public override void Train(List <Tuple <double[], double> > data,
DecisionTree.Splitter splitter)
{
List <Tuple <double, double[], double> > list = Initialize(data);
var sample = data;
var alpha = 0.0;
var epselonWrongCounter = 0.0;
var epselon = 0.0;
var n = data.Count;
for (int i = 0; i < ensembleSizeT; i++)
{
if (i > 0)
{
sample = BuildSample(list);
}
var classifier = new
DecisionTree.DecisionTreeClassifier(sample, splitter);
classifier.Train();
foreach (var value in sample)
{
var result = classifier.Classify(value.Item1);
if (result != value.Item2)
{
epselonWrongCounter++;
}
}
epselon = epselonWrongCounter / (double)n;
if (epselon == 0)
{
classifiers.Add(new Tuple <double, DecisionTree.DecisionTreeClassifier>(alpha, classifier));
break;
}
else
{
alpha = 0.5 * Math.Log(((1.0 - epselon) / epselon));
classifiers.Add(new Tuple <double, DecisionTree.DecisionTreeClassifier>(alpha, classifier));
}
list = Adjust(classifier, list, epselon);
epselonWrongCounter = 0.0;
}
}
public override void Train(List <Tuple <double[], double> > data,
DecisionTree.Splitter splitter)
{
var n = data.Count;
var bootstrapSampleSize = n * 0.8;
var dataArray = data.ToArray();
for (int t = 0; t < ensembleSizeT; t++)
{
var sample = new List <Tuple <double[], double> >();
Random random = new Random();
for (int i = 0; i < bootstrapSampleSize; i++)
{
var number = random.Next(0, n);
sample.Add(dataArray[number]);
}
var classifier = new
DecisionTree.DecisionTreeClassifier(sample, splitter);
classifier.Train();
classifiers.Add(classifier);
}
}