/// <summary>
/// Calculate the derivatives for this training set element.
/// </summary>
///
/// <param name="pair">The training set element.</param>
/// <returns>The sum squared of errors.</returns>
private double CalculateDerivatives(IMLDataPair pair)
{
// error values
double e = 0.0d;
double sum = 0.0d;
_network.Compute(pair.Input);
int fromLayer = _network.LayerCount - 2;
int toLayer = _network.LayerCount - 1;
int fromNeuronCount = _network.GetLayerTotalNeuronCount(fromLayer);
int toNeuronCount = _network.GetLayerNeuronCount(toLayer);
double output = _network.Structure.Flat.LayerOutput[0];
e = pair.Ideal[0] - output;
for (int i = 0; i < fromNeuronCount; i++)
{
double lastOutput = _network.GetLayerOutput(fromLayer, i);
_jacobian[_jacobianRow][_jacobianCol++] = CalcDerivative(
_network.GetActivation(toLayer), output) * lastOutput;
}
while (fromLayer > 0)
{
fromLayer--;
toLayer--;
fromNeuronCount = _network.GetLayerTotalNeuronCount(fromLayer);
toNeuronCount = _network.GetLayerNeuronCount(toLayer);
// this.network.getLayerOutput(fromLayer, neuronNumber) holder.getResult().get(lastSynapse);
// for each neuron in the input layer
for (int neuron = 0; neuron < toNeuronCount; neuron++)
{
output = _network.GetLayerOutput(toLayer, neuron);
IActivationFunction function = _network.GetActivation(toLayer);
double w = _network.GetWeight(toLayer, neuron, 0);
double val = CalcDerivative(function, output)
* CalcDerivative2(function, sum) * w;
// for each weight of the input neuron
for (int i = 0; i < fromNeuronCount; i++)
{
sum = 0.0d;
// for each neuron in the next layer
for (int j = 0; j < toNeuronCount; j++)
{
// for each weight of the next neuron
for (int k = 0; k < fromNeuronCount; k++)
{
sum += _network.GetWeight(fromLayer, k, j) * output;
}
}
_jacobian[_jacobianRow][_jacobianCol++] = val
* _network.GetLayerOutput(fromLayer, i);
}
}
}
// return error
return(e);
}