public override void Train(List <double[]> trainingData, List <double> trainingLabels, bool calculateError = true)
{
int[][] TrainingData = TypeCasters.DoubleMultiArrayToInt(trainingData).ToArray();
int[] TrainingLabels = TypeCasters.DoubleArrayToInt(trainingLabels).ToArray();
if (LearningAlgorithmName == DecisionTreeLearningAlgorithms.ID3Learning)
{
LearningAlgorithm = new ID3Learning();
}
else
{
LearningAlgorithm = new C45Learning();
}
Model = LearningAlgorithm.Learn(TrainingData, TrainingLabels);
if (calculateError == true)
{
CalculateTrainingError(trainingData, trainingLabels);
}
}
public override void Train(List <double[]> trainingData, List <double> trainingLabels, bool calculateError = true)
{
LearningAlgorithm = new RandomForestLearning();
if (NumTrees > 0)
{
LearningAlgorithm.NumberOfTrees = NumTrees;
}
if (SamplePropotion > 0)
{
LearningAlgorithm.SampleRatio = SamplePropotion;
}
int[][] TrainingData = TypeCasters.DoubleMultiArrayToInt(trainingData).ToArray();
int[] TrainingLabels = TypeCasters.DoubleArrayToInt(trainingLabels).ToArray();
Model = LearningAlgorithm.Learn(TrainingData, TrainingLabels);
if (calculateError == true)
{
TrainingError = new ZeroOneLoss(TrainingLabels).Loss(Model.Decide(TrainingData));
}
}
