static void ensembleLearningDemo()
{
try
{
DataSet ds = DataSetFactory.getRestaurantDataSet();
ICollection <DecisionTree> stumps = DecisionTree.getStumpsFor(ds, "Yes", "No");
ICollection <ILearner> learners = CollectionFactory.CreateQueue <ILearner>();
System.Console.WriteLine("\nStump Learners vote to decide in this algorithm");
foreach (object stump in stumps)
{
DecisionTree sl = (DecisionTree)stump;
StumpLearner stumpLearner = new StumpLearner(sl, "No");
learners.Add(stumpLearner);
}
AdaBoostLearner learner = new AdaBoostLearner(learners, ds);
learner.Train(ds);
var answer = learner.Predict(ds.getExample(0));
int[] result = learner.Test(ds);
System.Console.WriteLine("\nThis Ensemble Learner classifies the data set with "
+ result[0]
+ " successes"
+ " and "
+ result[1]
+ " failures");
System.Console.WriteLine("\n");
}
catch (Exception e)
{
throw e;
}
}
public void testAdaBoostEnablesCollectionOfStumpsToClassifyDataSetAccurately()
{
DataSet ds = DataSetFactory.getRestaurantDataSet();
List <DecisionTree> stumps = DecisionTree.getStumpsFor(ds, YES, "No");
List <Learner> learners = new List <Learner>();
foreach (Object stump in stumps)
{
DecisionTree sl = (DecisionTree)stump;
StumpLearner stumpLearner = new StumpLearner(sl, "No");
learners.Add(stumpLearner);
}
AdaBoostLearner learner = new AdaBoostLearner(learners, ds);
learner.train(ds);
int[] result = learner.test(ds);
Assert.AreEqual(12, result[0]);
Assert.AreEqual(0, result[1]);
}
