public void Train(double[][] learnInputData, double[][] learnOutputData, double alpha, double eps, int maxEpoch, bool log = false, int logInterval = 1000)
{
double error;
long epoch = 0;
do
{
error = 0;
for (int p = 0; p < learnInputData.Length; p++)
{
double[] results = GetOutputs(learnInputData[p]);
double[] sigmas = new double[outputs];
for (int i = 0; i < outputs; i++)
{
sigmas[i] = learnOutputData[p][i] - results[i];
error += sigmas[i] * sigmas[i];
}
double[][] deltas = new double[hiddenNeuronsSize.Length][];
for (int i = hiddenNeuronsSize.Length - 1; i >= 0; i--)
{
if (i == hiddenNeuronsSize.Length - 1)
{
deltas[i] = hiddenLayers[i].GetErrors(outputLayer, sigmas);
}
else
{
deltas[i] = hiddenLayers[i].GetErrors(hiddenLayers[i + 1], deltas[i + 1]);
}
}
for (int layer = 0; layer < hiddenNeuronsSize.Length; layer++)
{
for (int i = 0; i < hiddenNeuronsSize[layer]; i++)
{
for (int j = 0; j < hiddenLayers[layer].inputsSize; j++)
{
double weight = hiddenLayers[layer].GetWeight(j, i);
double output = layer == 0 ? inputLayer.GetOutput(j) : hiddenLayers[layer - 1].GetOutput(j);
double gradient = hiddenLayers[layer].GetDerivativeOutput(i);
hiddenLayers[layer].SetWeight(j, i, weight + alpha * deltas[layer][i] * output * gradient);
}
}
}
for (int i = 0; i < outputs; i++)
{
for (int j = 0; j < hiddenNeuronsSize[hiddenNeuronsSize.Length - 1]; j++)
{
double weight = outputLayer.GetWeight(j, i);
double output = hiddenLayers[hiddenNeuronsSize.Length - 1].GetOutput(j);
outputLayer.SetWeight(j, i, weight + alpha * sigmas[i] * output);
}
}
}
if (log && epoch % logInterval == 0)
{
Log(Math.Sqrt(error), epoch);
}
epoch++;
} while (Math.Sqrt(error) > eps && epoch < maxEpoch);
}