Serialized Form

Package weka.classifiers.functions

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

serialVersionUID:: -8620066949967678545L

Serialized Fields

m_NominalToBinary

weka.filters.unsupervised.attribute.NominalToBinary m_NominalToBinary

m_Filter
```
weka.filters.Filter m_Filter
```
m_filterType
```
int m_filterType
```

m_Missing

weka.filters.unsupervised.attribute.ReplaceMissingValues m_Missing

m_checksTurnedOff
```
boolean m_checksTurnedOff
```
m_delta
```
double m_delta
```
m_deltaSquared
```
double m_deltaSquared
```
m_Alin
```
double m_Alin
```
m_Blin
```
double m_Blin
```

m_kernel

weka.classifiers.functions.supportVector.Kernel m_kernel

m_actualKernel

weka.classifiers.functions.supportVector.Kernel m_actualKernel

m_NumTrain
```
int m_NumTrain
```
m_avg_target
```
double m_avg_target
```
m_L
```
no.uib.cipr.matrix.Matrix m_L
```
m_t
```
no.uib.cipr.matrix.Vector m_t
```
m_weights
```
double[] m_weights
```

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

serialVersionUID:

14172L
- Serialized Fields
  - m_Model
```
libsvm.svm_model m_Model
```
    LibSVM Model.
  - m_Filter
```
weka.filters.Filter m_Filter
```
    for normalizing the data.
  - m_NominalToBinary
```
weka.filters.Filter m_NominalToBinary
```
    for converting mult-valued nominal attributes to binary
  - m_ReplaceMissingValues
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_ReplaceMissingValues
```
    The filter used to get rid of missing values.
  - m_Normalize
```
boolean m_Normalize
```
    normalize input data.
  - m_x1
```
double m_x1
```
    coefficients used by normalization filter for doing its linear transformation
  - m_x0
```
double m_x0
```
  - m_noReplaceMissingValues
```
boolean m_noReplaceMissingValues
```
    If true, the replace missing values filter is not applied.
  - m_SVMType
```
int m_SVMType
```
    the SVM type.
  - m_KernelType
```
int m_KernelType
```
    the kernel type.
  - 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_Gamma
```
double m_Gamma
```
    for poly/rbf/sigmoid.
  - m_GammaActual
```
double m_GammaActual
```
    for poly/rbf/sigmoid (the actual gamma).
  - m_Coef0
```
double m_Coef0
```
    for poly/sigmoid.
  - m_CacheSize
```
double m_CacheSize
```
    in MB.
  - m_eps
```
double m_eps
```
    stopping criteria.
  - m_Cost
```
double m_Cost
```
    cost, for C_SVC, EPSILON_SVR and NU_SVR.
  - m_WeightLabel
```
int[] m_WeightLabel
```
    for C_SVC.
  - m_Weight
```
double[] m_Weight
```
    for C_SVC.
  - m_nu
```
double m_nu
```
    for NU_SVC, ONE_CLASS, and NU_SVR.
  - m_Loss
```
double m_Loss
```
    loss, for EPSILON_SVR.
  - m_Shrinking
```
boolean m_Shrinking
```
    use the shrinking heuristics.
  - m_ProbabilityEstimates
```
boolean m_ProbabilityEstimates
```
    whether to generate probability estimates instead of +1/-1 in case of classification problems.
  - m_ModelFile
```
java.io.File m_ModelFile
```
    the file to save the libsvm-internal model to.

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

serialVersionUID:: -3364580862046573747L

Serialized Fields

m_Coefficients
```
double[] m_Coefficients
```
m_SelectedAttributes
```
boolean[] m_SelectedAttributes
```
m_TransformedData
```
weka.core.Instances m_TransformedData
```

m_MissingFilter

weka.filters.unsupervised.attribute.ReplaceMissingValues m_MissingFilter

m_TransformFilter

weka.filters.supervised.attribute.NominalToBinary m_TransformFilter

m_ClassStdDev
```
double m_ClassStdDev
```
m_ClassMean
```
double m_ClassMean
```
m_ClassIndex
```
int m_ClassIndex
```
m_Means
```
double[] m_Means
```
m_StdDevs
```
double[] m_StdDevs
```
m_outputAdditionalStats
```
boolean m_outputAdditionalStats
```
m_AttributeSelection
```
int m_AttributeSelection
```
m_EliminateColinearAttributes
```
boolean m_EliminateColinearAttributes
```
m_checksTurnedOff
```
boolean m_checksTurnedOff
```
m_useQRDecomposition
```
boolean m_useQRDecomposition
```
m_Ridge
```
double m_Ridge
```
m_Minimal
```
boolean m_Minimal
```
m_ModelBuilt
```
boolean m_ModelBuilt
```
m_isZeroR
```
boolean m_isZeroR
```
m_df
```
int m_df
```
m_RSquared
```
double m_RSquared
```
m_RSquaredAdj
```
double m_RSquaredAdj
```
m_FStat
```
double m_FStat
```
m_StdErrorOfCoef
```
double[] m_StdErrorOfCoef
```
m_TStats
```
double[] m_TStats
```

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

serialVersionUID:: 3932117032546553727L

Serialized Fields

m_Par
```
double[][] m_Par
```
m_Data
```
double[][] m_Data
```
m_NumPredictors
```
int m_NumPredictors
```
m_ClassIndex
```
int m_ClassIndex
```
m_NumClasses
```
int m_NumClasses
```
m_Ridge
```
double m_Ridge
```

m_AttFilter

weka.filters.unsupervised.attribute.RemoveUseless m_AttFilter

m_NominalToBinary

weka.filters.unsupervised.attribute.NominalToBinary m_NominalToBinary

m_ReplaceMissingValues

weka.filters.unsupervised.attribute.ReplaceMissingValues m_ReplaceMissingValues

m_LL
```
double m_LL
```
m_MaxIts
```
int m_MaxIts
```
m_useConjugateGradientDescent
```
boolean m_useConjugateGradientDescent
```
m_doNotStandardizeAttributes
```
boolean m_doNotStandardizeAttributes
```
m_structure
```
weka.core.Instances m_structure
```
m_numModels
```
int m_numModels
```

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

serialVersionUID:: -5990607817048210779L

Serialized Fields

m_ZeroR
```
weka.classifiers.Classifier m_ZeroR
```
m_useDefaultModel
```
boolean m_useDefaultModel
```
m_instances
```
weka.core.Instances m_instances
```
m_currentInstance
```
weka.core.Instance m_currentInstance
```
m_numeric
```
boolean m_numeric
```
m_attributeRanges
```
double[] m_attributeRanges
```
m_attributeBases
```
double[] m_attributeBases
```

m_outputs

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

m_inputs

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

m_neuralNodes

weka.classifiers.functions.neural.NeuralConnection[] m_neuralNodes

m_numClasses
```
int m_numClasses
```
m_numAttributes
```
int m_numAttributes
```

m_nodePanel

weka.classifiers.functions.MultilayerPerceptron.NodePanel m_nodePanel

m_controlPanel

weka.classifiers.functions.MultilayerPerceptron.ControlPanel m_controlPanel

m_nextId
```
int m_nextId
```
m_selected
```
java.util.ArrayList<E> m_selected
```
m_numEpochs
```
int m_numEpochs
```
m_stopIt
```
boolean m_stopIt
```
m_stopped
```
boolean m_stopped
```
m_accepted
```
boolean m_accepted
```
m_win
```
javax.swing.JFrame m_win
```
m_autoBuild
```
boolean m_autoBuild
```
m_gui
```
boolean m_gui
```
m_valSize
```
int m_valSize
```
m_driftThreshold
```
int m_driftThreshold
```
m_randomSeed
```
int m_randomSeed
```
m_random
```
java.util.Random m_random
```
m_useNomToBin
```
boolean m_useNomToBin
```

m_nominalToBinaryFilter

weka.filters.unsupervised.attribute.NominalToBinary m_nominalToBinaryFilter

m_hiddenLayers
```
java.lang.String m_hiddenLayers
```
m_normalizeAttributes
```
boolean m_normalizeAttributes
```
m_decay
```
boolean m_decay
```
m_learningRate
```
double m_learningRate
```
m_momentum
```
double m_momentum
```
m_epoch
```
int m_epoch
```
m_numItsPerformed
```
int m_numItsPerformed
```
m_error
```
double m_error
```
m_reset
```
boolean m_reset
```
m_normalizeClass
```
boolean m_normalizeClass
```

m_sigmoidUnit

weka.classifiers.functions.neural.SigmoidUnit m_sigmoidUnit

m_linearUnit

weka.classifiers.functions.neural.LinearUnit m_linearUnit

m_resume
```
boolean m_resume
```
valSet
```
weka.core.Instances valSet
```
numInVal
```
int numInVal
```
totalWeight
```
double totalWeight
```
totalValWeight
```
double totalValWeight
```
driftOff
```
double driftOff
```
lastRight
```
double lastRight
```
bestError
```
double bestError
```
originalFormatData
```
weka.core.Instances originalFormatData
```

Class weka.classifiers.functions.MultilayerPerceptron.NeuralEnd extends weka.classifiers.functions.neural.NeuralConnection implements Serializable

serialVersionUID:

7305185603191183338L
- Serialized Fields
  - m_link
```
int m_link
```
  - m_input
```
boolean m_input
```

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

serialVersionUID:: -3732968666673530290L

Serialized Fields

m_replaceMissing

weka.filters.unsupervised.attribute.ReplaceMissingValues m_replaceMissing

m_nominalToBinary
```
weka.filters.Filter m_nominalToBinary
```

m_normalize

weka.filters.unsupervised.attribute.Normalize m_normalize

m_lambda
```
double m_lambda
```
m_learningRate
```
double m_learningRate
```
m_weights
```
double[] m_weights
```
m_epsilon
```
double m_epsilon
```
m_t
```
double m_t
```
m_numInstances
```
double m_numInstances
```
m_epochs
```
int m_epochs
```
m_dontNormalize
```
boolean m_dontNormalize
```
m_dontReplaceMissing
```
boolean m_dontReplaceMissing
```
m_data
```
weka.core.Instances m_data
```
m_loss
```
int m_loss
```
m_numModels
```
int m_numModels
```

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

serialVersionUID:: 7200171484002029584L

Serialized Fields

m_periodicP
```
int m_periodicP
```
m_minWordP
```
double m_minWordP
```
m_minAbsCoefficient
```
double m_minAbsCoefficient
```
m_wordFrequencies
```
boolean m_wordFrequencies
```
m_normalize
```
boolean m_normalize
```
m_norm
```
double m_norm
```
m_lnorm
```
double m_lnorm
```

m_dictionary

java.util.LinkedHashMap<K,V> m_dictionary

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_lambda
```
double m_lambda
```
m_learningRate
```
double m_learningRate
```
m_t
```
double m_t
```
m_bias
```
double m_bias
```
m_numInstances
```
double m_numInstances
```
m_data
```
weka.core.Instances m_data
```
m_epochs
```
int m_epochs
```
m_loss
```
int m_loss
```

m_svmProbs

weka.classifiers.functions.SGD m_svmProbs

m_fitLogistic
```
boolean m_fitLogistic
```

m_fitLogisticStructure

weka.core.Instances m_fitLogisticStructure

m_numModels
```
int m_numModels
```

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

serialVersionUID:

2104201532017340967L
- Serialized Fields
  - m_count
```
double m_count
```
  - m_weight
```
double m_weight
```
Class weka.classifiers.functions.SimpleLinearRegression extends weka.classifiers.AbstractClassifier implements Serializable

serialVersionUID:

1679336022895414137L
- Serialized Fields
  - m_attribute
```
weka.core.Attribute m_attribute
```
  - m_attributeIndex
```
int m_attributeIndex
```
  - m_slope
```
double m_slope
```
  - m_intercept
```
double m_intercept
```
  - m_classMeanForMissing
```
double m_classMeanForMissing
```
  - m_outputAdditionalStats
```
boolean m_outputAdditionalStats
```
  - m_df
```
int m_df
```
  - m_seSlope
```
double m_seSlope
```
  - m_seIntercept
```
double m_seIntercept
```
  - m_tstatSlope
```
double m_tstatSlope
```
  - m_tstatIntercept
```
double m_tstatIntercept
```
  - m_rsquared
```
double m_rsquared
```
  - m_rsquaredAdj
```
double m_rsquaredAdj
```
  - m_fstat
```
double m_fstat
```
  - m_suppressErrorMessage
```
boolean m_suppressErrorMessage
```

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

serialVersionUID:: 7397710626304705059L

Serialized Fields

m_boostedModel

weka.classifiers.trees.lmt.LogisticBase m_boostedModel

m_NominalToBinary

weka.filters.unsupervised.attribute.NominalToBinary m_NominalToBinary

m_ReplaceMissingValues

weka.filters.unsupervised.attribute.ReplaceMissingValues m_ReplaceMissingValues

m_numBoostingIterations
```
int m_numBoostingIterations
```
m_maxBoostingIterations
```
int m_maxBoostingIterations
```
m_heuristicStop
```
int m_heuristicStop
```
m_useCrossValidation
```
boolean m_useCrossValidation
```
m_errorOnProbabilities
```
boolean m_errorOnProbabilities
```
m_weightTrimBeta
```
double m_weightTrimBeta
```
m_useAIC
```
boolean m_useAIC
```

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

serialVersionUID:: -6585883636378691736L

Serialized Fields

m_classifiers

weka.classifiers.functions.SMO.BinarySMO[][] m_classifiers

m_C
```
double m_C
```
m_eps
```
double m_eps
```
m_tol
```
double m_tol
```
m_filterType
```
int m_filterType
```

m_NominalToBinary

weka.filters.unsupervised.attribute.NominalToBinary m_NominalToBinary

m_Filter
```
weka.filters.Filter m_Filter
```

m_Missing

weka.filters.unsupervised.attribute.ReplaceMissingValues m_Missing

m_classIndex
```
int m_classIndex
```
m_classAttribute
```
weka.core.Attribute m_classAttribute
```
m_KernelIsLinear
```
boolean m_KernelIsLinear
```
m_checksTurnedOff
```
boolean m_checksTurnedOff
```
m_fitCalibratorModels
```
boolean m_fitCalibratorModels
```

m_calibrator

weka.classifiers.Classifier m_calibrator

m_numFolds
```
int m_numFolds
```
m_randomSeed
```
int m_randomSeed
```

m_kernel

weka.classifiers.functions.supportVector.Kernel m_kernel

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

serialVersionUID:: -8246163625699362456L

Serialized Fields

m_alpha
```
double[] m_alpha
```
m_b
```
double m_b
```
m_bLow
```
double m_bLow
```
m_bUp
```
double m_bUp
```
m_iLow
```
int m_iLow
```
m_iUp
```
int m_iUp
```
m_data
```
weka.core.Instances m_data
```
m_weights
```
double[] m_weights
```
m_sparseWeights
```
double[] m_sparseWeights
```
m_sparseIndices
```
int[] m_sparseIndices
```

m_kernel

weka.classifiers.functions.supportVector.Kernel m_kernel

m_class
```
double[] m_class
```
m_errors
```
double[] m_errors
```

m_I0

weka.classifiers.functions.supportVector.SMOset m_I0

m_I1

weka.classifiers.functions.supportVector.SMOset m_I1

m_I2

weka.classifiers.functions.supportVector.SMOset m_I2

m_I3

weka.classifiers.functions.supportVector.SMOset m_I3

m_I4

weka.classifiers.functions.supportVector.SMOset m_I4

m_supportVectors

weka.classifiers.functions.supportVector.SMOset m_supportVectors

m_calibrator

weka.classifiers.Classifier m_calibrator

m_calibrationDataHeader

weka.core.Instances m_calibrationDataHeader

m_sumOfWeights
```
double m_sumOfWeights
```
m_nEvals
```
long m_nEvals
```
m_nCacheHits
```
int m_nCacheHits
```

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

serialVersionUID:: -7149606251113102827L

Serialized Fields

m_filterType
```
int m_filterType
```

m_NominalToBinary

weka.filters.unsupervised.attribute.NominalToBinary m_NominalToBinary

m_Filter
```
weka.filters.Filter m_Filter
```

m_Missing

weka.filters.unsupervised.attribute.ReplaceMissingValues m_Missing

m_onlyNumeric
```
boolean m_onlyNumeric
```
m_C
```
double m_C
```
m_x1
```
double m_x1
```
m_x0
```
double m_x0
```

m_optimizer

weka.classifiers.functions.supportVector.RegOptimizer m_optimizer

m_kernel

weka.classifiers.functions.supportVector.Kernel m_kernel

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

serialVersionUID:: -1072429260104568698L

Serialized Fields

m_MaxK
```
int m_MaxK
```
m_NumIterations
```
int m_NumIterations
```
m_Exponent
```
double m_Exponent
```
m_K
```
int m_K
```
m_Additions
```
int[] m_Additions
```
m_IsAddition
```
boolean[] m_IsAddition
```
m_Weights
```
int[] m_Weights
```
m_Train
```
weka.core.Instances m_Train
```
m_Seed
```
int m_Seed
```

m_NominalToBinary

weka.filters.unsupervised.attribute.NominalToBinary m_NominalToBinary

m_ReplaceMissingValues

weka.filters.unsupervised.attribute.ReplaceMissingValues m_ReplaceMissingValues

Serialized Form

Package weka.classifiers.functions

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

Serialized Fields

m_NominalToBinary

m_Filter

m_filterType

m_Missing

m_checksTurnedOff

m_delta

m_deltaSquared

m_Alin

m_Blin

m_kernel

m_actualKernel

m_NumTrain

m_avg_target

m_L

m_t

m_weights

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

Serialized Fields

m_Model

m_Filter

m_NominalToBinary

m_ReplaceMissingValues

m_Normalize

m_x1

m_x0

m_noReplaceMissingValues

m_SVMType

m_KernelType

m_Degree

m_Gamma

m_GammaActual

m_Coef0

m_CacheSize

m_eps

m_Cost

m_WeightLabel

m_Weight

m_nu

m_Loss

m_Shrinking

m_ProbabilityEstimates

m_ModelFile

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

Serialized Fields

m_Coefficients

m_SelectedAttributes

m_TransformedData

m_MissingFilter

m_TransformFilter

m_ClassStdDev

m_ClassMean

m_ClassIndex

m_Means

m_StdDevs

m_outputAdditionalStats

m_AttributeSelection

m_EliminateColinearAttributes

m_checksTurnedOff

m_useQRDecomposition

m_Ridge

m_Minimal

m_ModelBuilt

m_isZeroR

m_df

m_RSquared

m_RSquaredAdj

m_FStat

m_StdErrorOfCoef

m_TStats

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

Serialized Fields

m_Par

m_Data

m_NumPredictors

m_ClassIndex

m_NumClasses