Uses of Class
org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.Instances

Packages that use Instances

Package	Description
org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree
org.processmining.prediction

Uses of Instances in org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree

Fields in org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree declared as Instances

Modifier and Type	Field	Description
static Instances	Discrimination.data
protected Instances	Instance.m_Dataset	The dataset the instance has access to.
protected Instances	NaiveBayesSimple.m_Instances	The instances used for training.
protected Instances	CSVLoader.m_structure	Holds the determined structure (header) of the data set.
protected Instances	ClassifierTree.m_train	The training instances.
protected Instances	Massaging.massagedInst	The data set after tapplying massaging
static Instances	Discrimination.testWithSA
static Instances	Discrimination.trainInstsWithsa

Methods in org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree that return Instances

Modifier and Type	Method	Description
Instances	CostMatrix.applyCostMatrix(Instances data, java.util.Random random)	Applies the cost matrix to a set of instances.
Instances	Massaging.cndApplication(Instances instances)
Instances	Massaging.cndApplication(Instances instances, double promote, double demote)
Instances	Instance.dataset()	Returns the dataset this instance has access to.
abstract Instances	AbstractLoader.getDataSet()
Instances	CSVLoader.getDataSet()	Return the full data set.
Instances	Loader.getDataSet()	Return the full data set.
protected Instances	Filter.getInputFormat()	Gets the currently set inputformat instances.
Instances	Filter.getOutputFormat()	Gets the format of the output instances.
abstract Instances	AbstractLoader.getStructure()
Instances	CSVLoader.getStructure()	Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
Instances	Loader.getStructure()	Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
protected Instances	Filter.inputFormatPeek()	Returns a reference to the current input format without copying it.
static Instances	Instances.mergeInstances(Instances first, Instances second)	Merges two sets of Instances together.
Instances	Filter.outputFormat()	Deprecated. use getOutputFormat() instead.
protected Instances	Filter.outputFormatPeek()	Returns a reference to the current output format without copying it.
Instances	Instances.resample(java.util.Random random)	Creates a new dataset of the same size using random sampling with replacement.
Instances	Instances.resampleWithWeights(java.util.Random random)	Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
Instances	Instances.resampleWithWeights(java.util.Random random, double[] weights)	Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
Instances[]	ClassifierSplitModel.split(Instances data)	Splits the given set of instances into subsets.
Instances	Instances.stringFreeStructure()	Create a copy of the structure, but "cleanse" string types (i.e.
Instances	Instances.testCV(int numFolds, int numFold)	Creates the test set for one fold of a cross-validation on the dataset.
Instances	Instances.trainCV(int numFolds, int numFold)	Creates the training set for one fold of a cross-validation on the dataset.
Instances	Instances.trainCV(int numFolds, int numFold, java.util.Random random)	Creates the training set for one fold of a cross-validation on the dataset.
static Instances	Filter.useFilter(Instances data, Filter filter)	Filters an entire set of instances through a filter and returns the new set.

Methods in org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree with parameters of type Instances

Modifier and Type	Method	Description
void	Distribution.addInstWithUnknown(Instances source, int attIndex)	Adds all instances with unknown values for given attribute, weighted according to frequency of instances in each bag.
void	Distribution.addRange(int bagIndex, Instances source, int startIndex, int lastPlusOne)	Adds all instances in given range to given bag.
Instances	CostMatrix.applyCostMatrix(Instances data, java.util.Random random)	Applies the cost matrix to a set of instances.
void	BinC45Split.buildClassifier(Instances trainInstances)	Creates a C4.5-type split on the given data.
void	C45PruneableClassifierTree.buildClassifier(Instances data)	Method for building a pruneable classifier tree.
void	C45Split.buildClassifier(Instances trainInstances)	Creates a C4.5-type split on the given data.
abstract void	Classifier.buildClassifier(Instances data)	Generates a classifier.
abstract void	ClassifierSplitModel.buildClassifier(Instances instances)	Builds the classifier split model for the given set of instances.
void	ClassifierTree.buildClassifier(Instances data)	Method for building a classifier tree.
void	J48.buildClassifier(Instances instances)	Generates the classifier.
void	J48WithNDCs.buildClassifier(Instances instances)	Generates the classifier.
void	NaiveBayesSimple.buildClassifier(Instances instances)	Generates the classifier.
void	NoSplit.buildClassifier(Instances instances)	Creates a "no-split"-split for a given set of instances.
void	PruneableClassifierTree.buildClassifier(Instances data)	Method for building a pruneable classifier tree.
void	ClassifierTree.buildTree(Instances data, boolean keepData)	Builds the tree structure.
void	ClassifierTree.buildTree(Instances train, Instances test, boolean keepData)	Builds the tree structure with hold out set
void	ClassifierTree.cleanup(Instances justHeaderInfo)	Cleanup in order to save memory.
Instances	Massaging.cndApplication(Instances instances)
Instances	Massaging.cndApplication(Instances instances, double promote, double demote)
protected void	Instances.copyInstances(int from, Instances dest, int num)	Copies instances from one set to the end of another one.
protected void	Filter.copyStringValues(Instance instance, boolean instSrcCompat, Instances srcDataset, int[] srcStrAtts, Instances destDataset, int[] destStrAtts)	Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
protected void	Filter.copyStringValues(Instance instance, boolean instSrcCompat, Instances srcDataset, Instances destDataset)	Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
void	Evaluation.crossValidateModel(java.lang.String classifierString, Instances data, int numFolds, java.lang.String[] options, java.util.Random random)	Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
void	Evaluation.crossValidateModel(Classifier classifier, Instances data, int numFolds, java.util.Random random)	Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
void	Discrimination.datasetScan(Instances instances)
void	Distribution.delRange(int bagIndex, Instances source, int startIndex, int lastPlusOne)	Deletes all instances in given range from given bag.
void	Discrimination.discAttributeSelection(Instances instances)	used to find the discrimnatory attribut from the inout dataset
static double	Discrimination.discCalculation(Instances insts)
java.lang.Double[][]	J48WithNDCs.discriminationInfo(Instances instances)
java.lang.Double[][]	J48WithNDCs.discriminationInfo(Instances instances, Instances newInstances)	gives the information about the discrimination by classifier input : instances, the data set output : [0][0] number of favorable accepted by classifier [0][1] total number of favorable in data set [1][0] number of protected accepted by classifier [1][1] total number of protected in data set
java.lang.String	ClassifierSplitModel.dumpLabel(int index, Instances data)	Prints label for subset index of instances (eg class).
java.lang.String	ClassifierSplitModel.dumpModel(Instances data)	Prints the split model.
boolean	Instances.equalHeaders(Instances dataset)	Checks if two headers are equivalent.
double[]	Evaluation.evaluateModel(Classifier classifier, Instances data)	Evaluates the classifier on a given set of instances.
protected ClassifierTree	C45PruneableClassifierTree.getNewTree(Instances data)	Returns a newly created tree.
protected ClassifierTree	ClassifierTree.getNewTree(Instances data)	Returns a newly created tree.
protected ClassifierTree	ClassifierTree.getNewTree(Instances train, Instances test)	Returns a newly created tree.
protected ClassifierTree	PruneableClassifierTree.getNewTree(Instances train, Instances test)	Returns a newly created tree.
abstract Instance	AbstractLoader.getNextInstance(Instances structure)
Instance	CSVLoader.getNextInstance(Instances structure)	CSVLoader is unable to process a data set incrementally.
Instance	Loader.getNextInstance(Instances structure)	Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
protected int[]	Filter.getStringIndices(Instances insts)	Gets an array containing the indices of all string attributes.
java.util.Map<java.lang.Double,java.lang.Double[]>	J48WithNDCs.infoErrorSensitive(Instances instances)
java.util.Map<java.lang.Double,java.lang.Double[]>	J48WithNDCs.infoErrorSensitive(Instances instances, Instances newInstances)
boolean	Filter.inputFormat(Instances instanceInfo)	Deprecated. use setInputFormat(Instances) instead.
java.lang.String	BinC45Split.leftSide(Instances data)	Prints left side of condition..
java.lang.String	C45Split.leftSide(Instances data)	Prints left side of condition..
abstract java.lang.String	ClassifierSplitModel.leftSide(Instances data)	Prints left side of condition satisfied by instances.
java.lang.String	NoSplit.leftSide(Instances instances)	Does nothing because no condition has to be satisfied.
static Instances	Instances.mergeInstances(Instances first, Instances second)	Merges two sets of Instances together.
double[][]	C45Split.minsAndMaxs(Instances data, double[][] minsAndMaxs, int index)	Returns the minsAndMaxs of the index.th subset.
protected static java.lang.String	Evaluation.printClassifications(Classifier classifier, Instances train, java.lang.String testFileName, int classIndex, Range attributesToOutput)	Prints the predictions for the given dataset into a String variable.
void	Massaging.ranker(Instances instances)	ranker method produces sorted promotion and demotion list for CND
void	BinC45Split.resetDistribution(Instances data)	Sets distribution associated with model.
void	C45Split.resetDistribution(Instances data)	Sets distribution associated with model.
void	ClassifierSplitModel.resetDistribution(Instances data)	Sets distribution associated with model.
java.lang.String	BinC45Split.rightSide(int index, Instances data)	Prints the condition satisfied by instances in a subset.
java.lang.String	C45Split.rightSide(int index, Instances data)	Prints the condition satisfied by instances in a subset.
abstract java.lang.String	ClassifierSplitModel.rightSide(int index, Instances data)	Prints left side of condition satisfied by instances in subset index.
java.lang.String	NoSplit.rightSide(int index, Instances instances)	Does nothing because no condition has to be satisfied.
ClassifierSplitModel	BinC45ModelSelection.selectModel(Instances data)	Selects C4.5-type split for the given dataset.
ClassifierSplitModel	BinC45ModelSelection.selectModel(Instances train, Instances test)	Selects C4.5-type split for the given dataset.
ClassifierSplitModel	C45ModelSelection.selectModel(Instances data)
ClassifierSplitModel	C45ModelSelection.selectModel(Instances train, Instances test)	Selects C4.5-type split for the given dataset.
abstract ClassifierSplitModel	ModelSelection.selectModel(Instances data)	Selects a model for the given dataset.
ClassifierSplitModel	ModelSelection.selectModel(Instances train, Instances test)	Selects a model for the given train data using the given test data
void	Instance.setDataset(Instances instances)	Sets the reference to the dataset.
static void	Discrimination.setDepParameters(Instances insts)
void	C45PruneableClassifierTree.setDiscParam(Instances data)
static void	PruneableClassifierTree.setDiscParam(Instances data)
boolean	Filter.setInputFormat(Instances instanceInfo)	Sets the format of the input instances.
boolean	PrefrentialSamplingFilter.setInputFormat(Instances instanceInfo)	Sets the format of the input instances.
boolean	RemoveSAFilter.setInputFormat(Instances instanceInfo)	Sets the format of the input instances.
protected void	Filter.setOutputFormat(Instances outputFormat)	Sets the format of output instances.
void	Evaluation.setPriors(Instances train)	Sets the class prior probabilities
void	BinC45Split.setSplitPoint(Instances allInstances)	Sets split point to greatest value in given data smaller or equal to old split point.
void	C45Split.setSplitPoint(Instances allInstances)	Sets split point to greatest value in given data smaller or equal to old split point.
void	Distribution.shiftRange(int from, int to, Instances source, int startIndex, int lastPlusOne)	Shifts all instances in given range from one bag to another one.
java.lang.String	ClassifierSplitModel.sourceClass(int index, Instances data)
java.lang.String	BinC45Split.sourceExpression(int index, Instances data)	Returns a string containing java source code equivalent to the test made at this node.
java.lang.String	C45Split.sourceExpression(int index, Instances data)	Returns a string containing java source code equivalent to the test made at this node.
abstract java.lang.String	ClassifierSplitModel.sourceExpression(int index, Instances data)
java.lang.String	NoSplit.sourceExpression(int index, Instances data)	Returns a string containing java source code equivalent to the test made at this node.
Instances[]	ClassifierSplitModel.split(Instances data)	Splits the given set of instances into subsets.
static Instances	Filter.useFilter(Instances data, Filter filter)	Filters an entire set of instances through a filter and returns the new set.
void	PrefrentialSamplingFilter.weightCalculation(Instances instances)

Constructors in org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree with parameters of type Instances

Constructor	Description
BinC45ModelSelection(int minNoObj, Instances allData)	Initializes the split selection method with the given parameters.
C45ModelSelection(int minNoObj, Instances allData)	Initializes the split selection method with the given parameters.
Distribution(Instances source)	Creates a distribution with only one bag according to instances in source.
Distribution(Instances source, ClassifierSplitModel modelToUse)	Creates a distribution according to given instances and split model.
Evaluation(Instances data)	Initializes all the counters for the evaluation.
Evaluation(Instances data, CostMatrix costMatrix)	Initializes all the counters for the evaluation and also takes a cost matrix as parameter.
Instances(Instances dataset)	Constructor copying all instances and references to the header information from the given set of instances.
Instances(Instances dataset, int capacity)	Constructor creating an empty set of instances.
Instances(Instances source, int first, int toCopy)	Creates a new set of instances by copying a subset of another set.

Uses of Instances in org.processmining.prediction

Methods in org.processmining.prediction with parameters of type Instances

Modifier and Type	Method	Description
Instance	TreeConstructor.createInstance(java.util.Map<java.lang.String,java.lang.Object> inst, Instances DataSet)
double	EvaluationNDC.discriminationInData(Instances dataset)
double[][]	EvaluationNDC.evaluate(J48WithNDCs fairTree, J48WithNDCs normalTree, Instances dataset)

Constructors in org.processmining.prediction with parameters of type Instances

Constructor	Description
EvaluationNDC(J48WithNDCs fairTree, J48WithNDCs normalTree, Instances dataset, int sensitiveAttributeIndex)