@Deprecated public class RepTreeEstimator extends AbstractDecisionTreeFunctionEstimator
attributeIndexMap, attributeList, binarySplit, booleanValues, classAttributeName, confidenceThreshold, crossValidate, evaluation, FALSE_VALUE, instances, minNumInstancePerLeaf, name, nullValue, numFoldCrossValidation, numFoldErrorPruning, saveData, tree, TRUE_VALUE, unpruned, variableType
Constructor and Description |
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RepTreeEstimator(java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> attributeType,
java.util.Map<java.lang.String,java.util.Set<java.lang.String>> literalValues,
java.lang.String classAttributeName,
java.lang.String name,
int capacity)
Deprecated.
|
Modifier and Type | Method and Description |
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void |
addInstance(java.util.Map<java.lang.String,java.lang.Object> variableAssignment,
java.lang.Object outputValue,
float weight)
Deprecated.
|
double |
computeQualityMeasure()
Deprecated.
|
protected java.util.ArrayList<weka.core.Attribute> |
createAttributeList(java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> attributeType,
java.util.Map<java.lang.String,java.util.Set<java.lang.String>> literalValues,
java.lang.Object[] outputClasses)
Deprecated.
Create an
ArrayList with all attributes used (including the class
attribute) |
protected weka.classifiers.AbstractClassifier |
createClassifier(java.lang.Object[] option,
boolean saveData)
Deprecated.
Create the classifier
|
java.util.Map<java.lang.Object,org.processmining.models.FunctionEstimator.FunctionEstimation> |
getFunctionEstimation(java.lang.Object[] option)
Deprecated.
|
classify, computeFMeasure, createAndSetTree, createInstance, getAttributeByName, getConfidenceThreshold, getEstimation, getEvaluation, getMinNumInstancePerLeaf, getName, getNumFoldCrossValidation, getNumFoldErrorPruning, getNumInstances, getPrefuseTreeVisualization, getQualityMeasureName, getVisualization, isBinarySplit, isCrossValidate, isUnpruned, saveInstances, setBinarySplit, setConfidenceFactor, setCrossValidate, setMinNumObj, setNumFoldCrossValidation, setNumFolds, setSaveData, setUnpruned, toString
buildExpressionsFromLeafs, convertToExpression, getEstimation
public RepTreeEstimator(java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> attributeType, java.util.Map<java.lang.String,java.util.Set<java.lang.String>> literalValues, java.lang.String classAttributeName, java.lang.String name, int capacity)
protected java.util.ArrayList<weka.core.Attribute> createAttributeList(java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> attributeType, java.util.Map<java.lang.String,java.util.Set<java.lang.String>> literalValues, java.lang.Object[] outputClasses)
AbstractDecisionTreeFunctionEstimator
ArrayList
with all attributes used (including the class
attribute)createAttributeList
in class AbstractDecisionTreeFunctionEstimator
public double computeQualityMeasure()
public java.util.Map<java.lang.Object,org.processmining.models.FunctionEstimator.FunctionEstimation> getFunctionEstimation(java.lang.Object[] option) throws java.lang.Exception
java.lang.Exception
protected weka.classifiers.AbstractClassifier createClassifier(java.lang.Object[] option, boolean saveData) throws java.lang.Exception
AbstractDecisionTreeFunctionEstimator
createClassifier
in class AbstractDecisionTreeFunctionEstimator
java.lang.Exception
public void addInstance(java.util.Map<java.lang.String,java.lang.Object> variableAssignment, java.lang.Object outputValue, float weight)