/**
* Define a classification structure of the form P(A|B) = P(C)
*
* @param line
*/
public void defineClassificationStructure(String line) {
List<ParsedProbability> list = ParseProbability.parseProbabilityList(this, line);
if (list.size() > 1) {
throw new BayesianError("Must only define a single probability, not a chain.");
}
if (list.size() == 0) {
throw new BayesianError("Must define at least one probability.");
}
// first define everything to be hidden
for (BayesianEvent event : this.events) {
this.query.defineEventType(event, EventType.Hidden);
}
// define the base event
ParsedProbability prob = list.get(0);
if (prob.getBaseEvents().size() == 0) {
return;
}
BayesianEvent be = this.getEvent(prob.getChildEvent().getLabel());
this.classificationTarget = this.events.indexOf(be);
this.query.defineEventType(be, EventType.Outcome);
// define the given events
for (ParsedEvent parsedGiven : prob.getGivenEvents()) {
BayesianEvent given = this.getEvent(parsedGiven.getLabel());
this.query.defineEventType(given, EventType.Evidence);
}
this.query.locateEventTypes();
// set the values
for (ParsedEvent parsedGiven : prob.getGivenEvents()) {
BayesianEvent event = this.getEvent(parsedGiven.getLabel());
this.query.setEventValue(event, parseInt(parsedGiven.getValue()));
}
this.query.setEventValue(be, parseInt(prob.getBaseEvents().get(0).getValue()));
}
/**
* Define the structure of the Bayesian network as a string.
*
* @param line The string to define events and relations.
*/
public void setContents(String line) {
List<ParsedProbability> list = ParseProbability.parseProbabilityList(this, line);
List<String> labelList = new ArrayList<String>();
// ensure that all events are there
for (ParsedProbability prob : list) {
ParsedEvent parsedEvent = prob.getChildEvent();
String eventLabel = parsedEvent.getLabel();
labelList.add(eventLabel);
// create event, if not already here
BayesianEvent e = getEvent(eventLabel);
if (e == null) {
List<BayesianChoice> cl = new ArrayList<BayesianChoice>();
for (ParsedChoice c : parsedEvent.getList()) {
cl.add(new BayesianChoice(c.getLabel(), c.getMin(), c.getMax()));
}
createEvent(eventLabel, cl);
}
}
// now remove all events that were not covered
for (int i = 0; i < events.size(); i++) {
BayesianEvent event = this.events.get(i);
if (!labelList.contains(event.getLabel())) {
removeEvent(event);
}
}
// handle dependencies
for (ParsedProbability prob : list) {
ParsedEvent parsedEvent = prob.getChildEvent();
String eventLabel = parsedEvent.getLabel();
BayesianEvent event = requireEvent(eventLabel);
// ensure that all "givens" are present
List<String> givenList = new ArrayList<String>();
for (ParsedEvent given : prob.getGivenEvents()) {
if (!event.hasGiven(given.getLabel())) {
BayesianEvent givenEvent = requireEvent(given.getLabel());
this.createDependency(givenEvent, event);
}
givenList.add(given.getLabel());
}
// now remove givens that were not covered
for (int i = 0; i < event.getParents().size(); i++) {
BayesianEvent event2 = event.getParents().get(i);
if (!givenList.contains(event2.getLabel())) {
removeDependency(event2, event);
}
}
}
// finalize the structure
finalizeStructure();
if (this.query != null) {
this.query.finalizeStructure();
}
}