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
m_classLabel
int m_classLabel
- The class label.
m_ruleSupCounter
int m_ruleSupCounter
- The support of the rule.
m_premiseCount
int m_premiseCount
- The minimum support.
m_numRules
int m_numRules
- The maximum number of rules that are output.
m_Ls
FastVector m_Ls
- The set of all sets of itemsets.
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_priors
java.util.Hashtable<K,V> m_priors
- The hashtable containing the prior probabilities.
m_midPoints
double[] m_midPoints
- The mid points of the intervals used for the prior estimation.
m_expectation
double m_expectation
- The expected predictive accuracy a rule needs to be a candidate for the output.
m_best
java.util.TreeSet<E> m_best
- The n best rules.
m_bestChanged
boolean m_bestChanged
- Flag keeping track if the list of the n best rules has changed.
m_count
int m_count
- Counter for the time of generation for an association rule.
m_priorEstimator
PriorEstimation m_priorEstimator
- The prior estimator.
m_numRandRules
int m_numRandRules
- The number of rnadom rules.
m_numIntervals
int m_numIntervals
- The number of intervals.
m_randNum
java.util.Random m_randNum
- The random number generator.
m_instances
Instances m_instances
- The instances for which association rules are mined.
m_CARs
boolean m_CARs
- Flag indicating whether standard association rules or class association rules are mined.
m_distribution
java.util.Hashtable<K,V> m_distribution
- Hashtable to store the confidence values of randomly generated rules.
m_priors
java.util.Hashtable<K,V> m_priors
- Hashtable containing the estimated prior probabilities.
m_sum
double m_sum
- Sums up the confidences of all rules with a certain length.
m_midPoints
double[] m_midPoints
- The mid points of the discrete intervals in which the interval [0,1] is divided.
m_items
int[] m_items
- The items stored as an array of of integer.
m_counter
int m_counter
- Counter for how many transactions contain this item set.
m_totalTransactions
int m_totalTransactions
- The total number of transactions
m_change
boolean m_change
- Flag indicating whether the list fo the best rules has changed.
m_expectation
double m_expectation
- The minimum expected predictive accuracy that is needed to be a candidate for the list of the best rules.
m_minRuleCount
int m_minRuleCount
- The minimum support a rule needs to be a candidate for the list of the best rules.
m_midPoints
double[] m_midPoints
- Sorted array of the mied points of the intervals used for prior estimation.
m_priors
java.util.Hashtable<K,V> m_priors
- Hashtable conatining the estimated prior probabilities.
m_best
java.util.TreeSet<E> m_best
- The list of the actual n<\i> best rules.
m_count
int m_count
- Integer indicating the generation time of a rule.
m_instances
Instances m_instances
- The instances.
m_premise
ItemSet m_premise
- The premise of a rule.
m_consequence
ItemSet m_consequence
- The consequence of a rule.
m_accuracy
double m_accuracy
- The expected predictive accuracy of a rule.
m_genTime
int m_genTime
- The generation time of a rule.
m_results
SimpleLinkedList m_results
- The results.
m_hypotheses
int m_hypotheses
- Number of hypotheses considered.
m_explored
int m_explored
- Number of hypotheses explored.
m_time
java.util.Date m_time
- Time needed for the search.
m_valuesText
java.awt.TextField m_valuesText
- Field to output the current values.
m_instances
Instances m_instances
- Instances used for the search.
m_predicates
java.util.ArrayList<E> m_predicates
- Predicates used in the rules.
m_status
int m_status
- Status of the search.
m_best
int m_best
- Number of best confirmation values to search.
m_frequencyThreshold
double m_frequencyThreshold
- Frequency threshold for the body and the negation of the head.
m_confirmationThreshold
double m_confirmationThreshold
- Confirmation threshold for the rules.
m_noiseThreshold
double m_noiseThreshold
- Maximal number of counter-instances.
m_repeat
boolean m_repeat
- Repeat attributes ?
m_numLiterals
int m_numLiterals
- Number of literals in a rule.
m_negation
int m_negation
- Type of negation used in the rules.
m_classification
boolean m_classification
- Classification bias.
m_classIndex
int m_classIndex
- Index of class attribute.
m_horn
boolean m_horn
- Horn clauses bias.
m_equivalent
boolean m_equivalent
- Perform test on equivalent rules ?
m_sameClause
boolean m_sameClause
- Perform test on same clauses ?
m_subsumption
boolean m_subsumption
- Perform subsumption test ?
m_missing
int m_missing
- Way of handling missing values in the search.
m_roc
boolean m_roc
- Perform ROC analysis ?
m_partsString
java.lang.String m_partsString
- Name of the file containing the parts for individual-based learning.
m_parts
Instances m_parts
- Part instances for individual-based learning.
m_printValues
int m_printValues
- Type of values output.
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Package weka.associations.tertius |
m_value
java.lang.String m_value
m_index
int m_index
m_parts
Instances m_parts
m_type
int m_type
m_predicate
Predicate m_predicate
m_sign
int m_sign
m_negation
Literal m_negation
m_missing
int m_missing
m_literals
java.util.ArrayList<E> m_literals
- Literals contained in this set.
m_lastLiteral
Literal m_lastLiteral
- Last literal added to this set.
m_numInstances
int m_numInstances
- Number of instances in the data the set deals with.
m_counterInstances
java.util.ArrayList<E> m_counterInstances
- Set of counter-instances of this part of the rule.
m_counter
int m_counter
- Counter for the number of counter-instances.
m_type
int m_type
- Type of properties expressed in this set
(individual or parts properties).
m_literals
java.util.ArrayList<E> m_literals
m_name
java.lang.String m_name
m_index
int m_index
m_isClass
boolean m_isClass
m_body
Body m_body
- The body of the rule.
m_head
Head m_head
- The head of the rule.
m_repeatPredicate
boolean m_repeatPredicate
- Can repeat predicates in the rule ?
m_maxLiterals
int m_maxLiterals
- Maximal number of literals in the rule.
m_negBody
boolean m_negBody
- Can there be negations in the body ?
m_negHead
boolean m_negHead
- Can there be negations in the head ?
m_classRule
boolean m_classRule
- Is this rule a classification rule ?
m_singleHead
boolean m_singleHead
- Can there be only one literal in the head ?
m_numInstances
int m_numInstances
- Number of instances in the data this rule deals with.
m_counterInstances
java.util.ArrayList<E> m_counterInstances
- Set of counter-instances of this rule.
m_counter
int m_counter
- Counter for the counter-instances of this rule.
m_confirmation
double m_confirmation
- Confirmation of this rule.
m_optimistic
double m_optimistic
- Optimistic estimate of this rule.
readObject
private void readObject(java.io.ObjectInputStream s)
throws java.io.IOException,
java.lang.ClassNotFoundException
- Reconstitute this LinkedList instance from a stream (that is
deserialize it).
- Throws:
java.io.IOException
java.lang.ClassNotFoundException
writeObject
private void writeObject(java.io.ObjectOutputStream s)
throws java.io.IOException
- Save the state of this LinkedList instance to a stream (that
is, serialize it).
- Serial Data:
- The size of the list (the number of elements it
contains) is emitted (int), followed by all of its
elements (each an Object) in the proper order.
- Throws:
java.io.IOException
first
weka.associations.tertius.SimpleLinkedList.Entry first
last
weka.associations.tertius.SimpleLinkedList.Entry last
current
weka.associations.tertius.SimpleLinkedList.Entry current
lastReturned
weka.associations.tertius.SimpleLinkedList.Entry lastReturned
current
weka.associations.tertius.SimpleLinkedList.Entry current
lastReturned
weka.associations.tertius.SimpleLinkedList.Entry lastReturned
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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_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
Remove m_attributeFilter
- the attribute filter for processing instances with respect to
the most recent feature selection run
m_rankResults
double[][] m_rankResults
- hold statistics for repeated feature selection, such as
under cross validation
m_subsetResults
double[] m_subsetResults
m_trials
int m_trials
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
m_cacheSize
int m_cacheSize
- holds the maximum size of the lookup cache for evaluated subsets
m_data
java.lang.Object[] m_data
m_merit
double m_merit
m_MaxSize
int m_MaxSize
m_trainInstances
Instances m_trainInstances
- The training instances
m_disTransform
Discretize m_disTransform
- Discretise attributes when class in nominal
m_classIndex
int m_classIndex
- The class index
m_isNumeric
boolean m_isNumeric
- Is the class numeric
m_numAttribs
int m_numAttribs
- Number of attributes in the training data
m_numInstances
int m_numInstances
- Number of instances in the training data
m_missingSeperate
boolean m_missingSeperate
- Treat missing values as seperate values
m_locallyPredictive
boolean m_locallyPredictive
- Include locally predicitive attributes
m_corr_matrix
float[][] m_corr_matrix
- Holds the matrix of attribute correlations
m_std_devs
double[] m_std_devs
- Standard deviations of attributes (when using pearsons correlation)
m_c_Threshold
double m_c_Threshold
- Threshold for admitting locally predictive features
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
Discretize m_disTransform
- Discretise numeric attributes
m_table
java.util.Hashtable<K,V> m_table
- Hash table for evaluating feature subsets
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
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_evaluations
int m_evaluations
- the number of subsets evaluated during the search
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
weka.attributeSelection.GeneticSearch.GABitSet[] m_population
- the current population
m_popSize
int m_popSize
- the number of individual solutions
m_best
weka.attributeSelection.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<K,V> 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
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Class weka.attributeSelection.GeneticSearch.GABitSet extends java.lang.Object implements Serializable |
m_chromosome
java.util.BitSet m_chromosome
m_objective
double m_objective
- holds raw merit
m_fitness
double m_fitness
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_backward
boolean m_backward
- Use a backwards search instead of a forwards one
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_randomSeed
int m_randomSeed
- Random number seed
m_folds
int m_folds
- Number of folds for cross validation
m_evalUsingTrainingData
boolean m_evalUsingTrainingData
- Use training data to evaluate merit rather than x-val
m_minBucketSize
int m_minBucketSize
- Passed on to OneR
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
ReplaceMissingValues m_replaceMissingFilter
- Filters for original data
m_normalizeFilter
Normalize m_normalizeFilter
m_nominalToBinFilter
NominalToBinary m_nominalToBinFilter
m_attributeFilter
Remove m_attributeFilter
m_attribFilter
Remove 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_maxAttrsInName
int m_maxAttrsInName
- maximum number of attributes in the transformed attribute name
m_eTranspose
double[][] m_eTranspose
- holds the transposed eigenvectors for converting back to the
original space
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_attScores
double[] m_attScores
- The attribute scores
m_numToEliminate
int m_numToEliminate
- Constant rate of attribute elimination per iteration
m_percentToEliminate
int m_percentToEliminate
- Percentage rate of attribute elimination, trumps constant
rate (above threshold), ignored if = 0
m_percentThreshold
int m_percentThreshold
- Threshold below which percent elimination switches to
constant elimination
m_smoCParameter
double m_smoCParameter
- Complexity parameter to pass on to SMO
m_smoTParameter
double m_smoTParameter
- Tolerance parameter to pass on to SMO
m_smoPParameter
double m_smoPParameter
- Epsilon parameter to pass on to SMO
m_smoFilterType
int m_smoFilterType
- Filter parameter to pass on to SMO
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_Debug
boolean m_Debug
- Whether the classifier is run in debug mode.
m_Classifiers
Classifier[] m_Classifiers
- Array for storing the generated base classifiers.
m_NumIterations
int m_NumIterations
- The number of iterations.
m_Classifiers
Classifier[] m_Classifiers
- Array for storing the generated base classifiers.
m_Seed
int m_Seed
- The random number seed.
m_Seed
int m_Seed
- The random number seed.
m_Seed
int m_Seed
- The random number seed.
m_Seed
int m_Seed
- The random number seed.
m_Classifier
Classifier m_Classifier
- The base classifier to use
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Package weka.classifiers.bayes |
m_CondiCounts
double[][][] m_CondiCounts
- 3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues)
of attribute counts, i.e. the number of times an attribute value occurs
in conjunction with another attribute value and a class value.
m_ClassCounts
double[] m_ClassCounts
- The number of times each class value occurs in the dataset
m_SumForCounts
double[][] m_SumForCounts
- The sums of attribute-class counts
-- if there are no missing values for att, then m_SumForCounts[classVal][att]
will be the same as m_ClassCounts[classVal]
m_NumClasses
int m_NumClasses
- The number of classes
m_NumAttributes
int m_NumAttributes
- The number of attributes in dataset, including class
m_NumInstances
int m_NumInstances
- The number of instances in the dataset
m_ClassIndex
int m_ClassIndex
- The index of the class attribute
m_Instances
Instances m_Instances
- The dataset
m_TotalAttValues
int m_TotalAttValues
- The total number of values (including an extra for each attribute's
missing value, which are included in m_CondiCounts) for all attributes
(not including class). Eg. for three atts each with two possible values,
m_TotalAttValues would be 9 (6 values + 3 missing).
This variable is used when allocating space for m_CondiCounts matrix.
m_StartAttIndex
int[] m_StartAttIndex
- The starting index (in the m_CondiCounts matrix) of the values for each attribute
m_NumAttValues
int[] m_NumAttValues
- The number of values for each attribute
m_Frequencies
double[] m_Frequencies
- The frequency of each attribute value for the dataset
m_SumInstances
double m_SumInstances
- The number of valid class values observed in dataset
-- with no missing classes, this number is the same as m_NumInstances.
m_Limit
int m_Limit
- An att's frequency must be this value or more to be a superParent
m_Debug
boolean m_Debug
- If true, outputs debugging info
m_ParentSets
ParentSet[] m_ParentSets
- The parent sets.
m_Distributions
Estimator[][] m_Distributions
- The attribute estimators containing CPTs.
m_DiscretizeFilter
Discretize m_DiscretizeFilter
- filter used to quantize continuous variables, if any
m_nNonDiscreteAttribute
int m_nNonDiscreteAttribute
m_MissingValuesFilter
ReplaceMissingValues m_MissingValuesFilter
- filter used to fill in missing values, if any
m_NumClasses
int m_NumClasses
- The number of classes
m_Instances
Instances m_Instances
- The dataset header for the purposes of printing out a semi-intelligible
model
m_ADTree
ADNode m_ADTree
- Datastructure containing ADTree representation of the database.
This may result in more efficient access to the data.
m_otherBayesNet
BIFReader m_otherBayesNet
- Bayes network to compare the structure with.
m_bUseADTree
boolean m_bUseADTree
- Use the experimental ADTree datastructure for calculating contingency tables
m_SearchAlgorithm
SearchAlgorithm m_SearchAlgorithm
- Search algorithm used for learning the structure of a network.
m_BayesNetEstimator
BayesNetEstimator m_BayesNetEstimator
- Search algorithm used for learning the structure of a network.
wordWeights
double[][] wordWeights
- Weight of words for each class. The weight is actually the
log of the probability of a word (w) given a class (c)
(i.e. log(Pr[w|c])). The format of the matrix is:
wordWeights[class][wordAttribute]
smoothingParameter
double smoothingParameter
- Holds the smoothing value to avoid word probabilities of zero.
P.S.: According to the paper this is the Alpha i parameter
m_normalizeWordWeights
boolean m_normalizeWordWeights
- True if the words weights are to be normalized
numClasses
int numClasses
- Holds the number of Class values present in the set of specified
instances
header
Instances header
- The instances header that'll be used in toString
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_UseDiscretization
boolean m_UseDiscretization
- Whether to use discretization 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_Disc
Discretize m_Disc
- The discretization filter.
probOfWordGivenClass
double[][] probOfWordGivenClass
- probability that a word (w) exists in a class (H) (i.e. Pr[w|H])
The matrix is in the this format: probOfWordGivenClass[class][wordAttribute]
NOTE: the values are actually the log of Pr[w|H]
probOfClass
double[] probOfClass
- the probability of a class (i.e. Pr[H])
numAttributes
int numAttributes
- number of unique words
numClasses
int numClasses
- number of class values
lnFactorialCache
double[] lnFactorialCache
- cache lnFactorial computations
headerInfo
Instances headerInfo
- copy of header information for use in toString method
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.
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Package weka.classifiers.bayes.net |
m_VaryNodes
VaryNode[] m_VaryNodes
- list of VaryNode children
m_Instances
Instance[] m_Instances
- list of Instance children (either m_Instances or m_VaryNodes is instantiated)
m_nCount
int m_nCount
- count
m_nStartNode
int m_nStartNode
- first node in VaryNode array
m_nSeed
int m_nSeed
random
java.util.Random random
m_bGenerateNet
boolean m_bGenerateNet
m_nNrOfNodes
int m_nNrOfNodes
m_nNrOfArcs
int m_nNrOfArcs
m_nNrOfInstances
int m_nNrOfInstances
m_nCardinality
int m_nCardinality
m_sBIFFile
java.lang.String m_sBIFFile
m_nPositionX
int[] m_nPositionX
m_nPositionY
int[] m_nPositionY
m_order
int[] m_order
m_sFile
java.lang.String m_sFile