/// <summary>
/// Сгенерировать нейроны сети.
/// </summary>
/// <param name="attributes">Количество параметров входящего вектора.</param>
/// <param name="neuronsCount">Количество нейронов.</param>
public void GenerateNeurons(List <NetworkAttribute> attributes, int neuronsCount)
{
Neurons.Clear();
Weights.Clear();
InputAttributes.Clear();
InputAttributes.AddRange(attributes.Select(a => new InputAttributeBase
{
InputAttributeNumber = a.OrderNumber,
Name = a.Name
})
.ToList());
for (int i = 0; i < neuronsCount; i++)
{
Neurons.Add(new NeuronBase
{
NeuronNumber = i
});
}
foreach (var inputAttribute in InputAttributes)
{
foreach (var neuron in Neurons)
{
var randomWeight = NormalizationType.GetNeuronWeight(attributes.Count);
Weights.Add(new WeightBase
{
InputAttributeNumber = inputAttribute.InputAttributeNumber,
NeuronNumber = neuron.NeuronNumber,
Value = randomWeight
});
}
}
}
public void FromParents(Neat parent, Neat other)
{
lock (ThinkLock) {
OnRemove?.Invoke();
Neurons.Clear();
Genes.Clear();
}
if (other == null || R.NextDouble() > mutate_crossover)
{
CopyFrom(parent);
VaryColor(parent, null);
}
else
{
Dictionary <int, Gene> genes = new Dictionary <int, Gene>();
foreach (Gene gene in parent.Genes.Concat(other.Genes))
{
if (!genes.ContainsKey(gene.innovation))
{
Gene mutate = gene;
if (!mutate.enabled && R.NextDouble() < mutate_enable)
{
mutate.enabled = true;
}
genes[gene.innovation] = mutate;
}
}
int max = 0;
foreach (Gene gene in genes.Values)
{
max = Max(max, Max(gene.from, gene.axon.destination));
Genes.Add(gene);
}
addNeurons(max);
innovation = Max(parent.innovation, other.innovation);
VaryColor(parent, other);
}
MutateEnable();
Mutate();
}
protected void ConnectInnovNbNetwork()
{
if (Neurons == null)
{
Neurons = new Dictionary <int, Neuron>();
}
else
{
Neurons.Clear();
}
foreach (var neuron in NeuronLst)
{
if (!Neurons.ContainsKey(neuron.innovNb))
{
Neurons.Add(neuron.innovNb, neuron);
}
else if (Neurons[neuron.innovNb] != neuron)
{
throw new Exception("AAAAaAAAaaaaa: " + neuron.innovNb);
}
}
}
