/// <summary>
/// Calculates the output error for each node in the target layer and all hidden layers. Note that this is a wrapper
/// method which takes a signal array, converts it to a double array, and passes that on to the method below.
/// </summary>
/// <param name="layers">The discrete layers in the ANN.</param>
/// <param name="connections">Array of all connections in the ANN.</param>
/// <param name="nodeActivationValues">The neuron activation values resulting from the last forward pass.</param>
/// <param name="targetValues">The target values against which the network is being trained.</param>
/// <param name="nodeActivationFunctions">The activation function for each neuron (this will only differ with HyperNEAT).</param>
/// <returns>The errors for each output and hidden neuron.</returns>
public static double[] CalculateErrorSignals(LayerInfo[] layers, FastConnection[] connections,
double[] nodeActivationValues, ISignalArray outputValues, ISignalArray targetValues, IActivationFunction[] nodeActivationFunctions)
{
double[] targets = new double[targetValues.Length];
// Load the target double array from the input signal array
targetValues.CopyTo(targets, 0);
// Return the error signals
return CalculateErrorSignals(layers, connections, nodeActivationValues, (MappingSignalArray) outputValues, targets, nodeActivationFunctions);
}
