public double[] FeedForward(double[] x_inp)
{
Debug.Assert(x[0].Length == x_inp.Length + 1);
x[0][0] = bias;
for (int i = 0; i < x_inp.Length; i++)
{
x[0][i + 1] = x_inp[i];
}
Debug.Assert(w.Length + 1 == x.Length);
for (int lay = 0; lay < w.Length - 1; lay++)
{
x[lay + 1][0] = bias;
for (int neu = 0; neu < w[lay].Length; neu++)
{
s[lay][neu] = MatrixArithmetics.Sum(MatrixArithmetics.PairMult(x[lay], w[lay][neu]));
x[lay + 1][neu + 1] = F(s[lay][neu]);
}
}
// the last layer has no bias in outputs
int last = w.Length - 1;
{
for (int neu = 0; neu < w[last].Length; neu++)
{
s[last][neu] = MatrixArithmetics.Sum(MatrixArithmetics.PairMult(x[last], w[last][neu]));
x[last + 1][neu] = F(s[last][neu]);
}
}
return(x[x.Length - 1]);
}
public static double CalculateError(double[] etalonOut, double[] sampleOut)
{
double[] D = MatrixArithmetics.Minus(sampleOut, etalonOut);
return(MatrixArithmetics.Sum(MatrixArithmetics.PairMult(D, D)));
}
public void Train(double[][] xCookedSamples, double[][] yCookedSamples)
{
int outputs = w[w.Length - 1].Length;
//Debug.Assert(outputs == chars.Length);
int P = xCookedSamples.GetLength(0);
double netError;
int epoch = 0;
// ready to start
do
{
double localEta = const_Eta;
netError = 0;
// passing all samples
for (int p = 0; p < P; p++)
{
double[] y = FeedForward(xCookedSamples[p]);
double[] D = MatrixArithmetics.Minus(y, yCookedSamples[p]);
netError += MatrixArithmetics.Sum(MatrixArithmetics.PairMult(D, D));
// do the last layer
int last = w.Length - 1;
for (int j = 0; j < outputs; j++)
{
d[last][j] = D[j] * dF(s[last][j]);
}
// do inner layers
for (int lay = last - 1; lay >= 0; lay--)
{
for (int j = 0; j < d[lay].Length; j++)
{
double sum_dw = 0;
for (int k = 0; k < d[lay + 1].Length; k++)
{
sum_dw += d[lay + 1][k] * w[lay + 1][k][j];
}
// d = dF(s) * Sum(d * w)
d[lay][j] = dF(s[lay][j]) * sum_dw;
}
}
for (int lay = 0; lay < w.Length; lay++)
{
for (int neu = 0; neu < w[lay].Length; neu++)
{
for (int i = 0; i < w[lay][neu].Length; i++)
{
w[lay][neu][i] -= localEta * d[lay][neu] * x[lay][i];
}
}
}
}
epoch++;
//if (backgroundWorkerTrain.CancellationPending)
// goto stopTrain;
//if (epoch % 5 == 0)
//{
// backgroundWorkerTrain.ReportProgress(epoch);
// reportEpoc = epoch;
// reportError = netError;
//}
}while (Math.Abs(netError) > eps && epoch < maxEpoch);
//stopTrain:
//reportEpoc = epoch;
//reportError = netError;
//Debug.Print("Trainig finished with Error: {0}, at epoch #{1}", netError, epoch);
}
