// ====== METHODS ==================
//given two indiv A & B, this method will choose a random layer and a random neuron position in that layer and then do a crossover
//by cuting A and B at this pos and mixing them together, and it will return a list containing the 2 mixed indivs
public List<indiv> crossover(indiv A, indiv B)
{
int layer_num_Crossingover = rand.Next(1, A.getNbLayer()-1);//in the first layer all weigths are 1 and can't change so no need to crossover at this layer
int Neuron_num_Crossingover = rand.Next(0, A.getNbNeuronAtLayer(layer_num_Crossingover)-1);
List<Nn.Neuron> layer_CrossingoverA = new List<Nn.Neuron>(A.getLayer(layer_num_Crossingover).GetRange(0, Neuron_num_Crossingover));
layer_CrossingoverA.AddRange(B.getLayer(layer_num_Crossingover).GetRange(Neuron_num_Crossingover, B.getNbNeuronAtLayer(layer_num_Crossingover) - Neuron_num_Crossingover));
List<Nn.Neuron> layer_CrossingoverB = new List<Nn.Neuron>(B.getLayer(layer_num_Crossingover).GetRange(0, Neuron_num_Crossingover));
layer_CrossingoverB.AddRange(A.getLayer(layer_num_Crossingover).GetRange(Neuron_num_Crossingover, A.getNbNeuronAtLayer(layer_num_Crossingover) - Neuron_num_Crossingover));
List<List<Nn.Neuron>> NnA2 = new List<List<Nn.Neuron>>(A.getLayersRange(0, layer_num_Crossingover));
NnA2.Add(layer_CrossingoverA);
NnA2.AddRange(B.getLayersRange(layer_num_Crossingover+1, B.getNbLayer()));
List<List<Nn.Neuron>> NnB2 = new List<List<Nn.Neuron>>(B.getLayersRange(0, layer_num_Crossingover));
NnB2.Add(layer_CrossingoverB);
NnB2.AddRange(A.getLayersRange(layer_num_Crossingover+1, A.getNbLayer()));
indiv A2 = new indiv(NnA2);
indiv B2 = new indiv(NnB2);
List<indiv> newIndivs = new List<indiv>();
newIndivs.Add(A2);
newIndivs.Add(B2);
return newIndivs;
}
public void Init(indiv saved_neuroNet, GameObject mySelf, bool color = false)
{
neuroNet = saved_neuroNet;
this.mySelf = mySelf;
this.color = color;
//Error gestion
if(saved_neuroNet.getNbNeuronAtLayer(0) != 18)
{
Debug.Log("Initialized with a Neural network with wrong first layer size : "+ saved_neuroNet.getNbNeuronAtLayer(0));
throw new System.Exception();
}
isInit = true;
}
//copy an indiv but not its evalValue
public indiv(indiv I, double eval)
{
this.Nn = new Nn.Neural_network(I.getNn());
evalValue = eval;
}
// copy an indiv
public indiv(indiv I)
{
this.Nn = new Nn.Neural_network(I.getNn());
evalValue = I.getEvalValue();
}
//this method will choose a random weigth of a random neuron of a random layer and modify it by a random number between -0.1 and 0.1
public indiv mutate(indiv A)
{
int layer_num_mutation = rand.Next(1, A.getNbLayer());//weights of the first layer must stay 1
int Neuron_num_mutation = rand.Next(0, A.getNbNeuronAtLayer(layer_num_mutation));
int weigth_num_mutation = rand.Next(0, A.getNeuron(layer_num_mutation, Neuron_num_mutation).getNbWeigths());
double mutation = (rand.NextDouble()*2-1); //mutation between -0.1 and 0.1
indiv mutator = new indiv(A, -1);
double newWeigth = A.getNeuron(layer_num_mutation, Neuron_num_mutation).getWeigth(weigth_num_mutation) + mutation;
if (newWeigth > 1) newWeigth = 1;
else if (newWeigth < -1) newWeigth = -1;
mutator.setDataAtPos(layer_num_mutation, Neuron_num_mutation, weigth_num_mutation, newWeigth);
return mutator;
}
