internal LayerGene MateLayer(LayerGene moreNeurons, LayerGene lessNeurons, Random random)
{
LayerGene childGene = new LayerGene
{
Neurons = new List <NeuronGene>()
};
var sameNumberOfTerminalsPerNeuronForBothMates = moreNeurons.Neurons[0].Axon.Weights.Count == lessNeurons.Neurons[0].Axon.Weights.Count;
var maxTerminals = Math.Max(moreNeurons.Neurons[0].Axon.Weights.Count, lessNeurons.Neurons[0].Axon.Weights.Count);
for (int j = 0; j < moreNeurons.Neurons.Count; j++)
{
//only breed the neuron if both mates have it. Otherwise just leave add the extra neuron untouched.
if (j < lessNeurons.Neurons.Count)
{
var neuron = moreNeurons.Neurons[j];
var lessNeuron = lessNeurons.Neurons[j];
childGene.Neurons.Add(BreedNeuron(neuron, lessNeuron, random));
}
else
{
if (sameNumberOfTerminalsPerNeuronForBothMates)
{
childGene.Neurons.Add(moreNeurons.Neurons[j]);
}
else
{
childGene.Neurons.Add(AdjustAxonTerminalsOfNeuronGene(moreNeurons.Neurons[j], maxTerminals));
}
}
}
return(childGene);
}
internal ILayer CreateLayerFromGene(LayerGene layerGene, Dictionary <int, Dictionary <int, IList <Synapse> > > synapseMapping, int layerInNetwork)
{
IList <INeuron> layerNeurons = new List <INeuron>();
for (int i = 0; i < layerGene.Neurons.Count; i++)
{
layerNeurons.Add(CreateNeuronFromGene(layerGene.Neurons[i], synapseMapping, layerInNetwork, i));
}
return(Layer.GetInstance(layerNeurons));
}
internal LayerGene TryAddNeuronsToLayer(NeuralNetworkGene networkGenes, int hiddenLayerIndex, double mutateChance, out bool didMutate)
{
LayerGene hiddenLayer = networkGenes.HiddenGenes[hiddenLayerIndex];
didMutate = false;
while (_random.NextDouble() <= mutateChance)
{
didMutate = true;
//update layer-1 axon terminals
LayerGene previousLayer = GetPreviousLayerGene(networkGenes, hiddenLayerIndex);
foreach (NeuronGene neuron in previousLayer.Neurons)
{
neuron.Axon.Weights.Add(_weightInitializer.InitializeWeight());
}
hiddenLayer.Neurons.Add(GetRandomHiddenNeuronGene(networkGenes, hiddenLayerIndex));
}
return(hiddenLayer);
}
internal NeuralNetworkGene TryAddLayerToNetwork(NeuralNetworkGene genes, double mutateChance, out bool didMutate)
{
NeuralNetworkGene newGenes = genes;
didMutate = false;
while (_random.NextDouble() <= mutateChance)
{
didMutate = true;
int layerToReplace = _random.Next(newGenes.HiddenGenes.Count);
int hiddenLayerSize = DetermineNumberOfHiddenNeuronsInLayer(genes, mutateChance);
//update layer-1 axon terminals
LayerGene previousLayer = GetPreviousLayerGene(newGenes, layerToReplace);
foreach (NeuronGene neuron in previousLayer.Neurons)
{
neuron.Axon.Weights.Clear();
for (int i = 0; i < hiddenLayerSize; i++)
{
neuron.Axon.Weights.Add(_weightInitializer.InitializeWeight());
}
}
LayerGene newLayer = new LayerGene
{
Neurons = new List <NeuronGene>()
};
newGenes.HiddenGenes.Insert(layerToReplace, newLayer);
for (int i = 0; i < hiddenLayerSize; i++)
{
var newNeuron = GetRandomHiddenNeuronGene(newGenes, layerToReplace);
newGenes.HiddenGenes[layerToReplace].Neurons.Add(newNeuron);
}
}
return(newGenes);
}
internal NeuronGene GetRandomHiddenNeuronGene(NeuralNetworkGene networkGenes, int hiddenLayerIndex)
{
var neuronGene = new NeuronGene
{
Axon = new AxonGene
{
Weights = new List <double>(),
ActivationFunction = GetRandomActivationFunction().GetType()
},
Soma = new SomaGene
{
Bias = _weightInitializer.InitializeWeight(),
SummationFunction = GetRandomSummationFunction().GetType()
}
};
//update terminals for current neuron
LayerGene nextlayer = GetNextLayerGene(networkGenes, hiddenLayerIndex);
for (int i = 0; i < nextlayer.Neurons.Count; i++)
{
neuronGene.Axon.Weights.Add(_weightInitializer.InitializeWeight());
}
return(neuronGene);
}
internal Dictionary <int, IList <Synapse> > CreateSynapseMapLayerFromLayerGene(ISynapseFactory synapseFactory, LayerGene layerGene)
{
var mapLayer = new Dictionary <int, IList <Synapse> >();
for (int i = 0; i < layerGene.Neurons.Count; i++)
{
mapLayer[i] = CreateTerminalsFromWeightList(synapseFactory, layerGene.Neurons[i].Axon.Weights);
}
return(mapLayer);
}
internal Dictionary<int, IList<Synapse>> CreateSynapseMapLayerFromLayerGene(ISynapseFactory synapseFactory, LayerGene layerGene)
{
var mapLayer = new Dictionary<int, IList<Synapse>>();
for (int i = 0; i < layerGene.Neurons.Count; i++)
{
mapLayer[i] = CreateTerminalsFromWeightList(synapseFactory, layerGene.Neurons[i].Axon.Weights);
}
return mapLayer;
}
internal ILayer CreateLayerFromGene(LayerGene layerGene, Dictionary<int, Dictionary<int, IList<Synapse>>> synapseMapping, int layerInNetwork)
{
IList<INeuron> layerNeurons = new List<INeuron>();
for (int i = 0; i < layerGene.Neurons.Count; i++)
{
layerNeurons.Add(CreateNeuronFromGene(layerGene.Neurons[i], synapseMapping, layerInNetwork, i));
}
return Layer.GetInstance(layerNeurons);
}
internal NeuralNetworkGene TryAddLayerToNetwork(NeuralNetworkGene genes, double mutateChance, out bool didMutate)
{
NeuralNetworkGene newGenes = genes;
didMutate = false;
while (_random.NextDouble() <= mutateChance)
{
didMutate = true;
int layerToReplace = _random.Next(newGenes.HiddenGenes.Count);
int hiddenLayerSize = DetermineNumberOfHiddenNeuronsInLayer(genes, mutateChance);
//update layer-1 axon terminals
LayerGene previousLayer = GetPreviousLayerGene(newGenes, layerToReplace);
foreach (NeuronGene neuron in previousLayer.Neurons)
{
neuron.Axon.Weights.Clear();
for (int i = 0; i < hiddenLayerSize; i++)
{
neuron.Axon.Weights.Add(_weightInitializer.InitializeWeight());
}
}
LayerGene newLayer = new LayerGene
{
Neurons = new List<NeuronGene>()
};
newGenes.HiddenGenes.Insert(layerToReplace, newLayer);
for (int i = 0; i < hiddenLayerSize; i++)
{
var newNeuron = GetRandomHiddenNeuronGene(newGenes, layerToReplace);
newGenes.HiddenGenes[layerToReplace].Neurons.Add(newNeuron);
}
}
return newGenes;
}
