001 /*
002 * Created on Aug 2, 2005
003 *
004 */
005 package aima.learning.learners;
006
007 import java.util.List;
008
009 import aima.learning.framework.DataSet;
010 import aima.learning.framework.Example;
011 import aima.learning.framework.Learner;
012 import aima.learning.inductive.DLTest;
013 import aima.learning.inductive.DLTestFactory;
014 import aima.learning.inductive.DecisionList;
015
016 /**
017 * @author Ravi Mohan
018 *
019 */
020
021 public class DecisionListLearner implements Learner {
022 public static final String FAILURE = "Failure";
023
024 private DecisionList decisionList;
025
026 private String positive, negative;
027
028 private DLTestFactory testFactory;
029
030 public DecisionListLearner(String positive, String negative,
031 DLTestFactory testFactory) {
032 this.positive = positive;
033 this.negative = negative;
034 this.testFactory = testFactory;
035 }
036
037 public void train(DataSet ds) {
038 this.decisionList = decisionListLearning(ds);
039 }
040
041 public String predict(Example e) {
042 if (decisionList == null) {
043 throw new RuntimeException(
044 "learner has not been trained with dataset yet!");
045 }
046 return decisionList.predict(e);
047 }
048
049 public int[] test(DataSet ds) {
050 int[] results = new int[] { 0, 0 };
051
052 for (Example e : ds.examples) {
053 if (e.targetValue().equals(decisionList.predict(e))) {
054 results[0] = results[0] + 1;
055 } else {
056 results[1] = results[1] + 1;
057 }
058 }
059 return results;
060 }
061
062 private DecisionList decisionListLearning(DataSet ds) {
063 if (ds.size() == 0) {
064 return new DecisionList(positive, negative);
065 }
066 List<DLTest> possibleTests = testFactory
067 .createDLTestsWithAttributeCount(ds, 1);
068 DLTest test = getValidTest(possibleTests, ds);
069 if (test == null) {
070 return new DecisionList(null, FAILURE);
071 }
072 // at this point there is a test that classifies some subset of examples
073 // with the same target value
074 DataSet matched = test.matchedExamples(ds);
075 DecisionList list = new DecisionList(positive, negative);
076 list.add(test, matched.getExample(0).targetValue());
077 return list.mergeWith(decisionListLearning(test.unmatchedExamples(ds)));
078 }
079
080 private DLTest getValidTest(List<DLTest> possibleTests, DataSet ds) {
081 for (DLTest test : possibleTests) {
082 DataSet matched = test.matchedExamples(ds);
083 if (!(matched.size() == 0)) {
084 if (allExamplesHaveSameTargetValue(matched)) {
085 return test;
086 }
087 }
088
089 }
090 return null;
091 }
092
093 private boolean allExamplesHaveSameTargetValue(DataSet matched) {
094 // assumes at least i example in dataset
095 String targetValue = matched.getExample(0).targetValue();
096 for (Example e : matched.examples) {
097 if (!(e.targetValue().equals(targetValue))) {
098 return false;
099 }
100 }
101 return true;
102 }
103
104 /**
105 * @return Returns the decisionList.
106 */
107 public DecisionList getDecisionList() {
108 return decisionList;
109 }
110
111 }