public NeuralNetwork(int pNbInputs, int[] pNbHiddens, int pNbOutputs, ActivationFunctions.eActivationFunction pActivationFunction = ActivationFunctions.eActivationFunction.Sigmoid, bool pAllowNegative = false)
{
NbInputs = pNbInputs;
NbHiddens = pNbHiddens;
NbOutputs = pNbOutputs;
_allowNegative = pAllowNegative;
_rnd = new Random();
ActivationFunction = pActivationFunction;
_weights = new List <Matrix>();
_neurons = new List <Matrix>();
_bias = new List <Matrix>();
Matrix Weights_IH = new Matrix(pNbHiddens[0], pNbInputs);
Matrix Bias = new Matrix(pNbHiddens[0], 1);
Weights_IH.Randomize(_rnd, pAllowNegative);
Bias.Randomize(_rnd, pAllowNegative);
_weights.Add(Weights_IH);
_bias.Add(Bias);
for (int i = 0; i < pNbHiddens.Length; i++)
{
Matrix w;
Matrix b;
if (i < pNbHiddens.Length - 1)
{
w = new Matrix(pNbHiddens[i + 1], pNbHiddens[i]);
b = new Matrix(pNbHiddens[i + 1], 1);
}
else
{
w = new Matrix(pNbOutputs, pNbHiddens[i]);
b = new Matrix(pNbOutputs, 1);
}
// Poids
w.Randomize(_rnd, pAllowNegative);
_weights.Add(w);
// Biais
b.Randomize(_rnd, pAllowNegative);
_bias.Add(b);
}
}
/// <summary>
/// Créé une nouvelle génération de la population.
/// </summary>
/// <param name="pSavePopulation">Sauvegarde la population avant de générer une nouvelle.</param>
/// <param name="pPathFile">Chemin du fichier de destination.</param>
public void NextGeneration(bool pSavePopulation = false, string pPathFile = "Population")
{
int populationNumber = Genomes.Count;
#region Evaluation du score total
int totalFitness = 0;
int maxFitness = 0;
for (int i = 0; i < Genomes.Count; i++)
{
GeneticNeuralNetwork g = Genomes[i];
g.FitnessScore = (int)Math.Pow(g.FitnessScore, 4);
if (g.FitnessScore > 0)
{
totalFitness += g.FitnessScore;
}
if (g.FitnessScore > maxFitness)
{
maxFitness = g.FitnessScore;
}
}
#endregion
#region Sauvegarde la génération actuelle
if (pSavePopulation)
{
Export(totalFitness + "_" + pPathFile);
}
#endregion
// Si le Fitness max est supérieur au minimum acceptable,
// conserve le nombre de couches cachées du réseau de neurones.
// Sinon, génère un nouveau schéma de réseau de neurones.
if (maxFitness > MinimumAcceptableFitness)
{
#region Génère des enfants
List <GeneticNeuralNetwork> nextPopulation = new List <GeneticNeuralNetwork>();
while (nextPopulation.Count < populationNumber)
{
bool selected = false;
GeneticNeuralNetwork p1;
GeneticNeuralNetwork p2;
#region Sélectionne deux parents et les retire du sac
do
{
int indexP1 = _rnd.Next(Genomes.Count);
p1 = Genomes[indexP1];
if (_rnd.Next(totalFitness) < p1.FitnessScore)
{
selected = true;
}
} while (!selected);
selected = false;
do
{
int indexP2 = _rnd.Next(Genomes.Count);
p2 = Genomes[indexP2];
if (_rnd.Next(totalFitness) < p1.FitnessScore)
{
selected = true;
}
} while (!selected);
#endregion
GeneticNeuralNetwork[] childs = GeneticNeuralNetwork.CreateChilds(p1, p2, _rnd);
if (nextPopulation.Count == populationNumber - 1)
{
nextPopulation.Add(childs[0]);
}
else
{
nextPopulation.AddRange(childs);
}
}
#endregion
#region Effectue une mutation sur les enfants
for (int i = 0; i < nextPopulation.Count; i++)
{
GeneticNeuralNetwork c = nextPopulation[i];
c.Mutate(_rnd, MutationRate);
}
#endregion
OnGenomesDeletion?.Invoke(this, new PopulationManagerEventArgs(Genomes));
Genomes = nextPopulation;
}
else
{
GeneticNeuralNetwork g = Genomes[0];
int nbInputs = g.NbInputs;
int nbOutputs = g.NbOutputs;
int[] hidden = new int[_rnd.Next(1, 20)];
ActivationFunctions.eActivationFunction function = g.ActivationFunction;
for (int i = 0; i < hidden.Length; i++)
{
hidden[i] = _rnd.Next(nbOutputs, nbInputs + nbOutputs);
}
OnGenomesDeletion?.Invoke(this, new PopulationManagerEventArgs(Genomes));
Genomes = new List <GeneticNeuralNetwork>();
for (int i = 0; i < populationNumber; i++)
{
Genomes.Add(new GeneticNeuralNetwork(nbInputs, hidden, nbOutputs, function, true));
}
}
OnGenomesChanged?.Invoke(this, new PopulationManagerEventArgs(Genomes));
Generation++;
}