Serialized Form

Package weka.classifiers.bayes
- Class weka.classifiers.bayes.AODE extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  9197439980415113523L
  - Serialized Fields
    - m_CondiCounts
```
double[][][] m_CondiCounts
```
      3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts, i.e., the number of times an attribute value occurs in conjunction with another attribute value and a class value.
    - m_ClassCounts
```
double[] m_ClassCounts
```
      The number of times each class value occurs in the dataset
    - m_SumForCounts
```
double[][] m_SumForCounts
```
      The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal]
    - m_NumClasses
```
int m_NumClasses
```
      The number of classes
    - m_NumAttributes
```
int m_NumAttributes
```
      The number of attributes in dataset, including class
    - m_NumInstances
```
int m_NumInstances
```
      The number of instances in the dataset
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute
    - m_Instances
```
weka.core.Instances m_Instances
```
      The dataset
    - m_TotalAttValues
```
int m_TotalAttValues
```
      The total number of values (including an extra for each attribute's missing value, which are included in m_CondiCounts) for all attributes (not including class). E.g., for three atts each with two possible values, m_TotalAttValues would be 9 (6 values + 3 missing). This variable is used when allocating space for m_CondiCounts matrix.
    - m_StartAttIndex
```
int[] m_StartAttIndex
```
      The starting index (in the m_CondiCounts matrix) of the values for each attribute
    - m_NumAttValues
```
int[] m_NumAttValues
```
      The number of values for each attribute
    - m_Frequencies
```
double[] m_Frequencies
```
      The frequency of each attribute value for the dataset
    - m_SumInstances
```
double m_SumInstances
```
      The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances.
    - m_Limit
```
int m_Limit
```
      An att's frequency must be this value or more to be a superParent
    - m_Debug
```
boolean m_Debug
```
      If true, outputs debugging info
    - m_MEstimates
```
boolean m_MEstimates
```
      flag for using m-estimates
    - m_Weight
```
int m_Weight
```
      value for m in m-estimate
- Class weka.classifiers.bayes.AODEsr extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  5602143019183068848L
  - Serialized Fields
    - m_CondiCounts
```
double[][][] m_CondiCounts
```
      3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts, i.e. the number of times an attribute value occurs in conjunction with another attribute value and a class value.
    - m_CondiCountsNoClass
```
double[][] m_CondiCountsNoClass
```
      2D array (m_TotalAttValues * m_TotalAttValues) of attributes counts. similar to m_CondiCounts, but ignoring class value.
    - m_ClassCounts
```
double[] m_ClassCounts
```
      The number of times each class value occurs in the dataset
    - m_SumForCounts
```
double[][] m_SumForCounts
```
      The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal]
    - m_NumClasses
```
int m_NumClasses
```
      The number of classes
    - m_NumAttributes
```
int m_NumAttributes
```
      The number of attributes in dataset, including class
    - m_NumInstances
```
int m_NumInstances
```
      The number of instances in the dataset
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute
    - m_Instances
```
weka.core.Instances m_Instances
```
      The dataset
    - m_TotalAttValues
```
int m_TotalAttValues
```
      The total number of values (including an extra for each attribute's missing value, which are included in m_CondiCounts) for all attributes (not including class). Eg. for three atts each with two possible values, m_TotalAttValues would be 9 (6 values + 3 missing). This variable is used when allocating space for m_CondiCounts matrix.
    - m_StartAttIndex
```
int[] m_StartAttIndex
```
      The starting index (in the m_CondiCounts matrix) of the values for each attribute
    - m_NumAttValues
```
int[] m_NumAttValues
```
      The number of values for each attribute
    - m_Frequencies
```
double[] m_Frequencies
```
      The frequency of each attribute value for the dataset
    - m_SumInstances
```
double m_SumInstances
```
      The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances.
    - m_Limit
```
int m_Limit
```
      An att's frequency must be this value or more to be a superParent
    - m_Debug
```
boolean m_Debug
```
      If true, outputs debugging info
    - m_MWeight
```
double m_MWeight
```
      m value for m-estimation
    - m_Laplace
```
boolean m_Laplace
```
      Using LapLace estimation or not
    - m_Critical
```
int m_Critical
```
      the critical value for the specialization-generalization
- Class weka.classifiers.bayes.BayesNet extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  746037443258775954L
  - Serialized Fields
    - m_ParentSets
```
weka.classifiers.bayes.net.ParentSet[] m_ParentSets
```
    - m_Distributions
```
weka.estimators.Estimator[][] m_Distributions
```
    - m_DiscretizeFilter
```
weka.filters.supervised.attribute.Discretize m_DiscretizeFilter
```
    - m_nNonDiscreteAttribute
```
int m_nNonDiscreteAttribute
```
    - m_MissingValuesFilter
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_MissingValuesFilter
```
    - m_NumClasses
```
int m_NumClasses
```
    - m_Instances
```
weka.core.Instances m_Instances
```
    - m_NumInstances
```
int m_NumInstances
```
    - m_ADTree
```
weka.classifiers.bayes.net.ADNode m_ADTree
```
    - m_otherBayesNet
```
weka.classifiers.bayes.net.BIFReader m_otherBayesNet
```
    - m_bUseADTree
```
boolean m_bUseADTree
```
    - m_SearchAlgorithm
```
weka.classifiers.bayes.net.search.SearchAlgorithm m_SearchAlgorithm
```
    - m_BayesNetEstimator
```
weka.classifiers.bayes.net.estimate.BayesNetEstimator m_BayesNetEstimator
```
- Class weka.classifiers.bayes.NaiveBayes extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  5995231201785697655L
  - Serialized Fields
    - m_Distributions
```
weka.estimators.Estimator[][] m_Distributions
```
    - m_ClassDistribution
```
weka.estimators.Estimator m_ClassDistribution
```
    - m_UseKernelEstimator
```
boolean m_UseKernelEstimator
```
    - m_UseDiscretization
```
boolean m_UseDiscretization
```
    - m_NumClasses
```
int m_NumClasses
```
    - m_Instances
```
weka.core.Instances m_Instances
```
    - m_Disc
```
weka.filters.supervised.attribute.Discretize m_Disc
```
    - m_displayModelInOldFormat
```
boolean m_displayModelInOldFormat
```
- Class weka.classifiers.bayes.NaiveBayesMultinomial extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  5932177440181257085L
  - Serialized Fields
    - m_probOfWordGivenClass
```
double[][] m_probOfWordGivenClass
```
    - m_probOfClass
```
double[] m_probOfClass
```
    - m_numAttributes
```
int m_numAttributes
```
    - m_numClasses
```
int m_numClasses
```
    - m_headerInfo
```
weka.core.Instances m_headerInfo
```
- Class weka.classifiers.bayes.NaiveBayesMultinomialText extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  2139025532014821394L
  - Serialized Fields
    - m_data
```
weka.core.Instances m_data
```
    - m_probOfClass
```
double[] m_probOfClass
```
    - m_wordsPerClass
```
double[] m_wordsPerClass
```
    - m_probOfWordGivenClass
```
java.util.Map<K,V> m_probOfWordGivenClass
```
    - m_StopwordsHandler
```
weka.core.stopwords.StopwordsHandler m_StopwordsHandler
```
    - m_tokenizer
```
weka.core.tokenizers.Tokenizer m_tokenizer
```
    - m_lowercaseTokens
```
boolean m_lowercaseTokens
```
    - m_stemmer
```
weka.core.stemmers.Stemmer m_stemmer
```
    - m_periodicP
```
int m_periodicP
```
    - m_minWordP
```
double m_minWordP
```
    - m_wordFrequencies
```
boolean m_wordFrequencies
```
    - m_normalize
```
boolean m_normalize
```
    - m_norm
```
double m_norm
```
    - m_lnorm
```
double m_lnorm
```
    - m_leplace
```
double m_leplace
```
    - m_t
```
double m_t
```
    - m_numModels
```
int m_numModels
```
- Class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable extends weka.classifiers.bayes.NaiveBayesMultinomial implements Serializable
  
  serialVersionUID:
  
  -7204398796974263186L
  - Serialized Fields
    - m_wordsPerClass
```
double[] m_wordsPerClass
```
- Class weka.classifiers.bayes.NaiveBayesUpdateable extends weka.classifiers.bayes.NaiveBayes implements Serializable
  
  serialVersionUID:
  
  -5354015843807192221L
- Class weka.classifiers.bayes.WAODE extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  2170978824284697882L
  - Serialized Fields
    - m_ClassCounts
```
double[] m_ClassCounts
```
      The number of each class value occurs in the dataset
    - m_AttCounts
```
double[] m_AttCounts
```
      The number of each attribute value occurs in the dataset
    - m_AttAttCounts
```
double[][] m_AttAttCounts
```
      The number of two attributes values occurs in the dataset
    - m_ClassAttAttCounts
```
double[][][] m_ClassAttAttCounts
```
      The number of class and two attributes values occurs in the dataset
    - m_NumAttValues
```
int[] m_NumAttValues
```
      The number of values for each attribute in the dataset
    - m_TotalAttValues
```
int m_TotalAttValues
```
      The number of values for all attributes in the dataset
    - m_NumClasses
```
int m_NumClasses
```
      The number of classes in the dataset
    - m_NumAttributes
```
int m_NumAttributes
```
      The number of attributes including class in the dataset
    - m_NumInstances
```
int m_NumInstances
```
      The number of instances in the dataset
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute in the dataset
    - m_StartAttIndex
```
int[] m_StartAttIndex
```
      The starting index of each attribute in the dataset
    - m_mutualInformation
```
double[] m_mutualInformation
```
      The array of mutual information between each attribute and class
    - m_Header
```
weka.core.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:
      
      toString()
    - m_ZeroR
```
weka.classifiers.Classifier m_ZeroR
```
      a ZeroR model in case no model can be built from the data

Serialized Form

Package weka.classifiers.bayes

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

Serialized Fields

m_CondiCounts

m_ClassCounts

m_SumForCounts

m_NumClasses

m_NumAttributes

m_NumInstances

m_ClassIndex

m_Instances

m_TotalAttValues

m_StartAttIndex

m_NumAttValues

m_Frequencies

m_SumInstances

m_Limit

m_Debug

m_MEstimates

m_Weight

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

Serialized Fields

m_CondiCounts

m_CondiCountsNoClass

m_ClassCounts

m_SumForCounts

m_NumClasses

m_NumAttributes

m_NumInstances

m_ClassIndex

m_Instances

m_TotalAttValues

m_StartAttIndex

m_NumAttValues

m_Frequencies

m_SumInstances

m_Limit

m_Debug

m_MWeight

m_Laplace

m_Critical

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

Serialized Fields

m_ParentSets

m_Distributions

m_DiscretizeFilter

m_nNonDiscreteAttribute

m_MissingValuesFilter

m_NumClasses

m_Instances

m_NumInstances

m_ADTree

m_otherBayesNet

m_bUseADTree

m_SearchAlgorithm

m_BayesNetEstimator

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

Serialized Fields

m_Distributions

m_ClassDistribution

m_UseKernelEstimator

m_UseDiscretization

m_NumClasses

m_Instances

m_Disc

m_displayModelInOldFormat

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

Serialized Fields

m_probOfWordGivenClass

m_probOfClass

m_numAttributes

m_numClasses

m_headerInfo

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

Serialized Fields

m_data

m_probOfClass

m_wordsPerClass

m_probOfWordGivenClass