public Spawner(float distInterval, int expectCount, int fullCount, SpawnFunction function)
{
this.distInterval = distInterval;
this.expectCount = expectCount;
this.fullCount = fullCount;
this.function = function;
actualCount = 0;
intervalCount = 0;
dist = 0f;
}
public GeneticSimulation(FitnessFunction <TGenome> fitnessFunction, SpawnFunction <TGenome> spawnFunction, int populationSize)
{
_population = new List <Organism <TGenome> >();
_rand = new Random();
_fitnessFunction = fitnessFunction;
for (int i = 0; i < populationSize; i++)
{
_population.Add(spawnFunction(i));
}
}
public void SpawnSimpleProcess()
{
AutoResetEvent handle = new AutoResetEvent(false);
int count = 0;
IFunction func = new SpawnFunction();
IFunction lambda = new LambdaFunction(() => { count = 1; return(handle.Set()); });
var result = func.Apply(null, new object[] { lambda });
Assert.IsNotNull(result);
Assert.IsInstanceOfType(result, typeof(Process));
handle.WaitOne();
Assert.AreEqual(1, count);
}
public void Sin()
{
var rand = new Random();
var net = NeuralNetBuilder.From(new NeuralNetConfig()
{
InputLabels = new string[] { "x" },
OutputLabels = new string[] { "y" },
HiddenLayerCount = 3,
HiddenLayerNeuronCount = 3,
//PreActivationFunction = (i, d) =>
ActivationFunction = d => d > .5 ? 1 : 0
//ActivationFunction = d => 1 / (1 + Math.Pow(Math.E, -d))
});
var trainingData = new List <Tuple <double, double> >();
for (double i = 0; i < Math.PI * 2; i += .1)
{
var input = i;
var output = Math.Sin(i);
//var normalizedOutput = output / Math.PI * 2;
var normalizedOutput = .5 * (output + 1);
var normalizedInput = input / (Math.PI * 2);
trainingData.Add(new Tuple <double, double>(normalizedInput, normalizedOutput));
}
var weightCount = net.WeightCount;
var biasCount = net.BiasCount;
var weightOrgSpawner = new SpawnFunction <double[]>(index =>
{
var data = new double[weightCount + biasCount];
for (int i = 0; i < weightCount + biasCount; i++)
{
var scale = i >= weightCount ? 1 : 1;
data[i] = (rand.NextDouble()) * scale;
}
return(new Org(data));
});
var fitnessFunction = new FitnessFunction <double[]>(org =>
{
net.InitializeWeights(org.Genome.Take(weightCount).ToArray());
net.InitializeBiases(org.Genome.Skip(weightCount).ToArray());
var errorSum = trainingData.Select(trainingElement =>
{
var input = trainingElement.Item1;
var expectedOutput = trainingElement.Item2;
var actualOutput = net.Compute(new NamedValue[] { new NamedValue()
{
Name = "x", Value = input
} });
var error = expectedOutput - actualOutput[0].Value;
var convertedError = Math.Pow((Math.Abs(error) + 1), 2);
return(convertedError);
}).Sum();
//Trace.WriteLine(errorSum);
return(1 / errorSum);
});
var geneticSim = new GeneticSimulation <double[]>(fitnessFunction, weightOrgSpawner, 50);
var bestSet = geneticSim.Evolve(20000, false);
var theBest = geneticSim.GetBest();
net.InitializeWeights(theBest.Genome.Take(weightCount).ToArray());
net.InitializeBiases(theBest.Genome.Skip(weightCount).ToArray());
Trace.WriteLine("OUTPUT");
trainingData.ForEach(t =>
{
var input = t.Item1;
var expectedOutput = t.Item2;
var actualOutput = net.Compute(new NamedValue[] { new NamedValue()
{
Name = "x", Value = input
} });
var error = expectedOutput - actualOutput[0].Value;
//Trace.WriteLine($"INPUT:{input} EXPECT:{expectedOutput} REAL:{actualOutput[0].Value} ERR:{error}");
//Trace.WriteLine($"{input},{expectedOutput},{actualOutput[0].Value}");
Trace.WriteLine($"{actualOutput[0].Value}");
});
}
public void RegisterSpawnable(EventType type, SpawnFunction spawner)
{
spawnableEvents.Add(type, spawner);
}
public static void SpawnObjects(int amount, float radius, float clusterDist, Vector3 position, GameObject[] objs, SpawnFunction create, LayerMask mask)
{
if (amount <= 0)
{
return;
}
float clusterDistSqrd = clusterDist * clusterDist;
int created = 0;
do
{
Vector3 spawnPoint = new Vector3(UnityEngine.Random.Range(-radius, radius) + position.x, 0f, UnityEngine.Random.Range(-radius, radius) + position.z);
spawnPoint.x = Mathf.Clamp(spawnPoint.x, -GenerateLevel.xx, GenerateLevel.xx);
spawnPoint.z = Mathf.Clamp(spawnPoint.z, -GenerateLevel.zz, GenerateLevel.zz);
Vector3 nearestLoc;
if (!Physics.CheckSphere(spawnPoint, 2, mask))
{
if (objs[0] == null)
{
objs[created] = create(spawnPoint);
NetworkServer.Spawn(objs[created]);
created++;
}
else
{
nearestLoc = NearestTarget(objs, spawnPoint);
if ((nearestLoc - spawnPoint).sqrMagnitude > clusterDistSqrd)
{
objs[created] = create(spawnPoint);
NetworkServer.Spawn(objs[created]);
created++;
}
}
}
}while(created < amount);
}
