Serialized Form
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Package weka.associations |
m_minSupport
double m_minSupport
- The minimum support.
m_upperBoundMinSupport
double m_upperBoundMinSupport
- The upper bound on the support
m_lowerBoundMinSupport
double m_lowerBoundMinSupport
- The lower bound for the minimum support.
m_metricType
int m_metricType
- The selected metric type.
m_minMetric
double m_minMetric
- The minimum metric score.
m_numRules
int m_numRules
- The maximum number of rules that are output.
m_delta
double m_delta
- Delta by which m_minSupport is decreased in each iteration.
m_significanceLevel
double m_significanceLevel
- Significance level for optional significance test.
m_cycles
int m_cycles
- Number of cycles used before required number of rules was one.
m_Ls
FastVector m_Ls
- The set of all sets of itemsets L.
m_hashtables
FastVector m_hashtables
- The same information stored in hash tables.
m_allTheRules
FastVector[] m_allTheRules
- The list of all generated rules.
m_instances
Instances m_instances
- The instances (transactions) to be used for generating
the association rules.
m_outputItemSets
boolean m_outputItemSets
- Output itemsets found?
m_removeMissingCols
boolean m_removeMissingCols
m_verbose
boolean m_verbose
- Report progress iteratively
m_items
int[] m_items
- The items stored as an array of of ints.
m_counter
int m_counter
- Counter for how many transactions contain this item set.
m_totalTransactions
int m_totalTransactions
- The total number of transactions
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Package weka.attributeSelection |
m_trainInstances
Instances m_trainInstances
- the instances to select attributes from
m_ASEvaluator
ASEvaluation m_ASEvaluator
- the attribute/subset evaluator
m_searchMethod
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_threshold
double m_threshold
- cutoff value by which to select attributes for ranked results
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
AttributeTransformer m_transformer
- if a feature selection run involves an attribute transformer
m_attributeFilter
AttributeFilter 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
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
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
this$0
BestFirst this$0
m_MaxSize
int m_MaxSize
m_trainInstances
Instances m_trainInstances
- The training instances
m_disTransform
DiscretizeFilter 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_missingSeperate
boolean m_missingSeperate
- Treat missing values as seperate values
m_locallyPredictive
boolean m_locallyPredictive
- Include locally predicitive attributes
m_corr_matrix
Matrix 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
m_missing_merge
boolean m_missing_merge
- Treat missing values as a seperate value
m_Binarize
boolean m_Binarize
- Just binarize numeric attributes
m_ChiSquareds
double[] m_ChiSquareds
- The chi-squared value for each attribute
m_trainingInstances
Instances m_trainingInstances
- 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 training instances
m_Classifier
Classifier m_Classifier
- holds the classifier to use for error estimates
m_Evaluation
Evaluation m_Evaluation
- holds the evaluation object to use for evaluating the classifier
m_holdOutFile
java.io.File m_holdOutFile
- the file that containts hold out/test instances
m_holdOutInstances
Instances m_holdOutInstances
- the instances to test on
m_useTraining
boolean m_useTraining
- evaluate on training data rather than seperate hold out/test set
m_trainInstances
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_disTransform
DiscretizeFilter m_disTransform
- Discretise numeric attributes
m_table
java.util.Hashtable m_table
- Hash table for evaluating feature subsets
m_starting
int[] m_starting
- holds a starting set as an array of attributes.
m_startRange
Range m_startRange
- the start set as a Range
m_bestGroup
java.util.BitSet m_bestGroup
- the best feature set found during the search
m_bestMerit
double m_bestMerit
- the merit of the best subset found
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_verbose
boolean m_verbose
- if true, then ouput new best subsets as the search progresses
m_stopAfterFirst
boolean m_stopAfterFirst
- stop after finding the first subset equal to or better than the
supplied start set (set to true if start set is supplied).
m_evaluations
int m_evaluations
- the number of subsets evaluated during the search
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
ASEvaluation m_ASEval
m_Instances
Instances m_Instances
m_startRange
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_trainInstances
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
m_starting
int[] m_starting
- holds a starting set as an array of attributes. Becomes one member of the
initial random population
m_startRange
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_population
GeneticSearch.GABitSet[] m_population
- the current population
m_popSize
int m_popSize
- the number of individual solutions
m_best
GeneticSearch.GABitSet m_best
- the best population member found during the search
m_bestFeatureCount
int m_bestFeatureCount
- the number of features in the best population member
m_lookupTableSize
int m_lookupTableSize
- the number of entries to cache for lookup
m_lookupTable
java.util.Hashtable m_lookupTable
- the lookup table
m_random
java.util.Random m_random
- random number generation
m_seed
int m_seed
- seed for random number generation
m_pCrossover
double m_pCrossover
- the probability of crossover occuring
m_pMutation
double m_pMutation
- the probability of mutation occuring
m_sumFitness
double m_sumFitness
- sum of the current population fitness
m_maxFitness
double m_maxFitness
m_minFitness
double m_minFitness
m_avgFitness
double m_avgFitness
m_maxGenerations
int m_maxGenerations
- the maximum number of generations to evaluate
m_reportFrequency
int m_reportFrequency
- how often reports are generated
m_generationReports
java.lang.StringBuffer m_generationReports
- holds the generation reports
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
m_trainInstances
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_trainInstances
Instances m_trainInstances
- The data to transform analyse/transform
m_trainCopy
Instances m_trainCopy
- Keep a copy for the class attribute (if set)
m_transformedFormat
Instances m_transformedFormat
- The header for the transformed data format
m_originalSpaceFormat
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 matrix for the original data
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
ReplaceMissingValuesFilter m_replaceMissingFilter
- Filters for original data
m_normalizeFilter
NormalizationFilter m_normalizeFilter
m_nominalToBinFilter
NominalToBinaryFilter m_nominalToBinFilter
m_attributeFilter
AttributeFilter m_attributeFilter
m_attribFilter
AttributeFilter m_attribFilter
- used to remove the class column if a class column is set
m_outputNumAtts
int m_outputNumAtts
- The number of attributes in the pc transformed data
m_normalize
boolean m_normalize
- normalize the input data?
m_coverVariance
double m_coverVariance
- the amount of varaince 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_eTranspose
double[][] m_eTranspose
- holds the transposed eigenvectors for converting back to the
original space
m_Instances
Instances m_Instances
m_raceType
int m_raceType
- the selected search type
m_xvalType
int m_xvalType
- the selected xval type
m_classIndex
int m_classIndex
- the class index
m_numAttribs
int m_numAttribs
- the number of attributes in the data
m_totalEvals
int m_totalEvals
- the total number of partially/fully evaluated subsets
m_bestMerit
double m_bestMerit
- holds the merit of the best subset found
m_theEvaluator
HoldOutSubsetEvaluator m_theEvaluator
- the subset evaluator to use
m_sigLevel
double m_sigLevel
- the significance level for comparisons
m_delta
double m_delta
- threshold for comparisons
m_samples
int m_samples
- the number of samples above which to begin testing for similarity
between competing subsets
m_numFolds
int m_numFolds
- number of cross validation folds---equal to the number of instances
for leave-one-out cv
m_ASEval
ASEvaluation m_ASEval
- the attribute evaluator to generate the initial ranking when
doing a rank race
m_Ranking
int[] m_Ranking
- will hold the attribute ranking produced by the above attribute
evaluator if doing a rank search
m_debug
boolean m_debug
- verbose output for monitoring the search and debugging
m_rankingRequested
boolean m_rankingRequested
- If true then produce a ranked list of attributes by fully traversing
a forward hillclimb race
m_rankedAtts
double[][] m_rankedAtts
- The ranked list of attributes produced if m_rankingRequested is true
m_rankedSoFar
int m_rankedSoFar
- The number of attributes ranked so far (if ranking is requested)
m_numToSelect
int m_numToSelect
- The number of attributes to retain if a ranking is requested. -1
indicates that all attributes are to be retained. Has precedence over
m_threshold
m_calculatedNumToSelect
int m_calculatedNumToSelect
m_threshold
double m_threshold
- the threshold for removing attributes if ranking is requested
m_starting
int[] m_starting
- holds a starting set as an array of attributes.
m_startRange
Range m_startRange
- holds the start set as a range
m_bestGroup
java.util.BitSet m_bestGroup
- the best feature set found during the search
m_bestMerit
double m_bestMerit
- the merit of the best subset found
m_onlyConsiderBetterAndSmaller
boolean m_onlyConsiderBetterAndSmaller
- only accept a feature set as being "better" than the best if its
merit is better or equal to the best, and it contains fewer
features than the best (this allows LVF to be implimented).
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_seed
int m_seed
- seed for random number generation
m_searchSize
double m_searchSize
- percentage of the search space to consider
m_iterations
int m_iterations
- the number of iterations performed
m_random
java.util.Random m_random
- random number object
m_verbose
boolean m_verbose
- output new best subsets as the search progresses
m_starting
int[] m_starting
- Holds the starting set as an array of attributes
m_startRange
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
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_best_group
java.util.BitSet m_best_group
- the best subset found
m_ASEval
ASEvaluation m_ASEval
- the attribute evaluator to use for generating the ranking
m_SubsetEval
ASEvaluation m_SubsetEval
- the subset evaluator with which to evaluate the ranking
m_Instances
Instances m_Instances
- the training instances
m_bestMerit
double m_bestMerit
- the merit of the best subset found
m_Ranking
int[] m_Ranking
- will hold the attribute ranking
m_trainInstances
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
m_trainInstances
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
m_trainInstances
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
Evaluation m_Evaluation
- holds an evaluation object
m_BaseClassifier
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_Classifier
Classifier m_Classifier
- The model base classifier to use
m_Classifiers
Classifier[] m_Classifiers
- Array for storing the generated base classifiers.
m_Betas
double[] m_Betas
- Array for storing the weights for the votes.
m_MaxIterations
int m_MaxIterations
- The maximum number of boost iterations
m_NumIterations
int m_NumIterations
- The number of successfully generated base classifiers.
m_WeightThreshold
int m_WeightThreshold
- Weight Threshold. The percentage of weight mass used in training
m_Debug
boolean m_Debug
- Debugging mode, gives extra output if true
m_UseResampling
boolean m_UseResampling
- Use boosting with reweighting?
m_Seed
int m_Seed
- Seed for boosting with resampling.
m_NumClasses
int m_NumClasses
- The number of classes
m_Classifier
Classifier m_Classifier
- Base classifier.
m_classIndex
int m_classIndex
- Class index.
m_shrinkage
double m_shrinkage
- Shrinkage (Learning rate). Default = no shrinkage.
m_additiveModels
FastVector m_additiveModels
- The list of iteratively generated models.
m_debug
boolean m_debug
- Produce debugging output.
m_maxModels
int m_maxModels
- Maximum number of models to produce. -1 indicates keep going until the error
threshold is met.
m_Classifier
Classifier m_Classifier
- The classifier
m_AttributeSelection
AttributeSelection m_AttributeSelection
- The attribute selection object
m_Evaluator
ASEvaluation m_Evaluator
- The attribute evaluator to use
m_Search
ASSearch m_Search
- The search method to use
m_ReducedHeader
Instances m_ReducedHeader
- The header of the dimensionally reduced data
m_numClasses
int m_numClasses
- The number of class vals in the training data (1 if class is numeric)
m_numAttributesSelected
double m_numAttributesSelected
- The number of attributes selected by the attribute selection phase
m_selectionTime
double m_selectionTime
- The time taken to select attributes in milliseconds
m_totalTime
double m_totalTime
- The time taken to select attributes AND build the classifier
m_Classifier
Classifier m_Classifier
- The model base classifier to use
m_Classifiers
Classifier[] m_Classifiers
- Array for storing the generated base classifiers.
m_NumIterations
int m_NumIterations
- The number of iterations.
m_Seed
int m_Seed
- The seed for random number generation.
m_BagSizePercent
int m_BagSizePercent
- The size of each bag sample, as a percentage of the training size
m_Classifiers
Classifier[] m_Classifiers
- The classifiers. (One for each class.)
m_ClassFilters
MakeIndicatorFilter[] m_ClassFilters
- The filters used to transform the class.
m_Classifier
Classifier m_Classifier
- The class name of the base classifier.
m_MatrixSource
int m_MatrixSource
- Indicates the current cost matrix source
m_OnDemandDirectory
java.io.File m_OnDemandDirectory
- The directory used when loading cost files on demand, null indicates
current directory
m_CostFile
java.lang.String m_CostFile
- The name of the cost file, for command line options
m_CostMatrix
CostMatrix m_CostMatrix
- The cost matrix
m_Classifier
Classifier m_Classifier
- The classifier
m_Seed
int m_Seed
- Seed for reweighting using resampling.
m_MinimizeExpectedCost
boolean m_MinimizeExpectedCost
- True if the costs should be used by selecting the minimum expected
cost (false means weight training data by the costs)
m_Classifier
Classifier m_Classifier
- The generated base classifier
m_ClassifierOptions
java.lang.String[] m_ClassifierOptions
- The base classifier options (not including those being set
by cross-validation)
m_BestClassifierOptions
java.lang.String[] m_BestClassifierOptions
- The set of all classifier options as determined by cross-validation
m_BestPerformance
double m_BestPerformance
- The cross-validated performance of the best options
m_CVParams
FastVector m_CVParams
- The set of parameters to cross-validate over
m_NumAttributes
int m_NumAttributes
- The number of attributes in the data
m_TrainFoldSize
int m_TrainFoldSize
- The number of instances in a training fold
m_NumFolds
int m_NumFolds
- The number of folds used in cross-validation
m_Seed
int m_Seed
- Random number seed
m_Debug
boolean m_Debug
- Debugging mode, gives extra output if true
m_AttIndex
int m_AttIndex
- The attribute used for classification.
m_SplitPoint
double m_SplitPoint
- The split point (index respectively).
m_Distribution
double[][] m_Distribution
- The distribution of class values or the means in each subset.
m_Instances
Instances m_Instances
- The instances used for training.
m_entries
java.util.Hashtable m_entries
- The hashtable used to hold training instances
m_decisionFeatures
int[] m_decisionFeatures
- Holds the final feature set
m_disTransform
DiscretizeFilter m_disTransform
- Discretization filter
m_delTransform
AttributeFilter m_delTransform
- Filter used to remove columns discarded by feature selection
m_ibk
IBk m_ibk
- IB1 used to classify non matching instances rather than majority class
m_theInstances
Instances m_theInstances
- Holds the training instances
m_numAttributes
int m_numAttributes
- The number of attributes in the dataset
m_numInstances
int m_numInstances
- The number of instances in the dataset
m_classIsNominal
boolean m_classIsNominal
- Class is nominal
m_debug
boolean m_debug
- Output debug info
m_useIBk
boolean m_useIBk
- Use the IBk classifier rather than majority class
m_displayRules
boolean m_displayRules
- Display Rules
m_maxStale
int m_maxStale
- Maximum number of fully expanded non improving subsets for a best
first search.
m_CVFolds
int m_CVFolds
- Number of folds for cross validating feature sets
m_rr
java.util.Random m_rr
- Random numbers for use in cross validation
m_majority
double m_majority
- Holds the majority class
this$0
DecisionTable this$0
attributes
double[] attributes
- Array of attribute values for an instance
missing
boolean[] missing
- True for an index if the corresponding attribute value is missing.
values
java.lang.String[] values
- The values
key
int key
- The key
this$0
DecisionTable this$0
m_Classifier
Classifier m_Classifier
- The classifier.
m_Classifier
Classifier m_Classifier
- The classifier
m_Filter
Filter m_Filter
- The filter
m_FilteredInstances
Instances m_FilteredInstances
- The instance structure of the filtered instances
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_Instances
Instances m_Instances
- The structure of the training data
m_HyperPipes
weka.classifiers.HyperPipes.HyperPipe[] m_HyperPipes
- Stores the HyperPipe for each class
m_Train
Instances m_Train
- The training instances used for classification.
m_MinArray
double[] m_MinArray
- The minimum values for numeric attributes.
m_MaxArray
double[] m_MaxArray
- The maximum values for numeric attributes.
m_Train
Instances m_Train
- The training instances used for classification.
m_NumClasses
int m_NumClasses
- The number of class values (or 1 if predicting numeric)
m_ClassType
int m_ClassType
- The class attribute type
m_Min
double[] m_Min
- The minimum values for numeric attributes.
m_Max
double[] m_Max
- The maximum values for numeric attributes.
m_kNN
int m_kNN
- The number of neighbours to use for classification (currently)
m_kNNUpper
int m_kNNUpper
- The value of kNN provided by the user. This may differ from
m_kNN if cross-validation is being used
m_kNNValid
boolean m_kNNValid
- Whether the value of k selected by cross validation has
been invalidated by a change in the training instances
m_WindowSize
int m_WindowSize
- The maximum number of training instances allowed. When
this limit is reached, old training instances are removed,
so the training data is "windowed". Set to 0 for unlimited
numbers of instances.
m_DistanceWeighting
int m_DistanceWeighting
- Whether the neighbours should be distance-weighted
m_CrossValidate
boolean m_CrossValidate
- Whether to select k by cross validation
m_MeanSquared
boolean m_MeanSquared
- Whether to minimise mean squared error rather than mean absolute
error when cross-validating on numeric prediction tasks
m_Debug
boolean m_Debug
- True if debugging output should be printed
m_DontNormalize
boolean m_DontNormalize
- True if normalization is turned off
m_NumAttributesUsed
double m_NumAttributesUsed
- The number of attributes the contribute to a prediction
m_Successors
Id3[] m_Successors
- The node's successors.
m_Attribute
Attribute m_Attribute
- Attribute used for splitting.
m_ClassValue
double m_ClassValue
- Class value if node is leaf.
m_Distribution
double[] m_Distribution
- Class distribution if node is leaf.
m_ClassAttribute
Attribute m_ClassAttribute
- Class attribute of dataset.
m_Counts
double[] m_Counts
- The number of instances in each class (null if class numeric).
m_Instances
Instances m_Instances
- The instances used for "training".
m_MinArray
double[] m_MinArray
- The minimum values for numeric attributes.
m_MaxArray
double[] m_MaxArray
- The maximum values for numeric attributes.
m_Coefficients
double[] m_Coefficients
- Array for storing coefficients of linear regression.
m_SelectedAttributes
boolean[] m_SelectedAttributes
- Which attributes are relevant?
m_TransformedData
Instances m_TransformedData
- Variable for storing transformed training data.
m_MissingFilter
ReplaceMissingValuesFilter m_MissingFilter
- The filter for removing missing values.
m_TransformFilter
NominalToBinaryFilter m_TransformFilter
- The filter storing the transformation from nominal to binary attributes.
m_StdDev
double[] m_StdDev
- The standard deviations of the attributes
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
b_Debug
boolean b_Debug
- True if debug output will be printed
m_AttributeSelection
int m_AttributeSelection
- The current attribute selection method
m_LL
double m_LL
- The log-likelihood of the built model
m_LLn
double m_LLn
- The log-likelihood of the null model
m_Par
double[] m_Par
- The coefficients of the model
m_NumPredictors
int m_NumPredictors
- The number of attributes in the model
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_Ridge
double m_Ridge
- The ridge parameter.
m_NominalToBinary
NominalToBinaryFilter m_NominalToBinary
- The filter used to make attributes numeric.
m_ReplaceMissingValues
ReplaceMissingValuesFilter m_ReplaceMissingValues
- The filter used to get rid of missing values.
m_Debug
boolean m_Debug
- Debugging output
m_f
double m_f
fvec
double[] fvec
m_nn
int m_nn
m_check
int m_check
m_ALF
double m_ALF
m_TOLX
double m_TOLX
m_TOLMIN
double m_TOLMIN
m_STPMX
double m_STPMX
m_MAXITS
int m_MAXITS
m_Classifiers
Classifier[][] m_Classifiers
- Array for storing the generated base classifiers.
m_Classifier
Classifier m_Classifier
- An instantiated base classifier used for getting and testing options
m_MaxIterations
int m_MaxIterations
- The maximum number of boost iterations
m_NumClasses
int m_NumClasses
- The number of classes
m_NumIterations
int m_NumIterations
- The number of successfully generated base classifiers.
m_WeightThreshold
int m_WeightThreshold
- Weight thresholding. The percentage of weight mass used in training
m_Debug
boolean m_Debug
- Debugging mode, gives extra output if true
m_NumericClassData
Instances m_NumericClassData
- Dummy dataset with a numeric class
m_ClassAttribute
Attribute m_ClassAttribute
- The actual class attribute (for getting class names)
m_UseResampling
boolean m_UseResampling
- Use boosting with reweighting?
m_Seed
int m_Seed
- Seed for boosting with resampling.
m_Train
Instances m_Train
- The training instances used for classification.
m_Min
double[] m_Min
- The minimum values for numeric attributes.
m_Max
double[] m_Max
- The maximum values for numeric attributes.
m_Debug
boolean m_Debug
- True if debugging output should be printed
m_kNN
int m_kNN
- The number of neighbours used to select the kernel bandwidth
m_WeightKernel
int m_WeightKernel
- The weighting kernel method currently selected
m_UseAllK
boolean m_UseAllK
- True if m_kNN should be set to all instances
m_MatrixSource
int m_MatrixSource
- Indicates the current cost matrix source
m_OnDemandDirectory
java.io.File m_OnDemandDirectory
- The directory used when loading cost files on demand, null indicates
current directory
m_CostFile
java.lang.String m_CostFile
- The name of the cost file, for command line options
m_Classifier
Classifier m_Classifier
- The classifier
m_CostMatrix
CostMatrix m_CostMatrix
- The cost matrix
m_NumIterations
int m_NumIterations
- The number of iterations.
m_Seed
int m_Seed
- Seed for reweighting using resampling.
m_BagSizePercent
int m_BagSizePercent
- The size of each bag sample, as a percentage of the training size
m_Classifiers
Classifier[] m_Classifiers
- The classifiers. (One for each class.)
m_ClassFilters
MakeIndicatorFilter[] m_ClassFilters
- The filters used to transform the class.
m_Classifier
DistributionClassifier m_Classifier
- The class name of the base classifier.
m_ZeroR
ZeroR m_ZeroR
- ZeroR classifier for when all base classifier return zero probability.
m_ClassAttribute
Attribute m_ClassAttribute
- Internal copy of the class attribute for output purposes
m_RandomWidthFactor
double m_RandomWidthFactor
- The multiplier when generating random codes. Will generate
numClasses * m_RandomWidthFactor codes
m_ErrorMode
int m_ErrorMode
- The error-correcting output code method to use
m_Classifier
Classifier m_Classifier
- The classifier that had the best performance on training data.
m_Classifiers
Classifier[] m_Classifiers
- The list of classifiers
m_ClassifierIndex
int m_ClassifierIndex
- The index into the vector for the selected scheme
m_NumXValFolds
int m_NumXValFolds
- Number of folds to use for cross validation (0 means use training
error for selection)
m_Debug
boolean m_Debug
- Debugging mode, gives extra output if true
m_Seed
int m_Seed
- Random number seed
m_Distributions
Estimator[][] m_Distributions
- The attribute estimators.
m_ClassDistribution
Estimator m_ClassDistribution
- The class estimator.
m_UseKernelEstimator
boolean m_UseKernelEstimator
- Whether to use kernel density estimator rather than normal distribution
for numeric attributes
m_NumClasses
int m_NumClasses
- The number of classes (or 1 for numeric class)
m_Instances
Instances m_Instances
- The dataset header for the purposes of printing out a semi-intelligible
model
m_Counts
double[][][] m_Counts
- All the counts for nominal attributes.
m_Means
double[][] m_Means
- The means for numeric attributes.
m_Devs
double[][] m_Devs
- The standard deviations for numeric attributes.
m_Priors
double[] m_Priors
- The prior probabilities of the classes.
m_Instances
Instances m_Instances
- The instances used for training.
m_rule
weka.classifiers.OneR.OneRRule m_rule
- A 1-R rule
m_minBucketSize
int m_minBucketSize
- The minimum bucket size
m_rules
weka.classifiers.Prism.PrismRule m_rules
- The first rule in the list of rules
m_Classifier
DistributionClassifier m_Classifier
- The subclassifier.
m_Discretizer
DiscretizeFilter m_Discretizer
- The discretization filter.
m_NumBins
int m_NumBins
- The number of classes in the Discretized training data.
m_ClassMeans
double[] m_ClassMeans
- The mean values for each Discretized class interval.
m_Debug
boolean m_Debug
- Whether debugging output will be printed
m_OptimizeBins
boolean m_OptimizeBins
- Whether the Discretizer will optimise the number of bins
m_exponent
double m_exponent
- The exponent for the polnomial kernel.
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_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
Instances m_data
- The training data.
m_weights
double[] m_weights
- Weight vector for linear machine.
m_storage
double[] m_storage
- Kernel function cache
m_keys
int[] m_keys
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.SMO.SMOset m_I0
- The five different sets used by the algorithm.
m_I1
weka.classifiers.SMO.SMOset m_I1
m_I2
weka.classifiers.SMO.SMOset m_I2
m_I3
weka.classifiers.SMO.SMOset m_I3
m_I4
weka.classifiers.SMO.SMOset m_I4
m_supportVectors
weka.classifiers.SMO.SMOset m_supportVectors
- The set of support vectors
m_NominalToBinary
NominalToBinaryFilter m_NominalToBinary
- The filter used to make attributes numeric.
m_Normalization
NormalizationFilter m_Normalization
- The filter used to normalize all values.
m_Missing
ReplaceMissingValuesFilter m_Missing
- The filter used to get rid of missing values.
m_kernelEvals
int m_kernelEvals
- Counts the number of kernel evaluations.
m_cacheSize
int m_cacheSize
- The size of the cache (a prime number)
m_Normalize
boolean m_Normalize
- True if we don't want to normalize
m_rescale
boolean m_rescale
- Rescale?
m_lowerOrder
boolean m_lowerOrder
- Use lower-order terms?
m_onlyNumeric
boolean m_onlyNumeric
- Only numeric attributes in the dataset?
m_MetaClassifier
Classifier m_MetaClassifier
- The meta classifier.
m_BaseClassifiers
Classifier[] m_BaseClassifiers
- The base classifiers.
m_MetaFormat
Instances m_MetaFormat
- Format for meta data
m_BaseFormat
Instances m_BaseFormat
- Format for base data
m_NumFolds
int m_NumFolds
- Set the number of folds for the cross-validation
m_Seed
int m_Seed
- Random number seed
m_Classifier
DistributionClassifier m_Classifier
- The generated base classifier
m_HighThreshold
double m_HighThreshold
- The upper threshold used as the basis of correction
m_LowThreshold
double m_LowThreshold
- The lower threshold used as the basis of correction
m_BestThreshold
double m_BestThreshold
- The threshold that lead to the best performance
m_BestValue
double m_BestValue
- The best value that has been observed
m_NumXValFolds
int m_NumXValFolds
- The number of folds used in cross-validation
m_Seed
int m_Seed
- Random number seed
m_DesignatedClass
int m_DesignatedClass
- Designated class value, determined during building
m_ClassMode
int m_ClassMode
- Method to determine which class to optimize for
m_EvalMode
int m_EvalMode
- The evaluation mode
m_RangeMode
int m_RangeMode
- The range correction mode
m_tView
TreeVisualizer m_tView
- The tree display panel.
m_iView
VisualizePanel m_iView
- The instances display.
m_top
weka.classifiers.UserClassifier.TreeClass m_top
- Two references to the structure of the decision tree.
m_focus
weka.classifiers.UserClassifier.TreeClass m_focus
- Two references to the structure of the decision tree.
m_nextId
int m_nextId
- The next number that can be used as a unique id for a node.
m_treeFrame
javax.swing.JFrame m_treeFrame
- These two frames aren't used anymore.
m_visFrame
javax.swing.JFrame m_visFrame
m_reps
javax.swing.JTabbedPane m_reps
- The tabbed window for the tree and instances view.
m_mainWin
javax.swing.JFrame m_mainWin
- The window.
m_built
boolean m_built
- The status of whether there is a decision tree ready or not.
m_classifiers
GenericObjectEditor m_classifiers
- A list of other m_classifiers.
m_propertyDialog
PropertyDialog m_propertyDialog
- A window for selecting other classifiers.
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_NumClasses
int m_NumClasses
- The number of classes
m_Instances
Instances m_Instances
- The training data
m_counts
double[][][] m_counts
- The class counts for each interval of each attribute
m_globalCounts
double[] m_globalCounts
- The global class counts
m_intervalBounds
double[][] m_intervalBounds
- The lower bounds for each attribute
m_maxEntrop
double m_maxEntrop
- The maximum entropy for the class
m_weightByConfidence
boolean m_weightByConfidence
- Exponentially bias more confident intervals
m_bias
double m_bias
- Bias towards more confident intervals
TINY
double TINY
m_MaxK
int m_MaxK
- The maximum number of alterations to the perceptron
m_NumIterations
int m_NumIterations
- The number of iterations
m_Exponent
double m_Exponent
- The exponent
m_K
int m_K
- The actual number of alterations
m_Additions
int[] m_Additions
- The training instances added to the perceptron
m_IsAddition
boolean[] m_IsAddition
- Addition or subtraction?
m_Weights
int[] m_Weights
- The weights for each perceptron
m_Train
Instances m_Train
- The training instances
m_Seed
int m_Seed
- Seed used for shuffling the dataset
m_NominalToBinary
NominalToBinaryFilter m_NominalToBinary
- The filter used to make attributes numeric.
m_ReplaceMissingValues
ReplaceMissingValuesFilter m_ReplaceMissingValues
- The filter used to get rid of missing values.
m_ClassValue
double m_ClassValue
- The class value 0R predicts.
m_Counts
double[] m_Counts
- The number of instances in each class (null if class numeric).
m_Class
Attribute m_Class
- The class attribute.
|
Package weka.classifiers.adtree |
m_trainInstances
Instances m_trainInstances
- The instances used to train the tree
m_root
PredictionNode m_root
- The root of the tree
m_random
java.util.Random m_random
- The random number generator - used for the random search heuristic
m_lastAddedSplitNum
int m_lastAddedSplitNum
- The number of the last splitter added to the tree
m_numericAttIndices
int[] m_numericAttIndices
- An array containing the inidices to the numeric attributes in the data
m_nominalAttIndices
int[] m_nominalAttIndices
- An array containing the inidices to the nominal attributes in the data
m_trainTotalWeight
double m_trainTotalWeight
- The total weight of the instances - used to speed Z calculations
m_posTrainInstances
ReferenceInstances m_posTrainInstances
- The training instances with positive class - referencing the training dataset
m_negTrainInstances
ReferenceInstances m_negTrainInstances
- The training instances with negative class - referencing the training dataset
m_search_bestInsertionNode
PredictionNode m_search_bestInsertionNode
- The best node to insert under, as found so far by the latest search
m_search_bestSplitter
Splitter m_search_bestSplitter
- The best splitter to insert, as found so far by the latest search
m_search_smallestZ
double m_search_smallestZ
- The smallest Z value found so far by the latest search
m_search_bestPathPosInstances
Instances m_search_bestPathPosInstances
- The positive instances that apply to the best path found so far
m_search_bestPathNegInstances
Instances m_search_bestPathNegInstances
- The negative instances that apply to the best path found so far
m_nodesExpanded
int m_nodesExpanded
- Statistics - the number of prediction nodes investigated during search
m_examplesCounted
int m_examplesCounted
- Statistics - the number of instances processed during search
m_boostingIterations
int m_boostingIterations
- Option - the number of boosting iterations o perform
m_searchPath
int m_searchPath
- Option - the search mode
m_randomSeed
int m_randomSeed
- Option - the seed to use for a random search
m_saveInstanceData
boolean m_saveInstanceData
- Option - whether the tree should remember the instance data
value
double value
- The prediction value stored in this node
children
FastVector children
- The children of this node - any number of splitter nodes
orderAdded
int orderAdded
- The number this node was in the order of nodes added to the tree
attIndex
int attIndex
- The index of the attribute the split depends on
trueSplitValue
int trueSplitValue
- The attribute value that is compared against
children
PredictionNode[] children
- The children of this split
attIndex
int attIndex
- The index of the attribute the split depends on
splitPoint
double splitPoint
- The attribute value that is compared against
children
PredictionNode[] children
- The children of this split
|
Package weka.classifiers.evaluation |
m_ClassNames
java.lang.String[] m_ClassNames
- Stores the names of the classes
m_Distribution
double[] m_Distribution
- The predicted probabilities
m_Actual
double m_Actual
- The actual class value
m_Predicted
double m_Predicted
- The predicted class value
m_Weight
double m_Weight
- The weight assigned to this prediction
m_Actual
double m_Actual
- The actual class value
m_Predicted
double m_Predicted
- The predicted class value
m_Weight
double m_Weight
- The weight assigned to this prediction
|
Package weka.classifiers.j48 |
m_minNoObj
int m_minNoObj
- Minimum number of instances in interval.
m_allData
Instances m_allData
- The FULL training dataset.
m_attIndex
int m_attIndex
- Attribute to split on.
m_minNoObj
int m_minNoObj
- Minimum number of objects in a split.
m_splitPoint
double m_splitPoint
- Value of split point.
m_infoGain
double m_infoGain
- InfoGain of split.
m_gainRatio
double m_gainRatio
- GainRatio of split.
m_sumOfWeights
double m_sumOfWeights
- The sum of the weights of the instances.
m_minNoObj
int m_minNoObj
- Minimum number of objects in interval.
m_allData
Instances m_allData
- All the training data
m_pruneTheTree
boolean m_pruneTheTree
- True if the tree is to be pruned.
m_CF
float m_CF
- The confidence factor for pruning.
m_subtreeRaising
boolean m_subtreeRaising
- Is subtree raising to be performed?
m_cleanup
boolean m_cleanup
- Cleanup after the tree has been built.
CF
double CF
- CF
minNumObj
int minNumObj
- Minimum number of objects
m_complexityIndex
int m_complexityIndex
- Desired number of branches.
m_attIndex
int m_attIndex
- Attribute to split on.
m_minNoObj
int m_minNoObj
- Minimum number of objects in a split.
m_splitPoint
double m_splitPoint
- Value of split point.
m_infoGain
double m_infoGain
- InfoGain of split.
m_gainRatio
double m_gainRatio
- GainRatio of split.
m_sumOfWeights
double m_sumOfWeights
- The sum of the weights of the instances.
m_index
int m_index
- Number of split points.
m_toSelectModel
ModelSelection m_toSelectModel
- The model selection method.
m_localModel
ClassifierSplitModel m_localModel
- Local model at node.
m_sons
ClassifierDecList[] m_sons
- References to sons.
m_isLeaf
boolean m_isLeaf
- True if node is leaf.
m_isEmpty
boolean m_isEmpty
- True if node is empty.
m_train
Instances m_train
- The training instances.
m_test
Distribution m_test
- The pruning instances.
indeX
int indeX
- Which son to expand?
m_distribution
Distribution m_distribution
- Distribution of class values.
m_numSubsets
int m_numSubsets
- Number of created subsets.
m_toSelectModel
ModelSelection m_toSelectModel
- The model selection method.
m_localModel
ClassifierSplitModel m_localModel
- Local model at node.
m_sons
ClassifierTree[] m_sons
- References to sons.
m_isLeaf
boolean m_isLeaf
- True if node is leaf.
m_isEmpty
boolean m_isEmpty
- True if node is empty.
m_train
Instances m_train
- The training instances.
m_test
Distribution m_test
- The pruning instances.
m_id
int m_id
- The id for the node.
m_perClassPerBag
double[][] m_perClassPerBag
- Weight of instances per class per bag.
m_perBag
double[] m_perBag
- Weight of instances per bag.
m_perClass
double[] m_perClass
- Weight of instances per class.
totaL
double totaL
- Total weight of instances.
m_root
ClassifierTree m_root
- The decision tree
m_unpruned
boolean m_unpruned
- Unpruned tree?
m_CF
float m_CF
- Confidence level
m_minNumObj
int m_minNumObj
- Minimum number of instances
m_useLaplace
boolean m_useLaplace
- Determines whether probabilities are smoothed using
Laplace correction when predictions are generated
m_reducedErrorPruning
boolean m_reducedErrorPruning
- Use reduced error pruning?
m_numFolds
int m_numFolds
- Number of folds for reduced error pruning.
m_binarySplits
boolean m_binarySplits
- Binary splits on nominal attributes?
m_subtreeRaising
boolean m_subtreeRaising
- Subtree raising to be performed?
m_noCleanup
boolean m_noCleanup
- Cleanup after the tree has been built.
theRules
java.util.Vector theRules
- Vector storing the rules.
CF
double CF
- The confidence for C45-type pruning.
minNumObj
int minNumObj
- Minimum number of objects
toSelectModeL
ModelSelection toSelectModeL
- The model selection method.
numSetS
int numSetS
- How many subsets of equal size? One used for pruning, the rest for training.
reducedErrorPruning
boolean reducedErrorPruning
- Use reduced error pruning?
m_root
MakeDecList m_root
- The decision list
m_CF
float m_CF
- Confidence level
m_minNumObj
int m_minNumObj
- Minimum number of objects
m_reducedErrorPruning
boolean m_reducedErrorPruning
- Use reduced error pruning?
m_numFolds
int m_numFolds
- Number of folds for reduced error pruning.
m_binarySplits
boolean m_binarySplits
- Binary splits on nominal attributes?
pruneTheTree
boolean pruneTheTree
- True if the tree is to be pruned.
numSets
int numSets
- How many subsets of equal size? One used for pruning, the rest for training.
m_cleanup
boolean m_cleanup
- Cleanup after the tree has been built.
m_MinNumObj
int m_MinNumObj
- Minimum number of objects
|
Package weka.classifiers.kstar |
m_Train
Instances m_Train
- The training instances used for classification.
m_NumInstances
int m_NumInstances
- The number of instances in the dataset
m_NumClasses
int m_NumClasses
- The number of class values
m_NumAttributes
int m_NumAttributes
- The number of attributes
m_ClassType
int m_ClassType
- The class attribute type
m_RandClassCols
int[][] m_RandClassCols
- Table of random class value colomns
m_ComputeRandomCols
int m_ComputeRandomCols
- Flag turning on and off the computation of random class colomns
m_InitFlag
int m_InitFlag
- Flag turning on and off the initialisation of config variables
m_Cache
KStarCache[] m_Cache
- A custom data structure for caching distinct attribute values
and their scale factor or stop parameter.
m_MissingMode
int m_MissingMode
- missing value treatment
m_BlendMethod
int m_BlendMethod
- 0 = use specified blend, 1 = entropic blend setting
m_GlobalBlend
int m_GlobalBlend
- default sphere of influence blend setting
|
Package weka.classifiers.m5 |
numInstances
int numInstances
missingInstances
int missingInstances
sumErr
double sumErr
sumAbsErr
double sumAbsErr
sumSqrErr
double sumSqrErr
meanSqrErr
double meanSqrErr
rootMeanSqrErr
double rootMeanSqrErr
meanAbsErr
double meanAbsErr
terms
int[] terms
coeffs
double[] coeffs
m_root
Node[] m_root
- The root node
options
Options options
- The options
m_UseUnsmoothed
boolean m_UseUnsmoothed
- No smoothing?
m_PruningFactor
double m_PruningFactor
- Pruning factor
m_Model
int m_Model
- Type of model
m_Verbosity
int m_Verbosity
- Verbosity
m_ReplaceMissingValuesFilter
ReplaceMissingValuesFilter m_ReplaceMissingValuesFilter
- Filter for replacing missing values.
m_NominalToBinaryFilter
NominalToBinaryFilter m_NominalToBinaryFilter
- Filter for replacing nominal attributes with numeric binary ones.
type
boolean type
splitAttr
int splitAttr
splitValue
double splitValue
unsmoothed
Function unsmoothed
smoothed
Function smoothed
valueNode
boolean valueNode
upNode
Node upNode
leftNode
Node leftNode
rightNode
Node rightNode
errors
Errors errors
numParameters
int numParameters
sf
SplitInfo sf
lm
int lm
instances
Instances instances
model
int model
pruningFactor
double pruningFactor
deviation
double deviation
smooth
boolean smooth
randomSeed
int randomSeed
classcol
int classcol
verbosity
int verbosity
model
int model
numFolds
int numFolds
pruningFactor
double pruningFactor
trainFile
java.lang.String trainFile
testFile
java.lang.String testFile
lmNo
int lmNo
deviation
double deviation
number
int number
first
int first
last
int last
position
int position
maxImpurity
double maxImpurity
leftAve
double leftAve
rightAve
double rightAve
splitAttr
int splitAttr
splitValue
double splitValue
|
Package weka.classifiers.neural |
m_inputList
NeuralConnection[] m_inputList
- The list of inputs to this unit.
m_outputList
NeuralConnection[] m_outputList
- The list of outputs from this unit.
m_inputNums
int[] m_inputNums
- The numbering for the connections at the other end of the input lines.
m_outputNums
int[] m_outputNums
- The numbering for the connections at the other end of the out lines.
m_numInputs
int m_numInputs
- The number of inputs.
m_numOutputs
int m_numOutputs
- The number of outputs.
m_unitValue
double m_unitValue
- The output value for this unit, NaN if not calculated.
m_unitError
double m_unitError
- The error value for this unit, NaN if not calculated.
m_weightsUpdated
boolean m_weightsUpdated
- True if the weights have already been updated.
m_id
java.lang.String m_id
- The string that uniquely (provided naming is done properly) identifies
this unit.
m_type
int m_type
- The type of unit this is.
m_x
double m_x
- The x coord of this unit purely for displaying purposes.
m_y
double m_y
- The y coord of this unit purely for displaying purposes.
m_instances
Instances m_instances
- The training instances.
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
NeuralNetwork.NeuralEnd[] m_outputs
- The output units.(only feeds the errors, does no calcs)
m_inputs
NeuralNetwork.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.neural.NeuralNetwork.NodePanel m_nodePanel
- The panel the nodes are displayed on.
m_controlPanel
weka.classifiers.neural.NeuralNetwork.ControlPanel m_controlPanel
- The control panel.
m_nextId
int m_nextId
- The next id number available for default naming.
m_selected
FastVector m_selected
- A Vector list of the units currently selected.
m_graphers
FastVector 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
long 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
NominalToBinaryFilter 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_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.
this$0
NeuralNetwork this$0
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.
m_weights
double[] m_weights
- The weights for each of the input connections, and the threshold.
m_changeInWeights
double[] m_changeInWeights
- The change in the weights.
m_random
java.util.Random m_random
m_methods
NeuralMethod m_methods
- Performs the operations for this node. Currently this
defines that the node is either a sigmoid or a linear unit.
tree
weka.clusterers.Cobweb.CTree tree
- the cobweb tree
numClusters
int numClusters
- number of clusters
m_Clusterer
Clusterer m_Clusterer
- The clusterer.
m_model
Estimator[][] m_model
- hold the discrete estimators for each cluster
m_modelNormal
double[][][] m_modelNormal
- hold the normal estimators for each cluster
m_minStdDev
double m_minStdDev
- default minimum standard deviation
m_weights
double[][] m_weights
- hold the weights of each instance for each cluster
m_priors
double[] m_priors
- the prior probabilities for clusters
m_loglikely
double m_loglikely
- the loglikelihood of the data
m_theInstances
Instances m_theInstances
- training instances
m_num_clusters
int m_num_clusters
- number of clusters selected by the user or cross validation
m_initialNumClusters
int m_initialNumClusters
- the initial number of clusters requested by the user--- -1 if
xval is to be used to find the number of clusters
m_num_attribs
int m_num_attribs
- number of attributes
m_num_instances
int m_num_instances
- number of training instances
m_max_iterations
int m_max_iterations
- maximum iterations to perform
m_rr
java.util.Random m_rr
- random numbers and seed
m_rseed
int m_rseed
m_verbose
boolean m_verbose
- Verbose?
m_instances
Instances m_instances
- training instances
m_ReplaceMissingFilter
ReplaceMissingValuesFilter m_ReplaceMissingFilter
- replace missing values in training instances
m_NumClusters
int m_NumClusters
- number of clusters to generate
m_ClusterCentroids
Instances m_ClusterCentroids
- holds the cluster centroids
m_ClusterAssignments
int[] m_ClusterAssignments
- temporary variable holding cluster assignments while iterating
m_Seed
int m_Seed
- random seed
m_Min
double[] m_Min
- attribute min values
m_Max
double[] m_Max
- attribute max values
m_Name
java.lang.String m_Name
- The attribute's name.
m_Type
int m_Type
- The attribute's type.
m_Values
FastVector m_Values
- The attribute's values (if nominal or string).
m_Hashtable
java.util.Hashtable m_Hashtable
- Mapping of values to indices (if nominal or string).
m_Index
int m_Index
- The attribute's index.
m_Objects
java.lang.Object[] m_Objects
- The array of objects.
m_Size
int m_Size
- The current size;
m_CapacityIncrement
int m_CapacityIncrement
- The capacity increment
m_CapacityMultiplier
double m_CapacityMultiplier
- The capacity multiplier.
m_Dataset
Instances m_Dataset
- The dataset the instance has access to. Null if the instance
doesn't have access to any dataset. Only if an instance has
access to a dataset, it knows about the actual attribute types.
m_AttValues
double[] m_AttValues
- The instance's attribute values.
m_Weight
double m_Weight
- The instance's weight.
m_RelationName
java.lang.String m_RelationName
- The dataset's name.
m_Attributes
FastVector m_Attributes
- The attribute information.
m_Instances
FastVector m_Instances
- The instances.
m_ClassIndex
int m_ClassIndex
- The class attribute's index
m_ValueBuffer
double[] m_ValueBuffer
- Buffer of values for sparse instance
m_IndicesBuffer
int[] m_IndicesBuffer
- Buffer of indices for sparse instance
m_Elements
double[][] m_Elements
- The data in the matrix.
m_Head
Queue.QueueNode m_Head
- Store a reference to the head of the queue
m_Tail
Queue.QueueNode m_Tail
- Store a reference to the tail of the queue
m_Size
int m_Size
- Store the current number of elements in the queue
this$0
Queue this$0
m_Next
Queue.QueueNode m_Next
- The next node in the queue
m_Contents
java.lang.Object m_Contents
- The nodes contents
m_RangeStrings
java.util.Vector m_RangeStrings
- Record the string representations of the columns to delete
m_Invert
boolean m_Invert
- Whether matching should be inverted
m_SelectFlags
boolean[] m_SelectFlags
- The array of flags for whether an column is selected
m_Upper
int m_Upper
- Store the maximum value permitted in the range. -1 indicates that
no upper value has been set
m_Serialized
byte[] m_Serialized
- Stores the serialized object
m_Compressed
boolean m_Compressed
- True if the object has been compressed during storage
m_Indices
int[] m_Indices
- The index of the attribute associated with each stored value.
m_NumAttributes
int m_NumAttributes
- The maximum number of values that can be stored.
|
Package weka.core.converters |
m_Retrieval
int m_Retrieval
m_structure
Instances m_structure
- Holds the determined structure (header) of the data set.
m_structure
Instances m_structure
- Holds the determined structure (header) of the data set.
m_sourceFile
java.io.File m_sourceFile
- Holds the source of the data set. In this case the names file of the
data set. m_sourceFileData is the data file.
m_sourceFileData
java.io.File m_sourceFileData
- Describe variable
m_sourceFileData here.
m_fileStem
java.lang.String m_fileStem
- Holds the filestem.
m_numAttribs
int m_numAttribs
- Number of attributes in the data (including ignore and label attributes).
m_ignore
boolean[] m_ignore
- Which attributes are ignore or label. These are *not* included in the
arff representation.
m_structure
Instances m_structure
- Holds the determined structure (header) of the data set.
m_sourceFile
java.io.File m_sourceFile
- Holds the source of the data set.
m_cumulativeStructure
FastVector m_cumulativeStructure
- A list of hash tables for accumulating nominal values during parsing.
m_cumulativeInstances
FastVector m_cumulativeInstances
- Holds instances accumulated so far
m_Dataset
Instances m_Dataset
- Holds the structure (header) of the data set.
m_IncrementalIndex
int m_IncrementalIndex
- The current index position for incremental reading
m_Counts
double[] m_Counts
- Hold the counts
m_SumOfCounts
double m_SumOfCounts
- Hold the sum of counts
m_Values
double[] m_Values
- Vector containing all of the values seen
m_Weights
double[] m_Weights
- Vector containing the associated weights
m_NumValues
int m_NumValues
- Number of values stored in m_Weights and m_Values so far
m_SumOfWeights
double m_SumOfWeights
- The sum of the weights so far
m_StandardDev
double m_StandardDev
- The standard deviation
m_Precision
double m_Precision
- The precision of data values
m_AllWeightsOne
boolean m_AllWeightsOne
- Whether we can optimise the kernel summation
m_CovarianceInverse
Matrix m_CovarianceInverse
- The inverse of the covariance matrix
m_Determinant
double m_Determinant
- The determinant of the covariance matrix
m_ConstDelta
double m_ConstDelta
- The difference between the conditioning value and the conditioning mean
m_ValueMean
double m_ValueMean
- The mean of the values
m_SumOfWeights
double m_SumOfWeights
- The sum of the weights
m_SumOfValues
double m_SumOfValues
- The sum of the values seen
m_SumOfValuesSq
double m_SumOfValuesSq
- The sum of the values squared
m_Mean
double m_Mean
- The current mean
m_StandardDev
double m_StandardDev
- The current standard deviation
m_Precision
double m_Precision
- The precision of numeric values ( = minimum std dev permitted)
m_NumValues
double m_NumValues
- The number of values seen
m_SumOfValues
double m_SumOfValues
- The sum of the values seen
m_Lambda
double m_Lambda
- The average number of times
an event occurs in an interval.
m_Instances
Instances m_Instances
- The dataset of interest
m_ResultListener
ResultListener m_ResultListener
- The ResultListener to send results to
m_ResultProducer
ResultProducer m_ResultProducer
- The ResultProducer used to generate results
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
- The names of any additional measures to look for in SplitEvaluators
m_ExpectedResultsPerAverage
int m_ExpectedResultsPerAverage
- The number of results expected to average over for each run
m_CalculateStdDevs
boolean m_CalculateStdDevs
- True if standard deviation fields should be produced
m_CountFieldName
java.lang.String m_CountFieldName
- The name of the field that will contain the number of results
averaged over.
m_KeyFieldName
java.lang.String m_KeyFieldName
- The name of the key field to average over
m_KeyIndex
int m_KeyIndex
- The index of the field to average over in the resultproducers key
m_Keys
FastVector m_Keys
- Collects the keys from a single run
m_Results
FastVector m_Results
- Collects the results from a single run
m_Classifier
Classifier m_Classifier
- The classifier used for evaluation
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
- The names of any additional measures to look for in SplitEvaluators
m_doesProduce
boolean[] m_doesProduce
- Array of booleans corresponding to the measures in m_AdditionalMeasures
indicating which of the AdditionalMeasures the current classifier
can produce
m_numberAdditionalMeasures
int m_numberAdditionalMeasures
- The number of additional measures that need to be filled in
after taking into account column constraints imposed by the final
destination for results
m_result
java.lang.String m_result
- Holds the statistics for the most recent application of the classifier
m_ClassifierOptions
java.lang.String m_ClassifierOptions
- The classifier options (if any)
m_ClassifierVersion
java.lang.String m_ClassifierVersion
- The classifier version
m_IRclass
int m_IRclass
- Class index for information retrieval statistics (default 0)
m_OnDemandDirectory
java.io.File m_OnDemandDirectory
- The directory used when loading cost files on demand, null indicates
current directory
m_Instances
Instances m_Instances
- The dataset of interest
m_ResultListener
ResultListener m_ResultListener
- The ResultListener to send results to
m_NumFolds
int m_NumFolds
- The number of folds in the cross-validation
m_debugOutput
boolean m_debugOutput
- Save raw output of split evaluators --- for debugging purposes
m_ZipDest
OutputZipper m_ZipDest
- The output zipper to use for saving raw splitEvaluator output
m_OutputFile
java.io.File m_OutputFile
- The destination output file/directory for raw output
m_SplitEvaluator
SplitEvaluator m_SplitEvaluator
- The SplitEvaluator used to generate results
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
- The names of any additional measures to look for in SplitEvaluators
m_RP
ResultProducer m_RP
- The ResultProducer sending us results
m_OutputFile
java.io.File m_OutputFile
- The destination output file, null sends to System.out
m_ResultProducer
ResultProducer m_ResultProducer
- The ResultProducer to listen to
m_ResultsTableName
java.lang.String m_ResultsTableName
- The name of the current results table
m_Debug
boolean m_Debug
- True if debugging output should be printed
m_CacheKeyName
java.lang.String m_CacheKeyName
- Holds the name of the key field to cache upon, or null if no caching
m_CacheKeyIndex
int m_CacheKeyIndex
- Stores the index of the key column holding the cache key data
m_CacheKey
java.lang.Object[] m_CacheKey
- Stores the key for which the cache is valid
m_Cache
FastVector m_Cache
- Stores the cached values
m_Instances
Instances m_Instances
- The dataset of interest
m_ResultListener
ResultListener m_ResultListener
- The ResultListener to send results to
m_ResultProducer
ResultProducer m_ResultProducer
- The ResultProducer used to generate results
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
- The names of any additional measures to look for in SplitEvaluators
m_DatabaseURL
java.lang.String m_DatabaseURL
- Database URL
m_Connection
java.sql.Connection m_Connection
- The database connection
m_Statement
java.sql.Statement m_Statement
- The statement used for database queries
m_Debug
boolean m_Debug
- True if debugging output should be printed
m_ResultListener
ResultListener m_ResultListener
- Where results will be sent
m_ResultProducer
ResultProducer m_ResultProducer
- The result producer
m_RunLower
int m_RunLower
- Lower run number
m_RunUpper
int m_RunUpper
- Upper run number
m_Datasets
javax.swing.DefaultListModel m_Datasets
- An array of dataset files
m_UsePropertyIterator
boolean m_UsePropertyIterator
- True if the exp should also iterate over a property of the RP
m_PropertyPath
PropertyNode[] m_PropertyPath
- The path to the iterator property
m_PropertyArray
java.lang.Object m_PropertyArray
- The array of values to set the property to
m_Notes
java.lang.String m_Notes
- User notes about the experiment
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
- Method names of additional measures of objects contained in the
custom property iterator. Only methods names beginning with "measure"
and returning doubles are recognised
m_ClassFirst
boolean m_ClassFirst
- True if the class attribute is the first attribute for all
datasets involved in this experiment.
m_AdvanceDataSetFirst
boolean m_AdvanceDataSetFirst
- If true an experiment will advance the current data set befor
any custom itererator
m_m_AdvanceRunFirst
boolean m_m_AdvanceRunFirst
m_CreateSparseData
boolean m_CreateSparseData
- Determines whether sparse data is created
m_Query
java.lang.String m_Query
- Query to execute
m_Instances
Instances m_Instances
- The dataset of interest
m_ResultListener
ResultListener m_ResultListener
- The ResultListener to send results to
m_ResultProducer
ResultProducer m_ResultProducer
- The ResultProducer used to generate results
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
- The names of any additional measures to look for in SplitEvaluators
m_LowerSize
int m_LowerSize
- The minimum number of instances to use. If this is zero, the first
step will contain m_StepSize instances
m_UpperSize
int m_UpperSize
- The maximum number of instances to use. -1 indicates no maximum
(other than the total number of instances)
m_StepSize
int m_StepSize
- The number of instances to add at each step
m_CurrentSize
int m_CurrentSize
- The current dataset size during stepping
readObject
private void readObject(java.io.ObjectInputStream in)
throws java.io.IOException,
java.lang.ClassNotFoundException
writeObject
private void writeObject(java.io.ObjectOutputStream out)
throws java.io.IOException
value
java.lang.Object value
- The current property value
parentClass
java.lang.Class parentClass
- The class of the object with this property
property
java.beans.PropertyDescriptor property
- Other info about the property
m_Instances
Instances m_Instances
- The dataset of interest
m_ResultListener
ResultListener m_ResultListener
- The ResultListener to send results to
m_TrainPercent
double m_TrainPercent
- The percentage of instances to use for training
m_randomize
boolean m_randomize
- Whether dataset is to be randomized
m_SplitEvaluator
SplitEvaluator m_SplitEvaluator
- The SplitEvaluator used to generate results
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
- The names of any additional measures to look for in SplitEvaluators
m_debugOutput
boolean m_debugOutput
- Save raw output of split evaluators --- for debugging purposes
m_ZipDest
OutputZipper m_ZipDest
- The output zipper to use for saving raw splitEvaluator output
m_OutputFile
java.io.File m_OutputFile
- The destination output file/directory for raw output
m_Classifier
Classifier m_Classifier
- The classifier used for evaluation
m_AdditionalMeasures
java.lang.String[] m_AdditionalMeasures
- The names of any additional measures to look for in SplitEvaluators
m_doesProduce
boolean[] m_doesProduce
- Array of booleans corresponding to the measures in m_AdditionalMeasures
indicating which of the AdditionalMeasures the current classifier
can produce
m_result
java.lang.String m_result
- Holds the statistics for the most recent application of the classifier
m_ClassifierOptions
java.lang.String m_ClassifierOptions
- The classifier options (if any)
m_ClassifierVersion
java.lang.String m_ClassifierVersion
- The classifier version
m_HostName
java.lang.String m_HostName
- The name of the host that this engine is started on
m_TaskQueue
Queue m_TaskQueue
- A queue of waiting tasks
m_TaskIdQueue
Queue m_TaskIdQueue
- A queue of corresponding ID's for tasks
m_TaskStatus
java.util.Hashtable m_TaskStatus
- A hashtable of experiment status
m_TaskRunning
boolean m_TaskRunning
- Is there a task running
m_listeners
FastVector m_listeners
- The list of objects listening for remote experiment events
m_remoteHosts
javax.swing.DefaultListModel m_remoteHosts
- Holds the names of machines with remoteEngine servers running
m_remoteHostsQueue
Queue m_remoteHostsQueue
- The queue of available hosts
m_remoteHostsStatus
int[] m_remoteHostsStatus
- The status of each of the remote hosts
m_remoteHostFailureCounts
int[] m_remoteHostFailureCounts
- The number of times tasks have failed on each remote host
m_experimentAborted
boolean m_experimentAborted
- Set to true if MAX_FAILURES exceeded on all hosts or connections fail
on all hosts or user aborts experiment (via gui)
m_removedHosts
int m_removedHosts
- The number of hosts removed due to exceeding max failures
m_failedCount
int m_failedCount
- The count of failed sub-experiments
m_finishedCount
int m_finishedCount
- The count of successfully completed sub-experiments
m_baseExperiment
Experiment m_baseExperiment
- The base experiment to split up into sub experiments for remote
execution
m_subExperiments
Experiment[] m_subExperiments
- The sub experiments
m_subExpQueue
Queue m_subExpQueue
- The queue of sub experiments waiting to be processed
m_subExpComplete
int[] m_subExpComplete
- The status of each of the sub-experiments
m_splitByDataSet
boolean m_splitByDataSet
- If true, then sub experiments are created on the basis of data sets
rather than run number.
m_statusMessage
boolean m_statusMessage
- A status type message
m_logMessage
boolean m_logMessage
- A log type message
m_messageString
java.lang.String m_messageString
- The message
m_experimentFinished
boolean m_experimentFinished
- True if a remote experiment has finished
m_experiment
Experiment m_experiment
m_ExecutionStatus
int m_ExecutionStatus
- Holds current execution status.
m_StatusMessage
java.lang.String m_StatusMessage
- Holds current status message.
m_TaskResult
java.lang.Object m_TaskResult
- Holds task result. Set to null for no returnable result.
m_SelectedCols
Range m_SelectedCols
- Stores which columns to copy
m_FillWithMissing
boolean m_FillWithMissing
- True if missing values should be used rather than removing instances
where the translated value is not known (due to border effects).
m_InstanceRange
int m_InstanceRange
- The number of instances forward to translate values between.
A negative number indicates taking values from a past instance.
m_History
Queue m_History
- Stores the historical instances to copy values between
m_AttributeType
int m_AttributeType
- Record the type of attribute to insert
m_Name
java.lang.String m_Name
- The name for the new attribute
m_Insert
int m_Insert
- The location to insert the new attribute
m_Labels
FastVector m_Labels
- The list of labels for nominal attribute
m_infixExpression
java.lang.String m_infixExpression
- The infix expression
m_operatorStack
java.util.Stack m_operatorStack
- Operator stack
m_postFixExpVector
java.util.Vector m_postFixExpVector
- Holds the expression in postfix form
m_signMod
boolean m_signMod
- True if the next numeric constant or attribute index is negative
m_previousTok
java.lang.String m_previousTok
- Holds the previous token
m_attributeName
java.lang.String m_attributeName
- Name of the new attribute. "expression" length string will use the
provided expression as the new attribute name
m_Debug
boolean m_Debug
- If true, makes the attribute name equal to the postfix parse of the
expression
m_SelectCols
Range m_SelectCols
- Stores which columns to select as a funky range
m_SelectedAttributes
int[] m_SelectedAttributes
- Stores the indexes of the selected attributes in order, once the
dataset is seen
m_InputStringIndex
int[] m_InputStringIndex
- Contains an index of string attributes in the input format
that will survive the filtering process
m_trainSelector
AttributeSelection m_trainSelector
- the attribute selection evaluation object
m_ASEvaluator
ASEvaluation m_ASEvaluator
- the attribute evaluator to use
m_ASSearch
ASSearch m_ASSearch
- the search method if any
m_FilterOptions
java.lang.String[] m_FilterOptions
- holds a copy of the full set of valid options passed to the filter
m_SelectedAttributes
int[] m_SelectedAttributes
- holds the selected attributes
m_DeleteType
int m_DeleteType
- Stores which type of attribute to delete
m_CopyCols
Range m_CopyCols
- Stores which columns to copy
m_SelectedAttributes
int[] m_SelectedAttributes
- Stores the indexes of the selected attributes in order, once the
dataset is seen
m_InputStringIndex
int[] m_InputStringIndex
- Contains an index of string attributes in the input format
that survive the filtering process -- some entries may be duplicated
m_DiscretizeCols
Range m_DiscretizeCols
- Stores which columns to Discretize
m_NumBins
int m_NumBins
- The number of bins to divide the attribute into
m_CutPoints
double[][] m_CutPoints
- Store the current cutpoints
m_UseMDL
boolean m_UseMDL
- True if discretisation will be done by MDL rather than binning
m_MakeBinary
boolean m_MakeBinary
- Output binary attributes for discretized attributes.
m_UseBetterEncoding
boolean m_UseBetterEncoding
- Use better encoding of split point for MDL.
m_UseKononenko
boolean m_UseKononenko
- Use Kononenko's MDL criterion instead of Fayyad et al.'s
m_FindNumBins
boolean m_FindNumBins
- Find the number of bins using cross-validated entropy.
m_MinArray
double[] m_MinArray
- The minimum values for numeric attributes.
m_MaxArray
double[] m_MaxArray
- The maximum values for numeric attributes.
m_Keep
boolean[] m_Keep
- An array of attribute indices that shall be kept.
m_Debug
boolean m_Debug
- Debugging mode
m_OutputFormat
Instances m_OutputFormat
- The output format for instances
m_OutputQueue
Queue m_OutputQueue
- The output instance queue
m_OutputStringAtts
int[] m_OutputStringAtts
- Indices of string attributes in the output format
m_InputStringAtts
int[] m_InputStringAtts
- Indices of string attributes in the input format
m_InputFormat
Instances m_InputFormat
- The input format for instances
m_NewBatch
boolean m_NewBatch
- Record whether the filter is at the start of a batch
m_DeltaCols
Range m_DeltaCols
- Stores which columns to take differences between
m_AttributeSet
int m_AttributeSet
- Stores the attribute setting
m_Attribute
int m_Attribute
- Stores which attribute to be used for filtering
m_Values
Range m_Values
- Stores which values of nominal attribute are to be used for filtering.
m_Value
double m_Value
- Stores which value of a numeric attribute is to be used for filtering.
m_Inverse
boolean m_Inverse
- Inverse of test to be used?
m_MatchMissingValues
boolean m_MatchMissingValues
- True if missing values should count as a match
m_ModifyHeader
boolean m_ModifyHeader
- Modify header for nominal attributes?
m_NominalMapping
int[] m_NominalMapping
- If m_ModifyHeader, stores a mapping from old to new indexes
m_AttIndexSet
int m_AttIndexSet
- The attribute's index option setting.
m_AttIndex
int m_AttIndex
- The attribute's index
m_ValIndex
Range m_ValIndex
- The value's index
m_Numeric
boolean m_Numeric
- Make boolean attribute numeric.
m_AttIndexSet
int m_AttIndexSet
- The attribute's index setting.
m_FirstIndexSet
int m_FirstIndexSet
- The first value's index setting.
m_SecondIndexSet
int m_SecondIndexSet
- The second value's index setting.
m_AttIndex
int m_AttIndex
- The attribute's index.
m_FirstIndex
int m_FirstIndex
- The first value's index.
m_SecondIndex
int m_SecondIndex
- The second value's index.
m_Indices
int[][] m_Indices
- The sorted indices of the attribute values.
m_Numeric
boolean m_Numeric
- Are the new attributes going to be nominal or numeric ones?
m_MinArray
double[] m_MinArray
- The minimum values for numeric attributes.
m_MaxArray
double[] m_MaxArray
- The maximum values for numeric attributes.
m_Cols
Range m_Cols
- Stores which columns to transform.
m_Class
java.lang.Class m_Class
- Class containing transformation method.
m_Method
java.lang.reflect.Method m_Method
- Transformation method.
m_Seed
int m_Seed
- The random number seed
m_Random
java.util.Random m_Random
- The current random number generator
m_ModesAndMeans
double[] m_ModesAndMeans
- The modes and means
m_SampleSizePercent
double m_SampleSizePercent
- The subsample size, percent of original set, default 100%
m_RandomSeed
int m_RandomSeed
- The random number generator seed
m_BiasToUniformClass
double m_BiasToUniformClass
- The degree of bias towards uniform (nominal) class distribution
m_FirstBatchDone
boolean m_FirstBatchDone
- True if the first batch has been done
m_Range
Range m_Range
- Range of instances provided by user.
m_Inverse
boolean m_Inverse
- Indicates if inverse of selection is to be output.
m_NumFolds
int m_NumFolds
- Number of folds to split dataset into
m_Fold
int m_Fold
- Fold to output
m_Seed
long m_Seed
- Random number seed.
m_DontStratifyData
boolean m_DontStratifyData
- Don't stratify data if class index is set?
m_RandomSeed
int m_RandomSeed
- The random number generator seed
m_MaxCount
int m_MaxCount
- The maximum count of any class
m_FirstBatchDone
boolean m_FirstBatchDone
- True if the first batch has been done
m_DistributionSpread
double m_DistributionSpread
- True if the first batch has been done
m_AdjustWeights
boolean m_AdjustWeights
- True if instance weights will be adjusted to maintain
total weight per class.
m_AttIndexSet
int m_AttIndexSet
- The attribute index setting (allows -1 = last).
m_AttIndex
int m_AttIndex
- The attribute index.
m_AttIndexSet
int m_AttIndexSet
- The attribute's index setting.
m_FirstIndexSet
int m_FirstIndexSet
- The first value's index setting.
m_SecondIndexSet
int m_SecondIndexSet
- The second value's index setting.
m_AttIndex
int m_AttIndex
- The attribute's index.
m_FirstIndex
int m_FirstIndex
- The first value's index.
m_SecondIndex
int m_SecondIndex
- The second value's index.
m_IncludeAll
javax.swing.JButton m_IncludeAll
- Press to select all attributes
m_RemoveAll
javax.swing.JButton m_RemoveAll
- Press to deselect all attributes
m_Invert
javax.swing.JButton m_Invert
- Press to invert the current selection
m_Table
javax.swing.JTable m_Table
- The table displaying attribute names and selection status
m_Model
weka.gui.AttributeSelectionPanel.AttributeTableModel m_Model
- The table model containingn attribute names and selection status
m_AttributeNameLab
javax.swing.JLabel m_AttributeNameLab
- Displays the name of the relation
m_AttributeTypeLab
javax.swing.JLabel m_AttributeTypeLab
- Displays the type of attribute
m_MissingLab
javax.swing.JLabel m_MissingLab
- Displays the number of missing values
m_UniqueLab
javax.swing.JLabel m_UniqueLab
- Displays the number of unique values
m_DistinctLab
javax.swing.JLabel m_DistinctLab
- Displays the number of distinct values
m_StatsTable
javax.swing.JTable m_StatsTable
- Displays other stats in a table
m_Instances
Instances m_Instances
- The instances we're playing with
m_AttributeStats
AttributeStats[] m_AttributeStats
- Cached stats on the attributes we've summarized so far
m_Support
java.beans.PropertyChangeSupport m_Support
- Handles property change notification
m_Label
javax.swing.JLabel m_Label
- The label for when we can't edit that type
m_ElementList
javax.swing.JList m_ElementList
- The list component displaying current values
m_ElementClass
java.lang.Class m_ElementClass
- The class of objects allowed in the array
m_ListModel
javax.swing.DefaultListModel m_ListModel
- The defaultlistmodel holding our data
m_ElementEditor
java.beans.PropertyEditor m_ElementEditor
- The property editor for the class we are editing
m_DeleteBut
javax.swing.JButton m_DeleteBut
- Click this to delete the selected array values
m_AddBut
javax.swing.JButton m_AddBut
- Click to add the current object configuration to the array
m_InnerActionListener
java.awt.event.ActionListener m_InnerActionListener
- Listens to buttons being pressed and taking the appropriate action
m_InnerSelectionListener
javax.swing.event.ListSelectionListener m_InnerSelectionListener
- Listens to list items being selected and takes appropriate action
this$0
GenericObjectEditor this$0
m_ObjectChooser
javax.swing.JComboBox m_ObjectChooser
- The chooser component
m_ChildPropertySheet
PropertySheetPanel m_ChildPropertySheet
- The component that performs classifier customization
m_ObjectNames
javax.swing.DefaultComboBoxModel m_ObjectNames
- The model containing the list of names to select from
m_OpenBut
javax.swing.JButton m_OpenBut
- Open object from disk
m_SaveBut
javax.swing.JButton m_SaveBut
- Save object to disk
m_okBut
javax.swing.JButton m_okBut
- ok button
m_cancelBut
javax.swing.JButton m_cancelBut
- cancel button
m_FileChooser
javax.swing.JFileChooser m_FileChooser
- The filechooser for opening and saving object files
m_SimpleBut
java.awt.Button m_SimpleBut
- Click to open the simplecli
m_ExplorerBut
java.awt.Button m_ExplorerBut
- Click to open the Explorer
m_ExperimenterBut
java.awt.Button m_ExperimenterBut
- Click to open the Explorer
m_SimpleCLI
SimpleCLI m_SimpleCLI
- The SimpleCLI
m_ExplorerFrame
javax.swing.JFrame m_ExplorerFrame
- The frame containing the explorer interface
m_ExperimenterFrame
javax.swing.JFrame m_ExperimenterFrame
- The frame containing the experiment interface
m_weka
java.awt.Image m_weka
- The weka image
m_RelationNameLab
javax.swing.JLabel m_RelationNameLab
- Displays the name of the relation
m_NumInstancesLab
javax.swing.JLabel m_NumInstancesLab
- Displays the number of instances
m_NumAttributesLab
javax.swing.JLabel m_NumAttributesLab
- Displays the number of attributes
m_Instances
Instances m_Instances
- The instances we're playing with
m_SelectBut
javax.swing.JButton m_SelectBut
- Click to choose the currently selected property
m_CancelBut
javax.swing.JButton m_CancelBut
- Click to cancel the property selection
m_List
javax.swing.JList m_List
- The list component
m_Result
int m_Result
- Whether the selection was made or cancelled
m_StatusLab
javax.swing.JLabel m_StatusLab
- Displays the current status
m_LogText
javax.swing.JTextArea m_LogText
- Displays the log messages
m_First
boolean m_First
- An indicator for whether text has been output yet
m_TaskMonitor
WekaTaskMonitor m_TaskMonitor
- The panel for monitoring the number of running tasks (if supplied)
m_Editor
java.beans.PropertyEditor m_Editor
- The property editor
m_EditorComponent
java.awt.Component m_EditorComponent
- The custom editor component
m_Editor
java.beans.PropertyEditor m_Editor
- The property editor
m_PD
PropertyDialog m_PD
- The currently displayed property dialog, if any
m_SelectBut
javax.swing.JButton m_SelectBut
- Click to choose the currently selected property
m_CancelBut
javax.swing.JButton m_CancelBut
- Click to cancel the property selection
m_Root
javax.swing.tree.DefaultMutableTreeNode m_Root
- The root of the property tree
m_RootObject
java.lang.Object m_RootObject
- The object at the root of the tree
m_Result
int m_Result
- Whether the selection was made or cancelled
m_ResultPath
java.lang.Object[] m_ResultPath
- Stores the path to the selected property
m_Tree
javax.swing.JTree m_Tree
- The component displaying the property tree
m_Target
java.lang.Object m_Target
- The target object being edited
m_Properties
java.beans.PropertyDescriptor[] m_Properties
- Holds properties of the target
m_Methods
java.beans.MethodDescriptor[] m_Methods
- Holds the methods of the target
m_Editors
java.beans.PropertyEditor[] m_Editors
- Holds property editors of the object
m_Values
java.lang.Object[] m_Values
- Holds current object values for each property
m_Views
javax.swing.JComponent[] m_Views
- Stores GUI components containing each editing component
m_Labels
javax.swing.JLabel[] m_Labels
- The labels for each property
m_TipTexts
java.lang.String[] m_TipTexts
- The tool tip text for each property
m_HelpText
java.lang.StringBuffer m_HelpText
- StringBuffer containing help text for the object being edited
m_HelpFrame
javax.swing.JFrame m_HelpFrame
- Help frame
m_HelpBut
javax.swing.JButton m_HelpBut
- Button to pop up the full help text in a separate frame
m_NumEditable
int m_NumEditable
- A count of the number of properties we have an editor for
support
java.beans.PropertyChangeSupport support
- A support object for handling property change listeners
m_SingleText
javax.swing.text.JTextComponent m_SingleText
- An optional component for single-click display
m_SingleName
java.lang.String m_SingleName
- The named result being viewed in the single-click display
m_Model
javax.swing.DefaultListModel m_Model
- The list model
m_List
javax.swing.JList m_List
- The list component
m_Results
java.util.Hashtable m_Results
- A Hashtable mapping names to result buffers
m_FramedOutput
java.util.Hashtable m_FramedOutput
- A Hashtable mapping names to output text components
m_Objs
java.util.Hashtable m_Objs
- A hashtable mapping names to arbitrary objects
m_HandleRightClicks
boolean m_HandleRightClicks
- Let the result history list handle right clicks in the default
manner---ie, pop up a window displaying the buffer
m_OpenFileBut
javax.swing.JButton m_OpenFileBut
- Click to open instances from a file
m_OpenURLBut
javax.swing.JButton m_OpenURLBut
- Click to open instances from a URL
m_Summary
InstancesSummaryPanel m_Summary
- The instance summary component
m_ArffFilter
javax.swing.filechooser.FileFilter m_ArffFilter
- Filter to ensure only arff files are selected
m_FileChooser
javax.swing.JFileChooser m_FileChooser
- The file chooser for selecting arff files
m_LastURL
java.lang.String m_LastURL
- Stores the last URL that instances were loaded from
m_IOThread
java.lang.Thread m_IOThread
- The thread we do loading in
m_Support
java.beans.PropertyChangeSupport m_Support
- Manages sending notifications to people when we change the set of
working instances.
m_Instances
Instances m_Instances
- The current set of instances loaded
m_OutputArea
java.awt.TextArea m_OutputArea
- The output area canvas added to the frame
m_Input
java.awt.TextField m_Input
- The command input area
m_CommandHistory
java.util.Vector m_CommandHistory
- The history of commands entered interactively
m_HistoryPos
int m_HistoryPos
- The current position in the command history
m_POO
java.io.PipedOutputStream m_POO
- The new output stream for System.out
m_POE
java.io.PipedOutputStream m_POE
- The new output stream for System.err
m_OutRedirector
java.lang.Thread m_OutRedirector
- The thread that sends output from m_POO to the output box
m_ErrRedirector
java.lang.Thread m_ErrRedirector
- The thread that sends output from m_POE to the output box
m_RunThread
java.lang.Thread m_RunThread
- The thread currently running a class main method
m_ActiveTasks
int m_ActiveTasks
- The number of running weka threads
m_MonitorLabel
javax.swing.JLabel m_MonitorLabel
- The label for displaying info
m_iconStationary
javax.swing.ImageIcon m_iconStationary
- The icon for the stationary bird
m_iconAnimated
javax.swing.ImageIcon m_iconAnimated
- The icon for the animated bird
m_animating
boolean m_animating
- True if their are active tasks
|
Package weka.gui.experiment |
m_Exp
Experiment m_Exp
- The experiment to set the dataset list of
m_List
javax.swing.JList m_List
- The component displaying the dataset list
m_AddBut
javax.swing.JButton m_AddBut
- Click to add a dataset
m_DeleteBut
javax.swing.JButton m_DeleteBut
- Click to remove the selected dataset from the list
m_relativeCheck
javax.swing.JCheckBox m_relativeCheck
- Make file paths relative to the user (start) directory
m_ArffFilter
javax.swing.filechooser.FileFilter m_ArffFilter
- A filter to ensure only arff files get selected
m_UserDir
java.io.File m_UserDir
- The user (start) directory
m_FileChooser
javax.swing.JFileChooser m_FileChooser
- The file chooser component
m_Exp
RemoteExperiment m_Exp
- The experiment to configure.
m_enableDistributedExperiment
javax.swing.JCheckBox m_enableDistributedExperiment
- Distribute the current experiment to remote hosts
m_configureHostNames
javax.swing.JButton m_configureHostNames
- Popup the HostListPanel
m_hostList
HostListPanel m_hostList
- The host list panel
m_splitByDataSet
javax.swing.JRadioButton m_splitByDataSet
- Split experiment up by data set.
m_splitByRun
javax.swing.JRadioButton m_splitByRun
- Split experiment up by run number.
m_radioListener
java.awt.event.ActionListener m_radioListener
- Handle radio buttons
m_SetupPanel
SetupPanel m_SetupPanel
- The panel for configuring the experiment
m_RunPanel
RunPanel m_RunPanel
- The panel for running the experiment
m_ResultsPanel
ResultsPanel m_ResultsPanel
- The panel for analysing experimental results
m_TabbedPane
javax.swing.JTabbedPane m_TabbedPane
- The tabbed pane that controls which sub-pane we are working with
m_ClassFirst
boolean m_ClassFirst
- True if the class attribute is the first attribute for all
datasets involved in this experiment.
m_ConfigureBut
javax.swing.JButton m_ConfigureBut
- Click to select the property to iterate over
m_StatusBox
javax.swing.JComboBox m_StatusBox
- Controls whether the custom iterator is used or not
m_ArrayEditor
GenericArrayEditor m_ArrayEditor
- Allows editing of the custom property values
m_Exp
Experiment m_Exp
- The experiment this all applies to
m_Listeners
FastVector m_Listeners
- Listeners who want to be notified about editing status of this
panel
m_Exp
RemoteExperiment m_Exp
- The remote experiment to set the host list of
m_List
javax.swing.JList m_List
- The component displaying the host list
m_DeleteBut
javax.swing.JButton m_DeleteBut
- Click to remove the selected host from the list
m_HostField
javax.swing.JTextField m_HostField
- The field with which to enter host names
m_FromFileBut
javax.swing.JButton m_FromFileBut
- Click to load results from a file
m_FromDBaseBut
javax.swing.JButton m_FromDBaseBut
- Click to load results from a database
m_FromExpBut
javax.swing.JButton m_FromExpBut
- Click to get results from the destination given in the experiment
m_FromLab
javax.swing.JLabel m_FromLab
- Displays a message about the current result set
m_DatasetModel
javax.swing.DefaultComboBoxModel m_DatasetModel
- The model embedded in m_DatasetCombo
m_RunModel
javax.swing.DefaultComboBoxModel m_RunModel
- The model embedded in m_RunCombo
m_CompareModel
javax.swing.DefaultComboBoxModel m_CompareModel
- The model embedded in m_CompareCombo
m_TestsModel
javax.swing.DefaultListModel m_TestsModel
- The model embedded in m_TestsList
m_DatasetKeyLabel
javax.swing.JLabel m_DatasetKeyLabel
- Displays the currently selected column names for the scheme & options
m_DatasetKeyBut
javax.swing.JButton m_DatasetKeyBut
- Click to edit the columns used to determine the scheme
m_DatasetKeyModel
javax.swing.DefaultListModel m_DatasetKeyModel
- Stores the list of attributes for selecting the scheme columns
m_DatasetKeyList
javax.swing.JList m_DatasetKeyList
- Displays the list of selected columns determining the scheme
m_RunCombo
javax.swing.JComboBox m_RunCombo
- Lets the user select which column contains the run number
m_ResultKeyLabel
javax.swing.JLabel m_ResultKeyLabel
- Displays the currently selected column names for the scheme & options
m_ResultKeyBut
javax.swing.JButton m_ResultKeyBut
- Click to edit the columns used to determine the scheme
m_ResultKeyModel
javax.swing.DefaultListModel m_ResultKeyModel
- Stores the list of attributes for selecting the scheme columns
m_ResultKeyList
javax.swing.JList m_ResultKeyList
- Displays the list of selected columns determining the scheme
m_TestsButton
javax.swing.JButton m_TestsButton
- Lets the user select which scheme to base comparisons against
m_TestsList
javax.swing.JList m_TestsList
- Holds the list of schemes to base the test against
m_CompareCombo
javax.swing.JComboBox m_CompareCombo
- Lets the user select which performance measure to analyze
m_SigTex
javax.swing.JTextField m_SigTex
- Lets the user edit the test significance
m_ShowStdDevs
javax.swing.JCheckBox m_ShowStdDevs
- Lets the user select whether standard deviations are to be output
or not
m_PerformBut
javax.swing.JButton m_PerformBut
- Click to start the test
m_SaveOutBut
javax.swing.JButton m_SaveOutBut
- Click to save test output to a file
m_SaveOut
SaveBuffer m_SaveOut
- The buffer saving object for saving output
m_OutText
javax.swing.JTextArea m_OutText
- Displays the output of tests
m_History
ResultHistoryPanel m_History
- A panel controlling results viewing
m_ArffFilter
javax.swing.filechooser.FileFilter m_ArffFilter
- Filter to ensure only arff files are selected for result files
m_FileChooser
javax.swing.JFileChooser m_FileChooser
- The file chooser for selecting result files
m_TTester
PairedTTester m_TTester
- The PairedTTester object
m_Instances
Instances m_Instances
- The instances we're extracting results from
m_InstanceQuery
InstanceQuery m_InstanceQuery
- Does any database querying for us
m_LoadThread
java.lang.Thread m_LoadThread
- A thread to load results instances from a file or database
m_Exp
Experiment m_Exp
- An experiment (used for identifying a result source) -- optional
m_ConfigureListener
java.awt.event.ActionListener m_ConfigureListener
- An actionlisteners that updates ttest settings
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
m_LowerText
javax.swing.JTextField m_LowerText
- Configures the lower run number
m_UpperText
javax.swing.JTextField m_UpperText
- Configures the upper run number
m_Exp
Experiment m_Exp
- The experiment being configured
m_StartBut
javax.swing.JButton m_StartBut
- Click to start running the experiment
m_StopBut
javax.swing.JButton m_StopBut
- Click to signal the running experiment to halt
m_Log
LogPanel m_Log
m_Exp
Experiment m_Exp
- The experiment to run
m_RunThread
java.lang.Thread m_RunThread
- The thread running the experiment
m_Exp
Experiment m_Exp
- The experiment being configured
m_OpenBut
javax.swing.JButton m_OpenBut
- Click to load an experiment
m_SaveBut
javax.swing.JButton m_SaveBut
- Click to save an experiment
m_NewBut
javax.swing.JButton m_NewBut
- Click to create a new experiment with default settings
m_ExpFilter
javax.swing.filechooser.FileFilter m_ExpFilter
- A filter to ensure only experiment files get shown in the chooser
m_FileChooser
javax.swing.JFileChooser m_FileChooser
- The file chooser for selecting experiments
m_RPEditor
GenericObjectEditor m_RPEditor
- The ResultProducer editor
m_RPEditorPanel
PropertyPanel m_RPEditorPanel
- The panel to contain the ResultProducer editor
m_RLEditor
GenericObjectEditor m_RLEditor
- The ResultListener editor
m_RLEditorPanel
PropertyPanel m_RLEditorPanel
- The panel to contain the ResultListener editor
m_GeneratorPropertyPanel
GeneratorPropertyIteratorPanel m_GeneratorPropertyPanel
- The panel that configures iteration on custom resultproducer property
m_RunNumberPanel
RunNumberPanel m_RunNumberPanel
- The panel for configuring run numbers
m_DistributeExperimentPanel
DistributeExperimentPanel m_DistributeExperimentPanel
- The panel for enabling a distributed experiment
m_DatasetListPanel
DatasetListPanel m_DatasetListPanel
- The panel for configuring selected datasets
m_NotesText
javax.swing.JTextArea m_NotesText
- Area for user notes Default of 5 rows
m_Support
java.beans.PropertyChangeSupport m_Support
- Manages sending notifications to people when we change the experiment,
at this stage, only the resultlistener so the resultpanel can update.
m_advanceDataSetFirst
javax.swing.JRadioButton m_advanceDataSetFirst
- Click to advacne data set before custom generator
m_advanceIteratorFirst
javax.swing.JRadioButton m_advanceIteratorFirst
- Click to advance custom generator before data set
m_RadioListener
java.awt.event.ActionListener m_RadioListener
- Handle radio buttons
|
Package weka.gui.explorer |
m_AssociatorEditor
GenericObjectEditor m_AssociatorEditor
- Lets the user configure the associator
m_CEPanel
PropertyPanel m_CEPanel
- The panel showing the current associator selection
m_OutText
javax.swing.JTextArea m_OutText
- The output area for associations
m_Log
Logger m_Log
- The destination for log/status messages
m_SaveOut
SaveBuffer m_SaveOut
- The buffer saving object for saving output
m_History
ResultHistoryPanel m_History
- A panel controlling results viewing
m_StartBut
javax.swing.JButton m_StartBut
- Click to start running the associator
m_StopBut
javax.swing.JButton m_StopBut
- Click to stop a running associator
m_SaveOutBut
javax.swing.JButton m_SaveOutBut
- Click to save the output associated with the currently selected result
m_Instances
Instances m_Instances
- The main set of instances we're playing with
m_TestInstances
Instances m_TestInstances
- The user-supplied test set (if any)
m_RunThread
java.lang.Thread m_RunThread
- A thread that associator runs in
m_AttributeEvaluatorEditor
GenericObjectEditor m_AttributeEvaluatorEditor
- Lets the user configure the attribute evaluator
m_AttributeSearchEditor
GenericObjectEditor m_AttributeSearchEditor
- Lets the user configure the search method
m_AEEPanel
PropertyPanel m_AEEPanel
- The panel showing the current attribute evaluation method
m_ASEPanel
PropertyPanel m_ASEPanel
- The panel showing the current search method
m_OutText
javax.swing.JTextArea m_OutText
- The output area for attribute selection results
m_Log
Logger m_Log
- The destination for log/status messages
m_SaveOut
SaveBuffer m_SaveOut
- The buffer saving object for saving output
m_History
ResultHistoryPanel m_History
- A panel controlling results viewing
m_ClassCombo
javax.swing.JComboBox m_ClassCombo
- Lets the user select the class column
m_CVBut
javax.swing.JRadioButton m_CVBut
- Click to set evaluation mode to cross-validation
m_TrainBut
javax.swing.JRadioButton m_TrainBut
- Click to set test mode to test on training data
m_CVLab
javax.swing.JLabel m_CVLab
- Label by where the cv folds are entered
m_CVText
javax.swing.JTextField m_CVText
- The field where the cv folds are entered
m_SeedLab
javax.swing.JLabel m_SeedLab
- Label by where cv random seed is entered
m_SeedText
javax.swing.JTextField m_SeedText
- The field where the seed value is entered
m_RadioListener
java.awt.event.ActionListener m_RadioListener
- Alters the enabled/disabled status of elements associated with each
radio button
m_StartBut
javax.swing.JButton m_StartBut
- Click to start running the attribute selector
m_StopBut
javax.swing.JButton m_StopBut
- Click to stop a running classifier
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
- Stop the class combo from taking up to much space
m_CurrentVis
VisualizePanel m_CurrentVis
- The current visualization object
m_Instances
Instances m_Instances
- The main set of instances we're playing with
m_RunThread
java.lang.Thread m_RunThread
- A thread that attribute selection runs in
m_ClassifierEditor
GenericObjectEditor m_ClassifierEditor
- Lets the user configure the classifier
m_CEPanel
PropertyPanel m_CEPanel
- The panel showing the current classifier selection
m_OutText
javax.swing.JTextArea m_OutText
- The output area for classification results
m_Log
Logger m_Log
- The destination for log/status messages
m_SaveOut
SaveBuffer m_SaveOut
- The buffer saving object for saving output
m_History
ResultHistoryPanel m_History
- A panel controlling results viewing
m_ClassCombo
javax.swing.JComboBox m_ClassCombo
- Lets the user select the class column
m_CVBut
javax.swing.JRadioButton m_CVBut
- Click to set test mode to cross-validation
m_PercentBut
javax.swing.JRadioButton m_PercentBut
- Click to set test mode to generate a % split
m_TrainBut
javax.swing.JRadioButton m_TrainBut
- Click to set test mode to test on training data
m_TestSplitBut
javax.swing.JRadioButton m_TestSplitBut
- Click to set test mode to a user-specified test set
m_StorePredictionsBut
javax.swing.JCheckBox m_StorePredictionsBut
- Check to save the predictions in the results list for visualizing
later on
m_OutputModelBut
javax.swing.JCheckBox m_OutputModelBut
- Check to output the model built from the training data
m_OutputPerClassBut
javax.swing.JCheckBox m_OutputPerClassBut
- Check to output true/false positives, precision/recall for each class
m_OutputConfusionBut
javax.swing.JCheckBox m_OutputConfusionBut
- Check to output a confusion matrix
m_OutputEntropyBut
javax.swing.JCheckBox m_OutputEntropyBut
- Check to output entropy statistics
m_EvalWRTCostsBut
javax.swing.JCheckBox m_EvalWRTCostsBut
- Check to evaluate w.r.t a cost matrix
m_SetCostsBut
javax.swing.JButton m_SetCostsBut
m_CVLab
javax.swing.JLabel m_CVLab
- Label by where the cv folds are entered
m_CVText
javax.swing.JTextField m_CVText
- The field where the cv folds are entered
m_PercentLab
javax.swing.JLabel m_PercentLab
- Label by where the % split is entered
m_PercentText
javax.swing.JTextField m_PercentText
- The field where the % split is entered
m_SetTestBut
javax.swing.JButton m_SetTestBut
- The button used to open a separate test dataset
m_SetTestFrame
javax.swing.JFrame m_SetTestFrame
- The frame used to show the test set selection panel
m_SetCostsFrame
PropertyDialog m_SetCostsFrame
- The frame used to show the cost matrix editing panel
m_RadioListener
java.awt.event.ActionListener m_RadioListener
- Alters the enabled/disabled status of elements associated with each
radio button
m_MoreOptions
javax.swing.JButton m_MoreOptions
- Button for further output/visualize options
m_StartBut
javax.swing.JButton m_StartBut
- Click to start running the classifier
m_StopBut
javax.swing.JButton m_StopBut
- Click to stop a running classifier
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
- Stop the class combo from taking up to much space
m_CostMatrixEditor
CostMatrixEditor m_CostMatrixEditor
- The cost matrix editor for evaluation costs
m_Instances
Instances m_Instances
- The main set of instances we're playing with
m_TestInstances
Instances m_TestInstances
- The user-supplied test set (if any)
m_TestInstancesCopy
Instances m_TestInstancesCopy
- The user supplied test set after preprocess filters have been applied
m_RunThread
java.lang.Thread m_RunThread
- A thread that classification runs in
m_visXIndex
int m_visXIndex
- default x index for visualizing
m_visYIndex
int m_visYIndex
- default y index for visualizing
m_CurrentVis
VisualizePanel m_CurrentVis
- The current visualization object
m_Preprocess
PreprocessPanel m_Preprocess
- The preprocess panel through which filters can be applied to
user supplied test data sets
m_Summary
InstancesSummaryPanel m_Summary
- The instances summary panel displayed by m_SetTestFrame
m_ClustererEditor
GenericObjectEditor m_ClustererEditor
- Lets the user configure the clusterer
m_CLPanel
PropertyPanel m_CLPanel
- The panel showing the current clusterer selection
m_OutText
javax.swing.JTextArea m_OutText
- The output area for classification results
m_Log
Logger m_Log
- The destination for log/status messages
m_SaveOut
SaveBuffer m_SaveOut
- The buffer saving object for saving output
m_History
ResultHistoryPanel m_History
- A panel controlling results viewing
m_PercentBut
javax.swing.JRadioButton m_PercentBut
- Click to set test mode to generate a % split
m_TrainBut
javax.swing.JRadioButton m_TrainBut
- Click to set test mode to test on training data
m_TestSplitBut
javax.swing.JRadioButton m_TestSplitBut
- Click to set test mode to a user-specified test set
m_ClassesToClustersBut
javax.swing.JRadioButton m_ClassesToClustersBut
- Click to set test mode to classes to clusters based evaluation
m_ClassCombo
javax.swing.JComboBox m_ClassCombo
- Lets the user select the class column for classes to clusters based
evaluation
m_PercentLab
javax.swing.JLabel m_PercentLab
- Label by where the % split is entered
m_PercentText
javax.swing.JTextField m_PercentText
- The field where the % split is entered
m_SetTestBut
javax.swing.JButton m_SetTestBut
- The button used to open a separate test dataset
m_SetTestFrame
javax.swing.JFrame m_SetTestFrame
- The frame used to show the test set selection panel
m_ignoreBut
javax.swing.JButton m_ignoreBut
- The button used to popup a list for choosing attributes to ignore while
clustering
m_ignoreKeyModel
javax.swing.DefaultListModel m_ignoreKeyModel
m_ignoreKeyList
javax.swing.JList m_ignoreKeyList
m_RadioListener
java.awt.event.ActionListener m_RadioListener
- Alters the enabled/disabled status of elements associated with each
radio button
m_StartBut
javax.swing.JButton m_StartBut
- Click to start running the clusterer
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
- Stop the class combo from taking up to much space
m_StopBut
javax.swing.JButton m_StopBut
- Click to stop a running clusterer
m_Instances
Instances m_Instances
- The main set of instances we're playing with
m_TestInstances
Instances m_TestInstances
- The user-supplied test set (if any)
m_TestInstancesCopy
Instances m_TestInstancesCopy
- The user supplied test set after preprocess filters have been applied
m_CurrentVis
VisualizePanel m_CurrentVis
- The current visualization object
m_visXIndex
int m_visXIndex
- default x index for visualizing
m_visYIndex
int m_visYIndex
- default y index for visualizing
m_StorePredictionsBut
javax.swing.JCheckBox m_StorePredictionsBut
- Check to save the predictions in the results list for visualizing
later on
m_RunThread
java.lang.Thread m_RunThread
- A thread that clustering runs in
m_Preprocess
PreprocessPanel m_Preprocess
- The pre-process object from which to fetch filters for applying
to a user specified test set
m_Summary
InstancesSummaryPanel m_Summary
- The instances summary panel displayed by m_SetTestFrame
m_PreprocessPanel
PreprocessPanel m_PreprocessPanel
- The panel for preprocessing instances
m_ClassifierPanel
ClassifierPanel m_ClassifierPanel
- The panel for running classifiers
m_ClustererPanel
ClustererPanel m_ClustererPanel
- Label for a panel that still need to be implemented
m_AssociationPanel
AssociationsPanel m_AssociationPanel
- Label for a panel that still need to be implemented
m_AttributeSelectionPanel
AttributeSelectionPanel m_AttributeSelectionPanel
- Label for a panel that still need to be implemented
m_VisualizePanel
VisualizePanel m_VisualizePanel
- Label for a panel that still need to be implemented
m_TabbedPane
javax.swing.JTabbedPane m_TabbedPane
- The tabbed pane that controls which sub-pane we are working with
m_LogPanel
LogPanel m_LogPanel
- The panel for log and status messages
m_BaseInstPanel
InstancesSummaryPanel m_BaseInstPanel
- Displays simple stats on the base instances
m_WorkingInstPanel
InstancesSummaryPanel m_WorkingInstPanel
- Displays simple stats on the working instances
m_OpenFileBut
javax.swing.JButton m_OpenFileBut
- Click to load base instances from a file
m_OpenURLBut
javax.swing.JButton m_OpenURLBut
- Click to load base instances from a URL
m_OpenDBBut
javax.swing.JButton m_OpenDBBut
- Click to load base instances from a Database
m_DatabaseQueryEditor
GenericObjectEditor m_DatabaseQueryEditor
m_ApplyBut
javax.swing.JButton m_ApplyBut
- Click to apply filters and replace the working dataset
m_ReplaceBut
javax.swing.JButton m_ReplaceBut
- Click to replace the base dataset with the working dataset
m_SaveBut
javax.swing.JButton m_SaveBut
- Click to apply filters and save the results
m_AttPanel
AttributeSelectionPanel m_AttPanel
- Panel to let the user toggle attributes
m_Filters
GenericArrayEditor m_Filters
- Lets the user add a series of filters
m_AttSummaryPanel
AttributeSummaryPanel m_AttSummaryPanel
- Displays summary stats on the selected attribute
m_ArffFilter
javax.swing.filechooser.FileFilter m_ArffFilter
- Filter to ensure only arff files are selected
m_FileChooser
javax.swing.JFileChooser m_FileChooser
- The file chooser for selecting arff files
m_LastURL
java.lang.String m_LastURL
- Stores the last URL that instances were loaded from
m_SQLQ
java.lang.String m_SQLQ
- Stores the last sql query executed
m_BaseInstances
Instances m_BaseInstances
- The unadulterated instances
m_WorkingInstances
Instances m_WorkingInstances
- The working (filtered) copy
m_Support
java.beans.PropertyChangeSupport m_Support
- Manages sending notifications to people when we change the set of
working instances.
m_IOThread
java.lang.Thread m_IOThread
- A thread to loading/saving instances from a file or URL
m_Log
Logger m_Log
m_FiltersCopy
SerializedObject m_FiltersCopy
- A copy of the most recently applied filters
m_Count_Lab
javax.swing.JLabel m_Count_Lab
m_Count
int m_Count
m_Debug
boolean m_Debug
m_ID
int m_ID
listeners
java.util.Vector listeners
- The listeners
b_Debug
boolean b_Debug
- Debugging mode
m_InputFormat
Instances m_InputFormat
- The input format for instances
m_OutputInstance
Instance m_OutputInstance
- The current output instance
b_FirstInputFinished
boolean b_FirstInputFinished
- Whether the first input batch has finished
b_SecondInputFinished
boolean b_SecondInputFinished
m_Listeners
java.util.Vector m_Listeners
m_LoaderThread
java.lang.Thread m_LoaderThread
m_OutputInstance
Instance m_OutputInstance
m_OutputInstances
Instances m_OutputInstances
m_Debug
boolean m_Debug
m_StartBut
javax.swing.JButton m_StartBut
m_FileNameTex
javax.swing.JTextField m_FileNameTex
count_Lab
java.awt.Label count_Lab
m_Count
int m_Count
arffFile_Tex
java.awt.TextField arffFile_Tex
b_Debug
boolean b_Debug
outputWriter
java.io.PrintWriter outputWriter
m_InstanceTable
javax.swing.JTable m_InstanceTable
m_Debug
boolean m_Debug
m_Clear
boolean m_Clear
m_UpdateString
java.lang.String m_UpdateString
m_Instances
Instances m_Instances
m_OutputTex
javax.swing.JTextArea m_OutputTex
m_Debug
boolean m_Debug
m_Clear
boolean m_Clear
m_UpdateString
java.lang.String m_UpdateString
|
Package weka.gui.treevisualizer |
m_placer
NodePlace m_placer
- The placement algorithm for the Node structure.
m_topNode
Node m_topNode
- The top Node.
m_viewPos
java.awt.Dimension m_viewPos
- The postion of the view relative to the tree.
m_viewSize
java.awt.Dimension m_viewSize
- The size of the tree in pixels.
m_currentFont
java.awt.Font m_currentFont
- The font used to display the tree.
m_fontSize
java.awt.FontMetrics m_fontSize
- The size information for the current font.
m_numNodes
int m_numNodes
- The number of Nodes in the tree.
m_numLevels
int m_numLevels
- The number of levels in the tree.
m_nodes
weka.gui.treevisualizer.TreeVisualizer.NodeInfo[] m_nodes
- An array with the Nodes sorted into it and display information
about the Nodes.
m_edges
weka.gui.treevisualizer.TreeVisualizer.EdgeInfo[] m_edges
- An array with the Edges sorted into it and display information
about the Edges.
m_frameLimiter
javax.swing.Timer m_frameLimiter
- A timer to keep the frame rate constant.
m_mouseState
int m_mouseState
- Describes the action the user is performing.
m_oldMousePos
java.awt.Dimension m_oldMousePos
- A variable used to tag the start pos of a user action.
m_newMousePos
java.awt.Dimension m_newMousePos
- A variable used to tag the most current point of a user action.
m_clickAvailable
boolean m_clickAvailable
- A variable used to determine for the clicked method if any other
mouse state has already taken place.
m_nViewPos
java.awt.Dimension m_nViewPos
- A variable used to remember the desired view pos.
m_nViewSize
java.awt.Dimension m_nViewSize
- A variable used to remember the desired tree size.
m_scaling
int m_scaling
- The number of frames left to calculate.
m_winMenu
javax.swing.JPopupMenu m_winMenu
- A right (or middle) click popup menu.
m_topN
javax.swing.JMenuItem m_topN
- An option on the win_menu
m_fitToScreen
javax.swing.JMenuItem m_fitToScreen
- An option on the win_menu
m_autoScale
javax.swing.JMenuItem m_autoScale
- An option on the win_menu
m_selectFont
javax.swing.JMenu m_selectFont
- A ub group on the win_menu
m_selectFontGroup
javax.swing.ButtonGroup m_selectFontGroup
- A grouping for the font choices
m_size24
javax.swing.JRadioButtonMenuItem m_size24
- A font choice.
m_size22
javax.swing.JRadioButtonMenuItem m_size22
- A font choice.
m_size20
javax.swing.JRadioButtonMenuItem m_size20
- A font choice.
m_size18
javax.swing.JRadioButtonMenuItem m_size18
- A font choice.
m_size16
javax.swing.JRadioButtonMenuItem m_size16
- A font choice.
m_size14
javax.swing.JRadioButtonMenuItem m_size14
- A font choice.
m_size12
javax.swing.JRadioButtonMenuItem m_size12
- A font choice.
m_size10
javax.swing.JRadioButtonMenuItem m_size10
- A font choice.
m_size8
javax.swing.JRadioButtonMenuItem m_size8
- A font choice.
m_size6
javax.swing.JRadioButtonMenuItem m_size6
- A font choice.
m_size4
javax.swing.JRadioButtonMenuItem m_size4
- A font choice.
m_size2
javax.swing.JRadioButtonMenuItem m_size2
- A font choice.
m_size1
javax.swing.JRadioButtonMenuItem m_size1
- A font choice.
m_accept
javax.swing.JMenuItem m_accept
- An option on the win menu.
m_nodeMenu
javax.swing.JPopupMenu m_nodeMenu
- A right or middle click popup menu for nodes.
m_visualise
javax.swing.JMenuItem m_visualise
- A visualize choice for the node, may not be available.
m_addChildren
javax.swing.JMenuItem m_addChildren
- An add children to Node choice, This is only available if the tree
display has a treedisplay listerner added to it.
m_remChildren
javax.swing.JMenuItem m_remChildren
- Similar to add children but now it removes children.
m_classifyChild
javax.swing.JMenuItem m_classifyChild
- Use this to have J48 classify this node.
m_sendInstances
javax.swing.JMenuItem m_sendInstances
- Use this to dump the instances from this node to the vis panel.
m_focusNode
int m_focusNode
- The subscript for the currently selected node (this is an internal
thing, so the user is unaware of this).
m_highlightNode
int m_highlightNode
- The Node the user is currently focused on , this is similar to
focus node except that it is used by other
classes rather than this one.
m_listener
TreeDisplayListener m_listener
m_searchString
javax.swing.JTextField m_searchString
m_searchWin
javax.swing.JDialog m_searchWin
m_caseSen
javax.swing.JRadioButton m_caseSen
|
Package weka.gui.visualize |
m_plotInstances
Instances m_plotInstances
- The instances to be plotted
m_maxC
double m_maxC
- Holds the min and max values of the colouring attributes
m_minC
double m_minC
m_cIndex
int m_cIndex
m_xIndex
int m_xIndex
m_yIndex
int m_yIndex
m_colorList
FastVector m_colorList
- The colour map to use for colouring points
m_DefaultColors
java.awt.Color[] m_DefaultColors
- default colours for colouring discrete class
m_Listeners
FastVector m_Listeners
- The list of things listening to this panel
m_heights
int[] m_heights
- Holds the random height for each instance.
m_span
javax.swing.JPanel m_span
- The container window for the attribute bars, and also where the
X,Y or B get printed.
m_barColour
java.awt.Color m_barColour
- The default colour to use for the background of the bars if
a colour is not defined in Visualize.props
this$0
AttributePanel this$0
m_maxVal
double m_maxVal
- The min and max values for this attribute.
m_minVal
double m_minVal
m_attrib
Attribute m_attrib
- The attribute itself.
m_attribIndex
int m_attribIndex
- The index for this attribute.
m_cached
int[] m_cached
- The x position of each point.
m_pointDrawn
boolean[][] m_pointDrawn
- A temporary array used to strike any instances that would be
drawn redundantly.
m_oldWidth
int m_oldWidth
- Used to determine if the positions need to be recalculated.
m_isEnabled
boolean m_isEnabled
- True when the panel has been enabled (ie after
setNumeric or setNominal has been called
m_isNumeric
boolean m_isNumeric
- True if the colouring attribute is numeric
m_spectrumHeight
int m_spectrumHeight
- The height of the spectrum for numeric class
m_maxC
double m_maxC
- The maximum value for the colouring attribute
m_minC
double m_minC
- The minimum value for the colouring attribute
m_tickSize
int m_tickSize
- The size of the ticks
m_labelMetrics
java.awt.FontMetrics m_labelMetrics
- Font metrics
m_labelFont
java.awt.Font m_labelFont
- The font used in labeling
m_HorizontalPad
int m_HorizontalPad
- The amount of space to leave either side of the legend
m_precisionC
int m_precisionC
- The precision with which to display real values
m_fieldWidthC
int m_fieldWidthC
- Field width for numeric values
m_oldWidth
int m_oldWidth
- The old width.
m_Instances
Instances m_Instances
- Instances being plotted
m_cIndex
int m_cIndex
- Index of the colouring attribute
m_colorList
FastVector m_colorList
- the list of colours to use for colouring nominal attribute labels
m_Repainters
FastVector m_Repainters
- An optional list of Components that use the colour list
maintained by this class. If the user changes a colour
using the colour chooser, then these components need to
be repainted in order to display the change
m_DefaultColors
java.awt.Color[] m_DefaultColors
- default colours for colouring discrete class
m_plots
FastVector m_plots
- the list of plot elements
m_span
javax.swing.JPanel m_span
- the panel that contains the legend entries
m_Repainters
FastVector m_Repainters
- a list of components that need to be repainted when a colour is
changed
this$0
LegendPanel this$0
m_plotData
PlotData2D m_plotData
- the data for this legend entry
m_dataIndex
int m_dataIndex
- the index (in the list of plots) of the data for this legend---
used to draw the correct shape for this data
m_legendText
javax.swing.JLabel m_legendText
- the text part of this legend
m_pointShape
javax.swing.JPanel m_pointShape
- displays the point shape associated with this legend entry
m_axisColour
java.awt.Color m_axisColour
- Default colour for the axis
m_backgroundColour
java.awt.Color m_backgroundColour
- Default colour for the plot background
m_plots
FastVector m_plots
- The plots to display
m_masterPlot
PlotData2D m_masterPlot
- The master plot
m_masterName
java.lang.String m_masterName
- The name of the master plot
m_plotInstances
Instances m_plotInstances
- The instances to be plotted
m_plotCompanion
Plot2DCompanion m_plotCompanion
- An optional "compainion" of the panel. If specified, this
class will get to do its thing with our graphics context
before we do any drawing. Eg. the visualize panel may need
to draw polygons etc. before we draw plot axis and data points
m_InstanceInfo
javax.swing.JFrame m_InstanceInfo
- For popping up text info on data points
m_InstanceInfoText
javax.swing.JTextArea m_InstanceInfoText
m_colorList
FastVector m_colorList
- The list of the colors used
m_DefaultColors
java.awt.Color[] m_DefaultColors
- default colours for colouring discrete class
m_xIndex
int m_xIndex
- Indexes of the attributes to go on the x and y axis and the attribute
to use for colouring and the current shape for drawing
m_yIndex
int m_yIndex
m_cIndex
int m_cIndex
m_sIndex
int m_sIndex
m_maxX
double m_maxX
- Holds the min and max values of the x, y and colouring attributes
over all plots
m_minX
double m_minX
m_maxY
double m_maxY
m_minY
double m_minY
m_maxC
double m_maxC
m_minC
double m_minC
m_axisPad
int m_axisPad
- Axis padding
m_tickSize
int m_tickSize
- Tick size
m_XaxisStart
int m_XaxisStart
- the offsets of the axes once label metrics are calculated
m_YaxisStart
int m_YaxisStart
m_XaxisEnd
int m_XaxisEnd
m_YaxisEnd
int m_YaxisEnd
m_plotResize
boolean m_plotResize
- if the user resizes the window, or the attributes selected for
the attributes change, then the lookup table for points needs
to be recalculated
m_axisChanged
boolean m_axisChanged
- if the user changes attribute assigned to an axis
m_drawnPoints
int[][] m_drawnPoints
- An array used to show if a point is hidden or not.
This is used for speeding up the drawing of the plot panel
although I am not sure how much performance this grants over
not having it.
m_labelFont
java.awt.Font m_labelFont
- Font for labels
m_labelMetrics
java.awt.FontMetrics m_labelMetrics
m_JitterVal
int m_JitterVal
- the level of jitter
m_JRand
java.util.Random m_JRand
- random values for perterbing the data points
m_pointLookup
double[][] m_pointLookup
- lookup table for plotted points
m_DefaultColors
java.awt.Color[] m_DefaultColors
- default colours for colouring discrete class
m_XCombo
javax.swing.JComboBox m_XCombo
- Lets the user select the attribute for the x axis
m_YCombo
javax.swing.JComboBox m_YCombo
- Lets the user select the attribute for the y axis
m_ColourCombo
javax.swing.JComboBox m_ColourCombo
- Lets the user select the attribute to use for colouring
m_ShapeCombo
javax.swing.JComboBox m_ShapeCombo
- Lets the user select the shape they want to create for instance
selection.
m_submit
javax.swing.JButton m_submit
- Button for the user to enter the splits.
m_cancel
javax.swing.JButton m_cancel
- Button for the user to remove all splits.
m_saveBut
javax.swing.JButton m_saveBut
- Button for the user to save the visualized set of instances
COMBO_SIZE
java.awt.Dimension COMBO_SIZE
- Stop the combos from growing out of control
m_FileChooser
javax.swing.JFileChooser m_FileChooser
- file chooser for saving instances
m_ArffFilter
javax.swing.filechooser.FileFilter m_ArffFilter
- Filter to ensure only arff files are selected
m_JitterLab
javax.swing.JLabel m_JitterLab
- Label for the jitter slider
m_Jitter
javax.swing.JSlider m_Jitter
- The jitter slider
m_plot
VisualizePanel.PlotPanel m_plot
- The panel that displays the plot
m_attrib
AttributePanel m_attrib
- The panel that displays the attributes , using color to represent
another attribute.
m_legendPanel
LegendPanel m_legendPanel
- The panel that displays legend info if there is more than one plot
m_plotSurround
javax.swing.JPanel m_plotSurround
- Panel that surrounds the plot panel with a titled border
m_classSurround
javax.swing.JPanel m_classSurround
- Panel that surrounds the class panel with a titled border
listener
java.awt.event.ActionListener listener
- An optional listener that we will inform when ComboBox selections
change
m_splitListener
VisualizePanelListener m_splitListener
- An optional listener that we will inform when the user creates a
split to seperate instances.
m_plotName
java.lang.String m_plotName
- The name of the plot (not currently displayed, but can be used
in the containing Frame or Panel)
m_classPanel
ClassPanel m_classPanel
- The panel that displays the legend for the colouring attribute
m_colorList
FastVector m_colorList
- The list of the colors used
m_preferredXDimension
java.lang.String m_preferredXDimension
- These hold the names of preferred columns to visualize on---if the
user has defined them in the Visualize.props file
m_preferredYDimension
java.lang.String m_preferredYDimension
m_preferredColourDimension
java.lang.String m_preferredColourDimension
m_showAttBars
boolean m_showAttBars
- Show the attribute bar panel
m_Log
Logger m_Log
- the logger
this$0
VisualizePanel this$0
m_plot2D
Plot2D m_plot2D
- The actual generic plotting panel
m_plotInstances
Instances m_plotInstances
- The instances from the master plot
m_originalPlot
PlotData2D m_originalPlot
- The master plot
m_xIndex
int m_xIndex
- Indexes of the attributes to go on the x and y axis and the attribute
to use for colouring and the current shape for drawing
m_yIndex
int m_yIndex
m_cIndex
int m_cIndex
m_sIndex
int m_sIndex
m_axisPad
int m_axisPad
- Axis padding
m_tickSize
int m_tickSize
- Tick size
m_XaxisStart
int m_XaxisStart
- the offsets of the axes once label metrics are calculated
m_YaxisStart
int m_YaxisStart
m_XaxisEnd
int m_XaxisEnd
m_YaxisEnd
int m_YaxisEnd
m_createShape
boolean m_createShape
- True if the user is currently dragging a box.
m_shapes
FastVector m_shapes
- contains all the shapes that have been drawn for these attribs
m_shapePoints
FastVector m_shapePoints
- contains the points of the shape currently being drawn.
m_newMousePos
java.awt.Dimension m_newMousePos
- contains the position of the mouse (used for rubberbanding).