public static void decisionTreeDemo()
{
try
{
DataSet ds = DataSetFactory.getRestaurantDataSet();
DecisionTreeLearner learner = new DecisionTreeLearner();
learner.Train(ds);
System.Console.WriteLine("The Induced Decision Tree is ");
System.Console.WriteLine(learner.getDecisionTree());
int[] result = learner.Test(ds);
System.Console.WriteLine("\nThis Decision Tree classifies the data set with "
+ result[0]
+ " successes"
+ " and "
+ result[1]
+ " failures");
System.Console.WriteLine("\n");
}
catch (Exception e)
{
System.Console.WriteLine("Decision Tree Demo Failed ");
throw e;
}
}
public void testInducedDecisionTreeClassifiesRestaurantDataSetCorrectly()
{
DecisionTreeLearner learner = new DecisionTreeLearner(
createInducedRestaurantDecisionTree(), "Unable to clasify");
int[] results = learner.Test(DataSetFactory.getRestaurantDataSet());
Assert.AreEqual(12, results[0]);
Assert.AreEqual(0, results[1]);
}
public void testInducedTreeClassifiesDataSetCorrectly()
{
DataSet ds = DataSetFactory.getRestaurantDataSet();
DecisionTreeLearner learner = new DecisionTreeLearner();
learner.Train(ds);
int[] result = learner.Test(ds);
Assert.AreEqual(12, result[0]);
Assert.AreEqual(0, result[1]);
}
public void testStumpPredictionForDataSet()
{
DataSet ds = DataSetFactory.getRestaurantDataSet();
ICollection <string> unmatchedValues = CollectionFactory.CreateQueue <string>();
unmatchedValues.Add(NO);
DecisionTree tree = DecisionTree.getStumpFor(ds, "hungry", YES, YES,
unmatchedValues, "Unable to Classify");
DecisionTreeLearner learner = new DecisionTreeLearner(tree,
"Unable to Classify");
int[] result = learner.Test(ds);
Assert.AreEqual(5, result[0]);
Assert.AreEqual(7, result[1]);
}