public class J48Prediction extends org.processmining.models.FunctionEstimator.DecisionTreeFunctionEstimator implements Leafable
classIndexMap, classMapping, classValues, outputClassesattributeIndexMap, attributeList, binarySplit, booleanValues, classAttributeName, confidenceThreshold, crossValidate, evaluation, FALSE_VALUE, instances, minNumInstancePerLeaf, name, nullValue, numFoldCrossValidation, numFoldErrorPruning, saveData, tree, TRUE_VALUE, unpruned, variableType| Constructor and Description |
|---|
J48Prediction(java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> map,
java.util.Map<java.lang.String,java.util.Set<java.lang.String>> literalValues,
java.lang.Object[] outputValuesAsObjects,
java.lang.String name,
int capacity,
java.util.HashSet<java.lang.String> timeIntervalAttributes) |
J48Prediction(java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> map,
java.util.Map<java.lang.String,java.util.Set<java.lang.String>> literalValues,
java.lang.Object[] outputValuesAsObjects,
java.lang.String name,
int capacity,
java.util.HashSet<java.lang.String> timeIntervalAttributes,
boolean balanceInstances) |
| Modifier and Type | Method and Description |
|---|---|
void |
addInstance(java.util.Map<java.lang.String,java.lang.Object> variableAssignment,
java.lang.Object outputValue,
float weight)
Adds a new instance to the estimator's 'instances'.
|
void |
balanceInstances() |
java.util.List<org.processmining.framework.util.Pair<java.lang.String,org.processmining.models.guards.Expression>> |
getExpressionsAtLeaves() |
javax.swing.JComponent |
getPrefuseTreeVisualization()
Returns a JPanel containing a visualization of the weka J48 tree using
prefusetrees
|
javax.swing.JPanel |
getVisualization()
Returns a JPanel containing a visualization of the weka tree.
|
weka.core.Instances |
returnInstances() |
addWekaInstance, computeQualityMeasure, createAttributeList, createClassifier, getClassifier, getClassValue, getFunctionEstimation, isTreatNoLeafAsFalse, setTreatNoLeafAsFalseclassify, computeFMeasure, createAndSetTree, createInstance, getAttributeByName, getConfidenceThreshold, getEstimation, getEvaluation, getMinNumInstancePerLeaf, getName, getNumFoldCrossValidation, getNumFoldErrorPruning, getNumInstances, getQualityMeasureName, isBinarySplit, isCrossValidate, isUnpruned, saveInstances, setBinarySplit, setConfidenceFactor, setCrossValidate, setMinNumObj, setNumFoldCrossValidation, setNumFolds, setSaveData, setUnpruned, toStringbuildExpressionsFromLeafs, convertToExpression, getEstimationpublic J48Prediction(java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> map,
java.util.Map<java.lang.String,java.util.Set<java.lang.String>> literalValues,
java.lang.Object[] outputValuesAsObjects,
java.lang.String name,
int capacity,
java.util.HashSet<java.lang.String> timeIntervalAttributes)
public J48Prediction(java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> map,
java.util.Map<java.lang.String,java.util.Set<java.lang.String>> literalValues,
java.lang.Object[] outputValuesAsObjects,
java.lang.String name,
int capacity,
java.util.HashSet<java.lang.String> timeIntervalAttributes,
boolean balanceInstances)
public weka.core.Instances returnInstances()
public javax.swing.JComponent getPrefuseTreeVisualization()
getPrefuseTreeVisualization in class org.processmining.models.FunctionEstimator.AbstractDecisionTreeFunctionEstimatorpublic java.util.List<org.processmining.framework.util.Pair<java.lang.String,org.processmining.models.guards.Expression>> getExpressionsAtLeaves()
getExpressionsAtLeaves in interface Leafablepublic javax.swing.JPanel getVisualization()
org.processmining.models.FunctionEstimator.AbstractDecisionTreeFunctionEstimatorgetVisualization in class org.processmining.models.FunctionEstimator.AbstractDecisionTreeFunctionEstimatorpublic void balanceInstances()
balanceInstances in interface Leafablepublic void addInstance(java.util.Map<java.lang.String,java.lang.Object> variableAssignment,
java.lang.Object outputValue,
float weight)
org.processmining.models.FunctionEstimator.DecisionTreeFunctionEstimatoraddInstance in interface org.processmining.models.FunctionEstimator.FunctionEstimatoraddInstance in class org.processmining.models.FunctionEstimator.DecisionTreeFunctionEstimatorvariableAssignment - A MapoutputValue - The Transition which is to be executed with the variable
values of variableAssignment.weight - Parameter for weighted decision trees. Keep 1 for default.