BTGenotype genotypeByFullMethod(int maxDepth)
{
BTNode root = subTreeFullMethod(1, maxDepth, 0);
BTGenotype ans = new BTGenotype(root);
return(ans);
}
public void setBT(BTGenotype btGenotype, int _treeIndex)
{
treeIndex = _treeIndex;
BehaviorTree tree = gameObject.AddComponent <BehaviorTree>();
if (tree != null)
{
//Debug.Log("Creando árbol con index " + _treeIndex);
tree.StartWhenEnabled = false;
tree.GetBehaviorSource().HasSerialized = false;
EntryTask entryTask = new EntryTask();
entryTask.NodeData = new NodeData();
tree.GetBehaviorSource().EntryTask = entryTask;
//Debug.Log("Árbol con index " + _treeIndex + ": " + btGenotype.ToString());
Task rootTask = getBTfromGenotype(btGenotype.getRoot());
rootTask.NodeData = new NodeData();
tree.GetBehaviorSource().RootTask = rootTask;
tree.GetBehaviorSource().HasSerialized = true;
tree.RestartWhenComplete = true;
tree.EnableBehavior();
}
else
{
Debug.Log("No se pudo crear el árbol con index " + _treeIndex);
}
}
BTGenotype getCrossover(BTGenotype p1, BTGenotype p2)
{
BTGenotype parent1 = p1.getCopy();
BTGenotype parent2 = p2.getCopy();
/*Debug.Log("*******Performing Crossover*******");
* Debug.Log(parent1.ToString());
* Debug.Log(parent2.ToString());*/
BTNode n1 = parent1.getRandomSubTree();
BTNode n2 = parent2.getRandomSubTree();
//Debug.Log("N1: " + n1.ToString() + " N2: " + n2.ToString());
parent1.replaceSubTreeWithRoot(n1.index, n2);
parent2.replaceSubTreeWithRoot(n2.index, n1);
parent1.restructureBTIndexes();
parent2.restructureBTIndexes();
parent1.pruneGenotype(conditionals, actions);
parent2.pruneGenotype(conditionals, actions);
/*Debug.Log("*****Crossover Result*****");
* Debug.Log(parent1.ToString());
* Debug.Log(parent2.ToString());*/
if (Random.Range(0, 100) < 50)
{
return(parent1);
}
return(parent2);
}
public BTGenotype getCopy()
{
BTNode newRoot = getCopyAux(root);
BTGenotype ans = new BTGenotype(newRoot);
ans.maxDepthAllowed = maxDepthAllowed;
return(ans);
}
public BTGenotype getNextTree()
{
BTGenotype ans = population[obtainableTree];
numberOfTrees[obtainableTree]++;
obtainableTree++;
obtainableTree %= populationSize;
return(ans);
}
void recoverInitialPopulationFromFile()
{
string strPopulation = txtPopulation.text;
string[] lines = Regex.Split(strPopulation, "\n");
for (int i = 0; i < populationSize; i++)
{
string[] nodesStr = lines[i].Split('_');
population[i] = new BTGenotype(n, nodesStr, maxInitialDepth * 2);
//Debug.Log(population[i].ToString());
}
}
void recoverPopulation()
{
string populationStr = PlayerPrefs.GetString("populationStr");
string[] lines = Regex.Split(populationStr, "\n");
for (int i = 0; i < populationSize; i++)
{
string[] nodesStr = lines[i].Split('_');
population[i] = new BTGenotype(n, nodesStr, maxInitialDepth * 2);
//Debug.Log(population[i].ToString());
}
}
public int CompareTo(BTGenotype other)
{
if (other == null)
{
return(1);
}
if (fitness > other.getFitness())
{
return(1);
}
if (fitness < other.getFitness())
{
return(-1);
}
return(0);
}
void populationMutation()
{
int totalOfNodes = 0;
for (int i = 0; i < populationSize; i++)
{
totalOfNodes += population[i].numberOfNodes();
}
int nodesToModify = (int)(totalOfNodes * mutationFactor);
for (int i = 0; i < nodesToModify; i++)
{
int index = Random.Range(0, populationSize);
BTGenotype genotype = population[index];
BTNode nodeToChange = genotype.getRandomSubTree();
string newTaskName = "";
if (nodeToChange.isLeafNode())
{
int rand = Random.Range(0, 100);
if (rand < 15)
{
newTaskName = randomTerminalTaskName();
}
else
{
if (isActionTask(nodeToChange.taskName))
{
newTaskName = randomActionTaskName();
}
else
{
newTaskName = randomConditionalTaskName();
}
}
}
else
{
newTaskName = randomInternalTaskName();
}
nodeToChange.setTaskName(newTaskName);
}
}
BTGenotype getFitnessProportionate(List <BTGenotype> group)
{
double r = Random.Range(0.0f, 1.0f);
BTGenotype ans = null;
double total = 0.0f;
for (int i = 0; i < group.Count; i++)
{
total += group[i].getFitness();
if (total > r)
{
ans = group[i];
}
}
if (ans == null)
{
ans = group[group.Count - 1];
}
return(ans);
}
void evolvePopulation()
{
normalisePopulationFitness();
List <BTGenotype> list = new List <BTGenotype>();
for (int j = 0; j < populationSize; j++)
{
list.Add(population[j]);
}
list.Sort((a, b) => - 1 * a.CompareTo(b));
for (int j = 0; j < populationSize; j++)
{
population[j] = list[j];
}
// create two groups to pick genotypes based on fitness
List <BTGenotype> group1 = new List <BTGenotype>();
List <BTGenotype> group2 = new List <BTGenotype>();
int i = 0;
for (; i < populationSize * 0.2; i++)
{
group1.Add(population[i]);
}
for (; i < populationSize; i++)
{
group2.Add(population[i]);
}
BTGenotype [] temp = new BTGenotype[populationSize];
for (int j = 0; j < populationSize; j++)
{
BTGenotype parent1 = getRandomParent(group1, group2);
BTGenotype parent2 = getRandomParent(group1, group2);
temp[j] = getCrossover(parent1, parent2);
}
population = temp;
}
