static void testMatrix()
{
RandomUtils random = new RandomUtils();
int matrix_P_M = 3;
int matrix_P_N = 4;
int matrix_Q_M = 4;
int matrix_Q_N = 7;
Matrix matrix_P = newMatrix(matrix_P_M, matrix_P_N, random);
Matrix matrix_Q = newMatrix(matrix_Q_M, matrix_Q_N, random);
Matrix matrix_R = (matrix_P * (matrix_Q * (~matrix_Q))) + matrix_P;
writeMatrix(matrix_P, "matrix_P");
writeMatrix(matrix_Q, "matrix_Q");
writeMatrix(matrix_R, "matrix_R");
Matrix matrix_A = newMatrix(5, 6, random);
Matrix matrix_B = newMatrix(5, 6, random);
Matrix matrix_H = MatrixUtils.hadamardProduct(matrix_A, matrix_B);
writeMatrix(matrix_A, "matrix_A");
writeMatrix(matrix_B, "matrix_B");
writeMatrix(matrix_H, "matrix_H");
Console.ReadLine();
}
public Tuple <List <Matrix>, List <Matrix> > computeSingletonGradient(Matrix X, Matrix Y)
{
List <Matrix> wSingletonGradientList = new List <Matrix>();
List <Matrix> bSingletonGradientList = new List <Matrix>();
List <Matrix> deltaList = new List <Matrix>();
//Feed forward:
Tuple <List <Matrix>, List <Matrix> > zaLists = feedForward(X);
List <Matrix> zList = zaLists.Item1;
List <Matrix> aList = zaLists.Item2;
//Back propagate:
//BP1:
deltaList.Add(
MatrixUtils.hadamardProduct(
gradientCostOutput(aList.Last(), Y),
derivativeActivation(zList.Last())
)
);
//BP2:
Matrix BP2Matrix = null;
for (int l = layerSizes.Count - 2; l > 0; l--)
{
BP2Matrix =
MatrixUtils.hadamardProduct(
(~(wList.ElementAt(l + 1))) * deltaList.Last(),
derivativeActivation(zList.ElementAt(l))
);
deltaList.Add(BP2Matrix);
}
deltaList.Add(null);
deltaList.Reverse();
//BP3, BP4:
wSingletonGradientList.Add(null);
bSingletonGradientList.Add(null);
for (int l = 1; l < layerSizes.Count; l++)
{
wSingletonGradientList.Add(deltaList.ElementAt(l) * (~(aList.ElementAt(l - 1))));
bSingletonGradientList.Add(deltaList.ElementAt(l));
}
return(Tuple.Create(wSingletonGradientList, bSingletonGradientList));
}
