Serialized Form

Package weka.classifiers.mi
- Class weka.classifiers.mi.MDD extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -7273119490545290581L
  - Serialized Fields
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute
    - m_Par
```
double[] m_Par
```
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Data
```
double[][][] m_Data
```
      MI data
    - m_Attributes
```
weka.core.Instances m_Attributes
```
      All attribute names
    - m_Filter
```
weka.filters.Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither, default:standardize
    - m_Missing
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_Missing
```
      The filter used to get rid of missing values.
- Class weka.classifiers.mi.MIBoost extends weka.classifiers.SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -3808427225599279539L
  - Serialized Fields
    - m_Models
```
weka.classifiers.Classifier[] m_Models
```
      the models for the iterations
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Attributes
```
weka.core.Instances m_Attributes
```
      attributes name for the new dataset used to build the model
    - m_NumIterations
```
int m_NumIterations
```
      Number of iterations
    - m_Beta
```
double[] m_Beta
```
      Voting weights of models
    - m_MaxIterations
```
int m_MaxIterations
```
      the maximum number of boost iterations
    - m_DiscretizeBin
```
int m_DiscretizeBin
```
      the number of discretization bins
    - m_Filter
```
weka.filters.unsupervised.attribute.Discretize m_Filter
```
      filter used for discretization
    - m_ConvertToSI
```
weka.filters.unsupervised.attribute.MultiInstanceToPropositional m_ConvertToSI
```
      filter used to convert the MI dataset into single-instance dataset
- Class weka.classifiers.mi.MIDD extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  4263507733600536168L
  - Serialized Fields
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute
    - m_Par
```
double[] m_Par
```
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Data
```
double[][][] m_Data
```
      MI data
    - m_Attributes
```
weka.core.Instances m_Attributes
```
      All attribute names
    - m_Filter
```
weka.filters.Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither, default:standardize
    - m_Missing
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_Missing
```
      The filter used to get rid of missing values.
- Class weka.classifiers.mi.MIEMDD extends weka.classifiers.RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  3899547154866223734L
  - Serialized Fields
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute
    - m_Par
```
double[] m_Par
```
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Data
```
double[][][] m_Data
```
      MI data
    - m_Attributes
```
weka.core.Instances m_Attributes
```
      All attribute names
    - m_emData
```
double[][] m_emData
```
      MI data
    - m_Filter
```
weka.filters.Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither, default:standardize
    - m_Missing
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_Missing
```
      The filter used to get rid of missing values.
- Class weka.classifiers.mi.MILR extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  1996101190172373826L
  - Serialized Fields
    - m_Par
```
double[] m_Par
```
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Ridge
```
double m_Ridge
```
      The ridge parameter.
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Data
```
double[][][] m_Data
```
      MI data
    - m_Attributes
```
weka.core.Instances m_Attributes
```
      All attribute names
    - xMean
```
double[] xMean
```
    - xSD
```
double[] xSD
```
    - m_AlgorithmType
```
int m_AlgorithmType
```
      the type of processing
- Class weka.classifiers.mi.MINND extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -4512599203273864994L
  - Serialized Fields
    - m_Neighbour
```
int m_Neighbour
```
      The number of nearest neighbour for prediction
    - m_Mean
```
double[][] m_Mean
```
      The mean for each attribute of each exemplar
    - m_Variance
```
double[][] m_Variance
```
      The variance for each attribute of each exemplar
    - m_Dimension
```
int m_Dimension
```
      The dimension of each exemplar, i.e. (numAttributes-2)
    - m_Attributes
```
weka.core.Instances m_Attributes
```
      header info of the data
    - m_Class
```
double[] m_Class
```
      The class label of each exemplar
    - m_NumClasses
```
int m_NumClasses
```
      The number of class labels in the data
    - m_Weights
```
double[] m_Weights
```
      The weight of each exemplar
    - m_Rate
```
double m_Rate
```
      The learning rate in the gradient descent
    - m_MinArray
```
double[] m_MinArray
```
      The minimum values for numeric attributes.
    - m_MaxArray
```
double[] m_MaxArray
```
      The maximum values for numeric attributes.
    - m_STOP
```
double m_STOP
```
      The stopping criteria of gradient descent
    - m_Change
```
double[][] m_Change
```
      The weights that alter the dimnesion of each exemplar
    - m_NoiseM
```
double[][] m_NoiseM
```
      The noise data of each exemplar
    - m_NoiseV
```
double[][] m_NoiseV
```
      The noise data of each exemplar
    - m_ValidM
```
double[][] m_ValidM
```
      The noise data of each exemplar
    - m_ValidV
```
double[][] m_ValidV
```
      The noise data of each exemplar
    - m_Select
```
int m_Select
```
      The number of nearest neighbour instances in the selection of noises in the training data
    - m_Choose
```
int m_Choose
```
      The number of nearest neighbour exemplars in the selection of noises in the test data
    - m_Decay
```
double m_Decay
```
      The decay rate of learning rate
- Class weka.classifiers.mi.MIOptimalBall extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -6465750129576777254L
  - Serialized Fields
    - m_Center
```
double[] m_Center
```
      center of the optimal ball
    - m_Radius
```
double m_Radius
```
      radius of the optimal ball
    - m_Distance
```
double[][][] m_Distance
```
      the distances from each instance in a positive bag to each bag
    - m_Filter
```
weka.filters.Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither
    - m_ConvertToSI
```
weka.filters.unsupervised.attribute.MultiInstanceToPropositional m_ConvertToSI
```
      filter used to convert the MI dataset into single-instance dataset
    - m_ConvertToMI
```
weka.filters.unsupervised.attribute.PropositionalToMultiInstance m_ConvertToMI
```
      filter used to convert the single-instance dataset into MI dataset
- Class weka.classifiers.mi.MIRI extends MITI implements Serializable
  
  serialVersionUID:
  
  -218835168397644255L
  - Serialized Fields
    - rules
```
java.util.ArrayList<E> rules
```
- Class weka.classifiers.mi.MISMO extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -5834036950143719712L
  - Serialized Fields
    - m_classifiers
```
weka.classifiers.mi.MISMO.BinaryMISMO[][] m_classifiers
```
      The binary classifier(s)
    - m_C
```
double m_C
```
      The complexity parameter.
    - m_eps
```
double m_eps
```
      Epsilon for rounding.
    - m_tol
```
double m_tol
```
      Tolerance for accuracy of result.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither
    - m_minimax
```
boolean m_minimax
```
      Use MIMinimax feature space?
    - m_NominalToBinary
```
weka.filters.unsupervised.attribute.NominalToBinary m_NominalToBinary
```
      The filter used to make attributes numeric.
    - m_Filter
```
weka.filters.Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_Missing
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_Missing
```
      The filter used to get rid of missing values.
    - m_classIndex
```
int m_classIndex
```
      The class index from the training data
    - m_classAttribute
```
weka.core.Attribute m_classAttribute
```
      The class attribute
    - m_kernel
```
weka.classifiers.functions.supportVector.Kernel m_kernel
```
      Kernel to use
    - m_checksTurnedOff
```
boolean m_checksTurnedOff
```
      Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0.
    - m_fitLogisticModels
```
boolean m_fitLogisticModels
```
      Whether logistic models are to be fit
    - m_numFolds
```
int m_numFolds
```
      The number of folds for the internal cross-validation
    - m_randomSeed
```
int m_randomSeed
```
      The random number seed
- Class weka.classifiers.mi.MISMO.BinaryMISMO extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -7107082483475433531L
  - Serialized Fields
    - m_alpha
```
double[] m_alpha
```
      The Lagrange multipliers.
    - m_b
```
double m_b
```
      The thresholds.
    - m_bLow
```
double m_bLow
```
      The thresholds.
    - m_bUp
```
double m_bUp
```
      The thresholds.
    - m_iLow
```
int m_iLow
```
      The indices for m_bLow and m_bUp
    - m_iUp
```
int m_iUp
```
      The indices for m_bLow and m_bUp
    - m_data
```
weka.core.Instances m_data
```
      The training data.
    - m_weights
```
double[] m_weights
```
      Weight vector for linear machine.
    - m_sparseWeights
```
double[] m_sparseWeights
```
      Variables to hold weight vector in sparse form. (To reduce storage requirements.)
    - m_sparseIndices
```
int[] m_sparseIndices
```
    - m_kernel
```
weka.classifiers.functions.supportVector.Kernel m_kernel
```
      Kernel to use
    - m_class
```
double[] m_class
```
      The transformed class values.
    - m_errors
```
double[] m_errors
```
      The current set of errors for all non-bound examples.
    - m_I0
```
weka.classifiers.functions.supportVector.SMOset m_I0
```
      {i: 0 < m_alpha[i] < C}
    - m_I1
```
weka.classifiers.functions.supportVector.SMOset m_I1
```
      {i: m_class[i] = 1, m_alpha[i] = 0}
    - m_I2
```
weka.classifiers.functions.supportVector.SMOset m_I2
```
      {i: m_class[i] = -1, m_alpha[i] = C}
    - m_I3
```
weka.classifiers.functions.supportVector.SMOset m_I3
```
      {i: m_class[i] = 1, m_alpha[i] = C}
    - m_I4
```
weka.classifiers.functions.supportVector.SMOset m_I4
```
      {i: m_class[i] = -1, m_alpha[i] = 0}
    - m_supportVectors
```
weka.classifiers.functions.supportVector.SMOset m_supportVectors
```
      The set of support vectors {i: 0 < m_alpha[i]}
    - m_logistic
```
weka.classifiers.functions.Logistic m_logistic
```
      Stores logistic regression model for probability estimate
    - m_sumOfWeights
```
double m_sumOfWeights
```
      Stores the weight of the training instances
- Class weka.classifiers.mi.MISVM extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  7622231064035278145L
  - Serialized Fields
    - m_SparseFilter
```
weka.filters.Filter m_SparseFilter
```
      The filter used to transform the sparse datasets to nonsparse
    - m_SVM
```
weka.classifiers.mi.MISVM.SVM m_SVM
```
      The SMO classifier used to compute SVM soluton w,b for the dataset
    - m_kernel
```
weka.classifiers.functions.supportVector.Kernel m_kernel
```
      the kernel to use
    - m_C
```
double m_C
```
      The complexity parameter.
    - m_Filter
```
weka.filters.Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither
    - m_MaxIterations
```
int m_MaxIterations
```
      the maximum number of iterations to perform
    - m_ConvertToProp
```
weka.filters.unsupervised.attribute.MultiInstanceToPropositional m_ConvertToProp
```
      filter used to convert the MI dataset into single-instance dataset
- Class weka.classifiers.mi.MITI extends weka.classifiers.RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  -217735168397644244L
  - Serialized Fields
    - tree
```
weka.classifiers.mi.MITI.MultiInstanceDecisionTree tree
```
    - m_SplitMethod
```
int m_SplitMethod
```
    - m_scaleK
```
boolean m_scaleK
```
    - m_useBagCount
```
boolean m_useBagCount
```
    - m_unbiasedEstimate
```
boolean m_unbiasedEstimate
```
    - m_kBEPPConstant
```
int m_kBEPPConstant
```
    - m_AttributesToSplit
```
int m_AttributesToSplit
```
    - m_AttributeSplitChoices
```
int m_AttributeSplitChoices
```
    - m_bagInstanceMultiplier
```
double m_bagInstanceMultiplier
```
- Class weka.classifiers.mi.MITI.MultiInstanceDecisionTree extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  4037700809781784985L
  - Serialized Fields
    - root
```
TreeNode root
```
    - m_instanceBags
```
java.util.HashMap<K,V> m_instanceBags
```
    - numLeaves
```
int numLeaves
```
- Class weka.classifiers.mi.MIWrapper extends weka.classifiers.SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -7707766152904315910L
  - Serialized Fields
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_Method
```
int m_Method
```
      the test method
    - m_ConvertToProp
```
weka.filters.unsupervised.attribute.MultiInstanceToPropositional m_ConvertToProp
```
      Filter used to convert MI dataset into single-instance dataset
    - m_WeightMethod
```
int m_WeightMethod
```
      the single-instance weight setting method
- Class weka.classifiers.mi.QuickDDIterative extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  4263507733600536170L
  - Serialized Fields
    - m_ClassIndex
```
int m_ClassIndex
```
      The index of the class attribute
    - m_Par
```
double[] m_Par
```
      The target point and scaling vector learned by the algorithm. (comment by Jimmy)
    - m_CurrentCandidate
```
double[] m_CurrentCandidate
```
      The current guess at the target point, without scaling information. -Jimmy
    - m_NumClasses
```
int m_NumClasses
```
      The number of the class labels
    - m_BagWeights
```
double[] m_BagWeights
```
      The weights for each bag
    - m_Classes
```
int[] m_Classes
```
      Class labels for each bag
    - m_Data
```
double[][][] m_Data
```
      MI data
    - m_Attributes
```
weka.core.Instances m_Attributes
```
      All attribute names
    - m_Filter
```
weka.filters.Filter m_Filter
```
      The filter used to standardize/normalize all values.
    - m_filterType
```
int m_filterType
```
      Whether to normalize/standardize/neither, default:standardize
    - m_scaleFactor
```
double m_scaleFactor
```
      Initial scaling factor for Gaussian-like function at target point.
    - m_maxIterations
```
int m_maxIterations
```
      The maximum number of iterations to perform
    - m_maxProbNegativeClass
```
double m_maxProbNegativeClass
```
      The maximum probability for the negative class
    - m_considerBothClasses
```
boolean m_considerBothClasses
```
      Whether to consider both classes as "positive" class in turn
    - m_posClass
```
byte m_posClass
```
      The index of the positive class
    - m_Missing
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_Missing
```
      The filter used to get rid of missing values.
- Class weka.classifiers.mi.SimpleMI extends weka.classifiers.SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  9137795893666592662L
  - Serialized Fields
    - m_TransformMethod
```
int m_TransformMethod
```
      the method used in transformation
- Class weka.classifiers.mi.TLC extends weka.classifiers.SingleClassifierEnhancer implements Serializable
  
  serialVersionUID:
  
  -4444591375578585231L
  - Serialized Fields
    - m_partitionGenerator
```
weka.core.PartitionGenerator m_partitionGenerator
```
      The partition generator to use.
    - m_Filter
```
weka.filters.MultiFilter m_Filter
```
      The filter to use in conjunction with the partition generator.
- Class weka.classifiers.mi.TLD extends weka.classifiers.RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  6657315525171152210L
  - Serialized Fields
    - m_MeanP
```
double[][] m_MeanP
```
      The mean for each attribute of each positive exemplar
    - m_VarianceP
```
double[][] m_VarianceP
```
      The variance for each attribute of each positive exemplar
    - m_MeanN
```
double[][] m_MeanN
```
      The mean for each attribute of each negative exemplar
    - m_VarianceN
```
double[][] m_VarianceN
```
      The variance for each attribute of each negative exemplar
    - m_SumP
```
double[][] m_SumP
```
      The effective sum of weights of each positive exemplar in each dimension
    - m_SumN
```
double[][] m_SumN
```
      The effective sum of weights of each negative exemplar in each dimension
    - m_ParamsP
```
double[] m_ParamsP
```
      The parameters to be estimated for each positive exemplar
    - m_ParamsN
```
double[] m_ParamsN
```
      The parameters to be estimated for each negative exemplar
    - m_Dimension
```
int m_Dimension
```
      The dimension of each exemplar, i.e. (numAttributes-2)
    - m_Class
```
double[] m_Class
```
      The class label of each exemplar
    - m_NumClasses
```
int m_NumClasses
```
      The number of class labels in the data
    - m_Run
```
int m_Run
```
      The number of runs to perform
    - m_Cutoff
```
double m_Cutoff
```
    - m_UseEmpiricalCutOff
```
boolean m_UseEmpiricalCutOff
```
- Class weka.classifiers.mi.TLDSimple extends weka.classifiers.RandomizableClassifier implements Serializable
  
  serialVersionUID:
  
  9040995947243286591L
  - Serialized Fields
    - m_MeanP
```
double[][] m_MeanP
```
      The mean for each attribute of each positive exemplar
    - m_MeanN
```
double[][] m_MeanN
```
      The mean for each attribute of each negative exemplar
    - m_SumP
```
double[][] m_SumP
```
      The effective sum of weights of each positive exemplar in each dimension
    - m_SumN
```
double[][] m_SumN
```
      The effective sum of weights of each negative exemplar in each dimension
    - m_SgmSqP
```
double[] m_SgmSqP
```
      Estimated sigma^2 in positive bags
    - m_SgmSqN
```
double[] m_SgmSqN
```
      Estimated sigma^2 in negative bags
    - m_ParamsP
```
double[] m_ParamsP
```
      The parameters to be estimated for each positive exemplar
    - m_ParamsN
```
double[] m_ParamsN
```
      The parameters to be estimated for each negative exemplar
    - m_Dimension
```
int m_Dimension
```
      The dimension of each exemplar, i.e. (numAttributes-2)
    - m_Class
```
double[] m_Class
```
      The class label of each exemplar
    - m_NumClasses
```
int m_NumClasses
```
      The number of class labels in the data
    - m_Run
```
int m_Run
```
    - m_Cutoff
```
double m_Cutoff
```
    - m_UseEmpiricalCutOff
```
boolean m_UseEmpiricalCutOff
```
    - m_LkRatio
```
double[] m_LkRatio
```
    - m_Attribute
```
weka.core.Instances m_Attribute
```
Package weka.classifiers.mi.miti
- Class weka.classifiers.mi.miti.Bag extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  3890971117182125677L
  - Serialized Fields
    - positive
```
boolean positive
```
    - instances
```
weka.core.Instances instances
```
    - id
```
java.lang.String id
```
    - bagWeight
```
double bagWeight
```
    - bagWeightMultiplied
```
double bagWeightMultiplied
```
    - enabled
```
boolean enabled
```
- Class weka.classifiers.mi.miti.Split extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  147371323803237346L
  - Serialized Fields
    - attribute
```
weka.core.Attribute attribute
```
    - splitPoint
```
double splitPoint
```
    - score
```
double score
```
    - isNominal
```
boolean isNominal
```
- Class weka.classifiers.mi.miti.TreeNode extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  9050803921532593168L
  - Serialized Fields
    - instances
```
java.util.ArrayList<E> instances
```
    - nodeScore
```
double nodeScore
```
    - leafNode
```
boolean leafNode
```
    - positiveLeaf
```
boolean positiveLeaf
```
    - parent
```
TreeNode parent
```
    - left
```
TreeNode left
```
    - right
```
TreeNode right
```
    - nominalNodes
```
TreeNode[] nominalNodes
```
    - split
```
Split split
```
Package weka.classifiers.mi.supportVector
- Class weka.classifiers.mi.supportVector.MIPolyKernel extends weka.classifiers.functions.supportVector.PolyKernel implements Serializable
  
  serialVersionUID:
  
  7926421479341051777L
- Class weka.classifiers.mi.supportVector.MIRBFKernel extends weka.classifiers.functions.supportVector.RBFKernel implements Serializable
  
  serialVersionUID:
  
  -8711882393708956962L
  - Serialized Fields
    - m_kernelPrecalc
```
double[][] m_kernelPrecalc
```
      The precalculated dotproducts of <inst_i,inst_i>

Serialized Form

Package weka.classifiers.mi

Class weka.classifiers.mi.MDD extends weka.classifiers.AbstractClassifier implements Serializable

Serialized Fields

m_ClassIndex

m_Par

m_NumClasses

m_Classes

m_Data

m_Attributes

m_Filter

m_filterType

m_Missing

Class weka.classifiers.mi.MIBoost extends weka.classifiers.SingleClassifierEnhancer implements Serializable

Serialized Fields

m_Models

m_NumClasses

m_Classes

m_Attributes

m_NumIterations

m_Beta

m_MaxIterations

m_DiscretizeBin

m_Filter

m_ConvertToSI

Class weka.classifiers.mi.MIDD extends weka.classifiers.AbstractClassifier implements Serializable

Serialized Fields

m_ClassIndex

m_Par

m_NumClasses

m_Classes

m_Data

m_Attributes

m_Filter

m_filterType

m_Missing

Class weka.classifiers.mi.MIEMDD extends weka.classifiers.RandomizableClassifier implements Serializable

Serialized Fields

m_ClassIndex

m_Par

m_NumClasses

m_Classes

m_Data

m_Attributes

m_emData

m_Filter

m_filterType

m_Missing

Class weka.classifiers.mi.MILR extends weka.classifiers.AbstractClassifier implements Serializable

Serialized Fields

m_Par

m_NumClasses

m_Ridge

m_Classes

m_Data

m_Attributes

xMean

xSD

m_AlgorithmType

Class weka.classifiers.mi.MINND extends weka.classifiers.AbstractClassifier implements Serializable

Serialized Fields

m_Neighbour

m_Mean

m_Variance

m_Dimension

m_Attributes

m_Class

m_NumClasses

m_Weights

m_Rate

m_MinArray

m_MaxArray

m_STOP

m_Change

m_NoiseM

m_NoiseV

m_ValidM

m_ValidV

m_Select

m_Choose