wiring [] p = new wiring [20];
void setup(){
for (int i = 0; i < p.length; i++){
  p[i] = new wiring(50);
}
  background(255);
  size(400,400);
}
void loop(){
for (int i = 0; i < p.length; i++){
  p[i].load();
  p[i].fire();
  p[i].learn();
  p[i].update();
  }
}
void mousePressed(){
background(255);
}
class wiring{
  perceptron [] p;
  wiring(int n){
    p = new perceptron[n];
    for (int i = 0; i < p.length; i++){
      p[i] = new perceptron(0.01);
    }
  }
  void load(){
    for (int i1 = 0; i1 < p.length; i1++){
      for (int i2 = 0; i2 < p[i1].ins.length; i2++){
        p[i1].ins[i2] = p[(i1+(i2+1))%p.length].ot;
      }
    }
  }
  void fire(){
    for (int i = 0; i < p.length; i++){
      p[i].fire();
    }
  }
  void learn(){
    for (int i = 0; i < p.length; i++){
      float theta = atan2(mouseY-p[i].y,mouseX-p[i].x);
      //float theta = atan2(y-p[i].y,x-p[i].x) / TWO_PI;
      //float theta = atan2(200-p[i].y,200-p[i].x) / TWO_PI;
      float d = (theta + PI)/TWO_PI;
      for (int i2 = 0; i2 < p[i].w.length; i2++){
        p[i].learn(i2,d);
      }
    }
  }
  void update(){
    for (int i = 0; i < p.length; i++){
      p[i].x = fwrap(p[i].x + (cos(p[i].ot*TWO_PI) * 1),width-1);
      p[i].y = fwrap(p[i].y + (sin(p[i].ot*TWO_PI) * 1),height-1);
      color c = pixels[int(p[i].x)+int(p[i].y)*width];
      float r = red(c);
      float g = green(c);
      float b = blue(c);
      if (r > 0 && i > p.length/2){r -= 10;}else if (r < 255){r += 10;}
      if (g > 0 && i > p.length/2){g -= 10;}else if (g < 255){g += 10;}
      if (b > 0 && i > p.length/2){b -= 10;}else if (b < 255){b += 10;}
      stroke(r,g,b);
      point(p[i].x,p[i].y);
    }
    }
  
}
class perceptron{
  float x,y,ot,ln;
  float [] ins;
  float [] w;
  perceptron(float ln){
    this.x = random(400);
    //this.x = 0;
   this.y = random(400);
    //this.y = y;
    this.ot = 0.75;
    this.ln = ln;
    this.ins = new float[10];
    this.w = new float[10];
    for(int i = 0; i < w.length; i++){
      w[i] = 1.0;
    }
  }
  void fire(){
    ot = meanOut(ins,w);
  }
  void learn(int i, float d){
    w[i] += ln * (d - ot);
    //w[i] += ln * (d - (w[i]*ins[i]));
  }
}
float meanOut(float [] inputs, float [] weights){
  float sum = 0.0;
  for (int i = 0; i < inputs.length; i++){
    sum += inputs[i] * weights[i];
  }
  return sum / inputs.length;
}
float fwrap (float fvalu, float flimi){
  float fcar=fvalu;
if (fcar>flimi){ fcar=(fcar)%flimi; }
  if (fcar<0){
    fcar=flimi-fcar;
    fcar=(fcar)%flimi;
    fcar=flimi-fcar;
  }
  return fcar;
}