public void TrainGeneration(double[][] inputs, double[][] outputs)
{
//Cross Over 80% Of Nets & Randomize 10%
int OneTenthPopulation = NeuralNets.Length / 10;
for (int i = OneTenthPopulation; i < NeuralNets.Length; i++)
{
GeneticNeuralNetwork CurrentNet = NeuralNets[i];
if (i < 9 * OneTenthPopulation)
{
CurrentNet.CrossOverAndMutate(NeuralNets[i % OneTenthPopulation], MutationRate, Rand);
}
else
{
CurrentNet.Initialize(Rand);
}
}
//Calculate Fitnesses & Sort
Parallel.For(0, NeuralNets.Length, j =>
{
CalculateFitness(NeuralNets[j], inputs, outputs);
});
Array.Sort(NeuralNets, (a, b) => a.Fitness.CompareTo(b.Fitness));
BestNetwork = NeuralNets[0];
GenerationCount++;
}
private void CalculateFitness(GeneticNeuralNetwork neuralNetwork, double[][] inputs, double[][] desiredOutputs)
{
double MeanAbsoluteError = 0;
for (int i = 0; i < inputs.Length; i++)
{
double Output = neuralNetwork.Compute(inputs[i])[0];
MeanAbsoluteError += Math.Pow(desiredOutputs[i][0] - Output, 2);
}
neuralNetwork.Fitness = MeanAbsoluteError / inputs.Length;
}
public Genetics(Random rand, NeuralNetwork modelNetwork, int populationCount, double mutationRate = 0.05)
{
//Store Neural Network Data
Rand = rand;
MutationRate = mutationRate;
//Get Network Layer Neuron Counts
NeuralNets = new GeneticNeuralNetwork[populationCount];
for (int i = 0; i < populationCount; i++)
{
NeuralNets[i] = new GeneticNeuralNetwork(modelNetwork);
}
}
public void CrossOverAndMutate(GeneticNeuralNetwork BetterNetwork, double MutationRate, Random Rand)
{
Parallel.For(0, NeuralLayers.Count, i =>
{
//Cross Over The Neurons From Each Layer At Given Cut Off Point
int Flip = Rand.Next(2);
int CutPoint = Rand.Next(NeuralLayers[i].NeuronLength);
for (int j = Flip == 0 ? 0 : CutPoint; j < (Flip == 0 ? CutPoint : NeuralLayers[i].NeuronLength); j++)
{
//Get The Neurons
Neuron CurrentNeuron = NeuralLayers[i][j];
Neuron BetterNetworkNeuron = BetterNetwork.NeuralLayers[i][j];
for (int h = 0; h < CurrentNeuron.InputDendrites.Length; h++)
{
CurrentNeuron.InputDendrites[h] = BetterNetworkNeuron.InputDendrites[h];
}
CurrentNeuron.BiasValue = (BetterNetworkNeuron.BiasValue + CurrentNeuron.BiasValue) / 2;
}
//Mutate The Crossed Over Neurons
for (int j = 0; j < NeuralLayers[i].NeuronLength; j++)
{
ActivationFunc activationFunc = NeuralLayers[i].ActivationFunc;
Neuron CurrentNeuron = NeuralLayers[i][j];
for (int h = 0; h < CurrentNeuron.InputDendrites.Length; h++)
{
if (Rand.NextDouble() < MutationRate)
{
Mutate(activationFunc, ref CurrentNeuron.InputDendrites[h], Rand);
}
}
//Mutate The Bias
if (Rand.NextDouble() < MutationRate)
{
Mutate(activationFunc, ref CurrentNeuron.BiasValue, Rand);
}
}
});
}
