protected virtual void Reset()
{
pause = true;
Debug.Log("Entered resetting");
//Posoruj agentów pod względem funkcji przystosowania
List <GameObject> SortedList = agents.OrderByDescending(c => c.GetComponent <Fitness>().GetFitness()).ToList();
agents = SortedList;
Debug.Log("Sorted cars");
int chromosomeSize = agents[0].GetComponent <Core>().GetWeights().Length;
//Preserve car with best fitness
if (agents[0].GetComponent <Fitness>().GetFitness() > bestFitness)
{
bestFitness = agents[0].GetComponent <Fitness>().GetFitness();
bestCar = new double[chromosomeSize];
agents[0].GetComponent <Core>().GetWeights().CopyTo(bestCar, 0);
Debug.Log("New best fitness: " + bestFitness);
}
if (agents[0].GetComponent <Fitness>().GetFitness() >= 306)
{
//SavePopulation();
}
//save log
Debug.Log("Calculating avg fitness");
float avgFitness = 0;
for (int i = 0; i < agents.Count; i++)
{
avgFitness += agents[i].GetComponent <Fitness>().GetFitness();
}
avgFitness /= agents.Count;
string line = generation + " " + agents[0].GetComponent <Fitness>().GetFitness() + " " +
bestFitness + " " + avgFitness;
log.Add(line);
Debug.Log("Saving log");
SaveLog();
//Pobierz wartosci funkcji przystosowania
//od agentow
double[] fitness = new double[agents.Count];
for (int i = 0; i < agents.Count; i++)
{
fitness[i] = agents[i].GetComponent <Fitness>().GetFitness();
}
Debug.Log("Creating new children");
double[][] childrenWeights = new double[amount][];
for (int i = 0; i < amount; i++)
{
childrenWeights[i] = new double[chromosomeSize];
}
//Assign best car and best car from current generation
//childrenWeights[0] = cars[0].GetComponent<NeuralNetwork2>().GetWeights();
agents[0].GetComponent <Core>().GetWeights().CopyTo(childrenWeights[0], 0);
if (bestCar != null)
{
bestCar.CopyTo(childrenWeights[1], 0);
}
else
{
agents[1].GetComponent <Core>().GetWeights().CopyTo(childrenWeights[1], 0);
}
Debug.Log("Creating new generation");
int index = 2;
int limit = amount;
if (addNewEachGen)
{
limit = amount - 2;
}
while (index < limit)
{
//Wybierz indeksy rodzicow
int parent1index = genetics.rouletteSelect(fitness);
int parent2index = genetics.rouletteSelect(fitness);
//Upewnij sie ze rodzice nie sa tacy sami
while (parent1index == parent2index)
{
parent2index = genetics.rouletteSelect(fitness);
}
//Pobierz wagi rodzicow
double[] parent1 = agents[parent1index].GetComponent <Core>().GetWeights();
double[] parent2 = agents[parent2index].GetComponent <Core>().GetWeights();
//Dokonaj krzyzowania
double[] child1;
double[] child2;
genetics.Crossover(parent1, parent2, out child1, out child2, crossType);
child1.CopyTo(childrenWeights[index], 0);
index++;
child2.CopyTo(childrenWeights[index], 0);
index++;
//Debug.Log("Parent1: " + parent1index + ", parent2: " + parent2index);
}
while (index < amount)
{
Debug.Log("nowe typy");
double[] randWeights = agents[0].GetComponent <Core>().GetRandomWeights();
randWeights.CopyTo(childrenWeights[index], 0);
index++;
}
Debug.Log("Starting mutation");
//Mutate
for (int i = 0; i < childrenWeights.Length; i++)
{
System.Random rnd = new System.Random((int)DateTime.Now.Ticks);
if (rnd.NextDouble() < mutationProbability)
{
genetics.Mutation(childrenWeights[i], mutationProbability);
}
}
if (mutationType == Genetics.MutationType.IRREGULAR)
{
mutationProbability -= 0.0033f;
Debug.Log("Mutation probability: " + mutationProbability);
if (mutationProbability < 0.01)
{
mutationProbability = 0.01f;
}
}
for (int i = 0; i < agents.Count; i++)
{
agents[i].GetComponent <Fitness>().Reset();
agents[i].GetComponent <Core>().SetWeights(childrenWeights[i]);
agents[i].transform.position = spawnPoint.position + GetRandVec();
agents[i].transform.rotation = spawnPoint.rotation;
//cars[i].GetComponent<FitnessScript>().StartCar();
}
//agents[0].GetComponent<Renderer>().material.color = new Color(0, 1, 0);
generation++;
Debug.Log("Current generation: " + generation + ". Best fitness so far: " + bestFitness);
}