/// <summary>
/// Helper mehtod: Selects a pair of nurons at random from diffrent
/// levels of the network. The nurons are always listed in order. It
/// returns false if it was ubale to generate a valid pair.
/// </summary>
/// <param name="n1">The lower level nuron</param>
/// <param name="n2">The upper level nuron</param>
private bool RandPair(out NuronOld n1, out NuronOld n2)
{
//generates two random nurons
n1 = RandNuron();
n2 = RandNuron();
int count = 0;
//keeps searching while the nurons are on the same level
while (n1.Level == n2.Level && count < MAX_TRY)
{
NuronOld n3 = RandNuron();
n1 = n2;
n2 = n3;
count++;
}
//swaps the nurons if they are out of order
if (n1.Level > n2.Level)
{
NuronOld n3 = n1;
n1 = n2;
n2 = n3;
}
//indicates if we found a valid pair
return(count < MAX_TRY);
}
/// <summary>
/// Adds a nuron to the network at the given level and with the given
/// activation function. It returns null if it was unable to add the nuron.
/// </summary>
/// <param name="func">Activation function of the nuron</param>
/// <param name="level">Level of the nuron</param>
/// <returns>The inserted nuron</returns>
public NuronOld AddNuron(ActFunc func, int level)
{
//clamps the level to be within range
if (level > MAX_LV)
{
level = MAX_LV;
}
if (level < 0)
{
level = 0;
}
//tries to generate a random index
int index = RandIndex();
if (index < 0)
{
return(null);
}
//creates the node and adds it
NuronOld n = new NuronOld(this, func, level, index);
nurons.Add(index, n);
return(n);
}
/// <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>
/// Clones a given network by make a deep copy of the internal
/// structor, so that the clone may be manipulated without effecting
/// the original. It also replaces the internal random number generator
/// with a potentialy diffrent generator.
/// </summary>
/// <param name="rng">Random number generator</param>
/// <param name="other">Network to clone</param>
private NetworkCPP(VRandom rng, NetworkCPP other)
{
//copies the RNG by refrence
this.rng = rng;
//creates a new table with the same size
int size = other.nurons.Buckets;
nurons = new TableOpen <Int32, NuronOld>(size);
//makes a deep copy of the former network's structor
foreach (var pair in other.nurons)
{
NuronOld copy = new NuronOld(this, pair.Item);
nurons.Add(pair.Key, copy);
}
}
/// <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>
/// Preterbs the current neural net by some random amount without
/// creating a clone. The rate of mutaiton determins how many of the
/// network connections are preturbed. For exampe, a mutation rate
/// of 0.5 indicates that half the weights will be perturbed.
/// </summary>
/// <param name="rate">Rate of mutation</param>
public void MutateSelf(double rate)
{
//clamps the rate to be between zero and one
rate = VMath.Clamp(rate);
if (rng.RandBool(P_Node))
{
//changes the activation funciton of a single node
NuronOld node = RandNuron();
node.Func = RandFunc();
return;
}
//lists all the axons in the network
var axons = ListAxons();
foreach (Axon ax in axons)
{
//mutates weights based on the rate of mutation
if (rng.RandBool(1.0 - rate))
{
continue;
}
if (ax.Enabled)
{
//permutes the weight by a small amount
double delta = rng.RandGauss() * SDS;
ax.Weight = ax.Weight + delta;
}
else
{
//resets the neuron to a small weight
ax.Weight = rng.RandGauss() * SDS;
ax.Enabled = true;
}
}
}
