private void GenerateInitialNetsAsCopy(NEATNet net)
{
generationNumber = 0; //0 since simulaiton has no been started yet
testCounter = 0; //none have been tested
speciesManager = new Species(this);
for (int i = 0; i < populationSize; i++)
{ //run through population size
NEATNet netCopy = new NEATNet(net); //create net with consultor, perceptron information and test time
netCopy.SetNetFitness(0f);
netCopy.SetTimeLived(0f);
netCopy.SetTestTime(testTime);
netCopy.ClearNodeValues();
netCopy.GenerateNeuralNetworkFromGenome(); //< NEW LSES ADDITION
Population population = speciesManager.ClosestSpecies(netCopy);
if (population == null)
{
population = speciesManager.CreateNewSpecie(new Color(UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f)));
population.Add(netCopy);
}
else
{
population.Add(netCopy);
}
}
}
/// <summary>
/// Generate initial neural network, where all inputs perceptrons are connected to all output perceptrons.
/// Each of these nerual networks is mutated one time for slight change.
/// And then they are paired up into species.
/// </summary>
private void GenerateInitialNets()
{
generationNumber = 0; //0 since simulaiton has no been started yet
testCounter = 0; //none have been tested
speciesManager = new Species(this);
//species = new List<List<NEATNet>>(); //create an empty population list
for (int i = 0; i < populationSize; i++)
{ //run through population size
NEATNet net = new NEATNet(consultor, numberOfInputPerceptrons, numberOfOutputPerceptrons, testTime); //create net with consultor, perceptron information and test time
net.Mutate(); //mutate once for diversity (must make sure SJW's aren't triggered)
net.GenerateNeuralNetworkFromGenome(); //< NEW LSES ADDITION
Population population = speciesManager.ClosestSpecies(net);
if (population == null)
{
population = speciesManager.CreateNewSpecie(new Color(UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f)));
population.Add(net);
}
else
{
population.Add(net);
}
/*if (species.Count == 0) { //if nothing exists yet
* List<NEATNet> newSpecies = new List<NEATNet>(); //create a new species
* newSpecies.Add(net); //add net to this new species
* species.Add(newSpecies); //add new species to species list
* }
* else { //if at least one species exists
* int numberOfSpecies = species.Count; //save species count
* int location = -1; //-1 means no species match found, 0 >= will mean a species match has been found at a given index
*
* for (int j = 0; j < numberOfSpecies; j++) { //run through species count
* int numberOfNets = species[j].Count; //number of organisum in species at index j
* int randomIndex = UnityEngine.Random.Range(0, numberOfNets); //pick a random network within this species
*
* if (NEATNet.SameSpeciesV2(species[j][randomIndex], net)) { //check if new network and random network belong to the same species
* location = j; //new species can be added to index j
* break; //break out of loop
* }
* }
*
* if (location == -1) { //if no species found
* //create new species and add to that
* List<NEATNet> newSpecies = new List<NEATNet>();
* newSpecies.Add(net);
* species.Add(newSpecies);
* }
* else { //found species that match this network
* species[location].Add(net); //add net to the matched species list
* }
* }*/
}
}
/// <summary>
/// Generate initial neural network, where all inputs neurons are connected to all output neurons.
/// Each of these nerual networks is mutated one time for slight change.
/// And then they are paired up into species.
/// </summary>
private void GenerateInitialNets()
{
generationNumber = 0; //0 since simulaiton has no been started yet
testCounter = 0; //none have been tested
speciesManager = new Species(this);
//species = new List<List<NEATNet>>(); //create an empty population list
for (int i = 0; i < populationSize; i++)
{ //run through population size
NEATNet net = new NEATNet(consultor, numberOfInputNeurons, numberOfOutputNeurons, testTime); //create net with consultor, perceptron information and test time
net.Mutate(); //mutate once for diversity (must make sure SJW's aren't triggered)
net.GenerateNeuralNetworkFromGenome(); //< NEW LSES ADDITION
Population population = speciesManager.ClosestSpecies(net);
if (population == null)
{
population = speciesManager.CreateNewSpecie(new Color(UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f)));
}
population.Add(net);
}
}
/// <summary>
/// Create new generation of net's
/// </summary>
/// <param name="maxCap"></param>
public void GenerateNewGeneration(int maxCap)
{
float totalSharedFitness = 0f; //total shared fitness of the whole population
float totalNetworks = 0f; //total number of organisums (used for distribution)
List <float> distribution = new List <float>();
for (int i = 0; i < species.Count; i++)
{
float dist = species[i].GetDistribution(manager.beta);
distribution.Add(dist);
totalSharedFitness += dist;
species[i].RemoveWorst(manager.removeWorst);
}
for (int i = 0; i < distribution.Count; i++)
{
if (totalSharedFitness <= 0f)
{
distribution[i] = 0;
}
else
{
distribution[i] = (int)((distribution[i] / totalSharedFitness) * maxCap);
}
totalNetworks += distribution[i];
}
if (maxCap > totalNetworks)
{
Debug.Log("More added: " + totalNetworks + " " + maxCap + " " + (maxCap - totalNetworks));
for (int i = 0; i < (maxCap - totalNetworks); i++)
{
int highIndex = species.Count / 2;
int randomInsertIndex = UnityEngine.Random.Range(highIndex, species.Count);
distribution[randomInsertIndex] = distribution[randomInsertIndex] + 1;
}
}
else if (maxCap < totalNetworks)
{
Debug.Log("Some removed: " + totalNetworks + " " + maxCap + " " + (maxCap - totalNetworks));
for (int i = 0; i < (totalNetworks - maxCap); i++)
{
bool removed = false;
while (removed == false)
{
int randomInsertIndex = UnityEngine.Random.Range(0, species.Count);
if (distribution[randomInsertIndex] > 0)
{
distribution[randomInsertIndex] = distribution[randomInsertIndex] - 1;
removed = true;
}
}
}
}
for (int i = species.Count - 1; i >= 0; i--)
{
if (distribution[i] <= 0 || species[i].GetPopulation().Count == 0)
{
distribution.RemoveAt(i);
species.RemoveAt(i);
}
}
totalNetworks = maxCap;
List <Population> newSpecies = new List <Population>();
for (int i = 0; i < distribution.Count; i++)
{
int newDist = (int)distribution[i];
Population popOldGen = species[i];
newSpecies.Add(new Population(species[i].GetColor()));
for (int j = 0; j < newDist; j++)
{
NEATNet net = null;
if (j > newDist * manager.elite)
{
NEATNet organism1 = popOldGen.GetRandom();
NEATNet organism2 = popOldGen.GetRandom();
net = NEATNet.Corssover(organism1, organism2);
net.Mutate();
}
else
{
NEATNet netL = popOldGen.GetLast();
if (netL == null)
{
Debug.Log(newDist + " " + popOldGen.GetPopulation().Count);
}
net = new NEATNet(popOldGen.GetLast());
}
//reset copied stats
net.SetNetFitness(0f);
net.SetTimeLived(0f);
net.SetTestTime(manager.testTime);
net.ClearNodeValues();
net.GenerateNeuralNetworkFromGenome(); //< NEW LSES ADDITION
bool added = newSpecies[i].AddIfMatch(net);
if (added == false)
{
Population foundPopulation = Species.ClosestSpecies(newSpecies, net);
if (foundPopulation == null)
{
Population newSpecie = Species.CreateNewSpecie(newSpecies, new Color(UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f), UnityEngine.Random.Range(0f, 1f)));
newSpecie.Add(net);
}
else
{
foundPopulation.Add(net);
}
}
}
}
this.species = newSpecies;
}