void Start()
{
bird = GetComponent <Bird>();
neuralNet = new NeuralNetwork(GameConstants.defaultLearningRate);
neuralNet.AddDenseLayer(2, new ActivationFunction(Utils.ReLU));
neuralNet.AddDenseLayer(6, new ActivationFunction(Utils.ReLU));
neuralNet.AddDenseLayer(1, new ActivationFunction(Utils.Sigmoid));
neuralNet.FinishBuilding(Utils.LinearError);
this.dna = GameManager.instance.GetANewBirdDNA();
// if we dont have a valid DNA from the DNA Generator
if (dna == null)
{
this.dna = new BirdDNA(neuralNet.GetWeights(), neuralNet.GetBiases());
}
else
{
// we are here if we DID got a new DNA from the DNA Generator so we should actually apply it to the neural network
this.neuralNet.SetWeights(this.dna.weightsOfNeuralNetwork);
this.neuralNet.SetBiases(this.dna.biasesOfNeuralNetwork);
}
// we are adding this Evo bird to the static array that holds all Evo birds
instances.Add(this);
}
/// <summary>
/// A Simple DNA Generator based on the best DNA's that were before it
/// </summary>
/// <returns>A new Bird's DNA to use, at first will be null, then will generate better ones</returns>
public BirdDNA GetANewBirdDNA()
{
// MAIN LEARNING IS HERE AT GENERATING A BETTER BIRD'S DNA EACH GENERATION
BirdDNA birdDNAToReturn = null;
if (choosedBirdsDNAPool.Count < 2)
{
birdDNAToReturn = null;
}
else
{
// cross over the two dna's and loading the new bird's dna
birdDNAToReturn = BirdDNA.CrossOver(choosedBirdsDNAPool[0].val2, choosedBirdsDNAPool[1].val2);
// mutating the new dna a bit
birdDNAToReturn.Mutate(GameConstants.defaultMutationChance);
}
return(birdDNAToReturn);
}
// This function recieves a new dna and loads it to bird and its the neural network
private void LoadNewBirdDNA(BirdDNA newDna)
{
this.dna = newDna;
this.neuralNet.SetWeights(this.dna.weightsOfNeuralNetwork);
this.neuralNet.SetBiases(this.dna.biasesOfNeuralNetwork);
}
// static function to crossing over two bird dna
// crossing over meaning taking half dna of each bird
public static BirdDNA CrossOver(BirdDNA bird1, BirdDNA bird2)
{
// validating data dimensions
if (bird1.weightsOfNeuralNetwork.Count != bird2.weightsOfNeuralNetwork.Count)
{
throw new DataDimensionsMismatchException();
}
if (bird1.biasesOfNeuralNetwork.Count != bird2.biasesOfNeuralNetwork.Count)
{
throw new DataDimensionsMismatchException();
}
// initializing the new weights array
// now doing cross over algorithm, taking 1 info from bird and 1 from bird2
List <List <List <double> > > weightsInNewDNA = new List <List <List <double> > >();
// looping each layer and adding its weights to the 2D array
for (int curLayerIndex = 0; curLayerIndex < bird1.weightsOfNeuralNetwork.Count; curLayerIndex++)
{
List <List <double> > curLayerNodesDna = new List <List <double> >();
// looping each weight in a specific layer and adding its weight to the 2D array
for (int curNodeIndex = 0; curNodeIndex < bird1.weightsOfNeuralNetwork[curLayerIndex].Count; curNodeIndex++)
{
List <double> curNodeWeightsDna = new List <double>();
for (int curNodeWeightIndex = 0; curNodeWeightIndex < bird1.weightsOfNeuralNetwork[curLayerIndex][curNodeIndex].Count; curNodeWeightIndex++)
{
// add here a weight of either from bird1 or from bird2, one from each one or randomly
// returns a number which is between 0 and 1
if (Utils.randomGenerator.NextDouble() > 0.5)
{
curNodeWeightsDna.Add(bird1.weightsOfNeuralNetwork[curLayerIndex][curNodeIndex][curNodeWeightIndex]);
}
else
{
curNodeWeightsDna.Add(bird2.weightsOfNeuralNetwork[curLayerIndex][curNodeIndex][curNodeWeightIndex]);
}
}
curLayerNodesDna.Add(curNodeWeightsDna);
}
weightsInNewDNA.Add(curLayerNodesDna);
}
// initializing the new biases array
List <List <double> > biasesInNewDNA = new List <List <double> >();
// looping each layer in the neural network
for (int curLayerIndex = 0; curLayerIndex < bird1.biasesOfNeuralNetwork.Count; curLayerIndex++)
{
List <double> biasesOfNodesInCurLayer = new List <double>();
// looping each bias in a specific layer and adding its bias to the 1D array
for (int curBiasIndex = 0; curBiasIndex < bird1.biasesOfNeuralNetwork[curLayerIndex].Count; curBiasIndex++)
{
// add here a weight of either from bird1 or from bird2, one from each one or randomly
if (Utils.randomGenerator.NextDouble() > 0.5)
{
biasesOfNodesInCurLayer.Add(bird1.biasesOfNeuralNetwork[curLayerIndex][curBiasIndex]);
}
else
{
biasesOfNodesInCurLayer.Add(bird2.biasesOfNeuralNetwork[curLayerIndex][curBiasIndex]);
}
}
biasesInNewDNA.Add(biasesOfNodesInCurLayer);
}
// creating and returning the new bird dna that was cross over between the two dna's
BirdDNA birdDNAToReturn = new BirdDNA(weightsInNewDNA, biasesInNewDNA);
return(birdDNAToReturn);
}
