public void NodeMutate(double MutationProbability = 0.1, double MutationAmount = 2.0)
{
if (MutationProbability < 0 || MutationProbability > 1)
{
throw new ArgumentException("A probability must be a value between 0 and 1.", "MutationProbablity");
}
else if (MutationAmount <= 0)
{
throw new ArgumentException("The mutation amount must be greater than 0.", "MutationAmount");
}
for (int i = 0; i < AllNeurons.Count; i++)
{
foreach (NeatConnection aNeatConnection in (AllNeurons[i] as NeatNeuron).IncomingConnections)
{
if (aNeatConnection.Enabled && TheRandomizer.NextDouble() < MutationProbability)
{
int NewNeuronIndex = i;
if (NewNeuronIndex > TotalNeuronCount - OutputNeuronCount)
{
NewNeuronIndex = TotalNeuronCount - OutputNeuronCount;
}
NeatNeuron NewNeuron = InsertNeuron(NewNeuronIndex);
NewNeuron.IncomingConnections.Add(new NeatConnection(TheRandomizer.NextDouble() * (MutationAmount * 2) - MutationAmount, aNeatConnection.OtherNeuronKey));
aNeatConnection.OtherNeuronKey = NewNeuron.Key;
}
}
}
}
public void CalculateGpuArrays(List <NeatGpuDimensions> Dimensions,
List <NeatGpuIndicies> Indicies,
List <NeatGpuConnection> Connections,
int BufferInputNeuronIndex,
ref int BufferNeuronValuesIndex,
ref int BufferOutputNeuronIndex,
ref int BufferTotalNeuronCount,
ref int BufferOutputNeuronCount)
{
//Indicies.Add(new NeatGpuIndicies());
Indicies.Add(new NeatGpuIndicies(Connections.Count, BufferInputNeuronIndex, BufferNeuronValuesIndex, BufferOutputNeuronIndex));
int ConnectionsCount = 0;
for (int i = InputNeuronCount + 1; i < AllNeurons.Count; i++)
{
NeatNeuron CurrentNeuron = AllNeurons[i] as NeatNeuron;
foreach (NeatConnection aNeatConnection in CurrentNeuron.IncomingConnections)
{
if (aNeatConnection.Enabled)
{
Connections.Add(new NeatGpuConnection((int)aNeatConnection.OtherNeuronKey, (int)CurrentNeuron.Key, aNeatConnection.Weight));
ConnectionsCount++;
}
}
}
Dimensions.Add(new NeatGpuDimensions(InputNeuronCount, OutputNeuronCount, TotalNeuronCount, ConnectionsCount));
BufferNeuronValuesIndex += TotalNeuronCount;
BufferOutputNeuronIndex += OutputNeuronCount;
BufferTotalNeuronCount += TotalNeuronCount;
BufferOutputNeuronCount += OutputNeuronCount;
}
public void AddConnectionMutate(double MutationAmount = 2.0)
{
if (MutationAmount <= 0)
{
throw new ArgumentException("A the mutation amount must be greater than 0.", "MutationAmount");
}
int FirstNeuronIndex = TheRandomizer.Next(AllNeurons.Count - OutputNeuronCount);
int SecondNeuronIndex = FirstNeuronIndex;
while (FirstNeuronIndex == SecondNeuronIndex)
{
SecondNeuronIndex = TheRandomizer.Next(InputNeuronCount + 1, AllNeurons.Count);
}
if (FirstNeuronIndex > SecondNeuronIndex)
{
int Tmp = SecondNeuronIndex;
SecondNeuronIndex = FirstNeuronIndex;
FirstNeuronIndex = Tmp;
}
NeatNeuron FirstNeuron = AllNeurons[FirstNeuronIndex] as NeatNeuron;
NeatNeuron SecondNeuron = AllNeurons[SecondNeuronIndex] as NeatNeuron;
foreach (NeatConnection aNeatConnection in SecondNeuron.IncomingConnections)
{
if (aNeatConnection.OtherNeuronKey == FirstNeuron.Key)
{
return;
}
}
SecondNeuron.IncomingConnections.Add(new NeatConnection(TheRandomizer.NextDouble() * (MutationAmount * 2) - MutationAmount, FirstNeuron.Key));
}
private NeatNeuron InsertNeuron(int index, NeatNeuronType TheNeuronType = NeatNeuronType.Hidden)
{
NeatNeuron NewNeuron = new NeatNeuron((uint)AllNeurons.Count, TheNeuronType);
AllNeurons.Insert(index, (uint)AllNeurons.Count, NewNeuron);
return(NewNeuron);
}
private NeatNeuron AddNeuron(NeatNeuronType TheNeuronType = NeatNeuronType.Hidden)
{
NeatNeuron NewNeuron = new NeatNeuron((uint)AllNeurons.Count, TheNeuronType);
AllNeurons.Add((uint)AllNeurons.Count, NewNeuron);
return(NewNeuron);
}
public void Serialize(Stream TheStream)
{
using (BinaryWriter TheBinaryWriter = new BinaryWriter(TheStream))
{
TheBinaryWriter.Write(InputNeuronCount);
TheBinaryWriter.Write(OutputNeuronCount);
TheBinaryWriter.Write(TopAncestorSeed);
TheBinaryWriter.Write(TheRandomizer.Next());
TheBinaryWriter.Write(AllNeurons.Count);
foreach (DictionaryEntry aDictionaryEntry in AllNeurons)
{
TheBinaryWriter.Write((uint)aDictionaryEntry.Key);
NeatNeuron TheNeuron = aDictionaryEntry.Value as NeatNeuron;
TheBinaryWriter.Write((int)TheNeuron.TheNeuronType);
TheBinaryWriter.Write(TheNeuron.IncomingConnections.Count);
foreach (NeatConnection aNeatConnection in TheNeuron.IncomingConnections)
{
TheBinaryWriter.Write(aNeatConnection.Weight);
TheBinaryWriter.Write(aNeatConnection.OtherNeuronKey);
TheBinaryWriter.Write(aNeatConnection.Enabled);
}
}
}
}
public NeatNeuralNetwork(Stream TheStream)
{
using (BinaryReader TheBinaryReader = new BinaryReader(TheStream))
{
InputNeuronCount = TheBinaryReader.ReadInt32();
OutputNeuronCount = TheBinaryReader.ReadInt32();
AllNeurons = new OrderedDictionary(InputNeuronCount + OutputNeuronCount + 1);
TopAncestorSeed = TheBinaryReader.ReadInt32();
TheRandomizer = new System.Random(TheBinaryReader.ReadInt32());
int AllNeuronsCount = TheBinaryReader.ReadInt32();
for (int i = 0; i < AllNeuronsCount; i++)
{
uint Key = TheBinaryReader.ReadUInt32();
NeatNeuronType TheNeuronType = (NeatNeuronType)TheBinaryReader.ReadInt32();
NeatNeuron TheNeuron = new NeatNeuron(Key, TheNeuronType);
int IncomingConnectionsCount = TheBinaryReader.ReadInt32();
for (int j = 0; j < IncomingConnectionsCount; j++)
{
double TheWeight = TheBinaryReader.ReadDouble();
uint OtherNeuronKey = TheBinaryReader.ReadUInt32();
bool Enabled = TheBinaryReader.ReadBoolean();
TheNeuron.IncomingConnections.Add(new NeatConnection(TheWeight, OtherNeuronKey, Enabled));
}
AllNeurons.Add(Key, TheNeuron);
}
}
}
public NeatNeuron(NeatNeuron Main)
{
this.Key = Main.Key;
this.Value = Main.Value;
foreach (NeatConnection aNeatConnection in Main.IncomingConnections)
{
this.IncomingConnections.Add(new NeatConnection(aNeatConnection));
}
this.TheNeuronType = Main.TheNeuronType;
}
public double[] FeedForward(double[] Input)
{
// Validation Checks
if (Input == null)
{
throw new ArgumentException("The input array cannot be set to null.", "Input");
}
else if (Input.Length != InputNeuronCount)
{
throw new ArgumentException("The input array's length does not match the number of neurons in the input layer.", "Input");
}
double[] Result = new double[OutputNeuronCount];
for (int i = 0; i < InputNeuronCount; i++)
{
(AllNeurons[i] as NeatNeuron).Value = (Input[i]);
}
(AllNeurons[InputNeuronCount] as NeatNeuron).Value = 1;
for (int i = InputNeuronCount + 1; i < AllNeurons.Count; i++)
{
NeatNeuron CurrentNeuron = AllNeurons[i] as NeatNeuron;
CurrentNeuron.Value = 0;
foreach (NeatConnection aNeatConnection in CurrentNeuron.IncomingConnections)
{
if (aNeatConnection.Enabled)
{
CurrentNeuron.Value += aNeatConnection.Weight * ((AllNeurons[aNeatConnection.OtherNeuronKey] as NeatNeuron).Value);
}
}
CurrentNeuron.Value = ReLU(CurrentNeuron.Value);
}
for (int i = 0; i < OutputNeuronCount; i++)
{
Result[i] = ReLU((AllNeurons[i + AllNeurons.Count - OutputNeuronCount] as NeatNeuron).Value);
}
return(Result);
}