public NerualNetwork InitialiseCopy(int hiddenLayerCount, int hiddenNeuronCount)
{
NerualNetwork network = new NerualNetwork();
List <Matrix <float> > newWeights = new List <Matrix <float> >();
for (int i = 0; i < this.weights.Count; i++)
{
Matrix <float> currentWeight = Matrix <float> .Build.Dense(weights[i].RowCount, weights[i].ColumnCount);
for (int x = 0; x < currentWeight.RowCount; x++)
{
for (int y = 0; y < currentWeight.ColumnCount; y++)
{
currentWeight[x, y] = weights[i][x, y];
}
}
newWeights.Add(currentWeight);
}
List <float> newBiases = new List <float>();
newBiases.AddRange(biases);
network.weights = newWeights;
network.biases = newBiases;
network.InitialiseHidden(hiddenLayerCount, hiddenNeuronCount);
return(network);
}
private NerualNetwork[] PickBestPopulation()
{
NerualNetwork[] newPopulation = new NerualNetwork[population];
for (int i = 0; i < numOfBestAgentSelctions; i++)
{
newPopulation[naturalSelected] = neuralNetworkPopulation[i].InitialiseCopy(carController.neuralLayers, carController.neurons);
newPopulation[naturalSelected].fitness = 0;
naturalSelected++;
int f = Mathf.RoundToInt(neuralNetworkPopulation[i].fitness * 10);
// fitness times 10 to increase the change
// that the neuralnetwork be selected from the gene pool
for (int c = 0; c < f; c++)
{
genePool.Add(i);
}
}
for (int i = 0; i < numOfWorstAgentSelections; i++)
{
int last = neuralNetworkPopulation.Length - (i + 1);
// last -= i;
int f = Mathf.RoundToInt(neuralNetworkPopulation[last].fitness * 10);
for (int c = 0; c < f; c++)
{
genePool.Add(last);
}
}
return(newPopulation);
}
void Awake()
{
// Car accelaration and steering data initialization
accelerationRate = 1.5f;
steeringRate = 3f;
car = this.gameObject;
rb = car.GetComponent <Rigidbody2D>();
hitBorder = false;
distanceTravelled = 0;
// Record start postion and rotation
startPosition = transform.position;
startRotation = transform.eulerAngles;
lastPosition = startPosition;
// ignore car object's own collider when raycast
Physics2D.queriesStartInColliders = false;
neuralNetwork = GetComponent <NerualNetwork>();
neuralLayers = 3;
neurons = 10;
neuralNetwork.Initialize(neuralLayers, neurons);
}
private void RandomizeChildren(NerualNetwork[] networkPopulation, int startIndex)
{
while (startIndex < population)
{
networkPopulation[startIndex] = new NerualNetwork();
networkPopulation[startIndex].Initialize(carController.neuralLayers, carController.neurons);
startIndex++;
}
}
public void Death(float fitness, NerualNetwork network)
{
if (currentGenome < neuralNetworkPopulation.Length - 1)
{
neuralNetworkPopulation[currentGenome].fitness = fitness;
currentGenome++;
ResetToCurrentGenome();
}
else
{
RePopulate();
}
}
private void SortPopulation(NerualNetwork[] networks)
{
for (int i = 0; i < networks.Length; i++)
{
for (int j = i; j < networks.Length; j++)
{
if (networks[i].fitness < networks[j].fitness)
{
NerualNetwork temp = networks[i];
networks[i] = networks[j];
networks[j] = temp;
}
}
}
}
// private NerualNetwork[] CrossoverPopulation(){
// List<NerualNetwork> test = new List<NerualNetwork>(5);
// }
private void Crossover(NerualNetwork[] newPopulation)
{
// for (int i = 0; i < numberOfChildrenCrossovered; i+= 2){
for (int i = naturalSelected; i < population - 20; i++)
{
int AIndex = i;
int BIndex = i;
if (genePool.Count >= 1)
{
for (int l = 0; l < 100; l++)
// while(true)
{
AIndex = genePool[Random.Range(0, genePool.Count)];
BIndex = genePool[Random.Range(0, genePool.Count)];
if (AIndex != BIndex)
{
break;
}
}
}
NerualNetwork Child1 = new NerualNetwork();
NerualNetwork Child2 = new NerualNetwork();
Child1.Initialize(carController.neuralLayers, carController.neurons);
// Child2.Initialize(carController.neuralLayers, carController.neurons);
Child1.fitness = 0;
// Child2.fitness = 0;
for (int w = 0; w < Child1.weights.Count; w++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
Child1.weights[w] = neuralNetworkPopulation[AIndex].weights[w];
// Child2.weights[w] = neuralNetworkPopulation[BIndex].weights[w];
}
else
{
// Child2.weights[w] = neuralNetworkPopulation[AIndex].weights[w];
Child1.weights[w] = neuralNetworkPopulation[BIndex].weights[w];
}
}
for (int w = 0; w < Child1.biases.Count; w++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
Child1.biases[w] = neuralNetworkPopulation[AIndex].biases[w];
// Child2.biases[w] = neuralNetworkPopulation[BIndex].biases[w];
}
else
{
// Child2.biases[w] = neuralNetworkPopulation[AIndex].biases[w];
Child1.biases[w] = neuralNetworkPopulation[BIndex].biases[w];
}
}
newPopulation[naturalSelected] = Child1;
naturalSelected++;
// newPopulation[naturalSelected] = Child2;
// naturalSelected++;
}
}
static void Main(string[] args)
{
// объявления макета сети
int[] netMaket = new int[3];
netMaket[0] = 2;
netMaket[1] = 4;
netMaket[2] = 2;
// объявления потока для записи ошибок в эпохе
StreamWriter errorLog = new StreamWriter("log1.txt");
// Должно быть на выходе
double[][] needOut = new double[8][];
// Данные на вход
double[][] input = new double[8][];
#region dataInit
needOut[0] = new double[] { 0, 1 };
needOut[1] = new double[] { 1, 0 };
needOut[2] = new double[] { 0, 1 };
needOut[3] = new double[] { 1, 0 };
needOut[4] = new double[] { 1, 0 };
needOut[5] = new double[] { 0, 1 };
needOut[6] = new double[] { 1, 0 };
needOut[7] = new double[] { 1, 0 };
input[0] = new double[] { 1, 0 };
input[1] = new double[] { 6, 20 };
input[2] = new double[] { 2, 0 };
input[3] = new double[] { 4, 44 };
input[4] = new double[] { 6, 44 };
input[5] = new double[] { 8, 1 };
input[6] = new double[] { 10, 43 };
input[7] = new double[] { 11, 24 };
#endregion
int epohs;
// Создание конструктора сети
NetworkConstructor maket = new NetworkConstructor(netMaket);
// Объявление сети
NerualNetwork net = maket.GetNet(0.2);
//net.LoadData();
// Ввод кол-ва эпох
Console.Write("\nКоличество эпох (Больше 2000 нет смысла): ");
epohs = Convert.ToInt32(Console.ReadLine());
for (int i = 0; i < epohs; i++)
{
for (int j = 0; j < input.Length; j++)
{
net.SetData(input[j]);
net.UpdateData();
net.Learn(needOut[j]);
}
errorLog.WriteLine(net.GetError());
}
net.SaveData();
errorLog.Close();
Console.Write("Нажмите любую кнопку . . . ");
Console.ReadKey();
}
public void ResetWithNeuralNetwork(NerualNetwork network)
{
neuralNetwork = network;
Reset();
}