#include <LiquidCrystal.h>
#include <LCDKeypad.h>


//// VARIABILI
const int NX=1; //NUMERO INGRESSI
const int NH=4; //NUMERO NEURONI STATO CENTRALE
const int NY=1; //NUMERO USCITE
const int M=17; //NUMERO DI CAMPIONI D'INGRESSO (INPUT DATA SET)
double H[NH]; //VETTORE USCITE NEURONI
double Y[NY]; //VETTORE USCITE
long double A=0; //E^A "attivazione interna"
double X[NX]; //VETTORE VARIABILI INGRESSI

//matrici random:
int RM0[NX][NH];
int RM1[NH][NY];



void setup (){
  Random_Matrix_0(NX,NH);
  Random_Matrix_1(NX,NH);
}

void loop(){


  double Xi[]={0.0000,0.0625,0.1250,0.1875,0.2500,0.3125,0.3750,0.4375,0.5000,0.5625,0.6250,0.6875,0.7500,0.8125,0.8750,0.9375,1.0000};

  for(int l=0; l<16; l++){ //scorre il vettore d'ingresso
  Xi[l]=Xi[l+1];
  neural(1,4,1,Xi[l]);
  }
  }



void neural(int NX, int NH, int NY,int Zeta )
{

  //DEFINE VARIABLES




double wh[NH][NX+1]; //PESI
double wy[NY][NH+1]; //VETTORE DELLE USCITE DEI PESI DELLE USCITE DELL'IESIMO STRATO
int i,j,k=0;//INDICI



//CALCULATE THE LAYER H
for (k=1; k<=NH; k++){
  for (i=1; i<=NX;i++){
  A=A+(wh[k][i]*X[i]); //CALCULATE THE INTERNAL ACTIVATION FUNCTION A, that  is the sum wighted of all the input signals
  A=A+wh[k][NX+1];
  H[k]=1.0/(1.0+exp(-A)); //function of weight of the singolar layer
  }}


//CALCULATE THE LAYER Y //same thing but for the next layer(Y)
for (j=1; j<=NY; j++){
  A=0; //initzialize to zero
  for(k=1; k<=NH; k++){
  A += (wy[j][k]*H[k]);
  A += wy[j][NH+1];
  Y[j]=1.0/(1.0+exp(-A));
  }}

  //OPTIONAL STRUCTURE FOR LEARNING

const double DELTA=0.3; //speed learning
long double Err=0; //error variable (set point-real output)
double D [NY]; //set point output desired
double DeltaY [NY];  //Backpropagation Y
double DeltaH [NH]; // Backpropagation H

//BACK PROPAGATION

//Calcolo errore strato Y
for (j=1; j<=NY; j++) {
  Err=D[j]-Y[j];
  DeltaY[j]=Err*Y[j]*(1-Y[j]);
}

//Calcolo errore strato H
for (k=1; k<=NH; k++){
  Err=0;
 for (j=1; j<=NY; j++) {
 Err=Err+(DeltaY[j]*wy[j][k]);
  DeltaH[k]=Err*H[k]*(1-H[k]);
 }}

//Modifica pesi strato Y
for (j=1; j<=NY; j++){
 for (k=1; k<=NH; k++){
  wy[j][k]=wy[j][k]+(DELTA*DeltaY[j]*H[k]);
  wy[j][NH+1]=wy[j][NH+1]+(DELTA*DeltaY[j]);
 }}

//Modifica pesi strato H
for (k=1; k<=NH; k++){
 for (i=1; i<=NX; i++){
   wh[k][i]=wh[k][i]+(DELTA*DeltaH[k]*X[i]);
  wh[k][NX+1]=wh[k][NX+1]+(DELTA*DeltaH[k]);
 }}
 //implicitamente restituisce NY
}

void Random_Matrix_0(int A,int B){
  for (int a=1; a<=A;a++){
 for (int b=1; b<=B; b++){
   int rand =(double) random(0, 1000);
   RM0[a][b]= (rand/500)-1;
 }}
}
void Random_Matrix_1(int A,int B){
  for (int a=1; a<=A;a++){
 for (int b=1; b<=B; b++){
   int rand =(double) random(0, 1000);
   RM1[a][b]= (rand/500)-1;
 }}
}