public void NormalRandNominalBehavior()
{
List <float> list = new List <float>();
for (int i = 0; i < 1000; i++)
{
list.Add(ThreadSafeRandom.NormalRand());
}
List <List <float> > ranged = new List <List <float> >();
for (float n = -3; n < 3; n = n + 0.1f)
{
var r = new List <float>();
foreach (var l in list)
{
if (n < l && l < n + 0.2f)
{
r.Add(l);
}
}
ranged.Add(r);
}
foreach (var r in ranged)
{
if (r.Any())
{
Console.Write($"\t{r[^1]} : {r[0]} ; {r.Count}\t");
/// <summary>
/// Initialize a <see cref="Network"/> from a given size
/// </summary>
/// <param name="Sizes"></param>
private void InitializeNetwork(int[] Sizes)
{
this.Sizes = Sizes.ToArray();
NumberOfLayer = Sizes.Length;
Biases = new Matrix[Sizes.Length - 1];
Weights = new Matrix[Sizes.Length - 1];
// Initialize Biases
foreach ((int y, int l) in Sizes.Skip(1).Select((v, i) => (v, i)))
{
float[,] biases = new float[y, 1];
for (int i = 0; i < y; i++)
{
biases[i, 0] = ThreadSafeRandom.NormalRand();
}
Biases[l] = biases;
}
// Initialize Weights
foreach (((int x, int y), int l) in Sizes.SkipLast(1).Zip(Sizes.Skip(1)).Select((v, i) => (v, i)))
{
// x => number of neuron in previous layer
// y => number of neuron in actual layer
float[,] layer = new float[y, x];
for (int i = 0; i < y; i++)
{
for (int j = 0; j < x; j++)
{
layer[i, j] = ThreadSafeRandom.NormalRand();
}
}
Weights[l] = layer;
}
}
///<inheritdoc/>
public void MutateWeightRandom()
{
ConnectionGene connection = Connections.RandomElement();
if (connection != null)
{
connection.Weight = ThreadSafeRandom.NormalRand(0, 0.2f) * Constants.WEIGHT_RANDOM_STRENGTH;
}
}
///<inheritdoc/>
public void MutateNode()
{
ConnectionGene connection = Connections.RandomElement();
if (connection == null)
{
return;
}
NodeGene from = connection.In;
NodeGene to = connection.Out;
int replaceIndex = Neat.GetReplaceIndex(from, to);
NodeGene middle;
if (replaceIndex == 0)
{
ActivationEnumeration a = ActivationEnumeration.Random();
middle = Neat.CreateNode();
middle.X = (from.X + to.X) / 2;
middle.Y = ((from.Y + to.Y) / 2) + (ThreadSafeRandom.NormalRand(0, 0.02f) / 2);
middle.Activation = a.Activation;
middle.ActivationName = a.Name;
Neat.SetReplaceIndex(from, to, middle.InnovationNumber);
}
else
{
middle = Neat.GetNode(replaceIndex);
}
ConnectionGene connection1 = Neat.GetConnection(from, middle);
ConnectionGene connection2 = Neat.GetConnection(middle, to);
connection1.Weight = 1;
connection2.Weight = connection.Weight;
connection2.Enabled = connection.Enabled;
connection.Enabled = false;
Connections.Add(connection1);
Connections.Add(connection2);
Nodes.Add(middle);
}
///<inheritdoc/>
public void MutateLink()
{
for (int i = 0; i < 100; i++)
{
NodeGene a = Nodes.RandomElement();
NodeGene b = Nodes.RandomElement();
if (a == null || b == null)
{
continue;
}
if (a.X.Equals(b.X))
{
continue;
}
ConnectionGene connection;
if (a.X < b.X)
{
connection = new ConnectionGene(a, b);
}
else
{
connection = new ConnectionGene(b, a);
}
if (Connections.Contains(connection))
{
continue;
}
connection = Neat.GetConnection(connection.In, connection.Out);
connection.Weight += ThreadSafeRandom.NormalRand(0, 0.2f) * Constants.WEIGHT_SHIFT_STRENGTH;
AddSorted(connection);
return;
}
}
/// <summary>
/// TODO : move this to unit tests
/// </summary>
/// <returns></returns>
public RandomList <Client> CheckEvolutionProcess()
{
Neat neat = new Neat();
double[] inputs = new double[Constants.InputSize];
for (int i = 0; i < Constants.InputSize; i++)
{
inputs[i] = ThreadSafeRandom.NormalRand();
}
for (int i = 0; i < 100; i++)
{
foreach (Client c in neat.AllClients)
{
c.Score = c.Calculate(inputs)[0];
}
neat.Evolve();
//neat.TraceClients();
neat.TraceSpecies();
}
return(neat.AllClients);
}
