Exemple #1
0
        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);
        }
Exemple #2
0
        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);
        }
Exemple #3
0
        public void MutateAddConnection(NeuroNet nn)
        {
            //TODO: Добавляем новую случайную связь.
            List <Node> avF = nn.Nodes;
            List <Node> avT = nn.Nodes.FindAll(x => x.ID >= Inputs);

            /*
             *
             *      int maxT = 2*(avT.Count * (avT.Count - 1) / 2) + avT.Count + (Inputs* avT.Count);
             *      int numAs = 0;
             *      foreach (Connection c in nn.Connections)
             *      {
             *          if(c.Active)
             *          {
             *              numAs++;
             *          }
             *      }
             *
             *      if(maxT < numAs)
             *      {*/
            int f = 0;
            int t = 0;

            do
            {
                f = avF[GetRandomInt(avF.Count)].ID;
                t = avT[GetRandomInt(avT.Count)].ID;
            } while (nn.Connections.Exists(x => x.From == f && x.To == t));

            Connection ex = _generationConnections.Find(x => x.From == f && x.To == t);

            if (ex == null)
            {
                Connection con = new Connection(GetNewID(), f, t, GetRandom(-WeightDispersion, WeightDispersion));
                nn.AddConnection(con);
                _generationConnections.Add(con);
            }
            else
            {
                nn.AddConnection(new Connection(ex));
            }
        }