public CommunicationVisualizer(SharpNeatLib.CPPNs.SubstrateDescription _sd, ModularNetwork _net)
{
InitializeComponent();
//zlevel = _zlevel;
drawFont = new System.Drawing.Font("Arial", 8);
drawBrush = new System.Drawing.SolidBrush(System.Drawing.Color.Black);
drawFormat = new System.Drawing.StringFormat();
sd = _sd;
net = _net;
_net.UpdateNetworkEvent += networkUpdated;
activation = new activationLevels[200];
// outgoingActivation = new List<float>[200];
for (int i = 0; i < activation.Length; i++)
{
activation[i] = new activationLevels();
//outgoingActivation[i] = new List<float>();
}
penConnection = new Pen(Color.Black);
penRed = new Pen(Color.Red);
this.SetStyle(
ControlStyles.AllPaintingInWmPaint |
ControlStyles.UserPaint |
ControlStyles.DoubleBuffer, true);
}
// Schrum: This runs multiple time steps to evaluate the agent
public override double evaluateNetwork(SharpNeatLib.NeuralNetwork.INetwork network, out SharpNeatLib.BehaviorType behavior, System.Threading.Semaphore sem)
{
double result = base.evaluateNetwork(network, out behavior, sem);
// Schrum: remove reference to enemy list after each eval.
enemies = null;
return result;
}
//TODO: deal with threading
public double threadSafeEvaluateNetwork(SharpNeatLib.NeuralNetwork.INetwork network, System.Threading.Semaphore sem, out SharpNeatLib.BehaviorType behavior, int thread)
{
// try {
return exp.evaluateNetwork(network, out behavior,sem);
//} catch (Exception e) {
// behavior=new BehaviorType();
// Console.WriteLine(e.Message);
// Console.WriteLine(e.StackTrace);
// throw e;
// return 0.0001;
//}
}
// Schrum: Changed "internal" to "public". Probably a bad idea, but it works!
public virtual double evaluateNetwork(INetwork network, out SharpNeatLib.BehaviorType behavior, System.Threading.Semaphore sem) { behavior = null; return 0.0; }
internal override double evaluateNetwork(INetwork network, out SharpNeatLib.BehaviorType behavior, System.Threading.Semaphore sem)
{
double fitness = 0;//SharpNeatLib.Utilities.random.NextDouble();
NeatGenome tempGenome;
INetwork controller;
behavior = new SharpNeatLib.BehaviorType();
double[] accumObjectives = new double[6];
for (int i = 0; i < 6; i++) accumObjectives[i] = 0.0;
IFitnessFunction fit_copy;
IBehaviorCharacterization bc_copy;
CollisionManager cm;
instance_pack inst = new instance_pack();
inst.timestep=timestep;
foreach (Environment env2 in environmentList)
{
fit_copy = fitnessFunction.copy();
if (behaviorCharacterization != null)
{
bc_copy = behaviorCharacterization.copy();
inst.bc = bc_copy;
}
inst.ff = fit_copy;
double tempFit = 0;
double[] fitnesses = new double[timesToRunEnvironments];
SharpNeatLib.Maths.FastRandom evalRand = new SharpNeatLib.Maths.FastRandom(100);
for (int evals = 0; evals < timesToRunEnvironments; evals++)
{
int agent_trials = timesToRunEnvironments;
inst.num_rbts=this.numberRobots;
Environment env = env2.copy();
double evalTime = evaluationTime;
inst.eval = evals;
env.seed = evals;
env.rng = new SharpNeatLib.Maths.FastRandom(env.seed + 1);
int noise_lev = (int)this.sensorNoise+1;
float new_sn = evalRand.NextUInt() % noise_lev;
float new_ef = evalRand.NextUInt() % noise_lev;
inst.agentBrain = new AgentBrain(homogeneousTeam, inst.num_rbts, substrateDescription, network, normalizeWeights, adaptableANN, modulatoryANN, multibrain, evolveSubstrate, dirComm);
initializeRobots(agentBrain, env, headingNoise, new_sn, new_ef, inst);
inst.elapsed = 0;
inst.timestep = this.timestep;
//Console.WriteLine(this.timestep);
inst.timeSteps = 0;
inst.collisionManager = collisionManager.copy();
inst.collisionManager.Initialize(env, this, inst.robots);
try {
while (inst.elapsed < evalTime)
{
runEnvironment(env,inst,sem);
}
} catch( Exception e) {
for(int x=0;x<inst.robots.Count;x++) {
for(int y=0;y<inst.robots[x].history.Count;y++) {
Console.WriteLine(x+" " + y+ " "+ inst.robots[x].history[y].x + " " + inst.robots[x].history[y].y);
}
}
behavior=new SharpNeatLib.BehaviorType();
Console.WriteLine(e.Message);
Console.WriteLine(e.StackTrace);
throw(e);
return 0.0001;
}
double thisFit = inst.ff.calculate(this, env, inst, out behavior.objectives);
fitnesses[evals] = thisFit;
tempFit += thisFit;
if (behavior != null && behavior.objectives != null && inst.bc!=null)
{
for (int i = 0; i < behavior.objectives.Length; i++)
accumObjectives[i] += behavior.objectives[i];
if(behavior.behaviorList==null)
behavior.behaviorList=new List<double>();
behavior.behaviorList.AddRange(inst.bc.calculate(this, inst));
inst.bc.reset();
}
inst.ff.reset();
}
fitness += tempFit / timesToRunEnvironments;
}
behavior.objectives = accumObjectives;
return fitness / environmentList.Count;
}
public static float addNoise(float val,float percentage,SharpNeatLib.Maths.FastRandom rng)
{
percentage/=100.0f;
float p1 = 1.0f -percentage;
float p2 = percentage;
float rval = (float)rng.NextDouble();
return p1*val+p2*rval;
}
public void ResetEvaluator(SharpNeatLib.NeuralNetwork.IActivationFunction activationFn)
{
//populationEval = new SingleFilePopulationEvaluator(new NetworkEvaluator(simExp),null);
populationEval = new MultiThreadedPopulationEvaluator(new NetworkEvaluator(simExp),null);
}
//saves a CPPN in dot file format.
//Assumes that inputs are X1, Y1, X2, Y2, Z
public static void saveCPPNasDOT(SharpNeatLib.NeatGenome.NeatGenome genome, string filename)
{
StreamWriter SW = File.CreateText(filename);
SW.WriteLine("digraph g { ");
String activationType = "";
foreach (NeuronGene neuron in genome.NeuronGeneList)
{
switch (neuron.NeuronType)
{
case NeuronType.Bias: SW.WriteLine("N0 [shape=box, label=Bias]"); break;
case NeuronType.Input:
string str = "?";
switch (neuron.InnovationId)
{
case 1: str = "X1"; break;
case 2: str = "Y1"; break;
case 3: str = "X2"; break;
case 4: str = "Y2"; break;
case 5: str = "Z"; break;
}
SW.WriteLine("N" + neuron.InnovationId + "[shape=box label=" + str + "]");
break;
case NeuronType.Output: SW.WriteLine("N" + neuron.InnovationId + "[shape=triangle]"); break;
case NeuronType.Hidden:
if (neuron.ActivationFunction.FunctionDescription.Equals("bipolar steepend sigmoid")) activationType = "S";
if (neuron.ActivationFunction.FunctionDescription.Equals("bimodal gaussian")) activationType = "G";
if (neuron.ActivationFunction.FunctionDescription.Equals("Linear")) activationType = "L";
if (neuron.ActivationFunction.FunctionDescription.Equals("Sin function with doubled period")) activationType = "Si";
if (neuron.ActivationFunction.FunctionDescription.Equals("Returns the sign of the input")) activationType = "Sign";
SW.WriteLine("N" + neuron.InnovationId + "[shape=circle, label=N" + neuron.InnovationId + "_" + activationType + ", fillcolor=gray]");
break;
}
}
foreach (ConnectionGene gene in genome.ConnectionGeneList)
{
SW.Write("N" + gene.SourceNeuronId + " -> N" + gene.TargetNeuronId + " ");
if (gene.Weight > 0)
SW.WriteLine("[color=black] ");
else if (gene.Weight < -0)
SW.WriteLine("[color=red] [arrowType=inv]");
}
foreach (ModuleGene mg in genome.ModuleGeneList)
{
foreach (uint sourceID in mg.InputIds)
{
foreach (uint targetID in mg.OutputIds)
{
SW.Write("N" + sourceID + " -> N" + targetID + " ");
SW.WriteLine("[color=gray]");
}
}
}
SW.WriteLine(" { rank=same; ");
foreach (NeuronGene neuron in genome.NeuronGeneList)
{
if (neuron.NeuronType == NeuronType.Output)
{
SW.WriteLine("N" + neuron.InnovationId);
}
}
SW.WriteLine(" } ");
SW.WriteLine(" { rank=same; ");
foreach (NeuronGene neuron in genome.NeuronGeneList)
{
if (neuron.NeuronType == NeuronType.Input)
{
SW.Write("N" + neuron.InnovationId + " ->");
}
}
//Also the bias neuron on the same level
SW.WriteLine("N0 [style=invis]");
SW.WriteLine(" } ");
SW.WriteLine("}");
SW.Close();
}
/// <summary>
/// Threadsafe wrapper function for the experiment class's evaluteNetwork function.
/// </summary>
/// <param name="network">If using NEAT (with direct encoding), the network is the controller itself. Otherwise, if using HyperNEAT, the network is a CPPN that indirectly encodes the controller.</param>
/// <param name="sem">Semaphore for managing parallel processes.</param>
/// <param name="behavior">** Output parameter ** Returns a vector representation of the agent's behavior inside the experimental domain.</param>
public double threadSafeEvaluateNetwork(SharpNeatLib.NeuralNetwork.INetwork network, System.Threading.Semaphore sem, out SharpNeatLib.BehaviorType behavior, int thread)
{
//TODO: deal with threading
return Experiment.evaluateNetwork(network, out behavior,sem);
}
/// <summary>
/// Wrapper function for the experiment class's evaluteNetwork function.
/// </summary>
/// <param name="network">If using NEAT (with direct encoding), the network is the controller itself. Otherwise, if using HyperNEAT, the network is a CPPN that indirectly encodes the controller.</param>
/// <param name="behavior">** Output parameter ** Returns a vector representation of the agent's behavior inside the experimental domain.</param>
public double evaluateNetwork(SharpNeatLib.NeuralNetwork.INetwork network, out SharpNeatLib.BehaviorType behavior)
{
return Experiment.evaluateNetwork(network, out behavior,null);
}
/// <summary>
/// Runs a single individual (or single multiagent team) through the CurrentEnvironment(s) specified in the experiment file.
/// </summary>
internal override double evaluateNetwork(INetwork network, out SharpNeatLib.BehaviorType behavior, System.Threading.Semaphore sem)
{
double fitness = 0;
List<double> fitList = new List<double>();
behavior = new SharpNeatLib.BehaviorType();
List<Double> origBehavior = new List<Double>();
double[] accumObjectives = new double[6];
for (int i = 0; i < 6; i++) accumObjectives[i] = 0.0;
IFitnessFunction fit_copy;
IBehaviorCharacterization bc_copy;
instance_pack inst = new instance_pack();
inst.timestep = timestep;
foreach (Environment env2 in environmentList)
{
fit_copy = fitnessFunction.copy();
if (behaviorCharacterization != null)
{
bc_copy = behaviorCharacterization.copy();
inst.bc = bc_copy;
}
inst.ff = fit_copy;
double tempFit = 0;
double[] fitnesses = new double[timesToRunEnvironments];
SharpNeatLib.Maths.FastRandom evalRand;
if (noiseDeterministic) evalRand = new SharpNeatLib.Maths.FastRandom(100);
else
{
evalRand = new SharpNeatLib.Maths.FastRandom();
}
for (int evals = 0; evals < timesToRunEnvironments; evals++)
{
inst.num_rbts = this.numberRobots;
Environment env = env2.copy();
double evalTime = evaluationTime;
inst.eval = evals;
env.seed = evals;
if (!benchmark && noiseDeterministic)
env.rng = new SharpNeatLib.Maths.FastRandom(env.seed + 1);
else
{
env.rng = new SharpNeatLib.Maths.FastRandom();
}
float new_sn = this.sensorNoise;
float new_ef = this.effectorNoise;
float new_headingnoise = this.headingNoise;
inst.agentBrain = new AgentBrain(homogeneousTeam, inst.num_rbts, substrateDescription, network, normalizeWeights, adaptableANN, modulatoryANN, multibrain, evolveSubstrate, neatBrain, useCTRNNS);
inst.timestep = timestep;
initializeRobots(agentBrain, env, new_headingnoise, new_sn, new_ef, inst);
inst.elapsed = 0;
inst.timeSteps = 0;
inst.collisionManager = collisionManager.copy();
inst.collisionManager.initialize(env, this, inst.robots);
while (inst.elapsed < evalTime)
{
runEnvironment(env, inst, sem);
}
double thisFit = inst.ff.calculate(this, env, inst, out behavior.objectives);
fitnesses[evals] = thisFit;
tempFit += thisFit;
if (behavior != null && behavior.objectives != null && inst.bc != null)
{
for (int i = 0; i < behavior.objectives.Length; i++)
accumObjectives[i] += behavior.objectives[i];
if (behavior.behaviorList == null)
{
behavior.behaviorList = new List<double>();
}
behavior.behaviorList.AddRange(inst.bc.calculate(this, inst));
inst.bc.reset();
}
else if (behavior != null && inst.bc != null)
{
if (behavior.behaviorList == null)
{
behavior.behaviorList = new List<double>();
}
behavior.behaviorList.AddRange(inst.bc.calculate(this, inst));
inst.bc.reset();
}
inst.ff.reset();
}
fitness += tempFit / timesToRunEnvironments;
fitList.Add(tempFit / timesToRunEnvironments);
}
behavior.objectives = accumObjectives;
if (behaviorCharacterization != null)
{
behavior.wraparoundRange = behaviorCharacterization.wraparoundDistCalc;
}
double t = 0;
if (recordEndpoints)
{
behavior.finalLocation = new List<double>(2);
behavior.finalLocation.Add(inst.robots[0].Location.X);
behavior.finalLocation.Add(inst.robots[0].Location.Y);
}
if (recordTrajectories)
{
behavior.trajectory = new List<int>();
behavior.trajectory.AddRange(inst.robots[0].Trajectory);
}
return (fitness - t) / environmentList.Count;
}
