public Network(Network n)
{
for (int i = 0; i < _neurons.Length; i++){
_neurons[i] = new Neuron[NETWORK_BREADTH];
for (int j = 0; j < _neurons[i].Length; j++){
_neurons[i][j] = new Neuron(n._neurons[i][j]);
}
}
}
private bool AddToRated(float rating, Network n)
{
try{
_rated.Add(rating, n);
return true;
}
catch (Exception e){
return false;
}
}
private void Breed()
{
for (int i = _survivedSize; i + 1 < _popSize - _freshSize; i += 2){
int p1 = RandomParentId(INF,true);
int p2 = RandomParentId(p1,false);
_population[i] = new Network(_population[p1]);
_population[i + 1] = new Network(_population[p2]);
Network.Crossover(_population[i], _population[i + 1]);
}
for (int i = _popSize - _freshSize; i < _popSize; i++){
_population[i] = new Network();
}
for (int i = 0; i < _popSize - _freshSize; i++){
if (Program.RandomGenerator.Next()%100 < _mutationChance){
_population[i].Mutate();
}
}
}
public static void Crossover(Network s1, Network s2)
{
for (int i = 0; i < s1._neurons.Length; i++){
for (int j = 0; j < s1._neurons[i].Length; j++){
var los = Program.RandomGenerator.Next(3);
switch (los){
case 0:
break;
case 1:
var temp = s1._neurons[i][j];
s1._neurons[i][j] = s2._neurons[i][j];
s2._neurons[i][j] = temp;
break;
case 2:
Neuron.Crossover(s1._neurons[i][j], s2._neurons[i][j]);
break;
}
}
}
}
private void InitPopulation()
{
_population = new Network[_popSize];
for (int i = 0; i < _popSize; i++){
_population[i] = new Network();
}
}
private float RateNetwork(Network network)
{
float rating = 0;
foreach (var test in _tests){
float eval = network.Calculate(test) - test[0];
rating += Math.Abs(eval);
}
return rating;
}
