/// <summary>
/// Build a new Genetic NeuralNetwork from the Neural Network given as parameter
/// </summary>
/// <param name="n">The neural network model</param>
public GeneticNeuralNetwork(NeuralNetwork n)
{
nn = n;
int size = 0;
for(int i=0; i<nn.N_Layers; i++)
size += (nn[i].N_Inputs+1) * nn[i].N_Neurons;
genes = new float[size];
}
/// <summary>
/// GeneticLearningAlgorithm constructor
/// </summary>
/// <param name="nn">The neural network to train</param>
public GeneticLearningAlgorithm(NeuralNetwork nn) : base(nn)
{
population = new List<GeneticNeuralNetwork>();
for(int i=0; i<POPULATION_SIZE; i++)
population.Add(Muted_NeuralNetwork);
}
/// <summary>
/// Build a new BackPropagation learning algorithm instance
/// with alpha = 0,5 and gamma = 0,3
/// </summary>
/// <param name="nn">The neural network to train</param>
public BackPropagationLearningAlgorithm(NeuralNetwork nn) : base(nn)
{
}
/// <summary>
/// Learning algorithm constructor
/// </summary>
/// <param name="n">The neural network to train</param>
public LearningAlgorithm(NeuralNetwork n)
{
nn = n;
}
/// <summary>
/// Build a new BackPropagation learning algorithm instance
/// with alpha = 0,5 and gamma = 0,3
/// </summary>
/// <param name="nn">The neural network to train</param>
public BackPropagationLearningAlgorithm(NeuralNetwork nn) : base(nn)
{
}
/// <summary>
/// Learning algorithm constructor
/// </summary>
/// <param name="n">The neural network to train</param>
public LearningAlgorithm(NeuralNetwork n)
{
nn = n;
}
