public void testDecisionListMerge()
{
DecisionList dlist1 = new DecisionList("Yes", "No");
DecisionList dlist2 = new DecisionList("Yes", "No");
DataSet ds = DataSetFactory.getRestaurantDataSet();
aima.net.learning.inductive.DecisionListTest test1 = new aima.net.learning.inductive.DecisionListTest();
test1.add("type", "Thai"); // doesn't match first example
dlist1.add(test1, "test1success");
aima.net.learning.inductive.DecisionListTest test2 = new aima.net.learning.inductive.DecisionListTest();
test2.add("type", "French");
dlist2.add(test2, "test2success");// matches first example
DecisionList dlist3 = dlist1.mergeWith(dlist2);
Assert.AreEqual("test2success", dlist3.predict(ds.getExample(0)));
}
private DecisionList decisionListLearning(DataSet ds)
{
if (ds.size() == 0)
{
return(new DecisionList(positive, negative));
}
ICollection <DecisionListTest> possibleTests = testFactory.createDLTestsWithAttributeCount(ds, 1);
DecisionListTest test = getValidTest(possibleTests, ds);
if (test == null)
{
return(new DecisionList(null, FAILURE));
}
// at this point there is a test that classifies some subset of examples
// with the same target value
DataSet matched = test.matchedExamples(ds);
DecisionList list = new DecisionList(positive, negative);
list.add(test, matched.getExample(0).targetValue());
return(list.mergeWith(decisionListLearning(test.unmatchedExamples(ds))));
}