public class Predictor
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static java.lang.String |
CASE_ACTIVITY |
protected org.processmining.models.FunctionEstimator.AbstractDecisionTreeFunctionEstimator |
df |
| Constructor and Description |
|---|
Predictor(org.deckfour.xes.model.XLog log) |
Predictor(org.deckfour.xes.model.XLog log,
org.processmining.plugins.DataConformance.ResultReplay resReplay) |
| Modifier and Type | Method and Description |
|---|---|
boolean |
augmentLog(Augmentation[] augmentationCollection,
boolean useMapDB,
TaskForProgressBar task) |
double |
classify(java.util.Map<java.lang.String,java.lang.Object> variableAssignment) |
org.processmining.framework.util.Pair<java.lang.String[],org.deckfour.xes.model.XLog[]> |
clusterLog(boolean onlyCorrectlyClassified,
double maxDeviation) |
boolean |
configureAugmentation(Augmentation[] augmentationCollection) |
static java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> |
extractAttributeInformation(org.deckfour.xes.model.XLog log) |
java.util.Collection<java.lang.String> |
getActivities() |
java.util.List<java.lang.String> |
getAttributes() |
weka.classifiers.Evaluation |
getEvaluation() |
int |
getInstanceSetSize() |
java.util.Set<java.lang.String> |
getLiteralValues(java.lang.String attribute) |
static java.lang.String |
getName(org.deckfour.xes.model.XAttributable element) |
javax.swing.JComponent |
getNormalTreeVisualization() |
int |
getNumInstances() |
org.deckfour.xes.model.XLog |
getOriginalLog() |
javax.swing.JComponent |
getPrefuseTreeVisualization() |
org.processmining.plugins.DataConformance.ResultReplay |
getResReplay() |
java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> |
getTypes() |
void |
init() |
boolean |
isRegressionTree() |
void |
makePrediction(TaskForProgressBar task) |
void |
setActivitiesToConsider(java.util.Collection<java.lang.String> activitiesToConsider) |
void |
setBinarySplit(boolean binarySplit) |
void |
setConfidenceThreshold(float confidenceThreshold) |
void |
setMinNumInstancePerLeaf(double d) |
void |
setNumFolds(int numFoldErrorPruning) |
DiscretizationInterval[] |
setOutputAttribute(Augmentation attribute,
int numberIntervals,
DiscrMethod method,
boolean regressionTree) |
void |
setRegression(boolean regressionTree) |
void |
setSaveData(boolean saveData) |
void |
setUnPruned(boolean unPruned) |
protected org.processmining.models.FunctionEstimator.AbstractDecisionTreeFunctionEstimator df
public static final java.lang.String CASE_ACTIVITY
public Predictor(org.deckfour.xes.model.XLog log)
public Predictor(org.deckfour.xes.model.XLog log,
org.processmining.plugins.DataConformance.ResultReplay resReplay)
public javax.swing.JComponent getPrefuseTreeVisualization()
public javax.swing.JComponent getNormalTreeVisualization()
public double classify(java.util.Map<java.lang.String,java.lang.Object> variableAssignment)
throws java.lang.Exception
java.lang.Exceptionpublic org.processmining.framework.util.Pair<java.lang.String[],org.deckfour.xes.model.XLog[]> clusterLog(boolean onlyCorrectlyClassified,
double maxDeviation)
throws java.lang.Exception
java.lang.Exceptionpublic void init()
public static java.lang.String getName(org.deckfour.xes.model.XAttributable element)
public boolean configureAugmentation(Augmentation[] augmentationCollection)
public boolean augmentLog(Augmentation[] augmentationCollection, boolean useMapDB, TaskForProgressBar task)
public java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> getTypes()
public void setActivitiesToConsider(java.util.Collection<java.lang.String> activitiesToConsider)
public void setRegression(boolean regressionTree)
public void makePrediction(TaskForProgressBar task) throws java.lang.Exception
java.lang.Exceptionpublic DiscretizationInterval[] setOutputAttribute(Augmentation attribute, int numberIntervals, DiscrMethod method, boolean regressionTree)
public void setBinarySplit(boolean binarySplit)
public void setConfidenceThreshold(float confidenceThreshold)
public void setMinNumInstancePerLeaf(double d)
public void setSaveData(boolean saveData)
public void setUnPruned(boolean unPruned)
public int getNumInstances()
public void setNumFolds(int numFoldErrorPruning)
public weka.classifiers.Evaluation getEvaluation()
public java.util.Set<java.lang.String> getLiteralValues(java.lang.String attribute)
public java.util.Collection<java.lang.String> getActivities()
public org.deckfour.xes.model.XLog getOriginalLog()
public org.processmining.plugins.DataConformance.ResultReplay getResReplay()
public boolean isRegressionTree()
public static java.util.Map<java.lang.String,org.processmining.models.FunctionEstimator.Type> extractAttributeInformation(org.deckfour.xes.model.XLog log)
public java.util.List<java.lang.String> getAttributes()
public int getInstanceSetSize()