public void SerializeToXml(ref XmlElement strongLearnerNode)
{
foreach (KeyValuePair <int, WeakLearner> iterAndWeakLearner in _weakLearners)
{
WeakLearner learner = iterAndWeakLearner.Value;//_weakLearners[iter];
XmlElement learnerNote = strongLearnerNode.OwnerDocument.CreateElement("WeakLearner");
learnerNote.SetAttribute("type", learner.GetType().Name);
learnerNote.SetAttribute("iter", iterAndWeakLearner.Key.ToString());
learner.SerializeToXml(ref learnerNote);
strongLearnerNode.AppendChild(learnerNote);
}
}
public void DerializeFromXML(XmlElement strongLearnerNode)
{
_weakLearners = new SortedList <int, WeakLearner>();
foreach (XmlElement node in strongLearnerNode.ChildNodes)
{
if (node.Name != "WeakLearner")
{
continue;
}
int iter = int.Parse(node.Attributes["iter"].Value);
Assembly asm = Assembly.GetAssembly(typeof(StrongLeaner));
WeakLearner learner = (WeakLearner)asm.CreateInstance(typeof(StrongLeaner).Namespace + "." + node.Attributes["type"].Value);
learner.DeserializeFromXml(node);
_weakLearners.Add(iter, learner);
}
}
public void Train(Problem prob, string weakLearnerName, string[] weakLearnerArgs, int iter)
{
//Creat weaklearner and traindata
Assembly asm = Assembly.GetAssembly(typeof(WeakLearner));
WeakLearner srcLearner = (WeakLearner)asm.CreateInstance(typeof(WeakLearner).Namespace + "." + weakLearnerName, true);
srcLearner.InitLearningOptions(weakLearnerArgs);
TrainData traindata = srcLearner.CreateTrainData(prob);
// set the smoothing value to avoid numerical problem 1/N
//"Improved boosting algorithms using confidence-rated predictions". chapter 4.2
double smoothingVal = 1.0 / traindata.N;
//init weight
double[] weight = new double[traindata.N];
for (int t = 0; t < weight.Length; t++)
{
weight[t] = smoothingVal;
}
//show sth
Console.WriteLine("\tStrongLearner:{0}", this.GetType().Name);
Console.WriteLine("\tWeakLearner:{0}", weakLearnerName);
int cursorX = Console.CursorLeft;
int cursorY = Console.CursorTop;
//start iterating
_weakLearners = new SortedList <int, WeakLearner>(iter);
for (int t = 0; t < iter; t++)
{
//creat a new learner from srcLearner
WeakLearner subLearner = (WeakLearner)asm.CreateInstance(srcLearner.GetType().FullName);//srcLearner.CreateSubLearner();
//init args again
subLearner.InitLearningOptions(weakLearnerArgs);
//train the learner(the suboptimal solution with current weight)
double Pm = subLearner.Train(traindata, weight);
if (Pm >= 0.5)
{
throw new Exception(Messege.CouldNotClassify);
}
//calculate Alpha
//note : eps_min = Pm , eps_pls = 1-Pm
double eps_min = 0.0, eps_pls = 0.0;
double[] result = new double[prob.N];
for (int n = 0; n < prob.N; n++)
{
result[n] = subLearner.Classify(prob.X[n]);
if ((result[n] * prob.Y[n]) < 0)
{
eps_min += weight[n];
}
if ((result[n] * prob.Y[n]) > 0)
{
eps_pls += weight[n];
}
}
double Alpha = 0.5 * Math.Log((eps_pls + smoothingVal) / (eps_min + smoothingVal));
subLearner.Alpha = Alpha;
//update weight
double Z = 0;
for (int n = 0; n < prob.N; n++)
{
weight[n] = weight[n] * Math.Exp(-1 * prob.Y[n] * result[n] * Alpha);
Z += weight[n];
}
for (int n = 0; n < prob.N; n++)
{
weight[n] /= Z;
}
//test
double sum = 0;
for (int n = 0; n < prob.N; n++)
{
sum += weight[n];
}
//save
_weakLearners.Add(t, subLearner);
//show sth
Console.SetCursorPosition(cursorX, cursorY);
Console.WriteLine("\titerations {0}/{1}", t + 1, iter);
}
}
