public TrainPair getPair()
{
TrainPair aux = data[data.Count - 1];
data.Remove(aux);
return(aux);
}
private double error(TrainPair tp, double[] salida)
{
double aux = 0;
for (int i = 0; i < n_output; i++)
{
aux += Math.Pow(tp.getOutputs()[i] - salida[i], 2);
}
return(aux / 2);
}
public void train(TrainSet ts, double learning_rate)
{
double[,] aux_partial = new double[n_output, n_interior];
double[] salida = new double[n_output];
Tuple <double[], double[]> aux;
double[] hidden = new double[n_interior];
double total_error = 0, updated_weight;
while (!ts.finish())
{
TrainPair tp = ts.getPair();
aux = forward(tp.getInputs());
hidden = aux.Item1;
salida = aux.Item2;
total_error = error(tp, salida);
//Console.WriteLine("Current Network Error: {0}", total_error);
// Train output
for (int i = 0; i < n_output; i++)
{
for (int j = 0; j < n_interior; j++)
{
aux_partial[i, j] = -(tp.getOutputs()[i] - salida[i]) * derivada(salida[i]) * hidden[j];
updated_weight = output[i].getWeights()[j] - learning_rate * aux_partial[i, j];
output[i].updateWeight(j, updated_weight);
}
}
aux_partial = new double[n_interior, n_input];
// Train hidden
for (int i = 0; i < n_interior; i++)
{
for (int j = 0; j < n_input; j++)
{
double deltasum = 0;
for (int k = 0; k < n_output; k++)
{
deltasum += output[k].getWeights()[i] * (-(tp.getOutputs()[k] - salida[k]) * derivada(salida[k]));
}
aux_partial[i, j] = deltasum * derivada(hidden[i]) * input[j];
updated_weight = interior[i].getWeights()[j] - learning_rate * aux_partial[i, j];
interior[i].updateWeight(j, updated_weight);
}
}
}
Console.WriteLine("Net Error: " + total_error);
}
