public static NeuralNet ReadFromFile(string path, NeuralNet net)
{
string[] allWeightss = File.ReadAllLines(path);
List <string> keyList = new List <string>(net.weights.Keys);
int i = 0;
foreach (var key in keyList)
{
net.weights[key] = float.Parse(allWeightss[i]);
i++;
}
return(net);
}
public NeuralNet CrossWith(NeuralNet net)
{
List<string> keyList = new List<string>(weights.Keys);
NeuralNet LastNet = new NeuralNet(layers);
float thisValue;
float inputValue;
foreach (var key in keyList)
{
thisValue = this.weights[key];
inputValue = net.weights[key];
LastNet.weights[key] = (thisValue + inputValue) / 2;
}
return LastNet;
}
public void PlayBest(NeuralNet net)
{
int tmpFood = 1;
Snake snake = new Snake();
int genNum1 = food.Generate(0);
if (genNum1 == 0)
{
tmpFood++;
}
else
{
tmpFood += genNum1 + 1;
}
while (snake.IsLoose() == false)
{
snake.addFirst();
snake.removeLast();
float[] inputArr = new float[6] {
snake.IsFoodForward(), snake.IsFoodRight(), snake.IsFoodLeft(), snake.IsClearForward(), snake.IsClearRight(), snake.IsClearLeft()
};
string Direction = net.GetDirection(inputArr).ToString();
if (Direction == "Left")
{
snake.MoveLeft();
}
else if (Direction == "Right")
{
snake.MoveRight();
}
else
{
snake.KeepForward();
}
snake.hunger--;
if (Board.board[snake.nextPlace.X, snake.nextPlace.Y].whatsIn == Place.WhatsInEnum.Food)
{
Place tmpPlace = new Place();
tmpPlace.X = snake.snake.Last.Value.X;
tmpPlace.Y = snake.snake.Last.Value.Y;
tmpPlace.whatsIn = Place.WhatsInEnum.Snake;
snake.snake.AddLast(tmpPlace);
snake.score++;
snake.hunger += 50;
int genNum = food.Generate(tmpFood);
if (genNum == 0)
{
tmpFood++;
}
else
{
tmpFood += genNum + 1;
}
}
Console.Clear();
_board.Print();
Thread.Sleep(10);
}
}