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| Uses of FastVector in weka.associations |
|---|
| Methods in weka.associations that return FastVector | |
|---|---|
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[] |
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 |
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 |
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. |
FastVector[] |
Apriori.mineCARs(Instances data)
Method that mines all class association rules with minimum support and with a minimum confidence. |
FastVector[] |
CARuleMiner.mineCARs(Instances data)
Method for mining class association rules. |
FastVector[] |
PredictiveApriori.mineCARs(Instances data)
Method that mines the n best 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 |
AprioriItemSet.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 |
CaRuleGeneration.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). |
| Methods in weka.associations with parameters of type FastVector | |
|---|---|
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 |
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 |
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 |
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. |
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 |
| Uses of FastVector in weka.associations.gsp |
|---|
| Methods in weka.associations.gsp that return FastVector | |
|---|---|
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. |
static FastVector |
Element.getOneElements(Instances instances)
Returns all events of the given data set as Elements containing a single event. |
static FastVector |
Sequence.oneElementsToSequences(FastVector elements)
Converts a set of 1-Elements into a set of 1-Sequences. |
| Methods in weka.associations.gsp with parameters of type FastVector | |
|---|---|
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. |
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. |
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. |
| Constructors in weka.associations.gsp with parameters of type FastVector | |
|---|---|
Sequence(FastVector elements)
Constructor accepting a set of elements as parameter. |
|
| Uses of FastVector in weka.attributeSelection |
|---|
| Subclasses of FastVector in weka.attributeSelection | |
|---|---|
class |
BestFirst.LinkedList2
Class for handling a linked list. |
class |
LFSMethods.LinkedList2
Class for handling a linked list. |
| Uses of FastVector in weka.classifiers |
|---|
| Methods in weka.classifiers that return FastVector | |
|---|---|
FastVector |
Evaluation.predictions()
Returns the predictions that have been collected. |
| Uses of FastVector in weka.classifiers.bayes.net |
|---|
| Methods in weka.classifiers.bayes.net that return FastVector | |
|---|---|
FastVector |
EditableBayesNet.getChildren(int nTargetNode)
return list of children of a node |
| Methods in weka.classifiers.bayes.net with parameters of type FastVector | |
|---|---|
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 |
| Uses of FastVector in weka.classifiers.evaluation |
|---|
| Methods in weka.classifiers.evaluation that return FastVector | |
|---|---|
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. |
| Methods in weka.classifiers.evaluation with parameters of type FastVector | |
|---|---|
void |
ConfusionMatrix.addPredictions(FastVector predictions)
Includes a whole bunch of predictions in the confusion matrix. |
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)
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,
int classIndex)
Calculates the performance stats for the desired 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. |
| Uses of FastVector in weka.classifiers.rules |
|---|
| Methods in weka.classifiers.rules that return FastVector | |
|---|---|
FastVector |
JRip.getRuleset()
Get the ruleset generated by Ripper |
FastVector |
RuleStats.getRuleset()
Get the ruleset of the stats |
| Methods in weka.classifiers.rules with parameters of type FastVector | |
|---|---|
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 |
| Constructors in weka.classifiers.rules with parameters of type FastVector | |
|---|---|
RuleStats(Instances data,
FastVector rules)
Constructor that provides ruleset and data |
|
| Uses of FastVector in weka.classifiers.trees.adtree |
|---|
| Methods in weka.classifiers.trees.adtree that return FastVector | |
|---|---|
FastVector |
PredictionNode.getChildren()
Gets the children of this node. |
| Uses of FastVector in weka.classifiers.trees.m5 |
|---|
| Methods in weka.classifiers.trees.m5 with parameters of type FastVector | |
|---|---|
void |
RuleNode.returnLeaves(FastVector[] v)
Return a list containing all the leaves in the tree |
| Uses of FastVector in weka.clusterers |
|---|
| Methods in weka.clusterers that return FastVector | |
|---|---|
FastVector |
OPTICS.getResultVector()
Returns the resultVector |
| Uses of FastVector in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI |
|---|
| Methods in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI that return FastVector | |
|---|---|
FastVector |
SERObject.getResultVector()
Returns the resultVector |
| Methods in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI with parameters of type FastVector | |
|---|---|
void |
GraphPanel.setResultVector(FastVector resultVector)
Sets a new resultVector |
| Constructors in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI with parameters of type FastVector | |
|---|---|
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)
|
|
| Uses of FastVector in weka.core |
|---|
| Methods in weka.core with parameters of type FastVector | |
|---|---|
void |
FastVector.appendElements(FastVector toAppend)
Appends all elements of the supplied vector to this vector. |
| Constructors in weka.core with parameters of type FastVector | |
|---|---|
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. |
|
FastVector.FastVectorEnumeration(FastVector vector)
Constructs an enumeration. |
|
FastVector.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. |
|
| Uses of FastVector in weka.gui.boundaryvisualizer |
|---|
| Methods in weka.gui.boundaryvisualizer that return FastVector | |
|---|---|
FastVector |
BoundaryPanel.getColors()
Get the current vector of Color objects used for the classes |
| Methods in weka.gui.boundaryvisualizer with parameters of type FastVector | |
|---|---|
void |
BoundaryPanel.setColors(FastVector colors)
Set a vector of Color objects for the classes |
| Uses of FastVector in weka.gui.explorer |
|---|
| Methods in weka.gui.explorer with parameters of type FastVector | |
|---|---|
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. |
| Uses of FastVector in weka.gui.graphvisualizer |
|---|
| Methods in weka.gui.graphvisualizer that return FastVector | |
|---|---|
FastVector |
HierarchicalBCEngine.getNodes()
give access to set of graph nodes |
FastVector |
LayoutEngine.getNodes()
give access to set of graph nodes |
| Methods in weka.gui.graphvisualizer with parameters of type FastVector | |
|---|---|
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. |
| Constructors in weka.gui.graphvisualizer with parameters of type FastVector | |
|---|---|
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. |
|
| Uses of FastVector in weka.gui.visualize |
|---|
| Methods in weka.gui.visualize that return FastVector | |
|---|---|
FastVector |
Plot2D.getPlots()
Return the list of plots |
FastVector |
VisualizePanelEvent.getValues()
|
| Methods in weka.gui.visualize with parameters of type FastVector | |
|---|---|
void |
Plot2D.setColours(FastVector cols)
Set a list of colours to use when colouring points according to class values or cluster numbers |
void |
AttributePanel.setColours(FastVector cols)
Sets a list of colours to use for colouring data points |
void |
ClassPanel.setColours(FastVector cols)
Set a list of colours to use for colouring labels |
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 |
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 |
| Constructors in weka.gui.visualize with parameters of type FastVector | |
|---|---|
VisualizePanelEvent(FastVector ar,
Instances i,
Instances i2,
int at1,
int at2)
This constructor creates the event with all the parameters set. |
|
| Uses of FastVector in weka.gui.visualize.plugins |
|---|
| Methods in weka.gui.visualize.plugins with parameters of type FastVector | |
|---|---|
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. |
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