//public Axon AddAxon(Nuron input, double weight)
//{
// if (input.Level > this.Level)
// {
// //swaps the nurons if the level is swaped
// return input.AddAxon(this, weight);
// }
// //enshures that the nurons are on diffrent levels
// if (input.Level == this.Level) throw new
// InvalidOperationException();
// //enshures that the nurons belong to the same network
// if (input.network != this.network) throw new
// InvalidOperationException();
// //calls upon the internal method
// return AddAxonInit(input, weight);
//}
#endregion ////////////////////////////////////////////////////////////////////
#region Internal Methods...
/// <summary>
/// Connects the given input nuron to the current nuron with the
/// given weight. Note that it dose not check the topology of the
/// network before adding the axon, so care must be taken to avoid
/// redundent connections. If a connection already exists between
/// the two nurons, it returns that axon instead.
/// </summary>
/// <param name="input">Nuron to conect</param>
/// <param name="weight">Weight of the connection</param>
/// <returns>The axon between the nurons</returns>
internal Axon AddAxonInit(NuronOld input, double weight)
{
////enshures the nurons belong to the same network
//if (network == null || network != input.network)
// throw new InvalidOperationException();
//makes shure our parent network hasn't been disposed
if (network == null)
{
throw new InvalidOperationException();
}
//sees if we alreay contain a link to the input
Axon ax = GetAxon(input.Index);
if (ax == null)
{
//obtains an index to the sorce and target
int source = this.Index;
int target = input.Index;
//creates the axon and adds it to our list
ax = new Axon(source, target, weight);
inputs.Add(ax);
}
else
{
//sets the axons weight to our new weight
ax.Weight = weight;
}
return(ax);
}
/// <summary>
/// Compares the current genotype to the genotype of a diffrent
/// nural net. The result is a positive real value that indicates
/// how siimilar the genotypes are. This is tipicaly used to
/// seperate a population of individules into species.
/// </summary>
/// <param name="other">Nural net for comparison</param>
/// <returns>Mesure of similarity</returns>
public double Compare(NetworkCPP other)
{
//used in computing the distance
int match = 0;
int disjoint = 0;
double wbar = 0.0;
//lists the axons in each of the networks
var ittr1 = this.ListAxons();
var ittr2 = other.ListAxons();
foreach (Axon ax1 in ittr1)
{
//tries to find the matching axon
Axon ax2 = other.FindMatch(ax1);
if (ax2 != null)
{
//computes the distance between the weights
double w1 = ax1.Weight;
double w2 = ax2.Weight;
wbar += Math.Abs(w1 - w2);
match += 1;
}
else
{
//counts the disjoint nodes
disjoint += 1;
}
}
foreach (Axon ax1 in ittr2)
{
//only counts the missing disjoint edges
Axon ax2 = other.FindMatch(ax1);
if (ax2 == null)
{
disjoint += 1;
}
}
//determins the size of the larger network
int size = Math.Max(this.Axons, other.Axons);
size = (size > 20) ? size - 20 : 1;
//couputes the distance for specisation
double dist = (C0 * disjoint) / (double)size;
dist += C1 * (wbar / (double)match);
return(dist);
}
/// <summary>
/// Helper method: Finds an axon in the curent network that matches a
/// target axon from another network. If no sucth match can be found,
/// it returns null instead.
/// </summary>
/// <param name="other">Target axon</param>
/// <returns>A matching axon</returns>
private Axon FindMatch(Axon other)
{
//first atempts to find the target nuron
NuronOld n1 = nurons.GetValue(other.Target);
if (n1 == null)
{
return(null);
}
//obtains the axon from the target
return(n1.GetAxon(other.Source));
}
/// <summary>
/// Expands the curent nural net, in place, by either inserting a
/// new node along a pre-existing edge or creating a new edge. The
/// probibility of either event occoring is determined by the
/// existing topology.
/// </summary>
public void ExpandSelf()
{
NuronOld n1, n2;
//generates a pair of random nurons
bool test = RandPair(out n1, out n2);
Axon target = n2.GetAxon(n1.Index);
if (test && target == null)
{
//creates a new axon between the nurons
double weight = rng.RandGauss() * SDN;
n2.AddAxonInit(n1, weight);
}
else if (test && !target.Enabled)
{
//reinitilises the axon with a new weight
target.Weight = rng.RandGauss() * SDN;
target.Enabled = true;
}
else if ((n2.Level - n1.Level) > 3)
{
//disables the target edge
double weight = target.Weight;
target.Enabled = false;
//creates a new node with a random funciton
int index = RandIndex();
int level = (n2.Level + n1.Level) / 2;
ActFunc func = RandFunc();
//aborts the operation if we fail to insert
if (index < 0)
{
return;
}
//inserts the node into our data-structor
NuronOld nx = new NuronOld(this, func, level, index);
nurons.Add(index, nx);
//adds axons to replace the missing axon
nx.AddAxonInit(n1, 1.0);
n2.AddAxonInit(nx, weight);
}
}
/// <summary>
/// Combines the genes of the curent nural net with the genes of
/// another network to create a brand new offspring. The idea is
/// that the child network will possess trates from both its
/// parents, similar to sexual reproduction.
/// </summary>
/// <param name="rng">Random number generator</param>
/// <param name="mate">Mate of the curent network</param>
/// <returns>The child of both networks</returns>
public NetworkCPP Combine(VRandom rng, NetworkCPP mate)
{
//makes a clone of the dominate parent
NetworkCPP child = new NetworkCPP(rng, this);
//determins weather or not to do liniar crossover
bool liniar = rng.RandBool(P_Linear);
double a = rng.NextDouble();
//lists all the axons in the mate
var axons = mate.ListAxons();
foreach (Axon ax in axons)
{
//obtains the matching child axon
Axon axc = child.FindMatch(ax);
if (axc == null)
{
continue;
}
if (liniar)
{
//chooses a value between the two weights
double weight = axc.Weight * (1.0 - a);
axc.Weight = weight + (ax.Weight * a);
}
else
{
//determins the new weight based on crossover
bool cross = rng.RandBool();
if (cross)
{
axc.Weight = ax.Weight;
}
}
//has a chance of enabling if either are disabled
bool en = ax.Enabled && axc.Enabled;
axc.Enabled = en || rng.RandBool(0.25);
}
return(child);
}
/// <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);
}
}
