public void SimpleNetworkCheck()
{
using (var source = RandomSource.DrillIn())
{
// Arrange
for (var i = 0; i < 10; i++)
{
source.PushNextFloats(0.6f); // identity weights and density chances
}
source.GetParanoid();
var parameters = new NetworkParameters(3, 1)
{
InitialConnectionDensity = 1f
};
var tracker =
new InnovationTracker(NetworkParameters.BiasCount + parameters.SensorCount + parameters.EffectorCount);
var genome = new Genome(parameters, tracker, false);
var network = new RecurrentNetwork();
#region Network initialization
network.AddNeuron(0, 0);
network.AddNeuron(1, 0);
network.AddNeuron(2, 0);
network.AddNeuron(3, 0);
network.AddNeuron(4, 1);
network.AddConnection(0, 4, 1f);
network.AddConnection(1, 4, 1f);
network.AddConnection(2, 4, 1f);
network.AddConnection(3, 4, 1f);
network.AddConnection(4, 4, 1f);
#endregion
float[] result = null;
for (var i = 0; i < 5; i++)
{
for (var j = 0; j < 3; j++)
{
genome.Network.Sensors[j] = 0.5f;
}
genome.Network.Activate();
result = network.Activate(new[] { 1f, 0.5f, 0.5f, 0.5f });
}
Assert.Equal(result[0], genome.Network.Effectors[0]);
}
}
public void LayeredNetworkCheck()
{
var parameters = new NetworkParameters(3, 1)
{
InitialConnectionDensity = 1f
};
var tracker =
new InnovationTracker(NetworkParameters.BiasCount + parameters.SensorCount + parameters.EffectorCount);
var genome = new Genome(parameters, tracker, false);
genome.SplitConnection(0);
genome.SplitConnection(2);
genome.SplitConnection(genome.NeatChromosome.Single(g => g.SourceId == 5).Id);
while (genome.VacantConnectionCount > 0)
{
genome.AddConnection(Neat.Utils.RandomSource.Next(genome.VacantConnectionCount));
}
var network = new RecurrentNetwork();
void AddConnectionAndCopyWeight(int sourceId, int targetId)
{
var weight = genome.NeatChromosome.Single(g => g.SourceId == sourceId && g.TargetId == targetId).Weight;
network.AddConnection(sourceId, targetId, weight);
}
#region Network initialization
network.AddNeuron(0, 0);
network.AddNeuron(1, 0);
network.AddNeuron(2, 0);
network.AddNeuron(3, 0);
network.AddNeuron(5, 1);
network.AddNeuron(6, 1);
network.AddNeuron(7, 2);
network.AddNeuron(4, 3);
for (var i = 0; i < 4; i++)
{
for (var j = 4; j < 8; j++)
{
AddConnectionAndCopyWeight(i, j);
}
}
for (var i = 4; i < 8; i++)
{
for (var j = 4; j < 8; j++)
{
AddConnectionAndCopyWeight(i, j);
}
}
Assert.Equal(network.ConnectionsCount, genome.NeatChromosome.Count);
#endregion
float[] result = null;
for (var i = 0; i < 5; i++)
{
var values = new[]
{
1f,
Neat.Utils.RandomSource.Next(),
Neat.Utils.RandomSource.Next(),
Neat.Utils.RandomSource.Next()
};
for (var j = 0; j < 3; j++)
{
genome.Network.Sensors[j] = values[j + 1];
}
genome.Network.Activate();
result = network.Activate(values);
}
Assert.Equal(result[0], genome.Network.Effectors[0]);
}
