Serialized Form


Package weka.classifiers.bayes

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

serialVersionUID: 746037443258775954L

Serialized Fields

m_ParentSets

ParentSet[] m_ParentSets

m_Distributions

Estimator[][] m_Distributions

m_DiscretizeFilter

Discretize m_DiscretizeFilter

m_nNonDiscreteAttribute

int m_nNonDiscreteAttribute

m_MissingValuesFilter

ReplaceMissingValues m_MissingValuesFilter

m_NumClasses

int m_NumClasses

m_Instances

Instances m_Instances

m_ADTree

ADNode m_ADTree

m_otherBayesNet

BIFReader m_otherBayesNet

m_bUseADTree

boolean m_bUseADTree

m_SearchAlgorithm

SearchAlgorithm m_SearchAlgorithm

m_BayesNetEstimator

BayesNetEstimator m_BayesNetEstimator

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

serialVersionUID: 7246302925903086397L

Serialized Fields

wordWeights

double[][] wordWeights
Weight of words for each class. The weight is actually the log of the probability of a word (w) given a class (c) (i.e. log(Pr[w|c])). The format of the matrix is: wordWeights[class][wordAttribute]


smoothingParameter

double smoothingParameter
Holds the smoothing value to avoid word probabilities of zero.
P.S.: According to the paper this is the Alpha i parameter


m_normalizeWordWeights

boolean m_normalizeWordWeights
True if the words weights are to be normalized


numClasses

int numClasses
Holds the number of Class values present in the set of specified instances


header

Instances header
The instances header that'll be used in toString

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

serialVersionUID: 5995231201785697655L

Serialized Fields

m_Distributions

Estimator[][] m_Distributions

m_ClassDistribution

Estimator m_ClassDistribution

m_UseKernelEstimator

boolean m_UseKernelEstimator

m_UseDiscretization

boolean m_UseDiscretization

m_NumClasses

int m_NumClasses

m_Instances

Instances m_Instances

m_Disc

Discretize m_Disc

m_displayModelInOldFormat

boolean m_displayModelInOldFormat

Class weka.classifiers.bayes.NaiveBayesMultinomial extends 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_lnFactorialCache

double[] m_lnFactorialCache

m_headerInfo

Instances m_headerInfo

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

serialVersionUID: 2139025532014821394L

Serialized Fields

m_data

Instances m_data

m_probOfClass

double[] m_probOfClass

m_wordsPerClass

double[] m_wordsPerClass

m_probOfWordGivenClass

java.util.Map<K,V> m_probOfWordGivenClass

m_stopwordsFile

java.io.File m_stopwordsFile

m_tokenizer

Tokenizer m_tokenizer

m_lowercaseTokens

boolean m_lowercaseTokens

m_stemmer

Stemmer m_stemmer

m_useStopList

boolean m_useStopList

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

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

serialVersionUID: -7204398796974263186L

Serialized Fields

m_wordsPerClass

double[] m_wordsPerClass

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

serialVersionUID: -5354015843807192221L