public override void Train(List <double[]> trainingData, List <double> trainingLabels, bool calculateError = true)
{
int[] TrainingLabels = TypeCasters.DoubleArrayToInt(trainingLabels).ToArray();
double[][] TrainingData = trainingData.ToArray();
Model = LearningAlgorithm.Learn(TrainingData, TrainingLabels);
if (calculateError == true)
{
CalculateTrainingError(trainingData, trainingLabels);
}
}
public override dynamic Calculate(dynamic data)
{
var Mean = Accord.Statistics.Measures.Mean(data);
var STD = Accord.Statistics.Measures.StandardDeviation(data);
double[] newData = ((double[])data);
newData.Apply <double, double>((double value) => {
return((value - Mean) / STD);
});
return(new double[] { newData[1], TypeCasters.SubArray <double>(newData, 1, 5).Sum(), TypeCasters.SubArray <double>(newData, 6, 10).Sum() });
}
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));
}
}