Serialized Form
Package weka.attributeSelection |
Class weka.attributeSelection.ASEvaluation extends java.lang.Object implements Serializable |
serialVersionUID: 2091705669885950849L
Class weka.attributeSelection.ASSearch extends java.lang.Object implements Serializable |
serialVersionUID: 7591673350342236548L
Class weka.attributeSelection.AttributeSelection extends java.lang.Object implements Serializable |
serialVersionUID: 4170171824147584330L
m_trainInstances
weka.core.Instances m_trainInstances
- the instances to select attributes from
m_ASEvaluator
weka.attributeSelection.ASEvaluation m_ASEvaluator
- the attribute/subset evaluator
m_searchMethod
weka.attributeSelection.ASSearch m_searchMethod
- the search method
m_numFolds
int m_numFolds
- the number of folds to use for cross validation
m_selectionResults
java.lang.StringBuffer m_selectionResults
- holds a string describing the results of the attribute selection
m_doRank
boolean m_doRank
- rank features (if allowed by the search method)
m_doXval
boolean m_doXval
- do cross validation
m_seed
int m_seed
- seed used to randomly shuffle instances for cross validation
m_numToSelect
int m_numToSelect
- number of attributes requested from ranked results
m_selectedAttributeSet
int[] m_selectedAttributeSet
- the selected attributes
m_attributeRanking
double[][] m_attributeRanking
- the attribute indexes and associated merits if a ranking is produced
m_transformer
weka.attributeSelection.AttributeTransformer m_transformer
- if a feature selection run involves an attribute transformer
m_attributeFilter
weka.filters.unsupervised.attribute.Remove m_attributeFilter
- the attribute filter for processing instances with respect to
the most recent feature selection run
m_rankResults
double[][] m_rankResults
- hold statistics for repeated feature selection, such as
under cross validation
m_subsetResults
double[] m_subsetResults
m_trials
int m_trials
Class weka.attributeSelection.AttributeSetEvaluator extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: -5744881009422257389L
Class weka.attributeSelection.BestFirst extends weka.attributeSelection.ASSearch implements Serializable |
serialVersionUID: 7841338689536821867L
m_maxStale
int m_maxStale
- maximum number of stale nodes before terminating search
m_searchDirection
int m_searchDirection
- 0 == backward search, 1 == forward search, 2 == bidirectional
m_starting
int[] m_starting
- holds an array of starting attributes
m_startRange
weka.core.Range m_startRange
- holds the start set for the search as a Range
m_hasClass
boolean m_hasClass
- does the data have a class
m_classIndex
int m_classIndex
- holds the class index
m_numAttribs
int m_numAttribs
- number of attributes in the data
m_totalEvals
int m_totalEvals
- total number of subsets evaluated during a search
m_debug
boolean m_debug
- for debugging
m_bestMerit
double m_bestMerit
- holds the merit of the best subset found
m_cacheSize
int m_cacheSize
- holds the maximum size of the lookup cache for evaluated subsets
Class weka.attributeSelection.BestFirst.Link2 extends java.lang.Object implements Serializable |
serialVersionUID: -8236598311516351420L
m_data
java.lang.Object[] m_data
m_merit
double m_merit
Class weka.attributeSelection.BestFirst.LinkedList2 extends weka.core.FastVector implements Serializable |
serialVersionUID: 3250538292330398929L
m_MaxSize
int m_MaxSize
- Max number of elements in the list
Class weka.attributeSelection.CfsSubsetEval extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: 747878400813276317L
m_trainInstances
weka.core.Instances m_trainInstances
- The training instances
m_disTransform
weka.filters.supervised.attribute.Discretize m_disTransform
- Discretise attributes when class in nominal
m_classIndex
int m_classIndex
- The class index
m_isNumeric
boolean m_isNumeric
- Is the class numeric
m_numAttribs
int m_numAttribs
- Number of attributes in the training data
m_numInstances
int m_numInstances
- Number of instances in the training data
m_missingSeparate
boolean m_missingSeparate
- Treat missing values as separate values
m_locallyPredictive
boolean m_locallyPredictive
- Include locally predictive attributes
m_corr_matrix
float[][] m_corr_matrix
- Holds the matrix of attribute correlations
m_std_devs
double[] m_std_devs
- Standard deviations of attributes (when using pearsons correlation)
m_c_Threshold
double m_c_Threshold
- Threshold for admitting locally predictive features
Class weka.attributeSelection.GainRatioAttributeEval extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: -8504656625598579926L
m_trainInstances
weka.core.Instances m_trainInstances
- The training instances
m_classIndex
int m_classIndex
- The class index
m_numAttribs
int m_numAttribs
- The number of attributes
m_numInstances
int m_numInstances
- The number of instances
m_numClasses
int m_numClasses
- The number of classes
m_missing_merge
boolean m_missing_merge
- Merge missing values
Class weka.attributeSelection.GreedyStepwise extends weka.attributeSelection.ASSearch implements Serializable |
serialVersionUID: -6312951970168325471L
m_hasClass
boolean m_hasClass
- does the data have a class
m_classIndex
int m_classIndex
- holds the class index
m_numAttribs
int m_numAttribs
- number of attributes in the data
m_rankingRequested
boolean m_rankingRequested
- true if the user has requested a ranked list of attributes
m_doRank
boolean m_doRank
- go from one side of the search space to the other in order to generate
a ranking
m_doneRanking
boolean m_doneRanking
- used to indicate whether or not ranking has been performed
m_threshold
double m_threshold
- A threshold by which to discard attributes---used by the
AttributeSelection module
m_numToSelect
int m_numToSelect
- The number of attributes to select. -1 indicates that all attributes
are to be retained. Has precedence over m_threshold
m_calculatedNumToSelect
int m_calculatedNumToSelect
m_bestMerit
double m_bestMerit
- the merit of the best subset found
m_rankedAtts
double[][] m_rankedAtts
- a ranked list of attribute indexes
m_rankedSoFar
int m_rankedSoFar
m_best_group
java.util.BitSet m_best_group
- the best subset found
m_ASEval
weka.attributeSelection.ASEvaluation m_ASEval
m_Instances
weka.core.Instances m_Instances
m_startRange
weka.core.Range m_startRange
- holds the start set for the search as a Range
m_starting
int[] m_starting
- holds an array of starting attributes
m_backward
boolean m_backward
- Use a backwards search instead of a forwards one
m_conservativeSelection
boolean m_conservativeSelection
- If set then attributes will continue to be added during a forward
search as long as the merit does not degrade
Class weka.attributeSelection.HoldOutSubsetEvaluator extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: 8280529785412054174L
Class weka.attributeSelection.InfoGainAttributeEval extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: -1949849512589218930L
m_missing_merge
boolean m_missing_merge
- Treat missing values as a seperate value
m_Binarize
boolean m_Binarize
- Just binarize numeric attributes
m_InfoGains
double[] m_InfoGains
- The info gain for each attribute
serialVersionUID: 176853600006522257L
m_trainInstances
weka.core.Instances m_trainInstances
- The data to transform analyse/transform
m_trainHeader
weka.core.Instances m_trainHeader
- Keep a copy for the class attribute (if set) and for
checking for header compatibility
m_transformedFormat
weka.core.Instances m_transformedFormat
- The header for the transformed data format
m_hasClass
boolean m_hasClass
- Data has a class set
m_classIndex
int m_classIndex
- Class index
m_numAttributes
int m_numAttributes
- Number of attributes
m_numInstances
int m_numInstances
- Number of instances
m_transpose
boolean m_transpose
- Is transpose necessary because numAttributes < numInstances?
m_u
weka.core.matrix.Matrix m_u
- Will hold the left singular vectors
m_s
weka.core.matrix.Matrix m_s
- Will hold the singular values
m_v
weka.core.matrix.Matrix m_v
- Will hold the right singular vectors
m_transformationMatrix
weka.core.matrix.Matrix m_transformationMatrix
- Will hold the matrix used to transform instances to the new feature space
m_replaceMissingFilter
weka.filters.unsupervised.attribute.ReplaceMissingValues m_replaceMissingFilter
- Filters for original data
m_normalizeFilter
weka.filters.unsupervised.attribute.Normalize m_normalizeFilter
m_nominalToBinaryFilter
weka.filters.unsupervised.attribute.NominalToBinary m_nominalToBinaryFilter
m_attributeFilter
weka.filters.unsupervised.attribute.Remove m_attributeFilter
m_outputNumAttributes
int m_outputNumAttributes
- The number of attributes in the LSA transformed data
m_normalize
boolean m_normalize
- Normalize the input data?
m_rank
double m_rank
- The approximation rank to use (between 0 and 1 means coverage proportion)
m_sumSquaredSingularValues
double m_sumSquaredSingularValues
- The sum of the squares of the singular values
m_actualRank
int m_actualRank
- The actual rank number to use for computation
m_maxAttributesInName
int m_maxAttributesInName
- Maximum number of attributes in the transformed attribute name
Class weka.attributeSelection.OneRAttributeEval extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: 4386514823886856980L
m_trainInstances
weka.core.Instances m_trainInstances
- The training instances
m_classIndex
int m_classIndex
- The class index
m_numAttribs
int m_numAttribs
- The number of attributes
m_numInstances
int m_numInstances
- The number of instances
m_randomSeed
int m_randomSeed
- Random number seed
m_folds
int m_folds
- Number of folds for cross validation
m_evalUsingTrainingData
boolean m_evalUsingTrainingData
- Use training data to evaluate merit rather than x-val
m_minBucketSize
int m_minBucketSize
- Passed on to OneR
Class weka.attributeSelection.PrincipalComponents extends weka.attributeSelection.UnsupervisedAttributeEvaluator implements Serializable |
serialVersionUID: -3675307197777734007L
m_trainInstances
weka.core.Instances m_trainInstances
- The data to transform analyse/transform
m_trainHeader
weka.core.Instances m_trainHeader
- Keep a copy for the class attribute (if set)
m_transformedFormat
weka.core.Instances m_transformedFormat
- The header for the transformed data format
m_originalSpaceFormat
weka.core.Instances m_originalSpaceFormat
- The header for data transformed back to the original space
m_hasClass
boolean m_hasClass
- Data has a class set
m_classIndex
int m_classIndex
- Class index
m_numAttribs
int m_numAttribs
- Number of attributes
m_numInstances
int m_numInstances
- Number of instances
m_correlation
double[][] m_correlation
- Correlation/covariance matrix for the original data
m_means
double[] m_means
m_stdDevs
double[] m_stdDevs
m_center
boolean m_center
- If true, center (rather than standardize) the data and
compute PCA from covariance (rather than correlation)
matrix.
m_eigenvectors
double[][] m_eigenvectors
- Will hold the unordered linear transformations of the (normalized)
original data
m_eigenvalues
double[] m_eigenvalues
- Eigenvalues for the corresponding eigenvectors
m_sortedEigens
int[] m_sortedEigens
- Sorted eigenvalues
m_sumOfEigenValues
double m_sumOfEigenValues
- sum of the eigenvalues
m_replaceMissingFilter
weka.filters.unsupervised.attribute.ReplaceMissingValues m_replaceMissingFilter
- Filters for original data
m_nominalToBinFilter
weka.filters.unsupervised.attribute.NominalToBinary m_nominalToBinFilter
m_attributeFilter
weka.filters.unsupervised.attribute.Remove m_attributeFilter
m_centerFilter
weka.filters.unsupervised.attribute.Center m_centerFilter
m_standardizeFilter
weka.filters.unsupervised.attribute.Standardize m_standardizeFilter
m_outputNumAtts
int m_outputNumAtts
- The number of attributes in the pc transformed data
m_coverVariance
double m_coverVariance
- the amount of variance to cover in the original data when
retaining the best n PC's
m_transBackToOriginal
boolean m_transBackToOriginal
- transform the data through the pc space and back to the original
space ?
m_maxAttrsInName
int m_maxAttrsInName
- maximum number of attributes in the transformed attribute name
m_eTranspose
double[][] m_eTranspose
- holds the transposed eigenvectors for converting back to the
original space
Class weka.attributeSelection.Ranker extends weka.attributeSelection.ASSearch implements Serializable |
serialVersionUID: -9086714848510751934L
m_starting
int[] m_starting
- Holds the starting set as an array of attributes
m_startRange
weka.core.Range m_startRange
- Holds the start set for the search as a range
m_attributeList
int[] m_attributeList
- Holds the ordered list of attributes
m_attributeMerit
double[] m_attributeMerit
- Holds the list of attribute merit scores
m_hasClass
boolean m_hasClass
- Data has class attribute---if unsupervised evaluator then no class
m_classIndex
int m_classIndex
- Class index of the data if supervised evaluator
m_numAttribs
int m_numAttribs
- The number of attribtes
m_threshold
double m_threshold
- A threshold by which to discard attributes---used by the
AttributeSelection module
m_numToSelect
int m_numToSelect
- The number of attributes to select. -1 indicates that all attributes
are to be retained. Has precedence over m_threshold
m_calculatedNumToSelect
int m_calculatedNumToSelect
- Used to compute the number to select
Class weka.attributeSelection.ReliefFAttributeEval extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: -8422186665795839379L
m_trainInstances
weka.core.Instances m_trainInstances
- The training instances
m_classIndex
int m_classIndex
- The class index
m_numAttribs
int m_numAttribs
- The number of attributes
m_numInstances
int m_numInstances
- The number of instances
m_numericClass
boolean m_numericClass
- Numeric class
m_numClasses
int m_numClasses
- The number of classes if class is nominal
m_ndc
double m_ndc
- Used to hold the probability of a different class val given nearest
instances (numeric class)
m_nda
double[] m_nda
- Used to hold the prob of different value of an attribute given
nearest instances (numeric class case)
m_ndcda
double[] m_ndcda
- Used to hold the prob of a different class val and different att
val given nearest instances (numeric class case)
m_weights
double[] m_weights
- Holds the weights that relief assigns to attributes
m_classProbs
double[] m_classProbs
- Prior class probabilities (discrete class case)
m_sampleM
int m_sampleM
- The number of instances to sample when estimating attributes
default == -1, use all instances
m_Knn
int m_Knn
- The number of nearest hits/misses
m_karray
double[][][] m_karray
- k nearest scores + instance indexes for n classes
m_maxArray
double[] m_maxArray
- Upper bound for numeric attributes
m_minArray
double[] m_minArray
- Lower bound for numeric attributes
m_worst
double[] m_worst
- Keep track of the farthest instance for each class
m_index
int[] m_index
- Index in the m_karray of the farthest instance for each class
m_stored
int[] m_stored
- Number of nearest neighbours stored of each class
m_seed
int m_seed
- Random number seed used for sampling instances
m_weightsByRank
double[] m_weightsByRank
- used to (optionally) weight nearest neighbours by their distance
from the instance in question. Each entry holds
exp(-((rank(r_i, i_j)/sigma)^2)) where rank(r_i,i_j) is the rank of
instance i_j in a sequence of instances ordered by the distance
from r_i. sigma is a user defined parameter, default=20
m_sigma
int m_sigma
m_weightByDistance
boolean m_weightByDistance
- Weight by distance rather than equal weights
Class weka.attributeSelection.SymmetricalUncertAttributeEval extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: -8096505776132296416L
m_trainInstances
weka.core.Instances m_trainInstances
- The training instances
m_classIndex
int m_classIndex
- The class index
m_numAttribs
int m_numAttribs
- The number of attributes
m_numInstances
int m_numInstances
- The number of instances
m_numClasses
int m_numClasses
- The number of classes
m_missing_merge
boolean m_missing_merge
- Treat missing values as a seperate value
Class weka.attributeSelection.UnsupervisedAttributeEvaluator extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: -4100897318675336178L
Class weka.attributeSelection.UnsupervisedSubsetEvaluator extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: 627934376267488763L
Class weka.attributeSelection.WrapperSubsetEval extends weka.attributeSelection.ASEvaluation implements Serializable |
serialVersionUID: -4573057658746728675L
m_trainInstances
weka.core.Instances m_trainInstances
- training instances
m_classIndex
int m_classIndex
- class index
m_numAttribs
int m_numAttribs
- number of attributes in the training data
m_numInstances
int m_numInstances
- number of instances in the training data
m_Evaluation
weka.classifiers.Evaluation m_Evaluation
- holds an evaluation object
m_BaseClassifier
weka.classifiers.Classifier m_BaseClassifier
- holds the base classifier object
m_folds
int m_folds
- number of folds to use for cross validation
m_seed
int m_seed
- random number seed
m_threshold
double m_threshold
- the threshold by which to do further cross validations when
estimating the accuracy of a subset
m_evaluationMeasure
int m_evaluationMeasure
- The evaluation measure to use