public void Populate(int size, AbstractInputFunction inputFunction = null, AbstractActivationFunction activationFunction = null)
{
for (int toFill = size - Neurons.Count(); toFill > 0; toFill--)
{
Neurons.Add(new Neuron(inputFunction, activationFunction));
}
Size = Neurons.Count();
}
/// <summary>
/// Mutates a random link whithin the neural network.
/// </summary>
public void MutateLink()
{
int from;
int to;
Stopwatch sw = new Stopwatch();
sw.Start();
do
{
rand = new Random(Guid.NewGuid().GetHashCode());
do
{
rand = new Random(Guid.NewGuid().GetHashCode());
from = rand.Next(Neurons.Count());
} while (from > NumInputs - 1 && from < NumInputs + NumOutputs);
do
{
rand = new Random(Guid.NewGuid().GetHashCode());
to = rand.Next(NumInputs, Neurons.Count());
} while (to == from || to < NumInputs || (to < from && to >= NumInputs + NumOutputs));
if (sw.ElapsedMilliseconds > 10)
{
return;
}
} while (Connections.Contains(new Tuple <int, int>(from, to)));
if (Neurons.TryGetValue(to, out Neuron toNeuron))
{
if (Neurons.TryGetValue(from, out Neuron fromNeuron))
{
Tuple <int, int> fromToPair = new Tuple <int, int>(from, to);
Connection conn;
if (InnovationNumbers.ContainsKey(fromToPair))
{
lock (InnovationNumbers)
{
InnovationNumbers.TryGetValue(fromToPair, out int innoNumber);
conn = new Connection(innoNumber, fromNeuron, rand.NextDouble() * 4 - 2);
}
}
else
{
lock (InnovationNumbers)
{
conn = new Connection(InnovationNumbers.Count(), fromNeuron, rand.NextDouble() * 4 - 2);
InnovationNumbers.Add(new Tuple <int, int>(from, to), InnovationNumbers.Count());
}
}
Connections.Add(fromToPair);
toNeuron.AddConnection(conn.ID, conn);
}
}
}
/// <summary>
/// Returns a string in DOT language that can be used to visualize the neural network.
/// </summary>
/// <returns>A string in DOT language that can be used to visualize the neural network.</returns>
public string GenerateDOT()
{
StringBuilder sb = new StringBuilder("digraph NeuralNetwork{\n");
sb.Append("subgraph cluster_level1{\nlabel = \"Inputs\";\n");
for (int i = 1; i < NumInputs; i++)
{
Neuron neur = Neurons.Values.ElementAt(i);
sb.Append(neur.ID + ";\n");
}
sb.Append("}\nsubgraph cluster_level3{\nlabel = \"Outputs\";\n");
for (int i = NumInputs; i < NumInputs + NumOutputs; i++)
{
Neuron neur = Neurons.Values.ElementAt(i);
sb.Append(neur.ID + "[label=\"" + outputNames.ElementAt(i - NumInputs) + "\"];\n");
}
sb.Append("}\nsubgraph cluster_level2{\nlabel = \"Hidden Layers\";\nrankdir=LR;\n");
for (int i = NumInputs + NumOutputs; i < Neurons.Count(); i++)
{
Neuron neur = Neurons.Values.ElementAt(i);
sb.Append(neur.ID + ";\n");
}
sb.Append("}\n");
foreach (Neuron neuron in Neurons.Values)
{
foreach (Connection conn in neuron.Connections.Values)
{
if (conn.Enabled)
{
sb.Append(conn.From.ID + " -> " + neuron.ID + "[label=\"" + Math.Round(conn.Weight, 3) + "\"];\n");
}
else
{
sb.Append(conn.From.ID + " -> " + neuron.ID + "[label=\"" + Math.Round(conn.Weight, 3) + "\", style=dotted];\n");
}
}
}
sb.Append("}");
return(sb.ToString());
}
/// <summary>
/// Returns the count of all Neurons and all connections.
/// </summary>
/// <returns>The count of all neurons and all connections</returns>
public int Size()
{
return(Connections.Count() + Neurons.Count());
}
