static void Main(string[] args)
{
GARandomManager.Random = new RandomClassic((int)DateTime.Now.Ticks);
NeuralGenomeToJSONExtension.distBetweenNodes *= 5;
NeuralGenomeToJSONExtension.randomPosTries = 10;
NeuralGenomeToJSONExtension.xPadding = 0.03f;
NeuralGenomeToJSONExtension.yPadding = 0.03f;
NeuralNetDrawer.pyGraphDrawerPath = pyNeuralNetGraphDrawerPath;
PyDrawGraph.pyGraphDrawerFilePath = pyFitnessGraphPath;
var neuralNetDrawer = new NeuralNetDrawer(false);
//var fitnessCollector = new GraphDataCollector();
NeuralGenomeToJSONExtension.distBetweenNodes *= 5;
NeuralGenomeToJSONExtension.randomPosTries = 10;
var program = new Program();
for (var i = 0; i < maxIterations; i++)
{
program.Evaluate();
var best = program.BestGenome() as NeuralGenome;
//fitnessCollector.Tick(i, best.Fitness);
Console.WriteLine(String.Format(
"{0}) Best:{1:0.00}",
i,
best.Fitness));
if (targetReached)
{
foreach (var pair in program.samplePairs)
{
Console.WriteLine("{0} ^ {1} = {2} | {3:0.00}",
pair[0], pair[1],
pair[0] ^ pair[1],
program.FeedRNNPair(best, pair));
}
break;
}
program.Evolve();
}
neuralNetDrawer.QueueNeuralNetJson((program.BestGenome() as NeuralGenome).ToJson(
neuronRadius: 0.02f,
maxWeight: 7,
edgeWidth: 1f));
//fitnessCollector.Draw();
}
protected void Init()
{
GARandomManager.Random = new RandomClassic(TimeSinceEpochSeconds());
if (drawNetwork)
{
neuralNetDrawer = new NeuralNetDrawer(false);
}
agents = InitAgents().ToArray();
InitGeneticManager();
AssignBrains();
}
static void Main(string[] args)
{
GARandomManager.Random = new RandomClassic((int)DateTime.Now.Ticks);
NeuralGenomeToJSONExtension.distBetweenNodes *= 5;
NeuralGenomeToJSONExtension.randomPosTries = 10;
NeuralGenomeToJSONExtension.xPadding = 0.03f;
NeuralGenomeToJSONExtension.yPadding = 0.03f;
NeuralNetDrawer.pyGraphDrawerPath = pyNeuralNetGraphDrawerPath;
PyDrawGraph.pyGraphDrawerFilePath = pyFitnessGraphPath;
var neuralNetDrawer = new NeuralNetDrawer(false);
//var fitnessCollector = new GraphDataCollector();
NeuralGenomeToJSONExtension.distBetweenNodes *= 5;
NeuralGenomeToJSONExtension.randomPosTries = 10;
var program = new Program();
for (var i = 0; i < maxIterations; i++)
{
if (targetReached)
{
break;
}
program.Evaluate();
var fintessSum = program.geneticManager
.GenerationManager
.CurrentGeneration
.Genomes.Sum(x => x.Fitness);
var best = program.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));
program.Evolve();
}
neuralNetDrawer.QueueNeuralNetJson((program.BestGenome() as NeuralGenome).ToJson(
neuronRadius: 0.02f,
maxWeight: 7,
edgeWidth: 1f));
//fitnessCollector.Draw();
}
private void Start()
{
GARandomManager.Random = new RandomClassic(
(int)DateTime.Now.Subtract(new DateTime(1970, 1, 1, 0, 0, 0, DateTimeKind.Utc)).TotalSeconds);
NeuralNetDrawer.pyGraphDrawerPath = pyNeuralNetGraphDrawerPath;
NeuralNetDrawer.pyAssemblyCmd = "/usr/local/bin/python3";
PyDrawGraph.pyGraphDrawerFilePath = pyFitnessGraphPath;
neuralNetDrawer = new NeuralNetDrawer(false);
InitAgents();
InitGenetics();
AssignBrains();
DrawBestGenome();
}
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();
}