public nn InitialiseCopy(int hiddenLayerCount, int hiddenNeuronCount) //to add
{
nn n = new nn();
List <Matrix <float> > newWeights = new List <Matrix <float> >();
for (int i = 0; i < this.weights.Count; i++)
{
Matrix <float> currentWeight = Matrix <float> .Build.Dense(weights[i].RowCount, weights[i].ColumnCount);
for (int x = 0; x < currentWeight.RowCount; x++)
{
for (int y = 0; y < currentWeight.ColumnCount; y++)
{
currentWeight[x, y] = weights[i][x, y];
}
}
newWeights.Add(currentWeight);
}
List <float> newBiases = new List <float>();
newBiases.AddRange(biases);
n.weights = newWeights;
n.biases = newBiases;
n.InitialiseHidden(hiddenLayerCount, hiddenNeuronCount);
return(n);
}
private void initialFillPopulationWithRandomValues (nn[] newPopulation, int startingIndex)
{
if(fileexist)
{
int n=data.lastgenoms.Length;
for(int i=0;i<n;i++)
{
newPopulation[i]= data.lastgenoms[i];
}
currentGeneration=data.lastgeneration;
bestAgentSelection=0;
currentGenome=0;
fileexist=false;
}
else
{
while (startingIndex < initialPopulation)
{
newPopulation[startingIndex] = new nn();
newPopulation[startingIndex].Initialise(enemy.hiddenLayer, enemy.hiddenneurons);
startingIndex++;
}
currentGenome=0;
}
}
public void ResetWithNetwork(nn net)
{
NN = net;
centertime = 0f;
fitnessvalue = 0f;
ballhit = 0;
powerups = 0;
ball.GetComponent <ball>().reset();
}
private void FillPopulationWithRandomValues (nn[] newPopulation, int startingIndex)
{
while (startingIndex < initialPopulation)
{
newPopulation[startingIndex] = new nn();
newPopulation[startingIndex].Initialise(enemy.hiddenLayer, enemy.hiddenneurons);
startingIndex++;
}
}
public static int Main()
{
int N = 15;
Func <double, double> fit_func = (f) =>
{
// return Cos(5*f)*Exp(-f*f);
return(f * Exp(-f * f));
// return Exp(-f*f);
};
Func <double, double> activate_func = (f) =>
{
// return Cos(f);
return(Cos(5 * f) * Exp(-f * f));
};
nn NN = new nn(N / 3, activate_func);
double a = -3 / 2;
double b = 3 / 2;
vector x = new vector(N);
vector y = new vector(N);
System.IO.StreamWriter data = new System.IO.StreamWriter("xydata.txt");
for (int i = 0; i < N; i++)
{
x[i] = a + (b - a) * i / (N - 1);
y[i] = fit_func(x[i]);
data.WriteLine("{0} {1}", x[i], y[i]);
}
data.Close();
for (int i = 0; i < NN.n; i++)
{
NN.parameters[3 * i + 0] = a + (b - a) * i / (NN.n - 1);
NN.parameters[3 * i + 1] = 1;
NN.parameters[3 * i + 2] = 1;
}
NN.train(x, y);
vector fit_x = new vector(vector.linspace(a, b, 100));
WriteLine("fit data:");
System.IO.StreamWriter fit = new System.IO.StreamWriter("fitdata.txt");
for (int i = 0; i < fit_x.size; i++)
{
double feeded = NN.feedforwad(fit_x[i]);
// fit.WriteLine($"{feeded}");
fit.WriteLine("{0} {1}", fit_x[i], feeded);
}
fit.Close();
return(0);
}
private void SortPopulation()
{
for (int i = 0; i < numpopulation.Length; i++)
{
for (int j = i; j < numpopulation.Length; j++)
{
if (numpopulation[i].fitness < numpopulation[j].fitness)
{
nn temp = numpopulation[i];
numpopulation[i] = numpopulation[j];
numpopulation[j] = temp;
}
}
}
}
public void Death (float fitness, nn network,float maxfitness)
{
if (currentGenome < numpopulation.Length -1)
{
if(fitness>maxfitness)
{
bestAgentSelection++;
}
numpopulation[currentGenome].fitness = fitness;
currentGenome++;
ResetToCurrentGenome();
}
else
{
RePopulate();
//bestAgentSelection=0;
}
}
private nn[] PickBestPopulation()
{
nn[] newPopulation = new nn[initialPopulation];
for (int i = 0; i < bestAgentSelection; i++)
{
newPopulation[naturallySelected] = numpopulation[i];
newPopulation[naturallySelected].fitness = 0;
naturallySelected++;
int f = Mathf.RoundToInt(numpopulation[i].fitness /10);
for (int c = 0; c < f; c++)
{
genePool.Add(i);
}
}
int last = numpopulation.Length - 1;
for (int i = 0; i < worstAgentSelection; i++)
{
last -= i;
int f = Mathf.RoundToInt(numpopulation[last].fitness /10);
for (int c = 0; c < f; c++)
{
genePool.Add(last);
}
}
return newPopulation;
}
private void Crossover (nn[] newPopulation)
{
for (int i = 0; i < numberToCrossover; i+=2)
{
int AIndex = i;
int BIndex = i + 1;
if (genePool.Count >= 1)
{
for (int l = 0; l < 100; l++)
{
AIndex = genePool[Random.Range(0, genePool.Count)];
BIndex = genePool[Random.Range(0, genePool.Count)];
if (AIndex != BIndex)
break;
}
}
nn Child1 = new nn();
nn Child2 = new nn();
Child1.Initialise(enemy.hiddenLayer, enemy.hiddenneurons);
Child2.Initialise(enemy.hiddenLayer, enemy.hiddenneurons);
Child1.fitness = 0;
Child2.fitness = 0;
for (int w = 0; w < Child1.weights.Count; w++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
Child1.weights[w] = numpopulation[AIndex].weights[w];
Child2.weights[w] = numpopulation[BIndex].weights[w];
}
else
{
Child2.weights[w] = numpopulation[AIndex].weights[w];
Child1.weights[w] = numpopulation[BIndex].weights[w];
}
}
for (int w = 0; w < Child1.biases.Count; w++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
Child1.biases[w] = numpopulation[AIndex].biases[w];
Child2.biases[w] = numpopulation[BIndex].biases[w];
}
else
{
Child2.biases[w] = numpopulation[AIndex].biases[w];
Child1.biases[w] = numpopulation[BIndex].biases[w];
}
}
newPopulation[naturallySelected] = Child1;
naturallySelected++;
newPopulation[naturallySelected] = Child2;
naturallySelected++;
}
}
public static int Main()
{
int N = 15;
// Func<double,double> activate_func = (f) =>
// {
// // return Cos(f);
// // return Sin(f);
// return Exp(-f*f);
// // return Cos(5*f)*Exp(-f*f);
// };
Func <double, double> fit_func = (f) =>
{
// return Cos(5*f)*Exp(-f*f);
// return Exp(-f*f);
return(f * Exp(-f * f));
};
Func <double, double> derive_func = (f) =>
{
// return -2*f*Exp(-f*f);
// return -Exp(-f*f)*(5*Sin(5*f)+2*f*Cos(5*f));
return(Exp(-f * f) - 2 * f * f * Exp(-f * f));
};
Func <double, double> int_func = (f) =>
{
// return Sqrt(PI)/2*errorfunction(f);
// return (Sqrt(2)/2)*(2*errorfunction(f))/(Exp(24/4));
return(-1.0 / 2 * Exp(-f * f));
};
nn NN = new nn(N / 3, fit_func, derive_func, int_func);
double a = -3 / 2;
double b = 3 / 2;
vector x = new vector(N);
vector y = new vector(N);
vector dy = new vector(N);
vector Y = new vector(N);
System.IO.StreamWriter data = new System.IO.StreamWriter("xydata.txt");
for (int i = 0; i < N; i++)
{
x[i] = a + (b - a) * i / (N - 1);
y[i] = fit_func(x[i]);
dy[i] = derive_func(x[i]);
Y[i] = int_func(x[i]);
data.WriteLine("{0} {1} {2} {3}", x[i], y[i], dy[i], Y[i]);
}
data.Close();
for (int i = 0; i < NN.n; i++)
{
NN.parameters[3 * i + 0] = a + (b - a) * i / (NN.n - 1);
NN.parameters[3 * i + 1] = 1;
NN.parameters[3 * i + 2] = 1;
}
NN.train(x, y);
vector fit_x = new vector(vector.linspace(a, b, 100));
WriteLine("fit data:");
System.IO.StreamWriter fit = new System.IO.StreamWriter("fitdata.txt");
for (int i = 0; i < fit_x.size; i++)
{
double feeded = NN.feedforwad(fit_x[i]);
double derived = NN.ff_derivative(fit_x[i]);
double integrated = NN.ff_integrate(fit_x[i]);
// fit.WriteLine($"{feeded}");
fit.WriteLine("{0} {1} {2} {3}", fit_x[i], feeded, derived, 0.28 + integrated);
}
fit.Close();
return(0);
}
