public ConnectionNeurons GetConnectionNeurons(int vacantConnectionIdx,
NeatChromosome neatChromosome, HiddenNeuronList hiddenNeurons)
{
var outerLayersVacantConnectionCount =
_maxOuterLayersConnectionCount - neatChromosome.OuterLayersConnections.Count;
return(vacantConnectionIdx < outerLayersVacantConnectionCount
? GetOuterLayersVacantConnectionNeurons(vacantConnectionIdx, neatChromosome.OuterLayersConnections)
: GetHiddenLayersVacantConnectionNeurons(vacantConnectionIdx - outerLayersVacantConnectionCount,
neatChromosome, hiddenNeurons));
}
public NeatChromosome(NeatChromosome neatChromosome)
{
_outerLayersConnections = new List <ConnectionGene>(neatChromosome.OuterLayersConnections);
_hiddenLayersConnections = new List <ConnectionGene>(neatChromosome.HiddenLayersConnections);
InputCount = neatChromosome.InputCount;
OutputCount = neatChromosome.OutputCount;
OuterLayersNeuronCount = neatChromosome.OuterLayersNeuronCount;
HiddenNeuronCount = neatChromosome.HiddenNeuronCount;
_maxInnovationId = neatChromosome._maxInnovationId;
}
internal int GetVacantConnectionCount(NeatChromosome neatChromosome, HiddenNeuronList hiddenNeurons)
{
var maxInputToHiddenCount = _inputs.Count * hiddenNeurons.Count;
var maxHiddenToOutputCount = hiddenNeurons.Count * _outputs.Count;
var maxOutputToHiddenCount = _isRecurrent ? maxHiddenToOutputCount : 0;
var maxHiddenToHiddenCount = hiddenNeurons.Count * (_isRecurrent
? hiddenNeurons.Count
: hiddenNeurons.Count - 1);
return(_maxOuterLayersConnectionCount +
maxInputToHiddenCount +
maxHiddenToOutputCount +
maxOutputToHiddenCount +
maxHiddenToHiddenCount - neatChromosome.Count);
}
private ConnectionNeurons GetHiddenLayersVacantConnectionNeurons(int vacantConnectionIdx,
NeatChromosome neatChromosome, HiddenNeuronList hiddenNeurons)
{
NeuronGene GetSourceNeuronGene(int sourceNeuronIdx, int targetNeuronIdx)
{
if (sourceNeuronIdx < _inputs.Count)
{
return(_inputs[sourceNeuronIdx]);
}
if (_isRecurrent)
{
return(sourceNeuronIdx < _inputs.Count + _outputs.Count
? _outputs[sourceNeuronIdx - _inputs.Count]
: hiddenNeurons.Keys[sourceNeuronIdx - _inputs.Count - _outputs.Count]);
}
//feedforward
var sourceHiddenNeuronIdx = sourceNeuronIdx - _inputs.Count;
return(sourceHiddenNeuronIdx < targetNeuronIdx
? hiddenNeurons.Keys[sourceHiddenNeuronIdx]
: hiddenNeurons.Keys[sourceHiddenNeuronIdx + 1]);
}
var maxInConnectionCount = _inputs.Count + hiddenNeurons.Count + (_isRecurrent ? _outputs.Count : -1);
var vacantConnectionSum = 0;
int hiddenNeuronIdx;
var inConnectionCount = 0;
for (hiddenNeuronIdx = 0; hiddenNeuronIdx < hiddenNeurons.Count; hiddenNeuronIdx++)
{
inConnectionCount = hiddenNeurons.Values[hiddenNeuronIdx].@in;
vacantConnectionSum += maxInConnectionCount - inConnectionCount;
if (vacantConnectionSum > vacantConnectionIdx)
{
break;
}
}
var hiddenLayersConnections = neatChromosome.HiddenLayersConnections;
// There are no enough incoming vacant connections in hidden neurons, search in hidden to output connections
if (vacantConnectionSum <= vacantConnectionIdx)
{
return(GetHiddenToOuterLayerVacantConnectionNeurons(vacantConnectionIdx - vacantConnectionSum,
hiddenLayersConnections, hiddenNeurons));
}
var hiddenNeuronVacantConnectionIdx =
vacantConnectionIdx - (vacantConnectionSum - (maxInConnectionCount - inConnectionCount));
var targetHiddenNeuron = hiddenNeurons.Keys[hiddenNeuronIdx];
if (inConnectionCount == 0)
{
return(new ConnectionNeurons(GetSourceNeuronGene(hiddenNeuronVacantConnectionIdx, hiddenNeuronIdx), targetHiddenNeuron));
}
var connectionIdx = neatChromosome.FindFirstHiddenLayerConnectionIdx(targetHiddenNeuron.Id);
int connectionCount;
var connection = hiddenLayersConnections[connectionIdx];
for (connectionCount = 0;
connection.Target == targetHiddenNeuron;
connection = hiddenLayersConnections[connectionIdx])
{
if (!connection.Source.IsHidden)
{
var vacantConnectionCount = connection.SourceId - connectionCount;
if (vacantConnectionCount > hiddenNeuronVacantConnectionIdx)
{
break;
}
}
else //connection source is hidden neuron
{
var sourceNeuronIdx = hiddenNeurons.IndexOfKey(connection.Source);
var vacantConnectionCount = _inputs.Count +
(_isRecurrent ? _outputs.Count : 0) +
sourceNeuronIdx -
(!_isRecurrent && sourceNeuronIdx >= hiddenNeuronIdx ? 1 : 0) -
connectionCount;
if (vacantConnectionCount > hiddenNeuronVacantConnectionIdx)
{
break;
}
}
connectionCount++;
if (++connectionIdx == hiddenLayersConnections.Count)
{
break;
}
}
var sourceIdx = connectionCount + hiddenNeuronVacantConnectionIdx;
return(new ConnectionNeurons(GetSourceNeuronGene(sourceIdx, hiddenNeuronIdx), targetHiddenNeuron));
}