -
m_posTrainInstances
ReferenceInstances m_posTrainInstances
The training instances with positive class - referencing the training dataset -
m_random
java.util.Random m_random
The random number generator - used for the random search heuristic -
m_randomSeed
int m_randomSeed
Option - the seed to use for a random search -
m_root
PredictionNode m_root
The root of the tree -
m_saveInstanceData
boolean m_saveInstanceData
Option - whether the tree should remember the instance data -
m_search_bestInsertionNode
PredictionNode m_search_bestInsertionNode
The best node to insert under, as found so far by the latest search -
m_search_bestPathNegInstances
Instances m_search_bestPathNegInstances
The negative instances that apply to the best path found so far -
m_search_bestPathPosInstances
Instances m_search_bestPathPosInstances
The positive instances that apply to the best path found so far -
m_search_bestSplitter
Splitter m_search_bestSplitter
The best splitter to insert, as found so far by the latest search -
m_search_smallestZ
double m_search_smallestZ
The smallest Z value found so far by the latest search -
m_searchPath
int m_searchPath
Option - the search mode -
m_trainInstances
Instances m_trainInstances
The instances used to train the tree -
m_trainTotalWeight
double m_trainTotalWeight
The total weight of the instances - used to speed Z calculations
-
-
Class weka.classifiers.trees.BFTree extends RandomizableClassifier implements Serializable
- serialVersionUID:
- -7035607375962528217L
-
Serialized Fields
-
m_Attribute
Attribute m_Attribute
Attribute used for splitting. -
m_ClassAttribute
Attribute m_ClassAttribute
Class attribute of a dataset. -
m_ClassProbs
double[] m_ClassProbs
Class probabilities. -
m_ClassValue
double m_ClassValue
Class value for a node. -
m_Distribution
double[] m_Distribution
Class distributions. -
m_Dists
double[][][] m_Dists
Distributions of each attribute for two successor nodes. -
m_FixedExpansion
int m_FixedExpansion
Fixed number of expansions (if no pruning method is used, its value is -1. Otherwise, its value is gotten from internal cross-validation). -
m_Heuristic
boolean m_Heuristic
If use huristic search for binary split (default true). Note even if its value is true, it is only used when the number of values of a nominal attribute is larger than 4. -
m_isLeaf
boolean m_isLeaf
If the ndoe is leaf node. -
m_minNumObj
int m_minNumObj
Minimum number of instances at leaf nodes. -
m_numFoldsPruning
int m_numFoldsPruning
Number of folds for the pruning. -
m_Props
double[] m_Props
Branch proportions. -
m_PruningStrategy
int m_PruningStrategy
the pruning strategy -
m_SizePer
double m_SizePer
The training data size (0-1). Default 1. -
m_SortedIndices
int[][] m_SortedIndices
Sorted indices. -
m_SplitString
java.lang.String m_SplitString
Split subset (for nominal attributes). -
m_SplitValue
double m_SplitValue
Split point (for numeric attributes). -
m_Successors
BFTree[] m_Successors
Successor nodes. -
m_TotalWeight
double m_TotalWeight
Total weights. -
m_UseErrorRate
boolean m_UseErrorRate
If use error rate in internal cross-validation to fix the number of expansions - default (if not, root mean squared error is used). -
m_UseGini
boolean m_UseGini
If use Gini index as the splitting criterion - default (if not, information is used). -
m_UseOneSE
boolean m_UseOneSE
If use the 1SE rule to make the decision. -
m_Weights
double[][] m_Weights
Sorted weights.
-
-
Class weka.classifiers.trees.DecisionStump extends Classifier implements Serializable
- serialVersionUID:
- 1618384535950391L
-
Serialized Fields
-
m_AttIndex
int m_AttIndex
The attribute used for classification. -
m_Distribution
double[][] m_Distribution
The distribution of class values or the means in each subset. -
m_Instances
Instances m_Instances
The instances used for training. -
m_SplitPoint
double m_SplitPoint
The split point (index respectively). -
m_ZeroR
Classifier m_ZeroR
a ZeroR model in case no model can be built from the data
-
-
Class weka.classifiers.trees.FT extends Classifier implements Serializable
- serialVersionUID:
- -1113212459618105000L
-
Serialized Fields
-
m_convertNominal
boolean m_convertNominal
convert nominal attributes to binary ? -
m_errorOnProbabilities
boolean m_errorOnProbabilities
use error on probabilties instead of misclassification for stopping criterion of LogitBoost? -
m_minNumInstances
int m_minNumInstances
minimum number of instances at which a node is considered for splitting -
m_modelType
int m_modelType
Model Type, value: 0 is FT, 1 is FTLeaves, 2 is FTInner -
m_nominalToBinary
NominalToBinary m_nominalToBinary
Filter to replace nominal attributes -
m_numBoostingIterations
int m_numBoostingIterations
if non-zero, use fixed number of iterations for LogitBoost -
m_replaceMissing
ReplaceMissingValues m_replaceMissing
Filter to replace missing values -
m_tree
FTtree m_tree
root of the logistic model tree -
m_useAIC
boolean m_useAIC
If true, the AIC is used to choose the best LogitBoost iteration -
m_weightTrimBeta
double m_weightTrimBeta
Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.
-
-
Class weka.classifiers.trees.Id3 extends Classifier implements Serializable
- serialVersionUID:
- -2693678647096322561L
-
Serialized Fields
-
m_Attribute
Attribute m_Attribute
Attribute used for splitting. -
m_ClassAttribute
Attribute m_ClassAttribute
Class attribute of dataset. -
m_ClassValue
double m_ClassValue
Class value if node is leaf. -
m_Distribution
double[] m_Distribution
Class distribution if node is leaf. -
m_Successors
Id3[] m_Successors
The node's successors.
-
-
Class weka.classifiers.trees.J48 extends Classifier implements Serializable
- serialVersionUID:
- -217733168393644444L
-
Serialized Fields
-
m_binarySplits
boolean m_binarySplits
Binary splits on nominal attributes? -
m_CF
float m_CF
Confidence level -
m_minNumObj
int m_minNumObj
Minimum number of instances -
m_noCleanup
boolean m_noCleanup
Cleanup after the tree has been built. -
m_numFolds
int m_numFolds
Number of folds for reduced error pruning. -
m_reducedErrorPruning
boolean m_reducedErrorPruning
Use reduced error pruning? -
m_root
ClassifierTree m_root
The decision tree -
m_Seed
int m_Seed
Random number seed for reduced-error pruning. -
m_subtreeRaising
boolean m_subtreeRaising
Subtree raising to be performed? -
m_unpruned
boolean m_unpruned
Unpruned tree? -
m_useLaplace
boolean m_useLaplace
Determines whether probabilities are smoothed using Laplace correction when predictions are generated
-
-
Class weka.classifiers.trees.J48graft extends Classifier implements Serializable
- serialVersionUID:
- 8823716098042427799L
-
Serialized Fields
-
m_binarySplits
boolean m_binarySplits
Binary splits on nominal attributes? -
m_CF
float m_CF
Confidence level -
m_minNumObj
int m_minNumObj
Minimum number of instances -
m_noCleanup
boolean m_noCleanup
Cleanup after the tree has been built. -
m_numFolds
int m_numFolds
Number of folds for reduced error pruning. -
m_relabel
boolean m_relabel
relabel instances when grafting -
m_root
ClassifierTree m_root
The decision tree -
m_subtreeRaising
boolean m_subtreeRaising
Subtree raising to be performed? -
m_unpruned
boolean m_unpruned
Unpruned tree? -
m_useLaplace
boolean m_useLaplace
Determines whether probabilities are smoothed using Laplace correction when predictions are generated
-
-
Class weka.classifiers.trees.LADTree extends Classifier implements Serializable
- serialVersionUID:
- -4940716114518300302L
-
Serialized Fields
-
m_boostingIterations
int m_boostingIterations
-
m_examplesCounted
int m_examplesCounted
-
m_lastAddedSplitNum
int m_lastAddedSplitNum
-
m_nodesExpanded
int m_nodesExpanded
-
m_numericAttIndices
int[] m_numericAttIndices
-
m_numOfClasses
int m_numOfClasses
-
m_root
weka.classifiers.trees.LADTree.PredictionNode m_root
-
m_search_bestInsertionNode
weka.classifiers.trees.LADTree.PredictionNode m_search_bestInsertionNode
-
m_search_bestPathInstances
Instances m_search_bestPathInstances
-
m_search_bestSplitter
weka.classifiers.trees.LADTree.Splitter m_search_bestSplitter
-
m_search_smallestLeastSquares
double m_search_smallestLeastSquares
-
m_staticPotentialSplitters2way
FastVector m_staticPotentialSplitters2way
-
m_trainInstances
ReferenceInstances m_trainInstances
-
Z_MAX
double Z_MAX
-
-
Class weka.classifiers.trees.LADTree.LADInstance extends Instance implements Serializable
-
Serialized Fields
-
fVector
double[] fVector
-
pVector
double[] pVector
-
wVector
double[] wVector
-
zVector
double[] zVector
-
-
-
Class weka.classifiers.trees.LADTree.PredictionNode extends java.lang.Object implements Serializable
-
Serialized Fields
-
children
FastVector children
-
values
double[] values
-
-
-
Class weka.classifiers.trees.LADTree.Splitter extends java.lang.Object implements Serializable
-
Serialized Fields
-
attIndex
int attIndex
-
orderAdded
int orderAdded
-
-
-
Class weka.classifiers.trees.LADTree.TwoWayNominalSplit extends weka.classifiers.trees.LADTree.Splitter implements Serializable
-
Serialized Fields
-
children
weka.classifiers.trees.LADTree.PredictionNode[] children
-
trueSplitValue
int trueSplitValue
-
-
-
Class weka.classifiers.trees.LADTree.TwoWayNumericSplit extends weka.classifiers.trees.LADTree.Splitter implements Serializable
-
Serialized Fields
-
children
weka.classifiers.trees.LADTree.PredictionNode[] children
-
splitPoint
double splitPoint
-
-
-
Class weka.classifiers.trees.LMT extends Classifier implements Serializable
- serialVersionUID:
- -1113212459618104943L
-
Serialized Fields
-
m_convertNominal
boolean m_convertNominal
convert nominal attributes to binary ? -
m_errorOnProbabilities
boolean m_errorOnProbabilities
use error on probabilties instead of misclassification for stopping criterion of LogitBoost? -
m_fastRegression
boolean m_fastRegression
use heuristic that determines the number of LogitBoost iterations only once in the beginning? -
m_minNumInstances
int m_minNumInstances
minimum number of instances at which a node is considered for splitting -
m_nominalToBinary
NominalToBinary m_nominalToBinary
Filter to replace nominal attributes -
m_numBoostingIterations
int m_numBoostingIterations
if non-zero, use fixed number of iterations for LogitBoost -
m_replaceMissing
ReplaceMissingValues m_replaceMissing
Filter to replace missing values -
m_splitOnResiduals
boolean m_splitOnResiduals
split on residuals? -
m_tree
LMTNode m_tree
root of the logistic model tree -
m_useAIC
boolean m_useAIC
If true, the AIC is used to choose the best LogitBoost iteration -
m_weightTrimBeta
double m_weightTrimBeta
Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.
-
-
Class weka.classifiers.trees.M5P extends M5Base implements Serializable
- serialVersionUID:
- -6118439039768244417L
-
Class weka.classifiers.trees.NBTree extends Classifier implements Serializable
- serialVersionUID:
- -4716005707058256086L
-
Serialized Fields
-
m_minNumObj
int m_minNumObj
Minimum number of instances -
m_root
NBTreeClassifierTree m_root
The root of the tree
-
-
Class weka.classifiers.trees.RandomForest extends Classifier implements Serializable
- serialVersionUID:
- -2260823972777004705L
-
Serialized Fields
-
m_bagger
Bagging m_bagger
The bagger. -
m_KValue
int m_KValue
Final number of features that were considered in last build. -
m_MaxDepth
int m_MaxDepth
The maximum depth of the trees (0 = unlimited) -
m_numFeatures
int m_numFeatures
Number of features to consider in random feature selection. If less than 1 will use int(logM+1) ) -
m_numTrees
int m_numTrees
Number of trees in forest. -
m_randomSeed
int m_randomSeed
The random seed.
-
-
Class weka.classifiers.trees.RandomTree extends Classifier implements Serializable
- serialVersionUID:
- 8934314652175299374L
-
Serialized Fields
-
m_AllowUnclassifiedInstances
boolean m_AllowUnclassifiedInstances
Whether unclassified instances are allowed -
m_Info
Instances m_Info
The header information. -
m_KValue
int m_KValue
The number of attributes considered for a split. -
m_MaxDepth
int m_MaxDepth
The maximum depth of the tree (0 = unlimited) -
m_MinNum
double m_MinNum
Minimum number of instances for leaf. -
m_NumFolds
int m_NumFolds
Determines how much data is used for backfitting -
m_randomSeed
int m_randomSeed
The random seed to use. -
m_Tree
weka.classifiers.trees.RandomTree.Tree m_Tree
The Tree object -
m_zeroR
Classifier m_zeroR
a ZeroR model in case no model can be built from the data
-
-
Class weka.classifiers.trees.RandomTree.Tree extends java.lang.Object implements Serializable
- serialVersionUID:
- 3549573538656522569L
-
Serialized Fields
-
m_Attribute
int m_Attribute
The attribute to split on. -
m_ClassDistribution
double[] m_ClassDistribution
Class probabilities from the training data. -
m_Prop
double[] m_Prop
The proportions of training instances going down each branch. -
m_SplitPoint
double m_SplitPoint
The split point. -
m_Successors
weka.classifiers.trees.RandomTree.Tree[] m_Successors
The subtrees appended to this tree.
-
-
Class weka.classifiers.trees.REPTree extends Classifier implements Serializable
- serialVersionUID:
- -9216785998198681299L
-
Serialized Fields
-
m_MaxDepth
int m_MaxDepth
Upper bound on the tree depth -
m_MinNum
double m_MinNum
The minimum number of instances per leaf. -
m_MinVarianceProp
double m_MinVarianceProp
The minimum proportion of the total variance (over all the data) required for split. -
m_NoPruning
boolean m_NoPruning
Don't prune -
m_NumFolds
int m_NumFolds
Number of folds for reduced error pruning. -
m_Seed
int m_Seed
Seed for random data shuffling. -
m_Tree
weka.classifiers.trees.REPTree.Tree m_Tree
The Tree object -
m_zeroR
ZeroR m_zeroR
ZeroR model that is used if no attributes are present.
-
-
Class weka.classifiers.trees.REPTree.Tree extends java.lang.Object implements Serializable
- serialVersionUID:
- -1635481717888437935L
-
Serialized Fields
-
m_Attribute
int m_Attribute
The attribute to split on. -
m_ClassProbs
double[] m_ClassProbs
Class probabilities from the training data in the nominal case. Holds the mean in the numeric case. -
m_Distribution
double[] m_Distribution
The (unnormalized) class distribution in the nominal case. Holds the sum of squared errors and the weight in the numeric case. -
m_HoldOutDist
double[] m_HoldOutDist
Class distribution of hold-out set at node in the nominal case. Straight sum of weights plus sum of weighted targets in the numeric case (i.e. array has only two elements). -
m_HoldOutError
double m_HoldOutError
The hold-out error of the node. The number of miss-classified instances in the nominal case, the sum of squared errors in the numeric case. -
m_Info
Instances m_Info
The header information (for printing the tree). -
m_Prop
double[] m_Prop
The proportions of training instances going down each branch. -
m_SplitPoint
double m_SplitPoint
The split point. -
m_Successors
weka.classifiers.trees.REPTree.Tree[] m_Successors
The subtrees of this tree.
-
-
Class weka.classifiers.trees.SimpleCart extends RandomizableClassifier implements Serializable
- serialVersionUID:
- 4154189200352566053L
-
Serialized Fields
-
m_Alpha
double m_Alpha
Alpha-value (for pruning) at the node. -
m_Attribute
Attribute m_Attribute
Attribute used to split data. -
m_ClassAttribute
Attribute m_ClassAttribute
Class attriubte of data. -
m_ClassProbs
double[] m_ClassProbs
Class probabilities. -
m_ClassValue
double m_ClassValue
Class value if the node is leaf. -
m_Distribution
double[] m_Distribution
Distributions of leaf node (or temporary leaf node in minimal cost-complexity pruning) -
m_Heuristic
boolean m_Heuristic
If use huristic search for nominal attributes in multi-class problems (default true). -
m_isLeaf
boolean m_isLeaf
Indicate if the node is a leaf node. -
m_minNumObj
double m_minNumObj
Minimum number of instances in at the terminal nodes. -
m_numFoldsPruning
int m_numFoldsPruning
Number of folds for minimal cost-complexity pruning. -
m_numIncorrectModel
double m_numIncorrectModel
Number of training examples misclassified by the model (subtree rooted). -
m_numIncorrectTree
double m_numIncorrectTree
Number of training examples misclassified by the model (subtree not rooted). -
m_Props
double[] m_Props
Proportion for each branch. -
m_Prune
boolean m_Prune
If use minimal cost-compexity pruning. -
m_SizePer
double m_SizePer
Training data size. -
m_SplitString
java.lang.String m_SplitString
Split subset used to split data for nominal attributes. -
m_SplitValue
double m_SplitValue
Split point for a numeric attribute. -
m_Successors
SimpleCart[] m_Successors
Successor nodes. -
m_totalTrainInstances
int m_totalTrainInstances
Total number of instances used to build the classifier. -
m_train
Instances m_train
Training data. -
m_UseOneSE
boolean m_UseOneSE
If use the 1SE rule to make final decision tree.
-
-
Class weka.classifiers.trees.UserClassifier extends Classifier implements Serializable
- serialVersionUID:
- 6483901103562809843L
-
Serialized Fields
-
m_built
boolean m_built
The status of whether there is a decision tree ready or not. -
m_classifiers
GenericObjectEditor m_classifiers
A list of other m_classifiers. -
m_focus
weka.classifiers.trees.UserClassifier.TreeClass m_focus
Two references to the structure of the decision tree. -
m_nextId
int m_nextId
The next number that can be used as a unique id for a node. -
m_propertyDialog
PropertyDialog m_propertyDialog
A window for selecting other classifiers. -
m_top
weka.classifiers.trees.UserClassifier.TreeClass m_top
Two references to the structure of the decision tree.
-
-
-
Package weka.classifiers.trees.adtree
-
Class weka.classifiers.trees.adtree.PredictionNode extends java.lang.Object implements Serializable
- serialVersionUID:
- 6018958856358698814L
-
Serialized Fields
-
children
FastVector children
The children of this node - any number of splitter nodes -
value
double value
The prediction value stored in this node
-
-
Class weka.classifiers.trees.adtree.ReferenceInstances extends Instances implements Serializable
- serialVersionUID:
- -8022666381920252997L
-
Class weka.classifiers.trees.adtree.Splitter extends java.lang.Object implements Serializable
- serialVersionUID:
- 8190449848490055L
-
Serialized Fields
-
orderAdded
int orderAdded
The number this node was in the order of nodes added to the tree
-
-
Class weka.classifiers.trees.adtree.TwoWayNominalSplit extends Splitter implements Serializable
- serialVersionUID:
- -4598366190152721355L
-
Serialized Fields
-
attIndex
int attIndex
The index of the attribute the split depends on -
children
PredictionNode[] children
The children of this split -
trueSplitValue
int trueSplitValue
The attribute value that is compared against
-
-
Class weka.classifiers.trees.adtree.TwoWayNumericSplit extends Splitter implements Serializable
- serialVersionUID:
- 449769177903158283L
-
Serialized Fields
-
attIndex
int attIndex
The index of the attribute the split depends on -
children
PredictionNode[] children
The children of this split -
splitPoint
double splitPoint
The attribute value that is compared against
-
-
-
Package weka.classifiers.trees.ft
-
Class weka.classifiers.trees.ft.FTInnerNode extends FTtree implements Serializable
- serialVersionUID:
- -1125334488640233181L
-
Class weka.classifiers.trees.ft.FTLeavesNode extends FTtree implements Serializable
- serialVersionUID:
- 950601378326259315L
-
Class weka.classifiers.trees.ft.FTNode extends FTtree implements Serializable
- serialVersionUID:
- 2317688685139295063L
-
Class weka.classifiers.trees.ft.FTtree extends LogisticBase implements Serializable
- serialVersionUID:
- 1862737145870398755L
-
Serialized Fields
-
m_auxLocalModel
ClassifierSplitModel m_auxLocalModel
Auxiliary copy ClassifierSplitModel (for splitting) -
m_CF
float m_CF
Confidence level -
m_constError
double m_constError
Constructor error -
m_hasConstr
boolean m_hasConstr
True if node has or splits on constructor -
m_higherRegressions
SimpleLinearRegression[][] m_higherRegressions
Simple regression functions fit by LogitBoost at higher levels in the tree -
m_id
int m_id
Node id -
m_isLeaf
boolean m_isLeaf
True if node is leaf -
m_leafclass
int m_leafclass
Stores leaf class value -
m_leafModelNum
int m_leafModelNum
ID of logistic model at leaf -
m_localModel
ClassifierSplitModel m_localModel
The ClassifierSplitModel (for splitting) -
m_minNumInstances
int m_minNumInstances
minimum number of instances at which a node is considered for splitting -
m_modelSelection
ModelSelection m_modelSelection
ModelSelection object (for splitting) -
m_nominalToBinary
NominalToBinary m_nominalToBinary
Filter to convert nominal attributes to binary -
m_numHigherRegressions
int m_numHigherRegressions
Number of simple regression functions fit by LogitBoost at higher levels in the tree -
m_numInstances
int m_numInstances
Number of instances at the node -
m_sons
FTtree[] m_sons
Array of children of the node -
m_totalInstanceWeight
double m_totalInstanceWeight
Total number of training instances.
-
-
-
Package weka.classifiers.trees.j48
-
Class weka.classifiers.trees.j48.BinC45ModelSelection extends ModelSelection implements Serializable
- serialVersionUID:
- 179170923545122001L
-
Serialized Fields
-
m_allData
Instances m_allData
The FULL training dataset. -
m_minNoObj
int m_minNoObj
Minimum number of instances in interval.
-
-
Class weka.classifiers.trees.j48.BinC45Split extends ClassifierSplitModel implements Serializable
- serialVersionUID:
- -1278776919563022474L
-
Serialized Fields
-
m_attIndex
int m_attIndex
Attribute to split on. -
m_gainRatio
double m_gainRatio
GainRatio of split. -
m_infoGain
double m_infoGain
InfoGain of split. -
m_minNoObj
int m_minNoObj
Minimum number of objects in a split. -
m_splitPoint
double m_splitPoint
Value of split point. -
m_sumOfWeights
double m_sumOfWeights
The sum of the weights of the instances.
-
-
Class weka.classifiers.trees.j48.C45ModelSelection extends ModelSelection implements Serializable
- serialVersionUID:
- 3372204862440821989L
-
Serialized Fields
-
m_allData
Instances m_allData
All the training data -
m_minNoObj
int m_minNoObj
Minimum number of objects in interval.
-
-
Class weka.classifiers.trees.j48.C45PruneableClassifierTree extends ClassifierTree implements Serializable
- serialVersionUID:
- -4813820170260388194L
-
Serialized Fields
-
m_CF
float m_CF
The confidence factor for pruning. -
m_cleanup
boolean m_cleanup
Cleanup after the tree has been built. -
m_pruneTheTree
boolean m_pruneTheTree
True if the tree is to be pruned. -
m_subtreeRaising
boolean m_subtreeRaising
Is subtree raising to be performed?
-
-
Class weka.classifiers.trees.j48.C45PruneableClassifierTreeG extends ClassifierTree implements Serializable
- serialVersionUID:
- 66981207374331964L
-
Serialized Fields
-
m_BiProbCrit
double m_BiProbCrit
binomial probability critical value -
m_CF
float m_CF
The confidence factor for pruning. -
m_cleanup
boolean m_cleanup
Cleanup after the tree has been built. -
m_Debug
boolean m_Debug
-
m_pruneTheTree
boolean m_pruneTheTree
True if the tree is to be pruned. -
m_relabel
boolean m_relabel
flag for using relabelling when grafting -
m_subtreeRaising
boolean m_subtreeRaising
Is subtree raising to be performed?
-
-
Class weka.classifiers.trees.j48.C45Split extends ClassifierSplitModel implements Serializable
- serialVersionUID:
- 3064079330067903161L
-
Serialized Fields
-
m_attIndex
int m_attIndex
Attribute to split on. -
m_complexityIndex
int m_complexityIndex
Desired number of branches. -
m_gainRatio
double m_gainRatio
GainRatio of split. -
m_index
int m_index
Number of split points. -
m_infoGain
double m_infoGain
InfoGain of split. -
m_minNoObj
int m_minNoObj
Minimum number of objects in a split. -
m_splitPoint
double m_splitPoint
Value of split point. -
m_sumOfWeights
double m_sumOfWeights
The sum of the weights of the instances.
-
-
Class weka.classifiers.trees.j48.ClassifierSplitModel 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_id
int m_id
The id for the node. -
m_isEmpty
boolean m_isEmpty
True if node is empty. -
m_isLeaf
boolean m_isLeaf
True if node is leaf. -
m_localModel
ClassifierSplitModel m_localModel
Local model at node. -
m_sons
ClassifierTree[] m_sons
References to sons. -
m_test
Distribution m_test
The pruning instances. -
m_toSelectModel
ModelSelection m_toSelectModel
The model selection method. -
m_train
Instances m_train
The training instances.
-
-
Class weka.classifiers.trees.j48.Distribution extends java.lang.Object implements Serializable
- serialVersionUID:
- 8526859638230806576L
-
Serialized Fields
-
m_perBag
double[] m_perBag
Weight of instances per bag. -
m_perClass
double[] m_perClass
Weight of instances per class. -
m_perClassPerBag
double[][] m_perClassPerBag
Weight of instances per class per bag. -
totaL
double totaL
Total weight of instances.
-
-
Class weka.classifiers.trees.j48.EntropyBasedSplitCrit extends SplitCriterion implements Serializable
- serialVersionUID:
- -2618691439791653056L
-
Class weka.classifiers.trees.j48.EntropySplitCrit extends EntropyBasedSplitCrit implements Serializable
- serialVersionUID:
- 5986252682266803935L
-
Class weka.classifiers.trees.j48.GainRatioSplitCrit extends EntropyBasedSplitCrit implements Serializable
- serialVersionUID:
- -433336694718670930L
-
Class weka.classifiers.trees.j48.GraftSplit extends ClassifierSplitModel implements Serializable
- serialVersionUID:
- 722773260393182051L
-
Serialized Fields
-
m_attIndex
int m_attIndex
the attribute we are splitting on -
m_graftdistro
Distribution m_graftdistro
the distribution for graft values, from cases in atbop -
m_laplace
double m_laplace
laplace value of the subset specified by m_testType for m_maxClass -
m_leafdistro
Distribution m_leafdistro
leaf for the subset specified by m_testType -
m_maxClass
int m_maxClass
dominant class of the subset specified by m_testType -
m_otherLeafMaxClass
int m_otherLeafMaxClass
dominant class of the subset not specified by m_testType -
m_splitPoint
double m_splitPoint
value of split point (if numeric attribute) -
m_testType
int m_testType
type of test: 0: <= test 1: > test 2: = test 3: != test
-
-
Class weka.classifiers.trees.j48.InfoGainSplitCrit extends EntropyBasedSplitCrit implements Serializable
- serialVersionUID:
- 4892105020180728499L
-
Class weka.classifiers.trees.j48.ModelSelection extends java.lang.Object implements Serializable
- serialVersionUID:
- -4850147125096133642L
-
Class weka.classifiers.trees.j48.NBTreeClassifierTree extends ClassifierTree implements Serializable
- serialVersionUID:
- -4472639447877404786L
-
Class weka.classifiers.trees.j48.NBTreeModelSelection extends ModelSelection implements Serializable
- serialVersionUID:
- 990097748931976704L
-
Serialized Fields
-
m_allData
Instances m_allData
All the training data -
m_minNoObj
int m_minNoObj
Minimum number of objects in interval.
-
-
Class weka.classifiers.trees.j48.NBTreeNoSplit extends ClassifierSplitModel implements Serializable
- serialVersionUID:
- 7824804381545259618L
-
Serialized Fields
-
m_disc
Discretize m_disc
the discretizer used -
m_errors
double m_errors
errors on the training data at this node -
m_nb
NaiveBayesUpdateable m_nb
the naive bayes classifier
-
-
Class weka.classifiers.trees.j48.NBTreeSplit extends ClassifierSplitModel implements Serializable
- serialVersionUID:
- 8922627123884975070L
-
Serialized Fields
-
m_attIndex
int m_attIndex
Attribute to split on. -
m_c45S
C45Split m_c45S
-
m_complexityIndex
int m_complexityIndex
Desired number of branches. -
m_errors
double m_errors
The weight of the instances incorrectly classified by the naive bayes models arising from this split -
m_globalNB
NBTreeNoSplit m_globalNB
The global naive bayes model for this node -
m_minNoObj
int m_minNoObj
Minimum number of objects in a split. -
m_splitPoint
double m_splitPoint
Value of split point. -
m_sumOfWeights
double m_sumOfWeights
The sum of the weights of the instances.
-
-
Class weka.classifiers.trees.j48.NoSplit extends ClassifierSplitModel implements Serializable
- serialVersionUID:
- -1292620749331337546L
-
Class weka.classifiers.trees.j48.PruneableClassifierTree extends ClassifierTree implements Serializable
- serialVersionUID:
- -555775736857600201L
-
Serialized Fields
-
m_cleanup
boolean m_cleanup
Cleanup after the tree has been built. -
m_seed
int m_seed
The random number seed. -
numSets
int numSets
How many subsets of equal size? One used for pruning, the rest for training. -
pruneTheTree
boolean pruneTheTree
True if the tree is to be pruned.
-
-
Class weka.classifiers.trees.j48.SplitCriterion 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_alpha
double m_alpha
Alpha-value (for pruning) at the node -
m_fastRegression
boolean m_fastRegression
Use heuristic that determines the number of LogitBoost iterations only once in the beginning? -
m_higherRegressions
SimpleLinearRegression[][] m_higherRegressions
Simple regression functions fit by LogitBoost at higher levels in the tree -
m_id
int m_id
Node id -
m_isLeaf
boolean m_isLeaf
True if node is leaf -
m_leafModelNum
int m_leafModelNum
ID of logistic model at leaf -
m_localModel
ClassifierSplitModel m_localModel
The ClassifierSplitModel (for splitting) -
m_minNumInstances
int m_minNumInstances
minimum number of instances at which a node is considered for splitting -
m_modelSelection
ModelSelection m_modelSelection
ModelSelection object (for splitting) -
m_nominalToBinary
NominalToBinary m_nominalToBinary
Filter to convert nominal attributes to binary -
m_numHigherRegressions
int m_numHigherRegressions
Number of simple regression functions fit by LogitBoost at higher levels in the tree -
m_numIncorrectModel
double m_numIncorrectModel
Weighted number of training examples currently misclassified by the logistic model at the node -
m_numIncorrectTree
double m_numIncorrectTree
Weighted number of training examples currently misclassified by the subtree rooted at the node -
m_numInstances
int m_numInstances
Number of instances at the node -
m_sons
LMTNode[] m_sons
Array of children of the node -
m_totalInstanceWeight
double m_totalInstanceWeight
Total number of training instances.
-
-
Class weka.classifiers.trees.lmt.LogisticBase extends Classifier implements Serializable
- serialVersionUID:
- 168765678097825064L
-
Serialized Fields
-
m_errorOnProbabilities
boolean m_errorOnProbabilities
Use error on probabilities for stopping criterion of LogitBoost? -
m_fixedNumIterations
int m_fixedNumIterations
Use fixed number of iterations for LogitBoost? (if negative, cross-validate number of iterations) -
m_heuristicStop
int m_heuristicStop
Use heuristic to stop performing LogitBoost iterations earlier? If enabled, LogitBoost is stopped if the current (local) minimum of the error on a test set as a function of the number of iterations has not changed for m_heuristicStop iterations. -
m_maxIterations
int m_maxIterations
The maximum number of LogitBoost iterations -
m_numClasses
int m_numClasses
The number of different classes -
m_numericData
Instances m_numericData
Numeric version of the training data. Original class is replaced by a numeric pseudo-class. -
m_numericDataHeader
Instances m_numericDataHeader
Header-only version of the numeric version of the training data -
m_numParameters
double m_numParameters
Effective number of parameters used for AIC / BIC automatic stopping -
m_numRegressions
int m_numRegressions
The number of LogitBoost iterations performed. -
m_regressions
SimpleLinearRegression[][] m_regressions
Array holding the simple regression functions fit by LogitBoost -
m_train
Instances m_train
Training data -
m_useAIC
boolean m_useAIC
If true, the AIC is used to choose the best iteration -
m_useCrossValidation
boolean m_useCrossValidation
Use cross-validation to determine best number of LogitBoost iterations ? -
m_weightTrimBeta
double m_weightTrimBeta
Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.
-
-
Class weka.classifiers.trees.lmt.ResidualModelSelection extends ModelSelection implements Serializable
- serialVersionUID:
- -293098783159385148L
-
Serialized Fields
-
m_minInfoGain
double m_minInfoGain
Minimum information gain for split -
m_minNumInstances
int m_minNumInstances
Minimum number of instances for leaves
-
-
Class weka.classifiers.trees.lmt.ResidualSplit extends ClassifierSplitModel implements Serializable
- serialVersionUID:
- -5055883734183713525L
-
Serialized Fields
-
m_attIndex
int m_attIndex
The index of the attribute selected for the split -
m_attribute
Attribute m_attribute
The attribute selected for the split -
m_data
Instances m_data
The set of instances -
m_dataWs
double[][] m_dataWs
The LogitBoost-weights for the set of instances -
m_dataZs
double[][] m_dataZs
The Z-values (LogitBoost response) for the set of instances -
m_numClasses
int m_numClasses
Number of classed -
m_numInstances
int m_numInstances
Number of instances in the set -
m_splitPoint
double m_splitPoint
The split point (for numeric attributes)
-
-
-
Package weka.classifiers.trees.m5
-
Class weka.classifiers.trees.m5.CorrelationSplitInfo 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_maxImpurity
double m_maxImpurity
the maximum impurity reduction -
m_number
int m_number
the number of instances -
m_position
int m_position
-
m_splitAttr
int m_splitAttr
the attribute being tested -
m_splitValue
double m_splitValue
the best value on which to split
-
-
Class weka.classifiers.trees.m5.M5Base extends Classifier implements Serializable
- serialVersionUID:
- -4022221950191647679L
-
Serialized Fields
-
m_generateRules
boolean m_generateRules
generate a decision list instead of a single tree. -
m_instances
Instances m_instances
the instances covered by the tree/rules -
m_minNumInstances
double m_minNumInstances
The minimum number of instances to allow at a leaf node -
m_nominalToBinary
NominalToBinary m_nominalToBinary
filter to convert nominal attributes to binary -
m_regressionTree
boolean m_regressionTree
Make a regression tree/rule instead of a model tree/rule -
m_removeUseless
RemoveUseless m_removeUseless
for removing useless attributes -
m_replaceMissing
ReplaceMissingValues m_replaceMissing
filter to fill in missing values -
m_ruleSet
FastVector m_ruleSet
the rule set -
m_saveInstances
boolean m_saveInstances
Save instances at each node in an M5 tree for visualization purposes. -
m_unsmoothedPredictions
boolean m_unsmoothedPredictions
use unsmoothed predictions -
m_useUnpruned
boolean m_useUnpruned
Do not prune tree/rules
-
-
Class weka.classifiers.trees.m5.PreConstructedLinearModel extends Classifier implements Serializable
- serialVersionUID:
- 2030974097051713247L
-
Serialized Fields
-
m_coefficients
double[] m_coefficients
The coefficients -
m_instancesHeader
Instances m_instancesHeader
Holds the instances header for printing the model -
m_intercept
double m_intercept
The intercept -
m_numParameters
int m_numParameters
number of coefficients in the model
-
-
Class weka.classifiers.trees.m5.Rule extends java.lang.Object implements Serializable
- serialVersionUID:
- -4458627451682483204L
-
Serialized Fields
-
m_classIndex
int m_classIndex
the class index -
m_covered
Instances m_covered
the instances covered by this rule -
m_globalAbsDev
double m_globalAbsDev
the absolute deviation of the class for all the instances -
m_globalStdDev
double m_globalStdDev
the standard deviation of the class for all the instances -
m_instances
Instances m_instances
the instances covered by this rule -
m_internalNodes
RuleNode[] m_internalNodes
the corresponding internal nodes. Used for smoothing rules. -
m_minNumInstances
double m_minNumInstances
The minimum number of instances to allow at a leaf node -
m_notCovered
Instances m_notCovered
the instances not covered by this rule -
m_numAttributes
int m_numAttributes
the number of attributes -
m_numCovered
int m_numCovered
the number of instances covered by this rule -
m_numInstances
int m_numInstances
the number of instances in the dataset -
m_regressionTree
boolean m_regressionTree
Make a regression tree instead of a model tree -
m_relOps
int[] m_relOps
the corresponding relational operators (0 = "<=", 1 = ">") -
m_ruleModel
RuleNode m_ruleModel
the leaf encapsulating the linear model for this rule -
m_saveInstances
boolean m_saveInstances
Save instances at each node in an M5 tree for visualization purposes. -
m_smoothPredictions
boolean m_smoothPredictions
use the original m5 smoothing procedure -
m_splitAtts
int[] m_splitAtts
the indexes of the attributes used to split on for this rule -
m_splitVals
double[] m_splitVals
the corresponding values of the split points -
m_topOfTree
RuleNode m_topOfTree
the top of the m5 tree for this rule -
m_useTree
boolean m_useTree
use a pruned m5 tree rather than make a rule -
m_useUnpruned
boolean m_useUnpruned
Build unpruned tree/rule
-
-
Class weka.classifiers.trees.m5.RuleNode extends Classifier implements Serializable
- serialVersionUID:
- 1979807611124337144L
-
Serialized Fields
-
m_classIndex
int m_classIndex
the class index -
m_devFraction
double m_devFraction
a node will not be split if its class standard deviation is less than 5% of the class standard deviation of all the instances -
m_globalAbsDeviation
double m_globalAbsDeviation
the absolute deviation of the global class -
m_globalDeviation
double m_globalDeviation
a node will not be split if the class deviation of its instances is less than m_devFraction of the deviation of the global class -
m_id
int m_id
Node id. -
m_indices
int[] m_indices
Indices of the attributes to be used in generating a linear model at this node -
m_instances
Instances m_instances
instances reaching this node -
m_isLeaf
boolean m_isLeaf
Node is a leaf -
m_leafModelNum
int m_leafModelNum
the number assigned to the linear model if this node is a leaf. = 0 if this node is not a leaf -
m_left
RuleNode m_left
left child node -
m_nodeModel
PreConstructedLinearModel m_nodeModel
the linear model at this node -
m_numAttributes
int m_numAttributes
the number of attributes -
m_numInstances
int m_numInstances
the number of instances reaching this node -
m_numParameters
int m_numParameters
the number of paramters in the chosen model for this node---either the subtree model or the linear model. The constant term is counted as a paramter---this is for pruning purposes -
m_parent
RuleNode m_parent
the parent of this node -
m_pruningMultiplier
double m_pruningMultiplier
-
m_regressionTree
boolean m_regressionTree
Make a regression tree instead of a model tree -
m_right
RuleNode m_right
right child node -
m_rootMeanSquaredError
double m_rootMeanSquaredError
the mean squared error of the model at this node (either linear or subtree) -
m_saveInstances
boolean m_saveInstances
Save the instances at each node (for visualizing in the Explorer's treevisualizer. -
m_splitAtt
int m_splitAtt
attribute this node splits on -
m_splitNum
double m_splitNum
a node will not be split if it contains less then m_splitNum instances -
m_splitValue
double m_splitValue
the value of the split attribute
-
-
Class weka.classifiers.trees.m5.YongSplitInfo extends java.lang.Object implements Serializable
- serialVersionUID:
- 1864267581079767881L
-
Serialized Fields
-
first
int first
-
last
int last
-
leftAve
double leftAve
-
maxImpurity
double maxImpurity
-
number
int number
-
position
int position
-
rightAve
double rightAve
-
splitAttr
int splitAttr
-
splitValue
double splitValue
-
-
-
Package weka.clusterers
-
Class weka.clusterers.AbstractClusterer extends java.lang.Object implements Serializable
- serialVersionUID:
- -6099962589663877632L
-
Class weka.clusterers.AbstractDensityBasedClusterer extends AbstractClusterer implements Serializable
- serialVersionUID:
- -5950728041704213845L
-
Class weka.clusterers.CLOPE extends AbstractClusterer implements Serializable
- serialVersionUID:
- -567567567567588L
-
Serialized Fields
-
clusters
java.util.ArrayList<weka.clusterers.CLOPE.CLOPECluster> clusters
Array of clusters -
m_clusterAssignments
java.util.ArrayList<java.lang.Integer> m_clusterAssignments
-
m_numberOfClusters
int m_numberOfClusters
Number of clusters -
m_numberOfClustersDetermined
boolean m_numberOfClustersDetermined
whether the number of clusters was already determined -
m_numberOfInstances
int m_numberOfInstances
Number of instances -
m_processed_InstanceID
int m_processed_InstanceID
Counter for the processed instances -
m_Repulsion
double m_Repulsion
Specifies the repulsion -
m_RepulsionDefault
double m_RepulsionDefault
Specifies the repulsion default
-
-
Class weka.clusterers.ClusterEvaluation extends java.lang.Object implements Serializable
- serialVersionUID:
- -830188327319128005L
-
Serialized Fields
-
m_classToCluster
int[] m_classToCluster
will hold the mapping of classes to clusters (for class based evaluation) -
m_clusterAssignments
double[] m_clusterAssignments
holds the assigments of instances to clusters for a particular testing dataset -
m_Clusterer
Clusterer m_Clusterer
the clusterer -
m_clusteringResults
java.lang.StringBuffer m_clusteringResults
holds a string describing the results of clustering the training data -
m_logL
double m_logL
holds the average log likelihood for a particular testing dataset if the clusterer is a DensityBasedClusterer -
m_numClusters
int m_numClusters
holds the number of clusters found by the clusterer
-
-
Class weka.clusterers.Cobweb extends RandomizableClusterer implements Serializable
- serialVersionUID:
- 928406656495092318L
-
Serialized Fields
-
m_acuity
double m_acuity
Acuity (minimum standard deviation). -
m_cobwebTree
weka.clusterers.Cobweb.CNode m_cobwebTree
Holds the root of the Cobweb tree. -
m_cutoff
double m_cutoff
Cutoff (minimum category utility). -
m_numberMerges
int m_numberMerges
the number of merges that happened -
m_numberOfClusters
int m_numberOfClusters
Number of clusters (nodes in the tree). Must never be queried directly, only via the method numberOfClusters(). Otherwise it's not guaranteed that it contains the correct value. -
m_numberOfClustersDetermined
boolean m_numberOfClustersDetermined
whether the number of clusters was already determined -
m_numberSplits
int m_numberSplits
the number of splits that happened -
m_saveInstances
boolean m_saveInstances
Output instances in graph representation of Cobweb tree (Allows instances at nodes in the tree to be visualized in the Explorer).
-
-
Class weka.clusterers.DBSCAN extends AbstractClusterer implements Serializable
- serialVersionUID:
- -1666498248451219728L
-
Serialized Fields
-
clusterID
int clusterID
Holds the current clusterID -
database
Database database
The database that is used for DBSCAN -
database_distanceType
java.lang.String database_distanceType
Holds the distance-type that is used (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject) -
database_Type
java.lang.String database_Type
Holds the type of the used database (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase) -
elapsedTime
double elapsedTime
Holds the time-value (seconds) for the duration of the clustering-process -
epsilon
double epsilon
Specifies the radius for a range-query -
minPoints
int minPoints
Specifies the density (the range-query must contain at least minPoints DataObjects) -
numberOfGeneratedClusters
int numberOfGeneratedClusters
Holds the number of clusters generated -
processed_InstanceID
int processed_InstanceID
Counter for the processed instances -
replaceMissingValues_Filter
ReplaceMissingValues replaceMissingValues_Filter
Replace missing values in training instances
-
-
Class weka.clusterers.EM extends RandomizableDensityBasedClusterer implements Serializable
- serialVersionUID:
- 8348181483812829475L
-
Serialized Fields
-
m_displayModelInOldFormat
boolean m_displayModelInOldFormat
display model output in old-style format -
m_initialNumClusters
int m_initialNumClusters
the initial number of clusters requested by the user--- -1 if xval is to be used to find the number of clusters -
m_loglikely
double m_loglikely
the loglikelihood of the data -
m_max_iterations
int m_max_iterations
maximum iterations to perform -
m_maxValues
double[] m_maxValues
attribute max values -
m_minStdDev
double m_minStdDev
default minimum standard deviation -
m_minStdDevPerAtt
double[] m_minStdDevPerAtt
-
m_minValues
double[] m_minValues
attribute min values -
m_model
Estimator[][] m_model
hold the discrete estimators for each cluster -
m_modelNormal
double[][][] m_modelNormal
hold the normal estimators for each cluster -
m_modelNormalPrev
double[][][] m_modelNormalPrev
-
m_modelPrev
Estimator[][] m_modelPrev
-
m_num_attribs
int m_num_attribs
number of attributes -
m_num_clusters
int m_num_clusters
number of clusters selected by the user or cross validation -
m_num_instances
int m_num_instances
number of training instances -
m_priors
double[] m_priors
the prior probabilities for clusters -
m_priorsPrev
double[] m_priorsPrev
-
m_replaceMissing
ReplaceMissingValues m_replaceMissing
globally replace missing values -
m_rr
java.util.Random m_rr
random number generator -
m_theInstances
Instances m_theInstances
training instances -
m_verbose
boolean m_verbose
Verbose? -
m_weights
double[][] m_weights
hold the weights of each instance for each cluster
-
-
Class weka.clusterers.FarthestFirst extends RandomizableClusterer implements Serializable
- serialVersionUID:
- 7499838100631329509L
-
Serialized Fields
-
m_ClusterCentroids
Instances m_ClusterCentroids
holds the cluster centroids -
m_instances
Instances m_instances
training instances, not necessary to keep, could be replaced by m_ClusterCentroids where needed for header info -
m_Max
double[] m_Max
attribute max values -
m_Min
double[] m_Min
attribute min values -
m_NumClusters
int m_NumClusters
number of clusters to generate -
m_ReplaceMissingFilter
ReplaceMissingValues m_ReplaceMissingFilter
replace missing values in training instances
-
-
Class weka.clusterers.FilteredClusterer extends SingleClustererEnhancer implements Serializable
- serialVersionUID:
- 1420005943163412943L
-
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_bPrintNewick
boolean m_bPrintNewick
-
m_clusters
weka.clusterers.HierarchicalClusterer.Node[] m_clusters
-
m_DistanceFunction
DistanceFunction m_DistanceFunction
distance function used for comparing members of a cluster -
m_instances
Instances m_instances
training data -
m_nClusterNr
int[] m_nClusterNr
-
m_nLinkType
int m_nLinkType
Holds the Link type used calculate distance between clusters -
m_nNumClusters
int m_nNumClusters
number of clusters desired in clustering
-
-
Class weka.clusterers.MakeDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable
- serialVersionUID:
- -5643302427972186631L
-
Serialized Fields
-
m_minStdDev
double m_minStdDev
default minimum standard deviation -
m_model
DiscreteEstimator[][] m_model
discrete distributions fitted to each discrete attribute in each cluster -
m_modelNormal
double[][][] m_modelNormal
normal distributions fitted to each numeric attribute in each cluster -
m_priors
double[] m_priors
prior probabilities for the fitted clusters -
m_replaceMissing
ReplaceMissingValues m_replaceMissing
globally replace missing values -
m_theInstances
Instances m_theInstances
holds training instances header information -
m_wrappedClusterer
Clusterer m_wrappedClusterer
The clusterer being wrapped
-
-
Class weka.clusterers.OPTICS extends AbstractClusterer implements Serializable
- serialVersionUID:
- 274552680222105221L
-
Serialized Fields
-
database
Database database
The database that is used for OPTICS -
database_distanceType
java.lang.String database_distanceType
Holds the distance-type that is used (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject) -
database_Type
java.lang.String database_Type
Holds the type of the used database (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase) -
databaseOutput
java.io.File databaseOutput
the file to save the generated database object to. -
elapsedTime
double elapsedTime
Holds the time-value (seconds) for the duration of the clustering-process -
epsilon
double epsilon
Specifies the radius for a range-query -
minPoints
int minPoints
Specifies the density (the range-query must contain at least minPoints DataObjects) -
numberOfGeneratedClusters
int numberOfGeneratedClusters
Holds the number of clusters generated -
replaceMissingValues_Filter
ReplaceMissingValues replaceMissingValues_Filter
Replace missing values in training instances -
resultVector
FastVector resultVector
Holds the ClusterOrder (dataObjects with their r_dist and c_dist) for the GUI -
showGUI
boolean showGUI
whether to display the GUI after building the clusterer or not. -
writeOPTICSresults
boolean writeOPTICSresults
Flag that indicates if the results are written to a file or not
-
-
Class weka.clusterers.RandomizableClusterer extends AbstractClusterer implements Serializable
- serialVersionUID:
- -4819590778152242745L
-
Serialized Fields
-
m_Seed
int m_Seed
The random number seed. -
m_SeedDefault
int m_SeedDefault
the default seed value
-
-
Class weka.clusterers.RandomizableDensityBasedClusterer extends AbstractDensityBasedClusterer implements Serializable
- serialVersionUID:
- -5325270357918932849L
-
Serialized Fields
-
m_Seed
int m_Seed
The random number seed. -
m_SeedDefault
int m_SeedDefault
the default seed value
-
-
Class weka.clusterers.RandomizableSingleClustererEnhancer extends AbstractClusterer implements Serializable
- serialVersionUID:
- -644847037106316249L
-
Serialized Fields
-
m_Seed
int m_Seed
The random number seed. -
m_SeedDefault
int m_SeedDefault
the default seed value
-
-
Class weka.clusterers.sIB extends RandomizableClusterer implements Serializable
- serialVersionUID:
- -8652125897352654213L
-
Serialized Fields
-
bestT
weka.clusterers.sIB.Partition bestT
Holds the best partition built -
input
weka.clusterers.sIB.Input input
Holds the statistics about the input dataset -
m_data
Instances m_data
Training data -
m_maxLoop
int m_maxLoop
Max number of iterations during each restart -
m_minChange
int m_minChange
Minimum number of changes -
m_numAttributes
int m_numAttributes
Number of attributes -
m_numCluster
int m_numCluster
Number of clusters -
m_numInstances
int m_numInstances
Number of instances -
m_numRestarts
int m_numRestarts
Number of restarts -
m_replaceMissing
ReplaceMissingValues m_replaceMissing
Globally replace missing values -
m_uniformPrior
boolean m_uniformPrior
Uniform prior probability of the documents -
m_verbose
boolean m_verbose
Verbose? -
random
java.util.Random random
Randomly generate initial partition
-
-
Class weka.clusterers.SimpleKMeans extends RandomizableClusterer implements Serializable
- serialVersionUID:
- -3235809600124455376L
-
Serialized Fields
-
m_Assignments
int[] m_Assignments
Assignments obtained -
m_ClusterCentroids
Instances m_ClusterCentroids
holds the cluster centroids -
m_ClusterMissingCounts
int[][] m_ClusterMissingCounts
-
m_ClusterNominalCounts
int[][][] m_ClusterNominalCounts
For each cluster, holds the frequency counts for the values of each nominal attribute -
m_ClusterSizes
int[] m_ClusterSizes
The number of instances in each cluster -
m_ClusterStdDevs
Instances m_ClusterStdDevs
Holds the standard deviations of the numeric attributes in each cluster -
m_displayStdDevs
boolean m_displayStdDevs
Display standard deviations for numeric atts -
m_DistanceFunction
DistanceFunction m_DistanceFunction
the distance function used. -
m_dontReplaceMissing
boolean m_dontReplaceMissing
Replace missing values globally? -
m_FullMeansOrMediansOrModes
double[] m_FullMeansOrMediansOrModes
Stats on the full data set for comparison purposes In case the attribute is numeric the value is the mean if is being used the Euclidian distance or the median if Manhattan distance and if the attribute is nominal then it's mode is saved -
m_FullMissingCounts
int[] m_FullMissingCounts
-
m_FullNominalCounts
int[][] m_FullNominalCounts
-
m_FullStdDevs
double[] m_FullStdDevs
-
m_Iterations
int m_Iterations
Keep track of the number of iterations completed before convergence -
m_MaxIterations
int m_MaxIterations
Maximum number of iterations to be executed -
m_NumClusters
int m_NumClusters
number of clusters to generate -
m_PreserveOrder
boolean m_PreserveOrder
Preserve order of instances -
m_ReplaceMissingFilter
ReplaceMissingValues m_ReplaceMissingFilter
replace missing values in training instances -
m_squaredErrors
double[] m_squaredErrors
Holds the squared errors for all clusters
-
-
Class weka.clusterers.SingleClustererEnhancer extends AbstractClusterer implements Serializable
- serialVersionUID:
- 4893928362926428671L
-
Serialized Fields
-
m_Clusterer
Clusterer m_Clusterer
the clusterer
-
-
Class weka.clusterers.XMeans extends RandomizableClusterer implements Serializable
- serialVersionUID:
- -7941793078404132616L
-
Serialized Fields
-
m_Bic
double m_Bic
BIC-Score of the current model. -
m_BinValue
double m_BinValue
Distance value between true and false of binary attributes and "same" and "different" of nominal attributes (default = 1.0). -
m_ClusterAssignments
int[] m_ClusterAssignments
temporary variable holding cluster assignments while iterating. -
m_ClusterCenters
Instances m_ClusterCenters
cluster centers. -
m_CurrDebugFlag
boolean m_CurrDebugFlag
Flag: I'm debugging. -
m_CutOffFactor
double m_CutOffFactor
cutoff factor - percentage of splits done in Improve-Structure part only relevant, if all children lost. -
m_DebugLevel
int m_DebugLevel
level of debug output, 0 is no output. -
m_DebugVectors
Instances m_DebugVectors
all the debug vectors. -
m_DebugVectorsFile
java.io.File m_DebugVectorsFile
file name of the input file for the random vectors. -
m_DebugVectorsIndex
int m_DebugVectorsIndex
the index for the current debug vector. -
m_DebugVectorsInput
java.io.Reader m_DebugVectorsInput
input file for the random vectors --> USED FOR DEBUGGING. -
m_DistanceF
DistanceFunction m_DistanceF
the distance function used. -
m_InputCenterFile
java.io.File m_InputCenterFile
file name of the output file for the cluster centers. -
m_Instances
Instances m_Instances
training instances. -
m_IterationCount
int m_IterationCount
counts iterations done in main loop. -
m_KDTree
KDTree m_KDTree
KDTrees class if KDTrees are used. -
m_KMeansStopped
int m_KMeansStopped
counter to say how often kMeans was stopped by loop counter. -
m_MaxIterations
int m_MaxIterations
maximum overall iterations. -
m_MaxKMeans
int m_MaxKMeans
maximum iterations to perform Kmeans part if negative, iterations are not checked. -
m_MaxKMeansForChildren
int m_MaxKMeansForChildren
see above, but for kMeans of splitted clusters. -
m_MaxNumClusters
int m_MaxNumClusters
max number of clusters to generate. -
m_MinNumClusters
int m_MinNumClusters
min number of clusters to generate. -
m_Mle
double[] m_Mle
Distortion. -
m_Model
Instances m_Model
model information, should increase readability. -
m_NumClusters
int m_NumClusters
The actual number of clusters. -
m_NumSplits
int m_NumSplits
Number of splits prepared. -
m_NumSplitsDone
int m_NumSplitsDone
Number of splits accepted (including cutoff factor decisions). -
m_NumSplitsStillDone
int m_NumSplitsStillDone
Number of splits accepted just because of cutoff factor. -
m_OutputCenterFile
java.io.File m_OutputCenterFile
file name of the output file for the cluster centers. -
m_ReplaceMissingFilter
ReplaceMissingValues m_ReplaceMissingFilter
replace missing values in training instances. -
m_UseKDTree
boolean m_UseKDTree
whether to use the KDTree (the KDTree is only initialized to be configurable from the GUI).
-
-
-
Package weka.clusterers.forOPTICSAndDBScan.Databases
-
Class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase extends java.lang.Object implements Serializable
- serialVersionUID:
- 787245523118665778L
-
Serialized Fields
-
attributeMaxValues
double[] attributeMaxValues
Holds the maximum value for each attribute -
attributeMinValues
double[] attributeMinValues
Holds the minimum value for each attribute -
instances
Instances instances
Holds the original instances delivered from WEKA -
treeMap
java.util.TreeMap treeMap
Internal, sorted Treemap for storing all the DataObjects
-
-
-
Package weka.clusterers.forOPTICSAndDBScan.DataObjects
-
Class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject extends java.lang.Object implements Serializable
- serialVersionUID:
- -4408119914898291075L
-
Serialized Fields
-
c_dist
double c_dist
Holds the coreDistance for this DataObject -
clusterID
int clusterID
Holds the ID of the cluster, to which this DataObject is assigned -
database
Database database
Holds the database, that is the keeper of this DataObject -
instance
Instance instance
Holds the original instance -
key
java.lang.String key
Holds the (unique) key that is associated with this DataObject -
processed
boolean processed
Holds the status for this DataObject (true, if it has been processed, else false) -
r_dist
double r_dist
Holds the reachabilityDistance for this DataObject
-
-
Class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject extends java.lang.Object implements Serializable
- serialVersionUID:
- -3417720553766544582L
-
Serialized Fields
-
c_dist
double c_dist
Holds the coreDistance for this DataObject -
clusterID
int clusterID
Holds the ID of the cluster, to which this DataObject is assigned -
database
Database database
Holds the database, that is the keeper of this DataObject -
instance
Instance instance
Holds the original instance -
key
java.lang.String key
Holds the (unique) key that is associated with this DataObject -
processed
boolean processed
Holds the status for this DataObject (true, if it has been processed, else false) -
r_dist
double r_dist
Holds the reachabilityDistance for this DataObject
-
-
-
Package weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
-
Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel extends javax.swing.JComponent implements Serializable
- serialVersionUID:
- 7917937528738361470L
-
Serialized Fields
-
coreDistanceColor
java.awt.Color coreDistanceColor
Specifies the color for displaying core-distances -
reachabilityDistanceColor
java.awt.Color reachabilityDistanceColor
Specifies the color for displaying reachability-distances -
recentIndex
int recentIndex
Holds the index of the last toolTip -
resultVector
FastVector resultVector
Holds the clustering results -
showCoreDistances
boolean showCoreDistances
Holds the flag for showCoreDistances -
showReachabilityDistances
boolean showReachabilityDistances
Holds the flag for showrRechabilityDistances -
verticalAdjustment
int verticalAdjustment
Holds the value that is multiplied with the original values of core- and reachability distances in order to get better graphical views -
widthSlider
int widthSlider
Specifies the width for displaying the distances
-
-
Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel extends javax.swing.table.AbstractTableModel implements Serializable
- serialVersionUID:
- -7732711470435549210L
-
Serialized Fields
-
resultVector
FastVector resultVector
Holds the ClusterOrder (dataObjects with their r_dist and c_dist) for the GUI
-
-
Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject extends java.lang.Object implements Serializable
- serialVersionUID:
- -6022057864970639151L
-
Serialized Fields
-
database_distanceType
java.lang.String database_distanceType
-
database_Type
java.lang.String database_Type
-
databaseSize
int databaseSize
-
elapsedTime
java.lang.String elapsedTime
-
epsilon
double epsilon
-
minPoints
int minPoints
-
numberOfAttributes
int numberOfAttributes
-
numberOfGeneratedClusters
int numberOfGeneratedClusters
-
opticsOutputs
boolean opticsOutputs
-
resultVector
FastVector resultVector
-
-
-
Package weka.core
-
Class weka.core.AbstractStringDistanceFunction extends NormalizableDistance implements Serializable
-
Class weka.core.AlgVector extends java.lang.Object implements Serializable
- serialVersionUID:
- -4023736016850256591L
-
Serialized Fields
-
m_Elements
double[] m_Elements
The values of the matrix
-
-
Class weka.core.Attribute extends java.lang.Object implements Serializable
- serialVersionUID:
- -742180568732916383L
-
Serialized Fields
-
m_DateFormat
java.text.SimpleDateFormat m_DateFormat
Date format specification for date attributes -
m_Hashtable
java.util.Hashtable m_Hashtable
Mapping of values to indices (if nominal or string). -
m_HasZeropoint
boolean m_HasZeropoint
Whether the attribute has a zeropoint. -
m_Header
Instances m_Header
The header information for a relation-valued attribute. -
m_Index
int m_Index
The attribute's index. -
m_IsAveragable
boolean m_IsAveragable
Whether the attribute is averagable. -
m_IsRegular
boolean m_IsRegular
Whether the attribute is regular. -
m_LowerBound
double m_LowerBound
The attribute's lower numeric bound. -
m_LowerBoundIsOpen
boolean m_LowerBoundIsOpen
Whether the lower bound is open. -
m_Metadata
ProtectedProperties m_Metadata
The attribute's metadata. -
m_Name
java.lang.String m_Name
The attribute's name. -
m_Ordering
int m_Ordering
The attribute's ordering. -
m_Type
int m_Type
The attribute's type. -
m_UpperBound
double m_UpperBound
The attribute's upper numeric bound. -
m_UpperBoundIsOpen
boolean m_UpperBoundIsOpen
Whether the upper bound is open -
m_Values
FastVector m_Values
The attribute's values (if nominal or string). -
m_Weight
double m_Weight
The attribute's weight.
-
-
Class weka.core.AttributeExpression extends java.lang.Object implements Serializable
- serialVersionUID:
- 402130123261736245L
-
Serialized Fields
-
m_operatorStack
java.util.Stack m_operatorStack
Operator stack -
m_originalInfix
java.lang.String m_originalInfix
Holds the original infix expression -
m_postFixExpVector
java.util.Vector m_postFixExpVector
Holds the expression in postfix form -
m_previousTok
java.lang.String m_previousTok
Holds the previous token -
m_signMod
boolean m_signMod
True if the next numeric constant or attribute index is negative
-
-
Class weka.core.AttributeLocator 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<java.lang.Boolean> m_Attributes
contains the attribute locations, either true or false Boolean objects -
m_AttributesEfficient
java.util.BitSet m_AttributesEfficient
contains the attribute locations, either true or false (efficient replacement) -
m_Data
Instances m_Data
the referenced data -
m_Indices
int[] m_Indices
the indices -
m_LocatorIndices
int[] m_LocatorIndices
the indices of locator objects -
m_Locators
java.util.Vector<AttributeLocator> m_Locators
contains the locator locations, either null or a AttributeLocator reference -
m_Type
int m_Type
the type of the attribute
-
-
Class weka.core.AttributeStats extends java.lang.Object implements Serializable
- serialVersionUID:
- 4434688832743939380L
-
Serialized Fields
-
distinctCount
int distinctCount
The number of distinct values -
intCount
int intCount
The number of int-like values -
missingCount
int missingCount
The number of missing values -
nominalCounts
int[] nominalCounts
Counts of each nominal value -
numericStats
Stats numericStats
Stats on numeric value distributions -
realCount
int realCount
The number of real-like values (i.e. have a fractional part) -
totalCount
int totalCount
The total number of values (i.e. number of instances) -
uniqueCount
int uniqueCount
The number of values that only appear once
-
-
Class weka.core.BinarySparseInstance extends SparseInstance implements Serializable
- serialVersionUID:
- -5297388762342528737L
-
Class weka.core.Capabilities extends java.lang.Object implements Serializable
- serialVersionUID:
- -5478590032325567849L
-
Serialized Fields
-
m_AttributeTest
boolean m_AttributeTest
whether to perform attribute based tests -
m_Capabilities
java.util.HashSet m_Capabilities
the hashset for storing the active capabilities -
m_Dependencies
java.util.HashSet 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_InstancesTest
boolean m_InstancesTest
whether to perform data based tests -
m_MinimumNumberInstances
int m_MinimumNumberInstances
the minimum number of instances in a dataset -
m_MinimumNumberInstancesTest
boolean m_MinimumNumberInstancesTest
whether to test for minimum number of instances -
m_MissingClassValuesTest
boolean m_MissingClassValuesTest
whether to test for missing class values -
m_MissingValuesTest
boolean m_MissingValuesTest
whether to test for missing values -
m_Owner
CapabilitiesHandler m_Owner
the object that owns this capabilities instance -
m_Test
boolean m_Test
whether to perform any tests at all
-
-
Class weka.core.ChebyshevDistance extends NormalizableDistance implements Serializable
- serialVersionUID:
- -7739904999895461429L
-
Class weka.core.Debug extends java.lang.Object implements Serializable
- serialVersionUID:
- 66171861743328020L
-
Serialized Fields
-
m_Clock
Debug.Clock m_Clock
for clocking -
m_Enabled
boolean m_Enabled
whether logging is enabled -
m_Log
Debug.Log m_Log
for logging
-
-
Class weka.core.Debug.Clock extends java.lang.Object implements Serializable
- serialVersionUID:
- 4622161807307942201L
-
Serialized Fields
-
m_CanMeasureCpuTime
boolean m_CanMeasureCpuTime
whether the system can measure the CPU time -
m_OutputFormat
int m_OutputFormat
the format of the output -
m_Running
boolean m_Running
whether the time is still clocked -
m_Start
long m_Start
the start time -
m_Stop
long m_Stop
the end time -
m_ThreadID
long m_ThreadID
the thread ID -
m_UseCpuTime
boolean m_UseCpuTime
whether to use the CPU time (by default TRUE)
-
-
Class weka.core.Debug.DBO extends java.lang.Object implements Serializable
- serialVersionUID:
- -5245628124742606784L
-
Serialized Fields
-
m_outputTypes
Range m_outputTypes
range of outputtyp -
m_verboseOn
boolean m_verboseOn
enables/disables output of debug information
-
-
Class weka.core.Debug.Log extends java.lang.Object implements Serializable
- serialVersionUID:
- 1458435732111675823L
-
Serialized Fields
-
m_Filename
java.lang.String m_Filename
the filename, if any -
m_LoggerInitFailed
boolean m_LoggerInitFailed
whether the initialization of the logger failed -
m_NumFiles
int m_NumFiles
the number of files for rotating the logs -
m_Size
int m_Size
the size of the file (in bytes)
-
-
Class weka.core.Debug.Random 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_Format
java.lang.String m_Format
the format of the timestamp -
m_Formatter
java.text.SimpleDateFormat m_Formatter
handles the format of the output -
m_Stamp
java.util.Date m_Stamp
the actual date
-
-
Class weka.core.EditDistance extends AbstractStringDistanceFunction implements Serializable
-
Class weka.core.EuclideanDistance extends NormalizableDistance implements Serializable
- serialVersionUID:
- 1068606253458807903L
-
Class weka.core.FastVector extends java.lang.Object implements Serializable
- serialVersionUID:
- -2173635135622930169L
-
Serialized Fields
-
m_CapacityIncrement
int m_CapacityIncrement
The capacity increment -
m_CapacityMultiplier
int m_CapacityMultiplier
The capacity multiplier. -
m_Objects
java.lang.Object[] m_Objects
The array of objects. -
m_Size
int m_Size
The current size;
-
-
Class weka.core.Instance extends java.lang.Object implements Serializable
- serialVersionUID:
- 1482635194499365122L
-
Serialized Fields
-
m_AttValues
double[] m_AttValues
The instance's attribute values. -
m_Dataset
Instances m_Dataset
The dataset the instance has access to. Null if the instance doesn't have access to any dataset. Only if an instance has access to a dataset, it knows about the actual attribute types. -
m_Weight
double m_Weight
The instance's weight.
-
-
Class weka.core.InstanceComparator extends java.lang.Object implements Serializable
- serialVersionUID:
- -6589278678230949683L
-
Serialized Fields
-
m_IncludeClass
boolean m_IncludeClass
whether to include the class in the comparison
-
-
Class weka.core.Instances extends java.lang.Object implements Serializable
- serialVersionUID:
- -19412345060742748L
-
Serialized Fields
-
m_Attributes
FastVector m_Attributes
The attribute information. -
m_ClassIndex
int m_ClassIndex
The class attribute's index -
m_Instances
FastVector m_Instances
The instances. -
m_Lines
int m_Lines
The lines read so far in case of incremental loading. Since the StreamTokenizer will be re-initialized with every instance that is read, we have to keep track of the number of lines read so far.- See Also:
Instances.readInstance(Reader)
-
m_RelationName
java.lang.String m_RelationName
The dataset's name.
-
-
Class weka.core.Jython extends java.lang.Object implements Serializable
- serialVersionUID:
- -6972298704460209252L
-
Serialized Fields
-
m_Interpreter
java.lang.Object m_Interpreter
the interpreter
-
-
Class weka.core.ManhattanDistance extends NormalizableDistance implements Serializable
- serialVersionUID:
- 6783782554224000243L
-
Class weka.core.Matrix extends java.lang.Object implements Serializable
- serialVersionUID:
- -3604757095849145838L
-
Serialized Fields
-
m_Matrix
Matrix m_Matrix
Deprecated.The actual matrix
-
-
Class weka.core.NormalizableDistance extends java.lang.Object implements Serializable
-
Serialized Fields
-
m_ActiveIndices
boolean[] m_ActiveIndices
The boolean flags, whether an attribute will be used or not. -
m_AttributeIndices
Range m_AttributeIndices
The range of attributes to use for calculating the distance. -
m_Data
Instances m_Data
the instances used internally. -
m_DontNormalize
boolean m_DontNormalize
True if normalization is turned off (default false). -
m_Ranges
double[][] m_Ranges
The range of the attributes. -
m_Validated
boolean m_Validated
Whether all the necessary preparations have been done.
-
-
-
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_Size
int m_Size
Store the c m_Tail.m_Nexturrent number of elements in the queue -
m_Tail
weka.core.Queue.QueueNode m_Tail
Store a reference to the tail of the queue
-
-
Class weka.core.Queue.QueueNode extends java.lang.Object implements Serializable
- serialVersionUID:
- -5119358279412097455L
-
Serialized Fields
-
m_Contents
java.lang.Object m_Contents
The nodes contents -
m_Next
weka.core.Queue.QueueNode m_Next
The next node in the queue
-
-
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_Invert
boolean m_Invert
Whether matching should be inverted -
m_RangeStrings
java.util.Vector m_RangeStrings
Record the string representations of the columns to delete -
m_SelectFlags
boolean[] m_SelectFlags
The array of flags for whether an column is selected -
m_Upper
int m_Upper
Store the maximum value permitted in the range. -1 indicates that no upper value has been set
-
-
Class weka.core.RelationalLocator extends AttributeLocator implements Serializable
- serialVersionUID:
- 4646872277151854732L
-
Class weka.core.SerializedObject extends java.lang.Object implements Serializable
- serialVersionUID:
- 6635502953928860434L
-
Serialized Fields
-
m_isCompressed
boolean m_isCompressed
Whether or not the object is compressed. -
m_isJython
boolean m_isJython
Whether it is a Jython object or not -
m_storedObjectArray
byte[] m_storedObjectArray
The array storing the object.
-
-
Class weka.core.SingleIndex extends java.lang.Object implements Serializable
- serialVersionUID:
- 5285169134430839303L
-
Serialized Fields
-
m_IndexString
java.lang.String m_IndexString
Record the string representation of the number -
m_SelectedIndex
int m_SelectedIndex
The selected index -
m_Upper
int m_Upper
Store the maximum value permitted. -1 indicates that no upper value has been set
-
-
Class weka.core.SparseInstance extends Instance implements Serializable
- serialVersionUID:
- -3579051291332630149L
-
Serialized Fields
-
m_Indices
int[] m_Indices
The index of the attribute associated with each stored value. -
m_NumAttributes
int m_NumAttributes
The maximum number of values that can be stored.
-
-
Class weka.core.StringLocator extends AttributeLocator implements Serializable
- serialVersionUID:
- 7805522230268783972L
-
Class weka.core.Tag extends java.lang.Object implements Serializable
- serialVersionUID:
- 3326379903447135320L
-
Serialized Fields
-
m_ID
int m_ID
The ID -
m_IDStr
java.lang.String m_IDStr
The unique string for this tag, doesn't have to be numeric -
m_Readable
java.lang.String m_Readable
The descriptive text
-
-
Class weka.core.TestInstances extends java.lang.Object implements Serializable
- serialVersionUID:
- -6263968936330390469L
-
Serialized Fields
-
m_ClassIndex
int m_ClassIndex
the class index (-1 is LAST, -2 means no class) -
m_ClassType
int m_ClassType
the class type -
m_Data
Instances m_Data
the generated data -
m_Handler
CapabilitiesHandler m_Handler
the CapabilitiesHandler to get the Capabilities from -
m_MultiInstance
boolean m_MultiInstance
whether to generate Multi-Instance data or not -
m_NumClasses
int m_NumClasses
the number of classes (in case of NOMINAL class) -
m_NumDate
int m_NumDate
the number of date attributes -
m_NumInstances
int m_NumInstances
the number of instances -
m_NumInstancesRelational
int m_NumInstancesRelational
the number of instances in relational attributes (applies also for bags in multi-instance) -
m_NumNominal
int m_NumNominal
the number of nominal attributes -
m_NumNominalValues
int m_NumNominalValues
the number of values for nominal attributes -
m_NumNumeric
int m_NumNumeric
the number of numeric attributes -
m_NumRelational
int m_NumRelational
the number of relational attributes -
m_NumRelationalDate
int m_NumRelationalDate
the number of date attributes in a relational attribute -
m_NumRelationalNominal
int m_NumRelationalNominal
the number of nominal attributes in a relational attribute -
m_NumRelationalNominalValues
int m_NumRelationalNominalValues
the number of values for nominal attributes in relational attributes -
m_NumRelationalNumeric
int m_NumRelationalNumeric
the number of numeric attributes in a relational attribute -
m_NumRelationalString
int m_NumRelationalString
the number of string attributes in a relational attribute -
m_NumString
int m_NumString
the number of string attributes -
m_Random
java.util.Random m_Random
the random number generator -
m_Relation
java.lang.String m_Relation
the name of the relation -
m_RelationalClassFormat
Instances m_RelationalClassFormat
the format of the multi-instance data of the class -
m_RelationalFormat
Instances[] m_RelationalFormat
the format of the multi-instance data -
m_Seed
int m_Seed
the seed value -
m_Words
java.lang.String[] m_Words
for generating String attributes/classes -
m_WordSeparators
java.lang.String m_WordSeparators
for generating String attributes/classes
-
-
Class weka.core.Trie extends java.lang.Object implements Serializable
- serialVersionUID:
- -5897980928817779048L
-
Serialized Fields
-
m_HashCode
int m_HashCode
the hash code -
m_RecalcHashCode
boolean m_RecalcHashCode
whether the structure got modified and the hash code needs to be re-calculated -
m_Root
Trie.TrieNode m_Root
the root node
-
-
Class weka.core.Trie.TrieNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable
- serialVersionUID:
- -2252907099391881148L
-
Serialized Fields
-
m_Children
java.util.Hashtable<java.lang.Character,Trie.TrieNode> 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
-
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_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_outputFile
java.io.File m_outputFile
The destination file. -
m_prefix
java.lang.String m_prefix
The prefix for the filename (chosen in the GUI). -
m_useRelativePath
boolean m_useRelativePath
use relative file paths
-
-
-
Class weka.core.converters.AbstractLoader 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
-
-
Class weka.core.converters.C45Loader extends AbstractFileLoader implements Serializable
- serialVersionUID:
- 5454329403218219L
-
Serialized Fields
-
m_fileStem
java.lang.String m_fileStem
Holds the filestem. -
m_ignore
boolean[] m_ignore
Which attributes are ignore or label. These are *not* included in the arff representation. -
m_numAttribs
int m_numAttribs
Number of attributes in the data (including ignore and label attributes). -
m_sourceFileData
java.io.File m_sourceFileData
Describe variablem_sourceFileData
here.
-
-
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_BatchBuffer
Instances m_BatchBuffer
the batch buffer. -
m_BatchCounter
int m_BatchCounter
the instance counter for the batch case. -
m_File
java.io.File m_File
the file to load. -
m_Incremental
boolean m_Incremental
whether the loader is incremental. -
m_IncrementalBuffer
Instance m_IncrementalBuffer
the last internally read instance. -
m_Loader
Loader m_Loader
the loader. -
m_URL
java.net.URL m_URL
the URL to load.
-
-
Class weka.core.converters.CSVLoader extends AbstractFileLoader implements Serializable
- serialVersionUID:
- 5607529739745491340L
-
Serialized Fields
-
m_cumulativeInstances
FastVector m_cumulativeInstances
Holds instances accumulated so far. -
m_cumulativeStructure
FastVector m_cumulativeStructure
A list of hash tables for accumulating nominal values during parsing. -
m_dateAttributes
Range m_dateAttributes
The range of attributes to force to type date -
m_dateFormat
java.lang.String m_dateFormat
The formatting string to use to parse dates -
m_Enclosures
java.lang.String m_Enclosures
enclosure character(s) to use for strings -
m_FirstCheck
boolean m_FirstCheck
whether the first row has been read. -
m_formatter
java.text.SimpleDateFormat m_formatter
The formatter to use on dates -
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_StringAttributes
Range m_StringAttributes
The range of attributes to force to type string.
-
-
Class weka.core.converters.CSVSaver extends AbstractFileSaver implements Serializable
- serialVersionUID:
- 476636654410701807L
-
Class weka.core.converters.DatabaseConnection extends DatabaseUtils implements Serializable
- serialVersionUID:
- 1673169848863178695L
-
Class weka.core.converters.DatabaseLoader extends AbstractLoader implements Serializable
- serialVersionUID:
- -7936159015338318659L
-
Serialized Fields
-
m_checkForTable
boolean m_checkForTable
If true it checks whether or not the table exists in the database before loading depending on jdbc metadata information. Set flag to false if no check is required or if jdbc metadata is not complete. -
m_choice
int m_choice
Decides which SQL statement to limit the number of rows should be used. DBMS dependent. Algorithm just tries several possibilities. -
m_counter
int m_counter
Indicates how many rows has already been loaded incrementally -
m_DataBaseConnection
DatabaseConnection m_DataBaseConnection
The database connection -
m_datasetPseudoInc
Instances m_datasetPseudoInc
Used in pseudoincremental mode. The whole dataset from which instances will be read incrementally. -
m_firstTime
boolean m_firstTime
Flag indicating that incremental process wants to read first instance -
m_idColumn
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_inc
boolean m_inc
Flag indicating that incremental mode is chosen (for command line use only) -
m_Keys
java.lang.String m_Keys
the keys for unique ordering -
m_nominalIndexes
java.util.Hashtable[] m_nominalIndexes
Stores the index of a nominal value -
m_nominalStrings
FastVector[] m_nominalStrings
Stores the nominal value -
m_nominalToStringLimit
int m_nominalToStringLimit
Limit when an attribute is treated as string attribute and not as a nominal one because it has to many values. -
m_oldStructure
Instances m_oldStructure
Set of instances that equals m_structure except that the auto_generated_id column is not included as an attribute -
m_orderBy
FastVector m_orderBy
Contains the name of the columns that uniquely define a row in the ResultSet. Ensures a unique ordering of instances for indremental loading. -
m_Password
java.lang.String m_Password
the database password to use -
m_pseudoIncremental
boolean m_pseudoIncremental
Flag indicating that pseudo incremental mode is used (all instances load at once into main memeory and then incrementally from main memory instead of the database) -
m_query
java.lang.String m_query
The user defined query to load instances. (form: SELECT *|<column-list> FROM <table> [WHERE <condition>]) -
m_rowCount
int m_rowCount
The number of rows obtained by m_query, eg the size of the ResultSet to load -
m_structure
Instances m_structure
The header information that is retrieved in the beginning of incremental loading -
m_URL
java.lang.String m_URL
the JDBC URL to use -
m_User
java.lang.String m_User
the database user to use
-
-
Class weka.core.converters.DatabaseSaver extends AbstractSaver implements Serializable
- serialVersionUID:
- 863971733782624956L
-
Serialized Fields
-
m_count
int m_count
counts the rows and used as a primary key value. -
m_createDate
java.lang.String m_createDate
The database specific type for a date (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_createText
java.lang.String m_createText
The database specific type for a string (read in from the properties file). -
m_DataBaseConnection
DatabaseConnection m_DataBaseConnection
The database connection. -
m_DateFormat
java.text.SimpleDateFormat m_DateFormat
For converting the date value into a database string. -
m_id
boolean m_id
Flag indicating if a primary key column should be added. -
m_idColumn
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_inputFile
java.lang.String m_inputFile
An input arff file (for command line use). -
m_Password
java.lang.String m_Password
the password for the database. -
m_tableName
java.lang.String m_tableName
The name of the table in which the instances should be stored. -
m_tabName
boolean m_tabName
Flag indicating whether the default name of the table is the relaion name or not. -
m_Username
java.lang.String m_Username
the user name for the database.
-
-
Class weka.core.converters.LibSVMLoader extends AbstractFileLoader implements Serializable
- serialVersionUID:
- 4988360125354664417L
-
Serialized Fields
-
m_Buffer
java.util.Vector m_Buffer
the buffer of the rows read so far. -
m_URL
java.lang.String m_URL
the url.
-
-
Class weka.core.converters.LibSVMSaver extends AbstractFileSaver implements Serializable
- serialVersionUID:
- 2792295817125694786L
-
Serialized Fields
-
m_ClassIndex
SingleIndex m_ClassIndex
the class index
-
-
Class weka.core.converters.SerializedInstancesLoader extends AbstractFileLoader implements Serializable
- serialVersionUID:
- 2391085836269030715L
-
Serialized Fields
-
m_Dataset
Instances m_Dataset
Holds the structure (header) of the data set. -
m_IncrementalIndex
int m_IncrementalIndex
The current index position for incremental reading
-
-
Class weka.core.converters.SerializedInstancesSaver extends AbstractFileSaver implements Serializable
- serialVersionUID:
- -7717010648500658872L
-
Serialized Fields
-
m_objectstream
java.io.ObjectOutputStream m_objectstream
the output stream.
-
-
Class weka.core.converters.SVMLightLoader extends AbstractFileLoader implements Serializable
- serialVersionUID:
- 4988360125354664417L
-
Serialized Fields
-
m_Buffer
java.util.Vector m_Buffer
the buffer of the rows read so far. -
m_URL
java.lang.String m_URL
the url.
-
-
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_charSet
java.lang.String m_charSet
The charset to use when loading text files (default is to just use the default charset). -
m_Debug
boolean m_Debug
whether to print some debug information -
m_OutputFilename
boolean m_OutputFilename
whether to include the filename as an extra attribute -
m_sourceFile
java.io.File m_sourceFile
Holds the source of the data set. -
m_structure
Instances m_structure
Holds the determined structure (header) of the data set.
-
-
Class weka.core.converters.XRFFLoader 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_CompressOutput
boolean m_CompressOutput
whether to compress the output -
m_XMLInstances
XMLInstances m_XMLInstances
the generated XML document
-
-
-
Package weka.core.matrix
-
Class weka.core.matrix.CholeskyDecomposition extends java.lang.Object implements Serializable
- serialVersionUID:
- -8739775942782694701L
-
Serialized Fields
-
isspd
boolean isspd
Symmetric and positive definite flag.is symmetric and positive definite flag. -
L
double[][] L
Array for internal storage of decomposition.internal array storage. -
n
int n
Row and column dimension (square matrix).matrix dimension.
-
-
Class weka.core.matrix.EigenvalueDecomposition extends java.lang.Object implements Serializable
- serialVersionUID:
- 4011654467211422319L
-
Serialized Fields
-
d
double[] d
Arrays for internal storage of eigenvalues.internal storage of eigenvalues. -
e
double[] e
Arrays for internal storage of eigenvalues.internal storage of eigenvalues. -
H
double[][] H
Array for internal storage of nonsymmetric Hessenberg form.internal storage of nonsymmetric Hessenberg form. -
issymmetric
boolean issymmetric
Symmetry flag.internal symmetry flag. -
n
int n
Row and column dimension (square matrix).matrix dimension. -
ort
double[] ort
Working storage for nonsymmetric algorithm.working storage for nonsymmetric algorithm. -
V
double[][] V
Array for internal storage of eigenvectors.internal storage of eigenvectors.
-
-
Class weka.core.matrix.ExponentialFormat extends java.text.DecimalFormat implements Serializable
- serialVersionUID:
- -5298981701073897741L
-
Serialized Fields
-
digits
int digits
-
exp
int exp
-
nf
java.text.DecimalFormat nf
-
sign
boolean sign
-
trailing
boolean trailing
-
-
Class weka.core.matrix.FlexibleDecimalFormat extends java.text.DecimalFormat implements Serializable
- serialVersionUID:
- 110912192794064140L
-
Serialized Fields
-
decimalDigits
int decimalDigits
-
digits
int digits
-
exp
boolean exp
-
expDecimalDigits
int expDecimalDigits
-
grouping
boolean grouping
-
intDigits
int intDigits
-
nf
java.text.DecimalFormat nf
-
power
int power
-
sign
boolean sign
-
trailing
boolean trailing
-
-
Class weka.core.matrix.FloatingPointFormat extends java.text.DecimalFormat implements Serializable
- serialVersionUID:
- 4500373755333429499L
-
Serialized Fields
-
decimal
int decimal
-
nf
java.text.DecimalFormat nf
-
trailing
boolean trailing
-
width
int width
-
-
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. -
piv
int[] piv
Internal storage of pivot vector.pivot vector. -
pivsign
int pivsign
Row and column dimensions, and pivot sign.column dimension.
-
-
Class weka.core.matrix.Matrix 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
-
m
int m
Row and column dimensions.column dimension. -
n
int n
Row and column dimensions.column dimension. -
QR
double[][] QR
Array for internal storage of decomposition.internal array storage. -
Rdiag
double[] Rdiag
Array for internal storage of diagonal of R.diagonal of R.
-
-
Class weka.core.matrix.SingularValueDecomposition extends java.lang.Object implements Serializable
- serialVersionUID:
- -8738089610999867951L
-
Serialized Fields
-
m
int m
Row and column dimensions.row dimension. -
n
int n
Row and column dimensions.row dimension. -
s
double[] s
Array for internal storage of singular values.internal storage of singular values. -
U
double[][] U
Arrays for internal storage of U and V.internal storage of U. -
V
double[][] V
Arrays for internal storage of U and V.internal storage of U.
-
-
-
Package weka.core.neighboursearch
-
Class weka.core.neighboursearch.BallTree extends NearestNeighbourSearch implements Serializable
- serialVersionUID:
- 728763855952698328L
-
Serialized Fields
-
m_Distances
double[] m_Distances
Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours(). -
m_InstList
int[] m_InstList
The instances indices sorted inorder of appearence in the tree from left most leaf node to the right most leaf node. -
m_MaxInstancesInLeaf
int m_MaxInstancesInLeaf
The maximum number of instances in a leaf. A node is made into a leaf if the number of instances in it become less than or equal to this value. -
m_Root
BallNode m_Root
The root node of the BallTree. -
m_TreeConstructor
BallTreeConstructor m_TreeConstructor
The constructor method to use to build the tree. -
m_TreeStats
TreePerformanceStats m_TreeStats
Tree Stats variables.
-
-
Class weka.core.neighboursearch.CoverTree extends NearestNeighbourSearch implements Serializable
- serialVersionUID:
- 7617412821497807586L
-
Serialized Fields
-
il2
double il2
if we have base 2 then this can be viewed as 1/ln(2), which can be used later on to do il2*ln(d) instead of ln(d)/ln(2), to get log2(d), in get_scale method. -
m_Base
double m_Base
The base of our expansion constant. In other words the 2 in 2^i used in covering tree and separation invariants of a cover tree. P.S.: In paper it's suggested the separation invariant is relaxed in batch construction. -
m_DistanceList
double[] m_DistanceList
Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours(). -
m_EuclideanDistance
EuclideanDistance m_EuclideanDistance
The euclidean distance function to use. -
m_MaxDepth
int m_MaxDepth
Number of nodes in the tree. -
m_NumLeaves
int m_NumLeaves
Number of nodes in the tree. -
m_NumNodes
int m_NumNodes
Number of nodes in the tree. -
m_Root
CoverTree.CoverTreeNode m_Root
The root node. -
m_TreeStats
TreePerformanceStats m_TreeStats
Tree Stats variables.
-
-
Class weka.core.neighboursearch.CoverTree.CoverTreeNode extends java.lang.Object implements Serializable
- serialVersionUID:
- 1808760031169036512L
-
Serialized Fields
-
children
Stack<CoverTree.CoverTreeNode> children
The children of 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. -
nodeid
int nodeid
ID for the node. -
num_children
int num_children
The number of children node has. -
parent_dist
double parent_dist
The distance to the nodes parent. -
scale
int scale
The min i that makes base^i <= max_dist.
-
-
Class weka.core.neighboursearch.KDTree extends NearestNeighbourSearch implements Serializable
- serialVersionUID:
- 1505717283763272533L
-
Serialized Fields
-
m_DistanceList
double[] m_DistanceList
Array holding the distances of the nearest neighbours. It is filled up both by nearestNeighbour() and kNearestNeighbours(). -
m_EuclideanDistance
EuclideanDistance m_EuclideanDistance
The euclidean distance function to use. -
m_InstList
int[] m_InstList
Indexlist of the instances of this kdtree. Instances get sorted according to the splits. the nodes of the KDTree just hold their start and end indices -
m_MaxDepth
int m_MaxDepth
Tree stats. -
m_MaxInstInLeaf
int m_MaxInstInLeaf
maximal number of instances in a leaf. -
m_MinBoxRelWidth
double m_MinBoxRelWidth
minimal relative width of a KDTree rectangle. -
m_NormalizeNodeWidth
boolean m_NormalizeNodeWidth
flag for normalizing. -
m_NumLeaves
int m_NumLeaves
Tree stats. -
m_NumNodes
int m_NumNodes
Tree stats. -
m_Root
KDTreeNode m_Root
The root node of the tree. -
m_Splitter
KDTreeNodeSplitter m_Splitter
The node splitter. -
m_TreeStats
TreePerformanceStats m_TreeStats
Tree Stats variables.
-
-
Class weka.core.neighboursearch.LinearNNSearch 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_DistanceFunction
DistanceFunction m_DistanceFunction
the distance function used. -
m_Instances
Instances m_Instances
The neighbourhood of instances to find neighbours in. -
m_kNN
int m_kNN
The number of neighbours to find. -
m_MeasurePerformance
boolean m_MeasurePerformance
Should we measure Performance. -
m_Stats
PerformanceStats m_Stats
Performance statistics.
-
-
-
Class weka.core.neighboursearch.PerformanceStats extends java.lang.Object implements Serializable
- serialVersionUID:
- -7215110351388368092L
-
Serialized Fields
-
m_CoordCount
double m_CoordCount
The number of coordinates looked at for the current/last query. -
m_MaxC
double m_MaxC
The min and max coordinates(attributes) looked at per query. -
m_MaxP
double m_MaxP
The min and max data points looked for a query by the NNS algorithm. -
m_MinC
double m_MinC
The min and max coordinates(attributes) looked at per query. -
m_MinP
double m_MinP
The min and max data points looked for a query by the NNS algorithm. -
m_NumQueries
int m_NumQueries
The total number of queries looked at. -
m_PointCount
double m_PointCount
The number of data points looked at for the current/last query. -
m_SumC
double m_SumC
The sum of coordinates/attributes looked at for all the queries. -
m_SumP
double m_SumP
The sum of data points looked at for all the queries. -
m_SumSqC
double m_SumSqC
The squared sum of coordinates/attributes looked at for all the queries. -
m_SumSqP
double m_SumSqP
The squared sum of data points looked at for all the queries.
-
-
Class weka.core.neighboursearch.TreePerformanceStats extends PerformanceStats implements Serializable
- serialVersionUID:
- -6637636693340810373L
-
Serialized Fields
-
m_IntNodeCount
int m_IntNodeCount
The number of internal nodes looked at for the current/last query. -
m_LeafCount
int m_LeafCount
The number of leaf nodes looked at for the current/last query. -
m_MaxIntNodes
int m_MaxIntNodes
The min and max number internal nodes looked for a query by the tree based NNS algorithm. -
m_MaxLeaves
int m_MaxLeaves
The min and max number leaf nodes looked for a query by the tree based NNS algorithm. -
m_MinIntNodes
int m_MinIntNodes
The min and max number internal nodes looked for a query by the tree based NNS algorithm. -
m_MinLeaves
int m_MinLeaves
The min and max number leaf nodes looked for a query by the tree based NNS algorithm. -
m_SumIntNodes
int m_SumIntNodes
The sum of internal nodes looked at for all the queries. -
m_SumLeaves
int m_SumLeaves
The sum of leaf nodes looked at for all the queries. -
m_SumSqIntNodes
int m_SumSqIntNodes
The squared sum of internal nodes looked at for all the queries. -
m_SumSqLeaves
int m_SumSqLeaves
The squared sum of leaf nodes looked at for all the queries.
-
-
-
Package weka.core.neighboursearch.balltrees
-
Class weka.core.neighboursearch.balltrees.BallNode extends java.lang.Object implements Serializable
- serialVersionUID:
- -8289151861759883510L
-
Serialized Fields
-
m_End
int m_End
The end index of the portion of the master index array, which stores indices of the instances/points the node contains. -
m_Left
BallNode m_Left
The left child of the node. -
m_NodeNumber
int m_NodeNumber
The node number/id. -
m_NumInstances
int m_NumInstances
The number of instances/points in the node. -
m_Pivot
Instance m_Pivot
The pivot/centre of the ball. -
m_Radius
double m_Radius
The radius of this ball (hyper sphere). -
m_Right
BallNode m_Right
The right child of the node. -
m_SplitAttrib
int m_SplitAttrib
The attribute that splits this node (not always used). -
m_SplitVal
double m_SplitVal
The value of m_SpiltAttrib that splits this node (not always used). -
m_Start
int m_Start
The start index of the portion of the master index array, which stores the indices of the instances/points the node contains.
-
-
Class weka.core.neighboursearch.balltrees.BallSplitter extends java.lang.Object implements Serializable
-
Serialized Fields
-
m_DistanceFunction
EuclideanDistance m_DistanceFunction
The distance function (metric) from which the tree is (OR is to be) built. -
m_Instances
Instances m_Instances
The instance on which the tree is built. -
m_Instlist
int[] m_Instlist
The master index array that'll be reshuffled as nodes are split (and the tree is constructed).
-
-
-
Class weka.core.neighboursearch.balltrees.BallTreeConstructor extends java.lang.Object implements Serializable
-
Serialized Fields
-
m_DistanceFunction
DistanceFunction m_DistanceFunction
The distance function to use to build the tree. -
m_FullyContainChildBalls
boolean m_FullyContainChildBalls
Should a parent ball completely enclose the balls of its two children, or only the points inside its children. -
m_Instances
Instances m_Instances
The instances on which to build the tree. -
m_InstList
int[] m_InstList
The master index array. -
m_MaxDepth
int m_MaxDepth
The depth of the built tree. -
m_MaxInstancesInLeaf
int m_MaxInstancesInLeaf
The maximum number of instances allowed in a leaf. -
m_MaxRelLeafRadius
double m_MaxRelLeafRadius
The maximum relative radius of a leaf node (relative to the smallest ball enclosing all the data (training) points). -
m_NumLeaves
int m_NumLeaves
The number of leaf nodes in the built tree. -
m_NumNodes
int m_NumNodes
The number of internal and leaf nodes in the built tree.
-
-
-
Class weka.core.neighboursearch.balltrees.BottomUpConstructor extends BallTreeConstructor implements Serializable
- serialVersionUID:
- 5864250777657707687L
-
Class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint extends BallSplitter implements Serializable
- serialVersionUID:
- 5617378551363700558L
-
Serialized Fields
-
m_Rand
java.util.Random m_Rand
Random number generator for selecting an abitrary (random) point. -
m_RandSeed
int m_RandSeed
Seed for random number generator.
-
-
Class weka.core.neighboursearch.balltrees.MedianOfWidestDimension 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_RandomInitialAnchor
boolean m_RandomInitialAnchor
True if the initial anchor is chosen randomly. False if it is the furthest point from the mean/centroid. -
m_RSeed
int m_RSeed
Seed form random number generator. -
rand
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.
-
-
Class weka.core.neighboursearch.balltrees.MiddleOutConstructor.MyIdxList extends FastVector implements Serializable
- serialVersionUID:
- -2283869109722934927L
-
Class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren extends BallSplitter implements Serializable
- serialVersionUID:
- -2947177543565818260L
-
Class weka.core.neighboursearch.balltrees.TopDownConstructor extends BallTreeConstructor implements Serializable
- serialVersionUID:
- -5150140645091889979L
-
Serialized Fields
-
m_Splitter
BallSplitter m_Splitter
The BallSplitter algorithm used by the TopDown BallTree constructor, if it is selected.
-
-
-
Package weka.core.neighboursearch.covertrees
-
Class weka.core.neighboursearch.covertrees.Stack extends java.lang.Object implements Serializable
- serialVersionUID:
- 5604056321825539264L
-
Serialized Fields
-
elements
java.util.ArrayList<T> elements
The elements inside the stack. -
length
int length
The number of elements in the stack.
-
-
-
Package weka.core.neighboursearch.kdtrees
-
Class weka.core.neighboursearch.kdtrees.KDTreeNode extends java.lang.Object implements Serializable
- serialVersionUID:
- -3660396067582792648L
-
Serialized Fields
-
m_End
int m_End
The end index of the portion of the master index array, which stores indices of the instances/points the node contains. -
m_Left
KDTreeNode m_Left
left subtree; contains instances with smaller or equal to split value. -
m_NodeNumber
int m_NodeNumber
node number (only for debug). -
m_NodeRanges
double[][] m_NodeRanges
lowest and highest value and width (= high - low) for each dimension. -
m_NodesRectBounds
double[][] m_NodesRectBounds
The lo and high bounds of the hyper rectangle described by the node. -
m_Right
KDTreeNode m_Right
right subtree; contains instances with larger than split value. -
m_SplitDim
int m_SplitDim
attribute to split on. -
m_SplitValue
double m_SplitValue
value to split on. -
m_Start
int m_Start
The start index of the portion of the master index array, which stores the indices of the instances/points the node contains.
-
-
Class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter extends java.lang.Object implements Serializable
-
Serialized Fields
-
m_EuclideanDistance
EuclideanDistance m_EuclideanDistance
The distance function used for building the tree. -
m_Instances
Instances m_Instances
The instances that'll be used for tree construction. -
m_InstList
int[] m_InstList
The master index array that'll be reshuffled as nodes are split and the tree is constructed. -
m_NormalizeNodeWidth
boolean m_NormalizeNodeWidth
Stores whether if the width of a KDTree node is normalized or not.
-
-
-
Class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod extends KDTreeNodeSplitter implements Serializable
- serialVersionUID:
- -866783749124714304L
-
Class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension extends KDTreeNodeSplitter implements Serializable
- serialVersionUID:
- 1383443320160540663L
-
Class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension extends KDTreeNodeSplitter implements Serializable
- serialVersionUID:
- -7617277960046591906L
-
Class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide extends KDTreeNodeSplitter implements Serializable
- serialVersionUID:
- 852857628205680562L
-
-
Package weka.core.pmml
-
Class weka.core.pmml.BuiltInArithmetic extends Function implements Serializable
- serialVersionUID:
- 2275009453597279459L
-
Serialized Fields
-
m_operator
weka.core.pmml.BuiltInArithmetic.Operator m_operator
The operator for this function
-
-
Class weka.core.pmml.BuiltInMath extends Function implements Serializable
- serialVersionUID:
- -8092338695602573652L
-
Serialized Fields
-
m_func
weka.core.pmml.BuiltInMath.MathFunc m_func
The function to apply
-
-
Class weka.core.pmml.BuiltInString extends Function implements Serializable
- serialVersionUID:
- -7391516909331728653L
-
Serialized Fields
-
m_func
weka.core.pmml.BuiltInString.StringFunc m_func
The function to apply -
m_outputDef
Attribute m_outputDef
The output structure produced by this function
-
-
Class weka.core.pmml.Constant extends Expression implements Serializable
- serialVersionUID:
- -304829687822452424L
-
Serialized Fields
-
m_categoricalConst
java.lang.String m_categoricalConst
-
m_continuousConst
double m_continuousConst
-
-
Class weka.core.pmml.DefineFunction extends Function implements Serializable
- serialVersionUID:
- -1976646917527243888L
-
Serialized Fields
-
m_expression
Expression m_expression
The Expression for this function to use -
m_optype
FieldMetaInfo.Optype m_optype
The optype for this function -
m_parameters
java.util.ArrayList<weka.core.pmml.DefineFunction.ParameterField> m_parameters
The list of parameters expected by this function. We can use this to do some error/type checking when users call setParameterDefs() on us
-
-
Class weka.core.pmml.DefineFunction.ParameterField 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_expression
Expression m_expression
the single expression that defines the value of this field -
m_values
java.util.ArrayList<FieldMetaInfo.Value> m_values
the list of values (if the field is ordinal) - may be of size zero if none are specified. If none are specified, we may be able to construct this by querying the Expression in this derived field
-
-
-
Class weka.core.pmml.Discretize extends Expression implements Serializable
-
Serialized Fields
-
m_bins
java.util.ArrayList<weka.core.pmml.Discretize.DiscretizeBin> m_bins
The bins for this discretization -
m_defaultValue
java.lang.String m_defaultValue
The default value (if defined) -
m_defaultValueDefined
boolean m_defaultValueDefined
True if a default value has been specified -
m_fieldIndex
int m_fieldIndex
The index of the field -
m_fieldName
java.lang.String m_fieldName
The name of the field to be discretized -
m_mapMissingDefined
boolean m_mapMissingDefined
True if a replacement for missing values has been specified -
m_mapMissingTo
java.lang.String m_mapMissingTo
The value of the missing value replacement (if defined) -
m_outputDef
Attribute m_outputDef
The output structure of this discretization
-
-
-
Class weka.core.pmml.Discretize.DiscretizeBin extends java.lang.Object implements Serializable
- serialVersionUID:
- 5810063243316808400L
-
Serialized Fields
-
m_binValue
java.lang.String m_binValue
The bin value for this DiscretizeBin -
m_intervals
java.util.ArrayList<FieldMetaInfo.Interval> m_intervals
The intervals for this DiscretizeBin
-
-
Class weka.core.pmml.Expression extends java.lang.Object implements Serializable
- serialVersionUID:
- 4448840549804800321L
-
Serialized Fields
-
m_fieldDefs
java.util.ArrayList<Attribute> m_fieldDefs
The field defs -
m_opType
FieldMetaInfo.Optype m_opType
The optype of this Expression
-
-
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_closure
FieldMetaInfo.Interval.Closure m_closure
-
m_leftMargin
double m_leftMargin
The left boundary value -
m_rightMargin
double m_rightMargin
The right boundary value
-
-
Class weka.core.pmml.FieldMetaInfo.Value extends java.lang.Object implements Serializable
- serialVersionUID:
- -3981030320273649739L
-
Serialized Fields
-
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
-
m_value
java.lang.String m_value
The value
-
-
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<Attribute> 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_fieldsMappingText
java.lang.String m_fieldsMappingText
Holds a textual description of the fields mapping -
m_log
Logger m_log
For logging -
m_nominalValueMaps
int[][] m_nominalValueMaps
Map indexes for nominal values in incoming structure to those in the mining schema. There will be as many entries as there are attributes in this array. Non-nominal attributes will have null entries. Each non-null entry is an array of integer indexes. Each entry in a given array (for a given attribute) holds the index of the mining schema value that corresponds to this incoming value. UNKNOWN_NOMINAL_VALUE is used as the index for those incoming values that are not defined in the mining schema.
-
-
-
Class weka.core.pmml.MiningFieldMetaInfo extends FieldMetaInfo implements Serializable
- serialVersionUID:
- -1256774332779563185L
-
Serialized Fields
-
m_highValue
double m_highValue
outlier high value -
m_importance
double m_importance
importance (if defined) -
m_index
int m_index
the index of the field in the mining schema Instances -
m_lowValue
double m_lowValue
outlier low value -
m_miningSchemaI
Instances m_miningSchemaI
mining schema (needed for toString method) -
m_missingValueReplacementNominal
java.lang.String m_missingValueReplacementNominal
actual missing value replacements (if specified) -
m_missingValueReplacementNumeric
double m_missingValueReplacementNumeric
-
m_missingValueTreatmentMethod
weka.core.pmml.MiningFieldMetaInfo.Missing m_missingValueTreatmentMethod
missing values treatment method -
m_optypeOverride
FieldMetaInfo.Optype m_optypeOverride
optype overrides (override data dictionary type - NOT SUPPORTED AT PRESENT) -
m_outlierTreatmentMethod
weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod
outlier treatmemnt method -
m_usageType
weka.core.pmml.MiningFieldMetaInfo.Usage m_usageType
usage type
-
-
Class weka.core.pmml.MiningSchema extends java.lang.Object implements Serializable
- serialVersionUID:
- 7144380586726330455L
-
Serialized Fields
-
m_derivedMeta
java.util.ArrayList<DerivedFieldMetaInfo> m_derivedMeta
Meta information about derived fields (those defined in the TransformationDictionary followed by those defined in LocalTransformations) -
m_fieldInstancesStructure
Instances m_fieldInstancesStructure
The structure of all the fields (both mining schema and derived) as Instances -
m_miningMeta
java.util.ArrayList<MiningFieldMetaInfo> m_miningMeta
Meta information about the mining schema fields -
m_miningSchemaInstancesStructure
Instances m_miningSchemaInstancesStructure
Just the mining schema fields as Instances -
m_targetMetaInfo
TargetMetaInfo m_targetMetaInfo
target meta info (may be null if not defined) -
m_transformationDictionary
weka.core.pmml.TransformationDictionary m_transformationDictionary
The transformation dictionary (if defined)
-
-
Class weka.core.pmml.NormContinuous extends Expression implements Serializable
- serialVersionUID:
- 4714332374909851542L
-
Serialized Fields
-
m_fieldIndex
int m_fieldIndex
The index of the field -
m_fieldName
java.lang.String m_fieldName
The name of the field to use -
m_linearNormNorm
double[] m_linearNormNorm
norm values for the LinearNorm entries -
m_linearNormOrig
double[] m_linearNormOrig
original values for the LinearNorm entries -
m_mapMissingDefined
boolean m_mapMissingDefined
True if a replacement for missing values has been specified -
m_mapMissingTo
double m_mapMissingTo
The value of the missing value replacement (if defined) -
m_outlierTreatmentMethod
weka.core.pmml.MiningFieldMetaInfo.Outlier m_outlierTreatmentMethod
Outlier treatment method (default = asIs)
-
-
Class weka.core.pmml.NormDiscrete extends Expression implements Serializable
- serialVersionUID:
- -8854409417983908220L
-
Serialized Fields
-
m_field
Attribute m_field
The actual attribute itself -
m_fieldIndex
int m_fieldIndex
The index of the attribute -
m_fieldName
java.lang.String m_fieldName
The name of the field to lookup our value in -
m_fieldValue
java.lang.String m_fieldValue
The actual value (as a String) that will correspond to an output of 1 -
m_fieldValueIndex
int m_fieldValueIndex
If we are referring to a nominal (rather than String) attribute then this holds the index of the value in question. Will be faster than searching for the value each time. -
m_mapMissingDefined
boolean m_mapMissingDefined
True if a replacement for missing values has been specified -
m_mapMissingTo
double m_mapMissingTo
The value of the missing value replacement (if defined)
-
-
Class weka.core.pmml.TargetMetaInfo extends FieldMetaInfo implements Serializable
- serialVersionUID:
- 863500462237904927L
-
Serialized Fields
-
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_displayValues
java.util.ArrayList<java.lang.String> m_displayValues
corresponding display values -
m_max
double m_max
-
m_min
double m_min
min and max -
m_rescaleConstant
double m_rescaleConstant
re-scaling of target value (if defined) -
m_rescaleFactor
double m_rescaleFactor
-
m_values
java.util.ArrayList<java.lang.String> m_values
for categorical values. Actual values
-
-
-
Package weka.core.stemmers
-
Class weka.core.stemmers.IteratedLovinsStemmer extends LovinsStemmer implements Serializable
- serialVersionUID:
- 960689687163788264L
-
Class weka.core.stemmers.LovinsStemmer extends java.lang.Object implements Serializable
- serialVersionUID:
- -6113024782588197L
-
Class weka.core.stemmers.NullStemmer extends java.lang.Object implements Serializable
- serialVersionUID:
- -3671261636532625496L
-
Class weka.core.stemmers.SnowballStemmer extends java.lang.Object implements Serializable
- serialVersionUID:
- -6111170431963015178L
-
Serialized Fields
-
m_Stemmer
java.lang.Object m_Stemmer
the current stemmer.
-
-
-
Package weka.core.tokenizers
-
Class weka.core.tokenizers.AlphabeticTokenizer extends Tokenizer implements Serializable
- serialVersionUID:
- 6705199562609861697L
-
Serialized Fields
-
m_CurrentPos
int m_CurrentPos
the current position -
m_Str
char[] m_Str
the characters of the string
-
-
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_CurrentPosition
int m_CurrentPosition
the current position for returning elements -
m_MaxPosition
int m_MaxPosition
the number of strings available -
m_N
int m_N
the current length of the N-grams -
m_NMax
int m_NMax
the maximum number of N -
m_NMin
int m_NMin
the minimum number of N -
m_SplitString
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_Instances
Instances m_Instances
the underlying Instances -
m_Precision
int m_Precision
the precision for numbers
-
-
-
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
-
Class weka.datagenerators.DataGenerator extends java.lang.Object implements Serializable
- serialVersionUID:
- -3698585946221802578L
-
Serialized Fields
-
m_CreatingRelationName
boolean m_CreatingRelationName
flag, that indicates whether the relationname is currently assembled -
m_DatasetFormat
Instances m_DatasetFormat
The format for the generated dataset -
m_Debug
boolean m_Debug
Debugging mode -
m_NumExamplesAct
int m_NumExamplesAct
Number of instances that should be produced into the dataset this number is by default m_NumExamples, but can be reset by the generator -
m_Random
java.util.Random m_Random
random number generator -
m_RelationName
java.lang.String m_RelationName
Relation name the dataset should have -
m_Seed
int m_Seed
random number generator seed
-
-
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_Dataset
Instances m_Dataset
the dataset -
m_Not
boolean m_Not
whether to negate the test -
m_Split
double m_Split
the split
-
-
-
Package weka.datagenerators.classifiers.classification
-
Class weka.datagenerators.classifiers.classification.Agrawal extends ClassificationGenerator implements Serializable
- serialVersionUID:
- 2254651939636143025L
-
Serialized Fields
-
m_BalanceClass
boolean m_BalanceClass
whether to balance the class -
m_Function
int m_Function
the function to use for generating the data -
m_lastLabel
double m_lastLabel
the last class label that was generated -
m_nextClassShouldBeZero
boolean m_nextClassShouldBeZero
used for balancing the class -
m_PerturbationFraction
double m_PerturbationFraction
the perturabation fraction
-
-
Class weka.datagenerators.classifiers.classification.BayesNet 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_centroidClasses
int[] m_centroidClasses
the classes of the centroids -
m_centroids
double[][] m_centroids
the centroids -
m_centroidStdDevs
double[] m_centroidStdDevs
the stddevs of the centroids -
m_centroidWeights
double[] m_centroidWeights
the weights of the centroids -
m_NumAttributes
int m_NumAttributes
Number of attribute the dataset should have -
m_NumCentroids
int m_NumCentroids
the number of centroids to use for generation -
m_NumClasses
int m_NumClasses
Number of Classes the dataset should have
-
-
Class weka.datagenerators.classifiers.classification.RDG1 extends ClassificationGenerator implements Serializable
- serialVersionUID:
- 7751005204635320414L
-
Serialized Fields
-
m_AttList_Irr
boolean[] m_AttList_Irr
array defines which attributes are irrelevant, with: true = attribute is irrelevant; false = attribute is not irrelevant -
m_DecisionList
FastVector m_DecisionList
decision list -
m_MaxRuleSize
int m_MaxRuleSize
maximum rule size -
m_MinRuleSize
int m_MinRuleSize
minimum rule size -
m_NumAttributes
int m_NumAttributes
Number of attribute the dataset should have -
m_NumClasses
int m_NumClasses
Number of Classes the dataset should have -
m_NumIrrelevant
int m_NumIrrelevant
number of irrelevant attributes. -
m_NumNumeric
int m_NumNumeric
number of numeric attribute -
m_VoteFlag
boolean m_VoteFlag
flag that stores if voting is wished
-
-
-
Package weka.datagenerators.classifiers.regression
-
Class weka.datagenerators.classifiers.regression.Expression 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_MaxRange
double m_MaxRange
the upper boundary of the range, x is drawn from -
m_MinRange
double m_MinRange
the lower boundary of the range, x is drawn from -
m_NoiseRandom
java.util.Random m_NoiseRandom
the random number generator for the noise -
m_NoiseRate
double m_NoiseRate
the rate of the gaussian noise -
m_NoiseVariance
double m_NoiseVariance
the variance of the gaussian noise
-
-
-
Package weka.datagenerators.clusterers
-
Class weka.datagenerators.clusterers.BIRCHCluster extends ClusterGenerator implements Serializable
- serialVersionUID:
- -334820527230755027L
-
Serialized Fields
-
m_ClusterList
FastVector m_ClusterList
cluster list -
m_DistMult
double m_DistMult
distance multiplier (option M) -
m_GridSize
int m_GridSize
grid size -
m_GridWidth
double m_GridWidth
grid width -
m_InputOrder
int m_InputOrder
input order (changed with option O) -
m_MaxInstNum
int m_MaxInstNum
maximal number of instances per cluster (option N) -
m_MaxRadius
double m_MaxRadius
maximum radius (option R) -
m_MinInstNum
int m_MinInstNum
minimal number of instances per cluster (option N) -
m_MinRadius
double m_MinRadius
minimum radius (option R) -
m_NoiseRate
double m_NoiseRate
noise rate in percent (option P, between 0 and 30) -
m_NumClusters
int m_NumClusters
Number of Clusters the dataset should have -
m_NumCycles
int m_NumCycles
number of cycles (option C) -
m_Pattern
int m_Pattern
pattern (changed with options G or S)
-
-
Class weka.datagenerators.clusterers.SubspaceCluster extends ClusterGenerator implements Serializable
- serialVersionUID:
- -3454999858505621128L
-
Serialized Fields
-
m_Clusters
ClusterDefinition[] m_Clusters
cluster list -
m_globalMaxValue
double[] m_globalMaxValue
store global max values -
m_globalMinValue
double[] m_globalMinValue
store global min values -
m_NoiseRate
double m_NoiseRate
noise rate in percent (option P, between 0 and 30) -
m_numValues
int[] m_numValues
if nominal, store number of values
-
-
Class weka.datagenerators.clusterers.SubspaceClusterDefinition extends ClusterDefinition implements Serializable
- serialVersionUID:
- 3135678125044007231L
-
Serialized Fields
-
m_attributes
boolean[] m_attributes
attributes of this cluster -
m_AttrIndexRange
Range m_AttrIndexRange
range of atttributes -
m_attrIndices
int[] m_attrIndices
global indices of the attributes of the cluster -
m_clustersubtype
int m_clustersubtype
cluster subtypes -
m_clustertype
int m_clustertype
cluster type -
m_MaxInstNum
int m_MaxInstNum
maximal number of instances for this cluster -
m_maxValue
double[] m_maxValue
ranges of each attribute (max); not used if gaussian -
m_meanValue
double[] m_meanValue
mean ; only used if gaussian -
m_MinInstNum
int m_MinInstNum
minimal number of instances for this cluster -
m_minValue
double[] m_minValue
ranges of each attribute (min); not used if gaussian -
m_numClusterAttributes
int m_numClusterAttributes
number of attributes the cluster is defined for -
m_numInstances
int m_numInstances
number of instances for this cluster -
m_stddevValue
double[] m_stddevValue
standarddev; only used if gaussian
-
-
-
Package weka.estimators
-
Class weka.estimators.DiscreteEstimator 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_classValueIndex
double m_classValueIndex
The class value index is > -1 if subset is taken with specific class value only -
m_Debug
boolean m_Debug
Debugging mode -
m_noClass
boolean m_noClass
set if class is not important
-
-
Class weka.estimators.KernelEstimator extends Estimator implements Serializable
- serialVersionUID:
- 3646923563367683925L
-
Serialized Fields
-
m_AllWeightsOne
boolean m_AllWeightsOne
Whether we can optimise the kernel summation -
m_NumValues
int m_NumValues
Number of values stored in m_Weights and m_Values so far -
m_Precision
double m_Precision
The precision of data values -
m_StandardDev
double m_StandardDev
The standard deviation -
m_SumOfWeights
double m_SumOfWeights
The sum of the weights so far -
m_Values
double[] m_Values
Vector containing all of the values seen -
m_Weights
double[] m_Weights
Vector containing the associated weights
-
-
Class weka.estimators.MahalanobisEstimator extends Estimator implements Serializable
- serialVersionUID:
- 8950225468990043868L
-
Serialized Fields
-
m_ConstDelta
double m_ConstDelta
The difference between the conditioning value and the conditioning mean -
m_CovarianceInverse
Matrix m_CovarianceInverse
The inverse of the covariance matrix -
m_Determinant
double m_Determinant
The determinant of the covariance matrix -
m_ValueMean
double m_ValueMean
The mean of the values
-
-
Class weka.estimators.NormalEstimator extends Estimator implements Serializable
- serialVersionUID:
- 93584379632315841L
-
Serialized Fields
-
m_Mean
double m_Mean
The current mean -
m_Precision
double m_Precision
The precision of numeric values ( = minimum std dev permitted) -
m_StandardDev
double m_StandardDev
The current standard deviation -
m_SumOfValues
double m_SumOfValues
The sum of the values seen -
m_SumOfValuesSq
double m_SumOfValuesSq
The sum of the values squared -
m_SumOfWeights
double m_SumOfWeights
The sum of the weights
-
-
Class weka.estimators.PoissonEstimator extends Estimator implements Serializable
- serialVersionUID:
- 7669362595289236662L
-
Serialized Fields
-
m_Lambda
double m_Lambda
The average number of times an event occurs in an interval. -
m_NumValues
double m_NumValues
The number of values seen -
m_SumOfValues
double m_SumOfValues
The sum of the values seen
-
-
-
Package weka.experiment
-
Class weka.experiment.AveragingResultProducer extends java.lang.Object implements Serializable
- serialVersionUID:
- 2551284958501991352L
-
Serialized Fields
-
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators -
m_CalculateStdDevs
boolean m_CalculateStdDevs
True if standard deviation fields should be produced -
m_CountFieldName
java.lang.String m_CountFieldName
The name of the field that will contain the number of results averaged over. -
m_ExpectedResultsPerAverage
int m_ExpectedResultsPerAverage
The number of results expected to average over for each run -
m_Instances
Instances m_Instances
The dataset of interest -
m_KeyFieldName
java.lang.String m_KeyFieldName
The name of the key field to average over -
m_KeyIndex
int m_KeyIndex
The index of the field to average over in the resultproducers key -
m_Keys
FastVector m_Keys
Collects the keys from a single run -
m_ResultListener
ResultListener m_ResultListener
The ResultListener to send results to -
m_ResultProducer
ResultProducer m_ResultProducer
The ResultProducer used to generate results -
m_Results
FastVector m_Results
Collects the results from a single run
-
-
Class weka.experiment.ClassifierSplitEvaluator extends java.lang.Object implements Serializable
- serialVersionUID:
- -8511241602760467265L
-
Serialized Fields
-
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators -
m_attID
int m_attID
Attribute index of instance identifier (default -1) -
m_Classifier
Classifier m_Classifier
The classifier used for evaluation -
m_ClassifierOptions
java.lang.String m_ClassifierOptions
The classifier options (if any) -
m_ClassifierVersion
java.lang.String m_ClassifierVersion
The classifier version -
m_doesProduce
boolean[] m_doesProduce
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce -
m_IRclass
int m_IRclass
Class index for information retrieval statistics (default 0) -
m_numberAdditionalMeasures
int m_numberAdditionalMeasures
The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results -
m_predTargetColumn
boolean m_predTargetColumn
Flag for prediction and target columns output. -
m_result
java.lang.String m_result
Holds the statistics for the most recent application of the classifier -
m_Template
Classifier m_Template
The template classifier
-
-
Class weka.experiment.CostSensitiveClassifierSplitEvaluator 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_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_Instances
Instances m_Instances
The dataset of interest -
m_NumFolds
int m_NumFolds
The number of folds in the cross-validation -
m_OutputFile
java.io.File m_OutputFile
The destination output file/directory for raw output -
m_ResultListener
ResultListener m_ResultListener
The ResultListener to send results to -
m_SplitEvaluator
SplitEvaluator m_SplitEvaluator
The SplitEvaluator used to generate results -
m_ZipDest
OutputZipper m_ZipDest
The output zipper to use for saving raw splitEvaluator output
-
-
Class weka.experiment.CSVResultListener extends java.lang.Object implements Serializable
- serialVersionUID:
- -623185072785174658L
-
Serialized Fields
-
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. -
m_RP
ResultProducer m_RP
The ResultProducer sending us results
-
-
Class weka.experiment.DatabaseResultListener extends DatabaseUtils implements Serializable
- serialVersionUID:
- 7388014746954652818L
-
Serialized Fields
-
m_Cache
FastVector m_Cache
Stores the cached values -
m_CacheKey
java.lang.Object[] m_CacheKey
Stores the key for which the cache is valid -
m_CacheKeyIndex
int m_CacheKeyIndex
Stores the index of the key column holding the cache key data -
m_CacheKeyName
java.lang.String m_CacheKeyName
Holds the name of the key field to cache upon, or null if no caching -
m_Debug
boolean m_Debug
True if debugging output should be printed -
m_ResultProducer
ResultProducer m_ResultProducer
The ResultProducer to listen to -
m_ResultsTableName
java.lang.String m_ResultsTableName
The name of the current results table
-
-
Class weka.experiment.DatabaseResultProducer extends DatabaseResultListener implements Serializable
- serialVersionUID:
- -5620660780203158666L
-
Serialized Fields
-
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators -
m_Instances
Instances m_Instances
The dataset of interest -
m_ResultListener
ResultListener m_ResultListener
The ResultListener to send results to -
m_ResultProducer
ResultProducer m_ResultProducer
The ResultProducer used to generate results
-
-
Class weka.experiment.DatabaseUtils extends java.lang.Object implements Serializable
- serialVersionUID:
- -8252351994547116729L
-
Serialized Fields
-
DRIVERS
java.util.Vector DRIVERS
Holds the jdbc drivers to be used (only to stop them being gc'ed). -
m_checkForLowerCaseNames
boolean m_checkForLowerCaseNames
For databases where Tables and Columns are created in lower case. -
m_checkForUpperCaseNames
boolean m_checkForUpperCaseNames
For databases where Tables and Columns are created in upper case. -
m_createIndex
boolean m_createIndex
create index on the database? -
m_DatabaseURL
java.lang.String m_DatabaseURL
Database URL. -
m_Debug
boolean m_Debug
True if debugging output should be printed. -
m_doubleType
java.lang.String m_doubleType
double type for the create table statement. -
m_intType
java.lang.String m_intType
integer type for the create table statement. -
m_Keywords
java.util.HashSet<java.lang.String> 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). -
m_password
java.lang.String m_password
Database Password. -
m_setAutoCommit
boolean m_setAutoCommit
setAutoCommit on the database? -
m_stringType
java.lang.String m_stringType
string type for the create table statement. -
m_userName
java.lang.String m_userName
Database username. -
PROPERTIES
java.util.Properties PROPERTIES
Properties associated with the database connection.
-
-
Class weka.experiment.DensityBasedClustererSplitEvaluator extends java.lang.Object implements Serializable
-
Serialized Fields
-
m_additionalMeasures
java.lang.String[] m_additionalMeasures
The names of any additional measures to look for in SplitEvaluators -
m_clusterer
DensityBasedClusterer m_clusterer
The clusterer used for evaluation -
m_clustererOptions
java.lang.String m_clustererOptions
The clusterer options (if any) -
m_clustererVersion
java.lang.String m_clustererVersion
The clusterer version -
m_doesProduce
boolean[] m_doesProduce
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current clusterer can produce -
m_numberAdditionalMeasures
int m_numberAdditionalMeasures
The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results -
m_removeClassColumn
boolean m_removeClassColumn
Remove the class column (if set) from the data -
m_result
java.lang.String m_result
Holds the statistics for the most recent application of the clusterer
-
-
-
Class weka.experiment.Experiment extends java.lang.Object implements Serializable
- serialVersionUID:
- 44945596742646663L
-
Serialized Fields
-
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_AdvanceDataSetFirst
boolean m_AdvanceDataSetFirst
If true an experiment will advance the current data set befor any custom itererator -
m_ClassFirst
boolean m_ClassFirst
True if the class attribute is the first attribute for all datasets involved in this experiment. -
m_Datasets
javax.swing.DefaultListModel m_Datasets
An array of dataset files -
m_Notes
java.lang.String m_Notes
User notes about the experiment -
m_PropertyArray
java.lang.Object m_PropertyArray
The array of values to set the property to -
m_PropertyPath
PropertyNode[] m_PropertyPath
The path to the iterator property -
m_ResultListener
ResultListener m_ResultListener
Where results will be sent -
m_ResultProducer
ResultProducer m_ResultProducer
The result producer -
m_RunLower
int m_RunLower
Lower run number -
m_RunUpper
int m_RunUpper
Upper run number -
m_UsePropertyIterator
boolean m_UsePropertyIterator
True if the exp should also iterate over a property of the RP
-
-
Class weka.experiment.InstanceQuery extends DatabaseUtils implements Serializable
- serialVersionUID:
- 718158370917782584L
-
Serialized Fields
-
m_CreateSparseData
boolean m_CreateSparseData
Determines whether sparse data is created -
m_Query
java.lang.String m_Query
Query to execute
-
-
Class weka.experiment.InstancesResultListener extends CSVResultListener implements Serializable
- serialVersionUID:
- -2203808461809311178L
-
Class weka.experiment.LearningRateResultProducer extends java.lang.Object implements Serializable
- serialVersionUID:
- -3841159673490861331L
-
Serialized Fields
-
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators -
m_CurrentSize
int m_CurrentSize
The current dataset size during stepping -
m_Instances
Instances m_Instances
The dataset of interest -
m_LowerSize
int m_LowerSize
The minimum number of instances to use. If this is zero, the first step will contain m_StepSize instances -
m_ResultListener
ResultListener m_ResultListener
The ResultListener to send results to -
m_ResultProducer
ResultProducer m_ResultProducer
The ResultProducer used to generate results -
m_StepSize
int m_StepSize
The number of instances to add at each step -
m_UpperSize
int m_UpperSize
The maximum number of instances to use. -1 indicates no maximum (other than the total number of instances)
-
-
Class weka.experiment.PairedCorrectedTTester extends PairedTTester implements Serializable
- serialVersionUID:
- -3105268939845653323L
-
Class weka.experiment.PairedTTester extends java.lang.Object implements Serializable
- serialVersionUID:
- 8370014624008728610L
-
Serialized Fields
-
m_ColOrder
int[] m_ColOrder
The sorting of the columns (test base is always first) -
m_DatasetKeyColumns
int[] m_DatasetKeyColumns
An array containing the indexes of just the selected columns -
m_DatasetKeyColumnsRange
Range m_DatasetKeyColumnsRange
The range of columns that specify a unique "dataset" (eg: scheme plus configuration) -
m_DatasetSpecifiers
weka.experiment.PairedTTester.DatasetSpecifiers m_DatasetSpecifiers
The list of dataset specifiers -
m_DisplayedResultsets
int[] m_DisplayedResultsets
An array containing the indexes of the datasets to display -
m_FoldColumn
int m_FoldColumn
The option setting for the fold number column (-1 means none) -
m_Instances
Instances m_Instances
The set of instances we will analyse -
m_ResultMatrix
ResultMatrix m_ResultMatrix
the instance of the class to produce the output. -
m_ResultsetKeyColumns
int[] m_ResultsetKeyColumns
An array containing the indexes of just the selected columns -
m_ResultsetKeyColumnsRange
Range m_ResultsetKeyColumnsRange
The range of columns that specify a unique result set (eg: scheme plus configuration) -
m_Resultsets
FastVector m_Resultsets
Stores a vector for each resultset holding all instances in each set -
m_ResultsetsValid
boolean m_ResultsetsValid
Indicates whether the instances have been partitioned -
m_RunColumn
int m_RunColumn
The index of the column containing the run number -
m_RunColumnSet
int m_RunColumnSet
The option setting for the run number column (-1 means last) -
m_ShowStdDevs
boolean m_ShowStdDevs
Indicates whether standard deviations should be displayed -
m_SignificanceLevel
double m_SignificanceLevel
The significance level for comparisons -
m_SortColumn
int m_SortColumn
The column to sort on (-1 means default sorting) -
m_SortOrder
int[] m_SortOrder
The sorting of the datasets (according to the sort column)
-
-
Class weka.experiment.PropertyNode 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
-
parentClass
java.lang.Class parentClass
The class of the object with this property -
property
java.beans.PropertyDescriptor property
Other info about the property -
value
java.lang.Object value
The current property value
-
-
Class weka.experiment.RandomSplitResultProducer extends java.lang.Object implements Serializable
- serialVersionUID:
- 1403798165056795073L
-
Serialized Fields
-
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_Instances
Instances m_Instances
The dataset of interest -
m_OutputFile
java.io.File m_OutputFile
The destination output file/directory for raw output -
m_randomize
boolean m_randomize
Whether dataset is to be randomized -
m_ResultListener
ResultListener m_ResultListener
The ResultListener to send results to -
m_SplitEvaluator
SplitEvaluator m_SplitEvaluator
The SplitEvaluator used to generate results -
m_TrainPercent
double m_TrainPercent
The percentage of instances to use for training -
m_ZipDest
OutputZipper m_ZipDest
The output zipper to use for saving raw splitEvaluator output
-
-
Class weka.experiment.RegressionSplitEvaluator extends java.lang.Object implements Serializable
- serialVersionUID:
- -328181640503349202L
-
Serialized Fields
-
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
The names of any additional measures to look for in SplitEvaluators -
m_Classifier
Classifier m_Classifier
The classifier used for evaluation -
m_ClassifierOptions
java.lang.String m_ClassifierOptions
The classifier options (if any) -
m_ClassifierVersion
java.lang.String m_ClassifierVersion
The classifier version -
m_doesProduce
boolean[] m_doesProduce
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce -
m_result
java.lang.String m_result
Holds the statistics for the most recent application of the classifier -
m_Template
Classifier m_Template
The template classifier
-
-
Class weka.experiment.RemoteEngine 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_TaskIdQueue
Queue m_TaskIdQueue
A queue of corresponding ID's for tasks -
m_TaskQueue
Queue m_TaskQueue
A queue of waiting tasks -
m_TaskRunning
boolean m_TaskRunning
Is there a task running -
m_TaskStatus
java.util.Hashtable m_TaskStatus
A hashtable of experiment status
-
-
Class weka.experiment.RemoteExperiment extends Experiment implements Serializable
- serialVersionUID:
- -7357668825635314937L
-
Serialized Fields
-
m_baseExperiment
Experiment m_baseExperiment
The base experiment to split up into sub experiments for remote execution -
m_experimentAborted
boolean m_experimentAborted
Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts experiment (via gui) -
m_failedCount
int m_failedCount
The count of failed sub-experiments -
m_finishedCount
int m_finishedCount
The count of successfully completed sub-experiments -
m_listeners
FastVector m_listeners
The list of objects listening for remote experiment events -
m_remoteHostFailureCounts
int[] m_remoteHostFailureCounts
The number of times tasks have failed on each remote host -
m_remoteHosts
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_removedHosts
int m_removedHosts
The number of hosts removed due to exceeding max failures -
m_splitByDataSet
boolean m_splitByDataSet
If true, then sub experiments are created on the basis of data sets rather than run number. -
m_subExpComplete
int[] m_subExpComplete
The status of each of the sub-experiments -
m_subExperiments
Experiment[] m_subExperiments
The sub experiments -
m_subExpQueue
Queue m_subExpQueue
The queue of sub experiments waiting to be processed
-
-
Class weka.experiment.RemoteExperimentEvent extends java.lang.Object implements Serializable
- serialVersionUID:
- 7000867987391866451L
-
Serialized Fields
-
m_experimentFinished
boolean m_experimentFinished
True if a remote experiment has finished -
m_logMessage
boolean m_logMessage
A log type message -
m_messageString
java.lang.String m_messageString
The message -
m_statusMessage
boolean m_statusMessage
A status type message
-
-
Class weka.experiment.RemoteExperimentSubTask extends java.lang.Object implements Serializable
-
Serialized Fields
-
m_experiment
Experiment m_experiment
-
m_result
TaskStatusInfo m_result
-
-
-
Class weka.experiment.ResultMatrix extends java.lang.Object implements Serializable
- serialVersionUID:
- 4487179306428209739L
-
Serialized Fields
-
LEFT_PARENTHESES
java.lang.String LEFT_PARENTHESES
the left parentheses for enumerating cols/rows -
LOSS_STRING
java.lang.String LOSS_STRING
loss string -
m_ColHidden
boolean[] m_ColHidden
whether a column is hidden -
m_ColNames
java.lang.String[] m_ColNames
the column names -
m_ColNameWidth
int m_ColNameWidth
the size of the names of the columns -
m_ColOrder
int[] m_ColOrder
the ordering of the columns -
m_Counts
double[] m_Counts
the counts for the different datasets -
m_CountWidth
int m_CountWidth
the size of the counts -
m_EnumerateColNames
boolean m_EnumerateColNames
whether a "(x)" is printed before each column name with "x" as the index -
m_EnumerateRowNames
boolean m_EnumerateRowNames
whether a "(x)" is printed before each row name with "x" as the index -
m_HeaderKeys
java.util.Vector m_HeaderKeys
contains the keys for the header -
m_HeaderValues
java.util.Vector m_HeaderValues
contains the values for the header -
m_Mean
double[][] m_Mean
the values -
m_MeanPrec
int m_MeanPrec
the standard mean precision -
m_MeanWidth
int m_MeanWidth
the size of the mean columns -
m_NonSigWins
int[][] m_NonSigWins
the non-significant wins -
m_PrintColNames
boolean m_PrintColNames
whether the names or numbers are output as column declarations -
m_PrintRowNames
boolean m_PrintRowNames
whether the names or numbers are output as row declarations -
m_RankingDiff
int[] m_RankingDiff
the difference between wins and losses -
m_RankingLosses
int[] m_RankingLosses
the losses in ranking -
m_RankingWins
int[] m_RankingWins
the wins in ranking -
m_RemoveFilterName
boolean m_RemoveFilterName
whether to remove the filter name from the dataaset name -
m_RowHidden
boolean[] m_RowHidden
whether a row is hidden -
m_RowNames
java.lang.String[] m_RowNames
the row names -
m_RowNameWidth
int m_RowNameWidth
the size of the names of the rows -
m_RowOrder
int[] m_RowOrder
the ordering of the rows -
m_ShowAverage
boolean m_ShowAverage
whether the average for each column should be printed -
m_ShowStdDev
boolean m_ShowStdDev
whether std. deviations are printed as well -
m_Significance
int[][] m_Significance
the significance -
m_SignificanceWidth
int m_SignificanceWidth
the size of the significance columns -
m_StdDev
double[][] m_StdDev
the standard deviation -
m_StdDevPrec
int m_StdDevPrec
the standard std. deviation preicision -
m_StdDevWidth
int m_StdDevWidth
the size of the std dev columns -
m_Wins
int[][] m_Wins
the significant wins -
RIGHT_PARENTHESES
java.lang.String RIGHT_PARENTHESES
the right parentheses for enumerating cols/rows -
TIE_STRING
java.lang.String TIE_STRING
tie string -
WIN_STRING
java.lang.String WIN_STRING
win string
-
-
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 -
max
double max
The maximum value seen, or Double.NaN if no values seen -
mean
double mean
The mean of values at the last calculateDerived() call -
min
double min
The minimum value seen, or Double.NaN if no values seen -
stdDev
double stdDev
The std deviation of values at the last calculateDerived() call -
sum
double sum
The sum of values seen -
sumSq
double sumSq
The sum of values squared seen
-
-
Class weka.experiment.TaskStatusInfo 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_FirstBatchDone
boolean m_FirstBatchDone
True if the first batch has been done -
m_InputFormat
Instances m_InputFormat
The input format for instances -
m_InputRelAtts
RelationalLocator m_InputRelAtts
Indices of relational attributes in the input format -
m_InputStringAtts
StringLocator m_InputStringAtts
Indices of string attributes in the input format -
m_NewBatch
boolean m_NewBatch
Record whether the filter is at the start of a batch -
m_OutputFormat
Instances m_OutputFormat
The output format for instances -
m_OutputQueue
Queue m_OutputQueue
The output instance queue -
m_OutputRelAtts
RelationalLocator m_OutputRelAtts
Indices of relational attributes in the output format -
m_OutputStringAtts
StringLocator m_OutputStringAtts
Indices of string attributes in the output format
-
-
Class weka.filters.MultiFilter 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_ActualClassifier
Classifier m_ActualClassifier
The actual classifier used to do the classification. -
m_Classifier
Classifier m_Classifier
The classifier template used to do the classification. -
m_OutputClassification
boolean m_OutputClassification
whether to output the classification. -
m_OutputDistribution
boolean m_OutputDistribution
whether to output the class distribution. -
m_OutputErrorFlag
boolean m_OutputErrorFlag
whether to output the error flag. -
m_RemoveOldClass
boolean m_RemoveOldClass
whether to remove the old class attribute. -
m_SerializedClassifierFile
java.io.File m_SerializedClassifierFile
The file from which to load a serialized classifier. -
m_SerializedHeader
Instances m_SerializedHeader
the header of the file the serialized classifier was trained with.
-
-
Class weka.filters.supervised.attribute.AttributeSelection extends Filter implements Serializable
- serialVersionUID:
- -296211247688169716L
-
Serialized Fields
-
m_ASEvaluator
ASEvaluation m_ASEvaluator
the attribute evaluator to use -
m_ASSearch
ASSearch m_ASSearch
the search method if any -
m_FilterOptions
java.lang.String[] m_FilterOptions
holds a copy of the full set of valid options passed to the filter -
m_hasClass
boolean m_hasClass
True if the input format has a class attribute set -
m_SelectedAttributes
int[] m_SelectedAttributes
holds the selected attributes -
m_trainSelector
AttributeSelection m_trainSelector
the attribute selection evaluation object
-
-
Class weka.filters.supervised.attribute.ClassOrder extends Filter implements Serializable
- serialVersionUID:
- -2116226838887628411L
-
Serialized Fields
-
m_ClassAttribute
Attribute m_ClassAttribute
Class attribute of the data -
m_ClassCounts
double[] m_ClassCounts
This class can provide the class distribution in the sorted order as side effect -
m_ClassOrder
int m_ClassOrder
The class order to be sorted -
m_Converter
int[] m_Converter
The 1-1 converting table from the original class values to the new values -
m_Random
java.util.Random m_Random
The random object -
m_Seed
long m_Seed
The seed of randomization
-
-
Class weka.filters.supervised.attribute.Discretize extends Filter implements Serializable
- serialVersionUID:
- -3141006402280129097L
-
Serialized Fields
-
m_CutPoints
double[][] m_CutPoints
Store the current cutpoints -
m_DiscretizeCols
Range m_DiscretizeCols
Stores which columns to Discretize -
m_MakeBinary
boolean m_MakeBinary
Output binary attributes for discretized attributes. -
m_UseBetterEncoding
boolean m_UseBetterEncoding
Use better encoding of split point for MDL. -
m_UseKononenko
boolean m_UseKononenko
Use Kononenko's MDL criterion instead of Fayyad et al.'s
-
-
Class weka.filters.supervised.attribute.NominalToBinary extends Filter implements Serializable
- serialVersionUID:
- -5004607029857673950L
-
Serialized Fields
-
m_Indices
int[][] m_Indices
The sorted indices of the attribute values. -
m_needToTransform
boolean m_needToTransform
Whether we need to transform at all -
m_Numeric
boolean m_Numeric
Are the new attributes going to be nominal or numeric ones? -
m_TransformAll
boolean m_TransformAll
Are all values transformed into new attributes?
-
-
Class weka.filters.supervised.attribute.PLSFilter extends SimpleBatchFilter implements Serializable
- serialVersionUID:
- -3335106965521265631L
-
Serialized Fields
-
m_Algorithm
int m_Algorithm
the type of algorithm -
m_ClassMean
double m_ClassMean
the mean of the class -
m_ClassStdDev
double m_ClassStdDev
the standard deviation of the class -
m_Filter
Filter m_Filter
for centering the data -
m_Missing
Filter m_Missing
for replacing missing values -
m_NumComponents
int m_NumComponents
the maximum number of components to generate -
m_PerformPrediction
boolean m_PerformPrediction
whether to include the prediction, i.e., modifying the class attribute -
m_PLS1_b_hat
Matrix m_PLS1_b_hat
the b-hat vector for PLS1 -
m_PLS1_P
Matrix m_PLS1_P
the P matrix for PLS1 -
m_PLS1_RegVector
Matrix m_PLS1_RegVector
the regression vector "r-hat" for PLS1 -
m_PLS1_W
Matrix m_PLS1_W
the W matrix for PLS1 -
m_Preprocessing
int m_Preprocessing
the type of preprocessing -
m_ReplaceMissing
boolean m_ReplaceMissing
whether to replace missing values -
m_SIMPLS_B
Matrix m_SIMPLS_B
the B matrix for SIMPLS (used for prediction) -
m_SIMPLS_W
Matrix m_SIMPLS_W
the W matrix for SIMPLS
-
-
-
Package weka.filters.supervised.instance
-
Class weka.filters.supervised.instance.Resample extends Filter implements Serializable
- serialVersionUID:
- 7079064953548300681L
-
Serialized Fields
-
m_BiasToUniformClass
double m_BiasToUniformClass
The degree of bias towards uniform (nominal) class distribution. -
m_InvertSelection
boolean m_InvertSelection
Whether to invert the selection (only if instances are drawn WITHOUT replacement).- See Also:
Resample.m_NoReplacement
-
m_NoReplacement
boolean m_NoReplacement
Whether to perform sampling with replacement or without. -
m_RandomSeed
int m_RandomSeed
The random number generator seed. -
m_SampleSizePercent
double m_SampleSizePercent
The subsample size, percent of original set, default 100%.
-
-
Class weka.filters.supervised.instance.SMOTE extends Filter implements Serializable
- serialVersionUID:
- -1653880819059250364L
-
Serialized Fields
-
m_ClassValueIndex
java.lang.String m_ClassValueIndex
the index of the class value. -
m_DetectMinorityClass
boolean m_DetectMinorityClass
whether to detect the minority class automatically. -
m_NearestNeighbors
int m_NearestNeighbors
the number of neighbors to use. -
m_Percentage
double m_Percentage
the percentage of SMOTE instances to create. -
m_RandomSeed
int m_RandomSeed
the random seed to use.
-
-
Class weka.filters.supervised.instance.SpreadSubsample extends Filter implements Serializable
- serialVersionUID:
- -3947033795243930016L
-
Serialized Fields
-
m_AdjustWeights
boolean m_AdjustWeights
True if instance weights will be adjusted to maintain total weight per class. -
m_DistributionSpread
double m_DistributionSpread
True if the first batch has been done -
m_MaxCount
int m_MaxCount
The maximum count of any class -
m_RandomSeed
int m_RandomSeed
The random number generator seed
-
-
Class weka.filters.supervised.instance.StratifiedRemoveFolds extends Filter implements Serializable
- serialVersionUID:
- -7069148179905814324L
-
Serialized Fields
-
m_Fold
int m_Fold
Fold to output -
m_Inverse
boolean m_Inverse
Indicates if inverse of selection is to be output. -
m_NumFolds
int m_NumFolds
Number of folds to split dataset into -
m_Seed
long m_Seed
Random number seed.
-
-
-
Package weka.filters.unsupervised.attribute
-
Class weka.filters.unsupervised.attribute.AbstractTimeSeries extends Filter implements Serializable
- serialVersionUID:
- -3795656792078022357L
-
Serialized Fields
-
m_FillWithMissing
boolean m_FillWithMissing
True if missing values should be used rather than removing instances where the translated value is not known (due to border effects). -
m_History
Queue m_History
Stores the historical instances to copy values between -
m_InstanceRange
int m_InstanceRange
The number of instances forward to translate values between. A negative number indicates taking values from a past instance. -
m_SelectedCols
Range m_SelectedCols
Stores which columns to copy
-
-
Class weka.filters.unsupervised.attribute.Add extends Filter implements Serializable
- serialVersionUID:
- 761386447332932389L
-
Serialized Fields
-
m_AttributeType
int m_AttributeType
Record the type of attribute to insert. -
m_DateFormat
java.lang.String m_DateFormat
The date format. -
m_Insert
SingleIndex m_Insert
The location to insert the new attribute. -
m_Labels
FastVector m_Labels
The list of labels for nominal attribute. -
m_Name
java.lang.String m_Name
The name for the new attribute.
-
-
Class weka.filters.unsupervised.attribute.AddCluster extends Filter implements Serializable
- serialVersionUID:
- 7414280611943807337L
-
Class weka.filters.unsupervised.attribute.AddExpression extends Filter implements Serializable
- serialVersionUID:
- 402130384261736245L
-
Serialized Fields
-
m_attributeExpression
AttributeExpression m_attributeExpression
-
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_infixExpression
java.lang.String m_infixExpression
The infix expression
-
-
Class weka.filters.unsupervised.attribute.AddID extends Filter implements Serializable
- serialVersionUID:
- 4734383199819293390L
-
Serialized Fields
-
m_Counter
int m_Counter
the counter for the ID -
m_Index
SingleIndex m_Index
the index of the attribute -
m_Name
java.lang.String m_Name
the name of the attribute
-
-
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_Percent
int m_Percent
The subsample size, percent of original set, default 10% -
m_RandomSeed
int m_RandomSeed
The random number generator seed -
m_UseMissing
boolean m_UseMissing
Flag if missing values are taken as value.
-
-
Class weka.filters.unsupervised.attribute.AddValues extends Filter implements Serializable
- serialVersionUID:
- -8100622241742393656L
-
Serialized Fields
-
m_AttIndex
SingleIndex m_AttIndex
The attribute's index setting. -
m_Labels
java.util.Vector m_Labels
The values to add. -
m_Sort
boolean m_Sort
Whether to sort the values. -
m_SortedIndices
int[] m_SortedIndices
the array with the sorted label indices
-
-
Class weka.filters.unsupervised.attribute.Center 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_priors
double[] m_priors
The prior probability for each class -
m_removeAttributes
Filter m_removeAttributes
Filter for removing attributes
-
-
Class weka.filters.unsupervised.attribute.Copy 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_CutPoints
double[][] m_CutPoints
Store the current cutpoints -
m_DefaultCols
java.lang.String m_DefaultCols
The default columns to discretize -
m_DesiredWeightOfInstancesPerInterval
double m_DesiredWeightOfInstancesPerInterval
The desired weight of instances per bin -
m_DiscretizeCols
Range m_DiscretizeCols
Stores which columns to Discretize -
m_FindNumBins
boolean m_FindNumBins
Find the number of bins using cross-validated entropy. -
m_MakeBinary
boolean m_MakeBinary
Output binary attributes for discretized attributes. -
m_NumBins
int m_NumBins
The number of bins to divide the attribute into -
m_UseEqualFrequency
boolean m_UseEqualFrequency
Use equal-frequency binning if unsupervised discretization turned on
-
-
Class weka.filters.unsupervised.attribute.FirstOrder 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_AttributeIndices
int[] m_AttributeIndices
the generated indices (only for performance reasons) -
m_Attributes
Range m_Attributes
the attribute range to work on -
m_DetectionPerAttribute
boolean m_DetectionPerAttribute
whether to generate Outlier/ExtremeValue attributes for each attribute instead of a general one -
m_ExtremeValuesAsOutliers
boolean m_ExtremeValuesAsOutliers
whether extreme values are also tagged as outliers -
m_ExtremeValuesFactor
double m_ExtremeValuesFactor
the factor for detecting extreme values, by default 2*m_OutlierFactor -
m_IQR
double[] m_IQR
the interquartile range -
m_LowerExtremeValue
double[] m_LowerExtremeValue
the lower extreme value threshold (= Q1 - EVF*IQR) -
m_LowerOutlier
double[] m_LowerOutlier
the lower outlier threshold (= Q1 - OF*IQR) -
m_Median
double[] m_Median
the median -
m_OutlierAttributePosition
int[] m_OutlierAttributePosition
the position of the outlier attribute -
m_OutlierFactor
double m_OutlierFactor
the factor for detecting outliers -
m_OutputOffsetMultiplier
boolean m_OutputOffsetMultiplier
whether to add another attribute called "Offset", that lists the 'multiplier' by which the outlier/extreme value is away from the median, i.e., value = median + 'multiplier' * IQR
automatically enables m_DetectionPerAttribute! -
m_UpperExtremeValue
double[] m_UpperExtremeValue
the upper extreme value threshold (= Q3 + EVF*IQR) -
m_UpperOutlier
double[] m_UpperOutlier
the upper outlier threshold (= Q3 + OF*IQR)
-
-
Class weka.filters.unsupervised.attribute.KernelFilter extends SimpleBatchFilter implements Serializable
- serialVersionUID:
- 213800899640387499L
-
Serialized Fields
-
m_ActualFilter
Filter m_ActualFilter
for centering/standardizing the data (the actual filter to use) -
m_ActualKernel
Kernel m_ActualKernel
the Kernel which is actually used for computation -
m_checksTurnedOff
boolean m_checksTurnedOff
Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0. -
m_Filter
Filter m_Filter
for centering/standardizing the data -
m_InitFile
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_Kernel
Kernel m_Kernel
Kernel to use -
m_KernelFactor
double m_KernelFactor
the calculated kernel factor- See Also:
KernelFilter.m_KernelFactorExpression
-
m_KernelFactorExpression
java.lang.String m_KernelFactorExpression
optimizes the kernel with this formula (A = # of attributes, N = # of instances) -
m_Missing
ReplaceMissingValues m_Missing
The filter used to get rid of missing values. -
m_NominalToBinary
NominalToBinary m_NominalToBinary
The filter used to make attributes numeric. -
m_NumTrainInstances
int m_NumTrainInstances
The number of instances in the training data.
-
-
Class weka.filters.unsupervised.attribute.MakeIndicator extends Filter implements Serializable
- serialVersionUID:
- 766001176862773163L
-
Serialized Fields
-
m_AttIndex
SingleIndex m_AttIndex
The attribute's index setting. -
m_Numeric
boolean m_Numeric
Make boolean attribute numeric. -
m_ValIndex
Range m_ValIndex
The value's index
-
-
Class weka.filters.unsupervised.attribute.MathExpression extends PotentialClassIgnorer implements Serializable
- serialVersionUID:
- -3713222714671997901L
-
Serialized Fields
-
m_attStats
AttributeStats[] m_attStats
Attributes statistics -
m_expression
java.lang.String m_expression
The modification expression -
m_SelectCols
Range m_SelectCols
Stores which columns to select as a funky range
-
-
Class weka.filters.unsupervised.attribute.MergeTwoValues extends Filter implements Serializable
- serialVersionUID:
- 2925048980504034018L
-
Serialized Fields
-
m_AttIndex
SingleIndex m_AttIndex
The attribute's index setting. -
m_FirstIndex
SingleIndex m_FirstIndex
The first value's index setting. -
m_SecondIndex
SingleIndex m_SecondIndex
The second value's index setting.
-
-
Class weka.filters.unsupervised.attribute.MultiInstanceToPropositional extends Filter implements Serializable
- serialVersionUID:
- -4102847628883002530L
-
Serialized Fields
-
m_BagRelAtts
RelationalLocator m_BagRelAtts
Indices of relational attributes in the bag -
m_BagStringAtts
StringLocator m_BagStringAtts
Indices of string attributes in the bag -
m_NumBags
int m_NumBags
the total number of bags -
m_NumInstances
int m_NumInstances
the total number of the propositional instance in the dataset -
m_WeightMethod
int m_WeightMethod
the propositional instance weight setting method
-
-
Class weka.filters.unsupervised.attribute.NominalToBinary extends Filter implements Serializable
- serialVersionUID:
- -1130642825710549138L
-
Serialized Fields
-
m_Columns
Range m_Columns
Stores which columns to act on -
m_needToTransform
boolean m_needToTransform
Whether we need to transform at all -
m_Numeric
boolean m_Numeric
Are the new attributes going to be nominal or numeric ones? -
m_TransformAll
boolean m_TransformAll
Are all values transformed into new attributes?
-
-
Class weka.filters.unsupervised.attribute.NominalToString 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_MaxArray
double[] m_MaxArray
The maximum values for numeric attributes. -
m_MinArray
double[] m_MinArray
The minimum values for numeric attributes. -
m_Scale
double m_Scale
The scaling factor of the output range. -
m_Translation
double m_Translation
The translation of the output range.
-
-
Class weka.filters.unsupervised.attribute.NumericCleaner extends SimpleStreamFilter implements Serializable
- serialVersionUID:
- -352890679895066592L
-
Serialized Fields
-
m_CloseTo
double m_CloseTo
the number the values are checked for closeness to -
m_CloseToDefault
double m_CloseToDefault
the default replacement value for numbers "close-to" -
m_CloseToTolerance
double m_CloseToTolerance
the tolerance distance, below which numbers are considered being "close-to" -
m_Cols
Range m_Cols
Stores which columns to cleanse -
m_Decimals
int m_Decimals
the number of decimals to round to (-1 means no rounding) -
m_IncludeClass
boolean m_IncludeClass
whether to include the class attribute -
m_MaxDefault
double m_MaxDefault
the maximum default replacement value -
m_MaxThreshold
double m_MaxThreshold
the maximum threshold -
m_MinDefault
double m_MinDefault
the minimum default replacement value -
m_MinThreshold
double m_MinThreshold
the minimum threshold
-
-
Class weka.filters.unsupervised.attribute.NumericToBinary 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_Class
java.lang.String m_Class
Class containing transformation method. -
m_Cols
Range m_Cols
Stores which columns to transform. -
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
-
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_ClassIndex
int m_ClassIndex
Storing the class index -
m_IgnoreClass
boolean m_IgnoreClass
True if the class is to be unset
-
-
Class weka.filters.unsupervised.attribute.PrincipalComponents extends Filter implements Serializable
- serialVersionUID:
- -5649876869480249303L
-
Serialized Fields
-
m_AttributeFilter
Remove m_AttributeFilter
Filter for removing class attribute, nominal attributes with 0 or 1 value. -
m_center
boolean m_center
If true, center (rather than standardize) the data and compute PCA from covariance (rather than correlation) matrix. -
m_centerFilter
Center m_centerFilter
Filter for centering the data -
m_ClassIndex
int m_ClassIndex
Class index. -
m_Correlation
double[][] m_Correlation
Correlation matrix for the original data. -
m_CoverVariance
double m_CoverVariance
the amount of varaince to cover in the original data when retaining the best n PC's. -
m_Eigenvalues
double[] m_Eigenvalues
Eigenvalues for the corresponding eigenvectors. -
m_Eigenvectors
double[][] m_Eigenvectors
Will hold the unordered linear transformations of the (normalized) original data. -
m_HasClass
boolean m_HasClass
Data has a class set. -
m_MaxAttributes
int m_MaxAttributes
maximum number of attributes in the transformed data (-1 for all). -
m_MaxAttrsInName
int m_MaxAttrsInName
maximum number of attributes in the transformed attribute name. -
m_NominalToBinaryFilter
NominalToBinary m_NominalToBinaryFilter
Filter for turning nominal values into numeric ones. -
m_NumAttribs
int m_NumAttribs
Number of attributes. -
m_NumInstances
int m_NumInstances
Number of instances. -
m_OutputNumAtts
int m_OutputNumAtts
The number of attributes in the pc transformed data. -
m_ReplaceMissingFilter
ReplaceMissingValues m_ReplaceMissingFilter
Filters for replacing missing values. -
m_SortedEigens
int[] m_SortedEigens
Sorted eigenvalues. -
m_standardizeFilter
Standardize m_standardizeFilter
Filter for standardizing the data -
m_SumOfEigenValues
double m_SumOfEigenValues
sum of the eigenvalues. -
m_TrainCopy
Instances m_TrainCopy
Keep a copy for the class attribute (if set). -
m_TrainInstances
Instances m_TrainInstances
The data to transform analyse/transform. -
m_TransformedFormat
Instances m_TransformedFormat
The header for the transformed data format.
-
-
Class weka.filters.unsupervised.attribute.PropositionalToMultiInstance extends Filter implements Serializable
- serialVersionUID:
- 5825873573912102482L
-
Serialized Fields
-
m_BagRelAtts
RelationalLocator m_BagRelAtts
Indices of relational attributes in the bag -
m_BagStringAtts
StringLocator m_BagStringAtts
Indices of string attributes in the bag -
m_DoNotWeightBags
boolean m_DoNotWeightBags
do not weight bags by number of instances they contain -
m_Randomize
boolean m_Randomize
whether to randomize the output data -
m_Seed
int m_Seed
the seed for randomizing, default is 1
-
-
Class weka.filters.unsupervised.attribute.RandomProjection extends Filter implements Serializable
- serialVersionUID:
- 4428905532728645880L
-
Serialized Fields
-
m_distribution
int m_distribution
Stores the distribution to use for calculating the random matrix -
m_k
int m_k
Stores the number of dimensions to reduce the data to -
m_ntob
Filter m_ntob
The NominalToBinary filter applied to the data before this filter -
m_OutputFormatDefined
boolean m_OutputFormatDefined
Keeps track of output format if it is defined or not -
m_percent
double m_percent
Stores the dimensionality the data should be reduced to as percentage of the original dimension -
m_random
java.util.Random m_random
The random number generator used for generating the random matrix -
m_replaceMissing
Filter m_replaceMissing
The ReplaceMissingValues filter -
m_rmatrix
double[][] m_rmatrix
The random matrix -
m_rndmSeed
long m_rndmSeed
Stores the random seed used to generate the random matrix -
m_useGaussian
boolean m_useGaussian
Is the random matrix will be computed using Gaussian distribution or not -
m_useReplaceMissing
boolean m_useReplaceMissing
Should the missing values be replaced using unsupervised.ReplaceMissingValues filter
-
-
Class weka.filters.unsupervised.attribute.RandomSubset extends SimpleStreamFilter implements Serializable
- serialVersionUID:
- 2911221724251628050L
-
Serialized Fields
-
m_Indices
int[] m_Indices
The indices of the attributes that got selected. -
m_NumAttributes
double m_NumAttributes
The number of attributes to randomly choose (>= 1 absolute number of attributes, < 1 percentage). -
m_Seed
int m_Seed
The seed value.
-
-
Class weka.filters.unsupervised.attribute.RELAGGS extends SimpleBatchFilter implements Serializable
- serialVersionUID:
- -3333791375278589231L
-
Serialized Fields
-
m_AttStats
java.util.Hashtable<java.lang.String,AttributeStats> m_AttStats
stores the attribute statisticsatt_index-att_index_in_rel_att <-> AttributeStats
-
m_MaxCardinality
int m_MaxCardinality
the max. cardinality for nominal attributes -
m_SelectedRange
Range m_SelectedRange
the range of attributes to process (only relational ones will be processed)
-
-
Class weka.filters.unsupervised.attribute.Remove extends Filter implements Serializable
- serialVersionUID:
- 5011337331921522847L
-
Serialized Fields
-
m_SelectCols
Range m_SelectCols
Stores which columns to select as a funky range -
m_SelectedAttributes
int[] m_SelectedAttributes
Stores the indexes of the selected attributes in order, once the dataset is seen
-
-
Class weka.filters.unsupervised.attribute.RemoveType extends Filter implements Serializable
- serialVersionUID:
- -3563999462782486279L
-
Serialized Fields
-
m_attributeFilter
Remove m_attributeFilter
The attribute filter used to do the filtering -
m_attTypeToDelete
int m_attTypeToDelete
The type of attribute to delete -
m_invert
boolean m_invert
Whether to invert selection
-
-
Class weka.filters.unsupervised.attribute.RemoveUseless extends Filter implements Serializable
- serialVersionUID:
- -8659417851407640038L
-
Serialized Fields
-
m_maxVariancePercentage
double m_maxVariancePercentage
The type of attribute to delete -
m_removeFilter
Remove m_removeFilter
The filter used to remove attributes
-
-
Class weka.filters.unsupervised.attribute.Reorder extends Filter implements Serializable
- serialVersionUID:
- -1135571321097202292L
-
Serialized Fields
-
m_InputStringIndex
int[] m_InputStringIndex
Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated -
m_NewOrderCols
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
-
-
Class weka.filters.unsupervised.attribute.ReplaceMissingValues extends PotentialClassIgnorer implements Serializable
- serialVersionUID:
- 8349568310991609867L
-
Serialized Fields
-
m_ModesAndMeans
double[] m_ModesAndMeans
The modes and means
-
-
Class weka.filters.unsupervised.attribute.Standardize extends PotentialClassIgnorer implements Serializable
- serialVersionUID:
- -6830769026855053281L
-
Serialized Fields
-
m_Means
double[] m_Means
The means -
m_StdDevs
double[] m_StdDevs
The variances
-
-
Class weka.filters.unsupervised.attribute.StringToNominal extends Filter implements Serializable
- serialVersionUID:
- 4864084427902797605L
-
Serialized Fields
-
m_AttIndices
Range m_AttIndices
The attribute's range indices setting.
-
-
Class weka.filters.unsupervised.attribute.StringToWordVector extends Filter implements Serializable
- serialVersionUID:
- 8249106275278565424L
-
Serialized Fields
-
m_AvgDocLength
double m_AvgDocLength
Contains the average length of documents (among the first batch of instances aka training data). This is used in length normalization of documents which will be normalized to average document length. -
m_Dictionary
java.util.TreeMap m_Dictionary
Contains a mapping of valid words to attribute indexes. -
m_DocsCounts
int[] m_DocsCounts
Contains the number of documents (instances) a particular word appears in. The counts are stored with the same indexing as given by m_Dictionary. -
m_doNotOperateOnPerClassBasis
boolean m_doNotOperateOnPerClassBasis
whether to operate on a per-class basis. -
m_filterType
int m_filterType
The normalization to apply. -
m_IDFTransform
boolean m_IDFTransform
True if word frequencies should be transformed into fij*log(numOfDocs/numOfDocsWithWordi). -
m_lowerCaseTokens
boolean m_lowerCaseTokens
True if all tokens should be downcased. -
m_minTermFreq
int m_minTermFreq
the minimum (per-class) word frequency. -
m_NumInstances
int m_NumInstances
Contains the number of documents (instances) in the input format from which the dictionary is created. It is used in IDF transform. -
m_OutputCounts
boolean m_OutputCounts
True if output instances should contain word frequency rather than boolean 0 or 1. -
m_PeriodicPruningRate
double m_PeriodicPruningRate
The percentage at which to periodically prune the dictionary. -
m_Prefix
java.lang.String m_Prefix
A String prefix for the attribute names. -
m_SelectedRange
Range m_SelectedRange
Range of columns to convert to word vectors. -
m_Stemmer
Stemmer m_Stemmer
the stemming algorithm. -
m_Stopwords
java.io.File m_Stopwords
a file containing stopwords for using others than the default Rainbow ones. -
m_TFTransform
boolean m_TFTransform
True if word frequencies should be transformed into log(1+fi) where fi is the frequency of word i. -
m_Tokenizer
Tokenizer m_Tokenizer
the tokenizer algorithm to use. -
m_useStoplist
boolean m_useStoplist
True if tokens that are on a stoplist are to be ignored. -
m_WordsToKeep
int m_WordsToKeep
The default number of words (per class if there is a class attribute assigned) to attempt to keep.
-
-
Class weka.filters.unsupervised.attribute.SwapValues extends Filter implements Serializable
- serialVersionUID:
- 6155834679414275855L
-
Serialized Fields
-
m_AttIndex
SingleIndex m_AttIndex
The attribute's index setting. -
m_FirstIndex
SingleIndex m_FirstIndex
The first value's index setting. -
m_SecondIndex
SingleIndex m_SecondIndex
The second value's index setting.
-
-
Class weka.filters.unsupervised.attribute.TimeSeriesDelta extends TimeSeriesTranslate implements Serializable
- serialVersionUID:
- 3101490081896634942L
-
Class weka.filters.unsupervised.attribute.TimeSeriesTranslate extends AbstractTimeSeries implements Serializable
- serialVersionUID:
- -8901621509691785705L
-
Class weka.filters.unsupervised.attribute.Wavelet extends SimpleBatchFilter implements Serializable
- serialVersionUID:
- -3335106965521265631L
-
Serialized Fields
-
m_Algorithm
int m_Algorithm
the type of algorithm -
m_Filter
Filter m_Filter
an optional filter for preprocessing of the data -
m_Padding
int m_Padding
the type of padding
-
-
-
Package weka.filters.unsupervised.instance
-
Class weka.filters.unsupervised.instance.NonSparseToSparse extends Filter implements Serializable
- serialVersionUID:
- 4694489111366063852L
-
Class weka.filters.unsupervised.instance.Normalize extends Filter implements Serializable
- serialVersionUID:
- -7947971807522917395L
-
Serialized Fields
-
m_LNorm
double m_LNorm
The L-norm to use -
m_Norm
double m_Norm
The norm that each instance must have at the end
-
-
Class weka.filters.unsupervised.instance.Randomize extends Filter implements Serializable
- serialVersionUID:
- 8854479785121877582L
-
Serialized Fields
-
m_Random
java.util.Random m_Random
The current random number generator -
m_Seed
int m_Seed
The random number seed
-
-
Class weka.filters.unsupervised.instance.RemoveFolds extends Filter implements Serializable
- serialVersionUID:
- 8220373305559055700L
-
Serialized Fields
-
m_Fold
int m_Fold
Fold to output -
m_Inverse
boolean m_Inverse
Indicates if inverse of selection is to be output. -
m_NumFolds
int m_NumFolds
Number of folds to split dataset into -
m_Seed
long m_Seed
Random number seed.
-
-
Class weka.filters.unsupervised.instance.RemoveFrequentValues extends Filter implements Serializable
- serialVersionUID:
- -2447432930070059511L
-
Serialized Fields
-
m_AttIndex
SingleIndex m_AttIndex
The attribute's index setting. -
m_Invert
boolean m_Invert
whether to invert the matching sense. -
m_LeastValues
boolean m_LeastValues
whether to retain values with least instances instead of most. -
m_ModifyHeader
boolean m_ModifyHeader
Modify header for nominal attributes? -
m_NominalMapping
int[] m_NominalMapping
If m_ModifyHeader, stores a mapping from old to new indexes -
m_NumValues
int m_NumValues
the number of values to retain. -
m_Values
java.util.HashSet m_Values
contains the values to retain
-
-
Class weka.filters.unsupervised.instance.RemoveMisclassified extends Filter implements Serializable
- serialVersionUID:
- 5469157004717663171L
-
Serialized Fields
-
m_classIndex
int m_classIndex
The attribute to treat as the class for purposes of cleansing. -
m_cleansingClassifier
Classifier m_cleansingClassifier
The classifier used to do the cleansing -
m_firstBatchFinished
boolean m_firstBatchFinished
Have we processed the first batch (i.e. training data)? -
m_invertMatching
boolean m_invertMatching
Whether to invert the match so the correctly classified instances are discarded -
m_numericClassifyThreshold
double m_numericClassifyThreshold
The threshold for deciding when a numeric value is correctly classified -
m_numOfCleansingIterations
int m_numOfCleansingIterations
The maximum number of cleansing iterations to perform (<1 = until fully cleansed) -
m_numOfCrossValidationFolds
int m_numOfCrossValidationFolds
The number of cross validation folds to perform (<2 = no cross validation)
-
-
Class weka.filters.unsupervised.instance.RemovePercentage extends Filter implements Serializable
- serialVersionUID:
- 2150341191158533133L
-
Serialized Fields
-
m_Inverse
boolean m_Inverse
Indicates if inverse of selection is to be output. -
m_Percentage
double m_Percentage
Percentage of instances to select.
-
-
Class weka.filters.unsupervised.instance.RemoveRange 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_dontFilterAfterFirstBatch
boolean m_dontFilterAfterFirstBatch
Whether to filter instances after the first batch has been processed -
m_MatchMissingValues
boolean m_MatchMissingValues
True if missing values should count as a match -
m_ModifyHeader
boolean m_ModifyHeader
Modify header for nominal attributes? -
m_NominalMapping
int[] m_NominalMapping
If m_ModifyHeader, stores a mapping from old to new indexes -
m_Value
double m_Value
Stores which value of a numeric attribute is to be used for filtering. -
m_Values
Range m_Values
Stores which values of nominal attribute are to be used for filtering.
-
-
Class weka.filters.unsupervised.instance.Resample extends Filter implements Serializable
- serialVersionUID:
- 3119607037607101160L
-
Serialized Fields
-
m_InvertSelection
boolean m_InvertSelection
Whether to invert the selection (only if instances are drawn WITHOUT replacement)- See Also:
Resample.m_NoReplacement
-
m_NoReplacement
boolean m_NoReplacement
Whether to perform sampling with replacement or without -
m_RandomSeed
int m_RandomSeed
The random number generator seed -
m_SampleSizePercent
double m_SampleSizePercent
The subsample size, percent of original set, default 100%
-
-
Class weka.filters.unsupervised.instance.ReservoirSample extends Filter implements Serializable
- serialVersionUID:
- 3119607037607101160L
-
Serialized Fields
-
m_currentInst
int m_currentInst
The current instance being processed -
m_random
java.util.Random m_random
The random number generator -
m_RandomSeed
int m_RandomSeed
The random number generator seed -
m_SampleSize
int m_SampleSize
The subsample size, number of instances% -
m_subSample
Instance[] m_subSample
Holds the sub-sample (reservoir)
-
-
Class weka.filters.unsupervised.instance.SparseToNonSparse 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
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Class weka.gui.AttributeListPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -2030706987910400362L
-
Serialized Fields
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m_Model
weka.gui.AttributeListPanel.AttributeTableModel m_Model
The table model containing attribute names -
m_Table
javax.swing.JTable m_Table
The table displaying attribute names
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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_Invert
javax.swing.JButton m_Invert
Press to invert the current selection -
m_Model
weka.gui.AttributeSelectionPanel.AttributeTableModel m_Model
The table model containing attribute names and selection status -
m_Pattern
javax.swing.JButton m_Pattern
Press to enter a perl regular expression for selection -
m_PatternRegEx
java.lang.String m_PatternRegEx
The current regular expression. -
m_RemoveAll
javax.swing.JButton m_RemoveAll
Press to deselect all attributes -
m_Table
javax.swing.JTable m_Table
The table displaying attribute names and selection status
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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_AttributeStats
AttributeStats[] m_AttributeStats
Cached stats on the attributes we've summarized so far -
m_AttributeTypeLab
javax.swing.JLabel m_AttributeTypeLab
Displays the type of attribute -
m_DistinctLab
javax.swing.JLabel m_DistinctLab
Displays the number of distinct values -
m_Instances
Instances m_Instances
The instances we're playing with -
m_MissingLab
javax.swing.JLabel m_MissingLab
Displays the number of missing values -
m_StatsTable
javax.swing.JTable m_StatsTable
Displays other stats in a table -
m_UniqueLab
javax.swing.JLabel m_UniqueLab
Displays the number of unique values
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Class weka.gui.AttributeVisualizationPanel extends PrintablePanel implements Serializable
- serialVersionUID:
- -8650490488825371193L
-
Serialized Fields
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m_as
AttributeStats m_as
This holds the attribute stats of the current attribute on display. It is calculated in setAttribute(int idx) when it is called to set a new attribute index. -
m_asCache
AttributeStats[] m_asCache
Cache of attribute stats info for the current data set -
m_attribIndex
int m_attribIndex
This holds the index of the current attribute on display and should be set through setAttribute(int idx). -
m_barRange
double m_barRange
Contains the range of each bar in a histogram. It is used to work out the range of bar the mouse pointer is on in getToolTipText(). -
m_classIndex
int m_classIndex
Contains the current class index. -
m_colorAttrib
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_colorList
FastVector m_colorList
Contains discrete colours for colouring of subbars of histograms and bar plots when the class attribute is set and is nominal -
m_data
Instances m_data
This holds the current set of instances -
m_displayCurrentAttribute
boolean m_displayCurrentAttribute
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m_doneCurrentAttribute
boolean m_doneCurrentAttribute
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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_hc
java.lang.Thread m_hc
This stores the BarCalc or HistCalc thread while a new barplot or histogram is being calculated. -
m_histBarClassCounts
SparseInstance[] m_histBarClassCounts
This array holds the per class count (or per class height) of the each of the bars in a barplot or a histogram. For nominal attributes the format is:
m_histBarClassCounts[nominalValue][classValue+1]. For numeric attributes the format is:
m_histBarClassCounts[interval][classValues+1],
where the number of intervals is calculated by the Scott's method as mentioned above. The array is initialized to have 1+numClasses to accomodate for instances with missing class value. The ones with missing class value are displayed as a black sub par in a histogram or a barplot. NOTE: The values of this array are only calculated if the class attribute is set and it is nominal. -
m_histBarCounts
int[] m_histBarCounts
This array holds the count (or height) for the each of the bars in a barplot or a histogram. In case of barplots (and current attribute being nominal) its length (and the number of bars) is equal to the number of nominal values in the current attribute, with each field of the array being equal to the count of each nominal that it represents (the count of ith nominal value of an attribute is given by m_as.nominalCounts[i]). Whereas, in case of histograms (and current attribute being numeric) the width of its intervals is calculated by Scott's(1979) method:
intervalWidth = Max(1, 3.49*Std.Dev*numInstances^(1/3)) And the number of intervals by:
intervals = max(1, Math.round(Range/intervalWidth); Then each field of this array contains the number of values of the current attribute that fall in the histogram interval that it represents.
NOTE: The values of this array are only calculated if the class attribute is not set or if it is numeric. -
m_locker
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_maxValue
int m_maxValue
This holds the max value of the current attribute. In case of nominal attribute it is the highest count that a nominal value has in the attribute (given by m_as.nominalCounts[i]), otherwise in case of numeric attribute it is simply the maximum value present in the attribute (given by m_as.numericStats.max). It is used to calculate the ratio of the height of the bars with respect to the height of the display area. -
m_threadRun
boolean m_threadRun
True if the thread m_hc above is running.
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Class weka.gui.CheckBoxList extends javax.swing.JList implements Serializable
- serialVersionUID:
- -4359573373359270258L
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Class weka.gui.CheckBoxList.CheckBoxListModel extends javax.swing.DefaultListModel implements Serializable
- serialVersionUID:
- 7772455499540273507L
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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
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Serialized Fields
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m_CapabilitiesFilter
Capabilities m_CapabilitiesFilter
the Capabilities filter for the savers -
m_ConfigureButton
javax.swing.JButton m_ConfigureButton
the configure button -
m_CoreConvertersOnly
boolean m_CoreConvertersOnly
whether to display only core converters (hardcoded in ConverterUtils). Necessary for RMI/Remote Experiments for instance. -
m_CurrentConverter
java.lang.Object m_CurrentConverter
the converter that was chosen by the user -
m_DialogType
int m_DialogType
the type of dialog to display -
m_FileMustExist
boolean m_FileMustExist
whether the file to be opened must exist (only open dialog) -
m_LastFilter
javax.swing.filechooser.FileFilter m_LastFilter
the last filter that was used for opening/saving -
m_Listener
java.beans.PropertyChangeListener m_Listener
the propertychangelistener -
m_OverwriteWarning
boolean m_OverwriteWarning
whether to popup a dialog in case the file already exists (only save dialog) -
m_Self
ConverterFileChooser m_Self
the file chooser itself
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Class weka.gui.DatabaseConnectionDialog extends javax.swing.JDialog implements Serializable
- serialVersionUID:
- -1081946748666245054L
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Serialized Fields
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m_DbaseURLLab
javax.swing.JLabel m_DbaseURLLab
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m_DbaseURLText
javax.swing.JTextField m_DbaseURLText
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m_DebugCheckBox
javax.swing.JCheckBox m_DebugCheckBox
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m_DebugLab
javax.swing.JLabel m_DebugLab
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m_PasswordLab
javax.swing.JLabel m_PasswordLab
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m_PasswordText
javax.swing.JPasswordField m_PasswordText
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m_returnValue
int m_returnValue
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m_UserNameLab
javax.swing.JLabel m_UserNameLab
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m_UserNameText
javax.swing.JTextField m_UserNameText
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Class weka.gui.ExtensionFileFilter extends javax.swing.filechooser.FileFilter implements Serializable
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Serialized Fields
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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
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-
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Class weka.gui.GenericArrayEditor extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 3914616975334750480L
-
Serialized Fields
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m_AddBut
javax.swing.JButton m_AddBut
Click to add the current object configuration to the array. -
m_DeleteBut
javax.swing.JButton m_DeleteBut
Click this to delete the selected array values. -
m_DownBut
javax.swing.JButton m_DownBut
Click this to move the selected array value(s) one down. -
m_EditBut
javax.swing.JButton m_EditBut
Click this to edit the selected array value. -
m_Editor
java.beans.PropertyEditor m_Editor
The property editor for editing existing elements. -
m_ElementClass
java.lang.Class m_ElementClass
The class of objects allowed in the array. -
m_ElementEditor
java.beans.PropertyEditor m_ElementEditor
The property editor for the class we are editing. -
m_ElementList
javax.swing.JList m_ElementList
The list component displaying current values. -
m_InnerActionListener
java.awt.event.ActionListener m_InnerActionListener
Listens to buttons being pressed and taking the appropriate action. -
m_InnerMouseListener
java.awt.event.MouseListener m_InnerMouseListener
Listens to mouse events and takes appropriate action. -
m_InnerSelectionListener
javax.swing.event.ListSelectionListener m_InnerSelectionListener
Listens to list items being selected and takes appropriate action. -
m_Label
javax.swing.JLabel m_Label
The label for when we can't edit that type. -
m_ListModel
javax.swing.DefaultListModel m_ListModel
The defaultlistmodel holding our data. -
m_PD
PropertyDialog m_PD
The currently displayed property dialog, if any. -
m_Support
java.beans.PropertyChangeSupport m_Support
Handles property change notification. -
m_UpBut
javax.swing.JButton m_UpBut
Click this to move the selected array value(s) one up.
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Class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog extends javax.swing.JDialog implements Serializable
- serialVersionUID:
- -7845503345689646266L
-
Serialized Fields
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m_CancelButton
javax.swing.JButton m_CancelButton
the Cancel button. -
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_Popup
javax.swing.JPopupMenu m_Popup
the popup to display again. -
m_Self
javax.swing.JDialog m_Self
the dialog itself.
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Class weka.gui.GenericObjectEditor.GOEPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 3656028520876011335L
-
Serialized Fields
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m_cancelBut
javax.swing.JButton m_cancelBut
cancel button. -
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_FileChooser
javax.swing.JFileChooser m_FileChooser
The filechooser for opening and saving object files. -
m_okBut
javax.swing.JButton m_okBut
ok button. -
m_OpenBut
javax.swing.JButton m_OpenBut
Open object from disk. -
m_SaveBut
javax.swing.JButton m_SaveBut
Save object to disk.
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Class weka.gui.GenericObjectEditor.GOETreeNode extends javax.swing.tree.DefaultMutableTreeNode implements Serializable
- serialVersionUID:
- -1707872446682150133L
-
Serialized Fields
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m_Capabilities
Capabilities m_Capabilities
the Capabilities object to use for filtering. -
m_toolTipText
java.lang.String m_toolTipText
tool tip
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Class weka.gui.GenericObjectEditor.JTreePopupMenu extends javax.swing.JPopupMenu implements Serializable
- serialVersionUID:
- -3404546329655057387L
-
Serialized Fields
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m_CloseButton
javax.swing.JButton m_CloseButton
The button for closing the popup again. -
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_scroller
javax.swing.JScrollPane m_scroller
The scroller. -
m_Self
javax.swing.JPopupMenu m_Self
the popup itself. -
m_tree
javax.swing.JTree m_tree
The tree.
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Class weka.gui.GUIChooser extends javax.swing.JFrame implements Serializable
- serialVersionUID:
- 9001529425230247914L
-
Serialized Fields
-
m_ArffViewers
java.util.Vector m_ArffViewers
keeps track of the opened ArffViewer instancs -
m_BayesNetGUIFrame
javax.swing.JFrame m_BayesNetGUIFrame
The frame containing the Bayes net GUI -
m_BoundaryVisualizerFrame
javax.swing.JFrame m_BoundaryVisualizerFrame
The frame containing the boundary visualizer -
m_ChildFrames
java.util.HashSet<java.awt.Container> m_ChildFrames
contains the child frames (title <-> object). -
m_EnsembleLibraryFrame
javax.swing.JFrame m_EnsembleLibraryFrame
The frame containing the ensemble library 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_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_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_FileChooserTreeVisualizer
javax.swing.JFileChooser m_FileChooserTreeVisualizer
filechooser for the TreeVisualizer -
m_GraphVisualizers
java.util.Vector m_GraphVisualizers
keeps track of the opened graph visualizer instancs -
m_Icon
java.awt.Image m_Icon
the icon for the frames -
m_jMenuBar
javax.swing.JMenuBar m_jMenuBar
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m_jMenuHelp
javax.swing.JMenu m_jMenuHelp
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m_jMenuProgram
javax.swing.JMenu m_jMenuProgram
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m_jMenuTools
javax.swing.JMenu m_jMenuTools
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m_jMenuVisualization
javax.swing.JMenu m_jMenuVisualization
-
m_KnowledgeFlowBut
javax.swing.JButton m_KnowledgeFlowBut
Click to open the KnowledgeFlow -
m_KnowledgeFlowFrame
javax.swing.JFrame m_KnowledgeFlowFrame
The frame containing the knowledge flow interface -
m_MemoryUsageFrame
javax.swing.JFrame m_MemoryUsageFrame
The frame containing the memory usage -
m_PanelApplications
javax.swing.JPanel m_PanelApplications
the panel for the application buttons -
m_pendingKnowledgeFlowLoad
java.lang.String m_pendingKnowledgeFlowLoad
Pending file to load on startup of the KnowledgeFlow -
m_Plots
java.util.Vector m_Plots
keeps track of the opened plots -
m_ROCs
java.util.Vector m_ROCs
keeps track of the opened ROCs -
m_Self
GUIChooser m_Self
the GUIChooser itself -
m_SimpleBut
javax.swing.JButton m_SimpleBut
Click to open the simplecli -
m_SimpleCLI
SimpleCLI m_SimpleCLI
The SimpleCLI -
m_SqlViewerFrame
javax.swing.JFrame m_SqlViewerFrame
The frame containing the SqlViewer -
m_SystemInfoFrame
javax.swing.JFrame m_SystemInfoFrame
The frame containing the system info -
m_TreeVisualizers
java.util.Vector m_TreeVisualizers
keeps track of the opened tree visualizer instancs -
m_weka
java.awt.Image m_weka
The weka image
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Class weka.gui.GUIChooser.ChildFrameSDI extends javax.swing.JFrame implements Serializable
- serialVersionUID:
- 8588293938686425618L
-
Serialized Fields
-
m_Parent
GUIChooser m_Parent
the parent frame.
-
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Class weka.gui.HierarchyPropertyParser extends java.lang.Object implements Serializable
- serialVersionUID:
- -4151103338506077544L
-
Serialized Fields
-
m_Current
weka.gui.HierarchyPropertyParser.TreeNode m_Current
Keep track of the current node when traversing the tree -
m_Depth
int m_Depth
The depth of the tree -
m_Root
weka.gui.HierarchyPropertyParser.TreeNode m_Root
Keep track of the root of the tree -
m_Seperator
java.lang.String m_Seperator
The level separate in the path
-
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Class weka.gui.InstancesSummaryPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -5243579535296681063L
-
Serialized Fields
-
m_Instances
Instances m_Instances
The instances we're playing with -
m_NumAttributesLab
javax.swing.JLabel m_NumAttributesLab
Displays the number of attributes -
m_NumInstancesLab
javax.swing.JLabel m_NumInstancesLab
Displays the number of instances -
m_RelationNameLab
javax.swing.JLabel m_RelationNameLab
Displays the name of the relation -
m_showZeroInstancesAsUnknown
boolean m_showZeroInstancesAsUnknown
Whether to display 0 or ? for the number of instances in cases where a dataset has only structure. Depending on where this panel is used from, the user may have loaded a dataset with no instances or a Loader that can read incrementally may be being used (in which case we don't know how many instances are in the dataset... yet).
-
-
Class weka.gui.ListSelectorDialog extends javax.swing.JDialog implements Serializable
- serialVersionUID:
- 906147926840288895L
-
Serialized Fields
-
m_CancelBut
javax.swing.JButton m_CancelBut
Click to cancel the property selection -
m_List
javax.swing.JList m_List
The list component -
m_PatternBut
javax.swing.JButton m_PatternBut
Click to enter a regex pattern for selection -
m_PatternRegEx
java.lang.String m_PatternRegEx
The current regular expression. -
m_Result
int m_Result
Whether the selection was made or cancelled -
m_SelectBut
javax.swing.JButton m_SelectBut
Click to choose the currently selected property
-
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Class weka.gui.LogPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -4072464549112439484L
-
Serialized Fields
-
m_First
boolean m_First
An indicator for whether text has been output yet -
m_logButton
javax.swing.JButton m_logButton
The button for viewing the log -
m_LogText
javax.swing.JTextArea m_LogText
Displays the log messages -
m_StatusLab
javax.swing.JLabel m_StatusLab
Displays the current status -
m_TaskMonitor
WekaTaskMonitor m_TaskMonitor
The panel for monitoring the number of running tasks (if supplied)
-
-
Class weka.gui.LogWindow extends javax.swing.JFrame implements Serializable
- serialVersionUID:
- 5650947361381061112L
-
Serialized Fields
-
m_ButtonClear
javax.swing.JButton m_ButtonClear
the clear button -
m_ButtonClose
javax.swing.JButton m_ButtonClose
the close button -
m_CheckBoxWordwrap
javax.swing.JCheckBox m_CheckBoxWordwrap
whether to allow wordwrap or not -
m_LabelCurrentSize
javax.swing.JLabel m_LabelCurrentSize
the current size -
m_Output
javax.swing.JTextPane m_Output
the output -
m_SpinnerMaxSize
javax.swing.JSpinner m_SpinnerMaxSize
the spinner for the max number of chars -
m_UseWordwrap
boolean m_UseWordwrap
whether the JTextPane has wordwrap or not
-
-
Class weka.gui.Main extends javax.swing.JFrame implements Serializable
- serialVersionUID:
- 1453813254824253849L
-
Serialized Fields
-
jDesktopPane
javax.swing.JDesktopPane jDesktopPane
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jMenuApplications
javax.swing.JMenu jMenuApplications
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jMenuBar
javax.swing.JMenuBar jMenuBar
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jMenuExtensions
javax.swing.JMenu jMenuExtensions
-
jMenuHelp
javax.swing.JMenu jMenuHelp
-
jMenuItemApplicationsExperimenter
javax.swing.JMenuItem jMenuItemApplicationsExperimenter
-
jMenuItemApplicationsExplorer
javax.swing.JMenuItem jMenuItemApplicationsExplorer
-
jMenuItemApplicationsKnowledgeFlow
javax.swing.JMenuItem jMenuItemApplicationsKnowledgeFlow
-
jMenuItemApplicationsSimpleCLI
javax.swing.JMenuItem jMenuItemApplicationsSimpleCLI
-
jMenuItemHelpAbout
javax.swing.JMenuItem jMenuItemHelpAbout
-
jMenuItemHelpHomepage
javax.swing.JMenuItem jMenuItemHelpHomepage
-
jMenuItemHelpSourceforge
javax.swing.JMenuItem jMenuItemHelpSourceforge
-
jMenuItemHelpSystemInfo
javax.swing.JMenuItem jMenuItemHelpSystemInfo
-
jMenuItemHelpWekaWiki
javax.swing.JMenuItem jMenuItemHelpWekaWiki
-
jMenuItemProgramExit
javax.swing.JMenuItem jMenuItemProgramExit
-
jMenuItemProgramLogWindow
javax.swing.JMenuItem jMenuItemProgramLogWindow
-
jMenuItemProgramMemoryUsage
javax.swing.JMenuItem jMenuItemProgramMemoryUsage
-
jMenuItemToolsArffViewer
javax.swing.JMenuItem jMenuItemToolsArffViewer
-
jMenuItemToolsSqlViewer
javax.swing.JMenuItem jMenuItemToolsSqlViewer
-
jMenuItemVisualizationBoundaryVisualizer
javax.swing.JMenuItem jMenuItemVisualizationBoundaryVisualizer
-
jMenuItemVisualizationGraphVisualizer
javax.swing.JMenuItem jMenuItemVisualizationGraphVisualizer
-
jMenuItemVisualizationPlot
javax.swing.JMenuItem jMenuItemVisualizationPlot
-
jMenuItemVisualizationROC
javax.swing.JMenuItem jMenuItemVisualizationROC
-
jMenuItemVisualizationTreeVisualizer
javax.swing.JMenuItem jMenuItemVisualizationTreeVisualizer
-
jMenuProgram
javax.swing.JMenu jMenuProgram
-
jMenuTools
javax.swing.JMenu jMenuTools
-
jMenuVisualization
javax.swing.JMenu jMenuVisualization
-
jMenuWindows
javax.swing.JMenu jMenuWindows
-
m_ChildFrames
java.util.HashSet<java.awt.Container> m_ChildFrames
contains the child frames (title <-> object). -
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_FileChooserTreeVisualizer
javax.swing.JFileChooser m_FileChooserTreeVisualizer
filechooser for the TreeVisualizer. -
m_GUIType
int m_GUIType
the type of GUI to display. -
m_Self
Main m_Self
the frame itself.
-
-
Class weka.gui.Main.BackgroundDesktopPane 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_BackgroundColor
java.awt.Color m_BackgroundColor
the background color. -
m_ButtonGC
javax.swing.JButton m_ButtonGC
the button for running the garbage collector. -
m_Colors
java.util.Hashtable<java.lang.Double,java.awt.Color> m_Colors
the corresponding colors for the thresholds. -
m_DefaultColor
java.awt.Color m_DefaultColor
the default color. -
m_FrameLocation
java.awt.Point m_FrameLocation
the position for the dialog. -
m_History
java.util.Vector<java.lang.Double> m_History
the memory usage over time. -
m_Memory
Memory m_Memory
for monitoring the memory usage. -
m_Monitor
weka.gui.MemoryUsagePanel.MemoryMonitor m_Monitor
the thread for monitoring the memory usage. -
m_Percentages
java.util.Vector<java.lang.Double> m_Percentages
the threshold percentages to change color.
-
-
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_CustomPanel
javax.swing.JPanel m_CustomPanel
The custom panel (if any) -
m_Editor
java.beans.PropertyEditor m_Editor
The property editor -
m_HasCustomPanel
boolean m_HasCustomPanel
Whether the editor has provided its own panel -
m_PD
PropertyDialog m_PD
The currently displayed property dialog, if any
-
-
Class weka.gui.PropertySelectorDialog extends javax.swing.JDialog implements Serializable
- serialVersionUID:
- -3155058124137930518L
-
Serialized Fields
-
m_CancelBut
javax.swing.JButton m_CancelBut
Click to cancel the property selection -
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_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_SelectBut
javax.swing.JButton m_SelectBut
Click to choose the currently 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_aboutPanel
javax.swing.JPanel m_aboutPanel
The panel holding global info and help, if provided by the object being editied. -
m_CapabilitiesBut
javax.swing.JButton m_CapabilitiesBut
Button to pop up the capabilities in a separate dialog. -
m_CapabilitiesDialog
weka.gui.PropertySheetPanel.CapabilitiesHelpDialog m_CapabilitiesDialog
Capabilities Help dialog. -
m_CapabilitiesText
javax.swing.JTextArea m_CapabilitiesText
the TextArea of the Capabilities help dialog. -
m_Editors
java.beans.PropertyEditor[] m_Editors
Holds property editors of the object. -
m_HelpBut
javax.swing.JButton m_HelpBut
Button to pop up the full help text in a separate dialog. -
m_HelpDialog
javax.swing.JDialog m_HelpDialog
Help dialog. -
m_HelpText
java.lang.StringBuffer m_HelpText
StringBuffer containing help text for the object being edited. -
m_Labels
javax.swing.JLabel[] m_Labels
The labels for each property. -
m_Methods
java.beans.MethodDescriptor[] m_Methods
Holds the methods of the target. -
m_NumEditable
int m_NumEditable
A count of the number of properties we have an editor for. -
m_Properties
java.beans.PropertyDescriptor[] m_Properties
Holds properties of the target. -
m_Target
java.lang.Object m_Target
The target object being edited. -
m_TipTexts
java.lang.String[] m_TipTexts
The tool tip text for each property. -
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. -
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_FramedOutput
java.util.Hashtable m_FramedOutput
A Hashtable mapping names to output text components -
m_HandleRightClicks
boolean m_HandleRightClicks
Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer -
m_List
javax.swing.JList m_List
The list component -
m_Model
javax.swing.DefaultListModel m_Model
The list model -
m_Objs
java.util.Hashtable m_Objs
A hashtable mapping names to arbitrary objects -
m_Printer
PrintableComponent m_Printer
for printing the output to files -
m_Results
java.util.Hashtable m_Results
A Hashtable mapping names to result buffers -
m_SingleName
java.lang.String m_SingleName
The named result being viewed in the single-click display -
m_SingleText
javax.swing.text.JTextComponent m_SingleText
An optional component for single-click display
-
-
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_CloseBut
javax.swing.JButton m_CloseBut
Click to close the dialog -
m_CloseButPanel
javax.swing.JPanel m_CloseButPanel
the panel the Close-Button is located in -
m_FileChooser
ConverterFileChooser m_FileChooser
The file chooser for selecting arff files -
m_Instances
Instances m_Instances
The current set of instances loaded -
m_IOThread
java.lang.Thread m_IOThread
The thread we do loading in -
m_LastURL
java.lang.String m_LastURL
Stores the last URL that instances were loaded from -
m_Loader
Loader m_Loader
The current loader used to obtain the current instances -
m_OpenFileBut
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_ParentFrame
javax.swing.JFrame m_ParentFrame
the parent frame. if one is provided, the close-button is displayed -
m_readIncrementally
boolean m_readIncrementally
whether to read the instances incrementally, if possible. -
m_showZeroInstancesAsUnknown
boolean m_showZeroInstancesAsUnknown
whether to display zero instances as unknown ("?"). -
m_Summary
InstancesSummaryPanel m_Summary
The instance summary component -
m_Support
java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the set of working instances.
-
-
Class weka.gui.SimpleCLI extends java.awt.Frame implements Serializable
- serialVersionUID:
- -50661410800566036L
-
Class weka.gui.SimpleCLIPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -7377739469759943231L
-
Serialized Fields
-
m_CommandHistory
java.util.Vector m_CommandHistory
The history of commands entered interactively. -
m_Completion
SimpleCLIPanel.CommandlineCompletion m_Completion
The commandline completion. -
m_ErrRedirector
java.lang.Thread m_ErrRedirector
The thread that sends output from m_POE to the output box. -
m_HistoryPos
int m_HistoryPos
The current position in the command history. -
m_Input
javax.swing.JTextField m_Input
The command input area. -
m_OutputArea
javax.swing.JTextArea m_OutputArea
The output area canvas added to the frame. -
m_OutRedirector
java.lang.Thread m_OutRedirector
The thread that sends output from m_POO to the output box. -
m_POE
java.io.PipedOutputStream m_POE
The new output stream for System.err. -
m_POO
java.io.PipedOutputStream m_POO
The new output stream for System.out. -
m_RunThread
java.lang.Thread m_RunThread
The thread currently running a class main method.
-
-
Class weka.gui.SortedTableModel extends javax.swing.table.AbstractTableModel implements Serializable
- serialVersionUID:
- 4030907921461127548L
-
Serialized Fields
-
mAscending
boolean mAscending
whether sorting is ascending or descending -
mIndices
int[] mIndices
the mapping between displayed and actual index -
mModel
javax.swing.table.TableModel mModel
the actual table model -
mSortColumn
int mSortColumn
the sort column
-
-
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.
-
-
Class weka.gui.ViewerDialog extends javax.swing.JDialog implements Serializable
- serialVersionUID:
- 6747718484736047752L
-
Serialized Fields
-
m_ArffPanel
ArffPanel m_ArffPanel
the panel to display the Instances-object -
m_CancelButton
javax.swing.JButton m_CancelButton
Click to cancel the dialog -
m_OkButton
javax.swing.JButton m_OkButton
Click to activate the current set parameters -
m_Result
int m_Result
the result of the user's action, either OK or CANCEL -
m_UndoButton
javax.swing.JButton m_UndoButton
Click to undo the last action
-
-
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_animating
boolean m_animating
True if their are active tasks -
m_iconAnimated
javax.swing.ImageIcon m_iconAnimated
The icon for the animated bird -
m_iconStationary
javax.swing.ImageIcon m_iconStationary
The icon for the stationary bird -
m_MonitorLabel
javax.swing.JLabel m_MonitorLabel
The label for displaying info
-
-
-
Package weka.gui.arffviewer
-
Class weka.gui.arffviewer.ArffPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -4697041150989513939L
-
Serialized Fields
-
m_Changed
boolean m_Changed
flag for whether data got changed -
m_ChangeListeners
java.util.HashSet m_ChangeListeners
the listeners that listen for modifications -
m_CurrentCol
int m_CurrentCol
the currently selected column -
m_Filename
java.lang.String m_Filename
the filename used in the title -
m_LabelName
javax.swing.JLabel m_LabelName
displays the relation name -
m_LastReplace
java.lang.String m_LastReplace
the string used in the last replace -
m_LastSearch
java.lang.String m_LastSearch
the string used in the last search -
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_TableArff
ArffTable m_TableArff
the underlying table -
m_Title
java.lang.String m_Title
the title prefix -
menuItemAttributeAsClass
javax.swing.JMenuItem menuItemAttributeAsClass
-
menuItemClearSearch
javax.swing.JMenuItem menuItemClearSearch
-
menuItemCopy
javax.swing.JMenuItem menuItemCopy
-
menuItemDeleteAllSelectedInstances
javax.swing.JMenuItem menuItemDeleteAllSelectedInstances
-
menuItemDeleteAttribute
javax.swing.JMenuItem menuItemDeleteAttribute
-
menuItemDeleteAttributes
javax.swing.JMenuItem menuItemDeleteAttributes
-
menuItemDeleteSelectedInstance
javax.swing.JMenuItem menuItemDeleteSelectedInstance
-
menuItemMean
javax.swing.JMenuItem menuItemMean
-
menuItemOptimalColWidth
javax.swing.JMenuItem menuItemOptimalColWidth
-
menuItemOptimalColWidths
javax.swing.JMenuItem menuItemOptimalColWidths
-
menuItemRenameAttribute
javax.swing.JMenuItem menuItemRenameAttribute
-
menuItemReplaceValues
javax.swing.JMenuItem menuItemReplaceValues
-
menuItemSearch
javax.swing.JMenuItem menuItemSearch
-
menuItemSetAllValues
javax.swing.JMenuItem menuItemSetAllValues
-
menuItemSetMissingValues
javax.swing.JMenuItem menuItemSetMissingValues
-
menuItemSortInstances
javax.swing.JMenuItem menuItemSortInstances
-
menuItemUndo
javax.swing.JMenuItem menuItemUndo
-
-
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_ChangeListeners
java.util.HashSet m_ChangeListeners
the listeners for changes -
m_SearchString
java.lang.String m_SearchString
the search string
-
-
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_ColumnIndex
int m_ColumnIndex
the column index this editor is for -
m_CurrentInst
Instances m_CurrentInst
the current instances -
m_RowIndex
int m_RowIndex
the row index this editor is for
-
-
Class weka.gui.arffviewer.ArffTableCellRenderer extends javax.swing.table.DefaultTableCellRenderer implements Serializable
- serialVersionUID:
- 9195794493301191171L
-
Serialized Fields
-
highlightColor
java.awt.Color highlightColor
the color for highlighted values -
highlightColorSelected
java.awt.Color highlightColorSelected
the color for selected highlighted values -
missingColor
java.awt.Color missingColor
the color for missing values -
missingColorSelected
java.awt.Color missingColorSelected
the color for selected missing values
-
-
Class weka.gui.arffviewer.ArffViewer extends javax.swing.JFrame implements Serializable
- serialVersionUID:
- -7455845566922685175L
-
Serialized Fields
-
m_MainPanel
ArffViewerMainPanel m_MainPanel
the main panel
-
-
Class weka.gui.arffviewer.ArffViewerMainPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -8763161167586738753L
-
Serialized Fields
-
confirmExit
boolean confirmExit
-
exitOnClose
boolean exitOnClose
-
fileChooser
ConverterFileChooser fileChooser
-
frameTitle
java.lang.String frameTitle
-
height
int height
-
left
int left
-
menuBar
javax.swing.JMenuBar menuBar
-
menuEdit
javax.swing.JMenu menuEdit
-
menuEditAttributeAsClass
javax.swing.JMenuItem menuEditAttributeAsClass
-
menuEditClearSearch
javax.swing.JMenuItem menuEditClearSearch
-
menuEditCopy
javax.swing.JMenuItem menuEditCopy
-
menuEditDeleteAttribute
javax.swing.JMenuItem menuEditDeleteAttribute
-
menuEditDeleteAttributes
javax.swing.JMenuItem menuEditDeleteAttributes
-
menuEditDeleteInstance
javax.swing.JMenuItem menuEditDeleteInstance
-
menuEditDeleteInstances
javax.swing.JMenuItem menuEditDeleteInstances
-
menuEditRenameAttribute
javax.swing.JMenuItem menuEditRenameAttribute
-
menuEditSearch
javax.swing.JMenuItem menuEditSearch
-
menuEditSortInstances
javax.swing.JMenuItem menuEditSortInstances
-
menuEditUndo
javax.swing.JMenuItem menuEditUndo
-
menuFile
javax.swing.JMenu menuFile
-
menuFileClose
javax.swing.JMenuItem menuFileClose
-
menuFileCloseAll
javax.swing.JMenuItem menuFileCloseAll
-
menuFileExit
javax.swing.JMenuItem menuFileExit
-
menuFileOpen
javax.swing.JMenuItem menuFileOpen
-
menuFileProperties
javax.swing.JMenuItem menuFileProperties
-
menuFileSave
javax.swing.JMenuItem menuFileSave
-
menuFileSaveAs
javax.swing.JMenuItem menuFileSaveAs
-
menuView
javax.swing.JMenu menuView
-
menuViewAttributes
javax.swing.JMenuItem menuViewAttributes
-
menuViewOptimalColWidths
javax.swing.JMenuItem menuViewOptimalColWidths
-
menuViewValues
javax.swing.JMenuItem menuViewValues
-
parent
java.awt.Container parent
-
tabbedPane
javax.swing.JTabbedPane tabbedPane
-
top
int top
-
width
int width
-
-
-
Package weka.gui.beans
-
Class weka.gui.beans.AbstractDataSink extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 3956528599473814287L
-
Serialized Fields
-
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 -
m_visual
BeanVisual m_visual
Default visual for data sources
-
-
Class weka.gui.beans.AbstractDataSource extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -4127257701890044793L
-
Serialized Fields
-
m_bcSupport
java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support -
m_design
boolean m_design
True if this bean's appearance is the design mode appearance -
m_listeners
java.util.Vector m_listeners
Objects listening for events from data sources -
m_visual
BeanVisual m_visual
Default visual for data sources
-
-
Class weka.gui.beans.AbstractEvaluator extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 3983303541814121632L
-
Serialized Fields
-
m_listenee
java.lang.Object m_listenee
-
m_visual
BeanVisual m_visual
Default visual for evaluators
-
-
Class weka.gui.beans.AbstractTestSetProducer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -7905764845789349839L
-
Serialized Fields
-
m_listenee
java.lang.Object m_listenee
non null if this object is a target for any events. -
m_listeners
java.util.Vector m_listeners
Objects listening to us -
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.AbstractTrainAndTestSetProducer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -1809339823613492037L
-
Serialized Fields
-
m_listenee
java.lang.Object m_listenee
non null if this object is a target for any events. -
m_testListeners
java.util.Vector m_testListeners
Objects listening for test set events -
m_trainingListeners
java.util.Vector m_trainingListeners
Objects listening for trainin set events -
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.AbstractTrainingSetProducer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -7842746199524591125L
-
Serialized Fields
-
m_listenee
java.lang.Object m_listenee
non null if this object is a target for any events. -
m_listeners
java.util.Vector m_listeners
Objects listening for training set events -
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.Associator extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -7843500322130210057L
-
Serialized Fields
-
m_Associator
Associator m_Associator
-
m_buildThread
java.lang.Thread m_buildThread
-
m_globalInfo
java.lang.String m_globalInfo
Global info for the wrapped associator (if it exists). -
m_graphListeners
java.util.Vector m_graphListeners
Objects listening for graph events -
m_listenees
java.util.Hashtable m_listenees
Objects talking to us -
m_state
int m_state
-
m_textListeners
java.util.Vector m_textListeners
Objects listening for text events -
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.AssociatorCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 5767664969353495974L
-
Serialized Fields
-
m_AssociatorEditor
PropertySheetPanel m_AssociatorEditor
-
m_backup
Associator m_backup
Backup is user presses cancel -
m_dsAssociator
Associator m_dsAssociator
-
m_parentFrame
javax.swing.JFrame m_parentFrame
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
-
Class weka.gui.beans.AttributeSummarizer extends DataVisualizer implements Serializable
- serialVersionUID:
- -294354961169372758L
-
Serialized Fields
-
m_coloringIndex
int m_coloringIndex
Index on which to color the plots. -
m_gridWidth
int m_gridWidth
The number of plots horizontally in the display -
m_maxPlots
int m_maxPlots
The maximum number of plots to show
-
-
Class weka.gui.beans.BatchClassifierEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 878097199815991084L
-
Serialized Fields
-
m_classifier
Classifier m_classifier
The classifier -
m_groupIdentifier
long m_groupIdentifier
An identifier that can be used to group all related runs/sets together. -
m_maxRunNumber
int m_maxRunNumber
The maximum number of runs -
m_maxSetNumber
int m_maxSetNumber
The last set number for this series -
m_runNumber
int m_runNumber
The run number that this classifier was generated for -
m_setNumber
int m_setNumber
The set number for the test set -
m_testSet
DataSetEvent m_testSet
Instances that can be used for testing the classifier -
m_trainSet
DataSetEvent m_trainSet
Instances that were used to train the classifier (may be null if not available)
-
-
Class weka.gui.beans.BatchClustererEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 7268777944939129714L
-
Serialized Fields
-
m_clusterer
Clusterer m_clusterer
The clusterer -
m_maxSetNumber
int m_maxSetNumber
The last set number for this series -
m_setNumber
int m_setNumber
The set number for the test set -
m_testOrTrain
int m_testOrTrain
Indicates if m_testSet is a training or a test set. 0 for test, >0 for training -
m_testSet
DataSetEvent m_testSet
Training or Test Instances
-
-
Class weka.gui.beans.BeanConnection extends java.lang.Object implements Serializable
- serialVersionUID:
- 8804264241791332064L
-
Serialized Fields
-
m_eventName
java.lang.String m_eventName
The name of the event for this connection -
m_hidden
boolean m_hidden
-
m_source
BeanInstance m_source
-
m_target
BeanInstance m_target
-
-
Class weka.gui.beans.BeanInstance 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 occursjava.lang.ClassNotFoundException
- if an error occurs
-
-
Serialized Fields
-
m_animatedIconPath
java.lang.String m_animatedIconPath
Holds name (including path) of the animated icon -
m_connectorColor
java.awt.Color m_connectorColor
-
m_displayConnectors
boolean m_displayConnectors
-
m_iconPath
java.lang.String m_iconPath
Holds name (including path) of the static icon -
m_pcs
java.beans.PropertyChangeSupport m_pcs
-
m_stationary
boolean m_stationary
Container for the icon -
m_visualLabel
javax.swing.JLabel m_visualLabel
-
m_visualName
java.lang.String m_visualName
Name for the bean
-
-
Class weka.gui.beans.ChartEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 7812460715499569390L
-
Serialized Fields
-
m_dataPoint
double[] m_dataPoint
Y values of the data points -
m_legendText
java.util.Vector m_legendText
-
m_max
double m_max
-
m_min
double m_min
-
m_reset
boolean m_reset
-
-
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_dataFormatListeners
java.util.Vector m_dataFormatListeners
-
m_dataListeners
java.util.Vector m_dataListeners
-
m_dataProvider
java.lang.Object m_dataProvider
-
m_instanceListeners
java.util.Vector m_instanceListeners
-
m_instanceProvider
java.lang.Object m_instanceProvider
-
m_testListeners
java.util.Vector m_testListeners
-
m_testProvider
java.lang.Object m_testProvider
-
m_trainingListeners
java.util.Vector m_trainingListeners
-
m_trainingProvider
java.lang.Object m_trainingProvider
-
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.ClassAssignerCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 476539385765301907L
-
Serialized Fields
-
m_caEditor
PropertySheetPanel m_caEditor
-
m_classAssigner
ClassAssigner m_classAssigner
-
m_ClassCombo
javax.swing.JComboBox m_ClassCombo
-
m_displayColNames
boolean m_displayColNames
-
m_holderP
javax.swing.JPanel m_holderP
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
-
Class weka.gui.beans.Classifier extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 659603893917736008L
-
Serialized Fields
-
m_batchClassifierListeners
java.util.Vector m_batchClassifierListeners
Objects listening for batch classifier events -
m_binaryFilter
javax.swing.filechooser.FileFilter m_binaryFilter
-
m_block
boolean m_block
true if we should block any further training data sets. -
m_Classifier
Classifier m_Classifier
-
m_ClassifierTemplate
Classifier m_ClassifierTemplate
Template used for creating copies when building in parallel -
m_executionSlots
int m_executionSlots
Number of threads to use to train models with -
m_globalInfo
java.lang.String m_globalInfo
Global info for the wrapped classifier (if it exists). -
m_graphListeners
java.util.Vector m_graphListeners
Objects listening for graph events -
m_ie
IncrementalClassifierEvent m_ie
-
m_incrementalClassifierListeners
java.util.Vector m_incrementalClassifierListeners
Objects listening for incremental classifier events -
m_incrementalEvent
InstanceEvent m_incrementalEvent
Event to handle when processing incremental updates -
m_KOMLFilter
javax.swing.filechooser.FileFilter m_KOMLFilter
-
m_listenees
java.util.Hashtable m_listenees
Objects talking to us -
m_oldText
java.lang.String m_oldText
Holds original icon label text -
m_state
int m_state
-
m_textListeners
java.util.Vector m_textListeners
Objects listening for text events -
m_trainingSet
Instances m_trainingSet
Holds training instances for batch training. Not transient because header is retained for validating any instance events that this classifier might be asked to predict in the future. -
m_updateIncrementalClassifier
boolean m_updateIncrementalClassifier
If the classifier is an incremental classifier, should we update it (ie train it on incoming instances). This makes it possible incrementally test on a separate stream of instances without updating the classifier, or mix batch training/testing with incremental training/testing -
m_visual
BeanVisual m_visual
-
m_XStreamFilter
javax.swing.filechooser.FileFilter m_XStreamFilter
-
-
Class weka.gui.beans.Classifier.TrainingTask extends java.lang.Object implements Serializable
-
Serialized Fields
-
m_maxRunNum
int m_maxRunNum
-
m_maxSetNum
int m_maxSetNum
-
m_runNum
int m_runNum
-
m_setNum
int m_setNum
-
m_taskInfo
TaskStatusInfo m_taskInfo
-
m_train
Instances m_train
-
-
-
Class weka.gui.beans.ClassifierCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -6688000820160821429L
-
Serialized Fields
-
m_backup
Classifier m_backup
Copy of the current classifier in case cancel is selected -
m_ClassifierEditor
PropertySheetPanel m_ClassifierEditor
-
m_dsClassifier
Classifier m_dsClassifier
-
m_executionSlotsText
javax.swing.JTextField m_executionSlotsText
-
m_holderPanel
javax.swing.JPanel m_holderPanel
-
m_incrementalPanel
javax.swing.JPanel m_incrementalPanel
-
m_panelVisible
boolean m_panelVisible
-
m_parentFrame
javax.swing.JFrame m_parentFrame
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
m_updateIncrementalClassifier
javax.swing.JCheckBox m_updateIncrementalClassifier
-
-
Class weka.gui.beans.ClassifierPerformanceEvaluator extends AbstractEvaluator implements Serializable
- serialVersionUID:
- -3511801418192148690L
-
Serialized Fields
-
m_rocListenersConnected
boolean m_rocListenersConnected
-
m_textListeners
java.util.Vector m_textListeners
-
m_thresholdListeners
java.util.Vector m_thresholdListeners
-
m_visualizableErrorListeners
java.util.Vector m_visualizableErrorListeners
-
-
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_dataFormatListeners
java.util.Vector m_dataFormatListeners
-
m_dataListeners
java.util.Vector m_dataListeners
-
m_dataProvider
java.lang.Object m_dataProvider
-
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.ClassValuePickerCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 8213423053861600469L
-
Serialized Fields
-
m_backup
java.lang.String m_backup
-
m_ClassValueCombo
javax.swing.JComboBox m_ClassValueCombo
-
m_classValuePicker
ClassValuePicker m_classValuePicker
-
m_displayValNames
boolean m_displayValNames
-
m_holderP
javax.swing.JPanel m_holderP
-
m_messageLabel
javax.swing.JLabel m_messageLabel
-
m_parent
javax.swing.JFrame m_parent
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
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_batchClustererListeners
java.util.Vector m_batchClustererListeners
Objects listening for batch clusterer events -
m_buildThread
java.lang.Thread m_buildThread
-
m_Clusterer
Clusterer m_Clusterer
-
m_dummy
java.lang.Double m_dummy
-
m_globalInfo
java.lang.String m_globalInfo
Global info for the wrapped classifier (if it exists). -
m_graphListeners
java.util.Vector m_graphListeners
Objects listening for graph events -
m_listenees
java.util.Hashtable m_listenees
Objects talking to us -
m_state
int m_state
-
m_textListeners
java.util.Vector m_textListeners
Objects listening for text events -
m_trainingSet
Instances m_trainingSet
Holds training instances for batch training. -
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.ClustererCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -2035688458149534161L
-
Serialized Fields
-
m_backup
Clusterer m_backup
Backup if the user presses cancel -
m_ClustererEditor
PropertySheetPanel m_ClustererEditor
-
m_dsClusterer
Clusterer m_dsClusterer
-
m_parentFrame
javax.swing.JFrame m_parentFrame
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
-
Class weka.gui.beans.ClustererPerformanceEvaluator extends AbstractEvaluator implements Serializable
- serialVersionUID:
- 8041163601333978584L
-
Serialized Fields
-
m_textListeners
java.util.Vector m_textListeners
-
-
Class weka.gui.beans.CostBenefitAnalysis extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 8647471654613320469L
-
Serialized Fields
-
m_bcSupport
java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support -
m_design
boolean m_design
True if this bean's appearance is the design mode appearance -
m_framePoppedUp
boolean m_framePoppedUp
-
m_listenee
java.lang.Object m_listenee
The object sending us data (we allow only one connection at any one time) -
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 5364871945448769003L
-
Serialized Fields
-
m_benefitR
javax.swing.JRadioButton m_benefitR
-
m_classAttribute
Attribute m_classAttribute
The class attribute from the data that was used to generate the threshold curve -
m_classificationAccV
javax.swing.JLabel m_classificationAccV
Classification accuracy -
m_conf_aa
weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_aa
-
m_conf_ab
weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_ab
-
m_conf_actualA
javax.swing.JLabel m_conf_actualA
-
m_conf_actualB
javax.swing.JLabel m_conf_actualB
-
m_conf_ba
weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_ba
-
m_conf_bb
weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell m_conf_bb
-
m_conf_predictedA
javax.swing.JLabel m_conf_predictedA
-
m_conf_predictedB
javax.swing.JLabel m_conf_predictedB
-
m_cost_aa
javax.swing.JTextField m_cost_aa
-
m_cost_ab
javax.swing.JTextField m_cost_ab
-
m_cost_actualA
javax.swing.JLabel m_cost_actualA
-
m_cost_actualB
javax.swing.JLabel m_cost_actualB
-
m_cost_ba
javax.swing.JTextField m_cost_ba
-
m_cost_bb
javax.swing.JTextField m_cost_bb
-
m_cost_predictedA
javax.swing.JLabel m_cost_predictedA
-
m_cost_predictedB
javax.swing.JLabel m_cost_predictedB
-
m_costBenefit
PlotData2D m_costBenefit
Data for the cost/benefit curve -
m_costBenefitL
javax.swing.JLabel m_costBenefitL
-
m_costBenefitPanel
VisualizePanel m_costBenefitPanel
Displays the cost/benefit (profit/loss) graph -
m_costBenefitV
javax.swing.JLabel m_costBenefitV
-
m_costR
javax.swing.JRadioButton m_costR
-
m_fnPrevious
double m_fnPrevious
-
m_fpPrevious
double m_fpPrevious
-
m_gainV
javax.swing.JLabel m_gainV
-
m_masterPlot
PlotData2D m_masterPlot
Data for the threshold curve -
m_maximizeCB
javax.swing.JButton m_maximizeCB
-
m_minimizeCB
javax.swing.JButton m_minimizeCB
-
m_originalPopSize
int m_originalPopSize
-
m_percOfTarget
javax.swing.JRadioButton m_percOfTarget
-
m_percOfTargetLab
javax.swing.JLabel m_percOfTargetLab
-
m_percPop
javax.swing.JRadioButton m_percPop
-
m_percPopLab
javax.swing.JLabel m_percPopLab
-
m_performancePanel
VisualizePanel m_performancePanel
Displays the performance graphs(s) -
m_previousShapeIndex
int m_previousShapeIndex
The index of the previous plotted point that was highlighted -
m_randomV
javax.swing.JLabel m_randomV
-
m_shapeSizes
int[] m_shapeSizes
The size of the points being plotted -
m_threshold
javax.swing.JRadioButton m_threshold
-
m_thresholdLab
javax.swing.JLabel m_thresholdLab
-
m_thresholdSlider
javax.swing.JSlider m_thresholdSlider
The slider for adjusting the threshold -
m_tnPrevious
double m_tnPrevious
-
m_totalPopField
javax.swing.JTextField m_totalPopField
Population text field -
m_totalPopPrevious
int m_totalPopPrevious
-
m_tpPrevious
double m_tpPrevious
-
-
Class weka.gui.beans.CostBenefitAnalysis.AnalysisPanel.ConfusionCell extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 6148640235434494767L
-
Serialized Fields
-
m_conf_cell
javax.swing.JLabel m_conf_cell
-
m_conf_perc
javax.swing.JLabel m_conf_perc
-
m_percentage
double m_percentage
-
m_percentageP
javax.swing.JPanel m_percentageP
-
-
Class weka.gui.beans.CrossValidationFoldMaker extends AbstractTrainAndTestSetProducer implements Serializable
- serialVersionUID:
- -6350179298851891512L
-
Serialized Fields
-
m_numFolds
int m_numFolds
-
m_randomSeed
int m_randomSeed
-
-
Class weka.gui.beans.CrossValidationFoldMakerCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 1229878140258668581L
-
Serialized Fields
-
m_cvEditor
PropertySheetPanel m_cvEditor
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
-
Class weka.gui.beans.DataSetEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- -5111218447577318057L
-
Serialized Fields
-
m_dataSet
Instances m_dataSet
-
m_structureOnly
boolean m_structureOnly
-
-
Class weka.gui.beans.DataVisualizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 1949062132560159028L
-
Serialized Fields
-
m_bcSupport
java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support -
m_dataSetListeners
java.util.Vector m_dataSetListeners
Objects listening for data set events -
m_design
boolean m_design
True if this bean's appearance is the design mode appearance -
m_framePoppedUp
boolean m_framePoppedUp
-
m_visPanel
VisualizePanel m_visPanel
-
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.Filter extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 8249759470189439321L
-
Serialized Fields
-
m_dataListeners
java.util.Vector m_dataListeners
Objects listening for data set events -
m_Filter
Filter m_Filter
The filter to use. -
m_globalInfo
java.lang.String m_globalInfo
Global info for the wrapped filter (if it exists). -
m_ie
InstanceEvent m_ie
Instance event object for passing on filtered instance streams -
m_instanceListeners
java.util.Vector m_instanceListeners
Objects listening for instance events -
m_listenees
java.util.Hashtable m_listenees
Objects talking to us -
m_state
int m_state
-
m_structurePassedOn
boolean m_structurePassedOn
-
m_testListeners
java.util.Vector m_testListeners
Objects listening for test set events -
m_trainingListeners
java.util.Vector m_trainingListeners
Objects listening for training set events -
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.FilterCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 2049895469240109738L
-
Serialized Fields
-
m_backup
Filter m_backup
Backup if user presses cancel -
m_filter
Filter m_filter
-
m_filterEditor
PropertySheetPanel m_filterEditor
-
m_parentFrame
javax.swing.JFrame m_parentFrame
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
-
Class weka.gui.beans.GraphEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 2099494034652519986L
-
Serialized Fields
-
m_graphString
java.lang.String m_graphString
-
m_graphTitle
java.lang.String m_graphTitle
-
m_graphType
int m_graphType
-
-
Class weka.gui.beans.GraphViewer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -5183121972114900617L
-
Serialized Fields
-
m_bcSupport
java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support -
m_design
boolean m_design
True if this bean's appearance is the design mode appearance -
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.IncrementalClassifierEvaluator extends AbstractEvaluator implements Serializable
- serialVersionUID:
- -3105419818939541291L
-
Serialized Fields
-
m_ce
ChartEvent m_ce
-
m_dataLegend
java.util.Vector m_dataLegend
-
m_dataPoint
double[] m_dataPoint
-
m_instanceCount
int m_instanceCount
-
m_listeners
java.util.Vector m_listeners
-
m_max
double m_max
-
m_min
double m_min
-
m_outputInfoRetrievalStats
boolean m_outputInfoRetrievalStats
-
m_reset
boolean m_reset
-
m_statusFrequency
int m_statusFrequency
-
m_textListeners
java.util.Vector m_textListeners
-
-
Class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer extends javax.swing.JPanel implements Serializable
-
Serialized Fields
-
m_ieEditor
PropertySheetPanel m_ieEditor
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
for serialization
-
-
-
Class weka.gui.beans.IncrementalClassifierEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 28979464317643232L
-
Serialized Fields
-
m_classifier
Classifier m_classifier
-
m_currentInstance
Instance m_currentInstance
-
m_status
int m_status
-
m_structure
Instances m_structure
-
-
Class weka.gui.beans.InstanceEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 6104920894559423946L
-
Class weka.gui.beans.InstanceStreamToBatchMaker extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -7037141087208627799L
-
Serialized Fields
-
m_batch
java.util.ArrayList<Instance> m_batch
Collects up the instances. -
m_dataListeners
java.util.ArrayList<DataSourceListener> m_dataListeners
-
m_listenee
java.lang.Object m_listenee
Component connected to us. -
m_structure
Instances m_structure
-
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.KnowledgeFlowApp extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -7064906770289728431L
-
Serialized Fields
-
m_bcSupport
java.beans.beancontext.BeanContextSupport m_bcSupport
-
m_beanLayout
weka.gui.beans.KnowledgeFlowApp.BeanLayout m_beanLayout
The layout area -
m_editElement
BeanInstance m_editElement
Reference to bean being manipulated -
m_FileChooser
javax.swing.JFileChooser m_FileChooser
The file chooser for selecting layout files -
m_firstUserComponentOpp
boolean m_firstUserComponentOpp
-
m_flowEnvironment
Environment m_flowEnvironment
Environment variables for the current flow -
m_FlowHeight
int m_FlowHeight
the flow layout height -
m_FlowWidth
int m_FlowWidth
the flow layout width -
m_fontM
java.awt.FontMetrics m_fontM
-
m_helpB
javax.swing.JButton m_helpB
-
m_KfFilter
javax.swing.filechooser.FileFilter m_KfFilter
A filter to ensure only KnowledgeFlow files in binary format get shown in the chooser -
m_KOMLFilter
javax.swing.filechooser.FileFilter m_KOMLFilter
A filter to ensure only KnowledgeFlow files in KOML format get shown in the chooser -
m_loadB
javax.swing.JButton m_loadB
-
m_logPanel
LogPanel m_logPanel
-
m_mode
int m_mode
-
m_newB
javax.swing.JButton m_newB
-
m_oldX
int m_oldX
Used to record screen coordinates during move, select and connect operations -
m_oldY
int m_oldY
Used to record screen coordinates during move, select and connect operations -
m_pluginsBoxPanel
javax.swing.Box m_pluginsBoxPanel
-
m_pluginsToolBar
javax.swing.JToolBar m_pluginsToolBar
Stuff relating to plugin beans -
m_pointerB
javax.swing.JToggleButton m_pointerB
-
m_PreferredExtension
java.lang.String m_PreferredExtension
the preferred file extension -
m_saveB
javax.swing.JButton m_saveB
-
m_ScrollBarIncrementComponents
int m_ScrollBarIncrementComponents
the scrollbar increment of the components scrollpane -
m_ScrollBarIncrementLayout
int m_ScrollBarIncrementLayout
the scrollbar increment of the layout scrollpane -
m_showFileMenu
boolean m_showFileMenu
-
m_sourceEventSetDescriptor
java.beans.EventSetDescriptor m_sourceEventSetDescriptor
Event set descriptor for the bean being manipulated -
m_startX
int m_startX
-
m_startY
int m_startY
-
m_stopB
javax.swing.JButton m_stopB
-
m_toolBarBean
java.lang.Object m_toolBarBean
Holds the selected toolbar bean -
m_toolBarGroup
javax.swing.ButtonGroup m_toolBarGroup
Button group to manage all toolbar buttons -
m_toolBars
javax.swing.JTabbedPane m_toolBars
Tabbed pane to hold tool bars -
m_userBoxPanel
javax.swing.Box m_userBoxPanel
-
m_userComponents
java.util.Vector m_userComponents
-
m_UserComponentsInXML
boolean m_UserComponentsInXML
whether to store the user components in XML or in binary format -
m_userToolBar
javax.swing.JToolBar m_userToolBar
Stuff relating to user created meta beans -
m_XMLFilter
javax.swing.filechooser.FileFilter m_XMLFilter
A filter to ensure only KnowledgeFlow layout files in XML format get shown in the chooser -
m_XStreamFilter
javax.swing.filechooser.FileFilter m_XStreamFilter
A filter to ensure only KnowledgeFlow files in XStream format get shown in the chooser
-
-
Class weka.gui.beans.KnowledgeFlowApp.BeanLayout extends PrintablePanel implements Serializable
- serialVersionUID:
- -146377012429662757L
-
Class weka.gui.beans.Loader extends AbstractDataSource implements Serializable
- serialVersionUID:
- 1993738191961163027L
-
Serialization Methods
-
readObject
private void readObject(java.io.ObjectInputStream aStream) throws java.io.IOException, java.lang.ClassNotFoundException
- Throws:
java.io.IOException
java.lang.ClassNotFoundException
-
readResolve
private java.lang.Object readResolve() throws java.io.ObjectStreamException
- Throws:
java.io.ObjectStreamException
-
-
Serialized Fields
-
m_dataSetEventTargets
int m_dataSetEventTargets
-
m_dbSet
boolean m_dbSet
Flag indicating that a database has already been configured -
m_globalInfo
java.lang.String m_globalInfo
Global info for the wrapped loader (if it exists). -
m_ie
InstanceEvent m_ie
-
m_instanceEventTargets
int m_instanceEventTargets
Keep track of how many listeners for different types of events there are. -
m_ioThread
weka.gui.beans.Loader.LoadThread m_ioThread
Thread for doing IO in -
m_Loader
Loader m_Loader
Loader -
m_state
int m_state
-
m_stopped
boolean m_stopped
Asked to stop?
-
-
Class weka.gui.beans.LoaderCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 6990446313118930298L
-
Serialized Fields
-
m_dbaseURLText
javax.swing.JTextField m_dbaseURLText
-
m_dsLoader
Loader m_dsLoader
-
m_fileChooser
javax.swing.JFileChooser m_fileChooser
-
m_keyText
javax.swing.JTextField m_keyText
-
m_LoaderEditor
PropertySheetPanel m_LoaderEditor
-
m_parentFrame
javax.swing.JFrame m_parentFrame
-
m_passwordText
javax.swing.JPasswordField m_passwordText
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
m_queryText
javax.swing.JTextField m_queryText
-
m_relativeFilePath
javax.swing.JCheckBox m_relativeFilePath
-
m_userNameText
javax.swing.JTextField m_userNameText
-
-
Class weka.gui.beans.LogPanel extends javax.swing.JPanel implements Serializable
-
Serialized Fields
-
m_formatter
java.text.DecimalFormat m_formatter
For formatting timer digits -
m_logPanel
LogPanel m_logPanel
The log panel to delegate log messages to. -
m_table
javax.swing.JTable m_table
The table for the status area -
m_tableIndexes
java.util.HashMap<java.lang.String,java.lang.Integer> m_tableIndexes
Holds the index (line number) in the JTable of each component being tracked. -
m_tableModel
javax.swing.table.DefaultTableModel m_tableModel
The table model for the JTable used in the status area -
m_tabs
javax.swing.JTabbedPane m_tabs
Tabbed pane to hold both the status and the log -
m_timers
java.util.HashMap<java.lang.String,javax.swing.Timer> m_timers
Holds the timers associated with each component being tracked.
-
-
-
Class weka.gui.beans.MetaBean extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -6582768902038027077L
-
Serialized Fields
-
m_associatedConnections
java.util.Vector m_associatedConnections
-
m_inputs
java.util.Vector m_inputs
-
m_originalCoords
java.util.Vector m_originalCoords
-
m_outputs
java.util.Vector m_outputs
-
m_subFlow
java.util.Vector m_subFlow
-
m_subFlowPreview
javax.swing.ImageIcon m_subFlowPreview
-
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.ModelPerformanceChart extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -4602034200071195924L
-
Serialized Fields
-
m_bcSupport
java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support -
m_design
boolean m_design
True if this bean's appearance is the design mode appearance -
m_framePoppedUp
boolean m_framePoppedUp
-
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.PredictionAppender extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -2987740065058976673L
-
Serialized Fields
-
m_appendProbabilities
boolean m_appendProbabilities
Append classifier's predicted probabilities (if the class is discrete and the classifier is a distribution classifier) -
m_dataSourceListeners
java.util.Vector m_dataSourceListeners
Objects listenening for dataset events -
m_format
Instances m_format
Format of instances to be produced. -
m_instanceEvent
InstanceEvent m_instanceEvent
-
m_instanceListeners
java.util.Vector m_instanceListeners
Objects listening for instances events -
m_listenee
java.lang.Object m_listenee
Non null if this object is a target for any events. -
m_testSetListeners
java.util.Vector m_testSetListeners
Objects listening for test set events -
m_trainingSetListeners
java.util.Vector m_trainingSetListeners
Objects listening for training set events -
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.PredictionAppenderCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 6884933202506331888L
-
Serialized Fields
-
m_paEditor
PropertySheetPanel m_paEditor
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
-
Class weka.gui.beans.Saver extends AbstractDataSink implements Serializable
- serialVersionUID:
- 5371716690308950755L
-
Serialization Methods
-
readObject
private void readObject(java.io.ObjectInputStream aStream) throws java.io.IOException, java.lang.ClassNotFoundException
- Throws:
java.io.IOException
java.lang.ClassNotFoundException
-
-
Serialized Fields
-
m_count
int m_count
Count for structure available messages -
m_dataSet
Instances m_dataSet
Holds the instances to be saved -
m_fileName
java.lang.String m_fileName
The relation name that becomes part of the file name -
m_globalInfo
java.lang.String m_globalInfo
Global info for the wrapped loader (if it exists). -
m_isDBSaver
boolean m_isDBSaver
Flag indicating that instances will be saved to database. Used because structure information can only be sent after a database has been configured. -
m_relationNameForFilename
boolean m_relationNameForFilename
For file-based savers - if true (default), relation name is used as the primary part of the filename. If false, then the prefix is used as the filename. Useful for preventing filenames from getting too long when there are many filters in a flow. -
m_Saver
Saver m_Saver
Saver -
m_SaverTemplate
Saver m_SaverTemplate
-
m_structure
Instances m_structure
Holds the structure
-
-
Class weka.gui.beans.SaverCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -4874208115942078471L
-
Serialized Fields
-
m_dbaseURLText
javax.swing.JTextField m_dbaseURLText
-
m_dsSaver
Saver m_dsSaver
-
m_fileChooser
javax.swing.JFileChooser m_fileChooser
-
m_idBox
javax.swing.JComboBox m_idBox
-
m_parentFrame
javax.swing.JFrame m_parentFrame
-
m_passwordText
javax.swing.JPasswordField m_passwordText
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
m_prefixText
javax.swing.JTextField m_prefixText
-
m_relationNameForFilename
javax.swing.JCheckBox m_relationNameForFilename
-
m_relativeFilePath
javax.swing.JCheckBox m_relativeFilePath
-
m_SaverEditor
PropertySheetPanel m_SaverEditor
-
m_tabBox
javax.swing.JComboBox m_tabBox
-
m_tableText
javax.swing.JTextField m_tableText
-
m_userNameText
javax.swing.JTextField m_userNameText
-
-
Class weka.gui.beans.ScatterPlotMatrix extends DataVisualizer implements Serializable
- serialVersionUID:
- -657856527563507491L
-
Serialized Fields
-
m_matrixPanel
MatrixPanel m_matrixPanel
-
-
Class weka.gui.beans.SerializedModelSaver extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 3956528599473814287L
-
Serialization Methods
-
readObject
private void readObject(java.io.ObjectInputStream aStream) throws java.io.IOException, java.lang.ClassNotFoundException
- Throws:
java.io.IOException
java.lang.ClassNotFoundException
-
-
Serialized Fields
-
m_directory
java.io.File m_directory
The directory to hold the saved model(s) -
m_fileFormat
Tag m_fileFormat
File format stuff -
m_filenamePrefix
java.lang.String m_filenamePrefix
The prefix for the file name (model + training set info will be appended) -
m_listenee
java.lang.Object m_listenee
Non null if this object is a target for any events. Provides for the simplest case when only one incomming connection is allowed. -
m_useRelativePath
boolean m_useRelativePath
relative path for the directory (relative to the user.dir (startup directory))? -
m_visual
BeanVisual m_visual
Default visual for data sources
-
-
Class weka.gui.beans.SerializedModelSaverCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -4874208115942078471L
-
Serialized Fields
-
m_fileChooser
javax.swing.JFileChooser m_fileChooser
-
m_fileFormatBox
javax.swing.JComboBox m_fileFormatBox
-
m_parentFrame
javax.swing.JFrame m_parentFrame
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
m_prefixText
javax.swing.JTextField m_prefixText
-
m_relativeFilePath
javax.swing.JCheckBox m_relativeFilePath
-
m_SaverEditor
PropertySheetPanel m_SaverEditor
-
m_smSaver
SerializedModelSaver m_smSaver
-
-
Class weka.gui.beans.StripChart extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 1483649041577695019L
-
Serialization Methods
-
readObject
private void readObject(java.io.ObjectInputStream ois) throws java.io.IOException, java.lang.ClassNotFoundException
Provide some necessary initialization after object has been deserialized.- Throws:
java.io.IOException
- if an error occursjava.lang.ClassNotFoundException
- if an error occurs
-
-
Serialized Fields
-
m_BackgroundColor
java.awt.Color m_BackgroundColor
the background color. -
m_ce
ChartEvent m_ce
-
m_colorList
java.awt.Color[] m_colorList
default colours for colouring lines -
m_dataList
java.util.LinkedList m_dataList
Holds the data to be plotted. Entries in the list are arrays of y points. -
m_dataPoint
double[] m_dataPoint
-
m_iheight
int m_iheight
Width and height of the off screen image. -
m_iwidth
int m_iwidth
-
m_labelFont
java.awt.Font m_labelFont
-
m_labelMetrics
java.awt.FontMetrics m_labelMetrics
-
m_legendPanel
weka.gui.beans.StripChart.LegendPanel m_legendPanel
the legend. -
m_LegendPanelBorderColor
java.awt.Color m_LegendPanelBorderColor
the color of the legend panel's border. -
m_legendText
java.util.Vector m_legendText
-
m_listenee
java.lang.Object m_listenee
-
m_max
double m_max
Max value for the y axis. -
m_min
double m_min
Min value for the y axis. -
m_oldMax
double m_oldMax
-
m_oldMin
double m_oldMin
-
m_previousY
double[] m_previousY
-
m_Printer
PrintableComponent m_Printer
the class responsible for printing -
m_refreshFrequency
int m_refreshFrequency
Plot every m_refreshFrequency'th point -
m_refreshWidth
int m_refreshWidth
Shift the plot by this many pixels every time a point is plotted -
m_scalePanel
weka.gui.beans.StripChart.ScalePanel m_scalePanel
the scale. -
m_visual
BeanVisual m_visual
-
m_xCount
int m_xCount
-
m_xValFreq
int m_xValFreq
Print x axis labels every m_xValFreq points -
m_yScaleUpdate
boolean m_yScaleUpdate
Scale update requested.
-
-
Class weka.gui.beans.StripChartCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 7441741530975984608L
-
Serialized Fields
-
m_cvEditor
PropertySheetPanel m_cvEditor
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
-
Class weka.gui.beans.TestSetEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 8780718708498854231L
-
Serialized Fields
-
m_maxRunNumber
int m_maxRunNumber
Maximum number of runs. -
m_maxSetNumber
int m_maxSetNumber
Maximum number of sets (ie 10 in a 10 fold) -
m_runNumber
int m_runNumber
What run number is this training set from. -
m_setNumber
int m_setNumber
what number is this test set (ie fold 2 of 10 folds) -
m_structureOnly
boolean m_structureOnly
-
m_testSet
Instances m_testSet
The test set instances
-
-
Class weka.gui.beans.TestSetMaker extends AbstractTestSetProducer implements Serializable
- serialVersionUID:
- -8473882857628061841L
-
Serialized Fields
-
m_receivedStopNotification
boolean m_receivedStopNotification
-
-
Class weka.gui.beans.TextEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 4196810607402973744L
-
Serialized Fields
-
m_text
java.lang.String m_text
The text -
m_textTitle
java.lang.String m_textTitle
The title for the text. Could be used in a list component
-
-
Class weka.gui.beans.TextViewer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 104838186352536832L
-
Serialized Fields
-
m_bcSupport
java.beans.beancontext.BeanContextChildSupport m_bcSupport
BeanContextChild support -
m_design
boolean m_design
True if this bean's appearance is the design mode appearance -
m_textListeners
java.util.Vector m_textListeners
Objects listening for text events -
m_visual
BeanVisual m_visual
-
-
Class weka.gui.beans.ThresholdDataEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- -8309334224492439644L
-
Serialized Fields
-
m_classAttribute
Attribute m_classAttribute
-
m_dataSet
PlotData2D m_dataSet
-
-
Class weka.gui.beans.TrainingSetEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 5872343811810872662L
-
Serialized Fields
-
m_maxRunNumber
int m_maxRunNumber
Maximum number of runs. -
m_maxSetNumber
int m_maxSetNumber
Maximum number of sets (ie 10 in a 10 fold) -
m_runNumber
int m_runNumber
What run number is this training set from. -
m_setNumber
int m_setNumber
what number is this training set (ie fold 2 of 10 folds) -
m_structureOnly
boolean m_structureOnly
-
m_trainingSet
Instances m_trainingSet
The training instances
-
-
Class weka.gui.beans.TrainingSetMaker extends AbstractTrainingSetProducer implements Serializable
- serialVersionUID:
- -6152577265471535786L
-
Serialized Fields
-
m_receivedStopNotification
boolean m_receivedStopNotification
-
-
Class weka.gui.beans.TrainTestSplitMaker extends AbstractTrainAndTestSetProducer implements Serializable
- serialVersionUID:
- 7390064039444605943L
-
Serialized Fields
-
m_randomSeed
int m_randomSeed
-
m_splitThread
java.lang.Thread m_splitThread
-
m_trainPercentage
double m_trainPercentage
-
-
Class weka.gui.beans.TrainTestSplitMakerCustomizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -1684662340241807260L
-
Serialized Fields
-
m_pcSupport
java.beans.PropertyChangeSupport m_pcSupport
-
m_splitEditor
PropertySheetPanel m_splitEditor
-
-
Class weka.gui.beans.VisualizableErrorEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- -5811819270887223400L
-
Serialized Fields
-
m_dataSet
PlotData2D m_dataSet
-
-
-
Package weka.gui.boundaryvisualizer
-
Class weka.gui.boundaryvisualizer.BoundaryPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -8499445518744770458L
-
Serialized Fields
-
m_classifier
Classifier m_classifier
distribution classifier to use -
m_classIndex
int m_classIndex
index of the class attribute -
m_Colors
FastVector m_Colors
-
m_dataGenerator
DataGenerator m_dataGenerator
data generator to use -
m_dummy
java.lang.Double m_dummy
-
m_initialTiling
boolean m_initialTiling
is the main plot thread performing the initial coarse tiling -
m_listeners
java.util.Vector m_listeners
listeners to be notified when plot is complete -
m_maxX
double m_maxX
-
m_maxY
double m_maxY
-
m_minX
double m_minX
-
m_minY
double m_minY
-
m_numOfSamplesPerGenerator
int m_numOfSamplesPerGenerator
-
m_numOfSamplesPerRegion
int m_numOfSamplesPerRegion
-
m_osi
java.awt.Image m_osi
used for offscreen drawing -
m_panelHeight
int m_panelHeight
-
m_panelWidth
int m_panelWidth
-
m_pausePlotting
boolean m_pausePlotting
-
m_pixHeight
double m_pixHeight
-
m_pixWidth
double m_pixWidth
-
m_plotPanel
weka.gui.boundaryvisualizer.BoundaryPanel.PlotPanel m_plotPanel
the actual plotting area -
m_plotThread
java.lang.Thread m_plotThread
thread for running the plotting operation in -
m_plotTrainingData
boolean m_plotTrainingData
plot the training data -
m_probabilityCache
double[][][] m_probabilityCache
cache of probabilities for fast replotting -
m_random
java.util.Random m_random
A random number generator -
m_rangeX
double m_rangeX
-
m_rangeY
double m_rangeY
-
m_samplesBase
double m_samplesBase
-
m_size
int m_size
what size of tile is currently being plotted -
m_stopPlotting
boolean m_stopPlotting
Stop the plotting thread -
m_stopReplotting
boolean m_stopReplotting
Stop any replotting threads -
m_trainingData
Instances m_trainingData
training data -
m_xAttribute
int m_xAttribute
-
m_yAttribute
int m_yAttribute
-
-
Class weka.gui.boundaryvisualizer.BoundaryPanelDistributed extends BoundaryPanel implements Serializable
- serialVersionUID:
- -1743284397893937776L
-
Serialized Fields
-
m_failedCount
int m_failedCount
The count of failed sub-tasks -
m_finishedCount
int m_finishedCount
The count of successfully completed sub-tasks -
m_hostPollingTime
int[] m_hostPollingTime
-
m_listeners
java.util.Vector m_listeners
a list of RemoteExperimentListeners -
m_minTaskPollTime
int m_minTaskPollTime
number of seconds between polling server -
m_plottingAborted
boolean m_plottingAborted
Set to true if MAX_FAILURES exceeded on all hosts or connections fail on all hosts or user aborts plotting -
m_remoteHostFailureCounts
int[] m_remoteHostFailureCounts
The number of times tasks have failed on each remote host -
m_remoteHosts
java.util.Vector m_remoteHosts
Holds the names of machines with remoteEngine servers running -
m_remoteHostsQueue
Queue m_remoteHostsQueue
The queue of available hosts -
m_remoteHostsStatus
int[] m_remoteHostsStatus
The status of each of the remote hosts -
m_removedHosts
int m_removedHosts
The number of hosts removed due to exceeding max failures -
m_subExpQueue
Queue m_subExpQueue
The queue of sub-tasks waiting to be processed
-
-
Class weka.gui.boundaryvisualizer.BoundaryVisualizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 3933877580074013208L
-
Serialized Fields
-
chooseButton
javax.swing.JButton chooseButton
-
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
-
dataFileLabel
javax.swing.JLabel dataFileLabel
-
m_addPointsButton
javax.swing.JRadioButton m_addPointsButton
-
m_addRemovePointsButtonGroup
javax.swing.ButtonGroup m_addRemovePointsButtonGroup
-
m_addRemovePointsPanel
javax.swing.JPanel m_addRemovePointsPanel
-
m_arffFileFilter
ExtensionFileFilter m_arffFileFilter
-
m_boundaryPanel
BoundaryPanel m_boundaryPanel
the plotting panel -
m_classAttBox
javax.swing.JComboBox m_classAttBox
-
m_classifier
Classifier m_classifier
the classifier to use -
m_classifierEditor
GenericObjectEditor m_classifierEditor
-
m_ClassifierPanel
PropertyPanel m_ClassifierPanel
-
m_classPanel
ClassPanel m_classPanel
-
m_classValueSelector
javax.swing.JComboBox m_classValueSelector
-
m_controlPanel
javax.swing.JPanel m_controlPanel
-
m_dataGenerator
KDDataGenerator m_dataGenerator
-
m_FileChooser
javax.swing.JFileChooser m_FileChooser
The file chooser for selecting arff files -
m_generatorSamplesBase
int m_generatorSamplesBase
base for sampling in the non-fixed dimensions -
m_generatorSamplesText
javax.swing.JTextField m_generatorSamplesText
-
m_kernelBandwidth
int m_kernelBandwidth
Set the kernel bandwidth to cover this many nearest neighbours -
m_kernelBandwidthText
javax.swing.JTextField m_kernelBandwidthText
-
m_maxX
double m_maxX
-
m_maxY
double m_maxY
-
m_minX
double m_minX
-
m_minY
double m_minY
-
m_numberOfSamplesFromEachRegion
int m_numberOfSamplesFromEachRegion
-
m_plotAreaHeight
int m_plotAreaHeight
-
m_plotAreaWidth
int m_plotAreaWidth
-
m_plotTrainingData
javax.swing.JCheckBox m_plotTrainingData
-
m_regionSamplesText
javax.swing.JTextField m_regionSamplesText
-
m_removePointsButton
javax.swing.JRadioButton m_removePointsButton
-
m_startBut
javax.swing.JButton m_startBut
-
m_trainingInstances
Instances m_trainingInstances
the training instances -
m_xAttBox
javax.swing.JComboBox m_xAttBox
-
m_xAxisPanel
weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_xAxisPanel
-
m_xIndex
int m_xIndex
-
m_yAttBox
javax.swing.JComboBox m_yAttBox
-
m_yAxisPanel
weka.gui.boundaryvisualizer.BoundaryVisualizer.AxisPanel m_yAxisPanel
-
m_yIndex
int m_yIndex
-
removeAllButton
javax.swing.JButton removeAllButton
-
-
Class weka.gui.boundaryvisualizer.KDDataGenerator extends java.lang.Object implements Serializable
- serialVersionUID:
- -958573275606402792L
-
Serialized Fields
-
m_globalMeansOrModes
double[] m_globalMeansOrModes
global means or modes to use for missing values -
m_instances
Instances m_instances
the instances to use -
m_kernelBandwidth
int m_kernelBandwidth
Number of neighbours to use for kernel bandwidth -
m_kernelParams
double[][] m_kernelParams
standard deviations for numeric attributes computed from the m_kernelBandwidth nearest neighbours for each kernel. -
m_laplaceConst
double m_laplaceConst
Laplace correction for discrete distributions -
m_Max
double[] m_Max
The maximum values for numeric attributes. -
m_Min
double[] m_Min
The minimum values for numeric attributes. -
m_minStdDev
double m_minStdDev
minimum standard deviation for numeric attributes -
m_random
java.util.Random m_random
random number generator -
m_seed
int m_seed
random number seed -
m_standardDeviations
double[] m_standardDeviations
standard deviations of the normal distributions for numeric attributes in each KD estimator -
m_weightingDimensions
boolean[] m_weightingDimensions
which dimensions to use for computing a weight for each generated instance -
m_weightingValues
double[] m_weightingValues
the values for the weighting dimensions to use for computing the weight for the next instance to be generated
-
-
Class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask extends java.lang.Object implements Serializable
-
Serialized Fields
-
m_attsToWeightOn
boolean[] m_attsToWeightOn
-
m_classifier
Classifier m_classifier
-
m_dataGenerator
DataGenerator m_dataGenerator
-
m_dist
double[] m_dist
-
m_maxX
double m_maxX
-
m_maxY
double m_maxY
-
m_minX
double m_minX
-
m_minY
double m_minY
-
m_numOfSamplesPerGenerator
int m_numOfSamplesPerGenerator
-
m_numOfSamplesPerRegion
int m_numOfSamplesPerRegion
-
m_panelHeight
int m_panelHeight
-
m_panelWidth
int m_panelWidth
-
m_pixHeight
double m_pixHeight
-
m_pixWidth
double m_pixWidth
-
m_predInst
Instance m_predInst
-
m_random
java.util.Random m_random
-
m_result
RemoteResult m_result
-
m_rowNumber
int m_rowNumber
-
m_samplesBase
double m_samplesBase
-
m_status
TaskStatusInfo m_status
-
m_trainingData
Instances m_trainingData
-
m_vals
double[] m_vals
-
m_weightingAttsValues
double[] m_weightingAttsValues
-
m_xAttribute
int m_xAttribute
-
m_yAttribute
int m_yAttribute
-
-
-
Class weka.gui.boundaryvisualizer.RemoteResult extends java.lang.Object implements Serializable
- serialVersionUID:
- 1873271280044633808L
-
Serialized Fields
-
m_percentCompleted
int m_percentCompleted
progress on computing this row -
m_probabilities
double[][] m_probabilities
the result - ie. the probability distributions produced by the classifier for this row in the visualization -
m_rowLength
int m_rowLength
how many pixels in a row -
m_rowNumber
int m_rowNumber
the row number that this result corresponds to
-
-
-
Package weka.gui.experiment
-
Class weka.gui.experiment.AlgorithmListPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -7204528834764898671L
-
Serialized Fields
-
m_AddBut
javax.swing.JButton m_AddBut
Click to add an algorithm -
m_AlgorithmListModel
javax.swing.DefaultListModel m_AlgorithmListModel
The list model used -
m_ClassifierEditor
GenericObjectEditor m_ClassifierEditor
Lets the user configure the classifier -
m_DeleteBut
javax.swing.JButton m_DeleteBut
Click to remove the selected dataset from the list -
m_DownBut
javax.swing.JButton m_DownBut
Click to move the selected algorithm(s) one down -
m_EditBut
javax.swing.JButton m_EditBut
Click to edit the selected algorithm -
m_Editing
boolean m_Editing
Whether an algorithm is added or only edited -
m_Exp
Experiment m_Exp
The experiment to set the algorithm list of -
m_FileChooser
javax.swing.JFileChooser m_FileChooser
The file chooser for selecting experiments -
m_List
javax.swing.JList m_List
The component displaying the algorithm list -
m_LoadOptionsBut
javax.swing.JButton m_LoadOptionsBut
Click to edit the load the options for athe selected algorithm -
m_PD
PropertyDialog m_PD
The currently displayed property dialog, if any -
m_SaveOptionsBut
javax.swing.JButton m_SaveOptionsBut
Click to edit the save the options from selected algorithm -
m_UpBut
javax.swing.JButton m_UpBut
Click to move the selected algorithm(s) one up -
m_XMLFilter
javax.swing.filechooser.FileFilter m_XMLFilter
A filter to ensure only experiment (in XML format) files get shown in the chooser
-
-
Class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer extends javax.swing.DefaultListCellRenderer implements Serializable
- serialVersionUID:
- -5067138526587433808L
-
Class weka.gui.experiment.DatasetListPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 7068857852794405769L
-
Serialized Fields
-
m_AddBut
javax.swing.JButton m_AddBut
Click to add a dataset. -
m_DeleteBut
javax.swing.JButton m_DeleteBut
Click to remove the selected dataset from the list. -
m_DownBut
javax.swing.JButton m_DownBut
Click to move the selected dataset(s) one down. -
m_EditBut
javax.swing.JButton m_EditBut
Click to edit the selected algorithm. -
m_Exp
Experiment m_Exp
The experiment to set the dataset list of. -
m_FileChooser
ConverterFileChooser m_FileChooser
The file chooser component. -
m_List
javax.swing.JList m_List
The component displaying the dataset list. -
m_relativeCheck
javax.swing.JCheckBox m_relativeCheck
Make file paths relative to the user (start) directory. -
m_UpBut
javax.swing.JButton m_UpBut
Click to move the selected dataset(s) one up.
-
-
Class weka.gui.experiment.DistributeExperimentPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 5206721431754800278L
-
Serialized Fields
-
m_configureHostNames
javax.swing.JButton m_configureHostNames
Popup the HostListPanel -
m_enableDistributedExperiment
javax.swing.JCheckBox m_enableDistributedExperiment
Distribute the current experiment to remote hosts -
m_Exp
RemoteExperiment m_Exp
The experiment to configure. -
m_hostList
HostListPanel m_hostList
The host list panel -
m_radioListener
java.awt.event.ActionListener m_radioListener
Handle radio buttons -
m_splitByDataSet
javax.swing.JRadioButton m_splitByDataSet
Split experiment up by data set. -
m_splitByRun
javax.swing.JRadioButton m_splitByRun
Split experiment up by run number.
-
-
Class weka.gui.experiment.Experimenter extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -5751617505738193788L
-
Serialized Fields
-
m_ClassFirst
boolean m_ClassFirst
True if the class attribute is the first attribute for all datasets involved in this experiment. -
m_ResultsPanel
ResultsPanel m_ResultsPanel
The panel for analysing experimental results -
m_RunPanel
RunPanel m_RunPanel
The panel for running the experiment -
m_SetupPanel
SetupModePanel m_SetupPanel
The panel for configuring the experiment -
m_TabbedPane
javax.swing.JTabbedPane m_TabbedPane
The tabbed pane that controls which sub-pane we are working with
-
-
Class weka.gui.experiment.ExperimenterDefaults extends java.lang.Object implements Serializable
- serialVersionUID:
- -2835933184632147981L
-
Class weka.gui.experiment.GeneratorPropertyIteratorPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -6026938995241632139L
-
Serialized Fields
-
m_ArrayEditor
GenericArrayEditor m_ArrayEditor
Allows editing of the custom property values -
m_ConfigureBut
javax.swing.JButton m_ConfigureBut
Click to select the property to iterate over -
m_Exp
Experiment m_Exp
The experiment this all applies to -
m_Listeners
FastVector m_Listeners
Listeners who want to be notified about editing status of this panel -
m_StatusBox
javax.swing.JComboBox m_StatusBox
Controls whether the custom iterator is used or not
-
-
Class weka.gui.experiment.HostListPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 7182791134585882197L
-
Serialized Fields
-
m_DeleteBut
javax.swing.JButton m_DeleteBut
Click to remove the selected host from the list -
m_Exp
RemoteExperiment m_Exp
The remote experiment to set the host list of -
m_HostField
javax.swing.JTextField m_HostField
The field with which to enter host names -
m_List
javax.swing.JList m_List
The component displaying the host list
-
-
Class weka.gui.experiment.OutputFormatDialog extends javax.swing.JDialog implements Serializable
- serialVersionUID:
- 2169792738187807378L
-
Serialized Fields
-
m_CancelButton
javax.swing.JButton m_CancelButton
Click to cancel the dialog. -
m_MeanPrec
int m_MeanPrec
the number of digits after the period (= precision) for printing the mean. -
m_MeanPrecSpinner
javax.swing.JSpinner m_MeanPrecSpinner
the spinner to choose the precision for the mean from. -
m_OkButton
javax.swing.JButton m_OkButton
Click to activate the current set parameters. -
m_OutputFormatComboBox
javax.swing.JComboBox m_OutputFormatComboBox
lets the user choose the format for the output. -
m_RemoveFilterName
boolean m_RemoveFilterName
whether to remove the filter names from the names. -
m_RemoveFilterNameCheckBox
javax.swing.JCheckBox m_RemoveFilterNameCheckBox
the checkbox for the removing of filter classnames. -
m_Result
int m_Result
the result of the user's action, either OK or CANCEL. -
m_ResultMatrix
java.lang.Class m_ResultMatrix
the output format specific matrix. -
m_ShowAverage
boolean m_ShowAverage
whether to show the average too. -
m_ShowAverageCheckBox
javax.swing.JCheckBox m_ShowAverageCheckBox
the checkbox for outputting the average. -
m_StdDevPrec
int m_StdDevPrec
the number of digits after the period (= precision) for printing the std. deviation -
m_StdDevPrecSpinner
javax.swing.JSpinner m_StdDevPrecSpinner
the spinner to choose the precision for the std. deviation from
-
-
Class weka.gui.experiment.ResultsPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -4913007978534178569L
-
Serialized Fields
-
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
the size for a combobox. -
m_arffFileFilter
ExtensionFileFilter m_arffFileFilter
ARFF file filter. -
m_CompareCombo
javax.swing.JComboBox m_CompareCombo
Lets the user select which performance measure to analyze. -
m_CompareModel
javax.swing.DefaultComboBoxModel m_CompareModel
The model embedded in m_CompareCombo. -
m_csvFileFilter
ExtensionFileFilter m_csvFileFilter
CSV file filter. -
m_DatasetKeyBut
javax.swing.JButton m_DatasetKeyBut
Click to edit the columns used to determine the scheme. -
m_DatasetKeyLabel
javax.swing.JLabel m_DatasetKeyLabel
Displays the currently selected column names for the scheme & options. -
m_DatasetKeyList
javax.swing.JList m_DatasetKeyList
Displays the list of selected columns determining the scheme. -
m_DatasetKeyModel
javax.swing.DefaultListModel m_DatasetKeyModel
Stores the list of attributes for selecting the scheme columns. -
m_DatasetModel
javax.swing.DefaultComboBoxModel m_DatasetModel
The model embedded in m_DatasetCombo. -
m_DisplayedButton
javax.swing.JButton m_DisplayedButton
Lets the user select which schemes are compared to base. -
m_DisplayedList
javax.swing.JList m_DisplayedList
Holds the list of schemes to display. -
m_DisplayedModel
javax.swing.DefaultListModel m_DisplayedModel
The model embedded in m_DisplayedList. -
m_Exp
Experiment m_Exp
An experiment (used for identifying a result source) -- optional. -
m_FileChooser
javax.swing.JFileChooser m_FileChooser
The file chooser for selecting result files. -
m_FromDBaseBut
javax.swing.JButton m_FromDBaseBut
Click to load results from a database. -
m_FromExpBut
javax.swing.JButton m_FromExpBut
Click to get results from the destination given in the experiment. -
m_FromFileBut
javax.swing.JButton m_FromFileBut
Click to load results from a file. -
m_FromLab
javax.swing.JLabel m_FromLab
Displays a message about the current result set. -
m_History
ResultHistoryPanel m_History
A panel controlling results viewing. -
m_InstanceQuery
InstanceQuery m_InstanceQuery
Does any database querying for us. -
m_Instances
Instances m_Instances
The instances we're extracting results from. -
m_LoadThread
java.lang.Thread m_LoadThread
A thread to load results instances from a file or database. -
m_OutputFormatButton
javax.swing.JButton m_OutputFormatButton
lets the user choose the format for the output. -
m_OutText
javax.swing.JTextArea m_OutText
Displays the output of tests. -
m_PerformBut
javax.swing.JButton m_PerformBut
Click to start the test. -
m_ResultKeyBut
javax.swing.JButton m_ResultKeyBut
Click to edit the columns used to determine the scheme. -
m_ResultKeyLabel
javax.swing.JLabel m_ResultKeyLabel
Displays the currently selected column names for the scheme & options. -
m_ResultKeyList
javax.swing.JList m_ResultKeyList
Displays the list of selected columns determining the scheme. -
m_ResultKeyModel
javax.swing.DefaultListModel m_ResultKeyModel
Stores the list of attributes for selecting the scheme columns. -
m_ResultMatrix
ResultMatrix m_ResultMatrix
the initial result matrix. -
m_SaveOut
SaveBuffer m_SaveOut
The buffer saving object for saving output. -
m_SaveOutBut
javax.swing.JButton m_SaveOutBut
Click to save test output to a file. -
m_ShowStdDevs
javax.swing.JCheckBox m_ShowStdDevs
Lets the user select whether standard deviations are to be output or not. -
m_SigTex
javax.swing.JTextField m_SigTex
Lets the user edit the test significance. -
m_SortCombo
javax.swing.JComboBox m_SortCombo
Lets the user select which column to use for sorting. -
m_SortModel
javax.swing.DefaultComboBoxModel m_SortModel
The model embedded in m_SortCombo. -
m_TesterClasses
javax.swing.JComboBox m_TesterClasses
Lists all the available classes implementing the Tester-Interface.- See Also:
Tester
-
m_TesterClassesLabel
javax.swing.JLabel m_TesterClassesLabel
Displays the currently selected Tester-Class. -
m_TestsButton
javax.swing.JButton m_TestsButton
Lets the user select which scheme to base comparisons against. -
m_TestsList
javax.swing.JList m_TestsList
Holds the list of schemes to base the test against. -
m_TestsModel
javax.swing.DefaultListModel m_TestsModel
The model embedded in m_TestsList. -
m_TTester
Tester m_TTester
The PairedTTester object.
-
-
Class weka.gui.experiment.RunNumberPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -1644336658426067852L
-
Serialized Fields
-
m_Exp
Experiment m_Exp
The experiment being configured -
m_LowerText
javax.swing.JTextField m_LowerText
Configures the lower run number -
m_UpperText
javax.swing.JTextField m_UpperText
Configures the upper run number
-
-
Class weka.gui.experiment.RunPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 1691868018596872051L
-
Serialized Fields
-
m_Exp
Experiment m_Exp
The experiment to run -
m_Log
LogPanel m_Log
-
m_ResultsPanel
ResultsPanel m_ResultsPanel
A pointer to the results panel -
m_RunThread
java.lang.Thread m_RunThread
The thread running the experiment -
m_StartBut
javax.swing.JButton m_StartBut
Click to start running the experiment -
m_StopBut
javax.swing.JButton m_StopBut
Click to signal the running experiment to halt
-
-
Class weka.gui.experiment.SetupModePanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -3758035565520727822L
-
Serialized Fields
-
m_advancedPanel
SetupPanel m_advancedPanel
The advanced setup panel -
m_AdvancedSetupRBut
javax.swing.JRadioButton m_AdvancedSetupRBut
The button for choosing advanced setup mode -
m_simplePanel
SimpleSetupPanel m_simplePanel
The simple setup panel -
m_SimpleSetupRBut
javax.swing.JRadioButton m_SimpleSetupRBut
The button for choosing simple setup mode
-
-
Class weka.gui.experiment.SetupPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 6552671886903170033L
-
Serialized Fields
-
m_advanceDataSetFirst
javax.swing.JRadioButton m_advanceDataSetFirst
Click to advacne data set before custom generator -
m_advanceIteratorFirst
javax.swing.JRadioButton m_advanceIteratorFirst
Click to advance custom generator before data set -
m_DatasetListPanel
DatasetListPanel m_DatasetListPanel
The panel for configuring selected datasets -
m_DistributeExperimentPanel
DistributeExperimentPanel m_DistributeExperimentPanel
The panel for enabling a distributed experiment -
m_Exp
Experiment m_Exp
The experiment being configured -
m_ExpFilter
javax.swing.filechooser.FileFilter m_ExpFilter
A filter to ensure only experiment files get shown in the chooser -
m_FileChooser
javax.swing.JFileChooser m_FileChooser
The file chooser for selecting experiments -
m_GeneratorPropertyPanel
GeneratorPropertyIteratorPanel m_GeneratorPropertyPanel
The panel that configures iteration on custom resultproducer property -
m_KOMLFilter
javax.swing.filechooser.FileFilter m_KOMLFilter
A filter to ensure only experiment (in KOML format) files get shown in the chooser -
m_NewBut
javax.swing.JButton m_NewBut
Click to create a new experiment with default settings -
m_NotesButton
javax.swing.JButton m_NotesButton
A button for bringing up the notes -
m_NotesFrame
javax.swing.JFrame m_NotesFrame
Frame for the notes -
m_NotesText
javax.swing.JTextArea m_NotesText
Area for user notes Default of 10 rows -
m_OpenBut
javax.swing.JButton m_OpenBut
Click to load an experiment -
m_RadioListener
java.awt.event.ActionListener m_RadioListener
Handle radio buttons -
m_RLEditor
GenericObjectEditor m_RLEditor
The ResultListener editor -
m_RLEditorPanel
PropertyPanel m_RLEditorPanel
The panel to contain the ResultListener editor -
m_RPEditor
GenericObjectEditor m_RPEditor
The ResultProducer editor -
m_RPEditorPanel
PropertyPanel m_RPEditorPanel
The panel to contain the ResultProducer editor -
m_RunNumberPanel
RunNumberPanel m_RunNumberPanel
The panel for configuring run numbers -
m_SaveBut
javax.swing.JButton m_SaveBut
Click to save an experiment -
m_Support
java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update. -
m_XMLFilter
javax.swing.filechooser.FileFilter m_XMLFilter
A filter to ensure only experiment (in XML format) files get shown in the chooser
-
-
Class weka.gui.experiment.SimpleSetupPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 5257424515609176509L
-
Serialized Fields
-
m_AlgorithmListPanel
AlgorithmListPanel m_AlgorithmListPanel
The panel for configuring selected algorithms -
m_arffFileFilter
ExtensionFileFilter m_arffFileFilter
FIlter for choosing ARFF files -
m_BrowseDestinationButton
javax.swing.JButton m_BrowseDestinationButton
Button for browsing destination files -
m_csvFileFilter
ExtensionFileFilter m_csvFileFilter
Filter for choosing CSV files -
m_DatasetListPanel
DatasetListPanel m_DatasetListPanel
The panel for configuring selected datasets -
m_DestFileChooser
javax.swing.JFileChooser m_DestFileChooser
The file chooser for selecting result destinations -
m_destinationDatabaseURL
java.lang.String m_destinationDatabaseURL
The database destination URL to store results into -
m_destinationFilename
java.lang.String m_destinationFilename
The filename to store results into -
m_Exp
Experiment m_Exp
The experiment being configured -
m_ExpClassificationRBut
javax.swing.JRadioButton m_ExpClassificationRBut
Radio button for choosing classification experiment -
m_ExperimentParameterLabel
javax.swing.JLabel m_ExperimentParameterLabel
Label for parameter field -
m_ExperimentParameterTField
javax.swing.JTextField m_ExperimentParameterTField
Input field for experiment parameter -
m_ExperimentTypeCBox
javax.swing.JComboBox m_ExperimentTypeCBox
Combo box for choosing experiment type -
m_ExpFilter
javax.swing.filechooser.FileFilter m_ExpFilter
A filter to ensure only experiment files get shown in the chooser -
m_ExpRegressionRBut
javax.swing.JRadioButton m_ExpRegressionRBut
Radio button for choosing regression experiment -
m_FileChooser
javax.swing.JFileChooser m_FileChooser
The file chooser for selecting experiments -
m_KOMLFilter
javax.swing.filechooser.FileFilter m_KOMLFilter
A filter to ensure only experiment (in KOML format) files get shown in the chooser -
m_modePanel
SetupModePanel m_modePanel
The panel which switched between simple and advanced setup modes -
m_NewBut
javax.swing.JButton m_NewBut
Click to create a new experiment with default settings -
m_NotesButton
javax.swing.JButton m_NotesButton
A button for bringing up the notes -
m_NotesFrame
javax.swing.JFrame m_NotesFrame
Frame for the notes -
m_NotesText
javax.swing.JTextArea m_NotesText
Area for user notes Default of 10 rows -
m_NumberOfRepetitionsTField
javax.swing.JTextField m_NumberOfRepetitionsTField
Input field for number of repetitions -
m_numFolds
int m_numFolds
The number of folds for a cross-validation experiment -
m_numRepetitions
int m_numRepetitions
The number of times to repeat the sub-experiment -
m_OpenBut
javax.swing.JButton m_OpenBut
Click to load an experiment -
m_OrderAlgorithmsFirstRBut
javax.swing.JRadioButton m_OrderAlgorithmsFirstRBut
Radio button for choosing algorithms first in order of execution -
m_OrderDatasetsFirstRBut
javax.swing.JRadioButton m_OrderDatasetsFirstRBut
Radio button for choosing datasets first in order of execution -
m_ResultsDestinationCBox
javax.swing.JComboBox m_ResultsDestinationCBox
Combo box for choosing experiment destination type -
m_ResultsDestinationPathLabel
javax.swing.JLabel m_ResultsDestinationPathLabel
Label for destination field -
m_ResultsDestinationPathTField
javax.swing.JTextField m_ResultsDestinationPathTField
Input field for result destination path -
m_SaveBut
javax.swing.JButton m_SaveBut
Click to save an experiment -
m_Support
java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update. -
m_trainPercent
double m_trainPercent
The training percentage for a train/test split experiment -
m_userHasBeenAskedAboutConversion
boolean m_userHasBeenAskedAboutConversion
Whether or not the user has consented for the experiment to be simplified -
m_XMLFilter
javax.swing.filechooser.FileFilter m_XMLFilter
A filter to ensure only experiment (in XML format) files get shown in the chooser
-
-
-
Package weka.gui.explorer
-
Class weka.gui.explorer.AssociationsPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -6867871711865476971L
-
Serialized Fields
-
m_AssociatorEditor
GenericObjectEditor m_AssociatorEditor
Lets the user configure the associator -
m_CEPanel
PropertyPanel m_CEPanel
The panel showing the current associator selection -
m_Explorer
Explorer m_Explorer
the parent frame -
m_History
ResultHistoryPanel m_History
A panel controlling results viewing -
m_Instances
Instances m_Instances
The main set of instances we're playing with -
m_Log
Logger m_Log
The destination for log/status messages -
m_OutText
javax.swing.JTextArea m_OutText
The output area for associations -
m_RunThread
java.lang.Thread m_RunThread
A thread that associator runs in -
m_SaveOut
SaveBuffer m_SaveOut
The buffer saving object for saving output -
m_StartBut
javax.swing.JButton m_StartBut
Click to start running the associator -
m_StopBut
javax.swing.JButton m_StopBut
Click to stop a running associator -
m_TestInstances
Instances m_TestInstances
The user-supplied test set (if any)
-
-
Class weka.gui.explorer.AttributeSelectionPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 5627185966993476142L
-
Serialized Fields
-
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
Stop the class combo from taking up to much space -
m_AEEPanel
PropertyPanel m_AEEPanel
The panel showing the current attribute evaluation method -
m_ASEPanel
PropertyPanel m_ASEPanel
The panel showing the current search method -
m_AttributeEvaluatorEditor
GenericObjectEditor m_AttributeEvaluatorEditor
Lets the user configure the attribute evaluator -
m_AttributeSearchEditor
GenericObjectEditor m_AttributeSearchEditor
Lets the user configure the search method -
m_ClassCombo
javax.swing.JComboBox m_ClassCombo
Lets the user select the class column -
m_CVBut
javax.swing.JRadioButton m_CVBut
Click to set evaluation mode to cross-validation -
m_CVLab
javax.swing.JLabel m_CVLab
Label by where the cv folds are entered -
m_CVText
javax.swing.JTextField m_CVText
The field where the cv folds are entered -
m_Explorer
Explorer m_Explorer
the parent frame -
m_History
ResultHistoryPanel m_History
A panel controlling results viewing -
m_Instances
Instances m_Instances
The main set of instances we're playing with -
m_Log
Logger m_Log
The destination for log/status messages -
m_OutText
javax.swing.JTextArea m_OutText
The output area for attribute selection results -
m_RadioListener
java.awt.event.ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button -
m_RunThread
java.lang.Thread m_RunThread
A thread that attribute selection runs in -
m_SaveOut
SaveBuffer m_SaveOut
The buffer saving object for saving output -
m_SeedLab
javax.swing.JLabel m_SeedLab
Label by where cv random seed is entered -
m_SeedText
javax.swing.JTextField m_SeedText
The field where the seed value is entered -
m_StartBut
javax.swing.JButton m_StartBut
Click to start running the attribute selector -
m_StopBut
javax.swing.JButton m_StopBut
Click to stop a running classifier -
m_TrainBut
javax.swing.JRadioButton m_TrainBut
Click to set test mode to test on training data
-
-
Class weka.gui.explorer.ClassifierPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 6959973704963624003L
-
Serialized Fields
-
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
Stop the class combo from taking up to much space -
m_CEPanel
PropertyPanel m_CEPanel
The panel showing the current classifier selection -
m_ClassCombo
javax.swing.JComboBox m_ClassCombo
Lets the user select the class column -
m_ClassifierEditor
GenericObjectEditor m_ClassifierEditor
Lets the user configure the classifier -
m_CostMatrixEditor
CostMatrixEditor m_CostMatrixEditor
The cost matrix editor for evaluation costs -
m_CurrentVis
VisualizePanel m_CurrentVis
The current visualization object -
m_CVBut
javax.swing.JRadioButton m_CVBut
Click to set test mode to cross-validation -
m_CVLab
javax.swing.JLabel m_CVLab
Label by where the cv folds are entered -
m_CVText
javax.swing.JTextField m_CVText
The field where the cv folds are entered -
m_EvalWRTCostsBut
javax.swing.JCheckBox m_EvalWRTCostsBut
Check to evaluate w.r.t a cost matrix -
m_Explorer
Explorer m_Explorer
the parent frame -
m_FileChooser
javax.swing.JFileChooser m_FileChooser
The file chooser for selecting model files -
m_History
ResultHistoryPanel m_History
A panel controlling results viewing -
m_Instances
Instances m_Instances
The main set of instances we're playing with -
m_Log
Logger m_Log
The destination for log/status messages -
m_ModelFilter
javax.swing.filechooser.FileFilter m_ModelFilter
Filter to ensure only model files are selected -
m_MoreOptions
javax.swing.JButton m_MoreOptions
Button for further output/visualize options -
m_OutputAdditionalAttributesLab
javax.swing.JLabel m_OutputAdditionalAttributesLab
Label for the text field with additional attributes in the output -
m_OutputAdditionalAttributesRange
Range m_OutputAdditionalAttributesRange
the range of attributes to output -
m_OutputAdditionalAttributesText
javax.swing.JTextField m_OutputAdditionalAttributesText
Lists indices for additional attributes to output -
m_OutputConfusionBut
javax.swing.JCheckBox m_OutputConfusionBut
Check to output a confusion matrix -
m_OutputEntropyBut
javax.swing.JCheckBox m_OutputEntropyBut
Check to output entropy statistics -
m_OutputModelBut
javax.swing.JCheckBox m_OutputModelBut
Check to output the model built from the training data -
m_OutputPerClassBut
javax.swing.JCheckBox m_OutputPerClassBut
Check to output true/false positives, precision/recall for each class -
m_OutputPredictionsTextBut
javax.swing.JCheckBox m_OutputPredictionsTextBut
Check to output text predictions -
m_OutputSourceCode
javax.swing.JCheckBox m_OutputSourceCode
Whether to output the source code (only for classifiers importing Sourcable) -
m_OutText
javax.swing.JTextArea m_OutText
The output area for classification results -
m_PercentBut
javax.swing.JRadioButton m_PercentBut
Click to set test mode to generate a % split -
m_PercentLab
javax.swing.JLabel m_PercentLab
Label by where the % split is entered -
m_PercentText
javax.swing.JTextField m_PercentText
The field where the % split is entered -
m_PMMLModelFilter
javax.swing.filechooser.FileFilter m_PMMLModelFilter
-
m_PreserveOrderBut
javax.swing.JCheckBox m_PreserveOrderBut
Whether randomization is turned off to preserve order -
m_RadioListener
java.awt.event.ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button -
m_RandomLab
javax.swing.JLabel m_RandomLab
the label for the random seed textfield -
m_RandomSeedText
javax.swing.JTextField m_RandomSeedText
User specified random seed for cross validation or % split -
m_RunThread
java.lang.Thread m_RunThread
A thread that classification runs in -
m_SaveOut
SaveBuffer m_SaveOut
The buffer saving object for saving output -
m_SetCostsBut
javax.swing.JButton m_SetCostsBut
for the cost matrix -
m_SetCostsFrame
PropertyDialog m_SetCostsFrame
The frame used to show the cost matrix editing panel -
m_SetTestBut
javax.swing.JButton m_SetTestBut
The button used to open a separate test dataset -
m_SetTestFrame
javax.swing.JFrame m_SetTestFrame
The frame used to show the test set selection panel -
m_SourceCodeClass
javax.swing.JTextField m_SourceCodeClass
The name of the generated class (only applicable to Sourcable schemes) -
m_StartBut
javax.swing.JButton m_StartBut
Click to start running the classifier -
m_StopBut
javax.swing.JButton m_StopBut
Click to stop a running classifier -
m_StorePredictionsBut
javax.swing.JCheckBox m_StorePredictionsBut
Check to save the predictions in the results list for visualizing later on -
m_TestLoader
Loader m_TestLoader
The loader used to load the user-supplied test set (if any) -
m_TestSplitBut
javax.swing.JRadioButton m_TestSplitBut
Click to set test mode to a user-specified test set -
m_TrainBut
javax.swing.JRadioButton m_TrainBut
Click to set test mode to test on training data
-
-
Class weka.gui.explorer.ClustererPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -2474932792950820990L
-
Serialized Fields
-
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
Stop the class combo from taking up to much space -
m_ClassCombo
javax.swing.JComboBox m_ClassCombo
Lets the user select the class column for classes to clusters based evaluation -
m_ClassesToClustersBut
javax.swing.JRadioButton m_ClassesToClustersBut
Click to set test mode to classes to clusters based evaluation -
m_CLPanel
PropertyPanel m_CLPanel
The panel showing the current clusterer selection -
m_ClustererEditor
GenericObjectEditor m_ClustererEditor
Lets the user configure the clusterer -
m_CurrentVis
VisualizePanel m_CurrentVis
The current visualization object -
m_Explorer
Explorer m_Explorer
the parent frame -
m_FileChooser
javax.swing.JFileChooser m_FileChooser
The file chooser for selecting model files -
m_History
ResultHistoryPanel m_History
A panel controlling results viewing -
m_ignoreBut
javax.swing.JButton m_ignoreBut
The button used to popup a list for choosing attributes to ignore while clustering -
m_ignoreKeyList
javax.swing.JList m_ignoreKeyList
-
m_ignoreKeyModel
javax.swing.DefaultListModel m_ignoreKeyModel
-
m_Instances
Instances m_Instances
The main set of instances we're playing with -
m_Log
Logger m_Log
The destination for log/status messages -
m_ModelFilter
javax.swing.filechooser.FileFilter m_ModelFilter
Filter to ensure only model files are selected -
m_OutText
javax.swing.JTextArea m_OutText
The output area for classification results -
m_PercentBut
javax.swing.JRadioButton m_PercentBut
Click to set test mode to generate a % split -
m_PercentLab
javax.swing.JLabel m_PercentLab
Label by where the % split is entered -
m_PercentText
javax.swing.JTextField m_PercentText
The field where the % split is entered -
m_RadioListener
java.awt.event.ActionListener m_RadioListener
Alters the enabled/disabled status of elements associated with each radio button -
m_RunThread
java.lang.Thread m_RunThread
A thread that clustering runs in -
m_SaveOut
SaveBuffer m_SaveOut
The buffer saving object for saving output -
m_SetTestBut
javax.swing.JButton m_SetTestBut
The button used to open a separate test dataset -
m_SetTestFrame
javax.swing.JFrame m_SetTestFrame
The frame used to show the test set selection panel -
m_StartBut
javax.swing.JButton m_StartBut
Click to start running the clusterer -
m_StopBut
javax.swing.JButton m_StopBut
Click to stop a running clusterer -
m_StorePredictionsBut
javax.swing.JCheckBox m_StorePredictionsBut
Check to save the predictions in the results list for visualizing later on -
m_Summary
InstancesSummaryPanel m_Summary
The instances summary panel displayed by m_SetTestFrame -
m_TestInstances
Instances m_TestInstances
The user-supplied test set (if any) -
m_TestSplitBut
javax.swing.JRadioButton m_TestSplitBut
Click to set test mode to a user-specified test set -
m_TrainBut
javax.swing.JRadioButton m_TrainBut
Click to set test mode to test on training data
-
-
Class weka.gui.explorer.DataGeneratorPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -2520408165350629380L
-
Serialized Fields
-
m_GeneratorEditor
GenericObjectEditor m_GeneratorEditor
the GOE for the generators -
m_Instances
Instances m_Instances
the generated Instances -
m_Log
Logger m_Log
The destination for log/status messages -
m_Output
java.io.StringWriter m_Output
the generated output (as text)
-
-
Class weka.gui.explorer.Explorer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -7674003708867909578L
-
Serialized Fields
-
m_CapabilitiesFilterChangeListeners
java.util.HashSet<Explorer.CapabilitiesFilterChangeListener> m_CapabilitiesFilterChangeListeners
the listeners that listen to filter changes -
m_LogPanel
LogPanel m_LogPanel
The panel for log and status messages -
m_Panels
java.util.Vector<Explorer.ExplorerPanel> m_Panels
Contains all the additional panels apart from the pre-processing panel -
m_PreprocessPanel
PreprocessPanel m_PreprocessPanel
The panel for preprocessing instances -
m_TabbedPane
javax.swing.JTabbedPane m_TabbedPane
The tabbed pane that controls which sub-pane we are working with
-
-
Class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent extends javax.swing.event.ChangeEvent implements Serializable
- serialVersionUID:
- 1194260517270385559L
-
Serialized Fields
-
m_Filter
Capabilities m_Filter
the capabilities filter
-
-
Class weka.gui.explorer.ExplorerDefaults extends java.lang.Object implements Serializable
- serialVersionUID:
- 4954795757927524225L
-
Class weka.gui.explorer.PreprocessPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 6764850273874813049L
-
Serialized Fields
-
m_ApplyFilterBut
javax.swing.JButton m_ApplyFilterBut
Click to apply filters and save the results -
m_AttPanel
AttributeSelectionPanel m_AttPanel
Panel to let the user toggle attributes -
m_AttSummaryPanel
AttributeSummaryPanel m_AttSummaryPanel
Displays summary stats on the selected attribute -
m_AttVisualizePanel
AttributeVisualizationPanel m_AttVisualizePanel
The visualization of the attribute values -
m_DataGenerator
DataGenerator m_DataGenerator
The last generator that was selected -
m_EditBut
javax.swing.JButton m_EditBut
Click to open the current instances in a viewer -
m_Explorer
Explorer m_Explorer
the parent frame -
m_FileChooser
ConverterFileChooser m_FileChooser
The file chooser for selecting data files -
m_FilterEditor
GenericObjectEditor m_FilterEditor
Lets the user configure the filter -
m_FilterPanel
PropertyPanel m_FilterPanel
Filter configuration -
m_GenerateBut
javax.swing.JButton m_GenerateBut
Click to generate artificial data -
m_Instances
Instances m_Instances
The working instances -
m_InstSummaryPanel
InstancesSummaryPanel m_InstSummaryPanel
Displays simple stats on the working instances -
m_IOThread
java.lang.Thread m_IOThread
A thread for loading/saving instances from a file or URL -
m_LastURL
java.lang.String m_LastURL
Stores the last URL that instances were loaded from -
m_Log
Logger m_Log
The message logger -
m_OpenDBBut
javax.swing.JButton m_OpenDBBut
Click to load base instances from a Database -
m_OpenFileBut
javax.swing.JButton m_OpenFileBut
Click to load base instances from a file -
m_OpenURLBut
javax.swing.JButton m_OpenURLBut
Click to load base instances from a URL -
m_RemoveButton
javax.swing.JButton m_RemoveButton
Button for removing attributes -
m_SaveBut
javax.swing.JButton m_SaveBut
Click to apply filters and save the results -
m_SQLQ
java.lang.String m_SQLQ
Stores the last sql query executed -
m_Support
java.beans.PropertyChangeSupport m_Support
Manages sending notifications to people when we change the set of working instances. -
m_tempUndoFiles
java.io.File[] m_tempUndoFiles
Keeps track of undo points -
m_tempUndoIndex
int m_tempUndoIndex
The next available slot for an undo point -
m_UndoBut
javax.swing.JButton m_UndoBut
Click to revert back to the last saved point
-
-
Class weka.gui.explorer.VisualizePanel extends MatrixPanel implements Serializable
- serialVersionUID:
- 6084015036853918846L
-
Serialized Fields
-
m_Explorer
Explorer m_Explorer
the parent frame
-
-
-
Package weka.gui.graphvisualizer
-
Class weka.gui.graphvisualizer.BIFFormatException extends java.lang.Exception implements Serializable
- serialVersionUID:
- -4102518086411708140L
-
Class weka.gui.graphvisualizer.GraphVisualizer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -2038911085935515624L
-
Serialized Fields
-
fm
java.awt.FontMetrics fm
-
graphID
java.lang.String graphID
String containing graph's name -
ICONPATH
java.lang.String ICONPATH
path for icons -
jBtLayout
javax.swing.JButton jBtLayout
Button for laying out the graph again, necessary after changing node's size or some other property of the layout engine -
jTfNodeHeight
javax.swing.JTextField jTfNodeHeight
TextField for nodes height -
jTfNodeWidth
javax.swing.JTextField jTfNodeWidth
TextField for node's width -
m_edges
FastVector m_edges
Vector containing edges -
m_gp
weka.gui.graphvisualizer.GraphVisualizer.GraphPanel m_gp
Panel actually displaying the graph -
m_jBtSave
javax.swing.JButton m_jBtSave
Save button to save the current graph in DOT or XMLBIF format. The graph should be layed out again to get the original form if reloaded from command line, as the formats do not allow saving specific information for a properly layed out graph. -
m_js
javax.swing.JScrollPane m_js
this contains the m_gp GraphPanel -
m_le
LayoutEngine m_le
The current LayoutEngine -
m_nodes
FastVector m_nodes
Vector containing nodes -
maxStringWidth
int maxStringWidth
used for setting appropriate node size -
nodeHeight
int nodeHeight
-
nodeWidth
int nodeWidth
-
paddedNodeWidth
int paddedNodeWidth
-
scale
double scale
-
zoomPercents
int[] zoomPercents
used when using zoomIn and zoomOut buttons
-
-
Class weka.gui.graphvisualizer.LayoutCompleteEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 6172467234026258427L
-
-
Package weka.gui.hierarchyvisualizer
-
Class weka.gui.hierarchyvisualizer.HierarchyVisualizer extends PrintablePanel implements Serializable
- serialVersionUID:
- 1L
-
Serialized Fields
-
m_fHeight
double m_fHeight
-
m_fScaleX
double m_fScaleX
-
m_fScaleY
double m_fScaleY
-
m_fTmpLength
double m_fTmpLength
-
m_nLeafs
int m_nLeafs
-
m_sNewick
java.lang.String m_sNewick
-
m_tree
weka.gui.hierarchyvisualizer.HierarchyVisualizer.Node m_tree
-
-
-
Package weka.gui.sql
-
Class weka.gui.sql.ConnectionPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 3499317023969723490L
-
Serialized Fields
-
m_ButtonConnect
javax.swing.JButton m_ButtonConnect
the button for connecting to the database. -
m_ButtonDatabase
javax.swing.JButton m_ButtonDatabase
the button for the DB-Dialog. -
m_ButtonHistory
javax.swing.JButton m_ButtonHistory
the button for the history. -
m_ConnectionListeners
java.util.HashSet m_ConnectionListeners
the connection listeners. -
m_DbDialog
DatabaseConnectionDialog m_DbDialog
the databae connection dialog. -
m_DbUtils
DbUtils m_DbUtils
for connecting to the database. -
m_History
javax.swing.DefaultListModel m_History
the history of connections. -
m_HistoryChangedListeners
java.util.HashSet m_HistoryChangedListeners
the history listeners. -
m_LabelURL
javax.swing.JLabel m_LabelURL
the label for the URL. -
m_Parent
javax.swing.JFrame m_Parent
the parent frame. -
m_Password
java.lang.String m_Password
the password to use for connecting to the DB. -
m_TextURL
javax.swing.JTextField m_TextURL
the textfield for the URL. -
m_URL
java.lang.String m_URL
the URL to use. -
m_User
java.lang.String m_User
the user to use for connecting to the DB.
-
-
Class weka.gui.sql.DbUtils extends DatabaseUtils implements Serializable
- serialVersionUID:
- 103748569037426479L
-
Class weka.gui.sql.InfoPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -7701133696481997973L
-
Serialized Fields
-
m_ButtonClear
javax.swing.JButton m_ButtonClear
the button to clear the area -
m_ButtonCopy
javax.swing.JButton m_ButtonCopy
the button to copy the selected message -
m_Info
javax.swing.JList m_Info
the list the contains the messages -
m_Model
javax.swing.DefaultListModel m_Model
the model for the list -
m_Parent
javax.swing.JFrame m_Parent
the parent of this panel
-
-
Class weka.gui.sql.InfoPanelCellRenderer extends javax.swing.JLabel implements Serializable
- serialVersionUID:
- -533380118807178531L
-
Class weka.gui.sql.QueryPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 4348967824619706636L
-
Serialized Fields
-
m_ButtonClear
javax.swing.JButton m_ButtonClear
the clear button. -
m_ButtonExecute
javax.swing.JButton m_ButtonExecute
the execute button. -
m_ButtonHistory
javax.swing.JButton m_ButtonHistory
the history button. -
m_Connected
boolean m_Connected
whether we have a connection to a database or not. -
m_DbUtils
DbUtils m_DbUtils
for working on the database. -
m_History
javax.swing.DefaultListModel m_History
the query history. -
m_HistoryChangedListeners
java.util.HashSet m_HistoryChangedListeners
the history listeners. -
m_Parent
javax.swing.JFrame m_Parent
the parent of this panel. -
m_QueryExecuteListeners
java.util.HashSet m_QueryExecuteListeners
the connection listeners. -
m_SpinnerMaxRows
javax.swing.JSpinner m_SpinnerMaxRows
the spinner for the maximum number of rows. -
m_TextQuery
javax.swing.JTextArea m_TextQuery
the textarea for the query.
-
-
Class weka.gui.sql.ResultPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 278654800344034571L
-
Serialized Fields
-
m_ButtonClose
javax.swing.JButton m_ButtonClose
the close button -
m_ButtonCloseAll
javax.swing.JButton m_ButtonCloseAll
the close all button -
m_ButtonCopyQuery
javax.swing.JButton m_ButtonCopyQuery
the button that copies the query into the QueryPanel -
m_ButtonOptWidth
javax.swing.JButton m_ButtonOptWidth
the button for the optimal column width of the current table -
m_Listeners
java.util.HashSet m_Listeners
the result change listeners -
m_NameCounter
int m_NameCounter
the counter for the tab names -
m_Parent
javax.swing.JFrame m_Parent
the parent of this panel -
m_QueryPanel
QueryPanel m_QueryPanel
the panel where to output the queries -
m_TabbedPane
javax.swing.JTabbedPane m_TabbedPane
the tabbed pane for the results
-
-
Class weka.gui.sql.ResultSetTable extends javax.swing.JTable implements Serializable
- serialVersionUID:
- -3391076671854464137L
-
Serialized Fields
-
m_Password
java.lang.String m_Password
the password that was used to connect to the DB -
m_Query
java.lang.String m_Query
the query the table model is based on -
m_URL
java.lang.String m_URL
the connect string with which the query was run -
m_User
java.lang.String m_User
the user that was used to connect to the DB
-
-
Class weka.gui.sql.ResultSetTableCellRenderer extends javax.swing.table.DefaultTableCellRenderer implements Serializable
- serialVersionUID:
- -8106963669703497351L
-
Serialized Fields
-
missingColor
java.awt.Color missingColor
-
missingColorSelected
java.awt.Color missingColorSelected
-
-
Class weka.gui.sql.SqlViewer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -4395028775566514329L
-
Serialized Fields
-
m_ConnectionPanel
ConnectionPanel m_ConnectionPanel
the connection panel. -
m_History
java.util.Properties m_History
stores the history. -
m_InfoPanel
InfoPanel m_InfoPanel
the info panel. -
m_Parent
javax.swing.JFrame m_Parent
the parent of this panel. -
m_Password
java.lang.String m_Password
the password that was used to connect to the DB. -
m_Query
java.lang.String m_Query
the currently selected query. -
m_QueryPanel
QueryPanel m_QueryPanel
the query panel. -
m_ResultPanel
ResultPanel m_ResultPanel
the result panel. -
m_URL
java.lang.String m_URL
the connect string with which the query was run. -
m_User
java.lang.String m_User
the user that was used to connect to the DB.
-
-
Class weka.gui.sql.SqlViewerDialog extends javax.swing.JDialog implements Serializable
- serialVersionUID:
- -31619864037233099L
-
Serialized Fields
-
m_ButtonCancel
javax.swing.JButton m_ButtonCancel
the Cancel button -
m_ButtonOK
javax.swing.JButton m_ButtonOK
the OK button -
m_LabelQuery
javax.swing.JLabel m_LabelQuery
displays the current query -
m_PanelButtons
javax.swing.JPanel m_PanelButtons
the panel for the buttons -
m_Parent
javax.swing.JFrame m_Parent
the parent frame -
m_Password
java.lang.String m_Password
the password that was used to connect to the DB -
m_Query
java.lang.String m_Query
the currently selected query -
m_ReturnValue
int m_ReturnValue
the return value -
m_URL
java.lang.String m_URL
the connect string with which the query was run -
m_User
java.lang.String m_User
the user that was used to connect to the DB -
m_Viewer
SqlViewer m_Viewer
the SQL panel
-
-
-
Package weka.gui.sql.event
-
Class weka.gui.sql.event.ConnectionEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 5420308930427835037L
-
Serialized Fields
-
m_DbUtils
DbUtils m_DbUtils
the databaseutils instance reponsible for the connection -
m_Exception
java.lang.Exception m_Exception
a possible exception that occurred if not successful -
m_Type
int m_Type
the type of event, CONNECT or DISCONNECT
-
-
Class weka.gui.sql.event.HistoryChangedEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 7476087315774869973L
-
Serialized Fields
-
m_History
javax.swing.DefaultListModel m_History
the history model -
m_HistoryName
java.lang.String m_HistoryName
the name of the history
-
-
Class weka.gui.sql.event.QueryExecuteEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- -5556385019954730740L
-
Serialized Fields
-
m_DbUtils
DbUtils m_DbUtils
the Db utils instance for the current DB connection -
m_Exception
java.lang.Exception m_Exception
a possible exception, if the query failed -
m_MaxRows
int m_MaxRows
the maximum number of rows to retrieve -
m_Query
java.lang.String m_Query
the query that was executed -
m_ResultSet
java.sql.ResultSet m_ResultSet
the produced ResultSet, if any
-
-
Class weka.gui.sql.event.ResultChangedEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 36042516077236111L
-
Serialized Fields
-
m_Password
java.lang.String m_Password
the password that was used to connect to the DB -
m_Query
java.lang.String m_Query
the query that is associated with the active result table -
m_URL
java.lang.String m_URL
the connect string with which the query was run -
m_User
java.lang.String m_User
the user that was used to connect to the DB
-
-
-
Package weka.gui.streams
-
Class weka.gui.streams.InstanceCounter extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -6084967152645935934L
-
Serialized Fields
-
m_Count
int m_Count
-
m_Count_Lab
javax.swing.JLabel m_Count_Lab
-
m_Debug
boolean m_Debug
-
-
Class weka.gui.streams.InstanceEvent extends java.util.EventObject implements Serializable
- serialVersionUID:
- 3207259868110667379L
-
Serialized Fields
-
m_ID
int m_ID
-
-
Class weka.gui.streams.InstanceJoiner extends java.lang.Object implements Serializable
- serialVersionUID:
- -6529972700291329656L
-
Serialized Fields
-
b_Debug
boolean b_Debug
Debugging mode -
b_FirstInputFinished
boolean b_FirstInputFinished
Whether the first input batch has finished -
b_SecondInputFinished
boolean b_SecondInputFinished
-
listeners
java.util.Vector listeners
The listeners -
m_InputFormat
Instances m_InputFormat
The input format for instances -
m_OutputInstance
Instance m_OutputInstance
The current output instance
-
-
Class weka.gui.streams.InstanceLoader extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -8725567310271862492L
-
Serialized Fields
-
Class weka.gui.streams.InstanceSavePanel extends java.awt.Panel implements Serializable
- serialVersionUID:
- -6061005366989295026L
-
Serialized Fields
-
arffFile_Tex
java.awt.TextField arffFile_Tex
-
b_Debug
boolean b_Debug
-
count_Lab
java.awt.Label count_Lab
-
m_Count
int m_Count
-
outputWriter
java.io.PrintWriter outputWriter
-
-
Class weka.gui.streams.InstanceTable extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -2462533698100834803L
-
Serialized Fields
-
m_Clear
boolean m_Clear
-
m_Debug
boolean m_Debug
-
m_Instances
Instances m_Instances
-
m_InstanceTable
javax.swing.JTable m_InstanceTable
-
m_UpdateString
java.lang.String m_UpdateString
-
-
Class weka.gui.streams.InstanceViewer extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -4925729441294121772L
-
Serialized Fields
-
m_Clear
boolean m_Clear
-
m_Debug
boolean m_Debug
-
m_OutputTex
javax.swing.JTextArea m_OutputTex
-
m_UpdateString
java.lang.String m_UpdateString
-
-
-
Package weka.gui.treevisualizer
-
Class weka.gui.treevisualizer.TreeVisualizer extends PrintablePanel implements Serializable
- serialVersionUID:
- -8668637962504080749L
-
Serialized Fields
-
m_accept
javax.swing.JMenuItem m_accept
An option on the win menu. -
m_addChildren
javax.swing.JMenuItem m_addChildren
An add children to Node choice, This is only available if the tree display has a treedisplay listerner added to it. -
m_autoScale
javax.swing.JMenuItem m_autoScale
An option on the win_menu -
m_BackgroundColor
java.awt.Color m_BackgroundColor
the background color. -
m_caseSen
javax.swing.JRadioButton m_caseSen
-
m_classifyChild
javax.swing.JMenuItem m_classifyChild
Use this to have J48 classify this node. -
m_clickAvailable
boolean m_clickAvailable
A variable used to determine for the clicked method if any other mouse state has already taken place. -
m_currentFont
java.awt.Font m_currentFont
The font used to display the tree. -
m_edges
weka.gui.treevisualizer.TreeVisualizer.EdgeInfo[] m_edges
An array with the Edges sorted into it and display information about the Edges. -
m_fitToScreen
javax.swing.JMenuItem m_fitToScreen
An option on the win_menu -
m_focusNode
int m_focusNode
The subscript for the currently selected node (this is an internal thing, so the user is unaware of this). -
m_FontColor
java.awt.Color m_FontColor
the font color. -
m_fontSize
java.awt.FontMetrics m_fontSize
The size information for the current font. -
m_frameLimiter
javax.swing.Timer m_frameLimiter
A timer to keep the frame rate constant. -
m_highlightNode
int m_highlightNode
The Node the user is currently focused on , this is similar to focus node except that it is used by other classes rather than this one. -
m_LineColor
java.awt.Color m_LineColor
the line color. -
m_listener
TreeDisplayListener m_listener
-
m_mouseState
int m_mouseState
Describes the action the user is performing. -
m_newMousePos
java.awt.Dimension m_newMousePos
A variable used to tag the most current point of a user action. -
m_NodeColor
java.awt.Color m_NodeColor
the node color. -
m_nodeMenu
javax.swing.JPopupMenu m_nodeMenu
A right or middle click popup menu for nodes. -
m_nodes
weka.gui.treevisualizer.TreeVisualizer.NodeInfo[] m_nodes
An array with the Nodes sorted into it and display information about the Nodes. -
m_numLevels
int m_numLevels
The number of levels in the tree. -
m_numNodes
int m_numNodes
The number of Nodes in the tree. -
m_nViewPos
java.awt.Dimension m_nViewPos
A variable used to remember the desired view pos. -
m_nViewSize
java.awt.Dimension m_nViewSize
A variable used to remember the desired tree size. -
m_oldMousePos
java.awt.Dimension m_oldMousePos
A variable used to tag the start pos of a user action. -
m_placer
NodePlace m_placer
The placement algorithm for the Node structure. -
m_remChildren
javax.swing.JMenuItem m_remChildren
Similar to add children but now it removes children. -
m_scaling
int m_scaling
The number of frames left to calculate. -
m_searchString
javax.swing.JTextField m_searchString
-
m_searchWin
javax.swing.JDialog m_searchWin
-
m_selectFont
javax.swing.JMenu m_selectFont
A sub group on the win_menu -
m_selectFontGroup
javax.swing.ButtonGroup m_selectFontGroup
A grouping for the font choices -
m_sendInstances
javax.swing.JMenuItem m_sendInstances
Use this to dump the instances from this node to the vis panel. -
m_ShowBorder
boolean m_ShowBorder
whether to show the border or not. -
m_size1
javax.swing.JRadioButtonMenuItem m_size1
A font choice. -
m_size10
javax.swing.JRadioButtonMenuItem m_size10
A font choice. -
m_size12
javax.swing.JRadioButtonMenuItem m_size12
A font choice. -
m_size14
javax.swing.JRadioButtonMenuItem m_size14
A font choice. -
m_size16
javax.swing.JRadioButtonMenuItem m_size16
A font choice. -
m_size18
javax.swing.JRadioButtonMenuItem m_size18
A font choice. -
m_size2
javax.swing.JRadioButtonMenuItem m_size2
A font choice. -
m_size20
javax.swing.JRadioButtonMenuItem m_size20
A font choice. -
m_size22
javax.swing.JRadioButtonMenuItem m_size22
A font choice. -
m_size24
javax.swing.JRadioButtonMenuItem m_size24
A font choice. -
m_size4
javax.swing.JRadioButtonMenuItem m_size4
A font choice. -
m_size6
javax.swing.JRadioButtonMenuItem m_size6
A font choice. -
m_size8
javax.swing.JRadioButtonMenuItem m_size8
A font choice. -
m_topN
javax.swing.JMenuItem m_topN
An option on the win_menu -
m_topNode
Node m_topNode
The top Node. -
m_viewPos
java.awt.Dimension m_viewPos
The postion of the view relative to the tree. -
m_viewSize
java.awt.Dimension m_viewSize
The size of the tree in pixels. -
m_visualise
javax.swing.JMenuItem m_visualise
A visualize choice for the node, may not be available. -
m_winMenu
javax.swing.JPopupMenu m_winMenu
A right (or middle) click popup menu. -
m_ZoomBoxColor
java.awt.Color m_ZoomBoxColor
the color of the zoombox. -
m_ZoomBoxXORColor
java.awt.Color m_ZoomBoxXORColor
the XOR color of the zoombox.
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Package weka.gui.visualize
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Class weka.gui.visualize.AttributePanel extends javax.swing.JScrollPane implements Serializable
- serialVersionUID:
- 3533330317806757814L
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Serialized Fields
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m_backgroundColor
java.awt.Color m_backgroundColor
If set, it allows this panel to avoid setting a color in the color list that is equal to the background color -
m_barColour
java.awt.Color m_barColour
The default colour to use for the background of the bars if a colour is not defined in Visualize.props -
m_cIndex
int m_cIndex
-
m_colorList
FastVector m_colorList
The colour map to use for colouring points -
m_DefaultColors
java.awt.Color[] m_DefaultColors
default colours for colouring discrete class -
m_heights
int[] m_heights
Holds the random height for each instance. -
m_Listeners
FastVector m_Listeners
The list of things listening to this panel -
m_maxC
double m_maxC
Holds the min and max values of the colouring attributes -
m_minC
double m_minC
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m_plotInstances
Instances m_plotInstances
The instances to be plotted -
m_span
javax.swing.JPanel m_span
The container window for the attribute bars, and also where the X,Y or B get printed. -
m_xIndex
int m_xIndex
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m_yIndex
int m_yIndex
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Class weka.gui.visualize.AttributePanel.AttributeSpacing extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 7220615894321679898L
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Serialized Fields
-
m_attrib
Attribute m_attrib
The attribute itself. -
m_attribIndex
int m_attribIndex
The index for this attribute. -
m_cached
int[] m_cached
The x position of each point. -
m_maxVal
double m_maxVal
The min and max values for this attribute. -
m_minVal
double m_minVal
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m_oldWidth
int m_oldWidth
Used to determine if the positions need to be recalculated. -
m_pointDrawn
boolean[][] m_pointDrawn
A temporary array used to strike any instances that would be drawn redundantly.
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Class weka.gui.visualize.ClassPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -7969401840501661430L
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Serialized Fields
-
m_backgroundColor
java.awt.Color m_backgroundColor
if set, it allows this panel to steer away from setting up a color in the color list that is equal to the background color -
m_cIndex
int m_cIndex
Index of the colouring attribute -
m_colorList
FastVector m_colorList
the list of colours to use for colouring nominal attribute labels -
m_ColourChangeListeners
FastVector m_ColourChangeListeners
An optional list of listeners who want to know when a colour changes. Listeners are notified via an ActionEvent -
m_DefaultColors
java.awt.Color[] m_DefaultColors
default colours for colouring discrete class -
m_fieldWidthC
int m_fieldWidthC
Field width for numeric values -
m_HorizontalPad
int m_HorizontalPad
The amount of space to leave either side of the legend -
m_Instances
Instances m_Instances
Instances being plotted -
m_isEnabled
boolean m_isEnabled
True when the panel has been enabled (ie after setNumeric or setNominal has been called -
m_isNumeric
boolean m_isNumeric
True if the colouring attribute is numeric -
m_labelFont
java.awt.Font m_labelFont
The font used in labeling -
m_labelMetrics
java.awt.FontMetrics m_labelMetrics
Font metrics -
m_maxC
double m_maxC
The maximum value for the colouring attribute -
m_minC
double m_minC
The minimum value for the colouring attribute -
m_oldWidth
int m_oldWidth
The old width. -
m_precisionC
int m_precisionC
The precision with which to display real values -
m_Repainters
FastVector m_Repainters
An optional list of Components that use the colour list maintained by this class. If the user changes a colour using the colour chooser, then these components need to be repainted in order to display the change -
m_spectrumHeight
int m_spectrumHeight
The height of the spectrum for numeric class -
m_tickSize
int m_tickSize
The size of the ticks
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Class weka.gui.visualize.LegendPanel extends javax.swing.JScrollPane implements Serializable
- serialVersionUID:
- -1262384440543001505L
-
Serialized Fields
-
m_plots
FastVector m_plots
the list of plot elements -
m_Repainters
FastVector m_Repainters
a list of components that need to be repainted when a colour is changed -
m_span
javax.swing.JPanel m_span
the panel that contains the legend entries
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Class weka.gui.visualize.LegendPanel.LegendEntry extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 3879990289042935670L
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Serialized Fields
-
m_dataIndex
int m_dataIndex
the index (in the list of plots) of the data for this legend--- used to draw the correct shape for this data -
m_legendText
javax.swing.JLabel m_legendText
the text part of this legend -
m_plotData
PlotData2D m_plotData
the data for this legend entry -
m_pointShape
javax.swing.JPanel m_pointShape
displays the point shape associated with this legend entry
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Class weka.gui.visualize.MatrixPanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -1232642719869188740L
-
Serialized Fields
-
datapointSize
int datapointSize
This stores the size of the datapoint -
f
java.awt.Font f
font used in column and row names -
fontColor
java.awt.Color fontColor
color for the font used in column and row names -
jitterVals
int[][] jitterVals
Array containing precalculated jitter values -
jp
javax.swing.JSplitPane jp
Split pane for splitting the matrix and the buttons and bars -
m_attribList
javax.swing.JList m_attribList
The list for selecting the attributes to display the plot matrix -
m_classAttrib
javax.swing.JComboBox m_classAttrib
The combo box to allow user to select the colouring attribute -
m_classIndex
int m_classIndex
This contains the index of the currently selected colouring attribute -
m_colorList
FastVector m_colorList
Contains discrete colours for colouring for nominal attributes -
m_cp
ClassPanel m_cp
The panel that displays the legend of the colouring attribute -
m_data
Instances m_data
The dataset for which this panel will display the plot matrix for -
m_jitter
javax.swing.JSlider m_jitter
The slider to add jitter to the plots -
m_js
javax.swing.JScrollPane m_js
The scroll pane to scrolling the matrix -
m_missing
boolean[][] m_missing
Contains true for each attribute value (only the selected attributes+class attribute) that is missing, for each instance. m_missing[i][j] == true if m_selectedAttribs[j] is missing in instance i. m_missing[i][m_missing[].length-1] == true if class value is missing in instance i. -
m_plotLBSizeD
java.awt.Dimension m_plotLBSizeD
Stores the maximum size for PlotSize label to keep it's size constant -
m_plotSize
javax.swing.JSlider m_plotSize
The slider to adjust the size of the cells in the matrix -
m_plotSizeLb
javax.swing.JLabel m_plotSizeLb
Displays the current size beside the slider bar for cell size -
m_plotsPanel
weka.gui.visualize.MatrixPanel.Plot m_plotsPanel
The that panel contains the actual matrix -
m_pointColors
int[] m_pointColors
This is an array cache for the colour of each of the instances depending on the colouring attribute. If the colouring attribute is nominal then it contains the index of the colour in our colour list. Otherwise, for numeric colouring attribute, it contains the precalculated red component for each instance's colour -
m_pointLBSizeD
java.awt.Dimension m_pointLBSizeD
Stores the maximum size for PointSize label to keep it's size constant -
m_points
int[][] m_points
This is a local array cache for all the instance values for faster rendering -
m_pointSize
javax.swing.JSlider m_pointSize
The slider to adjust the size of the datapoints -
m_pointSizeLb
javax.swing.JLabel m_pointSizeLb
Displays the current size beside the slider bar for point size -
m_resampleBt
javax.swing.JButton m_resampleBt
The label for resample percentage -
m_resamplePercent
javax.swing.JTextField m_resamplePercent
The text area for percentage to resample data -
m_rseed
javax.swing.JTextField m_rseed
Random seed for random subsample -
m_selAttrib
javax.swing.JButton m_selAttrib
The button to display a window to select attributes -
m_selectedAttribs
int[] m_selectedAttribs
This array contains the indices of the attributes currently selected -
m_type
int[] m_type
This array contains for the classAttribute:
m_type[0] = [type of attribute, nominal, string or numeric]
m_type[1] = [number of discrete values of nominal or string attribute
or same as m_type[0] for numeric attribute] -
m_updateBt
javax.swing.JButton m_updateBt
The button that updates the display to reflect the changes made by the user. E.g. changed attribute set for the matrix -
optionsPanel
javax.swing.JPanel optionsPanel
The panel that contains all the buttons and tools, i.e. resize, jitter bars and sub-sampling buttons etc on the bottom of the panel -
rnd
java.util.Random rnd
For adding random jitter
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Class weka.gui.visualize.Plot2D extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- -1673162410856660442L
-
Serialized Fields
-
m_axisChanged
boolean m_axisChanged
if the user changes attribute assigned to an axis -
m_axisColour
java.awt.Color m_axisColour
Default colour for the axis -
m_axisPad
int m_axisPad
Axis padding -
m_backgroundColour
java.awt.Color m_backgroundColour
Default colour for the plot background -
m_cIndex
int m_cIndex
-
m_colorList
FastVector m_colorList
The list of the colors used -
m_DefaultColors
java.awt.Color[] m_DefaultColors
default colours for colouring discrete class -
m_drawnPoints
int[][] m_drawnPoints
An array used to show if a point is hidden or not. This is used for speeding up the drawing of the plot panel although I am not sure how much performance this grants over not having it. -
m_InstanceInfo
javax.swing.JFrame m_InstanceInfo
For popping up text info on data points -
m_InstanceInfoText
javax.swing.JTextArea m_InstanceInfoText
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m_JitterVal
int m_JitterVal
the level of jitter -
m_JRand
java.util.Random m_JRand
random values for perterbing the data points -
m_labelFont
java.awt.Font m_labelFont
Font for labels -
m_labelMetrics
java.awt.FontMetrics m_labelMetrics
-
m_masterName
java.lang.String m_masterName
The name of the master plot -
m_masterPlot
PlotData2D m_masterPlot
The master plot -
m_maxC
double m_maxC
-
m_maxX
double m_maxX
Holds the min and max values of the x, y and colouring attributes over all plots -
m_maxY
double m_maxY
-
m_minC
double m_minC
-
m_minX
double m_minX
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m_minY
double m_minY
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m_plotCompanion
Plot2DCompanion m_plotCompanion
An optional "compainion" of the panel. If specified, this class will get to do its thing with our graphics context before we do any drawing. Eg. the visualize panel may need to draw polygons etc. before we draw plot axis and data points -
m_plotInstances
Instances m_plotInstances
The instances to be plotted -
m_plotResize
boolean m_plotResize
if the user resizes the window, or the attributes selected for the attributes change, then the lookup table for points needs to be recalculated -
m_plots
FastVector m_plots
The plots to display -
m_pointLookup
double[][] m_pointLookup
lookup table for plotted points -
m_sIndex
int m_sIndex
-
m_tickSize
int m_tickSize
Tick size -
m_XaxisEnd
int m_XaxisEnd
-
m_XaxisStart
int m_XaxisStart
the offsets of the axes once label metrics are calculated -
m_xIndex
int m_xIndex
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing -
m_YaxisEnd
int m_YaxisEnd
-
m_YaxisStart
int m_YaxisStart
-
m_yIndex
int m_yIndex
-
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Class weka.gui.visualize.PrintableComponent.JComponentWriterFileFilter extends ExtensionFileFilter implements Serializable
-
Serialized Fields
-
m_Writer
JComponentWriter m_Writer
the associated writer.
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-
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Class weka.gui.visualize.PrintablePanel extends javax.swing.JPanel implements Serializable
- serialVersionUID:
- 6281532227633417538L
-
Serialized Fields
-
m_Printer
PrintableComponent m_Printer
the class responsible for printing
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Class weka.gui.visualize.ThresholdVisualizePanel extends VisualizePanel implements Serializable
- serialVersionUID:
- 3070002211779443890L
-
Serialized Fields
-
m_ROCString
java.lang.String m_ROCString
The string to add to the Plot Border. -
m_savePanelBorderText
java.lang.String m_savePanelBorderText
Original border text
-
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Class weka.gui.visualize.VisualizePanel extends PrintablePanel implements Serializable
- serialVersionUID:
- 240108358588153943L
-
Serialized Fields
-
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
Stop the combos from growing out of control -
listener
java.awt.event.ActionListener listener
An optional listener that we will inform when ComboBox selections change -
m_ArffFilter
javax.swing.filechooser.FileFilter m_ArffFilter
Filter to ensure only arff files are selected -
m_attrib
AttributePanel m_attrib
The panel that displays the attributes , using color to represent another attribute. -
m_cancel
javax.swing.JButton m_cancel
Button for the user to remove all splits. -
m_classPanel
ClassPanel m_classPanel
The panel that displays the legend for the colouring attribute -
m_classSurround
javax.swing.JPanel m_classSurround
Panel that surrounds the class panel with a titled border -
m_colorList
FastVector m_colorList
The list of the colors used -
m_ColourCombo
javax.swing.JComboBox m_ColourCombo
Lets the user select the attribute to use for colouring -
m_DefaultColors
java.awt.Color[] m_DefaultColors
default colours for colouring discrete class -
m_FileChooser
javax.swing.JFileChooser m_FileChooser
file chooser for saving instances -
m_Jitter
javax.swing.JSlider m_Jitter
The jitter slider -
m_JitterLab
javax.swing.JLabel m_JitterLab
Label for the jitter slider -
m_legendPanel
LegendPanel m_legendPanel
The panel that displays legend info if there is more than one plot -
m_Log
Logger m_Log
the logger -
m_openBut
javax.swing.JButton m_openBut
Button for the user to open the visualized set of instances -
m_plot
weka.gui.visualize.VisualizePanel.PlotPanel m_plot
The panel that displays the plot -
m_plotName
java.lang.String m_plotName
The name of the plot (not currently displayed, but can be used in the containing Frame or Panel) -
m_plotSurround
javax.swing.JPanel m_plotSurround
Panel that surrounds the plot panel with a titled border -
m_preferredColourDimension
java.lang.String m_preferredColourDimension
-
m_preferredXDimension
java.lang.String m_preferredXDimension
These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file -
m_preferredYDimension
java.lang.String m_preferredYDimension
-
m_saveBut
javax.swing.JButton m_saveBut
Button for the user to save the visualized set of instances -
m_ShapeCombo
javax.swing.JComboBox m_ShapeCombo
Lets the user select the shape they want to create for instance selection. -
m_showAttBars
boolean m_showAttBars
Show the attribute bar panel -
m_showClassPanel
boolean m_showClassPanel
Show the class panel -
m_splitListener
VisualizePanelListener m_splitListener
An optional listener that we will inform when the user creates a split to seperate instances. -
m_submit
javax.swing.JButton m_submit
Button for the user to enter the splits. -
m_XCombo
javax.swing.JComboBox m_XCombo
Lets the user select the attribute for the x axis -
m_YCombo
javax.swing.JComboBox m_YCombo
Lets the user select the attribute for the y axis
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Class weka.gui.visualize.VisualizePanel.PlotPanel extends PrintablePanel implements Serializable
- serialVersionUID:
- -4823674171136494204L
-
Serialized Fields
-
m_cIndex
int m_cIndex
-
m_createShape
boolean m_createShape
True if the user is currently dragging a box. -
m_newMousePos
java.awt.Dimension m_newMousePos
contains the position of the mouse (used for rubberbanding). -
m_originalPlot
PlotData2D m_originalPlot
The master plot -
m_plot2D
Plot2D m_plot2D
The actual generic plotting panel -
m_plotInstances
Instances m_plotInstances
The instances from the master plot -
m_shapePoints
FastVector m_shapePoints
contains the points of the shape currently being drawn. -
m_shapes
FastVector m_shapes
contains all the shapes that have been drawn for these attribs -
m_sIndex
int m_sIndex
-
m_XaxisEnd
int m_XaxisEnd
-
m_XaxisStart
int m_XaxisStart
the offsets of the axes once label metrics are calculated -
m_xIndex
int m_xIndex
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing -
m_YaxisEnd
int m_YaxisEnd
-
m_YaxisStart
int m_YaxisStart
-
m_yIndex
int m_yIndex
-
-