/// <summary>
/// Creates a new nuron on a given network, with a given activation
/// function and level depth. An index is required to be able to
/// refrence the nuron from outside the network.
/// </summary>
/// <param name="network">Network on wich to create the nuron</param>
/// <param name="func">Activation funciton of the nuron</param>
/// <param name="level">Level of the nuron</param>
/// <param name="index">Index of the nuron</param>
internal NuronOld(NetworkCPP network, ActFunc func, int level, int index)
{
this.network = network;
this.func = func;
this.level = level;
this.index = index;
inputs = new VListArray <Axon>();
value = 0.0;
}
/// <summary>
/// Clears all the data contained in this node. It is nessary to
/// call this when disposing of the parent network in order to
/// avoid cyclical refrences and potential memory leaks.
/// </summary>
internal void ClearData()
{
//desposes of the internal data sturctor
if (inputs != null)
{
inputs.Clear();
}
//deletes the network to avoid cyclic refrences
this.network = null;
this.inputs = null;
}
/// <summary>
/// Creates a copy of a given nuron for a given network. All of the
/// refrences to axons in the old network, now point to axons in
/// the new network. All other values remain the same.
/// </summary>
/// <param name="network">The network containing this nuron</param>
/// <param name="other">Another neuron to copy its values</param>
internal NuronOld(NetworkCPP network, NuronOld other)
{
//sets the network refrence to the new network
this.network = network;
//copies the other vlaues
func = other.func;
level = other.level;
value = other.value;
inputs = new VListArray <Axon>(other.inputs.Count);
//makes a deep copy of the list of inputs
foreach (Axon ax in other.inputs)
{
Axon clone = new Axon(ax);
this.inputs.Add(clone);
}
}
