static void Main()
{
WriteLine("______ Assignment A ______\n");
WriteLine("Testing the neural network for fitting to a function:\n");
Func <double, double> F_fit = delegate(double x){
return(Cos(5 * x - 1) * Exp(-x * x));
};
int n = 5;
var ann = new network(n);
double a = -1, b = 1;
int nx = 20;
vector xs = new vector(nx);
vector ys = new vector(nx);
for (int i = 0; i < nx; i++)
{
xs[i] = a + (b - a) * i / (nx - 1);
ys[i] = F_fit(xs[i]);
Error.Write($"{xs[i]} {ys[i]}\n");
}
Error.Write("\n\n");
for (int i = 0; i < ann.n; i++)
{
ann.p[3 * i + 0] = a + (b - a) * i / (ann.n - 1);
ann.p[3 * i + 1] = 1;
ann.p[3 * i + 2] = 1;
}
ann.p.print("Before training: p =");
(int nsteps, int ncalls) = ann.train(xs, ys);
ann.p.print("After training: p =");
WriteLine($"Minimization steps: {nsteps}");
WriteLine($"Function calls: {ncalls}");
for (double z = a; z <= b; z += 1.0 / 64)
{
Error.Write($"{z} {ann.feed(z)}\n");
}
Error.Write("\n\n");
WriteLine("The fitted function can be seen in the figure Fit.svg.");
Write("\n\n\n");
WriteLine("______ Assignment B ______\n");
WriteLine("We now use different feeders to get the derivative and antiderivative.\nThese can be seen in the figures Derivative.svg and Antiderivative.svg");
for (double z = a; z <= b; z += 1.0 / 64)
{
Error.Write($"{z} {ann.feedDeriv(z)}\n");
}
Error.Write("\n\n");
for (double z = a; z <= b; z += 1.0 / 64)
{
Error.Write($"{z} {ann.feedInt(z)}\n");
}
Error.Write("\n\n");
}