Modifier and Type | Class and Description |
---|---|
class |
StochasticNetImpl |
Modifier and Type | Method and Description |
---|---|
static java.lang.Object[] |
ToStochasticNet.asPetriNet(org.processmining.framework.plugin.PluginContext context,
StochasticNet net,
org.processmining.models.semantics.petrinet.Marking marking) |
static java.lang.Object[] |
ToStochasticNet.convertStochasticNetToType(org.processmining.framework.plugin.PluginContext context,
StochasticNet net,
org.processmining.models.semantics.petrinet.Marking marking,
StochasticNet.DistributionType type)
Converts all timed transitions (except immediate and deterministic transitions) to the specified type in the net.
|
static java.lang.Object[] |
ToStochasticNet.fromStochasticNet(org.processmining.framework.plugin.PluginContext context,
StochasticNet net,
org.processmining.models.semantics.petrinet.Marking marking) |
Modifier and Type | Method and Description |
---|---|
CaseTimeSeries |
TimeSeriesAlignmentPlugin.transform(org.processmining.contexts.uitopia.UIPluginContext context,
org.deckfour.xes.model.XLog log,
StochasticNet net) |
Modifier and Type | Method and Description |
---|---|
PNMLRoot |
StochasticNetToPNMLConverter.convertNet(StochasticNet net,
org.processmining.models.semantics.petrinet.Marking initialMarking,
org.processmining.models.connections.GraphLayoutConnection layout) |
void |
PnmlExportStochasticNet.exportPetriNetToPNMLFile(org.processmining.framework.plugin.PluginContext context,
StochasticNet net,
java.io.File file) |
Modifier and Type | Method and Description |
---|---|
static StochasticNet |
StochasticNetUtils.convertToGSPN(StochasticNet spn) |
static StochasticNet |
StochasticNetUtils.convertToNormal(StochasticNet spn) |
Modifier and Type | Method and Description |
---|---|
static StochasticNet |
StochasticNetUtils.convertToGSPN(StochasticNet spn) |
static StochasticNet |
StochasticNetUtils.convertToNormal(StochasticNet spn) |
static double |
StochasticNetUtils.getMeanDuration(StochasticNet net,
org.processmining.models.semantics.petrinet.Marking initialMarking)
Gets the mean duration of the model by a simple simulation.
|
static double |
StochasticNetUtils.getUpperBoundDuration(StochasticNet net,
org.processmining.models.semantics.petrinet.Marking initialMarking) |
Modifier and Type | Method and Description |
---|---|
StochasticNet |
CaseStatisticsAnalyzer.getStochasticNet() |
Modifier and Type | Method and Description |
---|---|
static double |
LikelihoodAnalyzer.getLogLikelihood(org.processmining.framework.plugin.PluginContext context,
org.deckfour.xes.model.XLog log,
StochasticNet net,
int traceIndex)
Computes the log likelihood of a trace given a net.
|
static CaseStatisticsList |
LikelihoodAnalyzer.getLogLikelihoods(org.processmining.framework.plugin.PluginContext context,
org.deckfour.xes.model.XLog log,
StochasticNet net)
Computes the log likelihood of a trace given a net.
|
CaseStatisticsList |
LikelihoodAnalyzerPlugin.transform(org.processmining.contexts.uitopia.UIPluginContext context,
org.deckfour.xes.model.XLog log,
StochasticNet net) |
Constructor and Description |
---|
CaseStatisticsAnalyzer(StochasticNet stochasticNet,
org.processmining.models.semantics.petrinet.Marking initialMarking,
CaseStatisticsList statistics) |
CaseStatisticsConnection(StochasticNet net,
org.deckfour.xes.model.XLog log,
CaseStatisticsList caseStatistics) |
Modifier and Type | Method and Description |
---|---|
AnomalyIntervals |
AnomalousIntervalsComputerPlugin.computeAnomalyIntervals(org.processmining.contexts.uitopia.UIPluginContext context,
StochasticNet net) |
java.util.Map<org.processmining.models.graphbased.directed.petrinet.elements.Transition,java.util.List<org.processmining.framework.util.Pair<java.lang.Double,java.lang.Double>>> |
AnomalousIntervalsComputer.getAnomalousIntervals(org.processmining.framework.plugin.PluginContext context,
StochasticNet net,
double outlierRate) |
Modifier and Type | Method and Description |
---|---|
static java.lang.Object[] |
ConvertDistributionsPlugin.convertStochasticNet(org.processmining.framework.plugin.PluginContext context,
StochasticNet net) |
static java.lang.Object[] |
ConvertDistributionsPlugin.stripStochasticInformation(org.processmining.framework.plugin.PluginContext context,
StochasticNet net) |
Modifier and Type | Method and Description |
---|---|
ModelComparisonResult |
PerformanceEnricherExperimentResult.getComparisonResult(StochasticNet net,
StochasticNet learnedNet,
PerformanceEnricher enricher) |
PerformanceEnricherExperimentResult |
PerformanceEnricherExperimentPlugin.performExperiment(org.processmining.contexts.uitopia.UIPluginContext context,
StochasticNet net,
org.processmining.models.semantics.petrinet.Marking initialMarking,
PerformanceEnricherExperimentPlugin.ExperimentType type)
We first simulate the net a number of times with a combination of given trace-sizes and policies, and then
enrich the base Petri net to be stochastic again.
|
PerformanceEnricherExperimentResult |
PerformanceEnricherExperimentPlugin.plugin(org.processmining.contexts.uitopia.UIPluginContext context,
StochasticNet net) |
Modifier and Type | Method and Description |
---|---|
static java.lang.Object[] |
AllocationBasedNetGenerator.generateNet(StochasticNet base,
PetrinetModelAllocations allocations,
java.util.Set<Allocatable> resources,
int numCases,
double meanTimeBetweenArrivals,
double noise,
long startTime)
Generates a net based on a basis and a number of cases that are drawn randomly from the model.
|
static java.lang.Object[] |
AllocationBasedNetGenerator.generateObservationAwareNet(StochasticNet base,
PetrinetModelAllocations allocations,
java.util.Set<Allocatable> resources,
double noise)
TOSO: Currently we neglect resources!
|
Modifier and Type | Method and Description |
---|---|
java.lang.Double |
TimePredictorPlugin.computeRisk(org.processmining.contexts.uitopia.UIPluginContext context,
StochasticNet model,
org.deckfour.xes.model.XTrace observedEvents,
java.util.Date currentTime,
java.util.Date targetTime) |
java.lang.Double |
AbstractTimePredictor.computeRiskToMissTargetTime(StochasticNet model,
org.deckfour.xes.model.XTrace observedEvents,
java.util.Date currentTime,
java.util.Date targetTime,
org.processmining.models.semantics.petrinet.Marking initialMarking,
boolean useOnlyPastTrainingData)
Maximum likelihood estimate for the risk of missing a deadline until the end of the process.
|
static org.processmining.models.semantics.Semantics<org.processmining.models.semantics.petrinet.Marking,org.processmining.models.graphbased.directed.petrinet.elements.Transition> |
AbstractTimePredictor.getCurrentState(StochasticNet model,
org.processmining.models.semantics.petrinet.Marking initialMarking,
org.deckfour.xes.model.XTrace observedEvents)
TODO: Maybe switch to alignment approach
|
static org.processmining.models.semantics.Semantics<org.processmining.models.semantics.petrinet.Marking,org.processmining.models.graphbased.directed.petrinet.elements.Transition> |
AbstractTimePredictor.getCurrentStateWithAlignment(StochasticNet model,
org.processmining.models.semantics.petrinet.Marking initialMarking,
org.deckfour.xes.model.XTrace observedEvents) |
protected abstract org.apache.commons.math3.stat.descriptive.DescriptiveStatistics |
AbstractTimePredictor.getPredictionStats(StochasticNet model,
org.deckfour.xes.model.XTrace observedEvents,
java.util.Date currentTime,
boolean useOnlyPastTrainingData,
org.processmining.models.semantics.Semantics<org.processmining.models.semantics.petrinet.Marking,org.processmining.models.graphbased.directed.petrinet.elements.Transition> semantics)
Computes some stats by running a Monte Carlo simulation of the process.
|
org.apache.commons.math3.stat.descriptive.DescriptiveStatistics |
TimePredictor.getPredictionStats(StochasticNet model,
org.deckfour.xes.model.XTrace observedEvents,
java.util.Date currentTime,
boolean useOnlyPastTrainingData,
org.processmining.models.semantics.Semantics<org.processmining.models.semantics.petrinet.Marking,org.processmining.models.graphbased.directed.petrinet.elements.Transition> semantics) |
org.processmining.models.semantics.Semantics<org.processmining.models.semantics.petrinet.Marking,org.processmining.models.graphbased.directed.petrinet.elements.Transition> |
AbstractTimePredictor.getSemantics(StochasticNet model,
org.deckfour.xes.model.XTrace observedEvents,
org.processmining.models.semantics.petrinet.Marking initialMarking) |
org.processmining.framework.util.Pair<java.lang.Double,java.lang.Double> |
AbstractTimePredictor.predict(StochasticNet model,
org.deckfour.xes.model.XTrace observedEvents,
java.util.Date currentTime,
boolean useOnlyPastTrainingData,
org.processmining.models.semantics.Semantics<org.processmining.models.semantics.petrinet.Marking,org.processmining.models.graphbased.directed.petrinet.elements.Transition> semantics) |
org.processmining.framework.util.Pair<java.lang.Double,java.lang.Double> |
AbstractTimePredictor.predict(StochasticNet model,
org.deckfour.xes.model.XTrace observedEvents,
java.util.Date currentTime,
org.processmining.models.semantics.petrinet.Marking initialMarking) |
org.processmining.framework.util.Pair<java.lang.Double,java.lang.Double> |
AbstractTimePredictor.predict(StochasticNet model,
org.deckfour.xes.model.XTrace observedEvents,
java.util.Date currentTime,
org.processmining.models.semantics.petrinet.Marking initialMarking,
boolean useOnlyPastTrainingData)
Does not care about final markings -> simulates net until no transitions are enabled any more...
|
java.lang.Double |
TimePredictorPlugin.predict(org.processmining.contexts.uitopia.UIPluginContext context,
StochasticNet model,
org.deckfour.xes.model.XTrace observedEvents,
java.util.Date currentTime) |
Modifier and Type | Method and Description |
---|---|
long |
PredictionExperimentPlugin.getProcessMeanDuration(StochasticNet model,
org.deckfour.xes.model.XLog log,
org.processmining.models.semantics.petrinet.Marking initialMarking,
PredictionExperimentConfig config) |
PredictionExperimentResult |
PredictionExperimentPlugin.predict(org.processmining.framework.plugin.PluginContext context,
StochasticNet model,
StochasticNet gspnModel,
org.processmining.plugins.tsanalyzer.annotation.time.TimeTransitionSystemAnnotation[] transitionSystemAnnotations,
org.deckfour.xes.model.XLog log,
PredictionExperimentConfig config,
double meanDuration) |
PredictionExperimentResult |
PredictionExperimentPlugin.predict(org.processmining.contexts.uitopia.UIPluginContext context,
StochasticNet model) |
Modifier and Type | Method and Description |
---|---|
protected org.apache.commons.math3.stat.descriptive.DescriptiveStatistics |
TimeseriesPredictor.getPredictionStats(StochasticNet model,
org.deckfour.xes.model.XTrace observedEvents,
java.util.Date currentTime,
boolean useOnlyPastTrainingData,
org.processmining.models.semantics.Semantics<org.processmining.models.semantics.petrinet.Marking,org.processmining.models.graphbased.directed.petrinet.elements.Transition> semantics) |
Modifier and Type | Method and Description |
---|---|
org.deckfour.xes.model.XLog |
LogLocationDelayInducer.induceLocationDelay(org.deckfour.xes.model.XLog log,
WorldConfiguration wc,
StochasticNet net) |
void |
AdvancedSimulator.simulateRealEntities(StochasticNet model,
WorldConfiguration wc)
Simulates real entities performing a process.
|
Constructor and Description |
---|
PNSimulatorConfig(long numberOfTraces,
StochasticNet net) |
Modifier and Type | Method and Description |
---|---|
javax.swing.JComponent |
PerformanceVisualization.visualize(org.processmining.framework.plugin.PluginContext context,
StochasticNet sNet) |