/// <summary>
/// Constructor
/// </summary>
/// <param name="ea"></param>
public Network(EvolutionaryAlogorithm ea, Genome genome)
{
this._ea = ea;
this._genome = genome;
this._neurons = new Neuron[this._ea.MaxNodes + this._ea.Outputs];
for (int i = 0; i < this._ea.Inputs; i++)
{
this._neurons[i] = new Neuron();
}
for (int i = 0; i < this._ea.Outputs; i++)
{
this._neurons[i + this._ea.MaxNodes] = new Neuron();
}
genome.Genes.Sort(delegate(Gene g1, Gene g2)
{
if (g1.Out == g2.Out)
{
return(0);
}
else if (g1.Out > g2.Out)
{
return(1);
}
else if (g1.Out < g2.Out)
{
return(-1);
}
else
{
return(0);
}
});
foreach (Gene gene in genome.Genes)
{
if (!gene.Enable)
{
continue;
}
if (this._neurons[gene.Out] == null)
{
this._neurons[gene.Out] = new Neuron();
}
if (this._neurons[gene.Into] == null)
{
this._neurons[gene.Into] = new Neuron();
}
this._neurons[gene.Out].Incomings.Add(gene);
}
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="ea"></param>
public Genome(EvolutionaryAlogorithm ea, Pool pool)
{
this._ea = ea;
this._pool = pool;
this._maxNeuron = this._ea.Inputs;
this._connectionsMutationChance = this._ea.ConnectionsMutationChance;
this._connectionsMutationStep = this._ea.ConnectionsMutationStep;
this._linkMutationChance = this._ea.LinkMutationChance;
this._biasMutationChance = this._ea.BiasMutationChance;
this._nodeMutationChance = this._ea.NodeMutationChance;
this._enableMutationChance = this._ea.EnableMutationChance;
this._disableMutationChance = this._ea.DisableMutationChance;
this.Mutate();
}
/// <summary>
/// Constructor (Crossover two genome)
/// </summary>
/// <param name="genome1"></param>
/// <param name="genome2"></param>
public Genome(Genome genome1, Genome genome2)
{
this._ea = genome1._ea;
this._pool = genome1._pool;
if (genome2._fitness > genome1._fitness)
{
Genome temp = genome1;
genome1 = genome2;
genome2 = temp;
}
Gene[] innovations = new Gene[this._ea.Pool.Innovation];
foreach (Gene g2 in genome2._genes)
{
innovations[g2.Innovation] = g2;
}
foreach (Gene g1 in genome1._genes)
{
Gene g2 = innovations[g1.Innovation];
if (g2 != null && g2.Enable && this._ea.Random.Next(0, 2) == 1)
{
this._genes.Add(new Gene(g2));
}
else
{
this._genes.Add(new Gene(g1));
}
}
this._maxNeuron = Math.Max(genome1._maxNeuron, genome2._maxNeuron);
this._connectionsMutationChance = genome1._connectionsMutationChance;
this._linkMutationChance = genome1._linkMutationChance;
this._biasMutationChance = genome1._biasMutationChance;
this._nodeMutationChance = genome1._nodeMutationChance;
this._enableMutationChance = genome1._enableMutationChance;
this._disableMutationChance = genome1._disableMutationChance;
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="ea"></param>
public Gene(EvolutionaryAlogorithm ea)
{
this._ea = ea;
}
/// <summary>
/// Constructor
/// </summary>
public Species(EvolutionaryAlogorithm ea)
{
this._ea = ea;
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="ea"></param>
public Pool(EvolutionaryAlogorithm ea)
{
this._ea = ea;
this._innovation = this._ea.Outputs;
}
