/// <summary>
/// Points and Movement.
/// </summary>
/// <param name="gameObject"></param>
/// <param name="id"></param>
public void UpdateCar(GameObject gameObject)
{
Rigidbody rigidbody = gameObject.GetComponent <Rigidbody>();
rigidbody.velocity = Vector3.zero;
rigidbody.angularVelocity = new Vector3(0, 0, 0);
Sensores(gameObject);
mutationUpdate();
//Update stats
//Used to create points
NeuralChildObject _stats = GetStats(gameObject);
time = Time.time - _stats.DriveTime;
if (timeLimit != 0)
{
if (time > timeLimit)
{
GameOver(gameObject, null, true);
}
}
if (_stats.AverageSpeed == 0)
{
_stats.AverageSpeed = rigidbody.velocity.magnitude;
}
else
{
_stats.AverageSpeed = (_stats.AverageSpeed + rigidbody.velocity.magnitude) / 2;
}
//End Update stats
if (newNetworkVersion)
{
try
{
results = newNetworks.Compute(sensors);
}
catch (Exception)
{
newNetworks = new NeuralNetwork.NetworkModels.Network(parameters[0], new int[] { 8, 8, 8 }, 4, 2, 1);
}
}
else
{
try
{
results = networks[_stats.Id].process(sensors);
}
catch (Exception)
{
networks[_stats.Id] = new Network(parameters);
results = networks[_stats.Id].process(sensors);
}
}
frontForce = (float)results[0];
backForce = (float)results[1];
leftForce = (float)results[2];
leftForce += (float)results[3];
rightForce = (float)results[4];
rightForce += (float)results[5];
if (frontForce >= maxSpeed)
{
frontForce = maxSpeed;
}
if (backForce >= maxSpeed)
{
backForce = maxSpeed;
}
_stats.Speed += (frontForce / 14);
_stats.Speed -= (backForce / 8) / (_stats.Speed * 16);
speed = _stats.Speed;
if (_stats.Speed <= -maxSpeed)
{
_stats.Speed = -maxSpeed;
}
else if (_stats.Speed >= maxSpeed)
{
_stats.Speed = maxSpeed;
}
_stats.DriveDistance = Vector3.Distance(gameObject.transform.position, startPosition);
_stats.LastPosition = gameObject.transform.position;
gameObject.transform.Rotate(0, (leftForce - rightForce) * 2, 0);
gameObject.transform.Translate(0, 0, _stats.Speed);
//gameObject.transform.position = Vector3.Lerp(gameObject.transform.position, new Vector3(0,0, _stats.speed), 0.5f * Time.deltaTime);
_stats.SenoreHistory.Add(sensors);
_stats.ResultHistory.Add(results);
SetStats(gameObject, _stats);
if (ignoreCollide)
{
ignoreCollide = false;
}
}
// Use this for initialization
void Start()
{
childFolder = new GameObject();
childFolder.name = "[" + transform.root.gameObject.name + "] Childs";
stats = new NeuralChildObject();
InvokeRepeating("mutationUpdate", 1, 1);
startPosition = transform.position;
startRotation = transform.rotation;
stats.LastPosition = startPosition;
ignoreCollide = ignoreFirstCollide;
Debug.Log("[" + transform.root.gameObject.name + "] Generation " + generation);
results = new double[4];
points = new double[population];
sensors = new double[10];
networks = new Network[population];
newNetworks = new NeuralNetwork.NetworkModels.Network(parameters[0], new int[] { 8, 8, 8 }, 4, 2, 1);
gameObjectsChilds = new GameObject[population];
gameObjectsChilds[0] = this.gameObject;
NeuralWeights loadedWeightsValues = null;
if (LoadLastNerual)
{
string m_Path = Application.dataPath + "/" + transform.root.gameObject.name + ".xml";
print(m_Path);
loadedWeightsValues = Load(m_Path);
if (loadedWeightsValues != null)
{
bestScore = loadedWeightsValues.bestScore;
generation = loadedWeightsValues.Generation;
print("[" + transform.root.gameObject.name + "] LoadedWeights");
}
}
if (newNetworkVersion)
{
if (loadedWeightsValues != null)
{
newNetworks.HiddenLayers = loadedWeightsValues.HiddenLayers;
newNetworks.InputLayer = loadedWeightsValues.InputLayer;
newNetworks.OutputLayer = loadedWeightsValues.OutputLayer;
}
for (int i = 1; i < population; i++)
{
//Spawn childs if wanted and can
if (spawnChild && child != null)
{
SpawnChild();
}
}
}
else
{
networks[0] = new Network(parameters);
for (int i = 1; i < population; i++)
{
//Spawn childs if wanted and can
if (spawnChild && child != null)
{
SpawnChild();
}
if (loadedWeightsValues == null)
{
networks[i] = new Network(parameters);
}
else
{
networks[i] = new Network(parameters);
networks[i].setWeights(loadedWeightsValues.weights);
networks[i] = new Network(this, networks[i]);
}
}
}
}
