/// <summary>
/// Obliczanie błędu
/// </summary>
/// <param name="trData">Dane treningowe</param>
/// <returns>Macierz</returns>
private double[,] ErrorCalculate(TrainingData trData)
{
double[,] y = MatrixHelper.ConvertToMatrix(trData.output);
var ysim = Propagate(trData.input);
return(MatrixHelper.MatrixSubstraction(y, ysim));
}
/// <summary>
/// Metoda propagacji - sporządza predykcję wektora wejściowego
/// </summary>
/// <param name="td"></param>
/// <returns></returns>
public double[,] Propagate(params double[] data)
{
trData = new TrainingData(data, new double[] { });
double[,] inputData = MatrixHelper.ConvertToMatrix(trData.input);
layerList[0].values = inputData;
for (int i = 1; i < layerList.Length; i++)
{
inputData = MatrixHelper.MatrixMultiply(inputData, layerList[i].perceptron.scalesMatrix);
inputData = ExecuteActivationFunction(inputData, layerList[i].perceptron.activationFunction);
layerList[i].values = inputData;
}
return(inputData);
}
