// Neuronales Netz aus 1 Neuron
// LTU = Linear Threshold Unit oder
// Adaline = Adaptive linear element


#define printXY nxtDisplayStringAt

const int nl0 = 20;

const int nd = 20;
const float th = 0.5;
float lbd = 0.2;

float dummy;

//**********************************************************************

typedef struct{
   float in[nd];
   float w[nd];
   float t[nd];
   float net;
   float thr;
   float act;
   float out;
} tNeuron;


tNeuron Neuron0[nl0];  // nl0=20 Neuronen in Eingabeschicht 0

//**********************************************************************

void ResetNeuron(tNeuron &neur){ // alles auf Null
   int d;

   for (d=0; d<nd; d++) {
      neur.in[d]=0;
     neur.w[d]=0;
     neur.t[d]=0;
   }
   neur.net=0;
   neur.thr=th;
   neur.act=0;
   neur.out=0;

}

//**********************************************************************



void initAllNeurons(){              // alle Netz-Neuronen auf Null


  ResetNeuron(Neuron0[0]);

}

//**********************************************************************

float  netPropag(tNeuron neur){      // DOESN'T WORK! [line 61]
  int   j=0;
  float s=0;

  for(j=0;j<nd;j++){
     s+= (neur.in[j]*neur.w[j]);      // gewichtete Summe
  }
  dummy=s;  // global variable to check the value of s
  printXY( 0,20, "s loc"); printXY(50,20,"%4.1f",s);
  return s;
}

//**********************************************************************


void pause() {
   while(true) wait1Msec(50);
}

task displayValues(){
   while(true) {

    printXY( 0,60, "in(0 1)");
    printXY( 50,60, "%2.0f", Neuron0[0].in[0]); printXY( 70,60, "%2.0f", Neuron0[0].in[1]);

    printXY( 0,50, "w (0 1)");
    printXY( 50,50, "%2.1f", Neuron0[0].w[0]);  printXY( 70,50, "%2.1f", Neuron0[0].w[1]); // FAULTY! shows 0/1, but must be 0.8/1.3 !!!

    printXY( 0,40, "net"); printXY(50,40,"%4.1f",Neuron0[0].net); // should change depending on Sensor Values
    printXY( 0,30, "dummy"); printXY(50,30,"%4.1f",dummy);        // should change depending on Sensor Values

    printXY( 0,10, "thr"); printXY(50,10,"%4.1f",Neuron0[0].thr); // FAULTY! shows 1.0, but must be 1.5 !!!

  }
  return;
}

void InitThisNeuronalNet(){
  Neuron0[0].w[0]=0.8;  // [line  99] FAULTY!!!  should be assigned to 0.8, but gets 0.0 !!!!
  Neuron0[0].w[1]=1.3;  // [line 100] FAULTY!!!  should be assigned to 1.3, but gets 1.0 !!!!
  Neuron0[0].thr=1.5;   // [line 101] FAULTY!!!  should be assigned to 1.5, but gets 1.0 !!!!
}

task RefreshNet(){
  while(true){
    Neuron0[0].in[0]=(float)SensorValue(0); // Input 0: Touch-Sensor an S1=0
    Neuron0[0].in[1]=(float)SensorValue(1); // Input 1: Touch-Sensor an S2=1

    Neuron0[0].net=netPropag(Neuron0[0]);   // [line 109] function call - -> [line 61]
  }
  return;
}

task main(){
   SensorType(S1)=SensorTouch;
   SensorType(S2)=SensorTouch;


   InitAllNeurons();
   InitThisNeuronalNet();

   StartTask (displayValues);
   StartTask (RefreshNet);
   pause();
}