public NeuroNet MergeNeuroNets(NeuroNet a, NeuroNet b)
{
NeuroNet nn = new NeuroNet(Inputs, Outputs);
a.Nodes.ForEach(x =>
{
if (x.ID > Inputs + Outputs)
{
nn.AddNode(new Node(x.ID));
}
});
a.Connections.ForEach(x => nn.AddConnection(x.ID, x.From, x.To, x.Weight, x.Active));
b.Nodes.ForEach(x =>
{
if (!nn.Nodes.Exists(y => y.ID == x.ID))
{
nn.AddNode(x);
}
});
b.Connections.ForEach(x =>
{
Connection con = nn.Connections.Find(y => y.From == x.From && y.To == x.To);
if (con == null)
{
nn.AddConnection(x);
}
else
{
con.Weight = (con.Weight / 2 + x.Weight / 2);
con.ID = GetNewID();
if (con.Active && !x.Active)
{
con.Active = false;
}
else if (!con.Active == !x.Active)
{
if (GetRandom(0.0, 1.0) < MergeMutationChance)
{
x.Active = !x.Active;
}
}
}
});
return(nn);
}
public NeuroNet GenerateNeuroNet(int additionalNodes = 0, int additionalConnections = 1)
{
NeuroNet res = new NeuroNet(Inputs, Outputs);
List <Node> nn = new List <Node>();
for (int i = 0; i < additionalNodes; ++i)
{
Node n = new Node(res.Nodes.Count);
nn.Add(n);
res.AddNode(n);
}
List <Node> avF = res.Nodes;
List <Node> avT = res.Nodes.FindAll(x => x.ID >= Inputs);
for (int i = 0; i < additionalNodes; ++i)
{
res.AddConnection(GetNewID(), avF[GetRandomInt(avF.Count)].ID, nn[i].ID, GetRandom(-WeightDispersion, WeightDispersion));
res.AddConnection(GetNewID(), nn[i].ID, avT[GetRandomInt(avT.Count)].ID, GetRandom(-WeightDispersion, WeightDispersion));
}
for (int i = 0; i < additionalConnections; ++i)
{
res.AddConnection(GetNewID(), avF[GetRandomInt(avF.Count)].ID, avT[GetRandomInt(avT.Count)].ID, GetRandom(-WeightDispersion, WeightDispersion));
}
return(res);
}