static void Main(string[] args)
{
PyDrawGraph.pyGraphDrawerFilePath = pyFitnessGraphPath;
GARandomManager.Random = new RandomClassic((int)DateTime.Now.Ticks);
var salesman = new Salesman(GetLocations());
var gif = new GifDrawer(
salesman.Locations,
() => salesman.GetBestPath(),
gifSlowMotionGradient: 1.02f,
gifSlowMotionCount: 0.8f);
var fitnessGrph = new GraphDataCollector();
for (var i = 1; i < maxIterations; i++)
{
salesman.Evolve();
// Debugging stuff...
var bestDist = salesman.CurrentBestDist();
Console.WriteLine(String.Format(
"{0}) dist = {1}", i, bestDist));
fitnessGrph.Tick(i, bestDist);
if (drawGif)
{
gif.Tick(i);
}
}
if (drawGif)
{
gif.ShowGif(maxPos);
}
if (drawResult)
{
TSGraph.DrawGrapghResult(
maxPos.X, maxPos.Y,
salesman.Locations, salesman.GetBestPath());
fitnessGrph.Draw("Distance", "b-");
}
}
static void Main(string[] args)
{
NeuralNetDrawer.pyGraphDrawerPath = pyNeuralNetGraphDrawerPath;
PyDrawGraph.pyGraphDrawerFilePath = pyFitnessGraphPath;
GARandomManager.Random = new RandomClassic((int)DateTime.Now.Ticks);
var neuralNetDrawer = new NeuralNetDrawer(false);
var fitnessCollector = new GraphDataCollector();
NeuralGenomeToJSONExtension.distBetweenNodes *= 5;
NeuralGenomeToJSONExtension.randomPosTries = 10;
NeuralGenomeToJSONExtension.xPadding = 0.03f;
NeuralGenomeToJSONExtension.yPadding = 0.03f;
var program = new Program();
var watch = new Stopwatch();
watch.Start();
for (var i = 0; i < maxIterations; i++)
{
//program.EvaluateAsync(25).Wait();
program.Evaluate();
//program.EvaluateAsyncEach().Wait();
if (i % 10 == 0)
{
watch.Stop();
Console.WriteLine(watch.Elapsed);
watch.Restart();
var fintessSum = program.geneticManager
.GenerationManager
.CurrentGeneration
.Genomes.Sum(x => x.Fitness);
var best = program.geneticManager
.GenerationManager
.CurrentGeneration
.BestGenome as NeuralGenome;
//fitnessCollector.Tick(i, best.Fitness);
Console.WriteLine(String.Format(
"{0}) Best:{1:0.00} Sum:{2:0.00}",
i,
best.Fitness,
fintessSum));
//neuralNetDrawer.QueueNeuralNetJson(program.GetBestJson());
if (best.Fitness > -0.01f)
{
generationsWithTargetReachedCount++;
if (generationsWithTargetReachedCount >= generationsWithTargetReached)
{
targetReached = true;
for (var j = 0; j < Math.Pow(2, inputs) - 1; j++)
{
best.FeedNeuralNetwork(GetBits(j).Select(x => (float)x).ToArray());
var expectedOutput = GetBits(j + 1);
var output = BitsToInt(best.Outputs.Select(x => x.Value).ToArray());
Console.WriteLine(String.Format("{0:0.00} | {1}", output, j));
}
break;
}
}
else
{
generationsWithTargetReachedCount = 0;
}
//neuralNetDrawer.QueueNeuralNetJson(program.GetBestJson());
}
program.Evolve();
}
neuralNetDrawer.QueueNeuralNetJson(program.GetBestJson());
//fitnessCollector.Draw();
}
