public IGenome <CNeuroNetWrapper> ProceedNonStructuralMutation()
{
NeuroNetGenome copy = new NeuroNetGenome(this);
foreach (var c in copy._neuroNet.Connections)
{
do
{
double ch = GetRandom(copy._nonStructuralMutations);
if (Math.Abs(c.Weight + ch) < 2)
{
c.Weight += ch;
}
else
{
if (Math.Abs(c.Weight - ch) < 2)
{
c.Weight -= ch;
}
}
} while (c.Weight > 2 || c.Weight < -2);
}
copy._nonStructuralMutations += 1;
copy._totalnonStructuralMutations += 1;
return(copy);
}
public static double TestFunction(NeuroNetGenome gen)
{
double result = 0.0;
const int maxTests = 10;
const double timestep = 0.02;
for (int i = 0; i < maxTests; ++i)
{
gen.Flush();
Random r = new Random(123454321 + i);
double difficultyCoef = i / (maxTests - 1.0);
Tester t = new Tester(
(r.NextDouble() - r.NextDouble()) * (Math.PI / 3) * (difficultyCoef + 0.1),
r.NextDouble() * 5 + 1,
(r.NextDouble() - r.NextDouble()) * (Math.PI / 4) * (difficultyCoef + 0.1),
r.NextDouble() * 5 + 1,
10 * (r.NextDouble() + 1) * (1.1 - difficultyCoef),
(difficultyCoef + 1) * (r.NextDouble() + 1) * 5);
do
{
double[] calcs = gen.Calculate(new double[] { t.rotation, t.rotation2, t.position, });
double cont = (calcs[0] - 0.5) * 2;
t.Update(timestep, cont);
} while (t.totalTime < 50.0 && Math.Abs(t.rotation) < (Math.PI / 2) && Math.Abs(t.rotation2) < (Math.PI / 2));
result += t.totalTime;
}
return(result);
}
public NeuroNetGenome(NeuroNetGenome copy)
{
this._neuroNet = new CNeuroNetWrapper(copy._neuroNet);
this._nonStructuralMutations = copy._nonStructuralMutations;
this._structuralMutations = copy._structuralMutations;
this._totalnonStructuralMutations = copy._totalnonStructuralMutations;
this.LastResult = LastResult;
}
public IGenome <CNeuroNetWrapper> ProceedStructuralMutation()
{
NeuroNetGenome copy = new NeuroNetGenome(this);
CNeuroNetWrapper.Connection con = null;
int maxCon = copy._neuroNet.Nodes.Count * (copy._neuroNet.Nodes.Count - copy._neuroNet.InputsCount);
double chance = copy._neuroNet.Connections.Count / (double)maxCon;
if (GenomeRandom.NextDouble() < chance)
{
do
{
foreach (var c in copy._neuroNet.Connections)
{
if (GenomeRandom.NextDouble() < (1.0 / copy._neuroNet.Connections.Count))
{
con = c;
break;
}
}
} while (con == null);
int from = con.From;
int to = con.To;
copy._neuroNet.Connections.Remove(con);
int nodeId = copy._neuroNet.Nodes.Count;
copy._neuroNet.AddNode(nodeId);
copy._neuroNet.AddConnection(from, nodeId, (GenomeRandom.NextDouble() - GenomeRandom.NextDouble()) * 2);
copy._neuroNet.AddConnection(nodeId, to, (GenomeRandom.NextDouble() - GenomeRandom.NextDouble()) * 2);
}
else
{
int from = GenomeRandom.Next(copy._neuroNet.Nodes.Count);
int to = GenomeRandom.Next(copy._neuroNet.InputsCount - 1, copy._neuroNet.Nodes.Count);
copy._neuroNet.AddConnection(from, to, (GenomeRandom.NextDouble() - GenomeRandom.NextDouble()) * 2);
}
copy._structuralMutations += 1;
copy._nonStructuralMutations = 0;
copy.ParentResult = this.LastResult;
return(copy);
}
