Serialized Form


Package weka.classifiers.functions

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

serialVersionUID: -8620066949967678545L

Serialized Fields

m_NominalToBinary

NominalToBinary m_NominalToBinary

m_Filter

Filter m_Filter

m_filterType

int m_filterType

m_Missing

ReplaceMissingValues m_Missing

m_checksTurnedOff

boolean m_checksTurnedOff

m_delta

double m_delta

m_Alin

double m_Alin

m_Blin

double m_Blin

m_kernel

Kernel m_kernel

m_NumTrain

int m_NumTrain

m_avg_target

double m_avg_target

m_L

double[][] m_L

m_t

Matrix m_t

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

serialVersionUID: 3171710488325239199L

Serialized Fields

m_coefficients

double[] m_coefficients

m_missingFilter

ReplaceMissingValues m_missingFilter

m_nominalToBinaryFilter

NominalToBinary m_nominalToBinaryFilter

m_normalizeFilter

Normalize m_normalizeFilter

m_kernel

Kernel m_kernel

m_kernelMatrix

double[][] m_kernelMatrix

m_classMean

double m_classMean

m_numInstances

int m_numInstances

m_checksTurnedOff

boolean m_checksTurnedOff

m_ridge

double m_ridge

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

serialVersionUID: -3364580862046573747L

Serialized Fields

m_Coefficients

double[] m_Coefficients

m_SelectedAttributes

boolean[] m_SelectedAttributes

m_TransformedData

Instances m_TransformedData

m_MissingFilter

ReplaceMissingValues m_MissingFilter

m_TransformFilter

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_AttributeSelection

int m_AttributeSelection

m_EliminateColinearAttributes

boolean m_EliminateColinearAttributes

m_checksTurnedOff

boolean m_checksTurnedOff

m_Ridge

double m_Ridge

m_Minimal

boolean m_Minimal

m_ModelBuilt

boolean m_ModelBuilt

Class weka.classifiers.functions.Logistic extends 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

RemoveUseless m_AttFilter

m_NominalToBinary

NominalToBinary m_NominalToBinary

m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues

m_Debug

boolean m_Debug

m_LL

double m_LL

m_MaxIts

int m_MaxIts

m_useConjugateGradientDescent

boolean m_useConjugateGradientDescent

m_structure

Instances m_structure

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

serialVersionUID: -5990607817048210779L

Serialized Fields

m_ZeroR

Classifier m_ZeroR

m_useDefaultModel

boolean m_useDefaultModel

m_instances

Instances m_instances

m_currentInstance

Instance m_currentInstance

m_numeric

boolean m_numeric

m_attributeRanges

double[] m_attributeRanges

m_attributeBases

double[] m_attributeBases

m_outputs

MultilayerPerceptron.NeuralEnd[] m_outputs

m_inputs

MultilayerPerceptron.NeuralEnd[] m_inputs

m_neuralNodes

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

FastVector<E> m_selected

m_graphers

FastVector<E> m_graphers

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

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_error

double m_error

m_reset

boolean m_reset

m_normalizeClass

boolean m_normalizeClass

m_sigmoidUnit

SigmoidUnit m_sigmoidUnit

m_linearUnit

LinearUnit m_linearUnit

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

serialVersionUID: 7305185603191183338L

Serialized Fields

m_link

int m_link

m_input

boolean m_input

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

serialVersionUID: 572250905027665169L

Serialized Fields

m_ZeroR

Classifier m_ZeroR
a ZeroR model in case no model can be built from the data


m_instances

Instances m_instances
The training instances.


m_valSetSource

ConverterUtils.DataSource m_valSetSource
Declaration of needed structures for loading and storing external validation and secondary task training files.


m_valSet

Instances m_valSet

m_valSetFileName

java.lang.String m_valSetFileName

m_secSetSource

ConverterUtils.DataSource m_secSetSource

m_secSet

Instances m_secSet

m_secSetFileName

java.lang.String m_secSetFileName

m_currentInstance

Instance m_currentInstance
The current instance running through the network.


m_numeric

boolean m_numeric
A flag to say that it's a numeric class.


m_attributeRanges

double[] m_attributeRanges
The ranges for all the attributes.


m_attributeBases

double[] m_attributeBases
The base values for all the attributes.


m_outputs

MultilayerPerceptronCS.NeuralEnd[] m_outputs
The output units.(only feeds the errors, does no calcs)


m_inputs

MultilayerPerceptronCS.NeuralEnd[] m_inputs
The input units.(only feeds the inputs does no calcs)


m_neuralNodes

NeuralConnection[] m_neuralNodes
All the nodes that actually comprise the logical neural net.


m_numClasses

int m_numClasses
The number of classes.


m_numAttributes

int m_numAttributes
The number of attributes.


m_nodePanel

weka.classifiers.functions.MultilayerPerceptronCS.NodePanel m_nodePanel
The panel the nodes are displayed on.


m_controlPanel

weka.classifiers.functions.MultilayerPerceptronCS.ControlPanel m_controlPanel
The control panel.


m_nextId

int m_nextId
The next id number available for default naming.


m_selected

FastVector<E> m_selected
A Vector list of the units currently selected.


m_graphers

FastVector<E> m_graphers
A Vector list of the graphers.


m_numEpochs

int m_numEpochs
The number of epochs to train through.


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_accepted

boolean m_accepted
a flag to state that the network should be accepted the way it is.


m_win

javax.swing.JFrame m_win
The window for the network.


m_autoBuild

boolean m_autoBuild
A flag to tell the build classifier to automatically build a neural net.


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_valSize

int m_valSize
An int to say how big the validation set should be.


m_driftThreshold

int m_driftThreshold
The number to to use to quit on validation testing.


m_randomSeed

int m_randomSeed
The number used to seed the random number generator.


m_random

java.util.Random m_random
The actual random number generator.


m_useNomToBin

boolean m_useNomToBin
A flag to state that a nominal to binary filter should be used.


m_nominalToBinaryFilter

NominalToBinary m_nominalToBinaryFilter
The actual filter.


m_hiddenLayers

java.lang.String m_hiddenLayers
The string that defines the hidden layers


m_normalizeAttributes

boolean m_normalizeAttributes
This flag states that the user wants the input values normalized.


m_decay

boolean m_decay
This flag states that the user wants the learning rate to decay.


m_learningRate

double m_learningRate
This is the learning rate for the network.


m_momentum

double m_momentum
This is the momentum for the network.


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_lowValError

double m_lowValError
Shows the lowest validation error


m_epochIndex

int m_epochIndex
Shows the epoch index when lowest validation error is found


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_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_sigmoidUnit

SigmoidUnit m_sigmoidUnit
this is a sigmoid unit.


m_linearUnit

LinearUnit m_linearUnit
This is a linear unit.

Class weka.classifiers.functions.MultilayerPerceptronCS.NeuralEnd extends NeuralConnection implements Serializable

serialVersionUID: 7305185603191183338L

Serialized Fields

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.


m_input

boolean m_input
True if node is an input, False if it's an output.

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

Serialized Fields

m_maxIterations

int m_maxIterations

m_weights

double[] m_weights

m_nominalToBinary

NominalToBinary m_nominalToBinary

m_replaceMissing

ReplaceMissingValues m_replaceMissing

m_train

Instances m_train

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

serialVersionUID: -3732968666673530290L

Serialized Fields

m_replaceMissing

ReplaceMissingValues m_replaceMissing

m_nominalToBinary

Filter m_nominalToBinary

m_normalize

Normalize m_normalize

m_lambda

double m_lambda

m_learningRate

double m_learningRate

m_weights

double[] m_weights

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

Instances m_data

m_loss

int m_loss

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

serialVersionUID: 7200171484002029584L

Serialized Fields

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_dictionary

java.util.LinkedHashMap<K,V> m_dictionary

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_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

Instances m_data

m_epochs

int m_epochs

m_loss

int m_loss

m_svmProbs

SGD m_svmProbs

m_fitLogistic

boolean m_fitLogistic

m_fitLogisticStructure

Instances m_fitLogisticStructure

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

serialVersionUID: 1679336022895414137L

Serialized Fields

m_attribute

Attribute m_attribute

m_attributeIndex

int m_attributeIndex

m_slope

double m_slope

m_intercept

double m_intercept

m_suppressErrorMessage

boolean m_suppressErrorMessage

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

serialVersionUID: 7397710626304705059L

Serialized Fields

m_boostedModel

LogisticBase m_boostedModel

m_NominalToBinary

NominalToBinary m_NominalToBinary

m_ReplaceMissingValues

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 AbstractClassifier implements Serializable

serialVersionUID: -6585883636378691736L

Serialized Fields

m_classifiers

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

NominalToBinary m_NominalToBinary

m_Filter

Filter m_Filter

m_Missing

ReplaceMissingValues m_Missing

m_classIndex

int m_classIndex

m_classAttribute

Attribute m_classAttribute

m_KernelIsLinear

boolean m_KernelIsLinear

m_checksTurnedOff

boolean m_checksTurnedOff

m_fitLogisticModels

boolean m_fitLogisticModels

m_numFolds

int m_numFolds

m_randomSeed

int m_randomSeed

m_kernel

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

Instances m_data

m_weights

double[] m_weights

m_sparseWeights

double[] m_sparseWeights

m_sparseIndices

int[] m_sparseIndices

m_kernel

Kernel m_kernel

m_class

double[] m_class

m_errors

double[] m_errors

m_I0

SMOset m_I0

m_I1

SMOset m_I1

m_I2

SMOset m_I2

m_I3

SMOset m_I3

m_I4

SMOset m_I4

m_supportVectors

SMOset m_supportVectors

m_logistic

Logistic m_logistic

m_sumOfWeights

double m_sumOfWeights

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

serialVersionUID: -7149606251113102827L

Serialized Fields

m_filterType

int m_filterType

m_NominalToBinary

NominalToBinary m_NominalToBinary

m_Filter

Filter m_Filter

m_Missing

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

RegOptimizer m_optimizer

m_kernel

Kernel m_kernel

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

serialVersionUID: -3732968666673530290L

Serialized Fields

m_replaceMissing

ReplaceMissingValues m_replaceMissing

m_nominalToBinary

NominalToBinary m_nominalToBinary

m_normalize

Normalize m_normalize

m_lambda

double m_lambda

m_weights

double[] m_weights

m_bias

double m_bias

m_wScale

double m_wScale

m_t

double m_t

m_epochs

int m_epochs

m_data

Instances m_data

Class weka.classifiers.functions.VotedPerceptron extends 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

Instances m_Train

m_Seed

int m_Seed

m_NominalToBinary

NominalToBinary m_NominalToBinary

m_ReplaceMissingValues

ReplaceMissingValues m_ReplaceMissingValues