private static JsonNeuron[] GetOutputNeurons(NeuralGenome target)
{
float x, y;
float deltaY = 0;
x = 1f - xPadding;
if (target.Outputs.Count() == 1)
{
y = 0.5f;
}
else
{
y = yPadding;
deltaY = (1f - 2 * yPadding) / target.Outputs.Count();
}
var outputNeurons = target.Outputs.Select(n =>
{
var pos = GetNeuronPos(n, x, y);
var result = new JsonNeuron
{
x = pos.X,
y = pos.Y,
innov = n.InnovationNb,
color = new[] { 1f, 0.917f, 0.721f },
label = "O"
};
y += deltaY;
return(result);
}).ToArray();
return(outputNeurons);
}
private static JsonNeuron[] GetRemainingNeurons(NeuralGenome target)
{
var remainingNodes = target.Neurons.Values
.Where(n =>
!target.Inputs.Contains(n) &&
!target.Outputs.Contains(n) &&
!target.Biasses.Contains(n) &&
n.group == null)
.ToArray();
// Compute the positions
foreach (var node in remainingNodes)
{
Vector2 pos;
if (!NeuronPos.ContainsKey(node.InnovationNb))
{
pos = GetRandomPos(randomPosTries);
NeuronPos.Add(node.InnovationNb, pos);
}
}
var remainingNeurons = remainingNodes.Select(n =>
{
JsonNeuron result;
if (typeof(MemoryNeuron).IsAssignableFrom(n.GetType()))
{
result = new JsonNeuron
{
x = NeuronPos[n.InnovationNb].X,
y = NeuronPos[n.InnovationNb].Y,
innov = n.InnovationNb,
color = new[] { 0.949f, 1, 0 },
label = string.Format("{0}<", (n as MemoryNeuron).TargetNeuron)
};
}
else
{
result = new JsonNeuron
{
x = NeuronPos[n.InnovationNb].X,
y = NeuronPos[n.InnovationNb].Y,
innov = n.InnovationNb,
};
}
return(result);
}).ToArray();
return(remainingNeurons);
}
private static JsonNeuron[] GetInputNeurons(NeuralGenome target)
{
float x, y;
float deltaY = 0;
// Inputs
x = xPadding;
y = yPadding;
var inputs = target.Inputs.Concat(target.Biasses);
deltaY = (1f - 2 * yPadding) / inputs.Count();
var inputNeurons = inputs.Select(n =>
{
JsonNeuron result;
var pos = GetNeuronPos(n, x, y);
if (target.Inputs.Contains(n))
{
result = new JsonNeuron
{
x = pos.X,
y = pos.Y,
innov = n.InnovationNb,
color = new[] { 1f, 0.721f, 0.992f },
label = "I"
};
}
else
{
result = new JsonNeuron
{
x = pos.X,
y = pos.Y,
innov = n.InnovationNb,
color = new[] { 0.627f, 0.160f, 1f },
label = "B"
};
}
y += deltaY;
return(result);
}).ToArray();
return(inputNeurons);
}
private static void ProcessNetworkGroups(
NeuralGenome target,
List <JsonNeuron> jsonNeurons,
List <JsonEdge> jsonEdges)
{
var groups = target.Neurons.Values.Where(x => x.group != null)
.GroupBy(x => x.group)
.ToArray();
if (groups == null || groups.Length == 0)
{
return;
}
foreach (var group in groups)
{
var innov = group.First().InnovationNb;
if (!NeuronPos.ContainsKey(innov))
{
NeuronPos.Add(innov, GetRandomPos(randomPosTries));
}
var pos = NeuronPos[innov];
var jsonNeuron = new JsonNeuron
{
x = pos.X,
y = pos.Y,
innov = innov,
color = new[] { 0.101F, 0.882F, 1 },
label = group.Key
};
jsonNeurons.Add(jsonNeuron);
var start = target.NeuralGenes
.Select(x => x.Synapse)
.Where(x =>
target.Neurons[x.incoming].group != group.Key &&
target.Neurons[x.outgoing].group == group.Key);
var end = target.NeuralGenes
.Select(x => x.Synapse)
.Where(x =>
target.Neurons[x.incoming].group == group.Key &&
target.Neurons[x.outgoing].group != group.Key);
foreach (var edge in start)
{
jsonEdges.Add(new JsonEdge
{
start = edge.incoming,
end = innov,
w = edge.Weight
});
}
foreach (var edge in end)
{
jsonEdges.Add(new JsonEdge
{
start = innov,
end = edge.outgoing,
w = edge.Weight
});
}
}
}