static Func<double,double> G=(x)=>-Cos(x); //anti-derivative of g
static void Main(){
int n=5; //number of sets of parameters
var ann=new network(n,f,df,F); //creating a new "network" using n and our activation function f
double a=-PI,b=2*PI; //a: "start" value, b: "end" value
int nx=30; //length of xs and ys
var xs=new double[nx];
var ys=new double[nx];
for(int i=0;i<nx;i++){
xs[i]=a+(b-a)*i/(nx-1); //interval of x inbetween a and b
ys[i]=g(xs[i]); //function values of function to fit
Write($"{xs[i]} {ys[i]} {dg(xs[i])} {G(xs[i])}\n");
}
Write("\n\n");
for(int i=0;i<ann.n;i++){
ann.p[3*i+0]=a+(b-a)*i/(ann.n-1); //a
ann.p[3*i+1]=1; //b
ann.p[3*i+2]=1; //w
}
ann.p.fprint(Console.Error,"p=");
ann.train(xs,ys); //calling "train" which minimizes the deviation given our x's and y's
double offset = G(xs[0])-ann.feedF(xs[0]);
ann.p.fprint(Console.Error,"p=");
for(double z=a;z<=b;z+=1.0/64)
Write($"{z} {ann.feed(z)} {ann.feeddf(z)} {ann.feedF(z)+offset}\n"); //calling "feed" which returns the output signal
}//Main
