// converts NN data to serializable object
public SerializableWeights(WeightsInfo weights)
{
weights1 = new List <List <double> >();
weights2 = new List <List <double> >();
for (int i = 0; i < weights.weights1.GetLength(0); i++)
{
var weightsList = new List <double>();
for (int j = 0; j < weights.weights1.GetLength(1); j++)
{
weightsList.Add(weights.weights1[i, j]);
}
weights1.Add(weightsList);
}
for (int i = 0; i < weights.weights2.GetLength(0); i++)
{
var weightsList = new List <double>();
for (int j = 0; j < weights.weights2.GetLength(1); j++)
{
weightsList.Add(weights.weights2[i, j]);
}
weights2.Add(weightsList);
}
}
private static void EventClosed(object sender, EventArgs e)
{
//Environment.Exit(0);
mainWindow.Close();
gameObjects = new List <GameObject>();
rand = new Random();
playerAlive = true;
playerPoints = 0;
playerBird = null;
isPlayerPlaying = false;
numberOfAliveBirds = 0;
deadBirds = new List <Bird>();
highScorePoints = 0;
isPretrained = false;
bestNetworkWeightsAlltime = null;
}
// converts back into NN data
public WeightsInfo ToWeightsInfo()
{
WeightsInfo weights = new WeightsInfo();
weights.weights1 = new double[NeuralNetwork.inputSize, NeuralNetwork.hiddenSize];
weights.weights2 = new double[NeuralNetwork.hiddenSize, NeuralNetwork.outputSize];
int i = 0;
int j = 0;
foreach (var w1 in weights1)
{
j = 0;
foreach (double weight in w1)
{
weights.weights1[i, j] = weight;
j++;
}
i++;
}
i = 0;
foreach (var w2 in weights2)
{
j = 0;
foreach (double weight in w2)
{
weights.weights2[i, j] = weight;
j++;
}
i++;
}
return(weights);
}
public void SetGenes(WeightsInfo weights)
{
Network.Weights = new WeightsInfo(weights.weights1, weights.weights2);
}
private static void NewGeneration()
{
gameObjects.Clear();
// update scores, highscore and keep track of the best performing bird from all previous generations
double maxScore = 0;
double scoreSum = 0;
foreach (var bird in deadBirds)
{
if (bird.Points > maxScore)
{
maxScore = bird.Points;
bestNetworkWeightsAlltime = new WeightsInfo(bird.brain.Network.Weights.weights1, bird.brain.Network.Weights.weights2);
}
scoreSum += bird.Points;
}
// add birds with their pick probability depending on their score
List <KeyValuePair <Bird, double> > elements = new List <KeyValuePair <Bird, double> >();
foreach (Bird bird in deadBirds)
{
elements.Add(new KeyValuePair <Bird, double>(bird, bird.Points / scoreSum));
}
// pick new birds, higher scoring birds have greater chances of being picked
Bird pickedBird = null;
for (int i = 0; i < WorldRules.BirdsPerGeneration - 1; i++)
{
Bird bird = new Bird();
double diceRoll = rand.NextDouble();
double cumulative = 0.0;
for (int j = 0; j < elements.Count; j++)
{
cumulative += elements[j].Value;
if (diceRoll < cumulative)
{
pickedBird = elements[j].Key;
break;
}
}
pickedBird.brain.Network.Mutate();
bird.brain.SetGenes(pickedBird.brain.GetGenes());
bird.brain = pickedBird.brain;
// in case all birds do very poorly, reset every bird to a new network
if (maxScore < 200)
{
bird = new Bird();
}
gameObjects.Add(bird);
playerBird = bird;
}
// elitism, the best bird from all generations is always picked
Bird bestBirdAlltime = new Bird();
bestBirdAlltime.brain.SetGenes(bestNetworkWeightsAlltime);
gameObjects.Add(bestBirdAlltime);
numberOfAliveBirds = WorldRules.BirdsPerGeneration;
// we reset the whole run, this includes new pipes
var pipe = new Pipe();
pipe.Position += new Vector2f(0, 0);
gameObjects.Add(pipe);
pipe = new Pipe();
pipe.Position += new Vector2f(333, 0);
gameObjects.Add(pipe);
pipe = new Pipe();
pipe.Position += new Vector2f(666, 0);
gameObjects.Add(pipe);
playerAlive = true;
deadBirds.Clear();
}