Modifier and Type | Field and Description |
---|---|
protected FastVector |
GeneralizedSequentialPatterns.m_AllSequentialPatterns
all generated frequent sequences, i.e.
|
protected FastVector[] |
Apriori.m_allTheRules
The list of all generated rules.
|
protected FastVector[] |
PredictiveApriori.m_allTheRules
The list of all generated rules.
|
protected FastVector |
GeneralizedSequentialPatterns.m_FilterAttrVector
Vector containing the attribute numbers that are used for result
filtering; -1 means no filtering
|
protected FastVector |
Apriori.m_hashtables
The same information stored in hash tables.
|
protected FastVector |
PredictiveApriori.m_hashtables
The same information stored in hash tables.
|
protected FastVector |
Apriori.m_Ls
The set of all sets of itemsets L.
|
protected FastVector |
PredictiveApriori.m_Ls
The set of all sets of itemsets.
|
Modifier and Type | Method and Description |
---|---|
static FastVector |
LabeledItemSet.deleteItemSets(FastVector itemSets,
int minSupport,
int maxSupport)
Deletes all item sets that don't have minimum support and have more than maximum support
|
static FastVector |
ItemSet.deleteItemSets(FastVector itemSets,
int minSupport,
int maxSupport)
Deletes all item sets that don't have minimum support.
|
protected FastVector |
GeneralizedSequentialPatterns.extractDataSequences(Instances originalDataSet,
int dataSeqID)
Extracts the data sequences out of the original data set according to
their sequence id attribute, which is removed after extraction.
|
FastVector[] |
LabeledItemSet.generateRules(double minConfidence,
boolean noPrune)
Generates rules out of item sets
|
FastVector[] |
AprioriItemSet.generateRules(double minConfidence,
FastVector hashtables,
int numItemsInSet)
Generates all rules for an item set.
|
FastVector[] |
AprioriItemSet.generateRulesBruteForce(double minMetric,
int metricType,
FastVector hashtables,
int numItemsInSet,
int numTransactions,
double significanceLevel)
Generates all significant rules for an item set.
|
FastVector[] |
Apriori.getAllTheRules()
returns all the rules
|
FastVector[] |
PredictiveApriori.getAllTheRules()
returns all the rules
|
static FastVector |
LabeledItemSet.mergeAllItemSets(FastVector itemSets,
int size,
int totalTrans)
Merges all item sets in the set of (k-1)-item sets
to create the (k)-item sets and updates the counters.
|
static FastVector |
ItemSet.mergeAllItemSets(FastVector itemSets,
int size,
int totalTrans)
Merges all item sets in the set of (k-1)-item sets
to create the (k)-item sets and updates the counters.
|
static FastVector |
AprioriItemSet.mergeAllItemSets(FastVector itemSets,
int size,
int totalTrans)
Merges all item sets in the set of (k-1)-item sets
to create the (k)-item sets and updates the counters.
|
FastVector[] |
Apriori.mineCARs(Instances data)
Method that mines all class association rules with minimum support and
with a minimum confidence.
|
FastVector[] |
PredictiveApriori.mineCARs(Instances data)
Method that mines the n best class association rules.
|
FastVector[] |
CARuleMiner.mineCARs(Instances data)
Method for mining class association rules.
|
static FastVector |
LabeledItemSet.pruneItemSets(FastVector toPrune,
Hashtable kMinusOne)
Prunes a set of (k)-item sets using the given (k-1)-item sets.
|
static FastVector |
ItemSet.pruneItemSets(FastVector toPrune,
Hashtable kMinusOne)
Prunes a set of (k)-item sets using the given (k-1)-item sets.
|
static FastVector |
CaRuleGeneration.singleConsequence(Instances instances)
generates a consequence of length 1 for a class association rule.
|
static FastVector |
RuleGeneration.singleConsequence(Instances instances,
int attNum,
FastVector consequences)
generates a consequence of length 1 for an association rule.
|
static FastVector |
CaRuleGeneration.singletons(Instances instances)
Converts the header info of the given set of instances into a set
of item sets (singletons).
|
static FastVector |
ItemSet.singletons(Instances instances)
Converts the header info of the given set of instances into a set
of item sets (singletons).
|
static FastVector |
AprioriItemSet.singletons(Instances instances)
Converts the header info of the given set of instances into a set
of item sets (singletons).
|
static FastVector |
LabeledItemSet.singletons(Instances instancesNoClass,
Instances classes)
Converts the header info of the given set of instances into a set
of item sets (singletons).
|
Modifier and Type | Method and Description |
---|---|
static FastVector |
LabeledItemSet.deleteItemSets(FastVector itemSets,
int minSupport,
int maxSupport)
Deletes all item sets that don't have minimum support and have more than maximum support
|
static FastVector |
ItemSet.deleteItemSets(FastVector itemSets,
int minSupport,
int maxSupport)
Deletes all item sets that don't have minimum support.
|
FastVector[] |
AprioriItemSet.generateRules(double minConfidence,
FastVector hashtables,
int numItemsInSet)
Generates all rules for an item set.
|
FastVector[] |
AprioriItemSet.generateRulesBruteForce(double minMetric,
int metricType,
FastVector hashtables,
int numItemsInSet,
int numTransactions,
double significanceLevel)
Generates all significant rules for an item set.
|
static Hashtable |
LabeledItemSet.getHashtable(FastVector itemSets,
int initialSize)
Return a hashtable filled with the given item sets.
|
static Hashtable |
ItemSet.getHashtable(FastVector itemSets,
int initialSize)
Return a hashtable filled with the given item sets.
|
static FastVector |
LabeledItemSet.mergeAllItemSets(FastVector itemSets,
int size,
int totalTrans)
Merges all item sets in the set of (k-1)-item sets
to create the (k)-item sets and updates the counters.
|
static FastVector |
ItemSet.mergeAllItemSets(FastVector itemSets,
int size,
int totalTrans)
Merges all item sets in the set of (k-1)-item sets
to create the (k)-item sets and updates the counters.
|
static FastVector |
AprioriItemSet.mergeAllItemSets(FastVector itemSets,
int size,
int totalTrans)
Merges all item sets in the set of (k-1)-item sets
to create the (k)-item sets and updates the counters.
|
static FastVector |
LabeledItemSet.pruneItemSets(FastVector toPrune,
Hashtable kMinusOne)
Prunes a set of (k)-item sets using the given (k-1)-item sets.
|
static FastVector |
ItemSet.pruneItemSets(FastVector toPrune,
Hashtable kMinusOne)
Prunes a set of (k)-item sets using the given (k-1)-item sets.
|
static void |
ItemSet.pruneRules(FastVector[] rules,
double minConfidence)
Prunes a set of rules.
|
protected boolean[] |
CheckAssociator.runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
protected boolean[] |
CheckAssociator.runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int classIndex,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
static FastVector |
RuleGeneration.singleConsequence(Instances instances,
int attNum,
FastVector consequences)
generates a consequence of length 1 for an association rule.
|
static void |
ItemSet.upDateCounters(FastVector itemSets,
Instances instances)
Updates counters for a set of item sets and a set of instances.
|
static void |
LabeledItemSet.upDateCounters(FastVector itemSets,
Instances instancesNoClass,
Instances instancesClass)
Updates counter of a specific item set
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
Sequence.m_Elements
ordered list of the comprised elements/itemsets
|
Modifier and Type | Method and Description |
---|---|
static FastVector |
Sequence.aprioriGen(FastVector kMinusOneSequences)
Generates all possible candidate k-Sequences and prunes the ones that
contain an infrequent (k-1)-Sequence.
|
static FastVector |
Sequence.deleteInfrequentSequences(FastVector sequences,
long minSupportCount)
Deletes Sequences of a given set which don't meet the minimum support
count threshold.
|
protected static FastVector |
Sequence.generateKCandidates(FastVector kMinusOneSequences)
Generates candidate k-Sequences on the basis of a given (k-1)-Sequence set.
|
protected FastVector |
Sequence.getElements()
Returns the Elements of the Sequence.
|
static FastVector |
Element.getOneElements(Instances instances)
Returns all events of the given data set as Elements containing a single
event.
|
protected static FastVector |
Sequence.merge(Sequence seq1,
Sequence seq2,
boolean oneElements,
boolean mergeElements)
Merges two Sequences in the course of candidate generation.
|
static FastVector |
Sequence.oneElementsToSequences(FastVector elements)
Converts a set of 1-Elements into a set of 1-Sequences.
|
protected static FastVector |
Sequence.pruneCadidates(FastVector allCandidates,
FastVector kMinusOneSequences)
Prunes a k-Sequence of a given candidate set if one of its (k-1)-Sequences
is infrequent.
|
Modifier and Type | Method and Description |
---|---|
static FastVector |
Sequence.aprioriGen(FastVector kMinusOneSequences)
Generates all possible candidate k-Sequences and prunes the ones that
contain an infrequent (k-1)-Sequence.
|
static FastVector |
Sequence.deleteInfrequentSequences(FastVector sequences,
long minSupportCount)
Deletes Sequences of a given set which don't meet the minimum support
count threshold.
|
protected static FastVector |
Sequence.generateKCandidates(FastVector kMinusOneSequences)
Generates candidate k-Sequences on the basis of a given (k-1)-Sequence set.
|
static FastVector |
Sequence.oneElementsToSequences(FastVector elements)
Converts a set of 1-Elements into a set of 1-Sequences.
|
static void |
Sequence.printSetOfSequences(FastVector setOfSequences)
Prints a set of Sequences as String output.
|
protected static FastVector |
Sequence.pruneCadidates(FastVector allCandidates,
FastVector kMinusOneSequences)
Prunes a k-Sequence of a given candidate set if one of its (k-1)-Sequences
is infrequent.
|
protected void |
Sequence.setElements(FastVector elements)
Sets the Elements of the Sequence.
|
static String |
Sequence.setOfSequencesToString(FastVector setOfSequences,
Instances dataSet,
FastVector filterAttributes)
Returns a String representation of a set of Sequences where the numeric
value of each event/item is represented by its respective nominal value.
|
static void |
Sequence.updateSupportCount(FastVector candidates,
FastVector dataSequences)
Updates the support count of a set of Sequence candidates according to a
given set of data sequences.
|
Constructor and Description |
---|
Sequence(FastVector elements)
Constructor accepting a set of elements as parameter.
|
Modifier and Type | Class and Description |
---|---|
class |
BestFirst.LinkedList2
Class for handling a linked list.
|
class |
LFSMethods.LinkedList2
Class for handling a linked list.
|
Modifier and Type | Method and Description |
---|---|
protected boolean[] |
CheckAttributeSelection.runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
protected boolean[] |
CheckAttributeSelection.runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int classIndex,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
Modifier and Type | Method and Description |
---|---|
FastVector |
Evaluation.predictions()
Returns the predictions that have been collected.
|
Modifier and Type | Method and Description |
---|---|
protected boolean[] |
CheckClassifier.runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numTest,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
protected boolean[] |
CheckClassifier.runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int classIndex,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numTest,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
EditableBayesNet.m_fMarginP
marginal distributions *
|
protected FastVector |
EditableBayesNet.m_nEvidence
evidence values, used for evidence propagation *
|
protected FastVector |
EditableBayesNet.m_nPositionX
location of nodes, used for graph drawing *
|
protected FastVector |
EditableBayesNet.m_nPositionY |
Modifier and Type | Method and Description |
---|---|
FastVector |
EditableBayesNet.getChildren(int nTargetNode)
return list of children of a node
|
Modifier and Type | Method and Description |
---|---|
void |
EditableBayesNet.addArc(String sParent,
FastVector nodes)
Add arc between parent node and each of the nodes in a given list.
|
void |
EditableBayesNet.alignBottom(FastVector nodes)
align set of nodes with the bottom most node in the list
|
void |
EditableBayesNet.alignLeft(FastVector nodes)
align set of nodes with the left most node in the list
|
void |
EditableBayesNet.alignRight(FastVector nodes)
align set of nodes with the right most node in the list
|
void |
EditableBayesNet.alignTop(FastVector nodes)
align set of nodes with the top most node in the list
|
void |
EditableBayesNet.centerHorizontal(FastVector nodes)
center set of nodes half way between left and right most node in the list
|
void |
EditableBayesNet.centerVertical(FastVector nodes)
center set of nodes half way between top and bottom most node in the list
|
void |
EditableBayesNet.deleteSelection(FastVector nodes)
Delete nodes with indexes in selection from the network, updating instances, parentsets,
distributions Conditional distributions are condensed by taking the
values for the target node to be its first value.
|
void |
EditableBayesNet.layoutGraph(FastVector nPosX,
FastVector nPosY)
set positions of all nodes
|
static ADNode |
ADNode.makeADTree(int iNode,
FastVector nRecords,
Instances instances)
create sub tree
|
static VaryNode |
ADNode.makeVaryNode(int iNode,
FastVector nRecords,
Instances instances)
create sub tree
|
void |
EditableBayesNet.setPosition(int nNode,
int nX,
int nY,
FastVector nodes)
Set position of node.
|
void |
EditableBayesNet.spaceHorizontal(FastVector nodes)
space out set of nodes evenly between left and right most node in the list
|
void |
EditableBayesNet.spaceVertical(FastVector nodes)
space out set of nodes evenly between top and bottom most node in the list
|
String |
EditableBayesNet.toXMLBIF03(FastVector nodes)
return fragment of network in XMLBIF format
|
Modifier and Type | Method and Description |
---|---|
FastVector |
EvaluationUtils.getCVPredictions(Classifier classifier,
Instances data,
int numFolds)
Generate a bunch of predictions ready for processing, by performing a
cross-validation on the supplied dataset.
|
FastVector |
EvaluationUtils.getTestPredictions(Classifier classifier,
Instances test)
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set assuming the classifier is already trained.
|
FastVector |
EvaluationUtils.getTrainTestPredictions(Classifier classifier,
Instances train,
Instances test)
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set after training on the given training set.
|
Modifier and Type | Method and Description |
---|---|
void |
ConfusionMatrix.addPredictions(FastVector predictions)
Includes a whole bunch of predictions in the confusion matrix.
|
Instances |
ThresholdCurve.getCurve(FastVector predictions)
Calculates the performance stats for the default class and return
results as a set of Instances.
|
Instances |
MarginCurve.getCurve(FastVector predictions)
Calculates the cumulative margin distribution for the set of
predictions, returning the result as a set of Instances.
|
Instances |
CostCurve.getCurve(FastVector predictions)
Calculates the performance stats for the default class and return
results as a set of Instances.
|
Instances |
ThresholdCurve.getCurve(FastVector predictions,
int classIndex)
Calculates the performance stats for the desired class and return
results as a set of Instances.
|
Instances |
CostCurve.getCurve(FastVector predictions,
int classIndex)
Calculates the performance stats for the desired class and return
results as a set of Instances.
|
Modifier and Type | Method and Description |
---|---|
protected boolean[] |
CheckKernel.runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
protected boolean[] |
CheckKernel.runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int classIndex,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
RacedIncrementalLogitBoost.m_committees
The committees
|
protected FastVector |
CVParameterSelection.m_CVParams
The set of parameters to cross-validate over
|
protected FastVector |
RacedIncrementalLogitBoost.Committee.m_models |
Modifier and Type | Method and Description |
---|---|
protected FastVector |
ThresholdSelector.getPredictions(Instances instances,
int mode,
int numFolds)
Collects the classifier predictions using the specified evaluation method.
|
Modifier and Type | Method and Description |
---|---|
protected void |
ThresholdSelector.findThreshold(FastVector predictions)
Finds the best threshold, this implementation searches for the
highest FMeasure.
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
ND.NDTree.m_indices
The indices associated with this node
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
JRip.RipperRule.m_Antds
The vector of antecedents of this rule
|
protected FastVector |
ConjunctiveRule.m_Antds
The vector of antecedents of this rule
|
Modifier and Type | Method and Description |
---|---|
FastVector |
JRip.RipperRule.getAntds()
Return the antecedents
|
FastVector |
RuleStats.getRuleset()
Get the ruleset of the stats
|
FastVector |
JRip.getRuleset()
Get the ruleset generated by Ripper
|
Modifier and Type | Method and Description |
---|---|
static Instances |
RuleStats.rmCoveredBySuccessives(Instances data,
FastVector rules,
int index)
Static utility function to count the data covered by the
rules after the given index in the given rules, and then
remove them.
|
void |
RuleStats.setRuleset(FastVector rules)
Set the ruleset of the stats, overwriting the old one if any
|
Constructor and Description |
---|
RuleStats(Instances data,
FastVector rules)
Constructor that provides ruleset and data
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
LADTree.m_staticPotentialSplitters2way |
Modifier and Type | Method and Description |
---|---|
protected FastVector |
BFTree.computeSplitInfo(BFTree node,
Instances data,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[][] props,
double[][] totalSubsetWeights,
boolean useHeuristic,
boolean useGini)
Compute the best splitting attribute, split point or subset and the best
gini gain or iformation gain for a given dataset.
|
FastVector |
LADTree.PredictionNode.getChildren() |
Modifier and Type | Method and Description |
---|---|
protected void |
BFTree.makeSuccessors(FastVector BestFirstElements,
Instances data,
int[][][] subsetSortedIndices,
double[][][] subsetWeights,
double[][][] dists,
Attribute att,
boolean useHeuristic,
boolean useGini)
Generate successor nodes for a node and put them into BestFirstElements
according to gini gain or information gain in a descending order.
|
protected void |
BFTree.makeTree(FastVector BestFirstElements,
BFTree root,
Instances train,
Instances test,
FastVector modelError,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[] classProbs,
double totalWeight,
double[] branchProps,
int minNumObj,
boolean useHeuristic,
boolean useGini,
boolean useErrorRate)
This method is to find the number of expansions based on internal
cross-validation for just post-pruning.
|
protected boolean |
BFTree.makeTree(FastVector BestFirstElements,
BFTree root,
Instances train,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[] classProbs,
double totalWeight,
double[] branchProps,
int minNumObj,
boolean useHeuristic,
boolean useGini)
This method is to find the number of expansions based on internal
cross-validation for just pre-pruning.
|
protected void |
BFTree.makeTree(FastVector BestFirstElements,
Instances data,
int[][] sortedIndices,
double[][] weights,
double[][][] dists,
double[] classProbs,
double totalWeight,
double[] branchProps,
int minNumObj,
boolean useHeuristic,
boolean useGini,
int preExpansion)
Recursively build a best-first decision tree.
|
Modifier and Type | Method and Description |
---|---|
FastVector |
PredictionNode.getChildren()
Gets the children of this node.
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
M5Base.m_ruleSet
the rule set
|
Modifier and Type | Method and Description |
---|---|
void |
RuleNode.returnLeaves(FastVector[] v)
Return a list containing all the leaves in the tree
|
Modifier and Type | Method and Description |
---|---|
FastVector |
OPTICS.getResultVector()
Returns the resultVector
|
Modifier and Type | Method and Description |
---|---|
protected boolean[] |
CheckClusterer.runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int missingLevel,
boolean predictorMissing,
int numTrain,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
Modifier and Type | Method and Description |
---|---|
FastVector |
SERObject.getResultVector()
Returns the resultVector
|
Modifier and Type | Method and Description |
---|---|
void |
GraphPanel.setResultVector(FastVector resultVector)
Sets a new resultVector
|
Constructor and Description |
---|
GraphPanel(FastVector resultVector,
int verticalAdjustment,
boolean showCoreDistances,
boolean showReachbilityDistances) |
ResultVectorTableModel(FastVector resultVector)
Constructs a default
DefaultTableModel
which is a table of zero columns and zero rows. |
SERObject(FastVector resultVector,
int databaseSize,
int numberOfAttributes,
double epsilon,
int minPoints,
boolean opticsOutputs,
String database_Type,
String database_distanceType,
int numberOfGeneratedClusters,
String elapsedTime) |
Modifier and Type | Field and Description |
---|---|
protected FastVector |
Instances.m_Attributes
The attribute information.
|
protected FastVector |
Instances.m_Instances
The instances.
|
Modifier and Type | Method and Description |
---|---|
void |
FastVector.appendElements(FastVector toAppend)
Appends all elements of the supplied vector to this vector.
|
Constructor and Description |
---|
Attribute(String attributeName,
FastVector attributeValues)
Constructor for nominal attributes and string attributes.
|
Attribute(String attributeName,
FastVector attributeValues,
int index)
Constructor for nominal attributes and string attributes with
a particular index.
|
Attribute(String attributeName,
FastVector attributeValues,
ProtectedProperties metadata)
Constructor for nominal attributes and string attributes, where
metadata is supplied.
|
FastVectorEnumeration(FastVector vector)
Constructs an enumeration.
|
FastVectorEnumeration(FastVector vector,
int special)
Constructs an enumeration with a special element.
|
Instances(String name,
FastVector attInfo,
int capacity)
Creates an empty set of instances.
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
CSVLoader.m_cumulativeInstances
Holds instances accumulated so far.
|
protected FastVector |
CSVLoader.m_cumulativeStructure
A list of hash tables for accumulating nominal values during parsing.
|
Modifier and Type | Method and Description |
---|---|
protected FastVector |
ArffLoader.ArffReader.parseAttribute(FastVector attributes)
Parses the attribute declaration.
|
Modifier and Type | Method and Description |
---|---|
protected FastVector |
ArffLoader.ArffReader.parseAttribute(FastVector attributes)
Parses the attribute declaration.
|
Modifier and Type | Class and Description |
---|---|
protected class |
MiddleOutConstructor.MyIdxList
Class implementing a list.
|
Modifier and Type | Method and Description |
---|---|
protected BallNode |
BottomUpConstructor.mergeNodes(FastVector list,
int startIdx,
int endIdx,
int[] instList)
Merges nodes into one top node.
|
Modifier and Type | Method and Description |
---|---|
protected FastVector |
XMLInstances.createAttributes(Element parent,
int[] classIndex)
returns a list of generated attributes
|
protected FastVector |
XMLInstances.createLabels(Element parent)
returns the labels listed underneath this (nominal) attribute in a
FastVector
|
Modifier and Type | Method and Description |
---|---|
protected boolean[] |
CheckEstimator.runBasicTest(CheckEstimator.AttrTypes attrTypes,
int numAtts,
int attrIndex,
int classType,
int missingLevel,
boolean attributeMissing,
boolean classMissing,
int numTrain,
int numTest,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
protected boolean[] |
CheckEstimator.runBasicTest(CheckEstimator.AttrTypes attrTypes,
int numAtts,
int attrIndex,
int classType,
int classIndex,
int missingLevel,
boolean attributeMissing,
boolean classMissing,
int numTrain,
int numTest,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics.
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
DatabaseResultListener.m_Cache
Stores the cached values
|
protected FastVector |
InstancesResultListener.m_Instances
Stores the instances created so far, before assigning to a header
|
protected FastVector |
AveragingResultProducer.m_Keys
Collects the keys from a single run
|
protected FastVector[] |
InstancesResultListener.m_NominalStrings
Contains strings seen so far for each nominal attribute
|
protected FastVector |
AveragingResultProducer.m_Results
Collects the results from a single run
|
protected FastVector |
PairedTTester.m_Resultsets
Stores a vector for each resultset holding all instances in each set
|
Modifier and Type | Method and Description |
---|---|
protected FastVector |
PairedTTester.Dataset.contents()
Returns a vector containing the instances in the dataset
|
FastVector |
PairedTTester.Resultset.dataset(Instance inst)
Returns a vector containing all instances belonging to one dataset.
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
Add.m_Labels
The list of labels for nominal attribute.
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
BoundaryPanel.m_Colors |
Modifier and Type | Method and Description |
---|---|
FastVector |
BoundaryPanel.getColors()
Get the current vector of Color objects used for the classes
|
Modifier and Type | Method and Description |
---|---|
void |
BoundaryPanel.setColors(FastVector colors)
Set a vector of Color objects for the classes
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
GeneratorPropertyIteratorPanel.m_Listeners
Listeners who want to be notified about editing status of this
panel
|
Modifier and Type | Method and Description |
---|---|
static void |
ClassifierPanel.processClassifierPrediction(Instance toPredict,
Classifier classifier,
Evaluation eval,
Instances plotInstances,
FastVector plotShape,
FastVector plotSize)
Process a classifier's prediction for an instance and update a set of
plotting instances and additional plotting info.
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
HierarchicalBCEngine.layoutCompleteListeners
FastVector containing listeners for
layoutCompleteEvent generated by this
LayoutEngine
|
protected FastVector |
DotParser.m_edges
These holds the nodes and edges of the graph
|
protected FastVector |
BIFParser.m_edges
These holds the nodes and edges of the graph
|
protected FastVector |
GraphVisualizer.m_edges
Vector containing edges
|
protected FastVector |
HierarchicalBCEngine.m_edges
FastVector containing nodes and edges
|
protected FastVector |
DotParser.m_nodes
These holds the nodes and edges of the graph
|
protected FastVector |
BIFParser.m_nodes
These holds the nodes and edges of the graph
|
protected FastVector |
GraphVisualizer.m_nodes
Vector containing nodes
|
protected FastVector |
HierarchicalBCEngine.m_nodes
FastVector containing nodes and edges
|
Modifier and Type | Method and Description |
---|---|
FastVector |
HierarchicalBCEngine.getNodes()
give access to set of graph nodes
|
FastVector |
LayoutEngine.getNodes()
give access to set of graph nodes
|
Modifier and Type | Method and Description |
---|---|
void |
HierarchicalBCEngine.setNodesEdges(FastVector nodes,
FastVector edges)
Sets the nodes and edges for this LayoutEngine.
|
void |
LayoutEngine.setNodesEdges(FastVector nodes,
FastVector edges)
This method sets the nodes and edges vectors of the LayoutEngine
|
static void |
DotParser.writeDOT(String filename,
String graphName,
FastVector nodes,
FastVector edges)
This method saves a graph in a file in DOT format.
|
static void |
BIFParser.writeXMLBIF03(String filename,
String graphName,
FastVector nodes,
FastVector edges)
This method writes a graph in XMLBIF ver.
|
Constructor and Description |
---|
BIFParser(InputStream instream,
FastVector nodes,
FastVector edges)
Constructor (if our input is an InputStream)
|
BIFParser(String input,
FastVector nodes,
FastVector edges)
Constructor (if our input is a String)
|
DotParser(Reader input,
FastVector nodes,
FastVector edges)
Dot parser Constructor
|
HierarchicalBCEngine(FastVector nodes,
FastVector edges,
int nodeWidth,
int nodeHeight)
Constructor - takes in FastVectors of nodes and edges, and the initial
width and height of a node
|
HierarchicalBCEngine(FastVector nodes,
FastVector edges,
int nodeWidth,
int nodeHeight,
boolean edgeConcentration)
Constructor - takes in FastVectors of nodes and edges, the initial width
and height of a node, and a boolean value to indicate if the edges
should be concentrated.
|
Modifier and Type | Field and Description |
---|---|
protected FastVector |
VisualizePanel.m_colorList
The list of the colors used
|
protected FastVector |
Plot2D.m_colorList
The list of the colors used
|
protected FastVector |
AttributePanel.m_colorList
The colour map to use for colouring points
|
protected FastVector |
AttributePanel.m_Listeners
The list of things listening to this panel
|
protected FastVector |
Plot2D.m_plots
The plots to display
|
protected FastVector |
LegendPanel.m_plots
the list of plot elements
|
protected FastVector |
LegendPanel.m_Repainters
a list of components that need to be repainted when a colour is
changed
|
Modifier and Type | Method and Description |
---|---|
FastVector |
Plot2D.getPlots()
Return the list of plots
|
FastVector |
VisualizePanel.PlotPanel.getShapes() |
FastVector |
VisualizePanelEvent.getValues() |
Modifier and Type | Method and Description |
---|---|
void |
VisualizePanel.PlotPanel.setColours(FastVector cols)
Set a list of colours to use for plotting points
|
void |
Plot2D.setColours(FastVector cols)
Set a list of colours to use when colouring points according
to class values or cluster numbers
|
void |
ClassPanel.setColours(FastVector cols)
Set a list of colours to use for colouring labels
|
void |
AttributePanel.setColours(FastVector cols)
Sets a list of colours to use for colouring data points
|
void |
PlotData2D.setConnectPoints(FastVector cp)
Set whether consecutive points should be connected by lines
|
void |
LegendPanel.setPlotList(FastVector pl)
Set the list of plots to generate legend entries for
|
void |
VisualizePanel.setShapes(FastVector l)
This will set the shapes for the instances.
|
void |
VisualizePanel.PlotPanel.setShapes(FastVector v)
This can be used to set the shapes that should appear.
|
void |
PlotData2D.setShapeSize(FastVector ss)
Set the shape sizes for the plot data
|
void |
PlotData2D.setShapeType(FastVector st)
Set the shape type for the plot data
|
Constructor and Description |
---|
VisualizePanelEvent(FastVector ar,
Instances i,
Instances i2,
int at1,
int at2)
This constructor creates the event with all the parameters set.
|
Modifier and Type | Method and Description |
---|---|
JMenuItem |
VisualizePlugin.getVisualizeMenuItem(FastVector preds,
Attribute classAtt)
Get a JMenu or JMenuItem which contain action listeners
that perform the visualization, using some but not
necessarily all of the data.
|
Copyright © 2015 University of Waikato, Hamilton, NZ. All rights reserved.