private void loadNetworkToolStripMenuItem_Click(object sender, EventArgs e)
{
string filename;
OpenFileDialog oDialog = new OpenFileDialog();
oDialog.AddExtension = true;
oDialog.DefaultExt = "xml";
oDialog.Title = "Load Seed Genome";
oDialog.RestoreDirectory = true;
// Show Open Dialog and Respond to OK
if (oDialog.ShowDialog() == DialogResult.OK)
{
filename = oDialog.FileName;
}
else
{
return;
}
NeatGenome.NeatGenome seedGenome = null;
try
{
XmlDocument doc = new XmlDocument();
doc.Load(filename);
seedGenome = XmlNeatGenomeReaderStatic.Read(doc);
}
catch (Exception ex)
{
MessageBox.Show("Problem loading genome. \n" + ex.Message);
return;
}
currentBest = GenomeDecoder.DecodeToFloatFastConcurrentNetwork(seedGenome, null);
}
private void ShowGenomeNetwork(NeatGenome genome)
{
NetworkControl networkControl = new NetworkControl();
networkControl.Dock = DockStyle.Fill;
panelNetWorkViewer.Controls.Clear();
panelNetWorkViewer.Controls.Add(networkControl);
/* create network model to draw the network */
NetworkModel networkModel = GenomeDecoder.DecodeToNetworkModel(genome);
GridLayoutManager layoutManager = new GridLayoutManager();
layoutManager.Layout(networkModel, networkControl.Size);
networkControl.NetworkModel = networkModel;
}
public static Stats postHocAnalyzer(NeatGenome.NeatGenome genome)
{
Stats stats = new Stats();
FloatFastConcurrentNetwork ffcn = GenomeDecoder.DecodeToFloatFastConcurrentNetwork(genome, null);
INetwork network;
network = substrate.generateNetwork(ffcn);
stats.NNconnections = ((FloatFastConcurrentNetwork)network).connectionArray.Length;
double avgSpeed = 0;
double totalTime = 0;
int numFood = 0;
double timetaken = 0;
for (int i = 0; i < FoodGatherParams.foodLocations.Length; i++)
{
Board testingArena = new Board(0, 500);
Robot tester = new Robot(new PointF(testingArena.Size.Width / 2.0F, testingArena.Size.Height / 2.0F), (int)FoodGatherParams.resolution, network);
testingArena.AddRobot(tester);
testingArena.AddFood(FoodGatherParams.foodLocations[i]);
timetaken = testingArena.game();
if (timetaken < 1000)
{
numFood++;
}
totalTime += timetaken;
}
avgSpeed = totalTime / FoodGatherParams.foodLocations.Length;
stats.numFoods = numFood;
stats.avgSpeed = avgSpeed;
return(stats);
}
public ESBodyInformation genomeIntoBodyObject(IGenome genome, out bool isEmpty)
{
INetwork net = GenomeDecoder.DecodeToModularNetwork((NeatGenome)genome);
isEmpty = false;
//we want the genome, so we can acknowledge the genomeID!
//now convert a network to a set of hidden neurons and connections
//we'll make body specific function calls later
var allBodyOutputs = new List <List <float> >();
var allBodyInputs = new List <PointPair>();
var indexToConnectionMap = new Dictionary <int, int>();
List <PointF> inputs, outputs, hiddenNeurons;
inputs = new List <PointF>();
outputs = new List <PointF>();
hiddenNeurons = new List <PointF>();
//inputs.Add(new PointF(0,0));
//int initialDepth, ESIterations;
//uint inputCount, outputCount;
//float varianceThreshold, bandThreshold;
ConnectionGeneList connections = new ConnectionGeneList();
//loop through a grid, defined by some resolution, and test every connection against another using leo
int resolution = 9;
//int resolutionHalf = resolution / 2;
List <PointF> queryPoints = gridQueryPoints(resolution);
float xDistanceThree = dXDistance(resolution, 3.0f);
float yDistanceThree = dYDistance(resolution, 3.0f);
bool useLeo = true;
int counter = 0;
Dictionary <long, PointF> conSourcePoints = new Dictionary <long, PointF>();
Dictionary <long, PointF> conTargetPoints = new Dictionary <long, PointF>();
//Dictionary<string, List<PointF>> pointsChecked = new Dictionary<string, List<PointF>>();
//List<PointF> pList;
int src, tgt;
//for each points we have
for (int p1 = 0; p1 < queryPoints.Count; p1++)
{
PointF xyPoint = queryPoints[p1];
//query against all other points (possibly limiting certain connection lengths
for (int p2 = p1; p2 < queryPoints.Count; p2++)
{
PointF otherPoint = queryPoints[p2];
if (p1 != p2 && (Math.Abs(xyPoint.X - otherPoint.X) < xDistanceThree && Math.Abs(xyPoint.Y - otherPoint.Y) < yDistanceThree))
{
//if(!pointsChecked.TryGetValue(xyPoint.ToString(), out pList))
//{
// pList = new List<PointF>();
// pointsChecked.Add(xyPoint.ToString(), pList);
//}
//pList.Add(otherPoint);
//if (!pointsChecked.TryGetValue(otherPoint.ToString(), out pList))
//{
// pList = new List<PointF>();
// pointsChecked.Add(otherPoint.ToString(), pList);
//}
//pList.Add(xyPoint);
//Console.WriteLine("Checking: ({0}, {1}) => ({2}, {3}) ", xyPoint.X, xyPoint.Y, otherPoint.X, otherPoint.Y);
float[] outs = queryCPPNOutputs((ModularNetwork)net, xyPoint.X, xyPoint.Y, otherPoint.X, otherPoint.Y, maxXDistanceCenter(xyPoint, otherPoint), minYDistanceGround(xyPoint, otherPoint));
float weight = outs[0];
allBodyInputs.Add(new PointPair(xyPoint, otherPoint));
allBodyOutputs.Add(new List <float>(outs));
if (useLeo)
{
if (outs[1] > 0)
{
//Console.WriteLine("XY: " + xyPoint + " Other: " + otherPoint + " LEO : " + outs[1]) ;
//Console.WriteLine(" XDist: " + sqrt(xDistanceSq(xyPoint, otherPoint))
// + " yDist : " + sqrt(yDistanceSq(xyPoint, otherPoint))
// + " MaxDist: " + maxXDistanceCenter(xyPoint, otherPoint))
//+ " MinY: " + minYDistanceGround(xyPoint, otherPoint));
//Console.WriteLine();
//add to hidden neurons
if (!hiddenNeurons.Contains(xyPoint))
{
hiddenNeurons.Add(xyPoint);
}
src = hiddenNeurons.IndexOf(xyPoint);
if (!hiddenNeurons.Contains(otherPoint))
{
hiddenNeurons.Add(otherPoint);
}
tgt = hiddenNeurons.IndexOf(otherPoint);
conSourcePoints.Add(counter, xyPoint);
conTargetPoints.Add(counter, otherPoint);
indexToConnectionMap.Add(allBodyOutputs.Count - 1, counter);
connections.Add(new ConnectionGene(counter++, (src), (tgt), weight * HyperNEATParameters.weightRange, new float[] { xyPoint.X, xyPoint.Y, otherPoint.X, otherPoint.Y }, outs));
}
}
else
{
//add to hidden neurons
if (!hiddenNeurons.Contains(xyPoint))
{
hiddenNeurons.Add(xyPoint);
}
src = hiddenNeurons.IndexOf(xyPoint);
if (!hiddenNeurons.Contains(otherPoint))
{
hiddenNeurons.Add(otherPoint);
}
tgt = hiddenNeurons.IndexOf(otherPoint);
conSourcePoints.Add(counter, xyPoint);
conTargetPoints.Add(counter, otherPoint);
indexToConnectionMap.Add(allBodyOutputs.Count - 1, counter);
connections.Add(new ConnectionGene(counter++, (src), (tgt), weight * HyperNEATParameters.weightRange, new float[] { xyPoint.X, xyPoint.Y, otherPoint.X, otherPoint.Y }, outs));
}
//PointF newp = new PointF(p.x2, p.y2);
//targetIndex = hiddenNeurons.IndexOf(newp);
//if (targetIndex == -1)
//{
// targetIndex = hiddenNeurons.Count;
// hiddenNeurons.Add(newp);
//}
//connections.Add(new ConnectionGene(counter++, (sourceIndex), (targetIndex + inputCount + outputCount), p.weight * HyperNEATParameters.weightRange, new float[] { p.x1, p.y1, p.x2, p.y2 }, p.Outputs));
}
}
}
//esSubstrate.generateSubstrate(inputs, outputs, net,
// HyperNEATParameters.initialDepth,
// (float)HyperNEATParameters.varianceThreshold,
// (float)HyperNEATParameters.bandingThreshold,
// HyperNEATParameters.ESIterations,
// (float)HyperNEATParameters.divisionThreshold,
// HyperNEATParameters.maximumDepth,
// (uint)inputs.Count, (uint)outputs.Count,
// ref connections, ref hiddenNeurons, true);
//generateSubstrate(List<System.Drawing.PointF> inputNeuronPositions, List<PointF> outputNeuronPositions,
//INetwork genome, int initialDepth, float varianceThreshold, float bandThreshold, int ESIterations,
// float divsionThreshold, int maxDepth,
// uint inputCount, uint outputCount,
// ref ConnectionGeneList connections, ref List<PointF> hiddenNeurons)
//blow out the object, we don't care about testing it
//foreach (var pPair in pointsChecked)
//{
// Console.WriteLine("Checking: " + pPair.Key + " processed: ");
// foreach (var xyPoint in pPair.Value)
// {
// Console.WriteLine("({0}, {1}) ", xyPoint.X, xyPoint.Y);
// }
//}
var beforeConn = connections.Count;
var beforeNeuron = hiddenNeurons.Count;
//var hiddenCopy = new List<PointF>(hiddenNeurons);
ensureSingleConnectedStructure(connections, hiddenNeurons, conSourcePoints, conTargetPoints);
if (hiddenNeurons.Count > 20 || connections.Count > 100)
{
hiddenNeurons = new List <PointF>();
connections = new ConnectionGeneList();
}
if (hiddenNeurons.Count == 0 || connections.Count == 0)
{
isEmpty = true;
}
NeatGenome ng = (NeatGenome)genome;
bool behaviorExists = (ng.Behavior != null);
ESBodyInformation esbody = new ESBodyInformation()
{
AllBodyOutputs = allBodyOutputs,
AllBodyInputs = allBodyInputs,
indexToConnection = indexToConnectionMap,
//PreHiddenLocations = hiddenCopy,
BeforeNeuron = beforeNeuron,
BeforeConnection = beforeConn,
GenomeID = genome.GenomeId,
Connections = connections,
HiddenLocations = hiddenNeurons,
InputLocations = inputs,
Objectives = ng.objectives,
Fitness = ng.Fitness,
Locality = ng.locality,
useLEO = useLeo
};
Console.WriteLine(" Nodes: " + hiddenNeurons.Count + " Connections: " + connections.Count);
return(esbody);
}
