package aima.core.learning.learners;
import java.util.ArrayList;
import java.util.List;
import aima.core.learning.framework.DataSet;
import aima.core.learning.framework.Example;
import aima.core.learning.framework.Learner;
import aima.core.learning.knowledge.CurrentBestLearning;
import aima.core.learning.knowledge.FOLDataSetDomain;
import aima.core.learning.knowledge.FOLExample;
import aima.core.learning.knowledge.Hypothesis;
import aima.core.logic.fol.inference.FOLOTTERLikeTheoremProver;
import aima.core.logic.fol.inference.InferenceResult;
import aima.core.logic.fol.kb.FOLKnowledgeBase;
/**
* @author Ciaran O'Reilly
*
*/
public class CurrentBestLearner implements Learner {
private String trueGoalValue = null;
private FOLDataSetDomain folDSDomain = null;
private FOLKnowledgeBase kb = null;
private Hypothesis currentBestHypothesis = null;
//
// PUBLIC METHODS
//
public CurrentBestLearner(String trueGoalValue) {
this.trueGoalValue = trueGoalValue;
}
//
// START-Learner
public void train(DataSet ds) {
folDSDomain = new FOLDataSetDomain(ds.specification, trueGoalValue);
List<FOLExample> folExamples = new ArrayList<FOLExample>();
int egNo = 1;
for (Example e : ds.examples) {
folExamples.add(new FOLExample(folDSDomain, e, egNo));
egNo++;
}
// Setup a KB to be used for learning
kb = new FOLKnowledgeBase(folDSDomain, new FOLOTTERLikeTheoremProver(
1000, false));
CurrentBestLearning cbl = new CurrentBestLearning(folDSDomain, kb);
currentBestHypothesis = cbl.currentBestLearning(folExamples);
}
public String predict(Example e) {
String prediction = "~" + e.targetValue();
if (null != currentBestHypothesis) {
FOLExample etp = new FOLExample(folDSDomain, e, 0);
kb.clear();
kb.tell(etp.getDescription());
kb.tell(currentBestHypothesis.getHypothesis());
InferenceResult ir = kb.ask(etp.getClassification());
if (ir.isTrue()) {
if (trueGoalValue.equals(e.targetValue())) {
prediction = e.targetValue();
}
} else if (ir.isPossiblyFalse() || ir.isUnknownDueToTimeout()) {
if (!trueGoalValue.equals(e.targetValue())) {
prediction = e.targetValue();
}
}
}
return prediction;
}
public int[] test(DataSet ds) {
int[] results = new int[] { 0, 0 };
for (Example e : ds.examples) {
if (e.targetValue().equals(predict(e))) {
results[0] = results[0] + 1;
} else {
results[1] = results[1] + 1;
}
}
return results;
}
// END-Learner
//
}