Serialized Form

Package weka.classifiers.rules
- Class weka.classifiers.rules.DecisionTable extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  2888557078165701326L
  - Serialized Fields
    - m_entries
```
java.util.Hashtable<K,V> m_entries
```
      The hashtable used to hold training instances
    - m_classPriorCounts
```
double[] m_classPriorCounts
```
      The class priors to use when there is no match in the table
    - m_classPriors
```
double[] m_classPriors
```
    - m_decisionFeatures
```
int[] m_decisionFeatures
```
      Holds the final feature set
    - m_disTransform
```
weka.filters.Filter m_disTransform
```
      Discretization filter
    - m_delTransform
```
weka.filters.unsupervised.attribute.Remove m_delTransform
```
      Filter used to remove columns discarded by feature selection
    - m_ibk
```
weka.classifiers.lazy.IBk m_ibk
```
      IB1 used to classify non matching instances rather than majority class
    - m_theInstances
```
weka.core.Instances m_theInstances
```
      Holds the original training instances
    - m_dtInstances
```
weka.core.Instances m_dtInstances
```
      Holds the final feature selected set of instances
    - m_numAttributes
```
int m_numAttributes
```
      The number of attributes in the dataset
    - m_numInstances
```
int m_numInstances
```
      The number of instances in the dataset
    - m_classIsNominal
```
boolean m_classIsNominal
```
      Class is nominal
    - m_useIBk
```
boolean m_useIBk
```
      Use the IBk classifier rather than majority class
    - m_displayRules
```
boolean m_displayRules
```
      Display Rules
    - m_CVFolds
```
int m_CVFolds
```
      Number of folds for cross validating feature sets
    - m_rr
```
java.util.Random m_rr
```
      Random numbers for use in cross validation
    - m_majority
```
double m_majority
```
      Holds the majority class
    - m_search
```
weka.attributeSelection.ASSearch m_search
```
      The search method to use
    - m_evaluator
```
weka.attributeSelection.ASEvaluation m_evaluator
```
      Our own internal evaluator
    - m_evaluation
```
weka.classifiers.Evaluation m_evaluation
```
      The evaluation object used to evaluate subsets
    - m_evaluationMeasure
```
int m_evaluationMeasure
```
    - m_saveMemory
```
boolean m_saveMemory
```
- Class weka.classifiers.rules.DecisionTableHashKey extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  5674163500154964602L
  - Serialized Fields
    - attributes
```
double[] attributes
```
      Array of attribute values for an instance
    - missing
```
boolean[] missing
```
      True for an index if the corresponding attribute value is missing.
    - key
```
int key
```
      The key
- Class weka.classifiers.rules.JRip extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -6589312996832147161L
  - Serialized Fields
    - m_Class
```
weka.core.Attribute m_Class
```
      The class attribute of the data
    - m_Ruleset
```
weka.core.FastVector<E> m_Ruleset
```
      The ruleset
    - m_Distributions
```
weka.core.FastVector<E> m_Distributions
```
      The predicted class distribution
    - m_Optimizations
```
int m_Optimizations
```
      Runs of optimizations
    - m_Random
```
java.util.Random m_Random
```
      Random object used in this class
    - m_Total
```
double m_Total
```
      # of all the possible conditions in a rule
    - m_Seed
```
long m_Seed
```
      The seed to perform randomization
    - m_Folds
```
int m_Folds
```
      The number of folds to split data into Grow and Prune for IREP
    - m_MinNo
```
double m_MinNo
```
      The minimal number of instance weights within a split
    - m_Debug
```
boolean m_Debug
```
      Whether in a debug mode
    - m_CheckErr
```
boolean m_CheckErr
```
      Whether check the error rate >= 0.5 in stopping criteria
    - m_UsePruning
```
boolean m_UsePruning
```
      Whether use pruning, i.e. the data is clean or not
    - m_Filter
```
weka.filters.Filter m_Filter
```
      The filter used to randomize the class order
    - m_RulesetStats
```
weka.core.FastVector<E> m_RulesetStats
```
      The RuleStats for the ruleset of each class value
- Class weka.classifiers.rules.JRip.Antd extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -8929754772994154334L
  - Serialized Fields
    - att
```
weka.core.Attribute att
```
      The attribute of the antecedent
    - value
```
double value
```
      The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)
    - maxInfoGain
```
double maxInfoGain
```
      The maximum infoGain achieved by this antecedent test in the growing data
    - accuRate
```
double accuRate
```
      The accurate rate of this antecedent test on the growing data
    - cover
```
double cover
```
      The coverage of this antecedent in the growing data
    - accu
```
double accu
```
      The accurate data for this antecedent in the growing data
- Class weka.classifiers.rules.JRip.NominalAntd extends weka.classifiers.rules.JRip.Antd implements Serializable
  
  serialVersionUID:
  
  -9102297038837585135L
  - Serialized Fields
    - accurate
```
double[] accurate
```
    - coverage
```
double[] coverage
```
- Class weka.classifiers.rules.JRip.NumericAntd extends weka.classifiers.rules.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 weka.classifiers.rules.Rule implements Serializable
  
  serialVersionUID:
  
  -2410020717305262952L
  - Serialized Fields
    - m_Consequent
```
double m_Consequent
```
      The internal representation of the class label to be predicted
    - m_Antds
```
weka.core.FastVector<E> m_Antds
```
      The vector of antecedents of this rule
- Class weka.classifiers.rules.LAD extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -7358699627342342455L
  - Serialized Fields
    - mCutpointTolerance
```
double mCutpointTolerance
```
    - mMinimumPurity
```
double mMinimumPurity
```
    - mPrintFile
```
boolean mPrintFile
```
    - mFeatureSelection
```
FeatureSelection mFeatureSelection
```
    - mRuleGenerator
```
RuleGenerator mRuleGenerator
```
    - mCutpoints
```
Cutpoints mCutpoints
```
    - mRuleManager
```
RuleManager mRuleManager
```
    - mFileManager
```
LADFileManager mFileManager
```
    - ERROR
```
java.lang.String ERROR
```
- Class weka.classifiers.rules.M5Rules extends weka.classifiers.trees.m5.M5Base implements Serializable
  
  serialVersionUID:
  
  -1746114858746563180L
- Class weka.classifiers.rules.OneR extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -3459427003147861443L
  - Serialized Fields
    - m_rule
```
weka.classifiers.rules.OneR.OneRRule m_rule
```
      A 1-R rule
    - m_minBucketSize
```
int m_minBucketSize
```
      The minimum bucket size
    - m_ZeroR
```
weka.classifiers.Classifier m_ZeroR
```
      a ZeroR model in case no model can be built from the data
- Class weka.classifiers.rules.PART extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  8121455039782598361L
  - Serialized Fields
    - m_root
```
weka.classifiers.rules.part.MakeDecList m_root
```
      The decision list
    - m_CF
```
float m_CF
```
      Confidence level
    - m_minNumObj
```
int m_minNumObj
```
      Minimum number of objects
    - m_useMDLcorrection
```
boolean m_useMDLcorrection
```
      Use MDL correction?
    - m_reducedErrorPruning
```
boolean m_reducedErrorPruning
```
      Use reduced error pruning?
    - m_numFolds
```
int m_numFolds
```
      Number of folds for reduced error pruning.
    - m_binarySplits
```
boolean m_binarySplits
```
      Binary splits on nominal attributes?
    - m_unpruned
```
boolean m_unpruned
```
      Generate unpruned list?
    - m_Seed
```
int m_Seed
```
      The seed for random number generation.
- Class weka.classifiers.rules.Rule extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  8815687740470471229L
- Class weka.classifiers.rules.RuleStats extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -5708153367675298624L
  - Serialized Fields
    - m_Data
```
weka.core.Instances m_Data
```
      The data on which the stats calculation is based
    - m_Ruleset
```
weka.core.FastVector<E> m_Ruleset
```
      The specific ruleset in question
    - m_SimpleStats
```
weka.core.FastVector<E> m_SimpleStats
```
      The simple stats of each rule
    - m_Filtered
```
weka.core.FastVector<E> m_Filtered
```
      The set of instances filtered by the ruleset
    - m_Total
```
double m_Total
```
      The total number of possible conditions that could appear in a rule
    - MDL_THEORY_WEIGHT
```
double MDL_THEORY_WEIGHT
```
      The theory weight in the MDL calculation
    - m_Distributions
```
weka.core.FastVector<E> m_Distributions
```
      The class distributions predicted by each rule
- Class weka.classifiers.rules.ZeroR extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  48055541465867954L
  - Serialized Fields
    - m_ClassValue
```
double m_ClassValue
```
      The class value 0R predicts.
    - m_Counts
```
double[] m_Counts
```
      The number of instances in each class (null if class numeric).
    - m_Class
```
weka.core.Attribute m_Class
```
      The class attribute.
Package weka.classifiers.rules.lad.binarization
- Class weka.classifiers.rules.lad.binarization.Cutpoints extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  3660537629035514316L
  - Serialized Fields
    - mCutpoints
```
java.util.Vector<E> mCutpoints
```
Package weka.classifiers.rules.lad.core
- Class weka.classifiers.rules.lad.core.BinaryData extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  2488234257854645289L
  - Serialized Fields
    - mbIArrayPositive
```
java.util.ArrayList<E> mbIArrayPositive
```
    - mbIArrayNegative
```
java.util.ArrayList<E> mbIArrayNegative
```
    - sCutpoints
```
Cutpoints sCutpoints
```
- Class weka.classifiers.rules.lad.core.BinaryInstance extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  977143245642898382L
  - Serialized Fields
    - mInstance
```
weka.core.Instance mInstance
```
    - sCutpoints
```
Cutpoints sCutpoints
```
- Class weka.classifiers.rules.lad.core.BinaryRule extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -5108679627773238991L
  - Serialized Fields
    - mClass
```
boolean mClass
```
    - mPurity
```
double mPurity
```
    - mLiterals
```
java.util.ArrayList<E> mLiterals
```
- Class weka.classifiers.rules.lad.core.Literal extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -3388849798203471234L
  - Serialized Fields
    - mAtt
```
int mAtt
```
    - mSign
```
java.lang.Boolean mSign
```
- Class weka.classifiers.rules.lad.core.NumericalRule extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  7849846023533714647L
  - Serialized Fields
    - mClass
```
boolean mClass
```
    - mPurity
```
double mPurity
```
    - mWeight
```
double mWeight
```
    - mConditions
```
java.util.ArrayList<E> mConditions
```
Package weka.classifiers.rules.lad.cutpointSelection
- Class weka.classifiers.rules.lad.cutpointSelection.FeatureSelection extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  6374730014984529250L
  - Serialized Fields
    - mSeparationLevel
```
int mSeparationLevel
```
    - mSelectedAttArray
```
java.util.ArrayList<E> mSelectedAttArray
```
- Class weka.classifiers.rules.lad.cutpointSelection.GreedySetCover extends FeatureSelection implements Serializable
  
  serialVersionUID:
  
  1L
- Class weka.classifiers.rules.lad.cutpointSelection.IteratedSampling extends FeatureSelection implements Serializable
  
  serialVersionUID:
  
  1557597598188635948L
  - Serialized Fields
    - mNumSamples
```
int mNumSamples
```
    - mSampleSize
```
double mSampleSize
```
    - mSolutionSize
```
int mSolutionSize
```
    - mSeed
```
long mSeed
```
    - fSampleCutpoints
```
boolean fSampleCutpoints
```
    - mRnd
```
java.util.Random mRnd
```
Package weka.classifiers.rules.lad.ruleGenerators
- Class weka.classifiers.rules.lad.ruleGenerators.MaxRuleGenerator extends RuleGenerator implements Serializable
  
  serialVersionUID:
  
  8175941261292479958L
  - Serialized Fields
    - mLiterals
```
java.util.ArrayList<E> mLiterals
```
    - mInstClass
```
boolean mInstClass
```
- Class weka.classifiers.rules.lad.ruleGenerators.RandomRuleGenerator extends RuleGenerator implements Serializable
  
  serialVersionUID:
  
  1448802467208122870L
  - Serialized Fields
    - mNumRules
```
int mNumRules
```
    - mRandomSeed
```
int mRandomSeed
```
    - mNumRandomFeatures
```
int mNumRandomFeatures
```
    - mMinRelativeCoverageOwnClass
```
double mMinRelativeCoverageOwnClass
```
    - mRandomObject
```
java.util.Random mRandomObject
```
    - NUM_POSITIVE_INSTANCES
```
int NUM_POSITIVE_INSTANCES
```
    - NUM_NEGATIVE_INSTANCES
```
int NUM_NEGATIVE_INSTANCES
```
- Class weka.classifiers.rules.lad.ruleGenerators.RuleGenerator extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -5645213683696957902L
  - Serialized Fields
    - mMinimumPurity
```
double mMinimumPurity
```
    - mTrainingData
```
BinaryData mTrainingData
```
    - mBinaryRules
```
java.util.ArrayList<E> mBinaryRules
```
Package weka.classifiers.rules.lad.ruleManager
- Class weka.classifiers.rules.lad.ruleManager.RuleManager extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  1956931334238019791L
  - Serialized Fields
    - mPositiveRules
```
java.util.ArrayList<E> mPositiveRules
```
    - mNegativeRules
```
java.util.ArrayList<E> mNegativeRules
```
    - largerClass
```
java.lang.Integer largerClass
```
    - mPSRepresentation
```
java.lang.String mPSRepresentation
```
    - mActiveRules
```
java.util.ArrayList<E> mActiveRules
```
    - mAttNames
```
java.lang.String[] mAttNames
```
Package weka.classifiers.rules.lad.setCovering
- Class weka.classifiers.rules.lad.setCovering.SetCovering extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -4580626902003828381L
  - Serialized Fields
    - mSets
```
java.util.ArrayList<E> mSets
```
    - mElements
```
java.util.ArrayList<E> mElements
```
    - mSolution
```
java.util.ArrayList<E> mSolution
```
Package weka.classifiers.rules.lad.util
- Class weka.classifiers.rules.lad.util.LADFileManager extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  940441734407639248L
  - Serialized Fields
    - pathname
```
java.lang.String pathname
```
    - writable
```
boolean writable
```
    - writer
```
java.io.BufferedWriter writer
```

Serialized Form

Package weka.classifiers.rules

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

Serialized Fields

m_entries

m_classPriorCounts

m_classPriors

m_decisionFeatures

m_disTransform

m_delTransform

m_ibk

m_theInstances

m_dtInstances

m_numAttributes

m_numInstances

m_classIsNominal

m_useIBk

m_displayRules

m_CVFolds

m_rr

m_majority

m_search

m_evaluator

m_evaluation

m_evaluationMeasure

m_saveMemory

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

Serialized Fields

attributes

missing

key

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

Serialized Fields

m_Class

m_Ruleset

m_Distributions

m_Optimizations

m_Random

m_Total

m_Seed

m_Folds

m_MinNo

m_Debug

m_CheckErr

m_UsePruning

m_Filter

m_RulesetStats

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

Serialized Fields

att

value

maxInfoGain

accuRate

cover

accu

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

Serialized Fields

accurate

coverage

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

Serialized Fields

splitPoint

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

Serialized Fields

m_Consequent

m_Antds

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

Serialized Fields

mCutpointTolerance

mMinimumPurity

mPrintFile

mFeatureSelection

mRuleGenerator

mCutpoints

mRuleManager

mFileManager

ERROR

Class weka.classifiers.rules.M5Rules extends weka.classifiers.trees.m5.M5Base implements Serializable

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

Serialized Fields