public void CrossoverWeights(NeuralNetworkController nncA, NeuralNetworkController nncB)
{
for (int r = 0; r < inputWeights.RowCount; r++)
{
for (int c = 0; c < inputWeights.ColumnCount; c++)
{
if (Random.Range(0, 2) == 1)
{
inputWeights[r, c] = nncA.inputWeights[r, c];
}
else
{
inputWeights[r, c] = nncB.inputWeights[r, c];
}
}
}
for (int r = 0; r < hiddenWeights.RowCount; r++)
{
for (int c = 0; c < hiddenWeights.ColumnCount; c++)
{
if (Random.Range(0, 2) == 1)
{
hiddenWeights[r, c] = nncA.hiddenWeights[r, c];
}
else
{
hiddenWeights[r, c] = nncB.hiddenWeights[r, c];
}
}
}
}
// -------- FUNCTIONS --------
// Start is called before the first frame update
void Start()
{
countTimeStop = 0;
// Initializing sensor parameters
sensorLength = 10f;
sensorAngle = 35f;
frontSensorPosition = 0.1f;
layer = 1 << LayerMask.NameToLayer("Circuit");
lap = 1;
// Initializing car parameters
rb = GetComponent <Rigidbody2D>();
rb.mass = 3f;
rb.drag = 1f;
rb.angularDrag = 1f;
//Checkpoints
currentCheckpoint = 1;
previewCheckpoint = 0;
// Have you finish the lap?
dataSizeTh = 1;
dataSizeSt = 1;
referee = GameObject.FindGameObjectWithTag("NNController").GetComponent <NeuralNetworkController>();
weightsThrust = referee.GetDecisionThrust();
weightsSteer = referee.GetDecisionSteer();
thetaThrust = referee.GetDecisionThrustTheta();
thetaSteer = referee.GetDecisionSteerTheta();
}
private void Crossover(NeuralNetworkController[] selectedGenomes)
{
for (int i = 0; i < selectedGenomes.Length - 1; i++)
{
NeuralNetworkController parentA = selectedGenomes[i];
NeuralNetworkController parentB = selectedGenomes[Random.Range(0, populationQuantity)];
population[i].CrossoverWeights(parentA, parentB);
population[i].CrossoverBiases(parentA, parentB);
}
}
public void CrossoverBiases(NeuralNetworkController nncA, NeuralNetworkController nncB)
{
if (Random.Range(0, 2) == 1)
{
inputBias = nncA.inputBias;
hiddentBias = nncB.hiddentBias;
}
else
{
inputBias = nncB.inputBias;
hiddentBias = nncA.hiddentBias;
}
}
private NeuralNetworkController[] RouletteWheelSelection()
{
float allFitness = 0f;
for (int i = 0; i < population.Length; i++)
{
allFitness += population[i].fitness;
}
float[] normalizedFitness = new float[populationQuantity];
float[] accumulatedFitness = new float[populationQuantity];
float allPreviousNormalizedFitness = 0f;
for (int i = population.Length - 1; i >= 0; i--)
{
normalizedFitness[i] = population[i].fitness / allFitness;
accumulatedFitness[i] = normalizedFitness[i] + allPreviousNormalizedFitness;
allPreviousNormalizedFitness += normalizedFitness[i];
}
NeuralNetworkController[] selectedGenomes = new NeuralNetworkController[populationQuantity];
for (int i = 0; i < topGenomes; i++)
{
selectedGenomes[i] = population[i];
}
int selectedIndex = topGenomes;
float selectionTreshold;
while (selectedIndex < populationQuantity)
{
selectionTreshold = Random.Range(0f, 1f);
for (int i = accumulatedFitness.Length - 1; i >= 0; i--)
{
if (accumulatedFitness[i] >= selectionTreshold)
{
selectedGenomes[selectedIndex] = population[i];
selectedIndex++;
break;
}
else if (i == 0)
{
Debug.LogError("Genome not selected with threshold: " + selectionTreshold + ", rounding problem?");
}
}
}
Array.Sort(selectedGenomes, neuralNetworkComparer);
return(selectedGenomes);
}
public void StartWithNetwork(NeuralNetworkController currentNetwork)
{
network = currentNetwork;
transform.position = initPosition;
transform.eulerAngles = initRotation;
fitness = 0f;
time = 0f;
distance = 0f;
avgSpeed = 0f;
lastPosition = initPosition;
started = true;
}
public void SetUp()
{
mockery = new Mockery();
mockNeuralNetwork = mockery.NewMock <IFeedForwardNeuralNetwork>();
testNeuralNetworkController = new NeuralNetworkController(mockNeuralNetwork);
}
