public virtual double[][] getNetOutputValues(NeuralNet trainedNet)
{
int rows = trainedNet.TrainSet.Length;
int cols = trainedNet.OutputLayer.NumberOfNeuronsInLayer;
double[][] matrixOutputValues = RectangularArrays.ReturnRectangularDoubleArray(rows, cols);
switch (trainedNet.trainType)
{
case TrainingTypesENUM.BACKPROPAGATION:
Backpropagation b = new Backpropagation();
for (int rows_i = 0; rows_i < rows; rows_i++)
{
for (int cols_i = 0; cols_i < cols; cols_i++)
{
matrixOutputValues[rows_i][cols_i] = b.forward(trainedNet, rows_i).OutputLayer.ListOfNeurons[cols_i].OutputValue;
}
}
break;
default:
throw new System.ArgumentException(trainedNet.trainType + " does not exist in TrainingTypesENUM");
}
return(matrixOutputValues);
}