/// <summary>
/// Generate a random number in the specified range.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="min">The minimum value.</param>
/// <param name="max">The maximum value.</param>
/// <returns>A random number.</returns>
public static double Randomize(EncogRandom rnd, double min, double max)
{
double range = max - min;
return((range * rnd.NextDouble()) + min);
}
/// <summary>
/// Generate a random number in the specified range.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="min">The minimum value.</param>
/// <param name="max">The maximum value.</param>
/// <returns>A random number.</returns>
public static double Randomize(EncogRandom rnd, double min, double max)
{
double range = max - min;
return (range * rnd.NextDouble()) + min;
}
/// <inheritdoc/>
public void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var mom = (NEATGenome)parents[parentIndex + 0];
var dad = (NEATGenome)parents[parentIndex + 1];
var best = FavorParent(rnd, mom, dad);
var notBest = (best == mom) ? mom : dad;
var selectedLinks = new List <NEATLinkGene>();
var selectedNeurons = new List <NEATNeuronGene>();
int curMom = 0; // current gene index from mom
int curDad = 0; // current gene index from dad
NEATLinkGene selectedGene = null;
// add in the input and bias, they should always be here
int alwaysCount = ((NEATGenome)parents[0]).InputCount
+ ((NEATGenome)parents[0]).OutputCount + 1;
for (int i = 0; i < alwaysCount; i++)
{
AddNeuronId(i, selectedNeurons, best, notBest);
}
while ((curMom < mom.NumGenes) || (curDad < dad.NumGenes))
{
NEATLinkGene momGene = null; // the mom gene object
NEATLinkGene dadGene = null; // the dad gene object
long momInnovation = -1;
long dadInnovation = -1;
// grab the actual objects from mom and dad for the specified
// indexes
// if there are none, then null
if (curMom < mom.NumGenes)
{
momGene = mom.LinksChromosome[curMom];
momInnovation = momGene.InnovationId;
}
if (curDad < dad.NumGenes)
{
dadGene = dad.LinksChromosome[curDad];
dadInnovation = dadGene.InnovationId;
}
// now select a gene for mom or dad. This gene is for the baby
if ((momGene == null) && (dadGene != null))
{
if (best == dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if ((dadGene == null) && (momGene != null))
{
if (best == mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (momInnovation < dadInnovation)
{
if (best == mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (dadInnovation < momInnovation)
{
if (best == dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if (dadInnovation == momInnovation)
{
selectedGene = rnd.NextDouble() < 0.5f ? momGene : dadGene;
curMom++;
curDad++;
}
if (selectedGene != null)
{
if (selectedLinks.Count == 0)
{
selectedLinks.Add(selectedGene);
}
else
{
if (selectedLinks[selectedLinks.Count - 1]
.InnovationId != selectedGene
.InnovationId)
{
selectedLinks.Add(selectedGene);
}
}
// Check if we already have the nodes referred to in
// SelectedGene.
// If not, they need to be added.
AddNeuronId(selectedGene.FromNeuronId, selectedNeurons,
best, notBest);
AddNeuronId(selectedGene.ToNeuronId, selectedNeurons,
best, notBest);
}
}
// now create the required nodes. First sort them into order
selectedNeurons.Sort();
// finally, create the genome
var factory = (INEATGenomeFactory)_owner
.Population.GenomeFactory;
var babyGenome = factory.Factor(selectedNeurons,
selectedLinks, mom.InputCount, mom.OutputCount);
babyGenome.BirthGeneration = _owner.IterationNumber;
babyGenome.Population = _owner.Population;
babyGenome.SortGenes();
offspring[offspringIndex] = babyGenome;
}