public void TestAdaBoost()
{
var models = new List <IAdaBootModel <double, bool> >()
{
new RegionalDivision(o => o.ToList()[0] > -0.5),
new RegionalDivision(o => o.ToList()[0] <= -0.5),
new RegionalDivision(o => o.ToList()[0] <= 0.5),
new RegionalDivision(o => o.ToList()[0] > 0.5),
new RegionalDivision(o => o.ToList()[1] > -0.5),
new RegionalDivision(o => o.ToList()[1] <= -0.5),
new RegionalDivision(o => o.ToList()[1] <= 0.5),
new RegionalDivision(o => o.ToList()[1] > 0.5)
};
var tuples = new List <LabeledTuple <double, bool> >()
{
new LabeledTuple <double, bool>(true, -1, 0),
new LabeledTuple <double, bool>(true, 1, 0),
new LabeledTuple <double, bool>(false, 0, -1),
new LabeledTuple <double, bool>(false, 0, 1)
};
var adaBoost = new AdaBoost <double, bool>(models);
adaBoost.Train(tuples, tuples.Count);
foreach (var tuple in tuples)
{
var expected = tuple.Target;
var actual = adaBoost.Predict(tuple.Properties);
Assert.AreEqual(expected, actual);
}
}
public void AdaBoost_Test()
{
Logger.Info("AdaBoost_Test");
var problem = ProblemFactory.CreateClassificationProblem(ClassificationProblemType.ChessBoard);
var tSet = problem.TrainingSet;
var vSet = problem.ValidationSet;
var classifier = new AdaBoost(tSet, 1000, problem.Dimension);
classifier.Train();
var hit = (from e in vSet.Examples let iResult = classifier.Predict(e.X) where e.Label.Id == iResult select e).Count();
var correctRatio = 1.0 * hit / vSet.Count;
Logger.Info("CorrectRatio: {0}", correctRatio);
Assert.IsTrue(correctRatio > 0.900, string.Format("AdaBoost (2-class) Correct Ratio, expected: greater than 0.900, actual: {0}.", correctRatio));
}
private void bwLoadData_DoWork(object sender, DoWorkEventArgs e)
{
try
{
Classifier classif = null;
Datas.Useable train = null;
Datas.Useable test = null;
ConfusionMatrix conf_train, conf_test;
if (e.Argument is ToBackgroundWorkerArgsTree)
{
var args = e.Argument as ToBackgroundWorkerArgsTree;
train = args._Train;
test = args._Test;
classif = new DecisionTree(args._MaxDepth);
classif.Train(train);
conf_train = new ConfusionMatrix(classif.Compile, train);
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else if (e.Argument is ToBackgroundWorkerArgsForest)
{
var args = e.Argument as ToBackgroundWorkerArgsForest;
train = args._Train;
test = args._Test;
classif = new RandomForest(args._MaxDepth, args._TreeCount);
classif.Train(train);
conf_train = new ConfusionMatrix(classif.Compile, train);
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else if (e.Argument is ToBackgroundWorkerArgsAdaBoost)
{
var args = e.Argument as ToBackgroundWorkerArgsAdaBoost;
train = args._Train;
test = args._Test;
classif = new AdaBoost(args._Factory, args._Boosts);
classif.Train(train);
conf_train = new ConfusionMatrix(classif.Compile, train);
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else if (e.Argument is ToBackgroundWorkerkNN)
{
var args = e.Argument as ToBackgroundWorkerkNN;
train = args._Train;
test = args._Test;
classif = new kNN(args._kNN);
classif.Train(train);
conf_train = null;
conf_test = new ConfusionMatrix(classif.Compile, test);
}
else
{
throw new Exception("Not recognized stuff");
}
if (this.bwLoadData.CancellationPending)
{
e.Result = null;
}
else
{
e.Result = new TrainerReturn(
conf_train,
conf_test,
classif);
}
}
catch (Exception exc)
{
e.Result = exc.ToString();
}
}