public static int Main()
{
Func <double, double> activate = delegate(double x){
return(Exp(-x * x));
};
Func <double, double> df = delegate(double x){
return(-2 * Exp(-x * x) * x);
};
Func <double, double> adf = delegate(double x){
return(Sqrt(PI) * math.erf(x) / 2);
};
Func <double, double> fitfun = delegate(double x){
return(x * Exp(-x * x)); // I'm not terribly imaginative with my functions
};
Write("Part A:\n");
int n = 8; // Wikibooks said this was a typical amount
var ann = new network(n, activate, df, adf);
double a = -2;
double b = 2;
int nx = 50;
double[] xs = new double[nx];
double[] ys = new double[nx];
for (int i = 0; i < nx; i++)
{
xs[i] = a + (b - a) * i / (nx - 1);
ys[i] = fitfun(xs[i]);
Error.Write("{0}\t{1}\n", xs[i], ys[i]);
}
Error.Write("\n\n");
for (int i = 0; i < n; i++)
{
ann.p[3 * i] = a + (b - a) * i / (n - 1);
ann.p[3 * i + 1] = 1.0;
ann.p[3 * i + 2] = 1.0;
}
ann.p.print("Initial p=");
vector time = ann.train(xs, ys); // time=[minimizeriterations, functioncalls]
ann.p.print("Post-training p=");
Write($"The minimiztion took {time[0]} iterations, and the deviation function was called {time[1]} times\n");
double z = a;
for (int i = 1; i <= 100; i++)
{
Error.Write($"{z}\t{ann.feed(z)}\n");
z += (b - a) / 100;
}
Write("\n The calculated function can be seen in A.svg\n\n");
Write("Part B:\n");
// Create derivative and anti-derivative functions
Func <double, double> dfun = delegate(double x){
return(Exp(-x * x) * (1 - 2 * x * x));
};
Func <double, double> adfun = delegate(double x){
return(-Exp(-x * x) / 2.0);
};
Error.Write("\n\n");
z = a;
for (int i = 1; i <= 100; i++) // For the derivative
{
Error.Write($"{z}\t{ann.dfeed(df, z)}\t{dfun(z)}\n");
z += (b - a) / 100;
}
Error.Write("\n\n");
z = a;
for (int i = 1; i <= 1000; i++) // For the antiderivative
{
Error.Write($"{z}\t{ann.adfeed(adf, z)}\t{adfun(z)}\n");
z += (b - a) / 1000;
}
Write("The resulting derivates and antiderivatives can be seen in B.svg\n");
return(0);
}
