/**
* Classify the input.
*
* @param input The input to classify.
*/
@Override
public int classify(MLData input) {
if (this.classificationTarget < 0 || this.classificationTarget >= this.events.size()) {
throw new BayesianError(
"Must specify classification target by calling setClassificationTarget.");
}
int[] d = this.determineClasses(input);
// properly tag all of the events
for (int i = 0; i < this.events.size(); i++) {
BayesianEvent event = this.events.get(i);
if (i == this.classificationTarget) {
this.query.defineEventType(event, EventType.Outcome);
} else if (this.inputPresent[i]) {
this.query.defineEventType(event, EventType.Evidence);
this.query.setEventValue(event, d[i]);
} else {
this.query.defineEventType(event, EventType.Hidden);
this.query.setEventValue(event, d[i]);
}
}
// loop over and try each outcome choice
BayesianEvent outcomeEvent = this.events.get(this.classificationTarget);
this.classificationProbabilities = new double[outcomeEvent.getChoices().size()];
for (int i = 0; i < outcomeEvent.getChoices().size(); i++) {
this.query.setEventValue(outcomeEvent, i);
this.query.execute();
classificationProbabilities[i] = this.query.getProbability();
}
return EngineArray.maxIndex(this.classificationProbabilities);
}
public int winner(MLData output) {
return EngineArray.maxIndex(output.getData());
}
