public void testDecisonListWithNoTestsReturnsDefaultValue()
{
DecisionList dlist = new DecisionList("Yes", "No");
DataSet ds = DataSetFactory.getRestaurantDataSet();
Assert.AreEqual("No", dlist.predict(ds.getExample(0)));
}
public void testDecisionListHandlesEmptyDataSet()
{
// tests first base case of recursion
DecisionList dlist = new DecisionList("Yes", "No");
aima.net.learning.inductive.DecisionListTest test1 = new aima.net.learning.inductive.DecisionListTest();
test1.add("type", "Thai"); // doesn't match first example
dlist.add(test1, "test1success");
}
public void testDecisionListWithSingleTestReturnsTestValueIfTestSuccessful()
{
DecisionList dlist = new DecisionList("Yes", "No");
DataSet ds = DataSetFactory.getRestaurantDataSet();
DLTest test = new DLTest();
test.add("type", "French");
dlist.add(test, "test1success");
Assert.AreEqual("test1success", dlist.predict(ds.getExample(0)));
}
public void testDecisionListFallsThruToDefaultIfNoTestMatches()
{
DecisionList dlist = 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
dlist.add(test1, "test1success");
aima.net.learning.inductive.DecisionListTest test2 = new aima.net.learning.inductive.DecisionListTest();
test2.add("type", "Burger");
dlist.add(test2, "test2success");// doesn't match first example
Assert.AreEqual("No", dlist.predict(ds.getExample(0)));
}
public void testDecisionListFallsThruToNextTestIfOneDoesntMatch()
{
DecisionList dlist = new DecisionList("Yes", "No");
DataSet ds = DataSetFactory.getRestaurantDataSet();
DLTest test1 = new DLTest();
test1.add("type", "Thai"); // doesn't match first example
dlist.add(test1, "test1success");
DLTest test2 = new DLTest();
test2.add("type", "French");
dlist.add(test2, "test2success");// matches first example
Assert.AreEqual("test2success", dlist.predict(ds.getExample(0)));
}
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))));
}
private DecisionList DecisionListLearning(DataSet ds)
{
if (ds.Count == 0)
{
return(new DecisionList(positive, negative));
}
var possibleTests = testFactory.CreateDLTestsWithAttributeCount(ds, 1);
DLTest test = this.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);
var list = new DecisionList(positive, negative);
list.Add(test, matched.GetExample(0).TargetValue());
return(list.MergeWith(this.DecisionListLearning(test.UnmatchedExamples(ds))));
}
private void FindVolume()
{
DecisionList.Add(new Formulas(cube.FindVolumes(Given)));
Answer.Add(answer.FindAnswerVolume(DecisionList, Find));
}
private void FindRib()
{
DecisionList.Add(new Formulas(cube.FindRibs(Given, Figure)));
Answer.Add(answer.FindAnswerRib(DecisionList, Find));
}
/// <summary>
/// Induces the decision list from the specified set of examples
/// </summary>
/// <param name="ds">a set of examples for constructing the decision list</param>
public void Train(DataSet ds)
{
this.decisionList = decisionListLearning(ds);
}
