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| Uses of FastVector in weka.associations |
| Fields in weka.associations declared as FastVector | |
protected FastVector |
Apriori.m_Ls
The set of all sets of itemsets L. |
protected FastVector |
Apriori.m_hashtables
The same information stored in hash tables. |
protected FastVector[] |
Apriori.m_allTheRules
The list of all generated rules. |
| Methods in weka.associations that return FastVector | |
static FastVector |
ItemSet.deleteItemSets(FastVector itemSets,
int minSupport,
int maxSupport)
Deletes all item sets that don't have minimum support. |
FastVector[] |
ItemSet.generateRules(double minConfidence,
FastVector hashtables,
int numItemsInSet)
Generates all rules for an item set. |
FastVector[] |
ItemSet.generateRulesBruteForce(double minMetric,
int metricType,
FastVector hashtables,
int numItemsInSet,
int numTransactions,
double significanceLevel)
Generates all significant rules for an item set. |
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 |
ItemSet.pruneItemSets(FastVector toPrune,
java.util.Hashtable kMinusOne)
Prunes a set of (k)-item sets using the given (k-1)-item sets. |
static FastVector |
ItemSet.singletons(Instances instances)
Converts the header info of the given set of instances into a set of item sets (singletons). |
private FastVector[] |
ItemSet.moreComplexRules(FastVector[] rules,
int numItemsInSet,
int numItemsInConsequence,
double minConfidence,
FastVector hashtables)
Generates rules with more than one item in the consequence. |
| Methods in weka.associations with parameters of type FastVector | |
static FastVector |
ItemSet.deleteItemSets(FastVector itemSets,
int minSupport,
int maxSupport)
Deletes all item sets that don't have minimum support. |
FastVector[] |
ItemSet.generateRules(double minConfidence,
FastVector hashtables,
int numItemsInSet)
Generates all rules for an item set. |
FastVector[] |
ItemSet.generateRulesBruteForce(double minMetric,
int metricType,
FastVector hashtables,
int numItemsInSet,
int numTransactions,
double significanceLevel)
Generates all significant rules for an item set. |
static java.util.Hashtable |
ItemSet.getHashtable(FastVector itemSets,
int initialSize)
Return a hashtable filled with the given item sets. |
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 |
ItemSet.pruneItemSets(FastVector toPrune,
java.util.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 void |
ItemSet.upDateCounters(FastVector itemSets,
Instances instances)
Updates counters for a set of item sets and a set of instances. |
private FastVector[] |
ItemSet.moreComplexRules(FastVector[] rules,
int numItemsInSet,
int numItemsInConsequence,
double minConfidence,
FastVector hashtables)
Generates rules with more than one item in the consequence. |
| Uses of FastVector in weka.attributeSelection |
| Subclasses of FastVector in weka.attributeSelection | |
class |
BestFirst.LinkedList2
Class for handling a linked list. |
| Uses of FastVector in weka.classifiers |
| Methods in weka.classifiers with parameters of type FastVector | |
protected boolean |
CheckClassifier.runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean numericClass,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numTest,
int numClasses,
FastVector accepts)
Runs a text on the datasets with the given characteristics. |
| Uses of FastVector in weka.classifiers.bayes |
| Methods in weka.classifiers.bayes with parameters of type FastVector | |
static VaryNode |
ADNode.MakeVaryNode(int iNode,
FastVector nRecords,
Instances instances)
create sub tree |
static ADNode |
ADNode.MakeADTree(int iNode,
FastVector nRecords,
Instances instances)
create sub tree |
| 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.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. |
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. |
| 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 |
ThresholdCurve.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. |
private double[] |
ThresholdCurve.getProbabilities(FastVector predictions,
int classIndex)
|
Instances |
CostCurve.getCurve(FastVector predictions)
Calculates the performance stats for the default 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. |
Instances |
MarginCurve.getCurve(FastVector predictions)
Calculates the cumulative margin distribution for the set of predictions, returning the result as a set of Instances. |
private double[] |
MarginCurve.getMargins(FastVector predictions)
Pulls all the margin values out of a vector of NominalPredictions. |
| Uses of FastVector in weka.classifiers.functions |
| Fields in weka.classifiers.functions declared as FastVector | |
private FastVector |
MultilayerPerceptron.m_selected
A Vector list of the units currently selected. |
private FastVector |
MultilayerPerceptron.m_graphers
A Vector list of the graphers. |
| Methods in weka.classifiers.functions with parameters of type FastVector | |
private void |
MultilayerPerceptron.NodePanel.selection(FastVector v,
boolean ctrl,
boolean left)
This function gets called when the user has clicked something It will amend the current selection or connect the current selection to the new selection. |
| Uses of FastVector in weka.classifiers.meta |
| Fields in weka.classifiers.meta declared as FastVector | |
protected FastVector |
CVParameterSelection.m_CVParams
The set of parameters to cross-validate over |
protected FastVector |
ND.NDTree.m_indices
The indices associated with this node |
protected FastVector |
RacedIncrementalLogitBoost.Committee.m_models
|
private FastVector |
AdditiveRegression.m_additiveModels
The list of iteratively generated models. |
protected FastVector |
RacedIncrementalLogitBoost.m_committees
The committees |
| Methods in weka.classifiers.meta that return FastVector | |
protected FastVector |
ThresholdSelector.getPredictions(Instances instances,
int mode,
int numFolds)
Collects the classifier predictions using the specified evaluation method. |
| Methods in weka.classifiers.meta with parameters of type FastVector | |
protected void |
ThresholdSelector.findThreshold(FastVector predictions)
Finds the best threshold, this implementation searches for the highest FMeasure. |
| Uses of FastVector in weka.classifiers.rules |
| Subclasses of FastVector in weka.classifiers.rules | |
class |
DecisionTable.LinkedList
Class for handling a linked list. |
| Fields in weka.classifiers.rules declared as FastVector | |
protected FastVector |
ConjunctiveRule.m_Antds
The vector of antecedents of this rule |
private FastVector |
ConjunctiveRule.m_Targets
The predicted classes recorded for each antecedent in the growing data |
private FastVector |
RuleStats.m_Ruleset
The specific ruleset in question |
private FastVector |
RuleStats.m_SimpleStats
The simple stats of each rule |
private FastVector |
RuleStats.m_Filtered
The set of instances filtered by the ruleset |
private FastVector |
RuleStats.m_Distributions
The class distributions predicted by each rule |
protected FastVector |
JRip.RipperRule.m_Antds
The vector of antecedents of this rule |
protected FastVector |
Ridor.RidorRule.m_Antds
The vector of antecedents of this rule |
private FastVector |
JRip.m_Ruleset
The ruleset |
private FastVector |
JRip.m_Distributions
The predicted class distribution |
private FastVector |
JRip.m_RulesetStats
The RuleStats for the ruleset of each class value |
| Methods in weka.classifiers.rules that return FastVector | |
FastVector |
RuleStats.getRuleset()
Get the ruleset of the stats |
FastVector |
JRip.getRuleset()
Get the ruleset generated by Ripper |
| Methods in weka.classifiers.rules with parameters of type FastVector | |
void |
RuleStats.setRuleset(FastVector rules)
Set the ruleset of the stats, overwriting the old one if any |
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. |
| 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 |
| Fields in weka.classifiers.trees declared as FastVector | |
FastVector |
UserClassifier.TreeClass.m_ranges
This contains the info for the coords of the shape converted to attrib coords, for polygon the first attrib is the number of points, This is not more object oriented because that would be over kill. |
| Methods in weka.classifiers.trees with parameters of type FastVector | |
void |
UserClassifier.TreeClass.setInfo(int at1,
int at2,
FastVector ar)
Call this to set this node with different information to what it was created with. |
private boolean |
UserClassifier.TreeClass.inPolyline(FastVector ob,
double x,
double y)
Call to find out if an instance is in a polyline. |
private boolean |
UserClassifier.TreeClass.inPoly(FastVector ob,
double x,
double y)
Call this to determine if an instance is in a polygon. |
| Constructors in weka.classifiers.trees with parameters of type FastVector | |
UserClassifier.TreeClass(FastVector r,
int a1,
int a2,
int id,
double w,
Instances i,
UserClassifier.TreeClass p)
Constructs a TreeClass node with all the important information. |
|
| Uses of FastVector in weka.classifiers.trees.adtree |
| Fields in weka.classifiers.trees.adtree declared as FastVector | |
private FastVector |
PredictionNode.children
The children of this node - any number of splitter nodes |
| 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 |
| Fields in weka.classifiers.trees.m5 declared as FastVector | |
protected FastVector |
M5Base.m_ruleSet
the rule set |
| 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 |
| Fields in weka.clusterers declared as FastVector | |
private FastVector |
Cobweb.CNode.m_children
Children of this node |
| Uses of FastVector in weka.core |
| Fields in weka.core declared as FastVector | |
private FastVector |
FastVector.FastVectorEnumeration.m_Vector
The vector. |
private FastVector |
Attribute.m_Values
The attribute's values (if nominal or string). |
protected FastVector |
Instances.m_Attributes
The attribute information. |
protected FastVector |
Instances.m_Instances
The instances. |
| Methods in weka.core with parameters of type FastVector | |
void |
FastVector.appendElements(FastVector toAppend)
Appends all elements of the supplied vector to this vector. |
double[] |
Optimization.lnsrch(double[] xold,
double[] gradient,
double[] direct,
double stpmax,
boolean[] isFixed,
double[][] nwsBounds,
FastVector wsBdsIndx)
Find a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently, the positive definiteness of B matrix (approximation of the inverse of the Hessian) is preserved and no bound constraints are violated. |
private boolean |
Optimization.equal(FastVector a,
FastVector b)
Check whether the two integer vectors equal to each other Two integer vectors are equal if all the elements are the same, regardless of the order of the elements |
| Constructors in weka.core with parameters of type FastVector | |
FastVector.FastVectorEnumeration(FastVector vector)
Constructs an enumeration. |
|
FastVector.FastVectorEnumeration(FastVector vector,
int special)
Constructs an enumeration with a special element. |
|
ClassRemoveableInstances(java.lang.String name,
FastVector attInfo,
int capacity)
|
|
Attribute(java.lang.String attributeName,
FastVector attributeValues)
Constructor for nominal attributes and string attributes. |
|
Attribute(java.lang.String attributeName,
FastVector attributeValues,
ProtectedProperties metadata)
Constructor for nominal attributes and string attributes, where metadata is supplied. |
|
Attribute(java.lang.String attributeName,
FastVector attributeValues,
int index)
Constructor for nominal attributes and string attributes with a particular index. |
|
Instances(java.lang.String name,
FastVector attInfo,
int capacity)
Creates an empty set of instances. |
|
| Uses of FastVector in weka.core.converters |
| Fields in weka.core.converters declared as FastVector | |
private FastVector |
CSVLoader.m_cumulativeStructure
A list of hash tables for accumulating nominal values during parsing. |
private FastVector |
CSVLoader.m_cumulativeInstances
Holds instances accumulated so far |
| Methods in weka.core.converters that return FastVector | |
private FastVector |
CSVLoader.getInstance(java.io.StreamTokenizer tokenizer)
Attempts to parse a line of the data set. |
| Methods in weka.core.converters with parameters of type FastVector | |
private void |
CSVLoader.checkStructure(FastVector current)
Checks the current instance against what is known about the structure of the data set so far. |
| Uses of FastVector in weka.datagenerators |
| Fields in weka.datagenerators declared as FastVector | |
private FastVector |
BIRCHCluster.m_ClusterList
|
private FastVector |
RDG1.m_DecisionList
|
private FastVector |
RDG1.RuleList.m_RuleList
|
| Methods in weka.datagenerators that return FastVector | |
private FastVector |
BIRCHCluster.defineClusters(java.util.Random random)
Defines the clusters |
private FastVector |
BIRCHCluster.defineClustersGRID(java.util.Random random)
Defines the clusters if pattern is GRID |
private FastVector |
BIRCHCluster.defineClustersRANDOM(java.util.Random random)
Defines the clusters if pattern is RANDOM |
private FastVector |
RDG1.generateTestList(java.util.Random random,
Instance example)
Generates a new rule for the decision list and classifies the new example. |
| Uses of FastVector in weka.experiment |
| Fields in weka.experiment declared as FastVector | |
(package private) FastVector |
PairedTTester.DatasetSpecifiers.m_Specifiers
|
protected FastVector |
DatabaseResultListener.m_Cache
Stores the cached values |
private FastVector |
RemoteExperiment.m_listeners
The list of objects listening for remote experiment events |
protected FastVector |
AveragingResultProducer.m_Keys
Collects the keys from a single run |
protected FastVector |
AveragingResultProducer.m_Results
Collects the results from a single run |
(package private) FastVector |
PairedTTester.Dataset.m_Dataset
|
(package private) FastVector |
PairedTTester.Resultset.m_Datasets
|
protected FastVector |
InstancesResultListener.m_Instances
Stores the instances created so far, before assigning to a header |
protected FastVector[] |
InstancesResultListener.m_NominalStrings
Contains strings seen so far for each nominal attribute |
protected FastVector |
PairedTTester.m_Resultsets
Stores a vector for each resultset holding all instances in each set |
| Methods in weka.experiment that return FastVector | |
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. |
| Uses of FastVector in weka.filters.unsupervised.attribute |
| Fields in weka.filters.unsupervised.attribute declared as FastVector | |
protected FastVector |
Add.m_Labels
The list of labels for nominal attribute |
| Methods in weka.filters.unsupervised.attribute with parameters of type FastVector | |
private int |
StringToWordVector.convertInstancewoDocNorm(Instance instance,
FastVector v)
|
| Uses of FastVector in weka.gui |
| Fields in weka.gui declared as FastVector | |
private FastVector |
AttributeVisualizationPanel.m_colorList
Contains discrete colours for colouring for nominal attributes |
| Uses of FastVector in weka.gui.boundaryvisualizer |
| Fields in weka.gui.boundaryvisualizer declared as FastVector | |
protected FastVector |
BoundaryPanel.m_Colors
|
| 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.experiment |
| Fields in weka.gui.experiment declared as FastVector | |
protected FastVector |
GeneratorPropertyIteratorPanel.m_Listeners
Listeners who want to be notified about editing status of this panel |
| Uses of FastVector in weka.gui.explorer |
| Methods in weka.gui.explorer with parameters of type FastVector | |
private void |
ClassifierPanel.processClassifierPrediction(Instance toPredict,
Classifier classifier,
Evaluation eval,
FastVector predictions,
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. plotInfo for nominal class datasets holds shape types (actual data points have automatic shape type assignment; classifier error data points have box shape type). |
private void |
ClassifierPanel.postProcessPlotInfo(FastVector plotSize)
Post processes numeric class errors into shape sizes for plotting in the visualize panel |
| Uses of FastVector in weka.gui.graphvisualizer |
| Fields in weka.gui.graphvisualizer declared as FastVector | |
protected FastVector |
GraphVisualizer.m_nodes
Vector containing nodes |
protected FastVector |
GraphVisualizer.m_edges
Vector containing edges |
protected FastVector |
HierarchicalBCEngine.m_nodes
FastVector containing nodes and edges |
protected FastVector |
HierarchicalBCEngine.m_edges
FastVector containing nodes and edges |
protected FastVector |
HierarchicalBCEngine.layoutCompleteListeners
FastVector containing listeners for layoutCompleteEvent generated by this LayoutEngine |
protected FastVector |
DotParser.m_nodes
These holds the nodes and edges of the graph |
protected FastVector |
DotParser.m_edges
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 |
BIFParser.m_edges
These holds the nodes and edges of the graph |
| 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(java.lang.String filename,
java.lang.String graphName,
FastVector nodes,
FastVector edges)
This method saves a graph in a file in DOT format. |
static void |
BIFParser.writeXMLBIF03(java.lang.String filename,
java.lang.String graphName,
FastVector nodes,
FastVector edges)
This method writes a graph in XMLBIF ver. 0.3 format to a file. |
| Constructors in weka.gui.graphvisualizer with parameters of type FastVector | |
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. |
|
DotParser(java.io.Reader input,
FastVector nodes,
FastVector edges)
Dot parser Constructor |
|
BIFParser(java.lang.String input,
FastVector nodes,
FastVector edges)
Constructor (if our input is a String) |
|
BIFParser(java.io.InputStream instream,
FastVector nodes,
FastVector edges)
Constructor (if our input is an InputStream) |
|
| Uses of FastVector in weka.gui.visualize |
| Fields in weka.gui.visualize declared as FastVector | |
private FastVector |
MatrixPanel.m_colorList
Contains discrete colours for colouring for nominal attributes |
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 |
protected FastVector |
Plot2D.m_plots
The plots to display |
protected FastVector |
Plot2D.m_colorList
The list of the colors used |
protected FastVector |
VisualizePanel.m_colorList
The list of the colors used |
private FastVector |
VisualizePanel.PlotPanel.m_shapes
contains all the shapes that have been drawn for these attribs |
private FastVector |
VisualizePanel.PlotPanel.m_shapePoints
contains the points of the shape currently being drawn. |
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 |
private FastVector |
VisualizePanelEvent.m_values
Contains FastVectors, each one containing the points for an object. |
private FastVector |
ClassPanel.m_colorList
the list of colours to use for colouring nominal attribute labels |
private FastVector |
ClassPanel.m_Repainters
An optional list of Components that use the colour list maintained by this class. |
private FastVector |
ClassPanel.m_ColourChangeListeners
An optional list of listeners who want to know when a colour changes. |
| Methods in weka.gui.visualize that return FastVector | |
FastVector |
Plot2D.getPlots()
Return the list of plots |
FastVector |
VisualizePanel.PlotPanel.getShapes()
|
private FastVector |
VisualizePanel.PlotPanel.makePolygon(FastVector v)
This will convert a polyline to a polygon for drawing purposes So that I can simply use the polygon drawing function. |
FastVector |
VisualizePanelEvent.getValues()
|
| Methods in weka.gui.visualize with parameters of type FastVector | |
void |
LegendPanel.setPlotList(FastVector pl)
Set the list of plots to generate legend entries for |
void |
Plot2D.setColours(FastVector cols)
Set a list of colours to use when colouring points according to class values or cluster numbers |
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. |
private boolean |
VisualizePanel.PlotPanel.inPolyline(FastVector ob,
double x,
double y)
Checks to see if the coordinate passed is inside the ployline passed, Note that this is done using attribute values and not there respective screen values. |
private boolean |
VisualizePanel.PlotPanel.inPoly(FastVector ob,
double x,
double y)
This checks to see if The coordinate passed is inside the polygon that was passed. |
void |
VisualizePanel.PlotPanel.setColours(FastVector cols)
Set a list of colours to use for plotting points |
private FastVector |
VisualizePanel.PlotPanel.makePolygon(FastVector v)
This will convert a polyline to a polygon for drawing purposes So that I can simply use the polygon drawing function. |
private int[] |
VisualizePanel.PlotPanel.getXCoords(FastVector v)
This will extract from a polygon shape its x coodrdinates so that an awt.Polygon can be created. |
private int[] |
VisualizePanel.PlotPanel.getYCoords(FastVector v)
This will extract from a polygon shape its y coordinates so that an awt.Polygon can be created. |
void |
PlotData2D.setShapeType(FastVector st)
Set the shape type for the plot data |
void |
PlotData2D.setShapeSize(FastVector ss)
Set the shape sizes for the plot data |
void |
PlotData2D.setConnectPoints(FastVector cp)
Set whether consecutive points should be connected by lines |
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 |
| 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. |
|
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